diff --git a/JetStreamDriver.js b/JetStreamDriver.js index eaf430e5..3b8e66b2 100644 --- a/JetStreamDriver.js +++ b/JetStreamDriver.js @@ -579,6 +579,7 @@ class Scripts { this.add(` performance.mark ??= function(name) { return { name }}; performance.measure ??= function() {}; + performance.timeOrigin ??= performance.now(); `); } @@ -2210,6 +2211,54 @@ let BENCHMARKS = [ worstCaseCount: 2, tags: ["Default", "Wasm"], }), + new AsyncBenchmark({ + name: "transformersjs-bert-wasm", + files: [ + "./polyfills/fast-text-encoding-1.0.3/text.js", + "./transformersjs/benchmark.js", + "./transformersjs/task-bert.js", + ], + preload: { + transformersJsModule: "./transformersjs/build/transformers.js", + + onnxJsModule: "./transformersjs/build/onnxruntime-web/ort-wasm-simd-threaded.mjs", + onnxWasmBinary: "./transformersjs/build/onnxruntime-web/ort-wasm-simd-threaded.wasm", + + modelWeights: "./transformersjs/build/models/Xenova/distilbert-base-uncased-finetuned-sst-2-english/onnx/model_uint8.onnx", + modelConfig: "./transformersjs/build/models/Xenova/distilbert-base-uncased-finetuned-sst-2-english/config.json", + modelTokenizer: "./transformersjs/build/models/Xenova/distilbert-base-uncased-finetuned-sst-2-english/tokenizer.json", + modelTokenizerConfig: "./transformersjs/build/models/Xenova/distilbert-base-uncased-finetuned-sst-2-english/tokenizer_config.json", + }, + iterations: 30, + tags: ["Default", "Wasm"], + }), + new AsyncBenchmark({ + name: "transformersjs-whisper-wasm", + files: [ + "./polyfills/fast-text-encoding-1.0.3/text.js", + "./transformersjs/benchmark.js", + "./transformersjs/task-whisper.js", + ], + preload: { + transformersJsModule: "./transformersjs/build/transformers.js", + + onnxJsModule: "./transformersjs/build/onnxruntime-web/ort-wasm-simd-threaded.mjs", + onnxWasmBinary: "./transformersjs/build/onnxruntime-web/ort-wasm-simd-threaded.wasm", + + modelEncoderWeights: "./transformersjs/build/models/Xenova/whisper-tiny.en/onnx/encoder_model_quantized.onnx", + modelDecoderWeights: "./transformersjs/build/models/Xenova/whisper-tiny.en/onnx/decoder_model_merged_quantized.onnx", + modelConfig: "./transformersjs/build/models/Xenova/whisper-tiny.en/config.json", + modelTokenizer: "./transformersjs/build/models/Xenova/whisper-tiny.en/tokenizer.json", + modelTokenizerConfig: "./transformersjs/build/models/Xenova/whisper-tiny.en/tokenizer_config.json", + modelPreprocessorConfig: "./transformersjs/build/models/Xenova/whisper-tiny.en/preprocessor_config.json", + modelGenerationConfig: "./transformersjs/build/models/Xenova/whisper-tiny.en/generation_config.json", + + inputFile: "./transformersjs/build/inputs/jfk.raw", + }, + iterations: 5, + worstCaseCount: 1, + tags: ["Default", "Wasm"], + }), new WasmLegacyBenchmark({ name: "tfjs-wasm", files: [ diff --git a/polyfills/fast-text-encoding-1.0.3/text.js b/polyfills/fast-text-encoding-1.0.3/text.js new file mode 100644 index 00000000..2438c769 --- /dev/null +++ b/polyfills/fast-text-encoding-1.0.3/text.js @@ -0,0 +1,286 @@ +/* + * Copyright 2017 Sam Thorogood. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); you may not + * use this file except in compliance with the License. You may obtain a copy of + * the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT + * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the + * License for the specific language governing permissions and limitations under + * the License. + */ + +/** + * @fileoverview Polyfill for TextEncoder and TextDecoder. + * + * You probably want `text.min.js`, and not this file directly. + */ + +//JSC +const global = (0,eval)("this"); + +(function(scope) { +'use strict'; + +// fail early +if (scope['TextEncoder'] && scope['TextDecoder']) { + return false; +} + +// used for FastTextDecoder +const validUtfLabels = ['utf-8', 'utf8', 'unicode-1-1-utf-8']; + +/** + * @constructor + */ +function FastTextEncoder() { + // This does not accept an encoding, and always uses UTF-8: + // https://www.w3.org/TR/encoding/#dom-textencoder +} + +Object.defineProperty(FastTextEncoder.prototype, 'encoding', {value: 'utf-8'}); + +/** + * @param {string} string + * @param {{stream: boolean}=} options + * @return {!Uint8Array} + */ +FastTextEncoder.prototype['encode'] = function(string, options={stream: false}) { + if (options.stream) { + throw new Error(`Failed to encode: the 'stream' option is unsupported.`); + } + + let pos = 0; + const len = string.length; + + let at = 0; // output position + let tlen = Math.max(32, len + (len >>> 1) + 7); // 1.5x size + let target = new Uint8Array((tlen >>> 3) << 3); // ... but at 8 byte offset + + while (pos < len) { + let value = string.charCodeAt(pos++); + if (value >= 0xd800 && value <= 0xdbff) { + // high surrogate + if (pos < len) { + const extra = string.charCodeAt(pos); + if ((extra & 0xfc00) === 0xdc00) { + ++pos; + value = ((value & 0x3ff) << 10) + (extra & 0x3ff) + 0x10000; + } + } + if (value >= 0xd800 && value <= 0xdbff) { + continue; // drop lone surrogate + } + } + + // expand the buffer if we couldn't write 4 bytes + if (at + 4 > target.length) { + tlen += 8; // minimum extra + tlen *= (1.0 + (pos / string.length) * 2); // take 2x the remaining + tlen = (tlen >>> 3) << 3; // 8 byte offset + + const update = new Uint8Array(tlen); + update.set(target); + target = update; + } + + if ((value & 0xffffff80) === 0) { // 1-byte + target[at++] = value; // ASCII + continue; + } else if ((value & 0xfffff800) === 0) { // 2-byte + target[at++] = ((value >>> 6) & 0x1f) | 0xc0; + } else if ((value & 0xffff0000) === 0) { // 3-byte + target[at++] = ((value >>> 12) & 0x0f) | 0xe0; + target[at++] = ((value >>> 6) & 0x3f) | 0x80; + } else if ((value & 0xffe00000) === 0) { // 4-byte + target[at++] = ((value >>> 18) & 0x07) | 0xf0; + target[at++] = ((value >>> 12) & 0x3f) | 0x80; + target[at++] = ((value >>> 6) & 0x3f) | 0x80; + } else { + continue; // out of range + } + + target[at++] = (value & 0x3f) | 0x80; + } + + // Use subarray if slice isn't supported (IE11). This will use more memory + // because the original array still exists. + return target.slice ? target.slice(0, at) : target.subarray(0, at); +} + +/** + * @constructor + * @param {string=} utfLabel + * @param {{fatal: boolean}=} options + */ +function FastTextDecoder(utfLabel='utf-8', options={fatal: false}) { + if (validUtfLabels.indexOf(utfLabel.toLowerCase()) === -1) { + throw new RangeError( + `Failed to construct 'TextDecoder': The encoding label provided ('${utfLabel}') is invalid.`); + } + // if (options.fatal) { + // throw new Error(`Failed to construct 'TextDecoder': the 'fatal' option is unsupported.`); + // } +} + +Object.defineProperty(FastTextDecoder.prototype, 'encoding', {value: 'utf-8'}); + +Object.defineProperty(FastTextDecoder.prototype, 'fatal', {value: false}); + +Object.defineProperty(FastTextDecoder.prototype, 'ignoreBOM', {value: false}); + +/** + * @param {!Uint8Array} bytes + * @return {string} + */ +function decodeBuffer(bytes) { + return Buffer.from(bytes.buffer, bytes.byteOffset, bytes.byteLength).toString('utf-8'); +} + +/** + * @param {!Uint8Array} bytes + * @return {string} + */ +function decodeSyncXHR(bytes) { + const b = new Blob([bytes], {type: 'text/plain;charset=UTF-8'}); + const u = URL.createObjectURL(b); + + // This hack will fail in non-Edgium Edge because sync XHRs are disabled (and + // possibly in other places), so ensure there's a fallback call. + try { + const x = new XMLHttpRequest(); + x.open('GET', u, false); + x.send(); + return x.responseText; + } catch (e) { + return decodeFallback(bytes); + } finally { + URL.revokeObjectURL(u); + } +} + +/** + * @param {!Uint8Array} bytes + * @return {string} + */ +function decodeFallback(bytes) { + let inputIndex = 0; + + // Create a working buffer for UTF-16 code points, but don't generate one + // which is too large for small input sizes. UTF-8 to UCS-16 conversion is + // going to be at most 1:1, if all code points are ASCII. The other extreme + // is 4-byte UTF-8, which results in two UCS-16 points, but this is still 50% + // fewer entries in the output. + const pendingSize = Math.min(256 * 256, bytes.length + 1); + const pending = new Uint16Array(pendingSize); + const chunks = []; + let pendingIndex = 0; + + for (;;) { + const more = inputIndex < bytes.length; + + // If there's no more data or there'd be no room for two UTF-16 values, + // create a chunk. This isn't done at the end by simply slicing the data + // into equal sized chunks as we might hit a surrogate pair. + if (!more || (pendingIndex >= pendingSize - 1)) { + // nb. .apply and friends are *really slow*. Low-hanging fruit is to + // expand this to literally pass pending[0], pending[1], ... etc, but + // the output code expands pretty fast in this case. + chunks.push(String.fromCharCode.apply(null, pending.subarray(0, pendingIndex))); + + if (!more) { + return chunks.join(''); + } + + // Move the buffer forward and create another chunk. + bytes = bytes.subarray(inputIndex); + inputIndex = 0; + pendingIndex = 0; + } + + // The native TextDecoder will generate "REPLACEMENT CHARACTER" where the + // input data is invalid. Here, we blindly parse the data even if it's + // wrong: e.g., if a 3-byte sequence doesn't have two valid continuations. + + const byte1 = bytes[inputIndex++]; + if ((byte1 & 0x80) === 0) { // 1-byte or null + pending[pendingIndex++] = byte1; + } else if ((byte1 & 0xe0) === 0xc0) { // 2-byte + const byte2 = bytes[inputIndex++] & 0x3f; + pending[pendingIndex++] = ((byte1 & 0x1f) << 6) | byte2; + } else if ((byte1 & 0xf0) === 0xe0) { // 3-byte + const byte2 = bytes[inputIndex++] & 0x3f; + const byte3 = bytes[inputIndex++] & 0x3f; + pending[pendingIndex++] = ((byte1 & 0x1f) << 12) | (byte2 << 6) | byte3; + } else if ((byte1 & 0xf8) === 0xf0) { // 4-byte + const byte2 = bytes[inputIndex++] & 0x3f; + const byte3 = bytes[inputIndex++] & 0x3f; + const byte4 = bytes[inputIndex++] & 0x3f; + + // this can be > 0xffff, so possibly generate surrogates + let codepoint = ((byte1 & 0x07) << 0x12) | (byte2 << 0x0c) | (byte3 << 0x06) | byte4; + if (codepoint > 0xffff) { + // codepoint &= ~0x10000; + codepoint -= 0x10000; + pending[pendingIndex++] = (codepoint >>> 10) & 0x3ff | 0xd800; + codepoint = 0xdc00 | codepoint & 0x3ff; + } + pending[pendingIndex++] = codepoint; + } else { + // invalid initial byte + } + } +} + +// Decoding a string is pretty slow, but use alternative options where possible. +let decodeImpl = decodeFallback; +if (typeof Buffer === 'function' && Buffer.from) { + // Buffer.from was added in Node v5.10.0 (2015-11-17). + decodeImpl = decodeBuffer; +} else if (typeof Blob === 'function' && typeof URL === 'function' && typeof URL.createObjectURL === 'function') { + // Blob and URL.createObjectURL are available from IE10, Safari 6, Chrome 19 + // (all released in 2012), Firefox 19 (2013), ... + decodeImpl = decodeSyncXHR; +} + +/** + * @param {(!ArrayBuffer|!ArrayBufferView)} buffer + * @param {{stream: boolean}=} options + * @return {string} + */ +FastTextDecoder.prototype['decode'] = function(buffer, options={stream: false}) { + if (options['stream']) { + throw new Error(`Failed to decode: the 'stream' option is unsupported.`); + } + + if (!buffer) + return; + + let bytes; + + if (buffer instanceof Uint8Array) { + // Accept Uint8Array instances as-is. + bytes = buffer; + } else if (buffer.buffer instanceof ArrayBuffer) { + // Look for ArrayBufferView, which isn't a real type, but basically + // represents all the valid TypedArray types plus DataView. They all have + // ".buffer" as an instance of ArrayBuffer. + bytes = new Uint8Array(buffer.buffer); + } else { + // The only other valid argument here is that "buffer" is an ArrayBuffer. + // We also try to convert anything else passed to a Uint8Array, as this + // catches anything that's array-like. Native code would throw here. + bytes = new Uint8Array(buffer); + } + + return decodeImpl(/** @type {!Uint8Array} */ (bytes)); +} + +scope['TextEncoder'] = FastTextEncoder; +scope['TextDecoder'] = FastTextDecoder; + +}(typeof window !== 'undefined' ? window : (typeof global !== 'undefined' ? global : this))); diff --git a/sqlite3/benchmark.js b/sqlite3/benchmark.js index 9ee176de..c5a36548 100644 --- a/sqlite3/benchmark.js +++ b/sqlite3/benchmark.js @@ -18,7 +18,7 @@ class TextEncoder { return Uint8Array.from(string, (char) => { let byte = char.codePointAt(0); if (byte > 0x7f) - throw new Error("TextEncoder polyfill only supports ASCII"); + throw new Error("TextEncoder polyfill only supports ASCII, got: " + char); return byte; }); } @@ -27,7 +27,7 @@ class TextDecoder { decode(array) { for (let byte of array) { if (byte > 0x7f) - throw new Error("TextDecoder polyfill only supports ASCII"); + throw new Error("TextDecoder polyfill only supports ASCII, got: " + byte); } return String.fromCharCode.apply(null, array); } diff --git a/transformersjs/.gitignore b/transformersjs/.gitignore new file mode 100644 index 00000000..0f47dc19 --- /dev/null +++ b/transformersjs/.gitignore @@ -0,0 +1,2 @@ +/util/node_modules/ +/util/package-lock.json diff --git a/transformersjs/README.md b/transformersjs/README.md new file mode 100644 index 00000000..549db7e9 --- /dev/null +++ b/transformersjs/README.md @@ -0,0 +1,12 @@ +- Two tasks: one text/NLP, one audio processing/speech-to-text. +- Everything in `build/` is generated or an upstream library. +- Everything in `util/` is tooling for building and preparing the benchmark. + +# Licenses + +- Transformers.js: Apache 2.0, https://github.com/huggingface/transformers.js/blob/main/LICENSE +- ONNX runtime: MIT, https://github.com/microsoft/onnxruntime/blob/main/LICENSE +- `text-encoding` Polyfill: Unlicense OR Apache 2.0, https://github.com/inexorabletash/text-encoding/blob/master/LICENSE.md +- Model `DistilBERT base uncased finetuned SST-2`: Apache 2.0, https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english +- Model `openai/whisper-tiny.en`: Apache 2.0, https://huggingface.co/openai/whisper-tiny.en +- Audio file for speech-to-text task: Public domain, https://www.jfklibrary.org/learn/about-jfk/historic-speeches/inaugural-address diff --git a/transformersjs/benchmark.js b/transformersjs/benchmark.js new file mode 100644 index 00000000..2c80a5a8 --- /dev/null +++ b/transformersjs/benchmark.js @@ -0,0 +1,116 @@ +// Copyright 2025 the V8 project authors. All rights reserved. +// Use of this source code is governed by a BSD-style license that can be +// found in the LICENSE file. + +// Polyfills that Transformers.js / the ONNX runtime needs in JavaScript shells. + +class URL { + href; + constructor(url, base) { + // DEBUG + // console.log('URL', url, base); + this.href = url; + } +} +globalThis.URL = URL; + +// Polyfill fetch for shell-compatibility and to cache / preload model weights etc. +let preload = { /* Initialized in init() below due to async. */ }; +const originalFetch = globalThis.fetch ?? function(url) { + throw new Error("no fetch available"); +} +globalThis.fetch = async function(url) { + // DEBUG + // console.log('fetch', url); + + // Redirect some paths to cached/preloaded resources. + if (preload[url]) { + return { + ok: true, + status: 200, + arrayBuffer() { return preload[url]; }, + async blob() { + return { + size: preload[url].byteLength, + async arrayBuffer() { return preload[url]; } + } + }, + }; + } + + // This should only be called in the browser, where fetch() is available. + return originalFetch(url); +}; + +// JetStream benchmark harness. Reuse for two different Transformers.js tasks. +// Assumes `preloadFiles(module)`, `initPipeline(pipelineFromTransformersJs)`, +// and `doTask(initializedPipeline, inputArrayBuffer)` is in the global scope. + +class Benchmark { + transformersJsModule; + wasmBinary; + pipeline; + inputFile; + output; + + async init() { + this.transformersJsModule = await JetStream.dynamicImport(JetStream.preload.transformersJsModule); + this.wasmBinary = await JetStream.getBinary(JetStream.preload.onnxWasmBinary); + + for (const url of Object.values(JetStream.preload)) { + preload[url] = await JetStream.getBinary(url); + } + + if ('inputFile' in JetStream.preload) { + this.inputFile = (await JetStream.getBinary(JetStream.preload.inputFile)).buffer; + // DEBUG + // console.log('inputFile', this.inputFile.byteLength, 'bytes'); + } + } + + async runIteration() { + // Initialize the inference pipeline in the first iteration. + if (!this.pipeline) { + // TODO: Profile startup only: What is taking so much time here? + let { env, pipeline } = this.transformersJsModule; + + env.allowRemoteModels = false; + env.allowLocalModels = true; + env.localModelPath = './transformersjs/build/models/'; + + // Always select the Wasm backend, nothing else. + delete env.backends.onnx.webgl; + delete env.backends.onnx.webgpu; + + // Single-threaded only for now, since we cannot spawn workers in shells. + // TODO: Implement sufficiently powerful workers in shells (or provide + // polyfills). + env.backends.onnx.wasm.numThreads = 1; + + // Do not specify path prefix, because this loads the JSEP build by default. + // TODO: Do we want the JSEP build because it's the default online, or the + // non-asyncified one, since it's the smaller / more performant one? + // env.backends.onnx.wasm.wasmPaths = 'build/onnxruntime-web/'; + // So instead, give the ONNX runtime files directly: + env.backends.onnx.wasm.wasmPaths = { + // The ONNX runtime module is dynamically imported relative to the + // Transformers.js module above, hence strip the prefix. + // With preloading, this is an (absolute) blob URL, so the replace is a nop. + mjs: JetStream.preload.onnxJsModule.replace('./transformersjs/build/', './') + }; + // Give it the wasmBinary directly instead of a path, such that the + // ONNX runtime uses asynchronous (not streaming) Wasm instantiation. + // (To keep the shell and browser results comparable, and streaming + // instantiation is not available in shells.) + env.backends.onnx.wasm.wasmBinary = this.wasmBinary; + + this.pipeline = await initPipeline(pipeline); + } + + this.output = await doTask(this.pipeline, this.inputFile); + } + + validate() { + validate(this.output); + } +} diff --git a/transformersjs/build.log b/transformersjs/build.log new file mode 100644 index 00000000..2aaa93cc --- /dev/null +++ b/transformersjs/build.log @@ -0,0 +1,9 @@ +Built on 2025-08-20T13:30:51Z +Installing Node dependencies... +Download and convert audio input(s)... +Converted 4.25s of audio + from 'jfk.wav', 2 channel(s), 44100 Hz, 16 bit, 176000 samples + to 'build/inputs/jfk.raw', 1 channel(s), 16000 Hz, 32 bit float, 68000 samples, 272000 bytes +Download and run model(s)... +Copy library files into build/... +Building done diff --git a/transformersjs/build.sh b/transformersjs/build.sh new file mode 100755 index 00000000..d6297eaf --- /dev/null +++ b/transformersjs/build.sh @@ -0,0 +1,50 @@ +#!/bin/bash + +set -euo pipefail + +rm -rf build/ +mkdir -p build/{models,inputs,onnxruntime-web}/ + +# Optional: clean all node packages as well. +rm -rf util/node_modules/ + +touch build.log +BUILD_LOG="$(realpath build.log)" +echo "Built on $(date -u '+%Y-%m-%dT%H:%M:%SZ')" | tee "$BUILD_LOG" + +echo "Installing Node dependencies..." | tee -a "$BUILD_LOG" +pushd util/ +npm install +popd + +echo "Download and convert audio input(s)..." | tee -a "$BUILD_LOG" +wget https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav | tee -a "$BUILD_LOG" +# Shorten the audio file to one sentence in the middle, to speed up a single iteration. +node util/convert-audio.mjs jfk.wav build/inputs/jfk.raw 52000 120000 | tee -a "$BUILD_LOG" +rm jfk.wav + +echo "Download and run model(s)..." | tee -a "$BUILD_LOG" +# This automatically places the model files in `build/models/`. +node util/test-models.mjs + +echo "Copy library files into build/..." | tee -a "$BUILD_LOG" + +cp util/node_modules/@huggingface/transformers/dist/transformers.js build/ +git apply transformers.js.patch + +# Transformers.js packages the ONNX runtime JSEP build by default, even when +# only using the Wasm backend, which would be fine with the non-JSEP build. +# JSEP uses ASYNCIFY, which isn't optimal. And it's a much larger Wasm binary. +# cp util/node_modules/@huggingface/transformers/dist/ort-wasm-simd-threaded.jsep.{mjs,wasm} build/ + +# There is also an ONNX runtime build in the onnxruntime-web package. +# TODO(dlehmann): Discuss with upstream Transformers.js folks, whether they can +# use the non-JSEP build if one requests the Wasm backend. +# TODO(dlehmann): Measure performance difference between the two. +cp util/node_modules/onnxruntime-web/dist/ort-wasm-simd-threaded.{mjs,wasm} build/onnxruntime-web/ + +# TODO: Compress model data (and maybe Wasm modules) with zstd. +# Either decompress with native APIs available in browsers or JS/Wasm polyfill? +# E.g., https://github.com/101arrowz/fzstd or https://github.com/fabiospampinato/zstandard-wasm or https://github.com/donmccurdy/zstddec-wasm + +echo "Building done" | tee -a "$BUILD_LOG" diff --git a/transformersjs/build/inputs/jfk.raw b/transformersjs/build/inputs/jfk.raw new file mode 100644 index 00000000..40b22bb5 Binary files /dev/null and b/transformersjs/build/inputs/jfk.raw differ diff --git a/transformersjs/build/models/Xenova/distilbert-base-uncased-finetuned-sst-2-english/config.json b/transformersjs/build/models/Xenova/distilbert-base-uncased-finetuned-sst-2-english/config.json new file mode 100644 index 00000000..3c36342e --- /dev/null +++ b/transformersjs/build/models/Xenova/distilbert-base-uncased-finetuned-sst-2-english/config.json @@ -0,0 +1,33 @@ +{ + "_name_or_path": "distilbert-base-uncased-finetuned-sst-2-english", + "activation": "gelu", + "architectures": [ + "DistilBertForSequenceClassification" + ], + "attention_dropout": 0.1, + "dim": 768, + "dropout": 0.1, + "finetuning_task": "sst-2", + "hidden_dim": 3072, + "id2label": { + "0": "NEGATIVE", + "1": "POSITIVE" + }, + "initializer_range": 0.02, + "label2id": { + "NEGATIVE": 0, + "POSITIVE": 1 + }, + "max_position_embeddings": 512, + "model_type": "distilbert", + "n_heads": 12, + "n_layers": 6, + "output_past": true, + "pad_token_id": 0, 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17515, + "Ġvolcan": 17516, + "ilia": 17517, + "Material": 17518, + "Ġformats": 17519, + "ĠBast": 17520, + "Ġparliamentary": 17521, + "Ġsnake": 17522, + "Ġservants": 17523, + "ĠTrudeau": 17524, + "ĠGrim": 17525, + "ĠArabic": 17526, + "ĠSCP": 17527, + "ĠBoys": 17528, + "station": 17529, + "Ġprospective": 17530, + "orde": 17531, + "initialized": 17532, + "Ġbored": 17533, + "ABLE": 17534, + "Ġaccessed": 17535, + "Ġtaxi": 17536, + "ĠShell": 17537, + "aiden": 17538, + "ursed": 17539, + "inates": 17540, + "ĠInsurance": 17541, + "ĠPete": 17542, + "September": 17543, + "650": 17544, + "Ġadventures": 17545, + "ĠCover": 17546, + "Ġtribute": 17547, + "Ġsketch": 17548, + "Ġempower": 17549, + "ĠØ": 17550, + "ĠGlenn": 17551, + "ĠDaw": 17552, + "=\\\"": 17553, + "ĠPolitics": 17554, + "Ġguides": 17555, + "Ġdioxide": 17556, + "ĠGore": 17557, + "ĠBright": 17558, + "ĠSierra": 17559, + "Ġvalued": 17560, + "cond": 17561, + "Ġpointer": 17562, + "Select": 17563, + "Ġrisky": 17564, + "Ġabsorb": 17565, + "images": 17566, + "Ġrefuses": 17567, + "Ġbonuses": 17568, + "___": 17569, + "Ġhilar": 17570, + "ĠFeatures": 17571, + "220": 17572, + "ĠCollector": 17573, + "Foot": 17574, + "Ġ1964": 17575, + "culus": 17576, + "Ġdawn": 17577, + "Ġworkout": 17578, + "ĠLO": 17579, + "Ġphilosophical": 17580, + "ĠSandy": 17581, + "ĠYouth": 17582, + "Ġliable": 17583, + "Af": 17584, + "blue": 17585, + "Ġoverturn": 17586, + "lessness": 17587, + "ĠTribune": 17588, + "ĠIng": 17589, + "Ġfactories": 17590, + "Ġcatches": 17591, + "Ġprone": 17592, + "Ġmatrix": 17593, + "Ġlogin": 17594, + "Ġinacc": 17595, + "Ġexert": 17596, + "sys": 17597, + "Ġneedle": 17598, + "ĠQur": 17599, + "Ġnotified": 17600, + "oulder": 17601, + "tx": 17602, + "Ġreminds": 17603, + "Ġpublishers": 17604, + "Ġnort": 17605, + "Ġgit": 17606, + "Ġflies": 17607, + "ĠEmily": 17608, + "Ġflowing": 17609, + "ĠAlien": 17610, + "ĠStrateg": 17611, + "Ġhardest": 17612, + "Ġmodification": 17613, + "API": 17614, + "ĠMY": 17615, + "Ġcrashes": 17616, + "stairs": 17617, + "number": 17618, + "Ġurging": 17619, + "channel": 17620, + "ĠFalcon": 17621, + "Ġinhabitants": 17622, + "Ġterrifying": 17623, + "Ġutilize": 17624, + "Ġbanner": 17625, + "Ġcigarettes": 17626, + "Ġsenses": 17627, + "ĠHolmes": 17628, + "Ġpractition": 17629, + "ĠPhillips": 17630, + "otto": 17631, + "Ġcompile": 17632, + "Model": 17633, + "ĠKo": 17634, + "Ġ[]": 17635, + "Americans": 17636, + "ĠTerms": 17637, + "Ġmedications": 17638, + "ĠAna": 17639, + "Ġfundamentally": 17640, + "ĠNotice": 17641, + "Ġweaker": 17642, + "Ġ0000": 17643, + "Ġgarlic": 17644, + "Ġoutbreak": 17645, + "Ġeconomist": 17646, + "ĠBirth": 17647, + "Ġobstacles": 17648, + "arcer": 17649, + "ĠOrthodox": 17650, + "Ġplacebo": 17651, + "ĠCrew": 17652, + "aspberry": 17653, + "ĠAngels": 17654, + "Ġdischarge": 17655, + "Ġdestructive": 17656, + "117": 17657, + "ĠRising": 17658, + "Ġdairy": 17659, + "late": 17660, + "Ġcollision": 17661, + "ĠTigers": 17662, + "eanor": 17663, + "ocumented": 17664, + "ĠInvalid": 17665, + 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17715, + "rak": 17716, + "Ġliv": 17717, + "ĠRegion": 17718, + "Ġbriefing": 17719, + "sers": 17720, + "cloud": 17721, + "ĠMik": 17722, + "usp": 17723, + "Ġprediction": 17724, + "azor": 17725, + "Ġportable": 17726, + "ĠGand": 17727, + "Ġpresenting": 17728, + "Ġ1080": 17729, + "»": 17730, + "ushi": 17731, + "ĠSpark": 17732, + "thereum": 17733, + "Ġjustification": 17734, + "ĠNy": 17735, + "Ġcontractors": 17736, + "mingham": 17737, + "ĠStyle": 17738, + "åħ": 17739, + "ĠChronicles": 17740, + "ĠPicture": 17741, + "Ġproving": 17742, + "Ġwives": 17743, + "sett": 17744, + "Ġmolecules": 17745, + "ĠFairy": 17746, + "Ġconsisting": 17747, + "Ġpier": 17748, + "alone": 17749, + "inition": 17750, + "Ġnucle": 17751, + "json": 17752, + "Ġgotta": 17753, + "Ġmobil": 17754, + "Ġverbal": 17755, + "arium": 17756, + "Ġmonument": 17757, + "ucked": 17758, + "Ġ256": 17759, + "Tech": 17760, + "minecraft": 17761, + "ĠTrack": 17762, + "Ġtile": 17763, + "Ġcompatibility": 17764, + "asis": 17765, + "Ġsadd": 17766, + "Ġinstructed": 17767, + "ĠMueller": 17768, + "Ġlethal": 17769, + "Ġhormone": 17770, + "Ġorche": 17771, + "else": 17772, + "Ġskelet": 17773, + "Ġentertaining": 17774, + "Ġminimize": 17775, + "again": 17776, + "Ġundergo": 17777, + "Ġconstraints": 17778, + "Ġcigarette": 17779, + "ĠIslamist": 17780, + "Ġtravels": 17781, + "ĠPanthers": 17782, + "lings": 17783, + "Care": 17784, + "Ġlawsuits": 17785, + "uras": 17786, + "Ġcryst": 17787, + "Ġlowered": 17788, + "Ġaerial": 17789, + "Ġcombinations": 17790, + "Ġhaun": 17791, + "Ġcha": 17792, + "Ġvine": 17793, + "Ġquantities": 17794, + "Ġlinking": 17795, + "bank": 17796, + "Ġsoy": 17797, + "Bill": 17798, + "ĠAngela": 17799, + "Ġrecipient": 17800, + "ĠProtest": 17801, + "Ġsocket": 17802, + "Ġsolidarity": 17803, + "ĠâĨ": 17804, + "mill": 17805, + "Ġvaries": 17806, + "ĠPakistani": 17807, + "Dragon": 17808, + "Ġune": 17809, + "Ġhorizon": 17810, + "³³³³³³³³": 17811, + "Ġprovinces": 17812, + "Ġfrankly": 17813, + "Ġenacted": 17814, + "notes": 17815, + "['": 17816, + "Ġ192": 17817, + "ocracy": 17818, + "Ġendorsement": 17819, + "Ġovertime": 17820, + "True": 17821, + "Lab": 17822, + "licted": 17823, + "ĠDNC": 17824, + "Ġbeats": 17825, + "ĠJamie": 17826, + "152": 17827, + "ĠINT": 17828, + "Contact": 17829, + "Ġaccounted": 17830, + "hash": 17831, + "ĠPackers": 17832, + "pires": 17833, + "Ġlesbian": 17834, + "Ġamendments": 17835, + "Ġhopeful": 17836, + "ĠFinland": 17837, + "Ġspotlight": 17838, + "Ġconfigured": 17839, + "Ġtroubled": 17840, + "Ġgaze": 17841, + "ĠCalgary": 17842, + "Ġreliability": 17843, + "Ġinsurg": 17844, + "swer": 17845, + "buy": 17846, + "ĠSkin": 17847, + "Ġpixels": 17848, + "Ġhandgun": 17849, + "Ġparas": 17850, + "Ġcategor": 17851, + "ĠEL": 17852, + "ĠRex": 17853, + "Indeed": 17854, + "Ġkinda": 17855, + "Ġconjunction": 17856, + "ĠBryan": 17857, + "ĠManufact": 17858, + "yang": 17859, + "Plus": 17860, + "SQL": 17861, + "ishment": 17862, + "Ġdominate": 17863, + "Ġnail": 17864, + "Ġoath": 17865, + "Ġerupt": 17866, + "ĠFine": 17867, + "itbart": 17868, + "ĠChip": 17869, + "ĠAbd": 17870, + "ĠNam": 17871, + "Ġbuyer": 17872, + "Ġdissent": 17873, + "Leaks": 17874, + "Contin": 17875, + "Ġrider": 17876, + "ĠSomeone": 17877, + "Ġillusion": 17878, + "cin": 17879, + "ĠBoeing": 17880, + "Ġinadequ": 17881, + "ovation": 17882, + "iants": 17883, + "Ġrebuild": 17884, + "450": 17885, + "ĠDestiny": 17886, + "SW": 17887, + "ĠTill": 17888, + "Hit": 17889, + "iaz": 17890, + "ĠBangl": 17891, + "achers": 17892, + "ĠReform": 17893, + "Ġsegments": 17894, + "Ġsystematic": 17895, + "dc": 17896, + "ĠConservatives": 17897, + "Ġportal": 17898, + "hor": 17899, + "ĠDragonbound": 17900, + "Ġdragged": 17901, + "omo": 17902, + "Ġthee": 17903, + "advert": 17904, + "ĠReports": 17905, + "ĠEt": 17906, + "Ġbarrels": 17907, + "August": 17908, + "Ġcomparisons": 17909, + "Ġhex": 17910, + "Ġanthrop": 17911, + "\"[": 17912, + "borough": 17913, + "abi": 17914, + "Ġpictured": 17915, + "playing": 17916, + "ĠAddress": 17917, + "ĠMirror": 17918, + "Smith": 17919, + "Ġtires": 17920, + "ĠNPR": 17921, + "AAAA": 17922, + "Ġclassification": 17923, + "ĠThan": 17924, + "ĠHarm": 17925, + "ĠRA": 17926, + "Ġrejection": 17927, + "mination": 17928, + "Ġranged": 17929, + "ĠFalls": 17930, + "DI": 17931, + "Host": 17932, + "ãĤ´": 17933, + "ĠExample": 17934, + "listed": 17935, + "thirds": 17936, + "Ġsafegu": 17937, + "brand": 17938, + "Ġprobable": 17939, + "Canada": 17940, + "ITION": 17941, + "ĠQaeda": 17942, + "Ġchick": 17943, + "Ġimports": 17944, + "hit": 17945, + "loc": 17946, + "WW": 17947, + "Ġblew": 17948, + "Ġanytime": 17949, + "Ġwholes": 17950, + "iked": 17951, + "Ġcalculation": 17952, + "create": 17953, + "ĠOri": 17954, + "Ġupgraded": 17955, + "Ġappar": 17956, + "utory": 17957, + "ĠMol": 17958, + "Brit": 17959, + "ĠJong": 17960, + "INAL": 17961, + "ĠStarting": 17962, + "Ġdice": 17963, + "urtle": 17964, + "Ġrelying": 17965, + "closure": 17966, + "Ġprofitable": 17967, + "Ġslaughter": 17968, + "ĠManual": 17969, + "caster": 17970, + "Ġ\"$": 17971, + "Ġfeather": 17972, + "ĠSimply": 17973, + "ieves": 17974, + "Ġdeterior": 17975, + "ĠPCI": 17976, + "Ġstamp": 17977, + "Ġflaws": 17978, + "Ġshade": 17979, + "hammer": 17980, + "Ġpassport": 17981, + "Ġconting": 17982, + "amel": 17983, + "Ġobservers": 17984, + "Ġneglect": 17985, + "ĠRB": 17986, + "ĠBrotherhood": 17987, + "Ġskeptical": 17988, + "family": 17989, + "usk": 17990, + "Ġemotionally": 17991, + "âĻ": 17992, + "ĠBeta": 17993, + "asonable": 17994, + "idity": 17995, + "ĠMul": 17996, + "Ġkicking": 17997, + "ĠCarm": 17998, + "ollah": 17999, + "VERTIS": 18000, + "ĠAthen": 18001, + "Ġladder": 18002, + "ĠBullet": 18003, + "å£": 18004, + "0001": 18005, + "ĠWildlife": 18006, + "ĠMask": 18007, + "ĠNan": 18008, + "Rev": 18009, + "Ġunacceptable": 18010, + "legal": 18011, + "Ġcrowded": 18012, + "agi": 18013, + "ĠCox": 18014, + "je": 18015, + "Ġmorality": 18016, + "Ġfuels": 18017, + "Ġcables": 18018, + "Ġmankind": 18019, + "ĠCaribbean": 18020, + "Ġanchor": 18021, + "Ġbyte": 18022, + "ĠOften": 18023, + "ĠOz": 18024, + "Ġcrafted": 18025, + "Ġhistorian": 18026, + "ĠWu": 18027, + "Ġtowers": 18028, + "ĠCitizens": 18029, + "Ġhelm": 18030, + "Ġcredentials": 18031, + "Ġsingular": 18032, + "ĠJesse": 18033, + "Ġtackles": 18034, + "Ġcontempt": 18035, + "Ġafore": 18036, + "ĠShadows": 18037, + "Ġnil": 18038, + "Ġurgent": 18039, + "apple": 18040, + "blood": 18041, + "Ġvon": 18042, + "Ġoffline": 18043, + "Ġbreathe": 18044, + "Ġjumps": 18045, + "Ġirrelevant": 18046, + "oxic": 18047, + "omal": 18048, + "important": 18049, + "Jim": 18050, + "Ġgloves": 18051, + "arming": 18052, + "depth": 18053, + "Ġtalents": 18054, + "ookie": 18055, + "ĠSB": 18056, + "Ġpalm": 18057, + "uffs": 18058, + "esta": 18059, + "IGH": 18060, + "Ġcanon": 18061, + "ĠVerizon": 18062, + "ĠPle": 18063, + "Ġcoupled": 18064, + "velt": 18065, + "Ġfundraising": 18066, + "ĠGetting": 18067, + "ĠDLC": 18068, + "Ġmathematical": 18069, + "ĠHS": 18070, + "ĠCardinals": 18071, + "telling": 18072, + "Ġsponsors": 18073, + "ĠÏ": 18074, + "ĠBulls": 18075, + "option": 18076, + "Ġpropose": 18077, + "Ġmemorable": 18078, + "Ġembraced": 18079, + "Ġdeclining": 18080, + "Health": 18081, + "eda": 18082, + "Ġ};": 18083, + "Ġspam": 18084, + "mile": 18085, + "Ġpitcher": 18086, + "ĠEight": 18087, + "Ġcaring": 18088, + "utic": 18089, + "role": 18090, + "Ġairline": 18091, + "ernandez": 18092, + "ĠAthlet": 18093, + "Ġcertification": 18094, + "uxe": 18095, + "riger": 18096, + "Ġempir": 18097, + "Ġsensation": 18098, + "Ġdism": 18099, + "Ġbolt": 18100, + "Ġevolve": 18101, + "House": 18102, + "Ġconsultation": 18103, + "ĠDuty": 18104, + "Ġtouches": 18105, + "ĠNathan": 18106, + "Ġfaint": 18107, + "had": 18108, + "\"(": 18109, + "ĠConsumer": 18110, + "ĠExtreme": 18111, + "Ġ127": 18112, + "ĠHerm": 18113, + "ĠSacrament": 18114, + "izoph": 18115, + "Ġanxious": 18116, + "ulously": 18117, + "Ġsocially": 18118, + "ĠUTC": 18119, + "Ġsolving": 18120, + "ĠLetter": 18121, + "History": 18122, + "educ": 18123, + "Price": 18124, + 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+ "Ġsuppress": 18175, + "Ġconcealed": 18176, + "Ġhappily": 18177, + "Ġaccepts": 18178, + "ĠEnjoy": 18179, + "Ġrivers": 18180, + "ĠExcept": 18181, + "225": 18182, + "ĠNHS": 18183, + "ĠMcConnell": 18184, + "Ġpussy": 18185, + "ferred": 18186, + "utable": 18187, + "Ġattain": 18188, + "Ġ>=": 18189, + "Ġdeposits": 18190, + "rophic": 18191, + "Ġnotorious": 18192, + "ĠShaw": 18193, + "ilitation": 18194, + "Ġepidemic": 18195, + "allic": 18196, + "Ġsmallest": 18197, + "ovich": 18198, + "Ġaccessories": 18199, + "perties": 18200, + "Ġsurplus": 18201, + "ĠMech": 18202, + "Ġambig": 18203, + "ĠImmigration": 18204, + "Ġchim": 18205, + "eval": 18206, + "Ġpracticing": 18207, + "ĠMystery": 18208, + "Ġdomains": 18209, + "ĠSilicon": 18210, + "apps": 18211, + "Ġkilometers": 18212, + "ea": 18213, + "ĠSmash": 18214, + "Ġwarranty": 18215, + "Ġnost": 18216, + "sil": 18217, + "rev": 18218, + "Jon": 18219, + "ĠDublin": 18220, + "Ġtastes": 18221, + "Ġbout": 18222, + "great": 18223, + "error": 18224, + "Ġswitches": 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18584, + "Ġamateur": 18585, + "Ġdorm": 18586, + "Ġconsuming": 18587, + "Ġarmour": 18588, + "ĠRoose": 18589, + "Ġintensive": 18590, + "Ġeliminating": 18591, + "ĠSunni": 18592, + "ĠAleppo": 18593, + "jin": 18594, + "Ġadvise": 18595, + "pal": 18596, + "ĠHalo": 18597, + "Ġdescent": 18598, + "Ġsimpler": 18599, + "Ġbooth": 18600, + "STR": 18601, + "Later": 18602, + "ĠCave": 18603, + "===": 18604, + "Ġmol": 18605, + "Ġfist": 18606, + "Ġshotgun": 18607, + "supp": 18608, + "Ġrobbery": 18609, + "Effect": 18610, + "Ġobscure": 18611, + "ĠProfessional": 18612, + "Ġembassy": 18613, + "Ġmilitant": 18614, + "Ġincarcer": 18615, + "Ġgenerates": 18616, + "Ġlaunches": 18617, + "Ġadministrators": 18618, + "Ġshaft": 18619, + "Ġcircular": 18620, + "Ġfreshman": 18621, + "ĠWes": 18622, + "ĠJoel": 18623, + "ĠDrew": 18624, + "ĠDuncan": 18625, + "ĠApparently": 18626, + "sight": 18627, + "ĠInternal": 18628, + "ĠIndividual": 18629, + "ĠFE": 18630, + "Ġbore": 18631, + "ĠMt": 18632, + "Ġbroadly": 18633, + "ĠOptions": 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"Ġdeclaring": 18684, + "Ġanonymity": 18685, + "Ġjuven": 18686, + "ĠNord": 18687, + "ĠDoom": 18688, + "ĠActually": 18689, + "Ok": 18690, + "phis": 18691, + "ĠDesert": 18692, + "Ġ116": 18693, + "IK": 18694, + "ĠFM": 18695, + "Ġincomes": 18696, + "VEL": 18697, + "okers": 18698, + "Ġpecul": 18699, + "Ġlightweight": 18700, + "gue": 18701, + "Ġaccent": 18702, + "Ġincrement": 18703, + "ĠChan": 18704, + "Ġcomplaining": 18705, + "ĠBaghd": 18706, + "Ġmidfielder": 18707, + "Ġoverhaul": 18708, + "Process": 18709, + "ĠHollow": 18710, + "ĠTitans": 18711, + "Small": 18712, + "manuel": 18713, + "ĠUnity": 18714, + "ĠEvents": 18715, + "Sty": 18716, + "Ġdisproportion": 18717, + "nesty": 18718, + "enes": 18719, + "ĠCod": 18720, + "Ġdemonstrations": 18721, + "ĠCrimson": 18722, + "ĠOH": 18723, + "Ġenrolled": 18724, + "Ġcel": 18725, + "ĠBrett": 18726, + "Ġaide": 18727, + "Ġheels": 18728, + "Ġbroadband": 18729, + "Ġmarking": 18730, + "Ġwizard": 18731, + "ĠNJ": 18732, + "ĠChiefs": 18733, + "Ġingredient": 18734, + "Ġdug": 18735, + "ĠShut": 18736, + "urchase": 18737, + "endor": 18738, + "Ġfarmer": 18739, + "ĠGoldman": 18740, + "129": 18741, + "155": 18742, + "Order": 18743, + "Ġlion": 18744, + "iably": 18745, + "Ġstain": 18746, + "array": 18747, + "ilitary": 18748, + "ĠFAQ": 18749, + "Ġexploded": 18750, + "ĠMcCarthy": 18751, + "ĠTweet": 18752, + "ĠGreens": 18753, + "eking": 18754, + "ln": 18755, + "ensen": 18756, + "Ġmotorcycle": 18757, + "Ġparticle": 18758, + "Ġcholesterol": 18759, + "Bron": 18760, + "Ġstair": 18761, + "Ġoxid": 18762, + "Ġdesirable": 18763, + "ibles": 18764, + "Ġtheor": 18765, + "forcing": 18766, + "Ġpromotional": 18767, + "ovo": 18768, + "boot": 18769, + "ĠBonus": 18770, + "rawling": 18771, + "Ġshortage": 18772, + "ĠPsy": 18773, + "Ġrecruited": 18774, + "Ġinfants": 18775, + "Ġtestosterone": 18776, + "Ġdeduct": 18777, + "Ġdistinctive": 18778, + "Ġfirmware": 18779, + "built": 18780, + "145": 18781, + "Ġexplored": 18782, + "Ġfactions": 18783, + "Ġvide": 18784, + "Ġtattoo": 18785, + "Ġfinancially": 18786, + "Ġfatigue": 18787, + "Ġproceeding": 18788, + "constitutional": 18789, + "Ġmiser": 18790, + "Ġchairs": 18791, + "gging": 18792, + "ipple": 18793, + "Ġdent": 18794, + "Ġdisreg": 18795, + "çĶ": 18796, + "stant": 18797, + "llo": 18798, + "bps": 18799, + "akening": 18800, + "Ġabnormal": 18801, + "ĠERA": 18802, + "士": 18803, + "ĠHBO": 18804, + "ĠMAR": 18805, + "Ġconcess": 18806, + "Ġservant": 18807, + "Ġaspir": 18808, + "lav": 18809, + "ĠPanel": 18810, + "amo": 18811, + "Ġprecip": 18812, + "Ġrecordings": 18813, + "Ġproceeded": 18814, + "Ġcolony": 18815, + "ĠTang": 18816, + "ablo": 18817, + "Ġstripped": 18818, + "Left": 18819, + "too": 18820, + "Ġpotatoes": 18821, + "Ġfinest": 18822, + "%).": 18823, + "Ġcrap": 18824, + "ĠZach": 18825, + "abases": 18826, + "ĠGoth": 18827, + "Ġbillionaire": 18828, + "wolf": 18829, + "Ġsanction": 18830, + "SK": 18831, + "Ġlogged": 18832, + "Po": 18833, + "eyed": 18834, + "unal": 18835, + "Ġcricket": 18836, + "Ġarmies": 18837, + "Ġuncovered": 18838, + "Cloud": 18839, + "ón": 18840, + "Ġrebounds": 18841, + "Ġmes": 18842, + "Oper": 18843, + "Pac": 18844, + "Ġnationally": 18845, + "Ġinserted": 18846, + "pict": 18847, + "Ġgovernance": 18848, + "и": 18849, + "Ġprivileges": 18850, + "GET": 18851, + "Ġfavorites": 18852, + "imity": 18853, + "Ġlover": 18854, + "them": 18855, + "empl": 18856, + "Ġgorgeous": 18857, + "Ann": 18858, + "Ġslipped": 18859, + "Ġveto": 18860, + "Bob": 18861, + "Ġslim": 18862, + "ucc": 18863, + "ĠFame": 18864, + "uddenly": 18865, + "Ġdenies": 18866, + "ĠMaur": 18867, + "Ġdistances": 18868, + "Ġwanna": 18869, + "tar": 18870, + "ĠSER": 18871, + "ĠâĪ": 18872, + "Ġlemon": 18873, + "athetic": 18874, + "Ġliteral": 18875, + "Ġdistinguished": 18876, + "Ġanswering": 18877, + "GI": 18878, + "Ġreligions": 18879, + "ĠPhilos": 18880, + "ĠLay": 18881, + "Ġcompos": 18882, + "irements": 18883, + "ĠKos": 18884, + "inez": 18885, + "rolling": 18886, + "Ġyoungest": 18887, + "andise": 18888, + 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"ĠHezbollah": 22871, + "category": 22872, + "cyclop": 22873, + "xton": 22874, + "ĠDot": 22875, + "urdy": 22876, + "tz": 22877, + "Ġenvelope": 22878, + "ĠNL": 22879, + "âķ": 22880, + "Ġwherein": 22881, + "Spec": 22882, + "184": 22883, + "Ġtelev": 22884, + "aliation": 22885, + "Ġmyths": 22886, + "å°": 22887, + "Ġrigorous": 22888, + "Ġcommunicating": 22889, + "Ġobserver": 22890, + "Ġrehe": 22891, + "ĠWash": 22892, + "Ġapologized": 22893, + "ĠTin": 22894, + "Ġexpenditures": 22895, + "workers": 22896, + "document": 22897, + "Ġhesitate": 22898, + "ĠLenin": 22899, + "Ġunpredictable": 22900, + "Ġrenewal": 22901, + "cler": 22902, + "okia": 22903, + "ĠCONT": 22904, + "Ġpostseason": 22905, + "Tokens": 22906, + "Ġexacerb": 22907, + "Ġbetting": 22908, + "Ġ147": 22909, + "Ġelevation": 22910, + "Wood": 22911, + "ĠSolomon": 22912, + "194": 22913, + "004": 22914, + "output": 22915, + "Ġredund": 22916, + "ĠMumbai": 22917, + "ĠpH": 22918, + "Ġreproduce": 22919, + "ĠDuration": 22920, + "MAX": 22921, + 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22972, + "Param": 22973, + "Ġlearns": 22974, + "Magic": 22975, + "Ġdetailing": 22976, + "moon": 22977, + "Ġbroadcasting": 22978, + "Ġbaked": 22979, + "265": 22980, + "holm": 22981, + "ĠSah": 22982, + "ĠHussein": 22983, + "ĠCourtesy": 22984, + "174": 22985, + "Ġ146": 22986, + "Ġgeographic": 22987, + "peace": 22988, + "Ġjudging": 22989, + "ĠStern": 22990, + "Bur": 22991, + "Ġstoryline": 22992, + "Gun": 22993, + "ĠStick": 22994, + "245": 22995, + "307": 22996, + "ãĤ´ãĥ³": 22997, + "ĠAdministrator": 22998, + "Ġburnt": 22999, + "Ġpave": 23000, + "choes": 23001, + "Exec": 23002, + "Ġcampuses": 23003, + "Result": 23004, + "Ġmutations": 23005, + "ĠCharter": 23006, + "Ġcaptures": 23007, + "Ġcompares": 23008, + "Ġbadge": 23009, + "Scient": 23010, + "Ġerad": 23011, + "iery": 23012, + "oi": 23013, + "ettes": 23014, + "ĠEstate": 23015, + "Ġstrap": 23016, + "Ġproudly": 23017, + "Ġfried": 23018, + "Ġwithdrawn": 23019, + "ĠVoy": 23020, + "phony": 23021, + "Items": 23022, + "ĠPierce": 23023, + "bard": 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"Ġoutrageous": 23077, + "ĠValentine": 23078, + "luence": 23079, + "ĠEverybody": 23080, + "Altern": 23081, + "Ġrelevance": 23082, + "Ġterminated": 23083, + "Ġdessert": 23084, + "Ġfulfilled": 23085, + "Ġprosecuted": 23086, + "ĠWords": 23087, + "Ġmigrant": 23088, + "Ġcultivation": 23089, + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ": 23090, + "idelity": 23091, + "ĠVern": 23092, + "ĠLogin": 23093, + "Ġmetaphor": 23094, + "ĠTip": 23095, + "Ġrecruits": 23096, + "ĠPig": 23097, + "ribing": 23098, + "Ġenthusiasts": 23099, + "exper": 23100, + "Ġfrightening": 23101, + "ĠHair": 23102, + "anson": 23103, + "strate": 23104, + "Ġhi": 23105, + "Height": 23106, + "Ġowning": 23107, + "none": 23108, + "Ġdislike": 23109, + "Ġknives": 23110, + "pherd": 23111, + "Ġloudly": 23112, + "ĠAPIs": 23113, + "Display": 23114, + "ĠLac": 23115, + "ĠUSS": 23116, + "abl": 23117, + "verages": 23118, + "Jew": 23119, + "Ġ172": 23120, + "ĠHistorical": 23121, + "atoon": 23122, + "ĠPhysics": 23123, + "intern": 23124, + "Ġwarmth": 23125, + "Ġtopp": 23126, + "DM": 23127, + "Ġgunman": 23128, + "Ġemperor": 23129, + "odi": 23130, + "ãĥ£": 23131, + "inatory": 23132, + "ĠRib": 23133, + "Ġ131": 23134, + "ĠSaturn": 23135, + "ĠShining": 23136, + "Ġwaking": 23137, + "Quotes": 23138, + "Ġcomedian": 23139, + "enberg": 23140, + "½": 23141, + "Ġbelievers": 23142, + "Ġpaperwork": 23143, + "custom": 23144, + "Ġlev": 23145, + "Ġlament": 23146, + "Ġpouring": 23147, + "222": 23148, + "political": 23149, + "ĠSupplement": 23150, + "maid": 23151, + "Ġcruelty": 23152, + "Ġtread": 23153, + "ysics": 23154, + "Aw": 23155, + "rites": 23156, + "Ġmodifier": 23157, + "ĠPosition": 23158, + "Adam": 23159, + "lb": 23160, + "ubs": 23161, + "Ġimperfect": 23162, + "Ġclusters": 23163, + "ĠEngineer": 23164, + "ĠCherry": 23165, + "Ġinauguration": 23166, + "ĠSau": 23167, + "Ġembodiment": 23168, + "ĠUncle": 23169, + "Ġoverr": 23170, + "Ġexplosions": 23171, + "cule": 23172, + "ĠPrinceton": 23173, + "ĠAndrea": 23174, + "Ġincorrectly": 23175, + "Ġearnest": 23176, + "Ġpilgr": 23177, + "ĠSprint": 23178, + "Ġsleeve": 23179, + "Ġhears": 23180, + "ĠAmazing": 23181, + "Ġbrowsing": 23182, + "agin": 23183, + "Ġhomeland": 23184, + "Ġhaw": 23185, + "Ġdiving": 23186, + "istered": 23187, + "178": 23188, + "Ġbargaining": 23189, + "ĠArcade": 23190, + "Ġdelegate": 23191, + "terson": 23192, + "................................................................": 23193, + "ĠJacksonville": 23194, + "275": 23195, + "Ġstagn": 23196, + "Ġadam": 23197, + "ĠSherman": 23198, + "CB": 23199, + "Ġsuburb": 23200, + "ĠFoods": 23201, + "Ġconverting": 23202, + "ĠArist": 23203, + "Ġchambers": 23204, + "love": 23205, + "Ġamino": 23206, + "ĠGan": 23207, + "Ġmadness": 23208, + "mc": 23209, + "ĠUSE": 23210, + "defined": 23211, + "Ġultr": 23212, + "indust": 23213, + "Ġwolves": 23214, + "lance": 23215, + "Additionally": 23216, + "Ġcracks": 23217, + "asia": 23218, + "ĠReason": 23219, + "ĠPump": 23220, + "Ġaccidental": 23221, + "ĠLaser": 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23429, + "Ġdos": 23430, + "ĠWhole": 23431, + "ĠReady": 23432, + "Try": 23433, + "ĠWinnipeg": 23434, + "earance": 23435, + "Ġincurred": 23436, + "renched": 23437, + "ĠNSW": 23438, + "ilot": 23439, + "raine": 23440, + "Ġcube": 23441, + "got": 23442, + "Ġrunway": 23443, + "etermined": 23444, + "ĠHawks": 23445, + "Ġsurvivor": 23446, + "ĠWish": 23447, + "ĠDin": 23448, + "ĠDEF": 23449, + "ĠVault": 23450, + "187": 23451, + "Ġmushrooms": 23452, + "Ġcrisp": 23453, + "bey": 23454, + "ĠDiscovery": 23455, + "Ġdevelopmental": 23456, + "Ġparadigm": 23457, + "Ġchaotic": 23458, + "ĠTsu": 23459, + "Ġ333": 23460, + "bons": 23461, + "Ġbacterial": 23462, + "Ġcommits": 23463, + "Ġcosmic": 23464, + "Ġmega": 23465, + "ocative": 23466, + "ĠPaint": 23467, + "ophobic": 23468, + "Ġvain": 23469, + "Ġcarved": 23470, + "ĠThief": 23471, + "ĠGul": 23472, + "owship": 23473, + "Ġcites": 23474, + "ĠEdinburgh": 23475, + "Ġdiminished": 23476, + "Ġacknowledges": 23477, + "ĠKills": 23478, + "Ġmicrow": 23479, + "ĠHera": 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"Ġdebugging": 28769, + "Ġgraphs": 28770, + "399": 28771, + "ĠNigel": 28772, + "Ġbun": 28773, + "Ġpillow": 28774, + "Ġprogressed": 28775, + "fashioned": 28776, + "Ġobedience": 28777, + "ERN": 28778, + "Ġrehears": 28779, + "Cell": 28780, + "tl": 28781, + "Sher": 28782, + "Ġherald": 28783, + "ĠPayment": 28784, + "ĠCory": 28785, + "ĠDept": 28786, + "Ġrepent": 28787, + "ĠWeak": 28788, + "uckland": 28789, + "Ġpleasing": 28790, + "Ġshortages": 28791, + "Ġjurors": 28792, + "ĠKab": 28793, + "qqa": 28794, + "Anti": 28795, + "Ġwow": 28796, + "ĠRCMP": 28797, + "Ġtsun": 28798, + "ĠSic": 28799, + "Ġcomprises": 28800, + "Ġspies": 28801, + "Ġprecinct": 28802, + "nu": 28803, + "Ġurges": 28804, + "Ġtimed": 28805, + "Ġstripes": 28806, + "ĠBoots": 28807, + "Ġyen": 28808, + "Advanced": 28809, + "Ġdiscrete": 28810, + "ĠArchangel": 28811, + "employment": 28812, + "Diff": 28813, + "Ġmonuments": 28814, + "Ġ209": 28815, + "worker": 28816, + "Ġ196": 28817, + "ĠIg": 28818, + "utterstock": 28819, + "TPS": 28820, + "Jac": 28821, + "Ġhomelessness": 28822, + "Ġcommentator": 28823, + "Ġracially": 28824, + "fing": 28825, + "seed": 28826, + "Ele": 28827, + "ellation": 28828, + "Ġethanol": 28829, + "Ġparish": 28830, + "ĠDong": 28831, + "ĠAwakening": 28832, + "Ġdeviation": 28833, + "ĠBearing": 28834, + "ĠTsuk": 28835, + "Ġrecess": 28836, + "Ġlymph": 28837, + "ĠCannabis": 28838, + "åľ": 28839, + "ĠNEWS": 28840, + "Ġdra": 28841, + "ĠStefan": 28842, + "ĠWrong": 28843, + "ĠSAM": 28844, + "Ġloosely": 28845, + "Ġinterpreter": 28846, + "ĠPlain": 28847, + "Government": 28848, + "Ġbigotry": 28849, + "Ġgrenades": 28850, + "avez": 28851, + "pictured": 28852, + "Ġmandated": 28853, + "ĠMonk": 28854, + "ĠPedro": 28855, + "Ġlava": 28856, + "274": 28857, + "Ġcynical": 28858, + "ĠScrolls": 28859, + "locks": 28860, + "Mp": 28861, + "Ġcongregation": 28862, + "ornings": 28863, + "phil": 28864, + "ĠIbid": 28865, + "Ġferv": 28866, + "Ġdisappearing": 28867, + "Ġarrogant": 28868, + "syn": 28869, + "ĠMaver": 28870, + "ĠSuit": 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"ĠThumbnails": 28924, + "Energy": 28925, + "ĠEthan": 28926, + "ĠQing": 28927, + "Ġopposes": 28928, + "WIND": 28929, + "vik": 28930, + "ĠMau": 28931, + "ĠSUB": 28932, + "667": 28933, + "GRE": 28934, + "ĠVolunte": 28935, + "nton": 28936, + "Cook": 28937, + "åIJ": 28938, + "esque": 28939, + "Ġplummet": 28940, + "Ġsuing": 28941, + "Ġpronounce": 28942, + "Ġresisting": 28943, + "ĠFishing": 28944, + "ĠTrials": 28945, + "Ġyell": 28946, + "Ġ310": 28947, + "Ġinduct": 28948, + "Ġpersonalized": 28949, + "often": 28950, + "Reb": 28951, + "EMBER": 28952, + "Ġviewpoint": 28953, + "Ġexistential": 28954, + "())": 28955, + "remove": 28956, + "MENTS": 28957, + "lasses": 28958, + "Ġevapor": 28959, + "Ġaisle": 28960, + "meta": 28961, + "Ġreflective": 28962, + "Ġentitlement": 28963, + "Ġdevised": 28964, + "music": 28965, + "ascade": 28966, + "Ġwinding": 28967, + "offset": 28968, + "Ġaccessibility": 28969, + "kered": 28970, + "Better": 28971, + "ĠJohnston": 28972, + "thinking": 28973, + "Snow": 28974, + "ĠCroatia": 28975, + "ĠAtomic": 28976, + "271": 28977, + "348": 28978, + "Ġtextbook": 28979, + "ĠSixth": 28980, + "ĠاÙĦ": 28981, + "Ġslider": 28982, + "ĠBurger": 28983, + "bol": 28984, + "Sync": 28985, + "Ġgrandchildren": 28986, + "Ġcerv": 28987, + "+)": 28988, + "Ġeternity": 28989, + "Ġtweeting": 28990, + "Ġspeculative": 28991, + "Ġpivotal": 28992, + "ĠWP": 28993, + "ĠTER": 28994, + "ynamic": 28995, + "Ġupl": 28996, + "ĠCats": 28997, + "perhaps": 28998, + "Ġclassmates": 28999, + "Ġblatant": 29000, + "'-": 29001, + "Ġlakh": 29002, + "antine": 29003, + "ĠBorg": 29004, + "iom": 29005, + "/(": 29006, + "ĠAthletic": 29007, + "Ġsar": 29008, + "OTA": 29009, + "ĠHoffman": 29010, + "Nevertheless": 29011, + "Ġadorable": 29012, + "Ġspawned": 29013, + "Associated": 29014, + "ĠDomestic": 29015, + "Ġimplant": 29016, + "ĠLuxem": 29017, + "ĠKens": 29018, + "Ġpumps": 29019, + "ĠSAT": 29020, + "Attributes": 29021, + "509": 29022, + "avour": 29023, + "Ġcentralized": 29024, + "ĠTN": 29025, + "Ġfreshly": 29026, + "ĠAchieve": 29027, + "Ġoutsiders": 29028, + "herty": 29029, + "ĠRee": 29030, + "ĠTowers": 29031, + "ĠDart": 29032, + "akable": 29033, + "Ġmp": 29034, + "ĠHeavenly": 29035, + "Ġripe": 29036, + "ĠCaroline": 29037, + "ryan": 29038, + "Ġclassics": 29039, + "Ġretiring": 29040, + "Ġ228": 29041, + "Ġah": 29042, + "Ġdealings": 29043, + "Ġpunching": 29044, + "ĠChapman": 29045, + "Options": 29046, + "maxwell": 29047, + "volume": 29048, + "Ġstal": 29049, + "Ġexported": 29050, + "ĠQuite": 29051, + "Ġnumerical": 29052, + "Burn": 29053, + "Fact": 29054, + "ĠKeystone": 29055, + "Ġtrending": 29056, + "Ġaltering": 29057, + "ĠAfricans": 29058, + "478": 29059, + "ĠMN": 29060, + "ĠKnock": 29061, + "Ġtemptation": 29062, + "Ġprestige": 29063, + "Overview": 29064, + "ĠTraditional": 29065, + "ĠBahrain": 29066, + "Private": 29067, + "ĠHOU": 29068, + "Ġbarr": 29069, + "ĠTat": 29070, + "Cube": 29071, + "USD": 29072, + "ĠGrande": 29073, + "ĠGat": 29074, + "ĠFlo": 29075, + "Ġresides": 29076, + "Ġindec": 29077, + "volent": 29078, + "Ġperpetual": 29079, + "ubes": 29080, + "Ġworldview": 29081, + "ĠQuantum": 29082, + "Ġfiltered": 29083, + "Ġensu": 29084, + "orgetown": 29085, + "ERSON": 29086, + "ĠMild": 29087, + "379": 29088, + "OTT": 29089, + "Ã¥": 29090, + "Ġvitamins": 29091, + "Ġribbon": 29092, + "Ġsincerely": 29093, + "ĠHin": 29094, + "Ġeighteen": 29095, + "Ġcontradictory": 29096, + "Ġglaring": 29097, + "Ġexpectancy": 29098, + "Ġconspir": 29099, + "Ġmonstrous": 29100, + "Ġ380": 29101, + "reci": 29102, + "Ġhandic": 29103, + "Ġpumped": 29104, + "Ġindicative": 29105, + "Ġrapp": 29106, + "Ġavail": 29107, + "ĠLEGO": 29108, + "ĠMarijuana": 29109, + "1985": 29110, + "erton": 29111, + "Ġtwentieth": 29112, + "################################": 29113, + "ĠSwamp": 29114, + "Ġvaluation": 29115, + "Ġaffiliates": 29116, + "adjusted": 29117, + "ĠFacility": 29118, + "262": 29119, + "Ġenzymes": 29120, + "itudinal": 29121, + "Ġimprint": 29122, + "Site": 29123, + "Ġinstaller": 29124, + "ĠTRA": 29125, + "mology": 29126, + "linear": 29127, + "ĠCollective": 29128, + "igating": 29129, + "ĠToken": 29130, + "Ġspeculated": 29131, + "KN": 29132, + "ĠCly": 29133, + "ority": 29134, + "Ġdefer": 29135, + "Ġinspectors": 29136, + "approved": 29137, + "RM": 29138, + "ĠSuns": 29139, + "Ġinforming": 29140, + "ĠSyracuse": 29141, + "ibli": 29142, + "765": 29143, + "Ġglove": 29144, + "Ġauthorize": 29145, + "âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦": 29146, + "ĠCruise": 29147, + "Ġcontracting": 29148, + "shell": 29149, + "IFE": 29150, + "ĠJewel": 29151, + "pract": 29152, + "ĠPhotoshop": 29153, + "ĠKnowing": 29154, + "harm": 29155, + "Ġattractions": 29156, + "adan": 29157, + "etus": 29158, + "018": 29159, + "wagen": 29160, + "Alt": 29161, + "Ġmultiply": 29162, + "Ġequilibrium": 29163, + ":{": 29164, + "ĠFighters": 29165, + "ĠEdgar": 29166, + "Ġfourteen": 29167, + "Govern": 29168, + "Ġmisuse": 29169, + "Ġabusing": 29170, + "Ġancestry": 29171, + "ramer": 29172, + "644": 29173, + "Ġworms": 29174, + "Ġthicker": 29175, + "ĠCombine": 29176, + "Ġpeasants": 29177, + "Ġvind": 29178, + "Ġconquest": 29179, + "Ġmocked": 29180, + "Ġcinnamon": 29181, + "ĠCald": 29182, + "ĠGallup": 29183, + "Ġavoidance": 29184, + "Ġincarnation": 29185, + "ĠStrat": 29186, + "Ġtasted": 29187, + "enta": 29188, + "ĠNeal": 29189, + "pared": 29190, + "Ġterminology": 29191, + "jection": 29192, + "Scientists": 29193, + "ĠINS": 29194, + "ĠDee": 29195, + "Ġdirectories": 29196, + "Road": 29197, + "ĠShap": 29198, + "bright": 29199, + "ĠDirectors": 29200, + "ĠColumn": 29201, + "Ġbob": 29202, + "Ġpreferably": 29203, + "Ġglitch": 29204, + "furt": 29205, + "Ġeg": 29206, + "idis": 29207, + "CBC": 29208, + "Ġsurrendered": 29209, + "Ġtestament": 29210, + "336": 29211, + "uggest": 29212, + "ĠNil": 29213, + "another": 29214, + "Ġpathetic": 29215, + "ĠDonna": 29216, + "Ġ218": 29217, + "ĠAvery": 29218, + "Ġwhiskey": 29219, + "Ġfixture": 29220, + "ĠConquest": 29221, + "Ġbets": 29222, + "Occ": 29223, + "ĠLeicester": 29224, + "].\"": 29225, + "Ġ));": 29226, + "Ġflashes": 29227, + "456": 29228, + "Ġmasked": 29229, + "gebra": 29230, + "Ġcomputed": 29231, + "chel": 29232, + "auder": 29233, + "Ġdefeats": 29234, + "ĠLiberation": 29235, + "ĠOsama": 29236, + "ĠVive": 29237, + "Changes": 29238, + "Channel": 29239, + "Ġtariffs": 29240, + "Ġmage": 29241, + "ĠSax": 29242, + "Ġinadvertently": 29243, + "ĠCRE": 29244, + "ĠReaper": 29245, + "inky": 29246, + "grading": 29247, + "Ġstereotyp": 29248, + "Ġcurl": 29249, + "ĠFANT": 29250, + "Ġframeworks": 29251, + "Mom": 29252, + "ĠAnch": 29253, + "Ġflavour": 29254, + "carbon": 29255, + "Ġpermitting": 29256, + "letcher": 29257, + "ĠMozilla": 29258, + "ĠParking": 29259, + "ĠChamp": 29260, + "Scroll": 29261, + "Ġmurderer": 29262, + "Ġrested": 29263, + "Ġowes": 29264, + "ĠPoss": 29265, + "ADD": 29266, + "IFF": 29267, + "resolution": 29268, + "ĠMining": 29269, + "Ġcomparative": 29270, + "Dim": 29271, + "Ġneighbouring": 29272, + "ĠAST": 29273, + "ĠToxic": 29274, + "Ġbiases": 29275, + "Ġgunfire": 29276, + "urous": 29277, + "ĠMoment": 29278, + "1983": 29279, + "Ġpervasive": 29280, + "ttp": 29281, + "ĠNormally": 29282, + "rir": 29283, + "Sarah": 29284, + "ĠAlbany": 29285, + "Ġunsett": 29286, + "ĠSMS": 29287, + "ipers": 29288, + "layer": 29289, + "ĠWhites": 29290, + "uple": 29291, + "Ġturbo": 29292, + "ĠLeeds": 29293, + "Ġthats": 29294, + "ĠMiner": 29295, + "MER": 29296, + "ĠReign": 29297, + "Ġperme": 29298, + "ĠBlitz": 29299, + "Ġ1934": 29300, + "Ġintimidating": 29301, + "tube": 29302, + "Ġeccentric": 29303, + "abolic": 29304, + "boxes": 29305, + "ĠAssociates": 29306, + "votes": 29307, + "Ġsimulate": 29308, + "umbo": 29309, + "astery": 29310, + "Ġshipments": 29311, + "FFFF": 29312, + "anth": 29313, + "Ġseasoned": 29314, + "Ġexperimentation": 29315, + "âĸł": 29316, + "laws": 29317, + "Meet": 29318, + "iddles": 29319, + "antics": 29320, + "Rating": 29321, + "ISIS": 29322, + "hift": 29323, + "Ġfronts": 29324, + "buf": 29325, + "017": 29326, + "Ġunatt": 29327, + "ĠDil": 29328, + "leases": 29329, + "ĠGardens": 29330, + "777": 29331, + "touch": 29332, + "vell": 29333, + "458": 29334, + "Ġ=====": 29335, + "saving": 29336, + "Ġerosion": 29337, + "ĠQuin": 29338, + "Ġearns": 29339, + "Ġaccomplishment": 29340, + "ĠWei": 29341, + "Ġ<[": 29342, + "_____": 29343, + "Ġirrig": 29344, + "ĠTeddy": 29345, + "Ġconquered": 29346, + "ĠArmored": 29347, + "Ġasserts": 29348, + "Ġmanipulating": 29349, + "ré": 29350, + "Ġtranscripts": 29351, + "Gallery": 29352, + "Ġplotting": 29353, + "Neil": 29354, + "Ġbetrayal": 29355, + "loader": 29356, + "ĠSul": 29357, + "Ġdisplacement": 29358, + "Ġroyalty": 29359, + "ĠWI": 29360, + "heit": 29361, + "ĠDevices": 29362, + "allel": 29363, + "Ġmunicipalities": 29364, + "Ġcanal": 29365, + "Stars": 29366, + "ĠUAE": 29367, + "Ġ\"âĢ¦": 29368, + "ĠCU": 29369, + "above": 29370, + "Ġresonance": 29371, + "ĠguiActiveUn": 29372, + "added": 29373, + "ĠBraves": 29374, + "ĠIbn": 29375, + "Ġhereby": 29376, + "ĠBRE": 29377, + "Ġshareholder": 29378, + "ĠHir": 29379, + "ĠJi": 29380, + "Ġstrangely": 29381, + "Ġadmired": 29382, + "Ġplight": 29383, + "Ġbachelor": 29384, + "ĠPole": 29385, + "ciplinary": 29386, + "Tony": 29387, + "ĠArmenian": 29388, + "Ġunman": 29389, + "ĠZionist": 29390, + "Stage": 29391, + "iscover": 29392, + "Ġautomotive": 29393, + "Ġsidelines": 29394, + "Ġslick": 29395, + "ĠRenaissance": 29396, + "ĠFUN": 29397, + "Images": 29398, + "ĠHaj": 29399, + "Ġping": 29400, + "Ġshortcut": 29401, + "ĠBlvd": 29402, + "ĠLooks": 29403, + "Ġbursts": 29404, + "Ġclamp": 29405, + "Ġmish": 29406, + "Ġsorting": 29407, + "Ġpatriot": 29408, + "Ġcorrectness": 29409, + "ĠScandinav": 29410, + "ĠCavaliers": 29411, + "python": 29412, + "azar": 29413, + "Ġ375": 29414, + "ĠJaune": 29415, + "409": 29416, + "Ġdetrimental": 29417, + "Ġstabbing": 29418, + "Ġpoisoned": 29419, + "Ġfountain": 29420, + "ocent": 29421, + "orst": 29422, + "ĠMari": 29423, + "Ġrains": 29424, + "ĠOvers": 29425, + "ĠInstitution": 29426, + "udget": 29427, + "AMY": 29428, + "tale": 29429, + "ĠKR": 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29685, + "Ġdiscounted": 29686, + "uckle": 29687, + "please": 29688, + "ĠLanding": 29689, + "ÃŃa": 29690, + "Ġ193": 29691, + "Ġante": 29692, + "Ġrepetition": 29693, + "Ġ+/-": 29694, + "Ġparody": 29695, + "Ġlively": 29696, + "AAA": 29697, + "ĠHorus": 29698, + "Ġpits": 29699, + "inders": 29700, + "LOC": 29701, + "ĠVenice": 29702, + "406": 29703, + "ĠDiscover": 29704, + "âĨ": 29705, + "ellectual": 29706, + "Ġpens": 29707, + "Ġeyel": 29708, + "iguous": 29709, + "Impl": 29710, + "Ġjoking": 29711, + "Ġinval": 29712, + "ĠBelfast": 29713, + "Ġcreditors": 29714, + "ĠSkywalker": 29715, + "ovsky": 29716, + "Ġceasefire": 29717, + "Ġseals": 29718, + "isoft": 29719, + ")).": 29720, + "ĠFelix": 29721, + "ITS": 29722, + "Ġtresp": 29723, + "ĠBlockchain": 29724, + "eware": 29725, + "ĠSchwar": 29726, + "enne": 29727, + "mounted": 29728, + "ĠBeacon": 29729, + "lesh": 29730, + "Ġimmensely": 29731, + "Ġcheering": 29732, + "Employ": 29733, + "scene": 29734, + "ishly": 29735, + "atchewan": 29736, + "ĠNicolas": 29737, + "Ġdrained": 29738, + "ĠExit": 29739, + "ĠAzerb": 29740, + "jun": 29741, + "Ġfloated": 29742, + "uania": 29743, + "Deep": 29744, + "Ġsuperv": 29745, + "Ġmystical": 29746, + "ĠDollar": 29747, + "ĠApostle": 29748, + "ĠREL": 29749, + "ĠProvided": 29750, + "ĠBucks": 29751, + "ãĥ´": 29752, + "cutting": 29753, + "Ġenhancements": 29754, + "ĠPenguins": 29755, + "ĠIsaiah": 29756, + "Ġjerk": 29757, + "ĠWyn": 29758, + "Ġstalled": 29759, + "Ġcryptocurrencies": 29760, + "ĠRoland": 29761, + "single": 29762, + "Ġlumin": 29763, + "ĠFellow": 29764, + "ĠCapacity": 29765, + "ĠKazakh": 29766, + "WN": 29767, + "Ġfinanced": 29768, + "389": 29769, + "Ġtid": 29770, + "Ġcollusion": 29771, + "ĠMyr": 29772, + "îĢ": 29773, + "Senator": 29774, + "Ġpediatric": 29775, + "Ġneatly": 29776, + "Ġsandwiches": 29777, + "ĠArchitecture": 29778, + "Ġtucked": 29779, + "Ġbalcony": 29780, + "Ġearthquakes": 29781, + "quire": 29782, + "Future": 29783, + "Ġhefty": 29784, + "éĹ": 29785, + "Ġspecializes": 29786, + 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31047, + "Ġpresses": 31048, + "Ġhumour": 31049, + "Ġspacing": 31050, + "Ġ'/": 31051, + "olkien": 31052, + "Coun": 31053, + "OPER": 31054, + "Tre": 31055, + "Son": 31056, + "ĠCambodia": 31057, + "ierre": 31058, + "mong": 31059, + "ozy": 31060, + "Ġliquidity": 31061, + "ĠSoviets": 31062, + "ĠFernando": 31063, + "Ġ229": 31064, + "Ġslug": 31065, + "ĠCatalan": 31066, + "electric": 31067, + "Ġscenery": 31068, + "ĠHearth": 31069, + "Ġconstrained": 31070, + "Ġgoalie": 31071, + "ĠGuidelines": 31072, + "ĠAmmo": 31073, + "ĠPearson": 31074, + "Ġtaxed": 31075, + "Ġfetus": 31076, + "Response": 31077, + "ĠAlexis": 31078, + "thia": 31079, + "Guy": 31080, + "Ġreconstruct": 31081, + "Ġextremes": 31082, + "Ġconcluding": 31083, + "ĠPeg": 31084, + "ooks": 31085, + "Ġdeductions": 31086, + "Rose": 31087, + "Ġgroundbreaking": 31088, + "ĠTarg": 31089, + "ãĥģ": 31090, + "ĠReve": 31091, + "resource": 31092, + "Ġmoons": 31093, + "Ġelectromagnetic": 31094, + "Ġamidst": 31095, + "ĠViktor": 31096, + "NESS": 31097, + "BACK": 31098, + "Ġcommute": 31099, + "ĠAnaheim": 31100, + "Ġfluctuations": 31101, + "640": 31102, + "Ġnoodles": 31103, + "ĠCopenhagen": 31104, + "ĠTide": 31105, + "ĠGrizz": 31106, + "ĠSEE": 31107, + "Ġpipelines": 31108, + "Ġscars": 31109, + "endo": 31110, + "agus": 31111, + "ĠETF": 31112, + "/#": 31113, + "ĠBecome": 31114, + "448": 31115, + "Ġvisc": 31116, + "ĠRecommended": 31117, + "Ġjumper": 31118, + "Ġcognition": 31119, + "Ġassassin": 31120, + "Ġwitnessing": 31121, + "ĠSetup": 31122, + "Ġlac": 31123, + "vim": 31124, + "ISM": 31125, + "pages": 31126, + "SSL": 31127, + "358": 31128, + "Ġadject": 31129, + "industrial": 31130, + "lore": 31131, + "chery": 31132, + "Ġglitter": 31133, + "Ġcalf": 31134, + "Florida": 31135, + "Ġspoilers": 31136, + "Ġsucceeds": 31137, + "Ġchanting": 31138, + "Ġslogans": 31139, + "ĠTracy": 31140, + "Visit": 31141, + "rology": 31142, + "Ġmornings": 31143, + "Ġlineage": 31144, + "Ġsip": 31145, + "Ġintensely": 31146, + "Ġflourish": 31147, + "ĠSleeping": 31148, + "ĠFem": 31149, + "orpor": 31150, + "ĠKlan": 31151, + "ĠDarth": 31152, + "hack": 31153, + "ĠNielsen": 31154, + "Ġtumors": 31155, + "Ġprocurement": 31156, + "ĠYorkshire": 31157, + "Ġraided": 31158, + "KY": 31159, + "Anna": 31160, + "Ġ//[": 31161, + "ĠDisorder": 31162, + "ĠMustang": 31163, + "ĠWen": 31164, + "ĠTrying": 31165, + "sq": 31166, + "Ġdeliveries": 31167, + "Ġshutter": 31168, + "Ġcerebral": 31169, + "Ġbipolar": 31170, + "ĠCN": 31171, + "lass": 31172, + "jet": 31173, + "Ġdebating": 31174, + ">:": 31175, + "Ġeagle": 31176, + "grades": 31177, + "ĠDixon": 31178, + "UGC": 31179, + "MAS": 31180, + "ĠDraco": 31181, + "ĠMachines": 31182, + "affer": 31183, + "Ġeman": 31184, + "²": 31185, + "pron": 31186, + "ĠGym": 31187, + "Ġcomparatively": 31188, + "ĠTribunal": 31189, + "PRO": 31190, + "Ġlex": 31191, + "Ġfertile": 31192, + "Ġdepressing": 31193, + "Ġsuperficial": 31194, + "essential": 31195, + "ĠHunters": 31196, + "gp": 31197, + "Ġprominence": 31198, + "Liber": 31199, + "ĠAncest": 31200, + "otechnology": 31201, + "Ġmocking": 31202, + "ĠTraff": 31203, + "ĸļ": 31204, + "Medium": 31205, + "Iraq": 31206, + "Ġpsychiatrist": 31207, + "Quantity": 31208, + "ĠLect": 31209, + "Ġnoisy": 31210, + "520": 31211, + "GY": 31212, + "Ġslapped": 31213, + "ĠMTV": 31214, + "Ġpara": 31215, + "pull": 31216, + "Multiple": 31217, + "asher": 31218, + "Ġnour": 31219, + "ĠSeg": 31220, + "Spell": 31221, + "vous": 31222, + "ordial": 31223, + "Senior": 31224, + "ĠGoldberg": 31225, + "ĠPlasma": 31226, + "need": 31227, + "Ġmessenger": 31228, + "eret": 31229, + "Ġteamed": 31230, + "Ġliteracy": 31231, + "ĠLeah": 31232, + "ĠDoyle": 31233, + "Ġemitted": 31234, + "UX": 31235, + "Ġevade": 31236, + "Ġmaze": 31237, + "Ġwrongly": 31238, + "ĠLars": 31239, + "Ġstereotype": 31240, + "Ġpledges": 31241, + "Ġaroma": 31242, + "ĠMET": 31243, + "Ġacre": 31244, + "ĠOD": 31245, + "Ġff": 31246, + "Ġbreweries": 31247, + "ĠHilton": 31248, + "undle": 31249, + "ĠKak": 31250, + "ĠThankfully": 31251, + "ĠCanucks": 31252, + "inctions": 31253, + "ĠAppears": 31254, + "Ġcoer": 31255, + "Ġundermined": 31256, + "rovers": 31257, + "Andre": 31258, + "Ġblaze": 31259, + "umers": 31260, + "Ġfamine": 31261, + "amphetamine": 31262, + "ulkan": 31263, + "Amount": 31264, + "Ġdesperation": 31265, + "wikipedia": 31266, + "development": 31267, + "ĠCorinth": 31268, + "ussia": 31269, + "Jackson": 31270, + "LI": 31271, + "Native": 31272, + "Rs": 31273, + "Ohio": 31274, + "ĠKathleen": 31275, + "Fortunately": 31276, + "Ġattendant": 31277, + "ĠPreferred": 31278, + "ĠDidn": 31279, + "ĠVs": 31280, + "Mis": 31281, + "Ġrespondent": 31282, + "Ġboun": 31283, + "stable": 31284, + "Ġpaved": 31285, + "Ġunexpl": 31286, + "ĠCheney": 31287, + "LM": 31288, + "ĠCull": 31289, + "blown": 31290, + "Ġconfronting": 31291, + "ocese": 31292, + "serving": 31293, + "Wi": 31294, + "ĠLithuania": 31295, + "anni": 31296, + "Ġstalk": 31297, + "hd": 31298, + "Ġvener": 31299, + "APH": 31300, + "ynchronous": 31301, + "URR": 31302, + "umably": 31303, + "historic": 31304, + "Half": 31305, + "Hay": 31306, + "Ġresilience": 31307, + "spection": 31308, + "Ġabandoning": 31309, + "Obs": 31310, + "ĠDebbie": 31311, + "Ġgradient": 31312, + "ĠPlaint": 31313, + "ĠCanal": 31314, + "ARCH": 31315, + "Ġexpansive": 31316, + "Ġfung": 31317, + "Ġbounced": 31318, + "Und": 31319, + "Ġprecautions": 31320, + "Ġclarification": 31321, + "Ġdagger": 31322, + "Ġgrips": 31323, + "Ġµ": 31324, + "ĠRivera": 31325, + "ĠUndead": 31326, + "isites": 31327, + "ĠFIRST": 31328, + "ño": 31329, + "audi": 31330, + "Ġhostages": 31331, + "Ġcompliant": 31332, + "Ġalumni": 31333, + "Seven": 31334, + "Ġcybersecurity": 31335, + "either": 31336, + "Collect": 31337, + "Ġinvariably": 31338, + "ĠSoci": 31339, + "Ġlawmaker": 31340, + "Ġale": 31341, + "ĠPersonally": 31342, + "Nazi": 31343, + "Ġcustomization": 31344, + "ĠProc": 31345, + "ĠSaskatchewan": 31346, + "eaturing": 31347, + "Ġspared": 31348, + "Ġdiscontinued": 31349, + "Ġcomputational": 31350, + "ĠMotorola": 31351, + "Ġsupremacist": 31352, + "governmental": 31353, + "Ġparadise": 31354, + "ĠDowning": 31355, + "ĠNikon": 31356, + "Ġcatalyst": 31357, + "berra": 31358, + "Toronto": 31359, + "875": 31360, + "beta": 31361, + "ĠMacron": 31362, + "Ġunrealistic": 31363, + "vector": 31364, + "ĠVehicles": 31365, + "itiveness": 31366, + "ĠRV": 31367, + "ĠColbert": 31368, + "sin": 31369, + "oji": 31370, + "entin": 31371, + "ĠKrish": 31372, + "hello": 31373, + "ffield": 31374, + "oky": 31375, + "ĠTate": 31376, + "Ġmaple": 31377, + "Ġaids": 31378, + "chemical": 31379, + "334": 31380, + "nuts": 31381, + "ĠWarp": 31382, + "Ġxx": 31383, + "ĠRobb": 31384, + "umerous": 31385, + "_-_": 31386, + "ftime": 31387, + "ĠVW": 31388, + "Ġwinger": 31389, + "ĠDome": 31390, + "tools": 31391, + "ĠPV": 31392, + "ĠGeorgetown": 31393, + "Ġgeared": 31394, + "Ġjihadists": 31395, + "Ġcp": 31396, + "Ġsteroids": 31397, + "Mother": 31398, + "clerosis": 31399, + "ĠDRM": 31400, + "nesia": 31401, + "Ġlinger": 31402, + "Ġimmersive": 31403, + "ĠCOUN": 31404, + "Ġoutweigh": 31405, + "ensual": 31406, + "Band": 31407, + "Ġtransforms": 31408, + "matched": 31409, + "psons": 31410, + "ĠJudicial": 31411, + "factor": 31412, + "Ġreferral": 31413, + "Ġoddly": 31414, + "ĠWenger": 31415, + "Bring": 31416, + "ĠBows": 31417, + "602": 31418, + "ICLE": 31419, + "Ġlions": 31420, + "ĠAcademic": 31421, + "ĠThorn": 31422, + "ĠRaider": 31423, + "kefeller": 31424, + "Storage": 31425, + "Lower": 31426, + "ĠOrt": 31427, + "ĠEquality": 31428, + "ALT": 31429, + "ĠSOC": 31430, + "Types": 31431, + "Ġlyn": 31432, + "ĠAsset": 31433, + "coat": 31434, + "TPP": 31435, + "CVE": 31436, + "ĠPioneer": 31437, + "application": 31438, + "Modern": 31439, + "ĠHK": 31440, + "Environment": 31441, + "Alright": 31442, + "Rain": 31443, + "IPP": 31444, + "ĠShiite": 31445, + "Ġmound": 31446, + "ĠAbilities": 31447, + "condition": 31448, + "Staff": 31449, + "Ġcompetence": 31450, + "ĠMoor": 31451, + "ĠDiablo": 31452, + "Ġwithheld": 31453, + "Ġostensibly": 31454, + "ĠBrom": 31455, + "Ġmsg": 31456, + "Ġdenomin": 31457, + "ĠReferences": 31458, + "ĠFP": 31459, + "Ġplunged": 31460, + "Ġpamph": 31461, + "moving": 31462, + "central": 31463, + "Ġdownright": 31464, + "Ġfading": 31465, + "Tal": 31466, + "Typ": 31467, + "ĠThy": 31468, + "ukes": 31469, + "ithe": 31470, + "Ġove": 31471, + "Ġbattled": 31472, + "Ġseafood": 31473, + "Ġfigur": 31474, + "ĠRD": 31475, + "crop": 31476, + "Ġsquads": 31477, + "{\\": 31478, + "à¹": 31479, + "ĠEh": 31480, + "Ġinterviewing": 31481, + "ĠQin": 31482, + "Ġaspiring": 31483, + "PLIC": 31484, + "Ġclauses": 31485, + "ĠGast": 31486, + "ĠNir": 31487, + "Ġluggage": 31488, + "Ġhose": 31489, + "Ġsystemd": 31490, + "Ġdescending": 31491, + "ĠRevised": 31492, + "ĠRails": 31493, + "align": 31494, + "709": 31495, + "337": 31496, + "Ġfug": 31497, + "charging": 31498, + "tags": 31499, + "Ġuter": 31500, + "kish": 31501, + "WARNING": 31502, + "490": 31503, + "profits": 31504, + "Ġvoyage": 31505, + "Ġace": 31506, + "ĠVanguard": 31507, + "ĠTanks": 31508, + "ĠMuk": 31509, + "Ġ226": 31510, + "Safe": 31511, + "Armor": 31512, + "Ġvolcanic": 31513, + "Ġwomb": 31514, + "ĠMIL": 31515, + "Ġbeginner": 31516, + "ĠRecogn": 31517, + "ĠAAP": 31518, + "PLAY": 31519, + ")!": 31520, + "Ġdetecting": 31521, + "cn": 31522, + "Ġbreaches": 31523, + "Basically": 31524, + "ĠPag": 31525, + "ĠMunicipal": 31526, + "ĠIndie": 31527, + "ĠLaf": 31528, + "ĠDisable": 31529, + "ĠOlson": 31530, + "Ġrestrained": 31531, + "Ġrulings": 31532, + "Ġhumane": 31533, + "events": 31534, + "ĠCinema": 31535, + "displayText": 31536, + "ĠHatch": 31537, + "actionDate": 31538, + "onnaissance": 31539, + "Ġassaulting": 31540, + "ĠLug": 31541, + "CHAT": 31542, + "Ġvigorous": 31543, + "ĠPerse": 31544, + "Ġintolerance": 31545, + "ĠSnapchat": 31546, + "ĠSharks": 31547, + "Ġdummy": 31548, + "ĠDiagn": 31549, + "ĠGuitar": 31550, + "imeters": 31551, + "403": 31552, + "REG": 31553, + "Ax": 31554, + "Ġseparates": 31555, + "ĠMahm": 31556, + "Ġtv": 31557, + "jah": 31558, + "OOL": 31559, + "Circ": 31560, + "ĠWindsor": 31561, + "ussian": 31562, + "Ġintuition": 31563, + "Ġdisdain": 31564, + "ĠDonovan": 31565, + "Ġ221": 31566, + "Emb": 31567, + "Ġcondemning": 31568, + "Ġgenerosity": 31569, + "zzy": 31570, + "Ġpanties": 31571, + "ĠPrevent": 31572, + "ActionCode": 31573, + "ANA": 31574, + "342": 31575, + "externalActionCode": 31576, + "Ġspecifying": 31577, + "Ġcrystall": 31578, + "Jere": 31579, + "Ġrupt": 31580, + "ĠApprentice": 31581, + "Ġprofiling": 31582, + "к": 31583, + "Strike": 31584, + "Ġsideline": 31585, + "Ġobligated": 31586, + "Ġoccult": 31587, + "Ġbureaucratic": 31588, + "antically": 31589, + "rupted": 31590, + "negative": 31591, + "ĠEthiopia": 31592, + "ĠCivic": 31593, + "Ġinsiders": 31594, + "eligible": 31595, + "ĠTVs": 31596, + "ĠBAR": 31597, + "ĠTI": 31598, + "iologist": 31599, + "ĠAIR": 31600, + "Ġsubstituted": 31601, + "Arab": 31602, + "ĠSaul": 31603, + "ĠYog": 31604, + "prem": 31605, + "Ġbuilders": 31606, + "Ġstationary": 31607, + "Ġdoubtful": 31608, + "Ġvigorously": 31609, + "Ġthrilling": 31610, + "Physical": 31611, + "ĠCarey": 31612, + "ĠHydra": 31613, + "geoning": 31614, + "ĠSly": 31615, + "yton": 31616, + "Ġborrowers": 31617, + "ĠParkinson": 31618, + "Ġë": 31619, + "ĠJamaica": 31620, + "Ġsatir": 31621, + "Ġinsurgents": 31622, + "ĠFirm": 31623, + "Ġisot": 31624, + "ĠKarn": 31625, + "ourning": 31626, + "akens": 31627, + "docs": 31628, + "little": 31629, + "ĠMonaco": 31630, + "CLASS": 31631, + "Turkey": 31632, + "Ly": 31633, + "ĠConan": 31634, + "assic": 31635, + "Ġstarred": 31636, + "ĠPacers": 31637, + "eties": 31638, + "Ġtipping": 31639, + "Moon": 31640, + "ĠRw": 31641, + "same": 31642, + "Ġcavity": 31643, + "Ġgoof": 31644, + "ĠZo": 31645, + "Shock": 31646, + "ummer": 31647, + "Ġemphasizes": 31648, + "Ġregrett": 31649, + "Ġnovelty": 31650, + "Ġenvy": 31651, + "ĠPassive": 31652, + "rw": 31653, + "505": 31654, + "Ġindifferent": 31655, + "ĠRica": 31656, + "ĠHimself": 31657, + "ĠFreddie": 31658, + "Ġadip": 31659, + "ä¸Ģ": 31660, + "Ġbreakout": 31661, + "Ġhurried": 31662, + "ĠHuang": 31663, + "ĠDisk": 31664, + "Ġroaming": 31665, + "?????-?????-": 31666, + "UV": 31667, + "ĠRicky": 31668, + "ĠSigma": 31669, + "Ġmarginalized": 31670, + "Ġedits": 31671, + "Ġ304": 31672, + "memory": 31673, + "Ġspecimen": 31674, + "293": 31675, + "ãģ¯": 31676, + "Ġvertically": 31677, + "Ġaudition": 31678, + "ĠHeck": 31679, + "Ġcaster": 31680, + "ĠHoldings": 31681, + "adal": 31682, + "ĠCron": 31683, + "ĠLiam": 31684, + "Ġdeflect": 31685, + "Pick": 31686, + "ĠDebug": 31687, + "REF": 31688, + "Ġversatility": 31689, + "othes": 31690, + "classified": 31691, + "ĠMahar": 31692, + "ĠHort": 31693, + "Counter": 31694, + "stasy": 31695, + "noticed": 31696, + "331": 31697, + "ĠShim": 31698, + "fuck": 31699, + "ĠBie": 31700, + "Ġairing": 31701, + "ĠProtein": 31702, + "ĠHolding": 31703, + "Ġspectators": 31704, + "iliated": 31705, + "ĠThatcher": 31706, + "nosis": 31707, + "ãĥ¼ãĥ³": 31708, + "Tele": 31709, + "Boston": 31710, + "ĠTempl": 31711, + 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35457, + "Ġfashioned": 35458, + "Ġreorgan": 35459, + "ivari": 35460, + "finger": 35461, + "Ġreluctantly": 35462, + "etting": 35463, + "ĠVand": 35464, + "young": 35465, + "ĠGarland": 35466, + "Ġpresumption": 35467, + "Ġamenities": 35468, + "ĠPleasant": 35469, + "onential": 35470, + "ĠOxy": 35471, + "Ġmorals": 35472, + "ĠYah": 35473, + "Ready": 35474, + "Simon": 35475, + "Enh": 35476, + "Demon": 35477, + "Ġclich": 35478, + "Monitor": 35479, + "ĠDU": 35480, + "Ġwelcomes": 35481, + "Ġstandout": 35482, + "Ġdreadful": 35483, + "Ġbananas": 35484, + "Ġballoons": 35485, + "hooting": 35486, + "basic": 35487, + "Ġsuffix": 35488, + "Ġduly": 35489, + "cano": 35490, + "Chain": 35491, + "atos": 35492, + "Ġgeopolitical": 35493, + "Ġ(&": 35494, + "ĠGemini": 35495, + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ": 35496, + "Ġacquitted": 35497, + "Luck": 35498, + "protect": 35499, + "1024": 35500, + "Ġscarcity": 35501, + 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"Ġo wn", + "Ġpro v", + "Ġs et", + "if e", + "ment s", + "b le", + "w ard", + "Ġsh ow", + "Ġp res", + "m s", + "om et", + "Ġo b", + "Ġs ay", + "ĠS h", + "t s", + "f ul", + "Ġe ff", + "Ġg u", + "Ġin st", + "u nd", + "re n", + "c ess", + "Ġ ent", + "ĠY ou", + "Ġgo od", + "Ġst art", + "in ce", + "Ġm ade", + "t t", + "st em", + "ol og", + "u p", + "Ġ |", + "um p", + "Ġhe l", + "ver n", + "ul ar", + "u ally", + "Ġa c", + "Ġm on", + "Ġl ast", + "Ġ2 00", + "1 0", + "Ġst ud", + "u res", + "ĠA r", + "sel f", + "ar s", + "mer ic", + "u es", + "c y", + "Ġm in", + "oll ow", + "Ġc ol", + "i o", + "Ġm od", + "Ġc ount", + "ĠC om", + "he s", + "Ġf in", + "a ir", + "i er", + "âĢ Ķ", + "re ad", + "an k", + "at ch", + "e ver", + "Ġst r", + "Ġpo int", + "or k", + "ĠN ew", + "Ġs ur", + "o ol", + "al k", + "em ent", + "Ġus ed", + "ra ct", + "we en", + "Ġs ame", + "ou n", + "ĠA l", + "c i", + "Ġdiff ere", + "Ġwh ile", + "---- ----", + "Ġg ame", + "ce pt", + "Ġs im", + ".. .", + "Ġin ter", + "e k", + "Ġre port", + "Ġpro du", + "Ġst ill", + "l ed", + "a h", + "Ġhe re", + "Ġwor ld", + "Ġth ough", + "Ġn um", + "ar ch", + "im es", + "al e", + "ĠS e", + "ĠI f", + "/ /", + "ĠL e", + "Ġre t", + "Ġre f", + "Ġtr ans", + "n er", + "ut ion", + "ter s", + "Ġt ake", + "ĠC l", + "Ġcon f", + "w ay", + "a ve", + "Ġgo ing", + "Ġs l", + "u g", + "ĠA meric", + "Ġspe c", + "Ġh and", + "Ġbet ween", + "ist s", + "ĠD e", + "o ot", + "I t", + "Ġe ar", + "Ġagain st", + "Ġh igh", + "g an", + "a z", + "at her", + "Ġex p", + "Ġo p", + "Ġin s", + "Ġg r", + "Ġhel p", + "Ġre qu", + "et s", + "in s", + "ĠP ro", + "is m", + "Ġf ound", + "l and", + "at a", + "us s", + "am es", + "Ġp erson", + "Ġg reat", + "p r", + "Ġs ign", + "ĠA n", + "' ve", + "Ġs omet", + "Ġs er", + "h ip", + "Ġr un", + "Ġ :", + "Ġt er", + "ire ct", + "Ġf ollow", + "Ġd et", + "ic es", + "Ġf ind", + "1 2", + "Ġm em", + "Ġc r", + "e red", + "e x", + "Ġex t", + "ut h", + "en se", + "c o", + "Ġte am", + "v ing", + "ou se", + "as h", + "at t", + "v ed", + "Ġsy stem", + "ĠA s", + "d er", + "iv es", + "m in", + "Ġle ad", + "ĠB l", + "c ent", + "Ġa round", + "Ġgo vern", + "Ġc ur", + "vel op", + "an y", + "Ġc our", + "al th", + "ag es", + "iz e", + "Ġc ar", + "od e", + "Ġl aw", + "Ġre ad", + "' m", + "c on", + "Ġre al", + "Ġsupp ort", + "Ġ1 2", + ".. ..", + "Ġre ally", + "n ess", + "Ġf act", + "Ġd ay", + "Ġb oth", + "y ing", + "Ġs erv", + "ĠF or", + "Ġth ree", + "Ġw om", + "Ġm ed", + "od y", + "ĠThe y", + "5 0", + "Ġex per", + "t on", + "Ġe ach", + "ak es", + "Ġc he", + "Ġc re", + "in es", + "Ġre p", + "1 9", + "g g", + "ill ion", + "Ġg rou", + "ut e", + "i k", + "W e", + "g et", + "E R", + "Ġm et", + "Ġs ays", + "o x", + "Ġd uring", + "er n", + "iz ed", + "a red", + "Ġf am", + "ic ally", + "Ġha pp", + "ĠI s", + "Ġch ar", + "m ed", + "v ent", + "Ġg ener", + "i ent", + "p le", + "i et", + "re nt", + "1 1", + "v es", + "pt ion", + "Ġ2 0", + "form ation", + "Ġc or", + "Ġoff ic", + "ie ld", + "Ġto o", + "is ion", + "Ġin f", + "Ġ Z", + "t he", + "o ad", + "Ġp ublic", + "Ġpro g", + "r ic", + "* *", + "Ġw ar", + "Ġp ower", + "v iew", + "Ġf ew", + "Ġl oc", + "Ġdiffere nt", + "Ġst ate", + "Ġhe ad", + "' ll", + "Ġp oss", + "Ġst at", + "re t", + "ant s", + "Ġv al", + "Ġis s", + "Ġc le", + "i vers", + "an c", + "Ġex pl", + "Ġan other", + "Ġ Q", + "Ġa v", + "th ing", + "n ce", + "W h", + "Ġch ild", + "Ġs ince", + "i red", + "l ess", + "Ġl ife", + "Ġde velop", + "itt le", + "Ġde p", + "Ġp ass", + "ã ĥ", + "Ġt urn", + "or n", + "Th is", + "b ers", + "ro ss", + "ĠA d", + "Ġf r", + "Ġres p", + "Ġsec ond", + "o h", + "Ġ /", + "Ġdis c", + "Ġ &", + "Ġsomet hing", + "Ġcomp le", + "Ġ ed", + "Ġf il", + "Ġmon th", + "a j", + "u c", + "Ġgovern ment", + "Ġwith out", + "Ġle g", + "Ġd ist", + "Ġp ut", + "Ġqu est", + "an n", + "Ġpro t", + "2 0", + "Ġne ver", + "i ence", + "Ġle vel", + "Ġar t", + "Ġth ings", + "Ġm ight", + "Ġeff ect", + "Ġcont ro", + "Ġc ent", + "Ġ1 8", + "Ġall ow", + "Ġbel ie", + "ch ool", + "ot t", + "Ġinc re", + "Ġfe el", + "Ġres ult", + "Ġl ot", + "Ġf un", + "ot e", + "Ġt y", + "ere st", + "Ġcont in", + "Ġus ing", + "Ġb ig", + "2 01", + "Ġas k", + "Ġb est", + "Ġ )", + "I N", + "Ġo pp", + "3 0", + "Ġnum ber", + "in ess", + "S t", + "le ase", + "Ġc a", + "Ġm ust", + "Ġd irect", + "Ġg l", + "Ġ <", + "Ġop en", + "Ġp ost", + "Ġcom e", + "Ġse em", + "ord ing", + "Ġwe ek", + "ate ly", + "it al", + "Ġe l", + "ri end", + "Ġf ar", + "Ġt ra", + "in al", + "Ġp ri", + "ĠU S", + "Ġpl ace", + "Ġfor m", + "Ġto ld", + "\" :", + "ain s", + "at ure", + "ĠTr ump", + "Ġst and", + "Ġ #", + "id er", + "ĠF r", + "Ġne xt", + "Ġs oc", + "Ġp ur", + "Ġle t", + "Ġl ittle", + "Ġh um", + "Ġ i", + "r on", + "1 5", + "Ġ1 5", + "Ġcomm un", + "Ġm ark", + "ĠThe re", + "Ġw r", + "ĠTh at", + "Ġin formation", + "w ays", + "Ġb us", + "a pp", + "Ġinv est", + "m e", + "Ġh ard", + "ain ed", + "e ad", + "Ġim port", + "Ġapp ro", + "Ġt est", + "Ġt ri", + "Ġre st", + "os ed", + "Ġf ull", + "Ġc are", + "ĠS p", + "Ġc ase", + "O N", + "Ġs k", + "Ġl ess", + "Ġ +", + "Ġpart ic", + "ĠP l", + "ab ly", + "u ck", + "is hed", + "ch n", + "b e", + "Ġl ist", + "at or", + "Ġto p", + "Ġad v", + "ĠB e", + "ru ct", + "Ġd em", + "r ation", + "l ing", + "g y", + "re en", + "g er", + "Ġh ome", + "Ġle ft", + "Ġbet ter", + "Ġd ata", + "Ġ1 1", + "Ġatt ack", + "Ġpro ble", + "l ine", + "ard s", + "Ġbe h", + "r al", + "ĠH ow", + "ĠS he", + "ar ge", + "Ġ --", + ": //", + "Ġb ro", + "ĠP h", + "at s", + "Ġbu ild", + "w w", + "id ed", + "a im", + "as es", + "en cy", + "Ġm ain", + "in ed", + "Ġinclud ing", + "Ġ {", + "Ġg ot", + "Ġint erest", + "Ġke ep", + "Ġ X", + "Ġe as", + "ain ing", + "Ġcl ass", + "âĢ ¦", + "ĠN o", + "Ġv ar", + "Ġsm all", + "amp le", + "A T", + "Ġ ide", + "ĠS o", + "Ġre ce", + "Ġpol it", + "Ġm ov", + "Ġpl an", + "Ġper cent", + "iv ing", + "Ġc amp", + "Ġp ay", + "1 4", + "s c", + "is ed", + "Ġu nt", + "one y", + "pl oy", + "== ==", + "Ġdid n", + "ĠI nd", + "el s", + "ert ain", + "Ġp os", + "__ __", + "i ver", + "Ġpro cess", + "Ġprog ram", + "if ied", + "ĠR ep", + "1 6", + "u ro", + "olog y", + "at ter", + "in a", + "Ġn ame", + "ĠA ll", + "Ġf our", + "Ġret urn", + "v ious", + "b s", + "Ġcall ed", + "Ġm ove", + "ĠS c", + "ir d", + "Ġgrou p", + "Ġb re", + "Ġm en", + "Ġc ap", + "t en", + "e e", + "Ġd ri", + "le g", + "he re", + "uth or", + "Ġp at", + "Ġcur rent", + "id es", + "Ġp op", + "t o", + "ent ion", + "Ġal ways", + "Ġm il", + "Ġwom en", + "Ġ1 6", + "Ġo ld", + "iv en", + "ra ph", + "ĠO r", + "r or", + "ent ly", + "Ġn ear", + "ĠE x", + "re am", + "s h", + "Ġ1 4", + "Ġf ree", + "iss ion", + "st and", + "ĠC on", + "al ity", + "us ed", + "1 3", + "Ġdes ign", + "Ġch ange", + "Ġch ang", + "Ġb o", + "Ġv is", + "em ber", + "Ġb ook", + "read y", + "Ġk ill", + "2 5", + "pp ed", + "Ġa way", + "Ġab le", + "Ġcount ry", + "Ġcon st", + "ar n", + "Ġor der", + "A R", + "i or", + "i um", + "or th", + "1 8", + "ail able", + "Ġs w", + "Ġm illion", + "Ġ1 3", + "at ic", + "t ed", + "ĠG o", + "Ġo per", + "en g", + "Ġth ing", + "aj or", + "con om", + "ĠCom m", + "Ġwh y", + "u red", + "ur al", + "Ġs chool", + "b y", + "ĠM ar", + "Ġa ff", + "Ġd ays", + "Ġan n", + "us h", + "an e", + "I f", + "e g", + "Ġpro f", + "Ġhe alth", + "ou th", + "B ut", + "ion al", + ". ,", + "Ġs ol", + "Ġal ready", + "Ġ3 0", + "Ġchar act", + "H e", + "Ġf riend", + "E S", + "i ans", + "ic le", + "' d", + "ĠO n", + "Ġle ast", + "Ġp rom", + "Ġd r", + "Ġh ist", + "it her", + "Ġ est", + "i qu", + "1 7", + "s on", + "Ġte ll", + "Ġt alk", + "oh n", + "o int", + "le ction", + "A N", + "Ġunt il", + "au gh", + "Ġl ater", + "Ġ ve", + "Ġv iew", + "end ing", + "iv ed", + "Ġwor d", + "w are", + "Ġc ost", + "Ġen ough", + "Ġg ive", + "ĠUn ited", + "Ġte chn", + "are nt", + "O R", + "Ġp ar", + "ĠD r", + "Ġ201 6", + "r ist", + "er ing", + "Ġ Â", + "Ġl arge", + "s ide", + "ac y", + "cc ess", + "Ġw in", + "Ġimport ant", + "Ġ19 9", + "Ġdoes n", + "Ġ1 7", + "Ġbus iness", + "Ġcle ar", + "Ġre se", + "\" ,", + "ur y", + "Ġe qu", + "as ter", + "al f", + "ĠAmeric an", + "n ect", + "Ġex pect", + "ivers ity", + "Ġo cc", + "ĠF l", + "Ġk ind", + "Ġme an", + "Ġp ast", + "Ġde v", + "Ġb as", + "le t", + "ra ft", + "Ġor gan", + "Ġde l", + "Ġper form", + "Ġst ory", + "Ġse ason", + "ĠC ol", + "Ġcl aim", + "Ġc ame", + "Ġwith in", + "Ġl ine", + "Ġpro ject", + "ĠA t", + "Ġcontro l", + "end ed", + "ĠS y", + "Ġa ir", + "iz ation", + "Ġ *", + "le y", + "Ġm oney", + "id d", + "Y ou", + "f or", + "Ġfam ily", + "Ġm aking", + "Ġb it", + "Ġpol ice", + "Ġhapp en", + "Ġ vers", + "on y", + "u ff", + "ĠW hen", + "Ġs it", + "ide o", + "l f", + "is on", + "Ġsu re", + "g in", + "Ġapp ear", + "Ġl ight", + "Ġ es", + "o f", + "Ġw ater", + "Ġt imes", + "n ot", + "Ġg row", + "Ġcomp any", + "ĠT e", + "ow s", + "Ġm ar", + "our ce", + "i ol", + "ar m", + "b r", + "Ġex ample", + "Ġcon c", + "Ġf ore", + "ĠT o", + "p ro", + "E N", + "ri es", + "Ġ2 5", + "ĠC an", + "ne y", + "Ġact ually", + "Ġe ver", + "ur ity", + "ak en", + "ap s", + "Ġt ax", + "Ġm ajor", + "am a", + "Ġof ten", + "er al", + "Ġhum an", + "Ġj ob", + "is ter", + "Ġav ailable", + "oc r", + "en n", + "a id", + "iv id", + "Ġrec ord", + "? \"", + "Ġs ing", + "ĠA m", + "id ence", + "Ġnew s", + "st er", + "Ġe conom", + "Ġfollow ing", + "ĠB r", + "is ing", + "Ġh our", + "m ost", + "um ent", + "Ġse x", + "Ġdes c", + "Ġbec ome", + "ĠE d", + "Ġto ok", + "Ġha ving", + "Ġprodu ct", + "a ult", + "A s", + "ar ing", + "Ġme ans", + "Ġh op", + "un e", + "Ġch o", + "Ġc ertain", + "Ġn on", + "Ġde al", + "2 4", + "le ment", + "oc i", + "en e", + "Ġs ide", + "ĠP r", + "ĠM ay", + "Ġre ason", + "u ed", + "c hed", + "ul ation", + "Ġe lect", + "Ġoffic ial", + "Ġposs ible", + "Ġh old", + "and s", + "ot s", + "Ġc ity", + "or ies", + "Ġse ver", + "Ġchild ren", + "Ġon ce", + "Ġact iv", + "l er", + "Ġn ight", + "it ions", + "ĠJ ohn", + "a pe", + "pl ay", + "Ġd one", + "Ġl im", + "Ġwork ing", + "ĠP res", + "or ld", + "e b", + "ĠC o", + "Ġb ody", + "ail s", + "ut es", + "ĠM r", + "Ġwhe ther", + "Ġa uthor", + "ro p", + "Ġpro per", + "Ġse en", + ") ;", + "Ġf ac", + "ĠS u", + "Ġcon d", + "it ing", + "Ġcour se", + "Ġ }", + "-------- --------", + "a ign", + "Ġev ent", + "Ġen g", + "Ġp ot", + "Ġin tern", + "i am", + "Ġsh ort", + "em pt", + "ã Ĥ", + "ĠG od", + "il ar", + "8 0", + "Ġor ig", + "I S", + "our n", + "ab ility", + "it ive", + "Ġd am", + "Ġ1 00", + "Ġp ress", + "Ġdo ing", + "Ġprot ect", + "r ing", + "Ġthough t", + "Ġquest ion", + "re w", + "ĠW ar", + "Ġsever al", + "ĠSt ate", + "Ġg iven", + "Ġf und", + "ĠT w", + "Ġw ent", + "an ces", + "w ork", + "p or", + "m y", + "4 0", + "Ġar g", + "art ment", + "ust om", + "Ġpol ic", + "Ġme et", + "Ġc reat", + "2 2", + "ĠSt ates", + "Ġg ames", + "ra w", + "ut ure", + "Ġunder stand", + "ur s", + "ĠO b", + "l ish", + "s y", + "Ġm akes", + "Ġw on", + "ag on", + "Ġh tt", + "Ġl ove", + "ent ial", + "Ġcomple te", + "p ar", + "ĠI m", + "A L", + "Ġacc ount", + " ł", + "ore d", + "ver t", + "Ġ ident", + "Ġ201 5", + "Ġother s", + "ĠM in", + "i ber", + "ver age", + "The re", + "ition al", + "d d", + "Ġpro b", + "Ġyou ng", + "Ġal ong", + "Ġacc ording", + "Ġy et", + "Ġmem bers", + "ĠWh at", + "o id", + "ĠM an", + "A nd", + "Ġam ong", + "a i", + "Ġem ploy", + "ĠR es", + "Ġ >", + "Ġinv ol", + "Ġl ow", + "a f", + "ĠC ar", + "Ġh ig", + "ĠO ne", + "ĠS ec", + "in ation", + "Ġlike ly", + "Ġan t", + "ag ed", + "ĠR uss", + "Ġb en", + "Ġre le", + "F or", + "b ack", + "ĠN ot", + "Ġpres ident", + "b all", + "Ġacc ess", + "ivid ual", + "ĠD em", + "ĠE uro", + "6 0", + "Ġkn own", + "ir l", + "ĠG r", + "Ġear ly", + "u se", + "iet y", + "âĢ ĵ", + "Ġf ight", + "Ġs ent", + "Ġto day", + "Ġmark et", + "\" .", + "Ġb ased", + "Ġstr ong", + "ur ther", + "Ġde b", + "m ber", + "Ġproble m", + "Ġde ath", + "Ġsoc ial", + "im ate", + "A S", + "ort un", + "Ġcamp aign", + "er y", + "C h", + "Ġe y", + "i ally", + "Ġm us", + "w h", + "p os", + "Ġ er", + "Ġsa f", + "Ġmonth s", + "ir on", + "Ġv iol", + "Ġf ive", + "Ġst re", + "Ġplay ers", + "in c", + "al d", + "y ear", + "a un", + "Ġsu ccess", + "Ġpres ent", + "ere nce", + "Ġ201 4", + "Ġsu gg", + "Ġpartic ular", + "Ġtr y", + "Ġsugg est", + "ĠCh rist", + "on es", + "Ġpri v", + "2 3", + "Ġc rit", + "Ġl and", + "Ġloc al", + "if y", + "2 9", + "Ġa ut", + "E D", + "ĠG u", + "Ġm ult", + "Ġpolit ical", + "Ġask ed", + "Ġfor mer", + "it ter", + "ri pt", + "Ġcl ose", + "Ġp ract", + "ĠY ork", + "Ġget ting", + "Ġac ross", + "Ġcom b", + "Ġbelie ve", + "Ġ z", + "Ġto get", + "Ġtoget her", + "ĠC ent", + "ir c", + "Ġind ividual", + "ĠM c", + "2 7", + "is k", + "ĠE ng", + "Ġf ace", + "Ġ2 4", + "Ġval ue", + "Ġare a", + "e v", + "Ġw rit", + "ĠPres ident", + "Ġv ot", + "Ġke y", + "Ġm om", + "p ut", + "Ġany thing", + "Ġexper ience", + "att le", + "Ġm ind", + "a ff", + "om m", + "Ġf uture", + "g ed", + "Ġc ut", + "Ġto t", + "it ch", + "Ġv ideo", + "Ġinvest ig", + "Ġn et", + "ĠM y", + "r ict", + "i en", + ". )", + "Ġimp ro", + "th ough", + "ward s", + "Ġcon nect", + "ĠM ed", + "sel ves", + "ens ive", + "m b", + "o ber", + "at ors", + "A n", + "Ġ5 0", + "Ġre du", + "res ent", + "Ġab ove", + "Ġf re", + "ĠEuro pe", + "s w", + "Ġam ount", + "ĠA pp", + "Ġe ither", + "Ġmil it", + "Ġan al", + "Ġf ail", + "ĠE n", + "al es", + "Ġspec ial", + "Ġbl ack", + "I T", + "c her", + "Ġlook ing", + "Ġf ire", + "y n", + "Ġal most", + "o on", + "Ġstud y", + "Ġm iss", + "c hes", + "ro wn", + "Ġt re", + "Ġcommun ity", + "Ġmed ia", + "Ġf ood", + "Ġcom es", + "ĠUn iversity", + "Ġsing le", + "Wh at", + "u ly", + "Ġh alf", + "ag ue", + "h od", + "ĠRep ublic", + "Ġstart ed", + "Ġqu ick", + "ot o", + "b ook", + "Ġiss ue", + "it or", + "Ġel se", + "Ġcons ider", + "2 6", + "ro du", + "Ġt aken", + "2 8", + "9 9", + "ĠW ith", + "Ġtr ue", + "Ġw a", + "Ġtr ad", + "Ġag o", + "Ġm ess", + "ie f", + "Ġadd ed", + "o ke", + "Ġb ad", + "Ġf av", + "3 3", + "Ġsim ilar", + "as k", + "ĠD on", + "Ġcharact er", + "ort s", + "ĠH ouse", + "Ġreport ed", + "Ġty pe", + "v al", + "i od", + "ĠHow ever", + "Ġt arg", + "Ġent ire", + "pp ing", + "Ġhist ory", + "Ġl ive", + "ff ic", + ".... ....", + "ed eral", + "Ġtr ying", + "Ġdisc uss", + "ĠH ar", + "ac es", + "l ished", + "Ġse lf", + "os p", + "re st", + "Ġro om", + "el t", + "Ġf all", + "ol ution", + "Ġe t", + "Ġ x", + "Ġis n", + "Ġide a", + "b o", + "Ġs ound", + "ĠD ep", + "Ġsome one", + "ci ally", + "ull y", + "Ġf oc", + "Ġob ject", + "if t", + "ap er", + "Ġplay er", + "Ġr ather", + "Ġserv ice", + "as hing", + "ĠD o", + "ĠP art", + "ru g", + "m on", + "p ly", + "Ġm or", + "Ġnot hing", + "Ġprov ide", + "I C", + "un g", + "Ġpart y", + "Ġex ist", + "Ġm ag", + "7 0", + "Ġr ul", + "Ġh ouse", + "Ġbeh ind", + "Ġhow ever", + "ĠW orld", + "Ġs um", + "Ġapp lic", + "Ġ ;", + "Ġfun ction", + "g r", + "ĠP ol", + "Ġfr ont", + "2 00", + "Ġser ies", + "Ġt em", + "Ġty p", + "ill s", + "Ġo pt", + "Ġpoint s", + "Ġbel ow", + "itt ed", + "Ġspec ific", + "Ġ201 7", + "um b", + "Ġr a", + "Ġpre vious", + "Ġpre t", + "re me", + "Ġc ustom", + "Ġcour t", + "ĠM e", + "Ġre pl", + "Ġwho le", + "g o", + "c er", + "Ġt reat", + "ĠA ct", + "Ġprob ably", + "Ġle arn", + "end er", + "ĠA ss", + "Ġvers ion", + "n ow", + "Ġche ck", + "ĠC al", + "R E", + "min ist", + "O n", + "our ces", + "Ġben ef", + "Ġd oc", + "Ġdet er", + "Ġen c", + "Ġsu per", + "Ġadd ress", + "Ġv ict", + "Ġ201 3", + "Ġme as", + "t r", + "Ġf ield", + "W hen", + "Ġsign ific", + "u ge", + "Ġfe at", + "Ġcomm on", + "l oad", + "Ġbe gin", + "Ġbr ing", + "Ġa ction", + "er man", + "Ġdesc rib", + "Ġind ust", + "Ġwant ed", + "ri ed", + "m ing", + "Ġatt empt", + "4 5", + "f er", + "Ġd ue", + "ress ion", + "# #", + "Ġsh all", + "Ġs ix", + "o o", + "Ġst ep", + "Ġp ub", + "Ġhim self", + "Ġ2 3", + "Ġc op", + "Ġd est", + "Ġst op", + "A C", + "ib ility", + "Ġl ab", + "ic ult", + "Ġhour s", + "Ġcre ate", + "Ġf urther", + "ĠAmeric a", + "ĠC ity", + "Ġd ou", + "he ad", + "S T", + "ĠN orth", + "c ing", + "Ġn ational", + "u le", + "ĠIn st", + "Ġt aking", + "ĠQ u", + "ir t", + "Ġre d", + "Ġrese arch", + "v iron", + "ĠG e", + "Ġbre ak", + "an a", + "Ġsp ace", + "ater ial", + "Ġrec ent", + "ĠA b", + "Ġgener al", + "Ġh it", + "Ġper iod", + "Ġevery thing", + "ive ly", + "Ġph ys", + "Ġsay ing", + "an ks", + "Ġc ou", + "Ġc ult", + "ac ed", + "e al", + "u ation", + "Ġc oun", + "l u", + "Ġinclud e", + "Ġpos ition", + "ĠA fter", + "ĠCan ad", + "ĠE m", + "Ġim m", + "ĠR ed", + "Ġp ick", + "Ġcom pl", + "Ġm atter", + "re g", + "e xt", + "ang u", + "is c", + "o le", + "a ut", + "Ġcomp et", + "e ed", + "f ect", + "Ġ2 1", + "ĠS en", + "ĠThe se", + "as ing", + "Ġcan not", + "Ġin it", + "Ġrel ations", + "ac hed", + "Ġb ar", + "Ġ4 0", + "ĠT H", + "Ġ201 2", + "Ġv ol", + "Ġg round", + "Ġsec urity", + "Ġup d", + "il t", + "3 5", + "Ġconc ern", + "ĠJ ust", + "Ġwh ite", + "Ġseem s", + "ĠH er", + "pe cially", + "i ents", + "Ġann oun", + "Ġf ig", + "ight s", + "Ġst ri", + "l ike", + "id s", + "Ġs us", + "Ġw atch", + "Ġ â", + "Ġw ind", + "ĠC ont", + "Ġit self", + "Ġm ass", + "A l", + "y le", + "iqu e", + "ĠN ational", + "Ġab s", + "Ġp ack", + "Ġout side", + "Ġan im", + "Ġp ain", + "et er", + "Ġman ag", + "du ct", + "og n", + "Ġ ]", + "ĠSe pt", + "se c", + "o ff", + "ĠJ an", + "Ġf oot", + "ad es", + "Ġth ird", + "Ġm ot", + "Ġev idence", + "int on", + "Ġth reat", + "a pt", + "pl es", + "c le", + "Ġl o", + "Ġde cl", + "Ġit em", + "med i", + "Ġrep resent", + "om b", + "am er", + "Ġsignific ant", + "og raph", + "s u", + "Ġc al", + "i res", + "00 00", + "I D", + "A M", + "Ġsim ply", + "Ġlong er", + "Ġf ile", + "O T", + "c he", + "S o", + "ate g", + "or g", + "ĠH is", + "Ġen er", + "Ġd om", + "Ġup on", + "il i", + "\": \"", + "Ġthem selves", + "Ġcom ing", + "Ġqu ite", + "Ġdiff icult", + "ĠB ar", + "il ities", + "re l", + "end s", + "c ial", + "6 4", + "Ġwom an", + "ra p", + "y r", + "Ġne cess", + "ip s", + "Ġte xt", + "Ġrequ ire", + "Ġmilit ary", + "Ġre view", + "Ġresp ons", + "7 5", + "Ġsub ject", + "Ġinst ead", + "Ġiss ues", + "Ġg en", + "\" ,\"", + "Ġmin utes", + "Ġwe ap", + "r ay", + "am ed", + "t ime", + "b l", + "H ow", + "Ġc ode", + "ĠS m", + "Ġhig her", + "ĠSt e", + "r is", + "Ġp age", + "Ġstud ents", + "ĠIn tern", + "Ġmet hod", + "ĠA ug", + "ĠP er", + "ĠA g", + "Ġpolic y", + "ĠS w", + "Ġex ec", + "Ġac cept", + "um e", + "rib ut", + "Ġword s", + "Ġfin al", + "Ġchang es", + "ĠDem ocr", + "Ġfriend s", + "Ġres pect", + "Ġe p", + "Ġcomp an", + "iv il", + "Ġdam age", + "** **", + "og le", + "viron ment", + "Ġne g", + "ent al", + "Ġa p", + "Ġtot al", + "iv al", + "! \"", + "l im", + "Ġneed s", + "Ġag re", + "Ġdevelop ment", + "Ġa ge", + "ip le", + "2 1", + "Ġresult s", + "ĠA f", + "S h", + "Ġg un", + "ĠOb ama", + "ro ll", + "Ġ @", + "Ġright s", + "ĠB rit", + "Ġrun ning", + "Ġwas n", + "Ġp ort", + "Ġr ate", + "Ġpret ty", + "Ġtarg et", + "Ġsa w", + "Ġc irc", + "Ġwor ks", + "ic ro", + "al t", + "o ver", + "ww w", + "Th at", + "l ier", + "Ġevery one", + "ud e", + "Ġp ie", + "idd le", + "ra el", + "Ġr ad", + "Ġbl ock", + "Ġw alk", + "T o", + "ã ģ", + "n es", + "ĠA ust", + "a ul", + "ro te", + "ĠS outh", + "ess ion", + "op h", + "Ġshow s", + "Ġs ite", + "Ġj o", + "Ġr isk", + "cl us", + "l t", + "Ġin j", + "id ing", + "ĠS pe", + "Ġch all", + "ir m", + "Ġ2 2", + "itt ing", + "st r", + "Ġh y", + "L E", + "ke y", + "Ġbe gan", + "at ur", + "ashing ton", + "l am", + "ĠD av", + "b it", + "Ġs ize", + "ĠP ar", + "3 8", + "ourn al", + "f ace", + "Ġdec ision", + "Ġl arg", + "Ġj ud", + "re ct", + "Ġcontin ue", + "ĠO ct", + "ove red", + "ĠI nt", + "==== ====", + "Ġp arent", + "ĠW ill", + "Ġeas y", + "Ġd rug", + "ang er", + "Ġs ense", + "Ġd i", + "id ay", + "Ġener gy", + "ist ic", + "Ġass oci", + "ar ter", + "ob al", + "e ks", + "ĠE l", + "ur ch", + "Ġg irl", + "o e", + "it le", + "Ġ2 8", + "ĠC he", + "Ġrequ est", + "Ġso on", + "Ġh ost", + "k y", + "Ġst ates", + "om es", + "Ġm aterial", + "le x", + "Ġmom ent", + "Ġan sw", + "on se", + "Ġes pecially", + "Ġn orm", + "Ġserv ices", + "p ite", + "r an", + "Ġro le", + "4 4", + ") :", + "Ġc red", + "C l", + "____ ____", + "Ġm at", + "Ġl og", + "ĠCl inton", + "O U", + "Ġoff ice", + "Ġ2 6", + "Ġch arg", + "Ġtr ack", + "m a", + "Ġhe art", + "Ġb all", + "Ġperson al", + "Ġbuild ing", + "n a", + "s et", + "b ody", + "ĠBl ack", + "Ġincre ase", + "itt en", + "Ġneed ed", + "3 6", + "3 2", + "= \"", + "Ġl ost", + "Ġbec ame", + "Ġgrou ps", + "ĠM us", + "Ġw rote", + "ĠP e", + "Ġpro p", + "j oy", + "à ©", + "ĠWh ite", + "Ġde ad", + ". '", + "Ġhtt p", + "Ġwe bs", + "O S", + "Ġins ide", + "Ġwr ong", + "Ġstat ement", + "Ġ ...", + "y l", + "Ġfil m", + "Ġmus ic", + "Ġsh are", + "ific ation", + "Ġre lease", + "Ġfor ward", + "Ġst ay", + "Ġcomp ut", + "it te", + "s er", + "Ġorig inal", + "Ġc ard", + "Ġc and", + "Ġd iv", + "at ural", + "Ġfav or", + "O M", + "Ġc ases", + "us es", + "Ġse ction", + "Ġle ave", + "g ing", + "ov ed", + "ĠW ashington", + "3 9", + "ĠG l", + "Ġrequ ired", + "act ion", + "ap an", + "o or", + "it er", + "ĠK ing", + "Ġcount ries", + "ĠG erman", + "ll ing", + "Ġ2 7", + "3 4", + "Ġquest ions", + "Ġpr im", + "Ġc ell", + "Ġsh oot", + "Ġany one", + "ĠW est", + "Ġaff ect", + "ep end", + "Ġon line", + "ĠIs rael", + "ĠSept ember", + "Ġab ility", + "Ġcont ent", + "is es", + "Ġre ve", + "Ġl aun", + "Ġind ic", + "Ġfor ce", + "c ast", + "Ġso ld", + "av ing", + "f l", + "Ġso ft", + "Ġcompan ies", + "ce ed", + "Ġart icle", + "Ġa ud", + "Ġre v", + "Ġed uc", + "Ġplay ing", + "0 5", + "Ġhe ld", + "ct or", + "Ġrele ased", + "Ġf ederal", + "3 7", + "Ġad minist", + "Ġinter view", + "Ġinst all", + "Ġrece ived", + "Ġs ource", + "u k", + "P h", + "Ġser ious", + "Ġcre ated", + "Ġc ause", + "Ġim medi", + "Ġdef in", + "u el", + "ĠDep artment", + "ct ions", + "ĠC our", + "ĠN ow", + "z e", + "it es", + "it ution", + "Ġl ate", + "Ġspe ak", + "n ers", + "Ġleg al", + "ar i", + "ĠC or", + "Ġwe eks", + "Ġmod el", + "Ġp red", + "Ġex act", + "B C", + "ĠB y", + "IN G", + "os ing", + "Ġt akes", + "Ġreg ard", + "Ġopp ortun", + "Ġpr ice", + "Ġ19 8", + "ĠA pr", + "f ully", + "Ġor d", + "Ġproble ms", + "ru ction", + "h am", + "ĠC ount", + "le ge", + "Ġlead ers", + "E T", + "le v", + "Ġde ep", + "olog ical", + "es e", + "h aps", + "ĠS ome", + "Ġp ers", + "Ġcont ract", + "Ġrelations hip", + "s p", + "ou d", + "Ġb ase", + "4 8", + "m it", + "A d", + "anc ial", + "Ġcons um", + "Ġpot ential", + "Ġl angu", + "re m", + "et h", + "Ġrel ig", + "ress ed", + "6 6", + "Ġl ink", + "Ġl ower", + "ay er", + "ĠJ une", + "Ġf em", + "un t", + "er c", + "ur d", + "Ġcont act", + "Ġ ill", + "Ġm other", + "Ġest ab", + "h tt", + "ĠM arch", + "ĠB ro", + "ĠCh ina", + "Ġ2 9", + "Ġs qu", + "Ġprov ided", + "Ġa verage", + "as ons", + "Ġ201 1", + "Ġex am", + "l in", + "5 5", + "n ed", + "Ġper fect", + "Ġt ou", + "al se", + "u x", + "Ġbu y", + "Ġsh ot", + "Ġcol lect", + "Ġph ot", + "Ġplay ed", + "Ġsur pr", + "Ġofficial s", + "Ġsim ple", + "av y", + "Ġindust ry", + "Ġhand s", + "g round", + "Ġp ull", + "Ġr ound", + "Ġus er", + "Ġr ange", + "u ary", + "Ġpriv ate", + "op s", + "e es", + "Ġw ays", + "ĠM ich", + "Ġve h", + "Ġex cept", + "Ġter ms", + "im um", + "pp er", + "I ON", + "ore s", + "ĠDr agon", + "ou l", + "Ġd en", + "Ġperform ance", + "Ġb ill", + "c il", + "4 7", + "Ġen vironment", + "Ġex c", + "ad d", + "Ġwor th", + "Ġp ict", + "Ġch ance", + "Ġ201 8", + "b or", + "Ġspe ed", + "ict ion", + "Ġal leg", + "ĠJ apan", + "at ory", + "re et", + "Ġm atch", + "ĠI I", + "Ġst ru", + "ord er", + "Ġst e", + "Ġl iving", + "Ġst ruct", + "in o", + "Ġse par", + "her n", + "Ġresp onse", + "Ġen joy", + "Ġv ia", + "A D", + "um ents", + "ace book", + "Ġmem ber", + "ib r", + "iz ing", + "Ġto ol", + "ĠM on", + "ĠWh ile", + "h ood", + "ĠA ng", + "ĠD ef", + "Ġoff er", + "T r", + "a ur", + "Ġturn ed", + "ĠJ uly", + "d own", + "an ced", + "Ġrec ently", + "ĠE ar", + "Ġc e", + "ĠSt ar", + "ĠC ong", + "rough t", + "Ġbl ood", + "Ġhop e", + "Ġcom ment", + "ain t", + "Ġar ri", + "il es", + "Ġpartic ip", + "ough t", + "ri ption", + "0 8", + "4 9", + "Ġg ave", + "Ġse lect", + "Ġkill ed", + "sy ch", + "Ġgo es", + "i j", + "Ġc oll", + "Ġimp act", + "at ives", + "ĠS er", + "0 9", + "ĠAug ust", + "Ġb oy", + "d e", + "ĠD es", + "Ġf elt", + "U S", + "Ġexpect ed", + "Ġim age", + "ĠM ark", + "cc ording", + "o ice", + "E C", + "ĠM ag", + "en ed", + "h old", + "ĠP ost", + "Ġpre vent", + "N o", + "Ġinvol ved", + "Ġey es", + "Ġquick ly", + "A t", + "un k", + "Ġbeh av", + "Ġ ur", + "Ġl ed", + "c ome", + "e y", + "Ġcand id", + "Ġear lier", + "Ġfoc us", + "et y", + "P ro", + "led ge", + "ix ed", + "ill ed", + "Ġpop ular", + "A P", + "Ġset t", + "l ight", + "Ġvar ious", + "in ks", + "Ġlevel s", + "Ġro ad", + "ell ig", + "ab les", + "he l", + "itte e", + "ĠG ener", + "y pe", + "Ġhe ard", + "ic les", + "Ġm is", + "Ġus ers", + "ĠS an", + "Ġimpro ve", + "Ġf ather", + "Ġse arch", + "The y", + "v il", + "Ġprof ess", + "Ġkn ew", + "Ġl oss", + "Ġev ents", + "6 5", + "Ġb illion", + "0 7", + "0 2", + "ĠNew s", + "ĠA M", + "Ġco ver", + "w here", + "ens ion", + "Ġb ott", + "Ġare as", + "en ces", + "op e", + "ĠTw itter", + "a el", + "Ġget s", + "ĠGo ogle", + "Ġs n", + "i ant", + "Ġv ote", + "Ġnear ly", + "Ġinclud ed", + "Ġrec ogn", + "z z", + "m m", + "al ed", + "Ġhappen ed", + "0 4", + "Ġh ot", + "Ġwho se", + "Ġc ivil", + "Ġsu ff", + "o es", + "it iz", + "ĠSy ri", + "Ġresp ond", + "Ġh on", + "Ġfeat ures", + "Ġeconom ic", + "ĠApr il", + "r im", + "Ġtechn ology", + "Ġo ption", + "ag ing", + "Ġpur ch", + "R e", + "Ġl at", + "ch ie", + "is l", + "Ġrec omm", + "u f", + "Ġtr aining", + "Ġeffect s", + "Ġf ast", + "Ġ201 0", + "Ġocc ur", + "Ġwebs ite", + "Ġem ail", + "Ġs ens", + "e ch", + "Ġo il", + "Ġinf lu", + "Ġcurrent ly", + "ĠS ch", + "ĠAd d", + "Ġgo al", + "Ġsc ient", + "Ġcon v", + "1 00", + "em y", + "Ġdec ided", + "Ġtra vel", + "Ġm ention", + "L L", + "0 3", + "Ġe lection", + "Ġph one", + "Ġlook s", + "Ġsit uation", + "Ġc y", + "Ġh or", + "b ed", + "ĠCour t", + "a ily", + "av es", + "Ġqu ality", + "ĠCom p", + "w ise", + "Ġt able", + "Ġst aff", + "ĠW ind", + "et t", + "Ġtri ed", + "ide red", + "Ġadd ition", + "Ġb ox", + "Ġl ack", + "ar ily", + "Ġw ide", + "Ġm id", + "Ġbo ard", + "ys is", + "Ġant i", + "h a", + "Ġd ig", + "en ing", + "Ġd ro", + "C on", + "6 8", + "Ġsl ow", + "b ased", + "se qu", + "Ġp ath", + "E x", + "ak er", + "Ġwork ed", + "Ġp en", + "Ġeng ine", + "Ġlook ed", + "ĠSu per", + "ĠS erv", + "Ġvict im", + "U n", + "Ġproper ty", + "Ġint rodu", + "Ġexec ut", + "ĠP M", + "L e", + "Ġcol or", + "ĠM ore", + "Ġ6 0", + "Ġnet work", + "Ġd ate", + "c ul", + "id ge", + "Ġext ra", + "3 1", + "Ġs le", + "6 7", + "Ġw ond", + "Ġreport s", + "j ust", + "ĠAust ral", + "Ġcap ital", + "Ġen s", + "Ġcomm and", + "Ġallow ed", + "Ġpre p", + "Ġca pt", + "h ib", + "Ġnum bers", + "ch an", + "Ġf air", + "m p", + "om s", + "Ġre ach", + "W ith", + "t ain", + "Ġbro ad", + "Ġcou ple", + "ec ause", + "ly ing", + "ĠF eb", + "Ġsc reen", + "Ġl ives", + "Ġpri or", + "ĠCong ress", + "A r", + "Ġappro ach", + "Ġe mer", + "ar ies", + "ĠD is", + "s erv", + "ĠN e", + "Ġbu ilt", + "c ies", + "Ġre pe", + "Ġrul es", + "for ce", + "ĠP al", + "Ġfin ancial", + "Ġcons idered", + "ĠCh ar", + "n ces", + "ĠI S", + "Ġb rought", + "Ġb i", + "i ers", + "ĠS im", + "O P", + "Ġproduct s", + "Ġvis it", + "Ġdoc ument", + "Ġcon duct", + "Ġcomplete ly", + "in ing", + "ĠCal if", + "ib ly", + "Ġwr itten", + "ĠT V", + "em ents", + "Ġd raw", + "O ne", + "Ġpub lished", + "Ġsec ret", + "r ain", + "he t", + "ĠF acebook", + "ond ay", + "ĠU p", + "Ġsex ual", + "Ġth ous", + "ĠP at", + "Ġ ess", + "Ġstand ard", + "Ġar m", + "g es", + "ect ion", + "Ġf ell", + "Ġfore ign", + "an i", + "ĠFr iday", + "Ġreg ular", + "in ary", + "Ġincre ased", + "Ġus ually", + "Ġdem on", + "Ġd ark", + "Ġadd itional", + "ro l", + "ĠO f", + "Ġprodu ction", + "! !", + "und red", + "Ġintern ational", + "id ents", + "ĠF ree", + "rou p", + "Ġr ace", + "Ġm ach", + "Ġh uge", + "A ll", + "le ar", + "ove mber", + "Ġto wn", + "Ġatt ention", + "ĠO ff", + "y ond", + "ĠThe n", + "f ield", + "Ġter ror", + "ra z", + "ĠB o", + "Ġmeet ing", + "ĠP ark", + "Ġar rest", + "Ġf ear", + "Ġa w", + "ĠV al", + "or ing", + "' ,", + "Ġext reme", + "ar r", + "Ġwork ers", + "A fter", + "Ġ3 1", + "n et", + "am ent", + "Ġdirect ly", + "Ġpop ulation", + "ub e", + "ĠOct ober", + "ĠI N", + "ĠJan uary", + "5 9", + "ĠDav id", + "Ġc ross", + "ce mber", + "ĠF irst", + "Ġmess age", + "ir it", + "Ġn ation", + "Ġp oll", + "is ions", + "Ġansw er", + "n y", + "is ode", + "Ġcar ry", + "ĠRuss ia", + "Ġhe ar", + "eng th", + "ro y", + "Ġn atural", + "in ally", + "Ġdo g", + "m itted", + "Ġtr ade", + "Ġsub st", + "Ġmult iple", + "ĠAf ric", + "Ġf ans", + "Ġs ort", + "Ġgl obal", + "ic ation", + "ĠW ed", + "ar a", + "Ġa chie", + "Ġlangu age", + "ve y", + "Ġt al", + "Ġnecess ary", + "Ġdet ails", + "Ġs en", + "ĠS und", + "ĠRe g", + "ĠR ec", + "0 6", + "Ġs il", + "ress ive", + "Ġmed ical", + "un ch", + "orn ia", + "Ġu nd", + "f ort", + "oc ks", + "ĠM onday", + "ues day", + "c raft", + "7 7", + "ur t", + "Ġ ver", + "ĠH ill", + "Ġrece ive", + "Ġmor ning", + "es tern", + "Ġb ank", + "Ġs at", + "ir th", + "ĠH igh", + "Ġdev ice", + "ĠTH E", + "ĠCent er", + "Ġsaf e", + "Ġp le", + "ĠCanad a", + "Ġsystem s", + "Ġass ist", + "Ġsur v", + "Ġb attle", + "ĠS oc", + "vert is", + "S he", + "Ġp aper", + "Ġgrow th", + "Ġc ast", + "S c", + "Ġpl ans", + "ll ed", + "Ġpart s", + "Ġw all", + "Ġmove ment", + "Ġpract ice", + "im ately", + "Ġdis play", + "Ġsomet imes", + "om p", + "ĠP aul", + "ĠY es", + "k ing", + "5 8", + "o ly", + "Ġs on", + "Ġav oid", + "ok es", + "ĠJ ew", + "Ġto wards", + "as c", + "Ġ //", + "ĠK ore", + "Ġtalk ing", + "Ġcor rect", + "Ġsp ent", + "ic ks", + "i able", + "e ared", + "Ġter m", + "Ġwant s", + "om ing", + "Ġ ut", + "Ġdou b", + "Ġfor ces", + "Ġp lease", + "6 9", + "ĠN ovember", + "at form", + "ond on", + "Ġon es", + "Ġimmedi ately", + "ĠRuss ian", + "ĠM et", + "Ġde g", + "Ġparent s", + "C H", + "ĠAmeric ans", + "al y", + "ĠM od", + "Ġsh own", + "Ġcond itions", + "Ġst uff", + "Ġre b", + "ĠY our", + "Ġinclud es", + "n own", + "ĠS am", + "Ġexper ien", + "m ission", + "ĠE ven", + "augh t", + "Ġannoun ced", + "ĠRepublic an", + "Ġdeter min", + "Ġdescrib ed", + "ĠCount y", + "( )", + "Ġdo or", + "Ġchang ed", + "Ġne igh", + "ĠH ere", + "Ġcle an", + "Ġp an", + "ĠDe cember", + "ĠEurope an", + "ir ing", + "ap ter", + "Ġcl ub", + "ĠT uesday", + "Ġp aid", + "ĠN et", + "Ġattack s", + "Ġcharact ers", + "Ġal one", + "Ġdirect or", + "d om", + "Ġ3 5", + "Ġl oad", + "Ġr out", + "ĠCalif ornia", + "Ġfin ally", + "Ġr ac", + "Ġcont r", + "Ġexact ly", + "res h", + "p ri", + "ĠIs lam", + "Ġn ature", + "Ġcare er", + "Ġlat est", + "Ġcon vers", + "ĠS l", + "p ose", + "ci ent", + "ĠIn c", + "iv ity", + "8 8", + "ĠA tt", + "ĠM or", + "nes day", + "Ġwe ight", + "k en", + "Ġnot e", + "Ġteam s", + "Ġ \\", + "air s", + "ĠG reen", + "Ġh undred", + "on ent", + "Ġstre ng", + "Ġcons ist", + "ic ated", + "Ġreg ul", + "Ġl ic", + "ast ic", + "Ġt en", + "urs day", + "ellig ence", + "ous ly", + "ĠU K", + "B I", + "Ġcost s", + "Ġind epend", + "ĠA P", + "Ġnorm al", + "Ġh om", + "Ġob vious", + "Ġs we", + "Ġst ar", + "Ġread y", + "ac her", + "Ġimp lement", + "g est", + "Ġs ong", + "ĠG et", + "ĠL ab", + "Ġinterest ing", + "us ing", + "Ġg iving", + "ĠSund ay", + "Ġet c", + "Ġm iddle", + "Ġrem ember", + "r ight", + "os ition", + "ut ions", + "Ġm ax", + "4 6", + "Ġyour self", + "Ġdem and", + "Ġtreat ment", + "Ġd anger", + "ĠC ons", + "Ġgu y", + "ĠBrit ish", + "Ġphys ical", + "Ġrel ated", + "Ġrem ain", + "Ġcould n", + "Ġref er", + "Ġc itiz", + "b ox", + "EN T", + "bo ard", + "Ġin n", + "I G", + "er o", + "ĠSt reet", + "osp ital", + "ren ch", + "cher s", + "Ġst ra", + "O L", + "ag er", + "ĠA N", + "Ġeas ily", + "I A", + "en ge", + "in y", + "Ġcl os", + "ock ed", + "Ġus es", + "ĠC oun", + "I m", + "u ild", + "? ?", + "m ore", + "Ġan g", + "Ġwr ite", + "ol ute", + "5 7", + "Ġlead er", + "Ġread ing", + "< /", + "Ġaut om", + "est s", + "4 3", + "Ġleg isl", + "ĠG old", + "Ġdesign ed", + "ĠS T", + "ĠLe g", + "a res", + "Ġbe aut", + "ĠT ex", + "Ġappear s", + "Ġstru gg", + "ĠR om", + "Ġ 00", + "Ġcho ice", + "Ġparticular ly", + "ĠF rom", + "op er", + "ĠL ondon", + "ann ed", + "Ġallow s", + "ob ile", + "Ġdiffere nce", + "âĢ ¢", + "ĠV iew", + "ĠWed nesday", + "Ġal though", + "Ġrel ative", + "Ġapplic ation", + "ate ver", + "Ġare n", + "Ġmy self", + "Ġim ag", + "Ġdis e", + "Ġsoc iety", + "Ġfre qu", + "ĠEng lish", + "Ġpo or", + "ĠD ay", + "Ġwrit ing", + "Ġse ven", + "Ġstart ing", + "Ġb ud", + "Ġpr int", + "ĠTr ans", + "uf act", + "ĠSt ud", + "n ew", + "Ġcr im", + "Ġg ives", + "Ġco ol", + "a e", + "i ance", + "ĠGener al", + "Ġthink ing", + "Ġsa ve", + "Ġlim ited", + "ĠPart y", + "Ġmean ing", + "p en", + "ow ers", + "ĠJ ack", + "E M", + "Ġn ice", + "ru pt", + "Ġg as", + "Ġe ight", + "Ġfe et", + "Ġeff ort", + "Ġ ign", + "ic it", + "B l", + "co in", + "Ġop in", + "Ġbr ain", + "Wh ile", + "he st", + "ĠTh ursday", + "Ġwould n", + "augh ter", + "Ġtou ch", + "le ments", + "Ġstud ies", + "Ġcent er", + "c ont", + "or ge", + "Ġcomput er", + "Ġinvestig ation", + "P l", + "or ks", + "Ġ200 8", + "Ġincre asing", + "Ġst ore", + "Ġcom ments", + "Ġb al", + "m en", + "Ġdo ll", + "Ġl iber", + "Ġw ife", + "Ġlaw s", + "atur day", + "it ness", + "Ġmod ern", + "ĠS k", + "Ġadminist ration", + "Ġopportun ity", + "Ġs al", + "Ġpower ful", + "M y", + "Ġclaim s", + "ĠEar th", + "ord s", + "Ġt itle", + "Ġes c", + "n ame", + "N ot", + "om en", + "Ġbe yond", + "Ġc amer", + "Ġse ll", + "it ute", + "ear ch", + "Ġapp l", + "im ent", + "4 2", + "ĠAr t", + "Ġun f", + "Ġviol ence", + "ur g", + "ĠE ast", + "Ġcomp ared", + "Ġopt ions", + "Ġthrough out", + "Ġv s", + "ig r", + ". [", + "ac hes", + "7 8", + "Ġfil es", + "F L", + "E L", + "ar ian", + "ĠJ ames", + "ĠA ir", + "an ch", + "Ġdet ail", + "Ġpie ce", + "P S", + "Ġn amed", + "Ġeduc ation", + "Ġdri ve", + "Ġitem s", + "Ġstud ent", + "ic ed", + ": :", + "ic o", + "Ġth row", + "Ġsc ene", + "Ġcomple x", + "Ġ200 9", + "Ġpre c", + "ĠB re", + "7 9", + "Ġcon cept", + "Ġstat us", + "am ing", + "Ġd ied", + "Ġknow ledge", + "Ġbegin ning", + "O D", + "ru ary", + "Ġcertain ly", + "Ġgu ys", + "Ġsl ight", + "in n", + "ound s", + "Ġf ine", + "Ġf at", + "ic ations", + "Ġper haps", + "ĠA nt", + "Ġinc ome", + "Ġhtt ps", + "Ġmajor ity", + "port s", + "st on", + "Ġgreat er", + "Ġfe ed", + "ent ially", + "Ġsaf ety", + "Ġun ique", + "and om", + "Ġg one", + "Ġshow ed", + "Ġhist or", + "Ġcoun ter", + "i us", + "id a", + "Ġlead ing", + "i pe", + "Ġs end", + "ĠDon ald", + "er ve", + "Ġdef ense", + "ines e", + "Ġy es", + "ĠF ire", + "ĠMus lim", + "ra q", + "Ġcontin ued", + "os h", + "Ġprov ides", + "Ġpr ison", + "ĠP re", + "Ġhapp y", + "Ġeconom y", + "Ġtr ust", + "ag s", + "ĠG ame", + "Ġweap ons", + "um an", + "ĠC le", + "it ation", + "Ġanal ysis", + "ĠT imes", + "Ġsc ience", + "- >", + "Ġfig ure", + "Ġdis app", + "ent y", + "Ġsoft ware", + "Ġu lt", + "Ġoffic ers", + "N ew", + "I s", + "Ġrem ains", + "ĠInd ia", + "Ġp sych", + "ri ef", + "Ġc at", + "es c", + "Ġob serv", + "Ġst age", + "ĠD ark", + "Ġent er", + "ch ange", + "Ġpass ed", + "Ġdes pite", + "ĠO ut", + "Ġmov ie", + "r s", + "Ġv oice", + "m ine", + "ĠPl ay", + "Ġto ward", + "ĠT er", + "Ġreg ion", + "Ġval ues", + "or ters", + "Ġm ount", + "Ġoffic er", + "ĠO ther", + "b an", + "Ġh ous", + "w ood", + "ro om", + "I V", + "ĠS un", + "se e", + "ĠO ver", + "ro g", + "9 0", + "Ġl ay", + "ĠT ur", + "a wn", + "Ġpress ure", + "ĠS ub", + "Ġbook s", + "ed om", + "ĠS and", + "A A", + "ag o", + "Ġre asons", + "f ord", + "Ġactiv ity", + "U T", + "N ow", + "ĠSen ate", + "ce ll", + "n ight", + "Ġcall s", + "in ter", + "Ġlet ter", + "ĠR ob", + "ĠJ e", + "Ġcho ose", + "ĠL aw", + "G et", + "B e", + "Ġro b", + "Ġtyp es", + "Ġpl atform", + "Ġqu arter", + "R A", + "ĠT ime", + "Ġmay be", + "ĠC r", + "9 5", + "p re", + "Ġmov ing", + "Ġl if", + "Ġgo ld", + "Ġs om", + "Ġpat ients", + "Ġtr uth", + "ĠK e", + "ur ance", + "ant ly", + "m ar", + "Ġchar ge", + "ĠG reat", + "Ġce le", + "---------------- ----------------", + "Ġro ck", + "ro id", + "an cy", + "Ġcred it", + "a ud", + "B y", + "ĠE very", + "Ġmov ed", + "ing er", + "rib ution", + "Ġn ames", + "Ġstra ight", + "ĠHe alth", + "ĠW ell", + "Ġfe ature", + "Ġr ule", + "Ġsc he", + "in ated", + "ĠMich ael", + "ber g", + "4 1", + "il ed", + "b and", + "Ġcl ick", + "ĠAng el", + "on ents", + " Ń", + "ĠI raq", + "ĠS aturday", + "Ġa ware", + "p art", + "Ġpat tern", + "O W", + "ĠL et", + "Ġgr ad", + "ign ed", + "Ġassoci ated", + "Ġst yle", + "n o", + "i ation", + "a ith", + "il ies", + "Ġst ories", + "ur ation", + "Ġindividual s", + "ĠâĢ ¦", + "m iss", + "ĠAss oci", + "ish ing", + "ab y", + "Ġsum mer", + "ĠB en", + "Ġ3 2", + "Ġar ch", + "ut y", + "ĠTex as", + "h ol", + "Ġfull y", + "Ġm ill", + "Ġfollow ed", + "ĠB ill", + "ĠInd ian", + "ĠSec ret", + "ĠB el", + "ĠFeb ruary", + "Ġjob s", + "Ġseem ed", + "ĠGo vern", + "i pped", + "Ġreal ity", + "Ġl ines", + "Ġp ark", + "Ġmeas ure", + "ĠO ur", + "I M", + "Ġbro ther", + "Ġgrow ing", + "Ġb an", + "Ġest im", + "Ġc ry", + "ĠS chool", + "Ġme chan", + "ĠO F", + "ĠWind ows", + "Ġr ates", + "ĠO h", + "Ġpos itive", + "Ġcult ure", + "ist ics", + "ic a", + "Ġh ar", + "y a", + "ite ly", + "i pp", + "Ġm ap", + "en cies", + "ĠWill iam", + "I I", + "ak ers", + "5 6", + "ĠM art", + "ĠR em", + "Ġal tern", + "it ude", + "Ġco ach", + "row d", + "D on", + "Ġk ids", + "Ġj ournal", + "Ġcor por", + "Ġf alse", + "Ġwe b", + "Ġsle ep", + "Ġcont ain", + "Ġst o", + "Ġb ed", + "iver se", + "ĠR ich", + "ĠCh inese", + "Ġp un", + "Ġme ant", + "k nown", + "Ġnot ice", + "Ġfavor ite", + "a ven", + "Ġcond ition", + "Ġpur pose", + ") )", + "Ġorgan ization", + "Ġchall eng", + "Ġman ufact", + "Ġsus p", + "ĠA c", + "Ġcrit ic", + "un es", + "uc lear", + "Ġm er", + "vent ion", + "Ġ8 0", + "Ġm ist", + "ĠU s", + "ĠT or", + "htt p", + "ol f", + "Ġlarg er", + "Ġadv ant", + "Ġrese ar", + "Ġact ions", + "m l", + "Ġke pt", + "Ġa im", + ", '", + "c ol", + "Ġbenef its", + "if ying", + "Ġact ual", + "ĠIntern ational", + "Ġveh icle", + "Ġch ief", + "Ġeff orts", + "ĠLe ague", + "ĠM ost", + "Ġwa it", + "Ġad ult", + "Ġover all", + "Ġspe ech", + "Ġhigh ly", + "Ġfem ale", + "Ġer ror", + "Ġeffect ive", + "5 4", + "Ġenc our", + "w ell", + "Ġfail ed", + "Ġcons erv", + "Ġprogram s", + "Ġt rou", + "Ġa head", + "5 00", + "vertis ement", + "I P", + "ĠF ound", + "p ir", + "Ġ %", + "Ġcr ime", + "and er", + "Ġloc ation", + "ĠI ran", + "Ġbehav ior", + "az ing", + "Ġr are", + "Ġem b", + "Ġca used", + "Ġsh ip", + "Ġact ive", + "Ġcont ribut", + "Ġg reen", + "Ġac qu", + "Ġref lect", + "ven ue", + "Ġf irm", + "Ġb irth", + "] .", + "Ġclear ly", + "Ġem ot", + "Ġag ency", + "ri age", + "Ġmem ory", + "9 8", + "S A", + "ĠSe e", + "ac ing", + "C C", + "Ġbig gest", + "Ġr ap", + "Ġbas ic", + "Ġb and", + "e at", + "Ġsus pect", + "ĠM ac", + "Ġ9 0", + "m ark", + "ist an", + "Ġsp read", + "am s", + "k i", + "as y", + "ra v", + "ĠR ober", + "Ġdemon str", + "r ated", + "Ġabs olute", + "Ġpl aces", + "Ġim pl", + "ibr ary", + "Ġc ards", + "Ġdest roy", + "Ġv irt", + "ve re", + "Ġapp eared", + "y an", + "p oint", + "Ġbe g", + "Ġtem per", + "s pe", + "ant ed", + "ear s", + "ĠD irect", + "Ġl ength", + "Ġbl og", + "am b", + "Ġint eg", + "Ġres ources", + "ac c", + "if ul", + "Ġsp ot", + "Ġfor ced", + "Ġthous ands", + "ĠMin ister", + "Ġqu al", + "ĠF rench", + "at ically", + "Ġgener ally", + "Ġdr ink", + "Ġth us", + "I L", + "od es", + "Ġappro pri", + "ĠRe ad", + "Ġwh om", + "Ġey e", + "Ġcol lege", + "Ġ4 5", + "ire ction", + "Ġens ure", + "Ġapp arent", + "id ers", + "Ġrelig ious", + "Ġmin or", + "ol ic", + "Ġt ro", + "ĠWh y", + "rib ute", + "m et", + "Ġprim ary", + "Ġdevelop ed", + "Ġpe ace", + "Ġsk in", + "st e", + "av a", + "Ġbl ue", + "Ġfam ilies", + "Ġ ir", + "Ġapp ly", + "Ġin form", + "ĠSm ith", + "C T", + "i i", + "Ġlim it", + "Ġres ist", + "........ ........", + "um n", + "Ġconf lic", + "Ġtw e", + "ud d", + "ĠT om", + "Ġl iter", + "qu e", + "b on", + "Ġha ir", + "Ġevent ually", + "Ġp us", + "Ġhelp ed", + "Ġag g", + "or ney", + "ĠApp le", + "Ġf it", + "ĠS ur", + "Ġpre m", + "Ġs ales", + "Ġsecond s", + "Ġstreng th", + "Ġfeel ing", + "¿ ½", + "Ġt our", + "Ġknow s", + "o om", + "Ġex erc", + "Ġsom ew", + "ï ¿½", + "> >", + "Ġsp okes", + "Ġide as", + "Ġreg ist", + "so ft", + "ĠD el", + "ĠP C", + "Ġpro pos", + "Ġlaun ch", + "Ġbott om", + "T H", + "ĠP lease", + "v est", + "it z", + "ĠIn ter", + "Ġsc ript", + "Ġr at", + "ar ning", + "Ġ il", + "ĠJ er", + "ĠA re", + "Ġwh atever", + "ok en", + "ci ence", + "Ġmod e", + "Ġag ree", + "Ġs ources", + "Ġinit ial", + "Ġrest rict", + "Ġwond er", + "us ion", + "## ##", + "ĠS il", + "vil le", + "Ġb urn", + "t w", + "as ion", + "Ġ £", + "Ġn or", + "u ing", + "Ġre ached", + "Ġs un", + "Ġc ateg", + "ig ration", + "Ġc ook", + "Ġprom ot", + "Ġm ale", + "Ġcl imate", + "Ġf ix", + "Ġalleg ed", + "U R", + "all ed", + "Ġim ages", + "C ont", + "ot a", + "Ġschool s", + "i os", + "Ġd rop", + "Ġst ream", + "ĠM o", + "Ġprevious ly", + "al ing", + "Ġp et", + "Ġdou ble", + "Ġ( @", + "ann el", + "Ġdef ault", + "t ies", + "Ġr ank", + "ĠD ec", + "ĠCoun cil", + "Ġweap on", + "Ġst ock", + "Ġanal y", + "ĠSt r", + "Ġpict ure", + "ĠPol ice", + "f erence", + "Ġcent ury", + "Ġcitiz ens", + "Ġon to", + "Ġexp and", + "Ġhe ro", + "ĠS ol", + "Ġw ild", + "Ġupd ate", + "Ġcustom ers", + "r ont", + "d ef", + "Ġl ik", + "Ġcrim inal", + "ĠChrist ian", + "S P", + "7 6", + "Ġle aving", + "Ġother wise", + "ĠD ist", + "Ġbas is", + "5 2", + "5 3", + "ic ip", + "ĠB er", + "Ġrecomm end", + "Ġfl oor", + "Ġc rowd", + "ol es", + "Ġ7 0", + "Ġcent ral", + "ĠE v", + "Ġd ream", + "Ġdown load", + "Ġconf ir", + "ĠTh om", + "Ġwind ow", + "Ġhapp ens", + "Ġun it", + "Ġt end", + "Ġs pl", + "Ġbec omes", + "Ġfight ing", + "Ġpred ict", + "ĠP ress", + "ĠP ower", + "Ġhe avy", + "ak ed", + "Ġf an", + "or ter", + "ate gy", + "B A", + "iz es", + "Ġsp end", + "H ere", + "Ġ200 7", + "Ġad op", + "ĠH am", + "Ġfoot ball", + "ĠP ort", + "od ay", + "5 1", + "amp ions", + "Ġtrans fer", + "h t", + "Ġ3 8", + "ter m", + "ac ity", + "Ġb ur", + "] ,", + "tern al", + "r ig", + "b ut", + "Ġthere fore", + "ĠB ecause", + "res p", + "re y", + "Ġm ission", + "S ome", + "Ġnot ed", + "Ġass um", + "Ġdise ase", + "Ġed it", + "Ġprog ress", + "r d", + "ĠB rown", + "oc al", + "Ġadd ing", + "Ġra ised", + "ĠAn y", + "Ġt ick", + "Ġsee ing", + "ĠPe ople", + "Ġagre ement", + "Ġser ver", + "Ġw at", + "Ġdeb ate", + "Ġsupp osed", + "il ing", + "Ġlarg est", + "Ġsuccess ful", + "ĠP ri", + "ĠDemocr atic", + "Ġj ump", + "ĠSyri a", + "Ġown ers", + "Ġoff ers", + "Ġshoot ing", + "Ġeff ic", + "se y", + "Ġha ven", + "ver se", + "te red", + "ĠL ight", + "im al", + "ĠB ig", + "Ġdef end", + "Ġbe at", + "Ġrecord s", + "% )", + "Ġsc en", + "Ġemploy ees", + "Ġdev ices", + "he m", + "Ġcom mer", + "ĠM ex", + "Ġbenef it", + "ĠPro f", + "Ġil leg", + "Ġsur face", + "ĠAl so", + "Ġh arm", + "ing ly", + "w ide", + "ĠA lex", + "Ġsh ut", + "ĠC ur", + "Ġl ose", + "p m", + "Ġchall enge", + "se mb", + "Ġst ation", + "Ġint elligence", + "Ġacc ur", + "ĠFl or", + "Ġrequ ires", + "ĠM al", + "b um", + "Ġh ospital", + "Ġsp irit", + "Ġoff ered", + "Ġprodu ce", + "ĠComm un", + "Ġcreat ing", + "Ġcr is", + "s pect", + "Ġend ed", + "Ġd aily", + "Ġvot ers", + "land s", + "i as", + "i h", + "on a", + "Ġsm art", + "ĠOff ice", + "ĠL ord", + "ri al", + "ĠIntern et", + "Ġcirc um", + "Ġextreme ly", + "' .", + "Ġopin ion", + "ĠM il", + "Ġg ain", + "B S", + "ĠF in", + "y p", + "Ġuse ful", + "Ġbud get", + "Ġcom fort", + "is f", + "Ġback ground", + "el ine", + "Ġep isode", + "Ġen emy", + "Ġtri al", + "Ġestab lish", + "d ate", + "ĠC ap", + "Ġcontin ues", + "Ġshow ing", + "ĠUn ion", + "w ith", + "Ġpost ed", + "ĠSy stem", + "Ġe at", + "ri an", + "Ġr ise", + "ĠGerman y", + "il s", + "Ġsign ed", + "Ġv ill", + "Ġgr and", + "m or", + "ĠEng land", + "Ġproject s", + "um ber", + "Ġconf erence", + "z a", + "Ġrespons ible", + "ĠAr ab", + "Ġlearn ed", + "âĢĶ âĢĶ", + "i pping", + "ĠGe orge", + "O C", + "Ġreturn ed", + "ĠAustral ia", + "Ġb rief", + "Q u", + "Ġbr and", + "ill ing", + "ab led", + "Ġhig hest", + "Ġtr ain", + "ĠComm ission", + "wh ile", + "Ġn om", + "cept ion", + "Ġm ut", + "ĠBl ue", + "Ġinc ident", + "v ant", + "8 6", + "ĠI D", + "Ġn uclear", + "7 4", + "ĠL ike", + "ĠR E", + "ĠM icro", + "l i", + "m ail", + "Ġcharg es", + "8 9", + "Ġad just", + "ad o", + "Ġear th", + "N A", + "Ġpr ices", + "P A", + "Ġd raft", + "Ġrun s", + "Ġcandid ate", + "ens es", + "Ġmanag ement", + "ĠPh il", + "ĠM iss", + "Ġte ach", + "g ram", + "Ġunderstand ing", + "a it", + "ic ago", + "A dd", + "ĠE p", + "sec ut", + "Ġsepar ate", + "Ġinst ance", + "Ġe th", + "Ġun less", + "**** ****", + "ĠF ore", + "in ate", + "Ġoper ations", + "S p", + "Ġf aith", + "g ar", + "ĠCh urch", + "ron ic", + "Ġconf ig", + "os ure", + "Ġactiv ities", + "Ġtrad itional", + "Ġ3 6", + "Ġd irection", + "Ġmach ine", + "Ġsur round", + "Ġp ush", + "un ction", + "ĠE U", + "Ġeas ier", + "Ġarg ument", + "G B", + "Ġm icro", + "Ġsp ending", + "iz ations", + "Ġthe ory", + "ad ow", + "Ġcall ing", + "ĠL ast", + "Ġd er", + "Ġinflu ence", + "Ġcomm it", + "Ġph oto", + "Ġun c", + "ist ry", + "g n", + "ast e", + "ack s", + "Ġdis p", + "ad y", + "d o", + "ĠG ood", + "Ġ `", + "Ġw ish", + "Ġreve aled", + "Âł Âł", + "l ig", + "Ġen force", + "ĠComm ittee", + "Ġche m", + "Ġmil es", + "Ġinterest ed", + "Ġsol ution", + "ic y", + "in ct", + "Ġ- >", + "ĠD et", + "Ġrem oved", + "Ġcomp ar", + "e ah", + "Ġpl ant", + "ĠS ince", + "Ġachie ve", + "Ġadvant age", + "Ġslight ly", + "b ing", + "Ġpl aced", + "u nder", + "201 5", + "ĠM ad", + "Ġt im", + "os es", + "Ġc ru", + "ĠR ock", + "Ġmost ly", + "Ġneg ative", + "Ġset ting", + "Ġprodu ced", + "Ġm ur", + "Ġconnect ion", + "ĠM er", + "Ġdri ver", + "Ġexecut ive", + "Ġass ault", + "Ġb orn", + "ĠV er", + "t ained", + "Ġstruct ure", + "Ġredu ce", + "Ġdec ades", + "Ġd ed", + "u ke", + "ĠM any", + "idd en", + "Ġle ague", + "S e", + "Ġjo in", + "Ġdis co", + "Ġd ie", + "c ks", + "act ions", + "Ġass ess", + "ag n", + "Ġgo als", + "our s", + "I R", + "Ġsen ior", + "ill er", + "m od", + "ip ment", + "oc ol", + "u y", + "ĠQ ue", + "Ġpart ies", + "ir gin", + "Ġle arning", + "it able", + "Ġstre et", + "Ġcamer a", + "A pp", + "Ġsk ills", + "b re", + "c ious", + "Ġcele br", + "ĠFr anc", + "Ġexist ing", + "Ġwill ing", + "l or", + "Ġ id", + "ĠSp ace", + "Ġcrit ical", + "ĠL a", + "ortun ately", + "Ġser ve", + "Ġc old", + "Ġspec ies", + "T S", + "Ġanim als", + "ĠB ay", + "Ġold er", + "ĠU nder", + "est ic", + "ĠT re", + "Ġte acher", + "Ġpre fer", + "v is", + "Ġth read", + "ĠM att", + "Ġmanag er", + "ãĥ »", + "Ġprofess ional", + "ĠV ol", + "Ġnot es", + "The se", + "ul a", + "Ġf resh", + "ent ed", + "u zz", + "ed y", + "clus ion", + "ĠR el", + "Ġdoub t", + "E O", + "Ġopen ed", + "ĠB it", + "Ad vertisement", + "Ġgu ess", + "ĠU N", + "Ġse qu", + "Ġexpl ain", + "ott en", + "Ġatt ract", + "ak s", + "Ġstr ing", + "Ġcont ext", + "oss ible", + "ĠRepublic ans", + "Ġsol id", + "Ġc ities", + "Ġask ing", + "Ġr andom", + "u ps", + "ur ies", + "ar ant", + "dd en", + "g l", + "ĠFlor ida", + "Ġdep end", + "ĠSc ott", + "Ġ3 3", + "Ġi T", + "ic on", + "Ġmention ed", + "Ġ2 000", + "Ġclaim ed", + "Ġdefin itely", + "ul f", + "Ġc ore", + "Ġopen ing", + "ĠCon st", + "wh ich", + "ĠT ra", + "A G", + "7 2", + "Ġbelie ved", + "ad a", + "Ġ4 8", + "ĠSec urity", + "yr ight", + "ĠP et", + "ĠL ou", + "Ġhold ing", + "======== ========", + "Ġ ice", + "Ġb row", + "Ġauthor ities", + "h ost", + "w ord", + "Ġsc ore", + "ĠD iv", + "Ġcell s", + "Ġtrans l", + "Ġneigh bor", + "Ġrem ove", + "u ct", + "Ġdist rict", + "ĠA ccording", + "Ġwor se", + "Ġconcern s", + "Ġpresident ial", + "Ġpolic ies", + "ĠH all", + "7 3", + "Ġh us", + "A Y", + "Ġ200 6", + "ĠJ ud", + "Ġindepend ent", + "ĠJust ice", + "ili ar", + "pr int", + "igh ter", + "Ġprotect ion", + "z en", + "Ġsu dden", + "h ouse", + "ĠJ es", + "P R", + "ĠIn f", + "Ġb ul", + "Ġ _", + "ĠServ ice", + "ĠP R", + "Ġstr ategy", + "ff ect", + "Ġgirl s", + "Ġmiss ing", + "oy al", + "ĠTe am", + "ul ated", + "Ġd at", + "Ġpolit ics", + "ab or", + "A ccording", + "Ġspe ll", + "Ġg raph", + "ort hern", + "T C", + "A b", + "Ġlab or", + "is her", + "Ġk ick", + "ĠiT unes", + "Ġstep s", + "pos es", + "Ġsmall er", + "E n", + "ber t", + "Ġro ll", + "Ġresear chers", + "Ġcl osed", + "Ġtrans port", + "Ġlaw y", + "________ ________", + "ĠCh icago", + "Ġas pect", + "Ġn one", + "Ġmar riage", + "9 6", + "Ġe lements", + "ĠF re", + "ĠS al", + "Ġd ram", + "F C", + "t op", + "e qu", + "Ġhe aring", + "Ġsupport ed", + "Ġtest ing", + "co hol", + "Ġmass ive", + "Ġst ick", + "Ġgu ard", + "is co", + "ph one", + "F rom", + "How ever", + "Ġb order", + "Ġcop y", + "ograph y", + "l ist", + "7 1", + "Ġown er", + "cl ass", + "ru it", + "r ate", + "ĠO nce", + "Ġdig ital", + "Ġt ask", + "ER S", + "Ġinc red", + "t es", + "+ +", + "ĠFr ance", + "Ġb reat", + "ow l", + "Ġiss ued", + "ĠW estern", + "Ġdet ect", + "Ġpart ners", + "Ġsh ared", + "ĠC all", + "Ġcan cer", + "ac he", + "rib e", + "Ġexpl ained", + "Ġhe at", + "{ \"", + "Ġinvest ment", + "ĠB ook", + "Ġw ood", + "Ġtool s", + "ĠAl though", + "Ġbelie f", + "Ġcris is", + "Ġg e", + "ĠM P", + "Ġoper ation", + "ty pe", + "~ ~", + "g a", + "Ġcont ains", + "ant a", + "Ġexp ress", + "ĠG roup", + "ĠJ ournal", + "k a", + "Ġam b", + "ĠUS A", + "Ġfind ing", + "Ġfund ing", + "h ow", + "Ġestab lished", + "ide os", + "Ġdeg ree", + "Ġdanger ous", + "ang ing", + "Ġfre edom", + "pp ort", + "out hern", + "Ġch urch", + "Ġc atch", + "ĠTw o", + "Ġpres ence", + "ĠGu ard", + "U p", + "Ġauthor ity", + "ĠPro ject", + "Ġbut ton", + "Ġcon sequ", + "Ġval id", + "Ġwe ak", + "Ġstart s", + "Ġref erence", + "ĠM em", + "\" )", + "U N", + "or age", + "ĠO pen", + "Ġcol lection", + "y m", + "g ency", + "Ġbeaut iful", + "ro s", + "Ġtell s", + "Ġwa iting", + "n el", + "Ġprov iding", + "ĠDemocr ats", + "Ġd aughter", + "Ġm aster", + "Ġpur poses", + "ĠJapan ese", + "Ġequ al", + "Ġturn s", + "Ġdoc uments", + "Ġwatch ing", + "R es", + "Ġr an", + "201 4", + "Ġre ject", + "ĠKore a", + "Ġvictim s", + "Le vel", + "ere nces", + "Ġw itness", + "Ġ3 4", + "Ġre form", + "com ing", + "Ġocc up", + "Ġc aught", + "Ġtra ffic", + "ad ing", + "Ġmod els", + "ar io", + "Ġserv ed", + "Ġb atter", + "u ate", + "ĠSecret ary", + "Ġagre ed", + "Ġtr uly", + "yn am", + "ĠR et", + "Ġun its", + "ĠRes earch", + "h and", + "az ine", + "ĠM ike", + "Ġvar iety", + "ot al", + "Ġam azing", + "Ġconfir med", + "Ġentire ly", + "Ġpurch ase", + "Ġe lement", + "Ġc ash", + "Ġdeter mine", + "D e", + "Ġc ars", + "ĠW all", + "â ĸ", + "Ġview s", + "Ġdrug s", + "Ġdep artment", + "ĠSt ep", + "u it", + "Ġ3 9", + "as ure", + "ĠCl ass", + "Ġc overed", + "ĠB ank", + "Ġme re", + "u ana", + "Ġmult i", + "Ġm ix", + "Ġun like", + "lev ision", + "Ġsto pped", + "Ġs em", + "ĠG al", + "ul es", + "Ġwe l", + "ĠJohn son", + "l a", + "Ġsk ill", + "Ġbec oming", + "ri e", + "Ġappropri ate", + "f e", + "ell ow", + "ĠPro t", + "ul ate", + "oc ation", + "Ġweek end", + "od ies", + "Ġsit es", + "Ġanim al", + "ĠT im", + "Ġsc ale", + "Ġcharg ed", + "Ġinst ruct", + "ill a", + "Ġmethod s", + "Ġc ert", + "Ġjud ge", + "ĠH el", + "Ġdoll ars", + "Ġstand ing", + "ĠS qu", + "Ġdeb t", + "l iam", + "Ġdri ving", + "ĠS um", + "ĠEd ition", + "Ġal bum", + "and on", + "I F", + "ĠU k", + "6 3", + "ad er", + "Ġcommer cial", + "es h", + "ĠGovern ment", + "Ġdisc overed", + "Ġout put", + "ĠHill ary", + "ĠCar ol", + "Ġ200 5", + "Ġab use", + "anc ing", + "Ġsw itch", + "Ġann ual", + "T w", + "Ġst ated", + "ag ement", + "in ner", + "Ġdem ocr", + "Ġres idents", + "Ġallow ing", + "Ġfact ors", + "od d", + "Ġf uck", + "em ies", + "Ġoccur red", + "ot i", + "Ġn orth", + "ĠP ublic", + "Ġinj ury", + "Ġins urance", + "C L", + "oll y", + "ã Ģ", + "Ġrepe ated", + "Ġar ms", + "ang ed", + "Ġconst ruction", + "Ġf le", + "P U", + "ic ians", + "Ġfor ms", + "ĠMc C", + "ant ic", + "Ġm ental", + "p ire", + "Ġequ ipment", + "Ġf ant", + "Ġdiscuss ion", + "Ġregard ing", + "k in", + "ar p", + "Ġch air", + "og ue", + "Ġpro ceed", + "ĠI d", + "O ur", + "Ġmur der", + "M an", + "Ġ4 9", + "as p", + "Ġsupp ly", + "Ġin put", + "Ġwe alth", + "liam ent", + "Ġpro ced", + "or ial", + "ĠSt at", + "ĠN FL", + "hen s", + "ĠInst itute", + "Ġput ting", + "ourn ament", + "et ic", + "Ġloc ated", + "Ġk id", + "er ia", + "r un", + "Ġpr inc", + "Ġ !", + "go ing", + "ĠB et", + "Ġcl ot", + "Ġtell ing", + "Ġprop osed", + "i ot", + "or ry", + "Ġfund s", + "g ment", + "ĠL ife", + "Ġb aby", + "ĠB ack", + "Ġsp oke", + "Im age", + "Ġear n", + "ĠA T", + "g u", + "Ġex change", + "ĠL in", + "ov ing", + "Ġp air", + "M ore", + "az on", + "Ġarrest ed", + "Ġkill ing", + "c an", + "ĠC ard", + "y d", + "Ġident ified", + "Ġm obile", + "Ġthan ks", + "ony m", + "ĠF orm", + "Ġhundred s", + "ĠCh ris", + "ĠC at", + "Ġtre nd", + "h at", + "ĠA v", + "om an", + "Ġelect ric", + "ĠW il", + "S E", + "O f", + "Ġrest aur", + "ot ed", + "Ġtr ig", + "Ġn ine", + "Ġb omb", + "Wh y", + " ¯", + "Ġco verage", + "Ġapp eal", + "ĠRober t", + "ĠS up", + "Ġfin ished", + "Ġfl ow", + "Ġdel iver", + "Ġcal cul", + "Ġphot os", + "Ġph il", + "Ġpie ces", + "Ġapp re", + "k es", + "Ġr ough", + "D o", + "Ġpart ner", + "Ġconcern ed", + "Ġ3 7", + "ĠG en", + "C ol", + "ct ors", + "Ġ= >", + "st ate", + "Ġsuggest ed", + "ĠFor ce", + "C E", + "Ġher self", + "ĠPl an", + "w orks", + "o oth", + "ren cy", + "Ġcor ner", + "Ġhus band", + "Ġintern et", + "ĠA ut", + "em s", + "os en", + "ĠAt l", + "g en", + "Ġbal ance", + "6 2", + "Ġsound s", + "te xt", + "Ġar r", + "ov es", + "Ġmill ions", + "Ġrad io", + "Ġsat isf", + "ĠD am", + "M r", + "G o", + "S pe", + "Ġcomb at", + "r ant", + "ĠG ree", + "Ġf uel", + "Ġdist ance", + "Ġtest s", + "Ġdec re", + "ĠE r", + "Ġman aged", + "D S", + "Ġt it", + "Ġmeas ures", + "ĠL iber", + "Ġatt end", + "as hed", + "ĠJ ose", + "ĠN ight", + "d it", + "ĠN ov", + "ĠE nd", + "out s", + "Ġgener ation", + "Ġadv oc", + "y th", + "Ġconvers ation", + "ĠS ky", + "act ive", + "ce l", + "ri er", + "ĠFr ank", + "Ġg ender", + "Ġcon cent", + "Ġcar ried", + "and a", + "ĠV irgin", + "Ġarri ved", + "ic ide", + "ad ed", + "Ġfail ure", + "Ġmin imum", + "le ts", + "Ġwor st", + "Ġkeep ing", + "Ġint ended", + "Ġilleg al", + "Ġsub sc", + "Ġdetermin ed", + "Ġtri p", + "Y es", + "Ġra ise", + "Ġ ~", + "Ġfeel s", + "Ġpack age", + "ĠJ o", + "h i", + "201 6", + "re al", + "Ġf ra", + "Ġsy mb", + "M e", + "uck y", + "p ret", + "ĠK h", + "ĠEd it", + "ĠWe b", + "em ic", + "ĠCol or", + "Ġjust ice", + "I nt", + "Ġfar m", + "ck now", + "\" >", + "el ess", + "Ġredu ced", + "Ġ5 00", + "x x", + "ĠR ad", + "ĠW ood", + "Ġcl in", + "Ġhy p", + "il er", + "ur a", + "k ins", + "8 5", + "6 1", + "ĠThe ir", + "ĠM ary", + "Ġs an", + "Ġno vel", + "ĠWh o", + "Ġcap acity", + "Ġimp ossible", + "Ġpl ays", + "Ġmin ister", + "ij uana", + "ic ate", + "ĠS et", + "Ġf ram", + "Ġ ing", + "Ġcommun ities", + "ĠF BI", + "it a", + "Ġb on", + "Ġstr ateg", + "Ġinterest s", + "l ock", + "g ers", + "m as", + "ĠAN D", + "Ġconflic t", + "Ġrequire ments", + "Ġs ac", + "Ġoper ating", + "in i", + "rel ated", + "Ġcomm itted", + "Ġrelative ly", + "Ġs outh", + "¯ ¯", + "Ġaff ord", + "Ġident ity", + "Ġdec isions", + "Ġacc used", + "pl ace", + "Ġvict ory", + "o ch", + "i at", + "N ame", + "C om", + "t ion", + "ed s", + "Ġsee k", + "Ġt ight", + "ĠIm ages", + "Ġinit i", + "Ġhum ans", + "Ġfam iliar", + "Ġaud ience", + "Ġintern al", + "vent ure", + "Ġs ides", + "ĠT O", + "Ġd im", + "Ġcon clud", + "Ġapp oint", + "Ġenforce ment", + "ĠJ im", + "ĠAssoci ation", + "Ġcircum st", + "ĠCanad ian", + "Ġjo ined", + "Ġdiffere nces", + "ĠL os", + "Ġprot est", + "Ġtw ice", + "w in", + "Ġgl ass", + "ars h", + "ĠAr my", + "Ġexp ression", + "Ġdec ide", + "Ġplan ning", + "an ia", + "Ġhand le", + "ĠMicro soft", + "ĠN or", + "Ġmax imum", + "ĠRe v", + "Ġse a", + "Ġev al", + "Ġhel ps", + "re f", + "Ġb ound", + "Ġm outh", + "Ġstand ards", + "Ġcl im", + "ĠC amp", + "ĠF ox", + "cl es", + "Ġar my", + "ĠTe chn", + "ack ing", + "x y", + "S S", + "Ġ4 2", + "Ġbu g", + "ĠUk rain", + "ĠM ax", + "ĠJ ones", + "ĠSh ow", + "l o", + "Ġplan et", + "Ġ7 5", + "Ġwin ning", + "Ġf aster", + "Ġspe ct", + "Ġbro ken", + "T R", + "Ġdef ined", + "Ġhealth y", + "Ġcompet ition", + "htt ps", + "ĠIs land", + "ĠF e", + "Ġannoun ce", + "ĠC up", + "ĠInst ead", + "Ġcl ient", + "Ġposs ibly", + "se ction", + "ock et", + "l ook", + "Ġfin ish", + "Ġcre w", + "Ġres erv", + "Ġed itor", + "Ġh ate", + "Ġs ale", + "Ġcontro vers", + "Ġp ages", + "w ing", + "Ġnum er", + "Ġopp osition", + "Ġ200 4", + "Ġref uge", + "Ġfl ight", + "Ġap art", + "ĠL at", + "A meric", + "ĠAfric a", + "Ġapplic ations", + "ĠPal est", + "ĠB ur", + "Ġg ar", + "ĠSoc ial", + "Ġup gr", + "Ġsh ape", + "Ġspe aking", + "ans ion", + "a o", + "ĠS n", + "Ġwor ry", + "ĠBrit ain", + "P lease", + "rou d", + "Ġh un", + "Ġintrodu ced", + "Ġd iet", + "I nd", + "ĠSec ond", + "Ġfun ctions", + "ut s", + "ĠE ach", + "ĠJe ff", + "Ġst ress", + "Ġaccount s", + "Ġgu arant", + "ĠAn n", + "ed ia", + "Ġhon est", + "Ġt ree", + "ĠAfric an", + "ĠB ush", + "} ,", + "Ġs ch", + "ĠOn ly", + "Ġf if", + "ig an", + "Ġexerc ise", + "ĠEx p", + "Ġscient ists", + "Ġlegisl ation", + "ĠW ork", + "ĠS pr", + "à Ĥ", + "ĠH uman", + "Ġ è", + "Ġsur vey", + "Ġr ich", + "ri p", + "Ġmain tain", + "Ġfl o", + "Ġleaders hip", + "st ream", + "ĠIslam ic", + "Ġ 01", + "ĠCol lege", + "Ġmag ic", + "ĠPr ime", + "Ġfig ures", + "201 7", + "ind er", + "x ual", + "ĠDe ad", + "Ġabsolute ly", + "Ġfour th", + "Ġpresent ed", + "resp ond", + "rib le", + "Ġal cohol", + "at o", + "ĠD E", + "por ary", + "Ġgr ab", + "Ġvar i", + "Ġqu ant", + "ĠPh oto", + "Ġpl us", + "r ick", + "ar ks", + "Ġaltern ative", + "Ġp il", + "Ġappro x", + "th at", + "Ġobject s", + "ĠR o", + "ĠAnd roid", + "Ġsignificant ly", + "ĠR oad", + "k ay", + "R ead", + "av or", + "Ġa cknow", + "ĠH D", + "ĠS ing", + "O r", + "ĠM ont", + "Ġun s", + "pro f", + "Ġneg oti", + "ĠAr ch", + "ik i", + "Ġte levision", + "ĠJew ish", + "Ġcomm ittee", + "Ġmot or", + "Ġappear ance", + "Ġs itting", + "Ġstri ke", + "ĠD own", + "com p", + "ĠH ist", + "Ġf old", + "ac ement", + "ĠLou is", + "Ġbel ong", + "ĠâĢ ¢", + "Ġm ort", + "Ġprep ared", + "Ġ6 4", + "ĠM aster", + "Ġind eed", + "ĠD en", + "Ġre nt", + "T A", + "our ney", + "ar c", + "S u", + "9 7", + "Ġadv ice", + "Ġchang ing", + "Ġlist ed", + "Ġlaun ched", + "is ation", + "ĠP eter", + "is hes", + "Ġl ived", + "ĠM el", + "ĠSup reme", + "ĠF ederal", + "Ġ) ;", + "ruct ure", + "Ġset s", + "Ġphil os", + "u ous", + "Ġ ł", + "Ġappl ied", + "ĠN OT", + "Ġhous ing", + "ĠM ount", + "Ġo dd", + "Ġsu st", + "D A", + "ffic ient", + "Ġ ?", + "ol ved", + "Ġp owers", + "Ġth r", + "Ġrem aining", + "ĠW ater", + "L C", + "Ġca uses", + "ãģ ®", + "Ġman ner", + "ad s", + "Ġsuggest s", + "Ġend s", + "stand ing", + "f ig", + "ĠD un", + "id th", + "Ġg ay", + "Ġter min", + "ĠAngel es", + "M S", + "Ġscient ific", + "Ġco al", + "ap ers", + "b ar", + "ĠThom as", + "Ġsy m", + "ĠR un", + "th is", + "P C", + "igr ants", + "Ġmin ute", + "ĠDist rict", + "cell ent", + "Ġle aves", + "Ġcomple ted", + "am in", + "Ġfoc used", + "Ġmon itor", + "Ġveh icles", + "M A", + "ĠM ass", + "ĠGr and", + "Ġaffect ed", + "itution al", + "Ġconst ruct", + "Ġfollow s", + "Ġt on", + "re ens", + "Ġh omes", + "ĠE xt", + "ĠLe vel", + "r ast", + "ĠI r", + "Ġel im", + "Ġlarge ly", + "ĠJ oe", + "Ġvot es", + "all s", + "Ġbusiness es", + "ĠFound ation", + "ĠCent ral", + "Ġy ards", + "Ġmaterial s", + "ul ner", + "Ġgu ide", + "Ġclos er", + "um s", + "Ġsp orts", + "ed er", + "J ust", + "Ġtax es", + "8 4", + "ĠO ld", + "Ġdec ade", + "ol a", + "Ġv ir", + "Ġdro pped", + "Ġdel ay", + "it ect", + "Ġsec ure", + "ste in", + "le vel", + "Ġtre ated", + "Ġfil ed", + "ain e", + "Ġv an", + "Ġm ir", + "Ġcol umn", + "ict ed", + "e per", + "Ġro t", + "Ġcons ult", + "Ġent ry", + "Ġmar ijuana", + "ĠD ou", + "Ġapparent ly", + "ok ing", + "clus ive", + "Ġincre ases", + "an o", + "Ġspecific ally", + "Ġte le", + "ens ions", + "Ġrelig ion", + "ab ilities", + "Ġfr ame", + "ĠN ote", + "ĠLe e", + "Ġhelp ing", + "Ġed ge", + "ost on", + "Ġorgan izations", + "à ĥ", + "ĠB oth", + "hip s", + "Ġbig ger", + "Ġbo ost", + "ĠSt and", + "Ġro w", + "ul s", + "ab ase", + "Ġr id", + "L et", + "are n", + "ra ve", + "Ġst ret", + "P D", + "Ġv ision", + "Ġwe aring", + "Ġappre ci", + "Ġa ward", + "ĠU se", + "Ġfact or", + "w ar", + "ul ations", + ") (", + "Ġg od", + "Ġter rit", + "Ġpar am", + "ast s", + "8 7", + "Ġen emies", + "ĠG ames", + "F F", + "Ġacc ident", + "W ell", + "ĠMart in", + "T ER", + "Ġat h", + "ĠHe ll", + "Ġfor g", + "Ġve ter", + "ĠMed ic", + "f ree", + "Ġst ars", + "Ġexp ensive", + "Ġac ad", + "ra wn", + "ĠW he", + "Ġl ock", + "Ġform at", + "Ġsold iers", + "s m", + "Ġag ent", + "Ġrespons ibility", + "or a", + "ĠS cience", + "Ġrap id", + "Ġt ough", + "ĠJes us", + "Ġbelie ves", + "M L", + "Ġwe ar", + "le te", + "Ãĥ ÃĤ", + "ĠD ri", + "Ġcomm ission", + "ĠB ob", + "O h", + "ap ed", + "Ġwar m", + "ÃĥÃĤ ÃĥÃĤ", + "Ġ200 3", + "ort ion", + "Ġhas n", + "ust er", + "Ġun ivers", + "ĠI ll", + "Ġk ing", + "olog ies", + "9 4", + "ĠT em", + "ĠM os", + "Ġpat ient", + "ĠMex ico", + "ce an", + "ĠDe ath", + "ĠSand ers", + "y ou", + "ĠC ast", + "ĠComp any", + "pt y", + "Ġhappen ing", + "F P", + "ĠB attle", + "Ġb ought", + "A m", + "M od", + "U s", + "ut ers", + "ĠC re", + "ĠTh ose", + "Ġ4 4", + "is er", + "Ġs oul", + "ĠT op", + "ĠHar ry", + "ĠA w", + "Ġse at", + "ff ee", + "Ġrev olution", + "Ġ( \"", + "ĠD uring", + "et te", + "Ġr ing", + "Ġoff ensive", + "Ġreturn s", + "Ġv ideos", + "Ġdis cl", + "Ġfam ous", + "en ced", + "ĠS ign", + "ĠR iver", + "Ġ3 00", + "P M", + "ĠB us", + "ĠC H", + "Ġcandid ates", + "ard en", + "Ġpercent age", + "Ġvis ual", + "Ġthan k", + "Ġtrou ble", + "ner gy", + "Ġ200 1", + "Ġpro ve", + "ash ion", + "Ġen h", + "ĠL ong", + "U M", + "Ġconnect ed", + "Ġposs ibility", + "O ver", + "Ġexper t", + "Ġl ibrary", + "art s", + "ĠDirect or", + "Ġfell ow", + "9 2", + "ir ty", + "Ġd ry", + "Ġsign s", + "ĠL ove", + "Ġqu iet", + "f oot", + "Ġp ure", + "ĠH un", + "Ġf illed", + "ph as", + "ĠE lect", + "end ment", + "ĠEx pl", + "Ġun able", + "n s", + "m o", + "Ġv ast", + "ob e", + "Ġident ify", + "app ing", + "ĠCarol ina", + "g ress", + "Ġpro te", + "Ġf ish", + "Ġcircumst ances", + "raz y", + "ĠPh ot", + "Ġb odies", + "ĠM ur", + "Ġdevelop ing", + "ĠA R", + "Ġexperien ced", + "Ġsubst ant", + "ĠBo ard", + "es ome", + "Ġdom estic", + "Ġcomb ined", + "ĠP ut", + "Ġchem ical", + "ĠCh ild", + "Ġpo ol", + "ĠC y", + "Ġe gg", + "c ons", + "st ers", + "Ġh urt", + "Ġmark ets", + "Ġconserv ative", + "Ġsupp orters", + "Ġag encies", + "id el", + "O b", + "ur b", + "Ġ4 3", + "ĠDef ense", + "y e", + "ĠA p", + "du le", + "Ġtemper ature", + "Ġconduct ed", + "ĠCh ief", + "Ġpull ed", + "Ġf ol", + "L ast", + "ont o", + "os is", + "V ER", + "D es", + "ĠP an", + "F irst", + "Ġadv ance", + "Ġlic ense", + "r ors", + "ĠJ on", + "Ġimag ine", + "Ġhe ll", + "Ġf ixed", + "Ġinc or", + "os ite", + "ĠL og", + "ick en", + "] :", + "Ġsurpr ise", + "h ab", + "Ġc raft", + "ol t", + "ĠJ ul", + "Ġd ial", + "Ġrele vant", + "Ġent ered", + "Ġlead s", + "ĠA D", + "ĠCle an", + "Ġpict ures", + "ess or", + "Ġal t", + "Ġpay ing", + "P er", + "ĠMark et", + "Ġupd ates", + "am ily", + "ĠT ype", + "ĠH ome", + "Ġ5 5", + "semb ly", + "rom e", + "8 3", + "Ġgreat est", + "Ġhe ight", + "Ġhe av", + "ain ts", + "Ġlist en", + "as er", + "ĠS H", + "Ġcap able", + "ac le", + "Ġpers pect", + "in ating", + "Ġoff ering", + "ry pt", + "ĠDe velop", + "ab in", + "r c", + "Ġbr ight", + "al ty", + "ar row", + "Ġsupp l", + "ind ing", + "ack ed", + "gy pt", + "ĠAn other", + "p g", + "ĠVirgin ia", + "ĠL u", + "Ġpl anned", + "Ġp it", + "Ġswe et", + "T ype", + "ĠD i", + "Ġtyp ically", + "ĠFranc isco", + "Ġpro spect", + "ĠD an", + "Ġte en", + "re es", + "Ġsc hed", + "Ġh ol", + "Ġsc r", + "Ġlot s", + "l ife", + "Ġnews p", + "Ġfor get", + "ĠN one", + "ĠM iddle", + "ĠR yan", + "ed d", + "Ġse vere", + "Ġsu it", + "ll er", + "9 3", + "Ġcor respond", + "Ġexpl os", + "u ations", + "Ġfl ag", + "g ame", + "r id", + "Ġpr in", + "ĠD ata", + "Ġde ploy", + "ĠEn ter", + "su it", + "gh an", + "ĠM en", + "Ġthough ts", + "Ġmat ters", + "Ġad apt", + "ĠA ri", + "Ġf ill", + "Ġfor th", + "Ġs am", + "Ġ4 1", + "Ġpay ment", + "ĠH or", + "Ġsp ring", + "du c", + "Ġl osing", + "Ġbring ing", + "F O", + "al a", + "Ġdist ribution", + "he red", + "b our", + "ĠIsrael i", + "om a", + "Ġcomb ination", + "Ġpl enty", + "V E", + "C an", + "ĠH aw", + "Ġper man", + "ĠSpe cial", + "Ġto w", + "Ġsee king", + "Ġexam ples", + "Ġclass es", + "c r", + "Ġbe er", + "Ġmov es", + "ĠI P", + "ĠK n", + "Ġpan el", + "E ven", + "Ġproper ly", + "Ġr is", + "Ġpl ug", + "Ġestim ated", + "E very", + "Ġdef ensive", + "ag raph", + "Ġpre gn", + "Ġinst it", + "ĠV ict", + "Ġvol ume", + "Ġpos itions", + "Ġl inks", + "ĠPro gram", + "ĠWe ek", + "ag ues", + "Ġtrans form", + "k er", + "ĠC EO", + "Ġc as", + "Ġopp onent", + "Ġtwe et", + "ĠC ode", + "Ġsh op", + "Ġf ly", + "Ġtal ks", + "Ġb ag", + "Ph one", + "Ġa id", + "Ġpl ants", + "Ġ6 5", + "Ġatt orney", + "ar ters", + "qu est", + "ĠMag ic", + "Ġbeg ins", + "Ġmy ster", + "Ġenvironment al", + "Ġst orage", + "N N", + "Ġm arg", + "Ġs ke", + "Ġmet al", + "ell y", + "Ġord ered", + "Ġrem ained", + "Ġl oved", + "Ġprom pt", + "Ġupd ated", + "Ġexper ts", + "Ġwalk ing", + "Ġan cient", + "Ġperform ed", + "AT E", + "Ġne ither", + "i ency", + "Ġmanufact ure", + "ĠP ak", + "Ġselect ed", + "Ġm ine", + "Ġult imately", + "Ġexpl an", + "Ġlab el", + "ĠServ ices", + "ribut ed", + "Tr ump", + "Ġsy n", + "ĠU lt", + "S C", + "Ġme at", + "Ġg iant", + "ĠW ars", + "ĠO N", + "Ġad m", + "Ġinter pret", + "Ġeven ing", + "Ġev il", + "ĠB oston", + "ĠW ild", + "Ġ Ã", + "ĠBit coin", + "ĠAm azon", + "D r", + "ĠIn formation", + "Ġobvious ly", + "Ġadv anced", + "Ph oto", + "ol ar", + "Ġwe ather", + "Ġsymb ol", + "Ġso le", + "Ġpot entially", + "ost er", + "Ġorig inally", + "m un", + "3 00", + "az e", + "ess ions", + "Ġde ck", + "Ġst ood", + "Ġyou th", + "ĠB ern", + "R ep", + "ĠT est", + "Ġbas ically", + "ot ic", + "Ġinvol ve", + "ol it", + "ly n", + "S ee", + "Ġair craft", + "Ġconf irm", + "E W", + "Ġmess ages", + "ĠRich ard", + "Ġk it", + "Ġpro hib", + "Ġv ulner", + "is ters", + "Ġexist ence", + "Ġturn ing", + "ĠS P", + "Ġdes ire", + "Ġfl at", + "Ġm ent", + "se ason", + "ang es", + "Ġneighbor hood", + "ĠL ake", + "AT ION", + "Ġpoint ed", + "b ur", + "Ġinn ov", + "uc ks", + "U L", + "Ġprofess or", + "Ġexp ressed", + "A B", + "ic ious", + "Ġ200 2", + "ĠDe v", + "Ġs ession", + "Ġb are", + "s en", + "Ġdis s", + "ĠC ath", + "ĠP ass", + "ĠP oint", + "Ġdo ctor", + "or row", + "ail ed", + "ĠR ub", + "ĠD C", + "ĠChar l", + "p erson", + "Ġwrit er", + "igh ters", + "ure au", + "Ġob lig", + "Ġrecord ed", + "Ġbro ke", + "Ġord ers", + "il ty", + "Ġmot ion", + "in ity", + "l aw", + "ad ium", + "Ġimm igration", + "Ġcontr ast", + "Ġb att", + "Ġex cellent", + "Ġtechn ical", + "am i", + "Ġt un", + "Ġcl oud", + "ĠY ear", + "ge on", + "Ġcre ation", + "Ġstr ange", + "Ġa uth", + "Ġfor t", + "b orn", + "Ġext ent", + "ĠT oday", + "ĠCl ub", + "Ġr ain", + "Ġs ample", + "Ġaccept ed", + "Ġt act", + "Ġf ired", + "ĠS on", + "Ġstand s", + "Ġb oot", + "Ġ4 7", + "Ġstat ements", + "Ġvers ions", + "Ġse lling", + "ound ed", + "Ġ199 0", + "Ġwere n", + "ĠW atch", + "Ġexper iment", + "P ost", + "Ġret ail", + "ul ed", + "In st", + "un te", + "ãĥ ¼", + "Ġdep art", + "Ġb ond", + "i very", + "om pl", + "Ġre action", + "ĠSyri an", + "ĠP ac", + "app ed", + "ani el", + "D P", + "Ġres olution", + "Ġre act", + "Ġappro ved", + "on om", + "m ond", + "ĠO ffic", + "-- -", + "Ġrepl ace", + "Ġt ack", + "Ġsp ort", + "Ġch ain", + "Ġemer gency", + "r ad", + "ĠPalest in", + "Ġ4 6", + "Ġautom atically", + "Ġrout e", + "Ġp al", + "Ġb anks", + "ĠPar is", + "ĠMed ia", + "ro ad", + "ic ing", + "i xt", + "ist ed", + "Ġg rew", + "Ġco ord", + "ĠW here", + "om in", + "Ġsub s", + "� �", + "Ġ ±", + "Ġcorpor ate", + "Ġse lection", + "n oon", + "ĠRep ort", + "c s", + "clud ing", + "ord ers", + "anc he", + "ĠIt s", + "Ġslow ly", + "ĠE gypt", + "ĠA cc", + "Ġcol le", + "iqu es", + "E X", + "Ġattempt s", + "ur l", + "ĠC ross", + "Ġfind ings", + "ĠS C", + "ĠO R", + "Ġind ex", + "ens ity", + "ĠW ay", + "ĠL and", + "Ġsh ock", + "d is", + "Ġd ynam", + "Ġc art", + "m osp", + "S ince", + "i est", + "ĠB oy", + "Ġst orm", + "ĠCont in", + "201 3", + "he w", + "il it", + "Ġess ential", + "iqu id", + "O ther", + "ive red", + "Ġreason able", + "A ct", + "Ġsub sequ", + "ĠP ack", + "ĠF ort", + "Ġconsider ing", + "Ġun iversity", + "l og", + "Ġmar ried", + "Ġill ust", + "ĠTr ue", + "£ ı", + "Ġnumer ous", + "rast ructure", + "Ġserious ly", + "Ġrefer red", + "u a", + "Ġconsist ent", + "on na", + "ĠRe al", + "ru ption", + "ci ples", + "Ġfact s", + "9 1", + "ot es", + "er g", + "The n", + "Ġacc ompl", + "N ote", + "Ġre venue", + "Ġpass ing", + "Ġm al", + "e en", + "ĠY et", + "Ġg ather", + "ter day", + "ew ork", + "ĠA uthor", + "P e", + "Ġopt im", + "Ġr ub", + "Ġè £ı", + "Ġun known", + "st one", + "Ġun ion", + "ol ve", + "Ġopportun ities", + "Ġbrow ser", + "ĠW al", + "ĠC ost", + "Ġreport ing", + "st s", + "p et", + "Ġs and", + "Ġsudden ly", + "Ġsurpr ising", + "ĠV R", + "Ġsomew hat", + "ĠB as", + "ult ure", + "iz z", + "ĠC D", + "Ġchalleng es", + "Ġsett ings", + "Ġexperien ces", + "ĠF ull", + "Ġcan n", + "Ġrece iving", + "ES T", + "Ġj oint", + "Ġcult ural", + "Ġa st", + "8 2", + "as tern", + "ce ived", + "ĠC ru", + "Ġb ull", + "p ired", + "am m", + "Ġfac ing", + "p ower", + "Ġb oss", + "ĠH ol", + "Ġinst r", + "Ġincreasing ly", + "Ġsh ift", + "Ġstre ets", + "ĠWilliam s", + "ab b", + "Ġl ie", + "Ġl augh", + "ĠC a", + "P L", + "Ġadult s", + "Ġcustom er", + "Ġob tained", + "Ġsupport ing", + "ht ml", + "f ire", + "Ġdetail ed", + "Ġpick ed", + "ĠR ight", + "ld er", + "E E", + "st ood", + "ĠK im", + "Ġw ire", + "Ġs ight", + "Ġdevelop ers", + "Ġpers ons", + "Ġs ad", + "Ġc up", + "Ġwar ning", + "Ġboy s", + "l ong", + "Ġb ird", + "f o", + "Ġw al", + "Ġobserv ed", + "Ġz one", + "iven ess", + "Ġch annel", + "c ript", + "Ġref used", + "ĠAg ain", + "Ġsu c", + "Ġspokes man", + "ĠRe f", + "r ite", + "ou ston", + "ãĥ ³", + "ĠS her", + "Ġact s", + "ĠN ame", + "Ġstrugg le", + "ar ry", + "omet imes", + "Ġdisc rim", + "H T", + "Ġcateg ory", + "Ġreal ize", + "Ġemploy ee", + "ĠAf ghan", + "en ger", + "Ġgun s", + "ĠSte ve", + "ĠM ot", + "ĠO l", + "ok ed", + "Ġth ick", + "Ġfair ly", + "ill y", + "Ġsur ve", + "ĠM at", + "we ight", + "â Ķ", + "Ġtro ops", + "Ġag ents", + "Ġbatter y", + "Ġmot iv", + "à ¡", + "S ec", + "d en", + "o very", + "L S", + "Ġfl u", + "Ġconf ident", + "ĠO per", + "Ġem pty", + "Ġp hen", + "Ġse ctor", + "Ġexc ited", + "Ġrem ote", + "ap h", + "o en", + "Ġdestroy ed", + "Ġmor al", + "ĠH P", + "ĠR on", + "Ġd ress", + "ĠB at", + "Ġl it", + "ĠM S", + "Ġa f", + "H L", + "r um", + "is ms", + "Ġshould n", + "Ġsym pt", + "ĠTor onto", + "het ic", + "Ġcar bon", + "Ġinstall ed", + "Ġviol ent", + "Ġsol ar", + "j a", + "Ġpract ices", + "Ġr ide", + "ĠP enn", + "Ġimpro ved", + "Ġaud io", + "Ġbehav i", + "ĠP S", + "Ġe ating", + "D ata", + "ĠRe view", + "p ass", + "cl aim", + "u ated", + "ang ers", + "c hen", + "Ġproper ties", + "Ġany where", + "An other", + "Ġbl ow", + "ĠJack son", + "Ġp roud", + "Ġplan e", + "l ines", + "Ġsqu are", + "Ġpro of", + "ans as", + "Ġtalk ed", + "m akers", + "Ġs ister", + "Ġhold s", + "Ġres ident", + "Ġ= =", + "Ġresist ance", + "Ġspl it", + "Ġpro secut", + "Ġconf idence", + "res ents", + "Ġcut s", + "Ġexcept ion", + "Ġz ero", + "Get ty", + "Ġcop yright", + "Ġtot ally", + "orm al", + "ific ations", + "ĠAustral ian", + "Ġs ick", + "Ġ1 50", + "Ġhouse hold", + "Ġfe es", + "Ġdri vers", + "og en", + "ĠN Y", + "Ġnecess arily", + "Ġregul ations", + "ear ing", + "s l", + "Ġperspect ive", + "c are", + "ic ial", + "H is", + "Ġesc ape", + "Ġsurpr ised", + "ĠV an", + "ur rent", + "Ġv ac", + "8 1", + "ĠTh us", + "Ġem phas", + "ĠCh ampions", + "ĠI ce", + "Ġn arr", + "Ġhead s", + "Ġca using", + "b el", + "f ortunately", + "ĠM a", + "Ġtarg ets", + "ci pl", + "Ġafter noon", + "Ġadd s", + "ĠMay be", + "ĠF our", + "ess ed", + "ple te", + "Ġus ual", + "ch o", + "ing u", + "Ġwith d", + "ĠE nergy", + "ĠE conom", + "O O", + "Ġart icles", + "Ġinj ured", + "Ġman age", + "Ġexpl ains", + "Ġdi agn", + "R ec", + "at ures", + "Ġlink ed", + "Ġdiscuss ed", + "Ġexpl o", + "Ġocc asion", + "ath an", + "Ġopp osite", + "Ġfac es", + "Ġden ied", + "ĠK night", + "Ġn ut", + "Ġapprox imately", + "Ġdisapp oint", + "onym ous", + "ĠB est", + "ĠL o", + "ĠH y", + "ĠA ff", + "Ġvot ing", + "an while", + "ĠII I", + "Ġinstit utions", + "ag ram", + "ĠD aily", + "Ġdr ag", + "Ġnear by", + "Ġgu ilty", + "Ġcon ver", + "P re", + "s hip", + "Ġre ward", + "Ġphilos oph", + "ĠS S", + "u gh", + "Ġapp s", + "f riend", + "Ġu pper", + "Ġad vert", + "Ġs now", + "Ġfr ust", + "Ġour selves", + "F r", + "ĠD ie", + "amp ion", + "Ġdis miss", + "Ġc ere", + "Ġsign al", + "f rom", + "Ġ ).", + "Ġ5 2", + "Ġcr imes", + "it ors", + "est ival", + "use um", + "Ġcoun cil", + "ĠS aud", + "M ay", + "ĠG un", + "ic ian", + "et her", + "Ġsu fficient", + "ĠH en", + "so le", + "Ġhistor ical", + "ĠF ar", + "ĠT urn", + "Ġp in", + "Ġsuc ceed", + "m at", + "ly mp", + "Ġtrad ition", + "ĠO k", + "Ġc ro", + "Ġdesc ription", + "al le", + "Ġsk y", + "T e", + "Ġwide ly", + "Ġw ave", + "Ġdefin ition", + "ĠJew s", + "Ġcy cle", + "Ġref ere", + "Ġbr ings", + "us al", + "Ġal ive", + "Ġfrequ ently", + "Ġint ention", + "ĠCont rol", + "l v", + "y stem", + "Ġpriv acy", + "g ent", + "ren ce", + "ĠQu est", + "ĠChrist mas", + "Ġr ail", + "Ġco oper", + "Ġtest ed", + "ĠC apt", + "as ks", + "Ġcomfort able", + "Ġdel ivered", + "sc ape", + "Ġdep th", + "ĠG OP", + "Ġwrit es", + "Ġass ets", + "Ġsa v", + "im ents", + "Ġtrans ition", + "Ġart ist", + "ĠL ook", + "Ġl ob", + "Ġcomp onents", + "ar ity", + "Ġwalk ed", + "Ġro ot", + "Ġparticip ants", + "Ġnot iced", + "Ġres c", + "Ġn av", + "ĠAd minist", + "d a", + "ut ral", + "pl ate", + "Ġimport ance", + "Ġass ert", + "ious ly", + "c ription", + "Ġinj uries", + "ĠChe ck", + "Ġregist ered", + "Ġint ent", + "Ġmiss ed", + "ograph ic", + "Ġsent ence", + "oun ter", + "Ġassist ance", + "ev in", + "Ġdat abase", + "Ġbuild ings", + "Ġclass ic", + "Ġth inks", + "ĠOh io", + "P r", + "ug g", + "Ġfe e", + "p an", + "Ġeffect ively", + "Ġfac ility", + "Ġbe ar", + "Ġch apter", + "Ġdog s", + "ĠCol umb", + "Ġl atter", + "it ial", + "Ġad mitted", + "T V", + "ĠGe org", + "Ġpost s", + "\\ \\", + "Ġlawy er", + "Ġequ ival", + "Ġm and", + "Ġcontro lled", + "ĠW alk", + "ĠAnd rew", + "Ġmen u", + "am ental", + "Ġprotect ed", + "v a", + "Ġadminist r", + "or al", + "Ġre in", + "ĠS ar", + "Ġamount s", + "Ġn ative", + "ĠM oon", + "Ġrep resents", + "Ġab andon", + "Ġcarry ing", + "Ġt ank", + "m ary", + "Ġdecl ared", + "T ube", + "Ġh at", + "Ġpun ish", + "el lect", + "m es", + "Ġun iverse", + "ĠR od", + "ph y", + "Ġinf rastructure", + "Ġ5 1", + "Ġopp osed", + "ow nt", + "c a", + "ĠM ake", + "Ġhard ware", + "Ġco ffee", + "R el", + "b al", + "w orld", + "ĠS af", + "ĠSe a", + "in als", + "Ġown ed", + "Ġh all", + "ers ion", + "Ġdescrib e", + "ĠP ot", + "Ġport ion", + "Ġat mosp", + "Ġgovern ments", + "Ġdep ending", + "Ġoff ense", + "Ġtr ick", + "aw a", + "ĠL ine", + "ĠV is", + "ĠH ard", + "ĠOr ig", + "ĠCl ick", + "Ġdes k", + "ĠVal ley", + "ĠS ov", + "Ġmov ies", + "Ġrem ark", + "Ġm ail", + "Ġcons cious", + "Ġrul ing", + "ĠR ights", + "Ġmed ic", + "he nt", + "ĠW omen", + "> <", + "Ġrepl aced", + "ĠP rem", + "ĠTh anks", + "Ġre new", + "ĠB all", + "if orm", + "Ġsh ots", + "C omm", + "Ġar med", + "Ġconst ant", + "Ġt aste", + "Ġreal ized", + "Ġbu ff", + "Ġm o", + "Ġeffic ient", + "M ost", + "or ation", + "if ies", + "Ġcommun ication", + "Ġfl ood", + "Ġconsequ ences", + "Ġany way", + "ig g", + "ĠG M", + "ĠTh ank", + "Ġ iron", + "Ġev olution", + "ĠC op", + "tw itter", + "Ġ9 5", + "Ġrelationship s", + "ad el", + "ĠYou ng", + "Ġpropos al", + "ay ers", + "uild ing", + "ĠH ot", + "OR E", + "c os", + "Ġcoll abor", + "P G", + "ax y", + "Ġknow ing", + "Ġsupport s", + "ow ed", + "Ġcontrol s", + "Ġmere ly", + "um er", + "Ġath let", + "Ġf ashion", + "p ath", + "Ġg ift", + "Ġer a", + "AN D", + "Ġkind s", + "ĠKore an", + "Ġleg it", + "ul ous", + "Ġess entially", + "Ġthe rap", + "n ic", + "Ġsuff ered", + "Ġh ur", + "Ġprom ise", + "Ġex cess", + "Ġover w", + "Ġpr ime", + "ĠH ouston", + "er ry", + "ĠM s", + "R S", + "201 2", + "Ġst ores", + "ĠO lymp", + "Ġj ourney", + "Al though", + "S ub", + "ĠE duc", + "ĠCh apter", + "Ġrequest s", + "Ġconsum ers", + "Ġt iny", + "Ġis ol", + "ĠF air", + "b a", + "ĠY OU", + "Ġcr ash", + "ce ler", + "Ġemot ional", + "Ġgood s", + "Ġelect ed", + "Ġmod er", + "ĠLin ux", + "Ġbl ocks", + "Ġis land", + "ĠSoc iety", + "Ġelect ions", + "Ġbroad cast", + "Ġche ap", + "Ġn ations", + "Ġse asons", + "4 00", + "Ġwas te", + "ĠS at", + "Ġfield s", + "em ploy", + "Ġprof ile", + "Ġauth ors", + "AL L", + "ĠG ra", + "w est", + "ĠT y", + "Ġdeath s", + "Ġv acc", + "Ġfor med", + "Ġd u", + "Ġon going", + "ĠMuslim s", + "el f", + "ig ure", + "Ġass ume", + "ĠUkrain e", + "w ater", + "Ġco ast", + "Ġvot ed", + "g or", + "ĠA S", + "ĠMich igan", + "az a", + "ĠAr m", + "i ro", + "Ġf lex", + "as ters", + "' '", + "Ġwel come", + "ar l", + "Ġloc ations", + "ig ation", + "ĠF il", + "Ġbu ying", + "Ġarch itect", + "Ġhard er", + "ĠC ub", + "Ġinter face", + "Ġrestaur ant", + "Ġdisco ver", + "Ġex ceed", + "Ġfav our", + "ger y", + "Ġd uty", + "Ġp itch", + "ad or", + "ĠM ach", + "b oy", + "Ġrespond ed", + "Ġext ended", + "her s", + "M any", + "ra id", + "if er", + "ĠIn s", + "S er", + "Ġmed ium", + "s he", + "ĠS ports", + "Ġmag azine", + "ut ation", + "Ġlim its", + "ĠG all", + "Ġex ternal", + "raz il", + "Ġyoung er", + "t le", + "Ġrem ind", + "ĠC ON", + "Ġimmedi ate", + "Ġh idden", + "Ġvol unte", + "Ġsim pl", + "od cast", + "Ġph ase", + "d r", + "Ġpl ot", + "Ġexp osure", + "R I", + "og rap", + "v in", + "an ish", + "ĠAc ad", + "ĠEng ine", + "Ġexp ansion", + "ĠP ay", + "Y our", + "Ġpus hed", + "ĠE ll", + "ĠHe ad", + "Ġmarket ing", + "ĠA C", + "k et", + "Ġh its", + "Ġg ro", + "ĠA ge", + "ĠSc ot", + "] [", + "Ġst im", + "Ġi Phone", + "Ī Ĵ", + "Ġn arrow", + "ĠGet ty", + "ĠTur key", + "Ġperfect ly", + "Ġen able", + "ut ch", + "Ġprec ise", + "Ġreg ime", + "Ġsh if", + "Ġcomp ens", + "g un", + "d iv", + "Ġch osen", + "ĠK en", + "An y", + "Ġtre es", + "Ġrecomm ended", + "ĠR en", + "u able", + "ĠH T", + "F ollow", + "E G", + "ĠH and", + "ĠK enn", + "Ġarg uments", + "Ġex ists", + "Ġb ike", + "ĠCons erv", + "Ġbre aking", + "ĠG ar", + "Ġc razy", + "Ġvirt ual", + "ay lor", + "ix el", + "Ġ19 80", + "Ġper mission", + "ĠSer ies", + "Ġconsum er", + "Ġclose ly", + "c alled", + "Ġ5 4", + "Ġhop es", + "Ġar ray", + "ĠW in", + "ĠLab our", + "Ġsp ons", + "ĠI re", + "Ġp ow", + "Ġread ers", + "Ġemploy ment", + "Ġcreat ure", + "Ġresult ing", + "Ġaccur ate", + "Ġmom ents", + "Ġarg ued", + "Ġp ed", + "D uring", + "Ġ5 3", + "ĠT al", + "Ġs ought", + "Ġsuff ering", + "Ġ icon", + "le e", + "Ġ( $", + "al ian", + " °", + "Ġp ra", + "Ġbon us", + "( \"", + "k o", + "Ġact ing", + "D E", + "f all", + "Ġcompar ison", + "Ġsm ooth", + "ĠN AS", + "u pp", + "ĠJose ph", + "ep ing", + "ĠT ake", + "ĠM id", + "Ġs ending", + "f ast", + "ĠF all", + "Ġdeal ing", + "us er", + "ĠOr gan", + "C o", + "Ġatt ached", + "Ġse es", + "% .", + "Ġtyp ical", + "AR T", + "Ġfind s", + "ĠAs ia", + "um in", + "ĠC ore", + "ĠE nt", + "in ent", + "u ce", + "ĠBl ood", + "ĠN ever", + "Ġem ails", + "Ġhigh light", + "Ġconf ront", + "at us", + "ut ed", + "Ġun us", + "Ġtop ic", + "ĠAd am", + "Ġb le", + "at i", + "Ġunder stood", + "S et", + "st ruct", + "T P", + "Ġm ob", + "a a", + "ĠSt art", + "pect ed", + "se ll", + "Ġded icated", + "ĠC A", + "u an", + "Ġsong s", + "esc ription", + "Ġte ch", + "Ġr ape", + "Ġas ide", + "Ġgr ant", + "Ġ5 6", + "s ub", + "Ġarg ue", + "Ġcont aining", + "Ġsche dule", + "Ġliber al", + "Ġpublic ly", + "Ġheav ily", + "ĠU t", + "in er", + "ĠS ection", + "ĠC are", + "we et", + "l s", + "D is", + "âĶ Ģ", + "ĠF ollow", + "B ack", + "ĠI T", + "Ġb es", + "j i", + "ĠH it", + "est ed", + "Ġevery body", + "ĠSw ed", + "Ġfem in", + "Ġfac ilities", + "Ġcon ven", + "C omp", + "ĠO S", + "c ore", + "Ġan x", + "Ġdiv ision", + "ĠC am", + "ĠSt an", + "m ates", + "Ġexpl ore", + "pl om", + "Ġsh ares", + "pl oad", + "an es", + "Ġide al", + "et ers", + "ĠB ase", + "Ġpl astic", + "Ġdist inct", + "ĠNet work", + "ĠSe attle", + "Ġtrad ing", + "ens us", + "int end", + "Ġex hib", + "Ġinit ially", + "ĠF ood", + "Ġthous and", + "ĠBus iness", + "act er", + "Ġpar agraph", + "Ġrough ly", + "Ġw ww", + "Ġcreat ive", + "ĠCon f", + "Ġconsum ption", + "Ġfil ms", + "ag an", + "Ġob tain", + "Ġt all", + "Ġt or", + "Ġacknow led", + "Ġg rown", + "al o", + "K E", + "Ġ4 00", + "end ers", + "t aining", + "U G", + "Ġsu icide", + "Ġwat ched", + "ĠL ist", + "al i", + "re hens", + "Ġsurround ing", + "Ġp ip", + "Ġf lying", + "ĠJ ava", + "ord an", + "Ġserv ing", + "in ations", + "p ost", + "Ġsh o", + "A v", + "Ġj ail", + "z y", + "Ġ199 9", + "Ġ< /", + "Ġliter ally", + "ĠS ir", + "Ġexp osed", + "Ġl ies", + "st ar", + "Ġb at", + "Ġear ned", + "ĠD ig", + "Ġspec ified", + "ĠSe ason", + "Ġdeg rees", + "Don ald", + "Ġcent re", + "Ġsh aring", + "Ġwin ter", + "ĠC O", + "C he", + "Ġ Î", + "M P", + "Ġun w", + "Ġfew er", + "ĠM ir", + "Ġsomew here", + "ĠK ey", + "Ġattack ed", + "ĠK ir", + "Ġdom ain", + "Ġstrong er", + "Ġ9 9", + "Ġpen alty", + "I d", + "Sc ript", + "Ġdecl ined", + "Ġne ck", + "Ġfra ud", + "Ġcur rency", + "Ġr ising", + "R C", + "âĢ¦ âĢ¦", + "H z", + "Ġt ab", + "Ġtal ent", + "n am", + "ĠN BA", + "Ġvill age", + "Ġleg s", + "ĠN ext", + "E d", + "Ġac id", + "Ġhy d", + "8 00", + "Ġinvol ving", + "ĠIm age", + "ĠBe fore", + "F l", + "Ġyes terday", + "S ource", + "Ġterror ist", + "Ġsu p", + "Ġsy nt", + "ĠSaud i", + "Ġw est", + "Ġr u", + "b urg", + "Ġvis ible", + "Ġstru ck", + "r ison", + "Ġaw esome", + "Ġd rawn", + "Ġansw ers", + "ĠG irl", + "ĠR am", + "Ġthreat s", + "Ġdef eat", + "os it", + "Ġv ent", + "atur ally", + "Americ an", + "end a", + "ĠH oly", + "Ġr um", + "% ,", + "c ase", + "ĠHist ory", + "ĠYou Tube", + "Ġsit uations", + "ĠD NA", + "S te", + "Ġsa ved", + "It em", + "Ġrec ip", + "olog ist", + "Ġfac ed", + "Ġel ig", + "O nce", + "ĠL i", + "u h", + "Ġmist ake", + "ĠDiv ision", + "ĠB ell", + "Ġsympt oms", + " ®", + "Ġdom in", + "Ġfall ing", + "Ġend ing", + "as hes", + "Ġmat ches", + "ĠOn line", + "Ġexplan ation", + "D ef", + "red it", + "Ġany more", + "ĠT otal", + "ĠF OR", + "us hed", + "Ġlet ters", + "Ġris ks", + "ĠO K", + "Ġreported ly", + ": \\", + "Ġpl ate", + "Ġsubject s", + "Ġattempt ed", + "if ier", + "ian a", + "Ġunlike ly", + "ĠTh ough", + "um a", + "ĠIn vest", + "ĠPr in", + "ic an", + "ĠD ar", + "ĠColor ado", + "au g", + "Ġve get", + "a os", + "ri a", + "Ġshe l", + "Ġmark ed", + "Ġ( )", + "Ġsp r", + "p o", + "ĠL ink", + "Ġdef e", + "ĠJ r", + "Ġthem e", + "Ġpass ion", + "ĠP en", + "Ġinf o", + "iz er", + "Ġsh it", + "ĠC ivil", + "ap se", + "c re", + "Ġpo ly", + "Ġcomp onent", + "ĠChar les", + "ĠIre land", + "ĠPro v", + "Ġdo ctors", + "Ġgr anted", + "Ġpain t", + "Ġhon or", + "Ġsm oke", + "Ġpay ments", + "Ġprim arily", + "ĠKing dom", + "r ich", + "ate ll", + "Ġde als", + "Ġsched uled", + "Ġfund amental", + "Ġprote in", + "Ġnewsp aper", + "Ġcl ients", + "yth on", + "ĠD ate", + "h us", + "Ġfeed back", + "Ġstret ch", + "Ġc ock", + "Ġhot el", + "ĠQue en", + "Ġsu gar", + "Ġj u", + "Ġmil k", + "Ġappro val", + "ĠL ive", + "Ġequival ent", + "ef ully", + "Ġins ert", + "z ona", + "Ġext ension", + "d ri", + "J ohn", + "Ġacc omp", + "S m", + "ĠF und", + "Ġconst antly", + "Ġ` `", + "Ġgener ated", + "ĠA ction", + "ĠP sych", + "ĠT ri", + "Ġrecogn ize", + "Ġv ary", + "ph a", + "ĠR a", + "d f", + "et ch", + "ĠSov iet", + "Tw o", + "Ġpattern s", + "Ġprof ession", + "an ing", + "T ime", + "ĠL im", + "Ġcol ors", + "ĠA z", + "ĠT R", + "Ġinf ect", + "Ġphen omen", + "Ġshe ll", + "Al so", + "Ġput s", + "Ġdel ivery", + "Ġbro wn", + "Ġprocess ing", + "Ġlight s", + "ess age", + "ĠBro ok", + "ĠA ud", + "l ation", + "Ġindust rial", + "L ike", + "ĠB razil", + "rou s", + "ES S", + "ĠL uc", + "Ġsome how", + "Ġ8 5", + "Ġpro port", + "Ġpolit icians", + "Ġindic ate", + "Ġh ole", + "Ġtechn iques", + "Ġcompet itive", + "Ġph r", + "Ġv o", + "ist ent", + "ĠD ream", + "Ġcamp us", + "Ġaspect s", + "Ġhelp ful", + "Ġsh ield", + "or se", + "Ġtrig ger", + "m al", + "Ġ5 8", + "Ġt ort", + "Ġperson ally", + "Ġt ag", + "Ġkeep s", + "ĠV ideo", + "Ġben ch", + "Ġg ap", + "a ire", + "Ġe ast", + "Ġrec overy", + "per ial", + "Ġprof it", + "ĠM ic", + "Ġ5 7", + "Ġcol on", + "Ġstrong ly", + "st yle", + "Ġalleg ations", + "h an", + "Ġrep orters", + "j o", + "r ine", + "arg et", + "and al", + "Ġ0 3", + "Ġfl ash", + "tr ans", + "Ġstr ict", + "Ġpark ing", + "ĠPak istan", + "Ġl i", + "Ġwe ird", + "ĠE ric", + "Ġreg ions", + "ĠJ un", + "Ġint ellect", + "ĠW H", + "od ing", + "rib utes", + "up id", + "ĠT it", + "Ġf inger", + "or ia", + "Ġe lev", + "ĠF ield", + "Ġcon clusion", + "; ;", + "Ġfeel ings", + "Ġext ensive", + "Ġm ixed", + "Ġne uro", + "v y", + "Ġhar ass", + "ĠC irc", + "ou ch", + "Ġterrit ory", + "Ġsuccess fully", + "M ar", + "Ġing red", + "Ġoverw hel", + "Ġl ayer", + "V iew", + "Ġall ies", + "ill ance", + "ĠTh ree", + "Ġb unch", + "Ġnorm ally", + "Ġnet works", + "Ġsac r", + "ĠC IA", + "b les", + "Ġch ose", + "Ġopp onents", + "Ġregard less", + "Ġfr anch", + "Ġpre f", + "ĠP o", + "Ġbr idge", + "ann a", + "ĠSil ver", + "Ġw age", + "p age", + "ri or", + "Ġrad ical", + "ĠL ittle", + "Ġman ip", + "Ġsecret ary", + "Ġg ang", + "D R", + "F A", + "Ġdec ent", + "ĠSp irit", + "Ġun cle", + "ĠDevelop ment", + "Ġinvest ors", + "Ġwall s", + "Ġpub lish", + "Ġgener ate", + "iss ions", + "c ar", + "Ġprom ote", + "Ġcut ting", + "Ġche st", + "Ġdrink ing", + "Ġcollect ed", + "Ġ7 2", + "Ġhop ing", + "Ġem br", + "gor ith", + "Ġwar ned", + "Ġinstruct ions", + "O G", + "ĠD id", + "ĠAg ency", + "Ġg ear", + "Ġcritic ism", + "ĠF urther", + "Ġut il", + "ann y", + "R ed", + "Ġcoun sel", + "ĠAs ian", + "Ġredu ction", + "p ool", + "Ġteach ing", + "Ġdeep ly", + "i y", + "Ġestim ates", + "Ġcho ices", + "Ġperman ent", + "in em", + "ke l", + "Ġf asc", + "p se", + "f ile", + "ĠL ow", + "ĠP erson", + "Ġt ournament", + "st al", + "Ġm el", + "U ST", + "ĠR ay", + "az i", + "V al", + "Ġcont ained", + "ĠH olly", + "Ġw ake", + "Ġreve al", + "Ġprocess es", + "ĠIS IS", + "Ġ0 9", + "Ġbl ind", + "Ġste el", + "ĠB ad", + "Ġcare fully", + "app y", + "ro it", + "Ġg aming", + "Ġhous es", + "ĠC oll", + "Ġtr uck", + "er m", + "Ġsc ored", + "Ġocc as", + "ret urn", + "b ound", + "v ar", + "Ġsh arp", + "Ġaf raid", + "ĠE X", + "am ber", + "c ific", + "Ġsche me", + "N C", + "ĠPol it", + "Ġdecl ine", + "Ġ199 8", + "Ġpus hing", + "Ġposs ession", + "Ġpriv ile", + "Ġteacher s", + "Ġy ield", + "H A", + "ĠDav is", + "it led", + "#### ####", + "Ġr ig", + "ĠD aniel", + "ac on", + "Ġh ide", + "ut en", + "Ġcolle agues", + "Ġprin ciples", + "Ġl oud", + "Ġs in", + "ĠDem on", + "Ġst one", + "Ġ0 2", + "Ġt aught", + "Ġter rible", + "Ġst uck", + "ĠPol icy", + "te en", + "Ġimplement ation", + "ĠB BC", + "ĠAP I", + "Ġwhe el", + "all as", + "Ġch ampions", + "ol ars", + "play er", + "Ġrepeated ly", + "ĠSt ill", + "Ġlik es", + "ast y", + "es ter", + "ĠCath olic", + "R L", + "Ġb ath", + "Ġno ise", + "t itle", + "Ġn orthern", + "P art", + "Ġmag n", + "Ġf ab", + "ĠAs h", + "Ġdis pl", + "Ġtick et", + "Ġm urd", + "Ġalong side", + "ĠMus ic", + "Ġr iver", + "ĠSte el", + "ĠC L", + "ĠPl ayer", + "ĠM ult", + "ow ing", + "re p", + "s ize", + "Ġt ur", + "ĠGeorg ia", + "isc al", + "ra ction", + "Ġc able", + "Ġ5 9", + "Ġw ins", + "Ġup coming", + "Ġsurv ive", + "Ġins pired", + "ĠEduc ation", + "Ġstat istics", + "ĠF oot", + "iam i", + "Ġy ellow", + "ĠP age", + ". -", + "ĠH as", + "Ġur ban", + "Ġa x", + "es sel", + "\\ \"", + "Ġquarter back", + "Ġreg ister", + "ĠLab or", + "Ġab ilities", + "ĠF amily", + "Ġvar iable", + "ĠPr ice", + "Ġcont em", + "Ġth in", + "ĠE qu", + "d ata", + "Ġg otten", + "Ġconst it", + "Ġas ks", + "Ġt ail", + "Ġexc iting", + "ĠE ffect", + "ĠSp anish", + "Ġencour age", + "ins on", + "ĠA h", + "Ġcommit ment", + "C S", + "Ġr ally", + "Ġ: :", + "Ġsubs id", + "Ġsp in", + "Ġcapt ured", + "201 8", + "Ġinn oc", + "Ġalleged ly", + "ĠC ome", + "Ġart ists", + "ĠN umber", + "Ġelect ronic", + "Ġreg ional", + "ap es", + "Ġw ra", + "Ġmy th", + "pr ise", + "ĠM iller", + "ĠC reat", + "ĠEp isode", + "b ell", + "Ġdirect ed", + "Ġext ract", + "Ġs orry", + "Ġv ice", + "ag ger", + "ĠSu pport", + "Ġ6 6", + "ĠI ron", + "Ġwonder ful", + "Ġg ra", + "N et", + "ion e", + "E ng", + "Ġsh ips", + "ik es", + "ĠK evin", + "it ar", + "Ġactiv ists", + "tr ue", + "ĠAri zona", + "ent h", + "ĠDes pite", + "ĠS E", + "Ġha bit", + "ern el", + "Ġin qu", + "Ġab ortion", + "Ġv oid", + "Ġexpl icit", + "Ġeng aged", + "Ġang ry", + "Ġr ating", + "Ġfr ag", + "b ro", + "ick ing", + "d ev", + "Ġwor ried", + "Ġob ser", + "Ġap artment", + "ĠG T", + "Ġest ate", + "ĠConst itution", + "em on", + "ĠS now", + "Ġcount y", + "Ġdis ag", + "ĠStep hen", + "Ġimm igrants", + "w ind", + "ĠN ations", + "Ġfol ks", + "O ut", + "Ġg all", + "Ġtarget ed", + "Ġst ead", + "ĠB on", + "ĠL ib", + "Ġinform ed", + "Ġ12 0", + "ch ain", + "idel ines", + "or ough", + "Ġdri ven", + "Ġregular ly", + "Ġbas ket", + "Ġprinc iple", + "oc ument", + "Ġst un", + "ib ilities", + "ĠRom an", + "ĠAb out", + "Ġal ert", + "Ġdemocr acy", + "Ġrepresent ed", + "H S", + "c ers", + "p arent", + "Ar t", + "p ack", + "Ġdi plom", + "re ts", + "ĠN O", + "Ġcapt ure", + "ĠAd v", + "Ħ ¢", + "Ġannounce ment", + "ĠL ear", + "Ġh ook", + "Ġpur s", + "ĠS uch", + "ĠC amer", + "Ġrefuge es", + "ĠV e", + "P ol", + "Ġrecogn ized", + "l ib", + "Ġhad n", + "A ss", + "Ġpil ot", + "us hing", + "Ġreturn ing", + "Ġtra il", + "ĠSt one", + "Ġrout ine", + "Ġcour ts", + "Ġdes per", + "Ġfriend ly", + "ĠIt aly", + "Ġpl ed", + "Ġbreat h", + "Ġstud io", + "N S", + "Ġimp ressive", + "ĠAfghan istan", + "Ġf ing", + "Ġd ownt", + "ink ing", + "ĠR og", + "i ary", + "col or", + "se x", + "ar on", + "Ġf ault", + "ĠN ick", + "D own", + "ĠR ose", + "ĠS outhern", + "X X", + "is odes", + "L ist", + "6 00", + "Ġout come", + "er r", + "Ġelse where", + "Ġret ire", + "Ġp ounds", + "ĠGl obal", + "Pe ople", + "Ġcommun ications", + "Ġlo an", + "Ġrat io", + "ĠEm pire", + "Ġg onna", + "Ġinv ent", + "D F", + "Ġ19 70", + "ĠComm on", + "p at", + "Ġprom ised", + "Ġd inner", + "ĠH om", + "Ġcreat es", + "Ġoper ate", + "ver ty", + "ĠJ ordan", + "et ime", + "Ġsust ain", + "R eg", + "Ġincred ible", + "im a", + "Ġwar rant", + "Ġm m", + "A tt", + "Ġlaw suit", + "Ġreview s", + "it ure", + "ĠS ource", + "l ights", + "ĠF ord", + "Ġ6 3", + "g roup", + "st ore", + "Ġfeat ured", + "Ġfore ver", + "Ġpo verty", + "ĠP op", + "ĠC NN", + "az z", + "ab is", + "ach ing", + "Ġl aid", + "ĠSu pp", + "Ġfil ter", + "en a", + "ĠCommun ity", + "Ġcreat ures", + "u ction", + "ĠR oyal", + "Ġassoci ation", + "ĠCon nect", + "ĠBr ad", + "âĸ Ī", + "l ers", + "the re", + "ĠG i", + "Ġval uable", + "AC K", + "ĠT aylor", + "Ġl iquid", + "ĠAtt orney", + "ĠCar l", + "ĠF inal", + "ag a", + "ĠWil son", + "B ecause", + "ĠProf essor", + "ak a", + "Ġincred ibly", + "r ance", + "! )", + "R ef", + "s k", + "Ġsol utions", + "Ġatmosp here", + "Ġbl ame", + "um es", + "ĠN ob", + "C A", + "um ps", + "r ical", + "ĠPut in", + "ĠD est", + "or ic", + "ĠP A", + "Ġrespect ively", + "w an", + "Ġfif th", + "â Ħ¢", + "ĠC ry", + "Ġgovern or", + "res ident", + "Ġpurch ased", + "Ġh ack", + "Ġint ense", + "ob s", + "Ġorig in", + "Ġdef ine", + "Ġcare ful", + "** *", + "Ġshould er", + "Cl ick", + "Ġt ied", + "Ġdest ruction", + "ou red", + "Ġno body", + "Ġh o", + "ĠEx per", + "Ġt ip", + "\" ;", + "Ġtechn ique", + "Ġj ur", + "ĠP ok", + "b ow", + "Ġleg end", + "Ġacc ord", + "Ġbus y", + "ĠInt el", + "Ġh ang", + "ak i", + ". ]", + "âĢĶâĢĶ âĢĶâĢĶ", + "Ġsur gery", + "Ġrep rodu", + "Ġun iform", + "Ġscen es", + "c ode", + "Ġ6 2", + "l isher", + "ĠH ave", + "ph ia", + "Ġcry pt", + "Ġrec on", + "Ġsc ream", + "Ġadop ted", + "Ġsc ores", + "N e", + "ĠIt alian", + "in cluding", + "B O", + "Ġindic ated", + "Ġent ertain", + "G u", + "T ext", + "i el", + "Ġtw enty", + "Ġeng age", + "off s", + "ĠPac ific", + "Ġsm ile", + "Ġperson nel", + "Ġto ler", + "Ġdo ors", + "Ġt one", + "Ġmach ines", + "Ġent ering", + "ten ance", + "C O", + "ĠJer sey", + "Ġfore st", + "Ġhor se", + "Ġcompl aint", + "ĠSpr ing", + "y o", + "ĠPl us", + "ed ing", + "ĠRet urn", + "qu arters", + "ial s", + "c ow", + "Ġacad emic", + "Ġf ruit", + "Ġ199 6", + "og ether", + "Ġw ine", + "Ġpur su", + "ĠSte ven", + "Ġlic ens", + "Wh o", + "Ġclot hes", + "re ction", + "Ġsqu ad", + "Ġst able", + "Ġr aw", + "z ens", + "St ar", + "ut ies", + "anc er", + "Ġke ys", + "ĠM u", + "Ġcompl icated", + "ig er", + "ĠTe xt", + "Ġabs or", + "Ġ6 8", + "Ġfun ny", + "Ġrel ief", + "ĠL ew", + "ĠC ook", + "Ġch art", + "Ġdraw ing", + "G E", + "Ġmod ule", + "ĠB ull", + "I LL", + "Ġs alt", + "0000 0000", + "il le", + "Ġres ource", + "aw ay", + "adel phia", + "ĠB ru", + "Ġ6 7", + "Ġsome body", + "Ġparticip ate", + "Ġro se", + "we red", + "Ġmus cle", + "Ġcons ent", + "Ġcontin uing", + "ĠGuard ian", + "ĠOr der", + "reg on", + "Ġre ar", + "Ġprov ision", + "Ġlik ed", + "ri ent", + "Ġb ra", + "Tr ans", + "Ġmeet ings", + "Ġto x", + "Ġcon vent", + "Ġaut o", + "Ġrec ording", + "ĠSo ft", + "00 1", + "ĠR oll", + "Ġprogram ming", + "Ġp ic", + "Ġprov ed", + "Ġst ab", + "ĠA st", + "Ġca ption", + "ul ating", + "ĠAtt ack", + "Ġnew ly", + "Ġ199 7", + "f r", + "Ġdis cipl", + "ĠGree k", + "Ġed ition", + "ĠDo es", + "ĠB ox", + "if le", + "ack et", + "Ġpass es", + "Ġgu est", + "Ġac celer", + "it als", + "U D", + "Ġaut hent", + "ĠR est", + "ov al", + "t a", + "u ine", + "Ġarm or", + "ĠT own", + "Ġcomp at", + "Ġinc hes", + "Des pite", + "Ġass ign", + "he rent", + "Ġprep are", + "ĠM eg", + "oc key", + "Ġdep ends", + "Ġtrack s", + "w atch", + "Ġl ists", + "ĠN orthern", + "Ġal ter", + "re c", + "ĠE astern", + "Ġcond em", + "Ġevery where", + "? '", + "Ġaff ili", + "Ġf ought", + "\": {\"", + "Ġm ac", + "it arian", + "Ġsc ope", + "ĠA L", + "aw s", + "ar ms", + "Ġqu e", + "Ġenjoy ed", + "nes ota", + "Ġagg ressive", + "ĠSt ory", + "ĠI V", + "Ġrec ipe", + "Ġrare ly", + "ĠMed ical", + "val ue", + "ang el", + "ay ing", + "omet hing", + "Ġsub section", + "Ġs outhern", + "Ġfrequ ency", + "re te", + "roll ed", + "ult s", + "ĠN ic", + "Ġbeh alf", + "Ġsequ ence", + "ab et", + "Ġcontrovers ial", + "Ġcomp rom", + "Ġwork er", + "Ġmain ly", + "Ġal gorith", + "ĠM ajor", + "or ce", + "g ender", + "Ġorgan ized", + "Ġf ake", + "Ġconclud ed", + "ĠE D", + "ĠEx ec", + "r age", + "Ġch ances", + "ber ry", + "ĠTr ad", + "Ġconfig uration", + "Ġwithd raw", + "Ġf ro", + "ud es", + "ĠBro ther", + "ĠB rian", + "Ġtri es", + "Ġsam ples", + "Ġb id", + "ĠGold en", + "Ġphot ograph", + "if est", + "ĠD O", + "ĠPar liament", + "******** ********", + "R em", + "Ġcont est", + "Ġsign ing", + "p x", + "ĠZ eal", + "âĶĢ âĶĢ", + "E ar", + "Ġex it", + "Be fore", + "ĠCor por", + "n ull", + "mon th", + "Ġrac ial", + "ott ed", + "ĠV eg", + "ĠRe uters", + "Ġsw ord", + "ps on", + "ĠRom ney", + "a ed", + "Ġt rib", + "Ġin ner", + "Ġprot ocol", + "ĠB i", + "ĠM iami", + "ever al", + "p ress", + "Ġsh ipping", + "ĠAm endment", + "ĠHow ard", + "con nect", + "ĠD isc", + "ĠJ ac", + "iam ond", + "ĠThere fore", + "s es", + "ĠPrin cess", + "ĠUS B", + "ĠAn th", + "Ġsurve illance", + "Ġap olog", + "Ġ6 1", + "ow a", + "Ġf ulf", + "j s", + "Ġl uck", + "ust ed", + "Ġ §", + "n i", + "Ġant icip", + "em an", + "Ġwin ner", + "Ġsil ver", + "ll a", + "ic ity", + "Ġunus ual", + "Ġcr ack", + "Ġt ies", + "e z", + "Ġpract ical", + "Ġprov ince", + "ĠPl ace", + "Ġprior ity", + "IC E", + "Ġdescrib es", + "Ġbr anch", + "F orm", + "ask a", + "miss ions", + "b i", + "Ġp orn", + "ĠTur k", + "Ġent hus", + "Ġf ighters", + "Ġ0 8", + "ĠDet roit", + "Ġfound ation", + "av id", + "A re", + "Ġjud gment", + "cl ing", + "Ġsol ve", + "ĠDes ign", + "W here", + "hes is", + "ĠT ro", + "a fter", + "Ġne utral", + "ĠPalestin ian", + "ĠHolly wood", + "Ġadv is", + "ĠN on", + "y es", + "ol is", + "Ġrep utation", + "Ġsm ell", + "Ġb read", + "ĠB ul", + "ĠBe ach", + "Ġclaim ing", + "Ġgen etic", + "Ġtechn ologies", + "Ġupgr ade", + "row s", + "Ġdevelop er", + "ĠJ osh", + "ĠDis ney", + "erv ed", + "ip al", + "Ġun ex", + "Ġbare ly", + "t hen", + "ĠP ub", + "Ġill ness", + "et ary", + "ĠB al", + "Ġp atch", + "Ġbut t", + "Ġst upid", + "ĠD og", + "ĠD allas", + "f ront", + "ie ce", + "Ġprot ests", + "Ġch at", + "oen ix", + "Ġw ing", + "Ġpar liament", + "Ġ7 7", + "ose xual", + "Ġre nder", + "pt ions", + "ĠCo ast", + "os a", + "ĠG reg", + "h op", + "ĠMan agement", + "Ġbit coin", + "Ġrec over", + "Ġincor por", + "or ne", + "ĠUs ing", + "Ġpre ced", + "Ġthreat ened", + "Ġspirit ual", + "ĠE vent", + "ĠF red", + "Ġadvert ising", + "Ġimprove ments", + "ĠC ustom", + "Ġer rors", + "Ġsens itive", + "ĠN avy", + "Ġcre am", + "L ook", + "Ġex clusive", + "Ġcomp rehens", + "Ġde leg", + "Ġcon ce", + "Ġrem em", + "Ġstruct ures", + "Ġst ored", + "N D", + "Ġ1 000", + "U P", + "ĠB udd", + "A F", + "w oman", + "ĠAcad emy", + "ð Ł", + "se a", + "Ġtem porary", + "Ab out", + "es ters", + "Ġtick ets", + "Ġposs ess", + "in ch", + "o z", + "Ġl a", + "Ġcontract s", + "Ġun p", + "Ġc ig", + "ĠK at", + "ult ural", + "as m", + "Ġmount ain", + "ĠCapt ain", + "St ep", + "m aking", + "ĠSp ain", + "Ġequ ally", + "Ġl ands", + "at ers", + "Ġreject ed", + "er a", + "im m", + "ri x", + "C D", + "Ġtrans action", + "g ener", + "less ly", + "Ġ| |", + "Ġc os", + "ĠHen ry", + "Ġprov isions", + "Ġg ained", + "Ġdirect ory", + "Ġra ising", + "ĠS ep", + "ol en", + "ond er", + "Ġcon sole", + "in st", + "Ġb om", + "Ġunc ertain", + "1 50", + "ock ing", + "Ġmeas ured", + "Ġpl ain", + "Ġse ats", + "Ġd ict", + "S L", + "af e", + "Ġest imate", + "iz on", + "at hered", + "Ġcontribut ed", + "Ġep isodes", + "omm od", + "G r", + "AN T", + "Ġ6 9", + "G ener", + "Ġ2 50", + "vious ly", + "rog en", + "Ġterror ism", + "Ġmove ments", + "ent le", + "oun ce", + "ĠS oul", + "Ġpre v", + "ĠT able", + "act s", + "ri ors", + "t ab", + "Ġsuff er", + "Ġn erv", + "Ġmain stream", + "ĠW olf", + "Ġfranch ise", + "b at", + "Ġdem ands", + "Ġag enda", + "Ġdo zen", + "Ġclin ical", + "iz ard", + "ĠO p", + "t d", + "Ġvis ited", + "ĠPer haps", + "Ġact or", + "Ġde lic", + "Ġcont ribute", + "Ġin ject", + "ĠE s", + "ac co", + "Ġlist ening", + "Ġcon gress", + "epend ent", + "Ġprem ium", + "Ġ7 6", + "ĠIr ish", + "Ġass igned", + "ĠPh ys", + "Ġworld wide", + "Ġnarr ative", + "ot ype", + "m ont", + "b ase", + "ĠB owl", + "ĠAdminist ration", + "Ġrel ation", + "ĠE V", + "C P", + "Ġco vers", + "Ġ7 8", + "Ġcert ific", + "Ġgr ass", + "Ġ0 4", + "pir acy", + "ir a", + "Ġengine ering", + "ĠM ars", + "Ġun employ", + "ĠFore ign", + "st ract", + "Ġv en", + "Ġst eal", + "Ġrepl ied", + "Ġult imate", + "Ġtit les", + "d ated", + "Ġj oy", + "a us", + "Ġhy per", + "ak u", + "Ġoffic ially", + "ĠPro duct", + "Ġdifficult y", + "per or", + "Ġresult ed", + "rib ed", + "l ink", + "wh o", + "~~ ~~", + "ĠSpe ed", + "ĠV iet", + "W ind", + "ĠBar ack", + "Ġrestrict ions", + "ĠSh are", + "Ġ199 5", + "ition ally", + "Ġbeaut y", + "op t", + "Ġm aps", + "ĠC R", + "ĠN ation", + "ĠCru z", + "W ill", + "Ġelectric ity", + "Ġor g", + "Ġb urd", + "Ġviol ation", + "Ġus age", + "Ġper mit", + "ĠCh ron", + "ĠF ant", + "Ġn aturally", + "Ġ0 7", + "Ġth rown", + "ĠAw oken", + "Ġal ien", + "ĠHer o", + "ĠK ent", + "ĠR ick", + "ri ke", + "Ġp ace", + "}, {\"", + "G L", + "Ġpo ison", + "ĠT ower", + "Ġform al", + "al ysis", + "Ġgen uine", + "Ġk il", + "a ver", + "Ġproced ure", + "ĠPro p", + "intend o", + "ĠM ain", + "as ant", + "Ġtr ained", + "G ame", + "ĠL oad", + "ĠM A", + "Ġcru cial", + "Ġle ts", + "ĠF R", + "Ġch ampion", + "1 01", + "ĠCon ference", + "Ġwrit ers", + "Ġconnect ions", + "Ġo kay", + "ir ms", + "ĠR and", + "Ġenc ounter", + "ĠB uff", + "Ġachie ved", + "Ġche cks", + "isc ons", + "Ġassist ant", + "Ġwhen ever", + "ĠA ccess", + "ĠU r", + "b in", + "Ġcl ock", + "is p", + "op her", + "Ġb orrow", + "Ġm ad", + "Ġperson ality", + "on ly", + "IS T", + "ab ama", + "Ġg ains", + "Ġcommon ly", + "Ġter r", + "Ġhyp ot", + "Ġre ly", + "Ġt iss", + "iscons in", + "Ġrid ic", + "f unction", + "ĠO regon", + "Ġun com", + "r ating", + "el and", + "ĠN C", + "Ġm oon", + "ann on", + "Ġvulner able", + "ut ive", + "³³ ³³", + "ĠRad io", + "Ġw estern", + "se ct", + "ĠT ony", + "Ġocc urs", + "ĠO s", + "ĠH on", + "à Ń", + "Ġv essel", + "ĠScot land", + "Ġdiscrim ination", + "Ġsubsequ ent", + "st ring", + "Ġfant asy", + "ĠSh adow", + "Ġtest im", + "W E", + "it i", + "r as", + "Ġbo at", + "Ġmar ks", + "Ġord inary", + "Ġre n", + "Ġrepresent ative", + "Ġpet ition", + "Ġ7 3", + "Ġad venture", + "Ġign ore", + "ĠPhil adelphia", + "ĠS av", + "V P", + "Ġfact ory", + "Ġt asks", + "Ġdep ression", + "z ed", + "................ ................", + "ĠSt orm", + "Ġc ogn", + "Ġelig ible", + "Ġredu cing", + "v ia", + "Ġ0 5", + "Ġstri king", + "Ġdoll ar", + "h o", + "O V", + "Ġinstr ument", + "Ġphilosoph y", + "ĠMo ore", + "ĠA venue", + "Ġrul ed", + "ĠFr ont", + "IN E", + "ĠM ah", + "Ġscen ario", + "ĠNAS A", + "Ġen orm", + "Ġdeb ut", + "Ġte a", + "T oday", + "Ġabs ence", + "S im", + "Ġh am", + "le ep", + "Ġt ables", + "ĠHe art", + "M I", + "K e", + "re qu", + "V D", + "m ap", + "Ġchair man", + "Ġp ump", + "Ġrapid ly", + "v i", + "Ġsubstant ial", + "E P", + "d es", + "ch ant", + "ili pp", + "ĠS anta", + "ri ers", + "anche ster", + "L oad", + "ĠC ase", + "Ġsa ving", + "Ġ7 4", + "ĠA FP", + "er ning", + "oun ced", + "ĠMin nesota", + "ĠW as", + "Ġrec ru", + "Ġassess ment", + "ĠB ron", + "U E", + "Ġdynam ic", + "Ġf urn", + "ul ator", + "Ġprop ag", + "h igh", + "Ġacc ommod", + "Ġst ack", + "ĠS us", + "w rit", + "Ġre ven", + "ĠGod d", + "ĠZeal and", + "ab s", + "Ġbr ut", + "Ġper pet", + "h ot", + "Ġhard ly", + "ĠB urn", + "ãĤ ¹", + "Ġst y", + "Ġtrans actions", + "Ġg ate", + "Ġsc reens", + "Ġsub mitted", + "Ġ1 01", + "Ġlangu ages", + "ugh t", + "em en", + "Ġfall s", + "Ġc oc", + "Ĥ ¬", + "Ġstri kes", + "p a", + "Ġdel iber", + "ĠI M", + "Ġrel ax", + "ann els", + "ĠSen ator", + "Ġext rem", + "Ġ} ,", + "ĠDe b", + "Ġbe ll", + "Ġdis order", + "c ut", + "Ġi OS", + "Ġl ocked", + "Ġem issions", + "Ġshort ly", + "\" ]", + "ĠJud ge", + "ĠS ometimes", + "Ġr ival", + "Ġd ust", + "Ġreach ing", + "F ile", + "¯¯ ¯¯", + "ino is", + "ĠJ ason", + "Ġs atell", + "are t", + "Ġst ations", + "Ġag ric", + "ĠTechn ology", + "com es", + "ĠUn fortunately", + "ĠChild ren", + "Ġappl ies", + "ast ed", + "Ġan ger", + "ail ability", + "ĠDam age", + "Ġcomp are", + "ĠStand ard", + "Ġaim ed", + "ĠB a", + "angu age", + "Ġreg ulation", + "Ġj ury", + "Ġair port", + "Ġse ctions", + "ĠPr ince", + "em ed", + "Ġmedic ine", + "Ġh itting", + "Ġsp ark", + "ol ves", + "Ġad s", + "St ate", + "Ġfood s", + "Ġrepl acement", + "Ġch icken", + "Ġlow est", + "Ġmind s", + "Ġinvol ves", + "u i", + "Ġarr ang", + "Ġproced ures", + "ĠWh ich", + "ivers ary", + "Ġb ills", + "Ġimprove ment", + "Ġin ev", + "Ġexpect ations", + "Ġintellect ual", + "Ġsp aces", + "Ġmechan ism", + "2 50", + "bre ak", + "ĠZ e", + "ĠT enn", + "ĠB alt", + "Ġbar rel", + "Ġstat ic", + "man n", + "Pol ice", + "Ġt ips", + "Ġhand ling", + "c us", + "od ed", + "il ton", + "ir y", + "Ġjournal ists", + "our se", + "Ġcom ic", + "Ġnom ine", + "IT Y", + "Ġvers us", + "Ġlo op", + "Ġsur f", + "ĠInd ust", + "ĠHun ter", + "Ġbelief s", + "is an", + "Ġset up", + "Ġbre w", + "im age", + "Ġcomput ers", + "f ol", + "} ,\"", + "ĠMed al", + "Ġtax p", + "Ġdisplay ed", + "Ġg rav", + "Ġf iscal", + "M on", + "ĠMos cow", + "ĠK ong", + "ĠCent re", + "Ġcamer as", + "ĠMr s", + "ĠH ay", + "Ġa ver", + "ĠK elly", + "p y", + "Ġrequire ment", + "Ġent itled", + "omb ie", + "Ġsh adow", + "ag ic", + "ĠA k", + "Ġel ite", + "Ġdiv ided", + "Ġhead ing", + "Ġcop ies", + "Ġloss es", + "Ġv it", + "k ed", + "ĠB ry", + "Ġan s", + "ĠSte am", + "Ġrep orter", + "he im", + "ĠIt em", + "Ġsuper ior", + "d on", + "ere nt", + "à ¶", + "Ġtherap y", + "Ġpe ak", + "ĠMod el", + "Ġl ying", + "Ġg am", + "z er", + "r itten", + "Ġrespons es", + "Ġconsider ation", + "ĠB ible", + "Ġl oyal", + "Ġinst ant", + "Ġp m", + "ĠFore st", + "à ¼", + "Ġext end", + "Ġconv icted", + "Ġfound er", + "Ġconv in", + "ĠO ak", + "che ck", + "Ġsch olars", + "p ed", + "Ġover se", + "T op", + "c ount", + "ĠAr k", + " ·", + "Ġ0 6", + "ĠL A", + "m d", + "ĠLat in", + "im ental", + "ĠC PU", + "Ġsubst ance", + "Ġminor ity", + "Ġmanufact uring", + "E r", + "ocol ate", + "Ġatt ended", + "ĠMan ager", + "r ations", + "Ġappreci ate", + "om y", + "GB T", + "id ency", + "B L", + "Ġguarant ee", + "pos ition", + "Ġo cean", + "clud e", + "Ġhead ed", + "Ġt ape", + "Ġlo ose", + "Ġlog ic", + "Ġpro ven", + "Ġsp ir", + "Ġad mit", + "is a", + "Ġinvestig ate", + "Ġ199 4", + "sy lv", + "ĠL ost", + "c est", + "Ġ7 1", + "Ġrequest ed", + "Ġwind ows", + "ĠPok é", + "ĠWith out", + "M et", + "Ġbehavi our", + "Ġread er", + "Ġh ung", + "ĠKe ep", + "Ġro les", + "Ġimplement ed", + "Ġbl ank", + "Ġserv es", + "ĠJ ay", + "Ġc ited", + "ĠF riend", + "prof it", + "ap on", + "Ġrep air", + "it em", + "arr ass", + "Ġcrit ics", + "ad i", + "ĠF ather", + "Ġsh out", + "Ġf ool", + "Ġ8 8", + "Ġprodu cing", + "Ġl ib", + "Ġround s", + "Ġcirc le", + "Ġpre par", + "Ġsub mit", + "Ġn ic", + "mor row", + "ãĥ «", + "U nder", + "Ġv ital", + "ater n", + "Ġpass word", + "Ġpublic ation", + "Ġprom inent", + "Ġspeak s", + "Ġb ars", + "Ġde eper", + "ĠM ill", + "port ed", + "Ġw id", + "Ġbut ter", + "Ġsm oking", + "Ġindic ates", + "K ey", + "rop ri", + "ĠF ile", + "all ing", + "ast ing", + "ĠR us", + "Ġad j", + "Ġ7 9", + "av al", + "Ġpres um", + "bur gh", + "on ic", + "Ġf ur", + "Ġpoll s", + "ik a", + "Ġsecond ary", + "Ġmon ster", + "ig s", + "ĠCur rent", + "E vent", + "Ġowners hip", + "end ar", + "Ġarri ve", + "ĠT ax", + "Ġn ull", + "ĠPri v", + "Ġth ro", + "Ġk iss", + "c at", + "Ġup set", + "ang le", + "it ches", + "ect or", + "olog ists", + "ĠGal axy", + "Ġcor ruption", + "Ġh int", + "ent er", + "ĠH ospital", + "Ġgreat ly", + "Ġbeg un", + "es y", + "Ġso il", + "ĠAnt on", + "Ġmain tenance", + "ãĥ ©", + "Ġdo zens", + "Ġhuman ity", + "ĠAl abama", + "Ġr om", + "w orth", + "ap ing", + "sylv ania", + "l ah", + "Ġg athered", + "G A", + "Ġattack ing", + "f ound", + "ĠSqu are", + "Ġar bit", + "ict ions", + "ĠW isconsin", + "Ġd ance", + "ĠS aint", + "arch y", + "Ġbase ball", + "Ġcontribut ions", + "Ġliter ature", + "Ġex ha", + "per ty", + "t est", + "Ġb ab", + "Ġcontain er", + "let ter", + "Ġfall en", + "Ġwebs ites", + "Ġbott le", + "ĠS ac", + "Ġbre ast", + "ĠP L", + "Ġveter an", + "Ġinterview s", + "ĠA le", + "Ġb anned", + "eng ers", + "ĠRev olution", + "in th", + "Ġconc erning", + "IV E", + "Ġexp enses", + "ĠMatt hew", + "ĠColumb ia", + "d s", + "ist ance", + "Ġent ity", + ".. .\"", + "Ġrel iable", + "Ġpar alle", + "ĠChrist ians", + "Ġopin ions", + "Ġin du", + "l ow", + "Ġcompet e", + "Ġth orough", + "Ġemploy ed", + "Ġestablish ment", + "ig en", + "ĠC ro", + "Ġlawy ers", + "ĠSt ation", + "T E", + "ĠL ind", + "ĠP ur", + "it ary", + "Ġeffic iency", + "âĢ IJ", + "ĠL y", + "Ġm ask", + "Ġdis aster", + "Ġag es", + "ER E", + "es is", + "ĠH old", + "Ġcas ual", + "b led", + "Ġen abled", + "ĠEn vironment", + "ĠInt elligence", + "i per", + "ĠM ap", + "ĠB E", + "Ġemer ged", + "is dom", + "Ġc abin", + "Ġregist ration", + "Ġfing ers", + "Ġro ster", + "Ġfram ework", + "ĠDo ctor", + "et ts", + "Ġtransport ation", + "Ġaware ness", + "H er", + "Ġattempt ing", + "O ff", + "ĠSt ore", + "ÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤ", + "ĠK now", + "Ġdef ence", + "Ġsc an", + "ĠT en", + "ĠCh air", + "ĠP H", + "ĠAtl anta", + "Ġfuck ing", + "Ġans wered", + "b n", + "ĠK ar", + "Ġcateg ories", + "Ġr ational", + "Ġc ust", + "Ġrob ot", + "Ġcorrect ly", + "Ġg if", + "Ġgraph ics", + "m ic", + "Ġground s", + "ĠO pp", + "i ate", + "Ġdist ributed", + "Ġsan ctions", + "Ġchalleng ing", + "ut o", + "Ġingred ients", + "Ġinv ited", + "Ġfound ed", + "ĠRe qu", + "d ed", + "Ġb owl", + "Ġbrother s", + "ĠH a", + "I O", + "Ġw ages", + "im ore", + "oc ial", + "Ġse ed", + "ative ly", + "Ġaddress es", + "ĠI owa", + "ab eth", + "Ġatt itude", + "is d", + "ch ild", + "Ġm ole", + "Ġdisco very", + "y ard", + "B r", + "Ġ8 2", + "Ġsuppl ies", + "ell ing", + "Ġdist ingu", + "C R", + "Ġre cept", + "Ġ vert", + "Ġsw im", + "b ec", + "d oor", + "ĠY eah", + "Ġg al", + "Ġinter act", + "ĠE SP", + "ĠC S", + "amp s", + "Ġconvin ced", + "Ġobject ive", + "Ġdis h", + "ĠPhot os", + "l ad", + "Ġdownt own", + "o il", + "in ction", + "Ġto morrow", + "ĠC OM", + "Ġsurv ival", + "sh ot", + "Ġsett lement", + "C ons", + "ĠX box", + "int erest", + "ĠS M", + "arg o", + "en ess", + "Ġeth nic", + "b ered", + "M in", + "ĠT ok", + "Ġinc ent", + "ĠComm and", + "Ġmain tained", + "Ġbreak s", + "br idge", + "at ar", + "ag g", + "ĠF inally", + "un icip", + "ĠO nt", + "le ft", + "Ġrecogn ition", + "Ġ* /", + "ĠP ers", + "Ġwe lf", + "Ġaddress ed", + "ĠK ansas", + "Ġvir us", + "Ġwhere as", + "Ġp apers", + "ram s", + "ĠMin istry", + "Ġple asure", + "Ġacqu ired", + "Ġd uration", + "j pg", + "Ġcal m", + "ĠN HL", + "Ġburn ing", + "Ġfold er", + "ick ed", + "ĠP y", + "ĠIll inois", + "Cl ass", + "ĠGodd ess", + "Ġperform ing", + "Ġwelf are", + "j ar", + "In ter", + "Ġl in", + "Ġenh ance", + "Ġnot ion", + "f are", + "yp es", + "ĠAre a", + "Ġcann abis", + "ĠDie go", + "f s", + "ĠM anchester", + "com m", + "in ite", + "Ġcover ing", + "ĠS ound", + "Ġ19 60", + "Ġ8 4", + "e lect", + "z ing", + "Ġcitiz en", + "Ġph ones", + "Ġr aid", + "Ġign ored", + "ĠOb ject", + "Ġu pload", + "c ard", + "Ġmod ified", + "Ġroom s", + "ia h", + "r ange", + "he ast", + "ach us", + "Ġsuggest ing", + "âĢ ĭ", + "gr ade", + "E l", + "Ġclot hing", + "Ġr h", + "ĠH an", + "un ity", + "en cing", + "ĠAust in", + "sec ution", + "t ra", + "d em", + "ĠQ ual", + "Ġhe aven", + "Ġst ages", + "Ġw edd", + "pl us", + "ific ial", + "ĠIm m", + "ĠH o", + "iet ies", + "Ġphr ase", + "Ġbr ill", + "act ory", + "Ġprov iders", + "Ġsil ence", + "Ġa er", + "ĠA I", + "ĠAd venture", + "Ġplatform s", + "Ġdemonstr ated", + "Ġinter f", + "ing ton", + "Ġr aces", + "Ġgr ade", + "ult ane", + "ĠTh rough", + "f alse", + "Ġb ow", + "ĠA B", + "Ġfl avor", + "Ġhistor ic", + "g ov", + "Ġcol our", + "Ġview ed", + "ĠEm ail", + "el come", + "Ġinter vention", + "Ġd iversity", + "Ġperiod s", + "Ġre verse", + "ĠV ery", + "Ġqu ote", + "ĠLe ft", + "th rough", + "Ġsc rew", + "Ġland ing", + "Ġp ill", + "Ġw et", + "Ġprot esters", + "Ġrepe at", + "av ed", + "er k", + "Ġsal ary", + "ĠPenn sylvania", + "St ill", + "Ġmay or", + "Ġkit chen", + "Ġfeat uring", + "ĠM useum", + "ĠT ournament", + "ĠF al", + "Ġser vers", + "U C", + "Ġany body", + "im g", + "ĠTr ade", + "ixt ure", + "the less", + "Ġfin ance", + "Ġcl osing", + "ĠPat ri", + "i ac", + "ab el", + "Ġ> >", + "or ous", + "Ġf irms", + "sc reen", + "un a", + "Ġemb arrass", + "ul se", + "Ġlet ting", + "Ġth rew", + "ile y", + "Ġch annels", + "l an", + "ĠVeg as", + "Ġse ar", + "Ġfant astic", + "ar re", + "uzz le", + "ĠD er", + "Th ose", + "Ġsw ing", + "Ġshe et", + "ind ex", + "co ver", + "og an", + "Ġvari ables", + "ĠTe ch", + "Ġsp oken", + "ac hel", + "ĠD a", + "ĠMount ain", + "Ġload ed", + "Ġfoot age", + "vers ion", + "Ġun l", + "ĠPh oenix", + "Ġthrow ing", + "Ġf iring", + "Ġtrack ing", + "Ġw idth", + "Ġstrugg ling", + "ro oms", + "ot ion", + "Ġmonth ly", + "ĠSer ver", + "Ġegg s", + "op en", + "M C", + "Ġ199 3", + "Ġh ired", + "Ġstay ed", + "ĠAll en", + "Ġst ro", + "Ġ9 8", + "st ep", + "ĠTurk ish", + "Ġfab ric", + "ist ing", + "ĠD om", + "Ġd ates", + "Ġpr on", + "Ġbasket ball", + "Ġl ucky", + "ĠArab ia", + "Ġassum ed", + "est y", + "Ġaff airs", + "Ġgl ad", + "ĠInd eed", + "ĠF A", + "ĠW ord", + "Ġjo ining", + "if ice", + "p read", + "ir ts", + "ĠSe lect", + "Ġpop ulations", + "aw are", + "Ġn ose", + "Ġcompl aints", + "st art", + "Ġsc oring", + "Th anks", + "Ġmin ing", + "Ġvisit ors", + "S H", + "Ġdam aged", + "Ġcharacter istics", + "ĠP ent", + "D C", + "Ġ8 3", + "ĠS ix", + "r ates", + "Ġfl ags", + "ĠB rew", + "d og", + "M ark", + "// //", + "Ġexec ution", + "Ġj oke", + "ph ones", + "Ġtestim ony", + "Ġob st", + "Q L", + "ĠC ut", + "Ġstud ied", + "ĠN intendo", + "ick et", + "ĠN BC", + "Ġl ad", + "ĠB ra", + "ĠM oh", + "Ġk ernel", + "Ġoverwhel ming", + "Ġag ed", + "Ġapplic able", + "ĠC ond", + "Ġroad s", + "ĠBl ock", + "m ade", + "od ge", + "Ġcomm ands", + "Ġoff ices", + "vel and", + "Ġt ut", + "Ġrece iver", + "ĠF ro", + "Ġsho pping", + "Ġi P", + "ĠSt re", + "ĠA BC", + "Ġentertain ment", + "ĠB ow", + "ort ed", + "M c", + "Ġread s", + "gr ad", + "ĠCol lect", + "Ġâ ĪĴ", + "ĠCap ital", + "eder ation", + "Ġemploy er", + "Ġinvolve ment", + "Ġanx iety", + "al ia", + "Ġro of", + "ĠAm ong", + "ĠDemocr at", + "Ġstat s", + "ĠV ill", + "Ġconst itutional", + "Ġrefer ring", + "itt y", + "Ġtack le", + "out ube", + "Ġback ed", + "ĠH ong", + "ĠBro ad", + "Ġe le", + "ĠO tt", + "Ġ199 2", + "h our", + "achus etts", + "C al", + "Ġdefe ated", + "Ġ8 1", + "es p", + "Ġseem ingly", + "w as", + "ĠJ enn", + "ĠK urd", + "Ġg ene", + "Ġdisc ount", + "R et", + "EC T", + "( );", + "Ġclub s", + "Ġs id", + "ĠM arsh", + "Che ck", + "Ġp p", + "ĠE ag", + "ides pread", + "Ġbe ings", + "F T", + "Ġintrodu ction", + "ĠCh ange", + "AR D", + "Ġ1 10", + "ad ows", + "ier ce", + "Ġme al", + "a uthor", + "ĠB ang", + "lah oma", + "Ġr anks", + "201 1", + "?? ??", + "m ax", + "Ġcoll apse", + "Ġop ens", + "Ġe cho", + "Ġs oph", + "Ġrac ist", + "Ġenorm ous", + "Ġw aves", + "Ġt ap", + "Ġcomprehens ive", + ". --", + "ĠR oy", + "Ġfarm ers", + "Rel ated", + "a ired", + "ron es", + "ĠC rim", + "Ġproport ion", + "Ġdesign s", + "Ġnegoti ations", + "Ġvirt ually", + "ĠBat man", + "Ġwar n", + "Ġlegit imate", + "m ate", + "Ġcon vention", + ", ,", + "net ic", + "ĠS D", + "Ġconsist ently", + "Ġcompens ation", + "Ġpunish ment", + "Ġy e", + "Ġt ie", + "ĠB ureau", + "ir lf", + "ĠB u", + "ĠA ren", + "ĠPh ilipp", + "Ġkn ife", + "Ġmem ories", + "ĠR oss", + "Ġang le", + "Ġ8 6", + "ĠTh under", + "Ġre nd", + "ĠT our", + "Ġcount s", + "s ung", + "ĠIm p", + "Ġeduc ational", + "Ġaccess ible", + "C OM", + "Ġd rew", + "y er", + "G l", + "am ine", + "OR T", + "O B", + "I B", + "m aster", + "Ġtri als", + "og y", + "h ar", + "ĠTr ust", + "Ġprefer red", + "irlf riend", + "ĠN ev", + "Ġb in", + "Ġc ow", + "P age", + "Ġsign ature", + "ĠB L", + "7 00", + "Ġret ired", + "Ġby tes", + "Ġneigh b", + "ĠLeg end", + "Ġdev ast", + "Ġsuspect ed", + "is ons", + "ĠPoké mon", + "sc ale", + "Ġcap abilities", + "Ġre vel", + "Ġche ese", + "d y", + "igr ant", + "Ġfail ing", + "b its", + "ĠHer oes", + "ĠG host", + "ĠS cient", + "Ġappoint ed", + "ur i", + "Ġinst itution", + "Ġexpand ed", + "g reg", + "Ġmonitor ing", + "Ġp odcast", + "Ġcoal ition", + "Ġ9 6", + "J o", + "Ġst olen", + "ĠS ab", + "Ġstop s", + "Ġhol iday", + "Ġint r", + "C ar", + "Bl ack", + "ĠL GBT", + "Ġwar ming", + "ĠAnd erson", + "Ġ8 9", + "Ġprodu cer", + "M ed", + "Ġaccur acy", + "ĠMar vel", + "iz abeth", + "ĠPat rick", + "m ony", + "Ġmin i", + "ac les", + "Ġover t", + "the y", + "Ġmembers hip", + "ĠV en", + "Ġex ch", + "Ġrem oval", + "ĠD ave", + "T Y", + "m ad", + "ĠF ind", + "Ġad equ", + "Ġe c", + "Ġte eth", + "Ġemot ion", + "Ġper m", + "Ġsole ly", + "d b", + "Ġextra ord", + "IG HT", + "c al", + "Ġgu idelines", + "Ġd ying", + "Ġsusp ended", + "ĠPrem ier", + "ĠAnth ony", + "el ve", + "Ġd ad", + "ĠE th", + "ĠFoot ball", + "Ġabandon ed", + "Ġ< <", + "Ġm arch", + "Ġhor ror", + "âĢ¦ \"", + "Ġchild hood", + "Ġcampaign s", + "Ġl unch", + "ĠAl bert", + "bl ock", + "âĸĪ âĸĪ", + "ound ing", + "Ġb one", + "or gan", + "ad ers", + "ĠFl ash", + "ĠDri ve", + "Ġton ight", + "Ġw ars", + "ĠF L", + "Ġform ation", + "con st", + "New s", + "Ġcom pe", + "or ious", + "ĠSt aff", + "Ġdiscuss ions", + "ĠProt ection", + "ĠJ am", + "Ġcrit eria", + "Ġinstall ation", + "Ġaccompl ish", + "iz za", + "Ġpub lisher", + "Ġresc ue", + "ĠT ry", + "U LL", + "ĠS om", + "ĠH op", + "ore t", + "th s", + "ord on", + "Ġp ocket", + "ĠIn v", + "Down load", + "ĠCr ime", + "Ġb ene", + "ĠGu ide", + "ĠAs sembly", + "Ġparam eters", + "I E", + "ĠAlex ander", + "Ġconc ert", + "ĠSc he", + "Ġsh oes", + "Ġvis iting", + "Ġrec all", + "Ġb ub", + "Ġr ural", + "Ġconc rete", + "ĠR os", + "N ext", + "R uss", + "Ġlo ans", + "ĠSh ield", + "Ġtre m", + "hem at", + "k g", + "ĠHar ris", + "is ition", + "ĠM ove", + "ĠF C", + "Ġf ate", + "ĠCh o", + "Ġt ired", + "Ġprinc ipal", + "h ist", + "ien ces", + "ath y", + "Ġse vent", + "Ġm ood", + "Ġstrateg ic", + "Ġdise ases", + "Ġfor um", + "Ġtem por", + "Ġhead quarters", + "P ar", + "ig e", + "fl ix", + "Ġgu itar", + "Ġ9 4", + "On ly", + "Ġrele ases", + "ro ph", + "================ ================", + "Ġ6 00", + "ĠContin ue", + "ig ate", + "ĠC rit", + "sy stem", + "Ġdis abled", + "Ġunex pected", + "ith ub", + "Ġuncle ar", + "ĠE st", + "Ġcontr ad", + "Ġstrateg ies", + "vent ures", + "Ġpass age", + "AM E", + "Ġimpro ving", + "Ġreve als", + "Ġdecre ase", + "ov a", + "Ġann oy", + "ĠSh ort", + "ĠL ibrary", + "Ġcy ber", + "n ell", + "ĠH ur", + "ĠC B", + "Ġphot ograp", + "U I", + "Ġs ed", + "G e", + "Ġ8 7", + "Ġd iverse", + "Ġencour aged", + "Ġcons piracy", + "Ġbird s", + "Ġoper ator", + "Ġhand ful", + "Ġclass ified", + "? )", + "Ġdram atic", + "Ġinvestig ators", + "it o", + "Ġw idespread", + "ĠR oom", + "-------------------------------- --------------------------------", + "Ġcollect ive", + "Ġjournal ist", + "St ring", + "Ġtemper atures", + "il a", + "Ġgu id", + "Ġins pect", + "Ġmiss ile", + "ĠMay or", + "Ġman ual", + "Ġsim ultane", + "Ġrat ings", + "Ġsu ck", + "Ġ9 7", + "Ġunivers al", + "Ġph arm", + "Ġdis rupt", + "ian o", + "A V", + "Ġf t", + "Ġstat ist", + "old s", + "ĠWalk er", + "ph p", + "Ġunder t", + "ĠL as", + "ish op", + "nt il", + "res hold", + "ĠWhe ther", + "M s", + "Ġden y", + "ĠCl oud", + "Ġprov ider", + "Ġsurv iv", + "ĠUp date", + "h as", + "Ġmist akes", + "ch arge", + "pl ed", + "r ity", + "Ġn ode", + "ĠMass achusetts", + "ool s", + "lic ation", + "Ġf ails", + "em ale", + "or i", + "back s", + "Ġsh irt", + "Ġ' '", + "ĠN AT", + "Ġwat ers", + "els on", + "Ġe ase", + "Ġsc ar", + "Ġcont ents", + "m ind", + "Ġcont ribution", + "Ġsh r", + "Ġhand ed", + "Ġst ability", + "Ġtra ve", + "E m", + "Ġmir ror", + "12 3", + "Ġwe igh", + "Ġf iction", + "ou ver", + "ist ant", + "r ition", + "ĠF ed", + "Ġphys ically", + "Ġst ake", + "ĠArt icle", + "ĠAr c", + "ĠLew is", + "ĠM ind", + "Ġdemonstr ate", + "Ġprof its", + "v ision", + "om ic", + "ol id", + "Ġbatt les", + "Ġdri ves", + "Ġeas tern", + "ĠS ony", + "!! !", + "ar ation", + "v ard", + "ĠG L", + "port ation", + "Ġ9 2", + "Ġlaw makers", + "Ġprotect ing", + "ĠE PA", + "Ġy eah", + "Ġsh ame", + "ol ph", + "e ven", + "x it", + "Ġatt ach", + "Ġrepresent ing", + "Ġob s", + "ĠUt ah", + "iff s", + "ĠFre edom", + "à ³", + "A K", + "Ġinc idents", + "it age", + "Ġview ers", + "c d", + "Ġm ouse", + "Ġcl ar", + "Ġaccord ance", + "Ġb ot", + "c or", + "ĠSum mer", + "he ld", + "Ġinnoc ent", + "Ġiniti ative", + "ol s", + "________________ ________________", + "Ġsp ots", + "p ace", + "Ġconvent ional", + "Ġcorpor ations", + "Ġblock ed", + "H D", + "at tered", + "Ġref ers", + "Ġbu ck", + "ĠDig ital", + "12 0", + "Ġtop ics", + "T F", + "Ä ģ", + "br id", + "re ement", + "Ġunder lying", + "ĠM ember", + "Ġinvestig ating", + "Ġpregn ancy", + "Ġtouch down", + "ĠB and", + "ĠCall er", + "Ġinst ances", + "P P", + "w a", + "G ood", + "Ġ199 1", + "ĠC old", + "Ġfear s", + "Ġrem arks", + "Ĩ Ĵ", + "at al", + "Ġm it", + "Ġexper iments", + "i pt", + "Col or", + "ind u", + "Up date", + "Ġ9 3", + "A g", + "Ġ å", + "anc ouver", + "B oth", + "Ġjud ges", + "Ob ject", + "Ġst ere", + "umb n", + "Ġparticip ation", + "ĠSt ars", + "ĠJ ere", + "Ġweek ly", + "ĠB an", + "Ġconvers ations", + "ĠP itt", + "u z", + "ĠIndian a", + "ĠK ick", + "Ġinf ection", + "Ġhero es", + "Ġsett led", + "Ġstri p", + "Ġh al", + "Ġd ump", + "ĠS ci", + "Ġl es", + "Ġref erences", + "ĠU RL", + "ĠBr idge", + "Ġwant ing", + "For ce", + "Ġex clus", + "Me anwhile", + "m n", + "Ġg entle", + "m aker", + "sen al", + "ĠG ro", + "ou ri", + "ĠR ain", + "ĠAll iance", + "Ġl ift", + "el a", + "S D", + "ĠCle veland", + "Ġrank ed", + "Ġst adium", + "Ġdead ly", + "ä ¸", + "Ġr iding", + "ar ia", + "ĠAr mor", + "Ġdocument ation", + "ĠGree ce", + "ree k", + "Ġl ens", + "ĠS a", + "Ġg ross", + "ĠE mer", + "ag ers", + "ĠD ub", + "ĠR h", + "ĠAM D", + "Ġarri val", + "Ġdes ert", + "Ġsupp lement", + "ĠRes p", + "Ġkn ee", + "Ġmarg in", + "f ont", + "og g", + "201 0", + "ĠP ir", + "ĠP rom", + "iv als", + "Ġint ake", + "Ġdifferent ly", + "ug s", + "Ġb its", + "clud ed", + "Ġsearch ing", + "ĠD u", + "um ble", + "Ġfunction al", + "ĠBalt imore", + "ĠC ould", + "Ġdes ired", + "Ġcirc uit", + "ĠL yn", + "ĠG O", + "ĠF alse", + "re pre", + "' :", + "alt ies", + "Ġmin im", + "Ġdro ve", + "ĠSh ould", + "Ġh ip", + "Ġpro s", + "Ġut ility", + "ĠN ature", + "ĠM ode", + "P resident", + "o pp", + "r at", + "form ance", + "Ġconcent ration", + "Ġf ont", + "ĠB ud", + "Ġam id", + "Ġre vers", + "ĠM L", + "B ar", + "Ġinter action", + "Ġjur isd", + "Ġspell s", + "d ep", + "f il", + "Ġcivil ians", + "ut ter", + "ĠCo oper", + "ĠBel ow", + "Ġent rance", + "Ġcon vert", + "Ġcontrovers y", + "ow ered", + "Ġcontr ary", + "Ġar c", + "ĠExec utive", + "ĠOffic er", + "Ġpack ages", + "Ġprog ressive", + "w idth", + "Ġreserv ed", + "v ol", + "ĠSam sung", + "Ġprint ed", + "Ġcent ers", + "Ġintrodu ce", + "ĠKenn edy", + "Ġodd s", + "Ġsure ly", + "Ġindepend ence", + "Ġpass engers", + "repre ne", + "ĠBe h", + "Ġl oves", + "ĠESP N", + "Ġfac ilit", + "Ġident ical", + "Ġdo ct", + "Ġpartners hip", + "con f", + "ĠH ide", + "Ġconf used", + "ĠC ow", + "M en", + "Ġw rest", + "ĠIraq i", + "Ġh oles", + "ĠStud ies", + "Ġpregn ant", + "h ard", + "Ġsign als", + "I X", + "Ġpull ing", + "Ġgrad uate", + "Ġnomine e", + "D ate", + "Ġper mitted", + "Ġâ Ĥ¬", + "ĠOk lahoma", + "St art", + "Ġauthor ized", + "Ġal arm", + "ĠC os", + "v an", + "Ġgener ations", + "c ular", + "Ġdr agon", + "ĠSoft ware", + "ĠEd ward", + "Ġcontro ller", + "S en", + "ge red", + "ĠV ik", + "Ġappro ached", + "Th ank", + "Ġcan ce", + "Ġform ula", + "ĠSm all", + "Ġweak ness", + "Ġr amp", + "it udes", + "j ud", + "Ġbrill iant", + "Ġacc us", + "s ource", + "Ġ8 00", + "ĠE vil", + "S w", + "Ġhom eless", + "we ek", + "i ens", + "r ics", + "ĠTh ird", + "T O", + "Ġorgan ic", + "Ġpresent ation", + "ag h", + "ĠDown load", + "v ation", + "Ġas sembly", + "or able", + "hold ers", + "ĠBern ie", + "ĠHel p", + "Ġt ong", + "ĠF ight", + "Ġbe ach", + "B ook", + "ĠL ic", + "Ġr ush", + "ĠR ound", + "ou p", + "ĠMar x", + "Ġcalcul ated", + "ĠDe vil", + "ĠSar ah", + "Ġoccasion ally", + "Ġbul let", + "Av ailable", + "g ate", + "Ġ9 1", + "Ġh osp", + "Ġprom ises", + "ĠH IV", + "ĠSt adium", + "ĠSt ock", + "ĠCorpor ation", + "g age", + "N G", + "ĠC redit", + "Ġs ne", + "ib l", + "Ġacc um", + "s uch", + "Ġterror ists", + "Ġconscious ness", + "ĠZ h", + "Ġdram a", + "ool a", + "pir ation", + "Ġlab our", + "ĠN in", + "Ġut ter", + "Ġdemocr atic", + "Ġass ass", + "il ation", + "Ġg est", + "Ġab road", + "Ġmet ab", + "Ġs orts", + "Ġfl av", + "U B", + "Ġm g", + "ĠNot hing", + "ĠO d", + "Ġmus ical", + "200 9", + "Ġdro ps", + "oc ated", + "ater al", + "0000 00", + "Ġg re", + "Ġequ ality", + "Ġburd en", + "Ġv ig", + "ĠLe ader", + "-------- ----", + "Ġcere mony", + "Ġf ighter", + "Ġact ors", + "Ġ æ", + "am an", + "F i", + "Ġal ign", + "put er", + "Ġe lder", + "ĠN SA", + "Ġrepresent ation", + "ĠOnt ario", + "IT H", + "usal em", + "Ġharass ment", + "itz er", + "Ġsy mp", + "Ġbox es", + "ĠD R", + "Ġman ifest", + "at re", + "Ġ ^", + "Ġd ies", + "le ton", + "Ġmiss ions", + "et he", + "Ġres olve", + "Ġfollow ers", + "Ġas c", + "Ġk m", + "l ord", + "am med", + "Ġsil ent", + "ĠAssoci ated", + "Ġtim ing", + "Ġprison ers", + "ĠK ings", + "ĠF ive", + "Ġtow er", + "Ġappro aches", + "Ġprecise ly", + "Ġb ureau", + "ĠM other", + "ĠI ss", + "Ġkey board", + "it ual", + "Ġfund ed", + "Ġstay ing", + "Ġpsych ological", + "Ġm ile", + "ĠLe on", + "ĠBar b", + "w ill", + "Ġw ider", + "ĠAtl antic", + "Ġt ill", + "ĠR ome", + "ro t", + "Ġaccomp an", + "Ġfl our", + "ac o", + "W orld", + "ĠExp ress", + "ĠY u", + "C or", + "Ġple ased", + "part y", + "Ġpoint ing", + "Ġinf lation", + "Ġro y", + "Ġ ),", + "ain er", + "Ġwedd ing", + "orm on", + "Ġrequ iring", + "Ġqual ified", + "Ġse gment", + "EN D", + "Ġs izes", + "e als", + "Ġcor rupt", + "ass ador", + "Ġcele b", + "Ġdream s", + "ĠM ess", + "Ġcheck ing", + "ĠV ersion", + "Ġprep aring", + "Ġact ively", + "ĠD iff", + "Ġl ux", + "ĠW inter", + "act eria", + "ĠN E", + "Ġdep uty", + "Ġtrans gender", + "Ġsum mary", + "Ġin her", + "er ies", + "ch ar", + "ĠY an", + "Ġkn ock", + "ĠP ath", + "Ġl ip", + "roll er", + "Ġimp ression", + "Ġcelebr ate", + "Ġsl ide", + "Ġgu ests", + "Ġcl ip", + "F S", + "Ġsav ings", + "Ġcapt ain", + "Ġleg acy", + "ĠDen ver", + "Ġw ounded", + "tab oola", + "AC T", + "Ġpurs ue", + "Ġo xy", + "Ġ q", + "Ġsem i", + "ĠN eed", + "ĠAff airs", + "Ġob sc", + "Ġcheck ed", + "Ġd ual", + "C ode", + "ĠM D", + "le m", + "ult y", + "Ġ ©", + "ĠEl izabeth", + "Ġcent uries", + "ard ed", + "s rc", + "Ġev ident", + "enn is", + "at in", + "Ġunemploy ment", + "ĠMar io", + "Ġint im", + "Ch rist", + "Ġbi ological", + "Ġsold ier", + "ĠAdd ed", + "Ġm ath", + "ĠG il", + "Ġbi as", + "Ġd ating", + "ĠO cean", + "Ġm ice", + "M us", + "h ire", + "ĠT es", + "Ser ver", + "lim ited", + "S ize", + "Ġmet ers", + "Ġrock et", + "es see", + "Ġcertific ate", + "ĠIran ian", + "AS S", + "Ġgr id", + "D ec", + "Ġro lling", + "com mun", + "ĠSwed en", + "b ury", + "Ġtiss ue", + "Ġrac ism", + "ĠL ocal", + "Ġmyster y", + "Ġexam ine", + "Ġst em", + "Ġs its", + "Ġhop ed", + "ot ing", + "Ġdial ogue", + "Ġpers u", + "W atch", + "l ay", + "M AN", + "Ġch ronic", + "ĠPort land", + "mark et", + "ĠS EC", + "Ġparalle l", + "Ġsc andal", + "Ġcar ries", + "Ġphenomen on", + "h uman", + "ack er", + "ĠO x", + "Ġretire ment", + "tain ment", + "ov ie", + "ĠG ear", + "Ġd uties", + "Ġdo se", + "Ġsc roll", + "M B", + "in f", + "Ġsa uce", + "Ġland scape", + "red dit", + "ĠChampions hip", + "ĠRed dit", + "al id", + "Ġco in", + "Ġover s", + "Ġpost ing", + "ab out", + "Ġf el", + "and y", + "Ġb old", + "Ġfocus ing", + "e ffect", + "G R", + "Ġde emed", + "Ġrecommend ations", + "Ġste pped", + "Ġvot er", + "ĠDe ep", + "ĠInst agram", + "Ġmoder ate", + "ĠMary land", + "Ġrestrict ed", + "ĠM B", + "ĠCh all", + "Ġto b", + "Ġc ir", + "ĠO cc", + "ĠE ver", + "Ġcoll aps", + "IN FO", + "= -", + "ĠP ict", + "ĠAcc ount", + "n c", + "Ġo ught", + "Ġex port", + "Ġdr unk", + "( '", + "Ġw ise", + "ĠM ort", + "ne cess", + "Ġan cest", + "ĠInc re", + "Ġfrequ ent", + "m ir", + "Ġinterpret ation", + "Ġdepend ent", + "Ġco ins", + "ĠB ol", + "V ideo", + "ĠJust in", + "Ġfat al", + "Ġcook ing", + "Ġconf usion", + "ip her", + "Ġcust ody", + "ĠMor gan", + "om ach", + "ĠGovern or", + "Ġrestaur ants", + "el ing", + "Ġacknowled ged", + "Ġthe r", + "Ġgen es", + "ch ing", + "He y", + "Ġtact ics", + "ĠMex ican", + "Ġv end", + "Ġhe s", + "qu er", + "Ġnot ing", + "ĠCamer on", + "Ġtarget ing", + "ro ck", + "Ġcred its", + "Ġemot ions", + "Ġrepresent atives", + "new s", + "Ġlegisl ative", + "Ġrem oving", + "Ġtweet ed", + "ĠCar ter", + "ĠF ixed", + "Ġfor cing", + "Ġspeak er", + "Ġm ales", + "ĠViet nam", + "l ined", + "Ġconcept s", + "Ġvo ices", + "o ir", + "ĠT rib", + "W he", + "ĠJer usalem", + "ĠS ant", + "Ġc ul", + "Ġl ady", + "ĠHaw ai", + "Ġar ts", + "ĠIn n", + "ĠMach ine", + "ĠEm peror", + "Ġsl ot", + "g ly", + "ĠPro cess", + "II I", + "Ġathlet es", + "ĠTem ple", + "ĠRep resent", + "Ġpres c", + "Ġt ons", + "Ġgold en", + "Ġp unch", + "ĠG R", + "iver pool", + "Ġen act", + "Ġlob by", + "Ġm os", + "Ġpick ing", + "Ġlif etime", + "Ġcogn itive", + "E ach", + "z o", + "Ġd ub", + "Ġcons ists", + "ol n", + "Ġf estival", + "am ous", + "Ġint ellig", + "w ords", + "ĠSm art", + "Ġde le", + "Ġl apt", + "Ġmag ical", + "ĠS in", + "b us", + "ur ities", + "igh th", + "ĠRub y", + "ĠS ure", + "ol ving", + "Ġj un", + "O ST", + "Ġimp osed", + "Ġast ron", + "Ġcor rel", + "ĠN S", + "ĠK it", + "ĠF uture", + "b urn", + "Ġimm une", + "oc us", + "Ġcour ses", + "ĠSt ring", + "Ġle an", + "Ġg host", + "Ġout comes", + "Ġexp ense", + "Ġevery day", + "Ġaccept able", + "A h", + "Ġequ ipped", + "Ġor ange", + "F R", + "ĠD utch", + "Th ough", + "ĠR ank", + "Q U", + "ĠRober ts", + "wh at", + "re nd", + "Ġdisapp ear", + "Ġsp awn", + "ĠL am", + "o is", + "Ġdes erve", + "Ġmin imal", + "Ġnerv ous", + "ĠW ould", + "Ġro ok", + "ĠV ancouver", + "Ġres ign", + "sh ire", + "ĠW orks", + "ĠB uild", + "Ġafford able", + "ĠG ary", + "ĠAren a", + "Ġh anging", + "Ġimpl ications", + "ĠS ong", + "Ġmain taining", + "Ġgu ards", + "C ON", + "Ġder ived", + "Ġexecut ed", + "Ġthe ories", + "Ġqu oted", + "ĠAnd re", + "og a", + "sel ess", + "in fo", + "ĠBel g", + "Ġt ears", + "ĠSur v", + "Ġbirth day", + "ig ious", + "im mer", + "Ġspect rum", + "Ġarchitect ure", + "Ġrec ruit", + "arm a", + "T able", + "Ġmon sters", + "ĠG ov", + "Ġdest ination", + "Ġattract ive", + "Ġf oss", + "ĠMore over", + "Ġpres ents", + "TH E", + "Ġrep ly", + "pt on", + "Ġc um", + "Ġdel ight", + "Ġaffect s", + "Ġdon ations", + "ĠT oy", + "ĠH im", + "M ENT", + "Ġover come", + "it ched", + "ĠFant asy", + "ĠH at", + "ĠBe ast", + "b ott", + "Ġinvestig ations", + "R un", + "Ġhun ting", + "d i", + "f und", + "Ġs essions", + "est yle", + "Ġport ray", + "oid s", + "Y eah", + "Ġcommun icate", + "Ġcom edy", + "ĠY ang", + "Ġbel t", + "ĠMar ine", + "Ġpredict ed", + "Pl ay", + "Ġimportant ly", + "Ġremark able", + "Ġelim inate", + "D avid", + "Ġb ind", + "V ID", + "Ġadvoc ates", + "ĠG aza", + "im p", + "D B", + "ĠN a", + "ĠSim ilar", + "I ES", + "Ġchar ity", + "v as", + "m ath", + "Ġâ ĸ", + "ok er", + "nd um", + "Ġcap s", + "ĠH al", + "2 000", + "e an", + "Ġfle et", + "Ġrec re", + "R ight", + "Ġsleep ing", + "ij ing", + "k ind", + "Ġdesign ated", + "à ¤", + "Ġanim ation", + "ke e", + "ĠInt rodu", + "Ġ/ >", + "Ġdelay ed", + "Ġtrem end", + "Ġcur ious", + "U se", + "Ġle ct", + "d am", + "Ġinnov ation", + "ĠPoint s", + "Ġload ing", + "Ġdisp ute", + "ct ic", + "ird s", + "ĠB Y", + "Ġn urs", + "ĠVal ue", + "ION S", + "ĠH um", + "Ġtem plate", + "m ers", + "Ġappear ances", + "ĠEnter tainment", + "Ġtransl ation", + "Ġsa ke", + "Ġbene ath", + "Ġin hib", + "Ġe uro", + "abet es", + "Ġstud ying", + "ĠM as", + "Ġper ceived", + "Ġexam ined", + "Ġe ager", + "Ġco aches", + "Ġim per", + "ch i", + "Ġprodu ces", + "\" ).", + "ĠEvery one", + "Ġm unicip", + "Ġg irlfriend", + "Ġh ire", + "ĠV ice", + "Ġsu itable", + "op y", + "Ġin equ", + "ĠD uke", + "f ish", + "f irst", + "ĠO bs", + "Ġinter ior", + "ĠBru ce", + "ĠR y", + "Ġanal ys", + "Ġconsider able", + "Ġfore cast", + "Ġf ert", + "ors hip", + "ĠD rug", + "ĠA LL", + ": \"", + "th ur", + "ĠM ail", + "Ġball ot", + "Ġinst antly", + "ĠCh annel", + "Ġp icks", + "Ġ198 9", + "Ġt ent", + "ol i", + "Ġcivil ian", + "b ling", + "ell o", + "b u", + "Ġin ch", + "Ġlog o", + "Ġcooper ation", + "Ġwal ks", + "Ġinvest ments", + "Ġimp rison", + "ĠF estival", + "ĠK y", + "Ġleg ally", + "Ġg ri", + "ch arg", + "S l", + "Ġthreat ening", + "du ction", + "fl ow", + "Ġdismiss ed", + "ibr aries", + "c ap", + "e le", + "ĠMc G", + "ĠHar vard", + "ĠConserv ative", + "ĠC BS", + "p ng", + "Ġro ots", + "ĠH aving", + "umb led", + "ĠF un", + "\\ /", + "ĠS earch", + "ple x", + "Ġdiscuss ing", + "Ġcontin u", + "ĠT ai", + "ĠW ik", + "F ree", + "f it", + "Ġref use", + "Ġmanag ing", + "Ġsy nd", + "ip edia", + "w alk", + "Ġprofession als", + "Ġguid ance", + "Ġunivers ities", + "Ġas semb", + "unt u", + "F inally", + "AS E", + "ĠAut o", + "ĠH ad", + "Ġann iversary", + "L D", + "ĠD ur", + "ĠUlt imate", + "ih ad", + "pro duct", + "Ġtrans it", + "Ġrest ore", + "Ġexpl aining", + "Ġass et", + "Ġtransfer red", + "Ġbur st", + "ap olis", + "ĠMag azine", + "ĠC ra", + "ĠB R", + "gg ed", + "ĠH E", + "M ich", + "b et", + "ĠL ady", + "yl um", + "erv es", + "Ġme ets", + "wh ite", + "L og", + "Ġcorrespond ing", + "Ġins isted", + "G G", + "Ġsurround ed", + "Ġt ens", + "Ġl ane", + "Ġco inc", + "h ome", + "Ġexist ed", + "ect ed", + "ĠDou ble", + "lam m", + "Ġske pt", + "ex p", + "Ġper ception", + "ie v", + "ĠBe ing", + "o ft", + "Ġadop t", + ". :", + "] ;", + "Wind ows", + "Ġsatell ite", + "AS H", + "Ġinf ant", + "d escription", + "ĠMe anwhile", + "c m", + "oc a", + "ĠT reat", + "act or", + "Ġtob acco", + "ĠN orm", + "em ption", + "Ġfl esh", + "Ġj e", + "o op", + "ĠHe aven", + "Ġbe ating", + "an im", + "Ġgather ing", + "Ġcult iv", + "G O", + "ab e", + "ĠJon athan", + "ĠSaf ety", + "Ġbad ly", + "pro t", + "Ġcho osing", + "Ġcontact ed", + "Ġqu it", + "Ġdist ur", + "Ġst ir", + "Ġto ken", + "D et", + "ĠP a", + "Ġfunction ality", + "00 3", + "s ome", + "Ġlimit ations", + "Ġmet h", + "b uild", + "con fig", + "N T", + "re ll", + "ble m", + "ĠM om", + "Ġveter ans", + "ĠH u", + "Ġtrend s", + "are r", + "ĠG iven", + "ĠCa ption", + "m ay", + "AS T", + "Ġwond ering", + "ĠCl ark", + "n ormal", + "Ġsepar ated", + "Ġdes p", + "st ic", + "b rew", + "Ġrel ating", + "ĠN ik", + "ĠF arm", + "Ġenthus i", + "g ood", + "d eb", + "Ġactiv ist", + "Ġm art", + "Ġexplos ion", + "ĠEconom ic", + "L ink", + "Ġins ight", + "Ġconven ient", + "Ġcounter part", + "su pport", + "ĠV irt", + "ag en", + "ĠTenn essee", + "ĠSim on", + "ĠA ward", + "OC K", + "ĠF igure", + "Ġoverse as", + "Ġpr ide", + "ĠC as", + "n ote", + "m g", + "C urrent", + "Ġdispl ays", + "cont ent", + "Ġtravel ing", + "Ġhosp itals", + "ĠFin ancial", + "ĠP ast", + "Ġdefend ant", + "Ġstream ing", + "m ble", + "ĠBer lin", + "uk i", + "Ġdist ribut", + "Ġant ib", + "Ġch ocolate", + "ĠCast le", + "Ġinter rupt", + "ĠR ow", + "Ġconvers ion", + "Ġbug s", + "ĠR ather", + "li est", + "L Y", + "ĠJe an", + "com mon", + "ak h", + "Ġ1 30", + "ot ton", + "ĠDe an", + "Ġam endment", + "Ġgame play", + "ĠWar ren", + "od a", + "Ġhigh lights", + "Ġir re", + "ĠNAT O", + "Ġball s", + "Ġdemand ing", + "U RE", + "ĠL uke", + "F igure", + "st op", + "on ia", + "z one", + "iz ers", + "ĠW R", + "Ġaward ed", + "Ġregul atory", + "ĠH art", + "ĠS N", + "pl ing", + "Ġs our", + "ĠP ixel", + "us ive", + "Ġf et", + "ĠS ent", + "Ġautom atic", + "Ġf er", + "vern ment", + "ĠKh an", + "T ON", + "f ather", + "Ġextraord inary", + "th rop", + "ĠP ython", + "ĠG PU", + "Ġsex ually", + "Ġdesk top", + "it ivity", + "ĠAnton io", + "Ġo rient", + "Ġe ars", + "ob by", + "ous es", + "vertis ements", + "Ġmanufacture rs", + "ic ient", + "min ute", + "Ġconv iction", + "Ġg arden", + "p ublic", + "Ġsatisf ied", + "f old", + "O K", + "Ġin hab", + "ĠTh ink", + "Ġprogram me", + "Ġst omach", + "Ġcoord in", + "Ġh oly", + "Ġth reshold", + "Ġr het", + "Ġser ial", + "Ġemploy ers", + "ĠEvery thing", + "ra h", + "Ġb other", + "Ġbr ands", + "Val ue", + "ĠT ed", + "ĠPlan et", + "Ġp ink", + "ĠFurther more", + "s a", + "P E", + "re ck", + "ĠUS D", + "ot te", + "Ġ& &", + "Ġland ed", + "g ets", + "Ġprodu cers", + "Ġhealth care", + "Ġdomin ant", + "Ġdest ro", + "Ġam ended", + "ch ron", + "Ġf its", + "ĠSy d", + "ĠAuthor ity", + "AT CH", + "Ġfight s", + "ĠL LC", + "Ġ-- -", + "ĠCor p", + "Ġtox ic", + "spe cific", + "ĠC orn", + "ĠChe l", + "Ġtele phone", + "ĠP ant", + "Ġmyster ious", + "aun ch", + "od ox", + "med ia", + "Ġwitness es", + "ag u", + "Ġquestion ed", + "ĠBre xit", + "ĠRem ember", + "ene z", + "Ġend orse", + "iat ric", + "ĠId ent", + "Ġridic ulous", + "1 10", + "Ġpr ayer", + "Ġscient ist", + "Ġ19 50", + "ĠA qu", + "Ġunder ground", + "ĠU FC", + "m are", + "ĠL ater", + "w ich", + "Ġsubsc rib", + "Ġhost s", + "Ġer r", + "Ġgr ants", + "ant om", + "Ġsum mon", + "ear ly", + "ĠC lear", + "ĠPr im", + "Ġsusp ension", + "Ġguarant eed", + "app er", + "Ġr ice", + "ĠSe an", + "ĠSh in", + "Ġrefere ndum", + "Ġfl ed", + "r ust", + "Ġ3 60", + "ter y", + "Ġsh ocked", + "B R", + "ĠO il", + "ĠAll ah", + "Ġpart ly", + "Ġign or", + "Ġtrans mission", + "Ġhom osexual", + "ivers al", + "Ġhop efully", + "ãĤ ¤", + "Ġless on", + "L eg", + "Ġ ..", + "Y et", + "t able", + "app ropri", + "re tt", + "Ġbo ards", + "Ġincor rect", + "Ġb acteria", + "ar u", + "am ac", + "Ġsn ap", + ".' \"", + "Ġpar ad", + "t em", + "he art", + "Ġav ailability", + "Ġw isdom", + "Ġ( +", + "Ġpri est", + "ĠÂł ĠÂł", + "O pen", + "Ġsp an", + "Ġparam eter", + "Ġconv ince", + "Ġ( %)", + "r ac", + "Ġf o", + "Ġsafe ly", + "Ġconver ted", + "ĠOlymp ic", + "Ġres erve", + "Ġhe aling", + "ĠM ine", + "M ax", + "Ġin herent", + "ĠGra ham", + "Ġinteg rated", + "D em", + "Ġpip eline", + "Ġapp lying", + "Ġem bed", + "ĠCharl ie", + "Ġc ave", + "200 8", + "Ġcons ensus", + "Ġre wards", + "P al", + "ĠHT ML", + "Ġpopular ity", + "look ing", + "ĠSw ord", + "ĠAr ts", + "' )", + "Ġelect ron", + "clus ions", + "Ġinteg rity", + "Ġexclus ively", + "Ġgr ace", + "Ġtort ure", + "Ġburn ed", + "tw o", + "Ġ18 0", + "P rodu", + "Ġent reprene", + "raph ics", + "Ġg ym", + "ric ane", + "ĠT am", + "Ġadministr ative", + "Ġmanufacture r", + "Ġ vel", + "ĠN i", + "Ġisol ated", + "ĠMedic ine", + "Ġback up", + "Ġpromot ing", + "Ġcommand er", + "Ġfle e", + "ĠRus sell", + "Ġforg otten", + "ĠMiss ouri", + "Ġres idence", + "m ons", + "Ġrese mb", + "Ġw and", + "Ġmeaning ful", + "P T", + "Ġb ol", + "Ġhe lic", + "Ġwealth y", + "Ġr ifle", + "str ong", + "row ing", + "pl an", + "as ury", + "âĢ¦ .", + "Ġexpand ing", + "ĠHam ilton", + "Ġrece ives", + "S I", + "eat ures", + "ĠAn im", + "RE E", + "P ut", + "Ġbrief ly", + "ri ve", + "Ġstim ul", + "Ġ`` (", + "Ġ __", + "Ġch ip", + "Ġha z", + "Ġpri ze", + "ĠTh ings", + "AC E", + "ul in", + "d ict", + "ok u", + "Ġassoci ate", + "ock ets", + "y outube", + "St ory", + "ateg ory", + "Ġm ild", + "ail ing", + "ĠY e", + "O rig", + "ĠK a", + "or ig", + "Ġpropag anda", + "Ġan onymous", + "Ġstrugg led", + "Ġout rage", + "AT ED", + "ĠBe ijing", + "r ary", + "Ġle ather", + "Ġworld s", + "Ġbroad er", + "12 5", + "id al", + "ĠBet ter", + "Ġt ear", + "E xt", + "Ġpropos als", + "Ġit er", + "ĠSqu ad", + "Ġvol unt", + "m i", + "D id", + "ĠP u", + "p in", + "Ġspeak ers", + "Ġb orders", + "Ġfig ured", + "= '", + "Ġsimultane ously", + "aed a", + "Ġcharg ing", + "Ġur ged", + "Ġcon j", + "25 6", + "ĠG ordon", + "mer ce", + "Ġdocument ary", + "Sh are", + "it ol", + "ON E", + "ĠG arden", + "h att", + "ĠThom pson", + "ane ous", + "ap ore", + "Ġt anks", + "Ġless ons", + "tr ack", + "Ġout standing", + "Ġvolunte ers", + "Ġsp ray", + "Ġmanag ers", + "l arge", + "Ġcamp s", + "Ġart ificial", + "ĠR u", + "Ġb ags", + "th al", + "Ġcompat ible", + "ĠBl ade", + "Ġf ed", + "Ġarg ues", + "F I", + "Ġunf air", + "Ġcor n", + "Ġoff set", + "Ġdirect ions", + "Ġdisappoint ed", + "ĠCon vention", + "Ġview ing", + "M E", + "oc ity", + "Ġtown s", + "Ġlay ers", + "Ġro lled", + "Ġjump ed", + "Ġatt ribute", + "Ġun necess", + "inc oln", + "Ġsupp ose", + "ĠNet her", + "ch a", + "Ġbur ied", + "Ġsix th", + "B en", + "ress ing", + "OU R", + "Ġw ound", + "Ġcy cl", + "Ġmechan isms", + "Ġcongress ional", + "ĠE lement", + "Ġagre ements", + "Ġdec or", + "Ġclos est", + "ĠM it", + "Go ogle", + "} }", + "Ġm ixture", + "Ġflu id", + "S ign", + "ĠSch olar", + "Ġp ist", + "ask et", + "ab ling", + "Ġrac ing", + "he ro", + "ri el", + "ass y", + "Ġche aper", + "b en", + "Ġvert ical", + "amac are", + "ĠRead ing", + "g ments", + "Ġhelic op", + "Ġsacr ifice", + "ay a", + "p aren", + "V A", + "ĠL es", + "ĠStud io", + "Ġviol ations", + "ĠAn na", + "ac er", + "é ¾", + "ĠR at", + "ĠBe ck", + "ĠD ick", + "ĠA CT", + "Ġcomp osition", + "Ġtext ure", + "ĠO wn", + "Ġsmart phone", + "ĠN A", + "Ġfor b", + "im port", + "Ġdef ending", + "il st", + "re r", + "Ġo h", + "ĠJere my", + "Ġbank ing", + "cept ions", + "Ġrespect ive", + "/ .", + "Ġdr inks", + "ĠW i", + "Ġb ands", + "ĠL iverpool", + "Ġg rip", + "ĠB uy", + "Ġopen ly", + "Ġreview ed", + "per t", + "Ġver ify", + "ĠCo le", + "ĠW ales", + "M O", + "Ġun pre", + "Ġshel ter", + "ĠIm perial", + "Ġgu i", + "ĠD ak", + "Ġsuggest ions", + "Ġexplicit ly", + "Ġsl ave", + "Ġblock chain", + "Ġcompet ing", + "Ġprom ising", + "S ON", + "Ġsoc cer", + "Ġconst itution", + "4 29", + "Ġdist ract", + "ĠU ser", + "es ides", + "ĠMet hod", + "ĠTok yo", + "Ġaccompan ied", + "Cl ient", + "s ur", + "al og", + "Ġident ification", + "Ġinv asion", + "as ma", + "Ġindust ries", + "pp ers", + "Ġsub tle", + "ĠUn it", + "n atural", + "Ġsurv ived", + "Ġfl aw", + "ĺ ħ", + "ĠH oll", + "Ġdef icit", + "Ġtut orial", + "ĠCh ance", + "Ġarg uing", + "Ġcontem porary", + "Ġinteg ration", + "for ward", + "Ġt um", + "it is", + "Ġh iding", + "ĠD omin", + "ĠT an", + "ĠB uilding", + "ĠV in", + "Ġspokes person", + "ĠNot es", + "Ġemer ging", + "Ġprepar ation", + "Ġpro st", + "Ġsuspect s", + "Ġaut onom", + "D escription", + "Ġdeal t", + "ĠP ear", + "Ġstead y", + "Ġdecre ased", + "Ġso vere", + "ĠCl in", + "Ġgrad ually", + "ors es", + "ĠW AR", + "S erv", + "ãĤ ¢", + "h r", + "Ġd irty", + "ĠB arn", + "ĠB C", + "Ġd il", + "Ġcal endar", + "Ġcompl iance", + "Ġch amber", + "b b", + "Ġpass enger", + "ate ful", + "ĠT itle", + "ĠSyd ney", + "ĠG ot", + "Ġdark ness", + "Ġdef ect", + "Ġpack ed", + "ass ion", + "Ġgod s", + "Ġh arsh", + "IC K", + "le ans", + "Ġalgorith m", + "Ġoxy gen", + "Ġvis its", + "Ġbl ade", + "Ġkil omet", + "ĠKent ucky", + "Ġkill er", + "P ack", + "enn y", + "Ġdiv ine", + "Ġnom ination", + "be ing", + "Ġeng ines", + "Ġc ats", + "Ġbuff er", + "ĠPh ill", + "Ġtra ff", + "AG E", + "Ġtong ue", + "Ġrad iation", + "ere r", + "m em", + "ĠExpl icit", + "é¾ į", + "Ġcou ples", + "Ġphys ics", + "ĠMc K", + "Ġpolit ically", + "aw ks", + "ĠBl oom", + "Ġwor ship", + "e ger", + "ut er", + "ĠF O", + "Ġmat hemat", + "Ġsent enced", + "Ġdis k", + "ĠM arg", + "Ġ/ *", + "P I", + "Ġoption al", + "Ġbab ies", + "Ġse eds", + "ĠScott ish", + "Ġth y", + "] ]", + "ĠHit ler", + "P H", + "ng th", + "Ġrec overed", + "ing e", + "Ġpow der", + "Ġl ips", + "Ġdesign er", + "Ġdis orders", + "Ġcour age", + "Ġch aos", + "\" },{\"", + "Ġcar rier", + "b ably", + "H igh", + "ĠR T", + "es ity", + "l en", + "Ġrout es", + "u ating", + "F il", + "N OT", + "w all", + "s burgh", + "Ġeng aging", + "ĠJava Script", + "ore r", + "li hood", + "Ġun ions", + "ĠF ederation", + "ĠTes la", + "Ġcomple tion", + "ĠT a", + "Ġprivile ge", + "ĠOr ange", + "Ġne ur", + "paren cy", + "Ġb ones", + "Ġtit led", + "Ġprosecut ors", + "ĠM E", + "Ġengine er", + "ĠUn iverse", + "ĠH ig", + "n ie", + "o ard", + "Ġheart s", + "ĠG re", + "uss ion", + "Ġmin istry", + "Ġpen et", + "ĠN ut", + "ĠO w", + "ĠX P", + "in stein", + "Ġbul k", + "S ystem", + "ic ism", + "ĠMarket able", + "Ġpre val", + "Ġpost er", + "Ġatt ending", + "ur able", + "Ġlicens ed", + "ĠG h", + "et ry", + "ĠTrad able", + "Ġbl ast", + "à ¤", + "ĠTit an", + "ell ed", + "d ie", + "H ave", + "ĠFl ame", + "Ġprof ound", + "Ġparticip ating", + "Ġan ime", + "ĠE ss", + "Ġspec ify", + "Ġregard ed", + "ĠSpe ll", + "Ġs ons", + "own ed", + "Ġm erc", + "Ġexper imental", + "land o", + "h s", + "ĠDun geon", + "in os", + "Ġcomp ly", + "ĠSystem s", + "ar th", + "Ġse ized", + "l ocal", + "ĠGirl s", + "ud o", + "on ed", + "ĠF le", + "Ġconstruct ed", + "Ġhost ed", + "Ġsc ared", + "act ic", + "ĠIs lands", + "ĠM ORE", + "Ġbl ess", + "Ġblock ing", + "Ġch ips", + "Ġev ac", + "P s", + "Ġcorpor ation", + "Ġo x", + "Ġlight ing", + "Ġneighb ors", + "ĠU b", + "ar o", + "Ġbe ef", + "ĠU ber", + "F acebook", + "ar med", + "it ate", + "ĠR ating", + "ĠQu ick", + "Ġoccup ied", + "Ġaim s", + "ĠAdd itionally", + "ĠInt erest", + "Ġdram atically", + "Ġhe al", + "Ġpain ting", + "Ġengine ers", + "M M", + "ĠM ust", + "Ġquant ity", + "P aul", + "Ġearn ings", + "ĠPost s", + "st ra", + "ãĥ¼ ãĥ", + "Ġst ance", + "Ġdro pping", + "sc ript", + "Ġd ressed", + "M ake", + "Ġjust ify", + "ĠL td", + "Ġprompt ed", + "Ġscr ut", + "Ġspeed s", + "ĠGi ants", + "om er", + "ĠEd itor", + "Ġdescrib ing", + "ĠL ie", + "ment ed", + "Ġnow here", + "oc aly", + "Ġinst ruction", + "fort able", + "Ġent ities", + "Ġc m", + "ĠN atural", + "Ġinqu iry", + "Ġpress ed", + "iz ont", + "for ced", + "Ġra ises", + "ĠNet flix", + "ĠS ide", + "Ġout er", + "Ġamong st", + "im s", + "ows ki", + "Ġclim b", + "ne ver", + "Ġcomb ine", + "d ing", + "Ġcomp r", + "Ġsignific ance", + "Ġremem bered", + "ĠNev ada", + "ĠT el", + "ĠSc ar", + "ĠWar riors", + "ĠJ ane", + "Ġcou p", + "b as", + "Ġtermin al", + ", -", + "O H", + "Ġt ension", + "Ġw ings", + "ĠMy ster", + "�� ��", + "ĠUn like", + "val id", + "viron ments", + "ĠAl i", + "Ġn aked", + "book s", + "ĠM un", + "ĠG ulf", + "Ġd ensity", + "Ġdim in", + "Ġdesper ate", + "Ġpres idency", + "Ġ198 6", + "h y", + "IN D", + "Ġun lock", + "im ens", + "Ġhand led", + "ĠE b", + "Ġdisapp eared", + "Ġgen re", + "Ġ198 8", + "Ġdetermin ation", + "St ream", + "ik o", + "ap ters", + "Ġacknow ledge", + "J an", + "Ġcapital ism", + "P at", + "Ġ20 20", + "Ġpain ful", + "Ġcur ve", + "Ġbom bs", + "st orm", + "ĠMet al", + "en cer", + "ĠF ig", + "ĠA aron", + "anc hes", + "Ġins piration", + "Ġexha ust", + "t ains", + "ash i", + "Ġdesc ript", + "Ġr itual", + "ĠChel sea", + "Ġpromot ion", + "ĠH ung", + "ĠW ard", + "iv a", + "ĠE T", + "Ġto ss", + "all ow", + "ĠFranc is", + "D ep", + "Ġhapp iness", + "ĠGl ass", + "Ġbet a", + "Ġstreng then", + "N E", + "o a", + "Ġbutt ons", + "ĠMur ray", + "Ġkick ed", + "Qu est", + "ĠT alk", + "ĠS everal", + "ĠZ ero", + "Ġdr one", + "ul k", + "Ġc am", + "ĠM obile", + "Ġprevent ing", + "Ġret ro", + "ĠA x", + "Ġcru el", + "Ġflo at", + ". ),", + "Ġfil ing", + "ĠGr ant", + "ĠB or", + "Ġr ib", + "Ġchampions hip", + "ĠM erc", + "Ġsty les", + "Ġc ake", + "Ġbuild s", + "ĠS elf", + "io x", + "Ġep ic", + "oy d", + "B el", + "ĠSt ew", + ". (", + "ah u", + "ĠBe yond", + "Ġout s", + "Ġsol o", + "ĠT ree", + "Ġpres erve", + "Ġt ub", + "AR E", + "ro c", + "ĠIm pro", + "ĠW right", + "Ġbu nd", + "Ġtr aged", + "Ġoccas ional", + "b ian", + "Sec ond", + "r ons", + "Ġinter actions", + "form ed", + "s ing", + "Ġown s", + "Ġh ockey", + "Gener al", + "Ġlog ical", + "Ġexp end", + "Ġesc al", + "ĠGr iff", + "ĠC rown", + "ĠRes erve", + "Ġsto pping", + "Ġexc use", + "sec ond", + "Ġoper ated", + "Ġre aches", + "ĠMal ays", + "Ġpoll ution", + "ĠBrook lyn", + "Ġde lete", + "Ġhas h", + "Bl ock", + "ah a", + "âĢ ³", + "Ġsh orter", + "p iece", + "> >>", + "ĠM ormon", + "t or", + "Ġpartic les", + "ĠB art", + "ry ption", + "Ġad min", + "Ġsqu ee", + "VID IA", + "Ġcreat or", + "iam eter", + "ic ular", + "N BC", + "Ġgrab bed", + "Ġn odd", + "Ġr ated", + "Ġrot ation", + "Ġgr asp", + "Ġexcess ive", + "ĠE C", + "ĠWh it", + "Ġinvent ory", + "ault s", + "ĠF B", + "Ġe cosystem", + "Ġbill ions", + "Ġvent ure", + "n amed", + "Ġdef ender", + "out e", + "Inst ead", + "ir able", + "W ar", + "Ġassum ption", + "Ġb ite", + "Ġearth qu", + "t ail", + "sp ace", + "Ġgif ts", + "boy s", + "Ġinev itable", + "Ġstruct ural", + "Ġbenef icial", + "Ġcompe lling", + "h ole", + "erv ation", + "Ġco at", + "o j", + "inc arn", + "ĠY ears", + "Ġdetermin ing", + "Ġrhet oric", + "Ġbound aries", + "Ġwh ites", + "A nt", + "add y", + ") -", + "ra ham", + "eter min", + "Ġhar vest", + "ĠCon c", + "Ġlapt op", + "ĠM atch", + "Ġenjoy ing", + "cc a", + "oll ar", + "Ġtri ps", + "Ġadd iction", + "ĠS ak", + "Ġpow ered", + "Ġc ous", + "ĠRuss ians", + "ie re", + "Ġret rie", + "qu ality", + "Ġdiff er", + "Ġking dom", + "ĠL aur", + "ĠCap itol", + "Ġcon clusions", + "ĠAl tern", + "ĠN av", + "Ġtrans parent", + "B ER", + "G roup", + "ĠCom plete", + "Ġinf er", + "Ġint rig", + "Ġins ane", + "R O", + "oph ob", + "is en", + "qu al", + "Mich ael", + "Ġm useum", + "ĠP ope", + "Ġres et", + "r ative", + "f ive", + "Ġagg reg", + "itte es", + "osit ory", + "Ġcar b", + "ĠRec ord", + "Ġdec ides", + "ĠF ix", + "Ġexcept ions", + "ĠCommission er", + "un s", + "ĠEnvironment al", + "Ġlegend ary", + "ist ence", + "Ġtun nel", + "k m", + "Ġins ult", + "Ġt roll", + "Ġsh ake", + "Ġdet ention", + "qu es", + "ĠCh rome", + "ĠF iles", + "Ġsub t", + "Ġprospect s", + "Ġpro l", + "re nder", + "pro of", + "Ġperform ances", + "St r", + "Ġh ref", + "ern ame", + "Ġachieve ment", + "Ġf ut", + "F ull", + "ĠLe ban", + "go ogle", + "ãĥ Ī", + "amp a", + "May be", + "Ġproject ed", + "ĠE mb", + "Ġcol leg", + "Ġa wards", + "Ġâ Ķ", + "G old", + "ĠBl ake", + "ĠR aj", + "if ting", + "Ġp ending", + "Ġinst inct", + "Ġdevelop ments", + "Con nect", + "ĠM and", + "ĠW ITH", + "ĠPhilipp ines", + "prof ile", + "Ġalt ogether", + "ĠB und", + "ĠT D", + "oo oo", + "amp ed", + "ip h", + "Ġste am", + "Ġold est", + "Ġdet ection", + "ul pt", + "Ġ ç", + "ĠWay ne", + "200 6", + "f a", + "Ġcir cles", + "ĠF u", + "Ġdon ors", + "appropri ate", + "ĠDak ota", + "j amin", + "Ġmotiv ated", + "Ġpurch ases", + "ĠLouis iana", + "ĠS pl", + "Ġgl obe", + "Ġ10 5", + "z ip", + "c all", + "Ġdepart ments", + "Ġsustain able", + "10 5", + "ĠO P", + "if iers", + "Ġprevent ed", + "Ġinc omp", + "ĠComm ander", + "Ġdom inated", + "Ġ »", + "Ġinvest ed", + "Ġcomplex ity", + "Ġin cl", + "Ġens uring", + "Ġreal m", + "yn c", + "ĠInd ependent", + "r ained", + "ĠJ en", + "ĠFl ight", + "Ġat he", + "Ġspec ulation", + "ĠT E", + "oc ate", + "t ic", + "Ġpl aint", + "her ry", + "Ġto y", + "Ġ1 11", + "Ġpl ates", + "st atus", + "ĠIs a", + "Ġdev oted", + "C op", + "ĠE S", + "25 5", + "ur rency", + "M ain", + "Ġsl aves", + "Ġpe pper", + "Ġqu otes", + "Ġce iling", + "ĠF ish", + "Ġtrans formation", + "Ġfra ction", + "Ġadvant ages", + "Ġto ile", + "Ġstun ning", + "Ġmo ist", + "bre aking", + "s i", + "ĠL ocation", + "ĠMed ium", + "Ġtext s", + "Ġu gly", + "Ġb io", + ". âĢĶ", + "ĠB ased", + "Ġtr ains", + "ĠW ing", + "ĠAn cient", + "ĠRec ords", + "ĠH ope", + "Spe cial", + "ades h", + "ob i", + "[ /", + "Ġtempor arily", + "V er", + "h u", + "os er", + "Ġover night", + "Ġm amm", + "ĠTre asury", + "ĠV enezuel", + "ĠMeg a", + "Ġt ar", + "Ġexpect s", + "bl ack", + "or ph", + "\\\\ \\\\", + "Ġaccept ance", + "Ġrad ar", + "s is", + "Ġjun ior", + "Ġfram es", + "Ġobserv ation", + "ac ies", + "P ower", + "ĠAdv anced", + "M ag", + "olog ically", + "ĠMe chan", + "Ġsent ences", + "Ġanaly sts", + "augh ters", + "force ment", + "Ġv ague", + "Ġcl ause", + "Ġdirect ors", + "Ġeval uate", + "Ġcabin et", + "M att", + "ĠClass ic", + "A ng", + "Ġcl er", + "ĠB uck", + "Ġresear cher", + "Ġ16 0", + "Ġpoor ly", + "Ġexperien cing", + "ĠP ed", + "ĠMan hattan", + "Ġfre ed", + "Ġthem es", + "ad vant", + "Ġn in", + "Ġpra ise", + "10 4", + "ĠLib ya", + "b est", + "Ġtrust ed", + "Ġce ase", + "Ġd ign", + "D irect", + "Ġbomb ing", + "Ġm igration", + "ĠSci ences", + "Ġmunicip al", + "ĠA verage", + "Ġgl ory", + "Ġreve aling", + "Ġare na", + "Ġuncertain ty", + "Ġbattle field", + "ia o", + "G od", + "Ġc inem", + "ra pe", + "el le", + "ap ons", + "Ġlist ing", + "Ġwa ited", + "Ġsp otted", + "ke ley", + "ĠAud io", + "e or", + "ard ing", + "idd ing", + "ig ma", + "ĠN eg", + "Ġl one", + "Ġ ----", + "ex e", + "d eg", + "Ġtrans f", + "Ġwas h", + "Ġsl avery", + "Ġexpl oring", + "ĠW W", + "ats on", + "Ġen cl", + "l ies", + "ĠC reek", + "Ġwood en", + "Man ager", + "ĠBr and", + "um my", + "ĠAr thur", + "Ġbureau cr", + "Ġbl end", + "ar ians", + "F urther", + "Ġsupposed ly", + "Ġwind s", + "Ġ19 79", + "Ġgrav ity", + "Ġanalys es", + "ĠTra vel", + "ĠV eter", + "Ġd umb", + "Ġaltern ate", + "g al", + "Ġconsum ed", + "Ġeffect iveness", + ".' '", + "Ġpath s", + "ond a", + "L A", + "ĠStr ong", + "Ġen ables", + "Ġesc aped", + "Ġ\" \"", + "Ġ1 12", + "Ġ198 3", + "Ġsm iled", + "Ġtend ency", + "F ire", + "Ġp ars", + "ĠR oc", + "Ġl ake", + "Ġf itness", + "ĠA th", + "ĠH orn", + "Ġh ier", + "Ġimp ose", + "m other", + "Ġp ension", + "ic ut", + "bor ne", + "ic iary", + ". _", + "ĠS U", + "Ġpol ar", + "is y", + "eng u", + "itial ized", + "AT A", + "w rite", + "Ġexerc ises", + "ĠD iamond", + "ot ypes", + "Ġharm ful", + "on z", + "Ġprint ing", + "st ory", + "Ġexpert ise", + "ĠG er", + "Ġtraged y", + "ĠF ly", + "Ġd ivid", + "amp ire", + "st ock", + "M em", + "Ġre ign", + "Ġun ve", + "Ġam end", + "ĠProp het", + "Ġmut ual", + "ĠF ac", + "Ġrepl acing", + "H ar", + "ĠCirc uit", + "Ġthro at", + "ĠSh ot", + "Ġbatter ies", + "Ġto ll", + "Ġaddress ing", + "ĠMedic aid", + "Ġp upp", + "ĠN ar", + "ol k", + "Ġequ ity", + "M R", + "ĠHis pan", + "ĠL arge", + "m id", + "D ev", + "Ġexp ed", + "Ġdem o", + "ĠMarsh all", + "erg us", + "Ġf iber", + "Ġdiv orce", + "ĠCre ate", + "Ġsl ower", + "ĠPark er", + "ĠStud ent", + "ĠTr aining", + "Ret urn", + "ĠT ru", + "Ġc ub", + "ĠRe ached", + "Ġpan ic", + "Ġqu arters", + "Ġre ct", + "Ġtreat ing", + "Ġr ats", + "ĠChristian ity", + "ol er", + "Ġsac red", + "Ġdecl are", + "ul ative", + "et ing", + "Ġdeliver ing", + "est one", + "Ġt el", + "ĠL arry", + "Ġmet a", + "ac cept", + "art z", + "ĠRog er", + "hand ed", + "Ġhead er", + "Ġtra pped", + "ĠCent ury", + "Ġkn ocked", + "ĠOx ford", + "Ġsurviv ors", + "b ot", + "Ġdemon stration", + "Ġd irt", + "Ġass ists", + "OM E", + "ĠD raft", + "ortun ate", + "fol io", + "pe red", + "ust ers", + "g t", + "ĠL ock", + "Ġjud icial", + "ver ted", + "Ġsec ured", + "out ing", + "ĠBook s", + "Ġhost ing", + "Ġlif ted", + "l ength", + "Ġj er", + "Ġwhe els", + "ĠR ange", + "umbn ails", + "Ġdiagn osis", + "te ch", + "ĠStew art", + "ĠP ract", + "Ġnation wide", + "Ġde ar", + "Ġoblig ations", + "Ġgrow s", + "Ġmand atory", + "Ġsusp icious", + "! '", + "A pr", + "G reat", + "Ġmort gage", + "Ġprosecut or", + "Ġeditor ial", + "ĠK r", + "Ġprocess ed", + "ung le", + "Ġflex ibility", + "Ear lier", + "ĠC art", + "ĠS ug", + "Ġfoc uses", + "Ġstart up", + "Ġbre ach", + "ĠT ob", + "cy cle", + "ãĢ Į", + "ro se", + "Ġb izarre", + "ãĢ į", + "Ġveget ables", + "$ $", + "Ġret reat", + "osh i", + "ĠSh op", + "ĠG round", + "ĠSt op", + "ĠHawai i", + "ĠA y", + "Per haps", + "ĠBe aut", + "uff er", + "enn a", + "Ġproduct ivity", + "F ixed", + "cont rol", + "Ġabs ent", + "ĠCamp aign", + "G reen", + "Ġident ifying", + "Ġreg ret", + "Ġpromot ed", + "ĠSe ven", + "Ġer u", + "ne ath", + "aug hed", + "ĠP in", + "ĠL iving", + "C ost", + "om atic", + "me ga", + "ĠN ig", + "oc y", + "Ġin box", + "Ġem pire", + "Ġhor izont", + "Ġbr anches", + "Ġmet aph", + "Act ive", + "ed i", + "ĠFil m", + "ĠS omething", + "Ġmod s", + "inc ial", + "ĠOrig inal", + "G en", + "Ġspir its", + "Ġear ning", + "H ist", + "Ġr iders", + "Ġsacr ific", + "M T", + "ĠV A", + "ĠS alt", + "Ġoccup ation", + "ĠM i", + "Ġdis g", + "lic t", + "Ġn it", + "Ġn odes", + "e em", + "ĠP ier", + "Ġhat red", + "ps y", + "ãĥ ī", + "Ġthe ater", + "Ġsophistic ated", + "Ġdef ended", + "Ġbes ides", + "Ġthorough ly", + "ĠMedic are", + "Ġbl amed", + "arent ly", + "Ġcry ing", + "F OR", + "pri v", + "Ġsing ing", + "ĠI l", + "Ġc ute", + "o ided", + "olit ical", + "ĠNe uro", + "å ¤", + "Ġdon ation", + "ĠEag les", + "ĠG ive", + "T om", + "Ġsubstant ially", + "ĠLic ense", + "ĠJ a", + "Ġg rey", + "ĠAn imal", + "ĠE R", + "ĠU nd", + "Ġke en", + "Ġconclud e", + "ĠMississ ippi", + "Eng ine", + "ĠStud ios", + "P ress", + "o vers", + "ll ers", + "Ġ3 50", + "ĠR angers", + "Ġr ou", + "ert o", + "E p", + "iss a", + "iv an", + "Ġse al", + "ĠReg ist", + "dis play", + "Ġwe aken", + "u um", + "ĠComm ons", + "ĠS ay", + "Ġcult ures", + "Ġl aughed", + "Ġsl ip", + "Ġtreat ments", + "iz able", + "m art", + "ĠR ice", + "Ġbe ast", + "Ġob esity", + "ĠLa ure", + "ig a", + "Wh ich", + "hold er", + "Ġelder ly", + "Ġp ays", + "Ġcompl ained", + "Ġc rop", + "Ġpro c", + "Ġexplos ive", + "ĠF an", + "ĠAr senal", + "A uthor", + "ef ul", + "Ġme als", + "Ġ( -", + "id ays", + "Ġimag ination", + "Ġann ually", + "Ġm s", + "as ures", + "H ead", + "ik h", + "m atic", + "Ġboy friend", + "ĠCom puter", + "Ġb ump", + "Ġsur ge", + "ĠCra ig", + "ĠKir k", + "D el", + "medi ate", + "Ġscen arios", + "ĠM ut", + "ĠSt ream", + "Ġcompet itors", + "Ù Ħ", + "ĠStan ford", + "ĠRes ources", + "az ed", + "b age", + "Ġorgan is", + "ĠRe lease", + "Ġsepar ately", + "Ġha bits", + "Ġmeasure ments", + "ĠCl ose", + "Ġaccomp any", + "Ġg ly", + "Ġt ang", + "ĠR ou", + "Ġplug in", + "Ġcon vey", + "ĠChall enge", + "oot s", + "j an", + "Ġcur s", + "ĠRel ations", + "ke eper", + "Ġapproach ing", + "p ing", + "Spe aking", + "Ġarrang ement", + "ĠV I", + "are ttes", + "Ġaffect ing", + "Ġperm its", + "b ecause", + "Ġu seless", + "ĠH us", + "!! !!", + "Ġdestro ying", + "Un fortunately", + "Ġfasc inating", + "S em", + "Ġelect oral", + "Ġtrans parency", + "ĠCh aos", + "Ġvolunte er", + "Ġstatist ical", + "Ġactiv ated", + "ro x", + "We b", + "H E", + "ĠHamp shire", + "is ive", + "M ap", + "Ġtr ash", + "ĠLaw rence", + "st ick", + "C r", + "Ġr ings", + "EX T", + "Ġoper ational", + "op es", + "D oes", + "ĠEv ans", + "Ġwitness ed", + "P ort", + "Ġlaunch ing", + "ec onom", + "w ear", + "ĠPart icip", + "um m", + "cul es", + "ĠR AM", + "ĠT un", + "Ġass ured", + "Ġb inary", + "Ġbet ray", + "Ġexpl oration", + "ĠF el", + "Ġad mission", + "it ated", + "S y", + "Ġav oided", + "ĠSim ulator", + "Ġcelebr ated", + "ĠElect ric", + "¥ ŀ", + "Ġcl uster", + "itzer land", + "he alth", + "L ine", + "ĠN ash", + "at on", + "Ġsp are", + "Ġenter prise", + "ĠD IS", + "clud es", + "Ġfl ights", + "Ġreg ards", + "Ġà Ĺ", + "h alf", + "Ġtr ucks", + "Ġcontact s", + "Ġunc ons", + "ĠCl imate", + "Ġimm ense", + "N EW", + "oc c", + "ect ive", + "Ġemb od", + "Ġpat rol", + "Ġbes ide", + "Ġv iable", + "Ġcre ep", + "Ġtrig gered", + "ver ning", + "Ġcompar able", + "q l", + "Ġg aining", + "ass es", + "Ġ( );", + "ĠG rey", + "ĠM LS", + "s ized", + "Ġpros per", + "\" ?", + "Ġpoll ing", + "Ġsh ar", + "ĠR C", + "Ġfire arm", + "or ient", + "Ġf ence", + "Ġvari ations", + "g iving", + "ĠP i", + "osp el", + "Ġpled ge", + "Ġc ure", + "Ġsp y", + "Ġviol ated", + "Ġr ushed", + "Ġstro ke", + "ĠBl og", + "sel s", + "ĠE c", + ",' '", + "Ġp ale", + "ĠColl ins", + "ter ror", + "ĠCanad ians", + "Ġt une", + "Ġlabor atory", + "Ġn ons", + "t arian", + "Ġdis ability", + "ĠG am", + "Ġsing er", + "al g", + "ĠSen ior", + "Ġtrad ed", + "ĠWar rior", + "Ġinf ring", + "ĠFrank lin", + "Ġstr ain", + "ĠSwed ish", + "Ġsevent h", + "ĠB enn", + "ĠT ell", + "Ġsynd rome", + "Ġwond ered", + "id en", + "++ ++", + "ig o", + "Ġpur ple", + "Ġjournal ism", + "Ġreb el", + "Ġf u", + "bl og", + "Ġinv ite", + "ren cies", + "ĠCont act", + "Is rael", + "ĠCont ent", + "Ġche er", + "Ġbed room", + "ĠEngine ering", + "ĠQue ens", + "Ġd well", + "ĠPlay Station", + "ĠD im", + "ĠCol on", + "l r", + "Ġoper ates", + "Ġmotiv ation", + "US A", + "ast ered", + "C ore", + "ĠTr uth", + "ol o", + "OS E", + "ĠMem ory", + "Ġpred ec", + "Ġan arch", + "Ġ19 20", + "ĠY am", + "à ¨", + "b id", + "Ġgr ateful", + "Ġexc itement", + "Ġtre asure", + "Ġlong est", + "ct ive", + "Ġdes erves", + "Ġreserv es", + "Ġcop s", + "ĠOtt awa", + "ĠEgypt ian", + "ank ed", + "Ġart if", + "Ġhypot hesis", + ": /", + "Ġpurch asing", + "Ġlove ly", + "H P", + "Ġdiv ide", + "Ġstrict ly", + "Ġquestion ing", + "Ġtaxp ayers", + "ĠJ oy", + "Ġroll s", + "ĠHe avy", + "Ġp orts", + "Ġmag netic", + "Ġinf lamm", + "Ġbr ush", + "t ics", + "â ĪĴ", + "Ġbott les", + "pp y", + "Ġp add", + "ãĤ ¯", + "m illion", + "Ġdevast ating", + "Ġcomp iled", + "Ġmed ication", + "Ġtw elve", + "ĠPer ry", + "Sp ace", + "im b", + "y our", + "Ġle aked", + "ĠT ar", + "Ġun ity", + "Ġinfect ed", + "Ġtravel ed", + "ID E", + "ĠMc Donald", + "t xt", + "ĠPr inc", + "Ġinter ven", + "ĠTai wan", + "ĠP ow", + "Ġbe aring", + "ĠTh read", + "Ġz ones", + "iz ards", + "un ks", + "Ch apter", + "ll or", + "Ġ ·", + "Ġw ounds", + "Ġdisc retion", + "Ġsucceed ed", + "ik ing", + "Ġicon ic", + "C all", + "Ġscreen ing", + "ĠM is", + "ict s", + "Ġmin isters", + "Ġsepar ation", + "Pl ayer", + "Ġb ip", + "Ġbel oved", + "Ġcount ing", + "ĠE ye", + "ar ound", + "ing ing", + "Ġtable t", + "Ġoff ence", + "in ance", + "h ave", + "ĠInf o", + "ĠNin ja", + "Ġprotect ive", + "ĠC ass", + "M ac", + "ĠQual ity", + "N orth", + "Ġ ic", + "ĠCub a", + "ĠChron icle", + "ĠPro perty", + "Ġfast est", + "ot os", + "ĠG erm", + "OW N", + "Ġbo om", + "ĠStan ley", + "ergus on", + "Ġcle ver", + "Ġent ers", + "m ode", + "ter ior", + "ĠS ens", + "Ġlin ear", + "AR K", + "Ġcomp aring", + "Ġpure ly", + "Ġsaf er", + "ĠPot ter", + "Ġc ups", + "R T", + "Ġgl uc", + "Ġatt ributed", + "Ġdu pl", + "ĠP ap", + "Ġprec ious", + "Ġp a", + "iction ary", + "ĠT ig", + "ĠTo o", + "ol utions", + "st an", + "Ġrob ots", + "Ġlob b", + "Ġstat ute", + "Ġprevent ion", + "w estern", + "16 0", + "ĠAct ive", + "ĠMar ia", + "h al", + "N one", + "ell ar", + "ĠK B", + "ĠPart ners", + "ĠSing le", + "ĠFollow ing", + "ang o", + "ac ious", + "Ġth ou", + "Ġk g", + "Ġinflu ential", + "ĠFriend s", + "S ur", + "ain ted", + "Ġfor ums", + "Ġst arter", + "Ġcitizens hip", + "ĠE lection", + "on ge", + "ot ation", + "os ph", + ";; ;;", + "ut ical", + "p ur", + "ere n", + "Ġaccus ations", + "bit ious", + "ab bit", + "ĠOr d", + "Post ed", + "ir k", + "Ġsens itivity", + "ic he", + "ĠAm y", + "ĠF ab", + "Ġsum mit", + "Ġped est", + "Ġrub ber", + "Ġagric ultural", + "Ġcan cel", + "A E", + "Ġin aug", + "Ġcont am", + "Ġfirm ly", + "i w", + "st age", + "ĠK an", + "Ġt ier", + "Ġinv ention", + "Ġtransl ated", + "ĠR ules", + "B ox", + "Tw itter", + "ID S", + "Ġp izza", + "Ġdeb ug", + "ĠD rop", + "v s", + "Ġh orses", + "b ig", + "Ġb oring", + "Ġh ood", + "ĠMcC ain", + "at ched", + "ĠBro s", + "Ġsk ip", + "Ġess ay", + "st at", + "ĠLeg ends", + "Ġam munition", + "au c", + "Ġshoot er", + "Ġun h", + "Ġsuppl ied", + "Ġgener ic", + "ĠS K", + "ib an", + "yr ics", + "Ġ25 5", + "Ġclim bing", + "Form er", + "Ġfl ip", + "Ġjump ing", + "Ġfrust ration", + "ĠTer ry", + "Ġneighborhood s", + "Ġmed ian", + "be an", + "Ġbr ains", + "Follow ing", + "Ġsh aped", + "Ġdraw s", + "Ġal tered", + "J ack", + "Ġrecip es", + "Ġsk illed", + "we alth", + "ach i", + "e lection", + "Ġbehavi ors", + "de als", + "ĠU ntil", + "F e", + "Ġdecl aration", + "mar ks", + "ĠBet ween", + "cel ona", + "Ġres on", + "Ġbub ble", + "Am ong", + "Ġim perial", + "G S", + "Ġfemin ist", + "200 5", + "ĠK yle", + "Ġaccount ing", + "ĠTe le", + "ĠT yr", + "Ġconnect ing", + "Ġre hab", + "ĠP red", + "s im", + "Ġmeant ime", + "Ġphys ician", + "M W", + "ĠCamp bell", + "ĠBr andon", + "Ġcontribut ing", + "ĠR ule", + "ĠWe ight", + "ĠN ap", + "Ġinter active", + "Ġv ag", + "Ġhel met", + "ĠCom b", + "f our", + "Ġsh ipped", + "Ġcomple ting", + "ĠP D", + "PD ATE", + "Ġspread ing", + "Ġsc ary", + "erv ing", + "ĠG as", + "Ġfr ank", + "s chool", + "Ġrom antic", + "Ġstab il", + "R ob", + "Ġaccur ately", + "Ġac ute", + "ĠH ann", + "Ġsymbol s", + "Ġcivil ization", + "ĠA W", + "Ġlight ning", + "Ġcons iders", + "Ġven ue", + "Ġ ×", + "Ġo ven", + "ĠS F", + "h is", + "Ġn u", + "ĠLear n", + "Ġpe oples", + "Ġst d", + "Ġsle e", + "Ġs lic", + "ĠStat istics", + "Ġcor ners", + "ĠB aker", + "Ġ: )", + "ment ation", + "ol ver", + "Ġlaugh ing", + "ĠT odd", + "ond e", + "ĠH ills", + "Ġn uts", + "ĠW oman", + "pl ane", + "Ġl iver", + "ĠIn side", + "S orry", + "Ġagre es", + "Ġfund ament", + "ĠF isher", + "Ġa uction", + "Ġthread s", + "gl as", + "ĠBas ic", + "ĠN at", + "Ġlack ing", + "Ġceleb ration", + "j u", + "Ġs illy", + "E uro", + "Ġt att", + "ight y", + "cont rolled", + "T est", + "ĠSing h", + "Ġr age", + "Ġrh yth", + "o ffic", + "ĠPh antom", + "Ġhead lines", + "Ġrespond ing", + "ĠMor ning", + "Ġvit amin", + "Ġboot s", + "ĠS ite", + "al in", + "p i", + "Ġvir al", + "ĠU C", + "D ER", + "ĠSe x", + "Ġst ocks", + "c urrent", + "Ġch urches", + "ĠR are", + "ĠMur phy", + "Ġden ial", + "ĠG aming", + "Ġtou g", + "Ġn ick", + "Ġm akers", + "ĠRon ald", + "Ġgener ous", + "ĠD oc", + "ĠMor ris", + "Ġtransform ed", + "ĠN ormal", + "Ġ10 4", + "ĠKick starter", + "ĠUp on", + "On line", + "ĠI RS", + "Ġw rap", + "Ġl oving", + "Ġarri ves", + "ĠD ue", + "Ġhe ter", + "ĠM ade", + "Ġrent al", + "Ġbelong s", + "Ġatt orneys", + "Ġcro ps", + "Ġmat ched", + "ul um", + "ol ine", + "10 9", + "Ġdis par", + "Ġbuy ers", + "ĠCam bridge", + "Ġeth ics", + "rou ps", + "Ġjust ified", + "Ġmarg inal", + "Ġrespect ed", + "win ning", + "Ġnodd ed", + "ĠSer ge", + "ĠForm er", + "C raft", + "######## ########", + "ĠWar ner", + "Ġd ash", + "et e", + "Ġent ert", + "ĠE scape", + "out heast", + "Ġkn ees", + "ĠB omb", + "Ġr ug", + "P ass", + "Ġatt itudes", + "go vernment", + "ĠPri or", + "Ġqual ities", + "Ġnot ification", + "ĠPh one", + "l ie", + "Ġanticip ated", + "ĠCom bat", + "ĠBar ry", + "Ġ198 2", + "Us ers", + "on er", + "Ġcomput ing", + "ĠConnect icut", + "Ġless er", + "Ġpe ers", + "ĠC u", + "Ġtechn ically", + "Ġsub mission", + "ĠUn iversal", + "Ġman ually", + "our ge", + "Ġrespond ents", + "ĠB TC", + "ĠH ost", + "Ġf are", + "ĠB ird", + "Ġrece ipt", + "al so", + "Ġj ack", + "Ġagric ulture", + "Ġsk ull", + "Ġ! =", + "Ġpass ive", + "ĠC I", + "Ġsoc ieties", + "Ġremind ed", + "Ġinter ference", + "B uy", + "Ġâ ľ", + "g on", + "Ġscrut iny", + "ĠW itch", + "Ġconduct ing", + "Ġ ãĥ", + "Ġexch anges", + "ĠMit chell", + "Ġinhab it", + "Ġtw ist", + "B D", + "Ġwhere ver", + "group on", + "Ġj okes", + "ĠBen jamin", + "ĠR andom", + "fr ame", + "ĠL ions", + "Ġhighlight ed", + "ĠArk ansas", + "E nt", + "Ġp ile", + "Ġpre lim", + "g s", + "mind ed", + "Ġfel ony", + "ĠG A", + "ĠL uck", + "Ġpract ically", + "ĠB os", + "Ġact ress", + "D am", + "ĠB ou", + "Ġvis a", + "Ġembed ded", + "Ġhy brid", + "Ġear liest", + "Ġsoon er", + "s ocial", + "ĠH A", + "Ġste ep", + "Ġdis advant", + "Ġexplo it", + "ĠE gg", + "ĠUlt ra", + "Ġnecess ity", + "L ocal", + "ie ge", + "Ġd ated", + "Ġmass es", + "Ġsubsc ription", + "pl ess", + "Ġan onym", + "Ġpresum ably", + "Bl ue", + "The ir", + "asket ball", + "ĠPhil ip", + "Ġcom ed", + "load ed", + "r ane", + "Ġref lection", + "Ch ina", + "Ġext ends", + "Ġform ing", + "Ġund ers", + "200 1", + "Ġgr at", + "Ġconcent rations", + "Ġins ulin", + "Ġsec ular", + "Ġwh ilst", + "Ġwin ners", + "Ad vertisements", + "Ġdeliber ately", + "ĠWork ing", + "Ġs ink", + "et ics", + "d ale", + "Ġmand ate", + "Ġg ram", + "Ġvac ation", + "Ġwarn ings", + "ri pp", + "ĠTH AT", + "Ġcomment ary", + "Ġint u", + "Ġa est", + "Ġreason ing", + "Ġbreak down", + "ĠZ ombie", + "Ġ-- >", + "ĠPolit ical", + "c ott", + "Ġthr ust", + "Ġtechn ological", + "Ġdec iding", + "Ġtraff icking", + "L ong", + "W elcome", + "pr ising", + "ĠCommun ications", + "Ġend ors", + "Ġsw ift", + "Ġmetab ol", + "co ins", + "res a", + "ĠHT TP", + "Ġen roll", + "ĠH appy", + "us r", + "int age", + "Ġ[ \"", + "u ably", + "ĠM aterial", + "Ġrepe al", + "Se pt", + "k h", + "ĠMod i", + "Ġunder neath", + "ĠI L", + "sh ore", + "Ġdiagn osed", + "ace utical", + "Ġsh ower", + "au x", + "ĠSw itch", + "ĠStre ngth", + "Ġj ihad", + "n ational", + "Ġtra uma", + "uss y", + "on i", + "Ġcons olid", + "Ġcal ories", + "ĠF lynn", + "ag ged", + "16 8", + "ĠP ink", + "Ġfulf ill", + "Ġch ains", + "Ġnot ably", + "ĠA V", + "L ife", + "ĠCh uck", + "m us", + "ĠUr ban", + "ĠH end", + "Ġdep osit", + "ĠS ad", + "Ġaff air", + "OR K", + "ie val", + "ĠF DA", + "Ġt rop", + "ĠOver all", + "Ġvirt ue", + "Ġsatisf action", + "au nd", + "Ġl un", + "ĠSw itzerland", + "ĠOper ation", + "pro cess", + "Ġsh ook", + "Ġcount ies", + "le ased", + "ĠCharl otte", + "1 12", + "Ġtrans cript", + "Ġre dd", + "p ush", + "ĠHe y", + "ĠAn alysis", + "[ \"", + "Ġaltern atives", + "ard less", + "Ġele ph", + "Ġpre jud", + "ĠLe af", + "H aving", + "ĠH ub", + "Ġexpress ions", + "ĠVol ume", + "Ġshock ing", + "ĠRed s", + "Ġread ily", + "Ġplan ets", + "ad ata", + "Ġcollaps ed", + "ĠMad rid", + "Ġir rit", + "i pper", + "ĠEn c", + "ĠW ire", + "Ġbu zz", + "ĠG P", + "ash a", + "Ġaccident ally", + "ur u", + "Ġfrust rated", + "ĠS A", + "Ġhung ry", + "ĠH uff", + "Ġlab els", + "ant o", + "ĠE P", + "Ġbar riers", + ") |", + "ĠBer keley", + "ĠJ ets", + "Ġp airs", + "ĠL an", + "J ames", + "ĠB ear", + "Ġhum or", + "ĠLiber ty", + "Ġmagn itude", + "Ġag ing", + "ĠM ason", + "Ġfriends hip", + "umb ling", + "Ġemer ge", + "Ġnewsp apers", + "Ġam bitious", + "ĠRich ards", + "atern al", + "Ġ198 1", + "Ġcook ies", + "Ġsc ulpt", + "Ġpur suit", + "L ocation", + "Ġscript s", + "p c", + "Ġarrang ements", + "Ġd iameter", + "Ġl oses", + "am ation", + "Ġl iqu", + "ĠJ ake", + "aret te", + "Ġunderstand s", + "ĠZ en", + "v m", + "Ġappro ve", + "Ġw ip", + "Ġult ra", + "Ġint end", + "ĠD I", + "asc ular", + "Ġst ays", + "ĠK or", + "ĠK l", + "Ġinvest ing", + "L a", + "Ġbelie ving", + "b ad", + "m outh", + "Ġtaxp ayer", + "ãĥ ĥ", + "ĠQue bec", + "Ġl ap", + "ĠSw iss", + "d rop", + "Ġdr ain", + "ir i", + "et c", + "ft en", + "ĠN ex", + "Ġst raw", + "Ġscream ing", + "Ġcount ed", + "Ġdam aging", + "Ġamb assador", + "cent ury", + "Ġpro x", + "Ġarrest s", + "u v", + "il ateral", + "ĠCh arg", + "Ġpresc ribed", + "Ġindepend ently", + "Ġf ierce", + "ĠB aby", + "Ġb rave", + "Ġsu its", + "= >", + "Ġbas eline", + "ĠR ate", + "Ġis lands", + "Ġ( (", + "g reen", + "ix els", + "Ġname ly", + "ĠVill age", + "th an", + "am y", + "V ersion", + "g mail", + "ential s", + "ĠS ud", + "ĠMel bourne", + "Ġarri ving", + "Ġquant um", + "e ff", + "rop olitan", + "T ri", + "Ġfun eral", + "ĠI R", + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", + "ĠC ob", + "it ably", + "Ġt urb", + "Ġcomb o", + "Re view", + "Ġdeploy ment", + "u ity", + "ĠB ott", + "Ġinv isible", + "Ġrender ing", + "Ġunl ocked", + "Ġa qu", + "ĠVlad imir", + "Ġp ad", + "ĠBr ain", + "ĠLeg acy", + "dr agon", + "ĠKurd ish", + "Ġsound ed", + "Ġdet ained", + "ĠD M", + "g ary", + "Ġd aughters", + "Ġdistur bing", + "uk a", + "ĠPar ad", + "Ġt ast", + "Ġunf ortunate", + "Ġu l", + "em in", + "Ġattend ance", + "tr l", + "Ġpar ks", + "ĠMem orial", + "ĠAl ice", + "oth y", + "gu ard", + "ĠD ise", + "ĠSh an", + "ĠFor um", + "R ich", + "Ġshif ted", + "ue z", + "Ġl ighter", + "ĠMag n", + "Ġc od", + "S ch", + "ham mad", + "P ub", + "3 50", + "ĠP okemon", + "Ġprot otype", + "Ġun re", + "B ase", + "ĠStud ents", + "ĠRep ly", + "ĠCommun ist", + "Ġg au", + "ĠTy ler", + "I Z", + "Ġparticip ated", + "Ġsup rem", + "ĠDet ails", + "Ġvessel s", + "ro d", + "Ġt ribe", + "ke ep", + "Ġassum ptions", + "Ġp ound", + "Ġcr ude", + "ĠAv ailable", + "Ġswim ming", + "Ġin clusion", + "Ġadv ances", + "c ulation", + "Ġconserv ation", + "Ġover d", + "ĠBuff alo", + "Art icle", + "ed ge", + "Ġaw a", + "ĠMad ison", + "Ġsid ew", + "Ġcat ast", + "ĠK rist", + "uc le", + "ĠHigh way", + "ĠTer ror", + "Ġactiv ation", + "Ġuncons cious", + "ĠSat an", + "ĠSus an", + "ill ery", + "Ġarr anged", + "i op", + "Ġrum ors", + "ur ring", + "th ink", + "ĠKe ith", + "ĠK ind", + "Ġavoid ing", + "by n", + "n ut", + "ĠSpe aker", + "r us", + "n ames", + "Ġgu ilt", + "ĠOlymp ics", + "Ġsa il", + "ĠM es", + "lev ant", + "ĠColumb us", + "a ft", + "C ity", + "S outh", + "ĠHar vey", + "ĠP un", + "S everal", + "Ġment ally", + "Ġimp ress", + "m ount", + "ĠUb untu", + "âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ", + "ĠSuper man", + "ĠMP s", + "Ġintent ions", + "ĠR acing", + "Ġlike lihood", + "Ġ2 40", + "T otal", + "Ġto ys", + "ĠW atson", + "Ġur ge", + "L ear", + "ĠP aper", + "Ġoccur ring", + "ĠB eng", + "ĠC ert", + "Ġst ones", + "T im", + "ĠTw in", + "z b", + "ĠD ynam", + "Ġpolit ician", + "k ens", + "ĠEnter prise", + "UT ERS", + "Ġab ol", + "Ġref resh", + "Ġarbit rary", + "pe ction", + "Ġtrou bles", + "Ġ} );", + "t v", + "Ġpil ots", + "Ġdist ribute", + "Ġaud it", + "Ġp ause", + "orig inal", + "Ġr ivals", + " £", + "F ig", + "T L", + "ab il", + "ry ing", + "L in", + "ion ed", + "l on", + "Ġf ancy", + "Ġcr ashed", + "Ġt ract", + "Ġshe d", + "Ġcons ume", + "B ased", + "down load", + "in it", + "Ġvolt age", + "Int rodu", + "Ġcondem ned", + "ĠFin ance", + "res pect", + "Ġex cluded", + "Ġestablish ing", + "her ic", + "Ġher itage", + "Ġspect acular", + "Ġun st", + "ĠSnow den", + "ĠL ane", + "S an", + "Ġprotect ions", + "st ruction", + "inc inn", + "Ġmac ro", + "C ustom", + "ios ity", + "Ġes p", + "Ġfunction ing", + "Ġm ush", + "Ġp uzzle", + "Ġeth ical", + "M al", + "Ġgo verning", + "ĠF erguson", + "Ġrest ored", + "Ġst ressed", + "ĠCoun ter", + "ĠK as", + "cl ip", + "AN S", + "Ġse iz", + "U K", + "by ss", + "old own", + "ap i", + "Ġperman ently", + "oun ters", + "W est", + "Th rough", + "L ight", + "at oes", + "Ġne at", + "Ġc ord", + "ure r", + "Ġsevere ly", + "ĠA ven", + "Ġinter rog", + "Ġtri ple", + "G iven", + "N umber", + "Ġar ise", + "Ġs her", + "pl ant", + "Ġfl ower", + "ĠC ou", + "Ġat e", + "Ġnew er", + "b ul", + "Ġmean while", + "ĠL air", + "Ġadjust ment", + "ĠCop yright", + "Ġd ivers", + "i ological", + "Ġgam ers", + "o at", + "Ġhistor ically", + "Ġanal og", + "Ġlong time", + "Ġpres cription", + "ĠM ist", + "ĠHy per", + "ĠM aine", + "ĠDe ity", + "Ġmulti pl", + "ĠRe incarn", + "ĠH yd", + "ĠP ic", + "S il", + "r ants", + "ĠC ris", + ". ;", + "( {", + "epend ence", + "Ġrec y", + "ate ur", + "Ġqu ad", + "Ġgl ob", + "Ġcon ced", + "te am", + "Ġcapital ist", + "ĠL ot", + "Ġroy al", + "ĠCy ber", + "Ġblack s", + "met ic", + "ri v", + "ĠD anny", + "Ġsp o", + "ĠR O", + "Ġanim ated", + "rypt ed", + "ĠDep uty", + "Ġrend ered", + "F E", + "Ġstre ak", + "Ġcloud s", + "ĠDou g", + "~~~~ ~~~~", + "Ġdisc our", + "ĠVe h", + "Ġpsych ology", + "ĠJ ourney", + "Ġcry stal", + "ĠFro st", + "Ġsuspic ion", + "Ġrel ate", + "or us", + "ĠC rypt", + "ĠN VIDIA", + "com ed", + "ut ing", + "incinn ati", + "Ġvulner ability", + "ost ic", + "Ġisol ation", + "Ġcool ing", + "ĠCoal ition", + "Ġ1 19", + "F our", + "ĠDe al", + "Ġâ ī", + "se mble", + "ram ent", + "ĠBar celona", + "Ġ10 2", + "Ġcoc aine", + "ocaly pse", + "F eb", + "ogen ic", + "Ġmut ation", + "Ġcrypt oc", + "ĠK el", + "ĠG it", + "a is", + "Ġs isters", + "AN K", + "Ġactiv ate", + "T er", + "Ġd read", + "yl on", + "Ġprop ri", + "A ust", + "ĠDef ault", + "Ġout door", + "Ġshe er", + "ce ive", + "Ġg ently", + "Ð ¾", + "Pro gram", + "Ġâ ĨĴ", + "Ġve gan", + "ĠCr us", + "Ġrespons ibilities", + "ĠH R", + "OL D", + "Ġprev ents", + "Ġst iff", + "ĠW ere", + "Ġathlet ic", + "ĠSc ore", + "Ġ) :", + "Ġcolumn s", + "ĠL oc", + "av ailable", + "ĠF ram", + "ĠS essions", + "Ġcompan ion", + "Ġpack s", + "14 0", + "ĠKn ights", + "Ġf art", + "Ġstream s", + "Ġsh ore", + "Ġapp eals", + "ĠPer formance", + "h aul", + "ĠSt ra", + "ĠN ag", + "10 3", + "ĠTrans portation", + "B B", + "E v", + "z an", + "P ublic", + "Ġtw in", + "uls ion", + "M ult", + "Ġelect ro", + "Ġstat ue", + "ation ally", + "ĠN ort", + "Ġins pection", + "/ *", + "ig ue", + "Ġcomp assion", + "ĠT ales", + "ĠSte in", + "ĠSc reen", + "ĠB ug", + "ĠL ion", + "g irl", + "Ġwithdraw al", + "Ġobject ives", + "Ġblood y", + "Ġprelim inary", + "Ġj acket", + "Ġdim ensions", + "ĠC ool", + "ĠOcc up", + "Ġw reck", + "Ġdoub led", + "ank ing", + "Ġ19 75", + "Ġglass es", + "ĠW ang", + "pro v", + "P ath", + "connect ed", + "ĠMult i", + "ĠNor way", + "agon ist", + "Ġfe ared", + "Ġtouch ing", + "Ġarg uably", + "¯¯¯¯ ¯¯¯¯", + "ĠNC AA", + "che m", + "Ġsp at", + "ĠW WE", + "ĠC el", + "ig ger", + "Ġattack er", + "ĠJo in", + "ob ject", + "ett a", + "Ġelim inated", + "d et", + "Ġdest ruct", + "ĠLuc as", + "ct uary", + "18 0", + "ĠBr ady", + "ĠBl ues", + "B ay", + "au kee", + "Ġtim eline", + "Ġdeleg ates", + "w ritten", + "uff icient", + "Ġsh apes", + "Cop yright", + "ou ble", + "serv ice", + "Ġp ione", + "Ġcolleg es", + "Ġrow s", + "Ġsp ite", + "Ġassess ed", + "3 60", + "Ġle ase", + "Ġconfident ial", + "ck er", + "ĠMan ning", + "ĠV oice", + "Ġse aled", + "Ġcalcul ate", + "N O", + "ĠAss istant", + "Ġteen ager", + "ul ent", + "ather ine", + "Ġm ock", + "Ġd iamond", + "Ġf est", + "Ġsw itched", + "Ġres ume", + "ĠPu erto", + "Ġl anes", + "ir ation", + "ĠSimilar ly", + "Ġro d", + "ĠS el", + "ĠPal ace", + "ĠLim ited", + "e ous", + "Ġvar iant", + "Ġw ard", + "Ġ) )", + "Sh ow", + "OO K", + "A lex", + "ĠN ep", + "br is", + "ĠWik ipedia", + "Ġexcept ional", + "Ġman ages", + "ĠD raw", + "Ag ain", + "Ġco pper", + "ut t", + "Ġex ports", + "Ġport folio", + "Ġelev ated", + "R ated", + "ĠOther wise", + "ĠT act", + "ĠShe l", + "ĠT X", + "\" âĢĶ", + "Ġres ur", + "ĠW a", + "ven ant", + "Ġmon etary", + "pe ople", + "E mail", + "Ġfif ty", + "ĠS weet", + "ĠMalays ia", + "Ġconf using", + "ĠR io", + "ud a", + "uten ant", + "\" );", + "Ġpra ised", + "Ġvol umes", + "t urn", + "Ġm ature", + "Ġnon profit", + "Ġpassion ate", + "ĠPriv ate", + "Ġ10 3", + "Ġdesc end", + "ç ¥ŀ", + "uff y", + "head ed", + "Whe ther", + "ri en", + "ze ch", + "be it", + "Ġch rom", + "ĠMc M", + "Ġd ancing", + "Ġe leg", + "ĠNot iced", + "11 5", + "Ġadvoc acy", + "ENT S", + "amb ling", + "ĠMin or", + "ĠF inn", + "Ġprior ities", + "Ġthere of", + "ĠSt age", + "ĠRog ers", + "Ġsubst itute", + "ĠJ ar", + "ĠJeff erson", + "Ġlight ly", + "10 2", + "ĠL isa", + "u its", + "ys ical", + "Ġshif ts", + "Ġd rones", + "Ġwork place", + "Ġres id", + "ens ed", + "ah n", + "Ġpref erences", + "ser ver", + "Ġdeb ates", + "d oc", + "ĠGod s", + "Ġhelicop ter", + "Ġhon our", + "Ġconsider ably", + "ed ed", + "ĠF emale", + "ĠAn ne", + "Ġre un", + "ĠF ace", + "ĠHall ow", + "ĠBud get", + "Ġcondem n", + "Ġt ender", + "Pro f", + "ocr atic", + "ĠTurn er", + "ĠAg ric", + "Ġ19 76", + "Ġa pt", + "d isc", + "ĠF ighter", + "ĠA ur", + "Ġgar bage", + "in put", + "ĠK arl", + "ĠOl iver", + "ĠL anguage", + "k n", + "N on", + "ĠCl ar", + "Ġtrad itions", + "Ġad vertisement", + "ĠS or", + "Ġarch ive", + "Ġvill ages", + "7 50", + "Ġimplement ing", + "w aukee", + "Ġdiet ary", + "Ġswitch ing", + "Rep ublic", + "Ġvel ocity", + "Ġc it", + "ĠA wards", + "Ġfin ancing", + "Ġlast ed", + ") ]", + "Ġrem inder", + "P erson", + "Ġprec ision", + "Ġdesign ers", + "ĠF ried", + "ĠB order", + "Ġtr agic", + "Ġw ield", + "Ġiniti atives", + "ĠT ank", + "w er", + "Ġjo ins", + "R o", + "in ery", + "Ġar row", + "Ġgener ating", + "found er", + "Ġsear ches", + "Ġrandom ly", + "A ccess", + "Ġb atch", + "Ġp osed", + "l at", + "Ġpursu ing", + "as a", + "Ġtest ified", + "form ing", + "ĠSh ar", + "w iki", + "ĠE ither", + "S ometimes", + "Ġsen ators", + "ĠJohn ny", + "ĠTal iban", + "ĠG PS", + "\":\" /", + "ãģ® å", + "Ġanaly zed", + "ĠRub io", + "ĠMove ment", + "op ard", + "ii i", + "St and", + "f ight", + "Ġign oring", + "i ang", + "ĠG N", + "so ever", + "ĠST AT", + "Ġref using", + "Ġswe at", + "Ġb ay", + "P ORT", + "ir med", + "ak y", + "Ġdis pro", + "Ġlabel ed", + "Ġ10 8", + "H ello", + "Ġple asant", + "ab a", + "Ġtri umph", + "Ġab oard", + "Ġinc om", + "ĠC row", + "le tt", + "Ġfol k", + "Ġch ase", + "` `", + "ĠBr us", + "Ġte ens", + "c ue", + "Ġter rain", + "h yd", + "il ight", + "OR Y", + "Su pport", + "ew s", + "ll i", + "rain ts", + "ĠC and", + "Ġab used", + "ach ment", + "l arg", + "B as", + "ĠC ancer", + "Ġ19 78", + "Ġsupp orter", + "ac cess", + "ĠTer min", + "ĠT ampa", + "ĠAN Y", + "Ġnew est", + "ĠCrim inal", + "ed u", + "Ġ19 30", + "Ġadm its", + "Ġend e", + "Ġfail ures", + "ur ate", + "ful ness", + "cy cl", + "ĠSub ject", + "Ġinf inite", + "th ree", + "W A", + "p it", + "ĠInst all", + "R ad", + "ili ation", + "G M", + "Ġcontin ent", + "Ġaccommod ate", + "ĠCl ay", + "Ġp up", + "ĠF unction", + "Ġham mer", + "ĠAlbert a", + "Ġrev ised", + "Ġminor ities", + "Ġmeasure ment", + "Con nell", + "Ġdis able", + "ĠM ix", + "In cre", + "Ġfor k", + "ĠR osen", + "Ġimpl ies", + "umb lr", + "AN G", + "Ġprote ins", + "Ġagg ression", + "Ġfacilit ate", + "S N", + "Ġilleg ally", + "u er", + "Ġacad em", + "Ġp uzz", + "ĠSh ift", + "p ay", + "oll o", + "Ġaud iences", + "B uild", + "Ġno ble", + "Ġsynt ax", + "â ĺħ", + "Ġbe am", + "ĠB ed", + "ĠA ld", + "Ġorig ins", + "v ideo", + "Ġ19 77", + "ĠAss ault", + "Ġgar age", + "Te am", + "Ġver dict", + "Ġd war", + "ĠVirt ual", + "e vent", + "Ke ep", + "Ġsent iment", + "Ġwild life", + "sh irt", + "Ġb urg", + "Ġrecommend ation", + "rep resent", + "Ġgall ery", + "own ers", + "Ġsch olar", + "Ġconven ience", + "ĠSw ift", + "Ġconv inc", + "C ap", + "Ġwar fare", + "ĠVis ual", + "Ġconst itute", + "Ġab ort", + "ĠWe ather", + "ĠLook ing", + "ĠH em", + "Ġmart ial", + "Ġinc oming", + "et ition", + "Ġtoler ance", + "ĠCre ated", + "Ġfl ows", + "ĠE lder", + "Ġsoul s", + "Ġf oul", + "ĠP ain", + "ĠC AN", + "Ġ2 20", + "b c", + "he nd", + "Ġgen ius", + "R eal", + "ĠW r", + "omet er", + "p ad", + "Ġlim iting", + "ĠS i", + "ĠL ore", + "ĠAd ventures", + "Ġvar ied", + "D isc", + "f in", + "ĠPerson al", + "Ch ris", + "Ġinv ented", + "Ġd ive", + "ĠR ise", + "Ġo z", + "ĠCom ics", + "Ġexp ose", + "ĠRe b", + "let ters", + "s ite", + "im ated", + "Ġh acking", + "Ġeduc ated", + "ĠNob ody", + "Ġdep ri", + "Ġincent ive", + "ãĤ ·", + "Ġovers ight", + "Ġtrib es", + "ĠBelg ium", + "Ġlicens ing", + "our t", + "Produ ct", + "ah l", + "ĠG em", + "Ġspecial ist", + "Ġc ra", + "ann ers", + "ĠCor byn", + "Ġ19 73", + "RE AD", + "Ġsum mar", + "Ġover look", + "ĠApp lication", + "Ġin appropriate", + "Ġdownload ed", + "Q ue", + "ĠB ears", + "Ġth umb", + "ĠChar acter", + "ĠReincarn ated", + "ĠS id", + "Ġdemonstr ates", + "s ky", + "ĠBloom berg", + "ĠAr ray", + "ĠRes ults", + "ĠFour th", + "ĠED T", + "ĠO scar", + "c end", + "Ġ10 6", + "ĠN ULL", + "ĠH ERE", + "m atch", + "ĠBr un", + "Ġgluc ose", + "ie g", + "eg u", + "Ġcert ified", + "Ġrel ie", + "Ġhuman itarian", + "Ġpr ayers", + "K ing", + "Ġn an", + "h ou", + "10 8", + "ul u", + "Ġrenew able", + "Ġdistingu ish", + "Ġd ense", + "ĠV ent", + "ĠPack age", + "ĠB oss", + "Ġedit ors", + "Ġm igr", + "T ra", + "ĠPet ers", + "ĠAr ctic", + "200 4", + "ĠC ape", + "Ġloc ally", + "Ġlast ing", + "Ġhand y", + ". ).", + "P an", + "ĠR ES", + "Ind ex", + "Ġt ensions", + "Ġformer ly", + "Ġide ological", + "Ġsens ors", + "Ġdeal ers", + "Ġdef ines", + "S k", + "Ġproceed s", + "Ġpro xy", + "az ines", + "ĠB ash", + "ĠP ad", + "ĠC raft", + "eal ous", + "Ġshe ets", + "omet ry", + "J une", + "cl ock", + "T T", + "ĠThe atre", + "ĠB uzz", + "Ġch apters", + "Ġmill enn", + "Ġd ough", + "ĠCongress ional", + "Ġimag ined", + "av ior", + "Ġclin ic", + "Ġ19 45", + "Ġhold er", + "ro ot", + "oles ter", + "Ġrest art", + "B N", + "ĠHam as", + "ĠJ ob", + "Ġor b", + "Ġr am", + "Ġdiscl ose", + "Ġtransl ate", + "Ġimm igrant", + "Ġannoy ing", + "Ġtreat y", + "an ium", + "ĠTe a", + "ĠLeg ion", + "Ġcrowd s", + "ĠB ec", + "ĠA er", + "oh yd", + "B ro", + "Look ing", + "Ġl bs", + "Ġagg ress", + "Ġse am", + "Ġinter cept", + "ĠM I", + "mer cial", + "act iv", + "ĠC it", + "Ġdim ension", + "Ġconsist ency", + "Ġr ushing", + "ĠDou glas", + "Ġtr im", + "Inst all", + "ick er", + "Ġsh y", + "10 6", + "Ġment ions", + "pe lled", + "ĠT ak", + "c ost", + "Ġclass room", + "Ġfort une", + "dri ven", + "Ġun le", + "ĠWhe el", + "Ġinvest or", + "ĠM asters", + "k it", + "Ġassoci ations", + "ĠEv olution", + "op ing", + "us cript", + "Ġprov incial", + "ĠWal ter", + "av i", + "S O", + "Ġun limited", + "Eng lish", + "ĠC ards", + "ĠEb ola", + "ne red", + "Ġreven ge", + "Ġout right", + "um per", + "Ġf itting", + "ĠSol id", + "Ġform ally", + "Ġproblem atic", + "Ġhaz ard", + "Ġenc ryption", + "Ġstraight forward", + "ĠA K", + "Ġp se", + "ĠOr b", + "ĠCh amber", + "ĠM ak", + "Cont ents", + "Ġloyal ty", + "Ġl yrics", + "ĠSy m", + "Ġwel comed", + "Ġcook ed", + "Ġmon op", + "Ġn urse", + "Ġmis leading", + "Ġe ternal", + "Ġshif ting", + "Ġ+ =", + "V is", + "Ġinst itutional", + "ill ary", + "Ġp ant", + "VER T", + "ĠA CC", + "ĠEn h", + "Ġinc on", + "ĠRE UTERS", + "Ġdon ated", + "âĢ¦âĢ¦ âĢ¦âĢ¦", + "In tern", + "Ġexhib it", + "Ġt ire", + "ĠR ic", + "ĠCh ampion", + "ĠMu hammad", + "N ING", + "ĠSoc cer", + "Ġmob ility", + "Ġvary ing", + "ĠM ovie", + "Ġl ord", + "o ak", + "F ield", + "Ġve ctor", + "us ions", + "Ġsc rap", + "Ġen abling", + "m ake", + "T or", + ". *", + "| |", + "ĠWe bsite", + "ĠN PC", + "Ġsocial ist", + "ĠBill y", + "ĠAdd itional", + "Ġc argo", + "Ġfar ms", + "ĠSo on", + "ĠPri ze", + "Ġmid night", + "Ġ9 00", + "se en", + "ĠSp ot", + "Ġshe ep", + "Ġspons ored", + "ĠH i", + "ĠJ ump", + "Ġ19 67", + "Micro soft", + "ĠAg ent", + "Ġch arts", + "d ir", + "Ġadj acent", + "Ġtr icks", + "Ġman ga", + "Ġex agger", + "/ >", + "foot ball", + "ĠF CC", + "G C", + "ĠT ier", + "and ra", + "OU ND", + "% ),", + "Ġfru its", + "V C", + "ĠA A", + "R ober", + "Ġmid st", + "â Ĺ", + "ank a", + "Ġlegisl ature", + "ĠNe il", + "Ġtour ists", + "\" \"", + "ĠWar ning", + "ĠNever theless", + "ĠOffic ial", + "ĠWh atever", + "Ġm old", + "Ġdraft ed", + "Ġsubst ances", + "Ġbre ed", + "Ġt ags", + "ĠT ask", + "Ġver b", + "Ġmanufact ured", + "com ments", + "ĠPol ish", + "Pro v", + "Ġdetermin es", + "Ob ama", + "k ers", + "Ġutter ly", + "Ġse ct", + "sc he", + "ĠG ates", + "ĠCh ap", + "Ġal uminum", + "Ġz ombie", + "ĠT ouch", + "ĠU P", + "Ġsatisf y", + "Ġpred omin", + "asc ript", + "Ġelabor ate", + "Ġ19 68", + "Ġmeas uring", + "ĠV ari", + "any ahu", + "Ġs ir", + "ul ates", + "id ges", + "ick ets", + "ĠSp encer", + "T M", + "oub ted", + "Ġpre y", + "Ġinstall ing", + "ĠC ab", + "re ed", + "re ated", + "Su pp", + "Ġwr ist", + "ĠK erry", + "10 7", + "ĠK le", + "ĠR achel", + "Ġc otton", + "ĠA RE", + "ĠE le", + "Cont rol", + "Ġload s", + "ĠD od", + "an as", + "b one", + "Ġclass ical", + "ĠReg ional", + "ĠInt eg", + "V M", + "Ġdes ires", + "Ġaut ism", + "support ed", + "ĠM essage", + "Ġcomp act", + "writ er", + "Ġ10 9", + "ĠHur ricane", + "c ision", + "Ġcy cles", + "Ġdr ill", + "Ġcolle ague", + "Ġm aker", + "G erman", + "Ġmist aken", + "S un", + "ĠG ay", + "Ġwhat soever", + "Ġsell s", + "ĠA irl", + "l iv", + "ĠO ption", + "Ġsol ved", + "Ġse ctors", + "Ġhorizont al", + "Ġequ ation", + "ĠSk ill", + "ĠB io", + "g ement", + "ĠSn ap", + "ĠLeg al", + "Ġtradem ark", + "Ġmake up", + "Ġassemb led", + "Ġsa ves", + "ĠHallow een", + "ĠVer mont", + "ĠFR OM", + "Ġfar ming", + "ĠP odcast", + "accept able", + "ĠHig her", + "Ġas leep", + "ull ivan", + "Ġrefere n", + "ĠLe v", + "Ġbul lets", + "ok o", + "H C", + "Ġst airs", + "Ġmain tains", + "ĠL ower", + "ĠV i", + "Ġmar ine", + "Ġac res", + "Ġcoordin ator", + "ĠJ oh", + "Ġcounterpart s", + "ĠBrother s", + "Ġind ict", + "b ra", + "Ġch unk", + "Ġc ents", + "H ome", + "ĠMon th", + "Ġaccording ly", + "if les", + "ĠGerm ans", + "ĠSy n", + "H ub", + "Ġey eb", + "âĶĢâĶĢ âĶĢâĶĢ", + "Ġr anges", + "ĠHoll and", + "ĠRob ot", + "f c", + "M ike", + "Ġpl asma", + "Ġsw ap", + "Ġath lete", + "ĠR ams", + ",' \"", + "Ġinfect ions", + "Ġcor rid", + "Ġv ib", + "Ġpat ches", + "Ġtradition ally", + "Ġrevel ation", + "Ġswe ep", + "Ġgl ance", + "Ġin ex", + "200 3", + "ĠR aw", + "work ing", + "os ures", + "ĠD at", + "ĠLyn ch", + "Ġle verage", + "ĠRe id", + "Ġcorrel ation", + "ian ces", + "av ascript", + "Ġrep ository", + "ret ty", + "Ġ19 72", + "24 0", + "Ġo un", + "p ol", + "ĠRe ed", + "Ġtact ical", + "is ite", + "App le", + "ĠQu inn", + "Ġrap ed", + "ill o", + "Euro pe", + "Ġalgorith ms", + "ĠRod rig", + "i u", + "Ġill um", + "Ġf ame", + "Ġintrodu cing", + "Ġdel ays", + "ĠRaid ers", + "Ġwh istle", + "Ġnovel s", + "ĠRe ally", + "Ġder iv", + "Ġpublic ations", + "ĠNe ither", + "ĠCom merce", + "Ġa ston", + "l anguage", + "Not es", + "ĠR oth", + "ĠF ear", + "Ġm ate", + "Ġpar ade", + "ĠQ B", + "Ġman eu", + "ĠC incinnati", + "m itting", + "Ġwa ist", + "ĠR ew", + "Ġdisc ont", + "Ð °", + "Ġst aring", + "Ġal ias", + "Ġsec urities", + "Ġtoile t", + "ĠJ edi", + "Ġun law", + "v ised", + "//// ////", + "] (", + "ĠWe iss", + "Ġpre st", + "ĠComp an", + "Ġmem o", + "ĠGr ace", + "J uly", + "ĠEl ite", + "cent er", + "ĠSt ay", + "Ġgal axy", + "Ġto oth", + "ĠS ettings", + "Ġsubject ed", + "ãĤ ¦", + "Ġline back", + "Ġretail ers", + "ĠW ant", + "Ġd angers", + "A ir", + "Ġvolunt ary", + "ew ay", + "Ġinterpret ed", + "ot ine", + "à §", + "Ġp el", + "Serv ice", + "ĠEvent ually", + "Ġcare ers", + "Ġthreat en", + "Ġmem or", + "ĠBrad ley", + "anc ies", + "s n", + "ĠUn known", + "N ational", + "Ġsh adows", + "ail and", + "ĠD ash", + "Every one", + "izz ard", + "M arch", + "= (", + "Ġpull s", + "Ġstr anger", + "Ġback wards", + "ĠBern ard", + "imens ional", + "Ġch ron", + "Ġtheoret ical", + "k top", + "Ġw are", + "ĠInvest ig", + "ĠIn iti", + "ĠOper ations", + "o ven", + "oc ide", + "* /", + "Ġfl ames", + "ĠC ash", + "sh it", + "Ġc ab", + "ĠAn aly", + "ĠSe ah", + "Ġdefin ing", + "Ġorder ing", + "Ġimm un", + "Ġpers istent", + "AC H", + "Russ ian", + "m ans", + "Ġh ind", + "Ġphot ography", + " ©", + "Ġh ug", + "Ġ10 7", + "ĠH ence", + "i ots", + "ude au", + "Ġsubsid ies", + "Ġroutine ly", + "ĠDev ice", + "it ic", + "Ġdisg ust", + "land er", + "Ġ19 40", + "Ġassign ment", + "ĠB esides", + "w ick", + "ĠD ust", + "us c", + "struct ed", + "11 1", + "de velop", + "Ġf ond", + "Ġinter section", + "Ġdign ity", + "Ġcommission er", + "With out", + "re ach", + "Ġcart oon", + "Ġsc ales", + "ãĥ Ń", + "F IG", + "Ġsurve ys", + "ĠIndones ia", + "Ġart work", + "Ġun ch", + "Ġcy cling", + "un ct", + "au er", + "or ate", + "ĠOb viously", + "Ġcharacter ized", + "fe ld", + "Ġaff irm", + "Ġinn ings", + "Ġ é", + "Ġal iens", + "Ġcl oth", + "et ooth", + "ĠC ertain", + " §", + "Ġdig est", + "k now", + "ĠX L", + "Ġpredict ions", + "Ġd in", + "W AR", + "Ġafter math", + "Ex ample", + "ĠSu ccess", + "ĠTh r", + "IG N", + "Ġmin er", + "B us", + "Ġcl arity", + "heim er", + "ĠO UT", + "ĠS end", + "ĠCirc le", + "ĠD iet", + "Ġpron ounced", + "Ġcreat ors", + "Ġearthqu ake", + "atter y", + "ge ons", + "Ġo d", + "Ġlay ing", + "or p", + "U lt", + "pro ject", + "Ġunder min", + "Ġsequ el", + "S am", + "ĠDark ness", + "Ġre ception", + "b ull", + "Y S", + "ĠV ir", + "Ġsequ ences", + "ĠCo in", + "Ġout fit", + "ĠW ait", + "1 19", + "Ġdel ivers", + ".... ..", + "Ġbl own", + "ĠE sc", + "ĠM ath", + "per m", + "ĠU l", + "Ġgl im", + "Ġfac ial", + "Ġgreen house", + "Ġto kens", + "/ -", + "ĠAnn ual", + "ĠON E", + "Ġteen age", + "ĠPhys ical", + "ĠL ang", + "ĠC elt", + "Ġsu ed", + "ivid ually", + "Ġpat ience", + "ch air", + "reg ular", + "Ġa ug", + "in v", + "ex cept", + "ĠL il", + "Ġn est", + "f d", + "s um", + "ĠCh ase", + "Russ ia", + "ĠJenn ifer", + "Ġoff season", + "Over all", + "F ore", + "Ġr iot", + "A ud", + "form er", + "Ġdefend ers", + "ĠC T", + "iot ic", + "rib ly", + "Ġautom ated", + "Ġpen is", + "Ġins ist", + "Ġdi agram", + "ĠS QL", + "ĠG arc", + "Ġw itch", + "cl ient", + "ier ra", + "am bers", + "Ġrec ount", + "f ar", + "V ery", + "oster one", + "Ġappreci ated", + "ĠPer fect", + "S ection", + "Ġd oses", + "oca ust", + "Ġcost ly", + "Ġg rams", + "ĠSh i", + "Ġwrest ling", + "Ġ19 71", + "Ġtro phy", + "Ġn erve", + "ĠK az", + "ĠExper ience", + "Ġpled ged", + "Ġplay back", + "Ġcreat ivity", + "by e", + "Ġattack ers", + "Ġhold ers", + "ĠCo ach", + "ĠPh D", + "Ġtransf ers", + "Ġcol ored", + "ĠH indu", + "Ġd rown", + "Ġlist ened", + "ĠW A", + "ias m", + "P O", + "Ġappeal ing", + "Ġdiscl osed", + "ĠCh icken", + "ag ging", + "Ġple aded", + "Ġnav igation", + "ĠReturn s", + "Ġ[ [", + "R OR", + "E A", + "Ġphotograp her", + "ĠR ider", + "ipp ers", + "Ġsl ice", + "Ġe rect", + "Ġhe d", + "iss ance", + "ĠVik ings", + "ur ious", + "Ġapp et", + "oubted ly", + "Ch ild", + "Ġauthent ic", + "o os", + "ĠM aking", + "Ġannoun cing", + "Ġb od", + "Ġmet er", + "ĠN ine", + "ĠR ogue", + "Ġwork force", + "Ġrenew ed", + "Ġorganis ations", + "ac s", + "P LE", + "Sh ort", + "Ġcomp ounds", + "ĠVis it", + "Ġen velop", + "ear th", + "Ġsupport ive", + "gg le", + "ĠBrus sels", + "ĠGu ild", + "Cre ate", + "RE L", + "Ġaver aged", + "Ġ19 69", + "ri ages", + "Ġlength y", + "Ġforg ot", + "O kay", + "ĠE rd", + "Ġdeal er", + "Ġrec ession", + "D D", + "Ġdesper ately", + "Ġhun ger", + "Ġst icks", + "Ġm ph", + "ĠF aith", + "Ġintention ally", + "Ġdem ol", + "ue ller", + "ĠS ale", + "Ġde bris", + "s pring", + "Ġle ap", + ">> >>", + "Ġcontain ers", + "se lling", + "rane an", + "atter ing", + "Ġcomment ed", + "ĠC M", + "on ut", + "Ġwood s", + "es pecially", + "Ġorgan ize", + "iv ic", + "ĠWood s", + "ang a", + "s qu", + "Ġm aj", + "am on", + "Ġax is", + "Ġ19 74", + "ĠDen mark", + "Ġwar rior", + "ĠP and", + "Ġout lined", + "ĠB O", + "ins ula", + "z illa", + "eb ook", + "Ġd are", + "Ġsear ched", + "Ġnav igate", + "S n", + "writ ing", + "Ġun ited", + "J apan", + "ĠHe brew", + "Ġfl ame", + "Ġrel ies", + "Ġcatch ing", + "ĠSh o", + "Ġimprison ment", + "Ġp ockets", + "Ġclos ure", + "ĠF am", + "t im", + "ade qu", + "Act ivity", + "Ġrecru iting", + "ĠW ATCH", + "ĠArgent ina", + "d est", + "Ġapolog ize", + "or o", + "Ġlack s", + "Ġtun ed", + "ĠGriff in", + "Ġinf amous", + "Ġcelebr ity", + "ss on", + "Ġ ----------------------------------------------------------------", + "ĠIs is", + "ĠDis play", + "Ġcred ibility", + "Ġeconom ies", + "Ġhead line", + "ĠCow boys", + "Ġind ef", + "Ġl ately", + "Ġincent ives", + "but ton", + "ĠM ob", + "A ut", + "Ġres igned", + "ĠO m", + "c amp", + "Ġprof iles", + "Ġsche mes", + "olph ins", + "ay ed", + "Cl inton", + "en h", + "ĠY ahoo", + "Ġab st", + "Ġan k", + "su its", + "Ġw ished", + "ĠMar co", + "udd en", + "Ġsp here", + "ĠB ishop", + "Ġincorpor ated", + "ĠPl ant", + "11 4", + "Ġh ated", + "p ic", + "Ġdon ate", + "Ġl ined", + "Ġbe ans", + "Ġsteal ing", + "Ġcost ume", + "Ġsher iff", + "Ġfor ty", + "Ġint act", + "Ġadapt ed", + "Ġtrave lling", + "b art", + "Ġnice ly", + "Ġdri ed", + "Ġsc al", + "os ity", + "NOT E", + "ĠB h", + "ĠBron cos", + "ĠI gn", + "Ġint imate", + "Ġchem istry", + "Ġopt imal", + "D eb", + "ĠGener ation", + "Ġ] ,", + "ich i", + "ĠW ii", + "ĠYOU R", + "vent ions", + "W rite", + "Ġpop ul", + "un ning", + "ĠW or", + "V ol", + "Ġqu een", + "head s", + "K K", + "Ġanaly ze", + "op ic", + "ear chers", + "Ġd ot", + "leg raph", + "ast ically", + "Ġupgr ades", + "Ġca res", + "Ġext ending", + "Ġfree ze", + "Ġin ability", + "Ġorg ans", + "Ġpret end", + "Ġout let", + "11 3", + "ol an", + "ĠM all", + "ul ing", + "t alk", + "Ġexpress ing", + "ĠAl ways", + "ĠBe gin", + "f iles", + "Ġlic enses", + "% %", + "ĠM itt", + "Ġfil ters", + "ĠMil waukee", + "G N", + "Ġunf old", + "M o", + "Ġnut rition", + "pp o", + "B o", + "Ġfound ing", + "Ġunder mine", + "Ġeas iest", + "ĠC zech", + "ĠM ack", + "Ġsexual ity", + "ĠN ixon", + "W in", + "ĠAr n", + "ĠK in", + "ãĤ £", + "ic er", + "Ġfort un", + "Ġsurf aces", + "agh d", + "Ġcar riers", + "ĠP ART", + "ĠT ib", + "Ġinter val", + "Ġfrust rating", + "ĠSh ip", + "ĠAr med", + "ff e", + "Ġbo ats", + "ĠAb raham", + "in is", + "Ġsu ited", + "th read", + "i ov", + "ab ul", + "ĠVenezuel a", + "Ġto m", + "su per", + "Ġcast le", + "alth ough", + "iox ide", + "ec hes", + "Ġevolution ary", + "Ġnegoti ate", + "Ġconfront ed", + "Rem ember", + "Ġ17 0", + "S uch", + "Ġ9 11", + "m ult", + "ĠA byss", + "ur ry", + "ke es", + "spe c", + "ĠBarb ara", + "Ġbelong ing", + "Ġvill ain", + "ist ani", + "Ġaccount able", + "Ġport ions", + "ĠDe cl", + "U r", + "ĠK ate", + "g re", + "Ġmag azines", + "UC K", + "Ġregul ate", + "om on", + "ĠAl most", + "Ġover view", + "Ġsc ram", + "Ġl oot", + "ĠF itz", + "Ġcharacter istic", + "ĠSn ake", + "s ay", + "ĠR ico", + "Ġtra it", + "ĠJo ined", + "au cus", + "Ġadapt ation", + "ĠAirl ines", + "Ġarch ae", + "ĠI de", + "Ġb ikes", + "Ġliter ary", + "Ġinflu ences", + "ĠUs ed", + "C reat", + "Ġple a", + "ĠDef ence", + "ĠAss ass", + "Ġp ond", + "UL T", + ") \"", + "Ġeval uated", + "Ġob taining", + "Ġdem ographic", + "Ġvig il", + "ale y", + "Ġsp ouse", + "ĠSeah awks", + "resp ons", + "ĠB elt", + "um atic", + "Ġr ises", + "run ner", + "ĠMichel le", + "Ġpot ent", + "r ace", + "ĠP AC", + "F ind", + "olester ol", + "IS S", + "ĠIntrodu ced", + "ress es", + "ign ment", + "O s", + "ĠT u", + "ĠDe x", + "ic ides", + "Ġspark ed", + "ĠLaur a", + "ĠBry ant", + "Ġsm iling", + "ĠNex us", + "Ġdefend ants", + "ĠCat al", + "Ġdis hes", + "sh aped", + "Ġpro long", + "m t", + "( $", + "ãĢ Ĥ", + "Ġcalcul ations", + "ĠS ame", + "Ġp iv", + "H H", + "Ġcance lled", + "Ġgr in", + "Ġterrit ories", + "ist ically", + "C ome", + "ĠP arent", + "Pro ject", + "Ġneg lig", + "ĠPriv acy", + "Ġam mo", + "LE CT", + "olute ly", + "ĠEp ic", + "Ġmis under", + "w al", + "Apr il", + "m os", + "path y", + "ĠC arson", + "Ġalbum s", + "ĠE asy", + "Ġpist ol", + "< <", + "Ġ\\ (", + "t arget", + "hel p", + "Ġinter pre", + "cons cious", + "ĠH ousing", + "ĠJ oint", + "12 7", + "Ġbe ers", + "s cience", + "ĠFire fox", + "effect ive", + "ĠC abin", + "ĠO kay", + "ĠApp lic", + "Ġspace craft", + "ĠS R", + "ve t", + "ĠStr ange", + "S B", + "Ġcor ps", + "iber al", + "e fficient", + "Ġpreval ence", + "Ġeconom ists", + "11 8", + "Th read", + "ord able", + "OD E", + "ĠC ant", + "=- =-", + "if iable", + "ĠA round", + "Ġpo le", + "Ġwilling ness", + "CL A", + "ĠK id", + "Ġcomple ment", + "Ġsc attered", + "Ġin mates", + "Ġble eding", + "e very", + "Ġque ue", + "ĠTr ain", + "Ġh ij", + "Ġme lee", + "ple ted", + "Ġdig it", + "Ġg em", + "offic ial", + "Ġlif ting", + "Ð µ", + "Re qu", + "it utes", + "Ġpack aging", + "ĠWork ers", + "h ran", + "ĠLeban on", + "ol esc", + "Ġpun ished", + "ĠJ uan", + "Ġj am", + "ĠD ocument", + "Ġm apping", + "ic ates", + "Ġinev itably", + "Ġvan illa", + "ĠT on", + "Ġwat ches", + "Ġle agues", + "Ġiniti ated", + "deg ree", + "port ion", + "Ġrec alls", + "Ġru in", + "Ġm elt", + "I AN", + "Ġhe m", + "Ex p", + "Ġb aking", + "ĠCol omb", + "at ible", + "Ġrad ius", + "pl ug", + "ĠI F", + "et ically", + "Ġf ict", + "H ER", + "ĠT ap", + "atin um", + "Ġin k", + "Ġco h", + "ĠW izard", + "b oth", + "te x", + "Ġsp ends", + "ĠCurrent ly", + "ĠP it", + "Ġneur ons", + "ig nt", + "Ġr all", + "Ġbus es", + "b uilding", + "Ġadjust ments", + "Ġc ried", + "ibl ical", + "att ed", + "ĠZ ion", + "ĠM atter", + "Ġmed itation", + "ĠD ennis", + "Ġour s", + "ĠT ab", + "Ġrank ings", + "ort al", + "Ġad vers", + "Ġsur render", + "ĠG ob", + "ci um", + "om as", + "im eter", + "Ġmulti player", + "Ġhero in", + "Ġoptim istic", + "Ġindic ator", + "ĠBr ig", + "Ġgro cery", + "Ġapplic ant", + "ĠRock et", + "v id", + "Ex ception", + "p ent", + "Ġorgan izing", + "Ġenc ounters", + "ĠT OD", + "Ġjew el", + "S ave", + "ĠChrist ie", + "Ġhe ating", + "Ġl azy", + "ĠC P", + "Ġcous in", + "Con fig", + "Ġreg ener", + "Ġne arest", + "Ġachie ving", + "EN S", + "th row", + "ĠRich mond", + "ant le", + "200 2", + "Ġan ten", + "b ird", + "13 3", + "Ġn arc", + "r aint", + "un ny", + "ĠHispan ic", + "ourn aments", + "Ġprop he", + "ĠTh ailand", + "ĠT i", + "Ġinject ion", + "Ġinher it", + "rav is", + "Ġmed i", + "Ġwho ever", + "ĠDE BUG", + "G P", + "ĠH ud", + "C ard", + "p rom", + "Ġp or", + "Ġover head", + "L aw", + "Ġviol ate", + "Ġhe ated", + "Ġdescript ions", + "Ġachieve ments", + "ĠBe er", + "ĠQu ant", + "W as", + "Ġe ighth", + "ĠI v", + "Ġspecial ized", + "U PDATE", + "ĠD elta", + "P op", + "J ul", + "ĠAs k", + "oph y", + "Ġnews letters", + "ĠT ool", + "Ġg ard", + "ĠConf eder", + "ĠGM T", + "ĠAb bott", + "Ġimm unity", + "ĠV M", + "Is lam", + "Ġimpl icit", + "w d", + "Ġ19 44", + "rav ity", + "omet ric", + "Ġsurv iving", + "ur ai", + "ĠPr ison", + "Ġr ust", + "ĠSk etch", + "Ġbe es", + "ĠThe ory", + "Ġmer it", + "T ex", + "ch at", + "Ġm im", + "Ġpast e", + "ĠK och", + "Ġignor ance", + "ĠSh oot", + "Ġbas ement", + "Un ited", + "ĠAd vis", + "he ight", + "Ġf oster", + "Ġdet ain", + "in formation", + "Ġne ural", + "' ;", + "Ġprov es", + "all ery", + "Ġinv itation", + "um bers", + "Ġc attle", + "Ġbicy cle", + "z i", + "Ġconsult ant", + "Ġap ology", + "ĠT iger", + "Ġ12 3", + "99 9", + "Ġind ividually", + "r t", + "ig ion", + "ĠBrazil ian", + "Ġdist urb", + "Ġentreprene urs", + "Ġfore sts", + "cer pt", + "pl ates", + "p her", + "clip se", + "Ġtw itter", + "Ġac ids", + "ograph ical", + "h um", + "ĠB ald", + "if ully", + "Ġcomp iler", + "ĠD A", + "Ġdon or", + "as i", + "Ġtrib al", + "l ash", + "ĠCon fig", + "Ġapplic ants", + "Ġsal aries", + "13 5", + "Put in", + "ĠF ocus", + "ir s", + "Ġmisc onduct", + "ĠH az", + "Ġeat en", + "M obile", + "Mus lim", + "ĠMar cus", + "v iol", + "Ġfavor able", + "Ġst ub", + "ad in", + "ĠH ob", + "Ġfaith ful", + "Ġelectron ics", + "Ġvac uum", + "w ait", + "back ed", + "econom ic", + "d ist", + "Ġten ure", + "Ġsince re", + "ĠT ogether", + "ĠW ave", + "Ġprog ression", + "Ġden ying", + "Ġdist ress", + "br aska", + "th ird", + "Ġmix ing", + "Ġcolon ial", + "Ġpriv ately", + "Ġun rest", + "atern ity", + "Ġprem ises", + "ant i", + "greg ation", + "Ġlic ence", + "ĠH ind", + "ĠSam uel", + "Ġconvinc ing", + "ĠA ce", + "ĠR ust", + "ĠNet anyahu", + "Ġhand les", + "ĠP atch", + "orient ed", + "ah o", + "ĠG onz", + "Ġhack ers", + "claim er", + "Ġcustom s", + "ĠGr an", + "f ighters", + "Ġl uc", + "Ġman uscript", + "aren thood", + "Ġdev il", + "Ġwar riors", + "Ġoff enders", + "Will iam", + "Ġhol idays", + "Ġnight mare", + "Ġle ver", + "iff erent", + "St at", + "Ġexhib ition", + "put ed", + "ĠP ure", + "Ġal pha", + "Ġenthus iasm", + "ĠRepresent atives", + "E AR", + "ĠT yp", + "Ġwhe at", + "ĠAl f", + "Ġcor rection", + "Ġev angel", + "AT T", + "M iss", + "Ġs oup", + "Ġimpl ied", + "par am", + "Ġsex y", + "ĠL ux", + "Ġrep ublic", + "p atch", + "ab lish", + "Ġic ons", + "Ġfather s", + "ĠG ET", + "ĠCar ib", + "Ġregul ated", + "ĠCo hen", + "ĠBob by", + "Ġn er", + "Ġb ent", + "vent ory", + "ĠAl ong", + "ĠE ST", + "ĠWall ace", + "Ġmurd ers", + "r ise", + "ke ll", + "ĠCommon wealth", + "Ġn asty", + "et a", + "ĠM IT", + "Ġadminist ered", + "Ġgenuine ly", + "Ed itor", + "n ick", + "Ġhyd ro", + "**************** ****************", + "ĠB le", + "Ġfin es", + "Ġg orge", + "aus ible", + "r h", + "Ġapp le", + "ment ioned", + "Ġro pe", + "ot yp", + "H R", + "Ġdisappoint ing", + "Ġc age", + "n ik", + "Ġdoub ts", + "ĠF REE", + "print s", + "ĠM UST", + "Ġvend ors", + "ĠIn qu", + "Ġliber als", + "Ġcontract or", + "Ġup side", + "child ren", + "Ġtrick y", + "Ġregul ators", + "charg ed", + "l iter", + "Ġ ***", + "Ġreb ell", + "l ang", + "Ġloc als", + "Ġphys icians", + "Ġhe y", + "ar se", + "t m", + "ĠLe x", + "Ġbehavior al", + "success ful", + "F X", + "Ġbr ick", + "ov ic", + "Ġcon form", + "Ġreview ing", + "Ġins ights", + "Ġbi ology", + "ĠRem ove", + "ĠExt ra", + "Ġcomm itting", + "indu ced", + "ignt y", + "ig m", + "Ġat omic", + "Comm on", + "ĠE M", + "ĠP ere", + "ĠIt ems", + "e h", + "Ġpres erved", + "ĠH ood", + "Ġprison er", + "Ġbankrupt cy", + "Ġg ren", + "us hes", + "Ġexplo itation", + "Ġsign atures", + "Ġfin an", + "] ,\"", + "ĠM R", + "Ġme g", + "rem lin", + "Ġmusic ians", + "Ġselect ing", + "Ġexam ining", + "IN K", + "l ated", + "H i", + "Ġart ic", + "Ġp ets", + "Ġimp air", + "ĠM AN", + "Ġtable ts", + "in clude", + "R ange", + "Ġca ut", + "Ġlog s", + "Ġmount ing", + "Ġun aware", + "Ġdynam ics", + "ĠPalest ine", + "ĠQu arter", + "ĠPur ple", + "Ġm a", + "ĠIm port", + "Ġcollect ions", + "ci ation", + "Ġsuccess or", + "Ġcl one", + "Ġaim ing", + "Ġposs essed", + "Ġstick ing", + "Ġsh aking", + "Ġloc ate", + "ĠH ockey", + "T urn", + "17 0", + "Ġfif teen", + "ĠHar rison", + "Ġcontinu ously", + "ĠT C", + "ĠVal ent", + "ĠRes cue", + "Ġby pass", + "am ount", + "Ġm ast", + "Ġprotect s", + "Ġart istic", + "Ġsomet ime", + "Ġsh oe", + "Ġshout ed", + "ific ant", + "et itive", + "ĠReg ister", + "ĠJ in", + "Ġconcent rated", + "ling ton", + "on ies", + "Ġgener ator", + "yr im", + "ĠAr men", + "Ġclear ing", + "id o", + "ĠT W", + "al ph", + "Ġlad ies", + "H ard", + "Ġdial og", + "Ġinput s", + "æ ľ", + "Ġpos es", + "Ġsl ots", + "ĠPrem ium", + "Ġle aks", + "Ġboss es", + "Ġ11 3", + "c ourse", + "A cc", + "ĠNew ton", + "ĠAust ria", + "ĠM age", + "Ġte aches", + "ab ad", + "Ġwe ars", + "Ġc yl", + "Ġcur se", + "ĠS ales", + "ĠW ings", + "Ġp sy", + "Ġg aps", + "ĠIce land", + "ĠP interest", + "Ġland lord", + "Ġdefin itions", + "ĠK er", + "Ġsufficient ly", + "ĠP ence", + "ĠArch itect", + "Ġsur pass", + "Ġ11 4", + "Ġsuper hero", + "ĠDise ase", + "Ġpri ests", + "ĠC ulture", + "Ġdefin itive", + "Ġsecret ly", + "ĠD ance", + "inst all", + "ch ief", + "ĠJess ica", + "W ould", + "Up dated", + "Ġlock er", + "ĠK ay", + "Ġmem orial", + "è ¦", + "f at", + "Ġdis gu", + "Ġflav ors", + "ĠBase ball", + "ĠRes istance", + "Ġk icks", + "Ġen v", + "Ġteen agers", + "D ark", + "ĠC AR", + "Ġh alt", + "ĠL G", + "ĠGab riel", + "Ġfe ver", + "Ġs atur", + "Ġm all", + "Ġaffili ate", + "ĠS leep", + "ĠSpe cific", + "ĠV el", + "Ġj ar", + "ĠSac red", + "ĠEd wards", + "ĠA CL", + "Ġret ained", + "ĠG iant", + "Ġlim itation", + "in ces", + "Ġref usal", + "ĠT ale", + "ĠBut ler", + "Ġacc idents", + "ĠC SS", + "Ġimport ed", + "ĠCop y", + "Î ±", + "ER T", + "z el", + "Ġdiv isions", + "h ots", + "ĠAl b", + "ĠD S", + "Load er", + "W ashington", + "at isf", + "ĠCreat ive", + "\\ .", + "ĠAut om", + "red ict", + "Ġrecept or", + "ĠCarl os", + "Met hod", + "ok a", + "Ġmal icious", + "Ġste pping", + ", [", + "ĠD ad", + "Ġatt raction", + "ĠEffect s", + "ĠPir ate", + "ĠC er", + "ĠIndust ry", + "ĠR ud", + "Ġchar ter", + "Ġd ining", + "Ġins ists", + "Ġconfig ure", + "Ġ( #", + "ĠSim ple", + "ĠSc roll", + "UT C", + "17 5", + "ĠK on", + "Ġmarket place", + "Ġ ãĤ", + "Ġref res", + "Ġg ates", + "er red", + "ĠP od", + "Ġbeh ave", + "Fr ank", + "n ode", + "Ġendors ed", + "he tt", + "as ive", + "ĠHom eland", + "Ġr ides", + "ĠLe ave", + "er ness", + "Ġflood ing", + "A FP", + "Ġris en", + "Ġcontin ually", + "Ġun anim", + "ĠCont ract", + "ĠP as", + "Ġgu ided", + "ĠCh ile", + "b d", + "Ġsu cc", + "pt ic", + "Ġcomm ittees", + "ĠL uther", + "ĠAny one", + "Ġs ab", + "12 4", + "Ġp ixel", + "ĠB ak", + "ĠT ag", + "ĠBenn ett", + "En ter", + "sm all", + "ĠPresident ial", + "Ġp ul", + "Ġcontr ace", + "arch ive", + "Ġcoast al", + "ĠK ids", + "19 2", + "âĢ ²", + "ick y", + "ING TON", + "Ġw olf", + "ĠSt alin", + "T ur", + "id get", + "am as", + "ĠUn less", + "Ġspons or", + "Ġmor ph", + "ĠCho ose", + "Ġrun ner", + "Ġun bel", + "Ġm ud", + "ĠMan a", + "Ġdub bed", + "Ġg odd", + "ure rs", + "wind ow", + "Ġrel ied", + "Ġcelebr ating", + "os c", + "Ġ13 5", + "Ġlobb ying", + "Ġincom plete", + "Ġrestrict ion", + "Ġinc ap", + "it us", + "Ġexpect ation", + "ĠAp ollo", + "Ġint ens", + "Ġsyn c", + "G H", + "Ġmanip ulation", + "B Y", + "Ġspe ar", + "Ġbre asts", + "Ġvol can", + "il ia", + "M aterial", + "Ġform ats", + "ĠB ast", + "Ġparliament ary", + "Ġsn ake", + "Ġserv ants", + "ĠTr udeau", + "ĠGr im", + "ĠArab ic", + "ĠSC P", + "ĠBoy s", + "st ation", + "Ġprospect ive", + "ord e", + "in itialized", + "Ġb ored", + "AB LE", + "Ġaccess ed", + "Ġtax i", + "ĠShe ll", + "aid en", + "urs ed", + "in ates", + "ĠIns urance", + "ĠPet e", + "Sept ember", + "6 50", + "Ġad ventures", + "ĠCo ver", + "Ġt ribute", + "Ġsk etch", + "Ġem power", + "Ġ Ø", + "ĠGl enn", + "ĠD aw", + "= \\\"", + "ĠPolit ics", + "Ġgu ides", + "Ġd ioxide", + "ĠG ore", + "ĠBr ight", + "ĠS ierra", + "Ġval ued", + "c ond", + "Ġpo inter", + "Se lect", + "Ġrisk y", + "Ġabsor b", + "im ages", + "Ġref uses", + "Ġbon uses", + "__ _", + "Ġh ilar", + "ĠF eatures", + "2 20", + "ĠCollect or", + "F oot", + "Ġ19 64", + "cul us", + "Ġd awn", + "Ġwork out", + "ĠL O", + "Ġphilosoph ical", + "ĠSand y", + "ĠYou th", + "Ġl iable", + "A f", + "bl ue", + "Ġovert urn", + "less ness", + "ĠTrib une", + "ĠIn g", + "Ġfact ories", + "Ġcat ches", + "Ġpr one", + "Ġmat rix", + "Ġlog in", + "Ġin acc", + "Ġex ert", + "s ys", + "Ġneed le", + "ĠQ ur", + "Ġnot ified", + "ould er", + "t x", + "Ġremind s", + "Ġpublisher s", + "Ġn ort", + "Ġg it", + "Ġfl ies", + "ĠEm ily", + "Ġflow ing", + "ĠAl ien", + "ĠStr ateg", + "Ġhard est", + "Ġmod ification", + "AP I", + "ĠM Y", + "Ġcr ashes", + "st airs", + "n umber", + "Ġur ging", + "ch annel", + "ĠFal con", + "Ġinhabit ants", + "Ġterr ifying", + "Ġutil ize", + "Ġban ner", + "Ġcig arettes", + "Ġsens es", + "ĠHol mes", + "Ġpract ition", + "ĠPhill ips", + "ott o", + "Ġcomp ile", + "Mod el", + "ĠK o", + "Ġ[ ]", + "Americ ans", + "ĠTer ms", + "Ġmed ications", + "ĠAn a", + "Ġfundament ally", + "ĠNot ice", + "Ġwe aker", + "Ġ 0000", + "Ġgar lic", + "Ġout break", + "Ġeconom ist", + "ĠB irth", + "Ġobst acles", + "ar cer", + "ĠOr thodox", + "Ġplace bo", + "ĠC rew", + "asp berry", + "ĠAng els", + "Ġdis charge", + "Ġdestruct ive", + "11 7", + "ĠR ising", + "Ġd airy", + "l ate", + "Ġcoll ision", + "ĠTig ers", + "ean or", + "ocument ed", + "ĠIn valid", + "Ġd ont", + "ĠL iter", + "ĠV a", + "Ġhyd rogen", + "Ġvari ants", + "ĠBrown s", + "Ġ19 65", + "Ġind igenous", + "Ġtrad es", + "Ġremain der", + "Ġswe pt", + "ĠImp act", + "Ġred ist", + "Ġun int", + "grad uate", + "ãĥ ķ", + "ĠW ILL", + "ãģ® ç", + "ĠCrit ical", + "Ġf isher", + "Ġv icious", + "Ġrevers ed", + "Y ear", + "ĠS ox", + "Ġshoot ings", + "Ġfil ming", + "Ġtouchdown s", + "ai res", + "m el", + "Ġgrand father", + "Ġaffect ion", + "ing le", + "Ġover ly", + "Add itional", + "Ġsup reme", + "ĠGr ad", + "Ġsport ing", + "Ġmer cy", + "ĠBrook s", + "ount y", + "Ġperform s", + "Ġtight ly", + "Ġdem ons", + "Ġkill ings", + "Ġfact ion", + "ĠNov a", + "aut s", + "Ġund oubtedly", + "ar in", + "Ġunder way", + "ra k", + "Ġl iv", + "ĠReg ion", + "Ġbrief ing", + "s ers", + "cl oud", + "ĠM ik", + "us p", + "Ġpred iction", + "az or", + "Ġport able", + "ĠG and", + "Ġpresent ing", + "Ġ10 80", + " »", + "ush i", + "ĠSp ark", + "there um", + "Ġjust ification", + "ĠN y", + "Ġcontract ors", + "ming ham", + "ĠSt yle", + "å ħ", + "ĠChron icles", + "ĠPict ure", + "Ġprov ing", + "Ġw ives", + "set t", + "Ġmole cules", + "ĠFair y", + "Ġconsist ing", + "Ġp ier", + "al one", + "in ition", + "Ġn ucle", + "j son", + "Ġg otta", + "Ġmob il", + "Ġver bal", + "ar ium", + "Ġmon ument", + "uck ed", + "Ġ25 6", + "T ech", + "mine craft", + "ĠTr ack", + "Ġt ile", + "Ġcompat ibility", + "as is", + "Ġs add", + "Ġinstruct ed", + "ĠM ueller", + "Ġle thal", + "Ġhorm one", + "Ġor che", + "el se", + "Ġske let", + "Ġentert aining", + "Ġminim ize", + "ag ain", + "Ġunder go", + "Ġconst raints", + "Ġcig arette", + "ĠIslam ist", + "Ġtravel s", + "ĠPant hers", + "l ings", + "C are", + "Ġlaw suits", + "ur as", + "Ġcry st", + "Ġlow ered", + "Ġaer ial", + "Ġcomb inations", + "Ġha un", + "Ġch a", + "Ġv ine", + "Ġquant ities", + "Ġlink ing", + "b ank", + "Ġso y", + "B ill", + "ĠAngel a", + "Ġrecip ient", + "ĠProt est", + "Ġs ocket", + "Ġsolid arity", + "Ġâ Ĩ", + "m ill", + "Ġvar ies", + "ĠPak istani", + "Dr agon", + "Ġun e", + "Ġhor izon", + "³³³³ ³³³³", + "Ġprov inces", + "Ġfrank ly", + "Ġenact ed", + "not es", + "[ '", + "Ġ19 2", + "ocr acy", + "Ġendorse ment", + "Ġover time", + "Tr ue", + "L ab", + "lic ted", + "ĠD NC", + "Ġbe ats", + "ĠJam ie", + "15 2", + "ĠIN T", + "Cont act", + "Ġaccount ed", + "h ash", + "ĠPack ers", + "p ires", + "Ġles bian", + "Ġamend ments", + "Ġhop eful", + "ĠFin land", + "Ġspot light", + "Ġconfig ured", + "Ġtrou bled", + "Ġg aze", + "ĠCal gary", + "Ġrel iability", + "Ġins urg", + "sw er", + "b uy", + "ĠSk in", + "Ġp ixels", + "Ġhand gun", + "Ġpar as", + "Ġcateg or", + "ĠE L", + "ĠRe x", + "Ind eed", + "Ġkind a", + "Ġconj unction", + "ĠBry an", + "ĠMan ufact", + "y ang", + "Pl us", + "S QL", + "ish ment", + "Ġdom inate", + "Ġn ail", + "Ġo ath", + "Ġeru pt", + "ĠF ine", + "it bart", + "ĠCh ip", + "ĠAb d", + "ĠN am", + "Ġbuy er", + "Ġdiss ent", + "Le aks", + "Cont in", + "Ġr ider", + "ĠSome one", + "Ġill usion", + "c in", + "ĠBoe ing", + "Ġin adequ", + "ov ation", + "i ants", + "Ġreb uild", + "4 50", + "ĠDest iny", + "S W", + "ĠT ill", + "H it", + "ia z", + "ĠBang l", + "acher s", + "ĠRe form", + "Ġse gments", + "Ġsystem atic", + "d c", + "ĠConserv atives", + "Ġport al", + "h or", + "ĠDragon bound", + "Ġdrag ged", + "om o", + "Ġthe e", + "ad vert", + "ĠRep orts", + "ĠE t", + "Ġbarrel s", + "Aug ust", + "Ġcompar isons", + "Ġhe x", + "Ġan throp", + "\" [", + "bor ough", + "ab i", + "Ġpict ured", + "play ing", + "ĠAdd ress", + "ĠMir ror", + "Sm ith", + "Ġt ires", + "ĠN PR", + "AA AA", + "Ġclass ification", + "ĠTh an", + "ĠH arm", + "ĠR A", + "Ġreject ion", + "min ation", + "Ġr anged", + "ĠF alls", + "D I", + "H ost", + "ãĤ ´", + "ĠEx ample", + "list ed", + "th irds", + "Ġsaf egu", + "br and", + "Ġprob able", + "Can ada", + "IT ION", + "ĠQ aeda", + "Ġch ick", + "Ġimport s", + "h it", + "l oc", + "W W", + "Ġble w", + "Ġany time", + "Ġwh oles", + "ik ed", + "Ġcal culation", + "cre ate", + "ĠO ri", + "Ġupgr aded", + "Ġapp ar", + "ut ory", + "ĠM ol", + "B rit", + "ĠJ ong", + "IN AL", + "ĠStart ing", + "Ġd ice", + "urt le", + "Ġre lying", + "cl osure", + "Ġprof itable", + "Ġsl aughter", + "ĠMan ual", + "c aster", + "Ġ\" $", + "Ġfe ather", + "ĠSim ply", + "ie ves", + "Ġdeter ior", + "ĠPC I", + "Ġst amp", + "Ġfl aws", + "Ġsh ade", + "ham mer", + "Ġpass port", + "Ġcont ing", + "am el", + "Ġobser vers", + "Ġneg lect", + "ĠR B", + "ĠBrother hood", + "Ġskept ical", + "f amily", + "us k", + "Ġemotion ally", + "â Ļ", + "ĠBet a", + "ason able", + "id ity", + "ĠM ul", + "Ġkick ing", + "ĠC arm", + "oll ah", + "VERT IS", + "ĠAt hen", + "Ġlad der", + "ĠBul let", + "å £", + "00 01", + "ĠWild life", + "ĠM ask", + "ĠN an", + "R ev", + "Ġun acceptable", + "leg al", + "Ġcrowd ed", + "ag i", + "ĠC ox", + "j e", + "Ġmor ality", + "Ġfu els", + "Ġc ables", + "Ġman kind", + "ĠCarib bean", + "Ġanch or", + "Ġby te", + "ĠO ften", + "ĠO z", + "Ġcraft ed", + "Ġhistor ian", + "ĠW u", + "Ġtow ers", + "ĠCitiz ens", + "Ġhel m", + "Ġcred entials", + "Ġsing ular", + "ĠJes se", + "Ġtack les", + "Ġcont empt", + "Ġa fore", + "ĠSh adows", + "Ġn il", + "Ġur gent", + "app le", + "bl ood", + "Ġv on", + "Ġoff line", + "Ġbreat he", + "Ġj umps", + "Ġirre levant", + "ox ic", + "om al", + "import ant", + "J im", + "Ġgl oves", + "arm ing", + "dep th", + "Ġtal ents", + "ook ie", + "ĠS B", + "Ġpal m", + "uff s", + "est a", + "IG H", + "Ġcan on", + "ĠVer izon", + "ĠP le", + "Ġcou pled", + "vel t", + "Ġfundra ising", + "ĠGet ting", + "ĠD LC", + "Ġmathemat ical", + "ĠH S", + "ĠCard inals", + "te lling", + "Ġspons ors", + "Ġ Ï", + "ĠBull s", + "op tion", + "Ġprop ose", + "Ġmem orable", + "Ġembr aced", + "Ġdecl ining", + "He alth", + "ed a", + "Ġ} ;", + "Ġsp am", + "m ile", + "Ġpit cher", + "ĠE ight", + "Ġcar ing", + "ut ic", + "ro le", + "Ġair line", + "ernand ez", + "ĠAth let", + "Ġcert ification", + "ux e", + "rig er", + "Ġem pir", + "Ġsens ation", + "Ġdis m", + "Ġb olt", + "Ġev olve", + "H ouse", + "Ġconsult ation", + "ĠD uty", + "Ġtou ches", + "ĠN athan", + "Ġf aint", + "h ad", + "\" (", + "ĠCons umer", + "ĠExt reme", + "Ġ12 7", + "ĠHer m", + "ĠSac rament", + "iz oph", + "Ġanx ious", + "ul ously", + "Ġsoc ially", + "ĠU TC", + "Ġsol ving", + "ĠLet ter", + "Hist ory", + "ed uc", + "Pr ice", + ") );", + "Ġrel oad", + "am ic", + "Ġp ork", + "Ġdisc ourse", + "Ġt ournaments", + "ai ro", + "ĠK ur", + "ĠCost a", + "Ġviol ating", + "Ġinterf ere", + "Ġrecre ational", + "uff le", + "Ġspe eches", + "Ġneed ing", + "Ġremem bers", + "Ġcred ited", + "n ia", + "f ocused", + "amer a", + "Ġb ru", + "um bs", + "ĠCub an", + "Ġpreced ing", + "Ġnons ense", + "ac ial", + "Ġsmart phones", + "ĠSt ories", + "S ports", + "ĠEmer gency", + "oun cing", + "ef ined", + "Ġb er", + "Ġconsult ing", + "Ġm asters", + "he astern", + ".\" [", + "ĠRun ning", + "Ġsus cept", + "ĠF eng", + "Americ a", + "pr ises", + "st itial", + "ĠWeek ly", + "ĠGreat er", + "mod ules", + "if ter", + "G raphics", + "ul er", + "Ġwho lly", + "Ġsupp ress", + "Ġconce aled", + "Ġhapp ily", + "Ġaccept s", + "ĠEn joy", + "Ġr ivers", + "ĠEx cept", + "2 25", + "ĠN HS", + "ĠMc Connell", + "Ġp ussy", + "fer red", + "ut able", + "Ġatt ain", + "Ġ> =", + "Ġdepos its", + "roph ic", + "Ġnot orious", + "ĠSh aw", + "il itation", + "Ġepid emic", + "all ic", + "Ġsmall est", + "ov ich", + "Ġaccess ories", + "per ties", + "Ġsur plus", + "ĠMe ch", + "Ġamb ig", + "ĠImm igration", + "Ġch im", + "ev al", + "Ġpract icing", + "ĠMyster y", + "Ġdom ains", + "ĠSil icon", + "app s", + "Ġkilomet ers", + "e a", + "ĠSm ash", + "Ġwarrant y", + "Ġn ost", + "s il", + "re v", + "J on", + "ĠDub lin", + "Ġtast es", + "Ġb out", + "g reat", + "er ror", + "Ġsw itches", + "ĠB apt", + "D O", + "ok i", + "Ġsour ced", + "pro du", + "Ġattach ment", + "ĠIss ue", + "ĠQuest ion", + "Jo in", + "Ġf itted", + "Ġunlaw ful", + "^ ^", + "ere k", + "Ġauthent ication", + "Ġst ole", + "Ġaccount ability", + "l abel", + "S earch", + "Ġal beit", + "atic an", + "fund ed", + "ĠAdd ing", + "ĠI Q", + "Ġsub mar", + "l it", + "a que", + "ĠLear ning", + "Ġint eger", + "M aster", + "ĠCh rom", + "Ġprem ier", + "O p", + "ĠLi u", + "Ġbl essed", + "ĠGl obe", + "ĠResp onse", + "Ġlegit im", + "ĠMer kel", + "Ġdispos al", + " ´", + "Ġgau ge", + "pe at", + "Ġindu ced", + "Ġquestion able", + "arth y", + "ĠV it", + "ĠF eed", + "U ntil", + "U t", + "worth y", + "R Y", + "ĠH erald", + "ĠHam mer", + "Ġmed al", + "ĠR ivers", + "ĠH ack", + "Ġclar ify", + "Ġtrack ed", + "Ġautonom ous", + "Ġten ant", + "ĠQ atar", + "er ie", + "Ġgr im", + "ĠMon itor", + "Ġresist ant", + "ĠSpe c", + "ĠWell s", + "N AS", + "14 8", + "Ġmin ers", + "iot ics", + "Ġmiss es", + "11 6", + "g ian", + "g it", + "ĠE yes", + "p res", + "Ġgrad uated", + "Ġang el", + "Ġsyn chron", + "Ġefficient ly", + "Ġtrans mitted", + "H arry", + "Ġglob ally", + "EN CE", + "ĠMont ana", + "r aged", + "ĠPre vention", + "Ġp iss", + "ĠL l", + "Ġshe lf", + "ĠB JP", + "ĠTest ament", + "ĠL ate", + "ik er", + "ĠH app", + "ĠJul ian", + "h all", + "Ġsp ont", + "Ġshut down", + "Ġincons istent", + "Ġsubscrib ers", + "Ġske leton", + "ĠNe braska", + "Ġins pire", + "ĠV oid", + "F eed", + "Ġang les", + "ĠSpr ings", + "Ġbench mark", + "Ġvacc ines", + "izoph ren", + "se xual", + "uff ed", + "Ġsh ine", + "ĠK ath", + "Ġgest ure", + "ine a", + "Ġr ip", + "Ġopp ression", + "Ġcons cience", + "b t", + "ĠL um", + "Ġinc idence", + "ĠF a", + "w r", + "Ġmin eral", + "ĠSp urs", + "alk y", + "Ġth under", + "Ġop io", + "Be ing", + "ĠPal m", + "Ġwas ted", + "Ġl b", + "i aries", + "ĠIniti ative", + "Ġcur ric", + "Ġmark er", + "ĠMc L", + "Ġext ensions", + "ĠP v", + "ĠAr ms", + "Ġoffer ings", + "Ġdef enses", + "Ġvend or", + "Ġcontrad ict", + "ĠCol in", + "Ġredd it", + "Ġper ipher", + "12 2", + "Ġs ins", + "E dit", + "IC T", + "So ft", + "ĠSh ah", + "Ġadministr ator", + "ĠT rip", + "Ġporn ography", + "Ġtu ition", + "in ence", + "ĠPro gress", + "Ġcat alog", + "Ġsu ite", + "Ġh ike", + "Ġreprodu ctive", + "eng ine", + "Ġd rought", + "ĠNo ah", + "Ġ2 30", + "Ġd ude", + "Ġrelax ed", + "Ġpart ition", + "Ġparticip ant", + "Ġtel esc", + "Ġfe as", + "ĠF F", + "own er", + "Ġswe eping", + "Ġl enses", + "Ġmatch up", + "ĠRe pl", + "ourn als", + "Ġcred ible", + "Ġgrand mother", + "Ġther mal", + "Ġsubscrib ing", + "Ġident ities", + "col m", + "U CT", + "Ġreluct ant", + "us ers", + "ĠC ort", + "Ġassist ed", + "OS S", + "ATION S", + "IS H", + "Ġpharm aceutical", + "ic able", + "ad ian", + "ĠSon ic", + "ĠF ury", + "ĠM ong", + "A H", + "ĠPsych ology", + "Ġph osph", + "Ġtreat s", + "Ń Ķ", + "Ġstead ily", + "ĠHell o", + "Ġrel ates", + "Ġcl ue", + "Ex pl", + "a uth", + "Ġrev ision", + "Ġe ld", + "os ion", + "Ġbr on", + "14 4", + "ri kes", + "Ġmin es", + "Ġblank et", + "ĠF ail", + "el ed", + "ĠIm agine", + "ĠPl anned", + "a ic", + "Re quest", + "M ad", + "ĠHor se", + "ĠEag le", + "Ġcap ac", + "15 7", + "Ġl ing", + "ĠN ice", + "ĠP arenthood", + "min ster", + "og s", + "ens itive", + "Not hing", + "Ġcar n", + "F in", + "ĠP E", + "Ġr ifles", + "ĠL P", + "S and", + "Ġgui Active", + "Ġtour ist", + "C NN", + "Ġunve iled", + "Ġpredec essor", + "} {", + "u ber", + "Ġoff shore", + "Ġopt ical", + "ĠR ot", + "ĠPear l", + "et on", + "Ġst ared", + "Ġfart her", + "at ility", + "cont in", + "ĠG y", + "ĠF oster", + "ĠC oc", + "ri ents", + "Ġdesign ing", + "ĠEconom y", + "ON G", + "W omen", + "ĠN ancy", + "er ver", + "Ġmas cul", + "Ġcasual ties", + "Ġ2 25", + "ĠS ullivan", + "ĠCh oice", + "Ġa ster", + "w s", + "Ġhot els", + "Ġconsider ations", + "Ġcou ch", + "ĠSt rip", + "ĠG n", + "Ġmanip ulate", + "l ied", + "Ġsynt hetic", + "Ġassault ed", + "Ġoff enses", + "ĠDra ke", + "Ġim pe", + "Oct ober", + "ĠHer itage", + "h l", + "ĠBl air", + "Un like", + "Ġg rief", + "Ġ4 50", + "Ġopt ed", + "Ġresign ation", + "il o", + "Ġver se", + "ĠT omb", + "Ġu pt", + "Ġa ired", + "ĠH ook", + "ĠML B", + "Ġassum es", + "out ed", + "ĠV ers", + "Ġinfer ior", + "Ġbund le", + "ĠD NS", + "ograp her", + "Ġmult ip", + "ĠSoul s", + "Ġillust rated", + "Ġtact ic", + "Ġdress ing", + "Ġdu o", + "Con f", + "Ġrel ent", + "Ġc ant", + "Ġscar ce", + "Ġcand y", + "ĠC F", + "Ġaffili ated", + "Ġspr int", + "yl an", + "ĠGarc ia", + "Ġj unk", + "Pr int", + "ex ec", + "C rit", + "Ġport rait", + "ir ies", + "ĠOF F", + "Ġdisp utes", + "W R", + "L ove", + "ãģ Ħ", + "ĠRe yn", + "Ġh ipp", + "op ath", + "Ġflo ors", + "ĠFe el", + "Ġwor ries", + "Ġsett lements", + "ĠP os", + "Ġmos que", + "Ġfin als", + "Ġcr ushed", + "ĠPro bably", + "ĠB ot", + "ĠM ans", + "ĠPer iod", + "Ġsovere ignty", + "Ġsell er", + "Ġap ost", + "Ġam ateur", + "Ġd orm", + "Ġconsum ing", + "Ġarm our", + "ĠRo ose", + "Ġint ensive", + "Ġelim inating", + "ĠSun ni", + "ĠAle ppo", + "j in", + "Ġadv ise", + "p al", + "ĠH alo", + "Ġdes cent", + "Ġsimpl er", + "Ġbo oth", + "ST R", + "L ater", + "ĠC ave", + "== =", + "Ġm ol", + "Ġf ist", + "Ġshot gun", + "su pp", + "Ġrob bery", + "E ffect", + "Ġobsc ure", + "ĠProf essional", + "Ġemb assy", + "Ġmilit ant", + "Ġinc arcer", + "Ġgener ates", + "Ġlaun ches", + "Ġadministr ators", + "Ġsh aft", + "Ġcirc ular", + "Ġfresh man", + "ĠW es", + "ĠJo el", + "ĠD rew", + "ĠDun can", + "ĠApp arently", + "s ight", + "ĠIntern al", + "ĠInd ividual", + "ĠF E", + "Ġb ore", + "ĠM t", + "Ġbroad ly", + "ĠO ptions", + "ount ain", + "ip es", + "ĠV ideos", + "20 4", + "Ġh ills", + "Ġsim ulation", + "Ġdisappoint ment", + "it an", + "ĠLabor atory", + "Ġup ward", + "Ġbound ary", + "Ġdark er", + "h art", + "Ġdomin ance", + "C ong", + "ĠOr acle", + "ĠL ords", + "Ġscholars hip", + "ĠVin cent", + "ed e", + "ĠR ah", + "Ġencour ages", + "ro v", + "Ġqu o", + "Ġprem ise", + "ĠCris is", + "ĠHol ocaust", + "Ġrhyth m", + "Ġmet ric", + "cl ub", + "Ġtransport ed", + "Ġn od", + "ĠP ist", + "Ġancest ors", + "ĠFred er", + "th umbnails", + "ĠC E", + "ON D", + "Ph il", + "ven ge", + "ĠProduct s", + "cast le", + "Ġqual ifying", + "ĠK aren", + "VERTIS EMENT", + "Ġmight y", + "Ġexplan ations", + "Ġfix ing", + "D i", + "Ġdecl aring", + "Ġanonym ity", + "Ġju ven", + "ĠN ord", + "ĠDo om", + "ĠAct ually", + "O k", + "ph is", + "ĠDes ert", + "Ġ11 6", + "I K", + "ĠF M", + "Ġinc omes", + "V EL", + "ok ers", + "Ġpe cul", + "Ġlight weight", + "g ue", + "Ġacc ent", + "Ġincre ment", + "ĠCh an", + "Ġcompl aining", + "ĠB aghd", + "Ġmidfield er", + "Ġover haul", + "Pro cess", + "ĠH ollow", + "ĠTit ans", + "Sm all", + "man uel", + "ĠUn ity", + "ĠEv ents", + "S ty", + "Ġdispro portion", + "n esty", + "en es", + "ĠC od", + "Ġdemonstr ations", + "ĠCrim son", + "ĠO H", + "Ġen rolled", + "Ġc el", + "ĠBre tt", + "Ġa ide", + "Ġhe els", + "Ġbroad band", + "Ġmark ing", + "Ġw izard", + "ĠN J", + "ĠChief s", + "Ġingred ient", + "Ġd ug", + "ĠSh ut", + "urch ase", + "end or", + "Ġfar mer", + "ĠGold man", + "12 9", + "15 5", + "Or der", + "Ġl ion", + "i ably", + "Ġst ain", + "ar ray", + "ilit ary", + "ĠFA Q", + "Ġexpl oded", + "ĠMcC arthy", + "ĠT weet", + "ĠG reens", + "ek ing", + "l n", + "ens en", + "Ġmotor cycle", + "Ġpartic le", + "Ġch olesterol", + "B ron", + "Ġst air", + "Ġox id", + "Ġdes irable", + "ib les", + "Ġthe or", + "for cing", + "Ġpromot ional", + "ov o", + "b oot", + "ĠBon us", + "raw ling", + "Ġshort age", + "ĠP sy", + "Ġrecru ited", + "Ġinf ants", + "Ġtest osterone", + "Ġded uct", + "Ġdistinct ive", + "Ġfirm ware", + "bu ilt", + "14 5", + "Ġexpl ored", + "Ġfact ions", + "Ġv ide", + "Ġtatt oo", + "Ġfinan cially", + "Ġfat igue", + "Ġproceed ing", + "const itutional", + "Ġmis er", + "Ġch airs", + "gg ing", + "ipp le", + "Ġd ent", + "Ġdis reg", + "ç Ķ", + "st ant", + "ll o", + "b ps", + "aken ing", + "Ġab normal", + "ĠE RA", + "å£ «", + "ĠH BO", + "ĠM AR", + "Ġcon cess", + "Ġserv ant", + "Ġas pir", + "l av", + "ĠPan el", + "am o", + "Ġprec ip", + "Ġrecord ings", + "Ġproceed ed", + "Ġcol ony", + "ĠT ang", + "ab lo", + "Ġstri pped", + "Le ft", + "to o", + "Ġpot atoes", + "Ġfin est", + "% ).", + "Ġc rap", + "ĠZ ach", + "ab ases", + "ĠG oth", + "Ġbillion aire", + "w olf", + "Ġsan ction", + "S K", + "Ġlog ged", + "P o", + "ey ed", + "un al", + "Ġcr icket", + "Ġarm ies", + "Ġunc overed", + "Cl oud", + "ó n", + "Ġreb ounds", + "Ġm es", + "O per", + "P ac", + "Ġnation ally", + "Ġinsert ed", + "p ict", + "Ġgovern ance", + "Ð ¸", + "Ġprivile ges", + "G ET", + "Ġfavor ites", + "im ity", + "Ġlo ver", + "the m", + "em pl", + "Ġgorge ous", + "An n", + "Ġsl ipped", + "Ġve to", + "B ob", + "Ġsl im", + "u cc", + "ĠF ame", + "udden ly", + "Ġden ies", + "ĠM aur", + "Ġdist ances", + "Ġw anna", + "t ar", + "ĠS ER", + "Ġâ Ī", + "Ġle mon", + "at hetic", + "Ġlit eral", + "Ġdistingu ished", + "Ġansw ering", + "G I", + "Ġrelig ions", + "ĠPhil os", + "ĠL ay", + "Ġcomp os", + "ire ments", + "ĠK os", + "ine z", + "roll ing", + "Ġyoung est", + "and ise", + "ĠB orn", + "Ġalt ar", + "am ina", + "ĠB oot", + "v oc", + "Ġdig ging", + "Ġpress ures", + "Ġl en", + "26 4", + "Ġassass ination", + "ĠBir mingham", + "ĠMy th", + "Ġsovere ign", + "ĠArt ist", + "ĠPhot ograph", + "Ġdep icted", + "Ġdisp ens", + "orth y", + "Ġamb ul", + "int eg", + "ĠC ele", + "ĠTib et", + "Ġhier archy", + "Ġc u", + "Ġpre season", + "ĠPet erson", + "Ġcol ours", + "Ġworry ing", + "Ġback ers", + "ĠPal mer", + "ĠÎ ¼", + "Ġcontribut or", + "Ġhear ings", + "Ġur ine", + "Ġ Ù", + "ourge ois", + "Sim ilar", + "ĠZ immer", + "s omething", + "ĠUS C", + "Ġstrength s", + "ĠF I", + "Ġlog ging", + "As ked", + "ĠTh ai", + "in qu", + "ĠW alt", + "Ġcrew s", + "it ism", + "3 01", + "Ġshar ply", + "um ed", + "Ġred irect", + "r ators", + "In f", + "ĠWe apons", + "Ġte asp", + "19 99", + "L ive", + "ĠEs pecially", + "ĠS ter", + "ĠVeter ans", + "Ġint ro", + "other apy", + "Ġmal ware", + "Ġbre eding", + "Ġmole cular", + "ĠR oute", + "ĠCom ment", + "oc hem", + "Ġa in", + "Se ason", + "Ġlineback er", + "Ä «", + "ĠEconom ics", + "es ar", + "ĠL ives", + "ĠEm ma", + "Ġk in", + "ĠTer rit", + "Ġpl anted", + "ot on", + "ĠBut ter", + "ĠSp ons", + "P ER", + "Ġdun geon", + "Ġsymb olic", + "Ġfil med", + "Ġdi ets", + "Ġconclud es", + "Ġcertain ty", + "ĠForm at", + "Ġstr angers", + "form at", + "ĠPh ase", + "Ġcop ied", + "Ġmet res", + "ld a", + "ĠUs ers", + "Ġdeliber ate", + "Ġwas hed", + "ĠL ance", + "im ation", + "Ġimpro per", + "ĠGen esis", + "ick r", + "ĠK ush", + "Ġreal ise", + "Ġembarrass ing", + "alk ing", + "b ucks", + "Ġver ified", + "Ġout line", + "year s", + "ĠIn come", + "20 2", + "Ġz ombies", + "F inal", + "ĠMill enn", + "Ġmod ifications", + "ĠV ision", + "ĠM oses", + "ver b", + "iter ranean", + "ĠJ et", + "Ġnav al", + "ĠA gg", + "Ġur l", + "Ġvict ories", + "Ġnon etheless", + "Ġinj ust", + "ĠF act", + "ç ļ", + "Ġins ufficient", + "re view", + "face book", + "Ġnegoti ating", + "Ġguarant ees", + "im en", + "uten berg", + "Ġg ambling", + "Ġcon gr", + "Load ing", + "Ġnever theless", + "Ġpres idents", + "ĠIndust rial", + "Ġ11 8", + "Ġp oured", + "ĠT ory", + "Ġ17 5", + "Ġ: =", + "Sc ott", + "ange red", + "T ok", + "Ġorgan izers", + "M at", + "ĠG rowth", + "Ġad ul", + "Ġens ures", + "Ġ11 7", + "é¾į å", + "Ġmass acre", + "Ġgr ades", + "be fore", + "AD VERTISEMENT", + "ĠSl ow", + "ĠM MA", + "âĢĶ \"", + "ĠV atican", + "Q aeda", + "Ġo we", + "66 66", + "ĠS orry", + "ĠGr ass", + "Ġbackground s", + "Ġexha usted", + "Ġcl an", + "Ġcomprom ised", + "ĠE lf", + "ĠIsa ac", + "ens on", + "In vest", + "IF A", + "Ġinterrupt ed", + "ãĥī ãĥ©", + "Ġtw isted", + "ĠDrag ons", + "M ode", + "ĠK remlin", + "Ġfert il", + "he res", + "ph an", + "ĠN ode", + "f ed", + "ĠOr c", + "Ġunw illing", + "C ent", + "Ġprior it", + "Ġgrad uates", + "Ġsubject ive", + "Ġiss uing", + "ĠL t", + "Ġview er", + "Ġw oke", + "Th us", + "bro ok", + "Ġdep ressed", + "Ġbr acket", + "ĠG or", + "ĠFight ing", + "Ġstri ker", + "Rep ort", + "ĠPortug al", + "Ġne o", + "w ed", + "19 9", + "Ġflee ing", + "sh adow", + "ident ified", + "US E", + "Ste am", + "Ġstret ched", + "Ġrevel ations", + "art ed", + "ĠD w", + "Ġalign ment", + "est on", + "ĠJ ared", + "S ep", + "Ġblog s", + "up date", + "g om", + "r isk", + "Ġcl ash", + "ĠH our", + "Ġrun time", + "Ġunw anted", + "Ġsc am", + "Ġr ack", + "Ġen light", + "on est", + "ĠF err", + "Ġconv ictions", + "Ġp iano", + "Ġcirc ulation", + "ĠW elcome", + "Ġback lash", + "ĠW ade", + "Ġrece ivers", + "ot ive", + "J eff", + "Ġnetwork ing", + "ĠPre p", + "ĠExpl orer", + "Ġlect ure", + "Ġupload ed", + "ĠMe at", + "B LE", + "ĠNaz is", + "ĠSy nd", + "st ud", + "ro ots", + "ri ans", + "Ġportray ed", + "Ġ ??", + "ĠBudd ha", + "s un", + "Rober t", + "ĠCom plex", + "Ġover see", + "Ġste alth", + "T itle", + "ĠJ obs", + "ĠK um", + "Ġappreci ation", + "ĠM OD", + "Ġbas ics", + "Ġcl ips", + "Ġnurs ing", + "Ġpropos ition", + "Ġreal ised", + "ĠNY C", + "Ġall ocated", + "ri um", + "ar an", + "ĠPro duction", + "ĠV ote", + "Ġsm ugg", + "Ġhun ter", + "az er", + "ĠCh anges", + "Ġfl uct", + "y on", + "Ar ray", + "Ġk its", + "W ater", + "Ġuncom mon", + "Ġrest ing", + "ell s", + "w ould", + "Ġpurs ued", + "Ġassert ion", + "omet own", + "ĠMos ul", + "ĠPl atform", + "io let", + "Ġshare holders", + "Ġtra ils", + "P ay", + "ĠEn forcement", + "ty pes", + "ĠAn onymous", + "Ġsatisf ying", + "il ogy", + "Ġ( '", + "w ave", + "c ity", + "Ste ve", + "Ġconfront ation", + "ĠE ld", + "C apt", + "ah an", + "ht m", + "ĠC trl", + "ON S", + "2 30", + "if a", + "hold ing", + "Ġdelic ate", + "Ġj aw", + "ĠGo ing", + "or um", + "S al", + "Ġd ull", + "ĠB eth", + "Ġpr isons", + "Ġe go", + "ĠEl sa", + "avor ite", + "ĠG ang", + "ĠN uclear", + "Ġsp ider", + "ats u", + "Ġsam pling", + "Ġabsor bed", + "ĠPh arm", + "iet h", + "Ġbuck et", + "ĠRec omm", + "O F", + "ĠF actory", + "AN CE", + "Ġb acter", + "H as", + "ĠObs erv", + "12 1", + "Ġprem iere", + "De velop", + "Ġcur rencies", + "C ast", + "Ġaccompany ing", + "ĠNash ville", + "Ġfat ty", + "ĠBre nd", + "Ġloc ks", + "Ġcent ered", + "ĠU T", + "augh s", + "or ie", + "ĠAff ordable", + "v ance", + "D L", + "em et", + "Ġthr one", + "ĠBlu etooth", + "Ġn aming", + "if ts", + "AD E", + "Ġcorrect ed", + "Ġprompt ly", + "ĠST R", + "Ġgen ome", + "Ġcop e", + "Ġval ley", + "Ġround ed", + "ĠK end", + "al ion", + "p ers", + "Ġtour ism", + "Ġst ark", + "v l", + "Ġblow ing", + "ĠSche dule", + "st d", + "Ġunh appy", + "Ġlit igation", + "ced es", + "Ġand roid", + "Ġinteg ral", + "ere rs", + "ud ed", + "t ax", + "Ġre iter", + "ĠMot ors", + "oci ated", + "Ġwond ers", + "ĠAp ost", + "uck ing", + "ĠRoose velt", + "f ram", + "Ġyield s", + "Ġconstit utes", + "aw k", + "Int erest", + "Ġinter im", + "Ġbreak through", + "ĠC her", + "Ġpro sec", + "ĠD j", + "ĠM T", + "Res p", + "ĠP T", + "Ġs perm", + "ed it", + "B T", + "Lin ux", + "count ry", + "le ague", + "Ġd ick", + "Ġo ct", + "Ġinsert ing", + "Ġsc ra", + "ĠBrew ing", + "Ġ19 66", + "Ġrun ners", + "Ġpl un", + "id y", + "ĠD ian", + "Ġdys function", + "Ġex clusion", + "Ġdis gr", + "Ġincorpor ate", + "Ġrecon c", + "Ġnom inated", + "ĠAr cher", + "d raw", + "achel or", + "Ġwrit ings", + "Ġshall ow", + "Ġh ast", + "ĠB MW", + "ĠR S", + "Ġth igh", + "Ġ19 63", + "Ġl amb", + "Ġfav ored", + "ag le", + "Ġcool er", + "ĠH ours", + "ĠG U", + "ĠOrig in", + "Ġglim pse", + "---------------- ----", + "L im", + "Ġche ek", + "Ġj ealous", + "- '", + "Ġhar ness", + "ĠPo ison", + "Ġdis abilities", + "ne apolis", + "Ġout look", + "Ġnot ify", + "ĠIndian apolis", + "Ġab rupt", + "ns ic", + "Ġenc rypted", + "Ġfor fe", + "reat h", + "Ġr abb", + "Ġfound ations", + "Ġcompl iment", + "ĠInter view", + "ĠS we", + "Ġad olesc", + "Ġmon itors", + "ĠSacrament o", + "Ġtime ly", + "Ġcontem pl", + "Ġposition ed", + "Ġpost ers", + "ph ies", + "iov ascular", + "v oid", + "ĠFif th", + "Ġinvestig ative", + "OU N", + "Ġinteg rate", + "ĠIN C", + "ish a", + "ibl ings", + "ĠRe quest", + "ĠRodrig uez", + "Ġsl ides", + "ĠD X", + "Ġfemin ism", + "Ġdat as", + "Ġb end", + "ir us", + "ĠNig eria", + "F ox", + "Ch ange", + "Ġair plane", + "ĠLad en", + "Ġpublic ity", + "ixt y", + "Ġcommit ments", + "Ġaggreg ate", + "Ġdisplay ing", + "ĠAr row", + "Ġ12 2", + "Ġrespect s", + "and roid", + "s ix", + "ĠSh a", + "Ġrest oration", + ") \\", + "W S", + "oy s", + "Ġillust rate", + "with out", + "12 6", + "ĠâĶ Ĥ", + "Ġpick up", + "n els", + "Ġ ....", + "f ood", + "ĠF en", + ") ?", + "Ġphenomen a", + "Ġcompan ions", + "ĠW rite", + "Ġsp ill", + "Ġbr idges", + "ĠUp dated", + "ĠF o", + "Ġinsect s", + "ASH INGTON", + "Ġsc are", + "il tr", + "ĠZh ang", + "Ġsever ity", + "Ġind ul", + "14 9", + "ĠCo ffee", + "Ġnorm s", + "Ġp ulse", + "ĠF T", + "Ġhorr ific", + "ĠDest roy", + "ĠJ SON", + "Ġo live", + "Ġdiscuss es", + "R est", + "E lect", + "ĠW inn", + "ĠSurv iv", + "ĠH ait", + "S ure", + "op ed", + "Ġro oted", + "ĠS ke", + "ĠBron ze", + "Ġl ol", + "Def ault", + "Ġcommod ity", + "red ited", + "Ġliber tarian", + "Ġforb idden", + "Ġgr an", + "à ¨", + "Ġl ag", + "en z", + "dri ve", + "Ġmathemat ics", + "Ġw ires", + "Ġcrit ically", + "Ġcarb ohyd", + "ĠChance llor", + "ĠEd die", + "Ġban ning", + "ĠF ri", + "Ġcompl ications", + "et ric", + "ĠBangl adesh", + "Ġband width", + "St op", + "ĠOrig inally", + "Ġhalf way", + "yn asty", + "sh ine", + "Ġt ales", + "rit ies", + "av ier", + "Ġspin ning", + "ĠWH O", + "Ġneighbour hood", + "b ach", + "Ġcommer ce", + "ĠS le", + "B U", + "Ġentreprene ur", + "Ġpecul iar", + "ĠCom ments", + "f re", + "3 20", + "IC S", + "Ġimag ery", + "ĠCan on", + "ĠElect ronic", + "sh ort", + "( (", + "D ig", + "Ġcomm em", + "u ced", + "Ġincl ined", + "ĠSum mon", + "Ġcl iff", + "ĠMed iterranean", + "Ġpo etry", + "Ġprosper ity", + "ĠRe ce", + "Ġp ills", + "m ember", + "Ġfin ale", + "un c", + "ĠG ig", + "ä ½", + "Ġl od", + "Ġback ward", + "- +", + "ĠFor ward", + "Ġth ri", + "s ure", + "Ġso ap", + "ĠF X", + "R ES", + "ĠSe xual", + "oul os", + "Ġfool ish", + "Ġright eous", + "Ġco ff", + "terror ism", + "ust ain", + "ot er", + "Ġab uses", + "ne xt", + "Ġab usive", + "Ġthere after", + "Ġprohib ition", + "ĠS UP", + "Ġd ip", + "Ġr ipped", + "Ġinher ited", + "Ġb ats", + "st ru", + "G T", + "Ġflaw ed", + "ph abet", + "Ġf og", + "do ors", + "Ġim aging", + "Ġdig its", + "ĠHung ary", + "Ġar rog", + "Ġteach ings", + "Ġprotocol s", + "ĠB anks", + "à ¸", + "p ound", + "ĠC urt", + ".\" )", + ". /", + "Ġex emption", + "end ix", + "ĠM ull", + "Ġimpro ves", + "ĠG amer", + "d imensional", + "I con", + "ĠMarg aret", + "St atus", + "d ates", + "Ġint ends", + "Ġdep ict", + "Ġpark ed", + "J oe", + "ĠMar ines", + "chn ology", + "! ).", + "Ġjud ged", + "Ġwe ights", + "R ay", + "Ġapart ments", + "he ster", + "Ġrein force", + "Ġoff ender", + "occ up", + "Ġs ore", + "e pt", + "ĠPH P", + "ĠB row", + "Ġauthor ization", + "ĠR isk", + "ĠDel aware", + "ĠQ U", + "Ġnot ifications", + "Ġsun light", + "Ġex clude", + "d at", + "Ġm esh", + "ĠSud an", + "Ġbelong ed", + "Ġsub way", + "Ġno on", + "ĠInter ior", + "ol ics", + "ĠL akers", + "Ġc oding", + "Dis claimer", + "Cal if", + "O ld", + "Ġdis l", + "???? ?", + "Ġconfir ms", + "Ġrecruit ment", + "Ġhom icide", + "Cons ider", + "ĠJeff rey", + "ft y", + "} ;", + "Ġobject ion", + "do ing", + "ĠLe o", + "W ant", + "Ġgl ow", + "ĠClar ke", + "ĠNorm an", + "Ġver ification", + "Ġpack et", + "ĠForm ula", + "Ġpl ag", + "es ville", + "Ġshout ing", + "Ġo v", + "ĠR EC", + "ĠB ub", + "Ġn inth", + "Ġener g", + "Ġvalid ity", + "Ġup s", + "j ack", + "Ġneighbor ing", + "ĠN ec", + "ew orks", + "ĠH ab", + "are z", + "Ġsp ine", + "Ġevent ual", + "ĠLe aders", + "ĠC arn", + "Ġprob ation", + "Ġrom ance", + "ms g", + "ĠMechan ical", + "ER Y", + "R ock", + "Ġpart isan", + "N ode", + "ass ets", + "min ent", + "Ġforeign ers", + "Ġtest ify", + "ĠUs ually", + "l ords", + "ĠG ren", + "ĠPow ell", + "BI L", + "Ġs r", + "Ġadd ict", + "Ġshell s", + "Ġs igh", + "ĠY ale", + "tern ity", + "Ġ7 50", + "E U", + "ĠR ifle", + "Ġpat ron", + "em a", + "ĠB annon", + "an ity", + "Ġtrop ical", + "ĠV II", + "c ross", + "Every thing", + "ĠIS O", + "Ġhum ble", + "ass ing", + "ĠF IG", + "Ġupd ating", + "ys on", + "Ġcal cium", + "Ġcompet ent", + "Ġste ering", + "Pro t", + "ĠS Y", + "ĠFin als", + "ĠR ug", + "15 9", + "13 7", + "ĠG olf", + "Ġ12 6", + "Ġaccommod ation", + "ĠHug hes", + "Ġaest hetic", + "art isan", + "ĠTw ilight", + "Ġpr ince", + "ĠAgric ulture", + "ĠDis co", + "Ġpreced ent", + "Ġtyp ing", + "author ized", + "O ption", + "ĠA ub", + "l ishes", + "ach t", + "m ag", + "P eter", + "ĠU FO", + "mont on", + "ĠL ith", + "Ġa rom", + "Ġsec uring", + "Ġconf ined", + "priv ate", + "Ġsw ords", + "Ġmark ers", + "Ġmetab olic", + "se lect", + "ĠCur se", + "ĠO t", + "g ressive", + "Ġinc umb", + "ĠS aga", + "Ġpr iced", + "Ġclear ance", + "Cont ent", + "Ġdr illing", + "Ġnot ices", + "Ġb ourgeois", + "Ġv est", + "Ġcook ie", + "ĠGuard ians", + "ry s", + "in yl", + "Ġ12 4", + "Ġpl ausible", + "on gh", + "ĠOd in", + "Ġconcept ion", + "ĠY uk", + "ĠBaghd ad", + "ĠFl ag", + "Aust ral", + "ĠI BM", + "Ġintern ationally", + "ĠWiki Leaks", + "I ED", + "Ġc yn", + "Ġcho oses", + "ĠP ill", + "Ġcomb ining", + "Ġrad i", + "ĠMoh ammed", + "def ense", + "atch ing", + "Sub ject", + "ic iency", + "Fr ame", + "Ġ{ \"", + "Ġche ss", + "Ġtim er", + "19 0", + "Ġt in", + "Ġord inance", + "emet ery", + "Ġacc using", + "Ġnotice able", + "Ġcent res", + "Ġl id", + "ĠM ills", + "img ur", + "Ġz oom", + "erg ic", + "Ġcomp ression", + "pr im", + "f ind", + "Ġsur g", + "Ġp and", + "ĠK ee", + "ĠCh ad", + "cell ence", + "oy le", + "Ġsocial ism", + "ĠT ravis", + "ĠM Hz", + "Ġgu ild", + "ALL Y", + "ĠSub scribe", + "ĠRel ated", + "Ġoccur rence", + "itch ing", + "Ġfict ional", + "Ġcr ush", + "ĠE A", + "c od", + "m ix", + "ĠTri ple", + "Ġretrie ve", + "Ġstimul us", + "Ġpsych iat", + "ĠDo or", + "Ġhomosexual ity", + "Ġelement ary", + "Ġcell ular", + "id ian", + "ĠL aun", + "Ġintrig uing", + "Ġfo am", + "ĠB ass", + "id i", + "its u", + "Ġass ure", + "Ġcongr at", + "Ġbusiness man", + "ĠBo ost", + "cl ose", + "Ġl ied", + "Ġsc iences", + "ĠO mega", + "ĠG raphics", + "Ġ< =", + "sp oken", + "Ġconnect ivity", + "S aturday", + "ĠAven gers", + "Ġto ggle", + "Ġank le", + "Ġnational ist", + "mod el", + "ĠP ool", + "ophob ia", + "V ar", + "ĠM ons", + "ator ies", + "Ġaggress ively", + "C lear", + "For ge", + "act ers", + "Ġhed ge", + "Ġpip es", + "Ġbl unt", + "Ġs q", + "Ġremote ly", + "W ed", + "as ers", + "Ġref riger", + "Ġt iles", + "Ġresc ued", + "Ġcompr ised", + "ins ky", + "Ġman if", + "avan augh", + "Ġprol ifer", + "Ġal igned", + "x ml", + "Ġtri v", + "Ġcoord ination", + "ĠP ER", + "ĠQu ote", + "13 4", + "b f", + "ĠS aw", + "Ġtermin ation", + "Ġ19 0", + "Ġadd itions", + "Ġtri o", + "Ġproject ions", + "Ġpositive ly", + "Ġin clusive", + "Ġmem br", + "19 90", + "old er", + "Ġpract iced", + "ink le", + "Ar ch", + "Ġstar ters", + "ari us", + "Ġinter mediate", + "ĠBen ef", + "ĠK iller", + "Ġinter ventions", + "ĠK il", + "ĠF lying", + "In v", + "Ġprem ature", + "Ġpsych iatric", + "Ġind ie", + "Ġcoll ar", + "ĠRain bow", + "af i", + "Ġdis ruption", + "ĠFO X", + "cast ing", + "Ġmis dem", + "c ro", + "Ġw ipe", + "ard on", + "Ġb ast", + "ĠTom my", + "ĠRepresent ative", + "Ġbell y", + "ĠP O", + "ĠBre itbart", + "13 2", + "Ġmess aging", + "Sh ould", + "Ref erences", + "ĠG RE", + "ist ical", + "L P", + "ĠC av", + "ĠC razy", + "Ġintu itive", + "ke eping", + "ĠM oss", + "Ġdiscont in", + "ĠMod ule", + "Ġun related", + "ĠPract ice", + "ĠTrans port", + "Ġstatist ically", + "orn s", + "Ġs ized", + "p u", + "Ġca f", + "ĠWorld s", + "ĠRod gers", + "ĠL un", + "ĠCom ic", + "l iving", + "Ġc ared", + "Ġclim bed", + ") {", + "Ġconsist ed", + "Ġmed ieval", + "fol k", + "Ġh acked", + "Ġd ire", + "ĠHerm ione", + "Ġt ended", + "ce ans", + "D aniel", + "w ent", + "Ġlegisl ators", + "Ġred es", + "g ames", + "Ġg n", + "am iliar", + "Ġ+ +", + "gg y", + "th reat", + "Ġmag net", + "Ġper ceive", + "Ġz ip", + "Ġindict ment", + "Ġcrit ique", + "g ard", + "ĠSaf e", + "ĠC ream", + "Ġad vent", + "ob a", + "Ġv owed", + "ous ands", + "Ġsk i", + "Ġabort ions", + "u art", + "Ġstun ned", + "Ġadv ancing", + "Ġlack ed", + "Ġ\\ \"", + "Ġsch izophren", + "Ġeleg ant", + "Ġconf erences", + "Ġcance led", + "ĠHud son", + "ĠHop efully", + "Ġtr ump", + "Ġfrequ encies", + "Ġmet eor", + "ĠJun ior", + "ĠFle et", + "ĠMal colm", + "ĠT ools", + "Ġ ........", + "Ġh obby", + "ĠEurope ans", + "Ġ15 00", + "ĠInt o", + "Ġs way", + "ĠApp ro", + "ĠCom pl", + "Comm unity", + "Ġt ide", + "ĠSum mit", + "ä »", + "Ġinter vals", + "ĠE ther", + "Ġhabit at", + "ĠSteven s", + "lish ing", + "ĠDom ain", + "Ġtrig gers", + "Ġch asing", + "Ġchar m", + "ĠFl ower", + "it ored", + "Ġbless ing", + "Ġtext ures", + "F ive", + "Ġliqu or", + "R P", + "F IN", + "Ġ19 62", + "C AR", + "Un known", + "Ġres il", + "ĠL ily", + "Ġabund ance", + "Ġpredict able", + "r ar", + "Ġbull shit", + "le en", + "che t", + "M or", + "M uch", + "ä ¹", + "Ġemphas ized", + "Ġcr ust", + "Ġprim itive", + "Ġenjoy able", + "ĠPict ures", + "Ġteam mate", + "pl er", + "ĠT ol", + "ĠK ane", + "Ġsummon ed", + "th y", + "ram a", + "ĠH onda", + "Ġreal izing", + "Ġquick er", + "Ġconcent rate", + "cle ar", + "Ġ2 10", + "ĠErd ogan", + "ar is", + "Ġrespond s", + "ĠB I", + "Ġelig ibility", + "Ġpus hes", + "ĠId aho", + "Ġagg rav", + "Ġru ins", + "ur ations", + "Ġb ans", + "Ġan at", + "sh are", + "Ġgr ind", + "h in", + "um en", + "Ġut ilities", + "ĠYan kees", + "Ġdat abases", + "ĠD D", + "Ġdispl aced", + "Ġdepend encies", + "Ġstim ulation", + "h un", + "h ouses", + "ĠP retty", + "ĠRaven s", + "ĠTOD AY", + "Ġassoci ates", + "Ġthe rape", + "cl ed", + "Ġde er", + "Ġrep airs", + "rent ice", + "Ġrecept ors", + "Ġrem ed", + "ĠC e", + "Ġmar riages", + "Ġball ots", + "ĠSold ier", + "Ġhilar ious", + "op l", + "13 8", + "Ġinherent ly", + "Ġignor ant", + "Ġb ounce", + "ĠE aster", + "REL ATED", + "ĠCur rency", + "E V", + "ãĥ ŀ", + "ĠLe ad", + "Ġdece ased", + "B rien", + "ĠMus k", + "J S", + "Ġmer ge", + "heart ed", + "c reat", + "m itt", + "m und", + "ĠâĢ ĭ", + "ĠB ag", + "Ġproject ion", + "Ġj ava", + "ĠStand ards", + "ĠLeon ard", + "Ġcoc onut", + "ĠPop ulation", + "Ġtra ject", + "Ġimp ly", + "Ġcur iosity", + "ĠD B", + "ĠF resh", + "ĠP or", + "Ġheav ier", + "ne ys", + "gom ery", + "Ġdes erved", + "Ġphr ases", + "ĠG C", + "Ġye ast", + "d esc", + "De ath", + "Ġreb oot", + "Ġmet adata", + "IC AL", + "Ġrep ay", + "ĠInd ependence", + "Ġsubur ban", + "ical s", + "Ġat op", + "Ġall ocation", + "gener ation", + "ĠG ram", + "Ġmoist ure", + "Ġp ine", + "ĠLiber als", + "Ġa ides", + "Ġund erest", + "ĠBer ry", + "Ġcere mon", + "3 70", + "ast rous", + "ĠPir ates", + "Ġt ense", + "ĠIndust ries", + "ĠApp eals", + "ĠN ear", + "Ġè£ı ç", + "Ġlo vers", + "ĠC AP", + "ĠC raw", + "Ġg iants", + "Ġeffic acy", + "E lement", + "ĠBeh avior", + "ĠToy ota", + "Ġint est", + "P riv", + "A I", + "Ġmaneu ver", + "Ġperfect ion", + "Ġb ang", + "p aper", + "r ill", + "Ge orge", + "b order", + "in ters", + "ĠS eth", + "Ġcl ues", + "ĠLe vi", + "ĠRe venue", + "14 7", + "Ġv apor", + "Ġfortun ate", + "Ġthreat ens", + "Ġve t", + "Ġdepend ency", + "ers ed", + "art icle", + "ĠBl izzard", + "Ġch lor", + "Ġmin us", + "ĠB ills", + "Ġcryptoc urrency", + "Ġmetabol ism", + "ter ing", + "Ġp estic", + "step s", + "ĠTre asure", + "ract ed", + "ĠConst ant", + "Ġtem p", + "13 9", + "ĠDet ective", + "ur ally", + "Ġrecover ing", + "Ġcort ex", + "Ġ14 4", + "cl osed", + "Ġprejud ice", + "aun ted", + "Ġstorm s", + "ĠN OW", + "Ġmach inery", + "Add ress", + "Ġcompe lled", + "27 0", + "Ġdesp air", + "b ane", + "Ġveget able", + "Ġbed s", + "Lear n", + "Ġcolor ful", + "Ġsp ike", + "Ġmarg ins", + "Ġsymp athy", + "Ġworks hop", + "ĠC BC", + "S at", + "Ġburn s", + "ĠG ender", + "Ġ12 9", + "ĠC able", + "Ġdeb ts", + "ĠThe resa", + "Ġreflect ing", + "Ġa irst", + "Ġr im", + "ram id", + "Ġweakness es", + "W rit", + "ogg le", + "t i", + "ĠCh arge", + "Ġwe ighed", + "Ġ( .", + "Ġl aughter", + "Ġrou ter", + "ĠDemocr acy", + "D ear", + "Ġhas ht", + "Ġd y", + "Ġhint s", + "run ning", + "Ġfin ishes", + "ar us", + "M ass", + "res ult", + "asc us", + "Ġv intage", + "Ġcon qu", + "Ġwild ly", + "ac ist", + "Ġl ingu", + "Ġprot agonist", + "st rom", + "te enth", + "ĠSol o", + "m ac", + "f illed", + "Ġre nown", + "it ives", + "Ġmot ive", + "ĠAnt ar", + "ĠM ann", + "ĠAd just", + "Ġrock ets", + "Ġtrou bling", + "e i", + "Ġorgan isms", + "ass is", + "Christ ian", + "Ġ14 5", + "ĠH ass", + "Ġsw all", + "Ġw ax", + "ĠSurv ival", + "V S", + "ĠM urd", + "v d", + "stand ard", + "Ġdrag ons", + "Ġacceler ation", + "r ational", + "f inal", + "Ġp aired", + "ĠE thereum", + "Ġinterf aces", + "Ġres ent", + "Ġartif acts", + "Å «", + "are l", + "Ġcompet itor", + "ĠNich olas", + "ĠSur face", + "c pp", + "ĠT ot", + "Ġeconom ically", + "Ġorgan ised", + "Ġen forced", + "in ho", + "Ġvar ieties", + "Ġab dom", + "ĠBa iley", + "id av", + "ĠSal v", + "p aid", + "Ġalt itude", + "ess ert", + "ĠG utenberg", + "are a", + "op oulos", + "Ġprofess ors", + "igg s", + "ĠF ate", + "he y", + "Ġ3 000", + "D ist", + "Ġtw ins", + "c ill", + "ĠM aps", + "Ġtra ps", + "Ġwe ed", + "ĠK iss", + "Ġy oga", + "Ġrecip ients", + "ĠWest minster", + "Ġpool s", + "ĠWal mart", + "18 8", + "ĠSchool s", + "att ack", + "ĠAR M", + "par agraph", + "W arning", + "j l", + "Ġself ish", + "anche z", + "ĠHe ights", + "F re", + "ĠS oph", + "Ġ --------------------------------", + "t ml", + "33 3", + "Ġraid s", + "Ġsatell ites", + "KE Y", + "Ġlast s", + "Ñ Ĥ", + "In s", + "ĠD ame", + "Ġunp redict", + "// /", + "gh ai", + "Ġart illery", + "Ġcru ise", + "Ġg el", + "ĠCabin et", + "Ġbl ows", + "ĠE sp", + "Ġprox imity", + "ot he", + "ĠSk ills", + "ĠU pper", + "ob o", + "ĠN DP", + "Ġenjoy s", + "Ġrepe ating", + "ĠConst ruction", + "ĠQuest ions", + "H illary", + "Ġu int", + "Ġprocess ors", + "ĠGib son", + "ĠMult iple", + "q a", + "ĠB om", + "ĠM iles", + "vent ional", + "Ġhur ts", + "s kin", + "ĠA IDS", + "Ġadvis ers", + "ĠR oot", + "Ġmethod ology", + "ĠD ale", + "Ġdet on", + "ĠKnow ledge", + "sequ ently", + "Ġ12 1", + "Ġconnect s", + "C y", + "ĠD anger", + "Ġcontribut ors", + "ĠB ent", + "Ġbr ass", + "ĠGun s", + "int o", + "ĠFort une", + "Ġbro ker", + "bal ance", + "Ġlength s", + "Ġv ic", + "Ġaver aging", + "Ġappropri ately", + "ĠCamer a", + "Ġsand wich", + "ĠCD C", + "Ġcoord inate", + "Ġnav ig", + "Ġgood ness", + "l aim", + "Ġbra ke", + "Ġextrem ist", + "ĠW ake", + "ĠM end", + "ĠT iny", + "ĠC OL", + "ĠR F", + "ĠD ual", + "ĠW ine", + "C ase", + "Ġref ined", + "Ġl amp", + "L ead", + "Ġb apt", + "ĠCar b", + "ĠS add", + "ĠMin neapolis", + "PD F", + "Ear ly", + "ĠH idden", + "I ts", + "ĠT IME", + "Ġp ap", + "Ġcommission ed", + "ĠF ew", + "ĠCol ts", + "ĠB ren", + "Ġbot hered", + "Ġlike wise", + "Ex per", + "ĠSch w", + "c ry", + "n n", + "ĠM itch", + "im on", + "M G", + "b m", + "UM P", + "r ays", + "Ġregist ry", + "Ġ2 70", + "ach ine", + "re lla", + "ant ing", + "00 000", + "Ġru ined", + "sp ot", + "Ġt a", + "Ġmaxim ize", + "Ġincon ven", + "D ead", + "H uman", + "En abled", + "ĠMar ie", + "Ġch ill", + "ĠParad ise", + "Ġstar ring", + "ĠLat ino", + "ĠProt ocol", + "ĠE VER", + "Ġsuppl iers", + "m essage", + "ĠBro ck", + "Ġser um", + "âĸĪâĸĪ âĸĪâĸĪ", + "Ġen comp", + "Ġamb ition", + "ues e", + "Ġar rows", + "And rew", + "Ġanten na", + "Ġ19 61", + "ĠB ark", + "Ġb ool", + "ãĤ ª", + "ĠSt orage", + "Ġrail way", + "Ġtoug her", + "ĠC ad", + "Ġwas hing", + "P y", + "' ]", + "em bed", + "ĠMem phis", + "ack le", + "Ġfam ously", + "ĠF ortunately", + "ov ies", + "Ġmind set", + "Ġsne ak", + "ĠD h", + "RA W", + "ĠSim pson", + "Ġliv est", + "Ġland mark", + "Ġc ement", + "L ow", + "Ġthr illed", + "ĠCour se", + "in el", + "Ġch uck", + "id ate", + "gl obal", + "Ġwh it", + "Ġ �", + "ad ays", + "s ki", + "ĠS V", + "Ġvir uses", + "30 6", + "ĠResp ons", + "Ġthe aters", + "ĠBr anch", + "ĠGene va", + "ĠM K", + "Ġunbel iev", + "Ġcommun ist", + "Orig inal", + "ĠRe ceived", + "ĠTrans fer", + "ĠAr g", + "In put", + "ĠStr ategy", + "Ġpal ace", + "the ning", + "D ri", + "Ġsent encing", + "umbn ail", + "Ġp ins", + "re cy", + "Ġs iblings", + "Get ting", + "ĠB U", + "ĠNorth west", + "Ġprolong ed", + "ĠSak ura", + "C omb", + "ĠB our", + "Ġinadequ ate", + "ĠK ash", + "Ġus ername", + "ĠImpro ve", + "Ġbatt ling", + "ĠM AC", + "Ġcurric ulum", + "Ġs oda", + "ĠC annon", + "Ġsens ible", + "sp ons", + "De cember", + "Ġw icked", + "ĠP engu", + "Ġdict ators", + "ĠHe arts", + "og yn", + "Ġsimilar ities", + "ĠSt ats", + "Ġh ollow", + "it ations", + "\": [", + "Ġh over", + "ĠList en", + "s ch", + "S und", + "Ġc ad", + "ĠPar ks", + "Ġl ur", + "Ġhy pe", + "ĠL em", + "N AME", + "is ure", + "Fr iday", + "Ġshoot s", + "Ġclos es", + "Ġd b", + "ĠR idge", + "ĠDiff erent", + "Ġrepl ies", + "ĠBroad way", + "op ers", + "Ġint oler", + "ĠZe us", + "akes pe", + "Ġpropri etary", + "Ġrequest ing", + "Ġcontro llers", + "ĠM IN", + "im edia", + "be cca", + "Ġexp ans", + "Ġoil s", + "B ot", + "ĠCh and", + "Ġpr inter", + "Ġto pped", + "ĠP OL", + "ĠEar lier", + "S ocial", + "av in", + "Ġdecre ases", + "ĠSe b", + "Ġspecific ations", + "ĠBl ast", + "ĠK urt", + "Ġfre el", + "B rown", + "Ġdil ig", + "ro e", + "ĠPro blem", + "ĠQu ad", + "Ġdecent ral", + "ĠV ector", + "an ut", + "Ġplug ins", + "ĠGreg ory", + "Ġfuck ed", + "el ines", + "ĠAmb assador", + "t ake", + "Ġcle ans", + "ong yang", + "An onymous", + "st ro", + "\" }", + "al ine", + "ĠO dd", + "ĠE ug", + "2 16", + "Ġbo il", + "ĠP owers", + "Ġnurs es", + "Ob viously", + "ĠTechn ical", + "Ġexceed ed", + "OR S", + "Ġextrem ists", + "Ġtr aces", + "ex pl", + "Ġcom r", + "ĠS ach", + ") /", + "Ġm asks", + "Ġsc i", + "B on", + "Ġreg ression", + "we gian", + "Ġadvis or", + "it ures", + "ĠV o", + "ex ample", + "ĠInst ruct", + "Ġs iege", + "Ġredu ctions", + "pt r", + "Ġstat utory", + "Ġrem oves", + "Ġp uck", + "red its", + "Ġbe e", + "Ġsal ad", + "Ġpromot ions", + "ĠJosh ua", + "with standing", + "ET H", + "ĠCh a", + "im us", + "Ġexpend iture", + "aun ting", + "Ġdelight ed", + "Ġ15 5", + "be h", + "Ġcar pet", + "ĠSp art", + "Ġj ungle", + "l ists", + "Ġbull ying", + "ĠNob el", + "ĠGl en", + "Ġreferen ced", + "Ġintrodu ces", + "se in", + "Ġcho pped", + "gl ass", + "ĠW rest", + "Ġneutral ity", + "Ġâ Ļ", + "Ġinvestig ator", + "Ġshel ves", + "Ġun constitutional", + "Ġreprodu ction", + "Ġmer chant", + "m ia", + "Ġmet rics", + "Ġexplos ives", + "ĠSon ia", + "Ġbod ily", + "Ġthick ness", + "Ġpredomin antly", + "ĠAb ility", + "Ġmon itored", + "IC H", + "Ġ] .", + "ĠMart inez", + "Ġvis ibility", + "Ġqu eries", + "Ġgen ocide", + "ĠWar fare", + "Qu ery", + "Ġstud ios", + "Ġemb ry", + "Ġcorrid or", + "Ġclean ed", + "com plete", + "ĠM H", + "Ġenroll ment", + "ING S", + "Ġimpact ed", + "Ġdis astrous", + "ĠY un", + "ĠCl aire", + "ĠBas ically", + "y t", + "uster ity", + "Ġindirect ly", + "w ik", + "Ġd od", + "ĠCar r", + "Ġam p", + "Ġprohib it", + "ĠIn itial", + "ĠR d", + "ij i", + "Ġeduc ate", + "c orn", + "i ott", + "ĠBeaut y", + "Ġdetect ive", + "ĠCon n", + "s ince", + "Ġst agger", + "Ġob ese", + "Ġb ree", + "olog ic", + "is se", + "walk er", + "Ġbl ades", + "Ġlaw ful", + "fun c", + "ĠBeh ind", + "Ġappet ite", + "Ġ( *", + "Ġt ennis", + "Ġoff spring", + "Ġj ets", + "Ġstruct ured", + "Ġafore mentioned", + "N ov", + "Ġsc aling", + "f ill", + "Ġst ew", + "Ġcur b", + "ĠStep han", + "ed In", + "S F", + "ob ic", + "é ŃĶ", + "ou g", + "ĠM M", + "Ġgen etically", + "ope z", + "13 6", + "Ġu mb", + "anc ers", + "Ġcoh ort", + "Ġmerch andise", + "Ġimp osing", + "ĠLegisl ature", + "ĠArch ive", + "iv ia", + "ĠN aval", + "Ġoff ences", + "Ġmir acle", + "Ġsn apped", + "Ġf oes", + "Ġextensive ly", + "ĠR af", + "Ġc ater", + "ed ience", + "K it", + "ĠB in", + "Ġrecomm ends", + "ĠC ities", + "Ġrig id", + "ĠRE AD", + "ĠNob le", + "ĠT ian", + "Ġcertific ates", + "ant is", + "o iler", + "ĠBudd hist", + "d id", + "Ġsurvey ed", + "Ġdown ward", + "Ġprint s", + "ĠMot ion", + "ron ics", + "ĠS ans", + "oss ibly", + "u ctions", + "Ġcolon ies", + "ĠDan ish", + "un it", + "Ġsp oil", + "Ġadvis ory", + "ber ries", + "Pl an", + "Ġspecific ation", + "op hers", + "ĠRes ource", + "Ġsh irts", + "prising ly", + "commun ications", + "Ġtriv ial", + "Ġmention ing", + "ise xual", + "Ġsupp lements", + "Ġsuper vision", + "B P", + "v or", + "Ġw it", + "Ġco oldown", + "Ġplaint iff", + "ĠReview s", + "ĠS ri", + "ĠM int", + "ĠSug ar", + "Ġafter ward", + "ĠPri est", + "ĠInvest ment", + "og ene", + "ĠT aking", + "Ġstretch ing", + "Ġinflamm ation", + "ĠTe hran", + "Ġl ining", + "Ġfree zing", + "ĠEnt ity", + "Ġins piring", + "spe cial", + "pr ice", + "Ġsu e", + "ĠP orter", + "oun ge", + "ET A", + "ĠD erek", + "ĠLu is", + "u o", + "ym ph", + "Ġex terior", + "ih il", + "ĠAsh ley", + "in ator", + "Ġnut rients", + "ĠTh rones", + "Ġfin ances", + "ĠIn spect", + "Ġspe cially", + "ĠRequ ired", + "ĠP TS", + "ĠViol ence", + "oint ed", + "sh ots", + "Ġex cerpt", + "co on", + "IN S", + "ĠG ri", + "Ġrecogn ised", + "We ek", + "You ng", + "Ġv om", + "is le", + "ĠCur ry", + "ĠBudd h", + "Ġnot ebook", + "Ġd urable", + "/ ?", + "ĠG ad", + "ĠP upp", + "Ġforg ive", + "p ark", + "Ġpersonal ities", + "an alysis", + "cl amation", + "Ġelev ator", + "Ġware house", + "ĠR ole", + "un n", + "Ġillust ration", + "ĠSc an", + "Ġatmosp heric", + "Im port", + "AN C", + "rict ed", + "f u", + "01 0", + "Ġar che", + "Ġreward ed", + "akespe are", + "Ġintern ally", + "ĠR BI", + "alk er", + "Ġeleph ant", + "ow itz", + "ĠP izza", + "Ġbip artisan", + "é s", + "Ġslow ed", + "ĠSt ark", + "Ġover ride", + "OU S", + "Ġ3 20", + "undred s", + "ĠDe ck", + "ĠC ensus", + "be e", + "14 6", + "ot or", + "Ġ ip", + "Ġu b", + "oc ations", + "ĠBut ton", + "r ice", + "Ġc ripp", + "ff f", + "Ġorig inated", + "Ġoverwhel med", + "app a", + "Ġfore most", + "âĢ ij", + "ĠL EG", + "re lease", + "eat ured", + "at ches", + "Ġre ps", + "Ġl ending", + "ĠRe ference", + "ĠCl ient", + "16 5", + "vent h", + "Com plete", + "ĠPat rol", + "Ġsw orn", + "c am", + "Ġshut tle", + "ĠR alph", + "Ġh ometown", + "- ,", + "on al", + "ĠB P", + "å ı", + "Ġpersu ade", + "ĠAlex and", + "Ġcomb ines", + "Ġv ivid", + "ĠL ag", + "Ġenc oding", + "Ġsal vation", + "w en", + "ĠRec overy", + "i ya", + "Un iversity", + "ĠB iden", + "Ġbud gets", + "ĠTex ans", + "f its", + "Ġhon ored", + "Ġp ython", + "T D", + "## #", + "cl one", + "Ġbl ink", + "ĠL iquid", + "Ġunemploy ed", + "Ġcl ashes", + "ĠCoun sel", + "Ġdirect ing", + "Ġpun ct", + "ĠFal cons", + "Ġsh ark", + "ĠDam ascus", + "Ġje ans", + "Ġemb ark", + "Ġse ize", + "Ġup wards", + "2 80", + "ĠE z", + "ĠAny thing", + "Ġex otic", + "l ower", + "ĠCreat or", + "ĠU m", + "Ġsubur bs", + "ber ger", + "ĠW end", + "Ġm int", + "ĠX X", + "ĠD ro", + "Ġsuff ers", + "Ġher b", + "t ree", + "Ġfrag ile", + "Ġflood ed", + "ĠAl cohol", + "ole an", + "ny der", + "ĠK O", + "F ram", + "Ġ13 6", + "Ġow ed", + "ĠMe lee", + "ĠH ash", + "Ġwh isk", + "Ġsu do", + "r r", + "Qu ick", + "app ro", + "Ġi i", + "ĠEx amples", + "he e", + "Ġpromot es", + "per ature", + "k ar", + "ĠHon or", + "Ġs odium", + "ĠL if", + "ros so", + "intend ent", + "Ġcorrespond ent", + "F ound", + "sec ret", + "Ġident ifies", + "ag ne", + "Ġl ou", + "ĠP P", + "Ġcoinc idence", + "m ove", + "Ġmilit ia", + "Ġinf iltr", + "ĠPrim ary", + "Ġpitch ing", + "ĠI b", + "ĠGO OD", + "ãĤ ¸", + "ĠW izards", + "ir al", + "ĠVen us", + "R R", + "ĠâĢ ķ", + "ĠCase y", + "Ġsad ly", + "Ġadm ire", + "Ġembarrass ed", + "c b", + "M el", + "Ġtub es", + "Ġbeaut ifully", + "ĠQueens land", + "Bel ow", + "re z", + "qu et", + "ple asant", + "Ġ «", + "C amp", + "Ġdec isive", + "19 98", + "ĠL amb", + "ut ton", + "h n", + "ĠJ agu", + "au nder", + "ĠC ord", + "Ġcl erk", + "Ġca ffe", + "Ġwip ed", + "Ġre im", + "ĠMount ains", + "Ġimprison ed", + "Ġdevelop s", + "ĠP ra", + "Ġmodel ing", + "Any one", + "ance l", + "ĠS it", + "Ġshield s", + "Ġl awn", + "Ġcard iovascular", + "Ġdemonstr ating", + "Ġpar se", + "ĠIsrael is", + "Ġeuro s", + "14 3", + "Ġgl orious", + "ins ki", + "ec d", + "Ġcondition ing", + "Ġhel pless", + "Ġmicro sc", + "ĠHar bor", + "Ġst akes", + "Ġ2 60", + "Ġun equ", + "ĠFl oyd", + "Ġd amp", + "Ġappar atus", + "ĠLaw s", + "Ġcoun ters", + "Ġindu ce", + "at able", + "ĠAh med", + "Ġsl am", + "N ovember", + "Ġpers ist", + "Ġim minent", + "á n", + "Ġsh red", + "Ġph ases", + "ĠEd monton", + "ĠArm strong", + "ĠMe et", + "ĠK itty", + "Ñ Ģ", + "c irc", + "ĠAd ult", + "Ġa rose", + "ĠX en", + "D an", + "g ow", + "Ġsuper f", + "ĠAd mir", + "Ġend ure", + "Ġkey word", + "yr us", + "Ġy arn", + "Ġpath way", + "ĠHop kins", + "mid t", + "Ġcens orship", + "d ependent", + "Ġinstruct or", + "S ources", + "Ġto e", + "Ġball oon", + "N ob", + "Ġsw ear", + "ĠCast ro", + "Ġgl oss", + "ĠK avanaugh", + "Ġremark ably", + "Ph otos", + "ĠN om", + "ĠS outheast", + "y ers", + "Ġvalid ation", + "Ġcann on", + "ĠVict ory", + "ĠPier re", + "Ġcaut ious", + "Aud io", + "Ġf etch", + "ĠG ift", + "ĠH yp", + "Ġrem edy", + "Z E", + "Ġsc ent", + "Ġbe ard", + "ĠR ut", + "- \"", + "Ġpat ents", + "H y", + "Ġun just", + "Ġpot ato", + "Ġforth coming", + "Ġche f", + "ĠR ift", + "aff e", + "ĠR OM", + "ĠL aunch", + "Ġp ads", + "ĠNe o", + "Ġon set", + "Ġsquee ze", + "s afe", + "Ġpref ix", + "ĠT M", + "ĠN early", + "ĠClin ical", + "ĠM ental", + "ot iation", + "ĠUn ic", + "ant ry", + "ĠC ir", + "Ġep it", + "à ¦", + "Ġextract ed", + "verse ly", + "ri ad", + "Ġstr ains", + "Ġto ps", + "Ġpo em", + "ĠRand y", + "ĠMap le", + "TH ER", + "up iter", + "ĠSS D", + "ļ é", + "Ġun con", + "per ing", + "Ġsle pt", + "in ers", + "Ġunder water", + "ĠEv idence", + "g one", + "20 5", + "Ġhistor ians", + "Ġsynt hesis", + "Ġf rog", + "b asketball", + "Ġvibr ant", + "Ġsub ord", + "Ġ3 65", + "ĠD ial", + "Ġcooper ate", + "HA HA", + "Ġgreet ed", + "15 8", + "Ġj azz", + "Ġinto x", + "ĠWalk ing", + "Ġsuper visor", + "ĠF usion", + "ĠMer cedes", + "s end", + "H am", + "s d", + "n l", + "Ġtour s", + "ĠF IFA", + "Ġcul p", + "g d", + "30 4", + "Ġple as", + "Ġillust rates", + "ĠColomb ia", + "Ġhighlight ing", + "ĠSum mary", + "Ġexp osing", + "ĠD ru", + "Ġir ony", + "r itional", + "ĠCar roll", + "ĠEll is", + "P ict", + "ĠR apt", + "Ġad apter", + "Ġun m", + "Ġcor pse", + "Ġceleb rities", + "D en", + "at um", + "ĠAp ocalypse", + "ĠW ag", + "lin ing", + "Ġhorm ones", + "R ub", + "ĠX i", + "ĠV aults", + "20 8", + "alky rie", + "inos aur", + "Ġfeed s", + "v ity", + "Ġdefe ating", + "W ait", + "Ġemphas ize", + "ĠSteel ers", + "yr inth", + "le ys", + "ĠWhe never", + "Current ly", + "ĠCl ock", + "Ġcollect ively", + "any on", + "ĠJ P", + "Ġment ality", + "Ġdownload s", + "Ġsurround ings", + "ĠBarn es", + "Ġflags hip", + "Ġindic ators", + "Ġgra pp", + "Jan uary", + "ĠElement al", + "ĠAthen a", + "ib al", + "Ġs ights", + "Ġcap ita", + "ĠTreat y", + "Ġvo iced", + "ĠG az", + "let te", + "Ġy a", + "Ġexp ired", + "Leg end", + "H ot", + "n ature", + "Ġunst able", + "Ġ2 80", + "à º", + "Com ment", + "AL E", + "Ġquest s", + "Ġhand ler", + "n is", + "Ġvers atile", + "Ġconce al", + "enge ance", + "ĠInter active", + "Ġobs essed", + "ĠDog s", + "Ġcr acked", + "S ound", + "s v", + "ĠD ylan", + "ro ads", + "f x", + "ĠCath olics", + "ĠH ag", + "Ġsl ammed", + "Ġgl owing", + "s ale", + "Ġtiss ues", + "ĠCh i", + "ne e", + "Ġc her", + "s ic", + "ur rection", + "Ġb acon", + "ul atory", + ") .\"", + "Ġir regular", + "FOR M", + "ass ed", + "Ġintention al", + "Ġcompens ate", + "ĠSpe aking", + "ĠS ets", + "15 3", + "Ġconvent ions", + "b ands", + "em ade", + "Ġe cc", + "ĠWin ston", + "ĠAssass in", + "ĠBelg ian", + "Ġdepend ence", + "Ġnic he", + "Ġb ark", + "ĠJ azz", + "Ġdisadvant age", + "Ġgas oline", + "Ġ16 5", + "çļ Ħ", + "ess a", + "mod ule", + "ang ular", + "O Y", + "ĠTreat ment", + "it as", + "ol ation", + "ĠArn old", + "Ġfe ud", + "ĠN est", + "Ġthe atre", + "ew ater", + "Ġmin ors", + "olic y", + "ĠH aven", + "div ision", + "Ġtr unk", + "F ar", + "ĠP ull", + "Ġcapt uring", + "Ġ18 00", + "ĠTe en", + "Ġex empl", + "Ġclin ics", + "ĠB urg", + "Ġsubst it", + "Ġpay load", + "ĠL av", + "ĠT roy", + "ĠW itness", + "Ġfrag ments", + "Ġpass words", + "Ġg ospel", + "ĠG in", + "Ġten ants", + "ol ith", + "S ix", + "Pre vious", + "ĠAg es", + "ĠDar win", + "Ġbl at", + "Ġem pathy", + "sm ith", + "b ag", + "ĠE cho", + "ĠC amb", + "ĠM add", + "ĠB oo", + "Ġred e", + "ĠBurn ing", + "Ġsmooth ly", + "ĠAd rian", + "ĠV ampire", + "ĠMon sters", + "ste am", + "Sty le", + "M a", + "re a", + "ĠD war", + "aly st", + "urs or", + "Ġelim ination", + "Ġcrypt o", + "ch t", + "ĠE ternal", + "âĢ¦ ]", + "ĠS orce", + "I ll", + "N ER", + "Ġu h", + "Con clusion", + "w age", + "Ġresp ir", + "Ġrem inis", + "het ical", + "Ġg y", + "Ġutil ized", + "ic idal", + "Ġ19 00", + "Ġhun ters", + "ĠSw an", + "ĠRe act", + "Ġvis itor", + "ĠThanks giving", + "30 8", + "Post s", + "Ġh ips", + "19 97", + "om ers", + "Ġkn ocking", + "ĠVeh icle", + "Ġt il", + "Ġ13 8", + "Ġm i", + "ĠInvest igation", + "ĠKen ya", + "Ġcas ino", + "Ġmot ives", + "Ġreg ain", + "re x", + "Ġweek ends", + "Ġstab bed", + "bor o", + "Ġexplo ited", + "ĠHA VE", + "ĠTe levision", + "c ock", + "Ġprepar ations", + "Ġende av", + "ĠRem ote", + "ĠM aker", + "ĠPro du", + "ĠEv an", + "Ġinform ational", + "ĠLouis ville", + "15 4", + "ĠDream s", + "Ġpl ots", + "ĠRun ner", + "Ġhur ting", + "Ġacad emy", + "ĠMont gomery", + "n m", + "ĠL anc", + "ĠAl z", + "2 10", + "el ong", + "Ġretail er", + "Ġar ising", + "Ġrebell ion", + "Ġbl onde", + "play ed", + "Ġinstrument al", + "C ross", + "Ġret ention", + "Ġtherape utic", + "Ġse as", + "Ġinfant ry", + "ĠCl int", + "Ġprompt ing", + "Ġbit ch", + "Ġst ems", + "ĠK ra", + "Ġthe sis", + "ĠB og", + "ru ed", + "Ġk ings", + "Ġcl ay", + "ific ent", + "ĠY ES", + "ĠTh ing", + "ĠCub s", + "vey ard", + "els h", + "in arily", + "ĠE y", + "ĠRoll ing", + "Ġev olving", + "Ind ia", + "Ġrecogn izes", + "Ġgrad uation", + "is ers", + "Ġfert ility", + "ĠMil an", + "Comm and", + "Ġbox ing", + "Ġ19 43", + "Ġgl uten", + "ĠEm ir", + "Ġid ol", + "Ġcon ceived", + "ĠCre ation", + "Mer it", + "udd y", + "uss ions", + "ĠLie utenant", + "iet al", + "Ġunch anged", + "ĠSc ale", + "ĠCrime a", + "ball s", + "ator ial", + "Ġdepth s", + "Ġempir ical", + "Ġtrans m", + "Ġuns afe", + "miss ible", + "com fort", + "15 6", + "Ġmechan ic", + "00 2", + "l ins", + "Ġsm oked", + "P os", + "Ġslow ing", + "Ġl av", + "Tex as", + "Ġche ating", + "ĠMet ropolitan", + "eth yl", + "Ġdiscover ing", + "as se", + "Ġpen cil", + "ĠPy ongyang", + "Ġclos et", + "ĠShe et", + "ĠEnt ry", + "ou stic", + "Ġmy st", + "er ate", + "ari at", + "Ġminer als", + "Ġmusic ian", + "ĠP ul", + "ĠM az", + "24 9", + "Ġper missions", + "Ġ iv", + "en ary", + "ick ers", + "ĠB ing", + "he a", + "en able", + "Ġgri ev", + "Ġassert ed", + "ĠColon el", + "Ġaff idav", + "w o", + "Ġse ated", + "ĠR ide", + "Ġpaint ings", + "ĠP ix", + "Ġ13 7", + "ish i", + "umb ai", + "g otten", + "ĠEar l", + "Ġin ning", + "Ġc ensus", + "Ġtrave lled", + "ĠCons ult", + "18 5", + "b ind", + "Ġsimpl icity", + "Ġoverlook ed", + "ĠHelp ful", + "Ġmon key", + "Ġoverwhelming ly", + "Bl ood", + "ĠFl int", + "ĠJ ama", + "ĠPres ent", + "ĠR age", + "ĠT A", + "pt ive", + "Ġturn out", + "w ald", + "ĠD olphins", + "ĠV PN", + "Ġon ion", + "Ġcraft ing", + "m ma", + "ĠMerc ury", + "Ġarr ange", + "Ġalert s", + "ĠO T", + "zb ollah", + "Ġg ases", + "ĠRichards on", + "s al", + "l ar", + "Ġfro st", + "Ġlower ing", + "Ġacc laim", + "Ġstart ups", + "ĠG ain", + "ess ment", + "Ġguard ian", + "äº º", + "ĠP ie", + "ĠL inks", + "Ġmer its", + "Ġaw ake", + "Ġparent al", + "Ġexceed s", + "Ġid le", + "ĠPil ot", + "Ġe Bay", + "ĠAc cept", + "ipe g", + "C am", + "ĠK ot", + "Ġtrad ers", + "olit ics", + "unk er", + "ĠP ale", + "os i", + "an mar", + "Ġ19 47", + "ĠF ell", + "est ial", + "it ating", + "G F", + "ĠS r", + "if ted", + "Ġconnect or", + "ĠB one", + "ill es", + "2 60", + "h ma", + "Ġoverl ap", + "ĠGit Hub", + "Ġclean er", + "ĠBapt ist", + "ĠW AS", + "Ġlung s", + "Ñ ģ", + "ĠB UT", + "Ġc ite", + "Ġpit ched", + "reat ment", + "Ġtro phies", + "ĠN u", + "38 6", + "ĠPr ide", + "Ġattend ees", + "[ ]", + "17 9", + "Ġspat ial", + "Ġpri zes", + "ĠRel igion", + "Ġshow case", + "ĠC ategory", + "vid ia", + "T arget", + "Pro perty", + "? ,", + "Ġf usion", + "p ie", + "ĠU CLA", + "Ġsound track", + "Ġprin cess", + "ĠC aval", + "sh ould", + "Ġlim bs", + "Back ground", + "Ġlone ly", + "Ġc ores", + "ĠT ail", + "she et", + "Ġ13 2", + "R a", + "ãĤ «", + "ĠB olt", + "Ġbook ed", + "Ġadmin ister", + "Ġequ als", + "w y", + "Ġobserv ing", + "ĠBar on", + "ĠAd obe", + "Ġv irgin", + "ĠSocial ist", + "M ove", + "gh azi", + "ĠLind a", + "2 12", + "Ġbre wing", + "Ġmerch ants", + "bur se", + "Ġdiv or", + "Ġmet als", + "ĠN er", + "Ġsum s", + "ĠEn emy", + "Ġen vision", + "Ġgrant ing", + "ĠH oney", + "ĠSk yrim", + "Ġsoc io", + "gr aded", + "Ġselect ive", + "W ASHINGTON", + "Ġ19 48", + "ĠSir ius", + "ĠG ross", + "act ivity", + "ĠI van", + "Ġfur ious", + "BS D", + "ĠPre vious", + "Ġrespons ive", + "Ġchar itable", + "Ġle aning", + "ĠP ew", + "Ġviol ates", + "\\\\\\\\ \\\\\\\\", + "ĠCom ing", + "w ire", + "Ġpo et", + "Ġres olutions", + "comm and", + "ĠPortug uese", + "Ġnick name", + "Ġde af", + "Feb ruary", + "Ġrecogn ise", + "Ġentire ty", + "Ġseason al", + "pl aced", + "ĠTe legraph", + "Ġmicro phone", + "our ing", + "Ġgr ains", + "Ġgovern ed", + "Ġpost p", + "ĠW aters", + "in ement", + "Ġund ocumented", + "ĠCom cast", + "Ġf ox", + "Ġassault s", + "re on", + "man y", + "ĠJen kins", + "ĠAny way", + "Ġassess ments", + "Ġdown s", + "ĠM ouse", + "Ġsuper b", + "k t", + "ĠD ow", + "Ġtax ation", + "4 01", + "Ġsm iles", + "Ġundert aken", + "Ġex h", + "Ġenthusi astic", + "Ġtw ent", + "Ġgovernment al", + "Ġautonom y", + "ĠTechn ologies", + "ĠCh ain", + "Ġpreval ent", + "f b", + "Ġnic otine", + "og ram", + "j ob", + "Ġawa iting", + "ĠMen u", + "Ġdep uties", + "k ov", + "ish ops", + "But ton", + "ĠShan ghai", + "Ġdies el", + "ĠD uck", + "R yan", + "ĠPC s", + "N F", + "j ury", + "ent e", + "Ġinacc urate", + "edd y", + "Wh atever", + "Ġshow c", + "ĠN ad", + "od us", + "et r", + "Ġplaint iffs", + "ĠW OR", + "ĠAss ange", + "Ġpriv at", + "Ġpremium s", + "Ġt am", + "UR L", + "Ġel ites", + "ĠR anger", + "otten ham", + "ĠH off", + "ĠAt hens", + "Ġdefin ite", + "Ġs ighed", + "Ġeven ly", + "2 11", + "ĠAm ber", + "ak ia", + "Ġmail ing", + "Ġcr ashing", + "ĠConfeder ate", + "ru gged", + "W al", + "ĠDep ths", + "Ġjuven ile", + "Ġreact or", + "Introdu ction", + "ĠDel uxe", + "19 95", + "ĠS anchez", + "ĠM ead", + "iv able", + ": -", + "ĠPlan ning", + "ĠT rap", + "qu in", + "ĠProt ect", + "ve red", + "In formation", + "Ġkid ney", + "inn amon", + "l as", + "Ġpolic ing", + "Ġtoler ate", + "ĠQ i", + "Ġbi ased", + "F ort", + "ĠK i", + "s ave", + "Ġprivile ged", + "Ġbe asts", + "ĠGl as", + "ĠC inem", + "Ġcome back", + "Sund ay", + "Ġext inction", + "h ops", + "Ġtrans mit", + "Ġdoub les", + "ĠFl at", + "16 7", + "Ġdis puted", + "Ġinjust ice", + "f oo", + "V ict", + "role um", + "ĠJul ie", + "Con text", + "ĠR arity", + "iss ue", + "Comp onent", + "Ġcounsel ing", + "an ne", + "d ark", + "Ġobject ions", + "u ilt", + "Ġg ast", + "Ġpl ac", + "Ġun used", + "ãĥ ĩ", + "ĠT rial", + "ĠJ as", + "hed ral", + "ob b", + "Ġtempor al", + "ĠPR O", + "ĠN W", + "ĠAnn iversary", + "L arge", + "Ġther m", + "Ġd avid", + "Ġsystem ic", + "ĠSh ir", + "m ut", + "ĠNe pt", + "add ress", + "Ġscan ning", + "Ġunderstand able", + "Ġcan vas", + "C at", + "ĠZ oo", + "Ġang els", + "L O", + "ĠStat ement", + "ĠS ig", + "ov able", + "ĠA way", + "sh aring", + "ocr ats", + "st ated", + "Ġweigh ing", + "N or", + "w ild", + "B ey", + "Ġaston ishing", + "ĠReyn olds", + "Ġop ener", + "Ġtrain er", + "Ġsurg ical", + "p n", + "Ġadjust ing", + "whe el", + "Ġf rown", + "erv ative", + "Ġsusp end", + "With in", + "te in", + "Ġobst acle", + "Ġliber ties", + "ym es", + "Ġur anium", + "ans om", + "an ol", + "ub a", + "ĠL oss", + "Ġa rous", + "ĠHend erson", + "W ow", + "s pl", + "c ur", + "Ġ Ń", + "Ġtheir s", + "Dam age", + "Ġdownload ing", + "Ġdisc ern", + "ĠSt o", + "ĠFl a", + "Ġh ath", + "ĠA j", + "Ġun pleasant", + "Europe an", + "exp ensive", + "Ġscreens hot", + "ĠU V", + "Ġall ied", + "ĠPers ian", + "Ġmonop oly", + "Ġat om", + "ĠReds kins", + "\"> <", + "Ġcan cell", + "Ġcinem a", + "13 1", + "f air", + "ĠAlf red", + "Ġd uck", + "arg s", + "22 3", + "ĠIS I", + "Ġsign aling", + "in ar", + "Ġlaugh s", + "Ġfor wards", + "Ġreck less", + "Ġlisten ers", + "at ivity", + "Ġvast ly", + "n ant", + "L ess", + "ĠHun ting", + "ĠScient ific", + "IT ED", + "Ġkn ight", + "ĠH TC", + "us a", + "t mp", + "Ġr ude", + "ĠLegend ary", + "Ġar ises", + "B ad", + "ĠCl aim", + "pe g", + "Ġreal ities", + "Th ink", + "Ġ °", + "Ġro de", + "Ġstri ve", + "Ġan ecd", + "Ġshort s", + "Ġhypot hes", + "Ġcoord inated", + "ĠGand hi", + "ĠF PS", + "R ED", + "Ġsuscept ible", + "Ġshr ink", + "ĠCh art", + "Hel p", + "Ġ ion", + "de ep", + "rib es", + "ĠK ai", + "ĠCustom er", + "Sum mary", + "Ġc ough", + "w ife", + "Ġl end", + "Ġposition ing", + "Ġlot tery", + "ĠC anyon", + "Ġf ade", + "Ġbron ze", + "ĠKenn y", + "Ġbo asts", + "ĠEnh anced", + "rec ord", + "Ġemer gence", + "Ġa kin", + "ĠB ert", + "it ous", + "âĸ ij", + "Ġst ip", + "Ġexch anged", + "om ore", + "als h", + "Ġreserv oir", + "Ġstand point", + "W M", + "Ġiniti ate", + "Ġdec ay", + "Ġbrew ery", + "Ġter ribly", + "Ġmort al", + "lev ard", + "Ġrev is", + "N I", + "el o", + "Ġconf ess", + "ĠMS NBC", + "Ġsub missions", + "Cont roller", + "Ġ20 2", + "ĠR uth", + "} );", + "ĠAz ure", + "Ġ .\"", + "20 6", + "ĠMarket ing", + "Ġl aund", + "ien cies", + "Ġrenown ed", + "ĠT rou", + "ĠN GO", + "ble ms", + "Ġterr ified", + "Ġwar ns", + "Ġper t", + "Ġuns ure", + "4 80", + "ale z", + "ult z", + "ĠOut side", + "Ġst yl", + "ĠUnder ground", + "Ġp anc", + "Ġd ictionary", + "Ġf oe", + "rim inal", + "ĠNor wegian", + "Ġj ailed", + "Ġm aternal", + "é e", + "ĠLu cy", + "c op", + "Ch o", + "Ġuns igned", + "ĠZe lda", + "ĠIns ider", + "ĠContin ued", + "Ġ13 3", + "ĠNar uto", + "ĠMajor ity", + "16 9", + "ĠW o", + "ãĤ ĵ", + "Ġpast or", + "Ġinform al", + "Ð ½", + "an throp", + "jo in", + "ãģ Ĺ", + "it ational", + "N P", + "ĠWrit ing", + "f n", + "ĠB ever", + "19 5", + "Ġy elling", + "Ġdr astically", + "Ġe ject", + "Ġne ut", + "Ġth rive", + "ĠFre qu", + "ou x", + "Ġpossess es", + "ĠSen ators", + "ĠD ES", + "ĠSh akespeare", + "ĠFran co", + "ĠL B", + "uch i", + "Ġinc arn", + "Ġfound ers", + "F unction", + "Ġbright ness", + "ĠB T", + "Ġwh ale", + "ĠThe ater", + "m ass", + "ĠD oll", + "S omething", + "Ġecho ed", + "ĠHe x", + "c rit", + "af ia", + "Ġgodd ess", + "Ġele ven", + "ĠPre view", + "ĠAur ora", + "Ġ4 01", + "uls ive", + "ĠLog an", + "in burgh", + "ĠCent ers", + "ĠON LY", + "ĠA id", + "Ġparad ox", + "Ġh urd", + "ĠL C", + "D ue", + "c ourt", + "Ġoff ended", + "Ġeval uating", + "ĠMatthew s", + "Ġto mb", + "Ġpay roll", + "Ġextra ction", + "ĠH ands", + "if i", + "Ġsuper natural", + "ĠCOM M", + "] =", + "dog s", + "Ġ5 12", + "ĠMe eting", + "Rich ard", + "ĠMax imum", + "Ġide als", + "Th ings", + "m and", + "ĠReg ardless", + "Ġhum ili", + "b uffer", + "L ittle", + "ĠD ani", + "ĠN ak", + "Ġliber ation", + "ĠA be", + "ĠO L", + "Ġstuff ed", + "ac a", + "ind a", + "raph ic", + "Ġmos qu", + "Ġcampaign ing", + "Ġoccup y", + "S qu", + "r ina", + "ĠW el", + "ĠV S", + "Ġphys ic", + "Ġp uls", + "r int", + "oad ed", + "ET F", + "ĠArch ives", + "Ġven ues", + "h ner", + "ĠTur bo", + "Ġl ust", + "Ġappeal ed", + "que z", + "il ib", + "ĠTim othy", + "Ġo mn", + "d ro", + "Ġobs ession", + "ĠSav age", + "19 96", + "Gl obal", + "J es", + "2 14", + "Ġsl iding", + "Ġdisapp ro", + "ĠMag ical", + "Ġvolunt arily", + "g b", + "ane y", + "Ġprop het", + "ĠRe in", + "ĠJul ia", + "ĠW orth", + "aur us", + "Ġb ounds", + "ie u", + ")) )", + "Ġcro re", + "ĠCitiz en", + "S ky", + "Ġcolumn ist", + "Ġseek ers", + "ond o", + "IS A", + "ĠL ength", + "Ġnost alg", + "Ġnew com", + "Ġdet rim", + "ent ric", + "3 75", + "ĠG E", + "Ġaut op", + "Ġacadem ics", + "App Data", + "ĠS hen", + "Ġid iot", + "ĠTrans it", + "Ġteasp oon", + "W il", + "K O", + "ĠCom edy", + "> ,", + "Ġpop ulated", + "W D", + "Ġp igs", + "ĠO culus", + "Ġsymp athetic", + "Ġmar athon", + "19 8", + "Ġseiz ure", + "s ided", + "Ġd op", + "irt ual", + "L and", + "ĠFl oor", + "osa urs", + "... ]", + "Ġl os", + "Ġsubsid iary", + "E Y", + "ĠPart s", + "ĠSt ef", + "ĠJud iciary", + "Ġ13 4", + "Ġmir rors", + "Ġk et", + "t imes", + "Ġneuro log", + "Ġc av", + "ĠGu est", + "Ġtum or", + "sc ill", + "ĠLl oyd", + "E st", + "Ġcle arer", + "Ġstere otypes", + "Ġd ur", + "not hing", + "Red dit", + "Ġnegoti ated", + "---------------- --------", + "23 5", + "Ġfl own", + "ĠSe oul", + "ĠRes ident", + "ĠS CH", + "Ġdisappear ance", + "ĠV ince", + "g rown", + "Ġgrab s", + "r il", + "ĠInf inite", + "ĠTw enty", + "Ġpedest rian", + "Ġjer sey", + "ĠF ur", + "ĠInf inity", + "ĠEll iott", + "Ġment or", + "Ġmor ally", + "Ġob ey", + "sec ure", + "iff e", + "Ġantib iotics", + "ang led", + "ĠFre eman", + "ĠIntrodu ction", + "J un", + "Ġm arsh", + "ic ans", + "ĠEV ENTS", + "och ond", + "W all", + "icult y", + "Ġmisdem eanor", + "Ġl y", + "Th omas", + "ĠRes olution", + "Ġanim ations", + "ĠD ry", + "Ġinter course", + "ĠNew castle", + "ĠH og", + "ĠEqu ipment", + "17 7", + "Ġterrit orial", + "Ġarch ives", + "20 3", + "Fil ter", + "ĠMun ich", + "Ġcommand ed", + "ĠW and", + "Ġpit ches", + "ĠCro at", + "Ġrat ios", + "ĠM its", + "Ġaccum ulated", + "ĠSpecific ally", + "Ġgentle man", + "acer b", + "Ġp enn", + "Ġa ka", + "ĠF uk", + "Ġinterven e", + "ĠRef uge", + "ĠAlz heimer", + "Ġsuccess ion", + "oh an", + "d oes", + "L ord", + "Ġsepar at", + "Ġcorrespond ence", + "Ġsh iny", + "P rior", + "Ġs ulf", + "Ġmiser able", + "Ġded ication", + "( ).", + "Ġspecial ists", + "Ġdefect s", + "ĠC ult", + "ĠX ia", + "Ġje opard", + "ĠO re", + "Ab ility", + "Ġle ar", + "Ġamb itions", + "ĠB MI", + "ĠArab s", + "Ġ19 42", + "Ġpres ervation", + "ific ate", + "Ġash amed", + "l oss", + "ĠRest aur", + "Ġrese mble", + "Ġen rich", + "ĠK N", + "ĠCl an", + "fl oat", + "Ġplay able", + "IT T", + "Ġharm ony", + "arr ison", + "ĠWe instein", + "w ere", + "Ġpoison ing", + "ĠCom put", + "ĠWord Press", + "m ajor", + "ĠVal ve", + "F an", + "ĠTh row", + "ĠRom ans", + "ĠDep ression", + "ad os", + "Ġtort ured", + "Ġbal ancing", + "bott om", + "Ġacqu iring", + "ĠMon te", + "ard i", + "Ġa ura", + "Ġ# #", + "ĠStand ing", + "ĠAtl as", + "C F", + "Ġintr ins", + "ĠBen ghazi", + "Ġcamp ing", + "Ġt apped", + "bl ade", + "st rous", + "ĠR abb", + "ĠW ritten", + "t ip", + "ĠNe igh", + "ster dam", + "ĠAll ow", + "ĠHe aling", + "ĠR hod", + "n um", + "Ġcaffe ine", + "ĠPer cent", + "Ġbo o", + "Ġapp les", + "30 5", + "Ġwel coming", + "Ġappl aud", + "Ġa usterity", + " ±", + "ĠRe ality", + "ef e", + "å ®", + "Ġsu cks", + "Ġtab s", + "ĠPay Pal", + "Ġback pack", + "Ġgif ted", + "abul ary", + "ĠSc out", + "ir teen", + "Ġch in", + "Ġo mitted", + "Ġnegative ly", + "Ġaccess ing", + "ĠE arn", + "Ġambul ance", + "Ġhead phones", + "Ġ20 5", + "ĠRef resh", + "p resident", + "ĠKit chen", + "ĠEnt ered", + "ĠS nyder", + "00 5", + "om ical", + "Ġborrow ed", + "ĠN em", + "Ġav iation", + "Ġst all", + "rim ination", + "Ġuniform s", + "it ime", + "ĠSim mons", + "ener gy", + "ab lished", + "y y", + "qual ified", + "Ġrall ies", + "ĠSt uart", + "fl ight", + "Ġgang s", + "r ag", + "Ġv ault", + "lu x", + "ĠCom par", + "Ġdesign ation", + "20 9", + "ĠJ os", + "d ollar", + "z ero", + "Ġwell s", + "30 3", + "Ġconstitu ents", + "Ġhe ck", + "Ġc ows", + "Ġcommand ers", + "Ġdifferent ial", + "ĠC atherine", + "29 9", + "Ġval ve", + "Ġbr ace", + "Ġperspect ives", + "c ert", + "f act", + "icular ly", + "ĠMc N", + "pl anes", + "Ġint ric", + "Ġpe as", + "ov an", + "Ġtoss ed", + "ret ch", + "ĠL opez", + "Ġunf amiliar", + "de ath", + "ĠA part", + "ĠCh ang", + "Ġrelie ved", + "rop he", + "Ġair ports", + "Ġfre ak", + "ut il", + "M ill", + "ĠCh in", + "ĠOw en", + "m ale", + "ĠBro ken", + "ĠWind s", + "ro b", + "r ising", + "Ġfire fighters", + "Ġauthor itarian", + "Ġ14 8", + "Bit coin", + "ex ternal", + "Ġbrow sers", + "iche ver", + "or ian", + "Ġun b", + "Ġpo ke", + "ĠZ ot", + "M id", + "ĠPop ular", + "Ġco vert", + "Ġcont ributes", + "Ġ6 50", + "Ġcont ention", + "G ate", + "Ġcons oles", + "Ġchrom os", + "ĠI X", + "Ġvis ually", + "ĠE isen", + "Ġjewel ry", + "Ġdeleg ation", + "Ġacceler ate", + "ĠR iley", + "Ġsl ope", + "Ġind oor", + "it ially", + "Ġhuge ly", + "Ġtun nels", + "Ġfin ed", + "Ġdirect ive", + "Ġfore head", + "ustom ed", + "Ġsk ate", + "Mus ic", + "g as", + "Ġrecogn izing", + "am bo", + "Ġover weight", + "ĠGr ade", + "Ù Ĭ", + "Ġsound ing", + "Ġlock ing", + "ĠR EM", + "St ore", + "Ġexc av", + "ĠLike wise", + "ĠL ights", + "Ġel bow", + "ĠSupp ly", + "w ic", + "Ġhands ome", + "19 94", + "C oll", + "Ġadequ ately", + "ĠAssoci ate", + "Ġstri ps", + "Ġcrack down", + "Ġmar vel", + "ĠK un", + "Ġpass ages", + "@@ @@", + "ĠT all", + "Ġthought ful", + "names e", + "Ġprost itution", + "bus iness", + "Ġball istic", + "person al", + "c ig", + "iz ational", + "R ound", + "ĠÂłĠÂł ĠÂłĠÂł", + "ĠCole man", + "Ġadm itting", + "ĠPl ug", + "Ġbit coins", + "ĠSu z", + "Ġfair ness", + "Ġsupp lier", + "Ġcatast rophic", + "ĠHel en", + "o qu", + "M arc", + "ĠArt icles", + "g ie", + "Ġend angered", + "Ġdest iny", + "ĠVol t", + "ol ia", + "ax is", + "Ġche at", + "Ġun ified", + "IC O", + "qu ote", + "30 2", + "ĠS ed", + "Ġsupp ression", + "Ġanaly zing", + "Ġsqu at", + "Ġfig uring", + "Ġcoordin ates", + "Ġch unks", + "Ġ19 46", + "Ġsub p", + "Ġw iki", + "ĠFor bes", + "ĠJ upiter", + "ĠE rik", + "im er", + "ĠCom mercial", + "\\ )", + "Ġlegitim acy", + "Ġd ental", + "ĠMe an", + "Ġdefic its", + "5 50", + "Orig inally", + "ĠHor ror", + "Ġcontam ination", + "ll ah", + "Ġconf isc", + "ĠCl are", + "T B", + "ĠF ailed", + "an ed", + "Ġrul er", + "ĠCont roller", + "Ġfemin ists", + "F ix", + "g ay", + "20 7", + "Ġr abbit", + "Th ird", + "ownt own", + "Ġgl ue", + "Ġvol atile", + "Ġsh ining", + "Ġf oll", + "Ġimp aired", + "Ġsup ers", + "æ Ī", + "Ġcl utch", + "ļé ĨĴ", + "Ġpro let", + "Ġ( !", + "Ġy elled", + "ĠK iev", + "ĠEr n", + "ĠSh ock", + "K B", + "Ġsit uated", + "qu ery", + "ĠN as", + "Ġan nex", + "char acter", + "ĠHol iday", + "Ġautom ation", + "ĠJ ill", + "ĠRem astered", + "Ġl inem", + "Ġwild erness", + "ĠHor izon", + "ĠGu inea", + "A Z", + "Ġmain land", + "Ġsec recy", + "LE ASE", + "Ġp unk", + "ĠProv ince", + "( ),", + "Spe ed", + "Ġhand ing", + "ĠSeb ast", + "S ir", + "r ase", + "Ġj ournals", + "Ġcon gest", + "ĠT ut", + "ir rel", + "Ġschizophren ia", + "Ġmis ogyn", + "health y", + "I ron", + "Ġreact ed", + "- $", + "25 2", + "Ġpl ural", + "Ġpl um", + "Ġbarg ain", + "Ġground ed", + "f inder", + "Ġdis se", + "ĠL az", + "O OD", + "Ġat roc", + "F actory", + "Ġmin ions", + "Ġo ri", + "ĠB rave", + "ĠP RE", + "ĠMy anmar", + "ĠH od", + "Ġexped ition", + "Ġexpl ode", + "ĠCo ord", + "Ġext r", + "ĠB rief", + "ĠAD HD", + "Ġhard core", + "feed ing", + "Ġd ile", + "ĠF ruit", + "Ġvacc ination", + "ĠM ao", + "osp here", + "Ġcont ests", + "- |", + "Ġf ren", + "isp here", + "R om", + "ĠSh arp", + "ĠTre nd", + "Ġdis connect", + "âĢ¢ âĢ¢", + "Ġper secution", + "Ear th", + "Ġhealth ier", + "38 4", + "Ġc ob", + "ĠTr inity", + "OW S", + "AN N", + "Ġspecial ty", + "Ġg ru", + "Ġcooper ative", + "wh y", + "Start ing", + "ĠIss ues", + "st re", + "ens or", + "Ġ18 5", + "Ad v", + "! ?", + "ĠRe vel", + "em ia", + "ĠH ulk", + "Ġcelebr ations", + "ĠS ou", + "ra ud", + "ĠKle in", + "Ġun real", + "con text", + "Ġpartners hips", + "Ġadop ting", + "t ical", + "Ġspl ash", + "ĠHe zbollah", + "c ategory", + "cycl op", + "xt on", + "ĠD ot", + "urd y", + "t z", + "Ġenvelop e", + "ĠN L", + "â ķ", + "Ġwhere in", + "Spe c", + "18 4", + "Ġte lev", + "al iation", + "Ġmyth s", + "å °", + "Ġrig orous", + "Ġcommun icating", + "Ġobser ver", + "Ġre he", + "ĠW ash", + "Ġapolog ized", + "ĠT in", + "Ġexpend itures", + "work ers", + "d ocument", + "Ġhes itate", + "ĠLen in", + "Ġunpredict able", + "Ġrenew al", + "cl er", + "ok ia", + "ĠCON T", + "Ġpost season", + "Tok ens", + "Ġex acerb", + "Ġbet ting", + "Ġ14 7", + "Ġelev ation", + "W ood", + "ĠSol omon", + "19 4", + "00 4", + "out put", + "Ġredu nd", + "ĠM umbai", + "Ġp H", + "Ġreprodu ce", + "ĠD uration", + "MA X", + "Ġb og", + "C BS", + "ĠBal ance", + "ĠS gt", + "ĠRec ent", + "Ġc d", + "Ġpo pped", + "Ġincomp et", + "pro p", + "ay an", + "g uy", + "Pac ific", + "Ġty r", + "Ġ{ {", + "ĠMy stic", + "ĠD ana", + "Ġmast urb", + "Ġge ometry", + "à ¢", + "ĠCor rect", + "Ġtraject ory", + "Ġdistract ed", + "Ġf oo", + "ĠW elsh", + "L uc", + "m ith", + "Ġrug by", + "Ġrespir atory", + "Ġtri angle", + "Ġ2 15", + "Ġunder graduate", + "ĠSuper ior", + "ch anging", + "_ -", + "Ġright ly", + "Ġrefere e", + "Ġluc rative", + "Ġun authorized", + "Ġresemb les", + "ĠGN U", + "ĠDer by", + "Ġpath ways", + "ĠL ed", + "Ġend urance", + "Ġst int", + "Ġcollect or", + "F ast", + "Ġd ots", + "Ġnational s", + "ĠSec urities", + "Ġwh ip", + "Par am", + "Ġlearn s", + "M agic", + "Ġdetail ing", + "m oon", + "Ġbroadcast ing", + "Ġb aked", + "26 5", + "hol m", + "ĠS ah", + "ĠHus sein", + "ĠCourt esy", + "17 4", + "Ġ14 6", + "Ġge ographic", + "pe ace", + "Ġjud ging", + "ĠS tern", + "B ur", + "Ġstory line", + "G un", + "ĠSt ick", + "24 5", + "30 7", + "ãĤ´ ãĥ³", + "ĠAdminist rator", + "Ġbur nt", + "Ġp ave", + "ch oes", + "Ex ec", + "Ġcamp uses", + "Res ult", + "Ġmut ations", + "ĠCh arter", + "Ġcapt ures", + "Ġcomp ares", + "Ġbad ge", + "S cient", + "Ġer ad", + "ier y", + "o i", + "ett es", + "ĠE state", + "Ġst rap", + "Ġproud ly", + "Ġf ried", + "Ġwithd rawn", + "ĠV oy", + "ph ony", + "It ems", + "ĠP ierce", + "b ard", + "Ġann otation", + "ant on", + "ill on", + "Im pro", + "... )", + "Ġhapp ier", + "---- --", + "ad just", + "Ġstaff ers", + "Ġactiv ism", + "Ġper f", + "Ġal right", + "N eed", + "Ġcomm ence", + "Ġopio id", + "ĠAm anda", + "E s", + "ĠP ars", + "ĠK aw", + "W orks", + "24 8", + "Ġind o", + "t c", + "end ant", + "ĠM oto", + "Ġlegal ization", + "OT E", + "Ġtask ed", + "Ġt sp", + "ĠACT IONS", + "16 6", + "Ġrefres hing", + "ĠN R", + "ĠPere z", + "Ġinfring ement", + "S Y", + "List en", + "in ning", + "k u", + "Ġrot ate", + "pro gram", + "ar ah", + "Des ign", + "Ġ( £", + "Ġst oring", + "Ġwar rants", + "Ġjud gement", + "ĠB rist", + "us ually", + "ph oto", + "ĠR an", + "ĠP ine", + "Ġoutrage ous", + "ĠValent ine", + "lu ence", + "ĠEvery body", + "Al tern", + "Ġrele vance", + "Ġtermin ated", + "Ġd essert", + "Ġfulf illed", + "Ġprosecut ed", + "ĠW ords", + "Ġm igrant", + "Ġcultiv ation", + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", + "idel ity", + "ĠV ern", + "ĠLog in", + "Ġmetaph or", + "ĠT ip", + "Ġrecru its", + "ĠP ig", + "rib ing", + "Ġenthusi asts", + "ex per", + "Ġfright ening", + "ĠH air", + "ans on", + "str ate", + "Ġh i", + "He ight", + "Ġown ing", + "n one", + "Ġdis like", + "Ġkn ives", + "pher d", + "Ġloud ly", + "ĠAP Is", + "Dis play", + "ĠL ac", + "ĠUS S", + "ab l", + "ver ages", + "J ew", + "Ġ17 2", + "ĠHist orical", + "at oon", + "ĠPhys ics", + "in tern", + "Ġwarm th", + "Ġto pp", + "D M", + "Ġgun man", + "Ġem peror", + "od i", + "ãĥ £", + "in atory", + "ĠR ib", + "Ġ13 1", + "ĠSat urn", + "ĠSh ining", + "Ġw aking", + "Qu otes", + "Ġcomed ian", + "en berg", + " ½", + "Ġbelie vers", + "Ġpaper work", + "c ustom", + "Ġle v", + "Ġl ament", + "Ġpour ing", + "22 2", + "p olitical", + "ĠSupp lement", + "m aid", + "Ġcruel ty", + "Ġt read", + "ys ics", + "A w", + "rit es", + "Ġmod ifier", + "ĠP osition", + "Ad am", + "l b", + "ub s", + "Ġimper fect", + "Ġcl usters", + "ĠEngine er", + "ĠC herry", + "Ġinaug uration", + "ĠS au", + "Ġembod iment", + "ĠUn cle", + "Ġover r", + "Ġexplos ions", + "c ule", + "ĠPrinc eton", + "ĠAndre a", + "Ġincorrect ly", + "Ġearn est", + "Ġpil gr", + "ĠS print", + "Ġslee ve", + "Ġhe ars", + "ĠAm azing", + "Ġbrow sing", + "ag in", + "Ġhom eland", + "Ġha w", + "Ġd iving", + "ist ered", + "17 8", + "Ġbarg aining", + "ĠArc ade", + "Ġdeleg ate", + "ters on", + "................................ ................................", + "ĠJackson ville", + "27 5", + "Ġst agn", + "Ġad am", + "ĠSher man", + "C B", + "Ġsub urb", + "ĠFood s", + "Ġconver ting", + "ĠAr ist", + "Ġch ambers", + "l ove", + "Ġam ino", + "ĠG an", + "Ġmad ness", + "m c", + "ĠUS E", + "def ined", + "Ġul tr", + "ind ust", + "Ġw olves", + "l ance", + "Add itionally", + "Ġcr acks", + "as ia", + "ĠRe ason", + "ĠP ump", + "Ġaccident al", + "ĠL aser", + "ĠR id", + "Ġinitial ized", + "ell i", + "Ġun named", + "Ġn oun", + "ĠPass ed", + "Ġhost age", + "ĠEth iop", + "sh irts", + "Ġun rel", + "ĠEmb assy", + "Ġ19 41", + "Ġat oms", + "Ġpur ported", + "16 4", + "ĠF i", + "Ġgall ons", + "ĠMon ica", + "Ġp g", + "en ment", + "Ġsort ed", + "ĠG ospel", + "Ġhe ights", + "Ġtr aced", + "Ġunder going", + "She ll", + "Ġs acks", + "Ġproport ions", + "Ġhall uc", + "F ont", + "ac et", + "Ġwar mer", + "ĠIN TER", + "Ġgrab bing", + "Pl ug", + "Ġreal ization", + "ĠBur ke", + "Ġen chant", + "AT ER", + "ĠSe ed", + "Ġabund ant", + "F M", + "Ġc ivic", + "V s", + "is i", + "Ġv ow", + "Ġre per", + "ĠPartners hip", + "Ġpenet ration", + "Ġax e", + "Ġsh attered", + "ĠZ ombies", + "Ġv inyl", + "ĠAl ert", + "e on", + "Ġoblig ed", + "ĠIll ust", + "ĠPl aza", + "ĠFront ier", + "Ġdavid jl", + "ĠSer ial", + "ĠH av", + "ĠNut rition", + "B i", + "Ġâĸ Ī", + "ĠJ ays", + "lin ux", + "Ġhur ry", + "Ġv oy", + "Ġhop eless", + "ĠSte alth", + "Ġ ãģ", + "ess ors", + "tt le", + "b org", + "ĠSaf ari", + "f ell", + "Ġw ary", + "d ue", + "ĠAb ove", + "H a", + "E LL", + "Ġnot or", + "ĠW on", + "T oo", + "Ġoccup ations", + "Ġposs essions", + "Ġinv iting", + "Ġpred ators", + "Ġacceler ated", + "Ġ15 7", + "uter te", + "ĠC ube", + "e ast", + "acc ount", + "G ive", + "Ġtrans plant", + "red ients", + "id able", + "Ġscreens hots", + "ĠG und", + "ĠF S", + "Ġtravel ers", + "Ġsens ory", + "ĠF iat", + "ĠRock ets", + "İ ĭ", + "_ {", + "F riend", + "Ġchar ming", + "AL S", + "Ġenjoy ment", + "m ph", + "Ġ5 000", + "ĠRE G", + "Ù Ĩ", + "b ia", + "Ġcomp ilation", + "ro st", + "ĠV P", + "ĠSch ne", + "201 9", + "Ġcop ying", + "M ORE", + "ĠFl ore", + "f alls", + "2 15", + "t otal", + "Ġdis ciples", + "d ouble", + "Ġexceed ing", + "Ġsm ashed", + "Ġconcept ual", + "ĠRom ania", + "ĠB rent", + "ĠI CE", + "ĠT ou", + "Ġg rap", + "Ġn ails", + "18 9", + "ãĥ ĺ", + "Ġproc ure", + "e ur", + "Ġconfir ming", + "ĠC ec", + "aw i", + "ĠEd en", + "Ġn g", + "Ġengine ered", + "at ics", + "Ġhook ed", + "Ġdisgust ing", + "ĠMur der", + "ãĤ ¿", + "L ibrary", + "Ġ16 8", + "Al most", + "hem atic", + "Men u", + "ĠNot re", + "ĠJ ur", + "Ġkidn apped", + "Ġhack er", + "ĠJ ade", + "Ġcreep y", + "Ġdraw ings", + "ĠSpons or", + "Ġcycl ists", + "ĠGob lin", + "Ġoptim ized", + "Ġst aged", + "ĠMc D", + "bet ween", + "A ge", + "en o", + "S ex", + "ĠW ide", + "n ings", + "av is", + "Ġincap able", + "ĠK ob", + "Ġreward ing", + "ĠL one", + "oles cent", + "Ġcontract ed", + "Ġstick y", + "J ose", + "B all", + "f est", + "ĠIn put", + "ĠRec ently", + "Ġto mat", + "squ are", + "App lication", + "Ġnit rogen", + "Ġdupl icate", + "ĠRec on", + "ĠD ear", + "L ondon", + "Ġint ra", + "Ġd ock", + "Ġout reach", + "ĠM illion", + "Ġmamm als", + "am pton", + "V AL", + "Ġsn aps", + "Ġd os", + "ĠWh ole", + "ĠRead y", + "T ry", + "ĠWinn ipeg", + "ear ance", + "Ġinc urred", + "ren ched", + "ĠNS W", + "il ot", + "rain e", + "Ġc ube", + "g ot", + "Ġrun way", + "etermin ed", + "ĠHaw ks", + "Ġsurviv or", + "ĠW ish", + "ĠD in", + "ĠDE F", + "ĠV ault", + "18 7", + "Ġmush rooms", + "Ġcris p", + "be y", + "ĠDisco very", + "Ġdevelopment al", + "Ġparad igm", + "Ġcha otic", + "ĠT su", + "Ġ3 33", + "b ons", + "Ġbacter ial", + "Ġcomm its", + "Ġcos mic", + "Ġme ga", + "oc ative", + "ĠP aint", + "ophob ic", + "Ġv ain", + "Ġcar ved", + "ĠTh ief", + "ĠG ul", + "ows hip", + "Ġc ites", + "ĠEd inburgh", + "Ġdimin ished", + "Ġacknowled ges", + "ĠK ills", + "Ġmic row", + "ĠHer a", + "Ġsen iors", + "Ġwhere by", + "H op", + "at ron", + "Ġun available", + "ĠN ate", + "Ġ4 80", + "Ġsl ated", + "ĠRe becca", + "ĠB attery", + "Ġgram mar", + "Ġhead set", + "Ġcurs or", + "Ġex cluding", + "any e", + "aunder ing", + "eb in", + "Ġfeas ible", + "ĠPub lishing", + "ĠLab s", + "ĠCl iff", + "ĠFerr ari", + "Ġp ac", + "vis ible", + "mark ed", + "pe ll", + "Ġpol ite", + "Ġstagger ing", + "ĠGal actic", + "Ġsuper st", + "Ġpar an", + "ĠOffic ers", + "ãĢ ģ", + "Ġspecific s", + "ul us", + "23 9", + "ĠP aste", + "AM P", + "ĠPan ama", + "ĠDe lete", + "angu ard", + "rest rial", + "Ġhero ic", + "ĠD y", + "ا ÙĦ", + "Ġincumb ent", + "Ġcr unch", + "t ro", + "Ġsc oop", + "Ġblog ger", + "Ġsell ers", + "ure n", + "Ġmedic ines", + "ĠC aps", + "ĠAnim ation", + "ox y", + "Ġout ward", + "Ġinqu iries", + "22 9", + "Ġpsych ologist", + "ĠS ask", + "ev il", + "Ġcontam inated", + "ãĤ ¨", + "he rence", + "Ġbrand ed", + "ĠAbd ul", + "z h", + "Ġparagraph s", + "Ġmin s", + "Ġcor related", + "er b", + "Ġimp art", + "Ġmil estone", + "ĠSol utions", + "ot le", + "Ġunder cover", + "Ġmar ched", + "ĠCharg ers", + "f ax", + "ĠSec rets", + "Ġr uth", + "we ather", + "Ġfemin ine", + "Ġsh am", + "Ġprest igious", + "igg ins", + "Ġs ung", + "hist ory", + "ett le", + "gg ie", + "Ġout dated", + "ol and", + "Ġper ceptions", + "ĠS ession", + "ĠDod gers", + "u j", + "ĠE ND", + "D oc", + "Ġdefic iency", + "Gr and", + "ĠJ oker", + "Ġretro spect", + "Ġdiagn ostic", + "Ġharm less", + "Ġro gue", + "ĠA val", + "E qu", + "Ġtrans c", + "ĠRoberts on", + "ĠDep ending", + "ĠBurn s", + "iv o", + "Ġhost ility", + "F eatures", + "ĵ ĺ", + "Ġdis comfort", + "ĠL CD", + "spec ified", + "ĠEx pect", + "3 40", + "Ġimper ative", + "ĠReg ular", + "Ch inese", + "Ġstate wide", + "Ġsy mm", + "Ġlo ops", + "Ġaut umn", + "N ick", + "Ġsh aping", + "Ġqu ot", + "Ġc herry", + "ĠCross ref", + "è¦ ļéĨĴ", + "Stand ard", + "he ed", + "ĠD ell", + "ĠViet namese", + "Ġo st", + "ĠV alkyrie", + "O A", + "Ass ad", + "Ġreb ound", + "ĠTra ffic", + "pl aces", + "æ ĺ", + "ĠB uc", + "17 2", + "Ġshel ters", + "Ġins isting", + "ĠCertain ly", + "ĠKenn eth", + "ĠT CP", + "Ġpen al", + "ĠRe play", + "he ard", + "Ġdial ect", + "iz a", + "ĠF Y", + "it cher", + "ĠD L", + "Ġspir al", + "Ġquarterback s", + "Ġh ull", + "Ġgo ogle", + "Ġto dd", + "ĠSter ling", + "ĠPl ate", + "Ġsp ying", + "mb ol", + "ĠReal m", + "ĠPro ced", + "ĠCr ash", + "Ġtermin ate", + "Ġprotest ing", + "C enter", + "gu ided", + "Ġun cover", + "Ġboy cott", + "Ġreal izes", + "s ound", + "Ġpret ending", + "ĠV as", + "19 80", + "Ġfram ed", + "Ġ13 9", + "Ġdesc ended", + "Ġrehab ilitation", + "Ġborrow ing", + "ĠB uch", + "Ġbl ur", + "R on", + "ĠFro zen", + "en za", + "Ch ief", + "ĠP oor", + "Ġtransl ates", + "M IN", + "Ġ2 12", + "J ECT", + "Ġerupt ed", + "Ġsuccess es", + "S EC", + "Ġpl ague", + "Ġg ems", + "d oms", + "Ġstret ches", + "ĠSp y", + "Ġstory telling", + "C redit", + "ĠP ush", + "Ġtra ction", + "Ġin effective", + "ĠL una", + "Ġt apes", + "Ġanaly tics", + "erc ise", + "Ġprogram mes", + "ĠCar bon", + "Ġbeh old", + "he avy", + "ĠConserv ation", + "ĠF IR", + "Ġs ack", + "ter min", + "ric ks", + "Ġhous ed", + "Ġunus ually", + "I ce", + "Ġexecut ing", + "ĠMor oc", + "ed ay", + "Ġed itions", + "Ġsm arter", + "ĠB A", + "Ġout law", + "Ġvan ished", + "ib a", + "AL SE", + "ĠSil va", + "23 8", + "C ould", + "Ġphilos opher", + "Ġevac uated", + "Sec ret", + "14 2", + "Ġvis as", + "ãĤ ¬", + "ĠM alt", + "ĠClear ly", + "ĠN iger", + "ĠC airo", + "ĠF ist", + "3 80", + "ĠX ML", + "aut o", + "it ant", + "Ġrein forced", + "Rec ord", + "ĠSurviv or", + "G Hz", + "Ġscrew s", + "parent s", + "Ġo ceans", + "ma res", + "Ġbra kes", + "vas ive", + "Ġhell o", + "ĠS IM", + "rim p", + "Ġo re", + "ĠArm our", + "24 7", + "Ġterr ific", + "Ġt ones", + "14 1", + "ĠMin utes", + "Ep isode", + "Ġcur ves", + "Ġinflamm atory", + "Ġbat ting", + "ĠBeaut iful", + "L ay", + "Ġunp op", + "v able", + "Ġr iots", + "ĠTact ics", + "b augh", + "ĠC ock", + "Ġorg asm", + "ĠS as", + "Ġconstruct or", + "et z", + "G ov", + "Ġant agon", + "Ġthe at", + "Ġde eds", + "ha o", + "c uts", + "ĠMc Cl", + "Ġu m", + "ĠScient ists", + "Ġgrass roots", + "ys sey", + "\"] =>", + "Ġsurf aced", + "Ġsh ades", + "Ġneighb ours", + "Ġad vertis", + "oy a", + "Ġmer ged", + "Up on", + "Ġg ad", + "Ġanticip ate", + "Any way", + "Ġsl ogan", + "Ġdis respect", + "I ran", + "ĠT B", + "act ed", + "Ġsubp oen", + "medi ately", + "OO OO", + "Ġwa iver", + "Ġvulner abilities", + "ott esville", + "ĠHuff ington", + "J osh", + "ĠD H", + "M onday", + "ĠEll en", + "K now", + "x on", + "it ems", + "22 8", + "Ġf ills", + "ĠN ike", + "Ġcum ulative", + "and als", + "I r", + "Ġ ì", + "Ġfr iction", + "ig ator", + "Ġsc ans", + "ĠVi enna", + "ld om", + "Ġperform ers", + "P rim", + "Ġb idding", + "M ur", + "Ġlean ed", + "ĠPri x", + "al ks", + "Ġ[ âĢ¦]", + "ĠTw itch", + "ĠDevelop er", + "ĠG ir", + "Ġcall back", + "Ab stract", + "Ġacc ustomed", + "Ġfreed oms", + "ĠP G", + "ur acy", + "Ġl ump", + "is man", + ",, ,,", + "19 92", + "ĠR ED", + "Ġwor m", + "M atch", + "ĠPl atinum", + "I J", + "ĠOwn er", + "Tri via", + "com pl", + "Ġnew born", + "Ġfant as", + "O wn", + "Ġ19 59", + "Ġsymp ath", + "Ġub iqu", + "Ġoutput s", + "Ġal lev", + "Ġpr ag", + "K evin", + "Ġfav ors", + "Ġbur ial", + "Ġn urt", + "so lete", + "c ache", + "Ġ15 6", + "Ġunl ocks", + "te chn", + "M aking", + "Ġcon quer", + "ad ic", + "æ ĸ", + "Ġel f", + "Ġelect orate", + "ĠKurd s", + "ĠSt ack", + "ĠSam urai", + "Ġâ ĺħ", + "Ġ{ }", + "ĠS aid", + "ĠFall out", + "Ġkind ness", + "ĠCustom s", + "ĠBou levard", + "Ġhelicop ters", + "ot ics", + "ĠVe get", + "com ment", + "Ġcritic ised", + "Ġpol ished", + "ĠRem ix", + "ĠC ultural", + "Ġrec ons", + "Ġdo i", + "at em", + "Sc reen", + "Ġbar red", + "Com ments", + "ĠGener ally", + "Ġsl ap", + "7 20", + "V ari", + "p ine", + "Ġem pt", + "Ġh ats", + "ĠPlay ing", + "l ab", + "a verage", + "form s", + "ĠC otton", + "Ġcan s", + "ĠD ON", + "ĠSom alia", + "C rypt", + "ĠIncre ases", + "E ver", + "mod ern", + "Ġsur geon", + "3 000", + "Ġrandom ized", + "================================ ================================", + "B ern", + "im pl", + "ĠC OR", + "Ġpro claim", + "th ouse", + "Ġto es", + "Ġam ple", + "Ġpres erving", + "Ġdis bel", + "gr and", + "B esides", + "Ġsil k", + "ĠPat tern", + "h m", + "Ġenter prises", + "Ġaffidav it", + "ĠAdvis ory", + "Ġadvert ised", + "ĠRel igious", + "se ctions", + "psy ch", + "ĠField s", + "aw ays", + "Ġhasht ag", + "ĠNight mare", + "Ġv ampire", + "Ġfore nsic", + "rosso ver", + "n ar", + "Ġn avy", + "Ġvac ant", + "ĠD uel", + "Ġhall way", + "Ġface book", + "ident ally", + "ĠN RA", + "Ġm att", + "Ġhur ricane", + "ĠKir by", + "ĠP uzzle", + "Ġsk irt", + "ou st", + "du llah", + "Ġanal ogy", + "in ion", + "Ġtomat oes", + "ĠN V", + "ĠPe ak", + "ĠMe yer", + "Ġappoint ments", + "Ġm asc", + "Ġal ley", + "re hend", + "Ġchar ities", + "Ġund o", + "Ġdest inations", + "ĠTest ing", + "\"> \"", + "c ats", + "* .", + "Ġgest ures", + "gener al", + "Le ague", + "Ġpack ets", + "ĠInspect or", + "ĠBer g", + "Ġfraud ulent", + "Ġcritic ize", + "F un", + "Ġbl aming", + "nd ra", + "Ġsl ash", + "ĠE ston", + "Ġpropos ing", + "Ġwh ales", + "Ġtherap ist", + "Ġsub set", + "Ġle isure", + "EL D", + "ĠC VE", + "ĠAct ivity", + "Ġcul min", + "sh op", + "ĠD AY", + "is cher", + "ĠAdmir al", + "ĠAtt acks", + "Ġ19 58", + "Ġmem oir", + "Ġfold ed", + "Ġsex ist", + "Ġ15 3", + "ĠL I", + "Ġread ings", + "Ġembarrass ment", + "ĠEmploy ment", + "w art", + "ch in", + "Ġcontin uation", + "l ia", + "Rec ently", + "Ġd uel", + "Ġevac uation", + "ĠKash mir", + "Ġdis position", + "ĠR ig", + "Ġbol ts", + "Ġins urers", + "4 67", + "M ex", + "Ġret aliation", + "Ġmis ery", + "Ġunre asonable", + "r aining", + "I mm", + "ĠP U", + "em er", + "Ġgen ital", + "ãĤ ³", + "ĠC andy", + "Ġon ions", + "ĠP att", + "lin er", + "Ġconced ed", + "Ġf a", + "Ġfor c", + "ĠH ernandez", + "ĠGe off", + "deb ian", + "ĠTe ams", + "Ġc ries", + "Ġhome owners", + "23 7", + "A BC", + "Ġst itch", + "Ġstat istic", + "Ġhead ers", + "ĠBi ology", + "Ġmot ors", + "ĠG EN", + "ĠL ip", + "Ġh ates", + "Ġhe el", + "S elf", + "i pl", + "ED IT", + "ort ing", + "Ġann ot", + "ĠSpe ech", + "old emort", + "ĠJ avascript", + "ĠLe Bron", + "Ġfoot print", + "Ġf n", + "Ġseiz ures", + "n as", + "h ide", + "Ġ19 54", + "ĠBe e", + "ĠDecl aration", + "ĠKat ie", + "Ġreserv ations", + "N R", + "f emale", + "Ġsatur ated", + "Ġb iblical", + "Ġtroll s", + "Dev ice", + "ph otos", + "Ġdr ums", + "ãĥīãĥ© ãĤ´ãĥ³", + "N ight", + "f ighter", + "ĠH ak", + "ri ber", + "Ġc ush", + "Ġdiscipl inary", + "ba um", + "ĠG H", + "ĠSch midt", + "ilib rium", + "Ġs ixty", + "ĠKush ner", + "ro ts", + "Ġp und", + "ĠR ac", + "Ġspr ings", + "Ġcon ve", + "Bus iness", + "F all", + "Ġqual ifications", + "Ġvers es", + "Ġnarc iss", + "ĠK oh", + "ĠW ow", + "ĠCharl ottesville", + "ed o", + "Ġinterrog ation", + "ĠW ool", + "36 5", + "B rian", + "Ġâľ ĵ", + "Ġalleg es", + "ond s", + "id ation", + "ĠJack ie", + "y u", + "Ġl akes", + "Ġworth while", + "Ġcryst als", + "ĠJud a", + "Ġcomp rehend", + "Ġfl ush", + "Ġabsor ption", + "ĠO C", + "Ġfright ened", + "ĠCh ocolate", + "Mart in", + "Ġbu ys", + "Ġbu cks", + "Ġapp ell", + "ĠChampions hips", + "Ġlist ener", + "ĠDef ensive", + "Ġc z", + "ud s", + "ĠM ate", + "Ġre play", + "Ġdecor ated", + "Ġs unk", + "ĠV IP", + "ĠAn k", + "Ġ19 5", + "aa aa", + "Nob ody", + "ĠMil k", + "ĠG ur", + "ĠM k", + "ĠS ara", + "Ġse ating", + "ĠW id", + "Tr ack", + "Ġemploy s", + "Ġgig antic", + "AP P", + "ãĤ §", + "in ventory", + "Ġtow el", + "at che", + "l asting", + "ĠT L", + "Ġlat ency", + "Ġkn e", + "B er", + "me aning", + "Ġup held", + "Ġplay ground", + "Ġm ant", + "S ide", + "Ġstere o", + "Ġnorth west", + "Ġexception ally", + "Ġr ays", + "Ġrec urring", + "D rive", + "Ġup right", + "Ġab duct", + "ĠMar athon", + "Ġgood bye", + "Ġal phabet", + "h p", + "Ġcourt room", + "ring ton", + "ot hing", + "T ag", + "Ġdiplom ats", + "Ġbar bar", + "ĠAqu a", + "18 3", + "33 33", + "Ġmat urity", + "Ġinst ability", + "ĠAp ache", + "Ġ= ==", + "Ġfast ing", + "ĠGr id", + "Mod Loader", + "Ġ15 2", + "A bs", + "ĠOper ating", + "ett i", + "Ġacqu aint", + "Don nell", + "ĠK em", + "ĠFor ge", + "Ġarm ored", + "M il", + "Ġphilos ophers", + "in vest", + "Pl ayers", + "â Ī", + "Ġmy riad", + "Ġcomr ades", + "R ot", + "Ġremember ing", + "Ġcorrespond s", + "Ġprogram mers", + "ĠLyn n", + "Ġo lig", + "Ġco herent", + "yn chron", + "ĠChem ical", + "Ġj ugg", + "p air", + "post s", + "E ye", + "ĠIn ner", + "Ġsem ester", + "ott est", + "ĠEmir ates", + "ric anes", + "or ously", + "m its", + "ĠW is", + "Ġd odge", + "l ocation", + "Ġf aded", + "Am azon", + "ĠPro ceed", + "ĠIN FO", + "j ournal", + "ĠTru ck", + "T en", + "Ġ2 17", + "Ġstat utes", + "m obile", + "ĠT ypes", + "Rec omm", + "b uster", + "pe x", + "Ġleg ends", + "Ġhead ache", + "f aced", + "ĠWi Fi", + "if ty", + "ĠH ER", + "Ġcirc uits", + "ER ROR", + "22 6", + "ol in", + "Ġcyl inder", + "osp ace", + "ik ers", + "P rem", + "Qu ant", + "Ġconflic ting", + "Ġslight est", + "Ġfor ged", + "ion age", + "Step hen", + "ĠK ub", + "ĠOpp ortun", + "ĠHe al", + "Ġbl o", + "Ġrul ers", + "Ġh uh", + "Ġsubmar ine", + "f y", + "ass er", + "Ġallow ance", + "ĠKas ich", + "ĠT as", + "ĠAustral ians", + "Forge ModLoader", + "ĠâĨ ij", + "ĠMat rix", + "am ins", + "Ġ12 00", + "ĠAc qu", + "23 6", + "D ocument", + "ĠBre aking", + "19 3", + "ĠSub st", + "ĠRoll er", + "ĠPro perties", + "ĠN I", + "t ier", + "Ġcr ushing", + "Ġadvoc ating", + "Further more", + "keep ers", + "Ġsex ism", + "x d", + "Ġcall er", + "ĠS ense", + "chie ve", + "ĠT F", + "Ġfuel ed", + "Ġreminis cent", + "Ġobs ess", + "ur st", + "Ġup hold", + "ĠF ans", + "het ics", + "Ġâ Ĺ", + "ĠB ath", + "Ġbe verage", + "Ġo scill", + "25 4", + "Ġpol es", + "Ġgrad ual", + "Ġex ting", + "ĠS uff", + "ĠS uddenly", + "Ġlik ing", + "Ġ19 49", + "un ciation", + "am ination", + "ĠO mar", + "ĠL V", + "ĠCon sequently", + "Ġsynt hes", + "ĠG IF", + "Ġp ains", + "Ġinteract ing", + "u ously", + "inc re", + "Ġrum or", + "ĠScient ology", + "19 7", + "ĠZ ig", + "Ġspe lling", + "ĠA SS", + "Ġexting u", + "ms on", + "Ġg h", + "Ġremark ed", + "ĠStrateg ic", + "ĠM ON", + "å ¥", + "g ae", + "ĠWH AT", + "E ric", + "ĠCamp us", + "Ġmeth ane", + "Ġimag in", + "J UST", + "ĠAl m", + "X T", + "i q", + "ĠR SS", + "Ġwrong doing", + "att a", + "Ġbig ot", + "Ġdemonstr ators", + "ĠCal vin", + "ĠV illa", + "Ġmembr ane", + "ĠAw esome", + "Ġbenef ic", + "26 8", + "Ġmagn ificent", + "ĠL ots", + "G reg", + "ĠBor is", + "Ġdetain ees", + "ĠH erman", + "Ġwhis pered", + "Ġa we", + "Prof essor", + "fund ing", + "Ġphys iological", + "ĠDest ruction", + "Ġlim b", + "Ġmanip ulated", + "Ġbub bles", + "Ġpse ud", + "Ġhyd ra", + "ĠBrist ol", + "Ġst ellar", + "ĠExp ansion", + "ĠK ell", + "ĠInterest ingly", + "Ġm ans", + "Ġdrag ging", + "Ġec ological", + "ĠF it", + "Ġg ent", + "Ġbenef ited", + "ĠHait i", + "Ġpoly g", + "ãĥ İ", + "Ġ20 30", + "Ġpro w", + "Ġrecon struction", + "Ġwas t", + "Ġpsych ic", + "ĠGree ks", + "Hand ler", + "16 2", + "ĠP ulse", + "Ġsol icit", + "Ġsy s", + "Ġinflu x", + "ĠG entle", + "per cent", + "Ġprolifer ation", + "Ġtax able", + "Ġdisreg ard", + "Ġesc aping", + "Ġg inger", + "Ġwith stand", + "Ġdevast ated", + "ĠD ew", + "ser ies", + "Ġinject ed", + "ela ide", + "Ġturn over", + "he at", + "Ļ Ĥ", + "H appy", + "ĠSil ent", + "ãĤ Ń", + "iv ism", + "Ġir rational", + "AM A", + "Ġre ef", + "r ub", + "Ġ16 2", + "Ġbank ers", + "ĠEth ics", + "v v", + "Ġcritic isms", + "K n", + "18 6", + "M ovie", + "ĠT ories", + "Ġno od", + "Ġdist ortion", + "F alse", + "od ore", + "Ġt asty", + "Res earch", + "ĠU ID", + "- )", + "Ġdivor ced", + "ĠM U", + "ĠHay es", + "ĠIs n", + "ian i", + "ĠH Q", + "Ġ\" #", + "ign ant", + "Ġtra umatic", + "ĠL ing", + "H un", + "Ġsab ot", + "on line", + "r andom", + "Ġren amed", + "ra red", + "K A", + "d ead", + "é t", + "ĠAss istance", + "Ġse af", + "++++ ++++", + "Ġse ldom", + "ĠWeb b", + "Ġbo olean", + "u let", + "Ġref rain", + "ĠDI Y", + "ru le", + "Ġshut ting", + "Ġutil izing", + "load ing", + "ĠPar am", + "co al", + "oot er", + "Ġattract ing", + "ĠD ol", + "Ġher s", + "ag netic", + "ĠRe ach", + "im o", + "Ġdisc arded", + "ĠP ip", + "01 5", + "ü r", + "Ġm ug", + "Im agine", + "C OL", + "Ġcurs ed", + "ĠSh ows", + "ĠCurt is", + "ĠSach s", + "spe aking", + "ĠV ista", + "ĠFram ework", + "ong o", + "Ġsub reddit", + "Ġcr us", + "ĠO val", + "R ow", + "g rowing", + "Ġinstall ment", + "Ġgl ac", + "ĠAdv ance", + "EC K", + "ĠLGBT Q", + "LE Y", + "Ġac et", + "Ġsuccess ive", + "ĠNic ole", + "Ġ19 57", + "Qu ote", + "Ġcircumst ance", + "ack ets", + "Ġ14 2", + "ort ium", + "Ġguess ed", + "ĠFr ame", + "Ġperpet rators", + "ĠAv iation", + "ĠBen ch", + "Ġhand c", + "A p", + "Ġ19 56", + "25 9", + "r and", + "Net Message", + "d in", + "urt les", + "h ig", + "ĠV III", + "ff iti", + "ĠSw ords", + "b ial", + "Ġkidn apping", + "dev ice", + "Ġb arn", + "ĠEl i", + "auc as", + "S end", + "Con structed", + "Ġ ½", + "Ġneed les", + "Ġad vertisements", + "Ġv ou", + "Ġexhib ited", + "ĠFort ress", + "As k", + "B erry", + "TY PE", + "Ġcan cers", + "ump ing", + "ĠTerrit ory", + "Ġpr ud", + "Ġn as", + "Ġathe ist", + "Ġbal ances", + "ãģ Ł", + "ĠSh awn", + "& &", + "Ġland sc", + "ĠR GB", + "Ġpet ty", + "Ġex cellence", + "Ġtransl ations", + "Ġpar cel", + "ĠChe v", + "E ast", + "ĠOut put", + "im i", + "Ġamb ient", + "ĠTh reat", + "Ġvill ains", + "Ġ5 50", + "IC A", + "Ġtall er", + "Ġle aking", + "c up", + "Ġpol ish", + "Ġinfect ious", + "ĠK C", + "Ġ@ @", + "back ground", + "Ġbureaucr acy", + "ĠS ai", + "un less", + "it ious", + "ĠSky pe", + "At l", + "ID ENT", + "00 8", + "Ġhyp ocr", + "Ġpit chers", + "Ġguess ing", + "ĠF INAL", + "Bet ween", + "Ġvill agers", + "Ġ25 2", + "f ashion", + "ĠTun is", + "Be h", + "ĠEx c", + "ĠM ID", + "28 8", + "ĠHas kell", + "19 6", + "ĠN OR", + "Ġspec s", + "Ġinv ari", + "Ġgl ut", + "ĠC ars", + "Ġimp ulse", + "Ġhon ors", + "g el", + "Ġjurisd ictions", + "ĠBund le", + "ul as", + "Calif ornia", + "ĠIncre ase", + "Ġp ear", + "Ġsing les", + "Ġc ues", + "Ġunder went", + "ĠW S", + "Ġexagger ated", + "Ġdub ious", + "Ġfl ashing", + "L OG", + ") ].", + "J ournal", + "t g", + "V an", + "ĠI stanbul", + "ĠIn sp", + "ĠFrank en", + "D raw", + "Ġsad ness", + "Ġiron ic", + "ĠF ry", + "x c", + "Ġ16 4", + "is ch", + "W ay", + "ĠProtest ant", + "h orn", + "Ġun aff", + "ĠV iv", + "ill as", + "ĠProduct ions", + "ĠH ogan", + "Ġper imeter", + "ĠS isters", + "Ġspont aneous", + "Ġdown side", + "Ġdescend ants", + "Ġor n", + "w orm", + "Japan ese", + "Ġ19 55", + "Ġ15 1", + "ĠDo ing", + "els en", + "umb les", + "Ġrad ically", + "ĠDr um", + "ĠB ach", + "Ġli abilities", + "ĠO B", + "ĠElement ary", + "Ġmem e", + "yn es", + "Ġfinger print", + "ĠGr ab", + "Ġundert ake", + "Mem bers", + "ĠRead er", + "ĠSim s", + "g od", + "Ġhypot hetical", + "s cient", + "ĠA J", + "Ġchar ism", + "Ġad missions", + "ĠMiss ile", + "tr ade", + "Ġexerc ising", + "ĠBack ground", + "W ritten", + "Ġvoc als", + "whe ther", + "Ġv i", + "ĠW inner", + "Ġl itter", + "ĠSh ooting", + "ST EM", + "ãĤ ¡", + "ĠA FL", + "Ġvari ability", + "Ġe ats", + "ĠD PS", + "b row", + "Ġeleph ants", + "Ġstr at", + "Ġ Å", + "Ġsett lers", + "Matt hew", + "Ġin advert", + "H I", + "ĠIM F", + "ĠGo al", + "Ġnerv es", + "John son", + "ey e", + "ablish ment", + "Th ursday", + "BIL ITY", + "H ad", + "am oto", + "het amine", + "ep s", + "Ġmit ochond", + "Ġcomp ressed", + "ĠTre vor", + "ĠAnim als", + "T ool", + "L ock", + "Ġtwe ak", + "Ġpin ch", + "Ġcancell ation", + "P ot", + "Ġfoc al", + "ĠAst ron", + "17 3", + "ĠA SC", + "ĠO THER", + "umn i", + "Ġdem ise", + "d l", + "Ù ħ", + "Sem itism", + "Ġcr acking", + "Ġcollabor ative", + "Ġexpl ores", + "s ql", + "Ġher bs", + "Ġconfig urations", + "m is", + "ĠRes ult", + "ace y", + "ĠSm oke", + "Ġsan ct", + "el ia", + "Ġdeg ener", + "Ġdeep est", + "Ġscream ed", + "Ġn ap", + "Soft ware", + "ĠST AR", + "E F", + "ĠX in", + "spons ored", + "mans hip", + "23 3", + "Ġprim aries", + "Ġfilter ing", + "Ġas semble", + "m il", + "ĠMy ers", + "b ows", + "Ġpun ched", + "M ic", + "Ġinnov ations", + "Ġfun c", + "and o", + "Ġfr acking", + "ĠV ul", + "о Ð", + "osh op", + "ĠIm mun", + "Ġsett ling", + "Ġadolesc ents", + "Ġreb uilding", + "Ġtransform ing", + "Ġpar ole", + "Ġhar bor", + "Ġbook ing", + "ot ional", + "onge vity", + "ĠY o", + "b ug", + "Ġemer ges", + "ĠMethod s", + "ĠCh u", + "P res", + "ĠDun geons", + "Ġtra iling", + "ĠR um", + "ĠH ugh", + "å¤ ©", + "ĠE ra", + "ĠBatt les", + "Res ults", + "ĠTr ading", + "Ġvers a", + "c ss", + "ax ies", + "he et", + "Ġgre ed", + "19 89", + "Ġgard ens", + "Ġconting ent", + "P ark", + "ĠLeaf s", + "h ook", + "ro be", + "Ġdiplom acy", + "ĠF uel", + "ĠInv asion", + "Ġupgr ading", + "M ale", + "Ġe lic", + "Ġrelent less", + "ĠCo venant", + "ap esh", + "ĠT rop", + "T y", + "pro duction", + "art y", + "Ġpun ches", + "ak o", + "cyclop edia", + "ĠR abbit", + "ĠHD MI", + "Ġ14 1", + "Ġf oil", + "Item Image", + "ĠF G", + "Ġimplement ations", + "ĠP om", + "ixt ures", + "Ġaw ait", + "Ġ3 30", + "am us", + "Ġumb rella", + "Ġfore see", + "se par", + "Ġcircum cision", + "Ġperipher al", + "S ay", + "ĠExper t", + "In c", + "Ġwithd rew", + "ĠAnd ers", + "f ried", + "Ġradio active", + "ĠOp ening", + "Ġboard ing", + "ĠN D", + "Ġover throw", + "Act iv", + "W P", + "ĠAct s", + "× Ļ", + "Ġmot ions", + "v ic", + "ĠM ighty", + "ĠDef ender", + "a er", + "Ġthank ful", + "ĠK illing", + "ĠBr is", + "mo il", + "Ġpredict ing", + "26 6", + "ch oice", + "Ġkill ers", + "Ġinc ub", + "ĠChe st", + "ather ing", + "Ġpro claimed", + "fl ower", + "oss om", + "umbled ore", + "ĠCy cling", + "ĠOccup y", + "AG ES", + "P en", + "ĠY ug", + "Ġpack aged", + "Ġheight ened", + "c ot", + "st ack", + "C ond", + "Ġst amps", + "m age", + "Ġpersu aded", + "Ġens l", + "ĠCard inal", + "Ġsol itary", + "Ġpossess ing", + "ĠC ork", + "Ġev id", + "ĠT ay", + "Ġbl ues", + "Ġextrem ism", + "Ġlun ar", + "Ġcl own", + "Te chn", + "Ġfest ivals", + "ĠPv P", + "ĠL ar", + "Ġconsequ ently", + "p resent", + "Ġsom eday", + "ç İĭ", + "ĠMet eor", + "Ġtour ing", + "c ulture", + "Ġbe aches", + "S hip", + "c ause", + "ĠFl ood", + "ãĥ ¯", + "Ġpur ity", + "th ose", + "Ġem ission", + "b olt", + "Ġch ord", + "ĠScript ure", + "L u", + "Ġ$ {", + "cre ated", + "Other s", + "25 8", + "Ġelement al", + "Ġannoy ed", + "ĠA E", + "d an", + "ĠS ag", + "Res earchers", + "Ġfair y", + "âĢĵ âĢĵ", + "======== ====", + "Sm art", + "GG GG", + "Ġskelet ons", + "Ġpup ils", + "link ed", + "Ġur gency", + "en abled", + "ĠF uck", + "Ġcoun cill", + "r ab", + "U AL", + "T I", + "Ġlif es", + "Ġconf essed", + "B ug", + "Ġharm on", + "ĠCON FIG", + "ĠNe utral", + "D ouble", + "Ġst aple", + "ĠSH A", + "Brit ish", + "ĠSN P", + "AT OR", + "oc o", + "Ġswing ing", + "ge x", + "ole on", + "pl ain", + "ĠMiss ing", + "ĠTro phy", + "v ari", + "ran ch", + "Ġ3 01", + "4 40", + "00000000 00000000", + "Ġrest oring", + "Ġha ul", + "uc ing", + "ner g", + "Ġfut ures", + "Ġstrateg ist", + "quest ion", + "Ġlater al", + "ĠB ard", + "Ġs or", + "ĠRhod es", + "ĠD owntown", + "????? -", + "ĠL it", + "ĠB ened", + "Ġco il", + "st reet", + "ĠPort al", + "FI LE", + "ĠG ru", + "* ,", + "23 1", + "ne um", + "Ġsuck ed", + "Ġr apper", + "Ġtend encies", + "ĠLaure n", + "cell aneous", + "26 7", + "Ġbrow se", + "Ġover c", + "head er", + "o ise", + "Ġbe et", + "ĠG le", + "St ay", + "Ġm um", + "Ġtyp ed", + "Ġdiscount s", + "T alk", + "ĠO g", + "ex isting", + "ĠS ell", + "u ph", + "C I", + "ĠAust rian", + "ĠW arm", + "Ġdismiss al", + "Ġaver ages", + "c amera", + "Ġalleg iance", + "L AN", + "=\" #", + "Ġcomment ators", + "ĠSet ting", + "ĠMid west", + "Ġpharm ac", + "ĠEX P", + "Ġstain less", + "Ch icago", + "Ġt an", + "24 4", + "Ġcountry side", + "ĠV ac", + "29 5", + "Ġpin ned", + "Ġcr ises", + "Ġstandard ized", + "T ask", + "ĠJ ail", + "ĠD ocker", + "col ored", + "f orth", + "\" },", + "Ġpat rons", + "Ġsp ice", + "Ġm ourn", + "ĠM ood", + "Ġlaund ry", + "Ġequ ip", + "ĠM ole", + "y ll", + "ĠTH C", + "n ation", + "ĠSher lock", + "Ġiss u", + "ĠK re", + "ĠAmeric as", + "ĠA AA", + "Ġsystem atically", + "Ġcont ra", + "ĠS ally", + "Ġrational e", + "Ġcar riage", + "Ġpe aks", + "Ġcontrad iction", + "ens ation", + "ĠFail ure", + "Ġpro ps", + "Ġnames pace", + "Ġc ove", + "field s", + "ãĤ ĭ", + "Ġw ool", + "ĠC atch", + "Ġpresum ed", + "ĠD iana", + "r agon", + "ig i", + "Ġh amm", + "Ġst unt", + "ĠG UI", + "ĠObserv atory", + "ĠSh ore", + "Ġsmell s", + "ann ah", + "Ġcock pit", + "ĠD uterte", + "8 50", + "Ġopp ressed", + "bre aker", + "ĠCont ribut", + "ĠPer u", + "ĠMons anto", + "ĠAtt empt", + "Ġcommand ing", + "Ġfr idge", + "ĠR in", + "ĠChe ss", + "ual ity", + "Ġo l", + "Republic an", + "ĠGl ory", + "ĠW IN", + ".... ...", + "ag ent", + "read ing", + "Ġin h", + "J ones", + "Ġcl icks", + "al an", + "Ġ[ ];", + "ĠMaj esty", + "ĠC ed", + "op us", + "ate l", + "à ª", + "AR C", + "ĠEc uador", + "ãĥ ł", + "ĠK uro", + "Ġritual s", + "Ġcapt ive", + "Ġoun ce", + "Ġdisag reement", + "Ġsl og", + "f uel", + "P et", + "M ail", + "Ġexerc ised", + "Ġsol ic", + "Ġrain fall", + "Ġdev otion", + "ĠAss essment", + "Ġrob otic", + "opt ions", + "ĠR P", + "ĠFam ilies", + "ĠFl ames", + "Ġassign ments", + "00 7", + "aked own", + "Ġvoc abulary", + "Re illy", + "Ġc aval", + "g ars", + "Ġsupp ressed", + "ĠS ET", + "ĠJohn s", + "Ġwar p", + "bro ken", + "Ġstat ues", + "Ġadvoc ated", + "Ġ2 75", + "Ġper il", + "om orph", + "ĠF emin", + "per fect", + "Ġh atch", + "L ib", + "5 12", + "Ġlif elong", + "3 13", + "Ġche eks", + "Ġnum bered", + "ĠM ug", + "B ody", + "ra vel", + "We ight", + "ĠJ ak", + "ĠHe ath", + "Ġkiss ing", + "ĠJ UST", + "Ġw aving", + "u pload", + "Ġins ider", + "ĠPro gressive", + "ĠFil ter", + "tt a", + "ĠBe am", + "Ġviol ently", + "ip ation", + "Ġskept icism", + "Ġ19 18", + "ĠAnn ie", + "ĠS I", + "Ġgen etics", + "Ġon board", + "at l", + "ĠFried man", + "ĠB ri", + "cept ive", + "Ġpir ate", + "ĠRep orter", + "27 8", + "Ġmyth ology", + "Ġe clipse", + "Ġsk ins", + "Ġgly ph", + "ing ham", + "F iles", + "C our", + "w omen", + "Ġreg imes", + "Ġphotograp hed", + "K at", + "ĠMA X", + "Offic ials", + "Ġunexpected ly", + "Ġimpress ions", + "F ront", + ";;;; ;;;;", + "Ġsuprem acy", + "Ġs ang", + "Ġaggrav ated", + "Ġabrupt ly", + "ĠS ector", + "Ġexc uses", + "Ġcost ing", + "ide press", + "St ack", + "ĠR NA", + "ob il", + "Ġghost s", + "ld on", + "at ibility", + "Top ics", + "Ġreim burse", + "ĠH M", + "ĠDe g", + "Ġth ief", + "y et", + "ogen esis", + "le aning", + "ĠK ol", + "ĠB asketball", + "Ġf i", + "ĠSee ing", + "Ġrecy cling", + "Ġ[ -", + "Cong ress", + "Ġlect ures", + "P sy", + "Ġne p", + "Ġm aid", + "Ġori ented", + "A X", + "Ġrespect ful", + "re ne", + "fl ush", + "ĠUn loaded", + "re quest", + "gr id", + "ĠAltern atively", + "ĠHug o", + "Ġdec ree", + "ĠBuddh ism", + "and um", + "And roid", + "ĠCong o", + "ĠJoy ce", + "Ġacknowled ging", + "hes ive", + "ĠTom orrow", + "ĠH iro", + "th ren", + "ĠM aced", + "Ġho ax", + "ĠIncre ased", + "ĠPr adesh", + "W ild", + "____ __", + "16 1", + "Ġa unt", + "Ġdistribut ing", + "ĠT ucker", + "ĠSS L", + "ĠW olves", + "B uilding", + "ou lt", + "ĠLu o", + "ĠY as", + "ĠSp ir", + "ĠSh ape", + "ĠCamb od", + "ĠIP v", + "Ġm l", + "Ġext rad", + "39 0", + "ĠPenn y", + "d ream", + "Ġstation ed", + "opt ional", + "ew orthy", + ". ", + "ĠWorks hop", + "ĠRet ail", + "ĠAv atar", + "6 25", + "N a", + "ĠV C", + "ĠSec ure", + "M Y", + "19 88", + "oss ip", + "Ġpro state", + "Ġund en", + "Ġg amer", + "ĠCont ents", + "ĠWar hammer", + "ĠSent inel", + "3 10", + "Ġse gregation", + "ĠF lex", + "ĠM AY", + "Ġdr ills", + "ĠDrug s", + "Islam ic", + "Ġsp ur", + "Ġca fe", + "Ġimag inary", + "Ġgu iding", + "Ġsw ings", + "ĠThe me", + "ob y", + "Ġn ud", + "Ġbe gging", + "Ġstr ongh", + "Ġreject ing", + "Ġpedest rians", + "ĠPro spect", + "R are", + "s le", + "Ġconcess ions", + "ĠConst itutional", + "Ġbe ams", + "Ġfib ers", + "p oon", + "Ġinstinct s", + "pro perty", + "ĠB IG", + "Sand ers", + "im ates", + "Ġco ating", + "Ġcorps es", + "ĠTR UE", + "check ed", + "Ġ16 6", + "A sh", + "ĠJ S", + "ĠF iction", + "Ġcommun al", + "Ġener getic", + "oooo oooo", + "Ġnow adays", + "IL D", + "ib o", + "ĠSU V", + "R en", + "Ġdwell ing", + "Sil ver", + "Ġt ally", + "ĠM oving", + "Ġcow ard", + "Ġgener als", + "Ġhorn s", + "Ġcirc ulated", + "Ġrob bed", + "ĠUn limited", + "Ġharass ed", + "Ġinhib it", + "Ġcomp oser", + "ĠSpot ify", + "Ġspread s", + "3 64", + "Ġsu icidal", + "Ġno ises", + "ĠSt ur", + "Ġs aga", + "ĠK ag", + "is o", + "Ġtheoret ically", + "M oney", + "Ġsimilar ity", + "Ġslic ed", + "ut ils", + "ing es", + "\" -", + "Ġan th", + "Ġimp ed", + "Mod ule", + "Through out", + "Ġmen us", + "comm ittee", + "and i", + "ob j", + "in av", + "f ired", + "ĠAb dullah", + "Ġund ead", + "Ġfont s", + "H old", + "EN G", + "Ġsustain ability", + "Ġfl ick", + "Ġr azor", + "ĠF est", + "ĠChar acters", + "Ġword ing", + "Ġpopul ist", + "Ġcritic izing", + "Ġm use", + "v ine", + "Ġcard board", + "Ġkind ly", + "Ġfr inge", + "ĠThe ft", + "icult ural", + "Ġgovern ors", + "Ġ ����", + "Ġ16 3", + "Ġtime out", + "ĠA uth", + "Child ren", + "A U", + "Ġred emption", + "ĠAl ger", + "Ġ19 14", + "Ġw aved", + "Ġastron auts", + "og rams", + "Ġsw amp", + "ĠFinn ish", + "Ġcand le", + "Ġton nes", + "ut m", + "Ġr ay", + "Ġsp un", + "Ġfear ful", + "art icles", + "Ġca us", + "or ically", + "ĠRequ ires", + "ĠG ol", + "Ġpop e", + "Ġinaug ural", + "Ġg le", + "AD A", + "ĠIS IL", + "ĠOff ensive", + "Ġwatch dog", + "Ġbal con", + "ent ity", + "ĠH oo", + "Ġgall on", + "AC C", + "Ġdoub ling", + "Ġimpl ication", + "ĠS ight", + "Ġdoct r", + "---- ---", + "Ġ\\ \\", + "Ġm alt", + "R oll", + "Ġâī ¥", + "Ġrec ap", + "add ing", + "u ces", + "ĠB end", + "fig ure", + "Ġtur key", + "Ġsoc ietal", + "ĠT ickets", + "Ġcommer cially", + "Ġsp icy", + "Ġ2 16", + "ĠR amp", + "Ġsuperior ity", + "à ¯", + "ĠTr acker", + "C arl", + "ĠC oy", + "ĠPatri ot", + "Ġconsult ed", + "Ġlist ings", + "Ġsle w", + "reens hot", + "ĠG one", + "Ġ[ ...]", + "30 9", + "Ġh ottest", + "Ø ±", + "Ġrock y", + "ĠD iaz", + "Ġmass age", + "Ġpar aly", + "Ġp ony", + "A z", + "Ġcart ridge", + "ĠN Z", + "Ġsn ack", + "ĠLam ar", + "ple ment", + "ĠLes lie", + "Ġm ater", + "Ġsn ipp", + "24 6", + "Ġjoint ly", + "ĠBris bane", + "ĠiP od", + "Ġpump ing", + "Ġgo at", + "ĠSh aron", + "eal ing", + "Ġcor on", + "Ġan omal", + "rah im", + "ĠConnect ion", + "Ġsculpt ure", + "Ġsched uling", + "ĠD addy", + "at hing", + "Ġeyeb rows", + "Ġcur ved", + "Ġsent iments", + "Ġdraft ing", + "D rop", + "( [", + "Ġnom inal", + "ĠLeaders hip", + "ĠG row", + "Ġ17 6", + "Ġconstruct ive", + "iv ation", + "Ġcorrupt ed", + "ger ald", + "ĠC ros", + "ĠChe ster", + "ĠL ap", + "ãģ ª", + "OT H", + "D ATA", + "Ġal mond", + "pro bably", + "I mp", + "Ġfe ast", + "ĠWar craft", + "F lor", + "Ġcheck point", + "Ġtrans cription", + "Ġ20 4", + "Ġtwe aks", + "Ġrel ieve", + "S cience", + "Ġperform er", + "Z one", + "Ġtur moil", + "ig ated", + "hib it", + "ĠC afe", + "the med", + "Ġflu or", + "ben ch", + "Ġde com", + "ĠU nt", + "ĠBar rett", + "ĠF acts", + "Ġt asting", + "ĠPTS D", + "ĠSe al", + "ĠJuda ism", + "ĠDynam ic", + "ĠC ors", + "V e", + "ĠM ing", + "ĠTrans form", + "v on", + "ĠDef enders", + "ĠTact ical", + "ĠV on", + "ĠUn ivers", + "Ġdist orted", + "ĠB reath", + "?' \"", + "Ġag on", + "ĠDead ly", + "Ġl an", + "ĠCy cle", + "orn ed", + "Ġrel iably", + "Ġgl or", + "ĠMon key", + "ãĥ ¡", + "Ġad ren", + "Ġmicrow ave", + "ĠAl ban", + "irc raft", + "dig it", + "sm art", + "ĠD read", + "¯¯¯¯¯¯¯¯ ¯¯¯¯¯¯¯¯", + "{ {", + "ĠRoc hester", + "Ġsimpl ified", + "Ġinf licted", + "Ġtake over", + "Ġyour selves", + "ad itional", + "Ġmus cular", + "K S", + "Ġing en", + "T ax", + "ĠFe ature", + "27 7", + "Ġcru c", + "Ġcr ate", + "Ġun identified", + "Ġacclaim ed", + "ĠM anga", + "ĠFr ances", + "ĠNep al", + "ĠG erald", + "ĠKu wait", + "Ġsl ain", + "ĠHe b", + "ĠG oku", + "ãģ® æ", + "28 6", + "M rs", + "ĠC ody", + "ĠSan ctuary", + "01 6", + "Ġdism ant", + "Ġdatas et", + "ĠH ond", + "b uck", + "ĠPat terson", + "Ġpal ette", + "ĠG D", + "ic ol", + "ĠL odge", + "Ġplanet ary", + "ak in", + "ĠRegist ered", + "ab we", + "ĠPeters burg", + "Ġha iled", + "ĠP iece", + "S che", + "ĠDO J", + "Ġen umer", + "18 1", + "ĠObs erver", + "ĠB old", + "f ounded", + "com merce", + "Ġexplo its", + "ĠF inding", + "UR N", + "ĠS ne", + "ĠAc id", + "ay ette", + "ĠVal ues", + "Ġdr astic", + "Ġarchitect ural", + "Ġ\" .", + "× ķ", + "ump ed", + "Ġwra pping", + "Ġwid ow", + "ĠSl ayer", + "l ace", + "on ce", + "German y", + "av oid", + "Ġtem ples", + "P AR", + "à ´", + "ĠLuc ifer", + "ĠFl ickr", + "l ov", + "for ces", + "Ġsc outing", + "Ġlou der", + "tes y", + "Ġbefore hand", + "Ä ĵ", + "ĠNe on", + "ĠW ol", + "ĠTyp ically", + "ĠPolit ico", + "-+ -+", + "Ġbuild er", + "Ġder ive", + "K ill", + "Ġp oker", + "Ġambig uous", + "Ġlif ts", + "Ġcy t", + "Ġrib s", + "ood le", + "ĠS ounds", + "h air", + "ĠSynd rome", + "t f", + "Ġproport ional", + "u id", + "Ġper taining", + "ĠKind le", + "ĠNeg ro", + "Ġreiter ated", + "ĠTon ight", + "oth s", + "ĠCorn ell", + "Ġo wing", + "Ġ20 8", + "elf are", + "oc ating", + "ĠB irds", + "Sub scribe", + "Ġess ays", + "Ġburd ens", + "Ġillust rations", + "ar ious", + "ER AL", + "ĠCal cul", + "Ġx en", + "ĠLink edIn", + "ĠJ ung", + "Ġredes ign", + "Con nor", + "29 6", + "Ġrevers al", + "ĠAd elaide", + "ĠL L", + "Ġs inking", + "Ġg um", + "US H", + "c apt", + "ĠGr imm", + "Ġfoot steps", + "ĠCB D", + "isp ers", + "Ġpro se", + "Wed nesday", + "ĠM ovies", + "ed in", + "Ġoverturn ed", + "Ġcontent ious", + "US B", + "~~~~~~~~ ~~~~~~~~", + "ĠCo pper", + "Ġpoint less", + "N V", + "val ues", + "olph in", + "d ain", + "Ġdepos ited", + "ĠG W", + "Ġpreced ed", + "ĠCl a", + "ĠGo lem", + "ĠN im", + "ĠÎ ²", + "ĠEngine ers", + "m iddle", + "Ġfl att", + "oper ative", + "Ġcouncil s", + "imb abwe", + "el in", + "Ġstress ful", + "ĠL D", + "Ġres h", + "l ake", + "Ġwheel chair", + "ĠAltern ative", + "Ġoptim ize", + "oper ation", + "Ġpe ek", + "Ġones elf", + "ig il", + "Ġtrans itions", + "op athy", + "bl ank", + "Ġ16 9", + "17 1", + "________________________________ ________________________________", + "Ġl aundering", + "En c", + "ĠD EC", + "Ġwork outs", + "Ġsp ikes", + "Ġdin osaurs", + "Ġdiscrim inatory", + "P ool", + "R ather", + "38 5", + "R NA", + "tes ters", + "et o", + "ĠIdent ity", + "Ġve in", + "ĠBur ton", + "Ġarc ade", + "4 20", + "Ult imately", + "ĠSad ly", + "à °", + "p ill", + "Ġcub ic", + "ĠSpect rum", + "the se", + "st ates", + "Ġun official", + "h awks", + "ĠEVER Y", + "Ġrain bow", + "Ġincarcer ation", + "and ing", + "Ġsy ll", + "ĠEver ton", + "Ġ17 9", + "ĠSer bia", + "Ġ18 9", + "m eter", + "ĠMic key", + "Ġant iqu", + "Ġfact ual", + "ne ck", + "ĠN are", + "n orm", + "m ust", + "Ġhigh ways", + "Ġgl am", + "Ġdivid ing", + "ĠSquad ron", + "ĠMar tha", + "Ġbirth s", + "C over", + "//////// ////////", + "ĠW ong", + "Ph ot", + "ĠA LS", + "ri o", + "ĠNon etheless", + "ĠL emon", + "Ġ20 6", + "ĠE E", + "Ġderiv ative", + "ĠWW II", + "v ote", + "Ġthere in", + "Ġsepar ating", + "44 6", + "sy nc", + "ĠStre ets", + "Ġr att", + "Ġmunicip ality", + "ĠShort ly", + "Ġmon k", + ") ,\"", + "Ġscr ub", + "Ġoper atives", + "Ne ither", + "Pl ace", + "ĠLim it", + "F emale", + "ĠAct or", + "Char acter", + "Ġconstit uted", + "35 7", + "Ġprotest ed", + "ĠSt raw", + "ĠHe ight", + "ild a", + "ĠTy ph", + "Ġflood s", + "Ġcos metic", + "W AY", + "pert ure", + "up on", + "t ons", + "ess ing", + "ĠP ocket", + "Ġro oft", + "ĠC aucas", + "Ġant idepress", + "Ġincomp atible", + "EC D", + "Ġoper a", + "ĠCont est", + "Ġgener ators", + "l ime", + "Def ense", + "19 87", + "for um", + "Ġsav age", + "ĠHung arian", + "n z", + "Ġmet allic", + "Ġex pelled", + "Ġres idency", + "Ġdress es", + "66 6", + "ĠC lement", + "f ires", + "C ategory", + "Ġge ek", + "al is", + "Ġc emetery", + "educ ated", + "Ġc rawl", + "ĠUn able", + "ĠT yson", + "ak is", + "Ġp ardon", + "ĠW ra", + "Ġstrengthen ed", + "ĠF ors", + "33 5", + "ĠH C", + "ĠM ond", + "Ġvisual s", + "ĠBeat les", + "ett lement", + "Ġ ï", + "g ro", + "Ġb ash", + "Ġpo orest", + "Ġex cel", + "Ġaspir ations", + "ĠM unicip", + "ens ible", + "Ġceremon ies", + "Ġintimid ation", + "ĠCON TR", + "be ck", + "ĠK ap", + "as u", + "Ġtradem arks", + "ĠS ew", + "ĠComp etition", + "net work", + "ĠAr ri", + "ĠT et", + "Ro aming", + "W C", + "D at", + "Ġso b", + "Ġpair ing", + "Ġoverd ose", + "SA Y", + "ab er", + "Ġrev olt", + "ĠF ah", + "act ing", + "e q", + "est ation", + "F ight", + "ĠMar ks", + "27 3", + "Ġ17 8", + "R aw", + "ãģ ĭ", + "34 9", + "bl ocks", + "Ġver ge", + "est ine", + "ĠPod esta", + "Ġinv asive", + "Ġprofound ly", + "ĠA o", + "e ach", + "Ġl est", + "inter pret", + "Ġshr inking", + "Ġerr one", + "Ġche es", + "ly s", + "ĠI vy", + "ĠDirect ory", + "Ġhint ed", + "V ICE", + "Ġcontact ing", + "ĠG ent", + "he i", + "Ġlabel ing", + "Ġmerc ury", + "ĠL ite", + "Ġexp ires", + "Ġdest abil", + "rit is", + "c u", + "Ġfeather s", + "Ġste er", + "Ġprogram med", + "ĠV ader", + "Go ing", + "ĠE lim", + "Ġy o", + "ĠMic he", + "Ġ20 3", + "Ġslee ves", + "Ġb ully", + "ĠHum ans", + "36 8", + "Ġcomp ress", + "ĠBan ner", + "AR S", + "Ġa while", + "Ġcal ib", + "Ġspons orship", + "ĠDiff iculty", + "ĠP apers", + "Ġident ifier", + "} .", + "Ġy og", + "ĠSh ia", + "Ġclean up", + "Ġvib e", + "int rodu", + "im ming", + "Austral ia", + "Ġout lines", + "ĠY outube", + "tr ain", + "ĠM akes", + "Ġde ported", + "Ġcent r", + "ĠD ug", + "ĠB oulder", + "ĠBuff y", + "Ġinj unction", + "ĠHar ley", + "ĠG roups", + "ĠD umbledore", + "ĠCl ara", + "Ġ\" -", + "Ġsacrific ed", + "ep h", + "Sh adow", + "ib ling", + "Ġfreel ance", + "Ġevident ly", + "ph al", + "Ġret ains", + "M ir", + "Ġfin ite", + "d ar", + "ĠC ous", + "Ġrep aired", + "Ġperiod ic", + "Ġchampions hips", + "Ġaster oid", + "bl ind", + "Ġexpress ly", + "ĠAst ros", + "Ġsc aled", + "Ġge ographical", + "ĠRap ids", + "En joy", + "Ġel astic", + "ĠMoh amed", + "Mark et", + "be gin", + "Ġdisco vers", + "Ġtele communications", + "Ġscan ner", + "Ġen large", + "Ġsh arks", + "Ġpsy chedel", + "ĠRou ge", + "Ġsnap shot", + "is ine", + "X P", + "Ġpestic ides", + "ĠL SD", + "ĠDist ribution", + "re ally", + "Ġde gradation", + "Ġdisgu ise", + "Ġbi om", + "ĠEX T", + "Ġequ ations", + "Ġhaz ards", + "ĠComp ared", + ") *", + "Ġvirt ues", + "Ġeld ers", + "Ġenh ancing", + "ĠAc ross", + "er os", + "ang ling", + "Ġcomb ust", + "ucc i", + "Ġconc ussion", + "Ġcontrace ption", + "ĠK ang", + "Ġexpress es", + "Ġa ux", + "ĠP ione", + "Ġexhib its", + "Deb ug", + "OT AL", + "ĠAl ready", + "ĠWheel er", + "Ġexp ands", + "? :", + "Ġreconc iliation", + "Ġpir ates", + "Ġpur se", + "Ġdiscour age", + "Ġspect acle", + "R ank", + "Ġwra ps", + "ĠTh ought", + "Ġimp ending", + "O pp", + "ĠAng lo", + "ĠE UR", + "Ġscrew ed", + "ret ched", + "Ġencour agement", + "mod els", + "Ġconf use", + "mm m", + "ĠVit amin", + "âĸij âĸij", + "C ru", + "Ġkn ights", + "Ġdisc ard", + "Ġb ishops", + "ĠW ear", + "ĠGar rett", + "k an", + "ãĥ Ł", + "Ġmascul ine", + "cap ital", + "ĠA us", + "Ġfat ally", + "th anks", + "ĠA U", + "ĠG ut", + "12 00", + "Ġ 00000000", + "Ġsur rog", + "ĠBI OS", + "ra its", + "ĠWat ts", + "Ġresur rection", + "ĠElect oral", + "ĠT ips", + "4 000", + "Ġnut rient", + "Ġdepict ing", + "Ġspr ink", + "Ġm uff", + "ĠL IM", + "ĠS ample", + "ps c", + "ib i", + "gener ated", + "Ġspec imens", + "Ġdiss atisf", + "Ġtail ored", + "Ġhold ings", + "ĠMonth ly", + "ĠE at", + "po ons", + "Ġne c", + "ĠC age", + "ĠLot us", + "ĠLan tern", + "Ġfront ier", + "Ġp ensions", + "Ġj oked", + "ĠHard y", + "=-=- =-=-", + "r ade", + "U ID", + "Ġr ails", + "Ġem it", + "Ġsl ate", + "Ġsm ug", + "Ġsp it", + "ĠCall s", + "ĠJac obs", + "f eat", + "ĠU E", + "Ġrest ruct", + "Ġregener ation", + "Ġenerg ies", + "ĠCon nor", + "OH N", + "ĠChe ese", + "Ġg er", + "Ġresur rect", + "man agement", + "N W", + "Ġpres ently", + "ĠBru ins", + "M ember", + "ĠM ang", + "id an", + "Ġboost ing", + "w yn", + "+ .", + "requ isite", + "ĠNY PD", + "ĠMe gan", + "ĠCond itions", + "Ġp ics", + "nes ium", + "ĠR ash", + "Ġ17 4", + "ĠD ucks", + "Ġemb ro", + "z u", + "on ian", + "rel igious", + "Ġc raz", + "ĠAC A", + "ĠZ ucker", + "EM A", + "ĠPro s", + "We apon", + "ĠKn ox", + "ĠAr duino", + "Ġst ove", + "Ġheaven s", + "ĠP urchase", + "Ġher d", + "Ġfundra iser", + "Dig ital", + "5 000", + "Ġprop onents", + "/ âĢĭ", + "Ġj elly", + "ĠVis a", + "Ġmon ks", + "Ġadvance ment", + "ĠW er", + "Ġ18 7", + "e us", + "ert ility", + "Ġfet al", + "Ġ19 36", + "L o", + "Ġout fits", + "Ġstair case", + "b omb", + "Ġcustom ized", + "cl air", + "T ree", + "Ġm apped", + "ĠConsider ing", + "ĠTor res", + "Ġmeth yl", + "Ġapprox imate", + "Ġdo om", + "ĠHans en", + "Ġc rossover", + "Ġstand alone", + "ä ¼", + "Ġinv ites", + "Ġgra veyard", + "Ġh p", + "Donald Trump", + "Ġesc ort", + "G ar", + "Ġpredec essors", + "Ġh ay", + "Ġen zyme", + "ĠStra ight", + "vis ors", + "I ng", + "ane ously", + "ĠApp lied", + "Ġf ec", + "ĠDur ant", + "Ġout spoken", + "or b", + "Ġz eal", + "Ġdisgr ace", + "' ).", + "ĠChe ng", + "28 9", + "ĠRen a", + "ĠSu icide", + "29 4", + "Ġout raged", + "ĠNew man", + "ĠN vidia", + "ĠA ber", + "ĠB ers", + "Ġrecre ation", + "Wind ow", + "ĠD P", + "x e", + "Ġped oph", + "Ġfall out", + "ambo o", + "Ġpresent ations", + "ĠApp s", + "Ġh tml", + "3 45", + "ĠX XX", + "Ġrub bing", + "ĠLe ather", + "Ġhum idity", + "se ys", + "est ablished", + "ĠUn its", + "64 6", + "Ġrespect able", + "A uto", + "Ġthri ving", + "ĠInn ovation", + "ang s", + "Ext ra", + "reg ulation", + "29 8", + "p ick", + "Ex amples", + "ĠC J", + "Att ack", + "Ġdr acon", + "L T", + "Ġstick er", + "re rs", + "Ġsun ny", + "I ss", + "reg ulated", + "d im", + "ĠAb stract", + "Ġhus bands", + "Off ice", + "om ination", + "it ars", + "AN GE", + "asc al", + "ĠK ris", + "ĠInf antry", + "Ġm alf", + "ĠA the", + "ĠR ally", + "bal anced", + "................ ........", + "OU P", + "Ġmole cule", + "met ics", + "ĠSpl it", + "ĠInstruct ions", + "ĠN ights", + "c ards", + "Ġt ug", + "Ġcon e", + "å Ń", + "Ġt x", + "ĠDisc ussion", + "Ġcatast rophe", + "pp e", + "g io", + "Ġcommun ism", + "Ġhal ted", + "ĠGu ant", + "cle an", + "ĠSc hed", + "ĠK anye", + "Ġw ander", + "ĠSer iously", + "Ġ18 8", + "enn ial", + "f ollow", + "product ive", + "ĠFl ow", + "ĠS ail", + "Ġc raw", + "Ġsim ulations", + "or u", + "ang les", + "ĠN olan", + "Ġmen stru", + "4 70", + "Ġ20 7", + "aj a", + "Ġcas ually", + "board ing", + "Ġ2 22", + "ov y", + "ĠN umbers", + "um at", + "O E", + "28 7", + "ĠCle mson", + "Ġcert s", + "Ġsl id", + "ĠT ribe", + "Ġto ast", + "Ġfort unes", + "Ġf als", + "ĠComm ittees", + "Ġg p", + "Ġf iery", + "ĠN ets", + "ĠAn ime", + "Pack age", + "ĠComp are", + "l aughter", + "in fect", + "Ġatroc ities", + "Ġjust ices", + "Ġins ults", + "ĠVern on", + "Ġsh aken", + "Ġperson a", + "est amp", + "36 7", + "br ain", + "Ġexperiment ing", + "K en", + "ĠElect ronics", + "Ġ16 1", + "dom ain", + "Ġgraph ical", + "b ishop", + "Ġwho pping", + "ĠEv angel", + "Ġadvertis ers", + "ĠSpe ar", + "Ġb ids", + "Ġdestro ys", + "ut z", + "Ġunders c", + "ĠAD D", + "Ġan ts", + "ĠC um", + "ipp les", + "ĠF ill", + "Ġgl anced", + "Ġind icted", + "ĠE ff", + "Ġmis con", + "ĠDes ktop", + "Ġab ide", + "ãĥ Ģ", + "ĠI o", + "ĠC oul", + "Ġcaps ule", + "ĠCh rys", + "M ON", + "Ġund es", + "ĠI RA", + "Ġc itation", + "Ġdict ate", + "ĠNet works", + "ĠConf lict", + "ĠSt uff", + "x a", + "is ec", + "ĠChem istry", + "Ġquarter ly", + "William s", + "an an", + "O pt", + "ĠAlexand ria", + "out heastern", + "ĠSpring field", + "ĠBlack s", + "Ġge ography", + "24 2", + "Ġut most", + "ĠEx xon", + "ab outs", + "E VA", + "ĠEn able", + "ĠBar r", + "Ġdisag reed", + "ĠCy prus", + "Ġdement ia", + "Ġlab s", + "Ġubiqu itous", + "ĠLO VE", + "Ġconsolid ated", + "s r", + "Ġcream y", + "ĠTim ber", + "Reg ardless", + "ĠCert ificate", + "Ġ\" ...", + "ogen ous", + "Capt ain", + "Ġinsult ing", + "ĠSor os", + "ĠInst r", + "ĠBulgar ia", + "bet ter", + "Ġsuck ing", + "ĠDavid son", + "at z", + "Ġcoll ateral", + "g if", + "Ġplag ued", + "ĠC ancel", + "ĠGard ner", + "R B", + "Ġsix teen", + "Rem ove", + "ur istic", + "c ook", + "R od", + "Ġcompr ising", + "f le", + ") âĢĶ", + "ĠVik ing", + "g rowth", + "agon al", + "Ġsr f", + "af ety", + "m ot", + "N early", + "st own", + "ĠF actor", + "Ġautom obile", + "Ġproced ural", + "m ask", + "amp ires", + "Ġdisapp ears", + "j ab", + "3 15", + "Ġ19 51", + "ne eded", + "Ġd aring", + "le ader", + "Ġp odium", + "Ġun healthy", + "Ġm und", + "Ġpy ramid", + "oc re", + "Ġkiss ed", + "Ġdream ed", + "ĠFant astic", + "ĠG ly", + "å Ĭ", + "Ġgreat ness", + "Ġsp ices", + "Ġmet ropolitan", + "Ġcomp uls", + "i ets", + "101 6", + "ĠSh am", + "ĠP yr", + "fl ies", + "ĠMid night", + "Ġswall owed", + "Ġgen res", + "ĠL ucky", + "ĠRew ards", + "Ġdisp atch", + "ĠI PA", + "ĠApp ly", + "Ġa ven", + "al ities", + "3 12", + "th ings", + "Ġ( ).", + "Ġm ates", + "ĠS z", + "ĠC OP", + "ol ate", + "O FF", + "Ġre charge", + "c aps", + "ĠYork er", + "ic one", + "Ġgal axies", + "ile aks", + "D ave", + "ĠP uzz", + "ĠCelt ic", + "ĠA FC", + "27 6", + "ĠS ons", + "Ġaffirm ative", + "H or", + "Ġtutorial s", + "ĠC ITY", + "ĠR osa", + "ĠExt ension", + "Ser ies", + "Ġf ats", + "Ġr ab", + "l is", + "Ġun ic", + "Ġe ve", + "ĠSp in", + "Ġadul thood", + "ty p", + "Ġsect arian", + "Ġcheck out", + "ĠCy cl", + "S ingle", + "Ġmart yr", + "Ġch illing", + "88 8", + "ou fl", + "Ġ] ;", + "Ġcongest ion", + "m k", + "ĠWhere as", + "Ġ19 38", + "ur rencies", + "er ion", + "Ġbo ast", + "ĠPat ients", + "Ġch ap", + "ĠB D", + "real DonaldTrump", + "Ġexam ines", + "h ov", + "Ġstart ling", + "ĠBab ylon", + "w id", + "om ew", + "br ance", + "ĠOd yssey", + "w ig", + "Ġtor ch", + "ĠV ox", + "ĠMo z", + "ĠT roll", + "ĠAn s", + "Similar ly", + "ĠF ul", + "00 6", + "Un less", + "ĠAl one", + "st ead", + "ĠPub lisher", + "r ights", + "t u", + "ĠDoes n", + "Ġprofession ally", + "Ġcl o", + "ic z", + "Ġste als", + "Ġ á", + "19 86", + "Ġst urdy", + "ĠJoh ann", + "Ġmed als", + "Ġfil ings", + "ĠFr aser", + "d one", + "Ġmult inational", + "Ġf eder", + "Ġworth less", + "Ġp est", + "Yes terday", + "ank ind", + "Ġg ays", + "Ġb orne", + "ĠP OS", + "Pict ure", + "Ġpercent ages", + "25 1", + "r ame", + "Ġpot ions", + "AM D", + "ĠLeban ese", + "Ġr ang", + "ĠL SU", + "ong s", + "Ġpen insula", + "ĠCl ause", + "AL K", + "oh a", + "ĠMac Book", + "Ġunanim ous", + "Ġl enders", + "Ġhang s", + "Ġfranch ises", + "ore rs", + "ĠUp dates", + "Ġisol ate", + "and ro", + "S oon", + "Ġdisrupt ive", + "ĠSur ve", + "Ġst itches", + "ĠSc orp", + "ĠDomin ion", + "Ġsupp lying", + "Ar g", + "Ġtur ret", + "ĠL uk", + "Ġbr ackets", + "* )", + "ĠRevolution ary", + "ĠHon est", + "Ġnot icing", + "ĠSh annon", + "Ġafford ed", + "Ġth a", + "ĠJan et", + "! --", + "ĠNare ndra", + "ĠPl ot", + "H ol", + "se ver", + "e enth", + "Ġobst ruction", + "Ġ10 24", + "st aff", + "j as", + "or get", + "sc enes", + "l aughs", + "ĠF argo", + "cr ime", + "Ġorche str", + "Ġde let", + "ili ary", + "rie ved", + "Ġmilit ar", + "ĠGreen e", + "âĹ ı", + "ãģ ¦", + "ĠGu ards", + "Ġunle ashed", + "ĠWe ber", + "Ġadjust able", + "Ġcal iber", + "Ġmotiv ations", + "Ġà ł", + "m Ah", + "ĠL anka", + "hand le", + "Ġp ent", + "ĠR av", + "ĠAng ular", + "ĠK au", + "umb ing", + "Ġphil anthrop", + "Ġde hyd", + "Ġtox icity", + "e er", + "ĠY ORK", + "w itz", + "å ¼", + "ĠI E", + "commun ity", + "ĠA H", + "Ġret ali", + "Ġmass ively", + "ĠDani els", + "ĠD EL", + "Ġcar cin", + "Ur l", + "Ġrout ing", + "ĠNPC s", + "ĠR AF", + "ry ce", + "Ġwa ived", + "ĠGu atem", + "Every body", + "Ġco venant", + "Ġ17 3", + "Ġrelax ing", + "Ġqu art", + "al most", + "Ġguard ed", + "ĠSold iers", + "ĠPL AY", + "Ġout going", + "L AND", + "Ġre write", + "ĠM OV", + "ĠIm per", + "ĠS olution", + "Ġphenomen al", + "Ġl ongevity", + "Ġimp at", + "ĠN issan", + "ir ie", + "Ġod or", + "ĠZ ar", + "ok s", + "Ġmilit ias", + "ĠSP EC", + "Ġtoler ated", + "ars er", + "ĠBrad ford", + "+ ,", + "Ġsur real", + "s f", + "Can adian", + "Ġresemb lance", + "Ġcarbohyd rate", + "VI EW", + "Ġaccess ory", + "me al", + "larg est", + "ieg el", + "Some one", + "Ġtoug hest", + "os o", + "Ġfun nel", + "Ġcondemn ation", + "lu ent", + "Ġw ired", + "ĠSun set", + "Jes us", + "ĠP ST", + "ĠP ages", + "ĠTy coon", + "ĠP F", + "Ġselect ions", + "Ġ à¤", + "part isan", + "Ġhigh s", + "ĠR une", + "Ġcraft s", + "le ad", + "ĠParent s", + "Ġre claim", + "ek er", + "ĠAll ied", + "ae per", + "Ġlo oming", + "Ġbenefic iaries", + "ĠH ull", + "Stud ents", + "Jew ish", + "d j", + "Ġp act", + "tem plate", + "ĠOffic ials", + "ĠBay lor", + "Ġhe mp", + "Ġyouth s", + "ĠLevel s", + "ĠX iao", + "ĠC hes", + "Ġende avor", + "ĠRem oved", + "Ġhipp ocamp", + "H ell", + "ãĤ Ĭ", + "80 5", + "Ġd inosaur", + "ĠWr ath", + "ĠIndones ian", + "Ġcalcul ator", + "ĠD ictionary", + "Ġ4 20", + "ĠM AG", + "( _", + "! ,", + "t arians", + "Ġrestrict ing", + "rac use", + "Ġweek day", + "OU NT", + "Ġsh rugged", + "leg round", + "Ġb ald", + "ĠDo ctors", + "Ġt outed", + "ĠMax well", + "Ġ2 14", + "Ġdiplom at", + "Ġrep ression", + "Ġconstitu ency", + "v ice", + "r anked", + "ĠNap oleon", + "g ang", + "ĠFore ver", + "t un", + "Ġbul b", + "ĠPD T", + "ĠC isco", + "V EN", + "Ġres umed", + "Ste ven", + "ĠManit oba", + "Ġfab ulous", + "ĠAg ents", + "19 84", + "Ġam using", + "ĠMyster ies", + "Ġor thodox", + "fl oor", + "Ġquestion naire", + "Ġpenet rate", + "Ġfilm makers", + "ĠUn c", + "Ġst amped", + "Ġth irteen", + "Ġout field", + "Ġforward ed", + "Ġapp ra", + "Ġa ided", + "t ry", + "Ġunf ocused", + "ĠL iz", + "ĠWend y", + "ĠSc ene", + "Ch arg", + "Ġreject s", + "Ġleft ist", + "ĠProv idence", + "ĠBr id", + "reg n", + "Ġprophe cy", + "ĠL IVE", + "4 99", + "Ġfor ge", + "ĠF ML", + "Ġintrins ic", + "ĠF rog", + "Ġw ont", + "ĠH olt", + "Ġfam ed", + "CL US", + "aeper nick", + "ĠH ate", + "ĠC ay", + "Ġregister ing", + "ort ality", + "rop y", + "ocaly ptic", + "a an", + "n av", + "Ġfasc ist", + "IF IED", + "Ġimpl icated", + "ĠRes ort", + "ĠChand ler", + "ĠBr ick", + "P in", + "ys c", + "Us age", + "ĠHel m", + "us ra", + "âĺħ âĺħ", + "ĠAb bas", + "Ġunanim ously", + "Ġke eper", + "Ġadd icted", + "?? ?", + "Ġhelm ets", + "Ġant ioxid", + "aps ed", + "80 8", + "gi ene", + "Ġwa its", + "Ġmin ion", + "ra ved", + "ĠP orsche", + "Ġdream ing", + "Ġ17 1", + "ĠC ain", + "Ġun for", + "ass o", + "ĠConfig uration", + "k un", + "hard t", + "Ġn ested", + "ĠL DS", + "L ES", + "Ġt ying", + "en os", + "Ġc ue", + "ĠMar qu", + "sk irts", + "Ġclick ed", + "Ġexp iration", + "ĠAccording ly", + "ĠW C", + "Ġbless ings", + "Ġaddict ive", + "ĠN arr", + "y x", + "ĠJagu ars", + "Ġrent s", + "ĠS iber", + "Ġt ipped", + "ous se", + "ĠFitz gerald", + "Ġhier arch", + "out ine", + "Ġwa velength", + "> .", + "ch id", + "ĠProcess ing", + "/ +", + "r anking", + "E asy", + "ĠConst ruct", + "Ġt et", + "ins ured", + "H UD", + "Ġqu oting", + "Ġcommun icated", + "in x", + "Ġin mate", + "Ġerect ed", + "ĠAbs olutely", + "ĠSure ly", + "Ġun im", + "ĠThr one", + "he id", + "Ġcl aws", + "Ġsuper star", + "ĠL enn", + "ĠWh is", + "U k", + "ab ol", + "Ġsk et", + "ĠN iet", + "Ġper ks", + "Ġaff inity", + "Ġopen ings", + "phas is", + "Ġdiscrim inate", + "T ip", + "v c", + "Ġgr inding", + "ĠJenn y", + "Ġast hma", + "hol es", + "ĠHom er", + "Ġreg isters", + "ĠGl ad", + "Ġcre ations", + "Ġlith ium", + "Ġappl ause", + "unt il", + "Just ice", + "ĠTur ks", + "Ġsc andals", + "Ġb ake", + "t ank", + "M ech", + "ĠMe ans", + "ĠM aid", + "Republic ans", + "is al", + "wind ows", + "ĠSant os", + "Ġveget ation", + "33 8", + "t ri", + "Ġfl ux", + "ins ert", + "Ġclar ified", + "Ġmort g", + "ĠCh im", + "ĠT ort", + "Ġdiscl aim", + "met al", + "ĠAs ide", + "Ġindu ction", + "Ġinf l", + "Ġathe ists", + "amp h", + "Ġe ther", + "ĠV ital", + "ĠBu ilt", + "M ind", + "Ġweapon ry", + "S ET", + "Ġ18 6", + "ad min", + "g am", + "cont ract", + "af a", + "Ġderiv atives", + "Ġsn acks", + "Ġch urn", + "E conom", + "Ġca pped", + "ĠUnder standing", + "ĠH ers", + "ĠI z", + "Ġd uct", + "I ENT", + "augh ty", + "Ġâľ Ķ", + "ĠN P", + "Ġsa iling", + "In itialized", + "Ġt ed", + "Ġreact ors", + "ĠL omb", + "Ġcho ke", + "ĠW orm", + "Ġadm iration", + "Ġsw ung", + "ens ibly", + "Ġr ash", + "ĠGo als", + "ĠImport ant", + "Sh ot", + "ĠR as", + "Ġtrain ers", + "ĠB un", + "Work ing", + "Ġhar med", + "ĠPand ora", + "ĠL TE", + "Ġmush room", + "ĠCH AR", + "ĠF ee", + "ĠM oy", + "B orn", + "ol iberal", + "ĠMart ial", + "Ġgentle men", + "Ġling ering", + "Offic ial", + "Ġgra ffiti", + "ĠN ames", + "D er", + "Ġqu int", + "ist rate", + "aze era", + "ĠNOT ICE", + "ĠFlore nce", + "Ġpay able", + "Ġdep icts", + "ĠSpe cies", + "He art", + "âĶĢâĶĢâĶĢâĶĢ âĶĢâĶĢâĶĢâĶĢ", + "Ġencl osed", + "Incre ases", + "D aily", + "ĠL is", + "Ġenact ment", + "ĠB acon", + "ĠSt eele", + "dem and", + "Ġ18 3", + "Ġmouth s", + "Ġstr anded", + "Ġenhance ment", + "01 1", + "ĠWh ats", + "Ġhe aled", + "en y", + "ĠR ab", + "Ġ3 40", + "ĠLab yrinth", + "ro ach", + "ĠY osh", + "ĠCl ippers", + "Ġconcert s", + "Intern et", + "35 5", + "Ġstick ers", + "Ġter med", + "ĠAx e", + "Ġgrand parents", + "Fr ance", + "ĠCl im", + "ĠU h", + "ul ic", + "Ġthr ill", + "cent ric", + "ĠOver view", + "ĠCond uct", + "Ġsubstant ive", + "Ġ18 2", + "m ur", + "Ġstr ay", + "ĠCo ff", + "Ġrep etitive", + "ĠFor gotten", + "Ġqual ification", + "ew itness", + "ĠZ imbabwe", + "Ġsim ulated", + "ĠJ D", + "25 3", + "ĠW are", + "Ġun sc", + "T imes", + "Ġsum mons", + "Ġdis connected", + "Ġ18 4", + "ci us", + "ĠGu jar", + "od ka", + "Ġer ase", + "ĠTob acco", + "elect ed", + "Ġun cont", + "ĠShe pard", + "ĠL amp", + "Ġalert ed", + "Ġoper ative", + "arn a", + "u int", + "Ġneglig ence", + "ac ements", + "Ġsup ra", + "Ġprev ail", + "ĠSh ark", + "Ġbel ts", + "ãģ «", + "Ġt ighter", + "Engine ers", + "Ġin active", + "Ġexp onent", + "ĠWill ie", + "a ples", + "Ġhe ir", + "ĠH its", + "ian n", + "ĠS ays", + "Ġcurrent s", + "ĠBeng al", + "Ġar ist", + "B uffer", + "Ġbree ze", + "ĠWes ley", + "Col a", + "Ġpron oun", + "Ġde ed", + "ĠK ling", + "Ġof t", + "Ġinf lict", + "Ġpun ishing", + "Ġn m", + "ik u", + "OD UCT", + "01 4", + "Ġsubsid y", + "ĠDE A", + "ĠHer bert", + "ĠJ al", + "B ank", + "Ġdef erred", + "Ġship ment", + "B ott", + "Ġal le", + "b earing", + "HT ML", + "Off line", + "Ġ2 13", + "Ġscroll ing", + "Ġsc anned", + "ĠLib yan", + "ĠT OP", + "ch rom", + "d t", + "col umn", + "Psy NetMessage", + "Z ero", + "Ġtor so", + "0 50", + "âķ IJ", + "Ġimp erson", + "ĠSchw artz", + "ud ic", + "Ġpiss ed", + "ĠS app", + "25 7", + "ĠIS Ps", + "og l", + "Ġsuper vised", + "Ġad olescent", + "Ġatt ained", + "ĠDel ivery", + "ĠB unny", + "Ġ19 37", + "Ġmini ature", + "Ġo s", + "Ġ3 70", + "60 8", + "ĠMour inho", + "Ġinn ate", + "Ġtem po", + "ĠN M", + "ĠFall en", + "00 9", + "Ġprov ocative", + "Stream er", + "ĠBened ict", + "ĠBol she", + "Ġt urtle", + "ĠPC B", + "ĠEqu al", + "Direct or", + "ĠR end", + "Ġflu ids", + "Author ities", + "Ġcous ins", + "requ ency", + "ĠNeigh bor", + "s ets", + "sh ared", + "Char les", + "pass word", + "Ġg ears", + "Ġ2 11", + "ĠHard ware", + "ri ka", + "Ġup stream", + "H om", + "Ġdisproportion ately", + "iv ities", + "Ġund efined", + "Ġelect rons", + "Ġcommem or", + "Event ually", + "Ġ> <", + "Ġir responsible", + "2 18", + "ĠRe leased", + "ĠO VER", + "ĠI GN", + "ĠB read", + "st ellar", + "ĠS age", + "tt ed", + "dam age", + "ed ition", + "ĠPre c", + "Ġl ime", + "Ġconf inement", + "Ġcal orie", + "we apon", + "Ġdiff ering", + "ĠS ina", + "m ys", + "am d", + "Ġintric ate", + "k k", + "ĠP AT", + "ã o", + "st ones", + "lin ks", + "Ġr anch", + "Sem itic", + "Ġdifferent iate", + "ĠS inger", + "occup ied", + "Ġfort ress", + "c md", + "Ġinter ception", + "ĠAnk ara", + "Ġre pt", + "ĠSol itaire", + "Ġrem ake", + "p red", + "Ġd ared", + "aut ions", + "ĠB ACK", + "Run ning", + "Ġdebug ging", + "Ġgraph s", + "3 99", + "ĠNig el", + "Ġb un", + "Ġpill ow", + "Ġprog ressed", + "fashion ed", + "Ġob edience", + "ER N", + "Ġrehe ars", + "C ell", + "t l", + "S her", + "Ġher ald", + "ĠPay ment", + "ĠC ory", + "ĠDe pt", + "Ġrep ent", + "ĠWe ak", + "uck land", + "Ġple asing", + "Ġshort ages", + "Ġjur ors", + "ĠK ab", + "q qa", + "Ant i", + "Ġw ow", + "ĠRC MP", + "Ġt sun", + "ĠS ic", + "Ġcomp rises", + "Ġsp ies", + "Ġprec inct", + "n u", + "Ġur ges", + "Ġtim ed", + "Ġstrip es", + "ĠB oots", + "Ġy en", + "Adv anced", + "Ġdisc rete", + "ĠArch angel", + "employ ment", + "D iff", + "Ġmon uments", + "Ġ20 9", + "work er", + "Ġ19 6", + "ĠI g", + "utter stock", + "T PS", + "J ac", + "Ġhomeless ness", + "Ġcomment ator", + "Ġrac ially", + "f ing", + "se ed", + "E le", + "ell ation", + "Ġeth anol", + "Ġpar ish", + "ĠD ong", + "ĠAw akening", + "Ġdev iation", + "ĠB earing", + "ĠTsu k", + "Ġrec ess", + "Ġl ymph", + "ĠCann abis", + "å ľ", + "ĠNEW S", + "Ġd ra", + "ĠStef an", + "ĠWr ong", + "ĠS AM", + "Ġloose ly", + "Ġinterpre ter", + "ĠPl ain", + "Go vernment", + "Ġbigot ry", + "Ġgren ades", + "ave z", + "pict ured", + "Ġmand ated", + "ĠMon k", + "ĠPed ro", + "Ġl ava", + "27 4", + "Ġcyn ical", + "ĠScroll s", + "l ocks", + "M p", + "Ġcon gregation", + "orn ings", + "ph il", + "ĠI bid", + "Ġf erv", + "Ġdisapp earing", + "Ġarrog ant", + "sy n", + "ĠMa ver", + "ĠSu it", + "24 1", + "Ġab bre", + "ack ers", + "P a", + "ĠY el", + "Whe never", + "Ġ23 5", + "ĠV ine", + "ĠAn at", + "Ġext inct", + "LE T", + "Ġexecut able", + "V ERS", + "ox ide", + "D NA", + "ĠP rel", + "Ġresent ment", + "Ġcompr ise", + "ĠAv iv", + "Ġinter ceptions", + "Ġprol ific", + "IN A", + "ĠEr in", + "though t", + "2 19", + "ĠPsychiat ry", + "un ky", + "chem ist", + "H o", + "ĠMcC oy", + "Ġbr icks", + "L os", + "ri ly", + "ĠUS SR", + "Ġr ud", + "Ġl aud", + "ĠW ise", + "ĠEmer ald", + "Ġrev ived", + "Ġdam ned", + "ĠRep air", + "id em", + "ct ica", + "Ġpatri arch", + "ĠN urs", + "me g", + "Ġcheap est", + "re ements", + "empt y", + "ĠCele br", + "Ġdepri vation", + "ch anted", + "ĠTh umbnails", + "E nergy", + "ĠEth an", + "ĠQ ing", + "Ġopp oses", + "W IND", + "v ik", + "ĠM au", + "ĠS UB", + "66 7", + "G RE", + "ĠVol unte", + "nt on", + "C ook", + "å IJ", + "es que", + "Ġplum met", + "Ġsu ing", + "Ġpron ounce", + "Ġresist ing", + "ĠF ishing", + "ĠTri als", + "Ġy ell", + "Ġ3 10", + "Ġin duct", + "Ġpersonal ized", + "oft en", + "R eb", + "EM BER", + "Ġview point", + "Ġexist ential", + "() )", + "rem ove", + "MENT S", + "l asses", + "Ġev apor", + "Ġa isle", + "met a", + "Ġreflect ive", + "Ġentit lement", + "Ġdev ised", + "mus ic", + "asc ade", + "Ġwind ing", + "off set", + "Ġaccess ibility", + "ke red", + "Bet ter", + "ĠJohn ston", + "th inking", + "S now", + "ĠCroat ia", + "ĠAt omic", + "27 1", + "34 8", + "Ġtext book", + "ĠSix th", + "Ġ اÙĦ", + "Ġsl ider", + "ĠBur ger", + "b ol", + "S ync", + "Ġgrand children", + "Ġc erv", + "+ )", + "Ġe ternity", + "Ġtweet ing", + "Ġspec ulative", + "Ġpiv otal", + "ĠW P", + "ĠT ER", + "ynam ic", + "Ġu pl", + "ĠC ats", + "per haps", + "Ġclass mates", + "Ġblat ant", + "' -", + "Ġl akh", + "ant ine", + "ĠB org", + "i om", + "/ (", + "ĠAthlet ic", + "Ġs ar", + "OT A", + "ĠHoff man", + "Never theless", + "Ġad orable", + "Ġspawn ed", + "Ass ociated", + "ĠDom estic", + "Ġimpl ant", + "ĠLux em", + "ĠK ens", + "Ġp umps", + "ĠS AT", + "Att ributes", + "50 9", + "av our", + "Ġcentral ized", + "ĠT N", + "Ġfresh ly", + "ĠA chieve", + "Ġouts iders", + "her ty", + "ĠRe e", + "ĠT owers", + "ĠD art", + "ak able", + "Ġm p", + "ĠHeaven ly", + "Ġr ipe", + "ĠCarol ine", + "ry an", + "Ġclass ics", + "Ġret iring", + "Ġ2 28", + "Ġa h", + "Ġdeal ings", + "Ġpunch ing", + "ĠChap man", + "O ptions", + "max well", + "vol ume", + "Ġst al", + "Ġex ported", + "ĠQu ite", + "Ġnumer ical", + "B urn", + "F act", + "ĠKey stone", + "Ġtrend ing", + "Ġalter ing", + "ĠAfric ans", + "47 8", + "ĠM N", + "ĠKn ock", + "Ġtempt ation", + "Ġprest ige", + "Over view", + "ĠTrad itional", + "ĠBah rain", + "Priv ate", + "ĠH OU", + "Ġbar r", + "ĠT at", + "C ube", + "US D", + "ĠGrand e", + "ĠG at", + "ĠFl o", + "Ġres ides", + "Ġind ec", + "vol ent", + "Ġperpet ual", + "ub es", + "Ġworld view", + "ĠQuant um", + "Ġfil tered", + "Ġen su", + "orget own", + "ERS ON", + "ĠM ild", + "37 9", + "OT T", + "à ¥", + "Ġvit amins", + "Ġrib bon", + "Ġsincere ly", + "ĠH in", + "Ġeight een", + "Ġcontradict ory", + "Ġgl aring", + "Ġexpect ancy", + "Ġcons pir", + "Ġmon strous", + "Ġ3 80", + "re ci", + "Ġhand ic", + "Ġpump ed", + "Ġindic ative", + "Ġr app", + "Ġav ail", + "ĠLEG O", + "ĠMar ijuana", + "19 85", + "ert on", + "Ġtwent ieth", + "################ ################", + "ĠSw amp", + "Ġval uation", + "Ġaffili ates", + "adjust ed", + "ĠFac ility", + "26 2", + "Ġenz ymes", + "itud inal", + "Ġimp rint", + "S ite", + "Ġinstall er", + "ĠT RA", + "m ology", + "lin ear", + "ĠCollect ive", + "ig ating", + "ĠT oken", + "Ġspec ulated", + "K N", + "ĠC ly", + "or ity", + "Ġdef er", + "Ġinspect ors", + "appro ved", + "R M", + "ĠSun s", + "Ġinform ing", + "ĠSy racuse", + "ib li", + "7 65", + "Ġgl ove", + "Ġauthor ize", + "âĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦", + "ĠCru ise", + "Ġcontract ing", + "she ll", + "IF E", + "ĠJew el", + "p ract", + "ĠPhot oshop", + "ĠKnow ing", + "h arm", + "Ġattract ions", + "ad an", + "et us", + "01 8", + "w agen", + "Al t", + "Ġmultip ly", + "Ġequ ilibrium", + ": {", + "ĠF ighters", + "ĠEd gar", + "Ġfour teen", + "Go vern", + "Ġmis use", + "Ġab using", + "Ġancest ry", + "ram er", + "64 4", + "Ġwor ms", + "Ġthick er", + "ĠComb ine", + "Ġpeas ants", + "Ġv ind", + "Ġcon quest", + "Ġm ocked", + "Ġc innamon", + "ĠC ald", + "ĠGall up", + "Ġavoid ance", + "Ġincarn ation", + "ĠStr at", + "Ġt asted", + "ent a", + "ĠN eal", + "p ared", + "Ġtermin ology", + "ject ion", + "Scient ists", + "ĠIN S", + "ĠDe e", + "Ġdirect ories", + "R oad", + "ĠSh ap", + "br ight", + "ĠDirect ors", + "ĠCol umn", + "Ġb ob", + "Ġprefer ably", + "Ġgl itch", + "f urt", + "Ġe g", + "id is", + "C BC", + "Ġsur rendered", + "Ġtest ament", + "33 6", + "ug gest", + "ĠN il", + "an other", + "Ġpat hetic", + "ĠDon na", + "Ġ2 18", + "ĠA very", + "Ġwhis key", + "Ġf ixture", + "ĠCon quest", + "Ġbet s", + "O cc", + "ĠLe icester", + "] .\"", + "Ġ) );", + "Ġfl ashes", + "45 6", + "Ġmask ed", + "ge bra", + "Ġcomput ed", + "che l", + "aud er", + "Ġdefe ats", + "ĠLiber ation", + "ĠOs ama", + "ĠV ive", + "Ch anges", + "Ch annel", + "Ġtar iffs", + "Ġm age", + "ĠS ax", + "Ġinadvert ently", + "ĠC RE", + "ĠRe aper", + "ink y", + "gr ading", + "Ġstere otyp", + "Ġcur l", + "ĠF ANT", + "Ġfram eworks", + "M om", + "ĠAn ch", + "Ġflav our", + "car bon", + "Ġperm itting", + "let cher", + "ĠMo zilla", + "ĠPark ing", + "ĠCh amp", + "Sc roll", + "Ġmurd erer", + "Ġrest ed", + "Ġow es", + "ĠP oss", + "AD D", + "IF F", + "res olution", + "ĠMin ing", + "Ġcompar ative", + "D im", + "Ġneighbour ing", + "ĠA ST", + "ĠT oxic", + "Ġbi ases", + "Ġgun fire", + "ur ous", + "ĠMom ent", + "19 83", + "Ġper vasive", + "tt p", + "ĠNorm ally", + "r ir", + "S arah", + "ĠAlb any", + "Ġun sett", + "ĠS MS", + "ip ers", + "l ayer", + "ĠWh ites", + "up le", + "Ġtur bo", + "ĠLe eds", + "Ġthat s", + "ĠMin er", + "M ER", + "ĠRe ign", + "Ġper me", + "ĠBl itz", + "Ġ19 34", + "Ġintimid ating", + "t ube", + "Ġecc entric", + "ab olic", + "box es", + "ĠAssoci ates", + "v otes", + "Ġsim ulate", + "um bo", + "aster y", + "Ġship ments", + "FF FF", + "an th", + "Ġseason ed", + "Ġexperiment ation", + "âĸ ł", + "law s", + "Me et", + "idd les", + "ant ics", + "R ating", + "IS IS", + "h ift", + "Ġfront s", + "b uf", + "01 7", + "Ġun att", + "ĠD il", + "le ases", + "ĠGard ens", + "77 7", + "t ouch", + "ve ll", + "45 8", + "Ġ= ====", + "s aving", + "Ġer osion", + "ĠQu in", + "Ġearn s", + "Ġaccomplish ment", + "ĠWe i", + "Ġ< [", + "____ _", + "Ġir rig", + "ĠT eddy", + "Ġconqu ered", + "ĠArm ored", + "Ġassert s", + "Ġmanip ulating", + "r é", + "Ġtranscript s", + "G allery", + "Ġplot ting", + "Ne il", + "Ġbetray al", + "load er", + "ĠS ul", + "Ġdispl acement", + "Ġroy alty", + "ĠW I", + "he it", + "ĠDev ices", + "alle l", + "Ġmunicipal ities", + "Ġcan al", + "St ars", + "ĠU AE", + "Ġ\" âĢ¦", + "ĠC U", + "ab ove", + "Ġreson ance", + "ĠguiActive Un", + "add ed", + "ĠBra ves", + "ĠI bn", + "Ġhere by", + "ĠB RE", + "Ġshare holder", + "ĠH ir", + "ĠJ i", + "Ġstrange ly", + "Ġadm ired", + "Ġpl ight", + "Ġb achelor", + "ĠP ole", + "cipl inary", + "T ony", + "ĠArmen ian", + "Ġun man", + "ĠZion ist", + "St age", + "isco ver", + "Ġautom otive", + "Ġs idelines", + "Ġsl ick", + "ĠRena issance", + "ĠF UN", + "Im ages", + "ĠH aj", + "Ġp ing", + "Ġshort cut", + "ĠBl vd", + "ĠLook s", + "Ġbur sts", + "Ġcl amp", + "Ġm ish", + "Ġsort ing", + "Ġpatri ot", + "Ġcorrect ness", + "ĠScand inav", + "ĠCaval iers", + "p ython", + "az ar", + "Ġ3 75", + "ĠJa une", + "40 9", + "Ġdetrim ental", + "Ġstab bing", + "Ġpoison ed", + "Ġf ountain", + "oc ent", + "or st", + "ĠMar i", + "Ġr ains", + "ĠO vers", + "ĠInst itution", + "ud get", + "AM Y", + "t ale", + "ĠK R", + "ĠPr ices", + "Ġhead aches", + "Ġlands l", + "ĠA ura", + "Bon us", + "ĠZ hao", + "ĠH ip", + "Ġhop s", + "ĠKurd istan", + "Ġexplo iting", + "ry n", + "Ġhypocr isy", + "op ening", + "Ġgun shot", + "Ġw ed", + "inter stitial", + "Inter stitial", + "Ġam en", + "Bre aking", + "Ġmarket ed", + "W ire", + "ĠC rowd", + "Contin ue", + "ĠK nown", + "ĠEffect ive", + "ore an", + "iz ons", + "Jose ph", + "Ġescal ation", + "us ername", + "Ġcur tain", + "AT ES", + "ĠP AR", + "ĠM iy", + "Ġcounter fe", + "l ene", + "Ġcont enders", + "d aily", + "ĠAs c", + "ĠPhill ip", + "most ly", + "Ġfil ename", + "he ne", + "Ġresemb ling", + "Ġst aging", + "ĠCh loe", + "Ġw iring", + "H on", + "ĠRen ew", + "ott age", + "ĠHy brid", + "m uch", + "Ġstro kes", + "Ġpolicy makers", + "AP TER", + "ĠArk ham", + "pl ot", + "Ġassist ants", + "Ġde port", + "ĠSe ga", + "Ġinflu enza", + "ĠC ursed", + "ĠK obe", + "Ġskin ny", + "Prov ider", + "ĠR ip", + "Ġincrement al", + "product s", + "B F", + "Ġd ome", + "ĠC redits", + "Ġlos ers", + "int s", + "ĠBet ty", + "ĠTal ent", + "ĠD AM", + "L v", + "E ss", + "Ġd ens", + "tem p", + "J udge", + "od ic", + "Ġ' (", + "UR ES", + "ets k", + "V O", + "Ġretrie ved", + "Ġarchitect s", + "Ù ĩ", + "Ġeth ic", + "ĠSecond ary", + "st ocks", + "ad ia", + "Ġ3 25", + "ĠOp inion", + "Ġsimultane ous", + "Ġd izz", + "ul p", + "Ġsmugg ling", + "ipp ery", + "R andom", + "f acing", + "ĠD as", + "Ġstock p", + "Ġdiscl osures", + "po inter", + "Ġcor al", + "ĠSe lection", + "ĠP ike", + "ival ent", + "Ġruth less", + "ĠR im", + "Ġensu ing", + "ĠExper iment", + "Ġcongress man", + "Ġbelie ver", + "Ġun specified", + "ĠM ord", + "Ġknowledge able", + "ĠV ERY", + "T X", + "Ġstra ps", + "Ġtur f", + "apesh ifter", + "Ġmar ital", + "Ġfl ock", + "ãģ Ĩ", + "26 3", + "AM ES", + "ĠOpp osition", + "Ġtre asures", + "ĠG OD", + "Ġmodel ed", + "ĠWOR LD", + "Ġ( [", + "ĠUs age", + "H F", + "Ġ$ (", + "uss ed", + "Ġpione er", + "E ight", + "par se", + "b read", + "rit z", + "ĠMir anda", + "ĠK ant", + "++ )", + "ore n", + "Ġprov oked", + "Ġbre eds", + "ĠIn cludes", + "ĠPast ebin", + "ĠFl ip", + "J ava", + "Ġbr ink", + "Ġrum ored", + "Ġun seen", + "Ġgar nered", + "ĠDef in", + "al ted", + "Ġtatt oos", + "Ġhes itation", + "is itions", + "ĠWe aver", + "ĠReport ing", + "Ġtherap ies", + "Ġconsult ants", + "Ġresid ual", + "ĠMal i", + "ĠRom a", + "i ago", + "ĠRes idents", + "ub i", + "Ġremed ies", + "Ġadapt ive", + "ĠAl ive", + "ĠBar cl", + "Ġwal lets", + "c rypt", + "etermin ation", + "ĠPel osi", + "Ġsl ipping", + "oton in", + "Ġall iances", + "pat rick", + "ir is", + "Ġor th", + "ĠPer kins", + "ĠDe V", + "ĠG ets", + "Ġdry ing", + "ge e", + "fore st", + "ĠFor get", + "ore m", + "33 9", + "Ġvague ly", + "ĠD ion", + "ĠP orn", + "ĠH OW", + "Ġp neum", + "Ġrub ble", + "ĠT aste", + "enc ia", + "ĠG el", + "Ġd st", + "Ġ24 5", + "ĠMoroc co", + "inf lamm", + "ĠTw ins", + "Ġb ots", + "d aughter", + "ĠB alk", + "Ġbre thren", + "Ġlog os", + "Ġgo bl", + "f ps", + "Ġsub division", + "Ġp awn", + "Ġsquee zed", + "Ġmor ale", + "ĠD W", + "' \"", + "Ġkn ot", + "ook y", + "Ġdiv isive", + "Ġboost ed", + "ch y", + "ãĥ IJ", + "if act", + "Ġnewcom ers", + "ĠWrest ling", + "Ġsc outs", + "w olves", + "R at", + "Ġnin eteenth", + "ĠOs borne", + "St ats", + "Ġem powered", + "Ġpsych opath", + "ĠO EM", + "ugg age", + "ĠP K", + "ĠMoh ammad", + "P ak", + "Ġanarch ists", + "ĠExt ract", + "est hes", + "ĠStock holm", + "l oo", + "ĠG raph", + "Ġdeploy ing", + "ĠStr anger", + "ĠM old", + "Ġstaff er", + "Ġdiscount ed", + "uck le", + "ple ase", + "ĠLand ing", + "ÃŃ a", + "Ġ19 3", + "Ġan te", + "Ġrep etition", + "Ġ+ /-", + "Ġpar ody", + "Ġlive ly", + "AA A", + "ĠHor us", + "Ġp its", + "ind ers", + "L OC", + "ĠVen ice", + "40 6", + "ĠDis cover", + "â Ĩ", + "ellect ual", + "Ġp ens", + "Ġey el", + "ig uous", + "Im pl", + "Ġj oking", + "Ġinv al", + "ĠBel fast", + "Ġcredit ors", + "ĠSky walker", + "ov sky", + "Ġcease fire", + "Ġse als", + "is oft", + ") ).", + "ĠFel ix", + "IT S", + "Ġt resp", + "ĠBlock chain", + "ew are", + "ĠSch war", + "en ne", + "mount ed", + "ĠBe acon", + "les h", + "Ġimmense ly", + "Ġche ering", + "Em ploy", + "sc ene", + "ish ly", + "atche wan", + "ĠNic olas", + "Ġdr ained", + "ĠEx it", + "ĠAz erb", + "j un", + "Ġflo ated", + "u ania", + "De ep", + "Ġsuper v", + "Ġmyst ical", + "ĠD ollar", + "ĠApost le", + "ĠR EL", + "ĠProv ided", + "ĠB ucks", + "ãĥ ´", + "cut ting", + "Ġenhance ments", + "ĠPengu ins", + "ĠIsa iah", + "Ġj erk", + "ĠW yn", + "Ġst alled", + "Ġcryptoc urrencies", + "ĠR oland", + "sing le", + "Ġl umin", + "ĠF ellow", + "ĠCap acity", + "ĠKaz akh", + "W N", + "Ġfin anced", + "38 9", + "Ġt id", + "Ġcoll usion", + "ĠMy r", + "î Ģ", + "Sen ator", + "Ġped iatric", + "Ġneat ly", + "Ġsandwic hes", + "ĠArchitect ure", + "Ġt ucked", + "Ġbalcon y", + "Ġearthqu akes", + "qu ire", + "F uture", + "Ġhe fty", + "é Ĺ", + "Ġspecial izes", + "Ġstress es", + "Ġs ender", + "Ġmisunder standing", + "Ġep ile", + "Ġprov oke", + "ĠCol ors", + "Ġdis may", + "uk o", + "[ _", + "58 6", + "ne utral", + "Ġdon ating", + "ĠRand all", + "Mult i", + "Ġconvenient ly", + "ĠS ung", + "ĠC oca", + "Ġt ents", + "ĠAc celer", + "Ġpart nered", + "27 2", + "ir ming", + "ĠB AS", + "s ometimes", + "Ġobject ed", + "ub ric", + "p osed", + "LC S", + "gr ass", + "Ġattribut able", + "V IS", + "Israel i", + "Ġrepe ats", + "ĠR M", + "v ag", + "ut a", + "in ous", + "Ġin ert", + "ĠMig uel", + "æ Ń", + "ĠHawai ian", + "B oard", + "Ġart ific", + "ĠAzerb ai", + "as io", + "ĠR ent", + "A IN", + "Ġappl iances", + "Ġnational ity", + "Ġass hole", + "ĠN eb", + "Ġnot ch", + "h ani", + "ĠBr ide", + "Av ailability", + "Ġintercept ed", + "Ġcontin ental", + "Ġsw elling", + "ĠPers pect", + "b ies", + ". <", + "ith metic", + "ĠL ara", + "Ġtempt ing", + "add r", + "Ġoversee ing", + "cl ad", + "ĠD V", + "ĠGing rich", + "Ġm un", + "ĠApp ropri", + "Ġalter ations", + "ĠPat reon", + "Ġha voc", + "Ġdiscipl ines", + "Ġnotor iously", + "aku ya", + "ier i", + "? ).", + "ĠW ent", + "Ġsil icon", + "Ġtre mb", + "Cont ainer", + "K nown", + "Ġmort ar", + "est e", + "ick a", + "Ar thur", + "ĠPre viously", + "ĠMart y", + "Ġsp arse", + "g ins", + "Ġin ward", + "ĠParticip ant", + "C opy", + "ĠM isc", + "Ġantib iotic", + "ĠRet ro", + "Ġel usive", + "Ġass ail", + "ĠBatt alion", + "ĠB ought", + "Ġdimin ish", + "ĠEuro pa", + "s ession", + "ĠDanger ous", + "ies el", + "Ġdisbel ief", + "Ġbl asts", + "ext reme", + "ĠBoy d", + "ĠProject s", + "ĠGu ys", + "Ġunder gone", + "Ġgr ill", + "ĠDw ight", + "Ġ19 7", + "US ER", + "Ġfiles ystem", + "Ġcl ocks", + "T aylor", + "Ġwra pper", + "Ġfold ing", + "ous and", + "ĠPhilipp ine", + "ATION AL", + "ĠPer th", + "Ġas hes", + "Ġaccum ulate", + "ĠGate way", + "Sh op", + "orks hire", + "H an", + "ĠBar rel", + "ĠLe h", + "ĠX V", + "Ġwh im", + "Ġrep o", + "ĠC G", + "ĠM am", + "Ġincorpor ating", + "Ġbail out", + "Ġlingu istic", + "Ġdis integ", + "C LE", + "Ġcinem atic", + "ĠF iber", + "S yn", + "il ion", + "ĠCom pos", + "c hens", + "Ġne oc", + "Ġbo iled", + "F INE", + "on o", + "un cle", + "ik en", + "ĠB M", + "Î ¹", + "Ġreceipt s", + "Ġdisp osed", + "ĠTh irty", + "ĠR ough", + "ĠA BS", + "Ġnot withstanding", + "oll en", + "# $", + "Ġunrel iable", + "Ġbl oom", + "Ġmedi ocre", + "Ġtr am", + "ĠTas man", + "Ġsh akes", + "Ġmanifest o", + "ĠM W", + "Ġsatisf actory", + "Ġsh ores", + "Ġcomput ation", + "Ġassert ions", + "orm ons", + "ar ag", + "ab it", + "Dem ocrats", + "ĠL oot", + "ĠVol ks", + "ha ired", + "Ġgrav itational", + "S ing", + "ĠM iz", + "Ġthro ttle", + "Ġtyr anny", + "ĠView s", + "Ġrob ber", + "ĠMinor ity", + "Ġsh rine", + "sc ope", + "pur pose", + "Ġnucle us", + "our cing", + "ĠUS DA", + "ĠD HS", + "w ra", + "ĠBow ie", + "Sc ale", + "ĠB EL", + "x i", + "I ter", + "Ġ( ),", + "w right", + "Ġsail ors", + "ous ed", + "NAS A", + "ĠPro of", + "ĠMin eral", + "t oken", + "ĠF D", + "R ew", + "Ġe ll", + "6 30", + "Ġchance llor", + "ĠG os", + "Ġamount ed", + "ĠRec re", + "ome z", + "ĠOpt im", + "ĠOl ive", + "Ġtrack er", + "ow ler", + "ĠUn ique", + "R oot", + "Ġmar itime", + "ĠQur an", + "ĠAd apt", + "Ġecosystem s", + "ĠRe peat", + "ĠS oy", + "ĠI MP", + "Ġgrad uating", + "and em", + "P ur", + "ĠRes et", + "ĠTr ick", + "ĠPh illy", + "ĠT ue", + "ĠMalays ian", + "Ġclim ax", + "Ġb ury", + "Ġcons pic", + "ĠSouth ampton", + "ĠFl owers", + "Ġesc orted", + "ĠEduc ational", + "ĠI RC", + "Ġbrut ally", + "e ating", + "Ġpill ar", + "ĠS ang", + "ĠJ ude", + "ar ling", + "ĠAm nesty", + "Ġrem inding", + "ĠAdminist rative", + "hes da", + "Ġfl ashed", + "ĠP BS", + "per ate", + "fe ature", + "Ġsw ipe", + "Ġgra ves", + "oult ry", + "26 1", + "bre aks", + "ĠGu er", + "Ġsh rimp", + "ĠV oting", + "qu ist", + "Ġanaly tical", + "Ġtables poons", + "ĠS OU", + "Ġresear ched", + "Ġdisrupt ed", + "Ġj our", + "Ġrepl ica", + "Ġcart oons", + "b ians", + "} )", + "c opy", + "G ot", + "ou ched", + "P UT", + "Ġsw arm", + "not ations", + "s aid", + "Ġreb uilt", + "Ġcollabor ate", + "Ġr aging", + "Ġn ar", + "Ġdem ographics", + "ĠD DR", + "Ġdist rust", + "oss ier", + "ĠK ro", + "Ġpump kin", + "Ġreg rets", + "Ġfatal ities", + "ĠL ens", + "ĠO le", + "p d", + "Ġpupp et", + "ĠOut look", + "ĠSt am", + "O l", + "F air", + "U U", + "Ġre written", + "Ä ±", + "Ġfasc inated", + "Ġve ctors", + "Ġtrib unal", + "u ay", + "ĠM ats", + "ĠCo ins", + "[ [", + "Ġ18 1", + "Ġrend ers", + "ĠK aepernick", + "Ġesp ionage", + "Ġsum m", + "Ġd itch", + "Acc ount", + "Ġspread sheet", + "Ġmut ant", + "p ast", + "40 7", + "Ġd ye", + "Ġinit iation", + "Ġ4 000", + "Ġpunish able", + "Ġth inner", + "ĠKh al", + "Ġinter medi", + "D un", + "ĠGoth am", + "Ġeager ly", + "Ġvag inal", + "p owers", + "V W", + "ĠWATCH ED", + "Ġpred ator", + "ams ung", + "Ġdispar ity", + "Ġ[ *", + "Ġam ph", + "Ġout skirts", + "ĠSpir its", + "Ġskelet al", + "Ð »", + "ĠR ear", + "Ġissu ance", + "ĠLog ic", + "re leased", + "Z Z", + "ĠB ound", + "Ent ry", + "Ġex its", + "is ol", + "ĠFound er", + "Ġw re", + "ĠGreen land", + "ĠM MO", + "t aker", + "IN C", + "ãģ ¾", + "Ġhour ly", + "hen ko", + "Ġfantas ies", + "Ġdis ob", + "Ġdemol ition", + "ãĥ ĭ", + "Ġen listed", + "rat ulations", + "Ġmis guided", + "Ġens ured", + "Ġdiscour aged", + "m ort", + "Ġfl ank", + "Ġc ess", + "Ġreact s", + "ĠS ere", + "s ensitive", + "ĠSer pent", + "ass ad", + "Ġ24 7", + "Ġcalm ly", + "b usters", + "Ġble ed", + "ĠSt ro", + "Ġamuse ment", + "ĠAntar ctica", + "Ġs cept", + "ĠG aw", + "a q", + "ason ic", + "Ġsp rawling", + "n ative", + "atur ated", + "ĠBattle field", + "IV ERS", + "E B", + "ĠG ems", + "ĠNorth western", + "ĠFil ms", + "ĠAut omatic", + "Ġappre hend", + "ãģ ¨", + "Ġgui Name", + "Ġback end", + "Ġevid enced", + "ge ant", + "01 2", + "ĠS iege", + "Ġexternal To", + "Ġunfocused Range", + "ĠguiActiveUn focused", + "Ġgui Icon", + "ĠexternalTo EVA", + "ĠexternalToEVA Only", + "F ri", + "ch ard", + "en aries", + "Ġchief s", + "Ġc f", + "ĠH UD", + "Ġcorro bor", + "Ġd B", + "ĠT aken", + "ĠPat ricia", + "ra il", + "ĠCh arm", + "ĠLiber tarian", + "rie ve", + "Person al", + "ĠO UR", + "ger ies", + "Ġdump ing", + "Ġneurolog ical", + "it imate", + "ĠClint ons", + "raft ed", + "ĠM olly", + "Ġtermin als", + "reg ister", + "Ġfl are", + "Ġenc oded", + "Ġautop sy", + "p el", + "m achine", + "Ġexempt ions", + "ĠRoy als", + "d istance", + "Ġdraft s", + "Ġl ame", + "ĠC unning", + "Ġsp ouses", + "ĠMark ets", + "ĠCar rier", + "Ġimp lying", + "ĠY ak", + "s id", + "Ġl oser", + "Ġvigil ant", + "Ġimpe achment", + "Ġaug mented", + "ĠEmploy ees", + "Ġunint ended", + "tern ally", + "ĠW att", + "Ġrecogn izable", + "ess im", + "æ Ŀ", + "Ġco ated", + "r ha", + "Ġlie utenant", + "ĠLegisl ation", + "pub lished", + "44 4", + "01 3", + "Ġide ally", + "ĠPass word", + "Ġsimpl ify", + "ĠMet a", + "ĠM RI", + "Ġple ading", + "organ ized", + "hand ler", + "Ġun ravel", + "cor rect", + "Ġ icy", + "Ġparan oid", + "Ġpass er", + "Ġinspect ions", + "of er", + "ĠHealth care", + "28 3", + "ĠBr ut", + "iol a", + "for ge", + "ĠMed ieval", + "MS N", + "ie vers", + "ĠProgram ming", + "å ī", + "Ġ2 23", + "m u", + "ĠC LE", + "ug a", + "Ġsho ppers", + "Ġinform ative", + "ĠPl ans", + "Ġsupplement ation", + "ĠT ests", + "ty ard", + "ocy tes", + "ĠVeg a", + "ĠGujar at", + "erman ent", + "Ex cept", + "ĠL OT", + "all a", + "ĠC umm", + "ĠO sw", + "Ġven om", + "ĠDeb t", + "ĠD OWN", + "Ġreun ion", + "Ġm uc", + "ĠRel ief", + "Ġge op", + "ĠðŁ ĺ", + "al ogue", + "An th", + "ech o", + "Ġcor ros", + "Ġrepl ication", + "ĠBl azing", + "ĠD aughter", + "Ġinf lic", + "ĠLind sey", + "Ù Ī", + "28 4", + "Ex it", + "Ġgl oom", + "TA IN", + "Ġundermin ing", + "Ġadv ising", + "h idden", + "Ġover flow", + "Ġg or", + "urd ue", + "Ġe choes", + "enh agen", + "Ġimp uls", + "d rug", + "c ash", + "Ġas ync", + "Ġmir ac", + "at ts", + "p unk", + "Ġpiv ot", + "ĠLegisl ative", + "Ġblog gers", + "ĠCl aw", + "s burg", + "d yl", + "ĠRecomm end", + "Ġver te", + "Ġprohib iting", + "ĠPant her", + "Jon athan", + "Ġo min", + "Ġhate ful", + "28 1", + "ĠOr che", + "ĠMurd och", + "down s", + "Ġas ymm", + "G ER", + "Al ways", + "Ġinform s", + "ĠW M", + "ĠP ony", + "ĠApp endix", + "ĠAr lington", + "J am", + "Ġmedic inal", + "ĠS lam", + "IT IES", + "Ġre aff", + "ĠR i", + "F G", + "S pring", + "b ool", + "Ġthigh s", + "Ġmark ings", + "ĠRa qqa", + "ĠL ak", + "p oll", + "ts ky", + "ĠMort y", + "ĠDef inition", + "Ġdeb unk", + "end ered", + "ĠLe one", + "a vers", + "Ġmortg ages", + "App arently", + "N ic", + "ha us", + "ĠTh ousands", + "au ld", + "Ġm ash", + "sh oot", + "Ġdi arr", + "Ġconscious ly", + "H ero", + "e as", + "ĠN aturally", + "ĠDestroy er", + "Ġdash board", + "serv ices", + "R og", + "Ġmillenn ials", + "Ġinv ade", + "- (", + "Ġcomm issions", + "ĠA uckland", + "Ġbroadcast s", + "Ġfront al", + "Ġcr ank", + "ĠHist oric", + "Ġrum ours", + "CT V", + "Ġster il", + "Ġboost er", + "rock et", + "ãĤ ¼", + "ut sche", + "ĠP I", + "Ġ2 33", + "ĠProdu cer", + "ĠAnaly tics", + "Ġinval uable", + "Ġunint ention", + "ĠC Y", + "Ġscrut in", + "Ġg igg", + "Ġeng ulf", + "Ġprolet ariat", + "Ġh acks", + "ĠH ew", + "ar ak", + "ĠSl ime", + "ield ing", + "ag her", + "ĠEll iot", + "Ġtele com", + "Ġ2 19", + "ult an", + "ĠAr bor", + "ĠSc outs", + "B an", + "Ġlifes pan", + "Ġbl asp", + "38 8", + "Ġjud iciary", + "ĠContin ental", + "ask ing", + "Mc C", + "L ED", + "Ġbag gage", + "ĠSorce rer", + "Ġrem nants", + "ĠGriff ith", + "ets u", + "ĠSub aru", + "ĠPerson ality", + "des igned", + "ush ima", + "agn ar", + "Ġrec oil", + "Ġpass ions", + "\\ \":", + "Ġte e", + "Ġabol ition", + "ĠCreat ing", + "j ac", + "Ġ19 4", + "01 9", + "Ġpill ars", + "ric hed", + "/ \"", + "t k", + "Ġlive lihood", + "Ġro asted", + "ah on", + "ĠH utch", + "ass ert", + "Ġdivid end", + "Ġkn it", + "Ġd aunting", + "Ġdisturb ance", + "Ġsh ale", + "Ġcultiv ated", + "Ġrefriger ator", + "L B", + "ĠN ET", + "Ġcommercial s", + "Ġthink ers", + "45 5", + "Ġch op", + "B road", + "Ġsuspic ions", + "Ġtag ged", + "l ifting", + "Ġsty lish", + "ĠShield s", + "Short ly", + "Ġt ails", + "A uth", + "ST E", + "ĠG AME", + "Ġse ism", + "ĠK is", + "olog ne", + "Ġcow ork", + "Ġforc ibly", + "Ġthy roid", + "ĠP B", + "AN E", + "mar ried", + "h orse", + "Ġpoly mer", + "ĠCh al", + "od or", + "DE BUG", + "ĠCon text", + "Ġbl iss", + "Ġpin point", + "ĠMat hemat", + "leg ram", + "ĠWeek end", + "Ġlab elled", + "Ġb art", + "it les", + "Ġest rogen", + "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ", + "\" '", + "Ġvis ibly", + "Ġouts ider", + "aid a", + "Are a", + "Ġdisse min", + "Ġdish onest", + "ĠCl osed", + "ĠBullet in", + "ĠRam sey", + "sw ord", + "ĠX I", + "our ced", + "S ame", + "34 6", + "ĠRe pe", + "ĠK ou", + "c ake", + "em is", + "C ache", + "ĠMe aning", + "ĠEn light", + "onom y", + "Ġmanifest ation", + "sw orth", + "J ay", + "Ġch ore", + "ö r", + "D ream", + "Ġsanction ed", + "Ġcult urally", + "ĠA ra", + "N av", + "Ġthe ological", + "Ġstr ut", + "ĠV O", + "ĠHand book", + "Ġconstruct ing", + "Ġ ¶", + "ĠBenef its", + "ĠPsych ological", + "s ac", + "å ¸", + "p olicy", + "ĠMat ters", + "ĠReport ed", + "ĠBy te", + "Ġvit ro", + "ĠM aiden", + "Ġl am", + "ĠJenn ings", + "Ġgar ment", + "ĠRut gers", + "ĠStaff ord", + "ĠWell ington", + "Ġinter mitt", + "Ġn pm", + "Ġord eal", + "Ġplug ged", + "o oming", + "in ished", + "fram ework", + "Ġtim ber", + "Ġc ass", + "Ġ8 50", + "il ess", + "ĠRed ux", + "7 68", + "St re", + "Ġsurpass ed", + "w hel", + "Ġparalle ls", + "Ġve il", + "ĠG I", + "ĠR EST", + "Ġread iness", + "s ort", + "Ġmod ifying", + "ĠSl ate", + "ru ff", + "Ġmar ble", + "Ġinf rared", + "Ġaud itor", + "ĠFANT ASY", + "ĠP overty", + "ĠS PD", + "Ġ\" (", + "K y", + "RA Y", + "Ġexecut ions", + "ĠBever ly", + "ĠMarx ism", + "ĠBur st", + "ĠK ali", + "est ones", + "Clear ly", + "E ll", + "ãģ §", + "ĠProceed ings", + "T oken", + "IF IC", + "ñ a", + "Cent ral", + "ĠH aley", + "ĠD rama", + "Ġform ations", + "OR N", + "Book s", + "Ġdom inating", + "ĠFly ers", + "ĠCompan ion", + "Ġdiscipl ined", + "ĠYug oslav", + "ĠSpell s", + "Ġv engeance", + "Ġland lords", + "L en", + "ĠO gre", + "ano ia", + "Ġpier cing", + "Ġcon greg", + "Ġscore r", + "ob ia", + "Ġnic kel", + "ĠLear ns", + "Ġre jo", + "Ġmaster piece", + "Fl ash", + "Ġinhab ited", + "ĠOpen GL", + "ĠD ud", + "ĠI CO", + "Ġar ter", + "Ġpl ur", + "Ġmaster y", + "Ġlong standing", + "st ed", + "Ġw ines", + "Ġtelev ised", + "ĠSh rine", + "ĠBay ern", + "Ġâ ĵĺ", + "Ġencl osure", + "j ohn", + "Ġprophe ts", + "ĠRes urrection", + "ĠOrd ers", + "Ġun even", + "r als", + "Ġd wind", + "ĠL ah", + "ĠSl oven", + "37 8", + "Ġins istence", + "aff le", + "ĠCl one", + "Ġhard ship", + "ĠCongress man", + "Ġple ad", + "Ġreview ers", + "Ġc ured", + "Ġ19 35", + "as ley", + "f ake", + "ĠTh inking", + "yd ia", + "P ART", + "ĠD ota", + "o it", + "Ġwh ipped", + "Ġb ouncing", + "ĠHispan ics", + "com ings", + "Ġcann abin", + "ĠCh ambers", + "ĠZ ack", + "Option al", + "Ġco ats", + "Ġprow ess", + "ĠNort on", + "Ġplain ly", + "Ġfre ight", + "Ġinhib ition", + "Ġcl am", + "Ġ30 3", + "ke f", + "ale igh", + "L uke", + "Ġpsych o", + "ator ium", + "M ED", + "Ġtreat ies", + "Ġind isc", + "Ġd c", + "OP S", + "Ġresil ient", + "ĠInter state", + "Ġsl ack", + "Ġmund ane", + "Ġestab lishes", + "35 9", + "Ġstr ained", + "Ġn ond", + "S us", + "Ġcast e", + "ar ate", + "ie ving", + "Ġunfair ly", + "Ġpars er", + "on ial", + "urs ive", + "V ia", + "ĠOtt o", + "ĠAuthor ities", + "stro ke", + "K R", + "ĠMer cy", + "Ġfurn ished", + "Ġout set", + "Ġmet ic", + "19 82", + "olith ic", + "ĠT ent", + "og ical", + "ĠA ircraft", + "Ġh ides", + "ĠBec ame", + "Ġeduc ators", + "re aching", + "Ġvol atility", + "Ġtodd ler", + "ĠNAS CAR", + "ĠTw elve", + "ĠHigh lights", + "Ġgra pe", + "Ġspl its", + "Ġpe asant", + "Ġre neg", + "ĠMS I", + "Tem p", + "st ars", + "Ġtre k", + "ĠHy de", + "b inding", + "Ġreal ism", + "Ġox ide", + "ĠH os", + "Ġmount s", + "Ġbit ing", + "Ġcollaps ing", + "Ġpost al", + "Ġmuse ums", + "Ġdet ached", + "Ġrespect ing", + "Ġmonop ol", + "Ġwork flow", + "ĠC ake", + "Tem plate", + "ĠOrgan isation", + "Ġpers istence", + "36 9", + "C oming", + "B rad", + "Ġredund ant", + "ĠG TA", + "Ġb ending", + "Ġrev oked", + "Ġoff ending", + "Ġfram ing", + "Ġprint f", + "Comm un", + "mem bers", + "Out side", + "Ġconst rued", + "Ġc oded", + "F ORE", + "Ġch ast", + "Ch at", + "Ind ian", + "ĠY ard", + "? !\"", + "ĠP orts", + "ĠX avier", + "ĠR ET", + "' .\"", + "ĠBo at", + "iv ated", + "ich t", + "umer able", + "D s", + "ĠDun n", + "Ġcoff in", + "Ġsecure ly", + "ĠRapt ors", + "ĠB es", + "Install ation", + "Ġin ception", + "ĠHealth y", + "end ants", + "Ġpsych ologists", + "ĠShe ikh", + "c ultural", + "ĠBlack Berry", + "sh ift", + "F red", + "oc he", + "Ġc akes", + "ĠS EO", + "ĠG ian", + "ĠAs ians", + "og ging", + "e lement", + "Ġpund its", + "ĠV augh", + "ĠG avin", + "Ġh itter", + "Ġdrown ed", + "Ġch alk", + "ĠZ ika", + "Ġmeas les", + "80 2", + "âĢ¦ ..", + "ĠAW S", + "] \"", + "Ġdist ort", + "ĠM ast", + "Ġantib odies", + "ĠM ash", + "Mem ory", + "ĠUg anda", + "ĠPro b", + "Ġvom iting", + "ĠTurn s", + "Ġoccup ying", + "Ġev asion", + "ĠTher apy", + "Ġprom o", + "Ġelect r", + "Ġblue print", + "ĠD re", + "pr iced", + "ĠDep ot", + "Ġallev iate", + "ĠSom ali", + "m arg", + "n ine", + "Ġnostalg ia", + "ĠShe pherd", + "Ġcaval ry", + "Ġtor ped", + "ĠBlood y", + "x b", + "Ġs ank", + "Ġgo alt", + "report print", + "embed reportprint", + "clone embedreportprint", + "ĠIn itially", + "ĠF ischer", + "Ġnot eworthy", + "c ern", + "Ġin efficient", + "raw download", + "rawdownload cloneembedreportprint", + "c ation", + "ĠD ynasty", + "l ag", + "D ES", + "Ġdistinct ly", + "ĠEston ia", + "Ġopen ness", + "Ġg ossip", + "ru ck", + "W idth", + "ĠIb rahim", + "Ġpet roleum", + "Ġav atar", + "ĠH ed", + "ath a", + "ĠHog warts", + "Ġc aves", + "67 8", + "Ġsafegu ard", + "ĠM og", + "iss on", + "ĠDur ham", + "sl aught", + "ĠGrad uate", + "Ġsub conscious", + "ĠEx cellent", + "ĠD um", + "---- -", + "Ġp iles", + "ĠW ORK", + "ĠG arn", + "ĠF ol", + "ĠAT M", + "Ġavoid s", + "ĠT ul", + "Ġble ak", + "EL Y", + "iv ist", + "light ly", + "P ers", + "ĠD ob", + "ĠL S", + "Ġins anity", + "Î µ", + "atal ie", + "En large", + "Ġtw ists", + "Ġfault y", + "Ġpir acy", + "Ġimp over", + "Ġrug ged", + "ĠF ashion", + "Ġs ands", + "' ?", + "sw ick", + "Ġn atives", + "Ġhe n", + "ĠNo ise", + "ãĥ Ĺ", + "Ġg reens", + "Ġfree zer", + "Ġd ynasty", + "ĠFather s", + "ĠNew ark", + "Ġarchae ological", + "Ġo t", + "ob ar", + "Ġblock ade", + "Ġall erg", + "L V", + "Ġdeb it", + "ĠR FC", + "ĠMil ton", + "ĠPress ure", + "Ġwill ingly", + "Ġdisproportion ate", + "Ġopp ressive", + "Ġdiamond s", + "Ġbelong ings", + "19 70", + "Ġbell s", + "Ġimperial ism", + "Ġ2 27", + "Ġexpl oding", + "ĠE clipse", + "Ġ19 19", + "Ġr ant", + "Ġnom inations", + "34 7", + "Ġpeace fully", + "ric a", + "ĠF UCK", + "Ġvib ration", + "mal ink", + "Ġro pes", + "ĠIv anka", + "ĠBrew ery", + "ĠBook er", + "ĠOw ens", + "go ers", + "Serv ices", + "ĠSn ape", + "Ġ19 1", + "39 5", + "Ġ2 99", + "just ice", + "Ġb ri", + "Ġdisc s", + "Ġprom inently", + "Ġvul gar", + "Ġsk ipping", + "l ves", + "Ġtsun ami", + "37 4", + "ĠU rug", + "ĠE id", + "rec ated", + "p hen", + "Ġfault s", + "ĠStart ed", + "9 50", + "Ġp i", + "Ġdetect or", + "Ġbast ard", + "Ġvalid ated", + "Space Engineers", + "OUR CE", + "Ġ( ~", + "Ġuns ur", + "Ġaff irmed", + "Ġfasc ism", + "Ġres olving", + "ĠCh avez", + "ĠC yn", + "Ġdet ract", + "L ost", + "Ġrig ged", + "Ġhom age", + "ĠBrun o", + "55 5", + "ec a", + "Ġpress es", + "Ġhum our", + "Ġsp acing", + "Ġ' /", + "olk ien", + "C oun", + "OP ER", + "T re", + "S on", + "ĠCambod ia", + "ier re", + "m ong", + "o zy", + "Ġliquid ity", + "ĠSov iets", + "ĠFernand o", + "Ġ2 29", + "Ġsl ug", + "ĠCatal an", + "elect ric", + "Ġsc enery", + "ĠH earth", + "Ġconst rained", + "Ġgoal ie", + "ĠGu idelines", + "ĠAm mo", + "ĠPear son", + "Ġtax ed", + "Ġfet us", + "Resp onse", + "ĠAlex is", + "th ia", + "G uy", + "Ġrecon struct", + "Ġextrem es", + "Ġconclud ing", + "ĠP eg", + "ook s", + "Ġded uctions", + "R ose", + "Ġground breaking", + "ĠT arg", + "ãĥ ģ", + "ĠRe ve", + "res ource", + "Ġmo ons", + "Ġelectrom agnetic", + "Ġamid st", + "ĠVik tor", + "N ESS", + "B ACK", + "Ġcomm ute", + "ĠAna heim", + "Ġfluct uations", + "6 40", + "Ġnood les", + "ĠCop enhagen", + "ĠT ide", + "ĠGri zz", + "ĠS EE", + "Ġpip elines", + "Ġsc ars", + "end o", + "ag us", + "ĠE TF", + "/ #", + "ĠBec ome", + "44 8", + "Ġvis c", + "ĠRecomm ended", + "Ġj umper", + "Ġcogn ition", + "Ġassass in", + "Ġwitness ing", + "ĠSet up", + "Ġl ac", + "v im", + "IS M", + "p ages", + "SS L", + "35 8", + "Ġad ject", + "indust rial", + "l ore", + "cher y", + "Ġgl itter", + "Ġc alf", + "Flor ida", + "Ġspoil ers", + "Ġsucceed s", + "Ġch anting", + "Ġslog ans", + "ĠTr acy", + "Vis it", + "rol ogy", + "Ġm ornings", + "Ġline age", + "Ġs ip", + "Ġintense ly", + "Ġflour ish", + "ĠSle eping", + "ĠF em", + "or por", + "ĠK lan", + "ĠDar th", + "h ack", + "ĠNi elsen", + "Ġtum ors", + "Ġprocure ment", + "ĠY orkshire", + "Ġra ided", + "K Y", + "An na", + "Ġ// [", + "ĠDis order", + "ĠMust ang", + "ĠW en", + "ĠTry ing", + "s q", + "Ġdeliver ies", + "Ġshut ter", + "Ġcere bral", + "Ġbip olar", + "ĠC N", + "l ass", + "j et", + "Ġdeb ating", + "> :", + "Ġe agle", + "gr ades", + "ĠD ixon", + "UG C", + "M AS", + "ĠDr aco", + "ĠMach ines", + "aff er", + "Ġem an", + " ²", + "pr on", + "ĠG ym", + "Ġcompar atively", + "ĠTrib unal", + "PR O", + "Ġle x", + "Ġfert ile", + "Ġdep ressing", + "Ġsuperf icial", + "ess ential", + "ĠHun ters", + "g p", + "Ġprom inence", + "L iber", + "ĠAn cest", + "ote chnology", + "Ġm ocking", + "ĠTra ff", + "ĸ ļ", + "Med ium", + "I raq", + "Ġpsychiat rist", + "Quant ity", + "ĠL ect", + "Ġno isy", + "5 20", + "G Y", + "Ġsl apped", + "ĠM TV", + "Ġpar a", + "p ull", + "Mult iple", + "as her", + "Ġn our", + "ĠSe g", + "Spe ll", + "v ous", + "ord ial", + "Sen ior", + "ĠGold berg", + "ĠPl asma", + "ne ed", + "Ġmess enger", + "ere t", + "Ġteam ed", + "Ġliter acy", + "ĠLe ah", + "ĠD oyle", + "Ġem itted", + "U X", + "Ġev ade", + "Ġm aze", + "Ġwrong ly", + "ĠL ars", + "Ġstere otype", + "Ġpled ges", + "Ġarom a", + "ĠM ET", + "Ġac re", + "ĠO D", + "Ġf f", + "Ġbrew eries", + "ĠH ilton", + "und le", + "ĠK ak", + "ĠThank fully", + "ĠCan ucks", + "in ctions", + "ĠApp ears", + "Ġco er", + "Ġundermin ed", + "ro vers", + "And re", + "Ġbl aze", + "um ers", + "Ġfam ine", + "amp hetamine", + "ulk an", + "Am ount", + "Ġdesper ation", + "wik ipedia", + "develop ment", + "ĠCor inth", + "uss ia", + "Jack son", + "L I", + "N ative", + "R s", + "Oh io", + "ĠKath leen", + "F ortunately", + "Ġattend ant", + "ĠPre ferred", + "ĠDid n", + "ĠV s", + "M is", + "Ġrespond ent", + "Ġb oun", + "st able", + "Ġp aved", + "Ġunex pl", + "ĠChe ney", + "L M", + "ĠC ull", + "bl own", + "Ġconfront ing", + "oc ese", + "serv ing", + "W i", + "ĠLith uania", + "ann i", + "Ġst alk", + "h d", + "Ġv ener", + "AP H", + "ynchron ous", + "UR R", + "um ably", + "hist oric", + "H alf", + "H ay", + "Ġresil ience", + "spe ction", + "Ġabandon ing", + "O bs", + "ĠDeb bie", + "Ġgrad ient", + "ĠPl aint", + "ĠCan al", + "AR CH", + "Ġexpans ive", + "Ġfun g", + "Ġb ounced", + "U nd", + "Ġprec autions", + "Ġclar ification", + "Ġd agger", + "Ġgri ps", + "Ġ µ", + "ĠRiver a", + "ĠUnd ead", + "is ites", + "ĠFIR ST", + "ñ o", + "aud i", + "Ġhost ages", + "Ġcompl iant", + "Ġal umni", + "Se ven", + "Ġcyber security", + "e ither", + "Col lect", + "Ġinvari ably", + "ĠS oci", + "Ġlaw maker", + "Ġa le", + "ĠPerson ally", + "N azi", + "Ġcustom ization", + "ĠPro c", + "ĠSask atchewan", + "eat uring", + "Ġsp ared", + "Ġdiscontin ued", + "Ġcomput ational", + "ĠMotor ola", + "Ġsuprem acist", + "government al", + "Ġparad ise", + "ĠDown ing", + "ĠNik on", + "Ġcat alyst", + "ber ra", + "Tor onto", + "8 75", + "bet a", + "ĠMac ron", + "Ġunreal istic", + "ve ctor", + "ĠVeh icles", + "it iveness", + "ĠR V", + "ĠCol bert", + "s in", + "o ji", + "ent in", + "ĠKr ish", + "hell o", + "ff ield", + "ok y", + "ĠT ate", + "Ġmap le", + "Ġa ids", + "chem ical", + "33 4", + "n uts", + "ĠWar p", + "Ġx x", + "ĠRob b", + "umer ous", + "_- _", + "ft ime", + "ĠV W", + "Ġw inger", + "ĠD ome", + "t ools", + "ĠP V", + "ĠGe orgetown", + "Ġg eared", + "Ġjihad ists", + "Ġc p", + "Ġster oids", + "M other", + "cler osis", + "ĠDR M", + "nes ia", + "Ġl inger", + "Ġimm ersive", + "ĠC OUN", + "Ġoutwe igh", + "ens ual", + "B and", + "Ġtransform s", + "mat ched", + "ps ons", + "ĠJud icial", + "f actor", + "Ġrefer ral", + "Ġodd ly", + "ĠW enger", + "B ring", + "ĠB ows", + "60 2", + "IC LE", + "Ġl ions", + "ĠAcad emic", + "ĠTh orn", + "ĠRa ider", + "kef eller", + "St orage", + "L ower", + "ĠOr t", + "ĠEqu ality", + "AL T", + "ĠS OC", + "T ypes", + "Ġl yn", + "ĠAss et", + "co at", + "TP P", + "C VE", + "ĠPione er", + "app lication", + "Mod ern", + "ĠH K", + "En vironment", + "Al right", + "R ain", + "IP P", + "ĠShi ite", + "Ġm ound", + "ĠAb ilities", + "cond ition", + "St aff", + "Ġcompet ence", + "ĠM oor", + "ĠDi ablo", + "Ġwith held", + "Ġost ensibly", + "ĠB rom", + "Ġms g", + "Ġden omin", + "ĠRef erences", + "ĠF P", + "Ġplun ged", + "Ġp amph", + "m oving", + "cent ral", + "Ġdown right", + "Ġf ading", + "T al", + "T yp", + "ĠTh y", + "uk es", + "it he", + "Ġo ve", + "Ġbatt led", + "Ġseaf ood", + "Ġfig ur", + "ĠR D", + "c rop", + "Ġsqu ads", + "{ \\", + "à ¹", + "ĠE h", + "Ġinterview ing", + "ĠQ in", + "Ġas piring", + "PL IC", + "Ġcla uses", + "ĠG ast", + "ĠN ir", + "Ġl uggage", + "Ġh ose", + "Ġsystem d", + "Ġdesc ending", + "ĠRev ised", + "ĠR ails", + "al ign", + "70 9", + "33 7", + "Ġf ug", + "charg ing", + "t ags", + "Ġut er", + "k ish", + "WAR NING", + "49 0", + "prof its", + "Ġvoy age", + "Ġa ce", + "ĠV anguard", + "ĠT anks", + "ĠM uk", + "Ġ2 26", + "S afe", + "Ar mor", + "Ġvolcan ic", + "Ġwom b", + "ĠM IL", + "Ġbegin ner", + "ĠRec ogn", + "ĠA AP", + "PL AY", + ") !", + "Ġdetect ing", + "c n", + "Ġbre aches", + "Bas ically", + "ĠP ag", + "ĠMunicip al", + "ĠInd ie", + "ĠL af", + "ĠDis able", + "ĠOl son", + "Ġrest rained", + "Ġrul ings", + "Ġhum ane", + "ev ents", + "ĠCinem a", + "display Text", + "ĠH atch", + "action Date", + "onna issance", + "Ġassault ing", + "ĠL ug", + "CH AT", + "Ġvig orous", + "ĠPer se", + "Ġintoler ance", + "ĠSnap chat", + "ĠSh arks", + "Ġd ummy", + "ĠDi agn", + "ĠGu itar", + "im eters", + "40 3", + "RE G", + "A x", + "Ġsepar ates", + "ĠMah m", + "Ġt v", + "j ah", + "O OL", + "C irc", + "ĠWinds or", + "uss ian", + "Ġintu ition", + "Ġdis dain", + "ĠDon ovan", + "Ġ2 21", + "E mb", + "Ġcondem ning", + "Ġgener osity", + "zz y", + "Ġpant ies", + "ĠPre vent", + "Action Code", + "AN A", + "34 2", + "external ActionCode", + "Ġspec ifying", + "Ġcryst all", + "J ere", + "Ġru pt", + "ĠApp rentice", + "Ġprof iling", + "Ð º", + "St rike", + "Ġsid eline", + "Ġoblig ated", + "Ġocc ult", + "Ġbureaucr atic", + "ant ically", + "rupt ed", + "neg ative", + "ĠEthiop ia", + "ĠC ivic", + "Ġins iders", + "el igible", + "ĠTV s", + "ĠB AR", + "ĠT I", + "i ologist", + "ĠA IR", + "Ġsubstit uted", + "Ar ab", + "ĠS aul", + "ĠY og", + "p rem", + "Ġbuild ers", + "Ġstation ary", + "Ġdoubt ful", + "Ġvig orously", + "Ġthr illing", + "Ph ysical", + "ĠCare y", + "ĠHyd ra", + "geon ing", + "ĠS ly", + "y ton", + "Ġborrow ers", + "ĠPark inson", + "Ġ ë", + "ĠJama ica", + "Ġsat ir", + "Ġinsurg ents", + "ĠF irm", + "Ġis ot", + "ĠK arn", + "our ning", + "ak ens", + "doc s", + "l ittle", + "ĠMon aco", + "CL ASS", + "Tur key", + "L y", + "ĠCon an", + "ass ic", + "Ġstar red", + "ĠPac ers", + "et ies", + "Ġt ipping", + "M oon", + "ĠR w", + "s ame", + "Ġcav ity", + "Ġgo of", + "ĠZ o", + "Sh ock", + "um mer", + "Ġemphas izes", + "Ġreg rett", + "Ġnovel ty", + "Ġen vy", + "ĠPass ive", + "r w", + "50 5", + "Ġind ifferent", + "ĠR ica", + "ĠHim self", + "ĠFred die", + "Ġad ip", + "ä¸ Ģ", + "Ġbreak out", + "Ġhur ried", + "ĠHu ang", + "ĠD isk", + "Ġro aming", + "?????- ?????-", + "U V", + "ĠRick y", + "ĠS igma", + "Ġmarginal ized", + "Ġed its", + "Ġ30 4", + "mem ory", + "Ġspec imen", + "29 3", + "ãģ ¯", + "Ġvert ically", + "Ġaud ition", + "ĠHe ck", + "Ġc aster", + "ĠHold ings", + "ad al", + "ĠC ron", + "ĠL iam", + "Ġdef lect", + "P ick", + "ĠDeb ug", + "RE F", + "Ġvers atility", + "ot hes", + "class ified", + "ĠMah ar", + "ĠH ort", + "C ounter", + "st asy", + "not iced", + "33 1", + "ĠSh im", + "f uck", + "ĠB ie", + "Ġair ing", + "ĠPro tein", + "ĠHold ing", + "Ġspect ators", + "ili ated", + "ĠThat cher", + "n osis", + "ãĥ¼ ãĥ³", + "Te le", + "B oston", + "ĠTem pl", + "st ay", + "Ġdecl arations", + "47 9", + "Vol ume", + "ĠDesign er", + "ĠOver watch", + "id ae", + "Ġon wards", + "Ġn ets", + "ĠMan ila", + "part icularly", + "Ġpolit ic", + "o other", + "Ġport raits", + "Ġpave ment", + "c ffff", + "Ġs aints", + "Ġbegin ners", + "ES PN", + "Ġshort comings", + "âķIJ âķIJ", + "Ġcom et", + "ĠOrgan ic", + "qu el", + "Ġhospital ized", + "Bre ak", + "Ġpe el", + "dyl ib", + "asp x", + "ur ances", + "ĠT IM", + "P g", + "Ġread able", + "ĠMal ik", + "Ġm uzzle", + "Ġbench marks", + "d al", + "ĠV acc", + "ĠH icks", + "60 9", + "ĠB iblical", + "he ng", + "Ġover load", + "ĠCivil ization", + "Ġimm oral", + "Ġf ries", + "ãĤ Ĵ", + "Ġreprodu ced", + "Ġform ulation", + "j ug", + "ire z", + "g ear", + "Ġco ached", + "Mp Server", + "ĠS J", + "ĠK w", + "In it", + "d eal", + "ĠO ro", + "ĠL oki", + "ĠSong s", + "Ġ23 2", + "ĠLou ise", + "asion ally", + "Ġunc ond", + "olly wood", + "Ġprogress ives", + "ĠEn ough", + "ĠDo e", + "Ġwreck age", + "Ġbr ushed", + "ĠBase Type", + "Ġz oning", + "ish able", + "het ically", + "ĠC aucus", + "ĠH ue", + "Ġk arma", + "ĠSport ing", + "Ġtrad er", + "Ġseem ing", + "ĠCapt ure", + "4 30", + "b ish", + "Ġt unes", + "Ġindo ors", + "ĠSp here", + "ĠD ancing", + "TER N", + "Ġno b", + "ĠG ST", + "m aps", + "Ġpe ppers", + "F it", + "Ġoverse es", + "ĠRabb i", + "ĠR uler", + "vert ising", + "off ice", + "xx x", + "Ġra ft", + "Ch anged", + "Ġtext books", + "L inks", + "ĠO mn", + "ãĢ ij", + "Ġinconven ience", + "ĠDon etsk", + "= ~", + "Ġimplicit ly", + "Ġboost s", + "ĠB ones", + "ĠBo om", + "Cour tesy", + "Ġsens ational", + "AN Y", + "Ġgre edy", + "ed en", + "Ġinex per", + "ĠL er", + "ĠV ale", + "Ġtight en", + "ĠE AR", + "ĠN um", + "Ġancest or", + "S ent", + "ĠH orde", + "urg ical", + "all ah", + "Ġsa p", + "amb a", + "ĠSp read", + "tw itch", + "Ġgrand son", + "Ġfract ure", + "Ġmoder ator", + "ĠSe venth", + "ĠRe verse", + "Ġestim ation", + "Cho ose", + "Ġpar ach", + "Ġbar ric", + "ãĢ IJ", + "Ġcomp ass", + "Ġall ergic", + "âĢ ķ", + "OT HER", + "err illa", + "Ġw agon", + "Ġz inc", + "Ġrub bed", + "ĠFull er", + "ĠLuxem bourg", + "ĠHoo ver", + "Ġli ar", + "ĠEven ing", + "ĠCob b", + "est eem", + "Ġselect or", + "ĠB rawl", + "is ance", + "ĠE k", + "Ġtro op", + "Ġg uts", + "ĠApp eal", + "ĠTibet an", + "Ġrout ines", + "ĠM ent", + "Ġsummar ized", + "steam apps", + "Ġtr anqu", + "Ġ19 29", + "or an", + "ĠAut hent", + "Ġg maxwell", + "Ġappre hens", + "Ġpo ems", + "Ġsa usage", + "ĠWeb ster", + "ur us", + "Ġthem ed", + "Ġl ounge", + "Ġcharg er", + "Sp oiler", + "Ġsp illed", + "h og", + "ĠSu nder", + "ĠA in", + "ĠAng ry", + "Ġdis qual", + "ĠFrequ ency", + "ĠEther net", + "Ġhel per", + "Per cent", + "Ġhorr ifying", + "Ġa il", + "ĠAll an", + "EE E", + "ĠCross ing", + "44 9", + "Ġh olog", + "ĠPuzz les", + "ĠGo es", + "eren n", + "60 4", + "ãģ ı", + "ĠRaf ael", + "Ġatt en", + "ĠE manuel", + "Ġup ro", + "ĠSus p", + "P sych", + "ĠTr ainer", + "ĠN ES", + "ĠHun ts", + "bec ue", + "Ġcounsel or", + "R ule", + "Ġtox ins", + "Ġb anners", + "r ifice", + "Ġgreet ing", + "Ġfren zy", + "Ġall ocate", + "Ġ* )", + "ex pr", + "50 3", + "ĠCh ick", + "ĠT orn", + "Ġconsolid ation", + "ĠF letcher", + "sw itch", + "fr ac", + "cl ips", + "ĠMcK in", + "ĠLun ar", + "Mon th", + "IT CH", + "Ġscholar ly", + "rap ed", + "39 8", + "Ġ19 10", + "Ġe greg", + "Ġin secure", + "Ġvict orious", + "cffff cc", + "Ġsing led", + "Ġel ves", + "ĠW ond", + "bur st", + "Ġcam oufl", + "ĠBL ACK", + "Ġcondition ed", + "ç ī", + "ans wered", + "Ġcompuls ory", + "asc ist", + "Ġpodcast s", + "ĠFrank furt", + "bn b", + "Ġne oliberal", + "ĠKey board", + "ĠBel le", + "w arm", + "Ġtrust s", + "Ġins ured", + "ĠBu cc", + "us able", + "60 7", + "ĠPl ains", + "Ġ18 90", + "Ġsabot age", + "Ġlod ged", + "f elt", + "Ġg a", + "ĠN arc", + "ĠSal em", + "Ġsevent y", + "ĠBl ank", + "p ocket", + "Ġwhis per", + "Ġm ating", + "om ics", + "ĠSal man", + "ĠK ad", + "Ġan gered", + "Ġcoll isions", + "Ġextraord inarily", + "Ġcoerc ion", + "G host", + "b irds", + "è Ģ", + "k ok", + "Ġper missible", + "avor able", + "Ġpo inters", + "Ġdiss ip", + "ac i", + "Ġtheat rical", + "ĠCos mic", + "Ġforget ting", + "Ġfinal ized", + "å¤ §", + "y out", + "l ibrary", + "Ġbo oming", + "ĠBel ieve", + "ĠTe acher", + "ĠL iv", + "ĠGOOD MAN", + "ĠDomin ican", + "OR ED", + "ĠPart ies", + "Ġprecip itation", + "ĠSl ot", + "R oy", + "ĠComb ined", + "Ġinteg rating", + "Ġch rome", + "Ġintest inal", + "ĠRe bell", + "Ġmatch ups", + "Ġblock buster", + "ĠLore n", + "ĠLe vy", + "Ġpre aching", + "ĠS ending", + "ĠPur pose", + "ra x", + "f if", + "Ġauthor itative", + "ĠP ET", + "ast ical", + "Ġdish on", + "Ġchat ting", + "Ġ\"$ :/", + "Connect ion", + "Ġrecre ate", + "Ġdel inqu", + "Ġbro th", + "ĠD irty", + "ĠAd min", + "z man", + "Ġscholars hips", + "Ġ25 3", + "cont act", + "als a", + "7 67", + "c reen", + "abb age", + "Ġ19 15", + "Ġbl ended", + "Ġal armed", + "L anguage", + "35 6", + "Ġbl ends", + "ĠCh anged", + "W olf", + "Ġhe pat", + "Creat ing", + "Ġper secut", + "Ġsweet ness", + "art e", + "Ġforfe iture", + "ĠRober to", + "im pro", + "N FL", + "ĠMag net", + "Det ailed", + "Ġinsign ificant", + "ĠPOL IT", + "ĠBB Q", + "ĠC PS", + "Ġse aw", + "amin er", + "m L", + "end if", + "f inals", + "Ġ26 5", + "u ish", + "Ġ} )", + "ĠPro blems", + "Ġem blem", + "Ġserious ness", + "Ġpars ing", + "Ġsubst itution", + "Ġpress ured", + "Ġrecy cled", + "ale b", + "Rub y", + "Ġprof iciency", + "Dri ver", + "ĠW ester", + ": '", + "AF TA", + "Ġm antle", + "ĠClay ton", + "fl ag", + "Ġpractition er", + "c overed", + "ĠSt ruct", + "add afi", + "4 25", + "ĠTown ship", + "ĠHyd ro", + "Lou is", + "34 3", + "Ġcond o", + "ĠT ao", + "Ġutil ization", + "Ġnause a", + "ĠDem s", + "rid ges", + "p ause", + "Ġform ulas", + "Ġchall enger", + "37 6", + "Ġdefect ive", + "ĠRail way", + "ĠPub Med", + "Ġyog urt", + "l bs", + "ĠNor folk", + "OP E", + "ĠMood y", + "Ġdistribut or", + "Ġscroll s", + "Ġextract s", + "St an", + "Ġv iability", + "Ġexp oses", + "Ġstar vation", + "ĠStep s", + "ĠD odd", + "f ew", + "ST D", + "33 2", + "Ġclos ures", + "Ġcomplement ary", + "ĠS asha", + "ump y", + "Ġmon et", + "Ġartic ulate", + "ĠDo ct", + "k iller", + "Ġsc rim", + "Ġ2 64", + "Ġprost itutes", + "Ġse vered", + "Ġattach ments", + "Ġcool ed", + "L ev", + "ĠF alk", + "f ail", + "Ġpolic eman", + "ĠD ag", + "Ġpray ed", + "ĠK ernel", + "Ġcl ut", + "Ġc ath", + "Ġan omaly", + "St orm", + "em aker", + "ĠBreak fast", + "ul i", + "o ire", + "J J", + "h z", + "Oper ation", + "ĠS ick", + "35 4", + "ĠGuatem ala", + "R ate", + "Ġexp osures", + "f aces", + "ĠArch ae", + "ra f", + "ĠM ia", + "Ġ20 25", + "Ġop aque", + "Ġdisgu ised", + "ĠHead quarters", + "S ah", + "Ġp ots", + "9 78", + "ĠM alf", + "Ġfrown ed", + "Ġpoison ous", + "ĠCon vers", + "ee ks", + "Ġcr ab", + ".\" \"", + "Ġtre ason", + "Ġr anc", + "Ġescal ating", + "Ġwar r", + "Ġmob s", + "Ġl amps", + "ĠSun shine", + "ĠBrun swick", + "Ph ones", + "Ġspe lled", + "ĠSk ip", + "Ġ20 50", + "Ġ19 11", + "ĠPl uto", + "ĠAm end", + "Ġme ats", + "38 7", + "Ġst omp", + "ĠZh ou", + "ĠLevi athan", + "ĠHaz ard", + "ad v", + "ĠOr well", + "Ġal oud", + "Ġb umper", + "ĠAn arch", + "ub untu", + "ĠSer ious", + "f itting", + "ĠOption al", + "ĠCec il", + "RE AM", + "Ġser otonin", + "Ġcultiv ate", + "ag ogue", + "} \\", + "Ġmos ques", + "ĠSun ny", + "Ġre active", + "rev olution", + "ĠL up", + "ĠFed ora", + "Ġdefense man", + "ĠV ID", + "ist ine", + "Ġdrown ing", + "ĠBroad casting", + "Ġthr iller", + "ĠS cy", + "Ġacceler ating", + "Ġdirect s", + "od ied", + "b ike", + "d uration", + "Ġpain fully", + "R edd", + "Ġproduct ions", + "Ġg ag", + "Ġwh ist", + "Ġs ock", + "Ġinf initely", + "ĠConc ern", + "ĠCit adel", + "Ġlie u", + "Ġcand les", + "ogene ous", + "arg er", + "Ġheaven ly", + "inflamm atory", + "Per formance", + "C s", + "ruct ose", + "az aki", + "Ġp essim", + "Ġinf erence", + "Ġpow d", + "ĠZ oe", + "Ġpain ts", + "Ġd azz", + "pt a", + "-------- ---", + "Ġins pir", + "ĠExper imental", + "ĠKn ife", + "reg or", + "b ors", + "Ġshow ers", + "rom eda", + "Ġs aint", + "Ġben ign", + "ĠJ iang", + "Ġenvision ed", + "Ġsh roud", + "IF T", + "H O", + "Ġsh uff", + "ĠI CC", + "Ġse greg", + "Ġrevis it", + "ighth ouse", + "L i", + "Ġsub strate", + "ĠSe as", + "ĠRew ard", + "ĠH ep", + "ĠBr ass", + "s bm", + "Ġelim inates", + "Ġst amina", + "ĠV AT", + "ĠLo an", + "Ġconst raint", + "Ġappropri ated", + "Ġp es", + "ĠA LE", + "r anging", + "Ġ40 4", + "39 2", + "Ġintellectual s", + "ach u", + "Ġrestruct uring", + "ĠLe vin", + "Ġrun es", + "Ġdelight ful", + "Ġcarbohyd rates", + "ĠMod els", + "ĠExp o", + "Ġtransport ing", + "all oc", + "Ġring ing", + "S amsung", + "Ġscarce ly", + "ĠURL s", + "ĠM AS", + "Ġprot otypes", + "Ġnarr ator", + "ĠCPU s", + "cd n", + "ĠBart on", + "Ġdecided ly", + "ĠSh u", + "ix ir", + "oc ious", + "ĠMy st", + "N intendo", + "Ġre use", + "Ġforg iven", + "F ew", + "in ical", + "n at", + "Ġseam less", + "ĠEv a", + "ĠE VE", + "ĠJ O", + "land ers", + "Ġso fter", + "neg ie", + "Ġtrans ient", + "Ġorb ital", + "Ġfulf il", + "ĠK om", + "Hop efully", + "Ġdynam ically", + "ĠHun ger", + "å Ľ", + "ĠArmen ia", + "el man", + "ber to", + "Ġp ige", + "ĠID s", + "lim it", + "Ġve ins", + "Ġso aring", + "p acks", + "Gold en", + "ĠCr ab", + "ist or", + "ĠR PM", + "Ġ$ $", + "g ression", + "Ġjihad ist", + "Ġgam ble", + "Ġcare g", + "Ġinf lated", + "F ace", + "ĠFire arms", + "ĠEm manuel", + "â Ŀ", + "Ġsh ocks", + "gr ab", + "Ġspl end", + "ĠHP V", + "ab ortion", + "Ab ove", + "Ent ity", + "play ers", + "Ġcomm enced", + "ul ence", + "Ġfulfill ment", + "Ġembod iments", + "ĠW elfare", + "Ġha il", + "Ġ< @", + "tt en", + "Ġcat cher", + "ĠJ azeera", + "Ġvolcan o", + "Ġstabil ize", + "ĠHand ler", + "Ġintens ified", + "ĠAb rams", + "Ġhum iliation", + "p aced", + "60 5", + "ĠCent OS", + "Spe cific", + "Ġhe ed", + "ĠC AM", + "ĠGal ile", + "D ie", + "Ġabol ished", + "ĠThom son", + "ĠTe achers", + "ĠW ass", + "j ong", + "ĠIS BN", + "ĠAll ies", + "sh ake", + "å ·", + "v ict", + "How ard", + "Ġde em", + "Ġexceed ingly", + "ĠSmart stocks", + "ib e", + "Ġdoor way", + "Ġcompet ed", + "ig mat", + "Ġnational ists", + "Ġg room", + "ĠKe en", + "Ġdispos able", + "de cl", + "ĠT olkien", + "ĠSche me", + "Ġb iod", + "Ġav id", + "ĠEl on", + "ag ar", + "ĠT SA", + "R oman", + "Ġartific ially", + "Ġadvis ors", + "X L", + "ĠInf erno", + "36 6", + "Ġted ious", + "ĠPhot ography", + "ĠCar rie", + "Ġtro pe", + "ĠSand ra", + "Ġdec imal", + "Que en", + "ĠGund am", + "ĠO M", + "ote ch", + "N BA", + "Ġ19 32", + "Ġent renched", + "ĠMar ion", + "Ġfr aternity", + "Lab our", + "Hen ry", + "Ġlat itude", + "E ither", + "Ġenh ances", + "ĠPot ential", + "Ġsh ines", + "id ad", + "Ġbread th", + "Ġcapac ities", + "ĠðŁ ĻĤ", + "ĠBron x", + "Ġsex es", + "Ġdifferent iation", + "Ġheavy weight", + "ĠT aj", + "d ra", + "Ġmigr ate", + "Ġexhaust ion", + "ĠR UN", + "els ius", + "ĠCu omo", + "Ġgu itars", + "Ġcl ones", + "ĠSom ew", + "ĠP ry", + "------------ -", + "Ġwarr anted", + "cy cles", + "Ġsalv age", + "Ġdis ks", + "R ANT", + "ĠNGO s", + "ĠMart ian", + "\":[ {\"", + "Ġadd icts", + "oj ure", + "il let", + "Ġamazing ly", + "art ments", + "p ixel", + "ĠGPU s", + "Lay out", + "è £", + "ĠTam il", + "ĠBas il", + "Ġimpart ial", + "ĠSt ructure", + "f ork", + "b ryce", + "Ġr idge", + "ĠHamb urg", + "ri ous", + "Ġbl itz", + "cig arettes", + "Ġcan ned", + "40 2", + "Ġiron ically", + "Ġcompassion ate", + "ĠHaw kins", + ". #", + "ĠCat hedral", + "Ġrall ied", + "in ternal", + "Ġqu ota", + "st akes", + "T EXT", + "m om", + "Ġcomple tes", + "Ġ23 8", + "Ġsh rug", + "ãĥ ij", + "ĠN inth", + "Ġrev ise", + "ĠProv ider", + "Ġtre acher", + "Ġqu asi", + "ĠPR ES", + "Ġdep osition", + "Ġconfidential ity", + "iss ors", + "Ġim balance", + "Ġspan ning", + "Ġang ular", + "ĠC ul", + "commun ication", + "ĠNor a", + "ĠGen ius", + "op ter", + "Ġs acked", + "Sp ot", + "Ġfine ly", + "ĠCH R", + "28 2", + "w aves", + "Pal est", + "ĠRo hing", + "N L", + "è ¿", + "Ġsh itty", + "ĠSc alia", + "4 75", + "Pro gress", + "Ġreferen cing", + "Ġclass rooms", + "ab ee", + "Ġs od", + "hes ion", + "70 8", + "ĠZucker berg", + "ĠFin ish", + "ĠScot ia", + "ĠSav ior", + "ĠInstall ation", + "an tha", + "( -", + "Ġ30 2", + "ĠP unk", + "Ġcr ater", + "yout u", + "Ġro ast", + "Ġinflu encing", + "Ġd up", + "ĠJ R", + "ĠG rav", + "Ġstat ure", + "Ġbath rooms", + "A side", + "W iki", + "me an", + "ĠZ ak", + "ĠOn es", + "ĠN ath", + "Ġhyper t", + "Ġcommence ment", + "C ivil", + "Ġmoder ately", + "Ġdistribut ors", + "Ġbreast feeding", + "Ġ9 80", + "ĠS ik", + "ĠC ig", + "ĠAM ER", + "R IP", + "ĠCare er", + "ust ing", + "Ġmess ed", + "Ġe h", + "ĠJ ensen", + "/ $", + "Ġblack mail", + "Ġconvers ions", + "Ġscientific ally", + "Ġmant ra", + "p aying", + "Ġiv ory", + "ĠCour ts", + "OU GH", + "aunt let", + "Ser ial", + "B row", + "ĠH undreds", + "3 23", + "Ġpe e", + "Ġlin ux", + "Ġsub mer", + "ĠPrinc ipal", + "48 5", + "ĠD SL", + "ĠCous ins", + "Ġdoctr ines", + "ĠAthlet ics", + "Ġ3 15", + "ĠK arma", + "Ġatt ent", + "ur ger", + "Ġpresc ribe", + "Ġenc aps", + "ĠC ame", + "Ġsecret ive", + "ĠCr imes", + "d n", + "C lean", + "ĠEgypt ians", + "ĠCar penter", + "Ġ ll", + "H um", + "ĠMil o", + "Ġcapital ists", + "Ġbrief ed", + "T we", + "ĠBas in", + "elve t", + "M os", + "Ġplun ge", + "ĠKa iser", + "ĠFu j", + "ill in", + "Ġsafegu ards", + "Ġo ste", + "ĠOpportun ity", + "ĠM afia", + "ĠCall ing", + "ap a", + "ur ban", + "br ush", + "ill ard", + "c é", + "int elligence", + "ĠL ob", + "ĠDru id", + "Ġsm oother", + "Ġfoot ing", + "Ġmotor ists", + "arc ity", + "Ġmascul inity", + "Ġm ism", + "Ġabdom inal", + "ĠTa vern", + "ĠR oh", + "Ġesc apes", + "s igned", + "Anth ony", + "Ġsacrific ing", + "Ġintim acy", + "Ġan terior", + "ĠK od", + "Ġmot if", + "Ġg raz", + "Ġvisual ization", + "Ġguitar ist", + "ĠTro tsky", + "m agic", + "D ar", + "ĠMor i", + "Ġw ards", + "Ġtoile ts", + "l est", + "Ġtele port", + "ĠSund ays", + "ĠPl at", + "ET S", + "Ġe Sports", + "Pat rick", + "ĠK atherine", + "en ko", + "Ġhas sle", + "ĠM ick", + "gg les", + "Ġh ob", + "aint ain", + "Ġair borne", + "Ġsp ans", + "Ġch ili", + "Ġa perture", + "Ġvolunte ered", + "ĠInc ident", + "ĠF res", + "ĠVeter an", + "augh tered", + "ing o", + "Ġun insured", + "CL OSE", + "Ġf use", + "Ġer otic", + "Ġadvert ise", + "ra ising", + "Text ure", + "Ġatt ends", + "ĠRE AL", + "udd led", + "Ġsm oot", + "Ġ30 5", + "ĠWill is", + "Ġbl ond", + "An alysis", + "ĠV T", + "on ica", + "Ġstrongh old", + "R F", + "N M", + ". >>", + "Ġprosper ous", + "Ġbo asted", + "29 2", + "ĠManufact uring", + "PR ESS", + "g ren", + "Ġpharm acy", + "ĠRoc kefeller", + "k ai", + "Ġth umbs", + "ĠH ut", + "Ġmother board", + "Ġguard ians", + "ĠAl ter", + "ll ular", + "Ġsh ack", + "Ġwise ly", + "Ġback bone", + "erv a", + "Ġsu icides", + "ĠMcG regor", + "ij ah", + "E mer", + "ĠB rav", + "Ġdesign ate", + "P OST", + "produ ced", + "Ġcleans ing", + "irl wind", + "ex istent", + "ĠHum ph", + "ĠPay ne", + "Ġv ested", + "Å ¡", + "Ġstring ent", + "ion a", + "Ġuns ub", + "Ġsum med", + "ĠHer cules", + "sub ject", + "ĠR agnar", + "ĠN os", + "Ġcharacter ization", + "Ġsav vy", + "ĠDaw son", + "ĠCas ino", + "Ġf ri", + "ĠBar rier", + "Ġmis information", + "Ġins ulation", + "Ġcorrid ors", + "Ġair planes", + "ĠNo ct", + "ah i", + "Ġ19 16", + "k b", + "arm ac", + "Ġsh un", + "Ġsche ma", + "Ġhorr ified", + "Ġ23 9", + "aund ers", + "N B", + "i ates", + "er ity", + "ĠSh ard", + "Ġr arity", + "Ġgroup ed", + "ĠGh ana", + "again st", + "ĠBi ological", + "ĠA ware", + "ow ell", + "Ï Ħ", + "ĠBe au", + "sh aw", + "H ack", + "ĠJul ius", + "US S", + "ol son", + "aun a", + "c ru", + "ĠMaur ice", + "ĠI k", + "Ġsequ encing", + "Ġradical s", + "Ġ( ?,", + "v irtual", + "Ġany ways", + "Ġreper c", + "Ġhand lers", + "Ġhes itant", + "é ĥ", + "ĠM F", + "ple mentation", + "ass ociated", + "Ġcampaign ed", + "ĠY ue", + "ut ations", + "ĠY oga", + "Ġsim mer", + "Ġro ds", + "Ġmel ody", + "Ġconv oy", + "v ideos", + "Ġscreen ed", + "N eg", + "ochem ical", + "Ġ( ))", + "Ġultr as", + "Ġant ip", + "ĠIsland ers", + "70 4", + "Ġfet ish", + "Ġridic ulously", + "ĠK art", + "Ġmitochond rial", + "Ġinterf ering", + "Build er", + "Ġover fl", + "Ġac ne", + "ĠM ud", + "ĠK err", + "f lex", + "ĠPost al", + "ĠBalt ic", + "47 7", + "ĠPers ons", + "our age", + "H B", + "ĠM use", + "ĠImm ortal", + "ĠDri ving", + "Ġpet itions", + "Ġsubsc ript", + "Ġs orce", + "ĠProcess or", + "ut on", + "S ony", + "Ġph on", + "Ġr aced", + "ĠAnth rop", + "Ġday time", + "ĠEx ercise", + "Add ing", + "Ġeng ages", + "ĠQual comm", + "Ġmir acles", + "Ġmem es", + "ĠDr ink", + "ĠOri oles", + "Ġhair s", + "ĠPol ar", + "ath om", + "Ġsl ippery", + "ĠR emy", + "Ġcar amel", + "ĠY EAR", + "Ġal k", + "I gn", + "a ution", + "ĠMer lin", + "ĠC ran", + "Ġap ologies", + "Ġ4 10", + "Ġout ing", + "ĠMem ories", + "app ointed", + "Ġcount ered", + "u ld", + "pos ing", + "Ġfire wall", + "ĠW ast", + "ĠW et", + "work ed", + "se ller", + "Ġrepe aled", + "ere o", + "ass uming", + "BL IC", + "m ite", + "ĠCEO s", + "ĠChap el", + "ellig ent", + "________________ ________", + "D og", + "Ġw art", + "Ġsubsc riber", + "s ports", + "Ġbe gged", + "ĠM V", + "Ġsem if", + "eth ical", + "Ġpre ach", + "Ġrev ital", + "Ġpun itive", + "Ġshort cuts", + "Ġinstit uted", + "ĠWars aw", + "Ġabdom en", + "ĠK ING", + "Ġsuper intendent", + "Ġf ry", + "ĠGe o", + "T OR", + "Ġcontrad ictions", + "apt ic", + "Ġlandsc apes", + "b ugs", + "Ġcl ust", + "Ġvol ley", + "c ribed", + "Ġt andem", + "Ġrob es", + "WH AT", + "Ġpromot er", + "Ġel oqu", + "review ed", + "ĠD K", + "ĠPl ato", + "Ġf ps", + "T ank", + "ĠDer rick", + "Ġpriorit ize", + "as per", + "ĠHond uras", + "ĠCom pleted", + "ne c", + "Ġm og", + "n ir", + "ĠMay o", + "DE F", + "st all", + "in ness", + "ĠVolks wagen", + "Ġprec aution", + "ĠM ell", + "i ak", + "ist ries", + "Ġ24 8", + "Ġoverl apping", + "Sen ate", + "ĠEnh ance", + "res y", + "rac ial", + "OR TS", + "ĠM ormons", + "Str ong", + "ĠCo ch", + "Mex ico", + "ĠMad uro", + "Ġj ars", + "Ġcan e", + "W ik", + "oll a", + "iff erence", + "Ġphysic ist", + "ĠMag gie", + "Ġ28 5", + "Ġdep iction", + "ĠMcL aren", + "J u", + "Ġsl ows", + "Ġcommission ers", + "ĠWill ow", + "ĠExpl os", + "hov ah", + "Ġtechn ician", + "Ġhom icides", + "ĠFl av", + "ĠTr uman", + "Ġ100 00", + "u ctor", + "Ġsh ader", + "News letter", + "45 7", + "Ġre ver", + "Ġhard ened", + "Ġwhere abouts", + "Ġrede velop", + "Ġcar bs", + "Ġtra vers", + "Ġsqu irrel", + "Ġfoll ower", + "Ġs ings", + "50 8", + "Ġrabb its", + "emon ium", + "Ġdocument ing", + "Ġmisunder stood", + ") '", + "R ick", + "gg ies", + "Ġprem ie", + "Ġsk ating", + "Ġpass ports", + "Ġf ists", + "aged don", + "H aw", + "AC P", + "0 80", + "ĠThough ts", + "ĠCarl son", + "Ġpriest hood", + "h ua", + "Ġdun geons", + "ĠLo ans", + "Ġant is", + "Ġfamiliar ity", + "ĠS abb", + "op al", + "ĠIn k", + "st rike", + "Ġc ram", + "Ġlegal ized", + "Ġcu isine", + "Ġfib re", + "Tra vel", + "ĠMon ument", + "OD Y", + "eth y", + "Ġinter state", + "ĠP UR", + "em porary", + "ĠArab ian", + "develop ed", + "Ġsadd le", + "Ġg ithub", + "ĠOff er", + "ĠIS P", + "ro let", + "ĠSUP ER", + "ĠDen is", + "Ġmultipl ier", + "Ġstir red", + "Interest ingly", + "Ġcustom ary", + "Ġbill ed", + "he x", + "Ġmultipl ied", + "Ġfl ipping", + "ĠCros by", + "Ġfundament als", + "ia e", + "ĠPlay ed", + "ĠAt om", + "am azon", + "ĠFl am", + "ee z", + "activ ated", + "Ġtables poon", + "Ġliberal ism", + "ĠPal in", + "ĠP atel", + "N um", + "ĠT AM", + "Ġs urn", + "ĠRel oaded", + "Ġco ined", + "\" ],", + "ĠCl ash", + "ĠAg u", + "Ġprag matic", + "ĠActiv ate", + "Ġ8 02", + "Ġtrail ers", + "Ġsil hou", + "Ġprob es", + "Ġcirc us", + "ĠB ain", + "ĠLind say", + "ĠAb bey", + "Del ivery", + "Ġconcess ion", + "Ġgast ro", + "ĠSpr ite", + "Ä Ł", + "and el", + "Ġg imm", + "Ġaut obi", + "ĠT urtle", + "Ġwonder fully", + "ĠHar am", + "ĠWorld wide", + "ĠHand le", + "Ġtheor ists", + "Ġsle ek", + "ĠZh u", + "ograph ically", + "EG A", + "ĠOwn ers", + "ath s", + "ĠAntar ctic", + "n atal", + "=\" \"", + "fl ags", + "`` ``", + "Ġs ul", + "K h", + "Ġpot assium", + "Ġlinem an", + "Ġcere al", + "ĠSe asons", + "Ġ20 22", + "Ġmat hematic", + "Ġastron omers", + "prof essional", + "Ġf ares", + "cknow led", + "Ġch i", + "Ġyoung sters", + "Ġmistaken ly", + "Ġhem isphere", + "ĠDiv inity", + "r one", + "Ġ\" ,", + "r ings", + "Ġattract s", + "v ana", + "å ¹", + "C AP", + "Ġplay list", + "Ġpor ch", + "ãģ £", + "Ġincorpor ates", + "Ġso ak", + "Ġassert ing", + "ĠTerror ism", + "ĠP ablo", + "J a", + "ces ter", + "Ġfear ing", + "ĠPr ayer", + "Ġescal ated", + "G W", + "Ġro be", + "ĠBright on", + "ac ists", + "ĠSym phony", + "ĠDwar f", + "ĠPar ade", + "ĠLe go", + "Ġinex pl", + "Ġl ords", + "le af", + "RA G", + "l iber", + "Ġcig ars", + "ĠJe hovah", + "60 6", + "WIND OWS", + "ĠLiber ia", + "eb us", + "He avy", + "Ġl ubric", + "ĠR W", + "angu ages", + "Ġnarrow ed", + "com puter", + "ĠE mber", + "Ġmurder ing", + "Ġdown stream", + "ĠT uls", + "ĠT ables", + "Top ic", + "ĠAcc uracy", + "= /", + "l ost", + "ĠRe i", + "Ġprogress es", + "b ear", + "Ġestablish ments", + "Just in", + "ĠPe ach", + "ĠG omez", + "å ¿", + "ĠTri angle", + "Id ent", + "ĠH ive", + "Res ources", + "Ġmix es", + "ĠAss uming", + "M u", + "Ġhyp oc", + "Ġs ane", + "ĠW an", + "id ious", + "Su ccess", + "Ġ io", + "Ang el", + "Ġdanger ously", + "ĠCreat ure", + "W ORK", + ": [", + "ĠKat rina", + "List ener", + "M iller", + "ĠId lib", + "h ang", + "Ġcircum vent", + "h ref", + "Ġcel estial", + "ĠWe eks", + "ĠP ug", + "ĠDal ton", + "Ġsubpoen a", + "uk u", + "Ġpers isted", + "pe i", + "old ing", + "ĠDoc uments", + "ĠH ast", + "ĠC ENT", + "Ġprim er", + "Ġsyn onymous", + "Ġn ib", + "om bs", + "Ġnot ation", + "ĠD ish", + "ĠAt mosp", + "Ġforb id", + "ĠAN G", + "pat tern", + "l os", + "Ġproject iles", + "b rown", + ".\" ,", + "ĠVen om", + "Ġfierce ly", + "ub lished", + "ĠU ran", + "ĠNic arag", + "4 10", + "ĠC AL", + "OT OS", + "ĠMir acle", + "ĠEn chant", + "Ġguard ing", + "app end", + "Att ach", + "Ġlevel ed", + "Ġcond oms", + "ih ilation", + "64 9", + "Ġnight mares", + "ĠTHE Y", + "ĠST ART", + "ĠK inn", + "Ġroomm ate", + "Ġhy giene", + "o pping", + "J ob", + "Ġl vl", + "ĠV ER", + "ĠKe eping", + "ab etic", + "Ġformat ting", + "eral a", + "Ġrev isions", + "Ġres urg", + "T el", + "ĠGood man", + "35 3", + "p od", + "Ġind isp", + "ĠTrans lation", + "Ġg own", + "ĠM und", + "Ġc is", + "Ġby stand", + "col lect", + "ĠPun jab", + "act ively", + "ĠG amb", + "te ll", + "Ġimport ing", + "g encies", + "Ġloc om", + "ĠBr ill", + "H oly", + "ĠBer ger", + "Ġshow down", + "Ġrespond ers", + "IL Y", + "Ġt akedown", + "le ted", + "Ġmat tered", + "Ġpredict ive", + "Ġover lay", + "G PU", + "ĠV ick", + "Ġconvey ed", + "T ab", + "pe er", + "Sc an", + "Ġdefensive ly", + "v ae", + "Ġappro ving", + "Ġt iers", + "ĠV ia", + "quer ade", + "ĠSaud is", + "Ġdemol ished", + "ĠProp he", + "Ġmon o", + "Ġhospital ity", + "H AM", + "ĠAri el", + "M OD", + "ĠTor ah", + "Ġbl ah", + "ĠBel arus", + "erent ial", + "ĠT uc", + "Ġbank er", + "39 7", + "Ġmosqu it", + "ĠScient ist", + "ĠMus ical", + "Ġh ust", + "Sh ift", + "Ġtor ment", + "Ġstand off", + "E duc", + "ĠF og", + "Ġampl ifier", + "Sh ape", + "Inst ance", + "ĠCrit ics", + "Ġda emon", + "H ouston", + "Ġmatt ress", + "ĠID F", + "Ġobsc ene", + "ĠA mer", + "hett i", + "Ġcomp iling", + "35 2", + "vere tt", + "ĠRed uction", + "ist ration", + "ĠBl essed", + "ĠB achelor", + "3 16", + "Ġpr ank", + "ĠVul can", + "dd ing", + "Ġm ourning", + "ĠQu int", + "ĠBl aster", + "test ing", + "Ġsed iment", + ">> >", + "ĠE ternity", + "ĠWH ERE", + "ĠM aze", + "Ġreact ing", + "ĠAl v", + "oms day", + "ĠC RA", + "Ġtransl ator", + "Ġbog us", + "at u", + "We bsite", + "oll s", + "Ġbapt ism", + "Ġs ibling", + "ĠAut umn", + "ve z", + "ãģ® é", + "gu ards", + "Ge org", + "assad ors", + "ĠFre ud", + "Ġcontin ents", + "ĠReg istry", + "Bern ie", + "ĸļ 士", + "Ġtoler ant", + "ĠU W", + "Ġhor ribly", + "99 5", + "ĠMID I", + "Ġimpat ient", + "oc ado", + "er i", + "ĠWor st", + "ĠNor ris", + "ĠTalk ing", + "Ġdef ends", + "ens able", + "Ġ20 21", + "Ġanat omy", + "L ew", + "Ġdraw er", + "ĠCan berra", + "Ġpatri otic", + "é¾įå ĸļ士", + "ĠAv g", + "AR M", + "Ġundis closed", + "Ġfare well", + "45 9", + "b able", + "ĠAll ison", + "OL OG", + "Ġcon co", + "t ight", + "ĠAC PI", + "ĠM ines", + "l ich", + "ĠâĶ ľ", + "represent ed", + "200 000", + "Ġenthusi ast", + "OT S", + "b il", + "ĠIng redients", + "Ġinvent or", + "ĠMy SQL", + "³³ Âł", + "ĠAB OUT", + "with in", + "Ġm k", + "B ul", + "ĠF ake", + "Ġdracon ian", + "W a", + "hel m", + "ĠTer ran", + "erv ille", + "Ġcommon place", + "SI ZE", + "Ġ\" <", + "re place", + "ograph s", + "ĠSE LECT", + "inc ible", + "ĠMost ly", + "ĠShe ffield", + "ĠID E", + "ugg le", + "Ġcit ations", + "h urst", + "ĠUn ix", + "Ġunle ash", + "ĠP iper", + "ĠN ano", + "Ġsucc umb", + "Ġreluct ance", + "Ġ25 00", + "ĠMer chant", + "Ġwire t", + "Ġcomb os", + "ĠBirth day", + "Ġchar coal", + "ĠU PS", + "ĠFair fax", + "Ġdrive way", + "ĠT ek", + "ĠP itch", + "ove re", + "Ġtechn icians", + "ĠAct ual", + "fl ation", + "ĠF iscal", + "ĠEm pty", + "an amo", + "Ġmag nesium", + "Ġsl ut", + "Ġgrow ers", + "Invest igators", + "( ):", + "ĠS atellite", + "ĠKe ynes", + "miss ive", + "l ane", + "Ġb orough", + "3 44", + "ĠTE AM", + "ĠBet hesda", + "C V", + "h ower", + "ĠR AD", + "Ġch ant", + "ĠR iy", + "Ġcompos itions", + "Ġmild ly", + "Ġmedd ling", + "Ġag ility", + "ane ers", + "5 01", + "Ġsyn th", + "ling er", + "29 1", + "Ġex claimed", + "Part y", + "Ġcont amin", + "ĠMan or", + "ĠResp ond", + "Ġpra ising", + "Ġman ners", + "fle et", + "Sum mer", + "ĠLy nd", + "ĠDef initely", + "gr im", + "Ġbow ling", + "st ri", + "ç Ľ", + "y nt", + "Ġmand ates", + "D IV", + "Ġreconc ile", + "view s", + "ĠDam on", + "vet te", + "F lo", + "ĠGreat est", + "il on", + "ic ia", + "Ġportray al", + "Ġcush ion", + "50 4", + "19 79", + "oss al", + "App lic", + "sc ription", + "Ġmit igation", + "AT S", + "p ac", + "Ġer ased", + "Ġdefic iencies", + "ĠHolland e", + "ĠX u", + "Ġb red", + "Ġpregn ancies", + "f emin", + "Ġem ph", + "Ġpl anners", + "Ġout per", + "utter ing", + "Ġperpet rator", + "Ġm otto", + "ĠEll ison", + "ĠNE VER", + "Ġadmitted ly", + "AR I", + "ĠAzerbai jan", + "Ġmill isec", + "Ġcombust ion", + "ĠBott le", + "ĠL und", + "ĠP s", + "ĠD ress", + "Ġfabric ated", + "Ġbat tered", + "Ġs idel", + "ĠNot ting", + "Fore ign", + "ĠJer ome", + "0 20", + "ĠAr bit", + "Ġkn ots", + "ĠR IGHT", + "M oving", + "ãģ Ļ", + "Ġsur geries", + "Ġcour thouse", + "Ġm astered", + "Ġhover ing", + "ĠBr an", + "ĠAl ison", + "Ġsaf est", + "m ilitary", + "Ġbull ied", + "Ġbar rage", + "Read er", + "ES E", + "ĠGe ographic", + "T ools", + "3 14", + "ĠGe ek", + "ro th", + "gl ers", + "ĠF IN", + "Ï ģ", + "ĠA ston", + "al tern", + "48 8", + "Ġveter in", + "G amer", + "Ġint el", + "ren ches", + "Sh ield", + "Ġam nesty", + "ĠB har", + "Ġp iled", + "Ġhonor able", + "ĠInst itutes", + "Ġso aked", + "Ġcom a", + "ĠE FF", + "34 1", + "by tes", + "ĠG mail", + "le in", + "ĠCanad iens", + "m aterial", + "I l", + "Ġinstruct ors", + "ĠK Y", + "Ġconce ive", + "ub b", + "ĠP ossible", + "Ġeas ing", + "ĠChrist ina", + "Ġcar ic", + "ĠHD R", + "R OM", + "Ġsho vel", + "de lete", + "Ġp uff", + "ĠCh anging", + "Ġseam lessly", + "Att ribute", + "Ġacqu isitions", + "ak ery", + "ĠE F", + "Ġaut istic", + "ĠT akes", + "ĠPow der", + "ĠSt ir", + "5 10", + "ĠBub ble", + "sett ings", + "ĠF owler", + "Ġmust ard", + "Ġmore over", + "Ġcopyright ed", + "ĠLED s", + "15 00", + "æ ī", + "ĠH IS", + "en f", + "Ġcust od", + "ĠH uck", + "G i", + "Ġim g", + "An swer", + "C t", + "j ay", + "ĠInf rastructure", + "Ġfeder ally", + "L oc", + "Ġmicro bes", + "Ġover run", + "dd s", + "ot ent", + "adi ator", + ">>>> >>>>", + "Ġtorn ado", + "Ġadj ud", + "Ġintrig ued", + "Ġs i", + "ĠRevel ation", + "pro gress", + "Ġburgl ary", + "ĠSai yan", + "ĠK athy", + "Ġser pent", + "ĠAndre as", + "Ġcomp el", + "ess ler", + "ĠPl astic", + "ĠAd vent", + "ĠPos itive", + "ĠQ t", + "ĠHind us", + "reg istered", + "ular ity", + "Ġrighteous ness", + "Ġdemon ic", + "u itive", + "ĠB DS", + "ĠGre gg", + "c ia", + "ĠCrus ade", + "ĠSina i", + "W ARE", + "+ (", + "Ġme ll", + "Ġder ail", + "y ards", + "A st", + "Ġnotice ably", + "ĠO ber", + "R am", + "Ġun noticed", + "Ġse q", + "av age", + "T s", + "Ġ6 40", + "Ġconced e", + "Ġ] )", + "F ill", + "Ġcapt ivity", + "ĠImprove ment", + "ĠCrus ader", + "ara oh", + "M AP", + "æ Ĺ", + "Ġstr ide", + "al ways", + "F ly", + "N it", + "Ġal gae", + "ĠCook ing", + "ĠDo ors", + "Mal ley", + "Ġpolic emen", + "ãģ į", + "Ġastron aut", + "access ible", + "49 5", + "ĠR AW", + "cl iffe", + "udic rous", + "Ġdep ended", + "al ach", + "Ġvent ures", + "ra ke", + "Ġt its", + "ĠH ou", + "Ġcond om", + "ormon al", + "Ġind ent", + "Ġupload ing", + "Foot note", + "Import ant", + "Ġ27 1", + "Ġmind ful", + "Ġcont ends", + "C ra", + "Ġcal ibr", + "ĠO ECD", + "plug in", + "F at", + "ĠIS S", + "ĠDynam ics", + "ans en", + "68 6", + "' ),", + "Ġsp rite", + "Ġhand held", + "ĠH ipp", + "=~ =~", + "Tr ust", + "Ġsem antics", + "ĠBund es", + "ĠRen o", + "ĠLiter ature", + "s ense", + "G ary", + "ĠA eg", + "ĠTr in", + "EE K", + "Ġcler ic", + "ĠSS H", + "Ġch rist", + "Ġinv ading", + "ib u", + "Ġen um", + "aur a", + "Ġal lege", + "ĠInc redible", + "B BC", + "Ġth ru", + "Ġsa iled", + "Ġem ulate", + "Ġin security", + "Ġc rou", + "Ġaccommod ations", + "Ġincompet ent", + "Ġsl ips", + "ĠEarth qu", + "s ama", + "IL LE", + "Ġi Phones", + "as aki", + "Ġby e", + "Ġar d", + "Ġext ras", + "Ġsl aughtered", + "Ġcrowd funding", + "res so", + "Ġfil ib", + "ĠER ROR", + "ĠT LS", + "e gg", + "ĠIt al", + "Ġen list", + "ĠCatal onia", + "ĠSc ots", + "Ġser geant", + "Ġdiss olve", + "N H", + "Ġstand ings", + "ri que", + "I Q", + "Ġbenef iciary", + "Ġaqu arium", + "You Tube", + "ĠPower Shell", + "Ġbright est", + "ĠWar rant", + "S old", + "Writ ing", + "Ġbegin nings", + "ĠRes erved", + "ĠLatin os", + "head ing", + "Ġ4 40", + "Ġrooft op", + "AT ING", + "Ġ3 90", + "VP N", + "G s", + "k ernel", + "turn ed", + "Ġprefer able", + "Ġturn overs", + "ĠH els", + "S a", + "ĠShin ji", + "ve h", + "ĠMOD ULE", + "V iol", + "Ġex iting", + "Ġj ab", + "ĠVan illa", + "Ġac ron", + "ĠG ap", + "ber n", + "A k", + "ĠMc Gu", + "Ġend lessly", + "ĠFar age", + "ĠNo el", + "V a", + "M K", + "Ġbr ute", + "ĠK ru", + "ĠES V", + "ĠOl ivia", + "âĢ ł", + "ĠK af", + "Ġtrust ing", + "Ġh ots", + "3 24", + "Ġmal aria", + "Ġj son", + "Ġp ounding", + "ort ment", + "Count ry", + "Ġpostp oned", + "Ġunequ iv", + "? ),", + "ĠRo oney", + "udd ing", + "ĠLe ap", + "ur rence", + "sh apeshifter", + "ĠH AS", + "os ate", + "Ġca vern", + "Ġconserv atism", + "ĠB AD", + "Ġmile age", + "Ġarrest ing", + "V aults", + "Ġmix er", + "Dem ocratic", + "ĠB enson", + "Ġauth ored", + "8 000", + "Ġpro active", + "ĠSpirit ual", + "t re", + "Ġincarcer ated", + "ĠS ort", + "Ġpe aked", + "Ġwield ing", + "re ciation", + "×Ļ ×", + "P atch", + "ĠEm my", + "Ġex qu", + "tt o", + "ĠRat io", + "ĠP icks", + "ĠG ry", + "ph ant", + "Ġf ret", + "Ġeth n", + "Ġarch ived", + "% -", + "c ases", + "ĠBl aze", + "Ġim b", + "c v", + "y ss", + "im ony", + "Ġcount down", + "Ġaw akening", + "ĠTunis ia", + "ĠRe fer", + "ĠM J", + "Ġun natural", + "ĠCar negie", + "iz en", + "ĠN uggets", + "he ss", + "Ġev ils", + "64 7", + "Ġintrodu ctory", + "l oving", + "ĠMcM ahon", + "Ġambig uity", + "L abel", + "ĠAlm ighty", + "Ġcolor ing", + "ĠCl aus", + "set ting", + "N ULL", + "ĠF avorite", + "ĠS IG", + "> (", + "ĠSh iva", + "ĠMay er", + "Ġstorm ed", + "ĠCo verage", + "we apons", + "igh am", + "Ġun answered", + "Ġle ve", + "Ġc oy", + "c as", + "b ags", + "as ured", + "Se attle", + "ĠSant orum", + "ser ious", + "Ġcourage ous", + "ĠS oup", + "Ġconfisc ated", + "Ġ// /", + "Ġuncon ventional", + "Ġmom s", + "ĠRohing ya", + "ĠOrche stra", + "ĠPot ion", + "Ġdisc redit", + "ĠF IL", + "f ixed", + "ĠDe er", + "do i", + "ĠDim ension", + "Ġbureaucr ats", + "et een", + "Ġaction Group", + "oh m", + "Ġb umps", + "ĠUt ility", + "Ġsubmar ines", + "ren heit", + "re search", + "ĠShap iro", + "Ġsket ches", + "Ġde ceptive", + "ĠV il", + "es ame", + "ĠEss entially", + "Ġramp age", + "isk y", + "Ġmut tered", + "th ritis", + "Ġ23 6", + "f et", + "b ars", + "Ġpup il", + "ĠTh ou", + "o S", + "s ong", + "Ġfract ured", + "Ġre vert", + "pict ure", + "Ġcrit erion", + "us her", + "Ġreperc ussions", + "ĠV intage", + "ĠSuper intendent", + "Offic ers", + "Ġflag ged", + "Ġbl ames", + "Ġin verse", + "ograp hers", + "Ġmakes hift", + "Ġdev oid", + "Ġfoss ils", + "ĠArist otle", + "ĠFund s", + "Ġde pleted", + "ĠFl u", + "ĠY uan", + "Ġw oes", + "Ġlip id", + "Ġsit u", + "requ isites", + "Ġfurn ish", + "ĠSam ar", + "Ġshame ful", + "Ġadverse ly", + "Ġad ept", + "Ġrem orse", + "Ġmurder ous", + "uck les", + "ĠE SL", + "Ġ3 14", + "s ent", + "Ġred ef", + "ĠC ache", + "ĠP urs", + "ig ans", + "Ġ4 60", + "Ġpres criptions", + "Ġf res", + "F uck", + "ocr ates", + "Tw enty", + "ĠWe ird", + "ĠT oggle", + "ĠC alled", + "itiz ens", + "Ġp oultry", + "Ġharvest ing", + "ãĤ¦ ãĤ¹", + "Bott om", + "Ġcaution ed", + "t n", + "39 6", + "ĠNik ki", + "Ġeval uations", + "Ġharass ing", + "Ġbind ings", + "ĠMon etary", + "Ġhit ters", + "Ġadvers ary", + "un ts", + "Ġset back", + "Ġenc rypt", + "ĠC ait", + "Ġl ows", + "eng es", + "ĠN orn", + "Ġbul bs", + "Ġbott led", + "ĠVoy ager", + "3 17", + "Ġsp heres", + "p olitics", + "Ġsubt ract", + "Ġsens ations", + "Ġapp alling", + "Ġ3 16", + "Ġenvironment ally", + "ĠST EM", + "Ġpub lishes", + "5 60", + "Ġdilig ence", + "48 4", + "Ġadv ises", + "Ġpet rol", + "Ġimag ining", + "Ġpatrol s", + "ĠInt eger", + "ĠAs hes", + "act us", + "ĠRad iant", + "ĠL T", + "it ability", + "ht aking", + "Set ting", + "Ġnu anced", + "ĠRe ef", + "ĠDevelop ers", + "N i", + "pie ces", + "99 0", + "Lic ense", + "Ġlow ers", + "ĠOtt oman", + "3 27", + "oo o", + "Ġqu itting", + "mark ets", + "Beh ind", + "Ġbas in", + "Ġdoc s", + "an ie", + "fl ash", + "ct l", + "Ġcivil ized", + "ĠFuk ushima", + "\"] ,\"", + "ĠK S", + "ĠHonest ly", + "ar at", + "Ġconstruct s", + "ĠL ans", + "ĠD ire", + "ĠLI KE", + "ĠTrou ble", + "Ġwith holding", + "ĠOb livion", + "Ġsan ity", + "any a", + "Con st", + "Ġgro cer", + "ĠC elsius", + "Ġrecount ed", + "ĠW ife", + "B order", + "ate red", + "h appy", + "Ġspo iler", + "Ġlog ically", + "H all", + "Ġsucceed ing", + "Ġpoly morph", + "Ġax es", + "ĠShot gun", + "ĠS lim", + "ĠPrin ciples", + "ĠL eth", + "art a", + "Ġsc or", + "Sc reenshot", + "Ġrelax ation", + "#$ #$", + "Ġdeter rent", + "idd y", + "Ġpower less", + "Ġles bians", + "Ġch ords", + "ĠEd ited", + "se lected", + "Ġseparat ists", + "000 2", + "Ġair space", + "Ġturn around", + "Ġc unning", + "P ATH", + "P oly", + "Ġbomb ed", + "Ġt ion", + "x s", + "Ġwith hold", + "Ġw aged", + "ĠLiber ties", + "Fl ag", + "Ġcomfort ing", + "45 4", + "ĠI ris", + "are rs", + "Ġr ag", + "Ġrel ocated", + "ĠGu arant", + "Ġstrateg ically", + "Ġgam ma", + "uber ty", + "ĠLock heed", + "g res", + "Ġgr illed", + "ĠLow e", + "st ats", + "ĠR ocks", + "Ġsens ing", + "Ġrent ing", + "ĠGe ological", + "ا Ø", + "ot rop", + "Ġse w", + "Ġimproper ly", + "48 6", + "Ġâĸ ł", + "Ġstar ving", + "ĠB j", + "Disc ussion", + "3 28", + "ĠCom bo", + "ĠFix es", + "N AT", + "Ġstri ving", + "th ora", + "Ġharvest ed", + "ĠP ing", + "Ġplay ful", + "Ġaven ues", + "Ġoccup ational", + "Ġw akes", + "ĠCou rier", + "Ġdrum mer", + "ĠBrow ser", + "ĠH outh", + "it u", + "Ġapp arel", + "p aste", + "Ġhun ted", + "ĠSecond ly", + "l ain", + "X Y", + "ĠP IN", + "ic ons", + "Ġcock tails", + "Ġs izable", + "Ġhurd les", + "est inal", + "ĠRecre ation", + "Ġe co", + "64 8", + "ĠD ied", + "m int", + "Ġfinger prints", + "Ġdis pose", + "ĠBos nia", + "ts y", + "22 00", + "Ġins pected", + "ĠF ou", + "Ġf uss", + "Ġamb ush", + "ĠR ak", + "Ġmanif ested", + "Pro secut", + "Ġsuff ice", + "ren ces", + "Ġcompens ated", + "ĠC yrus", + "Ġgen us", + "ĠWolver ine", + "ĠTrend s", + "Ġh ikes", + "ĠSe en", + "Ġen rol", + "C old", + "Ġpol itely", + "ĠSl av", + "ĠRu pert", + "Ġey ewitness", + "ĠAl to", + "Ġun comp", + "Ġposter ior", + "M ust", + "ĠHer z", + "Ġprogress ively", + "Ġ23 4", + "Ġind ifference", + "ĠCunning ham", + "Ġacadem ia", + "Ġse wer", + "Ġast ounding", + "ĠA ES", + "r ather", + "Ġeld est", + "Ġclim bs", + "ĠAdd s", + "Ġout cry", + "Ġcont ag", + "ĠH ouses", + "Ġpe pt", + "ĠMel ania", + "interest ed", + "ĠU CH", + "ĠR oots", + "ĠHub bard", + "ĠT BD", + "ĠRoman ian", + "fil ename", + "St one", + "ĠIm pl", + "Ġchromos ome", + "C le", + "d x", + "Ġscram bled", + "ĠP t", + "Ġ24 2", + "OP LE", + "Ġtremend ously", + "St reet", + "Ġcra ving", + "Ġbund led", + "ĠR G", + "p ipe", + "Ġinj uring", + "Ġarc ane", + "Part icip", + "ĠHero ic", + "st y", + "Ġto pping", + "ĠTemp est", + "rent ices", + "b h", + "Ġpar anoia", + "ĠUnic ode", + "Ġegreg ious", + "Ġ\\ '", + "ĠOsw ald", + "Ġgra vel", + "ĠSim psons", + "Ġbl and", + "ĠGuant anamo", + "Writ er", + "lin ers", + "ĠD ice", + "J C", + "Ġpar ity", + "Ġs ided", + "Ġ23 7", + "ĠPyr rha", + "at ters", + "d k", + "F ine", + "comp an", + "Ġform ulated", + "ĠId ol", + "il ers", + "hem oth", + "ĠF av", + "Ġintr usion", + "Ġcar rots", + "ĠL ayer", + "ĠH acker", + "Ġ ----------------", + "Ġmoder ation", + "é ģ", + "oc oc", + "Ġcharacter ize", + "ĠTe resa", + "Ġsocio economic", + "Ġper k", + "ĠParticip ation", + "tr aining", + "ĠPaul o", + "ph ys", + "Ġtrust worthy", + "Ġembod ied", + "ĠMer ch", + "c urrency", + "ĠPrior ity", + "Ġte asing", + "Ġabsor bing", + "Ġunf inished", + "ĠCompar ison", + "Ġdis ple", + "writ ers", + "Ġprofess ions", + "ĠPengu in", + "Ġang rily", + "ĠL INK", + "68 8", + "ĠCor respond", + "Ġprev ailed", + "Ġcart el", + "l p", + "as ms", + "ĠRed emption", + "ĠIslam ists", + "effect s", + "d ose", + "ĠL atter", + "ĠHal ifax", + "Ġv as", + "ĠTop ics", + "ĠN amed", + "advert ising", + "zz a", + "IC ES", + "Ġret arded", + "ach able", + "ĠPupp et", + "ĠItem Level", + "Ġret ract", + "Ġident ifiable", + "A aron", + "ĠB uster", + "s ol", + "hel le", + "as semb", + "H ope", + "r anged", + "B a", + "ĠP urch", + "é Ģ", + "ĠSir i", + "Ġarri vals", + "Ġ19 12", + "Ġshort ened", + "Ġ3 12", + "Ġdiscrep ancy", + "ĠTem perature", + "ĠWal ton", + "Ġkind erg", + "p olit", + "Ġrem ix", + "Ġconnect ors", + "ãĥĺ ãĥ©", + "ĠKazakh stan", + "dom inated", + "Ġsu gars", + "im ble", + "ĠPan ic", + "ĠDem and", + "ĠCol ony", + "on en", + "ĠM ER", + "7 75", + "ur ia", + "aza ar", + "ĠDeg ree", + "P ri", + "Ġsun shine", + "Ġ25 1", + "Ġpsychedel ic", + "Ġdigit ally", + "ĠBra un", + "Ġsh immer", + "Ġsh ave", + "ĠTel esc", + "ĠAst ral", + "ĠVenezuel an", + "ĠO G", + "Ġc rawling", + "Int eg", + "ĠFe ather", + "Ġunfold ing", + "Ġappropri ation", + "Ġè£ı è", + "ĠMob ility", + "ĠN ey", + "- .", + "b ilt", + "L IN", + "ĠT ube", + "ĠCon versely", + "Ġkey boards", + "ĠC ao", + "Ġover th", + "Ġla ure", + ">> \\", + "ĠV iper", + "ach a", + "Off set", + "ĠR aleigh", + "ĠJ ae", + "J ordan", + "j p", + "Ġtotal itarian", + "Connect or", + "Ġobserv es", + "ĠSpart an", + "ĠIm mediately", + "ĠSc al", + "C ool", + "Ġt aps", + "Ġro ar", + "P ast", + "Ġch ars", + "ĠB ender", + "ĠShe ldon", + "Ġpain ter", + "Ġbe acon", + "ĠCreat ures", + "Ġdownt urn", + "Ġh inder", + "ĠAnd romeda", + "à Ľ", + "cc oli", + "ĠF itness", + "et rical", + "Ġutil izes", + "Ġsen ate", + "Ġen semble", + "Ġche ers", + "T W", + "Ġaff luent", + "k il", + "ry lic", + "ord ering", + "Com puter", + "Ġgru esome", + "ost ics", + "ĠUb isoft", + "ĠKel ley", + "Ġw rench", + "Ġbourgeois ie", + "IB LE", + "ĠPrest on", + "w orn", + "ar ist", + "reat ing", + "Ġst ained", + "ar ine", + "Ġsl ime", + "EN N", + "Ġche sts", + "Ġground water", + "ann ot", + "ĠTr ay", + "ĠLoc ke", + "ĠC TR", + "Ġd udes", + "ĠEx ternal", + "ĠDec oder", + "Ġpar amed", + "ĠMed line", + "80 9", + "ĠD inner", + "rup al", + "g z", + "ĠG um", + "ĠDem o", + "j ee", + "Ġd h", + "ber man", + "arch s", + "Ġen qu", + "ĠEp stein", + "Ġdevast ation", + "Ġfriends hips", + "ĠAr d", + "Ġ23 1", + "ĠRub in", + "ĠDist ance", + "Ġsp urred", + "Ġd ossier", + "Ġover looking", + "\\\\\\\\\\\\\\\\ \\\\\\\\\\\\\\\\", + "Fore st", + "ĠCom es", + "\\ \",", + "ĠIran ians", + "Ġf ixtures", + "L aughs", + "Ġcur ry", + "ĠKing ston", + "Ġsqu ash", + "Ġcat alogue", + "Ġabnormal ities", + "Ġdigest ive", + ".... .....", + "Ġsubord inate", + "og ly", + "Ġ24 9", + "M iddle", + "Ġmass ac", + "Ġburg ers", + "Ġdown stairs", + "Ġ19 31", + "39 4", + "ĠV G", + "Ġl asers", + "ĠS ikh", + "ĠAlex a", + "der ived", + "Ġcycl ist", + "ãģ® éŃĶ", + "onel iness", + "!!!! !!!!", + "Ġbuff s", + "leg ate", + "Ġrap ing", + "Ġrecomm ending", + "ro red", + "Ġmult icultural", + "un ique", + "Ġbusiness men", + "Ġune asy", + "ĠM AP", + "Ġdisp ersed", + "cipl ine", + "J ess", + "ĠK erala", + "å §", + "Ġabst raction", + "Sur v", + "U h", + "Ġprin ters", + "ij a", + "ow der", + "Ġanalog ous", + "ĠA SP", + "af er", + "Ġunfold ed", + "Ġlevel ing", + "Ġbre ached", + "ĠH earing", + "Ġn at", + "Ġtransl ating", + "crit ical", + "Ġant agonist", + "ĠYes terday", + "Ġfuzz y", + "w ash", + "m ere", + "Ġbe wild", + "ĠM ae", + "V irgin", + "ph rase", + "Ġsign aled", + "ĠH IGH", + "Ġprot ester", + "Ġgar ner", + "unk nown", + "Ġk ay", + "Ġabduct ed", + "Ġst alking", + "am n", + "Ġdes erving", + "ĠR iv", + "ĠJ orge", + "Ġscratch ing", + "ĠS aving", + "ip ing", + "Ġte ase", + "Ġmission ary", + "ĠMor row", + "T IME", + "P resent", + "Ġchem otherapy", + "tern ess", + "ĠH omes", + "ĠP urdue", + "Ġst aunch", + "ĠWhit ney", + "ĠTH ERE", + "Î ¼", + "iat us", + "ĠErn est", + "ĠDe ploy", + "Ġcove ted", + "F ML", + "ĠDial ogue", + "Ġex ited", + "f ruit", + "Ġner d", + "\":\" \",\"", + "Ġv ivo", + "ru ly", + "4 60", + "ĠAm en", + "rehens ible", + "Ġâ ĺ", + "D IR", + "Ġad herence", + "Ġche w", + "ĠCo ke", + "ĠSerge i", + "dig ital", + "ĠNe ck", + "g ently", + "enth al", + "/ )", + "Ġwe ary", + "Ġgu ise", + "ĠConc ord", + "ĠOn ion", + "at cher", + "Ġb inge", + "ĠDirect ive", + "Ġman ned", + "ans k", + "Ġill usions", + "Ġbillion aires", + "38 3", + "oly n", + "odynam ic", + "ĠWhe at", + "ĠA lic", + "Ġcol oured", + "ĠN AFTA", + "ab o", + "Ġmac ros", + "ind ependent", + "s weet", + "Ġsp ac", + "ĠK abul", + "Ġ Ä", + "em e", + "Ġdict ated", + "Ġsh outs", + "= {", + "Ġr ipping", + "ĠSh ay", + "ĠCr icket", + "direct ed", + "Ġanalys ed", + "ĠWAR RANT", + "ag ons", + "ĠBlaz ers", + "Ġche ered", + "Ġar ithmetic", + "ĠTan z", + "37 3", + "ĠFl ags", + "Ġ29 5", + "Ġw itches", + "ĠIn cluded", + "ĠG ained", + "ĠBl ades", + "G am", + "ĠSam antha", + "ĠAtl antis", + "ĠPr att", + "Ġspo iled", + "ĠI B", + "ĠRam irez", + "Pro bably", + "re ro", + "ĠN g", + "ĠWar lock", + "t p", + "Ġover he", + "Ġadministr ations", + "Ġt int", + "Ġreg iment", + "Ġpist ols", + "Ġblank ets", + "Ġep ist", + "Ġbowl s", + "Ġhydra ulic", + "Ġde an", + "Ġj ung", + "Ġasc end", + "70 5", + "ĠSant iago", + "à ®", + "Ġun avoid", + "ĠSh aman", + "re b", + "Ġstem ming", + "99 8", + "ĠM G", + "st icks", + "esthes ia", + "ER O", + "Ġmor bid", + "ĠGr ill", + "ĠP oe", + "any l", + "Ġdele ting", + "ĠSurve illance", + "Ġdirect ives", + "Ġiter ations", + "ĠR ox", + "ĠMil ky", + "F ather", + "Ġpat ented", + "44 7", + "Ġprec ursor", + "Ġm aiden", + "ĠP hen", + "ĠVe gan", + "ĠPat ent", + "K elly", + "Redd itor", + "Ġn ods", + "Ġvent ilation", + "ĠSchwar z", + "Ġw izards", + "Ġomin ous", + "ĠHe ads", + "ĠB G", + "Ġl umber", + "ĠSp iel", + "Ġis Enabled", + "Ġancest ral", + "ĠSh ips", + "Ġwrest ler", + "ph i", + "Ġy uan", + "ĠRebell ion", + "Ġice berg", + "Ġmag ically", + "Ġdivers ion", + "ar ro", + "yth m", + "ĠR iders", + "ĠRob bie", + "ĠK ara", + "ĠMain tenance", + "ĠHer b", + "Ġhar ms", + "p acked", + "ĠFe instein", + "Ġmarry ing", + "Ġbl ending", + "ĠR ates", + "Ġ18 80", + "Ġwr ink", + "ĠUn ch", + "ĠTor ch", + "desc ribed", + "Ġhuman oid", + "ilit ating", + "ĠCon v", + "ĠFe ld", + "IGH TS", + "Ġwhistlebl ower", + "ort mund", + "ets y", + "arre tt", + "ĠMon o", + "ĠI ke", + "ĠC NBC", + "ĠW AY", + "ĠMD MA", + "ĠIndividual s", + "Ġsupplement al", + "Ġpower house", + "ĠSt ru", + "F ocus", + "aph ael", + "ĠCol leg", + "att i", + "Z A", + "Ġp erenn", + "ĠSign ature", + "ĠRod ney", + "Ġcub es", + "idd led", + "ĠD ante", + "ĠIN V", + "iling ual", + "ĠC th", + "Ġso fa", + "Ġintimid ate", + "ĠR oe", + "ĠDi plom", + "ĠCount ries", + "ays on", + "Ġextrad ition", + "Ġdis abling", + "ĠCard iff", + "Ġmemor andum", + "ĠTr ace", + "Ġ?? ?", + "se ctor", + "ĠRou hani", + "ĠY ates", + "ĠFree ze", + "Ġbl adder", + "M otor", + "ĠProm ise", + "ant asy", + "Ġforesee able", + "ĠC ologne", + "cont ainer", + "ĠTre es", + "ĠG ors", + "ĠSin clair", + "Ġbar ring", + "key e", + "Ġsl ashed", + "ĠStat istical", + "é ĩ", + "Ġâĸ º", + "All ows", + "Ġhum ility", + "Ġdr illed", + "ĠF urn", + "44 3", + "Ġse wage", + "Ġhome page", + "Ġcour tyard", + "Ġv ile", + "Ġsubsid iaries", + "aj o", + "direct ory", + "Ġam mon", + "V ers", + "charg es", + "Ġ} }", + "ĠCh ains", + "Ġ24 6", + "n ob", + "Ġper cept", + "Ġg rit", + "Ġfisher men", + "ĠIraq is", + "ĠDIS TR", + "ĠF ULL", + "ĠEval uation", + "g raph", + "at ial", + "Ġcooper ating", + "Ġmel an", + "Ġenlight ened", + "Ġal i", + "t ailed", + "Ġsal ute", + "Ġweak est", + "ĠBull dogs", + "U A", + "ĠAll oy", + "Ġsem en", + "oc ene", + "ĠWilliam son", + "s pr", + ", âĢĶ", + "ĠG F", + "itt ens", + "Be at", + "ĠJ unk", + "iph ate", + "ĠFarm ers", + "ĠBit coins", + "ig ers", + "d h", + "ĠL oyal", + "p ayer", + "Ġentert ained", + "Ġpenn ed", + "Ġcoup on", + "Que ue", + "Ġweaken ing", + "c arry", + "Ġunderest imate", + "Ġshoot out", + "Ġcharism atic", + "ĠProced ure", + "Ġprud ent", + "in ances", + "Ġric hes", + "Ġcort ical", + "Ġstr ides", + "Ġd rib", + "ĠOil ers", + "5 40", + "ĠPer form", + "ĠBang kok", + "Ġe uth", + "S ER", + "Ġsimpl istic", + "t ops", + "camp aign", + "Q uality", + "Ġimpover ished", + "ĠEisen hower", + "Ġaug ment", + "ĠH arden", + "Ġinterven ed", + "Ġlist ens", + "ĠK ok", + "Ġs age", + "Ġrub bish", + "ĠD ed", + "Ġm ull", + "pe lling", + "Ġvide ot", + "Produ ction", + "D J", + "m iah", + "Ġadapt ations", + "Ġmed ically", + "Ġboard ed", + "Ġarrog ance", + "Ġscra pped", + "Ġopp ress", + "FORM ATION", + "Ġj unction", + "4 15", + "EE EE", + "S kill", + "Ġsub du", + "ĠSug gest", + "ĠP ett", + "Ġle tt", + "ĠMan ip", + "ĠC af", + "ĠCooper ation", + "T her", + "Ġreg ained", + "¶ æ", + "ref lect", + "Ġth ugs", + "ĠShel by", + "Ġdict ates", + "ĠWe iner", + "ĠH ale", + "Ġbatt leground", + "s child", + "Ġcond ol", + "h unt", + "osit ories", + "Ġacc uses", + "Fil ename", + "Ġsh ri", + "Ġmotiv ate", + "Ġreflect ions", + "N ull", + "ĠL obby", + "¥ µ", + "ĠS ATA", + "ĠBack up", + "Ñ ĥ", + "n in", + "ĠCor rection", + "Ġju icy", + "ut ra", + "ĠP ric", + "Ġrest raining", + "ĠAir bnb", + "ĠAr rest", + "Ġappropri ations", + "Ġsl opes", + "Ġmans laughter", + "Ġwork ings", + "ĠH uss", + "ĠF rey", + "Le ave", + "ĠHarm ony", + "ĠF eder", + "Ġ4 30", + "Ġt rench", + "Ġglad ly", + "Ġbull pen", + "ĠG au", + "b ones", + "Ġgro ove", + "Ġpre text", + "ã ħĭ", + "Ġtransm itter", + "ĠComp onent", + "Ġunder age", + "ĠEm pires", + "T ile", + "Ġo y", + "ĠMar vin", + "ĠC AS", + "Ġbl oss", + "Ġrepl icated", + "ĠMar iners", + "Marc us", + "ĠBl ocks", + "Ġliber ated", + "Ġbutter fly", + "Fe el", + "Ġfer mentation", + "Ġyou tube", + "Ġoff end", + "ĠTer m", + "res ist", + "Ġcess ation", + "Ġinsurg ency", + "Ġb ir", + "ĠRa ise", + "59 5", + "Ġhypothes es", + "50 2", + "Ġpl aque", + "ocr at", + "Ġjack ets", + "ĠHuff Post", + "am ong", + "Ġconf er", + "48 7", + "ĠL illy", + "Ġadapt ing", + "ĠF ay", + "Ġsh oved", + "ve c", + "Ġref ine", + "Ġg on", + "Ġgun men", + "z ai", + "ĠShut tle", + "ĠI zan", + "Ġ19 13", + "Ġple thora", + "· ·", + "Ġ5 10", + "Ġp uberty", + "Ġ24 1", + "ĠWe alth", + "ĠAl ma", + "ĠM EM", + "ĠAd ults", + "C as", + "pr ison", + "R ace", + "Ġwater proof", + "Ġathlet icism", + "Ġcapital ize", + "ĠJu ice", + "Ġillum inated", + "ĠP ascal", + "Ġirrit ation", + "ĠWitness es", + "ad le", + "ĠAst ro", + "Ġf ax", + "ĠEl vis", + "Prim ary", + "ĠL ich", + "ĠEl ves", + "Ġres iding", + "Ġst umble", + "3 19", + "ĠP KK", + "Ġadvers aries", + "D OS", + "ĠR itual", + "Ġsm ear", + "Ġar son", + "ident al", + "Ġsc ant", + "Ġmon archy", + "Ġhal ftime", + "Ġresid ue", + "Ġind ign", + "ĠSh aun", + "ĠEl m", + "aur i", + "A ff", + "W ATCH", + "ĠLy on", + "hel ps", + "36 1", + "Ġlobby ist", + "Ġdimin ishing", + "Ġout breaks", + "Ġgo ats", + "f avorite", + "ĠN ah", + "son ian", + "ĠBo oster", + "Ġsand box", + "ĠF are", + "ĠMalt a", + "Ġatt Rot", + "ĠM OR", + "ld e", + "Ġnavig ating", + "T ouch", + "Ġunt rue", + "ĠDis aster", + "Ġl udicrous", + "Pass word", + "ĠJ FK", + "blog spot", + "4 16", + "ĠUN DER", + "ern al", + "Ġdelay ing", + "T OP", + "Ġimpl ants", + "ĠAV G", + "ĠH uge", + "att r", + "Ġjournal istic", + "ĠPe yton", + "ĠI A", + "R ap", + "go al", + "ĠProgram me", + "Ġsm ashing", + "w ives", + "print ln", + "ĠPl ague", + "in us", + "EE P", + "Ġcru iser", + "ĠPar ish", + "umin ium", + "Ġoccup ants", + "ĠJ ihad", + "m op", + "Ġp int", + "Ġhe ct", + "ĠMe cca", + "direct or", + "ĠFund ing", + "ĠM ixed", + "Ġst ag", + "T ier", + "Ġg ust", + "Ġbright ly", + "ors i", + "Ġup hill", + "R D", + "Ġles ions", + "ĠBund y", + "liv ious", + "Ġbi ologist", + "ĠFac ulty", + "ĠAuthor ization", + "Ġ24 4", + "All ow", + "ï ¸", + "ĠGi ul", + "Ġpert inent", + "ot aur", + "es se", + "ĠRo of", + "Ġunman ned", + "35 1", + "ĠSh ak", + "ĠO rient", + "Ġend anger", + "D ir", + "Ġrepl en", + "ed ient", + "Ġtail or", + "Ġgad gets", + "Ġaud ible", + "âĺ Ĩ", + "N ice", + "Ġbomb ard", + "ĠR ape", + "Ġdef iance", + "ĠTW O", + "ĠFilip ino", + "Ġunaff ected", + "erv atives", + "Ġso ared", + "ĠBol ton", + "Ġcomprom ising", + "ĠBrew ers", + "R AL", + "ĠA HL", + "icy cle", + "Ġv ampires", + "Ġdi pped", + "oy er", + "ĠX III", + "Ġsidew ays", + "ĠW aste", + "ĠD iss", + "ĠâĶľ âĶĢâĶĢ", + "$ .", + "Ġhabit ats", + "ĠBe ef", + "tr uth", + "tr ained", + "spl it", + "R us", + "And y", + "ĠB ram", + "RE P", + "p id", + "è£ ħ", + "ĠMut ant", + "An im", + "ĠMar ina", + "Ġfut ile", + "hig hest", + "f requency", + "Ġepile psy", + "Ġcop ing", + "Ġconc ise", + "Ġtr acing", + "ĠS UN", + "pan el", + "ĠSoph ie", + "ĠCrow ley", + "ĠAd olf", + "ĠShoot er", + "Ġsh aky", + "ĠI G", + "ĠL ies", + "ĠBar ber", + "p kg", + "Ġupt ake", + "Ġpred atory", + "UL TS", + "/ **", + "Ġintox icated", + "ĠWest brook", + "od der", + "he ment", + "Ġbas eman", + "AP D", + "st orage", + "ĠFif ty", + "ed itor", + "G EN", + "UT ION", + "ir ting", + "Ġse wing", + "r ift", + "Ġag ony", + "ĠS ands", + "Ġ25 4", + "C ash", + "Ġl odge", + "Ġp unt", + "N atural", + "ĠIde as", + "Ġerrone ous", + "ĠSens or", + "ĠHann ity", + "Ġ19 21", + "Ġm ould", + "ĠG on", + "kay a", + "Ġanonym ously", + "ĠK EY", + "Ġsim ulator", + "W inter", + "Ġstream ed", + "50 7", + "? \",", + "Ġte ased", + "Ġco efficient", + "Ġwart ime", + "ĠTH R", + "' '.", + "ĠBank ing", + "mp ire", + "Ġf andom", + "Ġl ia", + "G a", + "Ġdown hill", + "Ġinterpre ting", + "Ind ividual", + "N orm", + "Ġjealous y", + "bit coin", + "Ġple asures", + "ĠToy s", + "ĠChev rolet", + "ĠAd visor", + "IZ E", + "Ġrecept ions", + "70 6", + "C ro", + "Ġ26 2", + "Ġcit rus", + "ir u", + "Review er", + "ject ed", + "U ES", + "an z", + "19 81", + "ĠWork er", + "Ġcompl ied", + "ores cent", + "contin ental", + "T on", + "ĠPr ism", + "ĠShe ep", + "Ġ28 8", + "n ox", + "ĠV og", + "O rd", + "Ġreal ms", + "te k", + "Ġirrig ation", + "Ġbicy cles", + "Ġelectron ically", + "p oly", + "t all", + "() );", + "Ġaest hetics", + "ĠInteg rated", + "Expl ore", + "Ġd unk", + "47 6", + "p ain", + "ĠJac ques", + "ĠD mit", + "Fram es", + "Ġreun ited", + "Ġhum id", + "D ro", + "P olitical", + "Ġyouth ful", + "Ġent ails", + "Ġmosqu ito", + "36 3", + "spe cies", + "Ġcoord inating", + "ĠMay hem", + "ĠMagn us", + "M ount", + "Impro ved", + "ĠST ATE", + "ATT LE", + "Ġflow ed", + "Ġtack led", + "Ġfashion ed", + "Ġre organ", + "iv ari", + "f inger", + "Ġreluct antly", + "et ting", + "ĠV and", + "you ng", + "ĠGar land", + "Ġpresum ption", + "Ġamen ities", + "ĠPle asant", + "on ential", + "ĠO xy", + "Ġmor als", + "ĠY ah", + "Read y", + "Sim on", + "En h", + "D emon", + "Ġcl ich", + "Mon itor", + "ĠD U", + "Ġwel comes", + "Ġstand out", + "Ġdread ful", + "Ġban anas", + "Ġball oons", + "h ooting", + "bas ic", + "Ġsuff ix", + "Ġd uly", + "can o", + "Ch ain", + "at os", + "Ġgeop olitical", + "Ġ( &", + "ĠGem ini", + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", + "Ġacqu itted", + "L uck", + "prot ect", + "10 24", + "Ġsc arcity", + "Ġmind fulness", + "ec ided", + "D N", + "pr ime", + "ĠPres idents", + "ĠVID EO", + "Ġ( âĪĴ", + "add ock", + "N OR", + "ĠP ru", + "p un", + "ĠL OL", + ")) ))", + "ĠL iqu", + "ĠS AS", + "Ġsty ling", + "Ġpunish ments", + "Ġnum b", + "Ġasc ertain", + "ĠRock ies", + "f lu", + "Th umbnail", + "Ġperpet rated", + "ĠSem i", + "Ġdis arm", + "ĠOld er", + "ĠEx ception", + "Ġexponent ially", + "ĠCommun ities", + "Ġabol ish", + "ĠPart ner", + "pt oms", + "Ġ7 77", + "ĠFo ley", + "ĠC ases", + "Ġgre ase", + "ĠReb irth", + "G round", + "Ġ; )", + "ĠDoct rine", + "ik ini", + "Y e", + "ĠBl ossom", + "Ġpers ists", + "b ill", + "Ġinf usion", + "Ġbud dies", + "9 11", + "ĠPat ient", + "Ġdem os", + "Ġacquaint ance", + "ĠP aw", + "at ari", + "Ġx ml", + "Ġfasc ination", + "ĠSer ve", + "Ï Ĥ", + "br anded", + "Ġa z", + "Return s", + "Ġover shadow", + "Ġro am", + "Ġspeed y", + "n umbered", + "hel ial", + "Ġdisc iple", + "Ġass urances", + "g iven", + "pect ing", + "ĠN atalie", + "çĶ °", + "Ġmosquit oes", + "rote in", + "Ġnumer ic", + "Ġindepend ents", + "Ġtrans itional", + "Ġreaction ary", + "ĠMech dragon", + "do ctor", + "Ġshort est", + "Ġsequ ential", + "ĠB ac", + "ĠAccount s", + "ãģ Į", + "ach y", + "ract ive", + "ĠReg iment", + "Ġbreat htaking", + "ffic iency", + "ĠB ates", + "Ġ3 11", + "Ġward robe", + "ft s", + "ĠBer k", + "Sim ply", + "ĠRivers ide", + "iver ing", + "ident ial", + "lu cent", + "Ġen riched", + "ĠCon ver", + "ĠG iving", + "ãĥ Ļ", + "Ġlegal ize", + "ĠF TC", + "Ġfre aking", + "M ix", + "Ġter restrial", + "es ian", + "ci ents", + "W ing", + "LO AD", + "Ġled ge", + "ĠViol ent", + "ĠMet all", + "Ġ30 8", + "Ġs outheastern", + "hett o", + "M eat", + "Ġslow down", + "Ġret reated", + "Jere my", + "end as", + "**** *", + "er ic", + "Ġre ins", + "opp able", + "ĠHuman ity", + "ear ances", + "rig an", + "C amera", + "Ġwa ivers", + "s oc", + "Ġalter ation", + "trans form", + "ĠC emetery", + "50 6", + "Ġindef inite", + "Ġstim ulating", + "y g", + "60 3", + "ĠS op", + "Ġdescript ive", + "Ph ase", + "ĠEd mund", + "Ġpneum onia", + "vent us", + "A mb", + "Ġlabor atories", + "ĠEx clusive", + "ug ar", + "W ere", + "Ġmalf unction", + "Ġhomosexual s", + "Ġ---- ---", + "un i", + "Ġturb ines", + "ĠEqu ity", + "D u", + "Ġmind ed", + "ĠR H", + "ĠBlack hawks", + "Ġfe ats", + "Ġ17 00", + "re pl", + "36 2", + "lad en", + "Ġindisp ensable", + "ly ss", + "tt i", + "Ġre el", + "Ġdiver ted", + "Ġlik eness", + "Ġsubscript ions", + "Ġfing ert", + "Ġfil thy", + "dest ruct", + "d raft", + "ĠBernard ino", + "l aunch", + "Ġper plex", + "ĠS UM", + "car b", + "Ġswe ater", + "ĠVent ure", + "ĠJ ag", + "ĠCele b", + "ĠV oters", + "Ġstead fast", + "Ġathlet ics", + "ĠHans on", + "ĠDr ac", + "Tr acker", + "Ġcomm end", + "ĠPres idency", + "ĠD ID", + "in formed", + "Ġweb page", + "P retty", + "Ġforce fully", + "ãĥĥ ãĤ¯", + "Ġrel ocation", + "Ġsat ire", + "â ī", + "ĠSunder land", + "æ Ħ", + "V oice", + "???? ????", + "Ġinform ant", + "Ġbow el", + "ĠUn iform", + "Ġ ...\"", + "Ġpur ge", + "Ġpic nic", + "ĠU mb", + "ĠU PDATE", + "ĠSapp hire", + "ĠSt all", + "le arn", + "Ġobject ively", + "Ġob liter", + "Ġlooph ole", + "Ġjour neys", + "Ġo mission", + "Pro s", + "ĠSid ney", + "pl oma", + "Ġspray ed", + "Ġg uru", + "Ġtra itor", + "Ġtim et", + "Ġsn apping", + "ĠSe vent", + "urn al", + "ĠUk ip", + "Ġb owed", + "por al", + "l iberal", + "R os", + "Quest ions", + "i OS", + "Ġsummar ize", + "ST AT", + "Ġ18 50", + "ap est", + "Ġl ender", + "ĠVari able", + "br inging", + "ĠL ORD", + ", )", + "Ġcollaps es", + "x iety", + "ĠN ed", + "Y D", + "ĠSch a", + "Ġantib ody", + "Ġdis band", + "y re", + "ill usion", + "Ġro ver", + "s hed", + "ĠHiro sh", + "cc i", + "Ġcal am", + "ĠMort on", + "P interest", + "Ġ19 28", + "ĠE uras", + "ord es", + "Ġf ences", + "ĠIn ventory", + "ĠVal encia", + "ĠU d", + "ĠT iff", + "Ġsqu e", + "Ġqu otation", + "Ġtroubles ome", + "er ker", + "QU EST", + "ĠKing doms", + "s outh", + "Ġle vy", + "Pr ince", + "ĠSt ing", + "Ġnick named", + "Ġapp e", + "Ġphot ographic", + "Ġcorp us", + "re ference", + "ĠT rog", + "U nt", + ") =(", + "ĠLat via", + "Ġactiv ating", + "Ġlicense e", + "Ġdispar ities", + "ĠNews letter", + "ãĥĥ ãĥĪ", + "Ġfree ing", + "ĠJe ep", + "ĠPer ception", + "ins k", + "Ġsil icone", + "ĠHay den", + "Le an", + "ĠSuz uki", + "ibr arian", + "66 8", + "Ġsp or", + "Ġcorrel ations", + "ag hetti", + "Ġtu ber", + "ĠIP CC", + "il us", + "ĠV u", + "Ġwealth iest", + "ĠCarb uncle", + "an za", + "Ġfool ed", + "ĠZ ur", + "Ġd addy", + "ran o", + "il ian", + "Ġknock out", + "f man", + "requ ired", + "ĠWik ileaks", + "ĠD uffy", + "ON T", + "Ġins ol", + "ĠObject s", + "Ġb ou", + "ĠNord ic", + "ĠIns ert", + "sc an", + "Ġd ancers", + "Ġid iots", + "major ity", + "ĠNev ille", + "ĠFree BSD", + "Ġt art", + "pan ic", + "69 0", + "Ġcoc oa", + "Ġsam pled", + "Ġlook up", + "Ind ust", + "Ġinject ions", + "gen re", + "Ġa u", + "Ġroad way", + "Ġgen itals", + "K ind", + "ĠEx aminer", + "ĠY az", + "F resh", + "Ġpar alysis", + "ĠAl uminum", + "Ġre ap", + "ok é", + "Ġsl oppy", + "ĠTun nel", + "pos ium", + "ner y", + "en ic", + "Ġher bal", + "ĠOut er", + "ĠBuild er", + "Ġinc ur", + "Ġide ologies", + "Ġback ups", + "cons uming", + "ĠDet ect", + "de ck", + "ĠKN OW", + "ĠG ret", + "ĠM IC", + "Ġtough ness", + "ĠEx hibit", + "Ġh ive", + "L es", + "ĠSCH OOL", + "ĠAt ari", + "ald e", + "ĠN ull", + "and estine", + "m ouse", + "Ġbrig ade", + "48 9", + "Ġrev ol", + "ĠLaw son", + "ĠW ah", + "op oly", + "eb ted", + "ĠS aunders", + "Ġ3 13", + "ĠW inc", + "Ġtab oo", + "ĠHel met", + "Ġw edge", + "ch ip", + "ĠT ina", + "b g", + "Ġinf uri", + "r n", + "Ġanomal ies", + "ĠSy nc", + "ĠEx am", + "ĠComm it", + "ĠDi ary", + "ĠALS O", + "ĠDe bor", + "omed ical", + "Ġcomprehens ion", + "6 55", + "Ġempower ing", + "Ġ ire", + "Ġju ices", + "ĠE TH", + "ĠBox ing", + "=\" /", + "Ġfacilit ated", + "p oke", + "ĠPars ons", + "ĠMod er", + "tra vel", + "Ġcivil izations", + "Ġliber tarians", + "Ġrun e", + "ĠCl arks", + "at hed", + "Ġcampaign ers", + "ĠDis patch", + "ĠFah renheit", + "ĠCap com", + "-------- --", + "Ġl ace", + "Ġdr aining", + "Ġl iner", + "ĠArt ificial", + "é n", + "t ask", + "] ).", + "ĠGM O", + "ĠOper ator", + "ord inary", + "ĠInf luence", + "ĠU ps", + "Ġpot ency", + "uss en", + "osp ons", + "ĠSw im", + "ĠDead line", + "Un ity", + "Ġcul inary", + "Ġenlight enment", + "Ġwe arer", + "Ġmin ed", + "Ġp ly", + "Ġinc est", + "ĠDVD s", + "W alk", + "B TC", + "Tr ade", + "Ġdev al", + "ib and", + "ĠOvers ight", + "Palest inian", + "Ġd art", + "Ġm ul", + "L R", + "Ġrem ovable", + "ĠReal ms", + "ì Ŀ", + "Ġmisc ar", + "ĠV ulkan", + "68 5", + "è re", + "ĠS ap", + "Ġmer ging", + "ĠCar ly", + "che ster", + "Ġbr isk", + "Ġlux urious", + "ĠGener ator", + "Ġbit terness", + "Ġed ible", + "Ġ24 3", + "T G", + "Ġrect angle", + "With No", + "bel ow", + "J enn", + "Ġdark est", + "Ġh itch", + "Ġdos age", + "Ġsc aven", + "ĠK eller", + "ĠIllust rated", + "Certain ly", + "ĠMaver icks", + "Marg inal", + "Ġdiarr hea", + "Ġenorm ously", + "Ġ9 99", + "sh r", + "qu art", + "Ġadam ant", + "ĠM ew", + "Ġren ovation", + "Ġcerv ical", + "ĠPercent age", + "en ers", + "ĠKim ber", + "Ġflo ats", + "Ġde x", + "ĠW itcher", + "ĠSwan sea", + "d m", + "Ġsal ty", + "y ellow", + "Ġca pe", + "ĠDr ain", + "ĠPaul a", + "ĠTol edo", + "les i", + "Mag azine", + "ĠW ick", + "ĠM n", + "ĠA ck", + "ĠR iding", + "AS ON", + "Ġhom ophobic", + "AR P", + "Ġwand ered", + "C PU", + "ood oo", + "ĠP ipe", + "Ġtight ening", + "ĠBut t", + "3 18", + "Ġdesert ed", + "S ession", + "Ġfacilit ating", + "J ump", + "Ġemer gencies", + "OW ER", + "Ġexhaust ive", + "ĠAF TER", + "Ġheart beat", + "ĠLab el", + "ack y", + "ĠCert ified", + "ilt ration", + "Z e", + "ĠU tt", + "Ġ13 00", + "Ġpres ume", + "ĠDis p", + "Ġsur ged", + "Ġdoll s", + "Col umb", + "Ġchim pan", + "ĠR azor", + "Ġt icks", + "Ġcouncill or", + "Ġpilgr image", + "ĠReb els", + "ĠQ C", + "ĠA uction", + "x ia", + "ik k", + "b red", + "Ġinsert ion", + "Ġco arse", + "d B", + "SE E", + "ĠZ ap", + "ĠF oo", + "Ġcontem por", + "ĠQuarter ly", + "ot ions", + "ĠAl chemist", + "ĠT rey", + "ĠDu o", + "S weet", + "80 4", + "ĠGi ov", + "Ġfun n", + "N in", + "h off", + "Ġram ifications", + "Ġ19 22", + "ĠExper ts", + "az es", + "Ġgar ments", + "ar ial", + "ĠN ab", + "Ġ25 7", + "ĠV ed", + "Ġhum orous", + "ĠPom pe", + "Ġn ylon", + "Ġlur king", + "ĠSerge y", + "ĠMatt is", + "Ġmisogyn y", + "ĠComp onents", + "ĠWatch ing", + "ĠF olk", + "ract ical", + "B ush", + "Ġt aped", + "Ġgroup ing", + "Ġbe ads", + "Ġ20 48", + "Ġcon du", + "quer que", + "Read ing", + "Ġgriev ances", + "Ult ra", + "Ġend point", + "H ig", + "ĠSt atic", + "ĠScar borough", + "L ua", + "ĠMess i", + "a qu", + "ĠPsy Net", + "ĠR udd", + "Ġa venue", + "v p", + "J er", + "Ġsh ady", + "ĠRes ist", + "ĠArt emis", + "Ġcare less", + "Ġbro kers", + "Ġtemper ament", + "Ġ5 20", + "T ags", + "ĠTurn ing", + "Ġut tered", + "Ġp edd", + "Ġimpro vised", + "Ġ: (", + "Ġtab l", + "Ġpl ains", + "16 00", + "press ure", + "ĠEss ence", + "marg in", + "friend s", + "ĠRest oration", + "Ġpoll ut", + "ĠPok er", + "ĠAugust ine", + "ĠC IS", + "ĠSE AL", + "or ama", + "Ġth wart", + "se ek", + "Ġp agan", + " º", + "cp u", + "Ġg arn", + "Ġass ortment", + "ĠI LCS", + "t ower", + "Recomm ended", + "Ġun born", + "ĠRandom Redditor", + "ĠRandomRedditor WithNo", + "Ġparaly zed", + "Ġeru ption", + "Ġinter sect", + "ĠSt oke", + "ĠS co", + "B ind", + "å ¾", + "ĠP NG", + "ĠNeg ative", + "ĠNO AA", + "Le on", + "Ġall oy", + "ĠL ama", + "ĠD iversity", + "5 75", + "Ġunderest imated", + "ĠSc or", + "Ġm ural", + "Ġb usted", + "so on", + "l if", + "Ġnone x", + "Ġall ergy", + "ĠUnder world", + "ĠR ays", + "ĠBl asio", + "Ġh rs", + "ĠD ir", + "Ġ3 27", + "by ter", + "Ġrepl acements", + "Ġactiv ates", + "ri ved", + "M H", + "Ġp ans", + "ĠH I", + "Ġlong itudinal", + "Ġnu isance", + "al er", + "Ġsw ell", + "ĠS igned", + "s ci", + "ĠIs les", + "ĠA GA", + "Ġdef iant", + "Ġson ic", + "oc on", + "K C", + "ĠA im", + "t ie", + "ah ah", + "Ġm L", + "D X", + "Ġb isc", + "ĠBill board", + "ĠSY STEM", + "NE Y", + "ga ard", + "Ġdist ressed", + "former ly", + "Al an", + "Ġche fs", + "Ġopt ics", + "ĠC omet", + "ĠAM C", + "Ġredes igned", + "irm ation", + "Ġsight ings", + "38 2", + "3 11", + "ĠW B", + "Ġcont raction", + "ĠT OTAL", + "D ual", + "Ġstart led", + "Ġunderstand ably", + "Ġsung lasses", + "ETH OD", + "Ġd ocker", + "Ġsurf ing", + "ĠH EL", + "ĠSl ack", + "ton es", + "Ġsh alt", + "Vis ual", + "49 8", + "Dep artment", + "c ussion", + "Ġunrest ricted", + "Ġt ad", + "Ġre name", + "employ ed", + "Ġeduc ating", + "Ġgrin ned", + "bed room", + "ĠActiv ities", + "ĠV elvet", + "ĠSW AT", + "Ġsh uffle", + "ig or", + "Ġsatur ation", + "F inding", + "c ream", + "ic ter", + "Ġv odka", + "tr acking", + "te c", + "Ġfore ground", + "iest a", + "Ġve hement", + "ĠEC B", + "ĠT ie", + "E y", + "Ġt urtles", + "ĠRail road", + "ĠKat z", + "ĠFram es", + "Ġmen ace", + "ĠFell owship", + "ĠEss ential", + "ugg ish", + "Ġdri p", + "ch witz", + "ĠKy oto", + "s b", + "ĠN ina", + "Param eter", + "Ġal arms", + "ĠCl aud", + "Ġpione ering", + "Ġchief ly", + "ĠSc ream", + "Col lection", + "Ġthank fully", + "ĠRonald o", + "åŃ IJ", + "st rip", + "ĠDisney land", + "com mercial", + "See ing", + "S oul", + "Ġevac uate", + "Ġc iv", + "ĠAs he", + "Ġdiv ides", + "ĠD agger", + "rehens ive", + "Ġber ries", + "ĠD F", + "Ġs ushi", + "Ġplur ality", + "W I", + "Ġdisadvant aged", + "Ġbatt alion", + "ob iles", + "45 1", + "Ġcl ing", + "Ġunden iable", + "ĠL ounge", + "Ġha unt", + "p he", + "Ġquant ify", + "Ġdiff ered", + "Ġ[* ]", + "ĠV iz", + "c um", + "sl ave", + "Ġvide og", + "Ġqu ar", + "Ġbund les", + "ĠAl onso", + "t ackle", + "Ġneur onal", + "Ġlandsl ide", + "conf irmed", + "ĠDep th", + "Ġrenew ables", + "B ear", + "ĠMaced onia", + "Ġjer seys", + "Ġb unk", + "ĠSp awn", + "ĠControl s", + "ĠBuch anan", + "Ġrobot ics", + "Ġemphas izing", + "ĠTut orial", + "h yp", + "ist on", + "Ġmonument al", + "æ °", + "ĠCar ry", + "Ġt bsp", + "en ance", + "H ill", + "art hed", + "Ġro tten", + "De an", + "Ġtw isting", + "Ġgood will", + "Ġimm ersion", + "L iving", + "Ġbr ushes", + "ĠC GI", + "ĠAt k", + "tr aditional", + "Ġph antom", + "ĠSt amina", + "Ġexpans ions", + "ĠMar in", + "Ġembark ed", + "ĠE g", + "int estinal", + "ĠPE OPLE", + "ĠBo oth", + "ĠApp alach", + "Ġreleg ated", + "V T", + "M IT", + "Ġmust er", + "Ġwithdraw ing", + "Ġmicrosc ope", + "ĠG athering", + "ĠC rescent", + "ĠArgent ine", + "ĠDec re", + "ĠDomin ic", + "Ġbud s", + "ant age", + "ĠI on", + "Ġwid ened", + "ONS ORED", + "ĠGl oves", + "iann opoulos", + "raz en", + "fe el", + "Ġrepay ment", + "Ġhind sight", + "ĠRE ALLY", + "ĠPist ol", + "ĠBra h", + "Ġwat ts", + "Ġsurv ives", + "Ġfl urry", + "iss y", + "Al ert", + "ĠUrug uay", + "Ph oenix", + "S low", + "ĠG rave", + "ĠF ir", + "Ġmanage able", + "Ġtar iff", + "ĠU DP", + "ĠPist ons", + "ĠNiger ian", + "Ġstrike outs", + "Ġcos metics", + "whel ming", + "f ab", + "c ape", + "pro xy", + "Ġre think", + "Ġover coming", + "sim ple", + "Ġw oo", + "Ġdistract ing", + "ĠSt anton", + "ĠTuls a", + "ĠD ock", + "65 9", + "Ġdisc ord", + "ĠEm acs", + "ĠV es", + "ĠR OB", + "Ġreass uring", + "Ġcons ortium", + "Muslim s", + "3 21", + "Ġprompt s", + "se i", + "ĠH itch", + "imp osed", + "ĠF ool", + "Ġindisc rim", + "wr ong", + "bu querque", + "D avis", + "! ]", + "Ġtim eless", + "ĠNE ED", + "Ġpestic ide", + "Ġrally ing", + "ĠCal der", + "Ġå ¤", + "Ġx p", + "ĠUn le", + "ĠEx port", + "lu aj", + "B uff", + ") [", + "Ġsq or", + "S audi", + "Ġis tg", + "Ġindul ge", + "pro c", + "Ġdisg usted", + "Ġcomp ounded", + "Ġn em", + "Ġschool ing", + "ĠC ure", + "process ing", + "S ol", + "Ġpro verb", + "it ized", + "ĠAlv arez", + "Ġscar f", + "Ġrect angular", + "re ve", + "Ġh ormonal", + "ĠSt ress", + "itiz en", + "Ġ4 25", + "girl s", + "ĠNo ir", + "ĠR app", + "Ġmar ches", + "ch urch", + "ĠUs es", + "Ġ40 5", + "ĠBer m", + "Ġord inances", + "ĠJud gment", + "Charg es", + "ĠZ in", + "Ġdust y", + "Ġstraw berries", + "Ġper ce", + "ĠTh ur", + "ĠDebor ah", + "net flix", + "ĠLam bert", + "Ġam used", + "ĠGu ang", + "Y OU", + "R GB", + "ĠC CTV", + "Ġf iat", + "r ang", + "Ġf ederation", + "ĠM ant", + "ĠB ust", + "ĠM are", + "respect ive", + "ĠM igration", + "ĠB IT", + "59 0", + "Ġpatriot ism", + "Ġout lining", + "reg ion", + "ĠJos é", + "Ġbl asting", + "ĠEz ra", + "B s", + "Ġundermin es", + "ĠSm ooth", + "Ġcl ashed", + "rad io", + "Ġtransition ing", + "ĠBucc aneers", + "ĠOw l", + "Ġplug s", + "Ġh iatus", + "ĠPin ball", + "Ġm ig", + "ĠNut r", + "ĠWolf e", + "Ġinteg ers", + "Ġor bits", + "ĠEd win", + "ĠDirect X", + "b ite", + "Ġbl azing", + "v r", + "Ed ge", + "ĠP ID", + "ex it", + "ĠCom ed", + "ĠPath finder", + "ĠGu id", + "ĠSign s", + "ĠZ er", + "ĠAg enda", + "Ġreimburse ment", + "M esh", + "i Phone", + "ĠMar cos", + "ĠS ites", + "h ate", + "en burg", + "Ġs ockets", + "p end", + "Bat man", + "v ir", + "ĠSH OW", + "Ġprovision al", + "con n", + "ĠDeath s", + "AT IVE", + "Pro file", + "sy m", + "J A", + "Ġnin ja", + "inst alled", + "id ates", + "eb ra", + "ĠOm aha", + "Ġse izing", + "ĠBe asts", + "Ġsal ts", + "M ission", + "Gener ally", + "ĠTr ilogy", + "he on", + "leg ates", + "Ġd ime", + "Ġf aire", + "par able", + "G raph", + "Ġtotal ing", + "Ġdiagram s", + "ĠYan uk", + "ple t", + "ĠMe h", + "Ġmyth ical", + "ĠStep hens", + "aut ical", + "ochem istry", + "Ġkil ograms", + "Ġel bows", + "anc ock", + "ĠB CE", + "ĠPr ague", + "Ġimpro v", + "ĠDev in", + "Ġ\" \\", + "par alle", + "Ġsuprem acists", + "ĠB illion", + "Ġreg imen", + "inn acle", + "Ġrequ isite", + "ang an", + "ĠBur lington", + "ain ment", + "ĠObject ive", + "oms ky", + "G V", + "Ġun ilateral", + "Ġt c", + "Ġh ires", + "ment al", + "Ġinvol untary", + "Ġtrans pl", + "ĠASC II", + " ¨", + "Ev ents", + "Ġdoub ted", + "ĠKa plan", + "ĠCour age", + "ig on", + "ĠMan aging", + "ĠT art", + "Ġfalse hood", + "ĠV iolet", + "Ġair s", + "Ġfertil izer", + "Brit ain", + "Ġaqu atic", + "ou f", + "W ords", + "ĠHart ford", + "Ġeven ings", + "ĠV engeance", + "qu ite", + "G all", + "ĠP ret", + "Ġp df", + "ĠL M", + "ĠSo chi", + "ĠInter cept", + "9 20", + "Ġprofit ability", + "ĠId le", + "ĠMac Donald", + "ĠEst ablishment", + "um sy", + "Ġgather ings", + "ĠN aj", + "Charl ie", + "Ġas cent", + "ĠProt ector", + "Ġal gebra", + "Ġbi os", + "for ums", + "EL S", + "Introdu ced", + "Ġ3 35", + "Ġastron omy", + "Cont ribut", + "ĠPol ic", + "Pl atform", + "Ġcontain ment", + "w rap", + "Ġcoron ary", + "ĠJ elly", + "man ager", + "Ġheart breaking", + "c air", + "ĠChe ro", + "c gi", + "Med ical", + "ĠAccount ability", + "! !\"", + "oph ile", + "Ġpsych otic", + "ĠRest rict", + "Ġequ itable", + "iss ues", + "Ġ19 05", + "ĠN ek", + "c ised", + "ĠTr acking", + "Ġo zone", + "Ġcook er", + "ros is", + "Ġre open", + "Ġinf inity", + "ĠPharm aceutical", + "ens ional", + "Att empt", + "ĠR ory", + "Mar co", + "Ġawa its", + "H OW", + "t reated", + "Ġbol st", + "Ġreve red", + "Ġp ods", + "opp ers", + "00 10", + "Ġampl itude", + "ric an", + "SP ONSORED", + "Ġtrou sers", + "Ġhal ves", + "ĠK aine", + "ĠCut ler", + "ĠA UTH", + "Ġsplend id", + "Ġprevent ive", + "ĠDud ley", + "if acts", + "umin ati", + "ĠY in", + "Ġad mon", + "ĠV ag", + "Ġin verted", + "Ġhast ily", + "ĠH ague", + "L yn", + "Ġled ger", + "Ġastron omical", + "get ting", + "Ġcirc a", + "ĠC ic", + "ĠTenn is", + "Lim ited", + "Ġd ru", + "ĠBY U", + "Ġtrave llers", + "Ġp ane", + "ĠInt ro", + "Ġpatient ly", + "Ġa iding", + "Ġlo os", + "ĠT ough", + "Ġ29 3", + "Ġconsum es", + "Source File", + "Ġ\"\" \"", + "Ġbond ing", + "Ġtil ted", + "Ġmenstru al", + "ĠCel estial", + "UL AR", + "Plug in", + "Ġrisk ing", + "N az", + "ĠRiy adh", + "Ġacc redited", + "Ġsk irm", + "é Ľ", + "Ġexam iner", + "Ġmess ing", + "Ġnear ing", + "ĠC hern", + "ĠBeck ham", + "Ġsw apped", + "Ġgo ose", + "K ay", + "Ġlo fty", + "ĠWal let", + "Ġ[ '", + "Ġap ocalypse", + "Ġb amboo", + "ĠSP ACE", + "ĠEl ena", + "Ġ30 6", + "ac ons", + "Ġtight ened", + "Ġadolesc ence", + "Ġrain y", + "Ġvandal ism", + "ĠNew town", + "Ġcon ject", + "c akes", + "Ġche ated", + "Ġmoder ators", + "par ams", + "E FF", + "Ġdece it", + "ĠST L", + "ĠTanz ania", + "ĠR I", + "Ġ19 23", + "ĠEx ile", + "the l", + "Ġthe olog", + "Ġquir ky", + "ĠIr vine", + "Ġneed y", + "or is", + "U m", + "K a", + "Ġmail box", + "3 22", + "Ġb os", + "ĠPet ra", + "K ING", + "Ġenlarg ed", + "O ften", + "Ġbad ass", + "Ġ3 43", + "ĠPl aces", + "ĠC AD", + "Ġpr istine", + "Ġinterven ing", + "d irection", + "Ġl az", + "ĠD SM", + "Ġproject ing", + "ĠF unk", + "ag og", + "pay ment", + "n ov", + "Ġch atter", + "AR B", + "Ġexam inations", + "ĠHouse hold", + "ĠG us", + "F ord", + "4 14", + "B oss", + "Ġmy stic", + "Ġle aps", + "ĠB av", + "ul z", + "b udget", + "Foot ball", + "Ġsubsid ized", + "Ġfirst hand", + "Ġcoinc ide", + "oc ular", + "Con n", + "ĠColl abor", + "Ġfool s", + "am ura", + "ah ar", + "r ists", + "Ġsw ollen", + "Ġexp ended", + "ĠP au", + "s up", + "Ġsp ar", + "Ġkey note", + "s uff", + "Ġunequ al", + "Ġprogress ing", + "str ings", + "ĠGamer gate", + "Dis ney", + "ĠEle ven", + "om nia", + "Ġscript ed", + "Ġear ners", + "bro ther", + "ĠEn abled", + "æ ³", + "Ġlar vae", + "ĠL OC", + "m ess", + "Wil son", + "ĠTem plate", + "success fully", + "Ġparam ount", + "Ġcamoufl age", + "Ġbind s", + "ĠQu iet", + "ĠSh utterstock", + "r ush", + "Ġmasc ot", + "fort une", + "ĠCol t", + "ĠBe yon", + "hab i", + "Ġha irc", + "Ġ26 7", + "ĠDe us", + "Ġtw itch", + "Ġconcent rating", + "Ġn ipples", + "c ible", + "Ġg ir", + "N Z", + "M ath", + "n ih", + "Requ ired", + "Ġp onder", + "ĠS AN", + "Ġwedd ings", + "Ġl oneliness", + "N ES", + "ĠMah jong", + "69 5", + "add le", + "ĠGar ner", + "ĠC OUR", + "Br idge", + "Ġsp ree", + "ĠCald well", + "Ġbri bery", + "Ġ���� ����", + "plug ins", + "Ġr acket", + "Ġchamp agne", + "vers ible", + "V ote", + "Ġmod ifiers", + "May or", + "6 80", + "Ġassemb lies", + "ĠS ultan", + "ĠN ing", + "ĠLad ies", + "Ġsulf ur", + "Ġor bs", + "Ġ---- -", + "____ ___", + "ĠJournal ism", + "Ġes ports", + "Ġl ush", + "Ġh ue", + "Ġspect ral", + "H onest", + "ãĥ ı", + "Ġbus hes", + "Ġrein forcement", + "Ġre opened", + "ĠWhe els", + "ĠM org", + "rie ving", + "Ġaux iliary", + "Ġj Query", + "ĠB AT", + "tes que", + "Ġver tex", + "p ure", + "f rey", + "ãĤ º", + "d os", + "Ġty ph", + "Ġc ull", + "Ġe q", + "Ġdec on", + "Ġtoss ing", + "Ġdispar ate", + "ĠBr igham", + "print f", + "led ged", + "Ġsu nd", + "Ġco zy", + "Ġhepat itis", + "per forming", + "Ġav al", + "ĠG G", + "f uture", + "Ġpet ertodd", + "ĠKos ovo", + "Ġmagn ets", + "Al ready", + "ĠEd ison", + "ĠCe res", + "ĠRA ID", + "Ġbrill iance", + "57 6", + "Ġder ives", + "Ġhypert ension", + "ĠÎ Ķ", + "Ġlamb da", + "Ġfl air", + "Ġmission aries", + "Ġrap es", + "ĠSt arter", + "ĠMon ths", + "Ġdef y", + "Ġseism ic", + "ĠR aphael", + "Ġeuro zone", + "65 6", + "z sche", + "Ġscr atched", + "Ġb ows", + "ĠLenn on", + "ĠGa ia", + "Ġdri pping", + "f acts", + "A le", + "Ġfrog s", + "ĠBre ast", + "ogene ity", + "ĠProsecut or", + "Ġampl ified", + "ĠHod g", + "ĠF n", + "Th ousands", + "ĠNI H", + "ĠMonitor ing", + "FT WARE", + "ĠPri ebus", + "ĠG rowing", + "hun ter", + "Ġdiagn ose", + "ĠM ald", + "ĠL R", + "Ġcrown ed", + "Ġburst ing", + "Ġdiss olution", + "j avascript", + "Ġuseful ness", + "ĠExec ution", + ": (", + "ĠIv ory", + "a ah", + "Ġpersecut ed", + "viol ence", + "ist as", + "ĠCr ate", + "Ġimpuls es", + "ĠSp ani", + "ed es", + "Hand le", + "ĠZ erg", + "think able", + "Last ly", + "Ġspont aneously", + "Ġinconven ient", + "Ġdismiss ing", + "Ġpl otted", + "Ġeight y", + "Ġ7 37", + "r ish", + "ĠThor nton", + "ath am", + "Ġsit com", + "V en", + "Rec ipe", + "t el", + "l und", + "Ġcle ars", + "ĠSas uke", + "Ġ25 8", + "Ġopt ing", + "Ġen raged", + "est hetic", + "ĠA e", + "uch s", + "Pre p", + "Fl ow", + "Ġrun off", + "ĠE ating", + "ĠG iles", + "ĠAct ing", + "res ources", + "ib aba", + "Ġr pm", + "Ġske wed", + "ĠBl anc", + "ĠS akuya", + "Ġhot ter", + "Ġ19 24", + "op ian", + "ck o", + "Ġcr umbling", + "Ġcapt ains", + "ĠAppropri ations", + "le aders", + "dro pping", + "an uts", + "Ġrevers ing", + "ĠP ose", + "ĠS ek", + "Sc ot", + "ĠIde a", + "c ise", + "ĠSloven ia", + "Ġ3 17", + "Do ctor", + "Ġcro cod", + "ald i", + "Se a", + "ĠFar rell", + "Ġmerc enaries", + "ĠR NC", + "ĠGu ess", + "Ġp acing", + "M achine", + "Streamer Bot", + "ĠChar ity", + "Ġ29 8", + "Ġcann ons", + "ĠTob y", + "TPP StreamerBot", + "ĠPass ion", + "cf g", + "Th om", + "Ġbad ges", + "ĠBern stein", + ". âĢĵ", + "ĠP OP", + "ĠCon j", + "Ġinitial ization", + "Ġbiod iversity", + "D ub", + "Ġfeud al", + "Ġdisclaim er", + "Ġc row", + "Ġign ition", + "ar f", + "S HA", + "Ġk Hz", + "h azard", + "ĠArt ists", + "oe uv", + "67 9", + "ĠRud y", + "N ine", + "ĠRam adan", + "å ½", + "itt o", + "Ġadren aline", + "C ert", + "Ġsmell ed", + "Ġimp unity", + "Ġag endas", + "ĠRe born", + "ĠCon cent", + "ĠSe ems", + "Ġo mega", + "ĠDust in", + "Ġback er", + "ĠSau ce", + "ĠBoy le", + "W IN", + "Ġsp ins", + "Ġpa uses", + "u pt", + "Ġshred ded", + "Ġstra pped", + "ĠCor ruption", + "Ġscr atches", + "Ġn i", + "Ġatt ire", + "ĠS AF", + "Factory Reloaded", + "ĠI PS", + "Ġ( %", + "Ġsem inar", + "f ocus", + "c ivil", + "Ġ18 60", + "int osh", + "Ġcontin ual", + "Ġabbre vi", + "ĠS ok", + "oc obo", + "X M", + "Ġfr antic", + "Ġunavoid able", + "Ġar tery", + "Ġannot ations", + "b ath", + "Cl imate", + "Ġd ors", + "ĠSl ide", + "co ord", + "ĠRel oad", + "ĠL DL", + "ĠLove craft", + "Ġunim agin", + "Ġresemb led", + "Ġbarr acks", + "n p", + "Ġsurrog ate", + "Ġcategor ized", + "ãĤ ©", + "Ġvacc inated", + "Ġdrain age", + "Ġind ist", + "ĠWhats App", + "Ġ18 70", + "oler ance", + "inv oke", + "am orph", + "Ġrecon nect", + "Ġem anc", + "Ġblind ness", + "Ġ12 80", + "intern et", + "c ollar", + "Ġalt ru", + "Ġab yss", + "ĠT RI", + "65 7", + "Ġinf used", + "HE AD", + "Ġforest ry", + "ĠWood y", + "ĠC i", + "w i", + "s am", + "78 4", + "hol iday", + "Ġmog ul", + "ĠF ees", + "ĠD EN", + "In ternal", + "ur bed", + "f usc", + "at om", + "ĠIll usion", + "Ġpoll ed", + "Ġfl ap", + "Ġco ax", + "L GBT", + "An aly", + "ĠSect ions", + "ĠCalif orn", + "em n", + "Ġh ither", + "ĠN IGHT", + "Ġn ailed", + "ĠPip eline", + "39 1", + "o of", + "ĠPr imal", + "vere nd", + "Ġsl ashing", + "Ġret ri", + "avi our", + "Ġdepart ing", + "g il", + "IS C", + "Ġmid way", + "Ġultras ound", + "Ġbeh aving", + "ĠT ara", + "class es", + "V irtual", + "ĠColon ial", + "Ġstri pping", + "Ġorchestr ated", + "ĠGra ves", + "45 2", + "ĠIron ically", + "ĠWrit ers", + "Ġl ends", + "ĠMan z", + "Ġra ven", + "Ġoxid ative", + "Ġ26 6", + "EL F", + "act ually", + "asc ar", + "D raft", + "Ġfavour able", + "Ġhumili ating", + "Ġf idelity", + "ĠH of", + "ĠX uan", + "49 6", + "Ġlay ered", + "at is", + "79 0", + "Ġpay check", + "it on", + "K ar", + "ĠVM ware", + "ĠFar mer", + "Ġserv ic", + "gl omer", + "Ġsl ump", + "ĠFab ric", + "ĠD OC", + "est ing", + "Ġreass ure", + "Ġph yl", + "v olt", + "it ory", + "R ules", + "Ġoxid ation", + "Ġpri zed", + "Ġmist ress", + "ĠDj ango", + "WAR N", + "å ij", + "Ġenc ode", + "ĠFeed back", + "Ġstupid ity", + "I an", + "ĠYugoslav ia", + "× ¨", + "ac l", + "UT E", + "19 77", + "Ġqual ifies", + "Ġpuls es", + "pret ty", + "Ġfro ze", + "Ġs s", + "Iter ator", + "Ġur gently", + "Ġm ailed", + "ĠCh am", + "Ġsust aining", + "Ġbas il", + "Ġpupp ies", + "il ant", + "ĠP LEASE", + "l ap", + "ace ous", + "F ear", + "ĠMaster y", + "aut omatic", + "ĠT AG", + "Ġant im", + "ag les", + "47 3", + "fram es", + "Ġwh ispers", + "ĠWho ever", + "Ġbra very", + "ĠUK IP", + "ract ions", + "\"\" \"", + "Ġt ame", + "Ġpart ed", + "every thing", + "CON T", + "Ġind ebted", + "Ġadd r", + "re k", + "IR ED", + "Ġem inent", + "cl inton", + "Ġo usted", + "Ġreview er", + "Ġmelt down", + "Ġre arr", + "ĠY ao", + "the real", + "aby te", + "Ġst umbling", + "Ġbat ches", + "Ġ25 9", + "Ġcontrace ptive", + "Ġprost itute", + "ens is", + "De cl", + "ĠSt rikes", + "M ilitary", + "ĠO ath", + "v acc", + "pp ings", + "05 2", + "Ġpart Name", + "amp ing", + "Rep orts", + "K I", + "CH R", + "Ġsubt ly", + "sw ers", + "Bl ake", + "us ual", + "Ġcontest ants", + "Ġcart ridges", + "ĠGRE AT", + "Ġbl ush", + "ĠâĢ º", + "47 2", + "Ġreason ed", + "ãĥ ¤", + "paralle led", + "Ġd yn", + "ag ate", + "Ġnight ly", + "å Ĩ", + "55 6", + "Ġsem antic", + "ĠAdv oc", + "Ġ !!", + "Ġdisag rees", + "ĠB W", + "V eh", + "Ġharm ing", + "Ġembr aces", + "Ġstri ves", + "Ġin land", + "ĠK ard", + "Ġhe ats", + "ĠGin ny", + "ut an", + "ern aut", + "yl ene", + "ĠE lev", + "J D", + "Ġh ars", + "ĠStar r", + "Ġsk ysc", + "Ġcollabor ators", + "Us ually", + "Ġrev olutions", + "ĠSTAT S", + "Ġdism antle", + "Ġconfident ly", + "Ġkin etic", + "Al i", + "Ġpercent ile", + "Ġextract ing", + "ill ian", + "est ead", + "Ġphysic ists", + "ĠMarsh al", + "Ġfell owship", + "Ġd ashed", + "ĠU R", + "ĠSi oux", + "ĠComp act", + "am ide", + "P ython", + "ĠLe igh", + "ĠPharm ac", + "ist rates", + "her ical", + "Ġf ue", + "ĠE min", + "Ġ( {", + "ĠNeighbor hood", + "Ġdisrupt ing", + "ĠD up", + "Ġg land", + "ĠSe v", + "ĠMar ian", + "arg on", + "ĠD und", + "Ġ< !--", + "Ġstr and", + "Ġstadium s", + "z os", + "Ġpsych osis", + "ĠR ack", + "Ġbrilliant ly", + "ï¸ ı", + "Ġsubmer ged", + "ĠInst it", + "ĠCh ow", + "Ġc ages", + "ĠH ats", + "ĠU rs", + "Ġdil uted", + "us at", + "ien ne", + "ĠMembers hip", + "ĠBur k", + "Ġ ie", + "Ġarche type", + "D rug", + "ult on", + "ĠSp ock", + "ĠMcK ay", + "ĠDep end", + "F eatured", + "S oc", + "19 78", + "ĠB ere", + "Ġrelent lessly", + "Ġcripp ling", + "Ġar thritis", + "çĶ Ł", + "ĠTrop ical", + "ĠBul g", + "ĠCher yl", + "Ġadm irable", + "Ġsub title", + "Over ride", + "Ġorig inating", + "ĠC CP", + "Ġsw ore", + "ĠSo le", + "ĠDis orders", + "3 29", + "Ġprocess ion", + "Ġref urb", + "Ġimm ersed", + "requ ently", + "Ġskept ics", + "Ġcer amic", + "m itter", + "en stein", + "b elt", + "ĠT IT", + "b idden", + "Ġf ir", + "m ist", + "> ]", + "Ġwe ave", + "ĠParad ox", + "Ġentr usted", + "ĠBarcl ays", + "Ġnovel ist", + "og ie", + "80 6", + "Ġnin ety", + "Ġdisag reements", + "@@@@ @@@@", + "ĠAus chwitz", + "c ars", + "ĠL ET", + "t ub", + "arant ine", + "P OS", + "Ġback story", + "Ġcheer ful", + "ĠR ag", + "ek a", + "bi ased", + "Ġinexper ienced", + "ak ra", + "ĠW itt", + "t an", + "Ġrap ist", + "Ġplate au", + "ch al", + "ĠInqu is", + "exp ression", + "Ġc ipher", + "Ġsh aving", + "add en", + "re ly", + "( \\", + "ism a", + "ĠReg ulatory", + "CH AR", + "ily n", + "N VIDIA", + "G U", + "Ġmur m", + "la us", + "Christ opher", + "Ġcontract ual", + "ĠPro xy", + "ĠJa ime", + "ĠMethod ist", + "Ġstew ards", + "st a", + "per ia", + "Ġphys iology", + "Ġbump ed", + "Ġf ructose", + "Austral ian", + "ĠMet allic", + "ĠMas querade", + "ar b", + "Ġprom ul", + "Ġdown fall", + "Ġbut cher", + "Ġb our", + "ĠIN FORMATION", + "ĠB is", + "pect s", + "ad ena", + "Ġcontempl ating", + "ar oo", + "cent ered", + "ĠPe aks", + "Us ed", + "Ġmod em", + "Ġg enders", + "Ġ8 000", + "37 1", + "Ġm aternity", + "ĠR az", + "Ġrock ing", + "Ġhandgun s", + "ĠD ACA", + "Aut om", + "ĠN ile", + "Ġtum ult", + "ĠBenef it", + "ĠAppro ach", + "works hop", + "ĠLe aving", + "G er", + "inst ead", + "Ġvibr ations", + "Ġrep ositories", + "49 7", + "ĠA unt", + "ĠJ ub", + "ĠExp edition", + "Al pha", + "Ġs ans", + "Ġoverd ue", + "Ġoverc rowd", + "Ġlegisl atures", + "Ġp aternal", + "ĠLeon ardo", + "Ġexp ressive", + "Ġdistract ions", + "Ġsil enced", + "tr ust", + "Ġb iking", + "Ġ5 60", + "Ġpropri et", + "Ġimp osition", + "Ġcon glomer", + "Ġ= ================================================================", + "ĠTe aching", + "ĠY ose", + "int ensive", + "T own", + "Ġtroll ing", + "ĠGr ac", + "ĠAS US", + "Y o", + "Ġspecial s", + "ĠNep h", + "ĠGod zilla", + "Dat abase", + "ĠHe gel", + "Ġ27 2", + "19 76", + "ĠGl oria", + "Ġdis emb", + "ĠInvestig ations", + "ĠB ane", + "ag ements", + "St range", + "Ġtre asury", + "ĠPl ays", + "Ġundes irable", + "Ġwid ening", + "Ġverb ally", + "Ġinf ancy", + "Ġcut ter", + "f ml", + "Ġ21 00", + "prot otype", + "f ine", + "Ġdec riminal", + "Ġdysfunction al", + "Ġbes ie", + "ĠErn st", + "z eb", + "Ġnort heastern", + "Ġa ust", + "por ate", + "ĠMar lins", + "Ġsegreg ated", + "ew orld", + "ĠMa her", + "Ġtra verse", + "Ġmon astery", + "ur gy", + "G ear", + "s and", + "Com pl", + "ĠE MP", + "Ġpl ent", + "ĠMer cer", + "Ġ27 6", + "TA BLE", + "Config uration", + "H undreds", + "Ġpr ic", + "Ġcollabor ating", + "ĠPar amount", + "ĠCumm ings", + "Ġ( <", + "Ġrecord er", + "Ġfl ats", + "Ġ4 16", + "wh ose", + "Font Size", + "ĠOr bit", + "Y R", + "Ġwr ists", + "Ġb akery", + ") }", + "ĠB ounty", + "ĠLanc aster", + "Ġend ings", + "acc ording", + "ĠSal am", + "e asy", + "75 5", + "ĠBur r", + "ĠBarn ett", + "onom ous", + "Un ion", + "Ġpreced ence", + "ĠScholars hip", + "ĠU X", + "Ġroll out", + "Ġbo on", + "al m", + "ĠCan ter", + "æ µ", + "Ġround ing", + "Ġcl ad", + "Ġv ap", + "ĠF eatured", + "is ations", + "Ġ5 40", + "pol ice", + "Ġunsett ling", + "Ġdr ifting", + "ĠLum ia", + "ĠObama Care", + "ĠF avor", + "Hy per", + "ĠRoth schild", + "ĠMil iband", + "an aly", + "ĠJul iet", + "H u", + "Ġrec alling", + "a head", + "69 6", + "Ġunf avorable", + "Ġd ances", + "O x", + "Ġleg ality", + "Ġ40 3", + "rom ancer", + "Ġinqu ire", + "ĠM oves", + "\\ \">", + "ĠVari ant", + "ĠMess iah", + "ĠL CS", + "ĠBah á", + "75 6", + "Ġeyeb row", + "Ġ ¥", + "ĠMc F", + "ĠFort y", + "M as", + "Ġpan icked", + "Ġtransform ations", + "q q", + "Ġrev olves", + "ring e", + "ĠA i", + "ax e", + "Ġon ward", + "ĠC FR", + "ĠB are", + "log in", + "Ġliqu ids", + "Ġde comp", + "second ary", + "il an", + "ĠCon vert", + "ami ya", + "Ġprosecut ing", + "Ġâī ¡", + "ĠYork ers", + "ĠByr ne", + "sl ow", + "aw ei", + "J ean", + "Ġ26 9", + "ĠSky dragon", + "Ġ é", + "ĠNicarag ua", + "ĠHuck abee", + "ĠHigh ly", + "Ġamph ib", + "ĠPast or", + "ĠL ets", + "Ġbl urred", + "Ġvisc eral", + "ĠC BO", + "Ġcollabor ated", + "z ig", + "Leg al", + "Ġapart heid", + "Ġbr id", + "Ġpres et", + "ĠD ET", + "ĠAM A", + "× Ķ", + "arch ing", + "auc uses", + "build er", + "Ġpo etic", + "Ġem ulator", + "ĠMole cular", + "Ġhon oring", + "ise um", + "Ġtract or", + "ĠCl uster", + "ĠCal m", + "ared evil", + "Ġsidew alks", + "Ġviol in", + "Ġgeneral ized", + "ĠAle c", + "Ġemb argo", + "Ġfast ball", + "ĠHT TPS", + "ĠL ack", + "ĠCh ill", + "ri ver", + "C hel", + "ĠSw arm", + "ĠLev ine", + "ro ying", + "L aunch", + "Ġkick er", + "Ġadd itive", + "ĠDe als", + "W idget", + "cont aining", + "Ġescal ate", + "ĠOP EN", + "Ġtwe aked", + "Ġst ash", + "Ġsp arks", + "ĠEs sex", + "ĠE cc", + "Ġconv ict", + "Ġblog ging", + "I ER", + "ĠH L", + "Ġmurd erers", + "75 9", + "ĠH ib", + "Ġde pl", + "ĠJ ord", + "S ac", + "Ġdis sect", + "ĠHow e", + "os her", + "Ġcustom izable", + "ĠFran z", + "Ġat ro", + "Ä ĩ", + "Ġ000 4", + "Ġout post", + "R oss", + "Ġglyph osate", + "ĠHast ings", + "ĠBE FORE", + "Ġsh ove", + "o pped", + "ĠSc ala", + "Ġam ulet", + "an ian", + "Ġexacerb ated", + "Ġe ater", + "47 1", + "UM E", + "Ġpul p", + "izont al", + "ĠZ am", + "ĠAT I", + "imm une", + "aby tes", + "Ġunnecess arily", + "ĠC AT", + "ĠAx is", + "Ġvisual ize", + "à ī", + "ĠRad ical", + "f m", + "Doc uments", + "ĠFor rest", + "Ġcontext ual", + "ĠSy mbol", + "Ġtent ative", + "ĠDO ES", + "ĠGood s", + "Ġintermitt ent", + "} :", + "medi ated", + "Ġridic ule", + "Ġathe ism", + "Ġpath ogens", + "ĠM um", + "Ġre introdu", + "Ġ30 7", + "i HUD", + "Ġflash light", + "Ġsw earing", + "Ġp engu", + "B u", + "Ġrot ated", + "ĠCr ane", + "Ġ() );", + "Ġfashion able", + "Ġendors ing", + "46 3", + ") [", + "Ġingest ion", + "Ġcook s", + "Ġ9 50", + "ot omy", + "ĠIm am", + "Ġk a", + "Ġte aser", + "ĠGhost s", + "ĠãĤ µ", + "19 69", + "Ï ĥ", + "ub by", + "Ġconver ter", + "zan ne", + "end e", + "ĠPre par", + "ĠNic kel", + "ĠChim era", + "h im", + "ĠTyr ann", + "ĠSabb ath", + "ĠNich ols", + "Ġra pt", + "ih ar", + "Ġshe lling", + "Ġillum inate", + "Ġdent ist", + "ut or", + "ĠInteg ration", + "Ġwh ims", + "ĠLiter ary", + "Be aut", + "Ġp archment", + "ag ara", + "Br and", + "Ġder og", + "âĢ¦ )", + "ĠNor se", + "Ġunw itting", + "Ġc uc", + "Ġborder line", + "Ġupset ting", + "Ġrec ourse", + "Ġd raped", + "ĠRad ar", + "Ġcold er", + "ĠPep si", + "im inary", + "], [", + "65 8", + "V i", + "ĠF rem", + "ĠP es", + "Ġveter inary", + "ĠT ED", + "ĠEp idem", + "n ova", + "k id", + "Ġdev out", + "o ct", + "j ad", + "M oh", + "ĠP AY", + "Ġge ometric", + "Ġ3 23", + "Ġcircum ference", + "ich ick", + "19 75", + "ĠY uri", + "ĠSh all", + "ĠH over", + "un in", + "S pr", + "Ġg raft", + "ĠHapp iness", + "Ġdisadvant ages", + "att acks", + "Ġhub s", + "ĠStar Craft", + "é ĸ", + "Ġgall eries", + "ĠKor ra", + "Ġgrocer ies", + "ĠGors uch", + "Ġrap ists", + "Ġfun gi", + "ĠTyph oon", + "V ector", + "ĠEm press", + "b attle", + "4 68", + "Ġparas ite", + "ĠBom ber", + "S G", + "ex ist", + "ĠP f", + "Ġun se", + "Ġsurge ons", + "B irth", + "ĠUn sure", + "ĠPrint ed", + "ĠBehavior al", + "ĠA ster", + "Pak istan", + "Ġun ethical", + "Ġs v", + "ĠIo T", + "Ġlay outs", + "P ain", + "Ġconst ants", + "ĠL W", + "ĠB ake", + "Ġtow els", + "Ġdeterior ation", + "ĠBol ivia", + "Ġblind ed", + "ĠW arden", + "ĠMist ress", + "Ġon stage", + "Ġcl ans", + "ĠB EST", + "19 60", + "Ġant ique", + "Ġrhet orical", + "ĠPer cy", + "ĠRw anda", + ", .", + "B ruce", + "Ġtra umat", + "ĠParliament ary", + "Ġfoot note", + "id ia", + "ĠLear ned", + "se eking", + "gen ic", + "Ġdim ensional", + "H ide", + "èĢ ħ", + "Ġintrig ue", + "in se", + "Ġle ases", + "Ġapp rentices", + "w ashing", + "Ġ19 26", + "V ILLE", + "Ġsw oop", + "s cl", + "Ġbed rooms", + "on ics", + "ĠCr unch", + "comp atible", + "Ġincap ac", + "ĠYemen i", + "ash tra", + "z hou", + "d anger", + "Ġmanifest ations", + "ĠDem ons", + "AA F", + "Secret ary", + "ACT ED", + "L OD", + "Ġam y", + "ra per", + "eth nic", + "4 17", + "Ġpos itives", + "Ġ27 3", + "ĠRefuge es", + "Ġus b", + "ĠV ald", + "odd y", + "ĠMahm oud", + "As ia", + "Ġskull s", + "ĠEx odus", + "ĠComp et", + "ĠL IC", + "ĠM ansion", + "ĠA me", + "Ġconsolid ate", + "storm s", + "ont ent", + "99 6", + "Ġcl en", + "Ġm ummy", + "fl at", + "75 8", + "ĠV OL", + "oter ic", + "n en", + "ĠMin ute", + "S ov", + "Ġfin er", + "R h", + "ly cer", + "Ġreinforce ments", + "ĠJohann es", + "ĠGall agher", + "Ġgym n", + "S uddenly", + "Ġext ortion", + "k r", + "i ator", + "T a", + "Ġhippocamp us", + "N PR", + "ĠComput ing", + "Ġsquare ly", + "Ġmod elling", + "ĠFor ums", + "ĠL isp", + "ĠKrish na", + "Ġ3 24", + "Ġr ushes", + "Ġens ued", + "Ġcre eping", + "on te", + "n ai", + "il ater", + "ĠHorn ets", + "Ġob livious", + "IN ST", + "55 9", + "Ġjeopard y", + "Ġdistingu ishing", + "j ured", + "Ġbeg s", + "sim ilar", + "ph ot", + "5 30", + "ĠPark way", + "Ġs inks", + "ĠHearth stone", + "ib ur", + "ĠBat on", + "Av oid", + "Ġd ancer", + "Ġmag istrate", + "ary n", + "Ġdisturb ances", + "ĠRom ero", + "Ġpar aph", + "Ġmis chief", + "âĸ ĵ", + "ĠSh aria", + "Ġur inary", + "r oute", + "iv as", + "f itted", + "Ġeject ed", + "ĠAl buquerque", + "Ġ4 70", + "Ġirrit ated", + "ĠZ ip", + "ĠB iol", + "à į", + "Ġden ounce", + "Ġbin aries", + "ĠVer se", + "Ġopp os", + "ĠKend rick", + "ĠG PL", + "Ġsp ew", + "ĠEl ijah", + "ĠE as", + "Ġdr ifted", + "so far", + "Ġannoy ance", + "ĠB ET", + "47 4", + "ĠSt rongh", + "it ates", + "ĠCogn itive", + "oph one", + "ĠIdent ification", + "ocr ine", + "connect ion", + "Ġbox er", + "ĠAS D", + "ĠAre as", + "Y ang", + "t ch", + "ull ah", + "Ġdece ive", + "Comb at", + "ep isode", + "cre te", + "W itness", + "Ġcondol ences", + "ht ar", + "Ġhe als", + "Ġbuck ets", + "ĠLA W", + "B lu", + "Ġsl ab", + "ĠOR DER", + "oc l", + "att on", + "ĠSteven son", + "ĠG inger", + "ĠFriend ly", + "ĠVander bilt", + "sp irit", + "ig l", + "ĠReg arding", + "ĠPR OG", + "Ġse aling", + "start ing", + "Ġcard inal", + "ĠV ec", + "ĠBe ir", + "Ġmillisec onds", + "we ak", + "per se", + "Ġster ile", + "ĠCont emporary", + "ĠPh ant", + "ĠCl o", + "Ġout p", + "Ġex iled", + "Ġ27 7", + "Ġself ie", + "Ġman ic", + "Ġn ano", + "ter ms", + "Alex ander", + "Ġres olves", + "Ġmillenn ia", + "Ġexpl odes", + "Ġconst ellation", + "Ġadul tery", + "m otion", + "D OC", + "Ġbroad casters", + "Ġkinderg arten", + "ĠMay weather", + "ĠE co", + "ich o", + "Ġ28 7", + "l aun", + "Ġm ute", + "Ġdisc reet", + "Ġpres chool", + "Ġpre empt", + "De lete", + "ĠFre ed", + "P i", + "H K", + "Ġblock er", + "ĠC umber", + "Ġw rought", + "d ating", + "Ġins urer", + "Ġquot as", + "Ġpre ached", + "Ġev iction", + "ĠReg ina", + "ĠP ens", + "Ġsevent een", + "ĠN ass", + "D ick", + "Ġfold s", + "Ġd otted", + "ĠA ad", + "Un iversal", + "Ġp izz", + "ĠG uru", + "Ġso ils", + "Ġno vice", + "ĠNe ander", + "Ġst ool", + "Ġdeton ated", + "ĠPik achu", + "ĠMass ive", + "IV ER", + "ĠAb del", + "Ġsubdu ed", + "Ġtall est", + "Ġprec arious", + "Ġa y", + "r ification", + "ĠOb j", + "c ale", + "Ġun question", + "cul osis", + "ad as", + "igr ated", + "D ays", + "Ġque ens", + "ĠGaz ette", + "ĠCol our", + "ĠBow man", + "ĠJ J", + "ï ve", + "Ġdomin ates", + "Stud ent", + "Ġm u", + "Ġback log", + "ĠElect ro", + "Tr uth", + "48 3", + "Ġcond ensed", + "r ules", + "ĠCons piracy", + "Ġacron ym", + "hand led", + "ĠMat te", + "j ri", + "ĠImp ossible", + "l ude", + "cre ation", + "Ġwar med", + "ĠSl ave", + "Ġmis led", + "Ġfer ment", + "ĠK ah", + "ink i", + "ke leton", + "cy l", + "ĠKar in", + "Hun ter", + "Reg ister", + "ĠSur rey", + "Ġst ares", + "ĠW idth", + "ĠN ay", + "ĠSk i", + "Ġblack list", + "uck et", + "Ġexp ulsion", + "im et", + "Ġret weet", + "vant age", + "Fe ature", + "Ġtro opers", + "Ġhom ers", + "9 69", + "Ġconting ency", + "ĠW TC", + "ĠBrew er", + "fore ign", + "W are", + "S olar", + "Ġund ue", + "RE C", + "ulner able", + "path ic", + "ĠBo ise", + "Ġ3 22", + "Ġarous ed", + "ĠY ing", + "ä¸ į", + "uel ess", + "Ġp as", + "Ġmor p", + "Ġfl oral", + "Ex press", + "ud ging", + "k B", + "ĠGr anted", + "Ø ¯", + "ĠMich a", + "ĠGoth ic", + "ĠSPEC IAL", + "ĠRic ardo", + "F ran", + "Ġadminister ing", + "6 20", + "por a", + "Ġ ®", + "Ġcomprom ises", + "Ġb itten", + "Ac cept", + "Th irty", + "Ð ²", + "Ġmater ially", + "ĠTer r", + "ig matic", + "ch ains", + "Ġdo ve", + "stad t", + "Mar vel", + "FA ULT", + "Ġwind shield", + "Ġ3 36", + "ad ier", + "Ġsw apping", + "Ġflaw less", + "ĠPred ator", + "ĠMiche le", + "Ġprop ulsion", + "ĠPsych ic", + "Ġassign ing", + "Ġfabric ation", + "Ġbar ley", + "l ust", + "Ġtow ering", + "Ġalter cation", + "ĠBent ley", + "Sp here", + "Ġtun a", + "ĠClass es", + "Fre edom", + "un er", + "L ady", + "v oice", + "Ġcool est", + "or r", + "Ġpal p", + "$ {", + "Ġhyster ia", + "ĠMet atron", + "p ants", + "Ġspawn ing", + "Exper ts", + "ĠInvest ors", + "ĠAn archy", + "Ġshr unk", + "ĠVict im", + "Ġ28 9", + "Ġec stasy", + "ĠB inding", + "58 5", + "ĠMel ody", + "57 8", + "ot ally", + "ĠE tsy", + "lig a", + "Ġapplaud ed", + "Ġswe ating", + "Ġredist ributed", + "Ġpop corn", + "Ġsem inal", + "f ur", + "ĠNeuro science", + "R and", + "ĠO st", + "ĠMadd en", + "ĠIncre asing", + "ĠDaw kins", + "ĠSub way", + "Ġar sen", + "cons erv", + "B UR", + "Ġsp iked", + "ĠLy ft", + "ĠImper ium", + "ĠDrop box", + "Ġfav oured", + "Ġencomp asses", + "gh ost", + "Ġins pires", + "Ġbur geoning", + "ĠY oshi", + "ĠVert ical", + "ĠAud itor", + "Ġint ending", + "Ġfilib uster", + "Bl oom", + "f ac", + "ĠCav s", + "ign ing", + "Ġcowork ers", + "ĠBarb arian", + "rem ember", + "FL AG", + "Ġaudit ory", + "ason ry", + "Col lege", + "Ġmut ed", + "gem ony", + "ob in", + "ĠPsych o", + "9 68", + "Ġlav ish", + "Ġhierarch ical", + "ĠDr one", + "ou k", + "Ġcripp led", + "ĠMax im", + "Sl ot", + "Ġqu iz", + "ĠV id", + "if ling", + "Ġarchae ologists", + "Ġabandon ment", + "d ial", + "le on", + "ĠF as", + "T ed", + "Ġr aspberry", + "Ġmaneu vers", + "Ġbehavi ours", + "Ġins ure", + "Ġrem od", + "Sw itch", + "h oe", + "Ġsp aced", + "Ġafford ability", + "ĠF ern", + "not ation", + "ĠBal anced", + "Ġoccup ies", + "en vironment", + "Ġneck lace", + "Ġsed an", + "F U", + "ĠBrav o", + "Ġab users", + "ĠAn ita", + "met adata", + "ĠG ithub", + "ait o", + "ĠF aster", + "ĠWass erman", + "ĠF lesh", + "Ġth orn", + "r arily", + "ĠMer ry", + "w ine", + "Ġpopul ace", + "ĠL ann", + "Ġrepair ing", + "Ġpsy che", + "Ġmod ulation", + "aw aru", + "âĢĭ âĢĭ", + "ari j", + "Ġdecor ations", + "Ġapolog ise", + "ĠG arg", + "app ly", + "Ġgive away", + "ĠFl an", + "ĠWy att", + "U ber", + "Ġauthor ised", + "ĠMor al", + "HAHA HAHA", + "activ ate", + "Ġtorped o", + "ĠF AR", + "Ġam assed", + "ĠA ram", + "ark in", + "ĠVict ims", + "st ab", + "Ġo m", + "ĠE CO", + "Ġopio ids", + "Ġpurpose ly", + "ĠV est", + "Ġer g", + "at an", + "ĠSur gery", + "Ġcorrect ing", + "ĠOrt iz", + "ĠBe et", + "Ġrev oke", + "Ġfre eway", + "ĠH iggins", + "F ail", + "ĠFar ms", + "ĠAT P", + "h ound", + "Ġp oking", + "ĠCommun ists", + "mon ster", + "iment ary", + "Ġunlock ing", + "Ġunf it", + "we ed", + "en ario", + "at ical", + "ĠEnlight enment", + "ĠN G", + "ĠComp ensation", + "de en", + "ĠWid ow", + "ĠCind y", + "ĠAfter wards", + "Ġ6 000", + "ikh ail", + "ag ically", + "Ġrat ified", + "Ġcasual ty", + "H OME", + "p sey", + "f ee", + "Ġspark ling", + "Ġd é", + "Ġconcert ed", + "C atal", + "Ġcomp lying", + "ĠA res", + "ĠD ent", + "Sh ut", + "Ġsk im", + "ad minist", + "Ġhost ilities", + "ĠG ins", + "Ġ6 08", + "Ġm uddy", + "ĠMc Int", + "ĠDec ay", + "5 25", + "Ġconspic uous", + "ĠEx posure", + "Ġresc ind", + "Ġwear able", + "Ġ3 28", + "our met", + "ah s", + "ĠRob ots", + "Ġe clips", + "inst ance", + "ĠRE PORT", + "ĠApp l", + "0 30", + "ĠSk ies", + "01 00", + "Ġfall acy", + "S ocket", + "ĠRece iver", + "Ġsol ves", + "ĠButter fly", + "ĠSho pping", + "ĠFI RE", + "65 4", + "Med ic", + "Ġsing ers", + "ĠNeed less", + "'' ''", + "isher s", + "ĠD ive", + "58 8", + "Ġselect ively", + "Ġcl umsy", + "88 9", + "Ġpurch aser", + "ear ned", + "ard y", + "Ġbenef iting", + "eng lish", + "Ġyield ing", + "ĠP our", + "Ġspin ach", + "Ġdel ve", + "ĠC rom", + "6 10", + "Ġexport ing", + "ĠMA KE", + "Ġ26 3", + "Ġg rop", + "Ġenv oy", + "ĠInqu iry", + "ĠLu igi", + "d ry", + "ĠT uring", + "Thumbnail Image", + "ĠVar iety", + "Ġfac et", + "Ġfl uffy", + "Ġexcerpt s", + "Ġsh orth", + "ĠOl sen", + "CL UD", + "Ġrel iant", + "ĠUN C", + "T our", + "Ġbat hing", + "Comp any", + "Ġglobal ization", + "P red", + "ĠMalf oy", + "Ġh oc", + "j am", + "craft ed", + "ĠBond s", + "ĠKiss inger", + "Eng land", + "Ġorder ly", + "cat entry", + "Ġ26 1", + "Ġexch anging", + "ĠInt ent", + "ĠAmend ments", + "D OM", + "Ġst out", + "³³³³³³³³ ³³³³³³³³", + "ĠAir bus", + "Ġ27 8", + "hy de", + "P oll", + "Item ThumbnailImage", + "Ġlooph oles", + "ĠPill ar", + "Ġexpl or", + "St retch", + "A part", + "Ġun married", + "Lim it", + "ĠTransform ers", + "Ġintellect ually", + "unct ure", + "18 00", + "Ġd arn", + "B razil", + "Ġleft over", + "ber us", + "f red", + "Mine craft", + "3 26", + "ĠForm s", + "Ġproof s", + "ĠDes igned", + "Ġindex es", + "ĠSupp ose", + "EM S", + "ĠL oving", + "ĠBon nie", + "im ating", + "OT US", + "Ġconduct or", + "Ġbehav ed", + "ĠF ren", + "Ġsy nerg", + "Ġmillenn ium", + "Ġcater ing", + "ĠL auder", + "W r", + "ĠY iannopoulos", + "ĠAT F", + "Ġensl aved", + "Ġawaken ed", + "D VD", + "ĠED ITION", + "ĠConc ert", + "ĠChall enger", + "ĠH aku", + "umer ic", + "Ġdep recated", + "ĠSH AR", + "4 12", + "Ġdy stop", + "Ġtremb ling", + "Ġdread ed", + "ĠSp ac", + "p adding", + "Re pl", + "ĠG arrison", + "M ini", + "Ġun paralleled", + "am ar", + "URR ENT", + "w reck", + "c ertain", + "t al", + "ĠC LS", + "app ings", + "Ġsens ed", + "Ġf encing", + "ĠPas o", + "ĠDes k", + "Ġsc off", + "Ġcontem plate", + "ĠL iga", + "l iquid", + "75 7", + "Ġapp rentice", + "ĠUCH IJ", + "5 70", + "ĠTh ousand", + "ĠIll um", + "Ġchampion ed", + "ãĤ Į", + "Ġelect ors", + "Ġ3 98", + "ĠH ancock", + "round ed", + "ĠJ OHN", + "Ġuns atisf", + "Ġqual ifier", + "ĠGad get", + "EN E", + "Ġdead liest", + "ĠPl ants", + "Ġ ions", + "Ġacc ents", + "Ġtwe aking", + "Ġsh aved", + "F REE", + "ĠCh aser", + "Again st", + "9 60", + "Ġmeth amphetamine", + "Ġnormal ized", + "Ġ$ \\", + "ĠPre cision", + "ĠGu am", + "Ġch oked", + "ĠX II", + "ĠCast ing", + "Tor rent", + "Ġscal p", + "ĠJagu ar", + "w it", + "Ġsem ic", + "ix ie", + "ĠG ould", + "Ġconf ines", + "N usra", + "ĠL on", + "ĠJ ugg", + "y cle", + "ĠCod ec", + "E gypt", + "Ġrest rain", + "ĠAl iens", + "Ġch oking", + "ĠD unk", + "ĠBell a", + "ab c", + "Ġsl ang", + "Ġneuro trans", + "s av", + "Ġempower ment", + "â ĨĴ", + "Ġclim bers", + "ĠM im", + "ĠF ra", + "ros se", + "Cap ital", + "ĠCth ulhu", + "Inter face", + "Ġprof icient", + "ĠIN TO", + "Ġ3 18", + "ront al", + "5 80", + "ĠDes pair", + "K enn", + "Ġscrim mage", + "ĠCo at", + "as ions", + "Ġwall paper", + "ĠJ ol", + "Ġresurg ence", + "Ġant iv", + "ĠB alls", + "² ¾", + "Ġbuff ers", + "Ġsub system", + "ĠSt ellar", + "ĠL ung", + "A IDS", + "Ġerad icate", + "Ġblat antly", + "Ġbehav es", + "ĠN un", + "Ġant ics", + "ex port", + "DE V", + "w b", + "Ġph p", + "ĠInteg rity", + "Ġexplore r", + "Ġrev olving", + "auth ored", + "g ans", + "Ġbas k", + "Ġas ynchronous", + "å į", + "TH ING", + "69 8", + "G ene", + "ĠR acer", + "ĠN ico", + "iss ued", + "Ġser mon", + "p ossibly", + "Ġsize of", + "Ġentrepreneur ial", + "ox in", + "ĠMin erva", + "Ġpl atoon", + "n os", + "ri ks", + "A UT", + "ĠAval anche", + "ĠDes c", + "ij 士", + "ĠP oc", + "Ġconf erred", + "Î »", + "Ġpat ched", + "F BI", + "66 2", + "Ġfract ures", + "Ġdetect s", + "Ġded icate", + "Ġconstitu ent", + "Ġcos mos", + "W T", + "Ġswe ats", + "Ġspr ung", + "b ara", + "s olid", + "Ġuns us", + "Ġbul ky", + "ĠPhilipp e", + "ĠFen rir", + "Ġtherap ists", + "ore al", + "^^ ^^", + "Ġtotal ed", + "Ġboo ze", + "ĠR PC", + "Prosecut ors", + "Ġdis eng", + "ĠSh ared", + "Ġmotor cycles", + "Ġinvent ions", + "Ġlett uce", + "ĠMer ge", + "ĠJ C", + "Ġspiritual ity", + "ĠWAR NING", + "Ġunl ucky", + "ĠT ess", + "Ġtong ues", + "ĠD UI", + "T umblr", + "Ġle ans", + "Ġinv aders", + "Ġcan opy", + "ĠHur ricanes", + "ĠB ret", + "ĠAP PLIC", + "id ine", + "ick le", + "Reg arding", + "Ġve ggies", + "Ġe jac", + "ju ven", + "F ish", + "D EM", + "ĠD ino", + "Th row", + "ĠCheck ing", + "be ard", + "( &", + "Ġj ails", + "Ġh r", + "trans fer", + "iv ating", + "Ġfle ets", + "ĠIm ag", + "ĠMc Donnell", + "Ġsnipp et", + "Is a", + "ĠCh att", + "ĠSt ain", + "ĠSet FontSize", + "ĠO y", + "ĠMathemat ics", + "49 4", + "Ġelectro ly", + "ĠG ott", + "ĠBr as", + "B OOK", + "ĠF inger", + "d ump", + "Ġmut ants", + "Ġrent als", + "Ġinter tw", + "Ġc reek", + "ail a", + "Bro ther", + "ĠDisc ord", + "pe e", + "raw ler", + "Ġcar p", + "Ġ27 9", + "ãĤ· ãĥ£", + "rel ations", + "Ġcontr asts", + "Col umn", + "Ġrec onnaissance", + "Ġun know", + "Ġl ooting", + "Ġregul ates", + "Ġopt imum", + "ĠChero kee", + "ĠA ry", + "Lat est", + "Ġroad side", + "Ġd anced", + "ĠUnic orn", + "A cknowled", + "Ġuncont roll", + "ĠM US", + "at io", + "ch ance", + "ha ven", + "VAL UE", + "Ġfavour ites", + "Ġceremon ial", + "b inary", + "pe ed", + "wood s", + "EM P", + "Ġv ascular", + "Ġcontempl ated", + "Ġbar ren", + "ĠL IST", + "Y ellow", + "ospons ors", + "Ġwhisk y", + "ĠM amm", + "ĠDeV os", + "min imum", + "H ung", + "44 2", + "P ic", + "ĠSnap dragon", + "77 6", + "Ġcar ving", + "Ġund ecided", + "Ġadvantage ous", + "Ġpal ms", + "ĠA Q", + "Ġst arch", + "L oop", + "Ġpadd le", + "Ġfl aming", + "ĠHor izons", + "An imation", + "bo ost", + "Ġprob abilities", + "ĠM ish", + "Ġex odus", + "ĠEditor ial", + "Ġfung us", + "Ġdissent ing", + "ĠDel icious", + "rog ram", + "ĠD yn", + "d isk", + "t om", + "Ġfab rics", + "ĠC ove", + "ĠB ans", + "Ġsoft en", + "ĠCON S", + "Ġin eligible", + "Ġestim ating", + "ĠLex ington", + "pract ice", + "of i", + "Ġshe dding", + "ĠN ope", + "Ġbreat hed", + "ĠCorinth ians", + "y ne", + "ek i", + "B ull", + "Ġatt aching", + "reens hots", + "Ġanaly se", + "ĠK appa", + "Ġuns ustainable", + "Ġinter pol", + "ank y", + "he mer", + "Ġprot agonists", + "Ġform atted", + "ĠBry ce", + "ĠAch illes", + "ĠAb edin", + "sh ock", + "Ġb um", + "b os", + "qu a", + "ĠW arn", + "q t", + "ĠDi abetes", + "8 64", + "ĠIn visible", + "Ġvan ish", + "Ġtrans mitting", + "Ġmur ky", + "ĠFe i", + "Ġawa ited", + "ĠJur assic", + "umm ies", + "Ġmen acing", + "g all", + "C ath", + "B uilt", + "ild o", + "ĠV otes", + "Ġon t", + "Ġmun itions", + "ĠFre em", + "ÃŃ n", + "Ġdec ency", + "lo pp", + "ie ved", + "ĠG ord", + "Ġun thinkable", + "ĠNews week", + "Ġ3 21", + "He at", + "Ġpresent er", + "ji ang", + "Ġpl ank", + "ĠAval on", + "Ġben z", + "ĠR out", + "Ġslam ming", + "ĠD ai", + "ou ter", + "ĠCook ie", + "ĠAlic ia", + "ge y", + "Ġvan ity", + "Ġow l", + "á µ", + "t ested", + "ĠAw akens", + "Ġcan v", + "Ġblind ly", + "ĠRid ley", + "ĠEm ails", + "Requ ires", + "ĠSer bian", + "ograp hed", + "if rame", + "eter ia", + "Ġaltern ating", + "qu iet", + "Ġsoc iology", + "ĠUn lock", + "ĠCommun ism", + "Ġo ps", + "Ġatt ribution", + "Ġab duction", + "ĠAb ram", + "Ġsidel ined", + "ĠB OOK", + "Ġref ining", + "ĠFe eling", + "ĠOs lo", + "ĠPru itt", + "r ack", + "ang ible", + "Ġcaut iously", + "ĠM ARK", + "eed s", + "M ouse", + "ĠStep h", + "ĠP air", + "S ab", + "99 7", + "ĠBa al", + "B ec", + "Ġcomm a", + "ĠP all", + "ĠG ael", + "Ġmisunder stand", + "ĠP esh", + "Order able", + "Ġdis mal", + "ĠSh iny", + "% \"", + "Ġreal istically", + "Ġpat io", + "ĠG w", + "ĠVirt ue", + "Ġexhaust ing", + "wh atever", + "oph ys", + "y ip", + "4 18", + "Ad just", + "ĠWa iting", + "ess on", + "ĠMaz da", + "ĠDo zens", + "Ġstream lined", + "Ġincompet ence", + "ĠM eth", + "Ġeth os", + "ON ES", + "Ġincent iv", + "Ġgr itty", + "ĠBut cher", + "Head er", + "Ġexp onential", + "à Ł", + "Ġcorrel ate", + "Ġcons ensual", + "s ounding", + "R ing", + "Orig in", + "Ġcon clusive", + "fe et", + "ac ly", + "ĠF ernandez", + "Buy able", + "Ġd ucks", + "aunt lets", + "Ġel ong", + "Ġ28 6", + "Ġsim ul", + "G as", + "ĠK irst", + "Ġprot r", + "ĠRob o", + "ĠAo E", + "op ol", + "Ġpsych ologically", + "sp in", + "ilater ally", + "ĠCon rad", + "W ave", + "44 1", + "ĠAd vertisement", + "ĠHarm on", + "ĠOri ental", + "is Special", + "Ġpresum ptive", + "Ġw il", + "ĠK ier", + "ne a", + "Ġp pm", + "Ġhar bour", + "ĠW ired", + "comp any", + "Ġcor oner", + "atur days", + "ĠP roud", + "ĠN EXT", + "ĠFl ake", + "val ued", + "ce iver", + "Ġfra ught", + "Ġc asing", + "Ġrun away", + "Ġg in", + "ĠLaure nt", + "ĠHar lem", + "ĠCur iosity", + "qu ished", + "Ġneuro science", + "ĠH ulu", + "Ġborrow er", + "Ġpetition er", + "ĠCo oldown", + "W ARD", + "Ġinv oking", + "conf idence", + "For ward", + "Ġst s", + "pop ulation", + "Delivery Date", + "Fil m", + "ĠC ov", + "quick Ship", + "quickShip Available", + "prim ary", + "isSpecial Orderable", + "inventory Quantity", + "channel Availability", + "BO X", + "ĠMulti player", + "ĠJen ner", + "77 8", + "ĠM d", + "Ġ~ /.", + "M N", + "Ġchild ish", + "Ġantioxid ant", + "ĠChrom ebook", + "Ġ27 4", + "Ġscreen play", + "Ġadvent urous", + "ĠRelations hip", + "respons ive", + "ming ton", + "Ġcorner stone", + "ĠF ey", + "F IR", + "Ġrook ies", + "ĠF eaturing", + "Ġorig inate", + "Ġelectro des", + "ant es", + "Ġscript ures", + "Ġgl ued", + "Ġdiscont ent", + "Ġaff licted", + "lay out", + "B rave", + "Ġm osa", + "ĠQuant ity", + "ĠH ik", + "w inner", + "H ours", + "Ġent ail", + "ĠCell s", + "olog ue", + "Ġv il", + "Ġpre acher", + "Ġdecor ative", + "d ifferent", + "Ġprejud ices", + "ĠSm oking", + "ĠNotting ham", + "so Type", + "Ġrhyth ms", + "ĠAl ph", + "bl ast", + "Ste el", + "ĠDaniel le", + "Ġstr ife", + "Ġrem atch", + "so DeliveryDate", + "ĠF ork", + "t rip", + "ol ulu", + "hes es", + "C G", + "ĠPOLIT ICO", + "ost a", + "ĠDr ift", + "é¾įå ¥", + "é¾įå¥ ij士", + "Ġvet ting", + "ĠJin ping", + "ĠRec ession", + "Min or", + "ĠF raud", + "enf ranch", + "Ġconven ed", + "ĠNA ACP", + "ĠMill ions", + "ĠFarm ing", + "ĠW oo", + "ĠFl are", + "rit o", + "imm igrant", + "Ġvac ancy", + "ĠHE AD", + "ĠV aj", + "eg al", + "ĠV igil", + "Stud y", + "Ġru ining", + "Ġr acks", + "Ġhe ater", + "ĠRand olph", + "ĠBr ush", + "ĠT ir", + "Ø ¨", + "Ġc ov", + "% ]", + "Ġrecount s", + "ĠO PT", + "ĠM elt", + "Ġtr uce", + "Ġcas inos", + "Ġcrus ade", + "Ġcarn age", + "Ġstri pe", + "ĠK yl", + "Text ures", + "Ġ6 98", + "Ġpro clamation", + "Ġgood ies", + "Ġ........ ..", + "pro claimed", + "P olit", + "Ġtop ical", + "Ġspecial ize", + "ĠA min", + "g m", + "Ġanch ored", + "Ġbear ings", + "s ample", + "ĠHigh land", + "ĠAut ism", + "Ġmerc enary", + "Ġinterview er", + "L ER", + "ĠSom ers", + "Ġembry o", + "ĠAss y", + "Ġ28 1", + "ĠEd iting", + "ĠCh osen", + "6 60", + "Ġp ci", + "ĠThunder bolt", + "BI LL", + "Ġchuck led", + "jri wal", + "h of", + "Ġearth ly", + "() {", + "ind ependence", + "Ġdisp ers", + "ĠV endor", + "ĠG areth", + "Ġp als", + "P enn", + "ĠSub mit", + "ic um", + "Th u", + "Ġcl andestine", + "Ġcann ibal", + "ĠCl erk", + "E Stream", + "gal itarian", + "âĻ ¥", + "g ew", + "Ġhor rend", + "ĠL ov", + "ĠRe action", + "ocr in", + "Class ic", + "Ġecho ing", + "Ġdiscl osing", + "ĠIns ight", + "og un", + "ĠInc arn", + "upload s", + "pp erc", + "guy en", + "Ġ19 01", + "ĠB ars", + "68 7", + "Ġb ribes", + "ĠFres no", + "ur at", + "ĠRe ese", + "Ġintr usive", + "Ġgri pping", + "ĠBlue print", + "ĠR asm", + "un ia", + "man aged", + "ĠHeb do", + "Ġ3 45", + "Ġdec oding", + "Ġpo ets", + "Ġj aws", + "ĠF IGHT", + "am eless", + "ĠMead ows", + "ĠHar baugh", + "Inter view", + "ĠH osp", + "ĠB RA", + "Ġdelet ion", + "m ob", + "W alker", + "ĠMoon light", + "ĠJ ed", + "ĠSoph ia", + "Ġus ur", + "Ġfortun ately", + "ĠPut ting", + "ĠF old", + "Ġsan itation", + "Ġpart isans", + "IS ON", + "B ow", + "ĠCON C", + "ĠRed uced", + "ĠS utton", + "Ġtouch screen", + "Ġembry os", + "âĢ¢âĢ¢ âĢ¢âĢ¢", + "ĠK rug", + "com bat", + "ĠPet roleum", + "Ġam d", + "ĠCos mos", + "Ġpresc ribing", + "Ġconform ity", + "ours es", + "Ġplent iful", + "Ġdis illusion", + "ĠEc ology", + "itt al", + "Ġf anc", + "Ġassass inated", + "regn ancy", + "Ġperenn ial", + "ĠBul lets", + "Ġst ale", + "Ġc ached", + "ĠJud ith", + "ĠDise ases", + "All en", + "Ġl as", + "Ġsh ards", + "ĠSu arez", + "ĠFriend ship", + "inter face", + "ĠSupp orters", + "add ons", + "46 2", + "ĠIm ran", + "ĠW im", + "Ġnew found", + "ĠM b", + "An imal", + "Ġd arling", + "and e", + "Ġrh y", + "ĠTw isted", + "pos al", + "yn ski", + "Var ious", + "× ľ", + "ĠK iw", + "uy omi", + "Ġwell being", + "ĠL au", + "an os", + "Ġunm ist", + "Ġmac OS", + "Ġrest room", + "ĠOl iv", + "ĠAir ways", + "Ġtimet able", + "9 80", + "Ġrad ios", + "v oy", + "ias co", + "Ġcloud y", + "ĠDraw ing", + "Any thing", + "Sy ria", + "ĠH ert", + "st aking", + "Ġun checked", + "Ġb razen", + "ĠN RS", + "69 7", + "onom ic", + "est ablish", + "Ġl eng", + "Ġdi agonal", + "ĠF ior", + "L air", + "ĠSt ard", + "Ġdef icient", + "jo ining", + "be am", + "Ġomn ip", + "Ġbl ender", + "Ġsun rise", + "Mo ore", + "ĠF ault", + "ĠCost ume", + "ĠM ub", + "Fl ags", + "an se", + "Ġpay out", + "ĠGovern ors", + "ĠD illon", + "ĠBan ana", + "N ar", + "Ġtra iled", + "Ġimperial ist", + "um ann", + "ats uki", + "4 35", + "ĠRoad s", + "Ġsl ur", + "ĠIde ally", + "Ġt renches", + "C trl", + "Ġmir rored", + "ĠZ el", + "ĠC rest", + "Comp at", + "ĠRoll s", + "sc rib", + "ĠTra ils", + "omet ers", + "w inter", + "Ġimm ortality", + "il ated", + "Ġcontrad icts", + "un iversal", + "ill ions", + "ĠM ama", + "opt im", + "AT URE", + "Ġge o", + "et ter", + "ĠCar lo", + "4 24", + "Ġcanon ical", + "ĠStrongh old", + "n ear", + "Ġperf ume", + "Ġorche stra", + "od iac", + "Ġup he", + "Ġreign ing", + "vers ive", + "Ġc aucuses", + "ĠD EM", + "Ġinsult ed", + "Ġ---- --", + "ĠCr ush", + "Ġroot ing", + "ĠWra ith", + "Ġwh ore", + "Ġto fu", + "C md", + "ĠB ree", + "Ġ$ _", + "Ġr ive", + "ĠAd vertising", + "Ġw att", + "ĠH O", + "Ġpersu asive", + "ĠParam eters", + "Ġobserv ational", + "ĠN CT", + "ĠMo j", + "ĠSal on", + "Ġtr unc", + "Ġexqu isite", + "ĠMar a", + "Ġpo op", + "ĠAN N", + "Ex c", + "ĠWonder ful", + "ĠT aco", + "Ġhome owner", + "ĠSmith sonian", + "orpor ated", + "mm mm", + "Ġlo af", + "ĠYam ato", + "ĠInd o", + "Ġcl inging", + "á s", + "Ġimm utable", + "h ub", + "Or ange", + "Ġfingert ips", + "ĠWood en", + "ĠK idd", + "ĠJ PM", + "ĠDam n", + "C ow", + "c odes", + "48 2", + "Ġiniti ating", + "ĠEl k", + "ĠCut ting", + "Ġabsent ee", + "ĠV ance", + "ĠLil ith", + "G UI", + "Ġobsc ured", + "Ġdwar ves", + "ĠCh op", + "ĠB oko", + "Val ues", + "Ġmult imedia", + "Ġbrew ed", + "Reg ular", + "CRIP TION", + "ĠMort al", + "Ġa pex", + "Ġtravel er", + "Ġbo ils", + "Ġspray ing", + "Rep resent", + "ĠStars hip", + "4 28", + "Ġdisappro val", + "Ġshadow y", + "Ġlament ed", + "ĠRe place", + "ĠFran ç", + "67 7", + "d or", + "Ġunst oppable", + "Ġcoh orts", + "gy n", + "ĠClass ics", + "ĠAm ph", + "Ġsl uggish", + "ĠAdd iction", + "ĠPad res", + "Ġins cription", + "Ġin human", + "min us", + "ĠJere miah", + "at ars", + "Ter ror", + "ĠT os", + "ĠSh arma", + "ast a", + "c atch", + "Ġpl umbing", + "ĠTim bers", + "Sh ar", + "H al", + "ĠO sc", + "Ġcou pling", + "hum ans", + "Ġsp onge", + "Ġid ols", + "ĠSp a", + "ĠAdv ocate", + "ĠBe ats", + "lu a", + "Ġtick ing", + "Ġload er", + "ĠG ron", + "8 10", + "Ġstim ulated", + "Ġside bar", + "ĠManufact urer", + "ore And", + "19 73", + "Ġpra ises", + "ĠFl ores", + "dis able", + "ĠElect rical", + "ra ise", + "E th", + "Ġmigr ated", + "Ġlect urer", + "K ids", + "ĠCa vern", + "Ġk ettle", + "Ġgly c", + "ĠMand ela", + "ĠF ully", + "å§ «", + "FIN EST", + "Ġsquee zing", + "ĠRy der", + "amp oo", + "oreAnd Online", + "Inst oreAndOnline", + "Buyable InstoreAndOnline", + "Ġcommem orate", + "ĠRamp age", + "Aust in", + "ĠSh roud", + "ĠRu ins", + "9 15", + "ĠK H", + "Ġwater front", + "ĠE SC", + "b aby", + "ĠC out", + "ĠEm blem", + "Ġequival ents", + "49 2", + "Un ique", + "ĠNiet zsche", + "brow ser", + "Ġim itation", + "ĠWere wolf", + "ĠKir in", + "ac as", + "' ,\"", + "Ġà ¾", + "Review ed", + "Ġc unt", + "Ġvo ic", + "ĠLen ovo", + "Ġbond ed", + "48 1", + "Ġinhib itors", + "Ġendeav ors", + "ĠHav ana", + "ĠSt out", + "ĠJ olly", + "A ctor", + "*/ (", + "Ġoccur rences", + "ĠT ens", + "Incre ased", + "ĠACT ION", + "Ġ ãĢĮ", + "ĠRank ings", + "ĠB reat", + "Ġ30 9", + "D ou", + "Ġimpact ing", + "ĠDuc hess", + "pre fix", + "Q B", + "Ġsummon ing", + "Ġbest owed", + "ĠKe pler", + "ĠPOW ER", + "c ube", + "ĠK its", + "ĠG rip", + "Ġop ium", + "Ġrep utable", + "t oc", + "ich ael", + "ĠR ipple", + "Ġcaf é", + "ĠZ oom", + "ĠBur ma", + "Ġwa ive", + "Ġst alls", + "Ġdem eanor", + "inc erity", + "Ġfluor ide", + "ĠSH OULD", + "Par is", + "Ġlong ing", + "Ġpl at", + "Ġgross ly", + "Ġbull s", + "Ġshowc asing", + "ex pected", + "ĠG addafi", + "engine ering", + "Re peat", + "ĠK ut", + "Ġconce ivable", + "Ġtrim med", + "osc ope", + "ĠCand idate", + "ĠT ears", + "rol og", + "Lew is", + "S UP", + "Ġroad map", + "Ġsal iva", + "Ġtrump et", + "Jim my", + "Ġmirac ulous", + "Ġcolon ization", + "Ġam put", + "ĠGN OME", + "ate ch", + "D ifferent", + "ĠE LE", + "ĠGovern ments", + "ĠA head", + "ãħĭ ãħĭ", + "word press", + "L IB", + "ĠIn clude", + "ĠDor othy", + "0 45", + "ĠColomb ian", + "Ġle ased", + "88 4", + "Ġde grading", + "ĠDa isy", + "i ations", + "Ġbapt ized", + "Ġsurn ame", + "co x", + "Ġblink ed", + "ãĥ ¢", + "Ġpoll en", + "Ġder mat", + "Ġre gex", + "ĠNich olson", + "ĠE ater", + "ç ľ", + "rad or", + "Ġnarrow er", + "Ġhur ricanes", + "Ġhalluc inations", + "r idden", + "ISS ION", + "ĠFire fly", + "Ġattain ment", + "Ġnom inate", + "Ġav ocado", + "ĠM eredith", + "Ġt s", + "Ġreve rence", + "Ġe uph", + "Ġcr ates", + "ĠT EXT", + "Ġ4 43", + "Ġ3 19", + "J SON", + "iqu ette", + "Ġshort stop", + "ic key", + "Ġpro pelled", + "Ġap i", + "ĠTh ieves", + "77 9", + "Ġovers aw", + "Ġcol i", + "ĠNic ola", + "Ġover cl", + "ik awa", + "ĠC yr", + "Ġ38 4", + "78 9", + "ĠAll ows", + "10 27", + "Det roit", + "TR Y", + "set up", + "ĠSocial ism", + "Sov iet", + "s usp", + "ĠAP R", + "ĠShut down", + "Ġal uminium", + "zb ek", + "ĠL over", + "GGGG GGGG", + "Ġdemocr acies", + "Ġ19 08", + "ĠMer rill", + "ĠFranco is", + "gd ala", + "Ġtraff ickers", + "ĠT il", + "ĠGo at", + "Ġsp ed", + "ĠRes erv", + "Ġpro d", + "55 2", + "Ġc ac", + "ĠUn iv", + "ĠSch we", + "Ġsw irling", + "ĠWild erness", + "ĠEgg s", + "Ġsadd ened", + "Ġarch aic", + "H yd", + "Ġexcess ively", + "B RE", + "Ġaer ospace", + "ĠVo ices", + "Cra ig", + "Ġign ited", + "In itially", + "ĠMc A", + "Ġhand set", + "Ġreform ing", + "Ġfrust rations", + "ĠDead pool", + "ĠBel ichick", + "ract or", + "ĠRagnar ok", + "ĠD rupal", + "ĠApp roximately", + "19 20", + "ĠHub ble", + "arm or", + "ĠSar as", + "ĠJon as", + "Ġnostalg ic", + "Ġfeas ibility", + "Sah aran", + "Ġorb iting", + "Ġ9 70", + "R u", + "Ġsh in", + "ĠInvestig ators", + "Ġinconsist encies", + "ĠP AN", + "B G", + "Ġgraz ing", + "Ġdetect ors", + "ĠStart up", + "ĠFun ny", + "ĠNa omi", + "Consider ing", + "Ġh og", + "ut f", + "ce mic", + "Ġfort ified", + "ĠFun ctions", + "Ġcod ec", + "nut rition", + "H at", + "\" !", + "micro soft", + "55 8", + "ĠTh in", + "ĠA CE", + "Al ias", + "ĠO PS", + "p apers", + "P K", + "ãĢ İ", + "Ġimpro bable", + "N orthern", + "equ al", + "Ġlook out", + "Ġty res", + "ĠMod ified", + "ĠK op", + "Abs olutely", + "Ġbuild up", + "sil ver", + "Ġaud i", + "Ġgro tesque", + "ĠSab er", + "ĠPres byter", + "ON Y", + "Ġglac iers", + "ĠSho als", + "ĠK ass", + "ĠH RC", + "ĠNic ol", + "ĠL unch", + "ĠF oss", + "âĸ Ĵ", + "AD RA", + "ĠOne Plus", + "o ing", + "ground s", + "Ġincident al", + "Ġdatas ets", + "68 9", + "ĠClarks on", + "Ġassemb ling", + "ĠCorrect ions", + "Ġdrink ers", + "Ġqual ifiers", + "Ġle ash", + "Ġunf ounded", + "ĠH undred", + "Ġkick off", + "T i", + "Ġrecon cil", + "ĠGr ants", + "ĠCompl iance", + "ĠDexter ity", + "Ġ19 06", + "w arn", + "D allas", + "Max imum", + "n ard", + "av ia", + "be aut", + "ens itivity", + "tr ace", + "Ġpione ers", + "ĠF ract", + "ãĢ ı", + "Ġpre cept", + "Ġgloss y", + "ĠI EEE", + "Ac ross", + "Ġ6 80", + "S leep", + "che on", + "Ġsatir ical", + "ĠMin otaur", + "ĠCla ude", + "Ġr é", + "ape go", + "Ġcar rot", + "ĠSem in", + "ino a", + "Ġz o", + "Ind ependent", + "Ġdiagn oses", + "ĠC ue", + "M AR", + "Ġrend ition", + "ĠK ik", + "Ġpath ology", + "Ġselect s", + "Link edIn", + "Ġass ay", + "ĠD res", + "Ġtext ual", + "post ed", + "IT AL", + "ĠM aul", + "N eal", + "Ġinter connected", + "Ġerr atic", + "ĠVir us", + "Ġ5 30", + "Ġenvironmental ists", + "ĠP helps", + "Ġeng agements", + "ĠIN ST", + "Ġeconom ical", + "nox ious", + "Ġg earing", + "izz y", + "Ġfavor ably", + "ĠMcG ill", + "T erm", + "Ġh anged", + "Ġball park", + "ĠRe yes", + "Ġbe ware", + "ĠP sal", + "ĠMass acre", + "q i", + "Ġin accessible", + "acly sm", + "Ġfr ay", + "ill ac", + "Ġbitter ly", + "ĠCert ification", + "Mich igan", + "Ġir respective", + "al ore", + "Em pty", + "Ġendorse ments", + "Ġund et", + "f g", + "equ ipped", + "Ġmerc iless", + "ĠC ust", + "Ġimm ature", + "Ġvou cher", + "ĠBlack well", + "Ñ ı", + "h awk", + "dis ciplinary", + "ile e", + "ĠMak oto", + "ĠD ude", + "ãĥĩ ãĤ£", + "Y ears", + "Ġin ver", + "Ġsh aman", + "ĠY ong", + "ip el", + "ell en", + "ĠCath y", + "br ids", + "Ġs arc", + "65 1", + "N ear", + "Ġground work", + "Ġam az", + "Ġ4 15", + "ĠHunting ton", + "hew s", + "ĠB ung", + "Ġarbit rarily", + "ĠW it", + "ĠAl berto", + "Ġdis qualified", + "best os", + "46 1", + "Ġp c", + "Ġ28 4", + "ro bat", + "Rob in", + "Ġh ugs", + "ĠTrans ition", + "ĠOcc asionally", + "Ġ3 26", + "ĠWh ilst", + "ĠLe y", + "Ġspaces hip", + "cs v", + "Ġun successfully", + "ĠA u", + "le ck", + "ĠWing ed", + "ĠGrizz lies", + ". �", + "Ġne arer", + "ĠSorce ress", + "ĠInd igo", + "El se", + "8 40", + "let es", + "Co ach", + "Ġup bringing", + "ĠK es", + "Ġseparat ist", + "Ġrac ists", + "Ġch ained", + "Ġabst inence", + "lear ning", + "Ġrein stated", + "Ġsymm etry", + "Ġremind ers", + "ĠChe vy", + "Ġm ont", + "Ġexempl ary", + "ĠT OR", + "Z X", + "Ġqual itative", + "ĠSt amp", + "ĠSav annah", + "ĠRoss i", + "Ġp aed", + "Ġdispens aries", + "ĠWall s", + "ĠCh ronic", + "Ġcompliment ary", + "ĠBeir ut", + "Ġ+ ---", + "igs list", + "Ġcrypt ographic", + "mas ters", + "ĠCap itals", + "Ġmax imal", + "Ġent ropy", + "Point s", + "Ġcombat ants", + "l ip", + "ĠGl ob", + "ĠB MC", + "ph ase", + "th ank", + "HT TP", + "Ġcomm uter", + "Ġ\\( \\", + ".. /", + "ĠReg ener", + "ĠDO I", + "ĠActiv ision", + "Ġsl it", + "os al", + "RE M", + "Ġch ants", + "Y u", + "Ke ys", + "Bre xit", + "ĠFor ced", + "Ari zona", + "Ġsquad ron", + "IS O", + "ĠMal one", + "Ġ3 38", + "Ġcontrast ing", + "Ġt idal", + "Ġlib el", + "Ġimpl anted", + "Ġupro ar", + "ĠC ater", + "Ġpropos itions", + "M anchester", + "ĠEuro s", + "it amin", + "G il", + "ĠEl ven", + "ĠSe ek", + "ĠB ai", + "Ġredevelop ment", + "ĠTown s", + "ĠL ub", + "! \",", + "al on", + "K rist", + "Ġmeas urable", + "Ġimagin able", + "Ġapost les", + "Y N", + "7 60", + "Ġster oid", + "Ġspecific ity", + "ĠL ocated", + "ĠBeck er", + "ĠE du", + "ĠDiet ary", + "uts ch", + "ĠMar ilyn", + "Ġbl ister", + "ĠM EP", + "ĠK oz", + "ĠC MS", + "y ahoo", + "ĠCar ney", + "Ġbo asting", + "ĠC aleb", + "By te", + "read s", + "ad en", + "Pro blem", + "ĠWood ward", + "S we", + "S up", + "ĠK GB", + "Set up", + "Ġtac it", + "Ġret ribution", + "Ġd ues", + "ĠM ü", + ". ?", + "ä¸ Ń", + "p ots", + "Ġcame o", + "ĠP AL", + "educ ation", + "A my", + "like ly", + "g ling", + "Ġconstitution ally", + "ĠHam m", + "ĠSpe ak", + "Ġwid gets", + "br ate", + "Ġcra ppy", + "ĠI ter", + "Ġanticip ating", + "ĠB out", + "P ixel", + "ĠY ep", + "ĠLaur ie", + "Ġh ut", + "Ġbullet in", + "ĠSal vation", + "Ġch ats", + "ear able", + "Honest ly", + "AL TH", + "onse qu", + "c ult", + "isco very", + "ovy ch", + "Ġse lves", + "ĠSat oshi", + "S ounds", + "Ġconver gence", + "ĠRosen berg", + "19 74", + "Ġnas al", + "Ġfull est", + "Ġfer ocious", + "x us", + "ist e", + "AM S", + "Ġlobb ied", + "Ġso othing", + "ĠGun n", + "t oday", + "0 24", + "Ġinspir ational", + "ĠN BN", + "p b", + "g ewater", + "or ah", + "all owed", + "ĠCol iseum", + "Ġspecial izing", + "Ġinsane ly", + "ĠT ape", + "del ay", + "Ġt arn", + "ĠP ound", + "Ġmel anch", + "Ġdeploy ments", + "il and", + "Ġless en", + "Ġfur ry", + "ĠUE FA", + "Ġblood shed", + "ĠMe ier", + "ither ing", + "Ġhe irs", + "ĠJ aw", + "ax ter", + "ĠPublic ations", + "Ġal ters", + "int ention", + "ĠWinc hester", + "d etermination", + "ĠLif etime", + "th in", + "Mon ster", + "7 80", + "Ġapprox imation", + "Ġsuper markets", + "ĠSecond s", + "or os", + "h uge", + "Ġb ribe", + "ĠLIM ITED", + "un ed", + "Ġmis interpret", + "ĠIn jury", + "Ġ3 67", + "Ġthreshold s", + "ĠCarn ival", + "Ġgastro intestinal", + "Ġguid eline", + "Ġde ceived", + "f eatures", + "Ġpurported ly", + "ĠRon nie", + "ĠNew t", + "Ġsp acious", + "as us", + "Ġsuperhero es", + "ĠCyn thia", + "le gged", + "k amp", + "ch io", + "Ġth umbnail", + "ĠShir ley", + "ill ation", + "Ġshe ds", + "ĠZ y", + "E PA", + "Ġdam s", + "Ġy awn", + "n ah", + "ĠPe ggy", + "ĠE rie", + "ĠJu ventus", + "ĠF ountain", + "r x", + "don ald", + "al bum", + "ĠComp rehensive", + "Ġc aching", + "ĠU z", + "ulner ability", + "ĠPrinc iple", + "ĠJ ian", + "ing ers", + "cast s", + "ĠOs iris", + "ch art", + "t ile", + "ĠTiff any", + "ĠPatt on", + "ĠWh ip", + "Ġovers ized", + "J e", + "ĠCind erella", + "ĠB orders", + "ĠDa esh", + "M ah", + "Ġdog ma", + "Ġcommun ists", + "v u", + "Coun cil", + "Ġfresh water", + "Ġw ounding", + "Ġdeb acle", + "Ġyoung ster", + "Ġthread ed", + "ĠB ots", + "ĠSav ings", + "ãģ Ĥ", + "ol ing", + "oh o", + "Ġillum ination", + "M RI", + "Ġlo osen", + "tr ump", + "ag ency", + "ur ion", + "Ġmoment arily", + "ĠCh un", + "ĠBud apest", + "ĠAl ley", + "D isk", + "Ġaston ished", + "ĠCon quer", + "ĠAccount ing", + "h aving", + "ĠWe in", + "ĠAl right", + "Ġrev olver", + "Ġdel usion", + "Ġrelic s", + "Ġad herent", + "qu ant", + "Ġhand made", + "or io", + "Ġcomb ating", + "c oded", + "Ġquad ru", + "re th", + "N ik", + "ĠTrib al", + "ĠMyster ious", + "Ġin hal", + "ĠWin ning", + "ĠClass ification", + "ch anged", + "Ġun ab", + "Ġsc orn", + "icip ated", + "w l", + "ond uctor", + "Ġrein forcing", + "ĠChild hood", + "an ova", + "Ġadventure r", + "Ġdoctor al", + "ĠStrateg ies", + "Ġengulf ed", + "ĠEnc ounter", + "Ġl ashes", + "Crit ical", + "ric ular", + "ĠU TF", + "oci ation", + "check ing", + "ĠConsult ing", + "Run time", + "per iod", + "ĠAs gard", + "Ġdist illed", + "ĠPas adena", + "ĠD ying", + "ĠCOUN TY", + "Ġgran ite", + "Ġsm ack", + "Ġparach ute", + "ĠS UR", + "Virgin ia", + "ĠF urious", + "78 7", + "ĠO kin", + "Ġcam el", + "ĠM bps", + "19 72", + "ĠCh ao", + "ĠC yan", + "j oice", + "ef er", + "ĠW rap", + "ĠDeb ate", + "S eg", + "Ġfore arm", + "ĠIgn ore", + "Ġtim estamp", + "Ġprob ing", + "ĠNo on", + "ĠGra il", + "f en", + "Ġdorm ant", + "ĠFirst ly", + "ĠE ighth", + "ĠH UN", + "ĠDes ire", + "or as", + "Girl s", + "ĠDes mond", + "z ar", + "am ines", + "O AD", + "exec ute", + "Ġbo obs", + "ĠAT L", + "_ (", + "Chel sea", + "Ġmasturb ation", + "ĠCo C", + "Ġdestroy er", + "ĠCh omsky", + "Ġsc atter", + "ĠAss ets", + "79 6", + "ĠC argo", + "Ġrecept ive", + "ĠSc ope", + "Ġmarket ers", + "Ġlaun chers", + "Ġax le", + "ĠSE A", + "se q", + "ĠM off", + "f inding", + "ĠGib bs", + "Georg ia", + "extreme ly", + "N J", + "Ġlab orers", + "st als", + "Ġmed iation", + "ĠH edge", + "at own", + "Ġi od", + "des pite", + "v ill", + "J ane", + "ex istence", + "Ġcoinc ided", + "ĠUt ilities", + "ĠChe ap", + "Ġlog istical", + "Ġcul mination", + "ĠNic otine", + "p ak", + "F older", + "Ġrod ents", + "st uff", + "Ġlaw fully", + "Ġreper to", + "io ch", + "j j", + "Dial ogue", + "HH HH", + "lic tion", + "Look s", + "Ġ29 7", + "Ġtur rets", + "ĠAb andon", + "Ġinc ess", + "ĠTraff ord", + "Ġcur led", + "Ġprefer ring", + "Ġprivat ization", + "Ġir resist", + "ĠP anda", + "ĠSh ake", + "ĠMc Gr", + "ãĥ Ħ", + "und ers", + "Ġdiscrim inated", + "Ġbart ender", + "I LE", + "Atl antic", + "Ġprop ensity", + "ĠW iz", + "ĠG im", + "con ference", + "Ġrein forces", + "G h", + "w agon", + "Ġe erie", + "F al", + "Ġhug ged", + "rac ist", + "R IC", + "F u", + "Ġf iller", + "ĠSt ub", + "Ġeng raved", + "ĠWrest le", + "Ġimagin ative", + "ĠPe er", + "ĠFact ors", + "an us", + "ĠDrac ula", + "mon itor", + "Ġrou ters", + "ib ia", + "ĠBoo lean", + "end ale", + "ĠSl aughter", + "ĠSh ack", + "R FC", + "ĠSpiel berg", + "S ax", + "ĠPH OTO", + "ĠCl over", + "ĠR ae", + "Dep ending", + "ĠMem or", + "ar am", + "Ġpier ced", + "Ġcur tains", + "v ale", + "ĠInqu isition", + "ĠP oke", + "Ġforecast ing", + "Ġcompl ains", + "S ense", + "ĠHer mes", + "isc overed", + "Ġb ible", + "ĠMor ph", + "Ġg erm", + "78 5", + "D ON", + "Ġcon gen", + "Ġcr ane", + "ĠD PR", + "Ġrespect fully", + "R oom", + "ĠN aw", + "ĠDal ai", + "re ason", + "ĠAng us", + "Educ ation", + "ĠTitan ic", + "Ë ľ", + "Ġo val", + "un ited", + "Ġthird s", + "Ġmoist ur", + "ĠC PC", + "M iami", + "Ġtent acles", + "ĠPol aris", + "ex c", + "ex clusive", + "ĠPra irie", + "Ġcol ossal", + "ĠBl end", + "sur prisingly", + "ÃŃ s", + "Ġindo ctr", + "Ġbas al", + "ĠMP EG", + "und o", + "Spl it", + "Develop ment", + "Ġlan tern", + "19 71", + "Ġprov ocation", + "Ġang uish", + "ĠB ind", + "ĠLe ia", + "duc ers", + "ipp y", + "conserv ancy", + "Ġinitial ize", + "ĠTw ice", + "ĠSu k", + "Ġpred ic", + "Ġdi ploma", + "Ġsoc iop", + "Ing redients", + "Ġhamm ered", + "ĠIr ma", + "Q aida", + "Ġglim ps", + "ĠB ian", + "Ġst acking", + "Ġf end", + "gov track", + "Ġun n", + "dem ocratic", + "ig ree", + "Ġ5 80", + "Ġ29 4", + "Ġstraw berry", + "ID ER", + "Ġcher ished", + "ĠH ots", + "Ġinfer red", + "Ġ8 08", + "ĠS ocrates", + "O regon", + "ĠR oses", + "ĠFO IA", + "Ġins ensitive", + "Ġ40 8", + "Recomm end", + "ĠSh ine", + "Ġpain staking", + "UG E", + "ĠHell er", + "ĠEnter prises", + "I OR", + "ad j", + "N RS", + "L G", + "Ġalien ated", + "Ġacknowled gement", + "ĠA UD", + "ĠRen eg", + "Ġvou chers", + "Ġ9 60", + "Ġm oot", + "ĠDim ensions", + "Ġc abbage", + "B right", + "g at", + "ĠK lu", + "Ġlat ent", + "Ġz e", + "ĠM eng", + "Ġdis perse", + "Ġpand emonium", + "H Q", + "Ġvirt uous", + "ĠLoc ations", + "ee per", + "prov ided", + "Ġse ams", + "ĠW T", + "iz o", + "PR OV", + "Ġtit anium", + "Ġrecol lection", + "Ġcr an", + "Ġ7 80", + "ĠN F", + "49 1", + "64 2", + "p acking", + "59 8", + "text ure", + "Sp ider", + "fre edom", + "cipl ed", + "ĠTAM ADRA", + "âĻ ¦", + "aut hent", + "ĠW ANT", + "r ified", + "Ġr ites", + "Ġuter us", + "k iss", + "Ġâī ¤", + "Ġsk illet", + "Ġdis enfranch", + "ĠGa al", + "Comp an", + "Ġage ing", + "gu ide", + "B alt", + "Ġiter ator", + "Ġdiscretion ary", + "t ips", + "Ġprim ates", + "ĠTechn ique", + "ĠPay ments", + "az el", + "ĠR OCK", + "stant ial", + "0 60", + "Ġd mg", + "ĠJack ets", + "ĠPlay off", + "Ġnurs ery", + "ĠSy mb", + "art on", + "Ġannex ation", + "Color ado", + "Ġco ils", + "ĠSh oes", + "âĦ¢ :", + "ĠRo z", + "COM PLE", + "ĠEve rest", + "ĠTri umph", + "J oy", + "G rid", + "à ¼", + "process or", + "ĠPros per", + "ĠSever us", + "ĠSelect ed", + "r g", + "ĠTay yip", + "St ra", + "Ġski ing", + "Ġ? )", + "Ġpe g", + "Tes la", + "Ġtime frame", + "Ġmaster mind", + "ĠN B", + "scient ific", + "ĠSh it", + "gener ic", + "IN TER", + "N UM", + "Ġst roll", + "ĠEn ix", + "ĠM MR", + "ĠE MS", + "m ovie", + "Ĥ ª", + "Ġminim izing", + "idd ling", + "Ġilleg itimate", + "Ġprot otyp", + "Ġpremature ly", + "Ġmanual s", + "obb ies", + "ĠCass idy", + "D EC", + "des ktop", + "Ġaer os", + "Ġscreen ings", + "Ġdeb ilitating", + "ĠGr ind", + "nature conservancy", + "Ġf ades", + "ter mination", + "assets adobe", + "F actor", + "Ġdefinitive ly", + "P oké", + "ap ult", + "ĠLaf ayette", + "C orn", + "ĠCor al", + "Ġstagn ant", + "T ue", + "Ġdissatisf action", + "G ender", + "Ġkid neys", + "ĠG ow", + "ĠDef eat", + "ĠAsh ton", + "Ġcart els", + "Ġfore closure", + "ĠExpl ore", + "stre ngth", + "ot in", + "Ġveterin arian", + "Ġf umble", + "Ġpar ap", + "ĠSt rait", + "r ils", + "Ġpr ick", + "ĠBerm uda", + "ĠAm munition", + "skin ned", + "Ġab ound", + "ĠB raz", + "Ġshar per", + "ĠAsc ension", + "Ġ9 78", + "Ġpreview s", + "Ġcommun ion", + "ĠX Y", + "Ġph ony", + "Ġnewcom er", + "Ġ3 32", + ".\" ,\"", + "Ġredist ribution", + "Prot ect", + "ĠSo f", + "K al", + "Ġlip stick", + "w orst", + "Ġtang led", + "Ġretrospect ive", + "int eger", + "Ġvolunte ering", + "Ġ19 07", + "Ġ --------------------", + "ic hen", + "Ġunve iling", + "Ġsen seless", + "Ġfisher ies", + "\\ -", + "Ġh inges", + "Ġcalcul us", + "My th", + "Ġund efeated", + "Ġoptim izations", + "Ġdep ress", + "Ġbill board", + "ĠY ad", + "ĠPy ramid", + "Is n", + "I de", + "Ġleg ion", + "ĠK ramer", + "ent anyl", + "Ġpenet rating", + "ĠHaw th", + "ĠPR ODUCT", + "ĠGer ard", + "ĠP act", + "ĠIn cluding", + "ĠEl ias", + "ĠEl aine", + "vis ual", + "Ġhum ming", + "Ġcond esc", + "ĠF asc", + "ä¸ Ĭ", + "Ġe galitarian", + "Ġdev s", + "ĠD ahl", + "O ps", + "D H", + "ĠB ounce", + "id ated", + "ald o", + "Ġrepublic an", + "Ġh amb", + "ĠS ett", + "ograph ies", + "CH APTER", + "Ġtrans sexual", + "Ġsky rocket", + "ans wer", + "Ġmark up", + "Ø ª", + "Ġhero ine", + "Comp are", + "ĠT av", + "Be ast", + "Ġsuccess ors", + "Ġna ïve", + "ĠBuck ley", + "st ress", + "me at", + "Ġdownload able", + "Ġindex ed", + "Ġsc aff", + "ĠL ump", + "ĠHom o", + "Stud io", + "In sp", + "Ġr acked", + "far ious", + "ĠPet ty", + "Ex ternal", + "Ġ19 09", + "W ars", + "com mit", + "put ers", + "Ġun ob", + "ĠEr r", + "ĠE G", + "ĠAl am", + "ĠSiber ia", + "ĠAtmosp heric", + "IS TER", + "ĠSatan ic", + "trans lation", + "ĠL oud", + "tra umatic", + "l ique", + "Ġreson ate", + "ĠWel ch", + "Ġspark ing", + "ĠT OM", + "t one", + "Ġout l", + "Ġhandc uffed", + "ĠSer ie", + "8 01", + "Ġland marks", + "ĠRee ves", + "Ġsoft ened", + "Ġdazz ling", + "ĠW anted", + "month s", + "Mag ikarp", + "Ġunt reated", + "ĠBed ford", + "M i", + "ĠDynam o", + "O re", + "79 5", + "Ġwrong ful", + "Ġl ured", + "Ġcort isol", + "Ġve x", + "d rawn", + "ile t", + "Download ha", + "ĠF action", + "Ġlab yrinth", + "Ġhij acked", + "w aters", + "er ick", + "Ġsuper iors", + "ĠRow ling", + "ĠGu inness", + "Ġt d", + "99 2", + "Ġune arthed", + "Ġcentr if", + "Ġsham eless", + "P od", + "ĠF ib", + "Ġ icing", + "Ġpredict or", + "Ġ29 2", + "fore station", + "con struct", + "C and", + "@ #", + "Ġag itated", + "Ġre pr", + "OV A", + "Ġkn itting", + "ĠLim a", + "Ġf odder", + "68 4", + "ĠPerson a", + "k l", + "7 01", + "Ġbreak up", + "á ¸", + "Ġapp alled", + "Ġantidepress ants", + "ĠSus sex", + "Har ris", + "ĠTher mal", + "ee ee", + "U pload", + "Ġg ulf", + "Ġdoor step", + "ĠSh ank", + "L U", + "ĠM EN", + "ĠP ond", + "s orry", + "Ġmis fortune", + "n ance", + "Ġb ona", + "M ut", + "Ġde graded", + "ĠL OG", + "ĠN ess", + "an imal", + "Ġa version", + "und own", + "Ġsupplement ed", + "ĠC ups", + "Ġ50 4", + "Ġdep rive", + "ĠSpark le", + "Å Ĥ", + "ĠMed itation", + "auth ors", + "ĠSab an", + "ĠN aked", + "air d", + "ĠMand arin", + "ĠScript ures", + "ĠPerson nel", + "ĠMahar ashtra", + "Ġ19 03", + "ĠP ai", + "ĠMir age", + "omb at", + "Access ory", + "Ġfrag mented", + "T ogether", + "Ġbelie vable", + "ĠGl adiator", + "al igned", + "ĠSl ug", + "M AT", + "Ġconvert ible", + "ĠBour bon", + "amer on", + "ĠRe hab", + "nt ax", + "Ġpowd ered", + "pill ar", + "Ġsm oker", + "ĠMans on", + "ĠB F", + "5 11", + "ĠGood ell", + "ĠD AR", + "m ud", + "g art", + "Ġob edient", + "ĠTrans mission", + "ĠDon ation", + "8 80", + "Ġbother ing", + "Material s", + "ãĤ ±", + "dest roy", + "Ġfore going", + "Ġanarch ism", + "ĠK ry", + "ice ps", + "Ġl ittered", + "ĠSch iff", + "Ġanecd otal", + "un its", + "Ġf ian", + "ĠSt im", + "ĠS OME", + "ĠInv aders", + "Ġbehaviour al", + "ĠVent ures", + "Ġsub lime", + "Ġfru ition", + "ĠPen alty", + "Ġcorros ion", + "¶ ħ", + "Ġlik ened", + "Ġbesie ged", + "ween ey", + "ĠCre ep", + "Ġlinem en", + "mult i", + "ic ably", + "ud der", + "Ġvital ity", + "Ġshort fall", + "ĠP ants", + "ap ist", + "H idden", + "ĠDro ps", + "med ical", + "Ġpron unciation", + "ĠN RL", + "Ġinsight ful", + "J V", + "ĠBe ard", + "ĠCh ou", + "Ġchar ms", + "Ġb ins", + "Ġamb assadors", + "ĠS aturdays", + "Ġinhib itor", + "ĠFr anch", + "6 01", + "', '", + "ĠCon or", + "art ney", + "ĠX peria", + "g rave", + "be es", + "ĠProtest ants", + "Ġso aking", + "ĠM andal", + "Ġph ased", + "Ġ6 60", + "Ġsc ams", + "Ġbuzz ing", + "ĠItal ians", + "ĠLoren zo", + "ĠJ A", + "Ġhes itated", + "Ġcl iffs", + "ĠG OT", + "ingu ishable", + "Ġk o", + "Ġinter ruption", + "Z ip", + "Lear ning", + "Ġundersc ores", + "ĠBl ink", + "K u", + "57 9", + "ĠAut ob", + "I RE", + "Ġwater ing", + "Ġpast ry", + "8 20", + "Ġvision ary", + "ĠTempl ar", + "awa ited", + "Ġpist on", + "Ġant id", + "current ly", + "Ġp ard", + "Ġw aging", + "Ġnob ility", + "ĠY us", + "Ġinject ing", + "f aith", + "ĠP ASS", + "å º", + "Ġret ake", + "ĠPR OC", + "Ġcat hedral", + "b ash", + "Ġwrest lers", + "Ġpartner ing", + "Ġn oses", + "Ġ3 58", + "Trans form", + "am en", + "Ġb outs", + "ĠId eal", + "ĠConstant in", + "Ġse p", + "ĠMon arch", + "att en", + "ĠPe oples", + "mod ified", + "Ġmor atorium", + "Ġpen chant", + "Ġoffensive ly", + "Ġprox ies", + "ok ane", + "ĠTaiwan ese", + "ĠP oo", + "ĠH OME", + "us ional", + "Ġver bs", + "ĠO man", + "vis ory", + "Ġpersu asion", + "Ġmult it", + "Ġsc issors", + "G ay", + "ow ay", + "oph ysical", + "l us", + "gn u", + "Ġap ocalyptic", + "Ġabsurd ity", + "Ġplay book", + "Ġautobi ography", + "I UM", + "Ġsne aking", + "ĠSim ulation", + "pp s", + "ell ery", + "Plan et", + "Ġright fully", + "Ġn iece", + "ĠN EC", + "ĠIP O", + "ĠDis closure", + "lean or", + "ous y", + "ST ER", + "Ġ28 2", + "Cru z", + "Ch all", + "64 3", + "ĠSurv ive", + "ĠF atal", + "ĠAm id", + "ap o", + "We apons", + "D EN", + "7 70", + "ĠGreen wald", + "Ġlin en", + "al os", + "Ġpollut ants", + "ĠPCI e", + "k at", + "Ġp aw", + "ĠK raft", + "C hem", + "ĠTermin ator", + "Ġre incarn", + "Ġ] [", + "ĠSe eds", + "Ġsilhou ette", + "ĠSt ores", + "Ġgro oming", + "ĠD irection", + "ĠIs abel", + "ĠBr idges", + "ðŁ ij", + "E ED", + "ĠM orsi", + "Ġval ves", + "ĠRank ed", + "ĠPh arma", + "ĠOrgan izations", + "Ġpenet rated", + "ĠRod ham", + "ĠProt oss", + "Ġove rest", + "Ġex asper", + "ĠT J", + "Ġ 000000", + "Ġtrick le", + "Ġbour bon", + "WH O", + "Ġw retched", + "Ġmicrosc opic", + "Ġcheck list", + "Ġad orned", + "R oyal", + "Ad minist", + "ĠRet irement", + "ĠHig hest", + "We ather", + "ile ge", + "Ġincre ments", + "ĠC osponsors", + "Ġmas se", + "ĠS inn", + "r f", + "Ġh ordes", + "as sembly", + "75 4", + "ĠNat asha", + "ĠTY PE", + "ĠGEN ERAL", + "Ġarr anging", + "Ġ40 7", + "l ator", + "Ġg lean", + "Ġdisc redited", + "Ġclin icians", + "UN E", + "Ġachie ves", + "ĠEm erson", + "com plex", + "= [", + "Ġprincip ally", + "Ġfra il", + "p icked", + "Ġthan king", + "Ġre cl", + "ĠL AST", + "Ġsupp ressing", + "il ic", + "Ġantidepress ant", + "ĠLis bon", + "Ġth or", + "Ġsp a", + "Ġking doms", + "ĠPear ce", + "em o", + "Ġpl ung", + "Ġdiv est", + "Ġ ********************************", + "b is", + "osp els", + "ad r", + "Sp irit", + "hall a", + "P ink", + "end ez", + "Ġresurrect ed", + "esc ape", + "ĠRosen stein", + "Ġge ological", + "Ġnecess ities", + "Ġcarn iv", + "ĠE lys", + "ĠBar ney", + "Ġ29 6", + "dig y", + "ST ON", + "D OWN", + "Ġmil estones", + "Ġk er", + "Ġdismant ling", + "Ġre prim", + "Ġcross ings", + "19 45", + "Ġpatri archy", + "Ġblasp hemy", + "Ġ3 59", + "met ry", + "ĠOb esity", + "ĠDiff erences", + "bl ocking", + "ãĥķ ãĤ¡", + "ich ita", + "ĠSab ha", + "ph alt", + "ĠCol o", + "ual a", + "effic ients", + "ĠMed ina", + "con sole", + "55 7", + "ĠHann ibal", + "ĠHab it", + "ĠF ever", + "Ġthen ce", + "Ġsyn agogue", + "Ġessential s", + "Ġw ink", + "ĠTr ader", + "ID A", + "ĠSp oiler", + "ĠIceland ic", + "ĠHay ward", + "Ġpe ac", + "Ġmal ice", + "Ġflash back", + "Ġth w", + "Ġlay offs", + "L iquid", + "Ġtro oper", + "Ġh inge", + "ĠRead ers", + "Ph ill", + "ĠB auer", + "Cre ated", + "Ġaud its", + "ac compan", + "Ġunsus pecting", + "ier a", + "6666 6666", + "Ġbro ch", + "Ġapprehend ed", + "ĠM alk", + "cer ning", + "ĠCod ex", + "O VER", + "M arsh", + "ĠD eng", + "ĠExp ression", + "Ġdisrespect ful", + "Ġasc ending", + "t ests", + "ĠPlaint iff", + "ster y", + "ĠAl ibaba", + "din and", + "ĠDem psey", + "Applic ations", + "mor al", + "Ġthrough put", + "Ġquar rel", + "Ġm ills", + "Ġhe mor", + "ĠC ASE", + "terror ist", + "st im", + "ifest yle", + "ro zen", + "CE PT", + "Ar k", + "u ci", + "lect ic", + "Ġirrit ating", + "she ets", + "A y", + "Ġrede emed", + "Ġhorn y", + "ĠTe ach", + "ĠS ear", + "dem ocracy", + "4 65", + "ĠRest ore", + "Ġstand by", + "ĠP is", + "iff in", + "Ġsleep y", + "Ġextr ater", + "Ġcompl iments", + "Fram eworks", + "Ġinstall s", + "Ġb anging", + "sur face", + "found land", + "Ġmetaph ysical", + "Ġ28 3", + "oul s", + "dev ices", + "Ar gs", + "ĠSac rifice", + "ĠMcC orm", + "es on", + "Cons ervative", + "ĠM ikhail", + "see ing", + "is ively", + "ĠRo oms", + "ĠGener ic", + "Ġenthusi astically", + "Ġgri pped", + "Ġcomed ic", + "ĠElectric ity", + "Ġgu errilla", + "Ġdec oration", + "ĠPerspect ive", + "Ġconsult ations", + "Ġun amb", + "Ġplag iar", + "Ġmagic ian", + "Ġe rection", + "ĠTour ism", + "or ied", + "ro xy", + "11 00", + "T am", + "Ī è", + "Î ³", + "× ª", + "ĠPred ators", + "Nit rome", + "Ġtelesc opes", + "project s", + "Ġun protected", + "Ġst ocked", + "ĠEnt reprene", + "nex pected", + "Ġwast ewater", + "V ill", + "Ġint imately", + "Ġi Cloud", + "ĠConst able", + "Ġspo of", + "Ġne farious", + "Ġfin s", + "Ġcens or", + "ĠMod es", + "ĠEs per", + "ar bon", + "Ġinter sections", + "Ġlaud ed", + "Ġphys i", + "Ġgener ously", + "ĠThe Nitrome", + "ĠTheNitrome Fan", + "Ġar isen", + "ĠÙ Ī", + "Ġg lands", + "ĠPav ilion", + "ĠGu pta", + "Ġuniform ly", + "Ġr amps", + "ri et", + "ĠWH EN", + "ĠVan essa", + "Ġrout ed", + "Ġlim p", + "ĠC PI", + "p ter", + "int uitive", + "Ġv aping", + "Ġexperiment ed", + "ĠOlymp us", + "ĠAm on", + "Ġsight ing", + "Ġinfiltr ate", + "ĠGentle man", + "Ġsign ings", + "ĠMe ow", + "ĠNav igation", + "che cks", + "4 33", + "Ġel apsed", + "ĠBulg arian", + "esp ie", + "ĠS OM", + "d uring", + "Ġsp ills", + "anc a", + "ĠPly mouth", + "M AL", + "Ġdomest ically", + "ĠWater gate", + "ĠF AM", + "k illed", + "ed ited", + "ĠYour self", + "Ġsynchron ization", + "ĠPract ices", + "ST EP", + "Ġgen omes", + "ĠQ R", + "not ice", + "Ġloc ating", + "z in", + "Ġ3 29", + "al cohol", + "Ġk itten", + "V o", + "Ġr inse", + "Ġgrapp le", + "ĠSc rew", + "ĠD ul", + "A IR", + "Ġle asing", + "ĠCaf é", + "Ġro ses", + "ĠRes pect", + "Ġmis lead", + "Ġperfect ed", + "Ġnud ity", + "Ġnon partisan", + "ĠCons umption", + "Report ing", + "Ġnu ances", + "Ġdeduct ible", + "ĠSh ots", + "Ġ3 77", + "Ġæ ľ", + "ano oga", + "Ben ef", + "ĠB am", + "ĠS amp", + "if ix", + "Ġgal van", + "ĠMed als", + "rad ius", + "Ġno bles", + "Ġe aves", + "igr ate", + "K T", + "ĠHar bour", + "u ers", + "Ġrisk ed", + "re q", + "Ġneuro t", + "get table", + "ain a", + "Rom ney", + "Ġunder pin", + "Ġlo ft", + "ĠSub committee", + "ĠMong ol", + "b iz", + "Ġmanif ests", + "ass isted", + "ĠG aga", + "Ġsy nergy", + "Ġreligious ly", + "ĠPre f", + "ĠG erry", + "T AG", + "ĠCho i", + "4 66", + "beh ind", + "ĠO u", + "Gold Magikarp", + "Ġhemor rh", + "R iver", + "Ġtend on", + "Ġinj ure", + "ĠF iona", + "Ġp ag", + "Ġag itation", + "|| ||", + "ur an", + "ĠE SA", + "Ġest eem", + "Ġdod ging", + "Ġ4 12", + "r ss", + "Ġce ases", + "ex cluding", + "Ġint akes", + "Ġinsert s", + "Ġemb old", + "ĠO ral", + "up uncture", + "4 11", + "ĠUn ified", + "ĠDe le", + "Ġfurn ace", + "ĠCoy otes", + "ĠBr ach", + "L abor", + "Ġhand shake", + "Ġbru ises", + "Gr ade", + "éĹ ĺ", + "ĠGram my", + "ile en", + "St ates", + "ĠScandinav ian", + "ĠKard ash", + "8 66", + "Ġeffort lessly", + "ĠDI RECT", + "ĠTH EN", + "ĠMe i", + "ert ation", + "19 68", + "Ġgro in", + "w itch", + "Requ irements", + "98 5", + "Ġroof s", + "Ġest ates", + "ĠH F", + "Ġha ha", + "Ġdense ly", + "ĠO CT", + "Ġpl astics", + "Ġincident ally", + "ĠTr acks", + "ĠTax es", + "Ġch anted", + "Ġforce ful", + "ĠBie ber", + "ĠK ahn", + "K ent", + "ĠC ot", + "lic ts", + "F ed", + "Ġhide ous", + "ĠVer d", + "ĠSynd icate", + "ĠIl legal", + "J et", + "ĠD AV", + "re asonable", + "c rew", + "Ġfundamental ist", + "Ġtruth ful", + "ĠJ ing", + "Ġl il", + "Ġdown ed", + "Ġen chanted", + "ĠPolic ies", + "ĠMcM aster", + "ĠH are", + "ides how", + "Ġpar ams", + "en cers", + "gorith m", + "Ġallow ances", + "Ġturb ulent", + "Ġcomplex ities", + "ĠK T", + "Ġ3 37", + "ĠGen etic", + "F UN", + "D oug", + "t ick", + "Ġg igs", + "ument hal", + "Ġpatriarch al", + "Ġcal c", + ", ...", + "Ġc out", + "ĠGu an", + "Ġpath ological", + "ĠR ivals", + "Ġunder rated", + "Ġflu orescent", + "ĠJ iu", + "arna ev", + "ĠQu an", + "Ġ4 29", + "Ġ à¨", + "M ario", + "Con struct", + "ĠC itation", + "ĠR acial", + "ĠR SA", + "ĠF idel", + "Ġ3 95", + "Person ally", + "C ause", + "à »", + "rad ical", + "in en", + "Ġvehement ly", + "ĠPap a", + "Ġintern ship", + "Ġfl akes", + "ĠRe ck", + "Luck ily", + "B ra", + "20 20", + "rav ings", + "R N", + "W onder", + "Ser iously", + "Ġre usable", + "Ġpoll uted", + "ĠP eng", + "le igh", + "ind le", + "Ġcircuit ry", + "ĠMad onna", + "ĠB ART", + "Res idents", + "att ribute", + "Phil adelphia", + "Cl ub", + "Ġplan ner", + "Ġfr antically", + "Ġfaith fully", + "ĠTerrit ories", + "ĠL AT", + "ĠAnders en", + "an u", + "ĠP ARK", + "ĠS ora", + "i age", + "ĠPlay offs", + "ĠG CC", + "4 27", + "Ġab norm", + "ĠL ever", + "Ġdisob edience", + "As ync", + "ĠShe a", + "V ert", + "Ġsk irts", + "ĠSaw yer", + "x p", + "Ġwors ening", + "Ġsc apego", + "ĠAng le", + "oth al", + "Ġtro ve", + "ĠSt y", + "ĠN guyen", + "mar ine", + "ide on", + "Dep ths", + "Bl og", + "ĠIll uminati", + "Ġtract s", + "Ġorgan ise", + "Ġo str", + "F s", + "Ġlever aging", + "ĠD aredevil", + "as ar", + "Ġl ang", + "Ġex termin", + "urs ions", + "ĠRom o", + "ãĤ¤ ãĥĪ", + "Ġcont ended", + "Ġencounter ing", + "ĠTable t", + "ĠAltern ate", + "sk ill", + "Ġswe ets", + "Ġco hesive", + "cap acity", + "Ġrep ud", + "Ġl izard", + "ro o", + "Ġpilgr ims", + "ĠR uff", + "ĠInstr ument", + "ĠLog o", + "uit ous", + "E H", + "Ġsales man", + "Ġank les", + "L ed", + "ĠPat ty", + "ud os", + "Own er", + "Ġdiscrep ancies", + "k j", + "M U", + "Ġuncond itional", + "Dragon Magazine", + "i ard", + "O ak", + "ĠConvers ation", + "be er", + "ĠOs aka", + "D elta", + "us ky", + "Ġsecret ion", + "Ġpl aza", + "Ġm ing", + "Ġde pletion", + "ĠM ous", + "ĠI TS", + "ĠH imal", + "ĠFle ming", + "Ġcyt ok", + "ĠH ick", + "Ġbat ters", + "ĠInt ellectual", + "6 75", + "é r", + "IS ION", + "ĠQu entin", + "ĠCh apters", + "ih adi", + "Ġco aster", + "WAY S", + "ĠL izard", + "ĠY or", + "and ering", + "S kin", + "ha ust", + "ab by", + "Ġportray ing", + "Ġwield ed", + "d ash", + "Ġprop onent", + "Ġr ipple", + "Ġgrap hene", + "Ġfly er", + "Ġrec urrent", + "Ġdev ils", + "Ġwater fall", + "æĺ ¯", + "go o", + "Text Color", + "Ġtam pering", + "IV ES", + "TR UMP", + "ĠAb el", + "ĠS AL", + "ĠHend ricks", + "ĠLu cius", + "b ots", + "Ġ40 96", + "IST ORY", + "Gu est", + "ĠN X", + "in ant", + "Ben z", + "ĠLoad ed", + "ĠCle ver", + "t reatment", + "Ġta vern", + "Ġ3 39", + "ĠT NT", + "ific antly", + "Tem perature", + "F el", + "Ġunder world", + "ĠJud ges", + "Ġ< +", + "Ġst ump", + "Ġoccup ancy", + "Ġab er", + "ĠF inder", + ") \",", + "ĠN unes", + "res et", + "in et", + "ect omy", + "Ġwell ness", + "ĠP eb", + "quart ered", + "and an", + "Ġneg atives", + "ĠTh iel", + "ĠCl ip", + "ĠL TD", + "Ġbl ight", + "Ġreperto ire", + "K yle", + "Ġqu er", + "ĠC es", + "Ġha pl", + "98 9", + "ĠTh ames", + "isc opal", + "Des k", + "ivari ate", + "ĠEx cellence", + "found ation", + "Ġâ ĩ", + "X i", + "Ġmyster iously", + "esty les", + "Ġper ish", + "ĠEng els", + "ĠDE AD", + "09 0", + "}} }", + "ĠUn real", + "Ġrest less", + "ID ES", + "orth odox", + "ĠInter mediate", + "Ġdin ners", + "ĠTr out", + "ĠSe ym", + "ĠHall s", + "og ged", + "Ġtraged ies", + "Ġdid nt", + "67 6", + "Ġail ments", + "Ġobserv able", + "ĠV ide", + "ad apt", + "ĠD usk", + "Ġprofessional ism", + "ĠPres cott", + "ĠInd ies", + "p ox", + "ĠMe hran", + "W ide", + "Ġend emic", + "ĠPar an", + "B ird", + "Ġped als", + "ĠI U", + "ĠAdam ant", + "ĠH urt", + "Ġcorrel ates", + "urd en", + "Ġspons oring", + "cl imate", + "ĠUnivers ities", + "ĠK not", + "enn es", + "ĠDam ian", + "ĠAx el", + "S port", + "Ġbar b", + "ĠS no", + "sh own", + "ste en", + "ud ence", + "Ġnon violent", + "Ġhom ophobia", + "Ġbiom ass", + "ĠDet ail", + "Ġsrf N", + "ĠT une", + "accompan ied", + "I ENCE", + "Al bert", + "ĠMong o", + "z x", + "ĠCer berus", + "or bit", + "c ens", + "Ġsl ay", + "SH ARE", + "H Y", + "Ġb rawl", + "ĠPro be", + "Ġnonex istent", + "ĠClare nce", + "ĠBlack burn", + "Ġport als", + "ĠR ita", + "ĠRem ain", + "ĠLe vant", + "Ġtrick ed", + "ĠF erry", + "aver ing", + "ĠStraw berry", + "ĠAn swers", + "Ġhorrend ous", + "ĠA man", + "Supp lement", + "ĠT oad", + "Ġpe eled", + "Ġman oeuv", + "ĠU zbek", + "mond s", + "ĠH ector", + "Ġ40 2", + "pe es", + "fix es", + "Ġd j", + "Ġres umes", + "Ġaccount ant", + "Ġadvers ity", + "Ġham pered", + "ĠL arson", + "Ġd oping", + "part s", + "H ur", + "Ġbe arded", + "Ġy r", + "ĠPlug in", + "å¥ ³", + "Ġ/ **", + "rol ley", + "Ġwaters hed", + "ĠSub mission", + "if lower", + "AS C", + "Ġcho ir", + "Ġsculpt ures", + "m A", + "incre asing", + "ai i", + "Ġsne akers", + "Ġconfront s", + "ĠEle phant", + "ĠEl ixir", + "Ġrec al", + "ĠT TL", + "w idget", + "ĠW ax", + "ĠGr ayson", + "Ġha irst", + "Ġhumili ated", + "ĠWAR N", + "app iness", + "ĠT TC", + "F uel", + "Ġpol io", + "Ġcomplex es", + "Ġbab e", + "ĠX IV", + "P F", + "). [", + "P arts", + "Ġ4 35", + "M eg", + "ĠY ards", + "ĠAL P", + "Ġy ells", + "Ġprin ces", + "Ġbull ies", + "ĠCapital ism", + "ex empt", + "FA Q", + "ĠSp onge", + "ĠAl a", + "Ġpleas antly", + "Ġbu f", + "Ġden ote", + "Ġunp ublished", + "Ġkne eling", + "asc a", + "Ġl apse", + "al ien", + "99 4", + "Ġrefere es", + "ĠLaw yers", + "S anta", + "Ġpuzz ling", + "ĠProm etheus", + "ĠPh araoh", + "ĠDel ay", + "Ġfacilit ates", + "ĠC ES", + "Ġjew els", + "Ġbook let", + "ond ing", + "Ġpolar ization", + "ĠMor an", + "ĠSal ad", + "ĠS OS", + "ĠAdv ice", + "PH OTOS", + "IC AN", + "iat ures", + "ex press", + "ĠWonder land", + "ĠC ODE", + "ĠCL ASS", + "9 75", + "Ġg rep", + "ĠD iesel", + "ĠGl ac", + "! ?\"", + "Ġr m", + "o ine", + "disc rimination", + "ĠN urse", + "m allow", + "Ġv ortex", + "ĠCons ortium", + "Ġlarge Download", + "stra ight", + "augh lin", + "G rad", + "Ġpublic ized", + "ĠW aves", + "ĠRed d", + "Ġfest ivities", + "ĠM ane", + "ar ov", + "Ġfleet ing", + "ĠDr unk", + "ug en", + "C ele", + "Ġchromos omes", + "ĠD OT", + "-+-+ -+-+", + "Ġbus iest", + "ĠBe aver", + "Sy rian", + "ĠK yr", + "k as", + "ĠCross Ref", + "19 50", + "76 01", + "Ġrepe aling", + "ĠWin ners", + "ĠMac ro", + "ĠD OD", + "bl ance", + "S ort", + "64 1", + "Ġmet re", + "ĠD irk", + "Ġgo ggles", + "Ġdraw backs", + "Ġcomplain ant", + "Ġauthor izing", + "Ġantit rust", + "oper ated", + "Ġm ah", + "Ġexagger ation", + "Am azing", + "ĠSer aph", + "Ġha ze", + "w ow", + "Ġextingu ished", + "Ġcan yon", + "ĠB osh", + "Ġv ents", + "Ġsc rape", + "Cor rect", + "4 26", + "Ġav g", + "Dem and", + "ĠâĪ ¼", + "Ġmicrobi ota", + "\"} ],\"", + "ĠSt ev", + "B io", + "ĠPlan es", + "Ġsuggest ive", + "Ġdec ipher", + "ĠRefuge e", + "ĠKe jriwal", + "ĠGreen peace", + "Ġdecl ass", + "ĠSound ers", + "Ġth o", + "Ġdec rypt", + "Ġbr ushing", + "ĠJane iro", + "ip op", + "S i", + "8 77", + "ĠGeoff rey", + "Ġc pu", + "ĠHaz el", + "Ġview points", + "Ġcris py", + "ĠNot ification", + "Ġsold er", + "ĠMod est", + "ĠHem isphere", + "Ġcass ette", + "in cludes", + "Ġident ifiers", + "ĠC ALL", + "in cent", + "T odd", + "ĠSwe ep", + "Ġ3 34", + "b oss", + "Ġsm ir", + "gin x", + "Ġtown ship", + "Ġg rieving", + "ĠMos que", + "Net flix", + "AS ED", + "ĠMillenn ials", + "oc om", + "19 67", + "Ġbold ly", + "s leep", + "Ġes che", + "arij uana", + "Ġsw irl", + "ĠPen al", + "Ġneglig ent", + "ĠStephen son", + "K ER", + "ĠZ oro", + "ris is", + "Ġlocal ization", + "ĠSeym our", + "ĠAng lic", + "red itation", + "prot ection", + "ĠPa ige", + "Ġo mit", + "ĠR ousse", + "ĠT ub", + "Ġinv itations", + "t ty", + "Ġm oss", + "ph ysical", + "C redits", + "Ġan archy", + "Ġchild care", + "Ġl ull", + "ĠM ek", + "ĠL anguages", + "lat est", + "ĠSan ford", + "Ġus ability", + "Ġdiff use", + "ĠD ATA", + "Ġsp rites", + "ĠVeget a", + "ĠProm otion", + "ãĥ¼ ãĤ¯", + "rict ing", + "z ee", + "Tur kish", + "ĠTD s", + "pro ven", + "57 1", + "Ġsmug glers", + "707 10", + "Ġreform ed", + "ĠLo is", + "Ġun fl", + "ĠWITH OUT", + "ĠReturn ing", + "ann ie", + "ĠTom as", + "Fr anc", + "ĠProf it", + "ĠSER V", + "ĠR umble", + "ik uman", + "es an", + "Ġt esters", + "Ġgad get", + "Ġbrace let", + "ĠF SA", + "comp onent", + "Ġparamed ics", + "Ġj an", + "ĠRem em", + "ĠSk inner", + "Ġl ov", + "ĠQu ake", + "rom a", + "Ġfl ask", + "Pr inc", + "Ġover power", + "Ġlod ging", + "ĠK KK", + "ret te", + "Ġabsor bs", + "w rote", + "Ġ ,\"", + "K ings", + "ĠH ail", + "ĠFall ing", + "xt ap", + "ĠHel ena", + "ire ns", + "L arry", + "Ġpamph let", + "ĠC PR", + "G ro", + "ĠHirosh ima", + "Ġhol istic", + "\". [", + "Ġdet achment", + "Ġas pire", + "Ġcompl icit", + "ĠGreen wood", + "Ġresp awn", + "ĠSt upid", + "ĠFin ished", + "f al", + "b ass", + "Ġab hor", + "Ġmock ery", + "ĠFe ast", + "VID EO", + "Ġcon sec", + "ĠHung ry", + "P ull", + "ĠH ust", + "it ance", + "? ãĢį", + ") --", + "ĠPar allel", + "con v", + "4 69", + "ha ar", + "w ant", + "P aper", + "m ins", + "ĠTor o", + "ĠTR UMP", + "ĠR ai", + "D W", + "ĠW icked", + "ĠL ep", + "Ġfun ky", + "Ġdetrim ent", + "ios is", + "ache v", + "Ġde grade", + "im ilation", + "Ġret ard", + "Ġfrag mentation", + "Ġcow boy", + "ĠY PG", + "ĠH AL", + "Parent s", + "ĠS ieg", + "ĠStra uss", + "ĠRub ber", + "× IJ", + "Fr ag", + "Ġp t", + "Ġoption ally", + "ĠZ IP", + "ĠTrans cript", + "ĠD well", + "88 2", + "M erc", + "ĠM OT", + "ãĥ¯ ãĥ³", + "Ġhun ts", + "Ġexec utes", + "In cludes", + "Ġacid ic", + "ĠRespons ibility", + "ĠD umb", + "we i", + "And erson", + "ĠJas per", + "ight on", + "abs olutely", + "Ad ult", + "Ġpl under", + "Mor ning", + "ĠT ours", + "ĠD ane", + "Î º", + "ĠT EST", + "ĠG ina", + "Ġcan ine", + "aw an", + "Ġsocial ists", + "ĠS oda", + "Ġimp etus", + "ĠSupplement ary", + "oli ath", + "ĠKinn ikuman", + "mitted ly", + "second s", + "Ġorganis ers", + "Ġdocument aries", + "Vari able", + "GRE EN", + "Ġres orts", + "Ġbr agging", + "Ġ3 68", + "Art ist", + "w k", + "bl ers", + "Un common", + "ĠRet rieved", + "Ġhect ares", + "Ġtox in", + "r ank", + "Ġfaith s", + "ĠG raphic", + "Ġve c", + "ĠL IA", + "Af rican", + "Ġard ent", + "end iary", + "L ake", + "ĠD OS", + "cient ious", + "ĠOk awaru", + "ĠAll y", + "ĠTim eline", + "D ash", + "ĠI c", + "contin ue", + "Ġt idy", + "Ġinstinct ively", + "ĠP ossibly", + "ĠOut door", + "ĠWould n", + "Ġl ich", + "ĠBr ay", + "ĠA X", + "Ġà ī", + "Ġ+ #", + "\\ '", + "Direct ory", + "ab iding", + "Ġf eral", + "ic ative", + "but t", + "Ġper verse", + "S alt", + "Ġwar ped", + "Ġnin eteen", + "Ġcabin ets", + "Ġsrf Attach", + "ĠSl oan", + "Ġpower ing", + "reg ation", + "F light", + "se vere", + "Ġst ren", + "Ġc og", + "ap ache", + "Ġâ Ŀ", + "Ġcaf eteria", + "p aces", + "ĠGrim oire", + "uton ium", + "Ġr aining", + "Ġcir cling", + "Ġlineback ers", + "c redit", + "Ġrep atri", + "ĠCam den", + "lic ense", + "Ġly ric", + "Ġdescript or", + "Ġval leys", + "Ġre q", + "Ġback stage", + "ĠPro hibition", + "ĠK et", + "Op ening", + "S ym", + "æĸ ¹", + "Ġserv ings", + "Ġoverse en", + "Ġaster oids", + "ĠMod s", + "ĠSpr inger", + "ĠCont ainer", + "è »", + "ĠM ens", + "Ġmult im", + "Ġfire fighter", + "pe c", + "Ġchlor ine", + "Ð ¼", + "end i", + "Ġsp aring", + "Ġpolyg amy", + "ĠR N", + "ĠP ell", + "Ġt igers", + "Ġflash y", + "ĠMad ame", + "S word", + "Ġpref rontal", + "Ġpre requisite", + "uc a", + "Ġw ifi", + "Ġmiscon ception", + "Ġharsh ly", + "ĠStream ing", + "ot om", + "ĠGiul iani", + "foot ed", + "Ġtub ing", + "ind ividual", + "z ek", + "n uclear", + "m ol", + "Ġright ful", + "49 3", + "Ġspecial ization", + "Ġpassion ately", + "ĠVel ocity", + "ĠAv ailability", + "T enn", + "Ġl atch", + "ĠSome body", + "Ġhel ium", + "cl aw", + "Ġdi pping", + "XX X", + "Ġinter personal", + "7 10", + "Ġsub ter", + "Ġbi ologists", + "ĠLight ing", + "Ġopt ic", + "Ġden im", + "end on", + "ĠC orm", + "Ġ3 41", + "ĠC oup", + "Ġfear less", + "Ġal ot", + "ĠCliff ord", + "ĠRun time", + "ĠProv ision", + "up dated", + "lene ck", + "Ġneur on", + "Ġgrad ing", + "ĠC t", + "sequ ence", + "in ia", + "con cept", + "Ġro aring", + "ri val", + "ĠCaucas ian", + "Ġmon og", + "key es", + "Ġappell ate", + "Ġlia ison", + "EStream Frame", + "ĠPl um", + "! .", + "Ġsp herical", + "Ġper ished", + "Ġbl ot", + "Ġben ches", + "Ġ4 11", + "Ġpione ered", + "Ġhur led", + "Jenn ifer", + "ĠYose mite", + "Ch air", + "Ġreef s", + "Ġelect or", + "ĠAnt hem", + "65 2", + "Ġun install", + "Ġimp ede", + "Ġbl inking", + "Ġgot o", + "Dec re", + "A ren", + "Ġstabil ization", + "ĠDis abled", + "ĠYanuk ovych", + "Ġoutlaw ed", + "ĠVent ura", + "ten ess", + "Ġplant ation", + "Ġy acht", + "ĠHu awei", + "Ġsol vent", + "Ġgr acious", + "Ġcur iously", + "Ġcapac itor", + "Ġc x", + "ĠRef lex", + "Ph ys", + "ĠC f", + "pt in", + "cons ervative", + "Ġinv ocation", + "c our", + "F N", + "ĠNew ly", + "H our", + "As ian", + "ĠLe ading", + "ĠAer ospace", + "An ne", + "Ġpre natal", + "Ġdeterior ating", + "H CR", + "ĠNorm andy", + "ol ini", + "ĠAm bro", + "9 10", + "Ġset backs", + "ĠT RE", + "Ġs ig", + "ĠSc ourge", + "59 7", + "79 8", + "Game play", + "Ġm sec", + "M X", + "Ġprice y", + "ĠL LP", + "aker u", + "Ġover arching", + "ĠB ale", + "Ġworld ly", + "Cl ark", + "Ġscen ic", + "Ġdisl iked", + "ĠCont rolled", + "T ickets", + "ĠE W", + "ab ies", + "ĠPl enty", + "Non etheless", + "Ġart isan", + "Trans fer", + "ĠF amous", + "Ġinf ield", + "ble y", + "Ġunres olved", + "ĠML A", + "ãĤ Ĥ", + "Cor rection", + "Ġdemocr at", + "ĠMore no", + "ro cal", + "il ings", + "Ġsail or", + "Ġr ife", + "h ung", + "Ġtrop es", + "Ġsn atched", + "ĠL IN", + "ĠB ib", + "ES A", + "ĠPre v", + "ĠCam el", + "run time", + "Ġob noxious", + "4 37", + "Ġsum mers", + "Ġunexpl ained", + "ĠWal ters", + "cal iber", + "Ġg ull", + "ĠEnd urance", + "ä½ ľ", + "Ġ3 47", + "Ir ish", + "Ġaer obic", + "Ġcr amped", + "ĠHon olulu", + "à ©", + "us erc", + "ec ast", + "AC Y", + "ĠQu ery", + "ãĤ¹ ãĥĪ", + "Bet a", + "Ġsuscept ibility", + "ĠSh iv", + "ĠLim baugh", + "Ġà ĸ", + "ĠN XT", + "ĠM uss", + "ĠBrit ons", + "ES CO", + "EG IN", + "Ġ% %", + "Ġsec ession", + "ĠPat ron", + "ĠLu a", + "n aires", + "ĠJPM organ", + "us b", + "ocy te", + "Ġcouncill ors", + "ĠLi ang", + "f arm", + "Ġnerv ously", + "Ġattract iveness", + "ĠK ov", + "j ump", + "Pl ot", + "Ġst ains", + "ĠStat ue", + "ĠApost les", + "he ter", + "ĠSUP PORT", + "Ġoverwhel m", + "Y ES", + "Ġ29 1", + "d ensity", + "Ġtra pping", + "M it", + "Ġf ide", + "ĠPam ela", + "atl antic", + "Dam n", + "Ġp ts", + "OP A", + "Ġserv icing", + "Ġoverfl owing", + "ul o", + "ĠE rit", + "t icket", + "light ing", + "ĠH mm", + "ãĥ¼ ãĥ«", + "im oto", + "Ġchuck le", + "4 23", + "ãģ ķ", + "sh ape", + "Ġque ues", + "Ġanch ors", + "ãĤ¼ ãĤ¦ãĤ¹", + "F er", + "Ġaw oke", + "Ġ6 66", + "h ands", + "Ġdiver gence", + "Ġ50 5", + "T ips", + "Ġdep ot", + "Ġske w", + "ĠDel iver", + "op ot", + "Ġdiv ul", + "ĠE B", + "uns igned", + "ĠUn i", + "X box", + "Ġfor ks", + "Ġ7 02", + "å ¯", + "Ġpromot ers", + "ĠV apor", + "Ġlev ied", + "sl ot", + "Ġpig ment", + "Ġcyl inders", + "C RE", + "Ġsn atch", + "Ġperpet ually", + "Ġl icking", + "ĠFe et", + "ĠKra ken", + "ĠHold en", + "ĠCLS ID", + "m r", + "Ġproject or", + "Ġden otes", + "Ġchap el", + "ĠTor rent", + "b ler", + "R oute", + "ĠDef endant", + "ĠPublisher s", + "ĠM ales", + "ĠInn ov", + "ĠAg ility", + "rit er", + "ty mology", + "st ores", + "L ind", + "Ġf olly", + "ĠZur ich", + "B le", + "Ġnurt ure", + "Ġcoast line", + "uch in", + "D omin", + "Ġfri vol", + "ĠCons olid", + "res ults", + "M J", + "Ġphyl ogen", + "Ġha uled", + "ĠW iley", + "ĠJess ie", + "ĠPrep are", + "ĠE ps", + "Ġtreasure r", + "I AS", + "Ġcolon ists", + "Ġin und", + "ĠWW F", + "ĠCon verted", + "6 000", + "out side", + "ĠApp earance", + "ĠRel ic", + "ĠM ister", + "s aw", + "Ġresult ant", + "Ġadject ive", + "ĠLaure l", + "ĠHind i", + "b da", + "Pe ace", + "Ġreb irth", + "Ġmembr anes", + "Ġforward ing", + "Ġcoll ided", + "ĠCar olyn", + "K ansas", + "5 99", + "ĠSolid GoldMagikarp", + "Be ck", + "Ġstress ing", + "ĠGo o", + "ĠCooper ative", + "Ġf s", + "ĠAr chie", + "L iter", + "ĠK lopp", + "J erry", + "Ġfoot wear", + "War ren", + "Ġsc ree", + "h are", + "Under standing", + "P ed", + "Ġanth ology", + "ĠAnn ounce", + "M ega", + "Ġflu ent", + "Ġbond age", + "ĠDisc ount", + "il ial", + "C art", + "ĠNight mares", + "Sh am", + "ĠB oll", + "uss ie", + "H ttp", + "Atl anta", + "Ġun recogn", + "ĠB id", + "Ġunder grad", + "Ġforg iving", + "ĠGl over", + "AAAA AAAA", + "4 45", + "V G", + "pa io", + "kill ers", + "Ġrespons ibly", + "Ġmobil ize", + "Ġeffect ed", + "ĠL umin", + "Ġk ale", + "Ġinfring ing", + "ann ounced", + "Ġf itt", + "b atch", + "ĠT ackle", + "ĠL ime", + "ĠAP P", + "uke mia", + "Ġrub y", + "Ġex oner", + "ĠCas ual", + "0 70", + "Ġpel vic", + "Ġautom ate", + "ĠK ear", + "ĠCoast al", + "Ġcre ed", + "Ġbored om", + "ĠSt un", + "ri ott", + "Ĥ İ", + "Ġregener ate", + "Ġcomed ians", + "ĠOP ER", + "Sp ons", + "id ium", + "on is", + "L ocated", + "05 7", + "Ġsusp ense", + "ĠD ating", + "C ass", + "Ġneoc ons", + "ĠShin zo", + "Ġaw oken", + "ch rist", + "ĠMess ages", + "att led", + "ĠSpr ay", + "ĠSp ice", + "C W", + "Ġshield ing", + "ĠG aul", + "Am id", + "Ġparam ilitary", + "Ġmult if", + "ĠTan ner", + "il k", + "Ġgodd amn", + "g ements", + "Ġbe friend", + "m obi", + "Ġ3 88", + "fold er", + "acc a", + "Ġins in", + "g ap", + "N ev", + "fif th", + "Ġpsychiat ry", + "b anks", + "TH IS", + "Ġhar b", + "ac qu", + "Ġfac ade", + "ĠPower Point", + "80 3", + "Ġbl uff", + "Sh ares", + "Ġfavor ing", + "El izabeth", + "Ãį Ãį", + "Ġr anger", + "77 2", + "ĠAr che", + "h ak", + "ĠGen etics", + "ĠF EMA", + "Ġev olves", + "Ġest e", + "ĠP ets", + "ĠM é", + "ĠInterest ing", + "ĠCanter bury", + "ch apter", + "ĠStar fleet", + "Sp anish", + "Ġdraw back", + "ĠNor wich", + "9 70", + "n orth", + "ag anda", + "Ġtransform ative", + "ram ids", + "bi ology", + "ad ay", + "Ġpropag ation", + "ĠGam ma", + "ĠDen ise", + "ĠCalcul ator", + "ent imes", + "ĠB ett", + "Ġapp endix", + "ĠHD D", + "AK ING", + "Ġst igmat", + "Ġhol ster", + "Ġord inarily", + "Ch ance", + "ĠCont rary", + "Ġad hesive", + "Ġgather s", + "6 12", + "re au", + "ony ms", + "ew ays", + "Ġindu ces", + "Ġinterchange able", + "se m", + "Wh it", + "Ġtr ance", + "Ġincorpor ation", + "ĠExt ras", + "Fin ancial", + "Ġawkward ly", + "ĠStur geon", + "ĠH Y", + "Norm ally", + "ĠEnd ing", + "ĠAss ist", + "enc rypted", + "Ġsub jug", + "Ġn os", + "Ġfan atic", + "C ub", + "C U", + "?\" .", + "Ġirre versible", + "å Ĥ", + "03 1", + "ĠH AR", + "sp read", + "ul ia", + "= $", + "Sc ope", + "L ots", + "Ġlif estyles", + "ol on", + "Ġf eds", + "Ġcongrat ulate", + "web kit", + "Ġindist inguishable", + "ĠSw ing", + "Ġcommand ments", + "qu ila", + "ab ella", + "m ethyl", + "ann abin", + "Ġo vere", + "Ġlob ster", + "ĠQU EST", + "ĠCONT IN", + "bern atorial", + ":::: ::::", + "ĠTra ve", + "ĠSam oa", + "AN I", + "75 2", + "Ð ´", + "userc ontent", + "ĠMod erate", + "y eah", + "ĠK itt", + "Ġwe e", + "Ġstuff ing", + "ĠInter vention", + "ĠD ign", + "Ġware houses", + "ĠF iji", + "Ġpel lets", + "Ġtake away", + "ĠT ABLE", + "ĠClass ical", + "col lection", + "Ġland fall", + "ĠMus cle", + "Ġsett les", + "ĠAD V", + "Ġ3 44", + "L aura", + "Ġf ared", + "ĠPart ial", + "4 36", + "oss ibility", + "ĠD aly", + "ĠT arant", + "ĠFu ji", + "am l", + "c ence", + "55 1", + "ĠProced ures", + "ĠO CD", + "ĠU D", + "t in", + "Q UI", + "ach o", + "4 38", + "Ġgl itches", + "Ġenchant ment", + "Ġcalcul ates", + "IR O", + "ĠH ua", + "alys es", + "ĠL ift", + "um o", + "Ġle apt", + "Ġhypothes ized", + "ĠGust av", + "it ans", + "VERS ION", + "æ ł", + "Rog er", + "Ġr and", + "ĠAd apter", + "Ġ3 31", + "ĠPet ition", + "k ies", + "M ars", + "Ġunder cut", + "ze es", + "ĠLy ons", + "ĠDH CP", + "Miss ing", + "Ġretire es", + "Ġins idious", + "el i", + "> )", + ". ãĢį", + "Ġfinal ists", + "ĠA ure", + "Ġacc user", + "Ġwas tes", + "ĠY s", + "ĠL ori", + "Ġconstitu encies", + "Ġsupp er", + "Ġmay hem", + "or ange", + "Ġmis placed", + "Ġmanager ial", + "Ġex ce", + "ĠCL I", + "Ġprim al", + "ĠL ent", + "Cry stal", + "h over", + "ĠN TS", + "end um", + "Ġd w", + "ĠAl c", + "n ostic", + "Ġpres erves", + "ĠTs arnaev", + "Ġtri pled", + "rel ative", + "Arc ade", + "k illing", + "ĠW EEK", + "ĠH anna", + "D ust", + "Com pleted", + "ģ «", + "Ġappro ves", + "ĠSur f", + "ĠLuther an", + "ven ants", + "Ġrobber ies", + "we ights", + "soft ware", + "at ana", + "ug al", + "Ġgrav y", + "ĠC ance", + "OLOG Y", + "ly ak", + "Ton ight", + "Ġunve il", + "Ġ19 04", + "ĠMin ion", + "ent ious", + "st ice", + "pack ages", + "ĠG EAR", + "Ġg ol", + "ĠHutch inson", + "ĠProf ession", + "ĠG UN", + "ĠDiff erence", + "ĠTsuk uyomi", + "ĠLes bian", + "6 70", + "Ġfug itive", + "ĠPlan etary", + "-------------------------------- ------------------------", + "Ġacc rued", + "Ġch icks", + "Ġsto pp", + "Ġblock ers", + "C od", + "Ġcomment ers", + "ĠSomew here", + "ĠPhot ographer", + "the me", + "Ġmay oral", + "w u", + "Ġanten nas", + "Ġrev amped", + "ĠSubject s", + "it é", + "im ura", + "Ġentr ances", + "liter ally", + "Ġten ets", + "ĠO MG", + "ĠMP H", + "ĠDon key", + "ĠOff ense", + "Ġ\" +", + "Sn ap", + "ĠAF B", + "Ġan imate", + "ĠS od", + "His panic", + "Ġinconsist ency", + "D b", + "F Y", + "Ex port", + "Ġa pe", + "Ġpear l", + "ib el", + "ĠPAC s", + "Ġ{ \\", + "Ġact u", + "ĠHS BC", + "camp us", + "Ġpay off", + "Ġde ities", + "ĠN ato", + "ou ple", + "Ġcens ored", + "ĠCl ojure", + "Ġconf ounding", + "en i", + "Ġreck on", + "op he", + "Ġspot ting", + "Ġsign ifies", + "Ġprop el", + "Ġfest ive", + "S uggest", + "Ġpled ging", + "ĠB erman", + "Ġrebell ious", + "Ġovershadow ed", + "Ġinfiltr ated", + "j obs", + "67 2", + "Ġscal able", + "Ġdomin ion", + "ĠNew foundland", + "ĠMead ow", + "Ġpart itions", + "AM I", + "Ġsupplement ary", + "str ument", + "Ġhair y", + "Ġperpet uate", + "Ġnuts hell", + "ĠPot ato", + "ĠHob bit", + "Ġcur ses", + "Flo at", + "Ġquiet er", + "Ġfuel ing", + "Ġcaps ules", + "ĠL ust", + "ĠH aunted", + "Exec utive", + "Ġchild birth", + "G re", + "Ġrad iant", + "å İ", + "Ġm alls", + "Ġin ept", + "ĠWarrant y", + "Ġspect ator", + "E h", + "t hens", + "Ġculmin ating", + "æ ©", + "ary a", + "ãĤ ®", + "ilit arian", + "ĠOR IG", + "ĠSp ending", + "pt ives", + "ĠS iren", + "ĠRec ording", + "ay ne", + "Ġv im", + "Ġspr ang", + "T ang", + "ĠM FT", + "mor ning", + "ĠWe ed", + "m peg", + "cess ion", + "ĠCh ung", + "7 30", + "w arning", + "56 2", + "handed ly", + "P oor", + "P olitics", + ": #", + "Ġp ian", + "Ġfec es", + "ĠDocument ation", + "Ġban ished", + "Ġ3 99", + "ĠAR C", + "Ġhe inous", + "J ake", + "ĠAm ir", + "way ne", + "v re", + "os henko", + "Ġnotebook s", + "Ġfound ational", + "Ġmarvel ous", + "ixt ape", + "Ġwithdraw als", + "Ġh orde", + "ĠD habi", + "is able", + "ĠK D", + "Ġcontag ious", + "ĠD ip", + "ĠAr rows", + "Ġpronoun s", + "Ġmorph ine", + "ĠB US", + "68 2", + "Ġk osher", + "fin ished", + "ĠInstr uments", + "Ġf used", + "yd en", + "ĠSal mon", + "F ab", + "aff ected", + "K EN", + "C ENT", + "Dom ain", + "Ġpoke mon", + "ĠDr inking", + "G rowing", + "ĠInvestig ative", + "ĠA ether", + "em i", + "Ġtabl oid", + "Ġrep ro", + "ĠNot withstanding", + "ĠBers erker", + "Ġdram as", + "Ġclich é", + "Ġb ung", + "ĠU RI", + "ĠD os", + "0 44", + "Ġpast ors", + "Ġl s", + "Ġac rylic", + "aun ts", + "Ed ward", + "Ġmajor ities", + "B ang", + "Ġfield ing", + "ĠRepl acement", + "ĠAl chemy", + "pp ard", + "ĠRome o", + "ĠSan ct", + "ĠLav rov", + "ib ble", + "Inst ruct", + "Ġimp ractical", + "ĠPlay boy", + "ce phal", + "Ġsw aps", + "Ġk an", + "ĠThe o", + "Ġillust rating", + "Ġdismant led", + "ĠTrans gender", + "ĠG uth", + "UG H", + "Ġtriumph ant", + "Ġencomp ass", + "Ġbook mark", + "udd in", + "j er", + "Ġpred icate", + "ES H", + "Ġwhen ce", + "ĠAB E", + "Ġnon profits", + "Se qu", + "Ġdi abetic", + "Ġp end", + "Ġheart felt", + "sh i", + "Ġinter acts", + "ĠTele com", + "Ġbombard ment", + "dep ending", + "ĠLow ry", + "ĠAd mission", + "ĠBl ooming", + "ust ration", + "ene gger", + "B rew", + "Ġmol ten", + "ĠNer d", + "P IN", + "âĸ Ģ", + "ave ment", + "Ġtou red", + "Ġco efficients", + "ĠTray von", + "ans son", + "Ġsand y", + "t old", + "fl ows", + "Ġpop ulous", + "ĠT inder", + "ĠBl iss", + "R achel", + "Min imum", + "Ġcontest ant", + "ĠRed uce", + "ĠMor se", + "ĠGrass ley", + "ĠClick er", + "Ġexp r", + "Ġs incerity", + "Ġmar qu", + "Ġelic it", + "ĠPro position", + "ĠDemon ic", + "Ġtac os", + "G reek", + "Ġpost war", + "Ġin sofar", + "ĠP ork", + "Ġ35 2", + "doctor al", + "walk ing", + "Ġmid term", + "ĠSam my", + "sight ed", + "ĠTR ANS", + "ic i", + "AL D", + "ĠUS L", + "ĠF ISA", + "ĠAm pl", + "ĠAlex andra", + "ine lli", + "Tr ain", + "Ġsign ify", + "ĠVers us", + "Ġob fusc", + "Ġk h", + "Ġagg ro", + "ĠRen ault", + "Ġ3 48", + "5 18", + "ox icity", + "0 22", + "ĠTw ist", + "Ġgoof y", + "D ynamic", + "Ġbrief ings", + "m ight", + "8 99", + "Ġderog atory", + "T ro", + "Ġfor ging", + "ĠKor an", + "ĠMar ried", + "ĠBuc s", + "Ġpal ate", + "ĠCon version", + "m able", + "4 13", + "Ġ( _", + "Ġs iph", + "ĠN EO", + "col lege", + "Ġmarg inally", + "Ġfl irt", + "ĠTra ps", + "ĠP ace", + "é »Ĵ", + "Ġgoalt ender", + "Ġforb ids", + "Ġcler ks", + "ĠT ant", + "ĠRobb ins", + "ĠPrint ing", + "Ġpremie red", + "Ġmagn ification", + "ĠT G", + "ĠR ouse", + "ĠM ock", + "odynam ics", + "Ġpre clude", + "ism o", + "ĠPul itzer", + "Ġaval anche", + "ĠK odi", + "rib une", + "ĠL ena", + "Elect ric", + "Ġref inery", + "Ġend owed", + "Ġcounsel ors", + "Ġd olphin", + "ĠM ith", + "Ġarm oured", + "hib ited", + "Beg in", + "ĠP W", + "O il", + "ĠV or", + "ĠShar if", + "ĠFraz ier", + "est ate", + "Ġj ams", + "Pro xy", + "Ġband its", + "ĠPresbyter ian", + "ĠPrem iere", + "t iny", + "ĠCru el", + "Test ing", + "Ġhom er", + "ĠV ERS", + "ĠPro l", + "ĠDep osit", + "ĠCoff in", + "Ġsemin ars", + "Ġs ql", + "ĠDef endants", + "Altern atively", + "ĠR ats", + "ç «", + "ethy st", + "' >", + "Ġiss uer", + "58 9", + "Ġch aired", + "ĠAccess ories", + "man ent", + "Ġmar row", + "ĠPrim ordial", + "C N", + "Ġlimit less", + "ĠCarn age", + "Ġund rafted", + "q v", + "IN ESS", + "on ew", + "Ġco hesion", + "98 7", + "Ġne cks", + "Ġfootball er", + "ĠG ER", + "Ġdetect able", + "ĠSupport ing", + "ĠCS V", + "oc ally", + "k Hz", + "Ġund e", + "Ġsh one", + "Ġbud ding", + "tra k", + "Stand ing", + "ĠStar craft", + "ĠKem p", + "Ben ch", + "Ġthw arted", + "ĠGround s", + "ath i", + "L isa", + "Dial og", + "ĠS X", + "V ision", + "Ġingen ious", + "Ù IJ", + "Ġfost ering", + "ĠZ a", + "ĠIn gram", + "Ġ\" @", + "N aturally", + "6 16", + "0 35", + "ĠF AC", + "H mm", + "55 4", + "Ġacceler ator", + "ĠV end", + "Ġsun screen", + "Ġtuber culosis", + "rav iolet", + "ĠFunction al", + "ĠEr rors", + "ed ar", + "19 66", + "ĠSpect re", + "ĠRec ipes", + "88 5", + "ĠM ankind", + "L iverpool", + "Ġ| --", + "Ġsubst itutes", + "ĠX T", + "w ired", + "Ġinc o", + "ĠAf gh", + "E va", + "ic c", + "S ong", + "K night", + "Ġdilig ently", + "ĠBroad cast", + "A id", + "Ġaf ar", + "ĠH MS", + "aton in", + "ĠGr ateful", + "Ġfire place", + "ĠOm ni", + "e uro", + "ĠF RE", + "ĠSh ib", + "ĠDig est", + "t oggle", + "Ġheads ets", + "Ġdiff usion", + "ĠSqu irrel", + "ĠF N", + "Ġdark ened", + "out her", + "Ġsleep s", + "ĠX er", + "gun s", + "Ġset ups", + "Ġpars ed", + "Ġmamm oth", + "ĠCur ious", + "g ob", + "ĠFitz patrick", + "ĠEm il", + "im ov", + "........ .....", + "ĠB enny", + "Second ly", + "Ġheart y", + "Ġcons on", + "st ained", + "Ġgal actic", + "cl ave", + "Ġplummet ed", + "Ġp ests", + "Ġsw at", + "Ġrefer rals", + "ĠLion el", + "h oly", + "Ġunder dog", + "ĠSl ater", + "ĠProv ide", + "ĠAm ar", + "ress or", + "å Į", + "ong a", + "Ġtim id", + "Ġp iety", + "ĠD ek", + "Ġsur ging", + "az o", + "Ġ6 10", + "Ġdes ks", + "ĠSp okane", + "ĠAn field", + "Ġwars hips", + "ĠCob ra", + "Ġar ming", + "clus ively", + "ĠBad ge", + "ag ascar", + "ĠPR ESS", + "ĠMcK enzie", + "ĠFer dinand", + "burn ing", + "Af ee", + "Ġtyr ann", + "ĠI w", + "ĠBo one", + "100 7", + "ĠRe pt", + "Ċ Âł", + "Ġcar avan", + "ĠD ill", + "ĠBundes liga", + "Ch uck", + "Ġheal er", + "ãĥ¼ãĥ Ĩ", + "ĠH obby", + "Ġneg ate", + "Ġcrit iques", + "section al", + "mop olitan", + "Ġd x", + "Ġouts ourcing", + "ĠC ipher", + "t ap", + "Sh arp", + "Ġup beat", + "Ġhang ar", + "Ġcru ising", + "ĠNi agara", + "Ġ3 42", + "ill us", + "ĠS v", + "Ġsubt itles", + "Ġsqu ared", + "Ġbook store", + "Ġrevolution aries", + "ĠCarl ton", + "ab al", + "Ut ah", + "Ġdesp ise", + "ĠU M", + "cons ider", + "aid o", + "Ġc arts", + "ĠT urtles", + "Tr aining", + "Ġhonor ary", + " ¢", + "Ġtri angles", + "4 22", + "Ġreprint ed", + "Ġgrace ful", + "ĠMong olia", + "Ġdisrupt ions", + "ĠB oh", + "Ġ3 49", + "Ġdr ains", + "Ġcons ulate", + "Ġb ends", + "Ġm afia", + "ur on", + "ĠF ulton", + "m isc", + "Ġren al", + "Ġin action", + "ck ing", + "Ġphot ons", + "Ġbru ised", + "ĠC odes", + "og i", + "Ġn ests", + "ĠLove ly", + "ĠLib re", + "ĠD aryl", + "Ġ# ##", + "S ys", + ". ,\"", + "Ġfree zes", + "est ablishment", + "and owski", + "Ġcum bers", + "ĠSt arg", + "ĠBom bs", + "Ġleg ions", + "Ġhand writing", + "Ġgr un", + "ĠC ah", + "sequ ent", + "Ġm oth", + "ĠMS M", + "Ins ert", + "F if", + "Ġmot el", + "Ġdex ter", + "ĠB ild", + "hearted ly", + "Ġpro pe", + "ĠText ure", + "ĠJ unction", + "ynt hesis", + "oc ard", + "ĠVer a", + "ĠBar th", + "Ġμ g", + "Ġl ashed", + "Ġ35 1", + "ĠZ amb", + "ĠSt aples", + "ĠCort ex", + "ĠCork er", + "Ġcontinu um", + "ĠWR ITE", + "unt a", + "rid or", + "Ġde ems", + "0 33", + "ĠG OLD", + "p as", + "Ġrep ressive", + "ãĥĨ ãĤ£", + "Ġbaff led", + "Sc ar", + "Ġc rave", + "Ġ ______", + "Ġentrepreneurs hip", + "ĠDirector ate", + "Ġ' [", + "Ġv ines", + "Ġasc ended", + "ĠGR OUP", + "ĠGood bye", + "Ġdo gged", + "ãĥ´ ãĤ¡", + "Man ufact", + "Ġunimagin able", + "ri ots", + "ier rez", + "Ġrel ativity", + "ĠCraft ing", + "ra ught", + "ud en", + "c ookie", + "Ġassass ins", + "Ġdissatisf ied", + "ac ci", + "Ġcondu it", + "Sp read", + "ĠR ican", + "n ice", + "izz le", + "Ġsc ares", + "ĠWH Y", + "ph ans", + "5 35", + "Ġprot racted", + "ĠKrist en", + "5 36", + "ĠSc rib", + "ĠNe h", + "Ġtwent ies", + "Ġpredic ament", + "Ġhandc uffs", + "Ġfruit ful", + "ĠU L", + "ĠLud wig", + "Ġatt est", + "ĠBre aker", + "Ġbi ologically", + "ĠDeal er", + "Ġrenov ations", + "f w", + "ess en", + "Al ice", + "ĠHen ri", + "Ġun ilaterally", + "ĠS idd", + "h ai", + "ĠSt retch", + "S ales", + "Ġcumbers ome", + "ĠJ avier", + "Ġtrend y", + "Ġrot ting", + "ĠChall enges", + "Ġscra ps", + "Ġfac ets", + "ĠVer onica", + "ĠVer ge", + "ĠS ana", + "Al ien", + "ĠR ih", + "Ġrad ial", + "ect ar", + "Ġ6 30", + "cl i", + "Mar ie", + "Ġwild fire", + "ĠCat o", + "h ander", + "Ġwait ress", + "Ġch ops", + "ĠS ECTION", + "Ġblunt ly", + "ĠCat alog", + "n ian", + "stud y", + "Ġpat rolling", + "ĠT enth", + "nex us", + "ĠN ON", + "op sy", + "Ġsc athing", + "s ie", + "Ġdeterior ated", + "V B", + "Naz is", + "Ġdep ictions", + "Ġauthent icated", + "ĠCon ce", + "k rit", + "Ġpromul g", + "ĠL ONG", + "U FC", + "ĠVis itors", + "ĠRec all", + "Ġrehab ilit", + "ĠSL I", + "Ġglac ier", + "ĠB ite", + "Ġ50 3", + "Ġvom it", + "Ġfer mented", + "ĠKh alid", + "Ġgrad ed", + "ĠMag icka", + "ĠIch igo", + "power ful", + "ic ators", + "75 3", + "Ġsh rew", + "Ġ35 6", + "Ġlegal izing", + "Ġall otted", + "ĠArch demon", + "ith ing", + "igg urat", + "V OL", + "Le od", + "Ġo ily", + "Ġindu cing", + "Ġamy gdala", + "Ġadm ins", + "ĠAcqu isition", + "C AN", + "Ġsche matic", + "Ġmo an", + "ĠCamer oon", + "Ġt ink", + "Ġmer ry", + "Ġbutter flies", + "ĠGo ff", + "Ġworks pace", + "ĠCor ona", + "Ġj avascript", + "ĠD olphin", + "ĠCant or", + "4 64", + "to e", + "AP S", + "ĠAg ing", + "Ġpadd ed", + "ĠZ heng", + "ĠHe ld", + "Ġest ranged", + "Ġ7 70", + ". }", + "ĠDun ham", + "Ġsm okes", + "Ġcap itals", + "und ai", + "Sh in", + "ĠFound ing", + "Ġent itle", + "Ġcenter piece", + "D iscover", + "Ġthere to", + "al ert", + "ĠN ou", + "ĠAnaly st", + "l c", + "F H", + "FI ELD", + "ĠP OV", + "gr ay", + "Ġar cs", + "ĠH OT", + "Ġr s", + "Ġoblig atory", + "ĠArchitect s", + "ĠS ven", + "ĠF EC", + "0 200", + "Christ mas", + "ĠAlban ia", + "rat om", + "58 7", + "Ġhard ships", + "Ġaut os", + "ĠCharg es", + "Ġap es", + "Ġ3 76", + "wal let", + "Ġintox ication", + "Ġgobl in", + "Ġ5 70", + "++++++++ ++++++++", + "ĠYel p", + "ĠMag netic", + "ĠBr iggs", + "R ail", + "Ġspawn s", + "ĠW iggins", + "Ġshowc ased", + "Ġres orted", + "ub en", + "Ġwh ipping", + "Ġim itate", + "Ġdigest ion", + "ĠUS PS", + "ĠG est", + "Ġye a", + "ĠT ight", + "ind al", + "ic as", + "` .", + "C AST", + "'' ;", + "ĠF et", + "opath ic", + "In valid", + "Ġregrett ed", + "Ġbro ccoli", + "ĠSc ores", + "e ve", + "Ġpost ings", + "Ġaccum ulating", + "Ġneed less", + "elf th", + "Ġmay ors", + "Ġsc rib", + "Ġanecd otes", + "Ġbot ched", + "ĠRib bon", + "ĠConstant ine", + "i uses", + "ess es", + "Ġdev ise", + "Comp ared", + "Ġp udding", + "Ġg arg", + "Ġev oke", + "79 7", + "Ġdet ox", + "9 09", + "ĠPie ces", + "ĠMcC artney", + "Ġmet ast", + "ĠK rypt", + "P OR", + "Ġt ending", + "ĠMerch ants", + "Pro of", + "ĠV arg", + "ĠPort able", + "ãĥ¼ãĥĨ ãĤ£", + "B rain", + "25 00", + "Ġfol iage", + "Ø ¹", + "Ġment ors", + "ĠA ires", + "Ġminimal ist", + "Ġing ested", + "ĠTro jan", + "ĠQ ian", + "inv olved", + "0 27", + "Ġer oded", + "RA FT", + "Ġbl urry", + "M ob", + "Ġbuff et", + "ĠFn atic", + "ae a", + "KN OWN", + "ĠIn it", + "s afety", + "en um", + "ACT ION", + "ĠCrus her", + "ĠD ates", + "Ġ ................", + "c alling", + "ak ov", + "Ġvent ured", + "Ġ5 55", + "au ga", + "H art", + "ĠA ero", + "M AC", + "Ġthin ly", + "Ġar ra", + "ST ATE", + "ild e", + "ĠJac qu", + "ĠFem ales", + "Ġthe orem", + "Ġ3 46", + "Ġsmart est", + "ĠPU BLIC", + "ĠK ron", + "ĠB its", + "ĠV essel", + "ĠTele phone", + "Ġdec ap", + "Ġadj unct", + "ĠS EN", + "mer ga", + "Ġred acted", + "Ġpre historic", + "Ġexplan atory", + "ĠRun s", + "ĠUtt ar", + "ĠM anny", + "ĠAUTH OR", + "ĠUnle ashed", + "ĠBow ling", + "be ans", + "79 3", + "Ġunivers es", + "Ġsens it", + "ĠK ung", + "re peat", + "ctr l", + "Ġp aced", + "Ġfull er", + "Cl ock", + "Ġrec omb", + "ĠF aul", + "ĠB unker", + "Ġpool ed", + "Ġan a", + "ĠM outh", + "LL OW", + "hum ane", + "Ġbull do", + "ĠMicha els", + "f am", + "Ġwreck ed", + "Ġport rays", + "ĠWh ale", + "ĠH es", + "Ġguess es", + "ĠBrow se", + "ĠL APD", + "Ġconsequ ential", + "ĠInn ocent", + "ĠD RAG", + "Ġtrans gress", + "ĠO aks", + "Ġtri via", + "ĠRes on", + "ĠA DS", + "-- +", + "ĠT oll", + "Ġgrasp ing", + "ĠTHE M", + "ĠT ags", + "ĠCon clusion", + "Ġpract icable", + "Ġho op", + "Ġunintention ally", + "Ġign ite", + "ĠM ov", + "ur ized", + "le hem", + "Ter min", + "Ġcolour ful", + "ĠLin ear", + "ĠEll ie", + "G y", + "Ġman power", + "Ġj s", + "Ġem oji", + "ĠSHAR ES", + "_ .", + "0000 7", + "Ġsophistic ation", + "Ġunders core", + "Ġpract ise", + "Ġbl ob", + "op ens", + "Uk raine", + "Ke eping", + "Y C", + "J R", + "ult imate", + "Cl aim", + "Ġautom obiles", + "99 3", + "ste el", + "Ġpart ing", + "ĠL ank", + "... ?", + "Ġ38 5", + "Ġremem brance", + "Ġe ased", + "Ġcov ari", + "ĠS ind", + "Effect ive", + "Ġdisse mination", + "ĠMo ose", + "ĠCl apper", + "br ates", + "App ly", + "Ġinv is", + "Ġwors ened", + "âĢĶ -", + "Ġlegisl ator", + "ĠL ol", + "ĠRow e", + "Ġdealers hip", + "um ar", + "id ences", + "Ġinvestig ates", + "Ġc ascade", + "Ġbid der", + "ĠB EN", + "Iron ically", + "Ġpres iding", + "Ġd ing", + "Ġcontrad icted", + "Ġshut s", + "ĠF IX", + "Ġ3 66", + "Dist rict", + "Ġsin ful", + "ĠChar isma", + "o ops", + "Ġtot ality", + "Ġrest itution", + "ĠOpt imus", + "ĠD ah", + "Ġcl ueless", + "urn ed", + "Ġnut rit", + "Ġland owners", + "Ġfl ushed", + "Ġbroad en", + "m ie", + "Ġprint ln", + "Ġn ig", + "ĠCorp us", + "J en", + "Ġprot o", + "ĠWik imedia", + "ĠPal o", + "C OR", + "Ġstory lines", + "Ġevangel icals", + "ĠDar rell", + "Ġrot or", + "ĠH W", + "sk illed", + "ery l", + "Ġbe gg", + "ĠBl umenthal", + "Ġwe aving", + "Ġdown wards", + "ĠJack et", + "ĠANG EL", + "Te chnology", + "Ġes oteric", + "alde hyde", + "Ġfur iously", + "Ġforeign er", + "We ak", + "CH O", + "ĠH ound", + "Exper ience", + "ĠPlay station", + "ĠM IA", + "ĠU ng", + "cl oth", + "ag all", + "Ġcal ming", + "iz ens", + "St ruct", + "ĠW itches", + "ĠCeleb ration", + "Ġ........ ......", + "pt roller", + "ĠTC U", + "Ġb unny", + "ãĥ į", + "ut orial", + "Ġup scale", + "ĠSt a", + "ĠCol ossus", + "Ġchlor ide", + "ĠZ ac", + "ĠRe asons", + "ĠBrook ings", + "ĠWH ITE", + "][ /", + "ĠL ose", + "9 05", + "Ġunders ide", + "ern els", + "Ġv ape", + "do zen", + "upp et", + "ĠST OP", + "mat ical", + "ĠStat ements", + "hed dar", + "P AC", + "Custom er", + "Ġmem os", + "ĠP J", + "end ars", + "ĠLim its", + "l augh", + "Ġstabil ized", + "ĠALE C", + "Y A", + "Up grade", + "al am", + "Ġtechn o", + "Ġan ew", + "fore seen", + "Ġcolleg iate", + "ĠPy ro", + "ĠD ism", + "Ġfront line", + "Ġammon ia", + "I U", + "Qu ite", + "John ny", + "ass in", + "G OP", + "ĠSt yles", + "ĠSovere ign", + "acter ial", + "5 49", + "ĠR IP", + "ĠL ists", + "Ġ3 64", + "ĠRece p", + "s ocket", + "ĠByr d", + "ĠCand le", + "An cient", + "Ġappell ant", + "en forcement", + "ace a", + "ans ki", + "Ġold s", + "88 6", + "Ġsl urs", + "Ġem pires", + "Ġbuck le", + "Ġalien ation", + "ĠAber deen", + "Ġunic orn", + "Ġoverr iding", + "ĠL X", + "pp a", + "Ġdesp ised", + "ĠB ugs", + "ĠB ST", + "S outhern", + "5 33", + "Ġhall mark", + "ĠPost er", + "Ġstem med", + "Ġprincip als", + "ĠT ECH", + "ĠSand wich", + "It aly", + "Ġche esy", + "ĠSet TextColor", + "ĠProt ective", + "ĠC ohn", + "J O", + "apt op", + "Re ason", + "Lead er", + "ĠUnder stand", + "ĠFr idays", + "ĠContin uous", + "Ġcl ipping", + "ĠR ye", + "Ġber th", + "tim er", + "ann is", + "re act", + "Ġbuff alo", + "ĠPar as", + "Ġ6 55", + "Ġpres ided", + "ĠSun rise", + "Ġve ts", + "Ġcl oves", + "ĠMcC ull", + "Stre ngth", + "G AN", + "Ġill iter", + "ĠPric ing", + "l é", + "Ġresist or", + "Ġbr un", + "ĠSuff olk", + "Ñ ĭ", + "ĠL iver", + "Re leased", + "Ġwhat s", + "8 60", + "ĠMe asures", + "Ġden ouncing", + "ĠRy zen", + "Ġsou ven", + "Ġcareg ivers", + "ch ini", + "ĠScar lett", + "Ġt rough", + "Cong ratulations", + "Ġtax is", + "ĠTrad ition", + "j it", + "Ġtable top", + "Ġhither to", + "Ġdis information", + "off ensive", + "h ra", + "ĠDISTR ICT", + "Ġcompl icate", + "chen ko", + "ĠRecon struction", + "Ġpalp able", + "Ġa usp", + "Ġ4 28", + "Ġshowc ases", + "ĠPublic ation", + "know ledge", + "inn on", + "4 19", + "Ġretri eval", + "and ers", + "Ġref ute", + "Ġinqu ired", + "g ur", + "Ġneg ativity", + "Ġcons erve", + "Ġafter life", + "Ġpres upp", + "ĠGill espie", + "Ġm t", + "ĠD N", + "T ap", + "Ġper pend", + "ĠS my", + "does n", + "Ġsp illing", + "Ġhyp ers", + "K ate", + "® ,", + "ke pt", + "ĠP owered", + "Ġj a", + "ĠK lux", + "ard e", + "ab an", + "Ġ4 44", + "Ġflatt ened", + "ĠImprove ments", + "urg a", + "ĠK und", + "Ġins cribed", + "Ġfac ult", + "Ġunpre pared", + "ĠCons umers", + "Ġsatisf ies", + "Ġpul monary", + "Ġinf iltration", + "Ġex ternally", + "Ġcongrat ulations", + "ag han", + "Ġair liner", + "Ġfl ung", + "Ġfly ers", + "G D", + "Ġsnipp ets", + "Ġrec ursive", + "Ġmaster ing", + "L ex", + "Ġovert ly", + "v g", + "Ġluck ily", + "Ġenc ro", + "ĠLanc et", + "ĠAbyss al", + "function al", + "Ġs ow", + "Ġsqu id", + "Ġnar ration", + "Ġn aughty", + "ĠHon our", + "ĠSpart ans", + "Ġsh atter", + "ĠTac oma", + "ĠCal ories", + "ĠR aces", + "Sub mit", + "Ġpurpose fully", + "w av", + "ĠY ok", + "F est", + "ĠG err", + "Met ro", + "Ġit iner", + "f amous", + "Ġ\" {", + "in line", + "was her", + "Iss ue", + "ĠCL IENT", + "oz o", + "Vers ions", + "7 25", + "ĠGl ock", + "Ġshield ed", + "ĠPC R", + "ENC Y", + "ĠWe ld", + "ĠSim pl", + "Ġredirect ed", + "ĠK ham", + "Ġ( >", + "Ġlab ou", + "Ġdi apers", + "ss l", + "Ġcell ar", + "organ isms", + "ore sc", + "ĠBer ks", + "did n", + "Sh ipping", + "C hest", + "Ġund one", + "Ġmillion aire", + "Ġc ords", + "ĠYoung er", + "appropri ately", + "Ġsequ els", + "u ve", + "ant icipated", + "Ġle wd", + "ĠSh irt", + "ĠDmit ry", + "V eter", + "Ġsl aying", + "ĠY ar", + "Ġcompl ication", + "I owa", + "ĠEric a", + "ĠBL M", + "g irlfriend", + "b odied", + "6 26", + "19 63", + "Ġintermedi ary", + "Ġcons olation", + "M ask", + "ĠSi em", + "ow an", + "Beg inning", + "Ġfix me", + "Ġculmin ated", + "Ġcon duc", + "ĠVolunte er", + "Ġpos itional", + "Ġgre ets", + "ĠDefin itions", + "Ġthink er", + "Ġingen uity", + "Ġfresh men", + "ĠMom ents", + "Ġ35 7", + "ate urs", + "ĠFed Ex", + "s g", + "69 4", + "Ġdwind ling", + "ĠBO X", + "sel age", + "Ġt mp", + "Ġst en", + "ĠS ut", + "Ġneighbourhood s", + "Ġclass mate", + "f ledged", + "Ġleft ists", + "Ġclim ates", + "ATH ER", + "ĠScy the", + "ul iffe", + "Ġs ag", + "Ġho pped", + "ĠF t", + "ĠE ck", + "ĠC K", + "ĠDo omsday", + "k ids", + "Ġgas ped", + "Ġmon iker", + "ĠL od", + "ĠC FL", + "t ions", + "r ums", + "fol ios", + "Ġm d", + "Ġunc anny", + "Ġtrans ports", + "ĠLab rador", + "Ġrail ways", + "Ġappl iance", + "ĠCTR L", + "æ Ģ", + "Pop ulation", + "ĠConfeder acy", + "Ġunb earable", + "Ġdors al", + "ĠIn form", + "op ted", + "ĠK ILL", + "Mar x", + "Ġhypoc ritical", + "q us", + "ĠN umerous", + "ĠGeorg ian", + "ĠAmbro se", + "ĠL och", + "Ġgu bernatorial", + "ĠX eon", + "ĠSupp orts", + "ens er", + "ee ly", + "ĠAven ger", + "19 65", + "Ar my", + "Ġju xtap", + "Ġcho pping", + "ĠSpl ash", + "ĠS ustainable", + "ĠFin ch", + "Ġ18 61", + "ict ive", + "at meal", + "ĠG ohan", + "Ġlights aber", + "ĠG PA", + "ug u", + "ĠRE PL", + "vari able", + "Ġher pes", + "Ġdesert s", + "ac iously", + "Ġsitu ational", + "week ly", + "ob l", + "Ġtext ile", + "ĠCorn wall", + "Ġcontrace ptives", + "ĠA ke", + "] -", + "ä¹ ĭ", + ": ,", + "ĠW em", + "ĠB ihar", + "Ġ' .", + "Ġbe re", + "Ġanal ogue", + "ĠCook ies", + "Ġtake off", + "Whe el", + "Ġmaj estic", + "Ġcomm uting", + "0 23", + "ĠCor pse", + "ass ment", + "min i", + "Ġgor illa", + "ĠAl as", + "ere e", + "Ġacquaint ances", + "ĠAd vantage", + "Ġspirit ually", + "Ġey ed", + "pm wiki", + "ĠE nder", + "Ġtrans lucent", + "Ġnight time", + "ĠIM AGES", + "5 45", + "ĠK amp", + "ĠFre ak", + "Ġ ig", + "Port land", + "4 32", + "ĠM ata", + "Ġmar ines", + "Ġh ors", + "ater asu", + "ĠAtt ribution", + "Ġ-------- -", + "Ġk ins", + "ĠBEL OW", + "++ +", + "Ġre eling", + "ol ed", + "Ġcl utter", + "ĠRel ative", + "Ġ4 27", + "B US", + "Ġa vert", + "ĠChe ong", + "ĠA ble", + "ĠPry or", + "Develop er", + "Ġen cyclopedia", + "ĠUSA F", + "ĠG arry", + "Sp ain", + "Bl ocks", + "Ġexp osition", + "ĠGamer Gate", + "W OR", + "Ġstockp ile", + "Ġclot hed", + "ĠT one", + "ĠR ue", + "t umblr", + "Ġtreacher ous", + "Ġf rying", + "Ñ Į", + "ĠS ph", + "Ġrest raints", + "Ġemb odies", + "ĠG es", + "S afety", + "Ġnegoti ators", + "min ing", + "ĠAppalach ian", + "L OS", + "ĠJenn a", + "Ġpass ers", + "ç ĭ", + "sn ap", + "Ġshort en", + "creat or", + "Ġinn umerable", + "uther land", + "67 4", + "ĠW OM", + "ĠAs cend", + "ĠArm ory", + "ĠTrans action", + "K ick", + "Ġsuit case", + "day Name", + "Ġwaste ful", + "mar riage", + "ĠMcC abe", + "ite ch", + "ĠO ss", + "Cl osure", + "ĠTreasure r", + "Ġindec ent", + "ĠD ull", + "Ġresid ences", + "19 59", + "ĠS ettlement", + "Ham ilton", + "Ġself ies", + "ĠRank ing", + "ĠBark ley", + "ĠB ore", + "ĠW CS", + "ĠMar itime", + "ĠH uh", + "ĠForest ry", + "Ġcultiv ating", + "ĠBall ard", + "Ġg arrison", + "ĠSD L", + "9 30", + "Ġnas cent", + "Ġirresist ible", + "Ġaw fully", + "\\/ \\/", + "Ġequ ate", + "Ġanthrop ology", + "ĠSylv ia", + "Ġintest ine", + "Ġinnoc uous", + "cess ive", + "ag ra", + "ĠMet roid", + "G rant", + "8 55", + "ģ ĸ", + "Ġ\" _", + "ãĥĥ ãĥī", + "Ġappra isal", + "ĠFred dy", + "04 6", + "Ġ40 6", + "Ġ18 30", + "Ġd ocking", + "St atic", + "Ġp ont", + "ĠVolt age", + "ĠSt ead", + "ĠMort gage", + "ĠJon ah", + "Y L", + "CLASS IFIED", + "Ġas bestos", + "nik ov", + "Ġcoll agen", + "ĠOrb ital", + "P ocket", + "7 99", + "Ġhy brids", + "inc hes", + "Ġinv oice", + "und y", + "Ġinequ alities", + "T rend", + "w ashed", + "B ALL", + "Ġluc id", + "ĠComment ary", + "Ġw itty", + "Br andon", + "Ġbru ising", + "Ġ6 20", + "es cent", + "box ing", + "P OL", + "Ġ3 78", + "R ect", + "Ġlic ences", + "ĠMcG ee", + "p ressed", + "D anny", + "Ġj ammed", + "ord inate", + "Ġle th", + "Ġdistingu ishes", + "ĠYam aha", + "IL S", + "ĠH ume", + "ĠC ategories", + "Rober ts", + "Ch art", + "Ġbeet le", + "ĠGra veyard", + "Ġ($ )", + "o ÄŁ", + "Ġtw ilight", + "are lla", + "á ½", + "Ġbooth s", + "ĠH HS", + "ĠFeld man", + "Ġexcav ation", + "Ġphilosoph ies", + "at ography", + "ĠGar age", + "te chnology", + "Ġunfor gettable", + "Ġver ifying", + "Ġsubord inates", + "E ls", + "Ġne b", + "G aming", + "EN A", + "ĠAchieve ment", + "it ters", + "ĠG abe", + "Ġd umps", + "for cer", + "Ġpo ignant", + "ĠM BA", + "ĠHe idi", + "ime i", + "Ġm ages", + "Ġliber ate", + "Ġcircum cised", + "ĠMer maid", + "ĠMat th", + "t ogether", + "ĠW ichita", + "Ġstore front", + "ĠAd in", + "V II", + "Four th", + "Ġexplore rs", + "W ER", + "Not able", + "Bro ok", + "m ens", + "F aith", + "-------- -", + "ĠJ ou", + "¬ ¼", + "Ġpine apple", + "Ġam alg", + "el n", + "ark able", + "ĠãĤµ ãĥ¼ãĥĨãĤ£", + "ĠãĤµãĥ¼ãĥĨãĤ£ ãĥ¯ãĥ³", + "Ġov arian", + "ĠE choes", + "Ġhairc ut", + "Ġp av", + "Ġch illed", + "anas ia", + "Ġsty led", + "Ġd ab", + "ni per", + "Ġminister ial", + "ĠD UP", + "T an", + "Ġsul ph", + "ĠD eter", + "ĠBo hem", + "od an", + "Ġeduc ator", + "â ĵĺ", + "sp ir", + "Ch icken", + "ĠE leanor", + "Ġqu i", + "Ġheav iest", + "Ġgrasp ed", + "U RA", + "Ġcro oked", + "Jess ica", + "pro blem", + "Ġpred etermined", + "Ġman iac", + "Ġbreath s", + "ĠLauder dale", + "Ġh obbies", + "y z", + "Cr ime", + "Ġcharism a", + "d L", + "Ġle aping", + "Ġk ittens", + "Ang elo", + "ĠJ ACK", + "ĠSu zanne", + "Ġhal ting", + "ENT ION", + "Ġswall owing", + "ĠEarthqu ake", + "Ġeight eenth", + "ĠN IC", + "ĠIN F", + "ĠCons cious", + "Ġparticular s", + "circ le", + "7 40", + "Ġbene volent", + "Ġ7 47", + "Ġ4 90", + "Ġr undown", + "ĠVal erie", + "ĠB UR", + "Ġcivil isation", + "ĠS chn", + "W B", + "ot ide", + "intern ational", + "Ġj ohn", + "Ġ19 02", + "Ġpe anuts", + "Ġflav ored", + "k us", + "Ġro ared", + "Ġcut off", + "é £", + "Ġorn ament", + "Ġarchitect ures", + "Ġ3 69", + "ol or", + "ĠWild e", + "ĠC RC", + "ĠAdjust ed", + "Ġprov oking", + "land ish", + "Ġrational ity", + "Ġjust ifies", + "Ġdisp el", + "Ġa meric", + "ĠPol es", + "Ø ©", + "Ġen vis", + "ĠD oodle", + "ä½ ¿", + "igs aw", + "auld ron", + "Techn ical", + "T een", + "up hem", + "ĠX iang", + "Ġdetract ors", + "ĠZ i", + "ĠJournal ists", + "Ġconduc ive", + "ĠVolunte ers", + "Ġs d", + "Know ing", + "Ġtrans missions", + "ĠPL AN", + "ĠL IB", + "Ġall uded", + "Ġob e", + "Ġd ope", + "ĠGold stein", + "Ġwavelength s", + "ĠDest ination", + "nd a", + "ug i", + "Ġattent ive", + "ĠLe an", + "ral tar", + "Ġman g", + "mb uds", + "ak ings", + "b ender", + "Ġacc ol", + "Ġcraw led", + "N OW", + "Min nesota", + "Ġflour ished", + "ĠZ up", + "ĠSuper visor", + "ĠOliv ier", + "Ex cellent", + "Ġwid en", + "D one", + "Ġw ig", + "Ġmiscon ceptions", + "Cor p", + "W an", + "Ġvener able", + "ĠNot ably", + "ĠKling on", + "an imate", + "Bo ost", + "ĠS AY", + "miss ing", + "ibli ography", + "mel on", + "Ġpay day", + "Ø ³", + "bo le", + "Ġve iled", + "ĠAl phabet", + "It alian", + "Ġever lasting", + "ĠR IS", + "ĠC ree", + "rom pt", + "Ġh ating", + "Ġgrin ning", + "Ġge ographically", + "OS H", + "Ġwe eping", + "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", + "Ġimpe cc", + "Let ter", + "Ġblo ated", + "PL A", + "ĠFe in", + "Ġper sever", + "Th under", + "Ġa ur", + "ĠR L", + "Ġpit falls", + "âĸ º", + "Ġpredomin ant", + "Ġ5 25", + "7 18", + "AP E", + "7 14", + "Ġfarm land", + "ĠQ iao", + "Ġv iolet", + "ĠBah amas", + "Ġinflic ting", + "ĠE fficiency", + "Ġhome brew", + "Ġundert ook", + "Ġcur ly", + "ĠHard ing", + "man ia", + "59 6", + "Ġtem pered", + "Ġhar rowing", + "ĠP ledge", + "ĠFranken stein", + "è ª", + "M otion", + "Ġpredict ably", + "ĠExpl osion", + "oc using", + "er d", + "col o", + "FF ER", + "Ġback field", + "ĠV IDE", + "ue bl", + "N arr", + "ĠArg ument", + "Ġgen omic", + "Ġbout ique", + "Ġbatt ed", + "ĠB inary", + "Ġg amb", + "ĠRh ythm", + "67 3", + "Ġa float", + "ĠOlymp ia", + "Y ING", + "Ġend if", + "is in", + "Ġwin ters", + "Ġsc attering", + "I v", + "D istance", + "Ġtr u", + "ĠCom fort", + "Ġne xus", + "Ġair flow", + "ĠByz antine", + "p ayers", + "con i", + "ĠB etsy", + "D eal", + "ĠN ug", + "ĠContin ent", + "red ibly", + "Ġoptim izing", + "al beit", + "Ġec static", + "ĠPro to", + "ç ·", + "iv ot", + "âĸ Ħ", + "em p", + "rou nder", + "Ġcl out", + "ĠI ST", + "66 3", + "ĠDoll ars", + "ĠD AC", + "Ġsubsc ribed", + "Ġrehears al", + "Ġam ps", + "ĠSh ang", + "es m", + "Ġspr inkle", + "Ġassail ant", + "ĠO o", + "ĠCoin base", + "T act", + "Ġret ina", + "Ġn uns", + "R ON", + "att o", + "Ġj ug", + "ĠSV G", + "Ġb ikini", + "ĠFI LE", + "ĠFound ers", + "ep ort", + "ĠK P", + "Ġrest ores", + "ĠTh ick", + "Ġash ore", + "Ġappro vals", + "R ender", + "M AG", + "G raham", + "ĠCort ana", + "ãĥ³ ãĤ¸", + "ss h", + "or ians", + "ars ity", + "ĠInsp ired", + "u pper", + "Ġsign alling", + "Ġreb uke", + "Ġfl ares", + "Ġdownt ime", + "Stud ies", + "Ġstagn ation", + "ĠSequ ence", + "Ġgr unt", + "Ġass ures", + "ĠPL A", + "59 2", + "Ġintra ven", + "d epend", + "Sus an", + "ĠManz iel", + "Man ia", + "Cont ract", + "Ġsl ams", + "Ġcult ured", + "Ġcred itor", + "L IST", + "ĠH UM", + "ĠChatt anooga", + "serv ed", + "Ġclo aked", + "ĠF TP", + "p owder", + "ĠSt ella", + "uct ive", + "Ġcheap ly", + "ĠMU CH", + "ĠGalile o", + "Ġsu ites", + "spe ech", + "Ġdeliber ations", + "ĠCh ips", + "« ĺ", + "Bal ance", + "ĠWyn ne", + "ĠAk ron", + "Ass et", + "Ġhon oured", + "Ġed ged", + "Like wise", + "anim ous", + "ĠW age", + "ĠEz ek", + "ad vertisement", + "ĠRT X", + "ĠM AD", + "Ġmigr ating", + "ĠS QU", + "Ġ4 75", + "Ed ited", + "Ġshorth and", + "ĠBas ics", + "Ġcro tch", + "ĠEV EN", + "Ġv m", + "effic iency", + "Ġcal ves", + "ĠF rie", + "ĠBrill iant", + "Ġstri kers", + "Ġrepent ance", + "Ġarter ies", + "r l", + "B ed", + "h ap", + "Ġcrypt ography", + "ĠSab res", + "Ġ4 14", + "vi ks", + "ih ara", + "aps es", + "T alking", + "Ġintertw ined", + "Ġdoc ks", + "Ġalle le", + "ĠArt ifact", + "ĠH IM", + "t orn", + "ç ķ", + "Ġop acity", + "ĠE ly", + "os uke", + "Ġn ipple", + "Ġhand written", + "ĠV K", + "ĠChamber lain", + "ĠLa os", + "ig raph", + "g row", + "Ġtr illions", + "Ġdescend ant", + "ĠSail or", + "as uring", + "Ġce ilings", + "ĠWare house", + "f lying", + "ĠGl ow", + "Ġn ont", + "Ġmiscar riage", + "Ġrig s", + "Ġmin istries", + "Ġelabor ated", + "Ġdel usional", + "ĠHum ane", + "Ġ3 79", + "n ets", + "Ġblack out", + "add ers", + "Ġn p", + "ĠT ire", + "ro sc", + "Ġsub div", + "Ġlink age", + "Ġchron ological", + "ĠHER O", + "Ġres ettlement", + "ĠVin yl", + "Ġpast oral", + "ĠMob il", + "ĠBar bar", + "Co oldown", + "ĠF ritz", + "c riminal", + "re pe", + "Ġbell ig", + "ĠBre ed", + "Ġ4 18", + "Ġsem blance", + "ij k", + "Ġcur tail", + "Ġclin ch", + "cont ained", + "ĠProm pt", + "ast on", + "Ġw i", + "Ġpursu its", + "5 15", + "ĠGl oss", + "Ġfl ips", + "Ġcoup ons", + "Ġcl oning", + "ĠLike ly", + "Rem oved", + "ĠQu artz", + "r ices", + "ĠSpe ars", + "Ġp ious", + "Ġdep reciation", + "ĠD are", + "oun ces", + "am az", + "O nt", + "Ġp innacle", + "d ocker", + "0 26", + "ĠW yr", + "ĠPro per", + "Ë Ī", + "n il", + "By tes", + "Ġseek er", + "t rial", + "Ġunf olds", + "ĠMar se", + "Ġextravag ant", + "ĠSurviv ors", + "RED ACTED", + "ĠSpeed way", + "ĠCra igslist", + "sub mit", + "ĠGener ations", + "Ġup holding", + "Ġblood stream", + "ĠMiss ions", + "ĠL awn", + "Ġlim bo", + "ene i", + "H uh", + "ĠWild cats", + "pre p", + "ĠMark us", + "ĠFor bidden", + "rit ic", + "IN O", + "Ġexhib iting", + "requ ent", + "ch uk", + "Ġhabit ual", + "ĠComp atibility", + "Dr ag", + "RIP T", + "uj ah", + "GR OUND", + "Ġdelinqu ent", + "Ġburn er", + "Ġcontempor aries", + "Ġgimm ick", + "load s", + "Ġno zzle", + "p odcast", + "ĠW ak", + "ĠStat en", + "ĠK uh", + "ãģ ĵ", + "inter rupted", + "Ġinv incible", + "ĠBurn ett", + "cig arette", + "ĠPeb ble", + "ĠTem porary", + "ĠMar ino", + "58 2", + "Ġwast eland", + "ident ly", + "T x", + "Ġr ite", + "ĠPan asonic", + "ĠM iddles", + "ĠHort on", + "ae us", + "Ġc uring", + "Ġm ats", + "Ġadj ourn", + "Ġfears ome", + "pe z", + "bo ats", + "Ġpro pell", + "Ġconflic ted", + "ĠAng er", + "Ġinsurg ent", + "K arl", + "Ġco ales", + "Ġsouth western", + "Ġdis su", + "ĠO vert", + "******** ****", + "Ġbox ed", + "ĠBr une", + "aa a", + "Ġgard ening", + "ĠEng el", + "tr acks", + "Ġpur ified", + "Ġplace holder", + "ĠL ikes", + "Ġd an", + "G ab", + "Ġe ct", + "ĠF aw", + "ĠEl iot", + "Ġ' ,", + "otrop ic", + "ĠRu in", + "hed on", + "Ġca ul", + "Ġa ft", + "ĠCad illac", + "gh a", + "ass ian", + "ud eb", + "ĠT ick", + "Ġadjust s", + "AR GET", + "5 37", + "isc he", + "ant y", + "ĠFried rich", + "ĠBl izz", + "ĠA OL", + "Camp aign", + "Ġmamm al", + "ĠVe il", + "ĠK ev", + "ĠMaur it", + "ĠDam ien", + "N ation", + "E astern", + "Ġ{ :", + "Ġ= ================================", + "Ġstereotyp ical", + "Ġatt ic", + "ĠCy borg", + "requ ire", + "Ġaward ing", + "ĠPap ua", + "bt n", + "b ent", + "B oo", + "Ġ( =", + "ĠX ander", + "ĠSomers et", + "Ġcatch y", + "Ġcert ify", + "STR UCT", + "Ġit al", + "Ġt ides", + "ĠBr ands", + "G ray", + "comp etitive", + "Ġcur ator", + "ĠD G", + "omin ium", + "ĠGM Os", + "ci ating", + "ĠCarm en", + "ow ard", + "Balt imore", + "Ġr gb", + "C u", + "Ġwip es", + "spe ll", + "IT NESS", + "Ġsummar izes", + "ĠRe vis", + "Ġwhistlebl owers", + "ĠBre ach", + "Ġcro chet", + "k os", + "ews ki", + "Ġrep et", + "Ġcrim son", + "ĠKar achi", + "read able", + "dim ension", + "ĠI gor", + "ild ed", + "ĠZ ed", + "ĠKe ane", + "ĠCos metic", + "DE P", + "Ġretreat ing", + "ĠU A", + "ens ical", + "Ġd usk", + "ĠDick ens", + "Ġaren as", + "ĠPass age", + "level s", + "Ġcur v", + "P ope", + "Ġch ores", + "ĠEl ise", + "ĠComp ass", + "b ub", + "Ġmamm alian", + "ĠSans krit", + "ĠAN C", + "ĠCr ack", + "Q ual", + "L aun", + "amp unk", + "Ġlearn ers", + "Ġglam orous", + "Ġfur the", + "erm ott", + "c and", + "Gener ic", + "Ġnarr ated", + "Ġdisorder ly", + "ĠTrans actions", + "ĠDet ention", + "ĠR oku", + "Ä į", + "Ġunder statement", + "ĠS aur", + "ĠRodrig o", + "ĠAS AP", + "S in", + "Ġre joice", + "Method s", + "Ġelectro de", + "Ġworsh ipped", + "Ġid i", + "ĠPhys icians", + "Ġpop up", + "Ġde ft", + "ĠRem oval", + "ĠBu enos", + "ver bs", + "Ġfun k", + "ush a", + "rict ion", + "ore a", + "ĠBang alore", + "ĠKen obi", + "zz i", + "Ġnorm ative", + "Ġgobl ins", + "Ġcaf es", + "ĠUN CLASSIFIED", + "ĠF ired", + "S IGN", + "Ġs clerosis", + "ĠV oter", + "ĠSon ny", + "ĠExt end", + "ĠEV s", + "Ar senal", + "Ġp si", + "Ġwid est", + "ĠT us", + "Ġlo oms", + "Ġjust ifying", + "ĠGr anger", + "è ¯", + "Ref er", + "58 3", + "Ġflour ishing", + "ab re", + "Ġr ave", + "ĠCont ra", + "Ġ18 98", + "Add s", + "Ġf ul", + "ĠCo oke", + "some one", + "= #", + "67 1", + "Ġy ak", + "Ġar te", + "ĠMis cellaneous", + "ĠDet ection", + "ĠCl ancy", + "â ģ", + "ass ies", + "Ġval iant", + "ĠFemin ist", + "cor ruption", + "V el", + "P ear", + "Ġsucc inct", + "Ġquick est", + "k w", + "Ġsp itting", + "ĠL ibraries", + "åħ ī", + "ant z", + "D ad", + "ĠSpec ifications", + "rup ulous", + "and r", + "RES ULTS", + "Ġsnow ball", + "Ġpred is", + "ĠB axter", + "ĠNurs ing", + "ĠCh aff", + "s we", + "Ġout age", + "Ġnest ing", + "Ġnotor iety", + "tr igger", + "on ite", + "j on", + "Ġf ou", + "ook ed", + "ĠCelebr ity", + "re ality", + "Ġfat ig", + "Ġhug ging", + "Ġbother s", + "ĠPan zer", + "ĠCh andra", + "fig ured", + "Ġvol ts", + "ĠCloud s", + "Ġfee ble", + "ĠCur ve", + "ĠAs us", + "78 6", + "abs or", + "ĠV ICE", + "ĠH ess", + "Ġmanufact ures", + "Ġgri zz", + "ĠPower ful", + "ac id", + "Ġsub sections", + "ĠKrug man", + "ĠAl ps", + "is u", + "Ġsequ est", + "ĠUlt ron", + "ĠT inker", + "ĠGo ose", + "Ġmism atch", + "Att orney", + "Ġmorph ology", + "ĠSix ers", + "ut tered", + "ĠE LECT", + "gr an", + "Rus sell", + "ĠG SL", + "Ġfort night", + "Ġ. )", + "Ġapost le", + "pr one", + "el ist", + "Unt itled", + "ĠIm plementation", + "ist ors", + "Ġtank er", + "Ġpl ush", + "Ġattend ants", + "ĠT ik", + "ĠGreen wich", + "ĠY on", + "ĠSP L", + "cell s", + "unt led", + "S olution", + "ĠQu é", + "Ġvac ated", + "Ġupt ick", + "ĠMer idian", + "æ ĥ", + "ĠDr ill", + "9 25", + "58 4", + "Ġrenov ated", + "ĠKub rick", + "zy k", + "Ġl ousy", + "pp el", + "ohyd rate", + "ĠI zzy", + "lesi astical", + "CC C", + "ĠAj ax", + "Ġad apters", + "ĠPetra eus", + "Ġaffirm ation", + "ĠST OR", + "le ms", + "ad oes", + "ĠConstantin ople", + "Ġp onies", + "Ġl ighthouse", + "Ġadherent s", + "ĠBre es", + "omorph ic", + "Fight ing", + "Ġpl aster", + "ĠP VC", + "ĠOb st", + "Ġdear ly", + "ĠTo oth", + "icks on", + "Ġsh aming", + "P lex", + "A gg", + "ĠâĢ¦ \"", + "Ġsub reddits", + "Ġpige on", + "ĠResident ial", + "ĠPass ing", + "Ġl um", + "ĠP ension", + "Ġpessim istic", + "Ġ4 32", + "z inski", + "c ade", + "0 75", + "Ġapolog ised", + "iy ah", + "Put ting", + "Ġgloom y", + "ĠLy me", + "=-=-=-=- =-=-=-=-", + "ĠT ome", + "ĠPsych iatric", + "ĠH IT", + "c ms", + "ap olog", + "Ġbreak er", + "Ġdeep en", + "Ġtheor ist", + "ĠHigh lands", + "Ġb aker", + "Ġst aples", + "Ġinterf ered", + "ĠAb ortion", + "jo ined", + "ch u", + "Ġform ulate", + "Ġvacc inations", + "Ġban ter", + "phe us", + "Ġoutfield er", + "ĠM eter", + "Ġ# ####", + "Ġ18 95", + "Ġnarrow ing", + "ĠST ORY", + "f p", + "ĠC ST", + "ign ore", + "Ġproclaim ing", + "ĠR U", + "ĠB ALL", + "yn a", + "65 3", + "Ġpos it", + "P RE", + "59 4", + "ĠRegist rar", + "ĠPil grim", + "ic io", + "Ġpre tt", + "Ġlif eless", + "Ġ__ _", + "Ne igh", + "ĠCh urches", + "orn o", + "Ġor cs", + "Ġkind red", + "ĠAud it", + "Ġmillenn ial", + "ĠPers ia", + "g ravity", + "ĠDis ability", + "ĠD ARK", + "W s", + "od on", + "Ġgrand daughter", + "ĠBro oke", + "ĠA DA", + "ER A", + "Ġpick ups", + "ĠWil kinson", + "ĠSh ards", + "ĠN K", + "Ġexp el", + "ĠKis lyak", + "Ġj argon", + "Ġpolar ized", + "ian e", + "Pub lisher", + "Ġreb utt", + "Ġapprehens ion", + "ĠK essler", + "Ġpr ism", + "F UL", + "19 64", + "ĠL oll", + "ä ¿", + "le thal", + "Å Ł", + "Ġg hetto", + "Ġb oulder", + "ĠSlow ly", + "ĠOsc ars", + "ĠInst ruction", + "ĠUl tr", + "ĠM oe", + "N ich", + "ĠP ATH", + "( *", + "ĠRE LEASE", + "un ing", + "rou se", + "en eg", + "Ġre imb", + "ĠDet ected", + "Do S", + "Ġster ling", + "Ġaggreg ation", + "ĠLone ly", + "ĠAtt end", + "hig her", + "Ġairst rike", + "ks on", + "SE LECT", + "Ġdef lation", + "ĠHer rera", + "C ole", + "rit ch", + "Ġadvis able", + "F ax", + "Ġwork around", + "Ġp id", + "mort em", + "ers en", + "Ġtyp o", + "Ġal um", + "78 2", + "ĠJam al", + "script s", + "Ġcapt ives", + "ĠPres ence", + "ĠLie berman", + "angel o", + "Ġalcohol ism", + "ass i", + "Ġrec ite", + "Ġgap ing", + "Ġbask ets", + "ĠG ou", + "Brow ser", + "ne au", + "Ġcorrect ive", + "und a", + "sc oring", + "ĠX D", + "Ġfil ament", + "Ġdeep ening", + "ĠStain less", + "Int eger", + "Ġbu ggy", + "Ġten ancy", + "ĠMub arak", + "Ġt uple", + "ĠD roid", + "ĠS itting", + "Ġforfe it", + "ĠRasm ussen", + "ixt ies", + "es i", + "ĠKim mel", + "Ġmetic ulously", + "Ġap opt", + "ĠS eller", + "08 8", + "ec ake", + "hem atically", + "T N", + "Ġmind less", + "Ġdig s", + "ĠAcc ord", + "ons ense", + "em ing", + "br ace", + "Ġe Book", + "ĠDist ribut", + "ĠInvest ments", + "w t", + "] ),", + "beh avior", + "56 3", + "Ġbl inding", + "ĠPro testers", + "top ia", + "Ġreb orn", + "ĠKel vin", + "ĠDo ver", + "ĠD airy", + "ĠOut s", + "Ġ[ /", + "Ï Ģ", + "b p", + "ĠVan ity", + "ĠRec ap", + "ĠHOU SE", + "ĠF ACE", + "Ġ4 22", + "69 2", + "ĠAnt ioch", + "cook ed", + "Ġcoll ide", + "Ġa pr", + "Ġsle eper", + "ĠJar vis", + "Ġalternative ly", + "ĠLe aves", + "ĠM aw", + "Ġantiqu ity", + "ĠAdin ida", + "Ġab user", + "Poké mon", + "Ġass orted", + "ĠRev ision", + "ĠP iano", + "ĠG ideon", + "O cean", + "Ġsal on", + "Ġbust ling", + "ogn itive", + "ĠRah man", + "Ġwa iter", + "Ġpres ets", + "ĠO sh", + "ĠG HC", + "oper ator", + "Ġrept iles", + "Ġ4 13", + "ĠG arr", + "ĠCh ak", + "Ġhas hes", + "Ġfail ings", + "Ġfolk lore", + "Ġab l", + "ĠC ena", + "ĠMac Arthur", + "ĠCOUR T", + "Ġperipher y", + "app ers", + "Ġreck oned", + "ĠInf lu", + "ĠC ET", + "Ġ3 72", + "ĠDefin itive", + "ass ault", + "4 21", + "Ġreservoir s", + "Ġd ives", + "ĠCo il", + "DA Q", + "Ġvivid ly", + "ĠR J", + "ĠBel lev", + "Ġec lectic", + "ĠShow down", + "ĠK M", + "ip ed", + "reet ings", + "ĠAs uka", + "L iberal", + "ĠÏ Ħ", + "Ġbystand ers", + "ĠGood win", + "uk ong", + "S it", + "ĠT rem", + "Ġcrim inally", + "ĠCirc us", + "ch rome", + "88 7", + "Ġnan op", + "ĠOb i", + "ĠL OW", + "o gh", + "ĠAuth ors", + "ob yl", + "Ur ban", + "Ġt i", + "ĠWe ir", + "t rap", + "ag y", + "Ġparent heses", + "Ġout numbered", + "Ġcounter productive", + "ĠTob ias", + "ub is", + "P arser", + "ST AR", + "Ġsyn aptic", + "ĠG ears", + "Ġh iber", + "Ġdebunk ed", + "Ġex alted", + "aw atts", + "H OU", + "Ch urch", + "ĠPix ie", + "ĠU ri", + "ĠForm ation", + "ĠPred iction", + "C EO", + "Ġthro tt", + "ĠBrit ann", + "ĠMad agascar", + "ë ĭ", + "Ġbill boards", + "ĠRPG s", + "ĠBe es", + "complete ly", + "F IL", + "Ġdoes nt", + "ĠGreen berg", + "re ys", + "Ġsl ing", + "Ġempt ied", + "ĠPix ar", + "ĠDh arma", + "l uck", + "ingu ished", + "Ġend ot", + "Ġbab ys", + "05 9", + "che st", + "r ats", + "Ġr idden", + "Ġbeet les", + "Ġillum inating", + "Ġfict itious", + "ĠProv incial", + "Ġ7 68", + "Ġshe pherd", + "ĠR ender", + "Ġ18 96", + "C rew", + "Ġmold ed", + "ĠXia omi", + "ĠSp iral", + "Ġdel im", + "Ġorgan ising", + "Ġho ops", + "ĠBe i", + "z hen", + "Ġfuck in", + "Ġdec ad", + "Ġun biased", + "am my", + "sw ing", + "Ġsmugg led", + "Ġk ios", + "ĠP ERSON", + "ĠInquis itor", + "Ġsnow y", + "Ġscrap ing", + "ĠBurg ess", + "P tr", + "ag ame", + "R W", + "Ġdro id", + "ĠL ys", + "ĠCass andra", + "Jac ob", + "Ġ35 4", + "Ġpast ure", + "Ġfr anc", + "ĠScot ch", + "ĠEnd s", + "ĠI GF", + "def inition", + "Ġhyster ical", + "ĠBrown e", + "77 1", + "Ġmobil ization", + "æ ķ", + "iqu eness", + "Th or", + "Ġspear headed", + "Ġembro iled", + "Ġconject ure", + "jud icial", + "Ch oice", + "Ġpaper back", + "P ir", + "Ġrec overs", + "ĠSur ge", + "ĠSh ogun", + "ĠPed iatrics", + "ãģ ł", + "Ġsweep s", + "ĠLabor atories", + "ĠP acks", + "al us", + "add in", + "Ġhead lights", + "g ra", + "Ev idence", + "COL OR", + "Ad min", + "Ĭ ±", + "Ġconco ct", + "s ufficient", + "Ġun marked", + "Ġrich ness", + "Ġdiss ertation", + "Ġseason ing", + "Ġg ib", + "ĠM ages", + "un ctions", + "ĠN id", + "che at", + "ĠTM Z", + "c itizens", + "ĠCatholic ism", + "n b", + "Ġdisemb ark", + "ĠPROG RAM", + "a ques", + "Ty ler", + "Or g", + "ĠSl ay", + "ĠN ero", + "ĠTown send", + "IN TON", + "te le", + "Ġmes mer", + "9 01", + "Ġfire ball", + "ev idence", + "aff iliated", + "ĠFrench man", + "ĠAugust a", + "0 21", + "Ġs led", + "Ġre used", + "ĠImmun ity", + "Ġwrest le", + "assemb led", + "Mar ia", + "Ġgun shots", + "ĠBarb ie", + "Ġcannabin oids", + "ĠTo ast", + "ĠK inder", + "IR D", + "Ġre juven", + "Ġg ore", + "Ġrupt ure", + "Ġbre aching", + "ĠCart oon", + "Ġ4 55", + "ĠPale o", + "6 14", + "Ġspe ars", + "ĠAm es", + "ab us", + "Mad ison", + "GR OUP", + "Ġab orted", + "y ah", + "Ġfel on", + "Ġcaus ation", + "Ġprep aid", + "Ġp itted", + "op lan", + "ĠShel ley", + "ĠRus so", + "ĠP agan", + "Ġwill fully", + "ĠCan aver", + "und rum", + "ĠSal ary", + "ĠAr paio", + "read er", + "ĠR ational", + "ĠOver se", + "ĠCa uses", + "Ġ* .", + "Ġw ob", + "Ke ith", + "ĠCons ent", + "man ac", + "77 3", + "6 23", + "Ġfate ful", + "et imes", + "Ġspir ited", + "ĠD ys", + "Ġhe gemony", + "Ġboy cot", + "ĠEn rique", + "em outh", + "Ġtim elines", + "ĠSah ara", + "ĠRel ax", + "ĠQuin cy", + "ĠLess ons", + "ĠE QU", + "SE A", + "N K", + "ĠCost co", + "Incre ase", + "Ġmotiv ating", + "ĠCh ong", + "am aru", + "ĠDiv ide", + "Ġped igree", + "ĠTasman ia", + "ĠPrel ude", + "L as", + "9 40", + "57 4", + "Ġch au", + "ĠSp iegel", + "un ic", + "-- >", + "ĠPhil ips", + "ĠKaf ka", + "Ġuphe aval", + "Ġsent imental", + "Ġsa x", + "ĠAk ira", + "ser ial", + "Mat rix", + "Ġelect ing", + "Ġcomment er", + "ĠNeb ula", + "ple ts", + "ĠNad u", + "ĠAd ren", + "Ġen shr", + "ĠR AND", + "fin ancial", + "ĠCly de", + "uther ford", + "Ġsign age", + "Ġde line", + "Ġphosph ate", + "rovers ial", + "f ascist", + "ĠV all", + "ĠBeth lehem", + "Ġfor s", + "Ġeng lish", + "S olid", + "N ature", + "Ġv a", + "ĠGu ests", + "Ġtant al", + "Ġauto immune", + ";;;;;;;; ;;;;", + "ĠTot ally", + "ĠO v", + "Ġdef ences", + "ĠCoc onut", + "Ġtranqu il", + "Ġpl oy", + "Ġflav ours", + "ĠFl ask", + "ãĤ¨ ãĥ«", + "ĠWest on", + "ĠVol vo", + "8 70", + "Ġmicro phones", + "ver bal", + "R PG", + "Ġi ii", + "; }", + "0 28", + "Ġhead lined", + "Ġprim ed", + "Ġho ard", + "ĠSh ad", + "ĠEN TER", + "Ġtri angular", + "Ġcap it", + "l ik", + "ĠAn cients", + "Ġl ash", + "Ġconv ol", + "Ġcolon el", + "en emy", + "G ra", + "Ġpub s", + "ut ters", + "Ġassign s", + "ĠPen et", + "ĠMon strous", + "ĠBow en", + "il ver", + "H aunted", + "ĠD ing", + "start ed", + "pl in", + "Ġcontamin ants", + "ĠDO E", + "ff en", + "ĠTechn ician", + "R y", + "Ġrob bers", + "Ġhot line", + "ĠGuard iola", + "ĠKau fman", + "row er", + "ĠDres den", + "ĠAl pine", + "E lf", + "Ġf mt", + "ĠS ard", + "urs es", + "g pu", + "Un ix", + "Ġunequiv ocally", + "ĠCitizens hip", + "qu ad", + "m ire", + "ĠS weeney", + "B attery", + "6 15", + "Ġpanc akes", + "Ġo ats", + "M aps", + "ĠCont rast", + "mbuds man", + "ĠE PS", + "Ġsub committee", + "Ġsour cing", + "Ġs izing", + "ĠBuff er", + "ĠMand atory", + "Ġmoder ates", + "ĠPattern s", + "ĠCh ocobo", + "ĠZ an", + "ĠSTAT ES", + "ĠJud ging", + "ĠIn her", + "* :", + "Ġb il", + "ĠY en", + "Ġexh ilar", + "oll ower", + "z ers", + "Ġsn ug", + "max imum", + "Ġdesp icable", + "ĠP ACK", + "ĠAn nex", + "Ġsarcast ic", + "Ġlate x", + "Ġt amp", + "ĠS ao", + "b ah", + "ĠRe verend", + "ĠChin atown", + "ĠA UT", + "d ocumented", + "ĠGA BA", + "ĠCan aan", + "ĠÙ ħ", + "Ġgovern s", + "pre v", + "E sc", + "ĠEst imates", + "OS P", + "Ġendeav our", + "ĠCl osing", + "omet ime", + "every one", + "Ġwor sen", + "Ġsc anners", + "Ġdev iations", + "ĠRobot ics", + "ĠCom pton", + "Ġsorce rer", + "Ġend ogenous", + "Ġem ulation", + "ĠPier cing", + "ĠA ph", + "ĠS ocket", + "Ġb ould", + "ĠO U", + "ĠBorder lands", + "Ġ18 63", + "G ordon", + "ĠW TO", + "Ġrestrict s", + "Ġmosa ic", + "Ġmel odies", + "ç Ħ", + "T ar", + "Ġdis son", + "ĠProv ides", + "Ġ ......", + "b ek", + "F IX", + "Ġbro om", + "ans hip", + "Do ctors", + "Ġner ds", + "ĠReg ions", + "na issance", + "Ġmet e", + "Ġcre pt", + "pl ings", + "Ġgirlfriend s", + "kn it", + "ig ent", + "ow e", + "Ġus hered", + "ĠB az", + "M obil", + "4 34", + "ĠPres ents", + "orig in", + "Ġins omnia", + "ĠA ux", + "4 39", + "ĠCh ili", + "irs ch", + "G AME", + "Ġgest ation", + "alg ia", + "rom ising", + "$ ,", + "c row", + "ĠIn spection", + "at omic", + "Rel ations", + "J OHN", + "rom an", + "ĠClock work", + "ĠBak r", + "m one", + "M ET", + "Ġthirst y", + "Ġb c", + "Ġfacult ies", + "R um", + "Ġnu ance", + "ĠD arius", + "ple ting", + "fter s", + "etch up", + "Reg istration", + "ĠK E", + "R ah", + "Ġpref erential", + "ĠL ash", + "ĠH H", + "Val id", + "ĠN AV", + "Ġstar ve", + "ĠG ong", + "z ynski", + "ĠAct ress", + "Ġw ik", + "Ġun accompanied", + "lv l", + "Br ide", + "AD S", + "ĠCommand o", + "ĠVaugh n", + "Wal let", + "Ġho pping", + "ĠV ie", + "Ġcave ats", + "Ġal as", + "if led", + "ab use", + "66 1", + "Ġib n", + "Ġg ul", + "Ġrob bing", + "t il", + "IL A", + "Ġmit igating", + "Ġapt ly", + "Ġty rant", + "Ġmid day", + "ĠGil more", + "ĠDe cker", + "Ġ§ §", + "part ial", + "Ex actly", + "Ġphen otype", + "Ġ[+ ]", + "ĠP lex", + "ĠI ps", + "vers ions", + "Ġe book", + "Ġch ic", + "g ross", + "\":\" \"},{\"", + "ĠSur prisingly", + "M organ", + "Ġresid ues", + "ĠConf ederation", + "in feld", + "Ġl yr", + "mod erate", + "Ġperpend icular", + "V K", + "Ġsynchron ized", + "Ġrefres hed", + "Ġad ore", + "ĠTor ment", + "ol ina", + "Ġ26 00", + "Item Tracker", + "Ġp ies", + "ĠF AT", + "ĠR HP", + "0 48", + "ĠRES P", + "ĠB J", + "all ows", + "P and", + "Ġunw elcome", + "ĠV oc", + "ĠBast ard", + "ĠO W", + "ĠL AR", + "ĠHeal er", + "Environment al", + "ĠKen yan", + "ĠTr ance", + "ĠP ats", + "Ġali ases", + "ĠGar field", + "Ġcampaign er", + "Ġadvance ments", + "ĠOkin awa", + "ĠC oh", + "ows ky", + "Ġstar ved", + "Ġsize able", + "Ġ: -)", + "Ġm RNA", + "Ġsusp ensions", + "ist ar", + "Scot land", + "Pr in", + "-------------------------------- ----------------", + "Ġ50 2", + "Ġteasp oons", + "Ġ10 50", + "Ġcoerc ive", + "ĠMason ic", + "edd ed", + "ĠPass enger", + "Ġl att", + "Ġbr aces", + "ĠSt eal", + "ĠNY T", + "ĠK ats", + "ĠCel est", + "ae z", + "T u", + "ĠCoul ter", + "ðŁ ĺ", + "Fl ickr", + "ĠWil mington", + "ith s", + "++ ;", + "Ġv ending", + "Ġneg ro", + "ĠPh i", + "ĠYellow stone", + "Call back", + "Ġsh ampoo", + "ĠSh ades", + "w at", + "Ġsuper human", + "Ġridic uled", + "Ġhol iest", + "om bo", + "Ġintern s", + "Ġh one", + "ĠPar agu", + "UR I", + "Ġd angling", + "ãĤ »", + "so v", + "ict ional", + "av ailability", + "Ġrev ocation", + "Ġd ow", + "in ic", + "ĠTHE IR", + "Ġis o", + "Ġout ings", + "ĠLeth al", + "Ġ) ))", + "Ġinacc ur", + "Ġout landish", + "Ġan us", + "let ico", + "id on", + "l ol", + "Ġun regulated", + "Ġsuccumb ed", + "Ġc uff", + "ĠWast eland", + "let al", + "Ġsub str", + "Ġcoff ers", + "Ġautom akers", + "ov i", + "ĠX ue", + "ĠDayton a", + "Ġjar ring", + "Ġf umes", + "Ġdisband ed", + "z ik", + "itt on", + "Ġstriking ly", + "Ġsp ores", + "Ad apter", + ".) :", + "ĠLynd on", + "ival ry", + "Ġor ally", + "Ġtumult uous", + "Ġdisple asure", + "Ġcon es", + "or rect", + "Ġappe ase", + "Ġder by", + "ĠTrip oli", + "ĠAl ess", + "Ġp oked", + "ĠGu ilty", + "v P", + "En ough", + "Ġorig inals", + "6 99", + "Ġrabb i", + "Ġproverb ial", + "Ġpostp one", + "el ope", + "ĠMist y", + "Ġstaff ed", + "ĠUn employment", + "redit ary", + "Ġdilig ent", + "re comm", + "me asures", + "as in", + "8 25", + "Ġpond s", + "Ġmm ol", + "ĠS AR", + "ĠC ARE", + "Ġ3 71", + "Ġclen ched", + "ĠCors air", + "Ġcaric ature", + "z n", + "att ach", + "ĠSch ro", + "spe ak", + "p ainted", + "ĠS uc", + "ĠE NT", + "Ġcell ul", + "ĠP aid", + "di agn", + "WH ERE", + "Ġtext ed", + "B arn", + "Ġret racted", + "ĠRe ferred", + "S av", + "Ġup keep", + "Ġwork places", + "ĠTok ens", + "Ġampl ify", + "cl inical", + "Ġmult ic", + "mber g", + "Ġconvol uted", + "Reg ion", + "5 65", + "ĠTop ic", + "Ġsn ail", + "Ġsal ine", + "Ġins urrection", + "ĠPet r", + "f orts", + "B AT", + "ĠNav ajo", + "Ġrud imentary", + "ĠLak sh", + "OND ON", + "Me asure", + "Ġtransform er", + "ĠGodd ard", + "Ġcoinc ides", + "ir in", + "R ex", + "ĠB ok", + "qu it", + "Ġshotgun s", + "Ġprolet arian", + "Ġsc orp", + "ĠAd a", + "5 14", + "Ġsl ander", + "record ed", + "Ġemb ell", + "ris ome", + "Ġapolog izing", + "ĠMul cair", + "ĠGib raltar", + "Cl a", + "Ġall ot", + "ĠAtt ention", + "Ġ4 33", + "le ave", + "Ġwh ine", + "ĠIss a", + "ĠFa ust", + "ĠBar ron", + "hen y", + "Ġvictim ized", + "J ews", + "Ġnurt uring", + "ett el", + "W inged", + "ĠSub tle", + "Ġflavor ful", + "ĠRep s", + "eng ed", + "call back", + "Ġdirection al", + "Ġcl asp", + "ĠDirect ions", + "plan et", + "icult ure", + "Hel per", + "ic ion", + "ac ia", + "Ġç ¥ŀ", + "Ġsur ges", + "Ġcan oe", + "ĠPrem iership", + "be en", + "Ġdef ied", + "ĠTro oper", + "Ġtrip od", + "Ġgas p", + "ĠE uph", + "ĠAd s", + "vern ight", + "high ly", + "R ole", + "Ġent angled", + "ĠZe it", + "6 18", + "ĠRust y", + "Ġhaven s", + "ĠVaugh an", + "HA EL", + "ĠSER VICE", + "/ ,", + "Ġstr icken", + "Ġdel usions", + "Ġb is", + "ĠH af", + "Ġgrat ification", + "Ġent icing", + "UN CH", + "Ad ams", + "ĠOL ED", + "ĠBeet le", + "Ġ18 99", + "ĠSO FTWARE", + "ateg or", + "V L", + "ĠTot em", + "ĠG ators", + "AT URES", + "Ġimped ance", + "Reg istered", + "ĠC ary", + "ĠAer ial", + "on ne", + "en ium", + "Ġd red", + "ĠBe g", + "Ġconcurrent ly", + "Ġsuper power", + "ĠX an", + "j ew", + "imes ter", + "ĠDick inson", + "âĶ ģ", + "F la", + "Ġp ree", + "ĠRoll ins", + "© ¶æ", + "Ġden omination", + "ĠL ana", + "5 16", + "Ġinc iting", + "sc ribed", + "j uries", + "ĠWond ers", + "app roximately", + "Ġsusp ending", + "Ġmountain ous", + "ĠL augh", + "oid al", + "N s", + "Det ect", + ") =", + "ĠL uthor", + "ĠSchwarz enegger", + "ĠMull er", + "ĠDev i", + "ec ycle", + "J ar", + "6 13", + "ĠL ongh", + "B ah", + "ĠSP ORTS", + "n w", + "Ġref inement", + "Ġwater ways", + "Ġd iner", + "Bl ade", + "68 3", + "F ac", + "Ġinitial s", + "Ġro g", + "Ġparan ormal", + "B UT", + "Ġ[ (", + "ĠSw anson", + "ĠM esh", + "âĸ ¬", + "Impro ve", + "ĠRad iation", + "ĠEst her", + "ĠE sk", + "ĠA ly", + "ik y", + "Ġir rad", + "ĠBuck ingham", + "Ġref ill", + "Ġ. _", + "Re pe", + "CON CLUS", + "Ġdifferent iated", + "Ġchi rop", + "ĠAt kins", + "Pat tern", + "Ġexc ise", + "Ġcab al", + "N SA", + "ĠST A", + "ĠS IL", + "ĠPar aly", + "Ġr ye", + "ĠHow ell", + "ĠCount down", + "ness es", + "alys ed", + "Ġres ize", + "ãĤ ½", + "Ġbudget ary", + "ĠStr as", + "w ang", + "Ġap iece", + "Ġprecinct s", + "Ġpe ach", + "Ġsky line", + "Ġ35 3", + "pop ular", + "App earances", + "ĠMechan ics", + "ĠDev Online", + "S ullivan", + "Z en", + "Ġp u", + "op olis", + "5 44", + "Ġde form", + "Ġcounter act", + "ĠL ange", + "Ġ4 17", + "Con sole", + "77 4", + "Ġnodd ing", + "Ġpopul ism", + "Ġhe p", + "Ġcoun selling", + "compl iance", + "U FF", + "Ġunden iably", + "Ġrail ing", + "ĠHor owitz", + "ĠSim one", + "ĠBung ie", + "Ġa k", + "ĠTal ks", + "x ff", + "fl ake", + "Cr ash", + "Ġsweat y", + "Ġban quet", + "ĠOFF IC", + "Ġinvent ive", + "Ġastron omer", + "ĠStam ford", + "ĠSc are", + "ĠGRE EN", + "olic ited", + "Ġr usher", + "Ġcent rist", + "ight ing", + "Ġsub class", + "Ġdis av", + "Ġdef und", + "ĠN anto", + "oci ate", + "m ast", + "Ġpac if", + "Ġm end", + "e ers", + "imm igration", + "ESS ION", + "Ġnumber ing", + "Ġlaugh able", + "ĠEnd ed", + "v iation", + "em ark", + "P itt", + "Ġmetic ulous", + "ĠL F", + "Ġcongrat ulated", + "ĠBir ch", + "Ġsway ed", + "Ġsemif inals", + "Ġhum ankind", + "m atter", + "ĠEqu ip", + "opa usal", + "S aid", + "ĠLay out", + "Ġvo icing", + "Ġth ug", + "Ġporn ographic", + "I PS", + "Ġmo aning", + "Ġgriev ance", + "Ġconf essions", + "esc al", + "TEXT URE", + "Aut hent", + "os aurus", + "P urchase", + "Ġreleg ation", + "al ter", + "ĠÂł Âł", + "Ġr iddled", + "Ġo gre", + "ĠLow ell", + "Occ up", + "E at", + "ĠHy der", + "ĠAdvis er", + "Com merce", + "H unt", + "ĠOr th", + "ĠComp etitive", + "ĠCL A", + "CD C", + "Ġsal ads", + "F le", + "Ġindustrial ized", + "` ,", + "ĠO WN", + "Ġbec k", + "ĠPart icularly", + "oub t", + "Ġm M", + "ĠHuss ain", + "ĠChen nai", + "Ġ9 20", + "Ġappoint ing", + "ĠCull en", + ",,,, ,,,,", + "Ġp ores", + "ver ified", + "Ġbi ochemical", + "em ate", + "Ġcoward ly", + "ĠHels inki", + "ĠEthiop ian", + "S OURCE", + "ER C", + "est ro", + "Ġbi otech", + "ĠS our", + "Ġbrew er", + "Bloom berg", + "Ġintens ify", + "Gl ass", + "an co", + "ĠF DR", + "gre SQL", + "ĠF ires", + "©¶æ ¥µ", + "ec o", + "100 1", + "ĠHom eless", + "Ġinstant aneous", + "ĠH aste", + "ig el", + "D iamond", + "Ġp aving", + "Ġland fill", + "Ġd ads", + "h oun", + ": ]", + "Ġinc endiary", + "ĠLiving ston", + "ĠHil bert", + "ĠChe cks", + "st yles", + "in ators", + "ĠCl ive", + "ph rine", + "Ġchimpan zees", + "Ġp all", + "ĠJ M", + "ĠAad haar", + "ð Ŀ", + "Ġachie vable", + "dis abled", + "P ET", + "OOOO OOOO", + "M ot", + "Ġint angible", + "Ġbal let", + "ĠWe bs", + "ĠEst imated", + "Effect s", + "Ġb ailed", + "Josh ua", + "Ġturb ulence", + "Ġoccup ant", + "ĠDay light", + "Ġ36 1", + "me et", + "Ġstat ically", + "Ġon look", + "Ġk i", + "il legal", + "Ġvel vet", + "Ġdehyd ration", + "Ġacqu ies", + "ĠRe z", + "ak ura", + "ĠU pton", + "at ro", + "Ġincomp rehensible", + "Ġback door", + "ĠRh ino", + "7 27", + "Ġmath s", + ") +", + "Ġhe resy", + "Ġd f", + "ĠRoc he", + "ĠL ydia", + "Ġpanc reat", + "re ply", + "arre ll", + "Ġsolicit ation", + "Ġcirc adian", + "BI P", + "Ġfor ay", + "Ġcrypt ic", + "iz u", + "ime o", + "ĠTom ato", + "ĠH oms", + "ex amination", + "Ġqu arry", + "ĠVal iant", + "ĠJer icho", + "ĠIN CLUD", + "Ġ18 40", + "5 19", + "Ġres ists", + "Ġsnap shots", + "ĠSp ur", + "ĠAnt iqu", + "Log in", + "Ġbest selling", + "Ġant ic", + "ĠS utherland", + "ãĤ¢ ãĥ«", + "Ġ~ /", + "ĠP arm", + "è ĥ", + "P ages", + "int ensity", + "Ġimm obil", + "Ġ18 65", + "zz o", + "Ġn ifty", + "Ġf entanyl", + "ĠPres ervation", + "op hen", + "Ġd arts", + "ĠD inosaur", + "po inters", + "ĠR ite", + "s uggest", + "aware ness", + "ĠSher idan", + "Ġst ances", + "Ġsor cery", + "Ġper jury", + "ĠNik ola", + "ie ver", + "Ġf iance", + "ĠJordan ian", + "ĠBall oon", + "Ġn ab", + "Ġk b", + "Ġhuman ities", + "ĠTan aka", + "hill ary", + "Ġconsult ancy", + "ĠZ ub", + "Ġrem ission", + "Ġconf id", + "CH Q", + "ĠF ug", + "Ġimpro vis", + "Y ep", + "/ _", + "Ġunwilling ness", + "Ġport folios", + "05 5", + "ĠInstruct or", + "aim an", + "Ġclaim ants", + "M bps", + "ĠBy e", + "re ceived", + "T weet", + "Ġind emn", + "ri z", + "am ara", + "N at", + "Ġeval uates", + "ĠL ur", + "ep ad", + "FO X", + "ĠTh ro", + "Ġrust y", + "Ġbed rock", + "ĠOp rah", + "J B", + "Ġmanip ulative", + "Ġwill ful", + "Ġrel apse", + "Ġext ant", + "The me", + "S ensor", + "ĠSt ability", + "go vern", + "Ġpo ppy", + "Ġkn ack", + "Ġins ulated", + "ĠT ile", + "ĠExt rem", + "Ġunt old", + "Ġconver ge", + "Ġref uel", + "ig roup", + "Ġdistort ions", + "Ġrav aged", + "Ġmechan ically", + "ĠRe illy", + "ĠN ose", + "ĠIncarn ation", + "ĠBeck y", + "abb ling", + "Ġt aco", + "Ġr ake", + "Ġmelanch oly", + "Ġillust rious", + "ĠDart mouth", + "Gu ide", + "ĠR azer", + "ĠBen z", + "Ult imate", + "ĠSur prise", + "Ġpage ant", + "off er", + "Who ever", + "Ġw iser", + "Ġchem ist", + "ĠHE LL", + "ĠBul k", + "Ġpl utonium", + "ĠCO VER", + "Ö ¼", + "f ailed", + "Ġtire lessly", + "Ġinf ertility", + "ĠTr ident", + "ĠShow time", + "ĠC iv", + "V ice", + "requ ires", + "itt ance", + "Ġun controlled", + "interest ing", + "56 1", + "Ġinnov ate", + "ateg ic", + "L ie", + "ĠS elling", + "U l", + "Ġsav ior", + "ĠT osh", + "Ġsw ast", + "P ASS", + "Ġr ink", + "Ġcard io", + "ĠI ro", + "ud i", + "Ġv antage", + "Ġv ans", + "ĠNi ño", + "+ =", + "Ġpropag ate", + "< ?", + "Ġmethod ological", + "204 39", + "Ġtrig lycer", + "Ġing rained", + "ĠAn notations", + "arr anted", + "6 17", + "ĠS odium", + "ĠA AC", + "techn ical", + "mult ipl", + "Ġ3 73", + "å ĭ", + "Ġdec isively", + "Ġboost ers", + "Ġdessert s", + "ĠGren ade", + "Ġtest ifying", + "ĠSc ully", + "ID s", + "Ġlock down", + "ĠSc her", + "ĠR é", + "ĠWhit man", + "ĠRams ay", + "rem ote", + "Ġh ikers", + "ĠHy undai", + "Ġcons cientious", + "Ġcler ics", + "ĠSiber ian", + "ut i", + "is bury", + "Ġrel ayed", + "Ġqu artz", + "ĠC BI", + "seek ers", + "ull a", + "Ġweld ing", + "ĠSh al", + "ble acher", + "T ai", + "ĠSam son", + "Ġt umble", + "ĠInvest or", + "Ġsub contract", + "ĠShin ra", + "ow icz", + "j andro", + "d ad", + "Ġtermin ating", + "ĠNe ural", + "ä» £", + "Ġleak age", + "ĠMid lands", + "ĠCaucas us", + "í ķ", + "c it", + "ll an", + "iv ably", + "ĠAlb ion", + "Ġ4 57", + "Ġregist rations", + "Ġcomr ade", + "Ġclip board", + "0 47", + "Ġdiscour aging", + "ĠO ops", + "Ad apt", + "Ġem path", + "n v", + "ĠPR OT", + "ĠDon n", + "ĠP ax", + "ĠB ayer", + "t is", + "Squ are", + "Ġfoot prints", + "part icip", + "ĠChile an", + "B rend", + "ind ucing", + "M agn", + "Ġclub house", + "ĠMagn um", + "Ġenc amp", + "ĠEth nic", + "uch a", + "ere y", + "Ġw atered", + "ĠCal ais", + "Ġcomplex ion", + "Ġsect s", + "Ġren ters", + "Ġbr as", + "oÄŁ an", + "Time out", + "Man agement", + "Ġinf ographic", + "P okemon", + "Cl ar", + "Ġloc ality", + "Ġfl ora", + "as el", + "P ont", + "Ġpop ulate", + "ĠO ng", + "Ġsubs istence", + "Ġa uctions", + "ĠMcA uliffe", + "ĠL OOK", + "br inger", + "Ġtit an", + "Ġmanif old", + "ĠâĹ ı", + "Ġcalibr ated", + "Ġcal iphate", + "ĠSH E", + "ĠCommission ers", + "ce ivable", + "j c", + "W inner", + "5 24", + "Ġcond one", + "Other wise", + "Ġp iling", + "Ġem body", + "ĠCrime an", + "ut ics", + "ĠEx hibition", + "Ġ4 26", + "e ering", + "Ġv ying", + "ĠH UGE", + "* =-", + "Ġprin cipled", + "à ¦", + "Ġquir ks", + "ĠEdit ors", + "put ing", + "G ES", + "ĠF TA", + "ठ¾", + "add on", + "ĠH AM", + "ĠFrie za", + "W oman", + ". $", + "Ġc rib", + "ĠHer od", + "Ġtim ers", + "ĠSp aces", + "ĠMac intosh", + "at aka", + "Ġgl ide", + "Ġsmell ing", + "ĠB AL", + "Ġun su", + "Ġcond os", + "Ġbicy cl", + "ĠRev ival", + "55 3", + "Ġjugg ling", + "H ug", + "ĠKardash ian", + "ĠBalk ans", + "mult iple", + "Ġnutrit ious", + "oc ry", + "19 00", + "Ġinteg rates", + "Ġad joining", + "ĠF older", + "roll ment", + "ven ient", + "Ġu ber", + "y i", + "Ġwh iff", + "ĠJu ven", + "ĠB orough", + "net te", + "Ġb ilingual", + "ĠSp arks", + "ph thal", + "man ufact", + "Ġt outing", + "ĠPH I", + "Ke efe", + "Rew ard", + "Ġinf all", + "ĠTem per", + "typ ically", + "ĠNik ol", + "Ġregular s", + "Ġpseud onym", + "Ġexhib itions", + "Ġbl aster", + "Ġ40 9", + "w arming", + "Ġrever ber", + "Ġrecip rocal", + "Ġ6 70", + "ip ient", + "b ett", + "ĠBe gins", + "Ġit ching", + "ĠPh ar", + "Ass uming", + "Ġem itting", + "ĠML G", + "Ġbirth place", + "Ġt aunt", + "ĠL uffy", + "ĠAm it", + "Ġcir cled", + "ĠN ost", + "enn ett", + "Ġde forestation", + "ĠHist orically", + "ĠEvery day", + "Ġovert ake", + "79 2", + "Ġn un", + "ĠLuc ia", + "Ġaccompan ies", + "ĠSe eking", + "ĠTr ash", + "an ism", + "R ogue", + "Ġnorth western", + "ĠSupplement al", + "ĠNY U", + "ĠF RI", + "ĠSat isf", + "x es", + "5 17", + "Ġreass ured", + "Ġspor adic", + "Ġ7 01", + "Ġmed ial", + "Ġcannabin oid", + "Ġbarbar ic", + "Ġep is", + "ĠExplos ive", + "ĠD ough", + "Ġuns olved", + "Support ed", + "Ġacknowled gment", + "sp awn", + "Ġkit chens", + "Ġ- =", + "talk ing", + "ic ist", + "ĠPeg asus", + "ĠPS U", + "Ġphot on", + "ĠAuthent ication", + "R G", + "@# &", + "76 2", + "ĠCl air", + "Ġdi aper", + "Ġbr ist", + "ĠProsecut ors", + "ĠJ em", + "6 28", + "ĠEvery where", + "ĠJean ne", + "equ ality", + "ãĥ© ãĥ³", + "object s", + "ĠPel icans", + "Ġ39 2", + "Ġbl u", + "b ys", + "ĠA go", + "Ġinstruction al", + "Ġdiscrim inating", + "ĠTR AN", + "ĠCorn el", + "ag os", + "Ġty re", + "Ġas piration", + "ĠBrid gewater", + "\": -", + "! \".", + "ĠEn s", + "ĠCoc o", + "P ie", + "Ġdet ach", + "ĠC ouch", + "Ġphys ique", + "ĠOccup ations", + "osc opic", + "en ough", + "B uzz", + "App earance", + "Y P", + "Ġrac er", + "Ġcompl icity", + "r pm", + "T oy", + "Ġinterrupt s", + "ĠCat alyst", + "Ġut ilitarian", + "imp act", + "Ġsp aghetti", + "Ġp orous", + "Ġeste emed", + "Ġinc iner", + "ĠI OC", + "7 48", + "Ġesp resso", + "ĠSm ile", + "abil ia", + "6 35", + "Ġmathematic ian", + "Ġ4 24", + "ĠK L", + "ĠH IP", + "Ġover heard", + "ĠT ud", + "ĠT ec", + "Ġqu izz", + "Ġfl attering", + "Ġcon n", + "âĢ İ", + "Ġatt aches", + "ĠR OS", + "ĠAC S", + "Ġt cp", + "ĠSh ame", + "sk ip", + "res pected", + "ĠTrin idad", + "gr ain", + "Ġfooth old", + "ĠUnch arted", + "ĠJul io", + "z l", + "av ored", + "ĠAn xiety", + "er rors", + "ĠCent auri", + "its ch", + "D addy", + "Ġclutch ing", + "ĠIm plement", + "ĠGut ierrez", + "Ġ7 60", + "Ġtele portation", + "end ra", + "Ġrevers ible", + "st ros", + "Ad venture", + "08 3", + "Ġliber ating", + "Ġas phalt", + "ĠSp end", + "AR DS", + "im sy", + "PR ES", + "ĠEmer ging", + "Ġwild fires", + "Ġtechn ologically", + "Ġem its", + "ĠART ICLE", + "Ġirregular ities", + "Ġcher ish", + "çī Ī", + "Ġst ink", + "ĠR ost", + "Econom ic", + "Ġcough ing", + "ĠMcC ann", + "pro perties", + "ilant ro", + "Ġreneg oti", + "Trans lation", + "Ġin quest", + "ĠGra pe", + "oot ers", + "gu i", + "ĠSwords man", + "ace ae", + "h itting", + "Ġr c", + "Ġexert ed", + "ĠS AP", + "it ent", + "Ġperil ous", + "Ġobsc urity", + "Ġassass inate", + "Ġab original", + "Ġresc uing", + "ĠSh attered", + "lock ing", + "all ion", + "Ch anging", + "ĠHar rington", + "ĠB ord", + "ĠAfgh ans", + "Jam ie", + "aret z", + "ĠAugust us", + "Ġ38 6", + "8 30", + "Ġj og", + "ok ingly", + "Tr igger", + "ĠH OR", + "Stat istics", + "Ġviewers hip", + "Ġadd itives", + "h ur", + "Ġmaxim izing", + "ĠR ove", + "ĠLou ie", + "ĠBuck et", + "ĠCHR IST", + "ou sel", + "Ġstre aks", + "ir ted", + "Ġt ert", + "Ġcolonial ism", + "Ġbur ying", + "y k", + "Cond ition", + "ĠDPR K", + "By Id", + "75 1", + "âĹ ¼", + "Ġwor risome", + "Ġvoc ational", + "sl ice", + "Ġsa ils", + "ĠCorrection al", + "95 4", + "Ġt ul", + "K id", + "l uster", + "Ġfam ilial", + "ĠSp it", + "ĠEp iscopal", + "Specific ally", + "ĠVol cano", + "run s", + "q s", + "Ġve tted", + "Ġcram med", + "t rop", + "here r", + "Thank fully", + "Ġper cussion", + "Ġor anges", + "Ġround up", + "Ġ4 99", + "x ious", + "Char acters", + "ĠZion ism", + "ĠR ao", + "ÃĽ ÃĽ", + "W F", + "Ġunintention al", + "ONE Y", + "Gr ab", + "Com mercial", + "Ġglut amate", + "ĠMcK enna", + "ru ciating", + "ning ton", + "ih u", + "Ch an", + "ĠSw ap", + "Ġleaf lets", + "Ġfunction ally", + "er ous", + "F arm", + "Ġcal oric", + "ĠLiter ally", + "con cert", + "Ġshe nan", + "Ġrep aid", + "ey es", + "Ġbas hing", + "ĠG orge", + "Ġcollabor ations", + "Ġun account", + "itch ie", + "Ġteam work", + "pp elin", + "Ġpip ing", + "Ġmin ced", + "Ġd iam", + "ri eg", + "Ġmasc ara", + "Ġsuck er", + "ĠMo ons", + "App s", + "ĠPe ck", + "Ġper v", + "ĠFl oat", + "o ley", + "ĠN ish", + "im ize", + "Ġarom atic", + "u in", + "end ish", + "! /", + "ĠB icycle", + "ĠAS IC", + "ile ged", + "ĠQuad ro", + "ios yn", + "Ġlock out", + "ĠW ink", + "SP EC", + "Attempt s", + "Ġseed ed", + "red o", + "ias is", + "Ġsn ag", + "ãĥķ ãĤ©", + "ãĤ ¶", + "Ġground ing", + "Ġrelie ver", + "Ġfrivol ous", + "ĠG ifts", + "ĠF aces", + "Es pecially", + "Ġmicrobi ome", + "im ag", + "ĠSch l", + "ĠP les", + "ĠBle ach", + "ĠIr win", + "ĠE aton", + "ĠDisc iple", + "Ġmultipl ication", + "Ġcoer ced", + "Ġ4 19", + "st h", + "E vil", + "B omb", + "Ġex orc", + "Ġstag gered", + "L ESS", + "Ġinert ia", + "ĠED IT", + "Ġgo b", + "Tr aditional", + "Ġclass y", + "Lear y", + "ĠP AGE", + "yr s", + "Ġtrans porter", + "Ġmat ured", + "Ġhij ab", + "Ġbi ome", + "Where as", + "Ġex termination", + "ĠT ues", + "ĠT akeru", + "ĠAud rey", + "er ial", + "ĠAd en", + "aff les", + "Ġnarciss istic", + "ĠB aird", + "UT F", + "I re", + "ĠCon nie", + "Ch amp", + "Ġwhis pering", + "ĠH att", + "D K", + "Ġdis infect", + "Ġdeduct ed", + "Ġpart ake", + "Ġdown grade", + "ĠEs ports", + "ĠContin uing", + "Ġdemocr atically", + "icro bial", + "itt a", + "Ġlim estone", + "Ġexempt ed", + "ĠFren zy", + "H erm", + "7 28", + "Ġfled gling", + "Met a", + "765 61", + "69 3", + "% :", + "w ake", + "5 26", + "ĠDis cipline", + "Ġvirgin ity", + "ĠLeg ions", + "ĠFrank ie", + "int ent", + "Ġrest rooms", + "ĠRou ter", + "da q", + "Ġobjection able", + "âĨ ij", + "w ark", + "ĠRah ul", + "g ain", + "activ ation", + "abs olute", + "ĠAccess ed", + "Ġ24 00", + "ogg les", + "Ġsecond ly", + "ĠDEF ENSE", + "Ġpost age", + "wra pper", + "sh arp", + "7 29", + "Ġcommun icates", + "Ġadd on", + "ĠMil itia", + "H ong", + "Ġsl umped", + "ĠJP EG", + "ĠI car", + "ad ish", + "68 1", + "Ġmaj esty", + "ĠWolf gang", + "ĠEl astic", + "u per", + "Ġv iz", + "Ġunconscious ly", + "ĠST D", + "ĠS ass", + "Ġflower ing", + "ĠHel ic", + "ĠDra per", + "ĠAm ateur", + "Ġman ure", + "Ġdis ingen", + "ĠLe i", + "br ing", + "9 49", + "Ġinhib ited", + "Ġhead quartered", + "Ġen igmatic", + "�� �", + "Ġred ress", + "R H", + "Ġratt led", + "Ġd iction", + "l io", + "ĠT BA", + "ĠSN AP", + "C alling", + "Ġfasc ists", + "ĠD ove", + "iew icz", + "0 36", + "Ġco asts", + "ĠR ect", + "Ġ) ]", + "L ot", + "6 29", + "ĠS EM", + "ĠPeters en", + "ĠExpl ain", + "ĠBo ards", + "ĠBe zos", + "ĠJ ournals", + "Ġ20 24", + "p arser", + "Ġmist rust", + "Ġgr ate", + "ĠL ocked", + "bo a", + "S aint", + "g aming", + "Ġvow el", + "in ately", + "bl ow", + "All ah", + "Ġun matched", + "Ġb ordering", + "ĠExp end", + "n r", + "Or acle", + "rou ch", + "Ġcont iguous", + "ac us", + "Ġdist raught", + "58 1", + "Ġanat omical", + "O X", + "ap ixel", + "8 33", + "ĠPL US", + "Ġres usc", + "Ġab iding", + "57 3", + "Ġvac ancies", + "Em ily", + "Ġhyp othal", + "ĠWer ner", + "ĠWe e", + "ĠDJ s", + "5 13", + "Ġwitch craft", + "Ġac upuncture", + "ent ary", + "benef it", + "Product s", + "ĠP SP", + "ĠMP G", + "ĠJ inn", + "ĠJ arrett", + "Ġ4 45", + "ĠIm aging", + "ĠP yth", + "Fin ish", + "Ġte x", + "Ġjuven iles", + "Ġhero ism", + "Ġdoubt less", + "ĠA ki", + "ĠT end", + "ĠPatri arch", + "Ġbit ters", + "ĠTele communications", + "it atively", + "ag na", + "Ġr g", + "ĠS OLD", + "Ġcomp ulsion", + "ĠN asa", + "ĠKath ryn", + "Ġmillion aires", + "Ġintrins ically", + "Ġbolst ered", + "time out", + "fl o", + "Ġtut or", + "p our", + "Stat ement", + "Ġ{ *", + "ĠRud olph", + "ĠKimber ly", + "rog ens", + "adi q", + "] +", + "Ġindign ation", + "Ġfract uring", + "ĠRe leases", + "ĠGr ain", + "pro tein", + "L ago", + "Ġvac ations", + "Ġboot ed", + "ĠTH REE", + "ĠH G", + "oresc ence", + "Ġt f", + "Ġso ar", + "iosyn cr", + "Ġgl ances", + "ĠSp oon", + "ĠJ ury", + "ĠCow boy", + "Ġcreat ively", + "Hig her", + "Ġsolic itor", + "Ġhaw k", + "ac io", + "89 6", + "Ġsuperf lu", + "Ġbombs hell", + "ct ure", + "Ġbroker age", + "Ġraid ing", + "Ġf rench", + "Ġang led", + "Trans action", + "ĠGen ocide", + "u pe", + "ĠHait ian", + "57 2", + "! :", + "Ġunwitting ly", + "iter ator", + "sc roll", + "Ġtall ied", + "Ġbi omedical", + "ĠC ARD", + "Ġe uphem", + "Ġbrain storm", + "a quin", + "K o", + "Mic helle", + "ĠR unes", + "ĠBall istic", + "ud ers", + "Ġmod esty", + "ĠiP ads", + "ĠEzek iel", + "Y E", + "Ġstars hip", + "Ġpower fully", + "Ġper l", + "ĠSh ade", + "ĠQu art", + "ĠE EG", + "Ġfisher man", + "OS ED", + "ĠTyp ical", + "df x", + "Ġmes hes", + "Ġet ched", + "worth iness", + "Ġtopp led", + "Ġ3 96", + "or ius", + "We iss", + "Ġmy sql", + "ĠVal halla", + "Ù Ĵ", + "le asing", + "Ġrec omp", + "rap nel", + "S el", + "04 3", + "Ġder ailed", + "ĠGu ides", + "IR T", + "Ġde human", + "ĠBritt any", + "\" ))", + "Ġex claim", + "Ġb alk", + "Ġ8 40", + "CLA IM", + "int el", + "L AB", + "Ġpe gged", + "Ġast roph", + "sm oking", + "Ġrig ging", + "Ġfix ation", + "Ġcat apult", + "ins ide", + "ĠC ascade", + "ĠBolshe vik", + "G aza", + "Dep th", + "Ġloud spe", + "Ġalmond s", + "me yer", + "l eness", + "j en", + "f resh", + "Ġunbeat en", + "ĠSqu id", + "ĠPres umably", + "Tim er", + "B W", + "Ġro sters", + "Ġell ipt", + "ĠHar riet", + "dat abase", + "ĠMut ual", + "ĠComm odore", + "uk ed", + "kn ife", + "ĠCOMM UN", + "h ya", + "Ġmel ts", + "arch ives", + "Ġrat ification", + "Ġmultip lying", + "Ġinter oper", + "Ġasc ert", + "w ings", + "ver ting", + "ĠScorp ion", + "ay e", + "ĠPorts mouth", + "ĠM TA", + "n it", + "iaz ep", + "Ġqu arantine", + "Ġslides how", + "Ġcent imeters", + "Ġsyn opsis", + "Ġsp ate", + "th irst", + "Ġnom inating", + "ĠMel vin", + "Pre view", + "Ġthro b", + "Ġgener ational", + "ĠRad ius", + "rest ling", + "put able", + "aw ar", + "N ECT", + "Ġunlaw fully", + "ĠRevel ations", + "Wik ipedia", + "sur v", + "Ġeye ing", + "ij n", + "ĠF W", + "Ġbr unt", + "Ġinter stellar", + "Ġcl itor", + "ĠCroat ian", + "ĠCh ic", + "ev a", + "ĠDis app", + "ĠA kin", + "iner ies", + "d ust", + "Interest ed", + "Ġgen esis", + "ĠE ucl", + "ö n", + "p icking", + "Ġmut ated", + "Ġdisappro ve", + "ĠHD L", + "Ġ6 25", + "Ì ¶", + "c ancer", + "Ġsqu ats", + "Ġle vers", + "Disc uss", + "= ]", + "D ex", + "ĠVIDE OS", + "A UD", + "Ġtrans act", + "ĠKin ect", + "ĠK uala", + "ĠC yp", + "7 47", + "Ġsh attering", + "Ġarsen ic", + "ĠInt ake", + "ĠAngel o", + "ĠQu it", + "ĠK he", + "Ġ18 93", + "M aker", + "0 29", + "ĠPain ting", + "Dis able", + "9 16", + "Ġanal ges", + "Ġtact ile", + "Ġprop hes", + "Ġd iced", + "ĠTravel s", + "ĠHe ader", + "ĠClub s", + "Ass istant", + "Ġinc rim", + "Ġd ips", + "Ġcruc ifix", + "ĠShan ahan", + "ĠInter pret", + "Ġ40 90", + "al ogy", + "abb a", + "Ġsimul ac", + "hus band", + "S IM", + "Ġrecy cle", + "uc er", + "ed ged", + "Ġre naissance", + "ĠBomb ay", + "Cath olic", + "ĠL INE", + "ĠCl othing", + "re ports", + "Ġpl aus", + "Ġd ag", + "ĠM ace", + "Z I", + "Ġintr uder", + "ĠVeter inary", + "g ru", + "Ġsne aky", + "ĠS ie", + "ĠC innamon", + "P OSE", + "Ġcou rier", + "ĠC NS", + "Ġemanc ipation", + "s it", + "Ġplay through", + "ĠFac ilities", + "v irt", + "ĠG auntlet", + "Thom pson", + "Ġunbeliev ably", + "Param eters", + "Ġst itching", + "ign e", + "ĠTH ESE", + "Priv acy", + "Ġshenan igans", + "Ġvit ri", + "ĠVal id", + "59 1", + "Ń ·", + "ĠProt otype", + "ink a", + "SC P", + "ĠT id", + "è Ī", + "old ed", + "Ġindividual ity", + "Ġbark ing", + "Ġm ars", + "ĠW D", + "Ġ8 20", + "Ġt ir", + "Ġsl apping", + "Ġdisgr untled", + "ĠAng ola", + "ri us", + "ĠTorn ado", + "ĠTh urs", + "Ġcapt cha", + "Ġang st", + "ĠP og", + "ĠAssass ins", + "ĠAd idas", + "Ġjoy ful", + "Ġwh ining", + "Emer gency", + "Ġphosph orus", + "Ġatt rition", + "oph on", + "ĠTimber wolves", + "ĠJ ah", + "ĠBr inging", + "ĠW ad", + "ĠEn sure", + "oh l", + "ĠX ie", + "omm el", + "c mp", + "Ġz ipper", + "Ġrel at", + "ĠCor ridor", + "m ilo", + "T ING", + "Av g", + "Ġcro pped", + "] }", + "Ġr aged", + "ĠLump ur", + "ĠGuer rero", + "our ke", + "N ut", + "Ġoff sets", + "og lu", + "dr m", + "Ġmort als", + "lat able", + "Ġdismiss ive", + "ä¸ ī", + "Ġthro ats", + "Ġchips et", + "ĠSpot light", + "Catal og", + "art ist", + "G b", + "Ġch illy", + "Ġst oked", + "Ġ3 74", + "W ard", + "L atin", + "Ġf iasco", + "Ġble ach", + "Ġb rav", + "Enh anced", + "Ġin oc", + "ĠFior ina", + "_ >", + "Ġle ukemia", + "Ġel uc", + "Ġannoun cer", + "ĠLith uan", + "ĠArm ageddon", + "å ĩ", + "Len in", + "ĠR uk", + "Ġpe pp", + "ĠRom antic", + "ĠP IT", + "ĠInter stellar", + "ĠAt kinson", + "R aid", + "J s", + "Go al", + "C ourse", + "Ġvan ishing", + "es ley", + "ĠR ounds", + "Els a", + "59 3", + "Ġredund ancy", + "ĠST AND", + "Ġprop hetic", + "Ġhabit able", + "ry u", + "Ġfaint ly", + "M ODE", + "Ġfl anked", + "IR C", + "Aw esome", + "Ġsp urious", + "ĠZ ah", + "ĠMS G", + "Ġsh ading", + "Ġmotiv ational", + "ĠSant ana", + "ĠS PR", + "Ġexc ruciating", + "om ial", + "ĠM iko", + "ĠLe opard", + "A byss", + "Ġ[ |", + "d irty", + "Ġbath s", + "Ġdem oral", + "and re", + "P B", + "Ġun ification", + "Ġsac rament", + "Ġ[ &", + "Ġpric eless", + "Ġgel atin", + "Ġeman ating", + "ĠAll aah", + "98 6", + "Ġout burst", + "Ġer as", + "ĠX VI", + "ĠSP I", + "O tt", + "ĠLaz arus", + "PL IED", + "F lying", + "blog s", + "W isconsin", + "R aven", + "Ġreb ate", + "Ġcreep s", + "ĠSp an", + "ĠPain ter", + "ĠKir a", + "ĠAm os", + "ĠCor vette", + "Cons umer", + "ĠRec over", + "ck i", + "Ġpes ky", + "ĠIn vention", + "Compan ies", + "Ġchalleng ers", + "ad emic", + "ĠUkrain ians", + "ĠNeuro log", + "ĠFors aken", + "Ġent rants", + "Ġemb attled", + "Ġdef unct", + "ĠGlac ier", + "Ġpo isons", + "ĠH orses", + "m akes", + "ĠD irt", + "Ġ4 23", + "hh h", + "ĠTrans formation", + "QUI RE", + "................ ..", + "Ġtrave ller", + "ĠSe xy", + "ĠK ern", + "ip olar", + "Ġransom ware", + "oooooooo oooooooo", + "E c", + "rub y", + "Prof essional", + "ĠOut break", + "arg ument", + "G rey", + "ĠFif a", + "ĠCH O", + "ĠFOR M", + "ĠAm trak", + "- [", + "Ġcr adle", + "Ġantioxid ants", + "ãģ®å ®", + "7 36", + "ĠNAS L", + "ĠContribut ions", + "Ind iana", + "ĠST EP", + "C SS", + "Ġsal ient", + "Ġall ocations", + "yr ights", + "Ġm ashed", + "ĠCut ter", + "Sex ual", + "Ġp ounded", + "Ġfan base", + "Ġc asc", + "ĠTrans parency", + "Ġanaly tic", + "ĠSummon er", + "× ŀ", + "ĠAD C", + "det ail", + "Ġvan quished", + "Ġcr abs", + "ar ie", + "Dest roy", + "ĠS ack", + "Ġtrans istor", + "Al abama", + "ĠK oen", + "ĠFisher ies", + "c one", + "Ġannex ed", + "ĠM GM", + "es a", + "Ġf aked", + "ĠCong ratulations", + "Ġhind ered", + "Ġcorrection al", + "ĠI TV", + "lee ve", + "Ġin appropriately", + "lic ks", + "Ġtresp ass", + "Ġp aws", + "Ġnegoti ator", + "ĠChrist ensen", + "lim its", + "ĠDian ne", + "Ġeleg ance", + "ĠContract s", + "an ke", + "Ob j", + "Ġvigil ance", + "Ġcast les", + "ĠN AD", + "ĠHol o", + "Ġemph atically", + "ĠTit us", + "ĠServ ing", + "ĠRich ie", + "ĠP igs", + "5 68", + "Ġanim osity", + "ĠAtt ributes", + "ĠU riel", + "M Q", + "my ra", + "ĠApplic ant", + "Ġpsychiat rists", + "ĠV ij", + "ĠAb by", + "ag ree", + "P ush", + "Ġk Wh", + "hib a", + "Ġinc ite", + "ĠWe asley", + "ĠTax i", + "minist ic", + "hy per", + "ĠF arn", + "Ġ6 01", + "ĠNation wide", + "F ake", + "95 2", + "Ġma ize", + "Ġinteract ed", + "Ġtransition ed", + "Ġparas itic", + "Ġharm onic", + "Ġdec aying", + "Ġbas eless", + "ns ics", + "Ġtrans pired", + "Ġabund antly", + "ĠFore nsic", + "Ġtread mill", + "ĠJ av", + "ab and", + "Ġssh d", + "Ġfront man", + "ĠJak arta", + "oll er", + "dro ps", + "ĠSERV ICES", + "rompt u", + "oph ical", + "h ospital", + "bled on", + "6 45", + "Ġmid range", + "ĠEV ENT", + "cul ated", + "raw led", + "Ġper ched", + "Ġover board", + "ĠPe el", + "ĠP wr", + "ĠCar th", + "ĠCOM PLE", + "co e", + "sh all", + "Ġdeter rence", + "M ETHOD", + "ĠAbs ent", + "M EN", + "Ġs ill", + "ĠLE VEL", + "Y ork", + "Ġsin ners", + "ĠOP EC", + "ĠN ur", + "ĠDesign s", + "se lection", + "Ġunw orthy", + "CH A", + "Ġstreng thens", + "88 3", + "ed ly", + "Ġslic ing", + "Ġmal nutrition", + "Ġfilm making", + "ĠPol k", + "ur ated", + "Ġ4 21", + "bre akers", + "!' \"", + "Ġwet lands", + "ĠDisc rimination", + "Ġallow able", + "Ġste ered", + "ĠSic ily", + "S AM", + "Ġmust ache", + "Ġm ids", + "Ġcl ipped", + "Ġcirc ulate", + "Ġbr ittle", + "ĠBuild ings", + "ra ised", + "ĠRound up", + "Ġwealth ier", + "Ġoverw rite", + "Ġover powered", + "ĠGerr ard", + "s ites", + "PD ATED", + "Ġacute ly", + "ĠGam ble", + "Ġp im", + "ĠK us", + "Typ ically", + "De ploy", + "ĠMoroc can", + "p otion", + "com be", + "Ġvigil ante", + "Ġ36 3", + "St ew", + "ĠB agg", + "Ġres ided", + "ĠSp o", + "Ġrem nant", + "Ġempt iness", + "br ainer", + "Ġout patient", + "pri ority", + "Ġle ptin", + "ĠPay ton", + "ĠGle aming", + "ĠS hed", + "ĠPol o", + "ĠMormon ism", + "rest ricted", + "arl ane", + "w x", + "Ġcreat ine", + "ĠAn on", + "ĠST UD", + "ĠJ UL", + "ĠT ee", + "5 28", + "08 9", + "Ġhat ched", + "Dis patch", + "ĠCompos ite", + "Ġ45 1", + "p uff", + "ĠX COM", + "ĠOr n", + "ĠTH ANK", + "END ED", + "ĠAshe ville", + "Ġà ľ", + "Ġman go", + "ĠS lightly", + "world ly", + "ĠW ander", + "ĠExp and", + "ĠCh r", + "M ist", + "Ġorthodox y", + "ĠUN ESCO", + "reg ate", + "Else where", + "k ie", + "ir led", + "Ġtopp le", + "Ġadopt ive", + "ĠLeg s", + "d ress", + "ĠS agan", + "b are", + "ĠGl ou", + "Cr unch", + "Ġhelp ers", + "Ġchron ically", + "ĠH uma", + "1 0000", + "Ġaccommod ating", + "äº Ķ", + "Ġwrink les", + "Ġdod ged", + "four th", + "Ġpre con", + "Ġcompress or", + "ĠK are", + "Ġev ict", + "ĠWar wick", + "im ar", + "Ġmodern ization", + "Ġband wagon", + "Ġref uted", + "Ġnet ted", + "ĠNa ples", + "ĠGen ie", + "per ors", + "Ġfield ed", + "Ġde re", + "ĠPar ables", + "le es", + "Ġtr out", + "asp ers", + "Ġn ihil", + "Ġhapp iest", + "Ġflo ppy", + "ĠLo ft", + "ĠHe ard", + "Ġun ison", + "Ġl ug", + "ĠRed mond", + "class ic", + "Supp orters", + "SH IP", + "G MT", + "Ġfue lled", + "ç IJ", + "Ġd d", + "ĠEmin em", + "Ġ18 97", + "NY SE", + "Ġsecret aries", + "ĠF IA", + "ĠCanaver al", + "F avorite", + "Ġp omp", + "Ġdetain ee", + "ers hip", + "aim on", + "i our", + "ĠA pex", + "Ġplant ations", + "am ia", + "ac ion", + "R ust", + "Ġtow ed", + "ĠTru ly", + "5 77", + "Ġshel tered", + "r ider", + "W o", + "Ġl air", + "ĠInt elligent", + "impro ve", + "m atically", + "Ġet iquette", + "ad ra", + "all o", + "ĠJun o", + "any thing", + "ĠStru ggle", + "ĠPred ict", + "ĠGr imes", + "ĠAMER ICA", + "ct x", + "ĠSit uation", + "W OOD", + "Ġsol uble", + "me ier", + "Ġintoler able", + "ang ering", + "Ġun interrupted", + "Ġtool tip", + "Ġinterrog ated", + "Ġgun ned", + "ĠSne ak", + "æŃ ¦", + "Ġt ether", + "Ġcr umble", + "L ens", + "Ġclust ered", + "ĠSy l", + "ĠHas an", + "Ġdystop ian", + "w ana", + "Ġjoy stick", + "ĠTh ib", + "amm u", + "Tom orrow", + "5 46", + "Ġoverc ame", + "Ġminim ized", + "cept or", + "Run ner", + "ENG TH", + "ĠBrend a", + "ĠAchieve ments", + "Ġtor ches", + "Ġrapp ort", + "ĠInvestig ator", + "ĠHand ling", + "rel ation", + "g rey", + "8 15", + "Ġk cal", + "ĠComm ands", + "d q", + "Ġcur ls", + "Ġbe arer", + "Ġcyn icism", + "it ri", + "ĠUse ful", + "B ee", + "D CS", + "Ġab ras", + "P ract", + "BIL ITIES", + "7 12", + "Ġdebug ger", + "Ġdebt or", + "ĠL ia", + "ĠK ers", + "Ġexacerb ate", + "ĠSt acy", + "ĠB land", + "ĠSc enes", + "Ġbranch ing", + "âĸĪâĸĪâĸĪâĸĪ âĸĪâĸĪâĸĪâĸĪ", + "ape ake", + "Ġs alsa", + "Ġmish and", + "ĠKon ami", + "ĠN ib", + "Ġanecd ote", + "Ġagree able", + "Ï ī", + "ĠNath aniel", + "ĠHe isman", + "ĠB eware", + "Ġ18 86", + "spect ive", + "69 1", + "5 22", + "Ġinhib its", + "Ġhas hing", + "Ġ18 89", + "å° Ĩ", + "v ich", + "P ure", + "Ġsolid ly", + "Ġaspir in", + "im aru", + "Ġstreet car", + "ĠU CS", + "ĠJ udd", + "Ġflash backs", + "p ins", + "Ġ14 40", + "ĠUN HCR", + "ĠSym ptoms", + "T IT", + "5 38", + "F ra", + "% );", + "Ġo oz", + "Ġcur few", + "Ġcal med", + "Ġparticip ates", + "Te X", + "Ġnons ensical", + "Ġfull back", + "ĠDe L", + "mon key", + "h ari", + "Ġmetabol ites", + "Ġloot ed", + "ĠAL WAYS", + "ĠB CC", + "L t", + "oc het", + "B one", + "Ġveto ed", + "Ġg cc", + "ĠCL ICK", + "Ġ18 88", + "s af", + "Ġstiff ness", + "Ġlow ly", + "ĠGe h", + "vers on", + "ors et", + "Ġun foreseen", + "Ġan esthesia", + "ĠOpt ical", + "Ġrecon structed", + "ĠT up", + "sh ows", + "NEW S", + "ĠNewsp aper", + "ĠA SA", + "ter a", + "N umbers", + "Ġinexpl icable", + "× ij", + "Ġhard ness", + "unt arily", + "ĠA cer", + "grad ient", + "ARD IS", + "Ġwood land", + "Ġmetaph ors", + "ĠWem bley", + "ĠPa vel", + "phil is", + "Ġre writing", + "Ġpercept ual", + "Ġ10 70", + "worm s", + "ĠDown s", + "Ġunsur prisingly", + "Ġtag ging", + "fl ame", + "Ġlit res", + "Ġboun ces", + "ĠB abe", + "sh ut", + "Ġoverd oses", + "ĠShe ila", + "ĠCh au", + "ĠBl ess", + "Capt ure", + "ĠSign ificant", + "ĠSc ion", + "Ġ38 9", + "ĠMc H", + "ĠTitan ium", + "ĠMe al", + "amed a", + "ag ents", + "agg ressive", + "B illy", + "76 3", + "ĠS aying", + "DER R", + "it one", + "Coll ins", + "B ound", + "Ġbol ted", + "ĠDM CA", + "95 3", + "Ġun iqueness", + "Ġep igen", + "un ci", + "ant am", + "Ġreck oning", + "ch airs", + "OG R", + "ĠSen egal", + "Ġ18 62", + "re levant", + "Ġ ¯", + "Ġpharm acies", + "ĠG eral", + "v ier", + "Y an", + "OR PG", + "Ġrab id", + "b ending", + "ĠUN ITED", + "Ġ4 65", + "As sembly", + "Ġwe ep", + "Ġbe hest", + "ĠMother s", + "ĠJ ace", + "h id", + "Ġwh irlwind", + "ĠUN IVERS", + "Ġut opian", + "Ġkidn ap", + "Ph ilipp", + "K in", + "89 3", + "Ġlivest ream", + "ĠM ISS", + "Ġsub versive", + "ĠTechn iques", + "ĠJUST ICE", + "ĠB ASE", + "Ġ38 7", + "Ġassail ants", + "ĠHard core", + "Ġsprink led", + "ĠP se", + "é ļ", + "print ed", + "ĠH au", + "OR GE", + "ĠT OUR", + "Ġl aced", + "Ġit ch", + "G iving", + "Ġport ed", + "78 1", + "//////////////// ////////////////", + "bre eding", + "Ġlog ger", + "ĠH OL", + "inn ie", + "First ly", + "Ġembry onic", + "Ġdeleg ated", + "p ai", + "O IL", + "Ġcentr ally", + "ĠR x", + "ĠSc outing", + "D utch", + "Ġhe reditary", + "ĠCru iser", + "s at", + "5 29", + "ĠMar riott", + "other mal", + "Ġprohib itions", + "E arn", + "ĠSt ab", + "ĠColleg es", + "ĠBel ief", + "st retched", + "ĠL H", + "ĠEntity Item", + "C IA", + "Ġun rem", + "Ġlaure ate", + "Ġdenomin ations", + "sum mary", + "h ler", + "S pect", + "ĠK laus", + "ĠBe ans", + "Ġins ur", + "ĠPA X", + "Ġfield er", + "ĠV et", + "ĠSp arrow", + "z ie", + "ĠS Q", + "ĠMond ays", + "ĠOff line", + "ĠLer ner", + "ĠExt ensions", + "Ire land", + "Ġpatron age", + "Ġcontrast ed", + "ĠMan ia", + "h irt", + "Mos cow", + "Ġcondem ns", + "ĠAn ge", + "Ġcomp osing", + "ĠPe pe", + "ĠP addock", + "Ġheter ogeneity", + "Ġide ologically", + "Ġf ishes", + "Ġcur sing", + "ĠR utherford", + "ĠFlo ating", + "ĠAm elia", + "Te a", + "Syn opsis", + "Ġstun ts", + "Ġbe ad", + "Ġstock ing", + "ĠM ILL", + "ob ook", + "mass ive", + "\\ <", + "Ġh ump", + "ĠPref erences", + "Engine Debug", + "ge ist", + "ĠNiet o", + "ome ver", + "ish y", + "eval uate", + "col onial", + "Altern ative", + "ĠGo Pro", + "ĠV ortex", + "ĠNET WORK", + "ans ky", + "Sec ure", + "ĠTh rust", + "Sn ake", + "Ġparcel s", + "Ġsam urai", + "Ġactress es", + "N ap", + "M F", + "ifer ation", + "Be er", + "5 23", + "ĠI ly", + "oint ment", + "P ing", + "Ġstri ped", + "ĠMell on", + "oss ession", + "Ġneut ron", + "end ium", + "Ġa ph", + "ĠFlav oring", + "Ġ38 3", + "Ġrespons iveness", + "ĠJ indal", + "ĠHitch cock", + "Den ver", + "ĠDRAG ON", + "sm anship", + "ĠDu pl", + "Ġs ly", + "Ġweb cam", + "ĠTw ain", + "ĠDar ling", + "ili ate", + "cons umer", + "D IT", + "Ġnames ake", + "Ġun orthodox", + "Ġfun er", + "ĠPL oS", + "ĠCONTR OL", + "ozy g", + "ogl obin", + "F ACE", + "ER G", + "ĠD ia", + "ĠF iesta", + "ce le", + "0 34", + "Ġencl ave", + "âĸ¬ âĸ¬", + "on ement", + "al ist", + "M and", + "Ġhome grown", + "ĠF ancy", + "Ġconcept ions", + "ĠCont ains", + "ure en", + "Ġreiter ate", + "Ġme ager", + "Ġinstall ments", + "Sp awn", + "6 27", + "Ġphot oc", + "ĠCab rera", + "ĠRos enthal", + "ĠLans ing", + "is ner", + "Ġinvest s", + "ĠUFO s", + "EX P", + "Hard ware", + "Ġtr agically", + "Ġconced es", + "ie ft", + "ch am", + "bor gh", + "ĠSch r", + "ĠMel anie", + "ĠH oy", + "Ġvisit ation", + "Ġid iosyncr", + "Ġfract ions", + "Ġfore skin", + "ob os", + "Ġpo aching", + "ĠVI EW", + "Ġstimul ates", + "ĠG ork", + "can on", + "M IC", + "ĠNem esis", + "ĠInd ra", + "ĠDM V", + "Ġ5 29", + "Ġinspect ing", + "Ġgrand ma", + "ĠW hedon", + "ĠSh ant", + "ĠP 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+/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Environment: () => (/* binding */ Environment), +/* harmony export */ Interpreter: () => (/* binding */ Interpreter), +/* harmony export */ Template: () => (/* binding */ Template), +/* harmony export */ parse: () => (/* binding */ parse), +/* harmony export */ tokenize: () => (/* binding */ tokenize) +/* harmony export */ }); +// src/lexer.ts +var TOKEN_TYPES = Object.freeze({ + Text: "Text", + // The text between Jinja statements or expressions + NumericLiteral: "NumericLiteral", + // e.g., 123, 1.0 + StringLiteral: "StringLiteral", + // 'string' + Identifier: "Identifier", + // Variables, functions, statements, booleans, etc. + Equals: "Equals", + // = + OpenParen: "OpenParen", + // ( + CloseParen: "CloseParen", + // ) + OpenStatement: "OpenStatement", + // {% + CloseStatement: "CloseStatement", + // %} + OpenExpression: "OpenExpression", + // {{ + CloseExpression: "CloseExpression", + // }} + OpenSquareBracket: "OpenSquareBracket", + // [ + CloseSquareBracket: "CloseSquareBracket", + // ] + OpenCurlyBracket: "OpenCurlyBracket", + // { + CloseCurlyBracket: "CloseCurlyBracket", + // } + Comma: "Comma", + // , + Dot: "Dot", + // . + Colon: "Colon", + // : + Pipe: "Pipe", + // | + CallOperator: "CallOperator", + // () + AdditiveBinaryOperator: "AdditiveBinaryOperator", + // + - ~ + MultiplicativeBinaryOperator: "MultiplicativeBinaryOperator", + // * / % + ComparisonBinaryOperator: "ComparisonBinaryOperator", + // < > <= >= == != + UnaryOperator: "UnaryOperator", + // ! - + + Comment: "Comment" + // {# ... #} +}); +var Token = class { + /** + * Constructs a new Token. + * @param {string} value The raw value as seen inside the source code. + * @param {TokenType} type The type of token. + */ + constructor(value, type) { + this.value = value; + this.type = type; + } +}; +function isWord(char) { + return /\w/.test(char); +} +function isInteger(char) { + return /[0-9]/.test(char); +} +var ORDERED_MAPPING_TABLE = [ + // Control sequences + ["{%", TOKEN_TYPES.OpenStatement], + ["%}", TOKEN_TYPES.CloseStatement], + ["{{", TOKEN_TYPES.OpenExpression], + ["}}", TOKEN_TYPES.CloseExpression], + // Single character tokens + ["(", TOKEN_TYPES.OpenParen], + [")", TOKEN_TYPES.CloseParen], + ["{", TOKEN_TYPES.OpenCurlyBracket], + ["}", TOKEN_TYPES.CloseCurlyBracket], + ["[", TOKEN_TYPES.OpenSquareBracket], + ["]", TOKEN_TYPES.CloseSquareBracket], + [",", TOKEN_TYPES.Comma], + [".", TOKEN_TYPES.Dot], + [":", TOKEN_TYPES.Colon], + ["|", TOKEN_TYPES.Pipe], + // Comparison operators + ["<=", TOKEN_TYPES.ComparisonBinaryOperator], + [">=", TOKEN_TYPES.ComparisonBinaryOperator], + ["==", TOKEN_TYPES.ComparisonBinaryOperator], + ["!=", TOKEN_TYPES.ComparisonBinaryOperator], + ["<", TOKEN_TYPES.ComparisonBinaryOperator], + [">", TOKEN_TYPES.ComparisonBinaryOperator], + // Arithmetic operators + ["+", TOKEN_TYPES.AdditiveBinaryOperator], + ["-", TOKEN_TYPES.AdditiveBinaryOperator], + ["~", TOKEN_TYPES.AdditiveBinaryOperator], + ["*", TOKEN_TYPES.MultiplicativeBinaryOperator], + ["/", TOKEN_TYPES.MultiplicativeBinaryOperator], + ["%", TOKEN_TYPES.MultiplicativeBinaryOperator], + // Assignment operator + ["=", TOKEN_TYPES.Equals] +]; +var ESCAPE_CHARACTERS = /* @__PURE__ */ new Map([ + ["n", "\n"], + // New line + ["t", " "], + // Horizontal tab + ["r", "\r"], + // Carriage return + ["b", "\b"], + // Backspace + ["f", "\f"], + // Form feed + ["v", "\v"], + // Vertical tab + ["'", "'"], + // Single quote + ['"', '"'], + // Double quote + ["\\", "\\"] + // Backslash +]); +function preprocess(template, options = {}) { + if (template.endsWith("\n")) { + template = template.slice(0, -1); + } + if (options.lstrip_blocks) { + template = template.replace(/^[ \t]*({[#%-])/gm, "$1"); + } + if (options.trim_blocks) { + template = template.replace(/([#%-]})\n/g, "$1"); + } + return template.replace(/-%}\s*/g, "%}").replace(/\s*{%-/g, "{%").replace(/-}}\s*/g, "}}").replace(/\s*{{-/g, "{{").replace(/-#}\s*/g, "#}").replace(/\s*{#-/g, "{#").replace(/{%\s*(end)?generation\s*%}/gs, ""); +} +function tokenize(source, options = {}) { + const tokens = []; + const src = preprocess(source, options); + let cursorPosition = 0; + let curlyBracketDepth = 0; + const consumeWhile = (predicate) => { + let str = ""; + while (predicate(src[cursorPosition])) { + if (src[cursorPosition] === "\\") { + ++cursorPosition; + if (cursorPosition >= src.length) + throw new SyntaxError("Unexpected end of input"); + const escaped = src[cursorPosition++]; + const unescaped = ESCAPE_CHARACTERS.get(escaped); + if (unescaped === void 0) { + throw new SyntaxError(`Unexpected escaped character: ${escaped}`); + } + str += unescaped; + continue; + } + str += src[cursorPosition++]; + if (cursorPosition >= src.length) + throw new SyntaxError("Unexpected end of input"); + } + return str; + }; + main: + while (cursorPosition < src.length) { + const lastTokenType = tokens.at(-1)?.type; + if (lastTokenType === void 0 || lastTokenType === TOKEN_TYPES.CloseStatement || lastTokenType === TOKEN_TYPES.CloseExpression || lastTokenType === TOKEN_TYPES.Comment) { + let text = ""; + while (cursorPosition < src.length && // Keep going until we hit the next Jinja statement or expression + !(src[cursorPosition] === "{" && (src[cursorPosition + 1] === "%" || src[cursorPosition + 1] === "{" || src[cursorPosition + 1] === "#"))) { + text += src[cursorPosition++]; + } + if (text.length > 0) { + tokens.push(new Token(text, TOKEN_TYPES.Text)); + continue; + } + } + if (src[cursorPosition] === "{" && src[cursorPosition + 1] === "#") { + cursorPosition += 2; + let comment = ""; + while (src[cursorPosition] !== "#" || src[cursorPosition + 1] !== "}") { + if (cursorPosition + 2 >= src.length) { + throw new SyntaxError("Missing end of comment tag"); + } + comment += src[cursorPosition++]; + } + tokens.push(new Token(comment, TOKEN_TYPES.Comment)); + cursorPosition += 2; + continue; + } + consumeWhile((char2) => /\s/.test(char2)); + const char = src[cursorPosition]; + if (char === "-" || char === "+") { + const lastTokenType2 = tokens.at(-1)?.type; + if (lastTokenType2 === TOKEN_TYPES.Text || lastTokenType2 === void 0) { + throw new SyntaxError(`Unexpected character: ${char}`); + } + switch (lastTokenType2) { + case TOKEN_TYPES.Identifier: + case TOKEN_TYPES.NumericLiteral: + case TOKEN_TYPES.StringLiteral: + case TOKEN_TYPES.CloseParen: + case TOKEN_TYPES.CloseSquareBracket: + break; + default: { + ++cursorPosition; + const num = consumeWhile(isInteger); + tokens.push( + new Token(`${char}${num}`, num.length > 0 ? TOKEN_TYPES.NumericLiteral : TOKEN_TYPES.UnaryOperator) + ); + continue; + } + } + } + for (const [seq, type] of ORDERED_MAPPING_TABLE) { + if (seq === "}}" && curlyBracketDepth > 0) { + continue; + } + const slice2 = src.slice(cursorPosition, cursorPosition + seq.length); + if (slice2 === seq) { + tokens.push(new Token(seq, type)); + if (type === TOKEN_TYPES.OpenExpression) { + curlyBracketDepth = 0; + } else if (type === TOKEN_TYPES.OpenCurlyBracket) { + ++curlyBracketDepth; + } else if (type === TOKEN_TYPES.CloseCurlyBracket) { + --curlyBracketDepth; + } + cursorPosition += seq.length; + continue main; + } + } + if (char === "'" || char === '"') { + ++cursorPosition; + const str = consumeWhile((c) => c !== char); + tokens.push(new Token(str, TOKEN_TYPES.StringLiteral)); + ++cursorPosition; + continue; + } + if (isInteger(char)) { + let num = consumeWhile(isInteger); + if (src[cursorPosition] === "." && isInteger(src[cursorPosition + 1])) { + ++cursorPosition; + const frac = consumeWhile(isInteger); + num = `${num}.${frac}`; + } + tokens.push(new Token(num, TOKEN_TYPES.NumericLiteral)); + continue; + } + if (isWord(char)) { + const word = consumeWhile(isWord); + tokens.push(new Token(word, TOKEN_TYPES.Identifier)); + continue; + } + throw new SyntaxError(`Unexpected character: ${char}`); + } + return tokens; +} + +// src/ast.ts +var Statement = class { + type = "Statement"; +}; +var Program = class extends Statement { + constructor(body) { + super(); + this.body = body; + } + type = "Program"; +}; +var If = class extends Statement { + constructor(test, body, alternate) { + super(); + this.test = test; + this.body = body; + this.alternate = alternate; + } + type = "If"; +}; +var For = class extends Statement { + constructor(loopvar, iterable, body, defaultBlock) { + super(); + this.loopvar = loopvar; + this.iterable = iterable; + this.body = body; + this.defaultBlock = defaultBlock; + } + type = "For"; +}; +var Break = class extends Statement { + type = "Break"; +}; +var Continue = class extends Statement { + type = "Continue"; +}; +var SetStatement = class extends Statement { + constructor(assignee, value, body) { + super(); + this.assignee = assignee; + this.value = value; + this.body = body; + } + type = "Set"; +}; +var Macro = class extends Statement { + constructor(name, args, body) { + super(); + this.name = name; + this.args = args; + this.body = body; + } + type = "Macro"; +}; +var Comment = class extends Statement { + constructor(value) { + super(); + this.value = value; + } + type = "Comment"; +}; +var Expression = class extends Statement { + type = "Expression"; +}; +var MemberExpression = class extends Expression { + constructor(object, property, computed) { + super(); + this.object = object; + this.property = property; + this.computed = computed; + } + type = "MemberExpression"; +}; +var CallExpression = class extends Expression { + constructor(callee, args) { + super(); + this.callee = callee; + this.args = args; + } + type = "CallExpression"; +}; +var Identifier = class extends Expression { + /** + * @param {string} value The name of the identifier + */ + constructor(value) { + super(); + this.value = value; + } + type = "Identifier"; +}; +var Literal = class extends Expression { + constructor(value) { + super(); + this.value = value; + } + type = "Literal"; +}; +var IntegerLiteral = class extends Literal { + type = "IntegerLiteral"; +}; +var FloatLiteral = class extends Literal { + type = "FloatLiteral"; +}; +var StringLiteral = class extends Literal { + type = "StringLiteral"; +}; +var ArrayLiteral = class extends Literal { + type = "ArrayLiteral"; +}; +var TupleLiteral = class extends Literal { + type = "TupleLiteral"; +}; +var ObjectLiteral = class extends Literal { + type = "ObjectLiteral"; +}; +var BinaryExpression = class extends Expression { + constructor(operator, left, right) { + super(); + this.operator = operator; + this.left = left; + this.right = right; + } + type = "BinaryExpression"; +}; +var FilterExpression = class extends Expression { + constructor(operand, filter) { + super(); + this.operand = operand; + this.filter = filter; + } + type = "FilterExpression"; +}; +var FilterStatement = class extends Statement { + constructor(filter, body) { + super(); + this.filter = filter; + this.body = body; + } + type = "FilterStatement"; +}; +var SelectExpression = class extends Expression { + constructor(lhs, test) { + super(); + this.lhs = lhs; + this.test = test; + } + type = "SelectExpression"; +}; +var TestExpression = class extends Expression { + constructor(operand, negate, test) { + super(); + this.operand = operand; + this.negate = negate; + this.test = test; + } + type = "TestExpression"; +}; +var UnaryExpression = class extends Expression { + constructor(operator, argument) { + super(); + this.operator = operator; + this.argument = argument; + } + type = "UnaryExpression"; +}; +var SliceExpression = class extends Expression { + constructor(start = void 0, stop = void 0, step = void 0) { + super(); + this.start = start; + this.stop = stop; + this.step = step; + } + type = "SliceExpression"; +}; +var KeywordArgumentExpression = class extends Expression { + constructor(key, value) { + super(); + this.key = key; + this.value = value; + } + type = "KeywordArgumentExpression"; +}; +var SpreadExpression = class extends Expression { + constructor(argument) { + super(); + this.argument = argument; + } + type = "SpreadExpression"; +}; +var CallStatement = class extends Statement { + constructor(call, callerArgs, body) { + super(); + this.call = call; + this.callerArgs = callerArgs; + this.body = body; + } + type = "CallStatement"; +}; +var Ternary = class extends Expression { + constructor(condition, trueExpr, falseExpr) { + super(); + this.condition = condition; + this.trueExpr = trueExpr; + this.falseExpr = falseExpr; + } + type = "Ternary"; +}; + +// src/parser.ts +function parse(tokens) { + const program = new Program([]); + let current = 0; + function expect(type, error) { + const prev = tokens[current++]; + if (!prev || prev.type !== type) { + throw new Error(`Parser Error: ${error}. ${prev.type} !== ${type}.`); + } + return prev; + } + function expectIdentifier(name) { + if (!isIdentifier(name)) { + throw new SyntaxError(`Expected ${name}`); + } + ++current; + } + function parseAny() { + switch (tokens[current].type) { + case TOKEN_TYPES.Comment: + return new Comment(tokens[current++].value); + case TOKEN_TYPES.Text: + return parseText(); + case TOKEN_TYPES.OpenStatement: + return parseJinjaStatement(); + case TOKEN_TYPES.OpenExpression: + return parseJinjaExpression(); + default: + throw new SyntaxError(`Unexpected token type: ${tokens[current].type}`); + } + } + function is(...types) { + return current + types.length <= tokens.length && types.every((type, i) => type === tokens[current + i].type); + } + function isStatement(...names) { + return tokens[current]?.type === TOKEN_TYPES.OpenStatement && tokens[current + 1]?.type === TOKEN_TYPES.Identifier && names.includes(tokens[current + 1]?.value); + } + function isIdentifier(...names) { + return current + names.length <= tokens.length && names.every((name, i) => tokens[current + i].type === "Identifier" && name === tokens[current + i].value); + } + function parseText() { + return new StringLiteral(expect(TOKEN_TYPES.Text, "Expected text token").value); + } + function parseJinjaStatement() { + expect(TOKEN_TYPES.OpenStatement, "Expected opening statement token"); + if (tokens[current].type !== TOKEN_TYPES.Identifier) { + throw new SyntaxError(`Unknown statement, got ${tokens[current].type}`); + } + const name = tokens[current].value; + let result; + switch (name) { + case "set": + ++current; + result = parseSetStatement(); + break; + case "if": + ++current; + result = parseIfStatement(); + expect(TOKEN_TYPES.OpenStatement, "Expected {% token"); + expectIdentifier("endif"); + expect(TOKEN_TYPES.CloseStatement, "Expected %} token"); + break; + case "macro": + ++current; + result = parseMacroStatement(); + expect(TOKEN_TYPES.OpenStatement, "Expected {% token"); + expectIdentifier("endmacro"); + expect(TOKEN_TYPES.CloseStatement, "Expected %} token"); + break; + case "for": + ++current; + result = parseForStatement(); + expect(TOKEN_TYPES.OpenStatement, "Expected {% token"); + expectIdentifier("endfor"); + expect(TOKEN_TYPES.CloseStatement, "Expected %} token"); + break; + case "call": { + ++current; + let callerArgs = null; + if (is(TOKEN_TYPES.OpenParen)) { + callerArgs = parseArgs(); + } + const callee = parsePrimaryExpression(); + if (callee.type !== "Identifier") { + throw new SyntaxError(`Expected identifier following call statement`); + } + const callArgs = parseArgs(); + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + const body = []; + while (!isStatement("endcall")) { + body.push(parseAny()); + } + expect(TOKEN_TYPES.OpenStatement, "Expected '{%'"); + expectIdentifier("endcall"); + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + const callExpr = new CallExpression(callee, callArgs); + result = new CallStatement(callExpr, callerArgs, body); + break; + } + case "break": + ++current; + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + result = new Break(); + break; + case "continue": + ++current; + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + result = new Continue(); + break; + case "filter": { + ++current; + let filterNode = parsePrimaryExpression(); + if (filterNode instanceof Identifier && is(TOKEN_TYPES.OpenParen)) { + filterNode = parseCallExpression(filterNode); + } + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + const filterBody = []; + while (!isStatement("endfilter")) { + filterBody.push(parseAny()); + } + expect(TOKEN_TYPES.OpenStatement, "Expected '{%'"); + expectIdentifier("endfilter"); + expect(TOKEN_TYPES.CloseStatement, "Expected '%}'"); + result = new FilterStatement(filterNode, filterBody); + break; + } + default: + throw new SyntaxError(`Unknown statement type: ${name}`); + } + return result; + } + function parseJinjaExpression() { + expect(TOKEN_TYPES.OpenExpression, "Expected opening expression token"); + const result = parseExpression(); + expect(TOKEN_TYPES.CloseExpression, "Expected closing expression token"); + return result; + } + function parseSetStatement() { + const left = parseExpressionSequence(); + let value = null; + const body = []; + if (is(TOKEN_TYPES.Equals)) { + ++current; + value = parseExpressionSequence(); + } else { + expect(TOKEN_TYPES.CloseStatement, "Expected %} token"); + while (!isStatement("endset")) { + body.push(parseAny()); + } + expect(TOKEN_TYPES.OpenStatement, "Expected {% token"); + expectIdentifier("endset"); + } + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + return new SetStatement(left, value, body); + } + function parseIfStatement() { + const test = parseExpression(); + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + const body = []; + const alternate = []; + while (!isStatement("elif", "else", "endif")) { + body.push(parseAny()); + } + if (isStatement("elif")) { + ++current; + ++current; + const result = parseIfStatement(); + alternate.push(result); + } else if (isStatement("else")) { + ++current; + ++current; + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + while (!isStatement("endif")) { + alternate.push(parseAny()); + } + } + return new If(test, body, alternate); + } + function parseMacroStatement() { + const name = parsePrimaryExpression(); + if (name.type !== "Identifier") { + throw new SyntaxError(`Expected identifier following macro statement`); + } + const args = parseArgs(); + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + const body = []; + while (!isStatement("endmacro")) { + body.push(parseAny()); + } + return new Macro(name, args, body); + } + function parseExpressionSequence(primary = false) { + const fn = primary ? parsePrimaryExpression : parseExpression; + const expressions = [fn()]; + const isTuple = is(TOKEN_TYPES.Comma); + while (isTuple) { + ++current; + expressions.push(fn()); + if (!is(TOKEN_TYPES.Comma)) { + break; + } + } + return isTuple ? new TupleLiteral(expressions) : expressions[0]; + } + function parseForStatement() { + const loopVariable = parseExpressionSequence(true); + if (!(loopVariable instanceof Identifier || loopVariable instanceof TupleLiteral)) { + throw new SyntaxError(`Expected identifier/tuple for the loop variable, got ${loopVariable.type} instead`); + } + if (!isIdentifier("in")) { + throw new SyntaxError("Expected `in` keyword following loop variable"); + } + ++current; + const iterable = parseExpression(); + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + const body = []; + while (!isStatement("endfor", "else")) { + body.push(parseAny()); + } + const alternative = []; + if (isStatement("else")) { + ++current; + ++current; + expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token"); + while (!isStatement("endfor")) { + alternative.push(parseAny()); + } + } + return new For(loopVariable, iterable, body, alternative); + } + function parseExpression() { + return parseIfExpression(); + } + function parseIfExpression() { + const a = parseLogicalOrExpression(); + if (isIdentifier("if")) { + ++current; + const test = parseLogicalOrExpression(); + if (isIdentifier("else")) { + ++current; + const falseExpr = parseIfExpression(); + return new Ternary(test, a, falseExpr); + } else { + return new SelectExpression(a, test); + } + } + return a; + } + function parseLogicalOrExpression() { + let left = parseLogicalAndExpression(); + while (isIdentifier("or")) { + const operator = tokens[current]; + ++current; + const right = parseLogicalAndExpression(); + left = new BinaryExpression(operator, left, right); + } + return left; + } + function parseLogicalAndExpression() { + let left = parseLogicalNegationExpression(); + while (isIdentifier("and")) { + const operator = tokens[current]; + ++current; + const right = parseLogicalNegationExpression(); + left = new BinaryExpression(operator, left, right); + } + return left; + } + function parseLogicalNegationExpression() { + let right; + while (isIdentifier("not")) { + const operator = tokens[current]; + ++current; + const arg = parseLogicalNegationExpression(); + right = new UnaryExpression(operator, arg); + } + return right ?? parseComparisonExpression(); + } + function parseComparisonExpression() { + let left = parseAdditiveExpression(); + while (true) { + let operator; + if (isIdentifier("not", "in")) { + operator = new Token("not in", TOKEN_TYPES.Identifier); + current += 2; + } else if (isIdentifier("in")) { + operator = tokens[current++]; + } else if (is(TOKEN_TYPES.ComparisonBinaryOperator)) { + operator = tokens[current++]; + } else { + break; + } + const right = parseAdditiveExpression(); + left = new BinaryExpression(operator, left, right); + } + return left; + } + function parseAdditiveExpression() { + let left = parseMultiplicativeExpression(); + while (is(TOKEN_TYPES.AdditiveBinaryOperator)) { + const operator = tokens[current]; + ++current; + const right = parseMultiplicativeExpression(); + left = new BinaryExpression(operator, left, right); + } + return left; + } + function parseCallMemberExpression() { + const member = parseMemberExpression(parsePrimaryExpression()); + if (is(TOKEN_TYPES.OpenParen)) { + return parseCallExpression(member); + } + return member; + } + function parseCallExpression(callee) { + let expression = new CallExpression(callee, parseArgs()); + expression = parseMemberExpression(expression); + if (is(TOKEN_TYPES.OpenParen)) { + expression = parseCallExpression(expression); + } + return expression; + } + function parseArgs() { + expect(TOKEN_TYPES.OpenParen, "Expected opening parenthesis for arguments list"); + const args = parseArgumentsList(); + expect(TOKEN_TYPES.CloseParen, "Expected closing parenthesis for arguments list"); + return args; + } + function parseArgumentsList() { + const args = []; + while (!is(TOKEN_TYPES.CloseParen)) { + let argument; + if (tokens[current].type === TOKEN_TYPES.MultiplicativeBinaryOperator && tokens[current].value === "*") { + ++current; + const expr = parseExpression(); + argument = new SpreadExpression(expr); + } else { + argument = parseExpression(); + if (is(TOKEN_TYPES.Equals)) { + ++current; + if (!(argument instanceof Identifier)) { + throw new SyntaxError(`Expected identifier for keyword argument`); + } + const value = parseExpression(); + argument = new KeywordArgumentExpression(argument, value); + } + } + args.push(argument); + if (is(TOKEN_TYPES.Comma)) { + ++current; + } + } + return args; + } + function parseMemberExpressionArgumentsList() { + const slices = []; + let isSlice = false; + while (!is(TOKEN_TYPES.CloseSquareBracket)) { + if (is(TOKEN_TYPES.Colon)) { + slices.push(void 0); + ++current; + isSlice = true; + } else { + slices.push(parseExpression()); + if (is(TOKEN_TYPES.Colon)) { + ++current; + isSlice = true; + } + } + } + if (slices.length === 0) { + throw new SyntaxError(`Expected at least one argument for member/slice expression`); + } + if (isSlice) { + if (slices.length > 3) { + throw new SyntaxError(`Expected 0-3 arguments for slice expression`); + } + return new SliceExpression(...slices); + } + return slices[0]; + } + function parseMemberExpression(object) { + while (is(TOKEN_TYPES.Dot) || is(TOKEN_TYPES.OpenSquareBracket)) { + const operator = tokens[current]; + ++current; + let property; + const computed = operator.type === TOKEN_TYPES.OpenSquareBracket; + if (computed) { + property = parseMemberExpressionArgumentsList(); + expect(TOKEN_TYPES.CloseSquareBracket, "Expected closing square bracket"); + } else { + property = parsePrimaryExpression(); + if (property.type !== "Identifier") { + throw new SyntaxError(`Expected identifier following dot operator`); + } + } + object = new MemberExpression(object, property, computed); + } + return object; + } + function parseMultiplicativeExpression() { + let left = parseTestExpression(); + while (is(TOKEN_TYPES.MultiplicativeBinaryOperator)) { + const operator = tokens[current++]; + const right = parseTestExpression(); + left = new BinaryExpression(operator, left, right); + } + return left; + } + function parseTestExpression() { + let operand = parseFilterExpression(); + while (isIdentifier("is")) { + ++current; + const negate = isIdentifier("not"); + if (negate) { + ++current; + } + const filter = parsePrimaryExpression(); + if (!(filter instanceof Identifier)) { + throw new SyntaxError(`Expected identifier for the test`); + } + operand = new TestExpression(operand, negate, filter); + } + return operand; + } + function parseFilterExpression() { + let operand = parseCallMemberExpression(); + while (is(TOKEN_TYPES.Pipe)) { + ++current; + let filter = parsePrimaryExpression(); + if (!(filter instanceof Identifier)) { + throw new SyntaxError(`Expected identifier for the filter`); + } + if (is(TOKEN_TYPES.OpenParen)) { + filter = parseCallExpression(filter); + } + operand = new FilterExpression(operand, filter); + } + return operand; + } + function parsePrimaryExpression() { + const token = tokens[current++]; + switch (token.type) { + case TOKEN_TYPES.NumericLiteral: { + const num = token.value; + return num.includes(".") ? new FloatLiteral(Number(num)) : new IntegerLiteral(Number(num)); + } + case TOKEN_TYPES.StringLiteral: { + let value = token.value; + while (is(TOKEN_TYPES.StringLiteral)) { + value += tokens[current++].value; + } + return new StringLiteral(value); + } + case TOKEN_TYPES.Identifier: + return new Identifier(token.value); + case TOKEN_TYPES.OpenParen: { + const expression = parseExpressionSequence(); + expect(TOKEN_TYPES.CloseParen, "Expected closing parenthesis, got ${tokens[current].type} instead."); + return expression; + } + case TOKEN_TYPES.OpenSquareBracket: { + const values = []; + while (!is(TOKEN_TYPES.CloseSquareBracket)) { + values.push(parseExpression()); + if (is(TOKEN_TYPES.Comma)) { + ++current; + } + } + ++current; + return new ArrayLiteral(values); + } + case TOKEN_TYPES.OpenCurlyBracket: { + const values = /* @__PURE__ */ new Map(); + while (!is(TOKEN_TYPES.CloseCurlyBracket)) { + const key = parseExpression(); + expect(TOKEN_TYPES.Colon, "Expected colon between key and value in object literal"); + const value = parseExpression(); + values.set(key, value); + if (is(TOKEN_TYPES.Comma)) { + ++current; + } + } + ++current; + return new ObjectLiteral(values); + } + default: + throw new SyntaxError(`Unexpected token: ${token.type}`); + } + } + while (current < tokens.length) { + program.body.push(parseAny()); + } + return program; +} + +// src/utils.ts +function range(start, stop, step = 1) { + if (stop === void 0) { + stop = start; + start = 0; + } + const result = []; + for (let i = start; i < stop; i += step) { + result.push(i); + } + return result; +} +function slice(array, start, stop, step = 1) { + const direction = Math.sign(step); + if (direction >= 0) { + start = (start ??= 0) < 0 ? Math.max(array.length + start, 0) : Math.min(start, array.length); + stop = (stop ??= array.length) < 0 ? Math.max(array.length + stop, 0) : Math.min(stop, array.length); + } else { + start = (start ??= array.length - 1) < 0 ? Math.max(array.length + start, -1) : Math.min(start, array.length - 1); + stop = (stop ??= -1) < -1 ? Math.max(array.length + stop, -1) : Math.min(stop, array.length - 1); + } + const result = []; + for (let i = start; direction * i < direction * stop; i += step) { + result.push(array[i]); + } + return result; +} +function titleCase(value) { + return value.replace(/\b\w/g, (c) => c.toUpperCase()); +} +function strftime_now(format2) { + return strftime(/* @__PURE__ */ new Date(), format2); +} +function strftime(date, format2) { + const monthFormatterLong = new Intl.DateTimeFormat(void 0, { month: "long" }); + const monthFormatterShort = new Intl.DateTimeFormat(void 0, { month: "short" }); + const pad2 = (n) => n < 10 ? "0" + n : n.toString(); + return format2.replace(/%[YmdbBHM%]/g, (token) => { + switch (token) { + case "%Y": + return date.getFullYear().toString(); + case "%m": + return pad2(date.getMonth() + 1); + case "%d": + return pad2(date.getDate()); + case "%b": + return monthFormatterShort.format(date); + case "%B": + return monthFormatterLong.format(date); + case "%H": + return pad2(date.getHours()); + case "%M": + return pad2(date.getMinutes()); + case "%%": + return "%"; + default: + return token; + } + }); +} +function escapeRegExp(s) { + return s.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); +} +function replace(str, oldvalue, newvalue, count) { + if (count === 0) + return str; + let remaining = count == null || count < 0 ? Infinity : count; + const pattern = oldvalue.length === 0 ? new RegExp("(?=)", "gu") : new RegExp(escapeRegExp(oldvalue), "gu"); + return str.replaceAll(pattern, (match) => { + if (remaining > 0) { + --remaining; + return newvalue; + } + return match; + }); +} + +// src/runtime.ts +var BreakControl = class extends Error { +}; +var ContinueControl = class extends Error { +}; +var RuntimeValue = class { + type = "RuntimeValue"; + value; + /** + * A collection of built-in functions for this type. + */ + builtins = /* @__PURE__ */ new Map(); + /** + * Creates a new RuntimeValue. + */ + constructor(value = void 0) { + this.value = value; + } + /** + * Determines truthiness or falsiness of the runtime value. + * This function should be overridden by subclasses if it has custom truthiness criteria. + * @returns {BooleanValue} BooleanValue(true) if the value is truthy, BooleanValue(false) otherwise. + */ + __bool__() { + return new BooleanValue(!!this.value); + } + toString() { + return String(this.value); + } +}; +var IntegerValue = class extends RuntimeValue { + type = "IntegerValue"; +}; +var FloatValue = class extends RuntimeValue { + type = "FloatValue"; + toString() { + return this.value % 1 === 0 ? this.value.toFixed(1) : this.value.toString(); + } +}; +var StringValue = class extends RuntimeValue { + type = "StringValue"; + builtins = /* @__PURE__ */ new Map([ + [ + "upper", + new FunctionValue(() => { + return new StringValue(this.value.toUpperCase()); + }) + ], + [ + "lower", + new FunctionValue(() => { + return new StringValue(this.value.toLowerCase()); + }) + ], + [ + "strip", + new FunctionValue(() => { + return new StringValue(this.value.trim()); + }) + ], + [ + "title", + new FunctionValue(() => { + return new StringValue(titleCase(this.value)); + }) + ], + [ + "capitalize", + new FunctionValue(() => { + return new StringValue(this.value.charAt(0).toUpperCase() + this.value.slice(1)); + }) + ], + ["length", new IntegerValue(this.value.length)], + [ + "rstrip", + new FunctionValue(() => { + return new StringValue(this.value.trimEnd()); + }) + ], + [ + "lstrip", + new FunctionValue(() => { + return new StringValue(this.value.trimStart()); + }) + ], + [ + "startswith", + new FunctionValue((args) => { + if (args.length === 0) { + throw new Error("startswith() requires at least one argument"); + } + const pattern = args[0]; + if (pattern instanceof StringValue) { + return new BooleanValue(this.value.startsWith(pattern.value)); + } else if (pattern instanceof ArrayValue) { + for (const item of pattern.value) { + if (!(item instanceof StringValue)) { + throw new Error("startswith() tuple elements must be strings"); + } + if (this.value.startsWith(item.value)) { + return new BooleanValue(true); + } + } + return new BooleanValue(false); + } + throw new Error("startswith() argument must be a string or tuple of strings"); + }) + ], + [ + "endswith", + new FunctionValue((args) => { + if (args.length === 0) { + throw new Error("endswith() requires at least one argument"); + } + const pattern = args[0]; + if (pattern instanceof StringValue) { + return new BooleanValue(this.value.endsWith(pattern.value)); + } else if (pattern instanceof ArrayValue) { + for (const item of pattern.value) { + if (!(item instanceof StringValue)) { + throw new Error("endswith() tuple elements must be strings"); + } + if (this.value.endsWith(item.value)) { + return new BooleanValue(true); + } + } + return new BooleanValue(false); + } + throw new Error("endswith() argument must be a string or tuple of strings"); + }) + ], + [ + "split", + // follows Python's `str.split(sep=None, maxsplit=-1)` function behavior + // https://docs.python.org/3.13/library/stdtypes.html#str.split + new FunctionValue((args) => { + const sep = args[0] ?? new NullValue(); + if (!(sep instanceof StringValue || sep instanceof NullValue)) { + throw new Error("sep argument must be a string or null"); + } + const maxsplit = args[1] ?? new IntegerValue(-1); + if (!(maxsplit instanceof IntegerValue)) { + throw new Error("maxsplit argument must be a number"); + } + let result = []; + if (sep instanceof NullValue) { + const text = this.value.trimStart(); + for (const { 0: match, index } of text.matchAll(/\S+/g)) { + if (maxsplit.value !== -1 && result.length >= maxsplit.value && index !== void 0) { + result.push(match + text.slice(index + match.length)); + break; + } + result.push(match); + } + } else { + if (sep.value === "") { + throw new Error("empty separator"); + } + result = this.value.split(sep.value); + if (maxsplit.value !== -1 && result.length > maxsplit.value) { + result.push(result.splice(maxsplit.value).join(sep.value)); + } + } + return new ArrayValue(result.map((part) => new StringValue(part))); + }) + ], + [ + "replace", + new FunctionValue((args) => { + if (args.length < 2) { + throw new Error("replace() requires at least two arguments"); + } + const oldValue = args[0]; + const newValue = args[1]; + if (!(oldValue instanceof StringValue && newValue instanceof StringValue)) { + throw new Error("replace() arguments must be strings"); + } + let count; + if (args.length > 2) { + if (args[2].type === "KeywordArgumentsValue") { + count = args[2].value.get("count") ?? new NullValue(); + } else { + count = args[2]; + } + } else { + count = new NullValue(); + } + if (!(count instanceof IntegerValue || count instanceof NullValue)) { + throw new Error("replace() count argument must be a number or null"); + } + return new StringValue(replace(this.value, oldValue.value, newValue.value, count.value)); + }) + ] + ]); +}; +var BooleanValue = class extends RuntimeValue { + type = "BooleanValue"; +}; +var ObjectValue = class extends RuntimeValue { + type = "ObjectValue"; + /** + * NOTE: necessary to override since all JavaScript arrays are considered truthy, + * while only non-empty Python arrays are consider truthy. + * + * e.g., + * - JavaScript: {} && 5 -> 5 + * - Python: {} and 5 -> {} + */ + __bool__() { + return new BooleanValue(this.value.size > 0); + } + builtins = /* @__PURE__ */ new Map([ + [ + "get", + new FunctionValue(([key, defaultValue]) => { + if (!(key instanceof StringValue)) { + throw new Error(`Object key must be a string: got ${key.type}`); + } + return this.value.get(key.value) ?? defaultValue ?? new NullValue(); + }) + ], + ["items", new FunctionValue(() => this.items())], + ["keys", new FunctionValue(() => this.keys())], + ["values", new FunctionValue(() => this.values())] + ]); + items() { + return new ArrayValue( + Array.from(this.value.entries()).map(([key, value]) => new ArrayValue([new StringValue(key), value])) + ); + } + keys() { + return new ArrayValue(Array.from(this.value.keys()).map((key) => new StringValue(key))); + } + values() { + return new ArrayValue(Array.from(this.value.values())); + } +}; +var KeywordArgumentsValue = class extends ObjectValue { + type = "KeywordArgumentsValue"; +}; +var ArrayValue = class extends RuntimeValue { + type = "ArrayValue"; + builtins = /* @__PURE__ */ new Map([["length", new IntegerValue(this.value.length)]]); + /** + * NOTE: necessary to override since all JavaScript arrays are considered truthy, + * while only non-empty Python arrays are consider truthy. + * + * e.g., + * - JavaScript: [] && 5 -> 5 + * - Python: [] and 5 -> [] + */ + __bool__() { + return new BooleanValue(this.value.length > 0); + } +}; +var TupleValue = class extends ArrayValue { + type = "TupleValue"; +}; +var FunctionValue = class extends RuntimeValue { + type = "FunctionValue"; +}; +var NullValue = class extends RuntimeValue { + type = "NullValue"; +}; +var UndefinedValue = class extends RuntimeValue { + type = "UndefinedValue"; +}; +var Environment = class { + constructor(parent) { + this.parent = parent; + } + /** + * The variables declared in this environment. + */ + variables = /* @__PURE__ */ new Map([ + [ + "namespace", + new FunctionValue((args) => { + if (args.length === 0) { + return new ObjectValue(/* @__PURE__ */ new Map()); + } + if (args.length !== 1 || !(args[0] instanceof ObjectValue)) { + throw new Error("`namespace` expects either zero arguments or a single object argument"); + } + return args[0]; + }) + ] + ]); + /** + * The tests available in this environment. + */ + tests = /* @__PURE__ */ new Map([ + ["boolean", (operand) => operand.type === "BooleanValue"], + ["callable", (operand) => operand instanceof FunctionValue], + [ + "odd", + (operand) => { + if (!(operand instanceof IntegerValue)) { + throw new Error(`cannot odd on ${operand.type}`); + } + return operand.value % 2 !== 0; + } + ], + [ + "even", + (operand) => { + if (!(operand instanceof IntegerValue)) { + throw new Error(`cannot even on ${operand.type}`); + } + return operand.value % 2 === 0; + } + ], + ["false", (operand) => operand.type === "BooleanValue" && !operand.value], + ["true", (operand) => operand.type === "BooleanValue" && operand.value], + ["none", (operand) => operand.type === "NullValue"], + ["string", (operand) => operand.type === "StringValue"], + ["number", (operand) => operand instanceof IntegerValue || operand instanceof FloatValue], + ["integer", (operand) => operand instanceof IntegerValue], + ["iterable", (operand) => operand.type === "ArrayValue" || operand.type === "StringValue"], + ["mapping", (operand) => operand.type === "ObjectValue"], + [ + "lower", + (operand) => { + const str = operand.value; + return operand.type === "StringValue" && str === str.toLowerCase(); + } + ], + [ + "upper", + (operand) => { + const str = operand.value; + return operand.type === "StringValue" && str === str.toUpperCase(); + } + ], + ["none", (operand) => operand.type === "NullValue"], + ["defined", (operand) => operand.type !== "UndefinedValue"], + ["undefined", (operand) => operand.type === "UndefinedValue"], + ["equalto", (a, b) => a.value === b.value], + ["eq", (a, b) => a.value === b.value] + ]); + /** + * Set the value of a variable in the current environment. + */ + set(name, value) { + return this.declareVariable(name, convertToRuntimeValues(value)); + } + declareVariable(name, value) { + if (this.variables.has(name)) { + throw new SyntaxError(`Variable already declared: ${name}`); + } + this.variables.set(name, value); + return value; + } + // private assignVariable(name: string, value: AnyRuntimeValue): AnyRuntimeValue { + // const env = this.resolve(name); + // env.variables.set(name, value); + // return value; + // } + /** + * Set variable in the current scope. + * See https://jinja.palletsprojects.com/en/3.0.x/templates/#assignments for more information. + */ + setVariable(name, value) { + this.variables.set(name, value); + return value; + } + /** + * Resolve the environment in which the variable is declared. + * @param {string} name The name of the variable. + * @returns {Environment} The environment in which the variable is declared. + */ + resolve(name) { + if (this.variables.has(name)) { + return this; + } + if (this.parent) { + return this.parent.resolve(name); + } + throw new Error(`Unknown variable: ${name}`); + } + lookupVariable(name) { + try { + return this.resolve(name).variables.get(name) ?? new UndefinedValue(); + } catch { + return new UndefinedValue(); + } + } +}; +function setupGlobals(env) { + env.set("false", false); + env.set("true", true); + env.set("none", null); + env.set("raise_exception", (args) => { + throw new Error(args); + }); + env.set("range", range); + env.set("strftime_now", strftime_now); + env.set("True", true); + env.set("False", false); + env.set("None", null); +} +var Interpreter = class { + global; + constructor(env) { + this.global = env ?? new Environment(); + } + /** + * Run the program. + */ + run(program) { + return this.evaluate(program, this.global); + } + /** + * Evaluates expressions following the binary operation type. + */ + evaluateBinaryExpression(node, environment) { + const left = this.evaluate(node.left, environment); + switch (node.operator.value) { + case "and": + return left.__bool__().value ? this.evaluate(node.right, environment) : left; + case "or": + return left.__bool__().value ? left : this.evaluate(node.right, environment); + } + const right = this.evaluate(node.right, environment); + switch (node.operator.value) { + case "==": + return new BooleanValue(left.value == right.value); + case "!=": + return new BooleanValue(left.value != right.value); + } + if (left instanceof UndefinedValue || right instanceof UndefinedValue) { + if (right instanceof UndefinedValue && ["in", "not in"].includes(node.operator.value)) { + return new BooleanValue(node.operator.value === "not in"); + } + throw new Error(`Cannot perform operation ${node.operator.value} on undefined values`); + } else if (left instanceof NullValue || right instanceof NullValue) { + throw new Error("Cannot perform operation on null values"); + } else if (node.operator.value === "~") { + return new StringValue(left.value.toString() + right.value.toString()); + } else if ((left instanceof IntegerValue || left instanceof FloatValue) && (right instanceof IntegerValue || right instanceof FloatValue)) { + const a = left.value, b = right.value; + switch (node.operator.value) { + case "+": + case "-": + case "*": { + const res = node.operator.value === "+" ? a + b : node.operator.value === "-" ? a - b : a * b; + const isFloat = left instanceof FloatValue || right instanceof FloatValue; + return isFloat ? new FloatValue(res) : new IntegerValue(res); + } + case "/": + return new FloatValue(a / b); + case "%": { + const rem = a % b; + const isFloat = left instanceof FloatValue || right instanceof FloatValue; + return isFloat ? new FloatValue(rem) : new IntegerValue(rem); + } + case "<": + return new BooleanValue(a < b); + case ">": + return new BooleanValue(a > b); + case ">=": + return new BooleanValue(a >= b); + case "<=": + return new BooleanValue(a <= b); + } + } else if (left instanceof ArrayValue && right instanceof ArrayValue) { + switch (node.operator.value) { + case "+": + return new ArrayValue(left.value.concat(right.value)); + } + } else if (right instanceof ArrayValue) { + const member = right.value.find((x) => x.value === left.value) !== void 0; + switch (node.operator.value) { + case "in": + return new BooleanValue(member); + case "not in": + return new BooleanValue(!member); + } + } + if (left instanceof StringValue || right instanceof StringValue) { + switch (node.operator.value) { + case "+": + return new StringValue(left.value.toString() + right.value.toString()); + } + } + if (left instanceof StringValue && right instanceof StringValue) { + switch (node.operator.value) { + case "in": + return new BooleanValue(right.value.includes(left.value)); + case "not in": + return new BooleanValue(!right.value.includes(left.value)); + } + } + if (left instanceof StringValue && right instanceof ObjectValue) { + switch (node.operator.value) { + case "in": + return new BooleanValue(right.value.has(left.value)); + case "not in": + return new BooleanValue(!right.value.has(left.value)); + } + } + throw new SyntaxError(`Unknown operator "${node.operator.value}" between ${left.type} and ${right.type}`); + } + evaluateArguments(args, environment) { + const positionalArguments = []; + const keywordArguments = /* @__PURE__ */ new Map(); + for (const argument of args) { + if (argument.type === "SpreadExpression") { + const spreadNode = argument; + const val = this.evaluate(spreadNode.argument, environment); + if (!(val instanceof ArrayValue)) { + throw new Error(`Cannot unpack non-iterable type: ${val.type}`); + } + for (const item of val.value) { + positionalArguments.push(item); + } + } else if (argument.type === "KeywordArgumentExpression") { + const kwarg = argument; + keywordArguments.set(kwarg.key.value, this.evaluate(kwarg.value, environment)); + } else { + if (keywordArguments.size > 0) { + throw new Error("Positional arguments must come before keyword arguments"); + } + positionalArguments.push(this.evaluate(argument, environment)); + } + } + return [positionalArguments, keywordArguments]; + } + applyFilter(operand, filterNode, environment) { + if (filterNode.type === "Identifier") { + const filter = filterNode; + if (filter.value === "tojson") { + return new StringValue(toJSON(operand)); + } + if (operand instanceof ArrayValue) { + switch (filter.value) { + case "list": + return operand; + case "first": + return operand.value[0]; + case "last": + return operand.value[operand.value.length - 1]; + case "length": + return new IntegerValue(operand.value.length); + case "reverse": + return new ArrayValue(operand.value.reverse()); + case "sort": + return new ArrayValue( + operand.value.sort((a, b) => { + if (a.type !== b.type) { + throw new Error(`Cannot compare different types: ${a.type} and ${b.type}`); + } + switch (a.type) { + case "IntegerValue": + case "FloatValue": + return a.value - b.value; + case "StringValue": + return a.value.localeCompare(b.value); + default: + throw new Error(`Cannot compare type: ${a.type}`); + } + }) + ); + case "join": + return new StringValue(operand.value.map((x) => x.value).join("")); + case "string": + return new StringValue(toJSON(operand)); + case "unique": { + const seen = /* @__PURE__ */ new Set(); + const output = []; + for (const item of operand.value) { + if (!seen.has(item.value)) { + seen.add(item.value); + output.push(item); + } + } + return new ArrayValue(output); + } + default: + throw new Error(`Unknown ArrayValue filter: ${filter.value}`); + } + } else if (operand instanceof StringValue) { + switch (filter.value) { + case "length": + case "upper": + case "lower": + case "title": + case "capitalize": { + const builtin = operand.builtins.get(filter.value); + if (builtin instanceof FunctionValue) { + return builtin.value( + /* no arguments */ + [], + environment + ); + } else if (builtin instanceof IntegerValue) { + return builtin; + } else { + throw new Error(`Unknown StringValue filter: ${filter.value}`); + } + } + case "trim": + return new StringValue(operand.value.trim()); + case "indent": + return new StringValue( + operand.value.split("\n").map( + (x, i) => ( + // By default, don't indent the first line or empty lines + i === 0 || x.length === 0 ? x : " " + x + ) + ).join("\n") + ); + case "join": + case "string": + return operand; + case "int": { + const val = parseInt(operand.value, 10); + return new IntegerValue(isNaN(val) ? 0 : val); + } + case "float": { + const val = parseFloat(operand.value); + return new FloatValue(isNaN(val) ? 0 : val); + } + default: + throw new Error(`Unknown StringValue filter: ${filter.value}`); + } + } else if (operand instanceof IntegerValue || operand instanceof FloatValue) { + switch (filter.value) { + case "abs": + return operand instanceof IntegerValue ? new IntegerValue(Math.abs(operand.value)) : new FloatValue(Math.abs(operand.value)); + case "int": + return new IntegerValue(Math.floor(operand.value)); + case "float": + return new FloatValue(operand.value); + default: + throw new Error(`Unknown NumericValue filter: ${filter.value}`); + } + } else if (operand instanceof ObjectValue) { + switch (filter.value) { + case "items": + return new ArrayValue( + Array.from(operand.value.entries()).map(([key, value]) => new ArrayValue([new StringValue(key), value])) + ); + case "length": + return new IntegerValue(operand.value.size); + default: + throw new Error(`Unknown ObjectValue filter: ${filter.value}`); + } + } else if (operand instanceof BooleanValue) { + switch (filter.value) { + case "bool": + return new BooleanValue(operand.value); + case "int": + return new IntegerValue(operand.value ? 1 : 0); + case "float": + return new FloatValue(operand.value ? 1 : 0); + case "string": + return new StringValue(operand.value ? "true" : "false"); + default: + throw new Error(`Unknown BooleanValue filter: ${filter.value}`); + } + } + throw new Error(`Cannot apply filter "${filter.value}" to type: ${operand.type}`); + } else if (filterNode.type === "CallExpression") { + const filter = filterNode; + if (filter.callee.type !== "Identifier") { + throw new Error(`Unknown filter: ${filter.callee.type}`); + } + const filterName = filter.callee.value; + if (filterName === "tojson") { + const [, kwargs] = this.evaluateArguments(filter.args, environment); + const indent = kwargs.get("indent") ?? new NullValue(); + if (!(indent instanceof IntegerValue || indent instanceof NullValue)) { + throw new Error("If set, indent must be a number"); + } + return new StringValue(toJSON(operand, indent.value)); + } else if (filterName === "join") { + let value; + if (operand instanceof StringValue) { + value = Array.from(operand.value); + } else if (operand instanceof ArrayValue) { + value = operand.value.map((x) => x.value); + } else { + throw new Error(`Cannot apply filter "${filterName}" to type: ${operand.type}`); + } + const [args, kwargs] = this.evaluateArguments(filter.args, environment); + const separator = args.at(0) ?? kwargs.get("separator") ?? new StringValue(""); + if (!(separator instanceof StringValue)) { + throw new Error("separator must be a string"); + } + return new StringValue(value.join(separator.value)); + } else if (filterName === "int" || filterName === "float") { + const [args, kwargs] = this.evaluateArguments(filter.args, environment); + const defaultValue = args.at(0) ?? kwargs.get("default") ?? (filterName === "int" ? new IntegerValue(0) : new FloatValue(0)); + if (operand instanceof StringValue) { + const val = filterName === "int" ? parseInt(operand.value, 10) : parseFloat(operand.value); + return isNaN(val) ? defaultValue : filterName === "int" ? new IntegerValue(val) : new FloatValue(val); + } else if (operand instanceof IntegerValue || operand instanceof FloatValue) { + return operand; + } else if (operand instanceof BooleanValue) { + return filterName === "int" ? new IntegerValue(operand.value ? 1 : 0) : new FloatValue(operand.value ? 1 : 0); + } else { + throw new Error(`Cannot apply filter "${filterName}" to type: ${operand.type}`); + } + } else if (filterName === "default") { + const [args, kwargs] = this.evaluateArguments(filter.args, environment); + const defaultValue = args[0] ?? new StringValue(""); + const booleanValue = args[1] ?? kwargs.get("boolean") ?? new BooleanValue(false); + if (!(booleanValue instanceof BooleanValue)) { + throw new Error("`default` filter flag must be a boolean"); + } + if (operand instanceof UndefinedValue || booleanValue.value && !operand.__bool__().value) { + return defaultValue; + } + return operand; + } + if (operand instanceof ArrayValue) { + switch (filterName) { + case "selectattr": + case "rejectattr": { + const select = filterName === "selectattr"; + if (operand.value.some((x) => !(x instanceof ObjectValue))) { + throw new Error(`\`${filterName}\` can only be applied to array of objects`); + } + if (filter.args.some((x) => x.type !== "StringLiteral")) { + throw new Error(`arguments of \`${filterName}\` must be strings`); + } + const [attr, testName, value] = filter.args.map((x) => this.evaluate(x, environment)); + let testFunction; + if (testName) { + const test = environment.tests.get(testName.value); + if (!test) { + throw new Error(`Unknown test: ${testName.value}`); + } + testFunction = test; + } else { + testFunction = (...x) => x[0].__bool__().value; + } + const filtered = operand.value.filter((item) => { + const a = item.value.get(attr.value); + const result = a ? testFunction(a, value) : false; + return select ? result : !result; + }); + return new ArrayValue(filtered); + } + case "map": { + const [, kwargs] = this.evaluateArguments(filter.args, environment); + if (kwargs.has("attribute")) { + const attr = kwargs.get("attribute"); + if (!(attr instanceof StringValue)) { + throw new Error("attribute must be a string"); + } + const defaultValue = kwargs.get("default"); + const mapped = operand.value.map((item) => { + if (!(item instanceof ObjectValue)) { + throw new Error("items in map must be an object"); + } + return item.value.get(attr.value) ?? defaultValue ?? new UndefinedValue(); + }); + return new ArrayValue(mapped); + } else { + throw new Error("`map` expressions without `attribute` set are not currently supported."); + } + } + } + throw new Error(`Unknown ArrayValue filter: ${filterName}`); + } else if (operand instanceof StringValue) { + switch (filterName) { + case "indent": { + const [args, kwargs] = this.evaluateArguments(filter.args, environment); + const width = args.at(0) ?? kwargs.get("width") ?? new IntegerValue(4); + if (!(width instanceof IntegerValue)) { + throw new Error("width must be a number"); + } + const first = args.at(1) ?? kwargs.get("first") ?? new BooleanValue(false); + const blank = args.at(2) ?? kwargs.get("blank") ?? new BooleanValue(false); + const lines = operand.value.split("\n"); + const indent = " ".repeat(width.value); + const indented = lines.map( + (x, i) => !first.value && i === 0 || !blank.value && x.length === 0 ? x : indent + x + ); + return new StringValue(indented.join("\n")); + } + case "replace": { + const replaceFn = operand.builtins.get("replace"); + if (!(replaceFn instanceof FunctionValue)) { + throw new Error("replace filter not available"); + } + const [args, kwargs] = this.evaluateArguments(filter.args, environment); + return replaceFn.value([...args, new KeywordArgumentsValue(kwargs)], environment); + } + } + throw new Error(`Unknown StringValue filter: ${filterName}`); + } else { + throw new Error(`Cannot apply filter "${filterName}" to type: ${operand.type}`); + } + } + throw new Error(`Unknown filter: ${filterNode.type}`); + } + /** + * Evaluates expressions following the filter operation type. + */ + evaluateFilterExpression(node, environment) { + const operand = this.evaluate(node.operand, environment); + return this.applyFilter(operand, node.filter, environment); + } + /** + * Evaluates expressions following the test operation type. + */ + evaluateTestExpression(node, environment) { + const operand = this.evaluate(node.operand, environment); + const test = environment.tests.get(node.test.value); + if (!test) { + throw new Error(`Unknown test: ${node.test.value}`); + } + const result = test(operand); + return new BooleanValue(node.negate ? !result : result); + } + /** + * Evaluates expressions following the select operation type. + */ + evaluateSelectExpression(node, environment) { + const predicate = this.evaluate(node.test, environment); + if (!predicate.__bool__().value) { + return new UndefinedValue(); + } + return this.evaluate(node.lhs, environment); + } + /** + * Evaluates expressions following the unary operation type. + */ + evaluateUnaryExpression(node, environment) { + const argument = this.evaluate(node.argument, environment); + switch (node.operator.value) { + case "not": + return new BooleanValue(!argument.value); + default: + throw new SyntaxError(`Unknown operator: ${node.operator.value}`); + } + } + evaluateTernaryExpression(node, environment) { + const cond = this.evaluate(node.condition, environment); + return cond.__bool__().value ? this.evaluate(node.trueExpr, environment) : this.evaluate(node.falseExpr, environment); + } + evalProgram(program, environment) { + return this.evaluateBlock(program.body, environment); + } + evaluateBlock(statements, environment) { + let result = ""; + for (const statement of statements) { + const lastEvaluated = this.evaluate(statement, environment); + if (lastEvaluated.type !== "NullValue" && lastEvaluated.type !== "UndefinedValue") { + result += lastEvaluated.toString(); + } + } + return new StringValue(result); + } + evaluateIdentifier(node, environment) { + return environment.lookupVariable(node.value); + } + evaluateCallExpression(expr, environment) { + const [args, kwargs] = this.evaluateArguments(expr.args, environment); + if (kwargs.size > 0) { + args.push(new KeywordArgumentsValue(kwargs)); + } + const fn = this.evaluate(expr.callee, environment); + if (fn.type !== "FunctionValue") { + throw new Error(`Cannot call something that is not a function: got ${fn.type}`); + } + return fn.value(args, environment); + } + evaluateSliceExpression(object, expr, environment) { + if (!(object instanceof ArrayValue || object instanceof StringValue)) { + throw new Error("Slice object must be an array or string"); + } + const start = this.evaluate(expr.start, environment); + const stop = this.evaluate(expr.stop, environment); + const step = this.evaluate(expr.step, environment); + if (!(start instanceof IntegerValue || start instanceof UndefinedValue)) { + throw new Error("Slice start must be numeric or undefined"); + } + if (!(stop instanceof IntegerValue || stop instanceof UndefinedValue)) { + throw new Error("Slice stop must be numeric or undefined"); + } + if (!(step instanceof IntegerValue || step instanceof UndefinedValue)) { + throw new Error("Slice step must be numeric or undefined"); + } + if (object instanceof ArrayValue) { + return new ArrayValue(slice(object.value, start.value, stop.value, step.value)); + } else { + return new StringValue(slice(Array.from(object.value), start.value, stop.value, step.value).join("")); + } + } + evaluateMemberExpression(expr, environment) { + const object = this.evaluate(expr.object, environment); + let property; + if (expr.computed) { + if (expr.property.type === "SliceExpression") { + return this.evaluateSliceExpression(object, expr.property, environment); + } else { + property = this.evaluate(expr.property, environment); + } + } else { + property = new StringValue(expr.property.value); + } + let value; + if (object instanceof ObjectValue) { + if (!(property instanceof StringValue)) { + throw new Error(`Cannot access property with non-string: got ${property.type}`); + } + value = object.value.get(property.value) ?? object.builtins.get(property.value); + } else if (object instanceof ArrayValue || object instanceof StringValue) { + if (property instanceof IntegerValue) { + value = object.value.at(property.value); + if (object instanceof StringValue) { + value = new StringValue(object.value.at(property.value)); + } + } else if (property instanceof StringValue) { + value = object.builtins.get(property.value); + } else { + throw new Error(`Cannot access property with non-string/non-number: got ${property.type}`); + } + } else { + if (!(property instanceof StringValue)) { + throw new Error(`Cannot access property with non-string: got ${property.type}`); + } + value = object.builtins.get(property.value); + } + return value instanceof RuntimeValue ? value : new UndefinedValue(); + } + evaluateSet(node, environment) { + const rhs = node.value ? this.evaluate(node.value, environment) : this.evaluateBlock(node.body, environment); + if (node.assignee.type === "Identifier") { + const variableName = node.assignee.value; + environment.setVariable(variableName, rhs); + } else if (node.assignee.type === "TupleLiteral") { + const tuple = node.assignee; + if (!(rhs instanceof ArrayValue)) { + throw new Error(`Cannot unpack non-iterable type in set: ${rhs.type}`); + } + const arr = rhs.value; + if (arr.length !== tuple.value.length) { + throw new Error(`Too ${tuple.value.length > arr.length ? "few" : "many"} items to unpack in set`); + } + for (let i = 0; i < tuple.value.length; ++i) { + const elem = tuple.value[i]; + if (elem.type !== "Identifier") { + throw new Error(`Cannot unpack to non-identifier in set: ${elem.type}`); + } + environment.setVariable(elem.value, arr[i]); + } + } else if (node.assignee.type === "MemberExpression") { + const member = node.assignee; + const object = this.evaluate(member.object, environment); + if (!(object instanceof ObjectValue)) { + throw new Error("Cannot assign to member of non-object"); + } + if (member.property.type !== "Identifier") { + throw new Error("Cannot assign to member with non-identifier property"); + } + object.value.set(member.property.value, rhs); + } else { + throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(node.assignee)}`); + } + return new NullValue(); + } + evaluateIf(node, environment) { + const test = this.evaluate(node.test, environment); + return this.evaluateBlock(test.__bool__().value ? node.body : node.alternate, environment); + } + evaluateFor(node, environment) { + const scope = new Environment(environment); + let test, iterable; + if (node.iterable.type === "SelectExpression") { + const select = node.iterable; + iterable = this.evaluate(select.lhs, scope); + test = select.test; + } else { + iterable = this.evaluate(node.iterable, scope); + } + if (!(iterable instanceof ArrayValue || iterable instanceof ObjectValue)) { + throw new Error(`Expected iterable or object type in for loop: got ${iterable.type}`); + } + if (iterable instanceof ObjectValue) { + iterable = iterable.keys(); + } + const items = []; + const scopeUpdateFunctions = []; + for (let i = 0; i < iterable.value.length; ++i) { + const loopScope = new Environment(scope); + const current = iterable.value[i]; + let scopeUpdateFunction; + if (node.loopvar.type === "Identifier") { + scopeUpdateFunction = (scope2) => scope2.setVariable(node.loopvar.value, current); + } else if (node.loopvar.type === "TupleLiteral") { + const loopvar = node.loopvar; + if (current.type !== "ArrayValue") { + throw new Error(`Cannot unpack non-iterable type: ${current.type}`); + } + const c = current; + if (loopvar.value.length !== c.value.length) { + throw new Error(`Too ${loopvar.value.length > c.value.length ? "few" : "many"} items to unpack`); + } + scopeUpdateFunction = (scope2) => { + for (let j = 0; j < loopvar.value.length; ++j) { + if (loopvar.value[j].type !== "Identifier") { + throw new Error(`Cannot unpack non-identifier type: ${loopvar.value[j].type}`); + } + scope2.setVariable(loopvar.value[j].value, c.value[j]); + } + }; + } else { + throw new Error(`Invalid loop variable(s): ${node.loopvar.type}`); + } + if (test) { + scopeUpdateFunction(loopScope); + const testValue = this.evaluate(test, loopScope); + if (!testValue.__bool__().value) { + continue; + } + } + items.push(current); + scopeUpdateFunctions.push(scopeUpdateFunction); + } + let result = ""; + let noIteration = true; + for (let i = 0; i < items.length; ++i) { + const loop = /* @__PURE__ */ new Map([ + ["index", new IntegerValue(i + 1)], + ["index0", new IntegerValue(i)], + ["revindex", new IntegerValue(items.length - i)], + ["revindex0", new IntegerValue(items.length - i - 1)], + ["first", new BooleanValue(i === 0)], + ["last", new BooleanValue(i === items.length - 1)], + ["length", new IntegerValue(items.length)], + ["previtem", i > 0 ? items[i - 1] : new UndefinedValue()], + ["nextitem", i < items.length - 1 ? items[i + 1] : new UndefinedValue()] + ]); + scope.setVariable("loop", new ObjectValue(loop)); + scopeUpdateFunctions[i](scope); + try { + const evaluated = this.evaluateBlock(node.body, scope); + result += evaluated.value; + } catch (err) { + if (err instanceof ContinueControl) { + continue; + } + if (err instanceof BreakControl) { + break; + } + throw err; + } + noIteration = false; + } + if (noIteration) { + const defaultEvaluated = this.evaluateBlock(node.defaultBlock, scope); + result += defaultEvaluated.value; + } + return new StringValue(result); + } + /** + * See https://jinja.palletsprojects.com/en/3.1.x/templates/#macros for more information. + */ + evaluateMacro(node, environment) { + environment.setVariable( + node.name.value, + new FunctionValue((args, scope) => { + const macroScope = new Environment(scope); + args = args.slice(); + let kwargs; + if (args.at(-1)?.type === "KeywordArgumentsValue") { + kwargs = args.pop(); + } + for (let i = 0; i < node.args.length; ++i) { + const nodeArg = node.args[i]; + const passedArg = args[i]; + if (nodeArg.type === "Identifier") { + const identifier = nodeArg; + if (!passedArg) { + throw new Error(`Missing positional argument: ${identifier.value}`); + } + macroScope.setVariable(identifier.value, passedArg); + } else if (nodeArg.type === "KeywordArgumentExpression") { + const kwarg = nodeArg; + const value = passedArg ?? // Try positional arguments first + kwargs?.value.get(kwarg.key.value) ?? // Look in user-passed kwargs + this.evaluate(kwarg.value, macroScope); + macroScope.setVariable(kwarg.key.value, value); + } else { + throw new Error(`Unknown argument type: ${nodeArg.type}`); + } + } + return this.evaluateBlock(node.body, macroScope); + }) + ); + return new NullValue(); + } + evaluateCallStatement(node, environment) { + const callerFn = new FunctionValue((callerArgs, callerEnv) => { + const callBlockEnv = new Environment(callerEnv); + if (node.callerArgs) { + for (let i = 0; i < node.callerArgs.length; ++i) { + const param = node.callerArgs[i]; + if (param.type !== "Identifier") { + throw new Error(`Caller parameter must be an identifier, got ${param.type}`); + } + callBlockEnv.setVariable(param.value, callerArgs[i] ?? new UndefinedValue()); + } + } + return this.evaluateBlock(node.body, callBlockEnv); + }); + const [macroArgs, macroKwargs] = this.evaluateArguments(node.call.args, environment); + macroArgs.push(new KeywordArgumentsValue(macroKwargs)); + const fn = this.evaluate(node.call.callee, environment); + if (fn.type !== "FunctionValue") { + throw new Error(`Cannot call something that is not a function: got ${fn.type}`); + } + const newEnv = new Environment(environment); + newEnv.setVariable("caller", callerFn); + return fn.value(macroArgs, newEnv); + } + evaluateFilterStatement(node, environment) { + const rendered = this.evaluateBlock(node.body, environment); + return this.applyFilter(rendered, node.filter, environment); + } + evaluate(statement, environment) { + if (!statement) + return new UndefinedValue(); + switch (statement.type) { + case "Program": + return this.evalProgram(statement, environment); + case "Set": + return this.evaluateSet(statement, environment); + case "If": + return this.evaluateIf(statement, environment); + case "For": + return this.evaluateFor(statement, environment); + case "Macro": + return this.evaluateMacro(statement, environment); + case "CallStatement": + return this.evaluateCallStatement(statement, environment); + case "Break": + throw new BreakControl(); + case "Continue": + throw new ContinueControl(); + case "IntegerLiteral": + return new IntegerValue(statement.value); + case "FloatLiteral": + return new FloatValue(statement.value); + case "StringLiteral": + return new StringValue(statement.value); + case "ArrayLiteral": + return new ArrayValue(statement.value.map((x) => this.evaluate(x, environment))); + case "TupleLiteral": + return new TupleValue(statement.value.map((x) => this.evaluate(x, environment))); + case "ObjectLiteral": { + const mapping = /* @__PURE__ */ new Map(); + for (const [key, value] of statement.value) { + const evaluatedKey = this.evaluate(key, environment); + if (!(evaluatedKey instanceof StringValue)) { + throw new Error(`Object keys must be strings: got ${evaluatedKey.type}`); + } + mapping.set(evaluatedKey.value, this.evaluate(value, environment)); + } + return new ObjectValue(mapping); + } + case "Identifier": + return this.evaluateIdentifier(statement, environment); + case "CallExpression": + return this.evaluateCallExpression(statement, environment); + case "MemberExpression": + return this.evaluateMemberExpression(statement, environment); + case "UnaryExpression": + return this.evaluateUnaryExpression(statement, environment); + case "BinaryExpression": + return this.evaluateBinaryExpression(statement, environment); + case "FilterExpression": + return this.evaluateFilterExpression(statement, environment); + case "FilterStatement": + return this.evaluateFilterStatement(statement, environment); + case "TestExpression": + return this.evaluateTestExpression(statement, environment); + case "SelectExpression": + return this.evaluateSelectExpression(statement, environment); + case "Ternary": + return this.evaluateTernaryExpression(statement, environment); + case "Comment": + return new NullValue(); + default: + throw new SyntaxError(`Unknown node type: ${statement.type}`); + } + } +}; +function convertToRuntimeValues(input) { + switch (typeof input) { + case "number": + return Number.isInteger(input) ? new IntegerValue(input) : new FloatValue(input); + case "string": + return new StringValue(input); + case "boolean": + return new BooleanValue(input); + case "undefined": + return new UndefinedValue(); + case "object": + if (input === null) { + return new NullValue(); + } else if (Array.isArray(input)) { + return new ArrayValue(input.map(convertToRuntimeValues)); + } else { + return new ObjectValue( + new Map(Object.entries(input).map(([key, value]) => [key, convertToRuntimeValues(value)])) + ); + } + case "function": + return new FunctionValue((args, _scope) => { + const result = input(...args.map((x) => x.value)) ?? null; + return convertToRuntimeValues(result); + }); + default: + throw new Error(`Cannot convert to runtime value: ${input}`); + } +} +function toJSON(input, indent, depth) { + const currentDepth = depth ?? 0; + switch (input.type) { + case "NullValue": + case "UndefinedValue": + return "null"; + case "IntegerValue": + case "FloatValue": + case "StringValue": + case "BooleanValue": + return JSON.stringify(input.value); + case "ArrayValue": + case "ObjectValue": { + const indentValue = indent ? " ".repeat(indent) : ""; + const basePadding = "\n" + indentValue.repeat(currentDepth); + const childrenPadding = basePadding + indentValue; + if (input.type === "ArrayValue") { + const core = input.value.map((x) => toJSON(x, indent, currentDepth + 1)); + return indent ? `[${childrenPadding}${core.join(`,${childrenPadding}`)}${basePadding}]` : `[${core.join(", ")}]`; + } else { + const core = Array.from(input.value.entries()).map(([key, value]) => { + const v = `"${key}": ${toJSON(value, indent, currentDepth + 1)}`; + return indent ? `${childrenPadding}${v}` : v; + }); + return indent ? `{${core.join(",")}${basePadding}}` : `{${core.join(", ")}}`; + } + } + default: + throw new Error(`Cannot convert to JSON: ${input.type}`); + } +} + +// src/format.ts +var NEWLINE = "\n"; +var OPEN_STATEMENT = "{%- "; +var CLOSE_STATEMENT = " -%}"; +function getBinaryOperatorPrecedence(expr) { + switch (expr.operator.type) { + case "MultiplicativeBinaryOperator": + return 4; + case "AdditiveBinaryOperator": + return 3; + case "ComparisonBinaryOperator": + return 2; + case "Identifier": + if (expr.operator.value === "and") + return 1; + if (expr.operator.value === "in" || expr.operator.value === "not in") + return 2; + return 0; + } + return 0; +} +function format(program, indent = " ") { + const indentStr = typeof indent === "number" ? " ".repeat(indent) : indent; + const body = formatStatements(program.body, 0, indentStr); + return body.replace(/\n$/, ""); +} +function createStatement(...text) { + return OPEN_STATEMENT + text.join(" ") + CLOSE_STATEMENT; +} +function formatStatements(stmts, depth, indentStr) { + return stmts.map((stmt) => formatStatement(stmt, depth, indentStr)).join(NEWLINE); +} +function formatStatement(node, depth, indentStr) { + const pad = indentStr.repeat(depth); + switch (node.type) { + case "Program": + return formatStatements(node.body, depth, indentStr); + case "If": + return formatIf(node, depth, indentStr); + case "For": + return formatFor(node, depth, indentStr); + case "Set": + return formatSet(node, depth, indentStr); + case "Macro": + return formatMacro(node, depth, indentStr); + case "Break": + return pad + createStatement("break"); + case "Continue": + return pad + createStatement("continue"); + case "CallStatement": + return formatCallStatement(node, depth, indentStr); + case "FilterStatement": + return formatFilterStatement(node, depth, indentStr); + case "Comment": + return pad + "{# " + node.value + " #}"; + default: + return pad + "{{- " + formatExpression(node) + " -}}"; + } +} +function formatIf(node, depth, indentStr) { + const pad = indentStr.repeat(depth); + const clauses = []; + let current = node; + while (current) { + clauses.push({ test: current.test, body: current.body }); + if (current.alternate.length === 1 && current.alternate[0].type === "If") { + current = current.alternate[0]; + } else { + break; + } + } + let out = pad + createStatement("if", formatExpression(clauses[0].test)) + NEWLINE + formatStatements(clauses[0].body, depth + 1, indentStr); + for (let i = 1; i < clauses.length; ++i) { + out += NEWLINE + pad + createStatement("elif", formatExpression(clauses[i].test)) + NEWLINE + formatStatements(clauses[i].body, depth + 1, indentStr); + } + if (current && current.alternate.length > 0) { + out += NEWLINE + pad + createStatement("else") + NEWLINE + formatStatements(current.alternate, depth + 1, indentStr); + } + out += NEWLINE + pad + createStatement("endif"); + return out; +} +function formatFor(node, depth, indentStr) { + const pad = indentStr.repeat(depth); + let formattedIterable = ""; + if (node.iterable.type === "SelectExpression") { + const n = node.iterable; + formattedIterable = `${formatExpression(n.lhs)} if ${formatExpression(n.test)}`; + } else { + formattedIterable = formatExpression(node.iterable); + } + let out = pad + createStatement("for", formatExpression(node.loopvar), "in", formattedIterable) + NEWLINE + formatStatements(node.body, depth + 1, indentStr); + if (node.defaultBlock.length > 0) { + out += NEWLINE + pad + createStatement("else") + NEWLINE + formatStatements(node.defaultBlock, depth + 1, indentStr); + } + out += NEWLINE + pad + createStatement("endfor"); + return out; +} +function formatSet(node, depth, indentStr) { + const pad = indentStr.repeat(depth); + const left = formatExpression(node.assignee); + const right = node.value ? formatExpression(node.value) : ""; + const value = pad + createStatement("set", `${left}${node.value ? " = " + right : ""}`); + if (node.body.length === 0) { + return value; + } + return value + NEWLINE + formatStatements(node.body, depth + 1, indentStr) + NEWLINE + pad + createStatement("endset"); +} +function formatMacro(node, depth, indentStr) { + const pad = indentStr.repeat(depth); + const args = node.args.map(formatExpression).join(", "); + return pad + createStatement("macro", `${node.name.value}(${args})`) + NEWLINE + formatStatements(node.body, depth + 1, indentStr) + NEWLINE + pad + createStatement("endmacro"); +} +function formatCallStatement(node, depth, indentStr) { + const pad = indentStr.repeat(depth); + const params = node.callerArgs && node.callerArgs.length > 0 ? `(${node.callerArgs.map(formatExpression).join(", ")})` : ""; + const callExpr = formatExpression(node.call); + let out = pad + createStatement(`call${params}`, callExpr) + NEWLINE; + out += formatStatements(node.body, depth + 1, indentStr) + NEWLINE; + out += pad + createStatement("endcall"); + return out; +} +function formatFilterStatement(node, depth, indentStr) { + const pad = indentStr.repeat(depth); + const spec = node.filter.type === "Identifier" ? node.filter.value : formatExpression(node.filter); + let out = pad + createStatement("filter", spec) + NEWLINE; + out += formatStatements(node.body, depth + 1, indentStr) + NEWLINE; + out += pad + createStatement("endfilter"); + return out; +} +function formatExpression(node, parentPrec = -1) { + switch (node.type) { + case "SpreadExpression": { + const n = node; + return `*${formatExpression(n.argument)}`; + } + case "Identifier": + return node.value; + case "IntegerLiteral": + return `${node.value}`; + case "FloatLiteral": + return `${node.value}`; + case "StringLiteral": + return JSON.stringify(node.value); + case "BinaryExpression": { + const n = node; + const thisPrecedence = getBinaryOperatorPrecedence(n); + const left = formatExpression(n.left, thisPrecedence); + const right = formatExpression(n.right, thisPrecedence + 1); + const expr = `${left} ${n.operator.value} ${right}`; + return thisPrecedence < parentPrec ? `(${expr})` : expr; + } + case "UnaryExpression": { + const n = node; + const val = n.operator.value + (n.operator.value === "not" ? " " : "") + formatExpression(n.argument, Infinity); + return val; + } + case "CallExpression": { + const n = node; + const args = n.args.map(formatExpression).join(", "); + return `${formatExpression(n.callee)}(${args})`; + } + case "MemberExpression": { + const n = node; + let obj = formatExpression(n.object); + if (![ + "Identifier", + "MemberExpression", + "CallExpression", + "StringLiteral", + "IntegerLiteral", + "FloatLiteral", + "ArrayLiteral", + "TupleLiteral", + "ObjectLiteral" + ].includes(n.object.type)) { + obj = `(${obj})`; + } + let prop = formatExpression(n.property); + if (!n.computed && n.property.type !== "Identifier") { + prop = `(${prop})`; + } + return n.computed ? `${obj}[${prop}]` : `${obj}.${prop}`; + } + case "FilterExpression": { + const n = node; + const operand = formatExpression(n.operand, Infinity); + if (n.filter.type === "CallExpression") { + return `${operand} | ${formatExpression(n.filter)}`; + } + return `${operand} | ${n.filter.value}`; + } + case "SelectExpression": { + const n = node; + return `${formatExpression(n.lhs)} if ${formatExpression(n.test)}`; + } + case "TestExpression": { + const n = node; + return `${formatExpression(n.operand)} is${n.negate ? " not" : ""} ${n.test.value}`; + } + case "ArrayLiteral": + case "TupleLiteral": { + const elems = node.value.map(formatExpression); + const brackets = node.type === "ArrayLiteral" ? "[]" : "()"; + return `${brackets[0]}${elems.join(", ")}${brackets[1]}`; + } + case "ObjectLiteral": { + const entries = Array.from(node.value.entries()).map( + ([k, v]) => `${formatExpression(k)}: ${formatExpression(v)}` + ); + return `{${entries.join(", ")}}`; + } + case "SliceExpression": { + const n = node; + const s = n.start ? formatExpression(n.start) : ""; + const t = n.stop ? formatExpression(n.stop) : ""; + const st = n.step ? `:${formatExpression(n.step)}` : ""; + return `${s}:${t}${st}`; + } + case "KeywordArgumentExpression": { + const n = node; + return `${n.key.value}=${formatExpression(n.value)}`; + } + case "Ternary": { + const n = node; + const expr = `${formatExpression(n.trueExpr)} if ${formatExpression(n.condition, 0)} else ${formatExpression( + n.falseExpr + )}`; + return parentPrec > -1 ? `(${expr})` : expr; + } + default: + throw new Error(`Unknown expression type: ${node.type}`); + } +} + +// src/index.ts +var Template = class { + parsed; + /** + * @param {string} template The template string + */ + constructor(template) { + const tokens = tokenize(template, { + lstrip_blocks: true, + trim_blocks: true + }); + this.parsed = parse(tokens); + } + render(items) { + const env = new Environment(); + setupGlobals(env); + if (items) { + for (const [key, value] of Object.entries(items)) { + env.set(key, value); + } + } + const interpreter = new Interpreter(env); + const result = interpreter.run(this.parsed); + return result.value; + } + format(options) { + return format(this.parsed, options?.indent || " "); + } +}; + + + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/backend-impl.js": +/*!******************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/backend-impl.js ***! + \******************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ registerBackend: () => (/* binding */ registerBackend), +/* harmony export */ resolveBackendAndExecutionProviders: () => (/* binding */ resolveBackendAndExecutionProviders) +/* harmony export */ }); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. +const backends = new Map(); +const backendsSortedByPriority = []; +/** + * Register a backend. + * + * @param name - the name as a key to lookup as an execution provider. + * @param backend - the backend object. + * @param priority - an integer indicating the priority of the backend. Higher number means higher priority. if priority + * < 0, it will be considered as a 'beta' version and will not be used as a fallback backend by default. + * + * @ignore + */ +const registerBackend = (name, backend, priority) => { + if (backend && typeof backend.init === 'function' && typeof backend.createInferenceSessionHandler === 'function') { + const currentBackend = backends.get(name); + if (currentBackend === undefined) { + backends.set(name, { backend, priority }); + } + else if (currentBackend.priority > priority) { + // same name is already registered with a higher priority. skip registeration. + return; + } + else if (currentBackend.priority === priority) { + if (currentBackend.backend !== backend) { + throw new Error(`cannot register backend "${name}" using priority ${priority}`); + } + } + if (priority >= 0) { + const i = backendsSortedByPriority.indexOf(name); + if (i !== -1) { + backendsSortedByPriority.splice(i, 1); + } + for (let i = 0; i < backendsSortedByPriority.length; i++) { + if (backends.get(backendsSortedByPriority[i]).priority <= priority) { + backendsSortedByPriority.splice(i, 0, name); + return; + } + } + backendsSortedByPriority.push(name); + } + return; + } + throw new TypeError('not a valid backend'); +}; +/** + * Try to resolve and initialize a backend. + * + * @param backendName - the name of the backend. + * @returns the backend instance if resolved and initialized successfully, or an error message if failed. + */ +const tryResolveAndInitializeBackend = async (backendName) => { + const backendInfo = backends.get(backendName); + if (!backendInfo) { + return 'backend not found.'; + } + if (backendInfo.initialized) { + return backendInfo.backend; + } + else if (backendInfo.aborted) { + return backendInfo.error; + } + else { + const isInitializing = !!backendInfo.initPromise; + try { + if (!isInitializing) { + backendInfo.initPromise = backendInfo.backend.init(backendName); + } + await backendInfo.initPromise; + backendInfo.initialized = true; + return backendInfo.backend; + } + catch (e) { + if (!isInitializing) { + backendInfo.error = `${e}`; + backendInfo.aborted = true; + } + return backendInfo.error; + } + finally { + delete backendInfo.initPromise; + } + } +}; +/** + * Resolve execution providers from the specific session options. + * + * @param options - the session options object. + * @returns a promise that resolves to a tuple of an initialized backend instance and a session options object with + * filtered EP list. + * + * @ignore + */ +const resolveBackendAndExecutionProviders = async (options) => { + // extract backend hints from session options + const eps = options.executionProviders || []; + const backendHints = eps.map((i) => (typeof i === 'string' ? i : i.name)); + const backendNames = backendHints.length === 0 ? backendsSortedByPriority : backendHints; + // try to resolve and initialize all requested backends + let backend; + const errors = []; + const availableBackendNames = new Set(); + for (const backendName of backendNames) { + const resolveResult = await tryResolveAndInitializeBackend(backendName); + if (typeof resolveResult === 'string') { + errors.push({ name: backendName, err: resolveResult }); + } + else { + if (!backend) { + backend = resolveResult; + } + if (backend === resolveResult) { + availableBackendNames.add(backendName); + } + } + } + // if no backend is available, throw error. + if (!backend) { + throw new Error(`no available backend found. ERR: ${errors.map((e) => `[${e.name}] ${e.err}`).join(', ')}`); + } + // for each explicitly requested backend, if it's not available, output warning message. + for (const { name, err } of errors) { + if (backendHints.includes(name)) { + // eslint-disable-next-line no-console + console.warn(`removing requested execution provider "${name}" from session options because it is not available: ${err}`); + } + } + const filteredEps = eps.filter((i) => availableBackendNames.has(typeof i === 'string' ? i : i.name)); + return [ + backend, + new Proxy(options, { + get: (target, prop) => { + if (prop === 'executionProviders') { + return filteredEps; + } + return Reflect.get(target, prop); + }, + }), + ]; +}; +//# sourceMappingURL=backend-impl.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/backend.js": +/*!*************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/backend.js ***! + \*************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ registerBackend: () => (/* reexport safe */ _backend_impl_js__WEBPACK_IMPORTED_MODULE_0__.registerBackend) +/* harmony export */ }); +/* harmony import */ var _backend_impl_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./backend-impl.js */ "./node_modules/onnxruntime-common/dist/esm/backend-impl.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +//# sourceMappingURL=backend.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/env-impl.js": +/*!**************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/env-impl.js ***! + \**************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ env: () => (/* binding */ env) +/* harmony export */ }); +/* harmony import */ var _version_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./version.js */ "./node_modules/onnxruntime-common/dist/esm/version.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +let logLevelValue = 'warning'; +const env = { + wasm: {}, + webgl: {}, + webgpu: {}, + versions: { common: _version_js__WEBPACK_IMPORTED_MODULE_0__.version }, + set logLevel(value) { + if (value === undefined) { + return; + } + if (typeof value !== 'string' || ['verbose', 'info', 'warning', 'error', 'fatal'].indexOf(value) === -1) { + throw new Error(`Unsupported logging level: ${value}`); + } + logLevelValue = value; + }, + get logLevel() { + return logLevelValue; + }, +}; +// set property 'logLevel' so that they can be correctly transferred to worker by `postMessage()`. +Object.defineProperty(env, 'logLevel', { enumerable: true }); +//# sourceMappingURL=env-impl.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/env.js": +/*!*********************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/env.js ***! + \*********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ env: () => (/* binding */ env) +/* harmony export */ }); +/* harmony import */ var _env_impl_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./env-impl.js */ "./node_modules/onnxruntime-common/dist/esm/env-impl.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +/** + * Represent a set of flags as a global singleton. + */ +const env = _env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env; +//# sourceMappingURL=env.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/index.js": +/*!***********************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/index.js ***! + \***********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ InferenceSession: () => (/* reexport safe */ _inference_session_js__WEBPACK_IMPORTED_MODULE_2__.InferenceSession), +/* harmony export */ TRACE: () => (/* reexport safe */ _trace_js__WEBPACK_IMPORTED_MODULE_6__.TRACE), +/* harmony export */ TRACE_FUNC_BEGIN: () => (/* reexport safe */ _trace_js__WEBPACK_IMPORTED_MODULE_6__.TRACE_FUNC_BEGIN), +/* harmony export */ TRACE_FUNC_END: () => (/* reexport safe */ _trace_js__WEBPACK_IMPORTED_MODULE_6__.TRACE_FUNC_END), +/* harmony export */ Tensor: () => (/* reexport safe */ _tensor_js__WEBPACK_IMPORTED_MODULE_3__.Tensor), +/* harmony export */ env: () => (/* reexport safe */ _env_js__WEBPACK_IMPORTED_MODULE_1__.env), +/* harmony export */ registerBackend: () => (/* reexport safe */ _backend_js__WEBPACK_IMPORTED_MODULE_0__.registerBackend) +/* harmony export */ }); +/* harmony import */ var _backend_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./backend.js */ "./node_modules/onnxruntime-common/dist/esm/backend.js"); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./env.js */ "./node_modules/onnxruntime-common/dist/esm/env.js"); +/* harmony import */ var _inference_session_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./inference-session.js */ "./node_modules/onnxruntime-common/dist/esm/inference-session.js"); +/* harmony import */ var _tensor_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./tensor.js */ "./node_modules/onnxruntime-common/dist/esm/tensor.js"); +/* harmony import */ var _tensor_conversion_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ./tensor-conversion.js */ "./node_modules/onnxruntime-common/dist/esm/tensor-conversion.js"); +/* harmony import */ var _tensor_factory_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ./tensor-factory.js */ "./node_modules/onnxruntime-common/dist/esm/tensor-factory.js"); +/* harmony import */ var _trace_js__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! ./trace.js */ "./node_modules/onnxruntime-common/dist/esm/trace.js"); +/* harmony import */ var _onnx_model_js__WEBPACK_IMPORTED_MODULE_7__ = __webpack_require__(/*! ./onnx-model.js */ "./node_modules/onnxruntime-common/dist/esm/onnx-model.js"); +/* harmony import */ var _onnx_value_js__WEBPACK_IMPORTED_MODULE_8__ = __webpack_require__(/*! ./onnx-value.js */ "./node_modules/onnxruntime-common/dist/esm/onnx-value.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. +/** + * # ONNX Runtime JavaScript API + * + * ONNX Runtime JavaScript API is a unified API for all JavaScript usages, including the following NPM packages: + * + * - [onnxruntime-node](https://www.npmjs.com/package/onnxruntime-node) + * - [onnxruntime-web](https://www.npmjs.com/package/onnxruntime-web) + * - [onnxruntime-react-native](https://www.npmjs.com/package/onnxruntime-react-native) + * + * See also: + * - [Get Started](https://onnxruntime.ai/docs/get-started/with-javascript/) + * - [Inference examples](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/js) + * + * @packageDocumentation + */ + + + + + + + + + +//# sourceMappingURL=index.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/inference-session-impl.js": +/*!****************************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/inference-session-impl.js ***! + \****************************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ InferenceSession: () => (/* binding */ InferenceSession) +/* harmony export */ }); +/* harmony import */ var _backend_impl_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./backend-impl.js */ "./node_modules/onnxruntime-common/dist/esm/backend-impl.js"); +/* harmony import */ var _tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./tensor.js */ "./node_modules/onnxruntime-common/dist/esm/tensor.js"); +/* harmony import */ var _trace_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./trace.js */ "./node_modules/onnxruntime-common/dist/esm/trace.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + + + +class InferenceSession { + constructor(handler) { + this.handler = handler; + } + async run(feeds, arg1, arg2) { + (0,_trace_js__WEBPACK_IMPORTED_MODULE_2__.TRACE_FUNC_BEGIN)(); + const fetches = {}; + let options = {}; + // check inputs + if (typeof feeds !== 'object' || feeds === null || feeds instanceof _tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor || Array.isArray(feeds)) { + throw new TypeError("'feeds' must be an object that use input names as keys and OnnxValue as corresponding values."); + } + let isFetchesEmpty = true; + // determine which override is being used + if (typeof arg1 === 'object') { + if (arg1 === null) { + throw new TypeError('Unexpected argument[1]: cannot be null.'); + } + if (arg1 instanceof _tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor) { + throw new TypeError("'fetches' cannot be a Tensor"); + } + if (Array.isArray(arg1)) { + if (arg1.length === 0) { + throw new TypeError("'fetches' cannot be an empty array."); + } + isFetchesEmpty = false; + // output names + for (const name of arg1) { + if (typeof name !== 'string') { + throw new TypeError("'fetches' must be a string array or an object."); + } + if (this.outputNames.indexOf(name) === -1) { + throw new RangeError(`'fetches' contains invalid output name: ${name}.`); + } + fetches[name] = null; + } + if (typeof arg2 === 'object' && arg2 !== null) { + options = arg2; + } + else if (typeof arg2 !== 'undefined') { + throw new TypeError("'options' must be an object."); + } + } + else { + // decide whether arg1 is fetches or options + // if any output name is present and its value is valid OnnxValue, we consider it fetches + let isFetches = false; + const arg1Keys = Object.getOwnPropertyNames(arg1); + for (const name of this.outputNames) { + if (arg1Keys.indexOf(name) !== -1) { + const v = arg1[name]; + if (v === null || v instanceof _tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor) { + isFetches = true; + isFetchesEmpty = false; + fetches[name] = v; + } + } + } + if (isFetches) { + if (typeof arg2 === 'object' && arg2 !== null) { + options = arg2; + } + else if (typeof arg2 !== 'undefined') { + throw new TypeError("'options' must be an object."); + } + } + else { + options = arg1; + } + } + } + else if (typeof arg1 !== 'undefined') { + throw new TypeError("Unexpected argument[1]: must be 'fetches' or 'options'."); + } + // check if all inputs are in feed + for (const name of this.inputNames) { + if (typeof feeds[name] === 'undefined') { + throw new Error(`input '${name}' is missing in 'feeds'.`); + } + } + // if no fetches is specified, we use the full output names list + if (isFetchesEmpty) { + for (const name of this.outputNames) { + fetches[name] = null; + } + } + // feeds, fetches and options are prepared + const results = await this.handler.run(feeds, fetches, options); + const returnValue = {}; + for (const key in results) { + if (Object.hasOwnProperty.call(results, key)) { + const result = results[key]; + if (result instanceof _tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor) { + returnValue[key] = result; + } + else { + returnValue[key] = new _tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor(result.type, result.data, result.dims); + } + } + } + (0,_trace_js__WEBPACK_IMPORTED_MODULE_2__.TRACE_FUNC_END)(); + return returnValue; + } + async release() { + return this.handler.dispose(); + } + static async create(arg0, arg1, arg2, arg3) { + (0,_trace_js__WEBPACK_IMPORTED_MODULE_2__.TRACE_FUNC_BEGIN)(); + // either load from a file or buffer + let filePathOrUint8Array; + let options = {}; + if (typeof arg0 === 'string') { + filePathOrUint8Array = arg0; + if (typeof arg1 === 'object' && arg1 !== null) { + options = arg1; + } + else if (typeof arg1 !== 'undefined') { + throw new TypeError("'options' must be an object."); + } + } + else if (arg0 instanceof Uint8Array) { + filePathOrUint8Array = arg0; + if (typeof arg1 === 'object' && arg1 !== null) { + options = arg1; + } + else if (typeof arg1 !== 'undefined') { + throw new TypeError("'options' must be an object."); + } + } + else if (arg0 instanceof ArrayBuffer || + (typeof SharedArrayBuffer !== 'undefined' && arg0 instanceof SharedArrayBuffer)) { + const buffer = arg0; + let byteOffset = 0; + let byteLength = arg0.byteLength; + if (typeof arg1 === 'object' && arg1 !== null) { + options = arg1; + } + else if (typeof arg1 === 'number') { + byteOffset = arg1; + if (!Number.isSafeInteger(byteOffset)) { + throw new RangeError("'byteOffset' must be an integer."); + } + if (byteOffset < 0 || byteOffset >= buffer.byteLength) { + throw new RangeError(`'byteOffset' is out of range [0, ${buffer.byteLength}).`); + } + byteLength = arg0.byteLength - byteOffset; + if (typeof arg2 === 'number') { + byteLength = arg2; + if (!Number.isSafeInteger(byteLength)) { + throw new RangeError("'byteLength' must be an integer."); + } + if (byteLength <= 0 || byteOffset + byteLength > buffer.byteLength) { + throw new RangeError(`'byteLength' is out of range (0, ${buffer.byteLength - byteOffset}].`); + } + if (typeof arg3 === 'object' && arg3 !== null) { + options = arg3; + } + else if (typeof arg3 !== 'undefined') { + throw new TypeError("'options' must be an object."); + } + } + else if (typeof arg2 !== 'undefined') { + throw new TypeError("'byteLength' must be a number."); + } + } + else if (typeof arg1 !== 'undefined') { + throw new TypeError("'options' must be an object."); + } + filePathOrUint8Array = new Uint8Array(buffer, byteOffset, byteLength); + } + else { + throw new TypeError("Unexpected argument[0]: must be 'path' or 'buffer'."); + } + // resolve backend, update session options with validated EPs, and create session handler + const [backend, optionsWithValidatedEPs] = await (0,_backend_impl_js__WEBPACK_IMPORTED_MODULE_0__.resolveBackendAndExecutionProviders)(options); + const handler = await backend.createInferenceSessionHandler(filePathOrUint8Array, optionsWithValidatedEPs); + (0,_trace_js__WEBPACK_IMPORTED_MODULE_2__.TRACE_FUNC_END)(); + return new InferenceSession(handler); + } + startProfiling() { + this.handler.startProfiling(); + } + endProfiling() { + this.handler.endProfiling(); + } + get inputNames() { + return this.handler.inputNames; + } + get outputNames() { + return this.handler.outputNames; + } +} +//# sourceMappingURL=inference-session-impl.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/inference-session.js": +/*!***********************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/inference-session.js ***! + \***********************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ InferenceSession: () => (/* binding */ InferenceSession) +/* harmony export */ }); +/* harmony import */ var _inference_session_impl_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./inference-session-impl.js */ "./node_modules/onnxruntime-common/dist/esm/inference-session-impl.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +// eslint-disable-next-line @typescript-eslint/naming-convention +const InferenceSession = _inference_session_impl_js__WEBPACK_IMPORTED_MODULE_0__.InferenceSession; +//# sourceMappingURL=inference-session.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/onnx-model.js": +/*!****************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/onnx-model.js ***! + \****************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +//# sourceMappingURL=onnx-model.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/onnx-value.js": +/*!****************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/onnx-value.js ***! + \****************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +//# sourceMappingURL=onnx-value.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/tensor-conversion-impl.js": +/*!****************************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/tensor-conversion-impl.js ***! + \****************************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ tensorToDataURL: () => (/* binding */ tensorToDataURL), +/* harmony export */ tensorToImageData: () => (/* binding */ tensorToImageData) +/* harmony export */ }); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. +/** + * implementation of Tensor.toDataURL() + */ +const tensorToDataURL = (tensor, options) => { + const canvas = typeof document !== 'undefined' ? document.createElement('canvas') : new OffscreenCanvas(1, 1); + canvas.width = tensor.dims[3]; + canvas.height = tensor.dims[2]; + const pixels2DContext = canvas.getContext('2d'); + if (pixels2DContext != null) { + // Default values for height and width & format + let width; + let height; + if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') { + width = tensor.dims[2]; + height = tensor.dims[3]; + } + else { + // Default layout is NCWH + width = tensor.dims[3]; + height = tensor.dims[2]; + } + const inputformat = options?.format !== undefined ? options.format : 'RGB'; + const norm = options?.norm; + let normMean; + let normBias; + if (norm === undefined || norm.mean === undefined) { + normMean = [255, 255, 255, 255]; + } + else { + if (typeof norm.mean === 'number') { + normMean = [norm.mean, norm.mean, norm.mean, norm.mean]; + } + else { + normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 0]; + if (norm.mean[3] !== undefined) { + normMean[3] = norm.mean[3]; + } + } + } + if (norm === undefined || norm.bias === undefined) { + normBias = [0, 0, 0, 0]; + } + else { + if (typeof norm.bias === 'number') { + normBias = [norm.bias, norm.bias, norm.bias, norm.bias]; + } + else { + normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0]; + if (norm.bias[3] !== undefined) { + normBias[3] = norm.bias[3]; + } + } + } + const stride = height * width; + // Default pointer assignments + let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1; + // Updating the pointer assignments based on the input image format + if (inputformat === 'RGBA') { + rTensorPointer = 0; + gTensorPointer = stride; + bTensorPointer = stride * 2; + aTensorPointer = stride * 3; + } + else if (inputformat === 'RGB') { + rTensorPointer = 0; + gTensorPointer = stride; + bTensorPointer = stride * 2; + } + else if (inputformat === 'RBG') { + rTensorPointer = 0; + bTensorPointer = stride; + gTensorPointer = stride * 2; + } + for (let i = 0; i < height; i++) { + for (let j = 0; j < width; j++) { + const R = (tensor.data[rTensorPointer++] - normBias[0]) * normMean[0]; // R value + const G = (tensor.data[gTensorPointer++] - normBias[1]) * normMean[1]; // G value + const B = (tensor.data[bTensorPointer++] - normBias[2]) * normMean[2]; // B value + const A = aTensorPointer === -1 ? 255 : (tensor.data[aTensorPointer++] - normBias[3]) * normMean[3]; // A value + // eslint-disable-next-line @typescript-eslint/restrict-plus-operands + pixels2DContext.fillStyle = 'rgba(' + R + ',' + G + ',' + B + ',' + A + ')'; + pixels2DContext.fillRect(j, i, 1, 1); + } + } + if ('toDataURL' in canvas) { + return canvas.toDataURL(); + } + else { + throw new Error('toDataURL is not supported'); + } + } + else { + throw new Error('Can not access image data'); + } +}; +/** + * implementation of Tensor.toImageData() + */ +const tensorToImageData = (tensor, options) => { + const pixels2DContext = typeof document !== 'undefined' + ? document.createElement('canvas').getContext('2d') + : new OffscreenCanvas(1, 1).getContext('2d'); + let image; + if (pixels2DContext != null) { + // Default values for height and width & format + let width; + let height; + let channels; + if (options?.tensorLayout !== undefined && options.tensorLayout === 'NHWC') { + width = tensor.dims[2]; + height = tensor.dims[1]; + channels = tensor.dims[3]; + } + else { + // Default layout is NCWH + width = tensor.dims[3]; + height = tensor.dims[2]; + channels = tensor.dims[1]; + } + const inputformat = options !== undefined ? (options.format !== undefined ? options.format : 'RGB') : 'RGB'; + const norm = options?.norm; + let normMean; + let normBias; + if (norm === undefined || norm.mean === undefined) { + normMean = [255, 255, 255, 255]; + } + else { + if (typeof norm.mean === 'number') { + normMean = [norm.mean, norm.mean, norm.mean, norm.mean]; + } + else { + normMean = [norm.mean[0], norm.mean[1], norm.mean[2], 255]; + if (norm.mean[3] !== undefined) { + normMean[3] = norm.mean[3]; + } + } + } + if (norm === undefined || norm.bias === undefined) { + normBias = [0, 0, 0, 0]; + } + else { + if (typeof norm.bias === 'number') { + normBias = [norm.bias, norm.bias, norm.bias, norm.bias]; + } + else { + normBias = [norm.bias[0], norm.bias[1], norm.bias[2], 0]; + if (norm.bias[3] !== undefined) { + normBias[3] = norm.bias[3]; + } + } + } + const stride = height * width; + if (options !== undefined) { + if ((options.format !== undefined && channels === 4 && options.format !== 'RGBA') || + (channels === 3 && options.format !== 'RGB' && options.format !== 'BGR')) { + throw new Error("Tensor format doesn't match input tensor dims"); + } + } + // Default pointer assignments + const step = 4; + let rImagePointer = 0, gImagePointer = 1, bImagePointer = 2, aImagePointer = 3; + let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1; + // Updating the pointer assignments based on the input image format + if (inputformat === 'RGBA') { + rTensorPointer = 0; + gTensorPointer = stride; + bTensorPointer = stride * 2; + aTensorPointer = stride * 3; + } + else if (inputformat === 'RGB') { + rTensorPointer = 0; + gTensorPointer = stride; + bTensorPointer = stride * 2; + } + else if (inputformat === 'RBG') { + rTensorPointer = 0; + bTensorPointer = stride; + gTensorPointer = stride * 2; + } + image = pixels2DContext.createImageData(width, height); + for (let i = 0; i < height * width; rImagePointer += step, gImagePointer += step, bImagePointer += step, aImagePointer += step, i++) { + image.data[rImagePointer] = (tensor.data[rTensorPointer++] - normBias[0]) * normMean[0]; // R value + image.data[gImagePointer] = (tensor.data[gTensorPointer++] - normBias[1]) * normMean[1]; // G value + image.data[bImagePointer] = (tensor.data[bTensorPointer++] - normBias[2]) * normMean[2]; // B value + image.data[aImagePointer] = + aTensorPointer === -1 ? 255 : (tensor.data[aTensorPointer++] - normBias[3]) * normMean[3]; // A value + } + } + else { + throw new Error('Can not access image data'); + } + return image; +}; +//# sourceMappingURL=tensor-conversion-impl.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/tensor-conversion.js": +/*!***********************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/tensor-conversion.js ***! + \***********************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +//# sourceMappingURL=tensor-conversion.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/tensor-factory-impl.js": +/*!*************************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/tensor-factory-impl.js ***! + \*************************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ bufferToTensor: () => (/* binding */ bufferToTensor), +/* harmony export */ tensorFromGpuBuffer: () => (/* binding */ tensorFromGpuBuffer), +/* harmony export */ tensorFromImage: () => (/* binding */ tensorFromImage), +/* harmony export */ tensorFromMLTensor: () => (/* binding */ tensorFromMLTensor), +/* harmony export */ tensorFromPinnedBuffer: () => (/* binding */ tensorFromPinnedBuffer), +/* harmony export */ tensorFromTexture: () => (/* binding */ tensorFromTexture) +/* harmony export */ }); +/* harmony import */ var _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./tensor-impl.js */ "./node_modules/onnxruntime-common/dist/esm/tensor-impl.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +/** + * Create a new tensor object from image object + * + * @param buffer - Extracted image buffer data - assuming RGBA format + * @param imageFormat - input image configuration - required configurations height, width, format + * @param tensorFormat - output tensor configuration - Default is RGB format + */ +const bufferToTensor = (buffer, options) => { + if (buffer === undefined) { + throw new Error('Image buffer must be defined'); + } + if (options.height === undefined || options.width === undefined) { + throw new Error('Image height and width must be defined'); + } + if (options.tensorLayout === 'NHWC') { + throw new Error('NHWC Tensor layout is not supported yet'); + } + const { height, width } = options; + const norm = options.norm ?? { mean: 255, bias: 0 }; + let normMean; + let normBias; + if (typeof norm.mean === 'number') { + normMean = [norm.mean, norm.mean, norm.mean, norm.mean]; + } + else { + normMean = [norm.mean[0], norm.mean[1], norm.mean[2], norm.mean[3] ?? 255]; + } + if (typeof norm.bias === 'number') { + normBias = [norm.bias, norm.bias, norm.bias, norm.bias]; + } + else { + normBias = [norm.bias[0], norm.bias[1], norm.bias[2], norm.bias[3] ?? 0]; + } + const inputformat = options.format !== undefined ? options.format : 'RGBA'; + // default value is RGBA since imagedata and HTMLImageElement uses it + const outputformat = options.tensorFormat !== undefined ? (options.tensorFormat !== undefined ? options.tensorFormat : 'RGB') : 'RGB'; + const stride = height * width; + const float32Data = outputformat === 'RGBA' ? new Float32Array(stride * 4) : new Float32Array(stride * 3); + // Default pointer assignments + let step = 4, rImagePointer = 0, gImagePointer = 1, bImagePointer = 2, aImagePointer = 3; + let rTensorPointer = 0, gTensorPointer = stride, bTensorPointer = stride * 2, aTensorPointer = -1; + // Updating the pointer assignments based on the input image format + if (inputformat === 'RGB') { + step = 3; + rImagePointer = 0; + gImagePointer = 1; + bImagePointer = 2; + aImagePointer = -1; + } + // Updating the pointer assignments based on the output tensor format + if (outputformat === 'RGBA') { + aTensorPointer = stride * 3; + } + else if (outputformat === 'RBG') { + rTensorPointer = 0; + bTensorPointer = stride; + gTensorPointer = stride * 2; + } + else if (outputformat === 'BGR') { + bTensorPointer = 0; + gTensorPointer = stride; + rTensorPointer = stride * 2; + } + for (let i = 0; i < stride; i++, rImagePointer += step, bImagePointer += step, gImagePointer += step, aImagePointer += step) { + float32Data[rTensorPointer++] = (buffer[rImagePointer] + normBias[0]) / normMean[0]; + float32Data[gTensorPointer++] = (buffer[gImagePointer] + normBias[1]) / normMean[1]; + float32Data[bTensorPointer++] = (buffer[bImagePointer] + normBias[2]) / normMean[2]; + if (aTensorPointer !== -1 && aImagePointer !== -1) { + float32Data[aTensorPointer++] = (buffer[aImagePointer] + normBias[3]) / normMean[3]; + } + } + // Float32Array -> ort.Tensor + const outputTensor = outputformat === 'RGBA' + ? new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor('float32', float32Data, [1, 4, height, width]) + : new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor('float32', float32Data, [1, 3, height, width]); + return outputTensor; +}; +/** + * implementation of Tensor.fromImage(). + */ +const tensorFromImage = async (image, options) => { + // checking the type of image object + const isHTMLImageEle = typeof HTMLImageElement !== 'undefined' && image instanceof HTMLImageElement; + const isImageDataEle = typeof ImageData !== 'undefined' && image instanceof ImageData; + const isImageBitmap = typeof ImageBitmap !== 'undefined' && image instanceof ImageBitmap; + const isString = typeof image === 'string'; + let data; + let bufferToTensorOptions = options ?? {}; + const createCanvas = () => { + if (typeof document !== 'undefined') { + return document.createElement('canvas'); + } + else if (typeof OffscreenCanvas !== 'undefined') { + return new OffscreenCanvas(1, 1); + } + else { + throw new Error('Canvas is not supported'); + } + }; + const createCanvasContext = (canvas) => { + if (typeof HTMLCanvasElement !== 'undefined' && canvas instanceof HTMLCanvasElement) { + return canvas.getContext('2d'); + } + else if (canvas instanceof OffscreenCanvas) { + return canvas.getContext('2d'); + } + else { + return null; + } + }; + // filling and checking image configuration options + if (isHTMLImageEle) { + // HTMLImageElement - image object - format is RGBA by default + const canvas = createCanvas(); + canvas.width = image.width; + canvas.height = image.height; + const pixels2DContext = createCanvasContext(canvas); + if (pixels2DContext != null) { + let height = image.height; + let width = image.width; + if (options !== undefined && options.resizedHeight !== undefined && options.resizedWidth !== undefined) { + height = options.resizedHeight; + width = options.resizedWidth; + } + if (options !== undefined) { + bufferToTensorOptions = options; + if (options.tensorFormat !== undefined) { + throw new Error('Image input config format must be RGBA for HTMLImageElement'); + } + else { + bufferToTensorOptions.tensorFormat = 'RGBA'; + } + bufferToTensorOptions.height = height; + bufferToTensorOptions.width = width; + } + else { + bufferToTensorOptions.tensorFormat = 'RGBA'; + bufferToTensorOptions.height = height; + bufferToTensorOptions.width = width; + } + pixels2DContext.drawImage(image, 0, 0); + data = pixels2DContext.getImageData(0, 0, width, height).data; + } + else { + throw new Error('Can not access image data'); + } + } + else if (isImageDataEle) { + let height; + let width; + if (options !== undefined && options.resizedWidth !== undefined && options.resizedHeight !== undefined) { + height = options.resizedHeight; + width = options.resizedWidth; + } + else { + height = image.height; + width = image.width; + } + if (options !== undefined) { + bufferToTensorOptions = options; + } + bufferToTensorOptions.format = 'RGBA'; + bufferToTensorOptions.height = height; + bufferToTensorOptions.width = width; + if (options !== undefined) { + const tempCanvas = createCanvas(); + tempCanvas.width = width; + tempCanvas.height = height; + const pixels2DContext = createCanvasContext(tempCanvas); + if (pixels2DContext != null) { + pixels2DContext.putImageData(image, 0, 0); + data = pixels2DContext.getImageData(0, 0, width, height).data; + } + else { + throw new Error('Can not access image data'); + } + } + else { + data = image.data; + } + } + else if (isImageBitmap) { + // ImageBitmap - image object - format must be provided by user + if (options === undefined) { + throw new Error('Please provide image config with format for Imagebitmap'); + } + const canvas = createCanvas(); + canvas.width = image.width; + canvas.height = image.height; + const pixels2DContext = createCanvasContext(canvas); + if (pixels2DContext != null) { + const height = image.height; + const width = image.width; + pixels2DContext.drawImage(image, 0, 0, width, height); + data = pixels2DContext.getImageData(0, 0, width, height).data; + bufferToTensorOptions.height = height; + bufferToTensorOptions.width = width; + return bufferToTensor(data, bufferToTensorOptions); + } + else { + throw new Error('Can not access image data'); + } + } + else if (isString) { + return new Promise((resolve, reject) => { + const canvas = createCanvas(); + const context = createCanvasContext(canvas); + if (!image || !context) { + return reject(); + } + const newImage = new Image(); + newImage.crossOrigin = 'Anonymous'; + newImage.src = image; + newImage.onload = () => { + canvas.width = newImage.width; + canvas.height = newImage.height; + context.drawImage(newImage, 0, 0, canvas.width, canvas.height); + const img = context.getImageData(0, 0, canvas.width, canvas.height); + bufferToTensorOptions.height = canvas.height; + bufferToTensorOptions.width = canvas.width; + resolve(bufferToTensor(img.data, bufferToTensorOptions)); + }; + }); + } + else { + throw new Error('Input data provided is not supported - aborted tensor creation'); + } + if (data !== undefined) { + return bufferToTensor(data, bufferToTensorOptions); + } + else { + throw new Error('Input data provided is not supported - aborted tensor creation'); + } +}; +/** + * implementation of Tensor.fromTexture(). + */ +const tensorFromTexture = (texture, options) => { + const { width, height, download, dispose } = options; + // Always assume RGBAF32. TODO: support different texture format + const dims = [1, height, width, 4]; + return new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor({ location: 'texture', type: 'float32', texture, dims, download, dispose }); +}; +/** + * implementation of Tensor.fromGpuBuffer(). + */ +const tensorFromGpuBuffer = (gpuBuffer, options) => { + const { dataType, dims, download, dispose } = options; + return new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor({ location: 'gpu-buffer', type: dataType ?? 'float32', gpuBuffer, dims, download, dispose }); +}; +/** + * implementation of Tensor.fromMLTensor(). + */ +const tensorFromMLTensor = (mlTensor, options) => { + const { dataType, dims, download, dispose } = options; + return new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor({ location: 'ml-tensor', type: dataType ?? 'float32', mlTensor, dims, download, dispose }); +}; +/** + * implementation of Tensor.fromPinnedBuffer(). + */ +const tensorFromPinnedBuffer = (type, buffer, dims) => new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor({ location: 'cpu-pinned', type, data: buffer, dims: dims ?? [buffer.length] }); +//# sourceMappingURL=tensor-factory-impl.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/tensor-factory.js": +/*!********************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/tensor-factory.js ***! + \********************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +//# sourceMappingURL=tensor-factory.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/tensor-impl-type-mapping.js": +/*!******************************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/tensor-impl-type-mapping.js ***! + \******************************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP: () => (/* binding */ NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP), +/* harmony export */ NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP: () => (/* binding */ NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP), +/* harmony export */ checkTypedArray: () => (/* binding */ checkTypedArray) +/* harmony export */ }); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. +// a runtime map that maps type string to TypedArray constructor. Should match Tensor.DataTypeMap. +const NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP = new Map([ + ['float32', Float32Array], + ['uint8', Uint8Array], + ['int8', Int8Array], + ['uint16', Uint16Array], + ['int16', Int16Array], + ['int32', Int32Array], + ['bool', Uint8Array], + ['float64', Float64Array], + ['uint32', Uint32Array], + ['int4', Uint8Array], + ['uint4', Uint8Array], +]); +// a runtime map that maps type string to TypedArray constructor. Should match Tensor.DataTypeMap. +const NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP = new Map([ + [Float32Array, 'float32'], + [Uint8Array, 'uint8'], + [Int8Array, 'int8'], + [Uint16Array, 'uint16'], + [Int16Array, 'int16'], + [Int32Array, 'int32'], + [Float64Array, 'float64'], + [Uint32Array, 'uint32'], +]); +// the following code allows delaying execution of BigInt/Float16Array checking. This allows lazy initialization for +// NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP and NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP, which allows BigInt/Float16Array +// polyfill if available. +let isTypedArrayChecked = false; +const checkTypedArray = () => { + if (!isTypedArrayChecked) { + isTypedArrayChecked = true; + const isBigInt64ArrayAvailable = typeof BigInt64Array !== 'undefined' && BigInt64Array.from; + const isBigUint64ArrayAvailable = typeof BigUint64Array !== 'undefined' && BigUint64Array.from; + // eslint-disable-next-line @typescript-eslint/naming-convention, @typescript-eslint/no-explicit-any + const Float16Array = globalThis.Float16Array; + const isFloat16ArrayAvailable = typeof Float16Array !== 'undefined' && Float16Array.from; + if (isBigInt64ArrayAvailable) { + NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('int64', BigInt64Array); + NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array, 'int64'); + } + if (isBigUint64ArrayAvailable) { + NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('uint64', BigUint64Array); + NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array, 'uint64'); + } + if (isFloat16ArrayAvailable) { + NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('float16', Float16Array); + NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(Float16Array, 'float16'); + } + else { + // if Float16Array is not available, use 'Uint16Array' to store the data. + NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set('float16', Uint16Array); + } + } +}; +//# sourceMappingURL=tensor-impl-type-mapping.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/tensor-impl.js": +/*!*****************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/tensor-impl.js ***! + \*****************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Tensor: () => (/* binding */ Tensor) +/* harmony export */ }); +/* harmony import */ var _tensor_conversion_impl_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./tensor-conversion-impl.js */ "./node_modules/onnxruntime-common/dist/esm/tensor-conversion-impl.js"); +/* harmony import */ var _tensor_factory_impl_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./tensor-factory-impl.js */ "./node_modules/onnxruntime-common/dist/esm/tensor-factory-impl.js"); +/* harmony import */ var _tensor_impl_type_mapping_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./tensor-impl-type-mapping.js */ "./node_modules/onnxruntime-common/dist/esm/tensor-impl-type-mapping.js"); +/* harmony import */ var _tensor_utils_impl_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./tensor-utils-impl.js */ "./node_modules/onnxruntime-common/dist/esm/tensor-utils-impl.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + + + + +/** + * the implementation of Tensor interface. + * + * @ignore + */ +class Tensor { + /** + * implementation. + */ + constructor(arg0, arg1, arg2) { + // perform one-time check for BigInt/Float16Array support + (0,_tensor_impl_type_mapping_js__WEBPACK_IMPORTED_MODULE_2__.checkTypedArray)(); + let type; + let dims; + if (typeof arg0 === 'object' && 'location' in arg0) { + // + // constructing tensor from specific location + // + this.dataLocation = arg0.location; + type = arg0.type; + dims = arg0.dims; + switch (arg0.location) { + case 'cpu-pinned': { + const expectedTypedArrayConstructor = _tensor_impl_type_mapping_js__WEBPACK_IMPORTED_MODULE_2__.NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(type); + if (!expectedTypedArrayConstructor) { + throw new TypeError(`unsupported type "${type}" to create tensor from pinned buffer`); + } + if (!(arg0.data instanceof expectedTypedArrayConstructor)) { + throw new TypeError(`buffer should be of type ${expectedTypedArrayConstructor.name}`); + } + this.cpuData = arg0.data; + break; + } + case 'texture': { + if (type !== 'float32') { + throw new TypeError(`unsupported type "${type}" to create tensor from texture`); + } + this.gpuTextureData = arg0.texture; + this.downloader = arg0.download; + this.disposer = arg0.dispose; + break; + } + case 'gpu-buffer': { + if (type !== 'float32' && + type !== 'float16' && + type !== 'int32' && + type !== 'int64' && + type !== 'uint32' && + type !== 'uint8' && + type !== 'bool' && + type !== 'uint4' && + type !== 'int4') { + throw new TypeError(`unsupported type "${type}" to create tensor from gpu buffer`); + } + this.gpuBufferData = arg0.gpuBuffer; + this.downloader = arg0.download; + this.disposer = arg0.dispose; + break; + } + case 'ml-tensor': { + if (type !== 'float32' && + type !== 'float16' && + type !== 'int32' && + type !== 'int64' && + type !== 'uint32' && + type !== 'uint64' && + type !== 'int8' && + type !== 'uint8' && + type !== 'bool' && + type !== 'uint4' && + type !== 'int4') { + throw new TypeError(`unsupported type "${type}" to create tensor from MLTensor`); + } + this.mlTensorData = arg0.mlTensor; + this.downloader = arg0.download; + this.disposer = arg0.dispose; + break; + } + default: + throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`); + } + } + else { + // + // constructing tensor of location 'cpu' + // + let data; + let maybeDims; + // check whether arg0 is type or data + if (typeof arg0 === 'string') { + // + // Override: constructor(type, data, ...) + // + type = arg0; + maybeDims = arg2; + if (arg0 === 'string') { + // string tensor + if (!Array.isArray(arg1)) { + throw new TypeError("A string tensor's data must be a string array."); + } + // we don't check whether every element in the array is string; this is too slow. we assume it's correct and + // error will be populated at inference + data = arg1; + } + else { + // numeric tensor + const typedArrayConstructor = _tensor_impl_type_mapping_js__WEBPACK_IMPORTED_MODULE_2__.NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(arg0); + if (typedArrayConstructor === undefined) { + throw new TypeError(`Unsupported tensor type: ${arg0}.`); + } + if (Array.isArray(arg1)) { + if ((arg0 === 'float16' && typedArrayConstructor === Uint16Array) || arg0 === 'uint4' || arg0 === 'int4') { + // - 'float16': + // When no Float16Array polyfill is used, we cannot create 'float16' tensor from number array. + // + // Throw error here because when user try to use number array as data, + // e.g. new Tensor('float16', [1, 2, 3, 4], dims)), it will actually call + // Uint16Array.from(arg1) which generates wrong data. + // + // - 'uint4' and 'int4': + // Uint8Array.from(arg1) will generate wrong data for 'uint4' and 'int4' tensor. + // + throw new TypeError(`Creating a ${arg0} tensor from number array is not supported. Please use ${typedArrayConstructor.name} as data.`); + } + else if (arg0 === 'uint64' || arg0 === 'int64') { + // use 'as any' here because: + // 1. TypeScript's check on type of 'Array.isArray()' does not work with readonly arrays. + // see https://github.com/microsoft/TypeScript/issues/17002 + // 2. TypeScript's check on union type of '(BigInt64ArrayConstructor|BigUint64ArrayConstructor).from()' + // does not accept parameter mapFn. + // 3. parameters of 'SupportedTypedArrayConstructors.from()' does not match the requirement of the union + // type. + // assume 'arg1' is of type "readonly number[]|readonly bigint[]" here. + // eslint-disable-next-line @typescript-eslint/no-explicit-any + data = typedArrayConstructor.from(arg1, BigInt); + } + else { + // assume 'arg1' is of type "readonly number[]" here. + // eslint-disable-next-line @typescript-eslint/no-explicit-any + data = typedArrayConstructor.from(arg1); + } + } + else if (arg1 instanceof typedArrayConstructor) { + data = arg1; + } + else if (arg1 instanceof Uint8ClampedArray) { + if (arg0 === 'uint8') { + data = Uint8Array.from(arg1); + } + else { + throw new TypeError(`A Uint8ClampedArray tensor's data must be type of uint8`); + } + } + else if (arg0 === 'float16' && arg1 instanceof Uint16Array && typedArrayConstructor !== Uint16Array) { + // when Float16Array is available and data is of type Uint16Array. + // We allow Uint16Array to be passed in as data for 'float16' tensor until Float16Array is generally + // supported in JavaScript environment. + // eslint-disable-next-line @typescript-eslint/no-explicit-any + data = new globalThis.Float16Array(arg1.buffer, arg1.byteOffset, arg1.length); + } + else { + throw new TypeError(`A ${type} tensor's data must be type of ${typedArrayConstructor}`); + } + } + } + else { + // + // Override: constructor(data, ...) + // + maybeDims = arg1; + if (Array.isArray(arg0)) { + // only boolean[] and string[] is supported + if (arg0.length === 0) { + throw new TypeError('Tensor type cannot be inferred from an empty array.'); + } + const firstElementType = typeof arg0[0]; + if (firstElementType === 'string') { + type = 'string'; + data = arg0; + } + else if (firstElementType === 'boolean') { + type = 'bool'; + // 'arg0' is of type 'boolean[]'. Uint8Array.from(boolean[]) actually works, but typescript thinks this is + // wrong type. We use 'as any' to make it happy. + // eslint-disable-next-line @typescript-eslint/no-explicit-any + data = Uint8Array.from(arg0); + } + else { + throw new TypeError(`Invalid element type of data array: ${firstElementType}.`); + } + } + else if (arg0 instanceof Uint8ClampedArray) { + type = 'uint8'; + data = Uint8Array.from(arg0); + } + else { + // get tensor type from TypedArray + const mappedType = _tensor_impl_type_mapping_js__WEBPACK_IMPORTED_MODULE_2__.NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(arg0.constructor); + if (mappedType === undefined) { + throw new TypeError(`Unsupported type for tensor data: ${arg0.constructor}.`); + } + type = mappedType; + data = arg0; + } + } + // type and data is processed, now processing dims + if (maybeDims === undefined) { + // assume 1-D tensor if dims omitted + maybeDims = [data.length]; + } + else if (!Array.isArray(maybeDims)) { + throw new TypeError("A tensor's dims must be a number array"); + } + dims = maybeDims; + this.cpuData = data; + this.dataLocation = 'cpu'; + } + // perform check on dims + const size = (0,_tensor_utils_impl_js__WEBPACK_IMPORTED_MODULE_3__.calculateSize)(dims); + // if data is on CPU, check whether data length matches tensor size + if (this.cpuData && size !== this.cpuData.length) { + if ((type === 'uint4' || type === 'int4') && Math.ceil(size / 2) === this.cpuData.length) { + // for (u)int4, the data length is half of the tensor size. So we check this special case when size is odd. + } + else { + throw new Error(`Tensor's size(${size}) does not match data length(${this.cpuData.length}).`); + } + } + this.type = type; + this.dims = dims; + this.size = size; + } + // #endregion + // #region factory + static async fromImage(image, options) { + return (0,_tensor_factory_impl_js__WEBPACK_IMPORTED_MODULE_1__.tensorFromImage)(image, options); + } + static fromTexture(texture, options) { + return (0,_tensor_factory_impl_js__WEBPACK_IMPORTED_MODULE_1__.tensorFromTexture)(texture, options); + } + static fromGpuBuffer(gpuBuffer, options) { + return (0,_tensor_factory_impl_js__WEBPACK_IMPORTED_MODULE_1__.tensorFromGpuBuffer)(gpuBuffer, options); + } + static fromMLTensor(mlTensor, options) { + return (0,_tensor_factory_impl_js__WEBPACK_IMPORTED_MODULE_1__.tensorFromMLTensor)(mlTensor, options); + } + static fromPinnedBuffer(type, buffer, dims) { + return (0,_tensor_factory_impl_js__WEBPACK_IMPORTED_MODULE_1__.tensorFromPinnedBuffer)(type, buffer, dims); + } + // #endregion + // #region conversions + toDataURL(options) { + return (0,_tensor_conversion_impl_js__WEBPACK_IMPORTED_MODULE_0__.tensorToDataURL)(this, options); + } + toImageData(options) { + return (0,_tensor_conversion_impl_js__WEBPACK_IMPORTED_MODULE_0__.tensorToImageData)(this, options); + } + // #endregion + // #region properties + get data() { + this.ensureValid(); + if (!this.cpuData) { + throw new Error('The data is not on CPU. Use `getData()` to download GPU data to CPU, ' + + 'or use `texture` or `gpuBuffer` property to access the GPU data directly.'); + } + return this.cpuData; + } + get location() { + return this.dataLocation; + } + get texture() { + this.ensureValid(); + if (!this.gpuTextureData) { + throw new Error('The data is not stored as a WebGL texture.'); + } + return this.gpuTextureData; + } + get gpuBuffer() { + this.ensureValid(); + if (!this.gpuBufferData) { + throw new Error('The data is not stored as a WebGPU buffer.'); + } + return this.gpuBufferData; + } + get mlTensor() { + this.ensureValid(); + if (!this.mlTensorData) { + throw new Error('The data is not stored as a WebNN MLTensor.'); + } + return this.mlTensorData; + } + // #endregion + // #region methods + async getData(releaseData) { + this.ensureValid(); + switch (this.dataLocation) { + case 'cpu': + case 'cpu-pinned': + return this.data; + case 'texture': + case 'gpu-buffer': + case 'ml-tensor': { + if (!this.downloader) { + throw new Error('The current tensor is not created with a specified data downloader.'); + } + if (this.isDownloading) { + throw new Error('The current tensor is being downloaded.'); + } + try { + this.isDownloading = true; + const data = await this.downloader(); + this.downloader = undefined; + this.dataLocation = 'cpu'; + this.cpuData = data; + if (releaseData && this.disposer) { + this.disposer(); + this.disposer = undefined; + } + return data; + } + finally { + this.isDownloading = false; + } + } + default: + throw new Error(`cannot get data from location: ${this.dataLocation}`); + } + } + dispose() { + if (this.isDownloading) { + throw new Error('The current tensor is being downloaded.'); + } + if (this.disposer) { + this.disposer(); + this.disposer = undefined; + } + this.cpuData = undefined; + this.gpuTextureData = undefined; + this.gpuBufferData = undefined; + this.mlTensorData = undefined; + this.downloader = undefined; + this.isDownloading = undefined; + this.dataLocation = 'none'; + } + // #endregion + // #region tensor utilities + ensureValid() { + if (this.dataLocation === 'none') { + throw new Error('The tensor is disposed.'); + } + } + reshape(dims) { + this.ensureValid(); + if (this.downloader || this.disposer) { + throw new Error('Cannot reshape a tensor that owns GPU resource.'); + } + return (0,_tensor_utils_impl_js__WEBPACK_IMPORTED_MODULE_3__.tensorReshape)(this, dims); + } +} +//# sourceMappingURL=tensor-impl.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/tensor-utils-impl.js": +/*!***********************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/tensor-utils-impl.js ***! + \***********************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ calculateSize: () => (/* binding */ calculateSize), +/* harmony export */ tensorReshape: () => (/* binding */ tensorReshape) +/* harmony export */ }); +/* harmony import */ var _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./tensor-impl.js */ "./node_modules/onnxruntime-common/dist/esm/tensor-impl.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +/** + * calculate size from dims. + * + * @param dims the dims array. May be an illegal input. + */ +const calculateSize = (dims) => { + let size = 1; + for (let i = 0; i < dims.length; i++) { + const dim = dims[i]; + if (typeof dim !== 'number' || !Number.isSafeInteger(dim)) { + throw new TypeError(`dims[${i}] must be an integer, got: ${dim}`); + } + if (dim < 0) { + throw new RangeError(`dims[${i}] must be a non-negative integer, got: ${dim}`); + } + size *= dim; + } + return size; +}; +/** + * implementation of Tensor.reshape() + */ +const tensorReshape = (tensor, dims) => { + switch (tensor.location) { + case 'cpu': + return new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor(tensor.type, tensor.data, dims); + case 'cpu-pinned': + return new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor({ + location: 'cpu-pinned', + data: tensor.data, + type: tensor.type, + dims, + }); + case 'texture': + return new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor({ + location: 'texture', + texture: tensor.texture, + type: tensor.type, + dims, + }); + case 'gpu-buffer': + return new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor({ + location: 'gpu-buffer', + gpuBuffer: tensor.gpuBuffer, + type: tensor.type, + dims, + }); + case 'ml-tensor': + return new _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor({ + location: 'ml-tensor', + mlTensor: tensor.mlTensor, + type: tensor.type, + dims, + }); + default: + throw new Error(`tensorReshape: tensor location ${tensor.location} is not supported`); + } +}; +//# sourceMappingURL=tensor-utils-impl.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/tensor.js": +/*!************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/tensor.js ***! + \************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Tensor: () => (/* binding */ Tensor) +/* harmony export */ }); +/* harmony import */ var _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./tensor-impl.js */ "./node_modules/onnxruntime-common/dist/esm/tensor-impl.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +// eslint-disable-next-line @typescript-eslint/naming-convention +const Tensor = _tensor_impl_js__WEBPACK_IMPORTED_MODULE_0__.Tensor; +//# sourceMappingURL=tensor.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/trace.js": +/*!***********************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/trace.js ***! + \***********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ TRACE: () => (/* binding */ TRACE), +/* harmony export */ TRACE_FUNC_BEGIN: () => (/* binding */ TRACE_FUNC_BEGIN), +/* harmony export */ TRACE_FUNC_END: () => (/* binding */ TRACE_FUNC_END) +/* harmony export */ }); +/* harmony import */ var _env_impl_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./env-impl.js */ "./node_modules/onnxruntime-common/dist/esm/env-impl.js"); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. + +/** + * @ignore + */ +const TRACE = (deviceType, label) => { + if (typeof _env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env.trace === 'undefined' ? !_env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env.wasm.trace : !_env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env.trace) { + return; + } + // eslint-disable-next-line no-console + console.timeStamp(`${deviceType}::ORT::${label}`); +}; +const TRACE_FUNC = (msg, extraMsg) => { + const stack = new Error().stack?.split(/\r\n|\r|\n/g) || []; + let hasTraceFunc = false; + for (let i = 0; i < stack.length; i++) { + if (hasTraceFunc && !stack[i].includes('TRACE_FUNC')) { + let label = `FUNC_${msg}::${stack[i].trim().split(' ')[1]}`; + if (extraMsg) { + label += `::${extraMsg}`; + } + TRACE('CPU', label); + return; + } + if (stack[i].includes('TRACE_FUNC')) { + hasTraceFunc = true; + } + } +}; +/** + * @ignore + */ +const TRACE_FUNC_BEGIN = (extraMsg) => { + if (typeof _env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env.trace === 'undefined' ? !_env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env.wasm.trace : !_env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env.trace) { + return; + } + TRACE_FUNC('BEGIN', extraMsg); +}; +/** + * @ignore + */ +const TRACE_FUNC_END = (extraMsg) => { + if (typeof _env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env.trace === 'undefined' ? !_env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env.wasm.trace : !_env_impl_js__WEBPACK_IMPORTED_MODULE_0__.env.trace) { + return; + } + TRACE_FUNC('END', extraMsg); +}; +//# sourceMappingURL=trace.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-common/dist/esm/version.js": +/*!*************************************************************!*\ + !*** ./node_modules/onnxruntime-common/dist/esm/version.js ***! + \*************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ version: () => (/* binding */ version) +/* harmony export */ }); +// Copyright (c) Microsoft Corporation. All rights reserved. +// Licensed under the MIT License. +// This file is generated by /js/scripts/update-version.ts +// Do not modify file content manually. +const version = '1.21.0'; +//# sourceMappingURL=version.js.map + +/***/ }), + +/***/ "./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs?3a96": +/*!**************************************************************!*\ + !*** ./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs ***! + \**************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ InferenceSession: () => (/* binding */ Gp), +/* harmony export */ TRACE: () => (/* binding */ gr), +/* harmony export */ TRACE_FUNC_BEGIN: () => (/* binding */ Re), +/* harmony export */ TRACE_FUNC_END: () => (/* binding */ Oe), +/* harmony export */ Tensor: () => (/* binding */ Ge), +/* harmony export */ "default": () => (/* binding */ IS), +/* harmony export */ env: () => (/* binding */ ge), +/* harmony export */ registerBackend: () => (/* binding */ $t) +/* harmony export */ }); +/*! + * ONNX Runtime Web v1.22.0-dev.20250409-89f8206ba4 + * Copyright (c) Microsoft Corporation. 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q[de]=`fn ${de}(outputIndices: ${V.type.indices}) -> u32 { + return ${ze.length>0?ze.join("+"):"0u"}; + }`,`${de}(${C})`},W=(C,V)=>(()=>{if(f.storage===f.value)return`${e}[${C}]=${V};`;if(f.storage==="vec2"&&f.value==="i32")return`${e}[${C}]=vec2(u32(${V}), select(0u, 0xFFFFFFFFu, ${V} < 0));`;if(f.storage==="vec2"&&f.value==="u32")return`${e}[${C}]=vec2(u32(${V}), 0u);`;if(f.storage==="u32"&&f.value==="vec4")return`${e}[${C}]=dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(${V}));`;throw new Error(`not supported combination of storage type ${f.storage} and value type ${f.value} yet`)})(),Z=C=>(()=>{if(f.storage===f.value)return`${e}[${C}]`;if(f.storage==="vec2"&&f.value==="i32")return`i32(${e}[${C}].x)`;if(f.storage==="vec2"&&f.value==="u32")return`u32(${e}[${C}].x)`;if(f.storage==="u32"&&f.value==="vec4")return`vec4(bool(${e}[${C}] & 0xFFu), bool(${e}[${C}] & 0xFF00u), bool(${e}[${C}] & 0xFF0000u), bool(${e}[${C}] & 0xFF000000u))`;throw new Error(`not supported combination of storage 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new Error(`workgroup size [${r}, ${n}, ${o}] exceeds the maximum workgroup invocations ${this.limits.maxComputeInvocationsPerWorkgroup}.`);let i=this.normalizedDispatchGroup[1]===1&&this.normalizedDispatchGroup[2]===1,a=i?`@builtin(global_invocation_id) global_id : vec3, + @builtin(workgroup_id) workgroup_id : vec3, + @builtin(local_invocation_index) local_idx : u32, + @builtin(local_invocation_id) local_id : vec3`:`@builtin(global_invocation_id) global_id : vec3, + @builtin(local_invocation_id) local_id : vec3, + @builtin(local_invocation_index) local_idx : u32, + @builtin(workgroup_id) workgroup_id : vec3, + @builtin(num_workgroups) num_workgroups : vec3`,u=i?`let global_idx = global_id.x; + let workgroup_index = workgroup_id.x;`:`let workgroup_index = workgroup_id.z * num_workgroups[0] * num_workgroups[1] + + workgroup_id.y * num_workgroups[0] + workgroup_id.x; + let global_idx = workgroup_index * ${r*n*o}u + local_idx;`;return`@compute @workgroup_size(${r}, ${n}, ${o}) + fn main(${a}) { + ${u} + `}appendVariableUniforms(t){t.rank!==0&&(t.shape.startsWith("uniforms.")&&this.uniforms.push({name:t.shape.replace("uniforms.",""),type:"u32",length:t.rank}),t.strides.startsWith("uniforms.")&&this.uniforms.push({name:t.strides.replace("uniforms.",""),type:"u32",length:t.rank}))}declareVariable(t,r){if(t.usage==="internal")throw new Error("cannot use internal variable with declareVariable(). use registerInternalVariables() instead.");this.variables.push(t),this.appendVariableUniforms(t);let n=t.usage==="input"?"read":"read_write",o=t.usage==="atomicOutput"?"atomic":t.type.storage;return`@group(0) @binding(${r}) var ${t.name}: array<${o}>;`}declareVariables(...t){return t.map(r=>this.declareVariable(r,this.variableIndex++)).join(` +`)}registerInternalVariable(t){if(t.usage!=="internal")throw new Error("cannot use input or output variable with registerInternalVariable(). use declareVariables() 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t=r=>[12,10,1,6][["u32","f16","f32","i32"].indexOf(r)];return this.uniforms.map(r=>[t(r.type),r.length??1])}},ts=(e,t)=>new oo(e,t)});var pm,rs,mm,fm,hm,gm,Ee,ns,os,st=U(()=>{"use strict";ee();ne();Se();ie();pm=(e,t)=>{if(!e||e.length!==1)throw new Error("Transpose requires 1 input.");if(t.length!==0&&t.length!==e[0].dims.length)throw new Error(`perm size ${t.length} does not match input rank ${e[0].dims.length}`)},rs=(e,t)=>t.length!==0?t:[...new Array(e).keys()].reverse(),mm=(e,t)=>k.sortBasedOnPerm(e,rs(e.length,t)),fm=(e,t,r,n)=>{let o=`fn perm(i: ${n.type.indices}) -> ${r.type.indices} { + var a: ${r.type.indices};`;for(let i=0;i{let r=[],n=[];for(let o=0;o{let r=0;for(let n=0;n{let r=e.dataType,n=e.dims.length,o=rs(n,t),i=mm(e.dims,o),a=e.dims,u=i,d=n<2||gm(o,e.dims),c;if(d)return c=_=>{let S=P("input",r,a,4),$=M("output",r,u,4);return` + ${_.registerUniform("output_size","u32").declareVariables(S,$)} + ${_.mainStart()} + ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + output[global_idx] = input[global_idx]; + }`},{name:"TransposeCopy",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let _=k.size(i);return{outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(_/64/4)},programUniforms:[{type:12,data:Math.ceil(_/4)}]}},getShaderSource:c};let{newShape:p,newPerm:m}=hm(e.dims,o),f=k.areEqual(m,[2,3,1]),b=k.areEqual(m,[3,1,2]);if(p.length===2||f||b){a=f?[p[0],p[1]*p[2]]:b?[p[0]*p[1],p[2]]:p,u=[a[1],a[0]];let _=16;return c=S=>{let $=P("a",r,a.length),v=M("output",r,u.length);return` + ${S.registerUniform("output_size","u32").declareVariables($,v)} + var tile : array, ${_}>; + ${S.mainStart([_,_,1])} + let stride = (uniforms.output_shape[1] - 1) / ${_} + 1; + let workgroup_id_x = workgroup_index % stride; + let workgroup_id_y = workgroup_index / stride; + let input_col = workgroup_id_y * ${_}u + local_id.x; + let input_row = workgroup_id_x * ${_}u + local_id.y; + if (input_row < uniforms.a_shape[0] && input_col < uniforms.a_shape[1]) { + tile[local_id.y][local_id.x] = ${$.getByIndices(`${$.type.indices}(input_row, input_col)`)}; + } + workgroupBarrier(); + + let output_col = workgroup_id_x * ${_}u + local_id.x; + let output_row = workgroup_id_y * ${_}u + local_id.y; + if (output_row < uniforms.output_shape[0] && output_col < uniforms.output_shape[1]) { + ${v.setByIndices(`${v.type.indices}(output_row, output_col)`,"tile[local_id.x][local_id.y]")} + } + }`},{name:"TransposeShared",shaderCache:{inputDependencies:["type"]},getRunData:()=>{let S=k.size(i);return{outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(u[1]/_),y:Math.ceil(u[0]/_)},programUniforms:[{type:12,data:S},...N(a,u)]}},getShaderSource:c}}return c=_=>{let S=P("a",r,a.length),$=M("output",r,u.length);return` + ${_.registerUniform("output_size","u32").declareVariables(S,$)} + + ${fm(o,n,S,$)} + + ${_.mainStart()} + ${_.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${$.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${$.setByOffset("global_idx",S.getByIndices("aIndices"))} + }`},{name:"Transpose",shaderCache:{hint:`${t}`,inputDependencies:["rank"]},getRunData:()=>{let _=k.size(i);return{outputs:[{dims:i,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(_/64)},programUniforms:[{type:12,data:_},...N(a,u)]}},getShaderSource:c}},ns=(e,t)=>{pm(e.inputs,t.perm),e.compute(Ee(e.inputs[0],t.perm))},os=e=>J({perm:e.perm})});var bm,ym,_m,wm,vm,$m,xm,Sm,Tm,Im,et,is,as,ss,us,ds,ls,cs,ps,ms,fs,hs=U(()=>{"use strict";ee();ne();ie();Gr();st();bm={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate * candidate",logSumExp:"bestValue + exp(candidate)",l1:"bestValue + abs(candidate)",l2:"bestValue + candidate * candidate",logSum:"bestValue + candidate"},ym={max:"select(bestValue, candidate, candidate > bestValue)",min:"select(bestValue, candidate, candidate < bestValue)",mean:"bestValue + candidate",sum:"bestValue + candidate",prod:"bestValue * candidate",sumSquare:"bestValue + candidate",logSumExp:"bestValue + candidate",l1:"bestValue + candidate",l2:"bestValue + candidate",logSum:"bestValue + candidate"},_m={max:"_A[offset]",min:"_A[offset]",mean:"0",sum:"0",prod:"1",sumSquare:"0",logSumExp:"0",l1:"0",l2:"0",logSum:"0"},wm={max:"bestValue",min:"bestValue",sum:"bestValue",prod:"bestValue",sumSquare:"bestValue",logSumExp:"log(bestValue)",l1:"bestValue",l2:"sqrt(bestValue)",logSum:"log(bestValue)"},vm=(e,t)=>{let r=[];for(let n=t-e;n{let r=[],n=e.length;for(let i=0;ie[i]);return[r,o]},xm=(e,t)=>{let r=e.length+t.length,n=[],o=0;for(let i=0;i{for(let r=0;r{let r=[];if(!Sm(e,t)){for(let n=0;nr.push(n))}return r},Im=(e,t,r,n,o,i,a)=>{let u=r[0].dims,d=k.size(i),c=k.size(a),p=P("_A",r[0].dataType,u),m=M("output",o,i),f=64;d===1&&(f=256);let b=` + var aBestValues : array; + `,g=_=>` + ${_.registerUniform("reduceSize","u32").declareVariables(p,m)} + ${b} + fn DIV_CEIL(a : u32, b : u32) -> u32 { + return ((a - 1u) / b + 1u); + } + ${_.mainStart(f)} + + let outputIndex = global_idx / ${f}; + let offset = outputIndex * uniforms.reduceSize; + + var bestValue = f32(${_m[n]}); + let Length = uniforms.reduceSize; + for (var k = local_idx; k < Length; k = k + ${f}) { + let candidate = f32(${p.getByOffset("offset + k")}); + bestValue = ${bm[n]}; + } + aBestValues[local_idx] = bestValue; + workgroupBarrier(); + + var reduceSize = min(Length, ${f}u); + for (var currentSize = reduceSize / 2u; reduceSize > 1u; + currentSize = reduceSize / 2u) { + let interval = DIV_CEIL(reduceSize, 2u); + if (local_idx < currentSize) { + let candidate = aBestValues[local_idx + interval]; + bestValue = ${ym[n]}; + aBestValues[local_idx] = bestValue; + } + reduceSize = interval; + workgroupBarrier(); + } + + if (local_idx == 0u) { + ${m.setByOffset("outputIndex",`${n==="mean"?`${m.type.storage}(bestValue / f32(uniforms.reduceSize))`:`${m.type.storage}(${wm[n]})`}`)}; + } + }`;return{name:e,shaderCache:{hint:`${t};${f}`,inputDependencies:["type"]},getShaderSource:g,getRunData:()=>({outputs:[{dims:i,dataType:o}],dispatchGroup:{x:d},programUniforms:[{type:12,data:c}]})}},et=(e,t,r,n)=>{let o=e.inputs.length===1?r:so(e.inputs,r),i=o.axes;i.length===0&&!o.noopWithEmptyAxes&&(i=e.inputs[0].dims.map((b,g)=>g));let a=k.normalizeAxes(i,e.inputs[0].dims.length),u=a,d=e.inputs[0],c=Tm(u,e.inputs[0].dims.length);c.length>0&&(d=e.compute(Ee(e.inputs[0],c),{inputs:[0],outputs:[-1]})[0],u=vm(u.length,d.dims.length));let[p,m]=$m(d.dims,u),f=p;o.keepDims&&(f=xm(p,a)),e.compute(Im(t,o.cacheKey,[d],n,e.inputs[0].dataType,f,m),{inputs:[d]})},is=(e,t)=>{et(e,"ReduceMeanShared",t,"mean")},as=(e,t)=>{et(e,"ReduceL1Shared",t,"l1")},ss=(e,t)=>{et(e,"ReduceL2Shared",t,"l2")},us=(e,t)=>{et(e,"ReduceLogSumExpShared",t,"logSumExp")},ds=(e,t)=>{et(e,"ReduceMaxShared",t,"max")},ls=(e,t)=>{et(e,"ReduceMinShared",t,"min")},cs=(e,t)=>{et(e,"ReduceProdShared",t,"prod")},ps=(e,t)=>{et(e,"ReduceSumShared",t,"sum")},ms=(e,t)=>{et(e,"ReduceSumSquareShared",t,"sumSquare")},fs=(e,t)=>{et(e,"ReduceLogSumShared",t,"logSum")}});var tt,Cm,Hr,so,rt,Am,Em,km,Pm,zm,Om,Bm,Dm,Mm,Rm,nt,gs,bs,ys,_s,ws,vs,$s,xs,Ss,Ts,Gr=U(()=>{"use strict";ee();ne();Se();ie();hs();tt=e=>{if(!e||e.length===0||e.length>2)throw new Error("Reduce op requires 1 or 2 inputs.");if(e.length===2&&e[1].dims.length!==1)throw new Error("Invalid axes input dims.")},Cm=e=>["","",`var value = ${e.getByIndices("input_indices")};`,""],Hr=(e,t,r,n,o,i,a=!1,u=!1)=>{let d=[],c=r[0].dims,p=c.length,m=k.normalizeAxes(o,p),f=!u&&m.length===0;c.forEach((S,$)=>{f||m.indexOf($)>=0?a&&d.push(1):d.push(S)});let b=d.length,g=k.size(d);return{name:e,shaderCache:t,getShaderSource:S=>{let $=[],v=P("_A",r[0].dataType,p),x=M("output",i,b),T=n(v,x,m),E=T[2];for(let I=0,z=0;I=0?(a&&z++,E=`for(var j${I}: u32 = 0; j${I} < ${c[I]}; j${I}++) { + ${T[2].includes("last_index")?`let last_index = j${I};`:""} + ${v.indicesSet("input_indices",I,`j${I}`)} + ${E} + }`):($.push(`${v.indicesSet("input_indices",I,x.indicesGet("output_indices",z))};`),z++);return` + + ${S.registerUniform("output_size","u32").declareVariables(v,x)} + + ${S.mainStart()} + ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var input_indices: ${v.type.indices}; + let output_indices = ${x.offsetToIndices("global_idx")}; + + ${$.join(` +`)} + ${T[0]} // init ops for reduce max/min + ${T[1]} + ${E} + ${T[3]} + ${T.length===4?x.setByOffset("global_idx","value"):T.slice(4).join(` +`)} + }`},getRunData:()=>({outputs:[{dims:d,dataType:i}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:[{type:12,data:g},...N(c,d)]})}},so=(e,t)=>{let r=[];return e[1].dims[0]>0&&e[1].getBigInt64Array().forEach(n=>r.push(Number(n))),J({axes:r,keepDims:t.keepDims,noopWithEmptyAxes:t.noopWithEmptyAxes})},rt=(e,t,r,n)=>{let o=e.inputs,i=o.length===1?r:so(o,r);e.compute(Hr(t,{hint:i.cacheKey,inputDependencies:["rank"]},[o[0]],i.noopWithEmptyAxes&&i.axes.length===0?Cm:n,i.axes,o[0].dataType,i.keepDims,i.noopWithEmptyAxes),{inputs:[0]})},Am=(e,t)=>{tt(e.inputs),rt(e,"ReduceLogSum",t,(n,o)=>[`var value = ${o.type.storage}(0);`,"",`value += ${n.getByIndices("input_indices")};`,"value = log(value);"])},Em=(e,t)=>{tt(e.inputs),rt(e,"ReduceL1",t,(n,o)=>[`var value = ${o.type.storage}(0);`,"",`value += abs(${n.getByIndices("input_indices")});`,""])},km=(e,t)=>{tt(e.inputs),rt(e,"ReduceL2",t,(n,o)=>[`var t = ${o.type.value}(0); var value = ${o.type.value}(0);`,"",`t = ${n.getByIndices("input_indices")}; value += (t * t);`,"value = sqrt(value);"])},Pm=(e,t)=>{tt(e.inputs),rt(e,"ReduceLogSumExp",t,(n,o)=>[`var value = ${o.type.storage}(0);`,"",`value += exp(${n.getByIndices("input_indices")});`,"value = log(value);"])},zm=(e,t)=>{tt(e.inputs),rt(e,"ReduceMax",t,(n,o,i)=>{let a=[];for(let u=0;u=0||i.length===0)&&a.push(n.indicesSet("input_indices",u,0));return[`${a.join(` +`)}`,`var value = ${n.getByIndices("input_indices")};`,`value = max(value, ${n.getByIndices("input_indices")});`,""]})},Om=(e,t)=>{tt(e.inputs),rt(e,"ReduceMean",t,(n,o,i)=>{let a=1;for(let u=0;u=0||i.length===0)&&(a*=e.inputs[0].dims[u]);return["var sum = f32(0);","",`sum += f32(${n.getByIndices("input_indices")});`,`let value = ${o.type.value}(sum / ${a});`]})},Bm=(e,t)=>{tt(e.inputs),rt(e,"ReduceMin",t,(n,o,i)=>{let a=[];for(let u=0;u=0||i.length===0)&&a.push(`input_indices[${u}] = 0;`);return[`${a.join(` +`)}`,`var value = ${n.getByIndices("input_indices")};`,`value = min(value, ${n.getByIndices("input_indices")});`,""]})},Dm=(e,t)=>{tt(e.inputs),rt(e,"ReduceProd",t,(n,o)=>[`var value = ${o.type.storage}(1);`,"",`value *= ${n.getByIndices("input_indices")};`,""])},Mm=(e,t)=>{tt(e.inputs),rt(e,"ReduceSum",t,(n,o)=>[`var value = ${o.type.storage}(0);`,"",`value += ${n.getByIndices("input_indices")};`,""])},Rm=(e,t)=>{tt(e.inputs),rt(e,"ReduceSumSquare",t,(n,o)=>[`var t = ${o.type.value}(0); var value = ${o.type.value}(0);`,"",`t = ${n.getByIndices("input_indices")}; value += t * t;`,""])},nt=(e,t,r)=>{if(t.length===0)return r;let n=1,o=1;for(let i=0;i1024},gs=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Om(e,t):is(e,t)},bs=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Em(e,t):as(e,t)},ys=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?km(e,t):ss(e,t)},_s=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Pm(e,t):us(e,t)},ws=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?zm(e,t):ds(e,t)},vs=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Bm(e,t):ls(e,t)},$s=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Dm(e,t):cs(e,t)},xs=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Mm(e,t):ps(e,t)},Ss=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Rm(e,t):ms(e,t)},Ts=(e,t)=>{nt(e.inputs[0].dims,t.axes,t.noopWithEmptyAxes)?Am(e,t):fs(e,t)}});var Is,Cs,As,uo,Es=U(()=>{"use strict";ee();Se();Gr();Is=e=>{if(!e||e.length===0||e.length>2)throw new Error("ArgMinMaxOp op requires 1 or 2 inputs.");if(e[0].dataType!==1)throw new Error("Invalid input type.")},Cs=(e,t)=>{Is(e.inputs);let r=(n,o,i)=>{let a=[];for(let u=0;u=0||i.length===0)&&a.push(`input_indices[${u}] = 0;`);return[`${a.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?"<=":"<"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",o.setByOffset("global_idx","best_index")]};e.compute(Hr("ArgMin",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},As=(e,t)=>{Is(e.inputs);let r=(n,o,i)=>{let a=[];for(let u=0;u=0||i.length===0)&&a.push(`input_indices[${u}] = 0;`);return[`${a.join(` +`)}`,`var value = ${n.getByIndices("input_indices")}; +var best_index : i32 = 0;`,`if (${n.getByIndices("input_indices")} ${t.selectLastIndex>0?">=":">"} value) { + value = ${n.getByIndices("input_indices")}; + best_index = i32(last_index); + }`,"",o.setByOffset("global_idx","best_index")]};e.compute(Hr("argMax",{hint:t.cacheKey,inputDependencies:["rank"]},[e.inputs[0]],r,[t.axis],7,t.keepDims),{inputs:[0]})},uo=e=>J(e)});var Um,lo,Nm,Vm,Wm,Rt,Lm,ks,Fr=U(()=>{"use strict";ee();ne();Vr();ie();Um=(e,t)=>{let r=e[0],n=e[1],o=e[2],i=e[3],a=e[4],u=e[5];if(a&&u)throw new Error("Attention cannot have both past and attention_bias");if(r.dims.length!==3)throw new Error('Input "input" must have 3 dimensions');let d=r.dims[0],c=r.dims[1],p=r.dims[2];if(o.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimensions');if(n.dims.length!==2)throw new Error('Input "weights" is expected to have 2 dimensions');if(n.dims[0]!==p)throw new Error("Input 1 dimension 0 should have same length as dimension 2 of input 0");if(o.dims[0]!==n.dims[1])throw new Error('Input "bias" dimension 0 should have same length as dimension 1 of input "weights"');let m=o.dims[0]/3,f=m,b=f;if(t.qkvHiddenSizes.length>0){if(t.qkvHiddenSizes.length!==3)throw new Error("qkv_hidden_sizes attribute should have 3 elements");for(let x of t.qkvHiddenSizes)if(x%t.numHeads!==0)throw new Error("qkv_hidden_sizes should be divisible by num_heads");m=t.qkvHiddenSizes[0],f=t.qkvHiddenSizes[1],b=t.qkvHiddenSizes[2]}let g=c;if(m!==f)throw new Error("qkv_hidden_sizes first element should be same as the second");if(o.dims[0]!==m+f+b)throw new Error('Input "bias" dimension 0 should have same length as sum of Q/K/V hidden sizes');let _=0;if(a){if(f!==b)throw new Error('Input "past" expect k_hidden_size == v_hidden_size');if(a.dims.length!==5)throw new Error('Input "past" must have 5 dimensions');if(a.dims[0]!==2)throw new Error('Input "past" first dimension must be 2');if(a.dims[1]!==d)throw new Error('Input "past" second dimension must be batch_size');if(a.dims[2]!==t.numHeads)throw new Error('Input "past" third dimension must be num_heads');if(a.dims[4]!==f/t.numHeads)throw new Error('Input "past" fifth dimension must be k_hidden_size / num_heads');t.pastPresentShareBuffer||(_=a.dims[3])}let S=g+_,$=-1,v=0;if(i)throw new Error("Mask not supported");if(a)throw new Error("past is not supported");if(u){if(u.dims.length!==4)throw new Error('Input "attention_bias" must have 4 dimensions');if(u.dims[0]!==d||u.dims[1]!==t.numHeads||u.dims[2]!==c||u.dims[3]!==S)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:d,sequenceLength:c,pastSequenceLength:_,kvSequenceLength:g,totalSequenceLength:S,maxSequenceLength:$,inputHiddenSize:p,hiddenSize:m,vHiddenSize:b,headSize:Math.floor(m/t.numHeads),vHeadSize:Math.floor(b/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:v,scale:t.scale,broadcastResPosBias:!1,passPastInKv:!1,qkvFormat:1}},lo=(e,t,r)=>t&&e?` + let total_sequence_length_input = u32(${t.getByOffset("0")}); + let present_sequence_length = max(total_sequence_length_input, uniforms.past_sequence_length); + let is_subsequent_prompt: bool = sequence_length > 1 && sequence_length != total_sequence_length_input; + let is_first_prompt: bool = is_subsequent_prompt == false && sequence_length == total_sequence_length_input; + total_sequence_length = u32(${e?.getByOffset("batchIdx")}) + 1; + var past_sequence_length: u32 = 0; + if (is_first_prompt == false) { + past_sequence_length = total_sequence_length - sequence_length; + } + `:` + ${r?"let past_sequence_length = uniforms.past_sequence_length":""}; + let present_sequence_length = total_sequence_length; + `,Nm=(e,t,r,n,o,i,a,u)=>{let d=ce(a?1:i),c=64,p=i/d;p{let v=M("x",e.dataType,e.dims,d),x=[v],T=a?P("seq_lens",a.dataType,a.dims):void 0;T&&x.push(T);let E=u?P("total_sequence_length_input",u.dataType,u.dims):void 0;E&&x.push(E);let I=Ae(e.dataType),z=[{name:"batch_size",type:"u32"},{name:"num_heads",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"sequence_length",type:"u32"},{name:"total_sequence_length",type:"u32"},{name:"elements_per_thread",type:"u32"}];return` + var thread_max: array; + var thread_sum: array; + ${$.registerUniforms(z).declareVariables(...x)} + ${$.mainStart([c,1,1])} + let batchIdx = workgroup_id.z / uniforms.num_heads; + let headIdx = workgroup_id.z % uniforms.num_heads; + let sequence_length = uniforms.sequence_length; + var total_sequence_length = uniforms.total_sequence_length; + ${lo(T,E,!1)} + let local_offset = local_idx * uniforms.elements_per_thread; + let offset = (global_idx / ${c}) * uniforms.total_sequence_length + local_offset; + let seq_causal_length = ${a?"u32(past_sequence_length + workgroup_id.y + 1)":"total_sequence_length"}; + var thread_max_vector = ${g}(-3.402823e+38f); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + thread_max_vector = max(${g}(x[offset + i]), thread_max_vector); + } + thread_max[local_idx] = ${(()=>{switch(d){case 1:return"thread_max_vector";case 2:return"max(thread_max_vector.x, thread_max_vector.y)";case 4:return"max(max(thread_max_vector.x, thread_max_vector.y), max(thread_max_vector.z, thread_max_vector.w))";default:throw new Error(`Unsupported components: ${d}`)}})()}; + workgroupBarrier(); + + var max_value = f32(-3.402823e+38f); + for (var i = 0u; i < ${c}; i++) { + max_value = max(thread_max[i], max_value); + } + + var sum_vector = ${g}(0); + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + sum_vector += exp(${g}(x[offset + i]) - max_value); + } + thread_sum[local_idx] = ${(()=>{switch(d){case 1:return"sum_vector";case 2:return"sum_vector.x + sum_vector.y";case 4:return"sum_vector.x + sum_vector.y + sum_vector.z + sum_vector.w";default:throw new Error(`Unsupported components: ${d}`)}})()}; + workgroupBarrier(); + + var sum: f32 = 0; + for (var i = 0u; i < ${c}; i++) { + sum += thread_sum[i]; + } + + if (sum == 0) { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + x[offset + i] = ${v.type.value}(${I}(1.0) / ${I}(seq_causal_length)); + } + } else { + for (var i: u32 = 0; i < uniforms.elements_per_thread && i + local_offset < seq_causal_length; i++) { + var f32input = ${g}(x[offset + i]); + x[offset + i] = ${v.type.value}(exp(f32input - max_value) / sum); + } + } + ${a?` + for (var total_seq_id: u32 = seq_causal_length; total_seq_id + local_offset < uniforms.total_sequence_length; total_seq_id++) { + x[offset + total_seq_id] = ${v.type.value}(${I}(0)); + }`:""}; + }`};return{name:"AttentionProbsSoftmax",shaderCache:{hint:`${c};${b};${d}`,inputDependencies:_},getShaderSource:S,getRunData:()=>({outputs:[],dispatchGroup:{x:1,y:o,z:t*r},programUniforms:f})}},Vm=(e,t,r,n,o,i,a,u,d)=>{let c=a+i.kvSequenceLength,p=[i.batchSize,i.numHeads,i.sequenceLength,c],m=e>1&&n,f=i.kvNumHeads?i.kvNumHeads:i.numHeads,b=m?[i.batchSize,f,c,i.headSize]:void 0,g=i.nReps?i.nReps:1,_=i.scale===0?1/Math.sqrt(i.headSize):i.scale,S=ce(i.headSize),$=i.headSize/S,v=12,x={x:Math.ceil(c/v),y:Math.ceil(i.sequenceLength/v),z:i.batchSize*i.numHeads},T=[{type:12,data:i.sequenceLength},{type:12,data:$},{type:12,data:c},{type:12,data:i.numHeads},{type:12,data:i.headSize},{type:1,data:_},{type:12,data:a},{type:12,data:i.kvSequenceLength},{type:12,data:g}],E=m&&n&&k.size(n.dims)>0,I=["type","type"];E&&I.push("type"),o&&I.push("type"),u&&I.push("type"),d&&I.push("type");let z=[{dims:p,dataType:t.dataType,gpuDataType:0}];m&&z.push({dims:b,dataType:t.dataType,gpuDataType:0});let O=D=>{let L=P("q",t.dataType,t.dims,S),q=P("key",r.dataType,r.dims,S),Q=[L,q];if(E){let X=P("past_key",n.dataType,n.dims,S);Q.push(X)}o&&Q.push(P("attention_bias",o.dataType,o.dims));let W=u?P("seq_lens",u.dataType,u.dims):void 0;W&&Q.push(W);let Z=d?P("total_sequence_length_input",d.dataType,d.dims):void 0;Z&&Q.push(Z);let we=M("output",t.dataType,p),H=[we];m&&H.push(M("present_key",t.dataType,b,S));let j=Ae(1,S),te=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"alpha",type:"f32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return` + const TILE_SIZE = ${v}u; + + var tileQ: array<${L.type.storage}, ${v*v}>; + var tileK: array<${L.type.storage}, ${v*v}>; + ${D.registerUniforms(te).declareVariables(...Q,...H)} + ${D.mainStart([v,v,1])} + // x holds the N and y holds the M + let headIdx = workgroup_id.z % uniforms.num_heads; + let kvHeadIdx = ${g===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${g===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let m = workgroup_id.y * TILE_SIZE; + let n = workgroup_id.x * TILE_SIZE; + let sequence_length = uniforms.M; + var total_sequence_length = uniforms.N; + ${lo(W,Z,!0)} + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; + let qOffset = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + ${E&&m?"let pastKeyOffset = absKvHeadIdx * uniforms.past_sequence_length * uniforms.K;":""}; + let kOffset = absKvHeadIdx * uniforms.kv_sequence_length * uniforms.K; + ${m?"let presentKeyOffset = absKvHeadIdx * uniforms.N * uniforms.K;":""} + var value = ${j}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (global_id.y < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = q[qOffset + local_id.y * uniforms.K + w + local_id.x]; + } + if (n + local_id.y < uniforms.N && w + local_id.x < uniforms.K) { + var idx = TILE_SIZE * local_id.y + local_id.x; + ${E&&m?` + if (n + local_id.y < past_sequence_length) { + tileK[idx] = past_key[pastKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + } else if (n + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { + tileK[idx] = key[kOffset + (n + local_id.y - past_sequence_length) * uniforms.K + w + local_id.x]; + }`:` + if (n + local_id.y < uniforms.kv_sequence_length) { + tileK[idx] = key[kOffset + (n + local_id.y) * uniforms.K + w + local_id.x]; + }`} + ${m?`if (n + local_id.y < present_sequence_length) { + present_key[presentKeyOffset + (n + local_id.y) * uniforms.K + w + local_id.x] = tileK[idx]; + }`:""} + } + workgroupBarrier(); + + for (var k: u32 = 0u; k < TILE_SIZE && w+k < uniforms.K; k++) { + value += ${j}(tileQ[TILE_SIZE * local_id.y + k] * tileK[TILE_SIZE * local_id.x + k]); + } + + workgroupBarrier(); + } + + if (global_id.y < uniforms.M && global_id.x < total_sequence_length) { + let headOffset = workgroup_id.z * uniforms.M * uniforms.N; + let outputIdx = headOffset + global_id.y * uniforms.N + global_id.x; + var sum: f32 = ${(()=>{switch(S){case 1:return"value";case 2:return"value.x + value.y";case 4:return"value.x + value.y + value.z + value.w";default:throw new Error(`Unsupported components: ${S}`)}})()}; + output[outputIdx] = ${we.type.value} (sum * uniforms.alpha) + ${o?"attention_bias[outputIdx]":"0.0"}; + } + }`};return{name:"AttentionProbs",shaderCache:{hint:`${S};${o!==void 0};${n!==void 0};${e}`,inputDependencies:I},getRunData:()=>({outputs:z,dispatchGroup:x,programUniforms:T}),getShaderSource:O}},Wm=(e,t,r,n,o,i,a=void 0,u=void 0)=>{let d=i+o.kvSequenceLength,c=o.nReps?o.nReps:1,p=o.vHiddenSize*c,m=e>1&&n,f=o.kvNumHeads?o.kvNumHeads:o.numHeads,b=m?[o.batchSize,f,d,o.headSize]:void 0,g=[o.batchSize,o.sequenceLength,p],_=12,S={x:Math.ceil(o.vHeadSize/_),y:Math.ceil(o.sequenceLength/_),z:o.batchSize*o.numHeads},$=[{type:12,data:o.sequenceLength},{type:12,data:d},{type:12,data:o.vHeadSize},{type:12,data:o.numHeads},{type:12,data:o.headSize},{type:12,data:p},{type:12,data:i},{type:12,data:o.kvSequenceLength},{type:12,data:c}],v=m&&n&&k.size(n.dims)>0,x=["type","type"];v&&x.push("type"),a&&x.push("type"),u&&x.push("type");let T=[{dims:g,dataType:t.dataType,gpuDataType:0}];m&&T.push({dims:b,dataType:t.dataType,gpuDataType:0});let E=I=>{let z=P("probs",t.dataType,t.dims),O=P("v",r.dataType,r.dims),D=[z,O];v&&D.push(P("past_value",n.dataType,n.dims));let L=a?P("seq_lens",a.dataType,a.dims):void 0;a&&D.push(L);let q=u?P("total_sequence_length_input",u.dataType,u.dims):void 0;u&&D.push(q);let W=[M("output",t.dataType,g)];m&&W.push(M("present_value",t.dataType,b));let Z=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"v_hidden_size",type:"u32"},{name:"past_sequence_length",type:"u32"},{name:"kv_sequence_length",type:"u32"},{name:"n_reps",type:"u32"}];return` + const TILE_SIZE = ${_}u; + var tileQ: array<${z.type.value}, ${_*_}>; + var tileV: array<${z.type.value}, ${_*_}>; + ${I.registerUniforms(Z).declareVariables(...D,...W)} + ${I.mainStart([_,_,1])} + let headIdx = workgroup_id.z % uniforms.num_heads; + let batchIdx = workgroup_id.z / uniforms.num_heads; + let kvHeadIdx = ${c===1?"headIdx":"headIdx / uniforms.n_reps"}; + let kv_num_heads = ${c===1?"uniforms.num_heads":"uniforms.num_heads / uniforms.n_reps"}; + let m = global_id.y; + let n = global_id.x; + let sequence_length = uniforms.M; + var total_sequence_length = uniforms.K; + ${lo(L,q,!0)} + let offsetA = workgroup_id.z * uniforms.M * uniforms.K + m * uniforms.K; + let absKvHeadIdx = batchIdx * kv_num_heads + kvHeadIdx; // kvHeadIdx is relative to the batch + ${v&&m?"let pastValueOffset = absKvHeadIdx * uniforms.N * uniforms.past_sequence_length + n;":""}; + let vOffset = absKvHeadIdx * uniforms.N * uniforms.kv_sequence_length + n; + ${m?"let presentValueOffset = absKvHeadIdx * uniforms.N * uniforms.K + n;":""} + var value = ${z.type.storage}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileQ[TILE_SIZE * local_id.y + local_id.x] = probs[offsetA + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + var idx = TILE_SIZE * local_id.y + local_id.x; + ${v&&m?` + if (w + local_id.y < past_sequence_length) { + tileV[idx] = past_value[pastValueOffset + (w + local_id.y) * uniforms.N]; + } else if (w + local_id.y - past_sequence_length < uniforms.kv_sequence_length) { + tileV[idx] = v[vOffset + (w + local_id.y - past_sequence_length) * uniforms.N]; + } + `:` + if (w + local_id.y < uniforms.kv_sequence_length) { + tileV[idx] = v[vOffset + (w + local_id.y) * uniforms.N]; + }`} + ${m?` + if (w + local_id.y < present_sequence_length) { + present_value[presentValueOffset + (w + local_id.y) * uniforms.N] = tileV[idx]; + }`:""} + } + workgroupBarrier(); + for (var k: u32 = 0u; k < TILE_SIZE && w+k < total_sequence_length; k++) { + value += tileQ[TILE_SIZE * local_id.y + k] * tileV[TILE_SIZE * k + local_id.x]; + } + workgroupBarrier(); + } + + // we need to transpose output from BNSH_v to BSND_v + if (m < uniforms.M && n < uniforms.N) { + let outputIdx = batchIdx * uniforms.M * uniforms.v_hidden_size + m * uniforms.v_hidden_size + + headIdx * uniforms.N + n; + output[outputIdx] = value; + } + }`};return{name:"AttentionScore",shaderCache:{hint:`${n!==void 0};${e}`,inputDependencies:x},getRunData:()=>({outputs:T,dispatchGroup:S,programUniforms:$}),getShaderSource:E}},Rt=(e,t,r,n,o,i,a,u,d,c,p=void 0,m=void 0)=>{let f=Math.min(e.outputCount,1+(a?1:0)+(u?1:0)),b=f>1?c.pastSequenceLength:0,g=b+c.kvSequenceLength,_=d&&k.size(d.dims)>0?d:void 0,S=[t,r];f>1&&a&&k.size(a.dims)>0&&S.push(a),_&&S.push(_),p&&S.push(p),m&&S.push(m);let $=e.compute(Vm(f,t,r,a,_,c,b,p,m),{inputs:S,outputs:f>1?[-1,1]:[-1]})[0];e.compute(Nm($,c.batchSize,c.numHeads,b,c.sequenceLength,g,p,m),{inputs:p&&m?[$,p,m]:[$],outputs:[]});let v=[$,n];f>1&&u&&k.size(u.dims)>0&&v.push(u),p&&v.push(p),m&&v.push(m),e.compute(Wm(f,$,n,u,c,b,p,m),{inputs:v,outputs:f>1?[0,2]:[0]})},Lm=(e,t)=>{let r=[t.batchSize,t.numHeads,t.sequenceLength,t.headSize],n=t.sequenceLength,o=t.inputHiddenSize,i=t.headSize,a=12,u={x:Math.ceil(t.headSize/a),y:Math.ceil(t.sequenceLength/a),z:t.batchSize*t.numHeads},d=[e.inputs[0],e.inputs[1],e.inputs[2]],c=[{type:12,data:n},{type:12,data:o},{type:12,data:i},{type:12,data:t.numHeads},{type:12,data:t.headSize},{type:12,data:t.hiddenSize},{type:12,data:t.hiddenSize+t.hiddenSize+t.vHiddenSize}],p=m=>{let f=M("output_q",d[0].dataType,r),b=M("output_k",d[0].dataType,r),g=M("output_v",d[0].dataType,r),_=P("input",d[0].dataType,d[0].dims),S=P("weight",d[1].dataType,d[1].dims),$=P("bias",d[2].dataType,d[2].dims),v=_.type.storage,x=[{name:"M",type:"u32"},{name:"K",type:"u32"},{name:"N",type:"u32"},{name:"num_heads",type:"u32"},{name:"head_size",type:"u32"},{name:"hidden_size",type:"u32"},{name:"ldb",type:"u32"}];return` + const TILE_SIZE = ${a}u; + var tileInput: array<${v}, ${a*a}>; + var tileWeightQ: array<${v}, ${a*a}>; + var tileWeightK: array<${v}, ${a*a}>; + var tileWeightV: array<${v}, ${a*a}>; + ${m.registerUniforms(x).declareVariables(_,S,$,f,b,g)} + ${m.mainStart([a,a,1])} + let batchIndex = workgroup_id.z / uniforms.num_heads; + let headNumber = workgroup_id.z % uniforms.num_heads; + let m = global_id.y; + let n = global_id.x; + + let inputOffset = batchIndex * (uniforms.M * uniforms.K) + m * uniforms.K; + let biasOffsetQ = headNumber * uniforms.head_size; + let biasOffsetK = uniforms.hidden_size + biasOffsetQ; + let biasOffsetV = uniforms.hidden_size + biasOffsetK; + + var valueQ = ${v}(0); + var valueK = ${v}(0); + var valueV = ${v}(0); + for (var w: u32 = 0u; w < uniforms.K; w += TILE_SIZE) { + if (m < uniforms.M && w + local_id.x < uniforms.K) { + tileInput[TILE_SIZE * local_id.y + local_id.x] = input[inputOffset + w + local_id.x]; + } + if (n < uniforms.N && w + local_id.y < uniforms.K) { + let offset = n + (w + local_id.y) * uniforms.ldb; + tileWeightQ[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetQ + offset]; + tileWeightK[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetK + offset]; + tileWeightV[TILE_SIZE * local_id.y + local_id.x] = weight[biasOffsetV + offset]; + } + workgroupBarrier(); + for (var k: u32 = 0u; k({outputs:[{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0},{dims:r,dataType:e.inputs[0].dataType,gpuDataType:0}],dispatchGroup:u,programUniforms:c}),getShaderSource:p},{inputs:d,outputs:[-1,-1,-1]})},ks=(e,t)=>{let r=Um(e.inputs,t),[n,o,i]=Lm(e,r);return Rt(e,n,o,i,e.inputs[4],void 0,void 0,void 0,e.inputs[5],r)}});var Gm,Hm,Fm,Ps,zs=U(()=>{"use strict";We();ee();ne();Se();ie();Gm=(e,t)=>{if(!e||e.length!==5)throw new Error("BatchNormalization requires 5 inputs");let r=(n,o,i)=>{let a=o.length;if(a!==n.length)throw new Error(`${i}: num dimensions != ${a}`);o.forEach((u,d)=>{if(u!==n[d])throw new Error(`${i}: dim[${d}] do not match`)})};if(e[0].dims.length>1){let n=t.format==="NHWC"?t.spatial?e[0].dims.slice(-1):e[0].dims.slice(-1).concat(e[0].dims.slice(1,e[0].dims.length-1)):e[0].dims.slice(1,t.spatial?2:void 0);r(e[1].dims,n,"Invalid input scale"),r(e[2].dims,n,"Invalid input B"),r(e[3].dims,n,"Invalid input 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${x.registerUniform("outputSize","u32").declareVariables(m,f,b,g,_,S)} + ${x.mainStart()} + ${x.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${S.offsetToIndices(`global_idx * ${a}`)}; + ${$()} + let scale = ${f.getByOffset("cOffset")}; + let bias = ${b.getByOffset("cOffset")}; + let inputMean = ${g.getByOffset("cOffset")}; + let inputVar = ${_.getByOffset("cOffset")}; + let x = ${m.getByOffset("global_idx")}; + let value = (x - inputMean) * inverseSqrt(inputVar + epsilon) * scale + bias; + ${S.setByOffset("global_idx","value")} + }`;return{name:"BatchNormalization",shaderCache:{hint:`${t.epsilon}_${t.format}_${n}_${a}`,inputDependencies:c?["rank","type","type","type","type"]:void 0},getShaderSource:v,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c?[{type:12,data:d},...N(i)]:[{type:12,data:d}]})}},Fm=e=>J(e),Ps=(e,t)=>{let{inputs:r,outputCount:n}=e,o=Fm({...t,outputCount:n});if(ge.webgpu.validateInputContent&&Gm(r,o),t.trainingMode)throw new Error("BatchNormalization trainingMode is not supported yet.");e.compute(Hm(r,o))}});var qm,jm,Os,Bs=U(()=>{"use strict";ne();ie();qm=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![320,640,1280].includes(e[0].dims[2]))throw new Error("number of channels should be 320, 640 or 1280");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},jm=e=>{let t=e[0].dims,r=e[0].dims[2],n=k.size(t)/4,o=e[0].dataType,i=P("input",o,t,4),a=P("bias",o,[r],4),u=P("residual",o,t,4),d=M("output",o,t,4);return{name:"BiasAdd",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(n/64)}}),getShaderSource:p=>` + const channels = ${r}u / 4; + ${p.declareVariables(i,a,u,d)} + + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes(n)} + let value = ${i.getByOffset("global_idx")} + + ${a.getByOffset("global_idx % channels")} + ${u.getByOffset("global_idx")}; + ${d.setByOffset("global_idx","value")} + }`}},Os=e=>{qm(e.inputs),e.compute(jm(e.inputs))}});var Km,me,Ds,Ms,Rs,Us,Ns,Vs,Ws,Ls,Gs,Zm,Hs,Fs,qs,js,Yt,Ks,qr,Zs,Qs,Ys,Xs,Js,eu,tu,ru,nu,ou,iu,au,su,uu,du,lu,cu,pu,co,po,mu,fu,hu,Qm,Ym,gu,jr=U(()=>{"use strict";ee();ne();Se();ie();Km=(e,t,r,n,o,i,a)=>{let u=Math.ceil(t/4),d="";typeof o=="string"?d=`${o}(a)`:d=o("a");let c=P("inputData",r,[u],4),p=M("outputData",n,[u],4),m=[{name:"vec_size",type:"u32"}];return a&&m.push(...a),` + ${e.registerUniforms(m).declareVariables(c,p)} + + ${i??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + + let a = ${c.getByOffset("global_idx")}; + ${p.setByOffset("global_idx",d)} + }`},me=(e,t,r,n,o,i=e.dataType,a,u)=>{let d=[{type:12,data:Math.ceil(k.size(e.dims)/4)}];return a&&d.push(...a),{name:t,shaderCache:{hint:o,inputDependencies:["type"]},getShaderSource:c=>Km(c,k.size(e.dims),e.dataType,i,r,n,u),getRunData:c=>({outputs:[{dims:e.dims,dataType:i}],dispatchGroup:{x:Math.ceil(k.size(c[0].dims)/64/4)},programUniforms:d})}},Ds=e=>{e.compute(me(e.inputs[0],"Abs","abs"))},Ms=e=>{e.compute(me(e.inputs[0],"Acos","acos"))},Rs=e=>{e.compute(me(e.inputs[0],"Acosh","acosh"))},Us=e=>{e.compute(me(e.inputs[0],"Asin","asin"))},Ns=e=>{e.compute(me(e.inputs[0],"Asinh","asinh"))},Vs=e=>{e.compute(me(e.inputs[0],"Atan","atan"))},Ws=e=>{e.compute(me(e.inputs[0],"Atanh","atanh"))},Ls=e=>J(e),Gs=(e,t)=>{let r;switch(t.to){case 10:r="vec4";break;case 1:r="vec4";break;case 12:r="vec4";break;case 6:r="vec4";break;case 9:r="vec4";break;default:throw new RangeError(`not supported type (specified in attribute 'to' from 'Cast' operator): ${t.to}`)}e.compute(me(e.inputs[0],"Cast",r,void 0,t.cacheKey,t.to))},Zm=e=>{let t,r,n=e.length>=2&&e[1].data!==0,o=e.length>=3&&e[2].data!==0;switch(e[0].dataType){case 1:t=n?e[1].getFloat32Array()[0]:-34028234663852886e22,r=o?e[2].getFloat32Array()[0]:34028234663852886e22;break;case 10:t=n?e[1].getUint16Array()[0]:64511,r=o?e[2].getUint16Array()[0]:31743;break;default:throw new Error("Unsupport data type")}return J({min:t,max:r})},Hs=(e,t)=>{let r=t||Zm(e.inputs),n=Ae(e.inputs[0].dataType);e.compute(me(e.inputs[0],"Clip",o=>`clamp(${o}, vec4<${n}>(uniforms.min), vec4<${n}>(uniforms.max))`,void 0,r.cacheKey,void 0,[{type:e.inputs[0].dataType,data:r.min},{type:e.inputs[0].dataType,data:r.max}],[{name:"min",type:n},{name:"max",type:n}]),{inputs:[0]})},Fs=e=>{e.compute(me(e.inputs[0],"Ceil","ceil"))},qs=e=>{e.compute(me(e.inputs[0],"Cos","cos"))},js=e=>{e.compute(me(e.inputs[0],"Cosh","cosh"))},Yt=e=>J(e),Ks=(e,t)=>{let r=Ae(e.inputs[0].dataType);e.compute(me(e.inputs[0],"Elu",n=>`elu_vf32(${n})`,` + const elu_alpha_ = ${r}(${t.alpha}); + + fn elu_f32(a: ${r}) -> ${r} { + return select((exp(a) - 1.0) * elu_alpha_, a, a >= 0.0); + } + + fn elu_vf32(v: vec4<${r}>) -> vec4<${r}> { + return vec4(elu_f32(v.x), elu_f32(v.y), elu_f32(v.z), elu_f32(v.w)); + }`,t.cacheKey))},qr=(e="f32")=>` +const r0: ${e} = 0.3275911; +const r1: ${e} = 0.254829592; +const r2: ${e} = -0.284496736; +const r3: ${e} = 1.421413741; +const r4: ${e} = -1.453152027; +const r5: ${e} = 1.061405429; + +fn erf_vf32(v: vec4<${e}>) -> vec4<${e}> { + let absv = abs(v); + let x = 1.0 / (1.0 + r0 * absv); + return sign(v) * (1.0 - ((((r5 * x + r4) * x + r3) * x + r2) * x + r1) * x * exp(-absv * absv)); +}`,Zs=e=>{let t=Ae(e.inputs[0].dataType);e.compute(me(e.inputs[0],"Erf",r=>`erf_vf32(${r})`,qr(t)))},Qs=e=>{e.compute(me(e.inputs[0],"Exp","exp"))},Ys=e=>{e.compute(me(e.inputs[0],"Floor","floor"))},Xs=e=>{let t=Ae(e.inputs[0].dataType);e.compute(me(e.inputs[0],"Gelu",r=>`0.5 * ${r} * (1.0 + erf_vf32(${r} * 0.7071067811865475))`,qr(t)))},Js=(e,t)=>{let r=Ae(e.inputs[0].dataType);e.compute(me(e.inputs[0],"LeakyRelu",n=>`select(leaky_relu_alpha_ * ${n}, ${n}, ${n} >= vec4<${r}>(0.0))`,`const leaky_relu_alpha_ = ${r}(${t.alpha});`,t.cacheKey))},eu=e=>{e.compute(me(e.inputs[0],"Not",t=>`!${t}`))},tu=e=>{e.compute(me(e.inputs[0],"Neg",t=>`-${t}`))},ru=e=>{e.compute(me(e.inputs[0],"Reciprocal",t=>`1.0/${t}`))},nu=e=>{let t=Ae(e.inputs[0].dataType);e.compute(me(e.inputs[0],"Relu",r=>`select(vec4<${t}>(0.0), ${r}, ${r} > vec4<${t}>(0.0))`))},ou=e=>{e.compute(me(e.inputs[0],"Sigmoid",t=>`(1.0 / (1.0 + exp(-${t})))`))},iu=e=>J(e),au=(e,t)=>{let r=Ae(e.inputs[0].dataType);e.compute(me(e.inputs[0],"HardSigmoid",n=>`max(vec4<${r}>(0.0), min(vec4<${r}>(1.0), ${t.alpha} * ${n} + vec4<${r}>(${t.beta})))`,void 0,t.cacheKey))},su=e=>{e.compute(me(e.inputs[0],"Sin","sin"))},uu=e=>{e.compute(me(e.inputs[0],"Sinh","sinh"))},du=e=>{e.compute(me(e.inputs[0],"Sqrt","sqrt"))},lu=e=>{e.compute(me(e.inputs[0],"Tan","tan"))},cu=e=>`sign(${e}) * (1 - exp(-2 * abs(${e}))) / (1 + exp(-2 * abs(${e})))`,pu=e=>{e.compute(me(e.inputs[0],"Tanh",cu))},co=(e="f32")=>` +const fast_gelu_a: ${e} = 0.5; +const fast_gelu_b: ${e} = 0.7978845608028654; +const fast_gelu_c: ${e} = 0.035677408136300125; + +fn tanh_v(v: vec4<${e}>) -> vec4<${e}> { + return ${cu("v")}; +} +`,po=e=>`(fast_gelu_a + fast_gelu_a * tanh_v(${e} * (fast_gelu_c * ${e} * ${e} + fast_gelu_b))) * ${e}`,mu=e=>{let t=Ae(e.inputs[0].dataType);e.compute(me(e.inputs[0],"FastGelu",po,co(t),void 0,e.inputs[0].dataType))},fu=(e,t)=>{let r=Ae(e.inputs[0].dataType);return e.compute(me(e.inputs[0],"ThresholdedRelu",n=>`select(vec4<${r}>(0.0), ${n}, ${n} > thresholded_relu_alpha_)`,`const thresholded_relu_alpha_ = vec4<${r}>(${t.alpha});`,t.cacheKey)),0},hu=e=>{e.compute(me(e.inputs[0],"Log","log"))},Qm=(e,t)=>` +const alpha = vec4<${e}>(${t}); +const one = ${e}(1.0); +const zero = ${e}(0.0); + +fn quick_gelu_impl(x: vec4<${e}>) -> vec4<${e}> { + let v = x *alpha; + var x1 : vec4<${e}>; + for (var i = 0; i < 4; i = i + 1) { + if (v[i] >= zero) { + x1[i] = one / (one + exp(-v[i])); + } else { + x1[i] = one - one / (one + exp(v[i])); + } + } + return x * x1; +} +`,Ym=e=>`quick_gelu_impl(${e})`,gu=(e,t)=>{let r=Ae(e.inputs[0].dataType);e.compute(me(e.inputs[0],"QuickGelu",Ym,Qm(r,t.alpha),t.cacheKey,e.inputs[0].dataType))}});var Xm,Jm,yu,_u=U(()=>{"use strict";ne();ie();jr();Xm=e=>{if(e[0].dims.length!==3)throw new Error("input should have 3 dimensions");if(![2560,5120,10240].includes(e[0].dims[2]))throw new Error("hidden state should be 2560, 5120 or 10240");if(e[1].dims.length!==1)throw new Error("bias is expected to have 1 dimensions");if(e[0].dims[2]!==e[1].dims[0])throw new Error("last dimension of input and bias are not the same")},Jm=e=>{let t=e[0].dims.slice();t[2]=t[2]/2;let r=P("input",e[0].dataType,e[0].dims,4),n=P("bias",e[0].dataType,[e[0].dims[2]],4),o=M("output",e[0].dataType,t,4),i=k.size(t)/4,a=be(e[0].dataType);return{name:"BiasSplitGelu",getRunData:()=>({outputs:[{dims:t,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)}}),getShaderSource:d=>` + const M_SQRT2 = sqrt(2.0); + const halfChannels = ${e[0].dims[2]/4/2}u; + + ${d.declareVariables(r,n,o)} + + ${qr(a)} + + ${d.mainStart()} + ${d.guardAgainstOutOfBoundsWorkgroupSizes(i)} + let biasIdx = global_idx % halfChannels; + let batchIndex = global_idx / halfChannels; + let inputOffset = biasIdx + batchIndex * halfChannels * 2; + let valueLeft = input[inputOffset] + bias[biasIdx]; + let valueRight = input[inputOffset + halfChannels] + bias[biasIdx + halfChannels]; + let geluRight = valueRight * 0.5 * (erf_vf32(valueRight / M_SQRT2) + 1); + + ${o.setByOffset("global_idx","valueLeft * geluRight")} + }`}},yu=e=>{Xm(e.inputs),e.compute(Jm(e.inputs))}});var ef,tf,ot,wu,vu,$u,xu,Su,Tu,Iu,Cu,Au,Eu,ku=U(()=>{"use strict";ee();ne();ie();ef=(e,t,r,n,o,i,a,u,d,c,p,m)=>{let f,b;typeof u=="string"?f=b=(v,x)=>`${u}((${v}),(${x}))`:typeof u=="function"?f=b=u:(f=u.scalar,b=u.vector);let g=M("outputData",p,n.length,4),_=P("aData",d,t.length,4),S=P("bData",c,r.length,4),$;if(o)if(i){let v=k.size(t)===1,x=k.size(r)===1,T=t.length>0&&t[t.length-1]%4===0,E=r.length>0&&r[r.length-1]%4===0;v||x?$=g.setByOffset("global_idx",b(v?`${_.type.value}(${_.getByOffset("0")}.x)`:_.getByOffset("global_idx"),x?`${S.type.value}(${S.getByOffset("0")}.x)`:S.getByOffset("global_idx"))):$=` + let outputIndices = ${g.offsetToIndices("global_idx * 4u")}; + let offsetA = ${_.broadcastedIndicesToOffset("outputIndices",g)}; + let offsetB = ${S.broadcastedIndicesToOffset("outputIndices",g)}; + ${g.setByOffset("global_idx",b(a||T?_.getByOffset("offsetA / 4u"):`${_.type.value}(${_.getByOffset("offsetA / 4u")}[offsetA % 4u])`,a||E?S.getByOffset("offsetB / 4u"):`${S.type.value}(${S.getByOffset("offsetB / 4u")}[offsetB % 4u])`))} + `}else $=g.setByOffset("global_idx",b(_.getByOffset("global_idx"),S.getByOffset("global_idx")));else{if(!i)throw new Error("no necessary to use scalar implementation for element-wise binary op implementation.");let v=(x,T,E="")=>{let I=`aData[indexA${T}][componentA${T}]`,z=`bData[indexB${T}][componentB${T}]`;return` + let outputIndices${T} = ${g.offsetToIndices(`global_idx * 4u + ${T}u`)}; + let offsetA${T} = ${_.broadcastedIndicesToOffset(`outputIndices${T}`,g)}; + let offsetB${T} = ${S.broadcastedIndicesToOffset(`outputIndices${T}`,g)}; + let indexA${T} = offsetA${T} / 4u; + let indexB${T} = offsetB${T} / 4u; + let componentA${T} = offsetA${T} % 4u; + let componentB${T} = offsetB${T} % 4u; + ${x}[${T}] = ${E}(${f(I,z)}); + `};p===9?$=` + var data = vec4(0); + ${v("data",0,"u32")} + ${v("data",1,"u32")} + ${v("data",2,"u32")} + ${v("data",3,"u32")} + outputData[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:$=` + ${v("outputData[global_idx]",0)} + ${v("outputData[global_idx]",1)} + ${v("outputData[global_idx]",2)} + ${v("outputData[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(_,S,g)} + + ${m??""} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${$} + }`},tf=(e,t,r,n,o,i,a=r.dataType)=>{let u=r.dims.map(_=>Number(_)??1),d=n.dims.map(_=>Number(_)??1),c=!k.areEqual(u,d),p=u,m=k.size(u),f=!1,b=!1,g=[c];if(c){let _=Je.calcShape(u,d,!1);if(!_)throw new Error("Can't perform binary op on the given tensors");p=_.slice(),m=k.size(p);let S=k.size(u)===1,$=k.size(d)===1,v=u.length>0&&u[u.length-1]%4===0,x=d.length>0&&d[d.length-1]%4===0;g.push(S),g.push($),g.push(v),g.push(x);let T=1;for(let E=1;E_.toString()).join("_"),inputDependencies:["rank","rank"]},getShaderSource:_=>ef(_,u,d,p,f,c,b,o,r.dataType,n.dataType,a,i),getRunData:()=>({outputs:[{dims:p,dataType:a}],dispatchGroup:{x:Math.ceil(m/64/4)},programUniforms:[{type:12,data:Math.ceil(k.size(p)/4)},...N(u,d,p)]})}},ot=(e,t,r,n,o,i)=>{e.compute(tf(t,o??"",e.inputs[0],e.inputs[1],r,n,i))},wu=e=>{ot(e,"Add",(t,r)=>`${t}+${r}`)},vu=e=>{ot(e,"Div",(t,r)=>`${t}/${r}`)},$u=e=>{ot(e,"Equal",{scalar:(t,r)=>`u32(${t}==${r})`,vector:(t,r)=>`vec4(${t}==${r})`},void 0,void 0,9)},xu=e=>{ot(e,"Mul",(t,r)=>`${t}*${r}`)},Su=e=>{let t=P("input",e.inputs[0].dataType,e.inputs[0].dims).type.value;ot(e,"Pow",{scalar:(n,o)=>`pow_custom(${n},${o})`,vector:(n,o)=>`pow_vector_custom(${n},${o})`},` + fn pow_custom(a : ${t}, b : ${t}) -> ${t} { + if (b == ${t}(0.0)) { + return ${t}(1.0); + } else if (a < ${t}(0.0) && f32(b) != floor(f32(b))) { + return ${t}(pow(f32(a), f32(b))); // NaN + } + return select(sign(a), ${t}(1.0), round(f32(abs(b) % ${t}(2.0))) != 1.0) * ${t}(${t==="i32"?"round":""}(pow(f32(abs(a)), f32(b)))); + } + fn pow_vector_custom(a : vec4<${t}>, b : vec4<${t}>) -> vec4<${t}> { + // TODO: implement vectorized pow + return vec4<${t}>(pow_custom(a.x, b.x), pow_custom(a.y, b.y), pow_custom(a.z, b.z), pow_custom(a.w, b.w)); + } + `)},Tu=e=>{ot(e,"Sub",(t,r)=>`${t}-${r}`)},Iu=e=>{ot(e,"Greater",{scalar:(t,r)=>`u32(${t}>${r})`,vector:(t,r)=>`vec4(${t}>${r})`},void 0,void 0,9)},Cu=e=>{ot(e,"Less",{scalar:(t,r)=>`u32(${t}<${r})`,vector:(t,r)=>`vec4(${t}<${r})`},void 0,void 0,9)},Au=e=>{ot(e,"GreaterOrEqual",{scalar:(t,r)=>`u32(${t}>=${r})`,vector:(t,r)=>`vec4(${t}>=${r})`},void 0,void 0,9)},Eu=e=>{ot(e,"LessOrEqual",{scalar:(t,r)=>`u32(${t}<=${r})`,vector:(t,r)=>`vec4(${t}<=${r})`},void 0,void 0,9)}});var nf,of,af,sf,Pu,zu,Ou=U(()=>{"use strict";ee();ne();Se();ie();nf=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");let r=0,n=e[r],o=n.dataType,i=n.dims.length;e.forEach((a,u)=>{if(u!==r){if(a.dataType!==o)throw new Error("input tensors should be one type");if(a.dims.length!==i)throw new Error("input tensors should have the same shape");a.dims.forEach((d,c)=>{if(c!==t&&d!==n.dims[c])throw new Error("non concat dimensions must match")})}})},of=(e,t)=>` + fn calculateInputIndex(index: u32) -> u32 { + let sizeInConcatAxis = array(${t}); + for (var i: u32 = 0u; i < ${e}; i += 1u ) { + if (index < sizeInConcatAxis[i]) { + return i; + } + } + return ${e}u; + }`,af=(e,t)=>{let r=e.length,n=[];for(let o=0;o{let o=k.size(r),i=new Array(e.length),a=new Array(e.length),u=0,d=[],c=[],p=[{type:12,data:o}];for(let _=0;_`uniforms.sizeInConcatAxis${_}`).join(","),g=_=>` + + ${(()=>{_.registerUniform("outputSize","u32");for(let S=0;S(${b}); + ${f} -= sizeInConcatAxis[inputIndex - 1u]; + } + + ${af(a,m)} + }`;return{name:"Concat",shaderCache:{hint:`${t}`,inputDependencies:d},getRunData:()=>({outputs:[{dims:r,dataType:n}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:p}),getShaderSource:g}},Pu=(e,t)=>{let r=e.inputs,n=r[0].dims,o=k.normalizeAxis(t.axis,n.length);nf(r,o);let i=n.slice();i[o]=r.reduce((u,d)=>u+(d.dims.length>o?d.dims[o]:0),0);let a=r.filter(u=>k.size(u.dims)>0);e.compute(sf(a,o,i,r[0].dataType),{inputs:a})},zu=e=>J({axis:e.axis})});var Fe,qe,je,Kr,bt=U(()=>{"use strict";ee();ne();Fe=(e,t,r="f32")=>{switch(e.activation){case"Relu":return`value = max(value, ${t}(0.0));`;case"Sigmoid":return`value = (${t}(1.0) / (${t}(1.0) + exp(-value)));`;case"Clip":return`value = clamp(value, ${t}(${r}(uniforms.clip_min)), ${t}(${r}(uniforms.clip_max)));`;case"HardSigmoid":return`value = max(${t}(0.0), min(${t}(1.0), ${r}(uniforms.alpha) * value + ${r}(uniforms.beta)));`;case"LeakyRelu":return`value = select(${r}(uniforms.alpha) * value, value, value >= ${t}(0.0));`;case"Tanh":return`let e2x = exp(-2.0 * abs(value)); + value = sign(value) * (1.0 - e2x) / (1.0 + e2x); + `;case"":return"";default:throw new Error(`Unsupported activation ${e.activation}`)}},qe=(e,t)=>{e.activation==="Clip"?t.push({type:1,data:e.clipMax},{type:1,data:e.clipMin}):e.activation==="HardSigmoid"?t.push({type:1,data:e.alpha},{type:1,data:e.beta}):e.activation==="LeakyRelu"&&t.push({type:1,data:e.alpha})},je=(e,t)=>{e.activation==="Clip"?t.push({name:"clip_max",type:"f32"},{name:"clip_min",type:"f32"}):e.activation==="HardSigmoid"?t.push({name:"alpha",type:"f32"},{name:"beta",type:"f32"}):e.activation==="LeakyRelu"&&t.push({name:"alpha",type:"f32"})},Kr=e=>{let t=e?.activation||"";if(t==="HardSigmoid"){let[r,n]=e?.activation_params||[.2,.5];return{activation:t,alpha:r,beta:n}}else if(t==="Clip"){let[r,n]=e?.activation_params||[Wa,La];return{activation:t,clipMax:n,clipMin:r}}else if(t==="LeakyRelu"){let[r]=e?.activation_params||[.01];return{activation:t,alpha:r}}return{activation:t}}});var Ie,Bu,Zr=U(()=>{"use strict";Ie=(e,t)=>{switch(e){case 1:return t;case 2:return`vec2<${t}>`;case 3:return`vec3<${t}>`;case 4:return`vec4<${t}>`;default:throw new Error(`${e}-component is not supported.`)}},Bu=e=>` + ${e?"value = value + getBiasByOutputCoords(coords);":""} + `});var Du,Mu=U(()=>{"use strict";Du=e=>` +fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 { + return dot(coords, vec4( + shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1)); +} +fn getOutputIndexFromCoords(coords : vec4) -> i32 { + return dot(coords, vec4( + i32(${e}.x), i32(${e}.y), i32(${e}.z), 1)); +} +`});var Xt,Qr,Yr=U(()=>{"use strict";ee();ne();ie();bt();Xt=(e,t,r,n,o)=>{let i=n-r;return` + ${Array.from({length:r}).map((a,u)=>` + if (${F(t.shape,u,t.rank)} != 1) { + ${t.indicesSet(e,u,F(o,u+i,n))} + } else { + ${t.indicesSet(e,u,0)} + }`).join("")} +`},Qr=(e,t,r,n,o=!1,i)=>{let a=e[0].dims,u=e[1].dims,d=a[a.length-2],c=u[u.length-1],p=a[a.length-1],m=ce(c),f=ce(p),b=ce(d),g=k.size(r)/m/b,_=e.length>2,S=n?n.slice(0,-2):r.slice(0,-2),v=[k.size(S),d,c],x=[{type:12,data:g},{type:12,data:d},{type:12,data:c},{type:12,data:p}];qe(t,x),x.push(...N(S,a,u)),_&&x.push(...N(e[2].dims)),x.push(...N(v));let T=E=>{let I=Lr("batch_dims",e[0].dataType,S.length),z=P("a",e[0].dataType,a.length,f),O=P("b",e[1].dataType,u.length,m),D=M("output",e[0].dataType,v.length,m),L=be(D.type.tensor),q=Fe(t,D.type.value,L),Q=[z,O],W="";if(_){let H=o?m:1;Q.push(P("bias",e[2].dataType,e[2].dims.length,H)),W=`${o?`value += bias[col / ${H}];`:`value += ${D.type.value}(bias[row + i]);`}`}let Z=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"}];je(t,Z);let we=()=>{let H=`var a_data: ${z.type.value};`;for(let j=0;j; + for (var k: u32 = 0u; k < uniforms.K; k = k + ${f}) { + ${we()} + } + for (var i = 0u; i < ${b}u; i++) { + var value = values[i]; + ${W} + ${q} + let cur_indices = ${D.type.indices}(batch, row + i, col); + let offset = ${D.indicesToOffset("cur_indices")}; + ${D.setByOffset(`offset / ${m}`,"value")}; + } + } + `};return{name:"MatMulNaive",shaderCache:{hint:`${t.activation};${m};${f};${b};${o}`,inputDependencies:_?["rank","rank","rank"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:i?i(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(g/64)},programUniforms:x}),getShaderSource:T}}});var uf,df,mo,Ru,lf,fo,cf,Jt,Xr=U(()=>{"use strict";ee();ne();ie();bt();Yr();Zr();uf=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart / innerElementSize + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRow + innerRow, + kStart / innerElementSize + inputCol${t?", batchIndices":""}); + `,df=(e,t)=>e?` + let ACached0 = mm_Asub[k * innerElementSize][localRow]; + let ACached1 = mm_Asub[k * innerElementSize + 1][localRow]; + let ACached2 = mm_Asub[k * innerElementSize + 2][localRow]; + ${t===3?"":"let ACached3 = mm_Asub[k * innerElementSize + 3][localRow];"} + for (var i = 0; i < rowPerThread; i = i + 1) { + acc[i] = BCached0 * ACached0[i] + acc[i]; + acc[i] = BCached1 * ACached1[i] + acc[i]; + acc[i] = BCached2 * ACached2[i] + acc[i]; + ${t===3?"":"acc[i] = BCached3 * ACached3[i] + acc[i];"} + }`:` + for (var i = 0; i < rowPerThread; i = i + 1) { + let ACached = mm_Asub[tileRow + i][k]; + acc[i] = BCached0 * ACached.x + acc[i]; + acc[i] = BCached1 * ACached.y + acc[i]; + acc[i] = BCached2 * ACached.z + acc[i]; + ${t===3?"":"acc[i] = BCached3 * ACached.w + acc[i];"} + }`,mo=(e,t,r="f32",n,o=!1,i=32,a=!1,u=32)=>{let d=t[1]*e[1],c=t[0]*e[0],p=o?d:i,m=o?i:d,f=p/t[0],b=i/t[1];if(!((o&&f===4&&e[1]===4||!o&&(f===3||f===4))&&p%t[0]===0&&i%t[1]===0&&e[0]===4))throw new Error(`If transposeA ${o} is true, innerElementSize ${f} and workPerThread[1] ${e[1]} must be 4. + Otherwise, innerElementSize ${f} must be 3 or 4. + tileAWidth ${p} must be divisible by workgroupSize[0]${t[0]}. tileInner ${i} must be divisible by workgroupSize[1] ${t[1]}. colPerThread ${e[0]} must be 4.`);return` +var mm_Asub: array, ${p/f}>, ${m}>; +var mm_Bsub: array, ${c/e[0]}>, ${i}>; + +const rowPerThread = ${e[1]}; +const colPerThread = ${e[0]}; +const innerElementSize = ${f}; +const tileInner = ${i}; + +@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let localRow = i32(localId.y); + let tileRow = localRow * rowPerThread; + let tileCol = i32(localId.x); + + let globalRow =i32(globalId.y) * rowPerThread; + let globalCol = i32(globalId.x); + let batch = ${a?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let globalRowStart = i32(workgroupId.y) * ${d}; + + let num_tiles = ${a?`${Math.ceil(u/i)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${a?`i32(globalId.z) * ${u}`:"0"}; + + var acc: array, rowPerThread>; + + // Loop over shared dimension. + let tileRowB = localRow * ${b}; + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let inputRow = tileRow + innerRow; + let inputCol = tileCol; + ${uf(o,n)} + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${b}; innerRow = innerRow + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol${n?", batchIndices":""}); + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + for (var k = 0; k < tileInner / innerElementSize; k = k + 1) { + let BCached0 = mm_Bsub[k * innerElementSize][tileCol]; + let BCached1 = mm_Bsub[k * innerElementSize + 1][tileCol]; + let BCached2 = mm_Bsub[k * innerElementSize + 2][tileCol]; + ${f===3?"":"let BCached3 = mm_Bsub[k * innerElementSize + 3][tileCol];"} + + ${df(o,f)} + } + + workgroupBarrier(); + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]); + } +}`},Ru=(e,t)=>e?` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + kStart + inputRow, + globalRowStart + inputCol${t?", batchIndices":""}); + `:` + mm_Asub[inputRow][inputCol] = mm_readA(batch, + globalRowStart + inputRow, + kStart + inputCol${t?", batchIndices":""}); + `,lf=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];",fo=(e,t,r="f32",n,o=!1,i=32,a=!1,u=32,d=!1)=>{let c=e[1]*t[1],p=e[0]*t[0],m=o?c:i,f=o?i:c;if(!(f%t[1]===0&&m%t[0]===0&&i%t[1]===0))throw new Error(`tileAHight ${f} must be divisible by workgroupSize[1]${t[1]}, tileAWidth ${m} must be divisible by workgroupSize[0]${t[0]}, tileInner ${i} must be divisible by workgroupSize[1]${t[1]}`);let b=f/t[1],g=m/t[0],_=i/t[1],S=d?` + let localRow = i32(localId.y); + let localCol = i32(localId.x); + let globalRowStart = i32(workgroupId.y) * ${c}; + let globalColStart = i32(workgroupId.x) * ${p}; + + // Loop over shared dimension. + for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var inputRow = localRow; inputRow < ${f}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${m}; inputCol = inputCol + ${t[0]}) { + ${Ru(o,n)} + } + } + // Load one tile of B into local memory. + for (var inputRow = localRow; inputRow < ${i}; inputRow = inputRow + ${t[1]}) { + for (var inputCol = localCol; inputCol < ${p}; inputCol = inputCol + ${t[0]}) { + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalColStart + inputCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${r}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][localCol + inner * ${t[0]}]; + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let ACached = ${o?`mm_Asub[k][localRow + innerRow * ${t[1]}];`:`mm_Asub[localRow + innerRow * ${t[1]}][k];`} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + + ACached * BCached[innerCol]; + } + } + } + workgroupBarrier(); + } + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + let gRow = globalRowStart + localRow + innerRow * ${t[1]}; + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let gCol = globalColStart + localCol + innerCol * ${t[0]}; + mm_write(batch, gRow, gCol, acc[innerRow][innerCol]); + } + } + `:` +let tileRow = i32(localId.y) * rowPerThread; +let tileCol = i32(localId.x) * colPerThread; + +let globalRow = i32(globalId.y) * rowPerThread; +let globalCol = i32(globalId.x) * colPerThread; +let globalRowStart = i32(workgroupId.y) * ${c}; + +let tileRowA = i32(localId.y) * ${b}; +let tileColA = i32(localId.x) * ${g}; +let tileRowB = i32(localId.y) * ${_}; +// Loop over shared dimension. +for (var t = 0; t < num_tiles; t = t + 1) { + // Load one tile of A into local memory. + for (var innerRow = 0; innerRow < ${b}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < ${g}; innerCol = innerCol + 1) { + let inputRow = tileRowA + innerRow; + let inputCol = tileColA + innerCol; + ${Ru(o,n)} + } + } + + // Load one tile of B into local memory. + for (var innerRow = 0; innerRow < ${_}; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + let inputRow = tileRowB + innerRow; + let inputCol = tileCol + innerCol; + mm_Bsub[inputRow][inputCol] = mm_readB(batch, + kStart + inputRow, + globalCol + innerCol${n?", batchIndices":""}); + } + } + kStart = kStart + tileInner; + workgroupBarrier(); + + // Compute acc values for a single thread. + var BCached : array<${r}, colPerThread>; + for (var k = 0; k < tileInner; k = k + 1) { + for (var inner = 0; inner < colPerThread; inner = inner + 1) { + BCached[inner] = mm_Bsub[k][tileCol + inner]; + } + + for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + ${lf(o)} + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol]; + } + } + } + + workgroupBarrier(); +} + +for (var innerRow = 0; innerRow < rowPerThread; innerRow = innerRow + 1) { + for (var innerCol = 0; innerCol < colPerThread; innerCol = innerCol + 1) { + mm_write(batch, globalRow + innerRow, globalCol + innerCol, + acc[innerRow][innerCol]); + } +} +`;return` + var mm_Asub : array, ${f}>; + var mm_Bsub : array, ${i}>; + const rowPerThread = ${e[1]}; + const colPerThread = ${e[0]}; + const tileInner = ${i}; + +@compute @workgroup_size(${t[0]}, ${t[1]}, ${t[2]}) +fn main(@builtin(local_invocation_id) localId : vec3, + @builtin(global_invocation_id) globalId : vec3, + @builtin(workgroup_id) workgroupId : vec3) { + let batch = ${a?"0":"i32(globalId.z)"}; + ${n?`let batchIndices = ${n.offsetToIndices("u32(batch)")};`:""} + let num_tiles = ${a?`${Math.ceil(u/i)}`:"(uniforms.dim_inner - 1) / tileInner + 1"}; + var kStart = ${a?`i32(globalId.z) * ${u}`:"0"}; + + var acc : array, rowPerThread>; + ${S} + } +`},cf=(e,t,r,n,o=!1)=>{let[i,a,u,d]=n,c=be(n[0].type.tensor);return` + fn mm_readA(batch: i32, row: i32, colIn: i32, batchIndices: ${i.type.indices}) -> ${Ie(e,c)} { + var value = ${Ie(e,c)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_a_outer && col < uniforms.dim_inner) + { + var aIndices: ${a.type.indices}; + ${Xt("aIndices",a,a.rank-2,i.rank,"batchIndices")} + ${a.indicesSet("aIndices",a.rank-2,"u32(row)")} + ${a.indicesSet("aIndices",a.rank-1,"u32(colIn)")} + value = ${a.getByIndices("aIndices")}; + } + return value; + } + + fn mm_readB(batch: i32, row: i32, colIn: i32, batchIndices: ${i.type.indices}) -> ${Ie(e,c)} { + var value = ${Ie(e,c)}(0.0); + let col = colIn * ${e}; + if(row < uniforms.dim_inner && col < uniforms.dim_b_outer) + { + var bIndices: ${u.type.indices}; + ${Xt("bIndices",u,u.rank-2,i.rank,"batchIndices")} + ${u.indicesSet("bIndices",u.rank-2,"u32(row)")} + ${u.indicesSet("bIndices",u.rank-1,"u32(colIn)")} + value = ${u.getByIndices("bIndices")}; + } + return value; + } + + fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Ie(e,c)}) { + let col = colIn * ${e}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) { + var value = valueIn; + let coords = vec3(batch, row, colIn); + ${t?`value = value + ${o?"bias[colIn]":`${Ie(e,c)}(bias[row])`};`:""} + ${r} + ${d.setByIndices("vec3(coords)","value")} + } + } + `},Jt=(e,t,r,n,o=!1,i)=>{let a=e[0].dims,u=e[1].dims,d=a.slice(0,-2),c=u.slice(0,-2),p=n?n.slice(0,-2):r.slice(0,-2),m=k.size(p),f=a[a.length-2],b=a[a.length-1],g=u[u.length-1],_=b%4===0&&g%4===0,S=f<=8?[4,1,1]:[4,4,1],$=[8,8,1],v=[Math.ceil(g/$[0]/S[0]),Math.ceil(f/$[1]/S[1]),Math.ceil(m/$[2]/S[2])],x=_?4:1,T=[...d,f,b/x],E=T.length,I=[...c,b,g/x],z=I.length,O=[m,f,g/x],D=[{type:6,data:f},{type:6,data:g},{type:6,data:b}];qe(t,D),D.push(...N(p,T,I));let L=["rank","rank"],q=e.length>2;q&&(D.push(...N(e[2].dims)),L.push("rank")),D.push(...N(O));let Q=W=>{let Z=p.length,we=Lr("batchDims",e[0].dataType,Z,1),H=be(e[0].dataType),j=P("a",e[0].dataType,E,x),te=P("b",e[1].dataType,z,x),X=M("result",e[0].dataType,O.length,x),ue=[j,te];if(q){let V=o?x:1;ue.push(P("bias",e[2].dataType,e[2].dims.length,V))}let he=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"}];je(t,he);let ye=be(X.type.tensor),re=Fe(t,X.type.value,ye),C=cf(x,q,re,[we,j,te,X],o);return` + ${W.registerUniforms(he).registerInternalVariables(we).declareVariables(...ue,X)} + ${C} + ${_?mo(S,$,H,we):fo(S,$,H,we)} + `};return{name:"MatMul",shaderCache:{hint:`${S};${t.activation};${_};${o}`,inputDependencies:L},getRunData:()=>({outputs:[{dims:i?i(r):r,dataType:e[0].dataType}],dispatchGroup:{x:v[0],y:v[1],z:v[2]},programUniforms:D}),getShaderSource:Q}}});var pf,Uu,Nu=U(()=>{"use strict";ee();Xe();ie();bt();Zr();Mu();Xr();pf=(e,t,r,n,o=!1,i,a=4,u=4,d=4,c="f32")=>{let p=L=>{switch(L){case 1:return"resData = x[xIndex];";case 3:return`resData = vec3<${c}>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);`;case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${L} is not supported.`)}},m=L=>{switch(L){case 1:return"return w[row * i32(uniforms.w_shape[3]) + colIn];";case 4:return"return w[row * i32(uniforms.w_shape[3]) / 4 + colIn];";default:throw new Error(`innerElementSize ${L} is not supported.`)}},f=e?` + let coord = vec4(batch, xRow, xCol, xCh); + `:` + let coord = vec4(batch, xCh, xRow, xCol); + `,b=e?` + let coords = vec4( + batch, + row / outWidth, + row % outWidth, + col); + `:` + let coords = vec4( + batch, + row, + col / outWidth, + col % outWidth); + `,g=e?"i32(uniforms.x_shape[1])":"i32(uniforms.x_shape[2])",_=e?"i32(uniforms.x_shape[2])":"i32(uniforms.x_shape[3])",S=e?"row":"col",$=e?"col":"row",v=` + let inChannels = i32(uniforms.w_shape[2]); + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + let outRow = ${S} / outWidth; + let outCol = ${S} % outWidth; + + let WRow = ${$} / (i32(uniforms.w_shape[1]) * inChannels); + let WCol = ${$} / inChannels % i32(uniforms.w_shape[1]); + let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0]; + let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1]; + let xCh = ${$} % inChannels; + var resData = ${Ie(a,c)}(0.0); + // The bounds checking is always needed since we use it to pad zero for + // the 'same' padding type. + if (xRow >= 0 && xRow < ${g} && xCol >= 0 && xCol < ${_}) { + ${f} + let xIndex = getIndexFromCoords4D(coord, vec4(uniforms.x_shape)); + ${p(a)} + } + return resData;`,x=e?t&&n?` + let col = colIn * ${a}; + ${v}`:` + let col = colIn * ${a}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_inner) { + ${v} + } + return ${Ie(a,c)}(0.0);`:n&&r?` + let col = colIn * ${a}; + ${v}`:` + let col = colIn * ${a}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${v} + } + return ${Ie(a,c)}(0.0);`,T=e?n&&r?m(u):` + let col = colIn * ${u}; + if (row < uniforms.dim_inner && col < uniforms.dim_b_outer) { + ${m(u)} + } + return ${Ie(u,c)}(0.0);`:` + let col = colIn * ${u}; + if (row < uniforms.dim_inner && col < uniforms.dim_a_outer) { + ${m(u)} + } + return ${Ie(u,c)}(0.0);`,E=Ie(d,c),I=e?Ie(a,c):Ie(u,c),z=e?Ie(u,c):Ie(a,c),O=Fe(i,E,c);return` + fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${I} { + ${e?x:T} + } + + fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${z} { + ${e?T:x} + } + + fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${E}) { + let col = colIn * ${d}; + if (row < uniforms.dim_a_outer && col < uniforms.dim_b_outer) + { + var value = valueIn; + let outWidth = ${e?"i32(uniforms.result_shape[2])":"i32(uniforms.result_shape[3])"}; + ${b} + ${Bu(o)} + ${O} + setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value); + } + }`},Uu=(e,t,r,n,o,i,a,u,d)=>{let c=t.format==="NHWC",p=c?e[0].dims[3]:e[0].dims[1],m=r[0],f=c?r[2]:r[3],b=c?r[1]:r[2],g=c?r[3]:r[1],_=c&&(p%4===0||p%3===0)&&g%4===0,S=c?g:f*b,$=c?f*b:g,v=[8,8,1],x=n<=8?[4,1,1]:[4,4,1],T=[Math.ceil(S/v[0]/x[0]),Math.ceil($/v[1]/x[1]),Math.ceil(m/v[2]/x[2])];se("verbose",()=>`[conv2d_mm_webgpu] dispatch = ${T}`);let E=_?c&&p%4!==0?3:4:1,I=v[1]*x[1],z=v[0]*x[0],O=Math.max(v[0]*E,v[1]),D=n%I===0,L=o%z===0,q=i%O===0,Q=_?[E,4,4]:[1,1,1],W=[{type:6,data:n},{type:6,data:o},{type:6,data:i},{type:6,data:[t.pads[0],t.pads[1]]},{type:6,data:t.strides},{type:6,data:t.dilations}];qe(t,W),W.push(...N(e[0].dims,e[1].dims));let Z=["rank","rank"];a&&(W.push(...N(e[2].dims)),Z.push("rank")),W.push(...N(r));let we=H=>{let j=[{name:"dim_a_outer",type:"i32"},{name:"dim_b_outer",type:"i32"},{name:"dim_inner",type:"i32"},{name:"pad",type:"i32",length:2},{name:"stride",type:"i32",length:2},{name:"dilation",type:"i32",length:2}];je(t,j);let te=_?4:1,X=be(e[0].dataType),ue=` + fn setOutputAtIndex(flatIndex : i32, value : ${_?`vec4<${X}>`:X}) { + result[flatIndex] = ${_?`vec4<${X}>`:X}(value); + } + fn setOutputAtCoords(d0 : i32, d1 : i32, d2 : i32, d3 : i32, value : ${_?`vec4<${X}>`:X}) { + let flatIndex = getOutputIndexFromCoords(vec4(d0, d1, d2, d3)); + setOutputAtIndex(flatIndex ${_?"/ 4":""}, value); + }`,he=P("x",e[0].dataType,e[0].dims.length,E===3?1:E),ye=P("w",e[1].dataType,e[1].dims.length,te),re=[he,ye],C=M("result",e[0].dataType,r.length,te);if(a){let V=P("bias",e[2].dataType,e[2].dims.length,te);re.push(V),ue+=` + fn getBiasByOutputCoords(coords : vec4) -> ${_?`vec4<${X}>`:X} { + return bias[coords.${c?"w":"y"}${_?"/ 4":""}]; + }`}return` + ${Du("uniforms.result_strides")} + //struct Uniforms { xShape : vec4, wShape : vec4, outShape : vec4, + // outShapeStrides: vec3, filterDims : vec2, pad : vec2, stride : vec2, + // dilation : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32 }; + ${H.registerUniforms(j).declareVariables(...re,C)} + ${ue} + ${pf(c,D,L,q,a,t,Q[0],Q[1],Q[2],X)} + ${_?mo(x,v,X,void 0,!c,O):fo(x,v,X,void 0,!c,O,!1,void 0,u)}`};return{name:"Conv2DMatMul",shaderCache:{hint:`${t.cacheKey};${E};${_};${D};${L};${q};${I};${z};${O}`,inputDependencies:Z},getRunData:()=>({outputs:[{dims:d?d(r):r,dataType:e[0].dataType}],dispatchGroup:{x:T[0],y:T[1],z:T[2]},programUniforms:W}),getShaderSource:we}}});var mf,Vu,Jr,ff,Wu,hf,Lu,Gu,Hu=U(()=>{"use strict";ee();Xe();ne();ie();bt();Zr();mf=e=>{let t=1;for(let r=0;rtypeof e=="number"?[e,e,e]:e,Jr=(e,t)=>t<=1?e:e+(e-1)*(t-1),ff=(e,t,r,n=1)=>{let o=Jr(t,n);return Math.floor((e[0]*(r-1)-r+o)/2)},Wu=(e,t,r,n,o)=>{o==null&&(o=ff(e,t[0],n[0]));let i=[0,0,0,r];for(let a=0;a<3;a++)e[a]+2*o>=t[a]&&(i[a]=Math.trunc((e[a]-t[a]+2*o)/n[a]+1));return i},hf=(e,t,r,n,o,i,a,u,d,c)=>{let p,m,f,b;if(e==="VALID"&&(e=0),typeof e=="number"){p={top:e,bottom:e,left:e,right:e,front:e,back:e};let g=Wu([t,r,n,1],[u,d,c],1,[o,i,a],e);m=g[0],f=g[1],b=g[2]}else if(Array.isArray(e)){if(!e.every((_,S,$)=>_===$[0]))throw Error(`Unsupported padding parameter: ${e}`);p={top:e[0],bottom:e[1],left:e[2],right:e[3],front:e[4],back:e[5]};let g=Wu([t,r,n,1],[u,d,c],1,[o,i,a],e[0]);m=g[0],f=g[1],b=g[2]}else if(e==="SAME_UPPER"){m=Math.ceil(t/o),f=Math.ceil(r/i),b=Math.ceil(n/a);let g=(m-1)*o+u-t,_=(f-1)*i+d-r,S=(b-1)*a+c-n,$=Math.floor(g/2),v=g-$,x=Math.floor(_/2),T=_-x,E=Math.floor(S/2),I=S-E;p={top:x,bottom:T,left:E,right:I,front:$,back:v}}else throw Error(`Unknown padding parameter: ${e}`);return{padInfo:p,outDepth:m,outHeight:f,outWidth:b}},Lu=(e,t,r,n,o,i=!1,a="channelsLast")=>{let u,d,c,p,m;if(a==="channelsLast")[u,d,c,p,m]=e;else if(a==="channelsFirst")[u,m,d,c,p]=e;else throw new Error(`Unknown dataFormat ${a}`);let[f,,b,g,_]=t,[S,$,v]=Vu(r),[x,T,E]=Vu(n),I=Jr(b,x),z=Jr(g,T),O=Jr(_,E),{padInfo:D,outDepth:L,outHeight:q,outWidth:Q}=hf(o,d,c,p,S,$,v,I,z,O),W=i?f*m:f,Z=[0,0,0,0,0];return a==="channelsFirst"?Z=[u,W,L,q,Q]:a==="channelsLast"&&(Z=[u,L,q,Q,W]),{batchSize:u,dataFormat:a,inDepth:d,inHeight:c,inWidth:p,inChannels:m,outDepth:L,outHeight:q,outWidth:Q,outChannels:W,padInfo:D,strideDepth:S,strideHeight:$,strideWidth:v,filterDepth:b,filterHeight:g,filterWidth:_,effectiveFilterDepth:I,effectiveFilterHeight:z,effectiveFilterWidth:O,dilationDepth:x,dilationHeight:T,dilationWidth:E,inShape:e,outShape:Z,filterShape:t}},Gu=(e,t,r,n,o,i)=>{let a=i==="channelsLast",u=a?e[0].dims[3]:e[0].dims[1],d=!1,c=[64,1,1],p={x:r.map((v,x)=>x)},m=[Math.ceil(mf(p.x.map(v=>r[v]))/c[0]),1,1];se("verbose",()=>`[conv3d_naive_webgpu] dispatch = ${m}`);let f=d?a&&u%4!==0?3:4:1,b=k.size(r),g=[{type:12,data:b},{type:12,data:n},{type:12,data:o},{type:12,data:t.strides},{type:12,data:t.dilations}];qe(t,g),g.push(...N(e[0].dims,e[1].dims));let _=["rank","rank"],S=e.length===3;S&&(g.push(...N(e[2].dims)),_.push("rank")),g.push(...N(r));let $=v=>{let x=[{name:"output_size",type:"u32"},{name:"filter_dims",type:"u32",length:n.length},{name:"pads",type:"u32",length:o.length},{name:"strides",type:"u32",length:t.strides.length},{name:"dilations",type:"u32",length:t.dilations.length}];je(t,x);let T=d?4:1,E=be(e[0].dataType),I=P("x",e[0].dataType,e[0].dims.length,f===3?1:f),z=P("W",e[1].dataType,e[1].dims.length,T),O=[I,z],D=M("result",e[0].dataType,r.length,T),L="";if(S){let W=P("bias",e[2].dataType,e[2].dims.length,T);O.push(W),L+=` + fn getBiasByOutputCoords(coords : array) -> ${d?`vec4<${E}>`:E} { + return bias[${a?F("coords",4,5):F("coords",1,5)}${d?"/ 4":""}]; + }`}let q=Ie(f,E),Q=Fe(t,q,E);return` + ${L} + fn getX(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${I.getByIndices("aIndices")}; + } + fn getW(d0 : u32, d1 : u32, d2 : u32, d3 : u32, d4 : u32) -> f32 { + let aIndices = array(d0, d1, d2, d3, d4); + return ${z.getByIndices("aIndices")}; + } + ${v.registerUniforms(x).declareVariables(...O,D)} + ${v.mainStart()} + ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let coords = ${D.offsetToIndices("global_idx")}; + let batch = ${F("coords",0,I.rank)}; + let d2 = ${a?F("coords",I.rank-1,I.rank):F("coords",1,I.rank)}; + let xFRCCorner = vec3(${a?F("coords",1,I.rank):F("coords",2,I.rank)}, + ${a?F("coords",2,I.rank):F("coords",3,I.rank)}, + ${a?F("coords",3,I.rank):F("coords",4,I.rank)}) * uniforms.strides - uniforms.pads; + let xFCorner = xFRCCorner.x; + let xRCorner = xFRCCorner.y; + let xCCorner = xFRCCorner.z; + let xShapeY = ${a?F("uniforms.x_shape",1,I.rank):F("uniforms.x_shape",2,I.rank)}; + let xShapeZ = ${a?F("uniforms.x_shape",2,I.rank):F("uniforms.x_shape",3,I.rank)}; + let xShapeW = ${a?F("uniforms.x_shape",3,I.rank):F("uniforms.x_shape",4,I.rank)}; + let xShapeU = ${a?F("uniforms.x_shape",4,I.rank):F("uniforms.x_shape",1,I.rank)}; + let inputDepthNearestVec4 = (xShapeU / 4) * 4; + let inputDepthVec4Remainder = xShapeU % 4; + + var value = 0.0; + for (var wF = 0u; wF < uniforms.filter_dims[0]; wF++) { + let xF = xFCorner + wF * uniforms.dilations[0]; + if (xF < 0 || xF >= xShapeY) { + continue; + } + + for (var wR = 0u; wR < uniforms.filter_dims[1]; wR++) { + let xR = xRCorner + wR * uniforms.dilations[1]; + if (xR < 0 || xR >= xShapeZ) { + continue; + } + + for (var wC = 0u; wC < uniforms.filter_dims[2]; wC++) { + let xC = xCCorner + wC * uniforms.dilations[2]; + if (xC < 0 || xC >= xShapeW) { + continue; + } + + for (var d1 = 0u; d1 < inputDepthNearestVec4; d1 += 4) { + ${a?`let xValues = vec4( + getX(batch, xF, xR, xC, d1), + getX(batch, xF, xR, xC, d1 + 1), + getX(batch, xF, xR, xC, d1 + 2), + getX(batch, xF, xR, xC, d1 + 3)); + `:`let xValues = vec4( + getX(batch, d1, xF, xR, xC), + getX(batch, d1 + 1, xF, xR, xC), + getX(batch, d1 + 2, xF, xR, xC), + getX(batch, d1 + 3, xF, xR, xC)); + `} + let wValues = vec4( + getW(d2, d1, wF, wR, wC), + getW(d2, d1 + 1, wF, wR, wC), + getW(d2, d1 + 2, wF, wR, wC), + getW(d2, d1 + 3, wF, wR, wC)); + value += dot(xValues, wValues); + } + if (inputDepthVec4Remainder == 1) { + ${a?`value += getX(batch, xF, xR, xC, inputDepthNearestVec4) + * getW(d2, inputDepthNearestVec4, wF, wR, wC);`:`value += getX(batch, inputDepthNearestVec4, xF, xR, xC) + * getW(d2, inputDepthNearestVec4, wF, wR, wC);`} + } else if (inputDepthVec4Remainder == 2) { + ${a?`let xValues = vec2( + getX(batch, xF, xR, xC, inputDepthNearestVec4), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)); + `:`let xValues = vec2( + getX(batch, inputDepthNearestVec4, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC)); + `} + let wValues = vec2( + getW(d2, inputDepthNearestVec4, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC)); + value += dot(xValues, wValues); + } else if (inputDepthVec4Remainder == 3) { + ${a?`let xValues = vec3( + getX(batch, xF, xR, xC, inputDepthNearestVec4), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1), + getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)); + `:`let xValues = vec3( + getX(batch, inputDepthNearestVec4, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 1, xF, xR, xC), + getX(batch, inputDepthNearestVec4 + 2, xF, xR, xC)); + `} + let wValues = vec3( + getW(d2, inputDepthNearestVec4, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 1, wF, wR, wC), + getW(d2, inputDepthNearestVec4 + 2, wF, wR, wC)); + value += dot(xValues, wValues); + } + } + } + } + ${S?"value = value + getBiasByOutputCoords(coords)":""}; + ${Q} + result[global_idx] = f32(value); + }`};return{name:"Conv3DNaive",shaderCache:{hint:`${t.cacheKey};${a};${f};${S}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:m[0],y:m[1],z:m[2]},programUniforms:g}),getShaderSource:$}}});var Fu,qu,ju=U(()=>{"use strict";ee();ne();ie();bt();Fu=(e,t,r,n)=>{let o=e.length>2,i=o?"value += b[output_channel];":"",a=e[0].dims,u=e[1].dims,d=t.format==="NHWC",c=d?r[3]:r[1],p=c/t.group,m=d&&p>=4?ce(c):1,f=k.size(r)/m,b=[{type:12,data:f},{type:12,data:t.dilations},{type:12,data:[t.strides[0],t.strides[1]]},{type:12,data:[t.pads[0],t.pads[1]]},{type:12,data:p}];qe(t,b),b.push(...N(a,[u[0],u[1],u[2],u[3]/m]));let g=o?["rank","rank","rank"]:["rank","rank"];b.push(...N([r[0],r[1],r[2],r[3]/m]));let _=S=>{let $=M("output",e[0].dataType,r.length,m),v=be($.type.tensor),x=Fe(t,$.type.value,v),T=P("x",e[0].dataType,a.length),E=P("w",e[1].dataType,u.length,m),I=[T,E];o&&I.push(P("b",e[2].dataType,e[2].dims,m));let z=[{name:"output_size",type:"u32"},{name:"dilations",type:"u32",length:t.dilations.length},{name:"strides",type:"u32",length:2},{name:"pads",type:"u32",length:2},{name:"output_channels_per_group",type:"u32"}];je(t,z);let O=d?` + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[0]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[1]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[1]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[2]) { + continue; + } + + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[2]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + let xVal = ${T.get("batch","xHeight","xWidth","input_channel")}; + let wVal = ${E.get("wHeight","wWidth","wInChannel","output_channel")}; + value += xVal * wVal; + } + } + } + `:` + for (var wInChannel: u32 = 0u; wInChannel < uniforms.w_shape[1]; wInChannel++) { + let input_channel = in_channel_offset + wInChannel; + for (var wHeight: u32 = 0u; wHeight < uniforms.w_shape[2]; wHeight++) { + let xHeight = xRCCorner.x + wHeight * uniforms.dilations[0]; + + if (xHeight < 0u || xHeight >= uniforms.x_shape[2]) { + continue; + } + + for (var wWidth: u32 = 0u; wWidth < uniforms.w_shape[3]; wWidth++) { + let xWidth = xRCCorner.y + wWidth * uniforms.dilations[1]; + if (xWidth < 0u || xWidth >= uniforms.x_shape[3]) { + continue; + } + + let xVal = ${T.get("batch","input_channel","xHeight","xWidth")}; + let wVal = ${E.get("output_channel","wInChannel","wHeight","wWidth")}; + value += xVal * wVal; + } + } + } + `;return` + ${S.registerUniforms(z).declareVariables(...I,$)} + + ${S.mainStart()} + ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let outputIndices = ${$.offsetToIndices("global_idx")}; + let batch: u32 = outputIndices[0]; + let output_channel: u32 = outputIndices[${d?3:1}]; + let xRCCorner: vec2 = vec2(outputIndices[${d?1:2}], outputIndices[${d?2:3}]) * uniforms.strides - uniforms.pads; + let group_id: u32 = output_channel * ${m} / uniforms.output_channels_per_group; + var in_channel_offset = group_id * uniforms.w_shape[${d?2:1}]; + + var value: ${$.type.value} = ${$.type.value}(0); + ${O} + ${i} + ${x} + ${$.setByOffset("global_idx","value")} + }`};return{name:"GroupedConv",shaderCache:{hint:`${t.cacheKey}_${m}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:b}),getShaderSource:_}},qu=(e,t,r,n)=>{let o=e.length>2,i=ce(r[3]),a=ce(r[2]),u=k.size(r)/i/a,d=[e[0].dims[0],e[0].dims[1],e[0].dims[2],e[0].dims[3]/i],c=[e[1].dims[0],e[1].dims[1],e[1].dims[2],e[1].dims[3]/i],p=[r[0],r[1],r[2],r[3]/i],m=[{type:12,data:u},{type:6,data:[t.strides[0],t.strides[1]]},{type:6,data:[t.pads[0],t.pads[1]]}];qe(t,m),m.push(...N(d,c,p));let f=(a-1)*t.strides[1]+c[1],b=g=>{let _=M("output",e[0].dataType,p.length,i),S=be(_.type.tensor),$=Fe(t,_.type.value,S),v=P("x",e[0].dataType,d.length,i),x=P("w",e[1].dataType,c.length,i),T=[v,x];o&&T.push(P("b",e[2].dataType,e[2].dims,i));let E=o?"value += b[output_channel];":"",I=[{name:"output_size",type:"u32"},{name:"strides",type:"i32",length:2},{name:"pads",type:"i32",length:2}];return je(t,I),` + ${g.registerUniforms(I).declareVariables(...T,_)} + ${g.mainStart()} + ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let width0 = uniforms.output_shape[3]; + let output_channel = global_idx % width0; + var index1 = global_idx / width0; + let width1 = uniforms.output_shape[2] / ${a}u; + let col = (index1 % width1) * ${a}u; + index1 = index1 / width1; + let row = index1 % uniforms.output_shape[1]; + let batch = index1 / uniforms.output_shape[1]; + + let x_corner = vec2(i32(row), i32(col)) * uniforms.strides - uniforms.pads; + + var x_vals: array<${v.type.value}, ${f}>; + var values: array<${_.type.value}, ${a}>; + let input_channel = output_channel; + // Use constant instead of uniform can give better performance for w's height/width. + for (var w_height: u32 = 0u; w_height < ${c[0]}; w_height++) { + let x_height = x_corner.x + i32(w_height); + if (x_height >= 0 && u32(x_height) < uniforms.x_shape[1]) { + for (var i = 0; i < ${f}; i++) { + let x_width = x_corner.y + i; + if (x_width >= 0 && u32(x_width) < uniforms.x_shape[2]) { + x_vals[i] = ${v.get("batch","u32(x_height)","u32(x_width)","input_channel")}; + } else { + x_vals[i] = ${v.type.value}(0); + } + } + for (var w_width: u32 = 0u; w_width < ${c[1]}; w_width++) { + let w_val = ${x.get("w_height","w_width","0","output_channel")}; + for (var i = 0u; i < ${a}u; i++) { + values[i] = fma(x_vals[i * u32(uniforms.strides[1]) + w_width], w_val, values[i]); + } + } + } + } + + for (var i = 0u; i < ${a}u; i++) { + var value = values[i]; + ${E} + ${$} + ${_.set("batch","row","col + i","output_channel","value")}; + } + }`};return{name:"GroupedConv-Vectorize",shaderCache:{hint:`${t.cacheKey};${i};${a};${f};${c[0]};${c[1]}`,inputDependencies:o?["rank","rank","type"]:["rank","rank"]},getRunData:()=>({outputs:[{dims:n?n(r):r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:m}),getShaderSource:b}}});var gf,ho,bf,go,bo,Ku,yf,_f,yo,Zu=U(()=>{"use strict";ne();Nu();Hu();Xr();ju();bt();Yr();st();gf=(e,t,r,n,o,i)=>{let a=e[0],u=e.slice(i?1:2,i?3:4),d=u.length,c=t[0],m=t.slice(2).map((g,_)=>g+(g-1)*(r[_]-1)),b=u.map((g,_)=>g+n[_]+n[_+d]).map((g,_)=>Math.floor((g-m[_]+o[_])/o[_]));return b.splice(0,0,a),b.splice(i?3:1,0,c),b},ho=[2,3,1,0],bf=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length>5)throw new Error("greater than 5D is not supported");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[1]*t.group;if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(e.length===3&&(e[2].dims.length!==1||e[1].dims[0]!==e[2].dims[0]))throw new Error("invalid bias");let o=e[0].dims.length-2;if(t.dilations.length!==o)throw new Error(`dilations should be ${o}D`);if(t.strides.length!==o)throw new Error(`strides should be ${o}D`);if(t.pads.length!==o*2)throw new Error(`pads should be ${o*2}D`);if(t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape")},go=(e,t)=>{let r=e.kernelShape.slice();r.length{let t=Kr(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],o=e.dilations,i=e.group,a=e.kernel_shape,u=e.pads,d=e.strides,c=e.w_is_const();return{autoPad:n,format:r,dilations:o,group:i,kernelShape:a,pads:u,strides:d,wIsConst:c,...t,cacheKey:`${e.format};${t.activation};`}},Ku=(e,t,r,n)=>{let o=r.format==="NHWC",i=gf(t[0].dims,t[1].dims,r.dilations,r.pads,r.strides,o);if(r.group!==1){let I=[t[0]];if(o){let O=e.kernelCustomData.wT??e.compute(Ee(t[1],ho),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=O),I.push(O)}else I.push(t[1]);t.length===3&&I.push(t[2]),!e.adapterInfo.isArchitecture("ampere")&&o&&t[1].dims[0]===r.group&&t[1].dims[1]===1&&r.dilations[0]===1&&r.dilations[1]===1?e.compute(qu(I,r,i,n),{inputs:I}):e.compute(Fu(I,r,i,n),{inputs:I});return}let a=t.length===3,u=t[0].dims[o?1:2],d=t[0].dims[o?2:3],c=t[0].dims[o?3:1],p=t[1].dims[2],m=t[1].dims[3],f=i[o?1:2],b=i[o?2:3],g=i[o?3:1],_=o&&p===u&&m===d&&r.pads[0]===0&&r.pads[1]===0;if(_||p===1&&m===1&&r.dilations[0]===1&&r.dilations[1]===1&&r.strides[0]===1&&r.strides[1]===1&&r.pads[0]===0&&r.pads[1]===0){let I=i[0],z,O,D,L=[];if(o){let W=e.kernelCustomData.wT??e.compute(Ee(t[1],ho),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];if(r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=W),_){let Z=u*d*c;z=t[0].reshape([1,I,Z]),O=W.reshape([1,Z,g]),D=[1,I,g]}else z=t[0].reshape([I,u*d,c]),O=W.reshape([1,c,g]),D=[I,f*b,g];L.push(z),L.push(O)}else z=t[0].reshape([I,c,u*d]),O=t[1].reshape([1,g,c]),D=[I,g,f*b],L.push(O),L.push(z);a&&L.push(t[2]);let q=D[2],Q=L[0].dims[L[0].dims.length-1];q<8&&Q<8?e.compute(Qr(L,r,i,D,o,n),{inputs:L}):e.compute(Jt(L,r,i,D,o,n),{inputs:L});return}let S=!0,$=e.kernelCustomData.wT??e.compute(Ee(t[1],ho),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=$);let v=[t[0],$];a&&v.push(t[2]);let x=o?f*b:g,T=o?g:f*b,E=p*m*c;e.compute(Uu(v,r,i,x,T,E,a,S,n),{inputs:v})},yf=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let o=[0,t.pads[0],0,t.pads[1]],i=[1].concat(t.strides),a=[1].concat(t.dilations),u=[1].concat(t.kernelShape),d=go({...t,pads:o,strides:i,dilations:a,kernelShape:u},n);Ku(e,n,d,c=>r?[c[0],c[2],c[3]]:[c[0],c[1],c[3]])},_f=(e,t,r)=>{let n=r.format==="NHWC"?"channelsLast":"channelsFirst",o=go(r,t),i=r.autoPad==="NOTSET"?r.pads:r.autoPad,a=Lu(t[0].dims,t[1].dims,r.strides,r.dilations,i,!1,n);e.compute(Gu(t,o,a.outShape,[a.filterDepth,a.filterHeight,a.filterWidth],[a.padInfo.front,a.padInfo.top,a.padInfo.left],n))},yo=(e,t)=>{if(bf(e.inputs,t),e.inputs[0].dims.length===3)yf(e,t);else if(e.inputs[0].dims.length===5)_f(e,e.inputs,t);else{let r=go(t,e.inputs);Ku(e,e.inputs,r)}}});var Qu,Yu=U(()=>{"use strict";ee();Xe();ne();ie();Qu=(e,t,r)=>{let n=e.length>2,o=t.outputShape,i=t.format==="NHWC",a=t.group,u=e[1].dims,d=u[2]/a,c=u[3],p=i?ce(d):1,m=i&&c===1&&d>=4,f=m?Math.floor(d/4)*4:Math.floor(d/p)*p,b=d-f,g=i?ce(c):1,_=i?c===1?p:g:1,S=k.size(o)/g,$=[Math.ceil(S/64),1,1];se("verbose",()=>`[conv2d_backprop_webgpu] dispatch = ${$}`);let v=["rank","rank"],x=[t.strides[0],t.strides[1]],T=[t.kernelShape[i?1:2],t.kernelShape[i?2:3]],E=[t.dilations[0],t.dilations[1]],I=[T[0]+(t.dilations[0]<=1?0:(t.kernelShape[i?1:2]-1)*(t.dilations[0]-1)),T[1]+(t.dilations[1]<=1?0:(t.kernelShape[i?2:3]-1)*(t.dilations[1]-1))],z=[I[0]-1-Math.floor((t.pads[0]+t.pads[2])/2),I[1]-1-Math.floor((t.pads[1]+t.pads[3])/2)],O=[{type:12,data:S},{type:12,data:x},{type:12,data:T},{type:12,data:E},{type:12,data:I},{type:6,data:z},{type:12,data:f},{type:12,data:d},{type:12,data:c},...N(e[0].dims,e[1].dims)];n&&(O.push(...N(e[2].dims)),v.push("rank")),O.push(...N(o));let D=L=>{let q=[{name:"output_size",type:"u32"},{name:"strides",type:"u32",length:x.length},{name:"filter_dims",type:"u32",length:T.length},{name:"dilations",type:"u32",length:T.length},{name:"effective_filter_dims",type:"u32",length:I.length},{name:"pads",type:"i32",length:z.length},{name:"input_channels_per_group_int",type:"u32"},{name:"input_channels_per_group",type:"u32"},{name:"output_channels_per_group",type:"u32"}],Q=be(e[0].dataType),W=i?1:2,Z=i?2:3,we=i?3:1,H=P("W",e[1].dataType,e[1].dims.length,_),j=P("Dy",e[0].dataType,e[0].dims.length,p),te=[j,H];n&&te.push(P("bias",e[2].dataType,[o[we]].length,g));let X=M("result",e[0].dataType,o.length,g),ue=()=>{let re="";if(m)p===4?re+=` + let xValue = ${j.getByOffset("x_offset")}; + let wValue = ${H.getByOffset("w_offset")}; + dotProd = dotProd + dot(xValue, wValue); + x_offset += 1u; + w_offset += 1u;`:p===2?re+=` + dotProd = dotProd + dot(vec4<${Q}>(${j.getByOffset("x_offset")}, ${j.getByOffset("x_offset + 1u")}), vec4<${Q}>(${H.getByOffset("w_offset")}, ${H.getByOffset("w_offset + 1u")})); + x_offset += 2u; + w_offset += 2u;`:p===1&&(re+=` + dotProd = dotProd + dot(vec4<${Q}>(${j.getByOffset("x_offset")}, ${j.getByOffset("x_offset + 1u")}, ${j.getByOffset("x_offset + 2u")}, ${j.getByOffset("x_offset + 3u")}), vec4<${Q}>(${H.getByOffset("w_offset")}, ${H.getByOffset("w_offset + 1u")}, ${H.getByOffset("w_offset + 2u")}, ${H.getByOffset("w_offset + 3u")})); + x_offset += 4u; + w_offset += 4u;`);else if(re+=` + let xValue = ${i?j.getByOffset(`${j.indicesToOffset(`${j.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}`):j.get("batch","inputChannel","idyR","idyC")}; + `,p===1)re+=` + let w_offset = ${H.indicesToOffset(`${H.type.indices}(u32(wRPerm), u32(wCPerm), inputChannel, wOutChannel)`)}; + let wValue = ${H.getByOffset(`w_offset / ${_}`)}; + dotProd = dotProd + xValue * wValue;`;else for(let C=0;C{if(b===0)return"";if(!m)throw new Error(`packInputAs4 ${m} is not true.`);let re="";if(p===1){re+="dotProd = dotProd";for(let C=0;C(i32(r), i32(c)) - uniforms.pads; + let dyRCorner = dyCorner.x; + let dyCCorner = dyCorner.y; + let groupId = d1 / uniforms.output_channels_per_group; + let wOutChannel = d1 - groupId * uniforms.output_channels_per_group; + // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1). + // ? = to be determined. : = across all values in that axis. + var dotProd = ${X.type.value}(0.0); + var wR: u32 = 0; + if (uniforms.dilations.x == 1) { + // Minimum wR >= 0 that satisfies (dyRCorner + wR) % (uniforms.strides.x) == 0 + wR = u32(((dyRCorner + i32(uniforms.strides.x) - 1) / i32(uniforms.strides.x)) * i32(uniforms.strides.x) - dyRCorner); + } + for (; wR < uniforms.effective_filter_dims.x; wR = wR + 1) { + if (wR % uniforms.dilations.x != 0) { + continue; + } + let dyR = (${Q}(dyRCorner) + ${Q}(wR)) / ${Q}(uniforms.strides[0]); + let wRPerm = uniforms.filter_dims.x - 1 - wR / uniforms.dilations.x; + if (dyR < 0.0 || dyR >= ${Q}(uniforms.Dy_shape[${W}]) || fract(dyR) > 0.0 || + wRPerm < 0) { + continue; + } + let idyR: u32 = u32(dyR); + var wC: u32 = 0; + if (uniforms.dilations.y == 1) { + // Minimum wC >= 0 that satisfies (dyCCorner + wC) % (uniforms.strides.y) == 0 + wC = u32(((dyCCorner + i32(uniforms.strides.y) - 1) / i32(uniforms.strides.y)) * i32(uniforms.strides.y) - dyCCorner); + } + for (; wC < uniforms.effective_filter_dims.y; wC = wC + 1) { + if (wC % uniforms.dilations.y != 0) { + continue; + } + let dyC = (${Q}(dyCCorner) + ${Q}(wC)) / ${Q}(uniforms.strides.y); + let wCPerm = uniforms.filter_dims.y - 1 - wC / uniforms.dilations.y; + if (dyC < 0.0 || dyC >= ${Q}(uniforms.Dy_shape[${Z}]) || + fract(dyC) > 0.0 || wCPerm < 0) { + continue; + } + let idyC: u32 = u32(dyC); + var inputChannel = groupId * uniforms.input_channels_per_group; + ${m?` + var x_offset = ${j.indicesToOffset(`${j.type.indices}(batch, idyR, idyC, inputChannel)`)} / ${p}; + var w_offset = ${H.indicesToOffset(`${H.type.indices}(wRPerm, wCPerm, inputChannel, wOutChannel)`)} / ${_}; + `:""} + for (var d2: u32 = 0; d2 < uniforms.input_channels_per_group_int; d2 = d2 + ${m?4:p}) { + ${ue()} + inputChannel = inputChannel + ${m?4:p}; + } + ${he()} + wC = wC + uniforms.strides.y - 1; + } + wR = wR + uniforms.strides[0] - 1; + } + let value = dotProd${n?` + bias[d1 / ${g}]`:""}; + ${X.setByOffset("global_idx","value")}; + `;return` + ${L.registerUniforms(q).declareVariables(...te,X)} + ${L.mainStart()} + ${L.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")}; + ${ye}}`};return{name:"ConvTranspose2D",shaderCache:{hint:`${t.cacheKey};${p}${_}${g}${m}${b}`,inputDependencies:v},getRunData:()=>({dispatchGroup:{x:$[0],y:$[1],z:$[2]},outputs:[{dims:r?r(o):o,dataType:e[0].dataType}],programUniforms:O}),getShaderSource:D}}});var wf,vf,$f,Xu,Ju,xf,ed,Sf,td,rd=U(()=>{"use strict";Yu();bt();st();wf=(e,t,r,n,o,i)=>(e-1)*t+r+(n-1)*o+1-i,vf=(e,t,r,n,o)=>{let i=Math.floor(e/2);t==="SAME_UPPER"?(r[n]=i,r[o]=e-i):t==="SAME_LOWER"&&(r[n]=e-i,r[o]=i)},$f=(e,t,r,n,o,i,a,u,d,c)=>{let p=e.length-2,m=c.length===0;d.length{let r=e.kernelShape.slice();if(e.kernelShape.length===0||e.kernelShape.reduce((m,f)=>m*f,1)===0){r.length=0;for(let m=2;mm+f,0)===0){let m=t[0].dims.length-2;d=new Array(m).fill(1)}let c=e.strides.slice();if(c.reduce((m,f)=>m+f,0)===0){let m=t[0].dims.length-2;c=new Array(m).fill(1)}$f(u,r,d,e.autoPad,e.group,o,c,n,a,i);let p=Object.assign({},e);return Object.assign(p,{kernelShape:r,pads:o,outputPadding:a,outputShape:i,dilations:d,strides:c}),p},Ju=e=>{let t=Kr(e),r=e.format,n=["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][typeof e.autoPad>"u"?0:e.autoPad],o=e.dilations,i=e.group,a=e.kernelShape,u=e.pads,d=e.strides,c=e.wIsConst(),p=e.outputPadding,m=e.outputShape;return{autoPad:n,format:r,dilations:o,group:i,kernelShape:a,outputPadding:p,outputShape:m,pads:u,strides:d,wIsConst:c,...t,cacheKey:`${e.format};${t.activation};`}},xf=(e,t)=>{if(!e||e.length!==2&&e.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(e[0].dims.length!==4&&e[0].dims.length!==3)throw new Error("currently only support 2-dimensional conv");if(e[0].dims.length!==e[1].dims.length)throw new Error("filter does not have same dimension as input");let r=e[0].dims[t.format==="NHWC"?e[0].dims.length-1:1],n=e[1].dims[0];if(r!==n)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let o=e[1].dims[1]*t.group;if(e.length===3&&(e[2].dims.length!==1||e[2].dims[0]!==o))throw new Error("invalid bias");let i=e[0].dims.length-2;if(t.dilations.reduce((p,m)=>p+m,0)>0&&t.dilations.length!==i)throw new Error(`dilations should be ${i}D`);if(t.strides.reduce((p,m)=>p+m,0)>0&&t.strides.length!==i)throw new Error(`strides should be ${i}D`);if(t.pads.reduce((p,m)=>p+m,0)>0&&t.pads.length!==i*2)throw new Error(`pads should be ${i*2}D`);if(t.outputPadding.length!==i&&t.outputPadding.length!==0)throw new Error(`output_padding should be ${i}D`);if(t.kernelShape.reduce((p,m)=>p+m,0)>0&&t.kernelShape.length!==0&&t.kernelShape.length!==e[1].dims.length-2)throw new Error("invalid kernel shape");if(t.outputShape.length!==0&&t.outputShape.length!==e[0].dims.length-2)throw new Error("invalid output shape")},ed=(e,t,r,n)=>{let o=e.kernelCustomData.wT??e.compute(Ee(t[1],[2,3,0,1]),{inputs:[1],outputs:[r.wIsConst?-2:-1]})[0];r.wIsConst&&!e.kernelCustomData.wT&&(e.kernelCustomData.wT=o);let i=[t[0],o];t.length===3&&i.push(t[2]),e.compute(Qu(i,r,n),{inputs:i})},Sf=(e,t)=>{let r=t.format==="NHWC",n=[e.inputs[0].reshape(r?[e.inputs[0].dims[0],1,e.inputs[0].dims[1],e.inputs[0].dims[2]]:[e.inputs[0].dims[0],e.inputs[0].dims[1],1,e.inputs[0].dims[2]]),e.inputs[1].reshape([e.inputs[1].dims[0],e.inputs[1].dims[1],1,e.inputs[1].dims[2]])];e.inputs.length===3&&n.push(e.inputs[2]);let o=t.kernelShape;(o.length===0||o[0]===0)&&(o=[e.inputs[1].dims[2]]);let i=t.dilations;(i.length===0||i[0]===0)&&(i=[1]);let a=t.strides;(a.length===0||a[0]===0)&&(a=[1]);let u=t.pads;u.length===0&&(u=[0,0]),u=[0,u[0],0,u[1]],a=[1].concat(a),i=[1].concat(i),o=[1].concat(o);let d=t.outputPadding;d=[0].concat(d);let c=Xu({...t,pads:u,strides:a,dilations:i,kernelShape:o,outputPadding:d},n);ed(e,n,c,p=>r?[p[0],p[2],p[3]]:[p[0],p[1],p[3]])},td=(e,t)=>{if(xf(e.inputs,t),e.inputs[0].dims.length===3)Sf(e,t);else{let r=Xu(t,e.inputs);ed(e,e.inputs,r)}}});var Tf,nd,od,id=U(()=>{"use strict";ee();ne();Se();ie();Tf=(e,t,r,n)=>{let o=k.size(t),i=t.length,a=P("input",e,i),u=M("output",e,i),d=r.dataType===6?r.getInt32Array()[0]:Number(r.getBigInt64Array()[0]),c=k.normalizeAxis(d,i),p=m=>{let f=` i32(${a.indicesGet("inputIndices","uniforms.axis")}) `,b=F("uniforms.input_shape","uniforms.axis",i),g=n.reverse?f+(n.exclusive?" + 1":""):"0",_=n.reverse?b:f+(n.exclusive?"":" + 1");return` + ${m.registerUniform("outputSize","u32").registerUniform("axis","u32").declareVariables(a,u)} + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var inputIndices = ${u.offsetToIndices("global_idx")}; + var sum = ${u.type.value}(0); + let first : i32 = ${g}; + let last : i32 = ${_}; + for (var i : i32 = first; i < last; i++) { + ${a.indicesSet("inputIndices","uniforms.axis","u32(i)")}; + sum = sum + ${a.getByIndices("inputIndices")}; + } + ${u.setByOffset("global_idx","sum")}; + }`};return{name:"CumSum",shaderCache:{hint:n.cacheKey,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:t,dataType:e}],dispatchGroup:{x:Math.ceil(o/64)},programUniforms:[{type:12,data:o},{type:12,data:c},...N(t,t)]}),getShaderSource:p}},nd=(e,t)=>{let r=e.inputs[0].dims,n=e.inputs[0].dataType,o=e.inputs[1];e.compute(Tf(n,r,o,t),{inputs:[0]})},od=e=>{let t=e.exclusive===1,r=e.reverse===1;return J({exclusive:t,reverse:r})}});var If,Cf,Af,ad,sd,ud=U(()=>{"use strict";ee();ne();Se();ie();If=e=>{if(!e||e.length!==1)throw new Error("DepthToSpace requires 1 input.");if(e[0].dims.length!==4)throw new Error("DepthToSpace requires 4D input.")},Cf=(e,t,r,n)=>{let o=[];o.push(`fn perm(i: ${n.type.indices}) -> ${r.type.indices} { + var a: ${r.type.indices};`);for(let i=0;i{let r,n,o,i,a,u,d=t.format==="NHWC",c=t.blocksize,p=t.mode==="DCR";d?([r,n,o,i]=e.dims,a=p?[r,n,o,c,c,i/c**2]:[r,n,o,i/c**2,c,c],u=p?[0,1,3,2,4,5]:[0,1,4,2,5,3]):([r,n,o,i]=[e.dims[0],e.dims[2],e.dims[3],e.dims[1]],a=p?[r,c,c,i/c**2,n,o]:[r,i/c**2,c,c,n,o],u=p?[0,3,4,1,5,2]:[0,1,4,2,5,3]);let m=e.reshape(a),f=m.dims.length,b=e.dataType,g=P("a",b,f),_=M("output",b,f),S=$=>` + ${$.registerUniform("output_size","u32").declareVariables(g,_)} + + ${Cf(u,f,g,_)} + + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${_.offsetToIndices("global_idx")}; + let aIndices = perm(indices); + + ${_.setByOffset("global_idx",g.getByIndices("aIndices"))} + }`;return{name:"DepthToSpace",shaderCache:{hint:`${e.dims};${t.blocksize};${t.mode}`,inputDependencies:["rank"]},getRunData:$=>{let v=d?[r,n*c,o*c,i/c**2]:[r,i/c**2,n*c,o*c],x=k.size(v),T=m.dims,E=k.sortBasedOnPerm(T,u);return{outputs:[{dims:v,dataType:$[0].dataType}],dispatchGroup:{x:Math.ceil(x/64)},programUniforms:[{type:12,data:x},...N(T,E)]}},getShaderSource:S}},ad=(e,t)=>{If(e.inputs),e.compute(Af(e.inputs[0],t))},sd=e=>J({blocksize:e.blocksize,mode:e.mode,format:e.format})});var _o,en,dd,Ef,kf,wo,vo,ld,Pf,cd,pd,md=U(()=>{"use strict";ee();ne();Se();ie();_o="[a-zA-Z]|\\.\\.\\.",en="("+_o+")+",dd="^"+en+"$",Ef="("+en+",)*"+en,kf="^"+Ef+"$",wo=class{constructor(t=-1){this.symbolToIndices=new Map,this.inputIndex=t}addSymbol(t,r){let n=this.symbolToIndices.get(t);n===void 0?n=[r]:n.push(r),this.symbolToIndices.set(t,n)}},vo=class{constructor(t,r){this.equation=r;this.hasEllipsis=!1,this.symbolToInfo=new Map,this.lhs=new Array,this.outputDims=[];let[n,o]=r.includes("->")?r.split("->",2):[r,""];if(!n.match(RegExp(kf)))throw new Error("Invalid LHS term");if(n.split(",").forEach((u,d)=>{let c=t[d].dims.slice();if(!u.match(RegExp(dd)))throw new Error("Invalid LHS term");let p=this.processTerm(u,!0,c,d);this.lhs.push(p)}),o==="")o+=[...this.symbolToInfo.entries()].filter(([u,d])=>d.count===1||u==="...").map(([u])=>u).join("");else if(!o.match(RegExp(en)))throw new Error("Invalid RHS");o.match(RegExp(_o,"g"))?.forEach(u=>{if(u==="...")this.outputDims=this.outputDims.concat(this.ellipsisDims);else{let d=this.symbolToInfo.get(u);if(d===void 0)throw new Error("Invalid RHS symbol");this.outputDims.push(d.dimValue)}}),this.rhs=this.processTerm(o,!1,this.outputDims)}addSymbol(t,r,n){let o=this.symbolToInfo.get(t);if(o!==void 0){if(o.dimValue!==r&&o.count!==1)throw new Error("Dimension mismatch");o.count++,o.inputIndices.push(n)}else o={count:1,dimValue:r,inputIndices:[n]};this.symbolToInfo.set(t,o)}processTerm(t,r,n,o=-1){let i=n.length,a=!1,u=[],d=0;if(!t.match(RegExp(dd))&&!r&&t!=="")throw new Error("Invalid LHS term");let c=t.match(RegExp(_o,"g")),p=new wo(o);return c?.forEach((m,f)=>{if(m==="..."){if(a)throw new Error("Only one ellipsis is allowed per input term");a=!0;let b=i-c.length+1;if(b<0)throw new Error("Ellipsis out of bounds");if(u=n.slice(d,d+b),this.hasEllipsis){if(this.ellipsisDims.length!==u.length||this.ellipsisDims.toString()!==u.toString())throw new Error("Ellipsis dimensions mismatch")}else if(r)this.hasEllipsis=!0,this.ellipsisDims=u;else throw new Error("Ellipsis must be specified in the LHS");for(let g=0;ge+"_max",Pf=(e,t,r,n)=>{let i=e.map(p=>p.length).map((p,m)=>P(`input${m}`,t,p)),a=k.size(n),u=M("output",t,n.length),d=[...r.symbolToInfo.keys()].filter(p=>!r.rhs.symbolToIndices.has(p)),c=p=>{let m=[],f="var prod = 1.0;",b="var sum = 0.0;",g="sum += prod;",_=[],S=[],$=[],v=[],x=r.symbolToInfo.size===r.rhs.symbolToIndices.size;r.symbolToInfo.forEach((E,I)=>{if(r.rhs.symbolToIndices.has(I)){let z=r.rhs.symbolToIndices.get(I)?.[0];z!==void 0&&r.lhs.forEach((O,D)=>{if(E.inputIndices.includes(D)){let L=O.symbolToIndices.get(I);if(L===void 0)throw new Error("Invalid symbol error");L.forEach(q=>{m.push(`${i[D].indicesSet(`input${D}Indices`,q,u.indicesGet("outputIndices",z))}`)})}})}else r.lhs.forEach((z,O)=>{if(E.inputIndices.includes(O)){let D=z.symbolToIndices.get(I);if(D===void 0)throw new Error("Invalid symbol error");D.forEach(L=>{_.push(`${i[O].indicesSet(`input${O}Indices`,L,`${I}`)}`)}),v.push(`prod *= ${i[O].getByIndices(`input${O}Indices`)};`)}}),S.push(`for(var ${I}: u32 = 0; ${I} < uniforms.${ld(I)}; ${I}++) {`),$.push("}")});let T=x?[...m,`let sum = ${i.map((E,I)=>E.getByIndices(`input${I}Indices`)).join(" * ")};`]:[...m,b,...S,..._,f,...v,g,...$];return` + ${p.registerUniforms(d.map(E=>({name:`${ld(E)}`,type:"u32"}))).registerUniform("outputSize","u32").declareVariables(...i,u)} + + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + var outputIndices = ${u.offsetToIndices("global_idx")}; + ${i.map((E,I)=>`var input${I}Indices: ${i[I].type.indices};`).join(` +`)} + ${T.join(` +`)}; + ${u.setByOffset("global_idx","sum")}; + }`};return{name:"Einsum",shaderCache:{hint:r.equation,inputDependencies:e.map(()=>"rank")},getRunData:()=>{let p=d.filter(f=>r.symbolToInfo.has(f)).map(f=>({type:12,data:r.symbolToInfo.get(f)?.dimValue||0}));p.push({type:12,data:a});let m=e.map((f,b)=>[...N(f)]).reduce((f,b)=>f.concat(b),p);return m.push(...N(n)),{outputs:[{dims:n,dataType:t}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:m}},getShaderSource:c}},cd=(e,t)=>{let r=new vo(e.inputs,t.equation),n=r.outputDims,o=e.inputs.map((i,a)=>i.dims);e.compute(Pf(o,e.inputs[0].dataType,r,n))},pd=e=>{let t=e.equation.replace(/\s+/g,"");return J({equation:t})}});var zf,fd,Of,Bf,hd,gd=U(()=>{"use strict";ee();ne();ie();zf=e=>{if(!e||e.length!==2)throw new Error("Expand requires 2 input.");let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=r.length{let r=e.length-t.length,n=[];for(let o=0;oe.length>t.length?fd(e,t):fd(t,e),Bf=e=>{let t=e[0].dims,r=Array.from(e[1].getBigInt64Array(),Number),n=Of(t,r),o=e[0].dataType,i=o===9||k.size(t)===1,a=o===9||t.length>0&&t[t.length-1]%4===0?4:1,u=i||n.length>0&&n[n.length-1]%4===0?4:1,d=Math.ceil(k.size(n)/u),c=m=>{let f=P("input",o,t.length,a),b=M("output",o,n.length,u),g;if(o===9){let _=(S,$,v="")=>` + let outputIndices${$} = ${b.offsetToIndices(`outputOffset + ${$}u`)}; + let offset${$} = ${f.broadcastedIndicesToOffset(`outputIndices${$}`,b)}; + let index${$} = offset${$} / 4u; + let component${$} = offset${$} % 4u; + ${S}[${$}] = ${v}(${f.getByOffset(`index${$}`)}[component${$}]); + `;g=` + let outputOffset = global_idx * ${u}; + var data = vec4(0); + ${_("data",0,"u32")} + ${_("data",1,"u32")} + ${_("data",2,"u32")} + ${_("data",3,"u32")} + ${b.setByOffset("global_idx","data")} + }`}else g=` + let outputIndices = ${b.offsetToIndices(`global_idx * ${u}`)}; + let inputOffset = ${f.broadcastedIndicesToOffset("outputIndices",b)}; + let data = ${b.type.value}(${f.getByOffset(`inputOffset / ${a}`)}); + ${b.setByOffset("global_idx","data")} + }`;return` + ${m.registerUniform("vec_size","u32").declareVariables(f,b)} + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${g}`},p=[{type:12,data:d},...N(t,n)];return{name:"Expand",shaderCache:{hint:`${n.length};${a}${u}`,inputDependencies:["rank"]},getShaderSource:c,getRunData:()=>({outputs:[{dims:n,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:p})}},hd=e=>{zf(e.inputs),e.compute(Bf(e.inputs),{inputs:[0]})}});var Df,bd,yd=U(()=>{"use strict";ee();ne();ie();jr();Df=e=>{let t=e[0].dataType,r=k.size(e[0].dims),n=k.size(e[1].dims),o=n%4===0,i=a=>{let u=P("x",t,[1],4),d=P("bias",t,[1],4),c=M("y",t,[1],4),p=[{name:"output_vec_size",type:"u32"},{name:"bias_size",type:"u32"}],m=b=>` + let bias${b}_offset: u32 = (global_idx * 4 + ${b}) % uniforms.bias_size; + let bias${b} = ${d.getByOffset(`bias${b}_offset / 4`)}[bias${b}_offset % 4];`,f=o?` + let bias = ${d.getByOffset("global_idx % (uniforms.bias_size / 4)")};`:`${m(0)}${m(1)}${m(2)}${m(3)} + let bias = ${u.type.value}(bias0, bias1, bias2, bias3);`;return`${a.registerUniforms(p).declareVariables(u,d,c)} + + ${co(Ae(t))} + + ${a.mainStart(It)} + ${a.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_vec_size")} + + let x = ${u.getByOffset("global_idx")}; + ${f} + let x_in = x + bias; + ${c.setByOffset("global_idx",po("x_in"))} + }`};return{name:"FastGeluWithBias",shaderCache:{hint:`${o}`,inputDependencies:["type","type"]},getShaderSource:i,getRunData:a=>({outputs:[{dims:a[0].dims,dataType:a[0].dataType}],programUniforms:[{type:12,data:Math.ceil(r/4)},{type:12,data:n}],dispatchGroup:{x:Math.ceil(r/It/4)}})}},bd=e=>{e.inputs.length<2||k.size(e.inputs[1].dims)===0?mu(e):e.compute(Df(e.inputs))}});var Mf,Rf,_d,wd,vd=U(()=>{"use strict";ee();ne();Se();ie();Mf=e=>{if(!e||e.length!==2)throw new Error("Gather requires 2 inputs.")},Rf=(e,t)=>{let r=e[0].dims,n=e[1].dims,o=r.length,i=k.normalizeAxis(t.axis,o),a=r.slice(0);a.splice(i,1,...n);let u=r[i],d=e[0].dataType===9?4:1,c=Math.ceil(k.size(a)/d),p=[{type:12,data:c},{type:6,data:u},{type:12,data:i},...N(e[0].dims,e[1].dims,a)],m=f=>{let b=P("data",e[0].dataType,e[0].dims.length,d),g=P("inputIndices",e[1].dataType,e[1].dims.length),_=M("output",e[0].dataType,a.length,d),S=v=>{let x=n.length,T=`var indicesIndices${v} = ${g.type.indices}(0);`;for(let E=0;E1?`indicesIndices${v}[${E}]`:`indicesIndices${v}`} = ${a.length>1?`outputIndices${v}[uniforms.axis + ${E}]`:`outputIndices${v}`};`;T+=` + var idx${v} = ${g.getByIndices(`indicesIndices${v}`)}; + if (idx${v} < 0) { + idx${v} = idx${v} + uniforms.axisDimLimit; + } + var dataIndices${v} : ${b.type.indices}; + `;for(let E=0,I=0;E1?`dataIndices${v}[${E}]`:`dataIndices${v}`} = u32(idx${v});`,I+=x):(T+=`${o>1?`dataIndices${v}[${E}]`:`dataIndices${v}`} = ${a.length>1?`outputIndices${v}[${I}]`:`outputIndices${v}`};`,I++);return T},$;if(e[0].dataType===9){let v=(x,T,E="")=>` + let outputIndices${T} = ${_.offsetToIndices(`outputOffset + ${T}u`)}; + ${S(T)}; + let offset${T} = ${b.indicesToOffset(`dataIndices${T}`)}; + let index${T} = offset${T} / 4u; + let component${T} = offset${T} % 4u; + ${x}[${T}] = ${E}(${b.getByOffset(`index${T}`)}[component${T}]); + `;$=` + let outputOffset = global_idx * ${d}; + var value = vec4(0); + ${v("value",0,"u32")} + ${v("value",1,"u32")} + ${v("value",2,"u32")} + ${v("value",3,"u32")} + ${_.setByOffset("global_idx","value")} + `}else $=` + let outputIndices = ${_.offsetToIndices("global_idx")}; + ${S("")}; + let value = ${b.getByIndices("dataIndices")}; + ${_.setByOffset("global_idx","value")}; + `;return` + ${f.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(b,g,_)} + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + ${$} + }`};return{name:"Gather",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:a,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:p}),getShaderSource:m}},_d=e=>J({axis:e.axis}),wd=(e,t)=>{let r=e.inputs;Mf(r),e.compute(Rf(e.inputs,t))}});var Uf,$d,xd,Sd=U(()=>{"use strict";ee();ne();ie();Uf=(e,t,r,n,o,i,a,u,d)=>{let c=[{type:12,data:i},{type:12,data:n},{type:12,data:o},{type:12,data:r},{type:12,data:a},{type:12,data:u},{type:12,data:d}],p=[i];c.push(...N(t.dims,p));let m=f=>{let b=P("indices_data",t.dataType,t.dims.length),g=M("input_slice_offsets_data",12,1,1),_=[b,g],S=[{name:"output_size",type:"u32"},{name:"batch_dims",type:"u32"},{name:"input_dims",type:"u32",length:o.length},{name:"sizes_from_slice_dims_data",type:"u32",length:r.length},{name:"num_slices_per_batch",type:"u32"},{name:"input_batch_stride",type:"u32"},{name:"num_slice_dims",type:"u32"}];return` + ${f.registerUniforms(S).declareVariables(..._)} + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let batch_idx = global_idx / uniforms.num_slices_per_batch; + let base_offset = batch_idx * uniforms.input_batch_stride; + + let slice_indices_base_offset = global_idx * uniforms.num_slice_dims; + var relative_slice_offset = 0; + for (var dim_idx = 0u; dim_idx < uniforms.num_slice_dims; dim_idx ++) { + var index = i32(indices_data[dim_idx + slice_indices_base_offset].x); + let input_dim_idx = uniforms.batch_dims + dim_idx; + if (index < 0) { + ${o.length===1?"index += i32(uniforms.input_dims);":"index += i32(uniforms.input_dims[input_dim_idx]);"} + } + ${r.length===1?"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data);":"relative_slice_offset += index * i32(uniforms.sizes_from_slice_dims_data[dim_idx]);"} + } + + input_slice_offsets_data[global_idx] = base_offset + u32(relative_slice_offset); + }`};return e.compute({name:"computeSliceOffsets",shaderCache:{hint:`${o.length}_${r.length}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:p,dataType:e.inputs[1].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:c}),getShaderSource:m},{inputs:[t],outputs:[-1]})[0]},$d=(e,t)=>{let r=e.inputs,n=r[0].dims,o=r[0].dataType,i=r[1].dims,a=i[i.length-1],u=k.sizeToDimension(i,i.length-1),d=k.sizeFromDimension(n,t.batchDims+a),c=k.sizeToDimension(n,t.batchDims),p=k.sizeFromDimension(n,t.batchDims),m=u/c,f=new Array(a),b=d;for(let T=0;Tn.length)throw new Error("last dimension of indices must not be larger than rank of input tensor");let S=i.slice(0,-1).concat(n.slice(_)),$=k.size(S),v=[{type:12,data:$},{type:12,data:d},...N(r[0].dims,g.dims,S)],x=T=>{let E=P("data",r[0].dataType,r[0].dims.length),I=P("slice_offsets",12,g.dims.length),z=M("output",r[0].dataType,S.length);return` + ${T.registerUniform("output_size","u32").registerUniform("slice_size","u32").declareVariables(E,I,z)} + ${T.mainStart()} + ${T.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let slice_offset = slice_offsets[global_idx / uniforms.slice_size]; + output[global_idx] = data[u32(slice_offset) + global_idx % uniforms.slice_size]; + }`};e.compute({name:"GatherND",shaderCache:{hint:t.cacheKey,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:S,dataType:o}],dispatchGroup:{x:Math.ceil($/64)},programUniforms:v}),getShaderSource:x},{inputs:[r[0],g]})},xd=e=>({batchDims:e.batch_dims,cacheKey:""})});var Nf,Vf,Td,Id,Cd=U(()=>{"use strict";ee();ne();Se();ie();Nf=(e,t)=>{if(e.length<3||e.length>4)throw new Error("GatherBlockQuantized requires 3 or 4 inputs.");let r=k.normalizeAxis(t.quantizeAxis,e[0].dims.length),n=t.blockSize,o=e[0],i=e[2],a=e.length===4?e[3]:void 0;if(i.dims.length!==o.dims.length||!o.dims.map((u,d)=>d===r?Math.ceil(u/n)===i.dims[d]:u===i.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Scales must have the same rank as the input tensor and the dims should match except on gatherAxis.");if(a){if(a.dataType!==o.dataType)throw new Error("Zero point must have the same data type as the input tensor.");if(a.dims.length!==i.dims.length||!a.dims.map((u,d)=>u===i.dims[d]).reduce((u,d)=>u&&d,!0))throw new Error("Zero point must have the same rank as the input tensor and the dims should match except on quantizeAxis.")}},Vf=(e,t)=>{let r=e[0].dims,n=e[1].dims,o=r.length,i=k.normalizeAxis(t.gatherAxis,o),a=k.normalizeAxis(t.quantizeAxis,o),u=r.slice(0);u.splice(i,1,...n);let d=k.size(u),c=e[2].dataType,m=e[0].dataType===22,f=[{type:12,data:d},{type:12,data:a},{type:12,data:i},{type:12,data:t.blockSize},...N(...e.map((g,_)=>g.dims),u)],b=g=>{let _=P("data",e[0].dataType,e[0].dims.length),S=P("inputIndices",e[1].dataType,e[1].dims.length),$=P("scales",e[2].dataType,e[2].dims.length),v=e.length>3?P("zeroPoint",e[3].dataType,e[3].dims.length):void 0,x=M("output",c,u.length),T=[_,S,$];v&&T.push(v);let E=[{name:"output_size",type:"u32"},{name:"quantize_axis",type:"u32"},{name:"gather_axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${g.registerUniforms(E).declareVariables(...T,x)} + ${g.mainStart()} + let output_indices = ${x.offsetToIndices("global_idx")}; + var indices_indices = ${S.type.indices}(0); + ${n.length>1?` + for (var i: u32 = 0; i < ${n.length}; i++) { + let index = ${x.indicesGet("output_indices","uniforms.gather_axis + i")}; + ${S.indicesSet("indices_indices","i","index")}; + }`:`indices_indices = ${x.indicesGet("output_indices","uniforms.gather_axis")};`}; + var data_indices = ${_.type.indices}(0); + for (var i: u32 = 0; i < uniforms.gather_axis; i++) { + let index = ${x.indicesGet("output_indices","i")}; + ${_.indicesSet("data_indices","i","index")}; + } + var index_from_indices = ${S.getByIndices("indices_indices")}; + if (index_from_indices < 0) { + index_from_indices += ${r[i]}; + } + ${_.indicesSet("data_indices","uniforms.gather_axis","u32(index_from_indices)")}; + for (var i = uniforms.gather_axis + 1; i < ${u.length}; i++) { + let index = ${x.indicesGet("output_indices",`i + ${n.length} - 1`)}; + ${_.indicesSet("data_indices","i","index")}; + } + let data_offset = ${_.indicesToOffset("data_indices")}; + let data_index = data_offset % 8; + // Convert 4-bit packed data to 8-bit packed data. + let packed_4bit_quantized_data = ${_.getByOffset("data_offset / 8")}; + let packed_8bit_quantized_data = (packed_4bit_quantized_data >> (4 * (data_index % 2))) & 0x0f0f0f0f; + let quantized_data_vec = ${m?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_quantized_data)); + let quantized_data = quantized_data_vec[data_index / 2]; + var scale_indices = data_indices; + let quantize_axis_index = ${$.indicesGet("data_indices","uniforms.quantize_axis")} / uniforms.block_size; + ${$.indicesSet("scale_indices","uniforms.quantize_axis","quantize_axis_index")}; + var scale = ${$.getByIndices("scale_indices")}; + ${v?` + let zero_point_indices = scale_indices; + let zero_point_offset = ${v.indicesToOffset("zero_point_indices")}; + let zero_point_index = zero_point_offset % 8; + let packed_4bit_zero_points = ${v.getByOffset("zero_point_offset / 8")}; + let packed_8bit_zero_points = (packed_4bit_zero_points >> (4 * (zero_point_index % 2))) & 0x0f0f0f0f; + let zero_point_vec = ${m?"unpack4xI8":"unpack4xU8"}(u32(packed_8bit_zero_points)); + let zero_point = zero_point_vec[zero_point_index / 2];`:"var zero_point = 0"}; + let dequantized_data = ${Ae(c)}(quantized_data - zero_point) * scale; + ${x.setByOffset("global_idx","dequantized_data")}; + }`};return{name:"GatherBlockQuantized",shaderCache:{hint:`${t.cacheKey};${e.filter((g,_)=>_!==1).map(g=>g.dims.join("_")).join(";")}`,inputDependencies:Array.from({length:e.length},(g,_)=>"rank")},getRunData:()=>({outputs:[{dims:u,dataType:c}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:f}),getShaderSource:b}},Td=(e,t)=>{let r=e.inputs;Nf(r,t),e.compute(Vf(e.inputs,t))},Id=e=>J({blockSize:e.blockSize,gatherAxis:e.gatherAxis,quantizeAxis:e.quantizeAxis})});var Wf,Lf,Ad,Ed,kd=U(()=>{"use strict";ee();ne();Se();ie();Wf=e=>{if(!e||e.length!==2)throw new Error("GatherElements requires 2 inputs.");if(e[0].dims.length<1)throw new Error("GatherElements requires that the data input be rank >= 1.");if(e[0].dims.length!==e[1].dims.length)throw new Error(`GatherElements requires that the data input and + indices input tensors be of same rank.`)},Lf=(e,t)=>{let r=e[0].dims,n=e[0].dataType,o=r.length,i=e[1].dims,a=e[1].dataType,u=k.normalizeAxis(t.axis,o),d=r[u],c=i.slice(0),p=k.size(c),m=P("input",n,o),f=P("indicesInput",a,i.length),b=M("output",n,c.length),g=[{type:12,data:p},{type:6,data:d},{type:12,data:u}];return g.push(...N(r,i,c)),{name:"GatherElements",shaderCache:{inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:c,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:g}),getShaderSource:$=>` + ${$.registerUniform("outputSize","u32").registerUniform("axisDimLimit","i32").registerUniform("axis","u32").declareVariables(m,f,b)} + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let outputIndices = ${b.offsetToIndices("global_idx")}; + + var idx = ${f.getByOffset("global_idx")}; + if (idx < 0) { + idx = idx + uniforms.axisDimLimit; + } + var inputIndices = ${m.type.indices}(outputIndices); + ${m.indicesSet("inputIndices","uniforms.axis","u32(idx)")}; + let value = ${m.getByIndices("inputIndices")}; + + ${b.setByOffset("global_idx","value")}; + }`}},Ad=e=>J({axis:e.axis}),Ed=(e,t)=>{let r=e.inputs;Wf(r),e.compute(Lf(e.inputs,t))}});var Gf,Hf,Pd,zd,Od=U(()=>{"use strict";ee();ne();ie();Gf=e=>{if(!e)throw new Error("Input is missing");if(e.length<2||e.length>3)throw new Error("Invaid input number.");if(e.length===3&&e[2].dims.length>2)throw new Error("Invalid input shape of C");if(e[0].dataType!==e[1].dataType||e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("Input types are mismatched")},Hf=(e,t)=>{let r=e[0].dims.slice(),n=e[1].dims.slice(),[o,i,a]=Dr.getShapeOfGemmResult(r,t.transA,n,t.transB,e.length===3?e[2].dims:void 0),u=[o,i];if(!u)throw new Error("Can't use gemm on the given tensors");let d=16,c=Math.ceil(i/d),p=Math.ceil(o/d),m=!0,f=k.size(u),b=[{type:12,data:m?c:f},{type:12,data:o},{type:12,data:i},{type:12,data:a},{type:1,data:t.alpha},{type:1,data:t.beta}],g=["type","type"];e.length===3&&(b.push(...N(e[2].dims)),g.push("rank")),b.push(...N(u));let _=$=>{let v="";t.transA&&t.transB?v="value += a[k * uniforms.M + m] * b[n * uniforms.K + k];":t.transA&&!t.transB?v="value += a[k * uniforms.M + m] * b[k * uniforms.N + n];":!t.transA&&t.transB?v="value += a[m * uniforms.K + k] * b[n * uniforms.K + k];":!t.transA&&!t.transB&&(v="value += a[m * uniforms.K + k] * b[k * uniforms.N + n];");let x=t.alpha===1?"":"value *= uniforms.alpha;",T=P("a",e[0].dataType,e[0].dims),E=P("b",e[1].dataType,e[1].dims),I=T.type.value,z=null,O=[T,E];e.length===3&&(z=P("c",e[2].dataType,e[2].dims.length),O.push(z));let D=M("output",e[0].dataType,u.length);O.push(D);let L=[{name:"output_size",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}];return` + ${$.registerUniforms(L).declareVariables(...O)} + + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let m = global_idx / uniforms.N; + let n = global_idx % uniforms.N; + + var value = ${I}(0); + for (var k: u32 = 0u; k < uniforms.K; k++) { + ${v} + } + + ${x} + ${z!=null?`let cOffset = ${z.broadcastedIndicesToOffset("vec2(m, n)",D)}; value += ${I}(uniforms.beta) * ${z.getByOffset("cOffset")};`:""} + output[global_idx] = value; + }`},S=$=>{let v=P("a",e[0].dataType,e[0].dims),x=P("b",e[1].dataType,e[1].dims),T=null,E=[v,x];e.length===3&&(T=P("c",e[2].dataType,e[2].dims.length),E.push(T));let I=M("output",e[0].dataType,u.length);E.push(I);let z=[{name:"num_tile_n",type:"u32"},{name:"M",type:"u32"},{name:"N",type:"u32"},{name:"K",type:"u32"},{name:"alpha",type:"f32"},{name:"beta",type:"f32"}],O="",D="";t.transA&&t.transB?(D=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${v.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${x.type.value}(0); + } + `,O="value += tile_a[k][local_id.y] * tile_b[local_id.x][k];"):t.transA&&!t.transB?(D=` + var col = tile_row_start + local_id.x; + var row = k_start + local_id.y; + if (col < uniforms.M && row < uniforms.K) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.M + col]; + } else { + tile_a[local_id.y][local_id.x] = ${v.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${x.type.value}(0); + } + `,O="value += tile_a[k][local_id.y] * tile_b[k][local_id.x];"):!t.transA&&t.transB?(D=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${v.type.value}(0); + } + + col = k_start + local_id.x; + row = tile_col_start + local_id.y; + if (col < uniforms.K && row < uniforms.N) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.K + col]; + } else { + tile_b[local_id.y][local_id.x] = ${x.type.value}(0); + } + `,O="value += tile_a[local_id.y][k] * tile_b[local_id.x][k];"):!t.transA&&!t.transB&&(D=` + var col = k_start + local_id.x; + var row = tile_row_start + local_id.y; + if (col < uniforms.K && row < uniforms.M) { + tile_a[local_id.y][local_id.x] = a[row * uniforms.K + col]; + } else { + tile_a[local_id.y][local_id.x] = ${v.type.value}(0); + } + + col = tile_col_start + local_id.x; + row = k_start + local_id.y; + if (col < uniforms.N && row < uniforms.K) { + tile_b[local_id.y][local_id.x] = b[row * uniforms.N + col]; + } else { + tile_b[local_id.y][local_id.x] = ${x.type.value}(0); + } + `,O="value += tile_a[local_id.y][k] * tile_b[k][local_id.x];");let L=t.alpha===1?"":"value *= uniforms.alpha;";return` + ${$.registerUniforms(z).declareVariables(...E)} + var tile_a: array, ${d}>; + var tile_b: array, ${d}>; + ${$.mainStart([d,d,1])} + let tile_col_start = (workgroup_index % uniforms.num_tile_n) * ${d}; + let tile_row_start = (workgroup_index / uniforms.num_tile_n) * ${d}; + let num_tiles = (uniforms.K - 1) / ${d} + 1; + var k_start = 0u; + var value = ${I.type.value}(0); + for (var t: u32 = 0u; t < num_tiles; t++) { + ${D} + k_start = k_start + ${d}; + workgroupBarrier(); + + for (var k: u32 = 0u; k < ${d}; k++) { + ${O} + } + workgroupBarrier(); + } + + ${L} + let m = tile_row_start + local_id.y; + let n = tile_col_start + local_id.x; + ${T!=null?`let cOffset = ${T.broadcastedIndicesToOffset("vec2(m, n)",I)}; value += ${I.type.value}(uniforms.beta) * ${T.getByOffset("cOffset")};`:""} + if (m < uniforms.M && n < uniforms.N) { + output[m * uniforms.N + n] = value; + } + }`};return m?{name:"GemmShared",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:c*p},programUniforms:b}),getShaderSource:S}:{name:"Gemm",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:u,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:b}),getShaderSource:_}},Pd=e=>{let t=e.transA,r=e.transB,n=e.alpha,o=e.beta;return{transA:t,transB:r,alpha:n,beta:o,cacheKey:`${e.transA};${e.transB};${e.alpha===1}`}},zd=(e,t)=>{Gf(e.inputs),e.compute(Hf(e.inputs,t))}});var ut,yt,Ut,Nt,Ff,qf,jf,Kf,Zf,Qf,Yf,Xf,Bd,Dd,Md=U(()=>{"use strict";ee();ne();Se();ie();[ut,yt,Ut,Nt]=[0,1,2,3],Ff=e=>{if(e[0].dims.length!==4)throw new Error("only 4-D tensor is supported.");if(e[0].dims.length!==e[1].dims.length)throw new Error("input dimensions must be equal to grid dimensions");if(e[0].dims.length-2!==e[1].dims[e[1].dims.length-1])throw new Error(`last dimension of grid must be equal to ${e[0].dims.length-2}`);if(e[0].dims[0]!==e[1].dims[0])throw new Error("grid batch size must match input batch size")},qf=` + fn gs_get_cubic_coeffs(x: f32) -> vec4 { + let cubic_alpha = -0.75f; + let x_abs = abs(x); + var coeffs: vec4; + coeffs[0] = (((cubic_alpha * (x_abs + 1) - 5 * cubic_alpha) * (x_abs + 1) + 8 * cubic_alpha) * (x_abs + 1) - 4 * cubic_alpha); + coeffs[1] = (((cubic_alpha + 2) * x_abs - (cubic_alpha + 3)) * x_abs * x_abs + 1); + coeffs[2] = (((cubic_alpha + 2) * (1 - x_abs) - (cubic_alpha + 3)) * (1 - x_abs) * (1 - x_abs) + 1); + coeffs[3] = (((cubic_alpha * (2 - x_abs) - 5 * cubic_alpha) * (2 - x_abs) + 8 * cubic_alpha) * (2 - x_abs) - 4 * cubic_alpha); + return coeffs; + } +`,jf=e=>` + fn gs_bicubic_interpolate(p: mat4x4<${e}>, x: f32, y: f32) -> ${e} { + var v: vec4; + var coeffs = gs_get_cubic_coeffs(x); + for (var i = 0; i < 4; i++) { + v[i] = coeffs[0] * p[i][0] + coeffs[1] * p[i][1] + coeffs[2] * p[i][2] + coeffs[3] * p[i][3]; + } + coeffs = gs_get_cubic_coeffs(y); + let pixel = ${e}(coeffs[0] * v[0] + coeffs[1] * v[1] + coeffs[2] * v[2] + coeffs[3] * v[3]); + return pixel; + } +`,Kf=e=>` + fn gs_denormalize(n: f32, length: i32) -> f32 { + ${e.alignCorners===0?` + // alignCorners: false => [-1, 1] to [-0.5, length - 0.5] + return ((n + 1.0) * f32(length) - 1.0) / 2.0; + `:` + // alignCorners: true => [-1, 1] to [0, length - 1] + return (n + 1.0) / 2.0 * (f32(length - 1)); + `} + } +`,Zf=e=>` + ${e.paddingMode==="reflection"?` + fn gs_reflect(x: i32, x_min: f32, x_max: f32) -> u32 { + var dx = 0.0; + var fx = f32(x); + let range = x_max - x_min; + if (fx < x_min) { + dx = x_min - fx; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_min + r; + } else { + fx = x_max - r; + } + } else if (fx > x_max) { + dx = fx - x_max; + let n = u32(dx / range); + let r = dx - f32(n) * range; + if (n % 2 == 0) { + fx = x_max - r; + } else { + fx = x_min + r; + } + } + return u32(fx); + }`:""} +`,Qf=(e,t,r)=>` + fn pixel_at_grid(r: i32, c: i32, H: i32, W: i32, batch: u32, channel: u32, border: vec4) -> ${t} { + var pixel = ${t}(0); + var indices = vec4(0); + indices[${ut}] = batch; + indices[${yt}] = channel;`+(()=>{switch(r.paddingMode){case"zeros":return` + if (r >= 0 && r < H && c >=0 && c < W) { + indices[${Ut}] = u32(r); + indices[${Nt}] = u32(c); + } else { + return ${t}(0); + } + `;case"border":return` + indices[${Ut}] = u32(clamp(r, 0, H - 1)); + indices[${Nt}] = u32(clamp(c, 0, W - 1)); + `;case"reflection":return` + indices[${Ut}] = gs_reflect(r, border[1], border[3]); + indices[${Nt}] = gs_reflect(c, border[0], border[2]); + `;default:throw new Error(`padding mode ${r.paddingMode} is not supported`)}})()+` + return ${e.getByIndices("indices")}; + } +`,Yf=(e,t,r)=>(()=>{switch(r.mode){case"nearest":return` + let result = pixel_at_grid(i32(round(y)), i32(round(x)), H_in, W_in, indices[${ut}], indices[${yt}], border); + `;case"bilinear":return` + let x1 = i32(floor(x)); + let y1 = i32(floor(y)); + let x2 = x1 + 1; + let y2 = y1 + 1; + + let p11 = pixel_at_grid(y1, x1, H_in, W_in, indices[${ut}], indices[${yt}], border); + let p12 = pixel_at_grid(y1, x2, H_in, W_in, indices[${ut}], indices[${yt}], border); + let p21 = pixel_at_grid(y2, x1, H_in, W_in, indices[${ut}], indices[${yt}], border); + let p22 = pixel_at_grid(y2, x2, H_in, W_in, indices[${ut}], indices[${yt}], border); + + let dx2 = ${t}(f32(x2) - x); + let dx1 = ${t}(x - f32(x1)); + let dy2 = ${t}(f32(y2) - y); + let dy1 = ${t}(y - f32(y1)); + let result = dy2 * (dx2 * p11 + dx1 * p12) + dy1 * (dx2 * p21 + dx1 * p22); + `;case"bicubic":return` + let x0 = i32(floor(x)) - 1; + let y0 = i32(floor(y)) - 1; + var p: mat4x4<${t}>; + for (var h = 0; h < 4; h++) { + for (var w = 0; w < 4; w++) { + p[h][w] = pixel_at_grid(h + y0, w + x0, H_in, W_in, indices[${ut}], indices[${yt}], border); + } + } + + let dx = x - f32(x0 + 1); + let dy = y - f32(y0 + 1); + let result = gs_bicubic_interpolate(p, dx, dy); + `;default:throw new Error(`mode ${r.mode} is not supported`)}})()+`${e.setByOffset("global_idx","result")}`,Xf=(e,t)=>{let r=P("x",e[0].dataType,e[0].dims.length),n=[e[1].dims[0],e[1].dims[1],e[1].dims[2]],o=P("grid",e[1].dataType,n.length,2),i=[e[0].dims[0],e[0].dims[1],e[1].dims[1],e[1].dims[2]];t.format==="NHWC"&&(i=[e[0].dims[0],e[1].dims[1],e[1].dims[2],e[0].dims[3]],[ut,yt,Ut,Nt]=[0,3,1,2]);let a=M("output",e[0].dataType,i.length),u=r.type.value,d=k.size(i),c=[{type:12,data:d},...N(e[0].dims,n,i)],p=m=>` + ${m.registerUniform("output_size","u32").declareVariables(r,o,a)} + ${qf} + ${jf(u)} + ${Kf(t)} + ${Zf(t)} + ${Qf(r,u,t)} + + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let H_in = i32(uniforms.x_shape[${Ut}]); + let W_in = i32(uniforms.x_shape[${Nt}]); + + ${t.alignCorners===0?` + let x_min = -0.5; + let x_max = f32(W_in) - 0.5; + let y_min = -0.5; + let y_max = f32(H_in) - 0.5; + `:` + let x_min = 0.0; + let x_max = f32(W_in) - 1.0; + let y_min = 0.0; + let y_max = f32(H_in) - 1.0; + `}; + let border = vec4(x_min, y_min, x_max, y_max); + + let indices = ${a.offsetToIndices("global_idx")}; + var grid_indices = vec3(indices[${ut}], indices[${Ut}], indices[${Nt}]); + let nxy = ${o.getByIndices("grid_indices")}; + var x = gs_denormalize(f32(nxy[0]), W_in); + var y = gs_denormalize(f32(nxy[1]), H_in); + + ${Yf(a,u,t)} + }`;return{name:"GridSample",shaderCache:{hint:`${t.cacheKey}`,inputDependencies:["type","type"]},getRunData:m=>{let f=k.size(i);return{outputs:[{dims:i,dataType:m[0].dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:c}},getShaderSource:p}},Bd=(e,t)=>{Ff(e.inputs),e.compute(Xf(e.inputs,t))},Dd=e=>J({alignCorners:e.align_corners,mode:e.mode,paddingMode:e.padding_mode,format:e.format})});var Be,th,Ud,Rd,rh,er,Nd,$o=U(()=>{"use strict";ee();ne();Se();Vr();Fr();ie();st();Be=(e,t)=>e.length>t&&e[t].dims.length>0?e[t]:void 0,th=(e,t)=>{let r=e[0],n=Be(e,1),o=Be(e,2),i=Be(e,3),a=Be(e,4),u=Be(e,5),d=Be(e,6),c=Be(e,7);if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let p=r.dims[0],m=r.dims[1],f=r.dims.length===3?r.dims[2]:t.numHeads*r.dims[4],b=m,g=0,_=0,S=Math.floor(f/t.numHeads);if(d&&c&&k.size(d.dims)&&k.size(c.dims)){if(d.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(d.dims[0]!==p||d.dims[1]!==t.numHeads||d.dims[3]!==S)throw new Error('Input "past_key" shape (batch_size, num_heads, past_sequence_length, head_size)');if(c.dims[0]!==p||c.dims[1]!==t.numHeads||c.dims[3]!==S)throw new Error('Input "past_value" shape (batch_size, num_heads, past_sequence_length, head_size)');if(d.dims[2]!==c.dims[2])throw new Error('Input "past_key" and "past_value" shall have same dim 2 (past_sequence_length)');if(c.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');g=d.dims[2],_=d.dims[2]}else if(d&&k.size(d.dims)||c&&k.size(c.dims))throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let $;if(n&&k.size(n.dims)>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(n.dims[2]!==r.dims[2])throw new Error('Input "query" and "key" shall have same dim 2 (hidden_size)');$=2,b=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==S)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');$=5,b=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==S)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');$=0,b=n.dims[2]}}else{if(r.dims.length!==5)throw new Error('Input "query" is expected to have 5 dimensions when key is empty');if(r.dims[2]!==t.numHeads||r.dims[3]!==3)throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');$=3}if(i&&k.size(i.dims)>0){if(i.dims.length!==1)throw new Error('Input "bias" is expected to have 1 dimension');if(n&&n.dims.length===5&&n.dims[3]===2)throw new Error("bias is not allowed for packed kv.")}let v=g+b,x=0;if(a&&k.size(a.dims)>0){x=8;let z=a.dims;throw z.length===1?z[0]===p?x=1:z[0]===3*p+2&&(x=3):z.length===2&&z[0]===p&&z[1]===v&&(x=5),x===8?new Error('Input "key_padding_mask" shape shall be (batch_size) or (batch_size, total_sequence_length)'):new Error("Mask not supported")}let T=!1,E=f;if(o&&k.size(o.dims)>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(b!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');E=o.dims[2]}else{if(b!==o.dims[2])throw new Error('Input "key" and "value" shall have the same dim 2 (kv_sequence_length)');E=o.dims[1]*o.dims[3],T=!0}}let I=!1;if(a&&k.size(a.dims)>0)throw new Error("Key padding mask is not supported");if(u&&k.size(u.dims)>0){if(u.dims.length!==4)throw new Error('Input "attention_bias" is expected to have 4 dimensions');if(u.dims[0]!==p||u.dims[1]!==t.numHeads||u.dims[2]!==m||u.dims[3]!==v)throw new Error('Expect "attention_bias" shape (batch_size, num_heads, sequence_length, total_sequence_length)')}return{batchSize:p,sequenceLength:m,pastSequenceLength:g,kvSequenceLength:b,totalSequenceLength:v,maxSequenceLength:_,inputHiddenSize:0,hiddenSize:f,vHiddenSize:E,headSize:S,vHeadSize:Math.floor(E/t.numHeads),numHeads:t.numHeads,isUnidirectional:!1,pastPresentShareBuffer:!1,maskFilterValue:t.maskFilterValue,maskType:x,scale:t.scale,broadcastResPosBias:I,passPastInKv:T,qkvFormat:$}},Ud=e=>J({...e}),Rd=J({perm:[0,2,1,3]}),rh=(e,t,r,n,o,i,a)=>{let u=[n,o,i],d=k.size(u),c=[{type:12,data:d},{type:12,data:a},{type:12,data:i}],p=m=>{let f=M("qkv_with_bias",t.dataType,u),b=P("qkv",t.dataType,u),g=P("bias",r.dataType,u),_=[{name:"output_size",type:"u32"},{name:"bias_offset",type:"u32"},{name:"hidden_size",type:"u32"}];return` + ${m.registerUniforms(_).declareVariables(b,g,f)} + ${m.mainStart()} + ${m.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let bias_offset_idx = (global_idx % uniforms.hidden_size) + uniforms.bias_offset; + + qkv_with_bias[global_idx] = qkv[global_idx] + bias[bias_offset_idx]; + }`};return e.compute({name:"MultiHeadAttentionAddBias",shaderCache:{inputDependencies:["type","type"]},getRunData:()=>({outputs:[{dims:u,dataType:t.dataType,gpuDataType:0}],dispatchGroup:{x:Math.ceil(d/64)},programUniforms:c}),getShaderSource:p},{inputs:[t,r],outputs:[-1]})[0]},er=(e,t,r,n,o,i,a,u)=>{let d=i;if(a&&k.size(a.dims)>0){if(n===1)throw new Error("AddBiasReshape is not implemented. Please export your model with packed QKV or KV");return d=rh(e,i,a,t,n,r*o,u),d=d.reshape([t,n,r,o]),r===1||n===1?d:e.compute(Ee(d,Rd.perm),{inputs:[d],outputs:[-1]})[0]}else return i.dims.length===3&&(d=i.reshape([t,n,r,o])),r===1||n===1?d:e.compute(Ee(d,Rd.perm),{inputs:[d],outputs:[-1]})[0]},Nd=(e,t)=>{let r=th(e.inputs,t),n=e.inputs[0],o=Be(e.inputs,1),i=Be(e.inputs,2),a=Be(e.inputs,3),u=Be(e.inputs,4),d=Be(e.inputs,5),c=Be(e.inputs,6),p=Be(e.inputs,7);if(n.dims.length===5)throw new Error("Packed QKV is not implemented");if(o?.dims.length===5)throw new Error("Packed KV is not implemented");let m=o&&i&&o.dims.length===4&&i.dims.length===4,f=er(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,n,a,0);if(m)return Rt(e,f,o,i,u,void 0,c,p,d,r);if(!o||!i)throw new Error("key and value must be provided");let b=er(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.headSize,o,a,r.hiddenSize),g=er(e,r.batchSize,r.numHeads,r.kvSequenceLength,r.vHeadSize,i,a,2*r.hiddenSize);Rt(e,f,b,g,u,void 0,c,p,d,r)}});var nh,oh,ih,ah,xo,Vd,Wd,So=U(()=>{"use strict";ee();ne();Se();ie();nh=e=>{if(!e||e.length<1)throw new Error("too few inputs")},oh=(e,t)=>{let r=[],n=t.numOutputs;return e[1].dims[0]>0&&(e[1].getBigInt64Array().forEach(o=>r.push(Number(o))),n=r.length),J({numOutputs:n,axis:t.axis,splitSizes:r})},ih=e=>` +fn calculateOutputIndex(index: u32) -> u32 { + for (var i: u32 = 0u; i < ${e}u; i += 1u ) { + if (index < ${F("uniforms.size_in_split_axis","i",e)}) { + return i; + } + } + return ${e}u; +}`,ah=e=>{let t=e.length,r=[];for(let n=0;n{let r=e[0].dims,n=k.size(r),o=e[0].dataType,i=k.normalizeAxis(t.axis,r.length),a=new Array(t.numOutputs),u=P("input",o,r.length),d=new Array(t.numOutputs),c=[],p=[],m=0,f=[{type:12,data:n}];for(let g=0;g` + ${g.registerUniform("input_size","u32").registerUniform("size_in_split_axis","u32",d.length).declareVariables(u,...a)} + ${ih(d.length)} + ${ah(a)} + + ${g.mainStart()} + ${g.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.input_size")} + + var indices = ${u.offsetToIndices("global_idx")}; + var index = ${u.indicesGet("indices",i)}; + let output_number = calculateOutputIndex(index); + if (output_number != 0) { + index -= ${F("uniforms.size_in_split_axis","output_number - 1u",d.length)}; + ${u.indicesSet("indices",i,"index")}; + } + writeBufferData(output_number, indices, global_idx); + }`;return{name:"Split",shaderCache:{hint:t.cacheKey,inputDependencies:["rank"]},getShaderSource:b,getRunData:()=>({outputs:c,dispatchGroup:{x:Math.ceil(n/64)},programUniforms:f})}},Vd=(e,t)=>{nh(e.inputs);let r=e.inputs.length===1?t:oh(e.inputs,t);e.compute(xo(e.inputs,r),{inputs:[0]})},Wd=e=>{let t=e.axis,r=e.splitSizes,n=e.numOutputs<0?r.length:e.numOutputs;if(n!==r.length)throw new Error("numOutputs and splitSizes lengh must be equal");return J({axis:t,numOutputs:n,splitSizes:r})}});var sh,tn,Ld,To=U(()=>{"use strict";ee();ne();Se();ie();sh=(e,t)=>{let[r,n,o,i]=e,{numHeads:a,rotaryEmbeddingDim:u}=t;if(r.dims.length!==3&&r.dims.length!==4)throw new Error(`Input 'x' is expected to have 3 or 4 dimensions, got ${r.dims.length}`);if(!k.areEqual(n.dims,[])&&!k.areEqual(n.dims,[1])&&n.dims.length!==2)throw new Error(`Input 'position_ids' is expected to have 0, 1, or 2 dimensions, got ${n.dims.length}`);if(o.dims.length!==2)throw new Error(`Input 'cos_cache' is expected to have 2 dimensions, got ${o.dims.length}`);if(i.dims.length!==2)throw new Error(`Input 'sin_cache' is expected to have 2 dimensions, got ${i.dims.length}`);if(!k.areEqual(o.dims,i.dims))throw new Error("Inputs 'cos_cache' and 'sin_cache' are expected to have the same shape");if(u>0&&a===0)throw new Error("num_heads must be provided if rotary_embedding_dim is specified");let d=r.dims[0],c=r.dims[r.dims.length-2],p=o.dims[0],m=k.sizeFromDimension(r.dims,1)/c,f=u===0?o.dims[1]*2:m/a;if(u>f)throw new Error("rotary_embedding_dim must be less than or equal to head_size");if(n.dims.length===2){if(d!==n.dims[0])throw new Error(`Input 'position_ids' dimension 0 should be of size batch_size, got ${n.dims[0]}`);if(c!==n.dims[1])throw new Error(`Input 'position_ids' dimension 1 should be of size sequence_length, got ${n.dims[1]}`)}if(f/2!==o.dims[1]&&u/2!==o.dims[1])throw new Error(`Input 'cos_cache' dimension 1 should be same as head_size / 2 or rotary_embedding_dim / 2, got ${o.dims[1]}`);if(c>p)throw new Error("Updating cos_cache and sin_cache in RotaryEmbedding is not currently supported")},tn=(e,t)=>{let{interleaved:r,numHeads:n,rotaryEmbeddingDim:o,scale:i}=t,a=e[0].dims[0],u=k.sizeFromDimension(e[0].dims,1),d=e[0].dims[e[0].dims.length-2],c=u/d,p=e[2].dims[1],m=o===0?p*2:c/n,f=new Array(a,d,c/m,m-p),b=k.computeStrides(f),g=[{type:1,data:i},{type:12,data:f},{type:12,data:b},...e[0].dims.length===3?new Array({type:12,data:[u,c,m,1]}):[],...e[0].dims.length===4?new Array({type:12,data:[u,m,d*m,1]}):[],...N(e[0].dims,e[1].dims,e[2].dims,e[3].dims,e[0].dims)],_=S=>{let $=P("input",e[0].dataType,e[0].dims.length),v=P("position_ids",e[1].dataType,e[1].dims.length),x=P("cos_cache",e[2].dataType,e[2].dims.length),T=P("sin_cache",e[3].dataType,e[3].dims.length),E=M("output",e[0].dataType,e[0].dims.length);return S.registerUniforms([{name:"scale",type:"f32"},{name:"global_shape",type:"u32",length:f.length},{name:"global_strides",type:"u32",length:b.length},{name:"input_output_strides",type:"u32",length:b.length}]),` + ${S.declareVariables($,v,x,T,E)} + + ${S.mainStart(It)} + let half_rotary_emb_dim = uniforms.${x.name}_shape[1]; + let bsnh = global_idx / uniforms.global_strides % uniforms.global_shape; + let size = uniforms.global_shape[0] * uniforms.global_strides[0]; + ${S.guardAgainstOutOfBoundsWorkgroupSizes("size")} + + if (bsnh[3] < half_rotary_emb_dim) { + let position_ids_idx = + ${v.broadcastedIndicesToOffset("bsnh.xy",M("",v.type.tensor,2))}; + let position_id = + u32(${v.getByOffset("position_ids_idx")}) + select(0, bsnh[1], position_ids_idx == 0); + let i = dot(bsnh, uniforms.input_output_strides) + select(0, bsnh[3], ${r}); + let j = i + select(half_rotary_emb_dim, 1, ${r}); + let re = ${$.getByOffset("i")} * ${x.get("position_id","bsnh[3]")} - + ${$.getByOffset("j")} * ${T.get("position_id","bsnh[3]")}; + ${E.setByOffset("i","re")} + let im = ${$.getByOffset("i")} * ${T.get("position_id","bsnh[3]")} + + ${$.getByOffset("j")} * ${x.get("position_id","bsnh[3]")}; + ${E.setByOffset("j","im")} + } else { + let k = dot(bsnh, uniforms.input_output_strides) + half_rotary_emb_dim; + ${E.setByOffset("k",$.getByOffset("k"))} + } + }`};return{name:"RotaryEmbedding",shaderCache:{hint:J({interleaved:r}).cacheKey,inputDependencies:["rank","rank","rank","rank"]},getShaderSource:_,getRunData:()=>({outputs:[{dims:e[0].dims,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(k.size(f)/It)},programUniforms:g})}},Ld=(e,t)=>{sh(e.inputs,t),e.compute(tn(e.inputs,t))}});var uh,dh,Gd,lh,Hd,Fd=U(()=>{"use strict";Se();ee();Fr();$o();So();st();To();ie();uh=(e,t)=>{if(t.doRotary&&e.length<=7)throw new Error("cos_cache and sin_cache inputs are required if do_rotary is specified");let r=e[0],n=e[1],o=e[2],i=e[3],a=e[4];if(t.doRotary!==0&&e.length<=7)throw new Error("cos_cast and sin_cache are expected if do_rotary attribute is non-zero");if(t.localWindowSize!==-1)throw new Error("Local attention is not supported");if(t.softcap!==0)throw new Error("Softcap is not supported");if(t.rotaryInterleaved!==0)throw new Error("Rotary interleaved is not supported");if(t.smoothSoftmax)throw new Error("Smooth softmax is not supported");if(r.dims.length!==3&&r.dims.length!==5)throw new Error("Input query is expected to have 3 or 5 dimensions");let u=!1,d=r.dims[0],c=r.dims[1],p=r.dims.length===3?u?r.dims[2]/3:r.dims[2]:t.numHeads*r.dims[4],m=c,f=0,b=!n||n.dims.length===0,g=Math.floor(b?p/(t.numHeads+2*t.kvNumHeads):p/t.numHeads);b&&(p=g*t.numHeads);let _=i&&i.dims.length!==0,S=a&&a.dims.length!==0;if(_&&i.dims.length===4&&i.dims[0]===d&&i.dims[1]!==t.kvNumHeads&&i.dims[2]===t.kvNumHeads&&i.dims[3]===g)throw new Error("BSNH pastKey/pastValue is not supported");if(_&&S){if(i.dims.length!==4)throw new Error('Input "past_key" is expected to have 4 dimensions');if(a.dims.length!==4)throw new Error('Input "past_value" is expected to have 4 dimensions');f=i.dims[2]}else if(_||S)throw new Error('Input "past_key" and "past_value" shall be both present or both absent');let v=1;if(n&&n.dims.length>0){if(r.dims.length!==3)throw new Error('Input "query" is expected to have 3 dimensions when key is given');if(n.dims.length<3||n.dims.length>5)throw new Error('Input "key" is expected to have 3, 4, or 5 dimensions');if(r.dims[0]!==n.dims[0])throw new Error('Input "query" and "key" shall have same dim 0 (batch size)');if(n.dims.length===3){if(r.dims[2]%n.dims[2]!==0)throw new Error('Dimension 2 of "query" should be a multiple of "key"');m=n.dims[1]}else if(n.dims.length===5){if(n.dims[2]!==t.numHeads||n.dims[3]!==2||n.dims[4]!==g)throw new Error('Expect "key" shape (batch_size, kv_sequence_length, num_heads, 2, head_size) for packed kv');if(o)throw new Error('Expect "value" be none when "key" has packed kv format.');m=n.dims[1]}else{if(n.dims[1]!==t.numHeads||n.dims[3]!==g)throw new Error('Expect "key" shape (batch_size, num_heads, kv_sequence_length, head_size) for past_key');m=n.dims[2]}}else{if(r.dims.length!==3&&r.dims.length!==5)throw new Error('Input "query" is expected to have 3 or 5 dimensions when key is empty');if(r.dims.length===5&&(r.dims[2]!==t.numHeads||r.dims[3]!==3))throw new Error('Expect "query" shape (batch_size, kv_sequence_length, num_heads, 3, head_size) for packed kv');v=3}let x=0,T=!1,E=t.kvNumHeads?g*t.kvNumHeads:p;if(o&&o.dims.length>0){if(o.dims.length!==3&&o.dims.length!==4)throw new Error('Input "value" is expected to have 3 or 4 dimensions');if(r.dims[0]!==o.dims[0])throw new Error('Input "query" and "value" shall have same dim 0 (batch_size)');if(o.dims.length===3){if(m!==o.dims[1])throw new Error('Input "key" and "value" shall have the same dim 1 (kv_sequence_length)');E=o.dims[2]}else{if(m!==o.dims[2])throw new Error('Input "past_key" and "past_value" shall have the same dim 2 (kv_sequence_length)');E=o.dims[1]*o.dims[3],T=!0}}let I=e.length>4?e[5]:void 0;if(I&&I.dims.length!==1&&I.dims[0]!==d)throw new Error('Input "seqlens" is expected to have 1 dimension and the same dim 0 as batch_size');return{batchSize:d,sequenceLength:c,pastSequenceLength:f,kvSequenceLength:m,totalSequenceLength:-1,maxSequenceLength:-1,inputHiddenSize:0,hiddenSize:p,vHiddenSize:E,headSize:g,vHeadSize:Math.floor(E/t.kvNumHeads),numHeads:t.numHeads,kvNumHeads:t.kvNumHeads,nReps:t.numHeads/t.kvNumHeads,pastPresentShareBuffer:!1,maskType:x,scale:t.scale,broadcastResPosBias:!1,passPastInKv:T,qkvFormat:v}},dh=J({perm:[0,2,1,3]}),Gd=(e,t,r)=>{let n=t,o=r.kvNumHeads;return t.dims.length===3&&r.kvSequenceLength!==0&&(n=t.reshape([r.batchSize,r.kvSequenceLength,o,r.headSize]),n=e.compute(Ee(n,dh.perm),{inputs:[n],outputs:[-1]})[0]),n},lh=(e,t,r,n)=>{let o=7,i=["type","type"],a=[e*t],u=e*t,d=[{type:12,data:u},{type:12,data:t},{type:12,data:e}],c=p=>{let m=P("seq_lens",r.dataType,r.dims),f=P("total_seq_lens",n.dataType,n.dims),b=M("pos_ids",o,a),g=[{name:"output_size",type:"u32"},{name:"sequence_length",type:"u32"},{name:"batch_size",type:"u32"}];return` + ${p.registerUniforms(g).declareVariables(m,f,b)} + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let total_sequence_length = u32(${f.getByOffset("0")}); + let is_subsequent_prompt = uniforms.sequence_length > 1 && uniforms.sequence_length != total_sequence_length; + let is_first_prompt = !is_subsequent_prompt && uniforms.sequence_length == total_sequence_length; + let batch_idx = global_idx / uniforms.sequence_length; + let sequence_idx = i32(global_idx % uniforms.sequence_length); + var pos_id: i32 = 0; + let seqlen = ${m.getByOffset("batch_idx")}; + let total_seqlen = seqlen + 1; + if (is_first_prompt) { + if (sequence_idx < total_seqlen) { + pos_id = sequence_idx; + } else { + pos_id = 1; + } + ${b.setByOffset("global_idx","pos_id")} + } else if (is_subsequent_prompt) { + let past_seqlen = total_seqlen - i32(uniforms.sequence_length); + if (past_seqlen + sequence_idx < total_seqlen) { + pos_id = past_seqlen + sequence_idx; + } else { + pos_id = 1; + } + ${b.setByOffset("global_idx","pos_id")} + } else if (global_idx < uniforms.batch_size) { + ${b.setByOffset("global_idx","seqlen")} + }; + } + `};return{name:"GeneratePositionIds",shaderCache:{hint:`${e};${t}`,inputDependencies:i},getRunData:()=>({outputs:[{dims:a,dataType:o}],dispatchGroup:{x:Math.ceil(u/64)},programUniforms:d}),getShaderSource:c}},Hd=(e,t)=>{let r=uh(e.inputs,t);if(e.inputs[0].dims.length===5)throw new Error("Packed QKV is not implemented");if(e.inputs[1]?.dims.length===5)throw new Error("Packed KV is not implemented");let n=e.inputs[0],o=e.inputs[1]&&e.inputs[1].dims.length>0?e.inputs[1]:void 0,i=e.inputs[2]&&e.inputs[2].dims.length>0?e.inputs[2]:void 0,a=e.inputs[3]&&e.inputs[3].dims.length!==0?e.inputs[3]:void 0,u=e.inputs[4]&&e.inputs[4].dims.length!==0?e.inputs[4]:void 0,d=e.inputs.length>4?e.inputs[5]:void 0,c=e.inputs.length>5?e.inputs[6]:void 0,p=r.kvNumHeads?r.kvNumHeads:r.numHeads,m=J({axis:2,numOutputs:3,splitSizes:[r.numHeads*r.headSize,p*r.headSize,p*r.headSize]}),[f,b,g]=!o&&!i?e.compute(xo([n],m),{inputs:[n],outputs:[-1,-1,-1]}):[n,o,i],_,S;if(t.doRotary){let T=e.compute(lh(r.batchSize,r.sequenceLength,d,c),{inputs:[d,c],outputs:[-1]})[0],E=e.inputs[7],I=e.inputs[8],z=J({interleaved:t.rotaryInterleaved!==0,numHeads:r.numHeads,rotaryEmbeddingDim:0,scale:t.scale}),O=[f,T,E,I],D=[-1];_=e.compute(tn(O,z),{inputs:O,outputs:D})[0],O.splice(0,1,b);let L=J({interleaved:t.rotaryInterleaved!==0,numHeads:r.kvNumHeads,rotaryEmbeddingDim:0,scale:t.scale});S=e.compute(tn(O,L),{inputs:O,outputs:D})[0]}let $=er(e,r.batchSize,r.numHeads,r.sequenceLength,r.headSize,t.doRotary?_:f,void 0,0),v=Gd(e,t.doRotary?S:b,r),x=Gd(e,g,r);Rt(e,$,v,x,void 0,void 0,a,u,void 0,r,d,c)}});var qd,ch,ph,jd,Kd=U(()=>{"use strict";ee();ne();st();ie();qd=(e,t,r,n,o,i,a,u)=>{let d=ce(i),c=d===1?"f32":`vec${d}f`,p=d===1?"vec2f":`mat2x${d}f`,m=o*a,f=64;m===1&&(f=256);let b=[o,a,i/d],g=[o,a,2],_=["rank","type","type"],S=[];S.push(...N(b,g));let $=v=>{let x=P("x",t.dataType,3,d),T=P("scale",r.dataType,r.dims),E=P("bias",n.dataType,n.dims),I=M("output",1,3,2),z=[x,T,E,I];return` + var workgroup_shared : array<${p}, ${f}>; + const workgroup_size = ${f}u; + ${v.declareVariables(...z)} + ${v.mainStart(f)} + let batch = workgroup_index / uniforms.x_shape[1]; + let channel = workgroup_index % uniforms.x_shape[1]; + let hight = uniforms.x_shape[2]; + // initialize workgroup memory + var sum = ${c}(0); + var squared_sum = ${c}(0); + for (var h = local_idx; h < hight; h += workgroup_size) { + let value = ${c}(${x.get("batch","channel","h")}); + sum += value; + squared_sum += value * value; + } + workgroup_shared[local_idx] = ${p}(sum, squared_sum); + workgroupBarrier(); + + for (var currSize = workgroup_size >> 1; currSize > 0; currSize = currSize >> 1) { + if (local_idx < currSize) { + workgroup_shared[local_idx] = workgroup_shared[local_idx] + workgroup_shared[local_idx + currSize]; + } + workgroupBarrier(); + } + if (local_idx == 0) { + let sum_final = ${He("workgroup_shared[0][0]",d)} / f32(hight * ${d}); + let squared_sum_final = ${He("workgroup_shared[0][1]",d)} / f32(hight * ${d}); + + let inv_std_dev = inverseSqrt(squared_sum_final - sum_final * sum_final + f32(${u})); + let channel_scale = inv_std_dev * f32(scale[channel]); + let channel_shift = f32(bias[channel]) - sum_final * channel_scale; + output[workgroup_index] = vec2f(channel_scale, channel_shift); + } + }`};return e.compute({name:"InstanceNormComputeChannelScaleShift",shaderCache:{hint:`${d};${u};${f}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:g,dataType:1}],dispatchGroup:{x:m},programUniforms:S}),getShaderSource:$},{inputs:[t,r,n],outputs:[-1]})[0]},ch=(e,t,r)=>{let n=t[0].dims,o=n,i=2,a=n[0],u=n[1],d=k.sizeFromDimension(n,i),c=ce(d),p=k.size(o)/c,m=qd(e,t[0],t[1],t[2],a,d,u,r.epsilon),f=[a,u,d/c],b=[a,u],g=["type","none"],_=S=>{let $=P("x",t[0].dataType,f.length,c),v=P("scale_shift",1,b.length,2),x=M("output",t[0].dataType,f.length,c),T=[$,v,x];return` + ${S.registerUniform("output_size","u32").declareVariables(...T)} + ${S.mainStart()} + ${S.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let outputIndices = ${x.offsetToIndices("global_idx")}; + let batch = outputIndices[0]; + let channel = outputIndices[1]; + let scale_shift = ${v.getByIndices("vec2(batch, channel)")}; + let value = ${$.getByOffset("global_idx")} * ${x.type.value}(scale_shift.x) + ${x.type.value}(scale_shift.y); + ${x.setByOffset("global_idx","value")}; + }`};e.compute({name:"InstanceNormalization",shaderCache:{hint:`${c}`,inputDependencies:g},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(p/64)},programUniforms:[{type:12,data:p},...N(f,b,f)]}),getShaderSource:_},{inputs:[t[0],m]})},ph=(e,t,r)=>{let n=t[0].dims,o=n,i=n[0],a=n[n.length-1],u=k.sizeFromDimension(n,1)/a,d=ce(a),c=k.size(o)/d,p=[{type:12,data:u},{type:12,data:Math.floor(a/d)}],m=["type","type"],f=!1,b=[0,n.length-1];for(let $=0;$n[b[v]])),_=qd(e,g,t[1],t[2],i,u,a,r.epsilon),S=$=>{let v=be(t[0].dataType),x=d===1?"vec2f":`mat${d}x2f`,T=z=>{let O=z===0?"x":"y",D=d===1?"f32":`vec${d}f`;switch(d){case 1:return`${v}(${D}(scale.${O}))`;case 2:return`vec2<${v}>(${D}(scale[0].${O}, scale[1].${O}))`;case 4:return`vec4<${v}>(${D}(scale[0].${O}, scale[1].${O}, scale[2].${O}, scale[3].${O}))`;default:throw new Error(`Not supported compoents ${d}`)}},E=P("input",t[0].dataType,t[0].dims,d),I=M("output",t[0].dataType,o,d);return` + @group(0) @binding(0) var input : array<${E.type.storage}>; + @group(0) @binding(1) var scale_input : array<${x}>; + @group(0) @binding(2) var output : array<${I.type.storage}>; + struct Uniforms {H: u32, C : u32}; + @group(0) @binding(3) var uniforms: Uniforms; + + ${$.mainStart()} + let current_image_number = global_idx / (uniforms.C * uniforms.H); + let current_channel_number = global_idx % uniforms.C; + + let scale_offset = current_image_number * uniforms.C + current_channel_number; + let scale = scale_input[scale_offset]; + output[global_idx] = fma(input[global_idx], ${T(0)}, ${T(1)}); + }`};e.compute({name:"InstanceNormalizationNHWC",shaderCache:{hint:`${d}`,inputDependencies:m},getRunData:()=>({outputs:[{dims:o,dataType:t[0].dataType}],dispatchGroup:{x:Math.ceil(c/64)},programUniforms:p}),getShaderSource:S},{inputs:[t[0],_]})},jd=(e,t)=>{t.format==="NHWC"?ph(e,e.inputs,t):ch(e,e.inputs,t)}});var mh,fh,Zd,Qd=U(()=>{"use strict";ee();ne();ie();mh=e=>{if(!e||e.length<2)throw new Error("layerNorm requires at least 2 inputs.")},fh=(e,t,r)=>{let n=t.simplified,o=e[0].dims,i=e[1],a=!n&&e[2],u=o,d=k.normalizeAxis(t.axis,o.length),c=k.sizeToDimension(o,d),p=k.sizeFromDimension(o,d),m=k.size(i.dims),f=a?k.size(a.dims):0;if(m!==p||a&&f!==p)throw new Error(`Size of X.shape()[axis:] == ${p}. + Size of scale and bias (if provided) must match this. + Got scale size of ${m} and bias size of ${f}`);let b=[];for(let E=0;E1,v=r>2,x=E=>{let I=be(e[0].dataType),z=[P("x",e[0].dataType,e[0].dims,g),P("scale",i.dataType,i.dims,g)];a&&z.push(P("bias",a.dataType,a.dims,g)),z.push(M("output",e[0].dataType,u,g)),$&&z.push(M("mean_data_output",1,b)),v&&z.push(M("inv_std_output",1,b));let O=[{name:"norm_count",type:"u32"},{name:"norm_size",type:"f32"},{name:"norm_size_vectorized",type:"u32"},{name:"epsilon",type:"f32"}];return` + ${E.registerUniforms(O).declareVariables(...z)} + ${E.mainStart()} + ${E.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.norm_count")} + let offset = global_idx * uniforms.norm_size_vectorized; + var mean_vector = ${ao("f32",g)}; + var mean_square_vector = ${ao("f32",g)}; + + for (var h: u32 = 0u; h < uniforms.norm_size_vectorized; h++) { + let value = ${Ct(I,g,"x[h + offset]")}; + mean_vector += value; + mean_square_vector += value * value; + } + let mean = ${He("mean_vector",g)} / uniforms.norm_size; + let inv_std_dev = inverseSqrt(${He("mean_square_vector",g)} / uniforms.norm_size ${n?"":"- mean * mean"} + uniforms.epsilon); + + for (var j: u32 = 0; j < uniforms.norm_size_vectorized; j++) { + let f32input = ${Ct(I,g,"x[j + offset]")}; + let f32scale = ${Ct(I,g,"scale[j]")}; + output[j + offset] = ${z[0].type.value}((f32input ${n?"":"- mean"}) * inv_std_dev * f32scale + ${a?`+ ${Ct(I,g,"bias[j]")}`:""} + ); + } + + ${$?"mean_data_output[global_idx] = mean":""}; + ${v?"inv_std_output[global_idx] = inv_std_dev":""}; + }`},T=[{dims:u,dataType:e[0].dataType}];return $&&T.push({dims:b,dataType:1}),v&&T.push({dims:b,dataType:1}),{name:"LayerNormalization",shaderCache:{hint:`${g};${r};${n}`,inputDependencies:_},getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(c/64)},programUniforms:S}),getShaderSource:x}},Zd=(e,t)=>{mh(e.inputs),e.compute(fh(e.inputs,t,e.outputCount))}});var hh,Yd,Xd=U(()=>{"use strict";ne();Yr();Xr();hh=e=>{if(!e||e.length!==2)throw new Error("MatMul requires 2 inputs.");if(e[0].dims[e[0].dims.length-1]!==e[1].dims[e[1].dims.length-2])throw new Error("shared dimension does not match.")},Yd=e=>{hh(e.inputs);let t=Je.calcShape(e.inputs[0].dims,e.inputs[1].dims,!0);if(!t)throw new Error("Can't use matmul on the given tensors");let r=t[t.length-1],n=e.inputs[0].dims[e.inputs[0].dims.length-1];if(r<8&&n<8)e.compute(Qr(e.inputs,{activation:""},t));else{let o=t[t.length-2],i=k.size(e.inputs[0].dims.slice(0,-2)),a=k.size(e.inputs[1].dims.slice(0,-2));if(i!==1&&o===1&&a===1){let u=e.inputs[0].reshape([1,i,n]),d=e.inputs[1].reshape([1,n,r]),c=[1,i,r],p=[u,d];e.compute(Jt(p,{activation:""},t,c),{inputs:p})}else e.compute(Jt(e.inputs,{activation:""},t))}}});var gh,bh,yh,Jd,el,tl=U(()=>{"use strict";ee();ne();Se();ie();gh=(e,t)=>{if(e.length<3||e.length>4)throw new Error("MatMulNBits requires 3 or 4 inputs");let r=e[0],n=r.dims.length;if(r.dims[n-1]!==t.k)throw new Error("The last dim of input shape does not match the k value");let o=Math.floor((t.k+t.blockSize-1)/t.blockSize),i=t.blockSize/8*t.bits,a=e[1];if(!k.areEqual(a.dims,[t.n,o,i]))throw new Error("The second inputs must be 3D tensor with shape N X nBlocksPerCol X blobSize");let d=e[2].dims;if(k.size(d)!==t.n*o)throw new Error("scales input size error.");if(e.length===4){let p=e[3].dims,m=t.bits>4?t.n*o:t.n*Math.floor((o+1)/2);if(k.size(p)!==m)throw new Error("zeroPoints input size error.")}},bh=(e,t)=>{let r=e[0].dims,n=r.length,o=r[n-2],i=t.k,a=t.n,u=r.slice(0,n-2),d=k.size(u),p=e[1].dims[2]/4,m=e[0].dataType,f=ce(t.k),b=ce(p),g=ce(a),_=u.concat([o,a]),S=o>1&&a/g%2===0?2:1,$=k.size(_)/g/S,v=64,x=[],T=[d,o,i/f],E=k.convertShape(e[1].dims).slice();E.splice(-1,1,p/b),x.push(...N(T)),x.push(...N(E)),x.push(...N(e[2].dims)),e.length===4&&x.push(...N(k.convertShape(e[3].dims)));let I=[d,o,a/g];x.push(...N(I));let z=O=>{let D=T.length,L=P("a",e[0].dataType,D,f),q=P("b",12,E.length,b),Q=P("scales",e[2].dataType,e[2].dims.length),W=[L,q,Q],Z=e.length===4?P("zero_points",12,e[3].dims.length):void 0;Z&&W.push(Z);let we=I.length,H=M("output",e[0].dataType,we,g),j=be(e[0].dataType),te=(()=>{switch(f){case 1:return`array<${j}, 8>`;case 2:return`mat4x2<${j}>`;case 4:return`mat2x4<${j}>`;default:throw new Error(`${f}-component is not supported.`)}})(),X=()=>{let ye=` + // reuse a data + var input_offset = ${L.indicesToOffset(`${L.type.indices}(batch, row, word_offset)`)}; + var a_data: ${te}; + for (var j: u32 = 0; j < ${8/f}; j++) { + a_data[j] = ${L.getByOffset("input_offset")}; + input_offset++; + } + `;for(let re=0;re> 4) & b_mask); + b_quantized_values = ${te}(${Array.from({length:4},(C,V)=>`${j}(b_value_lower[${V}]), ${j}(b_value_upper[${V}])`).join(", ")}); + b_dequantized_values = ${f===1?`${te}(${Array.from({length:8},(C,V)=>`(b_quantized_values[${V}] - ${Z?`zero_point${re}`:"zero_point"}) * scale${re}`).join(", ")});`:`(b_quantized_values - ${te}(${Array(8).fill(`${Z?`zero_point${re}`:"zero_point"}`).join(",")})) * scale${re};`}; + workgroup_shared[local_id.x * ${S} + ${Math.floor(re/g)}]${g>1?`[${re%g}]`:""} += ${Array.from({length:8/f},(C,V)=>`${f===1?`a_data[${V}] * b_dequantized_values[${V}]`:`dot(a_data[${V}], b_dequantized_values[${V}])`}`).join(" + ")}; + `;return ye},ue=()=>{let ye=` + var col_index = col * ${g}; + ${Z?` + let zero_point_bytes_per_col = (nBlocksPerCol + 1) / 2; + var zero_point_byte_count: u32; + var zero_point_word_index: u32; + var zero_point_byte_offset: u32; + let zero_point_nibble_offset: u32 = block & 0x1u; + var zero_point_bits_offset: u32; + var zero_point_word: u32;`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${j}(8);`} + `;for(let re=0;re> 0x1u); + zero_point_word_index = zero_point_byte_count >> 0x2u; + zero_point_byte_offset = zero_point_byte_count & 0x3u; + zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + zero_point_word = ${Z.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point${re} = ${j}((zero_point_word) & 0xFu);`:""} + col_index += 1;`;return ye},he=()=>{let ye=`col_index = col * ${g};`;for(let re=0;re; + var b_value_upper: vec4; + var b_quantized_values: ${te}; + var b_dequantized_values: ${te};`,ye};return` + var workgroup_shared: array<${H.type.value}, ${S*v}>; + ${O.declareVariables(...W,H)} + ${O.mainStart([v,1,1])} + let output_indices = ${H.offsetToIndices(`(global_idx / ${v}) * ${S}`)}; + let col = output_indices[2]; + let row = output_indices[1]; + let batch = output_indices[0]; + let nBlocksPerCol = uniforms.b_shape[1]; + + for (var block = local_id.x; block < nBlocksPerCol; block += ${v}) { + //process one block + var word_offset: u32 = block * ${t.blockSize/f}; + ${ue()} + for (var word: u32 = 0; word < ${p}; word += ${b}) { + ${he()} + for (var i: u32 = 0; i < ${b}; i++) { + ${X()} + word_offset += ${8/f}; + } + } + } + workgroupBarrier(); + + if (local_id.x < ${S}) { + var output_value: ${H.type.value} = ${H.type.value}(0); + var workgroup_shared_offset: u32 = local_id.x; + for (var b: u32 = 0u; b < ${v}u; b++) { + output_value += workgroup_shared[workgroup_shared_offset]; + workgroup_shared_offset += ${S}; + } + ${H.setByIndices(`${H.type.indices}(batch, row, col + local_id.x)`,"output_value")}; + } + }`};return{name:"MatMulNBits",shaderCache:{hint:`${t.blockSize};${t.bits};${f};${b};${g};${S};${v}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:_,dataType:m}],dispatchGroup:{x:$},programUniforms:x}),getShaderSource:z}},yh=(e,t)=>{let r=e[0].dims,n=r.length,o=r[n-2],i=t.k,a=t.n,u=r.slice(0,n-2),d=k.size(u),p=e[1].dims[2]/4,m=e[0].dataType,f=ce(t.k),b=ce(p),g=u.concat([o,a]),_=128,S=a%8===0?8:a%4===0?4:1,$=_/S,v=$*b*8,x=v/f,T=v/t.blockSize,E=k.size(g)/S,I=[],z=[d,o,i/f],O=k.convertShape(e[1].dims).slice();O.splice(-1,1,p/b),I.push(...N(z)),I.push(...N(O)),I.push(...N(e[2].dims)),e.length===4&&I.push(...N(k.convertShape(e[3].dims)));let D=[d,o,a];I.push(...N(D));let L=q=>{let Q=z.length,W=P("a",e[0].dataType,Q,f),Z=P("b",12,O.length,b),we=P("scales",e[2].dataType,e[2].dims.length),H=[W,Z,we],j=e.length===4?P("zero_points",12,e[3].dims.length):void 0;j&&H.push(j);let te=D.length,X=M("output",e[0].dataType,te),ue=be(e[0].dataType),he=()=>{switch(f){case 1:return` + let a_data0 = vec4<${ue}>(sub_a[word_offset], sub_a[word_offset + 1], sub_a[word_offset + 2], sub_a[word_offset + 3]); + let a_data1 = vec4<${ue}>(sub_a[word_offset + 4], sub_a[word_offset + 5], sub_a[word_offset + 6], sub_a[word_offset + 7]);`;case 2:return` + let a_data0 = vec4<${ue}>(sub_a[word_offset], sub_a[word_offset + 1]); + let a_data1 = vec4<${ue}>(sub_a[word_offset + 2], sub_a[word_offset + 3]);`;case 4:return` + let a_data0 = sub_a[word_offset]; + let a_data1 = sub_a[word_offset + 1];`;default:throw new Error(`${f}-component is not supported.`)}};return` + var sub_a: array<${W.type.value}, ${x}>; + var inter_results: array, ${S}>; + ${q.declareVariables(...H,X)} + ${q.mainStart([$,S,1])} + let output_indices = ${X.offsetToIndices(`workgroup_index * ${S}`)}; + let col = output_indices[2]; + let row = output_indices[1]; + let batch = output_indices[0]; + let n_blocks_per_col = uniforms.b_shape[1]; + let num_tiles = (n_blocks_per_col - 1) / ${T} + 1; + + // Loop over shared dimension. + for (var tile: u32 = 0; tile < num_tiles; tile += 1) { + let a_col_start = tile * ${x}; + // load one tile A data into shared memory. + for (var a_offset = local_idx; a_offset < ${x}; a_offset += ${_}) + { + let a_col = a_col_start + a_offset; + if (a_col < uniforms.a_shape[2]) + { + sub_a[a_offset] = ${W.getByIndices(`${W.type.indices}(batch, row, a_col)`)}; + } else { + sub_a[a_offset] = ${W.type.value}(0); + } + } + workgroupBarrier(); + + // each thread process one block + let b_row = col + local_id.y; + let block = tile * ${T} + local_id.x; + ${j?` + let zero_point_bytes_per_col = (n_blocks_per_col + 1) / 2; + let zero_point_byte_count = b_row * zero_point_bytes_per_col + (block >> 0x1u); + let zero_point_word_index = zero_point_byte_count >> 0x2u; + let zero_point_byte_offset = zero_point_byte_count & 0x3u; + let zero_point_nibble_offset: u32 = block & 0x1u; + let zero_point_bits_offset = (zero_point_byte_offset << 3) + (zero_point_nibble_offset << 2); + let zero_point_word = ${j.getByOffset("zero_point_word_index")} >> zero_point_bits_offset; + let zero_point = ${ue}((zero_point_word) & 0xFu);`:` + // The default zero point is 8 for unsigned 4-bit quantization. + let zero_point = ${ue}(8);`} + let scale = ${we.getByOffset("b_row * n_blocks_per_col + block")}; + let b_data = ${Z.getByIndices(`${Z.type.indices}(b_row, block, 0)`)}; + var word_offset = local_id.x * ${t.blockSize/f}; + for (var i: u32 = 0; i < ${b}; i++) { + ${he()} + let b_value = ${b===1?"b_data":"b_data[i]"}; + let b_value_lower = unpack4xU8(b_value & 0x0F0F0F0Fu); + let b_value_upper = unpack4xU8((b_value >> 4) & 0x0F0F0F0Fu); + let b_quantized_values = mat2x4<${ue}>(${Array.from({length:4},(ye,re)=>`${ue}(b_value_lower[${re}]), ${ue}(b_value_upper[${re}])`).join(", ")}); + let b_dequantized_values = (b_quantized_values - mat2x4<${ue}>(${Array(8).fill("zero_point").join(",")})) * scale; + inter_results[local_id.y][local_id.x] += ${Array.from({length:2},(ye,re)=>`${`dot(a_data${re}, b_dequantized_values[${re}])`}`).join(" + ")}; + word_offset += ${8/f}; + } + workgroupBarrier(); + } + + if (local_idx < ${S}) { + var output_value: ${X.type.value} = ${X.type.value}(0); + for (var b = 0u; b < ${$}; b++) { + output_value += inter_results[local_idx][b]; + } + if (col + local_idx < uniforms.output_shape[2]) + { + ${X.setByIndices(`${X.type.indices}(batch, row, col + local_idx)`,"output_value")} + } + } + }`};return{name:"BlockwiseMatMulNBits32",shaderCache:{hint:`${t.blockSize};${f};${b};${$};${S}`,inputDependencies:Array(e.length).fill("rank")},getRunData:()=>({outputs:[{dims:g,dataType:m}],dispatchGroup:{x:E},programUniforms:I}),getShaderSource:L}},Jd=(e,t)=>{gh(e.inputs,t),t.blockSize===32&&e.adapterInfo.isVendor("intel")&&e.adapterInfo.isArchitecture("gen-12lp")?e.compute(yh(e.inputs,t)):e.compute(bh(e.inputs,t))},el=e=>J(e)});var _h,wh,vh,$h,xh,Sh,Th,Ih,rl,nl=U(()=>{"use strict";ee();ne();ie();_h=e=>{if(!e||e.length<1)throw new Error("Too few inputs");if(e[0].dataType!==1&&e[0].dataType!==10)throw new Error("Input type must be float or float16.");if(e.length>=2){let t=e[0].dims.length*2===e[1].dims[0];if(e.length===4&&(t=e[3].dims[0]*2===e[1].dims[0]),!t)throw new Error("The pads should be a 1D tensor of shape [2 * input_rank] or [2 * num_axes].")}},wh=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${F("uniforms.pads",o,r)}; + if (k < 0) { + break; + } + if (k >= i32(${F("uniforms.x_shape",o,t)})) { + break; + } + offset += k * i32(${F("uniforms.x_strides",o,t)}); + `;return` + value = ${e.type.value}(uniforms.constant_value); + for (var i = 0; i < 1; i++) { + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + } + `},vh=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${F("uniforms.pads",o,r)}; + if (k < 0) { + k = -k; + } + { + let _2n_1 = 2 * (i32(${F("uniforms.x_shape",o,t)}) - 1); + k = k % _2n_1; + if(k >= i32(${F("uniforms.x_shape",o,t)})) { + k = _2n_1 - k; + } + } + offset += k * i32(${F("uniforms.x_strides",o,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},$h=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${F("uniforms.pads",o,r)}; + if (k < 0) { + k = 0; + } + if (k >= i32(${F("uniforms.x_shape",o,t)})) { + k = i32(${F("uniforms.x_shape",o,t)}) - 1; + } + offset += k * i32(${F("uniforms.x_strides",o,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},xh=(e,t,r)=>{let n="";for(let o=t-1;o>=0;--o)n+=` + k = i32(${e.indicesGet("indices",o)}) - ${F("uniforms.pads",o,r)}; + if (k < 0) { + k += i32(${F("uniforms.x_shape",o,t)}]); + } + if (k >= i32(${F("uniforms.x_shape",o,t)})) { + k -= i32(${F("uniforms.x_shape",o,t)}); + } + offset += k * i32(${F("uniforms.x_strides",o,t)}); + `;return` + var offset = 0; + var k = 0; + ${n} + value = x[offset]; + `},Sh=(e,t,r)=>{switch(r.mode){case 0:return wh(e,t,r.pads.length);case 1:return vh(e,t,r.pads.length);case 2:return $h(e,t,r.pads.length);case 3:return xh(e,t,r.pads.length);default:throw new Error("Invalid mode")}},Th=(e,t)=>{let r=k.padShape(e[0].dims.slice(),t.pads),n=e[0].dims,o=k.size(r),i=[{type:12,data:o},{type:6,data:t.pads}],a=e.length>=3&&e[2].data;t.mode===0&&i.push({type:a?e[2].dataType:1,data:t.value}),i.push(...N(e[0].dims,r));let u=["rank"],d=c=>{let p=M("output",e[0].dataType,r.length),m=P("x",e[0].dataType,n.length),f=m.type.value,b=Sh(p,n.length,t),g=[{name:"output_size",type:"u32"},{name:"pads",type:"i32",length:t.pads.length}];return t.mode===0&&g.push({name:"constant_value",type:a?f:"f32"}),` + ${c.registerUniforms(g).declareVariables(m,p)} + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + + let indices = ${p.offsetToIndices("global_idx")}; + + var value = ${f}(0); + ${b} + output[global_idx] = value; + }`};return{name:"Pad",shaderCache:{hint:`${t.mode}${a}`,inputDependencies:u},getRunData:()=>({outputs:[{dims:r,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(k.size(r)/64)},programUniforms:i}),getShaderSource:d}},Ih=(e,t)=>{if(e.length>1){let r=e[1].getBigInt64Array(),n=e.length>=3&&e[2].data?e[2].dataType===10?e[2].getUint16Array()[0]:e[2].getFloat32Array()[0]:0,o=e[0].dims.length,i=new Int32Array(2*o).fill(0);if(e.length>=4){let u=e[3].getBigInt64Array();for(let d=0;di[Number(d)]=Number(u));let a=[];return i.forEach(u=>a.push(u)),{mode:t.mode,value:n,pads:a}}else return t},rl=(e,t)=>{_h(e.inputs);let r=Ih(e.inputs,t);e.compute(Th(e.inputs,r),{inputs:[0]})}});var rn,ol,il,al,sl,Ch,Ah,ul,dl,ll,cl,pl,ml,fl,hl,gl,bl,yl,_l,wl=U(()=>{"use strict";We();ee();ne();ie();rn=e=>{if(ge.webgpu.validateInputContent&&(!e||e.length!==1))throw new Error("Pool ops requires 1 input.")},ol=(e,t,r)=>{let n=t.format==="NHWC",o=e.dims.slice();n&&o.splice(1,0,o.pop());let i=Object.hasOwnProperty.call(t,"dilations"),a=t.kernelShape.slice(),u=t.strides.slice(),d=i?t.dilations.slice():[],c=t.pads.slice();Tt.adjustPoolAttributes(r,o,a,u,d,c);let p=Tt.computePoolOutputShape(r,o,u,d,a,c,t.autoPad),m=Object.assign({},t);i?Object.assign(m,{kernelShape:a,strides:u,pads:c,dilations:d,cacheKey:t.cacheKey}):Object.assign(m,{kernelShape:a,strides:u,pads:c,cacheKey:t.cacheKey});let f=p.slice();return f.push(f.splice(1,1)[0]),[m,n?f:p]},il=(e,t)=>{let r=t.format==="NHWC",n=k.size(e),o=k.size(t.kernelShape),i=[{type:12,data:n},{type:12,data:o}],a=[{name:"outputSize",type:"u32"},{name:"kernelSize",type:"u32"}];if(t.kernelShape.length<=2){let u=t.kernelShape[t.kernelShape.length-1],d=t.strides[t.strides.length-1],c=t.pads[t.pads.length/2-1],p=t.pads[t.pads.length-1],m=!!(c+p);i.push({type:12,data:u},{type:12,data:d},{type:12,data:c},{type:12,data:p}),a.push({name:"kw",type:"u32"},{name:"sw",type:"u32"},{name:"pwStart",type:"u32"},{name:"pwEnd",type:"u32"});let f=!1;if(t.kernelShape.length===2){let b=t.kernelShape[t.kernelShape.length-2],g=t.strides[t.strides.length-2],_=t.pads[t.pads.length/2-2],S=t.pads[t.pads.length-2];f=!!(_+S),i.push({type:12,data:b},{type:12,data:g},{type:12,data:_},{type:12,data:S}),a.push({name:"kh",type:"u32"},{name:"sh",type:"u32"},{name:"phStart",type:"u32"},{name:"phEnd",type:"u32"})}return[i,a,!0,m,f]}else{if(r)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let u=k.computeStrides(t.kernelShape);i.push({type:12,data:u},{type:12,data:t.pads},{type:12,data:t.strides}),a.push({name:"kernelStrides",type:"u32",length:u.length},{name:"pads",type:"u32",length:t.pads.length},{name:"strides",type:"u32",length:t.strides.length});let d=t.pads.reduce((c,p)=>c+p);return[i,a,!!d,!1,!1]}},al=(e,t,r,n,o,i,a,u,d,c,p,m)=>{let f=o.format==="NHWC",b=t.type.value,g=M("output",t.type.tensor,n);if(o.kernelShape.length<=2){let _="",S="",$="",v=r-(f?2:1);if(p?_=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${v}] = indices[${v}] * uniforms.sw - uniforms.pwStart + i; + if (xIndices[${v}] < 0 || xIndices[${v}] + >= uniforms.x_shape[${v}]) { + pad++; + continue; + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${i} + }`:_=` + for (var i: u32 = 0u; i < uniforms.kw; i++) { + xIndices[${v}] = indices[${v}] * uniforms.sw - uniforms.pwStart + i; + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${i} + }`,o.kernelShape.length===2){let T=r-(f?3:2);m?S=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${T}] = indices[${T}] * uniforms.sh - uniforms.phStart + j; + if (xIndices[${T}] < 0 || xIndices[${T}] >= uniforms.x_shape[${T}]) { + pad += i32(uniforms.kw); + continue; + } + `:S=` + for (var j: u32 = 0u; j < uniforms.kh; j++) { + xIndices[${T}] = indices[${T}] * uniforms.sh - uniforms.phStart + j; + `,$=` + } + `}return` + ${e.registerUniforms(d).declareVariables(t,g)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + + let indices = ${g.offsetToIndices("global_idx")}; + var xIndices = ${g.offsetToIndices("global_idx")}; + + var value = ${b}(${u}); + var pad = 0; + ${S} + ${_} + ${$} + ${a} + + output[global_idx] = value; + }`}else{if(f)throw new Error("Pooling with kernelShape.length > 2 is not supported for NHWC format.");let _=o.kernelShape.length,S=o.pads.length,$="";return c?$=` + if (xIndices[j] >= uniforms.x_shape[j]) { + pad++; + isPad = true; + break; + } + } + if (!isPad) { + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${i} + }`:$=` + } + let x_val = x[${t.indicesToOffset("xIndices")}]; + ${i} + `,` + ${e.registerUniforms(d).declareVariables(t,g)} + + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let indices = ${g.offsetToIndices("global_idx")}; + var xIndices = ${g.offsetToIndices("global_idx")}; + + var offsets: array; + + var value = ${b}(${u}); + var pad = 0; + var isPad = false; + + for (var i: u32 = 0u; i < uniforms.kernelSize; i++) { + var offset = i; + for (var j = 0u; j < ${_-1}u; j++) { + offsets[j] = offset / ${F("uniforms.kernelStrides","j",_)}; + offset -= offsets[j] * ${F("uniforms.kernelStrides","j",_)}; + } + offsets[${_-1}] = offset; + + isPad = false; + for (var j = ${r-_}u; j < ${r}u; j++) { + xIndices[j] = indices[j] * ${F("uniforms.strides",`j - ${r-_}u`,_)} + + offsets[j - ${r-_}u] - ${F("uniforms.pads","j - 2u",S)}; + ${$} + } + ${a} + + output[global_idx] = value; + }`}},sl=e=>`${e.format};${e.ceilMode};${e.autoPad};${e.kernelShape.length}`,Ch=e=>`${sl(e)};${e.countIncludePad}`,Ah=e=>`${sl(e)};${e.storageOrder};${e.dilations}`,ul=e=>({format:e.format,autoPad:["NOTSET","VALID","SAME_UPPER","SAME_LOWER"][e.auto_pad],ceilMode:e.ceil_mode,kernelShape:e.kernel_shape,strides:e.strides,pads:e.pads}),dl=(e,t,r,n)=>{let[o,i]=ol(t,n,r),a=P("x",t.dataType,t.dims.length),u=a.type.value,d="value += x_val;",c="";o.countIncludePad?c+=`value /= ${u}(uniforms.kernelSize);`:c+=`value /= ${u}(i32(uniforms.kernelSize) - pad);`;let[p,m,f,b,g]=il(i,o);p.push(...N(t.dims,i));let _=["rank"];return{name:e,shaderCache:{hint:`${n.cacheKey};${f};${b};${g}`,inputDependencies:_},getRunData:()=>({outputs:[{dims:i,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(k.size(i)/64)},programUniforms:p}),getShaderSource:S=>al(S,a,t.dims.length,i.length,o,d,c,0,m,f,b,g)}},ll=e=>{let t=e.count_include_pad!==0,r=ul(e);if(r.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");let n={countIncludePad:t,...r,cacheKey:""};return{...n,cacheKey:Ch(n)}},cl=(e,t)=>{rn(e.inputs),e.compute(dl("AveragePool",e.inputs[0],!1,t))},pl={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[]},ml=e=>{let t=e.format;return{format:t,...pl,cacheKey:t}},fl=(e,t)=>{rn(e.inputs),e.compute(dl("GlobalAveragePool",e.inputs[0],!0,t))},hl=(e,t,r,n)=>{let[o,i]=ol(t,n,r),a=` + value = max(x_val, value); + `,u="",d=P("x",t.dataType,t.dims.length),c=["rank"],[p,m,f,b,g]=il(i,o);return p.push(...N(t.dims,i)),{name:e,shaderCache:{hint:`${n.cacheKey};${f};${b};${g}`,inputDependencies:c},getRunData:()=>({outputs:[{dims:i,dataType:t.dataType}],dispatchGroup:{x:Math.ceil(k.size(i)/64)},programUniforms:p}),getShaderSource:_=>al(_,d,t.dims.length,i.length,o,a,u,t.dataType===10?-65504:-1e5,m,f,b,g)}},gl=(e,t)=>{rn(e.inputs),e.compute(hl("MaxPool",e.inputs[0],!1,t))},bl=e=>{let t=e.storage_order,r=e.dilations,n=ul(e);if(t!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(n.ceilMode!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");let o={storageOrder:t,dilations:r,...n,cacheKey:""};return{...o,cacheKey:Ah(o)}},yl=e=>{let t=e.format;return{format:t,...pl,cacheKey:t}},_l=(e,t)=>{rn(e.inputs),e.compute(hl("GlobalMaxPool",e.inputs[0],!0,t))}});var kh,Ph,vl,$l,xl=U(()=>{"use strict";ee();ne();Se();ie();kh=(e,t)=>{if(e.length<2||e.length>3)throw new Error("DequantizeLinear requires 2 or 3 inputs.");if(e.length===3&&e[1].dims===e[2].dims)throw new Error("x-scale and x-zero-point must have the same shape.");if(e.length===3&&e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[0].dataType===6&&e.length>2)throw new Error("In the case of dequantizing int32 there is no zero point.");if(e[1].dims.length!==0&&e[1].dims.length!==1&&e[1].dims.length!==e[0].dims.length)throw new Error("scale input must be a scalar, a 1D tensor, or have the same rank as the input tensor.");if(e.length>2){if(e[0].dataType!==e[2].dataType)throw new Error("x and x-zero-point must have the same data type.");if(e[1].dims.length!==e[2].dims.length)throw new Error("scale and zero-point inputs must have the same rank.");if(!e[1].dims.map((r,n)=>r===e[2].dims[n]).reduce((r,n)=>r&&n,!0))throw new Error("scale and zero-point inputs must have the same shape.")}if(t.blockSize>0){if(e[1].dims.length===0||e[1].dims.length===1&&e[1].dims[0]===1)throw new Error("blockSize must be set only for block quantization.");if(!e[1].dims.map((o,i)=>i===t.axis||o===e[0].dims[i]).reduce((o,i)=>o&&i,!0))throw new Error("For block qunatization, scale input shape to match the input shape except for the axis");if(e[1].dims.length!==e[0].dims.length)throw new Error("For block qunatization the scale input rank must be the same as the x rank.");let r=e[0].dims[t.axis],n=e[1].dims[t.axis];if(t.blockSizeMath.ceil(r/(n-1)-1))throw new Error("blockSize must be with in the range [ceil(dI / Si), ceil(dI / (Si - 1) - 1)].")}},Ph=(e,t)=>{let r=k.normalizeAxis(t.axis,e[0].dims.length),n=e[0].dataType,o=n===3,i=e[0].dims,a=e[1].dataType,u=k.size(i),d=n===3||n===2,c=d?[Math.ceil(k.size(e[0].dims)/4)]:e[0].dims,p=e[1].dims,m=e.length>2?e[2]:void 0,f=m?d?[Math.ceil(k.size(m.dims)/4)]:m.dims:void 0,b=p.length===0||p.length===1&&p[0]===1,g=b===!1&&p.length===1,_=ce(u),S=b&&(!d||_===4),$=S?_:1,v=S&&!d?_:1,x=P("input",d?12:n,c.length,v),T=P("scale",a,p.length),E=m?P("zero_point",d?12:n,f.length):void 0,I=M("output",a,i.length,$),z=[x,T];E&&z.push(E);let O=[c,p];m&&O.push(f);let D=[{type:12,data:u/$},{type:12,data:r},{type:12,data:t.blockSize},...N(...O,i)],L=q=>{let Q=[{name:"output_size",type:"u32"},{name:"axis",type:"u32"},{name:"block_size",type:"u32"}];return` + ${q.registerUniforms(Q).declareVariables(...z,I)} + ${q.mainStart()} + ${q.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${I.offsetToIndices("global_idx")}; + + // Set input x + ${d?` + let input = ${x.getByOffset("global_idx / 4")}; + let x_vec = ${o?"unpack4xI8(input)":"unpack4xU8(input)"}; + let x_value = ${$===1?"x_vec[global_idx % 4]":"x_vec"};`:`let x_value = ${x.getByOffset("global_idx")};`}; + + // Set scale input + ${b?`let scale_value= ${T.getByOffset("0")}`:g?` + let scale_index = ${I.indicesGet("output_indices","uniforms.axis")}; + let scale_value= ${T.getByOffset("scale_index")};`:` + var scale_indices: ${T.type.indices} = output_indices; + let index = ${T.indicesGet("scale_indices","uniforms.axis")} / uniforms.block_size; + ${T.indicesSet("scale_indices","uniforms.axis","index")}; + let scale_value= ${T.getByIndices("scale_indices")};`}; + + // Set zero-point input + ${E?b?d?` + let zero_point_input = ${E.getByOffset("0")}; + let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value= zero_point_vec[0]`:`let zero_point_value = ${E.getByOffset("0")}`:g?d?` + let zero_point_index = ${I.indicesGet("output_indices","uniforms.axis")}; + let zero_point_input = ${E.getByOffset("zero_point_index / 4")}; + let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_index % 4]`:` + let zero_point_index = ${I.indicesGet("output_indices","uniforms.axis")}; + let zero_point_value = ${E.getByOffset("zero_point_index")};`:d?` + let zero_point_offset = ${T.indicesToOffset("scale_indices")}; + let zero_point_input = ${E.getByOffset("zero_point_offset / 4")}; + let zero_point_vec = ${o?"unpack4xI8(zero_point_input)":"unpack4xU8(zero_point_input)"}; + let zero_point_value = zero_point_vec[zero_point_offset % 4];`:`let zero_point_value = ${E.getByIndices("scale_indices")};`:`let zero_point_value = ${d?o?"i32":"u32":x.type.value}(0);`}; + // Compute and write output + ${I.setByOffset("global_idx",`${I.type.value}(x_value - zero_point_value) * scale_value`)}; + }`};return{name:"DequantizeLinear",shaderCache:{hint:t.cacheKey,inputDependencies:E?["rank","rank","rank"]:["rank","rank"]},getShaderSource:L,getRunData:()=>({outputs:[{dims:i,dataType:a}],dispatchGroup:{x:Math.ceil(u/$/64),y:1,z:1},programUniforms:D})}},vl=(e,t)=>{kh(e.inputs,t),e.compute(Ph(e.inputs,t))},$l=e=>J({axis:e.axis,blockSize:e.blockSize})});var zh,Oh,Sl,Tl=U(()=>{"use strict";We();ee();ie();zh=(e,t,r)=>{let n=e===t,o=et&&r>0;if(n||o||i)throw new Error("Range these inputs' contents are invalid.")},Oh=(e,t,r,n)=>{let o=Math.abs(Math.ceil((t-e)/r)),i=[o],a=o,u=[{type:12,data:a},{type:n,data:e},{type:n,data:r},...N(i)],d=c=>{let p=M("output",n,i.length),m=p.type.value,f=[{name:"outputSize",type:"u32"},{name:"start",type:m},{name:"delta",type:m}];return` + ${c.registerUniforms(f).declareVariables(p)} + ${c.mainStart()} + ${c.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + output[global_idx] = uniforms.start + ${m}(global_idx) * uniforms.delta; + }`};return{name:"Range",shaderCache:{hint:`${n}`},getShaderSource:d,getRunData:()=>({outputs:[{dims:i,dataType:n}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:u})}},Sl=e=>{let t=0,r=0,n=0;e.inputs[0].dataType===6?(t=e.inputs[0].getInt32Array()[0],r=e.inputs[1].getInt32Array()[0],n=e.inputs[2].getInt32Array()[0]):e.inputs[0].dataType===1&&(t=e.inputs[0].getFloat32Array()[0],r=e.inputs[1].getFloat32Array()[0],n=e.inputs[2].getFloat32Array()[0]),ge.webgpu.validateInputContent&&zh(t,r,n),e.compute(Oh(t,r,n,e.inputs[0].dataType),{inputs:[]})}});var Bh,Il,Cl,Dh,Al,El,kl=U(()=>{"use strict";ee();ne();Se();ie();Bh=(e,t,r,n)=>{if(e!=="none"&&n!=="i32"&&n!=="u32"&&n!=="f32")throw new Error(`Input ${n} is not supported with reduction ${e}.`);let o=`{ + var oldValue = 0; + loop { + let newValueF32 =`,i=`; + let newValue = bitcast(newValueF32); + let res = atomicCompareExchangeWeak(&${t}, oldValue, newValue); + if res.exchanged { + break; + } + oldValue = res.old_value; + } + }`;switch(e){case"none":return`${t}=${r};`;case"add":return n==="i32"||n==="u32"?`atomicAdd(&${t}, bitcast<${n}>(${r}));`:` + ${o}bitcast<${n}>(oldValue) + (${r})${i}`;case"max":return n==="i32"||n==="u32"?`atomicMax(&${t}, bitcast<${n}>(${r}));`:` + ${o}max(bitcast(oldValue), (${r}))${i}`;case"min":return n==="i32"||n==="u32"?`atomicMin(&${t}, bitcast<${n}>(${r}));`:`${o}min(bitcast<${n}>(oldValue), (${r}))${i}`;case"mul":return`${o}(bitcast<${n}>(oldValue) * (${r}))${i}`;default:throw new Error(`Reduction ${e} is not supported.`)}},Il=(e,t)=>`${e===1?` + let element_count_dim = uniforms.output_strides; + let dim_value = uniforms.output_shape;`:` + let element_count_dim = uniforms.output_strides[${t?"i - indices_start":"i"}]; + let dim_value = uniforms.output_shape[${t?"i - indices_start":"i"} + uniforms.last_index_dimension];`} + + if (index >= 0) { + if (index >= i32(dim_value)) { + index = i32(dim_value - 1); + } + } else { + if (index < -i32(dim_value)) { + index = 0; + } else { + index += i32(dim_value); + } + } + data_offset += u32((u32(index) * element_count_dim));`,Cl=(e,t,r)=>`for (var i = 0u; i < uniforms.num_updates_elements; i++) { + let value = updates[uniforms.num_updates_elements * ${r?"global_idx":"idx"} + i]; + ${Bh(e.reduction,"output[data_offset + i]","value",t)} + }`,Dh=(e,t)=>{let r=e[0].dims,n=e[1].dims,o=r,i=1,a=Math.ceil(k.size(n)/i),u=n[n.length-1],d=k.sizeFromDimension(r,u),c=k.sizeFromDimension(n,0)/u,p=[{type:12,data:a},{type:12,data:u},{type:12,data:d},...N(e[1].dims,e[2].dims,o)],m=f=>{let b=P("indices",e[1].dataType,e[1].dims.length),g=P("updates",e[2].dataType,e[2].dims.length,i),_=t.reduction!=="none"&&t.reduction!==""?es("output",e[0].dataType,o.length):M("output",e[0].dataType,o.length,i);return` + ${f.registerUniform("output_size","u32").registerUniform("last_index_dimension","u32").registerUniform("num_updates_elements","u32").declareVariables(b,g,_)} + ${f.mainStart()} + ${f.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + var hasDuplicates = false; + if (${t.reduction==="none"}) { + for (var i = 0; i < ${c}; i = i + 1) { + for (var j = i + 1; j < ${c}; j = j + 1) { + var index_i = i32(indices[i].x); + var index_j = i32(indices[j].x); + if (index_i == index_j) { + hasDuplicates = true; + break; + } + } + if (hasDuplicates) { + break; + } + } + } + + if (${t.reduction==="none"} && hasDuplicates) { + if (global_idx != 0u) { + return; + } + // Process each index-update pair individually when duplicates exist + for (var idx = 0u; idx < ${c}u; idx++) { + var data_offset = 0u; + for (var i = 0u; i < uniforms.last_index_dimension; i++) { + var index = i32(indices[idx * uniforms.last_index_dimension + i].x); + ${Il(r.length,!1)} + } + ${Cl(t,_.type.value,!1)} + } + return; + } + + var data_offset = 0u; + var indices_start = uniforms.last_index_dimension * global_idx; + var indices_end = indices_start + uniforms.last_index_dimension; + for (var i = indices_start; i < indices_end; i++) { + var index = i32(indices[i].x); + ${Il(r.length,!0)} + } + ${Cl(t,_.type.value,!0)} + }`};return{name:"ScatterND",shaderCache:{hint:`${t.cacheKey}_${t.reduction}`,inputDependencies:["rank","rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(a/64)},programUniforms:p}),getShaderSource:m}},Al=e=>J({reduction:e.reduction}),El=(e,t)=>{e.compute(Dh(e.inputs,t),{inputs:[e.inputs[1],e.inputs[2]],outputs:[]})}});var Mh,Rh,Uh,Pl,Nh,Vh,Wh,Lh,Gh,Hh,Fh,qh,zl,jh,Kh,Zh,Qh,Yh,Ol,Bl,Dl=U(()=>{"use strict";ee();ne();Se();ie();Mh=(e,t)=>{if(e.every(r=>r>0||(()=>{throw new Error("Resize requires scales input values to be positive")})),e.length>0){if(t.mode==="linear"){if(!(e.length===2||e.length===3||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1||e.length===5&&e[0]===1&&e[1]===1))throw new Error(`For linear mode, Resize requires scales to be 2D, 3D, 4D with either two outermost or one innermost and + one outermost scale values equal to 1, or 5D with two outermost scale values equal to 1`)}else if(t.mode==="cubic"&&!(e.length===2||e.length===4&&e[0]===1&&e[1]===1||e.length===4&&e[0]===1&&e[3]===1))throw new Error("Resize requires scales input size to be 2 or 4 for cubic mode")}},Rh=(e,t,r)=>{t.every(o=>o>=0&&o{throw new Error("Resize requires axes input values to be positive and less than rank")}));let n=new Array(r).fill(1);return t.forEach((o,i)=>n[o]=e[i]),n},Uh=(e,t,r,n,o,i)=>{let[a,u,d]=r>10?[1,2,3]:[-1,e.length>1?1:-1,-1],c=e[0].dims.length;if(a>0&&e.length>a&&e[a].dims.length>0)e[a].getFloat32Array().forEach(p=>i.push(p));else if(t.coordinateTransformMode==="tf_crop_and_resize")throw new Error("Resize requires RoI input to be specified when coordinateTransformMode is tfCropAndResize");if(u>0&&e.length>u&&e[u].dims.length===1&&e[u].dims[0]>0){if(e[u].getFloat32Array().forEach(p=>n.push(p)),n.length!==0&&n.length!==c&&r>=18&&n.length!==t.axes.length)throw new Error("Resize requires scales input size to be same as input rank or axes size for opset 18 and up");Mh(n,t),t.axes.length>0&&Rh(n,t.axes,c).forEach((p,m)=>n[m]=p)}if(d>0&&e.length>d&&e[d].dims.length===1&&e[d].dims[0]>0&&(e[d].getBigInt64Array().forEach(p=>o.push(Number(p))),o.length!==0&&o.length!==c&&r>=18&&o.length!==t.axes.length))throw new Error("Resize requires sizes input size to be same as input rank or axes size for opset 18 and up");if(t.axes.length>0){if(n.length!==0&&n.length!==t.axes.length)throw new Error('Resize requires "scales" input size to be of axes rank when axes attributes is specified');if(o.length!==0&&o.length!==t.axes.length)throw new Error('Resize requires "sizes" input size to be of rank axes rank when axes attributes is specified')}if(typeof n<"u"&&typeof o<"u"&&n.length>0&&o.length>c)throw new Error("Resize requires only of scales or sizes to be specified")},Pl=(e,t,r,n)=>` + // The whole part and the fractional part are calculated separately due to inaccuracy of floating + // point division. As an example, f32(21) / f32(7) may evaluate to 2.99... instead of 3, causing an + // offset-by-one error later in floor(). + let big = (${e}) * (${t}); + let whole = ${n}(big / (${r})); + let fract = ${n}(big % (${r})) / ${n}(${r}); + return whole + fract; +`,Nh=(e,t)=>`fn getOriginalCoordinateFromResizedCoordinate(xResized: u32, xScale: f32, lengthResized: u32, + lengthOriginal: u32, roiStart: f32, roiEnd: f32) -> ${t} { `+(()=>{switch(e){case"asymmetric":return` + if (xScale < 1.0 || floor(xScale) != xScale) { + return ${t}(xResized) / ${t}(xScale); + } else { + ${Pl("xResized","lengthOriginal","lengthResized",t)} + } + `;case"pytorch_half_pixel":return`if (lengthResized > 1) { + return (${t}(xResized) + 0.5) / ${t}(xScale) - 0.5; + } else { + return 0.0; + }`;case"tf_half_pixel_for_nn":return`return (${t}(xResized) + 0.5) / ${t}(xScale);`;case"align_corners":return`if (lengthResized == 1) { + return 0.0; + } else { + ${Pl("xResized","lengthOriginal - 1","lengthResized - 1",t)} + }`;case"tf_crop_and_resize":return`if (lengthResized > 1) { + return ${t}(roiStart) * ${t}(lengthOriginal - 1) + + (${t}(xResized) * ${t}(roiEnd - roiStart) * ${t}(lengthOriginal - 1)) / + ${t}(lengthResized - 1); + } else { + return 0.5 * ${t}(roiStart + roiEnd) * ${t}(lengthOriginal - 1); + }`;case"half_pixel_symmetric":return`const outputWidth = ${t}xScale * ${t}(lengthResized); + const adjustment = ${t}(lengthResized) / outputWidth; + const center = ${t}(lengthOriginal) / 2; + const offset = center * (1 - adjustment); + return offset + ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;case"half_pixel":return`return ((${t}(xResized) + 0.5) / ${t}(xScale)) - 0.5;`;default:throw new Error(`Coordinate transform mode ${e} is not supported`)}})()+"}",Vh=(e,t,r)=>`fn getNearestPixelFromOriginal(xOriginal: ${r}, isDownSample: bool) -> ${r} {`+(()=>{switch(e){case"round_prefer_ceil":return"if (fract(xOriginal) == 0.5) { return ceil(xOriginal); } else { return round(xOriginal); }";case"floor":return"return floor(xOriginal);";case"ceil":return"return ceil(xOriginal);";case"round_prefer_floor":return"if (fract(xOriginal) == 0.5) { return floor(xOriginal); } else { return round(xOriginal); }";case"simple":default:if(t<11)return"if (isDownSample) { return ceil(xOriginal); } else { return xOriginal; }";throw new Error(`Nearest mode ${e} is not supported`)}})()+"}",Wh=(e,t,r)=>{let n=new Array(r).fill(0).concat(new Array(r).fill(1)),o=e.length===0?n:e.slice();return t.length>0?(t.forEach((i,a)=>{n[i]=o[a],n[a+r]=o[t.length+a]}),n):o},Lh=(e,t,r,n)=>{let o=[];if(r.length>0)if(n.length>0){if(e.forEach(i=>o.push(i)),Math.max(...n)>e.length)throw new Error("axes is out of bound");n.forEach((i,a)=>o[i]=r[a])}else r.forEach(i=>o.push(i));else{if(t.length===0)throw new Error("Resize requires either scales or sizes.");o=e.map((i,a)=>Math.round(i*t[a]))}return o},Gh=(e,t,r)=>{let n=(()=>{switch(r.keepAspectRatioPolicy){case"not_larger":return r.axes.length>0?Math.min(...r.axes.map(i=>t[i]),Number.MAX_VALUE):Math.min(...t,Number.MAX_VALUE);case"not_smaller":return r.axes.length>0?Math.max(...r.axes.map(i=>t[i]),Number.MIN_VALUE):Math.max(...t,Number.MIN_VALUE);default:throw new Error(`Keep aspect ratio policy ${r.keepAspectRatioPolicy} is not supported`)}})();t.fill(1,0,t.length);let o=e.slice();return r.axes.length>0?(r.axes.forEach(i=>t[i]=n),r.axes.forEach(i=>o[i]=Math.round(e[i]*t[i]))):(t.fill(n,0,t.length),o.forEach((i,a)=>o[a]=Math.round(i*t[a]))),o},Hh=(e,t,r,n,o)=>` + fn calculateOriginalIndicesFromOutputIndices(output_indices: ${e.type.indices}) -> array<${e.type.value}, ${r.length}> { + var original_indices: array<${e.type.value}, ${r.length}>; + for (var i:u32 = 0; i < ${r.length}; i++) { + var output_index = ${e.indicesGet("output_indices","i")}; + var scale = ${F("uniforms.scales","i",n)}; + var roi_low = ${F("uniforms.roi","i",o)}; + var roi_hi = ${F("uniforms.roi",`i + ${t.length}`,o)}; + if (scale == 1.0) { + original_indices[i] = ${e.type.value}(output_index); + } else { + var input_shape_i = ${F("uniforms.input_shape","i",t.length)}; + var output_shape_i = ${F("uniforms.output_shape","i",r.length)}; + original_indices[i] = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + } + } + return original_indices; + }`,Fh=(e,t,r,n,o,i,a)=>` + fn calculateInputIndicesFromOutputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + for (var i:u32 = 0; i < ${n.length}; i++) { + var output_index = ${t.indicesGet("output_indices","i")}; + var input_index: u32; + var scale = ${F("uniforms.scales","i",o)}; + if (scale == 1.0) { + input_index = output_index; + } else { + var roi_low = ${F("uniforms.roi","i",i)}; + var roi_hi = ${F("uniforms.roi",`i + ${r.length}`,i)}; + var input_shape_i = ${F("uniforms.input_shape","i",r.length)}; + var output_shape_i = ${F("uniforms.output_shape","i",n.length)}; + var original_idx = getOriginalCoordinateFromResizedCoordinate(output_index, scale, output_shape_i, + input_shape_i, roi_low, roi_hi); + if (!${a} || (original_idx >= 0 && original_idx < ${t.type.value}(input_shape_i))) { + if (original_idx < 0) { + input_index = 0; + } else if (original_idx > ${t.type.value}(input_shape_i - 1)) { + input_index = input_shape_i - 1; + } else { + input_index = u32(getNearestPixelFromOriginal(original_idx, scale < 1)); + } + } else { + input_index = u32(original_idx); + } + } + ${e.indicesSet("input_indices","i","input_index")} + } + return input_indices; + }`,qh=(e,t)=>` + fn checkInputIndices(input_indices: ${e.type.indices}) -> bool { + for (var i:u32 = 0; i < ${t.length}; i++) { + var input_index = ${e.indicesGet("input_indices","i")}; + if (input_index < 0 || input_index >= ${F("uniforms.input_shape","i",t.length)}) { + return false; + } + } + return true; + }`,zl=(e,t,r,n)=>e.rank>n?` + ${e.indicesSet("input_indices",t,"channel")}; + ${e.indicesSet("input_indices",r,"batch")}; +`:"",jh=(e,t,r,n,o)=>{let[a,u,d,c]=r.length===2?[-1,0,1,-1]:[0,2,3,1],p=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, row: u32, col: u32) -> ${p} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",u,`max(0, min(row, ${r[u]} - 1))`)}; + ${e.indicesSet("input_indices",d,`max(0, min(col, ${r[d]} - 1))`)}; + ${zl(e,c,a,2)} + return ${e.getByIndices("input_indices")}; + } + + fn bilinearInterpolation(output_indices: ${t.type.indices}) -> ${p} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var row:${p} = originalIndices[${u}]; + var col:${p} = originalIndices[${d}]; + ${n?`if (row < 0 || row > (${r[u]} - 1) || col < 0 || col > (${r[d]} - 1)) { + return ${o}; + }`:""}; + row = max(0, min(row, ${r[u]} - 1)); + col = max(0, min(col, ${r[d]} - 1)); + var row1: u32 = u32(row); + var col1: u32 = u32(col); + var row2: u32 = u32(row + 1); + var col2: u32 = u32(col + 1); + var channel: u32 = ${r.length>2?`u32(originalIndices[${c}])`:"0"}; + var batch: u32 = ${r.length>2?`u32(originalIndices[${a}])`:"0"}; + var x11: ${p} = getInputValue(batch, channel, row1, col1); + var x12: ${p} = getInputValue(batch, channel, row1, col2); + var x21: ${p} = getInputValue(batch, channel, row2, col1); + var x22: ${p} = getInputValue(batch, channel, row2, col2); + var dx1: ${p} = abs(row - ${p}(row1)); + var dx2: ${p} = abs(${p}(row2) - row); + var dy1: ${p} = abs(col - ${p}(col1)); + var dy2: ${p} = abs(${p}(col2) - col); + if (row1 == row2) { + dx1 = 0.5; + dx2 = 0.5; + } + if (col1 == col2) { + dy1 = 0.5; + dy2 = 0.5; + } + return (x11 * dx2 * dy2 + x12 * dx2 * dy1 + x21 * dx1 * dy2 + x22 * dx1 * dy1); + }`},Kh=(e,t,r,n,o,i,a,u,d,c)=>{let p=r.length===2,m=!0,[f,b]=p?[0,1]:m?[2,3]:[1,2],g=e.type.value,_=S=>{let $=S===f?"row":"col";return` + fn ${$}CubicInterpolation(input_indices: ${e.type.indices}, output_indices: ${t.type.indices}) -> ${g} { + var output_index = ${t.indicesGet("output_indices",S)}; + var originalIdx: ${g} = getOriginalCoordinateFromResizedCoordinate(output_index, ${o[S]}, + ${n[S]}, ${r[S]}, ${i[S]}, ${i[S]} + ${r.length}); + var fractOriginalIdx: ${g} = originalIdx - floor(originalIdx); + var coefs = getCubicInterpolationCoefs(fractOriginalIdx); + + if (${u} && (originalIdx < 0 || originalIdx > (${r[S]} - 1))) { + return ${d}; + } + var data: array<${g}, 4> = array<${g}, 4>(0.0, 0.0, 0.0, 0.0); + for (var i: i32 = -1; i < 3; i++) { + var ${$}: ${g} = originalIdx + ${g}(i); + if (${$} < 0 || ${$} >= ${r[S]}) { + ${c?`coefs[i + 1] = 0.0; + continue;`:u?`return ${d};`:`${$} = max(0, min(${$}, ${r[S]} - 1));`}; + } + var input_indices_copy: ${e.type.indices} = input_indices; + ${e.indicesSet("input_indices_copy",S,`u32(${$})`)}; + data[i + 1] = ${S===f?e.getByIndices("input_indices_copy"):"rowCubicInterpolation(input_indices_copy, output_indices)"}; + } + return cubicInterpolation1D(data, coefs); + }`};return` + ${_(f)}; + ${_(b)}; + fn getCubicInterpolationCoefs(s: ${g}) -> array<${g}, 4> { + var absS = abs(s); + var coeffs: array<${g}, 4> = array<${g}, 4>(0.0, 0.0, 0.0, 0.0); + var oneMinusAbsS: ${g} = 1.0 - absS; + var twoMinusAbsS: ${g} = 2.0 - absS; + var onePlusAbsS: ${g} = 1.0 + absS; + coeffs[0] = ((${a} * onePlusAbsS - 5 * ${a}) * onePlusAbsS + 8 * ${a}) * onePlusAbsS - 4 * ${a}; + coeffs[1] = ((${a} + 2) * absS - (${a} + 3)) * absS * absS + 1; + coeffs[2] = ((${a} + 2) * oneMinusAbsS - (${a} + 3)) * oneMinusAbsS * oneMinusAbsS + 1; + coeffs[3] = ((${a} * twoMinusAbsS - 5 * ${a}) * twoMinusAbsS + 8 * ${a}) * twoMinusAbsS - 4 * ${a}; + return coeffs; + } + + fn cubicInterpolation1D(x: array<${g}, 4>, coefs: array<${g}, 4>) -> ${g} { + var coefsSum: ${g} = coefs[0] + coefs[1] + coefs[2] + coefs[3]; + return (x[0] * coefs[0] + x[1] * coefs[1]+ x[2] * coefs[2]+ x[3] * coefs[3]) / coefsSum; + } + + fn bicubicInterpolation(output_indices: ${t.type.indices}) -> ${g} { + var input_indices: ${e.type.indices} = output_indices; + return colCubicInterpolation(input_indices, output_indices); + } + `},Zh=(e,t,r,n,o)=>{let[a,u,d,c,p]=r.length===3?[-1,0,1,2,-1]:[0,2,3,4,1],m=e.type.value;return` + fn getInputValue(batch: u32, channel: u32, depth:u32, height: u32, width: u32) -> ${m} { + var input_indices: ${e.type.indices}; + ${e.indicesSet("input_indices",u,`max(0, min(depth, ${r[u]} - 1))`)}; + ${e.indicesSet("input_indices",d,`max(0, min(height, ${r[d]} - 1))`)}; + ${e.indicesSet("input_indices",c,`max(0, min(width, ${r[c]} - 1))`)}; + ${zl(e,p,a,3)} + return ${e.getByIndices("input_indices")}; + } + + fn trilinearInterpolation(output_indices: ${t.type.indices}) -> ${m} { + var originalIndices = calculateOriginalIndicesFromOutputIndices(output_indices); + var depth:${m} = originalIndices[${u}]; + var height:${m} = originalIndices[${d}]; + var width:${m} = originalIndices[${c}]; + ${n?`if (depth < 0 || depth > (${r[u]} - 1) || height < 0 || height > (${r[d]} - 1) || width < 0 || (width > ${r[c]} - 1)) { + return ${o}; + }`:""}; + + depth = max(0, min(depth, ${r[u]} - 1)); + height = max(0, min(height, ${r[d]} - 1)); + width = max(0, min(width, ${r[c]} - 1)); + var depth1: u32 = u32(depth); + var height1: u32 = u32(height); + var width1: u32 = u32(width); + var depth2: u32 = u32(depth + 1); + var height2: u32 = u32(height + 1); + var width2: u32 = u32(width + 1); + var channel: u32 = ${r.length>3?`u32(originalIndices[${p}])`:"0"}; + var batch: u32 = ${r.length>3?`u32(originalIndices[${a}])`:"0"}; + + var x111: ${m} = getInputValue(batch, channel, depth1, height1, width1); + var x112: ${m} = getInputValue(batch, channel, depth1, height1, width2); + var x121: ${m} = getInputValue(batch, channel, depth1, height2, width1); + var x122: ${m} = getInputValue(batch, channel, depth1, height2, width2); + var x211: ${m} = getInputValue(batch, channel, depth2, height1, width1); + var x212: ${m} = getInputValue(batch, channel, depth2, height1, width2); + var x221: ${m} = getInputValue(batch, channel, depth2, height2, width1); + var x222: ${m} = getInputValue(batch, channel, depth2, height2, width2); + var dx1: ${m} = abs(depth - ${m}(depth1)); + var dx2: ${m} = abs(${m}(depth2) - depth); + var dy1: ${m} = abs(height - ${m}(height1)); + var dy2: ${m} = abs(${m}(height2) - height); + var dz1: ${m} = abs(width - ${m}(width1)); + var dz2: ${m} = abs(${m}(width2) - width); + if (depth1 == depth2) { + dx1 = 0.5; + dx2 = 0.5; + } + if (height1 == height2) { + dy1 = 0.5; + dy2 = 0.5; + } + if (width1 == width2) { + dz1 = 0.5; + dz2 = 0.5; + } + return (x111 * dx2 * dy2 * dz2 + x112 * dx2 * dy2 * dz1 + x121 * dx2 * dy1 *dz2 + x122 * dx2 * dy1 * dz1 + + x211 * dx1 * dy2 * dz2 + x212 * dx1 * dy2 * dz1 + x221 * dx1 * dy1 *dz2 + x222 * dx1 * dy1 * dz1); + }`},Qh=(e,t,r,n,o,i)=>{let a=e.dims,u=Wh(i,t.axes,a.length),d=Lh(a,n,o,t.axes),c=n.slice();n.length===0&&(c=a.map((v,x)=>v===0?1:d[x]/v),t.keepAspectRatioPolicy!=="stretch"&&(d=Gh(a,c,t)));let p=M("output",e.dataType,d.length),m=P("input",e.dataType,a.length),f=k.size(d),b=a.length===d.length&&a.every((v,x)=>v===d[x]),g=t.coordinateTransformMode==="tf_crop_and_resize",_=t.extrapolationValue,S=m.type.value,$=v=>` + ${b?"":` + ${Nh(t.coordinateTransformMode,S)}; + ${(()=>{switch(t.mode){case"nearest":return` + ${qh(m,a)}; + ${Vh(t.nearestMode,r,S)}; + ${Fh(m,p,a,d,c.length,u.length,g)}; + `;case"linear":return` + ${Hh(p,a,d,c.length,u.length)}; + ${(()=>{if(a.length===2||a.length===4)return`${jh(m,p,a,g,_)}`;if(a.length===3||a.length===5)return`${Zh(m,p,a,g,_)}`;throw Error("Linear mode only supports input dims 2, 3, 4 and 5 are supported in linear mode.")})()}; + `;case"cubic":return` + ${(()=>{if(a.length===2||a.length===4)return`${Kh(m,p,a,d,c,u,t.cubicCoeffA,g,t.extrapolationValue,t.excludeOutside)}`;throw Error("Cubic mode only supports input dims 2 and 4 are supported in linear mode.")})()}; + `;default:throw Error("Invalid resize mode")}})()}; + `} + ${v.registerUniform("output_size","u32").registerUniform("scales","f32",c.length).registerUniform("roi","f32",u.length).declareVariables(m,p)} + ${v.mainStart()} + ${v.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + ${b?"output[global_idx] = input[global_idx];":` + let output_indices = ${p.offsetToIndices("global_idx")}; + var input_indices: ${m.type.indices}; + ${(()=>{switch(t.mode){case"nearest":return`input_indices = calculateInputIndicesFromOutputIndices(output_indices); + if (checkInputIndices(input_indices)) { + output[global_idx] = ${m.getByIndices("input_indices")}; + } else { + output[global_idx] = ${t.extrapolationValue}; + }`;case"linear":return`output[global_idx] = ${a.length===2||a.length===4?"bilinearInterpolation":"trilinearInterpolation"}(output_indices);`;case"cubic":return"output[global_idx] = bicubicInterpolation(output_indices);";default:throw Error(`Unsupported resize mode: ${t.mode}`)}})()}; +`} + }`;return{name:"Resize",shaderCache:{hint:`${t.cacheKey}|${r}|${c.length>0?t.mode==="cubic"?c:c.length:""}|${o.length>0?o:""}|${u.length>0?u:""}|${b}|${t.mode==="nearest"?a.length:a}`,inputDependencies:["rank"]},getShaderSource:$,getRunData:()=>({outputs:[{dims:d,dataType:e.dataType}],dispatchGroup:{x:Math.ceil(f/64)},programUniforms:[{type:12,data:f},{type:1,data:c},{type:1,data:u},...N(a,d)]})}},Yh=e=>{let t=e.customDataBuffer;return new Uint32Array(t,t.byteOffset,1)[0]},Ol=(e,t)=>{let r=[],n=[],o=[],i=Yh(e);if(t.antialias!==0)throw Error("Only default value (0) for Antialias attribute is supported");Uh(e.inputs,t,i,r,n,o),e.compute(Qh(e.inputs[0],t,i,r,n,o),{inputs:[0]})},Bl=e=>{let t=e.antialias,r=e.axes,n=e.coordinateTransformMode,o=e.cubicCoeffA,i=e.excludeOutside!==0,a=e.extrapolationValue,u=e.keepAspectRatioPolicy,d=e.mode,c=e.nearestMode===""?"simple":e.nearestMode;return J({antialias:t,axes:r,coordinateTransformMode:n,cubicCoeffA:o,excludeOutside:i,extrapolationValue:a,keepAspectRatioPolicy:u,mode:d,nearestMode:c})}});var Xh,Jh,Ml,Rl=U(()=>{"use strict";ee();ne();ie();Xh=e=>{if(!e||e.length<3)throw new Error("layerNorm requires at least 3 inputs.");let t=e[0],r=e[1],n=e[2];if(t.dataType!==r.dataType||t.dataType!==n.dataType)throw new Error("All inputs must have the same data type");if(t.dims.length!==3&&t.dims.length!==2)throw new Error("Input must be 2D or 3D");if(r.dims.length!==3&&r.dims.length!==2)throw new Error("Skip must be 2D or 3D");let o=t.dims[t.dims.length-1],i=t.dims[t.dims.length-2];if(r.dims[r.dims.length-1]!==o)throw new Error("Skip must have the same hidden size as input");if(r.dims[r.dims.length-2]!==i)throw new Error("Skip must have the same sequence length as input");if(n.dims.length!==1)throw new Error("Gamma must be 1D");if(n.dims[n.dims.length-1]!==o)throw new Error("Gamma must have the same hidden size as input");if(e.length>3){let a=e[3];if(a.dims.length!==1)throw new Error("Beta must be 1D");if(a.dims[a.dims.length-1]!==o)throw new Error("Beta must have the same hidden size as input")}if(e.length>4){let a=e[4];if(a.dims.length!==1)throw new Error("Bias must be 1D");if(a.dims[a.dims.length-1]!==o)throw new Error("Bias must have the same hidden size as input")}},Jh=(e,t,r,n)=>{let o=t.simplified,i=e[0].dims,a=k.size(i),u=i,d=a,c=i.slice(-1)[0],p=n?i.slice(0,-1).concat(1):[],m=!o&&e.length>3,f=e.length>4,b=n&&r>1,g=n&&r>2,_=r>3,S=64,$=ce(c),v=[{type:12,data:d},{type:12,data:$},{type:12,data:c},{type:1,data:t.epsilon}],x=E=>{let I=[{name:"output_size",type:"u32"},{name:"components",type:"u32"},{name:"hidden_size",type:"u32"},{name:"epsilon",type:"f32"}],z=[P("x",e[0].dataType,e[0].dims,$),P("skip",e[1].dataType,e[1].dims,$),P("gamma",e[2].dataType,e[2].dims,$)];m&&z.push(P("beta",e[3].dataType,e[3].dims,$)),f&&z.push(P("bias",e[4].dataType,e[4].dims,$)),z.push(M("output",e[0].dataType,u,$)),b&&z.push(M("mean_output",1,p)),g&&z.push(M("inv_std_output",1,p)),_&&z.push(M("input_skip_bias_sum",e[0].dataType,u,$));let O=be(e[0].dataType),D=be(1,$);return` + + ${E.registerUniforms(I).declareVariables(...z)} + var sum_shared : array<${D}, ${S}>; + var sum_squared_shared : array<${D}, ${S}>; + + ${E.mainStart([S,1,1])} + let ix = local_id.x; + let iy = global_id.x / ${S}; + + let hidden_size_vectorized: u32 = uniforms.hidden_size / uniforms.components; + var stride = hidden_size_vectorized / ${S}; + let offset = ix * stride + iy * hidden_size_vectorized; + let offset1d = stride * ix; + if (ix == ${S-1}) { + stride = hidden_size_vectorized - stride * ix; + } + for (var i: u32 = 0; i < stride; i++) { + let skip_value = skip[offset + i]; + let bias_value = ${f?"bias[offset1d + i]":O+"(0.0)"}; + let input_value = x[offset + i]; + let value = input_value + skip_value + bias_value; + ${_?"input_skip_bias_sum[offset + i] = value;":""} + output[offset + i] = value; + let f32_value = ${Ct(O,$,"value")}; + sum_shared[ix] += f32_value; + sum_squared_shared[ix] += f32_value * f32_value; + } + workgroupBarrier(); + + var reduce_size : u32 = ${S}; + for (var curr_size = reduce_size >> 1; curr_size > 0; curr_size = reduce_size >> 1) { + reduce_size = curr_size + (reduce_size & 1); + if (ix < curr_size) { + sum_shared[ix] += sum_shared[ix + reduce_size]; + sum_squared_shared[ix] += sum_squared_shared[ix + reduce_size]; + } + workgroupBarrier(); + } + + let sum = sum_shared[0]; + let square_sum = sum_squared_shared[0]; + let mean = ${He("sum",$)} / f32(uniforms.hidden_size); + let inv_std_dev = inverseSqrt(${He("square_sum",$)} / f32(uniforms.hidden_size) ${o?"":"- mean * mean"} + uniforms.epsilon); + ${b?"mean_output[global_idx] = mean;":""} + ${g?"inv_std_output[global_idx] = inv_std_dev;":""} + + for (var i: u32 = 0; i < stride; i++) { + output[offset + i] = (output[offset + i] ${o?"":`- ${O}(mean)`}) * + ${O}(inv_std_dev) * gamma[offset1d + i] + ${m?"+ beta[offset1d + i]":""}; + } + }`},T=[{dims:u,dataType:e[0].dataType}];return r>1&&T.push({dims:p,dataType:1}),r>2&&T.push({dims:p,dataType:1}),r>3&&T.push({dims:i,dataType:e[0].dataType}),{name:"SkipLayerNormalization",shaderCache:{hint:`${$};${b};${g};${_}`,inputDependencies:e.map((E,I)=>"type")},getShaderSource:x,getRunData:()=>({outputs:T,dispatchGroup:{x:Math.ceil(d/c)},programUniforms:v})}},Ml=(e,t)=>{Xh(e.inputs);let n=[0];e.outputCount>1&&n.push(-3),e.outputCount>2&&n.push(-3),e.outputCount>3&&n.push(3),e.compute(Jh(e.inputs,t,e.outputCount,!1),{outputs:n})}});var eg,nn,tg,Ul,rg,ng,Nl,Vl,Wl=U(()=>{"use strict";ee();ne();Se();ie();eg=(e,t)=>{if(!e||e.length<1)throw new Error("too few inputs");if(t.axes.length!==0){if(t.axes.length!==t.starts.length||t.axes.length!==t.ends.length)throw new Error("axes, starts and ends must have the same length")}else if(t.starts.length!==t.ends.length)throw new Error("starts and ends must have the same length");e.slice(1).forEach((r,n)=>{if(e[n+1].dataType!==6&&e[n+1].dataType!==7)throw new Error(`Input ${n} must be an array of int32 or int64`)})},nn=(e,t)=>{let r=[];if(e.length>t)if(e[t].dataType===7)e[t].getBigInt64Array().forEach(n=>r.push(Number(n)));else if(e[t].dataType===6)e[t].getInt32Array().forEach(n=>r.push(Number(n)));else throw new Error(`Input ${t} must be an array of int32 or int64`);return r},tg=(e,t)=>{if(e.length>1){let r=nn(e,1),n=nn(e,2),o=nn(e,3);return o.length===0&&(o=[...Array(e[0].dims.length).keys()]),J({starts:r,ends:n,axes:o})}else return t},Ul=(e,t,r,n,o)=>{let i=e;return e<0&&(i+=r[n[t]]),o[t]<0?Math.max(0,Math.min(i,r[n[t]]-1)):Math.max(0,Math.min(i,r[n[t]]))},rg=(e,t,r)=>`fn calculateInputIndices(output_indices: ${t.type.indices}) -> ${e.type.indices} { + var input_indices: ${e.type.indices}; + var carry = 0u; + for (var i = ${r.length}; i >= 0; i--) { + let input_shape_i = ${F("uniforms.input_shape","i",r.length)}; + let steps_i = ${F("uniforms.steps","i",r.length)}; + let signs_i = ${F("uniforms.signs","i",r.length)}; + let starts_i = ${F("uniforms.starts","i",r.length)}; + var output_index = ${t.indicesGet("output_indices","i")}; + var input_index = output_index * steps_i + starts_i + carry; + carry = input_index / input_shape_i; + input_index = input_index % input_shape_i; + if (signs_i < 0) { + input_index = input_shape_i - input_index - 1u + starts_i; + } + ${e.indicesSet("input_indices","i","input_index")}; + } + return input_indices; + }`,ng=(e,t)=>{let r=e[0].dims,n=k.size(r),o=t.axes.length>0?k.normalizeAxes(t.axes,r.length):[...Array(r.length).keys()],i=nn(e,4);i.forEach($=>$!==0||(()=>{throw new Error("step cannot be 0")})),i.length===0&&(i=Array(o.length).fill(1));let a=t.starts.map(($,v)=>Ul($,v,r,o,i)),u=t.ends.map(($,v)=>Ul($,v,r,o,i));if(o.length!==a.length||o.length!==u.length)throw new Error("start, ends and axes should have the same number of elements");if(o.length!==r.length)for(let $=0;$Math.sign($));i.forEach(($,v,x)=>{if($<0){let T=(u[v]-a[v])/$,E=a[v],I=E+T*i[v];a[v]=I,u[v]=E,x[v]=-$}});let c=r.slice(0);o.forEach(($,v)=>{c[$]=Math.ceil((u[$]-a[$])/i[$])});let p={dims:c,dataType:e[0].dataType},m=M("output",e[0].dataType,c.length),f=P("input",e[0].dataType,e[0].dims.length),b=k.size(c),g=[{name:"outputSize",type:"u32"},{name:"starts",type:"u32",length:a.length},{name:"signs",type:"i32",length:d.length},{name:"steps",type:"u32",length:i.length}],_=[{type:12,data:b},{type:12,data:a},{type:6,data:d},{type:12,data:i},...N(e[0].dims,c)],S=$=>` + ${$.registerUniforms(g).declareVariables(f,m)} + ${rg(f,m,r)} + ${$.mainStart()} + ${$.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.outputSize")} + let output_indices = ${m.offsetToIndices("global_idx")}; + let input_indices = calculateInputIndices(output_indices); + ${m.setByOffset("global_idx",f.getByIndices("input_indices"))} + }`;return{name:"Slice",shaderCache:{hint:`${d.length}_${a.length}_${i.length}`,inputDependencies:["rank"]},getShaderSource:S,getRunData:()=>({outputs:[p],dispatchGroup:{x:Math.ceil(n/64)},programUniforms:_})}},Nl=(e,t)=>{eg(e.inputs,t);let r=tg(e.inputs,t);e.compute(ng(e.inputs,r),{inputs:[0]})},Vl=e=>{let t=e.starts,r=e.ends,n=e.axes;return J({starts:t,ends:r,axes:n})}});var og,ig,Ll,Gl,Hl=U(()=>{"use strict";ee();ne();Se();st();ie();og=e=>{if(!e||e.length!==1)throw new Error("Softmax op requires 1 input.")},ig=(e,t)=>{let r=e.inputs[0],n=r.dims,o=k.size(n),i=n.length,a=k.normalizeAxis(t.axis,i),u=aO),c[a]=i-1,c[i-1]=a,d=e.compute(Ee(r,c),{inputs:[r],outputs:[-1]})[0]):d=r;let p=d.dims,m=p[i-1],f=o/m,b=ce(m),g=m/b,_=64;f===1&&(_=256);let S=(z,O)=>O===4?`max(max(${z}.x, ${z}.y), max(${z}.z, ${z}.w))`:O===2?`max(${z}.x, ${z}.y)`:O===3?`max(max(${z}.x, ${z}.y), ${z}.z)`:z,$=P("x",d.dataType,d.dims,b),v=M("result",d.dataType,d.dims,b),x=$.type.value,T=be(d.dataType)==="f32"?`var threadMax = ${x}(-3.402823e+38f);`:`var threadMax = ${x}(-65504.0h);`,E=z=>` + var rowMaxShared : ${x}; + var rowSumShared : ${x}; + var threadShared : array<${x}, ${_}>; + + fn getValue(row: i32, col: i32, row_stride: i32) -> ${x} { + let index = row * row_stride + col; + return x[index]; + } + + fn setValue(row: i32, col: i32, row_stride: i32, value: ${x}) { + let index = row * row_stride + col; + result[index] = value; + } + ${z.registerUniform("packedCols","i32").declareVariables($,v)} + ${z.mainStart(_)} + let gindex = i32(global_idx); + let lindex = i32(local_idx); + const wg = ${_}; + let row = gindex / wg; + let cols = uniforms.packedCols; + let row_stride : i32 = uniforms.packedCols; + + // find the rows max + ${T} + for (var col = lindex; col < cols; col += wg) { + let value = getValue(row, col, row_stride); + threadMax = max(threadMax, value); + } + if (lindex < cols) { + threadShared[lindex] = threadMax; + } + workgroupBarrier(); + + var reduceSize = min(cols, wg); + for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) { + reduceSize = currSize + (reduceSize & 1); + if (lindex < currSize) { + threadShared[lindex] = max(threadShared[lindex], threadShared[lindex + reduceSize]); + } + workgroupBarrier(); + } + if (lindex == 0) { + rowMaxShared = ${x}(${S("threadShared[0]",b)}); + } + workgroupBarrier(); + + // find the rows sum + var threadSum = ${x}(0.0); + for (var col = lindex; col < cols; col += wg) { + let subExp = exp(getValue(row, col, row_stride) - rowMaxShared); + threadSum += subExp; + } + threadShared[lindex] = threadSum; + workgroupBarrier(); + + for (var currSize = wg >> 1; currSize > 0; currSize = currSize >> 1) { + if (lindex < currSize) { + threadShared[lindex] = threadShared[lindex] + threadShared[lindex + currSize]; + } + workgroupBarrier(); + } + if (lindex == 0) { + rowSumShared = ${x}(${He("threadShared[0]",b)}); + } + workgroupBarrier(); + + // calculate final value for each element in the row + for (var col = lindex; col < cols; col += wg) { + let value = exp(getValue(row, col, row_stride) - rowMaxShared) / rowSumShared; + setValue(row, col, row_stride, value); + } + }`,I=e.compute({name:"Softmax",shaderCache:{hint:`${b};${_}`,inputDependencies:["type"]},getRunData:()=>({outputs:[{dims:p,dataType:d.dataType}],dispatchGroup:{x:f},programUniforms:[{type:6,data:g}]}),getShaderSource:E},{inputs:[d],outputs:[u?-1:0]})[0];u&&e.compute(Ee(I,c),{inputs:[I]})},Ll=(e,t)=>{og(e.inputs),ig(e,t)},Gl=e=>J({axis:e.axis})});var Fl,ag,sg,ug,ql,jl=U(()=>{"use strict";ee();ne();ie();Fl=e=>Array.from(e.getBigInt64Array(),Number),ag=e=>{if(!e||e.length!==2)throw new Error("Tile requires 2 inputs.");if(e[0].dataType!==1&&e[0].dataType!==10&&e[0].dataType!==6&&e[0].dataType!==12)throw new Error("Tile only support float, float16, int32, and uint32 data types");if(e[1].dataType!==7)throw new Error("Tile `repeats` input should be of int64 data type");if(e[1].dims.length!==1)throw new Error("Tile `repeats` input should be 1-D");if(Fl(e[1]).length!==e[0].dims.length)throw new Error("Tile `repeats` input should have same number of elements as rank of input data tensor")},sg=(e,t)=>{let r=[];for(let n=0;n{let r=e[0].dims,n=t??Fl(e[1]),o=sg(r,n),i=k.size(o),a=e[0].dataType,u=P("input",a,r.length),d=M("output",a,o.length),c=p=>` + const inputShape = ${u.indices(...r)}; + ${p.registerUniform("output_size","u32").declareVariables(u,d)} + ${p.mainStart()} + ${p.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.output_size")} + let output_indices = ${d.offsetToIndices("global_idx")}; + var input_indices: ${u.type.indices}; + for (var i = 0; i < ${r.length}; i++) { + let input_dim_i = ${u.indicesGet("uniforms.input_shape","i")}; + let input_dim_value = ${d.indicesGet("output_indices","i")} % input_dim_i; + + ${u.indicesSet("input_indices","i","input_dim_value")} + } + ${d.setByOffset("global_idx",u.getByIndices("input_indices"))} + }`;return{name:"Tile",shaderCache:{hint:`${n}`,inputDependencies:["rank"]},getRunData:()=>({outputs:[{dims:o,dataType:e[0].dataType}],dispatchGroup:{x:Math.ceil(i/64)},programUniforms:[{type:12,data:i},...N(e[0].dims,o)]}),getShaderSource:c}},ql=e=>{ag(e.inputs),e.compute(ug(e.inputs),{inputs:[0]})}});var dg,lg,Kl,Zl=U(()=>{"use strict";ee();ne();ie();dg=(e,t,r,n,o)=>{let i=M("output_data",o,r.length,4),a=P("a_data",t[1].dataType,t[1].dims.length,4),u=P("b_data",t[2].dataType,t[2].dims.length,4),d=P("c_data",t[0].dataType,t[0].dims.length,4),c,p=(m,f,b)=>`select(${f}, ${m}, ${b})`;if(!n)c=i.setByOffset("global_idx",p(a.getByOffset("global_idx"),u.getByOffset("global_idx"),d.getByOffset("global_idx")));else{let m=(f,b,g="")=>{let _=`a_data[index_a${b}][component_a${b}]`,S=`b_data[index_b${b}][component_b${b}]`,$=`bool(c_data[index_c${b}] & (0xffu << (component_c${b} * 8)))`;return` + let output_indices${b} = ${i.offsetToIndices(`global_idx * 4u + ${b}u`)}; + let offset_a${b} = ${a.broadcastedIndicesToOffset(`output_indices${b}`,i)}; + let offset_b${b} = ${u.broadcastedIndicesToOffset(`output_indices${b}`,i)}; + let offset_c${b} = ${d.broadcastedIndicesToOffset(`output_indices${b}`,i)}; + let index_a${b} = offset_a${b} / 4u; + let index_b${b} = offset_b${b} / 4u; + let index_c${b} = offset_c${b} / 4u; + let component_a${b} = offset_a${b} % 4u; + let component_b${b} = offset_b${b} % 4u; + let component_c${b} = offset_c${b} % 4u; + ${f}[${b}] = ${g}(${p(_,S,$)}); + `};o===9?c=` + var data = vec4(0); + ${m("data",0,"u32")} + ${m("data",1,"u32")} + ${m("data",2,"u32")} + ${m("data",3,"u32")} + output_data[global_idx] = dot(vec4(0x1, 0x100, 0x10000, 0x1000000), vec4(data));`:c=` + ${m("output_data[global_idx]",0)} + ${m("output_data[global_idx]",1)} + ${m("output_data[global_idx]",2)} + ${m("output_data[global_idx]",3)} + `}return` + ${e.registerUniform("vec_size","u32").declareVariables(d,a,u,i)} + ${e.mainStart()} + ${e.guardAgainstOutOfBoundsWorkgroupSizes("uniforms.vec_size")} + ${c} + }`},lg=e=>{let t=e[1].dims,r=e[2].dims,n=e[0].dims,o=e[1].dataType,i=!(k.areEqual(t,r)&&k.areEqual(r,n)),a=t,u=k.size(t);if(i){let c=Je.calcShape(Je.calcShape(t,r,!1),n,!1);if(!c)throw new Error("Can't perform where op on the given tensors");a=c,u=k.size(a)}let d=Math.ceil(u/4);return{name:"Where",shaderCache:{inputDependencies:["rank","rank","rank"]},getShaderSource:c=>dg(c,e,a,i,o),getRunData:()=>({outputs:[{dims:a,dataType:o}],dispatchGroup:{x:Math.ceil(u/64/4)},programUniforms:[{type:12,data:d},...N(n,t,r,a)]})}},Kl=e=>{e.compute(lg(e.inputs))}});var Ql,Yl=U(()=>{"use strict";Es();Fr();zs();Bs();_u();ku();Ou();Zu();rd();id();ud();md();gd();yd();vd();Sd();Cd();kd();Od();Md();Fd();Kd();Qd();Xd();tl();$o();nl();wl();xl();Tl();kl();Gr();Dl();To();Rl();Wl();Hl();So();jl();st();jr();Zl();Ql=new 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on,Xl=U(()=>{"use strict";We();Xe();ie();on=class{constructor(t){this.backend=t;this.repo=new Map,this.attributesBound=!1}getArtifact(t){return this.repo.get(t)}setArtifact(t,r){this.repo.set(t,r)}run(t,r,n,o,i){Re(t.programInfo.name);let a=this.backend.device,u=this.backend.getComputePassEncoder();this.backend.writeTimestamp(this.backend.pendingDispatchNumber*2);let d=[];for(let p of r)d.push({binding:d.length,resource:{buffer:p.buffer}});for(let p of n)d.push({binding:d.length,resource:{buffer:p.buffer}});i&&d.push({binding:d.length,resource:i});let c=a.createBindGroup({layout:t.computePipeline.getBindGroupLayout(0),entries:d,label:t.programInfo.name});if(this.backend.sessionStatus==="capturing"){let 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All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +/** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +/** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ +//# sourceMappingURL=ort.bundle.min.mjs.map + + +/***/ }), + +/***/ "./src/backends/onnx.js": +/*!******************************!*\ + !*** ./src/backends/onnx.js ***! + \******************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +var onnxruntime_node__WEBPACK_IMPORTED_MODULE_1___namespace_cache; +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Tensor: () => (/* reexport safe */ onnxruntime_common__WEBPACK_IMPORTED_MODULE_3__.Tensor), +/* harmony export */ createInferenceSession: () => (/* binding */ createInferenceSession), +/* harmony export */ deviceToExecutionProviders: () => (/* binding */ deviceToExecutionProviders), +/* harmony export */ isONNXProxy: () => (/* binding */ isONNXProxy), +/* harmony export */ isONNXTensor: () => (/* binding */ isONNXTensor) +/* harmony export */ }); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../env.js */ "./src/env.js"); +/* harmony import */ var onnxruntime_node__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! onnxruntime-node */ "?2ce3"); +/* harmony import */ var onnxruntime_web__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! onnxruntime-web */ "./node_modules/onnxruntime-web/dist/ort.bundle.min.mjs?3a96"); +/* harmony import */ var onnxruntime_common__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! onnxruntime-common */ "./node_modules/onnxruntime-common/dist/esm/index.js"); +/** + * @file Handler file for choosing the correct version of ONNX Runtime, based on the environment. + * Ideally, we could import the `onnxruntime-web` and `onnxruntime-node` packages only when needed, + * but dynamic imports don't seem to work with the current webpack version and/or configuration. + * This is possibly due to the experimental nature of top-level await statements. + * So, we just import both packages, and use the appropriate one based on the environment: + * - When running in node, we use `onnxruntime-node`. + * - When running in the browser, we use `onnxruntime-web` (`onnxruntime-node` is not bundled). + * + * This module is not directly exported, but can be accessed through the environment variables: + * ```javascript + * import { env } from '@huggingface/transformers'; + * console.log(env.backends.onnx); + * ``` + * + * @module backends/onnx + */ + + + +// NOTE: Import order matters here. We need to import `onnxruntime-node` before `onnxruntime-web`. +// In either case, we select the default export if it exists, otherwise we use the named export. + + + + + +/** + * @typedef {import('onnxruntime-common').InferenceSession.ExecutionProviderConfig} ONNXExecutionProviders + */ + +/** @type {Record} */ +const DEVICE_TO_EXECUTION_PROVIDER_MAPPING = Object.freeze({ + auto: null, // Auto-detect based on device and environment + gpu: null, // Auto-detect GPU + cpu: 'cpu', // CPU + wasm: 'wasm', // WebAssembly + webgpu: 'webgpu', // WebGPU + cuda: 'cuda', // CUDA + dml: 'dml', // DirectML + + webnn: { name: 'webnn', deviceType: 'cpu' }, // WebNN (default) + 'webnn-npu': { name: 'webnn', deviceType: 'npu' }, // WebNN NPU + 'webnn-gpu': { name: 'webnn', deviceType: 'gpu' }, // WebNN GPU + 'webnn-cpu': { name: 'webnn', deviceType: 'cpu' }, // WebNN CPU +}); + +/** + * The list of supported devices, sorted by priority/performance. + * @type {import("../utils/devices.js").DeviceType[]} + */ +const supportedDevices = []; + +/** @type {ONNXExecutionProviders[]} */ +let defaultDevices; +let ONNX; +const ORT_SYMBOL = Symbol.for('onnxruntime'); + +if (ORT_SYMBOL in globalThis) { + // If the JS runtime exposes their own ONNX runtime, use it + ONNX = globalThis[ORT_SYMBOL]; + +} else if (_env_js__WEBPACK_IMPORTED_MODULE_0__.apis.IS_NODE_ENV) { + ONNX = onnxruntime_node__WEBPACK_IMPORTED_MODULE_1__ ?? /*#__PURE__*/ (onnxruntime_node__WEBPACK_IMPORTED_MODULE_1___namespace_cache || (onnxruntime_node__WEBPACK_IMPORTED_MODULE_1___namespace_cache = __webpack_require__.t(onnxruntime_node__WEBPACK_IMPORTED_MODULE_1__, 2))); + + // Updated as of ONNX Runtime 1.20.1 + // The following table lists the supported versions of ONNX Runtime Node.js binding provided with pre-built binaries. + // | EPs/Platforms | Windows x64 | Windows arm64 | Linux x64 | Linux arm64 | MacOS x64 | MacOS arm64 | + // | ------------- | ----------- | ------------- | ----------------- | ----------- | --------- | ----------- | + // | CPU | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | + // | DirectML | ✔️ | ✔️ | ❌ | ❌ | ❌ | ❌ | + // | CUDA | ❌ | ❌ | ✔️ (CUDA v11.8) | ❌ | ❌ | ❌ | + switch (process.platform) { + case 'win32': // Windows x64 and Windows arm64 + supportedDevices.push('dml'); + break; + case 'linux': // Linux x64 and Linux arm64 + if (process.arch === 'x64') { + supportedDevices.push('cuda'); + } + break; + case 'darwin': // MacOS x64 and MacOS arm64 + break; + } + + supportedDevices.push('cpu'); + defaultDevices = ['cpu']; +} else { + ONNX = onnxruntime_web__WEBPACK_IMPORTED_MODULE_2__; + + if (_env_js__WEBPACK_IMPORTED_MODULE_0__.apis.IS_WEBNN_AVAILABLE) { + // TODO: Only push supported providers (depending on available hardware) + supportedDevices.push('webnn-npu', 'webnn-gpu', 'webnn-cpu', 'webnn'); + } + + if (_env_js__WEBPACK_IMPORTED_MODULE_0__.apis.IS_WEBGPU_AVAILABLE) { + supportedDevices.push('webgpu'); + } + + supportedDevices.push('wasm'); + defaultDevices = ['wasm']; +} + +// @ts-ignore +const InferenceSession = ONNX.InferenceSession; + +/** + * Map a device to the execution providers to use for the given device. + * @param {import("../utils/devices.js").DeviceType|"auto"|null} [device=null] (Optional) The device to run the inference on. + * @returns {ONNXExecutionProviders[]} The execution providers to use for the given device. + */ +function deviceToExecutionProviders(device = null) { + // Use the default execution providers if the user hasn't specified anything + if (!device) return defaultDevices; + + // Handle overloaded cases + switch (device) { + case "auto": + return supportedDevices; + case "gpu": + return supportedDevices.filter(x => + ["webgpu", "cuda", "dml", "webnn-gpu"].includes(x), + ); + } + + if (supportedDevices.includes(device)) { + return [DEVICE_TO_EXECUTION_PROVIDER_MAPPING[device] ?? device]; + } + + throw new Error(`Unsupported device: "${device}". Should be one of: ${supportedDevices.join(', ')}.`) +} + + +/** + * To prevent multiple calls to `initWasm()`, we store the first call in a Promise + * that is resolved when the first InferenceSession is created. Subsequent calls + * will wait for this Promise to resolve before creating their own InferenceSession. + * @type {Promise|null} + */ +let wasmInitPromise = null; + +/** + * Create an ONNX inference session. + * @param {Uint8Array|string} buffer_or_path The ONNX model buffer or path. + * @param {import('onnxruntime-common').InferenceSession.SessionOptions} session_options ONNX inference session options. + * @param {Object} session_config ONNX inference session configuration. + * @returns {Promise} The ONNX inference session. + */ +async function createInferenceSession(buffer_or_path, session_options, session_config) { + if (wasmInitPromise) { + // A previous session has already initialized the WASM runtime + // so we wait for it to resolve before creating this new session. + await wasmInitPromise; + } + + const sessionPromise = InferenceSession.create(buffer_or_path, session_options); + wasmInitPromise ??= sessionPromise; + const session = await sessionPromise; + session.config = session_config; + return session; +} + +/** + * Check if an object is an ONNX tensor. + * @param {any} x The object to check + * @returns {boolean} Whether the object is an ONNX tensor. + */ +function isONNXTensor(x) { + return x instanceof ONNX.Tensor; +} + +/** @type {import('onnxruntime-common').Env} */ +// @ts-ignore +const ONNX_ENV = ONNX?.env; +if (ONNX_ENV?.wasm) { + // Initialize wasm backend with suitable default settings. + + // (Optional) Set path to wasm files. This will override the default path search behavior of onnxruntime-web. + // By default, we only do this if we are not in a service worker and the wasmPaths are not already set. + if ( + // @ts-ignore Cannot find name 'ServiceWorkerGlobalScope'.ts(2304) + !(typeof ServiceWorkerGlobalScope !== 'undefined' && self instanceof ServiceWorkerGlobalScope) + && !ONNX_ENV.wasm.wasmPaths + ) { + ONNX_ENV.wasm.wasmPaths = `https://cdn.jsdelivr.net/npm/@huggingface/transformers@${_env_js__WEBPACK_IMPORTED_MODULE_0__.env.version}/dist/`; + } + + // TODO: Add support for loading WASM files from cached buffer when we upgrade to onnxruntime-web@1.19.0 + // https://github.com/microsoft/onnxruntime/pull/21534 + + // Users may wish to proxy the WASM backend to prevent the UI from freezing, + // However, this is not necessary when using WebGPU, so we default to false. + ONNX_ENV.wasm.proxy = false; +} + +if (ONNX_ENV?.webgpu) { + ONNX_ENV.webgpu.powerPreference = 'high-performance'; +} + +/** + * Check if ONNX's WASM backend is being proxied. + * @returns {boolean} Whether ONNX's WASM backend is being proxied. + */ +function isONNXProxy() { + // TODO: Update this when allowing non-WASM backends. + return ONNX_ENV?.wasm?.proxy; +} + +// Expose ONNX environment variables to `env.backends.onnx` +_env_js__WEBPACK_IMPORTED_MODULE_0__.env.backends.onnx = ONNX_ENV; + + +/***/ }), + +/***/ "./src/base/feature_extraction_utils.js": +/*!**********************************************!*\ + !*** ./src/base/feature_extraction_utils.js ***! + \**********************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ FeatureExtractor: () => (/* binding */ FeatureExtractor), +/* harmony export */ validate_audio_inputs: () => (/* binding */ validate_audio_inputs) +/* harmony export */ }); +/* harmony import */ var _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../utils/constants.js */ "./src/utils/constants.js"); +/* harmony import */ var _utils_generic_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../utils/generic.js */ "./src/utils/generic.js"); +/* harmony import */ var _utils_hub_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../utils/hub.js */ "./src/utils/hub.js"); + + + + +/** + * Base class for feature extractors. + */ +class FeatureExtractor extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_1__.Callable { + /** + * Constructs a new FeatureExtractor instance. + * + * @param {Object} config The configuration for the feature extractor. + */ + constructor(config) { + super(); + this.config = config + } + + /** + * Instantiate one of the feature extractor classes of the library from a pretrained model. + * + * The feature extractor class to instantiate is selected based on the `feature_extractor_type` property of + * the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible) + * + * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either: + * - A string, the *model id* of a pretrained feature_extractor hosted inside a model repo on huggingface.co. + * Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a + * user or organization name, like `dbmdz/bert-base-german-cased`. + * - A path to a *directory* containing feature_extractor files, e.g., `./my_model_directory/`. + * @param {import('../utils/hub.js').PretrainedOptions} options Additional options for loading the feature_extractor. + * + * @returns {Promise} A new instance of the Feature Extractor class. + */ + static async from_pretrained(pretrained_model_name_or_path, options={}) { + const config = await (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_2__.getModelJSON)(pretrained_model_name_or_path, _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__.FEATURE_EXTRACTOR_NAME, true, options); + return new this(config); + } +} + + +/** + * Helper function to validate audio inputs. + * @param {any} audio The audio data. + * @param {string} feature_extractor The name of the feature extractor. + * @private + */ +function validate_audio_inputs(audio, feature_extractor) { + if (!(audio instanceof Float32Array || audio instanceof Float64Array)) { + throw new Error( + `${feature_extractor} expects input to be a Float32Array or a Float64Array, but got ${audio?.constructor?.name ?? typeof audio} instead. ` + + `If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.` + ) + } +} + + +/***/ }), + +/***/ "./src/base/image_processors_utils.js": +/*!********************************************!*\ + !*** ./src/base/image_processors_utils.js ***! + \********************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ ImageProcessor: () => (/* binding */ ImageProcessor), +/* harmony export */ center_to_corners_format: () => (/* binding */ center_to_corners_format), +/* harmony export */ post_process_instance_segmentation: () => (/* binding */ post_process_instance_segmentation), +/* harmony export */ post_process_object_detection: () => (/* binding */ post_process_object_detection), +/* harmony export */ post_process_panoptic_segmentation: () => (/* binding */ post_process_panoptic_segmentation), +/* harmony export */ post_process_semantic_segmentation: () => (/* binding */ post_process_semantic_segmentation) +/* harmony export */ }); +/* harmony import */ var _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../utils/generic.js */ "./src/utils/generic.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_maths_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../utils/maths.js */ "./src/utils/maths.js"); +/* harmony import */ var _utils_image_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../utils/image.js */ "./src/utils/image.js"); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ../utils/core.js */ "./src/utils/core.js"); +/* harmony import */ var _utils_hub_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ../utils/hub.js */ "./src/utils/hub.js"); +/* harmony import */ var _utils_constants_js__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! ../utils/constants.js */ "./src/utils/constants.js"); + + + + + + + + +/** + * Named tuple to indicate the order we are using is (height x width), + * even though the Graphics' industry standard is (width x height). + * @typedef {[height: number, width: number]} HeightWidth + */ + + +/** + * @typedef {object} ImageProcessorResult + * @property {Tensor} pixel_values The pixel values of the batched preprocessed images. + * @property {HeightWidth[]} original_sizes Array of two-dimensional tuples like [[480, 640]]. + * @property {HeightWidth[]} reshaped_input_sizes Array of two-dimensional tuples like [[1000, 1330]]. + */ + + + +/** + * Helper function to constrain a value to be a multiple of a number. + * @param {number} val The value to constrain. + * @param {number} multiple The number to constrain to. + * @param {number} [minVal=0] The minimum value to constrain to. + * @param {number} [maxVal=null] The maximum value to constrain to. + * @returns {number} The constrained value. + * @private + */ +function constraint_to_multiple_of(val, multiple, minVal = 0, maxVal = null) { + const a = val / multiple; + let x = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.bankers_round)(a) * multiple; + + if (maxVal !== null && x > maxVal) { + x = Math.floor(a) * multiple; + } + + if (x < minVal) { + x = Math.ceil(a) * multiple; + } + + return x; +} + +/** + * Rounds the height and width down to the closest multiple of size_divisibility + * @param {[number, number]} size The size of the image + * @param {number} divisor The divisor to use. + * @returns {[number, number]} The rounded size. + */ +function enforce_size_divisibility([width, height], divisor) { + return [ + Math.max(Math.floor(width / divisor), 1) * divisor, + Math.max(Math.floor(height / divisor), 1) * divisor + ]; +} + + +// Helper functions + +/** + * Converts bounding boxes from center format to corners format. + * + * @param {number[]} arr The coordinate for the center of the box and its width, height dimensions (center_x, center_y, width, height) + * @returns {number[]} The coodinates for the top-left and bottom-right corners of the box (top_left_x, top_left_y, bottom_right_x, bottom_right_y) + */ +function center_to_corners_format([centerX, centerY, width, height]) { + return [ + centerX - width / 2, + centerY - height / 2, + centerX + width / 2, + centerY + height / 2 + ]; +} + +/** + * Post-processes the outputs of the model (for object detection). + * @param {Object} outputs The outputs of the model that must be post-processed + * @param {Tensor} outputs.logits The logits + * @param {Tensor} outputs.pred_boxes The predicted boxes. + * @param {number} [threshold=0.5] The threshold to use for the scores. + * @param {[number, number][]} [target_sizes=null] The sizes of the original images. + * @param {boolean} [is_zero_shot=false] Whether zero-shot object detection was performed. + * @return {Object[]} An array of objects containing the post-processed outputs. + */ +function post_process_object_detection(outputs, threshold = 0.5, target_sizes = null, is_zero_shot = false) { + const out_logits = outputs.logits; + const out_bbox = outputs.pred_boxes; + const [batch_size, num_boxes, num_classes] = out_logits.dims; + + if (target_sizes !== null && target_sizes.length !== batch_size) { + throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits") + } + let toReturn = []; + for (let i = 0; i < batch_size; ++i) { + let target_size = target_sizes !== null ? target_sizes[i] : null; + let info = { + boxes: [], + classes: [], + scores: [] + } + let logits = out_logits[i]; + let bbox = out_bbox[i]; + + for (let j = 0; j < num_boxes; ++j) { + let logit = logits[j]; + + let indices = []; + let probs; + if (is_zero_shot) { + // Get indices of classes with high enough probability + probs = logit.sigmoid().data; + for (let k = 0; k < probs.length; ++k) { + if (probs[k] > threshold) { + indices.push(k); + } + } + + } else { + // Get most probable class + let maxIndex = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.max)(logit.data)[1]; + + if (maxIndex === num_classes - 1) { + // This is the background class, skip it + continue; + } + // Compute softmax over classes + probs = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.softmax)(logit.data); + + if (probs[maxIndex] < threshold) { + continue; + } + indices.push(maxIndex); + } + + for (const index of indices) { + + // Some class has a high enough probability + /** @type {number[]} */ + let box = bbox[j].data; + + // convert to [x0, y0, x1, y1] format + box = center_to_corners_format(box) + if (target_size !== null) { + box = box.map((x, i) => x * target_size[(i + 1) % 2]) + } + + info.boxes.push(box); + info.classes.push(index); + info.scores.push(probs[index]); + } + } + toReturn.push(info); + } + return toReturn; +} + + +/** + * Post-processes the outputs of the model (for semantic segmentation). + * @param {*} outputs Raw outputs of the model. + * @param {[number, number][]} [target_sizes=null] List of tuples corresponding to the requested final size + * (height, width) of each prediction. If unset, predictions will not be resized. + * @returns {{segmentation: Tensor; labels: number[]}[]} The semantic segmentation maps. + */ +function post_process_semantic_segmentation(outputs, target_sizes = null) { + + const logits = outputs.logits; + const batch_size = logits.dims[0]; + + if (target_sizes !== null && target_sizes.length !== batch_size) { + throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits") + } + + const toReturn = []; + for (let i = 0; i < batch_size; ++i) { + const target_size = target_sizes !== null ? target_sizes[i] : null; + + let data = logits[i]; + + // 1. If target_size is not null, we need to resize the masks to the target size + if (target_size !== null) { + // resize the masks to the target size + data = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.interpolate)(data, target_size, 'bilinear', false); + } + const [height, width] = target_size ?? data.dims.slice(-2); + + const segmentation = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor( + 'int32', + new Int32Array(height * width), + [height, width] + ); + + // Buffer to store current largest value + const buffer = data[0].data; + const segmentation_data = segmentation.data; + for (let j = 1; j < data.dims[0]; ++j) { + const row = data[j].data; + for (let k = 0; k < row.length; ++k) { + if (row[k] > buffer[k]) { + buffer[k] = row[k]; + segmentation_data[k] = j; + } + } + } + + // Store which objects have labels + // This is much more efficient that creating a set of the final values + const hasLabel = new Array(data.dims[0]); + for (let j = 0; j < segmentation_data.length; ++j) { + const index = segmentation_data[j]; + hasLabel[index] = index; + } + /** @type {number[]} The unique list of labels that were detected */ + const labels = hasLabel.filter(x => x !== undefined); + + toReturn.push({ segmentation, labels }); + } + return toReturn; +} + + +/** + * Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and `labels`. + * @param {Tensor} class_logits The class logits. + * @param {Tensor} mask_logits The mask logits. + * @param {number} object_mask_threshold A number between 0 and 1 used to binarize the masks. + * @param {number} num_labels The number of labels. + * @returns {[Tensor[], number[], number[]]} The binarized masks, the scores, and the labels. + * @private + */ +function remove_low_and_no_objects(class_logits, mask_logits, object_mask_threshold, num_labels) { + + const mask_probs_item = []; + const pred_scores_item = []; + const pred_labels_item = []; + + for (let j = 0; j < class_logits.dims[0]; ++j) { + const cls = class_logits[j]; + const mask = mask_logits[j]; + + const pred_label = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.max)(cls.data)[1]; + if (pred_label === num_labels) { + // Is the background, so we ignore it + continue; + } + + const scores = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.softmax)(cls.data); + const pred_score = scores[pred_label]; + if (pred_score > object_mask_threshold) { + mask_probs_item.push(mask); + pred_scores_item.push(pred_score); + pred_labels_item.push(pred_label); + } + } + + return [mask_probs_item, pred_scores_item, pred_labels_item]; +} + +/** + * Checks whether the segment is valid or not. + * @param {Int32Array} mask_labels Labels for each pixel in the mask. + * @param {Tensor[]} mask_probs Probabilities for each pixel in the masks. + * @param {number} k The class id of the segment. + * @param {number} mask_threshold The mask threshold. + * @param {number} overlap_mask_area_threshold The overlap mask area threshold. + * @returns {[boolean, number[]]} Whether the segment is valid or not, and the indices of the valid labels. + * @private + */ +function check_segment_validity( + mask_labels, + mask_probs, + k, + mask_threshold = 0.5, + overlap_mask_area_threshold = 0.8 +) { + // mask_k is a 1D array of indices, indicating where the mask is equal to k + const mask_k = []; + let mask_k_area = 0; + let original_area = 0; + + const mask_probs_k_data = mask_probs[k].data; + + // Compute the area of all the stuff in query k + for (let i = 0; i < mask_labels.length; ++i) { + if (mask_labels[i] === k) { + mask_k.push(i); + ++mask_k_area; + } + + if (mask_probs_k_data[i] >= mask_threshold) { + ++original_area; + } + } + let mask_exists = mask_k_area > 0 && original_area > 0; + + // Eliminate disconnected tiny segments + if (mask_exists) { + // Perform additional check + let area_ratio = mask_k_area / original_area; + mask_exists = area_ratio > overlap_mask_area_threshold; + } + + return [mask_exists, mask_k] +} + +/** + * Computes the segments. + * @param {Tensor[]} mask_probs The mask probabilities. + * @param {number[]} pred_scores The predicted scores. + * @param {number[]} pred_labels The predicted labels. + * @param {number} mask_threshold The mask threshold. + * @param {number} overlap_mask_area_threshold The overlap mask area threshold. + * @param {Set} label_ids_to_fuse The label ids to fuse. + * @param {number[]} target_size The target size of the image. + * @returns {[Tensor, Array<{id: number, label_id: number, score: number}>]} The computed segments. + * @private + */ +function compute_segments( + mask_probs, + pred_scores, + pred_labels, + mask_threshold, + overlap_mask_area_threshold, + label_ids_to_fuse = null, + target_size = null, +) { + const [height, width] = target_size ?? mask_probs[0].dims; + + const segmentation = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor( + 'int32', + new Int32Array(height * width), + [height, width] + ); + const segments = []; + + // 1. If target_size is not null, we need to resize the masks to the target size + if (target_size !== null) { + // resize the masks to the target size + for (let i = 0; i < mask_probs.length; ++i) { + mask_probs[i] = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.interpolate)(mask_probs[i], target_size, 'bilinear', false); + } + } + + // 2. Weigh each mask by its prediction score + // NOTE: `mask_probs` is updated in-place + // + // Temporary storage for the best label/scores for each pixel ([height, width]): + const mask_labels = new Int32Array(mask_probs[0].data.length); + const bestScores = new Float32Array(mask_probs[0].data.length); + + for (let i = 0; i < mask_probs.length; ++i) { + let score = pred_scores[i]; + + const mask_probs_i_data = mask_probs[i].data; + + for (let j = 0; j < mask_probs_i_data.length; ++j) { + mask_probs_i_data[j] *= score + if (mask_probs_i_data[j] > bestScores[j]) { + mask_labels[j] = i; + bestScores[j] = mask_probs_i_data[j]; + } + } + } + + let current_segment_id = 0; + + // let stuff_memory_list = {} + const segmentation_data = segmentation.data; + for (let k = 0; k < pred_labels.length; ++k) { + const pred_class = pred_labels[k]; + + // TODO add `should_fuse` + // let should_fuse = pred_class in label_ids_to_fuse + + // Check if mask exists and large enough to be a segment + const [mask_exists, mask_k] = check_segment_validity( + mask_labels, + mask_probs, + k, + mask_threshold, + overlap_mask_area_threshold + ) + + if (!mask_exists) { + // Nothing to see here + continue; + } + + // TODO + // if (pred_class in stuff_memory_list) { + // current_segment_id = stuff_memory_list[pred_class] + // } else { + // current_segment_id += 1; + // } + ++current_segment_id; + + + // Add current object segment to final segmentation map + for (const index of mask_k) { + segmentation_data[index] = current_segment_id; + } + + segments.push({ + id: current_segment_id, + label_id: pred_class, + // was_fused: should_fuse, TODO + score: pred_scores[k], + }) + + // TODO + // if(should_fuse){ + // stuff_memory_list[pred_class] = current_segment_id + // } + } + + return [segmentation, segments]; +} + +/** + * Rescales the image so that the following conditions are met: + * + * 1. Both dimensions (height and width) are divisible by 'factor'. + * 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. + * 3. The aspect ratio of the image is maintained as closely as possible. + * + * @param {number} height The height of the image. + * @param {number} width The width of the image. + * @param {number} [factor=28] The factor to use for resizing. + * @param {number} [min_pixels=56*56] The minimum number of pixels. + * @param {number} [max_pixels=14*14*4*1280] The maximum number of pixels. + * @returns {[number, number]} The new height and width of the image. + * @throws {Error} If the height or width is smaller than the factor. + */ +function smart_resize(height, width, factor = 28, min_pixels = 56 * 56, max_pixels = 14 * 14 * 4 * 1280) { + + if (height < factor || width < factor) { + throw new Error(`height:${height} or width:${width} must be larger than factor:${factor}`); + } else if (Math.max(height, width) / Math.min(height, width) > 200) { + throw new Error( + `absolute aspect ratio must be smaller than 200, got ${Math.max(height, width) / Math.min(height, width)}` + ); + } + + let h_bar = Math.round(height / factor) * factor; + let w_bar = Math.round(width / factor) * factor; + + if (h_bar * w_bar > max_pixels) { + const beta = Math.sqrt((height * width) / max_pixels); + h_bar = Math.floor((height / beta) / factor) * factor; + w_bar = Math.floor((width / beta) / factor) * factor; + } else if (h_bar * w_bar < min_pixels) { + const beta = Math.sqrt(min_pixels / (height * width)); + h_bar = Math.ceil((height * beta) / factor) * factor; + w_bar = Math.ceil((width * beta) / factor) * factor; + } + + return [h_bar, w_bar]; +} + + +/** + * Post-process the model output to generate the final panoptic segmentation. + * @param {*} outputs The model output to post process + * @param {number} [threshold=0.5] The probability score threshold to keep predicted instance masks. + * @param {number} [mask_threshold=0.5] Threshold to use when turning the predicted masks into binary values. + * @param {number} [overlap_mask_area_threshold=0.8] The overlap mask area threshold to merge or discard small disconnected parts within each binary instance mask. + * @param {Set} [label_ids_to_fuse=null] The labels in this state will have all their instances be fused together. + * @param {[number, number][]} [target_sizes=null] The target sizes to resize the masks to. + * @returns {Array<{ segmentation: Tensor, segments_info: Array<{id: number, label_id: number, score: number}>}>} + */ +function post_process_panoptic_segmentation( + outputs, + threshold = 0.5, + mask_threshold = 0.5, + overlap_mask_area_threshold = 0.8, + label_ids_to_fuse = null, + target_sizes = null, +) { + if (label_ids_to_fuse === null) { + console.warn("`label_ids_to_fuse` unset. No instance will be fused.") + label_ids_to_fuse = new Set(); + } + + const class_queries_logits = outputs.class_queries_logits ?? outputs.logits; // [batch_size, num_queries, num_classes+1] + const masks_queries_logits = outputs.masks_queries_logits ?? outputs.pred_masks; // [batch_size, num_queries, height, width] + + const mask_probs = masks_queries_logits.sigmoid() // [batch_size, num_queries, height, width] + + let [batch_size, num_queries, num_labels] = class_queries_logits.dims; + num_labels -= 1; // Remove last class (background) + + if (target_sizes !== null && target_sizes.length !== batch_size) { + throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits") + } + + let toReturn = []; + for (let i = 0; i < batch_size; ++i) { + let target_size = target_sizes !== null ? target_sizes[i] : null; + + let class_logits = class_queries_logits[i]; + let mask_logits = mask_probs[i]; + + let [mask_probs_item, pred_scores_item, pred_labels_item] = remove_low_and_no_objects(class_logits, mask_logits, threshold, num_labels); + + if (pred_labels_item.length === 0) { + // No mask found + let [height, width] = target_size ?? mask_logits.dims.slice(-2); + + let segmentation = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor( + 'int32', + new Int32Array(height * width).fill(-1), + [height, width] + ) + toReturn.push({ + segmentation: segmentation, + segments_info: [] + }); + continue; + } + + + // Get segmentation map and segment information of batch item + let [segmentation, segments] = compute_segments( + mask_probs_item, + pred_scores_item, + pred_labels_item, + mask_threshold, + overlap_mask_area_threshold, + label_ids_to_fuse, + target_size, + ) + + toReturn.push({ + segmentation: segmentation, + segments_info: segments + }) + } + + return toReturn; +} + + +/** + * Post-processes the outputs of the model (for instance segmentation). + * @param {*} outputs Raw outputs of the model. + * @param {number} [threshold=0.5] The probability score threshold to keep predicted instance masks. + * @param {[number, number][]} [target_sizes=null] List of tuples corresponding to the requested final size + * (height, width) of each prediction. If unset, predictions will not be resized. + * @returns {Array<{ segmentation: Tensor, segments_info: Array<{id: number, label_id: number, score: number}>}>} + */ +function post_process_instance_segmentation(outputs, threshold = 0.5, target_sizes = null) { + throw new Error('`post_process_instance_segmentation` is not yet implemented.'); +} + + +/** + * @typedef {Object} ImageProcessorConfig A configuration object used to create an image processor. + * @property {function} [progress_callback=null] If specified, this function will be called during model construction, to provide the user with progress updates. + * @property {number[]} [image_mean] The mean values for image normalization. + * @property {number[]} [image_std] The standard deviation values for image normalization. + * @property {boolean} [do_rescale] Whether to rescale the image pixel values to the [0,1] range. + * @property {number} [rescale_factor] The factor to use for rescaling the image pixel values. + * @property {boolean} [do_normalize] Whether to normalize the image pixel values. + * @property {boolean} [do_resize] Whether to resize the image. + * @property {number} [resample] What method to use for resampling. + * @property {number|Object} [size] The size to resize the image to. + * @property {number|Object} [image_size] The size to resize the image to (same as `size`). + * @property {boolean} [do_flip_channel_order=false] Whether to flip the color channels from RGB to BGR. + * Can be overridden by the `do_flip_channel_order` parameter in the `preprocess` method. + * @property {boolean} [do_center_crop] Whether to center crop the image to the specified `crop_size`. + * Can be overridden by `do_center_crop` in the `preprocess` method. + * @property {boolean} [do_thumbnail] Whether to resize the image using thumbnail method. + * @property {boolean} [keep_aspect_ratio] If `true`, the image is resized to the largest possible size such that the aspect ratio is preserved. + * Can be overidden by `keep_aspect_ratio` in `preprocess`. + * @property {number} [ensure_multiple_of] If `do_resize` is `true`, the image is resized to a size that is a multiple of this value. + * Can be overidden by `ensure_multiple_of` in `preprocess`. + * + * @property {number[]} [mean] The mean values for image normalization (same as `image_mean`). + * @property {number[]} [std] The standard deviation values for image normalization (same as `image_std`). + */ + +class ImageProcessor extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + + /** + * Constructs a new `ImageProcessor`. + * @param {ImageProcessorConfig} config The configuration object. + */ + constructor(config) { + super(); + + this.image_mean = config.image_mean ?? config.mean; + this.image_std = config.image_std ?? config.std; + + this.resample = config.resample ?? 2; // 2 => bilinear + this.do_rescale = config.do_rescale ?? true; + this.rescale_factor = config.rescale_factor ?? (1 / 255); + this.do_normalize = config.do_normalize; + + this.do_thumbnail = config.do_thumbnail; + this.size = config.size ?? config.image_size; + this.do_resize = config.do_resize ?? (this.size !== undefined); + // @ts-expect-error TS2339 + this.size_divisibility = config.size_divisibility ?? config.size_divisor; + + this.do_center_crop = config.do_center_crop; + // @ts-expect-error TS2339 + this.crop_size = config.crop_size; + // @ts-expect-error TS2339 + this.do_convert_rgb = config.do_convert_rgb ?? true; + // @ts-expect-error TS2339 + this.do_crop_margin = config.do_crop_margin; + + // @ts-expect-error TS2339 + this.pad_size = config.pad_size; + // @ts-expect-error TS2339 + this.do_pad = config.do_pad; + // @ts-expect-error TS2339 + this.min_pixels = config.min_pixels; + // @ts-expect-error TS2339 + this.max_pixels = config.max_pixels; + + if (this.do_pad && !this.pad_size && this.size && this.size.width !== undefined && this.size.height !== undefined) { + // Should pad, but no pad size specified + // We infer the pad size from the resize size + this.pad_size = this.size + } + + this.do_flip_channel_order = config.do_flip_channel_order ?? false; + + this.config = config; + } + + /** + * Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any + * corresponding dimension of the specified size. + * @param {RawImage} image The image to be resized. + * @param {{height:number, width:number}} size The size `{"height": h, "width": w}` to resize the image to. + * @param {string | 0 | 1 | 2 | 3 | 4 | 5} [resample=2] The resampling filter to use. + * @returns {Promise} The resized image. + */ + async thumbnail(image, size, resample = 2) { + const input_height = image.height; + const input_width = image.width; + + const output_height = size.height; + const output_width = size.width; + + // We always resize to the smallest of either the input or output size. + let height = Math.min(input_height, output_height) + let width = Math.min(input_width, output_width) + + if (height === input_height && width === input_width) { + return image; + } + if (input_height > input_width) { + width = Math.floor(input_width * height / input_height); + } else if (input_width > input_height) { + height = Math.floor(input_height * width / input_width); + } + return await image.resize(width, height, { resample }); + } + + + /** + * Crops the margin of the image. Gray pixels are considered margin (i.e., pixels with a value below the threshold). + * @param {RawImage} image The image to be cropped. + * @param {number} gray_threshold Value below which pixels are considered to be gray. + * @returns {Promise} The cropped image. + */ + async crop_margin(image, gray_threshold = 200) { + + const gray_image = image.clone().grayscale(); + + const minValue = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.min)(gray_image.data)[0]; + const maxValue = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.max)(gray_image.data)[0]; + const diff = maxValue - minValue; + + if (diff === 0) { + return image; + } + + const threshold = gray_threshold / 255; + + let x_min = gray_image.width, y_min = gray_image.height, x_max = 0, y_max = 0; + const gray_image_data = gray_image.data; + for (let j = 0; j < gray_image.height; ++j) { + const row = j * gray_image.width; + for (let i = 0; i < gray_image.width; ++i) { + if ((gray_image_data[row + i] - minValue) / diff < threshold) { + // We have a non-zero pixel, so we update the min/max values accordingly + x_min = Math.min(x_min, i); + y_min = Math.min(y_min, j); + x_max = Math.max(x_max, i); + y_max = Math.max(y_max, j); + } + } + } + + image = await image.crop([x_min, y_min, x_max, y_max]); + return image; + } + + /** + * Pad the image by a certain amount. + * @param {Float32Array} pixelData The pixel data to pad. + * @param {number[]} imgDims The dimensions of the image (height, width, channels). + * @param {{width:number; height:number}|number|'square'} padSize The dimensions of the padded image. + * @param {Object} options The options for padding. + * @param {'constant'|'symmetric'} [options.mode='constant'] The type of padding to add. + * @param {boolean} [options.center=false] Whether to center the image. + * @param {number|number[]} [options.constant_values=0] The constant value to use for padding. + * @returns {[Float32Array, number[]]} The padded pixel data and image dimensions. + */ + pad_image(pixelData, imgDims, padSize, { + mode = 'constant', + center = false, + constant_values = 0, + } = {}) { + const [imageHeight, imageWidth, imageChannels] = imgDims; + + let paddedImageWidth, paddedImageHeight; + if (typeof padSize === 'number') { + paddedImageWidth = padSize; + paddedImageHeight = padSize; + } else if (padSize === 'square') { + paddedImageWidth = paddedImageHeight = Math.max(imageHeight, imageWidth); + } else { + paddedImageWidth = padSize.width; + paddedImageHeight = padSize.height; + } + + // Only add padding if there is a difference in size + if (paddedImageWidth !== imageWidth || paddedImageHeight !== imageHeight) { + const paddedPixelData = new Float32Array(paddedImageWidth * paddedImageHeight * imageChannels); + if (Array.isArray(constant_values)) { + // Fill with constant values, cycling through the array + for (let i = 0; i < paddedPixelData.length; ++i) { + paddedPixelData[i] = constant_values[i % imageChannels]; + } + } else if (constant_values !== 0) { + paddedPixelData.fill(constant_values); + } + + const [left, top] = center + ? [Math.floor((paddedImageWidth - imageWidth) / 2), Math.floor((paddedImageHeight - imageHeight) / 2)] + : [0, 0]; + + // Copy the original image into the padded image + for (let i = 0; i < imageHeight; ++i) { + const a = (i + top) * paddedImageWidth; + const b = i * imageWidth; + for (let j = 0; j < imageWidth; ++j) { + const c = (a + j + left) * imageChannels; + const d = (b + j) * imageChannels; + for (let k = 0; k < imageChannels; ++k) { + paddedPixelData[c + k] = pixelData[d + k]; + } + } + } + + if (mode === 'symmetric') { + if (center) { + throw new Error('`center` padding is not supported when `mode` is set to `symmetric`.'); + // TODO: Implement this + } + const h1 = imageHeight - 1; + const w1 = imageWidth - 1; + for (let i = 0; i < paddedImageHeight; ++i) { + const a = i * paddedImageWidth; + const b = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.calculateReflectOffset)(i, h1) * imageWidth; + + for (let j = 0; j < paddedImageWidth; ++j) { + if (i < imageHeight && j < imageWidth) continue; // Do not overwrite original image + const c = (a + j) * imageChannels; + const d = (b + (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.calculateReflectOffset)(j, w1)) * imageChannels; + + // Copy channel-wise + for (let k = 0; k < imageChannels; ++k) { + paddedPixelData[c + k] = pixelData[d + k]; + } + } + } + } + + + // Update pixel data and image dimensions + pixelData = paddedPixelData; + imgDims = [paddedImageHeight, paddedImageWidth, imageChannels] + } + return [pixelData, imgDims]; + } + + /** + * Rescale the image' pixel values by `this.rescale_factor`. + * @param {Float32Array} pixelData The pixel data to rescale. + * @returns {void} + */ + rescale(pixelData) { + for (let i = 0; i < pixelData.length; ++i) { + pixelData[i] = this.rescale_factor * pixelData[i]; + } + } + + /** + * Find the target (width, height) dimension of the output image after + * resizing given the input image and the desired size. + * @param {RawImage} image The image to resize. + * @param {any} size The size to use for resizing the image. + * @returns {[number, number]} The target (width, height) dimension of the output image after resizing. + */ + get_resize_output_image_size(image, size) { + // `size` comes in many forms, so we need to handle them all here: + // 1. `size` is an integer, in which case we resize the image to be a square + + const [srcWidth, srcHeight] = image.size; + + let shortest_edge; + let longest_edge; + + if (this.do_thumbnail) { + // NOTE: custom logic for `Donut` models + const { height, width } = size; + shortest_edge = Math.min(height, width) + } + // Support both formats for backwards compatibility + else if (Number.isInteger(size)) { + shortest_edge = size; + // @ts-expect-error TS2339 + longest_edge = this.config.max_size ?? shortest_edge; + + } else if (size !== undefined) { + // Extract known properties from `size` + shortest_edge = size.shortest_edge; + longest_edge = size.longest_edge; + } + + // If `longest_edge` and `shortest_edge` are set, maintain aspect ratio and resize to `shortest_edge` + // while keeping the largest dimension <= `longest_edge` + if (shortest_edge !== undefined || longest_edge !== undefined) { + // http://opensourcehacker.com/2011/12/01/calculate-aspect-ratio-conserving-resize-for-images-in-javascript/ + // Try resize so that shortest edge is `shortest_edge` (target) + const shortResizeFactor = shortest_edge === undefined + ? 1 // If `shortest_edge` is not set, don't upscale + : Math.max(shortest_edge / srcWidth, shortest_edge / srcHeight); + + const newWidth = srcWidth * shortResizeFactor; + const newHeight = srcHeight * shortResizeFactor; + + // The new width and height might be greater than `longest_edge`, so + // we downscale again to ensure the largest dimension is `longest_edge` + const longResizeFactor = longest_edge === undefined + ? 1 // If `longest_edge` is not set, don't downscale + : Math.min(longest_edge / newWidth, longest_edge / newHeight); + + // To avoid certain floating point precision issues, we round to 2 decimal places + let finalWidth = Math.floor(Number((newWidth * longResizeFactor).toFixed(2))); + let finalHeight = Math.floor(Number((newHeight * longResizeFactor).toFixed(2))); + + if (this.size_divisibility !== undefined) { + [finalWidth, finalHeight] = enforce_size_divisibility([finalWidth, finalHeight], this.size_divisibility) + } + return [finalWidth, finalHeight]; + + } else if (size !== undefined && size.width !== undefined && size.height !== undefined) { + // If `width` and `height` are set, resize to those dimensions + + let newWidth = size.width; + let newHeight = size.height; + + // Custom for DPT models + if (this.config.keep_aspect_ratio && this.config.ensure_multiple_of) { + + // determine new height and width + let scale_height = newHeight / srcHeight; + let scale_width = newWidth / srcWidth; + + // scale as little as possible + if (Math.abs(1 - scale_width) < Math.abs(1 - scale_height)) { + // fit width + scale_height = scale_width; + } else { + // fit height + scale_width = scale_height; + } + + newHeight = constraint_to_multiple_of(scale_height * srcHeight, this.config.ensure_multiple_of); + newWidth = constraint_to_multiple_of(scale_width * srcWidth, this.config.ensure_multiple_of); + } + + return [newWidth, newHeight]; + + } else if (this.size_divisibility !== undefined) { + return enforce_size_divisibility([srcWidth, srcHeight], this.size_divisibility); + } else if (this.min_pixels !== undefined && this.max_pixels !== undefined) { + // Custom resize logic for Qwen2-VL models + // @ts-expect-error TS2339 + const factor = this.config.patch_size * this.config.merge_size; + return smart_resize(srcHeight, srcWidth, factor, this.min_pixels, this.max_pixels); + } else { + throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(size)}`); + } + } + + /** + * Resizes the image. + * @param {RawImage} image The image to resize. + * @returns {Promise} The resized image. + */ + async resize(image) { + const [newWidth, newHeight] = this.get_resize_output_image_size(image, this.size); + return await image.resize(newWidth, newHeight, { + // @ts-expect-error TS2322 + resample: this.resample, + }); + } + + /** + * @typedef {object} PreprocessedImage + * @property {HeightWidth} original_size The original size of the image. + * @property {HeightWidth} reshaped_input_size The reshaped input size of the image. + * @property {Tensor} pixel_values The pixel values of the preprocessed image. + */ + + /** + * Preprocesses the given image. + * + * @param {RawImage} image The image to preprocess. + * @param {Object} overrides The overrides for the preprocessing options. + * @returns {Promise} The preprocessed image. + */ + async preprocess(image, { + do_normalize = null, + do_pad = null, + do_convert_rgb = null, + do_convert_grayscale = null, + do_flip_channel_order = null, + } = {}) { + if (this.do_crop_margin) { + // NOTE: Specific to nougat processors. This is done before resizing, + // and can be interpreted as a pre-preprocessing step. + image = await this.crop_margin(image); + } + + const [srcWidth, srcHeight] = image.size; // original image size + + // Convert image to RGB if specified in config. + if (do_convert_rgb ?? this.do_convert_rgb) { + image = image.rgb(); + } else if (do_convert_grayscale) { + image = image.grayscale(); + } + + // TODO: + // For efficiency reasons, it might be best to merge the resize and center crop operations into one. + + // Resize all images + if (this.do_resize) { + image = await this.resize(image); + } + + // Resize the image using thumbnail method. + if (this.do_thumbnail) { + // @ts-expect-error TS2345 + image = await this.thumbnail(image, this.size, this.resample); + } + + if (this.do_center_crop) { + + let crop_width; + let crop_height; + if (Number.isInteger(this.crop_size)) { + crop_width = this.crop_size; + crop_height = this.crop_size; + } else { + crop_width = this.crop_size.width; + crop_height = this.crop_size.height; + } + + image = await image.center_crop(crop_width, crop_height); + } + + /** @type {HeightWidth} */ + const reshaped_input_size = [image.height, image.width]; + + // NOTE: All pixel-level manipulation (i.e., modifying `pixelData`) + // occurs with data in the hwc format (height, width, channels), + // to emulate the behavior of the original Python code (w/ numpy). + /** @type {Float32Array} */ + let pixelData = Float32Array.from(image.data); + let imgDims = [image.height, image.width, image.channels]; + + if (this.do_rescale) { + this.rescale(pixelData); + } + + if (do_normalize ?? this.do_normalize) { + let image_mean = this.image_mean; + if (!Array.isArray(this.image_mean)) { + image_mean = new Array(image.channels).fill(image_mean); + } + + let image_std = this.image_std; + if (!Array.isArray(this.image_std)) { + image_std = new Array(image.channels).fill(image_mean); + } + + if (image_mean.length !== image.channels || image_std.length !== image.channels) { + throw new Error(`When set to arrays, the length of \`image_mean\` (${image_mean.length}) and \`image_std\` (${image_std.length}) must match the number of channels in the image (${image.channels}).`); + } + + for (let i = 0; i < pixelData.length; i += image.channels) { + for (let j = 0; j < image.channels; ++j) { + pixelData[i + j] = (pixelData[i + j] - image_mean[j]) / image_std[j]; + } + } + } + + // do padding after rescaling/normalizing + if (do_pad ?? this.do_pad) { + if (this.pad_size) { + const padded = this.pad_image(pixelData, [image.height, image.width, image.channels], this.pad_size); + [pixelData, imgDims] = padded; // Update pixel data and image dimensions + } else if (this.size_divisibility) { + const [paddedWidth, paddedHeight] = enforce_size_divisibility([imgDims[1], imgDims[0]], this.size_divisibility); + [pixelData, imgDims] = this.pad_image(pixelData, imgDims, { width: paddedWidth, height: paddedHeight }); + } + } + + if (do_flip_channel_order ?? this.do_flip_channel_order) { + if (imgDims[2] !== 3) { + throw new Error('Flipping channel order is only supported for RGB images.'); + } + // Convert RGB to BGR + for (let i = 0; i < pixelData.length; i += 3) { + const temp = pixelData[i]; + pixelData[i] = pixelData[i + 2]; + pixelData[i + 2] = temp; + } + } + + const pixel_values = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor('float32', pixelData, imgDims) + .permute(2, 0, 1); // convert to channel dimension format (hwc -> chw) + + return { + original_size: [srcHeight, srcWidth], + reshaped_input_size: reshaped_input_size, + pixel_values, + } + } + + /** + * Calls the feature extraction process on an array of images, + * preprocesses each image, and concatenates the resulting + * features into a single Tensor. + * @param {RawImage[]} images The image(s) to extract features from. + * @param {...any} args Additional arguments. + * @returns {Promise} An object containing the concatenated pixel values (and other metadata) of the preprocessed images. + */ + async _call(images, ...args) { + if (!Array.isArray(images)) { + images = [images]; + } + /** @type {PreprocessedImage[]} */ + const imageData = await Promise.all(images.map(x => this.preprocess(x))); + + // Stack pixel values + const pixel_values = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.stack)(imageData.map(x => x.pixel_values), 0); + + return { + pixel_values, + + // Original sizes of images + original_sizes: imageData.map(x => x.original_size), + + // Reshaped sizes of images, before padding or cropping + reshaped_input_sizes: imageData.map(x => x.reshaped_input_size), + } + } + + + /** + * Instantiate one of the processor classes of the library from a pretrained model. + * + * The processor class to instantiate is selected based on the `image_processor_type` (or `feature_extractor_type`; legacy) + * property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible) + * + * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either: + * - A string, the *model id* of a pretrained processor hosted inside a model repo on huggingface.co. + * Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a + * user or organization name, like `dbmdz/bert-base-german-cased`. + * - A path to a *directory* containing processor files, e.g., `./my_model_directory/`. + * @param {import('../utils/hub.js').PretrainedOptions} options Additional options for loading the processor. + * + * @returns {Promise} A new instance of the Processor class. + */ + static async from_pretrained(pretrained_model_name_or_path, options={}) { + const preprocessorConfig = await (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_5__.getModelJSON)(pretrained_model_name_or_path, _utils_constants_js__WEBPACK_IMPORTED_MODULE_6__.IMAGE_PROCESSOR_NAME, true, options); + return new this(preprocessorConfig); + } +} + + +/***/ }), + +/***/ "./src/base/processing_utils.js": +/*!**************************************!*\ + !*** ./src/base/processing_utils.js ***! + \**************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Processor: () => (/* binding */ Processor) +/* harmony export */ }); +/* harmony import */ var _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../utils/constants.js */ "./src/utils/constants.js"); +/* harmony import */ var _utils_generic_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../utils/generic.js */ "./src/utils/generic.js"); +/* harmony import */ var _utils_hub_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../utils/hub.js */ "./src/utils/hub.js"); + +/** + * @file Processors are used to prepare inputs (e.g., text, image or audio) for a model. + * + * **Example:** Using a `WhisperProcessor` to prepare an audio input for a model. + * ```javascript + * import { AutoProcessor, read_audio } from '@huggingface/transformers'; + * + * const processor = await AutoProcessor.from_pretrained('openai/whisper-tiny.en'); + * const audio = await read_audio('https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac', 16000); + * const { input_features } = await processor(audio); + * // Tensor { + * // data: Float32Array(240000) [0.4752984642982483, 0.5597258806228638, 0.56434166431427, ...], + * // dims: [1, 80, 3000], + * // type: 'float32', + * // size: 240000, + * // } + * ``` + * + * @module processors + */ + + + + +/** + * @typedef {Object} ProcessorProperties Additional processor-specific properties. + * @typedef {import('../utils/hub.js').PretrainedOptions & ProcessorProperties} PretrainedProcessorOptions + * @typedef {import('../tokenizers.js').PreTrainedTokenizer} PreTrainedTokenizer + */ + + +/** + * Represents a Processor that extracts features from an input. + */ +class Processor extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_1__.Callable { + static classes = [ + 'image_processor_class', + 'tokenizer_class', + 'feature_extractor_class', + ] + static uses_processor_config = false; + static uses_chat_template_file = false; + + /** + * Creates a new Processor with the given components + * @param {Object} config + * @param {Record} components + * @param {string} chat_template + */ + constructor(config, components, chat_template) { + super(); + this.config = config; + this.components = components; + this.chat_template = chat_template; + } + + /** + * @returns {import('./image_processors_utils.js').ImageProcessor|undefined} The image processor of the processor, if it exists. + */ + get image_processor() { + return this.components.image_processor; + } + + /** + * @returns {PreTrainedTokenizer|undefined} The tokenizer of the processor, if it exists. + */ + get tokenizer() { + return this.components.tokenizer; + } + + /** + * @returns {import('./feature_extraction_utils.js').FeatureExtractor|undefined} The feature extractor of the processor, if it exists. + */ + get feature_extractor() { + return this.components.feature_extractor; + } + + /** + * @param {Parameters[0]} messages + * @param {Parameters[1]} options + * @returns {ReturnType} + */ + apply_chat_template(messages, options = {}) { + if (!this.tokenizer) { + throw new Error('Unable to apply chat template without a tokenizer.'); + } + return this.tokenizer.apply_chat_template(messages, { + tokenize: false, // default to false + chat_template: this.chat_template ?? undefined, + ...options, + }); + } + + /** + * @param {Parameters} args + * @returns {ReturnType} + */ + batch_decode(...args) { + if (!this.tokenizer) { + throw new Error('Unable to decode without a tokenizer.'); + } + return this.tokenizer.batch_decode(...args); + } + + /** + * @param {Parameters} args + * @returns {ReturnType} + */ + decode(...args) { + if (!this.tokenizer) { + throw new Error('Unable to decode without a tokenizer.'); + } + return this.tokenizer.decode(...args); + } + + + /** + * Calls the feature_extractor function with the given input. + * @param {any} input The input to extract features from. + * @param {...any} args Additional arguments. + * @returns {Promise} A Promise that resolves with the extracted features. + */ + async _call(input, ...args) { + for (const item of [this.image_processor, this.feature_extractor, this.tokenizer]) { + if (item) { + return item(input, ...args); + } + } + throw new Error('No image processor, feature extractor, or tokenizer found.'); + } + + + /** + * Instantiate one of the processor classes of the library from a pretrained model. + * + * The processor class to instantiate is selected based on the `image_processor_type` (or `feature_extractor_type`; legacy) + * property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible) + * + * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either: + * - A string, the *model id* of a pretrained processor hosted inside a model repo on huggingface.co. + * Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a + * user or organization name, like `dbmdz/bert-base-german-cased`. + * - A path to a *directory* containing processor files, e.g., `./my_model_directory/`. + * @param {PretrainedProcessorOptions} options Additional options for loading the processor. + * + * @returns {Promise} A new instance of the Processor class. + */ + static async from_pretrained(pretrained_model_name_or_path, options={}) { + + const [config, components, chat_template] = await Promise.all([ + // TODO: + this.uses_processor_config + ? (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_2__.getModelJSON)(pretrained_model_name_or_path, _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__.PROCESSOR_NAME, true, options) + : {}, + Promise.all( + this.classes + .filter((cls) => cls in this) + .map(async (cls) => { + const component = await this[cls].from_pretrained(pretrained_model_name_or_path, options); + return [cls.replace(/_class$/, ''), component]; + }) + ).then(Object.fromEntries), + this.uses_chat_template_file + ? (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_2__.getModelText)(pretrained_model_name_or_path, _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__.CHAT_TEMPLATE_NAME, true, options) + : null, + ]); + + return new this(config, components, chat_template); + } +} + + +/***/ }), + +/***/ "./src/configs.js": +/*!************************!*\ + !*** ./src/configs.js ***! + \************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ AutoConfig: () => (/* binding */ AutoConfig), +/* harmony export */ PretrainedConfig: () => (/* binding */ PretrainedConfig), +/* harmony export */ getCacheShapes: () => (/* binding */ getCacheShapes) +/* harmony export */ }); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./utils/core.js */ "./src/utils/core.js"); +/* harmony import */ var _utils_hub_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./utils/hub.js */ "./src/utils/hub.js"); + +/** + * @file Helper module for using model configs. For more information, see the corresponding + * [Python documentation](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoConfig). + * + * **Example:** Load an `AutoConfig`. + * + * ```javascript + * import { AutoConfig } from '@huggingface/transformers'; + * const config = await AutoConfig.from_pretrained('bert-base-uncased'); + * console.log(config); + * // PretrainedConfig { + * // "model_type": "bert", + * // "is_encoder_decoder": false, + * // "architectures": [ + * // "BertForMaskedLM" + * // ], + * // "vocab_size": 30522 + * // "num_attention_heads": 12, + * // "num_hidden_layers": 12, + * // "hidden_size": 768, + * // "max_position_embeddings": 512, + * // ... + * // } + * ``` + * + * @module configs + */ + + + + +/** + * @typedef {import('./utils/hub.js').PretrainedOptions} PretrainedOptions + */ + +/** + * @typedef {import('./utils/core.js').ProgressCallback} ProgressCallback + */ + +/** + * @typedef {import('./utils/core.js').ProgressInfo} ProgressInfo + */ + +/** + * Loads a config from the specified path. + * @param {string} pretrained_model_name_or_path The path to the config directory. + * @param {PretrainedOptions} options Additional options for loading the config. + * @returns {Promise} A promise that resolves with information about the loaded config. + */ +async function loadConfig(pretrained_model_name_or_path, options) { + return await (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_1__.getModelJSON)(pretrained_model_name_or_path, 'config.json', true, options); +} + +/** + * + * @param {PretrainedConfig} config + * @returns {Object} The normalized configuration. + */ +function getNormalizedConfig(config) { + const mapping = {}; + + let init_normalized_config = {}; + switch (config.model_type) { + // Sub-configs + case 'llava': + case 'paligemma': + case 'gemma3': + case 'florence2': + case 'llava_onevision': + case 'idefics3': + case 'ultravox': + case 'voxtral': + case 'smolvlm': + case 'gemma3n': + // @ts-expect-error TS2339 + init_normalized_config = getNormalizedConfig(config.text_config); + break; + case 'moondream1': + // @ts-expect-error TS2339 + init_normalized_config = getNormalizedConfig(config.phi_config); + break; + case 'musicgen': + // @ts-expect-error TS2339 + init_normalized_config = getNormalizedConfig(config.decoder); + break; + case 'multi_modality': + // @ts-expect-error TS2339 + init_normalized_config = getNormalizedConfig(config.language_config); + break; + + // Decoder-only models + case 'gpt2': + case 'gptj': + case 'jais': + case 'codegen': + case 'gpt_bigcode': + mapping['num_heads'] = 'n_head'; + mapping['num_layers'] = 'n_layer'; + mapping['hidden_size'] = 'n_embd'; + break; + case 'gpt_neox': + case 'stablelm': + case 'opt': + case 'falcon': + case 'modernbert-decoder': + mapping['num_heads'] = 'num_attention_heads'; + mapping['num_layers'] = 'num_hidden_layers'; + mapping['hidden_size'] = 'hidden_size'; + break; + case 'llama': + case 'arcee': + case 'lfm2': + case 'smollm3': + case 'olmo': + case 'olmo2': + case 'mobilellm': + case 'granite': + case 'cohere': + case 'mistral': + case 'starcoder2': + case 'qwen2': + case 'qwen2_vl': + case 'phi': + case 'phi3': + case 'phi3_v': + case 'llava_qwen2': + mapping['num_heads'] = 'num_key_value_heads'; + mapping['num_layers'] = 'num_hidden_layers'; + mapping['hidden_size'] = 'hidden_size'; + mapping['num_attention_heads'] = 'num_attention_heads'; + mapping['dim_kv'] = 'head_dim'; + break; + case 'qwen3': + case 'gemma': + case 'gemma2': + case 'gemma3_text': + case 'gemma3n_text': + case 'glm': + case 'helium': + case 'ernie4_5': + mapping['num_heads'] = 'num_key_value_heads'; + mapping['num_layers'] = 'num_hidden_layers'; + mapping['dim_kv'] = 'head_dim'; + break; + case 'openelm': + mapping['num_heads'] = 'num_kv_heads'; + mapping['num_layers'] = 'num_transformer_layers'; + mapping['dim_kv'] = 'head_dim'; + break; + case 'gpt_neo': + case 'donut-swin': + mapping['num_heads'] = 'num_heads'; + mapping['num_layers'] = 'num_layers'; + mapping['hidden_size'] = 'hidden_size'; + break; + case 'bloom': + mapping['num_heads'] = 'n_head'; + mapping['num_layers'] = 'n_layer'; + mapping['hidden_size'] = 'hidden_size'; + break; + case 'mpt': + mapping['num_heads'] = 'n_heads'; + mapping['num_layers'] = 'n_layers'; + mapping['hidden_size'] = 'd_model'; + break; + case 'exaone': + mapping['num_heads'] = 'num_key_value_heads'; + mapping['num_layers'] = 'num_layers'; + mapping['dim_kv'] = 'head_dim'; + mapping['num_attention_heads'] = 'num_attention_heads'; + break; + + // Encoder-decoder models + case 't5': + case 'mt5': + case 'longt5': + mapping['num_decoder_layers'] = 'num_decoder_layers'; + mapping['num_decoder_heads'] = 'num_heads'; + mapping['decoder_dim_kv'] = 'd_kv'; + mapping['num_encoder_layers'] = 'num_layers'; + mapping['num_encoder_heads'] = 'num_heads'; + mapping['encoder_dim_kv'] = 'd_kv'; + break; + case 'bart': + case 'mbart': + case 'marian': + case 'whisper': + case 'lite-whisper': + case 'm2m_100': + case 'blenderbot': + case 'blenderbot-small': + case 'florence2_language': + mapping['num_decoder_layers'] = 'decoder_layers'; + mapping['num_decoder_heads'] = 'decoder_attention_heads'; + mapping['decoder_hidden_size'] = 'd_model'; + mapping['num_encoder_layers'] = 'encoder_layers'; + mapping['num_encoder_heads'] = 'encoder_attention_heads'; + mapping['encoder_hidden_size'] = 'd_model'; + break; + case 'speecht5': + mapping['num_decoder_layers'] = 'decoder_layers'; + mapping['num_decoder_heads'] = 'decoder_attention_heads'; + mapping['decoder_hidden_size'] = 'hidden_size'; + mapping['num_encoder_layers'] = 'encoder_layers'; + mapping['num_encoder_heads'] = 'encoder_attention_heads'; + mapping['encoder_hidden_size'] = 'hidden_size'; + break; + case 'trocr': + mapping['num_encoder_layers'] = mapping['num_decoder_layers'] = 'decoder_layers'; + mapping['num_encoder_heads'] = mapping['num_decoder_heads'] = 'decoder_attention_heads'; + mapping['encoder_hidden_size'] = mapping['decoder_hidden_size'] = 'd_model'; + break; + case 'musicgen_decoder': + mapping['num_encoder_layers'] = mapping['num_decoder_layers'] = 'num_hidden_layers'; + mapping['num_encoder_heads'] = mapping['num_decoder_heads'] = 'num_attention_heads'; + mapping['encoder_hidden_size'] = mapping['decoder_hidden_size'] = 'hidden_size'; + break; + case 'moonshine': + mapping['num_decoder_layers'] = 'decoder_num_hidden_layers'; + mapping['num_decoder_heads'] = 'decoder_num_key_value_heads'; + mapping['num_encoder_layers'] = 'encoder_num_hidden_layers'; + mapping['num_encoder_heads'] = 'encoder_num_key_value_heads'; + mapping['encoder_hidden_size'] = mapping['decoder_hidden_size'] = 'hidden_size'; + break; + case 'vision-encoder-decoder': + // @ts-expect-error TS2339 + const decoderConfig = getNormalizedConfig(config.decoder); + + const add_encoder_pkv = 'num_decoder_layers' in decoderConfig; + const result = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_0__.pick)(config, ['model_type', 'is_encoder_decoder']); + if (add_encoder_pkv) { + // Decoder is part of an encoder-decoder model + result.num_decoder_layers = decoderConfig.num_decoder_layers; + result.num_decoder_heads = decoderConfig.num_decoder_heads; + result.decoder_hidden_size = decoderConfig.decoder_hidden_size; + + result.num_encoder_layers = decoderConfig.num_encoder_layers; + result.num_encoder_heads = decoderConfig.num_encoder_heads; + result.encoder_hidden_size = decoderConfig.encoder_hidden_size; + } else { + // Decoder is a decoder-only model + result.num_layers = decoderConfig.num_layers; + result.num_heads = decoderConfig.num_heads; + result.hidden_size = decoderConfig.hidden_size; + } + return result; + + } + + // NOTE: If `num_attention_heads` is not set, it is assumed to be equal to `num_heads` + const normalized_config = { + ...init_normalized_config, + ...(0,_utils_core_js__WEBPACK_IMPORTED_MODULE_0__.pick)(config, ['model_type', 'multi_query', 'is_encoder_decoder']), + }; + for (const key in mapping) { + normalized_config[key] = config[mapping[key]]; + } + return normalized_config; +} + +/** + * + * @param {PretrainedConfig} config + * @returns {Record} + */ +function getCacheShapes(config, options) { + if (config.model_type === 'lfm2') { + const pkv_prefix = options?.prefix ?? 'past_key_values'; + const conv_prefix = pkv_prefix === 'present' ? 'present' : 'past'; + + // Custom caching mechanism for LFM2 + /** @type {Record} */ + const cache_values = {}; + // @ts-expect-error TS2339 + const { layer_types, num_attention_heads, num_key_value_heads, hidden_size, conv_L_cache } = config; + const head_dim = hidden_size / num_attention_heads; + const batch_size = options?.batch_size ?? 1; + for (let i = 0; i < layer_types.length; ++i) { + if (layer_types[i] === 'full_attention') { + for (const kv of ['key', 'value']) { + cache_values[`${pkv_prefix}.${i}.${kv}`] = [batch_size, num_key_value_heads, 0, head_dim]; + } + } else if (layer_types[i] === 'conv') { + cache_values[`${conv_prefix}_conv.${i}`] = [batch_size, hidden_size, conv_L_cache]; + } else { + throw new Error(`Unsupported layer type: ${layer_types[i]}`); + } + } + return cache_values; + } + return getKeyValueShapes(config, options); +} + +/** @type {typeof getKeyValueShapes} */ +function getKeyValueShapes(config, { + prefix = 'past_key_values', + batch_size = 1, +} = {}) { + /** @type {Record} */ + const decoderFeeds = {}; + const normalized_config = config.normalized_config; + + if (normalized_config.is_encoder_decoder && ( + 'num_encoder_heads' in normalized_config && 'num_decoder_heads' in normalized_config + )) { + const encoder_dim_kv = normalized_config.encoder_dim_kv ?? ( + normalized_config.encoder_hidden_size / normalized_config.num_encoder_heads + ); + const decoder_dim_kv = normalized_config.decoder_dim_kv ?? ( + normalized_config.decoder_hidden_size / normalized_config.num_decoder_heads + ); + + const encoder_dims = [batch_size, normalized_config.num_encoder_heads, 0, encoder_dim_kv]; + const decoder_dims = [batch_size, normalized_config.num_decoder_heads, 0, decoder_dim_kv]; + for (let i = 0; i < normalized_config.num_decoder_layers; ++i) { + decoderFeeds[`${prefix}.${i}.encoder.key`] = encoder_dims; + decoderFeeds[`${prefix}.${i}.encoder.value`] = encoder_dims; + decoderFeeds[`${prefix}.${i}.decoder.key`] = decoder_dims; + decoderFeeds[`${prefix}.${i}.decoder.value`] = decoder_dims; + } + } else { // Decoders + const num_heads = normalized_config.num_heads; + const num_layers = normalized_config.num_layers; + const dim_kv = normalized_config.dim_kv ?? ( + normalized_config.hidden_size / + (normalized_config.num_attention_heads ?? num_heads) + ); + + if (normalized_config.model_type === 'falcon') { + // NOTE: Custom implementation for Falcon + const dims = [batch_size * num_heads, 0, dim_kv] + for (let i = 0; i < num_layers; ++i) { + decoderFeeds[`${prefix}.${i}.key`] = dims; + decoderFeeds[`${prefix}.${i}.value`] = dims; + } + } else if (normalized_config.multi_query) { // e.g., for `gpt_bigcode` + const dims = [batch_size * num_heads, 0, 2 * dim_kv] + + for (let i = 0; i < num_layers; ++i) { + decoderFeeds[`${prefix}.${i}.key_value`] = dims; + } + } else if (normalized_config.model_type === 'bloom') { + // NOTE: Custom implementation for Bloom + + const keyDims = [batch_size * num_heads, dim_kv, 0] // [batch_size x num_heads,64,past_sequence_length] + const valueDims = [batch_size * num_heads, 0, dim_kv] // [batch_size x num_heads,past_sequence_length,64] + for (let i = 0; i < num_layers; ++i) { + decoderFeeds[`${prefix}.${i}.key`] = keyDims; + decoderFeeds[`${prefix}.${i}.value`] = valueDims; + } + } else if (normalized_config.model_type === 'openelm') { + for (let i = 0; i < num_layers; ++i) { + const dims = [batch_size, num_heads[i], 0, dim_kv] + + decoderFeeds[`${prefix}.${i}.key`] = dims; + decoderFeeds[`${prefix}.${i}.value`] = dims; + } + } else { // Decoder-only + const dims = [batch_size, num_heads, 0, dim_kv] + for (let i = 0; i < num_layers; ++i) { + decoderFeeds[`${prefix}.${i}.key`] = dims; + decoderFeeds[`${prefix}.${i}.value`] = dims; + } + } + } + + return decoderFeeds; +} +/** + * Base class for all configuration classes. For more information, see the corresponding + * [Python documentation](https://huggingface.co/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig). + */ +class PretrainedConfig { + // NOTE: Typo in original + + /** @type {string|null} */ + model_type = null; + + /** @type {boolean} */ + is_encoder_decoder = false; + + /** @type {number} */ + max_position_embeddings; + + /** @type {TransformersJSConfig} */ + 'transformers.js_config'; + + /** + * Create a new PreTrainedTokenizer instance. + * @param {Object} configJSON The JSON of the config. + */ + constructor(configJSON) { + Object.assign(this, configJSON); + this.normalized_config = getNormalizedConfig(this); + } + + /** + * Loads a pre-trained config from the given `pretrained_model_name_or_path`. + * + * @param {string} pretrained_model_name_or_path The path to the pre-trained config. + * @param {PretrainedOptions} options Additional options for loading the config. + * @throws {Error} Throws an error if the config.json is not found in the `pretrained_model_name_or_path`. + * + * @returns {Promise} A new instance of the `PretrainedConfig` class. + */ + static async from_pretrained(pretrained_model_name_or_path, { + progress_callback = null, + config = null, + cache_dir = null, + local_files_only = false, + revision = 'main', + } = {}) { + if (config && !(config instanceof PretrainedConfig)) { + config = new PretrainedConfig(config); + } + + const data = config ?? await loadConfig(pretrained_model_name_or_path, { + progress_callback, + config, + cache_dir, + local_files_only, + revision, + }) + return new this(data); + } +} + +/** + * Helper class which is used to instantiate pretrained configs with the `from_pretrained` function. + * + * @example + * const config = await AutoConfig.from_pretrained('Xenova/bert-base-uncased'); + */ +class AutoConfig { + /** @type {typeof PretrainedConfig.from_pretrained} */ + static async from_pretrained(...args) { + return PretrainedConfig.from_pretrained(...args); + } +} + +/** + * Transformers.js-specific configuration, possibly present in config.json under the key `transformers.js_config`. + * @typedef {Object} TransformersJSConfig + * @property {Record} [device_config] Device-specific configurations. + * @property {import('./utils/tensor.js').DataType|Record} [kv_cache_dtype] The data type of the key-value cache. + * @property {Record} [free_dimension_overrides] Override the free dimensions of the model. + * See https://onnxruntime.ai/docs/tutorials/web/env-flags-and-session-options.html#freedimensionoverrides + * for more information. + * @property {import('./utils/devices.js').DeviceType} [device] The default device to use for the model. + * @property {import('./utils/dtypes.js').DataType|Record} [dtype] The default data type to use for the model. + * @property {import('./utils/hub.js').ExternalData|Record} [use_external_data_format=false] Whether to load the model using the external data format (used for models >= 2GB in size). + */ + +/** + * Device-specific configuration options. + * @typedef {Omit} DeviceConfig + */ + + +/***/ }), + +/***/ "./src/env.js": +/*!********************!*\ + !*** ./src/env.js ***! + \********************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ apis: () => (/* binding */ apis), +/* harmony export */ env: () => (/* binding */ env) +/* harmony export */ }); +/* harmony import */ var node_fs__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! node:fs */ "?db59"); +/* harmony import */ var node_path__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! node:path */ "?383f"); +/* harmony import */ var node_url__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! node:url */ "?fa4b"); +/** + * @file Module used to configure Transformers.js. + * + * **Example:** Disable remote models. + * ```javascript + * import { env } from '@huggingface/transformers'; + * env.allowRemoteModels = false; + * ``` + * + * **Example:** Set local model path. + * ```javascript + * import { env } from '@huggingface/transformers'; + * env.localModelPath = '/path/to/local/models/'; + * ``` + * + * **Example:** Set cache directory. + * ```javascript + * import { env } from '@huggingface/transformers'; + * env.cacheDir = '/path/to/cache/directory/'; + * ``` + * + * @module env + */ + + + + + +const VERSION = '3.7.2'; + +// Check if various APIs are available (depends on environment) +const IS_BROWSER_ENV = typeof window !== "undefined" && typeof window.document !== "undefined"; +const IS_WEBWORKER_ENV = typeof self !== "undefined" && (['DedicatedWorkerGlobalScope', 'ServiceWorkerGlobalScope', 'SharedWorkerGlobalScope'].includes(self.constructor?.name)); +const IS_WEB_CACHE_AVAILABLE = typeof self !== "undefined" && 'caches' in self; +const IS_WEBGPU_AVAILABLE = typeof navigator !== 'undefined' && 'gpu' in navigator; +const IS_WEBNN_AVAILABLE = typeof navigator !== 'undefined' && 'ml' in navigator; + +const IS_PROCESS_AVAILABLE = typeof process !== 'undefined'; +const IS_NODE_ENV = IS_PROCESS_AVAILABLE && process?.release?.name === 'node'; +const IS_FS_AVAILABLE = !isEmpty(node_fs__WEBPACK_IMPORTED_MODULE_0__); +const IS_PATH_AVAILABLE = !isEmpty(node_path__WEBPACK_IMPORTED_MODULE_1__); + +// Runtime detection +const IS_DENO_RUNTIME = typeof globalThis.Deno !== 'undefined'; +const IS_BUN_RUNTIME = typeof globalThis.Bun !== 'undefined'; + +/** + * A read-only object containing information about the APIs available in the current environment. + */ +const apis = Object.freeze({ + /** Whether we are running in a browser environment (and not a web worker) */ + IS_BROWSER_ENV, + + /** Whether we are running in a web worker environment */ + IS_WEBWORKER_ENV, + + /** Whether the Cache API is available */ + IS_WEB_CACHE_AVAILABLE, + + /** Whether the WebGPU API is available */ + IS_WEBGPU_AVAILABLE, + + /** Whether the WebNN API is available */ + IS_WEBNN_AVAILABLE, + + /** Whether the Node.js process API is available */ + IS_PROCESS_AVAILABLE, + + /** Whether we are running in a Node.js-like environment (node, deno, bun) */ + IS_NODE_ENV, + + /** Whether the filesystem API is available */ + IS_FS_AVAILABLE, + + /** Whether the path API is available */ + IS_PATH_AVAILABLE, +}); + +const RUNNING_LOCALLY = IS_FS_AVAILABLE && IS_PATH_AVAILABLE; + +let dirname__ = './'; +if (RUNNING_LOCALLY) { + // NOTE: We wrap `import.meta` in a call to `Object` to prevent Webpack from trying to bundle it in CommonJS. + // Although we get the warning: "Accessing import.meta directly is unsupported (only property access or destructuring is supported)", + // it is safe to ignore since the bundled value (`{}`) isn't used for CommonJS environments (we use __dirname instead). + const _import_meta_url = Object(import.meta).url; + + if (_import_meta_url) { + dirname__ = node_path__WEBPACK_IMPORTED_MODULE_1__.dirname(node_path__WEBPACK_IMPORTED_MODULE_1__.dirname(node_url__WEBPACK_IMPORTED_MODULE_2__.fileURLToPath(_import_meta_url))) // ESM + } else if (typeof __dirname !== 'undefined') { + dirname__ = node_path__WEBPACK_IMPORTED_MODULE_1__.dirname(__dirname) // CommonJS + } +} + +// Only used for environments with access to file system +const DEFAULT_CACHE_DIR = RUNNING_LOCALLY + ? node_path__WEBPACK_IMPORTED_MODULE_1__.join(dirname__, '/.cache/') + : null; + +// Set local model path, based on available APIs +const DEFAULT_LOCAL_MODEL_PATH = '/models/'; +const localModelPath = RUNNING_LOCALLY + ? node_path__WEBPACK_IMPORTED_MODULE_1__.join(dirname__, DEFAULT_LOCAL_MODEL_PATH) + : DEFAULT_LOCAL_MODEL_PATH; + +/** + * Global variable given visible to users to control execution. This provides users a simple way to configure Transformers.js. + * @typedef {Object} TransformersEnvironment + * @property {string} version This version of Transformers.js. + * @property {{onnx: Partial}} backends Expose environment variables of different backends, + * allowing users to set these variables if they want to. + * @property {boolean} allowRemoteModels Whether to allow loading of remote files, defaults to `true`. + * If set to `false`, it will have the same effect as setting `local_files_only=true` when loading pipelines, models, tokenizers, processors, etc. + * @property {string} remoteHost Host URL to load models from. Defaults to the Hugging Face Hub. + * @property {string} remotePathTemplate Path template to fill in and append to `remoteHost` when loading models. + * @property {boolean} allowLocalModels Whether to allow loading of local files, defaults to `false` if running in-browser, and `true` otherwise. + * If set to `false`, it will skip the local file check and try to load the model from the remote host. + * @property {string} localModelPath Path to load local models from. Defaults to `/models/`. + * @property {boolean} useFS Whether to use the file system to load files. By default, it is `true` if available. + * @property {boolean} useBrowserCache Whether to use Cache API to cache models. By default, it is `true` if available. + * @property {boolean} useFSCache Whether to use the file system to cache files. By default, it is `true` if available. + * @property {string} cacheDir The directory to use for caching files with the file system. By default, it is `./.cache`. + * @property {boolean} useCustomCache Whether to use a custom cache system (defined by `customCache`), defaults to `false`. + * @property {Object} customCache The custom cache to use. Defaults to `null`. Note: this must be an object which + * implements the `match` and `put` functions of the Web Cache API. For more information, see https://developer.mozilla.org/en-US/docs/Web/API/Cache. + * If you wish, you may also return a `Promise` from the `match` function if you'd like to use a file path instead of `Promise`. + */ + +/** @type {TransformersEnvironment} */ +const env = { + version: VERSION, + + /////////////////// Backends settings /////////////////// + // NOTE: These will be populated later by the backends themselves. + backends: { + // onnxruntime-web/onnxruntime-node + onnx: {}, + }, + + /////////////////// Model settings /////////////////// + allowRemoteModels: true, + remoteHost: 'https://huggingface.co/', + remotePathTemplate: '{model}/resolve/{revision}/', + + allowLocalModels: !(IS_BROWSER_ENV || IS_WEBWORKER_ENV), + localModelPath: localModelPath, + useFS: IS_FS_AVAILABLE, + + /////////////////// Cache settings /////////////////// + useBrowserCache: IS_WEB_CACHE_AVAILABLE && !IS_DENO_RUNTIME, + + useFSCache: IS_FS_AVAILABLE, + cacheDir: DEFAULT_CACHE_DIR, + + useCustomCache: false, + customCache: null, + ////////////////////////////////////////////////////// +} + + +/** + * @param {Object} obj + * @private + */ +function isEmpty(obj) { + return Object.keys(obj).length === 0; +} + + +/***/ }), + +/***/ "./src/generation/configuration_utils.js": +/*!***********************************************!*\ + !*** ./src/generation/configuration_utils.js ***! + \***********************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ GenerationConfig: () => (/* binding */ GenerationConfig) +/* harmony export */ }); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../utils/core.js */ "./src/utils/core.js"); + +/** + * @module generation/configuration_utils + */ + + + +/** + * Class that holds a configuration for a generation task. + */ +class GenerationConfig { + // Parameters that control the length of the output + /** + * The maximum length the generated tokens can have. + * Corresponds to the length of the input prompt + `max_new_tokens`. + * Its effect is overridden by `max_new_tokens`, if also set. + * @type {number} + * @default 20 + */ + max_length = 20; + + /** + * The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt. + * @type {number} + * @default null + */ + max_new_tokens = null; + + /** + * The minimum length of the sequence to be generated. + * Corresponds to the length of the input prompt + `min_new_tokens`. + * Its effect is overridden by `min_new_tokens`, if also set. + * @type {number} + * @default 0 + */ + min_length = 0; + + /** + * The minimum numbers of tokens to generate, ignoring the number of tokens in the prompt. + * @type {number} + * @default null + */ + min_new_tokens = null; + + /** + * Controls the stopping condition for beam-based methods, like beam-search. It accepts the following values: + * - `true`, where the generation stops as soon as there are `num_beams` complete candidates; + * - `false`, where an heuristic is applied and the generation stops when is it very unlikely to find better candidates; + * - `"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical beam search algorithm). + * @type {boolean|"never"} + * @default false + */ + early_stopping = false; + + /** + * The maximum amount of time you allow the computation to run for in seconds. + * Generation will still finish the current pass after allocated time has been passed. + * @type {number} + * @default null + */ + max_time = null; + + // Parameters that control the generation strategy used + /** + * Whether or not to use sampling; use greedy decoding otherwise. + * @type {boolean} + * @default false + */ + do_sample = false; + + /** + * Number of beams for beam search. 1 means no beam search. + * @type {number} + * @default 1 + */ + num_beams = 1; + + /** + * Number of groups to divide `num_beams` into in order to ensure diversity among different groups of beams. + * See [this paper](https://huggingface.co/papers/1610.02424) for more details. + * @type {number} + * @default 1 + */ + num_beam_groups = 1; + + /** + * The values balance the model confidence and the degeneration penalty in contrastive search decoding. + * @type {number} + * @default null + */ + penalty_alpha = null; + + /** + * Whether or not the model should use the past last key/values attentions (if applicable to the model) to speed up decoding. + * @type {boolean} + * @default true + */ + use_cache = true; + + // Parameters for manipulation of the model output logits + /** + * The value used to modulate the next token probabilities. + * @type {number} + * @default 1.0 + */ + temperature = 1.0; + + /** + * The number of highest probability vocabulary tokens to keep for top-k-filtering. + * @type {number} + * @default 50 + */ + top_k = 50; + + /** + * If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or higher are kept for generation. + * @type {number} + * @default 1.0 + */ + top_p = 1.0; + + /** + * Local typicality measures how similar the conditional probability of predicting a target token next is to the expected conditional probability of predicting a random token next, given the partial text already generated. + * If set to float < 1, the smallest set of the most locally typical tokens with probabilities that add up to `typical_p` or higher are kept for generation. + * See [this paper](https://huggingface.co/papers/2202.00666) for more details. + * @type {number} + * @default 1.0 + */ + typical_p = 1.0; + + /** + * If set to float strictly between 0 and 1, only tokens with a conditional probability greater than `epsilon_cutoff` will be sampled. + * In the paper, suggested values range from 3e-4 to 9e-4, depending on the size of the model. + * See [Truncation Sampling as Language Model Desmoothing](https://huggingface.co/papers/2210.15191) for more details. + * @type {number} + * @default 0.0 + */ + epsilon_cutoff = 0.0; + + /** + * Eta sampling is a hybrid of locally typical sampling and epsilon sampling. + * If set to float strictly between 0 and 1, a token is only considered if it is greater than either `eta_cutoff` or `sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits)))`. + * The latter term is intuitively the expected next token probability, scaled by `sqrt(eta_cutoff)`. In the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model. + * See [Truncation Sampling as Language Model Desmoothing](https://huggingface.co/papers/2210.15191) for more details. + * @type {number} + * @default 0.0 + */ + eta_cutoff = 0.0; + + /** + * This value is subtracted from a beam's score if it generates a token same as any beam from other group at a particular time. + * Note that `diversity_penalty` is only effective if `group beam search` is enabled. + * @type {number} + * @default 0.0 + */ + diversity_penalty = 0.0; + + /** + * The parameter for repetition penalty. 1.0 means no penalty. + * See [this paper](https://huggingface.co/papers/1909.05858) for more details. + * @type {number} + * @default 1.0 + */ + repetition_penalty = 1.0; + + /** + * The paramater for encoder_repetition_penalty. + * An exponential penalty on sequences that are not in the original input. + * 1.0 means no penalty. + * @type {number} + * @default 1.0 + */ + encoder_repetition_penalty = 1.0; + + /** + * Exponential penalty to the length that is used with beam-based generation. + * It is applied as an exponent to the sequence length, which in turn is used to divide the score of the sequence. + * Since the score is the log likelihood of the sequence (i.e. negative), `length_penalty` > 0.0 promotes longer sequences, while `length_penalty` < 0.0 encourages shorter sequences. + * @type {number} + * @default 1.0 + */ + length_penalty = 1.0; + + /** + * If set to int > 0, all ngrams of that size can only occur once. + * @type {number} + * @default 0 + */ + no_repeat_ngram_size = 0; + + /** + * List of token ids that are not allowed to be generated. + * In order to get the token ids of the words that should not appear in the generated text, use + * `tokenizer(bad_words, { add_prefix_space: true, add_special_tokens: false }).input_ids`. + * @type {number[][]} + * @default null + */ + bad_words_ids = null; + + /** + * List of token ids that must be generated. + * If given a `number[][]`, this is treated as a simple list of words that must be included, the opposite to `bad_words_ids`. + * If given `number[][][]`, this triggers a [disjunctive constraint](https://github.com/huggingface/transformers/issues/14081), where one can allow different forms of each word. + * @type {number[][]|number[][][]} + * @default null + */ + force_words_ids = null; + + /** + * Whether to renormalize the logits after applying all the logits processors or warpers (including the custom ones). + * It's highly recommended to set this flag to `true` as the search algorithms suppose the score logits are normalized but some logit processors or warpers break the normalization. + * @type {boolean} + * @default false + */ + renormalize_logits = false; + + /** + * Custom constraints that can be added to the generation to ensure that the output will contain the use of certain tokens as defined by `Constraint` objects, in the most sensible way possible. + * @type {Object[]} + * @default null + */ + constraints = null; + + /** + * The id of the token to force as the first generated token after the `decoder_start_token_id`. + * Useful for multilingual models like mBART where the first generated token needs to be the target language token. + * @type {number} + * @default null + */ + forced_bos_token_id = null; + + /** + * The id of the token to force as the last generated token when `max_length` is reached. + * Optionally, use a list to set multiple *end-of-sequence* tokens. + * @type {number|number[]} + * @default null + */ + forced_eos_token_id = null; + + /** + * Whether to remove possible *nan* and *inf* outputs of the model to prevent the generation method to crash. Note that using `remove_invalid_values` can slow down generation. + * @type {boolean} + */ + remove_invalid_values = false; + + /** + * This Tuple adds an exponentially increasing length penalty, after a certain amount of tokens have been generated. + * The tuple shall consist of: `(start_index, decay_factor)` where `start_index` indicates where penalty starts and `decay_factor` represents the factor of exponential decay. + * @type {[number, number]} + * @default null + */ + exponential_decay_length_penalty = null; + + /** + * A list of tokens that will be suppressed at generation. + * The `SuppressTokens` logit processor will set their log probs to `-inf` so that they are not sampled. + * @type {number[]} + * @default null + */ + suppress_tokens = null; + + /** + * A streamer that will be used to stream the generation. + * @type {import('./streamers.js').TextStreamer} + * @default null + */ + streamer = null; + + /** + * A list of tokens that will be suppressed at the beginning of the generation. + * The `SuppressBeginTokens` logit processor will set their log probs to `-inf` so that they are not sampled. + * @type {number[]} + * @default null + */ + begin_suppress_tokens = null; + + /** + * A list of pairs of integers which indicates a mapping from generation indices to token indices that will be forced before sampling. + * For example, `[[1, 123]]` means the second generated token will always be a token of index 123. + * @type {[number, number][]} + * @default null + */ + forced_decoder_ids = null; + + /** + * The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`. + * Higher guidance scale encourages the model to generate samples that are more closely linked to the input + * prompt, usually at the expense of poorer quality. + * @type {number} + * @default null + */ + guidance_scale = null; + + // Parameters that define the output variables of `generate` + /** + * The number of independently computed returned sequences for each element in the batch. + * @type {number} + * @default 1 + */ + num_return_sequences = 1; + + /** + * Whether or not to return the attentions tensors of all attention layers. + * See `attentions` under returned tensors for more details. + * @type {boolean} + * @default false + */ + output_attentions = false; + + /** + * Whether or not to return the hidden states of all layers. + * See `hidden_states` under returned tensors for more details. + * @type {boolean} + * @default false + */ + output_hidden_states = false; + + /** + * Whether or not to return the prediction scores. + * See `scores` under returned tensors for more details. + * @type {boolean} + * @default false + */ + output_scores = false; + + /** + * Whether or not to return a `ModelOutput` instead of a plain tuple. + * @type {boolean} + * @default false + */ + return_dict_in_generate = false; + + // Special tokens that can be used at generation time + /** + * The id of the *padding* token. + * @type {number} + * @default null + */ + pad_token_id = null; + + /** + * The id of the *beginning-of-sequence* token. + * @type {number} + * @default null + */ + bos_token_id = null; + + /** + * The id of the *end-of-sequence* token. + * Optionally, use a list to set multiple *end-of-sequence* tokens. + * @type {number|number[]} + * @default null + */ + eos_token_id = null; + + // Generation parameters exclusive to encoder-decoder models + /** + * If set to int > 0, all ngrams of that size that occur in the `encoder_input_ids` cannot occur in the `decoder_input_ids`. + * @type {number} + * @default 0 + */ + encoder_no_repeat_ngram_size = 0; + + /** + * If an encoder-decoder model starts decoding with a different token than *bos*, the id of that token. + * @type {number} + * @default null + */ + decoder_start_token_id = null; + + // Wild card + /** + * Additional generation kwargs will be forwarded to the `generate` function of the model. + * Kwargs that are not present in `generate`'s signature will be used in the model forward pass. + * @type {Object} + * @default {} + */ + generation_kwargs = {}; + + /** + * + * @param {GenerationConfig|import('../configs.js').PretrainedConfig} config + */ + constructor(config) { + Object.assign(this, (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_0__.pick)(config, Object.getOwnPropertyNames(this))); + } +} + + + +/***/ }), + +/***/ "./src/generation/logits_process.js": +/*!******************************************!*\ + !*** ./src/generation/logits_process.js ***! + \******************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ ClassifierFreeGuidanceLogitsProcessor: () => (/* binding */ ClassifierFreeGuidanceLogitsProcessor), +/* harmony export */ ForcedBOSTokenLogitsProcessor: () => (/* binding */ ForcedBOSTokenLogitsProcessor), +/* harmony export */ ForcedEOSTokenLogitsProcessor: () => (/* binding */ ForcedEOSTokenLogitsProcessor), +/* harmony export */ LogitsProcessor: () => (/* binding */ LogitsProcessor), +/* harmony export */ LogitsProcessorList: () => (/* binding */ LogitsProcessorList), +/* harmony export */ LogitsWarper: () => (/* binding */ LogitsWarper), +/* harmony export */ MinLengthLogitsProcessor: () => (/* binding */ MinLengthLogitsProcessor), +/* harmony export */ MinNewTokensLengthLogitsProcessor: () => (/* binding */ MinNewTokensLengthLogitsProcessor), +/* harmony export */ NoBadWordsLogitsProcessor: () => (/* binding */ NoBadWordsLogitsProcessor), +/* harmony export */ NoRepeatNGramLogitsProcessor: () => (/* binding */ NoRepeatNGramLogitsProcessor), +/* harmony export */ RepetitionPenaltyLogitsProcessor: () => (/* binding */ RepetitionPenaltyLogitsProcessor), +/* harmony export */ SuppressTokensAtBeginLogitsProcessor: () => (/* binding */ SuppressTokensAtBeginLogitsProcessor), +/* harmony export */ TemperatureLogitsWarper: () => (/* binding */ TemperatureLogitsWarper), +/* harmony export */ TopKLogitsWarper: () => (/* binding */ TopKLogitsWarper), +/* harmony export */ TopPLogitsWarper: () => (/* binding */ TopPLogitsWarper), +/* harmony export */ WhisperTimeStampLogitsProcessor: () => (/* binding */ WhisperTimeStampLogitsProcessor) +/* harmony export */ }); +/* harmony import */ var _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../utils/generic.js */ "./src/utils/generic.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_maths_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../utils/maths.js */ "./src/utils/maths.js"); + +/** + * @module generation/logits_process + */ + + + + + + +/** + * Abstract base class for all logit processors that can be applied during generation. + */ +class LogitsProcessor extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + /** + * Apply the processor to the input logits. + * + * @abstract + * @param {bigint[][]} input_ids The input ids. + * @param {Tensor} logits The logits to process. + * @throws {Error} Throws an error if `_call` is not implemented in the subclass. + */ + _call(input_ids, logits) { + throw Error("`_call` should be implemented in a subclass") + } +} + + +/** + * Abstract base class for all logit warpers that can be applied during generation with multinomial sampling. + */ +class LogitsWarper extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + /** + * Apply the processor to the input logits. + * + * @abstract + * @param {bigint[][]} input_ids The input ids. + * @param {Tensor} logits The logits to process. + * @throws {Error} Throws an error if `_call` is not implemented in the subclass. + */ + _call(input_ids, logits) { + throw Error("`_call` should be implemented in a subclass") + } +} + + +/** + * A class representing a list of logits processors. A logits processor is a function that modifies the logits + * output of a language model. This class provides methods for adding new processors and applying all processors to a + * batch of logits. + */ +class LogitsProcessorList extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + /** + * Constructs a new instance of `LogitsProcessorList`. + */ + constructor() { + super(); + this.processors = []; + } + + /** + * Adds a new logits processor to the list. + * + * @param {LogitsProcessor} item The logits processor function to add. + */ + push(item) { + this.processors.push(item); + } + + /** + * Adds multiple logits processors to the list. + * + * @param {LogitsProcessor[]} items The logits processor functions to add. + */ + extend(items) { + this.processors.push(...items); + } + + /** + * Applies all logits processors in the list to a batch of logits, modifying them in-place. + * + * @param {bigint[][]} input_ids The input IDs for the language model. + * @param {Tensor} logits + */ + _call(input_ids, logits) { + let toReturn = logits; + // NOTE: Most processors modify logits inplace + for (const processor of this.processors) { + toReturn = processor(input_ids, toReturn); + } + return toReturn; + } + + [Symbol.iterator]() { + return this.processors.values(); + } +} + +// DEPRECATED: https://github.com/huggingface/transformers/pull/29485 +// /** +// * A logits processor that forces a specific token to be generated by the decoder. +// */ +// export class ForceTokensLogitsProcessor extends LogitsProcessor { +// /** +// * Constructs a new instance of `ForceTokensLogitsProcessor`. +// * +// * @param {[number, number][]} forced_decoder_ids The ids of tokens that should be forced. +// */ +// constructor(forced_decoder_ids) { +// super(); +// // TODO: convert to `new Map(forced_decoder_ids)` +// this.force_token_map = Object.fromEntries(forced_decoder_ids ?? []); +// } + +// /** +// * Apply the processor to the input logits. +// * +// * @param {bigint[][]} input_ids The input ids. +// * @param {Tensor} logits The logits to process. +// * @returns {Tensor} The processed logits. +// */ +// _call(input_ids, logits) { +// console.log('this.force_token_map', this.force_token_map) +// console.log('call ForceTokensLogitsProcessor', input_ids, logits) +// console.log('input_ids.length', input_ids.length) +// let map = this.force_token_map[input_ids.length]; +// if (map) { // There exists a mapping +// logits.data.fill(-Infinity) +// logits.data[map] = 0; +// } +// console.log('map', map) +// // throw Error("Not implemented") +// return logits; +// } +// } + +/** + * A LogitsProcessor that forces a BOS token at the beginning of the generated sequence. + */ +class ForcedBOSTokenLogitsProcessor extends LogitsProcessor { + /** + * Create a ForcedBOSTokenLogitsProcessor. + * @param {number} bos_token_id The ID of the beginning-of-sequence token to be forced. + */ + constructor(bos_token_id) { + super(); + this.bos_token_id = bos_token_id; + } + + /** + * Apply the BOS token forcing to the logits. + * @param {bigint[][]} input_ids The input IDs. + * @param {Tensor} logits The logits. + * @returns {Tensor} The logits with BOS token forcing. + */ + _call(input_ids, logits) { + for (let i = 0; i < input_ids.length; ++i) { + if (input_ids[i].length === 1) { + const batch_logits_data = /** @type {Float32Array} */(logits[i].data); + batch_logits_data.fill(-Infinity); + batch_logits_data[this.bos_token_id] = 0; + } + } + return logits; + } +} + +/** + * A logits processor that enforces the specified token as the last generated token when `max_length` is reached. + */ +class ForcedEOSTokenLogitsProcessor extends LogitsProcessor { + /** + * Create a ForcedEOSTokenLogitsProcessor. + * @param {number} max_length The maximum length of the sequence to be generated. + * @param {number|number[]} eos_token_id The id(s) of the *end-of-sequence* token. + */ + constructor(max_length, eos_token_id) { + super(); + this.max_length = max_length; + this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id]; + } + + /** + * Apply the processor to input_ids and logits. + * + * @param {bigint[][]} input_ids The input ids. + * @param {Tensor} logits The logits tensor. + */ + _call(input_ids, logits) { + for (let i = 0; i < input_ids.length; ++i) { + if (input_ids[i].length === this.max_length - 1) { + const batch_logits_data = /** @type {Float32Array} */(logits[i].data); + batch_logits_data.fill(-Infinity); + for (const eos_token of this.eos_token_id) { + batch_logits_data[eos_token] = 0; + } + } + } + return logits; + } +} + +/** + * A LogitsProcessor that suppresses a list of tokens as soon as the `generate` function starts + * generating using `begin_index` tokens. This should ensure that the tokens defined by + * `begin_suppress_tokens` at not sampled at the begining of the generation. + */ +class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor { + /** + * Create a SuppressTokensAtBeginLogitsProcessor. + * @param {number[]} begin_suppress_tokens The IDs of the tokens to suppress. + * @param {number} begin_index The number of tokens to generate before suppressing tokens. + */ + constructor(begin_suppress_tokens, begin_index) { + super(); + this.begin_suppress_tokens = begin_suppress_tokens; + this.begin_index = begin_index; + } + + /** + * Apply the BOS token forcing to the logits. + * @param {bigint[][]} input_ids The input IDs. + * @param {Tensor} logits The logits. + * @returns {Tensor} The logits with BOS token forcing. + */ + _call(input_ids, logits) { + for (let i = 0; i < input_ids.length; ++i) { + if (input_ids[i].length === this.begin_index) { + const batch_logits_data = /** @type {Float32Array} */(logits[i].data); + for (const token_id of this.begin_suppress_tokens) { + batch_logits_data[token_id] = -Infinity; + } + } + } + return logits; + } +} + +/** + * A LogitsProcessor that handles adding timestamps to generated text. + */ +class WhisperTimeStampLogitsProcessor extends LogitsProcessor { + /** + * Constructs a new WhisperTimeStampLogitsProcessor. + * @param {import('../models/whisper/generation_whisper.js').WhisperGenerationConfig} generate_config The config object passed to the `generate()` method of a transformer model. + * @param {number[]} init_tokens The initial tokens of the input sequence. + */ + constructor(generate_config, init_tokens) { + super(); + this.eos_token_id = + Array.isArray(generate_config.eos_token_id) + ? generate_config.eos_token_id[0] + : generate_config.eos_token_id; + + this.no_timestamps_token_id = generate_config.no_timestamps_token_id; + this.timestamp_begin = this.no_timestamps_token_id + 1; + + this.begin_index = init_tokens.length; + if (init_tokens.at(-1) === this.no_timestamps_token_id) { + this.begin_index -= 1; + } + this.max_initial_timestamp_index = generate_config.max_initial_timestamp_index; + } + + /** + * Modify the logits to handle timestamp tokens. + * @param {bigint[][]} input_ids The input sequence of tokens. + * @param {Tensor} logits The logits output by the model. + * @returns {Tensor} The modified logits. + */ + _call(input_ids, logits) { + for (let i = 0; i < input_ids.length; ++i) { + const batch_logits_data = /** @type {Float32Array} */(logits[i].data); + + // suppress <|notimestamps|> which is handled by without_timestamps + batch_logits_data[this.no_timestamps_token_id] = -Infinity; + + if (input_ids[i].length === this.begin_index - 1) { + batch_logits_data.fill(-Infinity); + batch_logits_data[this.timestamp_begin] = 0; + continue; + } + + // timestamps have to appear in pairs, except directly before eos_token; mask logits accordingly + const seq = input_ids[i].slice(this.begin_index); + const last_was_timestamp = seq.length >= 1 && seq[seq.length - 1] >= this.timestamp_begin; + const penultimate_was_timestamp = seq.length < 2 || seq[seq.length - 2] >= this.timestamp_begin; + + if (last_was_timestamp) { + if (penultimate_was_timestamp) { // has to be non-timestamp + batch_logits_data.subarray(this.timestamp_begin).fill(-Infinity); + } else { // cannot be normal text tokens + batch_logits_data.subarray(0, this.eos_token_id).fill(-Infinity); + } + } + + // apply the `max_initial_timestamp` option + if (input_ids[i].length === this.begin_index && this.max_initial_timestamp_index !== null) { + const last_allowed = this.timestamp_begin + this.max_initial_timestamp_index; + batch_logits_data.subarray(last_allowed + 1).fill(-Infinity); + } + + // if sum of probability over timestamps is above any other token, sample timestamp + const logprobs = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.log_softmax)(batch_logits_data); + const timestamp_logprob = Math.log(logprobs.subarray(this.timestamp_begin).map(Math.exp).reduce((a, b) => a + b)); + const max_text_token_logprob = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.max)(logprobs.subarray(0, this.timestamp_begin))[0]; + + if (timestamp_logprob > max_text_token_logprob) { + batch_logits_data.subarray(0, this.timestamp_begin).fill(-Infinity); + } + } + + return logits; + } +} + +/** + * A logits processor that disallows ngrams of a certain size to be repeated. + */ +class NoRepeatNGramLogitsProcessor extends LogitsProcessor { + /** + * Create a NoRepeatNGramLogitsProcessor. + * @param {number} no_repeat_ngram_size The no-repeat-ngram size. All ngrams of this size can only occur once. + */ + constructor(no_repeat_ngram_size) { + super(); + this.no_repeat_ngram_size = no_repeat_ngram_size; + } + + /** + * Generate n-grams from a sequence of token ids. + * @param {bigint[]} prevInputIds List of previous input ids + * @returns {Map} Map of generated n-grams + */ + getNgrams(prevInputIds) { + const curLen = prevInputIds.length; + + /**@type {number[][]} */ + const ngrams = []; + for (let j = 0; j < curLen + 1 - this.no_repeat_ngram_size; ++j) { + const ngram = []; + for (let k = 0; k < this.no_repeat_ngram_size; ++k) { + ngram.push(prevInputIds[j + k]); + } + ngrams.push(ngram.map(Number)); + } + + /** @type {Map} */ + const generatedNgram = new Map(); + for (const ngram of ngrams) { + const prevNgram = ngram.slice(0, ngram.length - 1); + const prevNgramKey = JSON.stringify(prevNgram); + const prevNgramValue = generatedNgram.get(prevNgramKey) ?? []; + prevNgramValue.push(ngram[ngram.length - 1]); + generatedNgram.set(prevNgramKey, prevNgramValue); + } + return generatedNgram; + } + + /** + * Generate n-grams from a sequence of token ids. + * @param {Map} bannedNgrams Map of banned n-grams + * @param {bigint[]} prevInputIds List of previous input ids + * @returns {number[]} Map of generated n-grams + */ + getGeneratedNgrams(bannedNgrams, prevInputIds) { + const ngramIdx = prevInputIds.slice(prevInputIds.length + 1 - this.no_repeat_ngram_size, prevInputIds.length); + const banned = bannedNgrams.get(JSON.stringify(ngramIdx.map(Number))) ?? []; + return banned; + } + + /** + * Calculate banned n-gram tokens + * @param {bigint[]} prevInputIds List of previous input ids + * @returns {number[]} Map of generated n-grams + */ + calcBannedNgramTokens(prevInputIds) { + const bannedTokens = []; + if (prevInputIds.length + 1 < this.no_repeat_ngram_size) { + // return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet + return bannedTokens; + + } else { + const generatedNgrams = this.getNgrams(prevInputIds); + const bannedTokens = this.getGeneratedNgrams(generatedNgrams, prevInputIds); + return bannedTokens; + } + } + + /** + * Apply the no-repeat-ngram processor to the logits. + * @param {bigint[][]} input_ids The input IDs. + * @param {Tensor} logits The logits. + * @returns {Tensor} The logits with no-repeat-ngram processing. + */ + _call(input_ids, logits) { + for (let i = 0; i < input_ids.length; ++i) { + const batch_logits_data = /** @type {Float32Array} */(logits[i].data); + const bannedTokens = this.calcBannedNgramTokens(input_ids[i]); + for (const token of bannedTokens) { + batch_logits_data[token] = -Infinity; + } + } + return logits; + } +} + +/** + * A logits processor that prevents the repetition of previous tokens through a penalty. + * This penalty is applied at most once per token. Note that, for decoder-only models like most LLMs, + * the considered tokens include the prompt. + * + * In the original [paper](https://huggingface.co/papers/1909.05858), the authors suggest the use of a + * penalty of around 1.2 to achieve a good balance between truthful generation and lack of repetition. + * To penalize and reduce repetition, use `penalty` values above 1.0, where a higher value penalizes + * more strongly. To reward and encourage repetition, use `penalty` values between 0.0 and 1.0, where + * a lower value rewards more strongly. + */ +class RepetitionPenaltyLogitsProcessor extends LogitsProcessor { + /** + * Create a RepetitionPenaltyLogitsProcessor. + * @param {number} penalty The parameter for repetition penalty. + * - 1.0 means no penalty. Above 1.0 penalizes previously generated tokens. + * - Between 0.0 and 1.0 rewards previously generated tokens. + */ + constructor(penalty) { + super(); + this.penalty = penalty; + } + + /** + * Apply the repetition penalty to the logits. + * @param {bigint[][]} input_ids The input IDs. + * @param {Tensor} logits The logits. + * @returns {Tensor} The logits with repetition penalty processing. + */ + _call(input_ids, logits) { + for (let i = 0; i < input_ids.length; ++i) { + const batch_logits_data = /** @type {Float32Array} */(logits[i].data); + for (const input_id of new Set(input_ids[i])) { + const token = Number(input_id); + if (batch_logits_data[token] < 0) { + batch_logits_data[token] *= this.penalty; + } else { + batch_logits_data[token] /= this.penalty; + } + } + } + + return logits + } +} + +/** + * A logits processor that enforces a minimum number of tokens. + */ +class MinLengthLogitsProcessor extends LogitsProcessor { + /** + * Create a MinLengthLogitsProcessor. + * @param {number} min_length The minimum length below which the score of `eos_token_id` is set to negative infinity. + * @param {number|number[]} eos_token_id The ID/IDs of the end-of-sequence token. + */ + constructor(min_length, eos_token_id) { + super(); + this.min_length = min_length; + this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id]; + } + + /** + * Apply logit processor. + * @param {bigint[][]} input_ids The input IDs. + * @param {Tensor} logits The logits. + * @returns {Tensor} The processed logits. + */ + _call(input_ids, logits) { + for (let i = 0; i < input_ids.length; ++i) { + if (input_ids[i].length < this.min_length) { + const batch_logits_data = /** @type {Float32Array} */(logits[i].data); + + for (const eos_token of this.eos_token_id) { + batch_logits_data[eos_token] = -Infinity; + } + } + } + + return logits + } +} + +/** + * A logits processor that enforces a minimum number of new tokens. + */ +class MinNewTokensLengthLogitsProcessor extends LogitsProcessor { + /** + * Create a MinNewTokensLengthLogitsProcessor. + * @param {number} prompt_length_to_skip The input tokens length. + * @param {number} min_new_tokens The minimum *new* tokens length below which the score of `eos_token_id` is set to negative infinity. + * @param {number|number[]} eos_token_id The ID/IDs of the end-of-sequence token. + */ + constructor(prompt_length_to_skip, min_new_tokens, eos_token_id) { + super(); + this.prompt_length_to_skip = prompt_length_to_skip; + this.min_new_tokens = min_new_tokens; + this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id]; + } + + /** + * Apply logit processor. + * @param {bigint[][]} input_ids The input IDs. + * @param {Tensor} logits The logits. + * @returns {Tensor} The processed logits. + */ + _call(input_ids, logits) { + for (let i = 0; i < input_ids.length; ++i) { + const new_tokens_length = input_ids[i].length - this.prompt_length_to_skip; + if (new_tokens_length < this.min_new_tokens) { + const batch_logits_data = /** @type {Float32Array} */(logits[i].data); + + for (const eos_token of this.eos_token_id) { + batch_logits_data[eos_token] = -Infinity; + } + } + } + return logits + } +} + +class NoBadWordsLogitsProcessor extends LogitsProcessor { + /** + * Create a `NoBadWordsLogitsProcessor`. + * @param {number[][]} bad_words_ids List of list of token ids that are not allowed to be generated. + * @param {number|number[]} eos_token_id The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens. + */ + constructor(bad_words_ids, eos_token_id) { + super(); + this.bad_words_ids = bad_words_ids; + this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id]; + } + + /** + * Apply logit processor. + * @param {bigint[][]} input_ids The input IDs. + * @param {Tensor} logits The logits. + * @returns {Tensor} The processed logits. + */ + _call(input_ids, logits) { + for (let i = 0; i < input_ids.length; ++i) { + const batch_logits_data = /** @type {Float32Array} */(logits[i].data); + const ids = input_ids[i]; + for (const bad_word_ids of this.bad_words_ids) { + // There aren't enough tokens to match the banned sequence + if (ids.length < bad_word_ids.length - 1) continue; + + // Whether to modify the logits of the last token in the bad word id sequence + let mark = true; + + // For each bad word in the list, if the current sequence of input ids ends with this sequence (excluding the last), + // then we set the logits of the last bad word id to -Infinity. + for (let j = 1; j <= bad_word_ids.length - 1; ++j) { + // NOTE: We use != instead of !== to compare bigint and number + // @ts-ignore + if (bad_word_ids.at(-j - 1) != ids.at(-j)) { + // We have found a mismatch + mark = false; + break; + } + } + if (mark) { + batch_logits_data[bad_word_ids.at(-1)] = -Infinity; + } + } + } + return logits + } +} + +/** + * [`LogitsProcessor`] for classifier free guidance (CFG). The scores are split over the batch dimension, + * where the first half correspond to the conditional logits (predicted from the input prompt) and the second half + * correspond to the unconditional logits (predicted from an empty or 'null' prompt). The processor computes a + * weighted average across the conditional and unconditional logits, parameterised by the `guidance_scale`. + * + * See [the paper](https://huggingface.co/papers/2306.05284) for more information. + */ +class ClassifierFreeGuidanceLogitsProcessor extends LogitsProcessor { + + /** + * Create a `ClassifierFreeGuidanceLogitsProcessor`. + * @param {number} guidance_scale The guidance scale for classifier free guidance (CFG). CFG is enabled by setting `guidance_scale > 1`. + * Higher guidance scale encourages the model to generate samples that are more closely linked to the input + * prompt, usually at the expense of poorer quality. + */ + constructor(guidance_scale) { + super(); + if (guidance_scale <= 1) { + throw new Error( + `Require guidance scale >1 to use the classifier free guidance processor, got guidance scale ${guidance_scale}.` + ) + } + this.guidance_scale = guidance_scale; + } + + /** + * Apply logit processor. + * @param {bigint[][]} input_ids The input IDs. + * @param {Tensor} logits The logits. + * @returns {Tensor} The processed logits. + */ + _call(input_ids, logits) { + if (logits.dims[0] !== 2 * input_ids.length) { + throw new Error( + `Logits should have twice the batch size of the input ids, the first half of batches corresponding to ` + + `the conditional inputs, and the second half of batches corresponding to the unconditional inputs. Got ` + + `batch size ${logits.dims[0]} for the logits and ${input_ids.length} for the input ids.` + ) + } + + const unguided_bsz = input_ids.length; + const cond_logits = logits.slice([0, unguided_bsz], null); + const uncond_logits = logits.slice([unguided_bsz, logits.dims[0]], null); + + // Merge into uncond_logits (to save memory). This is equivalent to the following: + // scores = uncond_logits + (cond_logits - uncond_logits) * guidance_scale + for (let i = 0; i < uncond_logits.data.length; ++i) { + uncond_logits.data[i] += (cond_logits.data[i] - uncond_logits.data[i]) * this.guidance_scale; + } + + return uncond_logits; + } +} + +/** + * [`LogitsWarper`] for temperature (exponential scaling output probability distribution), which effectively means + * that it can control the randomness of the predicted tokens. Often used together with [`TopPLogitsWarper`] and [`TopKLogitsWarper`]. + */ +class TemperatureLogitsWarper extends LogitsWarper { + /** + * Create a `TemperatureLogitsWarper`. + * @param {number} temperature Strictly positive float value used to modulate the logits distribution. + * A value smaller than `1` decreases randomness (and vice versa), with `0` being equivalent to shifting + * all probability mass to the most likely token. + */ + constructor(temperature) { + super(); + + if (typeof temperature !== 'number' || temperature <= 0) { + let errorMessage = + `\`temperature\` (=${temperature}) must be a strictly positive float, otherwise your next token scores will be invalid.`; + + if (temperature === 0) { + errorMessage += " If you're looking for greedy decoding strategies, set `do_sample=false`." + } + } + this.temperature = temperature; + } + + /** + * Apply logit warper. + * @param {bigint[][]} input_ids The input IDs. + * @param {Tensor} logits The logits. + * @returns {Tensor} The processed logits. + */ + _call(input_ids, logits) { + const batch_logits_data = /** @type {Float32Array} */(logits.data); + for (let i = 0; i < batch_logits_data.length; ++i) { + batch_logits_data[i] /= this.temperature; + } + return logits; + } +} + +/** + * [`LogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off. + * Often used together with [`TemperatureLogitsWarper`] and [`TopKLogitsWarper`]. + */ +class TopPLogitsWarper extends LogitsWarper { + /** + * Create a `TopPLogitsWarper`. + * @param {number} top_p If set to < 1, only the smallest set of most probable tokens with + * probabilities that add up to `top_p` or higher are kept for generation. + * @param {Object} options Additional options for the top-p sampling. + * @param {number} [options.filter_value=-Infinity] All filtered values will be set to this float value. + * @param {number} [options.min_tokens_to_keep=1] Minimum number of tokens that cannot be filtered. + */ + constructor(top_p, { + filter_value = -Infinity, + min_tokens_to_keep = 1, + } = {}) { + super(); + if (top_p < 0 || top_p > 1.0) { + throw new Error(`\`top_p\` must be a float > 0 and < 1, but is ${top_p}`) + } + if (!Number.isInteger(min_tokens_to_keep) || min_tokens_to_keep < 1) { + throw new Error(`\`min_tokens_to_keep\` must be a positive integer, but is ${min_tokens_to_keep}`) + } + + this.top_p = top_p + this.filter_value = filter_value + this.min_tokens_to_keep = min_tokens_to_keep + } +} + +/** + * [`LogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements. + * Often used together with [`TemperatureLogitsWarper`] and [`TopPLogitsWarper`]. + */ +class TopKLogitsWarper extends LogitsWarper { + /** + * Create a `TopKLogitsWarper`. + * @param {number} top_k If set to > 0, only the top `top_k` tokens are kept for generation. + * @param {Object} options Additional options for the top-k sampling. + * @param {number} [options.filter_value=-Infinity] All filtered values will be set to this float value. + * @param {number} [options.min_tokens_to_keep=1] Minimum number of tokens that cannot be filtered. + */ + constructor(top_k, { + filter_value = -Infinity, + min_tokens_to_keep = 1, + } = {}) { + super(); + if (!Number.isInteger(top_k) || top_k < 0) { + throw new Error(`\`top_k\` must be a positive integer, but is ${top_k}`) + } + + this.top_k = Math.max(top_k, min_tokens_to_keep) + this.filter_value = filter_value + } +} + +/***/ }), + +/***/ "./src/generation/logits_sampler.js": +/*!******************************************!*\ + !*** ./src/generation/logits_sampler.js ***! + \******************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ LogitsSampler: () => (/* binding */ LogitsSampler) +/* harmony export */ }); +/* harmony import */ var _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../utils/generic.js */ "./src/utils/generic.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_maths_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../utils/maths.js */ "./src/utils/maths.js"); +/* harmony import */ var _generation_configuration_utils_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../generation/configuration_utils.js */ "./src/generation/configuration_utils.js"); + +/** + * @module generation/logits_sampler + */ + + + + + + + +/** + * Sampler is a base class for all sampling methods used for text generation. + */ +class LogitsSampler extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + /** + * Creates a new Sampler object with the specified generation config. + * @param {GenerationConfig} generation_config The generation config. + */ + constructor(generation_config) { + super(); + this.generation_config = generation_config; + } + + /** + * Executes the sampler, using the specified logits. + * @param {Tensor} logits + * @returns {Promise<[bigint, number][]>} + */ + async _call(logits) { + // Sample from logits, of dims [batch, sequence_length, vocab_size]. + // If index is specified, sample from [batch, index, vocab_size]. + return this.sample(logits); + } + + /** + * Abstract method for sampling the logits. + * @param {Tensor} logits + * @throws {Error} If not implemented in subclass. + * @returns {Promise<[bigint, number][]>} + */ + async sample(logits) { + throw Error("sample should be implemented in subclasses.") + } + + /** + * Returns the specified logits as an array, with temperature applied. + * @param {Tensor} logits + * @param {number} index + * @returns {Float32Array} + */ + getLogits(logits, index) { + let vocabSize = logits.dims.at(-1); + + let logs = /** @type {Float32Array} */(logits.data); + + if (index === -1) { + logs = logs.slice(-vocabSize); + } else { + let startIndex = index * vocabSize; + logs = logs.slice(startIndex, startIndex + vocabSize); + } + return logs; + } + + /** + * Selects an item randomly based on the specified probabilities. + * @param {import("../transformers.js").DataArray} probabilities An array of probabilities to use for selection. + * @returns {number} The index of the selected item. + */ + randomSelect(probabilities) { + // Return index of chosen item + let sumProbabilities = 0; + for (let i = 0; i < probabilities.length; ++i) { + sumProbabilities += probabilities[i]; + } + + let r = Math.random() * sumProbabilities; + for (let i = 0; i < probabilities.length; ++i) { + r -= probabilities[i]; + if (r <= 0) { + return i; + } + } + return 0; // return first (most probable) as a fallback + } + + /** + * Returns a Sampler object based on the specified options. + * @param {GenerationConfig} generation_config An object containing options for the sampler. + * @returns {LogitsSampler} A Sampler object. + */ + static getSampler(generation_config) { + // - *greedy decoding*: `num_beams=1` and `do_sample=False` + // - *contrastive search*: `penalty_alpha>0` and `top_k>1` + // - *multinomial sampling*: `num_beams=1` and `do_sample=True` + // - *beam-search decoding*: `num_beams>1` and `do_sample=False` + // - *beam-search multinomial sampling*: `num_beams>1` and `do_sample=True` + // - *diverse beam-search decoding*: `num_beams>1` and `num_beam_groups>1` + // - *constrained beam-search decoding*: `constraints!=None` or `force_words_ids!=None` + + // NOTE: beam search is implemented directly into the generation function + if (generation_config.do_sample) { + return new MultinomialSampler(generation_config); + + } else if (generation_config.num_beams > 1) { + return new BeamSearchSampler(generation_config); + + } else { + if (generation_config.num_return_sequences > 1) { + throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${generation_config.num_return_sequences}.`) + } + return new GreedySampler(generation_config); + } + } +} + +/** + * Class representing a Greedy Sampler. + */ +class GreedySampler extends LogitsSampler { + /** + * Sample the maximum probability of a given logits tensor. + * @param {Tensor} logits + * @returns {Promise<[bigint, number][]>} An array with a single tuple, containing the index of the maximum value and a meaningless score (since this is a greedy search). + */ + async sample(logits) { + // NOTE: no need to do log_softmax here since we only take the maximum + const argmax = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.max)(logits.data)[1]; + + // Note: score is meaningless in this context, since we are performing + // greedy search (p = 1 => log(p) = 0) + return [ + [BigInt(argmax), 0] + ]; + } +} + +/** + * Class representing a MultinomialSampler. + */ +class MultinomialSampler extends LogitsSampler { + + /** + * Sample from the logits. + * @param {Tensor} logits + * @returns {Promise<[bigint, number][]>} + */ + async sample(logits) { + let k = logits.dims.at(-1); // defaults to vocab size + if (this.generation_config.top_k > 0) { + k = Math.min(this.generation_config.top_k, k); + } + + // Get top k tokens + const [v, i] = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.topk)(logits, k); + + // Compute softmax over logits + const probabilities = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.softmax)(/** @type {Float32Array} */(v.data)); + + return Array.from({ length: this.generation_config.num_beams }, () => { + const sampledIndex = this.randomSelect(probabilities); + return [ + i.data[sampledIndex], // token id + Math.log(probabilities[sampledIndex]), // score + ]; + }); + } +} + + +/** + * Class representing a BeamSearchSampler. + */ +class BeamSearchSampler extends LogitsSampler { + + /** + * Sample from the logits. + * @param {Tensor} logits + * @returns {Promise<[bigint, number][]>} + */ + async sample(logits) { + let k = logits.dims.at(-1); // defaults to vocab size + if (this.generation_config.top_k > 0) { + k = Math.min(this.generation_config.top_k, k); + } + + // Get top k tokens + const [v, i] = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.topk)(logits, k); + + // Compute softmax over logits + const probabilities = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.softmax)(/** @type {Float32Array} */(v.data)); + + return Array.from({ length: this.generation_config.num_beams }, (_, x) => { + return [ + i.data[x], // token id + Math.log(probabilities[x]), // score + ]; + }); + } +} + + +/***/ }), + +/***/ "./src/generation/stopping_criteria.js": +/*!*********************************************!*\ + !*** ./src/generation/stopping_criteria.js ***! + \*********************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ EosTokenCriteria: () => (/* binding */ EosTokenCriteria), +/* harmony export */ InterruptableStoppingCriteria: () => (/* binding */ InterruptableStoppingCriteria), +/* harmony export */ MaxLengthCriteria: () => (/* binding */ MaxLengthCriteria), +/* harmony export */ StoppingCriteria: () => (/* binding */ StoppingCriteria), +/* harmony export */ StoppingCriteriaList: () => (/* binding */ StoppingCriteriaList) +/* harmony export */ }); +/* harmony import */ var _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../utils/generic.js */ "./src/utils/generic.js"); + +/** + * @module generation/stopping_criteria + */ + + + +// NOTE: +// Stopping Criteria returns a list of `batch_size` booleans, indicating whether each sequence in the batch should be stopped. + +/** + * Abstract base class for all stopping criteria that can be applied during generation. + */ +class StoppingCriteria extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + /** + * + * @param {number[][]} input_ids (`number[][]` of shape `(batch_size, sequence_length)`): + * Indices of input sequence tokens in the vocabulary. + * @param {number[][]} scores scores (`number[][]` of shape `(batch_size, config.vocab_size)`): + * Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax + * or scores for each vocabulary token after SoftMax. + * @returns {boolean[]} A list of booleans indicating whether each sequence should be stopped. + */ + _call(input_ids, scores) { + throw Error("StoppingCriteria needs to be subclassed"); + } +} +/** + */ +class StoppingCriteriaList extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + /** + * Constructs a new instance of `StoppingCriteriaList`. + */ + constructor() { + super(); + this.criteria = []; + } + + /** + * Adds a new stopping criterion to the list. + * + * @param {StoppingCriteria} item The stopping criterion to add. + */ + push(item) { + this.criteria.push(item); + } + + /** + * Adds multiple stopping criteria to the list. + * + * @param {StoppingCriteria|StoppingCriteriaList|StoppingCriteria[]} items The stopping criteria to add. + */ + extend(items) { + if (items instanceof StoppingCriteriaList) { + items = items.criteria; + } else if (items instanceof StoppingCriteria) { + items = [items]; + } + this.criteria.push(...items); + } + + _call(input_ids, scores) { + const is_done = new Array(input_ids.length).fill(false); + for (const criterion of this.criteria) { + const criterion_done = criterion(input_ids, scores); + for (let i = 0; i < is_done.length; ++i) { + is_done[i] ||= criterion_done[i]; + } + } + return is_done; + } + + [Symbol.iterator]() { + return this.criteria.values(); + } +} + +/** + * This class can be used to stop generation whenever the full generated number of tokens exceeds `max_length`. + * Keep in mind for decoder-only type of transformers, this will include the initial prompted tokens. + */ +class MaxLengthCriteria extends StoppingCriteria { + + /** + * + * @param {number} max_length The maximum length that the output sequence can have in number of tokens. + * @param {number} [max_position_embeddings=null] The maximum model length, as defined by the model's `config.max_position_embeddings` attribute. + */ + constructor(max_length, max_position_embeddings = null) { + super(); + this.max_length = max_length; + this.max_position_embeddings = max_position_embeddings; + } + + _call(input_ids) { + return input_ids.map(ids => ids.length >= this.max_length); + } +} + +// TODO: add MaxTimeCriteria + +/** + * This class can be used to stop generation whenever the "end-of-sequence" token is generated. + * By default, it uses the `model.generation_config.eos_token_id`. + */ +class EosTokenCriteria extends StoppingCriteria { + + /** + * + * @param {number|number[]} eos_token_id The id of the *end-of-sequence* token. + * Optionally, use a list to set multiple *end-of-sequence* tokens. + */ + constructor(eos_token_id) { + super(); + if (!Array.isArray(eos_token_id)) { + eos_token_id = [eos_token_id]; + } + this.eos_token_id = eos_token_id; + } + + /** + * + * @param {number[][]} input_ids + * @param {number[][]} scores + * @returns {boolean[]} + */ + _call(input_ids, scores) { + return input_ids.map(ids => { + const last = ids.at(-1); + // NOTE: We use == instead of === to allow for number/bigint comparison + return this.eos_token_id.some(eos_id => last == eos_id); + }); + } +} + +/** + * This class can be used to stop generation whenever the user interrupts the process. + */ +class InterruptableStoppingCriteria extends StoppingCriteria { + constructor() { + super(); + this.interrupted = false; + } + + interrupt() { + this.interrupted = true; + } + + reset() { + this.interrupted = false; + } + + _call(input_ids, scores) { + return new Array(input_ids.length).fill(this.interrupted); + } +} + + +/***/ }), + +/***/ "./src/generation/streamers.js": +/*!*************************************!*\ + !*** ./src/generation/streamers.js ***! + \*************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ BaseStreamer: () => (/* binding */ BaseStreamer), +/* harmony export */ TextStreamer: () => (/* binding */ TextStreamer), +/* harmony export */ WhisperTextStreamer: () => (/* binding */ WhisperTextStreamer) +/* harmony export */ }); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../utils/core.js */ "./src/utils/core.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../env.js */ "./src/env.js"); + +/** + * @module generation/streamers + */ + + + + + +class BaseStreamer { + /** + * Function that is called by `.generate()` to push new tokens + * @param {bigint[][]} value + */ + put(value) { + throw Error('Not implemented'); + } + + /** + * Function that is called by `.generate()` to signal the end of generation + */ + end() { + throw Error('Not implemented'); + } +} + +const stdout_write = _env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_PROCESS_AVAILABLE + ? x => process.stdout.write(x) + : x => console.log(x); + +/** + * Simple text streamer that prints the token(s) to stdout as soon as entire words are formed. + */ +class TextStreamer extends BaseStreamer { + /** + * + * @param {import('../tokenizers.js').PreTrainedTokenizer} tokenizer + * @param {Object} options + * @param {boolean} [options.skip_prompt=false] Whether to skip the prompt tokens + * @param {boolean} [options.skip_special_tokens=true] Whether to skip special tokens when decoding + * @param {function(string): void} [options.callback_function=null] Function to call when a piece of text is ready to display + * @param {function(bigint[]): void} [options.token_callback_function=null] Function to call when a new token is generated + * @param {Object} [options.decode_kwargs={}] Additional keyword arguments to pass to the tokenizer's decode method + */ + constructor(tokenizer, { + skip_prompt = false, + callback_function = null, + token_callback_function = null, + skip_special_tokens = true, + decode_kwargs = {}, + ...kwargs + } = {}) { + super(); + this.tokenizer = tokenizer; + this.skip_prompt = skip_prompt; + this.callback_function = callback_function ?? stdout_write; + this.token_callback_function = token_callback_function; + this.decode_kwargs = { skip_special_tokens, ...decode_kwargs, ...kwargs }; + + // variables used in the streaming process + this.token_cache = []; + this.print_len = 0; + this.next_tokens_are_prompt = true; + } + + /** + * Receives tokens, decodes them, and prints them to stdout as soon as they form entire words. + * @param {bigint[][]} value + */ + put(value) { + if (value.length > 1) { + throw Error('TextStreamer only supports batch size of 1'); + } + + const is_prompt = this.next_tokens_are_prompt; + if (is_prompt) { + this.next_tokens_are_prompt = false; + if (this.skip_prompt) return; + } + + const tokens = value[0]; + this.token_callback_function?.(tokens) + + // Add the new token to the cache and decodes the entire thing. + this.token_cache = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_0__.mergeArrays)(this.token_cache, tokens); + const text = this.tokenizer.decode(this.token_cache, this.decode_kwargs); + + let printable_text; + if (is_prompt || text.endsWith('\n')) { + // After the symbol for a new line, we flush the cache. + printable_text = text.slice(this.print_len); + this.token_cache = []; + this.print_len = 0; + } else if (text.length > 0 && (0,_tokenizers_js__WEBPACK_IMPORTED_MODULE_1__.is_chinese_char)(text.charCodeAt(text.length - 1))) { + // If the last token is a CJK character, we print the characters. + printable_text = text.slice(this.print_len); + this.print_len += printable_text.length; + } else { + // Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words, + // which may change with the subsequent token -- there are probably smarter ways to do this!) + printable_text = text.slice(this.print_len, text.lastIndexOf(' ') + 1); + this.print_len += printable_text.length; + } + + this.on_finalized_text(printable_text, false); + } + + /** + * Flushes any remaining cache and prints a newline to stdout. + */ + end() { + let printable_text; + if (this.token_cache.length > 0) { + const text = this.tokenizer.decode(this.token_cache, this.decode_kwargs); + printable_text = text.slice(this.print_len); + this.token_cache = []; + this.print_len = 0; + } else { + printable_text = ''; + } + this.next_tokens_are_prompt = true; + this.on_finalized_text(printable_text, true); + } + + /** + * Prints the new text to stdout. If the stream is ending, also prints a newline. + * @param {string} text + * @param {boolean} stream_end + */ + on_finalized_text(text, stream_end) { + if (text.length > 0) { + this.callback_function?.(text); + } + if (stream_end && this.callback_function === stdout_write && _env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_PROCESS_AVAILABLE) { + this.callback_function?.('\n'); + } + } +} + +/** + * Utility class to handle streaming of tokens generated by whisper speech-to-text models. + * Callback functions are invoked when each of the following events occur: + * - A new chunk starts (on_chunk_start) + * - A new token is generated (callback_function) + * - A chunk ends (on_chunk_end) + * - The stream is finalized (on_finalize) + */ +class WhisperTextStreamer extends TextStreamer { + /** + * @param {import('../tokenizers.js').WhisperTokenizer} tokenizer + * @param {Object} options + * @param {boolean} [options.skip_prompt=false] Whether to skip the prompt tokens + * @param {function(string): void} [options.callback_function=null] Function to call when a piece of text is ready to display + * @param {function(bigint[]): void} [options.token_callback_function=null] Function to call when a new token is generated + * @param {function(number): void} [options.on_chunk_start=null] Function to call when a new chunk starts + * @param {function(number): void} [options.on_chunk_end=null] Function to call when a chunk ends + * @param {function(): void} [options.on_finalize=null] Function to call when the stream is finalized + * @param {number} [options.time_precision=0.02] Precision of the timestamps + * @param {boolean} [options.skip_special_tokens=true] Whether to skip special tokens when decoding + * @param {Object} [options.decode_kwargs={}] Additional keyword arguments to pass to the tokenizer's decode method + */ + constructor(tokenizer, { + skip_prompt = false, + callback_function = null, + token_callback_function = null, + on_chunk_start = null, + on_chunk_end = null, + on_finalize = null, + time_precision = 0.02, + skip_special_tokens = true, + decode_kwargs = {}, + } = {}) { + super(tokenizer, { + skip_prompt, + skip_special_tokens, + callback_function, + token_callback_function, + decode_kwargs, + }); + this.timestamp_begin = tokenizer.timestamp_begin; + + this.on_chunk_start = on_chunk_start; + this.on_chunk_end = on_chunk_end; + this.on_finalize = on_finalize; + + this.time_precision = time_precision; + + this.waiting_for_timestamp = false; + } + + /** + * @param {bigint[][]} value + */ + put(value) { + if (value.length > 1) { + throw Error('WhisperTextStreamer only supports batch size of 1'); + } + const tokens = value[0]; + + // Check if the token is a timestamp + if (tokens.length === 1) { + const offset = Number(tokens[0]) - this.timestamp_begin; + if (offset >= 0) { + const time = offset * this.time_precision; + if (this.waiting_for_timestamp) { + this.on_chunk_end?.(time); + } else { + this.on_chunk_start?.(time); + } + this.waiting_for_timestamp = !this.waiting_for_timestamp; // Toggle + + // NOTE: Timestamp tokens should not be printed. Although, since they + // aren't classified as "special tokens", we need to handle them here. + this.token_callback_function?.(tokens); + return; + } + } + return super.put(value); + } + + end() { + super.end(); + this.on_finalize?.(); + } +} + + +/***/ }), + +/***/ "./src/models.js": +/*!***********************!*\ + !*** ./src/models.js ***! + \***********************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ ASTForAudioClassification: () => (/* binding */ ASTForAudioClassification), +/* harmony export */ ASTModel: () => (/* binding */ ASTModel), +/* harmony export */ ASTPreTrainedModel: () => (/* binding */ ASTPreTrainedModel), +/* harmony export */ AlbertForMaskedLM: () => (/* binding */ AlbertForMaskedLM), +/* harmony export */ AlbertForQuestionAnswering: () => (/* binding */ AlbertForQuestionAnswering), +/* harmony export */ AlbertForSequenceClassification: () => (/* binding */ AlbertForSequenceClassification), +/* harmony export */ AlbertModel: () => (/* binding */ AlbertModel), +/* harmony export */ AlbertPreTrainedModel: () => (/* binding */ AlbertPreTrainedModel), +/* harmony export */ ArceeForCausalLM: () => (/* binding */ ArceeForCausalLM), +/* harmony export */ ArceeModel: () => (/* binding */ ArceeModel), +/* harmony export */ ArceePreTrainedModel: () => (/* binding */ ArceePreTrainedModel), +/* harmony export */ AutoModel: () => (/* binding */ AutoModel), +/* harmony export */ AutoModelForAudioClassification: () => (/* binding */ AutoModelForAudioClassification), +/* harmony export */ AutoModelForAudioFrameClassification: () => (/* binding */ AutoModelForAudioFrameClassification), +/* harmony export */ AutoModelForAudioTextToText: () => (/* binding */ AutoModelForAudioTextToText), +/* harmony export */ AutoModelForCTC: () => (/* binding */ AutoModelForCTC), +/* harmony export */ AutoModelForCausalLM: () => (/* binding */ AutoModelForCausalLM), +/* harmony export */ AutoModelForDepthEstimation: () => (/* binding */ AutoModelForDepthEstimation), +/* harmony export */ AutoModelForDocumentQuestionAnswering: () => (/* binding */ AutoModelForDocumentQuestionAnswering), +/* harmony export */ AutoModelForImageClassification: () => (/* binding */ AutoModelForImageClassification), +/* harmony export */ AutoModelForImageFeatureExtraction: () => (/* binding */ AutoModelForImageFeatureExtraction), +/* harmony export */ AutoModelForImageMatting: () => (/* binding */ AutoModelForImageMatting), +/* harmony export */ AutoModelForImageSegmentation: () => (/* binding */ AutoModelForImageSegmentation), +/* harmony export */ AutoModelForImageTextToText: () => (/* binding */ AutoModelForImageTextToText), +/* harmony export */ AutoModelForImageToImage: () => (/* binding */ AutoModelForImageToImage), +/* harmony export */ AutoModelForMaskGeneration: () => (/* binding */ AutoModelForMaskGeneration), +/* harmony export */ AutoModelForMaskedLM: () => (/* binding */ AutoModelForMaskedLM), +/* harmony export */ AutoModelForNormalEstimation: () => (/* binding */ AutoModelForNormalEstimation), +/* harmony export */ AutoModelForObjectDetection: () => (/* binding */ AutoModelForObjectDetection), +/* harmony export */ AutoModelForPoseEstimation: () => (/* binding */ AutoModelForPoseEstimation), +/* harmony export */ AutoModelForQuestionAnswering: () => (/* binding */ AutoModelForQuestionAnswering), +/* harmony export */ AutoModelForSemanticSegmentation: () => (/* binding */ AutoModelForSemanticSegmentation), +/* harmony export */ AutoModelForSeq2SeqLM: () => (/* binding */ AutoModelForSeq2SeqLM), +/* harmony export */ AutoModelForSequenceClassification: () => (/* binding */ AutoModelForSequenceClassification), +/* harmony export */ AutoModelForSpeechSeq2Seq: () => (/* binding */ AutoModelForSpeechSeq2Seq), +/* harmony export */ AutoModelForTextToSpectrogram: () => (/* binding */ AutoModelForTextToSpectrogram), +/* harmony export */ AutoModelForTextToWaveform: () => (/* binding */ AutoModelForTextToWaveform), +/* harmony export */ AutoModelForTokenClassification: () => (/* binding */ AutoModelForTokenClassification), +/* harmony export */ AutoModelForUniversalSegmentation: () => (/* binding */ AutoModelForUniversalSegmentation), +/* harmony export */ AutoModelForVision2Seq: () => (/* binding */ AutoModelForVision2Seq), +/* harmony export */ AutoModelForXVector: () => (/* binding */ AutoModelForXVector), +/* harmony export */ AutoModelForZeroShotObjectDetection: () => (/* binding */ AutoModelForZeroShotObjectDetection), +/* harmony export */ BartForConditionalGeneration: () => (/* binding */ BartForConditionalGeneration), +/* harmony export */ BartForSequenceClassification: () => (/* binding */ BartForSequenceClassification), +/* harmony export */ BartModel: () => (/* binding */ BartModel), +/* harmony export */ BartPretrainedModel: () => (/* binding */ BartPretrainedModel), +/* harmony export */ BaseModelOutput: () => (/* binding */ BaseModelOutput), +/* harmony export */ BeitForImageClassification: () => (/* binding */ BeitForImageClassification), +/* harmony export */ BeitModel: () => (/* binding */ BeitModel), +/* harmony export */ BeitPreTrainedModel: () => (/* binding */ BeitPreTrainedModel), +/* harmony export */ BertForMaskedLM: () => (/* binding */ BertForMaskedLM), +/* harmony export */ BertForQuestionAnswering: () => (/* binding */ BertForQuestionAnswering), +/* harmony export */ BertForSequenceClassification: () => (/* binding */ BertForSequenceClassification), +/* harmony export */ BertForTokenClassification: () => (/* binding */ BertForTokenClassification), +/* harmony export */ BertModel: () => (/* binding */ BertModel), +/* harmony export */ BertPreTrainedModel: () => (/* binding */ BertPreTrainedModel), +/* harmony export */ BlenderbotForConditionalGeneration: () => (/* binding */ BlenderbotForConditionalGeneration), +/* harmony export */ BlenderbotModel: () => (/* binding */ BlenderbotModel), +/* harmony export */ BlenderbotPreTrainedModel: () => (/* binding */ BlenderbotPreTrainedModel), +/* harmony export */ BlenderbotSmallForConditionalGeneration: () => (/* binding */ BlenderbotSmallForConditionalGeneration), +/* harmony export */ BlenderbotSmallModel: () => (/* binding */ BlenderbotSmallModel), +/* harmony export */ BlenderbotSmallPreTrainedModel: () => (/* binding */ BlenderbotSmallPreTrainedModel), +/* harmony export */ BloomForCausalLM: () => (/* binding */ BloomForCausalLM), +/* harmony export */ BloomModel: () => (/* binding */ BloomModel), +/* harmony export */ BloomPreTrainedModel: () => (/* binding */ BloomPreTrainedModel), +/* harmony export */ CLIPModel: () => (/* binding */ CLIPModel), +/* harmony export */ CLIPPreTrainedModel: () => (/* binding */ CLIPPreTrainedModel), +/* harmony export */ CLIPSegForImageSegmentation: () => (/* binding */ CLIPSegForImageSegmentation), +/* harmony export */ CLIPSegModel: () => (/* binding */ CLIPSegModel), +/* harmony export */ CLIPSegPreTrainedModel: () => (/* binding */ CLIPSegPreTrainedModel), +/* harmony export */ CLIPTextModel: () => (/* binding */ CLIPTextModel), +/* harmony export */ CLIPTextModelWithProjection: () => (/* binding */ CLIPTextModelWithProjection), +/* harmony export */ CLIPVisionModel: () => (/* binding */ CLIPVisionModel), +/* harmony export */ CLIPVisionModelWithProjection: () => (/* binding */ CLIPVisionModelWithProjection), +/* harmony export */ CamembertForMaskedLM: () => (/* binding */ CamembertForMaskedLM), +/* harmony export */ CamembertForQuestionAnswering: () => (/* binding */ CamembertForQuestionAnswering), +/* harmony export */ CamembertForSequenceClassification: () => (/* binding */ CamembertForSequenceClassification), +/* harmony export */ CamembertForTokenClassification: () => (/* binding */ CamembertForTokenClassification), +/* harmony export */ CamembertModel: () => (/* binding */ CamembertModel), +/* harmony export */ CamembertPreTrainedModel: () => (/* binding */ CamembertPreTrainedModel), +/* harmony export */ CausalLMOutput: () => (/* binding */ CausalLMOutput), +/* harmony export */ CausalLMOutputWithPast: () => (/* binding */ CausalLMOutputWithPast), +/* harmony export */ ChineseCLIPModel: () => (/* binding */ ChineseCLIPModel), +/* harmony export */ ChineseCLIPPreTrainedModel: () => (/* binding */ ChineseCLIPPreTrainedModel), +/* harmony export */ ClapAudioModelWithProjection: () => (/* binding */ ClapAudioModelWithProjection), +/* harmony export */ ClapModel: () => (/* binding */ ClapModel), +/* harmony export */ ClapPreTrainedModel: () => (/* binding */ ClapPreTrainedModel), +/* harmony export */ ClapTextModelWithProjection: () => (/* binding */ ClapTextModelWithProjection), +/* harmony export */ CodeGenForCausalLM: () => (/* binding */ CodeGenForCausalLM), +/* harmony export */ CodeGenModel: () => (/* binding */ CodeGenModel), +/* harmony export */ CodeGenPreTrainedModel: () => (/* binding */ CodeGenPreTrainedModel), +/* harmony export */ CohereForCausalLM: () => (/* binding */ CohereForCausalLM), +/* harmony export */ CohereModel: () => (/* binding */ CohereModel), +/* harmony export */ CoherePreTrainedModel: () => (/* binding */ CoherePreTrainedModel), +/* harmony export */ ConvBertForMaskedLM: () => (/* binding */ ConvBertForMaskedLM), +/* harmony export */ ConvBertForQuestionAnswering: () => (/* binding */ ConvBertForQuestionAnswering), +/* harmony export */ ConvBertForSequenceClassification: () => (/* binding */ ConvBertForSequenceClassification), +/* harmony export */ ConvBertForTokenClassification: () => (/* binding */ ConvBertForTokenClassification), +/* harmony export */ ConvBertModel: () => (/* binding */ ConvBertModel), +/* harmony export */ ConvBertPreTrainedModel: () => (/* binding */ ConvBertPreTrainedModel), +/* harmony export */ ConvNextForImageClassification: () => (/* binding */ ConvNextForImageClassification), +/* harmony export */ ConvNextModel: () => (/* binding */ ConvNextModel), +/* harmony export */ ConvNextPreTrainedModel: () => (/* binding */ ConvNextPreTrainedModel), +/* harmony export */ ConvNextV2ForImageClassification: () => (/* binding */ ConvNextV2ForImageClassification), +/* harmony export */ ConvNextV2Model: () => (/* binding */ ConvNextV2Model), +/* harmony export */ ConvNextV2PreTrainedModel: () => (/* binding */ ConvNextV2PreTrainedModel), +/* harmony export */ DFineForObjectDetection: () => (/* binding */ DFineForObjectDetection), +/* harmony export */ DFineModel: () => (/* binding */ DFineModel), +/* harmony export */ DFinePreTrainedModel: () => (/* binding */ DFinePreTrainedModel), +/* harmony export */ DINOv3ConvNextModel: () => (/* binding */ DINOv3ConvNextModel), +/* harmony export */ DINOv3ConvNextPreTrainedModel: () => (/* binding */ DINOv3ConvNextPreTrainedModel), +/* harmony export */ DINOv3ViTModel: () => (/* binding */ DINOv3ViTModel), +/* harmony export */ DINOv3ViTPreTrainedModel: () => (/* binding */ DINOv3ViTPreTrainedModel), +/* harmony export */ DPTForDepthEstimation: () => (/* binding */ DPTForDepthEstimation), +/* harmony export */ DPTModel: () => (/* binding */ DPTModel), +/* harmony export */ DPTPreTrainedModel: () => (/* binding */ DPTPreTrainedModel), +/* harmony export */ DacDecoderModel: () => (/* binding */ DacDecoderModel), +/* harmony export */ DacDecoderOutput: () => (/* binding */ DacDecoderOutput), +/* harmony export */ DacEncoderModel: () => (/* binding */ DacEncoderModel), +/* harmony export */ DacEncoderOutput: () => (/* binding */ DacEncoderOutput), +/* harmony export */ DacModel: () => (/* binding */ DacModel), +/* harmony export */ DacPreTrainedModel: () => (/* binding */ DacPreTrainedModel), +/* harmony export */ DebertaForMaskedLM: () => (/* binding */ DebertaForMaskedLM), +/* harmony export */ DebertaForQuestionAnswering: () => (/* binding */ DebertaForQuestionAnswering), +/* harmony export */ DebertaForSequenceClassification: () => (/* binding */ DebertaForSequenceClassification), +/* harmony export */ DebertaForTokenClassification: () => (/* binding */ DebertaForTokenClassification), +/* harmony export */ DebertaModel: () => (/* binding */ DebertaModel), +/* harmony export */ DebertaPreTrainedModel: () => (/* binding */ DebertaPreTrainedModel), +/* harmony export */ DebertaV2ForMaskedLM: () => (/* binding */ DebertaV2ForMaskedLM), +/* harmony export */ DebertaV2ForQuestionAnswering: () => (/* binding */ DebertaV2ForQuestionAnswering), +/* harmony export */ DebertaV2ForSequenceClassification: () => (/* binding */ DebertaV2ForSequenceClassification), +/* harmony export */ DebertaV2ForTokenClassification: () => (/* binding */ DebertaV2ForTokenClassification), +/* harmony export */ DebertaV2Model: () => (/* binding */ DebertaV2Model), +/* harmony export */ DebertaV2PreTrainedModel: () => (/* binding */ DebertaV2PreTrainedModel), +/* harmony export */ DecisionTransformerModel: () => (/* binding */ DecisionTransformerModel), +/* harmony export */ DecisionTransformerPreTrainedModel: () => (/* binding */ DecisionTransformerPreTrainedModel), +/* harmony export */ DeiTForImageClassification: () => (/* binding */ DeiTForImageClassification), +/* harmony export */ DeiTModel: () => (/* binding */ DeiTModel), +/* harmony export */ DeiTPreTrainedModel: () => (/* binding */ DeiTPreTrainedModel), +/* harmony export */ DepthAnythingForDepthEstimation: () => (/* binding */ DepthAnythingForDepthEstimation), +/* harmony export */ DepthAnythingPreTrainedModel: () => (/* binding */ DepthAnythingPreTrainedModel), +/* harmony export */ DepthProForDepthEstimation: () => (/* binding */ DepthProForDepthEstimation), +/* harmony export */ DepthProPreTrainedModel: () => (/* binding */ DepthProPreTrainedModel), +/* harmony export */ DetrForObjectDetection: () => (/* binding */ DetrForObjectDetection), +/* harmony export */ DetrForSegmentation: () => (/* binding */ DetrForSegmentation), +/* harmony export */ DetrModel: () => (/* binding */ DetrModel), +/* harmony export */ DetrObjectDetectionOutput: () => (/* binding */ DetrObjectDetectionOutput), +/* harmony export */ DetrPreTrainedModel: () => (/* binding */ DetrPreTrainedModel), +/* harmony export */ DetrSegmentationOutput: () => (/* binding */ DetrSegmentationOutput), +/* harmony export */ Dinov2ForImageClassification: () => (/* binding */ Dinov2ForImageClassification), +/* harmony export */ Dinov2Model: () => (/* binding */ Dinov2Model), +/* harmony export */ Dinov2PreTrainedModel: () => (/* binding */ Dinov2PreTrainedModel), +/* harmony export */ Dinov2WithRegistersForImageClassification: () => (/* binding */ Dinov2WithRegistersForImageClassification), +/* harmony export */ Dinov2WithRegistersModel: () => (/* binding */ Dinov2WithRegistersModel), +/* harmony export */ Dinov2WithRegistersPreTrainedModel: () => (/* binding */ Dinov2WithRegistersPreTrainedModel), +/* harmony export */ DistilBertForMaskedLM: () => (/* binding */ DistilBertForMaskedLM), +/* harmony export */ DistilBertForQuestionAnswering: () => (/* binding */ DistilBertForQuestionAnswering), +/* harmony export */ DistilBertForSequenceClassification: () => (/* binding */ DistilBertForSequenceClassification), +/* harmony export */ DistilBertForTokenClassification: () => (/* binding */ DistilBertForTokenClassification), +/* harmony export */ DistilBertModel: () => (/* binding */ DistilBertModel), +/* harmony export */ DistilBertPreTrainedModel: () => (/* binding */ DistilBertPreTrainedModel), +/* harmony export */ DonutSwinModel: () => (/* binding */ DonutSwinModel), +/* harmony export */ DonutSwinPreTrainedModel: () => (/* binding */ DonutSwinPreTrainedModel), +/* harmony export */ EfficientNetForImageClassification: () => (/* binding */ EfficientNetForImageClassification), +/* harmony export */ EfficientNetModel: () => (/* binding */ EfficientNetModel), +/* harmony export */ EfficientNetPreTrainedModel: () => (/* binding */ EfficientNetPreTrainedModel), +/* harmony export */ ElectraForMaskedLM: () => (/* binding */ ElectraForMaskedLM), +/* harmony export */ ElectraForQuestionAnswering: () => (/* binding */ ElectraForQuestionAnswering), +/* harmony export */ ElectraForSequenceClassification: () => (/* binding */ ElectraForSequenceClassification), +/* harmony export */ ElectraForTokenClassification: () => (/* binding */ ElectraForTokenClassification), +/* harmony export */ ElectraModel: () => (/* binding */ ElectraModel), +/* harmony export */ ElectraPreTrainedModel: () => (/* binding */ ElectraPreTrainedModel), +/* harmony export */ Ernie4_5_ForCausalLM: () => (/* binding */ Ernie4_5_ForCausalLM), +/* harmony export */ Ernie4_5_Model: () => (/* binding */ Ernie4_5_Model), +/* harmony export */ Ernie4_5_PretrainedModel: () => (/* binding */ Ernie4_5_PretrainedModel), +/* harmony export */ EsmForMaskedLM: () => (/* binding */ EsmForMaskedLM), +/* harmony export */ EsmForSequenceClassification: () => (/* binding */ EsmForSequenceClassification), +/* harmony export */ EsmForTokenClassification: () => (/* binding */ EsmForTokenClassification), +/* harmony export */ EsmModel: () => (/* binding */ EsmModel), +/* harmony export */ EsmPreTrainedModel: () => (/* binding */ EsmPreTrainedModel), +/* harmony export */ ExaoneForCausalLM: () => (/* binding */ ExaoneForCausalLM), +/* harmony export */ ExaoneModel: () => (/* binding */ ExaoneModel), +/* harmony export */ ExaonePreTrainedModel: () => (/* binding */ ExaonePreTrainedModel), +/* harmony export */ FalconForCausalLM: () => (/* binding */ FalconForCausalLM), +/* harmony export */ FalconModel: () => (/* binding */ FalconModel), +/* harmony export */ FalconPreTrainedModel: () => (/* binding */ FalconPreTrainedModel), +/* harmony export */ FastViTForImageClassification: () => (/* binding */ FastViTForImageClassification), +/* harmony export */ FastViTModel: () => (/* binding */ FastViTModel), +/* harmony export */ FastViTPreTrainedModel: () => (/* binding */ FastViTPreTrainedModel), +/* harmony export */ Florence2ForConditionalGeneration: () => (/* binding */ Florence2ForConditionalGeneration), +/* harmony export */ Florence2PreTrainedModel: () => (/* binding */ Florence2PreTrainedModel), +/* harmony export */ GLPNForDepthEstimation: () => (/* binding */ GLPNForDepthEstimation), +/* harmony export */ GLPNModel: () => (/* binding */ GLPNModel), +/* harmony export */ GLPNPreTrainedModel: () => (/* binding */ GLPNPreTrainedModel), +/* harmony export */ GPT2LMHeadModel: () => (/* binding */ GPT2LMHeadModel), +/* harmony export */ GPT2Model: () => (/* binding */ GPT2Model), +/* harmony export */ GPT2PreTrainedModel: () => (/* binding */ GPT2PreTrainedModel), +/* harmony export */ GPTBigCodeForCausalLM: () => (/* binding */ GPTBigCodeForCausalLM), +/* harmony export */ GPTBigCodeModel: () => (/* binding */ GPTBigCodeModel), +/* harmony export */ GPTBigCodePreTrainedModel: () => (/* binding */ GPTBigCodePreTrainedModel), +/* harmony export */ GPTJForCausalLM: () => (/* binding */ GPTJForCausalLM), +/* harmony export */ GPTJModel: () => (/* binding */ GPTJModel), +/* harmony export */ GPTJPreTrainedModel: () => (/* binding */ GPTJPreTrainedModel), +/* harmony export */ GPTNeoForCausalLM: () => (/* binding */ GPTNeoForCausalLM), +/* harmony export */ GPTNeoModel: () => (/* binding */ GPTNeoModel), +/* harmony export */ GPTNeoPreTrainedModel: () => (/* binding */ GPTNeoPreTrainedModel), +/* harmony export */ GPTNeoXForCausalLM: () => (/* binding */ GPTNeoXForCausalLM), +/* harmony export */ GPTNeoXModel: () => (/* binding */ GPTNeoXModel), +/* harmony export */ GPTNeoXPreTrainedModel: () => (/* binding */ GPTNeoXPreTrainedModel), +/* harmony export */ Gemma2ForCausalLM: () => (/* binding */ Gemma2ForCausalLM), +/* harmony export */ Gemma2Model: () => (/* binding */ Gemma2Model), +/* harmony export */ Gemma2PreTrainedModel: () => (/* binding */ Gemma2PreTrainedModel), +/* harmony export */ Gemma3ForCausalLM: () => (/* binding */ Gemma3ForCausalLM), +/* harmony export */ Gemma3Model: () => (/* binding */ Gemma3Model), +/* harmony export */ Gemma3PreTrainedModel: () => (/* binding */ Gemma3PreTrainedModel), +/* harmony export */ Gemma3nForConditionalGeneration: () => (/* binding */ Gemma3nForConditionalGeneration), +/* harmony export */ Gemma3nPreTrainedModel: () => (/* binding */ Gemma3nPreTrainedModel), +/* harmony export */ GemmaForCausalLM: () => (/* binding */ GemmaForCausalLM), +/* harmony export */ GemmaModel: () => (/* binding */ GemmaModel), +/* harmony export */ GemmaPreTrainedModel: () => (/* binding */ GemmaPreTrainedModel), +/* harmony export */ GlmForCausalLM: () => (/* binding */ GlmForCausalLM), +/* harmony export */ GlmModel: () => (/* binding */ GlmModel), +/* harmony export */ GlmPreTrainedModel: () => (/* binding */ GlmPreTrainedModel), +/* harmony export */ GraniteForCausalLM: () => (/* binding */ GraniteForCausalLM), +/* harmony export */ GraniteModel: () => (/* binding */ GraniteModel), +/* harmony export */ GranitePreTrainedModel: () => (/* binding */ GranitePreTrainedModel), +/* harmony export */ GroundingDinoForObjectDetection: () => (/* binding */ GroundingDinoForObjectDetection), +/* harmony export */ GroundingDinoPreTrainedModel: () => (/* binding */ GroundingDinoPreTrainedModel), +/* harmony export */ GroupViTModel: () => (/* binding */ GroupViTModel), +/* harmony export */ GroupViTPreTrainedModel: () => (/* binding */ GroupViTPreTrainedModel), +/* harmony export */ HeliumForCausalLM: () => (/* binding */ HeliumForCausalLM), +/* harmony export */ HeliumModel: () => (/* binding */ HeliumModel), +/* harmony export */ HeliumPreTrainedModel: () => (/* binding */ HeliumPreTrainedModel), +/* harmony export */ HieraForImageClassification: () => (/* binding */ HieraForImageClassification), +/* harmony export */ HieraModel: () => (/* binding */ HieraModel), +/* harmony export */ HieraPreTrainedModel: () => (/* binding */ HieraPreTrainedModel), +/* harmony export */ HubertForCTC: () => (/* binding */ HubertForCTC), +/* harmony export */ HubertForSequenceClassification: () => (/* binding */ HubertForSequenceClassification), +/* harmony export */ HubertModel: () => (/* binding */ HubertModel), +/* harmony export */ HubertPreTrainedModel: () => (/* binding */ HubertPreTrainedModel), +/* harmony export */ IJepaForImageClassification: () => (/* binding */ IJepaForImageClassification), +/* harmony export */ IJepaModel: () => (/* binding */ IJepaModel), +/* harmony export */ IJepaPreTrainedModel: () => (/* binding */ IJepaPreTrainedModel), +/* harmony export */ Idefics3ForConditionalGeneration: () => (/* binding */ Idefics3ForConditionalGeneration), +/* harmony export */ Idefics3PreTrainedModel: () => (/* binding */ Idefics3PreTrainedModel), +/* harmony export */ ImageMattingOutput: () => (/* binding */ ImageMattingOutput), +/* harmony export */ JAISLMHeadModel: () => (/* binding */ JAISLMHeadModel), +/* harmony export */ JAISModel: () => (/* binding */ JAISModel), +/* harmony export */ JAISPreTrainedModel: () => (/* binding */ JAISPreTrainedModel), +/* harmony export */ JinaCLIPModel: () => (/* binding */ JinaCLIPModel), +/* harmony export */ JinaCLIPPreTrainedModel: () => (/* binding */ JinaCLIPPreTrainedModel), +/* harmony export */ JinaCLIPTextModel: () => (/* binding */ JinaCLIPTextModel), +/* harmony export */ JinaCLIPVisionModel: () => (/* binding */ JinaCLIPVisionModel), +/* harmony export */ Lfm2ForCausalLM: () => (/* binding */ Lfm2ForCausalLM), +/* harmony export */ Lfm2Model: () => (/* binding */ Lfm2Model), +/* harmony export */ Lfm2PreTrainedModel: () => (/* binding */ Lfm2PreTrainedModel), +/* harmony export */ LiteWhisperForConditionalGeneration: () => (/* binding */ LiteWhisperForConditionalGeneration), +/* harmony export */ LlamaForCausalLM: () => (/* binding */ LlamaForCausalLM), +/* harmony export */ LlamaModel: () => (/* binding */ LlamaModel), +/* harmony export */ LlamaPreTrainedModel: () => (/* binding */ LlamaPreTrainedModel), +/* harmony export */ LlavaForConditionalGeneration: () => (/* binding */ LlavaForConditionalGeneration), +/* harmony export */ LlavaOnevisionForConditionalGeneration: () => (/* binding */ LlavaOnevisionForConditionalGeneration), +/* harmony export */ LlavaPreTrainedModel: () => (/* binding */ LlavaPreTrainedModel), +/* harmony export */ LlavaQwen2ForCausalLM: () => (/* binding */ LlavaQwen2ForCausalLM), +/* harmony export */ LongT5ForConditionalGeneration: () => (/* binding */ LongT5ForConditionalGeneration), +/* harmony export */ LongT5Model: () => (/* binding */ LongT5Model), +/* harmony export */ LongT5PreTrainedModel: () => (/* binding */ LongT5PreTrainedModel), +/* harmony export */ M2M100ForConditionalGeneration: () => (/* binding */ M2M100ForConditionalGeneration), +/* harmony export */ M2M100Model: () => (/* binding */ M2M100Model), +/* harmony export */ M2M100PreTrainedModel: () => (/* binding */ M2M100PreTrainedModel), +/* harmony export */ MBartForCausalLM: () => (/* binding */ MBartForCausalLM), +/* harmony export */ MBartForConditionalGeneration: () => (/* binding */ MBartForConditionalGeneration), +/* harmony export */ MBartForSequenceClassification: () => (/* binding */ MBartForSequenceClassification), +/* harmony export */ MBartModel: () => (/* binding */ MBartModel), +/* harmony export */ MBartPreTrainedModel: () => (/* binding */ MBartPreTrainedModel), +/* harmony export */ MPNetForMaskedLM: () => (/* binding */ MPNetForMaskedLM), +/* harmony export */ MPNetForQuestionAnswering: () => (/* binding */ MPNetForQuestionAnswering), +/* harmony export */ MPNetForSequenceClassification: () => (/* binding */ MPNetForSequenceClassification), +/* harmony export */ MPNetForTokenClassification: () => (/* binding */ MPNetForTokenClassification), +/* harmony export */ MPNetModel: () => (/* binding */ MPNetModel), +/* harmony export */ MPNetPreTrainedModel: () => (/* binding */ MPNetPreTrainedModel), +/* harmony export */ MT5ForConditionalGeneration: () => (/* binding */ MT5ForConditionalGeneration), +/* harmony export */ MT5Model: () => (/* binding */ MT5Model), +/* harmony export */ MT5PreTrainedModel: () => (/* binding */ MT5PreTrainedModel), +/* harmony export */ MarianMTModel: () => (/* binding */ MarianMTModel), +/* harmony export */ MarianModel: () => (/* binding */ MarianModel), +/* harmony export */ MarianPreTrainedModel: () => (/* binding */ MarianPreTrainedModel), +/* harmony export */ MaskFormerForInstanceSegmentation: () => (/* binding */ MaskFormerForInstanceSegmentation), +/* harmony export */ MaskFormerModel: () => (/* binding */ MaskFormerModel), +/* harmony export */ MaskFormerPreTrainedModel: () => (/* binding */ MaskFormerPreTrainedModel), +/* harmony export */ MaskedLMOutput: () => (/* binding */ MaskedLMOutput), +/* harmony export */ Metric3DForDepthEstimation: () => (/* binding */ Metric3DForDepthEstimation), +/* harmony export */ Metric3DPreTrainedModel: () => (/* binding */ Metric3DPreTrainedModel), +/* harmony export */ Metric3Dv2ForDepthEstimation: () => (/* binding */ Metric3Dv2ForDepthEstimation), +/* harmony export */ Metric3Dv2PreTrainedModel: () => (/* binding */ Metric3Dv2PreTrainedModel), +/* harmony export */ MgpstrForSceneTextRecognition: () => (/* binding */ MgpstrForSceneTextRecognition), +/* harmony export */ MgpstrModelOutput: () => (/* binding */ MgpstrModelOutput), +/* harmony export */ MgpstrPreTrainedModel: () => (/* binding */ MgpstrPreTrainedModel), +/* harmony export */ MimiDecoderModel: () => (/* binding */ MimiDecoderModel), +/* harmony export */ MimiDecoderOutput: () => (/* binding */ MimiDecoderOutput), +/* harmony export */ MimiEncoderModel: () => (/* binding */ MimiEncoderModel), +/* harmony export */ MimiEncoderOutput: () => (/* binding */ MimiEncoderOutput), +/* harmony export */ MimiModel: () => (/* binding */ MimiModel), +/* harmony export */ MimiPreTrainedModel: () => (/* binding */ MimiPreTrainedModel), +/* harmony export */ MistralForCausalLM: () => (/* binding */ MistralForCausalLM), +/* harmony export */ MistralModel: () => (/* binding */ MistralModel), +/* harmony export */ MistralPreTrainedModel: () => (/* binding */ MistralPreTrainedModel), +/* harmony export */ MobileBertForMaskedLM: () => (/* binding */ MobileBertForMaskedLM), +/* harmony export */ MobileBertForQuestionAnswering: () => (/* binding */ MobileBertForQuestionAnswering), +/* harmony export */ MobileBertForSequenceClassification: () => (/* binding */ MobileBertForSequenceClassification), +/* harmony export */ MobileBertModel: () => (/* binding */ MobileBertModel), +/* harmony export */ MobileBertPreTrainedModel: () => (/* binding */ MobileBertPreTrainedModel), +/* harmony export */ MobileLLMForCausalLM: () => (/* binding */ MobileLLMForCausalLM), +/* harmony export */ MobileLLMModel: () => (/* binding */ MobileLLMModel), +/* harmony export */ MobileLLMPreTrainedModel: () => (/* binding */ MobileLLMPreTrainedModel), +/* harmony export */ MobileNetV1ForImageClassification: () => (/* binding */ MobileNetV1ForImageClassification), +/* harmony export */ MobileNetV1ForSemanticSegmentation: () => (/* binding */ MobileNetV1ForSemanticSegmentation), +/* harmony export */ MobileNetV1Model: () => (/* binding */ MobileNetV1Model), +/* harmony export */ MobileNetV1PreTrainedModel: () => (/* binding */ MobileNetV1PreTrainedModel), +/* harmony export */ MobileNetV2ForImageClassification: () => (/* binding */ MobileNetV2ForImageClassification), +/* harmony export */ MobileNetV2ForSemanticSegmentation: () => (/* binding */ MobileNetV2ForSemanticSegmentation), +/* harmony export */ MobileNetV2Model: () => (/* binding */ MobileNetV2Model), +/* harmony export */ MobileNetV2PreTrainedModel: () => (/* binding */ MobileNetV2PreTrainedModel), +/* harmony export */ MobileNetV3ForImageClassification: () => (/* binding */ MobileNetV3ForImageClassification), +/* harmony export */ MobileNetV3ForSemanticSegmentation: () => (/* binding */ MobileNetV3ForSemanticSegmentation), +/* harmony export */ MobileNetV3Model: () => (/* binding */ MobileNetV3Model), +/* harmony export */ MobileNetV3PreTrainedModel: () => (/* binding */ MobileNetV3PreTrainedModel), +/* harmony export */ MobileNetV4ForImageClassification: () => (/* binding */ MobileNetV4ForImageClassification), +/* harmony export */ MobileNetV4ForSemanticSegmentation: () => (/* binding */ MobileNetV4ForSemanticSegmentation), +/* harmony export */ MobileNetV4Model: () => (/* binding */ MobileNetV4Model), +/* harmony export */ MobileNetV4PreTrainedModel: () => (/* binding */ MobileNetV4PreTrainedModel), +/* harmony export */ MobileViTForImageClassification: () => (/* binding */ MobileViTForImageClassification), +/* harmony export */ MobileViTModel: () => (/* binding */ MobileViTModel), +/* harmony export */ MobileViTPreTrainedModel: () => (/* binding */ MobileViTPreTrainedModel), +/* harmony export */ MobileViTV2ForImageClassification: () => (/* binding */ MobileViTV2ForImageClassification), +/* harmony export */ MobileViTV2Model: () => (/* binding */ MobileViTV2Model), +/* harmony export */ MobileViTV2PreTrainedModel: () => (/* binding */ MobileViTV2PreTrainedModel), +/* harmony export */ ModelOutput: () => (/* binding */ ModelOutput), +/* harmony export */ ModernBertDecoderForCausalLM: () => (/* binding */ ModernBertDecoderForCausalLM), +/* harmony export */ ModernBertDecoderModel: () => (/* binding */ ModernBertDecoderModel), +/* harmony export */ ModernBertDecoderPreTrainedModel: () => (/* binding */ ModernBertDecoderPreTrainedModel), +/* harmony export */ ModernBertForMaskedLM: () => (/* binding */ ModernBertForMaskedLM), +/* harmony export */ ModernBertForSequenceClassification: () => (/* binding */ ModernBertForSequenceClassification), +/* harmony export */ ModernBertForTokenClassification: () => (/* binding */ ModernBertForTokenClassification), +/* harmony export */ ModernBertModel: () => (/* binding */ ModernBertModel), +/* harmony export */ ModernBertPreTrainedModel: () => (/* binding */ ModernBertPreTrainedModel), +/* harmony export */ Moondream1ForConditionalGeneration: () => (/* binding */ Moondream1ForConditionalGeneration), +/* harmony export */ MoonshineForConditionalGeneration: () => (/* binding */ MoonshineForConditionalGeneration), +/* harmony export */ MoonshineModel: () => (/* binding */ MoonshineModel), +/* harmony export */ MoonshinePreTrainedModel: () => (/* binding */ MoonshinePreTrainedModel), +/* harmony export */ MptForCausalLM: () => (/* binding */ MptForCausalLM), +/* harmony export */ MptModel: () => (/* binding */ MptModel), +/* harmony export */ MptPreTrainedModel: () => (/* binding */ MptPreTrainedModel), +/* harmony export */ MultiModalityCausalLM: () => (/* binding */ MultiModalityCausalLM), +/* harmony export */ MultiModalityPreTrainedModel: () => (/* binding */ MultiModalityPreTrainedModel), +/* harmony export */ MusicgenForCausalLM: () => (/* binding */ MusicgenForCausalLM), +/* harmony export */ MusicgenForConditionalGeneration: () => (/* binding */ MusicgenForConditionalGeneration), +/* harmony export */ MusicgenModel: () => (/* binding */ MusicgenModel), +/* harmony export */ MusicgenPreTrainedModel: () => (/* binding */ MusicgenPreTrainedModel), +/* harmony export */ NeoBertForMaskedLM: () => (/* binding */ NeoBertForMaskedLM), +/* harmony export */ NeoBertForQuestionAnswering: () => (/* binding */ NeoBertForQuestionAnswering), +/* harmony export */ NeoBertForSequenceClassification: () => (/* binding */ NeoBertForSequenceClassification), +/* harmony export */ NeoBertForTokenClassification: () => (/* binding */ NeoBertForTokenClassification), +/* harmony export */ NeoBertModel: () => (/* binding */ NeoBertModel), +/* harmony export */ NeoBertPreTrainedModel: () => (/* binding */ NeoBertPreTrainedModel), +/* harmony export */ NomicBertModel: () => (/* binding */ NomicBertModel), +/* harmony export */ NomicBertPreTrainedModel: () => (/* binding */ NomicBertPreTrainedModel), +/* harmony export */ OPTForCausalLM: () => (/* binding */ OPTForCausalLM), +/* harmony export */ OPTModel: () => (/* binding */ OPTModel), +/* harmony export */ OPTPreTrainedModel: () => (/* binding */ OPTPreTrainedModel), +/* harmony export */ Olmo2ForCausalLM: () => (/* binding */ Olmo2ForCausalLM), +/* harmony export */ Olmo2Model: () => (/* binding */ Olmo2Model), +/* harmony export */ Olmo2PreTrainedModel: () => (/* binding */ Olmo2PreTrainedModel), +/* harmony export */ OlmoForCausalLM: () => (/* binding */ OlmoForCausalLM), +/* harmony export */ OlmoModel: () => (/* binding */ OlmoModel), +/* harmony export */ OlmoPreTrainedModel: () => (/* binding */ OlmoPreTrainedModel), +/* harmony export */ OpenELMForCausalLM: () => (/* binding */ OpenELMForCausalLM), +/* harmony export */ OpenELMModel: () => (/* binding */ OpenELMModel), +/* harmony export */ OpenELMPreTrainedModel: () => (/* binding */ OpenELMPreTrainedModel), +/* harmony export */ OwlViTForObjectDetection: () => (/* binding */ OwlViTForObjectDetection), +/* harmony export */ OwlViTModel: () => (/* binding */ OwlViTModel), +/* harmony export */ OwlViTPreTrainedModel: () => (/* binding */ OwlViTPreTrainedModel), +/* harmony export */ Owlv2ForObjectDetection: () => (/* binding */ Owlv2ForObjectDetection), +/* harmony export */ Owlv2Model: () => (/* binding */ Owlv2Model), +/* harmony export */ Owlv2PreTrainedModel: () => (/* binding */ Owlv2PreTrainedModel), +/* harmony export */ PaliGemmaForConditionalGeneration: () => (/* binding */ PaliGemmaForConditionalGeneration), +/* harmony export */ PaliGemmaPreTrainedModel: () => (/* binding */ PaliGemmaPreTrainedModel), +/* harmony export */ PatchTSMixerForPrediction: () => (/* binding */ PatchTSMixerForPrediction), +/* harmony export */ PatchTSMixerModel: () => (/* binding */ PatchTSMixerModel), +/* harmony export */ PatchTSMixerPreTrainedModel: () => (/* binding */ PatchTSMixerPreTrainedModel), +/* harmony export */ PatchTSTForPrediction: () => (/* binding */ PatchTSTForPrediction), +/* harmony export */ PatchTSTModel: () => (/* binding */ PatchTSTModel), +/* harmony export */ PatchTSTPreTrainedModel: () => (/* binding */ PatchTSTPreTrainedModel), +/* harmony export */ Phi3ForCausalLM: () => (/* binding */ Phi3ForCausalLM), +/* harmony export */ Phi3Model: () => (/* binding */ Phi3Model), +/* harmony export */ Phi3PreTrainedModel: () => (/* binding */ Phi3PreTrainedModel), +/* harmony export */ Phi3VForCausalLM: () => (/* binding */ Phi3VForCausalLM), +/* harmony export */ Phi3VPreTrainedModel: () => (/* binding */ Phi3VPreTrainedModel), +/* harmony export */ PhiForCausalLM: () => (/* binding */ PhiForCausalLM), +/* harmony export */ PhiModel: () => (/* binding */ PhiModel), +/* harmony export */ PhiPreTrainedModel: () => (/* binding */ PhiPreTrainedModel), +/* harmony export */ PreTrainedModel: () => (/* binding */ PreTrainedModel), +/* harmony export */ PretrainedMixin: () => (/* binding */ PretrainedMixin), +/* harmony export */ PvtForImageClassification: () => (/* binding */ PvtForImageClassification), +/* harmony export */ PvtModel: () => (/* binding */ PvtModel), +/* harmony export */ PvtPreTrainedModel: () => (/* binding */ PvtPreTrainedModel), +/* harmony export */ PyAnnoteForAudioFrameClassification: () => (/* binding */ PyAnnoteForAudioFrameClassification), +/* harmony export */ PyAnnoteModel: () => (/* binding */ PyAnnoteModel), +/* harmony export */ PyAnnotePreTrainedModel: () => (/* binding */ PyAnnotePreTrainedModel), +/* harmony export */ QuestionAnsweringModelOutput: () => (/* binding */ QuestionAnsweringModelOutput), +/* harmony export */ Qwen2ForCausalLM: () => (/* binding */ Qwen2ForCausalLM), +/* harmony export */ Qwen2Model: () => (/* binding */ Qwen2Model), +/* harmony export */ Qwen2PreTrainedModel: () => (/* binding */ Qwen2PreTrainedModel), +/* harmony export */ Qwen2VLForConditionalGeneration: () => (/* binding */ Qwen2VLForConditionalGeneration), +/* harmony export */ Qwen2VLPreTrainedModel: () => (/* binding */ Qwen2VLPreTrainedModel), +/* harmony export */ Qwen3ForCausalLM: () => (/* binding */ Qwen3ForCausalLM), +/* harmony export */ Qwen3Model: () => (/* binding */ Qwen3Model), +/* harmony export */ Qwen3PreTrainedModel: () => (/* binding */ Qwen3PreTrainedModel), +/* harmony export */ RFDetrForObjectDetection: () => (/* binding */ RFDetrForObjectDetection), +/* harmony export */ RFDetrModel: () => (/* binding */ RFDetrModel), +/* harmony export */ RFDetrObjectDetectionOutput: () => (/* binding */ RFDetrObjectDetectionOutput), +/* harmony export */ RFDetrPreTrainedModel: () => (/* binding */ RFDetrPreTrainedModel), +/* harmony export */ RTDetrForObjectDetection: () => (/* binding */ RTDetrForObjectDetection), +/* harmony export */ RTDetrModel: () => (/* binding */ RTDetrModel), +/* harmony export */ RTDetrObjectDetectionOutput: () => (/* binding */ RTDetrObjectDetectionOutput), +/* harmony export */ RTDetrPreTrainedModel: () => (/* binding */ RTDetrPreTrainedModel), +/* harmony export */ RTDetrV2ForObjectDetection: () => (/* binding */ RTDetrV2ForObjectDetection), +/* harmony export */ RTDetrV2Model: () => (/* binding */ RTDetrV2Model), +/* harmony export */ RTDetrV2ObjectDetectionOutput: () => (/* binding */ RTDetrV2ObjectDetectionOutput), +/* harmony export */ RTDetrV2PreTrainedModel: () => (/* binding */ RTDetrV2PreTrainedModel), +/* harmony export */ ResNetForImageClassification: () => (/* binding */ ResNetForImageClassification), +/* harmony export */ ResNetModel: () => (/* binding */ ResNetModel), +/* harmony export */ ResNetPreTrainedModel: () => (/* binding */ ResNetPreTrainedModel), +/* harmony export */ RoFormerForMaskedLM: () => (/* binding */ RoFormerForMaskedLM), +/* harmony export */ RoFormerForQuestionAnswering: () => (/* binding */ RoFormerForQuestionAnswering), +/* harmony export */ RoFormerForSequenceClassification: () => (/* binding */ RoFormerForSequenceClassification), +/* harmony export */ RoFormerForTokenClassification: () => (/* binding */ RoFormerForTokenClassification), +/* harmony export */ RoFormerModel: () => (/* binding */ RoFormerModel), +/* harmony export */ RoFormerPreTrainedModel: () => (/* binding */ RoFormerPreTrainedModel), +/* harmony export */ RobertaForMaskedLM: () => (/* binding */ RobertaForMaskedLM), +/* harmony export */ RobertaForQuestionAnswering: () => (/* binding */ RobertaForQuestionAnswering), +/* harmony export */ RobertaForSequenceClassification: () => (/* binding */ RobertaForSequenceClassification), +/* harmony export */ RobertaForTokenClassification: () => (/* binding */ RobertaForTokenClassification), +/* harmony export */ RobertaModel: () => (/* binding */ RobertaModel), +/* harmony export */ RobertaPreTrainedModel: () => (/* binding */ RobertaPreTrainedModel), +/* harmony export */ SamImageSegmentationOutput: () => (/* binding */ SamImageSegmentationOutput), +/* harmony export */ SamModel: () => (/* binding */ SamModel), +/* harmony export */ SamPreTrainedModel: () => (/* binding */ SamPreTrainedModel), +/* harmony export */ SapiensForDepthEstimation: () => (/* binding */ SapiensForDepthEstimation), +/* harmony export */ SapiensForNormalEstimation: () => (/* binding */ SapiensForNormalEstimation), +/* harmony export */ SapiensForSemanticSegmentation: () => (/* binding */ SapiensForSemanticSegmentation), +/* harmony export */ SapiensPreTrainedModel: () => (/* binding */ SapiensPreTrainedModel), +/* harmony export */ SegformerForImageClassification: () => (/* binding */ SegformerForImageClassification), +/* harmony export */ SegformerForSemanticSegmentation: () => (/* binding */ SegformerForSemanticSegmentation), +/* harmony export */ SegformerModel: () => (/* binding */ SegformerModel), +/* harmony export */ SegformerPreTrainedModel: () => (/* binding */ SegformerPreTrainedModel), +/* harmony export */ Seq2SeqLMOutput: () => (/* binding */ Seq2SeqLMOutput), +/* harmony export */ SequenceClassifierOutput: () => (/* binding */ SequenceClassifierOutput), +/* harmony export */ SiglipModel: () => (/* binding */ SiglipModel), +/* harmony export */ SiglipPreTrainedModel: () => (/* binding */ SiglipPreTrainedModel), +/* harmony export */ SiglipTextModel: () => (/* binding */ SiglipTextModel), +/* harmony export */ SiglipVisionModel: () => (/* binding */ SiglipVisionModel), +/* harmony export */ SmolLM3ForCausalLM: () => (/* binding */ SmolLM3ForCausalLM), +/* harmony export */ SmolLM3Model: () => (/* binding */ SmolLM3Model), +/* harmony export */ SmolLM3PreTrainedModel: () => (/* binding */ SmolLM3PreTrainedModel), +/* harmony export */ SmolVLMForConditionalGeneration: () => (/* binding */ SmolVLMForConditionalGeneration), +/* harmony export */ SnacDecoderModel: () => (/* binding */ SnacDecoderModel), +/* harmony export */ SnacEncoderModel: () => (/* binding */ SnacEncoderModel), +/* harmony export */ SnacModel: () => (/* binding */ SnacModel), +/* harmony export */ SnacPreTrainedModel: () => (/* binding */ SnacPreTrainedModel), +/* harmony export */ SpeechT5ForSpeechToText: () => (/* binding */ SpeechT5ForSpeechToText), +/* harmony export */ SpeechT5ForTextToSpeech: () => (/* binding */ SpeechT5ForTextToSpeech), +/* harmony export */ SpeechT5HifiGan: () => (/* binding */ SpeechT5HifiGan), +/* harmony export */ SpeechT5Model: () => (/* binding */ SpeechT5Model), +/* harmony export */ SpeechT5PreTrainedModel: () => (/* binding */ SpeechT5PreTrainedModel), +/* harmony export */ SqueezeBertForMaskedLM: () => (/* binding */ SqueezeBertForMaskedLM), +/* harmony export */ SqueezeBertForQuestionAnswering: () => (/* binding */ SqueezeBertForQuestionAnswering), +/* harmony export */ SqueezeBertForSequenceClassification: () => (/* binding */ SqueezeBertForSequenceClassification), +/* harmony export */ SqueezeBertModel: () => (/* binding */ SqueezeBertModel), +/* harmony export */ SqueezeBertPreTrainedModel: () => (/* binding */ SqueezeBertPreTrainedModel), +/* harmony export */ StableLmForCausalLM: () => (/* binding */ StableLmForCausalLM), +/* harmony export */ StableLmModel: () => (/* binding */ StableLmModel), +/* harmony export */ StableLmPreTrainedModel: () => (/* binding */ StableLmPreTrainedModel), +/* harmony export */ Starcoder2ForCausalLM: () => (/* binding */ Starcoder2ForCausalLM), +/* harmony export */ Starcoder2Model: () => (/* binding */ Starcoder2Model), +/* harmony export */ Starcoder2PreTrainedModel: () => (/* binding */ Starcoder2PreTrainedModel), +/* harmony export */ StyleTextToSpeech2Model: () => (/* binding */ StyleTextToSpeech2Model), +/* harmony export */ StyleTextToSpeech2PreTrainedModel: () => (/* binding */ StyleTextToSpeech2PreTrainedModel), +/* harmony export */ Swin2SRForImageSuperResolution: () => (/* binding */ Swin2SRForImageSuperResolution), +/* harmony export */ Swin2SRModel: () => (/* binding */ Swin2SRModel), +/* harmony export */ Swin2SRPreTrainedModel: () => (/* binding */ Swin2SRPreTrainedModel), +/* harmony export */ SwinForImageClassification: () => (/* binding */ SwinForImageClassification), +/* harmony export */ SwinForSemanticSegmentation: () => (/* binding */ SwinForSemanticSegmentation), +/* harmony export */ SwinModel: () => (/* binding */ SwinModel), +/* harmony export */ SwinPreTrainedModel: () => (/* binding */ SwinPreTrainedModel), +/* harmony export */ T5ForConditionalGeneration: () => (/* binding */ T5ForConditionalGeneration), +/* harmony export */ T5Model: () => (/* binding */ T5Model), +/* harmony export */ T5PreTrainedModel: () => (/* binding */ T5PreTrainedModel), +/* harmony export */ TableTransformerForObjectDetection: () => (/* binding */ TableTransformerForObjectDetection), +/* harmony export */ TableTransformerModel: () => (/* binding */ TableTransformerModel), +/* harmony export */ TableTransformerObjectDetectionOutput: () => (/* binding */ TableTransformerObjectDetectionOutput), +/* harmony export */ TableTransformerPreTrainedModel: () => (/* binding */ TableTransformerPreTrainedModel), +/* harmony export */ TokenClassifierOutput: () => (/* binding */ TokenClassifierOutput), +/* harmony export */ TrOCRForCausalLM: () => (/* binding */ TrOCRForCausalLM), +/* harmony export */ TrOCRPreTrainedModel: () => (/* binding */ TrOCRPreTrainedModel), +/* harmony export */ UltravoxModel: () => (/* binding */ UltravoxModel), +/* harmony export */ UltravoxPreTrainedModel: () => (/* binding */ UltravoxPreTrainedModel), +/* harmony export */ UniSpeechForCTC: () => (/* binding */ UniSpeechForCTC), +/* harmony export */ UniSpeechForSequenceClassification: () => (/* binding */ UniSpeechForSequenceClassification), +/* harmony export */ UniSpeechModel: () => (/* binding */ UniSpeechModel), +/* harmony export */ UniSpeechPreTrainedModel: () => (/* binding */ UniSpeechPreTrainedModel), +/* harmony export */ UniSpeechSatForAudioFrameClassification: () => (/* binding */ UniSpeechSatForAudioFrameClassification), +/* harmony export */ UniSpeechSatForCTC: () => (/* binding */ UniSpeechSatForCTC), +/* harmony export */ UniSpeechSatForSequenceClassification: () => (/* binding */ UniSpeechSatForSequenceClassification), +/* harmony export */ UniSpeechSatModel: () => (/* binding */ UniSpeechSatModel), +/* harmony export */ UniSpeechSatPreTrainedModel: () => (/* binding */ UniSpeechSatPreTrainedModel), +/* harmony export */ ViTForImageClassification: () => (/* binding */ ViTForImageClassification), +/* harmony export */ ViTMAEModel: () => (/* binding */ ViTMAEModel), +/* harmony export */ ViTMAEPreTrainedModel: () => (/* binding */ ViTMAEPreTrainedModel), +/* harmony export */ ViTMSNForImageClassification: () => (/* binding */ ViTMSNForImageClassification), +/* harmony export */ ViTMSNModel: () => (/* binding */ ViTMSNModel), +/* harmony export */ ViTMSNPreTrainedModel: () => (/* binding */ ViTMSNPreTrainedModel), +/* harmony export */ ViTModel: () => (/* binding */ ViTModel), +/* harmony export */ ViTPreTrainedModel: () => (/* binding */ ViTPreTrainedModel), +/* harmony export */ VisionEncoderDecoderModel: () => (/* binding */ VisionEncoderDecoderModel), +/* harmony export */ VitMatteForImageMatting: () => (/* binding */ VitMatteForImageMatting), +/* harmony export */ VitMattePreTrainedModel: () => (/* binding */ VitMattePreTrainedModel), +/* harmony export */ VitPoseForPoseEstimation: () => (/* binding */ VitPoseForPoseEstimation), +/* harmony export */ VitPosePreTrainedModel: () => (/* binding */ VitPosePreTrainedModel), +/* harmony export */ VitsModel: () => (/* binding */ VitsModel), +/* harmony export */ VitsModelOutput: () => (/* binding */ VitsModelOutput), +/* harmony export */ VitsPreTrainedModel: () => (/* binding */ VitsPreTrainedModel), +/* harmony export */ VoxtralForConditionalGeneration: () => (/* binding */ VoxtralForConditionalGeneration), +/* harmony export */ Wav2Vec2BertForCTC: () => (/* binding */ Wav2Vec2BertForCTC), +/* harmony export */ Wav2Vec2BertForSequenceClassification: () => (/* binding */ Wav2Vec2BertForSequenceClassification), +/* harmony export */ Wav2Vec2BertModel: () => (/* binding */ Wav2Vec2BertModel), +/* harmony export */ Wav2Vec2BertPreTrainedModel: () => (/* binding */ Wav2Vec2BertPreTrainedModel), +/* harmony export */ Wav2Vec2ForAudioFrameClassification: () => (/* binding */ Wav2Vec2ForAudioFrameClassification), +/* harmony export */ Wav2Vec2ForCTC: () => (/* binding */ Wav2Vec2ForCTC), +/* harmony export */ Wav2Vec2ForSequenceClassification: () => (/* binding */ Wav2Vec2ForSequenceClassification), +/* harmony export */ Wav2Vec2Model: () => (/* binding */ Wav2Vec2Model), +/* harmony export */ Wav2Vec2PreTrainedModel: () => (/* binding */ Wav2Vec2PreTrainedModel), +/* harmony export */ WavLMForAudioFrameClassification: () => (/* binding */ WavLMForAudioFrameClassification), +/* harmony export */ WavLMForCTC: () => (/* binding */ WavLMForCTC), +/* harmony export */ WavLMForSequenceClassification: () => (/* binding */ WavLMForSequenceClassification), +/* harmony export */ WavLMForXVector: () => (/* binding */ WavLMForXVector), +/* harmony export */ WavLMModel: () => (/* binding */ WavLMModel), +/* harmony export */ WavLMPreTrainedModel: () => (/* binding */ WavLMPreTrainedModel), +/* harmony export */ WeSpeakerResNetModel: () => (/* binding */ WeSpeakerResNetModel), +/* harmony export */ WeSpeakerResNetPreTrainedModel: () => (/* binding */ WeSpeakerResNetPreTrainedModel), +/* harmony export */ WhisperForConditionalGeneration: () => (/* binding */ WhisperForConditionalGeneration), +/* harmony export */ WhisperModel: () => (/* binding */ WhisperModel), +/* harmony export */ WhisperPreTrainedModel: () => (/* binding */ WhisperPreTrainedModel), +/* harmony export */ XLMForQuestionAnswering: () => (/* binding */ XLMForQuestionAnswering), +/* harmony export */ XLMForSequenceClassification: () => (/* binding */ XLMForSequenceClassification), +/* harmony export */ XLMForTokenClassification: () => (/* binding */ XLMForTokenClassification), +/* harmony export */ XLMModel: () => (/* binding */ XLMModel), +/* harmony export */ XLMPreTrainedModel: () => (/* binding */ XLMPreTrainedModel), +/* harmony export */ XLMRobertaForMaskedLM: () => (/* binding */ XLMRobertaForMaskedLM), +/* harmony export */ XLMRobertaForQuestionAnswering: () => (/* binding */ XLMRobertaForQuestionAnswering), +/* harmony export */ XLMRobertaForSequenceClassification: () => (/* binding */ XLMRobertaForSequenceClassification), +/* harmony export */ XLMRobertaForTokenClassification: () => (/* binding */ XLMRobertaForTokenClassification), +/* harmony export */ XLMRobertaModel: () => (/* binding */ XLMRobertaModel), +/* harmony export */ XLMRobertaPreTrainedModel: () => (/* binding */ XLMRobertaPreTrainedModel), +/* harmony export */ XLMWithLMHeadModel: () => (/* binding */ XLMWithLMHeadModel), +/* harmony export */ XVectorOutput: () => (/* binding */ XVectorOutput), +/* harmony export */ YolosForObjectDetection: () => (/* binding */ YolosForObjectDetection), +/* harmony export */ YolosModel: () => (/* binding */ YolosModel), +/* harmony export */ YolosObjectDetectionOutput: () => (/* binding */ YolosObjectDetectionOutput), +/* harmony export */ YolosPreTrainedModel: () => (/* binding */ YolosPreTrainedModel) +/* harmony export */ }); +/* harmony import */ var _configs_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./configs.js */ "./src/configs.js"); +/* harmony import */ var _backends_onnx_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./backends/onnx.js */ "./src/backends/onnx.js"); +/* harmony import */ var _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./utils/dtypes.js */ "./src/utils/dtypes.js"); +/* harmony import */ var _utils_generic_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./utils/generic.js */ "./src/utils/generic.js"); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ./utils/core.js */ "./src/utils/core.js"); +/* harmony import */ var _utils_hub_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ./utils/hub.js */ "./src/utils/hub.js"); +/* harmony import */ var _utils_constants_js__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! ./utils/constants.js */ "./src/utils/constants.js"); +/* harmony import */ var _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__ = __webpack_require__(/*! ./generation/logits_process.js */ "./src/generation/logits_process.js"); +/* harmony import */ var _generation_configuration_utils_js__WEBPACK_IMPORTED_MODULE_8__ = __webpack_require__(/*! ./generation/configuration_utils.js */ "./src/generation/configuration_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__ = __webpack_require__(/*! ./utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_image_js__WEBPACK_IMPORTED_MODULE_10__ = __webpack_require__(/*! ./utils/image.js */ "./src/utils/image.js"); +/* harmony import */ var _utils_maths_js__WEBPACK_IMPORTED_MODULE_11__ = __webpack_require__(/*! ./utils/maths.js */ "./src/utils/maths.js"); +/* harmony import */ var _generation_stopping_criteria_js__WEBPACK_IMPORTED_MODULE_12__ = __webpack_require__(/*! ./generation/stopping_criteria.js */ "./src/generation/stopping_criteria.js"); +/* harmony import */ var _generation_logits_sampler_js__WEBPACK_IMPORTED_MODULE_13__ = __webpack_require__(/*! ./generation/logits_sampler.js */ "./src/generation/logits_sampler.js"); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_14__ = __webpack_require__(/*! ./env.js */ "./src/env.js"); +/* harmony import */ var _models_whisper_generation_whisper_js__WEBPACK_IMPORTED_MODULE_15__ = __webpack_require__(/*! ./models/whisper/generation_whisper.js */ "./src/models/whisper/generation_whisper.js"); +/* harmony import */ var _models_whisper_common_whisper_js__WEBPACK_IMPORTED_MODULE_16__ = __webpack_require__(/*! ./models/whisper/common_whisper.js */ "./src/models/whisper/common_whisper.js"); + +/** + * @file Definitions of all models available in Transformers.js. + * + * **Example:** Load and run an `AutoModel`. + * + * ```javascript + * import { AutoModel, AutoTokenizer } from '@huggingface/transformers'; + * + * let tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased'); + * let model = await AutoModel.from_pretrained('Xenova/bert-base-uncased'); + * + * let inputs = await tokenizer('I love transformers!'); + * let { logits } = await model(inputs); + * // Tensor { + * // data: Float32Array(183132) [-7.117443084716797, -7.107812881469727, -7.092104911804199, ...] + * // dims: (3) [1, 6, 30522], + * // type: "float32", + * // size: 183132, + * // } + * ``` + * + * We also provide other `AutoModel`s (listed below), which you can use in the same way as the Python library. For example: + * + * **Example:** Load and run an `AutoModelForSeq2SeqLM`. + * ```javascript + * import { AutoModelForSeq2SeqLM, AutoTokenizer } from '@huggingface/transformers'; + * + * let tokenizer = await AutoTokenizer.from_pretrained('Xenova/t5-small'); + * let model = await AutoModelForSeq2SeqLM.from_pretrained('Xenova/t5-small'); + * + * let { input_ids } = await tokenizer('translate English to German: I love transformers!'); + * let outputs = await model.generate(input_ids); + * let decoded = tokenizer.decode(outputs[0], { skip_special_tokens: true }); + * // 'Ich liebe Transformatoren!' + * ``` + * + * @module models + */ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +////////////////////////////////////////////////// +// Model types: used internally +const MODEL_TYPES = { + EncoderOnly: 0, + EncoderDecoder: 1, + Seq2Seq: 2, + Vision2Seq: 3, + DecoderOnly: 4, + MaskGeneration: 5, + ImageTextToText: 6, + Musicgen: 7, + MultiModality: 8, + Phi3V: 9, + AudioTextToText: 10, + AutoEncoder: 11, + ImageAudioTextToText: 12, +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Helper functions + +// NOTE: These will be populated fully later +const MODEL_TYPE_MAPPING = new Map(); +const MODEL_NAME_TO_CLASS_MAPPING = new Map(); +const MODEL_CLASS_TO_NAME_MAPPING = new Map(); + + +/** + * Constructs an InferenceSession using a model file located at the specified path. + * @param {string} pretrained_model_name_or_path The path to the directory containing the model file. + * @param {string} fileName The name of the model file. + * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the model. + * @returns {Promise<{buffer_or_path: Uint8Array|string, session_options: Object, session_config: Object}>} A Promise that resolves to the data needed to create an InferenceSession object. + * @private + */ +async function getSession(pretrained_model_name_or_path, fileName, options) { + let custom_config = options.config?.['transformers.js_config'] ?? {}; + + let device = options.device ?? custom_config.device; + if (device && typeof device !== 'string') { + if (device.hasOwnProperty(fileName)) { + device = device[fileName]; + } else { + console.warn(`device not specified for "${fileName}". Using the default device.`); + device = null; + } + } + + // If the device is not specified, we use the default (supported) execution providers. + const selectedDevice = /** @type {import("./utils/devices.js").DeviceType} */( + device ?? (_env_js__WEBPACK_IMPORTED_MODULE_14__.apis.IS_NODE_ENV ? 'cpu' : 'wasm') + ); + + const executionProviders = (0,_backends_onnx_js__WEBPACK_IMPORTED_MODULE_1__.deviceToExecutionProviders)(selectedDevice); + + // Update custom config with the selected device's config, if it exists + const device_config = custom_config.device_config ?? {}; + if (device_config.hasOwnProperty(selectedDevice)) { + custom_config = { + ...custom_config, + ...device_config[selectedDevice], + }; + } + + // If options.dtype is specified, we use it to choose the suffix for the model file. + // Otherwise, we use the default dtype for the device. + let dtype = options.dtype ?? custom_config.dtype; + if (typeof dtype !== 'string') { + if (dtype && dtype.hasOwnProperty(fileName)) { + dtype = dtype[fileName]; + } else { + dtype = _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DEFAULT_DEVICE_DTYPE_MAPPING[selectedDevice] ?? _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DATA_TYPES.fp32; + console.warn(`dtype not specified for "${fileName}". Using the default dtype (${dtype}) for this device (${selectedDevice}).`); + } + } + + if (dtype === _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DATA_TYPES.auto) { + // Try to choose the auto dtype based on the custom config + let config_dtype = custom_config.dtype; + if (typeof config_dtype !== 'string') { + config_dtype = config_dtype?.[fileName]; + } + + if (config_dtype && config_dtype !== _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DATA_TYPES.auto && _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DATA_TYPES.hasOwnProperty(config_dtype)) { + // Defined by the config, and is not "auto" + dtype = config_dtype; + } else { + // Choose default dtype based on device, falling back to fp32 + dtype = _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DEFAULT_DEVICE_DTYPE_MAPPING[selectedDevice] ?? _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DATA_TYPES.fp32; + } + } + + const selectedDtype = /** @type {import("./utils/dtypes.js").DataType} */(dtype); + + if (!_utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DEFAULT_DTYPE_SUFFIX_MAPPING.hasOwnProperty(selectedDtype)) { + throw new Error(`Invalid dtype: ${selectedDtype}. Should be one of: ${Object.keys(_utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DATA_TYPES).join(', ')}`); + } else if (selectedDtype === _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DATA_TYPES.fp16 && selectedDevice === 'webgpu' && !(await (0,_utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.isWebGpuFp16Supported)())) { + throw new Error(`The device (${selectedDevice}) does not support fp16.`); + } + + // Only valid for models with a decoder + const kv_cache_dtype_config = custom_config.kv_cache_dtype; + const kv_cache_dtype = kv_cache_dtype_config + ? (typeof kv_cache_dtype_config === 'string' + ? kv_cache_dtype_config + : kv_cache_dtype_config[selectedDtype] ?? 'float32') + : undefined; + + if (kv_cache_dtype && !['float32', 'float16'].includes(kv_cache_dtype)) { + throw new Error(`Invalid kv_cache_dtype: ${kv_cache_dtype}. Should be one of: float32, float16`); + } + + const session_config = { + dtype: selectedDtype, + kv_cache_dtype, + device: selectedDevice, + } + + // Construct the model file name + const suffix = _utils_dtypes_js__WEBPACK_IMPORTED_MODULE_2__.DEFAULT_DTYPE_SUFFIX_MAPPING[selectedDtype]; + const baseName = `${fileName}${suffix}.onnx`; + const modelFileName = `${options.subfolder ?? ''}/${baseName}`; + + const session_options = { ...options.session_options }; + + // Overwrite `executionProviders` if not specified + session_options.executionProviders ??= executionProviders; + + // Overwrite `freeDimensionOverrides` if specified in config and not set in session options + const free_dimension_overrides = custom_config.free_dimension_overrides; + if (free_dimension_overrides) { + session_options.freeDimensionOverrides ??= free_dimension_overrides; + } else if (selectedDevice.startsWith('webnn') && !session_options.freeDimensionOverrides) { + console.warn( + `WebNN does not currently support dynamic shapes and requires 'free_dimension_overrides' to be set in config.json, preferably as a field within config["transformers.js_config"]["device_config"]["${selectedDevice}"]. ` + + `When 'free_dimension_overrides' is not set, you may experience significant performance degradation.` + ); + } + + const return_path = _env_js__WEBPACK_IMPORTED_MODULE_14__.apis.IS_NODE_ENV && _env_js__WEBPACK_IMPORTED_MODULE_14__.env.useFSCache; + const bufferOrPathPromise = (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_5__.getModelFile)(pretrained_model_name_or_path, modelFileName, true, options, return_path); + + // Handle onnx external data files + const use_external_data_format = options.use_external_data_format ?? custom_config.use_external_data_format; + /** @type {Promise[]} */ + let externalDataPromises = []; + if (use_external_data_format) { + let external_data_format; + if (typeof use_external_data_format === 'object') { + if (use_external_data_format.hasOwnProperty(baseName)) { + external_data_format = use_external_data_format[baseName]; + } else if (use_external_data_format.hasOwnProperty(fileName)) { + external_data_format = use_external_data_format[fileName]; + } else { + external_data_format = false; + } + } else { + external_data_format = use_external_data_format; + } + + const num_chunks = +external_data_format; // (false=0, true=1, number remains the same) + if (num_chunks > _utils_hub_js__WEBPACK_IMPORTED_MODULE_5__.MAX_EXTERNAL_DATA_CHUNKS) { + throw new Error(`The number of external data chunks (${num_chunks}) exceeds the maximum allowed value (${_utils_hub_js__WEBPACK_IMPORTED_MODULE_5__.MAX_EXTERNAL_DATA_CHUNKS}).`); + } + for (let i = 0; i < num_chunks; ++i) { + const path = `${baseName}_data${i === 0 ? '' : '_' + i}`; + const fullPath = `${options.subfolder ?? ''}/${path}`; + externalDataPromises.push(new Promise(async (resolve, reject) => { + const data = await (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_5__.getModelFile)(pretrained_model_name_or_path, fullPath, true, options, return_path); + resolve(data instanceof Uint8Array ? { path, data } : path); + })); + } + + } else if (session_options.externalData !== undefined) { + externalDataPromises = session_options.externalData.map(async (ext) => { + // if the external data is a string, fetch the file and replace the string with its content + // @ts-expect-error TS2339 + if (typeof ext.data === "string") { + // @ts-expect-error TS2339 + const ext_buffer = await (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_5__.getModelFile)(pretrained_model_name_or_path, ext.data, true, options); + // @ts-expect-error TS2698 + return { ...ext, data: ext_buffer }; + } + return ext; + }); + } + + if (externalDataPromises.length > 0) { + const externalData = await Promise.all(externalDataPromises); + if (!_env_js__WEBPACK_IMPORTED_MODULE_14__.apis.IS_NODE_ENV) { + session_options.externalData = externalData; + } + } + + if (selectedDevice === 'webgpu') { + const shapes = (0,_configs_js__WEBPACK_IMPORTED_MODULE_0__.getCacheShapes)(options.config, { + prefix: 'present', + }); + if (Object.keys(shapes).length > 0 && !(0,_backends_onnx_js__WEBPACK_IMPORTED_MODULE_1__.isONNXProxy)()) { + // Only set preferredOutputLocation if shapes are present and we aren't proxying ONNX + /** @type {Record} */ + const preferredOutputLocation = {}; + for (const key in shapes) { + preferredOutputLocation[key] = 'gpu-buffer'; + } + session_options.preferredOutputLocation = preferredOutputLocation; + } + } + + const buffer_or_path = await bufferOrPathPromise; + + return { buffer_or_path, session_options, session_config }; +} + +/** + * Helper function to create multiple InferenceSession objects. + * + * @param {string} pretrained_model_name_or_path The path to the directory containing the model file. + * @param {Record} names The names of the model files to load. + * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the model. + * @returns {Promise>} A Promise that resolves to a dictionary of InferenceSession objects. + * @private + */ +async function constructSessions(pretrained_model_name_or_path, names, options) { + return Object.fromEntries(await Promise.all( + Object.keys(names).map(async (name) => { + const { buffer_or_path, session_options, session_config } = await getSession(pretrained_model_name_or_path, names[name], options); + const session = await (0,_backends_onnx_js__WEBPACK_IMPORTED_MODULE_1__.createInferenceSession)(buffer_or_path, session_options, session_config); + return [name, session]; + }) + )); +} + +/** + * Helper function to load multiple optional configuration files + * @param {string} pretrained_model_name_or_path The path to the directory containing the config file. + * @param {Record} names The names of the config files to load. + * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the configs. + * @returns {Promise>} A Promise that resolves to a dictionary of configuration objects. + * @private + */ +async function getOptionalConfigs(pretrained_model_name_or_path, names, options) { + return Object.fromEntries(await Promise.all( + Object.keys(names).map(async (name) => { + const config = await (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_5__.getModelJSON)(pretrained_model_name_or_path, names[name], false, options); + return [name, config]; + }) + )); +} + +/** + * Validate model inputs + * @param {Object} session The InferenceSession object that will be run. + * @param {Object} inputs The inputs to check. + * @returns {Record} The checked inputs. + * @throws {Error} If any inputs are missing. + * @private + */ +function validateInputs(session, inputs) { + /** + * NOTE: Create either a shallow or deep copy based on `onnx.wasm.proxy` + * @type {Record} + */ + const checkedInputs = Object.create(null); + const missingInputs = []; + for (const inputName of session.inputNames) { + const tensor = inputs[inputName]; + // Rare case where one of the model's input names corresponds to a built-in + // object name (e.g., toString), which would cause a simple (!tensor) check to fail, + // because it's not undefined but a function. + if (!(tensor instanceof _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor)) { + missingInputs.push(inputName); + continue; + } + // NOTE: When `env.wasm.proxy is true` the tensor is moved across the Worker + // boundary, transferring ownership to the worker and invalidating the tensor. + // So, in this case, we simply sacrifice a clone for it. + checkedInputs[inputName] = (0,_backends_onnx_js__WEBPACK_IMPORTED_MODULE_1__.isONNXProxy)() ? tensor.clone() : tensor; + } + if (missingInputs.length > 0) { + throw new Error( + `An error occurred during model execution: "Missing the following inputs: ${missingInputs.join(', ')}.`); + } + + const numInputsProvided = Object.keys(inputs).length; + const numInputsNeeded = session.inputNames.length; + if (numInputsProvided > numInputsNeeded) { + // No missing inputs, but too many inputs were provided. + // Warn the user and ignore the extra inputs. + let ignored = Object.keys(inputs).filter(inputName => !session.inputNames.includes(inputName)); + console.warn(`WARNING: Too many inputs were provided (${numInputsProvided} > ${numInputsNeeded}). The following inputs will be ignored: "${ignored.join(', ')}".`); + } + + return checkedInputs; +} + +// Currently, Transformers.js doesn't support simultaneous execution of sessions in WASM/WebGPU. +// For this reason, we need to chain the inference calls (otherwise we get "Error: Session already started"). +let webInferenceChain = Promise.resolve(); + +/** + * Executes an InferenceSession using the specified inputs. + * NOTE: `inputs` must contain at least the input names of the model. + * - If additional inputs are passed, they will be ignored. + * - If inputs are missing, an error will be thrown. + * + * @param {Object} session The InferenceSession object to run. + * @param {Object} inputs An object that maps input names to input tensors. + * @returns {Promise} A Promise that resolves to an object that maps output names to output tensors. + * @private + */ +async function sessionRun(session, inputs) { + const checkedInputs = validateInputs(session, inputs); + try { + // pass the original ort tensor + const ortFeed = Object.fromEntries(Object.entries(checkedInputs).map(([k, v]) => [k, v.ort_tensor])); + const run = () => session.run(ortFeed); + const output = await ((_env_js__WEBPACK_IMPORTED_MODULE_14__.apis.IS_BROWSER_ENV || _env_js__WEBPACK_IMPORTED_MODULE_14__.apis.IS_WEBWORKER_ENV) + ? (webInferenceChain = webInferenceChain.then(run)) + : run()); + return replaceTensors(output); + } catch (e) { + // Error messages can be long (nested) and uninformative. For this reason, + // we apply minor formatting to show the most important information + const formatted = Object.fromEntries(Object.entries(checkedInputs) + .map(([k, tensor]) => { + // Extract these properties from the underlying ORT tensor + const unpacked = { + type: tensor.type, + dims: tensor.dims, + location: tensor.location, + } + if (unpacked.location !== "gpu-buffer") { + // Only return the data if it's not a GPU buffer + unpacked.data = tensor.data; + } + return [k, unpacked]; + })); + + // This usually occurs when the inputs are of the wrong type. + console.error(`An error occurred during model execution: "${e}".`); + console.error('Inputs given to model:', formatted); + throw e; + } +} + +/** + * Replaces ONNX Tensor objects with custom Tensor objects to support additional functions. + * @param {Object} obj The object to replace tensor objects in. + * @returns {Object} The object with tensor objects replaced by custom Tensor objects. + * @private + */ +function replaceTensors(obj) { + for (let prop in obj) { + if ((0,_backends_onnx_js__WEBPACK_IMPORTED_MODULE_1__.isONNXTensor)(obj[prop])) { + obj[prop] = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor(obj[prop]); + } else if (typeof obj[prop] === 'object') { + replaceTensors(obj[prop]); + } + } + return obj; +} + + +/** + * Converts an array or Tensor of integers to an int64 Tensor. + * @param {any[]|Tensor} items The input integers to be converted. + * @returns {Tensor} The int64 Tensor with the converted values. + * @throws {Error} If the input array is empty or the input is a batched Tensor and not all sequences have the same length. + * @private + */ +function toI64Tensor(items) { + if (items instanceof _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor) { + return items; + } + // items is an array + if (items.length === 0) { + throw Error("items must be non-empty"); + } + + if (Array.isArray(items[0])) { + // batched + if (items.some(x => x.length !== items[0].length)) { + throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.") + } + + return new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', + BigInt64Array.from(items.flat().map(x => BigInt(x))), + [items.length, items[0].length] + ); + } else { + //flat + return new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', + BigInt64Array.from(items.map(x => BigInt(x))), + [1, items.length] + ); + } +} + +/** + * Creates a boolean tensor with a single value. + * @param {boolean} value The value of the tensor. + * @returns {Tensor} The boolean tensor. + * @private + */ +function boolTensor(value) { + return new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('bool', [value], [1]); +} + +// JS doesn't support mixins, so we define some reused functions here, and allow "this" to be passed in +/** + * Perform forward pass on the seq2seq model (both encoder and decoder). + * @param {Object} self The seq2seq model object. + * @param {Object} model_inputs The input object for the model containing encoder and decoder inputs. + * @returns {Promise} Promise that resolves with the output of the seq2seq model. + * @private + */ +async function seq2seqForward(self, model_inputs) { + let { encoder_outputs, input_ids, decoder_input_ids, ...other_decoder_inputs } = model_inputs; + // Encode if needed + if (!encoder_outputs) { + const encoder_inputs = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.pick)(model_inputs, self.sessions['model'].inputNames); + // Encoder outputs are not given, so we must compute them. + encoder_outputs = (await encoderForward(self, encoder_inputs)).last_hidden_state; + } + + other_decoder_inputs.input_ids = decoder_input_ids; + other_decoder_inputs.encoder_hidden_states = encoder_outputs; + + if (self.sessions['decoder_model_merged'].inputNames.includes('encoder_attention_mask')) { + other_decoder_inputs.encoder_attention_mask = model_inputs.attention_mask + } + + const decoderResults = await decoderForward(self, other_decoder_inputs, true); + + return decoderResults; +} + +/** + * Forward pass of an encoder model. + * @param {Object} self The encoder model. + * @param {Object} model_inputs The input data to be used for the forward pass. + * @returns {Promise} The model's outputs. + * @private + */ +async function encoderForward(self, model_inputs) { + const session = self.sessions['model']; + const encoderFeeds = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.pick)(model_inputs, session.inputNames); + + if (session.inputNames.includes('inputs_embeds') && !encoderFeeds.inputs_embeds) { + if (!model_inputs.input_ids) { + throw new Error('Both `input_ids` and `inputs_embeds` are missing in the model inputs.'); + } + encoderFeeds.inputs_embeds = await self.encode_text({ input_ids: model_inputs.input_ids }); + } + if (session.inputNames.includes('token_type_ids') && !encoderFeeds.token_type_ids) { + if (!encoderFeeds.input_ids) { + throw new Error('Both `input_ids` and `token_type_ids` are missing in the model inputs.'); + } + // Assign default `token_type_ids` (all zeroes) to the `encoderFeeds` if the model expects it, + // but they weren't created by the tokenizer. + encoderFeeds.token_type_ids = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.zeros_like)(encoderFeeds.input_ids); + } + if (session.inputNames.includes('pixel_mask') && !encoderFeeds.pixel_mask) { + if (!encoderFeeds.pixel_values) { + throw new Error('Both `pixel_values` and `pixel_mask` are missing in the model inputs.'); + } + // Assign default `pixel_mask` (all ones) to the `encoderFeeds` if the model expects it, + // but they weren't created by the processor. + const dims = encoderFeeds.pixel_values.dims; + encoderFeeds.pixel_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.ones)([dims[0], dims[2], dims[3]]); + } + + return await sessionRun(session, encoderFeeds); +} + +async function autoEncoderForward(self, model_inputs) { + const encoded = await self.encode(model_inputs); + const decoded = await self.decode(encoded); + return decoded; +} + +/** + * Forward pass of a decoder model. + * @param {Object} self The decoder model. + * @param {Object} model_inputs The input data to be used for the forward pass. + * @returns {Promise} The logits and past key values. + * @private + */ +async function decoderForward(self, model_inputs, is_encoder_decoder = false) { + + const session = self.sessions[ + is_encoder_decoder ? 'decoder_model_merged' : 'model' + ] + + const { past_key_values, ...new_model_inputs } = model_inputs; + + if (session.inputNames.includes('use_cache_branch')) { + new_model_inputs.use_cache_branch = boolTensor(!!past_key_values); + } + if (session.inputNames.includes('position_ids') && new_model_inputs.attention_mask && !new_model_inputs.position_ids) { + // NOTE: Handle a special case for paligemma/gemma3 models, where positions are 1-indexed + const start_index = ['paligemma', 'gemma3_text', 'gemma3'].includes(self.config.model_type) ? 1 : 0; + new_model_inputs.position_ids = createPositionIds(new_model_inputs, past_key_values, start_index); + } + + // Unpack the `past_key_values` object into model inputs + self.addPastKeyValues(new_model_inputs, past_key_values); + + // Select only the inputs that are needed for the current session + const fixed = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.pick)(new_model_inputs, session.inputNames); + return await sessionRun(session, fixed); +} + + + +function default_merge_input_ids_with_features({ + modality_token_id, + inputs_embeds, + modality_features, + input_ids, + attention_mask, +}) { + const token_positions = input_ids.tolist().map(ids => + ids.reduce((acc, x, idx) => { + if (x == modality_token_id) acc.push(idx); + return acc; + }, []) + ); + const n_tokens = token_positions.reduce((acc, x) => acc + x.length, 0); + const n_features = modality_features.dims[0]; + if (n_tokens !== n_features) { + throw new Error(`Number of tokens and features do not match: tokens: ${n_tokens}, features ${n_features}`); + } + + // Equivalent to performing a masked_scatter + let img = 0; + for (let i = 0; i < token_positions.length; ++i) { + const tokens = token_positions[i]; + const embeds = inputs_embeds[i]; + for (let j = 0; j < tokens.length; ++j) { + embeds[tokens[j]].data.set(modality_features[img++].data) + } + } + return { inputs_embeds, attention_mask } +} + + +function default_merge_input_ids_with_image_features({ + image_token_id, + inputs_embeds, + image_features, + input_ids, + attention_mask, +}) { + return default_merge_input_ids_with_features({ + modality_token_id: image_token_id, + inputs_embeds, + modality_features: image_features, + input_ids, + attention_mask, + }) +} + +function default_merge_input_ids_with_audio_features({ + audio_token_id, + inputs_embeds, + audio_features, + input_ids, + attention_mask, +}) { + return default_merge_input_ids_with_features({ + modality_token_id: audio_token_id, + inputs_embeds, + modality_features: audio_features, + input_ids, + attention_mask, + }) +} + +/** + * Abstract forward pass function for image-text-to-text or audio-text-to-text models. + * @param {Object} self The model object. + * @param {Object} params Additional parameters. + * @param {Function} [params.encode_function] The function to encode the modality values. + * @param {Function} [params.merge_function] The function to merge the modality features with the input embeddings. + * @param {string} [params.modality_input_name] The modality input name. + * @param {string} [params.modality_output_name] The modality output name. + * @param {Tensor} [params.input_ids=null] + * @param {Tensor} [params.attention_mask=null] + * @param {Tensor} [params.position_ids=null] + * @param {Tensor} [params.inputs_embeds=null] + * @param {Tensor} [params.past_key_values=null] + * @param {Object} [params.generation_config=null] + * @param {Object} [params.logits_processor=null] + * @returns {Promise} The model's output tensor + * @private + */ +async function genericTextToTextForward(self, { + // Generic parameters: + encode_function, + merge_function, + modality_input_name, + modality_output_name, + + // Produced by the tokenizer/processor: + input_ids = null, + attention_mask = null, + + // Used during generation: + position_ids = null, + inputs_embeds = null, + past_key_values = null, + + // Generic generation parameters + generation_config = null, + logits_processor = null, + + // Additional parameters + ...kwargs +}) { + const modality_values = kwargs[modality_input_name]; + if (!inputs_embeds) { + // 1. Extract the text embeddings. + inputs_embeds = await self.encode_text({ input_ids, ...kwargs }); + + // 2. Possibly, merge text and modality values + if (modality_values && input_ids.dims[1] !== 1) { + const modality_features = await encode_function({ + // Pass the modality values under its expected key. + // The caller knows whether this is audio or image. + [modality_input_name]: modality_values, + ...kwargs + }); + ({ inputs_embeds, attention_mask } = merge_function({ + [modality_output_name]: modality_features, + inputs_embeds, + input_ids, + attention_mask, + })); + + } else if (past_key_values && modality_values && input_ids.dims[1] === 1) { + // This branch handles the cache case. + const target_length = input_ids.dims[1]; // always 1 + const past_length = Object.values(past_key_values)[0].dims.at(-2); + + attention_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)([ + (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.ones)([input_ids.dims[0], past_length]), + attention_mask.slice(null, [attention_mask.dims[1] - target_length, attention_mask.dims[1]]), + ], 1); + } + } + + if (!position_ids) { + + if (self.config.model_type === 'qwen2_vl') { + // Special case for qwen2_vl models + // @ts-ignore + const { image_grid_thw, video_grid_thw } = kwargs; + [position_ids] = self.get_rope_index(input_ids, image_grid_thw, video_grid_thw, attention_mask) + } + } + + // 3. Call the decoder forward using the updated inputs. + const outputs = await decoderForward(self, { + inputs_embeds, + past_key_values, + attention_mask, + position_ids, + generation_config, + logits_processor, + }, true); + return outputs; +} + +/** + * Forward pass of an audio-text-to-text model. + * @param {Object} self The audio-text-to-text model. + * @param {Object} params The inputs for the audio-text-to-text forward pass. + * @returns {Promise} The model's output tensor. + * @private + */ +async function audioTextToTextForward(self, params) { + return await genericTextToTextForward(self, { + ...params, + modality_input_name: 'audio_values', + modality_output_name: 'audio_features', + encode_function: self.encode_audio.bind(self), + merge_function: self._merge_input_ids_with_audio_features.bind(self), + }); +} + +/** + * Forward pass of an image-text-to-text model. + * @param {Object} self The image-text-to-text model. + * @param {Object} params The inputs for the image-text-to-text forward pass. + * @returns {Promise} The model's output tensor. + * @private + */ +async function imageTextToTextForward(self, params) { + return await genericTextToTextForward(self, { + ...params, + modality_input_name: 'pixel_values', + modality_output_name: 'image_features', + encode_function: self.encode_image.bind(self), + merge_function: self._merge_input_ids_with_image_features.bind(self), + }); +} + +/** + * Helper function to perform the following: + * ```python + * x = attention_mask.long().cumsum(-1) - 1 + * x.masked_fill_(attention_mask == 0, 1) + * ``` + * @param {Tensor} attention_mask + * @returns {{data: BigInt64Array, dims: number[]}} + */ +function cumsum_masked_fill(attention_mask, start_index = 0) { + const [bz, seq_len] = attention_mask.dims; + const attn_mask_data = attention_mask.data; + + const data = new BigInt64Array(attn_mask_data.length); + for (let i = 0; i < bz; ++i) { + const start = i * seq_len; + let sum = BigInt(start_index); + for (let j = 0; j < seq_len; ++j) { + const index = start + j; + if (attn_mask_data[index] === 0n) { + data[index] = BigInt(1); + } else { // === 1n + data[index] = sum; + sum += attn_mask_data[index]; + } + } + } + return { data, dims: attention_mask.dims }; + +} + +/** + * If the model supports providing position_ids, we create position_ids on the fly for batch generation, + * by computing the cumulative sum of the attention mask along the sequence length dimension. + * + * Equivalent to: + * ```python + * position_ids = attention_mask.long().cumsum(-1) - 1 + * position_ids.masked_fill_(attention_mask == 0, 1) + * if past_key_values: + * position_ids = position_ids[:, -input_ids.shape[1] :] + * ``` + */ +function createPositionIds(model_inputs, past_key_values = null, start_index = 0) { + const { input_ids, inputs_embeds, attention_mask } = model_inputs; + + const { data, dims } = cumsum_masked_fill(attention_mask, start_index); + let position_ids = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', data, dims); + if (past_key_values) { + const offset = -(input_ids ?? inputs_embeds).dims.at(1); + position_ids = position_ids.slice(null, [offset, null]); + } + return position_ids; +} + +function decoder_prepare_inputs_for_generation(self, input_ids, model_inputs, generation_config) { + const past_length = model_inputs.past_key_values + ? Object.values(model_inputs.past_key_values)[0].dims.at(-2) + : 0; + + if (!model_inputs.attention_mask) { + // If the attention mask is not provided, we attempt to infer based on provided inputs + let dims; + for (const key of ['input_ids', 'inputs_embeds', 'position_ids']) { + if (model_inputs[key]) { + dims = model_inputs[key].dims; + break; + } + } + if (!dims) { + throw new Error("attention_mask is not provided, and unable to infer its shape from model inputs."); + } + model_inputs.attention_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.ones)([dims[0], past_length + dims[1]]); + } + + if (model_inputs.past_key_values) { + const { input_ids, attention_mask } = model_inputs; + + // Keep only the unprocessed tokens: + // 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where + // some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as + // input) + if (attention_mask && attention_mask.dims[1] > input_ids.dims[1]) { + // NOTE: not needed since we only pass the generated tokens to the next forward pass + // const offset = -(attention_mask.dims[1] - past_length); + // model_inputs.input_ids = input_ids.slice(null, [offset, null]); + } + // 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. + // We can discard input_ids based on the past_length. + else if (past_length < input_ids.dims[1]) { + // NOTE: Required for phi models. + // See https://github.com/huggingface/transformers/issues/30809#issuecomment-2111918479 for more information. + model_inputs.input_ids = input_ids.slice(null, [past_length, null]); + } + // 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. + else { + + } + } + + return model_inputs; +} + +function encoder_decoder_prepare_inputs_for_generation(self, input_ids, model_inputs, generation_config) { + if (model_inputs.past_key_values) { + input_ids = input_ids.map(x => [x.at(-1)]); + } + + return { + ...model_inputs, + decoder_input_ids: toI64Tensor(input_ids), + }; +} + +function multimodal_text_to_text_prepare_inputs_for_generation(self, ...args) { + if (self.config.is_encoder_decoder) { + return encoder_decoder_prepare_inputs_for_generation(self, ...args); + } else { + return decoder_prepare_inputs_for_generation(self, ...args); + } +} + +function multimodality_prepare_inputs_for_generation(self, input_ids, model_inputs, generation_config) { + const has_past_key_values = !!model_inputs.past_key_values; + + if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) { + if (has_past_key_values) { + model_inputs.input_ids = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)([ + model_inputs.input_ids, + model_inputs.input_ids, + ], 0) + // NOTE: attention_mask handled in generation + } else { + model_inputs.input_ids = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)([ + model_inputs.input_ids, + (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.full_like)(model_inputs.input_ids, BigInt(generation_config.pad_token_id)), + ], 0); + model_inputs.attention_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)([ + model_inputs.attention_mask, + (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.full_like)(model_inputs.attention_mask, 0n), + ], 0); + } + } + + if (has_past_key_values || !model_inputs.pixel_values) { + model_inputs.pixel_values = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.full)([0, 0, 3, 384, 384], 1.0); + } + + if (has_past_key_values) { + const num_img_tokens = 0; + const num_text_tokens = 1; + const has_image = num_img_tokens > 0 ? 1 : 0; + + const batch_size = 1; + model_inputs.images_seq_mask = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor( + 'bool', + new Array(num_img_tokens + num_text_tokens).fill(true).fill(false, 0, num_text_tokens), + [batch_size, num_img_tokens + num_text_tokens], + ); + model_inputs.images_emb_mask = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor( + 'bool', + new Array(num_img_tokens).fill(!!has_image), + [batch_size, 1, num_img_tokens], + ); + } + return model_inputs; +} + +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +/** + * A base class for pre-trained models that provides the model configuration and an ONNX session. + */ +class PreTrainedModel extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_3__.Callable { + main_input_name = 'input_ids'; + forward_params = ['input_ids', 'attention_mask']; + /** + * Creates a new instance of the `PreTrainedModel` class. + * @param {import('./configs.js').PretrainedConfig} config The model configuration. + * @param {Record} sessions The inference sessions for the model. + * @param {Record} configs Additional configuration files (e.g., generation_config.json). + */ + constructor(config, sessions, configs) { + super(); + + this.config = config; + this.sessions = sessions; + this.configs = configs; + + const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor); + const modelType = MODEL_TYPE_MAPPING.get(modelName); + + this.can_generate = false; + this._forward = null; + + this._prepare_inputs_for_generation = null; + switch (modelType) { + case MODEL_TYPES.DecoderOnly: + this.can_generate = true; + this._forward = decoderForward; + this._prepare_inputs_for_generation = decoder_prepare_inputs_for_generation; + break; + case MODEL_TYPES.Seq2Seq: + case MODEL_TYPES.Vision2Seq: + case MODEL_TYPES.Musicgen: + this.can_generate = true; + + this._forward = seq2seqForward; + this._prepare_inputs_for_generation = encoder_decoder_prepare_inputs_for_generation; + break; + + case MODEL_TYPES.EncoderDecoder: + this._forward = seq2seqForward; + break; + case MODEL_TYPES.ImageTextToText: + this.can_generate = true; + this._forward = imageTextToTextForward; + this._prepare_inputs_for_generation = multimodal_text_to_text_prepare_inputs_for_generation; + break; + case MODEL_TYPES.AudioTextToText: + this.can_generate = true; + this._forward = audioTextToTextForward; + this._prepare_inputs_for_generation = multimodal_text_to_text_prepare_inputs_for_generation; + break; + case MODEL_TYPES.Phi3V: + case MODEL_TYPES.ImageAudioTextToText: + this.can_generate = true; + this._prepare_inputs_for_generation = multimodal_text_to_text_prepare_inputs_for_generation; + break; + case MODEL_TYPES.MultiModality: + this.can_generate = true; + this._prepare_inputs_for_generation = multimodality_prepare_inputs_for_generation; + break; + case MODEL_TYPES.AutoEncoder: + this._forward = autoEncoderForward; + break; + default: + // should be MODEL_TYPES.EncoderOnly + this._forward = encoderForward; + break; + } + + if (this.can_generate) { + this.forward_params.push('past_key_values'); + } + + /** @type {import('./configs.js').TransformersJSConfig} */ + this.custom_config = this.config['transformers.js_config'] ?? {}; + } + + /** + * Disposes of all the ONNX sessions that were created during inference. + * @returns {Promise} An array of promises, one for each ONNX session that is being disposed. + * @todo Use https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/FinalizationRegistry + */ + async dispose() { + const promises = []; + for (const session of Object.values(this.sessions)) { + if (session?.handler?.dispose) { + promises.push(session.handler.dispose()) + } + } + return await Promise.all(promises); + } + + /** + * Instantiate one of the model classes of the library from a pretrained model. + * + * The model class to instantiate is selected based on the `model_type` property of the config object + * (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible) + * + * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either: + * - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + * Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a + * user or organization name, like `dbmdz/bert-base-german-cased`. + * - A path to a *directory* containing model weights, e.g., `./my_model_directory/`. + * @param {import('./utils/hub.js').PretrainedModelOptions} options Additional options for loading the model. + * + * @returns {Promise} A new instance of the `PreTrainedModel` class. + */ + static async from_pretrained(pretrained_model_name_or_path, { + progress_callback = null, + config = null, + cache_dir = null, + local_files_only = false, + revision = 'main', + model_file_name = null, + subfolder = 'onnx', + device = null, + dtype = null, + use_external_data_format = null, + session_options = {}, + } = {}) { + + let options = { + progress_callback, + config, + cache_dir, + local_files_only, + revision, + model_file_name, + subfolder, + device, + dtype, + use_external_data_format, + session_options, + } + + const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this); + const modelType = MODEL_TYPE_MAPPING.get(modelName); + + config = options.config = await _configs_js__WEBPACK_IMPORTED_MODULE_0__.AutoConfig.from_pretrained(pretrained_model_name_or_path, options); + + let info; + if (modelType === MODEL_TYPES.DecoderOnly) { + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, { + model: options.model_file_name ?? 'model', + }, options), + getOptionalConfigs(pretrained_model_name_or_path, { + generation_config: 'generation_config.json', + }, options), + ]); + + } else if (modelType === MODEL_TYPES.Seq2Seq || modelType === MODEL_TYPES.Vision2Seq) { + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, { + model: 'encoder_model', + decoder_model_merged: 'decoder_model_merged', + }, options), + getOptionalConfigs(pretrained_model_name_or_path, { + generation_config: 'generation_config.json', + }, options), + ]); + + } else if (modelType === MODEL_TYPES.MaskGeneration) { + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, { + model: 'vision_encoder', + prompt_encoder_mask_decoder: 'prompt_encoder_mask_decoder', + }, options), + ]); + + } else if (modelType === MODEL_TYPES.EncoderDecoder) { + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, { + model: 'encoder_model', + decoder_model_merged: 'decoder_model_merged', + }, options), + ]); + + } else if (modelType === MODEL_TYPES.ImageTextToText) { + const sessions = { + embed_tokens: 'embed_tokens', + vision_encoder: 'vision_encoder', + decoder_model_merged: 'decoder_model_merged', + } + if (config.is_encoder_decoder) { + sessions['model'] = 'encoder_model'; + } + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, sessions, options), + getOptionalConfigs(pretrained_model_name_or_path, { + generation_config: 'generation_config.json', + }, options), + ]); + + } else if (modelType === MODEL_TYPES.AudioTextToText) { + const sessions = { + embed_tokens: 'embed_tokens', + audio_encoder: 'audio_encoder', + decoder_model_merged: 'decoder_model_merged', + } + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, sessions, options), + getOptionalConfigs(pretrained_model_name_or_path, { + generation_config: 'generation_config.json', + }, options), + ]); + } else if (modelType === MODEL_TYPES.ImageAudioTextToText) { + const sessions = { + embed_tokens: 'embed_tokens', + audio_encoder: 'audio_encoder', + vision_encoder: 'vision_encoder', + decoder_model_merged: 'decoder_model_merged', + } + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, sessions, options), + getOptionalConfigs(pretrained_model_name_or_path, { + generation_config: 'generation_config.json', + }, options), + ]); + } else if (modelType === MODEL_TYPES.Musicgen) { + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, { + model: 'text_encoder', + decoder_model_merged: 'decoder_model_merged', + encodec_decode: 'encodec_decode', + }, options), + getOptionalConfigs(pretrained_model_name_or_path, { + generation_config: 'generation_config.json', + }, options), + ]); + + } else if (modelType === MODEL_TYPES.MultiModality) { + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, { + prepare_inputs_embeds: 'prepare_inputs_embeds', + model: 'language_model', + lm_head: 'lm_head', + gen_head: 'gen_head', + gen_img_embeds: 'gen_img_embeds', + image_decode: 'image_decode', + }, options), + getOptionalConfigs(pretrained_model_name_or_path, { + generation_config: 'generation_config.json', + }, options), + ]); + + } else if (modelType === MODEL_TYPES.Phi3V) { + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, { + prepare_inputs_embeds: 'prepare_inputs_embeds', + model: 'model', + vision_encoder: 'vision_encoder', + }, options), + getOptionalConfigs(pretrained_model_name_or_path, { + generation_config: 'generation_config.json', + }, options), + ]); + } else if (modelType === MODEL_TYPES.AutoEncoder) { + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, { + encoder_model: 'encoder_model', + decoder_model: 'decoder_model', + }, options), + ]); + } else { // should be MODEL_TYPES.EncoderOnly + if (modelType !== MODEL_TYPES.EncoderOnly) { + const type = modelName ?? config?.model_type; + if (type !== 'custom') { + console.warn(`Model type for '${type}' not found, assuming encoder-only architecture. Please report this at ${_utils_constants_js__WEBPACK_IMPORTED_MODULE_6__.GITHUB_ISSUE_URL}.`) + } + } + info = await Promise.all([ + constructSessions(pretrained_model_name_or_path, { + model: options.model_file_name ?? 'model', + }, options), + ]); + } + + // @ts-ignore + return new this(config, ...info); + } + + /** + * Runs the model with the provided inputs + * @param {Object} model_inputs Object containing input tensors + * @returns {Promise} Object containing output tensors + */ + async _call(model_inputs) { + return await this.forward(model_inputs); + } + + /** + * Forward method for a pretrained model. If not overridden by a subclass, the correct forward method + * will be chosen based on the model type. + * @param {Object} model_inputs The input data to the model in the format specified in the ONNX model. + * @returns {Promise} The output data from the model in the format specified in the ONNX model. + * @throws {Error} This method must be implemented in subclasses. + */ + async forward(model_inputs) { + return await this._forward(this, model_inputs); + } + + /** + * Get the model's generation config, if it exists. + * @returns {GenerationConfig|null} The model's generation config if it exists, otherwise `null`. + */ + get generation_config() { + return this.configs?.generation_config ?? null; + } + + /** + * This function returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsWarper`] + * instances used for multinomial sampling. + * @param {GenerationConfig} generation_config The generation config. + * @returns {LogitsProcessorList} generation_config + */ + _get_logits_warper(generation_config) { + + // instantiate warpers list + const warpers = new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.LogitsProcessorList(); + + if (generation_config.temperature !== null && generation_config.temperature !== 1.0) { + warpers.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.TemperatureLogitsWarper(generation_config.temperature)); + } + if (generation_config.top_k !== null && generation_config.top_k !== 0) { + // TODO: add min_tokens_to_keep + warpers.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.TopKLogitsWarper(generation_config.top_k)); + } + if (generation_config.top_p !== null && generation_config.top_p < 1.0) { + // TODO: add min_tokens_to_keep + warpers.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.TopPLogitsWarper(generation_config.top_p)); + } + + return warpers; + } + + /** + * @param {GenerationConfig} generation_config + * @param {number} input_ids_seq_length The starting sequence length for the input ids. + * @returns {LogitsProcessorList} + * @private + */ + _get_logits_processor( + generation_config, + input_ids_seq_length, + // encoder_input_ids, TODO + // prefix_allowed_tokens_fn, TODO + logits_processor = null + ) { + const processors = new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.LogitsProcessorList(); + + // if (generation_config.diversity_penalty !== null && generation_config.diversity_penalty > 0.0) { + // processors.push(new HammingDiversityLogitsProcessor( + // generation_config.diversity_penalty, + // generation_config.num_beams, + // generation_config.num_beam_groups + // )); + // } + + // if (generation_config.encoder_repetition_penalty !== null && generation_config.encoder_repetition_penalty !== 1.0) { + // processors.push(new EncoderRepetitionPenaltyLogitsProcessor( + // generation_config.encoder_repetition_penalty, + // encoder_input_ids + // )); + // } + + if (generation_config.repetition_penalty !== null && generation_config.repetition_penalty !== 1.0) { + processors.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.RepetitionPenaltyLogitsProcessor(generation_config.repetition_penalty)); + } + + if (generation_config.no_repeat_ngram_size !== null && generation_config.no_repeat_ngram_size > 0) { + processors.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size)); + } + + // if (generation_config.encoder_no_repeat_ngram_size !== null && generation_config.encoder_no_repeat_ngram_size > 0) { + // if (this.config.is_encoder_decoder) { + // processors.push(new EncoderNoRepeatNGramLogitsProcessor( + // generation_config.encoder_no_repeat_ngram_size, + // encoder_input_ids + // )); + // } else { + // throw new Error("It's impossible to use `encoder_no_repeat_ngram_size` with decoder-only architecture"); + // } + // } + + if (generation_config.bad_words_ids !== null) { + processors.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.NoBadWordsLogitsProcessor(generation_config.bad_words_ids, generation_config.eos_token_id)); + } + + if (generation_config.min_length !== null && generation_config.eos_token_id !== null && generation_config.min_length > 0) { + processors.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.MinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id)); + } + + if (generation_config.min_new_tokens !== null && generation_config.eos_token_id !== null && generation_config.min_new_tokens > 0) { + processors.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.MinNewTokensLengthLogitsProcessor( + input_ids_seq_length, + generation_config.min_new_tokens, + generation_config.eos_token_id + )); + } + + // if (prefix_allowed_tokens_fn !== null) { + // processors.push(new PrefixConstrainedLogitsProcessor( + // prefix_allowed_tokens_fn, + // generation_config.num_beams / generation_config.num_beam_groups + // )); + // } + + + if (generation_config.forced_bos_token_id !== null) { + processors.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.ForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id)); + } + + if (generation_config.forced_eos_token_id !== null) { + processors.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.ForcedEOSTokenLogitsProcessor( + generation_config.max_length, + generation_config.forced_eos_token_id + )); + } + + // if (generation_config.remove_invalid_values === true) { + // processors.push(new InfNanRemoveLogitsProcessor()); + // } + + // if (generation_config.exponential_decay_length_penalty !== null) { + // processors.push(new ExponentialDecayLengthPenalty( + // generation_config.exponential_decay_length_penalty, + // generation_config.eos_token_id, + // input_ids_seq_length + // )); + // } + + // if (generation_config.suppress_tokens !== null) { + // processors.push(new SuppressTokensLogitsProcessor(generation_config.suppress_tokens)); + // } + + if (generation_config.begin_suppress_tokens !== null) { + const begin_index = (input_ids_seq_length > 1 || generation_config.forced_bos_token_id === null) + ? input_ids_seq_length + : input_ids_seq_length + 1; + + processors.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index)); + } + + // DEPRECATED: https://github.com/huggingface/transformers/pull/29485 + // if (generation_config.forced_decoder_ids !== null) { + // processors.push(new ForceTokensLogitsProcessor(generation_config.forced_decoder_ids)); + // } + + + // 8. prepare batched CFG externally + if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) { + processors.push(new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale)); + } + + if (logits_processor !== null) { + processors.extend(logits_processor) + } + + // `LogitNormalization` should always be the last logit processor, when present + // if (generation_config.renormalize_logits === true) { + // processors.push(new LogitNormalization()); + // } + + return processors; + } + + /** + * This function merges multiple generation configs together to form a final generation config to be used by the model for text generation. + * It first creates an empty `GenerationConfig` object, then it applies the model's own `generation_config` property to it. Finally, if a `generation_config` object was passed in the arguments, it overwrites the corresponding properties in the final config with those of the passed config object. + * @param {GenerationConfig|null} generation_config A `GenerationConfig` object containing generation parameters. + * @param {Object} kwargs Additional generation parameters to be used in place of those in the `generation_config` object. + * @returns {GenerationConfig} The final generation config object to be used by the model for text generation. + */ + _prepare_generation_config(generation_config, kwargs, cls = _generation_configuration_utils_js__WEBPACK_IMPORTED_MODULE_8__.GenerationConfig) { + // Create empty generation config (contains defaults) + // We pass `this.config` so that if `eos_token_id` or `bos_token_id` exist in the model's config, we will use them + const config = { ...this.config }; + for (const key of ["decoder", "generator", "text_config"]) { + // Special case: some models have generation attributes set in the decoder. + // Use them if still unset in the generation config. + if (key in config) { + Object.assign(config, config[key]); + } + } + + const gen_config = new cls(config); + + // Apply model's generation config, if it exists + Object.assign(gen_config, this.generation_config ?? {}); + + // Next, use any generation config specified by the user + // when calling `generate` + if (generation_config) { + Object.assign(gen_config, generation_config); + } + + // Finally, if any kwargs were passed, use them to overwrite + if (kwargs) { + Object.assign(gen_config, (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.pick)(kwargs, Object.getOwnPropertyNames(gen_config))); + } + + return gen_config; + } + + /** + * + * @param {GenerationConfig} generation_config + * @param {StoppingCriteriaList} [stopping_criteria=null] + */ + _get_stopping_criteria(generation_config, stopping_criteria = null) { + const criteria = new _generation_stopping_criteria_js__WEBPACK_IMPORTED_MODULE_12__.StoppingCriteriaList(); + + if (generation_config.max_length !== null) { + criteria.push(new _generation_stopping_criteria_js__WEBPACK_IMPORTED_MODULE_12__.MaxLengthCriteria( + generation_config.max_length, + this.config.max_position_embeddings ?? null, + )); + } + // if (generation_config.max_time !== null) { + // criteria.push(new MaxTimeCriteria(generation_config.max_time)); + // } + if (generation_config.eos_token_id !== null) { + criteria.push(new _generation_stopping_criteria_js__WEBPACK_IMPORTED_MODULE_12__.EosTokenCriteria(generation_config.eos_token_id)); + } + + if (stopping_criteria) { + criteria.extend(stopping_criteria); + } + return criteria; + + } + + /** + * Confirms that the model class is compatible with generation. + * If not, raises an exception that points to the right class to use. + */ + _validate_model_class() { + if (!this.can_generate) { + const generate_compatible_mappings = [ + MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, + // MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, // TODO + MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES, + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, + MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES, + ]; + + const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor); + + const generate_compatible_classes = new Set(); + const modelType = this.config.model_type; + for (const model_mapping of generate_compatible_mappings) { + const supported_models = model_mapping.get(modelType); + if (supported_models) { + generate_compatible_classes.add(supported_models[0]); + } + } + + let errorMessage = `The current model class (${modelName}) is not compatible with \`.generate()\`, as it doesn't have a language model head.` + if (generate_compatible_classes.size > 0) { + errorMessage += ` Please use the following class instead: ${[...generate_compatible_classes].join(', ')}`; + } + throw Error(errorMessage); + } + } + + prepare_inputs_for_generation(...args) { + return this._prepare_inputs_for_generation(this, ...args); + } + + /** + * + * @param {Object} inputs + * @param {bigint[][]} inputs.generated_input_ids + * @param {Object} inputs.outputs + * @param {Object} inputs.model_inputs + * @param {boolean} inputs.is_encoder_decoder + * @returns {Object} The updated model inputs for the next generation iteration. + */ + _update_model_kwargs_for_generation({ generated_input_ids, outputs, model_inputs, is_encoder_decoder }) { + // update past_key_values + model_inputs['past_key_values'] = this.getPastKeyValues(outputs, model_inputs.past_key_values); + + // update inputs for next run + model_inputs['input_ids'] = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', generated_input_ids.flat(), [generated_input_ids.length, 1]); + + if (!is_encoder_decoder) { + // update attention mask + model_inputs.attention_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)( + [ + model_inputs.attention_mask, + (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.ones)([model_inputs.attention_mask.dims[0], 1]), + ], 1 + ); + } else if ('decoder_attention_mask' in model_inputs) { + // TODO: update decoder attention mask if the model requires it + } + + // force recreate position_ids in next iteration + model_inputs['position_ids'] = null; + + return model_inputs; + } + + /** + * This function extracts the model-specific `inputs` for generation. + * @param {Object} params + * @param {Tensor} [params.inputs=null] + * @param {number} [params.bos_token_id=null] + * @param {Record} [params.model_kwargs] + * @returns {{inputs_tensor: Tensor, model_inputs: Record, model_input_name: string}} The model-specific inputs for generation. + */ + _prepare_model_inputs({ inputs, bos_token_id, model_kwargs }) { + const model_inputs = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.pick)(model_kwargs, this.forward_params); + const input_name = this.main_input_name; + if (input_name in model_inputs) { + if (inputs) { + throw new Error( + "`inputs`: {inputs}` were passed alongside {input_name} which is not allowed. " + + "Make sure to either pass {inputs} or {input_name}=..." + ); + } + } else { + model_inputs[input_name] = inputs; + } + + const inputs_tensor = model_inputs[input_name]; + + return { inputs_tensor, model_inputs, model_input_name: input_name }; + } + + async _prepare_encoder_decoder_kwargs_for_generation({ inputs_tensor, model_inputs, model_input_name, generation_config }) { + if ( + this.sessions['model'].inputNames.includes('inputs_embeds') + && !model_inputs.inputs_embeds + && '_prepare_inputs_embeds' in this + ) { + // Encoder expects `inputs_embeds` instead of `input_ids` + const { input_ids, pixel_values, attention_mask, ...kwargs } = model_inputs; + // @ts-ignore + const prepared_inputs = await this._prepare_inputs_embeds(model_inputs); + model_inputs = { + ...kwargs, + ...(0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.pick)(prepared_inputs, ['inputs_embeds', 'attention_mask']), + }; + } + let { last_hidden_state } = await encoderForward(this, model_inputs); + + // for classifier free guidance we need to add a 'null' input to our encoder hidden states + if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) { + + last_hidden_state = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)([ + last_hidden_state, + (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.full_like)(last_hidden_state, 0.0), + ], 0); + + if ('attention_mask' in model_inputs) { + model_inputs['attention_mask'] = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)([ + model_inputs['attention_mask'], + (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.zeros_like)(model_inputs['attention_mask']), + ], 0); + } + + } else if (model_inputs.decoder_input_ids) { + // Ensure that the encoder outputs have the same batch size as the decoder inputs, + // allowing for more efficient batched generation for single inputs + const decoder_input_ids_batch_size = toI64Tensor(model_inputs.decoder_input_ids).dims[0]; + if (decoder_input_ids_batch_size !== last_hidden_state.dims[0]) { + if (last_hidden_state.dims[0] !== 1) { + throw new Error( + `The encoder outputs have a different batch size (${last_hidden_state.dims[0]}) than the decoder inputs (${decoder_input_ids_batch_size}).` + ) + } + last_hidden_state = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)(Array.from({ length: decoder_input_ids_batch_size }, () => last_hidden_state), 0); + } + } + model_inputs['encoder_outputs'] = last_hidden_state; + + return model_inputs; + } + + /** + * Prepares `decoder_input_ids` for generation with encoder-decoder models + * @param {*} param0 + */ + _prepare_decoder_input_ids_for_generation({ batch_size, model_input_name, model_kwargs, decoder_start_token_id, bos_token_id, generation_config }) { + let { decoder_input_ids, ...model_inputs } = model_kwargs; + + // Prepare input ids if the user has not defined `decoder_input_ids` manually. + if (!(decoder_input_ids instanceof _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor)) { + if (!decoder_input_ids) { + decoder_start_token_id ??= bos_token_id; + + if (this.config.model_type === 'musicgen') { + // Custom logic (TODO: move to Musicgen class) + decoder_input_ids = Array.from({ + // @ts-expect-error TS2339 + length: batch_size * this.config.decoder.num_codebooks + }, () => [decoder_start_token_id]); + + } else if (Array.isArray(decoder_start_token_id)) { + if (decoder_start_token_id.length !== batch_size) { + throw new Error( + `\`decoder_start_token_id\` expcted to have length ${batch_size} but got ${decoder_start_token_id.length}` + ) + } + decoder_input_ids = decoder_start_token_id; + } else { + decoder_input_ids = Array.from({ + length: batch_size, + }, () => [decoder_start_token_id]); + } + } else if (!Array.isArray(decoder_input_ids[0])) { + // Correct batch size + decoder_input_ids = Array.from({ + length: batch_size, + }, () => decoder_input_ids); + } + decoder_input_ids = toI64Tensor(decoder_input_ids); + } + + model_kwargs['decoder_attention_mask'] = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.ones_like)(decoder_input_ids); + + return { input_ids: decoder_input_ids, model_inputs }; + } + + /** + * Generates sequences of token ids for models with a language modeling head. + * @param {import('./generation/parameters.js').GenerationFunctionParameters} options + * @returns {Promise} The output of the model, which can contain the generated token ids, attentions, and scores. + */ + async generate({ + inputs = null, + generation_config = null, + logits_processor = null, + stopping_criteria = null, + streamer = null, + + // inputs_attention_mask = null, + ...kwargs + }) { + this._validate_model_class(); + + // Update generation config with defaults and kwargs + generation_config = this._prepare_generation_config(generation_config, kwargs); + + // 3. Define model inputs + let { inputs_tensor, model_inputs, model_input_name } = this._prepare_model_inputs({ + inputs, + model_kwargs: kwargs, + }); + + const is_encoder_decoder = this.config.is_encoder_decoder; + + // 4. Define other model kwargs + if (!is_encoder_decoder) { + // decoder-only models should use left-padding for generation + } else if (!('encoder_outputs' in model_inputs)) { + // if model is encoder decoder encoder_outputs are created + // and added to `model_kwargs` + model_inputs = await this._prepare_encoder_decoder_kwargs_for_generation( + { inputs_tensor, model_inputs, model_input_name, generation_config } + ) + } + + // 5. Prepare `input_ids` which will be used for auto-regressive generation + // TODO: Update to align with HF transformers' implementation + let input_ids; + if (is_encoder_decoder) { + // Generating from the encoder outputs + ({ input_ids, model_inputs } = this._prepare_decoder_input_ids_for_generation({ + batch_size: model_inputs[model_input_name].dims.at(0), + model_input_name, + model_kwargs: model_inputs, + decoder_start_token_id: generation_config.decoder_start_token_id, + bos_token_id: generation_config.bos_token_id, + generation_config, + })); + } else { + input_ids = model_inputs[model_input_name] + } + + // 6. Prepare `max_length` depending on other stopping criteria. + let input_ids_length = input_ids.dims.at(-1); + + if (generation_config.max_new_tokens !== null) { + generation_config.max_length = input_ids_length + generation_config.max_new_tokens; + } + + // input_ids_length = model_inputs[model_input_name].dims.at(1); + // // inputs instanceof Tensor ? : inputs.length; + + // // decoder-only + // if (input_ids_length === 0) { + // throw Error("Must supply a non-empty array of input token ids.") + // } + + // let decoder_input_ids = + // generation_config.decoder_input_ids + // ?? generation_config.decoder_start_token_id + // ?? generation_config.bos_token_id + // ?? generation_config.eos_token_id; + + // Update logits processor + // 8. prepare distribution pre_processing samplers + const prepared_logits_processor = this._get_logits_processor( + generation_config, + input_ids_length, + logits_processor, + ) + + // 9. prepare stopping criteria + const prepared_stopping_criteria = this._get_stopping_criteria( + generation_config, stopping_criteria + ) + + // /** @type {number[]} */ + // let eos_token_ids = generation_config.eos_token_id; + // if (eos_token_ids !== null && !Array.isArray(eos_token_ids)) { + // eos_token_ids = [eos_token_ids]; + // } + + const numInputs = model_inputs[model_input_name].dims.at(0); + + // TODO: + // done is a list of booleans to keep track of which inputs are done + // const done = new Array(numInputs).fill(false); + // For efficiency purposes, we remove completed rows from model_inputs + // when the beam is complete, and we keep track of the row index + // const rowIndexToBatchIndex = new Map(); + + const sampler = _generation_logits_sampler_js__WEBPACK_IMPORTED_MODULE_13__.LogitsSampler.getSampler(generation_config); + + // TODO make > numInputs + const scores = new Array(numInputs).fill(0); + /** @type {bigint[][]} */ + const all_input_ids = input_ids.tolist(); + if (streamer) { + streamer.put(all_input_ids); + } + // const all_generated_input_ids = Array.from({ length: numInputs }, () => []); + + // NOTE: For now, we don't support spawning new beams + // TODO: when we do, we simply copy past key values and accumulate into single large tensor + + //////////////////////////////////////////////////// + // Generic search which handles 4 generation modes: + // - GenerationMode.GREEDY_SEARCH + // - GenerationMode.SAMPLE + // - GenerationMode.BEAM_SEARCH + // - GenerationMode.BEAM_SAMPLE + //////////////////////////////////////////////////// + let outputs; + let attentions = {}; + while (true) { + // prepare model inputs + model_inputs = this.prepare_inputs_for_generation(all_input_ids, model_inputs, generation_config); + outputs = await this.forward(model_inputs); + + if (generation_config.output_attentions && generation_config.return_dict_in_generate) { + // Get attentions if they are present + const token_attentions = this.getAttentions(outputs); + for (const key in token_attentions) { + if (!(key in attentions)) { + attentions[key] = []; + } + attentions[key].push(token_attentions[key]); + } + } + + // Logits are of the form [batch_size, out_seq_length, vocab_size] + // In most cases, this will be [batch_size, 1, vocab_size] + // So, we select the last token's logits: + // (equivalent to `logits = outputs.logits[:, -1, :]`) + const logits = outputs.logits.slice(null, -1, null); + + const next_tokens_scores = prepared_logits_processor(all_input_ids, logits); + + /** @type {[bigint][]} */ + const generated_input_ids = []; + // const new_kv_cache = [];// NOTE: Only used for beam search when concatenating new kv + // Loop over each batch + for (let batch_idx = 0; batch_idx < next_tokens_scores.dims.at(0); ++batch_idx) { + const logs = next_tokens_scores[batch_idx]; + + const sampledTokens = await sampler(logs); + for (const [newTokenId, logProb] of sampledTokens) { + const bigint = BigInt(newTokenId); + // TODO: If branching, use previous beam as a starting point + // update generated ids, model inputs, and length for next step + scores[batch_idx] += logProb; + all_input_ids[batch_idx].push(bigint); + generated_input_ids.push([bigint]); + + // TODO: Support beam search + break; + } + } + if (streamer) { + streamer.put(generated_input_ids); + } + + const stop = prepared_stopping_criteria(all_input_ids); + if (stop.every(x => x)) { + break; + } + + model_inputs = this._update_model_kwargs_for_generation({ + generated_input_ids, outputs, model_inputs, is_encoder_decoder, + }); + } + + if (streamer) { + streamer.end(); + } + + // Retrieve and dispose all final past key values (including encoder attentions) + const past_key_values = this.getPastKeyValues(outputs, model_inputs.past_key_values, true); + + // TODO: ensure all_input_ids is padded correctly... + const sequences = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', all_input_ids.flat(), [all_input_ids.length, all_input_ids[0].length]); + + if (generation_config.return_dict_in_generate) { + return { + sequences, + past_key_values, + ...attentions, + // TODO: + // scores, + // logits, + } + } else { + // Dispose all remaining tensors + for (const tensor of Object.values(outputs)) { + if (tensor.location === 'gpu-buffer') { + tensor.dispose(); + } + } + return sequences; + } + } + + /** + * Returns an object containing past key values from the given decoder results object. + * + * @param {Object} decoderResults The decoder results object. + * @param {Object} pastKeyValues The previous past key values. + * @returns {Object} An object containing past key values. + */ + getPastKeyValues(decoderResults, pastKeyValues, disposeEncoderPKVs = false) { + const pkvs = Object.create(null); + + for (const name in decoderResults) { + if (name.startsWith('present')) { + const newName = name + .replace('present_conv', 'past_conv') // Hybrid cache architecture (e.g., LFM2) + .replace('present', 'past_key_values'); + const is_encoder_pkv = name.includes('encoder'); + if (is_encoder_pkv && pastKeyValues) { + // Optimization introduced by optimum to reuse past key values. + // So, we just replace the constant outputs (`decoderResults[name]`) with the previous past key values. + // https://github.com/huggingface/optimum/blob/0bf2c05fb7e1182b52d21b703cfc95fd9e4ea3dc/optimum/onnxruntime/base.py#L677-L704 + pkvs[newName] = pastKeyValues[newName]; + } else { // decoder or using first encoder PKVs + pkvs[newName] = decoderResults[name]; + } + + if (pastKeyValues && (!is_encoder_pkv || disposeEncoderPKVs)) { + // - Always dispose decoder PKVs + // - Only dispose encoder past key values when requested (after generation) + const t = pastKeyValues[newName]; + if (t.location === 'gpu-buffer') { + t.dispose(); + } + } + } + } + return pkvs; + } + + /** + * Returns an object containing attentions from the given model output object. + * + * @param {Object} model_output The output of the model. + * @returns {{cross_attentions?: Tensor[]}} An object containing attentions. + */ + getAttentions(model_output) { + const attentions = {}; + + for (const attnName of ['cross_attentions', 'encoder_attentions', 'decoder_attentions']) { + for (const name in model_output) { + if (name.startsWith(attnName)) { + if (!(attnName in attentions)) { + attentions[attnName] = []; + } + attentions[attnName].push(model_output[name]); + } + } + } + return attentions; + } + + /** + * Adds past key values to the decoder feeds object. If pastKeyValues is null, creates new tensors for past key values. + * + * @param {Object} decoderFeeds The decoder feeds object to add past key values to. + * @param {Object} pastKeyValues An object containing past key values. + */ + addPastKeyValues(decoderFeeds, pastKeyValues) { + if (pastKeyValues) { + Object.assign(decoderFeeds, pastKeyValues) + } else { + const session = this.sessions['decoder_model_merged'] ?? this.sessions['model']; + const batch_size = (decoderFeeds[this.main_input_name] ?? decoderFeeds.attention_mask)?.dims?.[0] ?? 1; + + const dtype = session?.config?.kv_cache_dtype ?? 'float32'; + const cls = (dtype === 'float16') ? _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.DataTypeMap.float16 : _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.DataTypeMap.float32; + const shapes = (0,_configs_js__WEBPACK_IMPORTED_MODULE_0__.getCacheShapes)(this.config, { batch_size }); + for (const name in shapes) { + const size = shapes[name].reduce((a, b) => a * b, 1); + decoderFeeds[name] = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor(dtype, new cls(size), shapes[name]); + } + } + } + + async encode_image({ pixel_values }) { + // image_inputs === { pixel_values } + return (await sessionRun(this.sessions['vision_encoder'], { pixel_values })).image_features; + } + + async encode_text({ input_ids }) { + // text_inputs === { input_ids, attention_mask } + return (await sessionRun(this.sessions['embed_tokens'], { input_ids })).inputs_embeds; + } + + async encode_audio({ audio_values }) { + // audio_inputs === { audio_values } + return (await sessionRun(this.sessions['audio_encoder'], { audio_values })).audio_features; + } +} + +////////////////////////////////////////////////// +// Base model output class +class ModelOutput { } + +/** + * Base class for model's outputs, with potential hidden states and attentions. + */ +class BaseModelOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.last_hidden_state Sequence of hidden-states at the output of the last layer of the model. + * @param {Tensor} [output.hidden_states] Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + * @param {Tensor} [output.attentions] Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + */ + constructor({ last_hidden_state, hidden_states = null, attentions = null }) { + super(); + this.last_hidden_state = last_hidden_state; + this.hidden_states = hidden_states; + this.attentions = attentions; + } +} +////////////////////////////////////////////////// +// Bert models +class BertPreTrainedModel extends PreTrainedModel { } +class BertModel extends BertPreTrainedModel { } + +/** + * BertForMaskedLM is a class representing a BERT model for masked language modeling. + */ +class BertForMaskedLM extends BertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * BertForSequenceClassification is a class representing a BERT model for sequence classification. + */ +class BertForSequenceClassification extends BertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * BertForTokenClassification is a class representing a BERT model for token classification. + */ +class BertForTokenClassification extends BertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * BertForQuestionAnswering is a class representing a BERT model for question answering. + */ +class BertForQuestionAnswering extends BertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// NeoBert models +class NeoBertPreTrainedModel extends PreTrainedModel { } +class NeoBertModel extends NeoBertPreTrainedModel { } + +class NeoBertForMaskedLM extends NeoBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +class NeoBertForSequenceClassification extends NeoBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +class NeoBertForTokenClassification extends NeoBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +class NeoBertForQuestionAnswering extends NeoBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// ModernBert models +class ModernBertPreTrainedModel extends PreTrainedModel { } +class ModernBertModel extends ModernBertPreTrainedModel { } + +class ModernBertForMaskedLM extends ModernBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +class ModernBertForSequenceClassification extends ModernBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +class ModernBertForTokenClassification extends ModernBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// ModernBERT Decoder models +class ModernBertDecoderPreTrainedModel extends PreTrainedModel { } +class ModernBertDecoderModel extends ModernBertDecoderPreTrainedModel { } +class ModernBertDecoderForCausalLM extends ModernBertDecoderPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// NomicBert models +class NomicBertPreTrainedModel extends PreTrainedModel { } +class NomicBertModel extends NomicBertPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// RoFormer models +class RoFormerPreTrainedModel extends PreTrainedModel { } + +/** + * The bare RoFormer Model transformer outputting raw hidden-states without any specific head on top. + */ +class RoFormerModel extends RoFormerPreTrainedModel { } + +/** + * RoFormer Model with a `language modeling` head on top. + */ +class RoFormerForMaskedLM extends RoFormerPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * RoFormer Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) + */ +class RoFormerForSequenceClassification extends RoFormerPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * RoFormer Model with a token classification head on top (a linear layer on top of the hidden-states output) + * e.g. for Named-Entity-Recognition (NER) tasks. + */ +class RoFormerForTokenClassification extends RoFormerPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * RoFormer Model with a span classification head on top for extractive question-answering tasks like SQuAD + * (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). + */ +class RoFormerForQuestionAnswering extends RoFormerPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +// TODO: Add RoFormerForCausalLM and RoFormerForMultipleChoice +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// ConvBert models +class ConvBertPreTrainedModel extends PreTrainedModel { } + +/** + * The bare ConvBERT Model transformer outputting raw hidden-states without any specific head on top. + */ +class ConvBertModel extends ConvBertPreTrainedModel { } + +/** + * ConvBERT Model with a language modeling head on top. + */ +class ConvBertForMaskedLM extends ConvBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * ConvBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) + */ +class ConvBertForSequenceClassification extends ConvBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * ConvBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) + * e.g. for Named-Entity-Recognition (NER) tasks. + */ +class ConvBertForTokenClassification extends ConvBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * ConvBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD + * (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`) + */ +class ConvBertForQuestionAnswering extends ConvBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Electra models +class ElectraPreTrainedModel extends PreTrainedModel { } + +/** + * The bare Electra Model transformer outputting raw hidden-states without any specific head on top. + * Identical to the BERT model except that it uses an additional linear layer between the embedding + * layer and the encoder if the hidden size and embedding size are different. + */ +class ElectraModel extends ElectraPreTrainedModel { } +// TODO add ElectraForPreTraining +/** + * Electra model with a language modeling head on top. + */ +class ElectraForMaskedLM extends ElectraPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * ELECTRA Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) + */ +class ElectraForSequenceClassification extends ElectraPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * Electra model with a token classification head on top. + */ +class ElectraForTokenClassification extends ElectraPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * LECTRA Model with a span classification head on top for extractive question-answering tasks like SQuAD + * (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). + */ +class ElectraForQuestionAnswering extends ElectraPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// CamemBERT models +class CamembertPreTrainedModel extends PreTrainedModel { } + +/** + * The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top. + */ +class CamembertModel extends CamembertPreTrainedModel { } + +/** + * CamemBERT Model with a `language modeling` head on top. + */ +class CamembertForMaskedLM extends CamembertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. + */ +class CamembertForSequenceClassification extends CamembertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. + */ +class CamembertForTokenClassification extends CamembertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * CamemBERT Model with a span classification head on top for extractive question-answering tasks + */ +class CamembertForQuestionAnswering extends CamembertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// DeBERTa models +class DebertaPreTrainedModel extends PreTrainedModel { } + +/** + * The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top. + */ +class DebertaModel extends DebertaPreTrainedModel { } + +/** + * DeBERTa Model with a `language modeling` head on top. + */ +class DebertaForMaskedLM extends DebertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) + */ +class DebertaForSequenceClassification extends DebertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. + */ +class DebertaForTokenClassification extends DebertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear + * layers on top of the hidden-states output to compute `span start logits` and `span end logits`). + */ +class DebertaForQuestionAnswering extends DebertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// DeBERTa-v2 models +class DebertaV2PreTrainedModel extends PreTrainedModel { } + +/** + * The bare DeBERTa-V2 Model transformer outputting raw hidden-states without any specific head on top. + */ +class DebertaV2Model extends DebertaV2PreTrainedModel { } + +/** + * DeBERTa-V2 Model with a `language modeling` head on top. + */ +class DebertaV2ForMaskedLM extends DebertaV2PreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * DeBERTa-V2 Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) + */ +class DebertaV2ForSequenceClassification extends DebertaV2PreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * DeBERTa-V2 Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. + */ +class DebertaV2ForTokenClassification extends DebertaV2PreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * DeBERTa-V2 Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear + * layers on top of the hidden-states output to compute `span start logits` and `span end logits`). + */ +class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// DistilBert models +class DistilBertPreTrainedModel extends PreTrainedModel { } +class DistilBertModel extends DistilBertPreTrainedModel { } + +/** + * DistilBertForSequenceClassification is a class representing a DistilBERT model for sequence classification. + */ +class DistilBertForSequenceClassification extends DistilBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * DistilBertForTokenClassification is a class representing a DistilBERT model for token classification. + */ +class DistilBertForTokenClassification extends DistilBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + + +/** + * DistilBertForQuestionAnswering is a class representing a DistilBERT model for question answering. + */ +class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} + +/** + * DistilBertForMaskedLM is a class representing a DistilBERT model for masking task. + */ +class DistilBertForMaskedLM extends DistilBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// ESM models +class EsmPreTrainedModel extends PreTrainedModel { } + +/** + * The bare ESM Model transformer outputting raw hidden-states without any specific head on top. + */ +class EsmModel extends EsmPreTrainedModel { } + +/** + * ESM Model with a `language modeling` head on top. + */ +class EsmForMaskedLM extends EsmPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * ESM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) + */ +class EsmForSequenceClassification extends EsmPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * ESM Model with a token classification head on top (a linear layer on top of the hidden-states output) + * e.g. for Named-Entity-Recognition (NER) tasks. + */ +class EsmForTokenClassification extends EsmPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// MobileBert models +class MobileBertPreTrainedModel extends PreTrainedModel { } +class MobileBertModel extends MobileBertPreTrainedModel { } + +/** + * MobileBertForMaskedLM is a class representing a MobileBERT model for masking task. + */ +class MobileBertForMaskedLM extends MobileBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) + */ +class MobileBertForSequenceClassification extends MobileBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * MobileBert Model with a span classification head on top for extractive question-answering tasks + */ +class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// MPNet models +class MPNetPreTrainedModel extends PreTrainedModel { } + +/** + * The bare MPNet Model transformer outputting raw hidden-states without any specific head on top. + */ +class MPNetModel extends MPNetPreTrainedModel { } + +/** + * MPNetForMaskedLM is a class representing a MPNet model for masked language modeling. + */ +class MPNetForMaskedLM extends MPNetPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for masked language modeling. + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * MPNetForSequenceClassification is a class representing a MPNet model for sequence classification. + */ +class MPNetForSequenceClassification extends MPNetPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * MPNetForTokenClassification is a class representing a MPNet model for token classification. + */ +class MPNetForTokenClassification extends MPNetPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * MPNetForQuestionAnswering is a class representing a MPNet model for question answering. + */ +class MPNetForQuestionAnswering extends MPNetPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for question answering. + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// SqueezeBert models +class SqueezeBertPreTrainedModel extends PreTrainedModel { } +class SqueezeBertModel extends SqueezeBertPreTrainedModel { } +class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} +class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Albert models +class AlbertPreTrainedModel extends PreTrainedModel { } +class AlbertModel extends AlbertPreTrainedModel { } +class AlbertForSequenceClassification extends AlbertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +class AlbertForQuestionAnswering extends AlbertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +class AlbertForMaskedLM extends AlbertPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// T5 models +class T5PreTrainedModel extends PreTrainedModel { + forward_params = [ + 'input_ids', + 'attention_mask', + 'encoder_outputs', + 'decoder_input_ids', + 'decoder_attention_mask', + 'past_key_values', + ]; +}; + +class T5Model extends T5PreTrainedModel { } + +/** + * T5Model is a class representing a T5 model for conditional generation. + */ +class T5ForConditionalGeneration extends T5PreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// LONGT5 models +/** + * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. + */ +class LongT5PreTrainedModel extends PreTrainedModel { }; + +/** + * The bare LONGT5 Model transformer outputting raw hidden-states without any specific head on top. + */ +class LongT5Model extends LongT5PreTrainedModel { } + +/** + * LONGT5 Model with a `language modeling` head on top. + */ +class LongT5ForConditionalGeneration extends LongT5PreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// MT5 models +class MT5PreTrainedModel extends PreTrainedModel { }; + +class MT5Model extends MT5PreTrainedModel { } + +/** + * A class representing a conditional sequence-to-sequence model based on the MT5 architecture. + */ +class MT5ForConditionalGeneration extends MT5PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Bart models +class BartPretrainedModel extends PreTrainedModel { }; + +/** + * The bare BART Model outputting raw hidden-states without any specific head on top. + */ +class BartModel extends BartPretrainedModel { } + +/** + * The BART Model with a language modeling head. Can be used for summarization. + */ +class BartForConditionalGeneration extends BartPretrainedModel { } + +/** + * Bart model with a sequence classification/head on top (a linear layer on top of the pooled output) + */ +class BartForSequenceClassification extends BartPretrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// MBart models +class MBartPreTrainedModel extends PreTrainedModel { }; + +/** + * The bare MBART Model outputting raw hidden-states without any specific head on top. + */ +class MBartModel extends MBartPreTrainedModel { } + +/** + * The MBART Model with a language modeling head. Can be used for summarization, after fine-tuning the pretrained models. + */ +class MBartForConditionalGeneration extends MBartPreTrainedModel { } + +/** + * MBart model with a sequence classification/head on top (a linear layer on top of the pooled output). + */ +class MBartForSequenceClassification extends MBartPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + + +class MBartForCausalLM extends MBartPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Blenderbot models +class BlenderbotPreTrainedModel extends PreTrainedModel { }; + +/** + * The bare Blenderbot Model outputting raw hidden-states without any specific head on top. + */ +class BlenderbotModel extends BlenderbotPreTrainedModel { } + +/** + * The Blenderbot Model with a language modeling head. Can be used for summarization. + */ +class BlenderbotForConditionalGeneration extends BlenderbotPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Blenderbot models +class BlenderbotSmallPreTrainedModel extends PreTrainedModel { }; + +/** + * The bare BlenderbotSmall Model outputting raw hidden-states without any specific head on top. + */ +class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel { } + +/** + * The BlenderbotSmall Model with a language modeling head. Can be used for summarization. + */ +class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Roberta models +class RobertaPreTrainedModel extends PreTrainedModel { } +class RobertaModel extends RobertaPreTrainedModel { } + +/** + * RobertaForMaskedLM class for performing masked language modeling on Roberta models. + */ +class RobertaForMaskedLM extends RobertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * RobertaForSequenceClassification class for performing sequence classification on Roberta models. + */ +class RobertaForSequenceClassification extends RobertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * RobertaForTokenClassification class for performing token classification on Roberta models. + */ +class RobertaForTokenClassification extends RobertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * RobertaForQuestionAnswering class for performing question answering on Roberta models. + */ +class RobertaForQuestionAnswering extends RobertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// XLM models +/** + * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. + */ +class XLMPreTrainedModel extends PreTrainedModel { } + +/** + * The bare XLM Model transformer outputting raw hidden-states without any specific head on top. + */ +class XLMModel extends XLMPreTrainedModel { } + +/** + * The XLM Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). + */ +class XLMWithLMHeadModel extends XLMPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * XLM Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) + */ +class XLMForSequenceClassification extends XLMPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * XLM Model with a token classification head on top (a linear layer on top of the hidden-states output) + */ +class XLMForTokenClassification extends XLMPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * XLM Model with a span classification head on top for extractive question-answering tasks + */ +class XLMForQuestionAnswering extends XLMPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// XLMRoberta models +class XLMRobertaPreTrainedModel extends PreTrainedModel { } +class XLMRobertaModel extends XLMRobertaPreTrainedModel { } + +/** + * XLMRobertaForMaskedLM class for performing masked language modeling on XLMRoberta models. + */ +class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new MaskedLMOutput(await super._call(model_inputs)); + } +} + +/** + * XLMRobertaForSequenceClassification class for performing sequence classification on XLMRoberta models. + */ +class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * XLMRobertaForTokenClassification class for performing token classification on XLMRoberta models. + */ +class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for token classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * XLMRobertaForQuestionAnswering class for performing question answering on XLMRoberta models. + */ +class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel { + /** + * Calls the model on new inputs. + * + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} returned object + */ + async _call(model_inputs) { + return new QuestionAnsweringModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Audio Spectrogram Transformer (AST) models +class ASTPreTrainedModel extends PreTrainedModel { }; + +/** + * The bare AST Model transformer outputting raw hidden-states without any specific head on top. + */ +class ASTModel extends ASTPreTrainedModel { } + +/** + * Audio Spectrogram Transformer model with an audio classification head on top + * (a linear layer on top of the pooled output) e.g. for datasets like AudioSet, Speech Commands v2. + */ +class ASTForAudioClassification extends ASTPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Whisper models +class WhisperPreTrainedModel extends PreTrainedModel { + + requires_attention_mask = false; + main_input_name = 'input_features'; + forward_params = [ + 'input_features', + 'attention_mask', + 'decoder_input_ids', + 'decoder_attention_mask', + 'past_key_values', + ]; +}; + +/** + * WhisperModel class for training Whisper models without a language model head. + */ +class WhisperModel extends WhisperPreTrainedModel { } + + +/** + * WhisperForConditionalGeneration class for generating conditional outputs from Whisper models. + */ +class WhisperForConditionalGeneration extends WhisperPreTrainedModel { + + _prepare_generation_config(generation_config, kwargs) { + return /** @type {WhisperGenerationConfig} */ (super._prepare_generation_config(generation_config, kwargs, _models_whisper_generation_whisper_js__WEBPACK_IMPORTED_MODULE_15__.WhisperGenerationConfig)); + } + + /** + * + * @param {WhisperGenerationConfig} generation_config + */ + _retrieve_init_tokens(generation_config) { + // prefix tokens are of the form: + // - Multilingual: <|startoftranscript|> <|lang_id|> <|task|> [<|notimestamps|>] + // - English-only: <|startoftranscript|> [<|notimestamps|>] + + // 1. Handle <|startoftranscript|> token + const init_tokens = [generation_config.decoder_start_token_id]; + + // 2. Handle <|lang_id|> and <|task> tokens + let language = generation_config.language; + const task = generation_config.task; + if (generation_config.is_multilingual) { + if (!language) { + // TODO: Implement language detection + console.warn('No language specified - defaulting to English (en).'); + language = 'en'; + } + + // Add language token + const language_code = (0,_models_whisper_common_whisper_js__WEBPACK_IMPORTED_MODULE_16__.whisper_language_to_code)(language); + const language_token = `<|${language_code}|>`; + init_tokens.push(generation_config.lang_to_id[language_token]) + + // Add task token + // NOTE: Defaults to 'transcribe' if no task is specified + init_tokens.push(generation_config.task_to_id[task ?? 'transcribe']); + + } else if (language || task) { + throw new Error( + "Cannot specify `task` or `language` for an English-only model. If the model is intended to be multilingual, pass `is_multilingual=true` to generate, or update the generation config." + ) + } + + // 3. Handle <|notimestamps|> token + if ( + !generation_config.return_timestamps + && generation_config.no_timestamps_token_id + && init_tokens.at(-1) !== generation_config.no_timestamps_token_id + ) { + init_tokens.push(generation_config.no_timestamps_token_id); + } else if ( + generation_config.return_timestamps + && + init_tokens.at(-1) === generation_config.no_timestamps_token_id + ) { + console.warn("<|notimestamps|> prompt token is removed from generation_config since `return_timestamps` is set to `true`."); + init_tokens.pop(); + } + + // let's make sure we don't pass `null` tokens as prompt tokens + return init_tokens.filter(token => token != null); + } + + /** + * Transcribes or translates log-mel input features to a sequence of auto-regressively generated token ids. + * @param {import('./models/whisper/generation_whisper.js').WhisperGenerationFunctionParameters} options + * @returns {Promise} The output of the model, which can contain the generated token ids, attentions, and scores. + */ + async generate({ + inputs = null, + generation_config = null, + logits_processor = null, + stopping_criteria = null, + + // Whisper-specific options (passed to kwargs) + // prompt_ids = null, + // language = null, + // task = null, + + ...kwargs + }) { + generation_config = this._prepare_generation_config(generation_config, kwargs); + + const init_tokens = kwargs.decoder_input_ids ?? this._retrieve_init_tokens(generation_config); + + if (generation_config.return_timestamps) { + logits_processor ??= new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.LogitsProcessorList(); + logits_processor.push( + new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.WhisperTimeStampLogitsProcessor(generation_config, init_tokens) + ); + } + + if (generation_config.begin_suppress_tokens) { + logits_processor ??= new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.LogitsProcessorList(); + logits_processor.push( + new _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_7__.SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, init_tokens.length) + ); + } + + if (generation_config.return_token_timestamps) { + if (!generation_config.alignment_heads) { + throw new Error( + "Model generation config has no `alignment_heads`, token-level timestamps not available. " + + "See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config." + ) + } + + if (generation_config.task === 'translate') { + console.warn("Token-level timestamps may not be reliable for task 'translate'.") + } + + generation_config.output_attentions = true; + generation_config.return_dict_in_generate = true; + } + + const outputs = await super.generate({ + inputs, + generation_config, + logits_processor, + decoder_input_ids: init_tokens, + ...kwargs + }); + + if (generation_config.return_token_timestamps) { + outputs["token_timestamps"] = this._extract_token_timestamps( + // @ts-expect-error TS2345 + outputs, + generation_config.alignment_heads, + generation_config.num_frames, + ); + } + + return outputs; + } + + /** + * Calculates token-level timestamps using the encoder-decoder cross-attentions and + * dynamic time-warping (DTW) to map each output token to a position in the input audio. + * If `num_frames` is specified, the encoder-decoder cross-attentions will be cropped before applying DTW. + * @param {Object} generate_outputs Outputs generated by the model + * @param {Tensor[][]} generate_outputs.cross_attentions The cross attentions output by the model + * @param {Tensor} generate_outputs.sequences The sequences output by the model + * @param {number[][]} alignment_heads Alignment heads of the model + * @param {number} [num_frames=null] Number of frames in the input audio. + * @param {number} [time_precision=0.02] Precision of the timestamps in seconds + * @returns {Tensor} tensor containing the timestamps in seconds for each predicted token + */ + _extract_token_timestamps(generate_outputs, alignment_heads, num_frames = null, time_precision = 0.02) { + if (!generate_outputs.cross_attentions) { + throw new Error( + "Model outputs must contain cross attentions to extract timestamps. " + + "This is most likely because the model was not exported with `output_attentions=True`." + ) + } + if (num_frames == null) { + console.warn( + "`num_frames` has not been set, meaning the entire audio will be analyzed. " + + "This may lead to inaccurate token-level timestamps for short audios (< 30 seconds)." + ); + } + + // @ts-expect-error TS2339 + let median_filter_width = this.config.median_filter_width; + if (median_filter_width === undefined) { + console.warn("Model config has no `median_filter_width`, using default value of 7.") + median_filter_width = 7; + } + + // TODO: Improve batch processing + const batch = generate_outputs.cross_attentions; + // Create a list with `decoder_layers` elements, each a tensor of shape + // (batch size, attention_heads, output length, input length). + // @ts-expect-error TS2339 + const cross_attentions = Array.from({ length: this.config.decoder_layers }, + // Concatenate the cross attentions for each layer across sequence length dimension. + (_, i) => (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)(batch.map(x => x[i]), 2) + ); + + const weights = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.stack)(alignment_heads.map(([l, h]) => { + if (l >= cross_attentions.length) { + throw new Error(`Layer index ${l} is out of bounds for cross attentions (length ${cross_attentions.length}).`) + } + return num_frames + ? cross_attentions[l].slice(null, h, null, [0, num_frames]) + : cross_attentions[l].slice(null, h); + })).transpose(1, 0, 2, 3); + + const [std, calculatedMean] = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.std_mean)(weights, -2, 0, true); + + // Normalize and smoothen the weights. + const smoothedWeights = weights.clone(); // [1, 8, seqLength, 1500] + + for (let a = 0; a < smoothedWeights.dims[0]; ++a) { + const aTensor = smoothedWeights[a]; // [8, seqLength, 1500] + + for (let b = 0; b < aTensor.dims[0]; ++b) { + const bTensor = aTensor[b]; // [seqLength, 1500] + + const stdTensorData = std[a][b][0].data; // [1500] + const meanTensorData = calculatedMean[a][b][0].data; // [1500] + + for (let c = 0; c < bTensor.dims[0]; ++c) { + + let cTensorData = bTensor[c].data; // [1500] + for (let d = 0; d < cTensorData.length; ++d) { + cTensorData[d] = (cTensorData[d] - meanTensorData[d]) / stdTensorData[d] + } + + // Apply median filter. + cTensorData.set((0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_11__.medianFilter)(cTensorData, median_filter_width)) + } + } + } + + // Average the different cross-attention heads. + const batchedMatrices = [(0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.mean)(smoothedWeights, 1)]; + + const timestampsShape = generate_outputs.sequences.dims; + + const timestamps = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor( + 'float32', + new Float32Array(timestampsShape[0] * timestampsShape[1]), + timestampsShape + ); + + // Perform dynamic time warping on each element of the batch. + for (let batch_idx = 0; batch_idx < timestampsShape[0]; ++batch_idx) { + // NOTE: Since we run only one batch at a time, we can squeeze to get the same dimensions + // as the python implementation + const matrix = batchedMatrices[batch_idx].neg().squeeze_(0); + const [text_indices, time_indices] = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_11__.dynamic_time_warping)(matrix.tolist()); + + const diffs = Array.from({ length: text_indices.length - 1 }, (v, i) => text_indices[i + 1] - text_indices[i]); + const jumps = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.mergeArrays)([1], diffs).map(x => !!x); // convert to boolean + + const jump_times = []; + for (let i = 0; i < jumps.length; ++i) { + if (jumps[i]) { + // NOTE: No point in rounding here, since we set to Float32Array later + jump_times.push(time_indices[i] * time_precision); + } + } + timestamps[batch_idx].data.set(jump_times, 1) + } + + return timestamps; + } +} +////////////////////////////////////////////////// + +class LiteWhisperForConditionalGeneration extends WhisperForConditionalGeneration { } + +////////////////////////////////////////////////// +// Moonshine models +class MoonshinePreTrainedModel extends PreTrainedModel { + + requires_attention_mask = false; + main_input_name = 'input_values'; + forward_params = [ + 'input_values', + 'decoder_input_ids', + 'past_key_values', + ]; +}; + +/** + * MoonshineModel class for training Moonshine models without a language model head. + */ +class MoonshineModel extends MoonshinePreTrainedModel { } + +class MoonshineForConditionalGeneration extends MoonshinePreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +/** + * Vision Encoder-Decoder model based on OpenAI's GPT architecture for image captioning and other vision tasks + */ +class VisionEncoderDecoderModel extends PreTrainedModel { + main_input_name = 'pixel_values'; + forward_params = [ + // Encoder inputs + 'pixel_values', + + // Decoder inpputs + 'decoder_input_ids', + 'encoder_hidden_states', + 'past_key_values', + ]; +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// LLaVa Models +class LlavaPreTrainedModel extends PreTrainedModel { + forward_params = [ + 'input_ids', + 'attention_mask', + 'pixel_values', + 'position_ids', + 'past_key_values', + ]; +} + +/** + * The LLAVA model which consists of a vision backbone and a language model. + */ +class LlavaForConditionalGeneration extends LlavaPreTrainedModel { + _merge_input_ids_with_image_features(kwargs) { + const vision_hidden_size = kwargs.image_features.dims.at(-1); + const reshaped_image_hidden_states = kwargs.image_features.view(-1, vision_hidden_size); + + return default_merge_input_ids_with_image_features({ + // @ts-ignore + image_token_id: this.config.image_token_index, + ...kwargs, + image_features: reshaped_image_hidden_states, + }) + } +} +////////////////////////////////////////////////// + +class LlavaOnevisionForConditionalGeneration extends LlavaForConditionalGeneration { } // NOTE: extends LlavaForConditionalGeneration +class Moondream1ForConditionalGeneration extends LlavaForConditionalGeneration { } // NOTE: extends LlavaForConditionalGeneration + +class Florence2PreTrainedModel extends PreTrainedModel { + forward_params = [ + // Encoder inputs + 'input_ids', + 'inputs_embeds', + 'attention_mask', + 'pixel_values', + + // Decoder inputs + 'encoder_outputs', + 'decoder_input_ids', + 'decoder_inputs_embeds', + 'decoder_attention_mask', + 'past_key_values', + ]; + main_input_name = 'inputs_embeds'; +} + +class Florence2ForConditionalGeneration extends Florence2PreTrainedModel { + + _merge_input_ids_with_image_features({ + inputs_embeds, + image_features, + input_ids, + attention_mask, + }) { + return { + inputs_embeds: (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)([ + image_features, // image embeds + inputs_embeds, // task prefix embeds + ], 1), + attention_mask: (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)([ + (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.ones)(image_features.dims.slice(0, 2)), // image attention mask + attention_mask, // task prefix attention mask + ], 1), + } + } + + async _prepare_inputs_embeds({ input_ids, pixel_values, inputs_embeds, attention_mask }) { + if (!input_ids && !pixel_values) { + throw new Error('Either `input_ids` or `pixel_values` should be provided.'); + } + + // 1. Possibly, extract the input embeddings + let text_features, image_features; + if (input_ids) { + text_features = await this.encode_text({ input_ids }); + } + if (pixel_values) { + image_features = await this.encode_image({ pixel_values }); + } + + // 2. Possibly, merge text and images + if (text_features && image_features) { + ({ inputs_embeds, attention_mask } = this._merge_input_ids_with_image_features({ + inputs_embeds: text_features, + image_features, + input_ids, + attention_mask, + })); + } else { + inputs_embeds = text_features || image_features; + } + + return { inputs_embeds, attention_mask }; + } + + async forward({ + input_ids, + pixel_values, + attention_mask, + decoder_input_ids, + decoder_attention_mask, + encoder_outputs, + past_key_values, + + inputs_embeds, + decoder_inputs_embeds, + }) { + if (!inputs_embeds) { + ({ inputs_embeds, attention_mask } = await this._prepare_inputs_embeds({ input_ids, pixel_values, inputs_embeds, attention_mask })); + } + + if (!encoder_outputs) { + // Must compute encoder outputs + let { last_hidden_state } = await encoderForward(this, { inputs_embeds, attention_mask }); + encoder_outputs = last_hidden_state; + } + + if (!decoder_inputs_embeds) { + if (!decoder_input_ids) { + throw new Error('Either `decoder_input_ids` or `decoder_inputs_embeds` should be provided.'); + } + decoder_inputs_embeds = await this.encode_text({ input_ids: decoder_input_ids }); + } + + const decoderFeeds = { + inputs_embeds: decoder_inputs_embeds, + attention_mask: decoder_attention_mask, + encoder_attention_mask: attention_mask, + encoder_hidden_states: encoder_outputs, + past_key_values, + }; + const decoder_outputs = await decoderForward(this, decoderFeeds, true); + return decoder_outputs; + } +} + +class PaliGemmaPreTrainedModel extends PreTrainedModel { + forward_params = [ + 'input_ids', + // 'inputs_embeds', + 'attention_mask', + 'pixel_values', + 'position_ids', + 'past_key_values', + ]; +} + +class PaliGemmaForConditionalGeneration extends PaliGemmaPreTrainedModel { + _merge_input_ids_with_image_features(kwargs) { + const vision_hidden_size = kwargs.image_features.dims.at(-1); + const reshaped_image_hidden_states = kwargs.image_features.view(-1, vision_hidden_size); + + return default_merge_input_ids_with_image_features({ + // @ts-ignore + image_token_id: this.config.image_token_index, + ...kwargs, + image_features: reshaped_image_hidden_states, + }) + } +} + +class LlavaQwen2ForCausalLM extends LlavaPreTrainedModel { + _merge_input_ids_with_image_features(kwargs) { + const vision_hidden_size = kwargs.image_features.dims.at(-1); + const reshaped_image_hidden_states = kwargs.image_features.view(-1, vision_hidden_size); + + return default_merge_input_ids_with_image_features({ + // @ts-ignore + image_token_id: this.config.image_token_index, + ...kwargs, + image_features: reshaped_image_hidden_states, + }) + } +} + +class Gemma3nPreTrainedModel extends PreTrainedModel { + forward_params = [ + 'input_ids', + 'attention_mask', + 'inputs_embeds', + 'per_layer_inputs', + + 'position_ids', + 'pixel_values', + 'input_features', + 'input_features_mask', + 'past_key_values', + ]; +} +class Gemma3nForConditionalGeneration extends Gemma3nPreTrainedModel { + + async forward({ + // Produced by the tokenizer/processor: + input_ids = null, + attention_mask = null, + pixel_values = null, + input_features = null, + input_features_mask = null, + + // Used during generation: + position_ids = null, + inputs_embeds = null, + per_layer_inputs=null, + past_key_values = null, + + // Generic generation parameters + generation_config = null, + logits_processor = null, + + // TODO: needed? + ...kwargs + }) { + if (!inputs_embeds || !per_layer_inputs) { + // 1. Extract the text embeddings. + ({ inputs_embeds, per_layer_inputs} = await sessionRun(this.sessions['embed_tokens'], { + input_ids, + })); + if (input_ids.dims[1] !== 1) { + if (pixel_values) { + // Encode the image + const { image_features } = await sessionRun(this.sessions['vision_encoder'], { + pixel_values, + }); + ({ inputs_embeds, attention_mask } = this._merge_input_ids_with_image_features({ + image_features, + inputs_embeds, + input_ids, + attention_mask, + })); + } + + if (input_features) { + // Encode the audio + const { audio_features } = await sessionRun(this.sessions['audio_encoder'], { + input_features, + input_features_mask, + }); + ({ inputs_embeds, attention_mask } = this._merge_input_ids_with_audio_features({ + audio_features, + inputs_embeds, + input_ids, + attention_mask, + })); + } + } + } + + const outputs = await decoderForward(this, { + inputs_embeds, + per_layer_inputs, + past_key_values, + attention_mask, + position_ids, + generation_config, + logits_processor, + }, true); + return outputs; + } + + _merge_input_ids_with_image_features(kwargs) { + const vision_hidden_size = kwargs.image_features.dims.at(-1); + const reshaped_image_hidden_states = kwargs.image_features.view(-1, vision_hidden_size); + return default_merge_input_ids_with_image_features({ + // @ts-ignore + image_token_id: this.config.image_token_id, + ...kwargs, + image_features: reshaped_image_hidden_states, + }); + } + _merge_input_ids_with_audio_features(kwargs) { + const audio_hidden_size = kwargs.audio_features.dims.at(-1); + const reshaped_audio_features = kwargs.audio_features.view(-1, audio_hidden_size); + + return default_merge_input_ids_with_audio_features({ + // @ts-ignore + audio_token_id: this.config.audio_token_id, + ...kwargs, + audio_features: reshaped_audio_features, + }) + } +} + + +////////////////////////////////////////////////// +// Idefics3 Models +class Idefics3PreTrainedModel extends PreTrainedModel { + forward_params = [ + 'input_ids', + 'attention_mask', + 'pixel_values', + 'pixel_attention_mask', + 'position_ids', + 'past_key_values', + ]; +} + +/** + * The Idefics3 model which consists of a vision backbone and a language model. + */ +class Idefics3ForConditionalGeneration extends Idefics3PreTrainedModel { + + async encode_image({ pixel_values, pixel_attention_mask }) { + const features = (await sessionRun(this.sessions['vision_encoder'], { pixel_values, pixel_attention_mask })).image_features; + return features; + } + + _merge_input_ids_with_image_features(kwargs) { + const vision_hidden_size = kwargs.image_features.dims.at(-1); + const reshaped_image_hidden_states = kwargs.image_features.view(-1, vision_hidden_size); + + return default_merge_input_ids_with_image_features({ + // @ts-ignore + image_token_id: this.config.image_token_id, + ...kwargs, + image_features: reshaped_image_hidden_states, + }) + } +} +////////////////////////////////////////////////// + +/** + * The SmolVLM Model with a language modeling head. + * It is made up a SigLIP vision encoder, with a language modeling head on top. + */ +class SmolVLMForConditionalGeneration extends Idefics3ForConditionalGeneration { } + +////////////////////////////////////////////////// +class Phi3VPreTrainedModel extends PreTrainedModel { + forward_params = [ + 'input_ids', + 'inputs_embeds', + 'attention_mask', + 'position_ids', + 'pixel_values', + 'image_sizes', + 'past_key_values', + ]; +} +class Phi3VForCausalLM extends Phi3VPreTrainedModel { + + async forward({ + // Produced by the tokenizer/processor: + input_ids = null, + attention_mask = null, + pixel_values = null, + image_sizes = null, + + // Used during generation: + position_ids = null, + inputs_embeds = null, + past_key_values = null, + + // Generic generation parameters + generation_config = null, + logits_processor = null, + + // TODO: needed? + ...kwargs + }) { + if (!inputs_embeds) { + let image_features; + if (pixel_values && input_ids.dims[1] !== 1) { + if (!image_sizes) { + throw new Error('`image_sizes` must be provided when `pixel_values` is provided.'); + } + + // Encode the image + ({ image_features } = await sessionRun(this.sessions['vision_encoder'], { + pixel_values, + image_sizes, + })); + } else { + const hidden_size = this.config.normalized_config.hidden_size; + image_features = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor( + 'float32', + [], + [0, hidden_size], + ); + } + + ({ inputs_embeds } = await sessionRun(this.sessions['prepare_inputs_embeds'], { + input_ids, + image_features, + })); + } + + const outputs = await decoderForward(this, { + inputs_embeds, + past_key_values, + attention_mask, + position_ids, + generation_config, + logits_processor, + }, false); + return outputs; + } +} + +////////////////////////////////////////////////// +class CLIPPreTrainedModel extends PreTrainedModel { } + +/** + * CLIP Text and Vision Model with a projection layers on top + * + * **Example:** Perform zero-shot image classification with a `CLIPModel`. + * + * ```javascript + * import { AutoTokenizer, AutoProcessor, CLIPModel, RawImage } from '@huggingface/transformers'; + * + * // Load tokenizer, processor, and model + * let tokenizer = await AutoTokenizer.from_pretrained('Xenova/clip-vit-base-patch16'); + * let processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch16'); + * let model = await CLIPModel.from_pretrained('Xenova/clip-vit-base-patch16'); + * + * // Run tokenization + * let texts = ['a photo of a car', 'a photo of a football match'] + * let text_inputs = tokenizer(texts, { padding: true, truncation: true }); + * + * // Read image and run processor + * let image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg'); + * let image_inputs = await processor(image); + * + * // Run model with both text and pixel inputs + * let output = await model({ ...text_inputs, ...image_inputs }); + * // { + * // logits_per_image: Tensor { + * // dims: [ 1, 2 ], + * // data: Float32Array(2) [ 18.579734802246094, 24.31830596923828 ], + * // }, + * // logits_per_text: Tensor { + * // dims: [ 2, 1 ], + * // data: Float32Array(2) [ 18.579734802246094, 24.31830596923828 ], + * // }, + * // text_embeds: Tensor { + * // dims: [ 2, 512 ], + * // data: Float32Array(1024) [ ... ], + * // }, + * // image_embeds: Tensor { + * // dims: [ 1, 512 ], + * // data: Float32Array(512) [ ... ], + * // } + * // } + * ``` + */ +class CLIPModel extends CLIPPreTrainedModel { } + +/** + * The text model from CLIP without any head or projection on top. + */ +class CLIPTextModel extends CLIPPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'text_model', + }); + } +} + +/** + * CLIP Text Model with a projection layer on top (a linear layer on top of the pooled output) + * + * **Example:** Compute text embeddings with `CLIPTextModelWithProjection`. + * + * ```javascript + * import { AutoTokenizer, CLIPTextModelWithProjection } from '@huggingface/transformers'; + * + * // Load tokenizer and text model + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clip-vit-base-patch16'); + * const text_model = await CLIPTextModelWithProjection.from_pretrained('Xenova/clip-vit-base-patch16'); + * + * // Run tokenization + * let texts = ['a photo of a car', 'a photo of a football match']; + * let text_inputs = tokenizer(texts, { padding: true, truncation: true }); + * + * // Compute embeddings + * const { text_embeds } = await text_model(text_inputs); + * // Tensor { + * // dims: [ 2, 512 ], + * // type: 'float32', + * // data: Float32Array(1024) [ ... ], + * // size: 1024 + * // } + * ``` + */ +class CLIPTextModelWithProjection extends CLIPPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'text_model', + }); + } +} + +/** + * The vision model from CLIP without any head or projection on top. + */ +class CLIPVisionModel extends CLIPPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'vision_model', + }); + } +} + +/** + * CLIP Vision Model with a projection layer on top (a linear layer on top of the pooled output) + * + * **Example:** Compute vision embeddings with `CLIPVisionModelWithProjection`. + * + * ```javascript + * import { AutoProcessor, CLIPVisionModelWithProjection, RawImage} from '@huggingface/transformers'; + * + * // Load processor and vision model + * const processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch16'); + * const vision_model = await CLIPVisionModelWithProjection.from_pretrained('Xenova/clip-vit-base-patch16'); + * + * // Read image and run processor + * let image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg'); + * let image_inputs = await processor(image); + * + * // Compute embeddings + * const { image_embeds } = await vision_model(image_inputs); + * // Tensor { + * // dims: [ 1, 512 ], + * // type: 'float32', + * // data: Float32Array(512) [ ... ], + * // size: 512 + * // } + * ``` + */ +class CLIPVisionModelWithProjection extends CLIPPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'vision_model', + }); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// SigLIP models +class SiglipPreTrainedModel extends PreTrainedModel { } + +/** + * SigLIP Text and Vision Model with a projection layers on top + * + * **Example:** Perform zero-shot image classification with a `SiglipModel`. + * + * ```javascript + * import { AutoTokenizer, AutoProcessor, SiglipModel, RawImage } from '@huggingface/transformers'; + * + * // Load tokenizer, processor, and model + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-224'); + * const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-224'); + * const model = await SiglipModel.from_pretrained('Xenova/siglip-base-patch16-224'); + * + * // Run tokenization + * const texts = ['a photo of 2 cats', 'a photo of 2 dogs']; + * const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true }); + * + * // Read image and run processor + * const image = await RawImage.read('http://images.cocodataset.org/val2017/000000039769.jpg'); + * const image_inputs = await processor(image); + * + * // Run model with both text and pixel inputs + * const output = await model({ ...text_inputs, ...image_inputs }); + * // { + * // logits_per_image: Tensor { + * // dims: [ 1, 2 ], + * // data: Float32Array(2) [ -1.6019744873046875, -10.720091819763184 ], + * // }, + * // logits_per_text: Tensor { + * // dims: [ 2, 1 ], + * // data: Float32Array(2) [ -1.6019744873046875, -10.720091819763184 ], + * // }, + * // text_embeds: Tensor { + * // dims: [ 2, 768 ], + * // data: Float32Array(1536) [ ... ], + * // }, + * // image_embeds: Tensor { + * // dims: [ 1, 768 ], + * // data: Float32Array(768) [ ... ], + * // } + * // } + * ``` + */ +class SiglipModel extends SiglipPreTrainedModel { } + +/** + * The text model from SigLIP without any head or projection on top. + * + * **Example:** Compute text embeddings with `SiglipTextModel`. + * + * ```javascript + * import { AutoTokenizer, SiglipTextModel } from '@huggingface/transformers'; + * + * // Load tokenizer and text model + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/siglip-base-patch16-224'); + * const text_model = await SiglipTextModel.from_pretrained('Xenova/siglip-base-patch16-224'); + * + * // Run tokenization + * const texts = ['a photo of 2 cats', 'a photo of 2 dogs']; + * const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true }); + * + * // Compute embeddings + * const { pooler_output } = await text_model(text_inputs); + * // Tensor { + * // dims: [ 2, 768 ], + * // type: 'float32', + * // data: Float32Array(1536) [ ... ], + * // size: 1536 + * // } + * ``` + */ +class SiglipTextModel extends SiglipPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'text_model', + }); + } +} + +/** + * The vision model from SigLIP without any head or projection on top. + * + * **Example:** Compute vision embeddings with `SiglipVisionModel`. + * + * ```javascript + * import { AutoProcessor, SiglipVisionModel, RawImage} from '@huggingface/transformers'; + * + * // Load processor and vision model + * const processor = await AutoProcessor.from_pretrained('Xenova/siglip-base-patch16-224'); + * const vision_model = await SiglipVisionModel.from_pretrained('Xenova/siglip-base-patch16-224'); + * + * // Read image and run processor + * const image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg'); + * const image_inputs = await processor(image); + * + * // Compute embeddings + * const { pooler_output } = await vision_model(image_inputs); + * // Tensor { + * // dims: [ 1, 768 ], + * // type: 'float32', + * // data: Float32Array(768) [ ... ], + * // size: 768 + * // } + * ``` + */ +class SiglipVisionModel extends CLIPPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'vision_model', + }); + } +} +////////////////////////////////////////////////// +// ChineseCLIP models +class ChineseCLIPPreTrainedModel extends PreTrainedModel { } + +class ChineseCLIPModel extends ChineseCLIPPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// JinaCLIP models +class JinaCLIPPreTrainedModel extends PreTrainedModel { } + +class JinaCLIPModel extends JinaCLIPPreTrainedModel { + async forward(model_inputs) { + const missing_text_inputs = !model_inputs.input_ids; + const missing_image_inputs = !model_inputs.pixel_values; + + if (missing_text_inputs && missing_image_inputs) { + throw new Error('Either `input_ids` or `pixel_values` should be provided.'); + } + + // If either `input_ids` or `pixel_values` aren't passed, we need to create dummy input since the model requires a value to be specified. + if (missing_text_inputs) { + // NOTE: We cannot pass zero-dimension tensor as input for input_ids. + // Fortunately, the majority of time is spent in the vision encoder, so this shouldn't significantly impact performance. + model_inputs.input_ids = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.ones)([model_inputs.pixel_values.dims[0], 1]); + } + + if (missing_image_inputs) { + // NOTE: Since we create a zero-sized tensor, this does not increase computation time. + // @ts-ignore + const { image_size } = this.config.vision_config; + model_inputs.pixel_values = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.full)([0, 3, image_size, image_size], 0.0); // (pass zero-dimension tensor) + } + + const { text_embeddings, image_embeddings, l2norm_text_embeddings, l2norm_image_embeddings } = await super.forward(model_inputs); + + const result = {}; + if (!missing_text_inputs) { + result.text_embeddings = text_embeddings; + result.l2norm_text_embeddings = l2norm_text_embeddings; + } + if (!missing_image_inputs) { + result.image_embeddings = image_embeddings; + result.l2norm_image_embeddings = l2norm_image_embeddings; + } + return result + } +} + +class JinaCLIPTextModel extends JinaCLIPPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'text_model', + }); + } +} + +class JinaCLIPVisionModel extends JinaCLIPPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'vision_model', + }); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// CLIPSeg models +class CLIPSegPreTrainedModel extends PreTrainedModel { } + +class CLIPSegModel extends CLIPSegPreTrainedModel { } + +/** + * CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation. + * + * **Example:** Perform zero-shot image segmentation with a `CLIPSegForImageSegmentation` model. + * + * ```javascript + * import { AutoTokenizer, AutoProcessor, CLIPSegForImageSegmentation, RawImage } from '@huggingface/transformers'; + * + * // Load tokenizer, processor, and model + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clipseg-rd64-refined'); + * const processor = await AutoProcessor.from_pretrained('Xenova/clipseg-rd64-refined'); + * const model = await CLIPSegForImageSegmentation.from_pretrained('Xenova/clipseg-rd64-refined'); + * + * // Run tokenization + * const texts = ['a glass', 'something to fill', 'wood', 'a jar']; + * const text_inputs = tokenizer(texts, { padding: true, truncation: true }); + * + * // Read image and run processor + * const image = await RawImage.read('https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true'); + * const image_inputs = await processor(image); + * + * // Run model with both text and pixel inputs + * const { logits } = await model({ ...text_inputs, ...image_inputs }); + * // logits: Tensor { + * // dims: [4, 352, 352], + * // type: 'float32', + * // data: Float32Array(495616) [ ... ], + * // size: 495616 + * // } + * ``` + * + * You can visualize the predictions as follows: + * ```javascript + * const preds = logits + * .unsqueeze_(1) + * .sigmoid_() + * .mul_(255) + * .round_() + * .to('uint8'); + * + * for (let i = 0; i < preds.dims[0]; ++i) { + * const img = RawImage.fromTensor(preds[i]); + * img.save(`prediction_${i}.png`); + * } + * ``` + */ +class CLIPSegForImageSegmentation extends CLIPSegPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// GPT2 models +class GPT2PreTrainedModel extends PreTrainedModel { } + +class GPT2Model extends GPT2PreTrainedModel { } + +/** + * GPT-2 language model head on top of the GPT-2 base model. This model is suitable for text generation tasks. + */ +class GPT2LMHeadModel extends GPT2PreTrainedModel { } +// export class GPT2ForSequenceClassification extends GPT2PreTrainedModel { +// TODO +// } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// JAIS models +class JAISPreTrainedModel extends PreTrainedModel { } + +/** + * The bare JAIS Model transformer outputting raw hidden-states without any specific head on top. + */ +class JAISModel extends JAISPreTrainedModel { } + +/** + * The JAIS Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). + */ +class JAISLMHeadModel extends JAISPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// GPTNeo models +class GPTNeoPreTrainedModel extends PreTrainedModel { } +class GPTNeoModel extends GPTNeoPreTrainedModel { } + +class GPTNeoForCausalLM extends GPTNeoPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// GPTNeoX models +class GPTNeoXPreTrainedModel extends PreTrainedModel { } +class GPTNeoXModel extends GPTNeoXPreTrainedModel { } + +class GPTNeoXForCausalLM extends GPTNeoXPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// GPT-J models +class GPTJPreTrainedModel extends PreTrainedModel { } + +class GPTJModel extends GPTJPreTrainedModel { } + +class GPTJForCausalLM extends GPTJPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// GPTBigCode models +class GPTBigCodePreTrainedModel extends PreTrainedModel { } + +class GPTBigCodeModel extends GPTBigCodePreTrainedModel { } + +class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// CodeGen models +class CodeGenPreTrainedModel extends PreTrainedModel { } +/** + * CodeGenModel is a class representing a code generation model without a language model head. + */ +class CodeGenModel extends CodeGenPreTrainedModel { } + +/** + * CodeGenForCausalLM is a class that represents a code generation model based on the GPT-2 architecture. It extends the `CodeGenPreTrainedModel` class. + */ +class CodeGenForCausalLM extends CodeGenPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// LLama models + +/** + * The bare LLama Model outputting raw hidden-states without any specific head on top. + */ +class LlamaPreTrainedModel extends PreTrainedModel { } +/** + * The bare LLaMA Model outputting raw hidden-states without any specific head on top. + */ +class LlamaModel extends LlamaPreTrainedModel { } + +class LlamaForCausalLM extends LlamaPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Arcee models +class ArceePreTrainedModel extends PreTrainedModel { } +class ArceeModel extends ArceePreTrainedModel { } +class ArceeForCausalLM extends ArceePreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// LFM2 models +class Lfm2PreTrainedModel extends PreTrainedModel { } +class Lfm2Model extends Lfm2PreTrainedModel { } +class Lfm2ForCausalLM extends Lfm2PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// SmolLM3 models +class SmolLM3PreTrainedModel extends PreTrainedModel { } +class SmolLM3Model extends SmolLM3PreTrainedModel { } +class SmolLM3ForCausalLM extends SmolLM3PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Helium models +class HeliumPreTrainedModel extends PreTrainedModel { } +class HeliumModel extends HeliumPreTrainedModel { } +class HeliumForCausalLM extends HeliumPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Glm models +class GlmPreTrainedModel extends PreTrainedModel { } +class GlmModel extends GlmPreTrainedModel { } +class GlmForCausalLM extends GlmPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// EXAONE models +class ExaonePreTrainedModel extends PreTrainedModel { } +class ExaoneModel extends ExaonePreTrainedModel { } +class ExaoneForCausalLM extends ExaonePreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// MobileLLM models +class MobileLLMPreTrainedModel extends PreTrainedModel { } +class MobileLLMModel extends MobileLLMPreTrainedModel { } +class MobileLLMForCausalLM extends MobileLLMPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// OLMo models +class OlmoPreTrainedModel extends PreTrainedModel { } +class OlmoModel extends OlmoPreTrainedModel { } +class OlmoForCausalLM extends OlmoPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// OLMo2 models +class Olmo2PreTrainedModel extends PreTrainedModel { } +class Olmo2Model extends Olmo2PreTrainedModel { } +class Olmo2ForCausalLM extends Olmo2PreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Granite models +class GranitePreTrainedModel extends PreTrainedModel { } +class GraniteModel extends GranitePreTrainedModel { } +class GraniteForCausalLM extends GranitePreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Cohere models + +/** + * The bare Cohere Model outputting raw hidden-states without any specific head on top. + */ +class CoherePreTrainedModel extends PreTrainedModel { } +class CohereModel extends CoherePreTrainedModel { } + +class CohereForCausalLM extends CoherePreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Gemma models + +/** + * The bare Gemma Model outputting raw hidden-states without any specific head on top. + */ +class GemmaPreTrainedModel extends PreTrainedModel { } +/** + * The bare Gemma Model outputting raw hidden-states without any specific head on top. + */ +class GemmaModel extends GemmaPreTrainedModel { } + +class GemmaForCausalLM extends GemmaPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Gemma2 models + +/** + * The bare Gemma2 Model outputting raw hidden-states without any specific head on top. + */ +class Gemma2PreTrainedModel extends PreTrainedModel { } +/** + * The bare Gemma2 Model outputting raw hidden-states without any specific head on top. + */ +class Gemma2Model extends Gemma2PreTrainedModel { } + +class Gemma2ForCausalLM extends Gemma2PreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Gemma3 models + +/** + * The bare Gemma3 Model outputting raw hidden-states without any specific head on top. + */ +class Gemma3PreTrainedModel extends PreTrainedModel { } +/** + * The bare Gemma3 Model outputting raw hidden-states without any specific head on top. + */ +class Gemma3Model extends Gemma3PreTrainedModel { } + +class Gemma3ForCausalLM extends Gemma3PreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class OpenELMPreTrainedModel extends PreTrainedModel { } +class OpenELMModel extends OpenELMPreTrainedModel { } + +class OpenELMForCausalLM extends OpenELMPreTrainedModel { } + + +////////////////////////////////////////////////// +// Qwen2 models + +/** + * The bare Qwen2 Model outputting raw hidden-states without any specific head on top. + */ +class Qwen2PreTrainedModel extends PreTrainedModel { } +/** + * The bare Qwen2 Model outputting raw hidden-states without any specific head on top. + */ +class Qwen2Model extends Qwen2PreTrainedModel { } + +class Qwen2ForCausalLM extends Qwen2PreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Qwen3 models + +/** + * The bare Qwen3 Model outputting raw hidden-states without any specific head on top. + */ +class Qwen3PreTrainedModel extends PreTrainedModel { } +/** + * The bare Qwen3 Model outputting raw hidden-states without any specific head on top. + */ +class Qwen3Model extends Qwen3PreTrainedModel { } + +class Qwen3ForCausalLM extends Qwen3PreTrainedModel { } +////////////////////////////////////////////////// + +class Qwen2VLPreTrainedModel extends PreTrainedModel { + forward_params = [ + // Text inputs + 'input_ids', + 'attention_mask', + 'position_ids', + 'past_key_values', + + // Vision inputs + 'pixel_values', + 'image_grid_thw', + ]; +} +class Qwen2VLForConditionalGeneration extends Qwen2VLPreTrainedModel { + + /** + * Calculate the 3D rope index based on image and video's temporal, height and width in LLM. + * + * Explanation: + * Each embedding sequence contains vision embedding and text embedding or just contains text embedding. + * + * For pure text embedding sequence, the rotary position embedding has no difference with mordern LLMs. + * Examples: + * input_ids: [T T T T T], here T is for text. + * temporal position_ids: [0, 1, 2, 3, 4] + * height position_ids: [0, 1, 2, 3, 4] + * width position_ids: [0, 1, 2, 3, 4] + * + * For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part + * and 1D rotary position embeddin for text part. + * Examples: + * Assume we have a video input with 3 temporal patches, 2 height patches and 2 width patches. + * input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision. + * vision temporal position_ids: [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2] + * vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] + * vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] + * text temporal position_ids: [3, 4, 5, 6, 7] + * text height position_ids: [3, 4, 5, 6, 7] + * text width position_ids: [3, 4, 5, 6, 7] + * Here we calculate the text start position_ids as the max vision position_ids plus 1. + * + * @param {Tensor} input_ids Indices of input sequence tokens in the vocabulary. Tensor of shape `(batch_size, sequence_length)`. + * @param {Tensor} image_grid_thw (Optional) The temporal, height and width of feature shape of each image in LLM. Tensor of shape `(num_images, 3)`. + * @param {Tensor} video_grid_thw (Optional) The temporal, height and width of feature shape of each video in LLM. Tensor of shape `(num_videos, 3)`. + * @param {Tensor} attention_mask (Optional) Mask to avoid performing attention on padding token indices. Tensor of shape `(batch_size, sequence_length)`. Mask values selected in `[0, 1]`: + * - 1 for tokens that are **not masked**, + * - 0 for tokens that are **masked**. + * @returns {[Tensor, Tensor]} [position_ids, mrope_position_deltas] with: + * - position_ids: Tensor of shape `(3, batch_size, sequence_length)`. + * - mrope_position_deltas: Tensor of shape `(batch_size)`. + */ + get_rope_index(input_ids, image_grid_thw, video_grid_thw, attention_mask) { + // @ts-ignore + const { vision_config, image_token_id, video_token_id, vision_start_token_id } = this.config; + const spatial_merge_size = vision_config.spatial_merge_size ?? 2; + + const mrope_position_deltas = []; + if (image_grid_thw || video_grid_thw) { + let total_input_ids = input_ids.tolist(); + if (!attention_mask) { + attention_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.ones_like)(input_ids); + } + + const attention_mask_list = attention_mask.tolist(); + const position_ids_list = Array.from({ length: 3 }, _ => Array.from({ length: input_ids.dims[0] }, _ => Array.from({ length: input_ids.dims[1] }, _ => 1))); + + const image_grid_thw_list = image_grid_thw ? image_grid_thw.tolist() : []; + const video_grid_thw_list = video_grid_thw ? video_grid_thw.tolist() : []; + + let image_index = 0; + let video_index = 0; + for (let i = 0; i < total_input_ids.length; ++i) { + const ids = total_input_ids[i].filter((_, j) => attention_mask_list[i][j] == 1); + + const vision_start_indices = ids.reduce((acc, x, idx) => { + if (x == vision_start_token_id) acc.push(idx); + return acc; + }, []); + + const vision_tokens = vision_start_indices.map(x => ids[x + 1]); + const image_nums = vision_tokens.filter(x => x == image_token_id).length; + const video_nums = vision_tokens.filter(x => x == video_token_id).length; + + /** @type {number[][]} */ + let llm_pos_ids_list = []; + let st = 0; + let remain_images = image_nums; + let remain_videos = video_nums; + for (let j = 0; j < vision_tokens.length; ++j) { + const next_image_token = ids.findIndex((x, i) => i > st && x == image_token_id); + const next_video_token = ids.findIndex((x, i) => i > st && x == video_token_id); + + const ed_image = (remain_images > 0 && next_image_token !== -1) + ? next_image_token + : ids.length + 1; + + const ed_video = (remain_videos > 0 && next_video_token !== -1) + ? next_video_token + : ids.length + 1; + + let ed; + let t, h, w; + if (ed_image < ed_video) { + ([t, h, w] = image_grid_thw_list[image_index]); + ++image_index; + --remain_images; + ed = ed_image; + } else { + ([t, h, w] = video_grid_thw_list[video_index]); + ++video_index; + --remain_videos; + ed = ed_video; + } + + const [llm_grid_t, llm_grid_h, llm_grid_w] = [ + Number(t), + Math.floor(Number(h) / spatial_merge_size), + Math.floor(Number(w) / spatial_merge_size) + ] + const text_len = ed - st; + const st_idx = llm_pos_ids_list.length > 0 + ? (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_11__.max)(llm_pos_ids_list.at(-1))[0] + 1 + : 0; + + llm_pos_ids_list.push( + Array.from({ length: 3 * text_len }, (_, i) => st_idx + (i % text_len)) + ) + + const offset = text_len + st_idx; + const grid_size = llm_grid_t * llm_grid_h * llm_grid_w; + const t_index = Array.from({ length: grid_size }, (_, i) => offset + Math.floor(i / (llm_grid_h * llm_grid_w))) + const h_index = Array.from({ length: grid_size }, (_, i) => offset + Math.floor(i / llm_grid_w) % llm_grid_h) + const w_index = Array.from({ length: grid_size }, (_, i) => offset + i % llm_grid_w) + + llm_pos_ids_list.push([t_index, h_index, w_index].flat()) + + st = ed + grid_size; + } + + if (st < ids.length) { + const st_idx = llm_pos_ids_list.length > 0 + ? (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_11__.max)(llm_pos_ids_list.at(-1))[0] + 1 + : 0; + const text_len = ids.length - st; + + llm_pos_ids_list.push( + Array.from({ length: 3 * text_len }, (_, i) => (st_idx + (i % text_len))) + ) + } + + // NOTE: Each item in llm_pos_ids_list is an array of shape (3, text_len), + // meaning to perform concatenation along dim=1, we can do the following: + const num_items = llm_pos_ids_list.reduce((acc, x) => acc + x.length, 0); + /** @type {number[]} */ + const llm_positions = new Array(num_items); + let index = 0; + for (let x = 0; x < 3; ++x) { + for (let y = 0; y < llm_pos_ids_list.length; ++y) { + const val = llm_pos_ids_list[y]; + const text_len = val.length / 3; + for (let z = x * text_len; z < (x + 1) * text_len; ++z) { + llm_positions[index++] = val[z]; + } + } + } + + let count = 0; + const attn_mask = attention_mask_list[i]; + for (let y = 0; y < attn_mask.length; ++y) { + if (attn_mask[y] == 1) { + for (let x = 0; x < 3; ++x) { + position_ids_list[x][i][y] = llm_positions[x * num_items / 3 + count]; + } + ++count; + } + } + + const max_llm_positions = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_11__.max)(llm_positions)[0]; + mrope_position_deltas.push(max_llm_positions + 1 - total_input_ids[i].length); + } + + return [ + new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', position_ids_list.flat(Infinity), [3, input_ids.dims[0], input_ids.dims[1]]), + new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', mrope_position_deltas, [mrope_position_deltas.length, 1]), + ]; + + } else { // Text-only + if (attention_mask) { + const { data, dims } = cumsum_masked_fill(attention_mask); + + const position_ids = BigInt64Array.from( + { length: 3 * data.length }, + (_, i) => data[i % data.length] + ); + /** @type {bigint[]} */ + const mrope_position_deltas = Array.from( + { length: dims[0] }, + (_, i) => (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_11__.max)(data.subarray(dims[1] * i, dims[1] * (i + 1)))[0] + 1n + BigInt(dims[1]) + ); + + return [ + new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', position_ids, [3, ...dims]), + new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', mrope_position_deltas, [mrope_position_deltas.length, 1]), + ] + } else { + const [batch_size, seq_length] = input_ids.dims; + const position_ids = BigInt64Array.from( + { length: 3 * batch_size * seq_length }, + (_, i) => BigInt(Math.floor(i % seq_length / batch_size)), + ); + + return [ + new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor('int64', position_ids, [3, ...input_ids.dims]), + (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.zeros)([batch_size, 1]), + ] + } + } + } + + async encode_image({ pixel_values, image_grid_thw }) { + const features = (await sessionRun(this.sessions['vision_encoder'], { pixel_values, grid_thw: image_grid_thw })).image_features; + return features; + } + + _merge_input_ids_with_image_features(kwargs) { + return default_merge_input_ids_with_image_features({ + // @ts-ignore + image_token_id: this.config.image_token_id, + ...kwargs + }) + } + + prepare_inputs_for_generation(input_ids, model_inputs, generation_config) { + // Overwritten -- in specific circumstances we don't want to forward image inputs to the model + if (model_inputs.attention_mask && !model_inputs.position_ids) { + // Calculate position_ids and rope_deltas + if (!model_inputs.past_key_values) { + ([model_inputs.position_ids, model_inputs.rope_deltas] = this.get_rope_index( + model_inputs.input_ids, + model_inputs.image_grid_thw, + model_inputs.video_grid_thw, + model_inputs.attention_mask, + )); + + } else { + model_inputs.pixel_values = null; + // model_inputs.pixel_values_videos = null; + + const delta = BigInt(Object.values(model_inputs.past_key_values)[0].dims.at(-2)); + const rope_deltas_list = model_inputs.rope_deltas.map(x => delta + x); + model_inputs.position_ids = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.stack)([rope_deltas_list, rope_deltas_list, rope_deltas_list], 0) + } + } + + return model_inputs; + } +} + + +////////////////////////////////////////////////// +// Phi models +class PhiPreTrainedModel extends PreTrainedModel { } +/** + * The bare Phi Model outputting raw hidden-states without any specific head on top. + */ +class PhiModel extends PhiPreTrainedModel { } + +class PhiForCausalLM extends PhiPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Phi3 models +class Phi3PreTrainedModel extends PreTrainedModel { } + +/** + * The bare Phi3 Model outputting raw hidden-states without any specific head on top. + */ +class Phi3Model extends Phi3PreTrainedModel { } + +class Phi3ForCausalLM extends Phi3PreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Bloom models +/** + * The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). + */ +class BloomPreTrainedModel extends PreTrainedModel { } + +/** + * The bare Bloom Model transformer outputting raw hidden-states without any specific head on top. + */ +class BloomModel extends BloomPreTrainedModel { } + +/** + * The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). + */ +class BloomForCausalLM extends BloomPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// MPT models +class MptPreTrainedModel extends PreTrainedModel { } + +/** + * The bare Mpt Model transformer outputting raw hidden-states without any specific head on top. + */ +class MptModel extends MptPreTrainedModel { } + +/** + * The MPT Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). + */ +class MptForCausalLM extends MptPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// OPT models +class OPTPreTrainedModel extends PreTrainedModel { } + +/** + * The bare OPT Model outputting raw hidden-states without any specific head on top. + */ +class OPTModel extends OPTPreTrainedModel { } + +/** + * The OPT Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). + */ +class OPTForCausalLM extends OPTPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class ViTPreTrainedModel extends PreTrainedModel { } +class ViTModel extends ViTPreTrainedModel { } +class ViTForImageClassification extends ViTPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class IJepaPreTrainedModel extends PreTrainedModel { } +class IJepaModel extends IJepaPreTrainedModel { } +class IJepaForImageClassification extends IJepaPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class VitPosePreTrainedModel extends PreTrainedModel { } + +/** + * The VitPose model with a pose estimation head on top. + */ +class VitPoseForPoseEstimation extends VitPosePreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class PvtPreTrainedModel extends PreTrainedModel { } +class PvtModel extends PvtPreTrainedModel { } +class PvtForImageClassification extends PvtPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class ViTMAEPreTrainedModel extends PreTrainedModel { } +class ViTMAEModel extends ViTMAEPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class ViTMSNPreTrainedModel extends PreTrainedModel { } +class ViTMSNModel extends ViTMSNPreTrainedModel { } +class ViTMSNForImageClassification extends ViTMSNPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class GroupViTPreTrainedModel extends PreTrainedModel { } +class GroupViTModel extends GroupViTPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class FastViTPreTrainedModel extends PreTrainedModel { } +class FastViTModel extends FastViTPreTrainedModel { } +class FastViTForImageClassification extends FastViTPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class VitMattePreTrainedModel extends PreTrainedModel { } + +/** + * ViTMatte framework leveraging any vision backbone e.g. for ADE20k, CityScapes. + * + * **Example:** Perform image matting with a `VitMatteForImageMatting` model. + * ```javascript + * import { AutoProcessor, VitMatteForImageMatting, RawImage } from '@huggingface/transformers'; + * + * // Load processor and model + * const processor = await AutoProcessor.from_pretrained('Xenova/vitmatte-small-distinctions-646'); + * const model = await VitMatteForImageMatting.from_pretrained('Xenova/vitmatte-small-distinctions-646'); + * + * // Load image and trimap + * const image = await RawImage.fromURL('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_image.png'); + * const trimap = await RawImage.fromURL('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_trimap.png'); + * + * // Prepare image + trimap for the model + * const inputs = await processor(image, trimap); + * + * // Predict alpha matte + * const { alphas } = await model(inputs); + * // Tensor { + * // dims: [ 1, 1, 640, 960 ], + * // type: 'float32', + * // size: 614400, + * // data: Float32Array(614400) [ 0.9894027709960938, 0.9970508813858032, ... ] + * // } + * ``` + * + * You can visualize the alpha matte as follows: + * ```javascript + * import { Tensor, cat } from '@huggingface/transformers'; + * + * // Visualize predicted alpha matte + * const imageTensor = image.toTensor(); + * + * // Convert float (0-1) alpha matte to uint8 (0-255) + * const alphaChannel = alphas + * .squeeze(0) + * .mul_(255) + * .clamp_(0, 255) + * .round_() + * .to('uint8'); + * + * // Concatenate original image with predicted alpha + * const imageData = cat([imageTensor, alphaChannel], 0); + * + * // Save output image + * const outputImage = RawImage.fromTensor(imageData); + * outputImage.save('output.png'); + * ``` + */ +class VitMatteForImageMatting extends VitMattePreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new ImageMattingOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class MobileViTPreTrainedModel extends PreTrainedModel { } +class MobileViTModel extends MobileViTPreTrainedModel { } +class MobileViTForImageClassification extends MobileViTPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +// TODO: MobileViTForSemanticSegmentation + +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class MobileViTV2PreTrainedModel extends PreTrainedModel { } +class MobileViTV2Model extends MobileViTV2PreTrainedModel { } +class MobileViTV2ForImageClassification extends MobileViTV2PreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +// TODO: MobileViTV2ForSemanticSegmentation + +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class OwlViTPreTrainedModel extends PreTrainedModel { } +class OwlViTModel extends OwlViTPreTrainedModel { } +class OwlViTForObjectDetection extends OwlViTPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class Owlv2PreTrainedModel extends PreTrainedModel { } +class Owlv2Model extends Owlv2PreTrainedModel { } +class Owlv2ForObjectDetection extends Owlv2PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Beit Models +class BeitPreTrainedModel extends PreTrainedModel { } +class BeitModel extends BeitPreTrainedModel { } +class BeitForImageClassification extends BeitPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class DetrPreTrainedModel extends PreTrainedModel { } +class DetrModel extends DetrPreTrainedModel { } +class DetrForObjectDetection extends DetrPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new DetrObjectDetectionOutput(await super._call(model_inputs)); + } +} + +class DetrForSegmentation extends DetrPreTrainedModel { + /** + * Runs the model with the provided inputs + * @param {Object} model_inputs Model inputs + * @returns {Promise} Object containing segmentation outputs + */ + async _call(model_inputs) { + return new DetrSegmentationOutput(await super._call(model_inputs)); + } +} + +class DetrObjectDetectionOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits Classification logits (including no-object) for all queries. + * @param {Tensor} output.pred_boxes Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). + * These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). + */ + constructor({ logits, pred_boxes }) { + super(); + this.logits = logits; + this.pred_boxes = pred_boxes; + } +} + +class DetrSegmentationOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits The output logits of the model. + * @param {Tensor} output.pred_boxes Predicted boxes. + * @param {Tensor} output.pred_masks Predicted masks. + */ + constructor({ logits, pred_boxes, pred_masks }) { + super(); + this.logits = logits; + this.pred_boxes = pred_boxes; + this.pred_masks = pred_masks; + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class RTDetrPreTrainedModel extends PreTrainedModel { } +class RTDetrModel extends RTDetrPreTrainedModel { } +class RTDetrForObjectDetection extends RTDetrPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new RTDetrObjectDetectionOutput(await super._call(model_inputs)); + } +} + +class RTDetrObjectDetectionOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits Classification logits (including no-object) for all queries. + * @param {Tensor} output.pred_boxes Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). + * These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). + */ + constructor({ logits, pred_boxes }) { + super(); + this.logits = logits; + this.pred_boxes = pred_boxes; + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class RTDetrV2PreTrainedModel extends PreTrainedModel { } +class RTDetrV2Model extends RTDetrV2PreTrainedModel { } +class RTDetrV2ForObjectDetection extends RTDetrV2PreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new RTDetrV2ObjectDetectionOutput(await super._call(model_inputs)); + } +} + +class RTDetrV2ObjectDetectionOutput extends RTDetrObjectDetectionOutput { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class RFDetrPreTrainedModel extends PreTrainedModel { } +class RFDetrModel extends RFDetrPreTrainedModel { } +class RFDetrForObjectDetection extends RFDetrPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new RFDetrObjectDetectionOutput(await super._call(model_inputs)); + } +} + +class RFDetrObjectDetectionOutput extends RTDetrObjectDetectionOutput { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class DFinePreTrainedModel extends PreTrainedModel { } +class DFineModel extends DFinePreTrainedModel { } +class DFineForObjectDetection extends DFinePreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new RTDetrObjectDetectionOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class TableTransformerPreTrainedModel extends PreTrainedModel { } + +/** + * The bare Table Transformer Model (consisting of a backbone and encoder-decoder Transformer) + * outputting raw hidden-states without any specific head on top. + */ +class TableTransformerModel extends TableTransformerPreTrainedModel { } + +/** + * Table Transformer Model (consisting of a backbone and encoder-decoder Transformer) + * with object detection heads on top, for tasks such as COCO detection. + */ +class TableTransformerForObjectDetection extends TableTransformerPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new TableTransformerObjectDetectionOutput(await super._call(model_inputs)); + } +} +class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class DeiTPreTrainedModel extends PreTrainedModel { } +class DeiTModel extends DeiTPreTrainedModel { } +class DeiTForImageClassification extends DeiTPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class HieraPreTrainedModel extends PreTrainedModel { } +class HieraModel extends HieraPreTrainedModel { } +class HieraForImageClassification extends HieraPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +/** + * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. + */ +class ResNetPreTrainedModel extends PreTrainedModel { } + +/** + * The bare ResNet model outputting raw features without any specific head on top. + */ +class ResNetModel extends ResNetPreTrainedModel { } + +/** + * ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. + */ +class ResNetForImageClassification extends ResNetPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class SwinPreTrainedModel extends PreTrainedModel { } +class SwinModel extends SwinPreTrainedModel { } +class SwinForImageClassification extends SwinPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +class SwinForSemanticSegmentation extends SwinPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class Swin2SRPreTrainedModel extends PreTrainedModel { } + +/** + * The bare Swin2SR Model transformer outputting raw hidden-states without any specific head on top. + */ +class Swin2SRModel extends Swin2SRPreTrainedModel { } + +/** + * Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration. + * + * **Example:** Super-resolution w/ `Xenova/swin2SR-classical-sr-x2-64`. + * + * ```javascript + * import { AutoProcessor, Swin2SRForImageSuperResolution, RawImage } from '@huggingface/transformers'; + * + * // Load processor and model + * const model_id = 'Xenova/swin2SR-classical-sr-x2-64'; + * const processor = await AutoProcessor.from_pretrained(model_id); + * const model = await Swin2SRForImageSuperResolution.from_pretrained(model_id); + * + * // Prepare model inputs + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/butterfly.jpg'; + * const image = await RawImage.fromURL(url); + * const inputs = await processor(image); + * + * // Run model + * const outputs = await model(inputs); + * + * // Convert Tensor to RawImage + * const output = outputs.reconstruction.squeeze().clamp_(0, 1).mul_(255).round_().to('uint8'); + * const outputImage = RawImage.fromTensor(output); + * // RawImage { + * // data: Uint8Array(786432) [ 41, 31, 24, ... ], + * // width: 512, + * // height: 512, + * // channels: 3 + * // } + * ``` + */ +class Swin2SRForImageSuperResolution extends Swin2SRPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class DPTPreTrainedModel extends PreTrainedModel { } + +/** + * The bare DPT Model transformer outputting raw hidden-states without any specific head on top. + */ +class DPTModel extends DPTPreTrainedModel { } + +/** + * DPT Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2. + * + * **Example:** Depth estimation w/ `Xenova/dpt-hybrid-midas`. + * ```javascript + * import { DPTForDepthEstimation, AutoProcessor, RawImage, interpolate_4d } from '@huggingface/transformers'; + * + * // Load model and processor + * const model_id = 'Xenova/dpt-hybrid-midas'; + * const model = await DPTForDepthEstimation.from_pretrained(model_id); + * const processor = await AutoProcessor.from_pretrained(model_id); + * + * // Load image from URL + * const url = 'http://images.cocodataset.org/val2017/000000039769.jpg'; + * const image = await RawImage.read(url); + * + * // Prepare image for the model + * const inputs = await processor(image); + * + * // Run model + * const { predicted_depth } = await model(inputs); + * + * // Interpolate to original size + * const prediction = (await interpolate_4d(predicted_depth.unsqueeze(1), { + * size: image.size.reverse(), + * mode: 'bilinear', + * })).squeeze(1); + * + * // Visualize the prediction + * const min = prediction.min().item(); + * const max = prediction.max().item(); + * const formatted = prediction.sub_(min).div_(max - min).mul_(255).to('uint8'); + * const depth = RawImage.fromTensor(formatted); + * // RawImage { + * // data: Uint8Array(307200) [ 85, 85, 84, ... ], + * // width: 640, + * // height: 480, + * // channels: 1 + * // } + * ``` + */ +class DPTForDepthEstimation extends DPTPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class DepthAnythingPreTrainedModel extends PreTrainedModel { } + +/** + * Depth Anything Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2. + */ +class DepthAnythingForDepthEstimation extends DepthAnythingPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class SapiensPreTrainedModel extends PreTrainedModel { } +class SapiensForSemanticSegmentation extends SapiensPreTrainedModel { } +class SapiensForDepthEstimation extends SapiensPreTrainedModel { } +class SapiensForNormalEstimation extends SapiensPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class DepthProPreTrainedModel extends PreTrainedModel { } +class DepthProForDepthEstimation extends DepthProPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class Metric3DPreTrainedModel extends PreTrainedModel { } +class Metric3DForDepthEstimation extends Metric3DPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class Metric3Dv2PreTrainedModel extends PreTrainedModel { } +class Metric3Dv2ForDepthEstimation extends Metric3Dv2PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class MaskFormerPreTrainedModel extends PreTrainedModel { } +class MaskFormerModel extends MaskFormerPreTrainedModel { } +class MaskFormerForInstanceSegmentation extends MaskFormerPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class GLPNPreTrainedModel extends PreTrainedModel { } + +/** + * The bare GLPN encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top. + */ +class GLPNModel extends GLPNPreTrainedModel { } + +/** + * import { GLPNForDepthEstimation, AutoProcessor, RawImage, interpolate_4d } from '@huggingface/transformers'; + * + * // Load model and processor + * const model_id = 'Xenova/glpn-kitti'; + * const model = await GLPNForDepthEstimation.from_pretrained(model_id); + * const processor = await AutoProcessor.from_pretrained(model_id); + * + * // Load image from URL + * const url = 'http://images.cocodataset.org/val2017/000000039769.jpg'; + * const image = await RawImage.read(url); + * + * // Prepare image for the model + * const inputs = await processor(image); + * + * // Run model + * const { predicted_depth } = await model(inputs); + * + * // Interpolate to original size + * const prediction = (await interpolate_4d(predicted_depth.unsqueeze(1), { + * size: image.size.reverse(), + * mode: 'bilinear', + * })).squeeze(1); + * + * // Visualize the prediction + * const min = prediction.min().item(); + * const max = prediction.max().item(); + * const formatted = prediction.sub_(min).div_(max - min).mul_(255).to('uint8'); + * const depth = RawImage.fromTensor(formatted); + * // RawImage { + * // data: Uint8Array(307200) [ 85, 85, 84, ... ], + * // width: 640, + * // height: 480, + * // channels: 1 + * // } + * ``` + */ +class GLPNForDepthEstimation extends GLPNPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class DonutSwinPreTrainedModel extends PreTrainedModel { } + +/** + * The bare Donut Swin Model transformer outputting raw hidden-states without any specific head on top. + * + * **Example:** Step-by-step Document Parsing. + * + * ```javascript + * import { AutoProcessor, AutoTokenizer, AutoModelForVision2Seq, RawImage } from '@huggingface/transformers'; + * + * // Choose model to use + * const model_id = 'Xenova/donut-base-finetuned-cord-v2'; + * + * // Prepare image inputs + * const processor = await AutoProcessor.from_pretrained(model_id); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/receipt.png'; + * const image = await RawImage.read(url); + * const image_inputs = await processor(image); + * + * // Prepare decoder inputs + * const tokenizer = await AutoTokenizer.from_pretrained(model_id); + * const task_prompt = ''; + * const decoder_input_ids = tokenizer(task_prompt, { + * add_special_tokens: false, + * }).input_ids; + * + * // Create the model + * const model = await AutoModelForVision2Seq.from_pretrained(model_id); + * + * // Run inference + * const output = await model.generate(image_inputs.pixel_values, { + * decoder_input_ids, + * max_length: model.config.decoder.max_position_embeddings, + * }); + * + * // Decode output + * const decoded = tokenizer.batch_decode(output)[0]; + * // CINNAMON SUGAR 17,000 1 x 17,000 17,000 17,000 20,000 3,000 + * ``` + * + * **Example:** Step-by-step Document Visual Question Answering (DocVQA) + * + * ```javascript + * import { AutoProcessor, AutoTokenizer, AutoModelForVision2Seq, RawImage } from '@huggingface/transformers'; + * + * // Choose model to use + * const model_id = 'Xenova/donut-base-finetuned-docvqa'; + * + * // Prepare image inputs + * const processor = await AutoProcessor.from_pretrained(model_id); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png'; + * const image = await RawImage.read(url); + * const image_inputs = await processor(image); + * + * // Prepare decoder inputs + * const tokenizer = await AutoTokenizer.from_pretrained(model_id); + * const question = 'What is the invoice number?'; + * const task_prompt = `${question}`; + * const decoder_input_ids = tokenizer(task_prompt, { + * add_special_tokens: false, + * }).input_ids; + * + * // Create the model + * const model = await AutoModelForVision2Seq.from_pretrained(model_id); + * + * // Run inference + * const output = await model.generate(image_inputs.pixel_values, { + * decoder_input_ids, + * max_length: model.config.decoder.max_position_embeddings, + * }); + * + * // Decode output + * const decoded = tokenizer.batch_decode(output)[0]; + * // What is the invoice number? us-001 + * ``` + */ +class DonutSwinModel extends DonutSwinPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class ConvNextPreTrainedModel extends PreTrainedModel { } + +/** + * The bare ConvNext model outputting raw features without any specific head on top. + */ +class ConvNextModel extends ConvNextPreTrainedModel { } + +/** + * ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. + */ +class ConvNextForImageClassification extends ConvNextPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class ConvNextV2PreTrainedModel extends PreTrainedModel { } + +/** + * The bare ConvNextV2 model outputting raw features without any specific head on top. + */ +class ConvNextV2Model extends ConvNextV2PreTrainedModel { } + +/** + * ConvNextV2 Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. + */ +class ConvNextV2ForImageClassification extends ConvNextV2PreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class Dinov2PreTrainedModel extends PreTrainedModel { } + +/** + * The bare DINOv2 Model transformer outputting raw hidden-states without any specific head on top. + */ +class Dinov2Model extends Dinov2PreTrainedModel { } + +/** + * Dinov2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. + */ +class Dinov2ForImageClassification extends Dinov2PreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class Dinov2WithRegistersPreTrainedModel extends PreTrainedModel { } + +/** + * The bare Dinov2WithRegisters Model transformer outputting raw hidden-states without any specific head on top. + */ +class Dinov2WithRegistersModel extends Dinov2WithRegistersPreTrainedModel { } + +/** + * Dinov2WithRegisters Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. + */ +class Dinov2WithRegistersForImageClassification extends Dinov2WithRegistersPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class DINOv3ViTPreTrainedModel extends PreTrainedModel { } +class DINOv3ViTModel extends DINOv3ViTPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class DINOv3ConvNextPreTrainedModel extends PreTrainedModel { } +class DINOv3ConvNextModel extends DINOv3ConvNextPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class GroundingDinoPreTrainedModel extends PreTrainedModel { } +class GroundingDinoForObjectDetection extends GroundingDinoPreTrainedModel { } + +////////////////////////////////////////////////// +class YolosPreTrainedModel extends PreTrainedModel { } +class YolosModel extends YolosPreTrainedModel { } +class YolosForObjectDetection extends YolosPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new YolosObjectDetectionOutput(await super._call(model_inputs)); + } +} + +class YolosObjectDetectionOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits Classification logits (including no-object) for all queries. + * @param {Tensor} output.pred_boxes Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). + * These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). + */ + constructor({ logits, pred_boxes }) { + super(); + this.logits = logits; + this.pred_boxes = pred_boxes; + } +} +////////////////////////////////////////////////// + + + + +////////////////////////////////////////////////// +class SamPreTrainedModel extends PreTrainedModel { } + +/** + * Segment Anything Model (SAM) for generating segmentation masks, given an input image + * and optional 2D location and bounding boxes. + * + * **Example:** Perform mask generation w/ `Xenova/sam-vit-base`. + * ```javascript + * import { SamModel, AutoProcessor, RawImage } from '@huggingface/transformers'; + * + * const model = await SamModel.from_pretrained('Xenova/sam-vit-base'); + * const processor = await AutoProcessor.from_pretrained('Xenova/sam-vit-base'); + * + * const img_url = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'; + * const raw_image = await RawImage.read(img_url); + * const input_points = [[[450, 600]]] // 2D localization of a window + * + * const inputs = await processor(raw_image, { input_points }); + * const outputs = await model(inputs); + * + * const masks = await processor.post_process_masks(outputs.pred_masks, inputs.original_sizes, inputs.reshaped_input_sizes); + * // [ + * // Tensor { + * // dims: [ 1, 3, 1764, 2646 ], + * // type: 'bool', + * // data: Uint8Array(14002632) [ ... ], + * // size: 14002632 + * // } + * // ] + * const scores = outputs.iou_scores; + * // Tensor { + * // dims: [ 1, 1, 3 ], + * // type: 'float32', + * // data: Float32Array(3) [ + * // 0.8892380595207214, + * // 0.9311248064041138, + * // 0.983696699142456 + * // ], + * // size: 3 + * // } + * ``` + */ +class SamModel extends SamPreTrainedModel { + + /** + * Compute image embeddings and positional image embeddings, given the pixel values of an image. + * @param {Object} model_inputs Object containing the model inputs. + * @param {Tensor} model_inputs.pixel_values Pixel values obtained using a `SamProcessor`. + * @returns {Promise<{ image_embeddings: Tensor, image_positional_embeddings: Tensor }>} The image embeddings and positional image embeddings. + */ + async get_image_embeddings({ pixel_values }) { + // in: + // - pixel_values: tensor.float32[batch_size,3,1024,1024] + // + // out: + // - image_embeddings: tensor.float32[batch_size,256,64,64] + // - image_positional_embeddings: tensor.float32[batch_size,256,64,64] + return await encoderForward(this, { pixel_values }) + } + + /** + * @typedef {Object} SamModelInputs Object containing the model inputs. + * @property {Tensor} pixel_values Pixel values as a Tensor with shape `(batch_size, num_channels, height, width)`. + * These can be obtained using a `SamProcessor`. + * @property {Tensor} [input_points] Input 2D spatial points with shape `(batch_size, num_points, 2)`. + * This is used by the prompt encoder to encode the prompt. + * @property {Tensor} [input_labels] Input labels for the points, as a Tensor of shape `(batch_size, point_batch_size, num_points)`. + * This is used by the prompt encoder to encode the prompt. There are 4 types of labels: + * - `1`: the point is a point that contains the object of interest + * - `0`: the point is a point that does not contain the object of interest + * - `-1`: the point corresponds to the background + * - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + * @property {Tensor} [input_boxes] Input bounding boxes with shape `(batch_size, num_boxes, 4)`. + * @property {Tensor} [image_embeddings] Image embeddings used by the mask decoder. + * @property {Tensor} [image_positional_embeddings] Image positional embeddings used by the mask decoder. + */ + + /** + * @param {SamModelInputs} model_inputs Object containing the model inputs. + * @returns {Promise} The output of the model. + */ + async forward(model_inputs) { + if (!model_inputs.image_embeddings || !model_inputs.image_positional_embeddings) { + // Compute the image embeddings if they are missing + model_inputs = { + ...model_inputs, + ...(await this.get_image_embeddings(model_inputs)) + } + } + + if (!model_inputs.input_labels && model_inputs.input_points) { + // Set default input labels if they are missing + const shape = model_inputs.input_points.dims.slice(0, -1); + const numElements = shape.reduce((a, b) => a * b, 1); + model_inputs.input_labels = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor( + 'int64', + new BigInt64Array(numElements).fill(1n), + shape + ); + } + + const decoder_inputs = { + image_embeddings: model_inputs.image_embeddings, + image_positional_embeddings: model_inputs.image_positional_embeddings, + }; + if (model_inputs.input_points) { + decoder_inputs.input_points = model_inputs.input_points; + } + if (model_inputs.input_labels) { + decoder_inputs.input_labels = model_inputs.input_labels; + } + if (model_inputs.input_boxes) { + decoder_inputs.input_boxes = model_inputs.input_boxes; + } + + // Returns: + // - iou_scores: tensor.float32[batch_size,point_batch_size,3] + // - pred_masks: tensor.float32[batch_size,point_batch_size,3,256,256] + return await sessionRun(this.sessions['prompt_encoder_mask_decoder'], decoder_inputs); + } + + /** + * Runs the model with the provided inputs + * @param {Object} model_inputs Model inputs + * @returns {Promise} Object containing segmentation outputs + */ + async _call(model_inputs) { + return new SamImageSegmentationOutput(await super._call(model_inputs)); + } +} + + +/** + * Base class for Segment-Anything model's output. + */ +class SamImageSegmentationOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.iou_scores The output logits of the model. + * @param {Tensor} output.pred_masks Predicted boxes. + */ + constructor({ iou_scores, pred_masks }) { + super(); + this.iou_scores = iou_scores; + this.pred_masks = pred_masks; + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// MarianMT models +class MarianPreTrainedModel extends PreTrainedModel { }; + +class MarianModel extends MarianPreTrainedModel { } + +class MarianMTModel extends MarianPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// M2M100 models +class M2M100PreTrainedModel extends PreTrainedModel { }; + +class M2M100Model extends M2M100PreTrainedModel { } + +class M2M100ForConditionalGeneration extends M2M100PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Wav2Vec2 models +class Wav2Vec2PreTrainedModel extends PreTrainedModel { }; + +/** + * The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top. + * + * **Example:** Load and run a `Wav2Vec2Model` for feature extraction. + * + * ```javascript + * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers'; + * + * // Read and preprocess audio + * const processor = await AutoProcessor.from_pretrained('Xenova/mms-300m'); + * const audio = await read_audio('https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac', 16000); + * const inputs = await processor(audio); + * + * // Run model with inputs + * const model = await AutoModel.from_pretrained('Xenova/mms-300m'); + * const output = await model(inputs); + * // { + * // last_hidden_state: Tensor { + * // dims: [ 1, 1144, 1024 ], + * // type: 'float32', + * // data: Float32Array(1171456) [ ... ], + * // size: 1171456 + * // } + * // } + * ``` + */ +class Wav2Vec2Model extends Wav2Vec2PreTrainedModel { } + +class Wav2Vec2ForCTC extends Wav2Vec2PreTrainedModel { + /** + * @param {Object} model_inputs + * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform. + * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1] + */ + async _call(model_inputs) { + return new CausalLMOutput(await super._call(model_inputs)); + } +} + +class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * Wav2Vec2 Model with a frame classification head on top for tasks like Speaker Diarization. + */ +class Wav2Vec2ForAudioFrameClassification extends Wav2Vec2PreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// PyAnnote models +class PyAnnotePreTrainedModel extends PreTrainedModel { }; + +/** + * The bare PyAnnote Model transformer outputting raw hidden-states without any specific head on top. + */ +class PyAnnoteModel extends PyAnnotePreTrainedModel { } + +/** + * PyAnnote Model with a frame classification head on top for tasks like Speaker Diarization. + * + * **Example:** Load and run a `PyAnnoteForAudioFrameClassification` for speaker diarization. + * + * ```javascript + * import { AutoProcessor, AutoModelForAudioFrameClassification, read_audio } from '@huggingface/transformers'; + * + * // Load model and processor + * const model_id = 'onnx-community/pyannote-segmentation-3.0'; + * const model = await AutoModelForAudioFrameClassification.from_pretrained(model_id); + * const processor = await AutoProcessor.from_pretrained(model_id); + * + * // Read and preprocess audio + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/mlk.wav'; + * const audio = await read_audio(url, processor.feature_extractor.config.sampling_rate); + * const inputs = await processor(audio); + * + * // Run model with inputs + * const { logits } = await model(inputs); + * // { + * // logits: Tensor { + * // dims: [ 1, 767, 7 ], // [batch_size, num_frames, num_classes] + * // type: 'float32', + * // data: Float32Array(5369) [ ... ], + * // size: 5369 + * // } + * // } + * + * const result = processor.post_process_speaker_diarization(logits, audio.length); + * // [ + * // [ + * // { id: 0, start: 0, end: 1.0512535626298245, confidence: 0.8220156481664611 }, + * // { id: 2, start: 1.0512535626298245, end: 2.3398869619825127, confidence: 0.9008811707860472 }, + * // ... + * // ] + * // ] + * + * // Display result + * console.table(result[0], ['start', 'end', 'id', 'confidence']); + * // ┌─────────┬────────────────────┬────────────────────┬────┬─────────────────────┐ + * // │ (index) │ start │ end │ id │ confidence │ + * // ├─────────┼────────────────────┼────────────────────┼────┼─────────────────────┤ + * // │ 0 │ 0 │ 1.0512535626298245 │ 0 │ 0.8220156481664611 │ + * // │ 1 │ 1.0512535626298245 │ 2.3398869619825127 │ 2 │ 0.9008811707860472 │ + * // │ 2 │ 2.3398869619825127 │ 3.5946089560890773 │ 0 │ 0.7521651315796233 │ + * // │ 3 │ 3.5946089560890773 │ 4.578039708226655 │ 2 │ 0.8491978128022479 │ + * // │ 4 │ 4.578039708226655 │ 4.594995410849717 │ 0 │ 0.2935352600416393 │ + * // │ 5 │ 4.594995410849717 │ 6.121008646925269 │ 3 │ 0.6788051309866024 │ + * // │ 6 │ 6.121008646925269 │ 6.256654267909762 │ 0 │ 0.37125512393851134 │ + * // │ 7 │ 6.256654267909762 │ 8.630452635138397 │ 2 │ 0.7467035186353542 │ + * // │ 8 │ 8.630452635138397 │ 10.088643060721703 │ 0 │ 0.7689364814666032 │ + * // │ 9 │ 10.088643060721703 │ 12.58113134631177 │ 2 │ 0.9123324509131324 │ + * // │ 10 │ 12.58113134631177 │ 13.005023911888312 │ 0 │ 0.4828358177572041 │ + * // └─────────┴────────────────────┴────────────────────┴────┴─────────────────────┘ + * ``` + */ +class PyAnnoteForAudioFrameClassification extends PyAnnotePreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// WeSpeakerResNet models +class WeSpeakerResNetPreTrainedModel extends PreTrainedModel { }; +class WeSpeakerResNetModel extends WeSpeakerResNetPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// UniSpeech models +class UniSpeechPreTrainedModel extends PreTrainedModel { }; + +/** + * The bare UniSpeech Model transformer outputting raw hidden-states without any specific head on top. + */ +class UniSpeechModel extends UniSpeechPreTrainedModel { } + +/** + * UniSpeech Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). + */ +class UniSpeechForCTC extends UniSpeechPreTrainedModel { + /** + * @param {Object} model_inputs + * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform. + * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1] + */ + async _call(model_inputs) { + return new CausalLMOutput(await super._call(model_inputs)); + } +} + +/** + * UniSpeech Model with a sequence classification head on top (a linear layer over the pooled output). + */ +class UniSpeechForSequenceClassification extends UniSpeechPreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// UniSpeechSat models +class UniSpeechSatPreTrainedModel extends PreTrainedModel { }; + +/** + * The bare UniSpeechSat Model transformer outputting raw hidden-states without any specific head on top. + */ +class UniSpeechSatModel extends UniSpeechSatPreTrainedModel { } + +/** + * UniSpeechSat Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). + */ +class UniSpeechSatForCTC extends UniSpeechSatPreTrainedModel { + /** + * @param {Object} model_inputs + * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform. + * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1] + */ + async _call(model_inputs) { + return new CausalLMOutput(await super._call(model_inputs)); + } +} + +/** + * UniSpeechSat Model with a sequence classification head on top (a linear layer over the pooled output). + */ +class UniSpeechSatForSequenceClassification extends UniSpeechSatPreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * UniSpeechSat Model with a frame classification head on top for tasks like Speaker Diarization. + */ +class UniSpeechSatForAudioFrameClassification extends UniSpeechSatPreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Wav2Vec2Bert models +class Wav2Vec2BertPreTrainedModel extends PreTrainedModel { }; + +/** + * The bare Wav2Vec2Bert Model transformer outputting raw hidden-states without any specific head on top. + */ +class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel { } + +/** + * Wav2Vec2Bert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). + */ +class Wav2Vec2BertForCTC extends Wav2Vec2BertPreTrainedModel { + /** + * @param {Object} model_inputs + * @param {Tensor} model_inputs.input_features Float values of input mel-spectrogram. + * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1] + */ + async _call(model_inputs) { + return new CausalLMOutput(await super._call(model_inputs)); + } +} + +/** + * Wav2Vec2Bert Model with a sequence classification head on top (a linear layer over the pooled output). + */ +class Wav2Vec2BertForSequenceClassification extends Wav2Vec2BertPreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Hubert models +class HubertPreTrainedModel extends PreTrainedModel { } + +/** + * The bare Hubert Model transformer outputting raw hidden-states without any specific head on top. + * + * **Example:** Load and run a `HubertModel` for feature extraction. + * + * ```javascript + * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers'; + * + * // Read and preprocess audio + * const processor = await AutoProcessor.from_pretrained('Xenova/hubert-base-ls960'); + * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav', 16000); + * const inputs = await processor(audio); + * + * // Load and run model with inputs + * const model = await AutoModel.from_pretrained('Xenova/hubert-base-ls960'); + * const output = await model(inputs); + * // { + * // last_hidden_state: Tensor { + * // dims: [ 1, 549, 768 ], + * // type: 'float32', + * // data: Float32Array(421632) [0.0682469978928566, 0.08104046434164047, -0.4975186586380005, ...], + * // size: 421632 + * // } + * // } + * ``` + */ +class HubertModel extends Wav2Vec2PreTrainedModel { } + +/** + * Hubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). + */ +class HubertForCTC extends Wav2Vec2PreTrainedModel { + /** + * @param {Object} model_inputs + * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform. + * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1] + */ + async _call(model_inputs) { + return new CausalLMOutput(await super._call(model_inputs)); + } +} + +/** + * Hubert Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB Keyword Spotting. + */ +class HubertForSequenceClassification extends Wav2Vec2PreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// WavLM models +/** + * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. + */ +class WavLMPreTrainedModel extends PreTrainedModel { }; + +/** + * The bare WavLM Model transformer outputting raw hidden-states without any specific head on top. + * + * **Example:** Load and run a `WavLMModel` for feature extraction. + * + * ```javascript + * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers'; + * + * // Read and preprocess audio + * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base'); + * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav', 16000); + * const inputs = await processor(audio); + * + * // Run model with inputs + * const model = await AutoModel.from_pretrained('Xenova/wavlm-base'); + * const output = await model(inputs); + * // { + * // last_hidden_state: Tensor { + * // dims: [ 1, 549, 768 ], + * // type: 'float32', + * // data: Float32Array(421632) [-0.349443256855011, -0.39341306686401367, 0.022836603224277496, ...], + * // size: 421632 + * // } + * // } + * ``` + */ +class WavLMModel extends WavLMPreTrainedModel { } + +/** + * WavLM Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). + */ +class WavLMForCTC extends WavLMPreTrainedModel { + /** + * @param {Object} model_inputs + * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform. + * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1] + */ + async _call(model_inputs) { + return new CausalLMOutput(await super._call(model_inputs)); + } +} + +/** + * WavLM Model with a sequence classification head on top (a linear layer over the pooled output). + */ +class WavLMForSequenceClassification extends WavLMPreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +/** + * WavLM Model with an XVector feature extraction head on top for tasks like Speaker Verification. + * + * **Example:** Extract speaker embeddings with `WavLMForXVector`. + * ```javascript + * import { AutoProcessor, AutoModel, read_audio } from '@huggingface/transformers'; + * + * // Read and preprocess audio + * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base-plus-sv'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav'; + * const audio = await read_audio(url, 16000); + * const inputs = await processor(audio); + * + * // Run model with inputs + * const model = await AutoModel.from_pretrained('Xenova/wavlm-base-plus-sv'); + * const outputs = await model(inputs); + * // { + * // logits: Tensor { + * // dims: [ 1, 512 ], + * // type: 'float32', + * // data: Float32Array(512) [0.5847219228744507, ...], + * // size: 512 + * // }, + * // embeddings: Tensor { + * // dims: [ 1, 512 ], + * // type: 'float32', + * // data: Float32Array(512) [-0.09079201519489288, ...], + * // size: 512 + * // } + * // } + * ``` + */ +class WavLMForXVector extends WavLMPreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits and speaker embeddings. + */ + async _call(model_inputs) { + return new XVectorOutput(await super._call(model_inputs)); + } +} + +/** + * WavLM Model with a frame classification head on top for tasks like Speaker Diarization. + * + * **Example:** Perform speaker diarization with `WavLMForAudioFrameClassification`. + * ```javascript + * import { AutoProcessor, AutoModelForAudioFrameClassification, read_audio } from '@huggingface/transformers'; + * + * // Read and preprocess audio + * const processor = await AutoProcessor.from_pretrained('Xenova/wavlm-base-plus-sd'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav'; + * const audio = await read_audio(url, 16000); + * const inputs = await processor(audio); + * + * // Run model with inputs + * const model = await AutoModelForAudioFrameClassification.from_pretrained('Xenova/wavlm-base-plus-sd'); + * const { logits } = await model(inputs); + * // { + * // logits: Tensor { + * // dims: [ 1, 549, 2 ], // [batch_size, num_frames, num_speakers] + * // type: 'float32', + * // data: Float32Array(1098) [-3.5301010608673096, ...], + * // size: 1098 + * // } + * // } + * + * const labels = logits[0].sigmoid().tolist().map( + * frames => frames.map(speaker => speaker > 0.5 ? 1 : 0) + * ); + * console.log(labels); // labels is a one-hot array of shape (num_frames, num_speakers) + * // [ + * // [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], + * // [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], + * // [0, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], + * // ... + * // ] + * ``` + */ +class WavLMForAudioFrameClassification extends WavLMPreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} An object containing the model's output logits for sequence classification. + */ + async _call(model_inputs) { + return new TokenClassifierOutput(await super._call(model_inputs)); + } +} + +class StyleTextToSpeech2PreTrainedModel extends PreTrainedModel { } +class StyleTextToSpeech2Model extends StyleTextToSpeech2PreTrainedModel { } + +////////////////////////////////////////////////// +// SpeechT5 models +/** + * An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. + */ +class SpeechT5PreTrainedModel extends PreTrainedModel { }; + +/** + * The bare SpeechT5 Encoder-Decoder Model outputting raw hidden-states without any specific pre- or post-nets. + */ +class SpeechT5Model extends SpeechT5PreTrainedModel { }; + +/** + * SpeechT5 Model with a speech encoder and a text decoder. + * + * **Example:** Generate speech from text with `SpeechT5ForSpeechToText`. + * ```javascript + * import { AutoTokenizer, AutoProcessor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, Tensor } from '@huggingface/transformers'; + * + * // Load the tokenizer and processor + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/speecht5_tts'); + * const processor = await AutoProcessor.from_pretrained('Xenova/speecht5_tts'); + * + * // Load the models + * // NOTE: We use the full-precision versions as they are more accurate + * const model = await SpeechT5ForTextToSpeech.from_pretrained('Xenova/speecht5_tts', { dtype: 'fp32' }); + * const vocoder = await SpeechT5HifiGan.from_pretrained('Xenova/speecht5_hifigan', { dtype: 'fp32' }); + * + * // Load speaker embeddings from URL + * const speaker_embeddings_data = new Float32Array( + * await (await fetch('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin')).arrayBuffer() + * ); + * const speaker_embeddings = new Tensor( + * 'float32', + * speaker_embeddings_data, + * [1, speaker_embeddings_data.length] + * ) + * + * // Run tokenization + * const { input_ids } = tokenizer('Hello, my dog is cute'); + * + * // Generate waveform + * const { waveform } = await model.generate_speech(input_ids, speaker_embeddings, { vocoder }); + * console.log(waveform) + * // Tensor { + * // dims: [ 26112 ], + * // type: 'float32', + * // size: 26112, + * // data: Float32Array(26112) [ -0.00043630177970044315, -0.00018082228780258447, ... ], + * // } + * ``` + */ +class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel { } + +/** + * SpeechT5 Model with a text encoder and a speech decoder. + */ +class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel { + + /** + * @typedef {Object} SpeechOutput + * @property {Tensor} [spectrogram] The predicted log-mel spectrogram of shape + * `(output_sequence_length, config.num_mel_bins)`. Returned when no `vocoder` is provided + * @property {Tensor} [waveform] The predicted waveform of shape `(num_frames,)`. Returned when a `vocoder` is provided. + * @property {Tensor} [cross_attentions] The outputs of the decoder's cross-attention layers of shape + * `(config.decoder_layers, config.decoder_attention_heads, output_sequence_length, input_sequence_length)`. returned when `output_cross_attentions` is `true`. + */ + + /** + * Converts a sequence of input tokens into a sequence of mel spectrograms, which are subsequently turned into a speech waveform using a vocoder. + * @param {Tensor} input_values Indices of input sequence tokens in the vocabulary. + * @param {Tensor} speaker_embeddings Tensor containing the speaker embeddings. + * @param {Object} options Optional parameters for generating speech. + * @param {number} [options.threshold=0.5] The generated sequence ends when the predicted stop token probability exceeds this value. + * @param {number} [options.minlenratio=0.0] Used to calculate the minimum required length for the output sequence. + * @param {number} [options.maxlenratio=20.0] Used to calculate the maximum allowed length for the output sequence. + * @param {Object} [options.vocoder=null] The vocoder that converts the mel spectrogram into a speech waveform. If `null`, the output is the mel spectrogram. + * @param {boolean} [options.output_cross_attentions=false] Whether or not to return the attentions tensors of the decoder's cross-attention layers. + * @returns {Promise} A promise which resolves to an object containing the spectrogram, waveform, and cross-attention tensors. + */ + async generate_speech(input_values, speaker_embeddings, { + threshold = 0.5, + minlenratio = 0.0, + maxlenratio = 20.0, + vocoder = null, + // output_cross_attentions = false, // TODO add + } = {}) { + + const model_inputs = { + input_ids: input_values + } + + const { encoder_outputs, encoder_attention_mask } = await encoderForward(this, model_inputs); + + // @ts-expect-error TS2339 + const r = encoder_outputs.dims[1] / this.config.reduction_factor; + const maxlen = Math.floor(r * maxlenratio); + const minlen = Math.floor(r * minlenratio); + + // @ts-expect-error TS2339 + const num_mel_bins = this.config.num_mel_bins; + + let spectrogramParts = []; + let past_key_values = null; + let decoder_outputs = null; + let idx = 0; + + while (true) { + ++idx; + + const use_cache_branch = boolTensor(!!decoder_outputs); + let output_sequence; + if (decoder_outputs) { + output_sequence = decoder_outputs.output_sequence_out; + } else { + output_sequence = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor( + 'float32', + new Float32Array(num_mel_bins), + [1, 1, num_mel_bins], + ) + } + let decoderFeeds = { + use_cache_branch, + output_sequence, + encoder_attention_mask: encoder_attention_mask, + speaker_embeddings: speaker_embeddings, + encoder_hidden_states: encoder_outputs, + }; + + this.addPastKeyValues(decoderFeeds, past_key_values); + decoder_outputs = await sessionRun(this.sessions['decoder_model_merged'], decoderFeeds); + past_key_values = this.getPastKeyValues(decoder_outputs, past_key_values); + + const { prob, spectrum } = decoder_outputs; + spectrogramParts.push(spectrum); + + if (idx >= minlen && ( + // Finished when stop token or maximum length is reached. + Array.from(prob.data).filter(p => p >= threshold).length > 0 || idx >= maxlen + )) { + break; + } + } + + const spectrogram = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.cat)(spectrogramParts); + const { waveform } = await sessionRun(vocoder.sessions['model'], { spectrogram }); + + return { + spectrogram, + waveform, + // cross_attentions: null, // TODO add + } + } +} + +/** + * HiFi-GAN vocoder. + * + * See [SpeechT5ForSpeechToText](./models#module_models.SpeechT5ForSpeechToText) for example usage. + */ +class SpeechT5HifiGan extends PreTrainedModel { + main_input_name = 'spectrogram'; +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// TrOCR models +class TrOCRPreTrainedModel extends PreTrainedModel { } + +/** + * The TrOCR Decoder with a language modeling head. + */ +class TrOCRForCausalLM extends TrOCRPreTrainedModel { } + +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Mistral models +/** + * The bare Mistral Model outputting raw hidden-states without any specific head on top. + */ +class MistralPreTrainedModel extends PreTrainedModel { } + +class MistralModel extends MistralPreTrainedModel { } + +class MistralForCausalLM extends MistralPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// ERNIE-4.5 models +class Ernie4_5_PretrainedModel extends PreTrainedModel { } + +class Ernie4_5_Model extends Ernie4_5_PretrainedModel { } + +class Ernie4_5_ForCausalLM extends Ernie4_5_PretrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Starcoder2 models +/** + * The bare Starcoder2 Model outputting raw hidden-states without any specific head on top. + */ +class Starcoder2PreTrainedModel extends PreTrainedModel { } + +class Starcoder2Model extends Starcoder2PreTrainedModel { } + +class Starcoder2ForCausalLM extends Starcoder2PreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Falcon models +/** + * The bare Falcon Model outputting raw hidden-states without any specific head on top. + */ +class FalconPreTrainedModel extends PreTrainedModel { } + +class FalconModel extends FalconPreTrainedModel { } + +class FalconForCausalLM extends FalconPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// CLAP models +class ClapPreTrainedModel extends PreTrainedModel { } + +class ClapModel extends ClapPreTrainedModel { } + +/** + * CLAP Text Model with a projection layer on top (a linear layer on top of the pooled output). + * + * **Example:** Compute text embeddings with `ClapTextModelWithProjection`. + * + * ```javascript + * import { AutoTokenizer, ClapTextModelWithProjection } from '@huggingface/transformers'; + * + * // Load tokenizer and text model + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/clap-htsat-unfused'); + * const text_model = await ClapTextModelWithProjection.from_pretrained('Xenova/clap-htsat-unfused'); + * + * // Run tokenization + * const texts = ['a sound of a cat', 'a sound of a dog']; + * const text_inputs = tokenizer(texts, { padding: true, truncation: true }); + * + * // Compute embeddings + * const { text_embeds } = await text_model(text_inputs); + * // Tensor { + * // dims: [ 2, 512 ], + * // type: 'float32', + * // data: Float32Array(1024) [ ... ], + * // size: 1024 + * // } + * ``` + */ +class ClapTextModelWithProjection extends ClapPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'text_model', + }); + } +} + +/** + * CLAP Audio Model with a projection layer on top (a linear layer on top of the pooled output). + * + * **Example:** Compute audio embeddings with `ClapAudioModelWithProjection`. + * + * ```javascript + * import { AutoProcessor, ClapAudioModelWithProjection, read_audio } from '@huggingface/transformers'; + * + * // Load processor and audio model + * const processor = await AutoProcessor.from_pretrained('Xenova/clap-htsat-unfused'); + * const audio_model = await ClapAudioModelWithProjection.from_pretrained('Xenova/clap-htsat-unfused'); + * + * // Read audio and run processor + * const audio = await read_audio('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cat_meow.wav'); + * const audio_inputs = await processor(audio); + * + * // Compute embeddings + * const { audio_embeds } = await audio_model(audio_inputs); + * // Tensor { + * // dims: [ 1, 512 ], + * // type: 'float32', + * // data: Float32Array(512) [ ... ], + * // size: 512 + * // } + * ``` + */ +class ClapAudioModelWithProjection extends ClapPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'audio_model', + }); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// VITS models +class VitsPreTrainedModel extends PreTrainedModel { } + +/** + * The complete VITS model, for text-to-speech synthesis. + * + * **Example:** Generate speech from text with `VitsModel`. + * ```javascript + * import { AutoTokenizer, VitsModel } from '@huggingface/transformers'; + * + * // Load the tokenizer and model + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/mms-tts-eng'); + * const model = await VitsModel.from_pretrained('Xenova/mms-tts-eng'); + * + * // Run tokenization + * const inputs = tokenizer('I love transformers'); + * + * // Generate waveform + * const { waveform } = await model(inputs); + * // Tensor { + * // dims: [ 1, 35328 ], + * // type: 'float32', + * // data: Float32Array(35328) [ ... ], + * // size: 35328, + * // } + * ``` + */ +class VitsModel extends VitsPreTrainedModel { + /** + * Calls the model on new inputs. + * @param {Object} model_inputs The inputs to the model. + * @returns {Promise} The outputs for the VITS model. + */ + async _call(model_inputs) { + return new VitsModelOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Segformer models +class SegformerPreTrainedModel extends PreTrainedModel { } + +/** + * The bare SegFormer encoder (Mix-Transformer) outputting raw hidden-states without any specific head on top. + */ +class SegformerModel extends SegformerPreTrainedModel { } + +/** + * SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden states) e.g. for ImageNet. + */ +class SegformerForImageClassification extends SegformerPreTrainedModel { } + +/** + * SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes. + */ +class SegformerForSemanticSegmentation extends SegformerPreTrainedModel { } + +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// StableLm models +class StableLmPreTrainedModel extends PreTrainedModel { } + +/** + * The bare StableLm Model transformer outputting raw hidden-states without any specific head on top. + */ +class StableLmModel extends StableLmPreTrainedModel { } + +/** + * StableLm Model with a `language modeling` head on top for Causal Language Modeling (with past). + */ +class StableLmForCausalLM extends StableLmPreTrainedModel { } +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +class EfficientNetPreTrainedModel extends PreTrainedModel { } + +/** + * The bare EfficientNet model outputting raw features without any specific head on top. + */ +class EfficientNetModel extends EfficientNetPreTrainedModel { } + +/** + * EfficientNet Model with an image classification head on top (a linear layer on top of the pooled features). + */ +class EfficientNetForImageClassification extends EfficientNetPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Musicgen models +class MusicgenPreTrainedModel extends PreTrainedModel { } + +/** + * The bare Musicgen decoder model outputting raw hidden-states without any specific head on top. + */ +class MusicgenModel extends MusicgenPreTrainedModel { } + +/** + * The MusicGen decoder model with a language modelling head on top. + */ +class MusicgenForCausalLM extends MusicgenPreTrainedModel { } + +/** + * The composite MusicGen model with a text encoder, audio encoder and Musicgen decoder, + * for music generation tasks with one or both of text and audio prompts. + * + * **Example:** Generate music from text with `Xenova/musicgen-small`. + * ```javascript + * import { AutoTokenizer, MusicgenForConditionalGeneration } from '@huggingface/transformers'; + * + * // Load tokenizer and model + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/musicgen-small'); + * const model = await MusicgenForConditionalGeneration.from_pretrained( + * 'Xenova/musicgen-small', { dtype: 'fp32' } + * ); + * + * // Prepare text input + * const prompt = '80s pop track with bassy drums and synth'; + * const inputs = tokenizer(prompt); + * + * // Generate audio + * const audio_values = await model.generate({ + * ...inputs, + * max_new_tokens: 512, + * do_sample: true, + * guidance_scale: 3, + * }); + * + * // (Optional) Write the output to a WAV file + * import wavefile from 'wavefile'; + * import fs from 'fs'; + * + * const wav = new wavefile.WaveFile(); + * wav.fromScratch(1, model.config.audio_encoder.sampling_rate, '32f', audio_values.data); + * fs.writeFileSync('musicgen_out.wav', wav.toBuffer()); + * ``` + */ +class MusicgenForConditionalGeneration extends PreTrainedModel { // NOTE: not MusicgenPreTrainedModel + forward_params = [ + 'input_ids', + 'attention_mask', + 'encoder_outputs', + 'decoder_input_ids', + 'decoder_attention_mask', + 'past_key_values', + ]; + + /** + * Apply the pattern mask to the final ids, + * then revert the pattern delay mask by filtering the pad token id in a single step. + * @param {Tensor} outputs The output tensor from the model. + * @returns {Tensor} The filtered output tensor. + */ + _apply_and_filter_by_delay_pattern_mask(outputs) { + const [bs_x_codebooks, seqLength] = outputs.dims; + // @ts-expect-error TS2339 + const num_codebooks = this.config.decoder.num_codebooks; + const upperBound = (seqLength - num_codebooks); + + let newDataSize = 0; + for (let i = 0; i < outputs.size; ++i) { + // @ts-expect-error TS2339 + if (outputs.data[i] === this.config.decoder.pad_token_id) { + continue; + } + + const row = (i % seqLength); + const col = Math.floor(i / seqLength) % num_codebooks; + + const diff = row - col; + if (diff > 0 && diff <= upperBound) { + outputs.data[newDataSize++] = outputs.data[i]; + } + } + + const batch_size = Math.floor(bs_x_codebooks / num_codebooks); + const inferred = newDataSize / (batch_size * num_codebooks); + // TODO: assert `inferred` is an integer + return new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_9__.Tensor( + outputs.type, + outputs.data.slice(0, newDataSize), + [batch_size, num_codebooks, inferred] + ); + } + + + prepare_inputs_for_generation(input_ids, model_inputs, generation_config) { + // apply the delay pattern mask + let clonedInputIds = structuredClone(input_ids); + for (let i = 0; i < clonedInputIds.length; ++i) { + for (let j = 0; j < clonedInputIds[i].length; ++j) { + // @ts-expect-error TS2339 + if ((i % this.config.decoder.num_codebooks) >= j) { + // @ts-expect-error TS2339 + clonedInputIds[i][j] = BigInt(this.config.decoder.pad_token_id); + } + } + } + // for classifier free guidance we need to replicate the decoder args across the batch dim + // (we'll split these before sampling) + if (generation_config.guidance_scale !== null && generation_config.guidance_scale > 1) { + // [batch, seqLength] -> [2 * batch, seqLength] + clonedInputIds = clonedInputIds.concat(clonedInputIds); + } + + const prepped = super.prepare_inputs_for_generation(clonedInputIds, model_inputs, generation_config); + return prepped; + } + + /** + * Generates sequences of token ids for models with a language modeling head. + * @param {import('./generation/parameters.js').GenerationFunctionParameters} options + * @returns {Promise} The output of the model, which can contain the generated token ids, attentions, and scores. + */ + async generate(options) { + + const output_ids = await super.generate(options); + + // apply the pattern mask to the final ids + // tensor: int64[1,batch_size,4,chunk_length] + const audio_codes = this._apply_and_filter_by_delay_pattern_mask( + /** @type {Tensor} */(output_ids) + ).unsqueeze_(0); // append the frame dimension back to the audio codes + + const { audio_values } = await sessionRun(this.sessions['encodec_decode'], { audio_codes }) + + return audio_values; + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// MobileNetV1 models +class MobileNetV1PreTrainedModel extends PreTrainedModel { } + +/** + * The bare MobileNetV1 model outputting raw hidden-states without any specific head on top. + */ +class MobileNetV1Model extends MobileNetV1PreTrainedModel { } + +/** + * MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), + * e.g. for ImageNet. + */ +class MobileNetV1ForImageClassification extends MobileNetV1PreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} + +class MobileNetV1ForSemanticSegmentation extends MobileNetV1PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// MobileNetV2 models +class MobileNetV2PreTrainedModel extends PreTrainedModel { } + +/** + * The bare MobileNetV2 model outputting raw hidden-states without any specific head on top. + */ +class MobileNetV2Model extends MobileNetV2PreTrainedModel { } + +/** + * MobileNetV2 model with an image classification head on top (a linear layer on top of the pooled features), + * e.g. for ImageNet. + */ +class MobileNetV2ForImageClassification extends MobileNetV2PreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +class MobileNetV2ForSemanticSegmentation extends MobileNetV2PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// MobileNetV3 models +class MobileNetV3PreTrainedModel extends PreTrainedModel { } + +/** + * The bare MobileNetV3 model outputting raw hidden-states without any specific head on top. + */ +class MobileNetV3Model extends MobileNetV3PreTrainedModel { } + +/** + * MobileNetV3 model with an image classification head on top (a linear layer on top of the pooled features), + * e.g. for ImageNet. + */ +class MobileNetV3ForImageClassification extends MobileNetV3PreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +class MobileNetV3ForSemanticSegmentation extends MobileNetV3PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// MobileNetV4 models +class MobileNetV4PreTrainedModel extends PreTrainedModel { } + +/** + * The bare MobileNetV4 model outputting raw hidden-states without any specific head on top. + */ +class MobileNetV4Model extends MobileNetV4PreTrainedModel { } + +/** + * MobileNetV4 model with an image classification head on top (a linear layer on top of the pooled features), + * e.g. for ImageNet. + */ +class MobileNetV4ForImageClassification extends MobileNetV4PreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new SequenceClassifierOutput(await super._call(model_inputs)); + } +} +class MobileNetV4ForSemanticSegmentation extends MobileNetV4PreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// Decision Transformer models +class DecisionTransformerPreTrainedModel extends PreTrainedModel { } + +/** + * The model builds upon the GPT2 architecture to perform autoregressive prediction of actions in an offline RL setting. + * Refer to the paper for more details: https://huggingface.co/papers/2106.01345 + */ +class DecisionTransformerModel extends DecisionTransformerPreTrainedModel { } + +////////////////////////////////////////////////// + +class MultiModalityPreTrainedModel extends PreTrainedModel { } +class MultiModalityCausalLM extends MultiModalityPreTrainedModel { + forward_params = [ + // prepare_inputs_embeds + 'input_ids', + 'pixel_values', + 'images_seq_mask', + 'images_emb_mask', + + // language_model + 'attention_mask', + 'position_ids', + 'past_key_values', + ]; + + /** + * @param {ConstructorParameters} args + */ + constructor(...args) { + super(...args); + + // State-based approach to switch out which heads to use during generation + this._generation_mode = 'text'; + } + + async forward(model_inputs) { + const mode = this._generation_mode ?? 'text'; + + // TODO support re-using PKVs for input_ids.dims[1] !== 1 + // if (model_inputs.past_key_values) { + // // && model_inputs.input_ids.dims[1] === 1 + // } + + let output_1; + if (mode === 'text' || !model_inputs.past_key_values) { + const session = this.sessions['prepare_inputs_embeds']; + const prep_inputs = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.pick)(model_inputs, session.inputNames); + output_1 = await sessionRun(session, prep_inputs); + } else { + const session = this.sessions['gen_img_embeds']; + const prep_inputs = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.pick)({ + image_ids: model_inputs.input_ids, + }, session.inputNames); + output_1 = await sessionRun(session, prep_inputs); + } + + const input_2 = { ...model_inputs, ...output_1 } + const output_2 = await decoderForward(this, input_2); + + const head = this.sessions[ + mode === 'text' + ? 'lm_head' + : 'gen_head' + ]; + if (!head) { + throw new Error(`Unable to find "${head}" generation head`); + } + + const output_3 = await sessionRun(head, (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.pick)(output_2, head.inputNames)) + + return { + ...output_1, + ...output_2, + ...output_3, + }; + } + + /** + * @param {import('./generation/parameters.js').GenerationFunctionParameters} options + */ + async generate(options) { + this._generation_mode = 'text'; + return super.generate(options); + } + + /** + * @param {import('./generation/parameters.js').GenerationFunctionParameters} options + */ + async generate_images(options) { + this._generation_mode = 'image'; + + const start_num_tokens = (options.inputs ?? options[this.main_input_name]).dims[1]; + const all_tokens = await super.generate(options); + + const generated_tokens = (/** @type {Tensor} */(all_tokens)).slice(null, [start_num_tokens, null]) + + const image_decode = this.sessions['image_decode']; + const { decoded_image } = await sessionRun(image_decode, { + generated_tokens, + }); + + // Equivalent to `np.clip((dec + 1) / 2 * 255, 0, 255)` + const clamped = decoded_image + .add_(1) + .mul_(255 / 2) + .clamp_(0, 255) + .to('uint8'); + + // Return as a list of images + const images = []; + for (const tensor of clamped) { + const img = _utils_image_js__WEBPACK_IMPORTED_MODULE_10__.RawImage.fromTensor(tensor); + images.push(img); + } + return images; + } +} + +class MgpstrModelOutput extends ModelOutput { + constructor({ char_logits, bpe_logits, wp_logits }) { + super(); + this.char_logits = char_logits; + this.bpe_logits = bpe_logits; + this.wp_logits = wp_logits; + } + + get logits() { + return [this.char_logits, this.bpe_logits, this.wp_logits]; + } +} + +class MgpstrPreTrainedModel extends PreTrainedModel { } + +/** + * MGP-STR Model transformer with three classification heads on top + * (three A^3 modules and three linear layer on top of the transformer encoder output) for scene text recognition (STR). + */ +class MgpstrForSceneTextRecognition extends MgpstrPreTrainedModel { + /** + * @param {any} model_inputs + */ + async _call(model_inputs) { + return new MgpstrModelOutput(await super._call(model_inputs)); + } +} + +////////////////////////////////////////////////// +// PatchTST Transformer models +class PatchTSTPreTrainedModel extends PreTrainedModel { } + +/** + * The bare PatchTST Model outputting raw hidden-states without any specific head. + */ +class PatchTSTModel extends PatchTSTPreTrainedModel { } + +/** + * The PatchTST for prediction model. + */ +class PatchTSTForPrediction extends PatchTSTPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// PatchTSMixer Transformer models +class PatchTSMixerPreTrainedModel extends PreTrainedModel { } + +/** + * The bare PatchTSMixer Model outputting raw hidden-states without any specific head. + */ +class PatchTSMixerModel extends PatchTSMixerPreTrainedModel { } + +/** + * The PatchTSMixer for prediction model. + */ +class PatchTSMixerForPrediction extends PatchTSMixerPreTrainedModel { } +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class UltravoxPreTrainedModel extends PreTrainedModel { + forward_params = [ + 'input_ids', + 'attention_mask', + 'position_ids', + 'audio_values', + 'past_key_values', + ]; +} + +class UltravoxModel extends UltravoxPreTrainedModel { + + _merge_input_ids_with_audio_features(kwargs) { + const audio_hidden_size = kwargs.audio_features.dims.at(-1); + const reshaped_audio_features = kwargs.audio_features.view(-1, audio_hidden_size); + + return default_merge_input_ids_with_audio_features({ + // @ts-ignore + audio_token_id: this.config.ignore_index ?? this.config.audio_token_id, + ...kwargs, + audio_features: reshaped_audio_features, + }) + } +} +////////////////////////////////////////////////// + +class VoxtralForConditionalGeneration extends UltravoxModel { } + +////////////////////////////////////////////////// +// Mimi models +class MimiPreTrainedModel extends PreTrainedModel { + main_input_name = 'input_values'; + forward_params = ['input_values']; +} + +class MimiEncoderOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.audio_codes Discrete code embeddings, of shape `(batch_size, num_quantizers, codes_length)`. + */ + constructor({ audio_codes }) { + super(); + this.audio_codes = audio_codes; + } +} + +class MimiDecoderOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.audio_values Decoded audio values, of shape `(batch_size, num_channels, sequence_length)`. + */ + constructor({ audio_values }) { + super(); + this.audio_values = audio_values; + } +} + +/** + * The Mimi neural audio codec model. + */ +class MimiModel extends MimiPreTrainedModel { + /** + * Encodes the input audio waveform into discrete codes. + * @param {Object} inputs Model inputs + * @param {Tensor} [inputs.input_values] Float values of the input audio waveform, of shape `(batch_size, channels, sequence_length)`). + * @returns {Promise} The output tensor of shape `(batch_size, num_codebooks, sequence_length)`. + */ + async encode(inputs) { + return new MimiEncoderOutput(await sessionRun(this.sessions['encoder_model'], inputs)); + } + + /** + * Decodes the given frames into an output audio waveform. + * @param {MimiEncoderOutput} inputs The encoded audio codes. + * @returns {Promise} The output tensor of shape `(batch_size, num_channels, sequence_length)`. + */ + async decode(inputs) { + return new MimiDecoderOutput(await sessionRun(this.sessions['decoder_model'], inputs)); + } +} + +class MimiEncoderModel extends MimiPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'encoder_model', + }); + } +} +class MimiDecoderModel extends MimiPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'decoder_model', + }); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Dac models +class DacPreTrainedModel extends PreTrainedModel { + main_input_name = 'input_values'; + forward_params = ['input_values']; +} + +class DacEncoderOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.audio_codes Discrete code embeddings, of shape `(batch_size, num_quantizers, codes_length)`. + */ + constructor({ audio_codes }) { + super(); + this.audio_codes = audio_codes; + } +} + +class DacDecoderOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.audio_values Decoded audio values, of shape `(batch_size, num_channels, sequence_length)`. + */ + constructor({ audio_values }) { + super(); + this.audio_values = audio_values; + } +} + +/** + * The DAC (Descript Audio Codec) model. + */ +class DacModel extends DacPreTrainedModel { + /** + * Encodes the input audio waveform into discrete codes. + * @param {Object} inputs Model inputs + * @param {Tensor} [inputs.input_values] Float values of the input audio waveform, of shape `(batch_size, channels, sequence_length)`). + * @returns {Promise} The output tensor of shape `(batch_size, num_codebooks, sequence_length)`. + */ + async encode(inputs) { + return new DacEncoderOutput(await sessionRun(this.sessions['encoder_model'], inputs)); + } + + /** + * Decodes the given frames into an output audio waveform. + * @param {DacEncoderOutput} inputs The encoded audio codes. + * @returns {Promise} The output tensor of shape `(batch_size, num_channels, sequence_length)`. + */ + async decode(inputs) { + return new DacDecoderOutput(await sessionRun(this.sessions['decoder_model'], inputs)); + } +} + +class DacEncoderModel extends DacPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'encoder_model', + }); + } +} +class DacDecoderModel extends DacPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'decoder_model', + }); + } +} +////////////////////////////////////////////////// + + +////////////////////////////////////////////////// +// Snac models +class SnacPreTrainedModel extends PreTrainedModel { + main_input_name = 'input_values'; + forward_params = ['input_values']; +} + +/** + * The SNAC (Multi-Scale Neural Audio Codec) model. + */ +class SnacModel extends SnacPreTrainedModel { + /** + * Encodes the input audio waveform into discrete codes. + * @param {Object} inputs Model inputs + * @param {Tensor} [inputs.input_values] Float values of the input audio waveform, of shape `(batch_size, channels, sequence_length)`). + * @returns {Promise>} The output tensors of shape `(batch_size, num_codebooks, sequence_length)`. + */ + async encode(inputs) { + return await sessionRun(this.sessions['encoder_model'], inputs); + } + + /** + * Decodes the given frames into an output audio waveform. + * @param {Record} inputs The encoded audio codes. + * @returns {Promise<{audio_values: Tensor}>} The output tensor of shape `(batch_size, num_channels, sequence_length)`. + */ + async decode(inputs) { + return await sessionRun(this.sessions['decoder_model'], inputs); + } +} + +class SnacEncoderModel extends SnacPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'encoder_model', + }); + } +} +class SnacDecoderModel extends SnacPreTrainedModel { + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options = {}) { + return super.from_pretrained(pretrained_model_name_or_path, { + ...options, + // Update default model file name if not provided + model_file_name: options.model_file_name ?? 'decoder_model', + }); + } +} +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +// AutoModels, used to simplify construction of PreTrainedModels +// (uses config to instantiate correct class) + +/** + * Base class of all AutoModels. Contains the `from_pretrained` function + * which is used to instantiate pretrained models. + */ +class PretrainedMixin { + /** + * Mapping from model type to model class. + * @type {Map[]} + */ + static MODEL_CLASS_MAPPINGS = null; + + /** + * Whether to attempt to instantiate the base class (`PretrainedModel`) if + * the model type is not found in the mapping. + */ + static BASE_IF_FAIL = false; + + + /** @type {typeof PreTrainedModel.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, { + progress_callback = null, + config = null, + cache_dir = null, + local_files_only = false, + revision = 'main', + model_file_name = null, + subfolder = 'onnx', + device = null, + dtype = null, + use_external_data_format = null, + session_options = {}, + } = {}) { + + const options = { + progress_callback, + config, + cache_dir, + local_files_only, + revision, + model_file_name, + subfolder, + device, + dtype, + use_external_data_format, + session_options, + } + options.config = await _configs_js__WEBPACK_IMPORTED_MODULE_0__.AutoConfig.from_pretrained(pretrained_model_name_or_path, options); + + if (!this.MODEL_CLASS_MAPPINGS) { + throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: " + this.name); + } + const model_type = options.config.model_type; + for (const MODEL_CLASS_MAPPING of this.MODEL_CLASS_MAPPINGS) { + let modelInfo = MODEL_CLASS_MAPPING.get(model_type); + if (!modelInfo) { + // As a fallback, we check if model_type is specified as the exact class + for (const cls of MODEL_CLASS_MAPPING.values()) { + if (cls[0] === model_type) { + modelInfo = cls; + break; + } + } + if (!modelInfo) continue; // Item not found in this mapping + } + return await modelInfo[1].from_pretrained(pretrained_model_name_or_path, options); + } + + if (this.BASE_IF_FAIL) { + if (!(CUSTOM_ARCHITECTURES.has(model_type))) { + console.warn(`Unknown model class "${model_type}", attempting to construct from base class.`); + } + return await PreTrainedModel.from_pretrained(pretrained_model_name_or_path, options); + } else { + throw Error(`Unsupported model type: ${model_type}`) + } + } +} + +const MODEL_MAPPING_NAMES_ENCODER_ONLY = new Map([ + ['bert', ['BertModel', BertModel]], + ['neobert', ['NeoBertModel', NeoBertModel]], + ['modernbert', ['ModernBertModel', ModernBertModel]], + ['nomic_bert', ['NomicBertModel', NomicBertModel]], + ['roformer', ['RoFormerModel', RoFormerModel]], + ['electra', ['ElectraModel', ElectraModel]], + ['esm', ['EsmModel', EsmModel]], + ['convbert', ['ConvBertModel', ConvBertModel]], + ['camembert', ['CamembertModel', CamembertModel]], + ['deberta', ['DebertaModel', DebertaModel]], + ['deberta-v2', ['DebertaV2Model', DebertaV2Model]], + ['mpnet', ['MPNetModel', MPNetModel]], + ['albert', ['AlbertModel', AlbertModel]], + ['distilbert', ['DistilBertModel', DistilBertModel]], + ['roberta', ['RobertaModel', RobertaModel]], + ['xlm', ['XLMModel', XLMModel]], + ['xlm-roberta', ['XLMRobertaModel', XLMRobertaModel]], + ['clap', ['ClapModel', ClapModel]], + ['clip', ['CLIPModel', CLIPModel]], + ['clipseg', ['CLIPSegModel', CLIPSegModel]], + ['chinese_clip', ['ChineseCLIPModel', ChineseCLIPModel]], + ['siglip', ['SiglipModel', SiglipModel]], + ['jina_clip', ['JinaCLIPModel', JinaCLIPModel]], + ['mobilebert', ['MobileBertModel', MobileBertModel]], + ['squeezebert', ['SqueezeBertModel', SqueezeBertModel]], + ['wav2vec2', ['Wav2Vec2Model', Wav2Vec2Model]], + ['wav2vec2-bert', ['Wav2Vec2BertModel', Wav2Vec2BertModel]], + ['unispeech', ['UniSpeechModel', UniSpeechModel]], + ['unispeech-sat', ['UniSpeechSatModel', UniSpeechSatModel]], + ['hubert', ['HubertModel', HubertModel]], + ['wavlm', ['WavLMModel', WavLMModel]], + ['audio-spectrogram-transformer', ['ASTModel', ASTModel]], + ['vits', ['VitsModel', VitsModel]], + ['pyannote', ['PyAnnoteModel', PyAnnoteModel]], + ['wespeaker-resnet', ['WeSpeakerResNetModel', WeSpeakerResNetModel]], + + ['detr', ['DetrModel', DetrModel]], + ['rt_detr', ['RTDetrModel', RTDetrModel]], + ['rt_detr_v2', ['RTDetrV2Model', RTDetrV2Model]], + ['rf_detr', ['RFDetrModel', RFDetrModel]], + ['d_fine', ['DFineModel', DFineModel]], + ['table-transformer', ['TableTransformerModel', TableTransformerModel]], + ['vit', ['ViTModel', ViTModel]], + ['ijepa', ['IJepaModel', IJepaModel]], + ['pvt', ['PvtModel', PvtModel]], + ['vit_msn', ['ViTMSNModel', ViTMSNModel]], + ['vit_mae', ['ViTMAEModel', ViTMAEModel]], + ['groupvit', ['GroupViTModel', GroupViTModel]], + ['fastvit', ['FastViTModel', FastViTModel]], + ['mobilevit', ['MobileViTModel', MobileViTModel]], + ['mobilevitv2', ['MobileViTV2Model', MobileViTV2Model]], + ['owlvit', ['OwlViTModel', OwlViTModel]], + ['owlv2', ['Owlv2Model', Owlv2Model]], + ['beit', ['BeitModel', BeitModel]], + ['deit', ['DeiTModel', DeiTModel]], + ['hiera', ['HieraModel', HieraModel]], + ['convnext', ['ConvNextModel', ConvNextModel]], + ['convnextv2', ['ConvNextV2Model', ConvNextV2Model]], + ['dinov2', ['Dinov2Model', Dinov2Model]], + ['dinov2_with_registers', ['Dinov2WithRegistersModel', Dinov2WithRegistersModel]], + ['dinov3_vit', ['DINOv3ViTModel', DINOv3ViTModel]], + ['dinov3_convnext', ['DINOv3ConvNextModel', DINOv3ConvNextModel]], + ['resnet', ['ResNetModel', ResNetModel]], + ['swin', ['SwinModel', SwinModel]], + ['swin2sr', ['Swin2SRModel', Swin2SRModel]], + ['donut-swin', ['DonutSwinModel', DonutSwinModel]], + ['yolos', ['YolosModel', YolosModel]], + ['dpt', ['DPTModel', DPTModel]], + ['glpn', ['GLPNModel', GLPNModel]], + + ['hifigan', ['SpeechT5HifiGan', SpeechT5HifiGan]], + ['efficientnet', ['EfficientNetModel', EfficientNetModel]], + + ['decision_transformer', ['DecisionTransformerModel', DecisionTransformerModel]], + ['patchtst', ['PatchTSTForPrediction', PatchTSTModel]], + ['patchtsmixer', ['PatchTSMixerForPrediction', PatchTSMixerModel]], + + ['mobilenet_v1', ['MobileNetV1Model', MobileNetV1Model]], + ['mobilenet_v2', ['MobileNetV2Model', MobileNetV2Model]], + ['mobilenet_v3', ['MobileNetV3Model', MobileNetV3Model]], + ['mobilenet_v4', ['MobileNetV4Model', MobileNetV4Model]], + + ['maskformer', ['MaskFormerModel', MaskFormerModel]], + ['mgp-str', ['MgpstrForSceneTextRecognition', MgpstrForSceneTextRecognition]], + + ['style_text_to_speech_2', ['StyleTextToSpeech2Model', StyleTextToSpeech2Model]], +]); + +const MODEL_MAPPING_NAMES_ENCODER_DECODER = new Map([ + ['t5', ['T5Model', T5Model]], + ['longt5', ['LongT5Model', LongT5Model]], + ['mt5', ['MT5Model', MT5Model]], + ['bart', ['BartModel', BartModel]], + ['mbart', ['MBartModel', MBartModel]], + ['marian', ['MarianModel', MarianModel]], + ['whisper', ['WhisperModel', WhisperModel]], + ['m2m_100', ['M2M100Model', M2M100Model]], + ['blenderbot', ['BlenderbotModel', BlenderbotModel]], + ['blenderbot-small', ['BlenderbotSmallModel', BlenderbotSmallModel]], +]); + +const MODEL_MAPPING_NAMES_AUTO_ENCODER = new Map([ + ['mimi', ['MimiModel', MimiModel]], + ['dac', ['DacModel', DacModel]], + ['snac', ['SnacModel', SnacModel]], +]); + +const MODEL_MAPPING_NAMES_DECODER_ONLY = new Map([ + ['bloom', ['BloomModel', BloomModel]], + ['jais', ['JAISModel', JAISModel]], + ['gpt2', ['GPT2Model', GPT2Model]], + ['gptj', ['GPTJModel', GPTJModel]], + ['gpt_bigcode', ['GPTBigCodeModel', GPTBigCodeModel]], + ['gpt_neo', ['GPTNeoModel', GPTNeoModel]], + ['gpt_neox', ['GPTNeoXModel', GPTNeoXModel]], + ['codegen', ['CodeGenModel', CodeGenModel]], + ['llama', ['LlamaModel', LlamaModel]], + ['arcee', ['ArceeModel', ArceeModel]], + ['lfm2', ['Lfm2Model', Lfm2Model]], + ['smollm3', ['SmolLM3Model', SmolLM3Model]], + ['exaone', ['ExaoneModel', ExaoneModel]], + ['olmo', ['OlmoModel', OlmoModel]], + ['olmo2', ['Olmo2Model', Olmo2Model]], + ['mobilellm', ['MobileLLMModel', MobileLLMModel]], + ['granite', ['GraniteModel', GraniteModel]], + ['cohere', ['CohereModel', CohereModel]], + ['gemma', ['GemmaModel', GemmaModel]], + ['gemma2', ['Gemma2Model', Gemma2Model]], + ['gemma3_text', ['Gemma3Model', Gemma3Model]], + ['helium', ['HeliumModel', HeliumModel]], + ['glm', ['GlmModel', GlmModel]], + ['openelm', ['OpenELMModel', OpenELMModel]], + ['qwen2', ['Qwen2Model', Qwen2Model]], + ['qwen3', ['Qwen3Model', Qwen3Model]], + ['phi', ['PhiModel', PhiModel]], + ['phi3', ['Phi3Model', Phi3Model]], + ['mpt', ['MptModel', MptModel]], + ['opt', ['OPTModel', OPTModel]], + ['mistral', ['MistralModel', MistralModel]], + ['ernie4_5', ['Ernie4_5_Model', Ernie4_5_Model]], + ['starcoder2', ['Starcoder2Model', Starcoder2Model]], + ['falcon', ['FalconModel', FalconModel]], + ['stablelm', ['StableLmModel', StableLmModel]], + ['modernbert-decoder', ['ModernBertDecoderModel', ModernBertDecoderModel]], +]); + +const MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = new Map([ + ['speecht5', ['SpeechT5ForSpeechToText', SpeechT5ForSpeechToText]], + ['whisper', ['WhisperForConditionalGeneration', WhisperForConditionalGeneration]], + ['lite-whisper', ['LiteWhisperForConditionalGeneration', LiteWhisperForConditionalGeneration]], + ['moonshine', ['MoonshineForConditionalGeneration', MoonshineForConditionalGeneration]], +]); + +const MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES = new Map([ + ['speecht5', ['SpeechT5ForTextToSpeech', SpeechT5ForTextToSpeech]], +]); + +const MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES = new Map([ + ['vits', ['VitsModel', VitsModel]], + ['musicgen', ['MusicgenForConditionalGeneration', MusicgenForConditionalGeneration]], +]); + +const MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = new Map([ + ['bert', ['BertForSequenceClassification', BertForSequenceClassification]], + ['neobert', ['NeoBertForSequenceClassification', NeoBertForSequenceClassification]], + ['modernbert', ['ModernBertForSequenceClassification', ModernBertForSequenceClassification]], + ['roformer', ['RoFormerForSequenceClassification', RoFormerForSequenceClassification]], + ['electra', ['ElectraForSequenceClassification', ElectraForSequenceClassification]], + ['esm', ['EsmForSequenceClassification', EsmForSequenceClassification]], + ['convbert', ['ConvBertForSequenceClassification', ConvBertForSequenceClassification]], + ['camembert', ['CamembertForSequenceClassification', CamembertForSequenceClassification]], + ['deberta', ['DebertaForSequenceClassification', DebertaForSequenceClassification]], + ['deberta-v2', ['DebertaV2ForSequenceClassification', DebertaV2ForSequenceClassification]], + ['mpnet', ['MPNetForSequenceClassification', MPNetForSequenceClassification]], + ['albert', ['AlbertForSequenceClassification', AlbertForSequenceClassification]], + ['distilbert', ['DistilBertForSequenceClassification', DistilBertForSequenceClassification]], + ['roberta', ['RobertaForSequenceClassification', RobertaForSequenceClassification]], + ['xlm', ['XLMForSequenceClassification', XLMForSequenceClassification]], + ['xlm-roberta', ['XLMRobertaForSequenceClassification', XLMRobertaForSequenceClassification]], + ['bart', ['BartForSequenceClassification', BartForSequenceClassification]], + ['mbart', ['MBartForSequenceClassification', MBartForSequenceClassification]], + ['mobilebert', ['MobileBertForSequenceClassification', MobileBertForSequenceClassification]], + ['squeezebert', ['SqueezeBertForSequenceClassification', SqueezeBertForSequenceClassification]], +]); + +const MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = new Map([ + ['bert', ['BertForTokenClassification', BertForTokenClassification]], + ['neobert', ['NeoBertForTokenClassification', NeoBertForTokenClassification]], + ['modernbert', ['ModernBertForTokenClassification', ModernBertForTokenClassification]], + ['roformer', ['RoFormerForTokenClassification', RoFormerForTokenClassification]], + ['electra', ['ElectraForTokenClassification', ElectraForTokenClassification]], + ['esm', ['EsmForTokenClassification', EsmForTokenClassification]], + ['convbert', ['ConvBertForTokenClassification', ConvBertForTokenClassification]], + ['camembert', ['CamembertForTokenClassification', CamembertForTokenClassification]], + ['deberta', ['DebertaForTokenClassification', DebertaForTokenClassification]], + ['deberta-v2', ['DebertaV2ForTokenClassification', DebertaV2ForTokenClassification]], + ['mpnet', ['MPNetForTokenClassification', MPNetForTokenClassification]], + ['distilbert', ['DistilBertForTokenClassification', DistilBertForTokenClassification]], + ['roberta', ['RobertaForTokenClassification', RobertaForTokenClassification]], + ['xlm', ['XLMForTokenClassification', XLMForTokenClassification]], + ['xlm-roberta', ['XLMRobertaForTokenClassification', XLMRobertaForTokenClassification]], +]); + +const MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = new Map([ + ['t5', ['T5ForConditionalGeneration', T5ForConditionalGeneration]], + ['longt5', ['LongT5ForConditionalGeneration', LongT5ForConditionalGeneration]], + ['mt5', ['MT5ForConditionalGeneration', MT5ForConditionalGeneration]], + ['bart', ['BartForConditionalGeneration', BartForConditionalGeneration]], + ['mbart', ['MBartForConditionalGeneration', MBartForConditionalGeneration]], + ['marian', ['MarianMTModel', MarianMTModel]], + ['m2m_100', ['M2M100ForConditionalGeneration', M2M100ForConditionalGeneration]], + ['blenderbot', ['BlenderbotForConditionalGeneration', BlenderbotForConditionalGeneration]], + ['blenderbot-small', ['BlenderbotSmallForConditionalGeneration', BlenderbotSmallForConditionalGeneration]], +]); + +const MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = new Map([ + ['bloom', ['BloomForCausalLM', BloomForCausalLM]], + ['gpt2', ['GPT2LMHeadModel', GPT2LMHeadModel]], + ['jais', ['JAISLMHeadModel', JAISLMHeadModel]], + ['gptj', ['GPTJForCausalLM', GPTJForCausalLM]], + ['gpt_bigcode', ['GPTBigCodeForCausalLM', GPTBigCodeForCausalLM]], + ['gpt_neo', ['GPTNeoForCausalLM', GPTNeoForCausalLM]], + ['gpt_neox', ['GPTNeoXForCausalLM', GPTNeoXForCausalLM]], + ['codegen', ['CodeGenForCausalLM', CodeGenForCausalLM]], + ['llama', ['LlamaForCausalLM', LlamaForCausalLM]], + ['arcee', ['ArceeForCausalLM', ArceeForCausalLM]], + ['lfm2', ['Lfm2ForCausalLM', Lfm2ForCausalLM]], + ['smollm3', ['SmolLM3ForCausalLM', SmolLM3ForCausalLM]], + ['exaone', ['ExaoneForCausalLM', ExaoneForCausalLM]], + ['olmo', ['OlmoForCausalLM', OlmoForCausalLM]], + ['olmo2', ['Olmo2ForCausalLM', Olmo2ForCausalLM]], + ['mobilellm', ['MobileLLMForCausalLM', MobileLLMForCausalLM]], + ['granite', ['GraniteForCausalLM', GraniteForCausalLM]], + ['cohere', ['CohereForCausalLM', CohereForCausalLM]], + ['gemma', ['GemmaForCausalLM', GemmaForCausalLM]], + ['gemma2', ['Gemma2ForCausalLM', Gemma2ForCausalLM]], + ['gemma3_text', ['Gemma3ForCausalLM', Gemma3ForCausalLM]], + ['helium', ['HeliumForCausalLM', HeliumForCausalLM]], + ['glm', ['GlmForCausalLM', GlmForCausalLM]], + ['openelm', ['OpenELMForCausalLM', OpenELMForCausalLM]], + ['qwen2', ['Qwen2ForCausalLM', Qwen2ForCausalLM]], + ['qwen3', ['Qwen3ForCausalLM', Qwen3ForCausalLM]], + ['phi', ['PhiForCausalLM', PhiForCausalLM]], + ['phi3', ['Phi3ForCausalLM', Phi3ForCausalLM]], + ['mpt', ['MptForCausalLM', MptForCausalLM]], + ['opt', ['OPTForCausalLM', OPTForCausalLM]], + ['mbart', ['MBartForCausalLM', MBartForCausalLM]], + ['mistral', ['MistralForCausalLM', MistralForCausalLM]], + ['ernie4_5', ['Ernie4_5_ForCausalLM', Ernie4_5_ForCausalLM]], + ['starcoder2', ['Starcoder2ForCausalLM', Starcoder2ForCausalLM]], + ['falcon', ['FalconForCausalLM', FalconForCausalLM]], + ['trocr', ['TrOCRForCausalLM', TrOCRForCausalLM]], + ['stablelm', ['StableLmForCausalLM', StableLmForCausalLM]], + ['modernbert-decoder', ['ModernBertDecoderForCausalLM', ModernBertDecoderForCausalLM]], + + // Also image-text-to-text + ['phi3_v', ['Phi3VForCausalLM', Phi3VForCausalLM]], +]); + +const MODEL_FOR_MULTIMODALITY_MAPPING_NAMES = new Map([ + ['multi_modality', ['MultiModalityCausalLM', MultiModalityCausalLM]], +]); + + +const MODEL_FOR_MASKED_LM_MAPPING_NAMES = new Map([ + ['bert', ['BertForMaskedLM', BertForMaskedLM]], + ['neobert', ['NeoBertForMaskedLM', NeoBertForMaskedLM]], + ['modernbert', ['ModernBertForMaskedLM', ModernBertForMaskedLM]], + ['roformer', ['RoFormerForMaskedLM', RoFormerForMaskedLM]], + ['electra', ['ElectraForMaskedLM', ElectraForMaskedLM]], + ['esm', ['EsmForMaskedLM', EsmForMaskedLM]], + ['convbert', ['ConvBertForMaskedLM', ConvBertForMaskedLM]], + ['camembert', ['CamembertForMaskedLM', CamembertForMaskedLM]], + ['deberta', ['DebertaForMaskedLM', DebertaForMaskedLM]], + ['deberta-v2', ['DebertaV2ForMaskedLM', DebertaV2ForMaskedLM]], + ['mpnet', ['MPNetForMaskedLM', MPNetForMaskedLM]], + ['albert', ['AlbertForMaskedLM', AlbertForMaskedLM]], + ['distilbert', ['DistilBertForMaskedLM', DistilBertForMaskedLM]], + ['roberta', ['RobertaForMaskedLM', RobertaForMaskedLM]], + ['xlm', ['XLMWithLMHeadModel', XLMWithLMHeadModel]], + ['xlm-roberta', ['XLMRobertaForMaskedLM', XLMRobertaForMaskedLM]], + ['mobilebert', ['MobileBertForMaskedLM', MobileBertForMaskedLM]], + ['squeezebert', ['SqueezeBertForMaskedLM', SqueezeBertForMaskedLM]], +]); + +const MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = new Map([ + ['bert', ['BertForQuestionAnswering', BertForQuestionAnswering]], + ['neobert', ['NeoBertForQuestionAnswering', NeoBertForQuestionAnswering]], + ['roformer', ['RoFormerForQuestionAnswering', RoFormerForQuestionAnswering]], + ['electra', ['ElectraForQuestionAnswering', ElectraForQuestionAnswering]], + ['convbert', ['ConvBertForQuestionAnswering', ConvBertForQuestionAnswering]], + ['camembert', ['CamembertForQuestionAnswering', CamembertForQuestionAnswering]], + ['deberta', ['DebertaForQuestionAnswering', DebertaForQuestionAnswering]], + ['deberta-v2', ['DebertaV2ForQuestionAnswering', DebertaV2ForQuestionAnswering]], + ['mpnet', ['MPNetForQuestionAnswering', MPNetForQuestionAnswering]], + ['albert', ['AlbertForQuestionAnswering', AlbertForQuestionAnswering]], + ['distilbert', ['DistilBertForQuestionAnswering', DistilBertForQuestionAnswering]], + ['roberta', ['RobertaForQuestionAnswering', RobertaForQuestionAnswering]], + ['xlm', ['XLMForQuestionAnswering', XLMForQuestionAnswering]], + ['xlm-roberta', ['XLMRobertaForQuestionAnswering', XLMRobertaForQuestionAnswering]], + ['mobilebert', ['MobileBertForQuestionAnswering', MobileBertForQuestionAnswering]], + ['squeezebert', ['SqueezeBertForQuestionAnswering', SqueezeBertForQuestionAnswering]], +]); + +const MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = new Map([ + ['vision-encoder-decoder', ['VisionEncoderDecoderModel', VisionEncoderDecoderModel]], + ['idefics3', ['Idefics3ForConditionalGeneration', Idefics3ForConditionalGeneration]], + ['smolvlm', ['SmolVLMForConditionalGeneration', SmolVLMForConditionalGeneration]], +]); + +const MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES = new Map([ + ['llava', ['LlavaForConditionalGeneration', LlavaForConditionalGeneration]], + ['llava_onevision', ['LlavaOnevisionForConditionalGeneration', LlavaOnevisionForConditionalGeneration]], + ['moondream1', ['Moondream1ForConditionalGeneration', Moondream1ForConditionalGeneration]], + ['florence2', ['Florence2ForConditionalGeneration', Florence2ForConditionalGeneration]], + ['qwen2-vl', ['Qwen2VLForConditionalGeneration', Qwen2VLForConditionalGeneration]], + ['idefics3', ['Idefics3ForConditionalGeneration', Idefics3ForConditionalGeneration]], + ['smolvlm', ['SmolVLMForConditionalGeneration', SmolVLMForConditionalGeneration]], + ['paligemma', ['PaliGemmaForConditionalGeneration', PaliGemmaForConditionalGeneration]], + ['llava_qwen2', ['LlavaQwen2ForCausalLM', LlavaQwen2ForCausalLM]], + ['gemma3n', ['Gemma3nForConditionalGeneration', Gemma3nForConditionalGeneration]], +]); + +const MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES = new Map([ + ['ultravox', ['UltravoxModel', UltravoxModel]], + ['voxtral', ['VoxtralForConditionalGeneration', VoxtralForConditionalGeneration]], +]); + + +const MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = new Map([ + ['vision-encoder-decoder', ['VisionEncoderDecoderModel', VisionEncoderDecoderModel]], +]); + +const MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = new Map([ + ['vit', ['ViTForImageClassification', ViTForImageClassification]], + ['ijepa', ['IJepaForImageClassification', IJepaForImageClassification]], + ['pvt', ['PvtForImageClassification', PvtForImageClassification]], + ['vit_msn', ['ViTMSNForImageClassification', ViTMSNForImageClassification]], + ['fastvit', ['FastViTForImageClassification', FastViTForImageClassification]], + ['mobilevit', ['MobileViTForImageClassification', MobileViTForImageClassification]], + ['mobilevitv2', ['MobileViTV2ForImageClassification', MobileViTV2ForImageClassification]], + ['beit', ['BeitForImageClassification', BeitForImageClassification]], + ['deit', ['DeiTForImageClassification', DeiTForImageClassification]], + ['hiera', ['HieraForImageClassification', HieraForImageClassification]], + ['convnext', ['ConvNextForImageClassification', ConvNextForImageClassification]], + ['convnextv2', ['ConvNextV2ForImageClassification', ConvNextV2ForImageClassification]], + ['dinov2', ['Dinov2ForImageClassification', Dinov2ForImageClassification]], + ['dinov2_with_registers', ['Dinov2WithRegistersForImageClassification', Dinov2WithRegistersForImageClassification]], + ['resnet', ['ResNetForImageClassification', ResNetForImageClassification]], + ['swin', ['SwinForImageClassification', SwinForImageClassification]], + ['segformer', ['SegformerForImageClassification', SegformerForImageClassification]], + ['efficientnet', ['EfficientNetForImageClassification', EfficientNetForImageClassification]], + ['mobilenet_v1', ['MobileNetV1ForImageClassification', MobileNetV1ForImageClassification]], + ['mobilenet_v2', ['MobileNetV2ForImageClassification', MobileNetV2ForImageClassification]], + ['mobilenet_v3', ['MobileNetV3ForImageClassification', MobileNetV3ForImageClassification]], + ['mobilenet_v4', ['MobileNetV4ForImageClassification', MobileNetV4ForImageClassification]], +]); + +const MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = new Map([ + ['detr', ['DetrForObjectDetection', DetrForObjectDetection]], + ['rt_detr', ['RTDetrForObjectDetection', RTDetrForObjectDetection]], + ['rt_detr_v2', ['RTDetrV2ForObjectDetection', RTDetrV2ForObjectDetection]], + ['rf_detr', ['RFDetrForObjectDetection', RFDetrForObjectDetection]], + ['d_fine', ['DFineForObjectDetection', DFineForObjectDetection]], + ['table-transformer', ['TableTransformerForObjectDetection', TableTransformerForObjectDetection]], + ['yolos', ['YolosForObjectDetection', YolosForObjectDetection]], +]); + +const MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = new Map([ + ['owlvit', ['OwlViTForObjectDetection', OwlViTForObjectDetection]], + ['owlv2', ['Owlv2ForObjectDetection', Owlv2ForObjectDetection]], + ['grounding-dino', ['GroundingDinoForObjectDetection', GroundingDinoForObjectDetection]], +]); + +const MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES = new Map([ + // TODO: Do not add new models here + ['detr', ['DetrForSegmentation', DetrForSegmentation]], + ['clipseg', ['CLIPSegForImageSegmentation', CLIPSegForImageSegmentation]], +]); + +const MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = new Map([ + ['segformer', ['SegformerForSemanticSegmentation', SegformerForSemanticSegmentation]], + ['sapiens', ['SapiensForSemanticSegmentation', SapiensForSemanticSegmentation]], + + ['swin', ['SwinForSemanticSegmentation', SwinForSemanticSegmentation]], + ['mobilenet_v1', ['MobileNetV1ForSemanticSegmentation', MobileNetV1ForSemanticSegmentation]], + ['mobilenet_v2', ['MobileNetV2ForSemanticSegmentation', MobileNetV2ForSemanticSegmentation]], + ['mobilenet_v3', ['MobileNetV3ForSemanticSegmentation', MobileNetV3ForSemanticSegmentation]], + ['mobilenet_v4', ['MobileNetV4ForSemanticSegmentation', MobileNetV4ForSemanticSegmentation]], +]); + +const MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES = new Map([ + ['detr', ['DetrForSegmentation', DetrForSegmentation]], + ['maskformer', ['MaskFormerForInstanceSegmentation', MaskFormerForInstanceSegmentation]], +]); + +const MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = new Map([ + ['sam', ['SamModel', SamModel]], +]); + +const MODEL_FOR_CTC_MAPPING_NAMES = new Map([ + ['wav2vec2', ['Wav2Vec2ForCTC', Wav2Vec2ForCTC]], + ['wav2vec2-bert', ['Wav2Vec2BertForCTC', Wav2Vec2BertForCTC]], + ['unispeech', ['UniSpeechForCTC', UniSpeechForCTC]], + ['unispeech-sat', ['UniSpeechSatForCTC', UniSpeechSatForCTC]], + ['wavlm', ['WavLMForCTC', WavLMForCTC]], + ['hubert', ['HubertForCTC', HubertForCTC]], +]); + +const MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = new Map([ + ['wav2vec2', ['Wav2Vec2ForSequenceClassification', Wav2Vec2ForSequenceClassification]], + ['wav2vec2-bert', ['Wav2Vec2BertForSequenceClassification', Wav2Vec2BertForSequenceClassification]], + ['unispeech', ['UniSpeechForSequenceClassification', UniSpeechForSequenceClassification]], + ['unispeech-sat', ['UniSpeechSatForSequenceClassification', UniSpeechSatForSequenceClassification]], + ['wavlm', ['WavLMForSequenceClassification', WavLMForSequenceClassification]], + ['hubert', ['HubertForSequenceClassification', HubertForSequenceClassification]], + ['audio-spectrogram-transformer', ['ASTForAudioClassification', ASTForAudioClassification]], +]); + +const MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = new Map([ + ['wavlm', ['WavLMForXVector', WavLMForXVector]], +]); + +const MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = new Map([ + ['unispeech-sat', ['UniSpeechSatForAudioFrameClassification', UniSpeechSatForAudioFrameClassification]], + ['wavlm', ['WavLMForAudioFrameClassification', WavLMForAudioFrameClassification]], + ['wav2vec2', ['Wav2Vec2ForAudioFrameClassification', Wav2Vec2ForAudioFrameClassification]], + ['pyannote', ['PyAnnoteForAudioFrameClassification', PyAnnoteForAudioFrameClassification]], +]); + +const MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES = new Map([ + ['vitmatte', ['VitMatteForImageMatting', VitMatteForImageMatting]], +]); + +const MODEL_FOR_TIME_SERIES_PREDICTION_MAPPING_NAMES = new Map([ + ['patchtst', ['PatchTSTForPrediction', PatchTSTForPrediction]], + ['patchtsmixer', ['PatchTSMixerForPrediction', PatchTSMixerForPrediction]], +]) + +const MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES = new Map([ + ['swin2sr', ['Swin2SRForImageSuperResolution', Swin2SRForImageSuperResolution]], +]) + +const MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = new Map([ + ['dpt', ['DPTForDepthEstimation', DPTForDepthEstimation]], + ['depth_anything', ['DepthAnythingForDepthEstimation', DepthAnythingForDepthEstimation]], + ['glpn', ['GLPNForDepthEstimation', GLPNForDepthEstimation]], + ['sapiens', ['SapiensForDepthEstimation', SapiensForDepthEstimation]], + ['depth_pro', ['DepthProForDepthEstimation', DepthProForDepthEstimation]], + ['metric3d', ['Metric3DForDepthEstimation', Metric3DForDepthEstimation]], + ['metric3dv2', ['Metric3Dv2ForDepthEstimation', Metric3Dv2ForDepthEstimation]], +]) + +const MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES = new Map([ + ['sapiens', ['SapiensForNormalEstimation', SapiensForNormalEstimation]], +]) + +const MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES = new Map([ + ['vitpose', ['VitPoseForPoseEstimation', VitPoseForPoseEstimation]], +]) + +// NOTE: This is custom to Transformers.js, and is necessary because certain models +// (e.g., CLIP) are split into vision and text components +const MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES = new Map([ + ['clip', ['CLIPVisionModelWithProjection', CLIPVisionModelWithProjection]], + ['siglip', ['SiglipVisionModel', SiglipVisionModel]], + ['jina_clip', ['JinaCLIPVisionModel', JinaCLIPVisionModel]], +]) + +const MODEL_CLASS_TYPE_MAPPING = [ + // MODEL_MAPPING_NAMES: + [MODEL_MAPPING_NAMES_ENCODER_ONLY, MODEL_TYPES.EncoderOnly], + [MODEL_MAPPING_NAMES_ENCODER_DECODER, MODEL_TYPES.EncoderDecoder], + [MODEL_MAPPING_NAMES_DECODER_ONLY, MODEL_TYPES.DecoderOnly], + [MODEL_MAPPING_NAMES_AUTO_ENCODER, MODEL_TYPES.AutoEncoder], + + [MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, MODEL_TYPES.Seq2Seq], + [MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES, MODEL_TYPES.Seq2Seq], + [MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_TYPES.DecoderOnly], + [MODEL_FOR_MULTIMODALITY_MAPPING_NAMES, MODEL_TYPES.MultiModality], + [MODEL_FOR_MASKED_LM_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES, MODEL_TYPES.Vision2Seq], + [MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES, MODEL_TYPES.ImageTextToText], + [MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES, MODEL_TYPES.AudioTextToText], + [MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_TIME_SERIES_PREDICTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_MASK_GENERATION_MAPPING_NAMES, MODEL_TYPES.MaskGeneration], + [MODEL_FOR_CTC_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES, MODEL_TYPES.Seq2Seq], + [MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + [MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], + + // Custom: + [MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly], +]; + +for (const [mappings, type] of MODEL_CLASS_TYPE_MAPPING) { + // @ts-ignore + for (const [name, model] of mappings.values()) { + MODEL_TYPE_MAPPING.set(name, type); + MODEL_CLASS_TO_NAME_MAPPING.set(model, name); + MODEL_NAME_TO_CLASS_MAPPING.set(name, model); + } +} + +const CUSTOM_MAPPING = [ + // OVERRIDE: + // TODO: Refactor to allow class to specify model + ['MusicgenForConditionalGeneration', MusicgenForConditionalGeneration, MODEL_TYPES.Musicgen], + ['Phi3VForCausalLM', Phi3VForCausalLM, MODEL_TYPES.Phi3V], + + ['CLIPTextModelWithProjection', CLIPTextModelWithProjection, MODEL_TYPES.EncoderOnly], + ['SiglipTextModel', SiglipTextModel, MODEL_TYPES.EncoderOnly], + ['JinaCLIPTextModel', JinaCLIPTextModel, MODEL_TYPES.EncoderOnly], + ['ClapTextModelWithProjection', ClapTextModelWithProjection, MODEL_TYPES.EncoderOnly], + ['ClapAudioModelWithProjection', ClapAudioModelWithProjection, MODEL_TYPES.EncoderOnly], + + ['DacEncoderModel', DacEncoderModel, MODEL_TYPES.EncoderOnly], + ['DacDecoderModel', DacDecoderModel, MODEL_TYPES.EncoderOnly], + ['MimiEncoderModel', MimiEncoderModel, MODEL_TYPES.EncoderOnly], + ['MimiDecoderModel', MimiDecoderModel, MODEL_TYPES.EncoderOnly], + ['SnacEncoderModel', SnacEncoderModel, MODEL_TYPES.EncoderOnly], + ['SnacDecoderModel', SnacDecoderModel, MODEL_TYPES.EncoderOnly], + + ['Gemma3nForConditionalGeneration', Gemma3nForConditionalGeneration, MODEL_TYPES.ImageAudioTextToText], +] +for (const [name, model, type] of CUSTOM_MAPPING) { + MODEL_TYPE_MAPPING.set(name, type); + MODEL_CLASS_TO_NAME_MAPPING.set(model, name); + MODEL_NAME_TO_CLASS_MAPPING.set(name, model); +} + +const CUSTOM_ARCHITECTURES = new Map([ + ['modnet', MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES], + ['birefnet', MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES], + ['isnet', MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES], + ['ben', MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES], +]); +for (const [name, mapping] of CUSTOM_ARCHITECTURES.entries()) { + mapping.set(name, ['PreTrainedModel', PreTrainedModel]) + MODEL_TYPE_MAPPING.set(name, MODEL_TYPES.EncoderOnly); + MODEL_CLASS_TO_NAME_MAPPING.set(PreTrainedModel, name); + MODEL_NAME_TO_CLASS_MAPPING.set(name, PreTrainedModel); +} + + +/** + * Helper class which is used to instantiate pretrained models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModel.from_pretrained('Xenova/bert-base-uncased'); + */ +class AutoModel extends PretrainedMixin { + /** @type {Map[]} */ + // @ts-ignore + static MODEL_CLASS_MAPPINGS = MODEL_CLASS_TYPE_MAPPING.map(x => x[0]); + static BASE_IF_FAIL = true; +} + +/** + * Helper class which is used to instantiate pretrained sequence classification models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForSequenceClassification.from_pretrained('Xenova/distilbert-base-uncased-finetuned-sst-2-english'); + */ +class AutoModelForSequenceClassification extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained token classification models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForTokenClassification.from_pretrained('Xenova/distilbert-base-multilingual-cased-ner-hrl'); + */ +class AutoModelForTokenClassification extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained sequence-to-sequence models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForSeq2SeqLM.from_pretrained('Xenova/t5-small'); + */ +class AutoModelForSeq2SeqLM extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained sequence-to-sequence speech-to-text models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForSpeechSeq2Seq.from_pretrained('openai/whisper-tiny.en'); + */ +class AutoModelForSpeechSeq2Seq extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained sequence-to-sequence text-to-spectrogram models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForTextToSpectrogram.from_pretrained('microsoft/speecht5_tts'); + */ +class AutoModelForTextToSpectrogram extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained text-to-waveform models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForTextToSpectrogram.from_pretrained('facebook/mms-tts-eng'); + */ +class AutoModelForTextToWaveform extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained causal language models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForCausalLM.from_pretrained('Xenova/gpt2'); + */ +class AutoModelForCausalLM extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_CAUSAL_LM_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained masked language models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForMaskedLM.from_pretrained('Xenova/bert-base-uncased'); + */ +class AutoModelForMaskedLM extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_MASKED_LM_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained question answering models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForQuestionAnswering.from_pretrained('Xenova/distilbert-base-cased-distilled-squad'); + */ +class AutoModelForQuestionAnswering extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained vision-to-sequence models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForVision2Seq.from_pretrained('Xenova/vit-gpt2-image-captioning'); + */ +class AutoModelForVision2Seq extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained image classification models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForImageClassification.from_pretrained('Xenova/vit-base-patch16-224'); + */ +class AutoModelForImageClassification extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained image segmentation models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForImageSegmentation.from_pretrained('Xenova/detr-resnet-50-panoptic'); + */ +class AutoModelForImageSegmentation extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained image segmentation models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForSemanticSegmentation.from_pretrained('nvidia/segformer-b3-finetuned-cityscapes-1024-1024'); + */ +class AutoModelForSemanticSegmentation extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained universal image segmentation models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForUniversalSegmentation.from_pretrained('hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation'); + */ +class AutoModelForUniversalSegmentation extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES]; +} + +/** + * Helper class which is used to instantiate pretrained object detection models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForObjectDetection.from_pretrained('Xenova/detr-resnet-50'); + */ +class AutoModelForObjectDetection extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES]; +} + +class AutoModelForZeroShotObjectDetection extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES]; +} + + +/** + * Helper class which is used to instantiate pretrained mask generation models with the `from_pretrained` function. + * The chosen model class is determined by the type specified in the model config. + * + * @example + * let model = await AutoModelForMaskGeneration.from_pretrained('Xenova/sam-vit-base'); + */ +class AutoModelForMaskGeneration extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_MASK_GENERATION_MAPPING_NAMES]; +} + +class AutoModelForCTC extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_CTC_MAPPING_NAMES]; +} + +class AutoModelForAudioClassification extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES]; +} + +class AutoModelForXVector extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES]; +} + +class AutoModelForAudioFrameClassification extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES]; +} + +class AutoModelForDocumentQuestionAnswering extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES]; +} + +class AutoModelForImageMatting extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES]; +} + +class AutoModelForImageToImage extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES]; +} + +class AutoModelForDepthEstimation extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES]; +} + +class AutoModelForNormalEstimation extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_NORMAL_ESTIMATION_MAPPING_NAMES]; +} + +class AutoModelForPoseEstimation extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_POSE_ESTIMATION_MAPPING_NAMES]; +} + +class AutoModelForImageFeatureExtraction extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES]; +} + +class AutoModelForImageTextToText extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES]; +} + +class AutoModelForAudioTextToText extends PretrainedMixin { + static MODEL_CLASS_MAPPINGS = [MODEL_FOR_AUDIO_TEXT_TO_TEXT_MAPPING_NAMES]; +} + +////////////////////////////////////////////////// + +////////////////////////////////////////////////// +class Seq2SeqLMOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits The output logits of the model. + * @param {Tensor} output.past_key_values An tensor of key/value pairs that represent the previous state of the model. + * @param {Tensor} output.encoder_outputs The output of the encoder in a sequence-to-sequence model. + * @param {Tensor} [output.decoder_attentions] Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. + * @param {Tensor} [output.cross_attentions] Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. + */ + constructor({ logits, past_key_values, encoder_outputs, decoder_attentions = null, cross_attentions = null }) { + super(); + this.logits = logits; + this.past_key_values = past_key_values; + this.encoder_outputs = encoder_outputs; + this.decoder_attentions = decoder_attentions; + this.cross_attentions = cross_attentions; + } +} + +/** + * Base class for outputs of sentence classification models. + */ +class SequenceClassifierOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits classification (or regression if config.num_labels==1) scores (before SoftMax). + * @param {Record} [output.attentions] Object of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + * Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. + */ + constructor({ logits, ...attentions }) { + super(); + this.logits = logits; + const attentions_list = Object.values(attentions); + if (attentions_list.length > 0) { + // Only set attentions if they are not empty + this.attentions = attentions_list; + } + } +} + +/** + * Base class for outputs of XVector models. + */ +class XVectorOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits Classification hidden states before AMSoftmax, of shape `(batch_size, config.xvector_output_dim)`. + * @param {Tensor} output.embeddings Utterance embeddings used for vector similarity-based retrieval, of shape `(batch_size, config.xvector_output_dim)`. + */ + constructor({ logits, embeddings }) { + super(); + this.logits = logits; + this.embeddings = embeddings; + } +} + +/** + * Base class for outputs of token classification models. + */ +class TokenClassifierOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits Classification scores (before SoftMax). + */ + constructor({ logits }) { + super(); + this.logits = logits; + } +} + +/** + * Base class for masked language models outputs. + */ +class MaskedLMOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + */ + constructor({ logits }) { + super(); + this.logits = logits; + } +} + +/** + * Base class for outputs of question answering models. + */ +class QuestionAnsweringModelOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.start_logits Span-start scores (before SoftMax). + * @param {Tensor} output.end_logits Span-end scores (before SoftMax). + */ + constructor({ start_logits, end_logits }) { + super(); + this.start_logits = start_logits; + this.end_logits = end_logits; + } +} + + +/** + * Base class for causal language model (or autoregressive) outputs. + */ +class CausalLMOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits Prediction scores of the language modeling head (scores for each vocabulary token before softmax). + */ + constructor({ logits }) { + super(); + this.logits = logits; + } +} + +/** + * Base class for causal language model (or autoregressive) outputs. + */ +class CausalLMOutputWithPast extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.logits Prediction scores of the language modeling head (scores for each vocabulary token before softmax). + * @param {Tensor} output.past_key_values Contains pre-computed hidden-states (key and values in the self-attention blocks) + * that can be used (see `past_key_values` input) to speed up sequential decoding. + */ + constructor({ logits, past_key_values }) { + super(); + this.logits = logits; + this.past_key_values = past_key_values; + } +} + +class ImageMattingOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.alphas Estimated alpha values, of shape `(batch_size, num_channels, height, width)`. + */ + constructor({ alphas }) { + super(); + this.alphas = alphas; + } +} + +/** + * Describes the outputs for the VITS model. + */ +class VitsModelOutput extends ModelOutput { + /** + * @param {Object} output The output of the model. + * @param {Tensor} output.waveform The final audio waveform predicted by the model, of shape `(batch_size, sequence_length)`. + * @param {Tensor} output.spectrogram The log-mel spectrogram predicted at the output of the flow model. + * This spectrogram is passed to the Hi-Fi GAN decoder model to obtain the final audio waveform. + */ + constructor({ waveform, spectrogram }) { + super(); + this.waveform = waveform; + this.spectrogram = spectrogram; + } +} + + +/***/ }), + +/***/ "./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js": +/*!******************************************************************************************************!*\ + !*** ./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js ***! + \******************************************************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ ASTFeatureExtractor: () => (/* binding */ ASTFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_audio_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../utils/audio.js */ "./src/utils/audio.js"); + + + + + +class ASTFeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + + constructor(config) { + super(config); + + const sampling_rate = this.config.sampling_rate; + const mel_filters = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.mel_filter_bank)( + 257, // num_frequency_bins + this.config.num_mel_bins, // num_mel_filters + 20, // min_frequency + Math.floor(sampling_rate / 2), // max_frequency + sampling_rate, // sampling_rate + null, // norm + "kaldi", // mel_scale + true, // triangularize_in_mel_space + ); + this.mel_filters = mel_filters; + + this.window = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.window_function)(400, 'hann', { + periodic: false, + }) + + this.mean = this.config.mean; + this.std = this.config.std; + } + + /** + * Computes the log-Mel spectrogram of the provided audio waveform. + * @param {Float32Array|Float64Array} waveform The audio waveform to process. + * @param {number} max_length The maximum number of frames to return. + * @returns {Promise} An object containing the log-Mel spectrogram data as a Float32Array and its dimensions as an array of numbers. + */ + async _extract_fbank_features(waveform, max_length) { + // NOTE: We don't pad/truncate since that is passed in as `max_num_frames` + return (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.spectrogram)( + waveform, + this.window, // window + 400, // frame_length + 160, // hop_length + { + fft_length: 512, + power: 2.0, + center: false, + preemphasis: 0.97, + mel_filters: this.mel_filters, + log_mel: 'log', + mel_floor: 1.192092955078125e-07, + remove_dc_offset: true, + + // Custom + max_num_frames: max_length, + transpose: true, + } + ) + } + + + /** + * Asynchronously extracts features from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @returns {Promise<{ input_values: Tensor }>} A Promise resolving to an object containing the extracted input features as a Tensor. + */ + async _call(audio) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'ASTFeatureExtractor'); + + const features = await this._extract_fbank_features(audio, this.config.max_length); + if (this.config.do_normalize) { + // Normalize the input audio spectrogram to have mean=0, std=0.5 + const denom = this.std * 2; + const features_data = features.data; + for (let i = 0; i < features_data.length; ++i) { + features_data[i] = (features_data[i] - this.mean) / denom; + } + } + + return { + input_values: features.unsqueeze_(0) + }; + } +} + + +/***/ }), + +/***/ "./src/models/auto/feature_extraction_auto.js": +/*!****************************************************!*\ + !*** ./src/models/auto/feature_extraction_auto.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ AutoFeatureExtractor: () => (/* binding */ AutoFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../utils/constants.js */ "./src/utils/constants.js"); +/* harmony import */ var _utils_hub_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/hub.js */ "./src/utils/hub.js"); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _feature_extractors_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../feature_extractors.js */ "./src/models/feature_extractors.js"); + + + + + + +class AutoFeatureExtractor { + + /** @type {typeof FeatureExtractor.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options={}) { + + const preprocessorConfig = await (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_1__.getModelJSON)(pretrained_model_name_or_path, _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__.FEATURE_EXTRACTOR_NAME, true, options); + + // Determine feature extractor class + const key = preprocessorConfig.feature_extractor_type; + const feature_extractor_class = _feature_extractors_js__WEBPACK_IMPORTED_MODULE_3__[key]; + + if (!feature_extractor_class) { + throw new Error(`Unknown feature_extractor_type: '${key}'. Please report this at ${_utils_constants_js__WEBPACK_IMPORTED_MODULE_0__.GITHUB_ISSUE_URL}.`); + } + + // Instantiate feature extractor + return new feature_extractor_class(preprocessorConfig); + } +} + + +/***/ }), + +/***/ "./src/models/auto/image_processing_auto.js": +/*!**************************************************!*\ + !*** ./src/models/auto/image_processing_auto.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ AutoImageProcessor: () => (/* binding */ AutoImageProcessor) +/* harmony export */ }); +/* harmony import */ var _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../utils/constants.js */ "./src/utils/constants.js"); +/* harmony import */ var _utils_hub_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/hub.js */ "./src/utils/hub.js"); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); +/* harmony import */ var _image_processors_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../image_processors.js */ "./src/models/image_processors.js"); + + + + + + +class AutoImageProcessor { + + /** @type {typeof ImageProcessor.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options={}) { + + const preprocessorConfig = await (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_1__.getModelJSON)(pretrained_model_name_or_path, _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__.IMAGE_PROCESSOR_NAME, true, options); + + // Determine image processor class + const key = preprocessorConfig.image_processor_type ?? preprocessorConfig.feature_extractor_type; + let image_processor_class = _image_processors_js__WEBPACK_IMPORTED_MODULE_3__[key?.replace(/Fast$/, '')]; + + if (!image_processor_class) { + if (key !== undefined) { + // Only log a warning if the class is not found and the key is set. + console.warn(`Image processor type '${key}' not found, assuming base ImageProcessor. Please report this at ${_utils_constants_js__WEBPACK_IMPORTED_MODULE_0__.GITHUB_ISSUE_URL}.`) + } + image_processor_class = _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_2__.ImageProcessor; + } + + // Instantiate image processor + return new image_processor_class(preprocessorConfig); + } +} + + +/***/ }), + +/***/ "./src/models/auto/processing_auto.js": +/*!********************************************!*\ + !*** ./src/models/auto/processing_auto.js ***! + \********************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ AutoProcessor: () => (/* binding */ AutoProcessor) +/* harmony export */ }); +/* harmony import */ var _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../utils/constants.js */ "./src/utils/constants.js"); +/* harmony import */ var _utils_hub_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/hub.js */ "./src/utils/hub.js"); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _processors_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../processors.js */ "./src/models/processors.js"); +/* harmony import */ var _image_processors_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ../image_processors.js */ "./src/models/image_processors.js"); +/* harmony import */ var _feature_extractors_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ../feature_extractors.js */ "./src/models/feature_extractors.js"); + + + + + + + + + + +/** + * Helper class which is used to instantiate pretrained processors with the `from_pretrained` function. + * The chosen processor class is determined by the type specified in the processor config. + * + * **Example:** Load a processor using `from_pretrained`. + * ```javascript + * let processor = await AutoProcessor.from_pretrained('openai/whisper-tiny.en'); + * ``` + * + * **Example:** Run an image through a processor. + * ```javascript + * let processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch16'); + * let image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg'); + * let image_inputs = await processor(image); + * // { + * // "pixel_values": { + * // "dims": [ 1, 3, 224, 224 ], + * // "type": "float32", + * // "data": Float32Array [ -1.558687686920166, -1.558687686920166, -1.5440893173217773, ... ], + * // "size": 150528 + * // }, + * // "original_sizes": [ + * // [ 533, 800 ] + * // ], + * // "reshaped_input_sizes": [ + * // [ 224, 224 ] + * // ] + * // } + * ``` + */ +class AutoProcessor { + + /** @type {typeof Processor.from_pretrained} */ + static async from_pretrained(pretrained_model_name_or_path, options={}) { + + // TODO: first check for processor.json + const preprocessorConfig = await (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_1__.getModelJSON)(pretrained_model_name_or_path, _utils_constants_js__WEBPACK_IMPORTED_MODULE_0__.IMAGE_PROCESSOR_NAME, true, options); + + const { image_processor_type, feature_extractor_type, processor_class } = preprocessorConfig; + if (processor_class && _processors_js__WEBPACK_IMPORTED_MODULE_3__[processor_class]) { + return _processors_js__WEBPACK_IMPORTED_MODULE_3__[processor_class].from_pretrained(pretrained_model_name_or_path, options); + } + + if (!image_processor_type && !feature_extractor_type) { + throw new Error('No `image_processor_type` or `feature_extractor_type` found in the config.'); + } + + const components = {}; + if (image_processor_type) { + // Some image processors are saved with the "Fast" suffix, so we remove that if present. + const image_processor_class = _image_processors_js__WEBPACK_IMPORTED_MODULE_4__[image_processor_type.replace(/Fast$/, '')]; + if (!image_processor_class) { + throw new Error(`Unknown image_processor_type: '${image_processor_type}'.`); + } + components.image_processor = new image_processor_class(preprocessorConfig); + } + + if (feature_extractor_type) { + const image_processor_class = _image_processors_js__WEBPACK_IMPORTED_MODULE_4__[feature_extractor_type]; + if (image_processor_class) { + // Handle legacy case where image processors were specified as feature extractors + components.image_processor = new image_processor_class(preprocessorConfig); + } else { + const feature_extractor_class = _feature_extractors_js__WEBPACK_IMPORTED_MODULE_5__[feature_extractor_type]; + if (!feature_extractor_class) { + throw new Error(`Unknown feature_extractor_type: '${feature_extractor_type}'.`); + } + components.feature_extractor = new feature_extractor_class(preprocessorConfig); + } + } + + const config = {}; + return new _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__.Processor(config, components, null); + } +} + + +/***/ }), + +/***/ "./src/models/beit/image_processing_beit.js": +/*!**************************************************!*\ + !*** ./src/models/beit/image_processing_beit.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ BeitFeatureExtractor: () => (/* binding */ BeitFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class BeitFeatureExtractor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/bit/image_processing_bit.js": +/*!************************************************!*\ + !*** ./src/models/bit/image_processing_bit.js ***! + \************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ BitImageProcessor: () => (/* binding */ BitImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class BitImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/chinese_clip/image_processing_chinese_clip.js": +/*!******************************************************************!*\ + !*** ./src/models/chinese_clip/image_processing_chinese_clip.js ***! + \******************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ ChineseCLIPFeatureExtractor: () => (/* binding */ ChineseCLIPFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class ChineseCLIPFeatureExtractor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/clap/feature_extraction_clap.js": +/*!****************************************************!*\ + !*** ./src/models/clap/feature_extraction_clap.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ ClapFeatureExtractor: () => (/* binding */ ClapFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_audio_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../utils/audio.js */ "./src/utils/audio.js"); + + + + + +class ClapFeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + + constructor(config) { + super(config); + + this.mel_filters = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.mel_filter_bank)( + this.config.nb_frequency_bins, // num_frequency_bins + this.config.feature_size, // num_mel_filters + this.config.frequency_min, // min_frequency + this.config.frequency_max, // max_frequency + this.config.sampling_rate, // sampling_rate + null, // norm + "htk", // mel_scale + ); + + this.mel_filters_slaney = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.mel_filter_bank)( + this.config.nb_frequency_bins, // num_frequency_bins + this.config.feature_size, // num_mel_filters + this.config.frequency_min, // min_frequency + this.config.frequency_max, // max_frequency + this.config.sampling_rate, // sampling_rate + "slaney", // norm + "slaney", // mel_scale + ); + + this.window = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.window_function)(this.config.fft_window_size, 'hann') + + } + + + /** + * Extracts the mel spectrogram and prepares it for the mode based on the `truncation` and `padding` arguments. + * + * Four different path are possible: + * - `truncation="fusion"` and the length of the waveform is greater than the max length: the mel spectrogram + * will be computed on the entire audio. 3 random crops and a dowsampled version of the full mel spectrogram + * are then stacked together. They will later be used for `feature_fusion`. + * - `truncation="rand_trunc"` and the length of the waveform is smaller than the max length: the audio is + * padded based on `padding`. + * - `truncation="fusion"` and the length of the waveform is smaller than the max length: the audio is padded + * based on `padding`, and is repeated `4` times. + * - `truncation="rand_trunc"` and the length of the waveform is greater than the max length: the mel + * spectrogram will be computed on a random crop of the waveform. + * + * @param {Float32Array|Float64Array} waveform The input waveform. + * @param {number} max_length The maximum length of the waveform. + * @param {string} truncation The truncation strategy to use. + * @param {string} padding The padding strategy to use. + * @returns {Promise} An object containing the mel spectrogram data as a Float32Array, its dimensions as an array of numbers, and a boolean indicating whether the waveform was longer than the max length. + * @private + */ + async _get_input_mel(waveform, max_length, truncation, padding) { + + /** @type {Tensor} */ + let input_mel; + let longer = false; + const diff = waveform.length - max_length; + if (diff > 0) { + if (truncation === 'rand_trunc') { + longer = true; + const idx = Math.floor(Math.random() * (diff + 1)); + waveform = waveform.subarray(idx, idx + max_length); + + input_mel = await this._extract_fbank_features(waveform, this.mel_filters_slaney, this.config.nb_max_samples); + } else { + // TODO implement fusion strategy + throw new Error(`Truncation strategy "${truncation}" not implemented`) + } + } else { + if (diff < 0) { + let padded = new Float64Array(max_length); // already padded with zeros + padded.set(waveform); + + if (padding === 'repeat') { + for (let i = waveform.length; i < max_length; i += waveform.length) { + padded.set(waveform.subarray(0, Math.min(waveform.length, max_length - i)), i); + } + } else if (padding === 'repeatpad') { + for (let i = waveform.length; i < -diff; i += waveform.length) { + padded.set(waveform, i); + } + } + waveform = padded; + } + + if (truncation === 'fusion') { + throw new Error(`Truncation strategy "${truncation}" not implemented`) + } + + input_mel = await this._extract_fbank_features(waveform, this.mel_filters_slaney, this.config.nb_max_samples); + } + + return input_mel.unsqueeze_(0); + } + + /** + * Compute the log-mel spectrogram of the provided `waveform` using the Hann window. + * In CLAP, two different filter banks are used depending on the truncation pattern: + * - `self.mel_filters`: they correspond to the default parameters of `torchaudio` which can be obtained from + * calling `torchaudio.transforms.MelSpectrogram().mel_scale.fb`. These filters are used when `truncation` + * is set to `"fusion"`. + * - `self.mel_filteres_slaney` : they correspond to the default parameters of `librosa` which used + * `librosa.filters.mel` when computing the mel spectrogram. These filters were only used in the original + * implementation when the truncation mode is not `"fusion"`. + * + * @param {Float32Array|Float64Array} waveform The audio waveform to process. + * @param {number[][]} mel_filters The mel filters to use. + * @param {number} [max_length=null] The maximum number of frames to return. + * @returns {Promise} An object containing the log-Mel spectrogram data as a Float32Array and its dimensions as an array of numbers. + */ + async _extract_fbank_features(waveform, mel_filters, max_length = null) { + // NOTE: We don't pad/truncate since that is passed in as `max_num_frames` + return (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.spectrogram)( + waveform, + this.window, // window + this.config.fft_window_size, // frame_length + this.config.hop_length, // hop_length + { + power: 2.0, + mel_filters, + log_mel: 'dB', + + // Custom + max_num_frames: max_length, + do_pad: false, + transpose: true, + } + ) + } + + + /** + * Asynchronously extracts features from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @returns {Promise<{ input_features: Tensor }>} A Promise resolving to an object containing the extracted input features as a Tensor. + */ + async _call(audio, { + max_length = null, + } = {}) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'ClapFeatureExtractor'); + + // convert to mel spectrogram, truncate and pad if needed. + const padded_inputs = await this._get_input_mel( + audio, + max_length ?? this.config.nb_max_samples, + this.config.truncation, + this.config.padding, + ); + + return { + input_features: padded_inputs.unsqueeze_(0), + } + } +} + + +/***/ }), + +/***/ "./src/models/clip/image_processing_clip.js": +/*!**************************************************!*\ + !*** ./src/models/clip/image_processing_clip.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ CLIPFeatureExtractor: () => (/* binding */ CLIPFeatureExtractor), +/* harmony export */ CLIPImageProcessor: () => (/* binding */ CLIPImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class CLIPImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } +class CLIPFeatureExtractor extends CLIPImageProcessor { } + + +/***/ }), + +/***/ "./src/models/convnext/image_processing_convnext.js": +/*!**********************************************************!*\ + !*** ./src/models/convnext/image_processing_convnext.js ***! + \**********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ ConvNextFeatureExtractor: () => (/* binding */ ConvNextFeatureExtractor), +/* harmony export */ ConvNextImageProcessor: () => (/* binding */ ConvNextImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class ConvNextImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + constructor(config) { + super(config); + + /** + * Percentage of the image to crop. Only has an effect if this.size < 384. + */ + // @ts-expect-error TS2339 + this.crop_pct = this.config.crop_pct ?? (224 / 256); + } + + async resize(image) { + const shortest_edge = this.size?.shortest_edge; + if (shortest_edge === undefined) { + throw new Error(`Size dictionary must contain 'shortest_edge' key.`); + } + + if (shortest_edge < 384) { + // maintain same ratio, resizing shortest edge to shortest_edge/crop_pct + const resize_shortest_edge = Math.floor(shortest_edge / this.crop_pct); + + const [newWidth, newHeight] = this.get_resize_output_image_size(image, { + shortest_edge: resize_shortest_edge, + }); + + image = await image.resize(newWidth, newHeight, { + resample: this.resample, + }); + + // then crop to (shortest_edge, shortest_edge) + image = await image.center_crop(shortest_edge, shortest_edge); + } else { + // warping (no cropping) when evaluated at 384 or larger + image = await image.resize(shortest_edge, shortest_edge, { + resample: this.resample, + }); + } + + return image; + } +} +class ConvNextFeatureExtractor extends ConvNextImageProcessor { } + + +/***/ }), + +/***/ "./src/models/dac/feature_extraction_dac.js": +/*!**************************************************!*\ + !*** ./src/models/dac/feature_extraction_dac.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ DacFeatureExtractor: () => (/* binding */ DacFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _encodec_feature_extraction_encodec_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../encodec/feature_extraction_encodec.js */ "./src/models/encodec/feature_extraction_encodec.js"); + + +class DacFeatureExtractor extends _encodec_feature_extraction_encodec_js__WEBPACK_IMPORTED_MODULE_0__.EncodecFeatureExtractor { } + + +/***/ }), + +/***/ "./src/models/deit/image_processing_deit.js": +/*!**************************************************!*\ + !*** ./src/models/deit/image_processing_deit.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ DeiTFeatureExtractor: () => (/* binding */ DeiTFeatureExtractor), +/* harmony export */ DeiTImageProcessor: () => (/* binding */ DeiTImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class DeiTImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } +class DeiTFeatureExtractor extends DeiTImageProcessor { } + +/***/ }), + +/***/ "./src/models/detr/image_processing_detr.js": +/*!**************************************************!*\ + !*** ./src/models/detr/image_processing_detr.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ DetrFeatureExtractor: () => (/* binding */ DetrFeatureExtractor), +/* harmony export */ DetrImageProcessor: () => (/* binding */ DetrImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + + + +/** + * @typedef {object} DetrFeatureExtractorResultProps + * @property {import('../../utils/tensor.js').Tensor} pixel_mask + * @typedef {import('../../base/image_processors_utils.js').ImageProcessorResult & DetrFeatureExtractorResultProps} DetrFeatureExtractorResult + */ + +class DetrImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + /** + * Calls the feature extraction process on an array of images, preprocesses + * each image, and concatenates the resulting features into a single Tensor. + * @param {import('../../utils/image.js').RawImage[]} images The image(s) to extract features from. + * @returns {Promise} An object containing the concatenated pixel values of the preprocessed images. + */ + async _call(images) { + const result = await super._call(images); + + // TODO support differently-sized images, for now assume all images are the same size. + // TODO support different mask sizes (not just 64x64) + // Currently, just fill pixel mask with 1s + const maskSize = [result.pixel_values.dims[0], 64, 64]; + const pixel_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.full)(maskSize, 1n); + + return { ...result, pixel_mask }; + } + + /** @type {typeof post_process_object_detection} */ + post_process_object_detection(...args) { + return (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.post_process_object_detection)(...args); + } + + /** @type {typeof post_process_panoptic_segmentation} */ + post_process_panoptic_segmentation(...args) { + return (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.post_process_panoptic_segmentation)(...args); + } + + /** @type {typeof post_process_instance_segmentation} */ + post_process_instance_segmentation(...args) { + return (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.post_process_instance_segmentation)(...args); + } +} + +class DetrFeatureExtractor extends DetrImageProcessor { } // NOTE: extends DetrImageProcessor + + +/***/ }), + +/***/ "./src/models/dinov3_vit/image_processing_dinov3_vit.js": +/*!**************************************************************!*\ + !*** ./src/models/dinov3_vit/image_processing_dinov3_vit.js ***! + \**************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ DINOv3ViTImageProcessor: () => (/* binding */ DINOv3ViTImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + +class DINOv3ViTImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/donut/image_processing_donut.js": +/*!****************************************************!*\ + !*** ./src/models/donut/image_processing_donut.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ DonutFeatureExtractor: () => (/* binding */ DonutFeatureExtractor), +/* harmony export */ DonutImageProcessor: () => (/* binding */ DonutImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class DonutImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + pad_image(pixelData, imgDims, padSize, options = {}) { + const [imageHeight, imageWidth, imageChannels] = imgDims; + + let image_mean = this.image_mean; + if (!Array.isArray(this.image_mean)) { + image_mean = new Array(imageChannels).fill(image_mean); + } + + let image_std = this.image_std; + if (!Array.isArray(image_std)) { + image_std = new Array(imageChannels).fill(image_mean); + } + + const constant_values = image_mean.map((x, i) => - x / image_std[i]); + + return super.pad_image(pixelData, imgDims, padSize, { + center: true, + + // Since normalization is done after padding, we need to use certain constant values to ensure the same behaviour is observed. + // For more information, see https://github.com/huggingface/transformers/blob/main/src/transformers/models/donut/image_processing_donut.py#L433-L451 + constant_values, + ...options, + }); + } +} +class DonutFeatureExtractor extends DonutImageProcessor { } + + +/***/ }), + +/***/ "./src/models/dpt/image_processing_dpt.js": +/*!************************************************!*\ + !*** ./src/models/dpt/image_processing_dpt.js ***! + \************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ DPTFeatureExtractor: () => (/* binding */ DPTFeatureExtractor), +/* harmony export */ DPTImageProcessor: () => (/* binding */ DPTImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class DPTImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } +class DPTFeatureExtractor extends DPTImageProcessor { } // NOTE: extends DPTImageProcessor + + +/***/ }), + +/***/ "./src/models/efficientnet/image_processing_efficientnet.js": +/*!******************************************************************!*\ + !*** ./src/models/efficientnet/image_processing_efficientnet.js ***! + \******************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ EfficientNetImageProcessor: () => (/* binding */ EfficientNetImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class EfficientNetImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + constructor(config) { + super(config); + // @ts-expect-error TS2339 + this.include_top = this.config.include_top ?? true; + if (this.include_top) { + this.image_std = this.image_std.map(x => x * x); + } + } +} + + +/***/ }), + +/***/ "./src/models/encodec/feature_extraction_encodec.js": +/*!**********************************************************!*\ + !*** ./src/models/encodec/feature_extraction_encodec.js ***! + \**********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ EncodecFeatureExtractor: () => (/* binding */ EncodecFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + + +class EncodecFeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + /** + * Asynchronously extracts input values from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @returns {Promise<{ input_values: Tensor; }>} The extracted input values. + */ + async _call(audio) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'EncodecFeatureExtractor'); + + if (audio instanceof Float64Array) { + audio = new Float32Array(audio); + } + + const num_channels = this.config.feature_size; + if (audio.length % num_channels !== 0) { + throw new Error(`The length of the audio data must be a multiple of the number of channels (${num_channels}).`); + } + + const shape = [ + 1, /* batch_size */ + num_channels, /* num_channels */ + audio.length / num_channels, /* num_samples */ + ]; + return { + input_values: new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor('float32', audio, shape), + }; + } +} + + +/***/ }), + +/***/ "./src/models/feature_extractors.js": +/*!******************************************!*\ + !*** ./src/models/feature_extractors.js ***! + \******************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ ASTFeatureExtractor: () => (/* reexport safe */ _audio_spectrogram_transformer_feature_extraction_audio_spectrogram_transformer_js__WEBPACK_IMPORTED_MODULE_0__.ASTFeatureExtractor), +/* harmony export */ ClapFeatureExtractor: () => (/* reexport safe */ _clap_feature_extraction_clap_js__WEBPACK_IMPORTED_MODULE_2__.ClapFeatureExtractor), +/* harmony export */ DacFeatureExtractor: () => (/* reexport safe */ _dac_feature_extraction_dac_js__WEBPACK_IMPORTED_MODULE_3__.DacFeatureExtractor), +/* harmony export */ EncodecFeatureExtractor: () => (/* reexport safe */ _encodec_feature_extraction_encodec_js__WEBPACK_IMPORTED_MODULE_1__.EncodecFeatureExtractor), +/* harmony export */ Gemma3nAudioFeatureExtractor: () => (/* reexport safe */ _gemma3n_feature_extraction_gemma3n_js__WEBPACK_IMPORTED_MODULE_4__.Gemma3nAudioFeatureExtractor), +/* harmony export */ ImageFeatureExtractor: () => (/* reexport safe */ _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_13__.ImageProcessor), +/* harmony export */ MoonshineFeatureExtractor: () => (/* reexport safe */ _moonshine_feature_extraction_moonshine_js__WEBPACK_IMPORTED_MODULE_5__.MoonshineFeatureExtractor), +/* harmony export */ PyAnnoteFeatureExtractor: () => (/* reexport safe */ _pyannote_feature_extraction_pyannote_js__WEBPACK_IMPORTED_MODULE_6__.PyAnnoteFeatureExtractor), +/* harmony export */ SeamlessM4TFeatureExtractor: () => (/* reexport safe */ _seamless_m4t_feature_extraction_seamless_m4t_js__WEBPACK_IMPORTED_MODULE_7__.SeamlessM4TFeatureExtractor), +/* harmony export */ SnacFeatureExtractor: () => (/* reexport safe */ _snac_feature_extraction_snac_js__WEBPACK_IMPORTED_MODULE_8__.SnacFeatureExtractor), +/* harmony export */ SpeechT5FeatureExtractor: () => (/* reexport safe */ _speecht5_feature_extraction_speecht5_js__WEBPACK_IMPORTED_MODULE_9__.SpeechT5FeatureExtractor), +/* harmony export */ Wav2Vec2FeatureExtractor: () => (/* reexport safe */ _wav2vec2_feature_extraction_wav2vec2_js__WEBPACK_IMPORTED_MODULE_10__.Wav2Vec2FeatureExtractor), +/* harmony export */ WeSpeakerFeatureExtractor: () => (/* reexport safe */ _wespeaker_feature_extraction_wespeaker_js__WEBPACK_IMPORTED_MODULE_11__.WeSpeakerFeatureExtractor), +/* harmony export */ WhisperFeatureExtractor: () => (/* reexport safe */ _whisper_feature_extraction_whisper_js__WEBPACK_IMPORTED_MODULE_12__.WhisperFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _audio_spectrogram_transformer_feature_extraction_audio_spectrogram_transformer_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js */ "./src/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.js"); +/* harmony import */ var _encodec_feature_extraction_encodec_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./encodec/feature_extraction_encodec.js */ "./src/models/encodec/feature_extraction_encodec.js"); +/* harmony import */ var _clap_feature_extraction_clap_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./clap/feature_extraction_clap.js */ "./src/models/clap/feature_extraction_clap.js"); +/* harmony import */ var _dac_feature_extraction_dac_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./dac/feature_extraction_dac.js */ "./src/models/dac/feature_extraction_dac.js"); +/* harmony import */ var _gemma3n_feature_extraction_gemma3n_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ./gemma3n/feature_extraction_gemma3n.js */ "./src/models/gemma3n/feature_extraction_gemma3n.js"); +/* harmony import */ var _moonshine_feature_extraction_moonshine_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ./moonshine/feature_extraction_moonshine.js */ "./src/models/moonshine/feature_extraction_moonshine.js"); +/* harmony import */ var _pyannote_feature_extraction_pyannote_js__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! ./pyannote/feature_extraction_pyannote.js */ "./src/models/pyannote/feature_extraction_pyannote.js"); +/* harmony import */ var _seamless_m4t_feature_extraction_seamless_m4t_js__WEBPACK_IMPORTED_MODULE_7__ = __webpack_require__(/*! ./seamless_m4t/feature_extraction_seamless_m4t.js */ "./src/models/seamless_m4t/feature_extraction_seamless_m4t.js"); +/* harmony import */ var _snac_feature_extraction_snac_js__WEBPACK_IMPORTED_MODULE_8__ = __webpack_require__(/*! ./snac/feature_extraction_snac.js */ "./src/models/snac/feature_extraction_snac.js"); +/* harmony import */ var _speecht5_feature_extraction_speecht5_js__WEBPACK_IMPORTED_MODULE_9__ = __webpack_require__(/*! ./speecht5/feature_extraction_speecht5.js */ "./src/models/speecht5/feature_extraction_speecht5.js"); +/* harmony import */ var _wav2vec2_feature_extraction_wav2vec2_js__WEBPACK_IMPORTED_MODULE_10__ = __webpack_require__(/*! ./wav2vec2/feature_extraction_wav2vec2.js */ "./src/models/wav2vec2/feature_extraction_wav2vec2.js"); +/* harmony import */ var _wespeaker_feature_extraction_wespeaker_js__WEBPACK_IMPORTED_MODULE_11__ = __webpack_require__(/*! ./wespeaker/feature_extraction_wespeaker.js */ "./src/models/wespeaker/feature_extraction_wespeaker.js"); +/* harmony import */ var _whisper_feature_extraction_whisper_js__WEBPACK_IMPORTED_MODULE_12__ = __webpack_require__(/*! ./whisper/feature_extraction_whisper.js */ "./src/models/whisper/feature_extraction_whisper.js"); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_13__ = __webpack_require__(/*! ../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + + + + + + + + + + + + + +// For legacy support, ImageFeatureExtractor is an alias for ImageProcessor + + + +/***/ }), + +/***/ "./src/models/florence2/processing_florence2.js": +/*!******************************************************!*\ + !*** ./src/models/florence2/processing_florence2.js ***! + \******************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Florence2Processor: () => (/* binding */ Florence2Processor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); + + + + +class Florence2Processor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + + constructor(config, components, chat_template) { + super(config, components, chat_template); + + const { + // @ts-expect-error TS2339 + tasks_answer_post_processing_type, + // @ts-expect-error TS2339 + task_prompts_without_inputs, + // @ts-expect-error TS2339 + task_prompts_with_input, + } = this.image_processor.config; + + /** @type {Map} */ + this.tasks_answer_post_processing_type = new Map(Object.entries(tasks_answer_post_processing_type ?? {})); + + /** @type {Map} */ + this.task_prompts_without_inputs = new Map(Object.entries(task_prompts_without_inputs ?? {})); + + /** @type {Map} */ + this.task_prompts_with_input = new Map(Object.entries(task_prompts_with_input ?? {})); + + this.regexes = { + quad_boxes: /(.+?)/gm, + bboxes: /([^<]+)?/gm, + } + this.size_per_bin = 1000; + } + + /** + * Helper function to construct prompts from input texts + * @param {string|string[]} text + * @returns {string[]} + */ + construct_prompts(text) { + if (typeof text === 'string') { + text = [text]; + } + + const prompts = []; + for (const t of text) { + // 1. fixed task prompts without additional inputs + if (this.task_prompts_without_inputs.has(t)) { + prompts.push(this.task_prompts_without_inputs.get(t)); + } + // 2. task prompts with additional inputs + else { + for (const [task, prompt] of this.task_prompts_with_input) { + if (t.includes(task)) { + prompts.push(prompt.replaceAll('{input}', t).replaceAll(task, '')); + break; + } + } + + // 3. default prompt + if (prompts.length !== text.length) { + prompts.push(t); + } + } + } + return prompts; + } + + /** + * Post-process the output of the model to each of the task outputs. + * @param {string} text The text to post-process. + * @param {string} task The task to post-process the text for. + * @param {[number, number]} image_size The size of the image. height x width. + */ + post_process_generation(text, task, image_size) { + const task_answer_post_processing_type = this.tasks_answer_post_processing_type.get(task) ?? 'pure_text'; + + // remove the special tokens + text = text.replaceAll('', '').replaceAll('', ''); + + let final_answer; + switch (task_answer_post_processing_type) { + case 'pure_text': + final_answer = text; + break; + + case 'description_with_bboxes': + case 'bboxes': + case 'phrase_grounding': + case 'ocr': + const key = task_answer_post_processing_type === 'ocr' ? 'quad_boxes' : 'bboxes'; + const matches = text.matchAll(this.regexes[key]); + const labels = []; + const items = []; + for (const [_, label, ...locations] of matches) { + // Push new label, or duplicate the last label + labels.push(label ? label.trim() : labels.at(-1) ?? ''); + items.push(locations.map((x, i) => + // NOTE: Add 0.5 to use the center position of the bin as the coordinate. + (Number(x) + 0.5) / this.size_per_bin * image_size[i % 2]) + ); + } + final_answer = { labels, [key]: items }; + break; + + default: + throw new Error(`Task "${task}" (of type "${task_answer_post_processing_type}") not yet implemented.`); + } + + return { [task]: final_answer } + } + + // NOTE: images and text are switched from the python version + // `images` is required, `text` is optional + async _call(images, text=null, kwargs = {}) { + + if (!images && !text){ + throw new Error('Either text or images must be provided'); + } + + const image_inputs = await this.image_processor(images, kwargs); + const text_inputs = text ? this.tokenizer(this.construct_prompts(text), kwargs) : {}; + + return { + ...image_inputs, + ...text_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/gemma3n/feature_extraction_gemma3n.js": +/*!**********************************************************!*\ + !*** ./src/models/gemma3n/feature_extraction_gemma3n.js ***! + \**********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Gemma3nAudioFeatureExtractor: () => (/* binding */ Gemma3nAudioFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_audio_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../utils/audio.js */ "./src/utils/audio.js"); + + + + +class Gemma3nAudioFeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + + constructor(config) { + super(config); + + const { + fft_length, feature_size, min_frequency, max_frequency, sampling_rate, frame_length + } = this.config; + + const mel_filters = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.mel_filter_bank)( + Math.floor(1 + fft_length / 2), // num_frequency_bins + feature_size, // num_mel_filters + min_frequency, // min_frequency + max_frequency, // max_frequency + sampling_rate, // sampling_rate + null, // norm + "htk", // mel_scale + false, // triangularize_in_mel_space + ); + this.mel_filters = mel_filters; + + this.window = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.window_function)(frame_length, 'hann') + } + + /** + * Computes the log-Mel spectrogram of the provided audio waveform. + * @param {Float32Array|Float64Array} waveform The audio waveform to process. + * @param {number} max_length The maximum number of frames to return. + * @returns {Promise} An object containing the log-Mel spectrogram data as a Float32Array and its dimensions as an array of numbers. + */ + async _extract_fbank_features(waveform, max_length) { + // NOTE: We don't pad/truncate since that is passed in as `max_num_frames` + return (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.spectrogram)( + waveform, + this.window, // window + this.config.frame_length, // frame_length + this.config.hop_length, // hop_length + { + fft_length: this.config.fft_length, + center: false, + onesided: true, + preemphasis: this.config.preemphasis, + preemphasis_htk_flavor: this.config.preemphasis_htk_flavor, + mel_filters: this.mel_filters, + log_mel: 'log', + mel_floor: this.config.mel_floor, + remove_dc_offset: false, + + // Custom + transpose: true, + } + ) + } + + /** + * Asynchronously extracts features from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @param {Object} options Optional parameters for feature extraction. + * @param {number} [options.max_length=480_000] If provided, defines the maximum length of the audio to allow. + * Audio longer than this will be truncated if `truncation=True`. + * @param {boolean} [options.truncation=true] Whether or not to truncate audio above `max_length`. + * @param {boolean} [options.padding=true] Whether to pad the sequence to a multiple of `pad_to_multiple_of`. + * @param {number} [options.pad_to_multiple_of=128] The number to pad the sequence to a multiple of. + * @returns {Promise<{ input_features: Tensor, input_features_mask: Tensor }>} A Promise resolving to an object containing the extracted input features and attention masks as Tensors. + */ + async _call(audio, { + max_length = 480_000, + truncation=true, + padding = true, + pad_to_multiple_of = 128, + } = {}) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'Gemma3nAudioFeatureExtractor'); + if (truncation && audio.length > max_length) { + audio = audio.slice(0, max_length); + } + if (padding && audio.length % pad_to_multiple_of !== 0) { + const padding_length = pad_to_multiple_of - (audio.length % pad_to_multiple_of); + const padded_audio = new Float64Array(audio.length + padding_length); + padded_audio.set(audio); + if (this.config.padding_value !== 0) { + padded_audio.fill(this.config.padding_value, audio.length); + } + audio = padded_audio; + } + + const features = await this._extract_fbank_features(audio, this.config.max_length); + const padded_attention_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.full)([1, features.dims[0]], true); + return { + input_features: features.unsqueeze_(0), + input_features_mask: padded_attention_mask, + } + } +} + + +/***/ }), + +/***/ "./src/models/gemma3n/processing_gemma3n.js": +/*!**************************************************!*\ + !*** ./src/models/gemma3n/processing_gemma3n.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Gemma3nProcessor: () => (/* binding */ Gemma3nProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../auto/feature_extraction_auto.js */ "./src/models/auto/feature_extraction_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _utils_image_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ../../utils/image.js */ "./src/utils/image.js"); +/* harmony import */ var _utils_audio_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ../../utils/audio.js */ "./src/utils/audio.js"); + + + + + + + + +class Gemma3nProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor; + static feature_extractor_class = _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoFeatureExtractor; + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.AutoTokenizer; + static uses_processor_config = true; + static uses_chat_template_file = true; + + constructor(config, components, chat_template) { + super(config, components, chat_template); + this.audio_seq_length = this.config.audio_seq_length; + this.image_seq_length = this.config.image_seq_length; + + const { + // Audio tokens + audio_token_id, boa_token, audio_token, eoa_token, + + // Image tokens + image_token_id, boi_token, image_token, eoi_token + } = this.tokenizer.config; + + this.audio_token_id = audio_token_id + this.boa_token = boa_token + this.audio_token = audio_token + const audio_tokens_expanded = audio_token.repeat(this.audio_seq_length); + this.full_audio_sequence = `\n\n${boa_token}${audio_tokens_expanded}${eoa_token}\n\n` + + this.image_token_id = image_token_id + this.boi_token = boi_token + this.image_token = image_token + const image_tokens_expanded = image_token.repeat(this.image_seq_length); + this.full_image_sequence = `\n\n${boi_token}${image_tokens_expanded}${eoi_token}\n\n` + } + + /** + * + * @param {string|string[]} text + * @param {RawImage|RawImage[]|RawImage[][]} images + * @param {RawAudio|RawAudio[]|RawAudio[][]} audio + * @returns {Promise} + */ + async _call(text, images = null, audio = null, options = {}) { + + if (typeof text === 'string') { + text = [text]; + } + + let audio_inputs; + if (audio) { + audio_inputs = await this.feature_extractor(audio, options); + + text = text.map(prompt => prompt.replaceAll(this.audio_token, this.full_audio_sequence)); + } + let image_inputs; + if (images) { + image_inputs = await this.image_processor(images, options); + text = text.map(prompt => prompt.replaceAll(this.image_token, this.full_image_sequence)); + } + + let text_inputs = this.tokenizer(text, options); + return { + ...text_inputs, + ...image_inputs, + ...audio_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/glpn/image_processing_glpn.js": +/*!**************************************************!*\ + !*** ./src/models/glpn/image_processing_glpn.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ GLPNFeatureExtractor: () => (/* binding */ GLPNFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class GLPNFeatureExtractor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/grounding_dino/image_processing_grounding_dino.js": +/*!**********************************************************************!*\ + !*** ./src/models/grounding_dino/image_processing_grounding_dino.js ***! + \**********************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ GroundingDinoImageProcessor: () => (/* binding */ GroundingDinoImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + + + +/** + * @typedef {object} GroundingDinoFeatureExtractorResultProps + * @property {import('../../utils/tensor.js').Tensor} pixel_mask + * @typedef {import('../../base/image_processors_utils.js').ImageProcessorResult & GroundingDinoFeatureExtractorResultProps} GroundingDinoFeatureExtractorResult + */ + +class GroundingDinoImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + /** + * Calls the feature extraction process on an array of images, preprocesses + * each image, and concatenates the resulting features into a single Tensor. + * @param {import('../../utils/image.js').RawImage[]} images The image(s) to extract features from. + * @returns {Promise} An object containing the concatenated pixel values of the preprocessed images. + */ + async _call(images) { + const result = await super._call(images); + + const dims = result.pixel_values.dims; + const pixel_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.ones)([dims[0], dims[2], dims[3]]); + + return { ...result, pixel_mask }; + } +} + + +/***/ }), + +/***/ "./src/models/grounding_dino/processing_grounding_dino.js": +/*!****************************************************************!*\ + !*** ./src/models/grounding_dino/processing_grounding_dino.js ***! + \****************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ GroundingDinoProcessor: () => (/* binding */ GroundingDinoProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + + + +/** + * Get token ids of phrases from posmaps and input_ids. + * @param {import('../../utils/tensor.js').Tensor} posmaps A boolean tensor of unbatched text-thresholded logits related to the detected bounding boxes of shape `(hidden_size, )`. + * @param {import('../../utils/tensor.js').Tensor} input_ids A tensor of token ids of shape `(sequence_length, )`. + */ +function get_phrases_from_posmap(posmaps, input_ids) { + + const left_idx = 0; + const right_idx = posmaps.dims.at(-1) - 1; + + const posmaps_list = posmaps.tolist(); + posmaps_list.fill(false, 0, left_idx + 1); + posmaps_list.fill(false, right_idx); + + const input_ids_list = input_ids.tolist(); + return posmaps_list + .map((val, idx) => val ? idx : null) + .filter(idx => idx !== null) + .map(i => input_ids_list[i]); +} + +class GroundingDinoProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + + /** + * @typedef {import('../../utils/image.js').RawImage} RawImage + */ + /** + * + * @param {RawImage|RawImage[]|RawImage[][]} images + * @param {string|string[]} text + * @returns {Promise} + */ + async _call(images, text, options = {}) { + + const image_inputs = images ? await this.image_processor(images, options) : {}; + const text_inputs = text ? this.tokenizer(text, options) : {}; + + return { + ...text_inputs, + ...image_inputs, + } + } + post_process_grounded_object_detection(outputs, input_ids, { + box_threshold = 0.25, + text_threshold = 0.25, + target_sizes = null + } = {}) { + const { logits, pred_boxes } = outputs; + const batch_size = logits.dims[0]; + + if (target_sizes !== null && target_sizes.length !== batch_size) { + throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits") + } + const num_queries = logits.dims.at(1); + + const probs = logits.sigmoid(); // (batch_size, num_queries, 256) + const scores = probs.max(-1).tolist(); // (batch_size, num_queries) + + // Convert to [x0, y0, x1, y1] format + const boxes = pred_boxes.tolist() // (batch_size, num_queries, 4) + .map(batch => batch.map(box => (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_3__.center_to_corners_format)(box))); + + const results = []; + for (let i = 0; i < batch_size; ++i) { + const target_size = target_sizes !== null ? target_sizes[i] : null; + + // Convert from relative [0, 1] to absolute [0, height] coordinates + if (target_size !== null) { + boxes[i] = boxes[i].map(box => box.map((x, j) => x * target_size[(j + 1) % 2])); + } + + const batch_scores = scores[i]; + const final_scores = []; + const final_phrases = []; + const final_boxes = []; + for (let j = 0; j < num_queries; ++j) { + const score = batch_scores[j]; + if (score <= box_threshold) { + continue; + } + const box = boxes[i][j]; + const prob = probs[i][j]; + + final_scores.push(score); + final_boxes.push(box); + + const phrases = get_phrases_from_posmap(prob.gt(text_threshold), input_ids[i]); + final_phrases.push(phrases); + } + results.push({ scores: final_scores, boxes: final_boxes, labels: this.batch_decode(final_phrases) }); + } + return results; + } +} + + +/***/ }), + +/***/ "./src/models/idefics3/image_processing_idefics3.js": +/*!**********************************************************!*\ + !*** ./src/models/idefics3/image_processing_idefics3.js ***! + \**********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Idefics3ImageProcessor: () => (/* binding */ Idefics3ImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + + + +class Idefics3ImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + constructor(config) { + super(config); + + this.do_image_splitting = config.do_image_splitting ?? true; + this.max_image_size = config.max_image_size; + } + + /** + * @typedef {import('../../utils/image.js').RawImage} RawImage + * @typedef {import('../../utils/tensor.js').Tensor} Tensor + */ + + /** + * Calculate size to resize images to, to be multiples of `vision_encoder_max_size` while preserving the aspect ratio. + * @param {Tensor} pixel_values Tensor of the image to resize. + * @param {number} vision_encoder_max_size Maximum size of the output image. If the image is larger than this size, + * it will be split into patches of this size, and the original image will be concatenated with the patches, resized to max_size. + */ + get_resize_for_vision_encoder(pixel_values, vision_encoder_max_size) { + let [height, width] = pixel_values.dims.slice(-2); + + const aspect_ratio = width / height; + if (width >= height) { + width = Math.ceil(width / vision_encoder_max_size) * vision_encoder_max_size; + height = Math.floor(width / aspect_ratio); + height = Math.ceil(height / vision_encoder_max_size) * vision_encoder_max_size; + } else { + height = Math.ceil(height / vision_encoder_max_size) * vision_encoder_max_size; + width = Math.floor(height * aspect_ratio); + width = Math.ceil(width / vision_encoder_max_size) * vision_encoder_max_size; + } + return { height, width }; + } + + /** @param {RawImage|RawImage[]|RawImage[][]} images */ + async _call(images, { + do_image_splitting = null, + return_row_col_info = false, + } = {}) { + + /** @type {RawImage[][]} */ + let batched_2d_images; + if (!Array.isArray(images)) { + batched_2d_images = [[images]]; + } else { + if (images.length === 0 || !images[0]) { + throw new Error("No images provided."); + } + if (!Array.isArray(images[0])) { + batched_2d_images = [/** @type {RawImage[]} */(images)]; + } else { + batched_2d_images = /** @type {RawImage[][]} */(images); + } + } + + // List of tensors, each with shape [patches, channels, height, width] + let all_pixel_values = []; + let images_list_rows = []; + let images_list_cols = []; + + const original_sizes = []; + const reshaped_input_sizes = []; + for (const image_batch of batched_2d_images) { + + let images_list = await Promise.all(image_batch.map(x => this.preprocess(x))); + + // Original sizes of images + original_sizes.push(...images_list.map(x => x.original_size)); + + // Reshaped sizes of images, before padding or cropping + reshaped_input_sizes.push(...images_list.map(x => x.reshaped_input_size)); + + // Convert images to 4D tensors for easier processing + images_list.forEach(x => x.pixel_values.unsqueeze_(0)); + + const { longest_edge } = this.max_image_size; + + /** @type {Tensor[]} */ + let images_tensor; + if (do_image_splitting ?? this.do_image_splitting) { + let image_rows = new Array(images_list.length); + let image_cols = new Array(images_list.length); + + // We first resize both height and width of each image to the nearest max_image_size multiple, disregarding the aspect ratio + images_tensor = await Promise.all(images_list.map(async (x, i) => { + const new_size = this.get_resize_for_vision_encoder(x.pixel_values, longest_edge); + + const resized = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.interpolate_4d)(x.pixel_values, { + size: [new_size.height, new_size.width], + }); + + const { frames, num_splits_h, num_splits_w } = await this.split_image(resized, this.max_image_size); + image_rows[i] = num_splits_h; + image_cols[i] = num_splits_w; + return (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.cat)(frames, 0); + })); + + images_list_rows.push(image_rows); + images_list_cols.push(image_cols); + + } else { + /** @type {[number, number]} */ + const size = [longest_edge, longest_edge]; + images_tensor = await Promise.all( + images_list.map(x => (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.interpolate_4d)(x.pixel_values, { size })) + ); + + images_list_rows.push(new Array(images_list.length).fill(0)); + images_list_cols.push(new Array(images_list.length).fill(0)); + } + + all_pixel_values.push((0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.cat)(images_tensor, 0)); + } + + const batch_size = all_pixel_values.length; + const [n, c, h, w] = all_pixel_values[0].dims; + + // Stack pixel values + let pixel_values; + let pixel_attention_mask; + if (batch_size === 1) { + pixel_values = all_pixel_values[0].unsqueeze_(0); + pixel_attention_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.full)([batch_size, n, h, w], true); + } else { + // Add padding (if necessary) to images with less patches than the maximum number of patches + const max_num_patches = Math.max(...all_pixel_values.map(x => x.dims.at(0))); + + pixel_attention_mask = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.full)([batch_size, max_num_patches, h, w], true); + const pixel_attention_mask_data = pixel_attention_mask.data; + const pixel_attention_mask_stride = max_num_patches * h * w; + for (let i = 0; i < batch_size; ++i) { + const num_patches = all_pixel_values[i].dims[0]; + if (num_patches < max_num_patches) { + all_pixel_values[i] = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.cat)([ + all_pixel_values[i], + (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.full)([max_num_patches - num_patches, c, h, w], 0), + ], 0); + + const start_offset = i * pixel_attention_mask_stride + num_patches * h * w; + const end_offset = (i + 1) * pixel_attention_mask_stride; + + // @ts-ignore + pixel_attention_mask_data.fill(false, start_offset, end_offset); + } + } + pixel_values = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.stack)(all_pixel_values, 0); + } + + return { + pixel_values, + pixel_attention_mask, + + original_sizes, + reshaped_input_sizes, + ...( + return_row_col_info + ? { rows: images_list_rows, cols: images_list_cols } + : {} + ), + } + } + + async split_image(pixel_values, { longest_edge }) { + const max_height = longest_edge; + const max_width = longest_edge; + + const frames = []; + + const [height, width] = pixel_values.dims.slice(-2); + + let num_splits_h = 0, num_splits_w = 0; + + if (height > max_height || width > max_width) { + // Calculate the number of splits + num_splits_h = Math.ceil(height / max_height); + num_splits_w = Math.ceil(width / max_width); + + // Calculate the optimal width and height for the sub-images + const optimal_height = Math.ceil(height / num_splits_h); + const optimal_width = Math.ceil(width / num_splits_w); + + // Iterate through each row and column + for (let r = 0; r < num_splits_h; ++r) { + for (let c = 0; c < num_splits_w; ++c) { + let start_x, start_y, end_x, end_y; + if (r === num_splits_h - 1) { // At bottom + start_y = height - optimal_height; + end_y = height; + } else { + start_y = r * optimal_height; + end_y = (r + 1) * optimal_height; + } + if (c === num_splits_w - 1) { // At right + start_x = width - optimal_width; + end_x = width; + } else { + start_x = c * optimal_width; + end_x = (c + 1) * optimal_width; + } + + const starts = [start_y, start_x]; + const ends = [end_y, end_x]; + + const patch = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.slice)(pixel_values, starts, ends, [2, 3]); + frames.push(patch); + } + } + + // Resize the global image to match max dimensions for memory efficiency + const global_image_height = max_height; + const global_image_width = max_width; + + if (height !== global_image_height || width !== global_image_width) { + pixel_values = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.interpolate_4d)(pixel_values, { + size: [global_image_height, global_image_width], + }) + } + } + + frames.push(pixel_values); + + return { frames, num_splits_h, num_splits_w }; + } +} + + +/***/ }), + +/***/ "./src/models/idefics3/processing_idefics3.js": +/*!****************************************************!*\ + !*** ./src/models/idefics3/processing_idefics3.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Idefics3Processor: () => (/* binding */ Idefics3Processor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _utils_image_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../../utils/image.js */ "./src/utils/image.js"); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ../../utils/core.js */ "./src/utils/core.js"); + + + + + + + +/** + * Prompt with expanded image tokens for when the image is split into patches. + * @private + */ +function _prompt_split_image(image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token) { + let text_split_images = ""; + for (let n_h = 0; n_h < image_rows; ++n_h) { + for (let n_w = 0; n_w < image_cols; ++n_w) { + text_split_images += ( + fake_token_around_image + + `` + + image_token.repeat(image_seq_len) + ); + } + text_split_images += "\n"; + } + + text_split_images += ( + `\n${fake_token_around_image}` + + `${global_img_token}` + + image_token.repeat(image_seq_len) + + `${fake_token_around_image}` + ); + return text_split_images; +} + +/** + * Prompt with expanded image tokens for a single image. + * @private + */ +function _prompt_single_image(image_seq_len, fake_token_around_image, image_token, global_img_token) { + return ( + `${fake_token_around_image}` + + `${global_img_token}` + + image_token.repeat(image_seq_len) + + `${fake_token_around_image}` + ); +} + +function get_image_prompt_string(image_rows, image_cols, image_seq_len, fake_token_around_image, image_token, global_img_token) { + if (image_rows === 0 && image_cols === 0) { + return _prompt_single_image( + image_seq_len, + fake_token_around_image, + image_token, + global_img_token + ); + } + return _prompt_split_image( + image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token + ); +} + + +class Idefics3Processor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + static uses_processor_config = true; + + fake_image_token = ""; + image_token = ""; + global_img_token = ""; + + /** + * + * @param {string|string[]} text + * @param {RawImage|RawImage[]|RawImage[][]} images + * @returns {Promise} + */ + async _call(text, images = null, options = {}) { + options.return_row_col_info ??= true; + + let image_inputs; + + if (images) { + image_inputs = await this.image_processor(images, options); + } + + // NOTE: We assume text is present + if (!Array.isArray(text)) { + text = [text]; + } + + const image_rows = image_inputs.rows ?? [new Array(text.length).fill(0)]; + const image_cols = image_inputs.cols ?? [new Array(text.length).fill(0)]; + + const image_seq_len = this.config.image_seq_len; + const n_images_in_text = [] + const prompt_strings = []; + for (let i = 0; i < text.length; ++i) { + const sample = text[i]; + const sample_rows = image_rows[i]; + const sample_cols = image_cols[i]; + + n_images_in_text.push((0,_utils_core_js__WEBPACK_IMPORTED_MODULE_4__.count)(sample, this.image_token)); + + // Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len` + const image_prompt_strings = sample_rows.map( + (n_rows, j) => get_image_prompt_string( + n_rows, + sample_cols[j], + image_seq_len, + this.fake_image_token, + this.image_token, + this.global_img_token, + ) + ); + + const split_sample = sample.split(this.image_token); + if (split_sample.length === 0) { + throw new Error("The image token should be present in the text."); + } + + // Place in the image prompt strings where the image tokens are + let new_sample = split_sample[0]; + for (let j = 0; j < image_prompt_strings.length; ++j) { + new_sample += image_prompt_strings[j] + split_sample[j + 1]; + } + prompt_strings.push(new_sample); + } + + const text_inputs = this.tokenizer(prompt_strings); + + return { + ...text_inputs, + ...image_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/image_processors.js": +/*!****************************************!*\ + !*** ./src/models/image_processors.js ***! + \****************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ BeitFeatureExtractor: () => (/* reexport safe */ _beit_image_processing_beit_js__WEBPACK_IMPORTED_MODULE_0__.BeitFeatureExtractor), +/* harmony export */ BitImageProcessor: () => (/* reexport safe */ _bit_image_processing_bit_js__WEBPACK_IMPORTED_MODULE_1__.BitImageProcessor), +/* harmony export */ CLIPFeatureExtractor: () => (/* reexport safe */ _clip_image_processing_clip_js__WEBPACK_IMPORTED_MODULE_3__.CLIPFeatureExtractor), +/* harmony export */ CLIPImageProcessor: () => (/* reexport safe */ _clip_image_processing_clip_js__WEBPACK_IMPORTED_MODULE_3__.CLIPImageProcessor), +/* harmony export */ ChineseCLIPFeatureExtractor: () => (/* reexport safe */ _chinese_clip_image_processing_chinese_clip_js__WEBPACK_IMPORTED_MODULE_2__.ChineseCLIPFeatureExtractor), +/* harmony export */ ConvNextFeatureExtractor: () => (/* reexport safe */ _convnext_image_processing_convnext_js__WEBPACK_IMPORTED_MODULE_4__.ConvNextFeatureExtractor), +/* harmony export */ ConvNextImageProcessor: () => (/* reexport safe */ _convnext_image_processing_convnext_js__WEBPACK_IMPORTED_MODULE_4__.ConvNextImageProcessor), +/* harmony export */ DINOv3ViTImageProcessor: () => (/* reexport safe */ _dinov3_vit_image_processing_dinov3_vit_js__WEBPACK_IMPORTED_MODULE_7__.DINOv3ViTImageProcessor), +/* harmony export */ DPTFeatureExtractor: () => (/* reexport safe */ _dpt_image_processing_dpt_js__WEBPACK_IMPORTED_MODULE_9__.DPTFeatureExtractor), +/* harmony export */ DPTImageProcessor: () => (/* reexport safe */ _dpt_image_processing_dpt_js__WEBPACK_IMPORTED_MODULE_9__.DPTImageProcessor), +/* harmony export */ DeiTFeatureExtractor: () => (/* reexport safe */ _deit_image_processing_deit_js__WEBPACK_IMPORTED_MODULE_5__.DeiTFeatureExtractor), +/* harmony export */ DeiTImageProcessor: () => (/* reexport safe */ _deit_image_processing_deit_js__WEBPACK_IMPORTED_MODULE_5__.DeiTImageProcessor), +/* harmony export */ DetrFeatureExtractor: () => (/* reexport safe */ _detr_image_processing_detr_js__WEBPACK_IMPORTED_MODULE_6__.DetrFeatureExtractor), +/* harmony export */ DetrImageProcessor: () => (/* reexport safe */ _detr_image_processing_detr_js__WEBPACK_IMPORTED_MODULE_6__.DetrImageProcessor), +/* harmony export */ DonutFeatureExtractor: () => (/* reexport safe */ _donut_image_processing_donut_js__WEBPACK_IMPORTED_MODULE_8__.DonutFeatureExtractor), +/* harmony export */ DonutImageProcessor: () => (/* reexport safe */ _donut_image_processing_donut_js__WEBPACK_IMPORTED_MODULE_8__.DonutImageProcessor), +/* harmony export */ EfficientNetImageProcessor: () => (/* reexport safe */ _efficientnet_image_processing_efficientnet_js__WEBPACK_IMPORTED_MODULE_10__.EfficientNetImageProcessor), +/* harmony export */ GLPNFeatureExtractor: () => (/* reexport safe */ _glpn_image_processing_glpn_js__WEBPACK_IMPORTED_MODULE_11__.GLPNFeatureExtractor), +/* harmony export */ GroundingDinoImageProcessor: () => (/* reexport safe */ _grounding_dino_image_processing_grounding_dino_js__WEBPACK_IMPORTED_MODULE_12__.GroundingDinoImageProcessor), +/* harmony export */ Idefics3ImageProcessor: () => (/* reexport safe */ _idefics3_image_processing_idefics3_js__WEBPACK_IMPORTED_MODULE_13__.Idefics3ImageProcessor), +/* harmony export */ JinaCLIPImageProcessor: () => (/* reexport safe */ _jina_clip_image_processing_jina_clip_js__WEBPACK_IMPORTED_MODULE_15__.JinaCLIPImageProcessor), +/* harmony export */ LlavaOnevisionImageProcessor: () => (/* reexport safe */ _llava_onevision_image_processing_llava_onevision_js__WEBPACK_IMPORTED_MODULE_16__.LlavaOnevisionImageProcessor), +/* harmony export */ Mask2FormerImageProcessor: () => (/* reexport safe */ _mask2former_image_processing_mask2former_js__WEBPACK_IMPORTED_MODULE_17__.Mask2FormerImageProcessor), +/* harmony export */ MaskFormerFeatureExtractor: () => (/* reexport safe */ _maskformer_image_processing_maskformer_js__WEBPACK_IMPORTED_MODULE_18__.MaskFormerFeatureExtractor), +/* harmony export */ MaskFormerImageProcessor: () => (/* reexport safe */ _maskformer_image_processing_maskformer_js__WEBPACK_IMPORTED_MODULE_18__.MaskFormerImageProcessor), +/* harmony export */ MobileNetV1FeatureExtractor: () => (/* reexport safe */ _mobilenet_v1_image_processing_mobilenet_v1_js__WEBPACK_IMPORTED_MODULE_19__.MobileNetV1FeatureExtractor), +/* harmony export */ MobileNetV1ImageProcessor: () => (/* reexport safe */ _mobilenet_v1_image_processing_mobilenet_v1_js__WEBPACK_IMPORTED_MODULE_19__.MobileNetV1ImageProcessor), +/* harmony export */ MobileNetV2FeatureExtractor: () => (/* reexport safe */ _mobilenet_v2_image_processing_mobilenet_v2_js__WEBPACK_IMPORTED_MODULE_20__.MobileNetV2FeatureExtractor), +/* harmony export */ MobileNetV2ImageProcessor: () => (/* reexport safe */ _mobilenet_v2_image_processing_mobilenet_v2_js__WEBPACK_IMPORTED_MODULE_20__.MobileNetV2ImageProcessor), +/* harmony export */ MobileNetV3FeatureExtractor: () => (/* reexport safe */ _mobilenet_v3_image_processing_mobilenet_v3_js__WEBPACK_IMPORTED_MODULE_21__.MobileNetV3FeatureExtractor), +/* harmony export */ MobileNetV3ImageProcessor: () => (/* reexport safe */ _mobilenet_v3_image_processing_mobilenet_v3_js__WEBPACK_IMPORTED_MODULE_21__.MobileNetV3ImageProcessor), +/* harmony export */ MobileNetV4FeatureExtractor: () => (/* reexport safe */ _mobilenet_v4_image_processing_mobilenet_v4_js__WEBPACK_IMPORTED_MODULE_22__.MobileNetV4FeatureExtractor), +/* harmony export */ MobileNetV4ImageProcessor: () => (/* reexport safe */ _mobilenet_v4_image_processing_mobilenet_v4_js__WEBPACK_IMPORTED_MODULE_22__.MobileNetV4ImageProcessor), +/* harmony export */ MobileViTFeatureExtractor: () => (/* reexport safe */ _mobilevit_image_processing_mobilevit_js__WEBPACK_IMPORTED_MODULE_23__.MobileViTFeatureExtractor), +/* harmony export */ MobileViTImageProcessor: () => (/* reexport safe */ _mobilevit_image_processing_mobilevit_js__WEBPACK_IMPORTED_MODULE_23__.MobileViTImageProcessor), +/* harmony export */ NougatImageProcessor: () => (/* reexport safe */ _nougat_image_processing_nougat_js__WEBPACK_IMPORTED_MODULE_24__.NougatImageProcessor), +/* harmony export */ OwlViTFeatureExtractor: () => (/* reexport safe */ _owlvit_image_processing_owlvit_js__WEBPACK_IMPORTED_MODULE_26__.OwlViTFeatureExtractor), +/* harmony export */ OwlViTImageProcessor: () => (/* reexport safe */ _owlvit_image_processing_owlvit_js__WEBPACK_IMPORTED_MODULE_26__.OwlViTImageProcessor), +/* harmony export */ Owlv2ImageProcessor: () => (/* reexport safe */ _owlv2_image_processing_owlv2_js__WEBPACK_IMPORTED_MODULE_25__.Owlv2ImageProcessor), +/* harmony export */ Phi3VImageProcessor: () => (/* reexport safe */ _phi3_v_image_processing_phi3_v_js__WEBPACK_IMPORTED_MODULE_27__.Phi3VImageProcessor), +/* harmony export */ PvtImageProcessor: () => (/* reexport safe */ _pvt_image_processing_pvt_js__WEBPACK_IMPORTED_MODULE_28__.PvtImageProcessor), +/* harmony export */ Qwen2VLImageProcessor: () => (/* reexport safe */ _qwen2_vl_image_processing_qwen2_vl_js__WEBPACK_IMPORTED_MODULE_29__.Qwen2VLImageProcessor), +/* harmony export */ RTDetrImageProcessor: () => (/* reexport safe */ _rt_detr_image_processing_rt_detr_js__WEBPACK_IMPORTED_MODULE_30__.RTDetrImageProcessor), +/* harmony export */ SamImageProcessor: () => (/* reexport safe */ _sam_image_processing_sam_js__WEBPACK_IMPORTED_MODULE_31__.SamImageProcessor), +/* harmony export */ SegformerFeatureExtractor: () => (/* reexport safe */ _segformer_image_processing_segformer_js__WEBPACK_IMPORTED_MODULE_32__.SegformerFeatureExtractor), +/* harmony export */ SegformerImageProcessor: () => (/* reexport safe */ _segformer_image_processing_segformer_js__WEBPACK_IMPORTED_MODULE_32__.SegformerImageProcessor), +/* harmony export */ SiglipImageProcessor: () => (/* reexport safe */ _siglip_image_processing_siglip_js__WEBPACK_IMPORTED_MODULE_33__.SiglipImageProcessor), +/* harmony export */ SmolVLMImageProcessor: () => (/* reexport safe */ _smolvlm_image_processing_smolvlm_js__WEBPACK_IMPORTED_MODULE_34__.SmolVLMImageProcessor), +/* harmony export */ Swin2SRImageProcessor: () => (/* reexport safe */ _swin2sr_image_processing_swin2sr_js__WEBPACK_IMPORTED_MODULE_35__.Swin2SRImageProcessor), +/* harmony export */ VLMImageProcessor: () => (/* reexport safe */ _janus_image_processing_janus_js__WEBPACK_IMPORTED_MODULE_14__.VLMImageProcessor), +/* harmony export */ ViTFeatureExtractor: () => (/* reexport safe */ _vit_image_processing_vit_js__WEBPACK_IMPORTED_MODULE_36__.ViTFeatureExtractor), +/* harmony export */ ViTImageProcessor: () => (/* reexport safe */ _vit_image_processing_vit_js__WEBPACK_IMPORTED_MODULE_36__.ViTImageProcessor), +/* harmony export */ VitMatteImageProcessor: () => (/* reexport safe */ _vitmatte_image_processing_vitmatte_js__WEBPACK_IMPORTED_MODULE_37__.VitMatteImageProcessor), +/* harmony export */ VitPoseImageProcessor: () => (/* reexport safe */ _vitpose_image_processing_vitpose_js__WEBPACK_IMPORTED_MODULE_38__.VitPoseImageProcessor), +/* harmony export */ YolosFeatureExtractor: () => (/* reexport safe */ 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__webpack_require__(/*! ./owlv2/image_processing_owlv2.js */ "./src/models/owlv2/image_processing_owlv2.js"); +/* harmony import */ var _owlvit_image_processing_owlvit_js__WEBPACK_IMPORTED_MODULE_26__ = __webpack_require__(/*! ./owlvit/image_processing_owlvit.js */ "./src/models/owlvit/image_processing_owlvit.js"); +/* harmony import */ var _phi3_v_image_processing_phi3_v_js__WEBPACK_IMPORTED_MODULE_27__ = __webpack_require__(/*! ./phi3_v/image_processing_phi3_v.js */ "./src/models/phi3_v/image_processing_phi3_v.js"); +/* harmony import */ var _pvt_image_processing_pvt_js__WEBPACK_IMPORTED_MODULE_28__ = __webpack_require__(/*! ./pvt/image_processing_pvt.js */ "./src/models/pvt/image_processing_pvt.js"); +/* harmony import */ var _qwen2_vl_image_processing_qwen2_vl_js__WEBPACK_IMPORTED_MODULE_29__ = __webpack_require__(/*! ./qwen2_vl/image_processing_qwen2_vl.js */ "./src/models/qwen2_vl/image_processing_qwen2_vl.js"); +/* harmony import */ var _rt_detr_image_processing_rt_detr_js__WEBPACK_IMPORTED_MODULE_30__ = __webpack_require__(/*! ./rt_detr/image_processing_rt_detr.js */ "./src/models/rt_detr/image_processing_rt_detr.js"); +/* harmony import */ var _sam_image_processing_sam_js__WEBPACK_IMPORTED_MODULE_31__ = __webpack_require__(/*! ./sam/image_processing_sam.js */ "./src/models/sam/image_processing_sam.js"); +/* harmony import */ var _segformer_image_processing_segformer_js__WEBPACK_IMPORTED_MODULE_32__ = __webpack_require__(/*! ./segformer/image_processing_segformer.js */ "./src/models/segformer/image_processing_segformer.js"); +/* harmony import */ var _siglip_image_processing_siglip_js__WEBPACK_IMPORTED_MODULE_33__ = __webpack_require__(/*! ./siglip/image_processing_siglip.js */ "./src/models/siglip/image_processing_siglip.js"); +/* harmony import */ var _smolvlm_image_processing_smolvlm_js__WEBPACK_IMPORTED_MODULE_34__ = __webpack_require__(/*! ./smolvlm/image_processing_smolvlm.js */ "./src/models/smolvlm/image_processing_smolvlm.js"); +/* harmony import */ var _swin2sr_image_processing_swin2sr_js__WEBPACK_IMPORTED_MODULE_35__ = __webpack_require__(/*! ./swin2sr/image_processing_swin2sr.js */ "./src/models/swin2sr/image_processing_swin2sr.js"); +/* harmony import */ var _vit_image_processing_vit_js__WEBPACK_IMPORTED_MODULE_36__ = __webpack_require__(/*! ./vit/image_processing_vit.js */ "./src/models/vit/image_processing_vit.js"); +/* harmony import */ var _vitmatte_image_processing_vitmatte_js__WEBPACK_IMPORTED_MODULE_37__ = __webpack_require__(/*! ./vitmatte/image_processing_vitmatte.js */ "./src/models/vitmatte/image_processing_vitmatte.js"); +/* harmony import */ var _vitpose_image_processing_vitpose_js__WEBPACK_IMPORTED_MODULE_38__ = __webpack_require__(/*! ./vitpose/image_processing_vitpose.js */ "./src/models/vitpose/image_processing_vitpose.js"); +/* harmony import */ var _yolos_image_processing_yolos_js__WEBPACK_IMPORTED_MODULE_39__ = __webpack_require__(/*! ./yolos/image_processing_yolos.js */ "./src/models/yolos/image_processing_yolos.js"); + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +/***/ }), + +/***/ "./src/models/janus/image_processing_janus.js": +/*!****************************************************!*\ + !*** ./src/models/janus/image_processing_janus.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ VLMImageProcessor: () => (/* binding */ VLMImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + +class VLMImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + constructor(config) { + super({ + do_pad: true, + pad_size: { + width: config.image_size, + height: config.image_size, + }, + ...config, + }); + // @ts-expect-error TS2339 + this.constant_values = this.config.background_color.map(x => x * this.rescale_factor) + } + + pad_image(pixelData, imgDims, padSize, options) { + return super.pad_image(pixelData, imgDims, padSize, { + constant_values: this.constant_values, + center: true, + ...options, + }); + } +} + + +/***/ }), + +/***/ "./src/models/janus/processing_janus.js": +/*!**********************************************!*\ + !*** ./src/models/janus/processing_janus.js ***! + \**********************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ VLChatProcessor: () => (/* binding */ VLChatProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../../utils/core.js */ "./src/utils/core.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_image_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ../../utils/image.js */ "./src/utils/image.js"); + + + + + + + + +class VLChatProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + static uses_processor_config = true; + + constructor(config, components, chat_template) { + super(config, components, chat_template); + + this.image_tag = this.config.image_tag; + this.image_start_tag = this.config.image_start_tag; + this.image_end_tag = this.config.image_end_tag; + this.num_image_tokens = this.config.num_image_tokens; + } + + /** + * @typedef {Object} MultimodalMessageProperties Additional properties for multimodal messages. + * @property {(RawImage | string | URL)[]} [images] The images in the message. + * @typedef {(import('../../tokenizers.js').Message & MultimodalMessageProperties)[]} MultimodalConversation The conversation possibly containing multimodal inputs. + */ + + /** + * @typedef {Object} VLCChatProcessorResult The processed input. + * @property {Tensor} input_ids The input IDs. + * @property {Tensor} attention_mask The attention mask. + * @property {Tensor} images_seq_mask The image sequence mask. + * @property {Tensor} images_emb_mask The image embedding mask. + */ + + /** + * @param {MultimodalConversation} conversation The chat messages to process. + * @param {Object} options Additional options for processing. + * @param {RawImage|RawImage[]} [options.images] The images to process, if not set in the conversation. + * @param {string} [options.chat_template="default"] The chat template to use. + * @returns {Promise} The processed input. + */ + async _call(conversation, { + images = null, + chat_template = "default", + }={}) { + if (!images) { + images = await Promise.all( + conversation + .filter((msg) => msg.images) + .flatMap((msg) => msg.images) + .map((img) => _utils_image_js__WEBPACK_IMPORTED_MODULE_5__.RawImage.read(img)) + ); + } else if (!Array.isArray(images)) { + images = [images]; + } + + const tokenizer = this.tokenizer; + const result = tokenizer.apply_chat_template(conversation, { + tokenize: false, + add_generation_prompt: true, + chat_template, + }); + + const encode = (text) => tokenizer.encode(text, { add_special_tokens: false }); + const parts = (/** @type {string} */(result)) + .split(this.image_tag); + const num_images = parts.length - 1; + if (images.length !== num_images) { + throw new Error(`Number of images provided (${images.length}) does not match number of "${this.image_tag}" image tags (${num_images})`); + } + + const [ + image_placeholder_tag_id, + image_start_tag_id, + image_end_tag_id, + ] = tokenizer.model.convert_tokens_to_ids([ + this.image_tag, + this.image_start_tag, + this.image_end_tag, + ]); + + let input_ids = encode(parts[0]); + let images_seq_mask = new Array(input_ids.length).fill(false); + for (let i = 1; i < parts.length; ++i) { + const placeholder_image_tokens = new Array(this.num_image_tokens).fill(image_placeholder_tag_id); + const tokens = encode(parts[i]); + input_ids = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_3__.mergeArrays)( + input_ids, + [image_start_tag_id], placeholder_image_tokens, [image_end_tag_id], + tokens, + ); + const image_mask = new Array(this.num_image_tokens).fill(true); + images_seq_mask = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_3__.mergeArrays)( + images_seq_mask, + [false], image_mask, [false], + new Array(tokens.length).fill(false), + ); + } + + const dims = [1, input_ids.length]; + const final = { + input_ids: new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor('int64', input_ids, dims), + attention_mask: new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor('int64', new Array(input_ids.length).fill(1), dims), + images_seq_mask: new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor('bool', images_seq_mask, dims), + images_emb_mask: new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor( + 'bool', + new Array(num_images * this.num_image_tokens).fill(true), + [1, num_images, this.num_image_tokens], + ), + } + + if (images && images.length > 0) { + const image_inputs = await this.image_processor(images); + // Set the batch_size dimension to 1 + image_inputs.pixel_values.unsqueeze_(0); + return { ...final, ...image_inputs }; + } + + return final; + } +} + + +/***/ }), + +/***/ "./src/models/jina_clip/image_processing_jina_clip.js": +/*!************************************************************!*\ + !*** ./src/models/jina_clip/image_processing_jina_clip.js ***! + \************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ JinaCLIPImageProcessor: () => (/* binding */ JinaCLIPImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class JinaCLIPImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + constructor(config) { + // JinaCLIPImageProcessor uses a custom preprocessor_config.json, so we configure it here + const { resize_mode, fill_color, interpolation, size, ...other } = config; + + const new_size = resize_mode === 'squash' + ? { width: size, height: size } + : resize_mode === 'shortest' + ? { shortest_edge: size } + : { longest_edge: size }; + + const resample = interpolation === 'bicubic' ? 3 : 2; + super({ + ...other, + size: new_size, + resample, + do_center_crop: true, + crop_size: size, + do_normalize: true, + }); + } +} + + +/***/ }), + +/***/ "./src/models/jina_clip/processing_jina_clip.js": +/*!******************************************************!*\ + !*** ./src/models/jina_clip/processing_jina_clip.js ***! + \******************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ JinaCLIPProcessor: () => (/* binding */ JinaCLIPProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); + + + + + +class JinaCLIPProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + + async _call(text=null, images=null, kwargs = {}) { + + if (!text && !images){ + throw new Error('Either text or images must be provided'); + } + + const text_inputs = text ? this.tokenizer(text, kwargs) : {}; + const image_inputs = images ? await this.image_processor(images, kwargs) : {}; + + return { + ...text_inputs, + ...image_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/llava/processing_llava.js": +/*!**********************************************!*\ + !*** ./src/models/llava/processing_llava.js ***! + \**********************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ LlavaProcessor: () => (/* binding */ LlavaProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); + + + + + +class LlavaProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + static uses_processor_config = true; + + /** + * @typedef {import('../../utils/image.js').RawImage} RawImage + */ + + // `images` is required, `text` is optional + async _call(/** @type {RawImage|RawImage[]} */ images, text = null, kwargs = {}) { + + const image_inputs = await this.image_processor(images, kwargs); + + if (text) { + const [height, width] = image_inputs.pixel_values.dims.slice(-2); + + const {image_token, patch_size, num_additional_image_tokens} = this.config; + const num_image_tokens = Math.floor( + height / patch_size + ) * Math.floor(width / patch_size) + num_additional_image_tokens; + + text = structuredClone(text); // Avoid modifying the original text input + if (!Array.isArray(text)) { + text = [text]; + } + for (let i = 0; i < text.length; ++i) { + text[i] = text[i].replace(image_token, image_token.repeat(num_image_tokens)); + } + } + + const text_inputs = text ? this.tokenizer(text, kwargs) : {}; + + return { + ...image_inputs, + ...text_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/llava_onevision/image_processing_llava_onevision.js": +/*!************************************************************************!*\ + !*** ./src/models/llava_onevision/image_processing_llava_onevision.js ***! + \************************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ LlavaOnevisionImageProcessor: () => (/* binding */ LlavaOnevisionImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class LlavaOnevisionImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor {} + + +/***/ }), + +/***/ "./src/models/mask2former/image_processing_mask2former.js": +/*!****************************************************************!*\ + !*** ./src/models/mask2former/image_processing_mask2former.js ***! + \****************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Mask2FormerImageProcessor: () => (/* binding */ Mask2FormerImageProcessor) +/* harmony export */ }); +/* harmony import */ var _maskformer_image_processing_maskformer_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../maskformer/image_processing_maskformer.js */ "./src/models/maskformer/image_processing_maskformer.js"); + + + +// NOTE: extends MaskFormerImageProcessor +class Mask2FormerImageProcessor extends _maskformer_image_processing_maskformer_js__WEBPACK_IMPORTED_MODULE_0__.MaskFormerImageProcessor { } + + +/***/ }), + +/***/ "./src/models/maskformer/image_processing_maskformer.js": +/*!**************************************************************!*\ + !*** ./src/models/maskformer/image_processing_maskformer.js ***! + \**************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MaskFormerFeatureExtractor: () => (/* binding */ MaskFormerFeatureExtractor), +/* harmony export */ MaskFormerImageProcessor: () => (/* binding */ MaskFormerImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class MaskFormerImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + + /** @type {typeof post_process_panoptic_segmentation} */ + post_process_panoptic_segmentation(...args) { + return (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.post_process_panoptic_segmentation)(...args); + } + /** @type {typeof post_process_instance_segmentation} */ + post_process_instance_segmentation(...args) { + return (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.post_process_instance_segmentation)(...args); + } +} +class MaskFormerFeatureExtractor extends MaskFormerImageProcessor { } + + +/***/ }), + +/***/ "./src/models/mgp_str/processing_mgp_str.js": +/*!**************************************************!*\ + !*** ./src/models/mgp_str/processing_mgp_str.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MgpstrProcessor: () => (/* binding */ MgpstrProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _utils_maths_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../../utils/maths.js */ "./src/utils/maths.js"); + + + + + +const DECODE_TYPE_MAPPING = { + 'char': ['char_decode', 1], + 'bpe': ['bpe_decode', 2], + 'wp': ['wp_decode', 102], +} +class MgpstrProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + + /** + * @returns {import('../../tokenizers.js').MgpstrTokenizer} The character tokenizer. + */ + get char_tokenizer() { + return this.components.char_tokenizer; + } + + /** + * @returns {import('../../tokenizers.js').GPT2Tokenizer} The BPE tokenizer. + */ + get bpe_tokenizer() { + return this.components.bpe_tokenizer; + } + + /** + * @returns {import('../../tokenizers.js').BertTokenizer} The WordPiece tokenizer. + */ + get wp_tokenizer() { + return this.components.wp_tokenizer; + } + + /** + * Helper function to decode the model prediction logits. + * @param {import('../../utils/tensor.js').Tensor} pred_logits Model prediction logits. + * @param {string} format Type of model prediction. Must be one of ['char', 'bpe', 'wp']. + * @returns {[string[], number[]]} The decoded sentences and their confidence scores. + */ + _decode_helper(pred_logits, format) { + if (!DECODE_TYPE_MAPPING.hasOwnProperty(format)) { + throw new Error(`Format ${format} is not supported.`); + } + + const [decoder_name, eos_token] = DECODE_TYPE_MAPPING[format]; + const decoder = this[decoder_name].bind(this); + + const [batch_size, batch_max_length] = pred_logits.dims; + const conf_scores = []; + const all_ids = []; + + /** @type {number[][][]} */ + const pred_logits_list = pred_logits.tolist(); + for (let i = 0; i < batch_size; ++i) { + const logits = pred_logits_list[i]; + const ids = []; + const scores = []; + + // Start and index=1 to skip the first token + for (let j = 1; j < batch_max_length; ++j) { + // NOTE: == to match bigint and number + const [max_prob, max_prob_index] = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.max)((0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.softmax)(logits[j])); + scores.push(max_prob); + if (max_prob_index == eos_token) { + break; + } + ids.push(max_prob_index); + } + + const confidence_score = scores.length > 0 + ? scores.reduce((a, b) => a * b, 1) + : 0; + + all_ids.push(ids); + conf_scores.push(confidence_score); + } + + const decoded = decoder(all_ids); + return [decoded, conf_scores]; + } + + /** + * Convert a list of lists of char token ids into a list of strings by calling char tokenizer. + * @param {number[][]} sequences List of tokenized input ids. + * @returns {string[]} The list of char decoded sentences. + */ + char_decode(sequences) { + return this.char_tokenizer.batch_decode(sequences).map(str => str.replaceAll(' ', '')); + } + + /** + * Convert a list of lists of BPE token ids into a list of strings by calling BPE tokenizer. + * @param {number[][]} sequences List of tokenized input ids. + * @returns {string[]} The list of BPE decoded sentences. + */ + bpe_decode(sequences) { + return this.bpe_tokenizer.batch_decode(sequences) + } + + /** + * Convert a list of lists of word piece token ids into a list of strings by calling word piece tokenizer. + * @param {number[][]} sequences List of tokenized input ids. + * @returns {string[]} The list of wp decoded sentences. + */ + wp_decode(sequences) { + return this.wp_tokenizer.batch_decode(sequences).map(str => str.replaceAll(' ', '')); + } + + /** + * Convert a list of lists of token ids into a list of strings by calling decode. + * @param {import('../../utils/tensor.js').Tensor[]} sequences List of tokenized input ids. + * @returns {{generated_text: string[], scores: number[], char_preds: string[], bpe_preds: string[], wp_preds: string[]}} + * Dictionary of all the outputs of the decoded results. + * - generated_text: The final results after fusion of char, bpe, and wp. + * - scores: The final scores after fusion of char, bpe, and wp. + * - char_preds: The list of character decoded sentences. + * - bpe_preds: The list of BPE decoded sentences. + * - wp_preds: The list of wp decoded sentences. + */ + // @ts-expect-error The type of this method is not compatible with the one + // in the base class. It might be a good idea to fix this. + batch_decode([char_logits, bpe_logits, wp_logits]) { + const [char_preds, char_scores] = this._decode_helper(char_logits, 'char'); + const [bpe_preds, bpe_scores] = this._decode_helper(bpe_logits, 'bpe'); + const [wp_preds, wp_scores] = this._decode_helper(wp_logits, 'wp'); + + const generated_text = []; + const scores = []; + for (let i = 0; i < char_preds.length; ++i) { + const [max_score, max_score_index] = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.max)([char_scores[i], bpe_scores[i], wp_scores[i]]); + generated_text.push([char_preds[i], bpe_preds[i], wp_preds[i]][max_score_index]); + scores.push(max_score); + } + + return { + generated_text, + scores, + char_preds, + bpe_preds, + wp_preds, + } + } + /** @type {typeof Processor.from_pretrained} */ + static async from_pretrained(...args) { + const base = await super.from_pretrained(...args); + + // Load Transformers.js-compatible versions of the BPE and WordPiece tokenizers + const bpe_tokenizer = await _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer.from_pretrained("Xenova/gpt2") // openai-community/gpt2 + const wp_tokenizer = await _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer.from_pretrained("Xenova/bert-base-uncased") // google-bert/bert-base-uncased + + // Update components + base.components = { + image_processor: base.image_processor, + char_tokenizer: base.tokenizer, + bpe_tokenizer: bpe_tokenizer, + wp_tokenizer: wp_tokenizer, + } + return base; + } + + async _call(images, text = null) { + const result = await this.image_processor(images); + + if (text) { + result.labels = this.tokenizer(text).input_ids + } + + return result; + } +} + + +/***/ }), + +/***/ "./src/models/mobilenet_v1/image_processing_mobilenet_v1.js": +/*!******************************************************************!*\ + !*** ./src/models/mobilenet_v1/image_processing_mobilenet_v1.js ***! + \******************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MobileNetV1FeatureExtractor: () => (/* binding */ MobileNetV1FeatureExtractor), +/* harmony export */ MobileNetV1ImageProcessor: () => (/* binding */ MobileNetV1ImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + +class MobileNetV1ImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } +class MobileNetV1FeatureExtractor extends MobileNetV1ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/mobilenet_v2/image_processing_mobilenet_v2.js": +/*!******************************************************************!*\ + !*** ./src/models/mobilenet_v2/image_processing_mobilenet_v2.js ***! + \******************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MobileNetV2FeatureExtractor: () => (/* binding */ MobileNetV2FeatureExtractor), +/* harmony export */ MobileNetV2ImageProcessor: () => (/* binding */ MobileNetV2ImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + +class MobileNetV2ImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } +class MobileNetV2FeatureExtractor extends MobileNetV2ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/mobilenet_v3/image_processing_mobilenet_v3.js": +/*!******************************************************************!*\ + !*** ./src/models/mobilenet_v3/image_processing_mobilenet_v3.js ***! + \******************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MobileNetV3FeatureExtractor: () => (/* binding */ MobileNetV3FeatureExtractor), +/* harmony export */ MobileNetV3ImageProcessor: () => (/* binding */ MobileNetV3ImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + +class MobileNetV3ImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } +class MobileNetV3FeatureExtractor extends MobileNetV3ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/mobilenet_v4/image_processing_mobilenet_v4.js": +/*!******************************************************************!*\ + !*** ./src/models/mobilenet_v4/image_processing_mobilenet_v4.js ***! + \******************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MobileNetV4FeatureExtractor: () => (/* binding */ MobileNetV4FeatureExtractor), +/* harmony export */ MobileNetV4ImageProcessor: () => (/* binding */ MobileNetV4ImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + +class MobileNetV4ImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } +class MobileNetV4FeatureExtractor extends MobileNetV4ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/mobilevit/image_processing_mobilevit.js": +/*!************************************************************!*\ + !*** ./src/models/mobilevit/image_processing_mobilevit.js ***! + \************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MobileViTFeatureExtractor: () => (/* binding */ MobileViTFeatureExtractor), +/* harmony export */ MobileViTImageProcessor: () => (/* binding */ MobileViTImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class MobileViTImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } +class MobileViTFeatureExtractor extends MobileViTImageProcessor { } + + +/***/ }), + +/***/ "./src/models/moonshine/feature_extraction_moonshine.js": +/*!**************************************************************!*\ + !*** ./src/models/moonshine/feature_extraction_moonshine.js ***! + \**************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MoonshineFeatureExtractor: () => (/* binding */ MoonshineFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + + +class MoonshineFeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + /** + * Asynchronously extracts input values from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @returns {Promise<{ input_values: Tensor; }>} The extracted input values. + */ + async _call(audio) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'MoonshineFeatureExtractor'); + + if (audio instanceof Float64Array) { + audio = new Float32Array(audio); + } + + const shape = [ + 1, /* batch_size */ + audio.length, /* num_samples */ + ]; + return { + input_values: new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor('float32', audio, shape), + }; + } +} + + +/***/ }), + +/***/ "./src/models/moonshine/processing_moonshine.js": +/*!******************************************************!*\ + !*** ./src/models/moonshine/processing_moonshine.js ***! + \******************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MoonshineProcessor: () => (/* binding */ MoonshineProcessor) +/* harmony export */ }); +/* harmony import */ var _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../auto/feature_extraction_auto.js */ "./src/models/auto/feature_extraction_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); + + + + +/** + * Represents a MoonshineProcessor that extracts features from an audio input. + */ +class MoonshineProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__.AutoTokenizer + static feature_extractor_class = _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_0__.AutoFeatureExtractor + + /** + * Calls the feature_extractor function with the given audio input. + * @param {any} audio The audio input to extract features from. + * @returns {Promise} A Promise that resolves with the extracted features. + */ + async _call(audio) { + return await this.feature_extractor(audio); + } +} + + +/***/ }), + +/***/ "./src/models/nougat/image_processing_nougat.js": +/*!******************************************************!*\ + !*** ./src/models/nougat/image_processing_nougat.js ***! + \******************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ NougatImageProcessor: () => (/* binding */ NougatImageProcessor) +/* harmony export */ }); +/* harmony import */ var _donut_image_processing_donut_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../donut/image_processing_donut.js */ "./src/models/donut/image_processing_donut.js"); + + + +// NOTE: extends DonutImageProcessor +class NougatImageProcessor extends _donut_image_processing_donut_js__WEBPACK_IMPORTED_MODULE_0__.DonutImageProcessor { } + + +/***/ }), + +/***/ "./src/models/owlv2/image_processing_owlv2.js": +/*!****************************************************!*\ + !*** ./src/models/owlv2/image_processing_owlv2.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Owlv2ImageProcessor: () => (/* binding */ Owlv2ImageProcessor) +/* harmony export */ }); +/* harmony import */ var _owlvit_image_processing_owlvit_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../owlvit/image_processing_owlvit.js */ "./src/models/owlvit/image_processing_owlvit.js"); + + + +// NOTE: extends OwlViTImageProcessor +class Owlv2ImageProcessor extends _owlvit_image_processing_owlvit_js__WEBPACK_IMPORTED_MODULE_0__.OwlViTImageProcessor { } + + +/***/ }), + +/***/ "./src/models/owlvit/image_processing_owlvit.js": +/*!******************************************************!*\ + !*** ./src/models/owlvit/image_processing_owlvit.js ***! + \******************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ OwlViTFeatureExtractor: () => (/* binding */ OwlViTFeatureExtractor), +/* harmony export */ OwlViTImageProcessor: () => (/* binding */ OwlViTImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class OwlViTImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + /** @type {typeof post_process_object_detection} */ + post_process_object_detection(...args) { + return (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.post_process_object_detection)(...args); + } +} +class OwlViTFeatureExtractor extends OwlViTImageProcessor { } + + +/***/ }), + +/***/ "./src/models/owlvit/processing_owlvit.js": +/*!************************************************!*\ + !*** ./src/models/owlvit/processing_owlvit.js ***! + \************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ OwlViTProcessor: () => (/* binding */ OwlViTProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); + + + +class OwlViTProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor +} + + +/***/ }), + +/***/ "./src/models/paligemma/processing_paligemma.js": +/*!******************************************************!*\ + !*** ./src/models/paligemma/processing_paligemma.js ***! + \******************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ PaliGemmaProcessor: () => (/* binding */ PaliGemmaProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); + + + + +const IMAGE_TOKEN = ""; + +function build_string_from_input( + prompt, + bos_token, + image_seq_len, + image_token, + num_images, +) { + return `${image_token.repeat(image_seq_len * num_images)}${bos_token}${prompt}\n` +} + +class PaliGemmaProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + static uses_processor_config = false; + + /** + * @typedef {import('../../utils/image.js').RawImage} RawImage + */ + + // `images` is required, `text` is optional + async _call(/** @type {RawImage|RawImage[]} */ images, text = null, kwargs = {}) { + if (!text) { + console.warn( + "You are using PaliGemma without a text prefix. It will perform as a picture-captioning model." + ) + text = "" + } + + if (!Array.isArray(images)) { + images = [images] + } + + if (!Array.isArray(text)) { + text = [text] + } + + const bos_token = this.tokenizer.bos_token; + // @ts-expect-error TS2339 + const image_seq_length = this.image_processor.config.image_seq_length; + let input_strings; + if (text.some((t) => t.includes(IMAGE_TOKEN))) { + input_strings = text.map( + sample => { + const expanded_sample = sample.replaceAll(IMAGE_TOKEN, IMAGE_TOKEN.repeat(image_seq_length)); + const bos_rfind_index = expanded_sample.lastIndexOf(IMAGE_TOKEN); + const bos_index = bos_rfind_index === -1 ? 0 : bos_rfind_index + IMAGE_TOKEN.length; + return expanded_sample.slice(0, bos_index) + bos_token + expanded_sample.slice(bos_index) + "\n"; + } + ) + } else { + console.warn( + "You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special " + + "image tokens in the text, as many tokens as there are images per each text. It is recommended to " + + "add `` tokens in the very beginning of your text. For this call, we will infer how many images " + + "each text has and add special tokens." + ) + + input_strings = text.map( + sample => build_string_from_input( + sample, + bos_token, + image_seq_length, + IMAGE_TOKEN, + images.length, + ) + ) + } + + const text_inputs = this.tokenizer(input_strings, kwargs); + const image_inputs = await this.image_processor(images, kwargs); + + return { + ...image_inputs, + ...text_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/phi3_v/image_processing_phi3_v.js": +/*!******************************************************!*\ + !*** ./src/models/phi3_v/image_processing_phi3_v.js ***! + \******************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Phi3VImageProcessor: () => (/* binding */ Phi3VImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + +const IMAGE_SIZE = 336; +const SLICE_AXES = [2, 3]; // axes to slice on +const { ceil, floor, sqrt } = Math; + +class Phi3VImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + constructor(config) { + super({ + ...config, + do_normalize: true, + do_pad: true, + pad_size: 'custom', + do_convert_rgb: true, + do_resize: true, // Smart resizing "hd_transform" + }); + + this._num_crops = config.num_crops; + } + calc_num_image_tokens_from_image_size(width, height) { + // @ts-expect-error + const { num_img_tokens } = this.config; + return floor(((floor((height / IMAGE_SIZE)) * floor((width / IMAGE_SIZE)) + 1) * num_img_tokens) + 1 + (floor(height / IMAGE_SIZE) + 1) * sqrt(num_img_tokens)); + } + + /** @type {ImageProcessor['get_resize_output_image_size']} */ + get_resize_output_image_size(image, size) { + const hd_num = this._num_crops; + const [width, height] = image.size + + let ratio = width / height; + let scale = 1; + + // Calculate the scaling factor + while (scale * Math.ceil(scale / ratio) <= hd_num) { + scale += 1; + } + scale -= 1; + + // Compute the new dimensions + const new_w = Math.floor(scale * 336); + const new_h = Math.floor(new_w / ratio); + + return [new_w, new_h] + } + + + /** @type {ImageProcessor['pad_image']} */ + pad_image(pixelData, imgDims, padSize, options = {}) { + // Phi3V uses a custom padding strategy: + // - Pad to a multiple of 336 + // - Pad with white pixels + const [imageHeight, imageWidth] = imgDims; + const height = IMAGE_SIZE * ceil(imageHeight / IMAGE_SIZE); + const width = IMAGE_SIZE * ceil(imageWidth / IMAGE_SIZE); + + // NOTE: Since padding is done after normalization, we need to fill with the normalized values + const constant_values = [1, 1, 1].map((x, i) => (x - this.image_mean[i]) / this.image_std[i]); + return super.pad_image(pixelData, imgDims, { width, height }, { + center: true, + constant_values, + ...options, + }); + } + + async _call(images, { + num_crops = null, + } = {}) { + // @ts-expect-error + this._num_crops = num_crops ??= this.config.num_crops; + if (num_crops < 4 || sqrt(num_crops) % 1 !== 0) { + throw new Error("num_crops must be a square number >= 4"); + } + + if (!Array.isArray(images)) { + images = [images]; + } + + const num_images = images.length; + const imageData = await Promise.all(images.map(x => this.preprocess(x))); + + const original_sizes = imageData.map(x => x.original_size); + const reshaped_input_sizes = imageData.map(x => x.reshaped_input_size); + + // Process each image in batch + const all_pixel_values = []; + for (const { pixel_values } of imageData) { + pixel_values.unsqueeze_(0); // Easier processing as 4D tensor + + const [height, width] = pixel_values.dims.slice(-2); + + // Global image (Tensor of shape [num_channels, height, width]) + const batch_pixel_values = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.interpolate_4d)(pixel_values, { + size: [IMAGE_SIZE, IMAGE_SIZE], + mode: 'bicubic', + }); + + if (num_crops > 0) { + const patches = []; + const sqrt_patches = sqrt(num_crops); + const patch_width = floor(width / sqrt_patches); + const patch_height = floor(height / sqrt_patches); + for (let y = 0; y < sqrt_patches; ++y) { + for (let x = 0; x < sqrt_patches; ++x) { + let start_x, start_y, end_x, end_y; + if (y === sqrt_patches - 1) { // At bottom + start_y = height - patch_height; + end_y = height; + } else { + start_y = y * patch_height; + end_y = (y + 1) * patch_height; + } + if (x === sqrt_patches - 1) { // At right + start_x = width - patch_width; + end_x = width; + } else { + start_x = x * patch_width; + end_x = (x + 1) * patch_width; + } + + const starts = [start_y, start_x]; + const ends = [end_y, end_x]; + const patch = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.slice)(pixel_values, starts, ends, SLICE_AXES); + patches.push(patch); + } + } + + const resized_tensors = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.interpolate_4d)((0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.cat)(patches, 0), { + size: [IMAGE_SIZE, IMAGE_SIZE], + mode: 'bicubic', + }); // [num_crops, 3, 336, 336] + + // Concatenate the global image with the patches + all_pixel_values.push((0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.cat)([batch_pixel_values, resized_tensors], 0)); + } else { + // Only use the global image + // NOTE: Not currently supported in modelling code + all_pixel_values.push(batch_pixel_values); + } + } + + // [num_images, 1 + num_crops, num_channels=3, height, width] + const pixel_values = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.stack)(all_pixel_values, 0); + + // Calculate padded image sizes + const sizes = reshaped_input_sizes.map(x => x.map(y => IMAGE_SIZE * ceil(y / IMAGE_SIZE))); + + const image_sizes = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor( + 'int64', + sizes.flat(), + [num_images, 2], + ); + + const num_img_tokens = sizes.map( + ([height, width]) => this.calc_num_image_tokens_from_image_size(width, height), + ); + + return { pixel_values, original_sizes, reshaped_input_sizes, image_sizes, num_img_tokens }; + } +} + + +/***/ }), + +/***/ "./src/models/phi3_v/processing_phi3_v.js": +/*!************************************************!*\ + !*** ./src/models/phi3_v/processing_phi3_v.js ***! + \************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Phi3VProcessor: () => (/* binding */ Phi3VProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _utils_image_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../../utils/image.js */ "./src/utils/image.js"); + + + + + +const IMAGE_TOKEN = "<|image|>"; +const IMAGE_TOKEN_PATTERN = /<\|image_\d+\|>/g; + +class Phi3VProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + + /** + * + * @param {string|string[]} text + * @param {RawImage|RawImage[]} images + * @param { { padding?: boolean, truncation?: boolean, num_crops?: number } | undefined } options + * @returns {Promise} + */ + async _call(text, images = null, { + padding = true, + truncation = true, + num_crops = null, + } = {}) { + + if (!Array.isArray(text)) { + text = [text]; + } + + let text_inputs, image_inputs; + if (images) { + image_inputs = await this.image_processor(images, { num_crops }); + const { num_img_tokens } = image_inputs; + + // The original implementation adds a bos_token before the image tokens + // TODO: Check if this affects performance, since it looks like a bug in the original implementation + const prompt_chunks = text.map((t, i) => t.split(IMAGE_TOKEN_PATTERN).join(IMAGE_TOKEN.repeat(num_img_tokens[i]))); + + text_inputs = this.tokenizer(prompt_chunks, { padding, truncation }); + + // The model expects image tokens to be negative, so we negate the image token ids + const image_token_id = this.tokenizer.model.convert_tokens_to_ids([IMAGE_TOKEN])[0]; + text_inputs.input_ids.map_(id => (id == image_token_id) ? -id : id); + } else { + text_inputs = this.tokenizer(text); + } + + return { + ...text_inputs, + ...image_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/processors.js": +/*!**********************************!*\ + !*** ./src/models/processors.js ***! + \**********************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Florence2Processor: () => (/* reexport safe */ _florence2_processing_florence2_js__WEBPACK_IMPORTED_MODULE_0__.Florence2Processor), +/* harmony export */ Gemma3nProcessor: () => (/* reexport safe */ _gemma3n_processing_gemma3n_js__WEBPACK_IMPORTED_MODULE_1__.Gemma3nProcessor), +/* harmony export */ GroundingDinoProcessor: () => (/* reexport safe */ _grounding_dino_processing_grounding_dino_js__WEBPACK_IMPORTED_MODULE_2__.GroundingDinoProcessor), +/* harmony export */ Idefics3Processor: () => (/* reexport safe */ _idefics3_processing_idefics3_js__WEBPACK_IMPORTED_MODULE_3__.Idefics3Processor), +/* harmony export */ JinaCLIPProcessor: () => (/* reexport safe */ _jina_clip_processing_jina_clip_js__WEBPACK_IMPORTED_MODULE_5__.JinaCLIPProcessor), +/* harmony export */ LlavaProcessor: () => (/* reexport safe */ _llava_processing_llava_js__WEBPACK_IMPORTED_MODULE_6__.LlavaProcessor), +/* harmony export */ MgpstrProcessor: () => (/* reexport safe */ _mgp_str_processing_mgp_str_js__WEBPACK_IMPORTED_MODULE_7__.MgpstrProcessor), +/* harmony export */ MoonshineProcessor: () => (/* reexport safe */ _moonshine_processing_moonshine_js__WEBPACK_IMPORTED_MODULE_8__.MoonshineProcessor), +/* harmony export */ OwlViTProcessor: () => (/* reexport safe */ _owlvit_processing_owlvit_js__WEBPACK_IMPORTED_MODULE_9__.OwlViTProcessor), +/* harmony export */ PaliGemmaProcessor: () => (/* reexport safe */ _paligemma_processing_paligemma_js__WEBPACK_IMPORTED_MODULE_11__.PaliGemmaProcessor), +/* harmony export */ Phi3VProcessor: () => (/* reexport safe */ _phi3_v_processing_phi3_v_js__WEBPACK_IMPORTED_MODULE_10__.Phi3VProcessor), +/* harmony export */ PyAnnoteProcessor: () => (/* reexport safe */ _pyannote_processing_pyannote_js__WEBPACK_IMPORTED_MODULE_12__.PyAnnoteProcessor), +/* harmony export */ Qwen2VLProcessor: () => (/* reexport safe */ _qwen2_vl_processing_qwen2_vl_js__WEBPACK_IMPORTED_MODULE_13__.Qwen2VLProcessor), +/* harmony export */ SamProcessor: () => (/* reexport safe */ _sam_processing_sam_js__WEBPACK_IMPORTED_MODULE_14__.SamProcessor), +/* harmony export */ SmolVLMProcessor: () => (/* reexport safe */ _smolvlm_processing_smolvlm_js__WEBPACK_IMPORTED_MODULE_15__.SmolVLMProcessor), +/* harmony export */ SpeechT5Processor: () => (/* reexport safe */ _speecht5_processing_speecht5_js__WEBPACK_IMPORTED_MODULE_16__.SpeechT5Processor), +/* harmony export */ UltravoxProcessor: () => (/* reexport safe */ _ultravox_processing_ultravox_js__WEBPACK_IMPORTED_MODULE_17__.UltravoxProcessor), +/* harmony export */ VLChatProcessor: () => (/* reexport safe */ _janus_processing_janus_js__WEBPACK_IMPORTED_MODULE_4__.VLChatProcessor), +/* harmony export */ VoxtralProcessor: () => (/* reexport safe */ _voxtral_processing_voxtral_js__WEBPACK_IMPORTED_MODULE_18__.VoxtralProcessor), +/* harmony export */ Wav2Vec2Processor: () => (/* reexport safe */ _wav2vec2_processing_wav2vec2_js__WEBPACK_IMPORTED_MODULE_19__.Wav2Vec2Processor), +/* harmony export */ Wav2Vec2ProcessorWithLM: () => (/* reexport safe */ _wav2vec2_with_lm_processing_wav2vec2_with_lm_js__WEBPACK_IMPORTED_MODULE_20__.Wav2Vec2ProcessorWithLM), +/* harmony export */ WhisperProcessor: () => (/* reexport safe */ _whisper_processing_whisper_js__WEBPACK_IMPORTED_MODULE_21__.WhisperProcessor) +/* harmony export */ }); +/* harmony import */ var _florence2_processing_florence2_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./florence2/processing_florence2.js */ "./src/models/florence2/processing_florence2.js"); +/* harmony import */ var _gemma3n_processing_gemma3n_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./gemma3n/processing_gemma3n.js */ "./src/models/gemma3n/processing_gemma3n.js"); +/* harmony import */ var _grounding_dino_processing_grounding_dino_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./grounding_dino/processing_grounding_dino.js */ "./src/models/grounding_dino/processing_grounding_dino.js"); +/* harmony import */ var _idefics3_processing_idefics3_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./idefics3/processing_idefics3.js */ "./src/models/idefics3/processing_idefics3.js"); +/* harmony import */ var _janus_processing_janus_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ./janus/processing_janus.js */ "./src/models/janus/processing_janus.js"); +/* harmony import */ var _jina_clip_processing_jina_clip_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ./jina_clip/processing_jina_clip.js */ "./src/models/jina_clip/processing_jina_clip.js"); +/* harmony import */ var _llava_processing_llava_js__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! ./llava/processing_llava.js */ "./src/models/llava/processing_llava.js"); +/* harmony import */ var _mgp_str_processing_mgp_str_js__WEBPACK_IMPORTED_MODULE_7__ = __webpack_require__(/*! ./mgp_str/processing_mgp_str.js */ "./src/models/mgp_str/processing_mgp_str.js"); +/* harmony import */ var _moonshine_processing_moonshine_js__WEBPACK_IMPORTED_MODULE_8__ = __webpack_require__(/*! ./moonshine/processing_moonshine.js */ "./src/models/moonshine/processing_moonshine.js"); +/* harmony import */ var _owlvit_processing_owlvit_js__WEBPACK_IMPORTED_MODULE_9__ = __webpack_require__(/*! ./owlvit/processing_owlvit.js */ "./src/models/owlvit/processing_owlvit.js"); +/* harmony import */ var _phi3_v_processing_phi3_v_js__WEBPACK_IMPORTED_MODULE_10__ = __webpack_require__(/*! ./phi3_v/processing_phi3_v.js */ "./src/models/phi3_v/processing_phi3_v.js"); +/* harmony import */ var _paligemma_processing_paligemma_js__WEBPACK_IMPORTED_MODULE_11__ = __webpack_require__(/*! ./paligemma/processing_paligemma.js */ "./src/models/paligemma/processing_paligemma.js"); +/* harmony import */ var _pyannote_processing_pyannote_js__WEBPACK_IMPORTED_MODULE_12__ = __webpack_require__(/*! ./pyannote/processing_pyannote.js */ "./src/models/pyannote/processing_pyannote.js"); +/* harmony import */ var _qwen2_vl_processing_qwen2_vl_js__WEBPACK_IMPORTED_MODULE_13__ = __webpack_require__(/*! ./qwen2_vl/processing_qwen2_vl.js */ "./src/models/qwen2_vl/processing_qwen2_vl.js"); +/* harmony import */ var _sam_processing_sam_js__WEBPACK_IMPORTED_MODULE_14__ = __webpack_require__(/*! ./sam/processing_sam.js */ "./src/models/sam/processing_sam.js"); +/* harmony import */ var _smolvlm_processing_smolvlm_js__WEBPACK_IMPORTED_MODULE_15__ = __webpack_require__(/*! ./smolvlm/processing_smolvlm.js */ "./src/models/smolvlm/processing_smolvlm.js"); +/* harmony import */ var _speecht5_processing_speecht5_js__WEBPACK_IMPORTED_MODULE_16__ = __webpack_require__(/*! ./speecht5/processing_speecht5.js */ "./src/models/speecht5/processing_speecht5.js"); +/* harmony import */ var _ultravox_processing_ultravox_js__WEBPACK_IMPORTED_MODULE_17__ = __webpack_require__(/*! ./ultravox/processing_ultravox.js */ "./src/models/ultravox/processing_ultravox.js"); +/* harmony import */ var _voxtral_processing_voxtral_js__WEBPACK_IMPORTED_MODULE_18__ = __webpack_require__(/*! ./voxtral/processing_voxtral.js */ "./src/models/voxtral/processing_voxtral.js"); +/* harmony import */ var _wav2vec2_processing_wav2vec2_js__WEBPACK_IMPORTED_MODULE_19__ = __webpack_require__(/*! ./wav2vec2/processing_wav2vec2.js */ "./src/models/wav2vec2/processing_wav2vec2.js"); +/* harmony import */ var _wav2vec2_with_lm_processing_wav2vec2_with_lm_js__WEBPACK_IMPORTED_MODULE_20__ = __webpack_require__(/*! ./wav2vec2_with_lm/processing_wav2vec2_with_lm.js */ "./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js"); +/* harmony import */ var _whisper_processing_whisper_js__WEBPACK_IMPORTED_MODULE_21__ = __webpack_require__(/*! ./whisper/processing_whisper.js */ "./src/models/whisper/processing_whisper.js"); + + + + + + + + + + + + + + + + + + + + + + + + +/***/ }), + +/***/ "./src/models/pvt/image_processing_pvt.js": +/*!************************************************!*\ + !*** ./src/models/pvt/image_processing_pvt.js ***! + \************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ PvtImageProcessor: () => (/* binding */ PvtImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class PvtImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/pyannote/feature_extraction_pyannote.js": +/*!************************************************************!*\ + !*** ./src/models/pyannote/feature_extraction_pyannote.js ***! + \************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ PyAnnoteFeatureExtractor: () => (/* binding */ PyAnnoteFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_maths_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../utils/maths.js */ "./src/utils/maths.js"); + + + + + +class PyAnnoteFeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + /** + * Asynchronously extracts features from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @returns {Promise<{ input_values: Tensor; }>} The extracted input features. + */ + async _call(audio) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'PyAnnoteFeatureExtractor'); + + if (audio instanceof Float64Array) { + audio = new Float32Array(audio); + } + + const shape = [ + 1, /* batch_size */ + 1, /* num_channels */ + audio.length, /* num_samples */ + ]; + return { + input_values: new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor('float32', audio, shape), + }; + } + + /** + * NOTE: Can return fractional values. `Math.ceil` will ensure correct value. + * @param {number} samples The number of frames in the audio. + * @returns {number} The number of frames in the audio. + */ + samples_to_frames(samples) { + return ((samples - this.config.offset) / this.config.step); + } + + /** + * Post-processes the speaker diarization logits output by the model. + * @param {import('../../utils/tensor.js').Tensor} logits The speaker diarization logits output by the model. + * @param {number} num_samples Number of samples in the input audio. + * @returns {Array>} The post-processed speaker diarization results. + */ + post_process_speaker_diarization(logits, num_samples) { + const ratio = ( + num_samples / this.samples_to_frames(num_samples) + ) / this.config.sampling_rate; + + const results = []; + for (const scores of logits.tolist()) { + const accumulated_segments = []; + + let current_speaker = -1; + for (let i = 0; i < scores.length; ++i) { + /** @type {number[]} */ + const probabilities = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.softmax)(scores[i]); + const [score, id] = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_2__.max)(probabilities); + const [start, end] = [i, i + 1]; + + if (id !== current_speaker) { + // Speaker has changed + current_speaker = id; + accumulated_segments.push({ id, start, end, score }); + } else { + // Continue the current segment + accumulated_segments.at(-1).end = end; + accumulated_segments.at(-1).score += score; + } + } + + results.push(accumulated_segments.map( + // Convert frame-space to time-space + // and compute the confidence + ({ id, start, end, score }) => ({ + id, + start: start * ratio, + end: end * ratio, + confidence: score / (end - start), + }) + )); + } + return results; + } + +} + + +/***/ }), + +/***/ "./src/models/pyannote/processing_pyannote.js": +/*!****************************************************!*\ + !*** ./src/models/pyannote/processing_pyannote.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ PyAnnoteProcessor: () => (/* binding */ PyAnnoteProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _feature_extraction_pyannote_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./feature_extraction_pyannote.js */ "./src/models/pyannote/feature_extraction_pyannote.js"); + + + +class PyAnnoteProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static feature_extractor_class = _feature_extraction_pyannote_js__WEBPACK_IMPORTED_MODULE_1__.PyAnnoteFeatureExtractor + + /** + * Calls the feature_extractor function with the given audio input. + * @param {any} audio The audio input to extract features from. + * @returns {Promise} A Promise that resolves with the extracted features. + */ + async _call(audio) { + return await this.feature_extractor(audio) + } + + /** @type {PyAnnoteFeatureExtractor['post_process_speaker_diarization']} */ + post_process_speaker_diarization(...args) { + return /** @type {PyAnnoteFeatureExtractor} */(this.feature_extractor).post_process_speaker_diarization(...args); + } + + get sampling_rate() { + return this.feature_extractor.config.sampling_rate; + } +} + + +/***/ }), + +/***/ "./src/models/qwen2_vl/image_processing_qwen2_vl.js": +/*!**********************************************************!*\ + !*** ./src/models/qwen2_vl/image_processing_qwen2_vl.js ***! + \**********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Qwen2VLImageProcessor: () => (/* binding */ Qwen2VLImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + +class Qwen2VLImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + async _call(images, ...args) { + const { pixel_values, original_sizes, reshaped_input_sizes } = await super._call(images, ...args); + + let patches = pixel_values; + + // @ts-ignore + const { temporal_patch_size, merge_size, patch_size } = this.config; + if (patches.dims[0] === 1) { + // Equivalent to np.tile(patches, (self.temporal_patch_size, 1, 1, 1)) + patches = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.cat)(Array.from({ length: temporal_patch_size }, () => patches), 0); + } + + const grid_t = patches.dims[0] / temporal_patch_size; + const channel = patches.dims[1]; + const grid_h = Math.floor(patches.dims[2] / patch_size); + const grid_w = Math.floor(patches.dims[3] / patch_size); + + const flatten_patches = patches + .view( + grid_t, + temporal_patch_size, + channel, + Math.floor(grid_h / merge_size), + merge_size, + patch_size, + Math.floor(grid_w / merge_size), + merge_size, + patch_size, + ) + .permute(0, 3, 6, 4, 7, 2, 1, 5, 8) + .view( + grid_t * grid_h * grid_w, + channel * temporal_patch_size * patch_size * patch_size, + ) + + const image_grid_thw = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor('int64', [grid_t, grid_h, grid_w], [1, 3]); + + return { + pixel_values: flatten_patches, + image_grid_thw, + original_sizes, + reshaped_input_sizes, + } + } +} + + + +/***/ }), + +/***/ "./src/models/qwen2_vl/processing_qwen2_vl.js": +/*!****************************************************!*\ + !*** ./src/models/qwen2_vl/processing_qwen2_vl.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Qwen2VLProcessor: () => (/* binding */ Qwen2VLProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _utils_image_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../../utils/image.js */ "./src/utils/image.js"); + + + + + +class Qwen2VLProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_2__.AutoTokenizer + + /** + * + * @param {string|string[]} text + * @param {RawImage|RawImage[]} images + * @param {...any} args + * @returns {Promise} + */ + async _call(text, images = null, ...args) { + + if (!Array.isArray(text)) { + text = [text]; + } + + let image_inputs, image_grid_thw; + + if (images) { + image_inputs = await this.image_processor(images); + image_grid_thw = image_inputs.image_grid_thw; + } + + if (image_grid_thw) { + // @ts-expect-error TS2551 + let merge_length = this.image_processor.config.merge_size ** 2; + let index = 0; + + const image_grid_thw_list = image_grid_thw.tolist(); + text = text.map(t => { + while (t.includes("<|image_pad|>")) { + const prod = Number(image_grid_thw_list[index++].reduce((a, b) => a * b, 1n)); + t = t.replace("<|image_pad|>", "<|placeholder|>".repeat(Math.floor(prod / merge_length))); + } + return t.replaceAll("<|placeholder|>", "<|image_pad|>"); + }); + } + + const text_inputs = this.tokenizer(text); + + return { + ...text_inputs, + ...image_inputs, + // TODO: ...videos_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/rt_detr/image_processing_rt_detr.js": +/*!********************************************************!*\ + !*** ./src/models/rt_detr/image_processing_rt_detr.js ***! + \********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ RTDetrImageProcessor: () => (/* binding */ RTDetrImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + +class RTDetrImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + /** @type {typeof post_process_object_detection} */ + post_process_object_detection(...args) { + return (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.post_process_object_detection)(...args); + } +} + + +/***/ }), + +/***/ "./src/models/sam/image_processing_sam.js": +/*!************************************************!*\ + !*** ./src/models/sam/image_processing_sam.js ***! + \************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SamImageProcessor: () => (/* binding */ SamImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/core.js */ "./src/utils/core.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + + + + +/** + * @typedef {object} SamImageProcessorResult + * @property {Tensor} pixel_values + * @property {import("../../base/image_processors_utils.js").HeightWidth[]} original_sizes + * @property {import("../../base/image_processors_utils.js").HeightWidth[]} reshaped_input_sizes + * @property {Tensor} [input_points] + * @property {Tensor} [input_labels] + * @property {Tensor} [input_boxes] + */ + +class SamImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + + /** + * + * @param {any} input_points + * @param {import("../../base/image_processors_utils.js").HeightWidth[]} original_sizes + * @param {import("../../base/image_processors_utils.js").HeightWidth[]} reshaped_input_sizes + * @returns {Tensor} + */ + reshape_input_points(input_points, original_sizes, reshaped_input_sizes, is_bounding_box = false) { + + // Make deep copy to avoid altering user's input + input_points = structuredClone(input_points); + let shape = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.calculateDimensions)(input_points); + + // TODO: add support for 2D input_points + if (shape.length === 3) { + // Correct user's input + if (!is_bounding_box) { + shape = [1, ...shape]; + } + input_points = [input_points]; + } else if (shape.length !== 4) { + throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.") + } + + // Reshape input points + for (let i = 0; i < input_points.length; ++i) { // batch_size + let originalImageSize = original_sizes[i]; + let reshapedImageSize = reshaped_input_sizes[i]; + + let resizeFactors = [ + reshapedImageSize[0] / originalImageSize[0], + reshapedImageSize[1] / originalImageSize[1] + ] + + for (let j = 0; j < input_points[i].length; ++j) { // point_batch_size + for (let k = 0; k < input_points[i][j].length; ++k) { // nb_points_per_image + for (let w = 0; w < input_points[i][j][k].length; ++w) { // 2 or 4 + input_points[i][j][k][w] *= resizeFactors[w % 2]; + } + } + } + } + + return new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_2__.Tensor( + 'float32', + Float32Array.from(input_points.flat(Infinity)), + shape + ) + + } + + /** + * + * @param {any} input_labels + * @param {Tensor} input_points + * @returns {Tensor} + */ + add_input_labels(input_labels, input_points) { + let shape = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.calculateDimensions)(input_labels); + if (shape.length === 2) { + // Correct user's input + shape = [1, ...shape]; + input_labels = [input_labels]; + } else if (shape.length !== 3) { + throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.") + } + + if (shape.some((x, i) => x !== input_points.dims[i])) { + throw Error(`The first ${shape.length} dimensions of 'input_points' and 'input_labels' must be the same.`) + } + return new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_2__.Tensor( + 'int64', + input_labels.flat(Infinity).map(BigInt), + shape, + ) + } + /** + * @param {any[]} images The URL(s) of the image(s) to extract features from. + * @param {Object} [options] Additional options for the processor. + * @param {any} [options.input_points=null] A 3D or 4D array, representing the input points provided by the user. + * - 3D: `[point_batch_size, nb_points_per_image, 2]`. In this case, `batch_size` is assumed to be 1. + * - 4D: `[batch_size, point_batch_size, nb_points_per_image, 2]`. + * @param {any} [options.input_labels=null] A 2D or 3D array, representing the input labels for the points, used by the prompt encoder to encode the prompt. + * - 2D: `[point_batch_size, nb_points_per_image]`. In this case, `batch_size` is assumed to be 1. + * - 3D: `[batch_size, point_batch_size, nb_points_per_image]`. + * @param {number[][][]} [options.input_boxes=null] A 3D array of shape `(batch_size, num_boxes, 4)`, representing the input boxes provided by the user. + * This is used by the prompt encoder to encode the prompt. Generally yields to much better generated masks. + * The processor will generate a tensor, with each dimension corresponding respectively to the image batch size, + * the number of boxes per image and the coordinates of the top left and botton right point of the box. + * In the order (`x1`, `y1`, `x2`, `y2`): + * - `x1`: the x coordinate of the top left point of the input box + * - `y1`: the y coordinate of the top left point of the input box + * - `x2`: the x coordinate of the bottom right point of the input box + * - `y2`: the y coordinate of the bottom right point of the input box + * @returns {Promise} + */ + async _call(images, { + input_points = null, + input_labels = null, + input_boxes = null + } = {}) { + // TODO allow user to use preprocessed images + /** @type {SamImageProcessorResult} */ + const processed = await super._call(images); + + if (input_points) { + processed.input_points = this.reshape_input_points( + input_points, processed.original_sizes, processed.reshaped_input_sizes + ); + } + + if (input_labels) { + if (!processed.input_points) { + throw Error("`input_points` must be provided if `input_labels` are provided.") + } + processed.input_labels = this.add_input_labels(input_labels, processed.input_points); + } + + if (input_boxes) { + processed.input_boxes = this.reshape_input_points( + input_boxes, processed.original_sizes, processed.reshaped_input_sizes, true, + ); + } + + return processed; + } + + /** + * Remove padding and upscale masks to the original image size. + * @param {Tensor} masks Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + * @param {[number, number][]} original_sizes The original sizes of each image before it was resized to the model's expected input shape, in (height, width) format. + * @param {[number, number][]} reshaped_input_sizes The size of each image as it is fed to the model, in (height, width) format. Used to remove padding. + * @param {Object} options Optional parameters for post-processing. + * @param {number} [options.mask_threshold] The threshold to use for binarizing the masks. + * @param {boolean} [options.binarize] Whether to binarize the masks. + * @param {Object} [options.pad_size] The target size the images were padded to before being passed to the model. If `null`, the target size is assumed to be the processor's `pad_size`. + * @param {number} [options.pad_size.height] The height the images were padded to. + * @param {number} [options.pad_size.width] The width the images were padded to. + * @returns {Promise} Batched masks in batch_size, num_channels, height, width) format, where (height, width) is given by original_size. + */ + async post_process_masks(masks, original_sizes, reshaped_input_sizes, { + mask_threshold = 0.0, + binarize = true, + pad_size = null, + } = {}) { + // masks: [1, 1, 3, 256, 256] + + const output_masks = []; + + pad_size = pad_size ?? this.pad_size; + + /** @type {[number, number]} */ + const target_image_size = [pad_size.height, pad_size.width]; + + for (let i = 0; i < original_sizes.length; ++i) { + const original_size = original_sizes[i]; + const reshaped_input_size = reshaped_input_sizes[i]; + + // Upscale mask to padded size + let interpolated_mask = (await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_2__.interpolate_4d)( + masks[i], + { mode: 'bilinear', size: target_image_size } + )); + + // Crop mask + interpolated_mask = interpolated_mask.slice(null, null, [0, reshaped_input_size[0]], [0, reshaped_input_size[1]]); + + // Downscale mask + interpolated_mask = (await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_2__.interpolate_4d)( + interpolated_mask, + { mode: 'bilinear', size: original_size } + )); + + if (binarize) { + const data = interpolated_mask.data; + const binarizedMaskData = new Uint8Array(data.length); + for (let i = 0; i < data.length; ++i) { + if (data[i] > mask_threshold) { + binarizedMaskData[i] = 1; + } + } + interpolated_mask = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_2__.Tensor( + 'bool', + binarizedMaskData, + interpolated_mask.dims + ) + } + + output_masks.push(interpolated_mask); + } + + return output_masks; + } + + /** + * Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer. + * @param {import("../../utils/image.js").RawImage} image Input original image + * @param {number} target_size Target size of the resized image + * @param {Object} options Options for generating crop boxes + * @param {number} [options.crop_n_layers] If >0, mask prediction will be run again on crops of the image. + * Sets the number of layers to run, where each layer has 2**i_layer number of image crops. + * @param {number} [options.overlap_ratio] Sets the degree to which crops overlap. In the first crop layer, + * crops will overlap by this fraction of the image length. Later layers with more crops scale down this overlap. + * @param {number} [options.points_per_crop] Number of points to sample from each crop. + * @param {number} [options.crop_n_points_downscale_factor] The number of points-per-side sampled in layer n is + * scaled down by crop_n_points_downscale_factor**n. + * @returns {Object} An object containing the crop boxes, number of points per crop, cropped images, and input labels. + */ + generate_crop_boxes(image, target_size, { + crop_n_layers = 0, + overlap_ratio = 512 / 1500, + points_per_crop = 32, + crop_n_points_downscale_factor = 1, + } = {}) { + // TODO: Implement + // return { crop_boxes, points_per_crop, cropped_images, input_labels } + } +} + + + +/***/ }), + +/***/ "./src/models/sam/processing_sam.js": +/*!******************************************!*\ + !*** ./src/models/sam/processing_sam.js ***! + \******************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SamProcessor: () => (/* binding */ SamProcessor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/image_processing_auto.js */ "./src/models/auto/image_processing_auto.js"); + + + +class SamProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static image_processor_class = _auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoImageProcessor + + async _call(...args) { + return await this.image_processor(...args); + } + + post_process_masks(...args) { + // @ts-ignore + return this.image_processor.post_process_masks(...args); + } + + reshape_input_points(...args) { + // @ts-ignore + return this.image_processor.reshape_input_points(...args); + } +} + +/***/ }), + +/***/ "./src/models/seamless_m4t/feature_extraction_seamless_m4t.js": +/*!********************************************************************!*\ + !*** ./src/models/seamless_m4t/feature_extraction_seamless_m4t.js ***! + \********************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SeamlessM4TFeatureExtractor: () => (/* binding */ SeamlessM4TFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_audio_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../utils/audio.js */ "./src/utils/audio.js"); + + + + +class SeamlessM4TFeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + + constructor(config) { + super(config); + + const sampling_rate = this.config.sampling_rate; + const mel_filters = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.mel_filter_bank)( + 257, // num_frequency_bins + this.config.num_mel_bins, // num_mel_filters + 20, // min_frequency + Math.floor(sampling_rate / 2), // max_frequency + sampling_rate, // sampling_rate + null, // norm + "kaldi", // mel_scale + true, // triangularize_in_mel_space + ); + this.mel_filters = mel_filters; + + this.window = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.window_function)(400, 'povey', { + periodic: false, + }) + } + + /** + * Computes the log-Mel spectrogram of the provided audio waveform. + * @param {Float32Array|Float64Array} waveform The audio waveform to process. + * @param {number} max_length The maximum number of frames to return. + * @returns {Promise} An object containing the log-Mel spectrogram data as a Float32Array and its dimensions as an array of numbers. + */ + async _extract_fbank_features(waveform, max_length) { + // NOTE: We don't pad/truncate since that is passed in as `max_num_frames` + + // Kaldi compliance: 16-bit signed integers + // 32768 == 2 ** 15 + waveform = waveform.map((/** @type {number} */ x) => x * 32768) + + return (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.spectrogram)( + waveform, + this.window, // window + 400, // frame_length + 160, // hop_length + { + fft_length: 512, + power: 2.0, + center: false, + preemphasis: 0.97, + mel_filters: this.mel_filters, + log_mel: 'log', + mel_floor: 1.192092955078125e-07, + remove_dc_offset: true, + + // Custom + max_num_frames: max_length, + transpose: true, + } + ) + } + + /** + * Asynchronously extracts features from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @param {Object} options Optional parameters for feature extraction. + * @param {boolean} [options.padding=true] Whether to pad the sequence to a multiple of `pad_to_multiple_of`. + * @param {number} [options.pad_to_multiple_of=2] The number to pad the sequence to a multiple of. + * @param {boolean} [options.do_normalize_per_mel_bins=true] Whether or not to zero-mean unit-variance normalize the input per mel-channel. + * @param {boolean} [options.return_attention_mask=true] Whether to return the attention mask. + * @returns {Promise<{ input_features: Tensor, attention_mask?: Tensor }>} A Promise resolving to an object containing the extracted input features and attention masks as Tensors. + */ + async _call(audio, { + padding = true, + pad_to_multiple_of = 2, + do_normalize_per_mel_bins = true, + return_attention_mask = true, + } = {}) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'SeamlessM4TFeatureExtractor'); + + let features = await this._extract_fbank_features(audio, this.config.max_length); + + if (do_normalize_per_mel_bins) { + const [num_features, feature_size] = features.dims; + const data = features.data; + for (let i = 0; i < feature_size; ++i) { + let sum = 0; + for (let j = 0; j < num_features; ++j) { + sum += data[j * feature_size + i]; + } + + const mean = sum / num_features; + + let variance = 0; + for (let j = 0; j < num_features; ++j) { + variance += (data[j * feature_size + i] - mean) ** 2; + } + variance /= num_features - 1; // NOTE: We use ddof=1 + + const std = Math.sqrt(variance + 1e-7); + for (let j = 0; j < num_features; ++j) { + const index = j * feature_size + i; + data[index] = (data[index] - mean) / std; + } + } + } + + let padded_attention_mask; + if (padding) { + const [num_frames, num_channels] = features.dims; + const data = /** @type {Float32Array} */(features.data); + + const pad_size = num_frames % pad_to_multiple_of; + if (pad_size > 0) { + const padded_data = new Float32Array(num_channels * (num_frames + pad_size)); + padded_data.set(data) + padded_data.fill(this.config.padding_value, data.length) + + const numPaddedFrames = num_frames + pad_size; + features = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor( + features.type, + padded_data, + [numPaddedFrames, num_channels], + ) + + if (return_attention_mask) { + padded_attention_mask = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor( + 'int64', + new BigInt64Array(numPaddedFrames), + [1, numPaddedFrames], + ); + /** @type {BigInt64Array} */ (padded_attention_mask.data).fill(1n, 0, num_frames); + } + } + } + + const [num_frames, num_channels] = features.dims; + + const stride = this.config.stride; + const remainder = num_frames % stride; + if (remainder !== 0) { + throw new Error(`The number of frames (${num_frames}) must be a multiple of the stride (${stride}).`) + } + + const input_features = features.view( + 1, + Math.floor(num_frames / stride), + num_channels * stride, + ); + + const result = { input_features } + + if (return_attention_mask) { + const reshapedNumFrames = input_features.dims[1]; + + const attention_mask_data = new BigInt64Array(reshapedNumFrames); + + if (padded_attention_mask) { + const padded_attention_mask_data = padded_attention_mask.data; + for (let i = 1, j = 0; i < num_frames; i += stride, ++j) { + attention_mask_data[j] = padded_attention_mask_data[i]; + } + } else { + attention_mask_data.fill(1n); + } + result.attention_mask = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor( + 'int64', + attention_mask_data, + [1, reshapedNumFrames], + ); + } + + return result; + } +} + + +/***/ }), + +/***/ "./src/models/segformer/image_processing_segformer.js": +/*!************************************************************!*\ + !*** ./src/models/segformer/image_processing_segformer.js ***! + \************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SegformerFeatureExtractor: () => (/* binding */ SegformerFeatureExtractor), +/* harmony export */ SegformerImageProcessor: () => (/* binding */ SegformerImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + + +class SegformerImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + /** @type {typeof post_process_semantic_segmentation} */ + post_process_semantic_segmentation(...args) { + return (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.post_process_semantic_segmentation)(...args); + } +} +class SegformerFeatureExtractor extends SegformerImageProcessor { } + + +/***/ }), + +/***/ "./src/models/siglip/image_processing_siglip.js": +/*!******************************************************!*\ + !*** ./src/models/siglip/image_processing_siglip.js ***! + \******************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SiglipImageProcessor: () => (/* binding */ SiglipImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class SiglipImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } + + +/***/ }), + +/***/ "./src/models/smolvlm/image_processing_smolvlm.js": +/*!********************************************************!*\ + !*** ./src/models/smolvlm/image_processing_smolvlm.js ***! + \********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SmolVLMImageProcessor: () => (/* reexport safe */ _idefics3_image_processing_idefics3_js__WEBPACK_IMPORTED_MODULE_0__.Idefics3ImageProcessor) +/* harmony export */ }); +/* harmony import */ var _idefics3_image_processing_idefics3_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../idefics3/image_processing_idefics3.js */ "./src/models/idefics3/image_processing_idefics3.js"); + + + + +/***/ }), + +/***/ "./src/models/smolvlm/processing_smolvlm.js": +/*!**************************************************!*\ + !*** ./src/models/smolvlm/processing_smolvlm.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SmolVLMProcessor: () => (/* reexport safe */ _idefics3_processing_idefics3_js__WEBPACK_IMPORTED_MODULE_0__.Idefics3Processor) +/* harmony export */ }); +/* harmony import */ var _idefics3_processing_idefics3_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../idefics3/processing_idefics3.js */ "./src/models/idefics3/processing_idefics3.js"); + + + + +/***/ }), + +/***/ "./src/models/snac/feature_extraction_snac.js": +/*!****************************************************!*\ + !*** ./src/models/snac/feature_extraction_snac.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SnacFeatureExtractor: () => (/* binding */ SnacFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _dac_feature_extraction_dac_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../dac/feature_extraction_dac.js */ "./src/models/dac/feature_extraction_dac.js"); + + +class SnacFeatureExtractor extends _dac_feature_extraction_dac_js__WEBPACK_IMPORTED_MODULE_0__.DacFeatureExtractor { } + + +/***/ }), + +/***/ "./src/models/speecht5/feature_extraction_speecht5.js": +/*!************************************************************!*\ + !*** ./src/models/speecht5/feature_extraction_speecht5.js ***! + \************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SpeechT5FeatureExtractor: () => (/* binding */ SpeechT5FeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); + + + +class SpeechT5FeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { } + + +/***/ }), + +/***/ "./src/models/speecht5/processing_speecht5.js": +/*!****************************************************!*\ + !*** ./src/models/speecht5/processing_speecht5.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ SpeechT5Processor: () => (/* binding */ SpeechT5Processor) +/* harmony export */ }); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../auto/feature_extraction_auto.js */ "./src/models/auto/feature_extraction_auto.js"); + + + + +class SpeechT5Processor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_0__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__.AutoTokenizer + static feature_extractor_class = _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoFeatureExtractor + + /** + * Calls the feature_extractor function with the given input. + * @param {any} input The input to extract features from. + * @returns {Promise} A Promise that resolves with the extracted features. + */ + async _call(input) { + return await this.feature_extractor(input) + } +} + + +/***/ }), + +/***/ "./src/models/swin2sr/image_processing_swin2sr.js": +/*!********************************************************!*\ + !*** ./src/models/swin2sr/image_processing_swin2sr.js ***! + \********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Swin2SRImageProcessor: () => (/* binding */ Swin2SRImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class Swin2SRImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + pad_image(pixelData, imgDims, padSize, options = {}) { + // NOTE: In this case, `padSize` represents the size of the sliding window for the local attention. + // In other words, the image is padded so that its width and height are multiples of `padSize`. + const [imageHeight, imageWidth, imageChannels] = imgDims; + + return super.pad_image(pixelData, imgDims, { + // NOTE: For Swin2SR models, the original python implementation adds padding even when the image's width/height is already + // a multiple of `pad_size`. However, this is most likely a bug (PR: https://github.com/mv-lab/swin2sr/pull/19). + // For this reason, we only add padding when the image's width/height is not a multiple of `pad_size`. + width: imageWidth + (padSize - imageWidth % padSize) % padSize, + height: imageHeight + (padSize - imageHeight % padSize) % padSize, + }, { + mode: 'symmetric', + center: false, + constant_values: -1, + ...options, + }) + } +} + +/***/ }), + +/***/ "./src/models/ultravox/processing_ultravox.js": +/*!****************************************************!*\ + !*** ./src/models/ultravox/processing_ultravox.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ UltravoxProcessor: () => (/* binding */ UltravoxProcessor) +/* harmony export */ }); +/* harmony import */ var _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../auto/feature_extraction_auto.js */ "./src/models/auto/feature_extraction_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); + + + + +/** + * Represents a UltravoxProcessor that extracts features from an audio input. + */ +class UltravoxProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__.AutoTokenizer + static feature_extractor_class = _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_0__.AutoFeatureExtractor + static uses_processor_config = true; + + /** + * @param {string} text The text input to process. + * @param {Float32Array} audio The audio input to process. + */ + async _call(text, audio = null, kwargs = {}) { + // TODO: Support batched inputs + if (Array.isArray(text)) { + throw new Error("Batched inputs are not supported yet."); + } + + let audio_inputs = {}; + if (audio) { + const audio_len = audio.length; + const { input_features } = await this.feature_extractor(audio, { + ...kwargs, + max_length: audio_len, + }); + const nb_encoder_frames = Math.round(audio_len / this.config.encoder_ds_factor + 1e-4); + + // NOTE: The python version appears to have an off-by-one error. + const audio_embed_frames = 1 + Math.ceil(nb_encoder_frames / this.config.stack_factor); + audio_inputs["audio_token_len"] = [audio_embed_frames]; + audio_inputs["audio_values"] = input_features; + + const image_token = this.config.audio_placeholder; + if (!text.includes(image_token)) { + throw new Error(`The input text does not contain the image token ${image_token}.`); + } + text = text.replaceAll(image_token, image_token.repeat(audio_embed_frames)); + } + + const text_inputs = this.tokenizer(text, { + add_special_tokens: false, + ...kwargs, + }); + + return { + ...text_inputs, + ...audio_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/vit/image_processing_vit.js": +/*!************************************************!*\ + !*** ./src/models/vit/image_processing_vit.js ***! + \************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ ViTFeatureExtractor: () => (/* binding */ ViTFeatureExtractor), +/* harmony export */ ViTImageProcessor: () => (/* binding */ ViTImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class ViTImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { } +class ViTFeatureExtractor extends ViTImageProcessor { } + + + +/***/ }), + +/***/ "./src/models/vitmatte/image_processing_vitmatte.js": +/*!**********************************************************!*\ + !*** ./src/models/vitmatte/image_processing_vitmatte.js ***! + \**********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ VitMatteImageProcessor: () => (/* binding */ VitMatteImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + + +class VitMatteImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + /** + * Calls the feature extraction process on an array of images, preprocesses + * each image, and concatenates the resulting features into a single Tensor. + * @param {import("../../utils/image.js").RawImage[]} images The image(s) to extract features from. + * @param {import("../../utils/image.js").RawImage[]} trimaps The trimaps(s) to extract features from. + * @returns {Promise} An object containing the concatenated pixel values of the preprocessed images. + */ + async _call(images, trimaps) { + if (!Array.isArray(images)) { + images = [images]; + } + if (!Array.isArray(trimaps)) { + trimaps = [trimaps]; + } + + const imageData = await Promise.all(images.map(x => this.preprocess(x))); + const trimapData = await Promise.all(trimaps.map(x => this.preprocess(x, { + do_normalize: false, + do_convert_rgb: false, + do_convert_grayscale: true, + }))); + + + // Stack pixel values + const pixel_values = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.stack)(imageData.map( + // Concatenate images and trimaps + (x, i) => (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.cat)([x.pixel_values, trimapData[i].pixel_values], 0) + ), 0); + + return { + pixel_values, + + // Original sizes of images + original_sizes: imageData.map(x => x.original_size), + + // Reshaped sizes of images, before padding or cropping + reshaped_input_sizes: imageData.map(x => x.reshaped_input_size), + } + } +} + + +/***/ }), + +/***/ "./src/models/vitpose/image_processing_vitpose.js": +/*!********************************************************!*\ + !*** ./src/models/vitpose/image_processing_vitpose.js ***! + \********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ VitPoseImageProcessor: () => (/* binding */ VitPoseImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class VitPoseImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + + /** + * Transform the heatmaps into keypoint predictions and transform them back to the image. + * NOTE: This is a naive implementation and does not include advanced post-processing techniques, + * so the results may not be as accurate as the original implementation. + * @param {import('../../utils/tensor.js').Tensor} outputs The model outputs. + * @param {[number, number, number, number][][]} boxes List or array of bounding boxes for each image. + * Each box should be a list of 4 floats representing the bounding box coordinates in COCO format (top_left_x, top_left_y, width, height). + * @returns {{ + * bbox: [number, number, number, number], + * scores: number[], + * labels: number[], + * keypoints: [number, number][] + * }[][]} List of keypoints predictions for each image. + */ + post_process_pose_estimation(outputs, boxes, { + threshold = null, + // TODO: + // kernel_size = 11, + // target_sizes = null, + } = {}) { + // NOTE: boxes are 3D (batch_size, num_boxes, 4) + const heatmaps = outputs.tolist(); + const [batch_size, num_classes, height, width] = outputs.dims; + + const results = []; + for (let b = 0; b < batch_size; ++b) { + const heatmap = heatmaps[b]; + const bboxes = boxes[b]; + + const batch_results = []; + for (let n = 0; n < bboxes.length; ++n) { + const bbox = bboxes[n]; + + const keypoints = []; + const scores = []; + const labels = []; + + const xScale = bbox.at(-2) / width; + const yScale = bbox.at(-1) / height; + for (let c = 0; c < heatmap.length; ++c) { + let [xWeightedSum, yWeightedSum] = [0, 0]; + let sum = 0; + let score = -Infinity; + const row = heatmap[c]; + for (let y = 0; y < row.length; ++y) { + const col = row[y]; + for (let x = 0; x < col.length; ++x) { + const value = col[x]; + sum += value; + + score = Math.max(score, value); + + // Get weighted sum of positions + // TODO: Determine best offsets + xWeightedSum += (x + 0.5) * value; + yWeightedSum += (y) * value; + } + } + + // Ignore low scores, if threshold is set + if (threshold != null && score < threshold) continue; + + /** @type {[number, number]} */ + const keypoint = [ + xScale * xWeightedSum / sum, + yScale * yWeightedSum / sum, + ] + keypoints.push(keypoint); + labels.push(c); + scores.push(score); + } + batch_results.push({ + bbox, + scores, + labels, + keypoints, + }); + } + results.push(batch_results); + } + return results; + } +} + + +/***/ }), + +/***/ "./src/models/voxtral/processing_voxtral.js": +/*!**************************************************!*\ + !*** ./src/models/voxtral/processing_voxtral.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ VoxtralProcessor: () => (/* binding */ VoxtralProcessor) +/* harmony export */ }); +/* harmony import */ var _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../auto/feature_extraction_auto.js */ "./src/models/auto/feature_extraction_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + + + +const AUDIO_TOKEN = "[AUDIO]"; +const BEGIN_AUDIO_TOKEN = "[BEGIN_AUDIO]"; +const NUM_AUDIO_TOKENS = 375; + +/** + * Helper function to split audio into non-overlapping chunks of n_samples + * @param {Float32Array} audio + * @param {number} n_samples + * @returns {Float32Array[]} + */ +function chunk(audio, n_samples) { + const chunks = []; + for (let i = 0; i < audio.length; i += n_samples) { + chunks.push(audio.subarray(i, Math.min(i + n_samples, audio.length))); + } + return chunks; +} + +/** + * Represents a VoxtralProcessor that extracts features from an audio input. + */ +class VoxtralProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__.AutoTokenizer + static feature_extractor_class = _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_0__.AutoFeatureExtractor + static uses_processor_config = false; + + /** + * @param {string} text The text input to process. + * @param {Float32Array|Float32Array[]} audio The audio input(s) to process. + */ + async _call(text, audio = null, kwargs = {}) { + if (Array.isArray(text)) { + throw new Error("Batched inputs are not supported yet."); + } + + const audio_inputs = {}; + if (audio) { + if (!text.includes(AUDIO_TOKEN)) { + throw new Error(`The input text does not contain the audio token ${AUDIO_TOKEN}.`); + } + if (!Array.isArray(audio)) { + audio = [audio]; + } + const text_parts = text.split(AUDIO_TOKEN); + const num_audio_tokens = text_parts.length - 1; + if (num_audio_tokens !== audio.length) { + throw new Error(`The number of audio inputs (${audio.length}) does not match the number of audio tokens in the text (${num_audio_tokens}).`); + } + + const n_samples = this.feature_extractor.config.n_samples; + + // Split each audio input into chunks and keep track of chunk counts + const audio_chunks = audio.map(a => chunk(a, n_samples)); + const chunk_counts = audio_chunks.map(chunks => chunks.length); + + // Flatten all chunks for feature extraction + const all_chunks = audio_chunks.flat(); + const features = (await Promise.all( + all_chunks.map((audio_input) => this.feature_extractor(audio_input, kwargs)) + )).map(x => x.input_features); + + audio_inputs["audio_values"] = features.length > 1 ? (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_3__.cat)(features, 0) : features[0]; + + // Replace text tokens for each audio input, expanding for chunk count + let new_text = text_parts[0]; + for (let i = 0; i < chunk_counts.length; ++i) { + new_text += BEGIN_AUDIO_TOKEN; + for (let j = 0; j < chunk_counts[i]; ++j) { + new_text += AUDIO_TOKEN.repeat(NUM_AUDIO_TOKENS); + } + new_text += text_parts[i + 1]; + } + text = new_text; + } + + const text_inputs = this.tokenizer(text, { + add_special_tokens: false, + ...kwargs, + }); + + return { + ...text_inputs, + ...audio_inputs, + } + } +} + + +/***/ }), + +/***/ "./src/models/wav2vec2/feature_extraction_wav2vec2.js": +/*!************************************************************!*\ + !*** ./src/models/wav2vec2/feature_extraction_wav2vec2.js ***! + \************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Wav2Vec2FeatureExtractor: () => (/* binding */ Wav2Vec2FeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); + + + +class Wav2Vec2FeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + + /** + * @param {Float32Array} input_values + * @returns {Float32Array} + */ + _zero_mean_unit_var_norm(input_values) { + // TODO support batch? + const sum = input_values.reduce((a, b) => a + b, 0); + const mean = sum / input_values.length; + const variance = input_values.reduce((a, b) => a + (b - mean) ** 2, 0) / input_values.length; + return input_values.map(x => (x - mean) / Math.sqrt(variance + 1e-7)); + } + + /** + * Asynchronously extracts features from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @returns {Promise<{ input_values: Tensor; attention_mask: Tensor }>} A Promise resolving to an object containing the extracted input features and attention mask as Tensors. + */ + async _call(audio) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'Wav2Vec2FeatureExtractor'); + + if (audio instanceof Float64Array) { + audio = new Float32Array(audio); + } + + let input_values = audio; + + // zero-mean and unit-variance normalization + if (this.config.do_normalize) { + input_values = this._zero_mean_unit_var_norm(input_values); + } + + // TODO: allow user to pass in attention mask + const shape = [1, input_values.length]; + return { + input_values: new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor('float32', input_values, shape), + attention_mask: new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor('int64', new BigInt64Array(input_values.length).fill(1n), shape) + }; + } +} + + +/***/ }), + +/***/ "./src/models/wav2vec2/processing_wav2vec2.js": +/*!****************************************************!*\ + !*** ./src/models/wav2vec2/processing_wav2vec2.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Wav2Vec2Processor: () => (/* binding */ Wav2Vec2Processor) +/* harmony export */ }); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/feature_extraction_auto.js */ "./src/models/auto/feature_extraction_auto.js"); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); + + + + +class Wav2Vec2Processor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer + static feature_extractor_class = _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoFeatureExtractor + + /** + * Calls the feature_extractor function with the given audio input. + * @param {any} audio The audio input to extract features from. + * @returns {Promise} A Promise that resolves with the extracted features. + */ + async _call(audio) { + return await this.feature_extractor(audio) + } +} + + +/***/ }), + +/***/ "./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js": +/*!********************************************************************!*\ + !*** ./src/models/wav2vec2_with_lm/processing_wav2vec2_with_lm.js ***! + \********************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Wav2Vec2ProcessorWithLM: () => (/* binding */ Wav2Vec2ProcessorWithLM) +/* harmony export */ }); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../auto/feature_extraction_auto.js */ "./src/models/auto/feature_extraction_auto.js"); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); + + + + +class Wav2Vec2ProcessorWithLM extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer + static feature_extractor_class = _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_1__.AutoFeatureExtractor + + /** + * Calls the feature_extractor function with the given audio input. + * @param {any} audio The audio input to extract features from. + * @returns {Promise} A Promise that resolves with the extracted features. + */ + async _call(audio) { + return await this.feature_extractor(audio) + } +} + + +/***/ }), + +/***/ "./src/models/wespeaker/feature_extraction_wespeaker.js": +/*!**************************************************************!*\ + !*** ./src/models/wespeaker/feature_extraction_wespeaker.js ***! + \**************************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ WeSpeakerFeatureExtractor: () => (/* binding */ WeSpeakerFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_audio_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../utils/audio.js */ "./src/utils/audio.js"); + + + + + +class WeSpeakerFeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + + constructor(config) { + super(config); + + const sampling_rate = this.config.sampling_rate; + const mel_filters = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.mel_filter_bank)( + 257, // num_frequency_bins + this.config.num_mel_bins, // num_mel_filters + 20, // min_frequency + Math.floor(sampling_rate / 2), // max_frequency + sampling_rate, // sampling_rate + null, // norm + "kaldi", // mel_scale + true, // triangularize_in_mel_space + ); + this.mel_filters = mel_filters; + + this.window = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.window_function)(400, 'hamming', { + periodic: false, + }) + this.min_num_frames = this.config.min_num_frames; + } + + /** + * Computes the log-Mel spectrogram of the provided audio waveform. + * @param {Float32Array|Float64Array} waveform The audio waveform to process. + * @returns {Promise} An object containing the log-Mel spectrogram data as a Float32Array and its dimensions as an array of numbers. + */ + async _extract_fbank_features(waveform) { + // Kaldi compliance: 16-bit signed integers + // 32768 == 2 ** 15 + waveform = waveform.map((/** @type {number} */ x) => x * 32768) + + return (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.spectrogram)( + waveform, + this.window, // window + 400, // frame_length + 160, // hop_length + { + fft_length: 512, + power: 2.0, + center: false, + preemphasis: 0.97, + mel_filters: this.mel_filters, + log_mel: 'log', + mel_floor: 1.192092955078125e-07, + remove_dc_offset: true, + + // Custom + transpose: true, + min_num_frames: this.min_num_frames, + } + ) + } + + + /** + * Asynchronously extracts features from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @returns {Promise<{ input_features: Tensor }>} A Promise resolving to an object containing the extracted input features as a Tensor. + */ + async _call(audio) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'WeSpeakerFeatureExtractor'); + + const features = (await this._extract_fbank_features(audio)).unsqueeze_(0); + + if (this.config.fbank_centering_span === null) { + // center features with global average + const meanData = /** @type {Float32Array} */ (features.mean(1).data); + const featuresData = /** @type {Float32Array} */(features.data); + const [batch_size, num_frames, feature_size] = features.dims; + + for (let i = 0; i < batch_size; ++i) { + const offset1 = i * num_frames * feature_size; + const offset2 = i * feature_size; + for (let j = 0; j < num_frames; ++j) { + const offset3 = offset1 + j * feature_size; + for (let k = 0; k < feature_size; ++k) { + featuresData[offset3 + k] -= meanData[offset2 + k]; + } + } + } + } + + return { + input_features: features + }; + } +} + + +/***/ }), + +/***/ "./src/models/whisper/common_whisper.js": +/*!**********************************************!*\ + !*** ./src/models/whisper/common_whisper.js ***! + \**********************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ WHISPER_LANGUAGE_MAPPING: () => (/* binding */ WHISPER_LANGUAGE_MAPPING), +/* harmony export */ WHISPER_TO_LANGUAGE_CODE_MAPPING: () => (/* binding */ WHISPER_TO_LANGUAGE_CODE_MAPPING), +/* harmony export */ whisper_language_to_code: () => (/* binding */ whisper_language_to_code) +/* harmony export */ }); + + +const WHISPER_LANGUAGES = [ + ["en", "english"], + ["zh", "chinese"], + ["de", "german"], + ["es", "spanish"], + ["ru", "russian"], + ["ko", "korean"], + ["fr", "french"], + ["ja", "japanese"], + ["pt", "portuguese"], + ["tr", "turkish"], + ["pl", "polish"], + ["ca", "catalan"], + ["nl", "dutch"], + ["ar", "arabic"], + ["sv", "swedish"], + ["it", "italian"], + ["id", "indonesian"], + ["hi", "hindi"], + ["fi", "finnish"], + ["vi", "vietnamese"], + ["he", "hebrew"], + ["uk", "ukrainian"], + ["el", "greek"], + ["ms", "malay"], + ["cs", "czech"], + ["ro", "romanian"], + ["da", "danish"], + ["hu", "hungarian"], + ["ta", "tamil"], + ["no", "norwegian"], + ["th", "thai"], + ["ur", "urdu"], + ["hr", "croatian"], + ["bg", "bulgarian"], + ["lt", "lithuanian"], + ["la", "latin"], + ["mi", "maori"], + ["ml", "malayalam"], + ["cy", "welsh"], + ["sk", "slovak"], + ["te", "telugu"], + ["fa", "persian"], + ["lv", "latvian"], + ["bn", "bengali"], + ["sr", "serbian"], + ["az", "azerbaijani"], + ["sl", "slovenian"], + ["kn", "kannada"], + ["et", "estonian"], + ["mk", "macedonian"], + ["br", "breton"], + ["eu", "basque"], + ["is", "icelandic"], + ["hy", "armenian"], + ["ne", "nepali"], + ["mn", "mongolian"], + ["bs", "bosnian"], + ["kk", "kazakh"], + ["sq", "albanian"], + ["sw", "swahili"], + ["gl", "galician"], + ["mr", "marathi"], + ["pa", "punjabi"], + ["si", "sinhala"], + ["km", "khmer"], + ["sn", "shona"], + ["yo", "yoruba"], + ["so", "somali"], + ["af", "afrikaans"], + ["oc", "occitan"], + ["ka", "georgian"], + ["be", "belarusian"], + ["tg", "tajik"], + ["sd", "sindhi"], + ["gu", "gujarati"], + ["am", "amharic"], + ["yi", "yiddish"], + ["lo", "lao"], + ["uz", "uzbek"], + ["fo", "faroese"], + ["ht", "haitian creole"], + ["ps", "pashto"], + ["tk", "turkmen"], + ["nn", "nynorsk"], + ["mt", "maltese"], + ["sa", "sanskrit"], + ["lb", "luxembourgish"], + ["my", "myanmar"], + ["bo", "tibetan"], + ["tl", "tagalog"], + ["mg", "malagasy"], + ["as", "assamese"], + ["tt", "tatar"], + ["haw", "hawaiian"], + ["ln", "lingala"], + ["ha", "hausa"], + ["ba", "bashkir"], + ["jw", "javanese"], + ["su", "sundanese"], +] + +// @ts-ignore +const WHISPER_LANGUAGE_MAPPING = new Map(WHISPER_LANGUAGES); +// @ts-ignore +const WHISPER_TO_LANGUAGE_CODE_MAPPING = new Map([ + ...WHISPER_LANGUAGES.map(([k, v]) => [v, k]), + ...[ + ["burmese", "my"], + ["valencian", "ca"], + ["flemish", "nl"], + ["haitian", "ht"], + ["letzeburgesch", "lb"], + ["pushto", "ps"], + ["panjabi", "pa"], + ["moldavian", "ro"], + ["moldovan", "ro"], + ["sinhalese", "si"], + ["castilian", "es"], + ] +]); + +/** + * @param {string} language The language name or code + * @returns {string} The language code + */ +function whisper_language_to_code(language) { + language = language.toLowerCase(); + + // Map to code from user-friendly name (e.g., "english" -> "en") + let language_code = WHISPER_TO_LANGUAGE_CODE_MAPPING.get(language); + + if (language_code === undefined) { + // User provided something that is not a language name + + // Perhaps the user passed the special token itself + const language_special_token = language.match(/^<\|([a-z]{2})\|>$/); + if (language_special_token) { + language = language_special_token[1]; + } + + if (WHISPER_LANGUAGE_MAPPING.has(language)) { + // User provided the language code directly (e.g., "en") + language_code = language; + + } else { + // User provided something that is not a language code or name + const is_language_code = language.length === 2; + const langs = is_language_code ? WHISPER_LANGUAGE_MAPPING.keys() : WHISPER_LANGUAGE_MAPPING.values(); + + throw new Error(`Language "${language}" is not supported. Must be one of: ${JSON.stringify(Array.from(langs))}`); + } + } + return language_code; +} + + +/***/ }), + +/***/ "./src/models/whisper/feature_extraction_whisper.js": +/*!**********************************************************!*\ + !*** ./src/models/whisper/feature_extraction_whisper.js ***! + \**********************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ WhisperFeatureExtractor: () => (/* binding */ WhisperFeatureExtractor) +/* harmony export */ }); +/* harmony import */ var _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/feature_extraction_utils.js */ "./src/base/feature_extraction_utils.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_audio_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../utils/audio.js */ "./src/utils/audio.js"); +/* harmony import */ var _utils_maths_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../../utils/maths.js */ "./src/utils/maths.js"); + + + + + +class WhisperFeatureExtractor extends _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.FeatureExtractor { + + constructor(config) { + super(config); + + // Prefer given `mel_filters` from preprocessor_config.json, or calculate them if they don't exist. + this.config.mel_filters ??= (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.mel_filter_bank)( + Math.floor(1 + this.config.n_fft / 2), // num_frequency_bins + this.config.feature_size, // num_mel_filters + 0.0, // min_frequency + 8000.0, // max_frequency + this.config.sampling_rate, // sampling_rate + "slaney", // norm + "slaney", // mel_scale + ); + + this.window = (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.window_function)(this.config.n_fft, 'hann'); + } + + /** + * Computes the log-Mel spectrogram of the provided audio waveform. + * @param {Float32Array|Float64Array} waveform The audio waveform to process. + * @returns {Promise} An object containing the log-Mel spectrogram data as a Float32Array and its dimensions as an array of numbers. + */ + async _extract_fbank_features(waveform) { + const features = await (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_2__.spectrogram)( + waveform, + this.window, // window + this.config.n_fft, // frame_length + this.config.hop_length, // hop_length + { + power: 2.0, + mel_filters: this.config.mel_filters, + log_mel: 'log10', + + // Custom + max_num_frames: Math.min( + Math.floor(waveform.length / this.config.hop_length), + this.config.nb_max_frames, // 3000 + ) + } + ) + + const data = features.data; + const maxValue = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.max)(/** @type {Float32Array} */(data))[0]; + + for (let i = 0; i < data.length; ++i) { + data[i] = (Math.max(data[i], maxValue - 8.0) + 4.0) / 4.0; + } + + return features; + } + + /** + * Asynchronously extracts features from a given audio using the provided configuration. + * @param {Float32Array|Float64Array} audio The audio data as a Float32Array/Float64Array. + * @returns {Promise<{ input_features: Tensor }>} A Promise resolving to an object containing the extracted input features as a Tensor. + */ + async _call(audio, { + max_length = null, + } = {}) { + (0,_base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_0__.validate_audio_inputs)(audio, 'WhisperFeatureExtractor'); + + let waveform; + const length = max_length ?? this.config.n_samples; + if (audio.length > length) { + if (audio.length > this.config.n_samples) { + console.warn( + "Attempting to extract features for audio longer than 30 seconds. " + + "If using a pipeline to extract transcript from a long audio clip, " + + "remember to specify `chunk_length_s` and/or `stride_length_s`." + ); + } + waveform = audio.slice(0, length); + } else { + // pad with zeros + waveform = new Float32Array(length); + waveform.set(audio); + } + + const features = await this._extract_fbank_features(waveform); + + return { + input_features: features.unsqueeze_(0) + }; + } +} + + +/***/ }), + +/***/ "./src/models/whisper/generation_whisper.js": +/*!**************************************************!*\ + !*** ./src/models/whisper/generation_whisper.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ WhisperGenerationConfig: () => (/* binding */ WhisperGenerationConfig) +/* harmony export */ }); +/* harmony import */ var _generation_configuration_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../generation/configuration_utils.js */ "./src/generation/configuration_utils.js"); + + +class WhisperGenerationConfig extends _generation_configuration_utils_js__WEBPACK_IMPORTED_MODULE_0__.GenerationConfig { + + /** + * Whether to return the timestamps with the text. This enables the `WhisperTimestampsLogitsProcessor`. + * @type {boolean} + */ + return_timestamps = null; + + /** + * Whether to return token-level timestamps + * with the text. This can be used with or without the `return_timestamps` option. To get word-level + * timestamps, use the tokenizer to group the tokens into words. + * @type {boolean} + */ + return_token_timestamps = null; + + /** + * The number of audio frames available in this chunk. This is only used generating word-level timestamps. + * @type {number} + */ + num_frames = null; + + /** + * Alignment heads to predict word-level timestamps. This is a list of [layer, head] pairs that + * select the cross-attention heads that are highly correlated to word-level timing. + * @type {[number, number][]} + */ + alignment_heads = null; + + /** + * Task to use for generation, either "translate" or "transcribe". + * @type {string} + */ + task = null; + + /** + * Language token to use for generation, can be either in the form of `<|en|>`, `en` or `english`. + * You can find all the possible language tokens in the `model.generation_config.lang_to_id` dictionary. + * @type {string} + */ + language = null; + + /** + * The id of the `"<|notimestamps|>"` token. + * @type {number} + */ + no_timestamps_token_id = null; + + /** + * Rank-1 list of token IDs created by passing text to [`~WhisperProcessor.get_prompt_ids`] that is + * provided as a prompt to each chunk. This can be used to provide or "prompt-engineer" a context for + * transcription, e.g. custom vocabularies or proper nouns to make it more likely to predict those words + * correctly. It cannot be used in conjunction with `decoder_start_token_id` as it overwrites this value. + * @type {number[]} + */ + prompt_ids = null; + + /** + * Whether the model is multilingual or not. + * @type {boolean} + */ + is_multilingual = null; + + /** + * (Optional) A mapping from language tokens to their corresponding IDs. + * Only required if the model is multilingual. + * @type {Record|null} + */ + lang_to_id = null; + + /** + * (Optional) A mapping from task tokens to their corresponding IDs. + * @type {Record|null} + */ + task_to_id = null; + + /** + * Used to set the maximum value of the initial timestamp. This is used to prevent the model from + * predicting timestamps that are too far in the future. + * @type {number} + */ + max_initial_timestamp_index = 1; +} + +/** + * @typedef {import('../../generation/parameters.js').GenerationFunctionParameters & {generation_config: WhisperGenerationConfig} & WhisperGenerationConfig} WhisperGenerationFunctionParameters + */ + + +/***/ }), + +/***/ "./src/models/whisper/processing_whisper.js": +/*!**************************************************!*\ + !*** ./src/models/whisper/processing_whisper.js ***! + \**************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ WhisperProcessor: () => (/* binding */ WhisperProcessor) +/* harmony export */ }); +/* harmony import */ var _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../auto/feature_extraction_auto.js */ "./src/models/auto/feature_extraction_auto.js"); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../../tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../../base/processing_utils.js */ "./src/base/processing_utils.js"); + + + + +/** + * Represents a WhisperProcessor that extracts features from an audio input. + */ +class WhisperProcessor extends _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_2__.Processor { + static tokenizer_class = _tokenizers_js__WEBPACK_IMPORTED_MODULE_1__.AutoTokenizer + static feature_extractor_class = _auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_0__.AutoFeatureExtractor + + /** + * Calls the feature_extractor function with the given audio input. + * @param {any} audio The audio input to extract features from. + * @returns {Promise} A Promise that resolves with the extracted features. + */ + async _call(audio) { + return await this.feature_extractor(audio); + } +} + + + +/***/ }), + +/***/ "./src/models/yolos/image_processing_yolos.js": +/*!****************************************************!*\ + !*** ./src/models/yolos/image_processing_yolos.js ***! + \****************************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ YolosFeatureExtractor: () => (/* binding */ YolosFeatureExtractor), +/* harmony export */ YolosImageProcessor: () => (/* binding */ YolosImageProcessor) +/* harmony export */ }); +/* harmony import */ var _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../../base/image_processors_utils.js */ "./src/base/image_processors_utils.js"); + + +class YolosImageProcessor extends _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.ImageProcessor { + /** @type {typeof post_process_object_detection} */ + post_process_object_detection(...args) { + return (0,_base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_0__.post_process_object_detection)(...args); + } +} +class YolosFeatureExtractor extends YolosImageProcessor { } + + +/***/ }), + +/***/ "./src/ops/registry.js": +/*!*****************************!*\ + !*** ./src/ops/registry.js ***! + \*****************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ TensorOpRegistry: () => (/* binding */ TensorOpRegistry) +/* harmony export */ }); +/* harmony import */ var _backends_onnx_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../backends/onnx.js */ "./src/backends/onnx.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../env.js */ "./src/env.js"); + + + + +const IS_WEB_ENV = _env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_BROWSER_ENV || _env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_WEBWORKER_ENV; +/** + * Asynchronously creates a wrapper function for running an ONNX inference session. + * + * @param {number[]} session_bytes The session data in bytes. + * @param {import('onnxruntime-common').InferenceSession.SessionOptions} session_options The options for the ONNX session. + * @template {string | [string] | string[]} T + * @param {T} names The name(s) of the output tensor(s). + * + * @returns {Promise): Promise>} + * The wrapper function for running the ONNX inference session. + */ +const wrap = async (session_bytes, session_options, names) => { + const session = await (0,_backends_onnx_js__WEBPACK_IMPORTED_MODULE_0__.createInferenceSession)( + new Uint8Array(session_bytes), session_options, + ); + + /** @type {Promise} */ + let chain = Promise.resolve(); + + return /** @type {any} */(async (/** @type {Record} */ inputs) => { + const proxied = (0,_backends_onnx_js__WEBPACK_IMPORTED_MODULE_0__.isONNXProxy)(); + const ortFeed = Object.fromEntries(Object.entries(inputs).map(([k, v]) => [k, (proxied ? v.clone() : v).ort_tensor])); + + // When running in-browser via WASM, we need to chain calls to session.run to avoid "Error: Session already started" + const outputs = await (chain = IS_WEB_ENV ? chain.then(() => session.run(ortFeed)) : session.run(ortFeed)); + + if (Array.isArray(names)) { + return names.map((n) => new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor(outputs[n])); + } else { + return new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_1__.Tensor(outputs[/** @type {string} */(names)]); + } + }) +} + +// In-memory registry of initialized ONNX operators +class TensorOpRegistry { + static session_options = { + // TODO: Allow for multiple execution providers + // executionProviders: ['webgpu'], + }; + + static get nearest_interpolate_4d() { + if (!this._nearest_interpolate_4d) { + this._nearest_interpolate_4d = wrap( + [8, 10, 18, 0, 58, 129, 1, 10, 41, 10, 1, 120, 10, 0, 10, 0, 10, 1, 115, 18, 1, 121, 34, 6, 82, 101, 115, 105, 122, 101, 42, 18, 10, 4, 109, 111, 100, 101, 34, 7, 110, 101, 97, 114, 101, 115, 116, 160, 1, 3, 18, 1, 114, 90, 31, 10, 1, 120, 18, 26, 10, 24, 8, 1, 18, 20, 10, 3, 18, 1, 98, 10, 3, 18, 1, 99, 10, 3, 18, 1, 104, 10, 3, 18, 1, 119, 90, 15, 10, 1, 115, 18, 10, 10, 8, 8, 7, 18, 4, 10, 2, 8, 4, 98, 31, 10, 1, 121, 18, 26, 10, 24, 8, 1, 18, 20, 10, 3, 18, 1, 98, 10, 3, 18, 1, 99, 10, 3, 18, 1, 104, 10, 3, 18, 1, 119, 66, 2, 16, 21], + this.session_options, + 'y', + ); + } + return this._nearest_interpolate_4d; + } + static get bilinear_interpolate_4d() { + if (!this._bilinear_interpolate_4d) { + this._bilinear_interpolate_4d = wrap( + [8, 9, 18, 0, 58, 128, 1, 10, 40, 10, 1, 120, 10, 0, 10, 0, 10, 1, 115, 18, 1, 121, 34, 6, 82, 101, 115, 105, 122, 101, 42, 17, 10, 4, 109, 111, 100, 101, 34, 6, 108, 105, 110, 101, 97, 114, 160, 1, 3, 18, 1, 114, 90, 31, 10, 1, 120, 18, 26, 10, 24, 8, 1, 18, 20, 10, 3, 18, 1, 98, 10, 3, 18, 1, 99, 10, 3, 18, 1, 104, 10, 3, 18, 1, 119, 90, 15, 10, 1, 115, 18, 10, 10, 8, 8, 7, 18, 4, 10, 2, 8, 4, 98, 31, 10, 1, 121, 18, 26, 10, 24, 8, 1, 18, 20, 10, 3, 18, 1, 98, 10, 3, 18, 1, 99, 10, 3, 18, 1, 104, 10, 3, 18, 1, 119, 66, 2, 16, 20], + this.session_options, + 'y', + ); + } + return this._bilinear_interpolate_4d; + } + + static get bicubic_interpolate_4d() { + if (!this._bicubic_interpolate_4d) { + this._bicubic_interpolate_4d = wrap( + [8, 9, 18, 0, 58, 127, 10, 39, 10, 1, 120, 10, 0, 10, 0, 10, 1, 115, 18, 1, 121, 34, 6, 82, 101, 115, 105, 122, 101, 42, 16, 10, 4, 109, 111, 100, 101, 34, 5, 99, 117, 98, 105, 99, 160, 1, 3, 18, 1, 114, 90, 31, 10, 1, 120, 18, 26, 10, 24, 8, 1, 18, 20, 10, 3, 18, 1, 98, 10, 3, 18, 1, 99, 10, 3, 18, 1, 104, 10, 3, 18, 1, 119, 90, 15, 10, 1, 115, 18, 10, 10, 8, 8, 7, 18, 4, 10, 2, 8, 4, 98, 31, 10, 1, 121, 18, 26, 10, 24, 8, 1, 18, 20, 10, 3, 18, 1, 98, 10, 3, 18, 1, 99, 10, 3, 18, 1, 104, 10, 3, 18, 1, 119, 66, 2, 16, 20], + this.session_options, + 'y', + ); + } + return this._bicubic_interpolate_4d; + } + + static get matmul() { + if (!this._matmul) { + this._matmul = wrap( + [8, 9, 18, 0, 58, 55, 10, 17, 10, 1, 97, 10, 1, 98, 18, 1, 99, 34, 6, 77, 97, 116, 77, 117, 108, 18, 1, 114, 90, 9, 10, 1, 97, 18, 4, 10, 2, 8, 1, 90, 9, 10, 1, 98, 18, 4, 10, 2, 8, 1, 98, 9, 10, 1, 99, 18, 4, 10, 2, 8, 1, 66, 2, 16, 20], + this.session_options, + 'c', + ); + } + return this._matmul; + } + + static get stft() { + if (!this._stft) { + this._stft = wrap( + [8, 7, 18, 0, 58, 148, 1, 10, 38, 10, 1, 115, 10, 1, 106, 10, 1, 119, 10, 1, 108, 18, 1, 111, 34, 4, 83, 84, 70, 84, 42, 15, 10, 8, 111, 110, 101, 115, 105, 100, 101, 100, 24, 1, 160, 1, 2, 18, 1, 115, 90, 26, 10, 1, 115, 18, 21, 10, 19, 8, 1, 18, 15, 10, 3, 18, 1, 98, 10, 3, 18, 1, 115, 10, 3, 18, 1, 99, 90, 11, 10, 1, 106, 18, 6, 10, 4, 8, 7, 18, 0, 90, 16, 10, 1, 119, 18, 11, 10, 9, 8, 1, 18, 5, 10, 3, 18, 1, 119, 90, 11, 10, 1, 108, 18, 6, 10, 4, 8, 7, 18, 0, 98, 31, 10, 1, 111, 18, 26, 10, 24, 8, 1, 18, 20, 10, 3, 18, 1, 98, 10, 3, 18, 1, 102, 10, 3, 18, 1, 100, 10, 3, 18, 1, 99, 66, 2, 16, 17], + this.session_options, + 'o', + ) + } + return this._stft; + } + + static get rfft() { + if (!this._rfft) { + this._rfft = wrap( + [8, 9, 18, 0, 58, 97, 10, 33, 10, 1, 120, 10, 0, 10, 1, 97, 18, 1, 121, 34, 3, 68, 70, 84, 42, 15, 10, 8, 111, 110, 101, 115, 105, 100, 101, 100, 24, 1, 160, 1, 2, 18, 1, 100, 90, 21, 10, 1, 120, 18, 16, 10, 14, 8, 1, 18, 10, 10, 3, 18, 1, 115, 10, 3, 18, 1, 99, 90, 11, 10, 1, 97, 18, 6, 10, 4, 8, 7, 18, 0, 98, 21, 10, 1, 121, 18, 16, 10, 14, 8, 1, 18, 10, 10, 3, 18, 1, 115, 10, 3, 18, 1, 99, 66, 2, 16, 20], + this.session_options, + 'y', + ) + } + return this._rfft; + } + + static get top_k() { + if (!this._top_k) { + this._top_k = wrap( + [8, 10, 18, 0, 58, 73, 10, 18, 10, 1, 120, 10, 1, 107, 18, 1, 118, 18, 1, 105, 34, 4, 84, 111, 112, 75, 18, 1, 116, 90, 9, 10, 1, 120, 18, 4, 10, 2, 8, 1, 90, 15, 10, 1, 107, 18, 10, 10, 8, 8, 7, 18, 4, 10, 2, 8, 1, 98, 9, 10, 1, 118, 18, 4, 10, 2, 8, 1, 98, 9, 10, 1, 105, 18, 4, 10, 2, 8, 7, 66, 2, 16, 21], + this.session_options, + [ /* Values */ 'v', /* Indices */ 'i'] + ) + } + return this._top_k; + } + + static get slice() { + if (!this._slice) { + this._slice = wrap( + [8, 7, 18, 0, 58, 96, 10, 25, 10, 1, 120, 10, 1, 115, 10, 1, 101, 10, 1, 97, 10, 1, 116, 18, 1, 121, 34, 5, 83, 108, 105, 99, 101, 18, 1, 114, 90, 9, 10, 1, 120, 18, 4, 10, 2, 8, 1, 90, 9, 10, 1, 115, 18, 4, 10, 2, 8, 7, 90, 9, 10, 1, 101, 18, 4, 10, 2, 8, 7, 90, 9, 10, 1, 97, 18, 4, 10, 2, 8, 7, 90, 9, 10, 1, 116, 18, 4, 10, 2, 8, 7, 98, 9, 10, 1, 121, 18, 4, 10, 2, 8, 1, 66, 2, 16, 13], + this.session_options, + 'y', + ) + } + return this._slice; + } +} + + +/***/ }), + +/***/ "./src/pipelines.js": +/*!**************************!*\ + !*** ./src/pipelines.js ***! + \**************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ AudioClassificationPipeline: () => (/* binding */ AudioClassificationPipeline), +/* harmony export */ AutomaticSpeechRecognitionPipeline: () => (/* binding */ AutomaticSpeechRecognitionPipeline), +/* harmony export */ BackgroundRemovalPipeline: () => (/* binding */ BackgroundRemovalPipeline), +/* harmony export */ DepthEstimationPipeline: () => (/* binding */ DepthEstimationPipeline), +/* harmony export */ DocumentQuestionAnsweringPipeline: () => (/* binding */ DocumentQuestionAnsweringPipeline), +/* harmony export */ FeatureExtractionPipeline: () => (/* binding */ FeatureExtractionPipeline), +/* harmony export */ FillMaskPipeline: () => (/* binding */ FillMaskPipeline), +/* harmony export */ ImageClassificationPipeline: () => (/* binding */ ImageClassificationPipeline), +/* harmony export */ ImageFeatureExtractionPipeline: () => (/* binding */ ImageFeatureExtractionPipeline), +/* harmony export */ ImageSegmentationPipeline: () => (/* binding */ ImageSegmentationPipeline), +/* harmony export */ ImageToImagePipeline: () => (/* binding */ ImageToImagePipeline), +/* harmony export */ ImageToTextPipeline: () => (/* binding */ ImageToTextPipeline), +/* harmony export */ ObjectDetectionPipeline: () => (/* binding */ ObjectDetectionPipeline), +/* harmony export */ Pipeline: () => (/* binding */ Pipeline), +/* harmony export */ QuestionAnsweringPipeline: () => (/* binding */ QuestionAnsweringPipeline), +/* harmony export */ SummarizationPipeline: () => (/* binding */ SummarizationPipeline), +/* harmony export */ Text2TextGenerationPipeline: () => (/* binding */ Text2TextGenerationPipeline), +/* harmony export */ TextClassificationPipeline: () => (/* binding */ TextClassificationPipeline), +/* harmony export */ TextGenerationPipeline: () => (/* binding */ TextGenerationPipeline), +/* harmony export */ TextToAudioPipeline: () => (/* binding */ TextToAudioPipeline), +/* harmony export */ TokenClassificationPipeline: () => (/* binding */ TokenClassificationPipeline), +/* harmony export */ TranslationPipeline: () => (/* binding */ TranslationPipeline), +/* harmony export */ ZeroShotAudioClassificationPipeline: () => (/* binding */ ZeroShotAudioClassificationPipeline), +/* harmony export */ ZeroShotClassificationPipeline: () => (/* binding */ ZeroShotClassificationPipeline), +/* harmony export */ ZeroShotImageClassificationPipeline: () => (/* binding */ ZeroShotImageClassificationPipeline), +/* harmony export */ ZeroShotObjectDetectionPipeline: () => (/* binding */ ZeroShotObjectDetectionPipeline), +/* harmony export */ pipeline: () => (/* binding */ pipeline) +/* harmony export */ }); +/* harmony import */ var _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./tokenizers.js */ "./src/tokenizers.js"); +/* harmony import */ var _models_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./models.js */ "./src/models.js"); +/* harmony import */ var _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./models/auto/processing_auto.js */ "./src/models/auto/processing_auto.js"); +/* harmony import */ var _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./base/processing_utils.js */ "./src/base/processing_utils.js"); +/* harmony import */ var _utils_generic_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ./utils/generic.js */ "./src/utils/generic.js"); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ./utils/core.js */ "./src/utils/core.js"); +/* harmony import */ var _utils_maths_js__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! ./utils/maths.js */ "./src/utils/maths.js"); +/* harmony import */ var _utils_audio_js__WEBPACK_IMPORTED_MODULE_7__ = __webpack_require__(/*! ./utils/audio.js */ "./src/utils/audio.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__ = __webpack_require__(/*! ./utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_image_js__WEBPACK_IMPORTED_MODULE_9__ = __webpack_require__(/*! ./utils/image.js */ "./src/utils/image.js"); +/** + * @file Pipelines provide a high-level, easy to use, API for running machine learning models. + * + * **Example:** Instantiate pipeline using the `pipeline` function. + * ```javascript + * import { pipeline } from '@huggingface/transformers'; + * + * const classifier = await pipeline('sentiment-analysis'); + * const output = await classifier('I love transformers!'); + * // [{'label': 'POSITIVE', 'score': 0.999817686}] + * ``` + * + * @module pipelines + */ + + + + + + + + + + + + + + + +/** + * @typedef {string | RawImage | URL | Blob | HTMLCanvasElement | OffscreenCanvas} ImageInput + * @typedef {ImageInput|ImageInput[]} ImagePipelineInputs + */ + +/** + * Prepare images for further tasks. + * @param {ImagePipelineInputs} images images to prepare. + * @returns {Promise} returns processed images. + * @private + */ +async function prepareImages(images) { + if (!Array.isArray(images)) { + images = [images]; + } + + // Possibly convert any non-images to images + return await Promise.all(images.map(x => _utils_image_js__WEBPACK_IMPORTED_MODULE_9__.RawImage.read(x))); +} + +/** + * @typedef {string | URL | Float32Array | Float64Array} AudioInput + * @typedef {AudioInput|AudioInput[]} AudioPipelineInputs + */ + +/** + * Prepare audios for further tasks. + * @param {AudioPipelineInputs} audios audios to prepare. + * @param {number} sampling_rate sampling rate of the audios. + * @returns {Promise} The preprocessed audio data. + * @private + */ +async function prepareAudios(audios, sampling_rate) { + if (!Array.isArray(audios)) { + audios = [audios]; + } + + return await Promise.all(audios.map(x => { + if (typeof x === 'string' || x instanceof URL) { + return (0,_utils_audio_js__WEBPACK_IMPORTED_MODULE_7__.read_audio)(x, sampling_rate); + } else if (x instanceof Float64Array) { + return new Float32Array(x); + } + return x; + })); +} + +/** + * @typedef {Object} BoundingBox + * @property {number} xmin The minimum x coordinate of the bounding box. + * @property {number} ymin The minimum y coordinate of the bounding box. + * @property {number} xmax The maximum x coordinate of the bounding box. + * @property {number} ymax The maximum y coordinate of the bounding box. + */ + +/** + * Helper function to convert list [xmin, xmax, ymin, ymax] into object { "xmin": xmin, ... } + * @param {number[]} box The bounding box as a list. + * @param {boolean} asInteger Whether to cast to integers. + * @returns {BoundingBox} The bounding box as an object. + * @private + */ +function get_bounding_box(box, asInteger) { + if (asInteger) { + box = box.map(x => x | 0); + } + const [xmin, ymin, xmax, ymax] = box; + + return { xmin, ymin, xmax, ymax }; +} + + +/** + * @callback DisposeType Disposes the item. + * @returns {Promise} A promise that resolves when the item has been disposed. + * + * @typedef {Object} Disposable + * @property {DisposeType} dispose A promise that resolves when the pipeline has been disposed. + */ + +/** + * The Pipeline class is the class from which all pipelines inherit. + * Refer to this class for methods shared across different pipelines. + */ +class Pipeline extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_4__.Callable { + /** + * Create a new Pipeline. + * @param {Object} options An object containing the following properties: + * @param {string} [options.task] The task of the pipeline. Useful for specifying subtasks. + * @param {PreTrainedModel} [options.model] The model used by the pipeline. + * @param {PreTrainedTokenizer} [options.tokenizer=null] The tokenizer used by the pipeline (if any). + * @param {Processor} [options.processor=null] The processor used by the pipeline (if any). + */ + constructor({ task, model, tokenizer = null, processor = null }) { + super(); + this.task = task; + this.model = model; + this.tokenizer = tokenizer; + this.processor = processor; + } + + /** @type {DisposeType} */ + async dispose() { + await this.model.dispose(); + } +} + +/** + * @typedef {Object} ModelTokenizerConstructorArgs + * @property {string} task The task of the pipeline. Useful for specifying subtasks. + * @property {PreTrainedModel} model The model used by the pipeline. + * @property {PreTrainedTokenizer} tokenizer The tokenizer used by the pipeline. + * + * @typedef {ModelTokenizerConstructorArgs} TextPipelineConstructorArgs An object used to instantiate a text-based pipeline. + */ + +/** + * @typedef {Object} ModelProcessorConstructorArgs + * @property {string} task The task of the pipeline. Useful for specifying subtasks. + * @property {PreTrainedModel} model The model used by the pipeline. + * @property {Processor} processor The processor used by the pipeline. + * + * @typedef {ModelProcessorConstructorArgs} AudioPipelineConstructorArgs An object used to instantiate an audio-based pipeline. + * @typedef {ModelProcessorConstructorArgs} ImagePipelineConstructorArgs An object used to instantiate an image-based pipeline. + */ + + +/** + * @typedef {Object} ModelTokenizerProcessorConstructorArgs + * @property {string} task The task of the pipeline. Useful for specifying subtasks. + * @property {PreTrainedModel} model The model used by the pipeline. + * @property {PreTrainedTokenizer} tokenizer The tokenizer used by the pipeline. + * @property {Processor} processor The processor used by the pipeline. + * + * @typedef {ModelTokenizerProcessorConstructorArgs} TextAudioPipelineConstructorArgs An object used to instantiate a text- and audio-based pipeline. + * @typedef {ModelTokenizerProcessorConstructorArgs} TextImagePipelineConstructorArgs An object used to instantiate a text- and image-based pipeline. + */ + +/** + * @typedef {Object} TextClassificationSingle + * @property {string} label The label predicted. + * @property {number} score The corresponding probability. + * @typedef {TextClassificationSingle[]} TextClassificationOutput + * + * @typedef {Object} TextClassificationPipelineOptions Parameters specific to text classification pipelines. + * @property {number} [top_k=1] The number of top predictions to be returned. + * + * @callback TextClassificationPipelineCallback Classify the text(s) given as inputs. + * @param {string|string[]} texts The input text(s) to be classified. + * @param {TextClassificationPipelineOptions} [options] The options to use for text classification. + * @returns {Promise} An array or object containing the predicted labels and scores. + * + * @typedef {TextPipelineConstructorArgs & TextClassificationPipelineCallback & Disposable} TextClassificationPipelineType + */ + +/** + * Text classification pipeline using any `ModelForSequenceClassification`. + * + * **Example:** Sentiment-analysis w/ `Xenova/distilbert-base-uncased-finetuned-sst-2-english`. + * ```javascript + * const classifier = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english'); + * const output = await classifier('I love transformers!'); + * // [{ label: 'POSITIVE', score: 0.999788761138916 }] + * ``` + * + * **Example:** Multilingual sentiment-analysis w/ `Xenova/bert-base-multilingual-uncased-sentiment` (and return top 5 classes). + * ```javascript + * const classifier = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment'); + * const output = await classifier('Le meilleur film de tous les temps.', { top_k: 5 }); + * // [ + * // { label: '5 stars', score: 0.9610759615898132 }, + * // { label: '4 stars', score: 0.03323351591825485 }, + * // { label: '3 stars', score: 0.0036155181005597115 }, + * // { label: '1 star', score: 0.0011325967498123646 }, + * // { label: '2 stars', score: 0.0009423971059732139 } + * // ] + * ``` + * + * **Example:** Toxic comment classification w/ `Xenova/toxic-bert` (and return all classes). + * ```javascript + * const classifier = await pipeline('text-classification', 'Xenova/toxic-bert'); + * const output = await classifier('I hate you!', { top_k: null }); + * // [ + * // { label: 'toxic', score: 0.9593140482902527 }, + * // { label: 'insult', score: 0.16187334060668945 }, + * // { label: 'obscene', score: 0.03452680632472038 }, + * // { label: 'identity_hate', score: 0.0223250575363636 }, + * // { label: 'threat', score: 0.019197041168808937 }, + * // { label: 'severe_toxic', score: 0.005651099607348442 } + * // ] + * ``` + */ +class TextClassificationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TextClassificationPipelineType} */ (Pipeline)) { + + /** + * Create a new TextClassificationPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {TextClassificationPipelineCallback} */ + async _call(texts, { + top_k = 1 + } = {}) { + + // Run tokenization + const model_inputs = this.tokenizer(texts, { + padding: true, + truncation: true, + }); + + // Run model + const outputs = await this.model(model_inputs) + + // TODO: Use softmax tensor function + const function_to_apply = + // @ts-expect-error TS2339 + this.model.config.problem_type === 'multi_label_classification' + ? batch => batch.sigmoid() + : batch => new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.Tensor( + 'float32', + (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(batch.data), + batch.dims, + ); // single_label_classification (default) + + // @ts-expect-error TS2339 + const id2label = this.model.config.id2label; + + const toReturn = []; + for (const batch of outputs.logits) { + const output = function_to_apply(batch); + + const scores = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.topk)(output, top_k); + + const values = scores[0].tolist(); + const indices = scores[1].tolist(); + const vals = indices.map((x, i) => ({ + label: id2label ? id2label[x] : `LABEL_${x}`, + score: values[i], + })); + if (top_k === 1) { + toReturn.push(...vals); + } else { + toReturn.push(vals); + } + } + + return Array.isArray(texts) || top_k === 1 ? /** @type {TextClassificationOutput} */ (toReturn) : /** @type {TextClassificationOutput[]} */ (toReturn)[0]; + } +} + +/** + * @typedef {Object} TokenClassificationSingle + * @property {string} word The token/word classified. This is obtained by decoding the selected tokens. + * @property {number} score The corresponding probability for `entity`. + * @property {string} entity The entity predicted for that token/word. + * @property {number} index The index of the corresponding token in the sentence. + * @property {number} [start] The index of the start of the corresponding entity in the sentence. + * @property {number} [end] The index of the end of the corresponding entity in the sentence. + * @typedef {TokenClassificationSingle[]} TokenClassificationOutput + * + * @typedef {Object} TokenClassificationPipelineOptions Parameters specific to token classification pipelines. + * @property {string[]} [ignore_labels] A list of labels to ignore. + * + * @callback TokenClassificationPipelineCallback Classify each token of the text(s) given as inputs. + * @param {string|string[]} texts One or several texts (or one list of texts) for token classification. + * @param {TokenClassificationPipelineOptions} [options] The options to use for token classification. + * @returns {Promise} The result. + * + * @typedef {TextPipelineConstructorArgs & TokenClassificationPipelineCallback & Disposable} TokenClassificationPipelineType + */ + +/** + * Named Entity Recognition pipeline using any `ModelForTokenClassification`. + * + * **Example:** Perform named entity recognition with `Xenova/bert-base-NER`. + * ```javascript + * const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER'); + * const output = await classifier('My name is Sarah and I live in London'); + * // [ + * // { entity: 'B-PER', score: 0.9980202913284302, index: 4, word: 'Sarah' }, + * // { entity: 'B-LOC', score: 0.9994474053382874, index: 9, word: 'London' } + * // ] + * ``` + * + * **Example:** Perform named entity recognition with `Xenova/bert-base-NER` (and return all labels). + * ```javascript + * const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER'); + * const output = await classifier('Sarah lives in the United States of America', { ignore_labels: [] }); + * // [ + * // { entity: 'B-PER', score: 0.9966587424278259, index: 1, word: 'Sarah' }, + * // { entity: 'O', score: 0.9987385869026184, index: 2, word: 'lives' }, + * // { entity: 'O', score: 0.9990072846412659, index: 3, word: 'in' }, + * // { entity: 'O', score: 0.9988298416137695, index: 4, word: 'the' }, + * // { entity: 'B-LOC', score: 0.9995510578155518, index: 5, word: 'United' }, + * // { entity: 'I-LOC', score: 0.9990395307540894, index: 6, word: 'States' }, + * // { entity: 'I-LOC', score: 0.9986724853515625, index: 7, word: 'of' }, + * // { entity: 'I-LOC', score: 0.9975294470787048, index: 8, word: 'America' } + * // ] + * ``` + */ +class TokenClassificationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TokenClassificationPipelineType} */ (Pipeline)) { + + /** + * Create a new TokenClassificationPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {TokenClassificationPipelineCallback} */ + async _call(texts, { + ignore_labels = ['O'], + } = {}) { + + const isBatched = Array.isArray(texts); + + // Run tokenization + const model_inputs = this.tokenizer(isBatched ? texts : [texts], { + padding: true, + truncation: true, + }); + + // Run model + const outputs = await this.model(model_inputs) + + const logits = outputs.logits; + // @ts-expect-error TS2339 + const id2label = this.model.config.id2label; + + const toReturn = []; + for (let i = 0; i < logits.dims[0]; ++i) { + const ids = model_inputs.input_ids[i]; + const batch = logits[i]; + + // List of tokens that aren't ignored + const tokens = []; + for (let j = 0; j < batch.dims[0]; ++j) { + const tokenData = batch[j]; + const topScoreIndex = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.max)(tokenData.data)[1]; + + const entity = id2label ? id2label[topScoreIndex] : `LABEL_${topScoreIndex}`; + if (ignore_labels.includes(entity)) { + // We predicted a token that should be ignored. So, we skip it. + continue; + } + + // TODO add option to keep special tokens? + const word = this.tokenizer.decode([ids[j].item()], { skip_special_tokens: true }); + if (word === '') { + // Was a special token. So, we skip it. + continue; + } + + const scores = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(tokenData.data); + + tokens.push({ + entity: entity, + score: scores[topScoreIndex], + index: j, + word: word, + + // TODO: Add support for start and end + // start: null, + // end: null, + }); + } + toReturn.push(tokens); + } + return isBatched ? toReturn : toReturn[0]; + } +} + +/** + * @typedef {Object} QuestionAnsweringOutput + * @property {number} score The probability associated to the answer. + * @property {number} [start] The character start index of the answer (in the tokenized version of the input). + * @property {number} [end] The character end index of the answer (in the tokenized version of the input). + * @property {string} answer The answer to the question. + * + * @typedef {Object} QuestionAnsweringPipelineOptions Parameters specific to question answering pipelines. + * @property {number} [top_k=1] The number of top answer predictions to be returned. + * + * @callback QuestionAnsweringPipelineCallback Answer the question(s) given as inputs by using the context(s). + * @param {string|string[]} question One or several question(s) (must be used in conjunction with the `context` argument). + * @param {string|string[]} context One or several context(s) associated with the question(s) (must be used in conjunction with the `question` argument). + * @param {QuestionAnsweringPipelineOptions} [options] The options to use for question answering. + * @returns {Promise} An array or object containing the predicted answers and scores. + * + * @typedef {TextPipelineConstructorArgs & QuestionAnsweringPipelineCallback & Disposable} QuestionAnsweringPipelineType + */ + +/** + * Question Answering pipeline using any `ModelForQuestionAnswering`. + * + * **Example:** Run question answering with `Xenova/distilbert-base-uncased-distilled-squad`. + * ```javascript + * const answerer = await pipeline('question-answering', 'Xenova/distilbert-base-uncased-distilled-squad'); + * const question = 'Who was Jim Henson?'; + * const context = 'Jim Henson was a nice puppet.'; + * const output = await answerer(question, context); + * // { + * // answer: "a nice puppet", + * // score: 0.5768911502526741 + * // } + * ``` + */ +class QuestionAnsweringPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => QuestionAnsweringPipelineType} */ (Pipeline)) { + + /** + * Create a new QuestionAnsweringPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {QuestionAnsweringPipelineCallback} */ + async _call(question, context, { + top_k = 1 + } = {}) { + + // Run tokenization + const inputs = this.tokenizer(question, { + text_pair: context, + padding: true, + truncation: true, + }); + + const { start_logits, end_logits } = await this.model(inputs); + const input_ids = inputs.input_ids.tolist(); + const attention_mask = inputs.attention_mask.tolist(); + + // TODO: add support for `return_special_tokens_mask` + const special_tokens = this.tokenizer.all_special_ids; + + /** @type {QuestionAnsweringOutput[]} */ + const toReturn = []; + for (let j = 0; j < start_logits.dims[0]; ++j) { + const ids = input_ids[j]; + const sepIndex = ids.findIndex(x => + // We use == to match bigint with number + // @ts-ignore + x == this.tokenizer.sep_token_id + ); + + + const valid_mask = attention_mask[j].map((y, ix) => ( + y == 1 + && ( + ix === 0 // is cls_token + || ( + ix > sepIndex + && special_tokens.findIndex(x => x == ids[ix]) === -1 // token is not a special token (special_tokens_mask == 0) + ) + ) + )); + + const start = start_logits[j].tolist(); + const end = end_logits[j].tolist(); + + // Now, we mask out values that can't be in the answer + // NOTE: We keep the cls_token unmasked (some models use it to indicate unanswerable questions) + for (let i = 1; i < start.length; ++i) { + if ( + attention_mask[j] == 0 // is part of padding + || i <= sepIndex // is before the sep_token + || special_tokens.findIndex(x => x == ids[i]) !== -1 // Is a special token + ) { + // Make sure non-context indexes in the tensor cannot contribute to the softmax + start[i] = -Infinity; + end[i] = -Infinity; + } + } + + // Normalize logits and spans to retrieve the answer + const start_scores = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(start).map((x, i) => [x, i]); + const end_scores = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(end).map((x, i) => [x, i]); + + // Mask CLS + start_scores[0][0] = 0; + end_scores[0][0] = 0; + + // Generate all valid spans and select best ones + const options = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_5__.product)(start_scores, end_scores) + .filter(x => x[0][1] <= x[1][1]) + .map(x => [x[0][1], x[1][1], x[0][0] * x[1][0]]) + .sort((a, b) => b[2] - a[2]); + + for (let k = 0; k < Math.min(options.length, top_k); ++k) { + const [start, end, score] = options[k]; + + const answer_tokens = ids.slice(start, end + 1) + + const answer = this.tokenizer.decode(answer_tokens, { + skip_special_tokens: true, + }); + + // TODO add start and end? + // NOTE: HF returns character index + toReturn.push({ + answer, score + }); + } + } + + // Mimic HF's return type based on top_k + return (top_k === 1) ? toReturn[0] : toReturn; + } +} + + +/** + * @typedef {Object} FillMaskSingle + * @property {string} sequence The corresponding input with the mask token prediction. + * @property {number} score The corresponding probability. + * @property {number} token The predicted token id (to replace the masked one). + * @property {string} token_str The predicted token (to replace the masked one). + * @typedef {FillMaskSingle[]} FillMaskOutput + * + * @typedef {Object} FillMaskPipelineOptions Parameters specific to fill mask pipelines. + * @property {number} [top_k=5] When passed, overrides the number of predictions to return. + * + * @callback FillMaskPipelineCallback Fill the masked token in the text(s) given as inputs. + * @param {string|string[]} texts One or several texts (or one list of prompts) with masked tokens. + * @param {FillMaskPipelineOptions} [options] The options to use for masked language modelling. + * @returns {Promise} An array of objects containing the score, predicted token, predicted token string, + * and the sequence with the predicted token filled in, or an array of such arrays (one for each input text). + * If only one input text is given, the output will be an array of objects. + * @throws {Error} When the mask token is not found in the input text. + * + * @typedef {TextPipelineConstructorArgs & FillMaskPipelineCallback & Disposable} FillMaskPipelineType + */ + +/** + * Masked language modeling prediction pipeline using any `ModelWithLMHead`. + * + * **Example:** Perform masked language modelling (a.k.a. "fill-mask") with `Xenova/bert-base-uncased`. + * ```javascript + * const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased'); + * const output = await unmasker('The goal of life is [MASK].'); + * // [ + * // { token_str: 'survival', score: 0.06137419492006302, token: 8115, sequence: 'The goal of life is survival.' }, + * // { token_str: 'love', score: 0.03902450203895569, token: 1567, sequence: 'The goal of life is love.' }, + * // { token_str: 'happiness', score: 0.03253183513879776, token: 9266, sequence: 'The goal of life is happiness.' }, + * // { token_str: 'freedom', score: 0.018736306577920914, token: 4438, sequence: 'The goal of life is freedom.' }, + * // { token_str: 'life', score: 0.01859794743359089, token: 1297, sequence: 'The goal of life is life.' } + * // ] + * ``` + * + * **Example:** Perform masked language modelling (a.k.a. "fill-mask") with `Xenova/bert-base-cased` (and return top result). + * ```javascript + * const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased'); + * const output = await unmasker('The Milky Way is a [MASK] galaxy.', { top_k: 1 }); + * // [{ token_str: 'spiral', score: 0.6299987435340881, token: 14061, sequence: 'The Milky Way is a spiral galaxy.' }] + * ``` + */ +class FillMaskPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => FillMaskPipelineType} */ (Pipeline)) { + + /** + * Create a new FillMaskPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {FillMaskPipelineCallback} */ + async _call(texts, { + top_k = 5 + } = {}) { + + // Run tokenization + const model_inputs = this.tokenizer(texts, { + padding: true, + truncation: true, + }); + + // Run model + const { logits } = await this.model(model_inputs) + + const toReturn = []; + + /** @type {bigint[][]} */ + const input_ids = model_inputs.input_ids.tolist(); + for (let i = 0; i < input_ids.length; ++i) { + const ids = input_ids[i]; + const mask_token_index = ids.findIndex(x => + // We use == to match bigint with number + // @ts-ignore + x == this.tokenizer.mask_token_id + ); + if (mask_token_index === -1) { + throw Error(`Mask token (${this.tokenizer.mask_token}) not found in text.`) + } + const itemLogits = logits[i][mask_token_index]; + + const scores = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.topk)(new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.Tensor( + 'float32', + (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(itemLogits.data), + itemLogits.dims, + ), top_k); + const values = scores[0].tolist(); + const indices = scores[1].tolist(); + + toReturn.push(indices.map((x, i) => { + const sequence = ids.slice(); + sequence[mask_token_index] = x; + + return { + score: values[i], + token: Number(x), + token_str: this.tokenizer.decode([x]), + sequence: this.tokenizer.decode(sequence, { skip_special_tokens: true }), + } + })); + } + return Array.isArray(texts) ? toReturn : toReturn[0]; + } +} + + +/** + * @typedef {Object} Text2TextGenerationSingle + * @property {string} generated_text The generated text. + * @typedef {Text2TextGenerationSingle[]} Text2TextGenerationOutput + * + * @callback Text2TextGenerationPipelineCallback Generate the output text(s) using text(s) given as inputs. + * @param {string|string[]} texts Input text for the encoder. + * @param {Partial} [options] Additional keyword arguments to pass along to the generate method of the model. + * @returns {Promise} + * + * @typedef {TextPipelineConstructorArgs & Text2TextGenerationPipelineCallback & Disposable} Text2TextGenerationPipelineType + */ + +/** + * Text2TextGenerationPipeline class for generating text using a model that performs text-to-text generation tasks. + * + * **Example:** Text-to-text generation w/ `Xenova/LaMini-Flan-T5-783M`. + * ```javascript + * const generator = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M'); + * const output = await generator('how can I become more healthy?', { + * max_new_tokens: 100, + * }); + * // [{ generated_text: "To become more healthy, you can: 1. Eat a balanced diet with plenty of fruits, vegetables, whole grains, lean proteins, and healthy fats. 2. Stay hydrated by drinking plenty of water. 3. Get enough sleep and manage stress levels. 4. Avoid smoking and excessive alcohol consumption. 5. Regularly exercise and maintain a healthy weight. 6. Practice good hygiene and sanitation. 7. Seek medical attention if you experience any health issues." }] + * ``` + */ +class Text2TextGenerationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => Text2TextGenerationPipelineType} */ (Pipeline)) { + /** @type {'generated_text'} */ + _key = 'generated_text'; + + /** + * Create a new Text2TextGenerationPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {Text2TextGenerationPipelineCallback} */ + async _call(texts, generate_kwargs = {}) { + if (!Array.isArray(texts)) { + texts = [texts]; + } + + + // Add global prefix, if present + // @ts-expect-error TS2339 + if (this.model.config.prefix) { + // @ts-expect-error TS2339 + texts = texts.map(x => this.model.config.prefix + x) + } + + // Handle task specific params: + // @ts-expect-error TS2339 + const task_specific_params = this.model.config.task_specific_params + if (task_specific_params && task_specific_params[this.task]) { + // Add prefixes, if present + if (task_specific_params[this.task].prefix) { + texts = texts.map(x => task_specific_params[this.task].prefix + x) + } + + // TODO update generation config + } + + const tokenizer = this.tokenizer; + const tokenizer_options = { + padding: true, + truncation: true, + } + let inputs; + if (this instanceof TranslationPipeline && '_build_translation_inputs' in tokenizer) { + // TODO: move to Translation pipeline? + // Currently put here to avoid code duplication + // @ts-ignore + inputs = tokenizer._build_translation_inputs(texts, tokenizer_options, generate_kwargs); + + } else { + inputs = tokenizer(texts, tokenizer_options); + } + + const outputTokenIds = await this.model.generate({ ...inputs, ...generate_kwargs }); + return tokenizer.batch_decode(/** @type {Tensor} */(outputTokenIds), { + skip_special_tokens: true, + }).map(text => ({ [this._key]: text })); + } +} + + +/** + * @typedef {Object} SummarizationSingle + * @property {string} summary_text The summary text. + * @typedef {SummarizationSingle[]} SummarizationOutput + * + * @callback SummarizationPipelineCallback Summarize the text(s) given as inputs. + * @param {string|string[]} texts One or several articles (or one list of articles) to summarize. + * @param {import('./generation/configuration_utils.js').GenerationConfig} [options] Additional keyword arguments to pass along to the generate method of the model. + * @returns {Promise} + * + * @typedef {TextPipelineConstructorArgs & SummarizationPipelineCallback & Disposable} SummarizationPipelineType + */ + +/** + * A pipeline for summarization tasks, inheriting from Text2TextGenerationPipeline. + * + * **Example:** Summarization w/ `Xenova/distilbart-cnn-6-6`. + * ```javascript + * const generator = await pipeline('summarization', 'Xenova/distilbart-cnn-6-6'); + * const text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, ' + + * 'and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. ' + + * 'During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest ' + + * 'man-made structure in the world, a title it held for 41 years until the Chrysler Building in New ' + + * 'York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to ' + + * 'the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the ' + + * 'Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second ' + + * 'tallest free-standing structure in France after the Millau Viaduct.'; + * const output = await generator(text, { + * max_new_tokens: 100, + * }); + * // [{ summary_text: ' The Eiffel Tower is about the same height as an 81-storey building and the tallest structure in Paris. It is the second tallest free-standing structure in France after the Millau Viaduct.' }] + * ``` + */ +class SummarizationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => SummarizationPipelineType} */ (/** @type {any} */ (Text2TextGenerationPipeline))) { + /** @type {'summary_text'} */ + _key = 'summary_text'; + + /** + * Create a new SummarizationPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } +} + + +/** + * @typedef {Object} TranslationSingle + * @property {string} translation_text The translated text. + * @typedef {TranslationSingle[]} TranslationOutput + * + * @callback TranslationPipelineCallback Translate the text(s) given as inputs. + * @param {string|string[]} texts Texts to be translated. + * @param {import('./generation/configuration_utils.js').GenerationConfig} [options] Additional keyword arguments to pass along to the generate method of the model. + * @returns {Promise} + * + * @typedef {TextPipelineConstructorArgs & TranslationPipelineCallback & Disposable} TranslationPipelineType + */ + +/** + * Translates text from one language to another. + * + * **Example:** Multilingual translation w/ `Xenova/nllb-200-distilled-600M`. + * + * See [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200) + * for the full list of languages and their corresponding codes. + * + * ```javascript + * const translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M'); + * const output = await translator('जीवन एक चॉकलेट बॉक्स की तरह है।', { + * src_lang: 'hin_Deva', // Hindi + * tgt_lang: 'fra_Latn', // French + * }); + * // [{ translation_text: 'La vie est comme une boîte à chocolat.' }] + * ``` + * + * **Example:** Multilingual translation w/ `Xenova/m2m100_418M`. + * + * See [here](https://huggingface.co/facebook/m2m100_418M#languages-covered) + * for the full list of languages and their corresponding codes. + * + * ```javascript + * const translator = await pipeline('translation', 'Xenova/m2m100_418M'); + * const output = await translator('生活就像一盒巧克力。', { + * src_lang: 'zh', // Chinese + * tgt_lang: 'en', // English + * }); + * // [{ translation_text: 'Life is like a box of chocolate.' }] + * ``` + * + * **Example:** Multilingual translation w/ `Xenova/mbart-large-50-many-to-many-mmt`. + * + * See [here](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt#languages-covered) + * for the full list of languages and their corresponding codes. + * + * ```javascript + * const translator = await pipeline('translation', 'Xenova/mbart-large-50-many-to-many-mmt'); + * const output = await translator('संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है', { + * src_lang: 'hi_IN', // Hindi + * tgt_lang: 'fr_XX', // French + * }); + * // [{ translation_text: 'Le chef des Nations affirme qu 'il n 'y a military solution in Syria.' }] + * ``` + */ +class TranslationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TranslationPipelineType} */ (/** @type {any} */ (Text2TextGenerationPipeline))) { + /** @type {'translation_text'} */ + _key = 'translation_text'; + + /** + * Create a new TranslationPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } +} + +function isChat(x) { + return Array.isArray(x) && x.every(x => 'role' in x && 'content' in x); +} + +/** + * @typedef {import('./tokenizers.js').Message[]} Chat + * + * @typedef {Object} TextGenerationSingle + * @property {string|Chat} generated_text The generated text. + * @typedef {TextGenerationSingle[]} TextGenerationOutput + * + * @typedef {Object} TextGenerationSpecificParams Parameters specific to text-generation pipelines. + * @property {boolean} [add_special_tokens] Whether or not to add special tokens when tokenizing the sequences. + * @property {boolean} [return_full_text=true] If set to `false` only added text is returned, otherwise the full text is returned. + * @typedef {import('./generation/configuration_utils.js').GenerationConfig & TextGenerationSpecificParams} TextGenerationConfig + * + * @callback TextGenerationPipelineCallback Complete the prompt(s) given as inputs. + * @param {string|string[]|Chat|Chat[]} texts One or several prompts (or one list of prompts) to complete. + * @param {Partial} [options] Additional keyword arguments to pass along to the generate method of the model. + * @returns {Promise} An array or object containing the generated texts. + * + * @typedef {TextPipelineConstructorArgs & TextGenerationPipelineCallback & Disposable} TextGenerationPipelineType + */ + +/** + * Language generation pipeline using any `ModelWithLMHead` or `ModelForCausalLM`. + * This pipeline predicts the words that will follow a specified text prompt. + * NOTE: For the full list of generation parameters, see [`GenerationConfig`](./utils/generation#module_utils/generation.GenerationConfig). + * + * **Example:** Text generation with `Xenova/distilgpt2` (default settings). + * ```javascript + * const generator = await pipeline('text-generation', 'Xenova/distilgpt2'); + * const text = 'I enjoy walking with my cute dog,'; + * const output = await generator(text); + * // [{ generated_text: "I enjoy walking with my cute dog, and I love to play with the other dogs." }] + * ``` + * + * **Example:** Text generation with `Xenova/distilgpt2` (custom settings). + * ```javascript + * const generator = await pipeline('text-generation', 'Xenova/distilgpt2'); + * const text = 'Once upon a time, there was'; + * const output = await generator(text, { + * temperature: 2, + * max_new_tokens: 10, + * repetition_penalty: 1.5, + * no_repeat_ngram_size: 2, + * num_beams: 2, + * num_return_sequences: 2, + * }); + * // [{ + * // "generated_text": "Once upon a time, there was an abundance of information about the history and activities that" + * // }, { + * // "generated_text": "Once upon a time, there was an abundance of information about the most important and influential" + * // }] + * ``` + * + * **Example:** Run code generation with `Xenova/codegen-350M-mono`. + * ```javascript + * const generator = await pipeline('text-generation', 'Xenova/codegen-350M-mono'); + * const text = 'def fib(n):'; + * const output = await generator(text, { + * max_new_tokens: 44, + * }); + * // [{ + * // generated_text: 'def fib(n):\n' + + * // ' if n == 0:\n' + + * // ' return 0\n' + + * // ' elif n == 1:\n' + + * // ' return 1\n' + + * // ' else:\n' + + * // ' return fib(n-1) + fib(n-2)\n' + * // }] + * ``` + */ +class TextGenerationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => TextGenerationPipelineType} */ (Pipeline)) { + + /** + * Create a new TextGenerationPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {TextGenerationPipelineCallback} */ + async _call(texts, generate_kwargs = {}) { + let isBatched = false; + let isChatInput = false; + + // By default, do not add special tokens, unless the tokenizer specifies otherwise + let add_special_tokens = generate_kwargs.add_special_tokens + ?? (this.tokenizer.add_bos_token || this.tokenizer.add_eos_token) + ?? false; + + // Normalize inputs + /** @type {string[]} */ + let inputs; + if (typeof texts === 'string') { + inputs = texts = [texts]; + } else if (Array.isArray(texts) && texts.every(x => typeof x === 'string')) { + isBatched = true; + inputs = /** @type {string[]} */(texts); + } else { + if (isChat(texts)) { + texts = [/** @type {Chat} */(texts)]; + } else if (Array.isArray(texts) && texts.every(isChat)) { + isBatched = true; + } else { + throw new Error('Input must be a string, an array of strings, a Chat, or an array of Chats'); + } + isChatInput = true; + + // If the input is a chat, we need to apply the chat template + inputs = /** @type {string[]} */(/** @type {Chat[]} */ (texts).map( + x => this.tokenizer.apply_chat_template(x, { + tokenize: false, + add_generation_prompt: true, + }) + )); + add_special_tokens = false; // Chat template handles this already + } + + // By default, return full text + const return_full_text = isChatInput + ? false + : generate_kwargs.return_full_text ?? true; + + this.tokenizer.padding_side = 'left'; + const text_inputs = this.tokenizer(inputs, { + add_special_tokens, + padding: true, + truncation: true, + }); + + const outputTokenIds = /** @type {Tensor} */(await this.model.generate({ + ...text_inputs, + ...generate_kwargs + })); + + const decoded = this.tokenizer.batch_decode(outputTokenIds, { + skip_special_tokens: true, + }); + + let promptLengths; + if (!return_full_text && text_inputs.input_ids.dims.at(-1) > 0) { + promptLengths = this.tokenizer.batch_decode(text_inputs.input_ids, { + skip_special_tokens: true, + }).map(x => x.length); + } + + /** @type {TextGenerationOutput[]} */ + const toReturn = Array.from({ length: texts.length }, _ => []); + for (let i = 0; i < decoded.length; ++i) { + const textIndex = Math.floor(i / outputTokenIds.dims[0] * texts.length); + + if (promptLengths) { + // Trim the decoded text to only include the generated part + decoded[i] = decoded[i].slice(promptLengths[textIndex]); + } + toReturn[textIndex].push({ + generated_text: isChatInput + ? [ + ...((/** @type {Chat[]} */(texts)[textIndex])), + { role: 'assistant', content: decoded[i] }, + ] + : decoded[i] + }); + } + return (!isBatched && toReturn.length === 1) ? toReturn[0] : toReturn; + } +} + +/** + * @typedef {Object} ZeroShotClassificationOutput + * @property {string} sequence The sequence for which this is the output. + * @property {string[]} labels The labels sorted by order of likelihood. + * @property {number[]} scores The probabilities for each of the labels. + * + * @typedef {Object} ZeroShotClassificationPipelineOptions Parameters specific to zero-shot classification pipelines. + * @property {string} [hypothesis_template="This example is {}."] The template used to turn each + * candidate label into an NLI-style hypothesis. The candidate label will replace the {} placeholder. + * @property {boolean} [multi_label=false] Whether or not multiple candidate labels can be true. + * If `false`, the scores are normalized such that the sum of the label likelihoods for each sequence + * is 1. If `true`, the labels are considered independent and probabilities are normalized for each + * candidate by doing a softmax of the entailment score vs. the contradiction score. + * + * @callback ZeroShotClassificationPipelineCallback Classify the sequence(s) given as inputs. + * @param {string|string[]} texts The sequence(s) to classify, will be truncated if the model input is too large. + * @param {string|string[]} candidate_labels The set of possible class labels to classify each sequence into. + * Can be a single label, a string of comma-separated labels, or a list of labels. + * @param {ZeroShotClassificationPipelineOptions} [options] The options to use for zero-shot classification. + * @returns {Promise} An array or object containing the predicted labels and scores. + * + * @typedef {TextPipelineConstructorArgs & ZeroShotClassificationPipelineCallback & Disposable} ZeroShotClassificationPipelineType + */ + +/** + * NLI-based zero-shot classification pipeline using a `ModelForSequenceClassification` + * trained on NLI (natural language inference) tasks. Equivalent of `text-classification` + * pipelines, but these models don't require a hardcoded number of potential classes, they + * can be chosen at runtime. It usually means it's slower but it is **much** more flexible. + * + * **Example:** Zero shot classification with `Xenova/mobilebert-uncased-mnli`. + * ```javascript + * const classifier = await pipeline('zero-shot-classification', 'Xenova/mobilebert-uncased-mnli'); + * const text = 'Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.'; + * const labels = [ 'mobile', 'billing', 'website', 'account access' ]; + * const output = await classifier(text, labels); + * // { + * // sequence: 'Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.', + * // labels: [ 'mobile', 'website', 'billing', 'account access' ], + * // scores: [ 0.5562091040482018, 0.1843621307860853, 0.13942646639336376, 0.12000229877234923 ] + * // } + * ``` + * + * **Example:** Zero shot classification with `Xenova/nli-deberta-v3-xsmall` (multi-label). + * ```javascript + * const classifier = await pipeline('zero-shot-classification', 'Xenova/nli-deberta-v3-xsmall'); + * const text = 'I have a problem with my iphone that needs to be resolved asap!'; + * const labels = [ 'urgent', 'not urgent', 'phone', 'tablet', 'computer' ]; + * const output = await classifier(text, labels, { multi_label: true }); + * // { + * // sequence: 'I have a problem with my iphone that needs to be resolved asap!', + * // labels: [ 'urgent', 'phone', 'computer', 'tablet', 'not urgent' ], + * // scores: [ 0.9958870956360275, 0.9923963400697035, 0.002333537946160235, 0.0015134138567598765, 0.0010699384208377163 ] + * // } + * ``` + */ +class ZeroShotClassificationPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => ZeroShotClassificationPipelineType} */ (Pipeline)) { + /** + * Create a new ZeroShotClassificationPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + + // Use model config to get label2id mapping + this.label2id = Object.fromEntries( + Object.entries((/** @type {any} */(this).model).config.label2id).map( + ([k, v]) => [k.toLowerCase(), v] + ) + ); + + this.entailment_id = this.label2id['entailment']; + if (this.entailment_id === undefined) { + console.warn("Could not find 'entailment' in label2id mapping. Using 2 as entailment_id."); + this.entailment_id = 2; + } + + this.contradiction_id = this.label2id['contradiction'] ?? this.label2id['not_entailment']; + if (this.contradiction_id === undefined) { + console.warn("Could not find 'contradiction' in label2id mapping. Using 0 as contradiction_id."); + this.contradiction_id = 0; + } + } + + /** @type {ZeroShotClassificationPipelineCallback} */ + async _call(texts, candidate_labels, { + hypothesis_template = "This example is {}.", + multi_label = false, + } = {}) { + + const isBatched = Array.isArray(texts); + if (!isBatched) { + texts = [/** @type {string} */ (texts)]; + } + if (!Array.isArray(candidate_labels)) { + candidate_labels = [candidate_labels]; + } + + // Insert labels into hypothesis template + const hypotheses = candidate_labels.map( + x => hypothesis_template.replace('{}', x) + ); + + // How to perform the softmax over the logits: + // - true: softmax over the entailment vs. contradiction dim for each label independently + // - false: softmax the "entailment" logits over all candidate labels + const softmaxEach = multi_label || candidate_labels.length === 1; + + /** @type {ZeroShotClassificationOutput[]} */ + const toReturn = []; + for (const premise of texts) { + const entails_logits = []; + + for (const hypothesis of hypotheses) { + const inputs = this.tokenizer(premise, { + text_pair: hypothesis, + padding: true, + truncation: true, + }) + const outputs = await this.model(inputs) + + if (softmaxEach) { + entails_logits.push([ + outputs.logits.data[this.contradiction_id], + outputs.logits.data[this.entailment_id] + ]) + } else { + entails_logits.push(outputs.logits.data[this.entailment_id]) + } + } + + /** @type {number[]} */ + const scores = softmaxEach + ? entails_logits.map(x => (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(x)[1]) + : (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(entails_logits); + + // Sort by scores (desc) and return scores with indices + const scores_sorted = scores + .map((x, i) => [x, i]) + .sort((a, b) => (b[0] - a[0])); + + toReturn.push({ + sequence: premise, + labels: scores_sorted.map(x => candidate_labels[x[1]]), + scores: scores_sorted.map(x => x[0]), + }); + } + return isBatched ? toReturn : toReturn[0]; + } +} + +/** + * @typedef {Object} FeatureExtractionPipelineOptions Parameters specific to feature extraction pipelines. + * @property {'none'|'mean'|'cls'|'first_token'|'eos'|'last_token'} [pooling="none"] The pooling method to use. + * @property {boolean} [normalize=false] Whether or not to normalize the embeddings in the last dimension. + * @property {boolean} [quantize=false] Whether or not to quantize the embeddings. + * @property {'binary'|'ubinary'} [precision='binary'] The precision to use for quantization. + * + * @callback FeatureExtractionPipelineCallback Extract the features of the input(s). + * @param {string|string[]} texts One or several texts (or one list of texts) to get the features of. + * @param {FeatureExtractionPipelineOptions} [options] The options to use for feature extraction. + * @returns {Promise} The features computed by the model. + * + * @typedef {TextPipelineConstructorArgs & FeatureExtractionPipelineCallback & Disposable} FeatureExtractionPipelineType + */ + +/** + * Feature extraction pipeline using no model head. This pipeline extracts the hidden + * states from the base transformer, which can be used as features in downstream tasks. + * + * **Example:** Run feature extraction with `bert-base-uncased` (without pooling/normalization). + * ```javascript + * const extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' }); + * const output = await extractor('This is a simple test.'); + * // Tensor { + * // type: 'float32', + * // data: Float32Array [0.05939924716949463, 0.021655935794115067, ...], + * // dims: [1, 8, 768] + * // } + * ``` + * + * **Example:** Run feature extraction with `bert-base-uncased` (with pooling/normalization). + * ```javascript + * const extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' }); + * const output = await extractor('This is a simple test.', { pooling: 'mean', normalize: true }); + * // Tensor { + * // type: 'float32', + * // data: Float32Array [0.03373778983950615, -0.010106077417731285, ...], + * // dims: [1, 768] + * // } + * ``` + * + * **Example:** Calculating embeddings with `sentence-transformers` models. + * ```javascript + * const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2'); + * const output = await extractor('This is a simple test.', { pooling: 'mean', normalize: true }); + * // Tensor { + * // type: 'float32', + * // data: Float32Array [0.09094982594251633, -0.014774246141314507, ...], + * // dims: [1, 384] + * // } + * ``` + * **Example:** Calculating binary embeddings with `sentence-transformers` models. + * ```javascript + * const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2'); + * const output = await extractor('This is a simple test.', { pooling: 'mean', quantize: true, precision: 'binary' }); + * // Tensor { + * // type: 'int8', + * // data: Int8Array [49, 108, 24, ...], + * // dims: [1, 48] + * // } + * ``` + */ +class FeatureExtractionPipeline extends (/** @type {new (options: TextPipelineConstructorArgs) => FeatureExtractionPipelineType} */ (Pipeline)) { + /** + * Create a new FeatureExtractionPipeline. + * @param {TextPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {FeatureExtractionPipelineCallback} */ + async _call(texts, { + pooling = /** @type {'none'} */('none'), + normalize = false, + quantize = false, + precision = /** @type {'binary'} */('binary'), + } = {}) { + + // Run tokenization + const model_inputs = this.tokenizer(texts, { + padding: true, + truncation: true, + }); + + // Run model + const outputs = await this.model(model_inputs) + + // TODO: Provide warning to the user that they might be using model which was not exported + // specifically for feature extraction + // console.log(this.model.config) + // console.log(outputs) + + /** @type {Tensor} */ + let result = outputs.last_hidden_state ?? outputs.logits ?? outputs.token_embeddings; + + switch (pooling) { + case 'none': + // Skip pooling + break; + case 'mean': + result = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.mean_pooling)(result, model_inputs.attention_mask); + break; + case 'first_token': + case 'cls': + result = result.slice(null, 0); + break; + case 'last_token': + case 'eos': + result = result.slice(null, -1); + break; + default: + throw Error(`Pooling method '${pooling}' not supported.`); + } + + if (normalize) { + result = result.normalize(2, -1); + } + + if (quantize) { + result = (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.quantize_embeddings)(result, precision); + } + + return result; + } +} + + +/** + * @typedef {Object} ImageFeatureExtractionPipelineOptions Parameters specific to image feature extraction pipelines. + * @property {boolean} [pool=null] Whether or not to return the pooled output. If set to `false`, the model will return the raw hidden states. + * + * @callback ImageFeatureExtractionPipelineCallback Extract the features of the input(s). + * @param {ImagePipelineInputs} images One or several images (or one list of images) to get the features of. + * @param {ImageFeatureExtractionPipelineOptions} [options] The options to use for image feature extraction. + * @returns {Promise} The image features computed by the model. + * + * @typedef {ImagePipelineConstructorArgs & ImageFeatureExtractionPipelineCallback & Disposable} ImageFeatureExtractionPipelineType + */ + +/** + * Image feature extraction pipeline using no model head. This pipeline extracts the hidden + * states from the base transformer, which can be used as features in downstream tasks. + * + * **Example:** Perform image feature extraction with `Xenova/vit-base-patch16-224-in21k`. + * ```javascript + * const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/vit-base-patch16-224-in21k'); + * const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png'; + * const features = await image_feature_extractor(url); + * // Tensor { + * // dims: [ 1, 197, 768 ], + * // type: 'float32', + * // data: Float32Array(151296) [ ... ], + * // size: 151296 + * // } + * ``` + * + * **Example:** Compute image embeddings with `Xenova/clip-vit-base-patch32`. + * ```javascript + * const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/clip-vit-base-patch32'); + * const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png'; + * const features = await image_feature_extractor(url); + * // Tensor { + * // dims: [ 1, 512 ], + * // type: 'float32', + * // data: Float32Array(512) [ ... ], + * // size: 512 + * // } + * ``` + */ +class ImageFeatureExtractionPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageFeatureExtractionPipelineType} */ (Pipeline)) { + /** + * Create a new ImageFeatureExtractionPipeline. + * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {ImageFeatureExtractionPipelineCallback} */ + async _call(images, { + pool = null, + } = {}) { + + const preparedImages = await prepareImages(images); + const { pixel_values } = await this.processor(preparedImages); + const outputs = await this.model({ pixel_values }); + + /** @type {Tensor} */ + let result; + if (pool) { + if (!('pooler_output' in outputs)) { + throw Error(`No pooled output was returned. Make sure the model has a 'pooler' layer when using the 'pool' option.`); + } + result = outputs.pooler_output; + + } else { + result = outputs.last_hidden_state ?? outputs.logits ?? outputs.image_embeds; + } + return result; + } +} + +// TODO +// export class SentenceSimilarityPipeline extends Pipeline { +// } + +/** + * @typedef {Object} AudioClassificationSingle + * @property {string} label The label predicted. + * @property {number} score The corresponding probability. + * @typedef {AudioClassificationSingle[]} AudioClassificationOutput + * + * @typedef {Object} AudioClassificationPipelineOptions Parameters specific to audio classification pipelines. + * @property {number} [top_k=5] The number of top labels that will be returned by the pipeline. + * If the provided number is `null` or higher than the number of labels available in the model configuration, + * it will default to the number of labels. + * + * @callback AudioClassificationPipelineCallback Classify the sequence(s) given as inputs. + * @param {AudioPipelineInputs} audio The input audio file(s) to be classified. The input is either: + * - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate + * to get the waveform using the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/AudioContext) API. + * If `AudioContext` is not available, you should pass the raw waveform in as a Float32Array of shape `(n, )`. + * - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done). + * @param {AudioClassificationPipelineOptions} [options] The options to use for audio classification. + * @returns {Promise} An array or object containing the predicted labels and scores. + * + * @typedef {AudioPipelineConstructorArgs & AudioClassificationPipelineCallback & Disposable} AudioClassificationPipelineType + */ + +/** + * Audio classification pipeline using any `AutoModelForAudioClassification`. + * This pipeline predicts the class of a raw waveform or an audio file. + * + * **Example:** Perform audio classification with `Xenova/wav2vec2-large-xlsr-53-gender-recognition-librispeech`. + * ```javascript + * const classifier = await pipeline('audio-classification', 'Xenova/wav2vec2-large-xlsr-53-gender-recognition-librispeech'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav'; + * const output = await classifier(url); + * // [ + * // { label: 'male', score: 0.9981542229652405 }, + * // { label: 'female', score: 0.001845747814513743 } + * // ] + * ``` + * + * **Example:** Perform audio classification with `Xenova/ast-finetuned-audioset-10-10-0.4593` and return top 4 results. + * ```javascript + * const classifier = await pipeline('audio-classification', 'Xenova/ast-finetuned-audioset-10-10-0.4593'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cat_meow.wav'; + * const output = await classifier(url, { top_k: 4 }); + * // [ + * // { label: 'Meow', score: 0.5617874264717102 }, + * // { label: 'Cat', score: 0.22365376353263855 }, + * // { label: 'Domestic animals, pets', score: 0.1141069084405899 }, + * // { label: 'Animal', score: 0.08985692262649536 }, + * // ] + * ``` + */ +class AudioClassificationPipeline extends (/** @type {new (options: AudioPipelineConstructorArgs) => AudioClassificationPipelineType} */ (Pipeline)) { + + /** + * Create a new AudioClassificationPipeline. + * @param {AudioPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {AudioClassificationPipelineCallback} */ + async _call(audio, { + top_k = 5 + } = {}) { + + const sampling_rate = this.processor.feature_extractor.config.sampling_rate; + const preparedAudios = await prepareAudios(audio, sampling_rate); + + // @ts-expect-error TS2339 + const id2label = this.model.config.id2label; + + const toReturn = []; + for (const aud of preparedAudios) { + const inputs = await this.processor(aud); + const output = await this.model(inputs); + const logits = output.logits[0]; + + const scores = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.topk)(new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.Tensor( + 'float32', + (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(logits.data), + logits.dims, + ), top_k); + + const values = scores[0].tolist(); + const indices = scores[1].tolist(); + + const vals = indices.map((x, i) => ({ + label: /** @type {string} */ (id2label ? id2label[x] : `LABEL_${x}`), + score: /** @type {number} */ (values[i]), + })); + + toReturn.push(vals); + }; + return Array.isArray(audio) ? toReturn : toReturn[0]; + } +} + +/** + * @typedef {Object} ZeroShotAudioClassificationOutput + * @property {string} label The label identified by the model. It is one of the suggested `candidate_label`. + * @property {number} score The score attributed by the model for that label (between 0 and 1). + * + * @typedef {Object} ZeroShotAudioClassificationPipelineOptions Parameters specific to zero-shot audio classification pipelines. + * @property {string} [hypothesis_template="This is a sound of {}."] The sentence used in conjunction with `candidate_labels` + * to attempt the audio classification by replacing the placeholder with the candidate_labels. + * Then likelihood is estimated by using `logits_per_audio`. + * + * @callback ZeroShotAudioClassificationPipelineCallback Classify the sequence(s) given as inputs. + * @param {AudioPipelineInputs} audio The input audio file(s) to be classified. The input is either: + * - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate + * to get the waveform using the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/AudioContext) API. + * If `AudioContext` is not available, you should pass the raw waveform in as a Float32Array of shape `(n, )`. + * - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done). + * @param {string[]} candidate_labels The candidate labels for this audio. + * @param {ZeroShotAudioClassificationPipelineOptions} [options] The options to use for zero-shot audio classification. + * @returns {Promise} An array of objects containing the predicted labels and scores. + * + * @typedef {TextAudioPipelineConstructorArgs & ZeroShotAudioClassificationPipelineCallback & Disposable} ZeroShotAudioClassificationPipelineType + */ + +/** + * Zero shot audio classification pipeline using `ClapModel`. This pipeline predicts the class of an audio when you + * provide an audio and a set of `candidate_labels`. + * + * **Example**: Perform zero-shot audio classification with `Xenova/clap-htsat-unfused`. + * ```javascript + * const classifier = await pipeline('zero-shot-audio-classification', 'Xenova/clap-htsat-unfused'); + * const audio = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/dog_barking.wav'; + * const candidate_labels = ['dog', 'vaccum cleaner']; + * const scores = await classifier(audio, candidate_labels); + * // [ + * // { score: 0.9993992447853088, label: 'dog' }, + * // { score: 0.0006007603369653225, label: 'vaccum cleaner' } + * // ] + * ``` + */ +class ZeroShotAudioClassificationPipeline extends (/** @type {new (options: TextAudioPipelineConstructorArgs) => ZeroShotAudioClassificationPipelineType} */ (Pipeline)) { + + /** + * Create a new ZeroShotAudioClassificationPipeline. + * @param {TextAudioPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {ZeroShotAudioClassificationPipelineCallback} */ + async _call(audio, candidate_labels, { + hypothesis_template = "This is a sound of {}." + } = {}) { + + const single = !Array.isArray(audio); + if (single) { + audio = [/** @type {AudioInput} */ (audio)]; + } + + // Insert label into hypothesis template + const texts = candidate_labels.map( + x => hypothesis_template.replace('{}', x) + ); + + // Run tokenization + const text_inputs = this.tokenizer(texts, { + padding: true, + truncation: true, + }); + + const sampling_rate = this.processor.feature_extractor.config.sampling_rate; + const preparedAudios = await prepareAudios(audio, sampling_rate); + + const toReturn = []; + for (const aud of preparedAudios) { + const audio_inputs = await this.processor(aud); + + // Run model with both text and audio inputs + const output = await this.model({ ...text_inputs, ...audio_inputs }); + + // Compute softmax per audio + const probs = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(output.logits_per_audio.data); + + toReturn.push([...probs].map((x, i) => ({ + score: x, + label: candidate_labels[i] + }))); + } + return single ? toReturn[0] : toReturn; + } +} + +/** + * @typedef {Object} Chunk + * @property {[number, number]} timestamp The start and end timestamp of the chunk in seconds. + * @property {string} text The recognized text. + */ + +/** + * @typedef {Object} AutomaticSpeechRecognitionOutput + * @property {string} text The recognized text. + * @property {Chunk[]} [chunks] When using `return_timestamps`, the `chunks` will become a list + * containing all the various text chunks identified by the model. + * + * @typedef {Object} AutomaticSpeechRecognitionSpecificParams Parameters specific to automatic-speech-recognition pipelines. + * @property {boolean|'word'} [return_timestamps] Whether to return timestamps or not. Default is `false`. + * @property {number} [chunk_length_s] The length of audio chunks to process in seconds. Default is 0 (no chunking). + * @property {number} [stride_length_s] The length of overlap between consecutive audio chunks in seconds. If not provided, defaults to `chunk_length_s / 6`. + * @property {boolean} [force_full_sequences] Whether to force outputting full sequences or not. Default is `false`. + * @property {string} [language] The source language. Default is `null`, meaning it should be auto-detected. Use this to potentially improve performance if the source language is known. + * @property {string} [task] The task to perform. Default is `null`, meaning it should be auto-detected. + * @property {number} [num_frames] The number of frames in the input audio. + * @typedef {import('./generation/configuration_utils.js').GenerationConfig & AutomaticSpeechRecognitionSpecificParams} AutomaticSpeechRecognitionConfig + * + * @callback AutomaticSpeechRecognitionPipelineCallback Transcribe the audio sequence(s) given as inputs to text. + * @param {AudioPipelineInputs} audio The input audio file(s) to be transcribed. The input is either: + * - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate + * to get the waveform using the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/AudioContext) API. + * If `AudioContext` is not available, you should pass the raw waveform in as a Float32Array of shape `(n, )`. + * - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done). + * @param {Partial} [options] Additional keyword arguments to pass along to the generate method of the model. + * @returns {Promise} An object containing the transcription text and optionally timestamps if `return_timestamps` is `true`. + * + * @typedef {TextAudioPipelineConstructorArgs & AutomaticSpeechRecognitionPipelineCallback & Disposable} AutomaticSpeechRecognitionPipelineType + */ + +/** + * Pipeline that aims at extracting spoken text contained within some audio. + * + * **Example:** Transcribe English. + * ```javascript + * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav'; + * const output = await transcriber(url); + * // { text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country." } + * ``` + * + * **Example:** Transcribe English w/ timestamps. + * ```javascript + * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav'; + * const output = await transcriber(url, { return_timestamps: true }); + * // { + * // text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country." + * // chunks: [ + * // { timestamp: [0, 8], text: " And so my fellow Americans ask not what your country can do for you" } + * // { timestamp: [8, 11], text: " ask what you can do for your country." } + * // ] + * // } + * ``` + * + * **Example:** Transcribe English w/ word-level timestamps. + * ```javascript + * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav'; + * const output = await transcriber(url, { return_timestamps: 'word' }); + * // { + * // "text": " And so my fellow Americans ask not what your country can do for you ask what you can do for your country.", + * // "chunks": [ + * // { "text": " And", "timestamp": [0, 0.78] }, + * // { "text": " so", "timestamp": [0.78, 1.06] }, + * // { "text": " my", "timestamp": [1.06, 1.46] }, + * // ... + * // { "text": " for", "timestamp": [9.72, 9.92] }, + * // { "text": " your", "timestamp": [9.92, 10.22] }, + * // { "text": " country.", "timestamp": [10.22, 13.5] } + * // ] + * // } + * ``` + * + * **Example:** Transcribe French. + * ```javascript + * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/french-audio.mp3'; + * const output = await transcriber(url, { language: 'french', task: 'transcribe' }); + * // { text: " J'adore, j'aime, je n'aime pas, je déteste." } + * ``` + * + * **Example:** Translate French to English. + * ```javascript + * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/french-audio.mp3'; + * const output = await transcriber(url, { language: 'french', task: 'translate' }); + * // { text: " I love, I like, I don't like, I hate." } + * ``` + * + * **Example:** Transcribe/translate audio longer than 30 seconds. + * ```javascript + * const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/ted_60.wav'; + * const output = await transcriber(url, { chunk_length_s: 30, stride_length_s: 5 }); + * // { text: " So in college, I was a government major, which means [...] So I'd start off light and I'd bump it up" } + * ``` + */ +class AutomaticSpeechRecognitionPipeline extends (/** @type {new (options: TextAudioPipelineConstructorArgs) => AutomaticSpeechRecognitionPipelineType} */ (Pipeline)) { + + /** + * Create a new AutomaticSpeechRecognitionPipeline. + * @param {TextAudioPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {AutomaticSpeechRecognitionPipelineCallback} */ + async _call(audio, kwargs = {}) { + switch (this.model.config.model_type) { + case 'whisper': + case 'lite-whisper': + return this._call_whisper(audio, kwargs) + case 'wav2vec2': + case 'wav2vec2-bert': + case 'unispeech': + case 'unispeech-sat': + case 'hubert': + return this._call_wav2vec2(audio, kwargs) + case 'moonshine': + return this._call_moonshine(audio, kwargs) + default: + throw new Error(`AutomaticSpeechRecognitionPipeline does not support model type '${this.model.config.model_type}'.`) + } + } + + /** + * @type {AutomaticSpeechRecognitionPipelineCallback} + * @private + */ + async _call_wav2vec2(audio, kwargs) { + // TODO use kwargs + + if (kwargs.language) { + console.warn('`language` parameter is not yet supported for `wav2vec2` models, defaulting to "English".'); + } + if (kwargs.task) { + console.warn('`task` parameter is not yet supported for `wav2vec2` models, defaulting to "transcribe".'); + } + + const single = !Array.isArray(audio); + if (single) { + audio = [/** @type {AudioInput} */ (audio)]; + } + + const sampling_rate = this.processor.feature_extractor.config.sampling_rate; + const preparedAudios = await prepareAudios(audio, sampling_rate); + + const toReturn = []; + for (const aud of preparedAudios) { + const inputs = await this.processor(aud); + const output = await this.model(inputs); + const logits = output.logits[0]; + + const predicted_ids = []; + for (const item of logits) { + predicted_ids.push((0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.max)(item.data)[1]) + } + const predicted_sentences = this.tokenizer.decode(predicted_ids) + toReturn.push({ text: predicted_sentences }) + } + return single ? toReturn[0] : toReturn; + } + + /** + * @type {AutomaticSpeechRecognitionPipelineCallback} + * @private + */ + async _call_whisper(audio, kwargs) { + const return_timestamps = kwargs.return_timestamps ?? false; + const chunk_length_s = kwargs.chunk_length_s ?? 0; + const force_full_sequences = kwargs.force_full_sequences ?? false; + let stride_length_s = kwargs.stride_length_s ?? null; + + const generation_config = { ...kwargs } + + if (return_timestamps === 'word') { + generation_config['return_token_timestamps'] = true; + generation_config['return_timestamps'] = false; // Do not predict timestamp tokens + } + + const single = !Array.isArray(audio); + if (single) { + audio = [/** @type {AudioInput} */ (audio)]; + } + + // @ts-expect-error TS2339 + const time_precision = this.processor.feature_extractor.config.chunk_length / this.model.config.max_source_positions; + const hop_length = this.processor.feature_extractor.config.hop_length; + + const sampling_rate = this.processor.feature_extractor.config.sampling_rate; + const preparedAudios = await prepareAudios(audio, sampling_rate); + + const toReturn = []; + for (const aud of preparedAudios) { + /** @type {{stride: number[], input_features: Tensor, is_last: boolean, tokens?: bigint[], token_timestamps?: number[]}[]} */ + let chunks = []; + if (chunk_length_s > 0) { + if (stride_length_s === null) { + stride_length_s = chunk_length_s / 6; + } else if (chunk_length_s <= stride_length_s) { + throw Error("`chunk_length_s` must be larger than `stride_length_s`.") + } + + // TODO support different stride_length_s (for left and right) + + const window = sampling_rate * chunk_length_s; + const stride = sampling_rate * stride_length_s; + const jump = window - 2 * stride; + let offset = 0; + + // Create subarrays of audio with overlaps + while (true) { + const offset_end = offset + window; + const subarr = aud.subarray(offset, offset_end); + const feature = await this.processor(subarr); + + const is_first = offset === 0; + const is_last = offset_end >= aud.length; + chunks.push({ + stride: [ + subarr.length, + is_first ? 0 : stride, + is_last ? 0 : stride + ], + input_features: feature.input_features, + is_last, + }) + if (is_last) break; + offset += jump; + } + + } else { + chunks = [{ + stride: [aud.length, 0, 0], + input_features: (await this.processor(aud)).input_features, + is_last: true + }] + } + + // Generate for each set of input features + for (const chunk of chunks) { + generation_config.num_frames = Math.floor(chunk.stride[0] / hop_length); + + // NOTE: doing sequentially for now + const data = await this.model.generate({ + inputs: chunk.input_features, + ...generation_config + }); + + // TODO: Right now we only get top beam + if (return_timestamps === 'word') { + // @ts-expect-error TS2339 + chunk.tokens = data.sequences.tolist()[0]; + // @ts-expect-error TS2339 + chunk.token_timestamps = data.token_timestamps.tolist()[0].map( + (/** @type {number} */ x) => (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.round)(x, 2) + ); + + } else { + chunk.tokens = (/** @type {Tensor} */(data))[0].tolist(); + } + + // convert stride to seconds + chunk.stride = chunk.stride.map(x => x / sampling_rate); + } + + // Merge text chunks + // @ts-ignore + const [full_text, optional] = this.tokenizer._decode_asr(chunks, { + time_precision, return_timestamps, force_full_sequences + }); + + toReturn.push({ text: full_text, ...optional }) + } + return single ? toReturn[0] : toReturn; + } + + /** + * @type {AutomaticSpeechRecognitionPipelineCallback} + * @private + */ + async _call_moonshine(audio, kwargs) { + const single = !Array.isArray(audio); + if (single) { + audio = [/** @type {AudioInput} */ (audio)]; + } + const sampling_rate = this.processor.feature_extractor.config.sampling_rate; + const preparedAudios = await prepareAudios(audio, sampling_rate); + const toReturn = []; + for (const aud of preparedAudios) { + const inputs = await this.processor(aud); + + // According to the [paper](https://huggingface.co/papers/2410.15608): + // "We use greedy decoding, with a heuristic limit of 6 output tokens + // per second of audio to avoid repeated output sequences." + const max_new_tokens = Math.floor(aud.length / sampling_rate) * 6; + const outputs = await this.model.generate({ max_new_tokens, ...kwargs, ...inputs }); + + const text = this.processor.batch_decode(/** @type {Tensor} */(outputs), { skip_special_tokens: true })[0]; + toReturn.push({ text }); + } + return single ? toReturn[0] : toReturn; + } + +} + +/** + * @typedef {Object} ImageToTextSingle + * @property {string} generated_text The generated text. + * @typedef {ImageToTextSingle[]} ImageToTextOutput + * + * @callback ImageToTextPipelineCallback Assign labels to the image(s) passed as inputs. + * @param {ImagePipelineInputs} texts The images to be captioned. + * @param {Partial} [options] Additional keyword arguments to pass along to the generate method of the model. + * @returns {Promise} An object (or array of objects) containing the generated text(s). + * + * @typedef {TextImagePipelineConstructorArgs & ImageToTextPipelineCallback & Disposable} ImageToTextPipelineType + */ + +/** + * Image To Text pipeline using a `AutoModelForVision2Seq`. This pipeline predicts a caption for a given image. + * + * **Example:** Generate a caption for an image w/ `Xenova/vit-gpt2-image-captioning`. + * ```javascript + * const captioner = await pipeline('image-to-text', 'Xenova/vit-gpt2-image-captioning'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg'; + * const output = await captioner(url); + * // [{ generated_text: 'a cat laying on a couch with another cat' }] + * ``` + * + * **Example:** Optical Character Recognition (OCR) w/ `Xenova/trocr-small-handwritten`. + * ```javascript + * const captioner = await pipeline('image-to-text', 'Xenova/trocr-small-handwritten'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/handwriting.jpg'; + * const output = await captioner(url); + * // [{ generated_text: 'Mr. Brown commented icily.' }] + * ``` + */ +class ImageToTextPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => ImageToTextPipelineType} */ (Pipeline)) { + + /** + * Create a new ImageToTextPipeline. + * @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {ImageToTextPipelineCallback} */ + async _call(images, generate_kwargs = {}) { + + const isBatched = Array.isArray(images); + const preparedImages = await prepareImages(images); + + const { pixel_values } = await this.processor(preparedImages); + + const toReturn = []; + for (const batch of pixel_values) { + batch.dims = [1, ...batch.dims] + const output = await this.model.generate({ inputs: batch, ...generate_kwargs }); + const decoded = this.tokenizer.batch_decode(/** @type {Tensor} */(output), { + skip_special_tokens: true, + }).map(x => ({ generated_text: x.trim() })) + toReturn.push(decoded); + } + + return isBatched ? toReturn : toReturn[0]; + } +} + +/** + * @typedef {Object} ImageClassificationSingle + * @property {string} label The label identified by the model. + * @property {number} score The score attributed by the model for that label. + * @typedef {ImageClassificationSingle[]} ImageClassificationOutput + * + * @typedef {Object} ImageClassificationPipelineOptions Parameters specific to image classification pipelines. + * @property {number} [top_k=1] The number of top labels that will be returned by the pipeline. + * + * @callback ImageClassificationPipelineCallback Assign labels to the image(s) passed as inputs. + * @param {ImagePipelineInputs} images The input images(s) to be classified. + * @param {ImageClassificationPipelineOptions} [options] The options to use for image classification. + * @returns {Promise} An array or object containing the predicted labels and scores. + * + * @typedef {ImagePipelineConstructorArgs & ImageClassificationPipelineCallback & Disposable} ImageClassificationPipelineType + */ + +/** + * Image classification pipeline using any `AutoModelForImageClassification`. + * This pipeline predicts the class of an image. + * + * **Example:** Classify an image. + * ```javascript + * const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg'; + * const output = await classifier(url); + * // [ + * // { label: 'tiger, Panthera tigris', score: 0.632695734500885 }, + * // ] + * ``` + * + * **Example:** Classify an image and return top `n` classes. + * ```javascript + * const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg'; + * const output = await classifier(url, { top_k: 3 }); + * // [ + * // { label: 'tiger, Panthera tigris', score: 0.632695734500885 }, + * // { label: 'tiger cat', score: 0.3634825646877289 }, + * // { label: 'lion, king of beasts, Panthera leo', score: 0.00045060308184474707 }, + * // ] + * ``` + * + * **Example:** Classify an image and return all classes. + * ```javascript + * const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg'; + * const output = await classifier(url, { top_k: 0 }); + * // [ + * // { label: 'tiger, Panthera tigris', score: 0.632695734500885 }, + * // { label: 'tiger cat', score: 0.3634825646877289 }, + * // { label: 'lion, king of beasts, Panthera leo', score: 0.00045060308184474707 }, + * // { label: 'jaguar, panther, Panthera onca, Felis onca', score: 0.00035465499968267977 }, + * // ... + * // ] + * ``` + */ +class ImageClassificationPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageClassificationPipelineType} */ (Pipeline)) { + + /** + * Create a new ImageClassificationPipeline. + * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {ImageClassificationPipelineCallback} */ + async _call(images, { + top_k = 5 + } = {}) { + + const preparedImages = await prepareImages(images); + + const { pixel_values } = await this.processor(preparedImages); + const output = await this.model({ pixel_values }); + + // @ts-expect-error TS2339 + const id2label = this.model.config.id2label; + + /** @type {ImageClassificationOutput[]} */ + const toReturn = []; + for (const batch of output.logits) { + const scores = await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.topk)(new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.Tensor( + 'float32', + (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(batch.data), + batch.dims, + ), top_k); + + const values = scores[0].tolist(); + const indices = scores[1].tolist(); + + const vals = indices.map((x, i) => ({ + label: /** @type {string} */ (id2label ? id2label[x] : `LABEL_${x}`), + score: /** @type {number} */ (values[i]), + })); + toReturn.push(vals); + } + + return Array.isArray(images) ? toReturn : toReturn[0]; + } + +} + +/** + * @typedef {Object} ImageSegmentationPipelineOutput + * @property {string|null} label The label of the segment. + * @property {number|null} score The score of the segment. + * @property {RawImage} mask The mask of the segment. + * + * @typedef {Object} ImageSegmentationPipelineOptions Parameters specific to image segmentation pipelines. + * @property {number} [threshold=0.5] Probability threshold to filter out predicted masks. + * @property {number} [mask_threshold=0.5] Threshold to use when turning the predicted masks into binary values. + * @property {number} [overlap_mask_area_threshold=0.8] Mask overlap threshold to eliminate small, disconnected segments. + * @property {null|string} [subtask=null] Segmentation task to be performed. One of [`panoptic`, `instance`, and `semantic`], + * depending on model capabilities. If not set, the pipeline will attempt to resolve (in that order). + * @property {number[]} [label_ids_to_fuse=null] List of label ids to fuse. If not set, do not fuse any labels. + * @property {number[][]} [target_sizes=null] List of target sizes for the input images. If not set, use the original image sizes. + * + * @callback ImageSegmentationPipelineCallback Segment the input images. + * @param {ImagePipelineInputs} images The input images. + * @param {ImageSegmentationPipelineOptions} [options] The options to use for image segmentation. + * @returns {Promise} The annotated segments. + * + * @typedef {ImagePipelineConstructorArgs & ImageSegmentationPipelineCallback & Disposable} ImageSegmentationPipelineType + */ + +/** + * Image segmentation pipeline using any `AutoModelForXXXSegmentation`. + * This pipeline predicts masks of objects and their classes. + * + * **Example:** Perform image segmentation with `Xenova/detr-resnet-50-panoptic`. + * ```javascript + * const segmenter = await pipeline('image-segmentation', 'Xenova/detr-resnet-50-panoptic'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg'; + * const output = await segmenter(url); + * // [ + * // { label: 'remote', score: 0.9984649419784546, mask: RawImage { ... } }, + * // { label: 'cat', score: 0.9994316101074219, mask: RawImage { ... } } + * // ] + * ``` + */ +class ImageSegmentationPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageSegmentationPipelineType} */ (Pipeline)) { + /** + * Create a new ImageSegmentationPipeline. + * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + + this.subtasks_mapping = { + // Mapping of subtasks to their corresponding post-processing function names. + panoptic: 'post_process_panoptic_segmentation', + instance: 'post_process_instance_segmentation', + semantic: 'post_process_semantic_segmentation' + } + } + + /** @type {ImageSegmentationPipelineCallback} */ + async _call(images, { + threshold = 0.5, + mask_threshold = 0.5, + overlap_mask_area_threshold = 0.8, + label_ids_to_fuse = null, + target_sizes = null, + subtask = null, + } = {}) { + const isBatched = Array.isArray(images); + + if (isBatched && images.length !== 1) { + throw Error("Image segmentation pipeline currently only supports a batch size of 1."); + } + + const preparedImages = await prepareImages(images); + const imageSizes = preparedImages.map(x => [x.height, x.width]); + + const inputs = await this.processor(preparedImages); + + const { inputNames, outputNames } = this.model.sessions['model']; + if (!inputNames.includes('pixel_values')) { + if (inputNames.length !== 1) { + throw Error(`Expected a single input name, but got ${inputNames.length} inputs: ${inputNames}.`); + } + + const newName = inputNames[0]; + if (newName in inputs) { + throw Error(`Input name ${newName} already exists in the inputs.`); + } + // To ensure compatibility with certain background-removal models, + // we may need to perform a mapping of input to output names + inputs[newName] = inputs.pixel_values; + } + + const output = await this.model(inputs); + + let fn = null; + if (subtask !== null) { + fn = this.subtasks_mapping[subtask]; + } else if (this.processor.image_processor) { + for (const [task, func] of Object.entries(this.subtasks_mapping)) { + if (func in this.processor.image_processor) { + fn = this.processor.image_processor[func].bind(this.processor.image_processor); + subtask = task; + break; + } + } + } + + // @ts-expect-error TS2339 + const id2label = this.model.config.id2label; + + /** @type {ImageSegmentationPipelineOutput[]} */ + const annotation = []; + if (!subtask) { + // We define an epsilon to safeguard against numerical/precision issues when detecting + // the normalization mode of the output (i.e., sigmoid already applied, or not). + // See https://github.com/microsoft/onnxruntime/issues/23943 for more information. + const epsilon = 1e-5; + + // Perform standard image segmentation + const result = output[outputNames[0]]; + for (let i = 0; i < imageSizes.length; ++i) { + const size = imageSizes[i]; + const item = result[i]; + if (item.data.some(x => x < -epsilon || x > 1 + epsilon)) { + item.sigmoid_(); + } + const mask = await _utils_image_js__WEBPACK_IMPORTED_MODULE_9__.RawImage.fromTensor(item.mul_(255).to('uint8')).resize(size[1], size[0]); + annotation.push({ + label: null, + score: null, + mask + }); + } + } else if (subtask === 'panoptic' || subtask === 'instance') { + const processed = fn( + output, + threshold, + mask_threshold, + overlap_mask_area_threshold, + label_ids_to_fuse, + target_sizes ?? imageSizes, // TODO FIX? + )[0]; + + const segmentation = processed.segmentation; + + for (const segment of processed.segments_info) { + const maskData = new Uint8ClampedArray(segmentation.data.length); + for (let i = 0; i < segmentation.data.length; ++i) { + if (segmentation.data[i] === segment.id) { + maskData[i] = 255; + } + } + + const mask = new _utils_image_js__WEBPACK_IMPORTED_MODULE_9__.RawImage(maskData, segmentation.dims[1], segmentation.dims[0], 1) + + annotation.push({ + score: segment.score, + label: id2label[segment.label_id], + mask: mask + }) + } + + } else if (subtask === 'semantic') { + const { segmentation, labels } = fn(output, target_sizes ?? imageSizes)[0]; + + for (const label of labels) { + const maskData = new Uint8ClampedArray(segmentation.data.length); + for (let i = 0; i < segmentation.data.length; ++i) { + if (segmentation.data[i] === label) { + maskData[i] = 255; + } + } + + const mask = new _utils_image_js__WEBPACK_IMPORTED_MODULE_9__.RawImage(maskData, segmentation.dims[1], segmentation.dims[0], 1); + + annotation.push({ + score: null, + label: id2label[label], + mask: mask + }); + } + } else { + throw Error(`Subtask ${subtask} not supported.`); + } + + return annotation; + } +} + + +/** + * @typedef {Object} BackgroundRemovalPipelineOptions Parameters specific to image segmentation pipelines. + * + * @callback BackgroundRemovalPipelineCallback Segment the input images. + * @param {ImagePipelineInputs} images The input images. + * @param {BackgroundRemovalPipelineOptions} [options] The options to use for image segmentation. + * @returns {Promise} The images with the background removed. + * + * @typedef {ImagePipelineConstructorArgs & BackgroundRemovalPipelineCallback & Disposable} BackgroundRemovalPipelineType + */ + +/** + * Background removal pipeline using certain `AutoModelForXXXSegmentation`. + * This pipeline removes the backgrounds of images. + * + * **Example:** Perform background removal with `Xenova/modnet`. + * ```javascript + * const segmenter = await pipeline('background-removal', 'Xenova/modnet'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/portrait-of-woman_small.jpg'; + * const output = await segmenter(url); + * // [ + * // RawImage { data: Uint8ClampedArray(648000) [ ... ], width: 360, height: 450, channels: 4 } + * // ] + * ``` + */ +class BackgroundRemovalPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => BackgroundRemovalPipelineType} */ (/** @type {any} */(ImageSegmentationPipeline))) { + /** + * Create a new BackgroundRemovalPipeline. + * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {BackgroundRemovalPipelineCallback} */ + async _call(images, options = {}) { + const isBatched = Array.isArray(images); + + if (isBatched && images.length !== 1) { + throw Error("Background removal pipeline currently only supports a batch size of 1."); + } + + const preparedImages = await prepareImages(images); + + // @ts-expect-error TS2339 + const masks = await super._call(images, options); + const result = preparedImages.map((img, i) => { + const cloned = img.clone(); + cloned.putAlpha(masks[i].mask); + return cloned; + }); + + return result; + } +} + +/** + * @typedef {Object} ZeroShotImageClassificationOutput + * @property {string} label The label identified by the model. It is one of the suggested `candidate_label`. + * @property {number} score The score attributed by the model for that label (between 0 and 1). + * + * @typedef {Object} ZeroShotImageClassificationPipelineOptions Parameters specific to zero-shot image classification pipelines. + * @property {string} [hypothesis_template="This is a photo of {}"] The sentence used in conjunction with `candidate_labels` + * to attempt the image classification by replacing the placeholder with the candidate_labels. + * Then likelihood is estimated by using `logits_per_image`. + * + * @callback ZeroShotImageClassificationPipelineCallback Assign labels to the image(s) passed as inputs. + * @param {ImagePipelineInputs} images The input images. + * @param {string[]} candidate_labels The candidate labels for this image. + * @param {ZeroShotImageClassificationPipelineOptions} [options] The options to use for zero-shot image classification. + * @returns {Promise} An array of objects containing the predicted labels and scores. + * + * @typedef {TextImagePipelineConstructorArgs & ZeroShotImageClassificationPipelineCallback & Disposable} ZeroShotImageClassificationPipelineType + */ + +/** + * Zero shot image classification pipeline. This pipeline predicts the class of + * an image when you provide an image and a set of `candidate_labels`. + * + * **Example:** Zero shot image classification w/ `Xenova/clip-vit-base-patch32`. + * ```javascript + * const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg'; + * const output = await classifier(url, ['tiger', 'horse', 'dog']); + * // [ + * // { score: 0.9993917942047119, label: 'tiger' }, + * // { score: 0.0003519294841680676, label: 'horse' }, + * // { score: 0.0002562698791734874, label: 'dog' } + * // ] + * ``` + */ +class ZeroShotImageClassificationPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => ZeroShotImageClassificationPipelineType} */ (Pipeline)) { + /** + * Create a new ZeroShotImageClassificationPipeline. + * @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {ZeroShotImageClassificationPipelineCallback} */ + async _call(images, candidate_labels, { + hypothesis_template = "This is a photo of {}" + } = {}) { + + const isBatched = Array.isArray(images); + const preparedImages = await prepareImages(images); + + // Insert label into hypothesis template + const texts = candidate_labels.map( + x => hypothesis_template.replace('{}', x) + ); + + // Run tokenization + const text_inputs = this.tokenizer(texts, { + padding: this.model.config.model_type === 'siglip' ? 'max_length' : true, + truncation: true, + }); + + // Run processor + const { pixel_values } = await this.processor(preparedImages); + + // Run model with both text and pixel inputs + const output = await this.model({ ...text_inputs, pixel_values }); + + const function_to_apply = + this.model.config.model_type === 'siglip' + ? batch => batch.sigmoid().data + : batch => (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_6__.softmax)(batch.data); + + // Compare each image with each candidate label + const toReturn = []; + for (const batch of output.logits_per_image) { + // Compute softmax per image + const probs = function_to_apply(batch); + + const result = [...probs].map((x, i) => ({ + score: x, + label: candidate_labels[i] + })); + result.sort((a, b) => b.score - a.score); // sort by score in descending order + toReturn.push(result); + } + + return isBatched ? toReturn : toReturn[0]; + } +} + + +/** + * @typedef {Object} ObjectDetectionPipelineSingle + * @property {string} label The class label identified by the model. + * @property {number} score The score attributed by the model for that label. + * @property {BoundingBox} box The bounding box of detected object in image's original size, or as a percentage if `percentage` is set to true. + * @typedef {ObjectDetectionPipelineSingle[]} ObjectDetectionPipelineOutput + * + * @typedef {Object} ObjectDetectionPipelineOptions Parameters specific to object detection pipelines. + * @property {number} [threshold=0.9] The threshold used to filter boxes by score. + * @property {boolean} [percentage=false] Whether to return the boxes coordinates in percentage (true) or in pixels (false). + * + * @callback ObjectDetectionPipelineCallback Detect objects (bounding boxes & classes) in the image(s) passed as inputs. + * @param {ImagePipelineInputs} images The input images. + * @param {ObjectDetectionPipelineOptions} [options] The options to use for object detection. + * @returns {Promise} A list of objects or a list of list of objects. + * + * @typedef {ImagePipelineConstructorArgs & ObjectDetectionPipelineCallback & Disposable} ObjectDetectionPipelineType + */ + +/** + * Object detection pipeline using any `AutoModelForObjectDetection`. + * This pipeline predicts bounding boxes of objects and their classes. + * + * **Example:** Run object-detection with `Xenova/detr-resnet-50`. + * ```javascript + * const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50'); + * const img = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg'; + * const output = await detector(img, { threshold: 0.9 }); + * // [{ + * // score: 0.9976370930671692, + * // label: "remote", + * // box: { xmin: 31, ymin: 68, xmax: 190, ymax: 118 } + * // }, + * // ... + * // { + * // score: 0.9984092116355896, + * // label: "cat", + * // box: { xmin: 331, ymin: 19, xmax: 649, ymax: 371 } + * // }] + * ``` + */ +class ObjectDetectionPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ObjectDetectionPipelineType} */ (Pipeline)) { + + /** + * Create a new ObjectDetectionPipeline. + * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {ObjectDetectionPipelineCallback} */ + async _call(images, { + threshold = 0.9, + percentage = false, + } = {}) { + + const isBatched = Array.isArray(images); + + if (isBatched && images.length !== 1) { + throw Error("Object detection pipeline currently only supports a batch size of 1."); + } + const preparedImages = await prepareImages(images); + + const imageSizes = percentage ? null : preparedImages.map(x => [x.height, x.width]); + + const { pixel_values, pixel_mask } = await this.processor(preparedImages); + const output = await this.model({ pixel_values, pixel_mask }); + + // @ts-ignore + const processed = this.processor.image_processor.post_process_object_detection(output, threshold, imageSizes); + + // Add labels + // @ts-expect-error TS2339 + const id2label = this.model.config.id2label; + + // Format output + /** @type {ObjectDetectionPipelineOutput[]} */ + const result = processed.map(batch => ( + batch.boxes.map((box, i) => ({ + score: batch.scores[i], + label: id2label[batch.classes[i]], + box: get_bounding_box(box, !percentage), + })) + )) + + return isBatched ? result : result[0]; + } +} + + +/** + * @typedef {Object} ZeroShotObjectDetectionOutput + * @property {string} label Text query corresponding to the found object. + * @property {number} score Score corresponding to the object (between 0 and 1). + * @property {BoundingBox} box Bounding box of the detected object in image's original size, or as a percentage if `percentage` is set to true. + * + * @typedef {Object} ZeroShotObjectDetectionPipelineOptions Parameters specific to zero-shot object detection pipelines. + * @property {number} [threshold=0.1] The probability necessary to make a prediction. + * @property {number} [top_k=null] The number of top predictions that will be returned by the pipeline. + * If the provided number is `null` or higher than the number of predictions available, it will default + * to the number of predictions. + * @property {boolean} [percentage=false] Whether to return the boxes coordinates in percentage (true) or in pixels (false). + * + * @callback ZeroShotObjectDetectionPipelineCallback Detect objects (bounding boxes & classes) in the image(s) passed as inputs. + * @param {ImagePipelineInputs} images The input images. + * @param {string[]} candidate_labels What the model should recognize in the image. + * @param {ZeroShotObjectDetectionPipelineOptions} [options] The options to use for zero-shot object detection. + * @returns {Promise} An array of objects containing the predicted labels, scores, and bounding boxes. + * + * @typedef {TextImagePipelineConstructorArgs & ZeroShotObjectDetectionPipelineCallback & Disposable} ZeroShotObjectDetectionPipelineType + */ + +/** + * Zero-shot object detection pipeline. This pipeline predicts bounding boxes of + * objects when you provide an image and a set of `candidate_labels`. + * + * **Example:** Zero-shot object detection w/ `Xenova/owlvit-base-patch32`. + * ```javascript + * const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/astronaut.png'; + * const candidate_labels = ['human face', 'rocket', 'helmet', 'american flag']; + * const output = await detector(url, candidate_labels); + * // [ + * // { + * // score: 0.24392342567443848, + * // label: 'human face', + * // box: { xmin: 180, ymin: 67, xmax: 274, ymax: 175 } + * // }, + * // { + * // score: 0.15129457414150238, + * // label: 'american flag', + * // box: { xmin: 0, ymin: 4, xmax: 106, ymax: 513 } + * // }, + * // { + * // score: 0.13649864494800568, + * // label: 'helmet', + * // box: { xmin: 277, ymin: 337, xmax: 511, ymax: 511 } + * // }, + * // { + * // score: 0.10262022167444229, + * // label: 'rocket', + * // box: { xmin: 352, ymin: -1, xmax: 463, ymax: 287 } + * // } + * // ] + * ``` + * + * **Example:** Zero-shot object detection w/ `Xenova/owlvit-base-patch32` (returning top 4 matches and setting a threshold). + * ```javascript + * const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/beach.png'; + * const candidate_labels = ['hat', 'book', 'sunglasses', 'camera']; + * const output = await detector(url, candidate_labels, { top_k: 4, threshold: 0.05 }); + * // [ + * // { + * // score: 0.1606510728597641, + * // label: 'sunglasses', + * // box: { xmin: 347, ymin: 229, xmax: 429, ymax: 264 } + * // }, + * // { + * // score: 0.08935828506946564, + * // label: 'hat', + * // box: { xmin: 38, ymin: 174, xmax: 258, ymax: 364 } + * // }, + * // { + * // score: 0.08530698716640472, + * // label: 'camera', + * // box: { xmin: 187, ymin: 350, xmax: 260, ymax: 411 } + * // }, + * // { + * // score: 0.08349756896495819, + * // label: 'book', + * // box: { xmin: 261, ymin: 280, xmax: 494, ymax: 425 } + * // } + * // ] + * ``` + */ +class ZeroShotObjectDetectionPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => ZeroShotObjectDetectionPipelineType} */ (Pipeline)) { + + /** + * Create a new ZeroShotObjectDetectionPipeline. + * @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {ZeroShotObjectDetectionPipelineCallback} */ + async _call(images, candidate_labels, { + threshold = 0.1, + top_k = null, + percentage = false, + } = {}) { + + const isBatched = Array.isArray(images); + const preparedImages = await prepareImages(images); + + // Run tokenization + const text_inputs = this.tokenizer(candidate_labels, { + padding: true, + truncation: true, + }); + + // Run processor + const model_inputs = await this.processor(preparedImages); + + // Since non-maximum suppression is performed for exporting, we need to + // process each image separately. For more information, see: + // https://github.com/huggingface/optimum/blob/e3b7efb1257c011db907ef40ab340e795cc5684c/optimum/exporters/onnx/model_configs.py#L1028-L1032 + const toReturn = []; + for (let i = 0; i < preparedImages.length; ++i) { + const image = preparedImages[i]; + const imageSize = percentage ? null : [[image.height, image.width]]; + const pixel_values = model_inputs.pixel_values[i].unsqueeze_(0); + + // Run model with both text and pixel inputs + const output = await this.model({ ...text_inputs, pixel_values }); + + let result; + if ('post_process_grounded_object_detection' in this.processor) { + // @ts-ignore + const processed = this.processor.post_process_grounded_object_detection( + output, + text_inputs.input_ids, + { + // TODO: support separate threshold values + box_threshold: threshold, + text_threshold: threshold, + target_sizes: imageSize, + }, + )[0]; + result = processed.boxes.map((box, i) => ({ + score: processed.scores[i], + label: processed.labels[i], + box: get_bounding_box(box, !percentage), + })) + } else { + // @ts-ignore + const processed = this.processor.image_processor.post_process_object_detection(output, threshold, imageSize, true)[0]; + result = processed.boxes.map((box, i) => ({ + score: processed.scores[i], + label: candidate_labels[processed.classes[i]], + box: get_bounding_box(box, !percentage), + })) + } + result.sort((a, b) => b.score - a.score); + + if (top_k !== null) { + result = result.slice(0, top_k); + } + toReturn.push(result) + } + + return isBatched ? toReturn : toReturn[0]; + } +} + +/** + * @typedef {Object} DocumentQuestionAnsweringSingle + * @property {string} answer The generated text. + * @typedef {DocumentQuestionAnsweringSingle[]} DocumentQuestionAnsweringOutput + * + * @callback DocumentQuestionAnsweringPipelineCallback Answer the question given as input by using the document. + * @param {ImageInput} image The image of the document to use. + * @param {string} question A question to ask of the document. + * @param {Partial} [options] Additional keyword arguments to pass along to the generate method of the model. + * @returns {Promise} An object (or array of objects) containing the answer(s). + * + * @typedef {TextImagePipelineConstructorArgs & DocumentQuestionAnsweringPipelineCallback & Disposable} DocumentQuestionAnsweringPipelineType + */ + +/** + * Document Question Answering pipeline using any `AutoModelForDocumentQuestionAnswering`. + * The inputs/outputs are similar to the (extractive) question answering pipeline; however, + * the pipeline takes an image (and optional OCR'd words/boxes) as input instead of text context. + * + * **Example:** Answer questions about a document with `Xenova/donut-base-finetuned-docvqa`. + * ```javascript + * const qa_pipeline = await pipeline('document-question-answering', 'Xenova/donut-base-finetuned-docvqa'); + * const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png'; + * const question = 'What is the invoice number?'; + * const output = await qa_pipeline(image, question); + * // [{ answer: 'us-001' }] + * ``` + */ +class DocumentQuestionAnsweringPipeline extends (/** @type {new (options: TextImagePipelineConstructorArgs) => DocumentQuestionAnsweringPipelineType} */ (Pipeline)) { + + /** + * Create a new DocumentQuestionAnsweringPipeline. + * @param {TextImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {DocumentQuestionAnsweringPipelineCallback} */ + async _call(image, question, generate_kwargs = {}) { + + // NOTE: For now, we only support a batch size of 1 + + // Preprocess image + const preparedImage = (await prepareImages(image))[0]; + const { pixel_values } = await this.processor(preparedImage); + + // Run tokenization + const task_prompt = `${question}`; + const decoder_input_ids = this.tokenizer(task_prompt, { + add_special_tokens: false, + padding: true, + truncation: true, + }).input_ids; + + // Run model + const output = await this.model.generate({ + inputs: pixel_values, + // @ts-expect-error TS2339 + max_length: this.model.config.decoder.max_position_embeddings, + decoder_input_ids, + ...generate_kwargs, + }); + + // Decode output + const decoded = this.tokenizer.batch_decode(/** @type {Tensor} */(output))[0]; + + // Parse answer + const match = decoded.match(/(.*?)<\/s_answer>/); + let answer = null; + if (match && match.length >= 2) { + answer = match[1].trim(); + } + return [{ answer }]; + } +} + + +/** + * @typedef {Object} VocoderOptions + * @property {PreTrainedModel} [vocoder] The vocoder used by the pipeline (if the model uses one). If not provided, use the default HifiGan vocoder. + * @typedef {TextAudioPipelineConstructorArgs & VocoderOptions} TextToAudioPipelineConstructorArgs + */ + +/** + * @typedef {Object} TextToAudioOutput + * @property {Float32Array} audio The generated audio waveform. + * @property {number} sampling_rate The sampling rate of the generated audio waveform. + * + * @typedef {Object} TextToAudioPipelineOptions Parameters specific to text-to-audio pipelines. + * @property {Tensor|Float32Array|string|URL} [speaker_embeddings=null] The speaker embeddings (if the model requires it). + * + * @callback TextToAudioPipelineCallback Generates speech/audio from the inputs. + * @param {string|string[]} texts The text(s) to generate. + * @param {TextToAudioPipelineOptions} options Parameters passed to the model generation/forward method. + * @returns {Promise} An object containing the generated audio and sampling rate. + * + * @typedef {TextToAudioPipelineConstructorArgs & TextToAudioPipelineCallback & Disposable} TextToAudioPipelineType + */ + +/** + * Text-to-audio generation pipeline using any `AutoModelForTextToWaveform` or `AutoModelForTextToSpectrogram`. + * This pipeline generates an audio file from an input text and optional other conditional inputs. + * + * **Example:** Generate audio from text with `Xenova/speecht5_tts`. + * ```javascript + * const synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { quantized: false }); + * const speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin'; + * const out = await synthesizer('Hello, my dog is cute', { speaker_embeddings }); + * // RawAudio { + * // audio: Float32Array(26112) [-0.00005657337896991521, 0.00020583874720614403, ...], + * // sampling_rate: 16000 + * // } + * ``` + * + * You can then save the audio to a .wav file with the `wavefile` package: + * ```javascript + * import wavefile from 'wavefile'; + * import fs from 'fs'; + * + * const wav = new wavefile.WaveFile(); + * wav.fromScratch(1, out.sampling_rate, '32f', out.audio); + * fs.writeFileSync('out.wav', wav.toBuffer()); + * ``` + * + * **Example:** Multilingual speech generation with `Xenova/mms-tts-fra`. See [here](https://huggingface.co/models?pipeline_tag=text-to-speech&other=vits&sort=trending) for the full list of available languages (1107). + * ```javascript + * const synthesizer = await pipeline('text-to-speech', 'Xenova/mms-tts-fra'); + * const out = await synthesizer('Bonjour'); + * // RawAudio { + * // audio: Float32Array(23808) [-0.00037693005288019776, 0.0003325853613205254, ...], + * // sampling_rate: 16000 + * // } + * ``` + */ +class TextToAudioPipeline extends (/** @type {new (options: TextToAudioPipelineConstructorArgs) => TextToAudioPipelineType} */ (Pipeline)) { + DEFAULT_VOCODER_ID = "Xenova/speecht5_hifigan" + + /** + * Create a new TextToAudioPipeline. + * @param {TextToAudioPipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + + // TODO: Find a better way for `pipeline` to set the default vocoder + this.vocoder = options.vocoder ?? null; + } + + + /** @type {TextToAudioPipelineCallback} */ + async _call(text_inputs, { + speaker_embeddings = null, + } = {}) { + + // If this.processor is not set, we are using a `AutoModelForTextToWaveform` model + if (this.processor) { + return this._call_text_to_spectrogram(text_inputs, { speaker_embeddings }); + } else { + return this._call_text_to_waveform(text_inputs); + } + } + + async _call_text_to_waveform(text_inputs) { + + // Run tokenization + const inputs = this.tokenizer(text_inputs, { + padding: true, + truncation: true, + }); + + // Generate waveform + const { waveform } = await this.model(inputs); + + // @ts-expect-error TS2339 + const sampling_rate = this.model.config.sampling_rate; + return new _utils_audio_js__WEBPACK_IMPORTED_MODULE_7__.RawAudio( + waveform.data, + sampling_rate, + ) + } + + async _call_text_to_spectrogram(text_inputs, { speaker_embeddings }) { + + // Load vocoder, if not provided + if (!this.vocoder) { + console.log('No vocoder specified, using default HifiGan vocoder.'); + this.vocoder = await _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModel.from_pretrained(this.DEFAULT_VOCODER_ID, { dtype: 'fp32' }); + } + + // Load speaker embeddings as Float32Array from path/URL + if (typeof speaker_embeddings === 'string' || speaker_embeddings instanceof URL) { + // Load from URL with fetch + speaker_embeddings = new Float32Array( + await (await fetch(speaker_embeddings)).arrayBuffer() + ); + } + + if (speaker_embeddings instanceof Float32Array) { + speaker_embeddings = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.Tensor( + 'float32', + speaker_embeddings, + [1, speaker_embeddings.length] + ) + } else if (!(speaker_embeddings instanceof _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.Tensor)) { + throw new Error("Speaker embeddings must be a `Tensor`, `Float32Array`, `string`, or `URL`.") + } + + // Run tokenization + const { input_ids } = this.tokenizer(text_inputs, { + padding: true, + truncation: true, + }); + + // NOTE: At this point, we are guaranteed that `speaker_embeddings` is a `Tensor` + // @ts-ignore + const { waveform } = await this.model.generate_speech(input_ids, speaker_embeddings, { vocoder: this.vocoder }); + + const sampling_rate = this.processor.feature_extractor.config.sampling_rate; + return new _utils_audio_js__WEBPACK_IMPORTED_MODULE_7__.RawAudio( + waveform.data, + sampling_rate, + ) + } +} + +/** + * @callback ImageToImagePipelineCallback Transform the image(s) passed as inputs. + * @param {ImagePipelineInputs} images The images to transform. + * @returns {Promise} The transformed image or list of images. + * + * @typedef {ImagePipelineConstructorArgs & ImageToImagePipelineCallback & Disposable} ImageToImagePipelineType + */ + +/** + * Image to Image pipeline using any `AutoModelForImageToImage`. This pipeline generates an image based on a previous image input. + * + * **Example:** Super-resolution w/ `Xenova/swin2SR-classical-sr-x2-64` + * ```javascript + * const upscaler = await pipeline('image-to-image', 'Xenova/swin2SR-classical-sr-x2-64'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/butterfly.jpg'; + * const output = await upscaler(url); + * // RawImage { + * // data: Uint8Array(786432) [ 41, 31, 24, 43, ... ], + * // width: 512, + * // height: 512, + * // channels: 3 + * // } + * ``` + */ +class ImageToImagePipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => ImageToImagePipelineType} */ (Pipeline)) { + /** + * Create a new ImageToImagePipeline. + * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {ImageToImagePipelineCallback} */ + async _call(images) { + + const preparedImages = await prepareImages(images); + const inputs = await this.processor(preparedImages); + const outputs = await this.model(inputs); + + /** @type {RawImage[]} */ + const toReturn = []; + for (const batch of outputs.reconstruction) { + const output = batch.squeeze().clamp_(0, 1).mul_(255).round_().to('uint8'); + toReturn.push(_utils_image_js__WEBPACK_IMPORTED_MODULE_9__.RawImage.fromTensor(output)); + } + + return toReturn.length > 1 ? toReturn : toReturn[0]; + } +} + +/** + * @typedef {Object} DepthEstimationPipelineOutput + * @property {Tensor} predicted_depth The raw depth map predicted by the model. + * @property {RawImage} depth The processed depth map as an image (with the same size as the input image). + * + * @callback DepthEstimationPipelineCallback Predicts the depth for the image(s) passed as inputs. + * @param {ImagePipelineInputs} images The images to compute depth for. + * @returns {Promise} An image or a list of images containing result(s). + * + * @typedef {ImagePipelineConstructorArgs & DepthEstimationPipelineCallback & Disposable} DepthEstimationPipelineType + */ + +/** + * Depth estimation pipeline using any `AutoModelForDepthEstimation`. This pipeline predicts the depth of an image. + * + * **Example:** Depth estimation w/ `Xenova/dpt-hybrid-midas` + * ```javascript + * const depth_estimator = await pipeline('depth-estimation', 'Xenova/dpt-hybrid-midas'); + * const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg'; + * const out = await depth_estimator(url); + * // { + * // predicted_depth: Tensor { + * // dims: [ 384, 384 ], + * // type: 'float32', + * // data: Float32Array(147456) [ 542.859130859375, 545.2833862304688, 546.1649169921875, ... ], + * // size: 147456 + * // }, + * // depth: RawImage { + * // data: Uint8Array(307200) [ 86, 86, 86, ... ], + * // width: 640, + * // height: 480, + * // channels: 1 + * // } + * // } + * ``` + */ +class DepthEstimationPipeline extends (/** @type {new (options: ImagePipelineConstructorArgs) => DepthEstimationPipelineType} */ (Pipeline)) { + /** + * Create a new DepthEstimationPipeline. + * @param {ImagePipelineConstructorArgs} options An object used to instantiate the pipeline. + */ + constructor(options) { + super(options); + } + + /** @type {DepthEstimationPipelineCallback} */ + async _call(images) { + + const preparedImages = await prepareImages(images); + + const inputs = await this.processor(preparedImages); + const { predicted_depth } = await this.model(inputs); + + const toReturn = []; + for (let i = 0; i < preparedImages.length; ++i) { + const batch = predicted_depth[i]; + const [height, width] = batch.dims.slice(-2); + const [new_width, new_height] = preparedImages[i].size; + + // Interpolate to original size + const prediction = (await (0,_utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.interpolate_4d)(batch.view(1, 1, height, width), { + size: [new_height, new_width], + mode: 'bilinear', + })).view(new_height, new_width); + + const minval = /** @type {number} */(prediction.min().item()); + const maxval = /** @type {number} */(prediction.max().item()); + const formatted = prediction.sub(minval).div_(maxval - minval).mul_(255).to('uint8').unsqueeze(0); + const depth = _utils_image_js__WEBPACK_IMPORTED_MODULE_9__.RawImage.fromTensor(formatted); + toReturn.push({ + predicted_depth: prediction, + depth, + }); + } + + return toReturn.length > 1 ? toReturn : toReturn[0]; + } +} + +const SUPPORTED_TASKS = Object.freeze({ + "text-classification": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": TextClassificationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForSequenceClassification, + "default": { + // TODO: replace with original + // "model": "distilbert-base-uncased-finetuned-sst-2-english", + "model": "Xenova/distilbert-base-uncased-finetuned-sst-2-english", + }, + "type": "text", + }, + "token-classification": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": TokenClassificationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForTokenClassification, + "default": { + // TODO: replace with original + // "model": "Davlan/bert-base-multilingual-cased-ner-hrl", + "model": "Xenova/bert-base-multilingual-cased-ner-hrl", + }, + "type": "text", + }, + "question-answering": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": QuestionAnsweringPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForQuestionAnswering, + "default": { + // TODO: replace with original + // "model": "distilbert-base-cased-distilled-squad", + "model": "Xenova/distilbert-base-cased-distilled-squad", + }, + "type": "text", + }, + + "fill-mask": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": FillMaskPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForMaskedLM, + "default": { + // TODO: replace with original + // "model": "bert-base-uncased", + "model": "Xenova/bert-base-uncased", + }, + "type": "text", + }, + "summarization": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": SummarizationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForSeq2SeqLM, + "default": { + // TODO: replace with original + // "model": "sshleifer/distilbart-cnn-6-6", + "model": "Xenova/distilbart-cnn-6-6", + }, + "type": "text", + }, + "translation": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": TranslationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForSeq2SeqLM, + "default": { + // TODO: replace with original + // "model": "t5-small", + "model": "Xenova/t5-small", + }, + "type": "text", + }, + "text2text-generation": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": Text2TextGenerationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForSeq2SeqLM, + "default": { + // TODO: replace with original + // "model": "google/flan-t5-small", + "model": "Xenova/flan-t5-small", + }, + "type": "text", + }, + "text-generation": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": TextGenerationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForCausalLM, + "default": { + // TODO: replace with original + // "model": "gpt2", + "model": "Xenova/gpt2", + }, + "type": "text", + }, + "zero-shot-classification": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": ZeroShotClassificationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForSequenceClassification, + "default": { + // TODO: replace with original + // "model": "typeform/distilbert-base-uncased-mnli", + "model": "Xenova/distilbert-base-uncased-mnli", + }, + "type": "text", + }, + "audio-classification": { + "pipeline": AudioClassificationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForAudioClassification, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "superb/wav2vec2-base-superb-ks", + "model": "Xenova/wav2vec2-base-superb-ks", + }, + "type": "audio", + }, + "zero-shot-audio-classification": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": ZeroShotAudioClassificationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModel, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "laion/clap-htsat-fused", + "model": "Xenova/clap-htsat-unfused", + }, + "type": "multimodal", + }, + "automatic-speech-recognition": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": AutomaticSpeechRecognitionPipeline, + "model": [_models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForSpeechSeq2Seq, _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForCTC], + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "openai/whisper-tiny.en", + "model": "Xenova/whisper-tiny.en", + }, + "type": "multimodal", + }, + "text-to-audio": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": TextToAudioPipeline, + "model": [_models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForTextToWaveform, _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForTextToSpectrogram], + "processor": [_models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, /* Some don't use a processor */ null], + "default": { + // TODO: replace with original + // "model": "microsoft/speecht5_tts", + "model": "Xenova/speecht5_tts", + }, + "type": "text", + }, + "image-to-text": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": ImageToTextPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForVision2Seq, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "nlpconnect/vit-gpt2-image-captioning", + "model": "Xenova/vit-gpt2-image-captioning", + }, + "type": "multimodal", + }, + + "image-classification": { + // no tokenizer + "pipeline": ImageClassificationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForImageClassification, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "google/vit-base-patch16-224", + "model": "Xenova/vit-base-patch16-224", + }, + "type": "multimodal", + }, + + "image-segmentation": { + // no tokenizer + "pipeline": ImageSegmentationPipeline, + "model": [_models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForImageSegmentation, _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForSemanticSegmentation, _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForUniversalSegmentation], + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "facebook/detr-resnet-50-panoptic", + "model": "Xenova/detr-resnet-50-panoptic", + }, + "type": "multimodal", + }, + "background-removal": { + // no tokenizer + "pipeline": BackgroundRemovalPipeline, + "model": [_models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForImageSegmentation, _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForSemanticSegmentation, _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForUniversalSegmentation], + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + "model": "Xenova/modnet", + }, + "type": "image", + }, + + "zero-shot-image-classification": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": ZeroShotImageClassificationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModel, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "openai/clip-vit-base-patch32", + "model": "Xenova/clip-vit-base-patch32", + }, + "type": "multimodal", + }, + + "object-detection": { + // no tokenizer + "pipeline": ObjectDetectionPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForObjectDetection, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "facebook/detr-resnet-50", + "model": "Xenova/detr-resnet-50", + }, + "type": "multimodal", + }, + "zero-shot-object-detection": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": ZeroShotObjectDetectionPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForZeroShotObjectDetection, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "google/owlvit-base-patch32", + "model": "Xenova/owlvit-base-patch32", + }, + "type": "multimodal", + }, + "document-question-answering": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": DocumentQuestionAnsweringPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForDocumentQuestionAnswering, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "naver-clova-ix/donut-base-finetuned-docvqa", + "model": "Xenova/donut-base-finetuned-docvqa", + }, + "type": "multimodal", + }, + "image-to-image": { + // no tokenizer + "pipeline": ImageToImagePipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForImageToImage, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "caidas/swin2SR-classical-sr-x2-64", + "model": "Xenova/swin2SR-classical-sr-x2-64", + }, + "type": "image", + }, + "depth-estimation": { + // no tokenizer + "pipeline": DepthEstimationPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForDepthEstimation, + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "default": { + // TODO: replace with original + // "model": "Intel/dpt-large", + "model": "Xenova/dpt-large", + }, + "type": "image", + }, + + // This task serves as a useful interface for dealing with sentence-transformers (https://huggingface.co/sentence-transformers). + "feature-extraction": { + "tokenizer": _tokenizers_js__WEBPACK_IMPORTED_MODULE_0__.AutoTokenizer, + "pipeline": FeatureExtractionPipeline, + "model": _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModel, + "default": { + // TODO: replace with original + // "model": "sentence-transformers/all-MiniLM-L6-v2", + "model": "Xenova/all-MiniLM-L6-v2", + }, + "type": "text", + }, + "image-feature-extraction": { + "processor": _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_2__.AutoProcessor, + "pipeline": ImageFeatureExtractionPipeline, + "model": [_models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModelForImageFeatureExtraction, _models_js__WEBPACK_IMPORTED_MODULE_1__.AutoModel], + "default": { + // TODO: replace with original + // "model": "google/vit-base-patch16-224", + "model": "Xenova/vit-base-patch16-224-in21k", + }, + "type": "image", + }, +}) + + +// TODO: Add types for TASK_ALIASES +const TASK_ALIASES = Object.freeze({ + "sentiment-analysis": "text-classification", + "ner": "token-classification", + // "vqa": "visual-question-answering", // TODO: Add + "asr": "automatic-speech-recognition", + "text-to-speech": "text-to-audio", + + // Add for backwards compatibility + "embeddings": "feature-extraction", +}); + +/** + * @typedef {keyof typeof SUPPORTED_TASKS} TaskType + * @typedef {keyof typeof TASK_ALIASES} AliasType + * @typedef {TaskType | AliasType} PipelineType All possible pipeline types. + * @typedef {{[K in TaskType]: InstanceType}} SupportedTasks A mapping of pipeline names to their corresponding pipeline classes. + * @typedef {{[K in AliasType]: InstanceType}} AliasTasks A mapping from pipeline aliases to their corresponding pipeline classes. + * @typedef {SupportedTasks & AliasTasks} AllTasks A mapping from all pipeline names and aliases to their corresponding pipeline classes. + */ + +/** + * Utility factory method to build a `Pipeline` object. + * + * @template {PipelineType} T The type of pipeline to return. + * @param {T} task The task defining which pipeline will be returned. Currently accepted tasks are: + * - `"audio-classification"`: will return a `AudioClassificationPipeline`. + * - `"automatic-speech-recognition"`: will return a `AutomaticSpeechRecognitionPipeline`. + * - `"depth-estimation"`: will return a `DepthEstimationPipeline`. + * - `"document-question-answering"`: will return a `DocumentQuestionAnsweringPipeline`. + * - `"feature-extraction"`: will return a `FeatureExtractionPipeline`. + * - `"fill-mask"`: will return a `FillMaskPipeline`. + * - `"image-classification"`: will return a `ImageClassificationPipeline`. + * - `"image-segmentation"`: will return a `ImageSegmentationPipeline`. + * - `"image-to-text"`: will return a `ImageToTextPipeline`. + * - `"object-detection"`: will return a `ObjectDetectionPipeline`. + * - `"question-answering"`: will return a `QuestionAnsweringPipeline`. + * - `"summarization"`: will return a `SummarizationPipeline`. + * - `"text2text-generation"`: will return a `Text2TextGenerationPipeline`. + * - `"text-classification"` (alias "sentiment-analysis" available): will return a `TextClassificationPipeline`. + * - `"text-generation"`: will return a `TextGenerationPipeline`. + * - `"token-classification"` (alias "ner" available): will return a `TokenClassificationPipeline`. + * - `"translation"`: will return a `TranslationPipeline`. + * - `"translation_xx_to_yy"`: will return a `TranslationPipeline`. + * - `"zero-shot-classification"`: will return a `ZeroShotClassificationPipeline`. + * - `"zero-shot-audio-classification"`: will return a `ZeroShotAudioClassificationPipeline`. + * - `"zero-shot-image-classification"`: will return a `ZeroShotImageClassificationPipeline`. + * - `"zero-shot-object-detection"`: will return a `ZeroShotObjectDetectionPipeline`. + * @param {string} [model=null] The name of the pre-trained model to use. If not specified, the default model for the task will be used. + * @param {import('./utils/hub.js').PretrainedModelOptions} [options] Optional parameters for the pipeline. + * @returns {Promise} A Pipeline object for the specified task. + * @throws {Error} If an unsupported pipeline is requested. + */ +async function pipeline( + task, + model = null, + { + progress_callback = null, + config = null, + cache_dir = null, + local_files_only = false, + revision = 'main', + device = null, + dtype = null, + subfolder = 'onnx', + use_external_data_format = null, + model_file_name = null, + session_options = {}, + } = {} +) { + // Helper method to construct pipeline + + // Apply aliases + // @ts-ignore + task = TASK_ALIASES[task] ?? task; + + // Get pipeline info + const pipelineInfo = SUPPORTED_TASKS[task.split('_', 1)[0]]; + if (!pipelineInfo) { + throw Error(`Unsupported pipeline: ${task}. Must be one of [${Object.keys(SUPPORTED_TASKS)}]`) + } + + // Use model if specified, otherwise, use default + if (!model) { + model = pipelineInfo.default.model + console.log(`No model specified. Using default model: "${model}".`); + } + + const pretrainedOptions = { + progress_callback, + config, + cache_dir, + local_files_only, + revision, + device, + dtype, + subfolder, + use_external_data_format, + model_file_name, + session_options, + } + + const classes = new Map([ + ['tokenizer', pipelineInfo.tokenizer], + ['model', pipelineInfo.model], + ['processor', pipelineInfo.processor], + ]); + + // Load model, tokenizer, and processor (if they exist) + const results = await loadItems(classes, model, pretrainedOptions); + results.task = task; + + (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_5__.dispatchCallback)(progress_callback, { + 'status': 'ready', + 'task': task, + 'model': model, + }); + + const pipelineClass = pipelineInfo.pipeline; + return new pipelineClass(results); +} + + +/** + * Helper function to get applicable model, tokenizer, or processor classes for a given model. + * @param {Map} mapping The mapping of names to classes, arrays of classes, or null. + * @param {string} model The name of the model to load. + * @param {import('./utils/hub.js').PretrainedOptions} pretrainedOptions The options to pass to the `from_pretrained` method. + * @private + */ +async function loadItems(mapping, model, pretrainedOptions) { + + const result = Object.create(null); + + /**@type {Promise[]} */ + const promises = []; + for (const [name, cls] of mapping.entries()) { + if (!cls) continue; + + /**@type {Promise} */ + let promise; + if (Array.isArray(cls)) { + promise = new Promise(async (resolve, reject) => { + let e; + for (const c of cls) { + if (c === null) { + // If null, we resolve it immediately, meaning the relevant + // class was not found, but it is optional. + resolve(null); + return; + } + try { + resolve(await c.from_pretrained(model, pretrainedOptions)); + return; + } catch (err) { + if (err.message?.includes('Unsupported model type')) { + // If the error is due to an unsupported model type, we + // save the error and try the next class. + e = err; + } else if (err.message?.includes('Could not locate file')) { + e = err; + } else { + reject(err); + return; + } + + } + } + reject(e); + }) + } else { + promise = cls.from_pretrained(model, pretrainedOptions); + } + + result[name] = promise; + promises.push(promise); + } + + // Wait for all promises to resolve (in parallel) + await Promise.all(promises); + + // Then assign to result + for (const [name, promise] of Object.entries(result)) { + result[name] = await promise; + } + + return result; +} + + +/***/ }), + +/***/ "./src/tokenizers.js": +/*!***************************!*\ + !*** ./src/tokenizers.js ***! + \***************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ AlbertTokenizer: () => (/* binding */ AlbertTokenizer), +/* harmony export */ AutoTokenizer: () => (/* binding */ AutoTokenizer), +/* harmony export */ BartTokenizer: () => (/* binding */ BartTokenizer), +/* harmony export */ BertTokenizer: () => (/* binding */ BertTokenizer), +/* harmony export */ BlenderbotSmallTokenizer: () => (/* binding */ BlenderbotSmallTokenizer), +/* harmony export */ BlenderbotTokenizer: () => (/* binding */ BlenderbotTokenizer), +/* harmony export */ BloomTokenizer: () => (/* binding */ BloomTokenizer), +/* harmony export */ CLIPTokenizer: () => (/* binding */ CLIPTokenizer), +/* harmony export */ CamembertTokenizer: () => (/* binding */ CamembertTokenizer), +/* harmony export */ CodeGenTokenizer: () => (/* binding */ CodeGenTokenizer), +/* harmony export */ CodeLlamaTokenizer: () => (/* binding */ CodeLlamaTokenizer), +/* harmony export */ CohereTokenizer: () => (/* binding */ CohereTokenizer), +/* harmony export */ ConvBertTokenizer: () => (/* binding */ ConvBertTokenizer), +/* harmony export */ DebertaTokenizer: () => (/* binding */ DebertaTokenizer), +/* harmony export */ DebertaV2Tokenizer: () => (/* binding */ DebertaV2Tokenizer), +/* harmony export */ DistilBertTokenizer: () => (/* binding */ DistilBertTokenizer), +/* harmony export */ ElectraTokenizer: () => (/* binding */ ElectraTokenizer), +/* harmony export */ Ernie4_5_Tokenizer: () => (/* binding */ Ernie4_5_Tokenizer), +/* harmony export */ EsmTokenizer: () => (/* binding */ EsmTokenizer), +/* harmony export */ FalconTokenizer: () => (/* binding */ FalconTokenizer), +/* harmony export */ GPT2Tokenizer: () => (/* binding */ GPT2Tokenizer), +/* harmony export */ GPTNeoXTokenizer: () => (/* binding */ GPTNeoXTokenizer), +/* harmony export */ GemmaTokenizer: () => (/* binding */ GemmaTokenizer), +/* harmony export */ Grok1Tokenizer: () => (/* binding */ Grok1Tokenizer), +/* harmony export */ HerbertTokenizer: () => (/* binding */ HerbertTokenizer), +/* harmony export */ LlamaTokenizer: () => (/* binding */ LlamaTokenizer), +/* harmony export */ M2M100Tokenizer: () => (/* binding */ M2M100Tokenizer), +/* harmony export */ MBart50Tokenizer: () => (/* binding */ MBart50Tokenizer), +/* harmony export */ MBartTokenizer: () => (/* binding */ MBartTokenizer), +/* harmony export */ MPNetTokenizer: () => (/* binding */ MPNetTokenizer), +/* harmony export */ MarianTokenizer: () => (/* binding */ MarianTokenizer), +/* harmony export */ MgpstrTokenizer: () => (/* binding */ MgpstrTokenizer), +/* harmony export */ MobileBertTokenizer: () => (/* binding */ MobileBertTokenizer), +/* harmony export */ NllbTokenizer: () => (/* binding */ NllbTokenizer), +/* harmony export */ NougatTokenizer: () => (/* binding */ NougatTokenizer), +/* harmony export */ PreTrainedTokenizer: () => (/* binding */ PreTrainedTokenizer), +/* harmony export */ Qwen2Tokenizer: () => (/* binding */ Qwen2Tokenizer), +/* harmony export */ RoFormerTokenizer: () => (/* binding */ RoFormerTokenizer), +/* harmony export */ RobertaTokenizer: () => (/* binding */ RobertaTokenizer), +/* harmony export */ SiglipTokenizer: () => (/* binding */ SiglipTokenizer), +/* harmony export */ SpeechT5Tokenizer: () => (/* binding */ SpeechT5Tokenizer), +/* harmony export */ SqueezeBertTokenizer: () => (/* binding */ SqueezeBertTokenizer), +/* harmony export */ T5Tokenizer: () => (/* binding */ T5Tokenizer), +/* harmony export */ TokenizerModel: () => (/* binding */ TokenizerModel), +/* harmony export */ VitsTokenizer: () => (/* binding */ VitsTokenizer), +/* harmony export */ Wav2Vec2CTCTokenizer: () => (/* binding */ Wav2Vec2CTCTokenizer), +/* harmony export */ WhisperTokenizer: () => (/* binding */ WhisperTokenizer), +/* harmony export */ XLMRobertaTokenizer: () => (/* binding */ XLMRobertaTokenizer), +/* harmony export */ XLMTokenizer: () => (/* binding */ XLMTokenizer), +/* harmony export */ is_chinese_char: () => (/* binding */ is_chinese_char) +/* harmony export */ }); +/* harmony import */ var _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./utils/generic.js */ "./src/utils/generic.js"); +/* harmony import */ var _utils_core_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./utils/core.js */ "./src/utils/core.js"); +/* harmony import */ var _utils_hub_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./utils/hub.js */ "./src/utils/hub.js"); +/* harmony import */ var _utils_maths_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./utils/maths.js */ "./src/utils/maths.js"); +/* harmony import */ var _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ./utils/tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var _utils_data_structures_js__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ./utils/data-structures.js */ "./src/utils/data-structures.js"); +/* harmony import */ var _huggingface_jinja__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! @huggingface/jinja */ "./node_modules/@huggingface/jinja/dist/index.js"); +/* harmony import */ var _models_whisper_common_whisper_js__WEBPACK_IMPORTED_MODULE_7__ = __webpack_require__(/*! ./models/whisper/common_whisper.js */ "./src/models/whisper/common_whisper.js"); + +/** + * @file Tokenizers are used to prepare textual inputs for a model. + * + * **Example:** Create an `AutoTokenizer` and use it to tokenize a sentence. + * This will automatically detect the tokenizer type based on the tokenizer class defined in `tokenizer.json`. + * ```javascript + * import { AutoTokenizer } from '@huggingface/transformers'; + * + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased'); + * const { input_ids } = await tokenizer('I love transformers!'); + * // Tensor { + * // data: BigInt64Array(6) [101n, 1045n, 2293n, 19081n, 999n, 102n], + * // dims: [1, 6], + * // type: 'int64', + * // size: 6, + * // } + * ``` + * + * @module tokenizers + */ + + + + + + + + + + + + + + + +/** + * @typedef {Object} TokenizerProperties Additional tokenizer-specific properties. + * @property {boolean} [legacy=false] Whether or not the `legacy` behavior of the tokenizer should be used. + * @typedef {import('./utils/hub.js').PretrainedOptions & TokenizerProperties} PretrainedTokenizerOptions + */ + +/** + * Loads a tokenizer from the specified path. + * @param {string} pretrained_model_name_or_path The path to the tokenizer directory. + * @param {PretrainedTokenizerOptions} options Additional options for loading the tokenizer. + * @returns {Promise} A promise that resolves with information about the loaded tokenizer. + */ +async function loadTokenizer(pretrained_model_name_or_path, options) { + + const info = await Promise.all([ + (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_2__.getModelJSON)(pretrained_model_name_or_path, 'tokenizer.json', true, options), + (0,_utils_hub_js__WEBPACK_IMPORTED_MODULE_2__.getModelJSON)(pretrained_model_name_or_path, 'tokenizer_config.json', true, options), + ]) + + // Override legacy option if `options.legacy` is not null + if (options.legacy !== null) { + info[1].legacy = options.legacy; + } + return info; +} + + +/** + * Helper function to split a string on a regex, but keep the delimiters. + * This is required, because the JavaScript `.split()` method does not keep the delimiters, + * and wrapping in a capturing group causes issues with existing capturing groups (due to nesting). + * @param {string} text The text to split. + * @param {RegExp} regex The regex to split on. + * @returns {string[]} The split string. + */ +function regexSplit(text, regex) { + const result = []; + let prev = 0; + for (const match of text.matchAll(regex)) { + const fullMatch = match[0]; + if (prev < match.index) { + result.push(text.slice(prev, match.index)); + } + if (fullMatch.length > 0) { + result.push(fullMatch); + } + prev = match.index + fullMatch.length; + } + if (prev < text.length) { + result.push(text.slice(prev)); + } + return result; +} + + +/** + * Helper method to construct a pattern from a config object. + * @param {Object} pattern The pattern object. + * @param {boolean} invert Whether to invert the pattern. + * @returns {RegExp|null} The compiled pattern. + */ +function createPattern(pattern, invert = true) { + + if (pattern.Regex !== undefined) { + // In certain cases, the pattern may contain unnecessary escape sequences (e.g., \# or \& or \~). + // i.e., valid in Python (where the patterns are exported from) but invalid in JavaScript (where the patterns are parsed). + // This isn't an issue when creating the regex w/o the 'u' flag, but it is when the 'u' flag is used. + // For this reason, it is necessary to remove these backslashes before creating the regex. + // See https://stackoverflow.com/a/63007777/13989043 for more information + let regex = pattern.Regex.replace(/\\([#&~])/g, '$1'); // TODO: add more characters to this list if necessary + + // We also handle special cases where the regex contains invalid (non-JS compatible) syntax. + for (const [key, value] of PROBLEMATIC_REGEX_MAP) { + regex = regex.replaceAll(key, value); + } + + return new RegExp(regex, 'gu'); + + } else if (pattern.String !== undefined) { + const escaped = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.escapeRegExp)(pattern.String); + // NOTE: if invert is true, we wrap the pattern in a group so that it is kept when performing .split() + return new RegExp(invert ? escaped : `(${escaped})`, 'gu'); + + } else { + console.warn('Unknown pattern type:', pattern) + return null; + } +} + +/** + * Helper function to convert an Object to a Map + * @param {Object} obj The object to convert. + * @returns {Map} The map. + */ +function objectToMap(obj) { + return new Map(Object.entries(obj)); +} + +/** + * Helper function to convert a tensor to a list before decoding. + * @param {Tensor} tensor The tensor to convert. + * @returns {number[]} The tensor as a list. + */ +function prepareTensorForDecode(tensor) { + const dims = tensor.dims; + switch (dims.length) { + case 1: + return tensor.tolist(); + case 2: + if (dims[0] !== 1) { + throw new Error('Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.'); + } + return tensor.tolist()[0]; + default: + throw new Error(`Expected tensor to have 1-2 dimensions, got ${dims.length}.`) + } +} + +/** + * Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms + * @param {string} text The text to clean up. + * @returns {string} The cleaned up text. + */ +function clean_up_tokenization(text) { + // Clean up a list of simple English tokenization artifacts + // like spaces before punctuations and abbreviated forms + return text.replace(/ \./g, '.') + .replace(/ \?/g, '?') + .replace(/ \!/g, '!') + .replace(/ ,/g, ',') + .replace(/ \' /g, "'") + .replace(/ n\'t/g, "n't") + .replace(/ \'m/g, "'m") + .replace(/ \'s/g, "'s") + .replace(/ \'ve/g, "'ve") + .replace(/ \'re/g, "'re"); +} + +/** + * Helper function to remove accents from a string. + * @param {string} text The text to remove accents from. + * @returns {string} The text with accents removed. + */ +function remove_accents(text) { + return text.replace(/\p{M}/gu, ''); +} + +/** + * Helper function to lowercase a string and remove accents. + * @param {string} text The text to lowercase and remove accents from. + * @returns {string} The lowercased text with accents removed. + */ +function lowercase_and_remove_accent(text) { + return remove_accents(text.toLowerCase()); +} + + +/** + * Checks whether the given Unicode codepoint represents a CJK (Chinese, Japanese, or Korean) character. + * + * A "chinese character" is defined as anything in the CJK Unicode block: + * https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + * + * Note that the CJK Unicode block is NOT all Japanese and Korean characters, despite its name. + * The modern Korean Hangul alphabet is a different block, as is Japanese Hiragana and Katakana. + * Those alphabets are used to write space-separated words, so they are not treated specially + * and are handled like all other languages. + * + * @param {number|bigint} cp The Unicode codepoint to check. + * @returns {boolean} True if the codepoint represents a CJK character, false otherwise. + */ +function is_chinese_char(cp) { + return ( + (cp >= 0x4E00 && cp <= 0x9FFF) + || (cp >= 0x3400 && cp <= 0x4DBF) + || (cp >= 0x20000 && cp <= 0x2A6DF) + || (cp >= 0x2A700 && cp <= 0x2B73F) + || (cp >= 0x2B740 && cp <= 0x2B81F) + || (cp >= 0x2B820 && cp <= 0x2CEAF) + || (cp >= 0xF900 && cp <= 0xFAFF) + || (cp >= 0x2F800 && cp <= 0x2FA1F) + ) +} + +/** + * Helper function to fuse consecutive unknown tokens. + * @param {string[]} arr The list of input tokens + * @param {Map} tokens_to_ids The mapping from tokens to token ids. + * @param {number} unk_token_id The value to fuse on. + * @private + */ +function fuse_unk(arr, tokens_to_ids, unk_token_id) { + const fused = []; + let i = 0; + while (i < arr.length) { + fused.push(arr[i]) + if ((tokens_to_ids.get(arr[i]) ?? unk_token_id) !== unk_token_id) { + ++i; + continue; + } + + while (++i < arr.length && (tokens_to_ids.get(arr[i]) ?? unk_token_id) === unk_token_id) { + if (tokens_to_ids.get(fused.at(-1)) !== unk_token_id) { + fused[fused.length - 1] += arr[i]; + } + } + } + + return fused; +} + +/** + * Split a string on whitespace. + * @param {string} text The text to split. + * @returns {string[]} The split string. + */ +function whitespace_split(text) { + return text.match(/\S+/g) || []; +} + +const PUNCTUATION_REGEX = '\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E'; +const PUNCTUATION_ONLY_REGEX = new RegExp(`^[${PUNCTUATION_REGEX}]+$`, 'gu'); +const BLOOM_SPLIT_CHARS = '.,!?\u2026\u3002\uff0c\u3001\u0964\u06d4\u060c'; + +// A mapping of regex patterns to their equivalent (but possibly longer) JS-compatible versions. +const PROBLEMATIC_REGEX_MAP = new Map([ + // This uses the case insensitive group modifier, which is not supported in JavaScript. + // When parsing the regex, an "Invalid group" error is thrown. + ["(?i:'s|'t|'re|'ve|'m|'ll|'d)", "(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"], + + // Used to override the default (invalid) regex of the bloom pretokenizer. + // For more information, see https://github.com/huggingface/transformers.js/issues/94 + [` ?[^(\\s|[${BLOOM_SPLIT_CHARS}])]+`, ` ?[^\\s${BLOOM_SPLIT_CHARS}]+`], +]) + + +/** + * Represent a token added by the user on top of the existing Model vocabulary. + * AddedToken can be configured to specify the behavior they should have in various situations like: + * - Whether they should only match single words + * - Whether to include any whitespace on its left or right + */ +class AddedToken { + /** + * Creates a new instance of AddedToken. + * @param {Object} config Added token configuration object. + * @param {string} config.content The content of the added token. + * @param {number} config.id The id of the added token. + * @param {boolean} [config.single_word=false] Whether this token must be a single word or can break words. + * @param {boolean} [config.lstrip=false] Whether this token should strip whitespaces on its left. + * @param {boolean} [config.rstrip=false] Whether this token should strip whitespaces on its right. + * @param {boolean} [config.normalized=false] Whether this token should be normalized. + * @param {boolean} [config.special=false] Whether this token is special. + */ + constructor(config) { + this.content = config.content; + this.id = config.id; + this.single_word = config.single_word ?? false; + this.lstrip = config.lstrip ?? false; + this.rstrip = config.rstrip ?? false; + this.special = config.special ?? false; + this.normalized = config.normalized ?? null; + } +} + +/** + * Abstract base class for tokenizer models. + * + * @extends Callable + */ +class TokenizerModel extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + /** + * Creates a new instance of TokenizerModel. + * @param {Object} config The configuration object for the TokenizerModel. + */ + constructor(config) { + super(); + this.config = config; + + /** @type {string[]} */ + this.vocab = []; + + /** + * A mapping of tokens to ids. + * @type {Map} + */ + this.tokens_to_ids = new Map(); + + this.unk_token_id = undefined; + this.unk_token = undefined; + this.end_of_word_suffix = undefined; + + /** @type {boolean} Whether to fuse unknown tokens when encoding. Defaults to false. */ + this.fuse_unk = this.config.fuse_unk ?? false; + } + + /** + * Instantiates a new TokenizerModel instance based on the configuration object provided. + * @param {Object} config The configuration object for the TokenizerModel. + * @param {...*} args Optional arguments to pass to the specific TokenizerModel constructor. + * @returns {TokenizerModel} A new instance of a TokenizerModel. + * @throws Will throw an error if the TokenizerModel type in the config is not recognized. + */ + static fromConfig(config, ...args) { + switch (config.type) { + case 'WordPiece': + return new WordPieceTokenizer(config); + case 'Unigram': + // @ts-ignore + return new Unigram(config, ...args); + case 'BPE': + return new BPE(config); + + default: + // Some older tokenizers, like `google-t5/t5-small`, `openai-community/gpt2`, and `distilbert/distilbert-base-uncased`, do not have a `type` field. + // In this case, we can infer the tokenizer type based on the structure of the `vocab` field and other properties. + if (config.vocab) { + if (Array.isArray(config.vocab)) { + // config.vocab is of type `[string, number][]` + // @ts-ignore + return new Unigram(config, ...args); + } else if (Object.hasOwn(config, 'continuing_subword_prefix') && Object.hasOwn(config, 'unk_token')) { + if (Object.hasOwn(config, 'merges')) { + return new BPE(config); + } else { + return new WordPieceTokenizer(config); + } + } else { + // @ts-ignore + return new LegacyTokenizerModel(config, ...args); + } + } + throw new Error(`Unknown TokenizerModel type: ${config.type}`); + } + } + + /** + * Internal function to call the TokenizerModel instance. + * @param {string[]} tokens The tokens to encode. + * @returns {string[]} The encoded tokens. + */ + _call(tokens) { + tokens = this.encode(tokens); + if (this.fuse_unk) { + // Fuse unknown tokens + tokens = fuse_unk(tokens, this.tokens_to_ids, this.unk_token_id); + } + return tokens; + } + + /** + * Encodes a list of tokens into a list of token IDs. + * @param {string[]} tokens The tokens to encode. + * @returns {string[]} The encoded tokens. + * @throws Will throw an error if not implemented in a subclass. + */ + encode(tokens) { + throw Error("encode should be implemented in subclass.") + } + + /** + * Converts a list of tokens into a list of token IDs. + * @param {string[]} tokens The tokens to convert. + * @returns {number[]} The converted token IDs. + */ + convert_tokens_to_ids(tokens) { + return tokens.map(t => this.tokens_to_ids.get(t) ?? this.unk_token_id); + } + + /** + * Converts a list of token IDs into a list of tokens. + * @param {number[]|bigint[]} ids The token IDs to convert. + * @returns {string[]} The converted tokens. + */ + convert_ids_to_tokens(ids) { + return ids.map(i => this.vocab[i] ?? this.unk_token); + } +} + +/** + * A subclass of TokenizerModel that uses WordPiece encoding to encode tokens. + * @extends TokenizerModel + */ +class WordPieceTokenizer extends TokenizerModel { + /** + * @param {Object} config The configuration object. + * @param {Object} config.vocab A mapping of tokens to ids. + * @param {string} config.unk_token The unknown token string. + * @param {string} config.continuing_subword_prefix The prefix to use for continuing subwords. + * @param {number} [config.max_input_chars_per_word=100] The maximum number of characters per word. + */ + constructor(config) { + super(config); + /** + * A mapping of tokens to ids. + * @type {Map} + */ + this.tokens_to_ids = objectToMap(config.vocab); + + /** + * The id of the unknown token. + * @type {number} + */ + this.unk_token_id = this.tokens_to_ids.get(config.unk_token); + + /** + * The unknown token string. + * @type {string} + */ + this.unk_token = config.unk_token; + + /** + * The maximum number of characters allowed per word. + * @type {number} + */ + this.max_input_chars_per_word = config.max_input_chars_per_word ?? 100; + + /** + * An array of tokens. + * @type {string[]} + */ + this.vocab = new Array(this.tokens_to_ids.size); + for (const [key, value] of this.tokens_to_ids) { + this.vocab[value] = key; + } + } + + /** + * Encodes an array of tokens using WordPiece encoding. + * @param {string[]} tokens The tokens to encode. + * @returns {string[]} An array of encoded tokens. + */ + encode(tokens) { + const outputTokens = []; + for (const token of tokens) { + const chars = [...token]; + if (chars.length > this.max_input_chars_per_word) { + outputTokens.push(this.unk_token); + continue; + } + + let isUnknown = false; + let start = 0; + const subTokens = []; + + while (start < chars.length) { + let end = chars.length; + let currentSubstring = null; + while (start < end) { + let substr = chars.slice(start, end).join(''); + + if (start > 0) { + substr = this.config.continuing_subword_prefix + substr; + } + if (this.tokens_to_ids.has(substr)) { + currentSubstring = substr; + break; + } + + --end; + } + if (currentSubstring === null) { + isUnknown = true; + break; + } + subTokens.push(currentSubstring); + start = end; + } + if (isUnknown) { + outputTokens.push(this.unk_token); + } else { + outputTokens.push(...subTokens); + } + } + + return outputTokens; + } + +} + +/** + * Class representing a Unigram tokenizer model. + * @extends TokenizerModel + */ +class Unigram extends TokenizerModel { + /** + * Create a new Unigram tokenizer model. + * @param {Object} config The configuration object for the Unigram model. + * @param {number} config.unk_id The ID of the unknown token + * @param {[string, number][]} config.vocab A 2D array representing a mapping of tokens to scores. + * @param {Object} moreConfig Additional configuration object for the Unigram model. + */ + constructor(config, moreConfig) { + super(config); + + const vocabSize = config.vocab.length; + this.vocab = new Array(vocabSize); + /** @type {number[]} */ + this.scores = new Array(vocabSize); + for (let i = 0; i < vocabSize; ++i) { + [this.vocab[i], this.scores[i]] = config.vocab[i]; + } + + this.unk_token_id = config.unk_id; + this.unk_token = this.vocab[config.unk_id]; + + this.tokens_to_ids = new Map(this.vocab.map((x, i) => [x, i])); + this.bos_token = ' '; // beginning of a sentence token + + this.bos_token_id = this.tokens_to_ids.get(this.bos_token); // NOTE: may be undefined + this.eos_token = moreConfig.eos_token; + + this.eos_token_id = this.tokens_to_ids.get(this.eos_token); + this.unk_token = this.vocab[this.unk_token_id]; + + this.minScore = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.min)(this.scores)[0]; + + this.unk_score = this.minScore - 10.0; + this.scores[this.unk_token_id] = this.unk_score; + + this.trie = new _utils_data_structures_js__WEBPACK_IMPORTED_MODULE_5__.CharTrie(); + this.trie.extend(this.vocab); + + // NOTE: `fuse_unk` is hardcoded to true for Unigram models + // See: https://github.com/huggingface/tokenizers/blob/b58227c7f1ccf8b73ee2268354336da56d91e492/tokenizers/src/models/unigram/model.rs#L119 + this.fuse_unk = true; + } + + /** + * Populates lattice nodes. + * @param {TokenLattice} lattice The token lattice to populate with nodes. + */ + populateNodes(lattice) { + const chars = lattice.chars; + const mblen = 1; + let beginPos = 0; + while (beginPos < chars.length) { + let hasSingleNode = false; + + const tokens = []; + const sliced = chars.slice(beginPos).join(''); + const prefixedTokens = this.trie.commonPrefixSearch(sliced); + for (const token of prefixedTokens) { + tokens.push(token); + const tokenId = this.tokens_to_ids.get(token); + const tokenScore = this.scores[tokenId]; + const n = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.len)(token); + lattice.insert(beginPos, n, tokenScore, tokenId); + if (!hasSingleNode && n === mblen) { + hasSingleNode = true; + } + } + if (!hasSingleNode) { + lattice.insert(beginPos, mblen, this.unk_score, this.unk_token_id); + } + beginPos += mblen; + } + } + + /** + * Encodes an array of tokens into an array of subtokens using the unigram model. + * + * @param {string} normalized The normalized string. + * @returns {string[]} An array of subtokens obtained by encoding the input tokens using the unigram model. + */ + tokenize(normalized) { + const lattice = new _utils_data_structures_js__WEBPACK_IMPORTED_MODULE_5__.TokenLattice(normalized, this.bos_token_id, this.eos_token_id); + this.populateNodes(lattice); + return lattice.tokens(); + } + + /** + * Encodes an array of tokens using Unigram encoding. + * @param {string[]} tokens The tokens to encode. + * @returns {string[]} An array of encoded tokens. + */ + encode(tokens) { + const toReturn = []; + for (const token of tokens) { + const tokenized = this.tokenize(token); + toReturn.push(...tokenized); + } + return toReturn; + } + +} + +/** + * Returns list of utf-8 byte and a mapping to unicode strings. + * Specifically avoids mapping to whitespace/control characters the BPE code barfs on. + * @returns {Object} Object with utf-8 byte keys and unicode string values. + */ +const BYTES_TO_UNICODE = (() => { + // Returns list of utf-8 byte and a mapping to unicode strings. + // We specifically avoids mapping to whitespace/control characters + // the bpe code barfs on. + + const bs = [ + ...Array.from({ length: "~".charCodeAt(0) - "!".charCodeAt(0) + 1 }, (_, i) => i + "!".charCodeAt(0)), + ...Array.from({ length: "¬".charCodeAt(0) - "¡".charCodeAt(0) + 1 }, (_, i) => i + "¡".charCodeAt(0)), + ...Array.from({ length: "ÿ".charCodeAt(0) - "®".charCodeAt(0) + 1 }, (_, i) => i + "®".charCodeAt(0)), + ]; + const cs = bs.slice(); + let n = 0; + for (let b = 0; b < 256; ++b) { + if (!bs.includes(b)) { + bs.push(b); + cs.push(256 + n); + n += 1; + } + } + const ccs = cs.map(n => String.fromCharCode(n)); + return Object.fromEntries(bs.map((b, i) => [b, ccs[i]])); +})(); + +const UNICODE_TO_BYTES = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.reverseDictionary)(BYTES_TO_UNICODE); + + +/** + * @typedef {Object} BPENode + * @property {string} token The token associated with the node + * @property {number} bias A positional bias for the node. + * @property {number} [score] The score of the node. + * @property {BPENode} [prev] The previous node in the linked list. + * @property {BPENode} [next] The next node in the linked list. + */ + +/** + * BPE class for encoding text into Byte-Pair-Encoding (BPE) tokens. + * @extends TokenizerModel + */ +class BPE extends TokenizerModel { + /** + * Create a BPE instance. + * @param {Object} config The configuration object for BPE. + * @param {Object} config.vocab A mapping of tokens to ids. + * @param {string[]|[string, string][]} config.merges An array of BPE merges as strings. + * @param {string} config.unk_token The unknown token used for out of vocabulary words. + * @param {string} config.end_of_word_suffix The suffix to place at the end of each word. + * @param {string} [config.continuing_subword_suffix] The suffix to insert between words. + * @param {boolean} [config.byte_fallback=false] Whether to use spm byte-fallback trick (defaults to False) + * @param {boolean} [config.ignore_merges=false] Whether or not to match tokens with the vocab before using merges. + */ + constructor(config) { + super(config); + + /** @type {Map} */ + this.tokens_to_ids = objectToMap(config.vocab); + + this.unk_token_id = this.tokens_to_ids.get(config.unk_token); + this.unk_token = config.unk_token; + + this.vocab = new Array(this.tokens_to_ids.size); + for (const [key, value] of this.tokens_to_ids) { + this.vocab[value] = key; + } + + // Tokenizers >= 0.20.0 serializes BPE merges as a [string, string][] instead of a string[], + // which resolves the ambiguity for merges containing spaces. + const use_new_merge_format = Array.isArray(config.merges[0]); + + /** @type {[string, string][]} */ + this.merges = use_new_merge_format + ? /** @type {[string, string][]} */(config.merges) + : (/** @type {string[]} */(config.merges)).map(x => /** @type {[string, string]} */(x.split(' ', 2))); + this.bpe_ranks = new Map(this.merges.map((x, i) => [JSON.stringify(x), i])); + + this.end_of_word_suffix = config.end_of_word_suffix; + + // NOTE: `continuing_subword_suffix` is custom (to support `BlenderbotSmallTokenizer`) + this.continuing_subword_suffix = config.continuing_subword_suffix ?? null; + + this.byte_fallback = this.config.byte_fallback ?? false; + + if (this.byte_fallback) { + this.text_encoder = new TextEncoder(); + } + + this.ignore_merges = this.config.ignore_merges ?? false; + + /** + * The maximum length we should cache in a model. + * Strings that are too long have minimal chances to cache hit anyway + */ + this.max_length_to_cache = 256; + + /** + * The default capacity for a `BPE`'s internal cache. + */ + this.cache_capacity = 10000; + this.cache = new _utils_data_structures_js__WEBPACK_IMPORTED_MODULE_5__.LRUCache(this.cache_capacity); + } + + /** + * Clears the cache. + */ + clear_cache() { + this.cache.clear(); + } + + /** + * Apply Byte-Pair-Encoding (BPE) to a given token. Efficient heap-based priority + * queue implementation adapted from https://github.com/belladoreai/llama-tokenizer-js. + * @param {string} token The token to encode. + * @returns {string[]} The BPE encoded tokens. + */ + bpe(token) { + if (token.length === 0) { + return []; + } + + const cached = this.cache.get(token); + if (cached !== undefined) { + return cached; + } + + const word = Array.from(token); + if (this.end_of_word_suffix) { + word[word.length - 1] += this.end_of_word_suffix; + } + + let result = []; + if (word.length > 1) { + // Create a priority queue to store the nodes that will be merged. + // The comparator function compares the scores of the nodes. + const queue = new _utils_data_structures_js__WEBPACK_IMPORTED_MODULE_5__.PriorityQueue((a, b) => a.score < b.score); + + // Construct a doubly-linked list of nodes that will be inserted into the priority queue, + // starting with the individual characters. We also populate each node with a positional + // bias to break ties in the priority queue. + let startingNode = { + token: word[0], + bias: 0, + prev: null, + next: null, + } + + let previousNode = startingNode + for (let i = 1; i < word.length; ++i) { + const currentNode = { + bias: i / word.length, // Add fractional component to break ties + token: word[i], + prev: previousNode, + next: null, + } + previousNode.next = currentNode + this._add_node(queue, previousNode) + previousNode = currentNode + } + + while (!queue.isEmpty()) { + // Get the next node with the highest priority + const node = queue.pop(); + + // Check that this merge is still possible + if (node.deleted || !node.next || node.next.deleted) continue; + + // Here, we mark the current node (left side of the merge) and the next node (right side of the merge) as deleted. + // This is because they will both be replaced by a new node representing the merge result. + node.deleted = true; + node.next.deleted = true; + + // Next, we fix the node that comes before the current node (i.e., left side of the merge). + if (node.prev) { + + // Make a shallow copy of the previous node + const newPreviousNode = { ...node.prev }; + + // Mark the old previous node as deleted. This avoids erroneous merges later, + // because there may still be references to this node in the priority queue. + node.prev.deleted = true; + node.prev = newPreviousNode; + + // Update the reference of the previous node, by pointing its previous node to this new previous node. + if (newPreviousNode.prev) { + newPreviousNode.prev.next = newPreviousNode; + } else { + // If the previous of the previous node does not exist, it means that + // `newPreviousNode` must be the new `startingNode`. + startingNode = newPreviousNode; + } + } + + // Create a new node which represents the result of the merge. + const merged = { + token: node.token + node.next.token, + bias: node.bias, + prev: node.prev, + next: node.next.next, + } + + // We now consider where we can add the new merged node to the priority queue: + // 1. prev <-> merged + if (merged.prev) { + merged.prev.next = merged; + this._add_node(queue, merged.prev); + } else { + // If `merged.prev` does not exist, then `merged` must be the new `startingNode`. + startingNode = merged; + } + + // 2. merged <-> next + if (merged.next) { + merged.next.prev = merged; + this._add_node(queue, merged); + } + } + + // Traverse the linked list, starting from the `startingNode`, and collect the tokens. + for (let currentNode = startingNode; currentNode !== null; currentNode = currentNode.next) { + result.push(currentNode.token); + } + } else { + result = word; + } + + // Possibly append suffix + if (this.continuing_subword_suffix) { + // Do not append suffix to the last token + for (let i = 0; i < result.length - 1; ++i) { + result[i] += this.continuing_subword_suffix; + } + } + + if (token.length < this.max_length_to_cache) { + // Save the result to the cache + this.cache.put(token, result); + } + + return result; + } + + + /** + * Helper function to add a node to the priority queue. + * @param {PriorityQueue} queue + * @param {BPENode} node + * @private + */ + _add_node(queue, node) { + // `score` is a measure of the merge priority: lower means higher priority + // We use the BPE rank as a measure of priority (i.e., the local of the merge in the merges list) + // We also add a fractional component to the score to break ties (with the earlier character having higher priority) + const rank = this.bpe_ranks.get(JSON.stringify([node.token, node.next.token])); + if (rank !== undefined) { + node.score = rank + node.bias; + queue.push(node); + } + } + + /** + * Encodes the input sequence of tokens using the BPE algorithm and returns the resulting subword tokens. + * @param {string[]} tokens The input sequence of tokens to encode. + * @returns {string[]} The resulting subword tokens after applying the BPE algorithm to the input sequence of tokens. + */ + encode(tokens) { + const outputTokens = []; + + for (const token of tokens) { + if (this.ignore_merges && this.tokens_to_ids.has(token)) { + outputTokens.push(token); + continue; + } + const bpe_token_list = this.bpe(token); + + for (const t of bpe_token_list) { + if (this.tokens_to_ids.has(t)) { + outputTokens.push(t); + } else if (this.byte_fallback) { + const byteTokens = Array.from(this.text_encoder.encode(t)) + .map(x => `<0x${x.toString(16).toUpperCase().padStart(2, '0')}>`); + if (byteTokens.every(x => this.tokens_to_ids.has(x))) { + // Ensure the byte tokens are actually in the vocabulary, otherwise + // we fall back to the unknown token. For more information, see + // https://github.com/huggingface/transformers/issues/28096. + outputTokens.push(...byteTokens); + } else { + outputTokens.push(this.unk_token); + } + } else { + outputTokens.push(this.unk_token); + } + } + } + + return outputTokens; + } + +} + +/** + * Legacy tokenizer class for tokenizers with only a vocabulary. + */ +class LegacyTokenizerModel extends TokenizerModel { + /** + * Create a LegacyTokenizerModel instance. + * @param {Object} config The configuration object for LegacyTokenizerModel. + * @param {Object} config.vocab A (possibly nested) mapping of tokens to ids. + * @param {Object} moreConfig Additional configuration object for the LegacyTokenizerModel model. + */ + constructor(config, moreConfig) { + super(config); + + /**@type {Map} */ + this.tokens_to_ids = objectToMap( + moreConfig.target_lang + ? config.vocab[moreConfig.target_lang] + : config.vocab + ); + + this.bos_token = moreConfig.bos_token; + this.bos_token_id = this.tokens_to_ids.get(this.bos_token); + + this.eos_token = moreConfig.eos_token; + this.eos_token_id = this.tokens_to_ids.get(this.eos_token); + + this.pad_token = moreConfig.pad_token; + this.pad_token_id = this.tokens_to_ids.get(this.pad_token); + + this.unk_token = moreConfig.unk_token; + this.unk_token_id = this.tokens_to_ids.get(this.unk_token); + + this.vocab = new Array(this.tokens_to_ids.size); + for (const [key, value] of this.tokens_to_ids) { + this.vocab[value] = key; + } + } + + encode(tokens) { + return tokens; + } +} + + +/** + * A base class for text normalization. + * @abstract + */ +class Normalizer extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + /** + * @param {Object} config The configuration object for the normalizer. + */ + constructor(config) { + super(); + this.config = config; + } + + /** + * Factory method for creating normalizers from config objects. + * @static + * @param {Object} config The configuration object for the normalizer. + * @returns {Normalizer} A Normalizer object. + * @throws {Error} If an unknown Normalizer type is specified in the config. + */ + static fromConfig(config) { + if (config === null) return null; + switch (config.type) { + case 'BertNormalizer': + return new BertNormalizer(config); + case 'Precompiled': + return new Precompiled(config); + case 'Sequence': + return new NormalizerSequence(config); + case 'Replace': + return new Replace(config); + case 'NFC': + return new NFC(config); + case 'NFD': + return new NFD(config); + case 'NFKC': + return new NFKC(config); + case 'NFKD': + return new NFKD(config); + case 'Strip': + return new StripNormalizer(config); + case 'StripAccents': + return new StripAccents(config); + case 'Lowercase': + return new Lowercase(config); + case 'Prepend': + return new Prepend(config); + default: + throw new Error(`Unknown Normalizer type: ${config.type}`); + } + } + + /** + * Normalize the input text. + * @abstract + * @param {string} text The text to normalize. + * @returns {string} The normalized text. + * @throws {Error} If this method is not implemented in a subclass. + */ + normalize(text) { + throw Error("normalize should be implemented in subclass.") + } + + /** + * Alias for {@link Normalizer#normalize}. + * @param {string} text The text to normalize. + * @returns {string} The normalized text. + */ + _call(text) { + return this.normalize(text); + } + +} + +/** + * Replace normalizer that replaces occurrences of a pattern with a given string or regular expression. + * @extends Normalizer + */ +class Replace extends Normalizer { + /** + * Normalize the input text by replacing the pattern with the content. + * @param {string} text The input text to be normalized. + * @returns {string} The normalized text after replacing the pattern with the content. + */ + normalize(text) { + const pattern = createPattern(this.config.pattern); + return pattern === null + ? text + : text.replaceAll(pattern, this.config.content); + } +} + +/** + * A normalizer that applies Unicode normalization to the input text. + * @extends Normalizer + * @abstract + */ +class UnicodeNormalizer extends Normalizer { + /** + * @type {string} The Unicode normalization form to apply. + * Should be one of: 'NFC', 'NFD', 'NFKC', or 'NFKD'. + */ + form = undefined; + + /** + * Normalize the input text by applying Unicode normalization. + * @param {string} text The input text to be normalized. + * @returns {string} The normalized text. + */ + normalize(text) { + text = text.normalize(this.form) + return text; + } +} + +/** + * A normalizer that applies Unicode normalization form C (NFC) to the input text. + * Canonical Decomposition, followed by Canonical Composition. + * @extends UnicodeNormalizer + */ +class NFC extends UnicodeNormalizer { + form = 'NFC'; +} + +/** + * A normalizer that applies Unicode normalization form D (NFD) to the input text. + * Canonical Decomposition. + * @extends UnicodeNormalizer + */ +class NFD extends UnicodeNormalizer { + form = 'NFD'; +} + +/** + * A normalizer that applies Unicode normalization form KC (NFKC) to the input text. + * Compatibility Decomposition, followed by Canonical Composition. + * @extends UnicodeNormalizer + */ +class NFKC extends UnicodeNormalizer { + form = 'NFKC'; +} + +/** + * A normalizer that applies Unicode normalization form KD (NFKD) to the input text. + * Compatibility Decomposition. + * @extends UnicodeNormalizer + */ +class NFKD extends UnicodeNormalizer { + form = 'NFKD'; +} + +/** + * A normalizer that strips leading and/or trailing whitespace from the input text. + */ +class StripNormalizer extends Normalizer { + /** + * Strip leading and/or trailing whitespace from the input text. + * @param {string} text The input text. + * @returns {string} The normalized text. + */ + normalize(text) { + if (this.config.strip_left && this.config.strip_right) { + // Fast path to avoid an extra trim call + text = text.trim(); + } else { + if (this.config.strip_left) { + text = text.trimStart(); + } + if (this.config.strip_right) { + text = text.trimEnd(); + } + } + return text; + } +} + +/** + * StripAccents normalizer removes all accents from the text. + * @extends Normalizer + */ +class StripAccents extends Normalizer { + /** + * Remove all accents from the text. + * @param {string} text The input text. + * @returns {string} The normalized text without accents. + */ + normalize(text) { + text = remove_accents(text); + return text; + } +} + +/** + * A Normalizer that lowercases the input string. + * @extends Normalizer + */ +class Lowercase extends Normalizer { + /** + * Lowercases the input string. + * @param {string} text The text to normalize. + * @returns {string} The normalized text. + */ + normalize(text) { + text = text.toLowerCase(); + return text; + } +} + +/** + * A Normalizer that prepends a string to the input string. + * @extends Normalizer + */ +class Prepend extends Normalizer { + /** + * Prepends the input string. + * @param {string} text The text to normalize. + * @returns {string} The normalized text. + */ + normalize(text) { + text = this.config.prepend + text; + return text; + } +} + +/** + * A Normalizer that applies a sequence of Normalizers. + * @extends Normalizer + */ +class NormalizerSequence extends Normalizer { + /** + * Create a new instance of NormalizerSequence. + * @param {Object} config The configuration object. + * @param {Object[]} config.normalizers An array of Normalizer configuration objects. + */ + constructor(config) { + super(config); + this.normalizers = config.normalizers.map(x => Normalizer.fromConfig(x)); + } + /** + * Apply a sequence of Normalizers to the input text. + * @param {string} text The text to normalize. + * @returns {string} The normalized text. + */ + normalize(text) { + return this.normalizers.reduce((t, normalizer) => { + return normalizer.normalize(t); + }, text); + } +} + +/** + * A class representing a normalizer used in BERT tokenization. + * @extends Normalizer + */ +class BertNormalizer extends Normalizer { + /** + * Adds whitespace around any CJK (Chinese, Japanese, or Korean) character in the input text. + * + * @param {string} text The input text to tokenize. + * @returns {string} The tokenized text with whitespace added around CJK characters. + */ + _tokenize_chinese_chars(text) { + /* Adds whitespace around any CJK character. */ + const output = []; + for (let i = 0; i < text.length; ++i) { + const char = text[i]; + const cp = char.charCodeAt(0); + if (is_chinese_char(cp)) { + output.push(" "); + output.push(char); + output.push(" "); + } else { + output.push(char); + } + } + return output.join(""); + } + + /** + * Strips accents from the given text. + * @param {string} text The text to strip accents from. + * @returns {string} The text with accents removed. + */ + stripAccents(text) { + // "Mark, Nonspacing" (Mn) + return text.normalize('NFD').replace(/\p{Mn}/gu, ''); + } + + + /** + * Checks whether `char` is a control character. + * @param {string} char The character to check. + * @returns {boolean} Whether `char` is a control character. + * @private + */ + _is_control(char) { + switch (char) { + case '\t': + case '\n': + case '\r': + // These are technically control characters but we count them as whitespace characters. + return false; + + default: + // Check if unicode category starts with C: + // Cc - Control + // Cf - Format + // Co - Private Use + // Cs - Surrogate + return /^\p{Cc}|\p{Cf}|\p{Co}|\p{Cs}$/u.test(char); + } + } + + /** + * Performs invalid character removal and whitespace cleanup on text. + * @param {string} text The text to clean. + * @returns {string} The cleaned text. + * @private + */ + _clean_text(text) { + const output = []; + for (const char of text) { + const cp = char.charCodeAt(0); + if (cp === 0 || cp === 0xFFFD || this._is_control(char)) { + continue; + } + if (/^\s$/.test(char)) { // is whitespace + output.push(" "); + } else { + output.push(char); + } + } + return output.join(""); + } + /** + * Normalizes the given text based on the configuration. + * @param {string} text The text to normalize. + * @returns {string} The normalized text. + */ + normalize(text) { + if (this.config.clean_text) { + text = this._clean_text(text); + } + + if (this.config.handle_chinese_chars) { + text = this._tokenize_chinese_chars(text); + } + + if (this.config.lowercase) { + text = text.toLowerCase(); + + if (this.config.strip_accents !== false) { + text = this.stripAccents(text); + } + } else if (this.config.strip_accents) { + text = this.stripAccents(text); + } + + return text; + } +} + +/** + * A callable class representing a pre-tokenizer used in tokenization. Subclasses + * should implement the `pre_tokenize_text` method to define the specific pre-tokenization logic. + * @extends Callable + */ +class PreTokenizer extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + /** + * Factory method that returns an instance of a subclass of `PreTokenizer` based on the provided configuration. + * + * @static + * @param {Object} config A configuration object for the pre-tokenizer. + * @returns {PreTokenizer} An instance of a subclass of `PreTokenizer`. + * @throws {Error} If the provided configuration object does not correspond to any known pre-tokenizer. + */ + static fromConfig(config) { + if (config === null) return null; + + switch (config.type) { + case 'BertPreTokenizer': + return new BertPreTokenizer(config); + case 'Sequence': + return new PreTokenizerSequence(config); + case 'Whitespace': + return new WhitespacePreTokenizer(config); + case 'WhitespaceSplit': + return new WhitespaceSplit(config); + case 'Metaspace': + return new MetaspacePreTokenizer(config); + + case 'ByteLevel': + return new ByteLevelPreTokenizer(config); + case 'Split': + return new SplitPreTokenizer(config); + case 'Punctuation': + return new PunctuationPreTokenizer(config); + case 'Digits': + return new DigitsPreTokenizer(config); + case 'Replace': + return new ReplacePreTokenizer(config); + default: + throw new Error(`Unknown PreTokenizer type: ${config.type}`); + } + } + + /** + * Method that should be implemented by subclasses to define the specific pre-tokenization logic. + * + * @abstract + * @param {string} text The text to pre-tokenize. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} The pre-tokenized text. + * @throws {Error} If the method is not implemented in the subclass. + */ + pre_tokenize_text(text, options) { + throw Error("pre_tokenize_text should be implemented in subclass.") + } + + /** + * Tokenizes the given text into pre-tokens. + * @param {string|string[]} text The text or array of texts to pre-tokenize. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of pre-tokens. + */ + pre_tokenize(text, options) { + return (Array.isArray(text) + ? text.map(x => this.pre_tokenize_text(x, options)) + : this.pre_tokenize_text(text, options) + ).flat(); + } + + /** + * Alias for {@link PreTokenizer#pre_tokenize}. + * @param {string|string[]} text The text or array of texts to pre-tokenize. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of pre-tokens. + */ + _call(text, options) { + return this.pre_tokenize(text, options); + } +} + +/** + * @extends PreTokenizer + */ +class BertPreTokenizer extends PreTokenizer { + /** + * A PreTokenizer that splits text into wordpieces using a basic tokenization scheme + * similar to that used in the original implementation of BERT. + * + * @param {Object} config The configuration object. + */ + constructor(config) { + super(); + // Construct a pattern which matches the rust implementation: + // https://github.com/huggingface/tokenizers/blob/b4fcc9ce6e4ad5806e82826f816acfdfdc4fcc67/tokenizers/src/pre_tokenizers/bert.rs#L11 + // Equivalent to removing whitespace and splitting on punctuation (both \p{P} and other ascii characters) + this.pattern = new RegExp(`[^\\s${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]`, 'gu'); + } + /** + * Tokenizes a single text using the BERT pre-tokenization scheme. + * + * @param {string} text The text to tokenize. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of tokens. + */ + pre_tokenize_text(text, options) { + return text.trim().match(this.pattern) || []; + } +} + +/** + * A pre-tokenizer that splits text into Byte-Pair-Encoding (BPE) subwords. + * @extends PreTokenizer + */ +class ByteLevelPreTokenizer extends PreTokenizer { + /** + * Creates a new instance of the `ByteLevelPreTokenizer` class. + * @param {Object} config The configuration object. + */ + constructor(config) { + super(); + this.config = config; + + /** + * @type {boolean} Whether to add a leading space to the first word. + * This allows to treat the leading word just as any other word. + */ + this.add_prefix_space = this.config.add_prefix_space; + + /** + * @type {boolean} Whether the post processing step should trim offsets + * to avoid including whitespaces. + * @todo Use this in the pretokenization step. + */ + this.trim_offsets = this.config.trim_offsets; + + /** + * @type {boolean} Whether to use the standard GPT2 regex for whitespace splitting. + * Set it to False if you want to use your own splitting. Defaults to true. + */ + this.use_regex = this.config.use_regex ?? true; + this.pattern = /'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+/gu; + + this.byte_encoder = BYTES_TO_UNICODE; + this.text_encoder = new TextEncoder(); + } + + /** + * Tokenizes a single piece of text using byte-level tokenization. + * @param {string} text The text to tokenize. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of tokens. + */ + pre_tokenize_text(text, options) { + // Add a leading space if the option is enabled + if (this.add_prefix_space && !text.startsWith(' ')) { + text = ' ' + text; + } + + // Split on whitespace and punctuation + const tokens = this.use_regex ? (text.match(this.pattern) || []) : [text]; + + // Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) + return tokens.map( + token => Array.from(this.text_encoder.encode(token), byte => this.byte_encoder[byte]).join('') + ); + } +} + +/** + * @typedef {'removed'|'isolated'|'mergedWithPrevious'|'mergedWithNext'|'contiguous'} SplitDelimiterBehavior + */ + +/** + * Splits text using a given pattern. + * @extends PreTokenizer + */ +class SplitPreTokenizer extends PreTokenizer { + /** + * @param {Object} config The configuration options for the pre-tokenizer. + * @param {Object} config.pattern The pattern used to split the text. Can be a string or a regex object. + * @param {string|undefined} config.pattern.String The string to use for splitting. Only defined if the pattern is a string. + * @param {string|undefined} config.pattern.Regex The regex to use for splitting. Only defined if the pattern is a regex. + * @param {SplitDelimiterBehavior} config.behavior The behavior to use when splitting. + * @param {boolean} config.invert Whether to split (invert=false) or match (invert=true) the pattern. + */ + constructor(config) { + super(); + this.config = config; + // TODO support all behaviours (config.behavior) + + this.pattern = createPattern(this.config.pattern, this.config.invert); + } + + /** + * Tokenizes text by splitting it using the given pattern. + * @param {string} text The text to tokenize. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of tokens. + */ + pre_tokenize_text(text, options) { + if (this.pattern === null) { + return []; + } + + if (this.config.invert) { + return text.match(this.pattern) || []; + } else if (this.config.behavior?.toLowerCase() === 'removed') { + return text.split(this.pattern).filter(x => x); + } else { + return regexSplit(text, this.pattern); + } + } +} + +/** + * Splits text based on punctuation. + * @extends PreTokenizer + */ +class PunctuationPreTokenizer extends PreTokenizer { + /** + * @param {Object} config The configuration options for the pre-tokenizer. + * @param {SplitDelimiterBehavior} config.behavior The behavior to use when splitting. + */ + constructor(config) { + super(); + this.config = config; + this.pattern = new RegExp(`[^${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]+`, 'gu'); + } + + /** + * Tokenizes text by splitting it using the given pattern. + * @param {string} text The text to tokenize. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of tokens. + */ + pre_tokenize_text(text, options) { + return text.match(this.pattern) || []; + } +} + + +/** + * Splits text based on digits. + * @extends PreTokenizer + */ +class DigitsPreTokenizer extends PreTokenizer { + /** + * @param {Object} config The configuration options for the pre-tokenizer. + * @param {boolean} config.individual_digits Whether to split on individual digits. + */ + constructor(config) { + super(); + this.config = config; + + // Construct a pattern which matches the rust implementation: + const digit_pattern = `[^\\d]+|\\d${this.config.individual_digits ? '' : '+'}`; + this.pattern = new RegExp(digit_pattern, 'gu'); + } + + /** + * Tokenizes text by splitting it using the given pattern. + * @param {string} text The text to tokenize. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of tokens. + */ + pre_tokenize_text(text, options) { + return text.match(this.pattern) || []; + } +} + +/** + * @typedef {Object} PostProcessedOutput + * @property {string[]} tokens List of token produced by the post-processor. + * @property {number[]} [token_type_ids] List of token type ids produced by the post-processor. + */ + + +/** + * @typedef {Object} EncodingSingle + * @property {number[]} input_ids List of token ids to be fed to a model. + * @property {number[]} attention_mask List of token type ids to be fed to a model + * @property {number[]} [token_type_ids] List of indices specifying which tokens should be attended to by the model + */ + + +/** + * @extends Callable + */ +class PostProcessor extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + + /** + * @param {Object} config The configuration for the post-processor. + */ + constructor(config) { + super(); + this.config = config; + } + + /** + * Factory method to create a PostProcessor object from a configuration object. + * + * @param {Object} config Configuration object representing a PostProcessor. + * @returns {PostProcessor} A PostProcessor object created from the given configuration. + * @throws {Error} If an unknown PostProcessor type is encountered. + */ + static fromConfig(config) { + if (config === null) return null; + switch (config.type) { + case 'TemplateProcessing': + return new TemplateProcessing(config); + + case 'ByteLevel': + return new ByteLevelPostProcessor(config); + + case 'RobertaProcessing': + return new RobertaProcessing(config); + case 'BertProcessing': + return new BertProcessing(config); + + case 'Sequence': + return new PostProcessorSequence(config); + default: + throw new Error(`Unknown PostProcessor type: ${config.type}`); + } + } + + /** + * Method to be implemented in subclass to apply post-processing on the given tokens. + * + * @param {Array} tokens The input tokens to be post-processed. + * @param {...*} args Additional arguments required by the post-processing logic. + * @returns {PostProcessedOutput} The post-processed tokens. + * @throws {Error} If the method is not implemented in subclass. + */ + post_process(tokens, ...args) { + throw Error("post_process should be implemented in subclass.") + } + + /** + * Alias for {@link PostProcessor#post_process}. + * @param {Array} tokens The text or array of texts to post-process. + * @param {...*} args Additional arguments required by the post-processing logic. + * @returns {PostProcessedOutput} The post-processed tokens. + */ + _call(tokens, ...args) { + return this.post_process(tokens, ...args); + } +} + +/** + * A post-processor that adds special tokens to the beginning and end of the input. + */ +class BertProcessing extends PostProcessor { + /** + * @param {Object} config The configuration for the post-processor. + * @param {string[]} config.cls The special tokens to add to the beginning of the input. + * @param {string[]} config.sep The special tokens to add to the end of the input. + */ + constructor(config) { + super(config); + // TODO use all of config: add_prefix_space, trim_offsets + + this.cls = config.cls[0]; + this.sep = config.sep[0]; + } + + /** + * Adds the special tokens to the beginning and end of the input. + * @param {string[]} tokens The input tokens. + * @param {string[]} [tokens_pair=null] An optional second set of input tokens. + * @returns {PostProcessedOutput} The post-processed tokens with the special tokens added to the beginning and end. + */ + post_process(tokens, tokens_pair = null, { + add_special_tokens = true, + } = {}) { + if (add_special_tokens) { + tokens = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)([this.cls], tokens, [this.sep]); + } + + let token_type_ids = new Array(tokens.length).fill(0); + if (tokens_pair !== null) { + // NOTE: It is intended to add 2 EOS tokens after the first set of tokens + // https://github.com/huggingface/tokenizers/issues/983 + const middle = (add_special_tokens && this instanceof RobertaProcessing) + ? [this.sep] + : []; + const after = add_special_tokens ? [this.sep] : []; + + tokens = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(tokens, middle, tokens_pair, after); + token_type_ids = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(token_type_ids, new Array(tokens_pair.length + middle.length + after.length).fill(1)); + } + return { tokens, token_type_ids }; + } +} +class RobertaProcessing extends BertProcessing { } // NOTE: extends BertProcessing + +/** + * Post processor that replaces special tokens in a template with actual tokens. + * @extends PostProcessor + */ +class TemplateProcessing extends PostProcessor { + /** + * Creates a new instance of `TemplateProcessing`. + * @param {Object} config The configuration options for the post processor. + * @param {Array} config.single The template for a single sequence of tokens. + * @param {Array} config.pair The template for a pair of sequences of tokens. + */ + constructor(config) { + super(config); + + this.single = config.single; + this.pair = config.pair; + } + + /** + * Replaces special tokens in the template with actual tokens. + * @param {string[]} tokens The list of tokens for the first sequence. + * @param {string[]} [tokens_pair=null] The list of tokens for the second sequence (optional). + * @returns {PostProcessedOutput} An object containing the list of tokens with the special tokens replaced with actual tokens. + */ + post_process(tokens, tokens_pair = null, { + add_special_tokens = true, + } = {}) { + const type = tokens_pair === null ? this.single : this.pair + + let processedTokens = []; + let types = []; + for (const item of type) { + if ('SpecialToken' in item) { + if (add_special_tokens) { + processedTokens.push(item.SpecialToken.id); + types.push(item.SpecialToken.type_id); + } + } else if ('Sequence' in item) { + if (item.Sequence.id === 'A') { + processedTokens = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(processedTokens, tokens); + types = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(types, new Array(tokens.length).fill(item.Sequence.type_id)); + + } else if (item.Sequence.id === 'B') { + processedTokens = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(processedTokens, tokens_pair); + types = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(types, new Array(tokens_pair.length).fill(item.Sequence.type_id)); + } + } + } + return { tokens: processedTokens, token_type_ids: types }; + } +} + +/** + * A PostProcessor that returns the given tokens as is. + * @extends PostProcessor + */ +class ByteLevelPostProcessor extends PostProcessor { + /** + * Post process the given tokens. + * @param {string[]} tokens The list of tokens for the first sequence. + * @param {string[]} [tokens_pair=null] The list of tokens for the second sequence (optional). + * @returns {PostProcessedOutput} An object containing the post-processed tokens. + */ + post_process(tokens, tokens_pair = null) { + if (tokens_pair) { + tokens = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(tokens, tokens_pair); + } + return { tokens }; + } +} + + +/** + * A post-processor that applies multiple post-processors in sequence. + */ +class PostProcessorSequence extends PostProcessor { + + /** + * Creates a new instance of PostProcessorSequence. + * @param {Object} config The configuration object. + * @param {Object[]} config.processors The list of post-processors to apply. + */ + constructor(config) { + super(config); + + this.processors = config.processors.map(x => PostProcessor.fromConfig(x)); + } + + /** + * Post process the given tokens. + * @param {string[]} tokens The list of tokens for the first sequence. + * @param {string[]} [tokens_pair=null] The list of tokens for the second sequence (optional). + * @returns {PostProcessedOutput} An object containing the post-processed tokens. + */ + post_process(tokens, tokens_pair = null, options = {}) { + let token_type_ids; + for (const processor of this.processors) { + if (processor instanceof ByteLevelPostProcessor) { + // Special case where we need to pass the tokens_pair to the post-processor + const output = processor.post_process(tokens); + tokens = output.tokens; + if (tokens_pair) { + const pair_output = processor.post_process(tokens_pair); + tokens_pair = pair_output.tokens; + } + } else { + const output = processor.post_process(tokens, tokens_pair, options); + tokens = output.tokens; + token_type_ids = output.token_type_ids; + } + } + return { tokens, token_type_ids }; + } +} + +/** + * The base class for token decoders. + * @extends Callable + */ +class Decoder extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + + /** + * Creates an instance of `Decoder`. + * + * @param {Object} config The configuration object. + */ + constructor(config) { + super(); + this.config = config; + + /** @type {AddedToken[]} */ + this.added_tokens = []; + this.end_of_word_suffix = null; + this.trim_offsets = config.trim_offsets; + } + + /** + * Creates a decoder instance based on the provided configuration. + * + * @param {Object} config The configuration object. + * @returns {Decoder} A decoder instance. + * @throws {Error} If an unknown decoder type is provided. + */ + static fromConfig(config) { + if (config === null) return null; + switch (config.type) { + case 'WordPiece': + return new WordPieceDecoder(config); + case 'Metaspace': + return new MetaspaceDecoder(config); + case 'ByteLevel': + return new ByteLevelDecoder(config); + + case 'Replace': + return new ReplaceDecoder(config); + case 'ByteFallback': + return new ByteFallback(config); + case 'Fuse': + return new FuseDecoder(config); + case 'Strip': + return new StripDecoder(config); + + case 'Sequence': + return new DecoderSequence(config); + + case 'CTC': + return new CTCDecoder(config); + case 'BPEDecoder': + return new BPEDecoder(config); + default: + throw new Error(`Unknown Decoder type: ${config.type}`); + } + } + + /** + * Calls the `decode` method. + * + * @param {string[]} tokens The list of tokens. + * @returns {string} The decoded string. + */ + _call(tokens) { + return this.decode(tokens); + } + + /** + * Decodes a list of tokens. + * @param {string[]} tokens The list of tokens. + * @returns {string} The decoded string. + */ + decode(tokens) { + return this.decode_chain(tokens).join(''); + } + + /** + * Apply the decoder to a list of tokens. + * + * @param {string[]} tokens The list of tokens. + * @returns {string[]} The decoded list of tokens. + * @throws {Error} If the `decode_chain` method is not implemented in the subclass. + */ + decode_chain(tokens) { + throw Error("`decode_chain` should be implemented in subclass.") + } + +} + +class ReplaceDecoder extends Decoder { + + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + const pattern = createPattern(this.config.pattern); + return pattern === null + ? tokens + : tokens.map(token => token.replaceAll(pattern, this.config.content)) + } +} + + +class ByteFallback extends Decoder { + constructor(config) { + super(config); + + this.text_decoder = new TextDecoder(); + } + + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + + const new_tokens = []; + let previous_byte_tokens = []; + + for (const token of tokens) { + let bytes = null; + if (token.length === 6 && token.startsWith('<0x') && token.endsWith('>')) { + const byte = parseInt(token.slice(3, 5), 16); + if (!isNaN(byte)) { + bytes = byte; + } + } + if (bytes !== null) { + previous_byte_tokens.push(bytes); + } else { + if (previous_byte_tokens.length > 0) { + const string = this.text_decoder.decode(Uint8Array.from(previous_byte_tokens)); + new_tokens.push(string); + previous_byte_tokens = []; + } + new_tokens.push(token); + } + } + if (previous_byte_tokens.length > 0) { + const string = this.text_decoder.decode(Uint8Array.from(previous_byte_tokens)); + new_tokens.push(string); + previous_byte_tokens = []; + } + + return new_tokens; + } +} + +/** + * Fuse simply fuses all tokens into one big string. + * It's usually the last decoding step anyway, but this decoder + * exists incase some decoders need to happen after that step + */ +class FuseDecoder extends Decoder { + + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + return [tokens.join('')]; + } +} + + +class StripDecoder extends Decoder { + constructor(config) { + super(config); + + this.content = this.config.content; + this.start = this.config.start; + this.stop = this.config.stop; + } + + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + return tokens.map(token => { + let start_cut = 0; + for (let i = 0; i < this.start; ++i) { + if (token[i] === this.content) { + start_cut = i + 1; + continue; + } else { + break; + } + } + + let stop_cut = token.length; + for (let i = 0; i < this.stop; ++i) { + const index = token.length - i - 1; + if (token[index] === this.content) { + stop_cut = index; + continue; + } else { + break; + } + } + + return token.slice(start_cut, stop_cut) + }); + } +} + +/** + * A decoder that decodes a list of WordPiece tokens into a single string. + * @extends Decoder + */ +class WordPieceDecoder extends Decoder { + + /** + * Creates a new instance of WordPieceDecoder. + * @param {Object} config The configuration object. + * @param {string} config.prefix The prefix used for WordPiece encoding. + * @param {boolean} config.cleanup Whether to cleanup the decoded string. + */ + constructor(config) { + super(config); + this.cleanup = config.cleanup; + } + + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + return tokens.map((token, i) => { + if (i !== 0) { + if (token.startsWith(this.config.prefix)) { + // NOTE: .replace() is intended; only replace first occurrence + token = token.replace(this.config.prefix, ''); + } else { + token = ' ' + token; + } + } + if (this.cleanup) { + token = clean_up_tokenization(token) + } + + return token; + }); + } +} + +/** + * Byte-level decoder for tokenization output. Inherits from the `Decoder` class. + * @extends Decoder + */ +class ByteLevelDecoder extends Decoder { + + /** + * Create a `ByteLevelDecoder` object. + * @param {Object} config Configuration object. + */ + constructor(config) { + super(config); + + this.byte_decoder = UNICODE_TO_BYTES; + this.text_decoder = new TextDecoder("utf-8", { + fatal: false, + ignoreBOM: true, + }); + + this.end_of_word_suffix = null; + } + + /** + * Convert an array of tokens to string by decoding each byte. + * @param {string[]} tokens Array of tokens to be decoded. + * @returns {string} The decoded string. + */ + convert_tokens_to_string(tokens) { + const text = tokens.join(''); + const byteArray = new Uint8Array([...text].map(c => this.byte_decoder[c])); + const decoded_text = this.text_decoder.decode(byteArray); + return decoded_text; + } + + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + // TODO move to base class (like HF) + // tokens === filtered_tokens + + // To avoid mixing byte-level and unicode for byte-level BPT + // we need to build string separately for added tokens and byte-level tokens + // cf. https://github.com/huggingface/transformers/issues/1133 + const sub_texts = []; + let current_sub_text = []; + for (const token of tokens) { + // tokens sent here are already filtered, so we don't need to do this + // if (skip_special_tokens && this.all_special_ids.includes(token)) { + // continue; + // } + + if (this.added_tokens.find(x => x.content === token) !== undefined) { + if (current_sub_text.length > 0) { + sub_texts.push(this.convert_tokens_to_string(current_sub_text)); + current_sub_text = []; + } + sub_texts.push(token); + } else { + current_sub_text.push(token); + } + } + if (current_sub_text.length > 0) { + sub_texts.push(this.convert_tokens_to_string(current_sub_text)); + } + + // TODO add spaces_between_special_tokens and clean_up_tokenization_spaces options + + return sub_texts; + } +} + +/** + * The CTC (Connectionist Temporal Classification) decoder. + * See https://github.com/huggingface/tokenizers/blob/bb38f390a61883fc2f29d659af696f428d1cda6b/tokenizers/src/decoders/ctc.rs + */ +class CTCDecoder extends Decoder { + + constructor(config) { + super(config); + + this.pad_token = this.config.pad_token; + this.word_delimiter_token = this.config.word_delimiter_token; + this.cleanup = this.config.cleanup; + } + /** + * Converts a connectionist-temporal-classification (CTC) output tokens into a single string. + * @param {string[]} tokens Array of tokens to be decoded. + * @returns {string} The decoded string. + */ + convert_tokens_to_string(tokens) { + if (tokens.length === 0) return ''; + + // group same tokens into non-repeating tokens in CTC style decoding + const grouped_tokens = [tokens[0]]; + for (let i = 1; i < tokens.length; ++i) { + if (tokens[i] !== grouped_tokens.at(-1)) { + grouped_tokens.push(tokens[i]); + } + } + + // filter self.pad_token which is used as CTC-blank token + const filtered_tokens = grouped_tokens.filter(token => token !== this.pad_token); + + let text = filtered_tokens.join(''); + if (this.cleanup) { + // cleanup and replace delimiter token + text = clean_up_tokenization(text) + .replaceAll(this.word_delimiter_token, ' ') + .trim(); + } + return text; + } + + + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + return [this.convert_tokens_to_string(tokens)]; + } +} + +/** + * Apply a sequence of decoders. + * @extends Decoder + */ +class DecoderSequence extends Decoder { + + /** + * Creates a new instance of DecoderSequence. + * @param {Object} config The configuration object. + * @param {Object[]} config.decoders The list of decoders to apply. + */ + constructor(config) { + super(config); + this.decoders = config.decoders.map(x => Decoder.fromConfig(x)); + } + + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + // Use reduce to apply each decoder to the tokens + return this.decoders.reduce((toks, decoder) => { + return decoder.decode_chain(toks); + }, tokens); + } + +} + +class BPEDecoder extends Decoder { + constructor(config) { + super(config); + + this.suffix = this.config.suffix; + } + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + return tokens.map((token, i) => { + return token.replaceAll(this.suffix, (i === tokens.length - 1) ? '' : ' ') + }); + } +} + +// Custom decoder for VITS +class VitsDecoder extends Decoder { + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + let decoded = ''; + for (let i = 1; i < tokens.length; i += 2) { + decoded += tokens[i]; + } + return [decoded]; + } +} + + +/** + * This PreTokenizer replaces spaces with the given replacement character, adds a prefix space if requested, + * and returns a list of tokens. + * @extends PreTokenizer + */ +class MetaspacePreTokenizer extends PreTokenizer { + /** + * @param {Object} config The configuration object for the MetaspacePreTokenizer. + * @param {boolean} config.add_prefix_space Whether to add a prefix space to the first token. + * @param {string} config.replacement The character to replace spaces with. + * @param {string} [config.str_rep=config.replacement] An optional string representation of the replacement character. + * @param {'first'|'never'|'always'} [config.prepend_scheme='always'] The metaspace prepending scheme. + */ + constructor(config) { + super(); + + this.addPrefixSpace = config.add_prefix_space; + this.replacement = config.replacement; + this.strRep = config.str_rep || this.replacement; + this.prepend_scheme = config.prepend_scheme ?? 'always'; + } + + /** + * This method takes a string, replaces spaces with the replacement character, + * adds a prefix space if requested, and returns a new list of tokens. + * @param {string} text The text to pre-tokenize. + * @param {Object} [options] The options for the pre-tokenization. + * @param {number} [options.section_index] The index of the section to pre-tokenize. + * @returns {string[]} A new list of pre-tokenized tokens. + */ + pre_tokenize_text(text, { + section_index = undefined, + } = {}) { + + let normalized = text.replaceAll(' ', this.strRep); + + if ( + // We add a prefix space if: + // (1) The addPrefixSpace option is enabled and the normalized + // token does not already start with the replacement character. + (this.addPrefixSpace && !normalized.startsWith(this.replacement)) + + // and (2) either: + // (a) prepend_scheme is 'always' + // (b) prepend_scheme is 'first' and this is the first section + && ( + this.prepend_scheme === 'always' || + (this.prepend_scheme === 'first' && section_index === 0) + ) + ) { + normalized = this.strRep + normalized; + } + return [normalized]; + } +} + +/** + * MetaspaceDecoder class extends the Decoder class and decodes Metaspace tokenization. + * @extends Decoder + */ +class MetaspaceDecoder extends Decoder { + /** + * Constructs a new MetaspaceDecoder object. + * @param {Object} config The configuration object for the MetaspaceDecoder. + * @param {boolean} config.add_prefix_space Whether to add a prefix space to the decoded string. + * @param {string} config.replacement The string to replace spaces with. + */ + constructor(config) { + super(config); + + this.addPrefixSpace = config.add_prefix_space; + this.replacement = config.replacement; + } + + /** @type {Decoder['decode_chain']} */ + decode_chain(tokens) { + const result = []; + for (let i = 0; i < tokens.length; ++i) { + let normalized = tokens[i].replaceAll(this.replacement, ' '); + if (this.addPrefixSpace && i == 0 && normalized.startsWith(' ')) { + normalized = normalized.substring(1); + } + result.push(normalized); + } + return result; + } +} + +/** + * A normalizer that applies a precompiled charsmap. + * This is useful for applying complex normalizations in C++ and exposing them to JavaScript. + * @extends Normalizer + * @param {Object} config The configuration object for the Precompiled normalizer. + * @param {Object} config.precompiled_charsmap The precompiled charsmap object. + */ +class Precompiled extends Normalizer { + /** + * Create a new instance of Precompiled normalizer. + * @param {Object} config The configuration object. + * @param {any} config.precompiled_charsmap Precompiled chars mapping. + */ + constructor(config) { + super(config); + this.charsmap = config.precompiled_charsmap; + } + + /** + * Normalizes the given text by applying the precompiled charsmap. + * @param {string} text The text to normalize. + * @returns {string} The normalized text. + */ + normalize(text) { + // As stated in the sentencepiece normalization docs (https://github.com/google/sentencepiece/blob/master/doc/normalization.md#use-pre-defined-normalization-rule), + // there are 5 pre-defined normalization rules: + // 1. nmt_nfkc: NFKC normalization with some additional normalization around spaces. (default) + // 2. nfkc: original NFKC normalization. + // 3. nmt_nfkc_cf: nmt_nfkc + Unicode case folding (mostly lower casing) + // 4. nfkc_cf: nfkc + Unicode case folding. + // 5. identity: no normalization + // + // For now, we only implement the default (nmt_nfkc). + // See https://raw.githubusercontent.com/google/sentencepiece/master/data/nmt_nfkc.tsv for the full list of rules. + // TODO: detect when a different `this.charsmap` is used. + + text = text.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm, ''); // Remove control characters + text = text.replace(/[\u0009\u000A\u000C\u000D\u00A0\u1680\u2000-\u200F\u2028\u2029\u202F\u205F\u2581\u3000\uFEFF\uFFFD]/gm, '\u0020'); // Replace certain characters with a space + + if (text.includes('\uFF5E')) { + // To match the sentencepiece implementation 100%, we must handle a very strange edge-case. + // For some reason, the "Fullwidth Tilde" character (\uFF5E) should not be converted to the standard Tilde character (\u007E). + // However, NFKC normalization does do this conversion. As a result, we split the string on the Fullwidth Tilde character, + // perform NFKC normalization on each substring, and then join them back together with the Fullwidth Tilde character. + const parts = text.split('\uFF5E'); + text = parts.map(part => part.normalize('NFKC')).join('\uFF5E'); + } else { + text = text.normalize('NFKC'); + } + + return text; + } +} + +/** + * A pre-tokenizer that applies a sequence of pre-tokenizers to the input text. + * @extends PreTokenizer + */ +class PreTokenizerSequence extends PreTokenizer { + /** + * Creates an instance of PreTokenizerSequence. + * @param {Object} config The configuration object for the pre-tokenizer sequence. + * @param {Object[]} config.pretokenizers An array of pre-tokenizer configurations. + */ + constructor(config) { + super(); + this.tokenizers = config.pretokenizers.map(x => PreTokenizer.fromConfig(x)); + } + + /** + * Applies each pre-tokenizer in the sequence to the input text in turn. + * @param {string} text The text to pre-tokenize. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} The pre-tokenized text. + */ + pre_tokenize_text(text, options) { + // Use reduce to apply each tokenizer to the text + return this.tokenizers.reduce((preTokenizedText, tokenizer) => { + return tokenizer.pre_tokenize(preTokenizedText, options); + }, [text]); + } +} + +/** + * Splits on word boundaries (using the following regular expression: `\w+|[^\w\s]+`). + */ +class WhitespacePreTokenizer extends PreTokenizer { + /** + * Creates an instance of WhitespacePreTokenizer. + * @param {Object} config The configuration object for the pre-tokenizer. + */ + constructor(config) { + super(); + } + /** + * Pre-tokenizes the input text by splitting it on word boundaries. + * @param {string} text The text to be pre-tokenized. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of tokens produced by splitting the input text on whitespace. + */ + pre_tokenize_text(text, options) { + return text.match(/\w+|[^\w\s]+/g) || []; + } +} + +/** + * Splits a string of text by whitespace characters into individual tokens. + * @extends PreTokenizer + */ +class WhitespaceSplit extends PreTokenizer { + /** + * Creates an instance of WhitespaceSplit. + * @param {Object} config The configuration object for the pre-tokenizer. + */ + constructor(config) { + super(); + } + /** + * Pre-tokenizes the input text by splitting it on whitespace characters. + * @param {string} text The text to be pre-tokenized. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of tokens produced by splitting the input text on whitespace. + */ + pre_tokenize_text(text, options) { + return whitespace_split(text); + } +} + +// NOTE: `ReplacePreTokenizer` is custom (to support `BlenderbotSmallTokenizer`) +class ReplacePreTokenizer extends PreTokenizer { + /** + * @param {Object} config The configuration options for the pre-tokenizer. + * @param {Object} config.pattern The pattern used to split the text. Can be a string or a regex object. + * @param {string} config.content What to replace the pattern with. + */ + constructor(config) { + super(); + this.config = config; + this.pattern = createPattern(this.config.pattern); + this.content = this.config.content; + } + + /** + * Pre-tokenizes the input text by replacing certain characters. + * @param {string} text The text to be pre-tokenized. + * @param {Object} [options] Additional options for the pre-tokenization logic. + * @returns {string[]} An array of tokens produced by replacing certain characters. + */ + pre_tokenize_text(text, options) { + if (this.pattern === null) { + return [text]; + } + return [text.replaceAll(this.pattern, this.config.content)]; + } +} + +const SPECIAL_TOKEN_ATTRIBUTES = [ + 'bos_token', + 'eos_token', + 'unk_token', + 'sep_token', + 'pad_token', + 'cls_token', + 'mask_token', + // additional_special_tokens (TODO) +] + +/** + * + * Helper function for padding values of an object, which are each arrays. + * NOTE: No additional checks are made here for validity of arguments. + * @param {Record} item The input object. + * @param {number} length The length to pad to. + * @param {(key: string) => any} value_fn Determine the value to fill the array, based on its key. + * @param {string} side Which side to pad the array. + * @private + */ +function padHelper(item, length, value_fn, side) { + for (const key of Object.keys(item)) { + const diff = length - item[key].length; + const value = value_fn(key); + + const padData = new Array(diff).fill(value); + item[key] = side === 'right' + ? (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(item[key], padData) + : (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(padData, item[key]); + } +} + +/** + * Helper function for truncating values of an object, which are each arrays. + * NOTE: No additional checks are made here for validity of arguments. + * @param {Record} item The input object. + * @param {number} length The length to truncate to. + * @private + */ +function truncateHelper(item, length) { + // Setting .length to a lower value truncates the array in-place: + // https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/length + for (const key of Object.keys(item)) { + item[key].length = length; + } +} + + +/** + * @typedef {Object} Message + * @property {string} role The role of the message (e.g., "user" or "assistant" or "system"). + * @property {string} content The content of the message. + */ + +class PreTrainedTokenizer extends _utils_generic_js__WEBPACK_IMPORTED_MODULE_0__.Callable { + return_token_type_ids = false; + + padding_side = 'right'; + /** + * Create a new PreTrainedTokenizer instance. + * @param {Object} tokenizerJSON The JSON of the tokenizer. + * @param {Object} tokenizerConfig The config of the tokenizer. + */ + constructor(tokenizerJSON, tokenizerConfig) { + super(); + + this.config = tokenizerConfig; + + // Construct parts of the tokenizer from the JSON + this.normalizer = Normalizer.fromConfig(tokenizerJSON.normalizer); + this.pre_tokenizer = PreTokenizer.fromConfig(tokenizerJSON.pre_tokenizer); + this.model = TokenizerModel.fromConfig(tokenizerJSON.model, tokenizerConfig); + this.post_processor = PostProcessor.fromConfig(tokenizerJSON.post_processor); + this.decoder = Decoder.fromConfig(tokenizerJSON.decoder); + + // Add added_tokens to model + this.special_tokens = []; + this.all_special_ids = []; + + /** @type {AddedToken[]} */ + this.added_tokens = []; + for (const addedToken of tokenizerJSON.added_tokens) { + const token = new AddedToken(addedToken); + this.added_tokens.push(token); + + this.model.tokens_to_ids.set(token.content, token.id); + this.model.vocab[token.id] = token.content; + + if (token.special) { + this.special_tokens.push(token.content); + this.all_special_ids.push(token.id); + } + } + + // Update additional_special_tokens + this.additional_special_tokens = tokenizerConfig.additional_special_tokens ?? []; + this.special_tokens.push(...this.additional_special_tokens); + this.special_tokens = [...new Set(this.special_tokens)]; // Remove duplicates + + if (this.decoder) { + // Slight hack, but it prevents code duplication: + this.decoder.added_tokens = this.added_tokens; + + // Another slight hack to add `end_of_word_suffix` (if present) to the decoder + // This is needed for cases where BPE model and ByteLevel decoder are used + // For more information, see https://github.com/huggingface/transformers.js/issues/74 + // TODO: save this to the decoder when exporting? + this.decoder.end_of_word_suffix = this.model.end_of_word_suffix; + } + + this.added_tokens_splitter = new _utils_data_structures_js__WEBPACK_IMPORTED_MODULE_5__.DictionarySplitter( + this.added_tokens.map(x => x.content), + ); + + /** @type {Map} */ + this.added_tokens_map = new Map(this.added_tokens.map(x => [x.content, x])) + + // Set mask token if present (otherwise will be undefined, which is fine) + this.mask_token = this.getToken('mask_token'); + this.mask_token_id = this.model.tokens_to_ids.get(this.mask_token); + + this.pad_token = this.getToken('pad_token', 'eos_token'); + this.pad_token_id = this.model.tokens_to_ids.get(this.pad_token); + + this.sep_token = this.getToken('sep_token'); + this.sep_token_id = this.model.tokens_to_ids.get(this.sep_token); + + this.unk_token = this.getToken('unk_token'); + this.unk_token_id = this.model.tokens_to_ids.get(this.unk_token); + + this.bos_token = this.getToken('bos_token'); + this.bos_token_id = this.model.tokens_to_ids.get(this.bos_token); + + this.eos_token = this.getToken('eos_token'); + this.eos_token_id = this.model.tokens_to_ids.get(this.eos_token); + + this.model_max_length = tokenizerConfig.model_max_length; + + /** @type {boolean} Whether or not to strip the text when tokenizing (removing excess spaces before and after the string). */ + this.remove_space = tokenizerConfig.remove_space; + + this.clean_up_tokenization_spaces = tokenizerConfig.clean_up_tokenization_spaces ?? true; + this.do_lowercase_and_remove_accent = tokenizerConfig.do_lowercase_and_remove_accent ?? false; + + if (tokenizerConfig.padding_side) { + this.padding_side = tokenizerConfig.padding_side; + } + + this.add_bos_token = tokenizerConfig.add_bos_token; + this.add_eos_token = tokenizerConfig.add_eos_token; + + this.legacy = false; + + this.chat_template = tokenizerConfig.chat_template ?? null; + if (Array.isArray(this.chat_template)) { + // Chat templates are stored as lists of dicts with fixed key names, + // we reconstruct that into a single dict while loading them. + const chat_template = Object.create(null); + for (const { name, template } of this.chat_template) { + if (typeof name !== 'string' || typeof template !== 'string') { + throw new Error('Chat template must be a list of objects with "name" and "template" properties'); + } + chat_template[name] = template; + } + this.chat_template = chat_template; + } + this._compiled_template_cache = new Map(); + } + + /** + * Returns the value of the first matching key in the tokenizer config object. + * @param {...string} keys One or more keys to search for in the tokenizer config object. + * @returns {string|null} The value associated with the first matching key, or null if no match is found. + * @throws {Error} If an object is found for a matching key and its __type property is not "AddedToken". + * @private + */ + getToken(...keys) { + for (const key of keys) { + const item = this.config[key]; + + if (!item) continue; + + if (typeof item === 'object') { + if (item.__type === 'AddedToken') { + return item.content; + } else { + throw Error(`Unknown token: ${item}`); + } + } else { + return item; + } + } + return null; + } + + /** + * Loads a pre-trained tokenizer from the given `pretrained_model_name_or_path`. + * + * @param {string} pretrained_model_name_or_path The path to the pre-trained tokenizer. + * @param {PretrainedTokenizerOptions} options Additional options for loading the tokenizer. + * + * @throws {Error} Throws an error if the tokenizer.json or tokenizer_config.json files are not found in the `pretrained_model_name_or_path`. + * @returns {Promise} A new instance of the `PreTrainedTokenizer` class. + */ + static async from_pretrained(pretrained_model_name_or_path, { + progress_callback = null, + config = null, + cache_dir = null, + local_files_only = false, + revision = 'main', + legacy = null, + } = {}) { + + const info = await loadTokenizer(pretrained_model_name_or_path, { + progress_callback, + config, + cache_dir, + local_files_only, + revision, + legacy, + }) + + // @ts-ignore + return new this(...info); + } + + /** + * @typedef {number[]|number[][]|Tensor} BatchEncodingItem + * + * @typedef {Object} BatchEncoding Holds the output of the tokenizer's call function. + * @property {BatchEncodingItem} input_ids List of token ids to be fed to a model. + * @property {BatchEncodingItem} attention_mask List of indices specifying which tokens should be attended to by the model. + * @property {BatchEncodingItem} [token_type_ids] List of token type ids to be fed to a model. + */ + + /** + * Encode/tokenize the given text(s). + * @param {string|string[]} text The text to tokenize. + * @param {Object} options An optional object containing the following properties: + * @param {string|string[]} [options.text_pair=null] Optional second sequence to be encoded. If set, must be the same type as text. + * @param {boolean|'max_length'} [options.padding=false] Whether to pad the input sequences. + * @param {boolean} [options.add_special_tokens=true] Whether or not to add the special tokens associated with the corresponding model. + * @param {boolean} [options.truncation=null] Whether to truncate the input sequences. + * @param {number} [options.max_length=null] Maximum length of the returned list and optionally padding length. + * @param {boolean} [options.return_tensor=true] Whether to return the results as Tensors or arrays. + * @param {boolean} [options.return_token_type_ids=null] Whether to return the token type ids. + * @returns {BatchEncoding} Object to be passed to the model. + */ + _call( + // Required positional arguments + text, + + // Optional keyword arguments + { + text_pair = null, + add_special_tokens = true, + padding = false, + truncation = null, + max_length = null, + return_tensor = true, // Different to HF + return_token_type_ids = null, + } = {}, + ) { + + const isBatched = Array.isArray(text); + + /** @type {EncodingSingle[]} */ + let encodedTokens; + + if (isBatched) { + if (text.length === 0) { + throw Error('text array must be non-empty') + } + + if (text_pair !== null) { + if (!Array.isArray(text_pair)) { + throw Error('text_pair must also be an array') + + } else if (text.length !== text_pair.length) { + throw Error('text and text_pair must have the same length') + } + + encodedTokens = text.map( + (t, i) => this._encode_plus(t, { text_pair: text_pair[i], add_special_tokens, return_token_type_ids }) + ) + + } else { + encodedTokens = text.map(x => this._encode_plus(x, { add_special_tokens, return_token_type_ids })); + } + + } else { + if (text === null || text === undefined) { + throw Error('text may not be null or undefined') + } + + if (Array.isArray(text_pair)) { + throw Error('When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).') + } + + // For single input, we just wrap in an array, and then unwrap later. + encodedTokens = [this._encode_plus(text, { text_pair, add_special_tokens, return_token_type_ids })]; + } + // At this point, `encodedTokens` is batched, of shape [batch_size, tokens]. + // However, array may be jagged. So, we may need pad to max_length. + if (max_length === null) { + max_length = this.model_max_length; + } else if (truncation === null) { + if (padding === true) { + console.warn( + "`max_length` is ignored when `padding: true` and there is no truncation strategy. " + + "To pad to max length, use `padding: 'max_length'`." + ) + max_length = this.model_max_length; + } else if (padding === false) { + console.warn("Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation: true` to explicitly truncate examples to max length."); + truncation = true; + } + } + + // padding: 'max_length' doesn't require any additional calculation + // but padding: true has to calculate max_length from the sequences + if (padding === true) { + max_length = Math.min((0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.max)(encodedTokens.map(x => x.input_ids.length))[0], max_length ?? Infinity); + } + + // Ensure it is less than model max length + max_length = Math.min(max_length, this.model_max_length ?? Infinity); + + if (padding || truncation) { + + // Perform padding and/or truncation + for (let i = 0; i < encodedTokens.length; ++i) { + if (encodedTokens[i].input_ids.length === max_length) { + continue; + + } else if (encodedTokens[i].input_ids.length > max_length) { + // possibly truncate + if (truncation) { + truncateHelper(encodedTokens[i], max_length); + } + + } else { // t.length < max_length + // possibly pad + if (padding) { + padHelper( + encodedTokens[i], + max_length, + key => key === 'input_ids' ? this.pad_token_id : 0, + this.padding_side + ); + } + } + } + } + + const result = {}; + + if (return_tensor) { + if (!(padding && truncation)) { + // Not, guaranteed that all items have same length, so + // we perform additional check + + if ( + encodedTokens.some(x => { + for (const key of Object.keys(x)) { + if (x[key].length !== encodedTokens[0][key]?.length) { + return true; + } + } + return false; + }) + ) { + throw Error( + "Unable to create tensor, you should probably activate truncation and/or padding " + + "with 'padding=true' and 'truncation=true' to have batched tensors with the same length." + ) + } + } + + // Now we actually convert to tensor + // NOTE: In the same way as the python library, we return a batched tensor, regardless of + // whether we have a single input or multiple inputs. + const dims = [encodedTokens.length, encodedTokens[0].input_ids.length]; + + for (const key of Object.keys(encodedTokens[0])) { + result[key] = new _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor('int64', + BigInt64Array.from(encodedTokens.flatMap(x => x[key]).map(BigInt)), + dims + ); + } + + } else { + for (const key of Object.keys(encodedTokens[0])) { + result[key] = encodedTokens.map(x => x[key]); + } + + // If not returning a tensor, we match the input type + if (!isBatched) { + // Input was not batched, so we unwrap + for (const key of Object.keys(result)) { + result[key] = result[key][0]; + } + } + } + + return /** @type {BatchEncoding} */(result); + } + + /** + * Encodes a single text using the preprocessor pipeline of the tokenizer. + * + * @param {string|null} text The text to encode. + * @returns {string[]|null} The encoded tokens. + */ + _encode_text(text) { + if (text === null) return null; + + // Actual function which does encoding, for a single text + // First, we take care of special tokens. Needed to avoid issues arising from + // normalization and/or pretokenization (which may not preserve special tokens) + const sections = this.added_tokens_splitter.split(text); + + // Process left/right stripping of added tokens + for (let i = 0; i < sections.length; ++i) { + const addedToken = this.added_tokens_map.get(sections[i]); + if (addedToken) { + if (addedToken.lstrip && i > 0) { + sections[i - 1] = sections[i - 1].trimEnd(); + } + if (addedToken.rstrip && i < sections.length - 1) { + sections[i + 1] = sections[i + 1].trimStart(); + } + } + } + + const tokens = sections.flatMap((x, section_index) => { + if (x.length === 0) return []; + if (this.added_tokens_map.has(x)) return [x]; // Return added tokens unchanged + + if (this.remove_space === true) { + x = x.trim().split(/\s+/).join(' '); + } + if (this.do_lowercase_and_remove_accent) { + x = lowercase_and_remove_accent(x); + } + + if (this.normalizer !== null) { + x = this.normalizer(x); + } + + // If, after normalization, this section is empty (e.g., trimming whitespace), + // we return an empty array + if (x.length === 0) { + return []; + } + + const sectionTokens = (this.pre_tokenizer !== null) ? this.pre_tokenizer(x, { + section_index, + }) : [x]; + + const tokens = this.model(sectionTokens); + + return tokens; + }); + + return tokens; + } + + /** + * Encodes a single text or a pair of texts using the model's tokenizer. + * + * @param {string} text The text to encode. + * @param {Object} options An optional object containing the following properties: + * @param {string} [options.text_pair=null] The optional second text to encode. + * @param {boolean} [options.add_special_tokens=true] Whether or not to add the special tokens associated with the corresponding model. + * @param {boolean} [options.return_token_type_ids=null] Whether to return token_type_ids. + * @returns {EncodingSingle} An object containing the encoded text. + * @private + */ + _encode_plus(text, { + text_pair = null, + add_special_tokens = true, + return_token_type_ids = null, + } = {}) { + + const { tokens, token_type_ids } = this._tokenize_helper(text, { pair: text_pair, add_special_tokens }); + + const input_ids = this.model.convert_tokens_to_ids(tokens); + + const result = { + input_ids, + attention_mask: new Array(input_ids.length).fill(1), + } + if ((return_token_type_ids ?? this.return_token_type_ids) && token_type_ids) { + result.token_type_ids = token_type_ids; + } + return result; + } + + /** + * Internal helper function to tokenize a text, and optionally a pair of texts. + * @param {string} text The text to tokenize. + * @param {Object} options An optional object containing the following properties: + * @param {string} [options.pair=null] The optional second text to tokenize. + * @param {boolean} [options.add_special_tokens=false] Whether or not to add the special tokens associated with the corresponding model. + * @returns {{tokens: string[], token_type_ids?: number[]}} An object containing the tokens and optionally the token type IDs. + */ + _tokenize_helper(text, { + pair = null, + add_special_tokens = false, + } = {}) { + const tokens = this._encode_text(text); + const tokens2 = this._encode_text(pair); + + return this.post_processor + ? this.post_processor(tokens, tokens2, { add_special_tokens }) + : { tokens: (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(tokens ?? [], tokens2 ?? []) }; + } + + /** + * Converts a string into a sequence of tokens. + * @param {string} text The sequence to be encoded. + * @param {Object} options An optional object containing the following properties: + * @param {string} [options.pair] A second sequence to be encoded with the first. + * @param {boolean} [options.add_special_tokens=false] Whether or not to add the special tokens associated with the corresponding model. + * @returns {string[]} The list of tokens. + */ + tokenize(text, { + pair = null, + add_special_tokens = false, + } = {}) { + return this._tokenize_helper(text, { pair, add_special_tokens }).tokens; + } + + /** + * Encodes a single text or a pair of texts using the model's tokenizer. + * + * @param {string} text The text to encode. + * @param {Object} options An optional object containing the following properties: + * @param {string} [options.text_pair=null] The optional second text to encode. + * @param {boolean} [options.add_special_tokens=true] Whether or not to add the special tokens associated with the corresponding model. + * @param {boolean} [options.return_token_type_ids=null] Whether to return token_type_ids. + * @returns {number[]} An array of token IDs representing the encoded text(s). + */ + encode(text, { + text_pair = null, + add_special_tokens = true, + return_token_type_ids = null, + } = {}) { + return this._encode_plus(text, { + text_pair, + add_special_tokens, + return_token_type_ids, + }).input_ids; + } + + /** + * Decode a batch of tokenized sequences. + * @param {number[][]|Tensor} batch List/Tensor of tokenized input sequences. + * @param {Object} decode_args (Optional) Object with decoding arguments. + * @returns {string[]} List of decoded sequences. + */ + batch_decode(batch, decode_args = {}) { + if (batch instanceof _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor) { + batch = batch.tolist(); + } + return batch.map(x => this.decode(x, decode_args)); + } + + /** + * Decodes a sequence of token IDs back to a string. + * + * @param {number[]|bigint[]|Tensor} token_ids List/Tensor of token IDs to decode. + * @param {Object} [decode_args={}] + * @param {boolean} [decode_args.skip_special_tokens=false] If true, special tokens are removed from the output string. + * @param {boolean} [decode_args.clean_up_tokenization_spaces=true] If true, spaces before punctuations and abbreviated forms are removed. + * + * @returns {string} The decoded string. + * @throws {Error} If `token_ids` is not a non-empty array of integers. + */ + decode( + token_ids, + decode_args = {}, + ) { + if (token_ids instanceof _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor) { + token_ids = prepareTensorForDecode(token_ids); + } + + if (!Array.isArray(token_ids) || token_ids.length === 0 || !(0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.isIntegralNumber)(token_ids[0])) { + throw Error("token_ids must be a non-empty array of integers."); + } + + return this.decode_single(token_ids, decode_args) + } + + /** + * Decode a single list of token ids to a string. + * @param {number[]|bigint[]} token_ids List of token ids to decode + * @param {Object} decode_args Optional arguments for decoding + * @param {boolean} [decode_args.skip_special_tokens=false] Whether to skip special tokens during decoding + * @param {boolean} [decode_args.clean_up_tokenization_spaces=null] Whether to clean up tokenization spaces during decoding. + * If null, the value is set to `this.decoder.cleanup` if it exists, falling back to `this.clean_up_tokenization_spaces` if it exists, falling back to `true`. + * @returns {string} The decoded string + */ + decode_single( + token_ids, + { + skip_special_tokens = false, + clean_up_tokenization_spaces = null, + } + ) { + let tokens = this.model.convert_ids_to_tokens(token_ids); + if (skip_special_tokens) { + tokens = tokens.filter(x => !this.special_tokens.includes(x)); + } + + // If `this.decoder` is null, we just join tokens with a space: + // https://github.com/huggingface/tokenizers/blob/8edec536a737cb04494b454805be16c020abb14f/tokenizers/src/tokenizer/mod.rs#L835 + /** @type {string} */ + let decoded = this.decoder ? this.decoder(tokens) : tokens.join(' '); + + // Slight hack, but prevents having to pass `skip_special_tokens` to + // each call to `decode`, which would lead to code duplication. + if (this.decoder && this.decoder.end_of_word_suffix) { + decoded = decoded.replaceAll(this.decoder.end_of_word_suffix, ' '); + if (skip_special_tokens) { + decoded = decoded.trim(); + } + } + + if (clean_up_tokenization_spaces ?? this.clean_up_tokenization_spaces) { + decoded = clean_up_tokenization(decoded); + } + + return decoded; + } + + /** + * Retrieve the chat template string used for tokenizing chat messages. This template is used + * internally by the `apply_chat_template` method and can also be used externally to retrieve the model's chat + * template for better generation tracking. + * + * @param {Object} options An optional object containing the following properties: + * @param {string} [options.chat_template=null] + * A Jinja template or the name of a template to use for this conversion. + * It is usually not necessary to pass anything to this argument, + * as the model's template will be used by default. + * @param {Object[]} [options.tools=null] + * A list of tools (callable functions) that will be accessible to the model. If the template does not + * support function calling, this argument will have no effect. Each tool should be passed as a JSON Schema, + * giving the name, description and argument types for the tool. See our + * [chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#automated-function-conversion-for-tool-use) + * for more information. + * @returns {string} The chat template string. + */ + get_chat_template({ + chat_template = null, + tools = null, + } = {}) { + + // First, handle the cases when the model has a dict of multiple templates + if (this.chat_template && typeof this.chat_template === 'object') { + const template_dict = this.chat_template; + + if (chat_template !== null && Object.hasOwn(template_dict, chat_template)) { + // The user can pass the name of a template to the chat template argument instead of an entire template + chat_template = template_dict[chat_template]; + } else if (chat_template === null) { + if (tools !== null && 'tool_use' in template_dict) { + chat_template = template_dict['tool_use']; + } else if ('default' in template_dict) { + chat_template = template_dict['default']; + } else { + throw Error( + `This model has multiple chat templates with no default specified! Please either pass a chat ` + + `template or the name of the template you wish to use to the 'chat_template' argument. Available ` + + `template names are ${Object.keys(template_dict).sort()}.` + ) + } + } + } else if (chat_template === null) { + // These are the cases when the model has a single template + // priority: `chat_template` argument > `tokenizer.chat_template` + if (this.chat_template) { + chat_template = this.chat_template; + } else { + throw Error( + "Cannot use apply_chat_template() because tokenizer.chat_template is not set and no template " + + "argument was passed! For information about writing templates and setting the " + + "tokenizer.chat_template attribute, please see the documentation at " + + "https://huggingface.co/docs/transformers/main/en/chat_templating" + ) + } + } + return chat_template; + } + + /** + * Converts a list of message objects with `"role"` and `"content"` keys to a list of token + * ids. This method is intended for use with chat models, and will read the tokenizer's chat_template attribute to + * determine the format and control tokens to use when converting. + * + * See [here](https://huggingface.co/docs/transformers/chat_templating) for more information. + * + * **Example:** Applying a chat template to a conversation. + * + * ```javascript + * import { AutoTokenizer } from "@huggingface/transformers"; + * + * const tokenizer = await AutoTokenizer.from_pretrained("Xenova/mistral-tokenizer-v1"); + * + * const chat = [ + * { "role": "user", "content": "Hello, how are you?" }, + * { "role": "assistant", "content": "I'm doing great. How can I help you today?" }, + * { "role": "user", "content": "I'd like to show off how chat templating works!" }, + * ] + * + * const text = tokenizer.apply_chat_template(chat, { tokenize: false }); + * // "[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]" + * + * const input_ids = tokenizer.apply_chat_template(chat, { tokenize: true, return_tensor: false }); + * // [1, 733, 16289, 28793, 22557, 28725, 910, 460, 368, 28804, 733, 28748, 16289, 28793, 28737, 28742, 28719, 2548, 1598, 28723, 1602, 541, 315, 1316, 368, 3154, 28804, 2, 28705, 733, 16289, 28793, 315, 28742, 28715, 737, 298, 1347, 805, 910, 10706, 5752, 1077, 3791, 28808, 733, 28748, 16289, 28793] + * ``` + * + * @param {Message[]} conversation A list of message objects with `"role"` and `"content"` keys, + * representing the chat history so far. + * @param {Object} options An optional object containing the following properties: + * @param {string} [options.chat_template=null] A Jinja template to use for this conversion. If + * this is not passed, the model's chat template will be used instead. + * @param {Object[]} [options.tools=null] + * A list of tools (callable functions) that will be accessible to the model. If the template does not + * support function calling, this argument will have no effect. Each tool should be passed as a JSON Schema, + * giving the name, description and argument types for the tool. See our + * [chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#automated-function-conversion-for-tool-use) + * for more information. + * @param {Record[]} [options.documents=null] + * A list of dicts representing documents that will be accessible to the model if it is performing RAG + * (retrieval-augmented generation). If the template does not support RAG, this argument will have no + * effect. We recommend that each document should be a dict containing "title" and "text" keys. Please + * see the RAG section of the [chat templating guide](https://huggingface.co/docs/transformers/main/en/chat_templating#arguments-for-RAG) + * for examples of passing documents with chat templates. + * @param {boolean} [options.add_generation_prompt=false] Whether to end the prompt with the token(s) that indicate + * the start of an assistant message. This is useful when you want to generate a response from the model. + * Note that this argument will be passed to the chat template, and so it must be supported in the + * template for this argument to have any effect. + * @param {boolean} [options.tokenize=true] Whether to tokenize the output. If false, the output will be a string. + * @param {boolean} [options.padding=false] Whether to pad sequences to the maximum length. Has no effect if tokenize is false. + * @param {boolean} [options.truncation=false] Whether to truncate sequences to the maximum length. Has no effect if tokenize is false. + * @param {number} [options.max_length=null] Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is false. + * If not specified, the tokenizer's `max_length` attribute will be used as a default. + * @param {boolean} [options.return_tensor=true] Whether to return the output as a Tensor or an Array. Has no effect if tokenize is false. + * @param {boolean} [options.return_dict=true] Whether to return a dictionary with named outputs. Has no effect if tokenize is false. + * @param {Object} [options.tokenizer_kwargs={}] Additional options to pass to the tokenizer. + * @returns {string | Tensor | number[]| number[][]|BatchEncoding} The tokenized output. + */ + apply_chat_template(conversation, { + tools = null, + documents = null, + chat_template = null, + add_generation_prompt = false, + tokenize = true, + padding = false, + truncation = false, + max_length = null, + return_tensor = true, + return_dict = false, + tokenizer_kwargs = {}, + ...kwargs + } = {}) { + + chat_template = this.get_chat_template({ chat_template, tools }); + + if (typeof chat_template !== 'string') { + throw Error(`chat_template must be a string, but got ${typeof chat_template}`); + } + + // Compilation function uses a cache to avoid recompiling the same template + let compiledTemplate = this._compiled_template_cache.get(chat_template); + if (compiledTemplate === undefined) { + compiledTemplate = new _huggingface_jinja__WEBPACK_IMPORTED_MODULE_6__.Template(chat_template); + this._compiled_template_cache.set(chat_template, compiledTemplate); + } + + const special_tokens_map = Object.create(null); + for (const key of SPECIAL_TOKEN_ATTRIBUTES) { + const value = this.getToken(key); + if (value) { + special_tokens_map[key] = value; + } + } + + const rendered = compiledTemplate.render({ + messages: conversation, + add_generation_prompt, + tools, + documents, + ...special_tokens_map, + ...kwargs, + }); + + if (tokenize) { + const out = this._call(rendered, { + add_special_tokens: false, + padding, + truncation, + max_length, + return_tensor, + ...tokenizer_kwargs, + }); + return return_dict ? out : out.input_ids; + } + + return rendered; + } +} + +/** + * BertTokenizer is a class used to tokenize text for BERT models. + * @extends PreTrainedTokenizer + */ +class BertTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} +/** + * Albert tokenizer + * @extends PreTrainedTokenizer + */ +class AlbertTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} +class MobileBertTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} +class SqueezeBertTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} +class DebertaTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} +class DebertaV2Tokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} +class HerbertTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} +class ConvBertTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} +class RoFormerTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} +class DistilBertTokenizer extends PreTrainedTokenizer { } +class CamembertTokenizer extends PreTrainedTokenizer { } +class XLMTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; + + constructor(tokenizerJSON, tokenizerConfig) { + super(tokenizerJSON, tokenizerConfig); + console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.') + } +} +class ElectraTokenizer extends PreTrainedTokenizer { + return_token_type_ids = true; +} + +class T5Tokenizer extends PreTrainedTokenizer { } +class GPT2Tokenizer extends PreTrainedTokenizer { } +class BartTokenizer extends PreTrainedTokenizer { } +class MBartTokenizer extends PreTrainedTokenizer { + constructor(tokenizerJSON, tokenizerConfig) { + super(tokenizerJSON, tokenizerConfig); + + this.languageRegex = /^[a-z]{2}_[A-Z]{2}$/; + this.language_codes = this.special_tokens.filter(x => this.languageRegex.test(x)); + this.lang_to_token = x => x; // Identity function + } + + /** + * Helper function to build translation inputs for an `MBartTokenizer`. + * @param {string|string[]} raw_inputs The text to tokenize. + * @param {Object} tokenizer_options Options to be sent to the tokenizer + * @param {Object} generate_kwargs Generation options. + * @returns {Object} Object to be passed to the model. + */ + _build_translation_inputs(raw_inputs, tokenizer_options, generate_kwargs) { + return _build_translation_inputs(this, raw_inputs, tokenizer_options, generate_kwargs); + } +} +class MBart50Tokenizer extends MBartTokenizer { } // NOTE: extends MBartTokenizer + +class RobertaTokenizer extends PreTrainedTokenizer { } + +class BloomTokenizer extends PreTrainedTokenizer { } + +const SPIECE_UNDERLINE = "▁"; + +class LlamaTokenizer extends PreTrainedTokenizer { + + padding_side = 'left'; + + constructor(tokenizerJSON, tokenizerConfig) { + super(tokenizerJSON, tokenizerConfig); + + this.legacy = tokenizerConfig.legacy ?? true; + if (!this.legacy) { + // See https://github.com/huggingface/transformers/pull/24565 for more information + this.normalizer = null; + this.pre_tokenizer = new MetaspacePreTokenizer({ + replacement: SPIECE_UNDERLINE, + add_prefix_space: true, + prepend_scheme: "first", + }); + } + } + + /** + * Helper function to handle legacy encoding of SPM tokenizers. + * Adapted from https://github.com/huggingface/transformers/blob/e6dcf8abd6f65bb4b6dfc1831b20d9ba49ce00e2/src/transformers/models/t5/tokenization_t5.py#L374-L387 + * @param {string} text The text to encode. + * @returns {string[]} The encoded tokens. + */ + _encode_text(text) { + if (text === null) return null; + + if (this.legacy || text.length === 0) { + return super._encode_text(text); + } + + let tokens = super._encode_text(SPIECE_UNDERLINE + text.replaceAll(SPIECE_UNDERLINE, " ")); + if (tokens.length > 1 && tokens[0] === SPIECE_UNDERLINE && this.special_tokens.includes(tokens[1])) { + tokens = tokens.slice(1); + } + return tokens; + } +} +class CodeLlamaTokenizer extends PreTrainedTokenizer { } + +class XLMRobertaTokenizer extends PreTrainedTokenizer { } +class MPNetTokenizer extends PreTrainedTokenizer { } + +class FalconTokenizer extends PreTrainedTokenizer { } + +class GPTNeoXTokenizer extends PreTrainedTokenizer { } + +class EsmTokenizer extends PreTrainedTokenizer { } + +class Qwen2Tokenizer extends PreTrainedTokenizer { } + +class GemmaTokenizer extends PreTrainedTokenizer { } + +class Grok1Tokenizer extends PreTrainedTokenizer { } + +/** + * Helper function to build translation inputs for an `NllbTokenizer` or `M2M100Tokenizer`. + * @param {PreTrainedTokenizer} self The tokenizer instance. + * @param {string|string[]} raw_inputs The text to tokenize. + * @param {Object} tokenizer_options Options to be sent to the tokenizer + * @param {Object} generate_kwargs Generation options. + * @returns {Object} Object to be passed to the model. + * @private + */ +function _build_translation_inputs(self, raw_inputs, tokenizer_options, generate_kwargs) { + if (!('language_codes' in self) || !Array.isArray(self.language_codes)) { + throw new Error('Tokenizer must have `language_codes` attribute set and it should be an array of language ids.') + } + if (!('languageRegex' in self) || !(self.languageRegex instanceof RegExp)) { + throw new Error('Tokenizer must have `languageRegex` attribute set and it should be a regular expression.') + } + if (!('lang_to_token' in self) || typeof self.lang_to_token !== 'function') { + throw new Error('Tokenizer must have `lang_to_token` attribute set and it should be a function.') + } + const src_lang_token = generate_kwargs.src_lang; + const tgt_lang_token = generate_kwargs.tgt_lang; + + // Check that the target language is valid: + if (!self.language_codes.includes(tgt_lang_token)) { + throw new Error(`Target language code "${tgt_lang_token}" is not valid. Must be one of: {${self.language_codes.join(', ')}}`); + } + + // Allow `src_lang` to be optional. If not set, we'll use the tokenizer's default. + if (src_lang_token !== undefined) { + // Check that the source language is valid: + if (!self.language_codes.includes(src_lang_token)) { + throw new Error(`Source language code "${src_lang_token}" is not valid. Must be one of: {${self.language_codes.join(', ')}}`); + } + + // In the same way as the Python library, we override the post-processor + // to force the source language to be first: + for (const item of self.post_processor.config.single) { + if ('SpecialToken' in item && self.languageRegex.test(item.SpecialToken.id)) { + item.SpecialToken.id = self.lang_to_token(src_lang_token); + break; + } + } + // TODO: Do the same for pair? + } + + // Override the `forced_bos_token_id` to force the correct language + generate_kwargs.forced_bos_token_id = self.model.convert_tokens_to_ids([self.lang_to_token(tgt_lang_token)])[0]; + + return self._call(raw_inputs, tokenizer_options); +} + +/** + * The NllbTokenizer class is used to tokenize text for NLLB ("No Language Left Behind") models. + * + * No Language Left Behind (NLLB) is a first-of-its-kind, AI breakthrough project + * that open-sources models capable of delivering high-quality translations directly + * between any pair of 200+ languages — including low-resource languages like Asturian, + * Luganda, Urdu and more. It aims to help people communicate with anyone, anywhere, + * regardless of their language preferences. For more information, check out their + * [paper](https://huggingface.co/papers/2207.04672). + * + * For a list of supported languages (along with their language codes), + * @see {@link https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200} + */ +class NllbTokenizer extends PreTrainedTokenizer { + + constructor(tokenizerJSON, tokenizerConfig) { + super(tokenizerJSON, tokenizerConfig); + + this.languageRegex = /^[a-z]{3}_[A-Z][a-z]{3}$/; + this.language_codes = this.special_tokens.filter(x => this.languageRegex.test(x)); + this.lang_to_token = x => x; // Identity function + } + + /** + * Helper function to build translation inputs for an `NllbTokenizer`. + * @param {string|string[]} raw_inputs The text to tokenize. + * @param {Object} tokenizer_options Options to be sent to the tokenizer + * @param {Object} generate_kwargs Generation options. + * @returns {Object} Object to be passed to the model. + */ + _build_translation_inputs(raw_inputs, tokenizer_options, generate_kwargs) { + return _build_translation_inputs(this, raw_inputs, tokenizer_options, generate_kwargs); + } +} + +/** + * The M2M100Tokenizer class is used to tokenize text for M2M100 ("Many-to-Many") models. + * + * M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many + * multilingual translation. It was introduced in this [paper](https://huggingface.co/papers/2010.11125) + * and first released in [this](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100) repository. + * + * For a list of supported languages (along with their language codes), + * @see {@link https://huggingface.co/facebook/m2m100_418M#languages-covered} + */ +class M2M100Tokenizer extends PreTrainedTokenizer { + constructor(tokenizerJSON, tokenizerConfig) { + super(tokenizerJSON, tokenizerConfig); + + this.languageRegex = /^__[a-z]{2,3}__$/; + this.language_codes = this.special_tokens + .filter(x => this.languageRegex.test(x)) + .map(x => x.slice(2, -2)); + this.lang_to_token = x => `__${x}__`; + } + + /** + * Helper function to build translation inputs for an `M2M100Tokenizer`. + * @param {string|string[]} raw_inputs The text to tokenize. + * @param {Object} tokenizer_options Options to be sent to the tokenizer + * @param {Object} generate_kwargs Generation options. + * @returns {Object} Object to be passed to the model. + */ + _build_translation_inputs(raw_inputs, tokenizer_options, generate_kwargs) { + return _build_translation_inputs(this, raw_inputs, tokenizer_options, generate_kwargs); + } +} + +/** + * WhisperTokenizer tokenizer + * @extends PreTrainedTokenizer + */ +class WhisperTokenizer extends PreTrainedTokenizer { + + get timestamp_begin() { + return this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0] + 1; + } + + /** + * Decodes automatic speech recognition (ASR) sequences. + * @param {Array<{tokens: bigint[], token_timestamps?: number[], stride: number[]}>} sequences The sequences to decode. + * @param {Object} options The options to use for decoding. + * @returns {Array, text: string}>}>} The decoded sequences. + */ + _decode_asr(sequences, { + return_timestamps = false, + return_language = false, + time_precision = null, + force_full_sequences = true + } = {}) { + // Set force_full_sequences=false if you want streaming + // TODO add support for `return_language` + + // Internal method meant to only be used by asr pipeline. + // Handles all the little quirks specific to whisper to handle + // the various options not allowed in other seq2seq models + + // =========== Overview ============ + // - iterate over all outputs + // - all tokens within output + // - Each token can be + // - language token + // - special token + // - timestamp token + // - text token + // - We accumulate the text tokens. + // - We split on end timestamps + // - Lots of complexity comes from stride and timestamps + + if (time_precision === null) { + throw Error("Must specify time_precision") + } + let last_language = null; + + const returnWordTimestamps = return_timestamps === "word"; + + function new_chunk() { + return { "language": last_language, "timestamp": [null, null], "text": "" }; + } + + // Welcome to the state machine! + const chunks = []; + let chunk = new_chunk(); + let time_offset = 0.0; + const timestamp_begin = this.timestamp_begin; + // Whisper timestamp tokens start from 0.00 and go to timestamp 30.00 in 0.02 increments. + // We can calculate the last time stamp token as timestamp_begin plus the number of tokens + // tokens from 0.00 to 30.00 which is 1500. + const total_timestamp_tokens = 1500; // (30.00 - 0.00) / 0.02 + const timestamp_end = timestamp_begin + total_timestamp_tokens; + + let previous_tokens = []; + let previous_token_timestamps = []; + + let skip = false; + let right_stride_start = null; + + + const all_special_ids = new Set(this.all_special_ids); + + for (const output of sequences) { + // NOTE: python version has batches, so it uses [0] + const token_ids = output.tokens; + const token_timestamps = returnWordTimestamps ? output.token_timestamps : null; + + // These keep track of timestamps within strides, which need + // to be skipped and resolve all tokens in a single chunk. + let last_timestamp = null; + let first_timestamp = timestamp_begin; + + if ("stride" in output) { + const [chunk_len, stride_left, stride_right] = output.stride; + + // Offset the timings to account for the other `model_outputs`. + time_offset -= stride_left; + right_stride_start = chunk_len - stride_right; + + // Keeping track of timestamps within strides + // We're going to NOT split on those, and delay until we're + // out of BOTH stride. Otherwise lots of issues occur and + // corner cases + if (stride_left) { + first_timestamp = stride_left / time_precision + timestamp_begin; + } + + if (stride_right) { + for (let i = token_ids.length - 1; i >= 0; --i) { + const token = Number(token_ids[i]); + if (token >= timestamp_begin) { + // There can be several token in the right stride + // But the last one is ALWAYS going to be skipped + if (last_timestamp !== null && (token - timestamp_begin) * time_precision < right_stride_start) { + break; + } + last_timestamp = token; + } + } + } + } + + let current_tokens = []; + let current_token_timestamps = []; + + // - all tokens within output + for (let i = 0; i < token_ids.length; ++i) { + const token = Number(token_ids[i]); + // 4 possible states for each token + // - 1/ Language code + // - 2/ all other special tokens (which we ignore) + // - 3/ Timestamp + // - 4/ Regular text + + if (all_special_ids.has(token)) { + const text = this.decode([token]); + const language = _models_whisper_common_whisper_js__WEBPACK_IMPORTED_MODULE_7__.WHISPER_LANGUAGE_MAPPING.get(text.slice(2, -2)); + + if (language !== undefined) { + // 1/ Indeed some language + // TODO Handle when language is different from the previous + // one, and we cannot use timestamped tokens to create chunks + if (last_language !== null && language !== last_language && !return_timestamps) { + previous_tokens.push(current_tokens); + const resolved_tokens = this.findLongestCommonSequence(previous_tokens)[0]; + const resolved_text = this.decode(resolved_tokens); + chunk.text = resolved_text; + chunks.push(chunk); + + // Flush all our temporary context + previous_tokens = []; + current_tokens = []; + chunk = new_chunk(); + } + + last_language = chunk.language = language; + } else { + // 2/ This is a regular special token, ignoring it + } + } else if (token >= timestamp_begin && token <= timestamp_end) { + // 3/ Timestamp token + const time = (token - timestamp_begin) * time_precision + time_offset; + const rounded_time = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.round)(time, 2); + + if (last_timestamp !== null && token >= last_timestamp) { + // Whisper outputted a timestamp token, but it falls within + // our stride, so we're going to skip it for the time being + // and resolve this later + // Skip is necessary because timestamp tokens always come + // by pair, so we need to skip the next one too (which would mark the start of another chunk). + skip = true; + } else if (skip || (previous_tokens.length > 0 && token < first_timestamp)) { + skip = false; + } else if (chunk.timestamp[0] === null) { + chunk.timestamp[0] = rounded_time; + } else { + // This is the end of the timestamp chunk + if (rounded_time === chunk.timestamp[0]) { + // This is a bug in timestamp token output + // where we're taking the duplicate token + // as a stop where it should be a start. + // This is an issue in the underlying model output + // Let's just skip it so it becomes de-factor a start agin + } else { + chunk.timestamp[1] = rounded_time; + + // Handling merges + previous_tokens.push(current_tokens) + + if (returnWordTimestamps) { + previous_token_timestamps.push(current_token_timestamps); + } + const [resolved_tokens, resolved_token_timestamps] = this.findLongestCommonSequence( + previous_tokens, previous_token_timestamps + ) + + const resolved_text = this.decode(resolved_tokens) + chunk.text = resolved_text + + if (returnWordTimestamps) { + chunk.words = this.collateWordTimestamps( + resolved_tokens, resolved_token_timestamps, last_language, + ) + } + + chunks.push(chunk) + + // Flush all our temporary context + previous_tokens = [] + current_tokens = [] + previous_token_timestamps = [] + current_token_timestamps = [] + chunk = new_chunk() + } + } + + } else { + // 4/ Regular token + // We just append to the list of all tokens so we can handle + // merges later and decode into text. + current_tokens.push(token) + + if (returnWordTimestamps) { + let start_time = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.round)(token_timestamps[i] + time_offset, 2); + + let end_time; + if (i + 1 < token_timestamps.length) { + end_time = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.round)(token_timestamps[i + 1] + time_offset, 2); + + // Do not allow punctuation-only tokens to have a duration. + // This prevents long pauses from messing up the timestamps. + const decoded_text = this.decode([token]); + if (PUNCTUATION_ONLY_REGEX.test(decoded_text)) { + // Add `time_precision` to avoid overlapping timestamps + end_time = (0,_utils_maths_js__WEBPACK_IMPORTED_MODULE_3__.round)(Math.min(start_time + time_precision, end_time), 2); + } + } else { + // should never happen + end_time = null; + } + current_token_timestamps.push([start_time, end_time]); + } + + } + } + + if ('stride' in output) { + const [chunk_len, stride_left, stride_right] = output.stride; + time_offset += chunk_len - stride_right + } + + // Leftover tokens + if (current_tokens.length > 0) { + previous_tokens.push(current_tokens) + if (returnWordTimestamps) { + previous_token_timestamps.push(current_token_timestamps); + } + } else if (previous_tokens.every(p => p.length === 0)) { + // Flushing previous tokens (END)" + chunk = new_chunk() + previous_tokens = [] + current_tokens = [] + previous_token_timestamps = []; + current_token_timestamps = []; + } + + } + + if (previous_tokens.length > 0) { + if (force_full_sequences && return_timestamps) { + // Last token should always be timestamps, so there shouldn't be + // leftover + throw new Error( + "Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. " + + "Also make sure WhisperTimeStampLogitsProcessor was used during generation." + ); + } + + // Happens when we don't use timestamps + const [resolved_tokens, resolved_token_timestamps] = this.findLongestCommonSequence(previous_tokens, previous_token_timestamps); + + // Flushing previous tokens (FINAL) + const resolved_text = this.decode(resolved_tokens); + chunk.text = resolved_text; + if (returnWordTimestamps) { + chunk.words = this.collateWordTimestamps( + resolved_tokens, resolved_token_timestamps, last_language, + ) + } + chunks.push(chunk); + } + + let optional = Object.create(null); + + // Preparing and cleaning up the pipeline output + const full_text = chunks.map(chunk => chunk.text).join(''); + if (return_timestamps || return_language) { + for (let i = 0; i < chunks.length; ++i) { + const chunk = chunks[i]; + if (!return_timestamps) { + delete chunk["timestamp"]; + } + + if (!return_language) { + delete chunk["language"]; + } + } + if (returnWordTimestamps) { + const new_chunks = []; + for (const chunk of chunks) { + for (const word of chunk.words) { + new_chunks.push(word); + } + } + optional = { "chunks": new_chunks }; + } else { + optional = { "chunks": chunks }; + } + } + return [full_text, optional]; + + } + + /** + * Finds the longest common sequence among the provided sequences. + * @param {number[][]} sequences An array of sequences of token ids to compare. + * @returns {number[][]} The longest common sequence found. + * @throws {Error} If there is a bug within the function. + * @private + */ + findLongestCommonSequence(sequences, token_timestamp_sequences = null) { + // It would be much harder to do O(n) because of fault tolerance. + // We actually have a really good property which is that the total sequence + // MUST be those subsequences in order. + // If token_timestamp_sequences is provided, will split those sequences in + // exactly the same way. + let leftSequence = sequences[0]; + let leftLength = leftSequence.length; + let totalSequence = []; + + const use_token_timestamp_sequences = Array.isArray(token_timestamp_sequences) && token_timestamp_sequences.length > 0; + let total_token_timestamp_sequence = use_token_timestamp_sequences ? [] : null; + let left_token_timestamp_sequence = use_token_timestamp_sequences ? token_timestamp_sequences[0] : null; + for (let i = 1; i < sequences.length; ++i) { + const rightSequence = sequences[i]; + let max = 0.0; + let maxIndices = [leftLength, leftLength, 0, 0]; + // Here we're sliding matches + // [a, b, c, d] + // [c, d, f] + // = [c] == [d] + + // [a, b, c, d] + // [c, d, f] + // = [c, d] == [c, d] + + + // [a, b, c, d] + // [c, d, f] + + // = [b, c, d] == [c, d, f] + + // [a, b, c, d] + // [c, d, f] + + // [a, b, c] == [c, d, f] + + // [a, b, c, d] + // [d, f] + + // [a, b] == [d, f] + + // [a, b, c, d] + // [f] + + // [a] == [f] + + const rightLength = rightSequence.length; + for (let j = 1; j < leftLength + rightLength; ++j) { + // Slightly convoluted because we don't want out of bound indices + // This will be necessary for a small conflict resolution optimization + // later + const leftStart = Math.max(0, leftLength - j); + const leftStop = Math.min(leftLength, leftLength + rightLength - j); + const left = leftSequence.slice(leftStart, leftStop); + const rightStart = Math.max(0, j - leftLength); + const rightStop = Math.min(rightLength, j); + const right = rightSequence.slice(rightStart, rightStop); + if (left.length !== right.length) { + throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference."); + } + + let matches; + if (use_token_timestamp_sequences) { + // Get length of longest subsequence of tokens that match + // and have timestamps that are in order + matches = left.filter((elem, idx) => ( + elem === right[idx] + && left_token_timestamp_sequence[leftStart + idx] <= token_timestamp_sequences[i][rightStart + idx] + )).length; + } else { + matches = left.filter((elem, idx) => elem === right[idx]).length; + } + + // epsilon to favor long perfect matches + const eps = j / 10000.0; + const matching = matches / j + eps; + if (matches > 1 && matching > max) { + max = matching; + maxIndices = [leftStart, leftStop, rightStart, rightStop]; + } + } + const [leftStart, leftStop, rightStart, rightStop] = maxIndices; + const leftMid = Math.floor((leftStop + leftStart) / 2); + const rightMid = Math.floor((rightStop + rightStart) / 2); + totalSequence.push(...leftSequence.slice(0, leftMid)); + leftSequence = rightSequence.slice(rightMid); + leftLength = leftSequence.length; + + if (use_token_timestamp_sequences) { + total_token_timestamp_sequence.push(...left_token_timestamp_sequence.slice(0, leftMid)); + left_token_timestamp_sequence = token_timestamp_sequences[i].slice(rightMid); + } + } + totalSequence.push(...leftSequence); + + if (use_token_timestamp_sequences) { + total_token_timestamp_sequence.push(...left_token_timestamp_sequence); + return [totalSequence, total_token_timestamp_sequence]; + } else { + return [totalSequence, []]; + } + } + + /** @private */ + collateWordTimestamps(tokens, token_timestamps, language) { + + const [words, _, token_indices] = this.combineTokensIntoWords(tokens, language); + + const timings = []; + for (let i = 0; i < words.length; ++i) { + const indices = token_indices[i]; + timings.push({ + text: words[i], + timestamp: [ + token_timestamps[indices.at(0)][0], + token_timestamps[indices.at(-1)][1], + ], + }); + } + return timings; + } + + /** + * Groups tokens by word. Returns a tuple containing a list of strings with the words, + * and a list of `token_id` sequences with the tokens making up each word. + * @param {number[]} tokens + * @param {string} [language] + * @param {string} prepend_punctionations + * @param {string} append_punctuations + * + * @private + */ + combineTokensIntoWords(tokens, language, prepend_punctionations = "\"'“¡¿([{-", append_punctuations = "\"'.。,，!！?？:：”)]}、") { + language = language ?? 'english'; + + let words, word_tokens, token_indices; + + if (["chinese", "japanese", "thai", "lao", "myanmar"].includes(language)) { + // These languages don't typically use spaces. + [words, word_tokens, token_indices] = this.splitTokensOnUnicode(tokens) + } else { + [words, word_tokens, token_indices] = this.splitTokensOnSpaces(tokens) + } + + return this.mergePunctuations(words, word_tokens, token_indices, prepend_punctionations, append_punctuations); + } + + /** @type {PreTrainedTokenizer['decode']} */ + decode( + token_ids, + decode_args, + ) { + let text; + // @ts-ignore + if (decode_args?.decode_with_timestamps) { + if (token_ids instanceof _utils_tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor) { + token_ids = prepareTensorForDecode(token_ids); + } + text = this.decodeWithTimestamps(token_ids, decode_args); + } else { + text = super.decode(token_ids, decode_args); + } + // TODO: implement offsets + // if (decode_args.output_offsets) { + // let offsets = this.computeOffsets + // } + return text; + } + + /** + * @param {number[]|bigint[]} token_ids List of token IDs to decode. + * @param {Object} decode_args Optional arguments for decoding + * @private + */ + decodeWithTimestamps(token_ids, decode_args) { + const time_precision = decode_args?.time_precision ?? 0.02; + + const timestamp_begin = Array.from(this.all_special_ids).at(-1) + 1; + /**@type {Array} */ + let outputs = [[]]; + for (let token of token_ids) { + token = Number(token); + if (token >= timestamp_begin) { + const timestamp = ((token - timestamp_begin) * time_precision).toFixed(2); + outputs.push(`<|${timestamp}|>`); + outputs.push([]); + } else { + outputs[outputs.length - 1].push(token); + } + } + outputs = outputs.map( + s => typeof s === 'string' ? s : super.decode(s, decode_args) + ) + + return outputs.join(''); + } + + /** + * Combine tokens into words by splitting at any position where the tokens are decoded as valid unicode points. + * @param {number[]} tokens + * @returns {*} + * @private + */ + splitTokensOnUnicode(tokens) { + const decoded_full = this.decode(tokens, { + // @ts-ignore + decode_with_timestamps: true, + }); + const replacement_char = '\uFFFD'; + + const words = [] + const word_tokens = [] + const token_indices = [] + let current_tokens = [] + let current_indices = [] + let unicode_offset = 0 + + for (let token_idx = 0; token_idx < tokens.length; ++token_idx) { + const token = tokens[token_idx]; + + current_tokens.push(token); + current_indices.push(token_idx); + + const decoded = this.decode(current_tokens, { + // @ts-ignore + decode_with_timestamps: true, + }); + + if (!decoded.includes(replacement_char) || decoded_full[unicode_offset + decoded.indexOf(replacement_char)] === replacement_char) { + words.push(decoded) + word_tokens.push(current_tokens) + token_indices.push(current_indices) + current_tokens = [] + current_indices = [] + unicode_offset += decoded.length; + } + + } + + return [words, word_tokens, token_indices] + } + + /** + * Combine tokens into words by splitting at whitespace and punctuation tokens. + * @param {number[]} tokens + * @private + */ + splitTokensOnSpaces(tokens) { + + const [subwords, subword_tokens_list, subword_indices_list] = this.splitTokensOnUnicode(tokens); + + const words = [] + const word_tokens = [] + const token_indices = [] + + const punctuationRegex = new RegExp(`^[${PUNCTUATION_REGEX}]$`, 'gu'); + + for (let i = 0; i < subwords.length; ++i) { + + const subword = subwords[i]; + const subword_tokens = subword_tokens_list[i]; + const subword_indices = subword_indices_list[i]; + + // @ts-ignore + const special = subword_tokens[0] >= this.model.tokens_to_ids.get('<|endoftext|>'); + const with_space = subword.startsWith(' '); + const trimmed = subword.trim(); + const punctuation = punctuationRegex.test(trimmed); + + if (special || with_space || punctuation || words.length === 0) { + words.push(subword); + word_tokens.push(subword_tokens); + token_indices.push(subword_indices); + } else { + const ix = words.length - 1; + words[ix] += subword; + word_tokens[ix].push(...subword_tokens); + token_indices[ix].push(...subword_indices); + } + } + + return [words, word_tokens, token_indices]; + + } + + /** + * Merges punctuation tokens with neighboring words. + * @param {string[]} words + * @param {number[][]} tokens + * @param {number[][]} indices + * @param {string} prepended + * @param {string} appended + * @private + */ + mergePunctuations(words, tokens, indices, prepended, appended) { + + const newWords = structuredClone(words); + const newTokens = structuredClone(tokens); + const newIndices = structuredClone(indices); + + + // prepend punctuations + let i = newWords.length - 2; + let j = newWords.length - 1; + + while (i >= 0) { + if (newWords[i].startsWith(' ') && prepended.includes(newWords[i].trim())) { + newWords[j] = newWords[i] + newWords[j]; + newTokens[j] = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(newTokens[i], newTokens[j]); + newIndices[j] = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(newIndices[i], newIndices[j]); + newWords[i] = ''; + newTokens[i] = []; + newIndices[i] = []; + } else { + j = i; + } + --i; + } + + // append punctuations + i = 0; + j = 1; + while (j < newWords.length) { + if (!newWords[i].endsWith(' ') && appended.includes(newWords[j])) { + newWords[i] += newWords[j]; + newTokens[i] = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(newTokens[i], newTokens[j]); + newIndices[i] = (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)(newIndices[i], newIndices[j]); + newWords[j] = ''; + newTokens[j] = []; + newIndices[j] = []; + } else { + i = j; + } + ++j; + } + + return [ + newWords.filter(x => x), + newTokens.filter(x => x.length > 0), + newIndices.filter(x => x.length > 0), + ] + } +} +class CodeGenTokenizer extends PreTrainedTokenizer { } +class CLIPTokenizer extends PreTrainedTokenizer { } +class SiglipTokenizer extends PreTrainedTokenizer { } + +/** + * @todo This model is not yet supported by Hugging Face's "fast" tokenizers library (https://github.com/huggingface/tokenizers). + * Therefore, this implementation (which is based on fast tokenizers) may produce slightly inaccurate results. + */ +class MarianTokenizer extends PreTrainedTokenizer { + /** + * Create a new MarianTokenizer instance. + * @param {Object} tokenizerJSON The JSON of the tokenizer. + * @param {Object} tokenizerConfig The config of the tokenizer. + */ + constructor(tokenizerJSON, tokenizerConfig) { + super(tokenizerJSON, tokenizerConfig); + + this.languageRegex = /^(>>\w+<<)\s*/g; + + this.supported_language_codes = this.model.vocab.filter( + x => this.languageRegex.test(x) + ); + + console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.') + } + + /** + * Encodes a single text. Overriding this method is necessary since the language codes + * must be removed before encoding with sentencepiece model. + * @see https://github.com/huggingface/transformers/blob/12d51db243a00726a548a43cc333390ebae731e3/src/transformers/models/marian/tokenization_marian.py#L204-L213 + * + * @param {string|null} text The text to encode. + * @returns {Array} The encoded tokens. + */ + _encode_text(text) { + if (text === null) return null; + + // Check if text starts with language code: + const [matchInfo, ...remainder] = text.trim().split(this.languageRegex); + + if (remainder.length === 0) { + // No language code, encode normally + return super._encode_text(matchInfo); + + } else if (remainder.length === 2) { + // Text starts with language code, so we do not encode it with sentencepiece. + const [language, text] = remainder; + + if (!this.supported_language_codes.includes(language)) { + console.warn(`Unsupported language code "${language}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`) + } + return (0,_utils_core_js__WEBPACK_IMPORTED_MODULE_1__.mergeArrays)([language], super._encode_text(text)); + } + } + +} + +class Wav2Vec2CTCTokenizer extends PreTrainedTokenizer { } + +class BlenderbotTokenizer extends PreTrainedTokenizer { } +class BlenderbotSmallTokenizer extends PreTrainedTokenizer { } + +class SpeechT5Tokenizer extends PreTrainedTokenizer { } + +class NougatTokenizer extends PreTrainedTokenizer { } + +class VitsTokenizer extends PreTrainedTokenizer { + + constructor(tokenizerJSON, tokenizerConfig) { + super(tokenizerJSON, tokenizerConfig); + + // Custom decoder function + this.decoder = new VitsDecoder({}); + } +} + +class CohereTokenizer extends PreTrainedTokenizer { } + +class MgpstrTokenizer extends PreTrainedTokenizer { } + +class Ernie4_5_Tokenizer extends PreTrainedTokenizer { } + +/** + * Helper class which is used to instantiate pretrained tokenizers with the `from_pretrained` function. + * The chosen tokenizer class is determined by the type specified in the tokenizer config. + * + * @example + * const tokenizer = await AutoTokenizer.from_pretrained('Xenova/bert-base-uncased'); + */ +class AutoTokenizer { + static TOKENIZER_CLASS_MAPPING = { + T5Tokenizer, + DistilBertTokenizer, + CamembertTokenizer, + DebertaTokenizer, + DebertaV2Tokenizer, + BertTokenizer, + HerbertTokenizer, + ConvBertTokenizer, + RoFormerTokenizer, + XLMTokenizer, + ElectraTokenizer, + MobileBertTokenizer, + SqueezeBertTokenizer, + AlbertTokenizer, + GPT2Tokenizer, + BartTokenizer, + MBartTokenizer, + MBart50Tokenizer, + RobertaTokenizer, + WhisperTokenizer, + CodeGenTokenizer, + CLIPTokenizer, + SiglipTokenizer, + MarianTokenizer, + BloomTokenizer, + NllbTokenizer, + M2M100Tokenizer, + LlamaTokenizer, + CodeLlamaTokenizer, + XLMRobertaTokenizer, + MPNetTokenizer, + FalconTokenizer, + GPTNeoXTokenizer, + EsmTokenizer, + Wav2Vec2CTCTokenizer, + BlenderbotTokenizer, + BlenderbotSmallTokenizer, + SpeechT5Tokenizer, + NougatTokenizer, + VitsTokenizer, + Qwen2Tokenizer, + GemmaTokenizer, + Grok1Tokenizer, + CohereTokenizer, + MgpstrTokenizer, + Ernie4_5_Tokenizer, + + // Base case: + PreTrainedTokenizer, + } + + + /** + * Instantiate one of the tokenizer classes of the library from a pretrained model. + * + * The tokenizer class to instantiate is selected based on the `tokenizer_class` property of the config object + * (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible) + * + * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either: + * - A string, the *model id* of a pretrained tokenizer hosted inside a model repo on huggingface.co. + * Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a + * user or organization name, like `dbmdz/bert-base-german-cased`. + * - A path to a *directory* containing tokenizer files, e.g., `./my_model_directory/`. + * @param {PretrainedTokenizerOptions} options Additional options for loading the tokenizer. + * + * @returns {Promise} A new instance of the PreTrainedTokenizer class. + */ + static async from_pretrained(pretrained_model_name_or_path, { + progress_callback = null, + config = null, + cache_dir = null, + local_files_only = false, + revision = 'main', + legacy = null, + } = {}) { + + const [tokenizerJSON, tokenizerConfig] = await loadTokenizer(pretrained_model_name_or_path, { + progress_callback, + config, + cache_dir, + local_files_only, + revision, + legacy, + }) + + // Some tokenizers are saved with the "Fast" suffix, so we remove that if present. + const tokenizerName = tokenizerConfig.tokenizer_class?.replace(/Fast$/, '') ?? 'PreTrainedTokenizer'; + + let cls = this.TOKENIZER_CLASS_MAPPING[tokenizerName]; + if (!cls) { + console.warn(`Unknown tokenizer class "${tokenizerName}", attempting to construct from base class.`); + cls = PreTrainedTokenizer; + } + return new cls(tokenizerJSON, tokenizerConfig); + } +} + + +/***/ }), + +/***/ "./src/utils/audio.js": +/*!****************************!*\ + !*** ./src/utils/audio.js ***! + \****************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ RawAudio: () => (/* binding */ RawAudio), +/* harmony export */ hamming: () => (/* binding */ hamming), +/* harmony export */ hanning: () => (/* binding */ hanning), +/* harmony export */ mel_filter_bank: () => (/* binding */ mel_filter_bank), +/* harmony export */ read_audio: () => (/* binding */ read_audio), +/* harmony export */ spectrogram: () => (/* binding */ spectrogram), +/* harmony export */ window_function: () => (/* binding */ window_function) +/* harmony export */ }); +/* harmony import */ var _hub_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./hub.js */ "./src/utils/hub.js"); +/* harmony import */ var _maths_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./maths.js */ "./src/utils/maths.js"); +/* harmony import */ var _core_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./core.js */ "./src/utils/core.js"); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../env.js */ "./src/env.js"); +/* harmony import */ var _tensor_js__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ./tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var node_fs__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! node:fs */ "?7992"); +/** + * @file Helper module for audio processing. + * + * These functions and classes are only used internally, + * meaning an end-user shouldn't need to access anything here. + * + * @module utils/audio + */ + + + + + + + + +/** + * Helper function to read audio from a path/URL. + * @param {string|URL} url The path/URL to load the audio from. + * @param {number} sampling_rate The sampling rate to use when decoding the audio. + * @returns {Promise} The decoded audio as a `Float32Array`. + */ +async function read_audio(url, sampling_rate) { + if (typeof AudioContext === 'undefined') { + // Running in node or an environment without AudioContext + throw Error( + "Unable to load audio from path/URL since `AudioContext` is not available in your environment. " + + "Instead, audio data should be passed directly to the pipeline/processor. " + + "For more information and some example code, see https://huggingface.co/docs/transformers.js/guides/node-audio-processing." + ) + } + + const response = await (await (0,_hub_js__WEBPACK_IMPORTED_MODULE_0__.getFile)(url)).arrayBuffer(); + const audioCTX = new AudioContext({ sampleRate: sampling_rate }); + if (typeof sampling_rate === 'undefined') { + console.warn(`No sampling rate provided, using default of ${audioCTX.sampleRate}Hz.`) + } + const decoded = await audioCTX.decodeAudioData(response); + + /** @type {Float32Array} */ + let audio; + + // We now replicate HuggingFace's `ffmpeg_read` method: + if (decoded.numberOfChannels === 2) { + // When downmixing a stereo audio file to mono using the -ac 1 option in FFmpeg, + // the audio signal is summed across both channels to create a single mono channel. + // However, if the audio is at full scale (i.e. the highest possible volume level), + // the summing of the two channels can cause the audio signal to clip or distort. + + // To prevent this clipping, FFmpeg applies a scaling factor of 1/sqrt(2) (~ 0.707) + // to the audio signal before summing the two channels. This scaling factor ensures + // that the combined audio signal will not exceed the maximum possible level, even + // if both channels are at full scale. + + // After applying this scaling factor, the audio signal from both channels is summed + // to create a single mono channel. It's worth noting that this scaling factor is + // only applied when downmixing stereo audio to mono using the -ac 1 option in FFmpeg. + // If you're using a different downmixing method, or if you're not downmixing the + // audio at all, this scaling factor may not be needed. + const SCALING_FACTOR = Math.sqrt(2); + + const left = decoded.getChannelData(0); + const right = decoded.getChannelData(1); + + audio = new Float32Array(left.length); + for (let i = 0; i < decoded.length; ++i) { + audio[i] = SCALING_FACTOR * (left[i] + right[i]) / 2; + } + + } else { + // If the audio is not stereo, we can just use the first channel: + audio = decoded.getChannelData(0); + } + + return audio; +} + +/** + * Helper function to generate windows that are special cases of the generalized cosine window. + * See https://www.mathworks.com/help/signal/ug/generalized-cosine-windows.html for more information. + * @param {number} M Number of points in the output window. If zero or less, an empty array is returned. + * @param {number} a_0 Offset for the generalized cosine window. + * @returns {Float64Array} The generated window. + */ +function generalized_cosine_window(M, a_0) { + if (M < 1) { + return new Float64Array(); + } + if (M === 1) { + return new Float64Array([1]); + } + + const a_1 = 1 - a_0; + const factor = 2 * Math.PI / (M - 1); + + const cos_vals = new Float64Array(M); + for (let i = 0; i < M; ++i) { + cos_vals[i] = a_0 - a_1 * Math.cos(i * factor); + } + return cos_vals; +} + +/** + * Generates a Hanning window of length M. + * See https://numpy.org/doc/stable/reference/generated/numpy.hanning.html for more information. + * + * @param {number} M The length of the Hanning window to generate. + * @returns {Float64Array} The generated Hanning window. + */ +function hanning(M) { + return generalized_cosine_window(M, 0.5); +} + + +/** + * Generates a Hamming window of length M. + * See https://numpy.org/doc/stable/reference/generated/numpy.hamming.html for more information. + * + * @param {number} M The length of the Hamming window to generate. + * @returns {Float64Array} The generated Hamming window. + */ +function hamming(M) { + return generalized_cosine_window(M, 0.54); +} + + +const HERTZ_TO_MEL_MAPPING = { + "htk": (/** @type {number} */ freq) => 2595.0 * Math.log10(1.0 + (freq / 700.0)), + "kaldi": (/** @type {number} */ freq) => 1127.0 * Math.log(1.0 + (freq / 700.0)), + "slaney": (/** @type {number} */ freq, min_log_hertz = 1000.0, min_log_mel = 15.0, logstep = 27.0 / Math.log(6.4)) => + freq >= min_log_hertz + ? min_log_mel + Math.log(freq / min_log_hertz) * logstep + : 3.0 * freq / 200.0, +} + +/** + * @template {Float32Array|Float64Array|number} T + * @param {T} freq + * @param {string} [mel_scale] + * @returns {T} + */ +function hertz_to_mel(freq, mel_scale = "htk") { + const fn = HERTZ_TO_MEL_MAPPING[mel_scale]; + if (!fn) { + throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".'); + } + + // @ts-expect-error ts(2322) + return typeof freq === 'number' ? fn(freq) : freq.map(x => fn(x)); +} + +const MEL_TO_HERTZ_MAPPING = { + "htk": (/** @type {number} */ mels) => 700.0 * (10.0 ** (mels / 2595.0) - 1.0), + "kaldi": (/** @type {number} */ mels) => 700.0 * (Math.exp(mels / 1127.0) - 1.0), + "slaney": (/** @type {number} */ mels, min_log_hertz = 1000.0, min_log_mel = 15.0, logstep = Math.log(6.4) / 27.0) => mels >= min_log_mel + ? min_log_hertz * Math.exp(logstep * (mels - min_log_mel)) + : 200.0 * mels / 3.0, +} + +/** + * @template {Float32Array|Float64Array|number} T + * @param {T} mels + * @param {string} [mel_scale] + * @returns {T} + */ +function mel_to_hertz(mels, mel_scale = "htk") { + const fn = MEL_TO_HERTZ_MAPPING[mel_scale]; + if (!fn) { + throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".'); + } + + // @ts-expect-error ts(2322) + return typeof mels === 'number' ? fn(mels) : mels.map(x => fn(x)); +} + +/** +* Creates a triangular filter bank. +* +* Adapted from torchaudio and librosa. +* +* @param {Float64Array} fft_freqs Discrete frequencies of the FFT bins in Hz, of shape `(num_frequency_bins,)`. +* @param {Float64Array} filter_freqs Center frequencies of the triangular filters to create, in Hz, of shape `(num_mel_filters,)`. +* @returns {number[][]} of shape `(num_frequency_bins, num_mel_filters)`. +*/ +function _create_triangular_filter_bank(fft_freqs, filter_freqs) { + const filter_diff = Float64Array.from( + { length: filter_freqs.length - 1 }, + (_, i) => filter_freqs[i + 1] - filter_freqs[i] + ); + + const slopes = Array.from({ + length: fft_freqs.length + }, () => new Array(filter_freqs.length)); + + for (let j = 0; j < fft_freqs.length; ++j) { + const slope = slopes[j]; + for (let i = 0; i < filter_freqs.length; ++i) { + slope[i] = filter_freqs[i] - fft_freqs[j]; + } + } + + const numFreqs = filter_freqs.length - 2; + const ret = Array.from({ length: numFreqs }, () => new Array(fft_freqs.length)); + + for (let j = 0; j < fft_freqs.length; ++j) { // 201 + const slope = slopes[j]; + for (let i = 0; i < numFreqs; ++i) { // 80 + const down = -slope[i] / filter_diff[i]; + const up = slope[i + 2] / filter_diff[i + 1]; + ret[i][j] = Math.max(0, Math.min(down, up)); + } + } + return ret; +} + +/** + * Return evenly spaced numbers over a specified interval. + * @param {number} start The starting value of the sequence. + * @param {number} end The end value of the sequence. + * @param {number} num Number of samples to generate. + * @returns `num` evenly spaced samples, calculated over the interval `[start, stop]`. + */ +function linspace(start, end, num) { + const step = (end - start) / (num - 1); + return Float64Array.from({ length: num }, (_, i) => start + step * i); +} + +/** + * Creates a frequency bin conversion matrix used to obtain a mel spectrogram. This is called a *mel filter bank*, and + * various implementation exist, which differ in the number of filters, the shape of the filters, the way the filters + * are spaced, the bandwidth of the filters, and the manner in which the spectrum is warped. The goal of these + * features is to approximate the non-linear human perception of the variation in pitch with respect to the frequency. + * @param {number} num_frequency_bins Number of frequency bins (should be the same as `n_fft // 2 + 1` + * where `n_fft` is the size of the Fourier Transform used to compute the spectrogram). + * @param {number} num_mel_filters Number of mel filters to generate. + * @param {number} min_frequency Lowest frequency of interest in Hz. + * @param {number} max_frequency Highest frequency of interest in Hz. This should not exceed `sampling_rate / 2`. + * @param {number} sampling_rate Sample rate of the audio waveform. + * @param {string} [norm] If `"slaney"`, divide the triangular mel weights by the width of the mel band (area normalization). + * @param {string} [mel_scale] The mel frequency scale to use, `"htk"` or `"slaney"`. + * @param {boolean} [triangularize_in_mel_space] If this option is enabled, the triangular filter is applied in mel space rather than frequency space. + * This should be set to `true` in order to get the same results as `torchaudio` when computing mel filters. + * @returns {number[][]} Triangular filter bank matrix, which is a 2D array of shape (`num_frequency_bins`, `num_mel_filters`). + * This is a projection matrix to go from a spectrogram to a mel spectrogram. + */ +function mel_filter_bank( + num_frequency_bins, + num_mel_filters, + min_frequency, + max_frequency, + sampling_rate, + norm = null, + mel_scale = "htk", + triangularize_in_mel_space = false, +) { + if (norm !== null && norm !== "slaney") { + throw new Error('norm must be one of null or "slaney"'); + } + + if (num_frequency_bins < 2) { + throw new Error(`Require num_frequency_bins: ${num_frequency_bins} >= 2`); + } + + if (min_frequency > max_frequency) { + throw new Error(`Require min_frequency: ${min_frequency} <= max_frequency: ${max_frequency}`); + } + + const mel_min = hertz_to_mel(min_frequency, mel_scale); + const mel_max = hertz_to_mel(max_frequency, mel_scale); + const mel_freqs = linspace(mel_min, mel_max, num_mel_filters + 2); + + let filter_freqs = mel_to_hertz(mel_freqs, mel_scale); + let fft_freqs; // frequencies of FFT bins in Hz + + if (triangularize_in_mel_space) { + const fft_bin_width = sampling_rate / ((num_frequency_bins - 1) * 2); + fft_freqs = hertz_to_mel(Float64Array.from({ length: num_frequency_bins }, (_, i) => i * fft_bin_width), mel_scale); + filter_freqs = mel_freqs; + } else { + fft_freqs = linspace(0, Math.floor(sampling_rate / 2), num_frequency_bins); + } + + const mel_filters = _create_triangular_filter_bank(fft_freqs, filter_freqs); + + if (norm !== null && norm === "slaney") { + // Slaney-style mel is scaled to be approx constant energy per channel + for (let i = 0; i < num_mel_filters; ++i) { + const filter = mel_filters[i]; + const enorm = 2.0 / (filter_freqs[i + 2] - filter_freqs[i]); + for (let j = 0; j < num_frequency_bins; ++j) { + // Apply this enorm to all frequency bins + filter[j] *= enorm; + } + } + } + + // TODO warn if there is a zero row + + return mel_filters; + +} + +/** + * @template {Float32Array|Float64Array} T + * Pads an array with a reflected version of itself on both ends. + * @param {T} array The array to pad. + * @param {number} left The amount of padding to add to the left. + * @param {number} right The amount of padding to add to the right. + * @returns {T} The padded array. + */ +function padReflect(array, left, right) { + // @ts-ignore + const padded = new array.constructor(array.length + left + right); + const w = array.length - 1; + + for (let i = 0; i < array.length; ++i) { + padded[left + i] = array[i]; + } + + for (let i = 1; i <= left; ++i) { + padded[left - i] = array[(0,_core_js__WEBPACK_IMPORTED_MODULE_2__.calculateReflectOffset)(i, w)]; + } + + for (let i = 1; i <= right; ++i) { + padded[w + left + i] = array[(0,_core_js__WEBPACK_IMPORTED_MODULE_2__.calculateReflectOffset)(w - i, w)]; + } + + return padded; +} + +/** + * Helper function to compute `amplitude_to_db` and `power_to_db`. + * @template {Float32Array|Float64Array} T + * @param {T} spectrogram + * @param {number} factor + * @param {number} reference + * @param {number} min_value + * @param {number} db_range + * @returns {T} + */ +function _db_conversion_helper(spectrogram, factor, reference, min_value, db_range) { + if (reference <= 0) { + throw new Error('reference must be greater than zero'); + } + + if (min_value <= 0) { + throw new Error('min_value must be greater than zero'); + } + + reference = Math.max(min_value, reference); + + const logReference = Math.log10(reference); + for (let i = 0; i < spectrogram.length; ++i) { + spectrogram[i] = factor * Math.log10(Math.max(min_value, spectrogram[i]) - logReference) + } + + if (db_range !== null) { + if (db_range <= 0) { + throw new Error('db_range must be greater than zero'); + } + const maxValue = (0,_maths_js__WEBPACK_IMPORTED_MODULE_1__.max)(spectrogram)[0] - db_range; + for (let i = 0; i < spectrogram.length; ++i) { + spectrogram[i] = Math.max(spectrogram[i], maxValue); + } + } + + return spectrogram; +} + +/** + * Converts an amplitude spectrogram to the decibel scale. This computes `20 * log10(spectrogram / reference)`, + * using basic logarithm properties for numerical stability. NOTE: Operates in-place. + * + * The motivation behind applying the log function on the (mel) spectrogram is that humans do not hear loudness on a + * linear scale. Generally to double the perceived volume of a sound we need to put 8 times as much energy into it. + * This means that large variations in energy may not sound all that different if the sound is loud to begin with. + * This compression operation makes the (mel) spectrogram features match more closely what humans actually hear. + * + * @template {Float32Array|Float64Array} T + * @param {T} spectrogram The input amplitude (mel) spectrogram. + * @param {number} [reference=1.0] Sets the input spectrogram value that corresponds to 0 dB. + * For example, use `np.max(spectrogram)` to set the loudest part to 0 dB. Must be greater than zero. + * @param {number} [min_value=1e-5] The spectrogram will be clipped to this minimum value before conversion to decibels, + * to avoid taking `log(0)`. The default of `1e-5` corresponds to a minimum of -100 dB. Must be greater than zero. + * @param {number} [db_range=null] Sets the maximum dynamic range in decibels. For example, if `db_range = 80`, the + * difference between the peak value and the smallest value will never be more than 80 dB. Must be greater than zero. + * @returns {T} The modified spectrogram in decibels. + */ +function amplitude_to_db(spectrogram, reference = 1.0, min_value = 1e-5, db_range = null) { + return _db_conversion_helper(spectrogram, 20.0, reference, min_value, db_range); +} + +/** + * Converts a power spectrogram to the decibel scale. This computes `10 * log10(spectrogram / reference)`, + * using basic logarithm properties for numerical stability. NOTE: Operates in-place. + * + * The motivation behind applying the log function on the (mel) spectrogram is that humans do not hear loudness on a + * linear scale. Generally to double the perceived volume of a sound we need to put 8 times as much energy into it. + * This means that large variations in energy may not sound all that different if the sound is loud to begin with. + * This compression operation makes the (mel) spectrogram features match more closely what humans actually hear. + * + * Based on the implementation of `librosa.power_to_db`. + * + * @template {Float32Array|Float64Array} T + * @param {T} spectrogram The input power (mel) spectrogram. Note that a power spectrogram has the amplitudes squared! + * @param {number} [reference=1.0] Sets the input spectrogram value that corresponds to 0 dB. + * For example, use `np.max(spectrogram)` to set the loudest part to 0 dB. Must be greater than zero. + * @param {number} [min_value=1e-10] The spectrogram will be clipped to this minimum value before conversion to decibels, + * to avoid taking `log(0)`. The default of `1e-10` corresponds to a minimum of -100 dB. Must be greater than zero. + * @param {number} [db_range=null] Sets the maximum dynamic range in decibels. For example, if `db_range = 80`, the + * difference between the peak value and the smallest value will never be more than 80 dB. Must be greater than zero. + * @returns {T} The modified spectrogram in decibels. + */ +function power_to_db(spectrogram, reference = 1.0, min_value = 1e-10, db_range = null) { + return _db_conversion_helper(spectrogram, 10.0, reference, min_value, db_range); +} + +/** + * Calculates a spectrogram over one waveform using the Short-Time Fourier Transform. + * + * This function can create the following kinds of spectrograms: + * - amplitude spectrogram (`power = 1.0`) + * - power spectrogram (`power = 2.0`) + * - complex-valued spectrogram (`power = None`) + * - log spectrogram (use `log_mel` argument) + * - mel spectrogram (provide `mel_filters`) + * - log-mel spectrogram (provide `mel_filters` and `log_mel`) + * + * In this implementation, the window is assumed to be zero-padded to have the same size as the analysis frame. + * A padded window can be obtained from `window_function()`. The FFT input buffer may be larger than the analysis frame, + * typically the next power of two. + * + * @param {Float32Array|Float64Array} waveform The input waveform of shape `(length,)`. This must be a single real-valued, mono waveform. + * @param {Float32Array|Float64Array} window The windowing function to apply of shape `(frame_length,)`, including zero-padding if necessary. The actual window length may be + * shorter than `frame_length`, but we're assuming the array has already been zero-padded. + * @param {number} frame_length The length of the analysis frames in samples (a.k.a., `fft_length`). + * @param {number} hop_length The stride between successive analysis frames in samples. + * @param {Object} options + * @param {number} [options.fft_length=null] The size of the FFT buffer in samples. This determines how many frequency bins the spectrogram will have. + * For optimal speed, this should be a power of two. If `null`, uses `frame_length`. + * @param {number} [options.power=1.0] If 1.0, returns the amplitude spectrogram. If 2.0, returns the power spectrogram. If `null`, returns complex numbers. + * @param {boolean} [options.center=true] Whether to pad the waveform so that frame `t` is centered around time `t * hop_length`. If `false`, frame + * `t` will start at time `t * hop_length`. + * @param {string} [options.pad_mode="reflect"] Padding mode used when `center` is `true`. Possible values are: `"constant"` (pad with zeros), + * `"edge"` (pad with edge values), `"reflect"` (pads with mirrored values). + * @param {boolean} [options.onesided=true] If `true`, only computes the positive frequencies and returns a spectrogram containing `fft_length // 2 + 1` + * frequency bins. If `false`, also computes the negative frequencies and returns `fft_length` frequency bins. + * @param {number} [options.preemphasis=null] Coefficient for a low-pass filter that applies pre-emphasis before the DFT. + * @param {boolean} [options.preemphasis_htk_flavor=true] Whether to apply the pre-emphasis filter in the HTK flavor. + * @param {number[][]} [options.mel_filters=null] The mel filter bank of shape `(num_freq_bins, num_mel_filters)`. + * If supplied, applies this filter bank to create a mel spectrogram. + * @param {number} [options.mel_floor=1e-10] Minimum value of mel frequency banks. + * @param {string} [options.log_mel=null] How to convert the spectrogram to log scale. Possible options are: + * `null` (don't convert), `"log"` (take the natural logarithm) `"log10"` (take the base-10 logarithm), `"dB"` (convert to decibels). + * Can only be used when `power` is not `null`. + * @param {number} [options.reference=1.0] Sets the input spectrogram value that corresponds to 0 dB. For example, use `max(spectrogram)[0]` to set + * the loudest part to 0 dB. Must be greater than zero. + * @param {number} [options.min_value=1e-10] The spectrogram will be clipped to this minimum value before conversion to decibels, to avoid taking `log(0)`. + * For a power spectrogram, the default of `1e-10` corresponds to a minimum of -100 dB. For an amplitude spectrogram, the value `1e-5` corresponds to -100 dB. + * Must be greater than zero. + * @param {number} [options.db_range=null] Sets the maximum dynamic range in decibels. For example, if `db_range = 80`, the difference between the + * peak value and the smallest value will never be more than 80 dB. Must be greater than zero. + * @param {boolean} [options.remove_dc_offset=null] Subtract mean from waveform on each frame, applied before pre-emphasis. This should be set to `true` in + * order to get the same results as `torchaudio.compliance.kaldi.fbank` when computing mel filters. + * @param {number} [options.max_num_frames=null] If provided, limits the number of frames to compute to this value. + * @param {number} [options.min_num_frames=null] If provided, ensures the number of frames to compute is at least this value. + * @param {boolean} [options.do_pad=true] If `true`, pads the output spectrogram to have `max_num_frames` frames. + * @param {boolean} [options.transpose=false] If `true`, the returned spectrogram will have shape `(num_frames, num_frequency_bins/num_mel_filters)`. If `false`, the returned spectrogram will have shape `(num_frequency_bins/num_mel_filters, num_frames)`. + * @returns {Promise} Spectrogram of shape `(num_frequency_bins, length)` (regular spectrogram) or shape `(num_mel_filters, length)` (mel spectrogram). + */ +async function spectrogram( + waveform, + window, + frame_length, + hop_length, + { + fft_length = null, + power = 1.0, + center = true, + pad_mode = "reflect", + onesided = true, + preemphasis = null, + preemphasis_htk_flavor = true, + mel_filters = null, + mel_floor = 1e-10, + log_mel = null, + reference = 1.0, + min_value = 1e-10, + db_range = null, + remove_dc_offset = null, + + // Custom parameters for efficiency reasons + min_num_frames = null, + max_num_frames = null, + do_pad = true, + transpose = false, + } = {} +) { + const window_length = window.length; + if (fft_length === null) { + fft_length = frame_length; + } + if (frame_length > fft_length) { + throw Error(`frame_length (${frame_length}) may not be larger than fft_length (${fft_length})`) + } + + if (window_length !== frame_length) { + throw new Error(`Length of the window (${window_length}) must equal frame_length (${frame_length})`); + } + + if (hop_length <= 0) { + throw new Error("hop_length must be greater than zero"); + } + + if (power === null && mel_filters !== null) { + throw new Error( + "You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram. " + + "Specify `power` to fix this issue." + ); + } + + if (!preemphasis_htk_flavor) { + throw new Error( + "`preemphasis_htk_flavor=false` is not currently supported." + ); + } + + if (center) { + if (pad_mode !== 'reflect') { + throw new Error(`pad_mode="${pad_mode}" not implemented yet.`) + } + const half_window = Math.floor((fft_length - 1) / 2) + 1; + waveform = padReflect(waveform, half_window, half_window); + } + + // split waveform into frames of frame_length size + let num_frames = Math.floor(1 + Math.floor((waveform.length - frame_length) / hop_length)) + if (min_num_frames !== null && num_frames < min_num_frames) { + num_frames = min_num_frames + } + const num_frequency_bins = onesided ? Math.floor(fft_length / 2) + 1 : fft_length + + let d1 = num_frames; + let d1Max = num_frames; + + // If maximum number of frames is provided, we must either pad or truncate + if (max_num_frames !== null) { + if (max_num_frames > num_frames) { // input is too short, so we pad + if (do_pad) { + d1Max = max_num_frames; + } + } else { // input is too long, so we truncate + d1Max = d1 = max_num_frames; + } + } + + // Preallocate arrays to store output. + const fft = new _maths_js__WEBPACK_IMPORTED_MODULE_1__.FFT(fft_length); + const inputBuffer = new Float64Array(fft_length); + const outputBuffer = new Float64Array(fft.outputBufferSize); + const transposedMagnitudeData = new Float32Array(num_frequency_bins * d1Max); + + for (let i = 0; i < d1; ++i) { + // Populate buffer with waveform data + const offset = i * hop_length; + const buffer_size = Math.min(waveform.length - offset, frame_length); + if (buffer_size !== frame_length) { + // The full buffer is not needed, so we need to reset it (avoid overflow from previous iterations) + // NOTE: We don't need to reset the buffer if it's full since we overwrite the first + // `frame_length` values and the rest (`fft_length - frame_length`) remains zero. + inputBuffer.fill(0, 0, frame_length); + } + + for (let j = 0; j < buffer_size; ++j) { + inputBuffer[j] = waveform[offset + j]; + } + + if (remove_dc_offset) { + let sum = 0; + for (let j = 0; j < buffer_size; ++j) { + sum += inputBuffer[j]; + } + const mean = sum / buffer_size; + for (let j = 0; j < buffer_size; ++j) { + inputBuffer[j] -= mean; + } + } + + if (preemphasis !== null) { + // Done in reverse to avoid copies and destructive modification + for (let j = buffer_size - 1; j >= 1; --j) { + inputBuffer[j] -= preemphasis * inputBuffer[j - 1]; + } + inputBuffer[0] *= 1 - preemphasis; + } + + // Apply window function + for (let j = 0; j < window.length; ++j) { + inputBuffer[j] *= window[j]; + } + + fft.realTransform(outputBuffer, inputBuffer); + + // compute magnitudes + for (let j = 0; j < num_frequency_bins; ++j) { + const j2 = j << 1; + + // NOTE: We transpose the data here to avoid doing it later + transposedMagnitudeData[j * d1Max + i] = outputBuffer[j2] ** 2 + outputBuffer[j2 + 1] ** 2; + } + } + + if (power !== null && power !== 2) { + // slight optimization to not sqrt + const pow = power / 2; // we use 2 since we already squared + for (let i = 0; i < transposedMagnitudeData.length; ++i) { + transposedMagnitudeData[i] **= pow; + } + } + + // TODO: What if `mel_filters` is null? + const num_mel_filters = mel_filters.length; + + // Perform matrix muliplication: + // mel_spec = mel_filters @ magnitudes.T + // - mel_filters.shape=(80, 201) + // - magnitudes.shape=(3000, 201) => magnitudes.T.shape=(201, 3000) + // - mel_spec.shape=(80, 3000) + let mel_spec = await (0,_tensor_js__WEBPACK_IMPORTED_MODULE_4__.matmul)( + // TODO: Make `mel_filters` a Tensor during initialization + new _tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor('float32', mel_filters.flat(), [num_mel_filters, num_frequency_bins]), + new _tensor_js__WEBPACK_IMPORTED_MODULE_4__.Tensor('float32', transposedMagnitudeData, [num_frequency_bins, d1Max]), + ); + if (transpose) { + mel_spec = mel_spec.transpose(1, 0); + } + + const mel_spec_data = /** @type {Float32Array} */(mel_spec.data); + for (let i = 0; i < mel_spec_data.length; ++i) { + mel_spec_data[i] = Math.max(mel_floor, mel_spec_data[i]); + } + + if (power !== null && log_mel !== null) { + const o = Math.min(mel_spec_data.length, d1 * num_mel_filters); + // NOTE: operates in-place + switch (log_mel) { + case 'log': + for (let i = 0; i < o; ++i) { + mel_spec_data[i] = Math.log(mel_spec_data[i]); + } + break; + case 'log10': + for (let i = 0; i < o; ++i) { + mel_spec_data[i] = Math.log10(mel_spec_data[i]); + } + break; + case 'dB': + if (power === 1.0) { + amplitude_to_db(mel_spec_data, reference, min_value, db_range); + } else if (power === 2.0) { + power_to_db(mel_spec_data, reference, min_value, db_range); + } else { + throw new Error(`Cannot use log_mel option '${log_mel}' with power ${power}`) + } + break; + default: + throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${log_mel}'`); + } + } + + return mel_spec; +} + +/** + * Returns an array containing the specified window. + * @param {number} window_length The length of the window in samples. + * @param {string} name The name of the window function. + * @param {Object} options Additional options. + * @param {boolean} [options.periodic=true] Whether the window is periodic or symmetric. + * @param {number} [options.frame_length=null] The length of the analysis frames in samples. + * Provide a value for `frame_length` if the window is smaller than the frame length, so that it will be zero-padded. + * @param {boolean} [options.center=true] Whether to center the window inside the FFT buffer. Only used when `frame_length` is provided. + * @returns {Float64Array} The window of shape `(window_length,)` or `(frame_length,)`. + */ +function window_function(window_length, name, { + periodic = true, + frame_length = null, + center = true, +} = {}) { + const length = periodic ? window_length + 1 : window_length; + let window; + switch (name) { + case 'boxcar': + window = new Float64Array(length).fill(1.0); + break; + case 'hann': + case 'hann_window': + window = hanning(length); + break; + case 'hamming': + window = hamming(length); + break; + case 'povey': + window = hanning(length).map(x => Math.pow(x, 0.85)); + break; + default: + throw new Error(`Unknown window type ${name}.`); + } + if (periodic) { + window = window.subarray(0, window_length); + } + if (frame_length === null) { + return window; + } + if (window_length > frame_length) { + throw new Error(`Length of the window (${window_length}) may not be larger than frame_length (${frame_length})`); + } + + return window; +} + +/** + * Encode audio data to a WAV file. + * WAV file specs : https://en.wikipedia.org/wiki/WAV#WAV_File_header + * + * Adapted from https://www.npmjs.com/package/audiobuffer-to-wav + * @param {Float32Array} samples The audio samples. + * @param {number} rate The sample rate. + * @returns {ArrayBuffer} The WAV audio buffer. + */ +function encodeWAV(samples, rate) { + let offset = 44; + const buffer = new ArrayBuffer(offset + samples.length * 4); + const view = new DataView(buffer); + + /* RIFF identifier */ + writeString(view, 0, "RIFF"); + /* RIFF chunk length */ + view.setUint32(4, 36 + samples.length * 4, true); + /* RIFF type */ + writeString(view, 8, "WAVE"); + /* format chunk identifier */ + writeString(view, 12, "fmt "); + /* format chunk length */ + view.setUint32(16, 16, true); + /* sample format (raw) */ + view.setUint16(20, 3, true); + /* channel count */ + view.setUint16(22, 1, true); + /* sample rate */ + view.setUint32(24, rate, true); + /* byte rate (sample rate * block align) */ + view.setUint32(28, rate * 4, true); + /* block align (channel count * bytes per sample) */ + view.setUint16(32, 4, true); + /* bits per sample */ + view.setUint16(34, 32, true); + /* data chunk identifier */ + writeString(view, 36, "data"); + /* data chunk length */ + view.setUint32(40, samples.length * 4, true); + + for (let i = 0; i < samples.length; ++i, offset += 4) { + view.setFloat32(offset, samples[i], true); + } + + return buffer; +} + +function writeString(view, offset, string) { + for (let i = 0; i < string.length; ++i) { + view.setUint8(offset + i, string.charCodeAt(i)); + } +} + + +class RawAudio { + + /** + * Create a new `RawAudio` object. + * @param {Float32Array} audio Audio data + * @param {number} sampling_rate Sampling rate of the audio data + */ + constructor(audio, sampling_rate) { + this.audio = audio + this.sampling_rate = sampling_rate + } + + /** + * Convert the audio to a wav file buffer. + * @returns {ArrayBuffer} The WAV file. + */ + toWav() { + return encodeWAV(this.audio, this.sampling_rate) + } + + /** + * Convert the audio to a blob. + * @returns {Blob} + */ + toBlob() { + const wav = this.toWav(); + const blob = new Blob([wav], { type: 'audio/wav' }); + return blob; + } + + /** + * Save the audio to a wav file. + * @param {string} path + */ + async save(path) { + let fn; + + if (_env_js__WEBPACK_IMPORTED_MODULE_3__.apis.IS_BROWSER_ENV) { + if (_env_js__WEBPACK_IMPORTED_MODULE_3__.apis.IS_WEBWORKER_ENV) { + throw new Error('Unable to save a file from a Web Worker.') + } + fn = _core_js__WEBPACK_IMPORTED_MODULE_2__.saveBlob; + } else if (_env_js__WEBPACK_IMPORTED_MODULE_3__.apis.IS_FS_AVAILABLE) { + fn = async (/** @type {string} */ path, /** @type {Blob} */ blob) => { + let buffer = await blob.arrayBuffer(); + node_fs__WEBPACK_IMPORTED_MODULE_5__.writeFileSync(path, Buffer.from(buffer)); + } + } else { + throw new Error('Unable to save because filesystem is disabled in this environment.') + } + + await fn(path, this.toBlob()) + } +} + + +/***/ }), + +/***/ "./src/utils/constants.js": +/*!********************************!*\ + !*** ./src/utils/constants.js ***! + \********************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ CHAT_TEMPLATE_NAME: () => (/* binding */ CHAT_TEMPLATE_NAME), +/* harmony export */ CONFIG_NAME: () => (/* binding */ CONFIG_NAME), +/* harmony export */ FEATURE_EXTRACTOR_NAME: () => (/* binding */ FEATURE_EXTRACTOR_NAME), +/* harmony export */ GENERATION_CONFIG_NAME: () => (/* binding */ GENERATION_CONFIG_NAME), +/* harmony export */ GITHUB_ISSUE_URL: () => (/* binding */ GITHUB_ISSUE_URL), +/* harmony export */ IMAGE_PROCESSOR_NAME: () => (/* binding */ IMAGE_PROCESSOR_NAME), +/* harmony export */ PROCESSOR_NAME: () => (/* binding */ PROCESSOR_NAME) +/* harmony export */ }); + +const GITHUB_ISSUE_URL = 'https://github.com/huggingface/transformers.js/issues/new/choose'; + +const CONFIG_NAME = "config.json" +const FEATURE_EXTRACTOR_NAME = "preprocessor_config.json" +const IMAGE_PROCESSOR_NAME = FEATURE_EXTRACTOR_NAME +const PROCESSOR_NAME = "processor_config.json" +const CHAT_TEMPLATE_NAME = "chat_template.jinja" +const GENERATION_CONFIG_NAME = "generation_config.json" + + +/***/ }), + +/***/ "./src/utils/core.js": +/*!***************************!*\ + !*** ./src/utils/core.js ***! + \***************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ calculateDimensions: () => (/* binding */ calculateDimensions), +/* harmony export */ calculateReflectOffset: () => (/* binding */ calculateReflectOffset), +/* harmony export */ count: () => (/* binding */ count), +/* harmony export */ dispatchCallback: () => (/* binding */ dispatchCallback), +/* harmony export */ escapeRegExp: () => (/* binding */ escapeRegExp), +/* harmony export */ isIntegralNumber: () => (/* binding */ isIntegralNumber), +/* harmony export */ isNullishDimension: () => (/* binding */ isNullishDimension), +/* harmony export */ isTypedArray: () => (/* binding */ isTypedArray), +/* harmony export */ len: () => (/* binding */ len), +/* harmony export */ mergeArrays: () => (/* binding */ mergeArrays), +/* harmony export */ pick: () => (/* binding */ pick), +/* harmony export */ pop: () => (/* binding */ pop), +/* harmony export */ product: () => (/* binding */ product), +/* harmony export */ reverseDictionary: () => (/* binding */ reverseDictionary), +/* harmony export */ saveBlob: () => (/* binding */ saveBlob) +/* harmony export */ }); + +/** + * @file Core utility functions/classes for Transformers.js. + * + * These are only used internally, meaning an end-user shouldn't + * need to access anything here. + * + * @module utils/core + */ + +/** + * @typedef {Object} InitiateProgressInfo + * @property {'initiate'} status + * @property {string} name The model id or directory path. + * @property {string} file The name of the file. + */ + +/** + * @typedef {Object} DownloadProgressInfo + * @property {'download'} status + * @property {string} name The model id or directory path. + * @property {string} file The name of the file. + */ + +/** + * @typedef {Object} ProgressStatusInfo + * @property {'progress'} status + * @property {string} name The model id or directory path. + * @property {string} file The name of the file. + * @property {number} progress A number between 0 and 100. + * @property {number} loaded The number of bytes loaded. + * @property {number} total The total number of bytes to be loaded. + */ + +/** + * @typedef {Object} DoneProgressInfo + * @property {'done'} status + * @property {string} name The model id or directory path. + * @property {string} file The name of the file. + */ + +/** + * @typedef {Object} ReadyProgressInfo + * @property {'ready'} status + * @property {string} task The loaded task. + * @property {string} model The loaded model. + */ + +/** + * @typedef {InitiateProgressInfo | DownloadProgressInfo | ProgressStatusInfo | DoneProgressInfo | ReadyProgressInfo} ProgressInfo + */ + +/** + * A callback function that is called with progress information. + * @callback ProgressCallback + * @param {ProgressInfo} progressInfo + * @returns {void} + */ + +/** + * Helper function to dispatch progress callbacks. + * + * @param {ProgressCallback | null | undefined} progress_callback The progress callback function to dispatch. + * @param {ProgressInfo} data The data to pass to the progress callback function. + * @returns {void} + * @private + */ +function dispatchCallback(progress_callback, data) { + if (progress_callback) progress_callback(data); +} + +/** + * Reverses the keys and values of an object. + * + * @param {Object} data The object to reverse. + * @returns {Object} The reversed object. + * @see https://ultimatecourses.com/blog/reverse-object-keys-and-values-in-javascript + */ +function reverseDictionary(data) { + // https://ultimatecourses.com/blog/reverse-object-keys-and-values-in-javascript + return Object.fromEntries(Object.entries(data).map(([key, value]) => [value, key])); +} + +/** + * Escapes regular expression special characters from a string by replacing them with their escaped counterparts. + * + * @param {string} string The string to escape. + * @returns {string} The escaped string. + */ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Check if a value is a typed array. + * @param {*} val The value to check. + * @returns {boolean} True if the value is a `TypedArray`, false otherwise. + * + * Adapted from https://stackoverflow.com/a/71091338/13989043 + */ +function isTypedArray(val) { + return val?.prototype?.__proto__?.constructor?.name === 'TypedArray'; +} + + +/** + * Check if a value is an integer. + * @param {*} x The value to check. + * @returns {boolean} True if the value is a string, false otherwise. + */ +function isIntegralNumber(x) { + return Number.isInteger(x) || typeof x === 'bigint' +} + +/** + * Determine if a provided width or height is nullish. + * @param {*} x The value to check. + * @returns {boolean} True if the value is `null`, `undefined` or `-1`, false otherwise. + */ +function isNullishDimension(x) { + return x === null || x === undefined || x === -1; +} + +/** + * Calculates the dimensions of a nested array. + * + * @param {any[]} arr The nested array to calculate dimensions for. + * @returns {number[]} An array containing the dimensions of the input array. + */ +function calculateDimensions(arr) { + const dimensions = []; + let current = arr; + while (Array.isArray(current)) { + dimensions.push(current.length); + current = current[0]; + } + return dimensions; +} + +/** + * Replicate python's .pop() method for objects. + * @param {Object} obj The object to pop from. + * @param {string} key The key to pop. + * @param {*} defaultValue The default value to return if the key does not exist. + * @returns {*} The value of the popped key. + * @throws {Error} If the key does not exist and no default value is provided. + */ +function pop(obj, key, defaultValue = undefined) { + const value = obj[key]; + if (value !== undefined) { + delete obj[key]; + return value; + } + if (defaultValue === undefined) { + throw Error(`Key ${key} does not exist in object.`) + } + return defaultValue; +} + +/** + * Efficiently merge arrays, creating a new copy. + * Adapted from https://stackoverflow.com/a/6768642/13989043 + * @param {Array[]} arrs Arrays to merge. + * @returns {Array} The merged array. + */ +function mergeArrays(...arrs) { + return Array.prototype.concat.apply([], arrs); +} + +/** + * Compute the Cartesian product of given arrays + * @param {...Array} a Arrays to compute the product + * @returns {Array} Returns the computed Cartesian product as an array + * @private + */ +function product(...a) { + // Cartesian product of items + // Adapted from https://stackoverflow.com/a/43053803 + return a.reduce((a, b) => a.flatMap(d => b.map(e => [d, e]))); +} + +/** + * Calculates the index offset for a given index and window size. + * @param {number} i The index. + * @param {number} w The window size. + * @returns {number} The index offset. + */ +function calculateReflectOffset(i, w) { + return Math.abs((i + w) % (2 * w) - w); +} + +/** + * Save blob file on the web. + * @param {string} path The path to save the blob to + * @param {Blob} blob The blob to save + */ +function saveBlob(path, blob){ + // Convert the canvas content to a data URL + const dataURL = URL.createObjectURL(blob); + + // Create an anchor element with the data URL as the href attribute + const downloadLink = document.createElement('a'); + downloadLink.href = dataURL; + + // Set the download attribute to specify the desired filename for the downloaded image + downloadLink.download = path; + + // Trigger the download + downloadLink.click(); + + // Clean up: remove the anchor element from the DOM + downloadLink.remove(); + + // Revoke the Object URL to free up memory + URL.revokeObjectURL(dataURL); +} + +/** + * + * @param {Object} o + * @param {string[]} props + * @returns {Object} + */ +function pick(o, props) { + return Object.assign( + {}, + ...props.map((prop) => { + if (o[prop] !== undefined) { + return { [prop]: o[prop] }; + } + }) + ); +} + +/** + * Calculate the length of a string, taking multi-byte characters into account. + * This mimics the behavior of Python's `len` function. + * @param {string} s The string to calculate the length of. + * @returns {number} The length of the string. + */ +function len(s) { + let length = 0; + for (const c of s) ++length; + return length; +} + +/** + * Count the occurrences of a value in an array or string. + * This mimics the behavior of Python's `count` method. + * @param {any[]|string} arr The array or string to search. + * @param {any} value The value to count. + */ +function count(arr, value) { + let count = 0; + for (const v of arr) { + if (v === value) ++count; + } + return count; +} + + +/***/ }), + +/***/ "./src/utils/data-structures.js": +/*!**************************************!*\ + !*** ./src/utils/data-structures.js ***! + \**************************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ CharTrie: () => (/* binding */ CharTrie), +/* harmony export */ DictionarySplitter: () => (/* binding */ DictionarySplitter), +/* harmony export */ LRUCache: () => (/* binding */ LRUCache), +/* harmony export */ PriorityQueue: () => (/* binding */ PriorityQueue), +/* harmony export */ TokenLattice: () => (/* binding */ TokenLattice) +/* harmony export */ }); + +/** + * @file Custom data structures. + * + * These are only used internally, meaning an end-user shouldn't + * need to access anything here. + * + * @module utils/data-structures + */ + + +/** + * Efficient Heap-based Implementation of a Priority Queue. + * It uses an array-based binary heap, where the root is at index `0`, and the + * children of node `i` are located at indices `2i + 1` and `2i + 2`, respectively. + * + * Adapted from the following sources: + * - https://stackoverflow.com/a/42919752/13989043 (original) + * - https://github.com/belladoreai/llama-tokenizer-js (minor improvements) + */ +class PriorityQueue { + + /** + * Create a new PriorityQueue. + * @param {function(any, any): boolean} comparator Comparator function to determine priority. Defaults to a MaxHeap. + */ + constructor(comparator = (a, b) => a > b, maxSize = Infinity) { + this._heap = []; + this._comparator = comparator; + this._maxSize = maxSize; + } + + /** + * The size of the queue + */ + get size() { + return this._heap.length; + } + + /** + * Check if the queue is empty. + * @returns {boolean} `true` if the queue is empty, `false` otherwise. + */ + isEmpty() { + return this.size === 0; + } + + /** + * Return the element with the highest priority in the queue. + * @returns {any} The highest priority element in the queue. + */ + peek() { + return this._heap[0]; + } + + /** + * Add one or more elements to the queue. + * @param {...any} values The values to push into the queue. + * @returns {number} The new size of the queue. + */ + push(...values) { + return this.extend(values); + } + + /** + * Add multiple elements to the queue. + * @param {any[]} values The values to push into the queue. + * @returns {number} The new size of the queue. + */ + extend(values) { + for (const value of values) { + if (this.size < this._maxSize) { + this._heap.push(value); + this._siftUp(); + } else { + // Get index of value with the lowest priority + const smallest = this._smallest(); + + // If the new value has higher priority than the smallest value in the heap + // then replace the smallest value with the new value and update the heap + if (this._comparator(value, this._heap[smallest])) { + this._heap[smallest] = value; + this._siftUpFrom(smallest); + } + } + } + return this.size; + } + + /** + * Remove and return the element with the highest priority in the queue. + * @returns {any} The element with the highest priority in the queue. + */ + pop() { + const poppedValue = this.peek(); + const bottom = this.size - 1; + if (bottom > 0) { + this._swap(0, bottom); + } + this._heap.pop(); + this._siftDown(); + return poppedValue; + } + + /** + * Replace the element with the highest priority in the queue with a new value. + * @param {*} value The new value. + * @returns {*} The replaced value. + */ + replace(value) { + const replacedValue = this.peek(); + this._heap[0] = value; + this._siftDown(); + return replacedValue; + } + + /** + * Compute the index for the parent of the node at index `i`. + * @param {number} i The index of the node to get the parent of. + * @returns {number} The index of the parent node. + * @private + */ + _parent(i) { + return ((i + 1) >>> 1) - 1; + } + + /** + * Compute the index for the left child of the node at index `i`. + * @param {number} i The index of the node to get the left child of. + * @returns {number} The index of the left child. + * @private + */ + _left(i) { + return (i << 1) + 1; + } + + /** + * Compute the index for the right child of the node at index `i`. + * @param {number} i The index of the node to get the right child of. + * @returns {number} The index of the right child. + * @private + */ + _right(i) { + return (i + 1) << 1; + } + + /** + * Check if the element at index `i` is greater than the element at index `j`. + * @param {number} i The index of the first element to compare. + * @param {number} j The index of the second element to compare. + * @returns {boolean} `true` if the element at index `i` is greater than the element at index `j`, `false` otherwise. + * @private + */ + _greater(i, j) { + return this._comparator(this._heap[i], this._heap[j]); + } + + /** + * Swap the elements at indices `i` and `j`. + * @param {number} i The index of the first element to swap. + * @param {number} j The index of the second element to swap. + * @private + */ + _swap(i, j) { + const temp = this._heap[i]; + this._heap[i] = this._heap[j]; + this._heap[j] = temp; + } + + /** + * Maintain the heap property by updating positions in the heap, + * starting at the last element and moving up the heap. + * @private + */ + _siftUp() { + this._siftUpFrom(this.size - 1); + } + + /** + * Helper function to sift up from a given node. + * @param {number} node The index of the node to start sifting up from. + */ + _siftUpFrom(node) { + while (node > 0 && this._greater(node, this._parent(node))) { + this._swap(node, this._parent(node)); + node = this._parent(node); + } + } + + /** + * Maintain the heap property by updating positions in the heap, + * starting at the first element and moving down the heap. + * @private + */ + _siftDown() { + let node = 0; + while ( + (this._left(node) < this.size && this._greater(this._left(node), node)) || + (this._right(node) < this.size && this._greater(this._right(node), node)) + ) { + const maxChild = (this._right(node) < this.size && this._greater(this._right(node), this._left(node))) + ? this._right(node) + : this._left(node); + this._swap(node, maxChild); + node = maxChild; + } + } + + /** + * Get the index of the smallest element in the heap. Since we use an array-based heap, + * the index can be computed without needing to traverse the heap. + * @private + */ + _smallest() { + return (2 ** (Math.floor(Math.log2(this.size))) - 1); + } +} + +/** + * A trie structure to efficiently store and search for strings. + */ +class CharTrie { + constructor() { + this.root = CharTrieNode.default(); + } + + /** + * Adds one or more `texts` to the trie. + * @param {string[]} texts The strings to add to the trie. + */ + extend(texts) { + for (const text of texts) { + this.push(text); + } + } + + /** + * Adds text to the trie. + * @param {string} text The string to add to the trie. + */ + push(text) { + let node = this.root; + for (const ch of text) { + let child = node.children.get(ch); + if (child === undefined) { + child = CharTrieNode.default(); + node.children.set(ch, child); + } + node = child; + } + node.isLeaf = true; + } + + /** + * Searches the trie for all strings with a common prefix of `text`. + * @param {string} text The common prefix to search for. + * @yields {string} Each string in the trie that has `text` as a prefix. + */ + *commonPrefixSearch(text) { + let node = this.root; + if (node === undefined) return; + + let prefix = ""; + for (const ch of text) { + prefix += ch; + node = node.children.get(ch); + if (node === undefined) return; + if (node.isLeaf) { + yield prefix; + } + } + } +} + +/** + * Represents a node in a character trie. + */ +class CharTrieNode { + /** + * Create a new CharTrieNode. + * @param {boolean} isLeaf Whether the node is a leaf node or not. + * @param {Map} children A map containing the node's children, where the key is a character and the value is a `CharTrieNode`. + */ + constructor(isLeaf, children) { + this.isLeaf = isLeaf; + this.children = children; + } + + /** + * Returns a new `CharTrieNode` instance with default values. + * @returns {CharTrieNode} A new `CharTrieNode` instance with `isLeaf` set to `false` and an empty `children` map. + */ + static default() { + return new CharTrieNode(false, new Map()); + } +} + +/** + * A lattice data structure to be used for tokenization. + */ +class TokenLattice { + /** + * Creates a new TokenLattice instance. + * + * @param {string} sentence The input sentence to be tokenized. + * @param {number} bosTokenId The beginning-of-sequence token ID. + * @param {number} eosTokenId The end-of-sequence token ID. + */ + constructor(sentence, bosTokenId, eosTokenId) { + this.chars = Array.from(sentence); + this.len = this.chars.length; + this.bosTokenId = bosTokenId; + this.eosTokenId = eosTokenId; + this.nodes = []; + this.beginNodes = Array.from({ length: this.len + 1 }, () => []); + this.endNodes = Array.from({ length: this.len + 1 }, () => []); + + const bos = new TokenLatticeNode(this.bosTokenId, 0, 0, 0, 0.0); + const eos = new TokenLatticeNode(this.eosTokenId, 1, this.len, 0, 0.0); + this.nodes.push(bos.clone()); + this.nodes.push(eos.clone()); + this.beginNodes[this.len].push(eos); + this.endNodes[0].push(bos); + } + + /** + * Inserts a new token node into the token lattice. + * + * @param {number} pos The starting position of the token. + * @param {number} length The length of the token. + * @param {number} score The score of the token. + * @param {number} tokenId The token ID of the token. + */ + insert(pos, length, score, tokenId) { + const nodeId = this.nodes.length; + const node = new TokenLatticeNode(tokenId, nodeId, pos, length, score); + this.beginNodes[pos].push(node); + this.endNodes[pos + length].push(node); + this.nodes.push(node); + } + + /** + * Implements the Viterbi algorithm to compute the most likely sequence of tokens. + * + * @returns {TokenLatticeNode[]} The most likely sequence of tokens. + */ + viterbi() { + const len = this.len; + let pos = 0; + while (pos <= len) { + if (this.beginNodes[pos].length == 0) { + return []; + } + for (let rnode of this.beginNodes[pos]) { + rnode.prev = null; + let bestScore = 0.0; + let bestNode = null; + for (let lnode of this.endNodes[pos]) { + const score = lnode.backtraceScore + rnode.score; + if (bestNode === null || score > bestScore) { + bestNode = lnode.clone(); + bestScore = score; + } + } + + if (bestNode !== null) { + rnode.prev = bestNode; + rnode.backtraceScore = bestScore; + } else { + return []; + } + } + ++pos; + } + + const results = []; + const root = this.beginNodes[len][0]; + const prev = root.prev; + if (prev === null) { + return []; + } + + let node = prev.clone(); + while (node.prev !== null) { + results.push(node.clone()); + const n = node.clone(); + node = n.prev.clone(); + } + + results.reverse(); + return results; + } + + /** + * @param {TokenLatticeNode} node + * @returns {string} The array of nodes representing the most likely sequence of tokens. + */ + piece(node) { + return this.chars.slice(node.pos, node.pos + node.length).join(''); + } + + /** + * @returns {string[]} The most likely sequence of tokens. + */ + tokens() { + const nodes = this.viterbi(); + return nodes.map(x => this.piece(x)); + } + + /** + * @returns {number[]} The most likely sequence of token ids. + */ + tokenIds() { + const nodes = this.viterbi(); + return nodes.map(x => x.tokenId); + } +} +class TokenLatticeNode { + /** + * Represents a node in a token lattice for a given sentence. + * @param {number} tokenId The ID of the token associated with this node. + * @param {number} nodeId The ID of this node. + * @param {number} pos The starting position of the token in the sentence. + * @param {number} length The length of the token. + * @param {number} score The score associated with the token. + */ + constructor(tokenId, nodeId, pos, length, score) { + this.tokenId = tokenId; + this.nodeId = nodeId; + this.pos = pos; + this.length = length; + this.score = score; + this.prev = null; + this.backtraceScore = 0.0; + } + + /** + * Returns a clone of this node. + * @returns {TokenLatticeNode} A clone of this node. + */ + clone() { + const n = new TokenLatticeNode(this.tokenId, this.nodeId, this.pos, this.length, this.score); + n.prev = this.prev; + n.backtraceScore = this.backtraceScore; + return n; + } +} + +/** + * A data structure which uses a trie to split a string into tokens based on a dictionary. + * It can also use a regular expression to preprocess the input text before splitting. + * + * NOTE: To ensure multi-byte characters are handled correctly, we operate at byte-level instead of character-level. + */ +class DictionarySplitter { + /** + * @param {string[]} dictionary The dictionary of words to use for splitting. + */ + constructor(dictionary) { + this.trie = this._buildTrie(dictionary); + } + + /** + * Builds a trie from the given dictionary. + * @param {string[]} dictionary The dictionary of words to build the trie from. + * @returns {Object} The root node of the trie. + * @private + */ + _buildTrie(dictionary) { + const trie = Object.create(null); + for (const word of dictionary) { + let node = trie; + for (let i = 0; i < word.length; ++i) { + node = (node[word[i]] ??= Object.create(null)); + } + node.end = word; + } + return trie; + } + + /** + * Splits the input text into tokens based on the dictionary. + * @param {string} text The input text to split. + * @returns {string[]} An array of tokens. + */ + split(text) { + const result = []; + const n = text.length; + let start = 0; + let i = 0; + + while (i < n) { + let node = this.trie; + let match = null; + let j = i; + + while (j < n && (node = node[text[j]])) { + if (node.end) { + // Always keep the last (i.e., longest) match. + match = node.end; + } + ++j; + } + + if (match) { + if (i > start) { + result.push(text.slice(start, i)); + } + result.push(match); + i += match.length; + start = i; + } else { + ++i; + } + } + if (start < n) { + result.push(text.slice(start)); + } + return result; + } +} + +/** +* A simple Least Recently Used (LRU) cache implementation in JavaScript. +* This cache stores key-value pairs and evicts the least recently used item +* when the capacity is exceeded. +*/ +class LRUCache { + /** + * Creates an LRUCache instance. + * @param {number} capacity The maximum number of items the cache can hold. + */ + constructor(capacity) { + this.capacity = capacity; + this.cache = new Map(); + } + + /** + * Retrieves the value associated with the given key and marks the key as recently used. + * @param {any} key The key to retrieve. + * @returns {any} The value associated with the key, or undefined if the key does not exist. + */ + get(key) { + if (!this.cache.has(key)) return undefined; + const value = this.cache.get(key); + this.cache.delete(key); + this.cache.set(key, value); + return value; + } + + /** + * Inserts or updates the key-value pair in the cache. + * If the key already exists, it is updated and marked as recently used. + * If the cache exceeds its capacity, the least recently used item is evicted. + * @param {any} key The key to add or update. + * @param {any} value The value to associate with the key. + */ + put(key, value) { + if (this.cache.has(key)) { + this.cache.delete(key); + } + this.cache.set(key, value); + if (this.cache.size > this.capacity) { + this.cache.delete(this.cache.keys().next().value); + } + } + + /** + * Clears the cache. + */ + clear() { + this.cache.clear(); + } +} + + +/***/ }), + +/***/ "./src/utils/devices.js": +/*!******************************!*\ + !*** ./src/utils/devices.js ***! + \******************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ DEVICE_TYPES: () => (/* binding */ DEVICE_TYPES) +/* harmony export */ }); + +/** + * The list of devices supported by Transformers.js + */ +const DEVICE_TYPES = Object.freeze({ + auto: 'auto', // Auto-detect based on device and environment + gpu: 'gpu', // Auto-detect GPU + cpu: 'cpu', // CPU + wasm: 'wasm', // WebAssembly + webgpu: 'webgpu', // WebGPU + cuda: 'cuda', // CUDA + dml: 'dml', // DirectML + + webnn: 'webnn', // WebNN (default) + 'webnn-npu': 'webnn-npu', // WebNN NPU + 'webnn-gpu': 'webnn-gpu', // WebNN GPU + 'webnn-cpu': 'webnn-cpu', // WebNN CPU +}); + +/** + * @typedef {keyof typeof DEVICE_TYPES} DeviceType + */ + + +/***/ }), + +/***/ "./src/utils/dtypes.js": +/*!*****************************!*\ + !*** ./src/utils/dtypes.js ***! + \*****************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ DATA_TYPES: () => (/* binding */ DATA_TYPES), +/* harmony export */ DEFAULT_DEVICE_DTYPE_MAPPING: () => (/* binding */ DEFAULT_DEVICE_DTYPE_MAPPING), +/* harmony export */ DEFAULT_DTYPE_SUFFIX_MAPPING: () => (/* binding */ DEFAULT_DTYPE_SUFFIX_MAPPING), +/* harmony export */ isWebGpuFp16Supported: () => (/* binding */ isWebGpuFp16Supported) +/* harmony export */ }); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../env.js */ "./src/env.js"); +/* harmony import */ var _devices_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./devices.js */ "./src/utils/devices.js"); +/// + + + + + +// TODO: Use the adapter from `env.backends.onnx.webgpu.adapter` to check for `shader-f16` support, +// when available in https://github.com/microsoft/onnxruntime/pull/19940. +// For more information, see https://github.com/microsoft/onnxruntime/pull/19857#issuecomment-1999984753 + +/** + * Checks if WebGPU fp16 support is available in the current environment. + */ +const isWebGpuFp16Supported = (function () { + /** @type {boolean} */ + let cachedResult; + + return async function () { + if (cachedResult === undefined) { + if (!_env_js__WEBPACK_IMPORTED_MODULE_0__.apis.IS_WEBGPU_AVAILABLE) { + cachedResult = false; + } else { + try { + const adapter = await navigator.gpu.requestAdapter(); + cachedResult = adapter.features.has('shader-f16'); + } catch (e) { + cachedResult = false; + } + } + } + return cachedResult; + }; +})(); + +const DATA_TYPES = Object.freeze({ + auto: 'auto', // Auto-detect based on environment + fp32: 'fp32', + fp16: 'fp16', + q8: 'q8', + int8: 'int8', + uint8: 'uint8', + q4: 'q4', + bnb4: 'bnb4', + q4f16: 'q4f16', // fp16 model with int4 block weight quantization +}); +/** @typedef {keyof typeof DATA_TYPES} DataType */ + +const DEFAULT_DEVICE_DTYPE_MAPPING = Object.freeze({ + // NOTE: If not specified, will default to fp32 + [_devices_js__WEBPACK_IMPORTED_MODULE_1__.DEVICE_TYPES.wasm]: DATA_TYPES.q8, +}); + +/** @type {Record, string>} */ +const DEFAULT_DTYPE_SUFFIX_MAPPING = Object.freeze({ + [DATA_TYPES.fp32]: '', + [DATA_TYPES.fp16]: '_fp16', + [DATA_TYPES.int8]: '_int8', + [DATA_TYPES.uint8]: '_uint8', + [DATA_TYPES.q8]: '_quantized', + [DATA_TYPES.q4]: '_q4', + [DATA_TYPES.q4f16]: '_q4f16', + [DATA_TYPES.bnb4]: '_bnb4', +}); + + +/***/ }), + +/***/ "./src/utils/generic.js": +/*!******************************!*\ + !*** ./src/utils/generic.js ***! + \******************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ Callable: () => (/* binding */ Callable) +/* harmony export */ }); + +/** + * A base class for creating callable objects. + * See [here](https://stackoverflow.com/q/76073890) for more information. + * + * @type {new () => {(...args: any[]): any, _call(...args: any[]): any}} + */ +const Callable = /** @type {any} */ (class { + /** + * Creates a new instance of the Callable class. + */ + constructor() { + /** + * Creates a closure that delegates to a private method '_call' with the given arguments. + * @type {any} + * @param {...any} args Zero or more arguments to pass to the '_call' method. + * @returns {*} The result of calling the '_call' method. + */ + let closure = function (...args) { + return closure._call(...args) + } + return Object.setPrototypeOf(closure, new.target.prototype) + } + + /** + * This method should be implemented in subclasses to provide the + * functionality of the callable object. + * + * @param {any[]} args + * @throws {Error} If the subclass does not implement the `_call` method. + */ + _call(...args) { + throw Error('Must implement _call method in subclass') + } +}); + + +/***/ }), + +/***/ "./src/utils/hub.js": +/*!**************************!*\ + !*** ./src/utils/hub.js ***! + \**************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ MAX_EXTERNAL_DATA_CHUNKS: () => (/* binding */ MAX_EXTERNAL_DATA_CHUNKS), +/* harmony export */ getFile: () => (/* binding */ getFile), +/* harmony export */ getModelFile: () => (/* binding */ getModelFile), +/* harmony export */ getModelJSON: () => (/* binding */ getModelJSON), +/* harmony export */ getModelText: () => (/* binding */ getModelText) +/* harmony export */ }); +/* harmony import */ var node_fs__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! node:fs */ "?7992"); +/* harmony import */ var node_path__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! node:path */ "?5af5"); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../env.js */ "./src/env.js"); +/* harmony import */ var _core_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./core.js */ "./src/utils/core.js"); + +/** + * @file Utility functions to interact with the Hugging Face Hub (https://huggingface.co/models) + * + * @module utils/hub + */ + + + + + + + +/** + * @typedef {boolean|number} ExternalData Whether to load the model using the external data format (used for models >= 2GB in size). + * If `true`, the model will be loaded using the external data format. + * If a number, this many chunks will be loaded using the external data format (of the form: "model.onnx_data[_{chunk_number}]"). + */ +const MAX_EXTERNAL_DATA_CHUNKS = 100; + +/** + * @typedef {Object} PretrainedOptions Options for loading a pretrained model. + * @property {import('./core.js').ProgressCallback} [progress_callback=null] If specified, this function will be called during model construction, to provide the user with progress updates. + * @property {import('../configs.js').PretrainedConfig} [config=null] Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when: + * - The model is a model provided by the library (loaded with the *model id* string of a pretrained model). + * - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named *config.json* is found in the directory. + * @property {string} [cache_dir=null] Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. + * @property {boolean} [local_files_only=false] Whether or not to only look at local files (e.g., not try downloading the model). + * @property {string} [revision='main'] The specific model version to use. It can be a branch name, a tag name, or a commit id, + * since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. + * NOTE: This setting is ignored for local requests. + */ + +/** + * @typedef {Object} ModelSpecificPretrainedOptions Options for loading a pretrained model. + * @property {string} [subfolder='onnx'] In case the relevant files are located inside a subfolder of the model repo on huggingface.co, + * you can specify the folder name here. + * @property {string} [model_file_name=null] If specified, load the model with this name (excluding the .onnx suffix). Currently only valid for encoder- or decoder-only models. + * @property {import("./devices.js").DeviceType|Record} [device=null] The device to run the model on. If not specified, the device will be chosen from the environment settings. + * @property {import("./dtypes.js").DataType|Record} [dtype=null] The data type to use for the model. If not specified, the data type will be chosen from the environment settings. + * @property {ExternalData|Record} [use_external_data_format=false] Whether to load the model using the external data format (used for models >= 2GB in size). + * @property {import('onnxruntime-common').InferenceSession.SessionOptions} [session_options] (Optional) User-specified session options passed to the runtime. If not provided, suitable defaults will be chosen. + */ + +/** + * @typedef {PretrainedOptions & ModelSpecificPretrainedOptions} PretrainedModelOptions Options for loading a pretrained model. + */ + +/** + * Mapping from file extensions to MIME types. + */ +const CONTENT_TYPE_MAP = { + 'txt': 'text/plain', + 'html': 'text/html', + 'css': 'text/css', + 'js': 'text/javascript', + 'json': 'application/json', + 'png': 'image/png', + 'jpg': 'image/jpeg', + 'jpeg': 'image/jpeg', + 'gif': 'image/gif', +} +class FileResponse { + + /** + * Creates a new `FileResponse` object. + * @param {string} filePath + */ + constructor(filePath) { + this.filePath = filePath; + this.headers = new Headers(); + + this.exists = node_fs__WEBPACK_IMPORTED_MODULE_0__.existsSync(filePath); + if (this.exists) { + this.status = 200; + this.statusText = 'OK'; + + let stats = node_fs__WEBPACK_IMPORTED_MODULE_0__.statSync(filePath); + this.headers.set('content-length', stats.size.toString()); + + this.updateContentType(); + + const stream = node_fs__WEBPACK_IMPORTED_MODULE_0__.createReadStream(filePath); + this.body = new ReadableStream({ + start(controller) { + stream.on('data', (chunk) => controller.enqueue(chunk)); + stream.on('end', () => controller.close()); + stream.on('error', (err) => controller.error(err)); + }, + cancel() { + stream.destroy(); + } + }); + } else { + this.status = 404; + this.statusText = 'Not Found'; + this.body = null; + } + } + + /** + * Updates the 'content-type' header property of the response based on the extension of + * the file specified by the filePath property of the current object. + * @returns {void} + */ + updateContentType() { + // Set content-type header based on file extension + const extension = this.filePath.toString().split('.').pop().toLowerCase(); + this.headers.set('content-type', CONTENT_TYPE_MAP[extension] ?? 'application/octet-stream'); + } + + /** + * Clone the current FileResponse object. + * @returns {FileResponse} A new FileResponse object with the same properties as the current object. + */ + clone() { + let response = new FileResponse(this.filePath); + response.exists = this.exists; + response.status = this.status; + response.statusText = this.statusText; + response.headers = new Headers(this.headers); + return response; + } + + /** + * Reads the contents of the file specified by the filePath property and returns a Promise that + * resolves with an ArrayBuffer containing the file's contents. + * @returns {Promise} A Promise that resolves with an ArrayBuffer containing the file's contents. + * @throws {Error} If the file cannot be read. + */ + async arrayBuffer() { + const data = await node_fs__WEBPACK_IMPORTED_MODULE_0__.promises.readFile(this.filePath); + return /** @type {ArrayBuffer} */ (data.buffer); + } + + /** + * Reads the contents of the file specified by the filePath property and returns a Promise that + * resolves with a Blob containing the file's contents. + * @returns {Promise} A Promise that resolves with a Blob containing the file's contents. + * @throws {Error} If the file cannot be read. + */ + async blob() { + const data = await node_fs__WEBPACK_IMPORTED_MODULE_0__.promises.readFile(this.filePath); + return new Blob([data], { type: this.headers.get('content-type') }); + } + + /** + * Reads the contents of the file specified by the filePath property and returns a Promise that + * resolves with a string containing the file's contents. + * @returns {Promise} A Promise that resolves with a string containing the file's contents. + * @throws {Error} If the file cannot be read. + */ + async text() { + const data = await node_fs__WEBPACK_IMPORTED_MODULE_0__.promises.readFile(this.filePath, 'utf8'); + return data; + } + + /** + * Reads the contents of the file specified by the filePath property and returns a Promise that + * resolves with a parsed JavaScript object containing the file's contents. + * + * @returns {Promise} A Promise that resolves with a parsed JavaScript object containing the file's contents. + * @throws {Error} If the file cannot be read. + */ + async json() { + return JSON.parse(await this.text()); + } +} + +/** + * Determines whether the given string is a valid URL. + * @param {string|URL} string The string to test for validity as an URL. + * @param {string[]} [protocols=null] A list of valid protocols. If specified, the protocol must be in this list. + * @param {string[]} [validHosts=null] A list of valid hostnames. If specified, the URL's hostname must be in this list. + * @returns {boolean} True if the string is a valid URL, false otherwise. + */ +function isValidUrl(string, protocols = null, validHosts = null) { + let url; + try { + url = new URL(string); + } catch (_) { + return false; + } + if (protocols && !protocols.includes(url.protocol)) { + return false; + } + if (validHosts && !validHosts.includes(url.hostname)) { + return false; + } + return true; +} + +const REPO_ID_REGEX = /^(\b[\w\-.]+\b\/)?\b[\w\-.]{1,96}\b$/; + +/** + * Tests whether a string is a valid Hugging Face model ID or not. + * Adapted from https://github.com/huggingface/huggingface_hub/blob/6378820ebb03f071988a96c7f3268f5bdf8f9449/src/huggingface_hub/utils/_validators.py#L119-L170 + * + * @param {string} string The string to test + * @returns {boolean} True if the string is a valid model ID, false otherwise. + */ +function isValidHfModelId(string) { + if (!REPO_ID_REGEX.test(string)) return false; + if (string.includes("..") || string.includes("--")) return false; + if (string.endsWith(".git") || string.endsWith(".ipynb")) return false; + return true; +} + +/** + * Helper function to get a file, using either the Fetch API or FileSystem API. + * + * @param {URL|string} urlOrPath The URL/path of the file to get. + * @returns {Promise} A promise that resolves to a FileResponse object (if the file is retrieved using the FileSystem API), or a Response object (if the file is retrieved using the Fetch API). + */ +async function getFile(urlOrPath) { + + if (_env_js__WEBPACK_IMPORTED_MODULE_2__.env.useFS && !isValidUrl(urlOrPath, ["http:", "https:", "blob:"])) { + return new FileResponse( + urlOrPath instanceof URL + ? urlOrPath.protocol === "file:" + ? urlOrPath.pathname + : urlOrPath.toString() + : urlOrPath, + ); + } else if (typeof process !== 'undefined' && process?.release?.name === 'node') { + const IS_CI = !!process.env?.TESTING_REMOTELY; + const version = _env_js__WEBPACK_IMPORTED_MODULE_2__.env.version; + + const headers = new Headers(); + headers.set('User-Agent', `transformers.js/${version}; is_ci/${IS_CI};`); + + // Check whether we are making a request to the Hugging Face Hub. + const isHFURL = isValidUrl(urlOrPath, ['http:', 'https:'], ['huggingface.co', 'hf.co']); + if (isHFURL) { + // If an access token is present in the environment variables, + // we add it to the request headers. + // NOTE: We keep `HF_ACCESS_TOKEN` for backwards compatibility (as a fallback). + const token = process.env?.HF_TOKEN ?? process.env?.HF_ACCESS_TOKEN; + if (token) { + headers.set('Authorization', `Bearer ${token}`); + } + } + return fetch(urlOrPath, { headers }); + } else { + // Running in a browser-environment, so we use default headers + // NOTE: We do not allow passing authorization headers in the browser, + // since this would require exposing the token to the client. + return fetch(urlOrPath); + } +} + +const ERROR_MAPPING = { + // 4xx errors (https://developer.mozilla.org/en-US/docs/Web/HTTP/Status#client_error_responses) + 400: 'Bad request error occurred while trying to load file', + 401: 'Unauthorized access to file', + 403: 'Forbidden access to file', + 404: 'Could not locate file', + 408: 'Request timeout error occurred while trying to load file', + + // 5xx errors (https://developer.mozilla.org/en-US/docs/Web/HTTP/Status#server_error_responses) + 500: 'Internal server error error occurred while trying to load file', + 502: 'Bad gateway error occurred while trying to load file', + 503: 'Service unavailable error occurred while trying to load file', + 504: 'Gateway timeout error occurred while trying to load file', +} +/** + * Helper method to handle fatal errors that occur while trying to load a file from the Hugging Face Hub. + * @param {number} status The HTTP status code of the error. + * @param {string} remoteURL The URL of the file that could not be loaded. + * @param {boolean} fatal Whether to raise an error if the file could not be loaded. + * @returns {null} Returns `null` if `fatal = true`. + * @throws {Error} If `fatal = false`. + */ +function handleError(status, remoteURL, fatal) { + if (!fatal) { + // File was not loaded correctly, but it is optional. + // TODO in future, cache the response? + return null; + } + + const message = ERROR_MAPPING[status] ?? `Error (${status}) occurred while trying to load file`; + throw Error(`${message}: "${remoteURL}".`); +} + +class FileCache { + /** + * Instantiate a `FileCache` object. + * @param {string} path + */ + constructor(path) { + this.path = path; + } + + /** + * Checks whether the given request is in the cache. + * @param {string} request + * @returns {Promise} + */ + async match(request) { + + let filePath = node_path__WEBPACK_IMPORTED_MODULE_1__.join(this.path, request); + let file = new FileResponse(filePath); + + if (file.exists) { + return file; + } else { + return undefined; + } + } + + /** + * Adds the given response to the cache. + * @param {string} request + * @param {Response} response + * @param {(data: {progress: number, loaded: number, total: number}) => void} [progress_callback] Optional. + * The function to call with progress updates + * @returns {Promise} + */ + async put(request, response, progress_callback = undefined) { + let filePath = node_path__WEBPACK_IMPORTED_MODULE_1__.join(this.path, request); + + try { + const contentLength = response.headers.get('Content-Length'); + const total = parseInt(contentLength ?? '0'); + let loaded = 0; + + await node_fs__WEBPACK_IMPORTED_MODULE_0__.promises.mkdir(node_path__WEBPACK_IMPORTED_MODULE_1__.dirname(filePath), { recursive: true }); + const fileStream = node_fs__WEBPACK_IMPORTED_MODULE_0__.createWriteStream(filePath); + const reader = response.body.getReader(); + + while (true) { + const { done, value } = await reader.read(); + if (done) { + break; + } + + await new Promise((resolve, reject) => { + fileStream.write(value, (err) => { + if (err) { + reject(err); + return; + } + resolve(); + }); + }); + + loaded += value.length; + const progress = total ? (loaded / total) * 100 : 0; + + progress_callback?.({ progress, loaded, total }); + } + + fileStream.close(); + } catch (error) { + // Clean up the file if an error occurred during download + try { + await node_fs__WEBPACK_IMPORTED_MODULE_0__.promises.unlink(filePath); + } catch { } + throw error; + } + } + + // TODO add the rest? + // addAll(requests: RequestInfo[]): Promise; + // delete(request: RequestInfo | URL, options?: CacheQueryOptions): Promise; + // keys(request?: RequestInfo | URL, options?: CacheQueryOptions): Promise>; + // match(request: RequestInfo | URL, options?: CacheQueryOptions): Promise; + // matchAll(request?: RequestInfo | URL, options?: CacheQueryOptions): Promise>; +} + +/** + * + * @param {FileCache|Cache} cache The cache to search + * @param {string[]} names The names of the item to search for + * @returns {Promise} The item from the cache, or undefined if not found. + */ +async function tryCache(cache, ...names) { + for (let name of names) { + try { + let result = await cache.match(name); + if (result) return result; + } catch (e) { + continue; + } + } + return undefined; +} + +/** + * Retrieves a file from either a remote URL using the Fetch API or from the local file system using the FileSystem API. + * If the filesystem is available and `env.useCache = true`, the file will be downloaded and cached. + * + * @param {string} path_or_repo_id This can be either: + * - a string, the *model id* of a model repo on huggingface.co. + * - a path to a *directory* potentially containing the file. + * @param {string} filename The name of the file to locate in `path_or_repo`. + * @param {boolean} [fatal=true] Whether to throw an error if the file is not found. + * @param {PretrainedOptions} [options] An object containing optional parameters. + * @param {boolean} [return_path=false] Whether to return the path of the file instead of the file content. + * + * @throws Will throw an error if the file is not found and `fatal` is true. + * @returns {Promise} A Promise that resolves with the file content as a Uint8Array if `return_path` is false, or the file path as a string if `return_path` is true. + */ +async function getModelFile(path_or_repo_id, filename, fatal = true, options = {}, return_path = false) { + + if (!_env_js__WEBPACK_IMPORTED_MODULE_2__.env.allowLocalModels) { + // User has disabled local models, so we just make sure other settings are correct. + + if (options.local_files_only) { + throw Error("Invalid configuration detected: local models are disabled (`env.allowLocalModels=false`) but you have requested to only use local models (`local_files_only=true`).") + } else if (!_env_js__WEBPACK_IMPORTED_MODULE_2__.env.allowRemoteModels) { + throw Error("Invalid configuration detected: both local and remote models are disabled. Fix by setting `env.allowLocalModels` or `env.allowRemoteModels` to `true`.") + } + } + + // Initiate file retrieval + (0,_core_js__WEBPACK_IMPORTED_MODULE_3__.dispatchCallback)(options.progress_callback, { + status: 'initiate', + name: path_or_repo_id, + file: filename + }) + + // First, check if the a caching backend is available + // If no caching mechanism available, will download the file every time + let cache; + if (!cache && _env_js__WEBPACK_IMPORTED_MODULE_2__.env.useCustomCache) { + // Allow the user to specify a custom cache system. + if (!_env_js__WEBPACK_IMPORTED_MODULE_2__.env.customCache) { + throw Error('`env.useCustomCache=true`, but `env.customCache` is not defined.') + } + + // Check that the required methods are defined: + if (!_env_js__WEBPACK_IMPORTED_MODULE_2__.env.customCache.match || !_env_js__WEBPACK_IMPORTED_MODULE_2__.env.customCache.put) { + throw new Error( + "`env.customCache` must be an object which implements the `match` and `put` functions of the Web Cache API. " + + "For more information, see https://developer.mozilla.org/en-US/docs/Web/API/Cache" + ) + } + cache = _env_js__WEBPACK_IMPORTED_MODULE_2__.env.customCache; + } + + if (!cache && _env_js__WEBPACK_IMPORTED_MODULE_2__.env.useBrowserCache) { + if (typeof caches === 'undefined') { + throw Error('Browser cache is not available in this environment.') + } + try { + // In some cases, the browser cache may be visible, but not accessible due to security restrictions. + // For example, when running an application in an iframe, if a user attempts to load the page in + // incognito mode, the following error is thrown: `DOMException: Failed to execute 'open' on 'CacheStorage': + // An attempt was made to break through the security policy of the user agent.` + // So, instead of crashing, we just ignore the error and continue without using the cache. + cache = await caches.open('transformers-cache'); + } catch (e) { + console.warn('An error occurred while opening the browser cache:', e); + } + } + + if (!cache && _env_js__WEBPACK_IMPORTED_MODULE_2__.env.useFSCache) { + if (!_env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_FS_AVAILABLE) { + throw Error('File System Cache is not available in this environment.'); + } + + // If `cache_dir` is not specified, use the default cache directory + cache = new FileCache(options.cache_dir ?? _env_js__WEBPACK_IMPORTED_MODULE_2__.env.cacheDir); + } + + const revision = options.revision ?? 'main'; + const requestURL = pathJoin(path_or_repo_id, filename); + + const validModelId = isValidHfModelId(path_or_repo_id); + const localPath = validModelId + ? pathJoin(_env_js__WEBPACK_IMPORTED_MODULE_2__.env.localModelPath, requestURL) + : requestURL; + const remoteURL = pathJoin( + _env_js__WEBPACK_IMPORTED_MODULE_2__.env.remoteHost, + _env_js__WEBPACK_IMPORTED_MODULE_2__.env.remotePathTemplate + .replaceAll('{model}', path_or_repo_id) + .replaceAll('{revision}', encodeURIComponent(revision)), + filename + ); + + /** @type {string} */ + let cacheKey; + const proposedCacheKey = cache instanceof FileCache + // Choose cache key for filesystem cache + // When using the main revision (default), we use the request URL as the cache key. + // If a specific revision is requested, we account for this in the cache key. + ? revision === 'main' ? requestURL : pathJoin(path_or_repo_id, revision, filename) + : remoteURL; + + // Whether to cache the final response in the end. + let toCacheResponse = false; + + /** @type {Response|FileResponse|undefined} */ + let response; + + if (cache) { + // A caching system is available, so we try to get the file from it. + // 1. We first try to get from cache using the local path. In some environments (like deno), + // non-URL cache keys are not allowed. In these cases, `response` will be undefined. + // 2. If no response is found, we try to get from cache using the remote URL or file system cache. + response = await tryCache(cache, localPath, proposedCacheKey); + } + + const cacheHit = response !== undefined; + if (response === undefined) { + // Caching not available, or file is not cached, so we perform the request + + if (_env_js__WEBPACK_IMPORTED_MODULE_2__.env.allowLocalModels) { + // Accessing local models is enabled, so we try to get the file locally. + // If request is a valid HTTP URL, we skip the local file check. Otherwise, we try to get the file locally. + const isURL = isValidUrl(requestURL, ['http:', 'https:']); + if (!isURL) { + try { + response = await getFile(localPath); + cacheKey = localPath; // Update the cache key to be the local path + } catch (e) { + // Something went wrong while trying to get the file locally. + // NOTE: error handling is done in the next step (since `response` will be undefined) + console.warn(`Unable to load from local path "${localPath}": "${e}"`); + } + } else if (options.local_files_only) { + throw new Error(`\`local_files_only=true\`, but attempted to load a remote file from: ${requestURL}.`); + } else if (!_env_js__WEBPACK_IMPORTED_MODULE_2__.env.allowRemoteModels) { + throw new Error(`\`env.allowRemoteModels=false\`, but attempted to load a remote file from: ${requestURL}.`); + } + } + + if (response === undefined || response.status === 404) { + // File not found locally. This means either: + // - The user has disabled local file access (`env.allowLocalModels=false`) + // - the path is a valid HTTP url (`response === undefined`) + // - the path is not a valid HTTP url and the file is not present on the file system or local server (`response.status === 404`) + + if (options.local_files_only || !_env_js__WEBPACK_IMPORTED_MODULE_2__.env.allowRemoteModels) { + // User requested local files only, but the file is not found locally. + if (fatal) { + throw Error(`\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${localPath}".`); + } else { + // File not found, but this file is optional. + // TODO in future, cache the response? + return null; + } + } + if (!validModelId) { + // Before making any requests to the remote server, we check if the model ID is valid. + // This prevents unnecessary network requests for invalid model IDs. + throw Error(`Local file missing at "${localPath}" and download aborted due to invalid model ID "${path_or_repo_id}".`); + } + + // File not found locally, so we try to download it from the remote server + response = await getFile(remoteURL); + + if (response.status !== 200) { + return handleError(response.status, remoteURL, fatal); + } + + // Success! We use the proposed cache key from earlier + cacheKey = proposedCacheKey; + } + + // Only cache the response if: + toCacheResponse = + cache // 1. A caching system is available + && typeof Response !== 'undefined' // 2. `Response` is defined (i.e., we are in a browser-like environment) + && response instanceof Response // 3. result is a `Response` object (i.e., not a `FileResponse`) + && response.status === 200 // 4. request was successful (status code 200) + } + + // Start downloading + (0,_core_js__WEBPACK_IMPORTED_MODULE_3__.dispatchCallback)(options.progress_callback, { + status: 'download', + name: path_or_repo_id, + file: filename + }) + + let result; + if (!(_env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_NODE_ENV && return_path)) { + /** @type {Uint8Array} */ + let buffer; + + if (!options.progress_callback) { + // If no progress callback is specified, we can use the `.arrayBuffer()` + // method to read the response. + buffer = new Uint8Array(await response.arrayBuffer()); + + } else if ( + cacheHit // The item is being read from the cache + && + typeof navigator !== 'undefined' && /firefox/i.test(navigator.userAgent) // We are in Firefox + ) { + // Due to bug in Firefox, we cannot display progress when loading from cache. + // Fortunately, since this should be instantaneous, this should not impact users too much. + buffer = new Uint8Array(await response.arrayBuffer()); + + // For completeness, we still fire the final progress callback + (0,_core_js__WEBPACK_IMPORTED_MODULE_3__.dispatchCallback)(options.progress_callback, { + status: 'progress', + name: path_or_repo_id, + file: filename, + progress: 100, + loaded: buffer.length, + total: buffer.length, + }) + } else { + buffer = await readResponse(response, data => { + (0,_core_js__WEBPACK_IMPORTED_MODULE_3__.dispatchCallback)(options.progress_callback, { + status: 'progress', + name: path_or_repo_id, + file: filename, + ...data, + }) + }) + } + result = buffer; + } + + if ( + // Only cache web responses + // i.e., do not cache FileResponses (prevents duplication) + toCacheResponse && cacheKey + && + // Check again whether request is in cache. If not, we add the response to the cache + (await cache.match(cacheKey) === undefined) + ) { + if (!result) { + // We haven't yet read the response body, so we need to do so now. + await cache.put(cacheKey, /** @type {Response} */(response), options.progress_callback); + } else { + // NOTE: We use `new Response(buffer, ...)` instead of `response.clone()` to handle LFS files + await cache.put(cacheKey, new Response(result, { + headers: response.headers + })) + .catch(err => { + // Do not crash if unable to add to cache (e.g., QuotaExceededError). + // Rather, log a warning and proceed with execution. + console.warn(`Unable to add response to browser cache: ${err}.`); + }); + } + } + (0,_core_js__WEBPACK_IMPORTED_MODULE_3__.dispatchCallback)(options.progress_callback, { + status: 'done', + name: path_or_repo_id, + file: filename + }); + + if (result) { + if (!_env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_NODE_ENV && return_path) { + throw new Error("Cannot return path in a browser environment.") + } + return result; + } + if (response instanceof FileResponse) { + return response.filePath; + } + + // Otherwise, return the cached response (most likely a `FileResponse`). + // NOTE: A custom cache may return a Response, or a string (file path) + const cachedResponse = await cache?.match(cacheKey); + if (cachedResponse instanceof FileResponse) { + return cachedResponse.filePath; + } else if (cachedResponse instanceof Response) { + return new Uint8Array(await cachedResponse.arrayBuffer()); + } else if (typeof cachedResponse === 'string') { + return cachedResponse; + } + + throw new Error("Unable to get model file path or buffer."); +} + +/** + * Fetches a text file from a given path and file name. + * + * @param {string} modelPath The path to the directory containing the file. + * @param {string} fileName The name of the file to fetch. + * @param {boolean} [fatal=true] Whether to throw an error if the file is not found. + * @param {PretrainedOptions} [options] An object containing optional parameters. + * @returns {Promise} The text content of the file. + * @throws Will throw an error if the file is not found and `fatal` is true. + */ +async function getModelText(modelPath, fileName, fatal = true, options = {}) { + const buffer = await getModelFile(modelPath, fileName, fatal, options, false); + if (buffer === null) { + return null; + } + + const decoder = new TextDecoder('utf-8'); + return decoder.decode(/** @type {Uint8Array} */(buffer)); +} + +/** + * Fetches a JSON file from a given path and file name. + * + * @param {string} modelPath The path to the directory containing the file. + * @param {string} fileName The name of the file to fetch. + * @param {boolean} [fatal=true] Whether to throw an error if the file is not found. + * @param {PretrainedOptions} [options] An object containing optional parameters. + * @returns {Promise} The JSON data parsed into a JavaScript object. + * @throws Will throw an error if the file is not found and `fatal` is true. + */ +async function getModelJSON(modelPath, fileName, fatal = true, options = {}) { + const text = await getModelText(modelPath, fileName, fatal, options); + if (text === null) { + // Return empty object + return {}; + } + + return JSON.parse(text); +} +/** + * Read and track progress when reading a Response object + * + * @param {Response|FileResponse} response The Response object to read + * @param {(data: {progress: number, loaded: number, total: number}) => void} progress_callback The function to call with progress updates + * @returns {Promise} A Promise that resolves with the Uint8Array buffer + */ +async function readResponse(response, progress_callback) { + + const contentLength = response.headers.get('Content-Length'); + if (contentLength === null) { + console.warn('Unable to determine content-length from response headers. Will expand buffer when needed.') + } + let total = parseInt(contentLength ?? '0'); + let buffer = new Uint8Array(total); + let loaded = 0; + + const reader = response.body.getReader(); + async function read() { + const { done, value } = await reader.read(); + if (done) return; + + const newLoaded = loaded + value.length; + if (newLoaded > total) { + total = newLoaded; + + // Adding the new data will overflow buffer. + // In this case, we extend the buffer + const newBuffer = new Uint8Array(total); + + // copy contents + newBuffer.set(buffer); + + buffer = newBuffer; + } + buffer.set(value, loaded); + loaded = newLoaded; + + const progress = (loaded / total) * 100; + + // Call your function here + progress_callback({ progress, loaded, total }); + + return read(); + } + + // Actually read + await read(); + + return buffer; +} + +/** + * Joins multiple parts of a path into a single path, while handling leading and trailing slashes. + * + * @param {...string} parts Multiple parts of a path. + * @returns {string} A string representing the joined path. + */ +function pathJoin(...parts) { + // https://stackoverflow.com/a/55142565 + parts = parts.map((part, index) => { + if (index) { + part = part.replace(new RegExp('^/'), ''); + } + if (index !== parts.length - 1) { + part = part.replace(new RegExp('/$'), ''); + } + return part; + }) + return parts.join('/'); +} + + +/***/ }), + +/***/ "./src/utils/image.js": +/*!****************************!*\ + !*** ./src/utils/image.js ***! + \****************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ RawImage: () => (/* binding */ RawImage), +/* harmony export */ load_image: () => (/* binding */ load_image) +/* harmony export */ }); +/* harmony import */ var _core_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./core.js */ "./src/utils/core.js"); +/* harmony import */ var _hub_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./hub.js */ "./src/utils/hub.js"); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../env.js */ "./src/env.js"); +/* harmony import */ var _tensor_js__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./tensor.js */ "./src/utils/tensor.js"); +/* harmony import */ var sharp__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! sharp */ "?2b25"); + +/** + * @file Helper module for image processing. + * + * These functions and classes are only used internally, + * meaning an end-user shouldn't need to access anything here. + * + * @module utils/image + */ + + + + + + +// Will be empty (or not used) if running in browser or web-worker + + +let createCanvasFunction; +let ImageDataClass; +let loadImageFunction; +const IS_BROWSER_OR_WEBWORKER = _env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_BROWSER_ENV || _env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_WEBWORKER_ENV; +if (IS_BROWSER_OR_WEBWORKER) { + // Running in browser or web-worker + createCanvasFunction = (/** @type {number} */ width, /** @type {number} */ height) => { + if (!self.OffscreenCanvas) { + throw new Error('OffscreenCanvas not supported by this browser.'); + } + return new self.OffscreenCanvas(width, height) + }; + loadImageFunction = self.createImageBitmap; + ImageDataClass = self.ImageData; + +} else if (sharp__WEBPACK_IMPORTED_MODULE_4__) { + // Running in Node.js, electron, or other non-browser environment + + loadImageFunction = async (/**@type {sharp.Sharp}*/img) => { + const metadata = await img.metadata(); + const rawChannels = metadata.channels; + + const { data, info } = await img.rotate().raw().toBuffer({ resolveWithObject: true }); + + const newImage = new RawImage(new Uint8ClampedArray(data), info.width, info.height, info.channels); + if (rawChannels !== undefined && rawChannels !== info.channels) { + // Make sure the new image has the same number of channels as the input image. + // This is necessary for grayscale images. + newImage.convert(rawChannels); + } + return newImage; + } + +} else { + throw new Error('Unable to load image processing library.'); +} + + +// Defined here: https://github.com/python-pillow/Pillow/blob/a405e8406b83f8bfb8916e93971edc7407b8b1ff/src/libImaging/Imaging.h#L262-L268 +const RESAMPLING_MAPPING = { + 0: 'nearest', + 1: 'lanczos', + 2: 'bilinear', + 3: 'bicubic', + 4: 'box', + 5: 'hamming', +} + +/** + * Mapping from file extensions to MIME types. + */ +const CONTENT_TYPE_MAP = new Map([ + ['png', 'image/png'], + ['jpg', 'image/jpeg'], + ['jpeg', 'image/jpeg'], + ['gif', 'image/gif'], +]); + +class RawImage { + + /** + * Create a new `RawImage` object. + * @param {Uint8ClampedArray|Uint8Array} data The pixel data. + * @param {number} width The width of the image. + * @param {number} height The height of the image. + * @param {1|2|3|4} channels The number of channels. + */ + constructor(data, width, height, channels) { + this.data = data; + this.width = width; + this.height = height; + this.channels = channels; + } + + /** + * Returns the size of the image (width, height). + * @returns {[number, number]} The size of the image (width, height). + */ + get size() { + return [this.width, this.height]; + } + + /** + * Helper method for reading an image from a variety of input types. + * @param {RawImage|string|URL|Blob|HTMLCanvasElement|OffscreenCanvas} input + * @returns The image object. + * + * **Example:** Read image from a URL. + * ```javascript + * let image = await RawImage.read('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg'); + * // RawImage { + * // "data": Uint8ClampedArray [ 25, 25, 25, 19, 19, 19, ... ], + * // "width": 800, + * // "height": 533, + * // "channels": 3 + * // } + * ``` + */ + static async read(input) { + if (input instanceof RawImage) { + return input; + } else if (typeof input === 'string' || input instanceof URL) { + return await this.fromURL(input); + } else if (input instanceof Blob) { + return await this.fromBlob(input); + } else if ( + (typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) + || + (typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas) + ) { + return this.fromCanvas(input); + } else { + throw new Error(`Unsupported input type: ${typeof input}`); + } + } + + /** + * Read an image from a canvas. + * @param {HTMLCanvasElement|OffscreenCanvas} canvas The canvas to read the image from. + * @returns {RawImage} The image object. + */ + static fromCanvas(canvas) { + if (!IS_BROWSER_OR_WEBWORKER) { + throw new Error('fromCanvas() is only supported in browser environments.') + } + + const ctx = /** @type {CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D} */ (canvas.getContext('2d')); + const data = ctx.getImageData(0, 0, canvas.width, canvas.height).data; + return new RawImage(data, canvas.width, canvas.height, 4); + } + + /** + * Read an image from a URL or file path. + * @param {string|URL} url The URL or file path to read the image from. + * @returns {Promise} The image object. + */ + static async fromURL(url) { + const response = await (0,_hub_js__WEBPACK_IMPORTED_MODULE_1__.getFile)(url); + if (response.status !== 200) { + throw new Error(`Unable to read image from "${url}" (${response.status} ${response.statusText})`); + } + const blob = await response.blob(); + return this.fromBlob(blob); + } + + /** + * Helper method to create a new Image from a blob. + * @param {Blob} blob The blob to read the image from. + * @returns {Promise} The image object. + */ + static async fromBlob(blob) { + if (IS_BROWSER_OR_WEBWORKER) { + // Running in environment with canvas + const img = await loadImageFunction(blob); + + const ctx = createCanvasFunction(img.width, img.height).getContext('2d'); + + // Draw image to context + ctx.drawImage(img, 0, 0); + + return new this(ctx.getImageData(0, 0, img.width, img.height).data, img.width, img.height, 4); + + } else { + // Use sharp.js to read (and possible resize) the image. + const img = sharp__WEBPACK_IMPORTED_MODULE_4__(await blob.arrayBuffer()); + + return await loadImageFunction(img); + } + } + + /** + * Helper method to create a new Image from a tensor + * @param {Tensor} tensor + */ + static fromTensor(tensor, channel_format = 'CHW') { + if (tensor.dims.length !== 3) { + throw new Error(`Tensor should have 3 dimensions, but has ${tensor.dims.length} dimensions.`); + } + + if (channel_format === 'CHW') { + tensor = tensor.transpose(1, 2, 0); + } else if (channel_format === 'HWC') { + // Do nothing + } else { + throw new Error(`Unsupported channel format: ${channel_format}`); + } + if (!(tensor.data instanceof Uint8ClampedArray || tensor.data instanceof Uint8Array)) { + throw new Error(`Unsupported tensor type: ${tensor.type}`); + } + switch (tensor.dims[2]) { + case 1: + case 2: + case 3: + case 4: + return new RawImage(tensor.data, tensor.dims[1], tensor.dims[0], tensor.dims[2]); + default: + throw new Error(`Unsupported number of channels: ${tensor.dims[2]}`); + } + } + + /** + * Convert the image to grayscale format. + * @returns {RawImage} `this` to support chaining. + */ + grayscale() { + if (this.channels === 1) { + return this; + } + + const newData = new Uint8ClampedArray(this.width * this.height * 1); + switch (this.channels) { + case 3: // rgb to grayscale + case 4: // rgba to grayscale + for (let i = 0, offset = 0; i < this.data.length; i += this.channels) { + const red = this.data[i]; + const green = this.data[i + 1]; + const blue = this.data[i + 2]; + + newData[offset++] = Math.round(0.2989 * red + 0.5870 * green + 0.1140 * blue); + } + break; + default: + throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`); + } + return this._update(newData, this.width, this.height, 1); + } + + /** + * Convert the image to RGB format. + * @returns {RawImage} `this` to support chaining. + */ + rgb() { + if (this.channels === 3) { + return this; + } + + const newData = new Uint8ClampedArray(this.width * this.height * 3); + + switch (this.channels) { + case 1: // grayscale to rgb + for (let i = 0, offset = 0; i < this.data.length; ++i) { + newData[offset++] = this.data[i]; + newData[offset++] = this.data[i]; + newData[offset++] = this.data[i]; + } + break; + case 4: // rgba to rgb + for (let i = 0, offset = 0; i < this.data.length; i += 4) { + newData[offset++] = this.data[i]; + newData[offset++] = this.data[i + 1]; + newData[offset++] = this.data[i + 2]; + } + break; + default: + throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`); + } + return this._update(newData, this.width, this.height, 3); + + } + + /** + * Convert the image to RGBA format. + * @returns {RawImage} `this` to support chaining. + */ + rgba() { + if (this.channels === 4) { + return this; + } + + const newData = new Uint8ClampedArray(this.width * this.height * 4); + + switch (this.channels) { + case 1: // grayscale to rgba + for (let i = 0, offset = 0; i < this.data.length; ++i) { + newData[offset++] = this.data[i]; + newData[offset++] = this.data[i]; + newData[offset++] = this.data[i]; + newData[offset++] = 255; + } + break; + case 3: // rgb to rgba + for (let i = 0, offset = 0; i < this.data.length; i += 3) { + newData[offset++] = this.data[i]; + newData[offset++] = this.data[i + 1]; + newData[offset++] = this.data[i + 2]; + newData[offset++] = 255; + } + break; + default: + throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`); + } + + return this._update(newData, this.width, this.height, 4); + } + + /** + * Apply an alpha mask to the image. Operates in place. + * @param {RawImage} mask The mask to apply. It should have a single channel. + * @returns {RawImage} The masked image. + * @throws {Error} If the mask is not the same size as the image. + * @throws {Error} If the image does not have 4 channels. + * @throws {Error} If the mask is not a single channel. + */ + putAlpha(mask) { + if (mask.width !== this.width || mask.height !== this.height) { + throw new Error(`Expected mask size to be ${this.width}x${this.height}, but got ${mask.width}x${mask.height}`); + } + if (mask.channels !== 1) { + throw new Error(`Expected mask to have 1 channel, but got ${mask.channels}`); + } + + const this_data = this.data; + const mask_data = mask.data; + const num_pixels = this.width * this.height; + if (this.channels === 3) { + // Convert to RGBA and simultaneously apply mask to alpha channel + const newData = new Uint8ClampedArray(num_pixels * 4); + for (let i = 0, in_offset = 0, out_offset = 0; i < num_pixels; ++i) { + newData[out_offset++] = this_data[in_offset++]; + newData[out_offset++] = this_data[in_offset++]; + newData[out_offset++] = this_data[in_offset++]; + newData[out_offset++] = mask_data[i]; + } + return this._update(newData, this.width, this.height, 4); + + } else if (this.channels === 4) { + // Apply mask to alpha channel in place + for (let i = 0; i < num_pixels; ++i) { + this_data[4 * i + 3] = mask_data[i]; + } + return this; + } + throw new Error(`Expected image to have 3 or 4 channels, but got ${this.channels}`); + } + + /** + * Resize the image to the given dimensions. This method uses the canvas API to perform the resizing. + * @param {number} width The width of the new image. `null` or `-1` will preserve the aspect ratio. + * @param {number} height The height of the new image. `null` or `-1` will preserve the aspect ratio. + * @param {Object} options Additional options for resizing. + * @param {0|1|2|3|4|5|string} [options.resample] The resampling method to use. + * @returns {Promise} `this` to support chaining. + */ + async resize(width, height, { + resample = 2, + } = {}) { + + // Do nothing if the image already has the desired size + if (this.width === width && this.height === height) { + return this; + } + + // Ensure resample method is a string + let resampleMethod = RESAMPLING_MAPPING[resample] ?? resample; + + // Calculate width / height to maintain aspect ratio, in the event that + // the user passed a null value in. + // This allows users to pass in something like `resize(320, null)` to + // resize to 320 width, but maintain aspect ratio. + const nullish_width = (0,_core_js__WEBPACK_IMPORTED_MODULE_0__.isNullishDimension)(width); + const nullish_height = (0,_core_js__WEBPACK_IMPORTED_MODULE_0__.isNullishDimension)(height); + if (nullish_width && nullish_height) { + return this; + } else if (nullish_width) { + width = (height / this.height) * this.width; + } else if (nullish_height) { + height = (width / this.width) * this.height; + } + + if (IS_BROWSER_OR_WEBWORKER) { + // TODO use `resample` in browser environment + + // Store number of channels before resizing + const numChannels = this.channels; + + // Create canvas object for this image + const canvas = this.toCanvas(); + + // Actually perform resizing using the canvas API + const ctx = createCanvasFunction(width, height).getContext('2d'); + + // Draw image to context, resizing in the process + ctx.drawImage(canvas, 0, 0, width, height); + + // Create image from the resized data + const resizedImage = new RawImage(ctx.getImageData(0, 0, width, height).data, width, height, 4); + + // Convert back so that image has the same number of channels as before + return resizedImage.convert(numChannels); + + } else { + // Create sharp image from raw data, and resize + let img = this.toSharp(); + + switch (resampleMethod) { + case 'box': + case 'hamming': + if (resampleMethod === 'box' || resampleMethod === 'hamming') { + console.warn(`Resampling method ${resampleMethod} is not yet supported. Using bilinear instead.`); + resampleMethod = 'bilinear'; + } + + case 'nearest': + case 'bilinear': + case 'bicubic': + // Perform resizing using affine transform. + // This matches how the python Pillow library does it. + img = img.affine([width / this.width, 0, 0, height / this.height], { + interpolator: resampleMethod + }); + break; + + case 'lanczos': + // https://github.com/python-pillow/Pillow/discussions/5519 + // https://github.com/lovell/sharp/blob/main/docs/api-resize.md + img = img.resize({ + width, height, + fit: 'fill', + kernel: 'lanczos3', // PIL Lanczos uses a kernel size of 3 + }); + break; + + default: + throw new Error(`Resampling method ${resampleMethod} is not supported.`); + } + + return await loadImageFunction(img); + } + + } + + async pad([left, right, top, bottom]) { + left = Math.max(left, 0); + right = Math.max(right, 0); + top = Math.max(top, 0); + bottom = Math.max(bottom, 0); + + if (left === 0 && right === 0 && top === 0 && bottom === 0) { + // No padding needed + return this; + } + + if (IS_BROWSER_OR_WEBWORKER) { + // Store number of channels before padding + const numChannels = this.channels; + + // Create canvas object for this image + const canvas = this.toCanvas(); + + const newWidth = this.width + left + right; + const newHeight = this.height + top + bottom; + + // Create a new canvas of the desired size. + const ctx = createCanvasFunction(newWidth, newHeight).getContext('2d'); + + // Draw image to context, padding in the process + ctx.drawImage(canvas, + 0, 0, this.width, this.height, + left, top, this.width, this.height + ); + + // Create image from the padded data + const paddedImage = new RawImage( + ctx.getImageData(0, 0, newWidth, newHeight).data, + newWidth, newHeight, 4 + ); + + // Convert back so that image has the same number of channels as before + return paddedImage.convert(numChannels); + + } else { + const img = this.toSharp().extend({ left, right, top, bottom }); + return await loadImageFunction(img); + } + } + + async crop([x_min, y_min, x_max, y_max]) { + // Ensure crop bounds are within the image + x_min = Math.max(x_min, 0); + y_min = Math.max(y_min, 0); + x_max = Math.min(x_max, this.width - 1); + y_max = Math.min(y_max, this.height - 1); + + // Do nothing if the crop is the entire image + if (x_min === 0 && y_min === 0 && x_max === this.width - 1 && y_max === this.height - 1) { + return this; + } + + const crop_width = x_max - x_min + 1; + const crop_height = y_max - y_min + 1; + + if (IS_BROWSER_OR_WEBWORKER) { + // Store number of channels before resizing + const numChannels = this.channels; + + // Create canvas object for this image + const canvas = this.toCanvas(); + + // Create a new canvas of the desired size. This is needed since if the + // image is too small, we need to pad it with black pixels. + const ctx = createCanvasFunction(crop_width, crop_height).getContext('2d'); + + // Draw image to context, cropping in the process + ctx.drawImage(canvas, + x_min, y_min, crop_width, crop_height, + 0, 0, crop_width, crop_height + ); + + // Create image from the resized data + const resizedImage = new RawImage(ctx.getImageData(0, 0, crop_width, crop_height).data, crop_width, crop_height, 4); + + // Convert back so that image has the same number of channels as before + return resizedImage.convert(numChannels); + + } else { + // Create sharp image from raw data + const img = this.toSharp().extract({ + left: x_min, + top: y_min, + width: crop_width, + height: crop_height, + }); + + return await loadImageFunction(img); + } + + } + + async center_crop(crop_width, crop_height) { + // If the image is already the desired size, return it + if (this.width === crop_width && this.height === crop_height) { + return this; + } + + // Determine bounds of the image in the new canvas + const width_offset = (this.width - crop_width) / 2; + const height_offset = (this.height - crop_height) / 2; + + + if (IS_BROWSER_OR_WEBWORKER) { + // Store number of channels before resizing + const numChannels = this.channels; + + // Create canvas object for this image + const canvas = this.toCanvas(); + + // Create a new canvas of the desired size. This is needed since if the + // image is too small, we need to pad it with black pixels. + const ctx = createCanvasFunction(crop_width, crop_height).getContext('2d'); + + let sourceX = 0; + let sourceY = 0; + let destX = 0; + let destY = 0; + + if (width_offset >= 0) { + sourceX = width_offset; + } else { + destX = -width_offset; + } + + if (height_offset >= 0) { + sourceY = height_offset; + } else { + destY = -height_offset; + } + + // Draw image to context, cropping in the process + ctx.drawImage(canvas, + sourceX, sourceY, crop_width, crop_height, + destX, destY, crop_width, crop_height + ); + + // Create image from the resized data + const resizedImage = new RawImage(ctx.getImageData(0, 0, crop_width, crop_height).data, crop_width, crop_height, 4); + + // Convert back so that image has the same number of channels as before + return resizedImage.convert(numChannels); + + } else { + // Create sharp image from raw data + let img = this.toSharp(); + + if (width_offset >= 0 && height_offset >= 0) { + // Cropped image lies entirely within the original image + img = img.extract({ + left: Math.floor(width_offset), + top: Math.floor(height_offset), + width: crop_width, + height: crop_height, + }) + } else if (width_offset <= 0 && height_offset <= 0) { + // Cropped image lies entirely outside the original image, + // so we add padding + const top = Math.floor(-height_offset); + const left = Math.floor(-width_offset); + img = img.extend({ + top: top, + left: left, + + // Ensures the resulting image has the desired dimensions + right: crop_width - this.width - left, + bottom: crop_height - this.height - top, + }); + } else { + // Cropped image lies partially outside the original image. + // We first pad, then crop. + + let y_padding = [0, 0]; + let y_extract = 0; + if (height_offset < 0) { + y_padding[0] = Math.floor(-height_offset); + y_padding[1] = crop_height - this.height - y_padding[0]; + } else { + y_extract = Math.floor(height_offset); + } + + let x_padding = [0, 0]; + let x_extract = 0; + if (width_offset < 0) { + x_padding[0] = Math.floor(-width_offset); + x_padding[1] = crop_width - this.width - x_padding[0]; + } else { + x_extract = Math.floor(width_offset); + } + + img = img.extend({ + top: y_padding[0], + bottom: y_padding[1], + left: x_padding[0], + right: x_padding[1], + }).extract({ + left: x_extract, + top: y_extract, + width: crop_width, + height: crop_height, + }) + } + + return await loadImageFunction(img); + } + } + + async toBlob(type = 'image/png', quality = 1) { + if (!IS_BROWSER_OR_WEBWORKER) { + throw new Error('toBlob() is only supported in browser environments.') + } + + const canvas = this.toCanvas(); + return await canvas.convertToBlob({ type, quality }); + } + + toTensor(channel_format = 'CHW') { + let tensor = new _tensor_js__WEBPACK_IMPORTED_MODULE_3__.Tensor( + 'uint8', + new Uint8Array(this.data), + [this.height, this.width, this.channels] + ); + + if (channel_format === 'HWC') { + // Do nothing + } else if (channel_format === 'CHW') { // hwc -> chw + tensor = tensor.permute(2, 0, 1); + } else { + throw new Error(`Unsupported channel format: ${channel_format}`); + } + return tensor; + } + + toCanvas() { + if (!IS_BROWSER_OR_WEBWORKER) { + throw new Error('toCanvas() is only supported in browser environments.') + } + + // Clone, and convert data to RGBA before drawing to canvas. + // This is because the canvas API only supports RGBA + const cloned = this.clone().rgba(); + + // Create canvas object for the cloned image + const clonedCanvas = createCanvasFunction(cloned.width, cloned.height); + + // Draw image to context + const data = new ImageDataClass(cloned.data, cloned.width, cloned.height); + clonedCanvas.getContext('2d').putImageData(data, 0, 0); + + return clonedCanvas; + } + + /** + * Split this image into individual bands. This method returns an array of individual image bands from an image. + * For example, splitting an "RGB" image creates three new images each containing a copy of one of the original bands (red, green, blue). + * + * Inspired by PIL's `Image.split()` [function](https://pillow.readthedocs.io/en/latest/reference/Image.html#PIL.Image.Image.split). + * @returns {RawImage[]} An array containing bands. + */ + split() { + const { data, width, height, channels } = this; + + /** @type {typeof Uint8Array | typeof Uint8ClampedArray} */ + const data_type = /** @type {any} */(data.constructor); + const per_channel_length = data.length / channels; + + // Pre-allocate buffers for each channel + const split_data = Array.from( + { length: channels }, + () => new data_type(per_channel_length), + ); + + // Write pixel data + for (let i = 0; i < per_channel_length; ++i) { + const data_offset = channels * i; + for (let j = 0; j < channels; ++j) { + split_data[j][i] = data[data_offset + j]; + } + } + return split_data.map((data) => new RawImage(data, width, height, 1)); + } + + /** + * Helper method to update the image data. + * @param {Uint8ClampedArray} data The new image data. + * @param {number} width The new width of the image. + * @param {number} height The new height of the image. + * @param {1|2|3|4|null} [channels] The new number of channels of the image. + * @private + */ + _update(data, width, height, channels = null) { + this.data = data; + this.width = width; + this.height = height; + if (channels !== null) { + this.channels = channels; + } + return this; + } + + /** + * Clone the image + * @returns {RawImage} The cloned image + */ + clone() { + return new RawImage(this.data.slice(), this.width, this.height, this.channels); + } + + /** + * Helper method for converting image to have a certain number of channels + * @param {number} numChannels The number of channels. Must be 1, 3, or 4. + * @returns {RawImage} `this` to support chaining. + */ + convert(numChannels) { + if (this.channels === numChannels) return this; // Already correct number of channels + + switch (numChannels) { + case 1: + this.grayscale(); + break; + case 3: + this.rgb(); + break; + case 4: + this.rgba(); + break; + default: + throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`); + } + return this; + } + + /** + * Save the image to the given path. + * @param {string} path The path to save the image to. + */ + async save(path) { + + if (IS_BROWSER_OR_WEBWORKER) { + if (_env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_WEBWORKER_ENV) { + throw new Error('Unable to save an image from a Web Worker.') + } + + const extension = path.split('.').pop().toLowerCase(); + const mime = CONTENT_TYPE_MAP.get(extension) ?? 'image/png'; + + // Convert image to Blob + const blob = await this.toBlob(mime); + + (0,_core_js__WEBPACK_IMPORTED_MODULE_0__.saveBlob)(path, blob) + + } else if (!_env_js__WEBPACK_IMPORTED_MODULE_2__.apis.IS_FS_AVAILABLE) { + throw new Error('Unable to save the image because filesystem is disabled in this environment.') + + } else { + const img = this.toSharp(); + return await img.toFile(path); + } + } + + toSharp() { + if (IS_BROWSER_OR_WEBWORKER) { + throw new Error('toSharp() is only supported in server-side environments.') + } + + return sharp__WEBPACK_IMPORTED_MODULE_4__(this.data, { + raw: { + width: this.width, + height: this.height, + channels: this.channels + } + }); + } +} + +/** + * Helper function to load an image from a URL, path, etc. + */ +const load_image = RawImage.read.bind(RawImage); + + + +/***/ }), + +/***/ "./src/utils/maths.js": +/*!****************************!*\ + !*** ./src/utils/maths.js ***! + \****************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ FFT: () => (/* binding */ FFT), +/* harmony export */ bankers_round: () => (/* binding */ bankers_round), +/* harmony export */ cos_sim: () => (/* binding */ cos_sim), +/* harmony export */ dot: () => (/* binding */ dot), +/* harmony export */ dynamic_time_warping: () => (/* binding */ dynamic_time_warping), +/* harmony export */ interpolate_data: () => (/* binding */ interpolate_data), +/* harmony export */ log_softmax: () => (/* binding */ log_softmax), +/* harmony export */ magnitude: () => (/* binding */ magnitude), +/* harmony export */ max: () => (/* binding */ max), +/* harmony export */ medianFilter: () => (/* binding */ medianFilter), +/* harmony export */ min: () => (/* binding */ min), +/* harmony export */ permute_data: () => (/* binding */ permute_data), +/* harmony export */ round: () => (/* binding */ round), +/* harmony export */ softmax: () => (/* binding */ softmax) +/* harmony export */ }); + +/** + * @file Helper module for mathematical processing. + * + * These functions and classes are only used internally, + * meaning an end-user shouldn't need to access anything here. + * + * @module utils/maths + */ + +/** + * @typedef {Int8Array | Uint8Array | Uint8ClampedArray | Int16Array | Uint16Array | Int32Array | Uint32Array | Float16Array | Float32Array | Float64Array} TypedArray + * @typedef {BigInt64Array | BigUint64Array} BigTypedArray + * @typedef {TypedArray | BigTypedArray} AnyTypedArray + */ + +/** + * @param {TypedArray} input + */ +function interpolate_data(input, [in_channels, in_height, in_width], [out_height, out_width], mode = 'bilinear', align_corners = false) { + // TODO use mode and align_corners + + // Output image dimensions + const x_scale = out_width / in_width; + const y_scale = out_height / in_height; + + // Output image + // @ts-ignore + const out_img = new input.constructor(out_height * out_width * in_channels); + + // Pre-calculate strides + const inStride = in_height * in_width; + const outStride = out_height * out_width; + + for (let i = 0; i < out_height; ++i) { + for (let j = 0; j < out_width; ++j) { + // Calculate output offset + const outOffset = i * out_width + j; + + // Calculate input pixel coordinates + const x = (j + 0.5) / x_scale - 0.5; + const y = (i + 0.5) / y_scale - 0.5; + + // Calculate the four nearest input pixels + // We also check if the input pixel coordinates are within the image bounds + let x1 = Math.floor(x); + let y1 = Math.floor(y); + const x2 = Math.min(x1 + 1, in_width - 1); + const y2 = Math.min(y1 + 1, in_height - 1); + + x1 = Math.max(x1, 0); + y1 = Math.max(y1, 0); + + + // Calculate the fractional distances between the input pixel and the four nearest pixels + const s = x - x1; + const t = y - y1; + + // Perform bilinear interpolation + const w1 = (1 - s) * (1 - t); + const w2 = s * (1 - t); + const w3 = (1 - s) * t; + const w4 = s * t; + + // Calculate the four nearest input pixel indices + const yStride = y1 * in_width; + const xStride = y2 * in_width; + const idx1 = yStride + x1; + const idx2 = yStride + x2; + const idx3 = xStride + x1; + const idx4 = xStride + x2; + + for (let k = 0; k < in_channels; ++k) { + // Calculate channel offset + const cOffset = k * inStride; + + out_img[k * outStride + outOffset] = + w1 * input[cOffset + idx1] + + w2 * input[cOffset + idx2] + + w3 * input[cOffset + idx3] + + w4 * input[cOffset + idx4]; + } + } + } + + return out_img; +} + + +/** + * Helper method to permute a `AnyTypedArray` directly + * @template {AnyTypedArray} T + * @param {T} array + * @param {number[]} dims + * @param {number[]} axes + * @returns {[T, number[]]} The permuted array and the new shape. + */ +function permute_data(array, dims, axes) { + // Calculate the new shape of the permuted array + // and the stride of the original array + const shape = new Array(axes.length); + const stride = new Array(axes.length); + + for (let i = axes.length - 1, s = 1; i >= 0; --i) { + stride[i] = s; + shape[i] = dims[axes[i]]; + s *= shape[i]; + } + + // Precompute inverse mapping of stride + const invStride = axes.map((_, i) => stride[axes.indexOf(i)]); + + // Create the permuted array with the new shape + // @ts-ignore + const permutedData = new array.constructor(array.length); + + // Permute the original array to the new array + for (let i = 0; i < array.length; ++i) { + let newIndex = 0; + for (let j = dims.length - 1, k = i; j >= 0; --j) { + newIndex += (k % dims[j]) * invStride[j]; + k = Math.floor(k / dims[j]); + } + permutedData[newIndex] = array[i]; + } + + return [permutedData, shape]; +} + + +/** + * Compute the softmax of an array of numbers. + * @template {TypedArray|number[]} T + * @param {T} arr The array of numbers to compute the softmax of. + * @returns {T} The softmax array. + */ +function softmax(arr) { + // Compute the maximum value in the array + const maxVal = max(arr)[0]; + + // Compute the exponentials of the array values + const exps = arr.map(x => Math.exp(x - maxVal)); + + // Compute the sum of the exponentials + // @ts-ignore + const sumExps = exps.reduce((acc, val) => acc + val, 0); + + // Compute the softmax values + const softmaxArr = exps.map(x => x / sumExps); + + return /** @type {T} */(softmaxArr); +} + +/** + * Calculates the logarithm of the softmax function for the input array. + * @template {TypedArray|number[]} T + * @param {T} arr The input array to calculate the log_softmax function for. + * @returns {T} The resulting log_softmax array. + */ +function log_softmax(arr) { + // Compute the maximum value in the array + const maxVal = max(arr)[0]; + + // Compute the sum of the exponentials + let sumExps = 0; + for(let i = 0; i < arr.length; ++i) { + sumExps += Math.exp(arr[i] - maxVal); + } + + // Compute the log of the sum + const logSum = Math.log(sumExps); + + // Compute the softmax values + const logSoftmaxArr = arr.map(x => x - maxVal - logSum); + + return /** @type {T} */(logSoftmaxArr); +} + +/** + * Calculates the dot product of two arrays. + * @param {number[]} arr1 The first array. + * @param {number[]} arr2 The second array. + * @returns {number} The dot product of arr1 and arr2. + */ +function dot(arr1, arr2) { + let result = 0; + for (let i = 0; i < arr1.length; ++i) { + result += arr1[i] * arr2[i]; + } + return result; +} + +/** + * Computes the cosine similarity between two arrays. + * + * @param {number[]} arr1 The first array. + * @param {number[]} arr2 The second array. + * @returns {number} The cosine similarity between the two arrays. + */ +function cos_sim(arr1, arr2) { + // Calculate dot product of the two arrays + const dotProduct = dot(arr1, arr2); + + // Calculate the magnitude of the first array + const magnitudeA = magnitude(arr1); + + // Calculate the magnitude of the second array + const magnitudeB = magnitude(arr2); + + // Calculate the cosine similarity + const cosineSimilarity = dotProduct / (magnitudeA * magnitudeB); + + return cosineSimilarity; +} + +/** + * Calculates the magnitude of a given array. + * @param {number[]} arr The array to calculate the magnitude of. + * @returns {number} The magnitude of the array. + */ +function magnitude(arr) { + return Math.sqrt(arr.reduce((acc, val) => acc + val * val, 0)); +} + + +/** + * Returns the value and index of the minimum element in an array. + * @template {number[]|bigint[]|AnyTypedArray} T + * @param {T} arr array of numbers. + * @returns {T extends bigint[]|BigTypedArray ? [bigint, number] : [number, number]} the value and index of the minimum element, of the form: [valueOfMin, indexOfMin] + * @throws {Error} If array is empty. + */ +function min(arr) { + if (arr.length === 0) throw Error('Array must not be empty'); + let min = arr[0]; + let indexOfMin = 0; + for (let i = 1; i < arr.length; ++i) { + if (arr[i] < min) { + min = arr[i]; + indexOfMin = i; + } + } + return /** @type {T extends bigint[]|BigTypedArray ? [bigint, number] : [number, number]} */([min, indexOfMin]); +} + + +/** + * Returns the value and index of the maximum element in an array. + * @template {number[]|bigint[]|AnyTypedArray} T + * @param {T} arr array of numbers. + * @returns {T extends bigint[]|BigTypedArray ? [bigint, number] : [number, number]} the value and index of the maximum element, of the form: [valueOfMax, indexOfMax] + * @throws {Error} If array is empty. + */ +function max(arr) { + if (arr.length === 0) throw Error('Array must not be empty'); + let max = arr[0]; + let indexOfMax = 0; + for (let i = 1; i < arr.length; ++i) { + if (arr[i] > max) { + max = arr[i]; + indexOfMax = i; + } + } + return /** @type {T extends bigint[]|BigTypedArray ? [bigint, number] : [number, number]} */([max, indexOfMax]); +} + +function isPowerOfTwo(number) { + // Check if the number is greater than 0 and has only one bit set to 1 + return (number > 0) && ((number & (number - 1)) === 0); +} + +/** + * Implementation of Radix-4 FFT. + * + * P2FFT class provides functionality for performing Fast Fourier Transform on arrays + * which are a power of two in length. + * Code adapted from https://www.npmjs.com/package/fft.js + */ +class P2FFT { + /** + * @param {number} size The size of the input array. Must be a power of two larger than 1. + * @throws {Error} FFT size must be a power of two larger than 1. + */ + constructor(size) { + this.size = size | 0; // convert to a 32-bit signed integer + if (this.size <= 1 || !isPowerOfTwo(this.size)) + throw new Error('FFT size must be a power of two larger than 1'); + + this._csize = size << 1; + + this.table = new Float64Array(this.size * 2); + for (let i = 0; i < this.table.length; i += 2) { + const angle = Math.PI * i / this.size; + this.table[i] = Math.cos(angle); + this.table[i + 1] = -Math.sin(angle); + } + + // Find size's power of two + let power = 0; + for (let t = 1; this.size > t; t <<= 1) + ++power; + + // Calculate initial step's width: + // * If we are full radix-4, it is 2x smaller to give inital len=8 + // * Otherwise it is the same as `power` to give len=4 + this._width = power % 2 === 0 ? power - 1 : power; + + // Pre-compute bit-reversal patterns + this._bitrev = new Int32Array(1 << this._width); + for (let j = 0; j < this._bitrev.length; ++j) { + this._bitrev[j] = 0; + for (let shift = 0; shift < this._width; shift += 2) { + const revShift = this._width - shift - 2; + this._bitrev[j] |= ((j >>> shift) & 3) << revShift; + } + } + } + + /** + * Create a complex number array with size `2 * size` + * + * @returns {Float64Array} A complex number array with size `2 * size` + */ + createComplexArray() { + return new Float64Array(this._csize); + } + + /** + * Converts a complex number representation stored in a Float64Array to an array of real numbers. + * + * @param {Float64Array} complex The complex number representation to be converted. + * @param {number[]} [storage] An optional array to store the result in. + * @returns {number[]} An array of real numbers representing the input complex number representation. + */ + fromComplexArray(complex, storage) { + const res = storage || new Array(complex.length >>> 1); + for (let i = 0; i < complex.length; i += 2) + res[i >>> 1] = complex[i]; + return res; + } + + /** + * Convert a real-valued input array to a complex-valued output array. + * @param {Float64Array} input The real-valued input array. + * @param {Float64Array} [storage] Optional buffer to store the output array. + * @returns {Float64Array} The complex-valued output array. + */ + toComplexArray(input, storage) { + const res = storage || this.createComplexArray(); + for (let i = 0; i < res.length; i += 2) { + res[i] = input[i >>> 1]; + res[i + 1] = 0; + } + return res; + } + + /** + * Performs a Fast Fourier Transform (FFT) on the given input data and stores the result in the output buffer. + * + * @param {Float64Array} out The output buffer to store the result. + * @param {Float64Array} data The input data to transform. + * + * @throws {Error} Input and output buffers must be different. + * + * @returns {void} + */ + transform(out, data) { + if (out === data) + throw new Error('Input and output buffers must be different'); + + this._transform4(out, data, 1 /* DONE */); + } + + /** + * Performs a real-valued forward FFT on the given input buffer and stores the result in the given output buffer. + * The input buffer must contain real values only, while the output buffer will contain complex values. The input and + * output buffers must be different. + * + * @param {Float64Array} out The output buffer. + * @param {Float64Array} data The input buffer containing real values. + * + * @throws {Error} If the input and output buffers are the same. + */ + realTransform(out, data) { + if (out === data) + throw new Error('Input and output buffers must be different'); + + this._realTransform4(out, data, 1 /* DONE */); + } + + /** + * Performs an inverse FFT transformation on the given `data` array, and stores the result in `out`. + * The `out` array must be a different buffer than the `data` array. The `out` array will contain the + * result of the transformation. The `data` array will not be modified. + * + * @param {Float64Array} out The output buffer for the transformed data. + * @param {Float64Array} data The input data to transform. + * @throws {Error} If `out` and `data` refer to the same buffer. + * @returns {void} + */ + inverseTransform(out, data) { + if (out === data) + throw new Error('Input and output buffers must be different'); + + this._transform4(out, data, -1 /* DONE */); + for (let i = 0; i < out.length; ++i) + out[i] /= this.size; + } + + /** + * Performs a radix-4 implementation of a discrete Fourier transform on a given set of data. + * + * @param {Float64Array} out The output buffer for the transformed data. + * @param {Float64Array} data The input buffer of data to be transformed. + * @param {number} inv A scaling factor to apply to the transform. + * @returns {void} + */ + _transform4(out, data, inv) { + // radix-4 implementation + + const size = this._csize; + + // Initial step (permute and transform) + const width = this._width; + let step = 1 << width; + let len = (size / step) << 1; + + let outOff; + let t; + const bitrev = this._bitrev; + if (len === 4) { + for (outOff = 0, t = 0; outOff < size; outOff += len, ++t) { + const off = bitrev[t]; + this._singleTransform2(data, out, outOff, off, step); + } + } else { + // len === 8 + for (outOff = 0, t = 0; outOff < size; outOff += len, ++t) { + const off = bitrev[t]; + this._singleTransform4(data, out, outOff, off, step, inv); + } + } + + // Loop through steps in decreasing order + const table = this.table; + for (step >>= 2; step >= 2; step >>= 2) { + len = (size / step) << 1; + const quarterLen = len >>> 2; + + // Loop through offsets in the data + for (outOff = 0; outOff < size; outOff += len) { + // Full case + const limit = outOff + quarterLen - 1; + for (let i = outOff, k = 0; i < limit; i += 2, k += step) { + const A = i; + const B = A + quarterLen; + const C = B + quarterLen; + const D = C + quarterLen; + + // Original values + const Ar = out[A]; + const Ai = out[A + 1]; + const Br = out[B]; + const Bi = out[B + 1]; + const Cr = out[C]; + const Ci = out[C + 1]; + const Dr = out[D]; + const Di = out[D + 1]; + + const tableBr = table[k]; + const tableBi = inv * table[k + 1]; + const MBr = Br * tableBr - Bi * tableBi; + const MBi = Br * tableBi + Bi * tableBr; + + const tableCr = table[2 * k]; + const tableCi = inv * table[2 * k + 1]; + const MCr = Cr * tableCr - Ci * tableCi; + const MCi = Cr * tableCi + Ci * tableCr; + + const tableDr = table[3 * k]; + const tableDi = inv * table[3 * k + 1]; + const MDr = Dr * tableDr - Di * tableDi; + const MDi = Dr * tableDi + Di * tableDr; + + // Pre-Final values + const T0r = Ar + MCr; + const T0i = Ai + MCi; + const T1r = Ar - MCr; + const T1i = Ai - MCi; + const T2r = MBr + MDr; + const T2i = MBi + MDi; + const T3r = inv * (MBr - MDr); + const T3i = inv * (MBi - MDi); + + // Final values + out[A] = T0r + T2r; + out[A + 1] = T0i + T2i; + out[B] = T1r + T3i; + out[B + 1] = T1i - T3r; + out[C] = T0r - T2r; + out[C + 1] = T0i - T2i; + out[D] = T1r - T3i; + out[D + 1] = T1i + T3r; + } + } + } + } + + /** + * Performs a radix-2 implementation of a discrete Fourier transform on a given set of data. + * + * @param {Float64Array} data The input buffer of data to be transformed. + * @param {Float64Array} out The output buffer for the transformed data. + * @param {number} outOff The offset at which to write the output data. + * @param {number} off The offset at which to begin reading the input data. + * @param {number} step The step size for indexing the input data. + * @returns {void} + */ + _singleTransform2(data, out, outOff, off, step) { + // radix-2 implementation + // NOTE: Only called for len=4 + + const evenR = data[off]; + const evenI = data[off + 1]; + const oddR = data[off + step]; + const oddI = data[off + step + 1]; + + out[outOff] = evenR + oddR; + out[outOff + 1] = evenI + oddI; + out[outOff + 2] = evenR - oddR; + out[outOff + 3] = evenI - oddI; + } + + /** + * Performs radix-4 transformation on input data of length 8 + * + * @param {Float64Array} data Input data array of length 8 + * @param {Float64Array} out Output data array of length 8 + * @param {number} outOff Index of output array to start writing from + * @param {number} off Index of input array to start reading from + * @param {number} step Step size between elements in input array + * @param {number} inv Scaling factor for inverse transform + * + * @returns {void} + */ + _singleTransform4(data, out, outOff, off, step, inv) { + // radix-4 + // NOTE: Only called for len=8 + const step2 = step * 2; + const step3 = step * 3; + + // Original values + const Ar = data[off]; + const Ai = data[off + 1]; + const Br = data[off + step]; + const Bi = data[off + step + 1]; + const Cr = data[off + step2]; + const Ci = data[off + step2 + 1]; + const Dr = data[off + step3]; + const Di = data[off + step3 + 1]; + + // Pre-Final values + const T0r = Ar + Cr; + const T0i = Ai + Ci; + const T1r = Ar - Cr; + const T1i = Ai - Ci; + const T2r = Br + Dr; + const T2i = Bi + Di; + const T3r = inv * (Br - Dr); + const T3i = inv * (Bi - Di); + + // Final values + out[outOff] = T0r + T2r; + out[outOff + 1] = T0i + T2i; + out[outOff + 2] = T1r + T3i; + out[outOff + 3] = T1i - T3r; + out[outOff + 4] = T0r - T2r; + out[outOff + 5] = T0i - T2i; + out[outOff + 6] = T1r - T3i; + out[outOff + 7] = T1i + T3r; + } + + /** + * Real input radix-4 implementation + * @param {Float64Array} out Output array for the transformed data + * @param {Float64Array} data Input array of real data to be transformed + * @param {number} inv The scale factor used to normalize the inverse transform + */ + _realTransform4(out, data, inv) { + // Real input radix-4 implementation + const size = this._csize; + + // Initial step (permute and transform) + const width = this._width; + let step = 1 << width; + let len = (size / step) << 1; + + let outOff; + let t; + const bitrev = this._bitrev; + if (len === 4) { + for (outOff = 0, t = 0; outOff < size; outOff += len, ++t) { + const off = bitrev[t]; + this._singleRealTransform2(data, out, outOff, off >>> 1, step >>> 1); + } + } else { + // len === 8 + for (outOff = 0, t = 0; outOff < size; outOff += len, ++t) { + const off = bitrev[t]; + this._singleRealTransform4(data, out, outOff, off >>> 1, step >>> 1, inv); + } + } + + // Loop through steps in decreasing order + const table = this.table; + for (step >>= 2; step >= 2; step >>= 2) { + len = (size / step) << 1; + const halfLen = len >>> 1; + const quarterLen = halfLen >>> 1; + const hquarterLen = quarterLen >>> 1; + + // Loop through offsets in the data + for (outOff = 0; outOff < size; outOff += len) { + for (let i = 0, k = 0; i <= hquarterLen; i += 2, k += step) { + const A = outOff + i; + const B = A + quarterLen; + const C = B + quarterLen; + const D = C + quarterLen; + + // Original values + const Ar = out[A]; + const Ai = out[A + 1]; + const Br = out[B]; + const Bi = out[B + 1]; + const Cr = out[C]; + const Ci = out[C + 1]; + const Dr = out[D]; + const Di = out[D + 1]; + + // Middle values + const MAr = Ar; + const MAi = Ai; + + const tableBr = table[k]; + const tableBi = inv * table[k + 1]; + const MBr = Br * tableBr - Bi * tableBi; + const MBi = Br * tableBi + Bi * tableBr; + + const tableCr = table[2 * k]; + const tableCi = inv * table[2 * k + 1]; + const MCr = Cr * tableCr - Ci * tableCi; + const MCi = Cr * tableCi + Ci * tableCr; + + const tableDr = table[3 * k]; + const tableDi = inv * table[3 * k + 1]; + const MDr = Dr * tableDr - Di * tableDi; + const MDi = Dr * tableDi + Di * tableDr; + + // Pre-Final values + const T0r = MAr + MCr; + const T0i = MAi + MCi; + const T1r = MAr - MCr; + const T1i = MAi - MCi; + const T2r = MBr + MDr; + const T2i = MBi + MDi; + const T3r = inv * (MBr - MDr); + const T3i = inv * (MBi - MDi); + + // Final values + out[A] = T0r + T2r; + out[A + 1] = T0i + T2i; + out[B] = T1r + T3i; + out[B + 1] = T1i - T3r; + + // Output final middle point + if (i === 0) { + out[C] = T0r - T2r; + out[C + 1] = T0i - T2i; + continue; + } + + // Do not overwrite ourselves + if (i === hquarterLen) + continue; + + const SA = outOff + quarterLen - i; + const SB = outOff + halfLen - i; + + out[SA] = T1r - inv * T3i; + out[SA + 1] = -T1i - inv * T3r; + out[SB] = T0r - inv * T2r; + out[SB + 1] = -T0i + inv * T2i; + } + } + } + + // Complete the spectrum by adding its mirrored negative frequency components. + const half = size >>> 1; + for (let i = 2; i < half; i += 2) { + out[size - i] = out[i]; + out[size - i + 1] = -out[i + 1]; + } + } + + /** + * Performs a single real input radix-2 transformation on the provided data + * + * @param {Float64Array} data The input data array + * @param {Float64Array} out The output data array + * @param {number} outOff The output offset + * @param {number} off The input offset + * @param {number} step The step + * + * @returns {void} + */ + _singleRealTransform2(data, out, outOff, off, step) { + // radix-2 implementation + // NOTE: Only called for len=4 + + const evenR = data[off]; + const oddR = data[off + step]; + + out[outOff] = evenR + oddR; + out[outOff + 1] = 0; + out[outOff + 2] = evenR - oddR; + out[outOff + 3] = 0; + } + + /** + * Computes a single real-valued transform using radix-4 algorithm. + * This method is only called for len=8. + * + * @param {Float64Array} data The input data array. + * @param {Float64Array} out The output data array. + * @param {number} outOff The offset into the output array. + * @param {number} off The offset into the input array. + * @param {number} step The step size for the input array. + * @param {number} inv The value of inverse. + */ + _singleRealTransform4(data, out, outOff, off, step, inv) { + // radix-4 + // NOTE: Only called for len=8 + const step2 = step * 2; + const step3 = step * 3; + + // Original values + const Ar = data[off]; + const Br = data[off + step]; + const Cr = data[off + step2]; + const Dr = data[off + step3]; + + // Pre-Final values + const T0r = Ar + Cr; + const T1r = Ar - Cr; + const T2r = Br + Dr; + const T3r = inv * (Br - Dr); + + // Final values + out[outOff] = T0r + T2r; + out[outOff + 1] = 0; + out[outOff + 2] = T1r; + out[outOff + 3] = -T3r; + out[outOff + 4] = T0r - T2r; + out[outOff + 5] = 0; + out[outOff + 6] = T1r; + out[outOff + 7] = T3r; + } +} + +/** + * NP2FFT class provides functionality for performing Fast Fourier Transform on arrays + * which are not a power of two in length. In such cases, the chirp-z transform is used. + * + * For more information, see: https://math.stackexchange.com/questions/77118/non-power-of-2-ffts/77156#77156 + */ +class NP2FFT { + + /** + * Constructs a new NP2FFT object. + * @param {number} fft_length The length of the FFT + */ + constructor(fft_length) { + // Helper variables + const a = 2 * (fft_length - 1); + const b = 2 * (2 * fft_length - 1); + const nextP2 = 2 ** (Math.ceil(Math.log2(b))) + this.bufferSize = nextP2; + this._a = a; + + // Define buffers + // Compute chirp for transform + const chirp = new Float64Array(b); + const ichirp = new Float64Array(nextP2); + this._chirpBuffer = new Float64Array(nextP2); + this._buffer1 = new Float64Array(nextP2); + this._buffer2 = new Float64Array(nextP2); + this._outBuffer1 = new Float64Array(nextP2); + this._outBuffer2 = new Float64Array(nextP2); + + // Compute complex exponentiation + const theta = -2 * Math.PI / fft_length; + const baseR = Math.cos(theta); + const baseI = Math.sin(theta); + + // Precompute helper for chirp-z transform + for (let i = 0; i < b >> 1; ++i) { + // Compute complex power: + const e = (i + 1 - fft_length) ** 2 / 2.0; + + // Compute the modulus and argument of the result + const result_mod = Math.sqrt(baseR ** 2 + baseI ** 2) ** e; + const result_arg = e * Math.atan2(baseI, baseR); + + // Convert the result back to rectangular form + // and assign to chirp and ichirp + const i2 = 2 * i; + chirp[i2] = result_mod * Math.cos(result_arg); + chirp[i2 + 1] = result_mod * Math.sin(result_arg); + + // conjugate + ichirp[i2] = chirp[i2]; + ichirp[i2 + 1] = - chirp[i2 + 1]; + } + this._slicedChirpBuffer = chirp.subarray(a, b); + + // create object to perform Fast Fourier Transforms + // with `nextP2` complex numbers + this._f = new P2FFT(nextP2 >> 1); + this._f.transform(this._chirpBuffer, ichirp); + } + + _transform(output, input, real) { + const ib1 = this._buffer1; + const ib2 = this._buffer2; + const ob2 = this._outBuffer1; + const ob3 = this._outBuffer2; + const cb = this._chirpBuffer; + const sb = this._slicedChirpBuffer; + const a = this._a; + + if (real) { + // Real multiplication + for (let j = 0; j < sb.length; j += 2) { + const j2 = j + 1 + const j3 = j >> 1; + + const a_real = input[j3]; + ib1[j] = a_real * sb[j]; + ib1[j2] = a_real * sb[j2]; + } + } else { + // Complex multiplication + for (let j = 0; j < sb.length; j += 2) { + const j2 = j + 1 + ib1[j] = input[j] * sb[j] - input[j2] * sb[j2]; + ib1[j2] = input[j] * sb[j2] + input[j2] * sb[j]; + } + } + this._f.transform(ob2, ib1); + + for (let j = 0; j < cb.length; j += 2) { + const j2 = j + 1; + + ib2[j] = ob2[j] * cb[j] - ob2[j2] * cb[j2]; + ib2[j2] = ob2[j] * cb[j2] + ob2[j2] * cb[j]; + } + this._f.inverseTransform(ob3, ib2); + + for (let j = 0; j < ob3.length; j += 2) { + const a_real = ob3[j + a]; + const a_imag = ob3[j + a + 1]; + const b_real = sb[j]; + const b_imag = sb[j + 1]; + + output[j] = a_real * b_real - a_imag * b_imag; + output[j + 1] = a_real * b_imag + a_imag * b_real; + } + } + + transform(output, input) { + this._transform(output, input, false); + } + + realTransform(output, input) { + this._transform(output, input, true); + } +} + +class FFT { + constructor(fft_length) { + this.fft_length = fft_length; + this.isPowerOfTwo = isPowerOfTwo(fft_length); + if (this.isPowerOfTwo) { + this.fft = new P2FFT(fft_length); + this.outputBufferSize = 2 * fft_length; + } else { + this.fft = new NP2FFT(fft_length); + this.outputBufferSize = this.fft.bufferSize; + } + } + + realTransform(out, input) { + this.fft.realTransform(out, input); + } + + transform(out, input) { + this.fft.transform(out, input); + } +} + + +/** + * Performs median filter on the provided data. Padding is done by mirroring the data. + * @param {AnyTypedArray} data The input array + * @param {number} windowSize The window size + */ +function medianFilter(data, windowSize) { + + if (windowSize % 2 === 0 || windowSize <= 0) { + throw new Error('Window size must be a positive odd number'); + } + + // @ts-ignore + const outputArray = new data.constructor(data.length); + + // @ts-ignore + const buffer = new data.constructor(windowSize); // Reusable array for storing values + + const halfWindowSize = Math.floor(windowSize / 2); + + for (let i = 0; i < data.length; ++i) { + let valuesIndex = 0; + + for (let j = -halfWindowSize; j <= halfWindowSize; ++j) { + let index = i + j; + if (index < 0) { + index = Math.abs(index); + } else if (index >= data.length) { + index = 2 * (data.length - 1) - index; + } + + buffer[valuesIndex++] = data[index]; + } + + buffer.sort(); + outputArray[i] = buffer[halfWindowSize]; + } + + return outputArray; +} + +/** + * Helper function to round a number to a given number of decimals + * @param {number} num The number to round + * @param {number} decimals The number of decimals + * @returns {number} The rounded number + */ +function round(num, decimals) { + const pow = Math.pow(10, decimals); + return Math.round(num * pow) / pow; +} + +/** + * Helper function to round a number to the nearest integer, with ties rounded to the nearest even number. + * Also known as "bankers' rounding". This is the default rounding mode in python. For example: + * 1.5 rounds to 2 and 2.5 rounds to 2. + * + * @param {number} x The number to round + * @returns {number} The rounded number + */ +function bankers_round(x) { + const r = Math.round(x); + const br = Math.abs(x) % 1 === 0.5 ? (r % 2 === 0 ? r : r - 1) : r; + return br; +} + + +/** + * Measures similarity between two temporal sequences (e.g., input audio and output tokens + * to generate token-level timestamps). + * @param {number[][]} matrix + * @returns {number[][]} + */ +function dynamic_time_warping(matrix) { + const output_length = matrix.length; + const input_length = matrix[0].length; + + const outputShape = [output_length + 1, input_length + 1]; + + const cost = Array.from( + { length: outputShape[0] }, + () => Array(outputShape[1]).fill(Infinity) + ); + cost[0][0] = 0; + + const trace = Array.from( + { length: outputShape[0] }, + () => Array(outputShape[1]).fill(-1) + ); + + for (let j = 1; j < outputShape[1]; ++j) { + for (let i = 1; i < outputShape[0]; ++i) { + const c0 = cost[i - 1][j - 1]; + const c1 = cost[i - 1][j]; + const c2 = cost[i][j - 1]; + + let c, t; + if (c0 < c1 && c0 < c2) { + c = c0; + t = 0; + } else if (c1 < c0 && c1 < c2) { + c = c1; + t = 1; + } else { + c = c2; + t = 2; + } + cost[i][j] = matrix[i - 1][j - 1] + c; + trace[i][j] = t; + } + } + + for (let i = 0; i < outputShape[1]; ++i) { // trace[0, :] = 2 + trace[0][i] = 2; + } + for (let i = 0; i < outputShape[0]; ++i) { // trace[:, 0] = 1 + trace[i][0] = 1; + } + + // backtrace + let i = output_length; + let j = input_length; + let text_indices = []; + let time_indices = []; + while (i > 0 || j > 0) { + text_indices.push(i - 1); + time_indices.push(j - 1); + + switch (trace[i][j]) { + case 0: + --i; --j; + break; + case 1: + --i; + break; + case 2: + --j; + break; + default: + throw new Error( + `Internal error in dynamic time warping. Unexpected trace[${i}, ${j}]. Please file a bug report.` + ) + } + } + + text_indices.reverse(); + time_indices.reverse(); + + return [text_indices, time_indices]; + +} + + +/***/ }), + +/***/ "./src/utils/tensor.js": +/*!*****************************!*\ + !*** ./src/utils/tensor.js ***! + \*****************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ DataTypeMap: () => (/* binding */ DataTypeMap), +/* harmony export */ Tensor: () => (/* binding */ Tensor), +/* harmony export */ cat: () => (/* binding */ cat), +/* harmony export */ full: () => (/* binding */ full), +/* harmony export */ full_like: () => (/* binding */ full_like), +/* harmony export */ interpolate: () => (/* binding */ interpolate), +/* harmony export */ interpolate_4d: () => (/* binding */ interpolate_4d), +/* harmony export */ layer_norm: () => (/* binding */ layer_norm), +/* harmony export */ matmul: () => (/* binding */ matmul), +/* harmony export */ mean: () => (/* binding */ mean), +/* harmony export */ mean_pooling: () => (/* binding */ mean_pooling), +/* harmony export */ ones: () => (/* binding */ ones), +/* harmony export */ ones_like: () => (/* binding */ ones_like), +/* harmony export */ permute: () => (/* binding */ permute), +/* harmony export */ quantize_embeddings: () => (/* binding */ quantize_embeddings), +/* harmony export */ rand: () => (/* binding */ rand), +/* harmony export */ rfft: () => (/* binding */ rfft), +/* harmony export */ slice: () => (/* binding */ slice), +/* harmony export */ stack: () => (/* binding */ stack), +/* harmony export */ std_mean: () => (/* binding */ std_mean), +/* harmony export */ topk: () => (/* binding */ topk), +/* harmony export */ zeros: () => (/* binding */ zeros), +/* harmony export */ zeros_like: () => (/* binding */ zeros_like) +/* harmony export */ }); +/* harmony import */ var _maths_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./maths.js */ "./src/utils/maths.js"); +/* harmony import */ var _backends_onnx_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../backends/onnx.js */ "./src/backends/onnx.js"); +/* harmony import */ var _ops_registry_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../ops/registry.js */ "./src/ops/registry.js"); +/** + * @file Helper module for `Tensor` processing. + * + * These functions and classes are only used internally, + * meaning an end-user shouldn't need to access anything here. + * + * @module utils/tensor + */ + + + + + + + +const DataTypeMap = Object.freeze({ + float32: Float32Array, + // @ts-ignore ts(2552) Limited availability of Float16Array across browsers: + // https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float16Array + float16: typeof Float16Array !== "undefined" ? Float16Array: Uint16Array, + float64: Float64Array, + string: Array, // string[] + int8: Int8Array, + uint8: Uint8Array, + int16: Int16Array, + uint16: Uint16Array, + int32: Int32Array, + uint32: Uint32Array, + int64: BigInt64Array, + uint64: BigUint64Array, + bool: Uint8Array, + uint4: Uint8Array, + int4: Int8Array, +}); + +/** + * @typedef {keyof typeof DataTypeMap} DataType + * @typedef {import('./maths.js').AnyTypedArray | any[]} DataArray + */ + + +class Tensor { + /** @type {number[]} Dimensions of the tensor. */ + get dims() { + // @ts-ignore + return this.ort_tensor.dims; + } + set dims(value) { + // FIXME: ONNXTensor declares dims as readonly so one needs to use the constructor() if dims change. + // @ts-ignore + this.ort_tensor.dims = value; + } + + /** @type {DataType} Type of the tensor. */ + get type() { + return this.ort_tensor.type; + }; + + /** @type {DataArray} The data stored in the tensor. */ + get data() { + return this.ort_tensor.data; + } + + /** @type {number} The number of elements in the tensor. */ + get size() { + return this.ort_tensor.size; + }; + + /** @type {string} The location of the tensor data. */ + get location() { + return this.ort_tensor.location; + }; + + ort_tensor; + + /** + * Create a new Tensor or copy an existing Tensor. + * @param {[DataType, DataArray, number[]]|[ONNXTensor]} args + */ + constructor(...args) { + if ((0,_backends_onnx_js__WEBPACK_IMPORTED_MODULE_1__.isONNXTensor)(args[0])) { + this.ort_tensor = /** @type {ONNXTensor} */ (args[0]); + } else { + // Create new tensor + this.ort_tensor = new _backends_onnx_js__WEBPACK_IMPORTED_MODULE_1__.Tensor( + /** @type {DataType} */(args[0]), + // @ts-expect-error ts(2769) Type 'number' is not assignable to type 'bigint'. + /** @type {Exclude} */(args[1]), + args[2], + ); + } + + return new Proxy(this, { + get: (obj, key) => { + if (typeof key === 'string') { + let index = Number(key); + if (Number.isInteger(index)) { + // key is an integer (i.e., index) + return obj._getitem(index); + } + } + // @ts-ignore + return obj[key]; + }, + set: (obj, key, value) => { + // TODO allow setting of data + + // @ts-ignore + return obj[key] = value; + } + }); + } + + dispose() { + this.ort_tensor.dispose(); + // this.ort_tensor = undefined; + } + + /** + * Returns an iterator object for iterating over the tensor data in row-major order. + * If the tensor has more than one dimension, the iterator will yield subarrays. + * @returns {Iterator} An iterator object for iterating over the tensor data in row-major order. + */ + *[Symbol.iterator]() { + const [iterLength, ...iterDims] = this.dims; + + if (iterDims.length > 0) { + const iterSize = iterDims.reduce((a, b) => a * b); + for (let i = 0; i < iterLength; ++i) { + yield this._subarray(i, iterSize, iterDims); + } + } else { + yield* this.data + } + + } + + /** + * Index into a Tensor object. + * @param {number} index The index to access. + * @returns {Tensor} The data at the specified index. + */ + _getitem(index) { + const [iterLength, ...iterDims] = this.dims; + + index = safeIndex(index, iterLength); + + if (iterDims.length > 0) { + const iterSize = iterDims.reduce((a, b) => a * b); + return this._subarray(index, iterSize, iterDims); + } else { + return new Tensor(this.type, [this.data[index]], iterDims); + } + } + + /** + * @param {number|bigint} item The item to search for in the tensor + * @returns {number} The index of the first occurrence of item in the tensor data. + */ + indexOf(item) { + const this_data = this.data; + for (let index = 0; index < this_data.length; ++index) { + // Note: == instead of === so we can match Ints with BigInts + if (this_data[index] == item) { + return index; + } + } + return -1; + } + + /** + * @param {number} index + * @param {number} iterSize + * @param {any} iterDims + * @returns {Tensor} + */ + _subarray(index, iterSize, iterDims) { + const o1 = index * iterSize; + const o2 = (index + 1) * iterSize; + + // We use subarray if available (typed array), otherwise we use slice (normal array) + const data = + ('subarray' in this.data) + ? this.data.subarray(o1, o2) + : this.data.slice(o1, o2); + return new Tensor(this.type, data, iterDims); + } + + /** + * Returns the value of this tensor as a standard JavaScript Number. This only works + * for tensors with one element. For other cases, see `Tensor.tolist()`. + * @returns {number|bigint} The value of this tensor as a standard JavaScript Number. + * @throws {Error} If the tensor has more than one element. + */ + item() { + const this_data = this.data; + if (this_data.length !== 1) { + throw new Error(`a Tensor with ${this_data.length} elements cannot be converted to Scalar`); + } + return this_data[0]; + } + + /** + * Convert tensor data to a n-dimensional JS list + * @returns {Array} + */ + tolist() { + return reshape(this.data, this.dims) + } + + /** + * Return a new Tensor with the sigmoid function applied to each element. + * @returns {Tensor} The tensor with the sigmoid function applied. + */ + sigmoid() { + return this.clone().sigmoid_(); + } + + /** + * Applies the sigmoid function to the tensor in place. + * @returns {Tensor} Returns `this`. + */ + sigmoid_() { + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + this_data[i] = 1 / (1 + Math.exp(-this_data[i])); + } + return this; + } + + /** + * Return a new Tensor with a callback function applied to each element. + * @param {Function} callback - The function to apply to each element. It should take three arguments: + * the current element, its index, and the tensor's data array. + * @returns {Tensor} A new Tensor with the callback function applied to each element. + */ + map(callback) { + return this.clone().map_(callback); + } + + /** + * Apply a callback function to each element of the tensor in place. + * @param {Function} callback - The function to apply to each element. It should take three arguments: + * the current element, its index, and the tensor's data array. + * @returns {Tensor} Returns `this`. + */ + map_(callback) { + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + this_data[i] = callback(this_data[i], i, this_data); + } + return this; + } + + /** + * Return a new Tensor with every element multiplied by a constant. + * @param {number} val The value to multiply by. + * @returns {Tensor} The new tensor. + */ + mul(val) { + return this.clone().mul_(val); + } + + /** + * Multiply the tensor by a constant in place. + * @param {number} val The value to multiply by. + * @returns {Tensor} Returns `this`. + */ + mul_(val) { + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + this_data[i] *= val; + } + return this; + } + + /** + * Return a new Tensor with every element divided by a constant. + * @param {number} val The value to divide by. + * @returns {Tensor} The new tensor. + */ + div(val) { + return this.clone().div_(val); + } + + /** + * Divide the tensor by a constant in place. + * @param {number} val The value to divide by. + * @returns {Tensor} Returns `this`. + */ + div_(val) { + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + this_data[i] /= val; + } + return this; + } + + /** + * Return a new Tensor with every element added by a constant. + * @param {number} val The value to add by. + * @returns {Tensor} The new tensor. + */ + add(val) { + return this.clone().add_(val); + } + + /** + * Add the tensor by a constant in place. + * @param {number} val The value to add by. + * @returns {Tensor} Returns `this`. + */ + add_(val) { + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + this_data[i] += val; + } + return this; + } + + /** + * Return a new Tensor with every element subtracted by a constant. + * @param {number} val The value to subtract by. + * @returns {Tensor} The new tensor. + */ + sub(val) { + return this.clone().sub_(val); + } + + /** + * Subtract the tensor by a constant in place. + * @param {number} val The value to subtract by. + * @returns {Tensor} Returns `this`. + */ + sub_(val) { + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + this_data[i] -= val; + } + return this; + } + + /** + * Creates a deep copy of the current Tensor. + * @returns {Tensor} A new Tensor with the same type, data, and dimensions as the original. + */ + clone() { + return new Tensor(this.type, this.data.slice(), this.dims.slice()); + } + + /** + * Performs a slice operation on the Tensor along specified dimensions. + * + * Consider a Tensor that has a dimension of [4, 7]: + * ``` + * [ 1, 2, 3, 4, 5, 6, 7] + * [ 8, 9, 10, 11, 12, 13, 14] + * [15, 16, 17, 18, 19, 20, 21] + * [22, 23, 24, 25, 26, 27, 28] + * ``` + * We can slice against the two dims of row and column, for instance in this + * case we can start at the second element, and return to the second last, + * like this: + * ``` + * tensor.slice([1, -1], [1, -1]); + * ``` + * which would return: + * ``` + * [ 9, 10, 11, 12, 13 ] + * [ 16, 17, 18, 19, 20 ] + * ``` + * + * @param {...(number|number[]|null)} slices The slice specifications for each dimension. + * - If a number is given, then a single element is selected. + * - If an array of two numbers is given, then a range of elements [start, end (exclusive)] is selected. + * - If null is given, then the entire dimension is selected. + * @returns {Tensor} A new Tensor containing the selected elements. + * @throws {Error} If the slice input is invalid. + */ + slice(...slices) { + // This allows for slicing with ranges and numbers + const newTensorDims = []; + const newOffsets = []; + + // slices is an array of numbers or arrays of numbers + // e.g., slices = [0, [1, 3], null, [0, 3]] + for (let sliceIndex = 0; sliceIndex < this.dims.length; ++sliceIndex) { + let slice = slices[sliceIndex]; + + if (slice === null || slice === undefined) { + // null or undefined means take the whole dimension + newOffsets.push([0, this.dims[sliceIndex]]); + newTensorDims.push(this.dims[sliceIndex]); + + } else if (typeof slice === 'number') { + slice = safeIndex(slice, this.dims[sliceIndex], sliceIndex); + + // A number means take a single element + newOffsets.push([slice, slice + 1]); + + } else if (Array.isArray(slice) && slice.length === 2) { + // An array of length 2 means take a range of elements + let [start, end] = slice; + start = start === null + ? 0 + : safeIndex(start, this.dims[sliceIndex], sliceIndex, false); + end = end === null + ? this.dims[sliceIndex] + : safeIndex(end, this.dims[sliceIndex], sliceIndex, false); + + if (start > end) { + throw new Error(`Invalid slice: ${slice}`); + } + + const offsets = [ + Math.max(start, 0), + Math.min(end, this.dims[sliceIndex]) + ]; + + newOffsets.push(offsets); + newTensorDims.push(offsets[1] - offsets[0]); + + } else { + throw new Error(`Invalid slice: ${slice}`); + } + } + + const newDims = newOffsets.map(([start, end]) => end - start); + const newBufferSize = newDims.reduce((a, b) => a * b); + + const this_data = this.data; + // Allocate memory + // @ts-ignore + const data = new this_data.constructor(newBufferSize); + + // Precompute strides + const stride = this.stride(); + + // Detect if the slice is contiguous + let isContiguous = true; + for (let i = 1; i < newDims.length; ++i) { + if (newOffsets[i][0] !== 0 || newOffsets[i][1] !== this.dims[i]) { + isContiguous = false; + break; + } + } + + if (isContiguous) { + // Perform bulk copy for contiguous slices to improve performance + const start = newOffsets[0][0] * stride[0]; + const end = newOffsets[0][1] * stride[0]; + + if (ArrayBuffer.isView(this_data)) { + // If this.data is a TypedArray, use subarray + // @ts-ignore + data.set(this_data.subarray(start, end)); + } else if (Array.isArray(this_data)) { + // If this.data is a plain array, use slice + const slicedData = this_data.slice(start, end); + for (let i = 0; i < slicedData.length; ++i) { + data[i] = slicedData[i]; + } + } else { + throw new Error("Unsupported data type for slicing"); + } + } else { + // Fallback to manual copying for non-contiguous slices + for (let i = 0; i < newBufferSize; ++i) { + let originalIndex = 0; + for (let j = newDims.length - 1, num = i; j >= 0; --j) { + const size = newDims[j]; + originalIndex += ((num % size) + newOffsets[j][0]) * stride[j]; + num = Math.floor(num / size); + } + data[i] = this_data[originalIndex]; + } + } + + return new Tensor(this.type, data, newTensorDims); + } + + /** + * Return a permuted version of this Tensor, according to the provided dimensions. + * @param {...number} dims Dimensions to permute. + * @returns {Tensor} The permuted tensor. + */ + permute(...dims) { + return permute(this, dims); + } + + // TODO: implement transpose. For now (backwards compatibility), it's just an alias for permute() + transpose(...dims) { + return this.permute(...dims); + } + + /** + * Returns the sum of each row of the input tensor in the given dimension dim. + * + * @param {number} [dim=null] The dimension or dimensions to reduce. If `null`, all dimensions are reduced. + * @param {boolean} keepdim Whether the output tensor has `dim` retained or not. + * @returns The summed tensor + */ + sum(dim = null, keepdim = false) { + return this.norm(1, dim, keepdim); + } + + /** + * Returns the matrix norm or vector norm of a given tensor. + * @param {number|string} [p='fro'] The order of norm + * @param {number} [dim=null] Specifies which dimension of the tensor to calculate the norm across. + * If dim is None, the norm will be calculated across all dimensions of input. + * @param {boolean} [keepdim=false] Whether the output tensors have dim retained or not. + * @returns {Tensor} The norm of the tensor. + */ + norm(p = 'fro', dim = null, keepdim = false) { + if (p === 'fro') { + // NOTE: Since we only support integer dims, Frobenius norm produces the same result as p=2. + p = 2; + } else if (typeof p === 'string') { + throw Error(`Unsupported norm: ${p}`); + } + + const this_data = this.data; + const fn = (a, b) => a + (b ** p); + + if (dim === null) { + // @ts-ignore + const val = this_data.reduce(fn, 0) ** (1 / p); + return new Tensor(this.type, [val], []); + } + + const [type, result, resultDims] = reduce_helper(fn, this, dim, keepdim); + + if (p !== 1) { + for (let i = 0; i < result.length; ++i) { + result[i] = result[i] ** (1 / p); + } + } + return new Tensor(type, result, resultDims); + } + + /** + * Performs `L_p` normalization of inputs over specified dimension. Operates in place. + * @param {number} [p=2] The exponent value in the norm formulation + * @param {number} [dim=1] The dimension to reduce + * @returns {Tensor} `this` for operation chaining. + */ + normalize_(p = 2.0, dim = 1) { + dim = safeIndex(dim, this.dims.length); + + const norm = this.norm(p, dim, true); + + const this_data = this.data; + const norm_data = norm.data; + for (let i = 0; i < this_data.length; ++i) { + + // Calculate the index in the resulting array + let resultIndex = 0; + + for (let j = this.dims.length - 1, num = i, resultMultiplier = 1; j >= 0; --j) { + const size = this.dims[j]; + if (j !== dim) { + const index = num % size; + resultIndex += index * resultMultiplier; + resultMultiplier *= this.dims[j]; + } + num = Math.floor(num / size); + } + + // Divide by normalized value + this_data[i] /= norm_data[resultIndex]; + } + + return this; + } + + /** + * Performs `L_p` normalization of inputs over specified dimension. + * @param {number} [p=2] The exponent value in the norm formulation + * @param {number} [dim=1] The dimension to reduce + * @returns {Tensor} The normalized tensor. + */ + normalize(p = 2.0, dim = 1) { + return this.clone().normalize_(p, dim); + } + + /** + * Compute and return the stride of this tensor. + * Stride is the jump necessary to go from one element to the next one in the specified dimension dim. + * @returns {number[]} The stride of this tensor. + */ + stride() { + return dimsToStride(this.dims); + } + + /** + * Returns a tensor with all specified dimensions of input of size 1 removed. + * + * NOTE: The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other. + * If you would like a copy, use `tensor.clone()` before squeezing. + * + * @param {number|number[]} [dim=null] If given, the input will be squeezed only in the specified dimensions. + * @returns {Tensor} The squeezed tensor + */ + squeeze(dim = null) { + return new Tensor( + this.type, + this.data, + calc_squeeze_dims(this.dims, dim) + ) + } + + /** + * In-place version of @see {@link Tensor.squeeze} + */ + squeeze_(dim = null) { + this.dims = calc_squeeze_dims(this.dims, dim); + return this; + } + + /** + * Returns a new tensor with a dimension of size one inserted at the specified position. + * + * NOTE: The returned tensor shares the same underlying data with this tensor. + * + * @param {number} dim The index at which to insert the singleton dimension + * @returns {Tensor} The unsqueezed tensor + */ + unsqueeze(dim = null) { + return new Tensor( + this.type, + this.data, + calc_unsqueeze_dims(this.dims, dim) + ); + } + + /** + * In-place version of @see {@link Tensor.unsqueeze} + */ + unsqueeze_(dim = null) { + this.dims = calc_unsqueeze_dims(this.dims, dim); + return this; + } + + /** + * In-place version of @see {@link Tensor.flatten} + */ + flatten_(start_dim = 0, end_dim = -1) { + // TODO validate inputs + end_dim = (end_dim + this.dims.length) % this.dims.length; + + let dimsToKeepBefore = this.dims.slice(0, start_dim); + let dimsToFlatten = this.dims.slice(start_dim, end_dim + 1); + let dimsToKeepAfter = this.dims.slice(end_dim + 1); + + this.dims = [...dimsToKeepBefore, dimsToFlatten.reduce((a, b) => a * b, 1), ...dimsToKeepAfter] + return this; + } + + /** + * Flattens input by reshaping it into a one-dimensional tensor. + * If `start_dim` or `end_dim` are passed, only dimensions starting with `start_dim` + * and ending with `end_dim` are flattened. The order of elements in input is unchanged. + * @param {number} start_dim the first dim to flatten + * @param {number} end_dim the last dim to flatten + * @returns {Tensor} The flattened tensor. + */ + flatten(start_dim = 0, end_dim = -1) { + return this.clone().flatten_(start_dim, end_dim); + } + + /** + * Returns a new tensor with the same data as the `self` tensor but of a different `shape`. + * @param {...number} dims the desired size + * @returns {Tensor} The tensor with the same data but different shape + */ + view(...dims) { + // TODO: validate dims + let inferredIndex = -1; + for (let i = 0; i < dims.length; ++i) { + if (dims[i] === -1) { + if (inferredIndex !== -1) { + throw new Error("Only one dimension can be inferred"); + } + inferredIndex = i; + } + } + + const this_data = this.data; + if (inferredIndex !== -1) { + // Some dimension must be inferred + const productOther = dims.reduce((product, curr, index) => { + return index !== inferredIndex ? product * curr : product + }, 1); + + dims[inferredIndex] = this_data.length / productOther; + } + return new Tensor(this.type, this_data, dims); // NOTE: uses same underlying storage + } + + neg_() { + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + this_data[i] = -this_data[i]; + } + return this; + } + neg() { + return this.clone().neg_(); + } + + /** + * Computes input > val element-wise. + * @param {number} val The value to compare with. + * @returns {Tensor} A boolean tensor that is `true` where input is greater than other and `false` elsewhere. + */ + gt(val) { + const mask = new Uint8Array(this.data.length); + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + mask[i] = this_data[i] > val ? 1 : 0; + } + return new Tensor('bool', mask, this.dims); + } + + /** + * Computes input < val element-wise. + * @param {number} val The value to compare with. + * @returns {Tensor} A boolean tensor that is `true` where input is less than other and `false` elsewhere. + */ + lt(val) { + const mask = new Uint8Array(this.data.length); + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + mask[i] = this_data[i] < val ? 1 : 0; + } + return new Tensor('bool', mask, this.dims); + } + + /** + * In-place version of @see {@link Tensor.clamp} + */ + clamp_(min, max) { + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + this_data[i] = Math.min(Math.max(this_data[i], min), max); + } + return this; + } + + /** + * Clamps all elements in input into the range [ min, max ] + * @param {number} min lower-bound of the range to be clamped to + * @param {number} max upper-bound of the range to be clamped to + * @returns {Tensor} the output tensor. + */ + clamp(min, max) { + return this.clone().clamp_(min, max); + } + + /** + * In-place version of @see {@link Tensor.round} + */ + round_() { + const this_data = this.data; + for (let i = 0; i < this_data.length; ++i) { + this_data[i] = Math.round(this_data[i]); + } + return this; + } + + /** + * Rounds elements of input to the nearest integer. + * @returns {Tensor} the output tensor. + */ + round() { + return this.clone().round_(); + } + + mean(dim = null, keepdim = false) { + return mean(this, dim, keepdim); + } + + min(dim = null, keepdim = false) { + if (dim === null) { + // None to reduce over all dimensions. + const val = (0,_maths_js__WEBPACK_IMPORTED_MODULE_0__.min)(this.data)[0]; + return new Tensor(this.type, [val], [/* scalar */]); + } + const [type, result, resultDims] = reduce_helper((a, b) => Math.min(a, b), this, dim, keepdim, Infinity); + return new Tensor(type, result, resultDims); + } + + max(dim = null, keepdim = false) { + if (dim === null) { + // None to reduce over all dimensions. + const val = (0,_maths_js__WEBPACK_IMPORTED_MODULE_0__.max)(this.data)[0]; + return new Tensor(this.type, [val], [/* scalar */]); + } + const [type, result, resultDims] = reduce_helper((a, b) => Math.max(a, b), this, dim, keepdim, -Infinity); + return new Tensor(type, result, resultDims); + } + + argmin(dim = null, keepdim = false) { + if (dim !== null) { + throw new Error("`dim !== null` not yet implemented."); + } + const index = (0,_maths_js__WEBPACK_IMPORTED_MODULE_0__.min)(this.data)[1]; + return new Tensor('int64', [BigInt(index)], []); + } + argmax(dim = null, keepdim = false) { + if (dim !== null) { + throw new Error("`dim !== null` not yet implemented."); + } + const index = (0,_maths_js__WEBPACK_IMPORTED_MODULE_0__.max)(this.data)[1]; + return new Tensor('int64', [BigInt(index)], []); + } + + /** + * Performs Tensor dtype conversion. + * @param {DataType} type The desired data type. + * @returns {Tensor} The converted tensor. + */ + to(type) { + // If the self Tensor already has the correct dtype, then self is returned. + if (this.type === type) return this; + + // Otherwise, the returned tensor is a copy of self with the desired dtype. + if (!DataTypeMap.hasOwnProperty(type)) { + throw new Error(`Unsupported type: ${type}`); + } + + // Handle special cases where a mapping function is needed (e.g., where one type is a bigint and the other is a number) + let map_fn; + const is_source_bigint = ['int64', 'uint64'].includes(this.type); + const is_dest_bigint = ['int64', 'uint64'].includes(type); + if (is_source_bigint && !is_dest_bigint) { + // TypeError: Cannot convert a BigInt value to a number + map_fn = Number; + } else if (!is_source_bigint && is_dest_bigint) { + // TypeError: Cannot convert [x] to a BigInt + map_fn = BigInt; + } + + // @ts-ignore + return new Tensor(type, DataTypeMap[type].from(this.data, map_fn), this.dims); + } +} + +/** + * This creates a nested array of a given type and depth (see examples). + * + * @example + * NestArray; // string[] + * @example + * NestArray; // number[][] + * @example + * NestArray; // string[][][] etc. + * @template T + * @template {number} Depth + * @template {never[]} [Acc=[]] + * @typedef {Acc['length'] extends Depth ? T : NestArray} NestArray + */ + +/** + * Reshapes a 1-dimensional array into an n-dimensional array, according to the provided dimensions. + * + * @example + * reshape([10 ], [1 ]); // Type: number[] Value: [10] + * reshape([1, 2, 3, 4 ], [2, 2 ]); // Type: number[][] Value: [[1, 2], [3, 4]] + * reshape([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]); // Type: number[][][] Value: [[[1, 2], [3, 4]], [[5, 6], [7, 8]]] + * reshape([1, 2, 3, 4, 5, 6, 7, 8], [4, 2 ]); // Type: number[][] Value: [[1, 2], [3, 4], [5, 6], [7, 8]] + * @param {T[]|DataArray} data The input array to reshape. + * @param {DIM} dimensions The target shape/dimensions. + * @template T + * @template {[number]|number[]} DIM + * @returns {NestArray} The reshaped array. + */ +function reshape(data, dimensions) { + + const totalElements = data.length; + const dimensionSize = dimensions.reduce((a, b) => a * b); + + if (totalElements !== dimensionSize) { + throw Error(`cannot reshape array of size ${totalElements} into shape (${dimensions})`); + } + + /** @type {any} */ + let reshapedArray = data; + + for (let i = dimensions.length - 1; i >= 0; i--) { + reshapedArray = reshapedArray.reduce((acc, val) => { + let lastArray = acc[acc.length - 1]; + + if (lastArray.length < dimensions[i]) { + lastArray.push(val); + } else { + acc.push([val]); + } + + return acc; + }, [[]]); + } + + return reshapedArray[0]; +} + +/** + * Permutes a tensor according to the provided axes. + * @param {any} tensor The input tensor to permute. + * @param {Array} axes The axes to permute the tensor along. + * @returns {Tensor} The permuted tensor. + */ +function permute(tensor, axes) { + const [permutedData, shape] = (0,_maths_js__WEBPACK_IMPORTED_MODULE_0__.permute_data)(tensor.data, tensor.dims, axes); + return new Tensor(tensor.type, permutedData, shape); +} + + +/** + * Interpolates an Tensor to the given size. + * @param {Tensor} input The input tensor to interpolate. Data must be channel-first (i.e., [c, h, w]) + * @param {number[]} size The output size of the image + * @param {string} mode The interpolation mode + * @param {boolean} align_corners Whether to align corners. + * @returns {Tensor} The interpolated tensor. + */ +function interpolate(input, [out_height, out_width], mode = 'bilinear', align_corners = false) { + + // Input image dimensions + const in_channels = input.dims.at(-3) ?? 1; + const in_height = input.dims.at(-2); + const in_width = input.dims.at(-1); + + let output = (0,_maths_js__WEBPACK_IMPORTED_MODULE_0__.interpolate_data)( + /** @type {import('./maths.js').TypedArray}*/(input.data), + [in_channels, in_height, in_width], + [out_height, out_width], + mode, + align_corners + ); + return new Tensor(input.type, output, [in_channels, out_height, out_width]); +} + + +/** + * Down/up samples the input. + * Inspired by https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html. + * @param {Tensor} input the input tensor + * @param {Object} options the options for the interpolation + * @param {[number, number]|[number, number, number]|[number, number, number, number]} [options.size=null] output spatial size. + * @param {"nearest"|"bilinear"|"bicubic"} [options.mode='bilinear'] algorithm used for upsampling + * @returns {Promise} The interpolated tensor. + */ +async function interpolate_4d(input, { + size = null, + mode = 'bilinear', +} = {}) { + + // Error checking + if (input.dims.length !== 4) { + throw new Error('`interpolate_4d` currently only supports 4D input.'); + } + if (!size) { + // TODO: support scale_factor + throw new Error('`interpolate_4d` requires a `size` argument.'); + } + + // Fill in missing dimensions + let targetDims; + if (size.length === 2) { + targetDims = [...input.dims.slice(0, 2), ...size]; + } else if (size.length === 3) { + targetDims = [input.dims[0], ...size]; + } else if (size.length === 4) { + targetDims = size; + } else { + throw new Error('`size` must be of length 2, 3, or 4.'); + } + + let op; + if (mode === 'nearest') { + op = await _ops_registry_js__WEBPACK_IMPORTED_MODULE_2__.TensorOpRegistry.nearest_interpolate_4d; + } else if (mode === 'bilinear') { + op = await _ops_registry_js__WEBPACK_IMPORTED_MODULE_2__.TensorOpRegistry.bilinear_interpolate_4d; + } else if (mode === 'bicubic') { + op = await _ops_registry_js__WEBPACK_IMPORTED_MODULE_2__.TensorOpRegistry.bicubic_interpolate_4d; + } else { + throw new Error(`Unsupported mode: ${mode}`); + } + + const sizeTensor = new Tensor('int64', new BigInt64Array(targetDims.map(BigInt)), [targetDims.length]); + return await op({ x: input, s: sizeTensor }); +} + +/** + * Matrix product of two tensors. + * Inspired by https://pytorch.org/docs/stable/generated/torch.matmul.html + * @param {Tensor} a the first tensor to be multiplied + * @param {Tensor} b the second tensor to be multiplied + * @returns {Promise} The matrix product of the two tensors. + */ +async function matmul(a, b) { + const op = await _ops_registry_js__WEBPACK_IMPORTED_MODULE_2__.TensorOpRegistry.matmul; + return await op({ a, b }); +} + +/** + * Computes the one dimensional Fourier transform of real-valued input. + * Inspired by https://pytorch.org/docs/stable/generated/torch.fft.rfft.html + * @param {Tensor} x the real input tensor + * @param {Tensor} a The dimension along which to take the one dimensional real FFT. + * @returns {Promise} the output tensor. + */ +async function rfft(x, a) { + const op = await _ops_registry_js__WEBPACK_IMPORTED_MODULE_2__.TensorOpRegistry.rfft; + return await op({ x, a }); +} + + +/** + * Returns the k largest elements of the given input tensor. + * Inspired by https://pytorch.org/docs/stable/generated/torch.topk.html + * @param {Tensor} x the input tensor + * @param {number} [k] the k in "top-k" + * @returns {Promise<[Tensor, Tensor]>} the output tuple of (Tensor, LongTensor) of top-k elements and their indices. + */ +async function topk(x, k) { + const op = await _ops_registry_js__WEBPACK_IMPORTED_MODULE_2__.TensorOpRegistry.top_k; + + if (k == null) { + k = x.dims.at(-1); + } else { + k = Math.min(k, x.dims.at(-1)); + } + return await op({ + x, + k: new Tensor( + 'int64', + [BigInt(k)], + [1] + ) + }); +} + + +const arrayToIndexTensor = (array) => new Tensor('int64', array, [array.length]); +/** + * Slice a multidimensional float32 tensor. + * @param {Tensor} data: Tensor of data to extract slices from + * @param {number[]} starts: 1-D array of starting indices of corresponding axis in axes + * @param {number[]} ends: 1-D array of ending indices (exclusive) of corresponding axis in axes + * @param {number[]} axes: 1-D array of axes that starts and ends apply to + * @param {number[]} [steps]: 1-D array of slice step of corresponding axis in axes. + * @returns {Promise} Sliced data tensor. + */ +async function slice(data, starts, ends, axes, steps) { + const op = await _ops_registry_js__WEBPACK_IMPORTED_MODULE_2__.TensorOpRegistry.slice; + return await op({ + x: data, + s: arrayToIndexTensor(starts), + e: arrayToIndexTensor(ends), + a: arrayToIndexTensor(axes), + t: arrayToIndexTensor(steps ?? new Array(axes.length).fill(1)), + }); +} + + +/** + * Perform mean pooling of the last hidden state followed by a normalization step. + * @param {Tensor} last_hidden_state Tensor of shape [batchSize, seqLength, embedDim] + * @param {Tensor} attention_mask Tensor of shape [batchSize, seqLength] + * @returns {Tensor} Returns a new Tensor of shape [batchSize, embedDim]. + */ +function mean_pooling(last_hidden_state, attention_mask) { + // last_hidden_state: [batchSize, seqLength, embedDim] + // attention_mask: [batchSize, seqLength] + const lastHiddenStateData = last_hidden_state.data; + const attentionMaskData = attention_mask.data; + + const shape = [last_hidden_state.dims[0], last_hidden_state.dims[2]]; + + // @ts-ignore + const returnedData = new lastHiddenStateData.constructor(shape[0] * shape[1]); + const [batchSize, seqLength, embedDim] = last_hidden_state.dims; + + let outIndex = 0; + for (let i = 0; i < batchSize; ++i) { + const offset = i * embedDim * seqLength; + + for (let k = 0; k < embedDim; ++k) { + let sum = 0; + let count = 0; + + const attnMaskOffset = i * seqLength; + const offset2 = offset + k; + // Pool over all words in sequence + for (let j = 0; j < seqLength; ++j) { + // index into attention mask + const attn = Number(attentionMaskData[attnMaskOffset + j]); + + count += attn; + sum += lastHiddenStateData[offset2 + j * embedDim] * attn; + } + + const avg = sum / count; + returnedData[outIndex++] = avg; + } + } + + return new Tensor( + last_hidden_state.type, + returnedData, + shape + ) +} + +/** + * Apply Layer Normalization for last certain number of dimensions. + * @param {Tensor} input The input tensor + * @param {number[]} normalized_shape input shape from an expected input of size + * @param {Object} options The options for the layer normalization + * @param {number} [options.eps=1e-5] A value added to the denominator for numerical stability. + * @returns {Tensor} The normalized tensor. + */ +function layer_norm(input, normalized_shape, { + eps = 1e-5, +} = {}) { + if (input.dims.length !== 2) { + throw new Error('`layer_norm` currently only supports 2D input.'); + } + + const [batchSize, featureDim] = input.dims; + + if (normalized_shape.length !== 1 && normalized_shape[0] !== featureDim) { + throw new Error('`normalized_shape` must be a 1D array with shape `[input.dims[1]]`.'); + } + + const [std, mean] = std_mean(input, 1, 0, true); + const stdData = /** @type {Float32Array} */(std.data); + const meanData = /** @type {Float32Array} */(mean.data); + + const inputData = /** @type {Float32Array} */(input.data); + + // @ts-ignore + const returnedData = new inputData.constructor(inputData.length); + + for (let i = 0; i < batchSize; ++i) { + const offset = i * featureDim; + for (let j = 0; j < featureDim; ++j) { + const offset2 = offset + j; + returnedData[offset2] = (inputData[offset2] - meanData[i]) / (stdData[i] + eps); + } + } + return new Tensor(input.type, returnedData, input.dims); +} + +/** + * Helper function to calculate new dimensions when performing a squeeze operation. + * @param {number[]} dims The dimensions of the tensor. + * @param {number|number[]|null} dim The dimension(s) to squeeze. + * @returns {number[]} The new dimensions. + * @private + */ +function calc_squeeze_dims(dims, dim) { + dims = dims.slice(); + if (dim === null) { + dims = dims.filter((d) => d !== 1); + } else if (typeof dim === 'number') { + if (dims[dim] === 1) { + dims.splice(dim, 1); + } + } else if (Array.isArray(dim)) { + dims = dims.filter((x, i) => { + return x !== 1 || !dim.includes(i); + }); + } + return dims; +} + +/** + * Helper function to calculate new dimensions when performing an unsqueeze operation. + * @param {number[]} dims The dimensions of the tensor. + * @param {number} dim The dimension to unsqueeze. + * @returns {number[]} The new dimensions. + * @private + */ +function calc_unsqueeze_dims(dims, dim) { + // Dimension out of range (e.g., "expected to be in range of [-4, 3], but got 4") + // + 1 since we allow inserting at the end (i.e. dim = -1) + dim = safeIndex(dim, dims.length + 1); + dims = dims.slice(); + // Insert 1 into specified dimension + dims.splice(dim, 0, 1); + return dims; +} + +/** + * Safely calculate the index for an array of a given size, allowing negative indexing. + * @param {number} index The index that will be used. + * @param {number} size The size of the array. + * @param {number} [dimension=null] The dimension that the index is for (optional). + * @returns {number} The index, guaranteed to be non-negative and less than `arrayLength`. + * + * @throws {Error} If the index is out of range. + * @private + */ +function safeIndex(index, size, dimension = null, boundsCheck = true) { + if (index < -size || index >= size) { + if (boundsCheck) { + throw new Error(`IndexError: index ${index} is out of bounds for dimension${dimension === null ? '' : ' ' + dimension} with size ${size}`); + } else { + return index < -size ? 0 : size; + } + } + + if (index < 0) { + // Negative indexing, ensuring positive index + index = ((index % size) + size) % size; + } + return index; +} + +/** + * Concatenates an array of tensors along a specified dimension. + * @param {Tensor[]} tensors The array of tensors to concatenate. + * @param {number} dim The dimension to concatenate along. + * @returns {Tensor} The concatenated tensor. + */ +function cat(tensors, dim = 0) { + dim = safeIndex(dim, tensors[0].dims.length); + + // TODO do validation of shapes + + const resultDims = tensors[0].dims.slice(); + resultDims[dim] = tensors.reduce((a, b) => a + b.dims[dim], 0); + + // Create a new array to store the accumulated values + const resultSize = resultDims.reduce((a, b) => a * b, 1); + // @ts-ignore + const result = new tensors[0].data.constructor(resultSize); + + // Create output tensor of same type as first + const resultType = tensors[0].type; + + if (dim === 0) { + // Handle special case for performance reasons + + let offset = 0; + for (const tensor of tensors) { + const tensorData = tensor.data; + result.set(tensorData, offset); + offset += tensorData.length; + } + + } else { + + let currentDim = 0; + + for (let t = 0; t < tensors.length; ++t) { + const { data, dims } = tensors[t]; + + // Iterate over the data array + for (let i = 0; i < data.length; ++i) { + // Calculate the index in the resulting array + let resultIndex = 0; + + for (let j = dims.length - 1, num = i, resultMultiplier = 1; j >= 0; --j) { + const size = dims[j]; + let index = num % size; + if (j === dim) { + index += currentDim; + } + resultIndex += index * resultMultiplier; + resultMultiplier *= resultDims[j]; + num = Math.floor(num / size); + } + // Accumulate the value at the current index + result[resultIndex] = data[i]; + } + + currentDim += dims[dim]; + } + } + return new Tensor(resultType, result, resultDims); +} + +/** + * Stack an array of tensors along a specified dimension. + * @param {Tensor[]} tensors The array of tensors to stack. + * @param {number} dim The dimension to stack along. + * @returns {Tensor} The stacked tensor. + */ +function stack(tensors, dim = 0) { + // TODO do validation of shapes + // NOTE: stack expects each tensor to be equal size + return cat(tensors.map(t => t.unsqueeze(dim)), dim); +} + + +/** + * @param {(previousValue: any, currentValue: any, currentIndex?: number, resultIndex?: number) => any} callbackfn + * @param {Tensor} input the input tensor. + * @param {number|null} dim the dimension to reduce. + * @param {boolean} keepdim whether the output tensor has dim retained or not. + * @returns {[DataType, any, number[]]} The reduced tensor data. + */ +function reduce_helper(callbackfn, input, dim = null, keepdim = false, initialValue = null) { + const inputData = input.data; + const inputDims = input.dims; + + // Negative indexing + dim = safeIndex(dim, inputDims.length); + + // Calculate the shape of the resulting array after summation + const resultDims = inputDims.slice(); // Copy the original dimensions + resultDims[dim] = 1; // Remove the specified axis + + // Create a new array to store the accumulated values + // @ts-ignore + const result = new inputData.constructor(inputData.length / inputDims[dim]); + if (initialValue !== null) { + result.fill(initialValue); + } + + // Iterate over the data array + for (let i = 0; i < inputData.length; ++i) { + + // Calculate the index in the resulting array + let resultIndex = 0; + + for (let j = inputDims.length - 1, num = i, resultMultiplier = 1; j >= 0; --j) { + const size = inputDims[j]; + if (j !== dim) { + const index = num % size; + resultIndex += index * resultMultiplier; + resultMultiplier *= resultDims[j]; + } + num = Math.floor(num / size); + } + + // Accumulate the value at the current index + result[resultIndex] = callbackfn(result[resultIndex], inputData[i], i, resultIndex); + } + + if (!keepdim) resultDims.splice(dim, 1); + + return [input.type, result, resultDims]; +} + + +/** + * Calculates the standard deviation and mean over the dimensions specified by dim. dim can be a single dimension or `null` to reduce over all dimensions. + * @param {Tensor} input the input tenso + * @param {number|null} dim the dimension to reduce. If None, all dimensions are reduced. + * @param {number} correction difference between the sample size and sample degrees of freedom. Defaults to Bessel's correction, correction=1. + * @param {boolean} keepdim whether the output tensor has dim retained or not. + * @returns {Tensor[]} A tuple of (std, mean) tensors. + */ +function std_mean(input, dim = null, correction = 1, keepdim = false) { + const inputData = /** @type {Float32Array} */(input.data); + const inputDims = input.dims; + + if (dim === null) { + // None to reduce over all dimensions. + const sum = inputData.reduce((a, b) => a + b, 0); + const mean = sum / inputData.length; + const std = Math.sqrt(inputData.reduce((a, b) => a + (b - mean) ** 2, 0) / (inputData.length - correction)); + + const meanTensor = new Tensor(input.type, [mean], [/* scalar */]); + const stdTensor = new Tensor(input.type, [std], [/* scalar */]); + + return [stdTensor, meanTensor]; + } + dim = safeIndex(dim, inputDims.length); + const meanTensor = mean(input, dim, keepdim); + const meanTensorData = meanTensor.data; + + // Compute squared sum + const [type, result, resultDims] = reduce_helper((a, b, i, j) => a + (b - meanTensorData[j]) ** 2, input, dim, keepdim); + + // Square root of the squared sum + for (let i = 0; i < result.length; ++i) { + result[i] = Math.sqrt(result[i] / (inputDims[dim] - correction)); + } + + const stdTensor = new Tensor(type, result, resultDims); + + return [stdTensor, meanTensor]; +} + +/** + * Returns the mean value of each row of the input tensor in the given dimension dim. + * @param {Tensor} input the input tensor. + * @param {number|null} dim the dimension to reduce. + * @param {boolean} keepdim whether the output tensor has dim retained or not. + * @returns {Tensor} A new tensor with means taken along the specified dimension. + */ +function mean(input, dim = null, keepdim = false) { + const inputDims = input.dims; + const inputData = /** @type {Float32Array} */(input.data); + + if (dim === null) { + // None to reduce over all dimensions. + const val = inputData.reduce((a, b) => a + b, 0); + return new Tensor(input.type, [val / inputData.length], [/* scalar */]); + } + dim = safeIndex(dim, inputDims.length); + + // Compute sum + const [type, result, resultDims] = reduce_helper((a, b) => a + b, input, dim, keepdim); + + // Divide by number of elements in the dimension + if (inputDims[dim] !== 1) { + for (let i = 0; i < result.length; ++i) { + result[i] /= inputDims[dim]; + } + } + + return new Tensor(type, result, resultDims); +} + + +function dimsToStride(dims) { + const stride = new Array(dims.length); + for (let i = dims.length - 1, s2 = 1; i >= 0; --i) { + stride[i] = s2; + s2 *= dims[i]; + } + return stride; +} + +function fullHelper(size, fill_value, dtype, cls) { + const numElements = size.reduce((a, b) => a * b, 1); + return new Tensor( + dtype, + new cls(numElements).fill(fill_value), + size + ) +} + +/** + * Creates a tensor of size size filled with fill_value. The tensor's dtype is inferred from fill_value. + * @param {number[]} size A sequence of integers defining the shape of the output tensor. + * @param {number|bigint|boolean} fill_value The value to fill the output tensor with. + * @returns {Tensor} The filled tensor. + */ +function full(size, fill_value) { + let dtype; + let typedArrayCls; + if (typeof fill_value === 'number') { + dtype = 'float32'; + typedArrayCls = Float32Array; + } else if (typeof fill_value === 'bigint') { + dtype = 'int64'; + typedArrayCls = BigInt64Array; + } else if (typeof fill_value === 'boolean') { + dtype = 'bool'; + typedArrayCls = Uint8Array; + } else { + // TODO: support other dtypes + throw new Error(`Unsupported data type: ${typeof fill_value}`); + } + return fullHelper(size, fill_value, dtype, typedArrayCls); +} + +function full_like(tensor, fill_value) { + return full(tensor.dims, fill_value); +} + +/** + * Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument size. + * @param {number[]} size A sequence of integers defining the shape of the output tensor. + * @returns {Tensor} The ones tensor. + */ +function ones(size) { + return fullHelper(size, 1n, 'int64', BigInt64Array); +} + +/** + * Returns a tensor filled with the scalar value 1, with the same size as input. + * @param {Tensor} tensor The size of input will determine size of the output tensor. + * @returns {Tensor} The ones tensor. + */ +function ones_like(tensor) { + return ones(tensor.dims); +} + +/** + * Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument size. + * @param {number[]} size A sequence of integers defining the shape of the output tensor. + * @returns {Tensor} The zeros tensor. + */ +function zeros(size) { + return fullHelper(size, 0n, 'int64', BigInt64Array); +} + +/** + * Returns a tensor filled with the scalar value 0, with the same size as input. + * @param {Tensor} tensor The size of input will determine size of the output tensor. + * @returns {Tensor} The zeros tensor. + */ +function zeros_like(tensor) { + return zeros(tensor.dims); +} + +/** + * Returns a tensor filled with random numbers from a uniform distribution on the interval [0, 1) + * @param {number[]} size A sequence of integers defining the shape of the output tensor. + * @returns {Tensor} The random tensor. + */ +function rand(size) { + const length = size.reduce((a, b) => a * b, 1); + return new Tensor( + "float32", + Float32Array.from({ length }, () => Math.random()), + size, + ) +} + +/** + * Quantizes the embeddings tensor to binary or unsigned binary precision. + * @param {Tensor} tensor The tensor to quantize. + * @param {'binary'|'ubinary'} precision The precision to use for quantization. + * @returns {Tensor} The quantized tensor. + */ +function quantize_embeddings(tensor, precision) { + if (tensor.dims.length !== 2) { + throw new Error("The tensor must have 2 dimensions"); + } + if (tensor.dims.at(-1) % 8 !== 0) { + throw new Error("The last dimension of the tensor must be a multiple of 8"); + } + if (!['binary', 'ubinary'].includes(precision)) { + throw new Error("The precision must be either 'binary' or 'ubinary'"); + } + + const signed = precision === 'binary'; + const dtype = signed ? 'int8' : 'uint8'; + + // Create a typed array to store the packed bits + const cls = signed ? Int8Array : Uint8Array; + const inputData = tensor.data; + const outputData = new cls(inputData.length / 8); + + // Iterate over each number in the array + for (let i = 0; i < inputData.length; ++i) { + // Determine if the number is greater than 0 + const bit = inputData[i] > 0 ? 1 : 0; + + // Calculate the index in the typed array and the position within the byte + const arrayIndex = Math.floor(i / 8); + const bitPosition = i % 8; + + // Pack the bit into the typed array + outputData[arrayIndex] |= bit << (7 - bitPosition); + if (signed && bitPosition === 0) { + outputData[arrayIndex] -= 128; + } + }; + + return new Tensor(dtype, outputData, [tensor.dims[0], tensor.dims[1] / 8]); +} + + +/***/ }), + +/***/ "./src/utils/video.js": +/*!****************************!*\ + !*** ./src/utils/video.js ***! + \****************************/ +/***/ ((__unused_webpack___webpack_module__, __webpack_exports__, __webpack_require__) => { + +__webpack_require__.r(__webpack_exports__); +/* harmony export */ __webpack_require__.d(__webpack_exports__, { +/* harmony export */ RawVideo: () => (/* binding */ RawVideo), +/* harmony export */ RawVideoFrame: () => (/* binding */ RawVideoFrame), +/* harmony export */ load_video: () => (/* binding */ load_video) +/* harmony export */ }); +/* harmony import */ var _image_js__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./image.js */ "./src/utils/image.js"); +/* harmony import */ var _env_js__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../env.js */ "./src/env.js"); + + + +class RawVideoFrame { + + /** + * @param {RawImage} image + * @param {number} timestamp + */ + constructor(image, timestamp) { + this.image = image; + this.timestamp = timestamp; + } +} + +class RawVideo { + /** + * @param {RawVideoFrame[]|RawImage[]} frames + * @param {number} duration + */ + constructor(frames, duration) { + if (frames.length > 0 && frames[0] instanceof _image_js__WEBPACK_IMPORTED_MODULE_0__.RawImage) { + // Assume uniform timestamps + frames = frames.map((image, i) => new RawVideoFrame(image, (i + 1) / (frames.length + 1) * duration)); + } + this.frames = /** @type {RawVideoFrame[]} */ (frames); + this.duration = duration; + } + + get width() { + return this.frames[0].image.width; + } + get height() { + return this.frames[0].image.height; + } + + get fps() { + return this.frames.length / this.duration; + } +} + + +/** + * Loads a video. + * + * @param {string|Blob|HTMLVideoElement} src The video to process. + * @param {Object} [options] Optional parameters. + * @param {number} [options.num_frames=null] The number of frames to sample uniformly. + * @param {number} [options.fps=null] The number of frames to sample per second. + * + * @returns {Promise} The loaded video. + */ +async function load_video(src, { num_frames = null, fps = null } = {}) { + if (!_env_js__WEBPACK_IMPORTED_MODULE_1__.apis.IS_BROWSER_ENV) { + throw new Error("`load_video` is currently only supported in browser environments."); + } + + // TODO: Support efficiently loading all frames using the WebCodecs API. + // Specfically, https://developer.mozilla.org/en-US/docs/Web/API/VideoDecoder + if (num_frames == null && fps == null) { + throw new Error("Either num_frames or fps must be provided."); + } + + const frames = []; + + const video = document.createElement("video"); + video.crossOrigin = "anonymous"; + video.muted = true; // mute to allow autoplay and seeking + + if (typeof src === 'string') { + video.src = src; + } else if (src instanceof Blob) { + video.src = URL.createObjectURL(src); + } else if (src instanceof HTMLVideoElement) { + video.src = src.src; + } else { + throw new Error("Invalid URL or video element provided."); + } + // Wait for metadata to load to obtain duration + await new Promise((resolve) => video.onloadedmetadata = resolve); + + if (video.seekable.start(0) === video.seekable.end(0)) { + // Fallback: Download entire video if not seekable + const response = await fetch(video.src); + const blob = await response.blob(); + video.src = URL.createObjectURL(blob); + await new Promise((resolve) => video.onloadedmetadata = resolve); + } + + const duration = video.duration; + + let count, step; + if (num_frames != null) { + count = num_frames; + step = num_frames === 1 ? 0 : duration / (num_frames - 1); + } else { + step = 1 / fps; + count = Math.floor(duration / step); + } + + // Build an array of sample times based on num_frames or fps + let sampleTimes = []; + for (let i = 0; i < count; ++i) { + sampleTimes.push(num_frames === 1 ? duration / 2 : i * step); + } + + const canvas = document.createElement("canvas"); + canvas.width = video.videoWidth; + canvas.height = video.videoHeight; + const ctx = canvas.getContext("2d", { willReadFrequently: true }); + for (const t of sampleTimes) { + video.currentTime = t; + await new Promise((resolve) => { + video.onseeked = resolve; + }); + ctx.drawImage(video, 0, 0, canvas.width, canvas.height); + const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height); + const frameData = new _image_js__WEBPACK_IMPORTED_MODULE_0__.RawImage(imageData.data, canvas.width, canvas.height, 4); + + const frame = new RawVideoFrame(frameData, t); + frames.push(frame); + } + + // Clean up video element. + video.remove(); + + return new RawVideo(frames, duration); +} + + +/***/ }) + +/******/ }); +/************************************************************************/ +/******/ // The module cache +/******/ var __webpack_module_cache__ = {}; +/******/ +/******/ // The require function +/******/ function __webpack_require__(moduleId) { +/******/ // Check if module is in cache +/******/ var cachedModule = __webpack_module_cache__[moduleId]; +/******/ if (cachedModule !== undefined) { +/******/ return cachedModule.exports; +/******/ } +/******/ // Create a new module (and put it into the cache) +/******/ var module = __webpack_module_cache__[moduleId] = { +/******/ // no module.id needed +/******/ // no module.loaded needed +/******/ exports: {} +/******/ }; +/******/ +/******/ // Execute the module function +/******/ __webpack_modules__[moduleId](module, module.exports, __webpack_require__); +/******/ +/******/ // Return the exports of the module +/******/ return module.exports; +/******/ } +/******/ +/************************************************************************/ +/******/ /* webpack/runtime/create fake namespace object */ +/******/ (() => { +/******/ var getProto = Object.getPrototypeOf ? 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&& value; typeof current == 'object' && !~leafPrototypes.indexOf(current); current = getProto(current)) { +/******/ Object.getOwnPropertyNames(current).forEach((key) => (def[key] = () => (value[key]))); +/******/ } +/******/ def['default'] = () => (value); +/******/ __webpack_require__.d(ns, def); +/******/ return ns; +/******/ }; +/******/ })(); +/******/ +/******/ /* webpack/runtime/define property getters */ +/******/ (() => { +/******/ // define getter functions for harmony exports +/******/ __webpack_require__.d = (exports, definition) => { +/******/ for(var key in definition) { +/******/ if(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) { +/******/ Object.defineProperty(exports, key, { enumerable: true, get: definition[key] }); +/******/ } +/******/ } +/******/ }; +/******/ })(); +/******/ +/******/ /* webpack/runtime/hasOwnProperty shorthand */ +/******/ (() => { +/******/ __webpack_require__.o = (obj, prop) => 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_models_js__WEBPACK_IMPORTED_MODULE_2__.ASTForAudioClassification), +/* harmony export */ ASTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ASTModel), +/* harmony export */ ASTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ASTPreTrainedModel), +/* harmony export */ AlbertForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AlbertForMaskedLM), +/* harmony export */ AlbertForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AlbertForQuestionAnswering), +/* harmony export */ AlbertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AlbertForSequenceClassification), +/* harmony export */ AlbertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AlbertModel), +/* harmony export */ AlbertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AlbertPreTrainedModel), +/* harmony export */ AlbertTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.AlbertTokenizer), +/* harmony export */ ArceeForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ArceeForCausalLM), +/* harmony export */ ArceeModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ArceeModel), +/* harmony export */ ArceePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ArceePreTrainedModel), +/* harmony export */ AudioClassificationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.AudioClassificationPipeline), +/* harmony export */ AutoConfig: () => (/* reexport safe */ _configs_js__WEBPACK_IMPORTED_MODULE_4__.AutoConfig), +/* harmony export */ AutoFeatureExtractor: () => (/* reexport safe */ _models_auto_feature_extraction_auto_js__WEBPACK_IMPORTED_MODULE_12__.AutoFeatureExtractor), +/* harmony export */ AutoImageProcessor: () => (/* reexport safe */ _models_auto_image_processing_auto_js__WEBPACK_IMPORTED_MODULE_15__.AutoImageProcessor), +/* harmony export */ AutoModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModel), +/* harmony export */ AutoModelForAudioClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForAudioClassification), +/* harmony export */ AutoModelForAudioFrameClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForAudioFrameClassification), +/* harmony export */ AutoModelForAudioTextToText: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForAudioTextToText), +/* harmony export */ AutoModelForCTC: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForCTC), +/* harmony export */ AutoModelForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForCausalLM), +/* harmony export */ AutoModelForDepthEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForDepthEstimation), +/* harmony export */ AutoModelForDocumentQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForDocumentQuestionAnswering), +/* harmony export */ AutoModelForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForImageClassification), +/* harmony export */ AutoModelForImageFeatureExtraction: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForImageFeatureExtraction), +/* harmony export */ AutoModelForImageMatting: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForImageMatting), +/* harmony export */ AutoModelForImageSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForImageSegmentation), +/* harmony export */ AutoModelForImageTextToText: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForImageTextToText), +/* harmony export */ AutoModelForImageToImage: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForImageToImage), +/* harmony export */ AutoModelForMaskGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForMaskGeneration), +/* harmony export */ AutoModelForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForMaskedLM), +/* harmony export */ AutoModelForNormalEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForNormalEstimation), +/* harmony export */ AutoModelForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForObjectDetection), +/* harmony export */ AutoModelForPoseEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForPoseEstimation), +/* harmony export */ AutoModelForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForQuestionAnswering), +/* harmony export */ AutoModelForSemanticSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForSemanticSegmentation), +/* harmony export */ AutoModelForSeq2SeqLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForSeq2SeqLM), +/* harmony export */ AutoModelForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForSequenceClassification), +/* harmony export */ AutoModelForSpeechSeq2Seq: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForSpeechSeq2Seq), +/* harmony export */ AutoModelForTextToSpectrogram: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForTextToSpectrogram), +/* harmony export */ AutoModelForTextToWaveform: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForTextToWaveform), +/* harmony export */ AutoModelForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForTokenClassification), +/* harmony export */ AutoModelForUniversalSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForUniversalSegmentation), +/* harmony export */ AutoModelForVision2Seq: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForVision2Seq), +/* harmony export */ AutoModelForXVector: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForXVector), +/* harmony export */ AutoModelForZeroShotObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.AutoModelForZeroShotObjectDetection), +/* harmony export */ AutoProcessor: () => (/* reexport safe */ _models_auto_processing_auto_js__WEBPACK_IMPORTED_MODULE_18__.AutoProcessor), +/* harmony export */ AutoTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.AutoTokenizer), +/* harmony export */ AutomaticSpeechRecognitionPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.AutomaticSpeechRecognitionPipeline), +/* harmony export */ BackgroundRemovalPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.BackgroundRemovalPipeline), +/* harmony export */ BartForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BartForConditionalGeneration), +/* harmony export */ BartForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BartForSequenceClassification), +/* harmony export */ BartModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BartModel), +/* harmony export */ BartPretrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BartPretrainedModel), +/* harmony export */ BartTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.BartTokenizer), +/* harmony export */ BaseModelOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BaseModelOutput), +/* harmony export */ BaseStreamer: () => (/* reexport safe */ _generation_streamers_js__WEBPACK_IMPORTED_MODULE_19__.BaseStreamer), +/* harmony export */ BeitFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.BeitFeatureExtractor), +/* harmony export */ BeitForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BeitForImageClassification), +/* harmony export */ BeitModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BeitModel), +/* harmony export */ BeitPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BeitPreTrainedModel), +/* harmony export */ BertForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BertForMaskedLM), +/* harmony export */ BertForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BertForQuestionAnswering), +/* harmony export */ BertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BertForSequenceClassification), +/* harmony export */ BertForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BertForTokenClassification), +/* harmony export */ BertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BertModel), +/* harmony export */ BertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BertPreTrainedModel), +/* harmony export */ BertTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.BertTokenizer), +/* harmony export */ BitImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.BitImageProcessor), +/* harmony export */ BlenderbotForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BlenderbotForConditionalGeneration), +/* harmony export */ BlenderbotModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BlenderbotModel), +/* harmony export */ BlenderbotPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BlenderbotPreTrainedModel), +/* harmony export */ BlenderbotSmallForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BlenderbotSmallForConditionalGeneration), +/* harmony export */ BlenderbotSmallModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BlenderbotSmallModel), +/* harmony export */ BlenderbotSmallPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BlenderbotSmallPreTrainedModel), +/* harmony export */ BlenderbotSmallTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.BlenderbotSmallTokenizer), +/* harmony export */ BlenderbotTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.BlenderbotTokenizer), +/* harmony export */ BloomForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BloomForCausalLM), +/* harmony export */ BloomModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BloomModel), +/* harmony export */ BloomPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.BloomPreTrainedModel), +/* harmony export */ BloomTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.BloomTokenizer), +/* harmony export */ CLIPFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.CLIPFeatureExtractor), +/* harmony export */ CLIPImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.CLIPImageProcessor), +/* harmony export */ CLIPModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CLIPModel), +/* harmony export */ CLIPPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CLIPPreTrainedModel), +/* harmony export */ CLIPSegForImageSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CLIPSegForImageSegmentation), +/* harmony export */ CLIPSegModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CLIPSegModel), +/* harmony export */ CLIPSegPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CLIPSegPreTrainedModel), +/* harmony export */ CLIPTextModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CLIPTextModel), +/* harmony export */ CLIPTextModelWithProjection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CLIPTextModelWithProjection), +/* harmony export */ CLIPTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.CLIPTokenizer), +/* harmony export */ CLIPVisionModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CLIPVisionModel), +/* harmony export */ CLIPVisionModelWithProjection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CLIPVisionModelWithProjection), +/* harmony export */ CamembertForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CamembertForMaskedLM), +/* harmony export */ CamembertForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CamembertForQuestionAnswering), +/* harmony export */ CamembertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CamembertForSequenceClassification), +/* harmony export */ CamembertForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CamembertForTokenClassification), +/* harmony export */ CamembertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CamembertModel), +/* harmony export */ CamembertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CamembertPreTrainedModel), +/* harmony export */ CamembertTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.CamembertTokenizer), +/* harmony export */ CausalLMOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CausalLMOutput), +/* harmony export */ CausalLMOutputWithPast: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CausalLMOutputWithPast), +/* harmony export */ ChineseCLIPFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.ChineseCLIPFeatureExtractor), +/* harmony export */ ChineseCLIPModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ChineseCLIPModel), +/* harmony export */ ChineseCLIPPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ChineseCLIPPreTrainedModel), +/* harmony export */ ClapAudioModelWithProjection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ClapAudioModelWithProjection), +/* harmony export */ ClapFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.ClapFeatureExtractor), +/* harmony export */ ClapModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ClapModel), +/* harmony export */ ClapPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ClapPreTrainedModel), +/* harmony export */ ClapTextModelWithProjection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ClapTextModelWithProjection), +/* harmony export */ ClassifierFreeGuidanceLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.ClassifierFreeGuidanceLogitsProcessor), +/* harmony export */ CodeGenForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CodeGenForCausalLM), +/* harmony export */ CodeGenModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CodeGenModel), +/* harmony export */ CodeGenPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CodeGenPreTrainedModel), +/* harmony export */ CodeGenTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.CodeGenTokenizer), +/* harmony export */ CodeLlamaTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.CodeLlamaTokenizer), +/* harmony export */ CohereForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CohereForCausalLM), +/* harmony export */ CohereModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CohereModel), +/* harmony export */ CoherePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.CoherePreTrainedModel), +/* harmony export */ CohereTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.CohereTokenizer), +/* harmony export */ ConvBertForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvBertForMaskedLM), +/* harmony export */ ConvBertForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvBertForQuestionAnswering), +/* harmony export */ ConvBertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvBertForSequenceClassification), +/* harmony export */ ConvBertForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvBertForTokenClassification), +/* harmony export */ ConvBertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvBertModel), +/* harmony export */ ConvBertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvBertPreTrainedModel), +/* harmony export */ ConvBertTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.ConvBertTokenizer), +/* harmony export */ ConvNextFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.ConvNextFeatureExtractor), +/* harmony export */ ConvNextForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvNextForImageClassification), +/* harmony export */ ConvNextImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.ConvNextImageProcessor), +/* harmony export */ ConvNextModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvNextModel), +/* harmony export */ ConvNextPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvNextPreTrainedModel), +/* harmony export */ ConvNextV2ForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvNextV2ForImageClassification), +/* harmony export */ ConvNextV2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvNextV2Model), +/* harmony export */ ConvNextV2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ConvNextV2PreTrainedModel), +/* harmony export */ DFineForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DFineForObjectDetection), +/* harmony export */ DFineModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DFineModel), +/* harmony export */ DFinePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DFinePreTrainedModel), +/* harmony export */ DINOv3ConvNextModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DINOv3ConvNextModel), +/* harmony export */ DINOv3ConvNextPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DINOv3ConvNextPreTrainedModel), +/* harmony export */ DINOv3ViTImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.DINOv3ViTImageProcessor), +/* harmony export */ DINOv3ViTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DINOv3ViTModel), +/* harmony export */ DINOv3ViTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DINOv3ViTPreTrainedModel), +/* harmony export */ DPTFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.DPTFeatureExtractor), +/* harmony export */ DPTForDepthEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DPTForDepthEstimation), +/* harmony export */ DPTImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.DPTImageProcessor), +/* harmony export */ DPTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DPTModel), +/* harmony export */ DPTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DPTPreTrainedModel), +/* harmony export */ DacDecoderModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DacDecoderModel), +/* harmony export */ DacDecoderOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DacDecoderOutput), +/* harmony export */ DacEncoderModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DacEncoderModel), +/* harmony export */ DacEncoderOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DacEncoderOutput), +/* harmony export */ DacFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.DacFeatureExtractor), +/* harmony export */ DacModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DacModel), +/* harmony export */ DacPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DacPreTrainedModel), +/* harmony export */ DataTypeMap: () => (/* reexport safe */ _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.DataTypeMap), +/* harmony export */ DebertaForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaForMaskedLM), +/* harmony export */ DebertaForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaForQuestionAnswering), +/* harmony export */ DebertaForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaForSequenceClassification), +/* harmony export */ DebertaForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaForTokenClassification), +/* harmony export */ DebertaModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaModel), +/* harmony export */ DebertaPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaPreTrainedModel), +/* harmony export */ DebertaTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.DebertaTokenizer), +/* harmony export */ DebertaV2ForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaV2ForMaskedLM), +/* harmony export */ DebertaV2ForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaV2ForQuestionAnswering), +/* harmony export */ DebertaV2ForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaV2ForSequenceClassification), +/* harmony export */ DebertaV2ForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaV2ForTokenClassification), +/* harmony export */ DebertaV2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaV2Model), +/* harmony export */ DebertaV2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DebertaV2PreTrainedModel), +/* harmony export */ DebertaV2Tokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.DebertaV2Tokenizer), +/* harmony export */ DecisionTransformerModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DecisionTransformerModel), +/* harmony export */ DecisionTransformerPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DecisionTransformerPreTrainedModel), +/* harmony export */ DeiTFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.DeiTFeatureExtractor), +/* harmony export */ DeiTForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DeiTForImageClassification), +/* harmony export */ DeiTImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.DeiTImageProcessor), +/* harmony export */ DeiTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DeiTModel), +/* harmony export */ DeiTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DeiTPreTrainedModel), +/* harmony export */ DepthAnythingForDepthEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DepthAnythingForDepthEstimation), +/* harmony export */ DepthAnythingPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DepthAnythingPreTrainedModel), +/* harmony export */ DepthEstimationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.DepthEstimationPipeline), +/* harmony export */ DepthProForDepthEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DepthProForDepthEstimation), +/* harmony export */ DepthProPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DepthProPreTrainedModel), +/* harmony export */ DetrFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.DetrFeatureExtractor), +/* harmony export */ DetrForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DetrForObjectDetection), +/* harmony export */ DetrForSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DetrForSegmentation), +/* harmony export */ DetrImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.DetrImageProcessor), +/* harmony export */ DetrModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DetrModel), +/* harmony export */ DetrObjectDetectionOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DetrObjectDetectionOutput), +/* harmony export */ DetrPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DetrPreTrainedModel), +/* harmony export */ DetrSegmentationOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DetrSegmentationOutput), +/* harmony export */ Dinov2ForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Dinov2ForImageClassification), +/* harmony export */ Dinov2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Dinov2Model), +/* harmony export */ Dinov2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Dinov2PreTrainedModel), +/* harmony export */ Dinov2WithRegistersForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Dinov2WithRegistersForImageClassification), +/* harmony export */ Dinov2WithRegistersModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Dinov2WithRegistersModel), +/* harmony export */ Dinov2WithRegistersPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Dinov2WithRegistersPreTrainedModel), +/* harmony export */ DistilBertForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DistilBertForMaskedLM), +/* harmony export */ DistilBertForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DistilBertForQuestionAnswering), +/* harmony export */ DistilBertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DistilBertForSequenceClassification), +/* harmony export */ DistilBertForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DistilBertForTokenClassification), +/* harmony export */ DistilBertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DistilBertModel), +/* harmony export */ DistilBertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DistilBertPreTrainedModel), +/* harmony export */ DistilBertTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.DistilBertTokenizer), +/* harmony export */ DocumentQuestionAnsweringPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.DocumentQuestionAnsweringPipeline), +/* harmony export */ DonutFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.DonutFeatureExtractor), +/* harmony export */ DonutImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.DonutImageProcessor), +/* harmony export */ DonutSwinModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DonutSwinModel), +/* harmony export */ DonutSwinPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.DonutSwinPreTrainedModel), +/* harmony export */ EfficientNetForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.EfficientNetForImageClassification), +/* harmony export */ EfficientNetImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.EfficientNetImageProcessor), +/* harmony export */ EfficientNetModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.EfficientNetModel), +/* harmony export */ EfficientNetPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.EfficientNetPreTrainedModel), +/* harmony export */ ElectraForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ElectraForMaskedLM), +/* harmony export */ ElectraForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ElectraForQuestionAnswering), +/* harmony export */ ElectraForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ElectraForSequenceClassification), +/* harmony export */ ElectraForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ElectraForTokenClassification), +/* harmony export */ ElectraModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ElectraModel), +/* harmony export */ ElectraPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ElectraPreTrainedModel), +/* harmony export */ ElectraTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.ElectraTokenizer), +/* harmony export */ EncodecFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.EncodecFeatureExtractor), +/* harmony export */ EosTokenCriteria: () => (/* reexport safe */ _generation_stopping_criteria_js__WEBPACK_IMPORTED_MODULE_20__.EosTokenCriteria), +/* harmony export */ Ernie4_5_ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Ernie4_5_ForCausalLM), +/* harmony export */ Ernie4_5_Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Ernie4_5_Model), +/* harmony export */ Ernie4_5_PretrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Ernie4_5_PretrainedModel), +/* harmony export */ Ernie4_5_Tokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.Ernie4_5_Tokenizer), +/* harmony export */ EsmForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.EsmForMaskedLM), +/* harmony export */ EsmForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.EsmForSequenceClassification), +/* harmony export */ EsmForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.EsmForTokenClassification), +/* harmony export */ EsmModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.EsmModel), +/* harmony export */ EsmPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.EsmPreTrainedModel), +/* harmony export */ EsmTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.EsmTokenizer), +/* harmony export */ ExaoneForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ExaoneForCausalLM), +/* harmony export */ ExaoneModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ExaoneModel), +/* harmony export */ ExaonePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ExaonePreTrainedModel), +/* harmony export */ FFT: () => (/* reexport safe */ _utils_maths_js__WEBPACK_IMPORTED_MODULE_9__.FFT), +/* harmony export */ FalconForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.FalconForCausalLM), +/* harmony export */ FalconModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.FalconModel), +/* harmony export */ FalconPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.FalconPreTrainedModel), +/* harmony export */ FalconTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.FalconTokenizer), +/* harmony export */ FastViTForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.FastViTForImageClassification), +/* harmony export */ FastViTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.FastViTModel), +/* harmony export */ FastViTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.FastViTPreTrainedModel), +/* harmony export */ FeatureExtractionPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.FeatureExtractionPipeline), +/* harmony export */ FeatureExtractor: () => (/* reexport safe */ _base_feature_extraction_utils_js__WEBPACK_IMPORTED_MODULE_10__.FeatureExtractor), +/* harmony export */ FillMaskPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.FillMaskPipeline), +/* harmony export */ Florence2ForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Florence2ForConditionalGeneration), +/* harmony export */ Florence2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Florence2PreTrainedModel), +/* harmony export */ Florence2Processor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.Florence2Processor), +/* harmony export */ ForcedBOSTokenLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.ForcedBOSTokenLogitsProcessor), +/* harmony export */ ForcedEOSTokenLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.ForcedEOSTokenLogitsProcessor), +/* harmony export */ GLPNFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.GLPNFeatureExtractor), +/* harmony export */ GLPNForDepthEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GLPNForDepthEstimation), +/* harmony export */ GLPNModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GLPNModel), +/* harmony export */ GLPNPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GLPNPreTrainedModel), +/* harmony export */ GPT2LMHeadModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPT2LMHeadModel), +/* harmony export */ GPT2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPT2Model), +/* harmony export */ GPT2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPT2PreTrainedModel), +/* harmony export */ GPT2Tokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.GPT2Tokenizer), +/* harmony export */ GPTBigCodeForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTBigCodeForCausalLM), +/* harmony export */ GPTBigCodeModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTBigCodeModel), +/* harmony export */ GPTBigCodePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTBigCodePreTrainedModel), +/* harmony export */ GPTJForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTJForCausalLM), +/* harmony export */ GPTJModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTJModel), +/* harmony export */ GPTJPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTJPreTrainedModel), +/* harmony export */ GPTNeoForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTNeoForCausalLM), +/* harmony export */ GPTNeoModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTNeoModel), +/* harmony export */ GPTNeoPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTNeoPreTrainedModel), +/* harmony export */ GPTNeoXForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTNeoXForCausalLM), +/* harmony export */ GPTNeoXModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTNeoXModel), +/* harmony export */ GPTNeoXPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GPTNeoXPreTrainedModel), +/* harmony export */ GPTNeoXTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.GPTNeoXTokenizer), +/* harmony export */ Gemma2ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Gemma2ForCausalLM), +/* harmony export */ Gemma2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Gemma2Model), +/* harmony export */ Gemma2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Gemma2PreTrainedModel), +/* harmony export */ Gemma3ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Gemma3ForCausalLM), +/* harmony export */ Gemma3Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Gemma3Model), +/* harmony export */ Gemma3PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Gemma3PreTrainedModel), +/* harmony export */ Gemma3nAudioFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.Gemma3nAudioFeatureExtractor), +/* harmony export */ Gemma3nForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Gemma3nForConditionalGeneration), +/* harmony export */ Gemma3nPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Gemma3nPreTrainedModel), +/* harmony export */ Gemma3nProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.Gemma3nProcessor), +/* harmony export */ GemmaForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GemmaForCausalLM), +/* harmony export */ GemmaModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GemmaModel), +/* harmony export */ GemmaPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GemmaPreTrainedModel), +/* harmony export */ GemmaTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.GemmaTokenizer), +/* harmony export */ GlmForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GlmForCausalLM), +/* harmony export */ GlmModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GlmModel), +/* harmony export */ GlmPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GlmPreTrainedModel), +/* harmony export */ GraniteForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GraniteForCausalLM), +/* harmony export */ GraniteModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GraniteModel), +/* harmony export */ GranitePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GranitePreTrainedModel), +/* harmony export */ Grok1Tokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.Grok1Tokenizer), +/* harmony export */ GroundingDinoForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GroundingDinoForObjectDetection), +/* harmony export */ GroundingDinoImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.GroundingDinoImageProcessor), +/* harmony export */ GroundingDinoPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GroundingDinoPreTrainedModel), +/* harmony export */ GroundingDinoProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.GroundingDinoProcessor), +/* harmony export */ GroupViTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GroupViTModel), +/* harmony export */ GroupViTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.GroupViTPreTrainedModel), +/* harmony export */ HeliumForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HeliumForCausalLM), +/* harmony export */ HeliumModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HeliumModel), +/* harmony export */ HeliumPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HeliumPreTrainedModel), +/* harmony export */ HerbertTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.HerbertTokenizer), +/* harmony export */ HieraForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HieraForImageClassification), +/* harmony export */ HieraModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HieraModel), +/* harmony export */ HieraPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HieraPreTrainedModel), +/* harmony export */ HubertForCTC: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HubertForCTC), +/* harmony export */ HubertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HubertForSequenceClassification), +/* harmony export */ HubertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HubertModel), +/* harmony export */ HubertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.HubertPreTrainedModel), +/* harmony export */ IJepaForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.IJepaForImageClassification), +/* harmony export */ IJepaModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.IJepaModel), +/* harmony export */ IJepaPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.IJepaPreTrainedModel), +/* harmony export */ Idefics3ForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Idefics3ForConditionalGeneration), +/* harmony export */ Idefics3ImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.Idefics3ImageProcessor), +/* harmony export */ Idefics3PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Idefics3PreTrainedModel), +/* harmony export */ Idefics3Processor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.Idefics3Processor), +/* harmony export */ ImageClassificationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ImageClassificationPipeline), +/* harmony export */ ImageFeatureExtractionPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ImageFeatureExtractionPipeline), +/* harmony export */ ImageFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.ImageFeatureExtractor), +/* harmony export */ ImageMattingOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ImageMattingOutput), +/* harmony export */ ImageProcessor: () => (/* reexport safe */ _base_image_processors_utils_js__WEBPACK_IMPORTED_MODULE_13__.ImageProcessor), +/* harmony export */ ImageSegmentationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ImageSegmentationPipeline), +/* harmony export */ ImageToImagePipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ImageToImagePipeline), +/* harmony export */ ImageToTextPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ImageToTextPipeline), +/* harmony export */ InterruptableStoppingCriteria: () => (/* reexport safe */ _generation_stopping_criteria_js__WEBPACK_IMPORTED_MODULE_20__.InterruptableStoppingCriteria), +/* harmony export */ JAISLMHeadModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.JAISLMHeadModel), +/* harmony export */ JAISModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.JAISModel), +/* harmony export */ JAISPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.JAISPreTrainedModel), +/* harmony export */ JinaCLIPImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.JinaCLIPImageProcessor), +/* harmony export */ JinaCLIPModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.JinaCLIPModel), +/* harmony export */ JinaCLIPPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.JinaCLIPPreTrainedModel), +/* harmony export */ JinaCLIPProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.JinaCLIPProcessor), +/* harmony export */ JinaCLIPTextModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.JinaCLIPTextModel), +/* harmony export */ JinaCLIPVisionModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.JinaCLIPVisionModel), +/* harmony export */ Lfm2ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Lfm2ForCausalLM), +/* harmony export */ Lfm2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Lfm2Model), +/* harmony export */ Lfm2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Lfm2PreTrainedModel), +/* harmony export */ LiteWhisperForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LiteWhisperForConditionalGeneration), +/* harmony export */ LlamaForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LlamaForCausalLM), +/* harmony export */ LlamaModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LlamaModel), +/* harmony export */ LlamaPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LlamaPreTrainedModel), +/* harmony export */ LlamaTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.LlamaTokenizer), +/* harmony export */ LlavaForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LlavaForConditionalGeneration), +/* harmony export */ LlavaOnevisionForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LlavaOnevisionForConditionalGeneration), +/* harmony export */ LlavaOnevisionImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.LlavaOnevisionImageProcessor), +/* harmony export */ LlavaPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LlavaPreTrainedModel), +/* harmony export */ LlavaProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.LlavaProcessor), +/* harmony export */ LlavaQwen2ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LlavaQwen2ForCausalLM), +/* harmony export */ LogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.LogitsProcessor), +/* harmony export */ LogitsProcessorList: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.LogitsProcessorList), +/* harmony export */ LogitsWarper: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.LogitsWarper), +/* harmony export */ LongT5ForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LongT5ForConditionalGeneration), +/* harmony export */ LongT5Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LongT5Model), +/* harmony export */ LongT5PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.LongT5PreTrainedModel), +/* harmony export */ M2M100ForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.M2M100ForConditionalGeneration), +/* harmony export */ M2M100Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.M2M100Model), +/* harmony export */ M2M100PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.M2M100PreTrainedModel), +/* harmony export */ M2M100Tokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.M2M100Tokenizer), +/* harmony export */ MBart50Tokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.MBart50Tokenizer), +/* harmony export */ MBartForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MBartForCausalLM), +/* harmony export */ MBartForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MBartForConditionalGeneration), +/* harmony export */ MBartForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MBartForSequenceClassification), +/* harmony export */ MBartModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MBartModel), +/* harmony export */ MBartPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MBartPreTrainedModel), +/* harmony export */ MBartTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.MBartTokenizer), +/* harmony export */ MPNetForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MPNetForMaskedLM), +/* harmony export */ MPNetForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MPNetForQuestionAnswering), +/* harmony export */ MPNetForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MPNetForSequenceClassification), +/* harmony export */ MPNetForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MPNetForTokenClassification), +/* harmony export */ MPNetModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MPNetModel), +/* harmony export */ MPNetPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MPNetPreTrainedModel), +/* harmony export */ MPNetTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.MPNetTokenizer), +/* harmony export */ MT5ForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MT5ForConditionalGeneration), +/* harmony export */ MT5Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MT5Model), +/* harmony export */ MT5PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MT5PreTrainedModel), +/* harmony export */ MarianMTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MarianMTModel), +/* harmony export */ MarianModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MarianModel), +/* harmony export */ MarianPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MarianPreTrainedModel), +/* harmony export */ MarianTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.MarianTokenizer), +/* harmony export */ Mask2FormerImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.Mask2FormerImageProcessor), +/* harmony export */ MaskFormerFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MaskFormerFeatureExtractor), +/* harmony export */ MaskFormerForInstanceSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MaskFormerForInstanceSegmentation), +/* harmony export */ MaskFormerImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MaskFormerImageProcessor), +/* harmony export */ MaskFormerModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MaskFormerModel), +/* harmony export */ MaskFormerPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MaskFormerPreTrainedModel), +/* harmony export */ MaskedLMOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MaskedLMOutput), +/* harmony export */ MaxLengthCriteria: () => (/* reexport safe */ _generation_stopping_criteria_js__WEBPACK_IMPORTED_MODULE_20__.MaxLengthCriteria), +/* harmony export */ Metric3DForDepthEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Metric3DForDepthEstimation), +/* harmony export */ Metric3DPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Metric3DPreTrainedModel), +/* harmony export */ Metric3Dv2ForDepthEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Metric3Dv2ForDepthEstimation), +/* harmony export */ Metric3Dv2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Metric3Dv2PreTrainedModel), +/* harmony export */ MgpstrForSceneTextRecognition: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MgpstrForSceneTextRecognition), +/* harmony export */ MgpstrModelOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MgpstrModelOutput), +/* harmony export */ MgpstrPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MgpstrPreTrainedModel), +/* harmony export */ MgpstrProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.MgpstrProcessor), +/* harmony export */ MgpstrTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.MgpstrTokenizer), +/* harmony export */ MimiDecoderModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MimiDecoderModel), +/* harmony export */ MimiDecoderOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MimiDecoderOutput), +/* harmony export */ MimiEncoderModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MimiEncoderModel), +/* harmony export */ MimiEncoderOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MimiEncoderOutput), +/* harmony export */ MimiModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MimiModel), +/* harmony export */ MimiPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MimiPreTrainedModel), +/* harmony export */ MinLengthLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.MinLengthLogitsProcessor), +/* harmony export */ MinNewTokensLengthLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.MinNewTokensLengthLogitsProcessor), +/* harmony export */ MistralForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MistralForCausalLM), +/* harmony export */ MistralModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MistralModel), +/* harmony export */ MistralPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MistralPreTrainedModel), +/* harmony export */ MobileBertForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileBertForMaskedLM), +/* harmony export */ MobileBertForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileBertForQuestionAnswering), +/* harmony export */ MobileBertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileBertForSequenceClassification), +/* harmony export */ MobileBertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileBertModel), +/* harmony export */ MobileBertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileBertPreTrainedModel), +/* harmony export */ MobileBertTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.MobileBertTokenizer), +/* harmony export */ MobileLLMForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileLLMForCausalLM), +/* harmony export */ MobileLLMModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileLLMModel), +/* harmony export */ MobileLLMPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileLLMPreTrainedModel), +/* harmony export */ MobileNetV1FeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileNetV1FeatureExtractor), +/* harmony export */ MobileNetV1ForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV1ForImageClassification), +/* harmony export */ MobileNetV1ForSemanticSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV1ForSemanticSegmentation), +/* harmony export */ MobileNetV1ImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileNetV1ImageProcessor), +/* harmony export */ MobileNetV1Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV1Model), +/* harmony export */ MobileNetV1PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV1PreTrainedModel), +/* harmony export */ MobileNetV2FeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileNetV2FeatureExtractor), +/* harmony export */ MobileNetV2ForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV2ForImageClassification), +/* harmony export */ MobileNetV2ForSemanticSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV2ForSemanticSegmentation), +/* harmony export */ MobileNetV2ImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileNetV2ImageProcessor), +/* harmony export */ MobileNetV2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV2Model), +/* harmony export */ MobileNetV2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV2PreTrainedModel), +/* harmony export */ MobileNetV3FeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileNetV3FeatureExtractor), +/* harmony export */ MobileNetV3ForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV3ForImageClassification), +/* harmony export */ MobileNetV3ForSemanticSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV3ForSemanticSegmentation), +/* harmony export */ MobileNetV3ImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileNetV3ImageProcessor), +/* harmony export */ MobileNetV3Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV3Model), +/* harmony export */ MobileNetV3PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV3PreTrainedModel), +/* harmony export */ MobileNetV4FeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileNetV4FeatureExtractor), +/* harmony export */ MobileNetV4ForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV4ForImageClassification), +/* harmony export */ MobileNetV4ForSemanticSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV4ForSemanticSegmentation), +/* harmony export */ MobileNetV4ImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileNetV4ImageProcessor), +/* harmony export */ MobileNetV4Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV4Model), +/* harmony export */ MobileNetV4PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileNetV4PreTrainedModel), +/* harmony export */ MobileViTFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileViTFeatureExtractor), +/* harmony export */ MobileViTForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileViTForImageClassification), +/* harmony export */ MobileViTImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.MobileViTImageProcessor), +/* harmony export */ MobileViTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileViTModel), +/* harmony export */ MobileViTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileViTPreTrainedModel), +/* harmony export */ MobileViTV2ForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileViTV2ForImageClassification), +/* harmony export */ MobileViTV2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileViTV2Model), +/* harmony export */ MobileViTV2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MobileViTV2PreTrainedModel), +/* harmony export */ ModelOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ModelOutput), +/* harmony export */ ModernBertDecoderForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ModernBertDecoderForCausalLM), +/* harmony export */ ModernBertDecoderModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ModernBertDecoderModel), +/* harmony export */ ModernBertDecoderPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ModernBertDecoderPreTrainedModel), +/* harmony export */ ModernBertForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ModernBertForMaskedLM), +/* harmony export */ ModernBertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ModernBertForSequenceClassification), +/* harmony export */ ModernBertForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ModernBertForTokenClassification), +/* harmony export */ ModernBertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ModernBertModel), +/* harmony export */ ModernBertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ModernBertPreTrainedModel), +/* harmony export */ Moondream1ForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Moondream1ForConditionalGeneration), +/* harmony export */ MoonshineFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.MoonshineFeatureExtractor), +/* harmony export */ MoonshineForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MoonshineForConditionalGeneration), +/* harmony export */ MoonshineModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MoonshineModel), +/* harmony export */ MoonshinePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MoonshinePreTrainedModel), +/* harmony export */ MoonshineProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.MoonshineProcessor), +/* harmony export */ MptForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MptForCausalLM), +/* harmony export */ MptModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MptModel), +/* harmony export */ MptPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MptPreTrainedModel), +/* harmony export */ MultiModalityCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MultiModalityCausalLM), +/* harmony export */ MultiModalityPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MultiModalityPreTrainedModel), +/* harmony export */ MusicgenForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MusicgenForCausalLM), +/* harmony export */ MusicgenForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MusicgenForConditionalGeneration), +/* harmony export */ MusicgenModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MusicgenModel), +/* harmony export */ MusicgenPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.MusicgenPreTrainedModel), +/* harmony export */ NeoBertForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.NeoBertForMaskedLM), +/* harmony export */ NeoBertForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.NeoBertForQuestionAnswering), +/* harmony export */ NeoBertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.NeoBertForSequenceClassification), +/* harmony export */ NeoBertForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.NeoBertForTokenClassification), +/* harmony export */ NeoBertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.NeoBertModel), +/* harmony export */ NeoBertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.NeoBertPreTrainedModel), +/* harmony export */ NllbTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.NllbTokenizer), +/* harmony export */ NoBadWordsLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.NoBadWordsLogitsProcessor), +/* harmony export */ NoRepeatNGramLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.NoRepeatNGramLogitsProcessor), +/* harmony export */ NomicBertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.NomicBertModel), +/* harmony export */ NomicBertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.NomicBertPreTrainedModel), +/* harmony export */ NougatImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.NougatImageProcessor), +/* harmony export */ NougatTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.NougatTokenizer), +/* harmony export */ OPTForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OPTForCausalLM), +/* harmony export */ OPTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OPTModel), +/* harmony export */ OPTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OPTPreTrainedModel), +/* harmony export */ ObjectDetectionPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ObjectDetectionPipeline), +/* harmony export */ Olmo2ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Olmo2ForCausalLM), +/* harmony export */ Olmo2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Olmo2Model), +/* harmony export */ Olmo2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Olmo2PreTrainedModel), +/* harmony export */ OlmoForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OlmoForCausalLM), +/* harmony export */ OlmoModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OlmoModel), +/* harmony export */ OlmoPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OlmoPreTrainedModel), +/* harmony export */ OpenELMForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OpenELMForCausalLM), +/* harmony export */ OpenELMModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OpenELMModel), +/* harmony export */ OpenELMPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OpenELMPreTrainedModel), +/* harmony export */ OwlViTFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.OwlViTFeatureExtractor), +/* harmony export */ OwlViTForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OwlViTForObjectDetection), +/* harmony export */ OwlViTImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.OwlViTImageProcessor), +/* harmony export */ OwlViTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OwlViTModel), +/* harmony export */ OwlViTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.OwlViTPreTrainedModel), +/* harmony export */ OwlViTProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.OwlViTProcessor), +/* harmony export */ Owlv2ForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Owlv2ForObjectDetection), +/* harmony export */ Owlv2ImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.Owlv2ImageProcessor), +/* harmony export */ Owlv2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Owlv2Model), +/* harmony export */ Owlv2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Owlv2PreTrainedModel), +/* harmony export */ PaliGemmaForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PaliGemmaForConditionalGeneration), +/* harmony export */ PaliGemmaPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PaliGemmaPreTrainedModel), +/* harmony export */ PaliGemmaProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.PaliGemmaProcessor), +/* harmony export */ PatchTSMixerForPrediction: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PatchTSMixerForPrediction), +/* harmony export */ PatchTSMixerModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PatchTSMixerModel), +/* harmony export */ PatchTSMixerPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PatchTSMixerPreTrainedModel), +/* harmony export */ PatchTSTForPrediction: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PatchTSTForPrediction), +/* harmony export */ PatchTSTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PatchTSTModel), +/* harmony export */ PatchTSTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PatchTSTPreTrainedModel), +/* harmony export */ Phi3ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Phi3ForCausalLM), +/* harmony export */ Phi3Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Phi3Model), +/* harmony export */ Phi3PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Phi3PreTrainedModel), +/* harmony export */ Phi3VForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Phi3VForCausalLM), +/* harmony export */ Phi3VImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.Phi3VImageProcessor), +/* harmony export */ Phi3VPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Phi3VPreTrainedModel), +/* harmony export */ Phi3VProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.Phi3VProcessor), +/* harmony export */ PhiForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PhiForCausalLM), +/* harmony export */ PhiModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PhiModel), +/* harmony export */ PhiPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PhiPreTrainedModel), +/* harmony export */ Pipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.Pipeline), +/* harmony export */ PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PreTrainedModel), +/* harmony export */ PreTrainedTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.PreTrainedTokenizer), +/* harmony export */ PretrainedConfig: () => (/* reexport safe */ _configs_js__WEBPACK_IMPORTED_MODULE_4__.PretrainedConfig), +/* harmony export */ PretrainedMixin: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PretrainedMixin), +/* harmony export */ Processor: () => (/* reexport safe */ _base_processing_utils_js__WEBPACK_IMPORTED_MODULE_16__.Processor), +/* harmony export */ PvtForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PvtForImageClassification), +/* harmony export */ PvtImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.PvtImageProcessor), +/* harmony export */ PvtModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PvtModel), +/* harmony export */ PvtPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PvtPreTrainedModel), +/* harmony export */ PyAnnoteFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.PyAnnoteFeatureExtractor), +/* harmony export */ PyAnnoteForAudioFrameClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PyAnnoteForAudioFrameClassification), +/* harmony export */ PyAnnoteModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PyAnnoteModel), +/* harmony export */ PyAnnotePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.PyAnnotePreTrainedModel), +/* harmony export */ PyAnnoteProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.PyAnnoteProcessor), +/* harmony export */ QuestionAnsweringModelOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.QuestionAnsweringModelOutput), +/* harmony export */ QuestionAnsweringPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.QuestionAnsweringPipeline), +/* harmony export */ Qwen2ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Qwen2ForCausalLM), +/* harmony export */ Qwen2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Qwen2Model), +/* harmony export */ Qwen2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Qwen2PreTrainedModel), +/* harmony export */ Qwen2Tokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.Qwen2Tokenizer), +/* harmony export */ Qwen2VLForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Qwen2VLForConditionalGeneration), +/* harmony export */ Qwen2VLImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.Qwen2VLImageProcessor), +/* harmony export */ Qwen2VLPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Qwen2VLPreTrainedModel), +/* harmony export */ Qwen2VLProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.Qwen2VLProcessor), +/* harmony export */ Qwen3ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Qwen3ForCausalLM), +/* harmony export */ Qwen3Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Qwen3Model), +/* harmony export */ Qwen3PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Qwen3PreTrainedModel), +/* harmony export */ RFDetrForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RFDetrForObjectDetection), +/* harmony export */ RFDetrModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RFDetrModel), +/* harmony export */ RFDetrObjectDetectionOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RFDetrObjectDetectionOutput), +/* harmony export */ RFDetrPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RFDetrPreTrainedModel), +/* harmony export */ RTDetrForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RTDetrForObjectDetection), +/* harmony export */ RTDetrImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.RTDetrImageProcessor), +/* harmony export */ RTDetrModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RTDetrModel), +/* harmony export */ RTDetrObjectDetectionOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RTDetrObjectDetectionOutput), +/* harmony export */ RTDetrPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RTDetrPreTrainedModel), +/* harmony export */ RTDetrV2ForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RTDetrV2ForObjectDetection), +/* harmony export */ RTDetrV2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RTDetrV2Model), +/* harmony export */ RTDetrV2ObjectDetectionOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RTDetrV2ObjectDetectionOutput), +/* harmony export */ RTDetrV2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RTDetrV2PreTrainedModel), +/* harmony export */ RawAudio: () => (/* reexport safe */ _utils_audio_js__WEBPACK_IMPORTED_MODULE_5__.RawAudio), +/* harmony export */ RawImage: () => (/* reexport safe */ _utils_image_js__WEBPACK_IMPORTED_MODULE_6__.RawImage), +/* harmony export */ RawVideo: () => (/* reexport safe */ _utils_video_js__WEBPACK_IMPORTED_MODULE_7__.RawVideo), +/* harmony export */ RawVideoFrame: () => (/* reexport safe */ _utils_video_js__WEBPACK_IMPORTED_MODULE_7__.RawVideoFrame), +/* harmony export */ RepetitionPenaltyLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.RepetitionPenaltyLogitsProcessor), +/* harmony export */ ResNetForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ResNetForImageClassification), +/* harmony export */ ResNetModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ResNetModel), +/* harmony export */ ResNetPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ResNetPreTrainedModel), +/* harmony export */ RoFormerForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RoFormerForMaskedLM), +/* harmony export */ RoFormerForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RoFormerForQuestionAnswering), +/* harmony export */ RoFormerForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RoFormerForSequenceClassification), +/* harmony export */ RoFormerForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RoFormerForTokenClassification), +/* harmony export */ RoFormerModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RoFormerModel), +/* harmony export */ RoFormerPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RoFormerPreTrainedModel), +/* harmony export */ RoFormerTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.RoFormerTokenizer), +/* harmony export */ RobertaForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RobertaForMaskedLM), +/* harmony export */ RobertaForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RobertaForQuestionAnswering), +/* harmony export */ RobertaForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RobertaForSequenceClassification), +/* harmony export */ RobertaForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RobertaForTokenClassification), +/* harmony export */ RobertaModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RobertaModel), +/* harmony export */ RobertaPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.RobertaPreTrainedModel), +/* harmony export */ RobertaTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.RobertaTokenizer), +/* harmony export */ SamImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.SamImageProcessor), +/* harmony export */ SamImageSegmentationOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SamImageSegmentationOutput), +/* harmony export */ SamModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SamModel), +/* harmony export */ SamPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SamPreTrainedModel), +/* harmony export */ SamProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.SamProcessor), +/* harmony export */ SapiensForDepthEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SapiensForDepthEstimation), +/* harmony export */ SapiensForNormalEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SapiensForNormalEstimation), +/* harmony export */ SapiensForSemanticSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SapiensForSemanticSegmentation), +/* harmony export */ SapiensPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SapiensPreTrainedModel), +/* harmony export */ SeamlessM4TFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.SeamlessM4TFeatureExtractor), +/* harmony export */ SegformerFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.SegformerFeatureExtractor), +/* harmony export */ SegformerForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SegformerForImageClassification), +/* harmony export */ SegformerForSemanticSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SegformerForSemanticSegmentation), +/* harmony export */ SegformerImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.SegformerImageProcessor), +/* harmony export */ SegformerModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SegformerModel), +/* harmony export */ SegformerPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SegformerPreTrainedModel), +/* harmony export */ Seq2SeqLMOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Seq2SeqLMOutput), +/* harmony export */ SequenceClassifierOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SequenceClassifierOutput), +/* harmony export */ SiglipImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.SiglipImageProcessor), +/* harmony export */ SiglipModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SiglipModel), +/* harmony export */ SiglipPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SiglipPreTrainedModel), +/* harmony export */ SiglipTextModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SiglipTextModel), +/* harmony export */ SiglipTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.SiglipTokenizer), +/* harmony export */ SiglipVisionModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SiglipVisionModel), +/* harmony export */ SmolLM3ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SmolLM3ForCausalLM), +/* harmony export */ SmolLM3Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SmolLM3Model), +/* harmony export */ SmolLM3PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SmolLM3PreTrainedModel), +/* harmony export */ SmolVLMForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SmolVLMForConditionalGeneration), +/* harmony export */ SmolVLMImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.SmolVLMImageProcessor), +/* harmony export */ SmolVLMProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.SmolVLMProcessor), +/* harmony export */ SnacDecoderModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SnacDecoderModel), +/* harmony export */ SnacEncoderModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SnacEncoderModel), +/* harmony export */ SnacFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.SnacFeatureExtractor), +/* harmony export */ SnacModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SnacModel), +/* harmony export */ SnacPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SnacPreTrainedModel), +/* harmony export */ SpeechT5FeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.SpeechT5FeatureExtractor), +/* harmony export */ SpeechT5ForSpeechToText: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SpeechT5ForSpeechToText), +/* harmony export */ SpeechT5ForTextToSpeech: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SpeechT5ForTextToSpeech), +/* harmony export */ SpeechT5HifiGan: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SpeechT5HifiGan), +/* harmony export */ SpeechT5Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SpeechT5Model), +/* harmony export */ SpeechT5PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SpeechT5PreTrainedModel), +/* harmony export */ SpeechT5Processor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.SpeechT5Processor), +/* harmony export */ SpeechT5Tokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.SpeechT5Tokenizer), +/* harmony export */ SqueezeBertForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SqueezeBertForMaskedLM), +/* harmony export */ SqueezeBertForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SqueezeBertForQuestionAnswering), +/* harmony export */ SqueezeBertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SqueezeBertForSequenceClassification), +/* harmony export */ SqueezeBertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SqueezeBertModel), +/* harmony export */ SqueezeBertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SqueezeBertPreTrainedModel), +/* harmony export */ SqueezeBertTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.SqueezeBertTokenizer), +/* harmony export */ StableLmForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.StableLmForCausalLM), +/* harmony export */ StableLmModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.StableLmModel), +/* harmony export */ StableLmPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.StableLmPreTrainedModel), +/* harmony export */ Starcoder2ForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Starcoder2ForCausalLM), +/* harmony export */ Starcoder2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Starcoder2Model), +/* harmony export */ Starcoder2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Starcoder2PreTrainedModel), +/* harmony export */ StoppingCriteria: () => (/* reexport safe */ _generation_stopping_criteria_js__WEBPACK_IMPORTED_MODULE_20__.StoppingCriteria), +/* harmony export */ StoppingCriteriaList: () => (/* reexport safe */ _generation_stopping_criteria_js__WEBPACK_IMPORTED_MODULE_20__.StoppingCriteriaList), +/* harmony export */ StyleTextToSpeech2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.StyleTextToSpeech2Model), +/* harmony export */ StyleTextToSpeech2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.StyleTextToSpeech2PreTrainedModel), +/* harmony export */ SummarizationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.SummarizationPipeline), +/* harmony export */ SuppressTokensAtBeginLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.SuppressTokensAtBeginLogitsProcessor), +/* harmony export */ Swin2SRForImageSuperResolution: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Swin2SRForImageSuperResolution), +/* harmony export */ Swin2SRImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.Swin2SRImageProcessor), +/* harmony export */ Swin2SRModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Swin2SRModel), +/* harmony export */ Swin2SRPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Swin2SRPreTrainedModel), +/* harmony export */ SwinForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SwinForImageClassification), +/* harmony export */ SwinForSemanticSegmentation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SwinForSemanticSegmentation), +/* harmony export */ SwinModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SwinModel), +/* harmony export */ SwinPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.SwinPreTrainedModel), +/* harmony export */ T5ForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.T5ForConditionalGeneration), +/* harmony export */ T5Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.T5Model), +/* harmony export */ T5PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.T5PreTrainedModel), +/* harmony export */ T5Tokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.T5Tokenizer), +/* harmony export */ TableTransformerForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.TableTransformerForObjectDetection), +/* harmony export */ TableTransformerModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.TableTransformerModel), +/* harmony export */ TableTransformerObjectDetectionOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.TableTransformerObjectDetectionOutput), +/* harmony export */ TableTransformerPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.TableTransformerPreTrainedModel), +/* harmony export */ TemperatureLogitsWarper: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.TemperatureLogitsWarper), +/* harmony export */ Tensor: () => (/* reexport safe */ _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.Tensor), +/* harmony export */ Text2TextGenerationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.Text2TextGenerationPipeline), +/* harmony export */ TextClassificationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.TextClassificationPipeline), +/* harmony export */ TextGenerationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.TextGenerationPipeline), +/* harmony export */ TextStreamer: () => (/* reexport safe */ _generation_streamers_js__WEBPACK_IMPORTED_MODULE_19__.TextStreamer), +/* harmony export */ TextToAudioPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.TextToAudioPipeline), +/* harmony export */ TokenClassificationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.TokenClassificationPipeline), +/* harmony export */ TokenClassifierOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.TokenClassifierOutput), +/* harmony export */ TokenizerModel: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.TokenizerModel), +/* harmony export */ TopKLogitsWarper: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.TopKLogitsWarper), +/* harmony export */ TopPLogitsWarper: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.TopPLogitsWarper), +/* harmony export */ TrOCRForCausalLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.TrOCRForCausalLM), +/* harmony export */ TrOCRPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.TrOCRPreTrainedModel), +/* harmony export */ TranslationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.TranslationPipeline), +/* harmony export */ UltravoxModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UltravoxModel), +/* harmony export */ UltravoxPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UltravoxPreTrainedModel), +/* harmony export */ UltravoxProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.UltravoxProcessor), +/* harmony export */ UniSpeechForCTC: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UniSpeechForCTC), +/* harmony export */ UniSpeechForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UniSpeechForSequenceClassification), +/* harmony export */ UniSpeechModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UniSpeechModel), +/* harmony export */ UniSpeechPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UniSpeechPreTrainedModel), +/* harmony export */ UniSpeechSatForAudioFrameClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UniSpeechSatForAudioFrameClassification), +/* harmony export */ UniSpeechSatForCTC: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UniSpeechSatForCTC), +/* harmony export */ UniSpeechSatForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UniSpeechSatForSequenceClassification), +/* harmony export */ UniSpeechSatModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UniSpeechSatModel), +/* harmony export */ UniSpeechSatPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.UniSpeechSatPreTrainedModel), +/* harmony export */ VLChatProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.VLChatProcessor), +/* harmony export */ VLMImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.VLMImageProcessor), +/* harmony export */ ViTFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.ViTFeatureExtractor), +/* harmony export */ ViTForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ViTForImageClassification), +/* harmony export */ ViTImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.ViTImageProcessor), +/* harmony export */ ViTMAEModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ViTMAEModel), +/* harmony export */ ViTMAEPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ViTMAEPreTrainedModel), +/* harmony export */ ViTMSNForImageClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ViTMSNForImageClassification), +/* harmony export */ ViTMSNModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ViTMSNModel), +/* harmony export */ ViTMSNPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ViTMSNPreTrainedModel), +/* harmony export */ ViTModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ViTModel), +/* harmony export */ ViTPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.ViTPreTrainedModel), +/* harmony export */ VisionEncoderDecoderModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.VisionEncoderDecoderModel), +/* harmony export */ VitMatteForImageMatting: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.VitMatteForImageMatting), +/* harmony export */ VitMatteImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.VitMatteImageProcessor), +/* harmony export */ VitMattePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.VitMattePreTrainedModel), +/* harmony export */ VitPoseForPoseEstimation: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.VitPoseForPoseEstimation), +/* harmony export */ VitPoseImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.VitPoseImageProcessor), +/* harmony export */ VitPosePreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.VitPosePreTrainedModel), +/* harmony export */ VitsModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.VitsModel), +/* harmony export */ VitsModelOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.VitsModelOutput), +/* harmony export */ VitsPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.VitsPreTrainedModel), +/* harmony export */ VitsTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.VitsTokenizer), +/* harmony export */ VoxtralForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.VoxtralForConditionalGeneration), +/* harmony export */ VoxtralProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.VoxtralProcessor), +/* harmony export */ Wav2Vec2BertForCTC: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Wav2Vec2BertForCTC), +/* harmony export */ Wav2Vec2BertForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Wav2Vec2BertForSequenceClassification), +/* harmony export */ Wav2Vec2BertModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Wav2Vec2BertModel), +/* harmony export */ Wav2Vec2BertPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Wav2Vec2BertPreTrainedModel), +/* harmony export */ Wav2Vec2CTCTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.Wav2Vec2CTCTokenizer), +/* harmony export */ Wav2Vec2FeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.Wav2Vec2FeatureExtractor), +/* harmony export */ Wav2Vec2ForAudioFrameClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Wav2Vec2ForAudioFrameClassification), +/* harmony export */ Wav2Vec2ForCTC: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Wav2Vec2ForCTC), +/* harmony export */ Wav2Vec2ForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Wav2Vec2ForSequenceClassification), +/* harmony export */ Wav2Vec2Model: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Wav2Vec2Model), +/* harmony export */ Wav2Vec2PreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.Wav2Vec2PreTrainedModel), +/* harmony export */ Wav2Vec2Processor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.Wav2Vec2Processor), +/* harmony export */ Wav2Vec2ProcessorWithLM: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.Wav2Vec2ProcessorWithLM), +/* harmony export */ WavLMForAudioFrameClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WavLMForAudioFrameClassification), +/* harmony export */ WavLMForCTC: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WavLMForCTC), +/* harmony export */ WavLMForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WavLMForSequenceClassification), +/* harmony export */ WavLMForXVector: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WavLMForXVector), +/* harmony export */ WavLMModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WavLMModel), +/* harmony export */ WavLMPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WavLMPreTrainedModel), +/* harmony export */ WeSpeakerFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.WeSpeakerFeatureExtractor), +/* harmony export */ WeSpeakerResNetModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WeSpeakerResNetModel), +/* harmony export */ WeSpeakerResNetPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WeSpeakerResNetPreTrainedModel), +/* harmony export */ WhisperFeatureExtractor: () => (/* reexport safe */ _models_feature_extractors_js__WEBPACK_IMPORTED_MODULE_11__.WhisperFeatureExtractor), +/* harmony export */ WhisperForConditionalGeneration: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WhisperForConditionalGeneration), +/* harmony export */ WhisperModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WhisperModel), +/* harmony export */ WhisperPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.WhisperPreTrainedModel), +/* harmony export */ WhisperProcessor: () => (/* reexport safe */ _models_processors_js__WEBPACK_IMPORTED_MODULE_17__.WhisperProcessor), +/* harmony export */ WhisperTextStreamer: () => (/* reexport safe */ _generation_streamers_js__WEBPACK_IMPORTED_MODULE_19__.WhisperTextStreamer), +/* harmony export */ WhisperTimeStampLogitsProcessor: () => (/* reexport safe */ _generation_logits_process_js__WEBPACK_IMPORTED_MODULE_21__.WhisperTimeStampLogitsProcessor), +/* harmony export */ WhisperTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.WhisperTokenizer), +/* harmony export */ XLMForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMForQuestionAnswering), +/* harmony export */ XLMForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMForSequenceClassification), +/* harmony export */ XLMForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMForTokenClassification), +/* harmony export */ XLMModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMModel), +/* harmony export */ XLMPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMPreTrainedModel), +/* harmony export */ XLMRobertaForMaskedLM: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMRobertaForMaskedLM), +/* harmony export */ XLMRobertaForQuestionAnswering: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMRobertaForQuestionAnswering), +/* harmony export */ XLMRobertaForSequenceClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMRobertaForSequenceClassification), +/* harmony export */ XLMRobertaForTokenClassification: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMRobertaForTokenClassification), +/* harmony export */ XLMRobertaModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMRobertaModel), +/* harmony export */ XLMRobertaPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMRobertaPreTrainedModel), +/* harmony export */ XLMRobertaTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.XLMRobertaTokenizer), +/* harmony export */ XLMTokenizer: () => (/* reexport safe */ _tokenizers_js__WEBPACK_IMPORTED_MODULE_3__.XLMTokenizer), +/* harmony export */ XLMWithLMHeadModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XLMWithLMHeadModel), +/* harmony export */ XVectorOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.XVectorOutput), +/* harmony export */ YolosFeatureExtractor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.YolosFeatureExtractor), +/* harmony export */ YolosForObjectDetection: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.YolosForObjectDetection), +/* harmony export */ YolosImageProcessor: () => (/* reexport safe */ _models_image_processors_js__WEBPACK_IMPORTED_MODULE_14__.YolosImageProcessor), +/* harmony export */ YolosModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.YolosModel), +/* harmony export */ YolosObjectDetectionOutput: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.YolosObjectDetectionOutput), +/* harmony export */ YolosPreTrainedModel: () => (/* reexport safe */ _models_js__WEBPACK_IMPORTED_MODULE_2__.YolosPreTrainedModel), +/* harmony export */ ZeroShotAudioClassificationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ZeroShotAudioClassificationPipeline), +/* harmony export */ ZeroShotClassificationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ZeroShotClassificationPipeline), +/* harmony export */ ZeroShotImageClassificationPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ZeroShotImageClassificationPipeline), +/* harmony export */ ZeroShotObjectDetectionPipeline: () => (/* reexport safe */ _pipelines_js__WEBPACK_IMPORTED_MODULE_1__.ZeroShotObjectDetectionPipeline), +/* harmony export */ bankers_round: () => (/* reexport safe */ _utils_maths_js__WEBPACK_IMPORTED_MODULE_9__.bankers_round), +/* harmony export */ cat: () => (/* reexport safe */ _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.cat), +/* harmony export */ cos_sim: () => (/* reexport safe */ _utils_maths_js__WEBPACK_IMPORTED_MODULE_9__.cos_sim), +/* harmony export */ dot: () => (/* reexport safe */ _utils_maths_js__WEBPACK_IMPORTED_MODULE_9__.dot), +/* harmony export */ dynamic_time_warping: () => (/* reexport safe */ _utils_maths_js__WEBPACK_IMPORTED_MODULE_9__.dynamic_time_warping), +/* harmony export */ env: () => (/* reexport safe */ _env_js__WEBPACK_IMPORTED_MODULE_0__.env), +/* harmony export */ full: () => (/* reexport safe */ _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.full), +/* harmony export */ full_like: () => (/* reexport safe */ _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.full_like), +/* harmony export */ getCacheShapes: () => (/* reexport safe */ _configs_js__WEBPACK_IMPORTED_MODULE_4__.getCacheShapes), +/* harmony export */ hamming: () => (/* reexport safe */ _utils_audio_js__WEBPACK_IMPORTED_MODULE_5__.hamming), +/* harmony export */ hanning: () => (/* reexport safe */ _utils_audio_js__WEBPACK_IMPORTED_MODULE_5__.hanning), +/* harmony export */ interpolate: () => (/* reexport safe */ _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.interpolate), +/* harmony export */ interpolate_4d: () => (/* reexport safe */ _utils_tensor_js__WEBPACK_IMPORTED_MODULE_8__.interpolate_4d), +/* 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__webpack_exports__.interpolate_4d; +var __webpack_exports__interpolate_data = __webpack_exports__.interpolate_data; +var __webpack_exports__is_chinese_char = __webpack_exports__.is_chinese_char; +var __webpack_exports__layer_norm = __webpack_exports__.layer_norm; +var __webpack_exports__load_image = __webpack_exports__.load_image; +var __webpack_exports__load_video = __webpack_exports__.load_video; +var __webpack_exports__log_softmax = __webpack_exports__.log_softmax; +var __webpack_exports__magnitude = __webpack_exports__.magnitude; +var __webpack_exports__matmul = __webpack_exports__.matmul; +var __webpack_exports__max = __webpack_exports__.max; +var __webpack_exports__mean = __webpack_exports__.mean; +var __webpack_exports__mean_pooling = __webpack_exports__.mean_pooling; +var __webpack_exports__medianFilter = __webpack_exports__.medianFilter; +var __webpack_exports__mel_filter_bank = __webpack_exports__.mel_filter_bank; +var __webpack_exports__min = __webpack_exports__.min; +var __webpack_exports__ones = __webpack_exports__.ones; +var __webpack_exports__ones_like = __webpack_exports__.ones_like; +var __webpack_exports__permute = __webpack_exports__.permute; +var __webpack_exports__permute_data = __webpack_exports__.permute_data; +var __webpack_exports__pipeline = __webpack_exports__.pipeline; +var __webpack_exports__quantize_embeddings = __webpack_exports__.quantize_embeddings; +var __webpack_exports__rand = __webpack_exports__.rand; +var __webpack_exports__read_audio = __webpack_exports__.read_audio; +var __webpack_exports__rfft = __webpack_exports__.rfft; +var __webpack_exports__round = __webpack_exports__.round; +var __webpack_exports__slice = __webpack_exports__.slice; +var __webpack_exports__softmax = __webpack_exports__.softmax; +var __webpack_exports__spectrogram = __webpack_exports__.spectrogram; +var __webpack_exports__stack = __webpack_exports__.stack; +var __webpack_exports__std_mean = __webpack_exports__.std_mean; +var __webpack_exports__topk = __webpack_exports__.topk; +var __webpack_exports__window_function = __webpack_exports__.window_function; +var __webpack_exports__zeros = __webpack_exports__.zeros; +var __webpack_exports__zeros_like = __webpack_exports__.zeros_like; +export { __webpack_exports__ASTFeatureExtractor as ASTFeatureExtractor, __webpack_exports__ASTForAudioClassification as ASTForAudioClassification, __webpack_exports__ASTModel as ASTModel, __webpack_exports__ASTPreTrainedModel as ASTPreTrainedModel, __webpack_exports__AlbertForMaskedLM as AlbertForMaskedLM, __webpack_exports__AlbertForQuestionAnswering as AlbertForQuestionAnswering, __webpack_exports__AlbertForSequenceClassification as AlbertForSequenceClassification, __webpack_exports__AlbertModel as AlbertModel, __webpack_exports__AlbertPreTrainedModel as AlbertPreTrainedModel, __webpack_exports__AlbertTokenizer as AlbertTokenizer, __webpack_exports__ArceeForCausalLM as ArceeForCausalLM, __webpack_exports__ArceeModel as ArceeModel, __webpack_exports__ArceePreTrainedModel as ArceePreTrainedModel, __webpack_exports__AudioClassificationPipeline as AudioClassificationPipeline, __webpack_exports__AutoConfig as AutoConfig, __webpack_exports__AutoFeatureExtractor as AutoFeatureExtractor, __webpack_exports__AutoImageProcessor as AutoImageProcessor, __webpack_exports__AutoModel as AutoModel, __webpack_exports__AutoModelForAudioClassification as AutoModelForAudioClassification, __webpack_exports__AutoModelForAudioFrameClassification as AutoModelForAudioFrameClassification, __webpack_exports__AutoModelForAudioTextToText as AutoModelForAudioTextToText, __webpack_exports__AutoModelForCTC as AutoModelForCTC, __webpack_exports__AutoModelForCausalLM as AutoModelForCausalLM, __webpack_exports__AutoModelForDepthEstimation as AutoModelForDepthEstimation, __webpack_exports__AutoModelForDocumentQuestionAnswering as AutoModelForDocumentQuestionAnswering, __webpack_exports__AutoModelForImageClassification as AutoModelForImageClassification, __webpack_exports__AutoModelForImageFeatureExtraction as AutoModelForImageFeatureExtraction, __webpack_exports__AutoModelForImageMatting as AutoModelForImageMatting, __webpack_exports__AutoModelForImageSegmentation as AutoModelForImageSegmentation, __webpack_exports__AutoModelForImageTextToText as AutoModelForImageTextToText, __webpack_exports__AutoModelForImageToImage as AutoModelForImageToImage, __webpack_exports__AutoModelForMaskGeneration as AutoModelForMaskGeneration, __webpack_exports__AutoModelForMaskedLM as AutoModelForMaskedLM, __webpack_exports__AutoModelForNormalEstimation as AutoModelForNormalEstimation, __webpack_exports__AutoModelForObjectDetection as AutoModelForObjectDetection, __webpack_exports__AutoModelForPoseEstimation as AutoModelForPoseEstimation, __webpack_exports__AutoModelForQuestionAnswering as AutoModelForQuestionAnswering, __webpack_exports__AutoModelForSemanticSegmentation as AutoModelForSemanticSegmentation, __webpack_exports__AutoModelForSeq2SeqLM as AutoModelForSeq2SeqLM, __webpack_exports__AutoModelForSequenceClassification as AutoModelForSequenceClassification, __webpack_exports__AutoModelForSpeechSeq2Seq as AutoModelForSpeechSeq2Seq, __webpack_exports__AutoModelForTextToSpectrogram as AutoModelForTextToSpectrogram, __webpack_exports__AutoModelForTextToWaveform as AutoModelForTextToWaveform, __webpack_exports__AutoModelForTokenClassification as AutoModelForTokenClassification, __webpack_exports__AutoModelForUniversalSegmentation as AutoModelForUniversalSegmentation, __webpack_exports__AutoModelForVision2Seq as AutoModelForVision2Seq, __webpack_exports__AutoModelForXVector as AutoModelForXVector, __webpack_exports__AutoModelForZeroShotObjectDetection as AutoModelForZeroShotObjectDetection, __webpack_exports__AutoProcessor as AutoProcessor, __webpack_exports__AutoTokenizer as AutoTokenizer, __webpack_exports__AutomaticSpeechRecognitionPipeline as AutomaticSpeechRecognitionPipeline, __webpack_exports__BackgroundRemovalPipeline as BackgroundRemovalPipeline, __webpack_exports__BartForConditionalGeneration as BartForConditionalGeneration, __webpack_exports__BartForSequenceClassification as BartForSequenceClassification, __webpack_exports__BartModel as BartModel, __webpack_exports__BartPretrainedModel as BartPretrainedModel, __webpack_exports__BartTokenizer as BartTokenizer, __webpack_exports__BaseModelOutput as BaseModelOutput, __webpack_exports__BaseStreamer as BaseStreamer, __webpack_exports__BeitFeatureExtractor as BeitFeatureExtractor, __webpack_exports__BeitForImageClassification as BeitForImageClassification, __webpack_exports__BeitModel as BeitModel, __webpack_exports__BeitPreTrainedModel as BeitPreTrainedModel, __webpack_exports__BertForMaskedLM as BertForMaskedLM, __webpack_exports__BertForQuestionAnswering as BertForQuestionAnswering, __webpack_exports__BertForSequenceClassification as BertForSequenceClassification, __webpack_exports__BertForTokenClassification as BertForTokenClassification, __webpack_exports__BertModel as BertModel, __webpack_exports__BertPreTrainedModel as BertPreTrainedModel, __webpack_exports__BertTokenizer as BertTokenizer, __webpack_exports__BitImageProcessor as BitImageProcessor, __webpack_exports__BlenderbotForConditionalGeneration as BlenderbotForConditionalGeneration, __webpack_exports__BlenderbotModel as BlenderbotModel, __webpack_exports__BlenderbotPreTrainedModel as BlenderbotPreTrainedModel, __webpack_exports__BlenderbotSmallForConditionalGeneration as BlenderbotSmallForConditionalGeneration, __webpack_exports__BlenderbotSmallModel as BlenderbotSmallModel, __webpack_exports__BlenderbotSmallPreTrainedModel as BlenderbotSmallPreTrainedModel, __webpack_exports__BlenderbotSmallTokenizer as BlenderbotSmallTokenizer, __webpack_exports__BlenderbotTokenizer as BlenderbotTokenizer, __webpack_exports__BloomForCausalLM as BloomForCausalLM, __webpack_exports__BloomModel as BloomModel, __webpack_exports__BloomPreTrainedModel as BloomPreTrainedModel, __webpack_exports__BloomTokenizer as BloomTokenizer, __webpack_exports__CLIPFeatureExtractor as CLIPFeatureExtractor, __webpack_exports__CLIPImageProcessor as CLIPImageProcessor, __webpack_exports__CLIPModel as CLIPModel, __webpack_exports__CLIPPreTrainedModel as CLIPPreTrainedModel, __webpack_exports__CLIPSegForImageSegmentation as CLIPSegForImageSegmentation, __webpack_exports__CLIPSegModel as CLIPSegModel, __webpack_exports__CLIPSegPreTrainedModel as CLIPSegPreTrainedModel, __webpack_exports__CLIPTextModel as CLIPTextModel, __webpack_exports__CLIPTextModelWithProjection as CLIPTextModelWithProjection, __webpack_exports__CLIPTokenizer as CLIPTokenizer, __webpack_exports__CLIPVisionModel as CLIPVisionModel, __webpack_exports__CLIPVisionModelWithProjection as CLIPVisionModelWithProjection, __webpack_exports__CamembertForMaskedLM as CamembertForMaskedLM, __webpack_exports__CamembertForQuestionAnswering as CamembertForQuestionAnswering, __webpack_exports__CamembertForSequenceClassification as CamembertForSequenceClassification, __webpack_exports__CamembertForTokenClassification as CamembertForTokenClassification, __webpack_exports__CamembertModel as CamembertModel, __webpack_exports__CamembertPreTrainedModel as CamembertPreTrainedModel, __webpack_exports__CamembertTokenizer as CamembertTokenizer, __webpack_exports__CausalLMOutput as CausalLMOutput, __webpack_exports__CausalLMOutputWithPast as CausalLMOutputWithPast, __webpack_exports__ChineseCLIPFeatureExtractor as ChineseCLIPFeatureExtractor, __webpack_exports__ChineseCLIPModel as ChineseCLIPModel, __webpack_exports__ChineseCLIPPreTrainedModel as ChineseCLIPPreTrainedModel, __webpack_exports__ClapAudioModelWithProjection as ClapAudioModelWithProjection, __webpack_exports__ClapFeatureExtractor as ClapFeatureExtractor, __webpack_exports__ClapModel as ClapModel, __webpack_exports__ClapPreTrainedModel as ClapPreTrainedModel, __webpack_exports__ClapTextModelWithProjection as ClapTextModelWithProjection, __webpack_exports__ClassifierFreeGuidanceLogitsProcessor as ClassifierFreeGuidanceLogitsProcessor, __webpack_exports__CodeGenForCausalLM as CodeGenForCausalLM, __webpack_exports__CodeGenModel as CodeGenModel, __webpack_exports__CodeGenPreTrainedModel as CodeGenPreTrainedModel, __webpack_exports__CodeGenTokenizer as CodeGenTokenizer, __webpack_exports__CodeLlamaTokenizer as CodeLlamaTokenizer, __webpack_exports__CohereForCausalLM as CohereForCausalLM, __webpack_exports__CohereModel as CohereModel, __webpack_exports__CoherePreTrainedModel as CoherePreTrainedModel, __webpack_exports__CohereTokenizer as CohereTokenizer, __webpack_exports__ConvBertForMaskedLM as ConvBertForMaskedLM, __webpack_exports__ConvBertForQuestionAnswering as ConvBertForQuestionAnswering, __webpack_exports__ConvBertForSequenceClassification as ConvBertForSequenceClassification, __webpack_exports__ConvBertForTokenClassification as ConvBertForTokenClassification, __webpack_exports__ConvBertModel as ConvBertModel, __webpack_exports__ConvBertPreTrainedModel as ConvBertPreTrainedModel, __webpack_exports__ConvBertTokenizer as ConvBertTokenizer, __webpack_exports__ConvNextFeatureExtractor as ConvNextFeatureExtractor, __webpack_exports__ConvNextForImageClassification as ConvNextForImageClassification, __webpack_exports__ConvNextImageProcessor as ConvNextImageProcessor, __webpack_exports__ConvNextModel as ConvNextModel, __webpack_exports__ConvNextPreTrainedModel as ConvNextPreTrainedModel, __webpack_exports__ConvNextV2ForImageClassification as ConvNextV2ForImageClassification, __webpack_exports__ConvNextV2Model as ConvNextV2Model, __webpack_exports__ConvNextV2PreTrainedModel as ConvNextV2PreTrainedModel, __webpack_exports__DFineForObjectDetection as DFineForObjectDetection, __webpack_exports__DFineModel as DFineModel, __webpack_exports__DFinePreTrainedModel as DFinePreTrainedModel, __webpack_exports__DINOv3ConvNextModel as DINOv3ConvNextModel, __webpack_exports__DINOv3ConvNextPreTrainedModel as DINOv3ConvNextPreTrainedModel, __webpack_exports__DINOv3ViTImageProcessor as DINOv3ViTImageProcessor, __webpack_exports__DINOv3ViTModel as DINOv3ViTModel, __webpack_exports__DINOv3ViTPreTrainedModel as DINOv3ViTPreTrainedModel, __webpack_exports__DPTFeatureExtractor as DPTFeatureExtractor, __webpack_exports__DPTForDepthEstimation as DPTForDepthEstimation, __webpack_exports__DPTImageProcessor as DPTImageProcessor, __webpack_exports__DPTModel as DPTModel, __webpack_exports__DPTPreTrainedModel as DPTPreTrainedModel, __webpack_exports__DacDecoderModel as DacDecoderModel, __webpack_exports__DacDecoderOutput as DacDecoderOutput, __webpack_exports__DacEncoderModel as DacEncoderModel, __webpack_exports__DacEncoderOutput as DacEncoderOutput, __webpack_exports__DacFeatureExtractor as DacFeatureExtractor, __webpack_exports__DacModel as DacModel, __webpack_exports__DacPreTrainedModel as DacPreTrainedModel, __webpack_exports__DataTypeMap as DataTypeMap, __webpack_exports__DebertaForMaskedLM as DebertaForMaskedLM, __webpack_exports__DebertaForQuestionAnswering as DebertaForQuestionAnswering, __webpack_exports__DebertaForSequenceClassification as DebertaForSequenceClassification, __webpack_exports__DebertaForTokenClassification as DebertaForTokenClassification, __webpack_exports__DebertaModel as DebertaModel, __webpack_exports__DebertaPreTrainedModel as DebertaPreTrainedModel, __webpack_exports__DebertaTokenizer as DebertaTokenizer, __webpack_exports__DebertaV2ForMaskedLM as DebertaV2ForMaskedLM, __webpack_exports__DebertaV2ForQuestionAnswering as DebertaV2ForQuestionAnswering, __webpack_exports__DebertaV2ForSequenceClassification as DebertaV2ForSequenceClassification, __webpack_exports__DebertaV2ForTokenClassification as DebertaV2ForTokenClassification, __webpack_exports__DebertaV2Model as DebertaV2Model, __webpack_exports__DebertaV2PreTrainedModel as DebertaV2PreTrainedModel, __webpack_exports__DebertaV2Tokenizer as DebertaV2Tokenizer, __webpack_exports__DecisionTransformerModel as DecisionTransformerModel, __webpack_exports__DecisionTransformerPreTrainedModel as DecisionTransformerPreTrainedModel, __webpack_exports__DeiTFeatureExtractor as DeiTFeatureExtractor, __webpack_exports__DeiTForImageClassification as DeiTForImageClassification, __webpack_exports__DeiTImageProcessor as DeiTImageProcessor, __webpack_exports__DeiTModel as DeiTModel, __webpack_exports__DeiTPreTrainedModel as DeiTPreTrainedModel, __webpack_exports__DepthAnythingForDepthEstimation as DepthAnythingForDepthEstimation, __webpack_exports__DepthAnythingPreTrainedModel as DepthAnythingPreTrainedModel, __webpack_exports__DepthEstimationPipeline as DepthEstimationPipeline, __webpack_exports__DepthProForDepthEstimation as DepthProForDepthEstimation, __webpack_exports__DepthProPreTrainedModel as DepthProPreTrainedModel, __webpack_exports__DetrFeatureExtractor as DetrFeatureExtractor, __webpack_exports__DetrForObjectDetection as DetrForObjectDetection, __webpack_exports__DetrForSegmentation as DetrForSegmentation, __webpack_exports__DetrImageProcessor as DetrImageProcessor, __webpack_exports__DetrModel as DetrModel, __webpack_exports__DetrObjectDetectionOutput as DetrObjectDetectionOutput, __webpack_exports__DetrPreTrainedModel as DetrPreTrainedModel, __webpack_exports__DetrSegmentationOutput as DetrSegmentationOutput, __webpack_exports__Dinov2ForImageClassification as Dinov2ForImageClassification, __webpack_exports__Dinov2Model as Dinov2Model, __webpack_exports__Dinov2PreTrainedModel as Dinov2PreTrainedModel, __webpack_exports__Dinov2WithRegistersForImageClassification as Dinov2WithRegistersForImageClassification, __webpack_exports__Dinov2WithRegistersModel as Dinov2WithRegistersModel, __webpack_exports__Dinov2WithRegistersPreTrainedModel as Dinov2WithRegistersPreTrainedModel, __webpack_exports__DistilBertForMaskedLM as DistilBertForMaskedLM, __webpack_exports__DistilBertForQuestionAnswering as DistilBertForQuestionAnswering, __webpack_exports__DistilBertForSequenceClassification as DistilBertForSequenceClassification, __webpack_exports__DistilBertForTokenClassification as DistilBertForTokenClassification, __webpack_exports__DistilBertModel as DistilBertModel, __webpack_exports__DistilBertPreTrainedModel as DistilBertPreTrainedModel, __webpack_exports__DistilBertTokenizer as DistilBertTokenizer, __webpack_exports__DocumentQuestionAnsweringPipeline as DocumentQuestionAnsweringPipeline, __webpack_exports__DonutFeatureExtractor as DonutFeatureExtractor, __webpack_exports__DonutImageProcessor as DonutImageProcessor, __webpack_exports__DonutSwinModel as DonutSwinModel, __webpack_exports__DonutSwinPreTrainedModel as DonutSwinPreTrainedModel, __webpack_exports__EfficientNetForImageClassification as EfficientNetForImageClassification, __webpack_exports__EfficientNetImageProcessor as EfficientNetImageProcessor, __webpack_exports__EfficientNetModel as EfficientNetModel, __webpack_exports__EfficientNetPreTrainedModel as EfficientNetPreTrainedModel, __webpack_exports__ElectraForMaskedLM as ElectraForMaskedLM, __webpack_exports__ElectraForQuestionAnswering as ElectraForQuestionAnswering, __webpack_exports__ElectraForSequenceClassification as ElectraForSequenceClassification, __webpack_exports__ElectraForTokenClassification as ElectraForTokenClassification, __webpack_exports__ElectraModel as ElectraModel, __webpack_exports__ElectraPreTrainedModel as ElectraPreTrainedModel, __webpack_exports__ElectraTokenizer as ElectraTokenizer, __webpack_exports__EncodecFeatureExtractor as EncodecFeatureExtractor, __webpack_exports__EosTokenCriteria as EosTokenCriteria, __webpack_exports__Ernie4_5_ForCausalLM as Ernie4_5_ForCausalLM, __webpack_exports__Ernie4_5_Model as Ernie4_5_Model, __webpack_exports__Ernie4_5_PretrainedModel as Ernie4_5_PretrainedModel, __webpack_exports__Ernie4_5_Tokenizer as Ernie4_5_Tokenizer, __webpack_exports__EsmForMaskedLM as EsmForMaskedLM, __webpack_exports__EsmForSequenceClassification as EsmForSequenceClassification, __webpack_exports__EsmForTokenClassification as EsmForTokenClassification, __webpack_exports__EsmModel as EsmModel, __webpack_exports__EsmPreTrainedModel as EsmPreTrainedModel, __webpack_exports__EsmTokenizer as EsmTokenizer, __webpack_exports__ExaoneForCausalLM as ExaoneForCausalLM, __webpack_exports__ExaoneModel as ExaoneModel, __webpack_exports__ExaonePreTrainedModel as ExaonePreTrainedModel, __webpack_exports__FFT as FFT, __webpack_exports__FalconForCausalLM as FalconForCausalLM, __webpack_exports__FalconModel as FalconModel, __webpack_exports__FalconPreTrainedModel as FalconPreTrainedModel, __webpack_exports__FalconTokenizer as FalconTokenizer, __webpack_exports__FastViTForImageClassification as FastViTForImageClassification, __webpack_exports__FastViTModel as FastViTModel, __webpack_exports__FastViTPreTrainedModel as FastViTPreTrainedModel, __webpack_exports__FeatureExtractionPipeline as FeatureExtractionPipeline, __webpack_exports__FeatureExtractor as FeatureExtractor, __webpack_exports__FillMaskPipeline as FillMaskPipeline, __webpack_exports__Florence2ForConditionalGeneration as Florence2ForConditionalGeneration, __webpack_exports__Florence2PreTrainedModel as Florence2PreTrainedModel, __webpack_exports__Florence2Processor as Florence2Processor, __webpack_exports__ForcedBOSTokenLogitsProcessor as ForcedBOSTokenLogitsProcessor, __webpack_exports__ForcedEOSTokenLogitsProcessor as ForcedEOSTokenLogitsProcessor, __webpack_exports__GLPNFeatureExtractor as GLPNFeatureExtractor, __webpack_exports__GLPNForDepthEstimation as GLPNForDepthEstimation, __webpack_exports__GLPNModel as GLPNModel, __webpack_exports__GLPNPreTrainedModel as GLPNPreTrainedModel, __webpack_exports__GPT2LMHeadModel as GPT2LMHeadModel, __webpack_exports__GPT2Model as GPT2Model, __webpack_exports__GPT2PreTrainedModel as GPT2PreTrainedModel, __webpack_exports__GPT2Tokenizer as GPT2Tokenizer, __webpack_exports__GPTBigCodeForCausalLM as GPTBigCodeForCausalLM, __webpack_exports__GPTBigCodeModel as GPTBigCodeModel, __webpack_exports__GPTBigCodePreTrainedModel as GPTBigCodePreTrainedModel, __webpack_exports__GPTJForCausalLM as GPTJForCausalLM, __webpack_exports__GPTJModel as GPTJModel, __webpack_exports__GPTJPreTrainedModel as GPTJPreTrainedModel, __webpack_exports__GPTNeoForCausalLM as GPTNeoForCausalLM, __webpack_exports__GPTNeoModel as GPTNeoModel, __webpack_exports__GPTNeoPreTrainedModel as GPTNeoPreTrainedModel, __webpack_exports__GPTNeoXForCausalLM as GPTNeoXForCausalLM, __webpack_exports__GPTNeoXModel as GPTNeoXModel, __webpack_exports__GPTNeoXPreTrainedModel as GPTNeoXPreTrainedModel, __webpack_exports__GPTNeoXTokenizer as GPTNeoXTokenizer, __webpack_exports__Gemma2ForCausalLM as Gemma2ForCausalLM, __webpack_exports__Gemma2Model as Gemma2Model, __webpack_exports__Gemma2PreTrainedModel as Gemma2PreTrainedModel, __webpack_exports__Gemma3ForCausalLM as Gemma3ForCausalLM, __webpack_exports__Gemma3Model as Gemma3Model, __webpack_exports__Gemma3PreTrainedModel as Gemma3PreTrainedModel, __webpack_exports__Gemma3nAudioFeatureExtractor as Gemma3nAudioFeatureExtractor, __webpack_exports__Gemma3nForConditionalGeneration as Gemma3nForConditionalGeneration, __webpack_exports__Gemma3nPreTrainedModel as Gemma3nPreTrainedModel, __webpack_exports__Gemma3nProcessor as Gemma3nProcessor, __webpack_exports__GemmaForCausalLM as GemmaForCausalLM, __webpack_exports__GemmaModel as GemmaModel, __webpack_exports__GemmaPreTrainedModel as GemmaPreTrainedModel, __webpack_exports__GemmaTokenizer as GemmaTokenizer, __webpack_exports__GlmForCausalLM as GlmForCausalLM, __webpack_exports__GlmModel as GlmModel, __webpack_exports__GlmPreTrainedModel as GlmPreTrainedModel, __webpack_exports__GraniteForCausalLM as GraniteForCausalLM, __webpack_exports__GraniteModel as GraniteModel, __webpack_exports__GranitePreTrainedModel as GranitePreTrainedModel, __webpack_exports__Grok1Tokenizer as Grok1Tokenizer, __webpack_exports__GroundingDinoForObjectDetection as GroundingDinoForObjectDetection, __webpack_exports__GroundingDinoImageProcessor as GroundingDinoImageProcessor, __webpack_exports__GroundingDinoPreTrainedModel as GroundingDinoPreTrainedModel, __webpack_exports__GroundingDinoProcessor as GroundingDinoProcessor, __webpack_exports__GroupViTModel as GroupViTModel, __webpack_exports__GroupViTPreTrainedModel as GroupViTPreTrainedModel, __webpack_exports__HeliumForCausalLM as HeliumForCausalLM, __webpack_exports__HeliumModel as HeliumModel, __webpack_exports__HeliumPreTrainedModel as HeliumPreTrainedModel, __webpack_exports__HerbertTokenizer as HerbertTokenizer, __webpack_exports__HieraForImageClassification as HieraForImageClassification, __webpack_exports__HieraModel as HieraModel, __webpack_exports__HieraPreTrainedModel as HieraPreTrainedModel, __webpack_exports__HubertForCTC as HubertForCTC, __webpack_exports__HubertForSequenceClassification as HubertForSequenceClassification, __webpack_exports__HubertModel as HubertModel, __webpack_exports__HubertPreTrainedModel as HubertPreTrainedModel, __webpack_exports__IJepaForImageClassification as IJepaForImageClassification, __webpack_exports__IJepaModel as IJepaModel, __webpack_exports__IJepaPreTrainedModel as IJepaPreTrainedModel, __webpack_exports__Idefics3ForConditionalGeneration as Idefics3ForConditionalGeneration, __webpack_exports__Idefics3ImageProcessor as Idefics3ImageProcessor, __webpack_exports__Idefics3PreTrainedModel as Idefics3PreTrainedModel, __webpack_exports__Idefics3Processor as Idefics3Processor, __webpack_exports__ImageClassificationPipeline as ImageClassificationPipeline, __webpack_exports__ImageFeatureExtractionPipeline as ImageFeatureExtractionPipeline, __webpack_exports__ImageFeatureExtractor as ImageFeatureExtractor, __webpack_exports__ImageMattingOutput as ImageMattingOutput, __webpack_exports__ImageProcessor as ImageProcessor, __webpack_exports__ImageSegmentationPipeline as ImageSegmentationPipeline, __webpack_exports__ImageToImagePipeline as ImageToImagePipeline, __webpack_exports__ImageToTextPipeline as ImageToTextPipeline, __webpack_exports__InterruptableStoppingCriteria as InterruptableStoppingCriteria, __webpack_exports__JAISLMHeadModel as JAISLMHeadModel, __webpack_exports__JAISModel as JAISModel, __webpack_exports__JAISPreTrainedModel as JAISPreTrainedModel, __webpack_exports__JinaCLIPImageProcessor as JinaCLIPImageProcessor, __webpack_exports__JinaCLIPModel as JinaCLIPModel, __webpack_exports__JinaCLIPPreTrainedModel as JinaCLIPPreTrainedModel, __webpack_exports__JinaCLIPProcessor as JinaCLIPProcessor, __webpack_exports__JinaCLIPTextModel as JinaCLIPTextModel, __webpack_exports__JinaCLIPVisionModel as JinaCLIPVisionModel, __webpack_exports__Lfm2ForCausalLM as Lfm2ForCausalLM, __webpack_exports__Lfm2Model as Lfm2Model, __webpack_exports__Lfm2PreTrainedModel as Lfm2PreTrainedModel, __webpack_exports__LiteWhisperForConditionalGeneration as LiteWhisperForConditionalGeneration, __webpack_exports__LlamaForCausalLM as LlamaForCausalLM, __webpack_exports__LlamaModel as LlamaModel, __webpack_exports__LlamaPreTrainedModel as LlamaPreTrainedModel, __webpack_exports__LlamaTokenizer as LlamaTokenizer, __webpack_exports__LlavaForConditionalGeneration as LlavaForConditionalGeneration, __webpack_exports__LlavaOnevisionForConditionalGeneration as LlavaOnevisionForConditionalGeneration, __webpack_exports__LlavaOnevisionImageProcessor as LlavaOnevisionImageProcessor, __webpack_exports__LlavaPreTrainedModel as LlavaPreTrainedModel, __webpack_exports__LlavaProcessor as LlavaProcessor, __webpack_exports__LlavaQwen2ForCausalLM as LlavaQwen2ForCausalLM, __webpack_exports__LogitsProcessor as LogitsProcessor, __webpack_exports__LogitsProcessorList as LogitsProcessorList, __webpack_exports__LogitsWarper as LogitsWarper, __webpack_exports__LongT5ForConditionalGeneration as LongT5ForConditionalGeneration, __webpack_exports__LongT5Model as LongT5Model, __webpack_exports__LongT5PreTrainedModel as LongT5PreTrainedModel, __webpack_exports__M2M100ForConditionalGeneration as M2M100ForConditionalGeneration, __webpack_exports__M2M100Model as M2M100Model, __webpack_exports__M2M100PreTrainedModel as M2M100PreTrainedModel, __webpack_exports__M2M100Tokenizer as M2M100Tokenizer, __webpack_exports__MBart50Tokenizer as MBart50Tokenizer, __webpack_exports__MBartForCausalLM as MBartForCausalLM, __webpack_exports__MBartForConditionalGeneration as MBartForConditionalGeneration, __webpack_exports__MBartForSequenceClassification as MBartForSequenceClassification, __webpack_exports__MBartModel as MBartModel, __webpack_exports__MBartPreTrainedModel as MBartPreTrainedModel, __webpack_exports__MBartTokenizer as MBartTokenizer, __webpack_exports__MPNetForMaskedLM as MPNetForMaskedLM, __webpack_exports__MPNetForQuestionAnswering 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__webpack_exports__WavLMPreTrainedModel as WavLMPreTrainedModel, __webpack_exports__WeSpeakerFeatureExtractor as WeSpeakerFeatureExtractor, __webpack_exports__WeSpeakerResNetModel as WeSpeakerResNetModel, __webpack_exports__WeSpeakerResNetPreTrainedModel as WeSpeakerResNetPreTrainedModel, __webpack_exports__WhisperFeatureExtractor as WhisperFeatureExtractor, __webpack_exports__WhisperForConditionalGeneration as WhisperForConditionalGeneration, __webpack_exports__WhisperModel as WhisperModel, __webpack_exports__WhisperPreTrainedModel as WhisperPreTrainedModel, __webpack_exports__WhisperProcessor as WhisperProcessor, __webpack_exports__WhisperTextStreamer as WhisperTextStreamer, __webpack_exports__WhisperTimeStampLogitsProcessor as WhisperTimeStampLogitsProcessor, __webpack_exports__WhisperTokenizer as WhisperTokenizer, __webpack_exports__XLMForQuestionAnswering as XLMForQuestionAnswering, __webpack_exports__XLMForSequenceClassification as XLMForSequenceClassification, __webpack_exports__XLMForTokenClassification as XLMForTokenClassification, __webpack_exports__XLMModel as XLMModel, __webpack_exports__XLMPreTrainedModel as XLMPreTrainedModel, __webpack_exports__XLMRobertaForMaskedLM as XLMRobertaForMaskedLM, __webpack_exports__XLMRobertaForQuestionAnswering as XLMRobertaForQuestionAnswering, __webpack_exports__XLMRobertaForSequenceClassification as XLMRobertaForSequenceClassification, __webpack_exports__XLMRobertaForTokenClassification as XLMRobertaForTokenClassification, __webpack_exports__XLMRobertaModel as XLMRobertaModel, __webpack_exports__XLMRobertaPreTrainedModel as XLMRobertaPreTrainedModel, __webpack_exports__XLMRobertaTokenizer as XLMRobertaTokenizer, __webpack_exports__XLMTokenizer as XLMTokenizer, __webpack_exports__XLMWithLMHeadModel as XLMWithLMHeadModel, __webpack_exports__XVectorOutput as XVectorOutput, __webpack_exports__YolosFeatureExtractor as YolosFeatureExtractor, __webpack_exports__YolosForObjectDetection as YolosForObjectDetection, __webpack_exports__YolosImageProcessor as YolosImageProcessor, __webpack_exports__YolosModel as YolosModel, __webpack_exports__YolosObjectDetectionOutput as YolosObjectDetectionOutput, __webpack_exports__YolosPreTrainedModel as YolosPreTrainedModel, __webpack_exports__ZeroShotAudioClassificationPipeline as ZeroShotAudioClassificationPipeline, __webpack_exports__ZeroShotClassificationPipeline as ZeroShotClassificationPipeline, __webpack_exports__ZeroShotImageClassificationPipeline as ZeroShotImageClassificationPipeline, __webpack_exports__ZeroShotObjectDetectionPipeline as ZeroShotObjectDetectionPipeline, __webpack_exports__bankers_round as bankers_round, __webpack_exports__cat as cat, __webpack_exports__cos_sim as cos_sim, __webpack_exports__dot as dot, __webpack_exports__dynamic_time_warping as dynamic_time_warping, __webpack_exports__env as env, __webpack_exports__full as full, __webpack_exports__full_like as full_like, __webpack_exports__getCacheShapes as getCacheShapes, __webpack_exports__hamming as hamming, __webpack_exports__hanning as hanning, __webpack_exports__interpolate as interpolate, __webpack_exports__interpolate_4d as interpolate_4d, __webpack_exports__interpolate_data as interpolate_data, __webpack_exports__is_chinese_char as is_chinese_char, __webpack_exports__layer_norm as layer_norm, __webpack_exports__load_image as load_image, __webpack_exports__load_video as load_video, __webpack_exports__log_softmax as log_softmax, __webpack_exports__magnitude as magnitude, __webpack_exports__matmul as matmul, __webpack_exports__max as max, __webpack_exports__mean as mean, __webpack_exports__mean_pooling as mean_pooling, __webpack_exports__medianFilter as medianFilter, __webpack_exports__mel_filter_bank as mel_filter_bank, __webpack_exports__min as min, __webpack_exports__ones as ones, __webpack_exports__ones_like as ones_like, __webpack_exports__permute as permute, __webpack_exports__permute_data as permute_data, __webpack_exports__pipeline as pipeline, __webpack_exports__quantize_embeddings as quantize_embeddings, __webpack_exports__rand as rand, __webpack_exports__read_audio as read_audio, __webpack_exports__rfft as rfft, __webpack_exports__round as round, __webpack_exports__slice as slice, __webpack_exports__softmax as softmax, __webpack_exports__spectrogram as spectrogram, __webpack_exports__stack as stack, __webpack_exports__std_mean as std_mean, __webpack_exports__topk as topk, __webpack_exports__window_function as window_function, __webpack_exports__zeros as zeros, __webpack_exports__zeros_like as zeros_like }; + +//# sourceMappingURL=transformers.js.map \ No newline at end of file diff --git a/transformersjs/task-bert.js b/transformersjs/task-bert.js new file mode 100644 index 00000000..04b08eb5 --- /dev/null +++ b/transformersjs/task-bert.js @@ -0,0 +1,35 @@ +// Copyright 2025 the V8 project authors. All rights reserved. +// Use of this source code is governed by a BSD-style license that can be +// found in the LICENSE file. + +// End-to-end task 1: Sentiment analysis, so NLP. + +globalThis.initPipeline = async function(pipeline) { + return await pipeline( + 'sentiment-analysis', + 'Xenova/distilbert-base-uncased-finetuned-sst-2-english', + // Use quantized models for smaller model weights. + { dtype: 'uint8' } + ); +} + +globalThis.doTask = async function(pipeline) { + const inputs = [ + 'I love transformers!', + 'Benchmarking is hard.', + ]; + const outputs = await pipeline(inputs); + return outputs; +} + +globalThis.validate = function(outputs) { + if (outputs.length !== 2) { + throw new Error('Expected output to be an array matching the inputs, but got:' + outputs); + } + if (outputs[0].label !== 'POSITIVE' || outputs[0].score < 0.9) { + throw new Error('Expected positive sentiment for first input, but got: ' + outputs[0]); + } + if (outputs[1].label !== 'NEGATIVE' || outputs[1].score < 0.9) { + throw new Error('Expected negative sentiment for second input, but got: ' + outputs[1]); + } +} diff --git a/transformersjs/task-whisper.js b/transformersjs/task-whisper.js new file mode 100644 index 00000000..bce526f1 --- /dev/null +++ b/transformersjs/task-whisper.js @@ -0,0 +1,28 @@ +// Copyright 2025 the V8 project authors. All rights reserved. +// Use of this source code is governed by a BSD-style license that can be +// found in the LICENSE file. + +// End-to-end task 2: Speech recognition. +// Based on the example https://huggingface.co/Xenova/whisper-tiny.en +// Convert audio inputs first with `convert-audio.mjs`. + +globalThis.initPipeline = async function(pipeline) { + return await pipeline( + 'automatic-speech-recognition', + 'Xenova/whisper-tiny.en', + // Use quantized model because of smaller weights. + { dtype: 'q8' } + ); +} + +globalThis.doTask = async function(pipeline, inputFileBuffer) { + const input = new Float32Array(inputFileBuffer); + const output = await pipeline(input); + return output.text.trim(); +} + +globalThis.validate = function(output) { + if (output !== 'Ask not what your country can do for you.') { + throw new Error('Wrong output, got: ' + output); + } +} diff --git a/transformersjs/transformers.js.patch b/transformersjs/transformers.js.patch new file mode 100644 index 00000000..cab7f388 --- /dev/null +++ b/transformersjs/transformers.js.patch @@ -0,0 +1,17 @@ +diff --git a/transformersjs/build/transformers.js b/transformersjs/build/transformers.js +index b8e1342..0c702e8 100644 +--- a/transformersjs/build/transformers.js ++++ b/transformersjs/build/transformers.js +@@ -37868,11 +37868,10 @@ function quantize_embeddings(tensor, precision) { + /******/ + /******/ /* webpack/runtime/publicPath */ + /******/ (() => { +-/******/ var scriptUrl; ++/******/ var scriptUrl = ''; + /******/ if (typeof import.meta.url === "string") scriptUrl = import.meta.url + /******/ // When supporting browsers where an automatic publicPath is not supported you must specify an output.publicPath manually via configuration + /******/ // or pass an empty string ("") and set the __webpack_public_path__ variable from your code to use your own logic. +-/******/ if (!scriptUrl) throw new Error("Automatic publicPath is not supported in this browser"); + /******/ scriptUrl = scriptUrl.replace(/#.*$/, "").replace(/\?.*$/, "").replace(/\/[^\/]+$/, "/"); + /******/ __webpack_require__.p = scriptUrl; + /******/ })(); diff --git a/transformersjs/util/convert-audio.mjs b/transformersjs/util/convert-audio.mjs new file mode 100644 index 00000000..b605af22 --- /dev/null +++ b/transformersjs/util/convert-audio.mjs @@ -0,0 +1,63 @@ +// Copyright 2025 the V8 project authors. All rights reserved. +// Use of this source code is governed by a BSD-style license that can be +// found in the LICENSE file. + +// Convert an WAV audio file to a format that Transformers.js Whisper model can +// work with. +// Write the output as raw float values, so we don't need any additional +// WAV/RIFF parser but can directly use it. +// See https://huggingface.co/docs/transformers.js/guides/node-audio-processing + +import wavefile from 'wavefile'; +import * as fs from 'fs'; + +if (process.argv.length < 5) { + throw new Error("Usage: node convert-audio.mjs input.wav output.raw "); +} +const inputFilename = process.argv[2]; +const outputFilename = process.argv[3]; +const outputStartSample = parseInt(process.argv[4]); +const outputEndSample = parseInt(process.argv[5]); + +const wav = new wavefile.WaveFile(fs.readFileSync(inputFilename)); +const inputChannelCount = wav.fmt.numChannels; +const inputSampleRate = wav.fmt.sampleRate; +const inputBitDepth = wav.fmt.bitsPerSample; + +// Pipeline expects input as a Float32Array. +wav.toBitDepth('32f'); + +// Whisper expects audio with a sampling rate of 16000. +const outputSampleRate = 16000; +wav.toSampleRate(outputSampleRate); +// Explicitly set output type to `Float32Array` to make the raw output smaller. +// (Otherwise, `Float64Array` is the default.) +let audioData = wav.getSamples(/* interleaved */ false, Float32Array); + +// Downmix to mono, if necessary. +const outputChannelCount = 1; +if (Array.isArray(audioData)) { + if (audioData.length > 1) { + const SCALING_FACTOR = Math.sqrt(2); + for (let i = 0; i < audioData[0].length; ++i) { + audioData[0][i] = SCALING_FACTOR * (audioData[0][i] + audioData[1][i]) / 2; + } + } + audioData = audioData[0]; +} + +const inputSampleCount = audioData.length; + +// Select samples as specified by outputStartSample and outputEndSample. +console.assert(outputStartSample < outputEndSample, "Given start is smaller than end."); +console.assert(outputEndSample <= audioData.length, "Given end is longer than input file."); +audioData = audioData.slice(outputStartSample, outputEndSample); + +const outputSampleCount = audioData.length; +const outputSizeBytes = audioData.byteLength; +fs.writeFileSync(outputFilename, audioData); + +const durationSeconds = outputSampleCount / outputSampleRate; +console.log(`Converted ${durationSeconds}s of audio`); +console.log(` from '${inputFilename}', ${inputChannelCount} channel(s), ${inputSampleRate} Hz, ${inputBitDepth} bit, ${inputSampleCount} samples`); +console.log(` to '${outputFilename}', ${outputChannelCount} channel(s), ${outputSampleRate} Hz, 32 bit float, ${outputSampleCount} samples, ${outputSizeBytes} bytes`); diff --git a/transformersjs/util/package.json b/transformersjs/util/package.json new file mode 100644 index 00000000..5ba21290 --- /dev/null +++ b/transformersjs/util/package.json @@ -0,0 +1,10 @@ +{ + "name": "transformersjs-wasm", + "version": "0.0.1", + "author": "Daniel Lehmann ", + "license": "MIT", + "dependencies": { + "@huggingface/transformers": "^3.7.2", + "wavefile": "^11.0.0" + } +} diff --git a/transformersjs/util/test-models.mjs b/transformersjs/util/test-models.mjs new file mode 100644 index 00000000..6b5537ae --- /dev/null +++ b/transformersjs/util/test-models.mjs @@ -0,0 +1,57 @@ +// Copyright 2025 the V8 project authors. All rights reserved. +// Use of this source code is governed by a BSD-style license that can be +// found in the LICENSE file. + +// This is set up as an NPM project, so we can test Transformers.js in the +// environment it is supposed to work in (Node.js or a full browser) and +// download the dist build and model data. +// Note that Transformers.js in Node.js actually uses a binding to a native +// implementation of ONNX runtime on either the CPU or CUDA, i.e., NOT Wasm. +// It also runs multi-threaded by default. +// Performance is thus NOT comparable to the Wasm version in shells / browsers. + +import { env, pipeline } from '@huggingface/transformers'; +import { readFileSync } from 'fs'; + +env.cacheDir = 'build/models/' +env.localModelPath = 'build/models/' +// Uncomment the following line, if you want to make sure it runs without +// internet connection. +// env.allowRemoteModels = false; + +{ + console.log('Sentiment analysis / text classification with BERT model.'); + await import('../task-bert.js'); + + let start = performance.now(); + let pipe = await initPipeline(pipeline); + console.log('initPipeline took ' + (performance.now() - start) + ' ms.'); + + for (let i = 0; i < 20; ++i) { + start = performance.now(); + const output = await doTask(pipe); + validate(output) + console.log('output:', output); + console.log('doTask took ' + (performance.now() - start) + ' ms.'); + } +} + +{ + console.log('Automatic speech recognition with Whisper model.'); + await import('../task-whisper.js'); + + const inputAudioBuffer = readFileSync('build/inputs/jfk.raw').buffer; + + let start = performance.now(); + let pipe = await initPipeline(pipeline); + console.log('initPipeline took ' + (performance.now() - start) + ' ms.'); + + for (let i = 0; i < 5; ++i) { + start = performance.now(); + const output = await doTask(pipe, inputAudioBuffer); + validate(output) + console.log('output:', output); + console.log('doTask took ' + (performance.now() - start) + ' ms.'); + } +} + \ No newline at end of file diff --git a/wasm-cli.js b/wasm-cli.js index e277443d..be27724e 100644 --- a/wasm-cli.js +++ b/wasm-cli.js @@ -38,6 +38,8 @@ testList = [ "Dart-flute-todomvc-wasm", "zlib-wasm", "Kotlin-compose-wasm", + "transformersjs-bert-wasm", + "transformersjs-whisper-wasm", ]; // Reuse the full CLI runner, just with the subset of Wasm line items above.