Cnn_model.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.5"
    },
    "colab": {
      "name": "Cnn_model.ipynb",
      "provenance": [],
      "collapsed_sections": [
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      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raaaouf/Covid-19-Detection-CNN/blob/master/Cnn_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-phKXjHA5edd",
        "colab_type": "text"
      },
      "source": [
        "this project is hosted on google colab to try it jusy press on the open in colab link"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "No_zsgpxDMZx",
        "colab_type": "text"
      },
      "source": [
        "# Dataset :\n",
        "[Covid-19 data on github  \n",
        "](https://github.com/ieee8023/covid-chestxray-dataset) \n",
        "\n",
        "[Normal data on kaggle](https://https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/data#)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WzSf8Knyf__x",
        "colab_type": "text"
      },
      "source": [
        "# Libraries "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "90fEnrDawOAn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "12ca9f36-e646-4d3c-f90a-9402483d2578"
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import AveragePooling2D\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from imutils import paths\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import argparse\n",
        "import cv2\n",
        "import os\n",
        "from tqdm import tqdm\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_QfakAdo4jY",
        "colab_type": "text"
      },
      "source": [
        "# Creat the Dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkpInOcPa8XT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DATADIR = \"dataset\"\n",
        "\n",
        "CATEGORIES = [\"Covid\", \"NotCovid\"] "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxD2G9Ope2CN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "417c42e0-5276-4937-a915-64b4f53e239f"
      },
      "source": [
        "# Just for test\n",
        "for category in CATEGORIES:  \n",
        "    path = os.path.join(DATADIR,category)\n",
        "    for img in os.listdir(path):  \n",
        "        print(category,img, \"\\n\") \n",
        "        img_array = cv2.imread(os.path.join(path,img)) \n",
        "        plt.imshow(img_array)  # graph it\n",
        "        plt.show()  # display!\n",
        "        break\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Covid ryct.2020200034.fig2.jpeg \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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PLXM0nsF3LMDxD5DfdS98hxI9evSo0XCKmiaTSVvJyz4cL/rEszbrRsUqn9vi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h9HPmCx3JvM16ExNl6UWvvM9UaRPH78OEdHR/35DmC4H1ZWVrK7u5tLl6ZbejohzwmiBnoDT+3D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\nRbkoL7T8/7Nl87mmiFirAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "NotCovid IM-0033-0001-0001.jpeg \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9otvrtLbbra6vrwMMXTrwseTsyscXfZZlWWRXXXRGd/S1lF53fEs0oCzLPifp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\nf/zy329qX3hJ4ZWdTjrzce9JB+Ih6AwvFAO48I7OLtLQyw9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BRidDzWkLMmwYK2leMmk8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1Er/GgEBWBDOLTNVFEwsDj6lFNLRqNxYivs+vT0tClMR9O4z+vM3GZkmr60Mu/7yYGayHWNOnse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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJoqvoNqY89W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "f85f0850-a84d-4a6d-c97d-040940a21cd2"
      },
      "source": [
        "dataSet = []\n",
        "IMG_SIZE = 224\n",
        "\n",
        "def create_training_data():\n",
        "    for category in CATEGORIES:  \n",
        "\n",
        "        path = os.path.join(DATADIR,category) \n",
        "        print(path)\n",
        "        class_num = CATEGORIES.index(category)  # get the classification  (0 or a 1). 0=Covid+ 1=Covid-\n",
        "        print(class_num)\n",
        "        for img in tqdm(os.listdir(path)):  \n",
        "            try:\n",
        "                img_array = cv2.imread(os.path.join(path,img))  # convert to array\n",
        "                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))  # resize to normalize data size\n",
        "                dataSet.append([new_array, class_num])  # add this to our dataset\n",
        "            except Exception as e:  # in the interest in keeping the output clean...\n",
        "            #except OSError as e:\n",
        "                print(\"OSErrroBad img most likely\", e, os.path.join(path,img))\n",
        "            #except Exception as e:\n",
        "                print(\"general exception\", e, os.path.join(path,img))\n",
        "\n",
        "create_training_data()\n",
        "\n",
        "print(len(dataSet))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  4%|▎         | 2/57 [00:00<00:04, 12.60it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "dataset/Covid\n",
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:02<00:00, 25.92it/s]\n",
            "  9%|▉         | 5/57 [00:00<00:01, 36.02it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "dataset/NotCovid\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 57/57 [00:01<00:00, 44.79it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "114\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KuCP4YuBOrOw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "\n",
        "random.shuffle(dataSet)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdkfY72IlDJF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "07987c1f-0fa5-4eb7-fa7f-b62ddf37f867"
      },
      "source": [
        "for sample in dataSet[:10]:\n",
        "    print(sample[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "0\n",
            "0\n",
            "1\n",
            "0\n",
            "1\n",
            "1\n",
            "0\n",
            "0\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDLVyWRoKcrV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# grab the list of images in our dataset directory, then initialize\n",
        "# the list of data (i.e., images) and class images\n",
        "\n",
        "X = []\n",
        "y = []\n",
        "\n",
        "for features,label in dataSet:\n",
        "  features = cv2.resize(features, (224, 224))\n",
        "  X.append(features)\n",
        "  y.append(label)\n",
        "  # y = np.array(label)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# X = np.array(X).reshape(IMG_SIZE, IMG_SIZE)  / 255.0\n",
        "# # X = np.array(X) / 255.0\n",
        "# y = np.array(y)\n",
        "# # X = X/255.0\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Z02nbltNJI9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "X = np.array(X) / 255.0\n",
        "y = np.array(y)\n",
        "# X = X/255.0\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O5PDOoLsOrOv",
        "colab_type": "text"
      },
      "source": [
        "### Create the *Training* and *Validation* datasets\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRrUmmUKqzdF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "(trainX, testX, trainY, testY) = train_test_split(X, y,\n",
        "\ttest_size=0.20, stratify=y, random_state=42)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zObnRLVdw_Mq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "8764b96b-617c-44a9-ce3d-7c30b0b89ac2"
      },
      "source": [
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
              "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1,\n",
              "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
              "       0, 0, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u3tQIhL8pPLC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "a0374428-d032-4c23-bc78-67ceb4330207"
      },
      "source": [
        "trainY"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0,\n",
              "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
              "       1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1,\n",
              "       0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trBMS-Bip-V1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "\n",
        "pickle_out = open(\"X.pickle\",\"wb\")\n",
        "pickle.dump(X, pickle_out)\n",
        "pickle_out.close()\n",
        "\n",
        "pickle_out = open(\"y.pickle\",\"wb\")\n",
        "pickle.dump(y, pickle_out)\n",
        "pickle_out.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OeoLromIphTF",
        "colab_type": "text"
      },
      "source": [
        "# Model settings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MINAP1vr-zy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# initialize the training data augmentation object\n",
        "trainAug = ImageDataGenerator(\n",
        "\trotation_range=15,\n",
        "\tfill_mode=\"nearest\")\n",
        "\n",
        "INIT_LR = 1e-3\n",
        "EPOCHS = 15\n",
        "BS = 8\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tMhD8LV1JqA",
        "colab_type": "text"
      },
      "source": [
        "# Model1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGH1aIp29p7S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "dca5d3c5-f0e3-4546-8e20-34f30b89998a"
      },
      "source": [
        "# 2ed Model\n",
        "\n",
        "\n",
        "nClasses = 2\n",
        "model2 = Sequential()\n",
        "\n",
        "model2.add ( Conv2D ( 32 , ( 3 , 3 ), padding= 'same' , activation= 'relu'))\n",
        "model2.add ( Conv2D ( 32 , ( 3 , 3 ), activation= 'relu' ))\n",
        "model2.add ( MaxPooling2D ( pool_size= ( 2 , 2 )))\n",
        "model2.add ( Dropout ( 0.5 ))\n",
        "model2.add ( Conv2D ( 64 , ( 3 , 3 ), padding= 'same' , activation= 'relu' ))\n",
        "model2.add ( Conv2D ( 64 , ( 3 , 3 ), activation= 'relu' ))\n",
        "model2.add ( MaxPooling2D ( pool_size= ( 2 , 2 )))\n",
        "model2.add ( Dropout ( 0.5 ))\n",
        "model2.add ( Conv2D ( 64 , ( 3 , 3 ), padding= 'same' , activation= 'relu' ))\n",
        "model2.add ( Conv2D ( 64 , ( 3 , 3 ), activation= 'relu' ))\n",
        "model2.add ( MaxPooling2D ( pool_size= ( 2 , 2 )))\n",
        "model2.add ( Dropout ( 0.5 ))\n",
        "model2.add ( Flatten ())\n",
        "model2.add ( Dense ( 512 , activation= 'sigmoid' ))\n",
        "model2.add ( Dropout ( 0.4 ))\n",
        "model2.add ( Dense ( nClasses , activation= 'sigmoid'))\n",
        "\n",
        "\n",
        "\n",
        "opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n",
        "model2.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "\n",
        "\n",
        "\n",
        "history2 = model2.fit_generator(\n",
        "\ttrainAug.flow(trainX, trainY, batch_size=BS),\n",
        "\tsteps_per_epoch=len(trainX) // BS,\n",
        "\tvalidation_data=(testX, testY),\n",
        "\tvalidation_steps=len(testX),\n",
        "\tepochs=EPOCHS)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.8610 - acc: 0.4491Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 67ms/sample - loss: 0.7002 - acc: 0.4783\n",
            "11/11 [==============================] - 28s 3s/step - loss: 0.8516 - acc: 0.4446 - val_loss: 0.7121 - val_acc: 0.4783\n",
            "Epoch 2/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.7320 - acc: 0.5227Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 70ms/sample - loss: 0.7059 - acc: 0.5217\n",
            "11/11 [==============================] - 26s 2s/step - loss: 0.7481 - acc: 0.5179 - val_loss: 0.6900 - val_acc: 0.5217\n",
            "Epoch 3/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.7494 - acc: 0.4646Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 67ms/sample - loss: 0.5630 - acc: 0.8478\n",
            "11/11 [==============================] - 27s 2s/step - loss: 0.7525 - acc: 0.4809 - val_loss: 0.5621 - val_acc: 0.8478\n",
            "Epoch 4/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.5678 - acc: 0.7674Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 70ms/sample - loss: 0.5924 - acc: 0.9130\n",
            "11/11 [==============================] - 25s 2s/step - loss: 0.5576 - acc: 0.7527 - val_loss: 0.5741 - val_acc: 0.9130\n",
            "Epoch 5/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.4414 - acc: 0.7674Epoch 1/15\n",
            "23/11 [==============================================================] - 1s 65ms/sample - loss: 0.4487 - acc: 0.7826\n",
            "11/11 [==============================] - 27s 2s/step - loss: 0.4218 - acc: 0.7758 - val_loss: 0.3914 - val_acc: 0.7826\n",
            "Epoch 6/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.2602 - acc: 0.8920Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 66ms/sample - loss: 0.4323 - acc: 0.8261\n",
            "11/11 [==============================] - 25s 2s/step - loss: 0.2469 - acc: 0.8929 - val_loss: 0.3508 - val_acc: 0.8261\n",
            "Epoch 7/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.2638 - acc: 0.8439Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 65ms/sample - loss: 0.3516 - acc: 0.8696\n",
            "11/11 [==============================] - 27s 2s/step - loss: 0.2639 - acc: 0.8481 - val_loss: 0.2861 - val_acc: 0.8696\n",
            "Epoch 8/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.3414 - acc: 0.8986Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 66ms/sample - loss: 0.2793 - acc: 0.8913\n",
            "11/11 [==============================] - 26s 2s/step - loss: 0.3193 - acc: 0.9015 - val_loss: 0.2310 - val_acc: 0.8913\n",
            "Epoch 9/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.2676 - acc: 0.9054Epoch 1/15\n",
            "23/11 [==============================================================] - 1s 65ms/sample - loss: 0.5360 - acc: 0.6957\n",
            "11/11 [==============================] - 25s 2s/step - loss: 0.2554 - acc: 0.9020 - val_loss: 0.4564 - val_acc: 0.6957\n",
            "Epoch 10/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.3528 - acc: 0.7984Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 66ms/sample - loss: 0.2484 - acc: 0.9348\n",
            "11/11 [==============================] - 27s 2s/step - loss: 0.3409 - acc: 0.8083 - val_loss: 0.2109 - val_acc: 0.9348\n",
            "Epoch 11/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.2125 - acc: 0.9275Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 66ms/sample - loss: 0.2406 - acc: 0.9565\n",
            "11/11 [==============================] - 26s 2s/step - loss: 0.2005 - acc: 0.9265 - val_loss: 0.2014 - val_acc: 0.9565\n",
            "Epoch 12/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.2595 - acc: 0.9587Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 82ms/sample - loss: 0.2709 - acc: 0.8696\n",
            "11/11 [==============================] - 26s 2s/step - loss: 0.2384 - acc: 0.9495 - val_loss: 0.2326 - val_acc: 0.8696\n",
            "Epoch 13/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.1780 - acc: 0.9838Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 65ms/sample - loss: 0.2000 - acc: 0.9565\n",
            "11/11 [==============================] - 25s 2s/step - loss: 0.2289 - acc: 0.9755 - val_loss: 0.1734 - val_acc: 0.9565\n",
            "Epoch 14/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.1838 - acc: 0.9325Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 66ms/sample - loss: 0.2165 - acc: 0.9565\n",
            "11/11 [==============================] - 26s 2s/step - loss: 0.1733 - acc: 0.9347 - val_loss: 0.1772 - val_acc: 0.9565\n",
            "Epoch 15/15\n",
            "10/11 [==========================>...] - ETA: 2s - loss: 0.2460 - acc: 0.8718Epoch 1/15\n",
            "23/11 [==============================================================] - 2s 65ms/sample - loss: 0.2945 - acc: 0.8478\n",
            "11/11 [==============================] - 27s 2s/step - loss: 0.2605 - acc: 0.8714 - val_loss: 0.2387 - val_acc: 0.8478\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-X9U7791oEh",
        "colab_type": "text"
      },
      "source": [
        "# Model 2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvqA43LxqBQA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "15806518-f49c-4a6b-f8e6-7cb7e7ac36a1"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "\n",
        "model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(256, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n",
        "\n",
        "model.add(Dense(64))\n",
        "\n",
        "model.add(Dense(1))\n",
        "model.add(Activation('sigmoid'))\n",
        "\n",
        "\n",
        "\n",
        "opt = Adam(lr=INIT_LR, decay=INIT_LR / 20)\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
        "\tmetrics=[\"accuracy\"])\n",
        "\n",
        "\n",
        "\n",
        "history1 = model.fit_generator(\n",
        "\ttrainAug.flow(trainX, trainY, batch_size=BS),\n",
        "\tsteps_per_epoch=len(trainX) // BS,\n",
        "\tvalidation_data=(testX, testY),\n",
        "\tvalidation_steps=len(testX),\n",
        "\tepochs=20)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 14.6659 - acc: 0.5600 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 259ms/sample - loss: 0.6804 - acc: 0.4783\n",
            "11/11 [==============================] - 83s 8s/step - loss: 13.5400 - acc: 0.5422 - val_loss: 0.6909 - val_acc: 0.4783\n",
            "Epoch 2/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.7028 - acc: 0.5067 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 256ms/sample - loss: 0.5940 - acc: 0.8696\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.6884 - acc: 0.5181 - val_loss: 0.5898 - val_acc: 0.8696\n",
            "Epoch 3/20\n",
            "10/11 [==========================>...] - ETA: 7s - loss: 0.4738 - acc: 0.8125 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 256ms/sample - loss: 0.7776 - acc: 0.6522\n",
            "11/11 [==============================] - 86s 8s/step - loss: 0.4542 - acc: 0.8068 - val_loss: 0.6536 - val_acc: 0.6522\n",
            "Epoch 4/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.3071 - acc: 0.8800 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 263ms/sample - loss: 3.3050 - acc: 0.6087\n",
            "11/11 [==============================] - 77s 7s/step - loss: 0.3219 - acc: 0.8718 - val_loss: 2.5530 - val_acc: 0.6087\n",
            "Epoch 5/20\n",
            "10/11 [==========================>...] - ETA: 7s - loss: 0.2462 - acc: 0.9250 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 257ms/sample - loss: 0.5782 - acc: 0.7826\n",
            "11/11 [==============================] - 86s 8s/step - loss: 0.2261 - acc: 0.9318 - val_loss: 0.4762 - val_acc: 0.7826\n",
            "Epoch 6/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.2934 - acc: 0.8714 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 257ms/sample - loss: 0.8773 - acc: 0.7826\n",
            "11/11 [==============================] - 77s 7s/step - loss: 0.2810 - acc: 0.8846 - val_loss: 0.7180 - val_acc: 0.7826\n",
            "Epoch 7/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.1930 - acc: 0.9467 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 257ms/sample - loss: 0.1898 - acc: 0.9130\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.1855 - acc: 0.9518 - val_loss: 0.1843 - val_acc: 0.9130\n",
            "Epoch 8/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.3137 - acc: 0.8933 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 258ms/sample - loss: 0.4474 - acc: 0.8261\n",
            "11/11 [==============================] - 82s 7s/step - loss: 0.4117 - acc: 0.8675 - val_loss: 0.3895 - val_acc: 0.8261\n",
            "Epoch 9/20\n",
            "10/11 [==========================>...] - ETA: 7s - loss: 0.3235 - acc: 0.9250 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 257ms/sample - loss: 0.5349 - acc: 0.7826\n",
            "11/11 [==============================] - 86s 8s/step - loss: 0.3750 - acc: 0.9091 - val_loss: 0.4781 - val_acc: 0.7826\n",
            "Epoch 10/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.3241 - acc: 0.9067 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 258ms/sample - loss: 0.5206 - acc: 0.7391\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.3393 - acc: 0.8916 - val_loss: 0.4649 - val_acc: 0.7391\n",
            "Epoch 11/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.3881 - acc: 0.8133 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 255ms/sample - loss: 0.4855 - acc: 0.7826\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.3866 - acc: 0.8193 - val_loss: 0.4455 - val_acc: 0.7826\n",
            "Epoch 12/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.3183 - acc: 0.8933 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 256ms/sample - loss: 0.4784 - acc: 0.7826\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.3202 - acc: 0.8916 - val_loss: 0.4379 - val_acc: 0.7826\n",
            "Epoch 13/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.3689 - acc: 0.8933 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 258ms/sample - loss: 0.3245 - acc: 0.8696\n",
            "11/11 [==============================] - 80s 7s/step - loss: 0.3403 - acc: 0.9036 - val_loss: 0.2700 - val_acc: 0.8696\n",
            "Epoch 14/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.7099 - acc: 0.9067 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 255ms/sample - loss: 0.1697 - acc: 0.9565\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.6660 - acc: 0.9036 - val_loss: 0.1653 - val_acc: 0.9565\n",
            "Epoch 15/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.2563 - acc: 0.9067 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 258ms/sample - loss: 0.6014 - acc: 0.8261\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.2375 - acc: 0.9157 - val_loss: 0.4950 - val_acc: 0.8261\n",
            "Epoch 16/20\n",
            "10/11 [==========================>...] - ETA: 7s - loss: 0.0784 - acc: 0.9750 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 256ms/sample - loss: 0.0340 - acc: 1.0000\n",
            "11/11 [==============================] - 85s 8s/step - loss: 0.0734 - acc: 0.9773 - val_loss: 0.0337 - val_acc: 1.0000\n",
            "Epoch 17/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.1111 - acc: 0.9600 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 258ms/sample - loss: 0.0630 - acc: 0.9565\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.1252 - acc: 0.9518 - val_loss: 0.0592 - val_acc: 0.9565\n",
            "Epoch 18/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.0376 - acc: 0.9867 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 257ms/sample - loss: 0.1724 - acc: 0.9130\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.0353 - acc: 0.9880 - val_loss: 0.1666 - val_acc: 0.9130\n",
            "Epoch 19/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.1153 - acc: 0.9333 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 256ms/sample - loss: 0.6403 - acc: 0.8261\n",
            "11/11 [==============================] - 81s 7s/step - loss: 0.1074 - acc: 0.9398 - val_loss: 0.5239 - val_acc: 0.8261\n",
            "Epoch 20/20\n",
            "10/11 [==========================>...] - ETA: 6s - loss: 0.2653 - acc: 0.9333 Epoch 1/20\n",
            "23/11 [==============================================================] - 6s 261ms/sample - loss: 0.1424 - acc: 1.0000\n",
            "11/11 [==============================] - 76s 7s/step - loss: 0.2436 - acc: 0.9359 - val_loss: 0.1149 - val_acc: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G3diWaFd2Mrx",
        "colab_type": "text"
      },
      "source": [
        "# Testing & Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vg7KUyAwQbF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# *** TO DO : testing & Prediction\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "\n",
        "# make predictions on the testing set\n",
        "predIdxs = model.predict(testX, batch_size=BS)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jM0Dro6NUTo-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "73c132bf-b018-4e36-f363-dcea6dcfc1fc"
      },
      "source": [
        "testY"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtcCrvqggeGN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Prediction\n",
        "\n",
        "import random\n",
        "import cv2 \n",
        "\n",
        "try:\n",
        "    from google.colab.patches import cv2_imshow\n",
        "except Exception:\n",
        "    def cv2_imshow(mat):\n",
        "        return cv2.imshow('img', mat)\n",
        "\n",
        "def url_to_image(url):\n",
        "    from skimage import io\n",
        "    return cv2.cvtColor(io.imread(url), cv2.COLOR_BGR2RGB)\n",
        "\n",
        "    \n",
        "def convertData(image):\n",
        "    img = image.astype('float32')\n",
        "    img /= 255\n",
        "    c = np.zeros(224*224*3).reshape((1,224,224,3))\n",
        "    c[0] = img\n",
        "    return c\n",
        "\n",
        "_labels = np.array([\n",
        "  'PosCovid-19', # class 0\n",
        "  'NegCovid-19'])# class 1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UD4JaiKssRmY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "def CasePrediction(url):\n",
        "  img = url_to_image(url)\n",
        "  img = resized = cv2.resize(img, (224,224)) \n",
        "\n",
        "  plt.figure(figsize=(16,16))\n",
        "  data = convertData(img)\n",
        "  plt.imshow(img)\n",
        "  ret = model.predict(data, batch_size=BS) \n",
        "  bestnum = 0.0\n",
        "  bestclass = 0\n",
        "\n",
        "  if  ret[0] < 0.5:\n",
        "      bestclass = 0\n",
        "  else :\n",
        "      bestclass = 1\n",
        "\n",
        "\n",
        "\n",
        "  plt.title(_labels[bestclass])\n",
        "                                                    \n",
        "  plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rcPuKHD1MvbM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e78dd695-b5d3-469c-b24e-3578e93ca816"
      },
      "source": [
        "# Pos Ex.\n",
        "urlp = 'https://a.top4top.io/p_15402pp5p3.png'\n",
        "# Neg Ex.\n",
        "urln = 'https://k.top4top.io/p_1540g20th1.jpeg'\n",
        "\n",
        "CasePrediction(urlp)\n",
        "CasePrediction(urln)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hghGzPzur8C3+dPxrv/ZrXTr/TP1TP/VT3TG242d/9me79C//8i93aVpcMLyU\noY78+f9973vf0t/8SZwhuL/zO7/TpRnWwBAKhiLwJ/Tz58936dwW5sWfzJXtBMM/GBZbfZr50qVL\nZdnMi+2i9Qs/E12FQUZE7Nu3b+nvBx98sDuWP28eEfH5z3++SzMcmGEvKuwtn68+f8+8Oe8Z3sOQ\nCba7Ck1Sn2JXoakqxEadn8ND1GfgObeUbYk6vwrhVpYYZDQUlXO/CtFkXmynCrPl+VUoI8N1KluZ\niNnxVGHV6nPqI3mrsFmVZlvY9ly+OleF6HL+8HgVRqdCTVWouwpF43yo5AxqrjEv9hvvo+wXjhH3\ntvy8QSspwj5lWskwqhD/0fBwPk8QSiUonSE5fxUOzrmq1oGy62CYdobzg/citS9yvNk21jXDeisb\nErUm1f1HyZUyfH5gXspyiWu2uqersGmGTXKM+PzJucsx4HN+vs/yuZvrn2Hy2QowYjYcXYVZ57aq\n5wHOY45RNc/nHa9CYVk2z2Veyp6lK+e6zzTGGGOMMcYY8z2JXxyNMcYYY4wxxpT4xdEYY4wxxhhj\nTMmq0Djedtttcddddy2llRbjscce69L5fMb1Uh959uzZLl3Fr8+j+mQxP+PLWGvGdTO2/tFHH+3S\nTzzxRJdmnDj7affu3Ut/s93UQ1Jvxz6lFlR9bp39mD+JTvsNalSpG2I/MUadujHG0+e6KT2l0mow\nLpx1YdnVJ+85Bjt27OjSP/dzP9elv/KVr3Tpr371q2Xd2JY8N3lMfQaafax0ILy+6ndlG1F9Ynpe\nmroCpdfLdVeWJsrao/psvKob+1TphAj7jeuM/cA1XX3Gnv3CdUMNipovlbaLGmheyz5U+imOmZoP\n+XzuPWr/ULpjNVcraxCuMWVDxHYTNTdzWtkYKX2lsszgJ/B5b6vKrmwB5tWNY8q6UOe+bdu2Lj2y\nXzCt9Nhqb8vXKwsMzlWOATWNfBZS6yj3w6j1j7rfVPcuXs95zP2D4610Yxwz9mN1f2Jd+MyndGNK\nO8z9pXpe4bm0uOEYUF+nyq40c5w7vBexT7nemTfnHu831BqfOnVq6W+2i/OBefE7FpwvXMMc8w0b\nNiz9zXZyHdC+jfBbIVeuXOnSSvub57L6VoD6tkiFf3E0xhhjjDHGGFPiF0djjDHGGGOMMSV+cTTG\nGGOMMcYYU7IqNI6vvvpqqedZWFjo0vTmyzHHjEdXMeSjPPDAA106xxRTu0eOHz/epanzYDsZo854\neRXrn3nmmWe69D333FPmffDgwS5Nnx3qQliXSj917ty5Ls14+Kx3jZjVAlITSfKYs16MCWdMuvLh\nGtVP5Thyxt4z1p5j8KM/+iFda3MAACAASURBVKNdmvPlC1/4QpeuPKUq7yF17bzz1fFK26O0FEo/\no/zqOCY8P9eV57Iu1BQofa0i95vycazm0jyqNXc9xzPKb05pt5SWOJ+vfPSUdo95V36mEbXXFssm\nXKNKC6jmNsltqfRuEbVGkXlFaH1mPl/Vm2VRNzjqzVqtQ5al+oV5cczo20YNVOXFpzSqSm+p+kF5\nt2Z47+K1fN7I3x2ImJ3rHGMez/krTzjCflLjT/IY8TlHabm4Z3O8WTbryvPzfGJdmObcU5pH5QOr\nNLUZanX53Kw8a9X8yHVT+wU1jRwTzmWWzX7gfMs6Qz5fUsNI3aD6TgHhGs5e8eo+yXZu3769S997\n771dms98nD98psxrXO3ZPM49vMK/OBpjjDHGGGOMKfGLozHGGGOMMcaYEr84GmOMMcYYY4wpWRUa\nx4g+Fpjxy4xJZtxw1pko7RVjwqmBJIxn/shHPlKen6F2j75J73vf+7r0oUOHujTjlxn3/UM/9ENd\nOsc/s530h6Gn4H333del6QPJujFevvKTodaG8e6s68MPP9yllU8fPX3uvvvupb+pj1S6UM4tjgHP\nVxq3StNGT0jGr3O86b3JMfzN3/zNLl15wrHPlY+W0r8oPV5uu/L4Y13YL9RTKH1mpZnktcorT/nZ\nKY1Drpvyo+O1RI2Z0grmuo/UO2K2H7j+R8aM64D7ptIdMm+idELVGlbnKh9YNb+YrnRkagxUPyjd\nauX7SjjezEtpg9W3CCq9DfMm3KP5rQCWxX5jP+W00jgqP0u1LirdKtcBr81aq4hZ/a26d6n5lsvj\n+Ix60PJ87n3UguV7vNI0ErZT+ecyf9Ylf2OBx5R3rnr24TrhfKl8opWmmc8LLHvU0zzPlxE/0nll\ns5+Uj3jWNBK2g8+qXDecq7xe6XFzP6hnD+6b/JYIn7Pf9a53dens1R4xq4HM5fO7JOq7A+pbAhn/\n4miMMcYYY4wxpsQvjsYYY4wxxhhjSvziaIwxxhhjjDGmZFVoHKdp6uKMlWaJsbg5jnjETy5C6yUI\n9Zb5evr9MP79x37sx7r0F7/4xS69f//+Lv21r32tS7MffuRHfmTZen71q18tr6UukGzevLlL79q1\nq0s/++yzXZreOXlMGFtNXx3lH0RtBvOjDvHAgQPL1pM6QeXxRL9KemFRs1B5X/FYpSGZVzce55j8\n/M//fJf+3d/93aW/H3vssTJvQh0Q16TyWqy815gX+1xpeZT/lFrzOT/2g7pWaSB5fZW/GoNR3aHS\nwFVeaZWua15ePM51Qio9FdfzqLaPeSudYYWai8yLuiHqZdSa5zrLc5n7mvL5JNQJqTWbUfVm3pwP\n9KhVuubKq5Xzg2NAv7r169dHhdJjsq1ZX0WNkhoD5ROstOZ5PrJspWnkfFF7OlE61eqY0s8pvRW9\nmvNxtQ9yfKmPY7tYNucu13geU+qxqSt78cUXuzT3F44Z6z6yh3N+qH7ic5XS+nHu5jXNYyO6wIjZ\nfmLdVNtyeRwvauiV1yrbwrpX913WS2k/Ofe4rz766KNdmhrId7/73V06+0Jy76HWk2VxLlf4F0dj\njDHGGGOMMSV+cTTGGGOMMcYYU+IXR2OMMcYYY4wxJatG45hjnpV/WeVtw1j7Sms1L695dct87nOf\n69I5Zp06jk9+8pNdetRDkD6PH/jAB7o0yzt16tTS39TeUTOgtFyMxadvzjvf+c4uzX46c+bM0t8q\nxpy6UcZiUz/JfiO5LsybulHGiO/du7dLK52H8gHN/co4fuWzxz5Vc5vajI997GNLf99///3dsd/7\nvd/r0hwj5dvIuipdcoZzi3NP6QqVnkJ5QuXzlccXx1dpHFVdKx9Htpt9zONqn1R6i+qY0n7zOPtN\n6ULy9UrrqTQpbCfnF8+vNNW8H4zqTFlXlsXjlaecWv+sy2jZlT5P6U7ZT8rPUn1boPIFZN7U51PT\npDwFuVcp/Xauu9K0jnrhqftJvmccP368OzaqxVJ6OeVJm9um/LHVfGHd+HxSzR+2m88Dak1yDPls\nw7pTt5ifRzherDfrpvwJlX8qyf3Ouch+UF6q1MQprXjuV7VPqnapPZr9zLbmdVJ902DecdZdfSel\n0oarbwGoe5fSofM57Stf+UqXzt+9yH7mEbPPh+rbABX+xdEYY4wxxhhjTIlfHI0xxhhjjDHGlPjF\n0RhjjDHGGGNMyarQOEb0ccjKl4exuLt37176mzoypX9hbP1IPSP6mGYV50+ob2Es/nvf+94uTa+0\nhYWFLv37v//7S38znp0+OEqTxvOZH3UCjGHfsmXL0t+M88/6x4hZ3y2mN23aVF5fxdMzVp488sgj\nXZrju2/fvmXzjtAa2tzPnGsqHp6x+Dxf+fBkzcKdd97ZHfvUpz7Vpf/gD/6gSx88eLCsW+U/FzHb\nT/l85X2m2q30eMpLL+evNI2VVjNivC05XWmI5uWt9BDUyyg/zEqPperC8VW+bJVn3Oi8V36GHH+u\nE+6buZ94f+CaYtnci6gjYV0IxzDvlUp/r9rJuTsyJrxWaTvVGuX84P2l0lTne0lExMaNG8uyuRex\nrkrbV+mSeEytOc4ftb9Qk3/hwoVYjsr7cl7eSnc6qs+qymbePK7GsNKZsg+ZZr153+X5yt+uWsNq\nHVTrex6cu8r3r1rDal0Q5q00+Hmus8/Unj3qr67ufbltHM/qOwOvp64sm/lX11bnRsyOEc9X70LZ\n2/X8+fPdMX6/g/63vPdV+BdHY4wxxhhjjDElfnE0xhhjjDHGGFPiF0djjDHGGGOMMSWrRuOYYZyv\nihPOcb6bN28u8668aK6HO+64o0vnuOD77ruvO6Z0IoxXzh4sEbOaRkL9xPvf//6lv6n7oXbi8OHD\nZV0I86NOgP2Y48SpMaAOhHW7fPlyl6bPEvWVbEvlAcZYfB5//PHHuzTjvqkVVL6gecyVdxnni/Jl\nYiw+xyDH+is/wv3793dpxsN//etf79IcI7ab5eV+Vu3gOmFd2W+8njqzSvPEuciyOO+ZVh6Ulb+V\n0pBwnrNf6OnE/YLzh+XlflSeTqMecGpuV/oYziXlX8j7AT1sOVepr8tzlXmpNcg+V9pPXq80tBnW\nm3VTPmxcBxzz3Hal41GaRqI09hzjrL+h5p3jz35Qa1T5n1b3QqU5Yx8r3RA1jLwX5vmhPGPVGKi5\nzbYw/zxmau8i1DQSpQ3MdeH+rvS3fE46e/ZsWZaaHyOaV86H5557rkuP6gqrtqo9Wx0nquycH+/3\no5pnlqX8dLNneUS/jpTvr9L+KqoxGPVlVPNe3R+qtvF7LwcOHOjS9HmkN3uFf3E0xhhjjDHGGFPi\nF0djjDHGGGOMMSV+cTTGGGOMMcYYU7IqNI6vvPJKp0uh1w01C4x/zsepWWLMMdNXr17t0son5ZOf\n/ORM/ZeDeWWfxYjZ2PsRH5WI3r9SsX379i5NLR+9EhknzjFRvo9V/DzHk7pRlkVNAsf4/vvv79KP\nPfbYsvVkLD6hJoFjxvnz4IMPdmnqQnI/Ki0Fy2bcv9IoUeOUy6a+hePFNUXvtI9//ONd+ty5c12a\nfpiVT5PSWij/QuVPx35grH/On3kpv0KOL1H+VLk8rjHqgFgX7hc8n2WpNZnPV/5hzJvrSPm2sa25\nLdSwMW/ui8rPlPuH8pjLafY5va44vpxrqs+5d1V1owaNY8KyucaVDrXyCFN5Ke240v6yLtSK5vOp\n+6OGlXVh3tQKV76uEfW9i+tfaaI570+fPt2lqVOu6sJ2VhrVCK3tIiNjprTcam9i3SpNPI9TF8p9\nkWmuo0q7OY9KY610x9WzasRsv7Duyrcx96O6r6p2qv2k8vlkPZV2U31bgPvJ8ePHuzS9Gqt+IKyL\net5Q35LI549eqzxn1bcFqrmp5s6TTz7ZpfkuVOFfHI0xxhhjjDHGlPjF0RhjjDHGGGNMyaoIVb3l\nllu6TyzzJ3L+3M+fc3OYJUMFmBd/GuannT/72c926R07dnRphk3mn45pG6HC2vjT8aFDh7r06OfV\nc91YNn/iZngY+5g/czM8SIWu5VACFf5HGLbEujFUhdxzzz1Lfx89erQ7pkIH1CfPcxjsvPw5Zrkt\nDBfeuXNnWTbnchUqMu947jf2OdcF+4FlM7SEdacNzpkzZ7r0kSNHlv5WIbesiwo1ZH4MRanChUbt\nNFRYrTo/jzHXIMvmGHGNcfzZT7TBYRhUZUXEPZbrQIX7qFC0yvKA5zIvZQ3E0FbuJ1W4GEMHlV0T\nw6D5uX3uB5QEsK653zmvmSbKRkB9Kj4fZx9xPijLLIY9cW4yFJ7Hc7+qstSaZKgr5wvXCeuS9z61\n93D+8L6p1gFDHyuLlMoeYV5dlc2RsuTKxxkOznnNuqh7G0MTufdl+wWuOc5NZWmgpA+VvCCiDtlV\nlklqDDk/uGdXKJsJJfFgXXjP59zM4zB6H2U7mffFixe7NNcwqcJw1X6hzlch3nkPUHIAtUerkG4e\n515X1UWVdfLkyfL87trrPtMYY4wxxhhjzPckfnE0xhhjjDHGGFPiF0djjDHGGGOMMSWrRuOYP5mt\nPgNcWSpQY6I+f868L1++3KWpcaEe4vz580t/M46bnwFXmiTG7ivtFmOvaZGQoSaBVh6jFgjqE9e5\n33mu0hWqNPV0HLPcryybn3FWug6ln+CYUdOSdacHDx7sjm3durVLv/vd7+7S1MxSq1d9Tp91GdWs\nEc5FjiHrduedd3bpbGtw+PDhZesZMat3UBoD9gPHnFRaLqXVUNoN9Un0fL3SxzBvtS+quUq9XdYV\n8Vql+2FeRF2f9wfmxXpTJ6QsVJgf52alHVSfP2dZyv5JWY1UmlilUSFKkzSiBWM7uKZ4n+U+yPsk\nvxWgtIJ5biq9Psdb2Vaoex333Twm7FOlQVP2TxyTylKD7SZKD6XGX8313M9cU+xDahZ5v2A7WRbn\nU6X1VDYkao8nqh/zXFXfDlDPm+p+ou4ReUyVJl7ZkKh7QFU39XzAdvCez/2Fx5l/ZcHDc9lu9VxN\nVD9lKquOCP29D1UXMjIflG2R0n5m/IujMcYYY4wxxpgSvzgaY4wxxhhjjCnxi6MxxhhjjDHGmJJV\noXGMqL3VlLdePq78pJTGRHlAseys3aLGZNRTUnnrKQ/CnB/bRd0G9ZfUFBDlMce6ZH8y6h2U153S\nDTDGnGOa9RccE2pSWDbHV/lLKT1Fhu3m3Pryl7/cpbMf5bw0x4RtyeUpn0alM1OaJKVZyePwwAMP\nlHnRK1V5o3G+MM0xzIz6JvG42j9Yl6w1Vn3M46oslR/TWSs8utdQ46So+rXaOyK0T1+ln4yY3fuq\n+aHyZh9zbrFf2DZq5Ljf5L1N7XOVh9e8uo/4erFd3Kt4n6Wmmb5+SjNJskZSaRSr54GI2X5U3z3g\nvTCPOe8fyqdN6QY5n+64444unbWkoz6+yi9Z9WOlY+SerOpW3aMjZjWzXCe5rmqNVvfBeWnWVT2n\n5fmkfF4rH995ZVdlXc/1GeWHrLR/7IfqOYzHCNe7GhP1nEWqfmBeo98pIJVGfqXrX92HVd0qnbry\ny1bfkujqdd1nGmOMMcYYY4z5nuR1vzi21u5srf1Oa+1ga+2J1tp/uvjvf6u1dqq19uji/378xlXX\nGGOMMcYYY8y3m5WEqr4cEX99mqYDrbXbI+Jftda+uHjsf5im6VdWXj1jjDHGGGOMMW80r/vFcZqm\nMxFxZvHv51trhyJiR33VfF599dUuxp0xydRfMRY3x9srPZzSAam48Mo7j/HHo36FSrPAtlBvkfvh\n4sWL3TFqVnbu3FkeZ/w7+4EaB/ojZu0nfRcvXLjQpZV/jIq9Zr9lDQu1Nvfdd1+XPnToUJdmPzBv\npbdlXXPblFaLHDlypEvTr5JaQepIct1VXD81JWwnNSlqXbDfsm6Ix+gxSp3P9u3buzT1MJxP1Cgp\n37aM0qwoDSTLuv3226+77BH9wrw066K8OvP+Maq1UPOJVH5k1Ixwv6/mdYTeR9WY5rpTi6M0SmoN\nU09HWLdcF7WmRn0eq70pot/T2Q+cx/RlXLduXZdWWkDmT11r7gel5eTc43xROiHWlftszp/3E+XL\nNzpGJH+LgHOF/aL0+EoryrpyH63u06P3i+x/rfKOqPdN5aVJlA6dx6u6jXoAjvoEV+3m8VGtntIl\nEtYtr2GWxfWtdKica2qvq/Zl9Zyt9nTlxVh970PpitW3ItQYqm+NVHkR5etYcUM0jq213RHxZyLi\n64v/9IuttX/dWvv7rbX1y15ojDHGGGOMMWbVs+IXx9ba2yPin0TEX52m6bmI+LsRcXdEvCeu/SL5\nq8tc9+nW2sOttYfVFz2NMcYYY4wxxrxxrOjFsbV2a1x7afw/pmn6pxER0zSdm6bplWmaXo2IvxcR\nD827dpqmX5+maf80TfsZqmaMMcYYY4wxZvXwujWO7VpA7P8aEYemafo76d/vWNQ/RkR8KiIev66K\npLhyxtoyjrfy4VJxuoxfpg6I8e2M1a80K9QY0CuRnpJK08i6KY1DTrOefDlnXY8ePRoVWbM4Lz/2\ne9adUf/CNOPh2U41/pWGjXmx3rt37+7SCwsLZXrU3zDrilT8O+EY8pf5xx/vl9bevXu79Pr1fxol\nrrS7Cl7PunEMqJlkP2XYLlU3rgvqdbmOqIHJGknWk/qmUV2y0qxUel3mzT5W+0WlK51Hpc9W7eL+\nQV3ZiK6E4znqrap06spLMe8/SovD+wH3LqXH5P7BuZ73DzWX2C7Oc6VprPQx1KVzz1aad1L5us67\nPs8P5cun9h5qZjmfuObZj/l6zmv2g9K0qTXLuuXjykOWOtFRbz313FXBZx32Ib13VT+SSqfMNcd7\nsPLOU2NGVrJvKv9Cwn6pnj+q72/cCKp+Wam2t9J6q7KJ8r9V+mulcRzRmiu9PY+Pfs9DpTNKRzwy\nX1byVdUfioifjYg/aK09uvhv/3lE/HRr7T0RMUXEsYj4j1ZQhjHGGGOMMcaYN5iVfFX1KxEx71X7\nt15/dYwxxhhjjDHGrDZuyFdVjTHGGGOMMcZ897KSUNUbxjRNXeyv8h+rYvErzUiE1uKouH7Gy+d6\ns2xqCEilZ5hXFxXPnOvCelKzwn5QOpGnn366S9Nrb9euXV0694XSHBClC1BjlPuFfaw0CdTeUD/H\n+HjqrUiOWVf6BqWnUzqzw4cPd+nsf7hly5buGNvN+cC5q+YiNS7UglU65FG/U6Wfo06A+qxcV85z\namCpzeH4cy5nXek88hiqdcC5yzHhXGU/cN2xnyuPUY6v0vIxb45BtQ5ZFvuc7VSaFKUFq+YL93Dl\nlUn9HOuq8qt8h5X+RWleOWa8nnXLXo1cz8xLaZjVfVZ5Dua28Nio9p/tvnLlSpc+c+ZMl+Z8yVrh\ntWvXdsc4/sqvUulWq3WonlVGNW3co5XuMPczz6Uvo9KBqfsw21Ld07nPjejh5tVFacHy+aNaPDXe\nyqdP7QFV3gp1PxrR9o14YUbodaHqUvlZcn6ovNR9s/pugfKMHvX1VHWtniGVB+hKNLD+xdEYY4wx\nxhhjTIlfHI0xxhhjjDHGlPjF0RhjjDHGGGNMyarQOK5Zs6aLW2fsNOO6K80K4/KptVAxxoz75/mV\npxTzUp4+2U8uYjw+muS6UGtDTaLS/aiyzp4926VZ93e+851LfytvmlGfxtGY9YzyG1OaSNaF51ca\nF6VpVChtIMs+fvz40t+XLl3qjt19991dmuOt5iKPU/vHfslaIKVJUfOec1tp2iqNQtZ1zasLvdFY\n95Hxj6j9ylQ/sM+ZN/cT6sioS6v0U6yn0l4o3Znyz81wfLMn7LzjylOU86G6nyjdBzVtoz5cat/N\n9yu1PyjPYXWv45rN/ci5wz5W60Bp2NSenteR0gEpvST9kx999NGo4NzP6a1bt3bHuObYDtWPSr+Z\nz1ffRFDznKgxqdYV26X0+MqLU2n7qmOj2t/RfbZa40pHpnx8lU5V7Ue53zkG6lshStM4sm8qz1ge\nV9rfUT/Mqh+UhlXp+9XczSifRnUfVc9hakyqdaPm6sjzqH9xNMYYY4wxxhhT4hdHY4wxxhhjjDEl\nfnE0xhhjjDHGGFOyKjSOrbUZLWJGxT9X56r490rPEDGraaGGIeef/Z4itKcX28zrVSx21S/UZtFf\n7vTp01HBurMfqJc6derUsnXLfoIR2htN6WN4fuXVqbw1lQccy6o0KPPqmrUg9KdT2jyVVl5Jue2c\nW0ePHu3SO3bs6NKcL0rDxNh66qey/krpjjlGLHvEZ2ve9XlMlF6W84f+djyuPAYrzaua11xjSkdI\nXTM1cJcvX176m/uF8sYjlQ9fxOx+kceE9Vqpv6nSU1YepawL9yrlT6j0ltwfWLfcFuVHxrIq79R5\nZXNPz21hu9UYcHwrLe+8unIMsy8g5z3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rcE8Vmsy5vrCw0KUvXry49DdDJtQn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kcUvvyZ/282fsTP/M8/N4SnPY91+zVP9az/98n/YbSUupyZSHR48enW1TmtyelrDW82lNLU2G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\nRPT0N9XI9JeQ0tpmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Km6Id+1qYPjlflfdN9pWhzFx3U6qQZBd+Ln8KWzoG56jblWGKbIthsRxXSsHE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WzpD8dSSimllFJKKVv6w7GUUkoppZRSypbnUuNoDrG52OZqz7xwc4aTbijV1TK/2Tzh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\nce3bsRpndzXj7t69e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bibm1oM9S7qW9SBJE3Txx9/vB27/U3cc9YX07ceP358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\nSDUl9eWkM536Ov3a9kcffXRo6y9qUsS5qMWYa+a8U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QvnbfJCY1nUKFAvw3XG\n672t1AWxLOqI2A/Uy3CMkydYHzNqO9kOjnfycUxzvY8Dx59lJb8x6pLp25U0T72uzDtp+Sbv1EP3\nT2ua/ZB0hcmfkHopMp0JK9rMQ3mlPZ/XOSZ9HJLvIvd0asOTnoZMukbWm3mlvSydR6TvN8nvlnMx\n1WXao6tmLdekUT5UV+bNdifNfd8vuN5JOquS5on76rRvJr0l+5hlJ10z94++ptPew7OOnsXp3WXl\n/GA903sWy+IYcT/huTt5TrLe3HvS9z04fwjfEXrdOH5pf6CuMGnseZ390OFcZD+wbmwX9900l6d9\nNX0rhKS8k5/2anlfFP/iKCIiIiIiIiP+cBQREREREZERfziKiIiIiIjIyKnUOJJJn8eYYN7LGHXG\nTie/wsk3JfmRJR+dpK+ZPGCYV9J5MK6b6aTNYQx6j72mL9KDBw82acagJ21nivPufodsB/ucsfaM\nd+cYUu9AT8FHjx5t0l2fRw0atZqpj6n1ox6G93ddCLV5HC/qG1gX6g7pZ0mtIPutjwOvnT9/fpPm\nvL558+YmfenSpU2a84dr+MKFC5t073eWRT0M82Y6aVI4P7rGhc8mLQXHIJHWTU8nH0a2myQPuMl/\nbFUvx7pQV5S04Ez3tk7XDl1f6eOqrNfs65B7EfdF6qV4PXmrkd625KPGNHWl6exL2p0+xuxjntlc\nR5wf6dsBHJPJ85jzgWUnLVZ6nul+P69x3lOjyD69fPnyWBc+z/3olVdeefJv7ve8l2cTz5+0Zifv\nPZ497GPq63m2se7Ji5X59304aVqTbj2t0eSl2OE8T9/U4P7C9zTez/2m143vWZN2u2p/fNO3AtL7\nZtd3p7nEucp1wHdCasfZr2z7RNKZk6R5PImm8SQ+j/7FUUREREREREb84SgiIiIiIiIj/nAUERER\nERGRkVOhcZz8x8hqDPGUV9V+PDTj57uejnkxfj35yTCmnLHXjJ+fPOcYA854ZqYZ707NCuPbk89O\n95yir1Ly5WI/Mf6dWkEy+W4lj56k/WPdOcZHR0ebdI+Xv3v37lPrWbWv3eB8IPfu3dukOUaT5pVe\niknTxOcZ989+pJ6m15Vzh/o49jk1jZybacw4t6d7qX9hn3JMqGmknpL9eObMmSf/Zr2p/eVcSv51\nSc/LtTBp3jjevJd7T/JWnPaypL1LmjXuZSx70o1VbfuVZTFv5pX0cakuk6aSe0vyWk17Msef13t5\nSavJfTDlzTOAYz5p7LlGOf5JR8q6JZ3ZyncMmE66U7aTeqpJ+zV906Bqv12cL9TfU2fI/Yb7Sz+/\nkiY+fZ+BnpTcR9nW3g/ss+R1yLOKmnrqMakl5/zp5Sf9HM+q9A0Fkt7jej+nfkhzMWneOKYr64L9\nlDxq0z7MdH934hrkfsF+unHjxljXSdtZNWsc01mV0iRp5KcxSXVbwb84ioiIiIiIyIg/HEVERERE\nRGTkVISqpj/39utTmEnV/p+hU0gFy+Ini/uf/1l2CnNLn40nzK+HvVVtw0dS6BDDN1JYUwpNY1t6\nP7HePby3av8z4KwLy2aYzBRGm8J/GfbKMBiWxbAEjhnDQXp4D+cSP1HOuZVCzxg2zX7soUi/9Vu/\nVRPMm+E67Jc0VxkW1e9nGCvbyfnBshjuwzFmP3D+9OcZssvwHvZbst/g/OK66PkxXOvixYs1kawf\nGCbF61OoewqLTqGo6XPsnPvsp07aRxn+layD+DzpoYpp70mfy1+9fzp/GK41hYpV7Y8h+4XXucf3\nMUv2G+mcZV1TuCjp45DCgZOdS5KQsG4pDHOC+wHXJM9dhulz3fQ9gvVmP7CeDMlkPzB8mO8T0xxg\nPdlO7m08T9gPLGs6b3hucrz4LPsl2Zak8OF+PdmK8IzmGc+5PdkWVe2PWa/7dNZU5XVA0r7Z80vS\nBl7nGZ7kaEn60O/numA7WFayjqJt2mS5cxKLi6ocjp7odU/WHVOYayxnqVYiIiIiIiLyG4c/HEVE\nRERERGTEH44iIiIiIiIycio0jiswtjp9kj7pIxgfnTRuHca/sy6M02bZTKe48a6JSRqTpFFJmqXU\nlqlsxojTwoCfx6YuIOU/fZKYOh/2KTUq6VPtd+7c2aSn2HvWk/eyLuwHtiVZg/T8OX7U1lA3Qo1K\n6jdqVjh/ut6GmhFqcVg25xrnD6F+4vbt25t0H0PWk3ON/UTdGe+nlueTTz7ZpPscSO3g/jFp0qr2\n2z3pYaq2Y8q5RN0QxzvpSNK66emktUmatvRJcz7Pfpv2Lmq3uA6Sni5ZRUz7xWTNULXfLtaV6WQl\n0rWjqU/T+ZA0sEm/PZWdtDhJC5wsELgW+pmQLAz4LMvmfkH91LSH064prTle535AeD/Pwt525sV+\n4HXumyyLbXvuuec26fv37z/5N9dM0kuybtzbks5w2i9Y1qp2j2Wn91HO3b5m07442bcdSqfvQ/T5\nsKpRTOfLyh5dNVsqMa9kcbL6ntbLS2dN2rtOqpHspHPzJPgXRxERERERERnxh6OIiIiIiIiM+MNR\nRERERERERk6FxjHpLTrJm4Rxv4whT5qFyYcp6T5SXQnzY10Zsz7FgbPdzDvFdSc/zMnHh3oF5rUa\n3558t/qYMa6bWoo0Xx4+fDg+n3QDXfNAzRr1EF3HcQjmnfztenmc1/SXYjvSfCEcE3pIda0P72W7\nkmcTdUNJV8Lyjo6Onvyb2s7VvDl3P/74402aOpE+H+kXxvVNj0n2A8eMacL8e7+zHcyLc4t1SR6B\n0z7MuZnqknTsCbalr6Pkyzjpyquyforp5MU5lZ32yeQbTHpb2KdJE892J40rn5/6kftB0hknzVK6\nPnltcv9f1ZES9tukDeN+kbSchHWjPp8+jjyv+vM8F+m9O+17VVkDl/bGDr8FQG0my6aPI/Pm2TXl\nx3bwXYdzlecNy2Kfc01PvsFpj2ZdVv1M+U4waeRZVvIU5J5O0vtoJ33XImmcOZ+4bjhXJ332SX0Z\nkyZ2Ki+dPaluUx/7F0cREREREREZ8YejiIiIiIiIjPjDUUREREREREZOhcZxJc6XsdDJ04vxzoyH\npsZp0ucl3xSm6XXEOHHGrDMOfPLGYl34bNJLsV8Ys8772ZauBWFerAv7hb6O1I2l+dDj59kP9M2i\nloLjz+epUWDbqHnpvn4si/3AdNIhUqNA/UOfT8nTi36D1PJQw0IdAP0L2dau/eC9hHWZ/Aer9tvG\n+cTyel2SPi5pL7g/sG5M935L+uvku8YxoF8ly+bcf/nll5/8m3sN03yW84NjxLYxP+4nE0kvl/aT\npNft91N7k3QfKT15Ah7Kf9L2Ee49yQ+TbeO66XC8uK8xL+5NHAOON9vGfuljlvyOuSdTJ5Z8HNO3\nCfr8Sp6AqZ/SuuHzva1J6//BBx+MeXO/IOwHzt0+xjwPVjWKHG/uo9xv+nzgfs52cu5yH+RcpFcv\n4TrpY868OCasW/IE5FymZpL057n3pH0zafd4nf3Q58fk+Xno2eRvS5ImctL3ckz4XsVvCXDurniW\nf9kaxpP4QK6WNeFfHEVERERERGTEH44iIiIiIiIy4g9HERERERERGTkVGscVf5Gkf0k+S4ylZvzz\npOVJMeJJd8iY9Mkf5lB5vS5sx2p8c6or/YeYX9csJA/IpAvi/ckbqWsc2A9Jw8p2s+xJg5LqynpS\nc0I/MuoCkgccdSFdH5G0nslLk7qhR48ejelXXnllk+7aHvqFUZvH+cC5luZm8ozq/fbCCy9srn34\n4Yeb9Llz5zZp9hv1NMmXq8+35KPEuUUNE9cB++XTTz/dpLumkeWxbM7V5OtHVvafpA1POlNe5xpP\n62b0qwrtSF66ySeYbe/Pp3YTlr3qOdn7nTqglTO4KvsCcwzY1l4e672icT9UNs8XwnO1r/G0J086\n80N1Sd9kuHr16pN/v/POO5trXN/s0+kbCIfgXsY50OvKfW3SqB7Ka3p3qdpfN/RenEjvYZz3PLvY\nFtal7y/ca9hOtou6db5/JN0hx6i3JWkaeT3Nj6Tt63M1ecRyvvDdJz1P2LbJm5Ht5jscdcesW+qH\nfj15J6a9K70TrNy/umev4F8cRUREREREZMQfjiIiIiIiIjLiD0cREREREREZORUax0SP5Z08ug6l\nGYtPPQU1DZMnIePVqVlLXleMSV71J+v3sx2MIWde1GLwOuP+GSdOrVivC8tmWcmjhzHq7GfG5vcx\nm7QzVfvx7UnbM+k+qvY1bg8ePHhqvc+fP79JU1fGvC9evLhJJ2/N7n9JbQW9q1g2tRu/+MUvNmmu\nC2oaOfd7P7IPWRZhXenryXXA+URdSOfevXubNPUvHAPWlZ6TR0dHmzTnes+fc++9997bpKkLSdoM\n1o3XJ60Gy0pavaSXSd5pk19h0n1xfDn+6f5p303eiUkjn+rC/Wjyu0wewiRpIlPder9zv6C32aoP\nG+vOc3TSuXMNcYy416x6OScdYl93STNPOCbUSKf3k163a9euPbVeVVlXyrrzTE/ravKz5J7OunAP\nZtncf/q5WbXdb5IGnuuGWlC2k16J3DeZX9dbcu4lr8Q0l6mpT/Nj+sYGSd8GSExn4+p7dfqex4p/\nOq9PfVS1/+7Cdx+u0eS9Oe3Lac9OmsdVXWJ/flVPuYJ/cRQREREREZERfziKiIiIiIjIiD8cRURE\nREREZORUaBxX4oAZS73qV8iykodLj5em5uDrX//6Jp1io6mXWInrrtrG7idPJ6aZN3Vl1LixbMaF\n97awLtQYMHae15O2izqzrqdgPdnu5C9FTx/OL/YTPaF6rH7yj2LM+eTZVLUfqz957XEuUvfBvNkv\n1JxQu0GdCLUcvZ+SHoZ6WfYT9VeE/ci5O2ke6THJuci5dvny5bGsyUOK/XDhhWHx5gAAIABJREFU\nwoVN+mc/+9kmTf0k9y56zjJ/amr7XE8alOQ3xj5NusKeP+dx0oInn9ek5eP1PmZcB9wXmWZefJ5r\nknvhNFd5HnAfZLsJx4TPk0knlNrJ+5PeKvm29bnL8eb+T/0l+4V7X9JATfsDWfVp45iy7twLu/aP\n48e82G7OVeZNneHKuw7zTvv/lFfV/rrgmPUxT3sR50das5wP9Iyc3tM4z9O3JVg3jgnT3IcnrXh6\nd017T/L5ZL/3dcI+Zj1TPyUNffJy7WPKdcL3hfQdk/TNhGlve9beien3y1Te6rMr+BdHERERERER\nGYk/HHe73T/e7XaPd7vdm+3//f3dbndvt9v9+LP//t127T/f7XY3drvdO7vd7t/+siouIiIiIiIi\nvxo+z18c/0lV/TsH/v9/eXx8/J3P/vvfqqp2u90bVfX7VfWtz575r3e73b924FkRERERERE5JUSN\n4/Hx8T/b7XbXPmd+/15V/Q/Hx8f/oqo+3O12N6rq36iqf/6Fa/g56LG6q16IjL1PejzS47z5bCqb\nGoXks5J0Qz0/tmvSv1Xta/WSf13ywurXk18QY+eTXob9QJ1Z9+ajZoAkX0a2kz5LjJefNApJa8G4\nfmr9qJ/jmFDjdvXq1afWi+NNjSrbxbLSuuD9XadIzy5qbainpA4oeYJRl0oNQ5+r7HP2IXWHSWfE\nfuW66P1MHQ/XQcqL2h7OF64bzgH6V3XYL9zbOHeT3orXJ0+4tEczb9Zt0gFVzXsVy+azhOuAZTO9\n4p3H8WKfc3zZj0k7Omm9kkaRazL55ZKkO+3lp7zSWcf9IPm8Uv/d68Y1yf2AeSWtMOH86/tFmmvU\nz3Ge0++Qc5FjOvk+sp6T/u1QXenFy/lz9uzZpz7PNcmy0vcYkq908qjt+XEP5V7Eucnzhe1M7wjs\nxz7/eK4mf0uuK+ZNWJfettTH6V0nvael99e+13E+cAw43nfv3h3z5vNJx9pZ1UCvei/y+oqPI1nR\nY55E4/if7Xa7n34WyvpLF9RLVXWn3XP3s/+3x263+9u73e7Pdrvdn52gDiIiIiIiIvIl80V/OP43\nVfVyVX2nqh5U1T9czeD4+PgPj4+Pv3d8fPy9L1gHERERERER+RXwhew4jo+Pn8Sd7Xa7/66q/tfP\nkveq6sV26+XP/t+JmP4cm+Cf/hmus/qn4IcPH27SPXSRfwJPoaXpegoXZAhF//N/6jOGSCRriBSK\nwNCDHsLBstNn5Fl2Ch1gSFYP0aB9Aj+1zbAXhppwTBmawrAI9msPm0mhABxfhp5wvFnXS5e2f9zv\n5XG8mHcP763aHyP28Te/+c2xbgybunjx4pN/s0/5KXfORc6fFHrC8LHpM+WsJ8Pa2C8pRJtpruE+\nl1nPZJnDdcDPjLPsZJHR25I+UZ/mA+uS9rre9hQGzzCmZDvANZ3kCz0/lp3kBiS1m22Z7D/YTj7L\nfkufwE8hnb3tSXaRwv3Sns+yuRZ6XTkmvJd1+drXvrZJJ1sr7uns536epD5ePScpEeD1vs44t9jO\n27dvb9IcwxSK+tJLL23SDKvs/cKwxxTKzuu0BmJ4KcNoez+n96ZkW5ZCvJnm+dTbznbyXOWaY7um\n8M+q/bBp9mNPMy/OrWRzRNL1TgolTftHsoLhuuP1FekD1zff6UmaT9PvkdXw0JPeP71jJruWlby+\n0F8cd7vdxZb896vql19c/V+q6vd3u93f2O1216vqlar6P79IGSIiIiIiIvLrQfyL4263+6Oq+r2q\nOrPb7e5W1d+rqt/b7XbfqarjqrpZVX+nqur4+Pgvdrvd/1hVP6uq/7uq/tPj4+P5yygiIiIiIiLy\na83n+arqHxz43/9ouP8fVNU/OEmlRERERERE5NeHL6Rx/Otm0iGmON5JF1iVY9Spkevx7ow/T5q0\n9Ll1xqQnq4he3mpdSPrEfYob7/qb1E7qpXh/+rTz9Glw6mGSvpJ9zLpQo0CtBz+BPdlQUF+X9DOM\ntef9kz6Leihq93id64JaLGraqO1kv9y58/9/bJk2FG+++eYmTb3U0dHRJk2tDucq20KtX68b5x7t\nWdin1BWx3ew3fna+z2Vqq5KFBecL5zbnA3VEk36X7aSuhxok2hKQ9Gn3vma5R3NMWDeS9lWOybRO\nku4j2dBwjbJswvJ6W5LOPO0P7JdkmdDzZ16ca9wPVnXr3EdZXp+bvJck2xm2M52zk16fljepLifV\nDvcxSdZRhHOV+yjLSvtRP7+mPbVqf91wT+f5wf2E3ybo5yrXAe2bOEbpWxGprry/p3mmU1fI8Wea\nfc6zMekvJ70l5/HqGuRcZlv7OZw0jOxDzmXC/SXp/ftexz6a3tmr8r5JJg1l0gmmb6jwOsd0xTLj\ny+QkdhwiIiIiIiLyG4A/HEVERERERGTEH44iIiIiIiIycio0jinOe/Kbmbyqqvbj+BkPT10R498n\nkt8YSR5ijBtnLHa/nuL4GefNdlJHxnj5pOWYYtiTL87k0VO13zbe39O8l+OXYtDTfGD+9F3qsfrU\n3lBzwusk9Qvr2r0Tk6cT82Y7L1++PF6nlo+auK5p4dxgn1PfkDQJ1MNQJ0JdYr+f/cKyqJehhpXz\nPj3ffSI5XkljkjRvbCfXLNO9vKRDZpoaJ86fpBPpehm2k6S8qNXi/OGan7wTkw9jOnvS3sSyOSb9\nfu7BaUzS2Zb86/p8Yp8m7U46R8mk5avatoXjl75TkHSpaa5OOiSOSVqj1IWxLbzOddjryj01jUEa\nk+TzyPz7Hk4NNDWKSX/J84P7MOnzhfs7xzedL2wn1yT3bNbtzJkzT80rab+5RtNelTxuez/Tg5hn\nFceM40vNY/JP7utumrdVWbu36ts4eX3zGsePmsdUtxUfx6RhTD6NaZ9N13vdU11Ogn9xFBERERER\nkRF/OIqIiIiIiMiIPxxFRERERERk5FRqHFdijJMujBoFxpynOOGu7aKnG+PVGTu9Gs9MGAfe9RLM\nK+nEkm4k6ekmvSX7IcXOM00dAeP+eX3SV3CMPvroo02a2hv6TbEt1Gp0/UPV1mOKmgPqIdkO1pXt\n5hhzjHpsP/NmvTkm3/72tzdprgvqSm7evLlJT36o1BwknzbWjfdT29c9I6v2+3nStFG7R50Qr6f7\np/2CY0ANE/uYa5LeesnPcNJYc54zb7Yz7W1MT3sA53HyHyPJpzHpEvvcTppnkjwAV30ge/lsx8qe\nW7Wv9eJ1ngn9OudK0mryetL2MM3zp/cr65J81zh32W6OMfdG7i99HVFHlvRQhPcn3WLvF2rUukdw\n1f548yzj+XPhwoXxOrXjvW3cU69duzbWheci1wnPNo5hP6fT+8TKGjt0f/qmQl+HSRNNPWb61sCk\n3avaX7OTfyqvcV5zDSftJ9s27Rd8luOZ3nWTBpJ16f3EucU19t577415pd8MpLdlde49S90h81/1\nfFy53784ioiIiIiIyIg/HEVERERERGTEH44iIiIiIiIycio0jitxwSs+J1VZk5Ri0LvOgLowxuJT\no0BdB+OjqStK+sspL7Y73U99RNJ6Uk81xVpTH5F0RdSwJA/J3hbG7bOe1HWkuH/2C/0LJ787ziXG\n/SfdYdKhUovR68q8qG+hJpHaDPbDvXv3xrLZL7189nFacz//+c83aWoab9++vUknfU2/nvxJqfOh\nT1fS9rHuXR/DPmU9CbUa7DfuF4R16+ss7UXUeXC+JI/JyYM2+eolz1Gmp3ZWzd6JbGfS5vH+Ve/F\naW9LZ9WkUaza7xc+P2koOR+ShpHznPMj+Z1Oem1eS76/rHvyJGa/cI/vexvbRc1j0gKzrtQWs219\nr+S5mTTN1Mhz7zp//vwmzfOI/dDz51x7++23N+lvfvObmzTX4KRpPXT/uXPnnlqv9G6SxoTaYWpH\nOXf72cg+ZbuSJjG9+/CdgHXpY8K5lXSFPLN7H1ft723TvprWYNLyJS3nyvlCHSnXP/NO50fyGZ7q\ntXo93Z/6sd+/cu8q/sVRRERERERERvzhKCIiIiIiIiP+cBQREREREZGRr4TGcYrVTR5QTCcfLmq/\nHj9+/OTfjJVPcd4sK/lyMbZ/8phju3gv476THxHjwNnn1Ff0/Kg5472MOWfs/aoeovcT60lNAvuU\nY0J9BNtCvQR54YUXnvyb7aS+kmUl/SR9mdjW3k+XLl3aXGM7qIdh2ezj5JU2abmoUWRdqBti2dQ0\n8v5Jb1u1HRM+m3z5CDVKk4do1VazRE0J60I9FTXSaYyoj2G/9Lmf9j3uTSt78KHrkzdWmlvJWzHt\nk9NelfYWtoP9kvbwpP2c9Nkp77RPJv/cnl/S27OPeT/3xeT7OJ3xfHbVHzn5svG8mXSsbGf67gDH\nhGVTt0x63ZPGlfsqy0qexZxfPJ+mvW3yEK7a14qzrmwbNfh9LXB/SBpHXmdd2Y/cN1leryvbxfdD\nan+5jtinqS2se38HmPaSqv19r7+7Vu2/G6W9sM8Xzh3WM2nmk39lqkvvJ87zDz74YJOefDmr9seE\n16e9atWXMem32W/p3J08JZ+lh6R/cRQREREREZERfziKiIiIiIjIiD8cRUREREREZORUaBxTXG+/\nnrQ0jI2efHGq9rU/1GN1jRO97agbYxx40sexLSk+vmsWWFbS8jHOn3Hd7Df2A7Ucvbzk8cO6pjGi\nbmDyO0saAY7B5OFVtR83Tj3EFNvPOP2kKySsC7UX1DH2MaQHGPUr1AVMXneH7qcej/QxYz9Qq5m8\nMwnHhP0weW9So5R0yZz39+/f36S5RietGMck7VVco5z3SWc0+fqldULYp2w31zTnUx/jpGFM/qVc\nw8l7cdLMpb2J/cLr3LOZH+f6pBtb0WYegu1OmrbJA2zVG5PnB/eqdKYnrfCUd9I8sl+Th2mvW6pX\n0nYm/SX3+J5OZ3bXblftn5M8X7g/cA2/+uqrT30+7ffsQ+rn2G562LKfenm8Rp1h0vrx7ON8YT+9\n9NJLT60bxyuNJ+vC61wHXEfcf/p1auBJet/kOuIYT3r+tBetajlJ+n5I38vYDs4P7nvpXTf5OE6e\n5ald6axK3x75vPX6Itcn/IujiIiIiIiIjPjDUUREREREREb84SgiIiIiIiIjp1LjmOKpOyk+OfnF\nTH50VVvdwZ07dzbXqBFIWpukr6LGhTHtPf49aU4ItTesC+PEGS8/ed8kjVqKKU9eatRm9H7jNT47\n6SOr9r31kh8i29rLY9589sGDB5s05wc1BkxzjHo/Uq+Q/IGSh1zSCXH+dX+rpCulv1Ty1aInWNIZ\n9bol3Rc1JawLoS8b113XJbEsjifHIOmCkl8Z+6G3hePFvSbpXZKmOmmHO6segGl+kKRpmcpK+8nk\npXqo7ORP1mE/JI0K97Z03vT0qg6M1zlfkm/jNGacD5ybaU1yb2N+acx625gX6811lPoteXP2Nfze\ne+9trk3+xXy2al9nSD9c3s/zpI9h0oXy3OT7Bd9duOfz/t6vXIPp3YXjzbnNvYxjlvp1qgvnKvMm\nSTM57fHsc6735EnNdnKMWHavG+d98nld9S9k2eyn/r2Ad955Z8wraT05fzgGHMOe/0k85w+R5svU\nj6uekCt19S+OIiIiIiIiMuIPRxERERERERnxh6OIiIiIiIiMnEqN4xSLu+L5eAjGFCf9Q8+P+hXG\nnNO3jfHwfJ6x91OMedU2hn3yQaraj9NnvHu6nnQi/Tr7jLH31AEw76R5pJ9Vj7dP+snkq8br7Jfk\nw9Tvpxbn7t27mzR9uKh/YT9RA8d+7WWznkmTdu7cufF68iSd+ontYJr9wHXDsukpSS3PpEngNep6\njo6ONmmuA/Y5NUyTZontZt70+fz2t7+9Sa/qRNjWPh+5P1C7NXl4HSqbcMx6XbhGeS/TSYNCLQ/3\nVepx+hrnvE06c+5tTHMMkr9lX2cn0TsdSrMfJo/BpImn/onrf1W3zr2x55c83NhOznPWjXs65+7k\nb8kzPXmnJu9Ftpv7R+8ntoPznOcJtd9sF/0J2ZbJm5nnAfdJahbZ7kePHm3S169f36TZb71fu16+\nan+/p38l1yzXUcpv0jSynUnbmd4veJZxfnCf7msj6QiTNpxjyrl88eLFTbqXl/bwtI+m75aks66P\nOfuQezLrtuIZe4he95PoBg8x+YIfIv2+eVb4F0cREREREREZ8YejiIiIiIiIjJyKUNX0Z+rPe61q\nP+yF4R4MkUjhP/3P1PyT+M2bNzdp/qmfoSQphC99drr/mTpZHqyGovJP4OkTxz0MivVmKAnrwn4k\nKZys588QO4Z/pc/K89PdDEVhuMf0yWOGfjAUkePJkE2OEcMqGXp2/vz5ehrJ+oXrhCE5ySJl+gw9\nw5YYfpNCLjl/uI44xrTI6GucZTGv1XZy/+CYXLhw4cm/k71GCi9On6XnGLKuPc01lz5Rn2wEeJ11\n6/sF28k+JCkcJ4WDTmFRqV2cq1xH3A8415kf031MGYrO8Uy2RDw/0tk4WQcxL4Yys1/SJ+/Tp+Gn\nT/1zvqT5wLJSqDP35b7nc+9hn3K8k20R68Yx7hYcydqB11lXnifsN74j0E7shz/84ZN/c6+ilRTr\nRrkB90WebXy+r1nOPZ7RDDXlOcjzgO8TnG/cC3s/XblyZXON48eQXI4Bz3DOlxRu2seQ45nCw9N5\nwfnE673fuN65H3B8U9g980vhyD/5yU+e/Du9y6QwfNYthQBP4aMr1oGH8k7Xp/dylp3CZlfCaP2L\no4iIiIiIiIz4w1FERERERERG/OEoIiIiIiIiI6dC40imT9yTFLfLuF9qFJLdQk9TY0KNCmPz02fi\nWVbS/vT706f4GcfNuqZ+Y/w069bj4/l5ZPZp0tvxenq+64xYL2ot0mfh03XqJahR6J88p96BeiqO\nAevKMaUej7qRDuc1P0H+9ttvb9JsF/uBcznNfWoSOtRSUAtMHQj1DrQGoB6Tc6DXndeozWBZ1D9x\nPvAT95NmhWuQ85pp5sWyk/6SYzTpIQjzSvqYtFf151PZzIvrhkxanEP59bXBPma7VyxwDtWVad7f\n50SyhmI66dgnfRSvc0/lek9nFcc0aSYnO6g0j5P1R/r0P+fytA+nfZDt5N6VtH/TecP9IJ3BJJ2r\n7Aem+37B82Q6e6pmG4mq/fNmWuOcKzwXqUlM1mSs+2QtVrWdT5yLrPfly5c36fv372/Sk1VU1f78\nYl17W1jvZB3EspLOkNd73dnHSRPPuZe0wfw+xGSDkr6BwbyTdjyxen/nWdtnTGdp0jyu4F8cRURE\nREREZMQfjiIiIiIiIjLiD0cREREREREZORUaR8bmTh5QyeckxfVSJ5DioXs8PTUkTL///vub9Llz\n5zZparUYB054vaeTNyL9C5O/VNJIkt7PzJvx7knfkLQ6THdfJ7abedHjifoI6ik4/qwrn+/lU5NI\nTRL1C9QFsi700pr0NdQ0/sVf/MVYNvuUWguuI2o3qP3oWgz2GbUYH3zwwSa96m10dHQ0Xu/zg/Wk\nzxb7gfOF64LzY9qrkvaCGkbCvNPeNenS2M7kZ0ttFuvKMeb86m1PGkfmxXXB60kLOPkb8t6kj2TZ\nyQOM+XFv7PMn+VtSq8V2Jf/CyUOS8zjpwjgmk+b9UNnTPp10XtxXqc1iPxLuw/fu3Rvv7yQ/46T9\nZr9yHXVtF3VeSdPIvG7cuLFJ80zgXtY9JKu22nO+P0xncFXWnXIMOP/6fpF0x9yb+F5F78Xkd8h+\n79rPtE74bsPzg9894HxKc7fDvSV5ziZ/Qo4J+7mvO+adzo9UdtJj/uhHP9qkp/nBPZr7SfKUTEzv\nJ8mvNr3bpH6a0qlsNY4iIiIiIiLypeEPRxERERERERnxh6OIiIiIiIiMnAqNY9Kd9Hhoxivz2RTn\nzfjm5JXU46epvaBeimUzdp6eccmPhjHsPSad2oqHDx+OZbMfEknL0+Phky6IcfwpVp/6COpK+jjw\nGnWBzIvx79QVUi/Buk+6Inp6JX8x9tNrr722SbNfbt26tUl3/QXHO/k00q+Q64j9RE0LvTv7HEh6\nBtaFWmBqmjimHDNqHrr2h2VT95N0Apzb1BVRF9LL43qnXoYkj8HJZ+tQXbt3Ftczy+JcZFlcs8nX\nr68L7pusN+ca68J+SxoV+tP1trMfkscsy2K7077K/Pq+nXTFaQyS5jX5RE55Jb/j5LW30q+sF8eI\n+2pao4Tzh2u453fnzp3NtfRNBa5/7pPUkVGf1/dp6uc5P7h3cc9n2Tx/OIbUrV+/fv3Jv5OHbPfV\nq9pfc1zz7CfS9/zUTubNPk1nW/qmRh8zlkXNIucD5xrfETkmTLO8nl96J+M5yT5nXTlfuOb7+0fa\ni9jupHlmW6i3nTxpk+44kc6ySRuYdIRJo5j6YeU8SRrGVS3nph5f+EkRERERERH5jcAfjiIiIiIi\nIjLiD0cREREREREZORUaR8LY3R6TvOqTwnhm6meSBrLrRCYdz6E0vZCo5WI8O+vCuPGuUWBZq95n\nzDt5iFFn0LU67GPGyiefnRQXfuHChac+T10P4/pZNutGrSjbQu3GpAVjnzL2Pvldsu7Ux7CuvWxq\nGjm3WNbt27c3ac5NlpU0kxyjDrVW1LBQ25P6iXVjW/uYcfy4xtJ+wLmYvPe6toPzIWnQku9j0v5N\nmmmWlXxg2a7k2zfpWHlv0kRz/DkGyVuR667XJXmAsU+TTjBpXqb9h/M2eaUlfS1hfr08znOuE5bN\nvYhacF7nfJh0Zkm7mzTSzJt7PNv24MGDTbr3I/cm6gA5Zkm/zzGgXrP3I/dYjhHP+FUfaNad9O8k\ncB4nnXL69gT3bOrYp+8WEGoaOT+oU+3+lFV5b+uk7xKwnewXQl0h72fb+lyefHo/T92SnyX36T6m\nyXs77YMcI2pk+f2GSRvIfY/rJOkE05ixXyZ9Zfr9kVj99siKp/1J8C+OIiIiIiIiMuIPRxERERER\nERnxh6OIiIiIiIiMfCU0jj3mOMVSp9hrxi8zdn/yPmEcP8tirDQ1C/S+u3bt2lj2pK+6cePG5lrS\nR6Z4aLaF2g1qFLqPF/uQeSc/w+TjNuk3k7dZ8rpjv7Hdk0dg1VazQp0P80raPd7P+cYx6GNMPW3S\nIFHLQ1826oKYvnz58ibdNSvJV411S3OTGsjz589v0tQ89PnFPqXWImmmWTaZfPomX6yq/fFNc3XS\nXlTNe1/SSyY/S5LGuEPNEvNO64LtIkkD1/cn5sV1kbSgnE+rXlq9bat9Pmneq/b7mff3+chrPKtW\nPYa5N3FN8oyY5s+q1xmvc4/m+cG69vOJ+yj7OHlOcx+dzs2q7fxju7hPJs00xzB5aXIf7flTa5fO\nJsJ3G2raeOb3trDdLIveiGxXOnd5nftNX0dJP5c00GnPTu9Z/dsSSZ/NPk261JXvg7CeHM/kV8h3\nwB//+MebdDqXez/yXYRrKu2jaV1Mz69qGld9Hlfq8mXiXxxFRERERERkxB+OIiIiIiIiMuIPRxER\nERERERk5FRrHFDe8EufLeHimk3fNVBfGYaf4d5ZF/6jXX399vH+KE09eZ+wz6iOS9pMx7Y8fP96k\ne7w8dUKEZSU/Il6njqRrEDgm7Bf6CzJvxscnbQ/1Ml1XwD6j5oTX6Y2ZfP3YT10zS80Rn6XejlBX\ncvbs2U2ac5E6oq6n4Bg8//zz47NsN/uFbSN8vpfH+ZGg9nPSPB+ir1HqI5PvFstKPo1k0mMlD6/k\n27aq9en5p3byWead2p3yn/zuku9a8kZL/peTvpPtpEaJdeOYJI098+/9kHSmbAf3D5bNPuc6Ir0f\nU1ncL5IGNukQJ8/ipJ/kWUc/XNadvo1T3ZP3Jc8yvk9QH8cx5X5CHWq/fzpzq7LuNPlfsl/6np/2\nTbZ79bsEhNf7GKb3KOotuYY5X5JvLNve52qaS+wnjlnSzPN9pc8n+k+mevN7Hm+99dZYdvLy7XVP\n50PSmabfBF8mJ9Uw9ratetqv4F8cRUREREREZMQfjiIiIiIiIjLiD0cREREREREZORUaxxTnO8X1\nkuQvxNhsxlYzlrvHTyefRWqxWBbjthkPz7KpWeix3UnLyX5i3D+1ndRXUPt37ty5Tfru3btP/s04\nfuaVfByTBxjj5/v9bHfSYkxarENpalYm/RS1FmfOnNmkk3aP+smkW+xzl31GqDm6dOnSeD/HhOuk\n+zZWbcec64J9znZxzKgb4fjzfubXy2ddqOUkSSvMucx+6XXhukgeoymdvBeZf5+bSf9G2O6k3+Ze\nN13j/sB1w/s5F5O+jv3U2572B3qEsazkT5Z8Yntdk66cc5HeaWwL+2nSzLHd7DO2ixo1Xmc7k49j\n15Ulj9mkx2TePFepsaaWq/dTGm/uTawL93yOwaNHjzbphw8fPrXspN1iP7EuHAPOH2r7Jt0p28E+\n5lzm8+ldqM8BelSznhxPfn+B71Gs27179zZpzv2XX375yb+5xibPx6r9M/zo6KhW4PtGn/sc7+Qb\nzbwm/9Kq/THubUnfGeA6ofY3nQHsR9Lrsnpuct2k3wjpHWCFpEP8MnWKJ8G/OIqIiIiIiMiIPxxF\nRERERERk5FSEqpLpz7mrthMM9+Cf8xmaxBCK/id43rsKQ2pSKAH/5N7/XM9nWe8UapZCjxiSwdDV\n/jz/9J/y4hikkDuGSbAuU17pk/Usi3X79NNPN2m2tYdcMBQ1feKedWX4J0Oq+BnyHqrCPn711Vc3\n6StXrmzSDFtiHzO8hyFYDAfq/cRnOde4hletH1Y+t88w1xQWmT71n0KweltYFucD06xrCrlheA9D\nl/oYJZuZ1KesC9cB29rnQFrvyW4j1T2FE/cxSXMvfcqddeGeneQM06ff2cep37hmp7OrartXpfDf\n1fFmP7IuHJMpL97Lc5drcDXcmOX1fuH6Z0gmr1+8eHEsi3v41atXN+m+5hlamixOUgger7Nf+Q7R\nx3zFHqFqv908NzkmPE96yG6yCrp58+YmnfZsWkMw9PXjjz/epPuefvkED3NNAAAgAElEQVTy5aVn\nOR/Yx0mmMdlHpbOMFihc4wyjTe++nSQPYDvSPkg4xny/6Pkz3Dv9Jkh7eHq/OAkpr2TBR6bfQtO9\nh9LT8/7FUUREREREREb84SgiIiIiIiIj/nAUERERERGRkVOpcVyBcbop3pnxzYz7Z2x1j5+nLozx\n7Okzv9RLUEdy69atTZqf/qaGqcNYempOUiw1686yUux1h/VONgLUdjA2nzqyPmbMmxqBdJ26EeoI\nqQNgXfunwNkuxv1z7p09e3aT5vyg5oDPnz9//sm/X3rppc21NO+TXobzh2nm1/uZ2guuKcI+TdYA\nySqk60iShpV5pTXKfuC66WVT88qy2Ycsm2uaGliuac7trr/htaSnS3rK9Mn7TtLPJdsB9vE096pm\nzRznA0n63MnioirrDHvbOa+TRi3pDlk269r3BO4PydIiWclwj6YWjNd7P09azKr9Ncq6c50kzfyk\n7WEfcm6xz7mHc0y5z1Jn1utCyyvuHxwjvn/cuXNnk/7+97+/SXebiap9G4v+7QDq6agTTO8LaS5z\nDfcx/+STT8a80jroesmq/f1gsuup2raFukG+23Bu0eqDczd952DaK1OfUzdK/SXnT9JX9n5K7yKc\n98leg/sB1zTfCfr1ZL/BPfyk9hsnseNINlZ/nfrKCf/iKCIiIiIiIiP+cBQREREREZERfziKiIiI\niIjIyKnQODLuN/mXTaRY+qS3Y9x4z4/x6Yzjpi4geXgl70XWpT/PspkX47oZa09dCOvK62x710Pw\nWcbWUydILyNeZ905Rs8999yTfzMWntoa1ps6Vc6HpPVhuo8Z807eZtRDsJ3UDdCLsWti2KdpDSXN\nUu/jqn1NAseoa94mP6hDpDHgukj6qd4XbFfyGE17EdvGudvXZRr/pBtJnpGsO/eEPkZpvFmX5PPH\nulAf09cJ65l0hswr6bHTnt7nT/J8S3rLBMd82sNJ6hfmndrC6yuapaTHZTu4/6T9pmv92EfJWzOd\nm6wbfX+nuc41xLpwXSTNK+tGHVrfP5IubOrDqn39Hevy4osvbtJcJ10zSW0mv7/APuZ84dnGc5M6\nxj4mPHvYzuT7yec53mzLpUuXNuk+Zh988MHmGsebfc6ziXOb70YXLlwY8+9tTR7lnKvPP//8Js11\nkPR2XWNLfT37dFXTyOeTtrz7grKezJvzfsUb8dDzK3mnb64kVuqyij6OIiIiIiIi8szwh6OIiIiI\niIiM+MNRRERERERERk6FxjHRY3NTzC/jvlMsddJEdj1N8txh3D7j3xljnDwGGbvNGPUpL2r/qGlK\nfkLUbv3iF7/YpHvsPfvh6Ohok6avEjUJyTOM/dDbwlj4pGlLPmspFp/6qp5/0vmwLOpI6ePFNPUQ\nXW9DbQXn+arPGvuNbeP9vS7U+lL/kLTB7GNqNZK2o/dz8mSa2nGoruyHSfvHZ9mn1Oqw3eyXpN3i\nmu39wrKSRpEkn8dpzTJvrinmxTFgv3GNJ+1GrxvHi6TzgHVZ9XWc9k3Csqgzorde8jPrZwTXTDov\nUp/z+aQV7f3CdjLvpFGi3yHnNufu5BtLnTrHl/M8XV/RSLLdrPfqu8zknVm1r+3rZzz3lmvXrm3S\n7Ce2m+mkievldT1b1b7fJMePnrJpf+G5y/ePPh85BlxzfJbjm/SafK+iBrbP7VWvXV7nmU+PSq7D\nnqZfKed90jhy7nF+JO/FvkbZLr4fcP6cVHe4onkkJ/Vt/DI1jxP+xVFERERERERG/OEoIiIiIiIi\nI/5wFBERERERkZFTqXE8SRwvY4IJY44Z101dUY+np+aM9WSsfYpvTp5xjMW/ffv2k38zBjzpHSZt\nXlX2oJy8s+gvSFI8O8uivoLXe7w7x4R6F8b1Mzaf9yfPr0m3lvwqqXehFpT6CGoamF8fg+RtxrIJ\ny+ZcJOzHyWOUcy/pfpIukfOB/T555bFP01xk3diWaV0xL/YpNUjJz5T5cT6QXh79xJJubNIVH0pP\ne1nSAU7amqr9fmDZnG8cs972NLfIpJ89dD1p+/r1pHdhvyVvXo7BtA4I28GyuE8mT1LWjbq0Pr84\nntSBJd0h92yOAec+x7zr1FjPpHFjHzNv+iFyjHv+/E4B4Tma9uw0PyZdO/cWztV0rvJ+avlYl/4O\nwblIfSU1bO++++4mnfZNfnti2l84l9JcS9pxjsmdO3c2ae591O89rZ5V+33K90euUY4J+7WPA/dY\nrgtep1aTazp9U4Fj0tdVek8mJ9UF9ufTnp3Ol7Qnp98IJ2GlH/yLo4iIiIiIiIz4w1FERERERERG\n/OEoIiIiIiIiI6dC45h0Jis+jowRTho16hIZP93jvBmHTc0BtRRJc8C6EfZLjxOn1oKaBGoWGWPO\n2HvmRw3DpJegLoR1uX79+iZ97969TZpeSOw39mvXgvFZ6hdSPyU/KsaYTz5+HE96H1GvkDRN1LxQ\n+9GvT/O2KmvzWLekU2Q/9LXBulAPkXRhfJ7X0zrq17kfsB28zvlEWLdJx5j0DRzf1A+se9IO9vuT\nTpR1S3643F8mH0i2k3Mreaky7+TzN/llsl2pz0nSobEuUzr5i7EfUl1ZN/ZDzz9p2JPmiHVjftRL\nccx73fks9/v0LQHu2dzz2W/TfFnVz9K7mXvdrVu3NumXX355k3706NGTfyddOseXdV3VR7Hfelt4\nHjCdzirWjR7EfGfobWW9qGFjHzPvmzdvbtK9j6vyHt/nF8ckeUTyPYvtZD+eP39+k+Z86/mxT3kG\ns11cs3zXSRr8/jy1ncm3k3ANpnebSac66YSr9vs86ZAT0/cA0rm6+r2GZ/l9l5Pk5V8cRURERERE\nZMQfjiIiIiIiIjLiD0cREREREREZORUax8RKrG7SbiQdwOTDlZ5NHnGEZVG7QR1J99Jiu6gbZCw9\n/aRI8uGhpqnHw1MPQ43Kxx9/vElTB5D6iW3p8fCM62ea/cT49+eee26TZp+zX5ju+k3m9bWvfW0s\ni95oSQNJ3Wm/nvxJqUGgPiLpbZN2q9eF7WafMxY/abV4P+cX9RHT/KA2h9cnLdahsiZ9Hq+xXZPH\n36Hnk1aD6V4ey05+ZGxn0qFN2s+kp0x+havevJzr/fmpjz5PWcmLM2kke12ThpV1YbuSv+XktUYP\nN857roukeeT8oS6N+fe9kGUlzSr3E97Pc/Tx48c10fdh9iHProsXL27SXCfUkdGblXq7vqezXUnr\nn7yaWTb1mQ8fPnzq86wn3wc4vq+++uomzW8N8Hxhv/X82A6uUY530luz7t0Pu2p/fvQx5JmbvhXA\nNcfnuV9wH6bmsc9NrhOOUTonWTb7kXXn/R2+P/BdhmPGMUnvVfw+RG8rdcVJt57ebciqBnJ6dlV3\nuKKZXNVH6uMoIiIiIiIiz4z4w3G32/3j3W73eLfbvdn+3z/d7XY//uy/m7vd7sef/f9ru93u/2rX\n/tsvs/IiIiIiIiLy5fN5QlX/SVX9V1X13//yfxwfH/9Hv/z3brf7h1XVYwvfPz4+/s6zqqCIiIiI\niIj89RJ/OB4fH/+z3W537dC13b8KqP0Pq+rffLbV2pL8zU7iR0J9RCqLMcY9Xpqx04xvZ96M42cc\nd9LfMXa/M3l0VVV9+OGHmzQ1LYy9pxcSY9gnfQW1edSFEJbNdvM6+73XhToN1oV9TJ0A28V+5fy4\nfPnyJv3iiy8+9V5qGgl1IEmrxTHu5VGPwD5kPzBvtptwPlB/c+7cuSf/5jznmHA8SVpn1OqQ3laO\nAfUsSaOWNNCTljT5OCZPyKS35HzjGPe6Jq0n6zr5Mlblfut1SdrM5I3GfZRlU5vDfpm0Osl3K3mK\nUieUNC79/lXPSOrK0nziuulzlfOBGqVpzz1UdpoPbEvf8/ks9yb2MevC66w79wB6TPa5zjXHuXX3\n7t1NmnOTGjXq+Xmm97ZzTLjnJu1V0udT+8m69/MonUWs609+8pOxrCtXrmzSk/776Ohoc43vLlyz\nrAu/55B0h5ybvbzkMc1+Ypp6PNaN58eDBw826T4HqJ/lvpk0jEnfzbb2MWferDfzTteTbpXvkH0u\nczyTx+TkCfl5mO5Pa3LVdz597+Mk33tZ4aQax79ZVY+Oj4/fa//v+m63+9Fut/s/drvd33zag7vd\n7m/vdrs/2+12f3bCOoiIiIiIiMiXyEm/qvoHVfVHLf2gqq4cHx9/stvtvltV//Nut/vW8fHxL/jg\n8fHxH1bVH1ZV7Xa7L/7TV0RERERERL5UvvBfHHe73b9eVf9BVf3TX/6/4+Pjf3F8fPzJZ//+YVW9\nX1WvHs5BRERERERETgMn+Yvjv1VVbx8fHz8J6t/tdmer6tPj4+P/Z7fbvVRVr1TVByes4x6r3icT\nk76laj8Wm/HTXU/B2Pjp3qp9LVbS7jE2m/n12HvqXRgzzrwmXUfVfhw4tWDUifS6sSySvK1eeuml\nTZo+S6xbj2lPcfzUR1BzwH7svoxV+75drHvX9iU9XNIBcX5QJ8L8+7rg+LNs5s25lXyXONcnbRjz\nYr2Tbxvvpy6IZbNuPT9qt5KfFK8njRvT/XlqUJIHINdR8jvk9Uk3lPSQ7EPui9zLODepU+75Je8z\nkjRtSTs6aSYn/9GqdZ9Gjhnvn/Q103o+9CzHn9oePs+53+vGPTlpd5MWNO0/nD99TJMeKmli01mW\n/M56/kl/zb3oo48+2qRXdcx9X6b/7f379zdptivti4R1o57u6tWrT/5NrWZqZ9rT//RP/3STZr90\nHTzXFL8FwH6a9v/Pw+uvv75Jd33dW2+9tbnGucq5l9bRrVu3NmmOIXWtPT1pMav2x4zvOn18q/b3\nB/ZjXxcc70k/fQjWlWt4xZM4nZPpPEi6xOl8OYnH46HnT5rfSlkresrPY8fxR1X1z6vqm7vd7u5u\nt/tPPrv0+7UNU62q+ltV9dPP7Dn+p6r6u8fHx5+WiIiIiIiInFo+z1dV/+Ap//8/PvD//riq/vjk\n1RIREREREZFfF076cZy/FqbPt6+GrfJP6vyzNUMPJ7sOfr6aIVQMO2AYJMMQGCaXQh37/WwXQ6TY\nTv6ZmqEFN2/e3KQZmsq69VA1hqmkEDz207vvvrtJJxsChll12C6Gc7FPv/Wtb23S/Fx2t9s4lH8f\nhxQunOxa2G9pfvW1wJAZrpNUNmFbVpisGQ6VnUJPU0gf+4nPT3VJYS4cw7Su+hgm6x8+m8JmOXdT\nuOB0L8cghb2y3exz1rX3G8OUSArpZbuThdIU/pP6kPsqr3ONpjGdxiSF1KVwQM5zyhG4Tvo48Fr6\n3H6yIUiSAc6X3s/Mm/OFc4tzl3VN9hsMq+7vAAypZYgm1yjHl21Jdhx9naTQZL5PpLD5ZPfC/LvV\nCM9BWioxzJF143inddfryvek999/f5Pm+DLNdzpeTxKSbk3GdnA+UNLD84J1SRZsnE/9/YX1vnbt\n2lg3hvQydJXzjfOlr7MUgk24RpN8Jc2PPkasN8tin3JunsRCI8noVkNPTyLD47NJhvFMQ1VFRERE\nRETkNxt/OIqIiIiIiMiIPxxFRERERERk5FRoHFc/YT2RYo7TJ+5Jj0lP+shk/ZHi36lhmawBqLVg\nnDd1gLS8YNw36/Lw4cNNmm1b0Z2ybtSwsC5sd/88dtU2xp1x24zzZ+z8d77znU2atgIsm2PO+3vb\nOdc4RtQcJL0U9TZTbD/rSdhPHJPVz2Mzv163FOfPZ7ne2c6kt5z0Wakdk9amal0n0HVn1F5N2u2q\nrANJOjP2Q//UP7VV3B+SPnf1E/ddG5TsErhGUx8ny4wVPW/SrCQNWxpT0vNLeluWxbrwvGA/UtvX\n8+e9SduZbKvS/kOtWNchsSzOF45vOuOp5Ur2LlPdqfXjs+xjav/Yb6xL78e0DtKY0LaC/Ui9JfPr\n5w37hGcZ9xPaWL3zzjubNOcux7BbcNFWgu8H1LDRtoR1+/a3vz3WZdrzeWZzzVGPyTFjnxPOJ+Y3\nvet0TWrV1hrsEOw33j/Z+/BdhOOXzouk109rvMPxSxZIJGkBp3eElXsP3Z/Siel98yR6SeJfHEVE\nRERERGTEH44iIiIiIiIy4g9HERERERERGTkVGkfGOyfdYWfVk4U6Esbyd10QrzOWOvlLUQ/HeHZq\nFFj2pCthnDbj0+nxw3Yzlp6x+Iz7Z13efvvtp+Z15cqVmkgeUOzns2fPbtI9tp/15ni/8sormzTb\nxX6kpoFaHo5hJ2l1kl9d8j6aYvNT2Um7x7L5PNvN8s6cOfPUa3yW6aTtojaDnmIsr8+npGehdiN5\noSX/qV5X9iHTrBvXGHUjHLPkd9r7OWlOki8Xy04eYL3s1M6kgeT4Jl/Q6XnWheOXPEZZl+QxuVIX\nkvS1yfeV+ffrrFfSna7WlecR697zZzuot096TM4HrukVPTf1cdS0JR16+tYAy+6afJ7/fH9I3Lp1\na5OmfyHhmHVtKPWShHsw5173Qqza38OpQ+1jwDOd+1w66/h+8dZbb23Sb7zxxibN+dbHOGkzOUaP\nHj3apPmtCM4vpifPW9aT335gP/Hdh2uQaZ43073Jc5jrJukKkw91h3ON9U5jljxoV3wcSdIZrmog\nVzyKV/Oe8C+OIiIiIiIiMuIPRxERERERERnxh6OIiIiIiIiMnAqNI0lxvtO9hPHL1BgwHpqx29Qd\nrJTNspLmLXkEdU8p6hmoxaMOhHHdTCctGHWIfUyoUaG/EPUS7GOmqY+gTqTrAPjs66+/PpZNTQq1\nGsn7iv3c+zF5FaX4+KQzm3z+qK1hWZxL1JmleHi2jeuqjxG1NckbL/n2Tbrjqlmnxn5IXqsrGrVD\nde1jmHSASfvHdrKfVrQcaS4mHz7Or6QL6bqTpKdN15Mmhden8yP1YdKJJL+ypLfr15OPJ+cD5w/L\n4nybNLHpuwJJW0MNY/JGnNZJ+u4A5wM1bCT5HTK/Xndq1qb9vmp/X039wLp0jRvPOeoAOfeS3prz\nhWfl1JYVz8eq7J3HfmVde78kP8vV9ybW7caNG5v01atXN+nJx5FwfnAdsc85P5LusPcz5wfL4tzi\nexg9SbmOJi1w+v5C0lumb2akfXc6n9I7fGJFl7jy2+Tz5D2VdSg9fddiNe8J/+IoIiIiIiIiI/5w\nFBERERERkRF/OIqIiIiIiMjIqdQ4TnG9K54rh/JKflWTJ1jSHCXvu+Q3lvyrelvp9/Pyyy9v0tQk\nMsacegneT+0f4+G7Vox9zHsZm5/0Eu+9995Y994PP/jBD8Z7qa2gb1LSsHFM2e/dv5Cx9smPlOOd\nNE+sax8jPss4f2r7kpaXZbEunC+9reyjpAPimNDza1Wn3NuatFvMmySd0OQ/RQ0R2815T9iupGmb\ndMpJN5bmHmHbuJ/0spNeJY3JpO09dP+K/ir5OvLZ5DmZ1nzvJ97LNZo0jUzzeWq/Jn1M8rd98ODB\nJp28EZMm+v79+0/+fe7cubEuyWOY+w11aewH7nVdr/npp5+OZSUvzaRD49ztZVPbz7nF/SN914Bz\nOWmBO1xD7Bf2Od8XuE/28a7anz/9jOjelrxWtV9vjif3Iurv0jtk1wLy/YHtZNlJj//mm29u0kmX\n2uuWfD6T/yXPbGoe+Xw/l5OONOnteaZz/NkPXFd9TLkO0jxP4530lf36igbx0HWSnp/uT9+lWP2t\ntHn2c98pIiIiIiIiv5H4w1FERERERERG/OEoIiIiIiIiI6dC43iSuN6UF+Od6R9DXcDkF5N0Qava\nLF5PPlz9+UuXLm2uMX49+Y/xfupMqGlgP3YYj04fP+o+CPuFZVML+K1vfevJvxlrTw3C5D9ZtR8f\nz7KZ/6RLS3oWjkGaDxxD1qXXPflyJo0bn0++XGxL79e0xlg36idWPQYnb8akn0vzgfdTV5TWeCd5\nqU56yaqsI2G6t4V5T7rQqqxBSf52fX4kDy+u78l/8NB15j/Nt6mPDuWVPENTP05tS3OR7aB3Ivsl\n6bX7mLKefJZ1S9rwtKY5P/o+yvlAuIezbtQ8pT2fc2Da6zje7HPWnTrFmzdvbtKTRy37jJoz5s2y\neYaz7uwnjlnXLXJ9M500b1xHaZ/t91OjSo0itX1JM811kzxt+3scz7lr165t0lzfZ8+eHcv+/ve/\nv0nTU5LvSn0uM6/0vpi0wP37DIfu7/tF8n1O/qXJ35Jtmdo2+bAeKjtpHhOTNvykPo3Te/Vq/iet\nW8e/OIqIiIiIiMiIPxxFRERERERkxB+OIiIiIiIiMnIqNI4rsbnJF4UwL+ofGGPM/HuMe/KiYbw7\nY6mTxin5sPS6J01SiiFnPPsnn3yySVOzMNWF127fvr1JJ99GtoXx8L/927+9SXe9Db2NqFE8f/78\nJs0+pnZj0ssdoutE2OfUYlAvkXSESV/ZdQfJXyx5jrLuJMXmT3q6pCNO2s6k36T+ovdbqnfShSSt\nBultmXSgVfs6oeQDyvu537AfeluSDpBjwLySPpfXez/zXq4LzlWOEdcB+yHtHyv67ORnyX5h3kl/\n08csaVxTvyTdKrVcve4c/9SHnD/0dUxaYo5Zz597D+/lHs3707lLbRfnaoftnPxqD+VFD0Lu2dSp\n9fyZF3WlnEusS9LyJb123wuTzpjfMXj48OEmTa0f72dd+9xmP1y8eHGTpk801yT3G5bF+TLpcfl+\nwfFN+yL7gWv4pZde2qR/9KMfbdJ9HFgW1yzrmvbJDz/8cJPmdzP6uxTrybnHucR3ZZLm1+QbPfmb\nV+2vk8SK9+JJfRpXf79M5Set5mpdO/7FUUREREREREb84SgiIiIiIiIj/nAUERERERGRkVOhcUz0\nWN4Vz8dDMAad6SluPMUrM047eSElzSNj2nu8PLUXSYvBdrEuhDoC0seBPkuEvkysC/vtjTfe2KS/\n/vWvb9K9n5gXvawY908NC72yki8b29J1BNRxMBafeSXNY/JW7PqZpBuc9E5VWVeYPEc7nIscv+Sj\nlfSWaa73MeR4Jy1f8uFj3Sa/MnpyUdvHspN+8iRQ98G5x3m/6nU1eRJOWqr0bFXW3yU/y742qEFk\nXpybSVfI8SfUZ/a2JM0S65I0s+znSXfIeZ38LFk3areocUtnZR8TavmoC0z6e64z7unp/Ll+/fqT\nf/Pc41yiXi55q1JnxvnXoS6M+wXnHtf0o0ePxrI4P3hm9LawbOpEmTfHMJ3LbEsfM+6L/G4B+5xr\nlOPPdqf50d8Z0rlJzeNrr722SXNM+a7E/Omt2NdV8uZO2k2u6cePH49l9zXO8U/vD3w/SN9/4Bqf\n8k97E+uazrJn6X9ITqIzPMSz1F9O+BdHERERERERGfGHo4iIiIiIiIz4w1FERERERERGvhIaxynm\neDWuN8ViT748vDdpbRiLnfyGJr+xqm1s/wsvvLC5dv/+/U2aOhG2a/JlrMoeYj1mnX1OXSE1KIy9\n/8EPfrBJ03uROoDeD2wHy04+bRwjajcmX8+q7RxIOkNqLajVSBom3t+1Gqw325V0IUlnlnSFPT9q\nkNgvSeO4qs9lfj2dtJlcs+ynNKYTSS/7/PPPb9Jp/XN+JI1k71fuB2wX+yXNvcnHk/dTF8a5yvlC\nXRDbNflVHqpbH7Pk68l00tNQH5U0Uf160hWnsyzpjCZ9b9K8Ugc2adKq9jVunC/U3/W6Js/ZdFZR\n20U4n7h/dN0ZtVjUZ1MDybokrSj7rdedZxfXJJ9lvyUtMPuJ2sGu0b979+7mGjWtacw4n5LurM99\nau/Sfs+5x/2Ec2/aJwnXFMef++jPfvazTfob3/jGJn3t2rUx/8kb/IMPPthc41zle1bSX3ON3rp1\na5Puvo5JR572YMJ1M30roGo7Zsn/Ol1f1e/3uqzqH9N5s/p7ZaUuacwm/IujiIiIiIiIjPjDUURE\nREREREZOZajqST9Z20l/GmbYA+mhCPxTL0NgGCKRwgFTuNcUCjuFTFbth70wNIAw5ILhIQwf6W1h\nuxiaxpCqy5cvb9L8NDPDYGhz0cPuVi0MUqgqw3lSKHRvG/uB84HXkwUCx5j0fmOfTZYEVdkaZjV0\nsYfNcExSaAjLTnYNKWS8w9CfZFOSPs2drEP6/amPuSbZp5w/hPdP6bSnJhsKhpczVJ739/mWQvq5\nj6b5wjWb5Aa9bat7MstK6yrZUExWMQwfJWwnn+cYkV42+4hhiyk0leuKZfP8ofzgzp07T/6dzkFe\nTyGZSSLAsMneLyyL514KVeaaThZbk83AtLccSqd9lHIWruF+/3TOHaorx5/3s8/ZD/35ycKmaj/E\nP1msMcyWdWH46SQJ4txK6//999/fpJnf0dHRJk1bip7fhx9+uLmW7HoYuko4hsyvtzWdk3yHY/gw\nbUs4pjy7VuAYkBT6TlZCOtO7yurzJ7l/1eZqwr84ioiIiIiIyIg/HEVERERERGTEH44iIiIiIiIy\ncio0jiufjU0aEsI432THQb1ET1NzwLjuVFeWzfwY990/h1y1jeXms9QBUjdEGP8+6YJYdtX2M+XU\nv1Dn8cYbb2zS169f36TZL4yPp3ajx8Ov6lumvKr220ktD7UbXY8xfUr70LOsC+vKMeGYd41C0m4m\nSwxqVDgmyUpkWgssm3lzDLkmV21L+vVV7Way0Enrrrct6YpJWnPU/rAfkxVEJ+k+kn6K/TDVLe3v\nLDvN3WRzMmkF+Wz6rHyqa0qz3/oekXRjbAfHN+lvuWb7mFGHPu0tVftaPmoi0z7KNf7KK688+Tc1\nZt0eo2p/b+L5wLOOdSXs1z5mSVfOfknnKMeIZ0KvC/uQ9UwaRj7PfuPcpO6s9yvnEutCrR6vs2zO\nXeoOp3WRzmD2eRpDWhNx/vW6My/Wm/3EdrNfuCbPnTu3SU+2Nt/97nc31/7kT/5kk072TIRnIcfo\nhz/84ZN//+7v/u6YdzoXeX/SPDLdYb2TzpQkXeH0fHqnT3tyej7Rn096/XSGT/gXRxERERERERnx\nh6OIiIiIiIiM+MNRRERERERERk6FxjEx+QtN934eUpx3L4/+QaF04jAAACAASURBVPTc6bq/quwZ\nN+kGD13v+SX9E/VVbCe9jJhf0gL2eHrGo9ObiLoAxvmzbtQFUD/X72csfdIBcQypUWE/UtPC631+\n8N6kf0r6zKRRmeY6xyT5TyWvPY4Z+7lrHKgL4fhSx8F2sm68n1qOaW5yHrOdyT+K/UC9DdvWSfOc\npDVHkk/TpIdIvp6cPxzT5I/Z28o+T7rSk3rvUsPU23pSzWvyFExerX2dJM/IpFlMukL2U29rypvj\nm7w2k+6Qe3rvh/StgHRu8qzjHk39Fe/v+fNZls3zhnnz/qTX7eUlnSDLSr6/6XyZ/O94L3056W+5\nosev2m9bv865xPei27dvb9Jcg9QsUlfK/WHSqfP7DTx76E/KdcQ0tcHUik7fDmBduk64at8zkmUn\nr1XuhRcuXHjy77Tn8jrHgHONfU7NNc/dvma5N63qDBPT+cO8Vj2qT8r0WyjpJ9Ma7fgXRxERERER\nERnxh6OIiIiIiIiM+MNRRERERERERr4SGseVGOVVnxXGrE/aL8YEM3Y+eYKxLMacX716dbx/0jwl\njRrLSj6PjFmfNFCM42bsPWPpWVf6bqXx7vdTr8C6sB1MUz+RNG2TzyfHmzHlSfNKrQbHm23r6UkP\nW7Xfp8kTjHObmthJN5J8tlhXXk/rhv3CdNdqJE1i8oxkXZLnZB9T9nnSlSaN7IrnLO9n2at63KT1\nm/wOkzaX8z7pzNivae/rsN7Jv5Z7PvVXiWkvS/pItov3c1/lHj/t+ckjcNK/Ve1rlAjLZlt6eamP\nOfcePny4SU9+lVX7a3qa2ywreSuyXWmvmtYwtZdsF886zodJs3SoLny+1z3twcl3j2d6+uZCX7Pp\nfYDfKeDZdPHixU2aczntN/06NYic13x/SD6wad/l9T6mZ86c2Vzj3Lt//35NcF3xbGTZXcfKffJ7\n3/veWBbbxfmTzoRJv8fxW/VxJMmrd/IkPqmPfNLzT6R3upPgXxxFRERERERkxB+OIiIiIiIiMuIP\nRxERERERERn5SmgcO6sxxknzmHRmPbabMeAJag6SH1nStPW6UN9CDQH1EPQAYsx6ihuf/Mhef/31\nMe+kt2M/8H76fPW6MHY++ZOxrKR5YtnJK63DMWHeJOlhJg+hpBOkNof3T/rJqv35Rvr8YZ8kDVpq\nJ7UYnKuT9x7Hi2VznbCf0txl3fv9vJdlpb1s1UOQ/dzHIWk9ky4srelJX5F05sn3M2mDk/aza5jY\nZ5OmqGq/31bPm6RrneBcTOuCGiZqATuci/SzTd6byecx+en2cWBZ9EdmXakr4x7PPqcGblrDnB9s\nZ/e2q5r9CKuyxqn7+rFs9iHXCdvNMaAOnXWZ5jbbwfmRPOToy0cdIc/tfr7QA5CkMzr5QCeNfe9X\n5pX0lRwT9jnnD8eYa7jPlzQ3L1++vEm/++67NcExuHLlyibdy6P/JEnznO8PnB/sh8mrMXk+pnTy\ny13xcVz1jX+WOsTVslfu9y+OIiIiIiIiMuIPRxERERERERnxh6OIiIiIiIiMfOU0jqskHVHSFfTY\nfMZxU4uR4vhX/QonHdFUz6rsFzP5ah2qK/V6PR7+/Pnzm2vUEDCvVa3f5GdH/QLryXaxn6iPSr6Q\nk4YlaRRTXZIH2KRDpDaL9Uz+Usm/kHD+9fuTjitpGlm3pBWeNHBpjSWPwKT1nXy4+CznVtIVTn18\n6DrL67qS5KuX2kWSVrznn7SbhPM89Qv1MHy+r6uTev5x7jK/tO/y+Q7PIsK6JU9i7i89nXQ9nC9p\nvFPZ1IJ1WBfqodgv1O7xDLhz585Yt0lznfbNR48ebdL0+eN8SR5zfUzZ56kPOR9YF+rzkg9kr1vS\nhidt3ur3IHr+1GombWa6P/XrpP/nXEznKPuUetw0Jqx7rwv3Kq7Zl19+eZPmfKFOcdLbsmx6SDLv\ndF7wHZHf3Ei+sb3tq57C6f1i5axb9YgkJ33+V1W2f3EUERERERGREX84ioiIiIiIyIg/HEVERERE\nRGTkK6dxTHG6Kb45aU74fNfMMbaecd9Jw5bqSn8a6gZ6W6itofaCeTOGnF5J9CdirD7j63vMOvvl\nueee26ST/iHFmPP+HntP/Qr1L2m+sK6pLiyvtz3NPY4Z80peitTm8Pmp7KQLTBqD5KXXtSDJ6zJp\nN5MPZPKMWiHpCNnHqZ/6fpLWP8tKWr70/OTzSC0NSfOF6aQb63VPmmaS9FHJS3PS2CZ9berTdJ35\nse1db5XWVPLeTPqqKU0tOD0f2Q76zyWdGefypPViu6h/4rPc46kb43XW5fHjx5t0X9O8l+mPPvpo\nk548Q6v22zJp2HgWJT0c+41neNKtT+8E7EPCucfxZ1s+/vjjTXp6L2O7eW5O7wOH6sZ+S+duz59a\nPvYL82afUl957ty58frR0dEm3ecPx5N9zLpy3XDeU785vW9yL3nrrbc26UuXLm3S3F+oQ+b7JNcR\n+7HvN1xTyRtx1Xs3pVfyWtVbPktOUpZ/cRQREREREZERfziKiIiIiIjIiD8cRUREREREZOQrp3Ek\nKW43xT9TV0LdQI+1TvHsSVeYfHcY980Y9R57n+rNulAnwLwZ988Y9W984xubdI+HT7rCpEni88kH\ncNIhJl3h5LtXtd9vSYc2zT+WzXYnzzCOCedHj/tnvfgsdSDUKCUPyqS/6teTT1LSCSZfvpOsebYz\naSuSTx/zm+7l3GTZyadxVYvR06mdTHP8k45oWldJy7fqjcbrbNvUD2kurea9qqnv6cmntyr7l1If\nRY0SNfP9fo538jN+8ODBJk2dETVO1E9xP+ptpzbr7NmzmzR1hdTApXVDqDPr2j72Kcvieuc5yXVD\nr2f2U68r28Hxpn6S43v//v1NmppH9gvrPsH5Qc/I5Fmcrvf5wHpyTPh9Burn0vPsN/ZzX3dco1yT\nHBPuk/Sw5ny6ePHiJs210Nc0daIcE55V9E68efPmJs12s279HZEaZ9bzjTfe2KT/8i//sibS/GDb\n+lw9qU59Vfs3+Th+mRrFZ12eGkcRERERERF5ZvjDUUREREREREa+cqGqq3+qTaFDyTqghyrwz+d8\nln/qZ14MD2Vd0uf4++eXP/300801hlDwOkMumE62Fgy56M8zXCP1MfNmOxkSzPCPnj/vTSF0TKdQ\ngBQ228Mm+GwKmeJ8YllsN+dXvz+FrZD0SXLC8NEpvJChJOmz8EyzLRwz9hPL6/en9T+Fmh7Km2VP\n82U1RIbtTnYsKcyyr7v0iftkccK6cN2xH/saTyHayWZmWv+H6j6FqvJe1ptjwLLTml4JZWY7uMbS\nfGBoKsMipzFiWZNNRNX+GLJsfuqfoaxsSw/RS+GfDEVMYfVJMsL8et3YLzxXWTbXFecu1xnP5V42\n68lw3ySj4Dmc9ni2rZ/xK/O4at+Og21J9j29LZwrbCdDctnHXP+cm2k/6XVLZzLLSlZTXDd8F+IY\n9jDtFOLPNcz1/93vfneTfvPNNzfpqW0M703nYpKncIwJ+63PnyTTIicN9+xtSWd6YrXslfufpa2I\nf3EUERERERGREX84ioiIiIiIyIg/HEVERERERGTkVGgcUyzus/wEbSqLsfhdw8CY8ZRX0scxRp1x\n4NQg9LokPWXSDaZ2/87v/M74fP/U86qmjWWnz9Kv6IRYT6bZb0l/xfun8pgX9TLp0+6E48+2cMw6\nSf/AdqzoPqr2694/z500iUnfwPs5l1k3Xp/WXeqHSS+Z8q7a9nuy40l6Wl7neCebm97PST9NkgYu\n6bEm0ufS036R4Pya1mjSNCat16TFOXS9l7dq15PsGDgG/Hx/18jfunVrc43jTTgGHH/2G/uJ99+4\ncePJv1988cXNNX4L4N69e2NdWFZas7y/ax7T3kSNIvPimCQNbSfZMbGPSdqbaKHA+de1pdTe8eyh\nPQvnT9IGsh/6WcdnJ0ubqqr33ntvk+Y6YJoa2jNnzmzSfQy5/pnXZGFRtd/HybqMViO9X1mXtGZZ\nFucX28L9p9edliZcY+xTjiHtObiXURP9/7V3bjGbnWd5fl4B5SCO44ln7Iw3dTaEiFAkgqKAlIKQ\nKBRQRdoeIFBFgSJRpFCBWqlselDEEaUFqZxQUYEEEtsKEFEFLZTSVj0IhaRhkwSKY8eQwbuxx4kt\nqraB1YP5bL3f9X/z3PPyzz//PzPXJUWZ9a+13v1mLX/Pve7u+YRtnp4nV3Xpq+f/stdeD907RvrG\nwXHK4i+OIiIiIiIi0uKLo4iIiIiIiLT44igiIiIiIiItt4TGkRzHKyXpI9Nxp5/gtdQzMOY8+bTR\nT4px4tQFzPkxFn5VD8F49je96U17x7MepupovPxcFsb1Uw/BePdV7WcX289rqSlhu/B88mnjeeoO\nZ+1O0lMmH6ak5WOfdfMi6d+SRok6gaQNm+uSNAarfpdJ+9f1YZrvLOs999yzd0xtR9INdHkzL8Lz\nHGvs03R+7pM0zpM/HbU6SRPbXcuxk8Y91x6OxVSXzmuVZeO8SP6WzCv5AM73c23icUoreRB3vn5c\nq5LvL9u8025WHd3LWLc5f/pPPvbYY3vH1ECy7Gm/oLav8w1lubkPsl2SNqvTmfL6pDtnO3F8cK2i\nZyDbiWvdnB/LwrzT+EhjufMNpfaOGte0jiZtOPufz3HzPEp703333bd3zDmX9h/OadZ9Hl/JhzN5\nTLK/P/uzP3vv+Hd+53f2jud2Yf/x+Kmnnto7Tt/reOihh/aOOU/YTtyPOlafP9L98/XpXST5Rq/q\n9Y/z7pO8uTv8xVFERERERERafHEUERERERGRFl8cRUREREREpOWW1DiuxgHPJK+01fvn2H3GaVMz\nkNKil03ST/H+WYdCLQY1JinWnhrJN77xjXvH1JFQmzFrnqgZYPw7dUBJV8r0WJY5Bn1VL0mSpmnF\nM4h9QpLOkO3EPuR4mO9nmyfdD9tt1RuNdenmGfuPmgPmnc4nD6lZ67Ea98+02a5J6zm3W/KMZP8n\n38fVdpnPJ10gSRpZjp9OV5LqzbFGPSWvT1pf9sl8nuWk/onnkx9h6pNO65O8D7nfJJ9YzjNqt7o1\nOx2znsm3jXSae+oEmdYzzzyzd0ztXuoDXs92nduF6z3bkONl9jOuOqrVYjt1ulOOlVQPthvvf+ml\nl9r7OZ7m5wvuH0lfS5K2r/N9TOVmWThekr7u3Llz7f3ztyc4tqgFvnDhwt4x2/R1r3vd3nHyXuT6\ns/Ksk9Ymrqv8NgW1xPPzatqb+Dz5yCOP7B1zXnAekE4bmPbV1W8JpHeEOf2U9qquML3rHMeL8Tia\nR39xFBERERERkRZfHEVERERERKTFF0cRERERERFpuSU1jiukGODk60gYo96dY8w4YWw9dYLUONJn\nh54+s96C2gnqWxhD/sQTT7R5Md6d/kTU38zeaoy97zRGVUfj/BmL3/VB1X6cOdNOvour+ktqPXj/\nXNfk8ZY8JUny9eN4ula5DqWVNI2sS9Klze2U/KZ4b9IlJ+0vx/5c1qT15dhNOmaOTbbrnF7SLPF8\nGntpXnAezWVjWsnnlSS9BNejWZfEsZh81kjyaWRZkq55Jmn5Vn1AV/Q3qd5pPeFYTevo7GeY1qJ0\nnn3KdZHjhV6K8/7BfbQbx4fKRg3kG97whr1jtiN9QTuPOPZfWqu4JrOdOv9bjts0jpOGLenS6SM9\nzwX2F9fYtI4mjT3LOq8fSbPKshBen8YX1+F5z+C11DSyLPfee+/eMccLxxqfw5jePMc57tN+kHzF\nCcsy6xI5btOzLPu/+xbA9Zyf+5T9xbxWfT5XvsGRvs9xnO+zrJI0i8cpm784ioiIiIiISIsvjiIi\nIiIiItLii6OIiIiIiIi03BYax+N4mRzn3qre7yz5BTF2mprF1Rjk2bcpxTdT90E9A72Mzp8/v3dM\n76ROS8i4feqnUr14nhoUlqXrU8bKs82Tl2LSmXX+Vbw3aRDYTmzHFU/B5MvHsjDv1A7J+2jOn9ey\nDTkvUtkI9TScC/P4WdHLHoL3c2xSCzTXjetB0p0mv8O0XnSaqFUdadKBMK9OS77qjZjmKNNL/nSz\nNoj9l/RUySOMY5FzeMVTMml5kw6IGqjOo5J5p3WTY5laLK5NzJvtOp9PHn+EumXuZdz7eH2n/aIe\nju2QxgM9CB944IG942efffaaedMLj2sLSf6FLBufAehJOM+j5I2a1mi2I/0w2Q7zPKTWLu3B7E+e\nZ71Zl+48x/nFixf3jtnG1PpxveGcTXvZ3I7UT7IezDt9S4Lzju0+35+0muxvXp++JcCysi7z9as6\nw3T96t7YpZXOr76PpLLPcJ50XpgJf3EUERERERGRlvjiOMZ4eIzxG2OMD40xPjjG+Lbd3187xvi1\nMcYf7f7/3O7vY4zxQ2OMR8cYvzvG+LyTroSIiIiIiIicHNfzi+Mnq+qfbNv21qr6gqp69xjjrVX1\nnVX169u2vbmqfn13XFX1FVX15t3/vrmqfviGl1pERERERERuGlHjuG3bk1X15O7fL44xPlxVD1bV\nu6rqi3eX/XhV/Zeq+o7d339iuxp8+94xxj1jjIu7dG4Ix9UlzqzGGDMOvNPHJL0DY+2pl6D3GWPY\nGUc+l4X1oj6CZWFe73znO/eO6eOYdCdzDPqqXooapORlxOu7PqT2IvmRMa3kA8lY/rmPk+8e7+X5\n5OvIPu/0eUybYzXpHZKnWOedlLSaJGlYqI8gnYfccfV11EdxHrHs87xIfZC0FUlXmHSp83GagxwP\nnAfJB7TTwCTNa/IUTX2YtOVzXZLOlKT9gusL0yedBjaND64fs+b9UHpduyZfPc7hNP85L9gHnKPz\n/dxXqX+jhpH1TOOLPP3003vH877LNmM7cG9if6e9ivvsDOv5kY98ZO84aZa4TrIs1DTSi2+uK3XD\nnf9k1dE+ZB9xHeXaNY+/5Nva6WUPlYW6U/Yhx898ns9knHP333//3jHrxbqwzfnss6LPpg6QfZb8\ntTmemN6s/UyaRJY7zQO2Q1pH5zGQ2pisfkuku37V1zdpFNP57jsIySuVnJjGcYzx+qp6W1X9ZlXd\nP70MPlVVL8+QB6vqT6bbPrb7m4iIiIiIiNyCXPdXVccYd1XVz1fVt2/b9gm8ZW9jjKWfAccY31xX\nQ1lFRERERETkDHNdvziOMT6trr40/uS2bb+w+/PTY4yLu/MXq+rlb1xfqqqHp9sf2v1tj23bfmTb\ntrdv2/b2v2zhRURERERE5OSJvziOqz8t/mhVfXjbth+cTr2nqr6+qr5v9/+/NP39W8cYP1NVn19V\nHz+uvnFF05i8z1bjfJP2Zz7POGzGGDP+nTohxoUnjUunaeI5xu0zLcbqJw8oahIYR77ilce82A48\nn2K35/NJL8XzjJ1n3uzj1Gedjiz56qT4eN7f6TeT/iXpxlZi69P5Tv9YlbU4nf7lUFlYt7mPU15J\ny5v86DgvZp0Qy83+o/aG1zPt1Accm3PdV72ryKrufC5b6q+kt+T1qeydVjDpXdL+kPzIkhfnXLek\ncU5anqQzu3z5cpv+TJpjHKtpbaMHIfenuawsZ/KfY7slv9u0L8/aP2qvmDbzZrswbY4XHs/Xs7/p\nGXjlypW9Y/YBNYxst+RnOH+DgW1ILV7yP026RM6reTzQ85F5cSylfTLpeTttIOc3x0daq/hdC+bF\nsvNbFR0sC8fm6rMO14/5eupvu/6rys8P3XcJDqU/9ynnXPI0T8/VaW9cYXUfTbrDriw3UtNIridU\n9Z1V9XVV9XtjjA/s/vbddfWF8efGGN9UVU9U1Vfvzv1yVX1lVT1aVX9WVd/4ly6diIiIiIiInDrX\n81XV/15V13o1/ZID129V9e5jlktERERERETOCEtfVRUREREREZE7j+v+qupZZo7VTfHHqxrIFX1M\n0trQ8yl5ofE4eYDN6SdNGvUSjzzyyN4xY/MZ/06NYxdPnfyBGFNOjUHSQ3Qec0mzljzgUjuwHTtN\nFDUDrCdJWh4ed5qnVG7mlfyGyIr/UJonHC9sN449zqNEF9uf5knSO6TrO688Xkv9C/U07H+2C7VA\nbKc5v6RJS+tkGptdXZMva/JWTXN2Zc5zntC7bHVOJo1856fL/kp6KXolprHZ1SVpnJO/bWpz6q9Y\n1ll3yHNpvnPeJC/W5Ck5jwmWJflTktSH3TzkOsg25DF1pKw3NY3Ja2+eC7yXUDfKPkjtxHk4X8/v\nMXCOcp3keGC9uP5cuHBh77jbnzgvOj/SqqNaP/p2UivIPuz0d+nZl/Vk2smLkf6mc5+mOUrdYdJ+\nJz/k7rsHSV+7qiMk3TNASivlfRzdYcorcWI+jiIiIiIiInLn4YujiIiIiIiItNwWoaqrP8neSOaf\nzBlix5/T+XP7aogVj5966qm94/mT2AwFSGFrDKHgcfoUPMPk5lAD3suysSwplDGF1MxhUenT/qvn\nGS5EGGIz91kKTU1t3NltHLp/Dk3h2GR/sY0764aqHHJF5vwZtpasXVhvzqME23HOn+2yapHCsq5Y\ni6R6pM+lp/Dj1G5zWdIayrG22ofd59VX7VjSusnrUxjlfJxCSZPUIdmzEKY3z9kU9pzCRVM7sE/m\n0DWeS2tu2qu6dbHqaGjjbEvANuW84HhgXryeeae1cG4LhtwxZPOZZ57ZO2b/MjSR4YPs4zl9rslp\nD2ZeTJvhgJxHtIaY243hoQwHpfUH00qync4+KoV/ss1ZL1qHnD9/fu+Y44fM55OlEvNiKCrDi9mu\naX+ax0CyLeM84jHbiX1I5rqzvzpbmaoc4s2xnZ515vHBvDnWyHHDQ+d2ZlpcJ48byroS6noc25CE\nvziKiIiIiIhIiy+OIiIiIiIi0uKLo4iIiIiIiLTcEhrHm/mJ2mQV0X3ql+eSppF6ifQJfH4+ubNM\nSNq8z/mcz9k7pn4iWUekz9DPuhDGs9933317x2ynZJHBuHHGtM95p/j2pKdMn3KnvoLtMKfHcia9\nFNs8aZq6z0Snz1knjQmvpwaKdH3INue1qawsW7LQ4Pk5PfYJ50F376GyMm9qVOb0kv4hzeGkeUx9\n2OljkkYtzZvUJ7O2K7Vpmgck1SUdd2mxrIRlTZ+dTxrZmbQPck5y7HEtZL3ndmc9Ll++vFQW9jfX\nSZ6n1ms+35XzEMmmhjrEtAbM7UpNIvfwtEazLEnfO5eV/UkNI8cW5yj1dWxH6kz5XQQ+f8yw3tT2\nsexsR2ogOT46S5Rka8a0HnzwwbZsnEes2zyW2QdpL0vWQhy71I522lCmxXGctKHs76RrntcItiHL\nTVZ1yWwXtkO3LqfnLLK6X8xrYdLIJ1JZk+ZxheOk5S+OIiIiIiIi0uKLo4iIiIiIiLT44igiIiIi\nIiItt4TG8Uay4oNy6PpOb9Xpmaqyt1nyAKM+grqAxx577JV/M2ac9yYvq+R9lbyTZl1B0kOxnkmr\nRTotGLUUrFenZ2FaVUf1FNQFsF1mku8WdUVsF95PzUJ3PdNO/cu0kr8hzyf/qY6k1WJd2C6km7PJ\n24qsenF23qxJF0jYR7w++aOS+XwaS0n72flqVfXacMK0kh8ZSWs6mdNP6z1hWenDlrzSOHbnMcGx\nlrSg1Nt1OrGqfi3jnONek7zSqHHjea7LnS8g82KbMi/2N9P+jM/4jL3jZ599du/43nvv3Tue+4F9\n8uSTT7b3so84vrjWcT/p8ub6wf5n2i+88MLeMfuAcC+b2z35+CafPo49ri/Me263NEdZb35TIfUB\n60avzjm95J3LeUMtL3WlzCvt+XO7Jn0t80p7Fdd4zqu53TgnOTapr037LtshfQdjJmkS05q++k2V\nOf0039Nz1qr+sntfWdUwJi/uGX9xFBERERERkRZfHEVERERERKTFF0cRERERERFpueM0juS4Mcdz\nvDRjyhlLzRjyFOfNslCT0MWc03eLfkPU1yWtHzULjN1nzPl8nu3A+HWmzbw7/7mqHKs/Q71M6pOk\nr0vtMOfHeqS8OJ5S/HznrbnibViV5wHbgToijq/5fo7j1AfJY5LnWVemP19P7VXyKyTMm2O50xWk\nccvzydcxaVI6zQPbLOkjkr8pfbw6bzTmtepvyXmT9Llsp3kM8N6k5WR/p3ZMfTSPv+SFyPHCeUXf\nvStXrrR5z33W+dEeOuZ6wP5m2dL9s16PujBCXSFhPbk3vvWtb907Ztnn8ZP6l+sJ68X1hfenPu7O\nsZ1YT85Jzot3vOMde8eXLl265v1sB+orSdJvp7VwbjfO99VvSyQvVT6fcI+/cOHCK//m/H/ggQfa\nsiXNIvfRtAfM62j6fgPnINPiesG6cazO+w81jMyb4/75559v0+Zaxb2Oe0SngU3avqRpTOnNfdjN\n16p1T8lEl16qlz6OIiIiIiIicmL44igiIiIiIiItvjiKiIiIiIhIyy2hcVyNWb6RJC+9OaaYcdeM\n62YMOmPpqa1gXozz7nwdGSvPGHTG1if/oc6nsar3eaPOg/VgrH3y2eH9nccgY855bdJqUXOSPKM6\nf0O2IccxNQhJ89rpQJhf0kemcc56UsvT6YJYtlU/SsLznFfUZrBd57onXWFq8+TF2PkhJv1k8u1L\n3ohpfKx4SiYPyaRT5PiY7+da03lfVh1tt6Rb5nHyMJ05rpdq0gmRuaxcN1mPpJ9Lfcbz89iknyC9\nErneU7PE8ZTGOq+f9XpJ55V0qOwj6v2T1mfeG9MezPHBfbjz4avqNXFcxzjHqFHjns81O/nAUjs6\n58c2evTRR/eO01hM+vxujedawrHJetO/kHD9YXqzprFqv1147aoX4sp6UNXrtZPXNvs/6SlT2ebx\nmLSa3fpf1a9Fh+5nXbqyJn12ehYix9EGrmoaU1m69I5TzoS/OIqIiIiIiEiLL44iIiIiIiLS4ouj\niIiIiIiItNwSGkfSxe6uerKkOGAeM7a600utxhQzLpy6Q+pOXve6110zreTZ1PmHHYKx1CwL9Zqd\nvi7pPJK3XqrbrAtJWorOd/HQ/ewT0unMUptTH0NNS/J5jEMkXwAAHJhJREFUXPH8Yr2TToj9T21P\n6tNZs5DmRadZPZR20sxSLzG3G9sw+VsmjQK1GJ3HWFovWJbkZ5l0I2S+n9eyTUnyZePY7nQlHPc8\n5tqSfDqZF/uwG+u8l/3H/iXMK83RTheffFyTFy9JPsDXKkfV0fHBdqCuLHntcXx13q5JF9Z9d+DQ\n9Z3mmXlX7bdF0v4zLWpck76Wa9e8z6Z5wPFADeyf/umf7h3zeSPpkueycqy95S1v2Tvm+GHe9H3s\ntOBV++2e9lHW4/z583vH1G4+9NBDe8fsf97f+Xomv1uOTbYx6520n3MfJ+/l5Ieb5hXLMo836msJ\n59wLL7ywd8yx+vjjj7dl6/bl9I2Mk9T+pbwS6fkiPRvNrNZLH0cRERERERG5YfjiKCIiIiIiIi2+\nOIqIiIiIiEjLLalxJCtxxKsxxykeeo6npsYgxYwzrT/+4z/eO37b2962d8x4+EuXLu0dz7H41C8k\nHdCqbiR5r80x7YylZ9o8nzx/eEzdyJxep0k9BHVFSeuXjud2TV6YSZu3qmGb00secNScMJY+eUxy\nPPD8rEPptJiHzrMs1DCkOcqyz2NiVW/J8ZF0p52vZ/J9XdXbpfGU5vQM65F0gkknxvTmsrNeSfub\n9LRc+zg+uvWDbZrupbYn9SmhXnu+n/XinGVZ6NtGks55bldq2JM2q/Ntraq6cuVKW9auLEkHuOpP\nyDYn3Rxe9WFjXmmdZbvNerykK+a4Z1pco+m9yXlG/0PO0xm2GfWYaW977rnn9o6pQ5z3k+Tjyrwf\nfvjhtqxp3eX4msdu0pknD+t0f1pHZw1t2uM5j9i/aX2g1nNeq9hGyd+Sbc5jaoOTb+Pcjul5IM3h\n9K2BlTUgfVMl7S/kOHmvlq3DXxxFRERERESkxRdHERERERERafHFUURERERERFpuSY1jFwecfBhv\ntLfJHHPMOG5qBhifzrhtagioC6GGkjqAOa6cZWE8O3VfnW/SoeuTlqfz4Up+Yp0XYlWOQZ+v7zSp\nh8qSfHKSnxD7aEUfw3qk+PfULvN51ot6GGqOUvz7qvZz1iwkL0ye59hkXVhWnuc8m8uWNEZJT5l8\n+ro+ZRumcvN8p+09VJauD5MeMs2jNB46DduqF17SKCVNXOcrSw1SSiuNvdQOLHunDV/1GOX9PF7R\nrCV9bKflrcra0a6dk9aXazhJZWE7duMjaX3TevGa17xm7zh5js5lZR/w2s5L+VBZeD01j9zL5nmY\n8uKanfZ47j/s01mvx3O8l/3LsZzGHu9nXbt70z7LObgKtaDzc1raN1NZWU+2E9fhzjeWabHcXGdX\n5mDV0TWAc2Mm7YPpeZJ055Me8riekSfpQbmin/QXRxEREREREWnxxVFERERERERafHEUERERERGR\nlltS49hxo2OMV3wcU2x00kfQE4wag6RpmWO5GdedPN9I8oyjVqPT/qW4fnpbJS9FtkPnEUQvI9Yr\n6QhT3HfyGJzHS/J0W9UNruiEko6DJB+/pM3p6pr6s9MkVvV6uUPpsZ3msiVNWtK0cR51Wl8eJ7/C\nzqvq0PmkQ+3Sp+YkrR9JO568VufzvJYapqQLTFrPLu+qXgvMsbSqeeX6kzwpu3mZ1r00r9iunY6Z\neqakOVrVfqbx0mlg2f/JWzXpddlHK95qaU1Pvq4sWzfPVj1GCZ8nOH7ovXfhwoVr5rfyLHLomPo4\nPvuQeX1iPdnGrCf3E36/gePhta997d5x53GdnlWSlyLbkaTn1XmeciwmzTzLRo1r9yxTtd+nvJba\nTsI25TNg2vu69YVzKu2rifS9h/l86q80R1fL0uW3Ws8V/MVRREREREREWnxxFBERERERkZZbMlS1\nC11L4Vw3OpR1/ol+1W6BefF+fsL44sWLe8es2xwecO7cub1zDAVhWRhiwbAHhrIxrCWF5MwwZIKh\nJSlksws9rNpvV5Y7feJ+NfwvhQPM6TOcg6EiyV6D9UxjeU4vhXul0KNVqxCGqsx1TaEkJFnHpLJ0\nYXHMm33C8ZOsRLqw6ar9dmbeKdwv9UkKbe5CV9P4WA0HJl2oYwrf6kLFDnEcyUBaL9imKWwy1YV1\nn/NPa02yJVkNs57TS9KGFMqcxirp5izTTp/yZzvwfq4fSRIyj9UUksm82Wcp5Ltb61b3j9WxmuQL\n8xhI4b5pjrIs58+f3zvuQjjZ/1xb2A733Xff3nEKbU7ho/P1aWyynToLnKqjdSHdmp7Cg5NdHJ/p\naOHGeTO3Y5J8pJD/F198sTrSejO3axdSe+h82rvSO8F8f3p+THmld4iV95MbadVB/MVRRERERERE\nWnxxFBERERERkRZfHEVERERERKTlltQ4kjkuePUT9tRWpPh2MseJM5Y6xXUzxpyfib506VJ7ntqM\nWR9BDUHSFfI8y0r7jaQr6T7dTaiHY9moG0h6mTnun3qG9An7RNINdTHtbAdqBpKeiuOlsxWo2ted\nsFy8lmkl24DUrp3Wa1WTmGxs0hwnXew/+yR9/jppHqlpmefJar2TPo7ppXnS6WlSm69YwVQdbac5\nPV7L+Z60mmne8HynBUsa15XP41cdXRc7PTbzZx+wDblmc+zeddddbVk7q4CkgU6WJxxbKb2uXVLe\nSbee9h+eZ5/PZUtrS/o2AFnRyLH/0xykXpL77N133713nJ5f5rpxf+CcSxYnbCfaMXDszvezXql/\n07MQxw/T5/WvetWrrnkv5yDLNt9bldeX9J2Defxcvnx57xzrkbS/SRPLPp3zY3+ttjH1lOl5lGN1\nJukryXE1j/P1aU9Pz12reXdlWbERuZ68Z/zFUURERERERFp8cRQREREREZEWXxxFRERERESk5ZbU\nOB4nNjf5Nq7GGM8x6oylZhx30tqQlF7nh5fi01Pa1Kwkj6fOx2lVo0SdENNm2bo+4znqJ1dj0Jl3\n0p108e+sd+d1V5U1b53v26ouMI3V1E6d1it5GyVtHln10prblfVgm6eyMa/UjnNZmNeqfjLpELk+\ndHXheKAWJ/nycb1h3p1uke2Qxl7SIJGk/Zx1SanN0v6R5njSQM59mnSDSTeWNPVcV+eyMS3WO3np\npXmz4vuWvBJZFo7d5H+ZyrZybfKbS/tNl1+6lrD/WRZqHlkX1nW+P+1NaW9LY5F0+tv0/Ya0rnKe\nJX/deTxR05i+Q5D8ktOc5xiY52zykOX59O0Izhuuu502nHnTRzx5UjO99EwwlyW16arWb8Wrm6x6\nKa6+n6w8+x7nvYn4i6OIiIiIiIi0+OIoIiIiIiIiLb44ioiIiIiISMstqXFMsbsr166kdYg55jzp\nwHicvM8Y7/z444/vHX/WZ33WNdOnfoEekPT8Sb6NbCfqJejDNOtMkp4u6cyoYWB8fKcVY72Srozn\nGWuf/IW6Pkw6saR/SDpFahjmsiT/yqRvSGXleY63ud3SnEt+hkkLmrRfc/rJV2t1rJKubsfxG6w6\nqjmh3iaN9Tm9VQ0ay5r0VJ2vY+p/5k2dYFpfmB51SF05E2leJE10p0tMmrMVT69D6aV5NZO0n6ks\nyY+u84nlmpvmf9e/h/JemTeck6vtwPGV9HTz+EkaNt7LOcixmOrSaabTmpu0watr33x+VdvLvBJp\nDeh8w1f3g9VvC3AMzM95aQ/n3pZ8HFf028m/kvfy+ZNjk3M+Pft0zzrH0fIdopvzN/p940aXvctb\nH0cRERERERG5YfjiKCIiIiIiIi2+OIqIiIiIiEjLLalxXCHFt6/6NnbXJ5+lzgOy6qh25+677947\nZhw448rn+Pfky3ju3Lm9Y8acJy0O69J5J7HNWJbk6ZO0HF27Jz+gpFFgGzO9pIGbNXRJS0OSRpZt\n3mlSkr8c2zj5MiX9DI/n+zkn77rrrvbepKdJGigy388512nxqrJHZNKddnqppN1K+ij2MfNmO83t\nwP5M8yLN2aSX6tYHHiedMde6zqfx0Pm5HZl30vYlHSr7IPnXze2c9JKp3Vb1VvPYTmOPsI3TGp32\n1bmuaU1O+yTbnH2SxvacHudJ0o6n8ZH6bC4r157kR8l5wrJzfKU+mdNP2qz0vJDGLss2tyPnDI/5\nfYc0bzpdaVX/LMV6Jo9RwvGQ9rpuv2JePE7PLpwnrDefEXl+Jj3DXblyZe847bvMm2vAPL5Sm5Pj\n6gg7X2hyXC/FpPW8kXm15bhhKYmIiIiIiMhtiS+OIiIiIiIi0uKLo4iIiIiIiLTcFhrHOXY3xd6v\n+jSu5L0a38w4btLpHaqOeifOMeeM87/33nvbvOhnRx8+xqgnTcJ8nueYFzUFhFo+ajcYHz/H8idd\nGEmaWJaVuhH2EWPxZ9guJHkjsiydh1jyxmS5X3zxxb3jpHHi+Y9//ON7x3OfJF+94/rPJV+3zkNw\ndbzw+qR57PQQq36nSW+V5t1c1lRujkXmxfOE6c06lHvuuae9Nnkhsp7Jx62rW/IuS7oxknSGrNuc\nXxr3HB/UHCWPwM4fN3m6JQ0kdUZJ88b85vHVre9VR/c65sXrk4dgVzfey7yS5jE9f3R9vOqlm/Rx\nqxr7uezMK2mkO8171dF27NZN7qkrvr2HjpPmle021z19h4DtxHZI3xogK96aSdOavHpXPAZZb/YR\n02ZZuFYlPS/n9Pws3K2ph8qyosevyu12HG6kz/xJekD6i6OIiIiIiIi0+OIoIiIiIiIiLb44ioiI\niIiISMttoXGcSTHBNzrut/OEe81rXrN3nPQOjPNOflTPP//83vGsBaRPI/WQ1DtRZ8QYdXrtJZ3R\nHPfNeifvouRHlPyr5j5hmzEWn7rBpM1J3kcrMejJG5H1Ytl4P9ul09CmsZi8rlhv6ow4vjodWdJi\nsc9YNo5V9inbofOIY9pJv5B0Rp1HXNLmdeU+lFfSCrMd53ZPOjCy6iHI9Oaxnuqdxl7yaUxrVTdn\nOeeoI0xrU2pHlnUeH0l/u+opSTqfz5Q25zt16CStdZ1HbVrD037Q6cKqsl6/00QnzWPKi+3Qjaek\nYe10olXZrzJ51M5lSVq+5AmY1j7Sac2Tpp1tnNot+Qh3XrxJ85p8gVm25GE9H3fehofSopci5zCf\nGbmvzjz77LN7x9xP+Kya9smksSfz+VUtb5rTie76VY1i0rUn/eVJ6hr3ynFTchEREREREZFbFl8c\nRUREREREpMUXRxEREREREWm57TSO5Dg+KNdz/3yctDNdrHzV0Xh4eulRt8j4+M4jrvNkOlR26jNT\nWTsvnKTNYlz/qo9OV7YUK796PnkdUeMw1z1pGJOekm1OHQHLMmuWeG3yLkpajeTD1WneOB5SXH4a\nD9RmpLE+90Nq46Qr43ighoV6iw6Wk/Ob7dB5hFYd1SV3uqSkWUw6QZLGS+fzuqrd47xKPq9dXZNX\nJvsgpb1atzm9NNZS3qta4fk46eWYNsciNW1sRx5/4hOf2Due25l58V6uJ2n+p7Wv05andTGVJe0v\n3RrOvNOanfo/9QnLNpclPT+kdkpzukufY4tpJZ/opKfj+U5fx3vTNw9Sn6VnITK3y6rejc+XbFfu\nH9zL5utZTvo4p3nANXv1eXNut3QvWX0m7LTD6XmRJI/R1P9dH6+8u1xPXnvXXveVIiIiIiIickfi\ni6OIiIiIiIi03PahquSkQ1dnVsNWSPe59KqjFhrzJ4+T3UIK/0mfge7sN6r2QyMZAsGQh2Q7kD5D\n3lkq8BzzSj/fp0+YJxgi2pGsPlKYWxcumkLPUhhUCjdm2Xj93K6pvznWSPp8dgrRmds1hfdxnKfx\nkD4FPufNe9Mn6hm6yvm/GjbVhYumeZI+E85jzuG5z1N4ZwpjY5gk17LUJ3N6q+Ggq3Y9yVqkC/FP\n4aHJEoNwPM2w/1N4KNs0haOz7N14S+teWotWrWNYtvn6FAad9qY0J7vQtlVpA9PmHGRdSBf6mvqA\ndi3JdoT3cyzP16ew11U7nrR/pDDJGY77FIqawgVT2bswSY5jloUhvUkq0T1DpH2Soavd88GhY6bX\nzavVkN10/crzxer7xap9RirryvvMce71F0cRERERERFp8cVRREREREREWnxxFBERERERkZY7TuO4\nqmlcgTHD1NbQ4mIVpnf33XfvHc9x4y+88MLeuYsXL+4dp5h0xnVTc8C6Ui8xx7+v2mmsfD7/UHoz\n1Aykz+N3esmq/Lnl7rP11CuwnsnaIcW3d3qqzpLiULmpb0iayJXY+6T7ov6BdP19KO9ubCdtZvqk\nfbJn6D7dzXuph026saSPYTuTFX3FqoUKy8a1ax4/ST+ZxjnbiWOX7dpp+5JWk9otps15RV1ZZ6FU\n1c9Zrg9JI5s00t0x72XayZ4jraNJUzvXNWmM0jqa5njS2817W9JAp3omrVan9WKb08IkwbFIDRzr\nxvTnsZ72QY7NdJ7jrVuH03xf1XpzDnOt6vokWTekZx+WLbVLZzWT7FS4dnV706FjPuN131CgppF9\nlJ6reD3HQ6fnTHrJpO1L60l65uvySudXy7qi51zVfnb4i6OIiIiIiIi0+OIoIiIiIiIiLb44ioiI\niIiISMttp3FMsdMnnd8MY+UZr975RVUd1RUyfp73nz9//pV/J90Q2+XcuXPt+dSOr371q/eOn376\n6Vf+zRjwpJdkrH7SAXQ6guTBlHy4VrQ4h8o2n1/1JuL1SVfUeW8m7cWK9qrqaDuxzzqtWNIzpDYm\nqR06XWJq0+QhmHQinS5x1b+QWpw0R1kW9slcdo7bTs9y6Jiwnbo+TZrX5F+XPGu5TnZaL9YrzQvm\n9eKLL7ZlZ127srDNOi3NobImf0OOl3msJu1upwOsOrrGdxrXql63nPaHpH9Kczj1UaczTDpTkvRR\nnackxwM1a8nvOGk/qWnsfGTZZuyT5CGZvqHQrYVpP0hey6t6284HdvX7C51G8VBZSOflmnzDuX9w\nzl6+fHnv+PWvf/3eMb+bMefHcrPN6BnJdZJjNflXcq2by5b0tSQ9G614Maa8Vr+xsqp5nM/fSE0j\n8RdHERERERERafHFUURERERERFp8cRQREREREZGW207juBKPfOj6GxkXzLQYg864/hSb38W3V+3H\neTOm/Lnnnts7vnDhwt4x4+EZY86yJ4+fu+6665V/X7lyZe9c0iwm3UfSqHR9lvQvq9oNlp06gjk2\nP8XeM+/k65g0cHNZkw6IZeP1zJtlTeOj0/ZxHiTfreTTlTxJ5/yTPpL1ZNrJx63z8eO97INVrSfz\nSjrFTuuZfPi6tKqO9gk1KfO8SmknP8o0j9iOXDfn65PeJfk8pvFBLXinW096uqRp5Nh+6aWX2rLO\n6a9qe9O+yj5IGuk5/7QvprGa2i3N+fl80kCv7l087tY+zudurzl0nPS3ab2Yy5L0lOlbAsmvsNO1\nprGX1gP2P/cf7mV8lprbKeltU58kT0rSrUer/qXpWWjl2xNJT8l685mQa3J6Hun0nMd9XryZXu/p\n/HHfZ04Kf3EUERERERGRFl8cRUREREREpMUXRxEREREREWkZNysmti3EGM9W1RNVdb6qLofL5WSx\nD04f++D0sQ/OBvbD6WMfnD72weljH5w+9sHN45Ft2y4cOnEmXhxfZozx29u2vf20y3EnYx+cPvbB\n6WMfnA3sh9PHPjh97IPTxz44feyDs4GhqiIiIiIiItLii6OIiIiIiIi0nLUXxx857QKIfXAGsA9O\nH/vgbGA/nD72weljH5w+9sHpYx+cAc6UxlFERERERETOHmftF0cRERERERE5Y5yZF8cxxpePMf5w\njPHoGOM7T7s8dwJjjIfHGL8xxvjQGOODY4xv2/39e8YYl8YYH9j97ytPu6y3M2OMj44xfm/X1r+9\n+9trxxi/Nsb4o93/nzvtct6ujDHeMo31D4wxPjHG+HbnwckyxvixMcYzY4zfn/52cNyPq/zQbn/4\n3THG551eyW8frtEH/3KM8Qe7dv7FMcY9u7+/fozxv6f58G9Or+S3D9fog2uuPWOM79rNgz8cY/zN\n0yn17cU1+uBnp/b/6BjjA7u/Ow9OgOZ51D3hjHEmQlXHGJ9SVf+rqr60qj5WVb9VVV+7bduHTrVg\ntzljjItVdXHbtvePMV5dVe+rqr9dVV9dVS9t2/avTrWAdwhjjI9W1du3bbs8/e37q+r5bdu+b/cf\nUs5t2/Ydp1XGO4XdWnSpqj6/qr6xnAcnxhjji6rqpar6iW3b/trubwfH/e7B+R9V1VfW1b7519u2\nff5plf124Rp98GVV9Z+3bfvkGONfVFXt+uD1VfXvX75ObgzX6IPvqQNrzxjjrVX101X1jqp6oKr+\nU1V95rZtf35TC32bcagPcP4Hqurj27Z9r/PgZGieR7+h3BPOFGflF8d3VNWj27Y9tm3b/62qn6mq\nd51ymW57tm17ctu29+/+/WJVfbiqHjzdUsmOd1XVj+/+/eN1dQGVk+dLquoj27Y9cdoFud3Ztu2/\nVdXz+PO1xv276upD3bZt23ur6p7dg4Ycg0N9sG3br27b9snd4Xur6qGbXrA7iGvMg2vxrqr6mW3b\n/s+2bY9X1aN19flJjkHXB2OMUVf/Y/pP39RC3WE0z6PuCWeMs/Li+GBV/cl0/LHyBeamsvuvaG+r\nqt/c/elbdz///5hhkifOVlW/OsZ43xjjm3d/u3/btid3/36qqu4/naLdcXxN7T8gOA9uLtca9+4R\np8M/qKpfmY7fMMb4n2OM/zrG+MLTKtQdwqG1x3lw8/nCqnp627Y/mv7mPDhB8DzqnnDGOCsvjnKK\njDHuqqqfr6pv37btE1X1w1X1pqr63Kp6sqp+4BSLdyfw17dt+7yq+oqqevcubOYVtqvx5KcfU36b\nM8b4K1X1VVX173Z/ch6cIo7702WM8c+q6pNV9ZO7Pz1ZVX9127a3VdU/rqqfGmPcfVrlu81x7Tk7\nfG3t/8dE58EJcuB59BXcE84GZ+XF8VJVPTwdP7T7m5wwY4xPq6uT9Ce3bfuFqqpt257etu3Pt237\ni6r6t2UozImybdul3f8/U1W/WFfb++mXwy52///M6ZXwjuErqur927Y9XeU8OCWuNe7dI24iY4xv\nqKq/VVV/b/ewVrvwyOd2/35fVX2kqj7z1Ap5G9OsPc6Dm8gY41Or6u9W1c++/Dfnwclx6Hm03BPO\nHGflxfG3qurNY4w37P6r/9dU1XtOuUy3PbvY/R+tqg9v2/aD09/nOPG/U1W/z3vlxjDGeNVOCF5j\njFdV1ZfV1fZ+T1V9/e6yr6+qXzqdEt5R7P2XZefBqXCtcf+eqvr7uy/pfUFd/VDFk4cSkOMxxvjy\nqvqnVfVV27b92fT3C7uPR9UY441V9eaqeux0Snl706w976mqrxljfPoY4w11tQ/+x80u3x3E36iq\nP9i27WMv/8F5cDJc63m03BPOHJ962gWoqtp9ve1bq+o/VtWnVNWPbdv2wVMu1p3AO6vq66rq917+\n1HRVfXdVfe0Y43PrakjAR6vqH55O8e4I7q+qX7y6ZtanVtVPbdv2H8YYv1VVPzfG+KaqeqKuivPl\nhNi9tH9p7Y/173cenBxjjJ+uqi+uqvNjjI9V1T+vqu+rw+P+l+vq1/Merao/q6tfvJVjco0++K6q\n+vSq+rXduvTebdu+paq+qKq+d4zx/6rqL6rqW7Ztu96Pusg1uEYffPGhtWfbtg+OMX6uqj5UV8OI\n3+0XVY/PoT7Ytu1H66jmvcp5cFJc63nUPeGMcSbsOEREREREROTsclZCVUVEREREROSM4oujiIiI\niIiItPjiKCIiIiIiIi2+OIqIiIiIiEiLL44iIiIiIiLS4oujiIiIiIiItPjiKCIiIiIiIi2+OIqI\niIiIiEjL/we6pr0ZgFvdgwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VuPfZv9J3Wc4",
        "colab_type": "text"
      },
      "source": [
        "# Ploting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JpmBeAcYUIW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e9d121f9-c518-481e-fb1e-2cb50c81ed00"
      },
      "source": [
        "history1.history.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tD_cHXYqOrPW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "301f1431-f710-4a2f-940f-862c11f45248"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "print(\"Model : 1\")\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.figure()\n",
        "plt.plot(history1.history['acc'], label='Training accuracy')\n",
        "plt.plot(history1.history['val_acc'], label='Validation accuracy')\n",
        "plt.plot(history1.history['loss'], label='Training loss')\n",
        "plt.plot(history1.history['val_loss'], label='Validation loss')\n",
        "plt.xlim(left=1)\n",
        "plt.ylim(top=1.1)\n",
        "plt.legend()\n",
        "plt.show()\n",
        "print(\"Model : 2\")\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.figure()\n",
        "plt.plot(history2.history['acc'], label='Training accuracy')\n",
        "plt.plot(history2.history['val_acc'], label='Validation accuracy')\n",
        "plt.plot(history2.history['loss'], label='Training loss')\n",
        "plt.plot(history2.history['val_loss'], label='Validation loss')\n",
        "plt.xlim(left=1)\n",
        "plt.ylim(top=1.1)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model : 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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H+3daxMlagiD0CwMu4IMno+bo0aPZuXMnn3/+eZdTL3RErZa4alIAU2cFUl3h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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model : 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZsWIFs2bN4qabbmLPnj2sXLmStrY23n33XTo6OoiOjuYPf/gDOp2Ouro61qxZ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\ntKxWKwrFwEK0GLY4AAqFgkWLFvHee+9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er5eGhgYqKysz0zfznU4nUVWjp6pGj39GY/Bm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\nUCD+H5XdUJZgcAzwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_8nhV5zGemW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a4bede78-b742-4cab-deeb-2ee39314057b"
      },
      "source": [
        "# To Do: Fix\n",
        "\n",
        "\"optionnal: confusion matrix\"\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'optionnal: confusion matrix'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etkWcmyh1UBz",
        "colab_type": "text"
      },
      "source": [
        "# Model 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJAESVyeOrPD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "73f8e8bd-edb5-4709-9a6f-07f8d89cd1a8"
      },
      "source": [
        "# TO DO: try this model \n",
        "\n",
        "img_width = 224\n",
        "img_height = 224\n",
        "epochs = 100\n",
        "\n",
        "base_model = tf.keras.applications.MobileNet(weights='imagenet', include_top=False, \n",
        "                                             input_shape=(img_width, img_height, 3))\n",
        "\n",
        "output = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)\n",
        "output = tf.keras.layers.Dense(2, activation='softmax')(output)\n",
        "\n",
        "model1 = tf.keras.Model(inputs=base_model.input, outputs=output)\n",
        "\n",
        "model1.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0001), \n",
        "              loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model1.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_5 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
            "_________________________________________________________________\n",
            "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
            "_________________________________________________________________\n",
            "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
            "_________________________________________________________________\n",
            "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
            "_________________________________________________________________\n",
            "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
            "_________________________________________________________________\n",
            "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
            "_________________________________________________________________\n",
            "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
            "_________________________________________________________________\n",
            "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
            "_________________________________________________________________\n",
            "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
            "_________________________________________________________________\n",
            "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
            "_________________________________________________________________\n",
            "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
            "_________________________________________________________________\n",
            "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
            "_________________________________________________________________\n",
            "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_4 ( (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 2)                 2050      \n",
            "=================================================================\n",
            "Total params: 3,230,914\n",
            "Trainable params: 3,209,026\n",
            "Non-trainable params: 21,888\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}