kd2718/resize_images.ipynb

## resize_images.ipynb

      
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## training.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!which python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gxKQc7hvK0PO",
    "outputId": "3a0c370b-24c5-4b64-9933-c1fc0707f975"
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AvVG92WsKeYm"
   },
   "source": [
    "# Introduction\n",
    "\n",
    "In this project, we will attempt to greate a Cycle Generative Adversarial Network (cycleGAN) in order to manipulate facial images. We will attempt to change the gender for a set of images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LYM61P4RKeYo"
   },
   "source": [
    "Our first step is to load the neccessary libraries we will be using for the entire process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/koryd/miniconda3/envs/utkface/bin/python\r\n"
     ]
    }
   ],
   "source": [
    "!which python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "2z7zHHmcKeYq"
   },
   "outputs": [],
   "source": [
    "#Loading the neccessary libraries\n",
    "%matplotlib inline\n",
    "\n",
    "import os, random, glob, itertools, shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import gc\n",
    "\n",
    "from PIL import Image \n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data as data\n",
    "from torch.autograd import Variable\n",
    "\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms, utils\n",
    "#from tqdm.notebook import tqdm\n",
    "from tqdm.autonotebook import tqdm\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9TLPoFq6S14N"
   },
   "source": [
    "Since we are using two platforms, we will be assigning a directory to the location of the data for each platform. This will make it easier to access the data, depending the platform we will be using.\n",
    "\n",
    "The two platforms will be:\n",
    "1. A local installation of  Jupyter Notebook\n",
    "2. A Google Colaboratory Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "AijKd1STKkUM"
   },
   "outputs": [],
   "source": [
    "windows_path = '''E:\\Data_Cloud\\GoogleDrive\\Dataset'''\n",
    "colab_path = '/content/drive/My Drive/Dataset/UTKFace_Resize'\n",
    "local = 'data/'\n",
    "basepath = local"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fp3g-Nh4KeZN"
   },
   "source": [
    "### Exploring the Images in the Data Folder\n",
    "\n",
    "Next, we will attempt to view the images in the data folder as is. There will be no preprocessing or manipulation at this stage, instead we will be viewing the images. \n",
    "\n",
    "We will view 5 images from the folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 193
    },
    "id": "6SOX1Xx4KeZP",
    "outputId": "0f1b8aee-46fa-4381-f70b-34dcc6495e0c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,30))\n",
    "\n",
    "#reassigning the name of the data location\n",
    "UTKFace_folder = 'data/testA_Male'\n",
    "\n",
    "#looping over the images and selecting random images to view\n",
    "for i in range(5):\n",
    "    file = np.random.choice(os.listdir(UTKFace_folder))\n",
    "    image_path= os.path.join(UTKFace_folder, file)\n",
    "    image=mpimg.imread(image_path)\n",
    "    plt.subplot(1,10,i+1)\n",
    "    plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AAF_SyGVKeZY"
   },
   "source": [
    "We will attempt the same procedure, but this time on a single image. We will look closely at just a single random image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "Nw8sbsPVKeZZ",
    "outputId": "fd14626c-75ad-42f1-b434-1b33799c03c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1):\n",
    "    file = np.random.choice(os.listdir(UTKFace_folder))\n",
    "    image_path = os.path.join(UTKFace_folder, file)\n",
    "    image=mpimg.imread(image_path)\n",
    "    plt.imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HN74qz6OT9Nc"
   },
   "source": [
    "### Creating the Dataset\n",
    "\n",
    "In this section, we will not attempt to organize and pre-process the data. It is also in this section that we will separate the images between male and female and remove any images with ages below 20 years old and above 60 years old.\n",
    "\n",
    "The images as been pre-labled and we will be using Regex in retrieving image labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "92UfV6apLtcH"
   },
   "outputs": [],
   "source": [
    "#creating a dataframe selecting gender and ages available\n",
    "face_df = pd.DataFrame(glob.glob(os.path.join(UTKFace_folder, '*.jpg')), columns=['names'])\n",
    "face_df['male'] = face_df.names.str.contains(r'\\d+_0_[0-4]_\\d+.jpg.chip.jpg$') + 0\n",
    "face_df['age'] = face_df.names.str.extract(r'(\\d+)_[0-1]_[0-4]_\\d+.jpg.chip.jpg$').astype(int) \n",
    "\n",
    "#Limiting Age Group between 20-70\n",
    "face_df = face_df[face_df['age'] <= 60] \n",
    "face_df = face_df[face_df['age'] >= 20] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m348gkM7WXm0"
   },
   "source": [
    "We will not view the images after the previous process. The purpose of this step is to verify that we are able to successfully separate the images accordingly.\n",
    "\n",
    "__Gender:__\n",
    "\n",
    "0 - Female\n",
    "\n",
    "1 - Male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "wkZtjh64e4OH",
    "outputId": "bab42a24-10e7-4ebc-8872-86e877b3a41a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9u92O0zhRlkIOC1ma5jHNFRCCBKoWpJntIJRmzttJqJUoYsKqQiqFUoNdpFCRBhdt9+kFwrr9zH3fuXhfpAtvu5DbyES6Dwi4dqvdV8VMu1/8IKELQvNFFelayk6NwUlVPEOmOKBUUBUKLbCsFK0GL5W5w3tFM1kruRaWZWGeZ5Zl6QJsVqNg0XvpwSjg2TqzajUER2SFSm0AnMFefl3ab75ovRICCjgcFInDyDAMDMPUfVCJAYkBCKgWu5CsYLJUM5UhOLTUsMcqFmB4oNSpB+FSiC60wT0m+j7t+TLBvd+v3Qq9CarHMP4hj85bFO8RvUgAMQfBYFjDfNUhnRaXVxfYirIm3hQNQq0BJNj7qpRqj1oVXJOV6g+t5JK7YM7ziWWZVwHdnK9aC6p+I6iux6LtENeU8ApEVHJWyryQ5/mFcvFKCKjIZaaiY3JBIKxFm+sJsKOvfgJihBghRQusUhz7tvybG188XPyvKheZpK1gboXsWfxvoyHdBN8rmPbB5wqo+Siy+isO9VjUbfsZo/RtuDHvN6iIUJrZbsEjzZy7fxsEiZVaBVWD7lpa1ED9djyuDV2ILG1aNzfvqhSegd025t5uumo3hj4bpavfPU2Lvmi9EgIKEMNgmtI1XWW9+yxQaifBL5abyxoghUhKgTFZvtxy5mMH6bMqUS0ZMGigFkWTESCiA9Utfbem8V6sQdu6T5HeDbiamdeGBrQvirg1cB0odrMSBKq9CpCCuFkFMMBeUM/ieEAVLFmgwQWkgkpFmh9aIyFUaqgIhv0GMVGPMWJQQ0I8SRLVlUUUYkqkCsNYGSsspV4iLdTNDagbYGJNq0aRfgTifnbLHr5ovRICeiGI1fwfc6C1pz+bk94id1vmhLeTNQ6JNDUXYbgA/bd3/fb/u8J5n3aAyyCpC9hncXwNanrGB91o1BDa3xEJlRZZxChoMV/bXFKPnDWuQSF4RqzQyFNIlxVCDdQQCdH8QbO1ESKkISPBYaNqv6EoyQkk0zRRJVLUXIhS4bxbujKIcSHWSIwFKXUNiFQvEY5mId77qXs1BLTn4gvdce5R5SaCdLAR2F7j0NOWnZE0XAqmvWefa0LbmUh3tGXzX7t/iP3TfMWGvTZhWzNIL1nS/9m+0M7A5vebKQ/9YyFYYEOtRt2r7fWAajAOgp8bCWumSEWQ6gFSEDTY3yFoz0AFINaEg1ZEMU1faiXGREyFAaEQyApFYcnKMAyMo2vQpISa7Xy7G+K7eHF2op9DFKcgvny9EgIK9Mh7WQrH45Hz7Zk8LwQghcSsJwJCauaDQigwJoGQSUNk2gV2I4wxE2VmCJkpFXZpJDq5AYloHFniCGGiEhnSiKREHQZKGAghIhJREao6Xur7mFCSKGMUxsl83XpeTMiTa+doplGGgMRhTQpU8eDFgyAVoBKCETWCBHsEvdDeIto1jyWEgmczlaoBNJt4BRM6QiQSDcIplZIrEEErRQO5VGq1gIkQWSgQBqoY5px1pgASlSSJRc92zBopJZJH4epKOS2ZUoRTPpKfFE7LbGlpKloMO0iqzICUapZPAK1MaeTqMJFeIqivhIAqoLnBGifyvPRUWM6eauufdH+uPZNAStLN+jiOTNPIbrcz4H8cXZO6zyp4ALFG76UUQjDfNIhfTFYNaVpC3OdyskljQokwjKOB5+I+WA+eLPhZU5kGvBg+aMGK+bhbd8Jz2t2HtVdbwGeafBVg+0ztVmbFgVetPgwDWTLq8A6sUI+qouczYDdgKQtLWemOtVZCEJJEprHhp8qyLEzpMuMXQyRGQwa2RMAWlm61qYgwxMgQ7+tkua5XQkAtatUukMbUzheRc0A6na5D334Rk0NUwzAwTRO73cRut2OaJsZhdGDYfDe7MEqM9RmQ2ExscrcgGaHEfeMgaeOfWnBSsl3gKIFaN0Km1bRmDUTx9Crtpgjd3IcmuFu0wXAjWOWcGCI1BIycHA1L1Ep1jLdW6a5Ry1JtfdsW6BXJ/Tw3fLPWSpgzIg5d6Sbt2fx0DQSH6ooL9tmVwW5ITLuBeZ44n8/+fiWIuQOC3e6bRK295kplml7cG+PVENDu+9lhxNg0ovuKF1ZgFVjDPdfgo/mbTZs2f7KUYpmUarAKkjsmqJ6CEwnEWIlVeypRnFTSNA2smrc69a9qJbdsjxhkhQgRRUICCqVDQE04jcwB7YI1bd4CGHMFROJKxGjH7n83VudaCWKWwFLCm4DO97MUs1Dn85nT6cTptJbUDKWYzxgMTTW+QrutMFy2oey+7ejss2nacZUDeV44nUZyrtRY7X9djzGwas8QAkNM7MaB3QfBB/2cllj0l1LwrNKeaZqYb29pBIU1aWjGYhuJt4dBTqmbxFwtL99gqVJbntp+VlW5PZ0ZilKxEoVBIVbTACEkJEQ0BNT9Ug0WMSOW3dFSvVYJL9XAVEbOPYdSbcc9kqX7nKbdDJ24AMC1ophvuizLhWYDrETDrc2czyzLwul0IrtWbCuoCUTOmdPtymLfCqgsi99c7jqE1d+tjhgg5oblnCmLYZfTmLg6HDhrYlkWjsczec7kXAjkLpRNvwQBYmAYYr/GV/sPBd3OmeNOJDgcDhwOOw67ifM4doBYumkH1DRoCvTygy3YLyLU0vzaxUoyVAkhMs8L57kwDAspJWoujONCKZW8VIahktJCSmOP+EuJ1FocERBCtJtJDSEH8aI8tVC3ajboR9UI127bQoiEZr6DCey2LKX7kbWSM/3m3GZwqufJ59lSlLenp8zzbEJ3Pq/lMurYaDPrp0vt2T4Xi7GTDPc0LyIEMYBf1GmKiVwKy3l2AVSmYUT2MGtgOR+5HQfOYyKezw5Z2dWNGKJgmUJTQNeHPdeHK26u3ycBFZEvBX4P1tW4Ar9dVX+LiPwqrGvv9/lHf5mTl1+4opgzf3XY8fDmitsHNzx954rbp9ZjyrhAnckIYpBIB9KrQs1ULZ25fQpQ8sL5eOwOf60VwTHPwQTwejcxDAO76cA0TZ5mHUlxuKjDSUPsQdIwDAyjw1pYoDCM0ZMD7hqoEkpBYgQKEpLdVMmDnQhBkrHfNwJY6rIR2jWosUfppRPns2nOd5+80+t8Tsdjf31ZFigrjpzn+3Prux7wKTEJEv2mT+aKZK0ebCZKrR7ERft8jOzGwG6cGNPAECJJDCQLLhhBQKIQR9Ocu8PEzdWeB9cHHlxd3ycOfX0+GjQDv1hV/xsRucEay36Lv/ebVfU3fDYbi0kYp8Rut+Pq6sDV1RX7vQU7Ucw32q4W04pY5Jmz9CBrWRbOUQgYC+r2eEQ9rZaXasxIaRho4tOaGccW9e+MPO0RaghevRnwiDWSBqsInabJXJLhwDjavk+7gWEcTXg1mfbMxjgKUYnR3LhYzT8MoRCxF0UL4qUdFIN8aq0cj0cX3NzTg82XnOcT3//O25zPZ54+fcpxY8LP5zPVzXGtlbKsyMhWa4+k1UpFHLEwYnejIo77HdO0d6wzElJF1M7xGL1SNgrRFG8PjBS3bCEyxMBucN9znNjv7PGi9TkLqFrvyO/2549F5K9i9fCf0xIRUgjsdpMzmSwjFIUOCGeHQ0ToNfCBwJiCp+sM6D+dTmjNZA9yzsdTh1WWpXCeZ3J2zVJh7/hlSiPjsHOoascwTD0BUGt1zZk67LTbGZx1ffWIq+s9Dx484JB3jGMmDJGda+YQKhIjqce0Ba1CKGq+bSg9bbkiGUvXkqUUbo9POZ1OnrhYelXksix8+t13eiHa6Xhknudu8otrzZyt9Hi7mlaeWhvTYG5WiLgVMH80Jbv5rq+v2R2uGKeJUezYxmFEA+x3O8YhEtyTT0GonvYMoqQo7KaJq8OOw9UV14c9u2Fk+IHAQUXkh2IN9v9rbGzMLxKRnwP8OUzLfuae7/TGDV/8pV+KODSSJFjNekzEAEjdULxaRGgBk1BBCjmbdl0WYQ6C1EItkVkM5lnOs6dMK+d5NiFd1gzV07wQQiQ62WQYRoY0uXm3gKuVPqTBNEuMkWkajFz98F1ubm54880Tjx494HBlVqDVQeVaiBWn0wlajdzS1yYR1cx6S/HO88zpfPT6nacsy5l5njd/L3zm8btdYM8uxO27ZV7oxXob+Gj7e3POPa0qooRY3Sc1F2raWaA0z7Odn5ggFgIDMWCmvaEnMVmgGgJFzB0YQmSMiTFFxjQwDYPxJhyledH6vAVURK6xtt3/mqq+KyK/DRuhov7/b2QzUWNzYnrjhq/4cT9Oje61oHggkiwUNiHKUOqaeuzLjEitFpTkLMwBtGSWGU/lVa99wS9cZc6W768eQIROxlVKqSxzReQMEu2G8ChY1UBrxP4fx4HDYc/jJ0cePnhg2q8WHtUHSAikYSQOxQM893+lYnXkDYgP1Gr5c0sDFlC1AG+eOZ+OPH73XS/T/UwXxMePH/PkyRPmeebJfOpas3hiA1gDJbF8aeu40kF/D8pC8WBPms9bXTFsmEa+T1qNFB0lmFCmAYmJ/eSCN0SGFCxz56Z+HCLjENkNI9NuZL/bsXcLOaQXd1/6vARUrMX1fwL8XlX9Q37Q37t5/3dg7bVfvFStpGBeyHmmlIXq/laZz71DxlY4Q8MdHcwOnicvuYAUFi0G/9Rt8wan6EU7uYjVfI8dk1wpeKWoCbUh/FQVSrVt1ppBsLISVZARkchuv2fcTd0/3e8XxmyZrbVMIiJyl9JXPLukF8K1LGamTSDf4e233+6C+fY7n+4CumyQgBhCJ8qISD8vIRjb6wIj9hXrYFpbi5/3xQO1TK2FNDTM0836lFbW2DCg1SzPmBJTssAyhUgMBa1G7hGBFAO7wcD9YYikYPv3ovX5RPGCDXj6q6r6mzavf5H7p2ATJv7Sy7altAtyvowyPZo1UuwG9I3NB7Xof0Of8QvldUyLad6VxhaJYkFMGoYVRtLWtEC6cC7LwjKvJQlG0jAAvVSlltz3S8KIpMj0xCpTY8to7aeefl0hsIgVsrXzpS6029Sl4bzNTB+PZuIfP37Mu+++y+Mn7/Duu+/y5MkTKxAMKzEmuc/c2vQMvu2UEvvJsmsW3KVViEuiVXDmspDzyeqQstUhKcUFPDJNe3bjxDQmhpRWFr1fixCFIYhfowYJqkNrQgqGgIwpEUXMYrxgfT4a9CdiPdD/ooj8BX/tlwFfKzb8QLEJaL/g5ZvSrjHn05nb4xNOt0fyvKA0H7RaiCGG0QWB1EqIG8Dt9K6Wc+44X4zOsjHtaRdwxzDtTTMEi7LBtGdeKufzQkiG+eWcPXOOQVkYFVCrwrJQHj9FVfoFF4E0BIbdRHJhCa7ZVrLyevSte8cKsK/lKhb03Xb88nS+5exYJ3hOoNaL7WyZWSEEb9I1sJt2awq4acCYGGVnRGfNlJrJeWbJJ3KeqbVwPt2CF8etAeTg+GxFqnQBjOLZvBDJMaJewxRESI6EjENkiInwYusOfH5R/J/i4jT39Z6aNVxuC+pi0MnT28c8ece0xOl0Wi+EJ2hMa1qEuWaQ6L5VcLqZ1taWRf1kbt6/ww2dppHGw1RVcqyO8wVizF37lZLJZUZmy4+oFkQic144zQbzDENkGBL7w8TV1RXDEA0qi/a//a5FSCmaFmoowRasXwV03hSsrYI4TVMX6mMutMYXwzB0RKOtZVkQYO6ZqzVjpQMMSaBp8mBdV9IglJLQWhiH6MdqN+E47hhj4ykoraooSuteZ+7MkCxuCE4ZTCmZnzqOjKMFW1HeRx/0C7VUa4dUjscjT5484cmTJ5xOJ0/zbXLL0h6rlkgOMw3Js0h1bZUjIk5ICKAB8ci8+q21MnZi3+5F+tRPuuAnWhQ8yjbSSSDEVoKLBWHzwvm09MBl22fKEAC7uIOjBq2suWyE8yKx4McaQzCNN1rnlBjthhIX0CYchjuGTsKZz7N1tavaQf4G5I/jSBY1kxw8uyWKYj6ooH7+ahfQKQ2EONhvl4I61trB/k4AD52Z3/zS9v3OgPpQsJlUOT39NPX8mFAz+XyknGbIBT0t1NNs3E9gDFiJQamIVqJEpnBDioEpjaQo1FqoEmHY09hOy2J58ZDMNxOsHUwgUuZClYo428dA7QUpmaAF8mwnSkBjYGBiL6lrtpDswqS8kEomlYosMzJnYrZ93Q+JQSDozGEaiFGodWYcQEtEgwH38/FEOR/R5UTUzBiEQWA/JA7TCGXHFBPsrxw+UuqydGFWVXKtlKWw3J4550IaB+a5kI+zZ8BG5uPC03ALwG53QLWQUmTaDQ7Sw7QbmKaRqsVcouaihADBMmBZIeiI5jOUxJQO7IbCnDIlZsBuiiEFg+WmHfv9FQ+vH3F9dc04fhjIIrLm0XvpRVy5kEB/bjl3Y203X8dMm/l6QwqIVkqJTuJoLCQoqqZhYkQc94wx9srCTkvztGDTfFumVAhWwbjVcI1c0bRCKWVNPZ72XZPmnElD8OMwfzHGSM7PNuXa/m1mdWS/32Ok7uXCFYhD3NSZK7Fk5kWsi55CWbLV5bkLU0ohxLkf7+PHT4HKMCR2+5FpGpimAWRHCDAMqbsQ63lIvfFDdmA/JbNmDSnIOUPHjCd/DJ7gsN8ZP6jmYZ/t2pZs9HJjabVK2ZlDLW1mxBETzGRpxnFi2hkAHDzo0lIp1bqpNYyyViVURWJm0MlZOvMFvFNLYckLizct2HnOfrcb2Y1TN0ud9Otp2ObXljxzfHrL7ZPHHPc7lgczy2yPcTAKXRRvkhsCRYt1qCveeMEfhuMK0zCiU0GvKinENbfu/yfWCsllWQxTLZUahBKcwa/NNaHve9PApYBqYU6BWiZqHkEnS5ikxH43MaTE5MiABZ3JaIoq5KGQhtgRi3GcGadErrH11mW3MxLQ1dXVxePDoUFhdcAdq0tOOoCVHW4EBKe5BSw7MZrQ7HY7dvuRIcYebS/LmeVcqEtmXk4sS0FCRuJMTAN1tAxLcigE6H6bXdCVrtZgmnZSxXmmxsFsYUJLpxrEleeFPC+WU8+Fmh2bdcGzykoD8akZau6pQsvLF6IouzESmYgBdmPq/mOD45qAnvNCPEfCPBui4Q1p21WurL41rFZgiIMrAS8Z2dwg0bmblgEaV4yVwCyC1soxFKsySJCGlg42f7hRC8dxZNoN7PYj+/3kPAvb3ovWKyOgUUwIximx66XDzmIXK8MSJ802bmeMDfaY7MCnnYG/KKoBLZnZMyNSjVRCLkhMvbNyEDNLjQ/XghGJ1ta7YCyr3Wgcgevra/bTDhFZC/uWE0BPT54dVRiipfwiDsNUo7/15wJkRWum5JlaFtBiAquFIMoQBYZEDGZC8zR04WxuQ/Ac/rAMLHHmnALJGVVpnk1TSrhwVdBAbv2ZFqsIUJQUgj0kMARzoaY0OMA+dpRAVaAqNeROHLf8PcSknZIYMJcspejWbmCcPmxBEga+D8PAbrdjv59WrG6yHG5W4yDiLQUjoZcM7Hcjh92e/d6YT1EC1MW06Jy8eVd1k1+gFoIqQQJRAhqNUWO1N8GqGv1i1lI47C23/uDmmocPbtjv9xcatM7jBW55PtuNtRsHhhi6VtLSGsv6w+pAoBQLdJYFqdX2TS0DM8ZoiESM6DBc5NkX//t0PPYhEUE2LXKAwX1ibUhAHAgprsFgLpyKtWwE89G7xhxHpsFu/N04MQ2r0qgVqhSzdAP+CF4FEbqQVnX/dAjdR00pkYZITIGXoEyvhoDiAcbkKUIreDOTMA0j4zQYvFOzERjQDnzvJhPqw2HP1X5vvNIoroEgL2fGcWCeEynOXTDESywA4y9uggDLKq1ByM3VNVdXV9zc3PDgwQNuDlc9GKq1Uufj2ro8n5lng4H208QQow09yJmaZ+oyorlAqmugU0p/oCvnNYaAOpmi+eOqlbxkhpS6BheHj0oqLMNAHjPjbMK1lGyuTQPwPcBpzKmSC0krpSxIwNO0A4erHTeHK24OV1x5dcPOEw7gXoAEMsZJsEdknCLzErugVoQhGTIwjB7IDitndptyvW+9EgIq0DGy3ZCYhpGd370tIrSieStlbdmk1ArlhsR+mthPIyHCGB0DLJnzcc/1ft87qsW4UCUQxG+GabS7O8RefNdPmpNJdruRq93E1W7ier/j6soyUDi7vSyxR+rzbMeRc+5MnQa45zxegO5aqxXM1YKWjLdPMU6oP5Iz29vFVHUysFjHkVojgZu+3abVl1w2jPtGU7TtqMTujuSc2Y+DuT+iXo4xsD9MXF+vgYw1aIi9ZAV3s0I0weuP2Ux56gIaNhWx6cK0h3DJrLpvvRICajFJ6XDEVls9fHjD7bsPOKeIlkwtM3k+MY0j19cHN/NmSmMS85scahrGZGUjV4deM3R7OlNVCENiSIZH7saha+RGGeuZKeDRo0fsdjvbp5srrq4OXgbiRWoJdtPAMo9GFE6xlzJHsfTs+XhiGka4UguevOdUKTPLfOyQTasGsF6az3ZAadDQWpZdCGG+YOSrWstJ207lpqwVsrXAUlcEoJTCyMGaO2i14HNK7HYje0/VTsNoqIOXqkRH9bVWSs4EXWEm22cnp0wDcUgMw9Tdtmap7Hn6kLRfFAsqRsfQxil1LG7vZcRarK13xUpw28WLiPtBxiFNUcyESKTmibwbKQ+ujcETA+PpTK0Q4kAcB1Ic2W8grpa3njwYEBFrCTlO7CcnMztjp4p1d84sxsoRpTKiVPISNjDZipl2VpU37bLmDY3J1LJk1lNKVbo237bkMQpd9PRtIaVxxU2lVZ1aijPX4gRue32phaHohSbfhYPzbc1vHMbYz38a1nPTz3k0wovh1YGgdO04jGtVbSnWCsfgp13XxBZoDXf4B/evV0JAQTqGdtjvORwOXB+uuD44XnbYe8DjzQXybBovmJmYBos0hyGSojiBtpnOa4KsFLTb6Uz1kuCUzKca41quvBvGDioboz/0E3t92HO1N7w1SKBU8R4M0UxvtJr5EEKvOVe1kTF3H9pq5bcWbpO+jY1ptNGcW/y1CazxaNeN9IJB1MpbSmG/u7K2Na4xczE8tMF3u7gDKhI2gLsHPMEbl22LEWOMVhHg7CwT4BVRmafM6CTxWJVp2lkh5P6aw+GaadozpNEs54chSBJnJpm5zlzt9uxdUPf7PVdXV2jOnLQyl+wjDhu00bJP7pNGo4WNQyTI3nzWJIwnE7KrxerUCdHnI0UGz2t3yGq369qywUXN/RjHkSEm03QAUUEsL11TtbY3SQjnQF6yFZlVvWOCveK8Pw/rWBgBCZEQk5NVYhfQVucvtaJeIq0SoK7aegvdLNO2iS8XjTFAemQ/qhXAhSgdKmq5ecSA/IuOf8FR3E4NXLHirkmHgVps6lwTzOtrCzb3uwPjuKOU1sHp+euVEFBYzXxrStWEYZoGDvs9y+lEyTPZ+502oqs4p1AwkgNe/JViNI4ie3bjyPmwcD1fk6sP/JKVVVROpy6g+90lJW0I64UZvGwhxpWzCYI6bBPUB4WJdO3Yaslb865eW+/frmCSoIBW7+WZCLFC5EI4e/AmSlDxXkeBhJlLSes4RyEyVtPWNmOzMi8LwXv928/acSWEVuIRop9HGxdmLCZiyzHbEcvaQmhtI+R81k3bIMs4WZmxZZFuOByu2O0Ona734RDQziVcG/RvA5YtaA+Xeer1b0sX2kfMBKYQiWJmcKyjaRSv6GwNuFQFdTrcmi++FM4YLT25zcc33DEGcWzWtJW1TQRF+/SPxjy623fUlnQMUttEDg1AY1C5qffGYvZc8d4qBCkUdRcgRBtW1rBKxdqbx0SsEFNL91rpdduXUUwoLVdRAasaqO4rW/C00heRS57Es1RBv9eCzScdholx2LHb7dlNpj1jTJZ6LS+m1L8aAsoKgfQWilGIqWmOdkGfpaKxGTYFJgxiCpKUzEctxe7wQewOD2mwPu5YwJIOhx5ZboWzrSGmNd3q+fP2W6UWNFQPCMSmqNL6aPoEYOitHns7c+seayz+4MV04rNGhT6hTYM4uSX0VpGqCsW2I6WguUE+idhguRAM8K8wDFC0Mnhdljqxu93wI7hgGmm5aoZi0+NEhVKz7YPjw+IWoB8HTUmsASEYZ7fdLCkNDGlaWVFEajAO6ovW51uT9J3AY6wKLKvqPyIibwL/EfBDMUb9z76vqvNyQ3hwYV2AtxHr6vfonbu0brRo7T3SN/vm38UJCQENQgwDIQ2oBPPPtJKq9qzUNE2djNt+LznvUlW9J9QqoDYw1ToWZ82g0Vnu5mbUxMrq3xzXatqebRvuG7/oZbRqYOuGLLISr5PT4JqLFIahg/GtIXCs0o+3RWbNTYk1A9VrkgJSI0Gqz/jcIAisswF00w78crfXPlMhJI/WL92UINGP+9kZqXfXF0KD/hOq+qnN378E+BOq+mtF5Jf439/wwi2oEGRHiIrWMyLDeqB4lsNHpSzL0uleOVdqCdSSOM8QQyHuIjIlIydLMEA7RqKzbSQmb+kiEIz0MeS1M14jL2xvgpTSBaN96w9qrYy5WN/NIOSamasgxYQUhRjNVRnEBl5ZCZVfGFXIi4H0tfojm2WgMe3p0JR6MzAl2kjwYTPwVYLxDCSisfWSsqyZZaoWpCqxDeLCe1SViBbLraPR9lEguusRQ+hJiUrr1WQJk9aWpxTlfD5zPh97zydTpGc0XCECSz6xlBOHsCdr5ZzPhPezqvM562cCP9mf/27gW3mZgHatpP1ug1Ujbjst90zJsjY2OJ/PLljTHW1jtvKu79dLPpwJv0tjF9AtlLMt291qi7sCqrqWbFx8LwRiGqCUrt227cnb55ZNDdKaDbKASV0wJBhLa605stGOjQVmNlsM8hKrWNXgY2p0o+1qQYISfBCt+uDZGtRI3B5odlAW+rVplX5WPbBt8bjGBdu+q7D2hVp8akgvj/aiwPc71anAN4uFY/+B17p/vFV1qup3i8jH7vuibBo3fMmXfDExJGid1FgFxHwmceWyBbu9fCGvrJ5WXivdlFhXthhjb6WIWGFXiJHoYPxOdj0wa7/fMkH9QP03t26H8SkrtcauXYvjiwYRrQHUXb5r23bbzjZNaVCQD5RpwdfmZolxQNJK7o4hudUOINHKWST6rKS27z4TXr2ExEuMa+s74EwxgreoNDIgWusqoN4pxOaErrOlar1UIl1YZb1OOWdmrzs7LTOoBa93ems+sz5fAf2JqvpdLoTfIiJ/7b1+UTeNG37cV36FitBxuX6xspJL3dyRl8FU6/rRwG6VtZV4izYbc71NzIiseOEQIjENBg89owlWf7Zp1X4D9AtjM+9WfzkSo/XFLCp9flM/5mYdwpqy3Gpo+w16wBTa67IJJFwztv6cVoc0OSIRnDnf2qyFLqCtyYV4PytqwAr/hBA8m6MOJVWj57WRceKm3vwBm98kVfp53zK5zAqs5640n7UpFG9mhkQrwXmJnHxeAqqq3+X/f1JE/jDw44HvFa+NF5EvAj750u0AYFCKb6/flbXWzltsZcRCgNiAYYeg0lq6u4LRw3NNSOOUbhsHbJGA/rk7gUDP1GxMurr2lCagtZIdeDfo6dlJI/awYuaeaw8RjWJ9SWmuxNpqvKGT2yNqAiwIGlqTXPFPKW3ysR2S9ofBSo4FB0F0dX8Qa1bRBok17NMgsUt470Lze+aqtFRsg6Z8vxsHYCkFgr0WX6JB30Nl8v1LRK7EutohIlfAT8eaNHwj8PX+sa8H/sh72Z5uGTsSL7RYKYV5WTgvM/Ns/szW1F9Gx5euAGwZ+d4yfMOXVJ79/l3Ia0vW2EbwFxenrP5ndnekqPUHjTEi0W+WsJayENYpI6Flj7Z1WSEhDitVtc4A/aZQpc2GV4eyDD9tuf2Gr/rgLtxNUmsVVDbgOv2G8fTYJpEAG0x341cWrRdmvfmZpRTbXrQBEsFz7hcuWfbW5Z2H8Pz1+WjQjwN/2C9qAn6fqv4xEfmzwB8UkZ8P/B3gn3v5pjxFJ2sJb8MLV8EJ1vEjZ5tzVaxvZa2VOXsLmmAnpgUPQDehd4Wswxt6OVED1kBkq/W2r29vgAZMt3LeeZ57Y7JcDAdN6fLGKdVmGLYbpxLMpAbto1qsVblf2FrNurQCPolmrA3Jx0y1C5R4Vso1dKPG2YS52kZ/msPTgi0PiJpQNjfJd2S90VE7z6xmfanFWgRl7Yys4Jq43WwhDR682YA2d3FNw6b3CQdV1b8FfMU9r38/8FM+2+2JxN71YxWmAYkDybWP3Ymsjn+1VFsn7somFx2k175vBS410LvDPas2bJ/dfueuib/MAkHTM821OJ5thHVpvixCriClkKu3s1kKA/mZ311vBKW1J1ZVclViKQ6bKTWt/jiuVVVWn7BVb3En49MSCHZTukfaXAfZfsr+UbEWija1YxXc2lAH9MKSrb7/BsOOgTDY7KqQksN8AZJd0zC8z93tvjBLO9Utz7NFwGGFZIZpRIN4BaV3vhjNdCxzRg/WyGsu2WqN+lWx5xaRr0FJFze1GqGsK/fyrmBesIY23MUG76iqjc7JmTm7VsXTf76v2yRAHKYVqXABiU4MyblQau5oRqmVufjzUqm6GHTlN0MH7qUSsBtc/Sb2/DFgIyC7xmqH7n2lailroSAtIxWxqct2k9R+sxVrae7BaCs9aShKKZXcgtM4MIw7pr31Aw2DxwkhMOfCsAtc39yw/3D0qPe7uFaKpZWI48DuYEVqDUAPcegXAVouXdc7u/uAhZyFZba+TUPDHEtFk0fOrorVT/iL1n2R/fb3lqIsG47l6lbE3igiDpcjGkMabC5mMfBdMKJG9N+pHjHT2Ug+5tr/DiFR1AY9RDFIp994sprkNh/UzHfpbdTtGCrrEbVJJO7aButjCmvGydyO1a9v2nRZFnOzHBI0j8EtQrQhFBJMQMOQSMNgWnUcCR+KunjH8NrYkpQS+/2em+uHHB/dcvWpTzlFy6cYS8MPnfeYTXvmrcPuHddEq9X2NOEs1fy0gGlQfYl0wguF8y5+uRSveXJ/d3CC7jiOJM9Dp3HqOfUaggcuDW+0mzUriGZAnL/ZslojuSpSC6lYW0oRYw1pm1rAdoxWOz53GVxUlWqCSLlIq969LKtLEzy7RTfxbaZqa7BWDASm0wS9GzXNdUsjaZiI48AwTnZupg9DXXzPo5v/aV00rMj/+vqaq5sbDodD78aGmrO+LAvn08xpODFNk93JJTOWyBJhKIHMpTC1ZYSSZ3PJ962t2b/Y7TuCmr14zUgVG7zWexFtB9x2woUEQiM2uwbKqoRi/aK206xb9Bw3N0bOhUomifYg3vxKWWVTnF5kouVQUaWNttma+DsH7q6BWFBnO2FDxJ7BPi3HL+J8h+jH2ho9OBUwDIk0DqTRykHih0GDKsa2STgndGdk5fnqiqdOWp72lu1BAqVa9uj29pZSMlMwssf5+oqc99Q6oGpkEA2XUffW14rbaP4Fa+uT3tWkayS+ju1WjIqnG5IEwQUzpo2wGwkmDINF9gSWqoSy1vZIVJJ6m5lNd7paK7kUQm7l2AMpBB9EF2gNcQ3T9LaUVKtmNafIBJRC3ejbu9elR/dbUL4WllbFuiwr7NdA+bCa9xgTQyv1mPz/0V22YSTGD4GA4tqjRqtynKaJcnXFssw8OD7tQ73SOBFTopSFZSk8VSMm7KN1sLs9n7iZZ5ZpYMjGfCsCJWeKPCuo5ovyUh+0A+V3IKmeclUuBJ8Njno3xdnTnBK6CU0xoXmh6ILkDYoQbOqxVYeGjZAoy1JRKSgLUV0QvW+qtKmt0gpLqmtRM+2mMYsL6ooWNEE1jFYuQ3v/zDZpYoiENxvOvqUgPeBtgeHhcM3ucG0VElcHdrsdw27qjYRftF4NAWXFCMU7jBwOB9BKnc/c3Nxwff3AywV25gsVD1Bq7c1dt/OBZrHJxKK1dwLOOTPGgoZivl81UPBlk6HvBkk9QLhj5npwtBHIFhy1uqbmR+umZEJqJTrzvf9WXQMNkUBlHUlei1Ilo4vtS/LflYYfq6cpmw9qEtmOhirN3Pvr9zmgrFjp3WPeliznnD1b1YbujuwPBw5XVxxubpiurnjw4AG76xsOfg3Hw6FXLaQPhQ+KuUilFmOOe0sb4YpyfsD19QOur/0OPByASp4Xis8R2p6sRskbAixiQVIuxaZOeMFYLIVQIiUU13a+D/dcKNXGGN/+fcf3bAIKqBfNhXu059YHbQKKCNxh9dz1B0MQpFp0rGr7UvoUImvhk0sm1cHoeKERTayoT5qfibo8aud2VlUCwZWldA0Ll3K7vUnvBoftGKvAOO16i6Crmxt219fsDmt92W63Y5gmxmm/9jx4wXolBLRSWdKCBrVCh2SzLTULV2+8wfWbj3j41kd486M/iKe3M9N4sG7M3uh2JnIslSdz5p3bM6TB5hINNrX43Jp7ReNQilrH4EEt6xIUu7ANK5SESKRUI+mW0tolGlm41oV5zpzPwjwnnmrhLEqODpxHu+BBhCSgMXWStFZstKA3gNWSkVipy8KcjxzPT7g93q6dpd01aWUmFqAYdhlCItREOE9ELUTJRGZSxTJyUSAGzudMFKEQmJeCajTOaI3Wa18rpZhmblxU0WwwmNok5B5glcp8Xjg+PXI+ZQKJpZwgWsONq6srrh4+4MHDG24eXbO7vuHBo4ek3YH9YWJ3ODDt9gy7PUOaCPIhENDmBGqtdv/WlY0kIrzxxhvcfvzjvPtD3iEgvPvOZ3j67mPLCokQYiYE40T2FNyysKREFEgiJFmhoHnJvaITr+sxU7pJYWrz1VYThyMC2+ZdDaRumtt6cAIayMkGh/VOy3MmhoxIJPrcp1oqNWcXeBvIejrNzwhoy7Bt0QibuSSEuObEQ7EpJwEvLFSDsTzjuYL47ZyDUe7UExHVmpeJVijuo258zrxcRu6lFGIarHV4SkyH/doi6NFDDtcPONw8IE0T4+GK3X7PME7EZOMm37dU5/uxmikVP5Mt8/HgwQPKD/pB1Fy42u351Pd9L5/8nu9ZgfF67lmdpVTmOTPHwBwXm3LmDPq4FGLMiCyWp3ZoO4bGlHftUlvbbaUNV4CVXbUlSx+PR56ejheDDWIYSGmhFJu7VKsgJGIYaESOQRXjRFaWcu7jZp48edInyG2hrS081TRqM7mDDBuzq4gUm1RsEyXc7EcXy4Z5ro53i8xLcWa/FgLF2jdqQdsNOGfOeY3cSzHWmXFwrQL3yn3OR48e8eCNNzhcP2B/fUMYBtJ0xTDtCHE0srhnll60XhkB7Tlpb08Y3LcKBHa7HW+89SZRgtfMWyeN09FSjGUZSKNFusucmWNmSZE5FVIMlKQUUUpSb6RlWsz4pNoF0vfkIiAKEVIcneqXu9bYjoe5PdsUjmW2QKnxCM7nwuk0M5+LBz2mXWuBaTJKWimFpZjmvL098eSJCWkTdttWYhjKhlAtxGj59C4gIn2aaUWtmpMChB58rnC8BY8NC20DeLsG9QifavhpbZbHWfFNOI2RpI7nCnGcmHYWJ+yvr7i6umF/bVpThsHSvONg8Ftjhb0EQnllBLSzZ6r6dGvDEgkGIY3JfJxpmggR5vPM8fYIwPH2CY2Lk3PlvCzMS6JONtOoFKUGWIoiRdGlQCjEwaoetxCRCWpF5JJm10x/zusczDa76Ml5HRNTSiMhW539btrz4MGZec7UIixzYb6qTNO85virCfqTx/Z4+nTVoCHYDapVfYSi5d8FgWisdxM1W9WiOKeFGJMe1twSbNCI4ulfP++GHljzXBsWYeKT1Ti5235OpdMZnRSepLfP3O0ONtNzt2PaHRh2O0iDuwIJDREldurhi9YrIqDaR+x107Nl1/hkuBSsoCvPJx4/fszTp09B4PbdPXk+M5+O5GwDuJaxsBRlqpCrkKo6fphRtaAn58IwGLYnaG9EYGSIQiBZi26MVjYXM3Gn5cRxPvP0dOTJ8ZZPv/02tz5leJ5Ny9SiXmu/5+Mf/zi3T0/M58rt7Ynr6xO73a6b61JPHI9H3nnnHR4/fsrxeFybj8WISKJW43OmZBOT20Cw1nyiNU+oWl1ajbXUS17ELFOxD1O9irPUxqqywWlaFkSrVW2qFWdvEYul5JX7KgEN3v5mSIzTnt1hz+7KI/bDlfEopglJA8TRvqORRqwW+RD4oOpAd6N5yR21n1IiIH3KxKP8Jh+7veV4OhGHxDvT2zx98i7v1Mq5FPNDs83lbCczK5YnxzItIdp7pUIpmRhtZmatm1ohzCc1X3K5CA601fTUzPF44ng8d+E6n2bm2QbUWgsYON5acuF4PPHkiSUfhOjQ17EPiN36nykltApLyJ1zmZIyDEqQyDhgGGTz57b82U4SMWuwKirrJYAjCFoytcFk2bRjE1BzCkrPuzfhLKWQ1cnPqNXgh2gpzMm05rg7ME4Tw25HmnbW8jFEqKbxS7Vgo8qHoLsd9Jv92RcR9vu9De9CvD+8WlBxOlkqbra7++nTp5xvj1Q1hrt10aDzKXPrb+mT4tp7XUOoVVACaxCCdeGwVS9oeC1wmaYdy2L9QEPXaAbdCJG3334bPCOkavzV82npVMB5OdLGiLemY03rGUza8uYtChdSqv0YLqajpEuzqe6y9HY3DhcpBiFVdb/aZ3NqNZ/UyCTmCpSWe3didlabYWq861YebhiwhIgkqw4Iw0BICfX0lrpXnItxCurFub1/fT6zOv9BrEFDWz8c+BXAI+BfBL7PX/9lqvqS6XPaa3AaFqJq+V8JYgSQkHoj1/3VgTc/8hbH88nKWW/zOlf9fCSh7KeROWeO5zNWbesneRgY2ZFUWUohLpk5KSFFYrHqxiEa22leFk7ns5cI0xuDGTolxCEx7XcrbzVdTnlrQrwsC0+fPvWeopb5GoddT3vmsjaYHYZhgyKsENe2WnLL6q+1cl5mJAbGcNkBr9qXncdqvn0braOq4MN2lXXUTqu2tPKMVt+ee8/Sc144ns6clmwFcTFCGIjjRBzGXurcInSVYO6Hk/2qNjKLte5536o6VfXbgK/0ExuBvwf8YeDnAr9ZVX/De91W65TmG3aCbDUyr1qBV3bd16b57q8OPHj0kHmeefypx6RpdKKtZVlaD6KiVmIRxZorBHW+aH9kzwxlJ5ms2ZLuI3bhsF0MIfhcpkTOAw8eXHeMshEmmlCHIOTFcNnj8QiIZbqGW1K09o+7/XCnkmCtW2pCvv17Sxhp7KmtMIPXcKohIb2p2h0WU3NRbNz5Gsm3QsBtpmzJlaWWXlZtWLGTo70aghCQ0FjzybBpMX+z8VLplmll/b9ofaFM/E8Bvl1V//Z7YQc9s2RjUv3EU4vVdUvtjrSd4GQtpSfLWsyPZg7X+969t2ilevFW4ysueUGohLzmv3MeHHyOlELXFBaMrHXuueQL7VXKKryNKb/bGTY4TQM577sgzfOM4Z4LYPgpmI+5bYedhkeISG/uepcC2MbeABdCCo3kEShqE+Zw1n9HJtALrY5Wx0armXM/vppt0nHJswVLuVD8vXleExGr/ymdjmfD4VOnFYZkJR4SB4itqtPsZKsiaEypH6gg6WuA37/5+xeJyM8B/hzwi/We3kyyadzwg7/4B9truIX3O9j+tiZUqDIH04ZJraGABms5OOwm4mDRdinmR83FRkkvVUjFmD0h20mVEMh1IdeFoolaw4XJNEEN3bSWvPTnBtSvJdC2DOhJKbDfT32Sxfl8th6ZZeewWQtWzNdVNfNXa0YkkVJgtxt7b/sGbQ2bup217so4BCJNk6r7qpbWXYkrTihJa6Vsj9qL8Rk0K8XNeFkskrf3StegswdIWR37FDFKnziCIA2wd83pLplKdGFsFaetdso06PsOM4nICPzPgF/qL/024Fe7rP1q4DcCP+/u93TTuOHHfsWPvSAkrogd5o/WShaxE1O9faKnQlszflgzIloWap1YysKQYRFFSOQAIQpVA1XXorWO8/ngAmqhlplaV5+ttug1txYu5o/NeUGCNcm11tbBJ16MLMve68V95nzbb12LArdEktY8dxiGvm8XND5WAd0OI2hhT6uq7LVXkd6UQr0TiBFs3Aet1guq1JbKnMkuoG08etUVubD9cR6rk+wFGzGJ+sByr8Y1yl7s19G0qFwKq3gD3hesL4QG/Wrgv1HV7zV5sv/9ZP4O4I++dAtrkti+Z1+250FWQcUaYrSLZHOUbMZRlWonLLRameq80Urw4Vg2wtv8w1wrBRsia9oiUoqdjq45izLP85rfn9dgYc3D1x55S7BGW+Ku1aoJ2UT3K7G59ejcH3zIwG7wYa7pwt3pLH2aibfjRKzJbOMe2PsrzzV4hF9roRSfz+5CKT7g7KKlZS4WzW+yZlUNA+65dw++SovcW22Ym/oQzLQ3YjbBrd1FQ4kuIO976xuAr2Vj3sW7ivif/zTWzOGlq3VtAxNK0U2VpQcexuAJiEeInQCc1nLjmMSgC6mUkjljuB7iqdMYCGVh1KYVHC91zVg3ZRvZSRxdKM/r+MGtT3aazy4g6ulTmwVvwdRE7xCC1yFtuqYAXF1ddXJv04zwLA+1vbYlxCiQvUN0w09rzTbGMLXOd2ZdUmyDGy6xx9Wt8YBwQ6dTF+Cia4cX4xdozwdIGNbylg3kJd5hueGHFyRCibx0iheff3/QA/DTgF+wefnXi8hX+v585533nruaz2SPYIyaFsUGecbUtfqchhnGuNb/UJs5M/KJdYUzoclbc0XteGUuhZhblslOnAno0sHzJqDbIjkDsU3QzPTahI4YI9No/EcItD5SqPTxMI2g3Jrmru27/fx64Niad9VNr3u7iejpyUaSBiwqB1JslsYSHGHj893n+pn7gZ+XlctcPQtn523z3Lcjycz53SoCuzGFfj90oqncA3rfvz7f3ky3wFt3Xvu6z3o7eDFYsHQmQM12VFECMQay9wUdYyJikzzCpJye3jKUSirKFAOHcc9CoFQ4q3qAhU/XOFODMAxjnyMUQiCMO3QuqBQGbC5CKYsx9OeZp7dPVg0712YlXdtgfTrTxp9MUy95GIaBq6ubDiGpC2ij6dVaGXBicwybnLnxSTWIw1mZUrKB204MWUol10zwDsvLRupCCJShjQd37TiNa/sehbkKi9pUj9I6NucBJKBSKLqQsxg2uswsi1Gd7T2DAZNr6IAB1zEmhrRjSAdgpGSjBFaEUsVJht44QuuHhCyiKyxyqUkvyw3uMs27NkiXvYwQjHSiViKx2L1OCFxMCW7T4HLOnrGxRrkaYPFWgfP53IVpmS0duM428mBhg1dO08Q02pSS/X7v0NHUtYqqdB+2+a4stb/fIr5e/xQuYSXKyhPt52RjQnuAFIKVJrtWTSkRNphpx1UlUDfQ1X2Ptj/tf7M+l9fhvu80VObimm1eu+/9u+vVEFDWAw9hDQbswb3CuV1WQbgdcBCo2AiYglr2wtIqpChdOHrHZlr+3e7qINqrRs/nM+fTaSOgRl0LwWYl9V6kLqB9wK2P0bE6pF3XriJxneu5LJRcqHPu5lFl7ZxnBycdd21/X0bVZn3AcuPtPRGbptcEtLWhaee296gKwctJtuMWN0Lea+3XurEG5DfQvX33vkERQYJT/xoJaMsCermdfyUEVGnpuLUDXbtApdh4vruQy0W+uWkbF1IJVspQajFWeMAai6EsXgJig18zMSWyNK1kAgowe+fm0+lkHYGbgFZshLenQ/uU4c1Faft0V/NvM0NbIQit99SmiUPPb7P9W9deqC3KLzbCsJUh9xaVIoSyNuUNYW3UdV9DtG2mqidLWDX1tg6p8RZEtQv7tubqQuPbVu697lu05nnrlRBQdJ0+EVtj/Z53dh74C7RoK0BLKVlp8rKQWx55WSAFm1YRo/lOaoP/+kmvFSnF/DARVNcs1FraUHvQVRHQjAYbm42nImOM5CWTYya7hrRgTTsT6mLSsEfMQXyiMavA2LFeaqdUU++T1IRQWZunLZtBXdYt0H1Tz1zFZd3Ppq3hxRbqrnAaF7Q4jLme94u2Pp2M7MsZarLlqYl8eDQod7RCkLV1taFPl6A1XGpRmxE5Me127PZ7yjJzno8suTKfzwgTpECqrf2LbTurNVVNNlAIKQ1Qd80njb7WGiEUj6StzYtIBgJjqZZ7z8Uf1mKnlsoyjoSwgvGtUUHXiqporC808Vui8MX3/NGQhdZMoWtsT9k24Wo94bcCeqEVN9u+K5R9Sl0t5tuHS65Ab+9zZ54V6KWv+R6EcrteCQEVVhZOF7xNENDglf75Owc57nfsrg4cDlfsDwfm+UR4asHP6XxmTAmJ3j7Q02zNlJZiAwRMW/lAVcGK0UKwJl8+b50QUO9BVEv2aL5ye3vLxZCEtA4Es9emTdOGdAG6i4ghEy1Iis8GSVsBKhsCxxq8zRcatG073gls7gpdE9DnPbaFcVvtqU0jbgR0yy3oFkBbDkkurlsz7XrPtby7Xg0BlcsJGhICaHOs6x0T9Gwk3+cb7a0ZQExG+8rVOjPPJRPKSqwwDSpeI18poTVdcMwxXO6XMYK8ZaJ689ycWXKhlkpeMiHIZnjDZR18G1q7FdBtULIbxu7HbZvv2r4YiWQbPW+BemucoF1oW5LhrsWBTQTe/r8joHc181awL15ndUfuwz670DnCIEaF8Ez85RV8UfALr4iAgqcFNycwgB+NHVKrC8q5kFLsZu3q6orz4Zppd+CjH/0ox+ORJ0+eEGPk6fFI0crxZGNqVALn85nHooRoHY1LqWi2kdF72ZEGBWmNDILv24BWIedKCJUSqtXTBKtxoho2CsUhm4o6Q2gJgRjmjUuy9ndqF3QbabdAb83GXNLvmoncCkxuFaAhdEbUMNi48buROZiAL8uCbKpEV015OX5na67bTaDeyXoYBvb7fa9FGsexC6g1CrOJfkhwv91ktirrmMiXycUXTMK+AGu9M+0P8VuvTY3Y3tWtKlBE2F8duLm54Xw+sz98ijAkqppAzXNmTIMz1w3njEvkOJ8NxE7WnEtEWFIm1TZ6MRKSMmAspBBsMkUMiWXZpEDFBHG77tLh5nneXOwVmjFYzWcTATb4K1DKBk8Mqza1cYqX2waILcjaRNTbznL3acmq2luK9yZgy0KeyzMm/u5DN0FrRyJCeOa4a/V+rzE4l8Lf25j9l61XSkDNZ5EunNKpWWXzkUvoJoiw2+24efiAopUH3/eAadpZR2atLMWyHnNeiLN3fAPirZ3kYRgYNKLazHDDNCNDHGAYEM/PT1NlWTJ5XjifF07HmWVYiJtKyaYlt7BTydtBDXemAYfQ2ViATw9e05nbmZoNSrsLZ8WWZvSmXU2LxtaF2gOnC9NdSq8EXZalD0TLc7lQBHd90KraEyuXeOmzLSpbv1dtwqkr6Rs+TDATl1BHgz/E1ak41ngXY+zE3qDs93tqrVw/eMi02z2Dxy05exJACQKnc+qm76hH02ZRLmhsMW38qqoeFCnLXDifToyDXdQpXpIetrMpRSzVt04MWX3Q1WdbL+Td7nFF64XJj8MaiLTvRC+RjnfgnibMDWO+e3NvA66WPGjUwO3nL6P8igHL7VhXzLr9vX1sO//1qSLrh18qF6+UgK7gL50zKEInUGzLHbY+UgiBaX+FBuH6+tpSjLuJONjgWPE+SMuSEWxkt0XdiRAKt/VMFRjOA+O4MAxG+tChTeq1DFB0Nz+P1uk4xCNpHKnxcmyOyJYsYfn3HjTF8ZmgYphWmt9SS9do8zyzlNyPtRXF3QXap2HXXYCea8e0ca314mbdYqjPROgbhlW/JlUvzb1qv6Ha2sJfsEkExIhW3VDzHHR67xb+1RDQli0ppayZJK9otMfaS1O1UnKFDSxl5chGL7u6uuJwfcX11Q1XV9eUZWEYEnk5Uxcrc7ABao2KFnyCsTDvMnPOjF4KknNy8wkhKMRICslai/sNFEOiqF5c4CagvaRjMwu0FcttYZk4rppoKaU3hjidTsx5g2uK9KBpewPsxqs1bSnrTKLF67LultN0OuFWU3chvUxtllIvPlO1InIHx90ojPtMvaIb7fls650XrVdCQFF6frc2AQ1WuyLgcIUNGygoVdaTYjU9AwlrZtCaV73xxhu88egNAkrNC0uePfcNtRaKZk9TBmK0up+6MXnLEolx7Y9kvfGjkZFdq47jhHW/WwFyyza1Xk+XOXrrNLK7jLRjgLgmEKKD6V3TbXBRxPisjazdTPkQdys8xzr4jLw4EcbaM/Yo3IWpCei2CVotl4Kcc7ngLVSfkpw2Zn8bIHXXA5xX6plAdNWgn8V6NQQUnHVNT3E2oLcfkIGXTozQfsEQIfqYleAwy+FwsAZWb7wBWnj38Ttw8oGnahkeqBxjIsXEbrc23zLCsgcHuZAlE50CGINDYGGduaQpEYYRVci5IE5SgTWVajhpuDSF3W+7wxpKaU1VxkjxgKado9pcHlndhjE1eMeCwOwCXnyicQakrJoVTBv3FOmG3VXzKqD2+qWWrZj12Zr950bxDU6yaMivo183bRf1QxEkKaMzr7uJUNaenRLWuhaviSlVqFWIIkxiPUZLLTx48IAHb7zJ9Rtv80Vf8iXWm4nIk6dnHp8eU3NhGAI7DdQANSqJM3GcGOdCOGaUwRoQIJSlIqUy7EdSGpASCdW6bkSBKHDSwVs8KhnrQmIcSMP8Fl2IEqmpUuLCAmgpFKf7hU1Zsapa590SkBoJxWCddo1FBdEENUKNiCYmIqJtlI8iS6EumZC98W4uBIWkQtbK4jyFNrpQToIuUM9eiowV9c2Ladbb05HTbJ1RlMighs9OOrGXK4omsiYyA4WRmkaWaMVyZypItGDJNWnDttGXVny8XEBF5HcCPwP4pKr+GH/tTaxpww/FWPM/W71yU0R+KfDzMWzof6Wq/9lnJ6z3r7v+TjODYOThDmYH63H/8OFD3njjDR6/8641ShiNk9maZeVsLWsAHgzJO7dl5jSTYmJJCylgbQvV+ovUWqmyMv1hnW63jZQbo2ie567VGxkm57zJMKXOAd1qnlKKzyV1wsv2xg1rBWXLreuAox33n7ft+dvm5XPx1OhmxpNpSe1uy9oHdRVQEPJUnkk4WCeUjcXb7E/Dtj0C7m7Gy9Z70aC/C/h3gd+zee2XAH9CVX+tiPwS//sbRORHYyXIXw78YOCPi8iP1LtFMC9Yz0t9bXG59velwA6EYBHr1dUVH/vYx9BcOB6PnE4n3nn7bdIwUJZ8IUQ5F047y5UPktDmAgjUumOMbRJxsBy9EzuGGKnVxhRaiy17ZH9tWXIX2nHJnOaF07x0QsXl3Pg1COrQjm6a6crqz8aUzK3xm8J6OxUiDS+1/RAf2dX+N1x+peQty9IHIIS8Vq82aGtZZk7nU79RzufZWleGRPCbtvnXMUaGlBjjShQRVTZ4AFZiDKvUbgT5BeulAqqq/4WI/NA7L/9M4Cf7898NfCvwDf76H1DVM/AdIvI3sRHd/9XLfuc5v33xvAnp3ZW1MohlfGIc+nS6oPDuu2/z5N3HfPr7v59xnFhO1mambIgXT26fduE4LzPL3mqTai7kYTCICzuXQ0wMMZJTRFr0nmUlYHStvubGmyZdo/p0EYnfiyXG1a+r0L+XRmuqm6KhGSVYu8lY60WDBt1o3XbuGoTVhLMFRrHI6ot6ffxpOXcUoRG8l1yJUS6Ecxvw9cnQ2iJ3NsfUn607+BxltF2fqw/6cfXKTbW58B/z178Y+NObz33CX3tmybZxww/+oov3nqdF/Xv3mq1cC8knnO32VxwOJjwf/fTH+dQnv4/rmxvrRDKfqctlBeP5vBDjERqoXdfsiHE416I3TRUdhm7BajX8Nlczja0Et6IXF/c+7Lb9xhBXAjPeX6lddNmkLsfdjh2tQW4kuu96DsFqopw/WrXNDNU+FW6pa7DTzHYvAFzWmv85W4XqaTai9pwXjuczOReqCiFon6LXHrvNmMeYNmlYEW+UG5xWYaiJeuDUUqYvWl/oIOk+p+LePdBt44Yf+2N08/r9G24ZpbDmsptwajUSSHaNtN/vGZJFzW+99RYP33iTt956i7c/+lFEhOOTpzx96oVwpXBeFsKcCGGmeCfkcTwzeMuZ87IwDAtzdvKFRjOpPlLRSpSCVTZ67Y+KtaOZnfK3LGuZMvjkDk/nDnG4EOA0JJuE4dppfzgwTiOVNv/TWFgxF5BAjNlUVBB3TayPZ6t5Oi3zRdvy+U5dlp5bs7CF2QXztJz7gK5Wji3BWtr0myeZ/5xS8ORGIkoAWSsC1nSmkXC6mZemYd8fAf1e8fp3Efki4JP++ieAL9187kuA73qvG71r0u860ZdE2PVzpSh5XkjJMkA71zhXOfPGG2/w8Y9/nPlkPeQPhwOf+tSnqN9TOZ3OlFI5LxkJM6owejmvzdacEez5nAtzziYgUi1P4HY/pcFRBuuTGeOAEpzHKs50mh303jYic9ObV9dARBi00iYZ5VpI4wBRGHTsFQEVfAS3+Bx2SyvWquRcOJ+XHmSdjrNrxcVJLtnhIyNgz/O5uyLn89EFeu6/JSKEFBiSQXhXV1dMh33nuyb3P2PapnfrJvjTPiVvK6Dbeqfnrc9VQL8R+Hrg1/r/f2Tz+u8Tkd+EBUk/AvgzL9uYvuAu2l647f/b94tWlpxBYUgjwxB7Xfrh5po333yzD/mKnpZ88uQJ7z5+DHPmPM+dQFvUWuvsdmemeSKKjW4xDToSQyHGQigZNHoUH4gyQNN+ZfT2g+Jz0iOn08lY6DE8E+h5BUX37SQknxJsjyKBJNF6u8cBGUbiMBKSpXJrEIq0M6m92ex5md1sLyx5w1i6w9DvjPxl4bSYls3eeVnFpndICEw7m4E0HfZ9+l+j2IUQvFzHp/epdxPxK+xEQ1DxvqDeGe/zNfEi8vuxgOgjIvIJ4FdigvkHReTnA38H+Of8hP9lEfmDwF8BMvCvvKcIXt+bID7ztU0Un/wkllLQxkuMgcP+mjff/MjmBAqn45HPfOYzvPPOO5yXmTAMLFWpp5nsrbtPc2Y6WzuZ5PXk41BIqRCWDBIIu8iYXDBjYpzG7npMe5uV/mBZePz4sfXWdMZQh49cUNvwhbKJ5teuKZHDtXceORwY9jvSOHoHuYG0mxAxQcq6Rv0FZS6Z2StSL+e6r5F86w1wPp+5PZ0scj+fOS9nE7yUmFLc9J/fcTgc+mC1kCLjEHtQGMChMAG1CljRFhMpqvXCgnzeJl5Vv/Y5b/2U53z+1wC/5mXb/UIu3dK/pGnVNUW5v7qilMLto1sePX7Mg4dvcHP9kKurz1iErZb+XGpFS2ZeCuc5W3QcI3mp5CF7C+yRGG0GUanWEqYx5rdReRwGhmki58zVzc3GhJ4vm8gCS95oU7lkqregaRxHJh8fOIwjaZoIw2ikmqAXiRmDG9eU8VyyBUnevmbb2sampFij37Zv87Kso9Edxx29sdnk0+LGyeamdkQiWR/SJoQ2HtEBphA8Pb3ZSYftX5b5fEUySZ/7Uoc0UM8+XWCjiWEaefjwIZOP3MvzwpMnT3j77bd59913KbmynI/uj2a0KMcwM51P1pczRPZ54bwMjG7qG3MoK0SEKsGDl8tx29u11VoNg+0atG5ohHcuWJvnmlIyzoFvP8RIjZEiK1zTMdSivf9+VSEvlZLVBr4WvJW3evudytnnnM7z3N2CKmq/N7rm3Bsp/Prmhuvr696Fr5NWEMM+q42tCSFgPbIEiWKzbB16slTvnSrP56wPvYDiB11rtbE1iPmRxQzmMEwMMfUmsOezTdP49Kc/zWc+Yxr06W1kKUqdTUhDXrg9nRlHa8p1nBdiXJjmmZNzOmOsLgA2L0hCIKgSRcAB9d4e8Z5CtC0bKIcNfsllUqJ4ucsWG23CX8WZYGUlGWuHi2yeUQPei16yj3KtzBszv/VNVa3RWuuUsjvsub665urBDTc3N+wO+4tGu3WTr895JuTk7dKtkVoQC4qq+5wrW+9DxAf9Qqx2cXPOhrk5oSM0WESVd9992Ef1PXjwwEbZBOHsUzaKrhWTx/NMSgPH06mnLGOMJAeprXZeqYN2DLRhjyr4RYq9JANWX3rLnzzLJhcvXArzFlLbXE8jY6jleT04qqV1lDb4rJFjuvvAWle0Fcw2vKG1TQdvcz6N7HY7rq6uuL6+7n5ng9+yVqRk5uVMmpM3wM2UoRAZuqsieslNNotn2aWXrQ+/gIqx7e9e+EWVMSWSC1VMkcWrP6f9gcPhup90VeXx48dGbcuQK5yXzHA6MabEMdk47yk1k5YIaWbyhghjXbtwtDzzVkNuwfm75RpVFW2lyNt8/CbVSdh091BdGU6eN49+3LmWnl/POZPr2hqnC+V57r7w8WgjHE/HI7lahxIRAZ82vd/vubq64ubmhqsb1577A20uqiUx7rChqvUsVfWJy3ZAnWzXoDW7Xo048vz1oRdQI9+nfueXahSzWisBqJ5+iyFepBqnaeLq6oarq3c5zYu1bcQ0IA69nM+Bo5sykdB9rugz7ecxk9LCHG20YkpqjWXxwQ/Z0MwYV2QihJXxbr6Y9j7u3W8VIWIuA4BE6+ekRmZFajUByT4swucbFe8lP8+LjYScM2W2lj3LYmMZz8dTJ0QfjycPjNaivmFIhCFxOOz7zM2HDx9yuDYBHcadtQIvl1CZEcrpxwnr1LsWuG0D9m1E/6L16gioagvu1v/bapmIO8eyEmRWZ1tdu9RSKCE6rnmpwexCDN4icU+Mj/2kBvNntVLUBtMu88IcI+eYmOeJZbS238uyMOdMWqqVFYdAGgxy0tD6souTjH2Qawi91LetoG0SxwZeEwPhxQMg+64g1f9WXaeUlExpjRuWhTKvU5iXO03S5nnmNJ85u2CePDgKrCUl4zgw7Caur6/NDXr0kMPhwOFwYLfbEdOIzDYZxdnkK3IhGyvhwZCoomqZNS+D7NfLUrIvFotXQkAFiC6AnZbFmkny82CvbRzr5pvl+eQpyOB/z2gpaIzQ8TnrkVRUkDgw7Q7s9ldMuwNT2hNroi5AiebUV6WelSwKQ4BFyCdljpVdLORUmG9nokamkNBBKUsxvuYgjMPorB+8ln9Ag6U/i6qPzLHnKSc35Yp401kNVt5S3V9LXvYSgTln6mmmHE/UPHNSG6czl4W5zOQ8c17OzN75WanczieenG45N6zz9uhTSJQwWJMLkcBhd8WDRw958803+ehHP8Zbb73FgwcPOBwOvTDxNp+pekarEnQmL5ORk1ktdpXW28B7X+GBHjYfNah6FcSHzMRvU5z3pTvvvg8rD3Lr+7XP3G1a0EDw1mB222xg3ebluhtUNA01D7OB+udkNeuhTRY2tnor14gxUtDVZG+gobUt4Yp/Wo9T8Xd1ZbT7FI6LqDtX5jxfJgJcQ56PJxuKoMrt7S23t7fMp/MFgaVlwla3Zw2Irq6uLvqcpg3bv1UIbH3/Wm04bS4FzYtVHmBpWQnOXfBIvmGiHwoNCi8WzLtZprvvbyll3a/j2SpQMGGZpqlrhP1+f9Hwyjd4gS3O80zEivOGmEheFWrdoK1KtO3PNE3dXLZ2h4ggee0OIm72wdlQHs2qGJbo4O7FMTV0omyDkc3VNfyzdBN/Op04n04snj26ffKEp0+fdk6C5nVY7eAEkOvrax49esSbb77Jm2++yaNHj7i5ubHIfQMrdRpg46uqzVtqNy9DNFK1gGi1igHzeeiOmbRjfLFcvCIC2qI7NtqwyclWcO95H2iRcxdE93G2vies2rM1mG355N5mZitA/blp0ByiRb8bAY0SPPJtmZvN74wD0Ukg2bvB9WORQIyWAa5VV+4mbe4QaNjUrfeLn8lutstiwtqEZJsIaNrzeDyyeMR+ur3l6GO+m3CuDXctYn/06BFvvPFGF9Bm2oceKK5c0K2QNv93Pp047yYkWU+pFIQgGGcAe94EUs3Z7xmr561XRECh52Ut6rl83iXznveRziu8gHVELgSzCXnToDtPG26Z7U1Qh5QQ9ZEysjLeWxlH9CZjgdb9uPFFk3nJMTEslrdXhZq9B6gGb+m9mrZaFYKxRYJ54pjrXPvAh9P5aIJ3PLGczuQyW+TuvNZj8Wa7T2954pry9snTLqBlXnplQXF/vGn5aRy7SX/jjTd61H5zc9P7LrVjby5UO4/VCx3r4rjx8Ugck82xb0FpCDYGSJvS0J5Rqg1qesF6dQR0a7bvPG9Ceu/7XDLtt76nfdTrcDzbBHQftLeI2ZBvh2FgGkdEzcmPMXrw1qoiM/Psv99G1jRuZzDkr2VhjKbmLPMYuqbc+qq1rr02q1oxnu2vspQWnZfORjJ/tFLyYr1Ia+U8m99p0NGR0+3R/j+6YN8ezbQvTubwxl+H/b6b9YcPH/LWW2/x6NGjtfnFuJJftlmwdn5DaG0sM8ty5nQUG7Qr6/kApYRIGOzYaMPMqNxtAHHfenUElPuDope+r1wERttH+05eFjTE3jtzW1feZtCvHUR85LdDJCkEa/vdSnlzYfZJbdKEPojnnEM3WcFLhi2HnixDtIG8JLXOcupfF5+yYfuenXTcOkUvy8Iyn8nnufuixeuezmfTjre3txyPx64tz+czZbZMUc2mpYdkDSSur642gvkGDx9a5N6Co523D9pSA7d+bz/PAFKpS+bEqUHzPiVRepOIlAeYjIUleDXn+z0n6Qu6XuYtv3BdBkzN/4RNB4y6jrSObs63xI7OHsJbQdZqQopYm8WmQbX2oWPRT3A8b1jm4i1ojqdO/1tKNlPoNeTEQCxr68PQqiG9x6gErMOIN144nW5Ncy4LZT6vwdJsrKjb+Smn08nM++MnJqg+AKLMC1Rn6ov3DXDN+eDBg27Ob7wkZp1MMvRj3t7wF+d581rVTJ0zbWbpOAwMISJaWSTArnoVAZaJCvdXod5dr4aAqkJ12mjTfFxmVmh/axNCz13zbLtDZC0NmefZScq194yfdjuiBAOeXZu2AKrBJ6LKEAJaTYu2qFoQZ+6nvv007rh9ekI1sBQ1d0KFnAvDZPDM6TSTpvHi4rf9TXGtIWrHmtVmIHUAvizkZaF66cgyn5hPFjDdzk+7cLbo/Xg8ks8zYIV+IQQOuz0PHzwwVtL1NY8ePLSHR+sPHjzg+vq6k7rBrM3pdOouyeVl84qA+UxjYrWSliiB4LwCC/wKMXgwGtSDUNYx3c9Zr4aAbtbL4KZn4Ce5LKK7z0UwgVvfjyGgm8K0zghvVZG19lqaO/cIQS4zUiLCnBcjYgjrCPBl4TSfu68bhsQ0r+MOm/+mqkg92QUOtd+YxZnxbSS2dTqZUWcOLeeZ2fmbT2+fcPv0KU+fPu3Dx2ZPVgSEONoAtNZx5eFDE8zV59xdIBptbQOiTmbRdVRk16KCKxlQMjXP5PnE+WhNiYWK5uLZo0KZRuPQpmdpiXfX59q44d8C/qfADHw78HNV9W2x8uS/Cnybf/1Pq+ovfNlvOMhEyxttn7MpCNlmkbRllfRZcP2uEDd/sb2XHJdr6c5t2ay5A3WzLfs1ccFMoY3CdvhKpPc7KqUyzwbzHM8ndqcj4zixvzaopk/gcF+3Bx+zdA1kpk83AlqsEtVBcJpW3Qjo46fvdCA+NzJ09qYSzkHYTTtu3Ky/+ch8zqY5Hz489DbqPTrf3PRbNKRVuW5b6ASBIlj7olLJc+AsR6hKXmwfp/1MqZmpZMZlx7jLBsVtboj71ufauOFbgF+qqllEfh02ivsb/L1vV9WvfA/bvbPu90EVw3Q7xru+6s8USwDe892N4DaNBxBTIjTg3YW0PVS1wzvNdwU1Fn3P5683i9XWeBq1WDHavGSWXMlLZZwKxW+GvFQWbzfe9q/WynIqq4kPzcTnjTa2icuWT6zkPLOcZwPil4Xbk3VPmc/nbiUaxrkbJx5em3/5VgPgHz5a05ejZY8a3NbO1fa8bS3TfRm3uEFUalVYZs5q05PneaaW2v3/SsvN1/dCB/3cGjeo6jdv/vzTwD/78p968VoRh22+vT1v2rIRFKSL593c/PYE3jVRzSSnZHhluyg7Lwbb7XagSl2WnrGhKjFY6YP9smdttIAqIShVN+0NXeM0szx78dowDIy7iek4dbSgg+zz2k2klaxkrcz53DVWrcXa9jRA/nzqGvR4fkxxLR6jmfNxHLk+XHE4HHjr0Rs8fPDAAPhHb3Qf9Hp/uGDFb9e2w95dAb2bOo7REhbWq6yiBYp6S52cDQnBuAadMyv4qPUXV3Z+IXzQn4f1aWrrh4nIfwu8C/zrqvon7/uSbBs3fNHHn+t7tufP80eVZ+9udM1QNDOHXLZD1I0GbaW019fXAMYCOptwaCkQA7X1aKptSrJ6nXrgXOhR+vamOOeF4by6D+k4dNKziLg2UcTJIkVzF9Cllo6DNty05twFtCzmh+YyMy+3/Te3YxgfPXjI9fU1D69veMOzRA9uLBB6cOXdV+6kg9uN3I71rjbdnuvgghfUiOFUhWim3rJE1k/0fD6R6zrOuz2vKNOd/v531+c7jvuXY9Wbv9df+m7gh6jq94vIPwz830Tky1X13bvf1U3jhh/z5T9K/bWXOs33fW5796u2ArL1hJdqDvpWg+KQyziOXTivrq5M651OnG5vfSZSAa2U4N2CxfzAWqs1wg2BuT4LZosIYT53UxtSvEgIbNu6NQG1riRONlHLJJXa+o6WLqCqaoPCHKivlE4h3O/3HTZ689EbPLp50NOYDx8+7Fq11RTZqs8I3t0WQ9sg6W76uOrSGWcC1Ba4WvsQzscTMRvcNufMuWb21UpSpml64bX+nAVURL4eC55+irq+V+vJdPbnf15Evh34kcCf+1x/5z3uC/6b+JOL1GcpxfPmLQ0akCibXPTU05/zPD8zLa3PR9eKENBqvUMtKlWUeCGgLYDAeyEdj8c+tbhf3MYBVZCSWta2Z2IqVutU1TRMKTbWsQvCxj8Oaa38vNqtLPgHDx7w8IHhnI8ePuThw4fsJ0vx7nd7RxIqGnIXwPvSw9u/t65S534Wa6yiIaAhOJnASo+r+iQ8rUYc0Up2OL8GnrkRnrm2dyPg5wjADwX+6CaK/yrgNwH/uKp+3+ZzHwU+rapFRH448CeBH6uqn37J9r8PeAp86qU78+qvj/D6OD7b9WWq+tH73vhcGzf8UmACvsXvsgYn/STg3xAbYlmAX/gy4QRQ1Y+KyJ9T1X/kPR7QK7teH8cXeD/eiwb9gVivygn5fNfr4/jCrpdn61+v1+sDXK+SgP72D3oHvkDr9XF8AdcrY+Jfr9frvvUqadDX6/V6Zn3gAioiXyUi3yYif1NsIMOHZonId4rIXxSRvyAif85fe1NEvkVE/ob//8YHvZ93l4j8ThH5pIj8pc1rz91vEfmlfn2+TUT+yR/Iff1ABVRs9O9vBb4a+NHA14pNCvkwrX9CVb9yE/G2CSg/AvgT/vertn4X8FV3Xrt3v+VycstXAf+etJHNPwDrg9agPx74m6r6t1R1Bv4ANinkw7x+Jjb5BP//Z31wu3L/UtX/AriLTz9vv38mPrlFVb8DaJNbfkDWBy2gXwz83c3fz50K8oouBb5ZRP68k1/gzgQU4GPP/fartZ633x/oNfqgGfX3MUM+TLDCT1TV7xIbw/MtIvLXPugdeh/WB3qNPmgN+nlNBfmgl6p+l///SeAPY6bve8UmnyCXE1Be9fW8/f5Ar9EHLaB/FvgRIvLDRGTEnPFv/ID36T0tEbkSkZv2HPjpwF9inYAClxNQXvX1vP3+RuBrRGQSkR/Ge5zc8gVbeoea9gP9AP4p4K9jtU2//IPen89iv3848P/xx19u+w68hUXBf8P/f/OD3td79v33Y9zdBdOQP/9F+w38cr8+3wZ89Q/kvr7OJL1er/T6oE386/V6vXC9FtDX65VerwX09Xql12sBfb1e6fVaQF+vV3q9FtDX65VerwX09Xql12sBfb1e6fX/A/Io939UGRsiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6d0Kxwxe1a2xenqqZiXM8cUniz0OIjj8aUQcJZq0jBIkYcdbzBL8Uu5dSIbKmk/TrHvHjpmrkAkWbPjsScpBA0UwuqxMppBQQSYQYKk9mv6ykFA1tWVcCZsFnVbcLHj+uCYFCnBKB2AmU2LioFiPgFEyLMcTXRG9KM9tt1UGTr1nX5UpW5zqVG5tOW8jNY1NGqEhHMd15mEow7FADSmmGlKIdKK//+maoZNowWHh0ORRyNn2sEYZUuM316IGDxWBemCDBHiGgGtpcixOqSQl1wvcNH6ITujihh7YZK5OoxirQ7k/OmeKSpn4WFFMZiCzLzqx9UeYUyTExY8aTEFiWhWU5Yrfbu3uUdoyL8ODFcU0IVJimTXtuNzTaTdSIBmlipok/VVRNzzGIqfvdq2hXdYdfhVyq5UkghmC+YhRCaecOAccY61wMculW/3hDD0nO0AYXu1ItX2lcon4zDNcAkHPu3MmJOYxSpV5bMHdhlzTBibHqcnUjOF4huI4Y+tm1/s51fAeQ7JiGAlSicUOedd33jaDBYS2/PofAJLhzRISpQoXRiPl8OWa/Lmx3m0bspWRzbT9hXBMCNXAX8BtoMFMI0QhUHZyngtCdGFW1cxr1nT1YtBVczyhaFHXnSRVRamDBoH/SiKwy0pJNWGWHpcSt3Or+rN+r5xMOcVjxuVWr10R+daOCJZw6sUhsojpIcsMk1olhrtvRIjf8stobUex9M+9cipSGOlJdnbiVrwVC4oBYDtAEVdZ1hVLqh4+I+smN2xyVpMl04pAI0dSN8/NzzucNR/OGZVlZc2ZZV7TY46pxLQgUMUPGOFwl0Ep0DjsRQKueNBG9JlUl0BaUMehSKSS69b6SPbpG1Ymw4qHSCa4ueueiB4lgyKAQdA/YoV42EmeohksDUisXt+Mp2ixzSbGJUDPI3O2rVdWpBKaU5scurGI4Y5BIiYomM+oKJmFElFy0Afsh1mAV1/GDuUvXkoml66D1MQ3SaSTcOuZ5NsnlNyZEI05DJYTtdsvRfmF7fsY+r+S8siwLJed3Cw4qviC+0wcOGMREXNXsU0rMaSbEyURFzo04qxg70N2AGCJaCovYglVxbRausmK7uBGWuPUtPZTDtN4hRkgumEKVo9egF+mcNIRgm0Hdkh7+NgIOAUmHPnF1ER387EUrKtMXVQHNjke6DlmKEkLEFRhzzwaafq1q7l1Y7ZbF7ltfc3Z3qW8uEYtmanq5n7d0BDlN/fMiTqAhUjSQSmGz2bDZ7Nluj9gtmWVZSLs967r6ejx+XBsCVTYt5EskOvZnvrkChDQzTRMpzRACWcVjDwOFiTdPF5YsTJsjsgbWImzCTC6FZc3kIuQQWFwH0kZUNFy0xgUVB+Wr/mjfrZqkWadohWcgioXk2Yao7sXQInzADXx3L0npnpgOM5lBIdE2pvFJI6TqXC3Z5lZKIaO4g4bsHDAq9ihKCLkbNEERLZ0D5oVYIkkTURNTOiZ5aCMSUQKTRJKH9klwnVYLWuxRd6kIaNggLrXmYJKuFGWWQpoSS1gpc2GZ9+xlx1mxuaJKXt8FIn4Em4OLZXEjAKRxw5QsRlFCwCLsbLEkWrxj/Rsni2ck2I3KWtzU0fYwqMkWrFTfSBPvlYOZiRSkY4WVg0IXc8W5qQcLEXQgMPpnwwXjvM91USf6eh6Hv4oYKK4CWqQR56rFpYdd0+ISoHLi0d1rBujUOHm73yWStRBLZvXvoLPFMrhIT2lmimZMBldZiivqXSXqKEC/vlGnF7bbDWvJbDYbpjkRYtWNy7uDg1YPkD13a13qDbZIBIuQ2TDPM0hkXddGoJVwx0ddlHoTctZmLZdSXJ/zG0Vu8yiDgRSom8cNjMoBRRuGqc4JUPMohQozEciOkdbg5HEMyKvHSYKU0ha/iDkTVGDNmVJohLnmTM7aItYX1q4jjzq4Q0bTND2iV8Zhw09qSAJ0lWNKiaq6mD4fquyw8+bcryWURpQtTsv9/CLC0dGWQuHofMNms3FmpJSSWdflStq4NgQ6TZPvyuhiL7lP2YyiSnRpMp88dJwupESaZ+I0mZvUQewisFsXJ1JYS7a0hJLRYeOab9pG1buMWGsIcaSywIoSiHTYqGhxkSVNT4weBaUluGXtBK9Vm/XfVyhIlVwNJ8dmqyNht9rGyqWwritLUVdvsv1WStuI1WisxHfRLXqRQGNK3Aixbd7qd7BQxkggsN3OTM6VRYude10pfn5tMbE1dHEwLEXMy7dGx6oNzlIypazm8rxiXA8CdbGimHcnesBICEZo1cVZYQu7wYlq5aZ5Yt5umLcbQjLjYC0ZUdgte+eY0hbVFqOL6BDVxbPHlzcr3DhDtTSr5d24lHOY7GpDprRQvyKBQiEFUHFvTlMB7LqrIVzK2uItLTDYiLMS7LKWgXsa7JNzZi2Ds6GUhjFWAryIDx94gQYiLjEyp4llWVjXFSmOjHgcwJFs7PsxIiRElgaSajM6lUJumK8OIY3VODMHmDgSUYaAlMePa0GgCOaLd70meN5KSD26SYJN1TwaFvyQ3P25OdpydHLM0cmxLawWlpwRlMUXU92o6gvZI3Ym7QtXY3WaC06UtRSz+D2mVEQ8L8nVkWicJCrkZv2qcxdYRbu6UIlk2CCUtSeYZQyzVQvZKwSzdt0oWrJdz1Jy53prPiBQnECXZfVg7s4926asumpKLJiadLTZcnx8TMmlEXEIgd286XCeB/JIyBbDCxTNrm+CYWg9KsrWbAUpBnMFbfNQMr3a5OXjUyZQEXkf8MexqsYF+EZV/b0i8luxwqQf9a/+Bg9efivHxKKWAhKreHKPhAo5KxIyKhhxOoHM2y1HJydsjo/Z73amT5VMEFvs3bo0r1NbyKyN4LSM57cRBqsmrIdYaBr0vBCDQTEohTKoCMHjQIvnWtnxs8NPYSDQden6dFaPKFL8GJl1zRQ1HbRyzrxaCoVJh/rcfQFqc8kKQSv+adfSDEtPwItxZVVL0Vj2uUE/dUOVbGjA0bpFgO08O9YbLegZV2dw6E2r48KxW4W8Ziwo2lN7xBIQ32kRvwJfq6rfLiI3scKy3+yffYOq/u5P/pDBxaOlHdRsRCFSamhXUSB6Xk0gxtRF/GZmzStLXj3Y2DxF2XNgGoEWTwArLbKx/QsujhSoN16hZYICq4gtrhaiRqboqglQxLFbKRZEXIq9x5AjJIZp1uMtuRPoWlN5JYCakF+LqQ+lqBOpE6sHqwSqY0OguXSleYp6ZKd701SB3DxrIS4HwLuJc7MBSilmmBYluV5ao/uMy0aQbmQ2R9UQrKOle/uqJ2usYHfV+JQJVK125Mv+/A0R+cdYPvynMASiT0WkRSwRoovTGqGT0dLBb2KkCBwfH3Pjxo0WA2pgdWG/3xvXytnchi5aci5utdpiZK36YId+ql5l4LoT22ith+CIQoC1WMJaUefsJu9WtHH7IlgWY7+B7fgglqeDL6x/qVR9NCiiRoAWQbQ2FaIEbdmRhsfHA0kAxh2LqnluRJvbsm7efV6RFJkd9Tg92wFvmvguFgO73FpaSOAcEzGJZTZEYwLtWhq9dX99yYNYHwjyrVS1+bTooCLyfqzA/t/C2sZ8jYj8UuBbMS77iUt+0wo3vPTi/a7A1wurUTc1TCy6K80DJrTGfLoxMM8z8zy77rW4qKIZDaP1Ot6Yaq1XINICTHJHO0UcEzIrR4sTr3RcVUoF9A2mCupwVTEsdVUT/Ro8X4rq5zfeVo0o3GNUk+Rw7ifZOWp1IMRoYE7AYztHHZqD6wU8FkBBohHnwAHrJVa4q0YvLcvC6c6CRDabDek0sZnOTK3ZbAlhpgTx4O5xQ/Tnvs8PNow4fNjTwq8eb5tAReQGVrb7P1DVByLyB7DisOp/fw9DR40++V644Qt+7I/W4XjUwg1N1W5GyezWLgcENxJpJUDTezrEUn3kPS0i9h0dutihKBa1YyLdMiYT1eqsRKzBsMpVC2NiYsNSEdS5XAOzK0Yqw7XSreqGZUaLGpLgHJtgBhcQVZxYnZuWguZHibMSzUg6B16h4V4ouaXRqBPN6kR6LoGzszNoYvrwvle37SB4fIEvrre5WE0iTl5EIvGOJs2Jlbj+H4E/oar/k01EXxk+/8NYVeMnDnUOVsQ9KWrhdAYbunWfEkEHrug5nAEheeJWlIAreIQgpDixxNQ8VBXGKgNwv6ZqQZQO5GsCh3GMeAqi2YjP51OTx0qp4k0JzlUDatfSuIcgxdbDGFYnoAPiFOmIBUoohTXQdWYNiOO2JQQzjlp4XEUmaK7YeswuNKQFcvSUkcTkOHMNvKkhdxllt9uhuTQAf55n9vNMLBHnJ/XyD+pa2LVXzNitf4ZzY6rbVePtWPGCNXj6x6r6nw/vv+T6KViHiX/wVo5XDRXjmt1/Xf3dOniW6kIRjIBHUPqiriMibDYbxhuhRSlxCMSdKzc1P3MpFYYKNBO/WNiahDHNrhs7KhawpiE0/bLG4kbp0VbO/oHudYqhL4OGGgXk9Y7sMh0DLbCuLE74oub50eGaqyE4uhFH7hpDD36uj5Qs0KTmPhE8MLpy07UgsrLf79nt9+z3Fo1U3dOxORjEou9dZemSrmdI1OgsPYD2Hj/eDgf9YqyZ1N8Xkb/r7/0G4CvFmh8oVjv9Vz3xSKO+VCfc9KghjThajKgjed3XHKNZ/WCLhpFjxAoKzNujA9egiqLBOFAuhbPgOqh61JEU0zcVt72d+Ar2vk+7GgZT9aXbmza7as2qOSLU4R8zrrXBZCFYGnXdQIarGkdds2G6uVhInRbDWcXhpbxWYD8anJO7AVjhoioBqlgmuZjG4k0tUdFUl4qhgjkXRoJXTU0/3e/37HazE5syD2Ja3JNUVapqLFVsWx0uXBfLuw/hahJ8O1b8X+NQxanjLRdrGEfdjZ0gUweTp8kzMN2/7B6lyjX2+73ftJ1b7GJG07Rp/uhlyazLQnb1oOpwIplS9geLkXOBkvsCo4OXhsYZKwap0aL/8RTpFqmuDkAWE9GiQJDmKZvnDXNMTMk5kLg49vyfZS2seWW33xM1sOjaiG/drx6ZpRCnRjzt4UA+gKgnBgYLYyZIC6pJKRE9o6BKkVIKRew+BHGTsSInYLrp+XljJkFrgTYPWBG1FOYBarL1TO33y7K2sMCrxrXwJAkesyjQApUPHoOxEyMxTQQJrHml5MLu7Jzz0zP25zvyslrWZ7T04+iQk2ZQcaeki55afKsMQSSrLzAlN8ywBNBsCydBjO4UB8czGicHAYYc+WEUL24GNOMhxsDG9b4pKkgnUBFhLZaeKyIsy0qursFcWFcD7/O6mpFGcNVkeGhpOrJFUx0aZOaL90eMqPrGPIB+DrHK0DbOQtz3SnqT79gQXM+WnrdlxqHlcwUPxctracE+IxFfNq4JgQqpAzDghk8FyEvJzEcbNCj7srCNiXA0s7xxzssffZkPP/goH3/4CR7uT9nrgpZMDEpeF8Q5SVAluU67WxerduHR+9vVEtf2uxXd7VizeTdSmkhxIi/F9CcPBtEMiAfzBmGne2IJZBGS62EaqnVvumhASaIkhEkzc87MubCZJmYtTGmCGNwPL+Qo7DRDXtnGgK6CZBOLkYkYAouA5hUJNq/s3iwN1TdezA2JOQeIEdKKxoUSIhoDJCGFBESmFJAS24bSUlAtpHlG88ru/AxBiboli5BjYKGwhNUkHZEoiSSFqInZ0iBRh7nmLJTFAndUJlQm9u+GYBGkZzRWJb1ZeqFHLjUoA8CB+NPTU1555RVeffVVHjx4YNHa6klrOuidByLcRGBFB9o0Bl24lMK6LpSiVtwAh2kGlyFAobje27mNqsdquoQ3Xdb0XmOvZThfYLMxiExS9Kgr8x4VEdaiPDzdtQCRsqyNg+rq4nxZBi5n+uh43dVwPHD1DtFP5vJM5rf332bPGQoq5HVlAVIUUy/i6qrEypoh52pc9cCUw4zRS7BZ6XEBV43rQaAtmgnT0WqwbewFDVQrSO66Z86cnp7y2muv8eEPf5iPfvSjPHjwwAMgLEfGIudt1IVZ19UWW0vDAzcbC4ZIaTExdS7s89oWMbgoqp4cxULsGqfR4pCSuTqLkaP55rHAY4PP5ADDDdHFbQq9qKsIitUzKmt2wlza3FuY3RBgktf1QCXCA1WzXyc4NpxXlsVE7WhEZfe3J0nmMQPfHCb2YwiECDnHA+IumqHE9nv8ftbCvlXvNd23uzqNSHvm/lXjmhAoDcCuBNludgxe8tCCIaovN+fM2dkZDx484MGDB7z55pvs93s2m00jAPPd241onDPnluJRb+bx8TFaCnt3lZailPPCzgMaAGJKBDdwqIEQhn+Z/ucivwCZQBSvZyogUsx+qhmbji6MHpUQAilEFhSyceh1Xcn7hR750yVCnX/A5hsEpiCkFBFJ7BXTrcva3LWaPaMgRHSy6h4VEWibsUkeC7vrRqXfL4usBinN755ztkIZSZE0uqI99lOk2Ywj/hpECe8KAjUryRx9gmN7w+ctuV8PxHVeDJs7Ozvj/Pyc/X7fI/Ndmy2XiDYciE7RI8pjokgZ6hj5OXJxffTQwBAxHVnFvEa1BnspxTDB4HWRnKNqUHedloYJxhTauacpMm8mgkS0WOheyIMYbNdgG0bRGraEiMVYxig9k8DvVi4LqhM557axoaon/THHRAqHrscYIiH16KPQ/KMVF/a1KAVdjehrdNUB9i7uvpWOBHiYxYHIf9y4HgRKdW9WHXTcgT0NefT01PdTiK4vro1A13llTlPzaV8EsO2G9bSIZtkO76UpMJc06Ixeblx7Ge1aznFIfsBcoYViLNNIwEviWDS+tnNMvkFqwdi6WKMu27hl6VzT1HSHjBDSZBhjrU1lx8jMaQKvajxa7/PkniN/jH7xILbBQoyWf6T5gIgu6rKrBzLXRgq1mUKIiSAFJXogTE9STMEkSIxSBdxjx7UgUOMC0WMyHFCO3UgS6WkYB3GZKbHZbDg6Omq52QdGkMMvj9xgr4/ewuc8L6ZWr4teSEIVYoksObfA3+Acwbi516bHjCA/A6rd/YepzdQU5Kp79pwgj8uUYAExxcg+qKefREjJYg2m/UpOhVBgjYWYLfGNaainpErNBbIYhUiMm/Y6JovC2mwsv6u5N5PYd2vNqtXVG40WapcSk+vMvZ6SpSKKg/jqToIsgRhr5bsaEGPFgCV0n34MhlZcNa4FgYK0IAYNckCcVekfK/VVworR8lxqEdtKpFXxN3dR57Zt9zuBVmJeFiPQWi7QKmkM4jPMlu8e2gQanGA4Y/UeuTir+onQgj0qFw4eQRCwMjspWM6PZataOkVWJReY18w2F1RWclHmdTYdtxSip/bmouQat1q80rMYRBe8+nKtlRq9PPc8zcxpYk6TFZ09stebjTk2pCjruqe4zr7uF6aqQsTUVYoQSFUtuKDLllI8fTt755K61O5p8rr5Ud4NBCogacidCd0vrIDEHkwxTRNBxOCklHjhhRd4z3veY4aO9oQxLcqaF0NVL1Rsq5mOpRQePnzIPE1NP1VwQwNkFdacHaAWj0wawvfcFVqjdEzg41wiMgXLhJyCibMUhTQZN9xsJo63W46OjpiSkKbJinsJrMVy+W1vulGVEpuNEd9+yciaWbC8q0DdeNriAEoxh8V2e9REa41AmjeJ7XbmyOGtzTS3aLApJUQLMQrLbodoYTrakFw9mKbov+n5Tsm9Qc2Ai9a2BpFW7nufrSZoTGKJc9T07qtJ43oQKLRCU4z6mOuj1Q1aBnEdRNjMcysDfnJywna7bce7zKPTrMuKu7buFqUp8WaoGbdNMRr2Km7HaWg58OoGSv1dRFq+TSUI8e/GGN3CTk2s9/ToSEqe5isBLWa0LCkb0UyFXGBSmFclRyswm6V4AQhDJUxi+LUP0f/g+mr1Hk1hmEdqiXUVAoruXYshoCkgJA85DExeomcefh9jYKqB5UPZH1uDvgoiFqJY1yClwDwl61RyxbgWBFoDJPrFWS78WFm4fq9GwcQYG0HeuXOHW7ducXx8zLIsbsxcNGykc07nJkE8ct8NgdpxosZU1XOua25IQsUy/UM7pprlX/37IdSaTJarFJN1v5imie00s503bOeZeU6uB6qlXWPuUyOcwtG8YXVoSINYNRW1KPicreBCUFjWTPG89hbwbWAtaCFIJNYy3Z5sWMMTUwjOyfQwlFHUSz0CzlErcVY3aX0eZSgPyaO+dYslqfCTWfAhwpQC6V2hg0rnksbFxhKDhxXsqjitonx2Lnp0dNTENtVYgEbco/i3+kHRFi5GQvHoIR2CRUIgOmHn7O1apHu0bLLuf64byPW+NMQTRKkJfqEbO9PUiHNOEykZAVh0D9bOxY2ZbbETSczAvhWf1aLkkikaCKXGBXhl/aYq2TRLrklrI6Kxktc9OViAvaiyOIEa1uviWsSw5GJwWYW2xvpTlF5xuSEQ5gWx70Qhim3UmnIcqcl87wJfPNAXuUJK0mEau8EVQzvMuakxj6MnxcLbOte0m0ZHBeghcwa7WKOpuObmQTp0RzqXd05TKKCW2hHEAql9MghGCLUah3UP9A0VXZxXIk0T05SYQm7lfOr1p5iYZti6ARlyIcQJSZMHNNu9CWFPENh7uFyLdB/Sp9XLbVspHiuhk4Ow1ODu2TZvKZkUE8W5mhT7Rck9D2t0kY4PVFvKSL1vlkomzYV70VitSMVV41oQaHU52osOitcxgvYVLqkXaaFby0ESXMsJ8mPYDTV9sEbHl1KcI2DJZmrBxmGloQBVnRix2LUuBBbZFGOyfL9Suq5aRZkbR31zdZzVCqElpjkxuw64L5lUknNSl4NhJU0z81qYppW0WZnSjpQmYkrsdjt2u53hv+7yjKkTkjHAEcs0jLZ+t27kGIMHYysVbTCHgBfXLbQQxArS18WrClEr1Fst9RC80osl1tVCvLUsjsFt7wYO6p6k/nJ4LsI0JbthAwY6wkn7/d69Je4HHty84y7vFerwWqGGb65r8fe8GIIHYdjN7IHF+ByKH68aHSE4Mur/VKMpRFzcyyNOgJFIN7PrjTmDWpB2UgiTEqZMLrBmmNbMvF+Zpg3Rf3u+23F2dkZKqWWxhtirqKjm1p7HrG49UHtMpbFmr7GYvG/GomewWi+kw40W3D8fk7BhImvHXg+k2ei6jkP8gat0rXDxY8bbzUn6fuANIAOrqn6RiNwD/gzwfiyi/hfqJVmdV41m+/mTyWGgGpM52uijf9oqJtd89I5TNsKusEbxAA+1ThZZ155ikS28r0UHCV6cIRirVW3FFfpCKKqxVSkeI3cq5NSCr8dHcgt/tvh/dTGvlOrJRKOVuwkZiMXEu8QmaYLHcvaNiwP1fi+VFrRtCW8X5icWnFLdt9UZYZkFAUJBtBPWKAnMQLLNVjMI2maWjl2L+zbq+epvRCyw/Krx6eCg/6qqfmx4/XXAX1bVrxeRr/PXv+6qA1g8aDqARipQQYF1v7Pdn7zGkRSPPoKjo4nbmxNubI6ZJJBXq1VXISCKBSSkKTG5SIuizNET4dadRR+VAqWQsPC6TezWqHESPyewSQPi4HF1pnMemYanZlFb2J9/d0rMR1u2N0/Y3rpBvLGhbBJLEiQdG3diablNxdiWxVOqFeoNWphSIMqGKQXmIJxPiTlljjbC6am608EClHMW1tVw3U7xpoJMEoy7I2zTZF1UgLAsSI1uismDZAbVRJJVUS0WSR/DRE7ZCDM5jhsXEFOZphQguh4shZCEzWbiaDO34PKrxjsh4r8c+On+/I8B38ITCPStjlEvrRBUD+A49GWD81nVRxTx8bs1SHoMlDjgunR14uD8wzGgtqJ2qP5CYLpVhU6PctDhYeeAGC2f37iiQVXbOBHjSlwT2VaetC4NlZBUGkfa7/emD7v6U9Z8cE21MNh4z8baAe36MNekDjbByDkvu19tjYZ1qAZUd72aQ2C73ZLj+s6KeIwG/pIYdvCH1HLdX1TP6lTVl0Xkhct+KEPhhvd99ksYTvbIdy77HRKCW7CHqSGjr95ckDbDppzTb2AzEBRADqraVQ3CNTEq2qLjHOrDyNsXDdBAkRr3aN+vi5oGIk1hOlhsW0DjdsbsihF6UVJMHkCDW+G0Y83zTIiFKVohr50bS5ozORdKKtY0S2vacoff7LxjGjfuxnXrfzCi6maoCMRl7WrqcaVelyML1UUcQmAzzRwdHXHj+MTC9N5hAv1iVf0RJ8JvFpF/8lZ/qEPhhp/4wS/Q2ljrcAzAvT9qcwHD2CyN9SKBdj+5/TN+dqB7qXq8g1bDsldCbl4Z2vEq2nDwn5jFXQlU3feuWlpAxAHHvmTTWfKYNC4TC1AiiMFWZSCAtnnEXIySYEkRnRLrlCCvJLyrsEOY4lWSSxZP43AvmLsk1F2848Mgo3rP6TjuxqCxlCIhSuuaPKoBYaqdPkLf2dKDcDaTcVCKtRC6arwtAlXVH/G/HxGRbwJ+EvCKeG68iLwEfORTOfbFRb0ogvy8B88vErh4EpdxRW1i90Dky8VNMagIj2yYy4dBSraI1lwgmGERoPcc0l5ysRYys9w8h8gqOutGlzgTLYZDai4WGFJM9KsbazX8MoqwiQnSxKLVaSANDy2uG9fOH9DVmyyPbpzRYq/cunHP6h5tnNX0z/rdNM+EFL2EulfTa5vZvrN14+gdI1AROQGCWuGwE+BnAb8N+PPAVwNf73//3CdxzLf0/mEQ72HOUeOS1BTY/v1aZa3KXjcu+yYfrM4DsV4hvwt47KgDm3sTLx1TS+Z4NBTD5mo55B5/6d4bq9vuxyra05QvXHeuaR+lOynKfiUU81iRplZpRdeMeoEKlWKJbjl7/ah+DUvUA4JsToUhxnS6UGJ91GGjF30YLfyQUo2OPZBcUQJzspbdUcJB/MRl4+1w0BeBb/Ibn4A/qar/m4j8beDPisgvB34Q+AVv5WDVSKjUcFEkHhhAbpGOaRy9hLUe3LiIdGC5ejAavILrslQDt87GjoUT8QFh2qNyR9NpS7fWqVyzV7qL0f3eaWoBwhZA4jWKvBYqWAidqfTmKLDqcjXleCUvK7vFWrioN/TKO8M/I4JELw+EeJR7YUVAsol9OldtBk3oIYkhBCTFA2JsuvMFnb8RqcdRjKpIXa9QPXhqIn5yvZmibCdzU1813k7hhu8FfsIl778KfMkndzTTM0VGsSrtr0it4dP1SFV3EQ+R3JWjQBdRVrjucpE92qzmfa5Cr1rsA6FeUDeCtyEUpNfEpHLdQZzVjXLwmJhCvGA8Jaysj1pwCu4Bq12BW8WQPctuz+KZnCog69I5oGBFE4p0BCMISvDwwENjz3acVrXeuL5AEstxstaIXY2RoAePsdemqrbq0E0E4bq9WHVpCziZiDNmLJ2cXEkZ18aTNLrd4FGCGOEkVW2BxRchjgN91S1Ss/ytbM7BaX1Bq5u0vS80bm7TeFT1OMBspboTa+Fd9xwlI+RaSSQFC1BJXqEYjyW1vz2gRWJEl9w2XG3UFXHRXLJnehpSEZzDjmkhDc0oPXtVHIS/GHAUYq/0J64eNH0zpgOkYVyXMcbWHA4zEo1r75elx/Q6olKt+JvHJ6zTYsd/t1QW6UQwCtrLdFI5+A5crqNWznfZzx7B/ATD6ipBOnHq+Hk/+iMzMgNIvKqzeM1MWkdgg5W8DlOIRK9TVMPeapquajXm5JENYNm7A4EURdfFxOogPSrh+MQOrP/xuqsOCiDhMFO0eoiqe3bUoe17w9yqC3XEVVWtpQ5AVQcwyTLHhG635JTa8a8a14JAYSTGakxgi+0449Dk3PzVXlRbxqJb9QgHFNXxVbn0c0s7qA2qUM+f4XIrvi3wSORanOP6HMVFoYz5OzA2wY21WW4YOWnvhTQagDVCq8Ri3d8E1IsrrOuKrr1PUjNSgqfQDBCZzcGJ0EMatTrbq/oUzfszzTW4uXrvFCoSUDzII4Qhx8iw6daFTsXq46uSvMUQ4Fw5UtbcOP5V4/oQ6CUW/OOs+vHzi16Ri6oBdOIP7fVwjCbKL4eyrpqrOLvVkRthxBn00eOZv7oTTn3kthEuZp/a9zZp8jLgSo7JVIRSyPs9y37Pbt0ZIcOQjOcd+wYaqL52kWCRXNFUkppZoPX302RFzRyQV+ilGX1Yvma7IU2dsAC9Q2dASqkRZIqRNM3IpKzLwrq8G0rfcDlhXAVuH0I8l1iW9QHNwm4dkRlwU6mv63Ht76DjXzpXe9TXfvxqSFwg9ijhQF9DawW8CjNVY8NA/+qxsfLzFrhc9fNlWZq+XhP+dvtd+zzGyFyLMNR7YbNE5VDaqEuLXrjNitumOgfPDVO/hvGaKqwnIi3yy97jgEBjtK6AZc0ExZP43KmRCyV0w/aycU0I9NFxGTe7KHK7i7BbxA0uqcSreBMGWmPXSqDm+qu6W893t8V+nPipuUh9niHUh+OAlYhdBx03TRX5qoquShalyHqALwaxvPElWmW+zWbTiGG/33tDLVq/9ZYwN9wjxYih3h9DKYq7itzKF/f/i5NwGDJAh5Lp1GMMPvi6QVShZKtlr9UpUdUI1y/zsnrDL/Hgnc7N3x1ZnT7eKhc9NCAe5aKVQEdCrcRRrfsG3gdpNd4rF6jHvUoHHblkSh0jbIQ5bLCc8wATWXXi/X5vum8psMJms+nBGCm518lcp5vNxpPilHN3qR5UCpEuEaTQOFpN34gxtmDqg98InhJc0QDaPRuv9eL9rWvQKrXU4g61U7PQAmDqPKSYtFJ3mKjQkvGuGteDQH3HjfUsR/n6COFKFavhEQK9CDUJVczX0DfojMSOU5xoqyhs0Mjwtw7l0TSFkYP65TT9FLQRZSXMaUpm7ODElKp4P9TzKoer+mRe14OFP9gsbuQUzDCx3Cyr0znjm/rC8aHq5wP31TrrTrj1eqrHbNSfFSukZhu/Avf1HhasjHo9op27aGntct4VKR8AjkMAg9ux3lCp6rjQEgskWzytp8JuPcd8t9tR8kpEmEQIWih5ZQozcwyWtqBWnDahqChZgjeeMUJrvTJL7ZbZ56ViQHhvRCBkWa0z3hQ92FfBW4CjCyEmclzYc85OhZALsqxk2ZF0Iq1H1uVtycyzF5FQS8nYbDasDshrDGSBfVF2eWVfMlkgSkJZXTLQWGVtM35edpZePCdrW1O6F43oUUu402OxtosiwjRPJIE0xRYcEsR+t6gXyVVlqe0Ms+udkojB7qf1sI+gQtLCrMpWTbTv95l8vr+SLK4PgfqQqg8Nynhzf7Z/+o4upbQSOJvNhmmaWDxVIefMqpk59g4fdb8qFmxRa2GNIn7827kh/b0LIvBifKR/6NzhkLPX46w5I+4NWlzFWJaltxHXtWWpquvJxoV3VkBs4GLj/EspLOtKGQpMTKnXlwdaGFyPX+jGU3HZ27ML3CArVqXaIvZqoMpg4IyMcMhm6HPrkNuyrpaC4XEFV41rRaBd/IxXGw4KNoh04lTVxj1v3brF3Xv3WJaFh29igRKq5CUTpoNyawewSOXKVGEmVaTV0mDF1Q9a7cxDIrXcHKmWtar7n4d4gDAaGMHyqFjQYkXPivfqtObOfa7zPHF0dMR+v3VreWn5V20eAWskFmMjwjVnVmhA+EgE9V4GzPqOMaDqKd/BUjuQrl82PBbregfdpSkNxaiRYl1vr2koqhb4kjzFWEth0R1kN54ucXyM4/oQaCXOwX1jEGXlFHidUCqVtoas2+2Wu3fv8sKLL7DsrabQst+jbliEkFDv9WluxtJuXFUaSoVhKpcMFjAsAzdvw+Q8SKTyZGMmFhCcgTRwTvvrFm0pyGrEtgQvi7iEg2CXUoygttstu92OzWZqhLtfzlnzHqQYQWehxI5k5pJbnf3ilTyWdW3vVW5vlr3SIkHFct+jRip0ZO7UQCyWlyQOIZkkAfGWj9IibQQpvblF5dA5r6bBhUBWpawZzdnL37xLPEmVQCXUoGPACms3AV8tT+ubaaJIMGjm9u3bPP/887zx+gNef/311sdHa1Ha7DnesRNLxUH1gnhHutHSAPP2/Q6l1IXKuRfJtblnSkxUBDKIEEpkWQsiC9l72deha+8BX0tJAmzOJ852p2w2m5a+nHNmt9ux5D2FDEFJMvktNDdjdv87WODx4sEkNYJ99op2VuA3s0qtrNKt9rUs5Dw1UV8ll2Gnapxbq87t11FXqqg/CipWzTlgunnWADm3jidPMOKvEYEyQDjmFa/vMop8cb3OaMi5F4GTk5vcvfscRycvm463rs4tHerx3PiUkh8/uNUZHjk/QmszKCH4b1tMlP1bvUBFWfeuE2KZpwA5WpnH2YONi3esLWXqKdReNVn3vbiuienF/86UsrLfn3cLX/p3G+xTew2FwMY5dtPvXBdHlejGT47RypqXYoUVLgSZSAytVPqyLIeVkb3Ej7mfu6XfyTP7ZvaSjG7Jry4VQlGv6S8NkrpqXCsCBYc9vOS3ukna6MaJE4eF6ogxcnzjBnfv3uXo6KQVdJBpQrA+P6V07tcIMRyK7+pON8iEpqv6xDpaQtetRGG37C4YLUqOmVImcvTIKyeqec0+faunqWpdkrsDYOTch5y5FieDnk8eYyA7MYQQ0BSJ80TeL+yXhXVZjHSK6cc1gDh7rlDJA+cTIZBhVdY1mNcqWvxoSFa+J8TB7+6EOuxrI9ZSN19ygq9qUHZVTQiSXK99t+igQDdWgMZF+82jVjTXzsVQYZomjo+POb5xo7U9XHMhJWUK0W9Y7akZmm4G9DrzzQ1qxGlnljaPGoxrBpO1fZFir/d56KhRLH3DJprNEHI8U7CS5NFFcc0PmhEkeL3NlNqxapmcJlmG6nmKdecopXC+1PaQ3R8v4qUPPd+o0KvfGaTmho0qIrleMMWxzHVdyWVlWRyp0IxoAkm+wYRaRS+47m6tws1I7ERayxF5fpQboUUKVVZeNd5OysePwQo01PGjgN8M3AF+BfBRf/83qOoTu89ldcFed2No3S2dm3S/cG2wBdZ8VhHmedsKiC155fx8ZxXl5sh+v2dOM9NsgQ9lVe/9CZVL1dHA5xq4oY1a22dSv1cf4tCJBz706PiFRSxQd7+zbnhHR0ccb48IQVhyZn9+zlkp3Lhxg83Rkef7yJCKnFsbHDPeTD+vVVCyKnFKsKytnn5LYJtnu5Y9rl64SLeCUVZ8zHvIV1TEEt6FjWgrKVTK2jbOhOm8QcwgEpQpxWbZ1/NIsWrVNdwzxMi0SVYqPHuTNDioKHPZeDsR9d8BfNBvXAR+GPgm4JcB36Cqv/tTPXbN15HGeXrasLqIbQq9CKREUWWet6R522pearF2MipmLFz096nI0G+9lxTTzrgfAeobGO7vWE12teS3usk8zSIAorCQLQ5AAtO0suQVWeH8fM/ufM+NzaYlpdUqx+aX73lHqvlgHkGEFG27nO9XsuYGhdUEttH4y0EQ11t7/KY7FFw/VLfGc17ZrysSA1Ez69oNwhB6X6TKSUuDsTyCX7QFyIQQOnwmYv5UrY4C++5V49Ml4r8E+B5V/YFHIJm3MHT8q13nNOIMrYxgN1I8gK5COC4Gp43VXt/MR4RkpWjyYjGUB0D5BSINfo7a2LjCWmXog65F6IHE3RxQhX3OrZ87/jvTLy3ETHPxtI5sNT4zaCnslj1n+x1HKaHFrjXFyUp0zzNyAJj3vkZ1g4aQSAQ0LJhurr1LsajV6lSlzDMh51avPziyYWUTsdTS2mUlj/ilKwP7vVedNiK1iCRp3r/aea+6XEFanGgrjVMbWhRHaprKdTVtfLoI9CuAPzW8/hoR+aXAtwJfq5fUZpKxcMP73ktRv7hqpIwGEU4k2klZa+vEIEi0ZlutSkey4rSqhazKPG0Ocmeg+7JVrVrdxdjZdiY/Z8X/KjSVB5yvVCs4BCdQ0/0qhitaSECIliQnwfqD5lXJq3J6vmPa7Nger6Q1E5Kai9ANRqvVJKx5nIMhESowz/NAvD73WjMeUwFCjI1Aa5STOToCUnVrVXLIFv0fexpOzitgRlWKtZlXoHmgRtXH189Wx3a8lYO0u4hrnaVZ+FdT6BNQqCcPEZmBfx347/2tPwD8aEz8vwz8nst+p6rfqKpfpKpfdP/+vUundtGz1GGoePDafPUeRBHH8iy1haFhnzoURVfHOmvAyWWcf9RH++seYNyJxeuOuhirTQNyKa07ntSWMykRYkJDpDih784XducL52d7ducL+93KuhTW1ct9tzbb9rdydFTMCPEIqK63WjHe1f3lYFyrZWimRJompnlm3my7m3i2Zg5NRWjX6Vy1Rfl7SUaltRuvrXSCV2rum9f08qLKWspBxsCyruy9gcXjxqeDg34Z8O2q+oov2iv1AxH5w8Bf+OQPeeiabAA6h8TZfL2GXZDizGY+4ujIHuuymOLuxGPw0WHmp825v26i2ocOnFPRViKnfl+CEHQouyMFVW+16AcqzlWkphe38zqk5g1kS4F1LayrEaMZce6UoJbTqRvKDh5UzUIHUxAPOLv1mm858J46XNOIbeO6KuWWu6nf2dUgRUv3cNlxVzSnLnIUd6MFi2LC9Mwx7qCUleIp1PYwbHZZF8pnIKL+KxnEu3hVEX/584F/8MQjVMD3AherRlJ77X70kTibT12slN+NGze4desWN27c4PT0lL0fp+tHBydo4NIjU7rAJVX1wN6veKWxy/5JKYW8mrsy+HzTkIYR49Si7pu1Hez9mptUF7ByxJqSHL3GYe3SBqZOlLwDevDF6jVTqxUeRJhqwEw7ZnRjsjsr9vsdgULOxm3NBWyuycoNS+nFcXO2TsrF72wYYhUCPVfJNgoH/v11tWbA1Wv2uPF264MeAz8T+FXD2/+ZiHwQ21vff+GzK4bpJAYIHKYft4zFMBKZW93qjxBavfobN25wfHxsC7auXYSPxC41XtGOVVHmx6tEDYY+eC4SGjgwBkdUP7+IkOJ0UAAhBINdmrWewwWO48hAKSa+Q0JCTx+uhIt4yFvu1n4l0t1+z95bi8/TbJB61QVDT8ew9JLQgH7xBg2WiSnkrKy6b/MaubM911a8Vjz204i1Vxlp38vFsNlsfQCyE/lV4+3WZjoFnrvw3ld9sscRgdSaUTkc74CwEAlpcvzT8Umqzmg6p5KRGDhb90zbDe/7nPfzxhtv8PDhQ6v9k81gkZoNKmYJFzc6Sgq9CQDWyq+UwloqIG0VOcqAhRqCEJEgrcBuWfdoUOIkBJ0I3qImbY/QOLHLGc7NbUmGMM1sQmQuQpinId4zo/tziiizzBSv/JGSGTsh1uuPiCob7zicNVBkx74UTteFs/2OXFYmCltRSnGOv5pxtI0zm63VUhKEtCzENLEuiyXl5YwWISVF8wolEphIMlvKSPb6TyVTVmsaNm82hDgjGpAioIF1X02oYGiIRCQJQaMVSbtiXDNP0tWjEofSRbDb+iCW0nrjxg3uPX+fu3fv8pGXP8zu/NwiuC/CTJeoFNDVgYbOc8B42zyKA+USLAqqzseaPPQkurGacF4ze923XCkRb9AlvV9RjSOohkjOuZUMH7nnqOZcep+GFJMogRwv6pK9o8nRtPHIoiFm1qOrqgSrHZ0fqYVVhsZmcrVfaFyzquK8O1I+Loy639rwkt4XdU/7bnUxGpe5cesmL774Ih998SV+8Pu+n1c/9jFT6L3FohSFHAzf8z7m6yULXQ2bdlP9hmaPjax6c1kLRWoUk6fxumizasiWiIYqy2q+8RisRlFtAjunufnWu46mrUHuNMfW5tEWNTUru/v/h/unPeNzWfcEJ+x1XclRLINg6D8/Zp1qKYjr21XUH3jNGnF3g7I1YbvgFaqldmiEa5u+oSqqhHdL4YY6RpUQaO25mzEr9F6YPkQD6uJ6u91y584d7t69y3y0dcJR0MCYf2MQUzRU7sICV+I0GNI3h6sVGkzXKgMmmauXR8yQC0N2pND71pd1dTdQ6I0JEEooB103qsuxBwvnA+JY13RQ+LW2iqy8qBHRmsnLyuKBy2taWAMEFdYQDxL4Dro+R1MdogRW9xgxcEcdgmwUHpFE9Z5XbhyixUNI0JbNqS5l3jUBy22aI3EycFIRd5Y8Kt5C8J0fIikxGEo3rKrFWrMcHVQexPsI0ItIh4TqhqAHHCsNIUIjg0FkWF7Qw55NxmgVrdmPxVJRKneSRSEXznMvf1hFZY3/FIHsm6FyvdpKsX5/2ljefLPcPVSufh9M1dhLJGhBJiWJsCSrjfTw4UPDQd3fHpCWYGeqivWRGquWjEYQlYH4xsDjUYMjLBXNiLVIWYrGVISG0z5uXAsC7c7DywCfwTAZPu2Nv+obtU6lteY7OTnhxo0b3L59l4dvPiBktYZUJbfv12iietixDLg4RBncW6IePN1OF6SlzqqKE34+4MYV/lqXBRQEw0ZrosmqxXOHcoPPKk4Jcdic6p6nlXVZGgpQCXTezRSMQN88e5PT01N2u12DcfK6mvGpUPJCmQzvnKL1yoznzukGvVB1SLMJVmIoeIO1EAKmYHspPOl2gard5wDommEaMyKM8K12aCCsy5M8ndeDQC8bzXXGGDBRPUE9mU3AUjsGRT0AabPh5OSEmzdvsu727PWcdclIVjeYwIPOqI1t2qjcU6uodwu+dBiJwV1auXITz5VIS3YCVq+83MF7E5gBlR4TKb6A4sRMKcSSEe29MKv7EroH5qE8NA66LpydPeTBm29w+vAh5+fn5GKtxbWYmpGXSJk7gYYQmObQctctbgEol6QoVwNyuNf49VkGqxJKIVDMfVyB+WW11O7gG3CaUAK7Zf/uKR42joYr+qMuuPrTA1tRhGW/Nr0pRPPMhBBIXgs9zZNbxjtvqe25MGJEV5Phqv47qqSK66EuZteG23Wcdm1ZlrnpXkbRBpKHejWl46OleYaGmIAamCweVkdNcmMQ/z2YuwLf+3Uhe5D26flDzs7OOD8/Z10WikNmQa1dIhoRhckbf22n2ZL8lAMV4zL3b6vdpt7gVnqwYteRFcSzZYtaUzTWlmoTm1rgLRvfFd2O6ZagNOXZPhCBGJOXvXblWyysrfjr2TtbzJsNGiLL+QNSSjz33HO88vKH2Wwf8PonXmNKMxosdzbNE7IYhrlTW0j1Tm92s62ZliV5WbTQWF5mtJ5XT9HAG7BWLhmqCeCYaXTjqVnjOPAu8RH9LiZp0NM8p4PPLQ2kg+dRgqVlRDt+ra1kn7tUUVc5suXxL7s969HaYK3G7Twbdl2Xgzyp6nnq6+Q6fCnNM10T5Mzr5Yl35+ekGSRZW2+dtK3tquWd9SS9U+OiJV/FWtVFazR4FaeLZyxObijhBVhrMYeUJmtFQ+eUIQTzYFejiNBrWmJctEYs5ZxbkYLR7dljATrHN2u/Ep706sLj+15/s77epPmSKsyHBNo6aARzTCzLwm63M0vcCel82Zt0cV32bL9jXZfWhmbkiKUUdHUjqhh3P0jkGwyemlfUDFMPumkSh6p5ycHvzVgyiM2yTIWQM8nXrQaMXDWuHYFeNJZMzHuQsFyQv+C73VyWpdQ2z4HN9piTWze5c+cOr7z8coNwLHA2ECQhcXUR3Ks7N84wBIkUV/JLKa0NInQkocI7leCMIE01qAlyVmnZNkYrWutwzXazbZ6i3lEj9l6em2nwnVt7mgoPrR4RtCwL2/2e8+3M0XLM6ekpm9NTzs/PWg1/wcp6pxqWOOiQF+9pcVjMuKsSauPdCyrASJyVAZRSGtwm1cDLGdVCmGZmt+73y8Lp7vxKergWBDpa8fAozGQ6GQdVQOxzPfAqIdF2rXjgyMktbt6+5eIONJu+2TFKjyzX3AMb6AW1zIDqNfHbfLXOzkZxWKWy0gpO1yXrleJ6IQfjksFLhvcQwUqk02TgfEqJ7XZz0HwWMdG42WwagVZxvBQj1jdPH/KJT3wCqcaKFzBL0Up8bycP7h51UKWlDFO6J6pu1APdtEJyaAu5q8Zl5Z5QvDhbadKqqRLRoqd2u92VtHEtCLRZzVyAmep7chgtg3gD1WKWoSQrU23Yo31nnme34m8TvcKvcZFgyWbO7UIIrRThCDM1SjuAobohUzm6YnhfI1jfVVqjzRuy0P/WzZBcH81rbyXeAzG6SO3RWLTvXQTWq/FXxKKE4pRY3EhaHZ4iF2KAOU1sppnNNFsd0hAOuGmP4i+toEQRc2BoMGeFSkdX8Gok4iqNfb8gYoHXlbhHJlRKYSmZ/G7AQS8bB+lDDphXeaIOETXII4TmrqsBwtNkZWNOTk4csE+s+8U8QKEbODXue4zzhG65g0NdDG4+NURUxulh0VF1rk1H9mO1wD4/Rw3qFSyJTIAi1kZRJZixEgtFzBtU9UQt2sLtRDk0ulz8xxjZLXu22y3runB+fu7IwUqKvZ5qJcyLbQ0vVnmu8x4rCHYjsceoxrpxG25Nkz5RLNSvAvuXuWgvG9eWQMdRdcAL/LVdpHWuqPny1gA1OsR0fHzMzZs32Wy37M53llWoDJzq0DKHin0e1kmqC2dpHaV5tMxCfvRGGyFqa1N9cc6VC1u6/uVR/c2VeqEsTkyDxS+htcqW0NOjA9LCDysInyUQRA86dwCtWZf4vR7vSwPrL+qe9RqoRGpV8uyqHZh3GEYwx4K4iiJuS1Siv2pcDwKtAGe1hetCVXHZdub4dU/H0E4ECi0nhtg76x4fH3v5mEgNO1a1tIjYYkJ9HADRHRtskfX4SUYCHTjN2MHN6PxyoutqAn4eaXrqaGzZWta8JwW8h6fUqXZoSulJdqhaqGGzpoUFS8lIwQuajdy3EsoFCK0uxeF83HRVrxA4BpRweM1271wVqWkpgzR5V9QHFSDVCrwUdy9Wzub/SHDdx2pNlmBgbwgTMZvudnaeiSGwmTeuByWOb94lbrY898J7eP2Nh+xOz9yShzDP5o+ppcGjGwslEEqgSHY81EtsiWV/Jk87aQtcHbHSi5EFpeXnRLGy3hFvIuB94K1eEeSwR6SQkiAzxCTEKRCmTIiWRoIvtBYoa0BlIqaNdeGYvc9RNRBDROcNQWElEpZCnIS9BouNBWJIpGnL9uiEdDxRotcYkAy6onm1/vQBlmUllUjUFdY9mUwZwjw2cUI0e+aH1wZNEznZ4haUNBmBWvt4U8uOEKZ8tQ76xKQ5EfkjIvIREfkHw3v3ROSbReS7/O/d4bNfLyLfLSLfISI/+0nHH34HPCrm+hcq99Lh1ihID2KoEI0RtOlam82G559/nueff56bN2+aUeLNW6O3/LtwIr8zF6zrGJmH1oDVor7YonpsuT0GVVwWaGHoxKFuZ5d42OZxBMxHvW3UF0fOPIri4JkG4+MwJ0m6uB8gtlEPHdekxtKOXDY3fdWvIQyRTzVJzqWRenG4tQW0XK2HvpWszj8KfOmF974O+Muq+nnAX/bXiMiPw1KQf7z/5veLPKG+3iWjE6IPkQHVcVE3GDlVFMfoOlCNqElGoPfu3eOFF17g7t27pJQM2C6ZXIrDKH5kz5Ss4H2ztlNimlJTGS4+albkZp4HYj0E3g+IUvo5DggEwwwPSoYvvSZo5eR2ezoRrw2vLI/qj0H6POfZMjkvIdJ6zPWS1pJdBbCOeGuxbNUWu+qJdfWcXS0KrGrY8XidxTHQ3WLXedV4oohX1f9LRN5/4e0vB366P/9jwLcAv87f/9OqugO+T0S+G2vR/TeeeB73sDeDvdY2r503wCz0kVb9y0WVGMTFsXUCrvVC0jxxfOOEO8/d47n7L/DKK69YxTYCpZj4snjisbxj6EEeA8cKQUmD/lQhpCnUbEor0KpVJy20uIFRLlQjorafqVyqOnVyKMTmYjwk7BCEqKFDQEDWXSMyy2fK5Lya+1aLxat6qJuVS/TEuRSIaSgQXNS5thtKdtUYbVbjqNoAxTFiCwdcw0RUZRJBpFe0VmpWbnA3qRFtdTTs9+9MCfAX1TM31frCv+Dvvxf4m8P3PuTvPTJkKNzwOe97b7P2atPThirVHzT4olsWteYkVDHUAXwJVkFNFG7dusX56Rl3797l5MYt3nzzTUouSLQ+5znv65wOUioe7eHZVZBRjEapRoL3cseLkQkWXK09VaW4+mEwlVUtjjJ0lPPMAcM2C6nElvNU/esZK2GYnYjWAxetN23Ia8vqrDGizZMWDvtxrroQ1MIPuypBM7wuljhXxTxqKKoWPVaiV+KD3tBW3IVcDV/HQo37W0zsbn3n8+LHcZkCeamSoarfCHwjwBd+4U/QVrlOaqEEbZ2KG1HQyyhKrToi2htRVTDcsT5LX4gcnRyzOdqyPTnm6OiIECfO9w+tq2+M5H3xRqteZsc5Ra3EDI/CLAeEWtEH7QhAkUqkFSKjRTLRrkuRDFkyUtzD5Tp1PXa1chtyUUW7GLsvClkGCzyXHim/LpQ1t8CSmpdVizfEeSJtptYsosUqqLoU8cCcYK7hil9akLZXyRPFAuwqPFU5p1ekDz3moHjcbHFptWppxX8fNz5VAn1FPP9dRF4CPuLvfwh43/C9zwZ+5K0csMZf2mIGJ/VRRR7ErQJ0LLKWhql94ZvCLyY+gySmzcytO7d54aXP4o3Th3zk5cUjaQJrKc2yrOm2GiwSCB7llhVuwWsVUbrIV8S7t5kOJmJWeE1uEK3OLiNcwSLuqw5qKSX9WitsVj0xVRw3DFHrBu5Ec2Bg5ZXGI5wzV8Nudp05enxBYSXk0gpd2LF10FODW+/a1BQGoi54Hr3LEHVsNgbr05RLaapCU0meQBefaumbPw98tT//auDPDe9/hYhsROQDwOcB/+9bO2StNRQOi3s1Y8jxwgPwW0EHQyGvVsigen1yZi0ZSZGj7Qn3n3+Rz/3A+3nPZ7+XG7dvQQxeisV0ULtxvjhhrFVqw7JHi+XWVHCc/ruefGbcOLqV3o47uk8v+K4rLlojpWAwOOJgyAzft3QJ58RiMBfUJD4rOKaaTfeMHb6ylJHgZR5DQyDClJAYCI6RGvfzlI4WwSSu91YYUJsHrDSMtBa49Qh8j/80a1+9UEqy9y5xcozjiRxURP4UZhDdF5EPAb8F+Hrgz4rILwd+EPgFPtl/KCJ/FvhHwAr8ezrWDXz8WcDrgTbg1o3/plk692o0U5mpKrmsTRyGaDqPQRgWypXmiWm74fY9Q8N2+5U3Hjzk7HzPw4cPWfJq0d4krzHk3TFiREpuVTZyWVgXq/hxvN0igqU3eIZikeIJftJrPwHEGjvgCaY48UXDR9eyWv3MmFpt0A5lxUeItPjG1BVf8B4k3SCnMLQizLlJB7u1wQqKJevtHtKhymLEmdr9tSgxmnpiOVLYJvW6UJNzY0mzuY4LRC+VGxLsl4UsgSkENpuZNBcyytnZ2ZWU8Vas+K98zEdf8pjv/w7gdzzpuJeOmph22UeY3nZowpuVX6vL1ejsIAE8J7142u+83XDkgRP3nr/PZ73vvTw8O4WPfpTXzk6pNUhr4a9SvJbmhcpx4tqy5ex0/zWqrX1h5faV24Rm/XbRHSQ0r9EUpyGaqQccx2lqpSPhkHvaQXyDLt07Vl2jPTVkaEt4AZc1whes6ofVOFXVhmYECa24rYhJm9XvoYqYkRlSn5+HLVo4RKkTZlkzGj0gPK+U3cLp6SkPHz7k4enbJNDP2BjhUrmMRC8n3KqP2sL49w4auNpunjYbTlz3eako+8WqMO/WlTdff42QEtlCdDw8zYWUBFyzc7ikEmrxuLyM5q6HXZytcfbQpPoBkB6EKFADPKahunINyZPU1QjTM/UAKA/eoODAwSGmgqiasVYrz8UYHhHxIpBLP15uLXYExAwl038VLeLla7Ql2MUYnRvXBmRmKFEKQYxYowSCQ25rVhbd8ebZKW8+POX09PRKsrg2BFo553ijR7Fz1ajgbw2mKCF2VNVRgehBCluEu8FE8muvvcarH/84D2/f5ezslP3eYhOTG0DWsaPrWjVHOaCe2uBxoi2pz0vs1OsZ5+/cdvTVN787+RG/+IHeGaqBZDpmjYJqumi0yPXoIjx44luQXtcqOTFN00ScDr1drV6+3+YWJ0AN+rBQO8TqTElMSKQdK3lGQHEr14qnFUtzUdjERPE5FFXWZWW/MyO1ux4uH9eGQN0sHgj1LfzE8bWUIlaKppfLtoK4NIMkRDN+ZrFUClXl+c96iRc/9irnD97glVdWHjx4AyjcODomRsfsnC1XnNM4sgHgLUOz5dH1nkp1flLt92qN16AQKhIASECaq3bwLA0Naqt6U12HzapHWcVKNVp6SXU3mrOh/j6EwDS4aKMbRCJW1RnVA9xX21/j2jVqKkpkisHrsLoB5IHewYmzRuFLKIQCcZ6soS3iSX5uvMbgTS8eP64HgQ6wg79BvUVy4bOOCtbXDrPUR+2462i/6YAdV0wpQQyc3IDnX3iBz3n/+9m9/jqnp6d8/OMfp+TSDIyah1QzI1ErQlC8QptiemgsDoYM/TGHS2scb9RRWpUUoVeGjpEwdTTAdEe6V8eU4wOoCe2Bw1ksWqll1NvJe5GHS12vTsg6EUJBZO/zqihGd2PWiKSQYvO3q9LCAVf1YrvLYsaR4JH53XgbK9ptt1tu3Lp1JWlcDwIdRr3pVA7zyKg+mfFHZi8bLtjdgtXBomKeD7zMeHBCvXv3Lu9///tZnEBff/11Th8+BHrzq5HgajiZarGUXs2G8Wl0zjgGati5a6R7nfrBsdRx1Fiv/VDPLo4bjoHDXIhhvRjOVx0HzdUovU7nZXEBj95/S4MJ2rToYUMEDxZ3bg6ou0cbTlqKt+fu6Sx1DSpcJUHYbDbcunWb8gSk83oQqFbzV/rfg4994cf3LhyiYoy1CSzU1tFe2bcRveXSFJSj4yOe4zkevue9vPrqq3zkIx/xYI09+/2CKKTQ56DVTanWYKByDpFiaSOhw2IG1BdzIbqoF9dXO9Ebl7ciXcKaq5zuUUnqlvDjR1cJWnAKFzfBJV4wpW2Qir9CdQw4wgBmKIWuupRSPAahF5xY1r21DarGIBZjEMS4bePcIVAQJkwFuaEGL141rgeBUjE25zYj1xnGANkfWMyhtZsw7tp1QHu/Gl81ZlLJCMLR0Ybtdmb/ns/i1U+8ymuvvwZS+MiHXyGXQgyBJWfmlBAiOS+c7/fkvKLZuUSYWEJpnMvwwtrVTbA2tbaBYvHiY/1iACXGwpptXqUkSowtX6nehyjBuXQcdHTLwZoc6oliZX+qjmu31JLgAoGgXp8z0wwawUrV6H6h7BbCWghFWoE2LUqMk987K0K77jqEVaTirFapTmIyYg0zGhNZEmGeOV9N6myOjtlsEnHNFDlH4tUkeG0I9KpxkVgvRqVreNQXMOp8clEB5PD13bt3efHFF3nttdesdMxrr/PGsrNcp8E1F4LFmmoJ3imttqLpqSeFWpABKoccJHyLLxiuhpzdw6Kdm+lQErFW97Na+BXoD+1R79HoAm0GnIg7gTpCcBD2hzV01RCIya6vdiupcFbVH9dW2Le0ZDdrVrs3RwNYGxzpzoY0m4Uf3No3L1UkRmvneKC+XDLeFQQ6jvGG9Ys7rHZh4v4wK1IHeGfkwIJw69YtXnrpJXa7HWcPT3nllVc4PT214gkxUGppxLrbi6IlH8wJql/ZDQrDmZq6Adr0t8PuwGb11rSJllHZijvUQg+D2A/Bo7iqzu4p1Fj7neBFIeoQ8U+jF9etfL7UnSNoiIgU8/A6pKbuSm1dmHNhnz14Gu8sB2RdiWptz0Oyv+YJs84hBQ8SKYV1sfTnfV5Z9taK56rxriLQkXMeZh2WR4izfr+qBJ0Ld2xSXSXIKDdu3ODFF1/k1Vdf5c6dOzx8402CVVBiydkLrRqcY7B972x8MEez7Y3bhYqHBirAr8M/VV8rWAFeXIxqdCNETJczKaEE1YZIZDEFZowYOOCgrip1/b1TbFbTmasa0AorlBpCpy0vvtRwPQ/uXopBRIp6cIxts+CpyNUjF8QDomMyyEsSEFiWlbUUdsvi2bH/PyDQTkwMYhB/rQecsnLT8fudmxxa5ELN71bSZsOtu3d48cUXeeml93J+fs7pm2+wOzuzlOQQiCQPIvHAYBZf/FHR70ZJkGgNYF3kivS5NoymcXssIquCGKpW775Bbh5vKvU6zD9QQo2TsqDtgwQ4EXcu9OpzXCZSS9XN+z1dS26VRfb7fc9jd+6pQTx8LxDjbOUgxSA8CcmdC3av580RRAvXK7mwX1dKAYmR6ZGUm8NxTQi02r5c8rcSY+V8o6h2I6jqX/Rg4wN9TrVZpuOxnYGSvHhBjJEXXvos3v+BD5Dzwg/9wA/y5oMHbr1jkT701IwKtQT3TFVjLAS8J5EHetSyMVqDmtWzA7pBqK6bZoVQyqAaVKwM41iN9ipWq4YU+PWaa9NTXxT7vasbilR6bdLDfQj0l+J0rCyeknF2du44pzfXjcGNNkcPJkcSpnnwzdv6ZBzuk4ISWBfDRSUGjrbHbI6PrqSMa0Kgb30cQCX1vdoW0cVb565jktnAuTqbsrVznW7ebLh//z4f+MAHWJaF119/nR/+4R9ya1ya/xmEsK7NL14NlTpafpTrkUYFFTgUKBmVAKFG4NeSivb7AhbrqqAqrU1koGO7wQs2l4J1DHFdFA1ot+Gp2NcYjEw1LsuhBGq6Z1b2S+Z8Z2kZp2c7U6tQlyRCSMYpgwe4SLJ2OSEm8PA6i/EN7JfscbBi8U0hst1suX3vLrdv371yva8NgV6EQes9G59XY6MSYtVBD/SuJsZ704V+TDWYZhDzABnTyyaJbI+OuXv/OZ7/xPOcnJx4B4+MoB5xL455JkQcoPbeTk3fC7XRgecteRdks46cYKT4bxopHcyplNIYqIiQcUeDBKC0Xkw6/D3Q0d1AU3X9kGGPNGK1DbzPq9Vj8oXILtbP9zuW3Z7dbufYJ0iKoMEIR8QKZoRo6kwM1mKGiEi0FPEQKQSSdH10Cokbt29x7/7z3L9//0q6uDYECnKBEPvzi25QE/NNHRx0wO4dqSLbxFXFKeNwLKsDD67jlmJQC7A9Oub2vbu873Pez4MHD3j5hz/E7uwMtFCAJa8Oih+zX86tGC50ApWqYtaNYlzX3LIuIqHVMdK8Q2v6Q3MJutVLYZ6CNToJkKQezx8SWHMBKciaUXH9rkqaoixDdmZrJAusiuGaq0fg75dGkPvzHdlzmvbL6oEhswHvnnBYsA52hOCwmFnuIUVL61C752u2vPyUZk6Oj7h15x73X3ieO3fuMM/zlVRxbQj0EBK6HKh//Hicu6xz0tGQuWhoiVhnNwm0AIY79+7y0me/l9OzN9ntdrz60Y+wO3voSqdFS4UQmGR7YLDVeqDiaHolBiom6s/FsU1r2e7iuYrkwbIVMb3RPFEe3+rEaRndoW3Uim6skluxMsBqf0r9XJuxVjMRTs/OKLV94rkR57JYPhNASMkftQ99Ty1Z10xMk92TOBHihISIBlM1igjbzYbNyQ1u3LrN0ckJt+/e4969+9y6c/vtE6iI/BHg5wIfUdUv8Pd+F/CvAXvge4BfpqqviaUn/2PgO/znf1NVf/WTzjGO7uLTT5FY28z9X2FMzW9JXU33Ump/H1VQCcR54s7tu7zvfSsR4Y3XH7A/P7NSgcXzh0SbizGvQ++kioOOOp8czkvqxqEXQlO5UOZRtOnVJrKHNjNSI/br9Rn2qUUs6NgJsjV3VpBCg6sAyppZlsyyrJyfW3eQ3dk5u51xTkrXZLfbbetyJzF4EwgnwOI11ooVLcm+AVBzQEhWTrZH3Lhxy7Jqb97mxq2bHB2fsJm3xHm6chU/1cIN3wx8gar+c8B3Ar9++Ox7VPWD/njrxOm00p4Pf+UJ/x2OARFQGMV+O9UBlmp6Yc2FUlVCiEzbDTdv3+Le8/d56b3v4fbde8zzTEzR00q8lqgADn437w69lpMZUjavaiMpxjG1Bgcj1HTlerVWQ797i0QsR6h6YoJXkiZYq0hCRCVgWoK7JHNpVvOSjdstS2a/XznfL5zt9pzt9pzvV873qxdSyBhAEayF+Lxx0T4R00yKM9O0Ic0bJE6WGqKBotbXM9e4hyCEZEUtbt64ze0797hz7z537t7j+OQG07SxPKYnZM19SoUbVPUvDS//JvBvPuk4Tx7uDRo4n52sf/bkMRDi8LsK2FfYyd7r8FVthmoQiSJqkeSbecvJyQnPPfccd+/e5ROfeJXTN950LLCgq3o3YQ/POIA4tWGQEuMgtWtOZOey4KpNFfuMUFo3AmM8LPp12cYDWhAH0L1WquxL6R4dN4TGKia1egli+upYfaS5SFMkpiE80ONWTWUJBPHy68cnnNy8zcmtm9x7/gXu3rvH3efusz06MmKfJ28b9M7joP8O8GeG1x8Qkb8DPAB+k6r+35f9SIbCDe/7nPc9Ch095vnFMWKkV3/n4Ox+3O5eNLjJoJ1aypCjLUcnx5zcvM1zz9/ntdc+zrLbc3b6ZkMQlmVhmvrmEjrsoO2fQSgUqNhmpqKFg7EmeP+lRyOQxOX2iEJYHfnh9/VMQxpHrRhSazzpUC5nXVdrXptre21xA9QzMqtjQqsnq2aaWlieCh4obvOK84ajkxvcuHWLO/ee49btuzz33HPcvHWbo+Njps3WG+JOTbW6arzddty/Ecve/BP+1svA56jqqyLyhcD/LCI/XlUfXPytHhRu+Il6kWO8/dG550igo3HkJ2sGhPHZWpImeH34TeOiH//Yc3z8Y6+yW7K5QUMgayFpN3q6jinDvnFFsOm+PcnNaK5+cXCJyhiRhKkERRxLNBw0Z3es7jv01oh3qK+UsxXB3e3c+FnWljdvfv86Y8ufSo07RlqVFLUNFRw/reknUDd0YLs95vbt29y5d5dbd+5x774R6O2795iOt+ZRCp5ePszxqvEpE6iIfDVmPH2JumxRq8m08+ffJiLfA3w+8K1v4XhvmWt+8qOS3iXnhda1DpfKa8mkujTi9e5v3ODk5AS8vYqphOGRORsHGutK9c+r6B/BcVWshPZVs78E5+xBwjYeqY5Xic/F+bq3riC73a4RaA26CRvzpAWRoX5+9KCTQ4PVei65a9Q3+vboiKOjI+7cucP9+/e5/8Lz3L77HHfv3efm7Vtsbt2CKYGKFR1r90Levg562RCRL8WKhf00tZ7x9f3ngY+rahaRH4UVbvjeJx3v27/973xsnm4+BD72qcznmo37PLuOT3Z87uM++FQLN/x6YAN8s+/aCif9VOC3iciKSYRfraoff9I5VPV5EflWVf2it3Ax13o8u45P8zz0QCF7euO63JC3O55dx6d3fKq1mZ6NZ+MzMq4TgX7j057Ap2k8u45P47g2Iv7ZeDYuG9eJgz4bz8Yj46kTqIh8qVhHkO8Wka972vP5ZIaIfL+I/H0R+bsi8q3+3mM7oFyXIZ+hzi2fjvFUCVQszOj3AV8G/DjgK8U6hbybxr/qgTHV4r20A8o1G3+Uz3Dnlk91PG0O+pOA71bV71XVPfCnsU4h7+bx5VjnE/zvz3t6U7l8qOr/BVzEpx837y/HO7eo6vcBtXPLZ2Q8bQJ9L/BDw+vHdgW5pkOBvyQi3+bBL3ChAwrwwmN/fb3G4+b9VNfoaUfUX+aEfjfBCl+sqj8i1obnm0XknzztCb0D46mu0dPmoJ9yV5DrMFT1R/zvR4BvwkTfK2KdT5DDDijXfTxu3k91jZ42gf5t4PNE5AMiMmPK+J9/ynN6S0NETkTkZn0O/CzgH/D4DijXfbwDnVs+DeOyUK7P5AP4OVjayPcAv/Fpz+eTmPePAv6eP/5hnTvwHGYFf5f/vfe053rJ3P8UFru7YBzyl181b+A3+vp8B/Bln8m5PvMkPRvXejxtEf9sPBtXjmcE+mxc6/GMQJ+Naz2eEeizca3HMwJ9Nq71eEagz8a1Hs8I9Nm41uMZgT4b13r8f/z8E6CyyqxYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, i in enumerate(face_df.sample(6).index):\n",
    "  plt.figure(figsize=[8,8])\n",
    "  im_row = face_df.loc[i,:]\n",
    "  my_image = Image.open(im_row.names).convert('RGB')\n",
    "  plt.subplot(3,2,idx+1)\n",
    "  plt.imshow(my_image)\n",
    "  plt.title(f'Gender {im_row.male} and Age {im_row.age}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "75e0vv2EW9ZA"
   },
   "source": [
    "With the images properly separated, we will first separate the images into two dataframes. One for dataframe for Males and another for Females."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "uBxMo0jGL-MT"
   },
   "outputs": [],
   "source": [
    "to_male = face_df[face_df['male'] == 1]\n",
    "to_female = face_df[face_df['male'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8j9vyFQWXO11"
   },
   "source": [
    "Once again we will verify that the dataframe assigned to the male gender is indeed pulling male images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "gltrzTllTOnF",
    "outputId": "916027d7-913e-446f-fb2a-477300d28a22"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, i in enumerate(to_male.sample(3).index):\n",
    "  plt.figure(figsize=[8,8])\n",
    "  im_row = face_df.loc[i,:]\n",
    "  my_image = Image.open(im_row.names).convert('RGB')\n",
    "  plt.subplot(3,2,idx+1)\n",
    "  plt.imshow(my_image)\n",
    "  plt.title(f'Gender {im_row.male} and Age {im_row.age}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9kd2GadIXZZy"
   },
   "source": [
    "We will repeat the step, but this time retrieve images of Females. This is once again to verify that the dataset is indeed retreiving the correct images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CL_4VfxFX1VY"
   },
   "source": [
    "We will now create global parameters which we can use at several points in this notebook. It will also be easier to remember number assigned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sqld3FCnL4Tc"
   },
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "otQKmH-brXxc"
   },
   "outputs": [],
   "source": [
    "#Creating the Parameters\n",
    "\n",
    "parameters = {'ngf':32,\n",
    "              'ndf':64,\n",
    "              'num_epochs':100,\n",
    "              'decay_epoch': 100,\n",
    "              'lgG':0.0002,\n",
    "              'lgD':0.0002,\n",
    "              'beta1':.5,\n",
    "              'beta2':.9999,\n",
    "              'lambdaA':10,\n",
    "              'lambdaB':10,\n",
    "              \n",
    "              'pool_size':20, \n",
    "              'img_width':256, \n",
    "              'img_height':256, \n",
    "              'img_depth':3,\n",
    "              'input_size':128,\n",
    "              'resize_size':286,\n",
    "              'crop_size':256,\n",
    "              'fliplr':True,\n",
    "              'num_resnet':6,\n",
    "              \n",
    "              'batch_size':55, # tuned to my GPU\n",
    "              'num_workers':4,\n",
    "              'pin_memory': True\n",
    "              }\n",
    "          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y4gcJSynYgR9"
   },
   "source": [
    "## Creating functions\n",
    "\n",
    "In the next set, we will create several functions that will be used throughout. The first function will convert our data into Numpy, which we can call at a later point.\n",
    "\n",
    "We will also set a variable so we can use a GPU for faster processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "iM56xhMDyBIu"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert to Numpy \n",
    "def to_numpy(x):\n",
    "  return x.data.cpu().numpy()\n",
    "\n",
    "#use GPU\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "in6MLzoYZBRb"
   },
   "source": [
    "### Transformer\n",
    "\n",
    "Since we will be using a __transformer__ at a later stage, we will set a transformer variable and assign it procedures which transforms an image size accordingly. This transformer will also convert the image into a Tensor and Normalize it as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "uDjZWE75jLwO"
   },
   "outputs": [],
   "source": [
    "trans = transforms.Compose([\n",
    "    #transforms.Resize(size=parameters['input_size']),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kt-ZRdnWZvZL"
   },
   "source": [
    "Defining a function which will __plot__ the result from training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Nah4HAftdxb3"
   },
   "outputs": [],
   "source": [
    "def plot_train_result(real_image, gen_image, recon_image, epoch, save=False,  \n",
    "                      show=True, fig_size=(15, 15)):\n",
    "   \n",
    "   fig, axes = plt.subplots(2, 3, figsize=fig_size)\n",
    "   \n",
    "   imgs = [to_numpy(real_image[0]), to_numpy(gen_image[0]), \n",
    "            to_numpy(recon_image[0]),\n",
    "            to_numpy(real_image[1]), to_numpy(gen_image[1]), \n",
    "            to_numpy(recon_image[1])]\n",
    "            \n",
    "   for ax, img in zip(axes.flatten(), imgs):\n",
    "     \n",
    "     ax.axis('off')\n",
    "     img = img.squeeze()\n",
    "     img = (((img - img.min()) * 255) / (img.max() - img.min())).transpose(1, 2, 0).astype(np.uint8)\n",
    "\n",
    "     ax.imshow(img, cmap=None, aspect='equal')\n",
    "    \n",
    "   plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "   title = 'Epoch {0}'.format(epoch + 1)\n",
    "   fig.text(0.5, 0.04, title, ha='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l_qDIcq5Z3KJ"
   },
   "source": [
    "Next, we will define an __ImagePool__ function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "VM0YNJl5y58U"
   },
   "outputs": [],
   "source": [
    "class ImagePool():\n",
    "    def __init__(self, pool_size):\n",
    "        self.pool_size = pool_size\n",
    "        if self.pool_size > 0:\n",
    "            self.num_imgs = 0\n",
    "            self.images = []\n",
    "\n",
    "    def query(self, images):\n",
    "        if self.pool_size == 0:\n",
    "            return images\n",
    "        return_images = []\n",
    "        for image in images.data:\n",
    "            image = torch.unsqueeze(image, 0)\n",
    "            if self.num_imgs < self.pool_size:\n",
    "                self.num_imgs = self.num_imgs + 1\n",
    "                self.images.append(image)\n",
    "                return_images.append(image)\n",
    "            else:\n",
    "                p = random.uniform(0, 1)\n",
    "                if p > 0.5:\n",
    "                    random_id = random.randint(0, self.pool_size-1)\n",
    "                    tmp = self.images[random_id].clone()\n",
    "                    self.images[random_id] = image\n",
    "                    return_images.append(tmp)\n",
    "                else:\n",
    "                    return_images.append(image)\n",
    "        \n",
    "        return_images = Variable(torch.cat(return_images, 0))\n",
    "        \n",
    "        return return_images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mWviChqSaQoP"
   },
   "source": [
    "### Creating the Dataloader\n",
    "\n",
    "In this section, we begin by creating the __dataloader__ which we will use to retrieve the data in parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "IYnaXef7zAFj"
   },
   "outputs": [],
   "source": [
    "class DatasetFromFolder(data.Dataset):\n",
    "    \n",
    "    def __init__(self, image_dir, subfolder='train', transform=None, \n",
    "                 resize_scale=None, crop_size=None, fliplr=False):\n",
    "        super(DatasetFromFolder, self).__init__()\n",
    "        self.input_path = os.path.join(image_dir, subfolder)\n",
    "        self.image_filenames = [x for x in sorted(os.listdir(self.input_path))]\n",
    "        self.transform = trans\n",
    "        \n",
    "        self.resize_scale = resize_scale\n",
    "        self.crop_size = crop_size\n",
    "        self.fliplr = fliplr\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.image_filenames)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "\n",
    "        # Load Images\n",
    "        img_fn = os.path.join(self.input_path, self.image_filenames[index])\n",
    "        img = Image.open(img_fn).convert('RGB')\n",
    "\n",
    "        # preprocessing\n",
    "        if self.resize_scale:\n",
    "            img = img.resize((self.resize_scale, self.resize_scale), \n",
    "                             Image.BILINEAR)\n",
    "\n",
    "        if self.crop_size:\n",
    "            x = random.randint(0, self.resize_scale - self.crop_size + 1)\n",
    "            y = random.randint(0, self.resize_scale - self.crop_size + 1)\n",
    "            img = img.crop((x, y, x + self.crop_size, y + self.crop_size))\n",
    "        \n",
    "        if self.fliplr:\n",
    "            if random.random() < 0.5:\n",
    "                img = img.transpose(Image.FLIP_LEFT_RIGHT)\n",
    "\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "\n",
    "        return img   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Es9nw6Xaecu"
   },
   "source": [
    "### Creating the Convolutional Block\n",
    "\n",
    "The __convolution__ block will consist of two things.\n",
    "\n",
    "1. Initialization\n",
    "\n",
    "2. Forward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "KLJ6rMcTpZRv"
   },
   "outputs": [],
   "source": [
    "class ConvBlock(torch.nn.Module):\n",
    "    \n",
    "    #Initialize a dunction\n",
    "    def __init__(self, input_size, output_size, kernel_size=3, stride=2,\n",
    "                 padding=1, activation='relu', batch_norm=True):\n",
    "        super(ConvBlock,self).__init__()\n",
    "        \n",
    "        self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,\n",
    "                                    stride, padding)\n",
    "        self.batch_norm = batch_norm\n",
    "        self.bn = torch.nn.InstanceNorm2d(output_size)\n",
    "        self.activation = activation\n",
    "        self.relu = torch.nn.ReLU(True)\n",
    "        self.lrelu = torch.nn.LeakyReLU(0.2,True)\n",
    "        self.tanh = torch.nn.Tanh()\n",
    "    \n",
    "    #Defining the forward function\n",
    "    def forward(self,x):\n",
    "        if self.batch_norm:\n",
    "            out = self.bn(self.conv(x))\n",
    "        else:\n",
    "            out = self.conv(x)\n",
    "        \n",
    "        if self.activation == 'relu':\n",
    "            return self.relu(out)\n",
    "        elif self.activation == 'lrelu':\n",
    "            return self.lrelu(out)\n",
    "        elif self.activation == 'tanh':\n",
    "            return self.tanh(out)\n",
    "        elif self.activation == 'no_act':\n",
    "            return out\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-XBNtHuHbZp9"
   },
   "source": [
    "### De-Convolution Function\n",
    "\n",
    "In this class we will create, we will need to bring back the convolution from the results previously processed. There will be two functions inside this class.\n",
    "\n",
    "1. Initialization\n",
    "\n",
    "2. Forward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "YF4rQT_VpiDU"
   },
   "outputs": [],
   "source": [
    "class DeconvBlock(torch.nn.Module):\n",
    "\n",
    "    #Initialization Function\n",
    "    def __init__(self, input_size, output_size, kernel_size=3, stride=2,\n",
    "                 padding=1, output_padding=1, activation='relu',\n",
    "                 batch_norm=True):\n",
    "      \n",
    "        super(DeconvBlock,self).__init__()\n",
    "\n",
    "        self.deconv = torch.nn.ConvTranspose2d(input_size, output_size,\n",
    "                                               kernel_size, stride, padding,\n",
    "                                               output_padding)\n",
    "        self.batch_norm = batch_norm\n",
    "        self.bn = torch.nn.InstanceNorm2d(output_size)\n",
    "        self.activation = activation\n",
    "        self.relu = torch.nn.ReLU(True)\n",
    "    \n",
    "    #Forward pass\n",
    "    def forward(self,x):\n",
    "        if self.batch_norm:\n",
    "            out = self.bn(self.deconv(x))\n",
    "        else:\n",
    "            out = self.deconv(x)\n",
    "        if self.activation == 'relu':\n",
    "            return self.relu(out)\n",
    "        elif self.activation == 'lrelu':\n",
    "            return self.lrelu(out)\n",
    "        elif self.activation == 'tanh':\n",
    "            return self.tanh(out)\n",
    "        elif self.activation == 'no_act':\n",
    "            return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yMTWZK_QcFI_"
   },
   "source": [
    "### Residual Learning Block\n",
    "\n",
    "In this secton, we will create a __residual learning__ block or __Resnet__. This class will also contain two functions.\n",
    "\n",
    "1. Initialization\n",
    "\n",
    "2. Forward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "mr_C4hnKpkyw"
   },
   "outputs": [],
   "source": [
    "class ResnetBlock(torch.nn.Module):\n",
    "    def __init__(self,num_filter,kernel_size=3,stride=1,padding=0):\n",
    "        super(ResnetBlock,self).__init__()\n",
    "\n",
    "        conv1 = torch.nn.Conv2d(num_filter, num_filter, kernel_size,\n",
    "                                stride, padding)\n",
    "        \n",
    "        conv2 = torch.nn.Conv2d(num_filter, num_filter, kernel_size,\n",
    "                                stride, padding)\n",
    "        \n",
    "        bn = torch.nn.InstanceNorm2d(num_filter)\n",
    "        relu = torch.nn.ReLU(True)\n",
    "        pad = torch.nn.ReflectionPad2d(1)\n",
    "        \n",
    "        self.resnet_block = torch.nn.Sequential(\n",
    "            pad,\n",
    "            conv1,\n",
    "            bn,\n",
    "            relu,\n",
    "            pad,\n",
    "            conv2,\n",
    "            bn\n",
    "            )\n",
    "    def forward(self,x):\n",
    "        out = self.resnet_block(x)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WL_Aa8m0g5OB"
   },
   "source": [
    "##Creating the Generator\n",
    "\n",
    "We will now create the __Generator__. The Generator will consist of the following:\n",
    "1. Encoder\n",
    "2. Transformer\n",
    "3. Decoder\n",
    "\n",
    "In addition, we will also be adding __weights__.\n",
    "\n",
    "After creating the class, we will create the actuall Generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "sO6cRW__hB-E"
   },
   "outputs": [],
   "source": [
    "class Generator(torch.nn.Module):\n",
    "    def __init__(self,input_dim,num_filter,output_dim,num_resnet):\n",
    "        super(Generator,self).__init__()\n",
    "        \n",
    "        #Reflection padding\n",
    "        self.pad = torch.nn.ReflectionPad2d(3)\n",
    "        \n",
    "        #Encoder\n",
    "        # Input Layer - NN Layer 1\n",
    "        self.conv1 = ConvBlock(input_dim, num_filter, kernel_size=7, stride=1, \n",
    "                               padding=0)\n",
    "        \n",
    "        # NN Layer 2\n",
    "        self.conv2 = ConvBlock(num_filter, num_filter*2)\n",
    "        \n",
    "        ## NN Layer 3\n",
    "        self.conv3 = ConvBlock(num_filter*2, num_filter*4)\n",
    "        \n",
    "        #Transformer\n",
    "        self.resnet_blocks = []\n",
    "        for i in range(num_resnet):\n",
    "            self.resnet_blocks.append(ResnetBlock(num_filter*4))\n",
    "        self.resnet_blocks = torch.nn.Sequential(*self.resnet_blocks)\n",
    "        \n",
    "        #Decoder\n",
    "        self.deconv1 = DeconvBlock(num_filter*4, num_filter*2)\n",
    "        self.deconv2 = DeconvBlock(num_filter*2, num_filter)\n",
    "        self.deconv3 = ConvBlock(num_filter, output_dim, kernel_size=7,\n",
    "                                 stride=1, padding=0, activation='tanh',\n",
    "                                 batch_norm=False)\n",
    "    \n",
    "    #Forward Function\n",
    "    def forward(self,x):\n",
    "        \n",
    "        #Encoder\n",
    "        enc1 = self.conv1(self.pad(x))\n",
    "        enc2 = self.conv2(enc1)\n",
    "        enc3 = self.conv3(enc2)\n",
    "        \n",
    "        #Transformer\n",
    "        res = self.resnet_blocks(enc3)\n",
    "        \n",
    "        #Decoder\n",
    "        dec1 = self.deconv1(res)\n",
    "        dec2 = self.deconv2(dec1)\n",
    "        out = self.deconv3(self.pad(dec2))\n",
    "        return out\n",
    "    \n",
    "    #Creating weights inside the Generator\n",
    "    \n",
    "    # def weights(m, mean=0.0, std = 0.02):\n",
    "    #   for m in self.children():\n",
    "    #     if isinstance(m, nn.Conv2d):\n",
    "    #       nn.init.normal_(m.weight, 0.0, 0.02)\n",
    "    #     elif isinstance(m, nn.BatchNorm2d):\n",
    "    #       nn.init.normal_(m.weight, 0.0, 0.02)\n",
    "    #       nn.init.constant_(m.bias, 0)\n",
    "    \n",
    "    def normal_weight_init(self,mean=0.0,std=0.02):\n",
    "        for m in self.children():\n",
    "            if isinstance(m,ConvBlock):\n",
    "                torch.nn.init.normal_(m.conv.weight,mean,std)\n",
    "            if isinstance(m,DeconvBlock):\n",
    "                torch.nn.init.normal_(m.deconv.weight,mean,std)\n",
    "            if isinstance(m,ResnetBlock):\n",
    "                torch.nn.init.normal_(m.conv.weight,mean,std)\n",
    "                torch.nn.init.constant_(m.conv.bias,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d_l3oAV-dZwA"
   },
   "source": [
    "### Creating the Generator and Testing the Neural Network\n",
    "\n",
    "Since we will be using a Cycle GAN, which requires 2 Neural Networks competing with each other, we will create 2 Generators as its requirement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JIJRhsDF9FBt"
   },
   "source": [
    "#### Generator A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GWqNSHCIdbFI",
    "outputId": "695e8a81-4b1b-4946-ad09-65c627d41117"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Generator(\n",
       "  (pad): ReflectionPad2d((3, 3, 3, 3))\n",
       "  (conv1): ConvBlock(\n",
       "    (conv): Conv2d(3, 32, kernel_size=(7, 7), stride=(1, 1))\n",
       "    (bn): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (tanh): Tanh()\n",
       "  )\n",
       "  (conv2): ConvBlock(\n",
       "    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "    (bn): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (tanh): Tanh()\n",
       "  )\n",
       "  (conv3): ConvBlock(\n",
       "    (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "    (bn): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (tanh): Tanh()\n",
       "  )\n",
       "  (resnet_blocks): Sequential(\n",
       "    (0): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (1): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (2): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (3): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (4): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (5): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (deconv1): DeconvBlock(\n",
       "    (deconv): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
       "    (bn): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "  )\n",
       "  (deconv2): DeconvBlock(\n",
       "    (deconv): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
       "    (bn): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "  )\n",
       "  (deconv3): ConvBlock(\n",
       "    (conv): Conv2d(32, 3, kernel_size=(7, 7), stride=(1, 1))\n",
       "    (bn): InstanceNorm2d(3, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (tanh): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Generator_A = Generator(3, parameters['ngf'], 3, \n",
    "                        parameters['num_resnet']).cuda()\n",
    "Generator_A.normal_weight_init(mean=0.0, std=0.02)\n",
    "Generator_A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MADq2MXY9JtS"
   },
   "source": [
    "#### Generator B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h4mRA2cZeJ4T",
    "outputId": "35204cf6-0430-46b4-f621-901ecdee7f7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Generator(\n",
       "  (pad): ReflectionPad2d((3, 3, 3, 3))\n",
       "  (conv1): ConvBlock(\n",
       "    (conv): Conv2d(3, 32, kernel_size=(7, 7), stride=(1, 1))\n",
       "    (bn): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (tanh): Tanh()\n",
       "  )\n",
       "  (conv2): ConvBlock(\n",
       "    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "    (bn): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (tanh): Tanh()\n",
       "  )\n",
       "  (conv3): ConvBlock(\n",
       "    (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "    (bn): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (tanh): Tanh()\n",
       "  )\n",
       "  (resnet_blocks): Sequential(\n",
       "    (0): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (1): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (2): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (3): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (4): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "    (5): ResnetBlock(\n",
       "      (resnet_block): Sequential(\n",
       "        (0): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (2): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "        (3): ReLU(inplace=True)\n",
       "        (4): ReflectionPad2d((1, 1, 1, 1))\n",
       "        (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "        (6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (deconv1): DeconvBlock(\n",
       "    (deconv): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
       "    (bn): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "  )\n",
       "  (deconv2): DeconvBlock(\n",
       "    (deconv): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n",
       "    (bn): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "  )\n",
       "  (deconv3): ConvBlock(\n",
       "    (conv): Conv2d(32, 3, kernel_size=(7, 7), stride=(1, 1))\n",
       "    (bn): InstanceNorm2d(3, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "    (relu): ReLU(inplace=True)\n",
       "    (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (tanh): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Generator_B = Generator(3, parameters['ngf'], 3, \n",
    "                        parameters['num_resnet']).cuda()\n",
    "Generator_B.normal_weight_init(mean=0.0, std=0.02)\n",
    "Generator_B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cWhXRqM4lS6v"
   },
   "source": [
    "##Creating the Discriminator\n",
    "\n",
    "The Discriminator will consist of the following\n",
    "1. Initialization\n",
    "2. Forward Pass\n",
    "3. Weights\n",
    "\n",
    "After creating the class, we will create the actuall Discriminator.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "eJsYjXHYg8AC"
   },
   "outputs": [],
   "source": [
    "class Discriminator(torch.nn.Module):\n",
    "    def __init__(self,input_dim,num_filter,output_dim):\n",
    "        super(Discriminator,self).__init__()\n",
    "\n",
    "        #Input - NN Layer 1\n",
    "        conv1 = ConvBlock(input_dim,num_filter,kernel_size=4,stride=2,padding=1,activation='lrelu',batch_norm=False)\n",
    "        \n",
    "        #NN Layer 2\n",
    "        conv2 = ConvBlock(num_filter,num_filter*2,kernel_size=4,stride=2,padding=1,activation='lrelu')\n",
    "        \n",
    "        #NN Layer 3\n",
    "        conv3 = ConvBlock(num_filter*2,num_filter*4,kernel_size=4,stride=2,padding=1,activation='lrelu')\n",
    "        \n",
    "        #NN Layer 4\n",
    "        conv4 = ConvBlock(num_filter*4,num_filter*8,kernel_size=4,stride=1,padding=1,activation='lrelu')\n",
    "        \n",
    "        #Output - NN Layer 5\n",
    "        conv5 = ConvBlock(num_filter*8,output_dim,kernel_size=4,stride=1,padding=1,activation='no_act',batch_norm=False)\n",
    "        \n",
    "        \n",
    "        self.conv_blocks = torch.nn.Sequential(\n",
    "            conv1,\n",
    "            conv2,\n",
    "            conv3,\n",
    "            conv4,\n",
    "            conv5\n",
    "            )\n",
    "        \n",
    "    def forward(self,x):\n",
    "        out = self.conv_blocks(x)\n",
    "        return out\n",
    "\n",
    "    #Creating the weights for the Discriminator\n",
    "    def normal_weight_init(self, mean=0.0, std=0.02):\n",
    "        for m in self.children():\n",
    "            if isinstance(m, ConvBlock):\n",
    "                torch.nn.init.normal_(m.conv.weight.data, mean, std)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "36IywNAtd-xY"
   },
   "source": [
    "### Creating the Discriminator and Testing the Neural Network\n",
    "\n",
    "Similar with the Generator, which requires 2 Neural Networks. We will create 2 Discriminators that compete with each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_YYXIKrz81I8"
   },
   "source": [
    "#### Discriminator A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1vcEqAoNeCKR",
    "outputId": "47b29023-78e1-4934-c159-3a7c02238515"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discriminator(\n",
       "  (conv_blocks): Sequential(\n",
       "    (0): ConvBlock(\n",
       "      (conv): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "    (1): ConvBlock(\n",
       "      (conv): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "    (2): ConvBlock(\n",
       "      (conv): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "    (3): ConvBlock(\n",
       "      (conv): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "    (4): ConvBlock(\n",
       "      (conv): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(1, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Discriminator_A = Discriminator(3, parameters['ndf'], 1).cuda()\n",
    "Discriminator_A.normal_weight_init(mean=0.0, std=0.02)\n",
    "Discriminator_A\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SJhcGbtZ88Nh"
   },
   "source": [
    "#### Discriminator B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RHLJKzoMemlT",
    "outputId": "263ae911-d788-4513-ffb9-74b2eceb2bd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discriminator(\n",
       "  (conv_blocks): Sequential(\n",
       "    (0): ConvBlock(\n",
       "      (conv): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "    (1): ConvBlock(\n",
       "      (conv): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "    (2): ConvBlock(\n",
       "      (conv): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "    (3): ConvBlock(\n",
       "      (conv): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "    (4): ConvBlock(\n",
       "      (conv): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(1, 1))\n",
       "      (bn): InstanceNorm2d(1, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (lrelu): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "      (tanh): Tanh()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Discriminator_B = Discriminator(3, parameters['ndf'], 1).cuda()\n",
    "Discriminator_B.normal_weight_init(mean=0.0, std=0.02)\n",
    "Discriminator_B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IM0cpsAcHpON"
   },
   "source": [
    "## Training Block / DataLoaders\n",
    "\n",
    "In this block, we will create the actual training block we will be using to train our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "hl_9r6z8q84A"
   },
   "outputs": [],
   "source": [
    "train_data_A = DatasetFromFolder(basepath, subfolder='trainA_Male', transform=trans,\n",
    "                                #resize_scale=parameters['resize_size'], \n",
    "                                #crop_size=parameters['crop_size'], \n",
    "                                #fliplr=parameters['fliplr']\n",
    "                                )\n",
    "\n",
    "train_data_loader_A = torch.utils.data.DataLoader(dataset=train_data_A, \n",
    "                                                  batch_size=parameters['batch_size'], \n",
    "                                                  num_workers=parameters['num_workers'],\n",
    "                                                  pin_memory=parameters['pin_memory'],\n",
    "                                                  shuffle=True)\n",
    "\n",
    "train_data_B = DatasetFromFolder(basepath, subfolder='trainB_Female', transform=trans,\n",
    "                                 #resize_scale=parameters['resize_size'], \n",
    "                                 #crop_size=parameters['crop_size'], \n",
    "                                 #fliplr=True\n",
    "                                )\n",
    "\n",
    "train_data_loader_B = torch.utils.data.DataLoader(dataset=train_data_B, \n",
    "                                                  batch_size=parameters['batch_size'], \n",
    "                                                  num_workers=parameters['num_workers'],\n",
    "                                                  pin_memory=parameters['pin_memory'],\n",
    "                                                  shuffle=True)\n",
    "\n",
    "#Load test data\n",
    "test_data_A = DatasetFromFolder(basepath, subfolder='testA_Male', transform=trans)\n",
    "\n",
    "test_data_loader_A = torch.utils.data.DataLoader(dataset=test_data_A, \n",
    "                                                 batch_size=parameters['batch_size'], \n",
    "                                                 shuffle=True)\n",
    "\n",
    "test_data_B = DatasetFromFolder(basepath, subfolder='trainB_Female', transform=trans)\n",
    "\n",
    "test_data_loader_B = torch.utils.data.DataLoader(dataset=test_data_B, \n",
    "                                                 batch_size=parameters['batch_size'], \n",
    "                                                 shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZMfrgqSn-CXU"
   },
   "source": [
    "## Retreiving Items\n",
    "\n",
    "In this block / section we will be retreiving an item for both the real data generated twice. We will also show this data not as an image, but as a Tensor array. We will do this to verify that there are some changes made compared to it's original state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7OtBW1kF-uTZ"
   },
   "source": [
    "### Test Real Data A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6vQIk46Ivk9a",
    "outputId": "86f402a6-9431-4ef2-b7d5-69dd291a4063"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[-0.0275, -0.0275, -0.0275,  ..., -0.0510,  0.1059,  0.2471],\n",
       "          [-0.0196, -0.0196, -0.0196,  ...,  0.0039,  0.0902,  0.1608],\n",
       "          [-0.0039, -0.0039,  0.0039,  ...,  0.0980,  0.1137,  0.1137],\n",
       "          ...,\n",
       "          [ 0.3333,  0.3333,  0.3333,  ...,  0.5373,  0.5608,  0.5686],\n",
       "          [ 0.3333,  0.3333,  0.3333,  ...,  0.5451,  0.5686,  0.5843],\n",
       "          [ 0.3333,  0.3333,  0.3333,  ...,  0.5529,  0.5765,  0.5922]],\n",
       "\n",
       "         [[-0.0196, -0.0196, -0.0196,  ..., -0.2157, -0.0588,  0.0824],\n",
       "          [-0.0118, -0.0118, -0.0118,  ..., -0.1608, -0.0745, -0.0039],\n",
       "          [ 0.0039,  0.0039,  0.0118,  ..., -0.0667, -0.0510, -0.0510],\n",
       "          ...,\n",
       "          [ 0.3412,  0.3412,  0.3412,  ...,  0.3255,  0.3490,  0.3569],\n",
       "          [ 0.3412,  0.3412,  0.3412,  ...,  0.3333,  0.3569,  0.3725],\n",
       "          [ 0.3412,  0.3412,  0.3412,  ...,  0.3412,  0.3647,  0.3804]],\n",
       "\n",
       "         [[-0.0667, -0.0667, -0.0667,  ..., -0.6471, -0.4902, -0.3490],\n",
       "          [-0.0588, -0.0588, -0.0588,  ..., -0.5922, -0.5059, -0.4353],\n",
       "          [-0.0431, -0.0431, -0.0353,  ..., -0.4824, -0.4667, -0.4667],\n",
       "          ...,\n",
       "          [ 0.2941,  0.2941,  0.2941,  ..., -0.1059, -0.0824, -0.0745],\n",
       "          [ 0.2941,  0.2941,  0.2941,  ..., -0.0980, -0.0588, -0.0431],\n",
       "          [ 0.2941,  0.2941,  0.2941,  ..., -0.0745, -0.0510, -0.0353]]]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_real_A_data = train_data_A.__getitem__(11).unsqueeze(0)\n",
    "test_real_A_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FHvYy0vb-z9a"
   },
   "source": [
    "### Test Real Data B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1-KfP8xiFOkC",
    "outputId": "b8902c9b-8d5a-4f62-bb46-d02bf7db643d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[-0.3098, -0.3020, -0.2941,  ...,  1.0000,  0.9765,  0.8431],\n",
       "          [-0.3255, -0.3098, -0.3020,  ...,  0.9686,  0.8902,  0.8588],\n",
       "          [-0.3412, -0.3255, -0.3020,  ...,  0.9373,  0.8745,  0.9608],\n",
       "          ...,\n",
       "          [-0.2157, -0.2627, -0.2784,  ...,  1.0000,  0.9765,  0.9373],\n",
       "          [-0.2078, -0.2549, -0.2706,  ...,  1.0000,  1.0000,  0.9608],\n",
       "          [-0.1922, -0.2314, -0.2549,  ...,  1.0000,  1.0000,  0.9765]],\n",
       "\n",
       "         [[-0.7490, -0.7804, -0.8039,  ...,  0.5686,  0.7020,  0.6863],\n",
       "          [-0.7647, -0.7882, -0.8118,  ...,  0.4980,  0.6157,  0.6863],\n",
       "          [-0.7804, -0.8039, -0.8118,  ...,  0.4510,  0.5686,  0.7569],\n",
       "          ...,\n",
       "          [-0.7255, -0.7725, -0.8039,  ...,  0.3412,  0.2392,  0.1686],\n",
       "          [-0.7176, -0.7647, -0.7882,  ...,  0.3490,  0.2627,  0.1922],\n",
       "          [-0.7020, -0.7569, -0.7725,  ...,  0.3569,  0.2706,  0.2078]],\n",
       "\n",
       "         [[-0.9294, -0.9451, -0.9451,  ...,  0.1765,  0.2627,  0.2078],\n",
       "          [-0.9451, -0.9529, -0.9529,  ...,  0.1059,  0.1765,  0.2157],\n",
       "          [-0.9608, -0.9686, -0.9529,  ...,  0.0667,  0.1373,  0.2941],\n",
       "          ...,\n",
       "          [-0.8824, -0.9294, -0.9529,  ..., -0.1608, -0.2627, -0.3412],\n",
       "          [-0.8745, -0.9216, -0.9608,  ..., -0.1451, -0.2392, -0.3176],\n",
       "          [-0.8588, -0.9059, -0.9451,  ..., -0.1451, -0.2314, -0.3020]]]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_real_B_data = train_data_B.__getitem__(11).unsqueeze(0)\n",
    "test_real_B_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-gZoWnJchfFq"
   },
   "source": [
    "## Creating the Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "aULDtm48JVS4"
   },
   "outputs": [],
   "source": [
    "Generator_optimizer = torch.optim.Adam(itertools.chain(Generator_A.parameters(),\n",
    "                                                       Generator_B.parameters()),\n",
    "                                                       betas=(parameters['beta1'], parameters['beta2']),\n",
    "                                                       lr=parameters['lgG'])\n",
    "\n",
    "Discriminator_A_optimizer = torch.optim.Adam(itertools.chain(Discriminator_A.parameters(),\n",
    "                                                           Discriminator_B.parameters()),\n",
    "                                                           betas=(parameters['beta1'], parameters['beta2']),\n",
    "                                                           lr=parameters['lgD'])\n",
    "\n",
    "Discriminator_B_optimizer = torch.optim.Adam(itertools.chain(Discriminator_A.parameters(),\n",
    "                                                           Discriminator_B.parameters()),\n",
    "                                                           betas=(parameters['beta1'], parameters['beta2']),\n",
    "                                                           lr=parameters['lgD'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hmY0_iVmEQaA"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4_S7jO44wNvk"
   },
   "source": [
    "## Defining Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "WGS3-iixJao8"
   },
   "outputs": [],
   "source": [
    "MSE_Loss = torch.nn.MSELoss().cuda()\n",
    "L1_Loss = torch.nn.L1Loss().cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "lLmXrBTMRB21"
   },
   "outputs": [],
   "source": [
    "Discriminator_A_avg_losses = []\n",
    "Discriminator_B_avg_losses = []\n",
    "Generator_A_avg_losses = []\n",
    "Generator_B_avg_losses = []\n",
    "\n",
    "cycle_A_avg_losses = []\n",
    "cycle_B_avg_losses = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZZ__inadh3-i"
   },
   "source": [
    "## Training the model and retreiving Images\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UR_L6bUV_NLa"
   },
   "source": [
    "Using the image pool created earliler, we will create a pool of variables which we will be using for training at the next cell block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "TSD7HSK6_ZUM"
   },
   "outputs": [],
   "source": [
    "num_pool = 50\n",
    "fake_A_Pool = ImagePool(num_pool)\n",
    "fake_B_Pool = ImagePool(num_pool)\n",
    "\n",
    "step = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G9sRRpj5_ho3"
   },
   "source": [
    "We will now run the Training sequence in the block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    },
    "id": "DqC8zlY-Hm3I",
    "outputId": "fba864be-793c-4f0f-e47e-795f1d24d1a1"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5b803db8422455c8806936d58e9e859",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=165.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/100], Step [1/165], Discriminator_A_losses: 0.5886, Discriminator_B_loss: 0.4861, Generator_A_loss: 0.6911, Generator_B_loss: 1.0921\n"
     ]
    }
   ],
   "source": [
    "scaler = torch.cuda.amp.GradScaler(enabled=True)\n",
    "\n",
    "best_gen = np.inf\n",
    "\n",
    "for epoch in range(parameters['num_epochs']):\n",
    "    Discriminator_A_losses = []\n",
    "    Discriminator_B_losses = []\n",
    "    Generator_A_losses = []\n",
    "    Generator_B_losses = []\n",
    "    cycle_A_losses = []\n",
    "    cycle_B_losses = []\n",
    "    \n",
    "    # Learing rate decay \n",
    "    if(epoch + 1) > parameters['decay_epoch']:\n",
    "        Discriminatoroptimizer.param_groups[0]['lr'] -= parameters['lgD'] / (parameters['num_epochs'] - parameters['decay_epoch'])\n",
    "        Discriminator_B_optimizer.param_groups[0]['lr'] -= parameters['lgD'] / (parameters['num_epochs'] - parameters['decay_epoch'])\n",
    "        G_optimizer.param_groups[0]['lr'] -= parameters['lrG'] / (parameters['num_epochs'] - parameters['decay_epoch'])\n",
    "        \n",
    "    \n",
    "    # training \n",
    "    for i, (real_A, real_B) in tqdm(enumerate(zip(train_data_loader_A, train_data_loader_B)), total=len(train_data_loader_A)):\n",
    "        \n",
    "        # input image data\n",
    "        real_A = real_A.to(device)\n",
    "        real_B = real_B.to(device)\n",
    "        \n",
    "        # Train The Generator\n",
    "        # A --> B\n",
    "        with torch.cuda.amp.autocast():\n",
    "            fake_B = Generator_A(real_A)\n",
    "            Discriminator_B_fake_decision = Discriminator_B(fake_B)\n",
    "            Generator_A_loss = MSE_Loss(Discriminator_B_fake_decision, Variable(torch.ones(Discriminator_B_fake_decision.size()).cuda()))\n",
    "        \n",
    "        \n",
    "            # Forward Cycle Loss\n",
    "            recon_A = Generator_B(fake_B)\n",
    "            cycle_A_loss = L1_Loss(recon_A, real_A) * parameters['lambdaA']\n",
    "        \n",
    "        # B --> A\n",
    "            fake_A = Generator_B(real_B)\n",
    "            Discriminator_fake_decision = Discriminator_A(fake_A)\n",
    "            Generator_B_loss = MSE_Loss(Discriminator_fake_decision, Variable(torch.ones(Discriminator_fake_decision.size()).cuda()))\n",
    "        \n",
    "        # Backward Cycle Loss\n",
    "            recon_B = Generator_A(fake_A)\n",
    "            cycle_B_loss = L1_Loss(recon_B, real_B) * parameters['lambdaB']\n",
    "        \n",
    "        # Back Propagation\n",
    "            Generator_loss = Generator_A_loss + Generator_B_loss + cycle_A_loss + cycle_B_loss\n",
    "        \n",
    "        \n",
    "        scaler.scale(Generator_loss).backward()\n",
    "        scaler.step(Generator_optimizer)\n",
    "        scaler.update()\n",
    "        Generator_optimizer.zero_grad(set_to_none=True)\n",
    "        \n",
    "        # fix faked half tensors\n",
    "        fake_A = fake_A.float()\n",
    "        fake_B = fake_B.float()\n",
    "        \n",
    "        \n",
    "        \n",
    "        # Train Discriminator_A \n",
    "        Discriminator_real_decision = Discriminator_A(real_A)\n",
    "        Discriminator_real_loss = MSE_Loss(Discriminator_real_decision, Variable(torch.ones(Discriminator_real_decision.size()).cuda()))\n",
    "        \n",
    "        fake_A = fake_A_Pool.query(fake_A)\n",
    "        \n",
    "        Discriminator_fake_decision = Discriminator_A(fake_A)\n",
    "        Discriminator_fake_loss = MSE_Loss(Discriminator_fake_decision, Variable(torch.zeros(Discriminator_fake_decision.size()).cuda()))\n",
    "        \n",
    "        # Back propagation\n",
    "        Discriminator_A_loss = (Discriminator_real_loss + Discriminator_fake_loss) * 0.5\n",
    "        Discriminator_A_optimizer.zero_grad()\n",
    "        Discriminator_A_loss.backward()\n",
    "        Discriminator_A_optimizer.step()\n",
    "        \n",
    "        # Train Discriminator_B \n",
    "        Discriminator_B_real_decision = Discriminator_B(real_B)\n",
    "        \n",
    "        try:\n",
    "          Discriminator_B_real_loss = MSE_Loss(Discriminator_B_real_decision, Variable(torch.ones(Discriminator_B_fake_decision.size()).cuda()))\n",
    "        except:\n",
    "          print(Discriminator_B_real_decision)\n",
    "\n",
    "        fake_B = fake_B_Pool.query(fake_B)\n",
    "        \n",
    "        Discriminator_B_fake_decision = Discriminator_B(fake_B)\n",
    "        Discriminator_B_fake_loss = MSE_Loss(Discriminator_B_fake_decision, Variable(torch.zeros(Discriminator_B_fake_decision.size()).cuda()))\n",
    "        \n",
    "        # Back Propagation\n",
    "        Discriminator_B_loss = (Discriminator_B_real_loss + Discriminator_B_fake_loss) * 0.5\n",
    "        Discriminator_B_optimizer.zero_grad()\n",
    "        Discriminator_B_loss.backward()\n",
    "        Discriminator_B_optimizer.step()\n",
    "        \n",
    "        #  Print \n",
    "\n",
    "\n",
    "        # loss values\n",
    "        Discriminator_A_losses.append(Discriminator_A_loss.item())\n",
    "        Discriminator_B_losses.append(Discriminator_B_loss.item())\n",
    "        Generator_A_losses.append(Generator_A_loss.item())\n",
    "        Generator_B_losses.append(Generator_B_loss.item())\n",
    "        cycle_A_losses.append(cycle_A_loss.item())\n",
    "        cycle_B_losses.append(cycle_B_loss.item())\n",
    "\n",
    "        if i%100 == 0:\n",
    "            print('Epoch [%d/%d], Step [%d/%d], Discriminator_A_losses: %.4f, Discriminator_B_loss: %.4f, Generator_A_loss: %.4f, Generator_B_loss: %.4f'\n",
    "                  % (epoch+1, parameters['num_epochs'], i+1, len(train_data_loader_A), Discriminator_A_loss.item(), Discriminator_B_loss.item(), Generator_A_loss.item(), Generator_B_loss.item()))\n",
    "            \n",
    "        step += 1\n",
    "        \n",
    "        del real_A\n",
    "        del real_B\n",
    "        del fake_A\n",
    "        del fake_B\n",
    "        del Discriminator_B_loss\n",
    "        del Discriminator_A_loss\n",
    "        gc.collect()\n",
    "        \n",
    "    Discriminator_A_avg_loss = torch.mean(torch.FloatTensor(Discriminator_A_losses))\n",
    "    Discriminator_B_avg_loss = torch.mean(torch.FloatTensor(Discriminator_B_losses))\n",
    "    Generator_A_avg_loss = torch.mean(torch.FloatTensor(Generator_A_losses))\n",
    "    Generator_B_avg_loss = torch.mean(torch.FloatTensor(Generator_B_losses))\n",
    "    cycle_A_avg_loss = torch.mean(torch.FloatTensor(cycle_A_losses))\n",
    "    cycle_B_avg_loss = torch.mean(torch.FloatTensor(cycle_B_losses))\n",
    "\n",
    "    # Average Loss Values\n",
    "    Discriminator_A_avg_losses.append(Discriminator_A_avg_loss.item())\n",
    "    Discriminator_B_avg_losses.append(Discriminator_B_avg_loss.item())\n",
    "    Generator_A_avg_losses.append(Generator_A_avg_loss.item())\n",
    "    Generator_B_avg_losses.append(Generator_B_avg_loss.item())\n",
    "    cycle_A_avg_losses.append(cycle_A_avg_loss.item())\n",
    "    cycle_B_avg_losses.append(cycle_B_avg_loss.item())\n",
    "    \n",
    "    # Test Image Results\n",
    "    test_real_A = test_real_A_data.cuda()\n",
    "    test_fake_B = Generator_A(test_real_A)\n",
    "    test_recon_A = Generator_B(test_fake_B)\n",
    "\n",
    "    test_real_B = test_real_B_data.cuda()\n",
    "    test_fake_A = Generator_B(test_real_B)\n",
    "    test_recon_B = Generator_A(test_fake_A)\n",
    "    \n",
    "    if best_gen > Generator_loss.item():\n",
    "        print('Saving Model')\n",
    "        torch.save(Generator_A, 'best_genA.bin')\n",
    "        torch.save(Generator_B, 'best_genB.bin')\n",
    "        torch.save(Discriminator_B, 'best_discB.bin')\n",
    "        torch.save(Discriminator_A, 'best_discA.bin')\n",
    "        best_gen = Generator_loss.item()\n",
    "\n",
    "        plot_train_result([test_real_A, test_real_B], [test_fake_B, test_fake_A], [test_recon_A, test_recon_B],\n",
    "                                epoch, save=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RiAvJvrFbwFG"
   },
   "outputs": [],
   "source": [
    "\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 231
    },
    "id": "HD1iaAfVKBwE",
    "outputId": "ebbe70ee-ab9e-4bc1-b39e-49be4f2303fa"
   },
   "outputs": [],
   "source": [
    "all_losses = pd.DataFrame()\n",
    "all_losses['Discriminator_A_avg_losses'] = Discriminator_A_avg_losses\n",
    "all_losses['Discriminator_B_avg_losses'] = Discriminator_B_avg_losses\n",
    "all_losses['Generator_A_avg_losses'] = Generator_A_avg_losses\n",
    "all_losses['Generator_B_avg_losses'] = Generator_B_avg_losses\n",
    "all_losses['cycle_A_avg_losses'] = cycle_A_avg_losses\n",
    "all_losses['cycle_B_avg_losses'] = cycle_B_avg_losses\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YRnk-OKcVnCS"
   },
   "outputs": [],
   "source": [
    "print(all_losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "q8H_0g7rx-PI"
   },
   "outputs": [],
   "source": [
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   ]
  },
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   "execution_count": null,
   "metadata": {
    "id": "iLlYcYMSMUfk"
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   "source": [
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   ]
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   "execution_count": null,
   "metadata": {
    "id": "lofwkqqPOpDl"
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   "source": [
    "PATH = '/content/drive/My Drive/Thinkful/CapstoneIV'\n",
    "\n",
    "torch.save(Generator_A.state_dict(), PATH)\n",
    "torch.save(Generator_B.state_dict(), PATH)\n",
    "torch.save(Discrimninator_A.state_dict(), PATH)\n",
    "torch.save(Discriminator_B.state_dict(), PATH)"
   ]
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   "execution_count": null,
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