best_448x448_colab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Vr7JQPTq_qla"
   },
   "source": [
    "# Approach\n",
    "\n",
    "* Firstly a convolutional neural network is used to segment the image, using the bounding boxes directly as a mask. \n",
    "* Secondly connected components is used to separate multiple areas of predicted pneumonia.\n",
    "* Finally a bounding box is simply drawn around every connected component.\n",
    "\n",
    "# Network\n",
    "\n",
    "* The network consists of a number of residual blocks with convolutions and downsampling blocks with max pooling.\n",
    "* At the end of the network a single upsampling layer converts the output to the same shape as the input.\n",
    "\n",
    "As the input to the network is 256 by 256 (instead of the original 1024 by 1024) and the network downsamples a number of times without any meaningful upsampling (the final upsampling is just to match in 256 by 256 mask) the final prediction is very crude. If the network downsamples 4 times the final bounding boxes can only change with at least 16 pixels.\n",
    "\n",
    "**Edit by EAS** - Change input image size to 320x320."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5jFPqYbl_tWH"
   },
   "outputs": [],
   "source": [
    "# install dependencies not included by Colab\n",
    "# use pip3 to ensure compatibility w/ Google Deep Learning Images \n",
    "!pip install -q pydicom \n",
    "!pip install -q imgaug \n",
    "!pip install -q kaggle\n",
    "!pip install -q tqdm \n",
    "!pip install -q PyDrive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zcb2HG8n_qlb"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "import random\n",
    "import pydicom\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from skimage import measure\n",
    "from skimage.transform import resize\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kvAZDt6q_ukM"
   },
   "outputs": [],
   "source": [
    "from google.colab import auth\n",
    "from googleapiclient.http import MediaFileUpload\n",
    "from googleapiclient.discovery import build\n",
    "\n",
    "auth.authenticate_user()\n",
    "\n",
    "drive_service = build('drive', 'v3')\n",
    "\n",
    "def save_file_to_drive(name, path):\n",
    "    file_metadata = {\n",
    "      'name': name,\n",
    "      'mimeType': 'application/octet-stream'\n",
    "     }\n",
    "\n",
    "    media = MediaFileUpload(path, \n",
    "                    mimetype='application/octet-stream',\n",
    "                    resumable=True)\n",
    "\n",
    "    created = drive_service.files().create(body=file_metadata,\n",
    "                                   media_body=media,\n",
    "                                   fields='id').execute()\n",
    "\n",
    "    print('File ID: {}'.format(created.get('id')))\n",
    "\n",
    "    return created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N7FbV28E_v4N"
   },
   "outputs": [],
   "source": [
    "# enter your Kaggle credentionals here\n",
    "os.environ['KAGGLE_USERNAME']=\"skooch\"\n",
    "os.environ['KAGGLE_KEY']=\"42f8a02ee92cc773d1dbe66565673ad3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 352
    },
    "colab_type": "code",
    "id": "XCkxN_zR_xHH",
    "outputId": "4956be94-a5cb-4f7a-b7fa-d48526eb82d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading GCP%20Credits%20Request%20Link%20-%20RSNA.txt to /content\n",
      "\r",
      "  0% 0.00/55.0 [00:00<?, ?B/s]\n",
      "100% 55.0/55.0 [00:00<00:00, 40.5kB/s]\n",
      "Downloading stage_1_detailed_class_info.csv.zip to /content\n",
      "  0% 0.00/561k [00:00<?, ?B/s]\n",
      "100% 561k/561k [00:00<00:00, 86.0MB/s]\n",
      "Downloading stage_1_sample_submission.csv to /content\n",
      "  0% 0.00/51.8k [00:00<?, ?B/s]\n",
      "100% 51.8k/51.8k [00:00<00:00, 38.2MB/s]\n",
      "Downloading stage_1_train_labels.csv.zip to /content\n",
      "  0% 0.00/634k [00:00<?, ?B/s]\n",
      "100% 634k/634k [00:00<00:00, 93.5MB/s]\n",
      "Downloading stage_1_test_images.zip to /content\n",
      " 96% 121M/126M [00:02<00:00, 42.7MB/s]\n",
      "100% 126M/126M [00:02<00:00, 50.7MB/s]\n",
      "Downloading stage_1_train_images.zip to /content\n",
      "100% 3.16G/3.17G [01:00<00:00, 69.9MB/s]\n",
      "100% 3.17G/3.17G [01:00<00:00, 56.2MB/s]\n"
     ]
    }
   ],
   "source": [
    "# If you are unable to download the competition dataset, check to see if you have \n",
    "# accepted the user agreement on the competition website. \n",
    "if not os.path.exists(\"./stage_1_detailed_class_info.csv.zip\"):\n",
    "    !kaggle competitions download -c rsna-pneumonia-detection-challenge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "colab_type": "code",
    "id": "f2IeRx6z_yTW",
    "outputId": "df36ab90-b8fc-4f4e-e18b-95284fa81f6f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unzipping...\n"
     ]
    }
   ],
   "source": [
    "# unzipping takes a few minutes\n",
    "if not os.path.exists(\"./stage_1_train_labels.csv\"):\n",
    "    print(\"Unzipping...\")\n",
    "    !unzip -q -o stage_1_test_images.zip -d stage_1_test_images\n",
    "    !unzip -q -o stage_1_train_images.zip -d stage_1_train_images\n",
    "    !unzip -q -o stage_1_train_labels.csv.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ltOidyEU_qle"
   },
   "source": [
    "# Load pneumonia locations\n",
    "\n",
    "Table contains [filename : pneumonia location] pairs per row. \n",
    "* If a filename contains multiple pneumonia, the table contains multiple rows with the same filename but different pneumonia locations. \n",
    "* If a filename contains no pneumonia it contains a single row with an empty pneumonia location.\n",
    "\n",
    "The code below loads the table and transforms it into a dictionary. \n",
    "* The dictionary uses the filename as key and a list of pneumonia locations in that filename as value. \n",
    "* If a filename is not present in the dictionary it means that it contains no pneumonia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "colab_type": "code",
    "id": "LaFD9zhyr2up",
    "outputId": "78d034ef-8a68-45cb-e995-dfd2a7cadc24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dir: ./stage_1_train_images\n"
     ]
    }
   ],
   "source": [
    "ROOT_DIR = \"./\"\n",
    "\n",
    "train_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_train_images')\n",
    "test_dicom_dir = os.path.join(ROOT_DIR, 'stage_1_test_images')\n",
    "print(\"Train dir:\", train_dicom_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bJaASoWC_qle"
   },
   "outputs": [],
   "source": [
    "# empty dictionary\n",
    "pneumonia_locations = {}\n",
    "# load table\n",
    "with open(os.path.join('./stage_1_train_labels.csv'), mode='r') as infile:\n",
    "    # open reader\n",
    "    reader = csv.reader(infile)\n",
    "    # skip header\n",
    "    next(reader, None)\n",
    "    # loop through rows\n",
    "    for rows in reader:\n",
    "        # retrieve information\n",
    "        filename = rows[0]\n",
    "        location = rows[1:5]\n",
    "        pneumonia = rows[5]\n",
    "        # if row contains pneumonia add label to dictionary\n",
    "        # which contains a list of pneumonia locations per filename\n",
    "        if pneumonia == '1':\n",
    "            # convert string to float to int\n",
    "            location = [int(float(i)) for i in location]\n",
    "            # save pneumonia location in dictionary\n",
    "            if filename in pneumonia_locations:\n",
    "                pneumonia_locations[filename].append(location)\n",
    "            else:\n",
    "                pneumonia_locations[filename] = [location]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2yLhAUNl_qli"
   },
   "source": [
    "# Load filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "mG2EOT-8_qlj",
    "outputId": "48305f5b-b1b8-4fed-8e99-62f55010116b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n train samples 23124\n",
      "n valid samples 2560\n"
     ]
    }
   ],
   "source": [
    "# load and shuffle filenames\n",
    "random.seed(1977)\n",
    "folder = './stage_1_train_images'\n",
    "filenames = os.listdir(folder)\n",
    "random.shuffle(filenames)\n",
    "# split into train and validation filenames\n",
    "n_valid_samples = 2560\n",
    "train_filenames = filenames[n_valid_samples:]\n",
    "valid_filenames = filenames[:n_valid_samples]\n",
    "print('n train samples', len(train_filenames))\n",
    "print('n valid samples', len(valid_filenames))\n",
    "n_train_samples = len(filenames) - n_valid_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vMmvXsFS_qlq"
   },
   "source": [
    " # Data generator\n",
    "\n",
    "The dataset is too large to fit into memory, so we need to create a generator that loads data on the fly.\n",
    "\n",
    "* The generator takes in some filenames, batch_size and other parameters.\n",
    "\n",
    "* The generator outputs a random batch of numpy images and numpy masks.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Z3SDWRfvAH4l"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 16\n",
    "IMAGE_SIZE = 448\n",
    "CHECKPOINT_PATH = \"model11_448.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 91
    },
    "colab_type": "code",
    "id": "rSwVxQKdBB3L",
    "outputId": "48200464-f395-461b-8806-f6b2297eadf9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated property [core/project].\n",
      "Copying gs://pneumonia/model10_448.h5...\n",
      "/ [1 files][ 66.9 MiB/ 66.9 MiB]                                                \n",
      "Operation completed over 1 objects/66.9 MiB.                                     \n"
     ]
    }
   ],
   "source": [
    "# upload checkpoint to GCS\n",
    "project_id = 'mammography-198911'\n",
    "bucket_name = 'pneumonia'\n",
    "\n",
    "!gcloud config set project {project_id}\n",
    "!gsutil cp gs://{bucket_name}/{CHECKPOINT_PATH} ./{CHECKPOINT_PATH}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TKZdiz76_qlr"
   },
   "outputs": [],
   "source": [
    "class generator(keras.utils.Sequence):\n",
    "    \n",
    "    def __init__(self, folder, filenames, pneumonia_locations=None, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, augment=False, predict=False):\n",
    "        self.folder = folder\n",
    "        self.filenames = filenames\n",
    "        self.pneumonia_locations = pneumonia_locations\n",
    "        self.batch_size = batch_size\n",
    "        self.image_size = image_size\n",
    "        self.shuffle = shuffle\n",
    "        self.augment = augment\n",
    "        self.predict = predict\n",
    "        self.on_epoch_end()\n",
    "        \n",
    "    def __load__(self, filename):\n",
    "        # load dicom file as numpy array\n",
    "        img = pydicom.dcmread(os.path.join(self.folder, filename)).pixel_array\n",
    "        # create empty mask\n",
    "        msk = np.zeros(img.shape)\n",
    "        # get filename without extension\n",
    "        filename = filename.split('.')[0]\n",
    "        # if image contains pneumonia\n",
    "        if filename in pneumonia_locations:\n",
    "            # loop through pneumonia\n",
    "            for location in pneumonia_locations[filename]:\n",
    "                # add 1's at the location of the pneumonia\n",
    "                x, y, w, h = location\n",
    "                msk[y:y+h, x:x+w] = 1\n",
    "        # if augment then horizontal flip half the time\n",
    "        if self.augment and random.random() > 0.5:\n",
    "            img = np.fliplr(img)\n",
    "            msk = np.fliplr(msk)\n",
    "            \n",
    "        # small shifts to images\n",
    "        if self.augment:\n",
    "            h_offset = np.random.randint(low=0, high=15)\n",
    "            v_offset = np.random.randint(low=0, high=15)\n",
    "            # crop the images\n",
    "            img = img[v_offset:,h_offset:]\n",
    "            msk = msk[v_offset:,h_offset:]\n",
    "            \n",
    "        # resize both image and mask\n",
    "        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n",
    "        msk = resize(msk, (self.image_size, self.image_size), mode='reflect') > 0.5\n",
    "        # add trailing channel dimension\n",
    "        img = np.expand_dims(img, -1)\n",
    "        msk = np.expand_dims(msk, -1)\n",
    "        return img, msk\n",
    "    \n",
    "    def __loadpredict__(self, filename):\n",
    "        # load dicom file as numpy array\n",
    "        img = pydicom.dcmread(os.path.join(self.folder, filename)).pixel_array\n",
    "        # resize image\n",
    "        img = resize(img, (self.image_size, self.image_size), mode='reflect')\n",
    "        # add trailing channel dimension\n",
    "        img = np.expand_dims(img, -1)\n",
    "        return img\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        # select batch\n",
    "        filenames = self.filenames[index*self.batch_size:(index+1)*self.batch_size]\n",
    "        # predict mode: return images and filenames\n",
    "        if self.predict:\n",
    "            # load files\n",
    "            imgs = [self.__loadpredict__(filename) for filename in filenames]\n",
    "            # create numpy batch\n",
    "            imgs = np.array(imgs)\n",
    "            return imgs, filenames\n",
    "        # train mode: return images and masks\n",
    "        else:\n",
    "            # load files\n",
    "            items = [self.__load__(filename) for filename in filenames]\n",
    "            # unzip images and masks\n",
    "            imgs, msks = zip(*items)\n",
    "            # create numpy batch\n",
    "            imgs = np.array(imgs)\n",
    "            msks = np.array(msks)\n",
    "            return imgs, msks\n",
    "        \n",
    "    def on_epoch_end(self):\n",
    "        if self.shuffle:\n",
    "            random.shuffle(self.filenames)\n",
    "        \n",
    "    def __len__(self):\n",
    "        if self.predict:\n",
    "            # return everything\n",
    "            return int(np.ceil(len(self.filenames) / self.batch_size))\n",
    "        else:\n",
    "            # return full batches only\n",
    "            return int(len(self.filenames) / self.batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zuDIxMEW_qlt"
   },
   "source": [
    "# Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2cENEbF7_qlu"
   },
   "outputs": [],
   "source": [
    "def create_downsample(channels, inputs):\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(inputs)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    x = keras.layers.Conv2D(channels, 1, padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.MaxPool2D(2)(x)\n",
    "    return x\n",
    "\n",
    "def create_resblock(channels, inputs):\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(inputs)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
    "    return keras.layers.add([x, inputs])\n",
    "\n",
    "def create_network(input_size, channels, n_blocks=2, depth=4):\n",
    "    # input\n",
    "    inputs = keras.Input(shape=(input_size, input_size, 1))\n",
    "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(inputs)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(channels, 3, padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(channels * 2, 3, strides=(2,2), padding='same', use_bias=False)(x)\n",
    "    \n",
    "    # residual blocks\n",
    "    for d in range(depth):\n",
    "        channels = channels * 2\n",
    "        if d > 0:\n",
    "          x = create_downsample(channels, x)\n",
    "          \n",
    "        for b in range(n_blocks):\n",
    "            x = create_resblock(channels, x)\n",
    "    # output\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    \n",
    "    # dilated convolutions\n",
    "    x = keras.layers.Conv2D(channels, 3, dilation_rate=(2,2), padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(channels, 3, dilation_rate=(2,2), padding='same', use_bias=False)(x)\n",
    "    x = keras.layers.BatchNormalization(momentum=0.9)(x)\n",
    "    x = keras.layers.LeakyReLU(0)(x)\n",
    "    \n",
    "    x = keras.layers.Dropout(0.15)(x)\n",
    "    \n",
    "    x = keras.layers.Conv2D(1, 1, activation='sigmoid')(x)\n",
    "    outputs = keras.layers.UpSampling2D(2**depth)(x)\n",
    "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NCUyPB3i_qlx"
   },
   "source": [
    "# Train network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3554
    },
    "colab_type": "code",
    "id": "QkzK5IBY_qly",
    "outputId": "5348807b-2252-43ce-cca2-e5924c8265d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 448, 448, 1)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 448, 448, 32) 288         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 448, 448, 32) 128         conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)         (None, 448, 448, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 448, 448, 32) 9216        leaky_re_lu[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 448, 448, 32) 128         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)       (None, 448, 448, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 224, 224, 64) 18432       leaky_re_lu_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 224, 224, 64) 256         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)       (None, 224, 224, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 224, 224, 64) 36864       leaky_re_lu_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 224, 224, 64) 256         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)       (None, 224, 224, 64) 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 224, 224, 64) 36864       leaky_re_lu_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 224, 224, 64) 0           conv2d_4[0][0]                   \n",
      "                                                                 conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 224, 224, 64) 256         add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)       (None, 224, 224, 64) 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 224, 224, 64) 36864       leaky_re_lu_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 224, 224, 64) 256         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)       (None, 224, 224, 64) 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 224, 224, 64) 36864       leaky_re_lu_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 224, 224, 64) 0           conv2d_6[0][0]                   \n",
      "                                                                 add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 224, 224, 64) 256         add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)       (None, 224, 224, 64) 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 224, 224, 128 8192        leaky_re_lu_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 112, 112, 128 0           conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 112, 112, 128 512         max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)       (None, 112, 112, 128 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 112, 112, 128 147456      leaky_re_lu_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 112, 112, 128 512         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)       (None, 112, 112, 128 0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 112, 112, 128 147456      leaky_re_lu_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 112, 112, 128 0           conv2d_9[0][0]                   \n",
      "                                                                 max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 112, 112, 128 512         add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)       (None, 112, 112, 128 0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 112, 112, 128 147456      leaky_re_lu_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 112, 112, 128 512         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_10 (LeakyReLU)      (None, 112, 112, 128 0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 112, 112, 128 147456      leaky_re_lu_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 112, 112, 128 0           conv2d_11[0][0]                  \n",
      "                                                                 add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 112, 112, 128 512         add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_11 (LeakyReLU)      (None, 112, 112, 128 0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 112, 112, 256 32768       leaky_re_lu_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 256)  0           conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 56, 56, 256)  1024        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_12 (LeakyReLU)      (None, 56, 56, 256)  0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 56, 56, 256)  589824      leaky_re_lu_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 56, 56, 256)  1024        conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_13 (LeakyReLU)      (None, 56, 56, 256)  0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 56, 56, 256)  589824      leaky_re_lu_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 56, 56, 256)  0           conv2d_14[0][0]                  \n",
      "                                                                 max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 56, 56, 256)  1024        add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_14 (LeakyReLU)      (None, 56, 56, 256)  0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 56, 56, 256)  589824      leaky_re_lu_14[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 56, 56, 256)  1024        conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_15 (LeakyReLU)      (None, 56, 56, 256)  0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 56, 56, 256)  589824      leaky_re_lu_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 56, 56, 256)  0           conv2d_16[0][0]                  \n",
      "                                                                 add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 56, 56, 256)  1024        add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_16 (LeakyReLU)      (None, 56, 56, 256)  0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 56, 56, 512)  131072      leaky_re_lu_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 28, 28, 512)  0           conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 28, 28, 512)  2048        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_17 (LeakyReLU)      (None, 28, 28, 512)  0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 28, 28, 512)  2359296     leaky_re_lu_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 28, 28, 512)  2048        conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_18 (LeakyReLU)      (None, 28, 28, 512)  0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 28, 28, 512)  2359296     leaky_re_lu_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           conv2d_19[0][0]                  \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 28, 28, 512)  2048        add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_19 (LeakyReLU)      (None, 28, 28, 512)  0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 28, 28, 512)  2359296     leaky_re_lu_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 28, 28, 512)  2048        conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_20 (LeakyReLU)      (None, 28, 28, 512)  0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 28, 28, 512)  2359296     leaky_re_lu_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           conv2d_21[0][0]                  \n",
      "                                                                 add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 28, 28, 512)  2048        add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_21 (LeakyReLU)      (None, 28, 28, 512)  0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 28, 28, 512)  2359296     leaky_re_lu_21[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 28, 28, 512)  2048        conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_22 (LeakyReLU)      (None, 28, 28, 512)  0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 28, 28, 512)  2359296     leaky_re_lu_22[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 28, 28, 512)  2048        conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_23 (LeakyReLU)      (None, 28, 28, 512)  0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 28, 28, 512)  0           leaky_re_lu_23[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 28, 28, 1)    513         dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d (UpSampling2D)    (None, 448, 448, 1)  0           conv2d_24[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 17,476,385\n",
      "Trainable params: 17,464,609\n",
      "Non-trainable params: 11,776\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# define iou or jaccard loss function\n",
    "def iou_loss(y_true, y_pred):\n",
    "    y_true = tf.reshape(y_true, [-1])\n",
    "    y_pred = tf.reshape(y_pred, [-1])\n",
    "    intersection = tf.reduce_sum(y_true * y_pred)\n",
    "    score = (intersection + 1.) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) - intersection + 1.)\n",
    "    return 1 - score\n",
    "\n",
    "# combine bce loss and iou loss\n",
    "def iou_bce_loss(y_true, y_pred):\n",
    "    return 0.5 * keras.losses.binary_crossentropy(y_true, y_pred) + 0.5 * iou_loss(y_true, y_pred)\n",
    "\n",
    "# mean iou as a metric\n",
    "def mean_iou(y_true, y_pred):\n",
    "    y_pred = tf.round(y_pred)\n",
    "    intersect = tf.reduce_sum(y_true * y_pred, axis=[1, 2, 3])\n",
    "    union = tf.reduce_sum(y_true, axis=[1, 2, 3]) + tf.reduce_sum(y_pred, axis=[1, 2, 3])\n",
    "    smooth = tf.ones(tf.shape(intersect))\n",
    "    return tf.reduce_mean((intersect + smooth) / (union - intersect + smooth))\n",
    "\n",
    "# mean iou with any image with no truth counted as 0\n",
    "def adj_mean_iou(y_true, y_pred):\n",
    "    y_pred = tf.round(y_pred)\n",
    "    y_true_pos = tf.reduce_max(y_true, axis=[1, 2, 3])\n",
    "    \n",
    "    intersect = tf.reduce_sum(y_true * y_pred, axis=[1, 2, 3])\n",
    "    union = tf.reduce_sum(y_true, axis=[1, 2, 3]) + tf.reduce_sum(y_pred, axis=[1, 2, 3])\n",
    "    smooth = tf.ones(tf.shape(intersect))\n",
    "    \n",
    "    return tf.reduce_mean((y_true_pos * (intersect + smooth)) / (union - intersect + smooth))      \n",
    "  \n",
    "# create network and compiler\n",
    "model = create_network(input_size=IMAGE_SIZE, channels=32, n_blocks=2, depth=4)\n",
    "model.compile(optimizer='adam',\n",
    "              loss=iou_bce_loss,\n",
    "              metrics=['accuracy', mean_iou, adj_mean_iou])\n",
    "\n",
    "# cosine learning rate annealing\n",
    "def cosine_annealing(x):\n",
    "    lr = 0.002\n",
    "    epochs = 40\n",
    "    return lr*(np.cos(np.pi*x/epochs)+1.)/2\n",
    "  \n",
    "learning_rate = tf.keras.callbacks.LearningRateScheduler(cosine_annealing)\n",
    "checkpoint = keras.callbacks.ModelCheckpoint(CHECKPOINT_PATH, monitor='val_loss', verbose=1, save_best_only=False, save_weights_only=True, mode='auto', period=1)\n",
    "\n",
    "# create train and validation generators\n",
    "folder = './stage_1_train_images'\n",
    "train_gen = generator(folder, train_filenames, pneumonia_locations, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=True, augment=True, predict=False)\n",
    "valid_gen = generator(folder, valid_filenames, pneumonia_locations, batch_size=BATCH_SIZE, image_size=IMAGE_SIZE, shuffle=False, predict=False)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qo-qjtrLHzQy"
   },
   "outputs": [],
   "source": [
    "model.load_weights(CHECKPOINT_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 782
    },
    "colab_type": "code",
    "id": "G369kKMh_ql2",
    "outputId": "d4a83316-a637-4b7a-e0ea-05ca08e76fe7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.5072 - acc: 0.9598 - mean_iou: 0.6083 - adj_mean_iou: 0.0290\n",
      "Epoch 00001: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2828s 2s/step - loss: 0.5071 - acc: 0.9598 - mean_iou: 0.6084 - adj_mean_iou: 0.0290 - val_loss: 0.4766 - val_acc: 0.9635 - val_mean_iou: 0.6087 - val_adj_mean_iou: 0.0457\n",
      "Epoch 2/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4678 - acc: 0.9640 - mean_iou: 0.6126 - adj_mean_iou: 0.0496\n",
      "Epoch 00002: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2803s 2s/step - loss: 0.4677 - acc: 0.9640 - mean_iou: 0.6126 - adj_mean_iou: 0.0496 - val_loss: 0.4585 - val_acc: 0.9661 - val_mean_iou: 0.6711 - val_adj_mean_iou: 0.0492\n",
      "Epoch 3/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4523 - acc: 0.9662 - mean_iou: 0.6448 - adj_mean_iou: 0.0550\n",
      "Epoch 00003: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2807s 2s/step - loss: 0.4523 - acc: 0.9662 - mean_iou: 0.6448 - adj_mean_iou: 0.0551 - val_loss: 0.4408 - val_acc: 0.9702 - val_mean_iou: 0.6660 - val_adj_mean_iou: 0.0569\n",
      "Epoch 4/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4409 - acc: 0.9677 - mean_iou: 0.6647 - adj_mean_iou: 0.0599\n",
      "Epoch 00004: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2804s 2s/step - loss: 0.4409 - acc: 0.9677 - mean_iou: 0.6647 - adj_mean_iou: 0.0599 - val_loss: 0.4342 - val_acc: 0.9707 - val_mean_iou: 0.6645 - val_adj_mean_iou: 0.0617\n",
      "Epoch 5/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4332 - acc: 0.9684 - mean_iou: 0.6728 - adj_mean_iou: 0.0617\n",
      "Epoch 00005: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2808s 2s/step - loss: 0.4333 - acc: 0.9684 - mean_iou: 0.6728 - adj_mean_iou: 0.0617 - val_loss: 0.4572 - val_acc: 0.9603 - val_mean_iou: 0.6461 - val_adj_mean_iou: 0.0582\n",
      "Epoch 6/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4287 - acc: 0.9690 - mean_iou: 0.6754 - adj_mean_iou: 0.0638\n",
      "Epoch 00006: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2807s 2s/step - loss: 0.4287 - acc: 0.9690 - mean_iou: 0.6753 - adj_mean_iou: 0.0638 - val_loss: 0.4242 - val_acc: 0.9717 - val_mean_iou: 0.6775 - val_adj_mean_iou: 0.0645\n",
      "Epoch 7/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4237 - acc: 0.9696 - mean_iou: 0.6813 - adj_mean_iou: 0.0659\n",
      "Epoch 00007: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2804s 2s/step - loss: 0.4237 - acc: 0.9696 - mean_iou: 0.6813 - adj_mean_iou: 0.0659 - val_loss: 0.4245 - val_acc: 0.9705 - val_mean_iou: 0.6943 - val_adj_mean_iou: 0.0634\n",
      "Epoch 8/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4203 - acc: 0.9698 - mean_iou: 0.6819 - adj_mean_iou: 0.0678\n",
      "Epoch 00008: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2796s 2s/step - loss: 0.4203 - acc: 0.9698 - mean_iou: 0.6820 - adj_mean_iou: 0.0679 - val_loss: 0.4500 - val_acc: 0.9635 - val_mean_iou: 0.6401 - val_adj_mean_iou: 0.0619\n",
      "Epoch 9/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4172 - acc: 0.9703 - mean_iou: 0.6849 - adj_mean_iou: 0.0680\n",
      "Epoch 00009: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2798s 2s/step - loss: 0.4171 - acc: 0.9703 - mean_iou: 0.6850 - adj_mean_iou: 0.0680 - val_loss: 0.4241 - val_acc: 0.9683 - val_mean_iou: 0.6460 - val_adj_mean_iou: 0.0741\n",
      "Epoch 10/10\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4134 - acc: 0.9706 - mean_iou: 0.6856 - adj_mean_iou: 0.0704\n",
      "Epoch 00010: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2795s 2s/step - loss: 0.4134 - acc: 0.9706 - mean_iou: 0.6857 - adj_mean_iou: 0.0704 - val_loss: 0.4384 - val_acc: 0.9670 - val_mean_iou: 0.6368 - val_adj_mean_iou: 0.0648\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "colab_type": "code",
    "id": "-d9VNT2rx3jH",
    "outputId": "01b9e51b-2cb2-4e7f-e5ca-94d9f496783b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/15\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4105 - acc: 0.9709 - mean_iou: 0.6887 - adj_mean_iou: 0.0723\n",
      "Epoch 00011: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2819s 2s/step - loss: 0.4106 - acc: 0.9709 - mean_iou: 0.6886 - adj_mean_iou: 0.0722 - val_loss: 0.4118 - val_acc: 0.9707 - val_mean_iou: 0.6629 - val_adj_mean_iou: 0.0773\n",
      "Epoch 12/15\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4068 - acc: 0.9712 - mean_iou: 0.6921 - adj_mean_iou: 0.0731\n",
      "Epoch 00012: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2790s 2s/step - loss: 0.4068 - acc: 0.9712 - mean_iou: 0.6921 - adj_mean_iou: 0.0731 - val_loss: 0.4201 - val_acc: 0.9744 - val_mean_iou: 0.7156 - val_adj_mean_iou: 0.0609\n",
      "Epoch 13/15\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4041 - acc: 0.9716 - mean_iou: 0.6973 - adj_mean_iou: 0.0727\n",
      "Epoch 00013: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2791s 2s/step - loss: 0.4040 - acc: 0.9716 - mean_iou: 0.6974 - adj_mean_iou: 0.0727 - val_loss: 0.4032 - val_acc: 0.9726 - val_mean_iou: 0.7110 - val_adj_mean_iou: 0.0734\n",
      "Epoch 14/15\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.4027 - acc: 0.9717 - mean_iou: 0.6991 - adj_mean_iou: 0.0752\n",
      "Epoch 00014: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2790s 2s/step - loss: 0.4026 - acc: 0.9717 - mean_iou: 0.6992 - adj_mean_iou: 0.0751 - val_loss: 0.4059 - val_acc: 0.9749 - val_mean_iou: 0.7264 - val_adj_mean_iou: 0.0658\n",
      "Epoch 15/15\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3981 - acc: 0.9720 - mean_iou: 0.7015 - adj_mean_iou: 0.0763\n",
      "Epoch 00015: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2785s 2s/step - loss: 0.3981 - acc: 0.9721 - mean_iou: 0.7015 - adj_mean_iou: 0.0762 - val_loss: 0.4266 - val_acc: 0.9773 - val_mean_iou: 0.7507 - val_adj_mean_iou: 0.0522\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=15, shuffle=True, initial_epoch=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 782
    },
    "colab_type": "code",
    "id": "33ILQ0lN4xr9",
    "outputId": "860d7ca5-0ff0-42e0-ff6e-dfbda26881df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3958 - acc: 0.9723 - mean_iou: 0.7036 - adj_mean_iou: 0.0778\n",
      "Epoch 00016: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2660s 2s/step - loss: 0.3958 - acc: 0.9723 - mean_iou: 0.7036 - adj_mean_iou: 0.0779 - val_loss: 0.4002 - val_acc: 0.9746 - val_mean_iou: 0.7171 - val_adj_mean_iou: 0.0721\n",
      "Epoch 17/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3950 - acc: 0.9722 - mean_iou: 0.7029 - adj_mean_iou: 0.0786\n",
      "Epoch 00017: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2628s 2s/step - loss: 0.3950 - acc: 0.9722 - mean_iou: 0.7030 - adj_mean_iou: 0.0786 - val_loss: 0.4047 - val_acc: 0.9773 - val_mean_iou: 0.7393 - val_adj_mean_iou: 0.0638\n",
      "Epoch 18/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3914 - acc: 0.9727 - mean_iou: 0.7086 - adj_mean_iou: 0.0787\n",
      "Epoch 00018: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2631s 2s/step - loss: 0.3915 - acc: 0.9727 - mean_iou: 0.7086 - adj_mean_iou: 0.0787 - val_loss: 0.4035 - val_acc: 0.9742 - val_mean_iou: 0.7081 - val_adj_mean_iou: 0.0694\n",
      "Epoch 19/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3893 - acc: 0.9731 - mean_iou: 0.7121 - adj_mean_iou: 0.0799\n",
      "Epoch 00019: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2631s 2s/step - loss: 0.3893 - acc: 0.9731 - mean_iou: 0.7121 - adj_mean_iou: 0.0799 - val_loss: 0.3988 - val_acc: 0.9759 - val_mean_iou: 0.7239 - val_adj_mean_iou: 0.0703\n",
      "Epoch 20/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3875 - acc: 0.9732 - mean_iou: 0.7133 - adj_mean_iou: 0.0810\n",
      "Epoch 00020: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2632s 2s/step - loss: 0.3875 - acc: 0.9732 - mean_iou: 0.7132 - adj_mean_iou: 0.0810 - val_loss: 0.4061 - val_acc: 0.9709 - val_mean_iou: 0.6887 - val_adj_mean_iou: 0.0762\n",
      "Epoch 21/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3842 - acc: 0.9734 - mean_iou: 0.7155 - adj_mean_iou: 0.0818\n",
      "Epoch 00021: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2633s 2s/step - loss: 0.3843 - acc: 0.9734 - mean_iou: 0.7154 - adj_mean_iou: 0.0818 - val_loss: 0.3971 - val_acc: 0.9756 - val_mean_iou: 0.7326 - val_adj_mean_iou: 0.0697\n",
      "Epoch 22/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3818 - acc: 0.9737 - mean_iou: 0.7178 - adj_mean_iou: 0.0822\n",
      "Epoch 00022: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2632s 2s/step - loss: 0.3819 - acc: 0.9737 - mean_iou: 0.7178 - adj_mean_iou: 0.0822 - val_loss: 0.3974 - val_acc: 0.9763 - val_mean_iou: 0.7372 - val_adj_mean_iou: 0.0684\n",
      "Epoch 23/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3829 - acc: 0.9739 - mean_iou: 0.7132 - adj_mean_iou: 0.0838\n",
      "Epoch 00023: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2634s 2s/step - loss: 0.3829 - acc: 0.9739 - mean_iou: 0.7132 - adj_mean_iou: 0.0838 - val_loss: 0.3988 - val_acc: 0.9782 - val_mean_iou: 0.7525 - val_adj_mean_iou: 0.0642\n",
      "Epoch 24/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3801 - acc: 0.9743 - mean_iou: 0.7195 - adj_mean_iou: 0.0834\n",
      "Epoch 00024: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2630s 2s/step - loss: 0.3802 - acc: 0.9743 - mean_iou: 0.7195 - adj_mean_iou: 0.0834 - val_loss: 0.3985 - val_acc: 0.9769 - val_mean_iou: 0.7339 - val_adj_mean_iou: 0.0659\n",
      "Epoch 25/25\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3773 - acc: 0.9747 - mean_iou: 0.7218 - adj_mean_iou: 0.0839\n",
      "Epoch 00025: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2629s 2s/step - loss: 0.3773 - acc: 0.9747 - mean_iou: 0.7218 - adj_mean_iou: 0.0839 - val_loss: 0.3976 - val_acc: 0.9760 - val_mean_iou: 0.7221 - val_adj_mean_iou: 0.0689\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=25, shuffle=True, initial_epoch=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "colab_type": "code",
    "id": "b-zKmWRie1_l",
    "outputId": "3409fb88-3227-4674-b68e-1660459d7087"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/30\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3740 - acc: 0.9749 - mean_iou: 0.7240 - adj_mean_iou: 0.0850\n",
      "Epoch 00026: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2631s 2s/step - loss: 0.3740 - acc: 0.9749 - mean_iou: 0.7240 - adj_mean_iou: 0.0849 - val_loss: 0.3962 - val_acc: 0.9757 - val_mean_iou: 0.7195 - val_adj_mean_iou: 0.0706\n",
      "Epoch 27/30\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3755 - acc: 0.9748 - mean_iou: 0.7223 - adj_mean_iou: 0.0849\n",
      "Epoch 00027: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2634s 2s/step - loss: 0.3756 - acc: 0.9748 - mean_iou: 0.7223 - adj_mean_iou: 0.0849 - val_loss: 0.3964 - val_acc: 0.9760 - val_mean_iou: 0.7203 - val_adj_mean_iou: 0.0698\n",
      "Epoch 28/30\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3727 - acc: 0.9752 - mean_iou: 0.7274 - adj_mean_iou: 0.0849\n",
      "Epoch 00028: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2634s 2s/step - loss: 0.3727 - acc: 0.9752 - mean_iou: 0.7274 - adj_mean_iou: 0.0849 - val_loss: 0.3967 - val_acc: 0.9766 - val_mean_iou: 0.7286 - val_adj_mean_iou: 0.0679\n",
      "Epoch 29/30\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3720 - acc: 0.9753 - mean_iou: 0.7298 - adj_mean_iou: 0.0853\n",
      "Epoch 00029: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2634s 2s/step - loss: 0.3719 - acc: 0.9753 - mean_iou: 0.7298 - adj_mean_iou: 0.0853 - val_loss: 0.3958 - val_acc: 0.9764 - val_mean_iou: 0.7225 - val_adj_mean_iou: 0.0693\n",
      "Epoch 30/30\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3723 - acc: 0.9752 - mean_iou: 0.7258 - adj_mean_iou: 0.0859\n",
      "Epoch 00030: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2635s 2s/step - loss: 0.3723 - acc: 0.9752 - mean_iou: 0.7258 - adj_mean_iou: 0.0859 - val_loss: 0.3966 - val_acc: 0.9754 - val_mean_iou: 0.7123 - val_adj_mean_iou: 0.0712\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=30, shuffle=True, initial_epoch=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 409
    },
    "colab_type": "code",
    "id": "3wuX8QaYD1yq",
    "outputId": "68e818a1-bc8f-4007-adeb-c2555c105650"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/35\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3760 - acc: 0.9748 - mean_iou: 0.7246 - adj_mean_iou: 0.0840\n",
      "Epoch 00031: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2668s 2s/step - loss: 0.3761 - acc: 0.9748 - mean_iou: 0.7246 - adj_mean_iou: 0.0840 - val_loss: 0.4007 - val_acc: 0.9731 - val_mean_iou: 0.6778 - val_adj_mean_iou: 0.0769\n",
      "Epoch 32/35\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3757 - acc: 0.9748 - mean_iou: 0.7209 - adj_mean_iou: 0.0857\n",
      "Epoch 00032: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2634s 2s/step - loss: 0.3757 - acc: 0.9748 - mean_iou: 0.7207 - adj_mean_iou: 0.0856 - val_loss: 0.3976 - val_acc: 0.9753 - val_mean_iou: 0.7167 - val_adj_mean_iou: 0.0713\n",
      "Epoch 33/35\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3732 - acc: 0.9750 - mean_iou: 0.7244 - adj_mean_iou: 0.0859\n",
      "Epoch 00033: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2637s 2s/step - loss: 0.3733 - acc: 0.9750 - mean_iou: 0.7243 - adj_mean_iou: 0.0859 - val_loss: 0.3945 - val_acc: 0.9768 - val_mean_iou: 0.7369 - val_adj_mean_iou: 0.0689\n",
      "Epoch 34/35\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3732 - acc: 0.9752 - mean_iou: 0.7239 - adj_mean_iou: 0.0862\n",
      "Epoch 00034: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2640s 2s/step - loss: 0.3732 - acc: 0.9752 - mean_iou: 0.7239 - adj_mean_iou: 0.0862 - val_loss: 0.3968 - val_acc: 0.9752 - val_mean_iou: 0.7112 - val_adj_mean_iou: 0.0720\n",
      "Epoch 35/35\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3697 - acc: 0.9756 - mean_iou: 0.7273 - adj_mean_iou: 0.0864\n",
      "Epoch 00035: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2638s 2s/step - loss: 0.3696 - acc: 0.9756 - mean_iou: 0.7274 - adj_mean_iou: 0.0865 - val_loss: 0.3957 - val_acc: 0.9771 - val_mean_iou: 0.7286 - val_adj_mean_iou: 0.0680\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=35, shuffle=True, initial_epoch=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "colab_type": "code",
    "id": "i5ZmErfR3QS7",
    "outputId": "a77dadc9-1937-4df8-cdc6-6828823e3918"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37/40\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3698 - acc: 0.9760 - mean_iou: 0.7208 - adj_mean_iou: 0.0870\n",
      "Epoch 00037: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2677s 2s/step - loss: 0.3697 - acc: 0.9760 - mean_iou: 0.7208 - adj_mean_iou: 0.0871 - val_loss: 0.3969 - val_acc: 0.9769 - val_mean_iou: 0.7269 - val_adj_mean_iou: 0.0678\n",
      "Epoch 38/40\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3716 - acc: 0.9758 - mean_iou: 0.7197 - adj_mean_iou: 0.0877\n",
      "Epoch 00038: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2648s 2s/step - loss: 0.3716 - acc: 0.9758 - mean_iou: 0.7197 - adj_mean_iou: 0.0876 - val_loss: 0.3971 - val_acc: 0.9768 - val_mean_iou: 0.7265 - val_adj_mean_iou: 0.0682\n",
      "Epoch 39/40\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3682 - acc: 0.9760 - mean_iou: 0.7235 - adj_mean_iou: 0.0880\n",
      "Epoch 00039: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2648s 2s/step - loss: 0.3682 - acc: 0.9760 - mean_iou: 0.7236 - adj_mean_iou: 0.0880 - val_loss: 0.3972 - val_acc: 0.9759 - val_mean_iou: 0.7154 - val_adj_mean_iou: 0.0704\n",
      "Epoch 40/40\n",
      "1444/1445 [============================>.] - ETA: 1s - loss: 0.3691 - acc: 0.9760 - mean_iou: 0.7214 - adj_mean_iou: 0.0877\n",
      "Epoch 00040: saving model to model10_448.h5\n",
      "1445/1445 [==============================] - 2649s 2s/step - loss: 0.3691 - acc: 0.9760 - mean_iou: 0.7213 - adj_mean_iou: 0.0877 - val_loss: 0.3968 - val_acc: 0.9760 - val_mean_iou: 0.7159 - val_adj_mean_iou: 0.0705\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_gen, validation_data=valid_gen, callbacks=[learning_rate, checkpoint], epochs=40, shuffle=True, initial_epoch=36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "fCE_u2jq_ql4",
    "outputId": "4c92b18c-d908-46a6-add4-e74049b0a0e1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wZLIiM/vfoQeL1c62/RU0tlpoaDbTYrbQFNwGMfC7t7/C3tR/n3ZlqAyVIhi9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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f30f8ccb128>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(131)\n",
    "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
    "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
    "plt.legend()\n",
    "plt.subplot(132)\n",
    "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
    "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
    "plt.legend()\n",
    "plt.subplot(133)\n",
    "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
    "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "5WiQuytBXOXJ",
    "outputId": "0eed57ed-f63e-4a92-b0d0-dccefb2496e2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3vSd+Dj4GPbZCoSA8/HKS7uHhgVwux8nJCQ8PD2pqasjOziYwMJCkpCQiIyO1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\n5hxpFUdZN3mlvcMZc3pMPeTUnkbn6kOINtDe4Qg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5fHW8nC3ropgaMnbbM8X5RuPhrCW9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\nWWMaUT0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f894902c9e8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(131)\n",
    "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
    "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
    "plt.legend()\n",
    "plt.subplot(132)\n",
    "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
    "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
    "plt.legend()\n",
    "plt.subplot(133)\n",
    "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
    "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "wdLSolR_41_u",
    "outputId": "c4ae18d4-45b6-400e-997f-e07b4f077a56"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bozZ4k0gkxHnPoEOnIa0202jHNZdxmTDrDXrez9yLzqDjrsjbcLS1rrbDEomE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Bs3jiZNmpR7PxlEZKwysXk5eHDp5u8kp2SUOmdr4q2rKCi42bnVyAFEUDPis/aCX1SeYcW/rOQy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f91eefccef0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(131)\n",
    "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
    "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
    "plt.legend()\n",
    "plt.subplot(132)\n",
    "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
    "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
    "plt.legend()\n",
    "plt.subplot(133)\n",
    "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
    "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "KI_mqH53fbfM",
    "outputId": "218d29f2-630c-4f1a-e5df-5f87526b3fc4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xxczMjOzsqskOoqLO1eOZQXllOfuTj2BiZExvl5pTRPrZ+hLYrCPJ+WkcSDlK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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9207f27f60>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(131)\n",
    "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
    "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
    "plt.legend()\n",
    "plt.subplot(132)\n",
    "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
    "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
    "plt.legend()\n",
    "plt.subplot(133)\n",
    "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
    "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 270
    },
    "colab_type": "code",
    "id": "1pYqgP6OD-wt",
    "outputId": "9b419e15-f9b9-4330-c0ad-917d64a6fe8f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YgtpbJR5tX3c1kbY3VWg0Qelo2amKigosLCyQy9s+/IODg4mKimLevHnq9p9/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\nDbMQQgghhBAuSMMshBBCCCGEC/8HKkEEvocu62oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec8e802e10>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(131)\n",
    "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
    "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
    "plt.legend()\n",
    "plt.subplot(132)\n",
    "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
    "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
    "plt.legend()\n",
    "plt.subplot(133)\n",
    "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
    "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "DOGYO3QW3Vi4",
    "outputId": "8cd9a687-70b9-4cf8-c485-7cabbd2ce395"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b0pzXhSp9+vTF19ePv/1tKmVlZfz1rzMwNzdn1KixLFz4Nu7u7ri5db5ldVJ0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6130c3cf8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(131)\n",
    "plt.plot(history.epoch, history.history[\"loss\"], label=\"Train loss\")\n",
    "plt.plot(history.epoch, history.history[\"val_loss\"], label=\"Valid loss\")\n",
    "plt.legend()\n",
    "plt.subplot(132)\n",
    "plt.plot(history.epoch, history.history[\"acc\"], label=\"Train accuracy\")\n",
    "plt.plot(history.epoch, history.history[\"val_acc\"], label=\"Valid accuracy\")\n",
    "plt.legend()\n",
    "plt.subplot(133)\n",
    "plt.plot(history.epoch, history.history[\"mean_iou\"], label=\"Train iou\")\n",
    "plt.plot(history.epoch, history.history[\"val_mean_iou\"], label=\"Valid iou\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zpfjJkeuApN9"
   },
   "source": [
    "## Sample Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1697
    },
    "colab_type": "code",
    "id": "vUKPS-jCAqTN",
    "outputId": "6f361b56-9886-441c-a9ef-7c49be9beeb7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5556L888/H//yL/8yvw6IWZyeJSoZ1WoVe/fuRbfbxfT0NOr1OlqtVji/0WiE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\nmcwRy/Yzx+B7Q/G+Jmb52iyQfK8x26+7uxsh5FIJIYS4WGKvoWLRBCC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3dNttt8Ua05df\nfhm9//77URRF0Z9//hktW7Ys9jEyXn/99ejee++Ndu7cWTIxjQSaE7loThSP5sTIozkxNKU6J+Kc\nD7Gk/Jqbm7F06VIAwJw5c3DixAmcPHlyTK6dTqfxwQcfoLKyMrutpaUFtbW1AICamho0Nzejra0N\n8+bNQ3l5OTKZDBYsWIDW1tZRien666/HG2+8AQC47LLL0NPTE2tMt99+Ox599FEAQHt7O2bMmBH7\nGAHAwYMHceDAASxZsgRA/N/bSKI5kYvmRHFoTowOmhNDU4pzIu75EIug6uzsxJQpU7K/V1RUoKOj\nY0yunUwmkclkcrb19PQgnU4DAKZOnYqOjg50dnaioqJiTGKcMGECJk6cCADYsWMHbr311thjAoD7\n778fzzzzDNauXVsS8axfvx4NDQ3Z30shppFCcyIXzYni0JwYHTQniqeU5kTc8yG2GiomKqG33xSK\nZSxi/Oabb7Bjxw5s3rwZy5Ytiz2mbdu24bfffsOzzz6b96LqsY5n165dqK6uxuzZsy/o2qX0t3Uh\nlFLcmhMDaE7ERynFrTkxQKnMiVKYD7EIqsrKSnR2dmZ/P3r0KKZPnx5HKACAiRMnore3F5lMBkeO\nHEFlZWUwxurq6lGL4YcffsC7776LDz/8EOXl5bHGtH//fkydOhVXXnkl5s6di7Nnz2LSpEmxjlFT\nUxMOHTqEpqYmHD58GOl0uiS+t5FCcyIfzYnB0ZwYW0phbDUnClMK8yGWlN+iRYuwZ88eAMCvv/6K\nyspKTJ48OY5QAAA333xzNp69e/di8eLFmD9/Pn755Rd0dXWhu7sbra2tWLhw4ahc/99//8WGDRvw\n3nvv4Yorrog9pn379mHz5s0Aztvup06din2MNm3ahJ07d+Kzzz5DXV0d6uvrY49pJNGcyEVzYmg0\nJ8aWuMdWc2JwSmE+JKKYfNSNGzdi3759SCQSePHFF1FVVTUm192/fz/Wr1+Pv/76C8lkEjNmzMDG\njRvR0NCAvr4+zJw5Ey+//DJSqRS+/vprfPTRR0gkEli5ciXuuuuuUYlp+/btaGxsxDXXXJPd9sor\nr2DdunWxxNTb24vnn38e7e3t6O3txapVq3DttddizZo1sY0R09jYiFmzZuGWW24pmZhGAs2JATQn\nLgzNiZFFc2JoSnlOxDUfYhNUQgghhBD/L6hTuhBCCCHEMJGgEkIIIYQYJhJUQgghhBDDRIJKCCGE\nEGKYSFAJIYQQQgwTCSohhBBCiGEiQSWEEEIIMUwkqIQQQgghhsn/AOfH1HUhHfLFAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff631326a20>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5X8cy1Pz8PKampkJRCyQA+sZMNWd2dla61zqdjpTaCoWC+G4ASCmR3XGj0UjG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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6130af518>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f1RyWxtihNl2qoslcp69T1WLpSpu/qTTp7jqqaFTH8vm8lFJ1sKhlWeh0OnJs\nlmVF1Cw9ykeTKJIelulyuRwACAkmQWq32zKsWud7aVM6CRfXie9P9Ww65oCvTaVS6Pf7kc8YyW8+\nn4dlWaIU8nozu8rAwMDAwGAtWHdCFYahlJOoRpEIcOwMVSfGDwyHQ/R6PcRiMTiOI+qRbt3XpnDe\nsHXWlQ69dBwn0vlHUhKLxaSspctRVD9YXtLeKhISnpv2Z3GfmniRMJRKJfT7/UiZUo91IVHwPA+O\n46BQKEieEs8JWB2EzPfXY2rY5cc11v4iXVKkWqdJGtWvaYKnc8IASNcfrwWPkY/1ej35vdlsolQq\nCZGaDkZlJ+FkMkGxWJSsK5JKXlNdNub6sruSSmWxWJQg1Uwmg/F4jH6/j3K5LF2BVCgNDAwMDAyO\nFutOqIDVGAKGQjLfSPtjSEZIBFgyGo1Gh5X4lpeXUa1WpRzneV7Ez6Q79gidZ8SbtDY8ax8Xy2fM\nmOLNW5eNSH70PMGZmRn4vh+Zw6c74viYjlrgtjy2eDwuMQFMLqdXSnuGut2uEDYd0cDfqWpxTA/f\nj00Cvu8jm80etk56vUiydEwC88K43kC0Q5HnzONsNpsSvsnrya5J7WtbXl4W1Y9qYBiG0tHHzwiz\nuWKxmBDC2dlZOffp+YKNRgOlUkkM6gYGBgYGBmvBhkgyZJmPhnSd4q09VPpxrUSw1MMbfyKRQKvV\niqR2+74Pz/OEOGi1hQSF5I0kh6+nYtbpdIRAMXFb5znp+AT6pHK5HCqVCrLZLBKJRCTgk8nielQM\nzfMkKjwn5muR+On4BEYVcHgyFR/dlUcfkx5xwyHKfB5YJW65XE6UJWAlJqHdbsvz9IdxO6pe7Axk\nh+W0OkfSSdWLpIYkmqqWzuFiOZjXsNfriapEAz4A2Z7XLgxDyRS744475HHdOUnVjFEdBgYGBgYG\na8G6K1QkBzQUa8VGJ5IDqyGPLInp7jIOwdWdXtq0ztex3EMTNhWxUqkUUS50fhKwOuiY7xcEQWSW\nHgfr8hzS6TTy+byUK7vdrvh+6Lli1+BwOBTPFEkfgIhXSpNKnrsOOJ1eI54fS3JUntLpNIIgQD6f\nl9dSFdNdh5p8MepAe6h05IMuo5HQMT6CRG0wGKBYLEbSyBmZwHXWQ56ny3YkVSSKmUxG1pokz/d9\nMezrz4b2YZFMsrTJzxKvpYGBgYGBwVqw7oSKJSX6Z8bjMVqtFgqFgnidtDmbKgJfw3ISO+Dox2KZ\njSpLq9VCJpORmypJhCZnvNnrrCYaoEkOSDZ4s89kMiiVShJCqbOZ2I3GLsZMJiOdi/F4HIVCQboO\naSQnUZnOjdJhmCx5ZTIZMeUvL9YqAAAgAElEQVRrMkhljbPuCJIMKmzcl+u6yOfzcg0syxIzOUMv\nSSb5eC6XE38ZiQlJVTwel5E0TKZnDpXuSgQg6+h5nrwPYzNI5PQ15xqzRMg1BlajGYIgwOzsrHQ8\nUkHj54jHyesei8WwvLyMTZs2PU6fcgMDAwODJzvWnVABq4oQS1RUVngT5I+e+Ufztt4Hb5As5VDZ\n0IGSOveJHWd8P61k8cZOpYoEjAntxWJRZvWl02npRGQ32nRsQyaTwcLCgoxdIRmg6b7VaolSZ1mW\nEEQ9LsWyLDFeU6HRg5tJFofDobyeI2T0uZE4knQyyoD5UDplXBv1uW48HqbQ6+vBjkbHcSLkkeSN\ngakkyrx2JLkM+WRXJT8X7LwEVlU6HZoKQI6Dz/N1VNX4N0un9HWReLOD1MDAwMDA4GixIQiVHgjs\nOE5kjAyfB1bzmkieNImi0sKSD9vgLcsSBYcEgeoEvVPa/E5Ml6KYHxUEAer1ujxPXw6N1fl8HrFY\nTGbIua6LE044AQcPHkS1Wo2cgx6MzC4/EgH6vKZn/LHDjeevAzGp+rBLTxM3Km0cEkx/F0knSRnV\nonw+j3Q6LeZ2bqejEvRomWw2K8SF6hJLd1ThMplMRIHi2uuMqHQ6LR4qlnCZG6UN9izVagWOx0Pf\n1A9+8INIJyOvPwkvFTG9ZgYGBgYGBmvBupvSSaRYVtPKlIbuuKMqQ+jyjY48IEnQ78Uflv90XAAJ\nCZUsTVBIAjjEmeU/lsCmDe3JZFJmzCWTSRSLRQBAu92G4ziHqVBU1Ujg9LgYHh+jAjQJ0cSTsQ0k\nFnobvQbM82KJlAoaVS6SWnYp8nrkcrnIbD2SOhI03UnHdaOSxrE0DDjla3mc+hzYXUjFjWRyOBzC\ndV10u130+33xn+nPAR9j7ISOvOB76UYHPu66LgaDwVF/fg0MDAwMDIANoFDxxqlzhahWsDUeWOng\nooLC7CVdCqS6QQWi3++Lh2c6PkDPygNWlQ2+l+u6UqIiSWCGEZUqlsZIsmgyZwmR8QDFYlF8PL/y\nK7+CXC6H+fl5SRpnPAGjBtgBx3KkHj3DKAGSF6ou2mitS3s8T+1XInFitAAVoCAI0Ol0UCwWIwb9\nXq+HWq2GxcXFCJEtl8vSlaevYT6fF3VQk1xNNEmeeQ1YhmU5LplMolQqSbCrbdvyGl5jDkLWGVgk\nc/yMnHbaaaKskQjyM8PrynWNx+Mm2NPAwMDAYM1Yd0IFRFUqrRLxxscbOVvodZmLP3rgrW6LZxCn\njlvQYZwkSdovxJs8S2zswGM5j0SHLfiFQgGu68rwXvqtSqWSEABmKGWzWczMzMh+SXps20a32xWD\nvVaagNWOP5YYeU46/0lHOOg14jbTw4ppNOexUNEiuaHB3/d9manHdaaaQ68TAOTzeQnjpF8pCIKI\nGgdEfVy8/gRjGmhmJ6nievD6U8VzHEdIHrO4+Fkh4dXNBzxnqow8b03yDAwMDAwMjhbrXvIDICUl\nloJ4Q9SqEUkAFQjekDVJ4t8aJE96jIw2m5M4aA8SsBruSU+WJmza70Pylc1mYdt2JKyT8Qg0ruvE\ncapa9CvpUh8Jnj6fIAgiI2l4DkyQ5/OPREwJkkSd1zWZTJDNZkUZIgniYGLO9iMxYomT68lzymaz\nQthyuZz41nj92GnHzkOdiM9rwmPjtScBorrEddAES+eJkURqwzvXhiDZ02umP28GBgYGBgZrwRER\nqgceeADnnnsuPvOZzwAADh48iNe//vW45JJL8Ja3vEUUoS9/+cu44IILcOGFF+Lf/u3fjvggeOPl\nDU6rLLzB8jFuS6MxSZaOPeC+qLjoNnudRK5vrtqjxX3ncjkkk0lks1npCiMmkwkymYyoVowKmG7p\n73Q6GAwGcF0XzWYT2WwWpVIJjUYDvu+jWq2KgkNVC1jNd5ILpVLHqaroEiXPh6RGv1ansPM8SALZ\ncUdlTA98pt9I+8No5M9ms5L9xedIDlmOpB9KG+t5bNwnr5P2vk2XaenR4vPa9washpKSpOlryPfT\n10SvqR51czR4vL8TBgbHG8x3wuCpjke9i7iui6uvvhrPf/7z5bEbb7wRl1xyCf7lX/4FJ510Enbv\n3g3XdXHTTTfhn//5n/HpT38at9xyC9rt9hEdxHg8RqfTWTkglQWlO/r0aBQdF6C9MPrx8Xgs5ICg\nssQOOACiVOkONpbxLMuKEAeWuRjymUqlkM1mkUqlUKlUUK/XZX+j0QgLCwtSBstmsyiXy0ilUiiV\nSpidncX27dsRj8dx6NAh6YBj+Kdt20IYAUj6Ost3OvFdz8/T2VOaZOncrEQigUKhECkDsjzJdRuN\nRmi32/L6crmMXC4nRnvXdSUMk6VOdizyHHgc7Exkx6VOwee5kIDpx0mEeE70fekAUX3dqDjqTkk+\nT0WP6h1/GDPh+/4Rd/k9Ed8JA4PjCeY7YWBwBIQqnU7jE5/4BOr1ujz2/e9/H+eccw4AYMeOHbjr\nrrtw33334YwzzkChUEAmk8GZZ56Je++994gOQt8ASaI0odLlLd2ZN12m0zELtm0DQCQskyqUJm1a\n7eJ+8/k8MplMJO2bRC2bzSKfz0eUFRIRxgXkcjmZ8xeLxVCv19HtduX9HMdBqVRCNpsVEzgjDHiD\np5JFdUdnUvHYObJHQ3f2aZWJ0QxcZ6pQmUxGzpXnxfIbt7UsC4VCIVJqtW1buv50lAGPlWQtHo9L\nKjvJH8u2OvpCz++jgsVz1NePpIrE6pFKtSRjOsZBq2K6nEwFlETzSPBEfCcMDI4nmO+EgcERmNKp\nbmiwAw0AarUalpaWZCAxUa1WsbS09KgHkEgkcOONNx7tcT+u+PjHP77eh3AYPvzhD6/3IURw6aWX\nrvchHIZPfvKTj+n173rXu45ou8f7O/FkgvGlPTVgvhMGBsegy+8X/QfzSP9DOh6Pcckll6BQKEQ6\n9tiFRrVDD/3V76Fb6dmuXyqVJHGc+9AeK+25Iaiw3HLLLXjzm98s3h8axW3bFj8VO9bq9boMO2Zg\nZjKZlBIfsNq1RrO6zlTicOVWq4V0Oo1WqyVjZei7SiQSeO9734u3vvWt8H1fynE8f3a9cc2o8DiO\nI2VAHnehUECtVpN8KD7PGXvsmOOoGF1y5DoWi0W87GUvw2c/+1m0222JfOCxUDXj3wCkBMjzHg6H\n6HQ6kQR1KmvAagchfU88V92d6bquREOMx2PceuutuOyyy0SlpLKoX8djYImU1wWAhIceCzzW78ST\nCdNNIscCT8V1PN5hvhMGTwWsqcsvl8tJOWVhYQH1eh31eh3Ly8uyzeLiYkT+/WXgjYzlJpb2fN+X\n0g/b8PVQXv5LYtXv9yNGdm1gB1b9Unporo4nIMFixxqzseiXYokpl8thdnZWylQ85oWFBbnRA6ud\ngto8r9v52UGoPUEsO5EsPFL3IskB10QTS53rxOe5X/qwmKHFc2UZjmU8djem0+lImc73ffG6cTYh\nPw+6a5JrRTP/9BgeEkCW4TgkejoCg/vU15lEjynq7PQjpo3nJFH8DGgyzWtOIvlYbv7H+jthYHC8\nw3wnDJ5qWBOhesELXoA77rgDAPCNb3wDL3rRi/CsZz0L999/P7rdLhzHwb333ouzzjrriPan1SM9\nYoXEgYqCDvHUN3D6mWgepzoxHRTK/fOmS/L1izrASJ6SyaQEcNJzxI42HW9gWRYWFxflZk81SQdT\n8gbP7KNEIhEhOHpUCvOhgNUU8FgsJmZ7rcbwHKjm6b919hawQioYh6CN3SRIJETav9Tr9SK5Wcyb\n4ja5XA6pVAq+78v+eZz0S037znROliaO03lSujGBj3EbklNgtZuRv5OIcy34N49Ddwvqz8hacKy/\nEwYGxzvMd8LgqYZHLfn93//9H6677jrs378fyWQSd9xxB/76r/8a7373u3Hbbbdhy5YtOP/885FK\npfD2t78db3rTmxCLxXDFFVegUCgc0UGwPKSN4bocN208Z1gjCQLLZNqEzYgArVDxJsp/adym2kNC\nwfIWu9iAVaM6iUwul0O325UZfnw9S4Uc9EvlTAdlkiy4rgvXdYVYkDQxJNTzPPk/PJJJjszRBEOX\n+0gcSMh0FyO3ASDp4CQ8jExwXVdIHAkXByhrgmXbNhzHkRwqdhhyxA7VO6pUVA35w5gGHjPJpR6/\noz8PJF6645LrPh0VofOvuK5aleR6AhB1jOtwJHgivhNPFpiSzlMD5jthYADEwnX+L954PMYb3/hG\nIUX6pq/LOpqEsISlu8V4I6b6wTIiH9ejZIDVbjB9HPl8Hh/72Mfw1re+VbrXZmZmYFkWhsMhKpUK\nstksXNdFKpVCGIbSrTKZTNDtdg8LAuV7WZaFXq+HmZkZNJtNlMtleQ1LTiRPyWQSBw8eRDKZRLPZ\nxIc+9CFcfvnlsl+dmu55nnQjUgXSg4d5nMlkEps2bUImk0Eul0O5XBa1bTAYCCkLggD5fF4e0+U0\nrverXvUq3HbbbXK8wGonne/74v1yHEf2z2Ry+s94vr1eT9aKQajMh+I5aM/YdJYVj/PWW2/FxRdf\nHOlw5PXXI4joc5sedUMyfcMNNxyTz7WBgYGBwVMLGyIpXfuaeENloKTOmgIQSRMHEPEGhWEoZmut\nTHGfurQ0Pb6GfiEA8p68wff7fWQyGfT7ffT7fdmOykgqlYLjOKhWq3KDp7qSSCSQz+dF+ZlMJigW\nizIOhSnqw+FQfFthGGJubg4AIsnkPC4SEqptOuaAStA0JpMJFhYW0Gw20el04Ps+giBAr9cTBUcH\nd5Jw9vt9+L6PdrsdiWmgqhiLxdDv98UDRoN+IpGQETv0OfE66/KeLmXq0i3XVn8mtIrEMt10jIbO\nsOJnSI8V4rqT0DGNXfusDAwMDAwMjhYbglDprCn+DUDUKpIIXaqjQkPjNZ/XN1ztl+KNV2claSM0\njdcAJDOJ+yFJy+fzQmx834fv+6JycCByPp8XgkblYzAYCBGhWkMio493MBig3W5L7lO5XI6MvNEh\nluwEpEdKEyyuA1+jDfpUkTzPEzLH/XE9tDrEUlyxWIz4rNgNSHM6uxhJCG3bFgKYz+cxHo+lBEr/\nFEmuJlU8Bn09+Xng/nj99fghgoRNzwukksaML+09Y5govXoGBgYGBgZrwYYYjgxEb4q6CjndMceo\nARrEtbGZLfHTo0tYJps2cfNf3nhJJKiwABA/FMmLbdtSKsvlcuL/yeVy8m8ymUS73Raywm49z/MA\nAPV6HZ7nYTgcIp/PC7kiOaEKxsBJYHUIsSZHVNlY5tOdffzR5xwEgXi2qCppgzgN5el0Gq7rymid\nTqeDcrmMdrstgansQqRPiueqDfK8Fp1OB6VSCYuLiyiXy1IyBVa7NflaKnw8N02seD7aZ8cyL6+V\nHjmkj6ff70tMRDqdlpgEqpnczsDAwMDAYC3YEAqVVgi0QZvPsaTVarXgeV6EILFcxRvltH9JD8rV\nnX4kJ5pQ6XE0AMSbNBgMhOwwA4nlRaaFDwYD9Ho9yVhKJBLodDpCLGZnZ5HL5TAzMwPHccTkzvdN\np9Myo48djYwqAFZN1Nq0zRIbfUI8H63OEVyX6dJgvV6XDDCO2Ukmk+h2u2LYHg6HcBwHs7OzMiaC\n6mA6nUYul0O73RZ1ikZ0EkjbtjEajVAqlYT4Ub1jGZTHyGOfJrvTMxK1D4qfFZZpmWzP12UyGVQq\nlYihX3dKPtbIBAMDAwMDgw1BqKZnq/GGp+fW0SxdLpfF3KxLQNqsPd0VqAcba3Klb6bal6Xn5pHs\nASuqDEMxSRIACLFi2S+bzcKyLGzbtk32EYahEArOkxsMBkJA+JyeqaeN8yxxAlFyoUkIfzQZ0WVN\nYLWMGgQBOp2O5GbxtY1GAwcPHkStVsN4vDLwmARINwzUajVRkuLxuMQwFAoFUb9IQukDI/lk56CO\ngWAH4XSPhP5b+6m4LcklAMkL4zUiQeX2sVhMRujouX2aWBkYGBgYGKwFG4JQUTHSUQYkTUSxWJTS\nGW+iNDuz3AcgUrphWUyXwgBIJhSAiGJBQkXCo5O+SX5YbgQgKg+79CzLkkgHkhmWw6hGWZaFmZkZ\nmQlIEsCgTw5PTiQSyOVyUmIjKeSNn8fDY+Sx6ABUbbTW5TEdnDoYDDA7O4tqtSrzBLkOuVwOtVoN\ns7OzsiZscY7H4yiXy8jn8wBWSKU2xLM0qst4zKliyc113Ujop45WIBGcLvNxW0L7nqZVOa43ySKv\nw8zMjKyDLhnqjkYDAwMDA4OjwYbwUHF8ijaI0wvDmyBLW1Q3dFAllRsgmjWllQsgmk+kO/FyuZwo\nXADEB0Xk8/lI+z1v2jRq6/ITy5A6rkCHXZKUMe2bfxeLRYlR4PFoJYWGahJHroEmIDxXvQa6S5Lr\nxzIej41kxnEcVCoVLC8vY25uTl7DiIdKpRJ5H3Y3alVRDzqm6kSDOgc0kzil02npoOx2u5Hz0M0H\n04/zd34+plVHTR6nS4ckeIVCQSIe6K3SBN7AwMDAwOBosCEI1XSnHrvnOB+PmC5pTYcx8gb6SEGP\n9PeQQPCmyzBOmrG5vS5zsbzkui5OOOEE6QwjqdB5RlSoSKjoI2q320LUSKSoklDZIqkCVkim53mR\n6Ae9fxIldq3p3/V6kEBNe6ssy8JoNEKv18NgMJCxM57noVQqCYFMp9MIggCzs7NyXAAiMwzpHfM8\n7zCSyTgFjhHSvjCdJUYyRjULQGQf2nDO68pz5WdEe6x4HXnO/Ezoz5Bt2xiPxxKkytKkgYGBgYHB\n0WJDlPyA1dlqVJs45oQGdd4EteqgfUJatQBWy318TKs0Or8pk8lEOsqAqEcpn89LXECtVhPPD+MR\n+H6aLFGh4eOZTAblclnev9lsCpli+ZE3/EKhIGoWO+S4PppA6jKn9iI90hgXXfbjMdDLRfWN29Hc\nzteTHJL48HhYwuMa0p9EgshoCZIZnQ3G49SkWF8/PsbPgvZA6evK454mWDrXTCtT0woWiRx9XQYG\nBgYGBmvFuitUmsg4jgPLslCpVCIBlrr9X5fytF9GxyRM+29isVgkKJRkieQlHo+Lx0nvdzweI5fL\nwbIsiUhIpVLIZrOyDTvKRqORlPRyuRyCIJC/dfZSJpOB67pC6IbDoWRPsRTlui4ajYb4rKbPb3ok\nC83y0+RErw3Ph2SD43kYQ8H9JJNJFAoFKZkxXoCxAxwto31aiUQC9Xo9MmqHQaa2baPf78O2bemO\nDIJAVCquu+u6ACAGdXrj9OxGnvN0iViTJpLFWCwmyp8mYJo08vXVahWxWAzdbvexfJQNDAwMDJ7C\n2BAKFUtx7OIj8eFzLMVpg7LuapsuAwKrvimW+qh+8eaqO8TY5aZJ1Xg8Rq1Wk+iARqMhpmzepHWH\noW3bSCaTsG0bYbgysobEYTwew3EcDAYDiVLgcGHXdVGv1zE7OwvP86TDL5VKodlsRoYv87inf6d6\npMthXBPdTZdMJqXzLZlMotfriUfJdV1ZXxKOwWCAhYUF7Nu3L9INCaxGPWSzWWzevFlKkiylkYCS\n7DFigWW9fD4vj5OUUSXS3Y08D02sp89fE0gSJa3kcT/8dzoYlNd/8+bNj/mzbGBgYGDw1MSGIFTd\nblfUGD2XjzdFmpt1FxiACFEiqMCQaNGfNX3jJSljyU97byzLEi9QqVQSAhKLxdDr9aRMpEtYNHqP\nRiM4joPFxUUxlTMlnN2CfF92FvJ12WxWCGQ2mxVyo48XWPUMaSM8yRRJzXSZT5f/2A3IciPn69Gz\nlUwmxawdi8VEJcvlcrLWTFunh0oTsTAMUS6XxaPUaDRE/aEvju/N9WGZkNeca0cip8fD6PN7pGuq\nU+91OZOv09eTa8kyq4GBgYGBwVqwIQhVuVyWDjIdzKnNxPTO8GbJm2cikUA2m41kIpEYcRQKX6+f\n06ZwlrZYdrIsS8pF/X4/ojwxZLTX6yEej6PVasmQXpqvSZQ4EJhkieGWJAskA57nodlsIpPJYDgc\nSjaU7jTURICjbKZn1HG9SLyo/pBITGc8DQYDzM/PYzAYwPd9LC4uot1u4+DBg5hMJti/fz8mkwk2\nb96MUqkUKbsBEFJJwkKvWDweh+M4GI1GkrjOGIrBYCBdjIlEAoVCQYJAOViaxIqeNEYw5PN52LYt\nnZ/T3imCnxOWQPX4HZ3bxTXSMQwGBgYGBgZrwbp7qKiAsGVdm61ZytIqw7RvSpe4ptUKmqWDIDjM\ni0NixhEqiUQCtVoNwKqPh8oJiUmxWEQqlRJjNv1Vnuchk8kIeWLHGkMtPc8TNYbZS51ORzKmGIq5\nuLgo8QiMWiCpIlnkgGLtDdO+oenUb/7O0psulwKQLKpqtQrP8+B5Hvr9vhCjwWCAhx9+GIlEApVK\nBdVqVdaIviPOAQSAfr8vY3UYiUDlLQgCLC8vI5vNigK3vLyMMAxlHXVXIodTsyTIMp1OxGeuFbBi\n6GeXKAApMXIt9Ygafma4DT8DBgYGBgYGa8GGUKj0yBCd88TntAeIv5Ng8EarW+a1N0arOExjZ1mN\npCqXy2Fubg6WZQFY7TCs1+uo1+tSVgzDEL1eT4zbVD6SySQ8z5OwTZJAnUEVhiG2bt2KdDqNvXv3\nIpvNYjgcCmnp9XryPt1uV27wvMmzBKkDPqmWTWczTSswutxH1YYEZzgc4v7778fevXuxsLCAfr+P\nIAjQbDbhOI6oQ7Zto9VqYf/+/bKWQRCIulQulyUoU59PMplEv98XEkefUiwWg+u6QiZJyugF0ySR\n/ixGVPAzYVkWSqXSYSN88vm8KJ58Hb1uVMB0hyB9ZYZQGRgYGBisFRuCUPGGxjIPiZMOdZzu4uPv\n02SC3YFahSG4Lx3AyZuuVrh0p9vy8rIoXA8//DBSqZR077G0R8LAG7duy+/1ehJ+SZVk8+bNGI1G\n4ieiWb3X64nCRT/TdAwE4wBI0qhS6e4/TSxJTLgP3QXI/QVBgIMHD0pIJsuNLHtybSuViqg/9EDp\nAM1ut4t2u43JZIKtW7eiVqsJeaGSlk6nUa1WsbS0BADyXrpcq2Mt+Hqqa/Q9kRCx5AsgQrjS6TQK\nhQLi8ZWxOLqcrI3q9FA9UnekgYGBgYHBkWLdS37AagAnO71IhKhYUenQCg0JBlUMYDXYkWRLK1c6\ng4nBnPF4HDMzM1Ku443WdV0pyXF0TDKZxOzsLFzXlWBMliaZ+N3pdEQh0fPkdEfZcDjE3Nwc9u/f\njzAMsW/fPul6Y5mRsQG65Z8ddlTcGFHA2AYdA0DVTJu6dZebLntSocnlcvB9H1u3bhW1rVwuC3ll\nVlO9Xpc1YtZWMpnE/v37USwWZWxOr9eLXJ96vY5msynm90qlIq+f7rrj9SRB4jWcHl7N0iq355gZ\ndu3l83kUCgUsLy9LbAWVKO5Dr4+e72dgYGBgYHA02BCEappEkfiQBLE0R88M/UlUtPS2Ws3SN2lg\nlUzwdewqnM61KpfLaDabyOfzQmpI0uj9GQwGclzxeBylUklKTiQ1DLaMxWLwPE/8WlRxOPA5CAK0\n220kEgmUSiUsLy+L34pJ6ywPJhIJOI4jSgxTxEmSdEYVz5nnpvOauE/ur1gsIpfLod1uy9zEer2O\nTqcTUX7oH+N10vtZWFiQ0qRt2/A8T0a8+L4P27alLFgoFDAcDtHtduXceIy8DixXalJFRY7XhGVa\nAKIcJpNJVKtVZLNZmY24f/9+tFqtw9LwgainysDAwMDAYC3YEIRK+2J0mCdVKF3esW37MI8Qt6ep\neZpI6VIX36dYLEpAp1ZzAEjQJg3PTP+2LAu9Xi8yP44qCW/kvDmTOJA86KHOjFggScvlctiyZQtc\n15XjpMGaJTcdcknj+nTZkwZuqmfT0QLTZnVuw5gErhlLdQw2tW1bSAyHIw8GA/EuMTcrm82K0kXS\nmEwmkc1mJTZiPB5jdnYWBw8elC6+5eVlxONxZLNZiXBg8Kgm27okO61KEnyOo3L4WLVaFd8XCSjL\njfx86P0YGBgYGBgcDTYEoQIg5EHf4CzLkpIUc6oYRcC2fE0q2FEHrIY+0iQ9XQ4j8WErv/YL0RDO\njKp+vy9kpVqtIggClEolOe7RaCQGb6pnJFqMXigWi7KNZVloNpsoFAqwbRvbtm1DEARieM/lcuj3\n+3AcR7KRdEcjSQeJ3HQuk35Ov27a0E8/UiaTQbFYlDLZzMwMWq0WisWiGO5JeOihYmcj1SnmVrGb\nEYDESIRhKCVNXg+ur+/7mJ2dxUMPPRTplOR5kViz3ElDPrDqASN0nAJVRyqQJ554IsrlMh544IFI\n7Ibu/JyOlTAwMDAwMDhSbAhCxfKNVh3S6bTMmrMsC+VyGcCK2kBvEsMlSSbY9TYej0V90i33wArR\nqlar4s9iMCVv2nx/lqm2b9+OSqUCYKVsRv8Vf+ijmvbi8DmazOm9AiBDhIMgwNzcHJaWliRCYPPm\nzRLboD1Uvu+LSXs6rFOXLUmUuKbD4TBS0qTHSJNKvp9t2yiXy+h0OhJCury8LNeDHX0ARFGjn2xm\nZkZyuTQxYSgouwapOvG627aN5eVlFItFGcmju/7Y/UnzO8+RfimtulWrVSF0upOP77VlyxZ0u10c\nOnRI1o8lw+l9GRgYGBgYHA02BKFiyYjKCYkDW+JZWqJvil4mlt7CMJRyH03rvGkzmRyAdIQxC4qK\nCpUokrZut4tNmzYBgHiiNAGxbVvCQG3bFl8Rs5TYdaZN7VR0+v1+xEPU7/elYy4MQywtLUl8gvYT\nsXxIBW6661ETOh3dQDKpx9NwfUj6tALVaDSwvLyMyWQiYaPMjaJ3S4PryP0Wi0UcPHhQSC9La1wH\nx3FExdPkh2S23W5Huhj5eh1aSjKVTqdRqVTkulDF0sR82ny+bds2JBIJzM/Pi8md3ZnGlG5gYGBg\nsFZsGBeu7r5iIGYul5ObKNURGqQJ5hhROdKRAQyGpEeLJIOqVzqdlpswACwvLwMATjjhhMhYGO5H\nq06e50kHH9O8GQCqf/7Jw/sAACAASURBVFguDMMQnU5HSpYcyUJ1jOpMrVaTAE2W1ABESnrT/+r4\nAqo5OnZCG9UZZMnfdQnU8zy0220Mh0NJdmeJj3MJNTnlfL5UKoVCoSCEi2s1GAwwHA4lOkKXJpnO\nTi8WSSqvOffB99THqQM+tc+Ma0ByB0RzzGKxlVFC2WwWxWIxEhSrze0GBgYGBgZHiw1BqEiASE50\nkjYVEt4kmVnFGzuJ13SnFr1NJFIApNOMI05SqZQQEHqGAEjXG5PMaTCn74dqis6FCoIAjuMIWdLE\nQo8+0cQPWEkWbzab6HQ6MlqF2zOCAYAQI6pMuoSpTdqaXEzHTACIdMjxd10WJJGkyjUcDrG8vIx+\nv49erxfJpeJAaxJMEsBCoYBSqQTHcaQMWy6XZYwPZwl2Oh0Z2UPFbNqYTgWLxJrHl0qlUC6XJS+L\n117HK/A8qW5xjarVKrZu3SoqJEk5X2dgYGBgYHC02BCEinlFJDvsFmPpjOSA8+R0KYflJN5cgdV4\nBN0Vxr+15wmA5CVt27ZN3i+fz4tiRBWHI1Z835cbPInWNDkDgMXFRSk7drtdWJYVSRznSBSa6SuV\nivivqGKRtOhzIKkiQePvLOlprxG30dlcHPHD5/XYFe3JWlpagm3bct6j0Qgnnngi2u02gJWohBNP\nPFGOLRaLyVryuMvlMnK5HLZt24ZOp4NCoRCJdmB3JaHXj+STSpQmqYlEAlu3bhUDO6HDVhmZQGVK\n55jV63UUi0Vs3rwZc3NzYqzX+zIwMDAwMDgabAhCFYvFZC4ey31UcrTKwJuq9r7QbwSsjGch0dKk\nimRCjx4BIMrM5s2bkclkhGww/4k3fCpmvu+j0+nAcRwZKUPFiKSEBnEmrTMUlOZtJq/r9HIeP31H\nqVRK8rb4nM5NYqmKxI6KD9eSxIKEUpvzdSlQqz9cD61wNZtNyanqdDoydgaAqE0AJFqCZVbbtuUa\nUm3L5XI4ePCg5E6R+HCdqFIlEomIN47nTpLILkheY/qfeO48Z75Gm9p5blxrDnNmWKoZPWNgYGBg\nsFZsCELF8SBUpxKJhJjUSVZKpZKoRjoAkqVAKk9s9det8DRy80ZMP1ahUEC9Xodt25hMJqJQUT1h\nFABLXewwpFG61WpheXkZnudhaWlJvFqO48jYEypQw+FQtnn44YexuLgowZbZbFY8WByMzEHMJAvc\nF0MweZx6kLQuLeo8Lq3QaFJF5ScWi8FxHACQ+AZgRVkbj8colUrwfR8HDx6UfCea8ffv34/RaIRW\nq4VarSaklwpTPp+X2IharYZ6vS5qEL1pLNlRfSsUCpHSJo+bn49SqRRRsrgeOoCU+2bWFRVE7nNm\nZgapVAqbNm3CySefHBnwbGBgYGBgcLRY9y6/WCyGfD4vXWA6K4k3TRIhKi0EFSFgNRyUj7GTjaZm\nnbju+z5qtZqoF47jIJPJyA01nU6j2+3KttVqFd1uV5LO+d6cwef7PmZmZiQck7lUNLU3m00cOnQI\n8Xgci4uLkfgAljJJaNrttgxVbjQaopqwXMf10aRKK3A6nJKqns5r4jqxm5Kdk/l8XkqYJJRBEEin\nHokIj5NZWb7vw3EcSYAHIOU2Kl65XA6u62Jubg6e52Hz5s3odrtoNBrSYalLeySe/HxwvTKZDEql\nUoT4TKtKeg30cyTmWqGcm5tDp9PB3NwcJpMJHnrooaP9+BoYGBgYGAA4QkJ1/fXX45577sFoNMKb\n3/xmnHHGGXjnO98pqdcf+tCHkE6n8eUvfxm33HIL4vE4LrroIlx44YWPum929VEl0qNnaDhmWUaT\nAhKM6RyhVColJRwSA13+4dy8YrEoN2pgxUvFmzijD5i+Pj8/L4OMe70eNm3aFDGop9PpSMyB7/tY\nWlrC5s2bxUw9mUxk3l06nUav1xNCR0UonU6j1WohlUqh0WgIoQEQiS3Qo1r0aByW/UiYtI+MJFOv\nFctsVMboSeL6cVumw1uWhcXFRQDA/v375bwPHTok10qXall2ZMYVCSk7Lbm+7ITkcfJzobs5U6lU\nJA+MalM2m434sHS5UweaUr0iqWaZslqtYnFxUQzqR4rH8zthYHA8wnwnDJ7qeFRC9b3vfQ8//elP\ncdttt6HVauGVr3wlnv/85+OSSy7BS1/6UvzN3/wNdu/ejfPPPx833XQTdu/ejVQqhVe/+tXYuXOn\nZDv9MnBUCmfhUYEhCZo2FvNmyYwoEgTLsqQsmEqlJCFdqzyZTAb5fF5uqFSwSA6AVfLR7XYRhiFs\n25awUJ0CTpLF1v/JZIJeryd+MCo0vu/LTd7zPDFlk4RRpeHwYD32hcfk+74oTdrYzbWYnktHP5JW\nnHT5jIRzNBoJ6WTJsNvtolKpSJgoSSBfC6zGL7TbbWSzWczNzcm6h2EYCV0tFAoyE5A+JprhqXTR\nR5VMJoX0aMM/Yxzo29J+Kd2dpwk1t9NkW5OqfD4vyhW7E48ET8R3wsDgeIL5ThgYHIGH6rnPfS7+\n7u/+DsCKP8nzPHz/+9/HOeecAwDYsWMH7rrrLtx3330444wzUCgUkMlkcOaZZ+Lee+89ooOg12Z2\ndha1Wg2FQgHZbBYnnngiSqWSkCve1KlysJwFQJQg/gsgolyQmNVqNSnz2bYt+2anFwAZ9MtxN/Q4\ndbtdKfmRQNGAzuHCtVoNtm3LDLv9+/eLp4cJ6hyEPDc3h+3bt8O2bTG6W5aFfr8vhnRtuNbp35pI\naeVOl//4Ot09B6ya2rnP0Wgk5T4mndOwTvM6S30HDx4EAMnUeu5zn4szzjgDk8kkEtZJIkPVLZfL\nIQgCdDodxONx2LYta7J582aJO+B+NHHK5/OiUnIbXeoldOQDQRKqybiOk+DnrFqtolarHdHn9Yn4\nThgYHE8w3wkDgyNQqBi2CAC7d+/Gb/3Wb+E73/mOEItarYalpSUsLy+jWq3K66rVKpaWlh71ABKJ\nBN7+9rev9fgfF/z+7//+MdvX6aefLr+/4hWvWPN+rr/++mNxOMcMx3KNjhV+7/d+b82v3bJlC372\ns58d0baP93fCwOB4g/lOGBgchSn9m9/8Jnbv3o1/+qd/wu/8zu/I479ooOyRDpodj8f4yle+IgGW\n7PQqlUrwPE8iBqjW0M/E94jH46LsdLtdeJ6HbrcL13VFdeF4lWc/+9miVlSrVTiOI6W4zZs3YzAY\n4IILLsDHP/5xZLNZ8UBls1kEQSAz5/L5PHK5nKhBLFkCkMG+o9EI2WwWnU4H4/EY+/fvx09+8hOk\n02kUCgWcdNJJKBQK6Pf7GI1GWFhYQKvVkpIgABkkfP311+Ntb3ubdNPpbj5g1ZyvTfr0RNFrpUtd\nOkGe0QUc50IVKZvNSockfUm5XA75fB7vete78NGPfhSbN2+W9+FrkskkWq0W6vU6Dh06hBNOOAHZ\nbBbdbhetVksS0Xu9HobDISqVCjqdDpLJJJrNJvbv3y+lUg6TPuuss2TEDEukLN+ynPnSl74UX/nK\nVyJDrIHVjCwqnNwHj5U+Nj52NHi8vhMGBscrzHfC4KmMI4pN+Pa3v41/+Id/wCc+8QkUCgXkcjkJ\nnFxYWEC9Xke9XpfRLcBKsGW9Xn/UfbMEQ3M3y2tygP+/jEUCQR+MLvXx5q99NRwNw5IPTe/02xw6\ndAi1Wk0eW1xcRL/fBwCceuqpOOWUU1AsFsU4DQCVSkXM6q1WS8zUJDMsNzEIk6Sh0WjA8zzk83kk\nEgnU6/XIWBmOYOn3+/jxj38spIzHzW183xdyQBJAPxSPQ5vJdVmM5nMdDMpSIElnEAQYDAao1WqR\n3C6SD5IlAHja054mpLfb7UqSuu/7yOVyaLVaEn7685//HMBK1MJgMEAQBJiZmUGlUon4p0h4MpmM\nkD/61vTga872A1bLfPp3eqj0GpE4k0Tq8h/BpPwjweP5nTAwOB5hvhMGT3U8KqHq9Xq4/vrr8bGP\nfUyMgy94wQtwxx13AAC+8Y1v4EUvehGe9axn4f7770e324XjOLj33ntx1llnPeoBxGIx2LYtalMq\nlUKpVJKbIAAxKtNYzOdIbniz14noNF0z86her0fGqzBkE1hRczZt2iRdZCQC02GgDKTkcGWSp8XF\nRbiui2azCdd14TgOPM/DAw88gPF4jGKxKJ15JCiu62Lfvn1C4n71V38VJ510khA5psZrczXN8toT\npU362pxOIkEiqgnXcDiUOXvsSmT8A71bNMrzuHO5XET5obm8Xq8jDENs3bpVsrPm5uZQr9dRKBTQ\nbrcxNzeHZrMpQakkzhySzPXlyB++Jwk0hzeTSAKr6uR0NAIAmQfIv7kmJFjTREqv4ZHg8f5OGBgc\nbzDfCQODIyj5fe1rX0Or1cKVV14pj33wgx/En//5n+O2227Dli1bcP755yOVSuHtb3873vSmNyEW\ni+GKK644oq4p3jR5A2c3F4BIgKe+2VGl0soVH9c3S+ZIkbRMd4sNBgOccMIJACCt/MBqZhG34c29\n3W6jWCwik8kAWJn5x+5Clud6vR46nQ5mZ2fF0N5oNPDwww8jFovhmc98JvL5PB544AFYloW5uTlR\ntEqlErZt2yYjXHq9XoQ4cV2o0LAMOk0cqMRQfdIdgHwd/wUgBAqAmOJZymTHnmVZUrIDgKWlJYRh\niO3bt6NcLst2Bw4cwOLiImzbloT5fr8Py7KwtLQk5dOlpaVIJ+L8/DzK5bKUTw8dOgTLsiShvtFo\noFarSXwDCSKbA3h+AIRcPxLR1CN2psnVkQZ7Pt7fCQOD4w3mO2FgcASE6jWveQ1e85rXHPb4Jz/5\nycMe27VrF3bt2nXUB8GbHElVEARyM9RdWYxKYIlNd65RndJKEjOVtO9mOBzCdV3kcjls375d/E62\nbYuBMhaLSRQCy27dbhfdbhcApHw2GAywb98+bNq0SUpXLC1Sxer1epHASN/3MRwOYVmWZDCVSiU0\nGg3Yti1khufQaDQAQMpYLNdNJ8Wzs04/x2PVwZ7ASociyRhn/zE+gmvJrkhghTiStDK8czQa4cCB\nA7AsC6VSSUqceuwOFcRGo4FSqYRNmzZhaWlJoiQYKppIJCSmgWU/ljLZIclyp87X4ugffW5UDnUp\nlNd0OhR1rXgivhMGBscTzHfCwGADjJ7R/intndIRAdyO5UGWdLSypfOGNEmYTCbI5/OiaqXTadTr\nddRqNQwGAwwGAxQKBSFTwMosv3w+j2azKcGUg8EAT3va09But9Hr9eA4Dubn5+E4Du677z70+30h\nU9VqFZPJBA8++CCWl5cl7oFlRZau5ubmJFWcw51pTF9YWIiMQ9ERCjTZc02YuM4yHLfVA4X5GH1U\n+nX0iDF3ajweS8mTZJSxCaVSSa5PGIaYn5+XFPhSqSRGfgZ3BkEg6eaO42A0GkWM/Xwf13UlIiOV\nSqFWqyEIAhQKBTk3XcKdntMIHD6/cVqx08ol10WrVEeqUBkYGBgYGExj3UfPACuKSaVSiQyvBVbH\niHAMih6bwpsrBxoPh8NIKY+EQpeFHMeRWXKPVP7jDTWTyWBpaQmVSkX2VSwW0ev14HkeKpUK2u02\nNm/eDMuyJGMpHo9jMBjAtm00m00hSZ7n4YQTTsCWLVtQrVYxGAyQTCZRLpeRyWQwPz8vY1Do0QrD\nEK1WS8pyyWRSVDD+TVVKKzckTVq10YOTp7sDh8NhpJzKYdCO48B1Xdk+m81iMplI+bTRaGBmZgbA\niuH0oYcewvbt22WEUBAE8DwPxWIRQRBId2YQBFIqZdL93r17ZU3ZhUgvGuc0koSSJFKJ0ueqy3nT\noad6rqEu8RkSZfBIONrPhZ5K8HjDdMYZGGxMrLtCBUACNrUfSEObqqlMsaNuOoSSpmoqOByOO5lM\nUC6XxVNFgzIjGUhigJUOwVwuJ/P7EomEdK1xH5ZlIZ/PS4RBuVwWtY3KF7BSqqMPqNPpyJw6EgUq\nNI1GA6PRCPv27cP8/Dza7bZ0t3E/miQw5JIE4pFKfVSc9CBhqj3j8VjWjaVREix2HzqOI0Z4vpfr\nugBWVLJmsyljaXK5HPbu3Yt9+/ZJJx4AHDhwAIlEAieddBLq9TpyuZwMZqaPK5/Pw3XdyPvzmmmF\nUn8eaLDndefjenwRz1enymu/2fRnzZArAwMDA4O1YkMoVByxok3EvEmSKFCZABC5QTKvSGczkRCM\nx2OZhdfv9zE7OwvbthGLxSJ+HXa8UXEhYfJ9X1LMi8WizOhjN1sYhqjVashkMvB9X7rvwjBEr9dD\ntVpFGIZoNptIp9PSrdbr9bB//35s27YNyWQSxWIRCwsLAIBWqyVkDIgO+KV/Clgdlqw7AalEkXDo\n9eTzJJIkqVRzSCp5Xsylcl0XQRCIOsR9UZWjakbVr9PpoNVqiU+M3YGVSgXdblciEebn56WtmiTI\ndV1Jsecx8przfUnwSAB935f8qEcyl2sixWszTa4MDAwMDAweKzaEQqUlbO174eO6zEXPkS5XUb3R\nRmtd+orFYqjX66LK0BPUbreFdE3HKCwuLsqNnvENJE2ZTAbValXISafTETWJ8QMnnXSSJAAz54od\nbJVKBdVqFaVSKVJWHI1G2LZtW2QeHqHn2PF9tcLmeZ5kPFH10t2SmlyRbOmyKNWi8XiMTqeD5eVl\nIVjdbhfNZhODwUCM+cvLy7LuVKN830e328WPf/xjGSWUTqfR7/dl4DSbBkhmDhw4gDAM4TiOdADq\nhoPpcFaei1bj9OdoOv5AkyZdMjZkysDAwMDgWGLdFappNYEqgvYG6Vl1JAme50m3ne6AY4yB53mi\nPhUKBRmNUKlUxIDeaDSwadMmIUN6ODIJw/33349SqYRms4kgCGDbNmzbRq/XQ6VSwemnn45arYZe\nr4cgCER1KZVKePrTn45kMinEzfd9ycMql8toNBoyA/DpT386HnzwQcTjcVHDNHRnm/ZBMbyT68hZ\ndyQcLJNSuSNhYXcg15wDnKmOaSM7QTM4AFH4aGifTCao1WqSqN5ut5HJZHDgwAGcfPLJGA6HMgeQ\n12k4HOLUU0/FgQMHpEzITk52CzabTZRKpQj5o5LFWAcSOpItkkV+ZvjDdfpFZMqQLAMDAwODtWLd\nCZUuSfGGpstVegwL4wGSyWSkvMXSFFv3dUs9AOTzeQwGA/i+j1arJflQlmWJn8j3ffR6PQDAPffc\nA8uy0Ol0gP/X3tvHWF7e5f/XOTNzzplzZmZnZ/ahPH0r8ocUQTaITUTRIpXEagxRIUgwUVtssmjU\nVAppq/6n0tamWqtolUgaTSlgCIlGiIkk/kFI6EbCkgoCFrrLPs3MzsN5nofz+2Pyuuf63DvAPrA7\nw4/3lZDZOQ+fz/25P/PpffV6X+/rllJmUqfT0Z49ezQ7O5t8T8eOHdOHPvQhDQ0N6YorrlClUtHO\nnTv1ve99LylaIyMj+oEf+AGNjY2lJPFer5fISLVaVavV0uLioqrVajKtS0oEB0KRG88pxzGHHhUA\niYB05p+DZEG+er1eMvE7oaWcirdJUtoWiA5N7g0qHMSHfKuVlZV0rvHx8fQZz7sidoGSrXuouG68\nY4yFkqVjMwO6q1dvF50QZt9AIBAInC22nFBJGwsZiyDqiMcgSCp0gEkbizTEaTAYqNlspgyqXq+n\nK664InXzLS0taXJyMnl5JKWQyWazqaNHj0pa3w4BMzMBkZQR5+bmkmcHZed73/uelpeX9cYbb2jv\n3r3q9/v60R/90bQ33UUXXZS6+Pr9vo4dO6ZSqZQ6/tj3b+fOnTpy5EghfNNN1BBMSBa+M66V68Zn\nxPWh8nlO1crKSoqS4FogIHwW0gPJgcRKSt40jlWr1dRutxPRmZiY0MzMjH7oh35InU5HrVZL09PT\n6f74vWWbivHxcb355pupq3BsbEyrq6sp6LTb7SYSibrG3wVwhcrT0iFRPqdhSg8EAoHAe4VtQ6i8\nZCVpU3UhLz+xvx0qEz8hEChArVYr7RGH+bter2thYUG9Xk9vvPFGOo+k5NUZDAZJ1cE0zSIvbQRg\norwMDQ3pjTfe0MjIiJ555hmVy2V99KMfLYx/165deu2119J2O5QoIUWUElGsXFHqdruFCIQ8DqDX\n6xXInitGvObZTZ647goO35NUIHfehellu6GhITWbzfT9brebdpV//fXX9YM/+IM6evRogeh6SRaF\nbm1tTZdddplefvnlpE6534t5oJMTP5w3K0hKHYT+NwRJZO78fud/b4FAIBAInCm2BaGSNkjTZt18\ndLSVSqWUro0BG/M16eqkgJdKpUR+iE6o1WqJtBw7dizlSpHc7Qvu2tpaYQPjXq+XFng28l1eXtbS\n0lJSVIhjQHUaGRnRwYMH1e129dGPflTHjh1Ts9nU5OSk5ufnVa1Wdemll0pSijDAuN1qtZIXTFLy\nODEWX/zdWJ4b9PlJaQ1iAtGClECwIB50TkLQ/FzSxl5+nk9FyCr3hrLq//7v/2p8fDyRJro6e71e\nipmo1WpaXFzU6uqqPvShD+mtt94qeLzIolpdXU0ZVD4n0kaJ0AmTe7LcS0Wp2EM9XekKBAKBQOBM\nsG0IFfDNfb3Lz5UVb/3HTE7aNp/t9/uq1+sp8wlysrCwoGazmVQSfE6+PQsLMseio5Dj4s9CcaHM\nBklh4W6324mozczMJPUJlYyNfyF6RBe0Wi1VKpWCIuVde8DjJdwT5KoWRKRUWk9Ex6+EsoQ6BiC1\nnj0FKfMSGoQKEktGFQR2dXU1BZNefPHFyQPXbDbTdjqdTifNF+Grk5OTKfgTguide/i5PHsrv243\n6r+dVyrvBnQvXyAQCAQCZ4ptEZsgFRdDFjtIE/EIEBtUG4zMBGnOz88nNQPjeKfTScc+fPiwjhw5\norm5Oc3OzibChKrjahTEQlIK/XRDeK/XU6vVUrfbTcSu2Wym8fA9iNLBgwcTGZmentbs7Kzq9brm\n5ua0sLCgt956S8eOHdPQ0FDa3obvShsmcI7NWDw+AJ+Xj51SIKQTYzcqHB40Pocy5STVy24QKchr\nv99PuVMQIEgimVQzMzPqdDqpzEcpk2uamJhICtfCwoIGg4F27NiRFLRcpeQeQWbzHC4USv5+MPi7\n0Z7f+Xmu+/sFAoFA4IONbaVQuTldUtpDj0BLAh1p/19dXVWj0VC73U7qEWSCchKL5tzcnBYXF1O4\nZrlcTmU3zu3eKBZ8/Dh03rlyJhXLb54NBUlbWlpKGwRDvi655BL9v//3/3T48GGVy2W99dZbSeWa\nnZ3V0NCQ2u32KaZ0TNnu7+r1ein41IkUn4NcUCpz8kBZk0R0jk+Ku2+eDJycuELEJsYQPOZuMBho\ncXFR5XJZJ0+e1K5duzQYrG+rQ4fg/Px8ipfIuzsp+3K/pqen0zY0/r5fs2dubVbGcwUPbNYtGAgE\nAoHA6WLbEConDr4w0klH559nT/nWLZ6Cvby8nIgS5vSTJ08WjuN+JVd4JBU2Bu52u6lsRnmwXC6n\nPCjIDGOntAZxoWQ2NjaWiMPU1JQajYbGx8f16quvql6v6+TJkylMlOt2ozSlNK7bSYNfA8TAS4F8\n3mMTIKjuDcsJI+VKSnqu4vX7fTUajRTfQEkSQkUAKx6no0ePql6vJ8M7c1yr1ZKnyslPvV5XqVRK\nDQUEe3okBN2HdGxCxCBZXsbl/fxvzZWsQCAQCATOFltOqDbrrHLCxCJer9eTakOZisWdji8WTzbV\nrVarGhoa0vHjxxM5oDwIIYIM+LYu3vXmSo2rJxAQzu25V+QkYYheW1vTwsJCInDLy8v6mZ/5Gb38\n8stqNBo6evRoUrQgY24ml9aJUqvVKozbx4wXya/F/WbMtZM15pjv1+v19LnBYJBM4XyPjkaukfJf\ntVpNXjOIKGTXyTGdmdw7NoJGaeMnHq6xsTEdOnRIY2NjyQPH99zU7sGe/GTc+Mec4OZ/e6FOBQKB\nQOBcseWEyokLv7Mg8tP3+VtcXFS/309J4pTKPJxyZWVFO3bsUK1W0xtvvFHoFMPUzDEpIUobCyxl\nNIhLbo72yAA+56oQ28TwGYgNPqKVlRV95zvf0fT0dNoyxsmhtK4AjY6OFqIcIDGQSggkYZxOLFDP\n3BvEHoU+7xwDcug/PQ8MQsrnISEQ3lJpPXm9Uqmk37l3qE9kVJEUz+bTzWZT0rovq16vJzLHPn+c\nx3O2IJSk1zNnmNu5F97MsFlMgqtTQaoCgUAgcLbYFk5cN6Cj/kB2UKUgHRCUtbW1tHGvh13iaarX\n68n8zKLvpmqUEEpPOXidz+Db4jh04DEeiBIkhnPxecheuVzW4uKiXn31Vb355puJ3C0tLSWyuLa2\nllQfiKMnmXMc5k1Smgf3gUFiJBU2QobseamSOUBVolQHoYTcuaojrWc+cV4IDmQHJZFj4eE6fvx4\n6rD0EiDp6ihl0roS5uGq/Jv7mKtL/O2A3N/mRnTmIjr8AoFAIHCu2HKFykFpiAWeBdy3XfHMKN5z\ntYnFv1Qqpf31yEbyIElImpMOz79CqXElg+NLSqTBW/AZH6Zuf50kcxSXEydOJMXp5MmTKb6AqAJf\n8CWlkqC0EX7qBMrnEPjm0LwHAfFSGOPG00QaOUoQXX+QK8awsLCg8fHxTf1ctVqtMIckrbfbbU1O\nTqZzoWChnC0tLaX7w/6LKGYeb9FoNNJ1oeq52T4nSj4+3ss7+4JUBQKBQOBssW0IFaSJBdn3j+N9\nfEooQv5570hbWVnRwsJCyqAif4nSEqU6QjopGXrmk0czUOryDYchTHk2FCoVvirUMZQcvEJDQ0M6\nfPiw5ufnE7FyIzXkEkJFAKabz/MAVObI4wMgfnwHooQCtrq6mgzkHMdjEggx5bhs+7OwsKBGo5FI\nl5NfAClirPV6Pd0ziC2eNgjnzp0707gpW/rv3EuIK9cvKZVluXbM6u4hy8M8o9wXCAQCgfcC26Lk\nJxXLfizOlIQkJYVIUvLouDpFYjmfofzkeUVOTtybg0rlfiWO4WZqV6ggVm7uZqxO9iAtkDfIBKW9\n1dXVZDZHQeN1P+wPcwAAIABJREFUziltlK74nXN6vpS0kcHkRnDmzMdNCZPxeum03++n/CkAGXEP\nlRMZSChj4fqYYwghzQK1Wq2Q4cU96nQ6Ghsbk7SRDu8NAVwj1+Dhp04IvTzp8+eGdf4eglQFAoFA\n4FyxLRQq71TzTi0WTu+ggxh4yCWfRenpdDrJ88Nii8EZAuMeIic70obKxHm8DMk5vSTnqhWKDYoK\n6hTjyBf0drudyl3EADhZgsDkoZ785NyVSqUwLl7Pt2tBbeM/9xx5RAPXTZcg98bVLhRAHw9jplPT\nFSuUQEmJWJXL5bTNDmXIubk5TUxMJA+WK4LezcixIUZ+/R454dfmr0dcQiAQCJxfbNbJfzZ4P/xv\n9bYgVNJG2YZyHwQHVYgF1JO8WWAhS3Sj4btxYsFnubl0yTl5YIGmTIeh2ktO7pkClNogKZQUvYwI\nMWLc/Jvjuhrjvi5Xy1wV8pwpxpyHcLrfS1LBjM7+ebyPwuQlMX8d+PFR0CCEjMPjLsrlsnbs2FEw\nuktSs9ksEOFWq5X2/hsfH09KHYTVox48MgLiKSndK/Z7dDXRuxJRJZ3svR8e1kAgEAhsX2wLQuXl\nGRY8VAkWeNLQIR+oH6urq2o2m4XsJUpJTiBcrfI2fl9UHRAyX7C9lJRHKPj3+Kyfi/IaRAuCJCmV\nN3u9nkZHRwtb4XgUAtfoZNJ9Um6Ud3LFWHyOpY3yIYoSPir8Scw3yhFdjRyTzafzDjpCQ7neVqul\nRqORPFTNZlPLy8saHx/Xrl271G63NTExkYz6KHtcR7fbTYn1bLrsqqCrkGNjY+p2uxodHT0lLDQn\nVD4X+b8DgUAgEDgTbAsPFcQEQoSCkhMkL8u5zwjyxEIKCH/EbwOR8e44V6wAaoj7nzxiwAkSpS8v\nG7q3ajPi5vvPcb28fuLEidTJSNmM91yd8tKXg3Ihih1jhDTxuxuz8+NSimN+qtVqii9AjSJlHZLD\nHHnEwejoaIE0ttttraysqF6va3x8XMPDw1pYWNDw8LBGRkY0OTmZ/GcQOoiZ+6eYCzfi+/VThuTv\nB+SxCfnfYCAQCAQCZ4ttoVC5ggTZQKVxJcZ9VdKG1wnVgd/dA+UKFWDB9y4yOvOkDSUD5Nu5QEic\n5OUp3H5Oz0pyAuDb1lB2dA+QkzzvYHNPk3f6SUoqU67MDA0NFXxWkDXUJf5jnMQUuCeLkpof08NP\nGW+lUkketqGhIY2NjaVyLd2FpN9z3OXl5RTKyf2AlKJYjY2NFZoWuBYv7fHTYxLy+5kT0SBTgUAg\nEDhXbAtCBVytyfOmIEYskO6ZYquXer1eUE0om0kqdOFJStlH+Iby0p+TJEgT5Sg3s0sbm/I6yXDV\nBILj3/WYB373GADO6YZ9zo+CxvecYHgEBGSDCAfGBTmrVqsF5Q0zv+/t52ra2tpaUqi8/AoBwvje\n6/W0tLSUUs/5XrVaVbvdVq1WS3lX/Jt9+hqNhlqtlgaDQeH+eKmVc+dhpj5Ofs/LnNyv/LXNfg8E\nAoFA4HSxLQiVK0ySkn8GckA7vrRBNigLNptN1ev19NM/k0cKsFccigZdc5TonHzxGc+Z8hgFaYNI\nsWi70dnN0/wOYYJIob44oUOtcaKTH9uDNF3RYnweMwAR28w3RamUa3NTfKPRULfbTWoY++6RQ3Xy\n5MlEonxrHuaZufZSK80D5HNB8gaD9W1lILBTU1Oam5tLm1H7PeW4pVIpJa/7vGBK59rZ+w/Quegl\n14hNCAQCgfODD9L/rr4roep0Orr//vs1OzurXq+n/fv368orr9RnP/tZra6uavfu3frSl76kSqWi\nJ598Ug8//LDK5bJuv/123Xbbbac1CCc9KA+uSOXqi98gFkz3y/gCK6mgWAHIhys47tdxAkTHm5vS\n85RtxuI5TZATFCqPFPAOP8bM8flObnjnc37udypdefaWe5n4PsQn94iVSqUUQIoxfnFxMSmBjIPP\ncfyxsbHkMeO8dBI2m80UjbC8vKyFhQUtLy9rbGxMpVJJ4+Pj6b70+31NTExobm4uJaz7XLli5apm\nfu/zMiyv5w0QZ4oL8UwEAu8XxPMQCKzjXQnVf/7nf+rqq6/W3XffrcOHD+s3f/M3dd111+nOO+/U\nz/3cz+krX/mKHnvsMd166636+te/rscee0wjIyP6lV/5Ff3sz/6sJicn33UQXm7z/CTgYZ2Y0CEb\nbDMzPj6elCsv+UDC3HPkZMpJjhON3Kvlize5Um/X7eflPI8RILXdt6ZhTGyF43vjocZIG2GmjJ8x\nehcerzvcjO6dgMyv7/UHsW21WqeY9rkmVMBGo1Ewr1erVa2traVUdRQ6CCvbzIDh4WEtLi6qXC6r\nWq1q9+7dmp2dTcqTq4B07FGmhTwvLy+nuApJqUvS1UzUPydPeewFfyunS64uxDMR2FqcDdH+IP0/\ncUc8D4HAOt61y+8Tn/iE7r77bknSkSNHtHfvXj333HO6+eabJUk33XSTnn32Wb3wwgu65pprND4+\nrlqtpuuuu04HDhw47YGwkXAeDQCR4L3BYJCIEARiZmYmlXu8ld4jGKQNg7r7ljBee9nNCQjqlKRU\npnJisJnxnJ8c003T+Xm8tMh1oQpBpvz4EBvmx/OtUJSYB++24zsoYHTpYVbvdrvqdDppg2audWJi\nQhdddJEuueQS7d69W7t27ZIkjY2NaWRkRLVarUCsmDefs7GxMTUajbTFjcc6LC0t6c0339Sbb76p\n8fHxQhTE6OjoKcZ8DOpc82YKHX9Dfs3Ay6X5f6eLC/VMBALvB8TzEAis47Q9VHfccYeOHj2qBx98\nUL/xG7+ROsamp6d14sQJzczMaGpqKn1+ampKJ06ceNfj5osdC2C73U6LPx4q7wZcW1tTs9lM5MCj\nF1h0UYKcuEjFLUpcTclLibzn8GwjIhHy/CdXttyv4wGUkDhKYJ5N5ePJ074hZG5oZ54YG6QThcgN\n826KJwBzcXExEbRqtZpUP/7NNThxohzqxM79XMy9J9vz/aWlpUKptVQq6cSJE5qdndXll19eaAAY\nHh5WpVJJZMpJa2449wgIn498/vK/u7P1UJ2vZyIQeD8inofABx2nTai+9a1v6bvf/a7uvffe02o5\nP93FqVwu68orrzzdYVwQvPjii1s9hFOw3cb00EMPbfUQTsGnPvWpc/r+wsLCGX3+fD0TgcD7EfE8\nBD7oeFdCdfDgQU1PT+uiiy7SRz7yEa2urqYOsFqtpmPHjmnPnj3as2ePZmZm0veOHz+uffv2vesA\n1tbW9D//8z/qdDpaWlrS8vKyZmdnNTs7q06nk9ryyU3qdDoprLPZbCaVYmFhIakSvO/xBqhUHrrJ\ne5TihoaG9N///d+66qqrCuqHm8op+eVmZ2mjIy9XkfBdUYJifzzf3oW5wPfj26u89NJLuuqqq06J\nZ2D8KEgezYCXqVKpFFQaVCs2fK5Wq6rX62o0GsnLhbo0OjqaDOBcd7lc1je+8Q3dc889BeN3p9NJ\nGx7jpaKjkpItatTw8LCOHz+ukZGR9P9ix8bGkh9qampKe/fu1fz8vGZmZnTkyJFUltu1a1cyvVer\nVe3YsUPDw8P6vd/7PT344IMaGRnR2NiYJiYmVKlUUogoStvk5GShsSHvljwdnO9nIhB4PyGeh0Bg\nHe/qoXr++eeTGjEzM6N2u60bbrhBTz31lCTp6aef1o033qhrr71WL774ohYXF9VqtXTgwAFdf/31\n7zqA3NPCXn2eN+VRCdL6Ijg/P19ox4fwtNvt5Lfy8hckBxLDvyE3nn4OvMwIEfOOMkibj50uRTfA\n8xPSMxhsbCuDZwpSREmT6ATvzoPQ4JXKAywlJULkZUaPdVheXlaz2Ux7Hlar1dTt52MmJ8pLk5VK\nJREgLytyfiIVMNjXarVT8q/IDsN47tvr9Ho9dbtdHTt2TK+88kqKwiAGYWhoKKWtMyerq6uFbX3c\nv0Z3pyfs5yZ1J1Z5Ftnb4Xw/E4HA+wnxPAQC63hXheqOO+7Q5z//ed15553qdrv6oz/6I1199dW6\n77779Mgjj+jiiy/WrbfeqpGREX3mM5/RJz/5SZVKJd1zzz2pFf6dwMLs6d1EHOSysStJKC1Ogrxj\nj+84CfIF1venk4qdhq5abDZe9yIxdrKaPH3cU8bdjJ6nuOPFcgIFEfJrkYphon5+FDGOwbVWq9Xk\nJ8PojvpEl5y0EfnAdz32weMVvPsOpYtr8QwuIhacJNbr9cJm18yJ+51Qwvr9vnbs2KGVlRVVKhW1\nWq10bLr+IEaeFo/K5X873hiwWYzCmZrSz/czEQi8nxDPQyCwjtJgiwvZg8FABw8eTOnaS0tLOnr0\nqHq9njqdjjqdTkHNWVtbS+XBkZERHT9+vFD+ojzoyhbnkYp79kF2vOT20ksv6Yd/+IcL33HDubRB\niFzR8QBKPuNEyhd0yJq/NjQ0lLKfKAtCqF5++WVdddVVaUycazNFxcuF7MHn3Y6U40ZHR5MKxfnI\nhMKULiltFQNKpZL+6q/+Sp/+9KcLShjXSegm/3k2F1vNULqlsaDRaKRzkd5OivquXbs0Nzenubm5\npCqSd0Upr1qt6t5779Vf//Vfq1wua3p6WqOjo6mrsNFoaOfOnapUKtqxY0ehtJrPo5tmA4FAIBA4\nXWyLzZF9YZY2vEQebsnCjHrhRCYvC3q2k4eCOlA2KGH5+3nkAkGjfMf9We6T4nc/Lx1vXnZy1Yxj\nQ/x8z8K3U83yzZbxSblaVq/X0xj6/X7hO6hPvV4vlek4h6tUkD7iFVD1/BiMs9vtpnvE66hLHIsM\nMZ+jvKzabrdTl6OTaN+WB7LlXjXmlSBRroHveFektEGKwxgbCAQCgfcCW06oKAd50CavS8WyGIuu\ntL5ItlqtTSMGnPTgV3IjuudQ5f4a/z7qle955z4uX7AZm6etu8I1MjKSFnmIC2oSpU4UGCIDyJTy\nefC5YNy9Xu+U+AmUuG63m1QjCKSTSEp1jJtjQ1gosXEsJ4KSCtu/kDaPId4zpSgbuqeLeWGuGc9g\nMFCn01G73dbhw4dT+Cefx0fl5nyOx3+QMeaTeck9Z0GoAoFAIPBeYMsJFXlHOUmQNgiOtKGI5KGR\n+SLNMTz80vfQg/jkxMC3NsHn46VDH2+urKDc+GchR7ky5oZ7DxDNyRUE0T1VucrCtXtHoJvFuU42\nNIb0oN6BlZWVRPggSPiufO58Tzyu371fruYxPogs29QwR9w7SBTjYENsDPhejuU4vudibtznuG6Y\n51hOrDimI8hVIBAIBM4WW06oJBXKc/lmuJSH3KgO6fBSGYs0/wbul4H08B0vKeUlNj8ex2GsUpGA\nSRsG+Nx87mU47zj0a+T4dPC52ubX4iTJiQKkykkSpNJJ6ujoqOr1evJKUc5jOxlpY/Ngnxs6GPOS\nH6GqfI/ry7ssmV88XT4uVyIhkHQ4cg10dG4W/yCpMIdOllyxlFToUOSc/jMQCAQCgbPFaQd7nk84\nkfKFG6UBZcbb41GfMFnTUs/7EBH3EbFgO1nyfCXgEQsQIv+ufw6yxOd9jz//d25OR5nhmCg57pVy\nL5aTN0gJx8RHthkBhHiQzVSv1zUxMaHdu3ensZPRNDw8rE6nk+4B1wXwRfkcM6+QKXKo/B4wTsp0\nOUH2DCgIY61WS3ESGPa5z678udKXe9f4u+FvyTsR+S5zdSY5VIFAIBAI5NhyQuU5R25UhohUKpVC\nZ56X2TCnO3mhVZ/vQ1byaAFJp5ArJ1WSUjjlZqQmL7/l27xAHJwo+Hjcx+XjcLM0xCY/B+9DXDwe\ngvKem/Z5j333VldXNT09nRQqQFmMDZoxsvO6j8eJ7tra+n6BmM79el0pYtxcSx7ACqmGPHNO/kbY\nGJnjeNcif0uMzT1sXqb1ceQKVShVgUAgEDhbbHnJbzAYaGRkJJW73Jjs5TVpY2Fst9unEBDgSpBn\nEnlIJfBMKi8FchwnUz5eFnFIy2Y5RrlR3okApA7FzX1H0gZpdBLgpcLcUE0J1BU0N8gPDQ2lctvw\n8HBhfz7iE6anp0/xa9VqtTRWzgm5zb1ueUkTIsf7jBHi6x4xSJWXbzkHvjK/Txwzv6f8HaHaYYan\nsYC/H74fCAQCgcB7hS1XqKSNhRAVAkWE1n7AQunkg8U67/hyxYZFGcWJY3PMvFzGIs+Y3OwNESCd\n3cuDjMcN1JAA/64b6RmHKzpuus9LYRzTyRZE1M3hlUolEapKpaJarZbmBAIEgYFUsWnz8vJy6qTz\nAFR8U9LG5tGAcqGbyZeXl1WpVNL2QV6uozORufFQTy+Z+rWiPnK9zLcrURzHje7c+7zDz0uwXqIM\nBAKBQOBMsS0IVa1WU7PZlKQUxjg8PJyCLlEhWLQp7Xk3G+THVZzx8fG0xUm73S50/7F4Ut7y0pN3\nzDnZ8rKdK2GuDEE4IG3uJ3LTNmOFZHGevFTpSgpqC2SNLWi8288JiHuImD/mE2LpJndJKZ1cWicZ\nS0tL6nQ6qfyWx0egYnU6HUnFslt+3TmJzVVBh5dC/R7n/iw/58rKSop4cAXMS6yO3MQfJb9AIBAI\nnC22vO7BYpdHIdCy7519KD9e7oFY8FkUKfaok1QohxENUK/XE5mCqAD3aTEm4KUuV4m8tOcxCnns\ngi/cXJsTKVet+D7Hd6N9bsz28pkTS/cH5WPlOlGSvEzKfFYqFTUajYIqBDCXowL5PHknpcc1eAmR\nXCvuE8TL584JIffaS5C5Wd+VK0mFmIjNTOccN0qAgUAgEDgXbLlC5YsoJAoFwmMHWGRdTWo0GgUD\neLVaTUZnL8ehUlECQ00ZGhpSvV7XysqKarVaIawSIgC54Nw5kcErlcc9SBtkEVXF1RjPj6JLj2Pm\nGVQcN+/84xqcTFHacxIBcXK/l/uRCMtEAaRUWq1WU5l0bW1N1Wo1zR1kFJLr15Gra5jMnTg2m81C\nd6N3LjKnzBlz2+12C9ELUlFlQtnMjfqQw83M8RDcPJMqEAgEAoEzwbb4v+UoIZCX4eHhQueWkyrI\nwNTUVCITfBZ4SQhS4hv88m8WUxZeCJUv7E5Y8Ha53wvk6lGuNjkZc/LkHjAH17qZAuZqC8fycpxf\n02AwULfb1fz8vObn59XtdnXo0CHNz8+n8zabzdTRByHt9XrpP47hChXxCHQDYjong4rXvRuPzy8s\nLJyi2m3Wvefz4PeSuXSDO8fJuzZRL/1vzecon/NAIBAIBM4GW65QSRtKj/ug8hIcxIPuOyc6rgB5\nOc39OygdlLhcIeIzHvzoZuxqtZp+djqdpM5IGyqUKyBugkeZ4biunvj3uTZP93bTt3f94aFyJapc\nLif/EMg9SAsLC2o2m8mv1W63UzL6YDDQxMREgWjgNSuV1tPsvQyKMuVkyLfA8XnxkutgMEhzCGH1\nuAxpowzn+VBOUt2nlpdjPXcK5IQq967l5DUQCAQCgTPFtiFUtVqtsEWIl91GRkbU7/cTkVhcXNTU\n1FRaFFGWpPVFFcWkXq8nwzYKFIu8L94YuIkEgKi434kFmiwk9xQ5ceF3N2RD+nIPkh/fiQzfYbNh\nXuOnKyle+vJMJh8P3/Ny6bFjx7R3797CPn+U0iCePj7ugZc7UbiYWyc/3j0nKRE5vuOlU8aLj8v3\n/+N1rqff7ycy1ul0VKvVCvdSWlfrSH/v9/uanJxMc7VZlEWU+wKBQCBwrthyQuWqTrVaVbvd1vLy\nciphufcI4sTGvSzqkJd+v5/KTENDQ2o2mxobG0tqj5f/coLjxnjeHxkZKaheriS5WoLq4Ys/JUEU\nHvaoo5wFQXNlDdXHz89iDylzZQVfk18fxMOTzjmv5zKVy2UdPXo0bWTc6/XU6XQ0PT2diKx7z1ZX\nV1OZj/sGCSXSAPLKdXvnJPEXXgrNSSSlRi/3ugKJCd7nwVUlFD4vAY+MjGh0dLSgKDqR8nMEAoFA\nIHC22HJC5QsiRIIF08tLlPk8U4j3vP0+98FAaPiv0+kkIuAdYZAgxuSZSLwPQSFCwBdhFn3IEt8H\neYwCJMg3Pea8eZSCpEI+E4RIWicqeYcapUpIiKtcKEmdTqfwPXKn2u22xsfH1Wg0kieKsiWdedK6\n4uR+J/b/GxoaUqvVOkXB8xgKiJHnUrnZniYBLwWzcTK5Vt7JyE/+NtwYz3w4AYOMM+de9gsEAoFA\n4Gyw5YTKgU+HRc7LR6g37qFxv5Evlu5barfbGh0dTa39lP/4LgsvnW7AF2TvQuP3zUzTEClpo4uP\nY3FdqF5cn+c65UbzPKg0z0qCqJRKpUIpzOfNS5Y+bk+QZ4NkSoxLS0up64/xlMvlQnktJ1M5vLNw\neXk5dQqiIOFr4jMobd5V6USJMXvMAdfm3YTcS+bGt+LJVS0nUEGmAoFAIHAu2BaEisVsdXVVU1NT\nmpmZSWGf3o3n3XsoMJSmWLBpk+ffpH6j2khKChPfp2zF4usmZc+ycjCubrdbKCeyoKPAcB5JqRQF\nCfHr8vZ9rnOzmAXveHPzdam0HtZJrADHQNXjO4wT0gnx6Ha7GhkZSYZxPs+xKQtChHw+KdXha3Kf\nlXf7Mf5+v5/Is8cccCz3unmZ0tUl73Z0pRCCXa1WVa/XVavV3vFvzol4lP0CgUAgcLbYFrEJgEWO\nchNdYxADSAeLMAs6JnQWay+VQaz4LEqNdw1KKpCTzeIUXFFhbJ1Op9BNCDi/EyJKUa6eefnPyRJw\nNQrlzTv7XKmhWw+iw5j8uokRoGsPIuqlP953BS4ns7wOGWVrGc5FB2G73U4lQ9Q7SBCestyg7p2M\neeYW95fxUn7NS56uGHpp0OHq19uFfgYCgUAgcLrYVoSKRXpsbEwjIyOq1WoaHh5OG9vmeUXur/FF\n2QlY3mFHeUlSISbBf9LJ56Si3++r1+sl0uC+G8qDPrY854gxEV7qHXIQBxQkJ2LekealNSeDvO6J\n45AJ911h9HdzvXvJUJogY04MOQ5KHUqUk8Nms5nIHOXHcrmcUun9HqKoMXeuYEkqkKbcvO7E2lUl\nPsfehU6Wc/i99XsfCAQCgcDZYFuU/ACluuXlZTUaDbVareTTQSkiXoEFkO4yV0/c70SpCIVI2jB4\n42VqNBpprz+p2E7vXi58PpzfiZjvpUfJCfWH3CtIXx7PwDgY02bbvNBp5wZzJ1V5lAPqnhM07zZ0\nAorRHHLh6eLMT7/fTynp0oYpnXIqJdN2u50IlbRO8kijd+LENTDWarWalEDmEgXKze2umHEvvURJ\niTAvB27ml8rzrgKBQCAQOFtsG0I1GAxUr9c1NzeXDNAe9kniNSTHNwamJV9aV52csEgbOVHeVYeC\nQrnQDeCSCj4eAJFCpfGSlAdoEjfg5TdJicx4FhMkwo/pCpV7fVyBclLB93KjO9fO+JgfxoT3y8dZ\nLpe1uLhYmA/mk5KjtE6ovENveHg4dfdBcNhXkXNw/ZKSYdy3pYH0vt2+gdxrPtfv9wtKFH8j7oHb\nLBLBS6PuEwuVKhAIBAJni21DqKQNnxGkADUK1YqkckgNJITQSdQYFmfPPnIFaXh4OJmw+Y7nH0nF\nkpMTK89BcjM5/h5P5UYlGR4eVrvdPiU2wLsI+byXDz3ywAmGxzt4FAMELycjeeckZU8vV+a5UahU\n/X5fi4uLSWXj+rxrD6IKQfMMLY7phnyPfPBNsPndFSrGwZx4ZEan01G9Xi+Y4HOFivvPHOTKopcs\nQ6UKBAKBwNliWxCqzVQYTMz4p2i373Q6iWR51IFvkyIp5SzRYdbr9ZLy4Z1j3oHmXWsQFfc75Z4n\nFny8PNVqNW3gy5hRoXzxdhM6RIhSIt+RVCBymLHz3Cbv+sPIjfLG9TG37oeCHPnce+ecz42XBD2N\nnHljPL69DGU8SSmN3U3rxDzwGoohgKxJKhDXcrmsbrebktBdJRwaGkpEGc/WZsRqM7+b/x0GAoFA\nIHCm2HJC5YuZEyoM4Gtra6rX61pYWJCkAhFxc7N/DyUKfw/n8XBQ/D5OHLwc5Z4m7xoErjAxfsbn\ncQrShkcJ4oHC5hEGlLRccXLVBCLDMbzjz5UmV6jcHO8qFcdxguMGbj9vbt6GoHGt7muCYA4Gg6Ru\n+dy7fyrf/DoP1/SE9Lzjr9FoJAJH6Y+x+Xucx68fuFLm9z4QCAQCgbPBtujyY0EulUoaHR09hRi4\nPylPNOd34hJQhNxA7cqQ+5n8eL1er9DB5uQAQK7yLjzPjfIcJY9mYEHnHPnWMH4e90PlHYgQF46/\nGVFwI72kwtz4e06ynKh6V50TmdxQnnckUmL1ZHTmjG1r8sBPVDmUOuaR7+WEbrPsqTzY09UslCyH\nl0j9eoNUBQKBQOBsseUKVU4I6AhzEsQC6l1kLH50gfGfh1eiirDQu3LjHX8QIO/uczJC2S9XUpwk\neU6Tb6nifiYIi+/dtxmZykkjrzt8jJ6fxWe9HOlxC/n58HhRUvTteFyp47qAl+M89wrCiNoIcXQy\n54of7zN+j8jgOPybzzK/5XJZ7Xa7oHZxPe5By9VFzu/J67lxPRAIBAKBM8G2UKikDeLg3hgWaVeB\nUFPwKLHo1+v1Qr6StLHo12q1FHtAZ5l7ojYrhzEmJzhOBHyRZs87lBXylSglMsacoLhaA5zo+e+Q\nlXy8KGaupvFvLym64gOBcILjpUEnPx4G6ioOSmCv10sKGKqT31PIrMdHMP68C5JuR47DtXnnXz52\nh5OooaH1zbY32xaHuXGCmKtegUAgEAicCU6LUHW7XX384x/Xv/zLv+jIkSP6tV/7Nd1555363d/9\n3eRfefLJJ/XLv/zLuu222/Too4+e0SByz4/HA+SGYhZSuvxYGPv9fvruZgoIPh4Wa9+8OF9M3UDt\n43My46QAgkY3Yr/fTyQDEgGR8LJgbtTOO+28bIfaJBX9P5BNj0AolUqFBPFcHfJSmm9d46Z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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6312ff358>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OtdLEMg8f16VDvX/txQJWyY3jOELKWEojOUkmk7IPRh8AEKO5ntuXy+XEF8T0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\n5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff63137f358>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pp8t66WWjCZ0qZjgcFjJFAz0AiWPgTMOpqSkkEglMTU2JqhkKhTA2NoZarSZq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff613284b38>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zKUSXg54rlQqKxaKolQYGBgYGBkeDVSdU9913n/hwms0mms0mpqenMTk5KSZo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6133f9f98>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Q4w0ymQzq9bqQGo6k4dBkKmGBQAB9fX1wu93I5XIA4CAyoVAIS5cuRblcRqFQ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9nY0jUagqklQTW567E00TxJCJJBovq9luHY1aihnHoeEkvdh3NGhUzT55PH9zwvFqTJc+g90O5E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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6134ef6d8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f8bXAasmPhHC03DduKtV5550n5Ur61sZpfQYGBgYGz20YQnUEcTC/FzvqWFIDVst/9Co1\nm01RhNrttryW37/oRS/C0tISlpeXpXRHM3i32xXFKRwOo9VqwXGcgCeKZCoUComixFJfIpFAJpOB\n4ziIRqNifI/FYuL1okJFQsV167Iij6+HM48LvvnNb0pXIs/dwMDAwMDgSGHsCdU4bs6HApIoepWA\nFaVoMBiIakVyAkDUo1arJR4lYLVzL5lMYmFhAd1uV3xRJGE6ToHlQypZ/EpjOYkQ31MrUlSd+I8q\nlS7t8fV8He/RuBKqTCYj34+SKaNUGRgYGBgcLsaeUI3rBq2x1tqoCvV6vYCPimoSf6by0+/30W63\n5fmJRCJAkqgcdTodKRf6vo9UKoV2uy3+p3A4LGrUcDhEp9MR75Truuh0OrBtW0p+lmXBdV1RbxjN\nwO/5O3b06a5ArXzxsXG+T78IhlQZGBgYGBwOxp5QjTNGyQMJxnA4DGzQVKQYccDfswwYDocl7mA4\nHCISiQSICoAAMeLrUqkUPM8LGMMjkYioV7ZtB4I+qVoBK+SHpTzLspBOp0WNsixLynv0G2kCxXPi\n4/1+H61WC71eT9aiVStDVgwMDAwMnu8Ye0I1buqUXguJkSZQa32lIkUyRFIFrJTOSIBIiJhQrk3n\n7Lbrdrtot9uSZZXJZAIEhqZ2kjSSKCalh0Ih1Ot1TExMyM86Q0qb53lMbXTX0OdNwsVORN2xOE73\nz8DAwMDA4NnA2BMqIKhwHIvNeS3itNZzDvZcEqperycEQ5MsYMWkPhwOxfTNeIR4PA5gxefU7/fh\neR7S6bSQJ12CYwmRx+axuIZOpyPKE8uJNMe3221kMhkp/emcKW04J1g6pMLG8mGv1xMlbfRejY4S\nOtr30ihlBgYGBgbPFp4ThEpD+5GeLaxVttPvPfoYH9fK0+jjAES1oUFd/+PvNInR55lIJOB5nmRW\nkbQwaZ3vEwqFJM6AnYB8f763ZVnI5/MBr9VwOBQixeOz/KfXQ9CrxXPVvx8tV+rHRzE6c/DZxMG8\nbgYGBgYGBoeL5xyhOpL5QdxgdRmO6eUa+v3WKumtRZ5Gv2fprt/vi/Fcvy9JCaMMOKyXChUHEFuW\nhUajIUqVZVkolUpP8zQxPd33ffR6PSFenAnI51OpSqVSMmOQHX1alWIOlT5vHoPr1qNuNMk6GFni\nvRwlZGuVFw0MDAwMDMYZzzlCdSTJ1KifafQrvz/Y4/r32miuSZ9WgfT7UaVay8jO55FYAathlIPB\nAPF4HLVaDY7joNVqIZ1OYzAYoNVqodPpoNPpBEbb8P1JsizLQiqVkuwqy7KQzWblPfgcTai4ZsY4\njJ73qFeM11i/fq14hbWgSdazTaxMwKeBgYGBwZHA2BOqIxmb8MtIlC7Njf6OGO3eGyVcfEwHaur3\n5+v4fKo5NKbz9zSSa/M6R8YwjLPX6yGVSqHVamE4HKLdbqPVagEA4vG4dN1pP9Py8jJOOOEE2LaN\naDQqIaDZbFbeZ3QGoD5Hna7Oa6XPV5OqUYXrYIrVwe7tqOn92SgJGjJlYGBgYHAkMLaEalTNeKav\n+UXm8IMpT5rgHOrPJBH0FI0SKa1CMeCTr6H3SZMU/b0OA+XsvHA4LKnqyWRS5us1Gg10Oh0xqTMI\n1Pd9WQcf46gbz/MQi8Xguq6U60bLoOwuHC2D8mftpdKElWnkOnJBkyt9z34RwVrr3h3Mc7XWc0df\ns9bjhlQZGBgYGBwuxpZQHQyasByMLB3s51G15WDfawULeLpiRaKgSQaJ0loKFgDJoNImdB2LQDKl\nXxMKhdDpdAAgMF6GHqjhcCijYWzbRrlcRqvVgu/7mJycFE8YSU8kEkEikQhkYvHYDPvU5UmeI/1c\nmhTpUiJ9UzpUVMcm8Fy032qtUuAokfplvisNxk2sRcZGXz+6nmPVdWhgYGBg8PzBc4JQrZXfpH9H\nrLUJr/X8tZQsfSxNckY72UZ9T6PvpUe+rLV+kimW+dYycOu1jJrCB4MBEolEQGkisSkWi6JI2bYd\nGAszHA7h+76oVK1WC/V6XdQuvU7d+afPS4/CIQFktyFVJ30++lz4HjwPnpsmVqOPjfqt9HUd/X70\nb0E/rq/v6Gv0PeK5GRgYGBgYHCrGdgfhLDviYCW8tdQKvfmuRbBGSRUQ3Iy12rSWCqbDNkfVMq06\n8biaSPE4Wo1aq5zG43Fd8XhcQj1jsRg8z8Py8jJisRh6vR5isRgGgwHa7Tai0SjC4TA8zwt4mlKp\nFGzbhu/7Eq/AkTLMkeJzR431xKhCNXqOWp3Sw5J5L0jMRr1Va5Er/X7PRD0aLc3qY4ySPD5Gkqq9\nbwYGBgYGBoeKsSVU3W43oAoBQT+S3vTpNWq1WpKZtBbR0coLhxLrTVyXwDQRGC0F6vWMKlV8bPRn\nTZp6vV5gph+fx6/0V2mvEclUt9vF0tISWq0WMplMoKxXKpVQqVTgui7a7TZSqVRAVev3+2g0GgGF\nyHVd6egDIIRHEzpmWo2eB3/m8zudztPGzmiMGsuZqL4WqeL6Rku1oySPr08mk6LOadLGsugoMdPn\ny+MZMmVgYGBgsF6MNaHSGzBVH63+9Pv9QMmKvx9Vd7jxM0WcmzI3Y4ZYsoQ2WtLTpTxN5nhsQpOX\n0RJhKBQKmNC1MqJfS88Uz4nn2+l0AuXCZDIp7xuJRCQyIZfLAQCy2ayQina7DQAolUpIp9OIRqOS\nhE6jfLvdhuM4gfKevhZreZ9Gv47+brQEp5+jlSyC95vXhvlZ+jrRKwVA7ifJEa8vlTDtVavVanI9\nOJCar+M5G4XKwMDAwGC9GGtCReWFm/5gMEClUpGN1nEceZ7e2DkCha8lWq2WkIJQaHVmXafTQbVa\nRbvdxubNm7G4uIhYLCYDi4GnG9X1+42WC/l8/RhVHJb6uIFr87cuoelUdQDwfR+NRgPdbhfxeByh\nUAitVktew+HJzWYTrusiEonAtu3ADL9oNIpGo4FUKiUp6Nr/xGOQ0GhiRP/UqFqoyScJGq/LqF9K\nq02jsQzhcFgGLOvrol8XDoeFHJIoalJ2MP9ao9GQdXEe4qhS2el0DJkyMDAwMFg3xpZQtdtt2RzL\n5TJisZiUprrdLjqdjhi7degls5WoulDp4Ny5SCQSMFJzJh0A+drpdFCv1+H7Pmzbhuu6SKfTgRKc\nJhI6HVz7iQhdctPddQDkfIAVNarZbMrxec58HokUCQHVtXa7LaQpk8mgUCjIzL5+vx8gKnqIMUkK\nyQ2JaiKRCPjFSKR4rUfPSytYekyN9lVpAquvGwA0m00hh7FYTLoO1+r602GnfL1WLWksHyWl+px5\nrnye7/smmd3AwMDA4LAw1oSKqd9UL4bDIVqtFrrdrmzwExMTAZUFWB1dQgICrI58aTabaDabAa8V\nPVMkVCwPkrwsLS2h2WzCtm1MTEwACHaLaYO59vaMqlskLPr1ujQ4Wk70fR+pVCpwTiQUVI1ICEOh\nECYnJxEOhzExMSFqnGVZolRFIhEhD5FIBLFYTM7Rsiy0Wi3Yti2zAPv9PmKxmHijuA6en+7eo2Ko\ny51aKdK+LJ5Lp9NBrVaD7/uBOAgqj3peIYkRyaNt2zImRw+I1teQ/2jO572i8qkJnud5R+gv18DA\nwMDghYixJVSzs7PI5XKyCdOQzS42z/PQbreRyWRE5dFKCaGN6MPhEK7rIplMyqbe7/extLQkZT8A\nKJfLQri63a6MbCmXy6hUKkilUhJd4Hne0zKmWErS6hUJwWgZjwZ1YLXMqTOeuCb+jmSDBIYkhMQj\nmUxiOBwikUgAgBAkKlSpVEoIVbVaFdKkyWCn05FYBpJNkqdRQqgDPqkERaPRAOEcfU25XEatVsNg\nMJCBzLwf3W5XrinPMZvNinlelyFHS5NUnXSZsNfrScco12fbtpT+9LqNh8rAwMDAYL0YW0LV7Xal\nvZ8dfPTQNBoNafvftWsXMpmMDBEejSzQSsWoz4qkJZvNBvw+juNgaWlJ4gjYSZdMJtHr9VAul7G4\nuIhoNIp0Oi0eLl3yo3dJK0q1Wg2tVkv8S6MlNGAlvZw+plFvWLPZRL/fR71el8R0TYZIBlgy40y/\nTqeD5eVlMbKTtHEIMsfTMPJBl1VZZqOCtBah0UocCR6vrzaMLywsoNvtotvtIp1OSxmOBIsRDpOT\nk0gkEkK0dIch33e08xKAqJkkcZVKBb7vY2FhAalUCpZlSeQEjfkA5BoaMmVgYGBgsF6MLaGi34n+\nHxIJYGVzpurSbrel0891XTFk6xZ/qh/8nsfXHWfa05NOp5FOpwEAtVoNs7OzomxQ+WG0wfLyMlzX\nRSKRQCqVChAgKmuzs7NSQiRZ4TnpiASt7pTLZQAQEkQy0Ol0xGekOwRZQqPRnATBtm00m00heVwf\nSQRLkXwdyYYu25Gg+b4vZbJGowHXdQMddfqas6TY6/VQKpXgeZ4oQzwGVcZwOIwzzjgjoEDxPnMd\nuvOQnZC8DzoCod/vY3l5WQzr7XYbhUJB1kTVjV/D4bAY/g0MDAwMDNaLsSVUJEq2baNWq6HRaMCy\nLNTrdUSjUZRKJdkwO50OpqamRLnJZDJS+tJmZpaAtLmZbfgkM3xvqlqJRAJbtmzBvn370Ov1MDs7\ni3g8Lpt7JBIRJajb7SKTyYhPp9lsolwuw3VdeQxYDfYcHT+TTCaRSCQCnWckD/R99Xq9QLBoq9WS\n0iTLV7prjaoRhyg7jhPwGfV6PXieJ+U3+qq4TnYH6k5Frsf3fSnLatWIBGhxcVGuSzweD5Th9uzZ\nAwB48YtfjEKhEFD1NCEkaSQR1UZ0fTxgpYRar9elYYFNCmt1FGqCrUmZgYGBgYHBejC2hIqkh4Qi\nFouh3W7DdV20Wi0JrWy322JWr9frSKfT0iqfyWREeeExWYbrdrsSBEqPlI4E4FduvpOTkwiFQpie\nnsYTTzwh6gkHC7MUWKlUJNWcigjJU61Wk/WGw2F533w+L4pYt9uVkmYmkwlck+FwKJ2HtVpNSoO+\n76NarWLTpk2IxWLyXD6fBKpWq6HZbIqxnteGr9GEj8oR18TuSRrAgZUYin379iGZTCKbzQq54ftQ\n1bNtW3KyPM9DoVDAWWedJaSOHjJgVTnUXZO6k5MYze8CgL179waiJ1jOrNVqSKfTMiqH959Kpvax\nGRgYGBgYrAdjS6g6nQ4qlYr8zLJTuVyWDZ8brCZdpVIJrusiFouh2WyiUCiImqQ333K5jHq9LiW8\nyclJUai0iZmlIV0um5ycFLVqdnYWhUIhEDZJ9WU4HCKTyUhJLJvNYmZmRkgf1TKSh3A4jGw2KyoS\niQkApFIplEol8QBxfbqDT6s3VJZISrvdLnK5nFwrlvB6vZ6EhlqWhXw+HzCV68ws+riAlZIfj9/r\n9fDEE0+IYZ5xDvRcUTXatGkTTjjhBLlno52AVKd47eiBYpee4zjYvHlzIAKBRI33lyVHEqZ6vY54\nPC5+M/2+JLQ8noGBgYGBwXoxtoSKBIEdfRz2G4vFxEul85dYCqPhmjPvGo2GzKwDVsiS53kS5Nnp\ndJBMJlEqlcRro0MrNZkiuej3++h0OrIht9ttybo6cOCAbP69Xg++7yMej2Pz5s2YmpqSc9PeJe3r\nImGJRCJIp9PioaJhmx4qqj5Ub0gWGLVAdUqXv3hukUhEwkuBFRN+OBxGKpVCOp0WAjYYDKS82Wq1\n5DwBBHxgVMGoCo7GOvR6PUxNTSGbzQa8a9obpbsgWYZkrEKr1RIi+fjjj6NYLMr5ep4X6DjUxJYK\nJJXGdruNdrstqhnjIHzfD/i1DAwMDAwMDhXPiFD97Gc/w7ve9S687W1vw5ve9CbMzs7i/e9/P/r9\nPiYnJ3HbbbchFovha1/7Gj772c8iHA7jyiuvxBVXXLHuhc3NzUmLP0lTLBaD67pSdiqVSmg0GrKh\n27YtRIBqSr1elw493VHX7XaRSCQwGAywvLwsZAJAQPGgN6hSqQSM6wCkvOc4DhYXF7G8vCw+o3g8\nDtu2ZZNut9uo1WpwXVc8TtzkeY7MgyJZocoCrJAeRhno0Mt8Pi8qjeM4on6Fw2EkEglRoUj4SFhI\nfCzLEsJqWRYymYx4x3id6D/qdrtC+JaXl+XaplIpIS4LCwvI5/MYDAZoNptIpVLYvHmz5EGxdKf9\nUjomgopRtVqV+5BMJmHbtjQAVCqVQPfk8vIyAKBarQqh1pEX0WhUmga0AZ/3k9f8UMjUsfhMGBiM\nM8xnwuCFjl9KqDzPw4c+9CGfzgpIAAAgAElEQVS84hWvkMfuuOMOvPGNb8SrX/1qfPzjH8eOHTtw\n2WWX4c4778SOHTsQjUZx+eWXY9u2bchms4e8qOFwiP379+PEE0/EYDBAIpGQGAVuflSrUqkUPM9D\nOByG53kIhUJiTqeiw/iDdrst+VKRSASe58lMN51gzg5DKiAAZCNmyYjdas1mU0JEqf64rotCoSAG\nc8uykM1mRREi+aGyRGLRarXQbDYDIZPsPuv3+0in06jVaqjX60IWFhcXhWDSHB+JRNBoNKQ8SnXJ\nsiwhXM1mE8lkErFYTDK12JWnyU80GhXvl/ZQnXnmmWg0Gti1axeq1ap43lzXxcLCAqamprBhwwbx\neNHsThWRZJPEhmRXxzDoOISnnnoKL3rRiwKxCFSyWK5jpAZJUjKZFJWvWq1KvhgN8rzHo9f8l+FY\nfCYMDMYZ5jNhYAD80tamWCyGT3/60ygWi/LYD37wA1x00UUAgK1bt+L+++/HI488gtNPPx2pVAqO\n4+Dss8/GQw89tK5F6eyjUCgU2NA9z5NSFzvI8vm8lLnq9bqUopaXl2WzJdngP27g1WpViAY38Lm5\nOezbtw+VSgWdTkc8RCyftdtt/OQnP0GtVkMoFMLu3btRKBRw7rnn4hWveAXOOOMMuK4rHizmPNE3\n5bquRAiw9McSVywWkyBLbZZutVqo1WpSciQhYWmRPqhSqSTJ7slkUubzsaxWr9dh2zZyuZwQvkQi\ngUwmI4GlrusilUrJ+JqJiQnxP7EsyhE309PTmJ6eRj6fF6M/ZwaSJLL0yXPsdDqiqpFAsczI86Pq\nFYvFkEwmccoppwRmDtK8TxUSgLwnA06ZEcY8M9/3AUBKtjpAtNVqPeO/z2PxmTAwGGeYz4SBwTNQ\nqBjoqEHFAQAKhQIWFxextLSEfD4vz8nn81hcXFz3wm666aZ1v/Zwcc011xyz9z4YrrrqqmO9hABe\n/vKXH+slPA3XXXfdUXmfY/WZMDAYV5jPhIHBETCl6y6tZ/L4M8UHP/hBbNy4MWA2p3GYyg/fhypO\nOBxGtVoV/w0HIlP5YQAn5/NNTEyIP8h1XWQyGbzrXe/Cpz71qUAHHk3Z3W4X5XIZiUQClUoFExMT\nOOecc8QXxG5Cfo3FYoE5caPz47SHh4ocAPFcscx58cUX42tf+xoqlYqUuJhVxXPnuJlYLCbDoYEV\nNcbzPJRKJczMzMDzPGSzWaTTafF/0X/GzjzGMWgfEgBRcc466yw89NBD4lcLh8OoVCpIJBLo9/vY\nvXs39u7dK9eCJVCuWw9RpkJGvxoDR6koep4nXjUAUprlyCDHcdBut/GBD3wAt99+OwqFAhzHged5\nUn7lvYzH42i32/J+vHae52F5efmIEeln6zNhYPBchflMGLwQsC5CxSwox3EwPz+PYrGIYrGIpaUl\nec7CwgLOOuusdS+MXWrMXWL0AAfm6pRvbsCxWEzIgGVZqFarkuZNk/r8/Dw6nY4MEKbZmyVCABJ0\nSaN1tVrF0tISkskkWq0WpqamkM/nxRelZ8cBq0ZuroP+AA54ZumPRnB6wlga0/MH9Xw6/l8gO/tI\nGlutlnQz0lBeqVSkfNbr9aT8NTU1JcSU15klQXbqAQiMv2F4qJ7lx4gHy7LQaDSQzWZlPSeffDI2\nb96M++67T0jX7OwsNm7cKPdXh63S+8Zrx2vFUitLleyCbDQakgHGFHhg5f+CdQwCSTDJG2cU1uv1\npw1gTiQSh9XldzQ+EwYGzyWYz4TBCw3rioc+77zzcM899wAAvvWtb+GCCy7AmWeeiR/96EeSnfTQ\nQw/hnHPOOazFkUyQWHBT5O8Gg4H4mgBImCQ72KgA0YvDLraZmRnxXLXbbSFZPM7y8jLK5TLm5+dR\nrVZRKpVkjt/ExAQSiQQKhQJyuZx0mzGqgWZ4ZjnpBHHXdWVuIOfIkVRQdYlGo4GxKdzgOcuQpIqx\nEABkDp7+vz2qXjwvEjiqZpzVx6+8nvR30a9EssX7QEKVTqdFmdNdhySS4XAY5557Lk466SS0Wi3Y\nto19+/aJ4Z+kUq9PRy2QQHN9nU4Hc3Nzch0Ye9BoNALZU/RGWZYlZJOZXKlUCrVaTci67qykIrde\nHK3PhIHBcwXmM2HwQsMvVah+/OMf46Mf/Sj279+PSCSCe+65Bx/72Mdw/fXX46677sLGjRtx2WWX\nIRqN4j3veQ/e+c53IhQK4ZprrkEqlVr3wrjB6uRvKi6axND4THWB5TUawXkshkAWCgVRrdjxRsLA\nSAA9LJhDhavVKnK5HIrFItLptARqsquO5nAqPf1+H9VqVdK54/F4oEOQ66GxnGRJB4hylAywOiya\npIv/MpkMKpWKlEanpqbQ6XQQj8fFgE7zO0kMlR4Slmw2K+RIJ5CT9LEEmc/n5Vpxna1WS+4BlUQq\ndFTEMpkMHn30UViWheXlZVG39L1m3AHLioPBINAUkMvlsGnTJmlO4HnxOQCkk5EqFbO7aJavVCrS\naUhliuogyfgzIVXH6jNhYDCuMJ8JAwMgNBzTIvZ1112HyclJKYnpxG8A0r2ly1DabzUYDCS4k2pG\nLBZDLpcDAFFV2GXGzfQDH/gAbrzxRmnBp/Ljui5mZmYQCoUkF2liYkLUDd/3sbi4iHg8jkKhIEoU\nfVgkDFwbyQqwQpyefPJJtFot5PN5LC8vy4YfDoexbds2fOYzn5FSJ71F09PT4jtqNpsoFotCnGq1\nGmq1muRQhcNh5PN5UYMSiYTM4WM3H6+TLic6jiNzEXmdTj75ZDz22GPyM5W5UqkkypiezUdlce/e\nvfjJT36CYrEI3/exYcMGIcYsDXIIdiQSEQWQfrlKpRLI3GLeVCaTwdVXX43t27cLqdV+NZIsy7KE\nkDGbClgh641GYyybEQwMDAwMnhsY26R0EiUSo9FxKNywqWQxpFIbqmk2J5FxXVfKf1SmmEekgx3D\n4TDK5TKSyaSU3ZLJpEQ0ZLNZCblcWFjAaaedhlQqhQ0bNkhgJ49FgsGsLG70JH40oh9//PFChEh+\nut0uSqUSAARKatFoFK7rBuIDmGdF1YsJ5jRoM8qAJnAqXFSEGC2QSqUwGAwwMTEB13XlOpEY6lws\nqmwcnqwHK1NZ4mPRaBTT09NwHAcPP/wwkskknnjiCYRCITiOI+tnZATPlb4yxjnwGvKesmEAQCBb\navQeUNlkDlWz2ZRoCWaYGRgYGBgYrBfr8lAdDegNk+UyjhlhECNN6ty09VgYKhVagGNpjd4skgKW\n4nTOUTwel0BQjmCJx+NwXRe+72NpaQmJRAJnnnmmqF0cBswyJTf1wWAAx3EC5SgAgZEw9Colk0lM\nTEzA931RaQBIWZG+IeYqsbSllTrtp2o2m0Ik9PUgCU2lUnBdVwIw4/G4lONoCudaSca4XpYu+b5c\nAzvqdJApuwcjkQg2btyIAwcOiIJk27Z0HDJfjPeRZV8+xvNjiVK/P/1qVNqokJGQU6WyLEuu0f79\n+yW7zMDAwMDAYL0YW4UKgMyO8zwPS0tLSKVSmJycRKFQEEJAD8/oOBF6fLgxx+NxIWl6Lp/uqGM5\nkYZllgxPOOEESRNvtVqYmJjAxo0b5T1JaFjW42M8Lv1WugylYw240dNHxWwWlhABoFgsotlsIpFI\n4PHHH5f3ow+I5xWLxQLn1W634fs+Nm7cKI9PTk5iampKFB2+N68dj0FlSYej6ll5wMrQZp5TvV6X\nc7YsS3xlJDhMpp+ZmZHOP8dxUCqVMDU1JYSMa9FmeBI7lnqZLu95nihaes4gVUc9MJr3IhqNIp1O\ni2q1a9cuzM3NPYt/yQYGBgYGz3eMLaFaWFiQdvyZmRlMT09LqU9HCwAQZUqPLNGRAXoAL8tB9BJx\nwyU5AVY232azKWUqrWrRR0X1haqP3vBJVHQidzwelwgIEgdGAyQSiUCpCoCY5xnlwDl0jUYDxWIx\nMJ+v0+kIqWA5jP6rSCQi8Qb0JNE3xpIjDd6MigBWS4u8nizh6fA+KnhU3RzHkXX0+32Uy+XAzD2q\nfvV6XQYcz83NSVs1SdXByBTvQTgcls4/XbZsNptCpJg1pcfcaG8VyVa328XGjRvN6AsDAwMDg8PC\n2BKqeDyOk08+WQIYSY4AiILC8g1b56k68SuVIRIcYHVTpiqkTez8HX1BsVhMOtVCoZCY0EnONHni\nMbhJs+QYi8UQi8WkfEXCwFKVLovp0hWzlkgWSJQYq0BFjcSARncqPACE5IzmYukwT20e5/nqkqju\nPiTJ4bH5lQR1lGwVi0V0u13Mzc1JeRAANm3ahMXFRem2JNk6cOCAKH+6HEoCSOKmy6Q65JUkmuU9\n+uXoxSIh1ESaZLndbh+hv1wDAwMDgxcixpZQ5fN52dhZyuMGr8tl9FjReE2FwnVdScsmKWB7/Wju\nE8kCjwusKE+bN2+WyINYLAbP88QjxQ2bHXcst5Go+b4vRnZCB5CS7PA9tWJG8kX/EwCUy2UAq34k\nPp8kguZ9vgdN8CRdDOikB4pm7lAohEajgUwmA2BF5dHESsdUaFWQDQDaLM/rqo3/oVAIxWIR7XYb\n1WpVrkmxWESlUpFk916vB8dxpExJJY9r5N8BySOjGvg4ryHheR7S6XRAkdRkls9nyVTfJwMDAwMD\ng0PF2JrSuYFzk6YCwg2Rmz3N6bqMR1VLB1Hy+Tw2j8vNtNfroVqtAlghahs2bEAsFsPk5KRs9Ez+\nZUmQCd80SbMMx5wj/TOVMq6XZEOXDqPRKDzPE0VJ+5fm5uZQqVQCqlQ8Hg+UrrQxnmSn2+0KGWy3\n2xKnUKvVAgGavFY8V66RZIWKjvaZsRxHhU53LVINozLmui6KxSIymYyQrkQigQ0bNsj5d7tdLCws\nyHXSw4z5vryvVPvodQNWjf36b0abzUnCeM5aceP5GRgYGBgYrAdjq1CRhLBsRtLDx3QIpY4+oBJB\nwqLjF4DgDD398/z8PJLJJICVCIOpqSkpjQGQ2XGTk5MSmDk6aobv2ev15FjxeFy6ERnpwH/AasmJ\nJnOW7bhGxhS0221MTk5iOBxKvpQu+7G0x2tE5YyGfCp07XY7MMPQ8zxMTEyI8qdjEIBVNW+UcNCX\nREWp3++LR4rXhOenS5yRSASFQkHypDjn76mnnkI0GpXZfCSHvJdcn1abdCaZvg9UuKhYMV1dh3aS\nbOkSooGBgYGBwXoxtoSKpIMbJICAakMS0u12Ua/XpSOPpSatUFAJ4sgSEh8a2fmVnV7ZbFa639hV\nRqPzcDiU0E2WDemrYiyDNnyTTAGQwEwAgbRuKknAajwBR7JQwUokEnAcB9VqVYYic116HqHv+6K8\nafVID1Rm2YylPcdxpDRKQuT7PjqdDlKpVKADcHl5GSeddFLAm9RoNOT9SX64Rr4Xy64kRizVMivM\ncRzJydJkVWdKaQJNIkTSrK8dZ/QxEFaXJbW6xb8hjsAxMDAwMDBYL8aWUAEIeJ50R1+5XJbSFrOR\n4vG4hGLqTCkSC5IxEi2mrA+HQ0n43rx5MwAIIQAgwz11xhLHzdD4zt97nodMJiMkTpcWtTKlO+MA\nBAijNruT8AArs/OYTbWwsIBcLicxCpFIRIIpNQkEIOU1Ps5xLLZtw3VdxONxKQ/qyAReW14PXnvH\nceS4JCKjuVW9Xk/IFM+HJIYqE31ixWIR4XAYU1NT8H0fzWZTgj61V27074DEkdeKv2fiul6zvnej\nCe7aT2VgYGBgYLBejLVxhMqPZVmo1+uYm5tDv9+XNG2mp+vNktAqBh/X401o3mb0wGAwQD6fl9d6\nnidlLKpMJCXpdBrZbFZmUJEsJBIJUUcABDZtkgLHcSSVnN4g+pZoQifpoAIDrMQo1Go1OI6DXC4n\nhIEmeSpIOjah1+vJdQqHwygUCsjn82JeZ0o4z4ElP60o6a5By7IwNTUl58ESJcuOmtiQ/DQaDSk1\nAisEh6NquK5kMolNmzZh48aNCIfDojiyJMfSph7mTHWM6+axSZZ0V+hokCtVKd35pzsUDQwMDAwM\nDhVjTahIlubn5+E4DorFYmBwMRUgkg49XFgrWvTh8Ge+hkNxOduOmyo7y/gclgyz2SxyuZx0EwKQ\nEqMmHp1OJ2B45sbfbrfRbDbh+z5834fneWIWH+2Y41eSMxJL/kxCRmLIdeocJ6pD/X5fcqx0lx6H\nCeuROPF4XK6fjhkYBUuGNOTrpgBg1Tulrzm78nguLO0xKf24444TIqYHPvN+85ryWvJaazLEcyfJ\noiLJa6YT9fk7/RwDAwMDA4P1YGz/t7xSqcCyLGSzWUn1psKkSRSwWmqib0bnPXFz5e+1kZoqB9PD\n2eXHDZpKzsaNGzEcDkWBoirD4/M9dJAo/Us8Pt+X5IWka3QeHX1E8XhcVCoiFAphcXERwGqaO8ui\nfC4DL1lSa7fbSKVS4sliZ+TU1BQajQZ83xelTa+VxINlUkZRaJLEcTK6xMbMLZIrnWHF2AkSJV5r\nkrNUKoUzzjgD3/ve95DJZCQQdZQwHYzwUfkbLanqOZD672GtXC0DAwMDA4P1YGwJ1fT0NFKplGye\n9CXpji9umtyU9UYLrKaWj+ZWEbrE1mg0MDk5Ka+LRqMoFArij2KZiAODaXJnqY6Gd66BZIBGb60s\naSLXbrelS46eqcFgdUiy7k7MZDJYXFyUiAdNZpiJxVJfpVKRciVLZM1mE/1+H8lkErt370Y+n5dZ\nfuympCFeRz/w/T3PEw+VVqW0X4znoUuB5XIZtVoNmUxGug81seFzeR5btmzBj3/840BzAO8jv2oS\npw3lOrlelzP1tQWCcxYNmTIwMDAwOFyMLaECIIQBgGyiOpNKp4vrlHGdN8XHqNbouXWarOmy4OTk\npBAvvkYrGsPhUFrxafIGgGq1ikQiAdu20Wq1hFTRvM0yVzKZlDEpJIoAxMytyQXPZXZ2Fv1+HwsL\nC0in06LYMRyU62HWEtUrjqbRpI1hnjodPpPJCHmhwqe7IUfH+2ivGMuO9Jlp75ruQqQvjaSUhLLR\naAQUrnQ6jeOOOw6e56HRaIhBnaRRXzPdgECT+ihR0iXiwWAg94slUQABom1gYGBgYHCoGFtCRXKg\nc4j0xqj9U3qjZ4ilLvmQGGglgl10tVpNogFYdtMz8hKJhKhPlmWhXC5Lu79WqNiZxk2bc/xYKmSk\nAqHN6SQiJC0M6gRW85w4C5AdfL1eD6lUCu12G61WS66TNn3z+Fyb9nbpUSuhUEi6CKkCAaskg1/1\ndSaJqtfrMkJGe5U0uQmFQojH47AsC7VaTdQvlmE534/3naGlu3fvlvDStUjS6GN8HxIu3TWpM6yo\nXjHGgd40/k0ZGBgYGBgcKsaWUHEjHJ3rxnKUNj2zhEaSRBVDh2fqrj++hsoIS3WMTaASEo1GUa/X\nAwSHRIhkge/T7XbRbDaRTqdl1Es+n0ez2RQVzLZtAKvGaZI0lrG0v4gERKtsfH2r1UK320WtVoPr\nupLgHo1GUavVAKwqSNpbFg6H4TiOkLhYLIZEIiGGe5I6Hajq+75kfLmuK0SM1053WrJbjsGiNNo7\njiNkleb/arUqBJKKY6PRQL1eFz8V86lIEnnfNMHmawE8rWGB0H42kitNwKl2GjJlYGBgYLBejC2h\nYvcYEOze051ZmijR46RLeQTVE278ur1+MFgZzMs4AYLlxn6/j2aziVQqJWpKt9tFq9VCtVpFNptF\nNpsVj5POoKISU6lUkE6nRTFhGZEqF31U3PjZaafTvflcmu55fo1GQ9ZDhYzvPRyujMuhgV6raLrM\nCKymjJOcaFM5CZkurzE2gdeRpIT/2P3IIFadrzUYDES1K5fL4v0i0VtaWhIy1el04Ps+isWi3Dc+\nl/ePZJfr1H83mkCz3Mtz4j3QpMzAwMDAwGA9GNvYBG7ILC3p7j5ddtK+JmA1cJIbtyZT9FcNh8NA\nl1w2mw0YwMPhMKrVqkQqsHOPBm+qPVNTU4jFYkKySNa4UbOUVCgUJNm81+tJCGev10Or1Qps5jom\nQBMfGsdJBJLJZCDviiSMxIXkjwoM855s20YikZDoB3Ya0nyvCRbJCtfLchzvj4butOv3+6hWq0Iu\ndVAqDf68Z67rSumSz2EiPI3/OmNMp53rvC4AgRKjjk7gmnktuR49d9CMnzEwMDAwOByMLaHSJmgd\njMkNebSkowkUDdEkV9rYztdGo1E0Gg0cd9xxclw+r1wuw7btwKiU2dlZtNttuK6LyclJ5PN58S6F\nw+GAj6jZbAbyqai8sPuPBnl6kJrNpsy20/EQVKuAFaJSq9XEzD46z1C/RpuvaVinGb3T6YhhntdE\nd8TRsM3SILsUSS5JpEhIqAK1Wi05b5YL9fXXyfUAhPSGQiEkEglMT08/bVYfzfws5bEJQd9rYJUo\nkfTyPUbJ26g3jPeN19PAwMDAwGC9GFtCBax4YnTopFantN9HqxfMTuLPekMlyapWqxgOhygUCjJ6\nhUQIWPEoVSoVDIdDVKtVFAoFHHfccbKewWCAer0eMMwzOoGKVavVkg2e6eP0UzHYk6B/p9Pp4Ikn\nnkC5XBZCyeHInudhdnYWnueh2+0KIaN3i118VPWo7FAlYqcfSQ5VpOXlZcmI4rpJOrW6R4LDa7Sw\nsCDnFY1GkUql4DgOms0mms0mXNeV69Xv91EqlYTEUanTSmM4HMb09DSSyaQofrx/9M3p53JdOjJB\nlxb1bERdTqSvS8dRWJYF13WP9J+vgYGBgcELCGNrHGHZiknY9B3pMiA9RaFQSAbh0vzNTjfd8cXO\nO86+KxaLQnCYlg6sEpxYLIaZmRkZscKNmaZ3Gq1ZkmSLfyQSQa1Wky5Bkr92uy2maxq/qVqlUilE\nIhFMTk6i1+vB930pOwLA3NwcKpWK+LnoXeK5al8RyRCvCZWyer0uSg9Lh+w+ZKlO+7C43kQiEfBT\nAUAqlZLSJ0HfG8kKoxk4O5B+KK6b94MYDodIp9OYmprCT37yEyGqjFnQKex8Pkkjf6bCN6pMad8d\nz5O/p6/KwMDAwMBgvRhbhYpkiARB5wsBq2nm2q+kfTBUaEbTy6mSkHBx0LH2BLmui+npaelOo5m9\nVqtJ2TAej8tMPXYC0tekBwOzI6/X68FxHGQyGSEBVKmoLrHsR7+VNp/zuSyxaS8RweszmsOlZxjS\nwF6r1USxo3pFQ7m+XrZtS6cfwz2B1dgCljxJVLV/i2SNieza/M2Q0VAoJL/T0Qs0+NNMr8H7oTs/\nuSZNnviPKqeO3tC+OL7WwMDAwMBgvRhbQgUER4YAqyZ0rVTxca1AcHMkIdOKBtvm2f3Gx4FVb00i\nkZAuN4ZK8jVUqBqNBhYWFmROHkt6jA9gJxu/p7m72+2KipVIJCRnicoKs6OoeC0tLQXOXZNEEiOu\nh3EFJFTAqtpGwsQyHUtpALC0tIQDBw7A930hSCRUrVYL8XhcTOu8liRPXKvOt+I5RqNRZDIZJJPJ\ngOdJNw00Gg3J9QIg4Z+MceC4HRJCTeR4LIIkVhNwEjj6sfTfifZgUXkzMDAwMDBYD8aeUOlSjB4p\nMxokCawSK/qo+By+jhu5TlMHVjbxZDKJiYkJAKvjSyzLQrPZFEKkzdyxWEwIEIkZ309nZWWzWems\ny2azYtr2fV/IzfT0tGRhcVwM10HCwIyqdDotZc1kMinHoxpEskaSYNt2YIbhqEmcCIVCWFpawsLC\nAur1OqrVKur1ulxb7ZcCVoz7pVJJVCw+zpE0PCbXOjExAdu2pftvOByi0WiIcZ6EB1jNDXvRi14E\n3/fhOA5qtZoMrSa54r3UQ5N1Jx9J6lqjiHgNuF5jSjcwMDAwOByMLaGissMNj6qETkYncQAQ+B03\nSK1m8Htu3FSuQqGQxCYwh4pRCa1WS8bILCwsSIcdsNodR4LSarXQbrfFI5RKpYQQMZpAB0x2u11R\nj+jz4u9I4PRoHQ4n7vf7SCQSYjRnqZDnxRKjTpnXhFP/HI1Gkc1mRd3yfR+Li4tSviPpYVlUl1xj\nsZiodxwqzbIdFT2WOnXJsVAoBGIrGCPh+76ocTqYk9dEm+VJiHSqO7Ba8tNEmveZhJI+Mx2doJsd\nDAwMDAwM1oOxJVRM8iYZ0a34wKoxWUci6HgCgsRE+2w4667VaiGTyUikgCYLDKqsVCpoNBpCzObm\n5lAqlVCr1eD7foBoua4rA5FJdnRZiX6kUCiEZDIpqlCtVhNfFtPQgRUViEilUkilUrKWURUIQECx\nY7mO58ZrqcNB2d1HU7rv+6jX65idnRVfWTqdDpQluSaW/Pr9PhzHEQM7z4/3SKtHJMn5fB7ZbDZQ\nnqSix+5Kzv/L5/MB8sxz1cGeOk+KyqLuDNXXiuvWQ5SNId3AwMDA4HAxtjsJO+s4f04rDnq2G7D2\nrD7+btTsTDM6AAncZAceicDi4qJ4nJLJZKCjkF6kbDYrJnP9ON83nU4H5urRx8Tk9VwuJwpPuVzG\n8vKylOxc18Xc3BwSiYSY0amoZDKZgAGfKpfucuS10eRSh2uSeDiOI918LGXysf3796NQKAjBYygo\ny6KtVkt8aAzlJIEhedUETnuWuLbJyUk0m03Mz8+L4sVICpZAE4kEKpUKotEo2u22rG/UbK6PC6wS\nbN35SZB461l++m/HwMDAwMDgUPGMCNWtt96KBx98EL1eD3/wB3+A008/He9///vR7/cxOTmJ2267\nDbFYDF/72tfw2c9+FuFwGFdeeSWuuOKKdS2KG6lOCmf0ABUF7VvSZSZupDo7iZ6der2OeDyOSqWC\n448/PpC4PRgMUCqV5P1J5vjenuchGo1iampKEsup4rC0RbWk1+uhUqnAsiwxrHPsCx9//PHHAUCy\nkWi2JwFLJBJoNBqBEiNN7zRX6ywrXdLU14ikgmQHCCaDU3nS16tSqYix3HVdNBoN8Xex4y6VSgV8\nWSzJMeYhl8thcXFR/GO8T+yCJHEaDAaYmprCrl27AkokR+q4ritkmuXD0VBTrQLyng6Hw6cNPNbf\nj/7tHOpw5KP9mTAwGOQ8W0QAACAASURBVHeYz4TBCx2/lFB9//vfx89//nPcddddKJfLeN3rXodX\nvOIVeOMb34hXv/rV+PjHP44dO3bgsssuw5133okdO3YgGo3i8ssvx7Zt25DNZg95UUzPBlaH/HKj\npu8GWG2f1ynYuiykVREAAa+S9tWQEFHpYEmQqgmHHuu5dvzK0TF8rTY509cUCoUkCHR+fl6IW7vd\nRi6Xw+TkpEQvMN9JqyvAinGe+Vf0J5EIsnzH1+nuR65BRxqQQPi+L+fBQE2Gd/b7fezduxeZTAb9\nfh/xeFw6+ADI+8XjcUlxZ9RBv98Xj1QoFILneTIah6qdNuD3+33MzMxg3759gY5NEkbmeXFGoA5v\n1SZ4AKJc6SHIo0Z1/XdGAnwoPqpj8ZkwMBhnmM+EgcEz8FC97GUvw9/+7d8CWClj+b6PH/zgB7jo\noosAAFu3bsX999+PRx55BKeffrokZp999tl46KGHDmtxoVAI8Xj8aVEJeqSLViTYYafJBjd4lgsb\njQYmJibE2xMKhVCpVCRvCoCMpaFf6YQTTkChUBBvVKvVEr8QO+xCoZB4qGKxGOLxOPr9Pubm5rB7\n925UKhXMzc3BsixkMhk4jiM5V/Pz80JWmLBOozaN8iQ+JCTNZlNIFw3kuqSmx7SQMPJ4PL7neajV\naqjVaiiVSqIKkbzMzs7ixz/+sZTSdA5Vo9EQ4mTbNnK5nCh7nMHH2IhOp4OlpSXMzs7C931JldfZ\nT7ZtY/PmzVJ+ZOlQq3S81+yiJJEcjc/odruivPEe8yvJFtUwqo2jWVe/CMfyM2FgMI4wnwkDg2eg\nUOmxHDt27MD/+3//D9/73vckCLNQKGBxcRFLS0vI5/Pyunw+j8XFxXUv7Ld/+7fX/drDxZ/8yZ8c\ns/c+GK655ppjvYQAtmzZ8qwc94ILLlj3a2+66aYjuJKD41h9JgwMxhXmM2FgcAim9G9/+9vYsWMH\n/umf/gm/8Ru/IY8fzMx7uCbfr371q3AcR1SK5eVlKXsxnoCdbHw/qjvamM38pFqtJp2DuVxOxrcw\nQ4mDcm+88UZs374drutKGYwlvVQqBWAltZymdapGzWYTGzZsCEQGcIzL/v37EYvFkEwm4XmeKDQc\ntsxOt1arhWq1KqNahsOVWYLvec97cMstt8D3femC00ZsXguW//T30WgUiUQi0BFHYzf9XXoMDr1Y\nei7gxMQEcrkcLrzwQvR6PZx88snYu3evXHt6mrSCFI1GJYIhlUqJH02nwDuOI9eY95Uq3VNPPYV6\nvY75+XnMz8+LgsTrEovFYNs2bNuGZVm47bbbcP3114vyyHJtKBQS/xqVqFgshkQiAdu2RVEcDAZ4\n+ctffkh/o0f7M2FgMO4wnwmDFzKeEaH67ne/i3/4h3/AP/7jPyKVSsF1XbRaLTiOg/n5eRSLRRSL\nRckRAlaG55511lnrXhg3QGA1QoEz91juYzmMXw+WmA6sxiyQHLXbbdmcWRojYQIgs+b4mkgkIoQt\nlUrB930xqi8sLCCbzWJ5eTkwxoZm7RNOOAH1eh0A5P/iaL7mGsLhMBzHkRInS3S65X9paQmWZUk+\nli5nsqTJ8+GIHHYe6sHO/J6eKZJGkig2AIRCIeTzeUxNTaHb7WJhYQFTU1NyT3h/RgNDFxYWhLhx\nDTxHxkqwq7JarUrplCSw2+1ienoas7OzEifh+35ghiHXoFPaWe7Tg7PpueI1ZHejbdtwHCfQEHAo\nOBafCQODcYb5TBi80PFLPVT1eh233norPvnJT4px8LzzzsM999wDAPjWt76FCy64AGeeeSZ+9KMf\noVarodls4qGHHsI555yz7oWRxIw+puMLgNU5dewA0/P/dBo6sJq0TkWHBMJxHBmUDEDCMfkerVZL\nhhPTZE0SRjIFrIys0QZ6qlw0rk9MTGDTpk2YmJjA4uIi9uzZg127dqFarcpYGnbvkRCQFNJAXq/X\nA8SRz9Op4DwPEhTmP+mohEQigXQ6jXQ6jXw+j3g8LiNiSDJ4Xefm5tBut7Fnzx45t1KpJKpSq9VC\ns9lEpVLB/Pw84vG4jNWhqZ3EyvO8QOciOwpJaBix0G63MTU1JUOxtSeMr+X3NO7r9HR2+Y3mVtF8\nTq9XLBYT1eqZ4lh9JgwMxhXmM2Fg8AwUqm984xsol8u49tpr5bFbbrkFH/jAB3DXXXdh48aNuOyy\nyxCNRvGe97wH73znOxEKhXDNNdcEFJ9DBVUpbog0EVNR4QZL0jGqzFCN0OUsTbJIvCYmJqTjjevV\nGzHjEUg2SNri8bjkUXU6HdTrdVE+isWilBNJfACIKvOzn/1MiEO9XsfCwgI2bNiAVCqFZDKJZrOJ\nTqcTiG5gYCbPj2NYeB30OBk9AofdklyD53lCqkgySLC63S5mZmYk/bzVaqFUKiGXy8nrH3vsMZx2\n2mlCKj3PEzXPsixJQmd6O0ujwOr4HN/3pSwYCoUCKfPZbFZ+PxwOkUwmhVTxeFw7uy0Jnc3FrkSW\n/XjvXdeVx4DVENdDKT0cq8+EgcG4wnwmDAyA0HBMi9jf/e53A2nlCwsLKJVKKJfLQjg0odIRAgCE\nfOgOt16vJ6TFsiwcd9xxQgbi8TgikQj+9E//FB/72MfEOMmoAd2Gr8uBVGL0mBldlqL3h/4g27ax\nZ88eeJ4nRKJSqcisv1QqJV1u4XAYpVIJ733ve/G+971POv/0OB2WFUfzlHSgKdUXBm3ato3JyUkp\nmelIA/45MEGd3jPbtkVx++M//mM88MAD8n7tdhsHDhzAli1b5P2oGFIR4prD4TA8z8Pi4qK8hybI\nVKfK5bJ4xnbt2iVqYTKZBLCSg8Wwzn6/j+3bt+Oaa64RTxxJIz1SzNRKJBLIZDIyY5Flv8FggFNO\nOeVo/GkbGBgYGDwPMbZJ6dx8qaA0m01p+dfJ5cDq0GMSHioZWrmiWgWsbMD5fF7CLKk2aSWpVCoh\nk8lIVlKr1UI0Gg3MAczn87JOkik9/oYECFiJGbBtG08++aQoWyQ9NI4PBoOAUb1araJWqwGAfAVW\nPUskbSQ2WsWhuZyDoLVZG4CUIanq8WcqgKFQSPKogJUuHQZxAsBPf/pTnHTSSRKVsHnzZlG/NLHT\nuVUkiv1+XxS/xcVF8ZW1Wi2JpeA50nSuS3ej5I8lRF1KbLfbEl2hSbae5cd/2oNlZvoZGBgYGKwH\nY0uo2CHHDbjZbIrKpI3OmiQBq1lFDI/U4Y583HEc2XxZhmOAJ7AaFkk1iIrUcDhEJpMRczXLclp9\noSrD8hJLdplMRgI9p6ampGSWSCRENQNWSnskFvPz8zJ4eC0VDlj1kHFED6+FzubS8+x0SVT7sUi8\nmCHFkhpVoGaziXg8Lu9brVaxZ88ebN68GbZty3NppLdtWzxvDP10HAeNRkPOJ5fLIR6PY+/evWIg\nr1QqoixyzalUSlLXeT9ZftWhnjqcUxvU+TfCxzkXcJRQGRgYGBgYrBdjOxyZmzG76IBVskTowcj6\n96Nz44bDoZSuAEjJj69lmCTVm8FgICUiEhxd2guFQlhYWJCYg3q9LgqXTiq3LAuJRAKO48g6YrGY\nlP/4uOu6EhHB1z/11FOo1WpS6mI8Ag3rJJM6NZ7lT5rFORRaRzlQfWJJbjgcCvmkEsdwVJIS27aF\n8PEaMZS00WggGo3KYGeO26Gxv9/vI51Oy7F1lyOxadMmhMNhLC0tBc6L8Qrs1NPz+PR9119JMElA\nqZBR2dSDlFnKpYHdqFMGBgYGBuvF2CpUVBC00gJADNlafdGdbjoVW3f5sfzjOE5gHAsAUWa4yUej\nUbRaLbRaLWSzWSSTSXktS4Su6yISicDzPCEeLHnxd1SOOOKE0Qr79u2T98lms1L2y+VyMihZzyUE\nIKNwdOcfADGf63l9jGRgGY/HWItY8XsqdWwGoPdLe9GoXgEQpZCp7YyLoKmcKlmr1YLnecjlcoGo\nC00KaTqPx+NoNpsBUsQGAZ5br9cLxCVQEQQgj5FU6/R8Kp4c38NrR2XRwMDAwMDgcDDWhCoSieDR\nRx9Fu90WYkXVQpe/NPEgdGmMCoTv+0in09Iyz5JQPB6XjjlgxctDjxVVLW7eVDa40buuK2tjbAIJ\nm/YpUflpNptYXFwU9SoSicB1XSE2s7OzKJVK0iWnSSFVMEYhcP3aJ8UyJ0mJHtHD69LtdlGv16Xb\nTXfO6bwuKkO6I4/HcRwHuVxO1jwzMwPP854Wz8B7sLy8jFarhYmJCXkPHotzDm3bllEzLM0ys4bk\ni2vlvdCEiKSaj5HQ0p+mlS8a4HkMTVINDAwMDAwOFWNLqIAVIzeTzqlCadWKJIpBjjqDiKWe4XCI\nRqMhpSrGL5TLZaTTaSFAutmRxIjPZyL4xMQE4vG4lIr4/jRgh8NhSTLv9XqSucS5e5yPx9IhS4+D\nwQD1el3CRvP5vJw3Te0klTxfro2KD4mbJhkkZIxvACCPUakDECATOsxUm9N935duQGAlh4pzDLvd\nLn72s5/hxS9+sZBeeql4HbmG5eVlhMNhpNNpDIcrSfDM9up2u5iYmMD+/fsBQEzwJFu8x1TPtArF\n8+j1ekgkEgHDOv9+tJJYq9WQzWYD99HAwMDAwGC9GFtCFYlE8H//938BAqXTzzXJ4s/6d1RqaBQn\nCQJWjN9UZ9i1p/OS8vl8IPGc5KLRaEg2kvbkMDG92WwG3pcdb7FYDAcOHJCcKvq2kskkCoVCgDTM\nzs4Guum0mVx7xPg9iaMe/aKvCT1bOtiSygzDU6lisYTH96YixG47XjcA4jsDIErW4uIiTjrpJCQS\nCSwvL2N5eRnJZBKu64oZ3HEczM3NodlsIpPJAIB4rdhFmUwmJVm+3++jVqtJyZEqHAdVUwkDVrv0\n6GUjAdcqFAklw0gzmYwxpRsYGBgYHDbGllAx8JHlLABPK8tonxVJFLA6goYkhKQhk8lIqUhHDzDR\nm8oHCRIJBfOKWCqq1+sIhUIolUrSpafN3trvRA9PJBJBJpOR4yaTSczMzKBerws5KJfL8DxPfEUk\njTxXHRlB5UWPiuG14jrorWK0Ab+nT0orWTSvs2NPB4mO+p6AFUK1vLwsnZfZbBae56Fer8u4GpKY\nxcVFyfOan59HKpVCpVLB/v37JZaC+VMs2RWLRTz55JNSCtVlXvqm9Igcrm+0/Mm/i3a7jWg0GiiH\n7tu3DzMzM3IsE5tgYGBgYLBejC2hosdH5y1xg9TkieoGsEqwuOHqzVWXfbh5tttt5PN5ZLNZCX8E\nIONoSKTYJUhSxRLVhg0bYNu2ZFTR5O04DsrlspSemJ5eLpeF4BUKBYkiYDmtXq/LwGLGOOgONpqz\nuUYqNSRzBMmk/kcywfKkvj6MLOD50xhOn9doxAKwUo51XVdKeQzppAo0HK7MRgyFQnBdF57noVqt\nikoWj8fR7XZRrVbFUK/JXbvdRiKRCJRLGTDKc2InqO7y5DXS3jGa7UnG2HSgO/7492NgYGBgYLAe\njC2hevDBB0VZImnSXWokV3xs1JjMTVbnVHHDpJk8k8lIK77eTBOJhPimdPcgx8vU63XYti1z6EgQ\ndNhnMplENBpFtVpFo9GQ2XmVSgUzMzNYWFiA7/uoVqvI5/Oo1WoBQsKSIdfB9+F56xBOkh+dKQWs\ndkTyK7Oz+D58HRUtqlc66LTRaEjJjONhAMhcPg4/9X0f3W4X2Ww2QHJzuRwKhYIMu/Y8D/Pz85K8\nrkfr9Ho9NJtNIX9U4UiqNKEDVj1TOj5DEyc2EpCEJhIJyaUi0X7sscdwxhlnHNG/XQMDAwODFx7G\nllDVajXkcjkAwVIfN3eWfzTJ0Ob0WCwmHYE6VZ3kiN4ZkpJYLBbYmKl+cC5cKpWSDX00yoE+qHQ6\nLeshiYlGo0KyKpUKPM/D1NSU+IS42TebTTFma1VN+3toqqYSQ7+Q7nTToZ7AatglyQWDSPk7rdox\n1oC+o36/j2w2i0qlAgCBrCZGGjAMlQSJ15nDnNvttoShktCwDFqv14UE6UBQrokBqzx3du3p4dc8\nnj4fnbPFrkA9GojX0XEczM7O4swzzwwQNQMDAwMDg0PF2BIqbqZacdFz4bR/iCSDRKparcpjhB7C\nWywWA6UgbsD0UDHegB13VKc4MiWZTCIWi0lukm3bsG1bCBiPyc2bqgtn9gEQb1a328Xc3BwajYYE\nfvK8dWYSu/54nixXkbjxNaPEUJMnPWKFv9ddgdr4z2tbq9UCJEpnd1mWJUZyqmo6fmJ+fl5CTzmB\n3nVdOa5t21heXobneXBdF+VyWTKjSIIZrgqsqoUkStFoVL7n+rRpnV4zrovfk4yxq5KEy8DAwMDA\nYL0YW0IFIKAqUbUgEdLt8NzwGaKpy3eWZQUyl/L5vGy8JGPsPtOdZcxLIunhBh4Oh6Wc1+12kc/n\npSSlvUqVSgWDwUAS2Hu9HnK5HDqdDnbt2oXBYIBUKgXLsrC8vIx2uy2kiYZ0qkDASu4Tuwi190n7\ngHSYKUkNyQnLXJqUanVH+9N0+ZTQGU+j96bX6yEej4v5nASt0Wggk8mgXq9jamoKmzZtwnA4xNzc\nnOR8sVzIc9T3kerhqEeKJI6EbrRpgWSKx+DfilaogNWssz179uDEE0887L9XAwMDA4MXLsY2IppE\nAVjd7DUZIKHQ4Zksk2mSoFUY+qX4HMuy5LWcDQhAjNiRSATValWGMjNBnOUjHeNAskIywvBQANKp\nVq/XUa/XMTs7G+g46/dXZhVy1AzVE64PWM2P8n0/UKKj10l3FJJMkZDoMSy6E47dbrxWjD+g74jl\nstEYAgBCUAHIufAxXY79/+19e4xc9X39mfed587uene9BkKgpMWtKRZKVJGWJIgEkaRKURsjhKjU\nktJKTqv+kRIsQtv/2pKX0qKo6QsFoSahMQEhNQqoVajayqIFRxAaigVpm9hZ9uWZnced987vj/2d\n75z79Ro/1vZMm8+RkHdn7tz7vXf25p6cz/mcT6VSQbvdxtLSEn70ox9hOBxifn7ekVMtefKzjDTQ\nrj9+R4T+rt+Bb8hXgq1KJsljLBZz5NdgMBgMhvPFxBIqn6iwzMcHJpUUqg9+BpHmNJF4TE9Pu4cu\nCRNn6LHUBMCNtun3+yiVSiiXyyiVSo5oqfpDAgJsdb5VKhWXV0XFipEM7J5bWFjA/Py883lxH8yt\nAuDS2EnyGHXAEpXvK2NApcYhUFXT8hwjEZRYAaMRNpqG7g9cptIFjIY481r2ej03v4+fD8PQzQXs\n9XpoNBpYWlqKlPBSqZRLjK/Vau58BoOBG6SswaT8HBVGzR9Tv5Sa9Llmze8CRgRrZWUFtVrNfFQG\ng8FgOG9MbMmPDz9iu9l+LI/52UMkVvwcANdRFgSB2359fR2lUskFdZJcNJtNxONxTE1NIZfLuX1Q\nOeGg4FarFfEUsfOOCeLHjx9HGIaoVCquy63T6aBUKiEMw0gkgnYvagK6psGzVEniwOtE0qjXgF4s\nEiGOcuE2WkIlOSVxZQwCsEXkeGy9rhoxweN3Oh0X4sljpFIpd51jsRharRauvvpqbGxsuPOJxWLI\nZDIun4rdhfxu9fry+9cOUFWgeP407fNzJOb696PqFeMwDAaDwWA4H0wsoeLD0Q+uBEbBnewE022V\neJE8hGGI3bt3u3IdSQAf/uxEo/JUKpWccsUyFxWcQqHgHuyNRsMZ0NvtdiSCYTAYIJvNAgA2NjZc\nlyCPy7gBYIvAzc7Ouv2GYej2qVEP9HzpQGE1YhNKIHh+HBGj5IcDg0mSVBGjx4rKHdU4jaPg2vje\n5uamK8F2u92I/4wdl/1+H8ePH8c111yDer3u4hLYvVcoFPA///M/EQ+V7o/g3wMT1Pka18/yYyaT\ncQSUpJF/Q/wMc6lIsgwGg8FgOFdMbMkPGA01ZqyBKjEkTvyP21Od0LEsug1LSVRG2G3HNHNgRCxy\nuZzbVywWQz6fd239zJrShzwHHbM0uLS0hPX1dQwGA+RyOXQ6HWSzWaTTaTdIeHFxEW9/+9sdOeCD\nX8MuATiCoSQGQESJU5JA0qQ/x2IxFAoFZLNZtNttp5Lxc4wl4P6UzHY6nYgy1m63T8n6CoLAKVos\nNdITRTWR3qhms+nM+vws98XvjWtSBY3H1DWqWsdrx9FB+j73x2Pwe6Ufy8iUwWAwGM4XE0uo1Cyt\n5nK/dKMPX/X7UDEZDLYGAdNbpIGP3LbX67m5esBInWHkwcrKiuuwYzmr2+2i0Wi4kM9isYhCoYBW\nq4UTJ05gfX0dyWQS2WwWu3fvdiNuNjY2nN+pXC670h7PtdlsungHn7Tkcjm3Zq6T/i/up9VquZ/9\nodEkDox/oIJGozsVIXqhqIbxeIlEwn0GiJYbgRHJI7HhdSepUnP+97//fReXoDP2er0eZmdnXblP\n5wsqYVTyx/Vp5IOGfpKMaVcoCavOKzQYDAaD4XwxsYSK6ozmA6nnSMkNyRfJAtWrRCKBZrPphgDT\nBA2MyEgsFkOxWIwQBRKAbreLYrGIyy+/HMVi0ZWtAGBlZQUAsGvXLpRKJecVooqTTqcdyer1epie\nnsbCwgIWFhbQ7XaxsLCAer2OcrnsspkARMgFrwMwigzQa0OljKUtAO78SCp1GDCvG4kGvUj0idE3\npcGp7XbbXTclT/F43KW4N5vNyP5IgJnSXi6X3XmoSrW2toZKpYJ4PI5areaUw+FwiOnpabdWljd5\nfbSTkK/p34Umv7Oz0e/i0/BQjacwGAwGg+F8MLGESh+cVBfUOK2GYpbF6HVSEpZKpVAsFiPZRFRS\nisWimw+nwZeZTMale8fjcdetxnl7S0tLiMViLsm92+06MzU746j6JJNJ7N69G41GA/1+H41GwxE/\nhnwqeaJ6RWKgXX4kUCSR9AnpMGXfU8Vt6QVjqYv/kghtbm66EqffJcfwUP4OREf1sFRK8qUm+0aj\n4fatKpN2GzLBndeb42/UkK/7JbHUvw/+znXz2qj6pOvnd01yyZKjwWAwGAzng4k1patZmmoMS1ca\n6gmMPEP64NTgR/qeGEUAwBEXGsQBRFQqpp83Gg3Mz8+j2Wy6xPNyuRzJg6Lp/OTJk0in08jlchED\nPI/HOIV4PB4xqgOjbjqSCy3TAXCkiZ4yztvT/C1Vp3h9SMxyuZzbVsklu+BIWmjUZ74VSarGUPB8\n+FoymYx0BcZiW7MNeW70b1HlYkRBIpFw5JZrYRBqr9dDqVTC8vKyO0/tSFTix++dXjsNAAVGih7X\nqvP/qLTNzMxcwL9eg8FgMPy4YWIVqsFgEFEtWLbThzz9OXyIKqhoMKqA/h8axPP5fGRmnc64435Z\nDmw2m2i1Wkin02g0GkgmkyiXywjDEI1GA71eDysrK24sDb1OVEHUUE617fLLL3cp3VdddRXy+TyC\nIHDlSpYkNSg0Fos5LxcjG5RcqE+IIBnh9eD7LInxWrJsx/+y2WyE5JHMcN+qGAJbZFTPnx2SNKNT\nrWPgaavVwvr6OqrVKur1uutoZII6/wYKhUIk4kCJIMmdKpKqgmnpV19j0wGvGwAXhWEwGAwGw/lg\nYgnVNddcAwBOydiunKddb1QrqDyxRBaPx934Ffp5SNTY/VYsFp0iAwCVSsX5fPr9PlqtFnK5nMum\noseIxKHdbiOfzyOfz6PT6SAMQ2SzWUdaNjY2XB5VPB53pcJkMonFxUV0u13n0yoUCpEASoZb0jzN\nDkISKR0grERJR8WwJMcIBXb3kSCxzKbQ8FK/QYDrIfkiuen1egjD0JFPro1Ejw0BTK1nXAHVrnw+\nj1qt5lSkZDLpVDxVonxvnSaoA6PuP26v5notf3Lbq6+++jz/Sg0Gg8Fg2MLEEqq3v/3t7kFKk7Nm\nCOnMOPUFMdNIx8tQ3WDsAR/yWlZTv1I2m3Up42EYOlLCUNBqtYper+eykebn591MP77G8hrN7SQR\nNMLT48OogSAIUCqVEI/H3X6ULJEEkCgBiChR7NCjmqOKlW7Pn0mqqJ6p8qclQQBuX3o8XnOSOKqA\nvFY0sJPQcv4efVYkjPQulUol5/8imfIVNyasK/TvQtdP+Mqang+v59ve9jYbPWMwGAyGHWFiCdWe\nPXtcyYveJz7k+dDVsSN8kJJAaCkPgCNDfLir2sKYAao0+XweicTWUOVarea8OSRXMzMzrkSVy+Wc\nokLjNglRp9NBtVrFxsYGms0mpqamnDrFtSUSCVSrVSwvLwMYjUMBECFPmUzGkR5CW/2VLAGIzCZU\nM7cSJx0Vo5/X/C6eO6871S6fiMXjcbRaLUc0SW75fXCtPCY/x8gJDehkJyAVpXw+HzHuk6xpNyQw\n6vbj+rWjU5sOCHq6zJBuMBgMhp1iYgkVFSM1oKsSoV1+NELTZ6UgIdMAzkKh4LoI9eHPB26tVsPq\n6ira7Tbm5+ddaY/qFgDMzc2hWCw6osOuv1wuFzG+79q1C7Ozs44sDIdDnDhxAoPBAMePH8dwOESh\nUEAsFnPjT5iS3ul0XMmPhEczmOih4nkCiJS0eA1VtdOgUpLAer0e6SpUoqYp6urp4vXmNWOJFRiR\nQs2y0g5KjTHgeB/6qnT/VCdpcM9msxFTup43oWVgEknN7eJneH30Z9+HZzAYDAbD2eKMXX6tVguH\nDh3C+vo6Op0ODh48iGuvvRaf/OQnMRgMMDc3h8985jNIp9N4+umn8eijjyIej+OOO+7AgQMHdrS4\nvXv34nvf+557uGqOkra/a+inn6wNADMzM+7BS7/OzMwM0um0y0piZxkArK+vY3p6GnNzc65sl8lk\n3IOd5Sj1aDEJvdfrOUM3VTCGenKm3sLCAsIwdDP9SqUSADhSpdv642w0vZzfD8mLdrXR18Tt+RpD\nSWmAJ1FliVH9Vkq8OD6G15HXS0NXtdSn/6qyRbDcyf2zw7JarbqS6NramiM9JHzaMQggMtSa6wVG\nRIuf2+79eDyO+fn5c05IH+c9YTBMGux+MBi2cEZC9e1vfxv79u3DvffeixMnTuCee+7BDTfcgLvu\nugsf/OAH8fnPzho3DgAAIABJREFUfx6HDx/G7bffji9+8Ys4fPgwUqkUPvrRj+IDH/gAyuXyeS0s\nHo9j7969ePnll90Dlh199CSRSGhpi0oDjdEMleTAYqoqHBRMgsVhvQBw5ZVXupiBTCaDIAgQBAHa\n7bZTSdgxyLgAtvtnMhm02233wK5WqwiCAMViET/4wQ/c+jjjrtPpoFKpoFgsOpVmdXXVEQcSJxqv\nSRi1hMVzIPmgKuNnMJFkFItF5PN5l4lFZS8MQ+fnUuM2yVWxWDwlTFPJDL8Xro8ENZPJIAxDp8Yx\nk6rVap0SrMncKh6fnZrsyux2u66LkiSO4Lb6N0TFT8NgeZ263S5+6qd+6pRcszNhXPeEwTCJsPvB\nYNjCGUt+H/rQh3DvvfcCAJaWlrCwsIDnn38et9xyCwDg5ptvxpEjR/DSSy/huuuuQ7FYRBAEuOGG\nG3D06NEdL/C2225zP+sDkb+z9ASMVAlNF9eQSpYPOZCXOVUkQouLiwDg5swVCgXn36rVaq482Gg0\n8F//9V9444038Nprr2F5eRnLy8s4efIkarUa2u02lpaWUKvVkMlkcOzYMTSbTWdKX11dxcrKiotl\nIOlrNBpOWSLp05wqngNJoUYiqKcJiJa3tFRIYkVFq1wuu2wrksBareZ+pl+N7/Nak4ipwZwlSO3s\nA+C8VcygajQarnMyFothfX0dzWYTJ0+edIOhgVHXIpU1xi/QK0dipFlfXCMbFvQ1bsv/+N2fq0I1\n7nvCYJgk2P1gMGzhrIM977zzTrz55pv40pe+hF//9V93ysTs7CxWV1extrYWCUecmZnB6urqeS+M\nD85CoeBKSm7R//+BqQ9CPviHwyHCMEQymcTMzIxTb1imi8e3ZsxpSjgTy6vVKgDg5Zdfxssvv+xC\nNkkGqGJQLaPxO51OIwgCNxOQqeXpdNolfr/wwgtuFM6uXbsAAK+99ppTcZrNJjY2NpxvimNfiNXV\nVUcsqI6RLJJsUInxZxqyw1C7JXWcDDsagaj/aTgcIpPJRIiSzh7kd0FyojEM3Bcw6prkgGWNRFhf\nX0cYhpEgzsFg4GIrwjB0UQpUHYGROsXrzL8BVZpUjWIeFr9rjcA4X//Upb4nDIZJht0Phh93nDWh\n+trXvoZXX30V9913X+Thc7oH0U4Nvrt373Y/33fffTva17nic5/73CU93tngn/7pn8a9hAi++tWv\njnsJp+CJJ57Y0edV/TwbXOp7wmCYZNj9YPhxxxkJ1SuvvILZ2VksLi5i7969bgZdu91GEARYXl7G\n/Pw85ufnsba25j63srKC/fv3n/fCWBJjWORXvvIVp1iEYYhOp+PKY51Ox+U3DQYDp/JcdtllKBQK\nbjYf/U9BEKDZbLpOPmDkt/nqV7+KO+64w5XE2FFG5UOzrViCpEpE5YedhTSFFwoFV2KMx+POA8S1\nEvo7zfedTgdPPvkk3vOe9zgfFRUjKlC5XA6tVsvFNlCFom+I/0+RKt3CwkLEfK6DozVKgl2NfI0x\nB4899hjuuecedxxNME+n0xETPI9BbxVVxyuuuAJBEKBSqUQiG5iczhLo2toaWq0WKpWKi1bo9/vI\n5/PuOhQKBTz55JM4cOCAM8BnMhnkcjkXTUEVLpvNus7CD3/4w5FcMh099FYY1z1hMEwi7H4wGLZw\nRg/VCy+8gEceeQQAsLa2hjAM8e53vxvPPPMMAODZZ5/FTTfdhOuvvx7f/e53UavV0Gw2cfToUbzz\nne8874Xp/3uJxWL4yEc+AmCLFDD5nP4pEgYArtw2PT2NqakpFItFZ/iu1+v43ve+h3//93/Hq6++\n6ozSvV4Pg8HAkSsSF3qUgNEwXwXVjE6ng1arhZMnT7rcKc6rGw6HqNfr2NzcRK1WQ7VadZ6hTqeD\ndDrtypLtdtv5mXhM/kzvFPfJcpp2//kBqJocTz8UyZ52IMZiscj8QE0yB7aiCDY2NiIRBPyXJIWj\nZ9gpyFInX1Nv0+WXX45SqYRMJoOFhQVn/GcXYTKZdAGo5XI5Ms6H5+WHiAKIeKtItvQceb16vR5u\nuummUzxfZ4tx3RMGwyTC7geDYQtnVKjuvPNOfOpTn8Jdd92FdruNP/iDP8C+fftw//334/HHH8ee\nPXtw++23I5VK4ROf+AQ+9rGPIRaL4eMf/ziKxeKOFseHHX++7bbb8OSTTzqPkL7HVnp6f6anpx0p\nCIIA3W4XtVrNdev1+303F48mbT5UtRWfqg8A5+MhAWHcAD9PpYWZTOwAVHWG3qhut4t8Pu+8R8BW\nNxwzo2j6VgLjp6Dz3Gnc1s4/qnYal8DOP4ZvTk9Pu2PT+6T/0WzOHC9//As/1+v1EI/HXdgpiR+7\nHHl9SIxyuZyLSlhZWUEqlcLa2prbB4dIM/uK58LzIajOEfyMhsCSTJI88fufmZk5p84+xTjvCYNh\n0mD3g8GwhdhwQgvZ6+vrALYemq1WC41GA2EY4sUXX8TLL7/s5tKxrMbyWLPZRKPRwFVXXeVI1crK\niiMdrVYrYuBWQgNs/b+p2267LWKC58O53++7YErNvtIcJo0uoFlc4wdIhvQBz5gClvl07lytVsM/\n/uM/4r3vfS+AUSmOREY7/sIwdGslAeO5kYwwgJSJ8VSjuFZuy/9o/OYQaAD45je/iQ9+8IMARrP+\nuB0N/ADceXHdLMGxs47jfGZnZ93oHs5FZMdjt9tFpVJBrVbDD37wA9f5yP8hDoIA6XQaTzzxBA4c\nOOAiFkgC9Ryz2SxyuRyGwyHuueceAIh8Z/zZYDAYDIZzxVmb0i81NPlbH/D79u1DrVbD66+/7sgQ\nOSHHwxQKBUdU/vu//9s9mDXVm8oTFS3llSQEAFzkArBVLqrX684nxbVRSdLRMFSOqN4Qw+HQdQey\nI5DBodsN8+WxmXOlKlEmk3FeMr6mnX3+WBYSh36/H1F2qIqRCALAxsaG81eRqFE1A+AiIarVKtLp\ntCNvVJ/8OIe5uTn3OjO/+J0uLy87orWwsOA6L7mWUqnkfFVAtPypJIjkku+rd4zEKgxDHDhwIOL7\nUjXLYDAYDIbzwUQTKu264oMyHo/jp3/6p5FKpXDs2DE3242lLHqSgiDAiRMnACAys05n+nEcC49H\nUqJlLQ2dZNu/liI1hZsKlGY6URlSksbSFEtk9HLl8/lTkr41AiEMQ1dq5LVRnxdfo0LF/0gmADiP\nEw38+XwetVrNzSRst9sRckjVimSK/jIlf4xLYFq8P0OwWCy6RHd6mEg2Abh8Ll7jmZkZZLNZlEol\nR3gWFxexsbER8bHp8GhgRJz4/WpsAkkvSZuGeaqCaKTKYDAYDOeDiSVU240QAeCM1Xv27EEQBPi3\nf/s3553i+8lkErVazXmFWB7T8p6qOoQ/PBeAK2ORMDFNnGSB6eYaJqrjWrgPvkeCollJNGBXq1U3\ntoZdgToUmMfQkTBUaVha1ER0Vd663a7Lg+I+OAA6Fos5oqKkkN4v7kPT00mi+B1xH+yk9N/ja8z/\n0o497pudfcBWmjtN7dyGpV2SJe5flSsNctXuSya533jjjaeoU/r3ZTAYDAbD+WBiCZXGFaifh8Zr\nBlz+3M/9HP71X/814k/q9/vO2MyHuPqOCBIGAC6dXI/Pz3Y6nch7VGt0jiBLXvydihhVJCUBfLhr\nByHVIp3Xp4N+WeLStVNh0zBPQrvb+D6T2YFRCZHXQxU2NeerfwwYdffxGFScuD4SM35n3W4X1Wr1\nlNKpGvjZEMBjr6ysoNVqoV6vY2ZmBlNTU+j1epienka/33cp69rFqN9bLBZzSiT32+v18JM/+ZO4\n4oorIiU+U6QMBoPBcCEw0YQKgHvoaZu8lvU2Nzfxrne9C+vr63jttdcQi8XQaDSQz+edssTynl/S\n0XZ7v4OMIIFgmY0KCYCIeqNDi0mESJo4YFnb+rkd/T46xmW7dVDJogmc50PljQRM18HzZSSCro/7\n4TpJLPmzqmGqJPF1rl2jGtTcz9d5nX01kPP8lNRwn/F43EVNkBgyv2swGDgVj2SKqhsN6rwemgW2\nb98+7N692w1D5vdtypTBYDAYLgQmllDxQatGb2DkP9LSDgDk83ns3bsX3/nOdwCMPD5Kxkho+L6f\nP6QRBewEBBCJLyAR4VoUJCtUt9QHpqUn7lMzobhGqlXAaKiwrldN5xqtQIVMjeVKcKiocRuSPf0s\n96Xkx1f0eE3ol+K6SAY1B0sN+gwYZamPnqkgCCJzCfmdDYdDNJtNpFIpbGxsIJvNunPL5XIRwq3n\nSdLN7yeRSGDXrl2YnZ1FsViMqJxGpgwGg8FwoTCxhMpXqPQhSFWCw23b7bZ7kC4uLqJer6PdbrsO\nOG5PokDVQxUm9Sup/2c71UhN63z4U9Xx95vJZNznWDpkmYwp3b5Pi+dbq9XcZ8MwdGtQbxPJhOZc\n0atF8kfSlM1mXSmSpUxeY9+HRa8YTfCEXgcqRoxsoA8qFos5VYvfEUuZCmZysXtxOByi0Wi4TsFM\nJoNWq4Vut4t6vY5isRgp/6ZSqYhCRaLN4NfNzU3MzMy4CI2ZmZltlSkjVgaDwWDYKSaWULEsQ6JA\nwsOHM0MfgyBArVZzWUPA1kO/XC4jn8+7XKpmsxkhVOo94u9UTWjmVnWMP2tkghInIErESEJUTQIQ\nKZ1pDIGWyFjKI8kB4H7W2AT1cfGaKQGkuqQlSm5LEkqiw9Jgq9Vy5JClSUYa0DzPY/F84vE4isVi\nZJgziQ6vEYkiDf1a/mT5kmSI58drxtfS6TRmZ2fd90B1i12YmgAPAHv27MH8/Dx27dqFqakplEql\nbcupvOYGg8FgMJwvJppQ+f9SpaJHKZVKIQxDpxJxdEs8Hkc+n3ft86VSyc2WInHiTEA+2NVsTWhW\nkqpSqliRxJCYMVpAjc8cqaL7pAlegz9TqZQjTerNAuAynDQlXk3rAFwSut9lx2vGcp5vBlczPQkP\nwWOQvBGMfQiCAMCoHMiAVX4njLPw1TuuVTvugJGix/NXFS4MQxSLRZftRZWKpJSerlKphOnpaeze\nvRuzs7Mol8uYm5tzxNLIk8FgMBguNCaaUPGh7D8ASXLUyxSLxZzxW4fcageelrWCIIioQ+x6A0aK\nDh/23W7XqSAsIWoXoJYiSaa4rcYCcN0kEzw2lSJ6kvQ19TCRHGlnIqMgdJ6hX8ZkqCmJDUuMJHa8\nzmpmJ+lh+YznxXXTIE8vmHYmaugmr5cSIw0YJQHUjkSNt9BGAKakA6OGAEYpAFuEqlAouOT1crmM\nXC6HxcVFd+2NTBkMBoPhYmBiCRUwIgaqUulDnQ9zzYBieKearTXzSRUmPqxJtnTgMN9XEkKFiJ8B\nRgqSlvU0b4kEiuUoEgxVgRgYqh163A9LfgQ/ryVO9SapD0vXRb8TAOdt4v6oFKmypR2FJGPsFgRG\npFPJKxVDfjdch5b9uGYqjL4SR5LGten3qN8zFTEl0Hv27EE+n8f8/DxmZ2dx9dVXO5+VEvTt/sYM\nBoPBYNgJJppQ+WU/VTi0847vae6Q33WmRIMPdxIgvscHuZa2uA8SLJbkVOXxS4YkAFra0rWTyCkp\nYHI410E/kvqqSLb85HUSIvVDkYhod18sFnPkje9rCCb/1YBM7pO5TkpmafQnOVMlj+ekZVNeJ/3u\neK1JfLSUSQLMffKa0eiez+dRLBZRKBQAAG9729uwsLCAxcXFiAKof0MGg8FgMFwMxM+8yfjgPwR9\npUrb8qnYzMzMuIc4W/X9DjgtUfEhz9IY968PYiUrnLmniesAIkqLT+aUbOnvJBadTueUspcf6cC1\nAdHIBxI7rkljF7guNYD7Kg2VNJYnVdEDtjxNVJuUxHHfGvrpd85RPaLPiaSJZnwStO26FYEoySJp\nTKVSyGazyOfzKBQKKJfL2LVrFwDgZ37mZzA/Px9Zm0/KDQaDwWC4GJhohcovx6jXR+fKpdNpN3A3\nkUggCAJnViepUd+TjqlhtIIavukrolJCNUc9UT6RAkbGcZISEib1W2mZi/PtVLEC4NQVfY3djb63\nTMmIHkv3q52S2k2omVgse5J0+iU/+p10tp8GhnLMj5rstVOQ5IhNA7FYzI3CIbGl+qQBn6lUyn0m\nHo+jVCph165duPzyy5HL5VAoFFzJj2vW3C/CcqcMBoPBcDEx0YRqO0XKfyiqokICwu3Y+acdeerL\n4QMYwCllKOZC+V4oHcVCYqdEi8oMABd9oB4nDQxVMuSb1fk+y10EYwx4PlTNSIi4DclXOp2ODCxW\n0sjffW+VKnscmaPhqlwbs6LYEEByyvPRbjxVoBjRoCNn6J3ie7y2hULBbZvL5dz2JKj0dunfg6a8\n+8TKPweDwWAwGC4EJrbkp23z6n1SfxMVD/Xx0PtDIqAxA1pqU18WO9l4zNN11mn3HktV/X4/MlPP\nX6cfwMl9qE9IjeYK7WQk+SB50vIl3yOxY0mQ58Nym4LnDCAylkcVOFXweC5qdmdEAl/3ByqTbPqq\nHaHlVl5Hbqskjt9zt9t1swL5OS39cluSXo2u2I5UWRnQYDAYDBcKE0uogFNN6eqZ0m1UgSJBoW9H\niQ234YPWN2yTgKjCRaKho2O01KaKCMmc+qR8IqWf5b8sh3EfJGpaniN8j5SSKxI5zeoiMdGuO/6n\nXXM0xOt1JUHi/lh64/lwrSSUvD7c53blRn1flTtVIf24DJriu91uJOeKx9Q18bN6nkacDAaDwXCx\nMbElP30Ibpc5pdCyj0YgqP9HiYiSIC3VbddNyG2ouPgGbO2m80mYb0hXv5FGMijoH0okEqcEgqri\npN4ukhrdt64NGMUcUHUioVI1jp/VbkISmGQyiUaj4c5/dXU1sq0SGZ+YdjodR8Z0bfRk6WtU0rS0\nx3OsVCpYWFjA1NSU25bjcfgZJVCqUFl5z2AwGAwXExOtUGmnmypF2uZPckTvFNvu6TVSBYf70Qe+\nHyTJ12mQ1pZ/EiuuyfdUaVYVj6XeL64FGJWxfEVGc5eYZk5oOW67MpzGO6gSpgRUfWZUnaj2cJ1h\nGKLT6aDRaKDT6aDb7aLdbqPRaLg4hzAMMRgMHMlSwqqkhoodr4WWQ1VV0/X5YancJ/1aSpTU5K/H\nVhXzdONmDAaDwWC4UJhoQqVQAqUPZpIBkgnOm1MSxIe07+MhmVIiwH/ZDcjjab6Slvr8PCdVjlSl\n0rBO9XUBIwWMpTnNmlJlzg8xpcKlhELnEaqpXokc16RdhaoCcjv6s7ZTBbluIBosynWqKqTGd1WR\nuIbNzdGgZg0s1e98c3MThUIhUhrkcdQ/p4RqO3JlMBgMBsPFwMQ/afxyjSowAFy5TQ3U+sDWeXHc\nTh+wqjyRIPB1AKeQMQUJgR8Eyn+pOPnqF0mHT3JUUWKZTMtheg24L5I15kWpwV7PQc3ePD/f8O6f\nm5ZGNQWex1aywuvtK4L8XT1cvGZUnIbDoYtg0M8CcOdE43wQBEilUm4f6j/Ta8mf/Twvg8FgMBgu\nBiaeUBF8OPpqCRWmXq+HXq93ShcYH9bqO6JqpUoSy3dAtFSnxEeN4sPh0B1bu9VUMeLrSrJUqWFX\nG9/zP3e6IE1uQ/LEsFFdm99Rpx2BqpapmkSyqZ16VMlIXLQBQLfTjjs9Ry3pkRzxZ5bxqCxpeZDn\nCCASXMrsLu3u8z1Tql4ZDAaDwXApMPGESlUc31OjD2uWhBjsyc4zqk5aDqKqpeNQSA64L41Y0K4/\nTVNnzpSqX4ws0JKhqjJ+SUrDNv24BlVfuD23IZnj+aunyjeHazeekiZ+zideXLOSWB5XFSr/OyKo\nrPlKE6MReHxNRCdB5bXXQM9YbCsEdDgcIpvNRkbQ6LlstxbzTxkMBoPhUmCiCZXfLaav83c++JPJ\npCsh6WgYJWAkNCQQWn4Dog9fKilKrnQfJDOaTK4lL3qv/LKYpqarYuQnqtND5assJFVKcFjmVFWO\n10f9WUqslPjp55WoalmQ+/LztTRWgdfQ92yRpPnBo9ye5FEVPjXg8zsNgsDN7WM3JNdCaDn0dGGw\n/vU0GAwGg2GnmGhCBUQfeKq4cDivluroy6lUKo44aQI3lRs1enNUiR9aqZELLBMqiaI6QgLjExR9\nkJMcaC4TXyPpIoHTkh7JBNeqxIFGbnqLlOCo2d0PMVUvlp6PkjA1ketxVSFUFSyZTCIIgogh3y99\nkgjqWkm8/HIrt9WAz1gshiAIkM1mI9eLfwe8drr96QzpFqFgMBgMhguNiSZUfju8PvjVm6RlMwZa\nni4EVAmBqjWqTvE4LE+pqZ0khwqRKlE+WVF1jP+SSKTT6QjB8pWq7br6lBT6cQ0kPZo8rkGcPtHg\na9yWUIVNO/J0rqFec/974vu6LlXoSIRY4uO+tMTJ7RuNhrsuuVwu4sXSmAxVw3wydSYFysiVwWAw\nGC4EJppQvdXDkARB4xMSiYQbiswwSt97tZ1KtZ3PRjsFuR0wIhIaoqleLZIj/qwKCkmckjVVgEgC\nVJ3yjddA1CelxJF+KJIVVW5IkPwRNErUVFHSBgCWU/0OPL5HEqgxFr55XU3svC6E+snoo+K5ZbNZ\npFIphGHojq9lWg1HVQJ4trCSn8FgMBguBCaaUPnwyQ8f6IlEwikfnPnmd3uxJMfXVe0AokoWMEoZ\nfysvFImCDhrmv1wPFSM1qCuZ86GlO32fa6DqwxLkdmSI5ETN39yu0+mctptOyYgqgpubW0OIWR4F\n4DoUB4NBpPzGSAOen/rgaODnNlwnt9VrQxWPBnUeL5PJnLKvc1WZjEQZDAaD4ULjrAhVu93G+9//\nfnzjG9/A0tISfvVXfxV33XUXfvd3f9d1vT399NP4lV/5FRw4cABf//rXd7ww34QORD0yvrGcP3MM\nTblcRq1Wi8yZ8wM2CfVO8T3fdK1EjgSJ25EgbFfyU7WJvys54vltV6JUs7fmOykZ0pKXGrKZXk61\niIZ99VPp9VRvkxI37o9DqJVU8rPqEVNFUE3j/trVz6URCFSp9HtvNpuOSLGzUkmZXjMl0ReTOI3j\nnjAYJhl2Txh+3HFWhOrP//zPMTU1BQD4sz/7M9x11134yle+giuvvBKHDx9GGIb44he/iC9/+ct4\n7LHH8Oijj6JarV6UBeuDXNUOjS/IZDLIZDIolUoRNUN9RMxw0jgD7WAjAeNDXImLD1V4SJi0e47v\nASMCqCSLpnY1avf7fYRh6Ea9aPcec7S4X984z3iCbreLTqeDdrvtQk8ZgaCkzCes+jvPz09F903z\nwEjt01Kr7p9KnhJVJWgkR5lMxpUsqTYCwPT0dGR2n68qbrf2i+WRmqR7wmCYBNg9YfhxxxkJ1Rtv\nvIHXX38d73vf+wAAzz//PG655RYAwM0334wjR47gpZdewnXXXYdisYggCHDDDTfg6NGjF2yRvtrg\nd9fxwUz/EP1T6XTamb+BkZkZiLbaa3CnHpPEgOqTX4ZTaCwAlRogqj6pKkUDtu9JUtO9Kjf6eRJE\nKmTsfNuuq02N9PzdV/+2UwP1Wm8XS8Brqfvmd6LXWeMaSH7VM+V393HbIAicssbPMgSU189PvX+r\nzr4LiUm4JwyGSYLdEwYDkDzTBg899BB+//d/H0899RQAoNVqOc/M7OwsVldXsba2hpmZGfeZmZkZ\nrK6u7mhhDHYcB/7lX/5lLMd9Kxw/fnzcS4jg2LFj417CKbjiiit29PmzVbPGdU8YDJMKuycMhjMQ\nqqeeegr79+8/7YPqdA+gC1Fm4bgRlrA6nQ76/T5arZYrabVaLTQaDdTrdbRaLVSrVWxsbGB9fR1T\nU1OIxWKoVCruc91u1/mKNF6A/7bbbXz729/GjTfe6NZBBYweAD8uQc3r3F6vg+95AqJlS+7LV+FY\nZmy1WlheXsbc3JxTafwIBS01Mq5Azedq3tfPqZ+r0+lEIg2AUXnSL7MeO3YM11xzTSTJnR2E6mFS\nZQ8Ypc4XCoWIYZ5qIkl0JpNBOp123/n09DTm5uaQz+dRLpcRBAGKxSJKpRKKxSISiQTe8Y534Ec/\n+pFTtVj6fStV0f+etPPwdBjnPWEwTCLsnjAYtvCWT5DnnnsOP/zhD/Hcc8/hzTffRDqdRi6XQ7vd\nRhAEWF5exvz8PObn57G2tuY+t7Kygv379+94cT4Z2a6cQxWLpCQIAmQyGXS7XeTzebc9R5/4o1pI\nlrR0FQTBKXP4uB6SEuZX+eZqnzxpmz9/JpHRGAItEbLcpxlO9IlxW+5nu3IoP8eYApInJUzaSchO\nOo2X8ImXHy2h56rRCGpI1xR1/R70+migKV9Xsz+9VCRc/BtgYKuWWv1rcTFM6eO+JwyGSYPdEwbD\nFt6SUH3hC19wPz/88MO47LLL8J3vfAfPPPMMfumXfgnPPvssbrrpJlx//fV48MEHUavVkEgkcPTo\nUTzwwAM7WpgSDPXoEEpSgJFXR/1Fw+HWYOTBYOCGJut7wEiBolKl++c2fvhnp9OJKDxcm67TJ0Qk\nQEpy/IRwDedkVx6VMf/8dc4d3/NjCjRCgeRD16fHVxXKH9jMtSpB0YRzvq9kSWMqOFZG4yo0s0qV\nLU235zqmpqYiYZ78jpXI6d+K/nehMc57wmCYRNg9YTBs4cw1Dg+/8zu/g/vvvx+PP/449uzZg9tv\nvx2pVAqf+MQn8LGPfQyxWAwf//jHUSwWd7y47cplhD5YtXNuONwaoFur1dDpdE5RqHScCaMP1LQO\nnDqihSSIpIcPe6pbJC7s1PPVHf3dJxzaCafxDVyTvq/m8MFggE6nE8l94utK5kgsdB6hqmIalskS\nnV5v7WBk6CavP0fb8Dx6vZ5TDJkhxfKb7q/f7zvS5GeDMRIhm82i0Wg4lUqVKVXruDZeNyWZev0u\nJi7lPWEw/G+A3ROGH0fEhhNayKZvip187XYbvV4P7XbbkaBer4d6vY5KpeL8VN1uF2EYotF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff61362bdd8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "THRESHOLD = 0.5\n",
    "# look at some sample predictions\n",
    "samples = np.random.choice(valid_filenames, size=8, replace=False)\n",
    "\n",
    "for filename in samples:\n",
    "    # load the image\n",
    "    img = pydicom.dcmread(os.path.join(train_dicom_dir, filename)).pixel_array\n",
    "    \n",
    "    msk = np.zeros(img.shape)\n",
    "    \n",
    "    filename = filename.split('.')[0]\n",
    "    # if image contains pneumonia\n",
    "    if filename in pneumonia_locations:\n",
    "        # loop through pneumonia\n",
    "        for location in pneumonia_locations[filename]:\n",
    "            # add 1's at the location of the pneumonia\n",
    "            x, y, w, h = location\n",
    "            msk[y:y+h, x:x+w] = 1\n",
    "        \n",
    "    # resize both image and mask\n",
    "    msk = resize(msk, (IMAGE_SIZE, IMAGE_SIZE), mode='reflect') > 0.5\n",
    "    img = resize(img, (IMAGE_SIZE, IMAGE_SIZE), mode='reflect')\n",
    "    \n",
    "    # predict the image\n",
    "    yhat = model.predict(img.reshape(1, IMAGE_SIZE, IMAGE_SIZE, 1))\n",
    "    yhat = yhat > THRESHOLD\n",
    "    # add trailing channel dimension\n",
    "    msk = np.expand_dims(msk, -1)\n",
    "\n",
    "    f, ax = plt.subplots(1, 3, figsize=(10, 4))\n",
    "    ax[0].imshow(img.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
    "    ax[0].set_title(\"Image\")\n",
    "    ax[1].imshow(msk.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
    "    ax[1].set_title(\"Label\")\n",
    "    ax[2].imshow(yhat.reshape(IMAGE_SIZE,IMAGE_SIZE))\n",
    "    ax[2].set_title(\"Prediction\")\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Att9nue5_ql7"
   },
   "source": [
    "# Predict test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "F6-gG8q__ql7",
    "outputId": "4279c874-9d33-47f4-9a55-97685096d284"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n test samples: 1000\n",
      "Done predicting!\n"
     ]
    }
   ],
   "source": [
    "# load and shuffle filenames\n",
    "folder = './stage_1_test_images'\n",
    "test_filenames = os.listdir(folder)\n",
    "print('n test samples:', len(test_filenames))\n",
    "\n",
    "# create test generator with predict flag set to True\n",
    "test_gen = generator(folder, test_filenames, None, batch_size=25, image_size=IMAGE_SIZE, shuffle=False, predict=True)\n",
    "\n",
    "# create submission dictionary\n",
    "submission_dict = {}\n",
    "# loop through testset\n",
    "for imgs, filenames in test_gen:\n",
    "    # predict batch of images\n",
    "    preds = model.predict(imgs)\n",
    "    # loop through batch\n",
    "    for pred, filename in zip(preds, filenames):\n",
    "        # resize predicted mask\n",
    "        pred = resize(pred, (1024, 1024), mode='reflect')\n",
    "        # threshold predicted mask\n",
    "        comp = pred[:, :, 0] > 0.5\n",
    "        # apply connected components\n",
    "        comp = measure.label(comp)\n",
    "        # apply bounding boxes\n",
    "        predictionString = ''\n",
    "        for region in measure.regionprops(comp):\n",
    "            # retrieve x, y, height and width\n",
    "            y, x, y2, x2 = region.bbox\n",
    "            height = y2 - y\n",
    "            width = x2 - x\n",
    "            # proxy for confidence score\n",
    "            conf = np.mean(pred[y:y+height, x:x+width])\n",
    "            # add to predictionString\n",
    "            predictionString += str(conf) + ' ' + str(x) + ' ' + str(y) + ' ' + str(width) + ' ' + str(height) + ' '\n",
    "        # add filename and predictionString to dictionary\n",
    "        filename = filename.split('.')[0]\n",
    "        submission_dict[filename] = predictionString\n",
    "    # stop if we've got them all\n",
    "    if len(submission_dict) >= len(test_filenames):\n",
    "        break\n",
    "\n",
    "print(\"Done predicting!\")        \n",
    "        \n",
    "# save dictionary as csv file\n",
    "sub = pd.DataFrame.from_dict(submission_dict,orient='index')\n",
    "sub.index.names = ['patientId']\n",
    "sub.columns = ['PredictionString']\n",
    "sub.to_csv('submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "colab_type": "code",
    "id": "1IxkJela_ql-",
    "outputId": "f4db7adb-ec1c-4bb1-d506-aa284a72211c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully submitted to RSNA Pneumonia Detection Challenge"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit -c rsna-pneumonia-detection-challenge -f submission.csv -m \"Colab segmentation 10 448x448 40 epochs\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 91
    },
    "colab_type": "code",
    "id": "lf8l38RrA1tN",
    "outputId": "65fde1a2-da2d-483b-d23c-40f372857761"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated property [core/project].\n",
      "Copying file://./model10_448.h5 [Content-Type=application/octet-stream]...\n",
      "|\n",
      "Operation completed over 1 objects/66.9 MiB.                                     \n"
     ]
    }
   ],
   "source": [
    "# upload checkpoint to GCS\n",
    "project_id = 'mammography-198911'\n",
    "bucket_name = 'pneumonia'\n",
    "\n",
    "!gcloud config set project {project_id}\n",
    "!gsutil cp ./{CHECKPOINT_PATH} gs://{bucket_name}/"
   ]
  }
 ],
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