HW4_CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HW4_CNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/javad-sheikh/CNN_Encoder_Decoder/blob/main/HW4_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fsfZGUp_LzCk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1a78e6a-8437-4a6a-82c7-4f13a688ed87"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "# TensorFlow and tf.keras\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# Helper libraries\n",
        "import numpy as np\n",
        "import os\n",
        "import re\n",
        "import cv2\n",
        "import pickle\n",
        "import glob\n",
        "from scipy.io import loadmat\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.python.keras.models import *\n",
        "from tensorflow.python.keras.layers import *\n",
        "from keras.utils import np_utils\n",
        "from keras.models import model_from_json\n",
        "from sklearn.metrics import log_loss\n",
        "print(tf.__version__)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDs0SXSmMA0s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2f92a17-dc6e-4a1f-a9cb-29a233009032"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "root_path = 'drive/My Drive/ANN/HW4_CNN/'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f93Z4IRzNLRd"
      },
      "source": [
        "os.chdir('/content/drive/MyDrive/ANN/HW4_CNN')  #change dir"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrMQQ-ddPbVH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff61e1f5-6bf1-40ca-a8b1-631897fb0b62"
      },
      "source": [
        "!wget https://s3.eu-central-1.amazonaws.com/avg-kitti/data_semantics.zip #download datasets\n",
        "!tar -xvf BSR_bsds500.tgz #unzip file\n",
        "!unzip data_semantics.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-01-17 13:59:52--  https://s3.eu-central-1.amazonaws.com/avg-kitti/data_semantics.zip\n",
            "Resolving s3.eu-central-1.amazonaws.com (s3.eu-central-1.amazonaws.com)... 52.219.75.223\n",
            "Connecting to s3.eu-central-1.amazonaws.com (s3.eu-central-1.amazonaws.com)|52.219.75.223|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 327699796 (313M) [application/zip]\n",
            "Saving to: ‘data_semantics.zip’\n",
            "\n",
            "data_semantics.zip  100%[===================>] 312.52M  18.8MB/s    in 19s     \n",
            "\n",
            "2021-01-17 14:00:12 (16.3 MB/s) - ‘data_semantics.zip’ saved [327699796/327699796]\n",
            "\n",
            "tar: BSR_bsds500.tgz: Cannot open: No such file or directory\n",
            "tar: Error is not recoverable: exiting now\n",
            "Archive:  data_semantics.zip\n",
            "   creating: testing/\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGnMsTntPhjD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "040df62a-e8b7-4bc9-fad0-5547e630f97a"
      },
      "source": [
        "%cd /content/drive/MyDrive/ANN/HW4_CNN"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/ANN/HW4_CNN\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeZv6iArBlrx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03b2e068-491b-4428-f916-300217287d10"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data_semantics.zip  HW4_CNN.ipynb  README.md  testing  training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMf-YwFeQecZ"
      },
      "source": [
        "path_train = 'training/image_2/'\n",
        "path_ground_train = 'training/semantic_rgb/'\n",
        "path_test = 'testing/image_2/'\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZEmuqAgou4w6"
      },
      "source": [
        "def sort_load_image(folder):\n",
        "  cv_img = []\n",
        "  X = []\n",
        "  print(\"image load|label semantic load\")\n",
        "  for img in glob.glob(folder + \"*.png\"):\n",
        "    cv_img.append(img)\n",
        "  cv_img.sort(key=lambda f: int(re.sub('\\D', '', f)))\n",
        "  dim = (1200, 320)\n",
        "  for image in cv_img:\n",
        "    n = cv2.resize(cv2.imread(image),dim, interpolation = cv2.INTER_AREA)\n",
        "    X.append(n)\n",
        "\n",
        "  return X"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyLLO1vIL0vS"
      },
      "source": [
        "###load groundTruth###"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ywsQfO7SG743",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "936a8673-cece-447e-9011-bbc6e4b65d43"
      },
      "source": [
        "X_train = sort_load_image(path_train)\n",
        "X_train = np.array(X_train) \n",
        "\n",
        "Y_train = sort_load_image(path_ground_train) \n",
        "Y_train = np.array(Y_train)\n",
        "# print(X_train[0].shape)\n",
        "\n",
        "X_test = sort_load_image(path_test)\n",
        "X_test = np.array(X_test) \n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image load|label semantic load\n",
            "image load|label semantic load\n",
            "image load|label semantic load\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEv-B_OJG_Bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "outputId": "397a168f-c5c0-4021-fc96-e26028883b18"
      },
      "source": [
        "print(X_train[0].shape)\n",
        "img_cvt=cv2.cvtColor(X_train[0], cv2.COLOR_BGR2RGB)\n",
        "plt.imshow(img_cvt)\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(320, 1200, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CjmV8pxFoCuswKBCC0PkIfIRNwFkSU2CVItEeOQLfj9BBCZVZnJNIJ9EOPM+nXJ9iI15ie22HxemjuLBKvSSoNRpYIxj1VgiiKebOvIP1mysUWhIpST7chrSDc2PF1uHwVIhVA0oipdGZ5sJLL/KuJ9+LHygsGufgyKEpfvz9j7F5Y5m1nR6WITtrIzw80t2U4eoSu8s7yKhCRSlGXv79MLW3GEFl+iQ2j3ntpS/TbEZI63Frq8/C6WNs3Wiz3vUYej6JdqiRYQKDZxK6OgGhUFMn2Vi/idq5TqNR497J4+QTi9hVxerqRZrTHaJogtcuXmfr1irW5Nx9z1PoPcfNzT6ZmqR1x1100s+xnW/R3z3HvQ/fT394i9bh49zczaip61SnI4phn4urirnGTSY8jwee+Ch3PgovnfsqTz18gvb8KTauX+Wx9/4ETmTY6mGGf/XbTJZ9qp5H//YG987P8+R//lGeW1rmXWfv4dnP3GAw6KIqBcY4/HKIlxe0GlV63RHGasYKucA5C8KNdfh9TXqsiYPNDafm51jZXWPh0Bwkmu14RB7n5ECaCqKqh8oDVACZNGyNEjoTJcqxQBNTRBOMtMULoCwku3lAVIHQ8wmlwI8LRKNOzZdUdcaW9qEUEk1OcOK+0wS+D5nmykqXxTmoeA5rNNnuNpFSpBt7rNZ8gsjRtNO4uMxY7d/HCfyJMqd+7HGQvEFWCTxB63Cbxx98HL/SZetGn8bPKD76yx/h/Ndv8vGvfYkffVIQRSVux5XvPmMVgLMIJ6hPHKMTTn1zH7HvzLWFoohxo11UnHFzVHvTQ/7AEqrfihMOYwqklHiyRH2iTjzYwaYpudWocX4TY0FJiS400uUoIRFAYcfJzrBcIy42CKXFhD5ZClpInMlQyo2NMYuhVEZJD6/qYXyN046w1MIEBc5YnLV4yiJ0QVAq4Uc1nPSYn5zixtYSIimzeOpHcH6AG62Q9XyywW2ynRF2xqB8DxmU8P0KpOMHI5wGp3GuwDjA+WANzhTo2KFFztr2OtqOZZtca27duE4UVCiKBJHX8FUJ6ZVQOkEog1MGbIEvLK2SZD1yyDBE+B5GlNEIpBcRRB5CToH2MDbACkk/Nty1OM2Nqcq+rjeOuISAMKwQTRicAGeGpPYbUhGsry7z+S98iad+/Ckee8/7mZxc5MK5K/zhH/w+SeajnODoybOcf/kFqi6n2Wqx3YsZpdkP0sS+N4RAVnzcYJu5qiNOlkGfZbZTZ1i0CfgC5SBlaqrFjeURcbZJeTAk8HIOtaG9t8X2cEheaEJdx0tS9PpVtnYv0Tx8isXFh7j+9efYHfr0RpJ3Pf5+SpUjfO3683imQkCFDz/+KP2ez3MvPcPLX/scP/euM1xYXeHGrXN86L0fpTMzx+qNq8x0OswcOQnNWabDEpWFKkIpHI473vUA+dISyeaQufk7EZOW3/+D3+P60r9mt7vKo2cnCKbfzemOR0TI7uBFsi48/dIznL7jOL4f0YszBJJQGYyEOE+ZnGqSrWyjrXlD2CgEWOQ4ycz4Pcu1RdXLLAYd9raHJPEOLoxoqzL1po/v55y+9xF49mW2422kJ5CRYpDk5FEVTzSp24Kq1twcJjgClLN4WYiNfDIDOYqar7BaEGuLLDeRKkBFU3jS2y+usKSiitx3lZ7UNGbLlDfAky0i4dGOBEHDY+6ew0zV/tq5C4BcQPlv2gl4DVBOcOqhk1zpzZMrwVp/i87kBLGImZ6tEUWKudMVdF76FonlTQg95h46y5waF3BkuaWf5cjRMlVdsJFNEYcRFS9ktlHlxNTfvLA38rZw7tYWDG8tIbBoaRjs5QSBj6w2cP1tjDFoN9alwGKFRdjxVNAKh3QFuuiydfsiXqkCvsAKifBCsBrl1fFCh81jdDEk3+wTBBVkEKC8FrLsY4ocSYqQPqbQiFIbF/gIZ9FZhgpAeAo/CNHDHCstse6yeeUakYQiywlUjlDgnKVAIlwOSDyrUEKRS4ez40jHugJtBdgRaWGxiWVva4PcFOAcNh5ScdP0+11Wl9fpD64j0xwhBNY6hPORMsTmKUJBIQR5LggdSOGhC4N2husXP4fvyrTnjlKO2pQ9n1KoaASOozNT/Op/8Y8ZxJoss4xScBgS62iEDdasx1a/oNJYQLjx4Fo4yRc+91WCrMIv/Orf486zZ2kdXuDTn/9jzj1/lYoSXDr3Ms4IFu88g6f67HUt8WCA26/neFvixn+KomDl4kusJQXx1i47yaf50Uf/M2z3OUaD63QHGtvfZHe7SznLsJHG9Auu9UNuS8GxckqBpNbxCIWHueMeslevsNKNaKU5Cypg9nSdC9vz5CJhaq7O8FKd0yVF9eQxvn51RCvcY+JQldC0SCeOoPM2RTykujCLDgv6WnPH4VMIFXN4sY1PgFBjDVY4R9n0KZ88w81rI7wJwaf+8A+hd4FH7miwHje49/45vPmj6Gvb2MLD7pS4e1Gyeq7Hmlwm8iN2But4CvLMkiQWlSd4wiMsVzGjIZZifNOsxSqBlA5hHbjx83XW8MUvf5GzRxZJRj2Mi2g1Iox22EGPxCtht5bJbUFY7SBERpymlJwmdIbMWaaaHWxvjVF9hliUUEEJkxYoPYDuOl40vh4rKmibIK3A6ZRk5PYrxAAEE6U6eeaIlEAMQZuYqZMzzB2fp6HGztxq0CWPnaDEBPtVdhKI9o3jm2PZG7Vu2e2xevEWdrFEY/U2n3/mApfTjHvveZiKVDSmYMLKbw4ub2qCFrZijyK9TcckpK5BGrSpVA/hR4rDytvP9o4j9u9WgPb2cO7aoo3e18QsusjBWTwvwBMKK9w4qWolUlqcEBgkgce+1uf2jyPxKJEm25AbhFDIsIbneSAdAkWRjpBkFGkfaSuIapnAL5NlOYGUmCJBj/qUOzVMFmOcj7EDIr81Lv9TUFgFxmDyFJen1JqTtA4d5fLXn6W9cojqkQWQBRiw0mBtiiYYl2zi4SHGCSkMFAku1zi1r6gZgzbgspxsNCTOYp793B9x6MgRFmY745dJO3QxIk9ich2jhCBUPuMJnEQ7h7EJUlhWr7/Oq594mofe/1Hued9ToAROyv3yRCiXI4KyI3ZQtY6dWBAPe1T8kH4/ZmLuLKUpn9SBlA7pB9RrVdI05ZnXX+PU4eMEoU+7WkGhybRAegFFkbC9e5vhrdu0TtxHt7fLTj9jshH9QG3tb+L2nTo4XGaI1y6Qb60SbO/S64/YWjNc8j5FsrdE4lt6mU+gc+6Uln4oGIqQdPIQUXScZv95ttIhaXfAnilTnagil3fpRGW6V7/IngoYIlF7I6bDLpu3VpkpwdlKj1sbknvvyDm+OMlwR7F6a4/QDjh9/yma8ipHyw2ypM/qtZwvv/gcJ06fIvTHFSDEkOUpUmg8r4QWdchzivQKLpulEsfc8+BHOX60gfRnuHx7hVsXXuHJ+9+DLyYZbd3gK1/7OGfvf5zJ6Srz4Z+xJgIGcR+tNdhxeWNvNKTa6qBchWHcRzs3lhktWCEI/bGEihiXZDpVxYkqMrKcbLcp0iFZZsg7E5TjlNHtNayvqFbKFEFAzTRxfopfSHS+w0pi6cnDWBVRVgXNuXnyQUK8VSDCEqrdwfMi8jijKBJwgjwv8JRFOQNOgpW4fAhmBKZGdyWFygxh0EONtZaxIuIceeGIi29xiePYbN86LMLJ/6BU0msEvPO+kwR3zTFzz1NEszH31TJmJjqEEoQSVOF7qhITEuplkJUFfAWRELQARPC3su23hXMXQhLUGuhsiDOAcOh8iLUlkGMZIfQV1hosHqEUWCHAl1hh0YXFJ6AxOYsfjXXrbLeHTQeYNCZs1rDOYYWP8msIr4SNu0ghUZ5Ax9voJAYZIgOPokhIt1apHLoTEYb4QqGkh3UKiUTnMcYKKvUO86fvpdjZo9qe48QjjyISD+ss0gDSYS0Ya/GtBDyMG46TIi7AOEuuPZw1GG2QaJzWWG2JhIdwFqTm9vIqLsmxHCUeJShtiEc9blx+jRNzi3SHfaQocLZAOoeQEkGKE5JmvUoQKLJ0hCw0WkiMLjBFPq5w2A9JpBAUPmx3d0n7m/h3PMjK9jYJMFU7yk4v5Va3i68TtBIYm3Hl1hZNv0PnyASlUkTJkwghEFajwpDeTozOczZXr/LClyK2Pvw+Jurhty0J+0FS5JDtreOV25i9FK0rdBaP4YY9Ruc3WLr0KrbRxFmNGYy4uVWnZCxesUVzYob6xAnKM8e48dmv0Atq9EpQ7W5Qs3vc9zM/Srg3wRc3tpAiZnOYki9dxfbHhQPbhwJ6q9dxg02u/b+XWHngVd79+E+jRztUqj568+u8/vJLeNUprj//7zj3+haPPvkBKpWAcWgpiI3htWf+lCJdZ/rwo2y89iqm7pOP1mn2TvDI+55kbZjSOXMIZRzXrj1LmWXmzizilxcZ3ZogevGLLLTq9IsK08ceI1v+Payx7Dd8gACjLf3dbbAScMj9unoxbvXAubEEKfd7TJwX4tcUdV0Gz2EiifAddS8jtJJ6DVhNiG2OJ0uEUlOK6mgcQa3NRL1DNCoYZAUTakjUvcjOxohCevjtGZSnAEGeJIjIAwtFMcL1LiOzTfDnIVIcunMOqiUQ0LyrxfxgkSRdeqNUIqEahETuWzT3fZ9ajCAJJHWfNyAAShLZnKbaOUmr4tM5EuJUDYEEZ3BIcivx1LgG/80QUlAK3rp3423h3P0oQHrgYvCER+CHyFKDIs+wNkOocaMIgELgZAkpDc7maKPxtMYKQzzaJNB1RCXAhT5SNVBWIkQEJiFQkCYDlJY4GZHlKfn2bYQxSCEplEdIh9LkMZTzkKG/r2OOm6IKa1G+hzYxojAoqpSbU4j6BAhQPuxtXCWXMeV6Gz8sYR3k1uEXDmEs0oIx+wljC9oVaK3JhWM3HmDEOLJOdUaUJSgBYVAlF2Wy4S4mHzHKwAyG1CZbvH7+HJuqz2hnd5xAFgJPBmAM0gl85RM6hxYhhcnJcosxOb0847WV63SXNzh919145SpYWL1+hc0LL7L6+vPIwMMLqtx9/wd56NQv0Kk3GPUGZMZQmIJQKrZzRVAoND6eUBipUMKhPIdzQwpnsFmKWV/iX//+n/CP/vFPszDZ4BvT2++Xm//WQoLveA4HWWqxRYGLOigl6UqJnDtDOugTdKbxmje5eWGTSa3oZgmNyRmOtQWDoWB3c4f1ZJe5dp/m8hXUkaMca89weXWP/PqLxPVpLt4y3NXYImy2sf0eNWFY21phavIwbmeTzZf/mO2bOyhlMJUVvvqVS1x66Rn07jpi7jE6pQ7L6xs8eHdI7t/PdrrOQkezdfUiIwwLx46ThWXK5ROIVoX5qROUDt+mawNefvnLFIv3sPHKZ1kPW2RuCTYyZH6T9dsFqW0SSEvULlGqTnB5c5l7nvhxTNSjEoWUfIH0NL1+BlIS+CF5no8TqALA7AcSEofFOjHuFxTjux7vbbO+DlU/IBVlykITpQX9nZx6fYrbWZ2d3Q28ckJYkgwLi93cAQ/KtTmkyplo+GjTpHB1irxHNl1Dpts4k+OkQnolarMV0iwnj2Oq7TadegMZNsbPXoAw+8l8IZDKUY8sgSujxF9biQTqYZXI995oNA78isDn28sgpfkp3jP7QaQfoPMRbv1lkuoplvMG88Ea7bCJC+t/Kxv+T+Vt4dyNNsiggpQZgayzcPgsquyz21+nu30bRn2UG4tgzlrIezgZgKcQxbj8ygmfLDUoHH5JoJMeIs1xKkAof9zRl40QUo2714SHK1KcBU9JMAZnfLQX40mwvo/X8/BqTfBLgBl3jvoCa3KsLhBFPjYeX45LwwrL7fMvkvS/AEGZ1vQ883c+RKU9hyXHGo1zCgQ4nY9nAVZTpI6g5FOPQurhuBRRu4Lh3jpOQLnkUDoiTyVZOsJohTaG0bDH+bUvcv32PPONDKM1WTxgZ2eD4XYDjCOxAUaEaKsonKKIhxS6IEkLkp2EP/jf/jl3v/MJfuE3fgWdSqqVKo12m/7eNtsbm/T3rrBy9QqBN+KX/8tfh8kWk+06pfYkolInEB5hIFBhRIFFqgoLs0fpD9fp7u4ioxBtC/JswNe+9lmWNq7z87/0EQ7PTwq8wJAAACAASURBVHPHzPQ3exveUhxgHVuJZaIi33Sm4CPQkUcaGxJr6VRr5CvLyHKNIlmnajKOHq5x/+NPcenGJt3ueeoLCzS3UhqlBc5dukpx+RXaMyVavYxSKtmKt5iadDTagvL6V1heq1NRivWBJbcDKipnZ6g4dHSGtZde5/iUTz2scn5vQMcIivUVrmx2yeMc264hwognfvSnSEWA6G1RDBydQ9Ok68v0sh5lnXP0rjm0m0CpiGz+FIIetakzrOzdYmld8dLNz3NksUFvT1CNu1wbZHzlU3/KE48/RJZYiuQG587dom/XwG4xOVEhzyW9dIh1gslmC70fyadpMr7J+4l4h/vmQO32HaZzDqkkjYl5fOchXcxg6Dhy5BhJtkXmlRHHZ5jMFVIlSBmQBQFrF17Cm+iQ9Q1WpwSBJleCXJWx/jTa5QgvQlmDX4rwowgjfSIboU1CliWI4QgrJEoIsBZX9GCspIOAKFBUM8e4OHkcjgsBpVZAKfgbTvxNAxCB1QqZrZKMOtzODMdrR2iUypTqHr46ND7f/nH+rnlbOHdrDEobCBRF1mNr9SJRvYEMBLbIILMUzuBQ+23GEmMtnhNIqcitQhJSrs1RqZVRJciSMs5v4HSMzgfo1BB5Ph4FmZZ4gUep3MQZgdfqkPQ2CYXCYkiHA0r1JhQai0G6gsIJLKCUj9EJhS2QWJwVuGKsyoVBiZmj95Bur7C5vcb1c+cplWYp1zvgxvKMkwYhxnXC2kqsHr8YSvrMLxwhnn6dNB5Q5DnagDYKV2hazRmG2W12d3fxVIhXbeFyTZ7EaHqs5JqBMZhBl9rOFheu+Ey88AI7q3s4J/DxwQaEpXH3rxBQb3ZoHjpJby8dDzjSMdXpYJ3B1wUlKdBRmXZ9hqZqoI2HCgKmTz1Ef69HW7RZ3VHU+1CqTNKZPcpolPPg449za/0Wr3/lWZJkl9FohBI+EzOz3Hz1Wf73/+k6977vKT78kZ+iUykzVxWU5FsfxoscXJnveFwnQEbgWcdet8fa5ctMh12WtSS5+lfkgcXzS4RKc/P8X+JPzXFMdugZRX/U49rGMrdzR2kwwJQkeyalZq/SLiR5qtkwCVPThynQCF8wiEoclj7Or7Kj21x45QqVrODyTk5faYSQ+JR453ydje0BexvLnLu5zNl3vouTDz7CF77wcSaOVFG6hNi0YKb4zMc+z2zjGGq0ivElx+8+RbVaYaYzy/STJfzKFM997bcw0qMWHCYpdVkjp727xyf+8I+4cOk57jnqs5v1WLqWMNr6OtoqatUK62sZ3Z0MKRXlUpnhoIfvS4r8r5f4MNbhsEgpmZxqkY8yBmmOAJRUdNodAkIq5QgVSaqH38n5r/45urtHqAuKYY9Sp4xXbiCFT6XRpjI5Te61CJoTqKBEJAQSD4dF5xmVMKFOwEqeIIRACYt2BiEFYVBG12pkQuHjcIxn9eJbU/lRSKTVG2USKZifLfBVH6clQlVA+N/ZeL5hY56CYJaqVJwSAmggBESOsWYffNdDfG84hzUOYw2ev78Mw/8fEqp+OSJNY0SRYp1CFA4RhMSDbQLrYyoKkxuUklhbIJD4MkJLByJDiRxpM0wxwvr1/bVgfKzLIdcI6VOuNEEpnC3jO4EpEvLcjNeHKRRRaxEPjdUjXG+bPE4gXUFhEVUfkhzKDWRHYZ3G5gbhMqwYDzjKCoTyx2vO7CcnB0M31sCNGxuKy8E4hPAQYlw6Bg6tQQWKUnuG1twiNhmidY7OcvZGMUePPcETv/CLfPaT/4xKpYJQIVnSI88E0oAnwDiHzh1JlrPe3eXajUvcXLrA9OwEFoO2IwwFwgmwjjTJyXqCzc1VylNNeiPICpiYmqA1d5ib62scmT/J3Y9+gB9973s4cd8xBtZja3uH1GWsr1xlUCtzx8IZlF+n0p7m8LHjDHoDLpz/OotH7uQDP/MRrlw9zwvPPI11GTvbtwjLZWxjki/+8R+w213h/R/8JUZzE9w97cH+ALofeJM6KAvGfQF8U/r97ohxImui9d0L0JwQ5Kkm3UmJR6v0KtO0Wh7zx38WN0oY7V5kTXforr5IeyunO9iiv3KTNJG0m7O0dldQNUnp8IN0V3e4snaTJO9SKXk8evcRhkOFP9S4cpnFu96Du/UlTrQ0i3aHF/qOq4nG9yXlIGQ0zMnLjulOmZPTLfrNFhc/90mOPvIQw9XzFINl3v2zP89U5yxOeczHhneahykLcGKaQWxo+lXW3Yj+1TVMnpEtv4Db20ZurmJOLXKi02L5tfOYqmaqbRDXLxE272Gi3OG22OMrr90mNpVxldl+1YhxmuX1FWrlCk6CH4boIh/3RQg71tql5NjcFOubPYb57jiK90KiapVG3uPJJz7MWjxE2BotOQ1hzPDqFWQg8HwFVhMqy6Fjx4itxzA3iCxBOk0lrLK9s04a71JkMXcfa1FSAW5rOC7L1A6jh+TxLtYZ1uJpcref+RSS1G8QivGCCQiQnsI3BcaOu2z3px9kI4GVHiXh41Dst5+8qZ2pQPCG2nj4a03wb5cH/fYY6H/9Va6nyxx/+H3UlMN4/pvu8rZw7jbPEVZTCIkvPHSk6PfWsXmK0ylOjJOEUobIKMIWBqUU0hfo2KCsQXiObLRLsZES+AphMiQVbGBxeUqWjRew8oMSCINSHrIcEoZ1hFJI6XAiwAmL35ikKCxOeRhS3DBBZJbN9Yv4to6loBgOSCoKX0WI0MdKQVbssXfzRfqxQBd9BAmFzhEOBB5C5hhjsFqPZ7XOYIoUmzmUEURK0NvdxpOCUqVGqVlHpCOqrTblVoXFw8fYnmmQjjLiIsUahyUi0AnJIENbS+AcWW+DfK/Hbi6YbddwQmGynCJL8Z0G5ePw0FbinEK5gsh3aCUJVcDk7Cy7rQXufeAhjr7nbjLZpxASnTnWli7y4h//Xwz6A1575jOcn7uD5m/+V8SFABMTRZJrr78Ow106R08xPbdAqdIgTYZkuz28egNrLc3mJK9++SW6lzZ4/Gd+icUP3kU9VGwUgoGBXEM3haoPUsFUCDP/kS/Lm72Yzlr0fm+FX/JRE1XC7G6OnZ2jFFXobW7w8rln2N3eYfqeBSaTQ2yt7pHu9cjiHNeZo2h0OBZ36Y22GepNqrU9GqU7Wbv4HK3pOqsb2ywkOxx74AmCmXuQsebl7nPo2gKXLp5jdzjiRDmilxXkgeLBx99NfusqN1+6ze3CkAxuYd029079fa7cWKZRn6FkZhllA6q1Fs5THD45T9jTiKnqeHC0luZOn831NUbhiGSQUlo4QTvTLE6fZGfpIrtblo1km0ffNcvC5AKnHn2UxeEJLi//EZX8JstpRq5hqFMcDmctxgjy3KCEQzr5jbX4xj0cwkNYx8uXlrDGjZucFKBTRLLB3Q88xsM/8iOkJiG+tkx38TDnb21QtREb3SV03ifyK+SeRIVlPCe59dKLbA42KbciPvz3f5WnP/Zpdvo36cy3mWi+l51ilzwv0AY8odBpghyHBZgsRBq373Mlmiah248MBFQPHeLkoTl86VMUDmdGBBqiWoVKqQEKit64ll2o7yl78x9iGPfni/2KrP0+kb+1BOkcFa/g9L0P4BvF8rNPMzxz95vu8rZw7lhBVG+gu7toaxFSkwz3cOlY00YKlDUURYbQEkQJS4RSHl4QUhiDsw4/9LHOYOIRxgmkcIhSDaQjT+Jx7WsR40sJpQ7WpOTDDEeOcx5BtYMLPZRXwYkUXeTIDIzRWHyiXJDtdEmyHa5f+isqvVkq1Tb1yQWicpvcGuK9DZau3waj8aI6cdwjH+0SNaoIN17BReuUJN3DjGIKb8TtGy8T1U7gq4hGtUN/a50by1fozB6ntjBNFI3XkKlOHWL22FGS/oA0TRmNEkrhMZ78ez/Hv//L/5vV7i7GJOTdFJ0MUOU6rdYU7lCMHq2R7m7BRAXjDJmGmhdSqk4jPIvGIYygUAqvWsJvTCOCkKWr5/j00y/y3/7TY7iwTbM2QbVeIR302NxYY29th7/4vSalI4ssr6+TZwXdYUJ3Z5PN3V1mj90DgYcdFbz7HffgtRtcuHQDXasy6A0ZFCP+ze/8r2jxK/zDDz3OrO8x+42ApPpmRvOfgHNoU7C8M6K7eg5CzcZaRBbvcGu3zMlZuLl3E1t4nDn7Ixw+dRpxxwJ7WnH9yxVuf/kz7N64Ra28SkMbJpTH5QsXmagJFk7NMVguM9doMjHbJkyrvPLiVTq1Jfr9bda3NmjpgtEopR3UqdiEqbmI7VSxsrJMXfpMzVXJuxEvXb3NbCvklc/9JZ8qMn7zN/4hF17+C+ZnjnH02F2EUZOgVEJgcRRY4SGl48bNS+zYG4iuR1llPPSeM9w0K5jOJO84ephR4vOXX/33yM672Gse43f/4ks8cH+bI7Mhq4vHUNfXGQy69Afx/iqj4ClBFEmqlQa7vQHSKJQzeEhyB1IKCq2xzuJLH4XCE471tS2UKOiurnDr9cuUSYjcCAnUahOkty+wkyaMyoJS5ONrUBR4LsHGXVy1jN+oI2WAJwTCDzA2QmeDcdBU5FglcU7jdI7OY9JBF0HKN0JnlTmk5ZsLnGcWMuMRFD0K0cD5IUEkqO1P9ATgt95gMPzHOHaH41tLcWwuWFuCqeMQvFmw/Y1x5Ducyp+cwK91yPdi1OFFjjebb3odbwvn7hw4VyMoCSw90kEfX0Z4tRK2KNACbDbEOYswoHyHEY5k0EO6AlyOtAonPPxKHZcO0NkIR4ZNHCIoE0ThuDSp6GONw8v6IBXagi4KrMkp8gTfCzFBGeFLjHEk2QhhJdIriBOHEh5pmrJ8+yqV9WU6h+6iFk7gSglxnDHKCoxzZGlORWny0Yist0rWqKM8hbMSZzX5aIdkdR1pHc/8yRKvf7ZFc+EsE4fuIaq3Se0lrPDxgzJSjpfwRYUMBykIn2qrRXXGp6wOc+Suezm1chK2lsiyjKzQuGabic4ck4eOM3vvwyR7mwz2VhH+IbCSQudEnsXhYa2m0JbCODwkKoj+P+beM8jS7Lzv+53wppv7du6JuxM3R+wusAABkwJIwUgSWVKpZFmyZdJUsMrfLFe5bJc/sewPLlmWZcqmRZOSbZoSLQIERZAAuMjABuzs7uyEndQ9nfPNbzrBH97exZLYAEKyC2dqqqffvnX7Tt/Tz/uc//MPIAJKH3Bq8R7WuyvVEBnozi4i4gbKOO4//ziPfuBZPvSZT/HFL/0+40lOb2eL3mhCa6SpRxH7d9dp1FqoImNmdo4nP/kpHnnqgOe/9mWywQ1kFDOcbHF3ZUh/4kj+fyIWKCE5OdOmI8+zdWeN7Ob3iBLFtRfuMHnoGez+DaYWTrH04AWsKajHTczyMnVZcOHMOdabK9RtQLGzxnIGq/s9Xk1mWNi/TjPusLKyjZcRt/czksO7jFuSOz3Ffq8kEn26dU9dNchUiC6GOFLagxVuFR1mIk8yV0Ot1xiYhMO7q0wvzJHkQxZVDTNus9LX1LZXESpnIWjRo2Tz7m0GG1f48le/wrA9QxmGHN/to6cdN2/sc3F+jfOPnOfCPSd57tVpPvbpT/DS80NW1jzPfMLy7DNneebiGf7T/+6fsz7Z4C1ZsgdroVGPiSQkSUSRpyS1gPsXp3n97j6FtRXTy1c2wLESaKnp7Rzwygvf5KGnnubcAz+Fvf09+r5B2TpDt3MPBwu7dKM6RZHhXE5pMoSWoBO0Au09ozzEywChNY6QtPCYPANpiXSAKD1FYfFSoXWIDCRS/KAg11TtT3TM9UBSSyRSTFGHN9WRf2KJd/3sHTr5P+3xUlp6e6sIPaY9ex8yVCycBvUjyVTfZSkBs9MgNUGrxrHumaPj0buvn4jiHtXqnL1wkfFol+HWJrnR1OptRK1k9+4dlLc4FeBKg5ICXE4gPEKWeKmrAp1brDMkKsTGEmlU5Z9uchgbvJLouA5BA5dnOBTeWqwFLxVKgpQa60pEMcQ6WbEV5NHE3SjSbIA8qPjJ1mcEjVkCW9Irthlfu8p4sM1Br4e3toJrRAzeMXaKcDIhigOcswhvj6yFDd47XJ4z2t0lCLZp37dA4KYQSQxaIwWMeysM+gdEYYel4/exvnyN9TtrLJ27QK1RR8UBswvH6d97Dp+lpOmYKL7AZ//232F/3KcsHLVmyOrll9CHLZQHl5U0E0lSk1hf4JwFT+VVEyUopbAipCzGeCkx1lb+5EGNpLtIq7nDX/17f5fTD54jbMwQqZxsMqJ0Bc6VbO0NmO/USeIJSXOOqfkue4NtVq9e4r5n/x26czPU6l/gxcuvMTUzjdIxxvzbjXx89yWQsnovZOjJ4jHWbDF94meYk56r1w8Yrixj4j6qZTl74l72r7xBVgzYGa1ghxts7I1oGEOjLTjXmeXACNbW96j5iNlORENIwjzjtNhnxRdcWcnIbEE3bnJnd4z2FqU8RaPJOe1QssmxTh0XCV58eZ3zxzpESvDYYw/x4vdf4KmHn2X65BMkRlHXCanqs7/zIqONu4jFeW5sGmS2y344oNmdZY4G2+mYBy7O841Xr2O85MxMQdzsEB+fZcdq+pNDLj5xP4X9DIvdKaQ5IDjRRif1Sm8iZWX16EHqgKTeQWvFrE6hzJnkKbcPD5htSXb7jomDei1CeoHuRnzm5z9N/40VZC1h/eqA76/+a+LmkPnpLoeJYmHWsbWtyesNYhsyHo/QmcPJAFWbIel6ajNdRllA68Rj1MUFatMtTJFhhgMybwmTHGHAmgJcBdF6O0CKH3gZCeWO8JBqCCzCP0uV/UFh90A56bFZFJxsz70rxOJ3hxxevsKNw7ucejjn/Pnz6Pi9fWD+1Ld6568lzaoRdkF1UivG7/l0PxHFXemAZPos9ZkZVBRzsLeDcxnlJMN4g8/LinYFSOEQHnye4i14HyCMQ4kAFbfJvcNmhrJMCZB4GWG9AxkiyhKX9hEyQEiDKWwVwmENJSBVgBGimrIXUJQOJQQWh7NFtUGkrSiMpmTk9rl55xLJ9i1G4wOkEPhsQhBo0Io4adCaPU17/jhJq411FpP2wQmE03hXhSgIDwQaqUO8kEhZQ6uwOllQcOeNb3L4j64xc+Is7dlTzLicwaREqJBCGC69+G227qywtrJKvdakPXucemOR6eNTTFZzpDXklJQuZXi4DuSYImeqM8OHnzzLcOPgSFjlmQwdtfZFjt1fozY3R6PTZensGK1jvAQpHVG9helM0Rusc+dSyb0f/iiBy+lvrGM9hFi0kOggwZmMshjhgoDeRHCwu8UbVy5xfO44H//Lf5kLD13gW1/9Y1be+C7DybP4qejfPjXynZaounc/hK3Lb+Ca99A+foKk2yW/u8wLrxxw4vgp/virr7J37z7bt18myCYMewc4m9FWKQ5JLWiQRxGRCHi806Grxpx2I2wr5FQ0oVAOOwhpNCIOB4dc2jpgsV4jjSLm5mKmVcxmb8TTn/5pzp25wKVXX6HXj0lNSdwoqdVHLHZDPviJJ5mZFuwPErrzHaI8ZPdGF7pTLN9aZy8bMV5/DdOqszB1mhP3Qvq9NV6+fYicSSh8xle++EW+8uWv88Fn/ion5hZw7XkSH3J6rks7jljvHdLsTlGPJF6KygXSaoyvsPaycASxRzfauP4Y73LGI0vciAm1xASGpVMRsqd4/MMf4pf+yi/yz/77X+Xcgws88bM/hVm+zX6+zosvbWAHljAaUGQTdscFWggauqA53WS7XzJ78hi1ch7pJFuvPsdst8TajHK4R7+/jcuziuxQxAgR4K1HB0endydQ9q23GdgH2kDyb7RlvIe0f8jNw4IT7TnEO8HxHoS23PPQRY7Fz7B8ZRWs/OFK+6Y9guBtQLx/0wWDCkVyCO/x3pMWJf2tVXpbt7h294A39tYY72+85+v9iSjuldHQNqPRACVjOu1FRoMR090FJs4w3txGeEkkFEEQYEWAtSXC5UitKoaN0yhfAxmBShHKUeQThMoxXiBsifAeKSTeWwqnUErjVFwRWbzBZCkIT+oEGo8S4IXC2coWCalABWipKuaGhHw8ROKIooR6rcGwt4UpPXiHcQNMdsBkuANFTtxo4PFYQEiH81UClJICpStAUAQKbSRKSpxzaCGR5KR7W/RImJ69FxnWECrCC8nB1nWUGSGCynRta30THTWJG4Y37uyyvnybjpYQCWxeMhofkhYDTDpGxzU+8um/QDYp8WGIyyGdFOxt79LfvQllju620VPzXLm5x4lzU0QKgkhXpmRBZeLmnWduaY5WErLfG5IkXR547CM89MA5ykDwh1/8PQ5v7/GGzbj56jX+3Gcts+0u+7e3Offok5w4cz9vXLqCPyjg2J8N3/yx1ptPLyEKRly8+DD9xXuYiiOENuRrr5KFQF3TCE4gJj3qKkfqHuO64ZT39IMao8LDOGMhHaGaET0O6XZaJBJu9PZIJx1ORYYyley4iLmTFwm5iS4MdQX13Zy7k11OPnCBmeM1xutXeeSpZzmMLpKtfZ8XXnmZpB/QmTnG+kvPY+/cg3Rr8NHPsBQIFi/cz6trm9w7t8D673+RG7sTPnqqS8gEl57i0Yc/gR6N+eI3v0p20GO3JRB2yB9//TfZX9mk9/rXkKeeJUhK7CTl8su3mD4esb2zi0LQDhUHpUBrTaPRIElCnvzgY8SyxR/+7u8TKAXlGOMcLvBQetZupsy0QmKZ8dKXfotssMGXvnqHb722QVsccurYEt+/NcSpmPHBmCIrKAbbyDAk04ZuK6BlMq5ffwUvDFDpWJyVWFH9TtRDQT3RBFFEvdnEiwDjPIESIDRTKqgw9jfXZAzN1juGinpfpWYJqaqTynswspzzFOMCM3jvzl80IrBdQlqc/0DrnYF2Z2FvCDOtCq/xYFPH2svf5fryJVZHPZqyIDOGdJKyvT1ibWWHYTnAorFWoN6nev9EFPcK1yvJ93ao19t4Z2m16jhZReGpoI73FXwgnCSqt5ChJk3HeGvRQlOkBQJF0pzC1kJGh9vkRYbLy6obJsOHGqejSnJvLcZYPGOQAuctyjrQEiEq4yElJKX3OBzWWoRz+LLAyYAgDLDCoZQkLxRhpBkPJnjrqsGvg9I61lcusbV3mVZ9js7CMaJ6gxIJwh9Jtz15mWO9w+U9tm98i+EoZzQYkDQiBAEWKiaAMhwcrJNOCoQrETj6+5v0V2/QXbqPZqfLKM8QcR2lAjpTbbZXdrl+6QqdhXvZ3t3HDMbkrmB0sEuaObyQ+CDCFuCKCaPBJuvLl3GDIXPH6hSHPQb9HbbKGtMLKaKm0DrCiohSBIhsTDYqaXdnsXGN40tzPPXxT+PTfcKGZG7hOFKWTMYjfJEzqLXR9SWsE5ii5OqNOzz54H189Gc/xLFuNcXy3v9/a1Hgj26xTiKlpX78HuKwCT5j69J3eP7b32AYTjPoZTw412a0fJf+1hbbkxFITzDTJKrBam+XmaJk2gkyrbmTapiLGe336Q369EXOqBUydepJHlOGrZVV9vOCE+enaXVnCScF/Q3Pi9fXGP6zLzM11eTE5oRJb0C9t87sVIOvXtqDKcF/+OCjzAXHWb46YLi5xfXoBDOy4OaNm0RzdVrTNaaHS1y5tM/UxVnuaRSMyozHP/Fz/Eyk+c6tb7F6Z4cT8zUmowgjG7z8R99HLWzz7IceIVIL5KLJl1+7ynaRYbxjkhVorWiEEqkEnekWHz/3CL/xu/+KT3/sEV56+Q1eXRkyLMvKl8lUEvrG9BRPf+xzXGye4vmXlmnXIctitnsDgumTFCKi1qzTbMxCNKQ9G5BEMaZMSfsT9na2iGsJYVQDqbFS4pGUQuCMJzbbhEJghabAgzEUhSG3BpxlMjSUVKpSPOSjAWJ24R391LGOw9UDom7jCHoK37W628KRH/YRueJI9fLDDxIVBRQd4fE4n+HTPjpuIuTbX4GHRlTdUI5WcWj58j/9Or997Q8ow5Q4iCmNRSqQXiPCiJm5OVwecbC3ixPv7bT6E1HcjSlI05yoXsdFCaIICVtTTCbbKBmANyhR+aCYfILxjiiOkAJKDFKAEI7CDHB9Sz4+xKQjsObIa9pgjsy6IH8LX1ZC86aVlxQCrx3eVTmhRiqsq1jXzphKGUtlouFsWR2djEFrjQLKUUitO0+WZog0wxqL85LDwz3MTsG+Wkdcf5U4jGm0mohaHWers6N1qjJdKgu2lpdZu7pMq3sMfyLk7kqP4e4eSRAj4j2Wb32fsjchmTlVyeaFQwUwGN/FOY8SCiECdjde4tUvQh5aXvyDzzO1dJGoFRClBUZKZFqQ5R6tDNZplHFYD6N+j43Xv0vaG7Kz8hrdbh0n68RPfpKseIDxaECjfoyZs5pSH2ezt0b6re9w7PQCxy48yn3zJ1i4/yzXntvgWD3h0je+TIjHZwWhFqSTCbvDgq1UENWmGZYltsh57dprNJ96El/m7E48SzNthA7REqKjU03hPFqKytPkx6aUVafh7f6IJM0J5AytWsykzBgNNujOniBzGplNiJLj6M4C6wd/xE6e0phqUTMSN065dHOX1OWshyF7GxNOduLKEylLsZ2AdF8z1YAy96h6RERKLraoIk8LljLL6sTw7IJkZ6lDaXL6g5A3bt3g9dtDFtpQ5o7t3ogZPK9/919y4ud+EVHs4vYyLt53nvXVdWrRPKPhHmfPHOfCRz+OzUue+/JXeO3Sq3SaU1wdfYFg/BqTtX1MmXM4NDRMzqDc59pdT/1gTCL2+MiHf4G5eMK3b75Ilg3pzEyzv7GN8yW5FWhjCHSXvatXWVvdoBt4xnmlsk5zg0EilSYMJJ/7mWf5+b/4C2TL25yfm+KWnufDT36Sa88N6MsWoTikVgjSwQHjwpKajCyrFNatmTrHFxbY9SlOxUdCWIMUAkX1+yJkl6IsGPX7uOGkyndwlkBqlBCM8yblm0NS59naOSQ+5Vk4ev/fWgJA4llrjQAAIABJREFU0pmZwxVDDvZykjPdd9060kBehMThLhylzFrrGaWDqkETEmM06fot7izfZnV5nYP9azS7gr/2N/5jmlOP/eDJVAD1t3X0AkLtaUeaJIoJOwmxDZlkE8KaRAcNFp/8AH/zZz/NznX4/L/6Kqvj599zq/9EFHdMQXGwhRcxscoxSiG8JKl3qTX7KKtxxuN8iplUtrylNThTAgrnJKGy4CZgKo67shYlA0QUYkyBNx53FOosvMRRRdghg0qpKGXlfucEnhLrFEqFgEd4iZSyooUd3dalqwKQy6JEG4MOFGWewpESzgmJMcWRO6XHWoUIE4q8oH/QQ2cpgVcIqSitYTR2aJ3hvKyGrC7ncG+Vw1eWEc4hdUCWTQiCHO0MtswZDPrsHu6RD7cIoxpxLcGaBs6W5NmY17/2hyxdfIhGWJmZjXb3yJ1HakcYwnh8yHe+9DssLZ3nwsMfpPQQKc1MO2F33KeYHPDi1Vdx1hNMJE986CNYDM5m9Pu7rN9axbsYO9sC6THU0Z05fBAShgGhChlnmrMPPczB1iZKRFx84AkGgz2e/94LnDpzkbChKa3hMDX8xm//Hps3rzPwAUbX0LUui7NTfPihE0y1O3z7yi3umZ7mmacepFOvuqAfp8N3Bhphm0E5JjQW7UYMe+vsbt5k88rrjNI+wXSHzc2rjPe/yp39HfKBZC4c0R9IpqdD7r+3w6Hs8vIrq8ycjZiNPeenI/Ymjn5mCYKIzfGIx+djzOqrHOSWWlFjqavRQQdtCuaDA7ZywaEvOXb6IlM5yNkmhVlh7/pljBnxgdMdelmdZt4l6+1j4waL8+cIupLF2Uf5zAVL7+o3uLayRyIzpuoN6gtzUIxYX7vDwcEOOhtw/sx9PLa0wB/83v/Dnb0NpjodPvqZnybIalx++Y/4v9MvsvLNl7m8uodUIXmREsUCKZs4U3JsepZQDnl+yzAwiq9euon1JaUtEV5iqdSTSSA4du4ERX5IWUsZyYDXXnuDnQPDeJzSakYkyjIVSHoTwSTzpPmIRCvarZhQStJIM10PKExZ0YcFKC3QKiAKAoSSjAdDsCmlNSgRgNcI4cBbVAjlUdaDc7B/aJku3vQB+JNLKIGqg4yazE3Bu9IePWgFJqyR+B8oV69/8wr/8Dd+lYNimyRQZLkhHw8o85xmq4k3gu5SHZe9P6FSekv1RxA2OiQkiCQgiEOEjOkunePs/Q9wsQnf+/qI5eHV99znPxHFvcxztpdvMSkc0gvarQXCMxE+MMhyCK5EEtCcOknZdZjSoH11tzZFSRhOIyJPnmZIHYKuUch9EJrI54hc4URR4eYCjHNVKAcW7wyEGiElXgQoNMLJt3zXpfCoMKg6PmtQOsRaA0qihccaiy0MzjqUNiAkSoL1byoWZDUElpW/u9QaGYRYI7BljpACjcIDu4dbpFmfEs9w3Mdno4qa6Txaa4o8pcwtER5pPKbI8dZRTEqK0YAizxFiiCvPwJE0vCgMufPUjzj21hnK3DDJB+RpzkvPfYVvT77G3/svziHa00gdIrREOI+ZjNBCEdTqnDzzCCqCwc4ed66+xGhvgw2X0is1Sj7E9PQSZZFyuLdMvBMiEDSm5qi3p1i8d4mrLzzPsx/5LCfPz7C6tsHe7gHr+ZCpRpeDxRny1NI/6HPvuQ9w685tXn7tZe6/9yKt6Sb/8//yW9RkwNLiLNl0m/28x7Ru8dSjF2jPtFB/xk5eaoEq0soGtlZj5ZVXuPPql1je6JON9rETCxPBQH6FK/0cYwtOzE8xyQ1LjZggchSNJsHBkKWpBV7fuMNWvEjuIUy6nOwWmMEO+4cTDgrD6a4gTJpkO2u8ujXmmGywLwV5Kel2AgrbRrcaPLhwke2DgiTcR8/Os7vWg2DCzFybBz73OTqzJ3lipkEytQDDPgNlUK5G7fhFTsgBYbtOoxZzcnCWIqnx2uFdglrIx/7Kf8m//j/+MVdf+x71rOCppz/Et156hYPeFo927+e6krj+Kk4aGnGdQTFElAbnPNYX1OsJizXF3toGl7b3SW0BDqythn2VeZhD64Bme4G724ov/M4XSHxBRIkdHdBfy4g6c4S6Bjan9BHjICRUgsJaPJbJRLJqJUorAi0wZY5QlYBKGBgPB4y8xVvDZNRnUtrKdbUsUVpWQkcUa72CXu6ZDQoEGpcLRP6mYpU/ETRSqaEt43xIvrdBZ6qLThbesWnweMbb+7haD++rPVcODKO1DdJoAKLBwXCfIJQ8dO4YDzz+Sa48v0Ue3kaIdwSF/tQ3cIhA0mgmqHqN0EQUZkSow8p6xYdIwGuwssAJ955P9xNR3MMo5L7HzvLii9fI+gWRGpIeruOVJohmUUmMyYaIsE6oPUIMSQ83sOMCEdZRvqAsJFk2xjuPVAlKxSAqfq4OJV4FuDLDu4p5I7TCelmlPDkoTYkQEoGvOoFAVbisNVhvqx88ILSuEpRE5VahtK4eYw1lYSqfdCXxsjLoD7TCIfHWorxGByHCVaEYlXLNYbxDC0U2PiQfDZE6wHlPo1ZHBSHO5DTiBp3ZKfqHW4yHBWq8TX9tiC8KtIjwyjMepQRSko8nSK3QQoIUWCHxpsSaEl+WlM6TGYPUIa3ZkxxsDqqbk7VIKszRlgVlaYnikCce+wgf/NRfwMqEoszYXr5GurfN+s0r9PoTguEus3M/zebKDe6+skn0zRbzM8e5/yMfBiSzC8e45+IjnLzvDCtXv0tzZpFXb69xMdJcfPQJ7jlzGu8cctjn2Olpvnf1ddpa8Bc/8Qg7ec7B7iai3qIVT7N29XmWL19m/r5HWOkPuee+p/nYuYQwkD9wD3kfSpkAlFLUkgatZsKyKUiWLtDee5mT00vMzxvWRyUzY0293aU33mU43GXsQ7ZETnJYsJ4H+HFBVJti51Cw+PRFJrdvM2Ad4T3ZcEgj0owmE7ypcbIL/+JGjqgHtI6F+KJNd7TDaFyQldvc/u4m0cV1gug8w94h3hm8tRR5SBzHzM92qDWb9EY1dL1FqBvEgz771mN2txC9NV66fptTJxfprazyyJMf5tWXlvjYZ3+KmWYdHUb8e//R3+Rg2SLOPskrd/5rbl25wUN/rkvSOMXSuTMc099j96U7XFnpY6zBC4gjQS2AzckQ40N6kxTvLEIIpBQ4J5ACpFcoKZiZSVjf2uHW6y8gigyN5OzSFE5qdkc9wqzPqDcgJeLmoMdgNGY0yHEuI4xKXLGLqiUkcZtibJg/2WaqcwzSkpWNFWqNBJdZcqFYardxxjMYDWg16+Ac1gla3SW0+IG7o5SqEi4e0VDWbm/zwvIVjAgw2ZiDN57nzso6Pt/ll375r3H+4c/xjooiCSdPnSHe3nzrkjETnPAknQ7deB5Z0+h6wjM//TSf/uQv8Me1Pb79xr+krxPekwx5ZMDmopA4DtBJk6TQ5GWCCgKc00hZ8fVtAE5J5Dvw89++3re4CyH+N+BTwI73/sGja13gt4DTwDLwl7z3h6K63f0D4JPABPgb3vvvv9/3iKOYe8/dx+VbdzH9gjCeQgQ1cuGx/RGyrojqHYpihMwrClvYrlJphHKMBntgHPItLLwk9b6K0ouSI7m9+AE2R5UBaYTFOIspKldzJT3WVT4tOhQoJfBeV7dKX5l9eSfAO5R/kxsvcUohrESKiCxP8dZVuL5UlKYaxAqhQAoMnkBYAqUoXdXdW2PRcYIKIsKYKvLI2WqCLwCl0Tqk2WhSeo8UQxAe5Q1xnFAmCUlSZzAakE9G7K9dJUha1KSmlqZ4D0o4Mjw2m2C8YpQbcmcorMMgyQpPbB1hWKMzvcSmWEXHDZqBY+dgjfGgT9iuE0hNo9OmHPUZHozR1tBNpsgdpMM9XF7iXI+1zLB2+xrjUZ9xXkJYY1LmDNMxtTzHjiZk9TG3NtZ5rLCcOn+O2zeu8Cv/8H9l/uzTqHaH/+nXf5vW1BxnH3iYyfpdTGOe5nyA21phtlly4/vfwvUO2Hm9xTMffoJmq8V8PXi7RdS7riAKaEcV5vnA0z/Dzs3rBMUhDFOO3/8Mw+e+wN5ozHB3m5kkplQBxneph45hb4A2krWRoW12Seohw8NbNKIRr93ZZOn8LCNiVkTGfGh4dWuPfrDEPgnToUWMcoQaoXVOSxQs91ISHRJORehhj3C8xskwIarV8MpxIrFkw12k6LL87e9y6qmPMn2qTnOqQ36Q8t3vfJ27qyu8tnmHh889COkByng6Ysj84gnqSYdP/vwnOfnAU4wv9LhxY5VwcMDCiZ9ib1Vz4p4HiW2PK1spmALhHWVZqU29sxzIgOm5WUobEATblIXHeo9w1TzQHwn/BZ6tO8uk21s0uzNcOPs4niFagS8LJDndIEdOd4hbNWR3kamZkjg2FHlOFEUU4yFxd4rpzjTj3ghZ09TbTSaioNVukNRDUsbIkWIySnHOohz0D3soHVUn4dTjLKADKD1KRQTyaE8Yz9XfeY5f/f1fI2p60jRFqph7zt2HmjS5XmrO8c79gRce60pWheEeIALiaIZWs045FdAM6kxMTFCr0YxqaC0J6xE+rjPQ79+5ewW6FhJHITrUhL5BqA+xpaEsCjwawZGzgQb/TjjT29aP0rn/OvA/Ar/xtmt/H/iK9/5XhBB//+jz/wz488C5o79PA//46ON7vwghOH5hEfG1GC8UUoXE7SWSEEbJkHw8YDxaxw4tydQMIqijlKNMS1AhxubY3KMkaO+w3mKNx8sSoapAAWcybJkjvUOgsEqDsYCrmDJa4a0BL1FKV1mntoJn8AaBIgjrdBZn2L17B60ESivq9QZCCNI8p8wtrXYN6y1FluOdINAWB5SFwRgoJiN0nFAqj3EeKTXuyJ5OKUUhqWwSpED4avYgKiMarJSoIKBeT3AqJLAFQRRSqpjpVoeuSVlf2UD5jLIYU0jYvH2dOIgZpyOyyYjxoIe1gs2dDQ43DylpIERehYQ4RxiHLJx5hBsvvUSnPs/ZJ57m2msvY9IUhUFJiZSassjxpePh+57gpz7zWbYGdwhMVnGivUd6Q9bfZrCzzO03pnj92iVevvR1Agkfe6ZOzecURcbd1bscbm6xk6WUh5s8Oqfp7VxiqzegrgyjwQGjg33aLmN/+XWSZgfTbvGNb3yHfHBIaFJUa4mvP3+JBx59kF/8+Y9Ti4P3xeLf/vW4FnPsvvvpnDrBG5e/w8zcEqFpsqsNBDE9ITDlhPu7nqg1Rz+J8ZMBnSjhxuGEWiEY7O1wMJpQ5AWzcQy1HtfuDlgtcj56Zp5hOmY0njAVa/qDEbOxozcsqI0zmi5gzzrWbi/z1IPnmVk8TT2JObneZ2dYcGXjEOVSRNFj8cw5JDv0djS1xnFCb5h/6CFub3r0zBKnjh/ntetXsN02yXCHoL/HysY6B6nHbl5mmhq1YEIz6nDsyad44sIj/PHnv8aNGy9hm13+zi/+u9z+b/4pt9augvdY54nqIVEoKMYZgY4qj5m3UI4KmpECtAqohQFKK6YXT/KRU/Nc7ivS8QgbaKybUHMDVg8njFNDUJvHpAU2z2nU21WX3QwohkOWDw8oC0OjrBMJwWA/Y3N9GWMzIi2Zmp1DqgTpDUVWMpn0qSW6mlMmnlBmVP4VHq3GaLkLfgkvPLqhaXeatBemGfZ7NLsz/Pt/6z/n8JVtysa7dMMCfGm4eecW10WPU0f7J6rXqDeaFHFIIBvoICZQEeFRMpoQkBnJ5EdJUdWSIK6T6AgfRMQuQCuN8BLjUuJQVqEoDrRxYN87dP59i7v3/utCiNN/6vJngY8d/ft/B56jKu6fBX7DV5PH7wohOkKIRe/9Ju+xhumIz//6/8lk/aCKkDOOzObo0tHv3Wb3jet4Y5FSI/dXUErghKMsTGX6ZQqEAysVFofxFV0P4bBFjg5DpKwGpOooYg4F1hm8qURRzniEVAhxZD9kbeWMFwRHPHmL8habjfF4CmtJtCYrS4RxSBnT6Eik8xiTEymFDEJKY/FljhAlzjgKC7IsCWWIkB6swJUGo9LKRMwdCaecx9icUIeAq2iTXlKPEpyOkFphnUV5QEQUR5TRsNVGkhDZHC8lOoxoNNrs7m/TrWnaySLCC/b2bvIv/of/ikx0CHXIzuYNam5Yva4gRIUJ9VaL7pmHEa+/ghMe4z1BlNDqdNggotZoM3/uMaaWTrA5XKUZakpXMilLWomi3ZlDizscbK6yunwTjKVWr7O7u8k4HdEsmmibU05GbKxuMNe2DLuaqbTg3FKTIJ7iYGufXuk53Z1lL0/Z3JtgRRvjA9TUedY2++zc6hPHitdGPdYunOXc4+d+KK3+vZYQAhWGNFSX08c/QGu+zmf++t8i+Mof8d1v/hHbd++gVcil3LIoBMcaAesjweERJS4KC06cOoHPJcvfuIzLLUPjcYOUoNmiuzBFOhmShJapRpPVXopoJZyePUP36ZO0Rvu88MrLtCaO9SvrxKLF4pQjUgEm26OwHdbuXGN6qqQ3sjScZv6BBfqbqzz33DfYG95Enn6YY9OPc+FigWg0WV6/ixiU3Lp+k89//neYSMl9T13g/MKDLJ15grTdor9+SOtxzRPP3sM4O0d/4xZjGVYYuK/Eet5Y9nf38cYgDZR5VtnPVtUBfHVOsq6iDFfOrZ6NW5f5tbvXiOoxjUaLQMcI6aiJnKimIYjIen1KX1ZVSGSVNYctsHnKQa+HDmLsTslgq1d5RnmJI0ZHEcXEYV0P5xxpmqGUZpwViFwjxm+6dVVLCoMyo6NPgGadWrNJfW4eZz2NbpvpmSaN8zUOG28WzHcaqnr21m7Sm8mPviqIQ0ekPS6EKIzRgwCEJaPECvBSUJSawqv3n6hqqNUCakkNE0ikFKhUV4EhuSYKRwhKHAFOy/eVg/y4mPv82wr2FjB/9O9jwOrbHrd2dO09i7uxnsOhOArCLrE2xeQTRsMR6TjFHm025xxkBoTHW4+QGi+qQGklNEiFlhFxoKjQE4fUAm9KvJQoPF4IPBab5eAsWscgJd5mVB5JtvKaEJVoQThTsWX0UWyWMWglj3D6AuugHiVESYiq1XC2SlifjCfEgWZqagovHKNhjywd4UyB89UNRghV/d+cY1JOyLJJJfcWIFAUuiByDbRSGFPiTclweMhkOMB4RaAkcRKiozrC1YkbTZKkRj1qYlyJdYJAR8RRyKQsSYRDqoB6rUazXaNIC9I+ZEWf3/3n/wCDwAtNFNdxDibFmP7+KmEUYIsc6wuQAd2lJVR0nQ987FPU6l2sKwnDBjaMcJOcE4tLtDsdgnqM8wbtSkIkQgcoY4nCBo8/9QCteh0b1qqEK2HpDzL2+wecnUsY5xN6+4fgPBem28R6zDAd0gwUU4vnECfn2Lp1nUy3qWfbCC+wpWf1cIfm9iwLi1N/JimUAJSE2VNzeDxNNrivNWR1ukWrcx/7h4fce7zJxp6hf+BIXcnB5IDd/TH3dhfZuLPH+TP30piZYae3h5YNZiJNqcEfDlnulWS5IM0sTec4GA6QgeCph3+aaZ7g8rXXOShbyNLSnvZMHTvLufktLi2v8YETJ4jr9xOpkosf/BAmN1hg09Y5/cQz7Hx5F0qD0BIdSRbvPcv5J56hzpjxxvM89lMfp9Oe4WAAu4eeYxtXaaucvbXL/Np/+zVWNrZ59rH7ub454td/+/eY73RY34owJgcENrfsbR2ArDpH4TxCiioLWwJHDK9ABzgnSQuDyBzW58iDjEYtJalFJEkdZw+wwiODmDK3aGGJghpplhJHLXSgiWoePc5BagwS5y2FsTibY52n1W0Sh1VnXBQpWSkIQ02gBcobIulQb+LRElSzg9JH6lQh0GFCPYlpNjpkYQ+hKwZOHIGy72496pFML3bZFO4tRL6eCEIVUWoIYokME8JQYsMEJwS1OiRxiC7fQUH1QxtQEDfq1BsxZVwjpoFVMyQqRncdp7sNhIAwgScfPcPMyc/xe1/4R+/6lP/GA1XvvRfiTwhxf6QlhPgl4JcAVBDQ6/UwaQZe4UxJPjlg0tumzEa8aV6EEAjhqCjnAUqHeGEwRYb1nsBJpA4qmb8t0FR4uCtThAyRIqjONLY8SmOSgK0ESrIaenrvq4GRr46cAFJVwxjnIS+PBqEWFIowiioaoJlAUUEn5bjAlzlxfRZEdQNZXDxJaQukMaRlymGvT5k7tKTyg9chSgWV37oUKCUrkZW1VRefF5gspTs1T1BrkQ4G2DInTTNEZhAtBZT0t7YYKiCIEELRmZql3Yzp1Oso6XFCIyQY64hqNaZUSm83JY4aZPnkiG8bEjSncCJk/fVrTM/N8err32Hq4DbGwqDXQwUh9c48hTEU3lNrdpheOE4w5Th1/3m2l+8wHBYUxtMGOnGEKS1xkjA/P8/c7AxRUmP+9P30x0OyrKQeBhxvWPJ0zGo/oz8oGQ5T9g/GnJibItSOulXo4QphrcWphmdtNEbUFI1mF0XB1uoyUyfPML/gjxql95Ac/vCmPNrTEEwf48QDH+XRPESPD1i/dYda0uXYUsHwYBOde6bbdR6a73JyvsbeQcnVazeYjuHlrQnHZyLU1BRZUSDqNQZ39zjWqZFYy8bEkdRhQVi2NlaZXow4sziN2Z+weOoDvHL5GmOxhZ6PuXhintbMAnOnTuEGG6gwQEcxvUPHkGnOHqsx/sCnSdtddo1EdwIiGTDfzRmnBa3zH+Gpe8as7UNj5wavvnKNP1zf4tDXufzCqyxKxygV2MYxHvnIvZxFs3Orzys3X8Pa/OjnISphvHMVUUBVPi1KHA0pfaUOjZOQpBajvUbooPpBSkEzVAhXgjeUWlAUYH1KaR02G+HLFOM9rshwR7Ec9bhFLkKCWkIY1PBWIWXFc5fCMhpuIrAIoQkUTPKUmmqjlEBFEil+wD1UtkQU6Vtvs9J1gloTnSSEQUwchQRS0p4NsK6CRU0xQQUhQugf7AsRcO99D9PfeYU3fcki5WlGIXkUEIURYZQQRp5JEB6dt8FlsnKz/RH2X6OuaIeCrNPhYz/zKTq1hGYU0gwkOooRaJIOfPwvLYCY55f/k3d/uh+3uG+/CbcIIRaBnaPr68CJtz3u+NG1H1re+38C/BOAKEl8p9miLFPy3B5xSmdIpmPSnRvVxhLirfBnZOVbLZxFRDGRVhTjtIIpjgaZQkiMs/i8QAqNcA7rJkipK9td5986unksEg1CVvmmsrouvECh8FSBBEiHDsEOPPUoIUlCgjBCxZUdgjeGvEgpi4KiyPDe02xOgeszODAYY2m3p/GBQgcR3mTg3RGdrOLpevcmnFCZHCFtdSR1JUVZEklBnAT4IsRowWScMju3hAgrF8ZG0iS3BSvLK7iipFNrMM4E/f11sjxH6oRWs03UaOBdSX6wS5QOUc02QgqsBxc2ccrg0ehmHVmTjIYHjHp3uXvjOjJsoaI6l195nvmFs2zdvUVZTJienSEsY06ePcfh3h6tdosg6dDuLqCihOPHFylD2N66zdruFu2paaZaTU7f9xjNmUXuXl4lVxZyzSg19EZDJmNLlpfoRNFqNTClpRzv0ZQWUQtoeU0cV/TSSCt8MeDq1etsrd7mQ088Smeq/iMNWN++BBDVY45deJhme5b117/LsROnEeU0W7u3uXp4l5lGRL1RY3d3zPW1IfuFodWpsd1PmQwKRNuyvT9CxorEZIwNLCjBQqfNVN0yUpIojImbCdFMG9s6TVvuMhxeY//wLlng2c4rhaN2h7ggYCJbFGuvYLOMvKhh1IPEjRAadXZ2V5g/f4Ybm0PWdjJu3d5k5/Y12ksNLlx4gDMnThPee5Jj987Sy6ZZHfwOnehx/vZ/8Bn2b+6xnB9yz6kZlo7dz+0vvchv/tZvslOOqaR7AuEd1lcnXwcESh+Fp/jq91NCLYmIopBWe4Y4DkAkZHmG8AblPXG9Rh42CZMSIxNq06exq2/QsyXtThucR8oQTMooHbLbWyUYBXzw5z7H6899n53d64hQcu/FR2m0GwRUp9MiLygnJUEIDof3KcL3wQByGun6CDP11rubxDGtWkwtjphEIUEQo6SgMx/R8Z7BxjbfeuEPefJjzzLbufetJs8JycRkZOM3Y+U93pegc3SY8Pgj53nyiWcIWzMsHm8SCMH9T3T45fN/ntmZ5H3pukJoavE0cdRANmucXJqnoQO8h9JVUFgMeAuD3SH9wXsCIj92cf888NeBXzn6+Ltvu/53hRD/F9Ugtf9+eDtUg5gzF8+QX0vZOdzHuhLncqRyaFUVdSU1Wlay/cJ7hLM4VyAKkKqCMrwrKUtJ5Ufhj2K0DMYrjoB1EA4pBd45hLe4I9Upb1IhlahEGa5EeoEVIIVEenDGMuoNcaZEBgEORVamuGJCFGiyPMd5SV4U2MIwKA6xeU7cqFOWBluWBEIR15ooITBC4NFHGbBHGa3egRegK8+do3z5qgtQijzLKH3GeHBAv99HozDdGaIgBC8IajUi1aDRPaDoTWh0ppA4ZuaP/b/MvVmMpFl23/e7935rrLlnVmZW1trVXV29Tk9P9yycvU1SI3GxaJESaQi2bMmCBYOADdAwYPvFL36SIfjBtiAJ8kZKXEVyhhwNZzjTs3dPr9VL7VVZS26RGZGxfetd/HCjmqQlzhgQYEy8ZAKVmRH1RXznnvM//4Xh6IhsPCbCYeqSUFm63S5l2sSZCkoPYeXjQ+rKECYB476mKltEtkJjkTJgcWULkhTlBFEn4ua1m5hy4i9vEBKFCeBQKiKJmyxtnGbz/OM8/8zj/PYf/SHZcUZz4zT9w6uE1tFd2qDRaSKNIS/8gaVmST9FXVNUhuqB40QBcWQY5RpkgrQl3aSLQ7A76HN7MMGpBs36Ab/9xqvULuRnX3qeh9qzH1XknbfcIz/sU+y/wthZmiJg8/yTSJFi3ITijiC8/AO6zT5Xb+xy815JEimCRNBshzTiOdJ2wcjUDPKS2MJV/8PiAAAgAElEQVRYdDnMJqzMpdzsh2wupZxKFKdPLbJ+4WM0uvNsPfcS5xYN03vXuXw/Y32ly+3377PTG/PpU58kFvPEiykIze7hNr3eAw7rIUet81y/8jp1f5vo9Ak6ekiCxaRrfPpTWxxsv0Lv+m2WwpDO6ilcso5otVlZ7WLWNlk9dZal1XX6f/r7rJ54ltW5lOLCCebnVzjOD8EaSq0JA0Vtne9YHZ55hvO4PLDQDnnphae5ces+0cIck8M+8fIW5fFV5uebSCdIQkmuJKqZEIZLTLM+Jp9gioxJv5ixcyRJEnPc22fn/l2SNKSymuzomP7+HmEzoTaC0TBDYTFWU+qaOEmxFpQMcHhrD4ITnrZcDhDupO/AnaPZTFlspzTnV2hfmmN+ZZ5GHHxQfK1VDHqK7cGI5bmZpBmHExLhLM4EfsIWMK29uqnRmufJj3yCbpJ6TzDhEasolcyr1NeuH/FwzjItx/THA/IHkj/4nd9nUNSIWnN8OOCJJzr89S/8ItN+wm/946v86yu/+8Pr6o96QiHEr+OXp0tCiPvAf48v6v9SCPF3gG3gb8x+/Et4GuQNPBXyP/qR/yOgqkruvXuFqjYYZ6l1SZYPmGQ96vEYJSVK+M6h1g6n8KpPK4jCwGPBznkcW2nfTVhHjX8zPIQu/VsuZ8sW598s6RzOCq9WRSKdQQUBThuw/sCwzlOrlIhI2l2cOKYwBpPXWGF8JF6cUOsa5yQPwUhjDNM8o7Laj4ci9KwX63nzDnDO+IWUCbCmRjmBcyVaK98TGYt1oMOIsphgKHlw7x5H+z2wXjjSSJusbqUUZUlxnNM/7jHY36OVdimmY+IkIpARSZKiK2h3O9RlQSWh0hqnDQ4fIlKWNdPhkKLIUVJ4KXTSQCVd0tBQ64rpaJdy3KScjBhPhqx0EqJmzNFoiiyPCa6GHA9HlLrEOIMTIUmzw2A4oH/QQ6eLtMMWVT3mYHjMsLfL9u0+RX9KKwjRYcUUx9GkxDhBbQSm1Iwmx8wvdSmN4fq9B8y1GlS9YxIZUmpBZWOu3rnJCwtdnjjR4PXvfJlLF06QdFbZnP/LPUMAcA5dTLn5zrd58OAW6eSQfn9CbGK2ntIkUcLe1Tc5zo5QQcTuVOMSQS1KilJwIm0gZUlLWQYLXaaTIc2kTUdMuX53gqsN6ytdklKSLHZprCyyX1iWhyXdSNNotomXT9JZfprWt65wGC5SiSNy7WgsCEYP7tJd7+CiBrbRoWwHhDZC50MiaoooZefq6yTnnubcqZTtPuzff4O33vwe/YHgnVde5u/96q/5zttBWfvULgAVhZzd2mCh3UIAK+dP8vRjH+Le4R1SJdHTEWGoaAjIiorKzJxZxcPPryJeWWd56zF29/ukC0uYHOJmh3RukY987MOsnjhDs9XlaHKIu3qNkZHMNzeoJxNUKKlMRqfRAsKZkVcPa/FwDQaNpyhjHVIp2vMtQiGoshw7zWl1WkinkUgiIZDGl3iERMUbf8HXJYyg3Wrz4SfPcf7soyghvenY7DG3ucTP/42/hoj9BA21/+oEth0RJOnso+TIp1PKvKK3/4Df/uIfo7OMPKvoXjzP3/rkT3D/zQG/8a3v8jO/+CmeXfsR6TMObGbJJyVFc8L+zWscTIeAZdQbc3JlDZyvB3lWkef/jpa/zrm/+Zf80+f+LT/rgP/8R/3Nf/P3LNsHhxhtkM7gLChCmkmDcZYhAonRBiEUMjBgwM1ECc4arJgp5WZ+L7MD2lsM4Au7d120f9ZxzKYAZwUyCHBGI9A+n8DWWPx04IRXa6ogRAUCXebgpP8bwiKCAOMs2tZIqXjYeIehp+NJEZJEDVBQ5RlZmdFUCiskxmokIIVCWI21hrr2fh0qVBgLDoGKErSuGR4PKOqMwcEArc0HAb/9/gCZ7hJGkrt3rjMZjBBSYXPNZH4Z0+nQH2yzt3sftKOhBI2FLnXtkDJEBAJtBSpMURbsdIKrK2oEdTFleOcWURizvrGJikN6u7fQWuB0RSKm7JQL1DsFu9v3ySdDrl57i0iFLKy9Ra+3y62r7zA47DM8usN0NERUmnHvHs1IUBvDtNZkRclCpOjVNbmRuBIwoLV3nXTCwzMyCqEyjHoTykITSEcmDK3OAvNxSKsJaaI48/xJzp8JuH7tTxiJD/H004/x6HL8Acvh/z0iO2B0cJtXv/V1hIMTrSbbewVpvcvgwU1sHBBEijo5QehqOjLA1AIZtZBlzl97donShfRGBiLJzqBirZkwqQSjPCdJFUZLKhVzeutRXvzCz/HVL32JV7/6m0gRcL83Yf+TP8NPffzTbKQdjqTm4nPPcuvaNRrpEvliF333BqvnznFua4mV9Yg39x3L8h6HvUO2nnyEjfkTtFpNrnz1d3g7OMGTxXV2jzPmG/PIUGJDkHJKJFusdxIOHtokCoFIUozw3yetmEeffJI/+PpvUKuAdiOh1UgJjCUrBwg5g2rEw2vnSJsNGs0FojSkMz+Pq+GJF7/AW299kb/+kx/m3FyDstK8fHuTFTvm69+7hlzbxBaG0fAQJySmBicNAY7KaJ8LbCy19sZhzvnU4aKwiOExQaA9S6c2HO7t0GqmgMQGBlv9OT/3OIFI8VCYVGvNKBtx5e41jvdukrTnePzxj9GIffA0whFEYN0EXOF/TyxSZyNGkzvIoPIfTicYTyf0s5JsaLj25mVGgwOs1GzNx1jrKPo5+9v3qUrz/6ESBgiZIpOYKA6QWhIEEmUdgdPIWvvXJx1K1Ujxw6eBHwuFqnMOW9dY7S+YtiWj8SHC5FR15QuwEzjnQ7K1tFijgQBtfTEXSngT5JnkH2G9sYQQ3hNZa892Ud6u11mLEAJrNbayKKkQEqyzYEMUGovD1YYgSnCYGRyTeCMjgCAAp2jEMVEaoE1JnRdQOqQShDJEW0USpzRbKVWz4eGZGVvGWYN1AiEUyhqkyTCV9oscBEJI/IrBkYQBURgynmpPqJH+uikpKKuCOs8oakP/cEgSKIJAUhrLaHBIVWfcvnWNcloQxxH7O3ssOMf8/Bza1EyHA9qd7uyyGaIgQCt/Q+WTMUkY0Gq1PZ/X1tSTiiqvcA6uvvMWQig2zjyCMzVaa2xVUImS737tSwgpuXntMsZFFPkAox1FVdEILIuL8zRaMTdu30CbmqEZkRSaulYclgl1PaQ2AonBuIBBYRld3yMKA4pCU9Q17UZIHAUcDQ/BCqos4V57m0+9dBFlB8x3Ne9cfoU+GUcbKSdPrHOyu+x9/P/sA4gzmt1bN5hkELucvi3Jki77/R2Wo4iEnIOR5PwpUOkc1XCAq0LCoCafWg7GFZ8+0+J3h4ZmMyJoJoTA8bEmiFMCZwhrzX5lCeYatDpdyrrC6hInNMYUDDNPDLg+rkjbq1w4d4YvJ7H3WYnnWLrwYcJGgBQSNdUocUT31AXWNrcZ71zhcm9C97CPHe6y+NjjqJ0uYWeNjbVFTJ4Rywa18MKjOFUzryT/EMaiLTMWl+DpM/O0m22cqphODVmWYyy00phxXnojPac+sKwu+kdUOqfVaZM2u9hmwcKJVZZ3lnkwmLK0cZHGYgszdHRFQLo4otUUVJFCNdq0222kBSMMsq5AKKz1S/Ha+F2UsV71LZUgjBWB9GZeBJaqsuhZhmyztrha4wsw2HEfyj/rmsdjw2F/xOj9Ht8/vM/61ilOPvIiobWUriJWhmxvh2QlALuClSkCSb1zyO6Vq7CkqIdvEXYustxZmHlHGcq6IMvGyFCQFf41j/pTJtmQoi6wruObzRm05ScTMNoRhgJnLL08Y5jX2MEYIyTjSYYSJePJmHLapDKW7TsFd/bvMhrt/dC6+mNR3BGgQj8uOmep9RR7bBHKYOsKzIx6JATS+roNASjPi7V/jqvjcTfvIfNBZ+E0Psrcc02lc74zF2qGz/kOwFmHDFKcEmBDhLIE4cOVkcWJEIvGmBJnHEaHJIkiCAMs4Cq890xtqGuH0BoZhSgBQRCjggipSpx1/tCaFXbwjBzhfERYGEUQKKTB52I6EEGESCImRU1ZaaTw04cTEivBFDmiGSOBQEiSMKRGUtYFSscoK0gbCThBXk4p8ylFmpBVhmlWkcRTpLNY7Z0wlZA4/BiSJA02T51ChIryOEeqABFCnY+RQtBuNGg0Uvbv3kFU3qUzEI65JMIKRW84RjtDVdcf+NsUxQRj5sEpqmxIqQ1ldsScC8hWXmBta43jr/9zpDnCGeeXZtbhpCBNIgqrMHnFpAhIE818IyIMY0bHI25eu8GX/rSg3Szo7dVkuaW39y5Hcws8/ezzrL/4k3/B2tsB5XiP21ff4uDwEGksq82IcnBEVgqOqjHHFXTOf4JHn3uK8c49XnllyIPxHnI0IS8F7x1mhHEDM7dJffSAB2PHWqtJLUfkeOOr9/OK1mKXWlZU5QhbwXhcI2RFlZdUtSFSkihuUS9sMChhPC14//YVWqcucvneLj/98Q+RCosIEoSsEQqWVk+gxznXb+8SJwIVBbSbCUkS0k5jNtfPc+/eNU8PjqfYOmJUWuxD03PnGFaaW/cGrKUDzq5vcPLMCmmU0BuOKWqNiBRVrX2WAWAfTj7C76yc08zpHo0oIomaFEGMiiQ7V+7yT258n9NPvMNLn/lZ9gYdor2SbLBHM4mZjwUmVvT6xxh8PJ4SnpEmpT+AjLYzhbmF2WdAyAgnHNZUTLMKEURMR5oo9i/p9u0bdFfXidSStwgw4oOgalF7vxrnwBpB6TQO6O8VfP3NN1jrDgkmPc4+tsn+rVfIw4Kz62vsvrnH5ctvUM8LWoMR554vuXr9kP1Bj6yM6UQho+MxyID0YIjta27f3GY4esDB+BZGLxIIBa4GF+D27qHjCYP7EUsXN6GAnRu77N64ysSOSYOE0XiAihXVtOZKcsBu723EGx1uH7xDnR3/0LL6Y1HcvZePl+hbMVuU1hZnBFiN0b47YAZl6JlVrrQPO3a/CEMoD1VYX4wBhJCoIPR0R2f9iTljVlqjPV/XWM9UEcpz4h/+vpCzDk/6LgWJCGKkKZHGEgaCvBgjckmYpgRKUpgKobW3E5450zlrsdZirMfLcN6UzBgN0iKcoHYQCeH5+NILoCwGZxy1q9GZpnc0ZTIcAjNGp3NMi4pmEBMGES4UfoyTgiSJwQiUCIiTlDCIabZSpnmGCpSPmdMZk7GjvbCGqQdUZYExGmchTRsUdUUQKlpz81R16acnWyJmC20lBWEcUVlHlU2QUhEFmqrWqCCmwncp0tUE1huX+aWZoN/bJwpDmq02SAhCwaiyFHXMZG+bXO1T5iOUnk1Q3vAPbSXTrEabgko78kIyKUpsHdEKFcYqDiZD/q9/dg+L4LEnHuf86XmGvWPevzJEmVU+9UxFWRvmmylBINH5mP7Vl4lEzlxLEs+9iDNDYnOLxggGEw0y4qMX19h87BKDs5dYGgw5/PY7FFOL1V4dLRvQbjmu9UNWF5cZH/fJasP7uzVbq11WVcDO4JidqeNwKAjCBKtCMp2yPy5oTXyMZBCG5IVGiiHdZoN+scDhgz3M4R4HvSnt/AFm4RROVwigvXEOeVhydbtHHCiKyvDgzh22VhYoxj0MClTgQUoX4YBuq8GhntFFhSQKm2zf2OWLr36N/+a//PvINKLdnac38LsdowVa1xi0pwsLRyD+3P1rJWlnnfpuDxdImksrdMKEjVOPMOwZXv3GdzixscRS8DS5KbCtZcqgQWNxnvkE4jVDUApGWU2WFcTzAZvJIipSZFPobmxwKtU4YaCqkcrRTQJ0oIkI0Y02L3z253jty19hONnlRgHn6zZ6lHNncMC8fYSkMlCMOBwfMtYWW0JWGLLjKdpUrCynnF1Y4PJ77xJM9+jrHVzW4MLZE/ze736N9y/vcqX3gPZmzKc/8gmWVMibky67exn6hGOpG/iCqgSUNQ4fwhQpw3IrJZihBg/hIbnUII06pI0mJCHVtMYd1eztHDDUY5QxCOvxSaEUd/YMh9lV2vYiUk8xQv/QuvpjUdwBJBKExCoHLkCEDYTTnnOOm1nhupmXt+94pXS+i5DOq9ucAScRUiCFmp3+wguDED4zE+EXsk75vwMegLECIWa42MwkDCXAemWsEwZwmLryyUwCtNMkYYyRXmRUmxorvH+MAAIpsULgkL6wzxapnm7gO2ycx/icU0ShxErlA6ofwkTGggw4Pj6knBY83CRI4a0Raqf9yOwcttae9qkioiiiLCwawbSaYIUgCkPyKsQK3/VImRAE/kOoy8Ir4oKQRDqyrCJsdGhaR9KaY+9ol8l4hBSKNApJkgZF5fxrceCEIJCCCjh96hE+8uIzlGXJaDLkuHfIsDC8e/WaF4856DbmuHjhCZLFRbA1SgjAUh0fMZk7RxgnqOYaweg+lS0ptEYlDVANTD7BWYvRFU5Ial1jdU0WJURKoC0UlUVFXg6/fzxifvMpjm9/ly99+as8OOgzzQyf+9Qn+cLnn6d48AbX3n6HrHLMdTukW12uX+3TBJbSEGESgmabjbUNwjAkaS2ytHWazuppuoObPDjM6bbnOLGyQT6JyAtDdy7l0bOP8Lt/PEA1U4KVFcxmxMWzDaq0hUmW6Y2GDPKMZHEdc1xQmRw3s62tdEVsQ4QMyETE7v4hjSRBmiNoJAyzEvKMvNbs7uyQTieIEBbWtpjsX2PaO+TqcMhiR5MNH/jgaetwJsAZQ3euiz3wVFwchPE83/m9/4Wlx7d4b1eyvX1IKxW0Gk3MRKOt9aJBo2f09Yd+Mv7j3D865re+8S1sLVH6MmdOneH997/DL//Nz/Cbvx4wkCFf/+L3eeRxSTS4h50copWhGB3TG+yArsl0QF5D7TRBGLAQRkhnOH7vu4SiphM7ylLTu3mZcWg57iSkKiUvJYWuicSA41xzNMz54z/8l3zu4y+yuniStdUP0XAKagN64lk2OidIHa00JiDH2mPidpNnP3SKu5df4b3+EWvPP8KlU0+RzkmKEnrGUeZTgoHmwc4eq5snKfSEUFiGWYZ1Do0lsFAWhvGk5Kj3gHI6xmR7CJ72V2zmECkay77ePHQUkwonA68Qjxexo0OUM9RWEAqYTjXVaBYuhPjzItx/6+PHorg753BO4pQFY7zdbpUhpWfAWOzM11l+4GMhA9/Foy1ChH7JYwVKhcgArK2x2sGMoSKdx9ON8+lOQjoEzhf5WVcpRODhER4uOy1SxbMPsb9UQdRA6wqBJVIJURT7yL8gwNoIqyCrNNo5DA4hHEVdoerSLzmtX0Q5KT2pxjkc0h8ESJSSBGGAExGBdRRFhjY1uqqw1o+PXhRo8LeWRdcl02mGEt5/XkqJDGKQJYEUKAHCaRAKbWqUdTSb8xAqRGDRznP6tXGUuqYZBVSBQwQxyVyX7fvbDPuHfiISEqUUQTjGlBVhGNBqdWi15ximAwQOGSdU1hInAYlsM+0dELSTD9gqDkdncQnTXGJhcY0sL1Bzp5hYyVFvglCOQksaSUw5chhj/XPKmQtns0M27s2CO8AIKMuKwAlEGFA7P+VJY3jm9EV+79uvMXj1X2OKCqTjle+9TJLMMdi/z/Prls1HL7F0MWP/9W+zd3zAvH2XrU6HqnRMqwpwBGFA7jRZUVHqKcOyjbWClTTknihI2susnT6NHB3x/SuwNxqwvHWGIAqJY8M07xN3NlFOcOGxhNruo4sBdVmiqgqjNXV9zLCYYBOFRbO4vsHyyjLKZmzfzZH2mFvb17iweY6prsCFfOtPvsFe/5Dzqyvc6/UR8nW2kjmigxE3b99kdX2RvtolUiUHD66yd/MWdmGJKRX3tt8knzzJ8eU9BkmEzqf0egO++Qd/QDndJqGk0WliqRmORkgU9exNNNYhhPH3IhKjLcO9PaRqEFVTdoYHvLb/R2zfeoJmtsD07vtMdMrpsxl5GTIJVwnUErWQ3Bkc0BGGg+GIXGvSSNNpLyLKEus0STtF6IhSW8JWmzRLCJSm00yJEEhZM+xPePWdyxhdEoUBe7sZ++Mha+kmyswEVZ5zjJSSCIhkgA4DlLB+cVqCSmKeuHCaix8OWNl8nOJQMLj9GosbbdZ7a9y8eRtbOCoRoPMRR4fXMLJGaom2M7QgDJFBRFMFJDLxPlFhwL8Z3vQXnSeFqBGqhLLE6T5SGALlldNSGGSlscPML9zikORHmJH9WBR38VDpZqzvvJ3BmNk+1PmwDB+yofB5TA5bVwjhC+NDuAWniOIUEUBZgdUVytRY/M3iFywz3eKMioi1PtRDziADJxFKoYIIrOeZO7z5F04SCEuYhtSjCbr2C8o0bXzAobVOY4QD4a16pREoGSARVFWB0ZYoisA66tL7YkdJjLPGTy4IHAqhAq+ozQyTbIKuPB1LSjwMZQVi9r1D0+v3iPKYsqxppl2/A3AWlPJdrpOzwhwighAZB5RVRW0M0llPkdMaaR0iTLFonBPs7dxn0Dvyh+pMH2CwOFsjnKCqKibTMd3EcS9UOB2wd7xH79uHOGEw1qLLcsbGUXjhoOPatfdIuouMDh8Qxgkr8Tqrj36Gha3n2H3r61x57V/hRM2gMERCYh004lWW1laIKLh5a0AYSoyN0WFAXVdUxqHLmkolzC0tszS3wNlHzvBCZdm7d5sH9w45Ot4lm0woS4PQmutXd3nkqY8x9+Rn6OSK9Pgr1ETYasAwr1mLUo4LS7MTc2N4l8P7c2xtLHPv7W2KccYwbqIYsz2EsY6ZW0hpz0fs9Y+4ujNGKsnnP/lRxr27tE5ssrKywh/98RXmpm8wGWbc2B7QGd9mMByztiv5n/7R/8z97bvMLR7gTs+xeWKd17/8LyirGOFq6lrz/oUPsX/UZ3lti/rBu2wuh9zMD1l9/tMsLC0jZIfz1T2uF7sYLch1xbm5gCw8hwhyjGtgVE01OcaGi/Rf+02Cp5+hnQha3Rj3ziukmy263RW62X2SxS6IgMPBsb9/ZnCjcBaplNdnIJBxjAoCPvqpn+DS+VN86fdeZnd/wGIkEVHI4vppLpw8wfXBkGza5+T6BmndIgwFotaUVY1xftCO0piokaKrgka3hSt8AQ2TeawYYt2QJAzJRyWZzcFq3nvnPrE2VHlF1au5/d57POIMuhpSmQXS2f0ShGOaiaLdEJikRbfVJgh8ApPNakZlzam5JtPeEX/ylVcoRvcpu4r5UJAoRRAHxHGEKnIOjntUWGzhqEVJ0u0Sp0t059uoZsDy6fOsFGcIws6ssM8gZCSYDGQI8qHlgSJwIdI5IuO1OiH4aU7g79O6JG5INrdO0l2I+cY3v/iX1tUfk+IuCJSjNDPoxEmiMPZQt7PYqvRGXtJ5PFr4I1DOVH44R6FrTGHRukCX9UyQYtBO+JMZAB+Q/dBUSswWrEIFIGYBHs5/lQRedj2DhRACa2uGox7O4SEIbYlqg5IWY2uUCpBKEQYKbcB48TE4iyFACIOaQUkPmT9OeghIysA3F1JgLH7xgl9i6cKrXb1S0NsViIf7AOFm3HlLkVfer8aj1N772TgS4WMEpVAIpfxUU3v/+UAGWGuptS/EpdbE1nvQlEYzPPbmTAJvCCUQYAUOi1R+CV7VJd95+wra+kDjEAG1pS59PJoxGociaoRYkVJPS+qioK4mHJQTQgl7D/53VBgQGgMy8H48k5oir2h2U4JonsIsIxvLTLNbBFGIqLy9b+Qk3YVFjrIKPR0T4KmoaXOe25d/wLy29KUgVJYgCKgLh4w7nDj1LJcPaz5b5BQu5LBK2Dj/HNfffpNoaYFTly7SKHocvhuiXczde3d5bP4kqao58ZFnSd/7AQxvICQsRCFxq8XhpM/iWsD+OCUJfNhL87FPMCle597X7jBeq5jWLebmQ5566gVefuXXubt/G2lqdKERap+sKGiGNcP+Dlub63znle9B0KSqfZbv9bdeJVzZQtclTghOzzW5O6pYa7TZiiwukujYsrTQoJgITp08wSPzEcvzIY21LtNogWI0YePUKo1QkiiJpSZUEpobpG6fuppSO8fCQgdnDPm4ZCCFN9EDcMIv853zEZUCGp0Gzz39KGe2zrG6NsfS4gLD8YjtB3tEEYRxTLfTJBKCMq+otCWws72SdYRhgNGaMAqpnaUdpz7DtdMhCmuOS0smAmwkyExCP8+RWhO4AFeXjIcHyHaKNY6om3Lm0fOkK4+i6c26ZgFhiAwdSRyydHKDn/rs4ywnbbqtjkdMZEAsO7zx+nvcPhxx9eZ9Vk9IRO4Y5DWVqUhUyuH1O3x/NOZwPEFgkWXNE5+4yE8+9RMEapOkrWgtBnz+bz3Fc6NTLC21oQICsOUOw35J/8ZrbD73UaLWyRll22KFm6nlFVhHNYuVlEJgnMBozdoTj/GrnzqLEJZ/8k//x7+0rv5YFHdwpHGECAVunOG0QMgQEQpwGmMqj2L/uYUozoF2uChCBBaha4TQWKsA3+k7Yb1+TggeRtp6homZaT594rkTDolCBQKrvXsj9mGBnKlbZwR2qxzOgVFq5oXhmBSBt/gMKuLZc0XOG5zVxi8YE1cjlUCKwEukmcHtVs12AcGMhumVs5Ia4TRlXfqQ8IcKNxGysLhKq92EsOGDuo2mnPaZ68wzqiqK4ZG3PcZS1gVB5XBKUNTeKVJbw/HgiLLSLC4vc1wM0dMxzlmKsgQB3dUNJnv3SaKYKIm9EtFZtHU440VkQjpiOdt92JJYSk+nq81sqvAHpTYGJ+wsYLxGqoCVsxd57EMfZXXtLGZywPH4kOEo5/7uEbqqUe1VZLaHCiLaG0+wdPI0eVEjhGDhxLMc3N71FFrh/X6eePpJ+t/5FnVdkyxtEXcuMNSSa7tjiDps79aknbNcevzDhItnCIJ5Du+9S+EUv/PqDp/50CmefOQMxX6b4x3N0mOnOLXeZZI4Tl+0vPPOd9nsWFx7jttXrnHv7jYr3UUivY9ZiRFlxEoU0p92eHzrOVU0KM4AACAASURBVE6fzzi8Lrn1RkASBTx5+hzfunmD/f0+i0uCH7x2m7WNS8yH0KsFYRARhTGtZgspBfOtJkpKmnMnCEOJFgllOQCpMOWEGIMMYiyOd248QM2t8sYXf5830Jx4/AUm179Lv6x9oZMVx3NnaciM0aSH6kIlLNZ4UaDQjryyGCeQLiRxgtv7e3QTx+EYMu09lBppymQ69Qf7LKxGeA8CcHB6o0sULbJ98yZzZRdT5YjKMjo6ZGElQRiBFRCmIc3OOpNpgStL3/wUJWVVewuM2oI2mGxKKAKiskJZS2oNQ1GR96dEjRplNWVRUFqfGuV0TW1DQjGDZ+M5hExQxGC0RwYmI1aWV/nY009z5vFTnF7dJC9qiumYKk4YHI5oLTvOdubpZSVRFDHMCl789AWOEst3XnmDfJpxMBxz73BIfzii2/ZmhQeTBgvdmIVYIuOA8aTAkrHULTGlIcstaXeBql9wcOs6rpjQ0zkbeQmxAJeh1JBWq4PVJVYqnCuRIkI6S9QIkbJJuCzYWu2A/HcM6/j/5eGgqB1IjQwcRguCIMBJizUPVWIzmwCkzxRVcqbszHE1WAPOKcIwwUmH0R5DE8KbiTkBUtdYHi5dfVFn1qlb8Wcyf2s0WOkXHLNSLIXxzz1LeDfGooSk1hXKGiIVEQYR03yMrT30YyRoY6iyMVWdEQQhYRhQ6RLroK5rsAKpZ3x779KBlFN/FDmHM4YoTT31CwFCkTRgbqmidgaLIpUwHYMyEvSUvJhiXI3DYZTEVQ1ay+seByQFGTApM1ydc/Pa+9TlBClmjpkORqNjer0dHjqkBUJ6wzZnkTOIJ4gatFpNgjia+eD7H0/ilNpU5JMBRkMQztHqzhOFEXHsQ8fb68/ykV/++6Qtx3GZM39yg/MxvHDhNF++vIutLM0mvP7Nb/Dg6i1EtEI/F8RiytH2XUxaESUBWVEj4i7F8SGvfvd7mAqCZpfFRz5K0t2kM71Gh4hrRwPOnjpFXjuwEj2uSJIjzp06yRPPPwlS8fbdjIV2QtxIqFbXuTs44KgRcnM/wk6P0McVAz3i6Likd+1dPv3E08RpzLMrP8nu1Vsc7N/l9mGXz3z+CwxszlvvfI8j/R5ShUxu3uSwlAR6QkWCtjGT4ZAsTvnF/+wf0IlyQuEwtgKdA45Ws83W1iO8+f4dHJLO2hZ5OSZorEI18sHx1lJXFUfVmAuNlB9cvUm31SBqNnjv7esUMqQhDeP9Q25evUEmFkn0Dm/e+Arnnvw4B3ce8KVf/y3ke++z8tijRI0WsbQ0Apje2aX76COccAH3Dgck83McHR8ThgoI0XXlKcJWeCabc9x99ya97TFlVVKuzbPTq1BLK0RhQCQCdJVTVBNkFIM07N9+nThukk8z2s6y0G746DTlMNorkheSBmNTsrQQ4qzjUuB4P5JMygpbFBSTIbXW1IQf8PYfefIR7GzS9vWlpDIKaos7OGTt4lkWLm2Ajdi/tc3t7WtUw12Cs1u8/ZXXuTne5r/9uy9xdphx490QG0BV1yRKEIaCOqu5vjvk7OkNjB1z+tJpRBHQ++of868mb9NJLnD68TVOnvwQd6+9w87eDfYHhrnWIT//C3+bbqvFhY9+AooSFwUwqHELiqAT8dO/9u/zQv6L1HYXp04ihu9Adwv6I5wq2dg8ya03r3Lmk8sE8YkfWlZ/LIq7kJJGIyIvKmztfSGc8+ERVnuPFyslnp34kPPq+a6u9nYBwumHRwBIsMaz051z3pEOhXAPRx5ffJ31QiKk9AEZQvnl0IwJgPNYu3QC91D2CjjjC72TzvvehCFlneNEjVAhGj0zN/OeN7YumVbQiZs0wjlke5njoztU02IGDwmftDJ78VIID/EEETJIUNSeiikC3ym5iKJexliFrsfUStKen4MoJhKWheUuUdr0IisC2u1FgrSJjBMK5wikojufkMQOQUA+GrBz9R32tq9j6xKHo7QWJcFZH9iAkgRKEQQJp579Kc49/1dZW1v0u4wgQkSCbHrME4sdttZj/sVv/HO+/9VXeO5X/muCzmmq6RRXj6ltRity3L12lyzPyAcDXN4ndhX15y9y/foeD+4f8Cs/9yLjoaYRxSxkd7n83luM6oq5uTarJx9n4cxJqklN1FrCyQCNIU0FMmgiVUyaCqrpIlurTTajD/HMs0+yMN/BhAFLjZgwCDy0E4gPpPQA8ekFHr90in/4v/0+t9//Jt97/W2EhFPzEWc/91k++pEX6Z+/xP67V7Gvf4OFFy7yp9/8OmJ9nrt1xRlTcne35LX3dsmHE4y2vHvjAT/9sSc5mX6caSh5UC3x3M+f4pd+4SWKieIf/sN/xI3bV7DWYOoxo1GGtYL/+Oc+xsraEyCh1Wwzkgpb9AgaHVxdEoQhSRwxnmqOTEa7EbDQDchHx2hp2DzVIdYhi802r93r8x+eW+OJ+S2oMmzSZm55gScf3+Ldl3M259t88mf+CmU24dTGk1w0Z3j/+h1u395BBYpO1CGMAlKZgosY14M/6+CFw6G43ZtwIV0hDBS3DkdkDs52JMWJVeL2EsfRHLdLQWd1lW61T5qssNTqMGd6OG2xRCRijihsEs11iYuMOGgzsiOSuYLETUmjDifKjKmNcSYlURFHVrNXgQxKwkaDk1FMcdBndON93NKjlHqK1gFECtOc4847lzlqKdyowA6GjPePuHvvBje+9hUGo5ozl86QHdXUruCFFz9KIzkgXInZuT9FG0GNxE5yfvDGNQZ7Yx4URzTTBD2ccveP9mmFN4i/qnj80T8l0pakmHLzYMr62ZgIye57N4guahZVGykVzLdwylH09zDZEUuRwNY5Tt2HZoBVI1wDaHbJrGav/4Ckbwiaf7k9MfyYFHdnHXUpUB9gwsYvA6XEON8xCuGFPkoF1M56WtOseNsZri2kQNc5QgukMZ63PlOPKeGQ8iEejSdNO68C9Xe2NyF6yEN3D0fOmW+Qsx7DtnghhRQedza1xVGgrKCqYpqNJo1Wi6o0hIFCCkNeaaypMSIiaj/Bpc/9Ci4a8u3f/h+Y9PZAxogoRYnAH0BqpnANE6I4Imp2CZXGGud93JMOQdoiUCCmCUoFhI15okbCXNpg5dR54nnLu2/eZm15neXHnkUqhZ0MORofM9nvobM9BsMxcavJ0uppPvzCC1y5/Trf+D9/A1tVM4GXwLp6dn0iRDTP/PKjPPe3fw3RWuf4aEQ+PKAeTqjyAXV2zLm5Ezzz4cc4tfVf8N/V/4zW0iZmOkDbIUWRkY8LBqMjGonmpV94ic2Vj/kEp3pCnGdclEvs77/M9197jeU4YxgJBgdDdkc9pDXsjvbZzzOeeu4neOyZzxIsLhLFASJJQSqU9N4j1imP5WKQFr57b4zc8Xk4rpySJC2aS/Nk/QOkruh0uvSHI6QrmEuaJKFjU2X83S9cYGeUMK4Nn/7cS2gc5eR9vvjyl3xE3muv8+b1N/l7/+mv8bnP/gz1YZ/vfPMP6YiYVitBCsPHz3dwN79DY/MScXORepLRai9x/dYBvWnIle0ddD5FVxnWWfa376NxHA4uES8AxtKZm+MoEGBrJtoShBoXhDirCCLLnimZ34q58GSXceEI+46585KNKKKxOo9613K8f4udIkV15iAQ6Cxn6eQGp9fOMLItPvmxp3B1iXQaIWHceY3Rn1ym005IXeQnW60xupqpvD0052Xwlu6JDj/5S/8B016PcekgSEk6Ef/Jr36GJF2gtIooDkiwFFmOKzOuvHXMO515Xnz+LJYuRivWgghDTpYfM6lLApWhiwnpUsDesEFr9ZhJ6dB1zbPhmMnwAUWZ8+6dQ24MLePEYOZCpsNDyukxtj7GuDZV5SAqePyZi8jOCfxSD3RZM9i+zSsv/wm3x/c4M99k+4232HGCZy49gi4X6B32GI37dNOQai5kZWUe/eCAPLCcXgs4fWKBMyef5Gt/eo14uctKqti7M+b0h5b48MbjbBU58/UR13dukT/osXlhEYoUp/BV2MFxv+b93TEinKPKx2DHiMM9rCsRZQ2RABnw3jsTpuoGadL9oXX1x6K4W+fIMm8vaoynDwqnEVYRSUk568QtFmMrBDM3Rwu+NZ5BGA5qXXiqoL+NkcJhUV4A5bzPDOA558J6v2aULwimRiGxbtZ1O+FVmw/VeMZbFggVzAAUX4SNqcF6eqauKwIEksDT9pwBawhVTBAtUjcN19/6PZLWGs3li8hwAytSwiAkiTRaT3G6pt1skrTmaLYVLkiQKsXJFMKEKEhprZygtbiOChWhHSCdJV2fp7t0gkqHXHv9TU5cOkXWu813f/d/5ejGe4QyoDvfwmRjyjpHa40gpNFocb0zz0u//EscfvwjXP7at7yoTCoEAQ5Ds7XEwvmncUYyvrtH2BlTHu9TZSPKylFOJmTHR7zxek6UDFnvzvO5l36Cr3z9W7SX2iRJi6gjWd84iWxsMt3Z4au/8Ts0OvNcfOHznFtMuXz5DW6++g3uXLnC3SCgyDUCQ5VPkWGTNE2JohBUyP3jjPrGdcS9XYKkQZi0kUIQxgFhEHkabZWBNigF1pVUeYmyNXOLXTrtTa5deRddTMAFnP/wc9y5ep0qr+lsXmA+XuAjn//36HabXGqt0Ol2We/GMN1jvrnE+sk1ZD7hu1dv0Xz8DGfPnGNqFMdE3DcLRGpE/+iAPK+4fG+PpTjmpXNbvP72dV7/9mu0Lz3LX/n0x7j67h4b7Zh7A0M5ynBO0J2bZ25xlU7nBGVpkNLRMH0CGSLw/iJCxV7TICSH/YrB3WOUtLz7ZoIQAu0cUaPLVLZ59w9fpshz/ujrb9LtpJx/5ll+/q9e4Nq7N/gHv/pf8bEy46M//TPEYQChLwnOOeaDiE6SEMkpB/1jhJAY7enJfoz2eguLREnLIyeX+Pmf/TmUSmYeTQZhCxpp6tlnfhHm2V7dFk7BybOn+Jx7agbtwEzN90Nqhb/tnXXsXrvK3t5VLi19hFw7mq/dJLl+nzIyhAsNHuyX/M7/8Y+ZCth+D3Zf/r+Zju6xtrXEqa0zOJo0kgCV9cjTNcr5OeYaOTJq8L3rD1g4N4czA3IihrtD7t/fwSpFPi2wsmAhkfSt4ZnPvMhn1x9Dy5J7Vwu2q4z9qSbXcOPumE9dkjxWpVx5xzF69Q3ILe2jM0TBPAd1gNOWdleyvrHKp9cVIuzCqAWqBnMK2m2IEpxtMxo32OjusfAJxYnOMvB3/tJr9WNR3J3zsXVYb8drHX65UnvaI8YTBJ2dsVaE3ywLEXiPFvcwcspTjJwIcCik8pxwKaWP5sNbDxhnkSpAKYmaSfg9HTOZcWHBCo+rRwJqJFWVY4rMc9+lnC3fBYGUhEFAEqYEgR/3nRMo9f8w9+ZBll33fd/nnHO3t7/X+zYbZgYYLCQw4AICXARukihSpCVZlGIrsWLRiqqUsrM4kUoVlyuJXXH+cKpctiq2YldiWXJELZQoiTQpcAeIjQAGwAAYzIJZerp7prfXb393OUv+OHcAkKJgpiqV4q3qej3vdb9+c++5v/NbvksESmBNjKpY4ijx20rvJvngJubGa8wmVXaClDTro9QM84uHOXr3aZZPLbB45CTV1XmCmYDUBOg8IJ8qssKrW4qaQCjILbcAOUwngq2eY/O5b7N35kXyyQE7l7/NwfXXkEIRhBH12h0EtQ56pAmEwuqUYtpj6iZ847O/x9ve8z6uLr1Gb2sTbm1hLmRu8R4e/oVfY/5Eg7maYnZWIZgnzWBiIiZGMOnnbJ15kf3r14j0mPPnz3Lmi/8Xwgo/7C4K5taOsLq8wP7+Fv29Hq35YyzV6lzpOJ5+5I+Z7B9wc2uTMKwQ11tUkoT5Y0fozB0miOuouIJSgrjWQgUSFcaoSoMgiJAyR0/7jKYHhFGNpFZHkDE52MXpAoRk7fY7qTU7XDp7hrwocKagUquw1K7z8tDLz0Y7lwkWO6zFmnZSQDUjrEE1FMjOMvX2Ep/5W8v0DkZcfPFZlt52G2r7CqPtG7z0rW9T7L3E3ErAFhkf+xuf5N0PfILnn3qSPTJOvOdubuwpDuyYR//od/mz3/kTik6dxsIscwuLhFIQBaHXuV+qsjPOcAgSYTm8dozXrl/hve+8nY9+4D2c2zRss8fu1nkCEVMUXmdpcWWOX/rbv8Tpd7yLdDDliWdf5vOf+x22t64x3BuxsZWycaVPd3+fOBBMYoNN3Ruwa3zCNRgXNOdXqOkb7Pd2qIQh4zBgkqZQSgUI4atr4eD8RkY/dxye8S5J/qh8790OFzahXkOstT3h8NbP5ha++Dy8dx7m197QD3nTUfLFQcGhO+5g5eghlNRYJVk58k7u2h0x7e9iyelPrjAjQlJRYSqbvHbzWSb9Ec88cp6z1cc56E4hFGRT7SvyOOCB99xN4Aqe/87LxJcTHp08RXc0wRQ5KRJqNfKe5anvXGE6HpPmOZ/7na/w0tILZNbQOyjY3t0lqCe04ip2LPijRxSztQQTVDnaOkq1ClvXR9yUlzzoINdso7kwnbJ7sEmt1kZmYyxe5sQpMDIAWWGcCdZf6tGZtFlaOPWWcfWHIriDwxXaD/Ssp9pa4Txs0Pr+uJSSOAqwMiC3zuOxZUioAq9tIbQXHkMiVIAQIbZ0URHCIeytnN0hndduRyqckL5377w6oykhh+BQgTfbxjiCxA9aBb5d0ooTqmGEQxJGdYQsRY6sF7kSxhv3pkkTValhhEboHJEPCYGGUMSqQBnD7Op9vOun/iYf/8jdqHrIrkzouoC4Bu22oABSDw4id560Y0s8sMH7fGcZFHuOYmvI3vmnGB5cZbp3mf7WZawuIJBeJKy3T7NVgckAjfBTeSRSGA5uvMaLj2pOv/9DPPYnf+ChdgjiuEJzdYksm2BswNZQc2NaJ0wC4kjS7w44uLrPSnuG5nCd9YvnePVMxs2tK6T9XZKkiQygyMZsnH+R7SsxUnj5gmH/gP3t16jGCc5qlIxYO3k/9UaHSrNBlDSQcYxwiiiuEtZbWGOoVOtESY08N2SjHUZZgXSOuJoQJm2KvE9+YxfhoT0EQcThd99OYUPOPvOkv5bOwzSX1hpok5JOpggMw36P0fwS39yx3D6zSt2EHMsKSCJuWTAG1RazlRazKysA9MM6g0KjE8uv/upP0WlH9Hd7hPOn+Isvf4vh/jY7z23QqTQ5fGiW7rUR9378J+lXDvPotx4j1T0y6+cua/cd48bmlM0b2zQXl4nrde44UeGBn/xRpsChI7PMrM0gvnGRSrRGW50mLCCTVXQccN+9t/Hh9z/IH/7O7/PE08/QUDk/877TNGc/TJDERI0FxkVMUKnwsz/xCe7ZXicsCThlfoRDsDDXYabuUHqBubmEom/YnaSIoCBwDis8Kk06cE6yuzdmMDqATvON2P7dtzn0C9xnfxs+eh+s/Thv1th3Cuh34bN/iPjMr0HlL7cd3oj3AqEEslrHAco5mh2otWoIucrFjX12xxU6ayuk13fYiXp86NM/zu21WfJun4M05aUnX+bFqxdJIotsKD76gYdYrjeIggB37AQHuaQpqqy/8DxZ0WU07EM+5LyesDsKWVrsUGk1EGnG1Ys3GTlN3GixcOQIkamzcqTFanuG65Mql/f3ONqyvPTkBfI8Y5AbsAXInIqDqOhTTDKubQ05mAxIjUBEAXku0DLE5QVxkLJQDejtT8gfARf+1RUO/LAE91JfHeeJ+wKJDP14VAURIpJYqylQ5QoQZfBXFHiKvjOlibQEdAHOYqVGyggZCAQFWImUMSJKMDbFZjkgCJImQRR6VqN1OGd9uVkKmjg7RpiQSmOJUEAtkARCMBIROQ5rjIc0OoszEIQh9cBhpmNMUMdFMcL5G1djwDr6NkUOJlSTCkG2z5XHn+JLM6tYp7n9jjXaq5IjTUleXr+inOcG+IDu8EG9MB5gMdmH8bZj//xZRtfOk+5dYbp3GZ0VeBSpBhUxHgxYnmuxeuoUg94eN25ukxhLICUyhL3tKxya3MOx+97B+osveB6ACOkfbLP+/Lc4/4xhkhk0IXHgWFpZRApDlBfUqkNePvcsN6/tArC1eYVQWNLxrp9vSEEgI4S1BJU6rbkVkmrDt1XCkDCMCaIQFYQlMihERm1v2JJExLUZokBhjSYddhnt3/A6Q1JSa3W8V+yoy/RgC4FE4bXGK4029338AS5vrrP57TN+fcnQz0y0pLZ0gt5elzwbA4JCa7Y2rmEGFXrXrhA26jxRn6E+s8ypGcfRuiGsRri4QVDtUEsS2gtHaGE59PO33UIGMtf0UhM/89c+xU9/8pMIk/N//ot/6qVmN65x/vx5zl+/SmoGCFUwSQsaKwu0gzHzdww5dmKR5cMJn6q+i9OnVjhxx2mUEmRun9ROsaHmr396DRfMEboKo2sNgiVNNegQ9ccc2jzLyzPzuAvP8KP/8O9z9M73IoT3FfqTP34MJwXv+NRPcfjV17hE6TdUIm6VEOioQb22yrB7lf7BHhOdodMpKpAIqzyE0WiklAjh0P2b5Bsv4g4d+b6xHYCNG0xvdnm2e4xjWz1W9BArayhruVHp8Fzj7bznq7/H7ENPI+7/yPfN3r/3EPjO7H7fEVYtg16XL3zxC2wrS7a8zXMXeyQri5w5eIKnQkNNxUzHE/au7/DazgGNZkSvF5Cf3SZWKbbImfRHKJlTuCFHjlSoz9yHU45aZLgvDdi42qc62eG2dx3l7Sfv4WB9xNb6OWS9ztxMh8hGxM2Q+qFjBGaZy6+eYeFEk6XFOxAYr4jiDI4UoXOkNpAbLj23xZ8/8gU2B1dJmjV0lpBWauQHE+LqiI9/8qcwZ3f5i28/ysbgCi++xXn54Qju4Es7IQmCiDAIMRis9b1vqW7h0SXCBahA4aSHMwqpwKTozAcOmdRwwuKc9rZ8TpYQSoGz3ipLhTESTxKwRYHJpqW6nfRaMk7j0GUf3hKGCmOm2KwgqHTIVcAQAI/xdkIh0Mgix+kCGcQEwmL0CD26gSpqBEmMzYeoIsdJh85zgqiOap3krp/4Oe572ynM6mF2h5YJispUMhWQAZnzjyk+e9cWcg15DnoM4wFMdx3p9oidF77B5GALMdx9vV0l8MxSiSQtUtJJymyzydJCizvvuYtvfuNRbJ5SDZskjQabW/u858EPM+z2yPopeTFh89yjbJ5/HGND4mqNKBAIcrbDGrVqQBxK1pOEXm+MkCHpdIxSFVaP340IagRKoQKFCsPSMzZGhlF57vzwOopCwuosigKtFZVWh2p7hiAKKPKUtL/HsDQ2dw7COKTamUfKkGy0w3i3C04RKImhQCNYWFjigU+8n+fPv8T1b7/s5zFKIo23N1SB5L7VGs9tDjDGh2WHYzTYo5LMsrG+TRQYVNxC167ynTjicBMaVYlsLdKaP8btswnzVUsl9GvZBRGEVSphiJSQ2wgpIFKKabTCN888AkGNf/g//nPyySZHDtW5/90r3PXQ2+isrDKTZIR1BYFlnF7ivadXqNcc1wd/QRwZjNT0uhNe2EywT19gZjUhosWNZwWtw1tMdY33rD6MdIKamjAWlthqv8ZLCex6PcQVllxb4ncc51ih8P09GEwt9URyaKlOZ67N9atDunt7dPs9glCiBGgFpqB8P+HbnmnGVm+X0291ow9HjE2df3ThBOJ8j3908V9zX+8Jrs68g1+78x9wLp/lX7ijfODsc3D6R0C8NSLk1iEkxJHg4vqIx7/6BN/89lmqswp99psEtBmsV3HTEet5igtDpgXUkojFdo1mNWQlUrjNK+RR1evDDyXGpKSiC7MRz11XfOfp84ymWzib05ivcv/aYV743Dm+1HmNEIELYkQEJw9pTrRixKSJSDcJao5obpmpCbCjHQKhCCtVZFgjVEnJwPGY9Xt/bI0733MCa1OPY3dexDAvBL0R9KYx9XtCjvzIu+iPRnzhKw/8lefkhyS4C6IoQSiFc4bcaG+84TUHPBLGSW7VgMakuMIjW4T0gmHCCGwQeKU86TyttxTUFyW1H6cRxQDc1PfNbYYzOcJOsGaCCBTC6dIpSaOkX/C2NAg22jIeW5Jo0b9mCqSTCJUgS8gjOJxJIZ+A1YisjzBjXBoinEGXAgNJ0uTw7R/iwb/5d/nUzxyiFcP1zPH4RUe7BnOHBEPh2zE5/ivVoHP/lWVlK2YAxdBhBpb9a6/Su/oyerRLVUIcRQRRRFHqo9hSl6c/HDEdjVGVDlevbzMzf5SpdsigTiA1w951+huL3P3Qj/DCN5/A9aaMBweoMPGBe7SHdMableBVDGWgECKgUauhZUQ2HtGaXSVpLqFiP4uQQnnilvSDuCCqUogEqVNEFBMkVZJam6Q+S6Uakk0GTLo3KNKRRz4Zf83DpEpjZplABox6N8hHu/hcOUBSkFs/x1k9dpx3/dgHeOrp77B15jzOWQIhEM4LwVkraHWaFK1VglevUeSZZyprf27ri8epHpll/eUzYG+Cu4EQcN260jQdAiWJA0EUCJRyJZs5BBkyk/hB48DWabSb1N2UM088xfalF2kfOsmkd8Ch43V+9lfu5+SRRQ4tz3KQGBItaURVLl3fQWUp7bUO670hMuzitGXGCjb3DEG4yt61Mdc2t5ikdRbyFc5duEpYX2I+f4XxaJfVux7k2sarXLj8GPHq7XQ6S37cH3p7R6szkuY8epqysXWJ4TQiHW+zGIORbcY7ewQqJ4orVJIxIlJEQjKejChMhtVeX93g2dXTbMLrgmLf76gkBDjiNOdc2ObV9Bj3Dr+IFRuE+5tMqyc4F7R5/85TCDN9EzX/Lx/OOYxx5JMJN/cnvHhhnce/+QLf+srn2RntICLPVsVcwhYpzhQ4q3Eu89ImeJKjFL7PaaUHXFgVg1REStCINHElIm4uM7VN8kIy2e+RMmH1kx/iky0BnUNs5YuM97bIdm4ghn3O2gWG/U12X/kWs6DUrQAAIABJREFU67s7xGqKVjGBDIijkGq7TdRa5vShiE6rTbBwB/VGg2MLktkkJohadF+9yHb3NVg5wvLR22kuxcxiCJ3j2NtOIsO3Dt8/FMFdSFFK7xqPqUYhwoonYMkIJXyzxokAK3ypK4Vvzyjle+HO5DgzQWSpV1YrGarCOJwMQVqs1QgrMO4N+V+sxrrC98N1qUwpFALvhPSG5K9DBhJrc/SoS6OaQJ57FJMcIJwgxCGcJcr6BFhqYUIHL5OrnWZfhAgBzc4idz7403zqv/hFTt01R7eA3tjRqwruOBESVt8I6DmQWig05CkUU8in/vt07EinYIYO3T1g/8KjZL19ZD6iOVej6CtasxGT0dQbjAiFdoo0S+l1ezSXb6N1dAU1qRJNd+ldfIZsso8TcPbpKcmhIzirCeMKjc6clwTOUxS+jy1U5DW3ZQAyQIUhRG0azTbzKxWiuOItC2WIdQZrLTJQxPUlQiEgiFDWETXb1GYWieMEnY8ZdK+xt97HulKUygFkVCo1GrOHkYFl3N0kGw0QVpcuWwLrTCmBIDl++m7ufvhtPP61J9l44RUUnh2spYPcb7DaaOLOLCtRn6/ubFOUqCc/p5HsXr1IXIlKuWEPohWmRGYVvtUlpWCKQuCwwpbALYMzlitGY432SqKVFnGQ0N/vM794GD0e0JmBX/rM/bz77pO05lrESkBUw6WbTJ1jfiFGZZ6R3ZqfQ2tJM3BcvHiNRx9fJ5y8QhQoLu8fsJPnvPe2Bl97fpvtvVf52tNnuLN9lHunknx1lq+ffYnHLv9jTt//MKfveggnApQQrJ9/gdFFwXB3i8loiEjqpAYmecYd7343FBPalSaB3EWEIXONGr1uHy2CktiXcysHM0azOyzePJf9y8fqIo2G42O9Z7h9Kvjx/h/zr0/9Y2Z2X+F/feV/4d8e+3vcn3YR9ZMgv3cYS5nJQjHRXH1tj2995wLnr22wsb9Lr3eDcVHBHHqQlg0R5AhyzKQLRQ+px+h8TDYdkub+9hcmQ2K9b4T1bGqrp1ijKSSMjMaoOlGySSSuYYwjSwt6exn/9Nf/Z2qJIqi1kI0lFtsVTjQt9ZkKYvmdxI0W993V4SMy4dwOPL8zwYWOU8kuLZfx9DDhkYsR98zscO3zj3Lm2gZxpInjCrOHV/iFn3o/+WvbfPn3vkzr6AInT91FgmTcLVi5c4UPvu99bxlXfyiCOwivN61TcAIVRqikAkGIZ6Q6fwGQSJtjRaniaIwv6a0rDTkkSF/24zzd3TseaYTx/E/j/ABIIDykSwYIF/jNo5Qb8Ka/DmdzbxAgvEiSLXKEVERuTCMwBNozOK0r1eCspnCGqXdawwmvQ16oiEGgyI1mbuE4H//pX+ajv/wTiGrEY89d5GAnpNFe4PB7qwSxb7+kQG4daQaZ9gHdTEBnPqDnA0s20Ix7E7KDXXqXH2fvlafIhvvMVBLCekLv+i7WhkS12de1dmSREVZb9CurvLYHgj5Sb6CG1ynyPiqIOXzoCP3dffpbO9SSKrPLq3QH816ZEunNOqQkUF68TEiv8RJI3y6TUiGdJ3AVOD8ckIogiJGRt0IjrFBttYiSmDzN6G1foZhOfMYnPFNYmAIjJHG9RaO9TBA4+nvrZKMD7x8bBkipEMbDA50BFUXc8dADHH/bcb71x4+x/9olnPNQVycMTgow2gdr51hdWiGXdfZ2utjCf07pNEhFNhkyv3g7s4dOsvnyk4QKT9pxGl1Yb7wOHjZrtUd9SDDWPxalxoQIA7LpPuMsJ5CWuZkmQaj58Ec+zAfvOEq9CkXeIw0lxo7RE0213WZsx+z2LM2qwomCas2wszHm2tU+g77m2OEm2xsD2jJibkEhGJIBy/UA1WjwN37uY1z+7CO8enmP7rUtyDRf+uJTvOe97+FH3v+3kVYwsgH3HD+GmZslzQuaNQVRiHYBrVqHJ3PN0EG9GtGdQKH9JhdFMfk081BjYwmURBvBC9c2MaMdvJxhFRXElL58vvKeqaEefIif/dw/JxwN+Mbxz/D8wx+hEXyQ/pf+lF9+9R/QDG4g7vl7ZfVdHs5n6tk049zZbR574TLn9oaMpEDMrVFpV6DfZO3U/aSDmPFeQffyswR2n1HepSgGVGLDRNVQMsJGjqh9lNAJ3PAi6B6FrTAyVaxJifIu1mSkeY4ToV/C0gESGVcJwwQpCyaijh3kyO4FDq5aXnYC6xSCpwkCRxDFyChBIojNBOskZ5xCBAFx/RWWl+vcuP0kaZBg0oKdcYpwKa9d2GCps8Pf/9W/y+rRo3zpa8/y4pNPcOKeO1hqzbC3vsujzz33llH1BzHIPgT8NrDoTzG/5Zz7Z0KIGeCzwFHgKvBp59yBEEIA/wxvlD0BftE599afwjlMXvgLaA06HSNFTNCIwOVYm2JL1zyMRZTUerBY6+3gkJ6BarVvQZSMIzzRqcSnC/e6Ropx5nViEnhdcl8dlGa4Qvj2jim1bYx7fX2iJFk+Is3f0KBBOJRzSPDyuUKgBUxFyCRKyPOUJOlQnb2TpXef4E+//A32LvTYne4TDBWH3nsfW11Jo2mYTPfpD2rkqUZnY7SpIlyAJCDXimKUUozG2GzqB80FZAeXcTrDpV22Rxkb+9uYtEAFVWw+RgpwIiSOE5qz89QTQZjfxGCZjA8oBgN/41rNoH/A3nhEVKmRVOuMck1cbZJUNUYopAPKwbcvaox/TgS+tRbWAEdUiQiNwzif5UdBRNKYJ0pqaDdg3Ntmbzj0q0oK1C2VT2txgaTeXqA+s4Y2U4a7l9GTAWBQIvQmLThPDrMGZwqCqMFDD/8kMwstHv2Tr7J/9WJZeXmzcyMUMjdYZzC2oBKGzMx3OBj1mPZ7OFsghU8IcAYpAvb3bnDv3fdgDlZYaldZ7oTMx5aRiJkmywRxwnwwJrIjRJQQyIjQOabaMXGC1AgKLch1gbOSSDlmayFL4U1O3tmmHi/hVJNhf4OD/ZuE1V1G2Qjt9glRGDNiPMpYiBqcuz7k8rNbJES02xH7W1N6NwqqcYZtRtRFC0MF6iFr8QxhtUXwoZ/EXP9NutsjklqALXKubL7GB5QhCBWryzF33X0MOAXjHi70DmSIhOJgwGDap7u9SbsiWTeCNM+RQYjUGQjpdfiFl8nGwe61XUw2ohgb5HSdymwNklkIZ/11EBniww8yd/15zGN/Rldq3qN3mAlDDpIaVmjiB07B6R8rQ4OvA6bDnCuX9vna2Stc6BaY+Zj5+5a5/egiC+0aJ1F0tCFLQq6MHVdu5pz5YoX1579MnheE7ePYbEo22SWuVUkMrNx9F3Z7wMHkCrp2lEr9JLK3Tbr/Kk5k5CpERXWUm/gBP8qvcSxBXKVSrSJUjJPg9DIWhXMCqUcU6RhrMiyaYpLhioxMGBwSZz2U2h4csL1heOGZC4gwIgwShPAcmnwy5c//YJur53+Dw6vHUJVlFBWunXmBS3v7jLOM0VuWSD9Y5q6B/9Y595wQogE8K4R4BPhF4KvOuX8ihPh14NeBXwM+Bpwsvx4A/vfy8a88nJCgIpxN/eRbOLSZYsfGS/7ekhEQomSkSl8GW8/a9B6qeLMN52UFbplhOyFLbcYyu74lIYBEyAAhQz+wLY06vHaXRnjqE0ZS4nD98FZIRY4gUzFhYhBp6su7W6Yb+M/hkJggJoti3+uWCmNSursv8Jv/w39PHLZw0jLqbiLDBlfOPc/K8XtYuGuNsFLFKokRNZRqE8S+ugiCOlVVh4UQnVmUUTg7oLdzjq2d5yn2rlEJFH1TwZgRzkGjs8b8idMoN2QmsrQWOqjsgN7BHts3dxHat8KyQpNOc/I8pzfKCMIqUSNBqQhjc6TzBg0CixNBiZQwpRSDIghCajO3IcjQOEw+QReGIKpSq7WJogqZLpj0rtHPUozzYmlShr7P7XzrIwwjmgurNObWMEXG/tY50sGet3cre/VeWtmBLbDWYS3UG3O87wN/nVg5vvYfvsLO5kXvTSutl4RwGqm178ebDGchWlpFBZLNriJNtV8hRuMEKKGwxjA6OODJL/wp0mbcCCwvhRGB8haETp4jEBCRIXDIqEq9Wme1FZK0GoSNGY40Y9YOdVhau4eZ2VlfVZYLLRtuMxlfI6ZPs32ExtxR8mJIS19kUKyTBFVGaUA67bIxHLB1qU9UabP94jV2+hOSVo1jcznT9gyXr4xZOx6xUKkRRQWf+rnP8LXHnmTSO2C324eKI56LqZiM1bmEWkWikFx94etcmBtxvN5CzZ5CqBicQ+gUjMBlEb3+mMOrR6lU+oShpJiO6abgbE4YBRhDqT0E++df4kBPWV48iZsOsTZHqjq83ol3UC/gVz6DPHwH/9mX/w3p7/0ryA1RJUd85F24n/9vvIjg6AqToeDZ9X0efyrnutqneniZkx86weLyHG0lWQbmhaAOJLHiJQf1CuT7gtxJCOsc/einCYuEg/VnaB65E+c0lZU7qTVXKapXCBoJRTHC6Ry5+iD55O24rEs+3GGws8V4tIcuCorJASLQJO0moQ0p0ilWZQRhBSkEgbSIIMaqBqGKEEZ7faxCY0zh45EFIQ1Cetc3Y7TXNNMpuRkhS9kU6wSTgebpx87zjLiAUIJABEjnCJREhe4t5xE/UHB3zt0AbpTfD4UQ54BV4FPAw+WP/VvgG/jg/ingt53fcp8UQrSFEMvl+/yVh3RAkHjDXeEQqlQ8EyAw3vfU3dICMOWJMq/v7M54cwwvQ1sSnm5ZRPLGoyyFxKRUyDD2SofGKy9GcQCxICAEF3jHJQtOOoTzG4Wzjsw4cptTE4bEeDil33REOVCT6LhCWq3gnCMUAmc9a3I6vIGUEUWwTxAERFGFIBR0koiHP/3XOPnxoxgJhRZMc9ATKCaQj0FPHPkU0rElG8FkNODgxgY3Xvk6++cfRUwPiOqzzC4eobv1Esb0mZmtkxTXSIQ3aN64uM7B3h794ZRaGGKdZZRmGF1uiELRrjdpzs9S68wjAoXNjdfKcfb1jVNYsDIglAkob3IyHe2hQkmU1Kl3VnBSYXXGdLDPKBthjfV8ABkiiL1+kPGVVpQ0aK/dRme2w2TYY//q80yGPQTGK3OqcrhtHbiiZCkWWCuYOXycd//IJ8i68Pjjf8je7k2Pb5cF1oUoB1ZrtCm83LEF6wqaszPMxRX2B33/mi58MiCUn8vggBCrM9ZOHKOiYGGmytzSKnP1gHZFQhSDioicpoomtRYbVrBBBYmX0602Eq96Wk4ZvaqFRJuIvjvCTKVAOYMcnycmQpoW1ekCO9GI0BZk3YxLZ7aIs4Bapca73n6Y67sHTLVjyCJzpCQNSXNlhdPDK7SSGq899SV+/w8e4aMf/Sj10FdDQS9FKsl97SYNcpwz7L78Ck/0dnlJag63D9E+2WCxHaGiE9BYIu50UNuCldWErRsVDqYTamFIV0xASir1CjPJDDf3d8iLnPXre3znya/xyQ/t4tIxLq8hiaB63CNfRAXCKoSC4sc/xpPdPjtn/4T7HnwfnUPv5P/+xmO8++oL3Hf8HVw68yRffmzEN7a2idfmeODnP8mRY8vMKsERoANUhGBSBijloG/h3BXD+Zf22Lv8IpXDc6ze9g40Ia3jp5FoCgNCG5w0RM15qkffAU776+/AZhOKdEw+2SNcv8KsmzDq7ZDUE5y2JJ157LTgYP0C0+EWSmbk45xpkRKGILTFmhRVS1DVNZwWVN2EPB0wzfymqFyBVCFBmCBciLSKIDBEoUEECUFYQRcB6WSMGQ+w+R7S5bScQxiY5JLUZm8Zu/9f9dyFEEeB08BTwOKbAvZNfNsGfOC//qZf2yif+67gLoT4ZeCXAYRUqDDC2dzLO9rSGNvdkhwAnIeulXgEr/2FKAes4o2M3KrXYY1h6Hu3CIEKvOivFAEi8CWkCAylbljJVHV+ai78XwmVwIS8vklo6z8DtsAKME5QKOXdo5zznqEqxFaa2Cj08EMpfEnnCozx1QK3BC4DSZhUaS2c4L5P/SJrHzxM7gT5FHQJuMmnkE8d2RSK4YjhzSFZLinSKf0bL7PxwiN0LzyGGfdRYZ3cCGTvCjVh6Kwu0QrHbO8fMO53KdKcJAoorCXPctLpFIH/fwshCIMKUaVCno+5+uo288tjOssnUHGIjFoUOsPlI38uw5hIKQLpdedVpUpU6RBGyjtDDfbIJ32c0zjCEo1anndjEMK3R6Jqm5ml49RmlpgON9h4+XHy8RCEwEnlVT2FQOucAIs2pUkyXhFz7e0PcNd776d/acjzT/4pg96ur8KkBBGUUhTaD1qtLhEWBVjL2toscVVx/twmtvAWhs6bsIELSrOWHKSivXSCOKzw5Dc+R8B3iAKfrRoCrIyoBI5OoojqVeqdDodadZq1EBdGBEmTVqfFWiMhriSISpWkUqdRqbLUrCNV2Vu2S4h0G7VzHtU1rK7exfzaGmk741jtGnL7ApPpDXIV8K61o4RVgexdZzxZonLPLDcmTc4fuYMPrTZ4eb3Hf/2ffIDt+CgP33GM1Ia8/VAVVxV0Zg4DPttszyywRkyydhtyr8f2dUE2SVE3v838+z7KWrvKGRHQrM5SqVwlCzq40YCqtewLS71aZTQYoK1nk6fTgq/9+Zf4sfcvEqs5aC94HXV90wvZFSGYEJeEvPjoDrvDiyx2FhnsbXHkk7dz+OUL/Oa//DPevnSZR849z8bVLr39ywStOjbWHP3pH0UETa6nhu1aQCYVMpRMQ4VRiu4g5qXHNth47mmmdofDpz6NVLGHvkrlOQ7WYaVAGW9vaQOHcGEpjaARSR0lFSpzVFp9smHE/KkVao0lnApRhLhiRGPtLrQuyAd7DPf3KUxBtSaR2pBORuT5hGqnTVxtYDPJeJwTd3cZdy8js22MFIQzh0jiJkkxoChyCicpbIARlrnbTxKGs5i9LrJ/nZODTSp6wIVcw3SKspO3jNc/cHAXQtSBPwL+K+fcQLyJXOCcc0KI/0gH6LsP59xvAb8FoJRyxeQAXJkdUpKabg055S1ayK25jDfa8GU6UMKZbkkJoJTvrztHoLx4WEl1QDqHc8b3l7W3bQvVLQKSfSMgSIF2t0xBHEJGhM5CKH1W7zQmK5iUGazAeb2PpOpx1M7hrC1dpCRCxoRxm/rsGoeOnaB9+l1U1jrMttrMHzlBMttifw/01JGPIc8seurIUsM0GzG49hrZwQAVRmR5j9HuFQ7Wz9C99Bw2n/qCt5ii5JQkDqk2WiwdXmVz/RoHB32KPEc4R5Zl3vbL4XumYUxcqRIldULlyPMpvcGIosgZ9QcsnVxAVBu4bB8hQIU1hJIEKiCo1okqTRABRT5merBOP5vgrEXJqAywvoXmDECBFdZTyOM6rfkjtGZXSYfbXH/xqxTZGCUkUgrfMuPWhu0dtPQtH1rrIE54x0c+waG3r7L70g7PPfYIo8EuUkVvCuweYSWsRpUkMFMu02a7SaU9Sz1pMNweYGxp3ygKAhkgQ0mt3qJZj1ma7fC21YB3PHSaj7+zSRKGJPUm1klyA2lhkXbi16KSWGtx6QBRymZIZ8EasjRnf1QwcRNk2KVZi1mYazPXantDcxlC9RDq6DJ0NnBZl2h3nVpVMXvbHXDP+8BpTEnwMtqg0L7lJyTt/S610X2srMwQ3xjS11Pi125y7HiNdx4/5OchYkJNtbm468hyQ/Xeh/ngB94NQqOHU4Kkg1Qj8kmPve0xzz3/IsPBkEs713GhAq3YHh6QGUuzUSNRkrxWJypyJmVC9tjTl7h2o+D2O08i8gnOjH3glMbDkvWUfL/O8499kR+/V7NAwW73Or3zX6ChX+Pg+g6/e/Y6/eE2QgYkzRrza/O4S2f4d//TM3T3B0zGYwjC0idZokJJtV4lrB7l+tmX6R9c4+hDH2D7mT8nWb6LRnMeZyTahqTjAs2EWjXBWYXJxhRZ4Z3I4oqvFvMJOy89wdZr36Sy1GRt8UMUg02sMZjCYowlHafeDk8XxMIRhhKfawbUGhUS24Cw4gfLrYjObIXO0iFMeswr3topMkwQQQzGUhQSm08ww22G+5vexCiKsJWE1WSVvzPX5PrONbb39ui6jML9f6DnLoQI8YH9d51znyuf3r7VbhFCLAM75fObwKE3/fpa+dxfebwetoV7PcM21kMQhcTTjIVDOFBKYpzDD0opM2Hrd2NkOZn3fWChvAqk1Na/h1MEaGTozbEzY7FOo7XwvVCpsFYTCO9OZCxgCqxQBMr3Yv1n87MAmSS4GH9Bra8QtNV+k5Jv9OGlVARxi7ixRHPxLhqrb2N25RRT3WF6ELBdRJgrffQwI88sJsspsgH5dEI23mF//SVcERAkFXR6wHS8S3pwk/HOJYpJjyAMEDYnDGOSSsRgMqXIU7oHe2S570s75/H6QgiUDImrDcKkisTj+ieDHYo8xzpLLamSJBFSxUzTPqoYoiSEKiwz9DZSBuhswHD3OjqbIKz0GZq41ZzSnptgXak94i3ZwqRJtbVEWG2h0y5b576OzcoBufDVFAQe3loqfTqrPQLF+QFqpTHHAx/9FPPLDTbO3ODsY48w7B94+VSc5x4IB1Z6RT0EVqdY6wfGGEFraQ1pCoZBjTiynLztMJ1Og8WFWQ6vLLKwssiRxRka9RrVSkwYhkghOD53763VWk7X3Rt9v+9d1TYrs5HkTT9S8vtfX/lv6kVTenk6A40lVGsZOezRvbFJY3SJcPUA+qDaRyCoosI3Wj0SaC2v8DaWAcfCao3ZMGb36nXyhTXaR+4H4423i/wmYsPDAidFhJw5AkDUMn5gmBuido9nvvIFxqrPoZkO6W6XQkMgRtRiT1aTODqdOpVpwfWeKO9h2Nod8u8/+1V+4797P1HFgYuxBylypoLbv4kz+9zYW6WI92nNrJLreV698BxX//wcF7YEtdW7GF/7FoVztDot6rOzJNWEg36GCBJcvY00Bp0XpJOUfDJBRhF71zYY9Z72fAUJF776eS7wecKwSlKvIZxCI8iNhKBKpR4ibYhOu2Cg0JYgCam2WlTiefauXGQyucl4UmW0eYViOsXpDGtzrBXlzKnA42LL2aGXyyzbswqhIlSUoJIKyewhqs0lmp0OKplDxLMEWEQQQgBB7EBD7ha9QXpcB+1QJudw7FhxKZeNRpmcKoaJs7zV8YOgZQTwb4Bzzrn/7U0v/Snwt4B/Uj5+/k3P/5dCiN/DD1L7/9F+uxCESYgCZCgoLER4OOPrMMNS/8U5v5CN86bX0hQY69BlFl86kKKUQJcXQZYszcJqsvJNFBA4h0FirB+aCmlx1htOe+icx+4K56n7MpBYUVrx6RLrLYLytnTeaMDhzb6F9aSdIEEJqFTqiKRGb+cqZnqTna3LhK3DVJttnMso8gkmnWLyFJ2PKLIJ2ajLqLtFGDVRcUKzKlmcP4SeO8xBVVEVY6K1DtfOvUJFBWjr6E8dYdxCRoZhb59CG4TwjNCw0iCOYpSUaJ0yHe1jdYHWXvgJKZEIFufmSJ0mLyoooQijCkmtikWR5xNGe1cxWY4TxmebQpYsYu9Na60r22QFAkmoYlS1Sb2zhgsExajLcPuiZ/MKL+0shPKZb2kQYg3eS1d7X1prwVhNZ/E2HvrkzxOYKedeXefcE1+hmE6RSpYzFXdLPs4P2IXP7CpRhUYtotFoMN9ucvz026hIyanFCsd/8ePMRCG1JCIIAwL5RlX65gr1jePNUxzxPYydW68JkEn5bw/Z5RZn4tbr3/vo8MgAZ8Fk3jO4XmPm+J0gHGI6YDxcJxR7WHce1TxJUIkQVMu1WAZY50AJlHS868HT2NL0XUhLFLcYjlKwGU5b+r1tzGQIMmEqYxB9lN3H9Cz7/W20Kbjt1CrNwtK/vEOaZgzHBXlhqLXnaHSOorN1FpYPUWxdZZKlmMLy7z77dTqVCj///rfTPPYQlXCKMzU2BjE3e2scHOxy6O772d2rsL21ze8OFrh2Y5eGmbJ9c59U95lZWyCszyHjBF04iumALOsy7e6TTjOsKGd1gB0PyNMUrQ3GFCgVI5Uf/Of5mHRn6If/0rdKhZDku64kQiqk9NLf6UAz3Y+9NWQ5u9MHE1J2kEKWwA7vteyc3wxxfiZnrQ/sTqjyumvIM4p0iO0Z+psXAYkIAkSYEKgqQaioNBtU5m8jiWewRmLyEaoS4JyjSAdIk3FbNsH2t9nJ+ziXEwtLRX6/tfnG8YNk7u8F/lPgrBDi+fK538AH9d8XQvwScA34dPnaF/EwyEt4KOR//gP8DZSQWGvQRQljFPC6rKj1WFrjEYcI4VAiQEqB1kXZV3de9MtZpPQnPJQKlCCzFqmNN9zAozIy5xDS/6xUjsCVPqFCYWTZWxd4ko7VZc9Xlf164WGZhSGudlBxQl6k5KODN8gueIggRpBJKHYuIffXCeMq+aBNMtiH+CwY63vAJveQPushc9Zk6HRMvX2YxVP38M7T9/KOB46zuXmTy5d3qakp1p3AdVJ2rl5HuJwiM2BGDHr7CCRRHKOqdc8ODQTaaCajA4oi9VmxLWcR0peT1hkKa8myDKugWklImh1MNmC4d4ApfValEng2aPC6ObJ1RalmZsB5P9kgqnoIZXsZEUZMD24yHWzjTO5tDXFeRqJssTnnvFaMKysko3FOl8S2kCP3PMgDn/wE+9s3uX7hKpef/pbX7xehr5AESBGRJDGtTpul+TluOzTPkUNLrKzMMdduUqk3qUSSjZ0JN2/u0LeSYzNtGtJ930B+a2Dvj1Lm6vveU98nfX+zCwg5uJjv3gzEd/+eAIhBxiCnYCflX5z6cxsFVI/djcUy2uxRXHyWzvIeoboNWrcDEaiK/5xRG4Emas6CMdjxAD3dQUVNVDFi8XCVX/iVX2B+IUZf/AJq5jDFtKAoduhevsRoGjHubvPA/fdSnW3Ru3yNu07czf7Vi2zvjYkixXKnwkzFoBPFwuKQ2y8TAAAgAElEQVQsB90dJlkBTjPan/B//Pv/QD2H++44IJmdpYhm+Zf/6nNc7Q3IJzmdE0v87C/8HfZHy1xe/zpb2y9RTAfsD3NEEpBezVDhTYppRtyoMdes0x96+QltLGFSpRgNcEKgc43OM5+MKYUS3m/UGb/BC+mTLWMtWmvfLhTSQxkdmMIT4GQgy+rcggGDQdgyAZFeR8rHJR/85a3rJjyRzjn/vHBgMX6Ddj5pdKV+li006CnW9ciQjLsOrr7imbII4kaTpDZD//IZCp3TkRDUYnbsmG4Rep0o7FspI5fLz32fRfn/86FU4Kr1BrK06/LleIiT1ht4OIeQeKMNZ8B6Bqnws0lfspe9VFFmf8EtCKQpyJ3wED7nBZGEUBTlRuCEz9qdK1UjpcCV/p84r/tetvX9m6sAKWOUEighqM3cyepdD5KZAaPdi2xfeZls0PPvXd7Yvn1UzgmUQkYNFAadDspqpMy48Po6YXWGMKmyduwh7nr4w4hOlelGj6P338vNG+vonXX0cAu9fZmr11/l5rWrFNoHiizNyvaDIw4Tqo0GWmdobb3RtfBMTin8xkeZnQgH1hZIJEePHMIISLOIxtKqJ2VI3wuH8nvh8O5QlP9Xg5ASYw1hEJM05qk255GBIBvsMO7t4IzGSb+Ri1uSsYHwrQDAau9ta403DdfWC7cpFXLnBz7G/R++n0vXr7Fx9jqb33kKbdISWug3eqUigkBSiUOq1YSFhQUSpwmTABklzM7N04wlYbNKVl/m1OI8aqHDcHfCkXhCFEcIFWEDhQglIorBSorxmNlOlVYU+Crw9U3gVkvlu49bs4LXeyavJ/oFjgxEFZDly29+r1uH/3/7Fo4FtIdMyQpOG5xLPSHLGJSzEFhcbsnWXyX6f6h701jLsuu+77f23uecO72x3qu5qrt6ZDe72c2ZYqiBFE1FUSwpkhHZRoIESBAgyIcAQb4n34IgQJwPmRA4hh0kkBzLQKRoDmlJFMVB4tBUs9ns7uqxqrqG9+rN7w7n7L1XPqx9brVhijKQ2GhfoKpeVd13733n7L32Wv/1///XpQ/iRptIvfbgtRfHdPtvc+f1b9E0m7CeOcpz/OwEbh9y7solXHvCvcOOG7dOqVfWufXuDnfvHzMervHm7n3a3Ttc+8BFpvtz7t2/w3yhPPrBC/jjlum8ZTgU7p2O+PZfvM7tgx3iYkHjhM1zG5y98jjiVrlz2nF8ekqMrdEDuylNMyBoxe133uH0eIfZ8S6nizmhrlk7u8n5i5dJ88zRaQspMj/dp1pdo53OCL7iaPeu9elzIpcqz1cBKQPuKT2eLM4gFPE2UtK6eYV91eeTivgAGTLRvJkw2DXnaHtFslVXRTMjEhBnpI6cOnLq0Pyeeyp9f+sB9GbO5QU6VLHBRJjQSx0Mx6vkhZLSAhHhsmT+o5EwCYFvulXebTv2YsssKt/ae/dbqvqxHxZX3ycKVSXnFsmBbKm5SbsRVGy+aU65+B55xEtxYizfnX0Zg2d+JznbsF8KR92TCrrp6FRAY8mIxIzCgi8WNtH0Lklw3uO9Q53hxvjKyjcpdEoVQj2iGg4YtHO2HjrPjfaUM1cOePd7R/RTjKSHZLOgolb2k3Des0gPPpcrN9f5mhA8w3qNi099jNrP0U6Y3nuNl3/vFQ5TzWznFWZ7b3Cye5fF9IiULQtpF3Nr4LkAZNq4QE8Kli3WBC1R0943JlugGuk6swfw3nP//jH1JCCuocuZ4CtEzUfeiZmroZlMQp0xjLxvCINVxqMJ1WgdIXO8f4s43S/sFus9SPHD9y6YkkDNqllJSzYUauynrJnB5Dwf/6m/wWPPj/iL6y/y7gs3uPPi96z0Lrcm+OJvoh2ahHi84PjomJ37+7TTU8QJOWcqH9DU8dBzH+bpDxyg1x7ilaOKl770VW699B3a7pRB7RjUNWE0ItQDqnqEtjM2hiCuZm3S8OxP/eu4GLnxyrcZDEasDhsmTcWwcQzGNlB8NFxh3DRUgwoJwcYmeo9HoNuBagB+hQcwjT0s4BdFp3qgs79XA8gd4mJRYBfKVVhF8xESlcEjH4EYSXsv4teuQr1mDqn1BmHrSUZzYff6W5zuHJIXcyZNR0fDbPeQkavZnx9wcjrn8hnlzOYZTu/cZbC64JPXGobPf5R1f4PuyZrtwY+xs9syXkm8fstz5+A+6zWc36p4+JEPc2Y85IVvvs2r08Rud4kDjbjZFJ+PWZsorhrSVEOkGkO1Tqwazj//LHmeuPPKW9y59w7z2SnDxlhjtR+wveJpU2Y0GiK+4iQ6um7GcHWDarJJd7RLt5gzOz0hdtF8fpxDk9ClFgS8Dwaz5ERWUzX7orZG1fp3lEQcI3eQiv5FyiB4MbKGJWRK0g6JpVJHC22XZTtFlwSR/E/nAtr/IYbNq+1JQx2czYbO5pJbeaVNcN05jgceFyrq1LBY/H/E3P9lPGxLO8yN3VuABsA2pXfWUDVYRs1iQCBny2ycJoL3dNHk/pYNq4kwnA33yBguJs4hKjjnqZy5K5Js6IDzjeHTCCKFX62lQeuEQLZNioKvScDJ3ru8MT3hQnyEw3s3ON47NkdKN8AFT0rFQhgLsClmNJ4Apto0BSUlcU/UVYPXjsV8n5e+/o/4QTpE4oJ2sSDFli57UlxYeagJ72yxxdiRSyAeDBtOT6cUVX45vISYMZsGNRlYPVhlMGzoujnt4b5lPzkzm58StWGyWaNRSZJBIy47VDo8mSiC4HGuphoPGUy2qSerdPNTju+9Qjs1KwHvTSHqqIBYcEurLFQ6NJlgQ5zh9JpjEZIp43MX+Jl/+9/n/LDlT15+mVsvXmf/+utQGBIislQcay6zULVk/mr+9agWe4BMlxc4lPPXHuN+5xm7QDyBuzdvcPPGm+Rk60m8lP6+IxcBXU+/dKJ8f9fz8FMf589+75vcv/0aQaXgswsbBRk8o7pi0DQ0Iwv2K8MBG+MBWytDmsk6VS2sbGyzMllnMgisrzZUwzGhbvBVRVXBoB5R+YD3UmYUZESnuFwhsoKQoT2CNCXPW9xwbHYa9RkOX3uNOH2LMxeeIEYIlz/MyvYVXv7+Db5/fY+TnT1OZ7u07QlPPH2ey+OGwZkNts+vk6sFk42G5z/3PIv9HWaHU9Y3V6lPznP7+pu46l3emTpyd8KkGXGpgTfu3OOxa4/y9mt3uTt/jVf34Nvfe5vT9luktmXRLkrVY9fTAAOH97UJdLzSDIfg18jVGhubQyYrNfWZx5msjMFVzGfC4b07TPffIDOj6+aIh9AMGV96guxWOL77DpoPkTgjxci8TVZpaSEW5A5QvPQUZSV1C0BwYuKiLFbJ20RNLYSOsm5TKmpvgwEt6SlBXMu/45YMP/eeEJv7RE+1OJu6Qu22/WiN2ExOCc3WZ6okExTeTcq7GyNOPvRRRmefYeVoxskrP4B3X/1L4+r7IriDGMZFwbZwRomTTETR5Ehi/jIxUdgwqSQvRlnULhNUyCYCtovWp3awzGb7EsyV3L0KlXF0+36WgngTsohvCF4MvehZGN4y3ew8bTtjdrDPoSbu3fouVtipNVlTi/hg8EXPxac/uCsoB1amwqZKCRLsM0dqoONk952SAIjZEKdMSi05mpulaiZq7l0WCM6zvrZGMx4zbxdLeMPgDltqfbkjzqG5YzYzdo+XgGpLJjFfzMgiNN0cTg9xwbJOEYdzgRQ8dbNC1UyoRxN8Mya3p+zffJlueliutzdaoiayiAWmAlPkUvaC4rNYhqqYG2e29uOFJ5/nc3/jl2kO7vCn13d569svcPj2TVCjWYqXZVKA7RVEbLC6JMgSDVLLuZS9duBrCDDY5Kn6iHdOIk3VcrBjpmGittEkCohZKuhS94D1WgTefvlbnL/2NFce/wh3371ufQK6MlUnkfKcfRRHwkllAU204LIZ1GPiLIeq2WJ4H6iqiiZYQ7eqlEkzpApCqDxhMGRYO4aDmvW1FVaGY8aDAcPVAVXtqQdDqmbMoKoZD5VBnRnpNea3jkntLnrjTW6/scPf+dUv8sq9+zz25DU+/9M/xvPPPsHZ9VV8OkZ3bnB0Kgw7Bykia2tsrW6wFxP7R8dozowmm9xtFT/2rNTrnPHK5PEhu9854XR6wOq4Im1cY/X4Fi4nFqeHpJSp6pph05DbGW1KNodAW2JqoUCi0+kUTTskzdy3WhbVP1kKDuvJNmvrGww3tqnP/TWCJJwc0cURs+Mp8eQtQsjEudJpwFcTJoNY7l0m5WBzkuOpJXXdwlhU5ELBddYiSdnuWU+VSIafa8HVk9qwoOSc+RBpgVece9AuVEo8yORsez/RlR6VK3259zTflyJIj0ZjihWslMoJO0m4NVPa4wM2x8dM9xecLvs5P/zxvgjufYbnxBXmRI+9l6ys96FGqILxna1Pkm0aDHZiqkBwdjAkcZYJOvMxF2+sGMvKBXUVg8ZBNtBGxeFCAxJwEk0GLIInFrOsAc47JNSo83TzY+azIzsoCvi/LJfLVRXxBGfOhnhn3uXiQaoyosy4+WhmdrJPbju6blYyAS3OlflBUC4ViWbK3+017Oe3TGQyHKBemayOOOyOSwCEXGiffY8lxogs5gWy8UDC1zXOB0JVMxiuoi6RU2cHWgYfKurxOtVoQjNcpR6tMT/e4/jOdbSdId6oi4jgHAQXjDWkftkLMU+t4tQZO2IGaNHoSJoIruLJj3yBz//1n2Pv1Zf57nTGG3/+dQ5uvoMoxq6RCFIjEkAUTckcQLNxzB02YcmRyBKMxYBVLJtnr7BWd0zOXkJOWqZMOTncMy56WWs9S0KLGE5IxFwmdOXM4f277L77Nucf/gBbrzzCvZuvoGJyfpwxhBwdaOkrOF9aOH0PJhrsVEgDOSltbJm3nilqxlZENO3b4ehs9oBgmW/WCOpK9gmuDJeR0CdGlm02ziTxqkI3n9Pu7XLYJWofOLrxFrffuchf++nP8ujDT6One8Qrn+D08A7tu7vcW0TW58ewuspaU+EPDjmp17m7v0/Vwvb2BpMzZ7m/c8i9l09wXEHPbnH10RVuvPQ6d+7d5969u5zMjhlOhrjQcHI6Zb6YWmWmGe/6flNJrGK59kuLEPu6U8jTE04Pdzm4RfE5d+ASoRkThluMV0YM19aorn6EgVckH3ByWhFP7tGe7tCeHhHjqTmNemiaCjewMYzqGwYrW3THh8ynR8R2DiRTsWZLzsQVvUapLhMZpxVZrVdmTLNC5+sFl6WXljWVtWuJgjmPFtRfelZg6b2rmopazarADpPETnbMgHj7Prfe/RqztkN6XPovebw/gruIiZBEcNnMIGM2fw+n1vjzStn8nqAZPDi1QR5xeco5WzDO43FUAtl5E8R4IZTRfOItK/AiJM2EnDBCY2scVq2Q0NCMVpDKmalZqG0jYQ2ZOGhJaUFa9Pi9+clAyf6rNYYb19g+N6aqKlY2zrEgEg8PCIOK+fERoRIGXslRef31feJ8blifvYp9JVIav6Wms8huhx9ask2WMNX+6ZzKRYM6NBM7iuOaHWCC4CWXubEGTxkePMD5Ab6uCaHCVwOcC4gzH/ZqsEazssFoZZMwGNBOD7l/40WjhGo0t0QsS7Vmk5g9wDKTycRkFrlZsI1TaKmpLOSqGfPMp3+Jn/npn+DFb3yLt9ojbr7wFY53bhW4UlEq61GkjMocVVk2rHOcmjjEeZRCjdT4YKGpcvbha9zem/LZZx/nrZM90nFF7GbmdaM9xc2uZ8qpHN4OUgvObBe6GHnzpT/l7MXLPPSBj7N/+01inBVYqMdq7WfusEHsWRRfRvShSpdS8URKOMzW2ibzQMqmrbB7WuCqcgWyiq3PpKjTwsjKJI1Il0HNSM+YGi0Q+l49HTVZOkK34OSo5fsvvMC3v/xFmCXanHjmuU/QrG/B1cz29JTu+F1yNTBGUqcMOmG8/VFW18esDEfk4TpPNAFSonOBQVXTtsq9O7/D9179x8zjlMeefpIPfegZHv3Ao7z+nVv8/pd+h9hN6Ret65uaIiWY5bKmtVhMpOUcguH6OrOjltRNjeGikW4xRw73OL5jB5/Il623UY8Jo3XG4xHN+lmG556gZkru5pzMHXm6Qzfbp13MQaY4OgaDCW7wMO3Bu6R8Sqo8gTGo0YVT8YO3flM2NauduGiKZW+WO6V9pWYxwSAgrJIsjfQet+8BeCceVzyNcrbXEIFZNj+Z0Qef5MLH/jb775xyuPM6XiPXv/+nf2lcfV8Ed4DgPQHBF/GRF5sc45eNDqEpWFiXwTuhrkdIPWIeT9GUDGoJwTxNsEPDl2alQ1Fp0ZQI2iEObGp7bdc/jHDBhrNlZxYG2XuUyqTKaiwPdXbIOPG4qiLPY2Hq9JuQImZweN9YxotysHcPlYxXRTvL4nPKTOdzcntCjJ3hxT14o9YQqqra7AG8J85n1t/R8izJZVlYORcqz+q4QanoZrMyJrDgnFg2bWpZX2CTiuBrXOVKgA+4UONChXeVNW6aMYPxuuHE41Xa+QkHO2+SU2uaJTFJt8h7cXNMpStqizklM9TTXJpFahl4oTySMtV4k+c++/P8xMc/wjf+8Ovc08Rb3/kip4dG6zQIy5rjsmQbhJLtJAtyGBafsiJl/Zi61aoJFcfmlWuw+waD8Sc5mVUc54VVRNmTsM26xISzUgz7cc7KcXLGiXD/1pt87Tf/HiqeLrakrsAslO919v2aMGYLCpLw2NR2FYeSIDqSM+V0WTzgs3kZYY08CmSZizirx3a1t8IW+4wxPzB3sx7PwFDJlMAp9WAMogyC58zGgLObW3S3b3Djz/6IjauXYHoRGW6BDPDjFfz4yeX+XP0he/a9vcEKO8WrKpNmB2iacfbqBZ5/9hkm558kTTNPf/R53nrjbb53/VuFaGDJC2LzHFBwBFvv5SjX7ApFODM/PgE8ThxdoQOCW0KqlHsWcyR2hyymR5zsgpNX8C4gwRPqCfVolWY0ZDB5nIkILHaYTjtOThe4+DqpndkVdBlXBcLwHGMHObXEJMRuQWoXNgEudsv+l31eiwWWnJvNhmZfnDcKuQFXdCD6HrasJSnGdOsHaWrJ6pXT1BFf/QHJ/T5uMULbKan5IX73742p/xxx91/4Q6AM2c1kV5Skzuw1U7Ygkl2gU7vl4gOIp3VWbkto8CEWSpQN6pCUDJeOyUbr+bJcfCAyIFSVWcAWq9mcIfsa1FnZmwWnC8jzMiSkZ+Mo6gTJQjMYEk9nxkQJFQrErjMP+O6I2f0fsNuuIhJJi1OzI5ZiS4DggiOljq41fJgycs42r3XyN7fOMV1M8WQO5zMLkMvyvj/5jU6VYmRvd4/RZMRsavCOlA6/74O6t6DuQrMM6D5Yhu69t+arqwlhgGtqRqtnaVY2iNN9Dm7eBk1L2wdVgZRtKpFhQ2XSEhZMe4aA9yb+woJykGTZugbAs7r1GM99/ud4+gOrfPWL32Q/d9z43h8xO7n7ILERDBLTAoH5Ci3NUjtQBHGlaVtKeztkvZWvKtZ4q5Su2mARF7joOD3cJ0Ub2BBxOLyJnzSjZDQJyZlhHFqGuGtGY2bn3m2jlKpN+rLqyuBBckC1Q0gFPzXKJqUvQJ/JleZaLlbGUQvmX3JX61WUjk0JYq7/P8ScATUjLuGz+fBoFpRAduC0wzhRphCeTAZc2lrl0WvnuDBZ49zVLS6sTOhuXif9QHHbV0i6Trj0sImwyppc0jr/kv3bP7wTnrm4ztW1Ee6hczz+6FNsPvEpDqffZ73b5tFHrvLm7TcYbQw53T+inc9IOeFcwHsKYcHWbXCC9cJNzJhiJiUlV446eWJy5MQDwVFZKkt6d6noVK3aktQSF3Pa6QHH4nHymjG4XEBcTT2oqSYTZO1hvDg8J8QIGucsZjZ0BRLOCW4wwPkVhs5sx1NUcpyTYgdxRoqd9RFzQl3R0JTKW8n4crBp2cdG4DDLay3QjpQmYFIhJuXk1g7Tnd9h4GoiYnqIH/F4XwR3gOArGyCdxQKsN8sn8RX4od0E7/GlnJNiEyvl1BQiOSqaFpbp+wbqIdXAVKiybOo5cja/dcuUvW08EWJnE5rEi10YdSSRJf8ewYY9FIx9OFqhPZ2SOxiMxmTN5HxAyhHVRLvYpW13S0O04IhSDii1yqJHzSwjSyUTNrgl5Y67t29aHpDTMisAGxzQZ5j91zknYk7M5jNi29oB4mu8c4ivLKh7jw8Vztelh+BL8zpAaKxUrhoGK2epR2PS4pTD29dxpKK8E8u6sQYUrsBHzjISyzb64FQwRRHLrMUty2zDPj3bT3ySj//Ez3Dh3A5f/+IrzGLL9Re+RDefl9fCqq9gTahMwiUtHkRuidEqCkkLG8gEbdZgx+xVNbG+dYG5Nlw+23DSrdBsj7j/9a8QW2MnWPA0GKfAnZBz6bskVNLSk0fUmWDG9Q3qHjgqsJwzQZzBheXAdmYVbF2cAE6IpUdAViJdgcocXZLlvAERwBvzy4sjlwakFvqcFCqsI9lYOSlHVbZqxXllWA9YXxuzff4sT1w9x7NPPc5zZ8cMRoG9u3fIZ1ZpLz6Pu3UD986fk7unYRKRZgtWngE/5kerdaWsY9j40LM89NBlDlfH3F3c5uefvMqXv3rEX//Cp7jz5n2u3rrJr/zHv8jhq1NevfU6d+4dcP/W2+zu3iPHE7x3VM4RXDAsuhyGyRcITzM51eTcgfN0XaSLHbFTUrLMN5U91NtS2V1yOG+Gfil3pJ7IUH6CbiZMDx3iblvADzUSBjRVTTMI5NEW3pl7aOpaclrQtqB5Dqp4lwkDQf02PgwJ3kNa0LaRrm2N5RZPTO+RjH7ZZy8W1rzBVOXT5qK2btFyrwXtWrLLtC6YT8+Piqk/8n//ZT3EIYOJwRAqFnREjPpYxEeJiGsXy8AQvDd5uqHgRD+wr5vaRAxlgadogSdLycpUrUFY2l5SBtOqc3gUXDaFaLnprmRXpm61HAi1Rq4LY0abm5zu73N8tGdluCZUzZ7WBD4F/S2LTMo7qyvNF8rJjXtPhx5Mgh97bZbhzN549kazcuZ7k8ohRyapo+06Yu5QFULV4MLAvGe8x7uqBHmHhMr4+s6DeAsYrqIZn6EZNqRuxmxvr2SFlQWP/r1L49gVQzYR8+VRXBF/SRGKOMtEU2shTR2SjSkS6hGXnvs8n/jJzyGL7/O137rPic5458UvkxZmNOW9swUu5gWE+AI3mKDNyLPlICn0SslaPIAgF1GYqOH7a+cuM797g4uf+jC7+zeYnXma1CkpG9PFlWbwEpYpUEnCGq1ZKzv8xSG0+HILMxnfCeIdWcxhdMnMciZ8ItuYR7Pgqeixm6pHXSSQscHu5ArXY86oGYWpFk2vFNJNuT5ZUZeWr5ddwongsnH/nQusrgw5e2bCxfPbXLt6jkuXLvDEo5c5d+U8zcom5z6YoJlAEpJOOeieYL674NLGw+z9yT9meOU7DJ74ORheLokV/NP5+nv3Mly4dokv/OzP8QevvMx86nEbI37hlz/Pq994gfE4sToa8Ov/499n2IzYvPwQH3rmcUaf+QyQuLc/484bL/HWm69zeOcWXTcnOAgiphYtB6iSS1PWM6gCTeXRITZTNSa6riN2nc0/Li6v/bULlcdnTxczpG5ZBeVkh6zNY4jIYoHqIYvyM6u7gXcGt7pqQmhqhsMadQNyNye1J8TYQjclcUzShK/HSL3CynhE8JDcNRIV2i6IixNiO0XTrFQbjjSbApYc9tFghlDjWBGoVNmQRCqQ5Ns/Iqy+P4K7s2BjPi0Opxa0cm7Rbo4tdQHnkWClovrKMHlLJgk4KlUSgOSCfdqGc75gBQpFIokUHDVgE3rEVTZxXCtysOaGFuzLGjzlTM1q4pEMokJVrTBcU47bu8baUMXIfD3wuvQ2tKyYgsjpEm0ojaBewPNeihSg/WbKUMzR+vdZ5hzaN3FscYOnagZUtQV3F7w1RyWUoSbORFl4Uo6EUNFM1nF1DXHOfHpMwOEcJKmK74a9gZTKQ6X/lIXimCwMaQLnE7kzXULWbA1w8eXgUwaTs1z98Of45Gc+xfEb3+HV75/Q+jlvvvRFhIwvENeyyik9F+nZQWpVQsSiqxgdyDLdB0vKYKMCFTnvOX/tSW6/9QMevbLB165PqVcy+7sHCEp27j05lB2mNjnIMmRxNs4RQPOiQCYseyRRTAhnltFGEHCuLlWlWV9IygUNL014CVTeoRQfeXVW4ks0G2zfM3mtyWgmdrY2rYo0/Fmyyd6zeEJp+IOnqgMbq+s8cnmFa5fPc/nyZT547SKjzQ0uXrpCPVhFqgEqEXQAHOG3NtjceAh0Du0Rk4efIboV9l/7M8b1Ldr1ipWzT4Kf0Mf496rcBXCh4vlPfZqbvuYXfvGXmUiNamLn1i3uHv+AQeO5e2vKXrzPnbu3efPV77G+sc6Zs5c5c+4hPvixT/KJz/40s+NT3rj+BtdfeoGbb73F/HSPQCYEscE55bC3tWFv7oMwDI5hE4hpSIyJrm1pk5Jysqw7lvu7/Niuz52RQn0FsXumiayu7LFEkoxjDnIMGKyLK8SE0ECzRjNqGA0CWYbE7NHuhO50n1nsQD14R9VUVM06g8kWlcxoozKbRsj3UVH7jCniROmcEFU4zda32U+ehsTo/wdvmX/xD1XiYkbpPhFchdQVlRO0XkXqBskG0VhAa+kVnZaxeHyFTTkpoK84b+ZgzlsT26Uy7MP46j3lrdVMji0hpFL+Fol5Vps4hBqPXo1GqcqSh6pqTdDKNQwnE+ZHx0VtWUqu8vP0mY5SWFBL5gtFzmyjt8xJTum9aZAeN7bDaFlciivnRvHHKKwX5zy+agjVAB/KrFNfmX9ODych1rRJvU3AGRMa5TkspiDeLG97qhZF/CWUz1ZgANT0XSrkbAcfCpKTBaRyWAmODuOaC7B6/mEe/cS/yUc+8gRvff0r7Kv89Y8AACAASURBVByMmMkpt17+CqQOBIPkFLt+GCymBa9M2Qa34IK9pvS8eTs8KUNVTFhY8l5NrGxeYvXcBfZe/zZrkxGxOyJ0HSd3b6Eplp/HP0gKNNnhIFYVGfibjMKJYNYLyTLyct/6mV9gOLFzWOmXpGTlhof3EHYmYf5z3uAgMVMpp8l6T84jWvzlixgOHEmKjYaa/7zRZq0ONcdrz2RQsb6+yuXtLR5/ZJtrVy7w9NWrbD58lWa8QjUcFziyQqgtM/Dr4NegAvIMqpr66c9S5xlDKuLuLie332RwdBvZeoqwfgV8DTmS2yNcqA3ao2ayfpbFNPHlP/8jfvwDH+fOW9f5wi9+jA8fPMt/81/9L9zwb+CyQI7E0xNuH59w++0beP8txitjVs5cZG19QlVPePyJD3Du/CVu3bnH3p1bHB7skWdH5NRS146mMnM+swzv0zDwzlM1jqYJpKhW1XaRmPqeT4Fa1S3rATst3DI1y+JLImZkAHJvG52BhCbhQb19ihzvM3eOQ+cIrsY3Nc1ggB9u0NQbVJXg4oyu7ezX9DbzNLc974eMxjVu/TxJVsmLGZfZ56Gu5d15y15OJnBzgdS2JN7DBPshj/dJcMeodz5ATtY4w7ITB6ivypTyFiUYZqyCaEcWB64yCwLUJMLYFHpc+ZPe+RGWJzIUzrlH6rDM8HJO5aYCuT/RFcFMs3IsKrJS6FHUkc4N8NXCNlp+0MWXPugghgGWDn+2f6KXmot7kJGhvcMfBY4oz5GwdLhURwneAeecsV58hfc2ChAXDGt3xhVKqqScLRuoa4ajVRsXpy3aRRv+KxUUFV6JTEACH8q16qEky0Q1FTlGsWPOKmha4CpjAlHgJsQ+w8bVZ3nskz/N08+c5Tv/5PdY5LMcHr7OnetfJ6cOJ4GsDpcM/hFflYwpFwaoNbOz9mwYh6gvB4sapp08TvsBLy0inkymmaxzfPc2W5vrzKJjZ++A4Od0iwVZc7FGeM9G9wbn9DATpVoxvLuy9y+VDwBerB+AVXyu9Bk0aYHNxARUmiDHkhX2994Z5Zf++npLeKJ59vgQig1GLGyY/jnFDkKETDTMtwpsbow5t32Gi9sTrp67wAcevcTlixfZ2hgzXF8pvjOK5NYonqGx18Ma3IKCG0MC7eZQWb/Kb13gwvZFQIh795i++VVk+yqD4RmcH4K3gfYohGFNUw348m//CU9cvsRDD1+knQ/49l/cIi9mxHaxdG1UNa5+zsoizYn3pxzs7vC2iDG4qgGC+c+Ldkwmq+DWmc3m5HTMyWJBjh3BKyEE08w4wRrgweDX4PDBIYOaNibaNrHoTOnd0xKUvEyibBsWWKAwsfpm+XLrStG19OuvUJYlO0hCJx1dO2Vx6sDt4MQZNFoN8fWEZjBgMJkg1TlUbFiRLg5oT49x8Q5nXOaxgfD5wQrXq8R34gLvAg1wkjPH/ypg7uIcvh6B2AANY4GkMuUH82jpFWRU2BQeyNQ4IpIj+LSEJrR4wCgmorFjtWTQhT9sOG3JjEspLX3zq2SaKXflppXPCYURYdhczhlSJHYzumj+FaGuDcZRY78gmCNcjsboKGmbLzajrg/ALrynuaOl2VoUb2JDSIJQqhLzw7DvA/G1vY6v7DAsDAcph1vbzckqVINVqsEKwZV5jsWfwLke1jFs2dzpyiL3oWTJlsk6B4hlaw6Ta0ehzH7MZrwWe4jJcmcnjvNP/QRPfPzHufJI4o9///+kDk9wcPMldt/5Dpo6FE+SnnloZbcNH7fA2PdC0K5kXB5VVzJi+3nE3tKGZeRSPUlCXODs409x+/49PnLtEY6Ojhk1I4buiJwizoVyS00MpKUckCJEseZqsEw9xeXhimLOpCnjcirX0aqyiDN8vpDmc6FJCoL6GtVc7r+imKeSd6CpzBbIC4qVnUFPKuAroiYCxQiPMhTGO+pQsTIZsbW5wvlzW1y5ssUTD13i0oVtLp/fZvXMOq5aMXYNZmOABuhaWBzCaBWkEB77Cyk1eXEASXCjIY4Is7swXEHGNSfTTTayg503IUfmXvDbT1FVI9Y2Jjx7do2HH/0cDz/0MNsr68zuz/jmH3yVe3vvcDKbc/7KOqmr0G5OSmqQYnFGNThU6doFaWaD4M1HLqFZSLmFLFTNgHowtmvYtizaOXl+iscOusYb7VZLRo4ITVVR+Yq6CsXfvojp8nKTl/TtAVmgp7dKiQe9KK98Q/n9AaGgp8QKavBu9mSJBrm1c9x0j1YcGcGJx3tPqGv8YIIPE56eeD7gI28cnPAP9/fI2jITqELDzAdmORLze+DbH/J4XwR3MJ5ripSg1Qc5gx8km2Q7F6WjUCNq5XlyVZ9D48WmLtlzIkJA8VbigmGL2fDfHjde3iCNxuDQMhgil7LhAWJeGmmKJgvWUk5r5x21G6BV3T8TciJ25i2N2ng/X5dfoSlyflmKiQxFdojEwuDpswPzjEc83kk5MKRMK6oKJq2F8uUKfGRZULeIZITQrDJsJjjpUD1dBl/xvryH/ekk0HtkIKUhJwralYWOLdxCTeul0kCxZrNeRO7FP+pQL5x78l/jqU/9OBsXDvmD3/4DVlc/wr2XX+DgzmvFm8MUxDlb7uvE8EZjSqiJRewnsQMkAxIRcaSutaCYnXngFAzYvNxNiVw1Qx7+4Ad57Y9/m49/4Vne3F3A0NHmU6Ynhyz9Y9S0R5CKqlAIPazWKwbLgZWzLKXkXlL57PKee+aXB04GXOHam72pqWEtoKXijhmWzXFPsa3OCSWblXUuh6v3dn01W3Ow9oyHQzZWR1y4tMmVSxtcuXSZJy5vc/HCZTa3L1KNGnDj5Tqz9HNgyfoQ0FgWWxED9vfSO9zKNujcKhU3hGYTfMAPMmcvjoixQ1auQpvY/+5XWY0t1bmHcfUmqxcucu3sgK/+5h/zoU8/QqjO8Df/w7/Ff/ff7tAuXsKNVnjs8Y+iZI7v3GL3zm3i/JSu88QYjTxBTyoQOs2WAPhitaeJ1LVo2xVoLDCoKxhsmo9SmjGNECTiA1R98VmCcRXENB1eSFno2s7YNqKGDBjnxuJQUTAbz96y/CUddRmBioq4P9AtsysJXaHsauyfRU97ztKRU0vXTuHkkKCJGwGyc6BW62eBkJUmneK8I6hjof8KKFQR84hxVTDcXJxRDjGVowWacvFKVqViAS2USUxWzfZNr0Qm4KS24JRbskrZfBlJfc89IlmMKklHxpE1kOmsYaMBxCh8TkBzZzCAk0JrM3fHTF3YEIkYO1JnI7sgU1UVYbJC8I1NXHEJr8WJrsAy/RhAY3YU6bIz50l8adQ5bxl1CRDGGimq02xlZCrZchfniPM043VcNbBsM8+X2TylGugrGi2KUqOKZsweweAUwXBim1WrBePPSCrExGzWqI6WWAy7+gM6DEacffIzfOQzn6Xhbb70m3/I1tmPcu97X+Pw7ltY3V/uRS/M8kXR54xZA1Y9iTPsU3DgIkaD7A8WYxoVEqlx6qV4AZEZT1YJYZXD05Yzly/y3W+8jpx9krvX3ybFUySpTVcuFhaQCoeqz9rt+lvfQC0G0reTbU2qCsPVNTbPnAMnHO/vcrq3RyE64nzpC+DIzhSsSZVQaLpkW1/qGoN6pN8bCmUEYEwmCHOVaRVGg8BkPGI0abhyfpMnn3ycR69u8djD5zl39iJh2BAGmyW49PdbWTpNLt/E4DhLPw1LBg/5FHFjkDFoi7oMMjL4ztWodHg/tzXUCOc+9AlcWkCcI+6Axy8GXnj1bcZrF7l3e87v/tbf5bO/9AU2Bhu0i8SLf/YCh/f2eOyxD7K9fZH18w9xejLnaPcW+zvvEhenSALtEm2XCU7J2RWdiCVAweUCqQpJW1ORdloOv4CvAkhtFiOa8QhBIkn6eG1ra+BN6Difq0n/keUhoKX30+sOTP0NS7iV/uuSCBaKmytVuinJefD/5SDom7qZHl0AkcRIlfUMBykzxyDSiVeG4pipw2XIRNqltfAPf7w/gjuUYFk2CgZlFDJ4KYmtJDdVqQP1GEnMzHiMGuiLWq+cpbogEkoxLHi1LMi40FIEU/ogoJWNiJQMvJg9WYgz7BkEyfY61tBMEBNdMo69ZssGqsGQqqnN7lVqK6Olz3+lKNMKrEIvF3fLgy1rLJWCqfYcmeQsi7ck6wEzJ0tth0o7w/uKwWTL5jLGFo3Twly0UtPhjJ1UrpF1+32BezIQLLCXa5ZyZ3CP2kBwTQnnDI7ouqIW1aKS7BkxIjSTbS498zk+8pmPMb/xIn/++g/YWHuSd7/9FaaH98y4jIRXRy4GTF4qew0syKpTw6KFos4sPHOMZ67ZeizFwBicLxtF8cWzQ7MwWj/D/ftvcXYc2BwPuHHvHlXa4uDOu6SUSNlsdENJKGLy5lWDWkWiDsmprDObOIV2y6pKNeLI+GrMEz/2b9E0FTvvXOe7/+RXSzNfKC0IPDZBLIvgrCFk+zpHnATjy2cpjqc920pMdesdXrBhJKsTtjfW2T67ysbqOtvrIzYGFVubq5w7e55mvIJUNriDcnjSv1s6LhWOzW2VHlteBnuj8lpk3YU8LvSjYI1lWWBiNoVcg2REA7lpIE/sOrnIeHMVd/wi2x/c5sLmY6xMXuQP/+/f4qVXv00Xp7TdgtdfeY0779xiY3ODte0t1ja3OXPhMucefZL26D737u1wcuctmM7oWsH7rkB/1s8w6qIlCEHV2HMes8Ank+PCGEoKUXKhppZ5ApoLDGZXOXaZpDajd6k2RwqTrSRElOerLEO0QXG6hHTon1v0F33V1nO5ZAnZ2EFgEFBJ2LIQBC6JsifKsQrzrMyyMnJKrZEFwlzUWDw/4vG+CO7Sq/foeaeJ7GxmpfQeG6g1HHMua9BK3qQQ1AKB0KLqjRrmetwrIymSsIlKzvUXP9jNpUiDVctNLnx3KUd7YdWI9oLo4g2hAlqT9JSknsor0qyblL9XUTpZPlco4+gkl46+CYAKS9o+E4X9Qkakwv7XFoUuA7L1AxxmNhSzkuaHON/QTLYIdSCnBbIwUzPfl+IOOzz6RVWmVVFEM6qUTNmenjSVeFmDmge21YeZjry0AMjwYBETQITh2kWuPPc5PvzpH+PWN7/ED269werWFW5860+Js8PyHp56dJY83y+mXeWwtdwbctEYiP2Lx/BvSwACfaM1l6xNRHDZfFw0QcQwcHGBrYeeYnHzFc5cuUybHIPxOtX6Nutbh9SDCRqjFdnJNqH0TptF22CZYmFY5fLvCiLZjl1VcMLsaI/j/bsMH36a7WsfYu3id9m79Wr5/GI2ruWQNAg4mKBFzcJCk4lY1DlT/Ra1tFOQIFS1YzKsWVlZ48KZNc5fOMPVcxucP7PBtccvsbpxzqA0zZAjSFEw6qIkLGWPuSGwKL8ExTxohPcqHh3IegnkpYIF8CslGSqHj5sDHhUb3ygoxCmaW9xAeP6nPsk//Ee/wcb693j60pDf+L19bt2+QcoLRG3C2tHREYdHx4Rb7zJZXeHs1uuc2dpiuHGWS5fOEq88wcGtN7hz4y1kvkeKiewr1Jn0X0rSlMXWTxYTQQaNxUbcbHTJkFMiigNMJ4NmavHMpnMW3ZSkPVxSGDBiryAFvtWirejdPCl9Me03jphaulx4q5iAJVdzCeyXDF4pyaMuIeJGhCTCIqtNkXOOGscQNbV++bMi886PiKvvi+CuanQ11zcvCMY2EMtmVWMpx+2XFlGRYauhzDWl8JrtZrgwtgteLHINkpDSAQdbvB6cNQFFK3qhjGBTcMzis3fjM96ybyqyQreYk7s53g1pBhm8NVIzvkBIkNUbqapg6+biZ9xk7c28EMt8JBTmZFGB/jPXyLIvKyw9bYp0C2ODNJMN6sHQRvy1x3gnS2hE+8ZoyYD7AdYi2aCv4lluJ5wDTaVJmcxLJxs7CHU2mIBis4uZVTkR1Fu26bxjfPZJrn3o0zz1qef4/u/+X9w+OWA4PsfNP/sTcnsK0tcPme70HjbZqWwWzUjOpiJ0IKm1Azpbs9lTeP4KhrkHIGKik2BeXWq6hp4mWDc16+eu8cp3/5h/52ee5s7RCWHSIL7icOcG3eyYpfG9t2Zy8mbURAShIms0qwqsv9KblaHeDMCcAAFi4sZL32Tj7DUGgwmPPvfjHN17g5haVCpTAWsuh5BRWh804XqoRwtLSzAxSyR4z3DQsDIesrE+5tz2Bpcun+PypXM8cfkKq1srbG5tMRkNCaEBNyCf3CfHHarhWqk4W3q+mAV6s2XozbqIe0ADfgCuWsIE1mmO4EuF20M6GjFqbVP2iHsQwIIHRmgeM96An//ZX+Tv/f3/g29842u8dfM2u0f3y2SwWFhNtv7atmN/9z4Hewc0b7/NxsYG65tbrK9OoFph7cwZ3HSNk6NdmJ9S1xBbB64kcdmRkgXylE3JG2PigchPLdsX0xZkbFbEfN4xb410oH2ALhWPWUr3zLgimlPoTWFE0zIrX5J/y1N6Vh70FYCtz5KqlvtdUAd5gMRX2FD5hRrpu1KbV7pAaRAqKULLvyKuvi+Cu5TyMxf+OYCT0rDDgdb0/iBOylBbykGnkYjgQ6CqR1TDDQbDhqiObrbL4miO+qpc9sJI6eFuoZTalulbUDfIxmhNHtyI0aAi1ivU+YDpTNHuGMFTBctUnRqsZCBeCZTSN0gV1WoZ5Psxd5a4m49Or0UU6ZFz1wMC9OP6TG0ZyNlmRYYwYLy6jXjzZSfOgGIYRSiLz94/v4drb7/3NUFdvnYs6V7Sc+kpzdXWqo+U8S7RKYQcbTrWUjAjSBiyefU5Hv7wp3nouS2++qv/gHmsGI7Xefe7XyaVYQ05RZPIqxp8VTdk9QRRawg7b1RHFZIaPELuUE3WVHIVKh6XfGm2epvClE04ZldTSsIAYXKejo64+xqXH/kldndO6doB9ekBJ0eHlnWLbWJ1nlTgi9RZktA3zoIz1o8xMiOpVHZSLBW8t3u0f+dNDu5dZ/vKM6xfeIiNi49w/+1XweXyOa1qsmZdNBgqmed9QC3hKD+3+eV4nKuMnZUiJI+0iRgVkjI7PmI8aNDJCbGpCG5g0MNoE5dPSO1tNK8SxkOEKWgL1EsGGGWfEdYNamqPIIwgjCxLVcvSJXvL+LWztSU9dRKKBSY9ZdQCfrMkRmw99BD/yX/+n/Kzr/0sf/7VP+S3v/IC7/zgL7h5e4fj0yma1RISKOwwIcbIzr177O3sMBiN2N5YIwxXLD6kSFSlS95EcinSW+emmI3HniMx5/dY5xobKqsaw8h2JHiIXbT7Km7JfffBl76TJVuaIymlZb/PKp3O9j0P4pIxvKRstd4AregfStwSCWR9APXYxLkyn0IpiW1vNq4k9XgxUeCU98SsvyKu/pXBXUQGwJfLHQvAr6vqfyEi14BfA84A3wL+XVVtRaQB/jfgo8B94FdU9a0f9R7mC1IwTPzyJLQpO/34teLPXsoa5x3Zj6ibEfVoHUJFiIdMZyccHN0m5wUpmwmR80blo59WLg8aHLLE+i2uJRVCaPDDMzQhsoiebr5PnB7QqpZDx/xVxPWktMKVdd78HrS09pYe7KlYEYSCpeoSb5OcylguXSZWy0Q6C0iFOCV1ZoRWNSOatTOIhxxnmAomlHF3lgWbL3l5OfEEJybIUhN/5XKdBTNVE7FFKQg5R+sFFKCl5y2bj7W3eZIAogQFYsYNVzlz7Tke+8RnWLua+MP/9e8iaQMNM27+xXdI8QRVC2ymlrUDx/ueY5bJXnA5YgoaO+S8Kwe+y2i00XzQ9zz6TWTrw4lAdiQ6a5JlG+jSiCce3EXCBo9eOMdvfuUbhHCVPD8lzo6N9ePsWJCUSgNUCuxW/Dycyf5zP/ijiKe0XAcn/QjCith23L7+HTbOP46rB1x99ic5uHOD2M3JKdqGzrJkvKgU6qPaUGaNLcGb1XU/wKbTOTEKbbvg+GTKzk7D+K3bvLjScGZ1na3NMavrq6ydXeWx7Qusr67gR2MqiayOR8ARo7UNXLPOYOSRaoZELZnlvMBaazZcezCwTF0FqFCJJZhga0EayIfQdSU4Vhb/qEAGy3tniZLx5iEzHg959vkP8szzj/O3/r0TDm69yiu373Dr1Vt8/+WX+cZLN9lei3zyc/8Ga+ub3N2bcuvd+7zw9a9x8/svcPPmLaI6xuOG0WjF1kLXkRQbzOF9oUu2xNQRUy5W0gJFzZ2K6V5GChSbiZ0NCsrF3M15RxWCNfDBBsILuNAYtTgl87TRVNCChLi6hPBcaMi5GA32Aiezh+iHfbjCyTJQh6KtsF4TIhx7x54oOfeDRgo8K6aFsBGUfRT5yx//PJn7Avicqp6ISAV8RUR+F/jPgL+jqr8mIv8z8B8A/1P5c19VHxORvwn818Cv/Oi3EINcco8pmX1AlhJcXAI1Z0c/WKcerltJlpU4PeFk/yYkE+hYhurBVzhXzK5EsDMn43IuCkq3PGkFcPUAN5owqpRFDDC/x/GRYsMmDAB2RdgjvZwew9T7Eg6Mr04RsKSsBfMuvHWNuOIQZ4vNoABQU4QqQItmhzrjPsfWIKV6OKauhyhK7E6RlAs+WzBWw6Dom9JGMcxIMlEYJStQMZqdUAahoGhrQcc7QAJedWlrnCWZAjUXC2bv7IxUY1o3ky3OPvZJnvr0p0Fv8pX/4Tcg1uRwl5Pdm8SUy/eUzKUwXypf4b0pMrWMOcS50kiusGG2pTOWLFBISW1yjuU+mEzfpiWxxEP77EnFkaXj9u1bnNseUk3G3DmoyCsVpycttAuCD0v9Qc5mZ2Hnr9hEnhLobSaYWACQAHlmEE7/XllLcxXu3brB5f0d1s9fZePiQ5y59CR33vgu6ssBAWVMoCPHcpi6JU8PshJ9pKqqEpCskmlTpKNjHmH3aI7uRMTdxktFXXmGTc144BkPJ0zGI+qmZm3csDKpGfgBZ9a32NxaZ3uzYX2lYThZYTAKCBHnGgb1GLeyZXMVXMI1tmfwAaUrUIxHZATBjN2UktD4AGlmkI6AwTfQI8p9kiCiDAdDmqvPsXnhcZpPOrIXuhPo7r3M3v1jtp55jOH4IjEO+ebXv8H//mu/zpe+9Efcuf0OBwenHByeEKqKYdNQBRO+pQ68r0jB9DLoArpZEdP1VXGxZO6rbZVl49QgWcVXpSOrgvMwbCo6hdiV9qovtuC5YPJqLCqrFsv9K4m79od+mfVgUEwslWkxM9SeRVUauN4xHY+46Wt8TkiyfRaqAVWwGby562jbuR0yi9O/NKr+lcFdjZJxUv5alV8KfA742+Xf/wHwX2LB/RfK1wC/Dvz3IiL6XgOKf/ZdlqdcmyP9TEEXHL5eoRk2SL2Kc5nYnjI92eW4mxYecGGP9Hh8TiA279PKpd5sKJfMS0uTzoEfM24SXXWWkO8xmx9xfHyK5IJJC7gizQ6lJNdeQUgxHVs2FsuPJ+U2aeGNu4xYew+XFUKFYqwejZnsFMnRNokTVIfkMunJhRGj1S1775xIcYFoxOe+LLbssjfpEm88de9KIF2agimS2tKvcMbGQAsNrGgEnCsCHiCqVTiKZRw5loa24a/9Qp1sXuXsYx/nuZ/8cQ7e/Drf+J3/h9QJp9NbpWpRvC9e8f1gCe/pOwpajJuyC4aTY+peJ5kYO5Bg10YVpbUNFi3wO7EN42JnjpFKgRAonPEW52ouPvtpDl7/Bh/4+KdRMsO1VUbntzj49rd59/p3SVGLTa71GkS9IWulytOCe9vhUsps6VCsIY8YzmtNvYxKYjE/4c7b32OyeYG6GXLtQz/B7q1XSe2pJQjZDlVroPuSmcUCWxnzSLKjdaeIq3DiiWpcfIlC5W2GLblUszkRU8fJ9IR7qlRub2mOR1muTagI4gjeMZkMWZ/UTAY1g7rizMoaw5GnrgdMNjdYHaywuT5gsrpKaAY01YDx5oRx5alX1vF1Q1XVBnOGpsB2c3CDJaOklCgUlj+U1qb1WGpctaCpViHXOFqatUCz/hnGquhihzs/+FPC8Az/b3tnGiPZdd3337n3vXq19FI9PZyFM9QMR6RMUxQtKpIpKoISWIo3BP4UwxZsRHEMyzZixFmAwEI+GAEMwwbkJQGMxEKU2BACy7ZExTKdSKApmoYhiTEl0RLNRRxRXGaGs3KWXqveu/f4wzmvu0VT1AxBptmN9wca0/WqpvrdurfOPfec//mfu+66hbe/89c4+dzzfOaz9/Gpu+/m4UceZmnpCvW0IYRAWUYz7NEUQbOYGmooF4jJBMRSM8E2yewhV69ZwQIcgphqKiYZXvYq+jMjRv0ea9MSqsjkwiorK88b6SGWmycw2BINwL6LoTRfPG/y2uNGLN/oz9rG+GlN+4Y1pC6ERntoVFdzLUkxkmMgFQWpX1nY6OKZb2tVryrmLsaV+hJwE/A7wDeAS9oSjeEEcMh/PwQ8hxnCRkQuY6Gb8y96zw8CHwRr35Zzg4aSIgpSjekPZugVwjQpzfoS9dIJUm4V3CwZZLujLSLTGRG3eaZx7eThLeL3QlH2oH+AflxlWgur66uw/CR1+2UJ2d7D25ghkCWSWrVBj7VayTp2PMOD+OpfqNwaV7A3KClitoSdQmOdYY1dluzUorGHNjCdLhOqIYOFwxRFSZ4uU0+XkdwgwTxw48irSyu0YmZO1zNLwQYtRowtoKi7pG0yCUSdnSHBizcsOJmT+xJiPI/gC9aafwQk9Jjdf4yDN7+d299zF9/44p/x+INfYea6o1y68AxMlEDpXo7FMQOZGAeWvPaNWJIaH91libWZIi4vYLahZpNdYN5xLEC8fZ+pB7Xk0mx9drOQtSZkmF9cYHZhgacvX+K7bz3IpdXA0nSWcl2IsY8EJdD4SQ5CNhnfjcIV8Zg+2frQRrxS1oxXypZkE/LmxqhCSJkzx7/GG950J1VZMLfvIAeO3cZzTzxoLQcRJwlYw3XrIjVB1QpkXH4TTTgnbwAAE3VJREFUqMipla31MFRONJ4jCNE2hkBywTzb+Orscr9qayUnoZ5kIw9I4PzyGkGn9ifUGGlRlCIKRSyoeiWDQY9BWdHvlVS9gj3zcwxHFcO5eWbnhsz2+uxZ7DOYuY7F2b0wqrhuPKbsKcOZGYJUgBB7Cck90AlSL8FoH8gQ4tA2w5iBykKmmpEs0FukOPAeZP0ceek05WDE0aOL/NzP/wt+/Cd/jC899BB3f+rT3Hvv/Zw+9QyTSQM0SIReNAXURO2n2JKy16fXH5JzTT0xyYXNAIk5fyIYjZJELPu2yaIM9t/E9NmzXDz9FLN7DlLN3sylM8/YySCU5pRqG8bCN20/nYeCUA4teqDZWFjZKJxBjdlFG2lw9VJUSE2mKCxcmVQoSpMSsUbvCtnqebR8+aj7VRl3NRb/W0VkDHwKuOVq/t93eM+PAB8BqIazOtxziFDOEmWd1fWataVzrEzXrP+lYsqO4HF5S46iUIilEUWMe63ZvLCA+FG9QMp5epWgWqJ5Sl55livJjrqh8NijYt6wlKDTjcIi/Kil2hC8krM9nqtXn23w7CVjFIvW6/Uerx4mgQC9HiGtexl7AWWPuq5J9RpVf46ZucMQIE8mTNcu+JlDKCVsxKo3mTYticqOdUkg5EyTk3l0TU1uMg0NuTb6pGbI1CBxo+tVkEAsvem0YjRNyab3AhtiDxoCxWCOxcO3sf+73sItd76Fh+7+fZ4/fpo917+Ji+e+zmTprAubRQ9FgSXhShrBt4rgTDqbWyGRsxVTRbG4vnnF9YbYm4oQChBqW9zBJJ3JzjTOIJottdfU5gLMHWR5dY16orzxyJu48MJpggiT9ch0ajkRbb03xXImKKTGnYbg9EX3szUjubAGJWJ9egO19WlV9ba2QgOsLV/kzHOP0J/9PoIkbrz93Zx59nGmK5cBo7dJbtlSAtJDaIi9gqTuEfqcN6m9y4BEaJpEWQIIJiyGxb5TbXkR/46J90QIMSLJE37N1E4joXBnBCQojSrTHNBpg6w3cGUVUO8R2rJAIrGwgsIq9hhWFf1Kma1mGM6PGQ0qhv3Invkhw96A+bl5FvaPGYchvQpmx2NidYIYYX48SzkaMBj2yblHEAu9pqSEsMZiFWBmllDuA21Ia+vI+b+hGpbcfniRd334V3n+9Fnu/uSf8YUH/pTP//XXuHL5EpPpxEJnAUKIaFCa1NjJtCjpD0ZknUc0WcizXrOkvYix26KxY+rpOlVvBunPMjNueOHCCa5cfJ7FG44w3xzh8vmnbAP5Fjpkm0AFUvIwa/ZNF5dZKc3wKx5NANLUC6VM0qReW6eZ1MQioiJMVlecLdaaJNmIw78crokto6qXROR+4C5gLCKFe++HgZP+spPADcAJMa7aPJZY/bYQCUwmSzSXzzqT0eV1W1pb23k8uE61BK9A9A/PGQ+aIWUzHrEo6A1myHFArC+yulIjaX3DmxbaJhweLxdFwhSl9A8teO9Gj2GLiRFlLDfQ0t9M693vIwHBwkQa7dgXsUw4WthpI2dUC1RK6ukaMp1Qjcb0B0MKgTRZoZmsAIqETJBIcE2XnNNGk4qo6tzb5F1flJwShSSk7GO5iojGIcKUoixMx72ZkpspTRhSMKHOWAhkcsU+Nymp+kMr/c4We40hkrJQze7luqN3cMOtd3DglgU+99HfYLo6YN/Rt3D+xMOsXDxlR0+xOUs1zgDIhGhhmYS4MBjgjIWodrQNvuCNVhacnhpoSJAmG8yCJAmpFUpjG8VgSn0ZRdSoelL0GcztZXL2KRbmKxb2jvibr5why4DVumF16QLNtPa/I6C1HbjasF025pSxnXwdeFhBgxeeu8yuRSEC5InlLEJERHn+yUc4dOxtVIMRc+P9vOHY7Xzj0b9CNHhfggbRRHRJUztd2bpOKdump2Ia7WoJcfWivGmTrQxfgmsBQYglog2NWrhGNFMUBanJTiu2/gdI6UVtkTKUUFgFbG5y+z1342EnNW1lb2nQ2tbiiq5zsSUlyFli7G0wTspoMglVWVCVhXn8RcHi3Jh+1acsA+O5ioXxHHPzYxb6BYPZIePxAqHODK6bpZgmqIReOaLfE/qlEpsJa5cSoX6BSuc4dngv/+4XfoqVn/1JHnrwy3zyDz/O//nzz3H2zFmalHyTTt5XuSBkNtohFqWt86YoyGtXNrzo2B9Q9QeoROo6cfgd7+PSl/+WC+dfIPRXoM4Uo8CwPsDSpVMbrLI2aQteLY8J9UnOSGjsM1eB5A1ZWomRaDLm0UUMVRXSGqmekBqlGA5Be6T1VZuPjfd3m/MyuBq2zHVA7YZ9APwTLEl6P/DPMMbMB4A/8f/yaX/8BX/+cy8fb4fc1ExWlj18EFrTazQ7965AkcY7KQV/nRqdLKkSKQjVHMVgjkqmXFmHZvmM0dS86rINZygYpVvbphK2KFWCFyeUyJYMt7hHl7zIwHIv4l9qm1C2bDgq2RMl0Y/tPg4pmKba5I0p6c/MU/ZmLIE8ucykmbouhSIxohp97/GFIn6SyBOmdUPSkhiMjx6G8/QilGWPsuoTUIrYIxYDkmSrvgxiKoUkJk1i7fJFmvWa+b03oukizSSTmlWaJm3UXuRkC2i4cIS9R+/g5u99F9XeZe79bx+mX93AwuE3cuaJB1hfvmibSWrssxXrayr00FBaYU4R0CZjMrylFb2IoO6B5za05om3No7e9hMFoQm2NqTskZsGiV7N6mEOi50FquGQ8fVHee6xB7jpyK2MZwacPHmapnczafk8l04+TjOtNzfIjCXiCsGEHiMb6o0xkBorgAteJZwbo9/FINagHdzAeWI8K1cunODCicc58Ma3koJyw613cuqph5msXfGaix5giTdL1E7J2dZOFKPjQvKmIMaNF6++zbmmyZbTMDfVPj8wJk+QYIm3PMUVOrAFa81kQiyJagVaNJnWNKlv6q3UgtK44bJq5pSSMW3IZD89C5mUptbOUkxoUlVZcuE040UUxOJZk8ENJf1SLTEtQlVYgnl2NKLfK5kZVhSq9Ec9+tUMc+MRe+ZGLCyOma9KFvctcmn6TWbkUTh1muLIzdx4/T5+4ed+hve+51383888wF988QucOnPK9Z3sRCthal6FWBOZHKaklEwDyCOrvUHJTe9+H8N0gOMP/yXP3P8J+nM3sO/G21i58HWm08tM16aEIlMWfep6pfVKzF7gxU1ieRsheFHTxGnyrXChn45z6QfYtkq8IMaSYji0fAyZ2AuoDJg2U4sg+HeFV0F+4CDw+x53D8Afqeo9IvIo8HER+RXgK8BH/fUfBT4mIseBF4Afv4q/4R508DhtspCFOK3Qv7zkgIbGGkLQM/XHckC/GpHCiLh+ktVLK0ycmy2ujZLJVsYe1N4Xa/XQGlxpZTtztESdsqFMKASsx6W2BWQ2OR5HY4Pl4hATexIt/C8nNPSMCri2hhQF/dFeYq/n3OVV0nQJSS210mvq0tRLnDf7L5pEQ0C1ohyMPH4bGYwqyuEMpTe4Nu5yRiiN8RHCRoVtqArqpiZOjLscZWIeZ5OoBvM0K5nEusUGUWKvz3jfMcaH38yb/9F7WF45zr2//VEWDtzO7MIhTj7xAJMr560HQU4k9bkUsSbbEpDC+ON2+o3mYVuttbGCckEQS1pqa1DATkGunoinEFpv0nIeCiJYCh5CKI3zH2G09xDjvft56oWzvPm9d9EonD53lpkjt7O6tsRkuk4rXYwm32Ss8QWtuqOUpDQl5GxtIKNJXiAWJrQkd3bOabTNPGBH8mzG8eTXH2Lx8BsJeUhvdpF9R27j6Ue/iGqDuhicpuShP/PFoiUWbH0l05mRrbUIsaWFehl9EO/PG905wMKX0aQRmmTVw6KNpzAa22RDQVUKqbaYfVvpiRfzpRAIKaO5JkuC7JpItLITaaNAy3bfhhRcZTRBSuvEIpKTfYdSDSlmRNapJ0ISy59IyjRaw7kLBClMxVFtk0s5EUj0iz6xiMyMRoyHA0bzM+ybG7C4bw97vnaOKxfXOL98AQKMBwXvuP27Of7UiGdOnGJpdZlWEC+rx9hJqJa2niwjCghpbcIzX/48x269k6Pf817OP/0Q55/+Kv25vVSDPuQpK+sXqdOEsleQUiSllq3WJkrZTL7TkrfFnY/kxVLQVuTnAI00FrCUSGoisIqIOXdh3dlVRZ8YB+bc2NmRleVvHxS5GrbMV4E7XuL6U8D3vsT1deBHv9P7fgs8Xt42cxb3WEQsiaYYjVBdHbI3PEhvZkjML7CyXrJ65RzanELEWBcaI1EDKSghZwJeDOMcatXGC8xcGhfx2KLHC1RtXwlx02t0ZgRi6oUSapNDQMwt0hqLHqtl0QMQS/JEma6sUPYHDGb3EPsDNGXS+jKpWXWvzEIwpEyTs8eTjX6YNdE0DcO5A8Sqol66QrWwl3rpNGltldHC9YxmZpHSwkVBWg+PDWZLngYayUyWL5inEiBP1iDOMjscUU9XyKrUS2e8o5IVexT9Wfa+4RZGew/xD37khzjzxIN8/uMf48BN76Dsz/HsYw+wvnSBoggEiSiQpEZEKRHLHQCtQidY1WxwO5XUOOvmrW9yqkOsrHQoq7NWMkH6Zrgz/nnj/OSGICUmdpzJwbT7x/sPMV27wPLahGNvegur61NW12bY068olq7Q7w1sfalxkluZadVADOYxJaz1nqhXv+ZMGaI5alGoM4jUqEbbIIsekjz/4rzrF848y9nnHmP/G96KFsp1t9zF6eceY+3SBUJMSGp9psZjqeYQSDSdmZS9uEuN3RSj0/k0GmWx2CyVVy/SydpYwVNwQTs1ydsggVCol+Jbx6GUo1VpiBr7lAziSpX0UAJNxkOYU2o/rYRQeox5gPMQoZ4avVfFGqiEYpOKiOW/VDJFhEmDfY/E6cJOBW1UoVHLc6Tkc6Ksri2TUc5cvLxRgxG8iUxVGNMsp4SmCeuTKUFhkhRioNfrUTfevcyNvDbJJErcsNvZxcKGq2fPc3r8DLOywvLFM0yWL7G6dJ5qZt7sURKa9RXCcAhSIGL6NaK2IbeOfJYNboyFtyRs5HeswtrZdlaNScv0sTXVhozbuwpInBCwht6eZHx5s/odIib/XyAiS8AT230fryH28iK20C5DN76di908Ntj94zuiqte91BOvC/kB4AlVfft238RrBRF5qBvfzsVuHt9uHhvs/vG9HF7er+/QoUOHDjsSnXHv0KFDh12I14tx/8h238BrjG58Oxu7eXy7eWyw+8f3bfG6SKh26NChQ4dXF68Xz71Dhw4dOryK2HbjLiI/KCJPiMhxEfml7b6fa4WI3CAi94vIoyLytyLyi359j4jcKyJP+r8Lfl1E5L/4eL8qIm/b3hFcHUQkishXROQef3yjiDzo4/hDESu3FJHKHx/3549u531fDURkLCKfEJHHReQxEblrN82fiPxbX5uPiMgfiEh/J8+fiPwPETkrIo9suXbN8yUiH/DXPykiH9iOsbyW2Fbj7lWvvwP8EHAr8H4RuXU77+kVoAH+vareCrwT+Fc+hl8C7lPVm4H7/DHYWG/2nw9iMsk7Ab8IPLbl8a9jev43ARcxHX/YoucP/Ja/7vWO/wx8RlVvAb4HG+eumD8ROQT8a+Dtqnobplfd9lnYqfP3e8APvujaNc2XiOwBfhm4EyvG/OV2Q9g1UNeJ3o4fTIDss1sefwj40Hbe06swpj/B9HeeAA76tYMYlx/gd4H3b3n9xuterz+YMNx9mIb/PVit9nmgePE8Ap8F7vLfC3+dbPcYXmZs88A3X3yPu2X+2JTg3uPzcQ/wAzt9/oCjwCOvdL6A9wO/u+X6t7xuN/xsd1hmQ/vdsVUXfsfBj7B3AA8C+1X1eX/qNLDff9+JY/5t4D+wKUO3yFXq+QOtnv/rFTcC54D/6WGn/y4iI3bJ/KnqSeDDwLPA89h8fIndM38trnW+dtQ8vhJst3HfNRCRGeCTwL9R1Stbn1NzDXYkLUlE/ilwVlW/tN338hqhAN4G/FdVvQNYYfNID+z4+VvAuqPdCFwPjPj7IY1dhZ08X68mttu4t9rvLbbqwu8YiDX9/CTwv1T1br98RkQO+vMHgbN+faeN+R8CPyIiT2Pyzt+HxajHYnr98NJ6/shV6vlvM04AJ1T1QX/8CczY75b5ex/wTVU9p6o1cDc2p7tl/lpc63zttHm8Zmy3cf9r4GbP3PewRM+nt/mergkiIpjM8WOq+ptbnmp17eHv693/c8/ivxO4vOU4+bqDqn5IVQ+r6lFsfj6nqj/Bpp4/vLSeP1ylnv92QlVPA8+JyHf5pfcCj7JL5g8Lx7xTRIa+Vtvx7Yr524Jrna/PAt8vIgt+uvl+v7Z7sN1Bf+CHga9jfVn/43bfzyu4/3djR8CvAg/7zw9jccr7gCeBPwf2+OuFzT60X8NYDNs+jqsc6z8G7vHfjwH/DzgO/DFQ+fW+Pz7uzx/b7vu+inG9FXjI5/B/Awu7af6A/wQ8DjwCfAyodvL8AX+A5Q9q7OT1069kvoB/6eM8DvzUdo/r1f7pKlQ7dOjQYRdiu8MyHTp06NDhNUBn3Dt06NBhF6Iz7h06dOiwC9EZ9w4dOnTYheiMe4cOHTrsQnTGvUOHDh12ITrj3qFDhw67EJ1x79ChQ4ddiL8DbkoofA5fg84AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICfL8h7hsV75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "outputId": "f1d50279-40f1-4843-e212-98b80ba5648d"
      },
      "source": [
        "img_cvt=cv2.cvtColor(Y_train[0], cv2.COLOR_BGR2RGB)\n",
        "plt.imshow(img_cvt)\n",
        "plt.show()\n",
        "print(len(Y_train))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ReHNk6q8BWxA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8812f2bb-717c-4f8c-ade4-b3e7d8a33b69"
      },
      "source": [
        "f = []\n",
        "for y in Y_train:\n",
        "  f.append(len(np.unique(y)))\n",
        "print(max(f))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "256\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sydh8FZw8frW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02fee978-dd25-4435-bf7e-14fa5d942135"
      },
      "source": [
        "Y_train[0][..., 0].max()\n",
        "print(Y_train[0][..., 0].max())\n",
        "#green\n",
        "Y_train[0][..., 1].max()\n",
        "print(Y_train[0][..., 1].max())\n",
        "#blue\n",
        "Y_train[0][..., 2].max()\n",
        "print(Y_train[0][..., 2].max())\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "232\n",
            "220\n",
            "244\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jIMZwet27sL"
      },
      "source": [
        "X_train =X_train.astype('float32') / 255\n",
        "Y_train = Y_train.astype('float32') / 255"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NB6zrWPFHv9D"
      },
      "source": [
        "## **CREATE** **MODEL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EVLuHWVH155",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2f54002-f559-4128-b2e1-420dd0c072b1"
      },
      "source": [
        "input_height = 320\n",
        "input_width = 1200\n",
        "n_classes = 29\n",
        "#DECODER\n",
        "#--------------------------------------------------------->\n",
        "img_input = Input(shape=(input_height,input_width , 3 ))\n",
        "conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(img_input)\n",
        "conv1 = Dropout(0.2)(conv1)\n",
        "conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv1)\n",
        "pool1 = MaxPooling2D((2, 2))(conv1)\n",
        "\n",
        "conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool1)\n",
        "conv2 = Dropout(0.2)(conv2)\n",
        "conv2 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv2)\n",
        "pool2 = MaxPooling2D((2, 2))(conv2)\n",
        "\n",
        "conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool2)\n",
        "conv3 = Dropout(0.2)(conv3)\n",
        "conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv3)\n",
        "conv3 = Dropout(0.1)(conv3)\n",
        "conv3 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv3)\n",
        "pool3 = MaxPooling2D((2, 2))(conv3)\n",
        "\n",
        "conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool3)\n",
        "conv4 = Dropout(0.2)(conv4)\n",
        "conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv4)\n",
        "conv4 = Dropout(0.1)(conv4)\n",
        "conv4 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv4)\n",
        "pool4 = MaxPooling2D((2, 2))(conv4)\n",
        "\n",
        "#ENCODER\n",
        "#--------------------------------------------------------->\n",
        "conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same')(pool4)\n",
        "conv5 = Dropout(0.2)(conv5)\n",
        "conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same')(conv5)\n",
        "conv5 = Dropout(0.2)(conv5)\n",
        "conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same')(conv5)\n",
        "\n",
        "up1 = concatenate([UpSampling2D((2, 2))(conv5), conv4], axis=-1)\n",
        "conv6 = Conv2D(512, (3, 3), activation='relu', padding='same')(up1)\n",
        "conv6 = Dropout(0.2)(conv6)\n",
        "conv6 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv6)\n",
        "conv6 = Dropout(0.2)(conv6)\n",
        "conv6 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv6)\n",
        "\n",
        "up2 = concatenate([UpSampling2D((2, 2))(conv6), conv3], axis=-1)\n",
        "conv7 = Conv2D(256, (3, 3), activation='relu', padding='same')(up2)\n",
        "conv7 = Dropout(0.2)(conv7)\n",
        "conv7 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv7)\n",
        "\n",
        "up3 = concatenate([UpSampling2D((2, 2))(conv7), conv2], axis=-1)\n",
        "conv8 = Conv2D(128, (3, 3), activation='relu', padding='same')(up3)\n",
        "conv8 = Dropout(0.2)(conv8)\n",
        "conv8 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv8)\n",
        "\n",
        "up4 = concatenate([UpSampling2D((2, 2))(conv8), conv1], axis=-1)\n",
        "conv9 = Conv2D(64, (3, 3), activation='relu', padding='same')(up4)\n",
        "conv9 = Dropout(0.2)(conv9)\n",
        "conv9 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv9)\n",
        "\n",
        "out = Conv2D(3, (1, 1) , padding='same')(conv9)\n",
        "\n",
        "encoder = keras.Model(img_input, out, name='encoder')\n",
        "encoder.summary()\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"encoder\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 320, 1200, 3 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 320, 1200, 64 1792        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 320, 1200, 64 0           conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 320, 1200, 64 36928       dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 160, 600, 64) 0           conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 160, 600, 128 73856       max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 160, 600, 128 0           conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 160, 600, 128 147584      dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 80, 300, 128) 0           conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 80, 300, 256) 295168      max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 80, 300, 256) 0           conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 80, 300, 256) 590080      dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 80, 300, 256) 0           conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 80, 300, 256) 590080      dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 40, 150, 256) 0           conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 40, 150, 512) 1180160     max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 40, 150, 512) 0           conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 40, 150, 512) 2359808     dropout_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_5 (Dropout)             (None, 40, 150, 512) 0           conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 40, 150, 512) 2359808     dropout_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 20, 75, 512)  0           conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 20, 75, 1024) 4719616     max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dropout_6 (Dropout)             (None, 20, 75, 1024) 0           conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 20, 75, 1024) 9438208     dropout_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_7 (Dropout)             (None, 20, 75, 1024) 0           conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 20, 75, 1024) 9438208     dropout_7[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d (UpSampling2D)    (None, 40, 150, 1024 0           conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 40, 150, 1536 0           up_sampling2d[0][0]              \n",
            "                                                                 conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 40, 150, 512) 7078400     concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dropout_8 (Dropout)             (None, 40, 150, 512) 0           conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 40, 150, 512) 2359808     dropout_8[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_9 (Dropout)             (None, 40, 150, 512) 0           conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 40, 150, 512) 2359808     dropout_9[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2D)  (None, 80, 300, 512) 0           conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 80, 300, 768) 0           up_sampling2d_1[0][0]            \n",
            "                                                                 conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 80, 300, 256) 1769728     concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_10 (Dropout)            (None, 80, 300, 256) 0           conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 80, 300, 256) 590080      dropout_10[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_2 (UpSampling2D)  (None, 160, 600, 256 0           conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 160, 600, 384 0           up_sampling2d_2[0][0]            \n",
            "                                                                 conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 160, 600, 128 442496      concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_11 (Dropout)            (None, 160, 600, 128 0           conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 160, 600, 128 147584      dropout_11[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_3 (UpSampling2D)  (None, 320, 1200, 12 0           conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 320, 1200, 19 0           up_sampling2d_3[0][0]            \n",
            "                                                                 conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 320, 1200, 64 110656      concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_12 (Dropout)            (None, 320, 1200, 64 0           conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 320, 1200, 64 36928       dropout_12[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 320, 1200, 3) 195         conv2d_21[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 46,126,979\n",
            "Trainable params: 46,126,979\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewtbiCCLN3Ct",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fb06218-34a1-4c8b-a691-f132e156ca90"
      },
      "source": [
        "encoder.compile(loss='categorical_crossentropy',\n",
        "              optimizer=keras.optimizers.Adam(),\n",
        "              metrics=['accuracy'])\n",
        "history = encoder.fit(X_train, Y_train,batch_size=1,epochs=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "200/200 [==============================] - 100s 439ms/step - loss: 1.4894 - accuracy: 0.3038\n",
            "Epoch 2/50\n",
            "200/200 [==============================] - 93s 466ms/step - loss: 1.4235 - accuracy: 0.3492\n",
            "Epoch 3/50\n",
            "200/200 [==============================] - 93s 465ms/step - loss: 1.3918 - accuracy: 0.4310\n",
            "Epoch 4/50\n",
            "200/200 [==============================] - 93s 466ms/step - loss: 1.3989 - accuracy: 0.6156\n",
            "Epoch 5/50\n",
            "200/200 [==============================] - 93s 465ms/step - loss: 1.3854 - accuracy: 0.6913\n",
            "Epoch 6/50\n",
            "200/200 [==============================] - 93s 465ms/step - loss: 1.3670 - accuracy: 0.7278\n",
            "Epoch 7/50\n",
            "200/200 [==============================] - 93s 463ms/step - loss: 1.3534 - accuracy: 0.7760\n",
            "Epoch 8/50\n",
            "200/200 [==============================] - 93s 464ms/step - loss: 1.3589 - accuracy: 0.7937\n",
            "Epoch 9/50\n",
            "200/200 [==============================] - 93s 463ms/step - loss: 1.3685 - accuracy: 0.7966\n",
            "Epoch 10/50\n",
            "200/200 [==============================] - 92s 462ms/step - loss: 1.3583 - accuracy: 0.7876\n",
            "Epoch 11/50\n",
            "200/200 [==============================] - 93s 463ms/step - loss: 1.3742 - accuracy: 0.7838\n",
            "Epoch 12/50\n",
            "200/200 [==============================] - 92s 462ms/step - loss: 1.3533 - accuracy: 0.7639\n",
            "Epoch 13/50\n",
            "200/200 [==============================] - 92s 462ms/step - loss: 1.3538 - accuracy: 0.7264\n",
            "Epoch 14/50\n",
            "200/200 [==============================] - 92s 462ms/step - loss: 1.3387 - accuracy: 0.7826\n",
            "Epoch 15/50\n",
            "195/200 [============================>.] - ETA: 2s - loss: 1.3561 - accuracy: 0.7828"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0ZHvy7uY7cG"
      },
      "source": [
        "tf.keras.utils.plot_model(encoder,show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pw9Bd952_9ZY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}