tf_pix2pix.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tf_pix2pix.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/zaidalyafeai/Notebooks/blob/master/tf_pix2pix.ipynb)"
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "aKKTe7lWfjWC"
      },
      "cell_type": "markdown",
      "source": [
        "# Download the Dataset"
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "UOU61m4nfHNa"
      },
      "cell_type": "code",
      "source": [
        "!wget https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/facades.tar.gz\n",
        "!tar -xzvf facades.tar.gz"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "08QODL4HcDV7"
      },
      "cell_type": "markdown",
      "source": [
        "# Imports\n"
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "zQvjEWNIwv_-"
      },
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import argparse\n",
        "import os\n",
        "import json\n",
        "import glob\n",
        "import random\n",
        "import collections\n",
        "import math\n",
        "import time\n",
        "import matplotlib.pyplot as plt \n",
        "from IPython.display import Image \n",
        "import matplotlib.pyplot as plt \n",
        "import cv2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "Km7-Le2sfq-v"
      },
      "cell_type": "markdown",
      "source": [
        "# Show Some Images "
      ]
    },
    {
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "colab_type": "code",
        "id": "YVYP3qqIf5P9",
        "outputId": "1260ad6a-ad35-475e-f265-fd4db6f2e646"
      },
      "cell_type": "code",
      "source": [
        "train_directory = 'facades/train/'\n",
        "images = os.listdir(train_directory)\n",
        "sample = np.random.choice(images)\n",
        "\n",
        "image = cv2.imread(train_directory+sample)\n",
        "plt.imshow(image)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff38f0a2e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I37b8CXf+9n7aAHZM+333CXc//vu97XuZq+W5vd/P7YNs+7DM8ck2A8a6NqMAeNe0kb7d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zkImth675Jilz/5+rzQjXquYKQUB7rhBT3jqq5xQbFPCEcSiekQ4zW6wusFh1gG3qFdBbaS\n3zc6lfmoUJ4kwa6sG0r55wp0dXlxCfU0Dm2D7oOtGFnHbrFkoBZsBamQxgQjp0L+VbdUxoJXtN7Q\no2CpG7aVp68MyhqUN3htxSnxMo9CgqyIT39GPWDFe0h9jcO+yqGDE+LbEmMLNmjZ9Qt2WNIQbIRy\npD5kCipVsPA9Nryl54V4Zr3COEEg8vPJnHUpDuU1Xmm8kuuMxliMNKEiRQEl1rZoUzBUjl1ds6+W\nbKgGrVuW4bK2TYPR8vtoqTFaYypxeFoKGu9RHaj1zPYLUfaUmMpeFk9RTNKUwetfzufgPYXSVEXJ\na1/4ArP5nHOlA8NUHtTJ5FzYfIWmLCqePHrE6fgEvGI06GOAZtmkPGy/qmgWS65ducIv/dIv8ejp\nE37wgx/w6uc/z/2PPsR7z+HRCdPZghdffJGDgwMePnxIa0PrvLZl7mtBPHSPwaBg7hseHj3mwfET\nhqMR2wc7zBdzdq7sUy8WzGYzNg62uLp/ha3RBh998AGDosKoAte0jHZ2mJ5f0LiYP1PYVlCDXPxC\noq7V+rtoSPItQppxUV42jTz02abXjHAOhUMnERm/m9+7yN6NddjnZ2dshBrvtm3Z29vDe8/FxQWz\nyUXKP8cyrEFVsWwDlyAcC6VWnIfs5ClqjizljpDV9XJeLpdMp1NGo5HUqHufBEKOjg8BhKl8dsre\n3h6L5Zy2EdJZPIbUFku9dkFnkI0xHcuTVfBQ3tSr8xedAaF+CsGwrgWmNoZlXdO0Lf3BIDWe0CFy\nzvW0xSHxCV0YDoc0uzscHR0xGo2kRlpr5rNF0rw2xkBZoDAdXJ05eDJ3Hu3EECsPVanB1lRKUSlF\nG8qUlAdDAyrGPPAs4/rT4FNwIdr1Hin7czYIXwwYaOg1HlPLs9DXLTgoPv2Qz92aYAxKPHjLQLX0\nsGgvC75CIe0WXdcQAOig159nEyegcJ6er+n7hso36SGRHlop6Szj8zV9b6l8SxnKjUhjXNuSBy5N\n/iLbOpa+plGojufgPOhA3Iqs4cIpesozoGWICBPhxSnzyuN8HWldlDGPjaPvLAMslUIMmdehBEpg\n38Ry91H7QK61M8Zhfp+5rvgq90N7hXIa46DnXbh3kpOVORYHIE5l42t6OHpYiqge5jUKLeiVXz+X\nDnnkzKvDBMMb51SuRvlOz815A87QR9HH0qemogFvkxNX+chB9xQGcI6YhDemt6KncNn22TaXyHKS\nqSZX69SyymKF3dcs0U76BBsU+9tbvPbqK7z19ttsbUlZCF7hXMPmcMhyucA5z8ZwQFMPKItK8o7O\noYqSXk8ijdZ6mkXN4wePuHXzJp97+RW+9vWv8+1/+s+YL2quvfAiX799m1df/Rzv3r3Lxx9/gi4N\n3tdM7YLGONRmyee//CVUr2K6mKGrikmzDH3JPYvZjK2tLV77yhe4d+8eVluuvXidXtXHa03jLe2i\nYVj28M5xfHIIumBpa7mx1mFMQVUWnQCH9ywWddfycDDCOotRnnYp5Uk2lAJ4BL5VYaFRphQDnwye\n3IPGNWyUw3QO7QXqtHiUEaJZG6DSerFgZ2cHbRSL2ZyNjQ2mFxOapmE4HDI5P2O0scnGxgYnJyds\nbW0JC1gLY3heL2hqkfMcDAYpCuz1+ymCC/ZLomAD1luU0phI8HJBlEWT8sXWOXqDARfHJ5iyoj8c\noYxhNjsHo9ga9CgG0pt52sxwxuG1x2IZT86oqkoIhGUPZx3KyPwWZYGvbRIXSYCWBgJS4ryIOlRK\nymti3lyFEhelxTvf37/CeDxm2TY01lH1Bxxcvcrh4SEv3brFeHzG2WSCNkUiJ/WGA2ZzcTC00jgb\nVOSURilDY2XBjRkxraU9Y1mWKCvIQ3QmVOBeOO8DcU7K6crEanHCZvHy78ILKUbWrBWKTre2pnyb\nk3z1pTlCjc4W7rhIOWPRVjS1Cw16ZUWK1vtnX1uKDpvA+7ybnAdl8aE8Jm9Ir7i8D+/Pskl5jxMS\nWx4MQjh3Zz09QZ407Ki1Srlv5zsgt0tV59S53CHMktzKS1SX3lF4NCUNJsCyhW5RynaQcWQv+dAD\nGJmjvM7XeYVXkrMVApddczB018oznD13WDsXxJOZvWycpOdX6rgF8jZK0EClFMoJ6UzFKQjT4ZRJ\nv7NoPh2KNq19OSjezeDKvfbdlYAPaXuTzW6oZtEqkR0dYJULY6X7bnzuXfamAuU9VVGAXfK87bPV\nsl6TRIyvPrw656Qbk7WiX61kodMoXrh6laZpeOfHP6ZQmuVyydZoI0hZDmiahtHGkLpZEtv++UBa\nMWVJvywptaG3s8vuzg5ff/11br/6KtVoiO/1+Pjjj/n+97/P8Q//GK80b731Fienp+wf7FOOSnxl\n2L26z8Jbji/G7B1sYZY96lAWdHF+zmQ8xqDoVSUfvH8PG/S6Hzx+yM72Lt5ahtsbbA83GfX61PMl\ny/mcs7MznG9ZzOa0rWN3a1tqfIMIRFEUNHUthjnkXgeDQTKYmGfzyGmevYqaIamkxPo2RbQRlcDH\niJwVudD5fM52qDuumyXn51JrvLW1xdOnT1P5jVKKRV3Thlzzzu4upycnVFXFbNayubkpDoWCUpfo\neF4trQvz50EZg/aexrYd+1l31xdh+qJXcT6ZULet1J4PBmAUI7PNjdsv8cknn1CMBmxtDmnblps3\nXqAyFY8+ekDZn3F2cc5gNEJphQ6weRsUsPCaxazGBSY8dOmAKjLms1yw1z5EqV3NtEPz+OkRqNDc\nIhiJs4sJWmuePn1KXXdNT5qmwTrp7zxZzFMbzZh/7g0GbGxvMTkdr9RPa2PY2NgQVr8xHaqgu8Ya\ncbvMANnWi++h47IZliSVLWvq2dcYibhsgQpPWvcvv2pUnPKy4AZOjc+MgFpZzv9fbrGmNBrFzPh2\nS/almMfPtOXX7VLEdfn4HWtNGNbY2Lk5y0cWHQevfBbBPbt5OmhWK1LRlMrMo0TkKhxLTqLUKuch\nkp8ul5GMlQf+ufdrfXyXHiWdP3YC8xLZ5/uFseWwt0Ti3ZbwMi/55djbO77G58orGXfW5yxzfNad\nHd85L16tuUXPv6b1TVJseu23sbr9QhhkxaoOcfzMqE62UYyDx1uB73q9HleuXKF87z3McEhVSU1m\nr9cPhmlBv9+naRpcK4vadqxRVqBR9IuCYVlx7YWr/Pm/8BfwSjGez3jxpRtMZhf82q//ee786B2+\n973vcXR0hFKKs3mPL33xy+xfv0prYLi3Te/hA8bjMc2i4ezolHo2l2YAdUOv6nH88DEO6A+HLJpa\nYMvFIhi3bab1ksFoRI2HskBj2B5usdEbSb1wUXByckLT1jR1zebmJkLuECGPojQoDa1taG1Dj34i\nuPX6ZYB9AzvYWnwbcuA4vPNJSCNC16U26EKnEhsgKW9VVUWvL9Kf1nuKqmQymTAaDALhyDFbLqh6\nfVprGY1GnI/H9Ho9hoNRIm/FnHHbtqkZx2w2o7y4oGdXOxzF56TqS/9qrYMIhtaoQrxgU2jqekld\nL+n1KwbDPrP5lNlyyebeLjNvaQrNdFqzs7PNFz/3OXCOT+5/SDUccK3/IufjCUvfUpoK5yw6ok1K\nhDKqqkDpVUWvIjSP8E70yBsP/UGvQyC075Z+rbB1k3pLg095+EGvRx2eU+jgcevECG9sbIh3DbSB\nFR9bQp6dnCYSZK5/HtEGlArPvEC1ZWEA8fQvMxfaEJK0kkNvsTSOjnCm6AxHZimkzrMjzMTNrb3/\nzIKkdST4dmQtL8ZzpQXjn25/uv1/fPtsSV3htQzSki4IPuAFklNFCVVJ09TYAFlXVUUPKQ862N3n\nV3/l17h79y7Hx8f0y4o2iFhsDIYsZwuWixpTSD661KwoKlWmwIQ6y6PTI4pBH4dmc2eHL331q9y9\ne5epa7j12udRwx6nx8fcuH2Dn/z4XXoffsjW/g7TxZzjs9MUtZRFwcu3btHOl5wtjjl9fExZGvb3\nrwgz2yhKCvx0wXZ/wOTwlF5R8cHxmN2dHbaHI86aFtuKrndRFIxnM5QxbO3tYZuGxlp293fX2MIt\ng1CvrZSSel1C1yJFWiDX88PeewbVQLo2BXa3AmwwlE0rGtbOOkYbw5Rjds5RlT0OrlxL7SqvD0RT\nfKttsd4zCsInu1s7khPV0rf6qXnC6dkZg8FAlKYWC7yDfm/A5uYWpiyTwdVao4PRtU0rxgVYtLW0\nYwwlUgoY9AqGm0Pxjp2jXw0o+hXKKA4fPwEFRpecn0358Z330DimkxnKSqnYwcEBp6enVFXF9tYu\nTdOiCkPpS7TuGmnECHnY7zOdTrEuyqlCWUinMl0avCVdB4hh6g37ScPdKIVrWwaVoBORyS2OkSAU\nvbLAWUe/qrpn14TmJkvRZZ/P5yldMJ/PGQ6H9Ho9oUMZg9ZFGoMPLH7ZBBKWnFv2lnyTJOKkNbp0\naGVQUSLQ6wySXmU+r0QUqqvV1V6nzjdR/Urqf6RKQtsOFTdaUj7RafjT7U+3/z9sP5VB/jt/5+/w\nxhtv0LYtf+Nv/A1ef/11/tbf+ltYa7ly5Qp/9+/+Xaqq4h/+w3/I7/zO76C15q//9b/OX/trf+1T\njxuhvFS+4n0SwojCGSCLSGGEVX2xkOgTrXDWs7u7yyuvvELTNJJLbqUFYqFEZnM0GmEKLcpDvmv1\nF1nMhHxl6xwb/T6TRR1a6MHp+Rmf/+IX+fGdn1D1+1SDHlvDLfQ1OD4+ZvL0hBbPZtFjvlzimprG\nNJwcHjMsJVeJlcX5YnzG5vYWWMdkdkpR9nGtZ39nh9OTMc1yyeFyyXlR0qsqarugNxwyr2ucVhil\nab2nGPQpAOs8Za8nTSWUkkYONrQk9A7vu/yQ0ToZ5FieAyRotK5rYQZ7iZZVUUheXyleefllbty8\nyeuvv05/IJrf4/GY8XjMvffv8+6779ICperESmKtcy7GkZPBYq1tVVWMNjaYzWY0dZsK/qPmshiz\nruQrHiOVEhWF5De95KM3Rv8Pe28Wa1l63ff9vmEPZ75z1e3qkezmKFEyJZoiIyUSDcuiHJsUIkCJ\njDzZgBEYCIwY8IOhF70ZhmIoDwb8YMh+JUAjjh8s21AYxDRjKaJIkSKbUlfPNdyqO5/57L2/IQ/r\n2/veapKiHMPpjnQXUHXnc/beZ59vfWut/zBge3ubuq45evDoygELoXrFECltJmYiqzXVei3dFpsz\nX0x5/Pgx+/v7Yr8ZA7OTNYNeD2UN1ubUdU1oGi6mUwBWq1V3PdtRS4uQDkGEWHSnKqFJY66upa/T\nuRBDd5zXkexyzqZ7n7QbMJR5wrHq8PCQ1WrFcinqVp7IYi3SpLYsCLHpRg7y2N+raZbmycjGoRXJ\n8BG8D0QHKhi5n6/7z7Xj4usN39j+LIAOGB1RQSfayrU2rKJDD2uTKvMWkBylY36TjG/iz1L8wIT8\n27/929y9e5cvfOELXFxc8Au/8At86lOf4pd/+Zf57Gc/yz/8h/+QL37xi3z+85/nH/2jf8QXv/hF\nsizjF3/xF/mLf/EvsrW19X0fO6bFKiallJaeFdPMN5jQ+RLP53MCqqtEo9d4JQvh4a1bzOdzHj16\nRFxFgs2eWMS89+SZQeh/V4AmSHzLIO5BdROwJsPEyPnZBecXUy4vL8Wasa6p5itOHx3j6goqz6au\naJyjCQ1RK0ZbY55/7lmOj49ZzRYCjDIZ/d6ApmmYnk9Zr9fYwmJ0w2a5Zr1YCeI4QmHFK9hHhykt\nm1ATLMmFSarSyjeUWY6vHdGlBKYSz1e1IwD1xDhAK3OF9HxHhWxIetFaUyZEttaan/z0p/jLf+Wv\n8NJLL7G9t0tZlghy0OKDZ11tmC9W/K//8n/jy1/+Mq/+0SvUTUNZlp0lZStq4aqEnC1Lqrqmqhq8\nC5yenjOfi7BI5ZquJWqNSHq2EqhKKbxSRCWbragVbhWSi4okpdDUrM9WPHj0UIBZeaIwOY/ygcyL\ns06ZlewNJ1xcXDCe7CRVLEtVVfT6BR/48IdwrkHbDB814+FY7q2HD4kxsjWa0BsM5N5RgYgYPFhr\nZUxhDOu66lruxgiY0KT7tmnq7rVpE3KLjtda09Sue08ARC/KaDolfO89JhNva2ty+j2D6vXQmcVF\n4SGXZSljgDwnKNO19omqS+Lf+w0pSdmQsFRROtdGazIdwFvRDzTuOmpIcjlKQDCd53BI80uPCldz\nvy4hp5mhiQraTUjaN3az0qCeqMJv4ib+tMcPTMif+MQn+NjHPgbQyU3+zu/8Dr/6q78KwM/8zM/w\nG7/xG7zwwgv88A//sMw3gY9//ON87Wtf4zOf+cwPPIjrPNk2rgs/zGYzXn/9dcrRmMFkTPCxA6fU\ndU3TNNy6dasT+wBZuG7fvs1LL73UISyz3OJdjVapAky/1xsMGKTjDkE0rFerlXCXvcdqzXK1ZtDv\nc3r0iP3dXcZbO8wWCyrXsNyssFlGz+Tcf+PtjuaT60Lavo0nukiZwErL5ZKYi+l8nuf0+31ZwBtZ\nfNdVhbKG0faEvOxR9Hr0ioKLiws2SxEiGYxGLGZLQiPtUu8COtNXfORUDXVSktfEQK5fY0XLVZSf\nlVnOz/7sz/Lf/fJ/y3A4JITAxekZWVmQZbYTs2icaLh+7nOf4yd/8if5yr/7Mv/8n/9zTk5Orjyt\nEVELjVS9y+USlcYOW9vbTyh7RQVFWbLabPDKYJxodgsYSSrtIjdMdrZpvGe6mCXglCYvMmazGbGW\npF4kU4jNumLY72N8wJo8gaIyfKr+W23y86kIk2Dhy1/5Cja3FEVBiIajx8cMej2yomBvZ4fFfMls\nNuveD/P5HOs9RZbLPNg3rGtBupdFIeC3uiY4B0HkLVthkLZOtXku8p4oyJKIi0riJkbR7/eFVkbq\nJhmZOzsvX1+cn5BlGU899RRnZ2c03ouFpZHKPnSbsXe+81IV+47vt0j+GNOmAU2sA/F8A40HHZ4Q\nYfKIEYFLCGuT6CvyYAGvII9SETfqSalNgHxW4CoRoOgYO+1/CehzEzfxZyFUfGfJ9MfEF77wBb76\n1a/y7//9v+c//If/AMDbb7/N3/27f5e/9tf+Gn/wB3/A3/t7fw+AX//1X+fw8JBf+qVf+r6Pd/fu\nK7z00gf+E0/hJm7iJm7iJm7i/x9xrx5NPXwAACAASURBVK94ZvW90+6fGNT1W7/1W3zxi1/kN37j\nN/jZn/3Z7vvfL5//SfL853/h83z7Wy/zoQ98gOl0mtDU0jod9wosiq3hgP/qZz7D3/ibf5NZtWHT\neI5OTnl0ciwqUKld29r4te3u67697exNtfKEzmG0prAZ4/GYrd0dQaz2+ywXKx6cnvD73/oDjh6f\nsFwumZ2f0lQ1ZZ4zn045Oz7BO9dZIOrMUg56lMO+0HZaBSsvM9ksuUA1TSOVTQxSwWpN3hdkbH84\n5GBvjwCcnp3Q7/fZuIqoFJPJhKoSFayeybk8O2N9uYDaC9hHmc5WDyM6xGjNa9/4Bh/4sU90hhfO\nOUJz5aFrraWqNxRG0L/b4zG9Iucvf/azPP/MsxhjKAd9YoyMJpNuNgqIdKc2rGsRothsNkynU/7P\nL/0f3L17VyrztsvBVbWcZRmVazr50fY1mk0XLFYrDg4OGG9vdQIxbXs3xojzNZWruXXnkE21ohz0\ngcjjoyMuL86YDCe4umaxWHBwcIC2Gb28YLVY0B+Msday2QjKu+2kKGPoD3qsq4qXPvgilWt4895b\n1I3n4OA2jx8eEZyjzApGoxHr5Yr79+/TPHzMR37q0/T7fR7cu0dR5FitObu4QOUZw36fnckWi9mM\nerVOAgGOuqrw6V5dVRsGoxHaWoJz6EbKzrZV3yLblRJktEs61SFqdvf3KfvDNH6JzKdTptMpy+US\nm+fcvn2bst8TXrAVLIZvR0FN4Fe//e/4r/dLyoEnxupKcKZVMureyMJjDdrjTEzqbgpNUiQLAa1F\n89txTT0NhI+ceLZZAnk1Sqrp9vdagYYQxf7RuUD2jYj7MY3xaTL9/6JCvsKoaTbechHH/N+rl/gf\n7/0NTnyfTbySS4qx6wN8F1/1P+YZh7riMJvx472X+e93v8KPlne5befdbP6KRiQfvFI03nActvlO\n/Sxfnr+fLy0+wcurQ+ah1/GOW5R+hzi/5j+sn6A9xTQKaIlNqhsN9FXNoT3nbv0/8Gt3fpGfHn+T\nl/L7DFWFiS2l7QoT8ASHOAqn98wNuO9v8X/NX+JfLj7Nl6cfoME8cSzXjxeeRPG3H6/Tuq54v61z\nk2fXzPhg/4hP9v+QvzL+Bh/J32aSzYkhWVxGRdu+icA0DHnsdvjK8iW+cPGT/N76fSxdQZ3kUa/4\n3HI0GhLtSX8X7akjlXXvg0SJU56chueKM/6ng9/kU4M/5Dl7xEBXHb+br0f4Uf3979fYo57/8Sn3\nT5SQv/zlL/OP//E/5p/8k3/CaDSSZLERWtHjx485ODjg4OCA09PT7m+Oj4/50R/90T/2cb1v0kf3\nRAJXSiVQkGFd1dy7d49//a//NYvGsagrNs6zWC67eVjbZm4FIuBqDt0CgCRJJd5zCJh4pWxVlCUf\n/ehHefrpp6XdPF2go+40mItyQN0Eah/Zu32bynsWsxlFas2uNht29vexvZKYWqGDohTlmsZhlcgg\nVqs1azSraiMqWzGSY/Eo2DhWF3NMpsm04ejoAaNtsd87OzmhqipeeO451os5KkQOtneIjSzQVgmI\nCg1V0yS/5DSzs4pAwNocYhBEdGZoqopNvRGvaRXJykLmd0rxO7/7u8xmU/b39zswUNkf0OpDa2Wo\nvcPFSBOkzd4Ex2w24/TshJPTY6zWVGtxI0IleUofGU7G7Owe0AqW1HXdcXJbyUdrMnwIqdUqiHEf\nxLTO+YZHjx4xGg+E8nMhwiPPPHWbb3ztG/imEapO47lz+BQXFxeYssAnhFAwitV6TZUsV3paM+qL\nfeRiOuXxyQk7k4lQt7KCH/2RP8fxo0c0VYVRGtPvS/sZQEWOHj1ktDWm1y8Y9QdsHezgg6IsRHnN\nNTmGIOj4jUcZAdnlRcbtO0+xtbPDg6MjlvM5NlMdrqJdDnVCSbX3cTtzbjnn3nvxq/YebS0uBNxm\nQ1U1Qo/SGhIwrKVpfU9M1xPPyhNcT5DkmvtIMFB7OQ4XBYPgvPBFC2FTXS3kie+ro5KWtkpCGBGy\n1A7XwaANOO9EqS0Nz3UkcZ/+2CXkJm7iT1X8wIQ8n8/5B//gH/DP/tk/6wBan/70p/k3/+bf8LnP\nfY5/+2//LT/1Uz/Fj/zIj/Arv/IrzGYzjDF87Wtf69rX3/fJW9RnlJ2PJxCj7ORdC4TJc/LegJ/6\nmb/AclNxMpuyaWrqhKrO87yr+Dr3pZSks0SfCSGI+41KC5cPsq8zGps8ZF0I1L6h9g33H7wNSsQd\nmugwRc642KUoM6arJbc+8D4OYsRmhpPjU3JXUyWE+Ga9FkOFtjrMhdZTrzyuaQhazmmz2TAYDMB5\n/GaDNobZai2czDJjkPXxlcMjIhr72ztcnl/w+P4R/bzgfNPgk3Viu9AGrWhCw2C8RZb4rGtXY/OM\n9WaOUjKPXE1XmFyD09RR6GSZNjituFjNeXD8iG9959vEGBkkcFBZ9Oj3B+KlnPcYjQfip5wZHp+c\n8Mabr3N6eoqrBdDVhECms7RpavfB0q3YNHUyx0Bey7rGOU9V1ayrGp1mzUEJ+l08oGWeOhqN+LFP\nfILTs2PqumZrOOL89JSd4YhPf/zHePXVV5nNlzy6/4D55ZyP//iP8/aD+9w+OADg61//OoXNeOmF\n5/nOyy9js4w/+sY32dQ1m9mcF198kTfefJO8LNFDR71Y4lZr8J7pYsHZ2SnjoYC6MqPYGg85On5M\nsch5e7NJ5holOoqP93qzkgq97KGUYjQYUNc1J+dnXM5n6Lfe6Ob8KJF8DSGglUVreb1aRbbaifi/\n8lGAW4OhdGliZDgei2xjJmYfB7duAYkDnBTGYhDhkOBcAl9dSR+0n0edmA5pSJx6TPJ7QWNcoJfD\nBqg0rGLEFrJxsC2Oi9aOr43YGT+EtPGudMtldtQBej3QVcCkah4fZeNwA7O+iT9D8QMT8r/6V/+K\ni4sL/vbf/tvd9/7+3//7/Mqv/Apf+MIXeOqpp/j85z9PlmX8nb/zd/jrf/2vo5Tib/2tv9UBvL5f\nXDcpyLKM2ruO69nqNrdt3rPLC0wuIKmoFU1SUGrVofJkvfdOXebWAcd5nygcgrqNXgQXTJGjjOHw\n8JDJZCJ2glmG34goRgAq1+BCIOQapyJnKRkYqyhsQRMaDIaqqcSLNmkN++DZVGJsIS3L0NF9Jrs7\nqYIP2F6JsZZmI61DH2Ay2WbZrMmyjMlkws7ODtPpBX7TYLVmkOWsZsv0GIpNs6Gpa24dHnI2vWSd\n5NlsbukN++z0dqmSSErWKyB41o0YqPuECLZNg0EEONZLofS4+ZwATKczqXS8JwZF2RPZSxc9Ra+Q\njYFVFNlAKl7niEE8i0nOVDI6aDqhF0DawOs19bpKSG4YDod4xB7SJnoT0ZMZxWDYIzjHoNeTVvX5\nGeenp5w/PuLpg6cY9Qd88AMfIiqFCzAaDNjb3uE73/o2d+7cQftA2c8Z9PvU6w0Lkh1kEL3w+/fv\nkyda0sHePl/76leZT6ccHtzi4vQMFUMn0LFeLlFWc7C3y9nlGTbTjIcDHr59xOGdO5RlyWg8pNne\nZjabMZlMGPcHwpnv97tNm1WauqoxCeXukz1m+/5o1c/a94wxGmU1WoMPCpNZVIhoY7h9+3a3Sets\nOFvVM31FKeuiTcyqUyK+1raMXVLVrQVgpgk2UFkYPT8As6ISe6Ukc3hV2AbozN91yvExXn2v1fgv\nImQbzeyVwMAFchLQUMfvVi25iZv4Uxw/MCH/0i/90vcEZv3Tf/pPv+t7P/dzP8fP/dzP/YmfPF5D\nYr4TYd042S2vNxseHR/zL/7Fv2AwnlArWFdrFqsVs4sp9+/fTw8mTj7t3xtU12K1WaJ+pNadRVyN\nil6P3niAzXO0UUQN5xdn1E2F8w3WGkyesXdwmyp4VosZGULlKTLDeDTAKsNqZekXPR4dHTPcHouA\ng9GYqLAxg+uGBU1DfzQiK9JMciUWevs7uwTv2Ww2jHsDWThnlyxXc17+znfo9Xp45/BNRa8oWKuM\nOlXZRluMslgVmS1nfOxHfhhTSoW8e2sXbS3T6RSb52BgMB7img21dzSbSiQpTY5X4tc7m60wRLIi\nJ3rhNBtraaoGpTV5XmBty2lF+NZAjB6vFNoo8jyj2dTCk46JAqUsvV5PEm5Cg9tE9zk/u6B2ThTa\njEFr1bmBKZPh6g0hNKzXa7761a+yf7DLei2c9Fv7B/zVn/95JuWA+XwuqlLAxXwhiPCq4vHREceP\nHkH05Ebx6OF97jx1i2rTiMJbCKxWa9Aywshsxre++U2i9+ADDx88oN/vE1zDYj4HYL6YUSRxk8Nb\ntzqbzh/+4A9jcsO9Bw9QSjFfLjG5qGq9fe8eygeKMqNe1xgUvV4pGt3XjD1aB6x2Y+pDg6uSRSa6\no/8RVTJj93gfOqWvzmgk4QpAKmR5LFEK1yIE/n3fn0Fdq3JVxASFi4GFBncI+i99iMleDXYBWQ14\nCIn6lCwGSdrREK9oUQDayc+CgjCBtz2bi7v443ZxUH/ssd3ETfxpjHfXD7kFksSID0038/VEMhCA\nkvcsl0uhc4TIvK64XMyYLRbC2GiFKKoKkxbw4D2rasNmuSJqhdWif+yTLWOmDSpGaYcPStCaLMuY\nzufizVvX1M7ho/i3bnzN7TtPcfePLljXG559/hli8KwWCzabDaenp5R5TjEosSYnSx7BRb+gdC4V\nx4G9vT0ym7N7sAeIsMSrr71CDIYNgcVizvTigs1wwrNPP024DISg2N7aYTQacXF5Tn97m63RmKMH\nD4hG0xsM0jzP89StA2aLBW++/TZZ2QPg+PRU2p55znw+R1lNcE4S//aY0fApZrMF0XkOdrZYT5c0\nTcV6sSY6mdvmec4nP/lJ5rMZ52cXYjBR18mf1cjvWQUkf1/v2XiP1q0eeevbLD1IYxRaWxGKj55h\nv+TZp+9IMk78YZTMWr33EFLnJHgWyzm3b99ia7jF+eNjLs4u+eBLLzG/mKEGnto5Ts8uOHr8mNo5\nLmYCwisTN/19L75PXrPj406jejQaCfCozMnLnJ39PV577dUkTdmnPxpQbzZUTUWWZWRK7rOyLCFG\nprMLZnPFyckx/f6An/8Lf4l1skz81ssvc3FxwWDYY7lcsre3R2Ezzs/OKHo9Br0e0Xls4mDrJPrR\ncuXfKSkLdDPk9XpN5QIBSc4qRtbrVdclatv+7dzZmowsjWi+WwE5PAFGaUfImiulLZAZss9hYWCv\nv4bBAnozScixkYTr7XUy8dW/eCVQ88T3Vg76BYsIk06lX0Ya3+P0b+ImvmfoPwX7t3dXOjPNkK01\nRAIhiFRhUIE6OrIoXGFlFc88c4fJzh6XywU78YCzywvWiyVN8B0oiNQCdN6LApeKOO/QOsc7h20F\n5jUEpai9Y30pVc69t+9jshyb59gip7SGRRL0Pz19xPnladosyKJ/+/AWF9NLHj5+jPcOX0EZQamK\nMjSMt7bojwYsVksMmtVySW/Y44c++jEup+f8/u//fprRllRKM59PUSoyHA7ZVGtef+sNmY3nOcZk\nrDY1SufcvvU0B7f2qOuGzXLFelOxXq5434vvRxnDdLmi7A+YTHYAaQlvNhtMbslySznsERSM7W7X\nDr/YLBltbXF0ekLP5IRME9SVv7K1GqOVtCRNq+gUqL3HWIWxAkaK+MRpTsA6oiCKjfCvsyxn2OtD\nCCxnM2Jq/yqlJImva7TN2Nm/JS1bwCTgnPMNDVIRXl5OWc+X1GtHvxiwnC158+379IpCxDdCw9v3\n3mS1WqG0iK2QkM0nx2di0VivpRI3sGo21LEhbmr2Dw84Pn6MzTRaK2bLOePtMTGXxP3+972Ps3MB\nL06nF2xvbzOZTLi8vGS8tYOKkUXCUfSyHItCec/+zj7r9Vr0y7d3WS7XKAKTnW3OT09RUaOCeNBk\nuZWKeFOLQ0wuY5W249MisLXWZJkhGnvFtad4Ipn7a8p3IMm8SQYobQUb47X8Ga8+trmxw1WlojVG\nIEN0LvUazEaScVsNqyuvWomUeLUntvPorjsWwTbJEOXaoqr8zfj4z3Aoru7BHwSy/5P8zn+2iKkT\n1Mb1A2lnMt/1N9//4d7VhNyG917EEexGqBkxkmWGjXOMipJNXfORH/ooWzs7PLo4pwrwXPCsVyu+\n/vWv8/DhQ3pmgHMNzabG5BZqKHolYb3GBU9mLf08JzSOmGz8PDLTras1B3t7PP30He6+9jqqyAlJ\nwzdqcK5mOO7R1A7daF557S7ffvlbZFlGXhY8/b73YZTi5NFxJ8qhrCYvMzbTDYe373BycsLdV19l\nU1WcnZ1x9PCBSHsm7e2yLBmUUom98eAhTV3zoQ99iK3JNsv1ivl8jin7TBdzHjw6Ej1k75ldTLEG\n3nzzTTaVA6NxHrSWmeNyPicrCup1lRbUyO7eHueXl6yrivrsjMV6RV3X3NrfJ6w3cu0XC5z3GGVZ\nLpd89Xd/m/VKKsTeYIgxGmOv6G1KixWgSitqO790qQ09Ho/xLnJyckLUitVqSWYMZSEgpnW1Aa1Z\nrWs8UvkrpdP81HaSmsYYDJrNcs3ycsZ4POL+vQe8/PLLWCst8bKXk+d5N5/OiqJT02qBgMPhkLIU\nVPzxqYiZ9IdDDg4OGIzXXJ6ds1qtsIVYMob1EltkvP72m0wvLgDpcFhryXvSZdlsNuxub5Pnlrp2\nnJ+diR91EGrWLHVU3nrrrfR9x+3DQ0aTCZfn56jUSREMhe08kI0Rq0AVwXmPtqL+lhc9GYdEvqua\nbr/Orn3ejnTE5etqXhy5Rqm5Fu+k6VyPoBDx6S4BizOE6Hh8rxUnPJntO29fkdf8XgIlN3ETf7ri\n+yjkXYt3t2Wd0Le9Xo9FJejc+XKBjx5tNbk2rEPDKNP84Wt3+ennPsO2jjht8DGwXC7Z2dkiBMd6\nXbFaLSiKAudrVFDMpxfUtSJLvODL+UoSmbaysFuF0RnGai6nFwRfs7e/y/2zU2ocVXQMt0bc2X+O\n79z9QzbLDYOihOjQBHKjuHWwR5Ebjo+PeeEDLxJj5PT0lMo7Ti4v2L91i6gUH/mhH4JWXayq0ECv\nKJhPp2ituXN4iEJTrdd85MMf5vL8HIAQPS+88DxZlnH37l2iVuzt7fH44RGb9ZrBYIBrNpK4ckvt\nJQG2c12x0pPqsKrWHB7cJqwc50cnYhBRFsTKYfo5eDFOWCyXaGup6zUYg7EaF4LYHipFCB4xSLuy\nZdTKplaqjB5sJmpTZVmitOXxyTGuDuzs77G9u8v56Qnnp6dUmzUAVVPTGwx4+umnuZxOObu4YG9v\nj8FggG1pPdBV7a1bVL8ssVoTvMh23rlzh8b7zre5XtfUbiXHkeauSimyokChyItMAHgJof+tb31L\n5vLGkOmM2os7VwROH59grKa4Br5aLpfs7O+x1+/z6NEjVivZ3OR5TuNk8xVjFFBWCDxz5w7OOc7P\nz0Fp3nz9LXZ3JhQ2g9QBiDHKfdlKbGqN8zVae3xTdzZ48ntitn49GV+nELZV9fWq+QcvCzdxEzfx\nbsS7mpDbhQ1gd3+fz37ur/LxT/557r72GvPlgrt/9Aomeg4ODjivl3zn3utsbW/z8N5bfOlLX+Jj\nH/sYw8mQ3rDg0aNjlI1kRnSJF7MZ2iiUCWS5xWSG0+klIVNoHQk05NZS9CzOKh6sLjj/6le4nC14\n7sMfYr5Z4TI4vrzk4b0LQm7Ym+yzPxqjY8Picopvah7dv8doNGK53vDmg7c4fOYZnvnQ+/nKV77C\n6uwcjGGQ99ks1/i6ljamMRA8j84uiCkRfPtiKgAd7xkNJ+R5zvT0jEf37vOHL7/Mcr1GG5GOHI/H\nGKvY2d/hheeeYzab4b3nqaef5t79+6yqDVUtrUFb9mSuenrGZrNhPl2xPZlgm+Q6unCU2lAv1ixC\nZG97h8lwzLjXpzBWZEPnc6zJuirLNyEltRyuoYGtsuTplspLETyZzxas1wustYwnI6yVJL27uysA\nrFrQ10Ve8uL7X2I4nrC1rjh6/JjFbEavKOhPRmzqCtcEopKNWoieXBtm0ymr1Yrc5kQfefzomJA8\nmPM8JwYY9sWlarlYUCUkfq/XYzgeM7uYsT3Z4dHjx+Qmp7AF1Urmv1mW0R8OmZ1eJjejyDArBTkO\nPP/sC2zqiun5lOWqYmtri6ZpmM1mPPWMcNqHQxHvOD89Yzwa8fD+g6Sx7djd3WU5n/PW65cUNqPf\n65En2lJd1518bJbJ7FdpuU5oEQlRzrHYVASu/K/fyee/zlrQWiewWPH/xdv7Jm7iJv4j411NyO1c\nS2tRl/q93/s9vvoH3+TOc88SY+Teo/usZnM2TY0ucv73L32JD//QR+mXBU/dOezEQHq9Hh/96Ic5\nPj7m7OS8+155IFVcnmU47ymbPk0MQlPKcmyvpD+ZkJcls+WCGDw7d25x//EDhltbrNwaW2RiIFHX\nnJ6esjq/YHF5SbNak2nL1tYWBwcHvHXvHvfuH3E6m7K3f5u9gwM2ZY9mtWa1kGSsQ6TabKidp8gF\nhBMaR1aW+KpGK0OvKBgPBlhrWc7ngqiuBFmdFQWZNlilyQYDijzn4aNH4mSVqjtjLaOdbQ5HydQj\nUZp292WG2aw2NLWjsGJQsEzUnabe0GjF0cOHqBiZTy+F3hOEl5ppS5HaqHVscL5mta7ITNYt9m0o\npVgsFiwWC5RSjMcjQkhKXUWBMSIs0jRXhhJKG3Rm0dYyHBeM12se3LvH2dkZg35JmResfJU0n+W8\nnHOMhwOaTcVqs6Hf7zMZj/HXaD9ZluGDiGg0TSNzVe9Zr9cyd80yfN0w6PchRuqUsKuqotpsYLFg\nmvyei6Kg2TRs7cl8/vzsgunskmeff46nn3+evf0dmqZhWIid5WQyYbMWrfWyLBkOh5ydnGCzjKzX\no6kqtkZj5iiqxGFur2OeXV1Xa614aDcn1JsNMSTFrMJiUZis+J7gr+te0u15g3CRr/sV38RN3MR7\nI97VhNzKMAbv0cnebl0tefS1x2ySacSkP+Dew/scnTxma2eH1996g+g9zz/3HKPRiPtHR9y+fZvR\n1gSdWRpfs1zNWS7mbA2GjEYj8ixjvliwd7DLqqlZbTaYfkEdA6vocNHRWIXDo13Nsy88hwuBrd1t\nLmbi0LQ13qKeL3j05lv4dYWvGoajAZnOuDif0jSewWDIwa3b3Dp8CmsNy9mMo/sPCF6Bj+xtbTHq\n9amXK6rNiueeeZY3XnuNOrkhKZPhXODo/gORmKwqIh5lDKPRkOF4jCdyfHzEiy++iLWWB/fugQ+c\nTy9RxmCsxZQFrpFr2+/3GU3GUlUbQ6w9r3z7ZVaLBQqxAozOM+z1McqSZYrNZoM1GTEIgkcl68AW\n/ZsVlgyLsZq6anDO4a9RziTBKnEcspbGe4KHvCyxRc4y+fdep/BEEOWpMAWT0ev1On/iV199VTi8\n4/E1IKClTI+3s78H5+ds7+wwHI1YzJdkVir0upFqt+PkQidHKWYTaa5ciFhLcW3eHKPCaMPOZBti\nxDXim+wqUerqFQVV0eta4ZcXM95++23+/A99lD59+v0+j4+PePDgAb1ywOOjIw729jDaslmuhJNu\ncyajCYwmTxiAxHDlytQq0XnvOyvIwWBA0esTosZfmyF3M311ZVvZOUk18lrFm571TdzEezLeEwnZ\nWouLEaUVm/WG6XxGXpb0+32M1XzqJ36CxjnOzs54/Pgx49GI+XzOyeMT7tw+5NZTh7z6R6/wne98\nh62tLba3t9meTFDOs1gscFUNwHw2Y+MavFI89/T7WVQbNlWFT5Vinuc8Pjri6OFDqromzwsm29vc\nOjzk6OgxynkG5YDZqqapA6vlmsyULNYr+sMhRZkzu1jQ1PfJioIyM+zt7VMc3ObeG2+Ke1LjBN08\n3Keua8bjMcvFSqpJLwnU5OJWRAgcHOyJ+xOK+XRK1TREBffeektUoWzGxXxKnqrXCFyenJIVQnuy\nWjO9nHJ6eiriImfnzOdz9ne2cVVD3VTkeY6vRMu62VSYpD6ttIjFttKjETCRjivcOLmuLZDtCf1y\nJa9vVVWYLEfpQN1UmDrrKDseEQ5prvGPxV5R+OL7+/vs7Owwm14wm80EyTwcsrZW1NaMYTqdClp9\nMOgSUpXEWNr59mYtn49GIy4uLrpkVRSFAMWyjODF6ao9lt3dXRbJGlJsExs2040gmZNIh2sCvV6P\n1XLNq6+8wuVsRmYETzAcDrFKMx4MefuaB/R8PmfcH5FlGTvb21RVRZ402JtrUq8xhu79YYxgJsqy\nRAVPiIaoFE3tUQaUNt9VIV9PzNc3I9er5pu4iZt4b8W7mpCbpAlsjBG6U/BkOmNrtMX23jYxRuaX\nU/pFj8eXj/nwBz/MZ376M9y9e5ft7W1ee/UNLi9m/M7v/C57O7vs7ezzgQ98gMvLS6r1mjzTlEWf\n2DhcgEI3OO/Y3h7z8I23mWxvoV3D5eUlNgRW0zl5AK1qJnmOiRazcVwenXK4s4+KkbtHZ2yPdvDF\nGOccm7phOBjx5z7+47x19JCTszO0Uzz7wtOMxyO+9c1vsnf4FKP+iJ3xDpvlktfu/hF4x2Q0wmpN\nnQBYk8lEaEgn0prMrcVVNT2b4WLAGotGERXkyRPX1TX9shTf36BoNg2ZBaXl2q6mM4bDAZu6ZjGb\nk1vDZDwk+IZBv6TXWEFClyWhcRQJfBRLAUb1etJ+DclvOWrVmXVEAk3jOk9moEPSaqW7kYQyRmwH\nnYDGxuMx69VCqt0E7CvLHsPBiKzo4SJdUjJKMR6J3ORyteHiXOhnyhouzs7ERKEsO09lF0SXe1Vt\nxDe5CWilyDLbzXSdE+10n8RagnPS2g8Bm3S3L07PGI3GrFcbNkuhv0XvUdrw6MERIIpiTfDUTcPF\n+SVbuzuMhwJCy4zh7EI2Eq3PdXAOfKCu0iaoaUSty/kOjNekFn6W5U9sUrQ1zGcLabvrK9EblOqM\nRVokOtCp1F1Pvtd/dhM3cRPvLrIzRgAAIABJREFUvXhXE/L1FmJ/PKLx4l5kgeVsTlVV7Gxv842v\nfZ3BYMBrd++Sm5znn3kegP/yv/gpXnnlFU5PT1EBnJfk9PzHPsZqseDtN96EGLFFThY8blPRrNac\nV2tMWXAyX1D7pvMFNrV8/pEPfITR1oTgI5eLOSfTOZvLJVvjCfvb+1xeXnLr4CkuLy/p9/sMh0Pe\nfONtotLc2j0gy3NWF0tmZ+dkxvDo4UNeePZ5Lk5PeP3uq/jaU1iZF2dZhttI+3Y+nQoYDdAo+mVP\nULRKYdFd0qidEwehFC3PNtcZeVGwXq2hlurVir4ik9EIk2UcP3xAVW2oAeVkpqiVITeW+eWMwbCf\n+K0ZRZazWi8FxGStMFNiJAQBYj0hJNFVx/FqXpmq6pgkQyUPpITd6jcTrjyPU+u6TRytCw+AMgLE\ncqNR5ySVJ74vRuPT87bKVu3jV1XAZqaT6yzaa56MSYbDoTgkaf0E+rjVSG9Da8321g7r5aKbxRZF\nyc54yOnZGcoYhuWQzXLFGWe89NJL+KZhNBiwmUyYzRYyNkDhatcl0FbatZcXNMnNqT2P6wm0Topi\n7c9CCGw2QhO8jrpuP76zQm7Poe0K3MRN3MR7L94ToK5eUisqsozlfM1qs2a8tcX+9h5lnvPpT/4E\n29vbQjPZrHl47wFlWTLuTzg8OGR3d5dPf/LTfP33f48//PZ3eOapp3nq+fdz8vCxVF25IK9bc/g8\nz6ldhdaGZ/YPuoVxMpkwGk7Ien1q53BEdBMpMRgXWVxM2dveYzKUZL012U7yjxmZDiiToUJE1R4f\nPc7XFDpjvVrx1utvcvr4Ea5qCE2DyUpynZHpjCIrsTaXamyzwYjFD0WWEfEd8Mk3DkPAqvjkohs1\nJqhOwSF6T16ILvRkMsEBtiiompr+aChJYrnCrdZSuflIXVWMhkNsZlitVqxWS9GMHgwoc6nGm40A\no7qKDKHdyDGk44kxKXTFJ8QpWiCRUYoyl/mur5tOdKJF/2plcGjhM8cIwacK3NKECqXFNtIocTJq\nlahUMichSaBGDcbo7jpdRyE3zVVy843QtJRST5gt6KhY1xVExc7OztX8VWl6AxkHzGcLGa30BuRl\nSZ7nDPoFbrHA1R6iZmd7D6JmOVvSbCrIhGZnM3PlTpZD1AoV6fStXSNdkwDJeEI2E1YrGifzdoWA\n4LQxT5zfdYpTnued3FV7nu2G4iZu4ibeW/EuC4Mk1OdmxWS8jdeK/+Yv/1Xe/6EPkpUlWmsmkwm3\nDw6JMZKXBXVdU1dCLeFam857x4997ON86sd/IskEws985qcZ/57Mm7e3t3ntlbvcW67w1ZqdyYSt\n8Zhnn32Wfr8PTpyHbFYw3zR4wEdHaUtq7QhNJLMZsfZYLa1bbOKIVo7oIkZJ+xbnMQpsNEQN1hSs\n1yu2tveJw4rcGrLMMBoMWS1nYA2k+bEyhjK5Qdk8R6srYE+M4teaG+GrSpJLbp/qqprSVtrQALPp\nnCp4/Exz6/CQ84sphdLovEQ1AXzAKk9RCFI3N5ZaaTKlsHlBqGo2jcOnOahNrlptV4HuuCR0FABb\nJIHBlCJ44V/7ROuaXlywmS+Sy5fBe0doArPZjKgNLhq0FZ1lzZXPNSnBhhix+mpmmmUZnoCO0qKt\nVmuWszlmOCS3Fu9dukdSR+Ba5blcLinTvebSuQQvSVtbS6ZFbGQ2m8nfact4S1DWz73vRVbrBb6q\n6ZU9iLAz2mbr4LZ4/YbI0b37PHh0JHzx9Hq1fshojfOeLM9pKkGQh1QlF0WZzluOqQmR5XJNHSJZ\nXjIej7GmSK5KV6Ig3/VRX+leN00jQMHvKdxxEzdxE+92vCeUunJr6RUFTsFkNOKDL77EdLXEZhkq\n6s5TV6Fxjcc714l96AjBO3KrxXIwhFQVBKxRTCYT5vM5W1tb3LlzB1fXlL2cfhKK6PWk2smyjPlq\nSd1IC9B7z2q5ZrVYsJwtyXtlt1CLiISlTp+3LUTi1YIfFaKi5D2lteQ2I3pP1BlaHLK7yle0hcEk\n2s98PpcE5j1ehbRQy0cx3RFrvLY6DV4cgUww8vMQ0Mn7eTgYkHvP5GCfTdVQ2h54D86hyXChIkZB\nUPeS25KKERVEJtQYk5x3xAjCXwMahXdUWlJlpuqdiPKBkOadksAlSWxvb3F6/FiuUxC3o9FoxP7e\nAbWPBG1SVRc6NyQfGtGeTs/Ttnbb5NIKewDdtbve4r1eKbe2nO3ftG5Kreaz9wFlZX4bQ7yiUBUF\n2uY8ePgQkCr18mLG+fk5W9tjMmPJjSaPgysxEGDY62OzjPl8/kQy7PcGXcUeAHvtWOtabCnbEUDQ\nctwh6bF77zE6bXq4SsLv1MBW77hOeZ7ftKxv4ibeo/GeUepq6hqdZZwen/Dg3j02LrCpa7Q2vPb6\nGxS5VDFbW1usVgusEq5omRYYFQMmz8iN5fLyEqUiu4cHZFlG2S9o6prdrRH2uWdE49iKmpX3nrLf\nJzhpgVZVw6qq8UqkIK3JQc2ZTi/Y3d1nOJkI4rjTxRbJRGOSCEOaiUoiE0OD2cUlJiixE9QaawTF\nbLUm6IxGNSjVVoRXyaVNviFVoIJ69lfJTWdpoXUisagFDW2UwqaEPO71qWKgWqxxPtDPe2IDGSuq\nyuMDZNYkxPcQo1qN5IyYkq8GsqQTLd7FAaU0SgW0zhJ9KbWsdejAUS6E5IUsCbS7ZoBP1SLxqpXa\nWQtqI566AVCBoMQj+7riVDtj7b5GEQloIr0yx48GaKSLoK15ggZ0fabaGjF09+S1+bNSYmuZlyVZ\nUTCfLykLEe8A2NoRS8umaZiMtzFaM+oP8HWNrx0G3QHOYowYFHmv7EBxzieAlpeka43BIhuKFnHd\nHmdUOpmjqK7tvGpWSUGtnbnH7zqPtl3dRusgdRM3cRPvvXhP0J6895R5rxO+D87hG09wjrxfpLam\nODgNh0OKoiA6LxShtDi5GGg2G1YhpGpH5HGlqvCo5DjkvU92h7c67WPnPc7JwuZcJXxbY8mtmA+0\nlXSndmQtTe0FSBQiRSGVUJuMpU2b5rsIkEYsn8RyLgYQjFaq/oPkpqraJE9ng86uFs3rFU67uAcf\nuwq1TaAQpNI2tluIY4xYLbNtk1m8jsS6YR0jKkBuLM41FEUhlKxej9xaNt7jvadfljjnKJP+87qq\niEGATSpJZoojU+iqUN3ylkNIEsURlEEjz9meg7UWoy0qXQepsJ+UNDbaYhKlyqladM4hbYqu/lmb\nUdcVRimGwyF5nrNZrVksFl0n4ntVhiF1VFpesvgHR6J36NQqF96ydEFW6zWjrQkA0+mU4XAkGyBr\n8VUtilr1lRpYlmXd84/HY5rgRdgl8b+V1ej0OgnwTd4TZZ5fWS/GSBM8Td0ISC6KIURUCozGaNud\nm+LqvmoTcvt12wEIId7IZ97ETbwH4122X0xJR4mgh8Lg6hqrDT2ryWzOYDhmMN6SdqFBLOdWa1yA\nxjegRXg/RAESaW1pnCyyxhh6eYFVGpVlNAoupxfUdc1iOqMsS0IdMNags4zpfMHx8TGvv/U273/p\nJYaTMUZdCVfUdc1kOBat4kwEL6w11K4VdJCEkmWWynuiNlS+oYkBryNV8JTWEKO431SNbDxiVFRV\nw2ZTJ+CQYTCQjoAIc4BBWqutkIZS10BLaSGPWpyBVuuKPFXImc6oY8AocWxywbEODcYolNW4Oqa5\nsJMqnStgUGYM0Ttya3BOhD+MUmRW9MG9l4qz3bQI+jpVakFhjBVFqRjxARQK33iWixVN48mygqre\n4AkEFZivpgRtabxKCTclTGMl0aCvtapFh1xhugrZao1OidNaS1SgrUlCGN8N7hJgmiYGiMmTW9yI\nItrIKKVCX7kepSrfJOOOrChEOSxtGNp7LsZI42rZGFjLZrUiS0C26MTFyFqLzjRV06BikESbqGLt\neIIQOt9olVrnjZPXut/vkxd9XIi4lF7lOugnNh7aqC5hO+cBT924/xxv55u4iZv4T4x3NSG3rbPr\nFA3vveg7Y1NFlVqhKqBUooIYLdaraccPglJFaZS2xBBwKekZY9BR0zjhfo63tmgSD3S5XKKVZXd3\nl03dML+c8vrrr3M5X7BarRiMR2gjsztpn+tOY7hVmlolIYnaN506lGtkBlk1DS6EzgJPQEFa0Lxa\nUL3WZh1tx8WAjwqtZNZHdEnsQ9rARVF0YK12tnhdECKo0FWebffy/PycBnHXkWMJENoqNnFZY+gS\nm07XWDjCoavO7bUqq0NPX5shtxVom+hCCKASrQmNQfyQhXqUuiFpdttWu71ej40LZEUp9otKy2ZK\nKbxzeLe5uo6ADw2xnXNHOopYmxS7FnWQxPRES1csjkQABanKtVzW7to1myp5Iveo68fs7R0wHm8x\nWywAKAoRcGl8IDMaf817uENlX2u1G2M6ERDvXDdeICXh2jlRWzNiO+oq2aBlZUHdNCiTJ3yEnK/3\nHueDOJMhmzR0JEQZGVx/beT1UURlMe4G1HUTN/FejHdXGCTJLfrQoMi7RFfXNb1+KZWk0ehMEpmP\nkaBil5TxKvmoKkyQxbedt5JafTqzKGNQXh570C9xWSZtSg1lLydEh9IwHA248/QhT2nL7v4ueZ6x\nrqonFtjFYoFrgshBpupQJ11lm9DNMchxRRBaSlrhrZZqK7TVH2CNILF1ZhjmI/KsoGkaYpoDx+Ag\nKnRUhMZjVFLEUoqQKqOgpMLMjLmataakZYwBrVBodJsko7SjXbiqrODKSalLyCCL+DtQue25Xs1u\npS39BJhIiT+1fK3SximgNGSpHS/o6AjasLt/iyzvY3IIyuBCxKgroJiKETwYHTEarFb0ihxjtCRD\n50hPiUbjgxe9saggygYv4olpM2K0SpaGQdwAkyewbB0iBoPNMrQW2crDw0OyrOD09Iz9W7fkuqdq\ntKUeydYJaufwLqCV6Wa27ez6+rVuxTu662vaubu8libP0JnQvLxSVHXAB49O71qrNVobXAL5gXQh\nSF9245PkXx3l5us8iW/iJm7ivRXvakJ216UX00K1mi9EOGO7INSCYpalHAKBoFT3tY+gUhXXCixY\na1GoJ4wrYowYK3O71WrFerXpvHL7PVGB0jan1+vx0vveTx0AraiaRihKRkQ8dATn5blHSaCi9k6E\nTYZ98TXuD+n3Bsynl2gVGI36+KZhtfLUPlkH6jTbNYo8g8xCLEzyiW3IehmZzaFxKG0hBnzthLvq\nAlEFGjxaX0uUIeDSDFMQ6XI9jTEsVksimqJXMhqOCL4RUwljcFqjaZHRCcyUZuUdYEiYTHjvxQM6\nzzvglspMB1Jqk0v7esrsNBKUQtMmIgU6sL23Q6/X59HjU5pQked9lM5FRCSAtQajks45Cp1lKOd5\ncO8hm82KW7s7SXwkWT3qQL3wHbe3qipRFkuVtsxpW860bBJU+pnRsmlSMeJ9IEbQXqFyzcX5Badn\nlxwc3KJyDu8juvVDXq+7rodPyPLY0fCESpYnR7O2jd4m4euymJo0E1eQabme3gubgGt61u29rYwl\neM96ucT5iMqzq05JTBsqkzoWaKxSIm2KVMk3KOubuIn3ZrwnQF0xtfMU0DSpemtkcdXXKrZOHtBa\nrBfQV2yFI64hbtvkDHS2gSEVBZPJhCIXpGsLpCkHfUKafca0YGlrpdVcN93jZVax2VT0+5J8g7pq\n1TrnumOQjUGGa9aEEHFBWsZdm1cJJcYqjQotFxiyLEdpzXLjUJkCa6jXlehUW4XzAaNNErDwUomr\nqwqrpe74EFBKTrgsS1SekRc9olCku2tzfVTQfhQEt07tZvcECrlNtnmei1vStce4/vNOQzqZOLQo\nLaUUhEBT1fzar/0aaMPjRyf8z7/+vzCdz3EoItL6BXFc1u1r6hyZNuR5H60tWd4nNhsaF8RSEVHz\nap2aBGlvnqBiJSydtIrTfce1sUdIgC7ilXXheDxmvLWFMpbq8pLRaMTh4SEgY4WWYqVTqznPcxor\nftvD4ZDxeMxisegoWC3N7TogTRDyulM0q+u6m02b9FhWa6o6iDSosTKvtjkRTc2VsMlVezrN202S\nNVWqU1qzNwn5Jm7iPRnvrlJXcWX03ipotfKGz9y5gyOCzVH9nKA1dVOJsELVdPZx4Vrb9XpLMIRA\nICU+a4mhEUee6Zz5fMHBwQF5iKAtWlnmqwWNl1mqc41USy4wn8+lNY1UM2Vp6fX6LFYrqaqUQhuT\nFlxD03hcvQYVsVmPugkcHR1Trdfi2NM0aCXCFrs7E56+c5s8y2gqDz4QAgx6PZZVTVM7VvMV1oov\ncYt21loMBQiBGJQk5igiE+2CXye+6nK9po6B+Wp91c72VwlWrldE6fjEYi4L/JVhRLvpkGsgHO7N\nctXNSa8jrFueb5eQaVv7ApqKMfKbv/mb/KXP/jyDwaDTbO71ejQuYBMISSnVKYm50BAwbG0fsqmX\nuOBxQVNmpYACXSUJViuqumY6m1HkJUpDFB8vlLaCMI7tRspIEmwxxyrpqxgZASgMg1GfGCPLhNYe\njQb0B8LXLouMipBax3KtmiS8sVwuhSLWeJradS3k9t6MMdlIpg2BdChko5RZRZn1xM+5bS+7SN3U\nhGsb0BgjPjq0Valdr9GRRElreyRglXQ9fAw474iuE3W7iZu4ifdQvKsJuUp6y5UTYYQYxalntZTk\n5aIiuIBzDTpLycFHiGnWqWQu+E5wU0sXaWetUSsiBh+hdvL76/Uan+bOs/mSVbWRNriX+XAM0oJc\nVxWubghE6nWFznvyt4kr7LxHhdDNVb0P0mLVmtoHVIjS5lYWVKBuNsIljlCWIs2oVQbxCsWsoiCG\ny9GEzJbMFwvOp0u0gapxlHnGsFekNmRAqYDSAZ042U16XkBs+hT4IDxrMbOo8N5TeUEUk9TAtIpP\nVFfXRwnei3PWer3uWtZABzK7LuXZJYskmtK+Ji1tbNjv882vf4MXXngfg+GEpmnY2i0oy4IcRRMi\n1uTUVcN0Nmc2nbNarrGmYDwco7OS+fyCqAN53qcJYGxBU8+7TYfgCTx40AYiGhUFne0DKG0xOkMb\nReMqmSVrEJeliI6RzNoOSNd2cHZ3d7sK02pNlTZrWZoht+fqr22OWhpYJ5WJJOXRcPj/tPfmwbZd\ndb3vZ4wxu9Xs/vRNmpOc5CQmJCC5SqMQhBiQh8KVxirKsi7YVISSUsQUYum1XskFsSvKV4KC+NR7\nxQevlKcorUjogkkgnSHdSXP6s/vVzXaM8f4YY861TpMOj/fEe9e36py992rmHHOsucZv/Lrvl8XF\nRUflWhQ8/OjD6LL0kSLHqoUwRElCoS1I29zbXo4LARjfmw5nkoMo6yINypGKOyYwMW16mmKKZyPO\nq0GWoTt9nVsrtUZJRa/Xc3KEQQRSIdstysx5uJGKIHQtOZkx5MarDSlXtFUvyIEvpKkpIV1YMWJ2\nbpE+m+SFpjKWbkdirEBXFqFcHlfJAINgOBoikD6PbVHCsGVx0eUI/ZpWGWd0Sq2JgoAiH0sJlpUh\njlsk4QytdkwSx2yurzHsbzqDm4Q+PBpirGMiU4EzdrFUiCBgfqHL3OIWhmnG+uoKq8vLaGvZsjjP\ncDBovGatnWHsdDpkZQXKGfg4DJFYVlbXSfOMvXv3EgZujuu2KRUoV+xTG4y6cM6j9qKtta5f23rq\nR+FaspyHNzbEaeqFL3z1eG2gqqpiZmaGqqp4y3/5L1x40UX864MPsNnbYMfunZ6/2iB91VJvMEIF\nMVdeeQmDfkpRWr7v+65jNOyxsXGChx+6n8cfe4CtWxaII+k8Q7zxw5GVKKUwWKcbXAkEAWWpsSag\nKsGYkqzIiEJBFDs1LakCF11JnOBDfzigKArm5+dpd1qUdXW5b2PLsUjrNkHuHpBIKdi3/1LyquTR\nQ4+j7HjTItCEgURYzYZXr2r5/u9RmjklKO3UpYxwxrkyIKXCCOfPj0YjKt8epvGRD1xlfBAE2Mrn\n02Xdg+3Y4oIgoBxljLevU5xPnO5MTP4+xf9+eFa0PQFoLIF/LC1ywjhmNCqwCrJs04WeY4sQvs0o\nL0niFmXpQoSlF4+PfBtNLegOwrErRRGBcExfWmu+/e07MVVObzBiMBq5Y2hXRJW0O/R6m/QGbiFO\nksS1OWlNHIRkZYFUIQaLRLr2qSDAVpbE00/qyrge4DTjuuc/n527thMISZGnfOdf7+Fbd9yO1QGz\nSwsNM1SggiZ8L4Rg145dtLqzaAul1uR797K5usLxo4d55LFDCKvptNu0ohZJEpJlKWsbm5ReBAGc\nEVhbWaEyllacgLEEoaPqNMYQhU4CsRGHEJZQKQKp0EHQFI4Zc2pl7mTRkjGGsqgc+1RNruE/y0l6\nUGtddCNSIfsuuojKOjnE+bkZkjjA6sqRiFiBkK5FKElmmZtdYOfOC6m0Ym29z8kTRynyEYP+iFFa\nUlSamU6LKk/Hko92PE4hXE+7lpL+puMUv/TS/a4tTZc89OC9bGyus7gwQxS7nmchA5Ikcapjo5Qs\ny9i1axdrq6uNbGg2HLoIgTYEgS9A873TVeW0oGv6zJoD3BhDu9NpahhqFao0TbnmqqsZ9Pusrq2R\n1gVjyvUqB9ZgtETFEqkilpeXkSp27VLGEkeJI9TxylHaVEShIolDQiVdLbw2lFVGkY1gSg0yxRTP\nOpzfoq5ivCjUC1OZpvzrd77Dg48cYn2zhyVgbbPnVG9kSKsds2vHdha3zLF1yxYuu/wSwLW6lLoi\n10WTw6yqijTN2djcxPj+5jrcvNkbkqYpVeUE66tKU2mNDELCUUpvMCDP80YHV1uLIGBzc5NhmjEz\nu4AVNBXaYRDSbrl8aByGjIZD1ldWWV1eZri2TKfbYtDvs7m+hjUVxlToXHKiGrlmGVthtBOhN8qR\nVQx6PbJSo8II5XPhs7OzjAazhNhGqag3GKKUYmFxFiElvV6PvHQGtBW32LKw5NSElCIIXb47EJJW\nEjKqCoJQYnWFMC6vWRNkaFMihTo1BDqRDihNidFe1i+JCE3gcqZlSVYWYCTWiFrymLo1Z3Nzk9/8\nzd8kL0vW+5v0+z3Wlk/SancxWNLchaCrUpIOSx5/9AiVllx33Yv42te/SiuRJLHj/G61WrSSdtO/\nnqcpwkKnHROGAQJFWVTMtNrMz29jvT1kfmEL27fucvegrSiLEQcfqWglbYoyx2JoBRFFmpGNRmSj\nEZ1uC1vm9NdzpDf66bBPf3OTKi8YhiG6ckaw3Yrp9/scP3aySQHUbXPz8/PEkXJFgN5rrkPjZZlT\n5QVJHHPB3r202m2ysmBlbY3CGqoSNgYjRqmjlM2yjNmZORY7c6yurGE1zM8uILEUReo2XVWJtq5e\nI4oUVaUxJkNOSGdOMcUUzw6cXz1kn4ccDlLm5uYY5TllBSsPP8TKep+8shSFISscEf/FF+9jx8JO\nvnnrv5CNNomTkPf82q9ijCFpRa6aFqevLAIFUtLqOLWnQ4cOIe24IllJy+xMm+HIyTIiHXWiNmAE\nbN2ySFk5NaBBr4fJc+dlGMNgMGB5ZZ0gjJp+ZGstc3NzZFmGrSxRpIjQWJ1x/3ceIRQWYwqKLCNU\nEiVc/3UcBqRFjhCCJEnodDosLi4ShDHDwQZBWaICF2bt9zdBV1RlRhIFdBcXieOYtfU+vd4Gh4+d\n8DrGITu37QDgyMFHQEpEoGi32wyqAiUc7eby8eOUeeaoQoVgdqZLt5WghCRUikIIrHW81YAryMJT\nUPtipFE6cl4oteH2/c8ybHLIdb9zt9tlfnaWUTokzQpn1NOcuVabwFhMOkRKSWIcvacSMZUpme9E\n9PopX/3S37Nt+xZGwwHDjRE6H9IOBW0lCIV16kxYtnTbzMUXUmQ5K8snaEcgyx4nD6/Tbs3RWx7y\nzw/f44QWQsVmf5VuO0bnAxSuerrbSlg5epjBZo+dO3eyY/cuwJN+GOch56M+RTr0BW65Y/YaDgiD\nGaIocqmXYc/n4/1mxlqGvb4rfvOGOE1TVz9RuHB1J2kjhGA0GjHM3IatzHKyXFNlGaFQqCBk19Zd\n3Hb7t+h25hCeEObY6ioAcRJyxYH9qEATxaErqDMl/X7O/Ez7f84XfIoppnhGOL9qT7V4gZSUVeWW\ne6GoBBRGEyRtLtx3ERdffIAgSkjiFsPNDS666CIGvTUOHnyYIsuYnZ2lMtUpRURCCPKsQLYl8/OL\nWGPobaxz7NgxX3DkekHbrS4Li4sURUFeFujK020oSaAct3YgJTKOieIOl1xyCYWxrB18HFVWqDBs\njLzzuCuSMCFNR7QX5rGeoCTNRkTSUWggHLWiq962mLJyock8JwgCxxI2oygri5IhcRITBwG2SqAq\nKJT1nMWOhGKYDRlmqS+AS9m9cyezMzOAIx4x1hIgKPOMVuJVjYocq53Ag2roKMd55LFX7D4q99yY\nE3rSY3b5y9pzplFMmpQ8jKLI6VFbi7Ca+x/4DnGUkGU5CwsLLM7NI7TG6hKQSAVFWZGmJd2ZedqR\nQUeG3tpxBJrZbsLICtJ+j2IY05mfQSmJMpa8KMlHI6zWzHe6SFFirSRRgfOItWax20ZjWd1YpcyH\nRBKWvOZ2HLWoRiWBhblOh107t9Ntt+gN+2ANie8tttq1RbWTxJF5yIC41aLVavHYY4/xzW9+k1Y7\ndjn0smiMbByOFZ3qdqjBYMAwHToVs9JFJuJ2y82/cnUORguSKGYwSFlb3SAb5ugiY1gZrjxwwI0h\nDNm/fz/LJ0+wZesSm/0VT+Liu8+sPCP9MMUUUzw7cH7bnjyXdRJFjgZRhQgl0UVJEEUUuiItctZ6\nA1qJodfLWDl+hHTjGMVo0xme2Vl3rCBAhSHD4RBtDFVZkCQxYRiytLREkaeu2lRawsApF2VFgdaG\nqmbc0mC9apMSnn3KmnGVrRBsbGy45702MEJhjCu0mZtbYH19ndnZWWS4SGdmht17djLIM1ZPHGc0\n2CAJIsc+JhTokjTLMUZhhyDUAAAgAElEQVQgrKQsNUKWwCZRkji93CKjyBUKhS1zhNHYqmRxaYko\n6bC6vun6cH01bzYaIqygzHIAx6SlNWXl255o+VB8QStJ0GpsNIW1jq5TjBWP6iZiJ1TgPreGPlOO\n6wCkCHzRl24EDJQMCIOIMInpdDp0Oh1n1LVrnapJLBSCJAgIAoVAkyQJRVWxujFg1F+jzFNKbQlV\njFSCOArQWZ9itMmgv8pJMhZn9jPbcVKaykToPCdNMxQWU5YkcYSVAVaGhNJSFJao06HMU7bMz7mx\nEtGdnXGV9hTkYkBrtk02GCK0QXsimzDyeWppCSPlaF2lRWPQpqKoNJ1Oh6WlJbQp6ff7TeFipTUi\nGvch1+IWxhhH1OIZ1IoiIysddWcQR8RJ20eC2oRBTLvdZTTIufTifUSRYteu7Vx++eWMhk5QY9uW\nJVZXVhHKgnIdA5V1PfFSxL6XfYoppng24fwWdXmS/rKoIASk8/yCUGF0QVUIqrzg6LHDQICuLLpM\nWTl6BGULZhfmuPeuu1lccoVadYg6iCJ0qSmKElsZWnGCsJZ2ktBJtjmDoR0XcVEUVMbS7/fJrSWQ\nCukpMOuCpG6r7fuZI44fOYywgm6k0FbTbneIkoRWp83szByJX2yDKCCMY06srnPlVddydOEQ6ysn\nWVs+TlEVmLKkyAviMCQbpYSetzlLc6zVdNOUufkOBsuwt+kFG5yqUhRI3xYGaZ6PCVF0ReD7gOti\nImtcBbkKFO1umywfYuq+70ChRdQUXzWG+bTWmfoxi25edwqPuLVUumbqcp+tU9myxHFM0m6TJElD\nLRoEAa/7sdfwmc98Dl2WzHTbbF1aZG62y2DYZ9jvE0rBloUZwkBycnWDtZMnqErD4uIistuit75B\nXmR0k5jd27dhyhwrY3cN2rAwO0ekAlZXVgiMq1QWgaLMcy6+eD+791xIfzSk3++za+8uNjY2WJzf\nSrvdocg0q2srfOGf/47RaESWZe56MGRZxs49jhgkiSKCMMT4kL8QXmvZVz23Owlahwx6mwgpUYHw\nHqpXBfNV6UVRNLzaeVmCcF0EslZ70tqRhwigKqhyTTdKeOWrX8lrXvMa4kgyNzePUoqN9R5f+drX\n+OY3vwk6Z6bbJStdm5sMAgIVnoWpq+barBlcGEtu2YnHJ3C2PmY78fMMcz95jLNuBs7tBuFJj+bb\n34Qfl8Cek7NL32LWiJG4k5127icenfTj8UTrzctF/YE08+a5A/BKas1jZ4HlzM9PGNfiaN1ZrScR\naj538URHc8c54+MT9pTRPhXO9qrT/7ZnOU9zpRPXU79k/FrbtB+6vyYq2Jvx21PeK06Z5TPfO+7o\ntw09rvusnwJnq9MQ5kmn6Lwa5DQv/M+cVhijpMUajawqlNZEQkCV8ZKX3cDjR4+zvt5jMJRsj/ay\ncuwwB488xkc/8iHaXuZupjvLzj0XcvVzn8tF+y4llCHCCidlZw1KQllpdFm6Sl5jiKQLPQpriAKF\nFZK8cuFsFYVOzxZoRTFVpelGirjV4RUveSHP/d7ncdHFlyDjCGMFm8MRwzRlc3OTe//1Pu685z4e\nO3SYUIa02rMs7l/iB198Pasrx7n3rjtZXT1O2t9AhgojPE+3cJ5yluYsLYIRrhXI9cdWyMB5tMM0\nJV3foNCCubk58jynt57R7bSIPf8xeClGqR2jU26I4w5Fse7Dm0HDSNX0DlcWQtkINOB7qo3R3thK\nUBZpnMqU9QpX2odBXc83aKvpzMwQx3FzrEF/CMKi84yvfvWrBBL+0/dew97de1CBZH1thdFo4Dir\noxC0pRsntHdsYyYOOXbsGHt3LLFv3z4ePXiQ4XBIK0mIA0mAK2DCQKErRlkBCKJWh+XjRynKTUpt\nETIkmVtk2+4LmZ1bpCo1jz/6CEEccfTYMSotyHOLsZoLLr2Eo4cPs7657kPzgqIoWMpdDtlJZcqG\nHtNWGlM5Xe8oCNi5bTsbGxusSekIPqz7QgsvU2wt6EqjfHW9sIIoiChK10EQRhGtdkJWFFR5xs6d\nO3nNq1/DkUcOcc1V13L9S65HWMMo28SKkkBaOltm+KEXPZ9Ldm9h284dJO02f/2J/5d77ruPKInI\nygKLv68m8BTLun+RcGIvZuINmMaAGDGWdZTgep/9++ped2G9znfzSuVIfmqt7ImBnM2WPG3UK7Sw\nSC9LWkO70QFOVSvw5CtGgGnG90zP565ZAoFjsT/laeFf4y5MYYVBNnPnOAAcM512j7umPf9eP77x\nkcafUz1BE3M9NiQ+tVWz3vvXBqJq5kRaRSU9q2G9SbF2YlPmHpPWcSRYz1VgrMT4e9hrrCJxynun\nmrt6kzdhGq3g9I9VTBo4r4F+Sh+AFVRYhICg3iP6gxhhMPX94+e13rJYhP//bJsJ97gUBqw6ZY7c\n/eHFj6jcHAjjxoZw5E71AJ5g0zo+C6fMyJPhvBrkJpMlHbWlrFy/r6kqTFWRpRlrlUAXGa+64eWE\nrTbrG2sMBxsMNlf567/8vxmsrJMNB3Q6XQabfTY2Bgz6I/bsvgDRiRtBB5fHy50akCeOCKMAaxwx\nSZ1DrXSF1XW/6ESfoHGKPjNJwite8TLe9KY3UeiKXBtGec4oK4gWZtmxfQmlLuT5130vy8tr/MNn\nv8Bf/Y//hwHQbrfptLq8+Aeu54aXv5xvfes2/vuf/bGv8q4wgXRKV0IyGrliHokgCgVagwhC1y8t\nLKUXSWjFEe1OCxVIqlGClGP2MnCylPWXtG7HMRqMtlh1KqFH854JwYMn6pE0xjgtZBxBSt1GRT3X\nflOTZZljxLJuh1/pEl1qXv3KV3P4yOMceuwxHnn0IFmWEQWSSEUkSUIoFUWeEUcRWWFYmO0SqB0c\nO3qIRw4+SKgCduzYQTuJGmPpFJNcv3Oal+R5QVkWpGVBbzBAhTHGVNx6+23cfvudREFIHAV0Z2M3\n12HE3PwWWp0FMHD3d+5j2O+ji9IxrwUB27YsEbWS5jpP79eu1baybMTRo0dpJZEnkKkwRjtFqgk6\n0nou6zRIfZyiKDDWkqZDkiTmJS9+ET/1kz/JTLvL+hWr/PMXvszxi/axtLSE1SVlOaISgi9/+ctc\nffXVXHNgP4WuKC385x99NRsbaxxfXiUKFBv9wZOHrCcNaf3TLziWehUGTw828b7Tfx8vxpOYcABh\n0ijBKauWNace/mnjLNdmzvLb+BEJwjhD/W9wlU3zUzhuA6uajQWcLWrA2ef6DDhj7czH6d6z/yls\nY+jEae9txuePbah/Toz9rN7cKW93D9UGuD72ExoiR3trJ69PnO7lc9rvvsXSszCeGmk4bQzj0/jx\nu3/1C55sH2f9eFyExDSUupPX53jvx0eROANcbzLPuIueahdnxVmu5EycZ4PsOYSByhokgSM3AK/r\n6wzll/7pCzz66ONceull7Ny1g1akGOiKQAmCOHKqQEohkJRlzqHHHuGOf/kGr7jx5YB1RgqXZy6K\nAmNcPVllXXWwo5/E0SpqcBq5GoNr4RkMh4yEoBsnLHRnefD+Bzn40EH27duHUIawpWjHMfc9dJDD\nR48yPz+PDCOC9gzXXnMV//B3n0aXJa04REjLo489wsJsm8suv5Qbb7yRf/qnL4xFBYyTmYQx0Ubd\nwlUTcUgUlfWFUp0u7W6XQEjyOKa+VSYNahA4ogsXZp4IyXgjeXqI+lTWp3rddV6OEAKlBRW+6nri\nPE6Qwsn/KaUmesGhKHKiyClZbVlY5Mqrrubb3/42xkrWNzYJpCLLKiJV0pmZJS9dP/NgMGjyr/Pz\n84RhxMmTJ+n3+wzTEe22a3mqpCQOQ4qqYjQasbm5SZFXxO0WUSvGjgZoW9FuzTA7G5EOcrQuKbWr\nIxCBZDRKWV4+iQoSOnPzXH7gSvJsxHA4ZKbTIQwDOu3EUXRC4/nrwnnKylN9hnIcatbWUhlXpyCE\ndOQz3iAHdZ7ef5mt1aRZiRE0ql55noPRvPFHX8OOuQWOHT1OO25zxRXfwyf/9lO89KXXs7gwy2g0\n4Mv//E8856qrmet2KbMUXVVU2rC6ssaley/g5MkVTF4Sq3C8NDRCGxMLytkW2XpjJy1W1oufbbwj\nhPOI1SkLj/eX6ocsY4EPxgWO9U83kvp8tVf5zC2kiwC435SE0uA2+v55JZwoiTuzE6vBBs27nymt\naO2sVQgKBJVVlCjPzT5evBvJF6Gplbfx12iswFhF5f9NXIkLwdYRBqSbR/+XD2J5wzc21A28p6lF\nHRkAbSIqG2CRGFFhpJtsY31UQ4zP7uRvvffp/wmL8+SFQVjVnM+MT4mcOIadPGDzCcvT3kWjaw/O\nI3des792YQnMRASliWkLjJAYYanqY5w2D81p7Xi+miyAcyXqVbNhsdOO+N2n6gwBBoVxXrF1PniF\nIDx9m3W6t9xEHU65u58Q57ftyc9uvahVFoQKqLQgijvkVY+NlVXSzDDqD7nv7jtJhwNGg3WsqJCh\np3e0lrwaUeY5ykK7FfOFz/wdz3/+1czOtJFhQJQkjAYDSgPWCiorsC7tDEFIZWrhBUNl3Q0ufdER\nUhImEXmlOXbiBNko532/9T4u37+fH3vtf2b3nl2EUnDtZftJhOHRRx9FJTFdIVg+epiX/eALue++\n++hvbNJfPcnW2Taro3X2bLuKyy+9hONHDnPw4QcpCosUlk4r5sIL9zovXXoVIQy68rlHLEIqtDaM\nBn2GQ9dTrfOcOIr85sR9qfMi89rEvipaSMdeJi2BZ+rSvo2nNrqny/MJIQiUm7c691n34upgLLFY\nVRWidOHvyOeMwzBEWEuWOYUtIRxRy8c+9jF6vR5lWRIFsSNn0RVFYLjtttvYuXMncRi6kLcALSxW\nW0aDIVsWl9iyZYvrd86dXGcQBBwfnMRg0WXFyvqaq4DutpiZ61IZQ6vVQiCp8or20gyBN3pCGaxw\ni2J3JnDV7YFEq4DZpW10FzTWlEgLWVXS7XbdvCjlhBt8Rbn04f9KFyifEx70HdubkBFFkSNFQBjF\nbvWTEiUVpipRUjIYjCAM0FWFMQJlDe04YfviAt++5at89sTfcuVVV5N0F3jk8Am2X3gZv/OHH2V+\nZpH1tZO86obryXI4fnSZ5ROHePiB+/nK127h+3/wpURJhx0L8/R6PUIVPfGXcnIxOSP36BfuxqGp\nPWBz5msnFj4ra2PowrXj0KRGS8eAh7CnOhHGV/h/FzFrgd8Y1KFwq5zX6scuhSYQ7p43VvlF2RUw\n2mZxfqYwaOmuwQiLEppAOlnXhv7I1B6im8fcBj587d5TSU0lNUZVSHKsVYBE2zrMbcdruhXjqTlj\nipyRqUPNEomy1imt+fdGFmJrUFhUvafyY7NNuNhtwgygpaG0ULogr7sHjJ87K041vODG7I8xGaCu\nffwnJ6YRCOt07JsL9PdQc8kWFI7ONhCayDoP1iC9hy2bM59pECdiFpba9/XH1U2yoKZzBklF6Ljg\nbeANu/Qhbv3U35nGSJ8Zvzgd55c60/8U1udM9DjUUBhNlLTpzCYsLW5jrjvDiRMnSEcDlLQgJHlW\nIkLnPRFKR70ZBE4Osd3illtuYcfePURhwijLXew/CDBWIK2mFBptNEXl2pcia9HGIrSZCCkKpNfv\nRQjSvEToHq0k4fY77mLX7gt5TvkcojjmxPJRTqycZG1tjdnFRdLS0l9fZdhbp7e+wsmTJ9FlxeOP\nPkykLHd+61+49KIL2bN7J0ePHPJ5XcWOHTucPGSR+tahU3WLtamcl2ZAU2FwRrEW58iyDOHX3Jod\nqlYyEirE+pB07b3C2BuvW5madibvHYuJG6kO0wohqBrGKVdNbI2g2+2iPJNXXZBUVyJbazhx7Bhh\nGDYKWfg2qo3+gEFvk/k5x28tca1kaToijmPCOGmiBTWdadRKnMJSWTIYDRtv2p0X8jQjihLiKGE4\nHLpiKaEQVlJpl0MssorKaoyQlEajgojKVhhrycuSLMtIAsXSlgXSQf8UHm/j+cFrgzwZxk7TlFan\nwyjPXArCG7BOp0MnnKGVhH7D4o73+OHDjqs8doIWylp0OuKqK6/kggsu4Nav3cptt3+bfVc8h90X\nX8Y3vvJVciIOHd+kzAu+c/AIO3fu5MGDj/EvX/8SDz9wHwtLs7RbEbsv2kulJPfc/x0688n4S+jV\nrU5dVPzPs0Uxhc+uCf8NNgqsgtM2cc4D8d5HfXzr41/N4ugtpHVRAiMcVX2Dp8jNPSV8bFxaGiIU\nY0/1f90I64utf/vuzjk5VIlFTR7nLIeU3ssTRkxsUiaN19gTHh+j8Y1P+U5OnkacYezMKZsmURey\nCesMSr35qaMldXjCCu+hgp70bK3weW7/N5qmOO60kK7wY52c9fF8j280Z0LdBmGyKKveSNVnqj8l\nK9x1uq2URYg6J+4cKbd3GQf43edcO4DjsU3O2+TPse8smhlvvG/hz9cU4J0FZwn3Px08LYP8/ve/\nn9tvv52qqvjZn/1ZvvjFL3LvvfcyPz8PwFve8hZe+tKX8qlPfYo/+7M/Q0rJG97wBl7/+tc/6XHl\nxO1Wt0C5J1xFcNydpzszi0CSZhnDUR+DIQ5Dp28cx6wPUnbs2IkSlo3VFRCWVqvNMEt5+JFH6A+H\nzC0sEkVthkUfISWr62suv9fpEEWRbwNyTF3WuPC5MQZdWTY21yjKjC1btpClKYEKMErSG2Tkec7f\nfvofuO/gI6yvr1PoEe0kYmFhgS07drJzz06OHD/G8557DZ/77D+yvrpKJ2kxPzdDkeUcO3KEjZMn\nuPrqq7j04n3cddddbN+5jW63OyFEYH0f6fj2KcsSra2XdtSU2kk4RnFM6NufaqOgAumiil720WKa\nCmkhnECCEaLJa0opnbpULYogvF4141xzbZCKqnJhfm8AjVefMtq6z9MY8ixrQttaa4SxmKpkmKW0\nWi2qyrFr1VzXAmec1zf7SNxxtywuEEURJ1dWuPjii9no9RoVLz1R9V2VBqiw2tBKYrZuWWRmZgZT\nCZIoYH1jjSIbYW1J2eTSARGQlxWFyYiShDCJMdYiZUBVuuNX+GpoaIq4BM64KyXQWHSliYBAjnu1\nk6SNNYJ0lLuedSGxws11nueO8rXS7nPxbGoqjLCVgbJidnaWfZfsZ3ltFRsovvPQQYqwg00WuO/h\nR8lKSVXEmHLEwuETqFtv5/Zbv4zNe+zYto19l17A3OI823dsJex0uf3uu9hMs+/a0Hlac5QGtASt\nQEdYqd3i7r/ZY+9NIa0BWY2dhHqVtL6gq1JgFJoJ7/vfjLH3LoQm8OFGiy9ImliOx6ZlHDp/xmez\ndXHQuEBtwtX0r5mYF6wrgbICaRXSjP9hhfvpS69q701SOY/ObzTOtnE4/RHnxeH3Pn4wssJK7T1u\nOx7rhOMovfEXwhmmEJ+SMG4boLC+Utt4A6V9WLeWOFWnbAtOYYZrctn+OfDz7j6jwJu+urDL+nmU\nhsaKjrd/px5X+nMFGDQWI0xTEe3L6JrzBtSpDG+oqefDgNAE1vX/Vz56YoTFCgOyRIoKKU7bhJ4D\nPKVB/sY3vsGDDz7Ixz/+cdbX13nta1/L93//9/OLv/iLXH/99c3rRqMRf/iHf8gnPvEJwjDkx3/8\nx3nFK17RGO2zQda5OFHn0NxNobEEShFHEUVVIaxldXWFtMiccIB03lkUJ2xNZti6bQdFnpKlKaYq\nMVJhrOH/+LEfpTMzy3CYIpQLFVqjmZmb48I4ZnNzswmbGus8HuuZulwbkXtMlyV5mhIoxWiQk0tD\nGERUUvHgoUOUynm13e4S7SQCKVldWccIQW9thdUTx9B5RrcT04pisC7/0opiRsM+d911F61Wq/Hu\nJn8WhaO2BMY9q9oivEGVUqK0xfhCoDiKmJntEAcxAGGkqCqLCkOM1UiXgkJVXlnKs3FNFndN5pKl\nFI2HLJrPyTYGthaCaLVaRGGCrtwYrbVN6xXCNPzc1hpaSYggodSaqsgpy4rV1dWG2GVxcdF578ZR\nTZZlyYmTJ13UIE2Zm5lxmwBb7/SdZxqGIWEg0bJCyZBQCYTVxEmLhaUdSCU4ejTF+EiAMRpjLHmZ\no+IIm1uCOHQREd+CJoXvky9KTGmII8dr3tzDUmKtoSgKRqMRCsHSwnyTP3aSjTMM09Rdv3H3lcVt\nqMLAgnGLx/z8PFUlMAhCGYIxbF+cIwgjHjv0GNt27yHs3E/UnuWhRx6lKAyFERTGtbGt9Hq0lkO2\n7t7DC//TNey/eDd3fuubPPb4YUqrWNi6jSuuuIJ/uePbE7m4CePRFN08sbG2FgILugLKCPIWSBBS\nu5uqTthZ2eT5hLQgfYbPBP4L7AsArYK8A4VyOcLTT/bdoomY1obDe29WMq65ZcKTMa6C2OLGVo9x\nYqxnPDb5HBY5EVGevA7bzPHYH0dAgHESs340rkLb/XP0OM4CmeZ5V4VdRxPMaeeZsFcNpM+Lun/a\nP2YwSCoEWkxUw09656d56nVLVyQ0HVGwKIdslX1yEzkCB6EJ8RXtfsNjOHWD1Xi+Vpyx8ZI+ihJg\nmFcD5oM+HZkSCD0OOfv7Qfpfx9fuDHksKlqioCNTpLLEtmwMsnuVPOXOFv6erx8zE5uWAIsyAoT0\nxXmaSGa01YhQVARCn3a003C279e5yCFfd911POc5zwFgdna24YI+HXfeeSdXX301M54h6nnPex53\n3HEHL3vZy57w2Kb2uKDJcxo0KGeQbFWhgoDhcEiWp4SBBOlYh5Jul6q0/Nf/+n/SmZnh2JEj/I//\n/uccPPgg3YV5nnPN1Vz+PVdx8uQKQoWO8Sp2RiqK3aKqjWUwHGFLV6IURRG6csYjDEIIJD0EqBCM\n222VxlCiyfKMpa3baNstHF/fJLeCbicmCgJmux1EeJK40+aF3/f9fOvbd9CKQ6rKYnTpcufGFZZs\nWVhkY3OdbDRqmJzSNGVmtgsSH2J3BWl5njd5ZOfNKkxZNdW5URCQ5b7n1F0q7XarqeKtdIEVlrIo\n0KZyfa5SIu2YHKTeCIwLu5yXbIzL1StflKRxBTNFpT3TmvX8zE51q9VqURnDzNwcxhjWN1apjCEO\nAuJul421dQajIUWWY5HOq8RRfO7cuZM0TdnY2OD4yRWKoqDbbjEzM0Poe3Nrkg2lQpS1GAPWGPc3\nFUmrzbC/gcQRbaRFBlIShLV8psuGGVthhaYsK4JIUZkcWxhGWcX2Xfsw2otmlKWfR9FITtbzVWpN\nnpcMRzkCxz2+b98+0jwninzEZNt2Ty5Th940wkdi8J9PHRKzxpG5CCFYXVvnHz73BagK1jf6RDNz\nXHPddTxy6ASHV/oU/YzcGuYWunQW5tgYDZltt7jt7nu55Wu3QD7iwIHLsUKSxC0O7D/AXXfd4yhO\nrecw98VpQkq/6NVqVn7Jqz0p3/YkDQQlkLahE0DZczFsWY0NlS8CA9zfykt91sdsViwFdgaynEhP\nFO0o2xj0Z4zT+qnr8KqTajU+5ynHNrvxjL2Js5JmU9H85CyPjZ+TaJR0OUgl3TkDeerYxekelQSj\nKmeWRUUgLMqUJECBRUsDaBdlwBLIyhtP6cO2Z/fmTzE63iAHQhP7eVBCUFlJbgMkkkQ6udEnS3G6\n3LshwhnjPcEqB5JlShO5Cm1f5FUXfWl/nMlWojPC1hOfrfThEykM83LI3nCZbWGPFpl7n/EGTbh3\ni4Z6TqCsJbIVbZmxGPbZbjbJZO4823rOrRgbZFHn1+s8sCGwdUGYRPsIgbICaQUVEik0kcjYEW7S\nFRmhBSVCF/2ZrB430rvy9fW575Tre/fa9U+CpzTISjkOZIBPfOIT/OAP/iBKKf7iL/6CP/3TP2Vp\naYlf+7VfY2VlhcXFxeZ9i4uLLC8vP+mxtb91Kq0JghC8sHplDFVRMNN2IeWVEwOKfISoNNZogiCg\n1x+wML/EF7/4Rfq9IcNRn9n5BX7izW/mssv3s7RlgaNHjzMaZaxv9EizEXk6REjn9W5ubrKxsUGe\n557Yv/ShT2f86rCwNQZdlMTzIaU2dGZn0EZw8aX7mZmb564774E4YlDkxK0Iqw0ra+tsbGyQJDF7\ndu/kwQcecD3EUhAHEaaqwGso28hX8glBHMfEkVNQql3ZQEWUlfMirYFCFwRR5G8wZyClUGh8RS6Q\nZiPiWmUodNkZrTVCezUpxrq8k0pZdX62Ju+YmZlpdJ+l9OEgHwovvFGqjKfaxJGLKB9yVkoRJlET\nBgcnxJEWGbKsCMOQbrtDFcUMBgO63S67d+8mCAIefvhhut0uCwsLzM3NORY2pZAY8jQlG41cEZbW\nVJWji3TRAvfFbndaXLR3D8Jqjh4/zrHlk8iwwygtwUpGWb27NURSoNAEUUTUaqGFIkhaxC23KYyC\ngDIvG2GSIgdP1914yvX44thtyDY2NprPUAin5CSzgkJ7AhEfkZAYXzXvohLaQFZ4OVErmJ+f53uu\nuobPf+GzbKytIqVi/4GryKzh0JHDBFHE3HxIe2GeMs944KH7mOu2OG5Kti/N8YqXvpS8v8FXbrmF\n3rAgjLpccsklzHcXXJQDjVBu4qwX5wDGK+e4Z6X5W1oIDZRrMPziPfSjAVUMStFUsE4wxDZvL+tO\nB5ytlU040tnqVhoges77biDBVhMh1ckCmYk+4zOf8+u2Tzq6Qh/38piCUERUk601AhD1cq3Ofr4n\nGkPznCZEE1KhrIuOlC74gZWT3qE/YeOVaqTQxKJgNkjZFvQZhBt0RY6W2vV2W8cGV7fdSKO8h1l7\n23W+cwwrQBjhIwSuSnhrsAlAR6RE/rPVVviK4vFmqN4HNTVkFhDue9KVKduDda6ID5FYjTaB8yon\nNm1weuph/KHW4zRNzrp+3DafQVembAt77A2XmVEjAlG5uh/GFf2TuweBJRYF24M+l4ePEpNSBrHr\nHpa6Mbzj7YuPBFrRXOe4xmC8WQuaj134zUbJfNBjh9ygZXMw1Tht4Ntkbb2x8Y/h6YYbCVvMk7by\nCXt6E+oT4POf/+/Q2UIAABLkSURBVDwf+tCH+OhHP8o999zD/Pw8V1xxBR/+8Ic5fvw4z33uc7n7\n7rt597vfDcDv/d7vsWvXLt74xjc+4THvf+B+Lr/s8qdz+immmGKKKab4D4/DXcGewdnN7tMq6rrl\nllv4oz/6I/7kT/6EmZkZXvCCFzTPvexlL+M3fuM3+OEf/mFWVlaax0+ePMm11177pMe98cYf5pGD\nj3LJpfuw0nmKUgRI4SpVW602RVEw2OyBMZRlSVHmZFWFCn0ZcSXI0sLnlAPmlxZ5/Rtfz2A0YrO3\n7oQbfM6wLlyqdEmaZU3oUUrpKpM9sYOuxkxM/X7fhbC9FxbGEQaYWVxg565dXHvNc5FSsr6+wa1f\nuRWFoMorhKmwpgKh+c799xHHIXNzc8RhyPr6eqOd/Nofex0qClnb2ODwkUPcd//9rK6u0ul02HPB\nBYBThar1hEejEVGYoALnAWdFSVEaRumAwEKn2yaJY2yl+dpXvsLzvu86Qi+AkWWZo6/MnRSgxNGH\nzsx2m0Kt0hdX1R5wFMV+nixCOE85EAqEdfl9329b9yjXxUxY55VYa51WsvHh3Txn0Bugy8JXUhv2\n7N7Jjm1bKcuSMJDMzc2xurpKr9dz15iXrK2tMb8wy+LCPEmS+Dlfp7fZxwqQIiAKwFYZc0nIgcsu\nJhKV6wNWimG5SBDNc/n3PJ9Ou8vsbMKffuT/4vCj93DphduIkoTO3Bbac4us9lJWBylhdw6dZwgM\nVZqRxMp7/PCVf/oSz3/B9xFFEVu3bnOtZAaqqiT1bWjGGC6+8ALSNOWuu+5ikI4AmoK9IIxJElcl\nHgQBCwsLlHlOq9XiygOXc+B7riSOY0ZZxv/36X8gTVOuOHAl1goeuP9Bjh8/jpKSymSIiWK8hbkF\nLty7lzgIEUYw7A1YX9tk165dLC0tceLESX7kbz/MD29LiNsGi9MUl0o5BbbJnKn7pfF+rLSgoKp8\nGFP5hqESAiEbj9FMeCMSyBuH1rmttUcijQSjkUiaIq+7M+w1df3Bd1E401QTOze8MoqCkBPVAp8b\nXENfx5QT7Vd1RllL85QhxbPCOpWzWELbpuwOV7gqeZyd0TqxzJr+37qIC+s5nqRjgKpEQGpiNnXM\nmumQ2wSMIJRjn9dYgfbtRu44tYd/akW18zxPryR3FcFKaK7+14c5ftUW5uWQROZjr/a01IAVlkpa\n5MRzsvYxhfWto4E/p/dufRFUiSLwr629bheJmLyeyQyw9+Qtvg947C2Luh3Op8zcwSYiFlZQCprP\n3LHIKbTxfPOqbELH2kofMdD+5RNZ4Ca07YrRpBU+j+4LG8S4jSzEjqNHvlQCKxDfNthrVXOd1tCQ\nIrnvV5t8cFonwWl4SoPc7/d5//vfz8c+9rGmQOvtb38773rXu9i7dy+33nor+/fv55prruE973kP\nvV4PpRR33HFH4y0/Eep51UVJEAcIJX24S/ue0z5lVhAqBYEkikM6tMnrVh5j6K33CUMIhHC9o2XB\n+toKKggwZYW1ugnJaq2RQmGsbiTv6seVUk2lcJNbEoJWq+V4n6MIKQXalEShYsfiHDu2zPOVf/4c\nhw8fZnN1g2xU0IoSbOULjYT1ggsVVeUMUJ0bFMYZr89+4fOsrK0xHA6hFraQAXHkWlOKovCsYZay\nymm3ug2TlhVOZ7coNEZDpgviMka1FVK60FtdWFWLYQRBANo2XN1lWTYUkFprCp8rrcUl6nl2gRQf\nfhZ1IVXQKAdN1hU0BXHaLUC1NrNAYqyi1AJrFYaKKEooK8OxEycZ9DbJs1Ez5rpavNJu87K+vs7a\n2iqxF2MoyhKl3EalyCu67YhWIIhm22SDPkQglYDSMhpa1nonOXZslUsPXInROVk+YmlxK4NeyqyK\nKQtNf5CyORigopbrXS9ysFDoEql9MZhvQ6uJT44ePcpoNKK3vuk40dMRZVmysLDArh3bCYLAbR56\nPdfa46vCEQqUbIRLrrjsAN12i7IqaLfbxEFImqauct4bl6NHjjAcDun1Nmi1QkIl6Q9SWnFEXpYY\nLegPehw8eNDJebZn6K1v0ko63HfwIDtGI8rKUJQWUwm30IkAozV1S0sTk28IKsYtM1VVESiF0JpQ\nSldciEDYELQGhK8Lc4U70ofplHChY4w7pvELrrHKfeeDwLH1+W4LYUKMqRAqOKXY6GnB4hdYtzGU\nVpIIWBQjnpccoiBET+SQa2pJKz0t6DOFdfrlgYBQFMwyYinIcGUcym9Oa2JJ3BwIN63WuvaolsiQ\nsqQtMjQBoQYpx5XbLpvs6ElDDLb5rOrwqx9KPfcTw3PZbtsYinmRkQhXswPuHkDIcZxZuGYfaTXo\nyBlloccGypNdhNTGy4WFm49JWCLr19Ha8NYEMj7UPLmpgLo9yUykSaBJaFuBsIGvP/AUvnX+3Apv\nqB1ng5LCX7/fREg/KdalSOu0iaiNphW+b9yfzlr3Xiv8HSLRVrv7qN4s1LNq5Znt1EaNvz/Gvc6N\n00IFuvSdHU+ApzTIn/70p1lfX+cd73hH89jrXvc63vGOd9BqtWi327z3ve8lSRJ+6Zd+ibe85S0I\nIfj5n//5psDriRA1O0eaYgDp23ICbyAdC5fAGOt6b4UlVq76uJUkVFnmFJ6soNIFEsOXv/BF5pcW\nG2IBYyvPjBS7wijcAg8gA7cgptnIeYLGNjF/hXTKTNJJ4gksgbLs2bWTyy69iHvuvZvDjz5CnhV0\nYkXZL0g3U5QMXIGYdJ5+kiT0+5uk2YhAui9vFCYUvR4nl9cIkwghBKXvh16an/eL+AYW13dcG6fB\nIHOVvQaipI0UAUr5hVK6wq/RMGh6W/v9QVMQVlWVq4CuHKWlMI75qypyZwSVpPLGVyhX6yl8VMH4\nfDp4z0NYb6x1k4+chDWCsnIboMqA0ZYwbrFr524OXHktx48e5vChx8hGPfJSY3zvd5IktFsxQgin\nYiUltqzQ2imA5UVGUZauLxkoihJtrcuDWkur3WJxcQ5tSqrS0FIBkRJIPSAbDDAYbrv9FtZWT2LK\nlE7sRB+K0iIrSzkc0StSWkmCiiNEFFJlpeeAdj3VWrvowfr6BkVRMOz13X3lCUKMMU3xWU2lKdAY\nWxGquMnf1wa59kCWl5fJZrrMzc9SmpK1zTX27NqF0VAMRywfPcbhxx9nZqaLCiR5lpEaw/Lxk3Q6\nHbqzs+RV3kQ7Wt22Uw2LRvTyIbv3XUhelhTWEAZt0KErElQCaS3W16qJxgOZZIyqFwyBSBWBBqEk\niAxrNNiwyT3b2iDYcSdn0BRMjQ1yU+wjtOuptwptXYsNMvKFRnKiEOxpQtS8XBJt3CZQCUgQXKTW\nqHxeufEMm/whbkF/hqiNuVQCZTURJZEFTOiMqQjqifFGUXnH1DZenwRHbiEda5q1EuX5Geqq6NIb\nmXDC4Iy9V/eaScau+tqEz9nXcx9ZiTERmAAjfK7UyPH7DCAsymoofc+6KmkK9twkU5nA8+9rV7Dm\niwhCIV3+emIop5DI1PB82LXBbDYNE21ptmlWUs1GppIC0ITegAe23uB4g27dfVY7XPV8CuGMoyuu\ns35jIX0rn27ucosr+Ko9ayUERhi0jwQEZvwcYtyh7S4vofbwhZVNfaQAKi1BS0I50eJ7Gp7SIL/x\njW88ax74ta997RmP3Xjjjdx4441PdcgxvKehEL4twVV6SkfIjKlqjWOJFYIgkGjr+ve67cQRgCQx\npiopi6rhTp7z8ol5VVC33ERRRFkVjYxgnufOKyw0RVE0i6cSYzUeaYXrDRXKFWZYy/atM6TDAV/6\n/Oc4cfJY401K4UTtc5kTq8SHYaTbomkvU+iLL1So3AKkndQgSvjvgAv/5nlBtzvDMBu5UKQK3Xs9\n+YS1liB0fcK5L0ZTPtxordPWVfX3wVduA9TKQi4cI4ii0G2MvadcFZ7XVUpXVT7B8IUV3ouqW31s\n8946VF2XIzSaytL1VAbSsdsEUcKuPXsIghZHDh9FBRFx7AoGuzNzCAG2Kti9ezePP/54UxzWjRNy\n78kXZU5NPRlFETIIHb2mL6oqioI8zwljp5iURCGBEJiyRxQLHn74fioR0GrFdJIYawqMFmRpThUM\nyZVjToo7CcZoujMzZBKy0ZCizDFFQVW5Yq6TJ0+6iIO1dDodlAqaqEoUjKvBW62Wb4vrYqyrUBdK\nEagQFYVI6d63ODeH1hVJO2RjY4NKl6wuL9Ptdiky1/fe628y2+0QSNfSZq1kptMhK7xXHcdcfuUV\ntFoter0eJ04ss3X7duJ2y1GJRiGtrqLIKzY3cmy/RAY094vWLgw90Y3jbyT3w1HMQmBdpEkFoAKw\nJqcyrrrW2Dp0StPDGuH+Ns5RmKDfhDCAsnIvrErYAywf33Rrtzh1GE8Lwo0R6dqzjPBGXkAl1v09\nWl/feDk1/t59pqgLgLX72lBayBi355T1dVrvfPj50WJ8dt9a7FouAVt5Iyrce7TAbSQExH78p3jB\nft7r63AXOX6uNqPbgNUTfeo2cgQo4z5/4wu7Qj+WUoIofZWzsm7M/viBhayuKZMQCWj5MeWMW6nq\n3vJmSzVhvU4fr5y8Dn+uugAw8HNSCiiUu7RY+GJ846VChHP6tb92bSHwWRTL+L4M/BxLO3E9Ynw7\nmPq8/n61QOU/GytcEXUwce31NW0Flk/03d9ivEcAbwY0mCIgS5/YRX7aRV1TTDHFFFNMMcW/H55h\nLGiKKaaYYooppvj3wNQgTzHFFFNMMcWzAFODPMUUU0wxxRTPAkwN8hRTTDHFFFM8CzA1yFNMMcUU\nU0zxLMDUIE8xxRRTTDHFswBPizrz3wO/9Vu/xZ133okQgne/+92NotQUzwwPPPAAN910Ez/1Uz/F\nm9/8Zo4dO8a73vUutNZs3bqV3/7t3yaKomesVT3FmTrgV1999XRuzwHSNOXmm29mdXWVPM+56aab\nOHDgwHRuzyGyLOPVr341N910Ey94wQumc3sOcOutt/ILv/AL7N+/H4DLLruMt771red2bu15wK23\n3mp/5md+xlpr7UMPPWTf8IY3nI9h/IfHcDi0b37zm+173vMe++d//ufWWmtvvvlm++lPf9paa+3v\n/M7v2L/8y7+0w+HQ3nDDDbbX69k0Te2P/MiP2PX19fM59Gc9vv71r9u3vvWt1lpr19bW7Ete8pLp\n3J4j/P3f/7398Ic/bK219vDhw/aGG26Yzu05xu/+7u/a173udfaTn/zkdG7PEb7xjW/Yt7/97ac8\ndq7n9ryErL/+9a/z8pe/HIBLLrmEzc1NBoPB+RjKf2hEUcQf//Efs23btuaxW2+9lR/6oR8C4Prr\nr+frX//6KVrVSZI0WtVTPDGuu+46/uAP/gAY64BP5/bc4FWvehU//dM/DcCxY8fYvn37dG7PIR5+\n+GEeeughXvrSlwLTNeHfE+d6bs+LQV5ZWWFhYaH5++loJ09xJoIgaDi5a6Rp6rR4gaWlJZaXl78r\nrer/3XE2HfDp3J5bvOlNb+Kd73wn7373u6dzew7xvve9j5tvvrn5ezq35w4PPfQQP/dzP8dP/MRP\n8NWvfvWcz+15yyFPwk7ZO/9d8ETzOp3vp4/Pf/7zfOITn+CjH/0oN9xwQ/P4dG7/7firv/or7rvv\nPn75l3/5lHmbzu13j7/5m7/h2muvZe/evWd9fjq33z0uuugi3va2t/HKV76SQ4cO8ZM/+ZOnqNyd\ni7k9LwZ527ZtZ2gnb9269XwM5X85tNttsiwjSRJOnDjBtm3bzjrfT6VVPcWZOuDTuT03uOeee1ha\nWmLnzp1cccUVaK3pdDrTuT0H+NKXvsShQ4f40pe+xPHjx4miaHrfniNs376dV73qVQBccMEFbNmy\nhbvvvvuczu15CVm/6EUv4jOf+QwA9957L9u2baPb7Z6Pofwvhxe+8IXN3H72s5/lB37gB7jmmmu4\n++676fV6DIdD7rjjDp7//Oef55E+u1HrgH/oQx9qdMCnc3tucNttt/HRj34UcOmr0Wg0ndtzhN//\n/d/nk5/8JH/913/N61//em666abp3J4jfOpTn+IjH/kI4KRSV1dXed3rXndO5/a8qT194AMf4Lbb\nbkMIwa//+q9z4MCB8zGM/9C45557eN/73seRI0cIgoDt27fzgQ98gJtvvpk8z9m1axfvfe97CcOQ\nf/zHf+QjH/kIQgje/OY385rXvOZ8D/9ZjY9//ON88IMf5OKLL24e+2//7b/xnve8Zzq3/0ZkWcav\n/uqvcuzYMbIs421vextXXXUVv/IrvzKd23OID37wg+zevZsXv/jF07k9BxgMBrzzne+k1+tRliVv\ne9vbuOKKK87p3E7lF6eYYoopppjiWYApU9cUU0wxxRRTPAswNchTTDHFFFNM8SzA1CBPMcUUU0wx\nxbMAU4M8xRRTTDHFFM8CTA3yFFNMMcUUUzwLMDXIU0wxxRRTTPEswNQgTzHFFFNMMcWzAFODPMUU\nU0wxxRTPAvz/kjMn2uxzcW4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff3a214f0b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "ggeyIXl3cKjI"
      },
      "cell_type": "markdown",
      "source": [
        "# Default Parameters"
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "g08ra2aQF_tg"
      },
      "cell_type": "code",
      "source": [
        "parser = argparse.ArgumentParser()\n",
        "parser.add_argument(\"--input_dir\", default= train_directory, help=\"path to folder containing images\")\n",
        "parser.add_argument(\"--mode\", default = 'train', choices=[\"train\", \"test\", \"export\"])\n",
        "parser.add_argument(\"--output_dir\", default = 'facades', help=\"where to put output files\")\n",
        "parser.add_argument(\"--seed\", type=int)\n",
        "parser.add_argument(\"--checkpoint\", default= None, help=\"directory with checkpoint to resume training from or use for testing\")\n",
        "\n",
        "parser.add_argument(\"--max_steps\", type=int, default = None, help=\"number of training steps (0 to disable)\")\n",
        "parser.add_argument(\"--max_epochs\", type=int, default = 2000, help=\"number of training epochs\")\n",
        "parser.add_argument(\"--summary_freq\", type=int, default=0, help=\"update summaries every summary_freq steps\")\n",
        "parser.add_argument(\"--progress_freq\", type=int, default=500, help=\"display progress every progress_freq steps\")\n",
        "parser.add_argument(\"--trace_freq\", type=int, default=0, help=\"trace execution every trace_freq steps\")\n",
        "parser.add_argument(\"--display_freq\", type=int, default=500, help=\"write current training images every display_freq steps\")\n",
        "parser.add_argument(\"--save_freq\", type=int, default=5000, help=\"save model every save_freq steps, 0 to disable\")\n",
        "\n",
        "parser.add_argument(\"--separable_conv\", default = False, action=\"store_true\", help=\"use separable convolutions in the generator\")\n",
        "parser.add_argument(\"--aspect_ratio\", type=float, default=1.0, help=\"aspect ratio of output images (width/height)\")\n",
        "parser.add_argument(\"--batch_size\", type=int, default=4, help=\"number of images in batch\")\n",
        "parser.add_argument(\"--which_direction\", type=str, default=\"BtoA\", choices=[\"AtoB\", \"BtoA\"])\n",
        "parser.add_argument(\"--ngf\", type=int, default=32, help=\"number of generator filters in first conv layer\")\n",
        "parser.add_argument(\"--ndf\", type=int, default=32, help=\"number of discriminator filters in first conv layer\")\n",
        "parser.add_argument(\"--scale_size\", type=int, default=286, help=\"scale images to this size before cropping to 256x256\")\n",
        "parser.add_argument(\"--flip\", dest=\"flip\", action=\"store_true\", help=\"flip images horizontally\")\n",
        "parser.add_argument(\"--no_flip\", dest=\"flip\", action=\"store_false\", help=\"don't flip images horizontally\")\n",
        "parser.set_defaults(flip=True)\n",
        "parser.add_argument(\"--lr\", type=float, default=0.0002, help=\"initial learning rate for adam\")\n",
        "parser.add_argument(\"--beta1\", type=float, default=0.5, help=\"momentum term of adam\")\n",
        "parser.add_argument(\"--l1_weight\", type=float, default=50.0, help=\"weight on L1 term for generator gradient\")\n",
        "parser.add_argument(\"--gan_weight\", type=float, default=1.0, help=\"weight on GAN term for generator gradient\")\n",
        "\n",
        "# export options\n",
        "parser.add_argument(\"--output_filetype\", default=\"png\", choices=[\"png\", \"jpeg\"])\n",
        "a = parser.parse_args('')\n",
        "\n",
        "\n",
        "\n",
        "EPS = 1e-12\n",
        "CROP_SIZE = 256\n",
        "\n",
        "Examples = collections.namedtuple(\"Examples\", \"paths, inputs, targets, count, steps_per_epoch\")\n",
        "Model = collections.namedtuple(\"Model\", \"outputs, predict_real, predict_fake, discrim_loss, discrim_grads_and_vars, gen_loss_GAN, gen_loss_L1, gen_grads_and_vars, train\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "AXDZiGZOeBG1"
      },
      "cell_type": "markdown",
      "source": [
        "# Loading the images"
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "CsJu8Clfd80S"
      },
      "cell_type": "code",
      "source": [
        "def load_examples():\n",
        "    if a.input_dir is None or not os.path.exists(a.input_dir):\n",
        "        raise Exception(\"input_dir does not exist\")\n",
        "\n",
        "    input_paths = glob.glob(os.path.join(a.input_dir, \"*.jpg\"))\n",
        "    decode = tf.image.decode_jpeg\n",
        "    if len(input_paths) == 0:\n",
        "        input_paths = glob.glob(os.path.join(a.input_dir, \"*.png\"))\n",
        "        decode = tf.image.decode_png\n",
        "\n",
        "    if len(input_paths) == 0:\n",
        "        raise Exception(\"input_dir contains no image files\")\n",
        "\n",
        "    def get_name(path):\n",
        "        name, _ = os.path.splitext(os.path.basename(path))\n",
        "        return name\n",
        "\n",
        "    # if the image names are numbers, sort by the value rather than asciibetically\n",
        "    # having sorted inputs means that the outputs are sorted in test mode\n",
        "    if all(get_name(path).isdigit() for path in input_paths):\n",
        "        input_paths = sorted(input_paths, key=lambda path: int(get_name(path)))\n",
        "    else:\n",
        "        input_paths = sorted(input_paths)\n",
        "\n",
        "    with tf.name_scope(\"load_images\"):\n",
        "        path_queue = tf.train.string_input_producer(input_paths, shuffle=a.mode == \"train\")\n",
        "        reader = tf.WholeFileReader()\n",
        "        paths, contents = reader.read(path_queue)\n",
        "        raw_input = decode(contents)\n",
        "        raw_input = tf.image.convert_image_dtype(raw_input, dtype=tf.float32)\n",
        "\n",
        "        assertion = tf.assert_equal(tf.shape(raw_input)[2], 3, message=\"image does not have 3 channels\")\n",
        "        with tf.control_dependencies([assertion]):\n",
        "            raw_input = tf.identity(raw_input)\n",
        "\n",
        "        raw_input.set_shape([None, None, 3])\n",
        "\n",
        "        # break apart image pair and move to range [-1, 1]\n",
        "        width = tf.shape(raw_input)[1] # [height, width, channels]\n",
        "        a_images = preprocess(raw_input[:,:width//2,:])\n",
        "        b_images = preprocess(raw_input[:,width//2:,:])\n",
        "\n",
        "    if a.which_direction == \"AtoB\":\n",
        "        inputs, targets = [a_images, b_images]\n",
        "    elif a.which_direction == \"BtoA\":\n",
        "        inputs, targets = [b_images, a_images]\n",
        "    else:\n",
        "        raise Exception(\"invalid direction\")\n",
        "\n",
        "    # synchronize seed for image operations so that we do the same operations to both\n",
        "    # input and output images\n",
        "    seed = random.randint(0, 2**31 - 1)\n",
        "    def transform(image, scale):\n",
        "        r = image\n",
        "        if a.flip:\n",
        "            r = tf.image.random_flip_left_right(r, seed=seed)\n",
        "\n",
        "        # area produces a nice downscaling, but does nearest neighbor for upscaling\n",
        "        # assume we're going to be doing downscaling here\n",
        "        \n",
        "        r = tf.image.resize_images(r, [scale[0], scale[0]], method=tf.image.ResizeMethod.AREA)\n",
        "\n",
        "        offset = tf.cast(tf.floor(tf.random_uniform([2], 0, tf.cast(scale[0], dtype=tf.float32) - CROP_SIZE + 1, seed=seed)), dtype=tf.int32)\n",
        "        if a.scale_size > CROP_SIZE:\n",
        "            r = tf.image.crop_to_bounding_box(r, offset[0], offset[1], CROP_SIZE, CROP_SIZE)\n",
        "        elif a.scale_size < CROP_SIZE:\n",
        "            raise Exception(\"scale size cannot be less than crop size\")\n",
        "        return r\n",
        "    \n",
        "    scale = tf.random_uniform([1], minval=286, maxval=300, dtype=tf.int32)\n",
        "    with tf.name_scope(\"input_images\"):\n",
        "        input_images = transform(inputs, scale)\n",
        "\n",
        "    with tf.name_scope(\"target_images\"):\n",
        "        target_images = transform(targets, scale)\n",
        "\n",
        "    paths_batch, inputs_batch, targets_batch = tf.train.batch([paths, input_images, target_images], batch_size=a.batch_size)\n",
        "    steps_per_epoch = int(math.ceil(len(input_paths) / a.batch_size))\n",
        "\n",
        "    return Examples(\n",
        "        paths=paths_batch,\n",
        "        inputs=inputs_batch,\n",
        "        targets=targets_batch,\n",
        "        count=len(input_paths),\n",
        "        steps_per_epoch=steps_per_epoch,\n",
        "    )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "UEWy6JAeeNcz"
      },
      "cell_type": "markdown",
      "source": [
        "# Helper Functions"
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ov1MwNGlGDQu"
      },
      "cell_type": "code",
      "source": [
        "def preprocess(image):\n",
        "    with tf.name_scope(\"preprocess\"):\n",
        "        # [0, 1] => [-1, 1]\n",
        "        return image * 2 - 1\n",
        "\n",
        "\n",
        "def deprocess(image):\n",
        "    with tf.name_scope(\"deprocess\"):\n",
        "        # [-1, 1] => [0, 1]\n",
        "        return (image + 1) / 2\n",
        "\n",
        "def discrim_conv(batch_input, out_channels, stride):\n",
        "    padded_input = tf.pad(batch_input, [[0, 0], [1, 1], [1, 1], [0, 0]], mode=\"CONSTANT\")\n",
        "    return tf.layers.conv2d(padded_input, out_channels, kernel_size=4, strides=(stride, stride), padding=\"valid\", kernel_initializer=tf.random_normal_initializer(0, 0.02))\n",
        "\n",
        "\n",
        "def gen_conv(batch_input, out_channels):\n",
        "    # [batch, in_height, in_width, in_channels] => [batch, out_height, out_width, out_channels]\n",
        "    initializer = tf.random_normal_initializer(0, 0.02)\n",
        "    if a.separable_conv:\n",
        "        return tf.layers.separable_conv2d(batch_input, out_channels, kernel_size=4, strides=(2, 2), padding=\"same\", depthwise_initializer=initializer, pointwise_initializer=initializer)\n",
        "    else:\n",
        "        return tf.layers.conv2d(batch_input, out_channels, kernel_size=4, strides=(2, 2), padding=\"same\", kernel_initializer=initializer)\n",
        "\n",
        "\n",
        "def gen_deconv(batch_input, out_channels):\n",
        "    # [batch, in_height, in_width, in_channels] => [batch, out_height, out_width, out_channels]\n",
        "    initializer = tf.random_normal_initializer(0, 0.02)\n",
        "    if a.separable_conv:\n",
        "        _b, h, w, _c = batch_input.shape\n",
        "        resized_input = tf.image.resize_images(batch_input, [h * 2, w * 2], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n",
        "        return tf.layers.separable_conv2d(resized_input, out_channels, kernel_size=4, strides=(1, 1), padding=\"same\", depthwise_initializer=initializer, pointwise_initializer=initializer)\n",
        "    else:\n",
        "        return tf.layers.conv2d_transpose(batch_input, out_channels, kernel_size=4, strides=(2, 2), padding=\"same\", kernel_initializer=initializer)\n",
        "\n",
        "\n",
        "def lrelu(x, a):\n",
        "    with tf.name_scope(\"lrelu\"):\n",
        "        # adding these together creates the leak part and linear part\n",
        "        # then cancels them out by subtracting/adding an absolute value term\n",
        "        # leak: a*x/2 - a*abs(x)/2\n",
        "        # linear: x/2 + abs(x)/2\n",
        "\n",
        "        # this block looks like it has 2 inputs on the graph unless we do this\n",
        "        x = tf.identity(x)\n",
        "        return (0.5 * (1 + a)) * x + (0.5 * (1 - a)) * tf.abs(x)\n",
        "\n",
        "def batchnorm(inputs):\n",
        "    return tf.layers.batch_normalization(inputs, axis=3, epsilon=1e-5, momentum=0.1, training=True, gamma_initializer=tf.random_normal_initializer(1.0, 0.02))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "hZhEz0Adekvc"
      },
      "cell_type": "markdown",
      "source": [
        "# Generator Network "
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "eQ412k4OeT4k"
      },
      "cell_type": "code",
      "source": [
        "def create_generator(generator_inputs, generator_outputs_channels):\n",
        "    layers = []\n",
        "\n",
        "    # encoder_1: [batch, 256, 256, in_channels] => [batch, 128, 128, ngf]\n",
        "    with tf.variable_scope(\"encoder_1\"):\n",
        "        output = gen_conv(generator_inputs, a.ngf)\n",
        "        layers.append(output)\n",
        "\n",
        "    layer_specs = [\n",
        "        a.ngf * 2, # encoder_2: [batch, 128, 128, ngf] => [batch, 64, 64, ngf * 2]\n",
        "        a.ngf * 4, # encoder_3: [batch, 64, 64, ngf * 2] => [batch, 32, 32, ngf * 4]\n",
        "        a.ngf * 8, # encoder_4: [batch, 32, 32, ngf * 4] => [batch, 16, 16, ngf * 8]\n",
        "        a.ngf * 8, # encoder_5: [batch, 16, 16, ngf * 8] => [batch, 8, 8, ngf * 8]\n",
        "        a.ngf * 8, # encoder_6: [batch, 8, 8, ngf * 8] => [batch, 4, 4, ngf * 8]\n",
        "        a.ngf * 8, # encoder_7: [batch, 4, 4, ngf * 8] => [batch, 2, 2, ngf * 8]\n",
        "        a.ngf * 8, # encoder_8: [batch, 2, 2, ngf * 8] => [batch, 1, 1, ngf * 8]\n",
        "    ]\n",
        "\n",
        "    for out_channels in layer_specs:\n",
        "        with tf.variable_scope(\"encoder_%d\" % (len(layers) + 1)):\n",
        "            rectified = lrelu(layers[-1], 0.2)\n",
        "            # [batch, in_height, in_width, in_channels] => [batch, in_height/2, in_width/2, out_channels]\n",
        "            convolved = gen_conv(rectified, out_channels)\n",
        "            output = batchnorm(convolved)\n",
        "            layers.append(output)\n",
        "\n",
        "    layer_specs = [\n",
        "        (a.ngf * 8, 0.5),   # decoder_8: [batch, 1, 1, ngf * 8] => [batch, 2, 2, ngf * 8 * 2]\n",
        "        (a.ngf * 8, 0.5),   # decoder_7: [batch, 2, 2, ngf * 8 * 2] => [batch, 4, 4, ngf * 8 * 2]\n",
        "        (a.ngf * 8, 0.5),   # decoder_6: [batch, 4, 4, ngf * 8 * 2] => [batch, 8, 8, ngf * 8 * 2]\n",
        "        (a.ngf * 8, 0.0),   # decoder_5: [batch, 8, 8, ngf * 8 * 2] => [batch, 16, 16, ngf * 8 * 2]\n",
        "        (a.ngf * 4, 0.0),   # decoder_4: [batch, 16, 16, ngf * 8 * 2] => [batch, 32, 32, ngf * 4 * 2]\n",
        "        (a.ngf * 2, 0.0),   # decoder_3: [batch, 32, 32, ngf * 4 * 2] => [batch, 64, 64, ngf * 2 * 2]\n",
        "        (a.ngf, 0.0),       # decoder_2: [batch, 64, 64, ngf * 2 * 2] => [batch, 128, 128, ngf * 2]\n",
        "    ]\n",
        "\n",
        "    num_encoder_layers = len(layers)\n",
        "    for decoder_layer, (out_channels, dropout) in enumerate(layer_specs):\n",
        "        skip_layer = num_encoder_layers - decoder_layer - 1\n",
        "        with tf.variable_scope(\"decoder_%d\" % (skip_layer + 1)):\n",
        "            if decoder_layer == 0:\n",
        "                # first decoder layer doesn't have skip connections\n",
        "                # since it is directly connected to the skip_layer\n",
        "                input = layers[-1]\n",
        "            else:\n",
        "                input = tf.concat([layers[-1], layers[skip_layer]], axis=3)\n",
        "\n",
        "            rectified = tf.nn.relu(input)\n",
        "            # [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]\n",
        "            output = gen_deconv(rectified, out_channels)\n",
        "            output = batchnorm(output)\n",
        "\n",
        "            if dropout > 0.0:\n",
        "                output = tf.nn.dropout(output, keep_prob=1 - dropout)\n",
        "\n",
        "            layers.append(output)\n",
        "\n",
        "    # decoder_1: [batch, 128, 128, ngf * 2] => [batch, 256, 256, generator_outputs_channels]\n",
        "    with tf.variable_scope(\"decoder_1\"):\n",
        "        input = tf.concat([layers[-1], layers[0]], axis=3)\n",
        "        rectified = tf.nn.relu(input)\n",
        "        output = gen_deconv(rectified, generator_outputs_channels)\n",
        "        output = tf.tanh(output)\n",
        "        layers.append(output)\n",
        "\n",
        "    return layers[-1]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "mOaBW62qenTF"
      },
      "cell_type": "markdown",
      "source": [
        "# Discriminator Network"
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "0gFFs5NXeZpP"
      },
      "cell_type": "code",
      "source": [
        "def create_discriminator(discrim_inputs, discrim_targets):\n",
        "        n_layers = 3\n",
        "        layers = []\n",
        "\n",
        "        # 2x [batch, height, width, in_channels] => [batch, height, width, in_channels * 2]\n",
        "        input = tf.concat([discrim_inputs, discrim_targets], axis=3)\n",
        "\n",
        "        # layer_1: [batch, 256, 256, in_channels * 2] => [batch, 128, 128, ndf]\n",
        "        with tf.variable_scope(\"layer_1\"):\n",
        "            convolved = discrim_conv(input, a.ndf, stride=2)\n",
        "            rectified = lrelu(convolved, 0.2)\n",
        "            layers.append(rectified)\n",
        "\n",
        "        # layer_2: [batch, 128, 128, ndf] => [batch, 64, 64, ndf * 2]\n",
        "        # layer_3: [batch, 64, 64, ndf * 2] => [batch, 32, 32, ndf * 4]\n",
        "        # layer_4: [batch, 32, 32, ndf * 4] => [batch, 31, 31, ndf * 8]\n",
        "        for i in range(n_layers):\n",
        "            with tf.variable_scope(\"layer_%d\" % (len(layers) + 1)):\n",
        "                out_channels = a.ndf * min(2**(i+1), 8)\n",
        "                stride = 1 if i == n_layers - 1 else 2  # last layer here has stride 1\n",
        "                convolved = discrim_conv(layers[-1], out_channels, stride=stride)\n",
        "                normalized = batchnorm(convolved)\n",
        "                rectified = lrelu(normalized, 0.2)\n",
        "                layers.append(rectified)\n",
        "\n",
        "        # layer_5: [batch, 31, 31, ndf * 8] => [batch, 30, 30, 1]\n",
        "        with tf.variable_scope(\"layer_%d\" % (len(layers) + 1)):\n",
        "            convolved = discrim_conv(rectified, out_channels=1, stride=1)\n",
        "            output = tf.sigmoid(convolved)\n",
        "            layers.append(output)\n",
        "\n",
        "        return layers[-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "bb_RcLa0eq5f"
      },
      "cell_type": "markdown",
      "source": [
        "# Intitialize the Model"
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "2s8gGjNkGtyN"
      },
      "cell_type": "code",
      "source": [
        "def create_model(inputs, targets):\n",
        "    with tf.variable_scope(\"generator\"):\n",
        "        out_channels = int(targets.get_shape()[-1])\n",
        "        outputs = create_generator(inputs, out_channels)\n",
        "\n",
        "    # create two copies of discriminator, one for real pairs and one for fake pairs\n",
        "    # they share the same underlying variables\n",
        "    with tf.name_scope(\"real_discriminator\"):\n",
        "        with tf.variable_scope(\"discriminator\"):\n",
        "            # 2x [batch, height, width, channels] => [batch, 30, 30, 1]\n",
        "            predict_real = create_discriminator(inputs, targets)\n",
        "\n",
        "    with tf.name_scope(\"fake_discriminator\"):\n",
        "        with tf.variable_scope(\"discriminator\", reuse=True):\n",
        "            # 2x [batch, height, width, channels] => [batch, 30, 30, 1]\n",
        "            predict_fake = create_discriminator(inputs, outputs)\n",
        "\n",
        "    with tf.name_scope(\"discriminator_loss\"):\n",
        "        # minimizing -tf.log will try to get inputs to 1\n",
        "        # predict_real => 1\n",
        "        # predict_fake => 0\n",
        "        discrim_loss = tf.reduce_mean(-(tf.log(predict_real + EPS) + tf.log(1 - predict_fake + EPS)))\n",
        "\n",
        "    with tf.name_scope(\"generator_loss\"):\n",
        "        # predict_fake => 1\n",
        "        # abs(targets - outputs) => 0\n",
        "        gen_loss_GAN = tf.reduce_mean(-tf.log(predict_fake + EPS))\n",
        "        gen_loss_L1 = tf.reduce_mean(tf.abs(targets - outputs))\n",
        "        gen_loss = gen_loss_GAN * a.gan_weight + gen_loss_L1 * a.l1_weight\n",
        "\n",
        "    with tf.name_scope(\"discriminator_train\"):\n",
        "        discrim_tvars = [var for var in tf.trainable_variables() if var.name.startswith(\"discriminator\")]\n",
        "        discrim_optim = tf.train.AdamOptimizer(a.lr, a.beta1)\n",
        "        discrim_grads_and_vars = discrim_optim.compute_gradients(discrim_loss, var_list=discrim_tvars)\n",
        "        discrim_train = discrim_optim.apply_gradients(discrim_grads_and_vars)\n",
        "\n",
        "    with tf.name_scope(\"generator_train\"):\n",
        "        with tf.control_dependencies([discrim_train]):\n",
        "            gen_tvars = [var for var in tf.trainable_variables() if var.name.startswith(\"generator\")]\n",
        "            gen_optim = tf.train.AdamOptimizer(a.lr, a.beta1)\n",
        "            gen_grads_and_vars = gen_optim.compute_gradients(gen_loss, var_list=gen_tvars)\n",
        "            gen_train = gen_optim.apply_gradients(gen_grads_and_vars)\n",
        "\n",
        "    ema = tf.train.ExponentialMovingAverage(decay=0.99)\n",
        "    update_losses = ema.apply([discrim_loss, gen_loss_GAN, gen_loss_L1])\n",
        "\n",
        "    global_step = tf.train.get_or_create_global_step()\n",
        "    incr_global_step = tf.assign(global_step, global_step+1)\n",
        "\n",
        "    return Model(\n",
        "        predict_real=predict_real,\n",
        "        predict_fake=predict_fake,\n",
        "        discrim_loss=ema.average(discrim_loss),\n",
        "        discrim_grads_and_vars=discrim_grads_and_vars,\n",
        "        gen_loss_GAN=ema.average(gen_loss_GAN),\n",
        "        gen_loss_L1=ema.average(gen_loss_L1),\n",
        "        gen_grads_and_vars=gen_grads_and_vars,\n",
        "        outputs=outputs,\n",
        "        train=tf.group(update_losses, incr_global_step, gen_train),\n",
        "    )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "sOkAQR79evVP"
      },
      "cell_type": "markdown",
      "source": [
        "# Save and Display Inputs and Outputs"
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "90pHlkSjeyA1"
      },
      "cell_type": "code",
      "source": [
        "def save_images(fetches, step=None):\n",
        "    image_dir = os.path.join(a.output_dir, \"images\")\n",
        "    if not os.path.exists(image_dir):\n",
        "        os.makedirs(image_dir)\n",
        "    filesets = []\n",
        "    for i, in_path in enumerate(fetches[\"paths\"][0:1]):\n",
        "        name, _ = os.path.splitext(os.path.basename(in_path.decode(\"utf8\")))\n",
        "        fileset = {\"name\": name, \"step\": step}\n",
        "        for kind in [\"inputs\", \"outputs\", \"targets\"]:\n",
        "            print(kind)\n",
        "            filename = name + \"-\" + kind + \".png\"\n",
        "            if step is not None:\n",
        "                filename = \"%08d-%s\" % (step, filename)\n",
        "            fileset[kind] = filename\n",
        "            out_path = os.path.join(image_dir, filename)\n",
        "            \n",
        "            contents = fetches[kind][i]\n",
        "            \n",
        "            with open(out_path, \"wb\") as f:\n",
        "                f.write(contents)\n",
        "            img = cv2.imread(out_path)\n",
        "            plt.imshow(img[:,:,::-1])\n",
        "            plt.show()\n",
        "        filesets.append(fileset)\n",
        "    return filesets\n",
        "\n",
        "\n",
        "def append_index(filesets, step=False):\n",
        "    index_path = os.path.join(a.output_dir, \"index.html\")\n",
        "    if os.path.exists(index_path):\n",
        "        index = open(index_path, \"a\")\n",
        "    else:\n",
        "        index = open(index_path, \"w\")\n",
        "        index.write(\"<html><body><table><tr>\")\n",
        "        if step:\n",
        "            index.write(\"<th>step</th>\")\n",
        "        index.write(\"<th>name</th><th>input</th><th>output</th><th>target</th></tr>\")\n",
        "\n",
        "    for fileset in filesets:\n",
        "        index.write(\"<tr>\")\n",
        "\n",
        "        if step:\n",
        "            index.write(\"<td>%d</td>\" % fileset[\"step\"])\n",
        "        index.write(\"<td>%s</td>\" % fileset[\"name\"])\n",
        "\n",
        "        for kind in [\"inputs\", \"outputs\", \"targets\"]:\n",
        "            index.write(\"<td><img src='images/%s'></td>\" % fileset[kind])\n",
        "\n",
        "        index.write(\"</tr>\")\n",
        "    return index_path"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "text",
        "id": "eLGuzeuqvQ_k"
      },
      "cell_type": "markdown",
      "source": [
        "# Main "
      ]
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "N9ni1Em_G14g"
      },
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    if a.seed is None:\n",
        "        a.seed = random.randint(0, 2**31 - 1)\n",
        "\n",
        "    tf.set_random_seed(a.seed)\n",
        "    np.random.seed(a.seed)\n",
        "    random.seed(a.seed)\n",
        "\n",
        "    if not os.path.exists(a.output_dir):\n",
        "        os.makedirs(a.output_dir)\n",
        "\n",
        "    if a.mode == \"test\" or a.mode == \"export\":\n",
        "        if a.checkpoint is None:\n",
        "            raise Exception(\"checkpoint required for test mode\")\n",
        "\n",
        "        # load some options from the checkpoint\n",
        "        options = {\"which_direction\", \"ngf\", \"ndf\"}\n",
        "        with open(os.path.join(a.checkpoint, \"options.json\")) as f:\n",
        "            for key, val in json.loads(f.read()).items():\n",
        "                if key in options:\n",
        "                    print(\"loaded\", key, \"=\", val)\n",
        "                    setattr(a, key, val)\n",
        "        # disable these features in test mode\n",
        "        a.scale_size = CROP_SIZE\n",
        "        a.flip = False\n",
        "\n",
        "    for k, v in a._get_kwargs():\n",
        "        print(k, \"=\", v)\n",
        "\n",
        "    with open(os.path.join(a.output_dir, \"options.json\"), \"w\") as f:\n",
        "        f.write(json.dumps(vars(a), sort_keys=True, indent=4))\n",
        "\n",
        "    if a.mode == \"export\":\n",
        "\n",
        "        input = tf.placeholder(tf.float32, shape=[CROP_SIZE, CROP_SIZE, 3])\n",
        "        batch_input = tf.expand_dims(input, axis=0)\n",
        "\n",
        "        with tf.variable_scope(\"generator\"):\n",
        "            output = deprocess(create_generator(preprocess(batch_input), 3))\n",
        "\n",
        "        key = tf.placeholder(tf.string, shape=[1])\n",
        "        inputs = {\n",
        "            \"key\": key.name,\n",
        "            \"input\": input.name\n",
        "        }\n",
        "        tf.add_to_collection(\"inputs\", json.dumps(inputs))\n",
        "        outputs = {\n",
        "            \"key\":  tf.identity(key).name,\n",
        "            \"output\": output.name,\n",
        "        }\n",
        "        tf.add_to_collection(\"outputs\", json.dumps(outputs))\n",
        "\n",
        "        init_op = tf.global_variables_initializer()\n",
        "        restore_saver = tf.train.Saver()\n",
        "        export_saver = tf.train.Saver()\n",
        "\n",
        "        with tf.Session() as sess:\n",
        "            sess.run(init_op)\n",
        "            print(\"loading model from checkpoint\")\n",
        "            checkpoint = tf.train.latest_checkpoint(a.checkpoint)\n",
        "            restore_saver.restore(sess, checkpoint)\n",
        "            print(\"exporting model\")\n",
        "            export_saver.export_meta_graph(filename=os.path.join(a.output_dir, \"export.meta\"))\n",
        "            export_saver.save(sess, os.path.join(a.output_dir, \"export\"), write_meta_graph=False)\n",
        "\n",
        "        return\n",
        "\n",
        "    examples = load_examples()\n",
        "    print(\"examples count = %d\" % examples.count)\n",
        "\n",
        "    # inputs and targets are [batch_size, height, width, channels]\n",
        "    model = create_model(examples.inputs, examples.targets)\n",
        "\n",
        "    inputs = deprocess(examples.inputs)\n",
        "    targets = deprocess(examples.targets)\n",
        "    outputs = deprocess(model.outputs)\n",
        "\n",
        "    def convert(image):\n",
        "        if a.aspect_ratio != 1.0:\n",
        "            # upscale to correct aspect ratio\n",
        "            size = [CROP_SIZE, int(round(CROP_SIZE * a.aspect_ratio))]\n",
        "            image = tf.image.resize_images(image, size=size, method=tf.image.ResizeMethod.BICUBIC)\n",
        "\n",
        "        return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)\n",
        "\n",
        "    # reverse any processing on images so they can be written to disk or displayed to user\n",
        "    with tf.name_scope(\"convert_inputs\"):\n",
        "        converted_inputs = convert(inputs)\n",
        "\n",
        "    with tf.name_scope(\"convert_targets\"):\n",
        "        converted_targets = convert(targets)\n",
        "\n",
        "    with tf.name_scope(\"convert_outputs\"):\n",
        "        converted_outputs = convert(outputs)\n",
        "\n",
        "    with tf.name_scope(\"encode_images\"):\n",
        "        display_fetches = {\n",
        "            \"paths\": examples.paths,\n",
        "            \"inputs\": tf.map_fn(tf.image.encode_png, converted_inputs, dtype=tf.string, name=\"input_pngs\"),\n",
        "            \"targets\": tf.map_fn(tf.image.encode_png, converted_targets, dtype=tf.string, name=\"target_pngs\"),\n",
        "            \"outputs\": tf.map_fn(tf.image.encode_png, converted_outputs, dtype=tf.string, name=\"output_pngs\"),\n",
        "        }\n",
        "\n",
        "    # summaries\n",
        "    with tf.name_scope(\"inputs_summary\"):\n",
        "        tf.summary.image(\"inputs\", converted_inputs)\n",
        "\n",
        "    with tf.name_scope(\"targets_summary\"):\n",
        "        tf.summary.image(\"targets\", converted_targets)\n",
        "\n",
        "    with tf.name_scope(\"outputs_summary\"):\n",
        "        tf.summary.image(\"outputs\", converted_outputs)\n",
        "\n",
        "    with tf.name_scope(\"predict_real_summary\"):\n",
        "        tf.summary.image(\"predict_real\", tf.image.convert_image_dtype(model.predict_real, dtype=tf.uint8))\n",
        "\n",
        "    with tf.name_scope(\"predict_fake_summary\"):\n",
        "        tf.summary.image(\"predict_fake\", tf.image.convert_image_dtype(model.predict_fake, dtype=tf.uint8))\n",
        "\n",
        "    tf.summary.scalar(\"discriminator_loss\", model.discrim_loss)\n",
        "    tf.summary.scalar(\"generator_loss_GAN\", model.gen_loss_GAN)\n",
        "    tf.summary.scalar(\"generator_loss_L1\", model.gen_loss_L1)\n",
        "\n",
        "    for var in tf.trainable_variables():\n",
        "        tf.summary.histogram(var.op.name + \"/values\", var)\n",
        "\n",
        "    for grad, var in model.discrim_grads_and_vars + model.gen_grads_and_vars:\n",
        "        tf.summary.histogram(var.op.name + \"/gradients\", grad)\n",
        "\n",
        "    with tf.name_scope(\"parameter_count\"):\n",
        "        parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])\n",
        "\n",
        "    saver = tf.train.Saver(max_to_keep=1)\n",
        "\n",
        "    logdir = a.output_dir if (a.trace_freq > 0 or a.summary_freq > 0) else None\n",
        "    sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=0, saver=None)\n",
        "    with sv.managed_session() as sess:\n",
        "        print(\"parameter_count =\", sess.run(parameter_count))\n",
        "\n",
        "        if a.checkpoint is not None:\n",
        "            print(\"loading model from checkpoint\")\n",
        "            checkpoint = tf.train.latest_checkpoint(a.checkpoint)\n",
        "            saver.restore(sess, checkpoint)\n",
        "\n",
        "        max_steps = 2**32\n",
        "        if a.max_epochs is not None:\n",
        "            max_steps = examples.steps_per_epoch * a.max_epochs\n",
        "        if a.max_steps is not None:\n",
        "            max_steps = a.max_steps\n",
        "\n",
        "        if a.mode == \"test\":\n",
        "            # lab_colorizationtesting\n",
        "            # at most, process the test data once\n",
        "            start = time.time()\n",
        "            max_steps = min(examples.steps_per_epoch, max_steps)\n",
        "            for step in range(max_steps):\n",
        "                results = sess.run(display_fetches)\n",
        "                filesets = save_images(results)\n",
        "                for i, f in enumerate(filesets):\n",
        "                    print(\"evaluated image\", f[\"name\"])\n",
        "                index_path = append_index(filesets)\n",
        "            print(\"wrote index at\", index_path)\n",
        "            print(\"rate\", (time.time() - start) / max_steps)\n",
        "        else:\n",
        "            # training\n",
        "            start = time.time()\n",
        "\n",
        "            for step in range(max_steps):\n",
        "                def should(freq):\n",
        "                    return freq > 0 and ((step + 1) % freq == 0 or step == max_steps - 1)\n",
        "\n",
        "                options = None\n",
        "                run_metadata = None\n",
        "                if should(a.trace_freq):\n",
        "                    options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n",
        "                    run_metadata = tf.RunMetadata()\n",
        "\n",
        "                fetches = {\n",
        "                    \"train\": model.train,\n",
        "                    \"global_step\": sv.global_step,\n",
        "                }\n",
        "\n",
        "                if should(a.progress_freq):\n",
        "                    fetches[\"discrim_loss\"] = model.discrim_loss\n",
        "                    fetches[\"gen_loss_GAN\"] = model.gen_loss_GAN\n",
        "                    fetches[\"gen_loss_L1\"] = model.gen_loss_L1\n",
        "\n",
        "                if should(a.summary_freq):\n",
        "                    fetches[\"summary\"] = sv.summary_op\n",
        "\n",
        "                if should(a.display_freq):\n",
        "                    fetches[\"display\"] = display_fetches\n",
        "\n",
        "                results = sess.run(fetches, options=options, run_metadata=run_metadata)\n",
        "\n",
        "                if should(a.summary_freq):\n",
        "                    print(\"recording summary\")\n",
        "                    sv.summary_writer.add_summary(results[\"summary\"], results[\"global_step\"])\n",
        "\n",
        "                if should(a.display_freq):\n",
        "                    print(\"Display saved images\")\n",
        "                    filesets = save_images(results[\"display\"], step=results[\"global_step\"])\n",
        "                    append_index(filesets, step=True)\n",
        "\n",
        "                if should(a.trace_freq):\n",
        "                    print(\"recording trace\")\n",
        "                    sv.summary_writer.add_run_metadata(run_metadata, \"step_%d\" % results[\"global_step\"])\n",
        "\n",
        "                if should(a.progress_freq):\n",
        "                    # global_step will have the correct step count if we resume from a checkpoint\n",
        "                    train_epoch = math.ceil(results[\"global_step\"] / examples.steps_per_epoch)\n",
        "                    train_step = (results[\"global_step\"] - 1) % examples.steps_per_epoch + 1\n",
        "                    rate = (step + 1) * a.batch_size / (time.time() - start)\n",
        "                    remaining = (max_steps - step) * a.batch_size / rate\n",
        "                    print(\"progress  epoch %d  step %d  image/sec %0.1f  remaining %dm\" % (train_epoch, train_step, rate, remaining / 60))\n",
        "                    print(\"discrim_loss\", results[\"discrim_loss\"])\n",
        "                    print(\"gen_loss_GAN\", results[\"gen_loss_GAN\"])\n",
        "                    print(\"gen_loss_L1\", results[\"gen_loss_L1\"])\n",
        "\n",
        "                if should(a.save_freq):\n",
        "                    print(\"saving model\")\n",
        "                    saver.save(sess, os.path.join(a.output_dir, \"model\"), global_step=sv.global_step)\n",
        "\n",
        "                if sv.should_stop():\n",
        "                    break\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "N1N7xi_MetRp",
        "outputId": "6a9a4151-2cb5-404d-8a54-7d250441165c"
      },
      "cell_type": "code",
      "source": [
        "main()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loaded ndf = 32\n",
            "loaded ngf = 32\n",
            "loaded which_direction = BtoA\n",
            "aspect_ratio = 1.0\n",
            "batch_size = 4\n",
            "beta1 = 0.5\n",
            "checkpoint = scene\n",
            "display_freq = 500\n",
            "flip = False\n",
            "gan_weight = 1.0\n",
            "input_dir = \n",
            "l1_weight = 50.0\n",
            "lr = 0.0002\n",
            "max_epochs = 2000\n",
            "max_steps = None\n",
            "mode = export\n",
            "ndf = 32\n",
            "ngf = 32\n",
            "output_dir = scene\n",
            "output_filetype = png\n",
            "progress_freq = 500\n",
            "save_freq = 5000\n",
            "scale_size = 256\n",
            "seed = 965562106\n",
            "separable_conv = False\n",
            "summary_freq = 0\n",
            "trace_freq = 0\n",
            "which_direction = BtoA\n",
            "loading model from checkpoint\n",
            "INFO:tensorflow:Restoring parameters from scene/model-179000\n",
            "exporting model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7H2Q88AsklS3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}