pneumoniaClassifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "version": "3.6.4",
      "file_extension": ".py",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "name": "python",
      "mimetype": "text/x-python"
    },
    "colab": {
      "name": "COVID -19 Detection Using X-rays.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQvr4PvvFXEb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "fa990da3-0765-42ec-8f1f-dccbfbb350b5"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atJf5yj2EYYX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip -uq '/content/drive/My Drive/COVID_XRAY/548681_1157383_bundle_archive.zip' -d \"/content/drive/My Drive/COVID_XRAY/Kaggle data\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "j0K2jBXmEQNI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "# https://www.kaggle.com/ffyyytt/covid-19-normal-classification\n",
        "\n",
        "import os\n",
        "imagePaths = []\n",
        "for dirname, _, filenames in os.walk('/content/drive/My Drive/COVID_XRAY/Kaggle data/images/'):\n",
        "    for filename in filenames:\n",
        "        if (filename[-3:] == 'png'):\n",
        "            imagePaths.append(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "trusted": true,
        "id": "_x8RJuk0EQNM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "9734cf70-94be-4d9d-fd4a-684ed7e85af7"
      },
      "source": [
        "import cv2\n",
        "import os\n",
        "import keras\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from keras.preprocessing import image\n",
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "from keras import applications\n",
        "from keras.optimizers import Adam\n",
        "from keras.models import Sequential, Model \n",
        "from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization\n",
        "from keras import backend as k \n",
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping\n",
        "from tqdm import tqdm\n",
        "from keras.optimizers import SGD\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.utils import np_utils\n",
        "\n",
        "from keras import backend as K"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "IPRYC3FsEQNO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "7f6c2b95-813d-4818-8783-82e9e16b69d5"
      },
      "source": [
        "image = cv2.imread(imagePaths[0])\n",
        "plt.imshow(image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fdbcd85b710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+uLgY2JZjUCnXyA8rmmxUT7CiDssa44Vbi2uabl7GMgnie0wsryln/qmq0aJFSx+Tsa0qDxLAoI7f+Z3fWTs7O/WP/tE/qs9+9rP1Mz/zM/WN3/iN9e3f/u31/ve/v37xF3+xuq4bDX6WVawolaIFUP4/P18FXFZuW7P7loMda/LzWtuSDFgZ2LfQpIVMJW2BaUvwLLjEbKpqWM3a3NwcDAmWHqawtrY25BnRRuIatrxe8ZpOp8Pz3L4EUf9kbM5jlYsBnusERO61EnusuabrblMSCMwDXLSF/hD3ItcNNw5AMLs6Pz8f3EWAK+Nv9M99JB1je3t7aLdXRd0v+sEcmsmmHAF8/mF+zbhT51quf7Ixsy/PD8/g/+elUFQ9IACzizSZTOr4+Lh+62/9rTWbzepv/+2/Xbu7u/UP/sE/qO/+7u+u9773vfXhD3+4Xn/99ZU0MwONfJZgs8oN4bMENn6vAq9kSxakFMBsU6tu7ydsPavV//va5etTKewStVYBq6q2trZGQeK+72tra2uIgWVKRbqEmau0WCzq8vJyUEYSYA1GKCbFCpnxKAONrXjGwrIe/5+rpB6bzc3NevToUT169Ghwg33fZDIZgArgYkGEGNbFxUWdnp7WdDodXEOfWeYE2lafmYfd3d1RjC3TKnDd+J62mb3xTOf5LRaL2traqu3t7WHVtKqGeJvz9fjf7UvG5mLDmAYyvYlMFG6VBwFgXXd74J0b/7t/9++ut73tbbVYLOr3/J7fU7/tt/222t3draurq9rf36+PfOQj9eM//uPNOA51GsRW/XBt63dVO3aSAGiATKFz/IA6klH571Vg6++TVbXYm5+V/UoASPB0+82E+r6v8/PzwS2ym7GxsTFiwragq5gf85PbXrLtxIaqxq5lC4Bac4mS2Bi0il3DbO/GxkYdHR3Vo0ePBqXOgLNXvHGRJ5NJ3dzc1NOnT+v09LSePHlS5+fnI7fQYGK3rmUUt7a27uSQ+b6W/DB27o8NLKDGGLFqalnIGBnPPDg4qK7rBvZI3NNud465XVPrfMbmSOG4rzwYALPgb2xs1Dd90zcNSvKt3/qtd5jRBz/4wfr6r//6+uxnP3tH+avu7q1bxUioswUCvmeVordAIv39ZEspDNxHe03puS+VL9lh9sWC4LZn/1q/HctIa22FbrloVj6KARyWTT3Oi8rxbrHfVl/dZn/OMxwUtsFIN4e2GjDW19fr4OCgjo6Ohsx8G6b0HLzda7FY1Pn5eZ2entbJyckIuDxPjKHHjWuImeGuZ+zQLAbX3WOdzNTAgZuPrOzs7AzxSY+5jQvsmbHhOp5FcvLZ2dmQpGwZ8ribqRmwPDZeJW+VBwFgVWMFeN/73le/43f8juq6rv78n//z9a//9b8ervvABz4wsLMPfOAD9S//5b+8k0tTVcNqV8ZN7lNsSgJetm9V8URxTyo4k9KySFYurzjymb9vKW9e636uKjkONgZYedqCYKdgmeX6Gn7DsKgjwQ23EQa3ubk52k7jjHSDTY5Fzo/jWK0FESuV56nqNgXh8PCwDg4Oant7exhn7wxwQnQeXEks6+TkpJ4+fTokYyZzwqW2weu6bjh9Ymtra7RqS398pA5jfHNzM8TUyMlbLBZDUJ/n4r4zb4D0/v5+U8b5jA3XBi7LL67z/v5+HR8f1/n5eb3++ut1dXU1qstzYjlknhlHxwBXlQcJYL/xN/7Getvb3lZPnz6tv/7X/3r9/M///HDN3/k7f6e+7du+rd7xjnfU7/pdv6t+8id/sn7wB3+wvu3bvm2Y6KqqP/En/kT9oT/0h+qHfuiH6u/+3b87KLQn3sqa1p/nOYaVFinbz3erYlwtep999zV2ZZIl+bkWbAOX6/L1gIkB18/JdA2ABxeG76D3dntoN9Yaq4zCVC3ZQNeN3ZscD8dJvK8vn9maOyto1pt/mwWgzMfHx4MsZUC/BWA8C3f3/Py8zs7OhtxEx6J4vkGU3+w1ZAGH4L9BknuRD+pjjr70pS8NbO/y8rK5a8LPPDo6GsALuUhZ9Oolq5ZVNYDTYrGo6XQ6zDuMbmdnp774xS8OG8h5bvZ7FZN/UwLYN33TN9Wv/Mqv1C/8wi/Uq6++OqLq0+m0fvqnf7p+02/6TXV4eFj7+/vV9/0QYJxOpyMwePvb316/4Tf8hoGG2wUzGHjFxgILE3BSIX/nUq/rdp/4LplRurKrGFfLVVy1uua+p6WknlxU8L1VdccN4TqfYMpnADz/A3KkUWxsbNT5+fkAVDn2gCntdAJkGgqPTSb02oWkHRnvMYPzChqytbe3V4eHh8PmaruK1G1w5/nUP5vN6urqalhVpM/eSWCAoH1+N4BBaW1tbYgN45ayEsxqoOcf93JjY6OePHlSFxcXtb6+PrSDttqo7O/v197e3vC8ZLdmQ/TT7qJ1BLnxlrK1tbV6xzveMTDRTMtx/ZZns+b7yhvyYtsvt6yvr/eHh4dN9lA1PlOr67ohwxkB8IFv29vbA/pjubBkzg3LWAgTAK23cFu5uZbs6Ovr67q8vBxAjRWalkVxWkTWazbBc91/nosiZppEMq5Vq5f0wUrOZwmerKKl0hqgeeb5+fmQ1Pr48eMhnWJ/f7+ur6+H0wsAN+7n2VU1GjfnImE4WrFBfuySUa8D3AYi9xXG5cA1ypNbiCw/gAdlPp/XdDodzunKfZGOH+ZY+3PaXVV1fHw89BfvwoaJeWG7Ftesr6/X2dnZUA/ACiPECK+vr9ejR49GwOZ6GR8KIA2RAKim0+md8eU6fjY3N+vVV1+t09PTEcM2QWCcMrfw05/+9D/r+/6bq1EeDANruVVVNVIc03QHkbkPFjaZTIbTFKjbcR3AAtBDWb1nD6Vgidgg6rOybF2ZEMCMpETiExkgTrrcAhuKBd35M7kgYAvm5yQjYWzNYFbt+/OYJHOi/1tbW0NQeGdnpy4uLkZGYhWQGjAobuNisbiTQ5V5RrSBVA6DxGQyuZPZjau2t7dXu7u7Q26ax9djx99WatrH3E6n00GpMVYGJjN76sj+uF9k1M9ms9EZYQYvPI/Uk77vh6NzANUvfelLw0GIs9msNjY26vDw8F49tOxQNjc3azabDfMxnU5HY2EWnbHLt7zlLYOLbeadMsh4vWlWIdfW1urw8HCkmBZ+fP+qJQOazWbD5lR3nHu8isOkpwtlqgvAWZCtSP4+raaXz3PCsUZmaN787P56hY5++G/XZ4C3G2jFboEGv4llOcVgc3NzlOOTLDEtNcCDdScDH/ea/rIqNZlMRid6YjjsfrrPtNcsOeckQdtMx322LOzu7g4Z61xjF4mS7MMsF0BiPyKMPFksstj3yyNobHyoKxklJ7K6TrufBnz3zQdJsoiAUQcEp9PpcKSP2Y51zrpmmXc8zzLQmiPXN5/Pa3d3t97ylrfUF7/4xZEM4w63tkhZl1rlwQDYo0ePRrEQW0Ln1LDF4urqqo6Pjwc6azDBglUtzxRP183Kk5OAoFmRPCn22T1hCJeft7GxMaxiWRlRcG8d8Z41Srpsfp4FINtppTZwG4yok/tgJjBPFCPBz/XRF5gmRxZTZ9/3w1HB6TJ7Xs0QrVQZyGWuzIiSydJmWC9se39/f7QTgHwlA6fZVbp5BnTmzEc683Yl9x0ZdKyHPlIsa8gemfVmdFXj00OcIFtVo+1JnN7KvT5ssu/72t3dHXQvjR3F8Ti33UbTssq4puFl3K6vr2tnZ6cODw+HEyZS59E/M8v7ylcFYF3X/XJVnVbVvKpmfd9/c9d1j6vqr1XV+6rql6vqY33frz4PQ8WD6AxmL+Nvb2/X1dVVvfrqq7VYLGp7e3v0QgAEG+UgJoC19TI+wmALh/A658YWz8yM57RiZxaEBB2Eim0g1AuokRAI3U+XqVUvJf/32JoBmL6n20Qs0ZYQK+r5MBvGtQA4UBDihAZv9x9wQUFwLzKLnrqSERu8yBtEaWAcyBJW3vEt5spAjywkg2MM/OIM2pIrrCi6WeF9BgTwraqBrS8Wi2Fs/Ayea+Da29urxWIxZPqzmAUgECu7vr4e2Gf22W1ALtJIMObeSmQ5ZBXSK88eO2J1XGf5s/dj8LyvfC0Y2Lf3ff+q/v9EVf39vu8/2XXdJ579/733VYDF8YSnsvMmls3NzXr8+HEdHx/XxcVFHRwc1PX19bCHjvoyvuMVE1vqjO/4PoCR565a9fEEVdVISZgcuyNmGlZEXt6wv79fVcvgK4FhWA51GBBcrxWuanmQXQoL92DpGbu0yAAr9eB+GmhQbECCwC6/GW+2qMAwGEPcTtpk4GQsqpYACxgjLx7LjG8Cog6st/qZbIz6YC8YF5iXx95zYrnyPFEYD6fJeBEJJkycyfNgEOcNRTCf6XQ6BOrN3J0Cc3NzM+ha1ZI1JQNziKTrlikzXjRIffO4YUwoKefsp6ROCowyDeuq8ka4kB+tqg8/+/svV9U/rOcA2GQyGQKqPqbFyA7bYdJhLqwi4RIQUL24uKjz8/PRipAFFMGkXgQGxTDDQGkBMdpRdSuMJBl6ZcqrYjATT4YF1sLiIHrf90N2NK4B50DZ7XTMygwRYYF9mtEkm0u2QRstjHYJzXIZJ8DWrgR9tiGyYgKezlCnfWZBjmNmwB2FJ/2Bec2YqhmQ41B+WQmf2TABgIyz3blkr2aauUeQOi2HZoPUbVnnc88VYMQ4+ige8rIMnmbPyHnOP230PZ5b5s/v/7SLnHKL7KRBpZ7WolUm9HqeVpWvFsD6qvqpruv6qvpf+r7/VFW9ve/7X332/a9V1dufV8lisajT09O6ubkZ9nkBJhZmBpRBxCKyP8+0fGdnZ5QjZsX3QXxd1w2biZlcr0wxsFwPgHkJ2FYFdmEGwPK8QcAAl1TZ/9vq8mzcMxgaK00GfoNXugm52oW7ZaHMuFoqHsLmjPGqurMqZpfVLNbMo+ruAoQtuN34vl8u0ZuF8dsyZTC3+2zmaCbu/jpO5jkww+Nag5jlwiAOWDhGB6jyve9jvLLfjBOMnDaQf3ZzczPaCsQ4AWyMvRm4x9pjznMdF3Rcjj547MxgE9T4Oz0X2uU6aUsrHOLy1QLYt/Z9//mu695WVX+v67pf8Jd93/fPwO1O6bru41X18aoaEvNIRIVe8rljYFZ4C+CzOu+4gVU1bIDlPlaP+HFM4fLycgCrnZ2dYXIt+Bw0B2Aw6DyX57HaRT12kWOcRnESKwMFwOAzgACwraphNYyXIeD6WCDMzuzq2BWjP1byjN/YWCDgtCmZjsEbJkD9Vmies7a2zPTObP+04mnd0w3iN0qUr4WzItMf3BgMpGXL/WMcHCIAaOirWQ5MxcwjX12HHmB8GAtAG1fdLigbqGmr41MYKOQJYpDjmW41/zO39hbSWNAfh2esp67XwEy9lqUXiee6fFUA1vf955/9/vWu6368qj5UVV/ouu6dfd//atd176yqX19x76eq6lNVVbu7u70Fgp/MI2FlzB1Py1pVg2KYelsZmRTO2UdxTY8RGq8oeqI9wOka4E4ByBxgB9jkG1080fQLAfTRNWYVzmxnTGCunBCAEvlNzQYxW8vWVh27yXaB7I5zr1mxdwBkuw0qGbviOxgvTKylOBk45nlp2f1csz4fsZzsg2C3FdLuOXXYwBgAPL65YudFEOTX85irggAORvfy8nLwFKwHXpDyGCRD9MKTx9vGifuQJzZlW35wJ83MPO6WJ48bZXNzs87Pz+/olWX/DQWwruv2qmrS9/3ps7//s6r676vqb1XV91TVJ5/9/pvPq4uJZLJcTIdblqPq7rEvLauS13Afz8eN4nMHp+1C5LMtMLkwwCTgtl5dXQ1B7Nzz5rYh6CkA6f6YyflFEcmKfDSKY4XOfs5nm23QT+bDbqBjeQBY1TJrnXZl3Qblq6urAbwBBbNyu2/Ml+cA5U3W4HG1EtFX6uW5MGraYDbSUkKuSfbmV5NZzhKEGZ/7DKLH3PJF33AlLZ8eI+bCoOXQiBm5x9SrtiYI/BigbIBs7FwMoGzr8/Ns6NJY3Fe+Ggb29qr68WcPWK+qv9L3/f/Zdd0/raof67ruD1bVr1TVx16kMpS56m7yarIuJgA3g79RLFP7BBYPUlWNJrFqnCtVtXTVUifWHrUAACAASURBVPA9eRaqDE66/VU1YnaAC3Ekr5o5NsKPg/W5asV4JZvg+V51AhxgnWaedmEd+0Bo6RvXOf5E/z0uVePN0rTH4JAM2i5PxghbcUMzFtwvK73ZHe6N66pasq60+h5/FDQNjkGyxTz8txU2r7cbSv9h8k5TcTb+zs7OsOvE7iJzjlyRn+f+reqD482WwclkMrinBhrup00tI2gdSObpcV71/6ryFQNY3/f/qqr+k8bnX6qq3/7l1GUhsTJQjPBpDTMVgUkjI9zWDaCw5XY9MBOEOicHQbBQEmdKxkc/7C66XyhnMjMDpmNFVUvlc+Db4OliV5zntMaQ2BzXmI3kAYUAhONYFnLPJWMFu3L/09IzloxnstuqcSpMy+JzPcAMOCALxItgpTnfMHAbRdebbMDsl7lM95XnGKR9b4K/mZ+fC+umOHwwmUyG8ERr1ZO6yaG0TtiIpB7muDrdwq61jfoq2eNay/XR0VF9/vOfv6NnNphvqAv5tSzr6+v1jne8Y2Sxva/RgOU9Zu6cVwfTbSBfhkA+gOXC/2QxOw6TsSLH0VrWrGqc92JWwm/ab0bDooLrT0rtoHO2r8X60o1GYXMpnLbZBTbD434Mhtky9zlozUqdn+GAve/N9ppNub6MDxmw6EMaQ/rMmFYtk2szJ5Bn5QqsF0LcXveNa/mdgJ799Zxa0T3GNnYeF7eLlXSfWeaFL56DQeEa9Mh7fbONlgvGANAyu7S+ZV8N4vZ6SL5lk7/vMdC9afZCHhwcjDZIo9BkFVeNO2Yl9+qXA77kb73rXe+6E+fpum60AmlFWV9fH/aQ8Zmfb2uaCgVlb9FfCzv3ekkZoYSVWbEsuJS8121Ldw13xNbbJd05sybGwDEc+ur4lNkUe+26bunqo0gtdxswwXBgkBB8r94ZyKpq5FqhpPQdRQWM3V8bQ8Z1e3t7iFnmuNq18zMSxFLOuCZBItmw+2r58kp7gt1kskxCdna7x8oy5JVQjI4BJlky7UrW7w3yKTfuu/tNXdfX10PCtk+nyPnJVdxWeRAAVrWMzcAyAJ/Dw8O6urqqk5OTYXuEA5oAhmNmDMTOzk698soro5hPvsEIxbXQkFDLy3PNdsxYcrK8tG9QTaGuulUUFNxBeQuuNwmjcFZ6C5qFj2KBQbD9hvN0kSmZJkCxoDvRsqoGMEPo2ALDPGEsvEqZ7a9arkBaHrzCZiNm98jfZf0wFdgzCa926ew++8UXHmszrmSP6Qa1jIhDE9kf32uZM1Bk5n7LsPmaZHwZY3OdyTLtdRhIzMhSrn3/KmPP3FVVHR0d1Re+8IWRAchxfVO4kFVL5tOi2VtbW/XKK6/U6enpcERt0nxeFPro0aPh5aLs+To9PR2ewfVra2ujLRW4VuSB7ezsjCxxLpszuIAnn+UKHZ8TWGaxAivmPCdKWjJcV+8oACCsZAY3BAzgYm8jSuqx4Dd9BIyYA/rkwyJT+Vjid27XxsZGXVxc1M7OzvDeQ4LsxJzMInM7kzO8E7jNLIjZOTxg5kmeFGM7m82GE0jpp0/QgKFwH+PIOCU4JPPNsbGiW675zKES15fzkqvw9CdZcjJlyyrXI0ewXa+008Zsu42273Gb3S6vprpvGIK9vb3h6CUzNo/r88qDADBb+7QslL6/3UHf93299tprw33+HqG3ElshLbD8dp4R1hdh8fEyZkDJenKS3Jau60aKWlWDUtEOuxDUuyrGYIG+vr4e3AD3G2NAno6VmfvTFQWEnQLgWArft05WbbGU6+vr0ZYV5opldFxEDECyEq+aejcB19l1bsVeFovlWVhmlIxXxi4N6J5bxjFjptn3qnGCb8pnymrWRZsS6NyW6+vrQV4dKrERQxayDrePVI9kf4w345eAkqviuKwef8uuWaz7huzu7OzUo0ePhjni/mR395UHA2DOVPeAVI139u/v79fa2lqdnJyMVm0YjI2NjSEIT0Ln7u7uILhVSyVlgBgw591YkbgOVuEtQlXLbHHaYmXx0TyOBZklrbLQZpk8n98O8No1cZsQMgTe7aJNtvSABcv2GUiGyXncWcqHBXtJn1NzqWdnZ2dYaSUPjrY4sGylS0bOb8bZrM2M5uLios7OzkbXwPpYscstNAZG/20XKtM4Uqk9rgZl+tVSyha4uT/0wcrt75m7vl/upTR4WH69moiM2PDwP8/1Mw3OHqdM6PW4uI3JwqpuT5390pe+NLwcORnd88qDALB0xapqpHSAD4Owt7dXXdcNZ2xvbGwM7+uruu18vgmFnfgtty2tCgLhWBvfwSoAk6plvpSZX9X4BQ4WZJ6dP63vESCvKqVl5jsE9/T0dBgX2maBS/BMd+W+grFJJmdFYasVeUfE8Zhjn6bLXCdomd24nalgyIYXXEgBcSwMF56tXZ4fW/9kDBiFTIcgVudxSdD1uPqzHPsWSFMYZ+SNOJ6BzG21QTCrsptplmbju1gshqOQctED1mYjku1yQRZzFdtMkb3Ph4eHdX5+fid+6zFcVR4EgOFmeaULBcyl/KoaQKzvb1+0ur6+XoeHh3eUiheKVt1ODhnwxGL43IqCUsAgsFJ+Nx9tdqZ1nqBhS+RJtyuUxQrdWjSAQSAIuSl2MpkMwIEwVo3fEJ19pS1WMDMcvk9mxN8+BqeqBhd8NpsNVtWg4ON4XB8lXblkMR4nNuj7wMTpdFpf+tKXRjFLzrvnfxgmMuc4H8/mGZZJnu8VUty/ZNE5fv4N48m+r3I/0322YfN85aknCYaMkTfu4xmke8dpsFU1MGmekYYOncldF4wNbck2c7TP48eP67XXXhtSQnz/89zIBwNgOzs7g/XEWttaeeKqbifj8PBwiAsALAkkgBiDvrW1VY8ePRqdV+/VM1Nwtjv4KB3q5D5vyUmLavZQVUPcwWDg681iWq60QchZ8maO9Mv5PRcXF0OdgAgswikJdnvNRhD8jGOwr9NHzVQtA7wODNNeXEjaXbVM0MXdzr15Bjrmaj6/PXOMl4nQzpOTk9HZ+AAN88SJIj7oEtfZLm3OhRlNAkPV3VVDl2QWBmI/o1U3/WUcbIByjlwnuuNwRMod843eUAeLNT5JuKqGM/n8VnEz09RX7rVRpz2OnQFi//bf/tsXdh0pDwLAqsZbT1C+VOCWm3FwcDDy/5lMKCoWBaBjGd8pG6a6MAQUylsnptPpkL1dNU54NFgZaJPOr/rOFj4ZEgDFyqOtbtWSYV1eXg6B66oa2AkAg/Xd29sbCX26yV4ttdL4qJxcxQSE7O7irjO3dmcBFwu+FzqYC8bZlrxqeWqprfzTp0+HRQZWQzOREgViXlkIMTv0uy9piw1K/phhYRwoVnIrMNfbJbac57O43m5iylAG+C2bLYBLxsd1LICk/Jk1Y0SYC2Qg03OsxzYEPIfY6OPHj4dXwSVzva88GADDarYG1fEmW4+q27cA47IghFYc02RADTCyZUNhyZPi8EADGM9JQEoGlf+bVvuaVfGmZHFO+UjXks9IUyDnivPVLAQo02w2G1JM7P4CLAgnYAbQ0Af6RmoG7kDm4cG8YHUwHPfDG+idZMlnVgD+5uA+wLLv+zo7OxudCMv9MGuuJSzhADZtRn54JR8ASh8dS7JhtXzmd46HIoOW4WR5Vv7W7xwTz4mZVhpAy0wG3X26LD8wXPSDRQ/OuquqOyvpVTVatbUhtqGxTCKzm5uboxXJF3Efqx4IgLlzVXeTRLE6FCs4L6HIWBOCjWBwtPHR0dGgONwHNV5bWxuOp0530JYr0zJSqMyS6IP7lULpcWCy7co4QJ/KYveX71sutZ9BjIx3cfp7FB13yIc42oqa0eBKO4BspgSQuc8OEQDSdv2rbhlkuk7uD5/zP3GbdMPNuhhL4jUGtMlkMigf4IWbjYuJ29yKV9Ge3N/JuG5sbAyGM3Oj+L0qRsn3BmUH831dylaCJPfN58u3KnFdGn0A2Dq6ublZh4eHdXh4WBsbyxcXm+kmCAGKNuA8A+/i+Pi4zs7OhjQpAPm+8iAArGppCSwMTmJMi2cAm81mdX5+PjAVZ97jQhr9cRG8UnZ1dTUCNYNHZoLThlx5sWVtMbQWYLX+T4V3cqzroY9MMoDDahuCdHl5OWJjjPXZ2Vnt7u4OypDukNueYzCZTAY2mmkkdk89Hp5bM9B0P52Ymi6X448IOAm0ZslmqIBQVQ2AnEzFY+kN3TaK6+vLE3dZEMpTgwFRj5f7yekRBrEWW2rNd/6dzNT1OI5I+2kTcnJ1dTVaKV/lujI31Mtr9J48eVKPHj0a4pq8NNftshxQF+3hOgwV77vgbP/Ul1Z5MACGZbQgutMMiC0fSpfKV7U8ndRWBDCwL+5nWQi4PwOwFFhGunQ5+Xy2qrQmmhwss5yqMYPDJSFlgPbaNeZagrG5QR3wIa0AN85uY7Ic2pEg5fFx/Y6vJSt133PMuMZL/owHAIQy+YgiA+5kMhm5mjmWTijmb/rkNxqZWeNWsbDDljDeFk9bOQQwmfza2u0uCk7pyKC3xyLHxGPWMpDc72cyJzZgGDTuz+v9HF/jWN5isainT5/W+fn5wMaQIx8YQHu8SGB33Gy772+T1Y+Pj4fFmeeB2IMAMIMI2e+2CA7sVi2F03k+mSVvtwv3kOA1gtr3/WgDud0hb1fyZKZbkm7iKjeA7wx4FkK7f44T8Uy7Z+wrBKBhJQ5qu+2LxWKI8TF2eQ4Z4F5VdwAUq43gZhwomWQqXgKYBTfHwDLRUih/Rpt2dnaGVWy/NYhMcccyCRkgG35PAu5p67BJfgPs1O+Fkuvr69rb26uDg4Pa29sb5Mh9BExpW46fWdkqJp+MNhmcA/bJ0JyWRF0Ze7Pby/X+SWB89dVX6+Lioo6Pj4eVe5895rnF8CJH/p65OTg4qKOjo3r11VfvyFaWBwFgVctVJcDH2xTsBzNZTuyEOVAAAoMXhaChV934TcqF35CTwJSg5JKC5fZ6ElNZARQDsoHRq3q8Yg2lRJFbbleChtvmzdbEfJyk67bmCyH8t5VsletIukKyAyudx9VuO4W4pPvC37yUxcCYOxD43mkDvLnKsRnCDgBZGk9YOezLc4Xx483XBLzT1Xf7PM7Jmvg8DYavo902LB5T/1SNk6tTDnke1xkIHas0wPH79PS0rq+v6/j4eDi+CrlxaIh5MJuj/bA0XMnz8/M6OTmp+8qDADAmAiXFbfD+vGQsWEcDGu4PCu9tPAyYlcgvVMAdMYia4rcCpi03EQvLZ6tYCvd7T6DrqRrH+jg62NbeW6IM9LSBLT5mH33fj5TKqRGMu4EJIbQbZQDJOA71Mh8YGZIn7cIadBxnYVxyrHHpHHvziiLzjDtJW+gf9dNv9mtmOobTeJzEiXJ5NRKm4fF1fWa4aZTcf+dTZdyvqn36iUHITMahDY+r29gCL8aGa8y+DI6tlVRika+//nr1/a0r6PdVuCQzdFvp2/b2dr31rW8dUoJWlQcBYFXjXeoGGi99pyvBfWZk/ABOCJODrbictmL44PksK6mFMKmxhYfvq+4GLSlOK0g2UrXc/sFRQigXSokLmTEUM1OUi2vpizeo0yevytF2Mzr3y8IMOPl63Fza5e0vXkGzq+3xNLNOYGKs3W8fCYPS0Xfm1EqJawfQAd6AWcbJUGC/SIMf+uWYknd58Aq8BO3WSqIVHYVO2Uj32zKXnorlMmNSBrVkfKmDBk+npbitjNfV1VW99tprw4kfzouzoaPtjC2fk75BbPFtb3tb3VceBIDRcCxpuiQ50bbssB2zMuo0i6I+fif6r1r1WmXl3HaudbGSu66q8UsaWkwO9vD06dNhXyErRqbzLYbhtsNSTPkZC6yjc7cAOvJ83D8YlUEOdgcAAKjeYI2ryrPtwlhBzc68Ksl4e3GHevjMybL0F3lwcqdz+wx0BoCMHbKymC44/QWM8qytFshSt4/s8RhbVu1mWz74LgHe9yB/jnU5xGBGyX3JIhPY6JPvNxjZtZzNZnVyclKLxaL29vaGOcD45Qb87AcgtrGxMbzAelV5EABWtTp+5HwhJtzWG+E3+7L7YYpuBkX9nuSqu2kAbp8nzJNPXWYrDoJbiDO7PQFybW2tptPpAF4onoPTgIDdbBKBnb/E/ZkBn26J+2C2xPcGH+71eWSMv4/0NtNhH6LbnS4kzKvl2nguDJCAbyumYsPw5MmTAay436DFsw04gLPdIIDb7B0gwu00QDt2CBNz3+xSun8GLoMT1ybLzzl1HfeBUX7fCke0ZDTjaHb/XN/JyUn1/fJIdz/Pz0rj6znPvMcsDwLA0h003U5fu2oZG8qgJUoKQKCEpstVd90gT0i2hb9NdZlIT7Jpdg56xoPMfKqWwjSfz+vJkyfDQgJW00F7Wy8Ayu1wCkRrjOzu0DavwNp99n0JLCgJbBImBrtwbM3KNZlMBkbjera3t0eurdubz/WZZQTNOXa8avmGIYCTQLBdPhsT2mbgS0Nnw0NdKaeMn/di0k76mOPKvNOGBPGWC5nzkvObXgH/G6A9tq7HQJaEwobbbcs2mPVhPDjSylu7Ui9znufz+cgbaJXnAljXdX+hqr6jqn697/tvfPbZ46r6a1X1vqr65ar6WN/3r3e3Lf/TVfU7q+qiqv5A3/f//HnPqLq7z7FlaegYVrDq7qqirYLzglBQ6rU1qLrrgqUwpLW0q2Omla7j2tryCGO+T0Y0m92+AJe9YASYUYbpdDra+1e1zOkygNlt2tnZGaURACxYPM7Eoi3E41C4vb29QdHThbFwp3U2Y6b9BnCzF7uKeVa/ldty4fiV35DO94DMfD4f9tV1XTeMAyAG+HVdN5w64jQWz3e6qnkyqmWC6/p+eaij58ULSwZ2l1VGxLKYIOd58e8EUe5JlpYMzbLu/93mVhqIx4G+cJgCemAPybHdfIYZ/6ryIgzsL1XVn62qH9Znn6iqv9/3/Se7rvvEs/+/t6o+UlVf9+znN1fVn3v2+7mlBV62OGY3UHksnd1HU2gmOlfKuMenUiaTSaBzO5OJ+Xq7jFU1ZCnTDoPy1tbWsLrIdgyvNAJssBmUzgJu6w0gOyGTe902QMVnzgOIgExVjc7V8n5Ij0nGDKuWrq3jil3XDflVjjsSi/KKa8stMWCbLVIApvPz87q4uBi+84oZbiHjtFgshleNmd3ThkzN8TYmt9mszffQTkB3Op0OAJ7MKl3A1AuKZasFonbr0x1cNV++LkMnHj+3iTm0/K0CHIPYzs7O6Dy5ZH9uT9Xdc8ayPBfA+r7/dNd174uPP1pVH37291+uqn9YtwD20ar64f726f+k67rjruve2ff9rz7vOQiAYzbJplAG7ynzErWTWT2BtlyAl9mSBeF5AgRrMGjZNfVk+2W9dj8ABHK6zCTMImg/iufl/Wyjg9sIMGNJvApXj3qc52R25nodc7KLbsZllsSz042vGr/b0fU7bmhQArjMsui7456z2WyQiadPn47aY5cUA8C4LBa3S/8s+S8Wi+Hoob7vR+eoAVjEu8y2umeutMfcgMI4XF1dDQcG2LXmOuQQhmiGlWBnuUwXLAHAHoK/8/OThZkwJONG/jN+7LoybgmI4U76yCXX67asqt/lK42BvV2g9Gt1+5buqqp3V9W/0XWfe/bZcwHMge0McjP4CJSXts280hpaADzoSflbbMvf5SC2hCldYEA3++FzuxzjcpDeu/nT8jHpDrLzf9USJFAsXFCYoAHo8vJyZcY59RkMEmxok/PpYMWt1UaPPXX5gEgYAMJr1zcVkbnvum5gNxcXF4OyWPj57RQZz7mZnuODVctEXvoJwDs2A2gzX4BmS9ZgtZ6HNLLUafaRXkn2z15HGrGWruVYu63OmePZCY6pF2l4GAeD02w2G16ww+kqBmvrU4vJtcpXHcTv+77vuu7FnqbSdd3Hq+rjVVWHh4eD5alaMhmSCD1gPirHP46BJb130NDM6Vn7R4JgthDtHa7nf1tZ32/WYWWzUKOgp6endXFxMQTq7aKgwBYg14Nr4vFZBT5nZ2eDAvqFJX4xSL6XwGNB3WTEI3xWHHKvXNxn/zjZl3Hix+ki6f54vmmb0zjMXPJsKu7LuKdBz0zUgMPqLkYIULNLmAs1zAH32/jwfCu+GWm6iR4Dy6k/p9DuBKFW0B5g8bwlu885dLGx8nx67jMUwN9sZ/O8JQt7oxjYF3ANu657Z1X9+rPPP19V79V173n22Z3S9/2nqupTVVXvete7egDHW3/SYjNQBEeJ1zinqmr5brwMcCebMYOxO2i6nLS69XnGvryowPdVNXKDrq+v6+nTp3V2dlaXl5ejeBA/uJYtup75UHzvNwF5pQxGBujt7e2NcmxQRMfMbFSqlqupBlNb1xxDxsN7MB0HTNfTbc0x51neVYBcpKFy/SgN48KOAPrI2HlblgsuKmCDTPLZzc3N6GW6zk+0N8FcAYi4k8QEkzV6LD0fBmT67zFnLlrxqHQZDS6er5a8+f5kY56/FuNznTD/vu+Hl1kTEyPtJPX+vnL/WyNXl79VVd/z7O/vqaq/qc+/u7st31JVJ/0Lxr8ALhTJLghWD5eIIHWmU/B3i/LyfWvQ030aDVDEtfL7BEV+G8h4lgGTfXi4kggwDDMPU3RsD4VzrMnMzAF5Mx73D7fLMccWE0mwYkz4rEX1DeRc67BA1ZjVeexaLgp1JYvzc3zMDWzJLAimmkDgcXObVrkyMCqPn0/FyPlM0AZob25uhm0yAJldVMbM7n3KWEsOzbQMSBTPrUsChefLXoPZFvfZmLgNZlUJShcXF8PLqjEG1uf0eFaVF0mj+NG6Ddi/0nXd56rqB6rqk1X1Y13X/cGq+pWq+tizy3+iblMofqlu0yj+y+fV/+wZQzwEq+YXVjAZuDxVy+NKWO1ybhF15kTaqmQMwayFz5J9ZZt9bz57laAtFovhvCMfO+JTRjPmY8vNlh/6Y3fEVjuZJMFjWI/dHfKT+r4fvbOROuwmmZkZEKz4jj/S56pxsm8yL4OGiw3SZLJ8C7eZIDJwcXFRp6enow36uTJdVaMDK3O/HnUa6GBvVTVib3YzmSezRJ7BPZxgy/wAfJzJxviiA+lStWQwWZmBwvdbFs0k+T7ledX9q5gZPznXre8Bcd6e1ff9sPEdPed5XzWA9X3/+1d89dsb1/ZV9UefV2er2FIBRmtra8OmZb9JiAH0G5cvLy9rZ2dnEAJTa1NxnpWxLk+OYyMtobGQZEBbY3EnY7zv+2GlzGeyu3/eT8gPrBMgg2Hw/PX15TG+jvNMJrfnjTum5XZD58/Pz2t/f3948xKGwxvM/WPlpV4+Q1lbDMdWPRWv6+6+mdvuoJkJDMdH4HCN5zAZHP1BUVwHsmBmbxabriIG1gzX7fd5/pYX0gjon2NkHtu1tbVR+CDDG+kOuj4HxXOc+W0wsewnE7bc+zkZnslYl9NYTCLSsJ2dnQ2fsUiX4YH7yoPIxK8an+lVdQtOZ2dnAytpTYb/xpr5HPNVboCFgvhMVY3cV9+bwEQdtDvdzGwfz5rNZnV2djacX2930ZOPizidTgfXhGewpG/GaOZWtVzhIVhvl8YuKX2HDR4dHY0WUjwvTlFxYio/HhfaSxvSeNjIeNy4J09vaMW53BfmgH7btWQsU+HIJbTbdnFxMcrO95j6qBfAppU2YcPjFU23GyPF+xcSyG3MAbGUO8uiWarjnlxj2fI8+LOW/N73t+O6/r71rAQ1fwcT6/u+Dg8PB1faC1j3lQcBYF033vR7dnY2Oqep5Z6lNQEgMtZS1d4qZNaUk+fvcwBtsRCyFgOrGh/k1/f9wKQALb/WDcXDlfRBeWaDXJfxJ7MmH5fDmDhfC0bBmJH7dHFxMWIWHl+Uwvsvea5/+1ovyJgFUpzl7yx3z0eCiN09u20A32SyfI1cxp5gOoAL/WRsyRS3C5/1283JFJ787ZVafjA2VmjG1AdJ9v3yLes+VXYVIzFopHHgf8bf40fhWp7D2Hv8bES4J42MPZT87XaaGMxmsyF/j5NdwYPcoZHlwQBYVQ3uFWdkW3laPnrVEmAYbGIHeXxO3uu/EWRbL9fbYnFWGD/DfbIw88ozr3KhLAi241z0w24R7AbrT/vW1taGzbIek6plIqcBgc/t5nKf+w3L8vso+Z/+zufz5palfJbBwmPVYkxWAo+tXVGupX+A9vHxcV1eXtba2tpoe5TdTfptJprP4xnEpxx4tzKaLToVxeMPQCUDcbuQWV68DODBoH0kdMZv7XLlXCYTytCIDY/HOAmCDXfrmvydz3Zx26h3Pp/Xa6+9NuggrvbzyoMAsKoxeMEkHMT3oJgdWAEYYDMxSoJXfmbhzclsUW+encV12A3ATfNz1tbWhiRWMvLdP1/rdmJJYa58DoCb0WWfUQq3164roOCVvHT7YDLe11hVA4DxmefB88K4WIA91p5XWKJTLtLlpN2O+bHChxW3QWk9gzYBxF3XDXtK7SaZVRgMACqzs4w94oYCmBxfhOu6vb09ylJnXJ1DtkoXWmDl+fXnHoNWaX2eY+/Pzez9bI9Xq635Xd/3w8b7t7zlLcPbwu4rDwLAFovFCH2N9LZaSXHtPrrApuxCVC3BB0Gze5cuo5fBUziScbXSCXAfcNF8BpQVHrfSlsi+v/tbVaPAumOGXrlBObtumT3O517xhLnxdu3Nzc0hNWB/f3/ETFIJGB/vbdvd3R3cHRQV5mZXFgC1u59JsDYiCR7UA+Poum60uZu43nQ6HRYlzGgBXp+Ei6zZTWa7F32iLWzQNiuyLFmGqsavtrMbZxY/n8/r8vKyzs7OhjO0fKIHcuM+Wy/8XYKc5baVjpJl1TPSmPs5njMTA7epBbYGMsbq9ddfr8lkUo8fP14JspQHAWAEopN+p/JaSKyULbcSEECAHWhvrW7wfYsF5ApLgmLV+Gwqsw5eoWVwZnuPX6+Fgvhe2uhVNrs8PNvBYu6FaaFs2ET/7gAAIABJREFUV1dXdX5+PjAT6sddtUtGjtn+/n7t7+8PbKGq7rAuwIP6YIQOWtN3Mq89n+me5Lykki0Wi9GKrF8PxvjwfgGDebYJGcFty5VdWBzhDM89DMkhBD8D2SNJlTwnrs1FCveLBNfWKme6iBSztfQiPNZ+phNnc9z9LDMvx7sYJ8Y9Ux8MqM4dazG51DEIzdraWh0dHdV95UEAmAfLP+mvZ/ykapwVnG4C7k6Lafle/92yUOmKmRX4mbgifE5cCwUG0EhgtUBmu71IQMDdLlwGUqmf4iOizUzslji+4vdiEpfhZRlVNdrW5TYDULagZr5WftiOrTL18LvFCAAbr9g6/cGLKbPZrPb29urs7GwAEucM2hDlJnXGlphexkRpNy4lwJXBe+q0JwEIEainX1Zax0DNfqiP3waQHEuX+4x0gvKqufVzEnxSP1psKcGMYhc8mRn/v/baa3f0PcuDADCKWVIyoGRiBhU+47cHeFU8zGCW9J8JzdVLnmmmVDU+sJBCXMtgwIZjux60M5WZPjsVwTEqvs+gvMfEfeeFqz5OmbF1//y5wQ0wTPaZSuQxh+GZKSbTtIKmu0p/fAw0n9tY0FfGhGTc09PTlczYgW+zkouLiyGpkjfreOcHrCPdT+TCLBA3szXPZjMwdNx75irlLl2zBJyWHPl7zxv9ybia29p6jvuRPyYiLf20nHiuLXO+7+bm9pVt95UHA2DpbyfF9EqWO54gxPWeSLOwtCK2sL7fVtfPsJWtWh6N42twbXCNAS6zIDMhM0WfH46ApZW1G5MukWM1i8Xtufq8Ah6QNTgyJqQvZCpKMpD9/f3hbUe0x7lmZjlVd9+qblfE7aTtZnPpWqQyAyauH8VHSb1NxYsPdqFwH2FqZmbkF9oj8AkaBinCFc4Lc2oEY+QN+IyR+4obaZBDJt2/Vka9P+d/jwt/ez6SlXlMqc/tcN8TeLi/xbpac+q59m9Kng6S5UEA2CoXz99X3c2aT3dwlfWpWp4ZX7V8ocWqdlTdPcjNv7HqrBwl3YcxEOsicdXgZffY4EeQmwRW6rN1p31mM2Zn8/m8dnd3h8Dw2tpaHRwcDM9gawy7AABEwMbJwBY0Dl6sWh4nYyZj1rBqnj1PydwMHh5PxtzXJ9AZ3LneDIP2M2+TyTKJ1YzMuVcsvlQt449m6X5ZMDliZhqwwWQmxMnsavMZLmwazvQG8qQNz1VLpj32/t/XMZa+Nn+yJJPygQXJdFOe0lA5VnafLrs8CACrqjuDamHNYGfVOAmS61xX0mImPdML8p6sJ5ma3QWftso1JKCen5/X6enpkCZBTMUKhYtmdxRgyRiBn2EGlfVSHMPxlqtW/KVlRRO4LXQoWSZptubUwOJ6WoHmBKJVVjyvd9wRhkQKxGKxGJ0DRlu3trYGdgRL7vt+dMwQgIeBwHXMfnllExD36jCGyUzdhsjpNc5bM1M0A/O4ef485lzrcW7JNt9nKKDFtqqWQES7VrmCKReWn/RCfG3K25sqBmba2vL1E7AYLAbYFs0KaOX0RPO7pUitwKUBjDPn7Vbe3NwMG4pJR0gr40mnXlhR3/dDPlgqQ9/3dzKyZ7PbI6cttDyHV355e5Stus+LcjKlhQvlNGh6fNI1TAaWYNgyHPdZWoyDWZDda+6lzfP5fLTS6dU8jAjtBNCd7+ZtLGlQmWfGgDQUnkM7zLLyPv9mbpBxu8+EILwNK8fHgJOlFfD3fLTGPQ0N8pUybHeSunPFmd/IVIvFWb8TwDy39xmwqgcGYFV1B4AQElsh++pWMgbR4GNBtItl1mOkb1kpx5VwHbwqh6U06yKp07EIWy765va6fd4MPJlMRsF3wCmD6LbWtrA+I4w6UTYrIS4PrpFTEexaGwgNSsmqbH0TDDy+LaZlkDIDchsAXmJVVjSe4fw7+kb9jsHRd7YTcUS1V3MBHLu51H9zczO4kHkEjt1Pxr11+CHgtba2NqwKm9l5XlvGYlVpeSpptHP8kZ1WrMpAlSEE5sWAzH0JYn4W9yJDL8K+qh4YgFnpEjhaTMkK4mBxdr5FgdMatNgB35n2I+j+nkTQjHN5dSmfa8XhGhiCTyZAOSisVGUMx89wP1GUnZ2duri4qMnkNlu9avnSjsViMZyldXh4OBx2aIbJPBjYULYWyMH6UnlSERN0vAXMwGegdp3pItkVA0g4OpuEW+bRx2yTh8g1PDMPeMS1M7DA6HiufzN+XkDIUz4cf2S+/eajlgx7HFZ5KC35t8uY+tDSMY97upiWb0DLhjjdXbuYqetuM9/bA1pVHhSAraKZCTQtdM5JSPemFfvJYhcgrZuFzULO6+jJJOd7x9sQcL5PN472weR4zZetbbI5tzdBwUpPGzgPbDqd3lnqXyxuM+p3dnZqd3e39vf3hyONfC5bMjqPu0EHJWm5PZ5Dz6uFmZ90w7jHCuB5NxNkh8HW1ladnZ2NcrC8cZq5MytmVXU2m41ei+aUEBucXFyh3YwdssA80gb6slgsD5W0R0Ff3N9kSmavLX0xwHtF0brBd76v5XqmoUivwXtNW+GTNGBJTuxBrSIUWR4MgGVjUyhzUlrInSCWlpySloB681oPNNd4awyCRnzFz0f5YVlkhzPpfqW9hWo+nw/H6HiyXZz/5P7QB3LHPK4GRtq6vb1dVcvVVJTeL/qwYrmNpLXkwgbM0+1sja1ByyCe1tnz6VVF9zn7zorg1tZWbW9vD4CLUtLHvl9uBwLoz87OBqVMluV54l0CgB2AS124/IwzR/1U1ZBOk8yLcTQgrgKfHEeusWFOA9Ey2i0dS7eR7yzffIYs5Psb+G6Vjq76Oz970zCwHDBb4lWsKEEsO96y8ghLxrVWuSeuD/qfgXQsjyl21ZIKm3VVLV82gZCT6MpnZM6TQOozwKjfymM3E4Xl+Qg1543Tbsfd+r4f8rtgJiiRQQolZ1UTpfTigecvx8/PtFEA7PP8J7s5lgEH0ynMCUCztbVVR0dHw3jO5/PhmJaqGm24Pjg4GJgSJ4YwxrQRg4jM2JV3G/jeCwE+QYT5oZ0Zi7WBNptN8LdMM1ZmrQkGnheu93xZd3JcHe/KsEh+tgoEW4yu9fyc6xboujwoAKsaA00yqJzotNT+sfVOFkUdLaRP+pyCS+G6lv8PqBnQ+N9gl/TeFDwZja+rqmEfnzPPu64b4jiTyfgtQVh9mKBdAIDIDIY+ouR81vf9cFaTFdCM1q4tAL5KIBlfs5e8No2M5wvwBlT5fzqd1unp6fCiW77f39+vra2tO8m4uUneyumkYY81rIOCoYLVMW7MA89i4/zu7u5okcqyZZfK1xjkAfLMCbMeGRBS/lOX+N6uu+fJbTJo2XjbKCYLa7GpZGCpZ28aALO1N+BQ/L+vsXB71cSMiv/TDcrExFSwFgDyvS2QgcnZ8AZW7k0XiSA6dfpo6N3d3ZEr4smlPbhJzieaTqcDkzIgw54Apqrl6qOFk5LPsmGwu8M1jMUqd9FjzT2Al+fP4O1il8UHQ6ZB83jjFnPqJ+MM2Gc8yizOLhFKjSHY2dmp7e3t0Ts8q5avw0tFBOgwPJPJZJRxz8IK7WgxJ8aa/DT3O+XWrC3ZVWuODXYGMb5LGU4AMwPzPDE2Bkm3s1XsbSQOZHkwAEZpCX4L0Fo02srRmlT/nRbI9D9Ly2r5xxuMDZRmOSibl5hhal4NpN0WoGSiTsLkHla4cJe82ODFAgu1Fy28akSiqtuRgWXYks8j86F9LbcPxmBGl8CXcTDmlDFjlwNKCACYqRKk39vbq+3t7To/P6/z8/NhZbVqmWBqGcgXCs/n84FxwaIWi8WwS2Jvb29YxGEDuLcuOYeupbwwRcZhb29vtPBjmfb9PKtqfNqwSyselobFrDZ/024DqoHL/9sDsV6kzmQ77d1YZl7EdRzG4oWueoNLCzQotvpJdRNYXFdSaK7zAHnA0h319V4tSYExEFUtg62OjbmNWF7nDwE6qcQoA/d564o3TGOdM8dpa2urdnd3aza7fSOyc8m6bhm/I69tPp8P+/4AGxYiACwnGaL06Wom68Mty7E3iNqImOXBUu1aMwYWdMfovEo8m83qrW99a1XVKDnUgOdYZNXyhbMHBwe1vr4+JK1mqOD6+np0XJPz1pJx0v/c42gg9Vw6vcbjgrEySFhuU3ZbQNByKf33Ki+kBWTp8VAS0FIH+Tvb3Pq5r7zIa9X+QlV9R1X9et/33/jssx+sqv+6qr747LLv7/v+J559931V9Qeral5V/23f9z/5vGfkRLToZg60KXtOlK81I7OgcK+DwS3anq4JE2UhzM/SOlUtrSZbi7zXEVZQtUyMNPMgrkXel91M4mDEpQ4PD2s+v02E3d/fr8lkMqxqAmLEwbxlyf0HhHd2dkagZcBx/3NM82QOGwuDYc6zn58gZxnJrUMeQwCPQHnf364CPn78uJ4+fTpyVc2WmZ88Aol5YAx8ZHnLiHozu4HL7z31QopZGgstVTU6y41+eazMrj1eGTx3aXkkOf95n+fBz0C+kzV5TuxCuv0GtQS/rzmAVdVfqqo/W1U/HJ//z33f/4/+oOu6/7iqfl9VfUNVvauq/q+u6/6jvu/ndU9JkDCIJfVtCU26bQlUqSgJPqsAKp9pJaUYQO0K0haSJA0WZhRcA2hQFyDk15yZgVhhiangPqHgl5eXg2JwZjyrb1XjrToke8LYGCMH6tNQOOBuF7M1vy3FcEpKxs+oNxc1+MxjRltQKtru3Cz6ni9G5lq+c1tef/316rpuAPyq5YkTVcvVZIOQDSasmLb5GKWW7HKNt4d5ccGyzfwBev7esWA/K0lAsjWPv4GNcUUu/R3fW3c816141vPAlf9brneWF3kv5Ke7rnvf8657Vj5aVX+17/urqvp/u677par6UFX99HOeccf6Utz5BKaqcTKn/elVLM4gWTVONfB3FvDWxHvS0x2yBeZcMECstT8sc8gAqEy/oB2cF8UkX11dDe/FZEw4soW6yY2izwZQnkNdPq2Da5NRGbjs8vBZMgfamjFKM07Hz/LUUsYzFcXz6I31ABlsqe9vj7y2a56sIWM4MDnGs6qGrUYJqvQJGUR+ADgYHgDciv0YUOfz+bAnFiD0mFUtz6Hzm3sMRMlsXDyHjoUlSGZgfhXLox4bYYNQAqR1z3rpdrXaneWriYH9sa7rvruqPlNVf7zv+9er6t1V9U90zeeefXZvSbBxJ1e5kga27HDLDbXA+BmtAfLAttpEfVzrWJdjOnYZfdImOU+m48SsuMasB4DwyQZVy9dteWWOl/vCHDKXiWRWJ4U6hQK3l2fZhaiq0Rn7dsdeZB4t4BTPnZlDxid9vetLgU+2jhsHwNG/zIej/z6ryydIoLwYC+YL1x55MEj59+7u7p1zxMx0Mm7G57PZbHjd3e7u7h1WxTY212tm6nlJhpQ6lr/NwLyxu8Xuco7zuZ7LBKsWY2vV3ypfKYD9uar6H6qqf/b7f6qq/+rLqaDruo9X1cerqg4PD1/IBfH3GXsxgLUEv/W3r8m/W7/tmiCcCLbjXbPZ7QtsOT7aQMEE7+7uDhuGu255EgJHu/AcBAbhhxkgVN6s3HXdkCFu5aJtrJgh5N5ag+tJrMaraBT6xx5CW/iWgfCY5f8ec8aGvmVJVu5+oPxmOLTF4M3KrJ+JshCTPDs7G8XbHKez4fBYMAe7u7vD9iuYE/3CNaSdzrJnXFrybwCYTqc1mUyG8+dQfMfnKK635dY9z9X0vPK/mZc/z7oTqGhHxsL8rFV6b8a+qnxFANb3/RfU4P+1qv6PZ/9+vqreq0vf8+yzVh2fqqpPVVW9853v7BGwZ9+NOkDHXWx9W2wtgcmCG+240zZb95ZFT+EAmDY2NoaX8hI498trbW1xMY6Pj0fxH8CEFUqeRVxqsVgMR1W7rRwDA9XPzcXpniJQxHfW1taG7UT89lurU/gcd3O2fosRpRuY487nDmp7vm3B7ZbZja1angzrZ7OHEVDf2tqqi4uLkUvE4YUJPK2FCjPnjBViPByjrFqmejj9wMaCbV+O33ksLTvIjxNozc4NMoyZQZm6W26g5T9dxQSwlm74Oa26k3SkPFh2fM195SsCsK7r3tn3/a8++/e/qKr/59nff6uq/krXdT9Ut0H8r6uqn3lefR6gBIoErpycFvq3Op0Dky5HBnXz2VhP35NWkKxvkh2TgiN0uCd8R3thdPxt1tL3yzd7+zrGibpJE+i67g6bshB6EQCFg0VYeMhxas2X90l6vNKFaM1TsqpkE/5+1bxxfQaRMYYGOe6DPQLyxLaYM/cl6yAMwLjxA3jZrSMHjfgbc215q1rGsVynx91MEeDyJvGqGjHLVWxmVclxZS4MXGmAKf47DVXO/Sqi4XhdPjdlpFVeJI3iR6vqw1X1Std1n6uqH6iqD3dd94G6dSF/uar+m2cN/9mu636sqn6uqmZV9Uf756xAZqHzSR+N+HRcbRyu8XfPC8Bn3b4ufwxuPANFub6+rtPT08EFifEbBJLYC3EqgMi5XgAXG63tnnqV0wfymZUgSCS2orAXFxd3FB2GRawHhfUCAkoNCDoNIBmpmVTOqcejxcgci8p5sFGx4lshHPfMGCgruvP5fGCXrATP5/PhhA6AgHy7TCptpT8kACwWi2GVcz6fD+94BDQNkGbXgJ2ZP+3Ls+HsKts1sxyvAgzqWcW8cr6SVfm3+96a12SwsN5k86m3BrKvGsD6vv/9jY//t3uu/1NV9aeeV2/jvpG18WAauFJobJ18TQuUUpH8bP+dCmCBNQAiTLxRPOm13SJAi/sRZoMKltz5QFU1Aq8EVKyi43IeG8BoZ2dn9FYgruFN1hYqFM6syHsAvU1pFfC3hD3jH/mZrTD983in4nC9GWmreKHBsSbAzKyPNrPySOzQ84mBAFi8mGJZJoywsbFRR0dHw/YjnsVqL8/P3D76a2bbiglRn5ObM46VYJHg0PouZTNBJYEmdazF1qyXLZ3NerMfWR5EJr4HKy0IHcuYVIJRS8CpJz9rCQGK7R+zGddPW25ubur09HSkPC3wgunQbrt7pFYQL2PV0grnXKh0mRBaAsh85rHMsen72xgWJyP4paQ+RgcANHhXLa1yi23l8/jfDCtd8+ynwTEVBuXMwLJliGt9CoRfc+e2bG5u1uPHj0dMw656ulBVyyRTvz2bZ8Pk/IzFYlGnp6cDA9/d3R3uYY7M9hgDb7zHpbTh8njk+Kfst2TU7ebzlBt/7r9dXxpFPzvv5XqX/D7H5r7yIACsagliFBQVQW65F1Xt5DgHyj2wyer8eTIrPod1cC31cXx0ngRgRgGwACo+pcDMgax8n6iQIO1Yl2MxBiS/i5CNwvSJUxh4/u7u7pBpX1UDo2AfIYpi99SsIAE/x80KkvNMm3I+cZv9BimDQM4dMsP44mrb7eYav1iF53nxhWN0AL3d3d3hOZln1ff9AIbMA6vEzINTZnx6xcnJSV1cXNTe3t7wDNqbIQCPL2GDXJRpAXyOrdvdmosErhzbvI/68u+WgVnF+GyUsz2MRX7fKg8CwFpuQFptl5xA6uC7LDnArQmuuh1Unx+fQf2qW2Dl9WKtOBz3AgJMFGdHWTl5Ew5t84STK8a1jI9Xwew2+GwqxwCJk0yn00FpUDIAzkcdEw/zeAFMDjZ7HG0EPCYp+E52pZ4Ef/pFfQmarttK4Tk2kwPs3RYzYp5HagbPuby8HOKGsC2YzGw2Gxgd42fGlIsIlmHcUnIEj46OhuOMzN4cYnC8zUBnJuQFAsbSY5H6kICSBKAFWjkHrZLz4s+yDsfDAHG3Le9rlQcBYFXLYDWlJeR0yHEMg1ECk12LqvHWFwu+mZUHLV0nVn6w1nk9FhfwsXBlv+yW8Df3I6hYbYMb97ifrgfrzdhZ8C8uLoZYlrc80TdcRwNSa9zJ6m8ZATMm/vbqbQv0WDm1QPNcSsbK3GeYkE+x9fHPgJkBGjZDTMqrkdSFQfMqLP0BZOxm4uoxVhm7MsBgiKpuY5yPHz8eGKiZSEs2V7Faxum+eGCLiaUc8QyvpPt+s2HLot1sty3nMo2d+2XS4metKg8CwJh4+/fpgmSw3qDFb39fdXf7UbouXGtF83cO7tqCepL9bNwIZ7F7q4wnhnosoAYW2JctEqyQMbOAs1CQbhSxHA7Ps8vr3DsLvd1VMzBiYgk+lAS+ZLJ2Q81sLcj+zLGyVCwn8bZcRqejAEawH8CNOh0fcx/7vh9YausMLr8X1ImtyQot194i5VjZkydPajab1fHx8ZCUSxgFAGQ+YHzub7qCZmCeH+vcfW5lFoDRTLcFqC0G3gKxZFiux3qJvK8qDw7AEHgGKl/8mh23v8z3SZFXMbT8jvur7gb619fXB5fBLg2D7uNobMnoD/fxpm6vCKLcq+5NJbd75dQH2oUi2hVhtQ03DibCszj5IJmeV/A8NgiXFdorhwlk9HOVe9ECPhsxGy3PjceJVIlMGAUQMAyAAueEzWazOj8/HwEtSbFmxjmfAEzV8v2SzrdD4S1btDMXR7quG5j9wcFB7e3tjRTZxtKpLWZy1pGU69bfHvcWm06w4u90MT1OzJfn0Z953vhtbwRZb7m5rfIgAKxqea5W1bJzCFGCSgp7/p2AlRNhRuFr7XLlMravdztIKXCQ2JMDiPi0Awd/AW+fSeUYgZfOqRcFc4DX1tafmfWZpS4WiwHAvLjAc+3eYO19VAyGw0pqq98yFOl6ZAyMa1JwraDOp/L/jAnbsDznrNBamdhjyHwwHmZKxMZgo9Tr+BaHVJo1cqKH54L+YTxSLj3nZ2dnVVX16NGjQabNpEhmNZAnm0rFNyDcd51Z1X2gtYpB2bivalu2hXFML6vVviwPAsDMwLLBUEijM5+nYrrc5zpaWQ1IeT/Xoai+DoVJCk1BucjrYoWRWIsn3EmVKfgUn3zqiU6mSj+9N3Bvb294ZZpPNcAt8hE7gDD9RZFzPJmTVUKd1j3ZsMfRSmPrjVy04jCAiA2B22w3EgZKfJFDG2m/++5tObiIjoFNJpPh5SdVNexdBZiYG85nA7SSvbJKzLPtdbD4cHJyUgcHBwOzBHgxir7XHknL+/DYZZzMLIj/mV8bFDPhFtB5zP28LPkcdIC+pxzdVx4MgHnZuWo5eH7bcdXqPJN0MRnkpKMZWKXOVr1mK443ITwsv7fSHrruNgDPCyVYuaIeu4YWArMnpzjANLI/jBkswcq9u7s7nMrKAX8nJyejcfeZY5eXl03DQDtyTCm4XglYNhx2Of1jN8ntQql8DcXjgIGxO25mulgshn5tbm7W9vb28Bai8/PzOjk5GdrJPBMrs3vtl6UAYFzDi2gJA5gh2UDateSZgJ5z92j/9fV1PX36tHZ3d4ejgLjfLNRGjHmyK+9E2Rz/BAjqt1yaAdvI2NtZZYRSz/2bvzHcHo9kYqvKgwCwqhq5j3aRYBYeIAfb7Y5UtbPwuY7fHhzHc1qsLa0MMRW/TMJxLOpkczCuY1ovnuM+0EfHR/jeAuvvGDvaZVeIOBdMBjAxbfcGeuc7mX0kpTfbcOwCwfe1Ga/KOcFiJ5i13HXXncyacTFoYGwMDoBY13X1yiuvDAcI2qABRCcnJ3V1dVU7OzsD0JO7RfLx+fl5XV5ejl4SzJwAfmZWGJxksXYrk2mRsnNwcDC00RvJaTPzylw7AJ6r5gav5/3vzwxqvj5Zsj2mVj3+2zFb+tUylK3yIAAMSo3gpvXy0ct2I6vGy+utGBmA6Hsy6I/w+38LGJ8bsKrGwXKvNHEahRNOrWym6xm3yqNqLAgwMRiXBZ06WHmkP5zAYEHi+QZKu6+wR1gACoZSpgI6nmWAMbO1ILaMjP92DG3V/S4Gdq8EM0e07fr6egDl6XQ6pIMA5p5b3svp7USLxW1O2MnJycCo6XuLdWBYaJPli7icM+yTzXi8eUfo8fHx8B3GJeX4vtwv5CaNmZ+bJQ1s1pnMChaXYRDX57n2a+d876r2uDwIALMA2U3DimRnTGmTgVGfKXTVGNS8iuN7UjGcv8N3tugktWJVLy4u6uLiYjgDzCCUkwz7MPvzmV/8po2cFgH7yxVLH6WzWCyG01mvr6+H+8yYDPh85mX6ZIp2d6vGr8EzuKcr7vv52+PdMjYooa/3vCabNaMmB89BfsdmDNIE8LvuNk5oNxJgs/Fj36ufBYO1/PIdhoG55X8MqAEURbYBp9ilPDk5qb29veE+gwjttbFxGMTzkoDk/z3vBiCDdMvDyR+vBvsaz7/dR57BGL1pXMgW+hJYzphRxmRSwFuWOj/3pKXls0L5M8fCPNlY5rOzs8FtdNtawGF6vLOzMzACK57djkzD8Pn63jfp2ArHIbNAkOd3mdldXl4O+yIRPBcbCsdTcqzTImeMscWWc55on5Uon+F66IsVwgssAAyZ7zZ+vOD21VdfHbZQMVaHh4eDIZ1MlsnBhAXMqpAls1rnkvEW80xdgREC2i29aK3oMhZekEmPIl039CbBwjE5Pls13jYUbmOr3fmz6hqHYswQTR7uKw8CwKqWdNt01Uv0Pu2BybD7loNk0GrFYaxwjoe1lNAsw3lGTCjngM3n85Glxdo7D4m2+mx1gvxWZNgDz2GDcVq2Vg6OLTgA5e9ZaaSv5EKxpQU3C8Win/SNk14ZRws4da6y1gaQLCie55fPPd7UY/lw/hX939jYGDbHn56e1tOnTwegqrpdHHn8+HGdnp4Ox0TbHd7f3x/GxsruUIb3JwJ4Zme0C0Xlx+AIyPoASdphw+kFCxYmePNUrnR6o3m686tAxUzJcUjf02JR/t96Z8Nt/bORbZ3A63tbcuLyYACMYqBwMBLKb5CrGmdtp3/PvQlaHvRkCVXL5NhMNvRkcP3JyclIyPnOVptrLdQwArMOgyfC6gUABNlMzYII4PgcsKplioXTPmALbgeraQS6/To3/s5tSvSPYqNJY2SLAAAgAElEQVRhq91y6ZlvjxHXZAIvda4yVGblCYQsTrAbAZlAvvb39+v1118fmKdZieXHgI6Rwig5hoqs+h0G8/l85PqZhRoovEhDGy0P9A0QY/+lWTLMs5Wpz/M8fvxeBTZ5jedsFbtqeRt5Dd5BC6Ra7WmVBwNgaRUQXjriCTK9dKzEoNRyLV1/K+BvAGndY4ElT8d5XWZnCA/37+3tjawKAWGSXOkfAAdQ8UwvbSPMVnKvoPlFE4wRMRaUejK5PbvdhzD2/W3Spzd9Az7+8by00i6SdfG3XVcHn5mPVLJkd2ZkZsHMDd95VwIMaX9/f7iOOvl9fHw8BOthBm474wUDpj2EB3JXheOJtIHntlYl7QWksjsPz4YVmbi8vKz9/f0R+Jutuh30p+V2Ws5bupglXdRWsdwmSBH+SNbmNqwCN5cHA2AGLANHUmeDWE6GrZrrXBVfMBuw8GRw2j/r67enm/KS1OwD1pfriXlULZWUREoSXXle1XLDOCAIwCWdh0Elc/JzADlYBAAGoE2n0yH+Zfdxf39/FEinbx5bs81kJr7GwEcxeLXAzt9byZlL5IB+OmBtQOT63d3dUSyQOeIwR/r6hS98YZgDt4VnAJzpBXASCCw7+wBbos2sDNuI2i31whDfmZV5lQ6ml2kc2Q+Pc7q7rXhZiyHzfwtwXMy8aK/rqKrRbpTW/WmYVpUHAWB2NarGFsB+tC1PC2C4vuWmWOBbz3c7zGxy4i8vL4dldAMqf1uZvBJodkVszG95ZnXRZ4JZsAwY/A8Q+gcG4gxyx1XsflLW129PbCVj3y459SV4osSAr12ojKUAmqkUHn+7aZ4rK5DjYnYVYRrJIlB+K1SOI4H1o6Oj6vu+njx5MtRnQ2nXPZmSFwqqxiyQewDJ8/PzUcpGrrbRRo+n2Zs/p/7r6+uBcdMGxpIxypKeBs9OfWrNVzK3ZHP+zmOeOYfeTWA5SKN1X3kQAEajbTGqli8r5ZocWD63W2YFoNgFNWtLJlBVo3iCrTkuxPn5+cjaI2C4aFU1HB/t4O319XVdXFxUVQ2Wlfc40kYH/lNxUV4YmccJ9pVn3HsPoEHWriGrjwS3zQxwRx3jcVu4DobYiuMlCHnebLTs9tulzblKmTG7Zf68QOF+sILo67l2MrnNsD85ORlceqeTkGaRp4VwnU+jIE+OMecZjD8pMOfn57W1tVV7e3sDANlo8jd9ZIxze5dZuGWe4pSeHENf39KtZGMt5tXyVPjc8UTGnCTgVj2WmftYHuVBAFjVcjC9UTZR3UpowTBbqGq/e7BliQyQ3Nda0cTlmE6nd6yUmZJX70yf3e6qZboD15FCYUpvIaYN7pctMUK9ubk5bDtJYXaeEYDr00d3dnZG+W3+8fN9HAzbvDwHafUBSl9Dm8wEkwUkw/KYAxreSeBnOrzgMSStxEqZoHt0dDS805P+ERPc398fve8TsHIMzAYGeXDb7QaT7DydTof6bfxsbFP2DWRmKgbu9DpsSBhfx1lXsa+W15L6kWCTxp3EW4x5nihsGch67isPBsBScKvurgymIHtCk4G1mEAOtJ+DclKSrQEyKSz8wIAMUrZ8aTWdrU8aBQptILP1TaCmjQDN9vZ2PXr0aDjax6e/Vi2PfOHsq4xd+Zk+YjqViecuFovh/ZFmOxZKSgKK3QT3JRlbxsPStfZ8mcXaIFA3bJd6ALqrq6th/F555ZWaz+f1a7/2a4PSwaw3Njbq+Pi4bm5uand3t548eTJs1md+DRj0Ebmh7QCj410YjqoarVbaaLRkPw0DfWvJ8ComlYD1vAC92WTOQf5Gnsh5IwzjWGJr3lvtbJUXea3ae6vqh6vq7VXVV9Wn+r7/013XPa6qv1ZV76vbV6t9rO/717vbJ/7pqvqdVXVRVX+g7/t//rznpAWBDWWiaVoJWw53ntJyP7jOwXOzrxRAn9DpicOKcO49QopF9pYgW6TpdDrEQrjWK5wJfu5LWmOyuXF3ON7FTMMJrfTLY0DKha2/QSsD8SiVGYgVN+Ma+eMxSVBMkPN1dg1dhwEpn4kbT8yF9lM/8kWccjKZ1CuvvFLT6bROT0+r7/shlwx3G/d8e3u7Tk9PhzE3CMFuWm50voPSR1bzHWwM1mJvoeXWtQyRASDHnfk12LSALHWG71plFYNCvs/PzwdX3t+3DJ6/v6+8CAObVdUf7/v+n3ddd1BV/6zrur9XVX+gqv5+3/ef7LruE1X1iar63qr6SN2+0Pbrquo3V9Wfe/Z7ZUnETstRVXcmxvfeF+hLdzILzyFukdYfymvQMCXms3SbCNYjWBy7YhcEoSRug4J6FdMxJysJIIIiocSnp6cja+wVM9ppV+rRo0e1v78/1ONx7fvxQZMey4wzmVXlnBowc64c9/F85BxZ+fjMTKNqvKE5WTdKTp8y4O753dzcrMPDw+GlLX67UdUtm4a17e/vj9xzrucexo45zJidVw9ZzcS9rKo6PDwc9rJ6/NOAp66kITAb5f4cU8879fjzVawrSUXL0FbV6D0D2ZfnscRV5UXeC/mrVfWrz/4+7bru56vq3VX10bp94W1V1V+uqn9YtwD20ar64f621f+k67rjbvwm72bxOVRmLlXLZWYmF+vlwD/spdH+kSDzWSqalcSsg2BjuliuD7Zkt8X3kOwKk8OF48eneRqg3B5+7+3tDeO1trY2uBu0w8vOVgregUifYBPORfMLTXg2Z8bTLi+q8IwEMZdUApTZAJi/8ycZuIFgFfP2Z4AUAIsLw/MMOAD8o0eP6ubmpl599dXR9rDLy8uBLXG92Rxs3EzYCcZeLW3FeqmXBGOMK4sIOb9emaWNtCuNjGXyPhbVci9bhmUV0Pm3+0XeWp5w4rn1nL8IiH1ZMbCu695XVR+sqv+7qt4uUPq1unUxq27B7d/ots89++xeAEuGg6Bm4l7GWBL1UxhaA2LXAXC04lownZ3N57w2C7ckARLwOz8/rydPngxWh3bZCiOsrJTRhhSy7e3t4Uwotr3s7+/X7u5u7e3t1Xw+H5JSPXabm5vD+VWLxaIuLi4GN6Xrunr69Gnt7OwMIJiC6lgVOWueAzM7B60NuinoLeHOuULg+Tzjgp5Lx0BbrAu5wTA4BaZq+do0j/v6+no9fvy4JpPb1WeO3AH8MAC0z2Dk3DtOkUBpMRZOTTGQ5hz0/e1Wr+vr6zo+Ph7im5Z394U+t+ryeKfOua4saZBXMbasMw0NuuT46yqQWgWwWV4YwLqu26+qv1FV/13f909jUPqu617sicv6Pl5VH69avlOPwfDZVVVjN8OuVmvw05VxO1ehuVf0fG0qDWDHM6wMsMKdnZ2aTqd1cnJST58+HZ2xRbxrc3Nz2NuJ8gMQ/szZ9LRvfX29Dg4OhlVHH64Hw6LNfN89c2EvLi5GK6mcUrG3tze8NxKLj8K1XMgWM+J3xrdWuYNpWBry0XQdWwX5cGa8v/NKZdctX3kH8+J/Myeux1A4bsWKNDEuwMjzayPF1qytra2azWZ1eno6MoiOxaLotIGxurm5qSdPntT+/n7t7e2NgCNXctNl9bxYp7xa7tJiwRlToy4vGrT00Tpt9ohe4EI/b45XlRcCsK7rNuoWvH6k7/v//dnHX8A17LrunVX1688+/3xVvVe3v+fZZ6PS9/2nqupTVVX7+/s9HXXw2pbeypLZ0C32pbb7mU2lymAxgotA2kXItuRnT548qdPT02Flkfu9VxJmBFC737bWPMNWHZCBsf1/7b19jGVbWt73rFP9UVXdXX27e0ZozFwFGxNZ2MRDIARE/kBjWZpYUbAlkoAijOyRx5GwbEuWP4ZI+ZCDZEs2E0eKrEw0iSFyPMHgiNGICGFgFCHZYDDXMGZCPGAs8+GZO/fe7tt9q7q6q87OH+c8+/z2U+86dbrv5fbpTi3p6Jyz99rr413v+7wf62M7/kIANRhaqJyHG4oNxl68ypkuurWklQXc7eQJF3Sr+XEZpFmOQY6R+15ZolkWnyFocFY6y+Z5c6Yt42LS6uy5a9eujbEb75f0OjvzBNdgcRJmGIZxNnc+Xyw8vnr1qg4ODiZr57i9yGVk7NMxWluCVmLmV54LZ7r0Fv6SlqnsK4WTlhfpn+WR1hwf8r+vObxhz4PPVQZIlTaZhWySPiHpc8MwfB9ufUrSd0n6a8vvH8H1P9Na+6QWwft7wznxL3aK1pc1MEGEeSvg4v6pJDRniFLzOT+FxZae8zFoTLfFYPRbv/Vb48zV6enqaGLGPVyHF41SO5nR7aolg0krzcUYwqNHjyar+T1rZncjV8mfnCzefCMtAqu2gOfz1bIIHlqY65mc0hUmHfNajhvzVq4lXUBaw1kPx9736Tb6PvmHFrPDAdl2ujgPHz4ct13du3dv5AuHEVwut4UZGM1H5h9bY44ttjY9CpzWDGdVrZxsTT548EAHBwdjOZUiT0WStCfNeT3pnLxbAVlaXrTsuDYu63Z8MY8nsrI5zyrbxAL7ZknfKemXWmuvLK99jxbA9YOttQ9L+leS/tPlvR/VYgnF57VYRvEnNqhj4i5IZ9cT9UxTEjUHgxZVDmpq78qVodvkZEvDjLqzszhb/Qtf+MI4GNa2PAKI4Om+efmBXQv/N6PSrZnPFyu33X7Omjrv48eP9eDBg1HzG6y4zsyBYYK3gVZarYL36n7XxdX7fp5LXHpKIZk9QY00Z6jA3+SHjCOmBUweoiWdi4l9z9YpgdrPeAfC48ePdePGjcm2r7t3707OBWM8lUsnbK2ZhhxvzlwavNhPAq8tOirn2WxxbDnHqVL6lVwksPlaZU2xLam8KoWWsmiFTPcxx99ywwkj33vbADYMw09L6tlxf6jIP0j67vPKLZ47E8MiM1erq/2hpiXI0fJIAaJrmm6Nr3nWyGVwHZjLvnfvnr70pS9NhETSxIIZhtXRITkZYZBxmXxNmxnejEtwMLCR0SwcPFL6+PhY9+/fH7X7o0ePxpda2LI5Pj4ez3p3HsfTKJweFws5BZ7gxpRCUllQOQ5WKhw7gybvpSKjpcz3OGY5Tlzsm4qQL5nxsgqP62w20+uvvy5ptXDUlmzuXHCbbYHkmkKXwfGl9UU6OB5J+j148GDcKZCK2ckWaY5LAoTlxrR1yvgZ++3nshx/TON1ySDWWptYptWEQqatWolvbcglExaqDLTnTIe1HgEsBYraN7UQZ7GctwoK06K7e/eu7t69K2n1sg+XbbfAs1beeE3gSvfY9eVLQGwROJlOdl+8kNX3Hj58OHmVm+nhWMre3t7EvXG/GF8zTSRNArDeN+l77ndPq2dKi4z1EGToQpAXetY0J1qcp7L6zEtUjmyv6Uor0/T3sTVu65tvvjlxVTkBwJeimB/Mm3TRvZ7QbTNoesbZ48cYnUHVfHB0dDTGRy0bTD13njTM/5SjHLfqOseX41FZX5V76n4QxKisemmrAIy/KyuKcSdfW+c+pmawpqvy+TctAWpQastLly7p3r1747IFWlXDMIwalv25fv36yIicSZVWW3zcb9dry8vuzHw+H9eQWfh8SJ+Z2TNkBkxbD95ATGvFcS3HZTxjyW1EdBUr64l0TZc7rSpeY5yS99Kq8rgkKHEtmZ91Yr6MndlKMvibZm6DQd/AZXo4nxeV+oTc+/fvT86/J+hfvnx5fMu2F7/SzWSYwPxjZWmaO75qK+Xq1avjWjP31f3hWj7yuF1UehNU1syfllSOZSVrLJOuqzdtJ8j1rPRhGMYDD7yoOp/NtBUAZlOTjFr50/mbg5jms8uUpqeD+r602sxdgRhjPdQGs9lsss0k28VtJcMwjPETX6c7ZCDjOjADIo/DIQO6XxQ+WwyeLeOJCe7f5cuXx2UadlV9eGFaX94qY0vBglbt9+PsI2NiTjmbmi5dpXjSKqJVxWA8y8xv1mGgy5lH95Pgb7rRovTYe8+nJ0EsoHTZW2uj+2krzUrC7Xb8zOeuSZrMJNPSMg0d5N7Z2RndVYKVrXy6oaRrzjbyO2ntVMW/aCU79QyJ6sjoij98zeB3eHioYRhGnlyXtgLApKkrQyZ0IoHS4pGmR+Y4DwXEZTtfrr2RVjN7XFIgrabUd3Z2dHR0pLt3745MbsGWVudB+UWxaU67fLuVKWxmGLowtBD8m20z4/rz1ltvjYxta8yg5/jX7du3xzP5c9M2T9QwcPrY4soqoqCngkiAp3JgWfnNQDXLZixUmh7+mIJHvmFgnJa1ZwV9z2vrDACu37O7tppMk5s3b+r09FRvvPHGhL88LgYkj7dByLQ8OjoagcjWr2NL5GHTg5vAuT7P/OU6c+kI+ZhLRVI2evKWcpTGQ+Y3n3rNIeuoxpohAo+V3Um7xb20FQDmDksrAPPgs4P+ZjyHZiuRnFteUmvzW5q6GVXgf2dnR3t7e3rw4MG4Jaia6Tk5OZlswHXddgO8opuWSmurjcTe/2YBtytDC9VT0hYsAp/LtJvpfnCNmMGIM51uh90rrnFyYj63kdZYWjlVAJbWFMc+LSMKCPeb+j9PBaE1yHpS0aXF4D6Yh8xbpptPT+DEjN1wbno/ODjQfL6YIXbbDawcX1vaBj/vXyX9yM+SxvwGzrQEKyuWEz4EK/c3eT8BJmnE30nnBC3yvF3sHPsMQ6QsMDZpEFuXtgLApFUQv9prl0DBZzgAufWk0hxJSPr2tKac38/bFcnYFl8L5Tc+s+10YzlzNwwrt05anZtOC4FrwRiP4XINggbBga/x4stefXR0a20M5pNGvmda2FKlqe//tt6qJRWka2VZcYz5m24bZ7/SWiBj2yrlfQow11kRNAyGLI9bfOzGODZ1//790YWz27i7uztaYgRLt58zhzx2x+1zHJMBeNOMZ+O5XzljmXSUdMYK49gSJBMAK2+A45Pyt876stXJMWHyGFExpnyenp6OSr2XtgLACDjrZhUZ1Jemr4FK94X3WVYFYKzTKYXRm7qvXLkymsZus017a1q6Hwzy00TmdUmTwfT2oGQKxstINzPblStXdPPmzTGOYjdSWp0Cy21bFghaKu67tX0KAoGSs64EUSsDjwGtYzJqCk7FxGxrKgd+O59pwZAE6evlJLSiOU6uKw8qdEDeAMXX3EkL9+7VV18dZ2n39/cnSyUcC5rPp2+hd8xyf39/MpFC3jOP5nIh8xP7zd90sTnevkeAqYApx6a6n3xopc4jc7IM94mTQxwzKsze+jGnrQAwqf8OOd9jPsZBSDxaIJX2Ts1NS6HSFgyUewuHpHGtlZnN7p6Bw4KfwOX7XsHd2mqrjwPrXhJhIXPZh4eHk3ZRQB1wd3+4QNI0sVvomIutDwsoraoEpDTzCWx2WVif28gxqJg4v6mIciwIepzMMB1SIfg9nW6H+0Wr1kLM+AsFPSeUpNU2MC6pcPsuXbqkBw8ejCvtfd908GymQwOSRsvOymk2m40n6tJtTrfb5bKtVJ72ZjgpYODqKQqmdBtdj+mbnpHzGKB7L+xIEGPIIa11f9atI9sqAKOllYzjAaEZSzeD12kBpbaQpv64LY1KUCRNCEiN7YWNXnOV92nV+ePYFIE612F5fY/dFPafAV4Hfq21XYbXC7lPFlLHaAysnnLPwLVpakAmCFnoqcnTjXDenA0zfXKCpAItjlOOq/vP57KNpBVBzUsZsiz/p1uWaw+pGB1/tXtpvrx169YYJ7X7SR5LC5/hErtdkkblePPmzTFv8jWv02JJy9TjxfBEhhzsWubLo3Ncq3hZ3nc/elZc1WZ6GOSVpFmVtgLA0u0jcFmguC5KmjJYAl0V2JXOanMTyQNoRiOIUcu7TC+087Eo1B6uw9bT/v6+Hj16pAcPHoxT7hZkrrZ3cN2uS5rOXObgl84y5mJ3weXZuiMIXrp0SUdHRzo4OBhn0xjoNQP7nvvngPbVq1dHN4r0tUAn7Uwz0ibdSefxuKZFxvLoNnHtFvNX1iBjdBYWf2wJs+1plbhcbuKWNHlV2t7ent773vfq5OREr7766hlL3FaX1zd5nLgHcBhWr9xzIPzWrVva398f+09QTbChwjDv0nr2f8YiM45KPu+5lbxGo8Nt5gs7ErxyTHLGlb8zrFOlrQAwaUVcWl4mSgZWK+HgdWsbazlaW3QzGbCnu0BNxYPupEV8yksSpJXWt+XEwP4wDOMmYGsngwwH0hrS4OH4iQHFAMsFrTy6xSDqfnr9jYGQweD79+/r+vXr45t0XKeP+LHrmi6XpPEFFNTeVBbpetJKqlzDdPNTgJw4boxVcewyblqNL61CLq2o3uhjWrM8u9i0It96661Jm9/znveMMSD3ke42x8mzjGyn22ieuHv3rk5PT3Xnzp0zvJ1yQICy7Jg/OKllpZQKvQcWHLvKO2L9nAHvleG6aKxwfFOJrUtbAWAmAD/UJBkIJjJXz9LCks66jPxI0zPcCZgMhtpK4vS46+eslrSwlt56663xzdcGsHwTsd0PMzkF2Zramp91pSDbWjo9PR3P/CLD8oW2h4eHeumll3T79u2xr3R3TW/uGODxyJxIIF2T+VOLp6tmGqYQMLncFAhacwxKV9Y7gdaJsS4qTVrdwzCc2U/J/NzuwxDB6eniPPuMQXqMrHicuJXI4+0FtqbX0dGR3njjDd25c2e0mgnIzufx8kxjNWvOkAaVdWUB0WKikkojwt9cd5hyx//kEdOCS4KeOwCTpuZoAozvS5oQ2v8T5FLbV/UQSAiG/GbsxBaK65SmkwCSxgD566+/rgcPHkxW4BNwbLHRevFUvd/47WOEDUxmdNdt68kzonarWmuTAxNpzmdgPl0r99kC5+dpgdgqpfCwXRTaFAD3nTRmSkGq8rEtqeh67orHjGu7mI95nEx3BsNZh9cGcnbRQnzjxg09evRIDx8+1M7OzngG2Gy22ATu8azcJ/OMl1bwHZKPHz/We97znvEE3lTiVLq2spyPfM74E13HynpOUKkMDYMl48Gmb/WboLvO8s7nqrQVAJbAxd9k5mTQBDhrmArIktkJkB54PkPiXrlypVyUx2n52Wxx1vzrr78+HiHNEw24QZ0MzPiIpHFl+NHR0ST25PgV91n6m3vgXKZPDJVWwWsvVGUw3snxOVostFw4S+b2mg5e8pGWrjQ9t8y0r645JVCQ5nyW9eWYpttqIaNLSAuMMU732a6++YaxTo8VrXI/58mVGzdujGPrwLbf2+m6ub3MIQivffIkgfnKdP/CF76gW7du6fbt2xO+NXi6v9y6RKudSiTpmzTvjRGBzbTsuY9VuZQD/2c9z50F1rO40rVhgJ7uQqJ4ajWCW2r3JDBnc1Kw/Iw0BT2fEfWlL31pZCQGmR3ncIDebyTyjKTztNYmJ0icnJzo4OBg1MzDsDp2OoXSU9cET9/nIktPQOQaIC72dP96FhtBxGUlIJLJmRispyXEthAEGfPhM34uQTPrrBQdLW9bSNySRJql+2nQ4QLf/f39cULAAOFxe+2118alEg72cyLAK/I9ju6v441eIkP5uHfv3lhHWmHmK/Mc+ZUysrOzc+ZwzHVu4zqvx3zJRaeV5UT+MphmudUz69LWAFi6jpWpylkWPsv7eZ0Df56JSuFirKsXB5AWwPPGG2/ozTffHN1A10kXi8sj/FIOT8M7fuKd+GQQxxRo0RlsGJ+SNAFEC5dB029+9mF6nLF03yoXw4LAtUhppSUdSVvSrtLE1b204KhMMlCfFptp5TgQx7yKvWV/K8vPiWNIYLfl5VMibAXfuHFjfAdoa220cr38xWNpADOweaxtRXuCgX2+f/++jo+PdefOncm58q7f/2kpU35S4XC8kg6pINwW1plbh6pyc8+wU8plz/Ws0lYBWF6rQEw6OxOSVpc0Dcz3XAsLLQencj9oeqcl9tprr43rfmx5kTksTMfHx7p3755aa7px48a4jSiDvQYma0m7H9aYtNxOTk50/fr1CWhyNtNWGBdWMp9jXQkQdl94nlW1rcWJdKkAMZmQ4+T7pDsFpidMVRyFQeqcPU0+cF4vR+FEiV26DGu4HIcNjo+Px/CANLWm7I57ssRrvGxNE/xms9kIAF7EauvGSoZWm8fPL/q4ffv2ZIeFAZTWMunJMWNYhWPVs2rT8jLf031MINwEvFLuON7r0lYAmLR6Y48HoDcr4ZTAVVlrOWjUGJVbQk1AF8hto9ZtrY3vDHR8wW6IY2NcSe/jU8yIPPzO1pCFyXXs7u5OzoLy0c9m3p2dnTEmZqFiXOHGjRvjbBZnFO2mPn78eNyD5z2TBrXqTHyulSO9KlCrXErnTQXi+zlOed39Yr353/QnKHtJhIGfY2t+4ARLa220evIoJOZzmTn1b4C9evWqXnrppTGYzoC/Y2LO6w3/fvFHnh83n89H99MupmXlzTff1I0bN8YXuljpmjZpLbOf6yzP9DgIXvysO/fLvGw+Tz6p6nqStDUAlnu2yJR0Dys30CnvpQvhAZemblA+X1lhvDefz/Xqq6+O58+b4efz+fiSiMPDw0mMgRrI7kSuG+O+Ra6c9uvZCJJuy4MHD7S3tzeeLmFw8llftrAMYI8ePdJrr72mmzdvji/J9ZlgtGrM2N464w/jVoxHUqgzSJxAk+PLPDl26dKwndJqIsN5cxLH17lwUlotNE4Qc8A++Ymg6LI9S+gZS1pGR0dHIzDduXNHe3t74wuObZ25LC8Yprt8eHioK1eujGN0//59HR4ejpv0bakbFDlW5jOeF5Y7I0wryoO/0yNIjydjYD4mu0oZ21tnYZEvyDvr0tYAGF21tKxoIZFwTrTUKBC0sOji9LR9CpHrY9rZ2dFrr702BizdRm8DOjw8HAPpjk14xfYwLLYAcYre5VtAGKfxfj5bXgZE55U0Ho1jBt3d3dXR0dHoitiys0V07969sS8+zdUAbPpZgBkbcr22jPPUjsqaJZ05ZrxGQa5cHbsf5AOCGPmE7XF7q50ZbCvjhlzWkOPvmUm2gdpbPY4AACAASURBVECf8Uj3l8tnTGsG150cCyN4Ot7mWU2+kJiLY23R379/XwcHB2eC/gR3Bs+THszLcUoL120zrfzSGJZnZWZa9JLpyDrPAy2mrQMwf1fxlgSU9MWdJ+NaqWkzsJnP5UyntBKGu3fvjrOIDJi3tjgR1SuzHSzniQYGHm+3cMxMWr1WyhaArZ2HDx9qf39/pIMZ3MDklfsWsJ2dnXGBpYHU7bM14P8ug4FinjCR21bojnBPpOnlccsxS5ee5RDo6PIQCEy3ZO4URF7nN8fT4+12ctbV/GceZD5av621EcwYr0wrg6vdCT4sg8tnGGckPd1uK0S3k3uDbV0fHh5OwMPJvMMwAwHe9KUCTyDzeLh+SaNiJbCnNZ8plVR1f9O0yXshX5b0A5K+TNIg6ePDMPyt1tp/I+lPSXp1mfV7hmH40eUzH5X0YUmnkv7sMAw/tq6ORHj+JkhlXunsmecUEufjbFuWQauH7k9qpNaajo6OdHh4OPr8XCRoq8pMZpeQmldaMLUBsLU2WkE8l97HHHtlM4HSrqqkkeGtxebz+ThTxgAzBcOzmmyby7CF5X7TuujwxmSbk9tIutGqrYQjlVQPpFiWBdb504IwmFfjbeFiyMJ0NajYokiL3haM6Z1uqNtgYMm9qakcvcbPSsRWOxc500o3oHHfKbeiUVFytwGB3Cv53feeAqAMVR6Qr5vX0sLl8zlGvFbJ7pOkTSywE0l/YRiGf9pauyHp51trP76897FhGP4GM7fWvlrSt0v6/ZJ+l6R/2Fr7t4dhWH+wjzTRgHQNEsSo6aXa1XMyQKUG9nMkYsZpaN76uGZaAlwomtYcT52wpeM2OOB6/fr1EaxsdbleB2/zRAO7e24nXzjh9tgKG4bVm4sMTF7q4b4OwzAG6GltUBhdTwVEpjHzUmA4PgQzPpsM72dNC1o2CWgeK5fNOJXLJsBYSeXuCN53DNI0JfhYedGiJq/Soh6GQdeuXZtYsl4v5bVaBBwrGwODxzbX7HHsuAbP9XuCIGWC/U+XnuNdAUrPoDBIc9zIAwmQOXaZ3nEXcli8Vfu3l7/vt9Y+J+nL1zzyrZI+OQzDsaR/2Vr7vKRvkPSPzqlnwgj5YR5Jk0H2PROXDCPVb80hk3tQKUgUFscXGIPiM5LO7HPkQHnf3O7u7rg8gfEQMyG1br5dhoLJzcSO43hBJmd86C7bpeV5X9R+FtwEEreLSyrShXRb0h2pNHzF2KTZunwey8oSsCDZgjJwe/cBxzuVoxOFx4qFOxSYz9/mOdPItLl58+YIdBzX+Xw+KkOCj5eucPmGlZePVuLuAE5KcJxND0/meDW/6zeIuc0uq3LRK3ozTk26V2O1LlXA1ru3Lj1RDKy19hWSvlbSz2jxxu4/01r745J+Tgsr7Q0twO0f47Hf0HrAG1MFWk5V0N7XKZgJdj1ByHLpOjLvfD6fnCjBGArfqG3tZwvHWvPOnTvjSzRoDZBJrbUT/BIIuVp+NpuNkwOOgRls5/P5KIAWPsbYbEl4a8v169fH9hn4DADz+eowQNOJ1hFB30KQQJRjyUmIBLocD44dQZuxHP+2xWRQsYBZaH0MEV0iWm/kJyoY94uWKdcGenmDr7v9dKtdhkFlZ2dH9+7dG11J08QxT7d1Npvp+vXr41g6rOAYKt1f98m04uGKdKkdZqjcOsoNrbQ0LHyNvN6ztrIe8sbbTRsDWGvtuqQflvTnh2F4s7X2tyX9VS3iYn9V0t+U9CefoLyPSPoIryWAmTHpmvSsMpq1LG9Z1/id7o2vp2tpzWOG4SkCs9lsPOeLQuQ1Vru7u7p165auXbumvb290Y3wmiJaAtyGYu3NfqSQ0eLxMgkLyGw2myyOpSD6OWv5YRjGbSq5SJZrnySN0/y2sPhKL2poabpIlaBEsEurx/3krFUKU/6vxj2FON19x5jSBUt6u88uw9YV12DZYk3rnZYvecpA6hnD3d3d0UoybegynpycjMBoBW3guX79+rhzwzPV9ETMQ55U8gJb06qK9dJNruhJQKMSYQCfdKwAq5LDXt5N00YA1lq7rAV4/d1hGP7BsrFfwP3/WdKnl39/U9LLePz9y2uTNAzDxyV9XJJms9mwvFaClIGMM1w030nwtLro5mXZtCrQrklZDtrT3GbQ1W0z0PqoGq/LYmyJdQxL15LxCwuK7/serQrGTOgWW7AMcu4XX0u1u7s7WmsGK9KWtDJ9CWR+zgs46XLSiknXsnIjnYfauxKuVEyVJWA3iu4RlzQQ/Gkx+HrPjXR5nAjxSbx0p7Jd3IfoMcvQRWttjIW5Ho6tpMmb112Wae4JgP39fd2/f18nJydjPp7/ZWvNwO2+X7lyRQ8ePBj76fFNZU4aso9WkLmyftNUWV+sa1NQ22QWskn6hKTPDcPwfbj+vmERH5OkPybps8vfn5L0v7fWvk+LIP5XSfrZjVqj6foZE5xnVqUr6W8TmNPQSSSasmQkCi6ZzQfTOS9fbErXsbU2AteNGzdGZic4WcA4bc9DA71i3m2gFej+M8jr59n/XJtlQLTF5HVhfGu3ND1zPWNpbjvjf9wKUzGi6Zngk4xJACAQ8L/Hp5rcIb8kH/hZ0sH5bSlLqzd1c0wc62NciTzExZkES1pedu0S/Ammtqj82/k4Hn6e70rwWNui3tvbG/dGZvD+5ORkXABrAHcfrIg4ZgQwKuiKxgSwTQFnk3xPYpFtYoF9s6TvlPRLrbVXlte+R9J3tNY+oIUL+euS/rQkDcPwz1trPyjpl7WYwfzu4ZwZyET31LIZ70qh8HXO4pkQ/FB70DqgdrTGbG31vkZpuv/N7qKtkFu3bung4GBcUMqV0mR0W28GI5rsrFdaMZT7xa0tbg+1vAWDS0IuXbo0vkaN8SE/Z0BNa4nXcgmFBc6ugy3JtJJMf46T25iWWYJY5WJU1nbykL8tnIxZDsMweWsUhdOutNdisZ9su38nTzmYnssvDFRJQ4OUz/Ui3XN9nZNf48bdEFRw3m/rUIeVnJUPrTDXx1M4UmGkEWA+5n9b8TkeKXfr0ttxH6XNZiF/WlJVy4+ueeZ7JX3vkzQkCZkWV7p/vsZvAkOCk6QzTEGg8zddm0qbm1El6fr16+OWHDOHmcZM69/JnKyXGpXWF5naqXprUGp23zegW3AJGs5TgRXLzoWpGONJkJzKI60y/6dgEBz8nUJQWdE5XqnM/OGSkmFY7UclMLn/dMl5j4BJwadgGij8LBUWFab5IK1+jw3HwYqIrmVrqwkaKzTLjF3L3d3d8bgmhh1OT0/HrUk+7NLxQC98JbBXYRcqEH/SMjOd1qVqPNeN6Xlpa1biU8BIMM4KkXCcdbTwEWwSJJwIbGkFkLHMHAYmu4xmwmvXrunmzZtjoNVWje9bABIEKtB0/XQ97CrSTXUez07R1Pe9fO9jamvfy+C+28mAe+VSkE4Murvc7FMCDMtySkutGjfmJXikgEkrZcixTHfHi0UZe+KYEMxJ/xy7dPWk6b5J84752NfNJ/v7+5N9hARJjzHjYnYJ/Qo+Tv64X56U8RHXBvDj42Ndu3ZtBDBb8Y7xkr49AONYr7OIc8zO+/20aWsAzHEJE1WaWmHpQhDU8n6P0Wgh0GyXpgDm8qXVSzzMCDs7O7px48ZodTFIT63PwfF9M1pqdTOFXQSuBTKQsR9uc24Qz5ibn2EMsWeFmgYZyE9rli5aAhzpngJfadW0uggUydyp2SlgrFOabr7PbyqJpAVBle1hP9MLIM39jGlkK4fv+SSIzufz8QQJj2GOM/tHfnds1q/UY/220C5fvjzOUs7n83GPrKTJ2kGvpCfNaYmmEqrkcRPLqTfebydtDYDZV2dgU1rNQHIWkgRMy2KdRnAcysxj9yHXxDBmNQyLGM+bb76p2Wyma9eujed5WQgS4CjAKSTsg4HFwMY3DBE0qB0TOFyHLQmuTyKD8K07phVnAAnybHfP0qNC4DqmtCiTyXvW1ibMnKBVtZ3CzwWsSYN0t2nJJwCzDPahilna4mP8zRb1lStXxvWE5jPnsVXE8UwriMqCoOgJII+py7D3wLdiHR8fj3RxDJd9Tc/Flmu6j2mBbeL2rbOqn/QZp60BMEnjCup8dVUGXulOsoO+n9P70tS68DfBhMBgkzrjZ7u7u7p+/fpoJdk9cDssIAZIMkMCgQWMyxyq+IGfY3ku098EZTI5BYCCmpZGalsDcR5iyOdIS8bvCKpuQ1pyadX5Wm9Wk5ZZz7q2MDke5JgSLfa0MFh3Bax8nvTnDG32w4lKlnX41FafF2fem81WZ8Sll8AxN53YZx+EmBYsFaM39h8eHo5vFT85ORmP5yGAJk3SfUxgfdq0DtA2LXerAGw+n49TwdXbohlHqIKK0lkrzJ8kvnTWGmIQloI2n891cHAwLgJlcNzCzvoqxqNw2mrhEcLp/qRQ96b00xqRzgJhgknGCyVNwCrdLVtqnEwgbXtWm2mXwEHNzbGg0JIeOW7p0qQF7fbY1eUhkknrBB72LWmcYEZ6mx9dJ8eMytNgb5C1FUXQIei5DVROaVkOw2LRqi2u5AXXff/+fd2/f39cAG3+9ZowjiWtLNKacpdxzB7oVNb1eZbVpnm2CsAkjVO+dqfSAqs+leaTzq4PYyIzOS8tJwuFXUgvADVTei1VVW5qJgo63T0yqL/T6kuNmEJORve93LlA+hCMfN1amgDOREvFZWRM0f1yoovBMSLY0KJmewkYlZCQlh4DCn2631xSwQ34tJyTtv5m3Ilj5fqyPeyHaZu0JB/RjTR9SA+CV7U8xInKPwV/Z2fxCrhHjx6Nh2Nevnx5PAaaJ9YmCOUETY4Rx7uXKiCqQO1p0tYB2DAs3rDjjc9mHs7gMOXyAAoOr6Wm9TUypxmclldrixlHvpLMK5g59c6BNdPl0TTcs+aUGs/tSCsy71NAe8xUWRIWBC6W9LV1WpJWJmnZiyMmeFIAKJRUOOluERRsQRF0GfhmHTkmbmdrq+UeOcmR7assINKVvMPrWYbHntdJ06zLvE4F2hsfjpPbcnx8PAb2qSgcv3UczG80csjFbiTHzr+5JIO8Wln/VWK/Sft3Im0dgEmrGUnvwZPq2Y8KuDIvjy1xohCne0CwmM1m43ldzm/tlYzIAaXFYQHxb37z93maLV0szgSy36mxk05cNsBn2JaMRfl6AgMF0DTh5Ef2j2Wka0JgrP7bsuTY84A+5/fZ8qm0uBTB7aIFTyuOgMMxTmswLWOPvftJ4CONqz5KmrzMgwuFE+QYT03+lTTux818rTVdv359cvqr3epqMXJlEa+ztN6p9CSW2VYC2DAsDt7jMgXORnJ7QwKaNVgPGBI8aDn4vxMtFb9FyMKajJMMlbEgDjzzUqAr7dYDAANRavUUinRJkzko6OsYJyc16AZzTVm6rwkK2Y78TkXiVAWQPRYuk/Vl32k1eeGopDPuMtuX9We73QaOAS3iCqTSY2DbvNVrPp+PW4LM57SW031MK89rFv0mqtbaaL0ybML6eO4c2+9y0zjgWGyaKsVdpScBya0CMDKNrTCenmAhJ3illkstmeX3rAy6Ktbu3BPHgH1afil0tExYZg+MUlvnjBnbbgZOQDCTs36eVGFmTIuMZdJ642/SslIEBCvSIfO4noxbsly6lE5pabrPpLGn+x20d3+t0Az6PJ2XliP5qwIpjmFl+TMmZh7pgWPS0W31rDTLtkVZhU/IG5l8Ygbfmp7KlhM3GVLwWFQWXqXsSO91vLJJSuWxLm0NgCXTDMNiZsVnGdHkTzBjoLUy/6t4RQpAggwHnlowNWplXfWsCTJGuh8U/kqrp+VR0S3bV8WmMnGRLcHAlgIZOGNnGeerFAhdINKI9CGdaAlXa96SZ7jlyyEDC1Med1PF2Eg71kEwS4uH487YYFosvpdbiLLv/k3wdRmml/vHmdR0uZOHGD6hgnS7CboGMbc3vYHK06lSxWfr+G9Tq6yXtgbAcmOytFoX5lNMGeCkJdZz23KgpZUrkG4cQTAtKGtwDzw1TeankNHaoVXD+qtYUFpFThUI9CwpMnRaFQRrWkXc8pIA7fIcl6wC0A4Kux4KdM+iTEGoAMNt4JhRmFgeBTLXWWUdaV1KmljYFPCkNfMkv7mttH5zjBkHZF6DmMeAoMv1Ygn+lWJzUN/xMMsMeZn09DY00pwf3iPNmT/HMtuWebLdT5q2BsBs/udqe8+qcKaqssR4v7cxtbK4pOk783jwnLVYMj+/GUxPJnY9+d91c8YzBSW1a9ZtIUnAywmFSkjZFws6NWtaJ8xrweAbdagcCCKpeZN5K1cyY0gVIPN5ui60PFmn+YFv0c6+Jf0ZYySAVuNF/qjGK133tJTYHsaouMQin/P+zqRvgobHlwdQWja4h5eTL5UbyPYn/Xr/STNe6z3zNGC2NQBmgc4Oe3VyBu35nyZuzq71LAAyFQGIjMlBYzm8lu3ldDe1L5k6Z5cS7Ni+FKy0pOg2OhHg1lkP6QpU1k9VhmlD2tOqTAAjHUnbjLfRCmGb+Dvbm3VxXAl+BjJJZ2aF3c9ck0XAqVx8jzfHrwdSed3l59hQwaWVxfHhm4kqPjNfG8ByFtZ5HRekAmCfySM9i7eXEkxJt6RxdS3zVmlrAIwCRUBhMN/TzA7YckYyrZ91Zi6vJcMQfNIaSSsqBzHN6IzbkBmSsZm/Z8Fku5Phq77zex0wk4HZzrSI2BeCAoE7gTD7lGUReLKv2WYGxrOvtkArkKNl70A+rR33lfSiy5qLbavxqiwYSeOJD5ImW8/Ia26jrzmYzzCJy5/NZmN8yzRhvaSR2+az9t0nAuHh4eE4hozb+dMbjx549SzUTQCJZWyStgbAJJ0RQGnREa+JSesrjzXOWZOe8FPAuOeSrghPqvQ386X7VAGO80oqVzlT26XQEBwrK4N96PWz0mQVgCbYJAjzGdbj8tJKzDHI+z16VZq4usf2MO5Gq6vnnjLxHK7KCnbiLKi/E4xTObFOW3akFdeuuW4qzNlsNp4UYZ5MupCGtJjII/5/erp6yzfv2Y3MNwtV41PVn4kyTODi9VRmVXruLDCpPwvBV0RJ06nrXkyMg8jyq7hRWkGSzrgTDAavW+hJJvaAJbOngCVYsUzWk1uQsm7nryywFHr3qcqTtKKrkvVXMY4KiJIhKUTnafMejZLmVduTF1JJcqIn6Z3KgPSoJhFIV1pEpKEtZr5nIRUZ25trDg1GfJaTFHxHYwKHT+XldqPWFrsTvEwo+TY/ORasqxrHHggmnz9t2hoAq4TSBONhgmltpVWWllQCB695WYDvOw+D2nQfWqtX1TMPrSK2l3X2Jhcqwc/29pjhPABwPoKRdHZJB/tEhk5LlDRNAXU+xrUqayw1bGU1n6fle7EptilnjlmG/1d0yJ0MCZxucx5hlHVVMda0nhNQ2Qcf/cx6nNflz+erJS18GzitoWEYxpfeeonNMCwO7XzrrbcmAG3+zVnlVAA9ZVXx4yZW15OmrQEwp17HDWAUvCq475SC2osTuXzn4dYUMjeXDuRA5NYZAmwluFX/KgAnKKbLWtEtyyHzpsbruZ9MSbN0CarrCU6VK1c9U1lYPY2e/7PPtkrY92xHKjO2uTdeCTbZVtI7y2R8jjSqYoX+TeuQMbx1/eL7GLJvftbn41PJelW++9RbPJzxtep3j27npfMArkpbA2A95vB/u5E5yNaABjL/5gtMnTIGkoHnnZ2dcUMr609mNlP5dyW0FZMneLDMbBvroDD2AqIp9AR0Mj3rSyB3WytXMunOe6np14EirVW2paJTT+OT3hUdcnyyHT0QTwsu+1rRmtfSsurFt0wDbivqCW72nTE1tiVdYbuplTzZXaR1vLe3N85G9pRe9tUyQ7e1Got16WlAi2lrAMypRzT773laau6NzFkqziZS+Gez6ZG9abG5DWkV9H673AoQeN91rRPMSqArgKusyATFddowLalev1xWr+5M6Wbyucrly2tZVs6EsT0pAHTP2f8ERfY5aetyWEaOE8E+lVlaRdWMLsvtjaVTWlPp2qZFy/76BTTJ21by7gPPprOizAkOl5OAylTxRcUvbxe4nDZ5L+SupP9b0tVl/h8ahuG/bq39bkmflHRH0s9L+s5hGB611q5K+gFJXyfpNUn/2TAMv75BPZJ0RpCowbwmjEF7glTGwrgFKAfYcREvJqU5La0EhEfmuB258p4ftzutt0rwGIBO64hlVAJGulXglfSsLMa8RoDhvZ51xLKrWVl+p+CvA0u2OwU8A92kWcbhaJ24zNz6U1kX2ddNQL/qC2N/tIgNymk9kV6sj/QmP/t/tsPeh2O8pJmf9wp97/P1EelZVhW6qLyLKlXgTFqdl85TlJLUl4xVOpb0wWEY/qCkD0j6UGvtGyX9dUkfG4bh90p6Q9KHl/k/LOmN5fWPLfNtnHqMNQyrWZReAD/3xSUBK81BIewF19MiyvIqK6cCr9x+09p0BslAwPq4cjzpkQPMcl0G3eQEnao/bAPBu2cd5UJO1uM28VoqnKRZjhvDAmmJpRLh2GYfkiZVOVUfsx+0nqsySBvSrvfdA4F1QM+1jyyHitbXuOCV4MsZSfeTE1rS9DSWTOnVVKkCvXc6nQtgwyI9WP69vPwMkj4o6YeW179f0h9d/v7W5X8t7/+htgncFikHly4khYHWljRdhOpykoA8kdPB1TxKJ4VfWgkp6+U9glHFUBUo2lw3s/RiQvydwNxzQZi/F7hPAUjBSwuz6lfONObYJYDxeoIQx9V1EEyrfnhMKmE2kFVgRtpk/CrbWCmzKlkZVmNe7Ztl/VXbqrGyR+IJpuQdt0Nardrntfl88WYj0yUnqqr2smz21TTK9DSg1TMKemkTC0yttZ22eCv3FyX9uKRflXR3GAa/DO83JH358veXS/rXy0acSLqnhZu5rvxS8+U1xrrSAqNWklazlCzPdUlnXTYzHIGrEkYSmEHYFDbmT81LIaCWqzQuNV0v3pDaPMtKxqv61hOgpFkl5Al+tBQqYE2FkgCcdDaNEoTYl2wH22068xV4CYrkheQ7AnQCa0VTl8PrBAOnysLNfZzVbyfzOMMgHEM/19rKkqfitsz4TWDDMOjatWsTWvI1h0mTHqhlvicBsnVlVWmjIP4wDKeSPtBae0nS/ynp9z1RLUVqrX1E0kfiWhlXIChVR0vzvoGFby1m3vl8teueWpJnpXOWJy0vApOfpymfAelKmJyfwOl7vJaWVM6qkl4JbqnlGTNhXVU8I8ehYkL2ORf49gSc7le6NVm2U4ILty7lYZSpeLIPfo6KIZWP62ebKhplvl45mcexKZbBceXaOYJhgr+BfRiGkde57MZ5/Hs2W7xVK61cno9/fHys/f39M6Bb0TVpwnKTRrxGhZ/0rNJ5959oFnIYhruttZ+S9E2SXmqtXVpaWe+X9JvLbL8p6WVJv9FauyTpphbB/Czr45I+Lkmz2WzkWBIo0d3glLEuApa0WsjndS2p4W3xGKhSQB0ETW3mfFUsxikF7jxhZv09kPBvMgHrr8Cgsg7yPttHoKPwkA7JuOsAg6kSbtKK481n+J2zgtLZ9Xe+X1lUKWC9sWFZCXQclwrsegqAv2kBevkB3bosK8cnFbLrojvpfKSRwYhvlx+GxZIKn6HvrUZ+f2V6D1Wq7m0CTj1l8aTpXBeytfbetrC81Frbk/SHJX1O0k9J+rZltu+S9CPL359a/tfy/k8OT2JDIvXcSAaBczAfP348DiYD+7TGMv5F4a/OumI70i1yO3tLAciQlRZLQCFgVgCez/QGP4UzF0VS4M+7Tksg3a6qHrazsqZ6DF9p9R6w8zlagrRuem45+1tZyBX498a0AvfscworLdaMy1XlJU3TVXdb7E4mDcnXXC5Bj8aeR2tt8h6I7Kf7RIX3JHGwBOa3mzaxwN4n6ftbaztaAN4PDsPw6dbaL0v6ZGvtv5P0C5I+scz/CUn/W2vt85Jel/TtT9MwEp8DyjiYNaTz2SS2T+/nciHn5cuXJ+d+GZTI0LmSuwdSlcbkM5VVUAksBzQFtHJNki5kKiczKtu8zlogU0orLZ4Beo4RU1odWW7lGlf5SO91oJdlsK2mh0G5ih8SzMgv5/VvnRBWlkW670ln0tqu8c7O6jVnOY7k91Rox8fHOj09Hd/olUpT0vgGIsvTo0ePRjqdnJzo2rVrevXVV8+AfdJknQJNmvX+n5fOy3sugA3D8IuSvra4/muSvqG4/lDSf7JxC5cpB74nxNRG/u/nfVpoxhS4zMLvm+T78OxqGrQ8aLTwOLNZgYDbmILSC6DzeedjHgp7lT/rrWjocivLJgErgYd9T1pLqzO1egCTba/uMSU4OFa0juEdxOax4hw7C2RaYuw/FZY0fclH5q+sziqW1usntxIlqLrNfFUcjwYiiGUdpoXbcXp6OnmjF/tGa8/PWOm7vX5bd2UZZt8q5fBOpvMsta1biV8lEi6D+B40rjj2twWR8THPrBwfH0/2jVUzj05pfWWb8rcth2rjdxWIpyXlNjJvzzKoQCPLrepysoBQCCpri4LKbwt7NcGRAk/QSOsp25kWrMuiS0mQpeVcpaSdy6rAP+OC59G4F2DvAXcFdmy7wYYeQm/NXCpAKj1bWDwuinX7qB4rfruWlgW/6Jj0r2i8DsDIW5UFX6W04t+2BfZupxzcygRNYbffTwGsTmy1leW8JGoFNh5QasWKoBUzrQtu5nfV/96MI9vQc2EqSyuFjW01PfJ5X2ffTT/TmIJfffeAK+nC9vt59pmgxfO/qr5X9fj5dS5pCk9PMLM/qZiSfmm5EUCrmWw/x2eS73sWn8s+PT3V8fHxmQMIOPHB7Xiu3+6kj65iuRWInQdKmwLX06atA7AqcZCpeW09Vfek6eCb8VMAaVZL0wHxgJoJyXi+X5nVqfV8j21ifqYc8MptSXBy6rWnul5ZpEozfgAAFuxJREFUNBWYMpaUIEeB8nfGufKbbWUb+YzpXdEmgZDlVLSurI+KJnyuspT8TM+NY3sra8ltZf2pHFiXX+xBl5a/07VkyIP08Zu9UgGaT+fz1RY9Wnu5rYiWGOmzDpwqRX2eVVUplnXpuQCwJAAHnoT3PQqa83JfZA40189Qw1SnZvYIXJnZObg9MOtpqXy2smQq4M3y83pq+arurCcZz/3NtlWWh3R2qUBq8p4wZ6osyQpw8hn2wXySIJZWV96rvIEKILMd1bi5Hi6Gzr740wvmuw3ZV1tarS3cQx8n7XrJ/5599Is/3C4et826elZfRYcnTU9jpf3ORd/e4ZSuCEGJA8L7FRPlFLC/K1DhsdI9K811MU/PmuF3XqsAwuVXVhY1YiWI6W7wQ5CvmG+TD9vZc02zTNKdfU+rprIYeS/dwHXCX9GTIJuhhB5A5pKL7FOOUa+NmT8VT/6fzWbjRutsW6VUc1LClvOjR48mY+L+eCKLgD6fz8eN3ilzTlX7q372rlVpnfLppefCAstkcKniJBROu42VdUImqxiBAWoKWYJfTtNXTMjvdZZFT8OvA8O8l2VkIJ1gUVlItEIqJUBLyc9V4FsJds8SSbBNiyT7VfWPY1TlS+XGemmxkD+y/uxbtr3qA8EyhT7XFFb0peLllh96E5ncP7rix8fHms1mk4kb1+cTX3MNXWXBV2DL51xmek05Hj1gOs/FzLTVFljVEQ88Bz+ZKF25nP43sHmQcnkET2X187lvjQxKq8htrKzAngleCVzSoLIA0xrLMshAaZlW4MdnKvDKPqUSIL16TEitnmOT7ajKzrZXrmT2nfeqcnoWZNLS/c0+9Hgg87lermUkn5J32TYqyUuXLp1547jbm8CaY/bw4cMJr/tZz0BSWbveHOfkqaRV9reXnsbFrNLWWmDUqE4eDB+pQ2KmG0mN5+RB8XX7/7nmiquZc+DYvgpEqDGp5dxOf1dB8Qo0EhTWgR2fqSwg9im1YjJ/lletS/Nzac1wrKq2VP3MPvWUU68M1pm/Tb+KH5JGFPhefbRw2K51wpt9S57I9uT4ci0Xz77POtNjID96ZnJ3d7cM2lM2JE02cdMyz/5UntA6K2rd/SexvqQtBrCKwX2NR+f4ejJ6Hj/tciiM1bG+af6yfpbhQau0d2UdpFVCwa/ys5wExkw56C6zEtoqf9XmtNAqV8/flWWWAp95uBmb9K8URY4JlRWfYRvT2q3oUE1icFFs0vQ8IK7oYmVF0MoAPvuZNEg5YGxrNlvMkjPIz7ypGFprE1eSdTjGxrw8hqfqY9aT6TwgeyfSVruQTiRSZWWR6Cm0afWYsbwin2VKmgBfpYU9+Gk2s74Eg3WA6PvrmJfAkM9TqKtnEgSTrvyurLJKgNKysYuR8Tbfz4mWDPAn6GRfsr/ZprTQeuBT8UaPltmevNajaZUyv3mHtKwUsqSJonQiD/LAwuoAwgpEDg8PSwvVZSVN8hoTZYKWX88S3ZRWm6attcCcaBk5JQNnnurY6RSGy5cvj//NAPlGokr4c4NrJWTr3IieVqpiGPlstiWtnl79vYBsz0J1GS6zFygm+LieytrK/7RGe2eq5bIKtiPBnrHMBM/zrIO07Px9Xp9Ih8o6zf6T5nm9yuO6q0RAszLmAm3OnqfS8DPHx8e6du3ahEYuh/xP0GVbc2wrxfJOpPMAbessMDbYQcuKcaRaiKoDD31ChZ+x5mIZnmpOYEpLK4O1vs7fHNxcb7Tu2aSB6+sFSRPMqmvntZ2pF+NgGVxOkALfUywpqGlpsc/+nVYd6+wBAeujAsv+ZOwqQS95qAJY84wVn/P0wIwgWE2+pNtd0aQaT1tdtMTyRJXKArRMWMbMp94E7g95kNZg1ZYKhH+n09ZaYJzyzYC9v32vms4mI0pTLV6t6fFiPgKbU2ogxg+Yt2I4akBfT2ZknzMuk+5WAlb+Zt2sP/Owzb1yMz+FzIKboFZZFxT2tHwqK9B08DeFVVpZbXmAYgIjgT/bw3Fh+7LPLLNSFgS+SrCd1lmxqfRIk2rcqzr5HA99dL7kzdPTUx0eHurg4GCk7enpqfb29sbZSoPhOt4znXvW4iap4k/SaF3aKgAjgvvMLpq/1HY854vPV4taeb+11evaXfbOzo4ODw/HOIAZIkGOvysriMJTadZkNqcKTNbNSPF/Cl2C17q8LKtilLxXAZCPHE5rZ13/eiCXQJZCXZ31RWHu9b+y1Pw/FWTSy7zmewTVCmSSBv5PN9f/q3KqyZ4eXQjgCbI9ECfdfIChtxr5VWt7e3u6d+/exALL2Gf+TqXea/87nbbKhTSxDCLSFN3JpAkUvs77PUC7cuXKaKHxWN5qgMic1SBRCCpQSyBJ7Vq5q5Url1qQ9fNTuZs9YeP/CvBzbNKaMVPT0q2URwXA6Y5kW7LvLjtn0CqaVHWavmyjpHGzPo9c6sVPPdbck1i1gUooFVHSMCdmEnAqJVz1j4cj5jHQ3BbEMubzud56660Jb8/nc+3v70+APT+VcupNav1Op62ywAxeHLSMSUhnZ5XW3UtGyinsnZ2d8egRa0CedZVHtVTC2BM4t6fXV9+vACXLzjrdP1qn5zFOxYDZr17dPUsw278ujnZen/k/g/tV3VXfem1hP9KSNL9k+6pZblrm6/rC+07pRlZ9SyWYiqBShsm3tqQMQtWsuft7cnKix48fa39/X8fHx+OBhslTFZAl71f9+Z0Gs60BMIIXp96r2IF0diak95/XGOPiAHmfGBmYQcsUjMolqQY8//PbaZPrvJfuznnAdR4DUQjWgUQFepXFyTKl6WkJfIYa32UwX/axBxysn2WnRejfvVlPlsn8lcVRuWfVeDF+l4o26dejZ06QkM4ul3FSW18GNB6VY8CiYp/P5zo+PtatW7cmK/WrepNOTL0YWM9iZDlvB+S2xoVkwD5dsir1wIuJzOfpZZ7c6YHizJZTNWPXq8MfPtMDBg5oFWPLuntWD/NWFmDv+cpiYtur9lT/05Jh+esmLJJO68CD34zdZLyLbWSdpG8uL/DzqSAr0Mr7VLLZt8pCYVsqEDuP1nye19k35/GspMHMRgFnElNerMBJE66RrMYuU44rx+68tImc99LWWGDSejDq5ZXOauBK6+bqfQ+wByvrTWDKgUwN37NeNrFSmD+/mTLut067VZYE65bO7nFLbZ6amFo720FhzX6k0FYM3gNW0tg0yOcyjshnKODsZ453glG2L9vO5wgKDHaTd/i/B2KVhcN+V+3IsZI0nqcvaTw+nXt7SSeXbTmwgrenwpQKt2rDu522AsDIVJXL2HNfqo/LY8qD4ZyntcW+Rw/yOgsmXSD/7gWhq0GtwIzP97RbjwY9QEzg4vU8Z57tZZ/YVgJUjhGBz0rB+ZLhCTL+ze1cbG8CUaUk1i1ncTv5XG8Me+vRKvr2wKUCW4InrSQ/s24WMmctScdsJ+t1Hp6/z/dEuMyqXo5NBWBpCVYKvxon0u+dTlvjQlad71kj1eD5OweSkwA2p6XpDFoKQsZhEhCSGf1M1c7KUqz6VS2xyH72tJzbksLPMhJEeu3Idq+zhpnHGt80ypkv3qv6kMsinD/DCiloKdSc2MnybWGQVt56ky5apRCzbawvQZ6gmYJOd45jkUqDVm9adlkmFU8+49+c+GF/rl69emYSwO5nTpCRHqz/PAusx7dvN50LYK213dbaz7bW/llr7Z+31v7b5fW/01r7l621V5afDyyvt9ba/9Ba+3xr7Rdba//uJg2phLV3L69zQHuWg5nVO/i9+96D5cHPc6Gyrp4llAyVx+9kfCAnCbK8FMqKFvlsJWCkJQGusiSYN/vOUzuyPCqLFGz3lTRIYGR5VATpqlJx9AC4pwjZdy45yLGqYoIsIxVM0j+tVIJs9jOBs6q3SqQdQb1SVslz1Zo3vmbQ45gv9mC5+bv6v67NT5LOo8UmLuSxpA8Ow/CgtXZZ0k+31v6v5b2/OAzDD0X+/1DSVy0//76kv738fqpGZ3BxnfXF76q82Ww2Hp1L4bV2TmuMWo3XepYEAdP19oSMs3PVM5VwpKbehG4JAlV7+L+yZlNIekwsrV7d5for+vF3TqD0xpcgVj1T9bOnhNy2PHVineXbK7MqPxVn1QcqkgSgXqr6w5ik87B8Hsx5+fJlPXr0aHIKhi0tv2bQM5X2WKo3PvWUEN83sY4+mwB0pVyrtMl7IQdJD5Z/Ly8/60r9Vkk/sHzuH7fWXmqtvW8Yht8+r9G9VAm0Z1vsAiTwpLBTy1bLKehKUAAZpGXZ/q6OXqnAgaa97/dWL7NdLIOAl3lTYPxdBbgrBkwh6i1hyZegZDuyTXlkTmUV9mjH/BTUPMwy66ElxXZRGVY0JAhmO1NpOa+tlR4tWA+BN5UN71Xty7Yknela87r50wB25coVPXz4cAQ3u9Weibx8+fL4NqNcUF4B+Kbgvi497XPShjGw1tpOa+0VSV+U9OPDMPzM8tb3toWb+LHWmt/D9OWS/jUe/43ltbWp0szVxwOcb+j2YPmTW5H88boXugr2/3kmUjUgVUC1ctV67sd5cSrW0xOgimYZs8vvbE9lXXEFumetKmvIwrIOiBKo02rLMtf1jXn8uxevIvgm+LNMt488kG5kflhGNYlhOmd8ie0imJDHGK8i3yXPsN2VG225qHjN//kCW2l1aCGttuPj4xGYyavrlE3SYpNU5X1SINwIwIZhOB2G4QOS3i/pG1prf0DSRyX9Pkn/nqTbkv7yxrUuGvqR1trPtdZ+rqdxzmnTOGDcCiKttlXYh0/m4ItuqRE585ICkfEX9ONMHMPfyXgVwJDBqudYRzJzavF8PtvXsyaccseD87KObIOvU+NXQJ0WX5a/bsFmtoNjX4EHrSTer+KS3KyccbHe+Pm5nBSo+kY6ZQyRbUs+yLFcB26kGZdMsA/sl/l8Nptpd3d34h3kBvkqhlvxX3Utxy1Tj2abPOv0RLOQwzDclfRTkj40DMNvD4t0LOl/lfQNy2y/KellPPb+5bUs6+PDMHz9MAxfn9bBJolMwI+3Rjx+/HjUIjxexMLm+yMhZtODClPrpxWRmjY1tfNVDBd0ONOXZLoeY5DpWF8PRKsgO/vaA7Zee6trlRXCGbQEstamL7dgORY4afoiYv/PvibNXV9vKUJeS7qvs36cUkEyZYwrFQ75LRVc73fSrspri5pjynZevXp1AsLpvg7DMBoBVEw93ui177xE+aruHRwc6ODgYG0Zm8xCvre19tLy956kPyzp/2mtvW95rUn6o5I+u3zkU5L+eFukb5R0bzgn/uUGP0nnq+fzP91OzjT66BxJE6utten0Pwe1Yvq0evg7NX0KJvO6ralRe/Wk0FJAKiaqYlkVjUhH1sV7bA9plEzO/+x3fuxSVfFH58klGZWyqICRdaZlWSkbX6ellrTofdKlYwwx6yFN8znGdbMcPp9tTp7NyQ7T2Qqdhxdm/MzlcLa2ki+2IXnz7SS3/2u+5mv0Ld/yLWvzbjIL+T5J399a29EC8H5wGIZPt9Z+srX2XklN0iuS/otl/h+V9EckfV7SoaQ/8SSNd0xqk5SMk9ZMvlmImp4vBmEAn8+lUNBCyQGjNVMBVAptAkOVekF7l91zvVNIsk7W61mqdQxauWnsM9/iXAHeJm5E1Y8KlKrfFk73Z51LX42T8/PbQlSBBcvLekgrPr+uj77utVqZL5UKaVOBozRVWrPZajbSdezt7Z1x2U0/7p/MuhJwmarY5NMCmt3bX/u1X1ubb5NZyF+U9LXF9Q928g+SvnvDdkpaHGUrSS+//LK+8iu/Up/5zGck9U3SJGBP+Cr3zPEvA1prbTxeJwenZ32xbXmvGtwE2Wxj5RawLzk7ltqPfSWjZVyJLgI/68rKPiagMw+n0dnfaq1Sjo/plODD+Jqf4SvxWIeFbpMxqe5VoEgLxe3hWJAOTNU6sIov2XcmLrSu6OX8Bhzmy3PC2F73YXd3V/P5fAyluK987yT7nOeBnUdL0vNpgGxnZ0ef+cxntLu7uzbfVmwlunnzpr7u675OV69e1dHR0bmWie9VVlcOHGMlqZ0ZJ5OmWvbk5GTcZuR7bFdOiefvfKZyEdm2qj9OlQnfs3SoSdnWtJzIpGxLBVjJ/Al6FvYU+BzHnkKits9y/Z9WRNKFgO3rHNMKpP1/nbXDepw3+1CBRK+P2T/+7ylLgxEtM/ebysHPEsg9wcU+uB3m6+Pj4wm9qfTIz6Zxuu+9vrzddHp6qtu3b+vll1/WF7/4xW6+rQCwo6MjtdZ0584dvfLKKxsh9jprpvqfyzCsfRzQTEYkACQg0bTOdniAyUi5cJFtXtefnuVA5kpXIfvuMrhuyXko9JWApRYmYFAz5zM9Qa7qcBvovjEumQBVKQ7SqmeFZb1ua8a6kocq6zmFlH3PsUu+SloYaHrxT/afdJc0WTJRxcqcrIhNz+oIcI+l68utS/yw/HXAvC6tk3H3+969e3rzzTfXltPeCbR8u6m19qqktyR96Vm35V1M79FFf1/kdNHfdy79W8MwvLe6sRUAJkltsR7s6591O96tdNHfFztd9PfdSVtzGsVFukgX6SI9aboAsIt0kS7Sc5u2CcA+/qwb8C6ni/6+2Omiv+9C2poY2EW6SBfpIj1p2iYL7CJdpIt0kZ4oPXMAa619qLX2K21xgutfedbteSdSa+1/aa19sbX2WVy73Vr78dbav1h+31peb+0pTrDdptRae7m19lOttV9ui1N7/9zy+gvZ59Y/pfh3t9Z+Ztmv/6O1dmV5/ery/+eX97/iWbb/aVNbHKv1C621Ty//P/P+PlMAa4v9lf+jFqe4frWk72itffWzbNM7lP6OpA/Ftb8i6SeGYfgqST+x/C9NT7D9iBYn2D5v6UTSXxiG4aslfaOk716O44vaZ59S/AclfUDSh9ri4IK/LuljwzD8XklvSPrwMv+HJb2xvP6xZb7nMf05SZ/D/2ff39x+825+JH2TpB/D/49K+uizbNM72LevkPRZ/P8VSe9b/n6fpF9Z/v6fJH1Hle95/Uj6ES1OLXnh+yxpX9I/1eLY9C9JurS8PvK2pB+T9E3L35eW+dqzbvsT9vP9WiihD0r6tBaHODzz/j5rF/KpTm99TtOXDatjhf6NpC9b/n6haLB0F75W0s/oBe5zi1OKJf2qpLvDMJwss7BPY3+X9+9JuvPutvhtp/9e0l+S5P1Kd7QF/X3WAPb/yzQsVNMLN/3bWrsu6Ycl/flhGCab2F60Pg9xSrEWpxO/kKm19h9J+uIwDD//rNuS6VkD2Eant74g6QttdQjk+7TQ3NILQoO2eGPVD0v6u8Mw/IPl5Re6z9LklOJvkvRSa80HJLBPY3+X929Keu1dburbSd8s6T9urf26pE9q4Ub+LW1Bf581gP0TSV+1nM24IunbtTjR9UVMn5L0Xcvf36VFnMjXn/gE221KbXGswCckfW4Yhu/DrReyz60+pfhzWgDZty2zZX9Nh2+T9JNLi/S5SMMwfHQYhvcPw/AVWsjoTw7D8J9rG/q7BcHBPyLp/9UihvBfPuv2vEN9+nuSflvSYy1iAx/WIgbwE5L+haR/KOn2Mm/TYib2VyX9kqSvf9btf4r+/gdauIe/qMXpvK8sx/WF7LOkf0fSLyz7+1lJ/9Xy+u+R9LNanEb89yVdXV7fXf7//PL+73nWfXgbff8WSZ/elv5erMS/SBfpIj236Vm7kBfpIl2ki/TU6QLALtJFukjPbboAsIt0kS7Sc5suAOwiXaSL9NymCwC7SBfpIj236QLALtJFukjPbboAsIt0kS7Sc5suAOwiXaSL9Nym/w+p3faMb5H8kAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "vv7huoSrEQNQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "661950f8-5222-4de6-a043-ad091841b444"
      },
      "source": [
        "image.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(362, 439, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "36q1DZlREQNS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0ea6e05-59ed-416c-f8fa-9fc445d4f3cb"
      },
      "source": [
        "# https://www.kaggle.com/ffyyytt/covid-19-normal-classification\n",
        "\n",
        "Data = []\n",
        "Target = []\n",
        "resize = 224\n",
        "\n",
        "for imagePath in tqdm(imagePaths):\n",
        "    label = imagePath.split(os.path.sep)[-2]\n",
        "    image = cv2.imread(imagePath)\n",
        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "    image = cv2.resize(image, (resize, resize)) /255\n",
        "\n",
        "    Data.append(image)\n",
        "    Target.append(label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:03<00:00, 16.68it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "w6TMsB3bEQNW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "56ab0eb7-a442-4225-acc4-8410118210b6"
      },
      "source": [
        "df = pd.DataFrame(Target,columns=['Labels'])\n",
        "sns.countplot(df['Labels'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdbcd1c1898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Gd0w7PT9EQNY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# encode class values as integers\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(Target)\n",
        "encoded_Target = encoder.transform(Target)\n",
        "# convert integers to dummy variables (i.e. one hot encoded)\n",
        "encoded_Target = np_utils.to_categorical(encoded_Target)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "KasoDKr7EQNa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d22cdec9-8d3a-45d8-d2a5-f7dea9d1ee76"
      },
      "source": [
        "encoder.classes_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['images'], dtype='<U6')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Oz6G0ykkEQNc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8be1dbb2-b311-4dd8-a6a1-32231ce66aff"
      },
      "source": [
        "encoded_Target[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "s2CTszK7EQNe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Train Test Split\n",
        "X_train, X_1, y_train, y_1 = train_test_split(np.array(Data), np.array(encoded_Target), test_size=0.2, random_state=42,stratify=encoded_Target)\n",
        "\n",
        "#Train Test Split\n",
        "X_cv, X_test, y_cv, y_test = train_test_split(X_1, y_1, test_size=0.2, random_state=42,stratify=y_1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "TCyKSFOQEQNg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del Data\n",
        "del Target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "MeJVv4xtEQNi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e9e97b46-c825-4926-9d32-3bdd177ad034"
      },
      "source": [
        "X_train.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "4WmT2nLpEQNk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ed163552-1d89-4beb-c31e-211ac4665165"
      },
      "source": [
        "X_cv.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "MMVEy7ntEQNm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "49bb244d-d3f5-4a29-b013-897a4ccfad51"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "jI9Uf4yNEQNo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_width = img_height = 224\n",
        "\n",
        "if K.image_data_format() == 'channels_first':\n",
        "    input_shape = (3, img_width, img_height)\n",
        "    X_train=X_train.reshape(X_train.shape[0],3,img_width,img_height)\n",
        "    X_cv=X_cv.reshape(X_cv.shape[0],3,img_width,img_height)\n",
        "    X_test=X_test.reshape(X_test.shape[0],3,img_width,img_height)\n",
        "    \n",
        "else:\n",
        "    input_shape = (img_width, img_height, 3)\n",
        "    X_train=X_train.reshape(X_train.shape[0],img_width,img_height,3)\n",
        "    X_cv=X_cv.reshape(X_cv.shape[0],img_width,img_height,3)\n",
        "    X_test=X_test.reshape(X_test.shape[0],img_width,img_height,3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "qizaq0Q6EQNq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Function to Plott train and Test loss\n",
        "\n",
        "def plt_dynamic(x,vy,ty,ax,colors=['b']):\n",
        "  ax.plot(x,vy,'b',label='Validation Loss')\n",
        "  ax.plot(x,ty,'r',label='Train Loss')\n",
        "  plt.legend()\n",
        "  plt.grid()\n",
        "  fig.canvas.draw()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "Gg4Y7lGyEQNt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "36f64ce5-74e5-4e89-bd5d-13fba2100c08"
      },
      "source": [
        "X_train[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.45098039, 0.45098039, 0.45098039],\n",
              "        [0.39607843, 0.39607843, 0.39607843],\n",
              "        [0.3372549 , 0.3372549 , 0.3372549 ],\n",
              "        ...,\n",
              "        [0.38431373, 0.38431373, 0.38431373],\n",
              "        [0.35686275, 0.35686275, 0.35686275],\n",
              "        [0.32156863, 0.32156863, 0.32156863]],\n",
              "\n",
              "       [[0.30980392, 0.30980392, 0.30980392],\n",
              "        [0.2745098 , 0.2745098 , 0.2745098 ],\n",
              "        [0.23921569, 0.23921569, 0.23921569],\n",
              "        ...,\n",
              "        [0.35294118, 0.35294118, 0.35294118],\n",
              "        [0.32941176, 0.32941176, 0.32941176],\n",
              "        [0.30588235, 0.30588235, 0.30588235]],\n",
              "\n",
              "       [[0.18431373, 0.18431373, 0.18431373],\n",
              "        [0.16078431, 0.16078431, 0.16078431],\n",
              "        [0.14509804, 0.14509804, 0.14509804],\n",
              "        ...,\n",
              "        [0.32941176, 0.32941176, 0.32941176],\n",
              "        [0.30588235, 0.30588235, 0.30588235],\n",
              "        [0.29019608, 0.29019608, 0.29019608]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[0.61176471, 0.61176471, 0.61176471],\n",
              "        [0.59607843, 0.59607843, 0.59607843],\n",
              "        [0.58823529, 0.58823529, 0.58823529],\n",
              "        ...,\n",
              "        [0.34117647, 0.35686275, 0.35686275],\n",
              "        [0.3372549 , 0.34901961, 0.34901961],\n",
              "        [0.35294118, 0.35294118, 0.36078431]],\n",
              "\n",
              "       [[0.59607843, 0.59607843, 0.59607843],\n",
              "        [0.58823529, 0.58823529, 0.58823529],\n",
              "        [0.58431373, 0.58431373, 0.58431373],\n",
              "        ...,\n",
              "        [0.34901961, 0.36078431, 0.36470588],\n",
              "        [0.34509804, 0.34509804, 0.3372549 ],\n",
              "        [0.35294118, 0.3372549 , 0.32156863]],\n",
              "\n",
              "       [[0.58039216, 0.58039216, 0.58039216],\n",
              "        [0.58039216, 0.58039216, 0.58039216],\n",
              "        [0.58039216, 0.58039216, 0.58039216],\n",
              "        ...,\n",
              "        [0.36078431, 0.36078431, 0.36078431],\n",
              "        [0.35294118, 0.3372549 , 0.3254902 ],\n",
              "        [0.35294118, 0.32156863, 0.29019608]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "TCEK9GopEQNv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Variables defined\n",
        "epoch = 25\n",
        "batch = 32\n",
        "num_classes=3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "x2L4fZl8EQNy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "2d4bb25d-422e-4064-99b6-f3e27bb7af23"
      },
      "source": [
        "#Model Defining\n",
        "model=Sequential()\n",
        "\n",
        "model.add(Conv2D(32,kernel_size=(3,3),\n",
        "                activation='relu',\n",
        "                input_shape=input_shape,\n",
        "                kernel_initializer='he_normal'))  \n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Conv2D(64,kernel_size=(3,3),\n",
        "                activation='relu',\n",
        "                kernel_initializer='he_normal'))\n",
        "model.add(MaxPooling2D(pool_size=(3,3)))\n",
        "model.add(Conv2D(64,kernel_size=(3,3),\n",
        "                activation='relu',\n",
        "                kernel_initializer='he_normal'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512,activation='relu',kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Dense(256,activation='relu',kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(128,activation='relu',kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(64,activation='relu',kernel_initializer='he_normal'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes,activation='softmax',kernel_initializer='glorot_normal'))\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 222, 222, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 111, 111, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 109, 109, 64)      18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 34, 34, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 17, 17, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 18496)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               9470464   \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 3)                 195       \n",
            "=================================================================\n",
            "Total params: 9,699,971\n",
            "Trainable params: 9,699,715\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "gwGs2Uu0EQN0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "outputId": "96a65bee-ff1c-4387-f517-08747e291ac1"
      },
      "source": [
        "#Model Compile\n",
        "model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])\n",
        "\n",
        "# Train\n",
        "#training = model.fit_generator(generator.flow(X_train,y_train, batch_size=batch)\n",
        "                              #,epochs=epoch\n",
        "                               # ,validation_data=[X_cv, y_cv]\n",
        "                                #,steps_per_epoch=10,verbose=1)\n",
        "his=model.fit(X_train,y_train,batch_size=batch,epochs=epoch,verbose=1,validation_data=(X_cv,y_cv))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-1aa8e99a1bcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m                                \u001b[0;31m# ,validation_data=[X_cv, y_cv]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m                                 \u001b[0;31m#,steps_per_epoch=10,verbose=1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mhis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_cv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_cv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1152\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1153\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1154\u001b[0;31m             batch_size=batch_size)\n\u001b[0m\u001b[1;32m   1155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1156\u001b[0m         \u001b[0;31m# Prepare validation data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[1;32m    619\u001b[0m                 \u001b[0mfeed_output_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m                 \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# Don't enforce the batch size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 621\u001b[0;31m                 exception_prefix='target')\n\u001b[0m\u001b[1;32m    622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m             \u001b[0;31m# Generate sample-wise weight values given the `sample_weight` and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[0;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[1;32m    143\u001b[0m                             \u001b[0;34m': expected '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' to have shape '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                             \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' but got array with shape '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m                             str(data_shape))\n\u001b[0m\u001b[1;32m    146\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Error when checking target: expected dense_5 to have shape (3,) but got array with shape (1,)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "YmHtoCgfEQN1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Plotting Train and Validation Loss\n",
        "fig,ax=plt.subplots(1,1)\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Binary Cross Entropy')\n",
        "\n",
        "x=list(range(1,epoch+1))\n",
        "\n",
        "vy=his.history['val_loss']\n",
        "ty=his.history['loss']\n",
        "plt_dynamic(x,vy,ty,ax)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "WHnDwzm0EQN3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Test Accuracy\n",
        "score=model.evaluate(X_test,y_test,verbose=0)\n",
        "print(\"The test accuracy for the model is %f \"%(score[1]*100))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "nU6P5y_eEQN5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred=model.predict(X_test).round()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "fYM25BZIEQN7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder.classes_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "9lXzHxNYEQN9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Plotting Confusion Matrix\n",
        "x=confusion_matrix(y_test.argmax(axis=1),y_pred.argmax(axis=1))\n",
        "Cm_df=pd.DataFrame(x,index=encoder.classes_,columns=encoder.classes_)\n",
        "\n",
        "sns.set(font_scale=1.5,color_codes=True,palette='deep')\n",
        "sns.heatmap(Cm_df,annot=True,annot_kws={'size':16},fmt='d',cmap='YlGnBu')\n",
        "plt.ylabel(\"True Label\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.title('Confusion Matrix')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "QfuWg3YSEQN_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "model.save('my_model_v1.h5') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": true,
        "id": "UJgpGk3dEQOB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}