# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
#
# 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,
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import argparse
from tqdm.auto import tqdm
import torch
import torch.nn as nn
from einops import rearrange
from latentsync.models.syncnet import SyncNet
from latentsync.data.syncnet_dataset import SyncNetDataset
from diffusers import AutoencoderKL
from omegaconf import OmegaConf
from accelerate.utils import set_seed


def main(config):
    set_seed(config.run.seed)

    device = "cuda" if torch.cuda.is_available() else "cpu"

    if config.data.latent_space:
        vae = AutoencoderKL.from_pretrained(
            "runwayml/stable-diffusion-inpainting", subfolder="vae", revision="fp16", torch_dtype=torch.float16
        )
        vae.requires_grad_(False)
        vae.to(device)

    # Dataset and Dataloader setup
    dataset = SyncNetDataset(config.data.val_data_dir, config.data.val_fileslist, config)

    test_dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=config.data.batch_size,
        shuffle=False,
        num_workers=config.data.num_workers,
        drop_last=False,
        worker_init_fn=dataset.worker_init_fn,
    )

    # Model
    syncnet = SyncNet(OmegaConf.to_container(config.model)).to(device)

    print(f"Load checkpoint from: {config.ckpt.inference_ckpt_path}")
    checkpoint = torch.load(config.ckpt.inference_ckpt_path, map_location=device)

    syncnet.load_state_dict(checkpoint["state_dict"])
    syncnet.to(dtype=torch.float16)
    syncnet.requires_grad_(False)
    syncnet.eval()

    global_step = 0
    num_val_batches = config.data.num_val_samples // config.data.batch_size
    progress_bar = tqdm(range(0, num_val_batches), initial=0, desc="Testing accuracy")

    num_correct_preds = 0
    num_total_preds = 0

    while True:
        for step, batch in enumerate(test_dataloader):
            ### >>>> Test >>>> ###

            frames = batch["frames"].to(device, dtype=torch.float16)
            audio_samples = batch["audio_samples"].to(device, dtype=torch.float16)
            y = batch["y"].to(device, dtype=torch.float16).squeeze(1)

            if config.data.latent_space:
                frames = rearrange(frames, "b f c h w -> (b f) c h w")

                with torch.no_grad():
                    frames = vae.encode(frames).latent_dist.sample() * 0.18215

                frames = rearrange(frames, "(b f) c h w -> b (f c) h w", f=config.data.num_frames)
            else:
                frames = rearrange(frames, "b f c h w -> b (f c) h w")

            if config.data.lower_half:
                height = frames.shape[2]
                frames = frames[:, :, height // 2 :, :]

            with torch.no_grad():
                vision_embeds, audio_embeds = syncnet(frames, audio_samples)

            sims = nn.functional.cosine_similarity(vision_embeds, audio_embeds)

            preds = (sims > 0.5).to(dtype=torch.float16)
            num_correct_preds += (preds == y).sum().item()
            num_total_preds += len(sims)

            progress_bar.update(1)
            global_step += 1

            if global_step >= num_val_batches:
                progress_bar.close()
                print(f"Accuracy score: {num_correct_preds / num_total_preds*100:.2f}%")
                return


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Code to test the accuracy of expert lip-sync discriminator")

    parser.add_argument("--config_path", type=str, default="configs/syncnet/syncnet_16_latent.yaml")
    args = parser.parse_args()

    # Load a configuration file
    config = OmegaConf.load(args.config_path)

    main(config)