{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hache22/PrediccionImagenes/blob/main/FINAL_TRANSFERENCIA_APRENDIZAJE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Este Script de Python realiza la transferencia de aprendizaje de un modelo ya entrenado para el procesamiento de imagenes, incluye el entrenamiento de las nuevas capas de entrenamiento para la prediccion de imagenes -> si corresponde a un ventilador o a un colectivo(autobus)* 🙌.\n",
        "Previamente se realizo la busqueda de las imagenes dentro de google sobre ventiladores y autobuses para entrenar al modelo (primera linea)"
      ],
      "metadata": {
        "id": "87QqdEia6M_6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/dataset/colectivos | wc -l\n",
        "!ls /content/dataset/ventiladores | wc -l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfZg0JxZb5bw",
        "outputId": "862b681d-5a5d-4733-839a-852cb1ab13ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://th.bing.com/th/id/OIP.u_k7dnKroJnwvlqI8SMjhQHaFj?w=960&h=720&rs=1&pid=ImgDetMain325\n",
            "407\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# IMPORTACION DE LIBRERIAS\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import numpy as np\n",
        "\n",
        "#Crear el dataset generador\n",
        "datagen = ImageDataGenerator(\n",
        "    rescale=1. / 255,\n",
        "    rotation_range = 30,\n",
        "    width_shift_range=0.15,\n",
        "    height_shift_range = 0.15,\n",
        "    shear_range = 5,\n",
        "    zoom_range = [0.7, 1.3],\n",
        "    validation_split = 0.2\n",
        ")\n",
        "\n",
        "data_gen_entrenamiento = datagen.flow_from_directory(\"/content/dataset\",\n",
        "                                                     target_size=(224,224),\n",
        "                                                     batch_size=32, shuffle=True,\n",
        "                                                     subset=\"training\")\n",
        "\n",
        "data_gen_pruebas = datagen.flow_from_directory(\"/content/dataset\",\n",
        "                                                     target_size=(224,224),\n",
        "                                                     batch_size=32, shuffle=True,\n",
        "                                                     subset=\"validation\")"
      ],
      "metadata": {
        "id": "SQl5pmmCcrOw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afade207-e3d4-4b14-b12e-4499d6e6bb9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 586 images belonging to 3 classes.\n",
            "Found 146 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "for imagenes, etiquetas in data_gen_entrenamiento:\n",
        "  for i in range(10):\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.imshow(imagenes[i])\n",
        "\n",
        "  break\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "6S3S4cZ1doS2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "b244efe3-402d-43ed-c92c-4fc10ad88a2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "url =\"https://www.tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\"\n",
        "mobilenetv2 = hub.KerasLayer(url, input_shape=(224, 224, 3))"
      ],
      "metadata": {
        "id": "LgYMc76ReLIY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# congelamiento de las capas para evitar overfitting\n",
        "mobilenetv2.trainable = False"
      ],
      "metadata": {
        "id": "1UEGVF52fS_Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo = tf.keras.Sequential([\n",
        "    mobilenetv2,\n",
        "    tf.keras.layers.Dense(3, activation=\"softmax\")\n",
        "])"
      ],
      "metadata": {
        "id": "KL3ecQYGfiet"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgNJrak6eFtO",
        "outputId": "ff59f650-300e-435c-c2f3-ab84c9ebb707"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " keras_layer (KerasLayer)    (None, 1280)              2257984   \n",
            "                                                                 \n",
            " dense (Dense)               (None, 3)                 3843      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2261827 (8.63 MB)\n",
            "Trainable params: 3843 (15.01 KB)\n",
            "Non-trainable params: 2257984 (8.61 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.compile(\n",
        "    optimizer=\"adam\",\n",
        "    loss=\"categorical_crossentropy\",\n",
        "    metrics=[\"accuracy\"]\n",
        ")"
      ],
      "metadata": {
        "id": "G3sRdQaOfuwk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCAS = 10\n",
        "historial = modelo.fit(\n",
        "    data_gen_entrenamiento, epochs=EPOCAS, batch_size=32,\n",
        "    validation_data=data_gen_pruebas\n",
        ")"
      ],
      "metadata": {
        "id": "d5JuXMaxf0Y4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5b9f9ab-1292-4895-8069-6a5098645c65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 9/19 [=============>................] - ETA: 4s - loss: 0.6891 - accuracy: 0.6562"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - ETA: 0s - loss: 0.4397 - accuracy: 0.7969"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/19 [==============================] - 22s 760ms/step - loss: 0.4397 - accuracy: 0.7969 - val_loss: 0.0865 - val_accuracy: 0.9863\n",
            "Epoch 2/10\n",
            " 2/19 [==>...........................] - ETA: 9s - loss: 0.0814 - accuracy: 0.9762"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/19 [===========================>..] - ETA: 0s - loss: 0.1021 - accuracy: 0.9621"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 13s 686ms/step - loss: 0.0992 - accuracy: 0.9625 - val_loss: 0.0376 - val_accuracy: 0.9932\n",
            "Epoch 3/10\n",
            "11/19 [================>.............] - ETA: 3s - loss: 0.0790 - accuracy: 0.9515"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17/19 [=========================>....] - ETA: 1s - loss: 0.0695 - accuracy: 0.9655"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - ETA: 0s - loss: 0.0699 - accuracy: 0.9642"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/19 [==============================] - 14s 745ms/step - loss: 0.0699 - accuracy: 0.9642 - val_loss: 0.0297 - val_accuracy: 0.9932\n",
            "Epoch 4/10\n",
            "14/19 [=====================>........] - ETA: 2s - loss: 0.0664 - accuracy: 0.9718"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/19 [========================>.....] - ETA: 1s - loss: 0.0630 - accuracy: 0.9735"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 13s 691ms/step - loss: 0.0578 - accuracy: 0.9778 - val_loss: 0.0179 - val_accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5/10\n",
            " 9/19 [=============>................] - ETA: 5s - loss: 0.0461 - accuracy: 0.9812"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/19 [========================>.....] - ETA: 1s - loss: 0.0441 - accuracy: 0.9837"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - ETA: 0s - loss: 0.0468 - accuracy: 0.9829"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/19 [==============================] - 13s 685ms/step - loss: 0.0468 - accuracy: 0.9829 - val_loss: 0.0338 - val_accuracy: 0.9863\n",
            "Epoch 6/10\n",
            "10/19 [==============>...............] - ETA: 5s - loss: 0.0333 - accuracy: 0.9906"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/19 [========================>.....] - ETA: 1s - loss: 0.0348 - accuracy: 0.9857"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - ETA: 0s - loss: 0.0363 - accuracy: 0.9863"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/19 [==============================] - 13s 697ms/step - loss: 0.0363 - accuracy: 0.9863 - val_loss: 0.0129 - val_accuracy: 1.0000\n",
            "Epoch 7/10\n",
            " 3/19 [===>..........................] - ETA: 8s - loss: 0.0287 - accuracy: 0.9896"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/19 [===================>..........] - ETA: 3s - loss: 0.0379 - accuracy: 0.9898"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - ETA: 0s - loss: 0.0388 - accuracy: 0.9881"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/19 [==============================] - 13s 670ms/step - loss: 0.0388 - accuracy: 0.9881 - val_loss: 0.0164 - val_accuracy: 1.0000\n",
            "Epoch 8/10\n",
            " 9/19 [=============>................] - ETA: 5s - loss: 0.0273 - accuracy: 0.9931"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - ETA: 0s - loss: 0.0255 - accuracy: 0.9932"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/19 [==============================] - 13s 678ms/step - loss: 0.0255 - accuracy: 0.9932 - val_loss: 0.0163 - val_accuracy: 1.0000\n",
            "Epoch 9/10\n",
            "10/19 [==============>...............] - ETA: 3s - loss: 0.0281 - accuracy: 0.9906"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - ETA: 0s - loss: 0.0308 - accuracy: 0.9915"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/19 [==============================] - 12s 655ms/step - loss: 0.0308 - accuracy: 0.9915 - val_loss: 0.0155 - val_accuracy: 1.0000\n",
            "Epoch 10/10\n",
            " 5/19 [======>.......................] - ETA: 5s - loss: 0.0343 - accuracy: 0.9855"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/19 [==============>...............] - ETA: 3s - loss: 0.0261 - accuracy: 0.9899"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - ETA: 0s - loss: 0.0268 - accuracy: 0.9932"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r19/19 [==============================] - 13s 697ms/step - loss: 0.0268 - accuracy: 0.9932 - val_loss: 0.0155 - val_accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import cv2\n",
        "\n",
        "def categorizar(ruta):\n",
        "  img = Image.open(ruta)\n",
        "  img = img.convert(\"RGB\")\n",
        "  img = np.array(img).astype(float)/255\n",
        "\n",
        "  img = cv2.resize(img, (224, 224))\n",
        "  prediccion = modelo.predict(img.reshape(-1, 224, 224, 3))\n",
        "  return np.argmax(prediccion[0], axis=-1)"
      ],
      "metadata": {
        "id": "dhWcPFvZhZHK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# graficar precision\n",
        "acc= historial.history['accuracy']\n",
        "val_acc = historial.history['val_accuracy']\n",
        "\n",
        "loss= historial.history['loss']\n",
        "val_loss = ['val_loss']\n",
        "\n",
        "rango_epocas=range(10)\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(rango_epocas , acc , label = 'Precisión Entrenamiento')\n",
        "plt.plot(rango_epocas, val_acc , label='Precisión Pruebas')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Precisión del entrenamiento y Pruebas')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(rango_epocas , loss , label='Perdida de Entrenamiento')\n",
        "# plt.plot(rango_epocas, val_loss, label='Pérdida de Pruebas')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Precisión del entrenamiento y Pruebas')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "Uyje9tiAj8sY",
        "outputId": "03a6b427-b51a-4bda-9dee-a284ef3cd6af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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3D3v37jWpNTRez8fHB3369MHIkSOxYMECfPjhh+jbty+GDRuGcePGQaVSWf2aqsJ5lPPo3XAe5Txqqbowj9aqgLhHjx7i6ujKqFSqCpO7Xq9HYGCgyV9Y5Vn7F5FRYGAgjhw5gt9++w2//vorfv31V6xcuRITJkzAl19+WaNrGykUCptcpyp6vR4PPfQQXnzxRbP3W9roXq/Xo2PHjkhMTDR7f1hYmMnXlb1GQRCq/Zzl/5o0jgEA/v3vf1f6g9qpUyeLxqHT6fDQQw/hxo0beOmll9CmTRt4eHjgypUrmDRpUoW/giu7njWv13jt999/H126dDF7jqenZ42fx1bGjBmDmTNnYs2aNXjllVfw9ddfo1u3bmZ/OZjj7++P6Ojou5535787UHk28s4FVNa8p3d7rnPnzqF///5o06YNEhMTERYWBqVSiS1btuDDDz8Un1Mmk+H777/Hvn378PPPP+O3337DE088gQ8++AD79u276/Nag/OofXEerd44OI9WH+dRx8yjtSogtlZERAS2bduG+++/3+w/ePnzAODEiRNo0aKFRc+hVCoxdOhQDB06FHq9HtOnT8enn36K1157zey1mjZtCgBITU0VP/oySk1NFe+vKeN10tLSTD6uyc7OrvAXdEREBAoKCqr1g1MdEREROHr0KPr372+zJvmWXicgIABeXl7Q6XQ2e13Hjx/HmTNn8OWXX2LChAnicUs+6rWW8XvU29vbZq8HMP++BgQEwN3d3ewWuKdPn4ZcLq/wy/hODRs2xJAhQ7BmzRqMHz8ee/bssXhRkbWMmYg7Nx8wZvuMrHlP09LSTLIpZ8+ehV6vF7sm/Pzzz9BoNNi0aZNJZunOj7eN7r33Xtx777146623sHbtWowfPx7ffvutyUfEUuM8ynmU82jVOI/W7nm0VtUQW2vUqFHQ6XR44403KtxXWloq/kMPGDAAXl5eSEhIqND6o6q/Aq9fv27ytVwuF/9avrMtjlG3bt0QGBiI5cuXm5zz66+/4tSpUxgyZEi1XtvdREdHw9XVFR9//LHJazD3wzRq1Cjs3bsXv/32W4X7bt68idLSUouee9SoUbhy5Qo+++yzCvfdunULhYWFFl0PgNgLtbo7LCkUCowcORI//PADTpw4UeH+7Oxsi8dgzBSUfz8FQTBpD2UvkZGRiIiIwMKFC1FQUFDhfmteD2B4X+98TxUKBQYMGICffvrJ5COszMxMrF27Fg888ECVHxUaPf744zh58iT+7//+DwqFAmPGjLFqjJYyTtDlazZ1Ol2Fj3yteU+N7Z6MPv74YwCGHr+A+e+R3NxcrFy50uRx//zzT4W5xZhdqWzukArnUc6jnEerxnm0ds+j9SJD3KdPHzz11FNISEjAkSNHMGDAALi6uiItLQ3r16/H4sWL8eijj8Lb2xsffvghJk+ejO7du2PcuHFo0KABjh49iqKioko/tps8eTJu3LiBfv36oXHjxrh06RI+/vhjdOnSBW3btjX7GFdXV7z77ruIjY1Fnz59MHbsWLFdUHh4OGbOnGmT127ss5mQkICHH34YgwcPxuHDh/Hrr7+K7XaM/u///g+bNm3Cww8/jEmTJiEyMhKFhYU4fvw4vv/+e1y8eLHCY6ry+OOP47vvvsO0adOwfft23H///dDpdDh9+jS+++47/Pbbb3f96PZOkZGRAIBXX30VY8aMgaurK4YOHVrlpgHvvPMOtm/fjqioKEyZMgXt2rXDjRs3cOjQIWzbtg03btywaAxt2rRBREQEZs2ahStXrsDb2xs//PCDRTV61pLL5fj8888xaNAgtG/fHrGxsQgNDcWVK1ewfft2eHt74+eff7b4upGRkdi2bRsSExPRqFEjNGvWTKw727p1Kx544AFMnz4dLi4u+PTTT6HRaPDee+9V69pDhgyBn58f1q9fj0GDBlVag2pr7du3x7333ovZs2eLLYu+/fbbCgGJNe/phQsX8K9//QsDBw7E3r178fXXX2PcuHHo3LkzAENQaMx2PvXUUygoKMBnn32GwMBAXLt2TbzOl19+iaVLl2L48OGIiIhAfn4+PvvsM3h7e2Pw4MH2f5MswHmU8yjn0apxHq3l82iNelQ4iLHtx91aakycOFHw8PCo9P7//Oc/QmRkpODm5iZ4eXkJHTt2FF588UXh6tWrJudt2rRJuO+++wQ3NzfB29tb6NGjh/DNN9+YPE/5dkHff/+9MGDAACEwMFBQKpVCkyZNhKeeekq4du2aeM6dLUqM1q1bJ3Tt2lVQqVRCw4YNhfHjx4vtj+72uoytS+5Gp9MJCxYsEEJCQgQ3Nzehb9++wokTJ4SmTZtWaBGTn58vzJ49W2jRooWgVCoFf39/4b777hMWLlwoaLVa8TxUo12QIBjaIr377rtC+/btBZVKJTRo0ECIjIwUFixYIOTm5ppcb8aMGRUeb26Mb7zxhhAaGirI5XKTdjCVXUMQBCEzM1OYMWOGEBYWJri6ugrBwcFC//79hf/85z/iOcZ/o/Xr15s81tiSZuXKleKxkydPCtHR0YKnp6fg7+8vTJkyRWxvVP68yv7t+vTpI7Rv397s6x0yZEiFMd35fXP48GFhxIgRgp+fn6BSqYSmTZsKo0aNEpKTk8VzjN8f2dnZJo8110bn9OnTQu/evQU3N7cKbaQOHTokxMTECJ6enoK7u7vw4IMPCn/88UeFsVfF2E5n7dq11X7Mne+FOcZ/m/fff9/s/efOnROio6MFlUolBAUFCa+88oqwdevWGr+nJ0+eFB599FHBy8tLaNCggRAXF1ehxdimTZuETp06CWq1WggPDxfeffddYcWKFSbv/aFDh4SxY8cKTZo0EVQqlRAYGCg8/PDDwsGDB6v9PlUX51HOo+VxHuU8asR51EAmCA6oCCeiem3mzJn44osvkJGRIW7yQERE1cd51L7qRQ0xEUmnuLgYX3/9NUaOHMlJnIjICpxH7a9e1BATkeNlZWVh27Zt+P7773H9+vUqt98lIqKKOI86DgNiIrKLkydPYvz48QgMDMRHH31UaW9KIiIyj/Oo47CGmIiIiIjqNdYQExEREVG9xoCYiIiIiOq1elNDrNfrcfXqVXh5edls+0siovIEQUB+fj4aNWoEubzu5Rs4jxKRvUk1j9abgPjq1at33S+ciMgWLl++jMaNG0s9DJvjPEpEjuLoebTeBMReXl4ADG9wdfYNJyKyVF5eHsLCwsT5pq7hPEpE9ibVPFpvAmLjx3ve3t6cyInIrupqOQHnUSJyFEfPo3WvyI2IiIiIyAIMiImIiIioXmNATERERET1Wr2pIa5PdDodSkpKpB4GUZ3j6uoKhUIh9TDICpwXiZyDs86jDIjrEEEQkJGRgZs3b0o9FKI6y9fXF8HBwXV24Vxdw3mRyPk44zzKgLgOMU76gYGBcHd3d6pvNKLaThAEFBUVISsrCwAQEhIi8YioOjgvEjkPZ55HGRDXETqdTpz0/fz8pB4OUZ3k5uYGAMjKykJgYKBTfuxHZTgvEjkfZ51HuaiujjDWxrm7u0s8EqK6zfgzxnpU58d5kcg5OeM8yoC4juHHgUT2xZ+x2of/ZkTOxRl/JhkQE9XAwYMH8eGHH0Kv10s9FCIiIrISA2Kql2QyGTZu3Fijc7Ozs/HYY4+hQ4cOkMv5o2RLkyZNwrBhw6QeBlGdFx4ejkWLFolf321uvHjxImQyGY4cOWLTcezYsQMymYzdQGyI86hl+FucJDVp0iTIZDLIZDIolUq0aNECr7/+OkpLS+36vNeuXcOgQYOsPlev1+Pxxx/HvHnz8NBDD9V4PKtWrRLfh/I3tVpt0XUsCfSd2eLFi7Fq1SqbXnP+/Pno0qWLTa9JZA9SzYuAZXOjs+E8aorzqGXYZYIkN3DgQKxcuRIajQZbtmzBjBkz4OrqitmzZ1c4V6vVQqlU1vg5g4ODa3SuXC5HUlJSjcdRnre3N1JTU02O2aPOylbvoT35+PhIPQQiSVkyL96NTqeDTCar1idZlsyNzojzaBnOo5Zhhpgkp1KpEBwcjKZNm+Lpp59GdHQ0Nm3aBKDsI5+33noLjRo1QuvWrQEAly9fxqhRo+Dr64uGDRvikUcewcWLF02uu2LFCrRv3x4qlQohISGIi4sT7yufAdBqtYiLi0NISAjUajWaNm2KhIQEs+cCwPHjx9GvXz+4ubnBz88PU6dORUFBgXi/ccwLFy5ESEgI/Pz8MGPGjLuuppXJZAgODja5BQUFiff37dsXzz77LF588UU0bNgQwcHBmD9/vnh/eHg4AGD48OGQyWTi18a/6D///HM0a9ZMzJbcvHkTkydPRkBAALy9vdGvXz8cPXpUvJ7xcatXr0Z4eDh8fHwwZswY5Ofni+ckJSXhgQcegK+vL/z8/PDwww/j3Llz4v3Gj1e/++479OrVC25ubujevTvOnDmDP//8E926dYOnpycGDRqE7OzsCu+hkV6vR0JCApo1awY3Nzd07twZ33//vXi/8ePW5ORkdOvWDe7u7rjvvvvEX4yrVq3CggULcPToUTFrZMycpKen45FHHoGnpye8vb0xatQoZGZmVvlvRWRvVc2LGo0Gs2bNQmhoKDw8PBAVFYUdO3aIj121ahV8fX2xadMmtGvXDiqVCunp6cjKysLQoUPh5uaGZs2aYc2aNRWe98757sCBA+jatSvUajW6deuGw4cPm5yv0+nw5JNPij+brVu3xuLFi+/6+rZs2YJWrVrBzc0NDz74YIX5GwB2794tzhthYWF49tlnUVhYWOV1OY9yHrUWA+I6ShAEFGlLJbkJglCjsbu5uUGr1YpfJycnIzU1FVu3bsUvv/yCkpISxMTEwMvLC7t27cKePXvg6emJgQMHio9btmwZZsyYgalTp+L48ePYtGkTWrRoYfb5PvroI2zatAnfffcdUlNTsWbNGnESvFNhYSFiYmLQoEED/Pnnn1i/fj22bdtmEmwDwPbt23Hu3Dls374dX375JVatWmWTj66+/PJLeHh4YP/+/Xjvvffw+uuvY+vWrQCAP//8EwCwcuVKXLt2TfwaAM6ePYsffvgBGzZsEGv/HnvsMWRlZeHXX39FSkoK7rnnHvTv3x83btwQH3fu3Dls3LgRv/zyC3755Rfs3LkT77zzjsn7ER8fj4MHDyI5ORlyuRzDhw+vsMhw3rx5mDNnDg4dOgQXFxeMGzcOL774IhYvXoxdu3bh7NmzmDt3bqWvOyEhAV999RWWL1+Ov/76CzNnzsS///1v7Ny50+S8V199FR988AEOHjwIFxcXPPHEEwCA0aNH44UXXkD79u1x7do1XLt2DaNHj4Zer8cjjzyCGzduYOfOndi6dSvOnz+P0aNHW/GvQ7WBVHOjLefFuLg47N27F99++y2OHTuGxx57DAMHDkRaWpp4flFREd599118/vnn+OuvvxAYGIhJkybh8uXL2L59O77//nssXbpU3CDBnIKCAjz88MNo164dUlJSMH/+fMyaNcvkHL1ej8aNG2P9+vU4efIk5s6di1deeQXfffddpde9fPkyRowYgaFDh+LIkSOYPHkyXn75ZZNzzp07h4EDB2LkyJE4duwY1q1bh927d1eYa63BeZTzqDksmaijbpXo0G7ub5I898nXY+CutPxbSxAEJCcn47fffsMzzzwjHvfw8MDnn38ufjz19ddfQ6/X4/PPPxc/Clu5ciV8fX2xY8cODBgwAG+++SZeeOEFPPfcc+J1unfvbvZ509PT0bJlSzzwwAOQyWRo2rRppWNcu3YtiouL8dVXX8HDwwMA8Mknn2Do0KF49913xUxEgwYN8Mknn0ChUKBNmzYYMmQIkpOTMWXKlEqvnZubC09PT5NjvXr1wq+//ip+3alTJ8ybNw8A0LJlS3zyySdITk7GQw89hICAAABlW2KWp9Vq8dVXX4nn7N69GwcOHEBWVhZUKhUAYOHChdi4cSO+//57TJ06FYDhl92qVavg5eUFAHj88ceRnJyMt956CwAwcuRIk+dZsWIFAgICcPLkSXTo0EE8PmvWLMTExAAAnnvuOYwdOxbJycm4//77AQBPPvlkpX8waDQavP3229i2bRt69uwJAGjevDl2796NTz/9FH369BHPfeutt8SvX375ZQwZMgTFxcVwc3ODp6cnXFxcTN6brVu34vjx47hw4QLCwsIAAF999RXat2+PP//8s9LvGaq9pJobbTUvpqenY+XKlUhPT0ejRo0AGH6+kpKSsHLlSrz99tsADP1dly5dis6dOwMAzpw5g19//RUHDhwQv6+/+OILtG3bttLnXrt2LfR6Pb744guo1Wq0b98ef//9N55++mnxHFdXVyxYsED8ulmzZti7dy++++47jBo1yux1ly1bhoiICHzwwQcAgNatW+P48eN49913xXMSEhIwfvx4PP/88wAM891HH32EPn36YNmyZZXWBXMeXWX2feE8encMiElyv/zyCzw9PVFSUgK9Xo9x48aZfITVsWNHk1qto0eP4uzZs+LkYlRcXIxz584hKysLV69eRf/+/av1/JMmTcJDDz2E1q1bY+DAgXj44YcxYMAAs+eeOnUKnTt3FoNhALj//vuh1+uRmpoqBsTt27c32X0nJCQEx48fr3IcXl5eOHTokMkx444+Rp06dTL5OiQkpMoMj1HTpk3FSRwwvIcFBQUVdu+6deuWyUd14eHhJu/znc+XlpaGuXPnYv/+/cjJyREzGunp6SYTeflxG9+jjh07mhyr7HWcPXsWRUVFFRYvarVadO3a1eRY+ecxbgmalZWFJk2amL32qVOnEBYWJk7iANCuXTv4+vri1KlTtWYip7qnsnlxx44d0Ol0aNWqlcn5Go3G5OdZqVSa/DycOnUKLi4uiIyMFI+1adMGvr6+lY7h1KlT6NSpk0nwaQymyluyZAlWrFiB9PR03Lp1C1qttsqFV6dOnUJUVJTJsTuve/ToURw7dsykrEMQBOj1ely4cKHSQJ7zKOdRazEgrqPcXBU4+XqMZM9tiQcffBDLli2DUqlEo0aN4OJi+m1ZPvgEDB/jRUZGmq1/CwgIsLgF2j333IMLFy7g119/xbZt2zBq1ChER0eb1FZZytXV1eRrmUx2117Fcrm80rKOmlwXMP8ehoSEmNQdGpX/BXm35xs6dCiaNm2Kzz77DI0aNYJer0eHDh1MSl7uvI4xq3/nscpeh7E+e/PmzQgNDTW5z5iVqep52COaypNqbrTVvFhQUACFQoGUlJQKW96Wz4y6ubk5ZPODb7/9FrNmzcIHH3yAnj17wsvLC++//z72799fo+sWFBTgqaeewrPPPlvhvsoCM4DzKOdR6zEgrqNkMplVH89JwcPD464TWHn33HMP1q1bh8DAQHh7e5s9Jzw8HMnJyXjwwQerdU1vb2+MHj0ao0ePxqOPPoqBAwfixo0baNiwocl5bdu2xapVq1BYWChOjnv27IFcLhcX/EnJ1dUVOp3urufdc889yMjIgIuLS6X10ndz/fp1pKam4rPPPkOvXr0AGD5CtLXyi4LKf6xnKaVSWeG9adu2LS5fvozLly+L2Y2TJ0/i5s2baNeuXY3GTc6ptsyNlc2LXbt2hU6nQ1ZWlvhzVx1t2rRBaWkpUlJSxIxdampqlX1/27Zti9WrV6O4uFjMEu/bt8/knD179uC+++7D9OnTxWPls6OVXde4QNDozuvec889OHnypEW/G2yF82jl6vI8ykV1VOuMHz8e/v7+eOSRR7Br1y5cuHABO3bswLPPPou///4bgGFl7wcffICPPvoIaWlpOHToED7++GOz10tMTMQ333yD06dP48yZM1i/fj2Cg4PNfpQ4fvx4qNVqTJw4ESdOnMD27dvxzDPP4PHHHzdZyWwNQRCQkZFR4WbJX+bGPwQyMjLwzz//VHpedHQ0evbsiWHDhuG///0vLl68iD/++AOvvvoqDh48WK3natCgAfz8/PCf//wHZ8+exe+//474+Phqj7W6vLy8MGvWLMycORNffvklzp07J/57fvnll9W+Tnh4OC5cuIAjR44gJycHGo0G0dHR6NixI8aPH49Dhw7hwIEDmDBhAvr06YNu3brZ/LUQ1VSrVq0wfvx4TJgwARs2bMCFCxdw4MABJCQkYPPmzZU+zlgS9tRTT2H//v1ISUnB5MmTK5QTlDdu3DjIZDJMmTIFJ0+exJYtW7Bw4UKTc1q2bImDBw/it99+w5kzZ/Daa6+ZLEQzZ9q0aUhLS8P//d//ITU1FWvXrq1Q+/rSSy/hjz/+QFxcHI4cOYK0tDT89NNPd11Ux3nUPM6jd8eAmGodd3d3/O9//0OTJk0wYsQItG3bFk8++SSKi4vFjPHEiROxaNEiLF26FO3bt8fDDz9ssgK7PC8vL7z33nvo1q0bunfvjosXL2LLli1mSy/c3d3x22+/4caNG+jevTseffRR9O/fH5988kmNX1deXh5CQkIq3KpT22b0wQcfYOvWrQgLC6tQF1aeTCbDli1b0Lt3b8TGxqJVq1YYM2YMLl26VO3AXi6X49tvv0VKSgo6dOiAmTNn4v3336/2WC3xxhtv4LXXXkNCQgLatm2LgQMHYvPmzWjWrFm1rzFy5EgMHDgQDz74IAICAvDNN99AJpPhp59+QoMGDdC7d29ER0ejefPmWLdunV1eB5EtrFy5EhMmTMALL7yA1q1bY9iwYfjzzz+rLCUwPq5Ro0bo06cPRowYgalTpyIwMLDS8z09PfHzzz/j+PHj6Nq1K1599VWThW8A8NRTT2HEiBEYPXo0oqKicP36dZNssTlNmjTBDz/8gI0bN6Jz585Yvny5uBjQqFOnTti5cyfOnDmDXr16oWvXrpg7d664kLAynEcrx3n0LgQL7dy5U3j44YeFkJAQAYDw448/3vUx27dvF7p27SoolUohIiJCWLlyZYVzPvnkE6Fp06aCSqUSevToIezfv9/k/lu3bgnTp08XGjZsKHh4eAgjRowQMjIyqj3u3NxcAYCQm5tb7cfUJrdu3RJOnjwp3Lp1S+qhENVpVf2s1fV5pra9Ps6LRM7JGedRizPEhYWF6Ny5M5YsWVKt8y9cuIAhQ4bgwQcfxJEjR/D8889j8uTJ+O23srY369atQ3x8PObNm4dDhw6hc+fOiImJMfmLbubMmfj555+xfv167Ny5E1evXsWIESMsHT4RERERkQmLVxYMGjTIon3Oly9fjmbNmon9Btu2bYvdu3fjww8/FPvpJSYmYsqUKYiNjRUfs3nzZqxYsQIvv/wycnNz8cUXX2Dt2rXo168fAMNHP23btsW+fftw7733WvoyiIiIiIgAOKDLxN69exEdHW1yLCYmRmy2rdVqkZKSYrI/u1wuR3R0NPbu3QsASElJQUlJicl12rRpgyZNmmDv3r1mA2KNRgONRiN+nZeXZ8uXRdYQBODWP0BxLuAVAriab6xe6xXnAQWZhtdLtZ9PKKD0uPt5ZNbMdUdwPrsA7z7aCW2CzXeFISKSmt0D4oyMjArF5UFBQcjLy8OtW7fwzz//QKfTmT3n9OnT4jWUSmWFVf9BQUHIyMgw+7wJCQkmu+eQxHRa4OZlQHP7D5OSW0BAa0BuWW9Op1dSDPxzARDqfs/GekN/9/ZLVLlT1/JwOiMf2fkatAm++/lERFJw/maMVpo9e7ZJ65K8vDyTXVTIQQQBuHUDyL0CCDoAMkMQrNMYAuQGTQEHNI93CL2uLBhWegIeAXd/DDk/18rbUtHdeasNTf7zi0slHgkRUeXsHhAHBwcjMzPT5FhmZia8vb3h5uYGhUIBhUJh9hzjXtnBwcHQarW4efOmSZa4/Dl3UqlUFXZfIQe7Myvs6gb4NjUEjDlpQPE/QJFH3Qkcc/8GSosBuQvQIBxQuN71IUR1nbeb4ddM3q0SycZQH3bZIqpNnPFn0u4Bcc+ePbFlyxaTY1u3bhX3LVcqlYiMjERycjKGDRsGwPBGJScniw24IyMj4erqiuTkZIwcORKAYYed9PR0s/uqk8TMZYW9ggHPoLJssHcjIO+K4RxXD0DpLumQa6zouuE1AwyGicoxZojzih0fECuVSsjlcly9ehUBAQFQKpUO2c6YiMwTBAFarRbZ2dmQy+VQKpVSD0lkcUBcUFCAs2fPil8bdyxp2LAhmjRpgtmzZ+PKlSv46quvABh2pPnkk0/w4osv4oknnsDvv/+O7777zmRHnfj4eEycOBHdunVDjx49sGjRIhQWFopdJ3x8fPDkk08iPj4eDRs2hLe3N5555hn07NmTHSacTakWyC2fFXYHfJtU/NjZIwDQFACaXEOZQUBrQ2a1Niq5Bdw07JAHrxBA5SXteIiciLfb7YD4luNLJuRyOZo1a4Zr167h6tWrDn9+IjLP3d0dTZo0MbsBllQsjkAOHjyIBx98UPzaWKc7ceJErFq1CteuXUN6erp4f7NmzbB582bMnDkTixcvRuPGjfH555+LLdcAYPTo0cjOzsbcuXORkZGBLl26ICkpyWSh3Ycffgi5XI6RI0dCo9EgJiYGS5cutepFkx0IgiFLmne1XFY4BPAMNF8jLJMBDZoA2allpRUNwmtdPfHBA/uxK+lHPPfEY5CrfQxZcCISeatvl0xIkCEGDFniJk2aoLS0FDodF0gSSU2hUMDFxcXpPq2RCUL96A2Vl5cHHx8f5Obmitv71iXFxcW4cOECmjVrBrXawe3MSrVAbjqgyTd87epuqBWuTls1baGhnhgC4N0Y8HRMPbFMJsOPP/4olulYc252VhZ6dI/Ef959BQ/17WXIctfCUolVq1bh+eefx82bN6UeSq1Q1c9aXZ9nrHl9n/3vPN7acgrDujTCojGVb4NLRARIN486T66aah9BAApzgOxTt4NhmaE22L9VtXsMT5o6A7LQrpCF3gNlw0ZoERGB119/HaWl9v149dq1a9XeYMbcuXq9Ho+PH4N5z0/GQ73vrXHd8KpVqyCTySCTySCXy9G4cWPExsaa7NZIVBsZF9WxywQRObNaWrRJkiu93TZNa8wKe9yuFbY8Oz1w4ECsTJwPTX4Otmzfhxmz58PV1dVksxYjrVZrkyL8yrqTVPdcuU6DpC8TAQi364Y9azwmb29vpKamQq/X4+jRo4iNjcXVq1dNtjk30ul0YvBM5MykXFRHRFRd/G1KlhGzwqdvB8MywDsU8G9p9c5zKpUKwa0j0bRpOJ5+fASi+9yPTZs2AQAmTZqEYcOG4a233kKjRo3QunVrAMDly5cxatQo+Pr6omHDhnjkkUdw8eJFk+uuWLEC7du3h0qlQkhIiNi1BDCUQWzcuBGAIciOi4tDSEgI1Go1mjZtioSEBLPnAsDxo0fQ78G+cIu4F34d+mFq/GsoKCgQ7zeOeeHChQgJCYGfnx9mzJiBkpKqAwKZTIbg4GA0atQIgwYNwrPPPott27bh1q1bWLVqFXx9fbFp0ya0a9cOKpUK6enp6Nu3r7jro9GwYcMwadIk8WuNRoNZs2YhNDQUHh4eiIqKwo4dOyo8/8aNG9GyZUuo1WrExMTg8uXL4n3nzp3DI488gqCgIHh6eqJ79+7Ytm2byeOXLl0qPj4oKAiPPvpola+X6gcpF9UREVUXA+K6ShAM9bm2vBXdADKOAzlnDF9DDviEGba1LSkqO8+asnS5C9CgGQAZ3JQKaIuLxLuSk5ORmpqKrVu34pdffkFJSQliYmLg5eWFXbt2Yc+ePfD09MTAgQOh1WoBAMuWLcOMGTMwdepUHD9+HJs2bUKLFi3MPvVHH32ETZs24bvvvkNqairWrFmD8PBws+cWFhQgJiYGDXw88eev32D9uu+wrVyLQKPt27fj3Llz2L59O7788kusWrUKq1atsugtcXNzg16vF8tHioqK8O677+Lzzz/HX3/9hcDAwGpdJy4uDnv37sW3336LY8eO4bHHHsPAgQORlpYmnlNUVIS33noLX331Ffbs2YObN29izJgx4v0FBQUYPHgwkpOTcfjwYQwcOBBDhw4VF9AePHgQzz77LF5//XWkpqYiKSkJvXv3tuj1Ut3EDDER1QYsmairSoqAtxtJ89yvXDUEyRYSXN2QfOgsftu5F8/EjrkddAMeHh74/PPPxVKJr7/+Gnq9Hp9//rm4SnXlypXw9fXFjh07MGDAALz55pt44YUX8Nxzz4nX7969u9nnTU9PR8uWLfHAAw9AJpOhadOmlY5x7arPUKwpxleL34BHWCdA5YlPPvkEQ4cOxbvvvit2RmnQoAE++eQTKBQKtGnTBkOGDEFycjKmTJlSrfciLS0Ny5cvR7du3eDlZWjjVlJSgqVLl6Jz587Vuobxta1cuRLp6elo1Mjw/TBr1iwkJSVh5cqVePvtt8Vrf/LJJ4iKigIAfPnll2jbti0OHDiAHj16oHPnzibP+8Ybb+DHH3/Epk2bEBcXh/T0dHh4eODhhx+Gl5cXmjZtiq5duYCKnGNjDiKiu2FATJL75Zdf4OnpiZKSEuj1eowb+S/Mf+Ep4J+LgKBHx44dTeqGjx49irNnz4qBolFxcTHOnTuHrKwsXL16Ff3796/W80+aNAkPPfQQWrdujYEDB+Lhhx/GgAEDKp6oLcKpYyno3LYVPIIjxLrh+++/H3q9HqmpqWJA3L59eygUCvGhISEhOH78eJXjyM3NhaenJ/R6PYqLi/HAAw/g888/F+9XKpXo1KlTtV6T0fHjx6HT6dCqVSuT4xqNBn5+fuLXLi4uJn8wtGnTBr6+vjh16hR69OiBgoICzJ8/H5s3b8a1a9dQWlqKW7duiRnihx56CE2bNkXz5s0xcOBADBw4EMOHD4e7ey3fcIVqzOt2hrhQq0OpTg8XBT+YJCLnw4C4rnJ1N2RqrSUIQOF1oOCaYatlmRzwDAE8/O7eK9jVsiDowQcfxLJly6BUKtGoUSO4yGW3+xNrAG0hPDxMs80FBQWIjIzEmjVrKlwrICDA4oVm99xzDy5cuIBff/0V27Ztw6hRoxAdHY3vv/++7CS93hCgA4BcAXhUXa7g6mracUImk911q0ovLy8cOnQIcrkcISEhcHMz3czEzc2tQt9GuVyOOzsnlq9VLigogEKhQEpKikmADgCentVfCDhr1ixs3boVCxcuRIsWLeDm5oZHH31ULFExjn3Hjh3473//i7lz52L+/Pn4888/TbZbp/rHS132a6ZAUwpfd+fZmYqIyIgBcV0lk1lVtgDgdgeJdEBbALioAKWnoYOEi8q2Y7zNw8OjYn1vg3BDrbKuBCg1/aj1nnvuwbp16xAYGFhpj8Lw8HAkJyebbCJTFW9vb4wePRqjR4/Go48+ioEDB+LGjRto2LCh4YTCbEDXDG1bt8Sq77egsKhIDNT37NkDuVwuLvizllwur7TOuTIBAQG4du2a+LVOp8OJEyfE1921a1fodDpkZWWhV69elV6ntLQUBw8eRI8ePQAYtka/efMm2rZtC8DwGidNmoThw4cDMATady5idHFxQXR0NKKjozFv3jz4+vri999/x4gRIyx6TVS3uCrkcFcqUKTVIe8WA2Iick787IrKCAJQkAVknTYEwzI54NMY8Gtht2C4Ukp3w3MDQGmxWE8MAOPHj4e/vz8eeeQR7Nq1CxcuXMCOHTvw7LPP4u+/DVsoz58/Hx988AE++ugjpKWl4dChQ/j444/NPlViYiK++eYbnD59GmfOnMH69esRHBxsmtksKQQgw/gnp0OtVmPixIk4ceIEtm/fjmeeeQaPP/64yc6KjtKvXz9s3rwZmzdvxunTp/H000+bbLDRqlUrjB8/HhMmTMCGDRtw4cIFHDhwAAkJCSbbp7u6uuKZZ57B/v37kZKSgkmTJuHee+8VA+SWLVtiw4YNOHLkCI4ePYpx48aZZLx/+eUXfPTRRzhy5AguXbqEr776Cnq9vsZ/JFDdwIV1ROTsGBCTQWkxcD0NyLsCQG/ICge0ATwCpNtO2d0PUNzOJt24AOgM3Rbc3d3xv//9D02aNMGIESPQtm1bPPnkkyguLhYzxhMnTsSiRYuwdOlStG/fHg8//LBJV4XyvLy88N5776Fbt27o3r07Ll68iC1bthhKL7Rl3S7g3QjuvgH47bffcOPGDXTv3h2PPvoo+vfvj08++cSub0VlnnjiCUycOBETJkxAnz590Lx58wpZ8ZUrV2LChAl44YUX0Lp1awwbNgx//vknmjRpIp7j7u6Ol156CePGjcP9998PT09PrFu3Trw/MTERDRo0wH333YehQ4ciJiYG99xzj3i/r68vNmzYgH79+qFt27ZYvnw5vvnmG7Rv397+bwI5PS6sIyJnx62b6wirt24WBEM5QN41ALdrhb0bAe7+0gXC5el1ZfXEKm+gYXPHjUtfevu5tYDKB2jYzDneE3IYvSBABpjUbnPrZstf36PL/sDBS/9g+b/vwcAOIXYcIRHVdlLNo6whdhZ6PfDnZ8CfnwMlxZY/3i0E6PoSkFMKuFqQ+Bf0hsAPsHutsFXkCkMgmp0KaPIMJR1eDihNEARDHbVOa8hSN2jCYPi2W1odsvKLUaTVQamQQ+Uqh9pVAbWLHCpXBVzksgqL/5ydXhCgKdVDU6JDcYkemlLDf7WlerQI9ICbklNlTRgX1nFzDiJyVpzlncH1c8BPcUD6H9ZfQycYAlt9CaCzMBiRyQ27zblXo4OEFFzdDPXEuZeB/Ns9jm2wVXKVCrOB4lwAMsMCPzl/VG5pS5GVr0FuuY+9S3R6FGpNz1PIZVC7KKBylUPlooDaVQ61iwIuCukDZb3+duBbWjHwFWD+wzJNqR5uXAdWI+JudawhJiInxd/yUtLrgf3LgeTXgdJbgKsHED0PaNzN8muVAshzAXzDALWFGV4XlfMHfO5+hoV+t/4xtD8LaA0oXO/6MKtoC4G82y3rvBtZ362jjrilLUVmnsYkmPFxc4WfhxIleqFcVlUPbakOOr2AQm2p2UBZ5VKWSVbfDphd7RAoGwJfHYrvyPoaAl/zFDKZYVxmxkc1U7aojhliInJOTh4F1WE5Z4GfZgCX9xm+btYb+NfHhmykNYqLgaILhu4MSgtqiGsLmcywTXRJ0e22cJeAhhG2z2jrS2/3GxYAtY9hUWE9VaQtRdYdgbCvmxKB3iqoXRVmH1NVBlanF1CkLUXRnYHy7UBU5SIXg1C1qxyuCvldA2Xd7cBXU6JHcbn/aksr7/lcPoNd/r/OkMGuq7iojoicHQNiR9PrgH3LgN/fMHR2UHoCA94AImNtEtzV6TWScgXQoBmQfQbQ5AMFmYBXsO2uLwjAP+Xqhn3rZ93wnYGwDICPuxKBXpUHwkZyuQxuSgXclKbn6QUB2lI9iktMs7baUj10gvlAWS6TVQhaS8sHwCU6aHXVDHxtWONcp3/G7IRt14jI2TEgdqScNGDjdODvA4avm/c1ZIV9m1T5sOow7oxWVFRUYYezOsXVDfBtbFjwln/tdj2x190fVx2F2YDGWDfczPnLSGysSFOKzHwN8ssFwr7uSgRUIxC+G7lMZghKXSsPlDXl/qsp1UMvCLil1eGWVlfltV3k8jsCZ/su7isqMrTiu3M3QqqcWEPMRXVE5KTq1298qeh1wN4lwO9vGtqHKb2AmLeAeybYLAOpUCjg6+uLrKwsAIa+snX241+5B6DwNgSvWRcMXShqWk+sLQJuXgEgAJ5BgF5uKEOpB4q0pbhRqEWhxhCsyCCDl9oFfh5KKF3lgK4ExTr7ZvbUckCtBHyUCgAK6AUBJbdrkjU6AdoSPbQ6PRRyQOmigFIhh9JVDpVCDhdF+a4qAgAddCU6VB1GW04QBBQVFSErKwu+vr4VtsKmyoldJpghJiInxYDY3rJTDbXCf/9p+DqiPzB0sWHxm40FBxvKB4xBcZ0m6IGCm4atnTP+qdkGIno9UJBhqB92dQcKcwHk2nK0TklTqkf+rRIU3663lQFwVyrgpXZB8S05rvwj7fjMKb19K7rbiXbk6+sr/qxR9RhLJvK5qI6InBQDYnvRlQJ7Pwa2J5RtKhHzNtD133arS5XJZAgJCUFgYCBKSupBJuaGHPhugqFDR4+phpul9HpgywvAxV2GRXujVtu/pZvEjl7+B6v3XsLhyzcBGGptB7QLxrgeTdCoQR0ut7EBV1dXZoatUFYyUQ/mJSKqlRgQ20PWKUOt8NVDhq9bDgAeXgT4hDrk6RUKRf34pd2oLfDgC8CPTwG/zwUadzLUZVtiz2LgxFpAoQLGrwV8/O0yVGew99x1LE4+g33nbwAAXOQyPNatMab3bYGwhu4Sj47qMm+WTBCRk2NAbEu6UuCPxcCOd8q2+x30DtB5bL3sVuAQnccAF3cDh1cDP0wBpu2u/k526fuAbQsM/z/oHSCkk/3GKRFBELD33HUsSk7DgQuGQNhVIcNj3cIwvW8EGjdgIEz2Z8wQF2hKodcLkMs5HxKRc2FAbCuZJ4GfpgNXDxu+bhkDDF1k2NiB7Gvw+8CVQ0DWX8APTwITfjK0aKtK4XVgfSwg6IAOjxra3tUhgiBgz1lDRvjPi4ZiYKVCjlHdG+Ppvi0Q6svSCHIc46I6QQDyNaXwcWOHDiJyLgyIa0pXAuxZBOx417BtstoHGPQe0Gk0s8KO4uoGjPoS+E9fQy3wjneAfq9Wfr5ebyizyL8K+LUw/OFSR/6tBEHArrQcLE5OQ8qlskB4TI8wPN03AiE+DITJ8VQuho1XNKV65N0qYUBMRE6HAXFNZJwANj4NZBwzfN16MPDwh7bdLIKqx7+loXvHD08C/3sfaHIv0KK/+XP3LALObgVc1MBjX9quj7GEBEHAzjPZWJychsPpNwEAShc5xvVogml9IhDsUwd3L6RaxdvNFdn5GnaaICKnxIDYGroSYFeiIfDSlwBuDYBB7wMdH60zmcZaqeOjhnrilJXAhqmGemLvENNzLv1h6AcNGEotgjs4fpw1pNcLuHLzFs5mFeBMZj7Ssgpw/O9cpGbmAwBULnKMj2qKp/o0R5A3A2FyDt5qF2Tna7iwjoicEgNiS107ZqgVzjhu+LrNw8CQxOov5CL7GvgOcOWg4d/n+yeAiT8Ditvf5gXZhmOCDug0Buj6uLRjvQu9XsDf/9xCWlY+zmQWIC0rH2ezCnA2qwBFZnZvU7vK8e+oppjapzkCvRgIk3Nh6zUicmYMiKurVAvs+gDYtdCwgYNbQ0OGscNIZoWdievtMohP+wDpfwDb3wKi592uG55q2O7ZvzXwcKLT/Lvp9AIu3yhC2u2M79mssuC3uERv9jGuChma+3uiZZAnWgZ6oWWQJ6KaNYSfp8rBoyeqHuPmHHksmSAiJ8SAuDquHTX0Fc48Yfi67VBDVtgzUNpxkXl+EcAjHwPrJwG7E4Gm9wFXjwDnfjfsRDfqS0Dp4fBh6fQCLl0vRNrtLO+ZzHykZRbgXHYBNKXmA1+lQo7mAR5oFeSFloG3A+AgLzRt6H7HlsVEzk3cvpkZYiJyQgyIq1KqMdQJ70o0fMzu7gcMXgi0H+402UWqRPvhwMU9wJ+fAd8/CWgN9bUY8gEQ2NauTy0IAs7nFCLtdsB7JqsAaZn5OJ9TCG0lga/KRY4WgZ63g14v8b9hDdwY+FKdIJZMsIaYiJwQA+LK3DgPfDseyDpp+Lr9cEMw7FF3dzKrc2LeAv4+YMjwA0CX8UCXcXZ5Kr1ewOHL/2DzsQwknbiGq7nFZs9zc1WIgW+LIE+0ul3u0LiBOxTcrIDqMGPJBLtMEJEzYkBcGY8AQFMAuPsbsorth0k9IrKUi8pQT/zlvwwbpAxeaNPL6/UCUtL/weZj15B0IgMZeWVBsJurAq2CPNHidsDb6natb6ivG3fponrJ240lE0TkvBgQV0blBYz5GvBuDHj4ST0aslbDZsCzhwC5i03KXHR6AQcv3sCW49fw64kMZOVrxPu8VC6IbheEwR1D0KulP9Sud9ktj6geKVtUx4CYiJwPA+KqhHSWegRkC4qa7Yql0wvYf+E6fj2egaS/MpBdPghWu+ChdkEY0jEED7T0h8qFQTCROWVt11gyQUTOhwExkRmlOj32XzBkgn/7KwM5BVrxPm+1Cwa0D8aQjiG4v4U/lC5c9EZ0N2KXCWaIicgJMSAmuq1Up8fe89ex5XgG/vtXBq4XlgXBvu6uiGkXjEEdg3FfBINgIkuxZIKInBkDYqrXSnR6/HHuOrYcu4b/nszAP0Vlv6wbuLtiYIdgDOoQgp4RfnBl+zMiq/ncXlTHLhNE5IwYEFO9oy3VY8+5nNtBcCZyy6169/NQIqZDMAZ3CMG9zRuyBzCRjYgZ4lslEAQBMvZyJyInwoCY6gVNqQ57zuZg87EMbD2ZYbJ9rL+nEgNvB8E9mjEIJrIH46I6vQAUanXwVPHXDxE5D85IVKedyy7ApzvP4dcTGSYf1QZ4qTDodjlEj2YNuSkGkZ2pXORQKuTQ6vTIu1XCgJiInApnJKqTzmYV4OPf0/Dz0avQC4ZjQd4qDOoQgkEdgtEtnEEwkSPJZDJ4qV1wvVCLvOISNIKb1EMiIhIxIKY6JS0zHx/9fha/HLsK4XYgHN02EFN6NUf38IbcJY5IQt5uroaAmL2IicjJMCCmOiE1Ix8f/Z6GLceviYHwQ+2C8Fz/lugQ6iPt4IgIgKGHNwDks/UaETkZrh6iWu10Rh6mr0lBzKL/YfMxQzAc0z4Im599AJ9N6MZgmOq8JUuWIDw8HGq1GlFRUThw4EC1Hvftt99CJpNh2LBh9h1gOeJudQyIicjJMENMtdLJq3n4KDkNSX9liMcGdQjGs/1bom2It4QjI3KcdevWIT4+HsuXL0dUVBQWLVqEmJgYpKamIjAwsNLHXbx4EbNmzUKvXr0cONryrddYMkFEzoUBMdUqJ67k4qPkNPz3ZCYAQCYDBncIwTP9W6BNMANhql8SExMxZcoUxMbGAgCWL1+OzZs3Y8WKFXj55ZfNPkan02H8+PFYsGABdu3ahZs3bzpsvN63N+fIu8UMMRE5FwbEVCsc/zsXi5PTsO1UWSD8cKdGeKZfC7QK8pJ4dESOp9VqkZKSgtmzZ4vH5HI5oqOjsXfv3kof9/rrryMwMBBPPvkkdu3aVeVzaDQaaDQa8eu8vLwajdmL2zcTkZNiQExO7djfN7F4WxqST2cBAOQyYGhnQyDcIpCBMNVfOTk50Ol0CAoKMjkeFBSE06dPm33M7t278cUXX+DIkSPVeo6EhAQsWLCgpkMVGRfVsWSCiJwNA2JySkcu38TibWewPTUbgCEQfqRLKOL6tUBEgKfEoyOqffLz8/H444/js88+g7+/f7UeM3v2bMTHx4tf5+XlISwszOoxGBfV5WuYISYi58KAmJxKyqV/sDg5Df87UxYID+sairgHW6A5A2Eikb+/PxQKBTIzM02OZ2ZmIjg4uML5586dw8WLFzF06FDxmF6vBwC4uLggNTUVERERJo9RqVRQqVQ2GzMX1RGRs2JATE7h4MUbWJychl1pOQAAhVyG4bcD4XB/D4lHR+R8lEolIiMjkZycLLZO0+v1SE5ORlxcXIXz27Rpg+PHj5scmzNnDvLz87F48eIaZX6rS1xUxxpiInIyDIhJUgcu3MDi5DPYc/Y6AMBFLsOIe0Ix48EWaOrHQJioKvHx8Zg4cSK6deuGHj16YNGiRSgsLBS7TkyYMAGhoaFISEiAWq1Ghw4dTB7v6+sLABWO20tZhpgBMRE5FwbEJIl9569j8bY07D1fFgg/GtkYMx5sgbCG7hKPjqh2GD16NLKzszF37lxkZGSgS5cuSEpKEhfapaenQy53nv2XyrpMsGSCiJwLA2JyqPPZBXjlx+PYd/4GAMBVIcOjkWGY3jeCgTCRFeLi4syWSADAjh07qnzsqlWrbD+gKpTvQywIAmQymUOfn4ioMgyIyWHyiksQu+pPXLpeBFeFDKO6hWH6gy0Q6usm9dCIyAGMJROlegHFJXq4KRUSj4iIyIABMTmEIAh4+YdjuHS9CKG+blj31L1o3IAZYaL6xF2pgEIug04vIK+4hAExETkN5ykuozrtyz8uYsvxDLgqZPhkXFcGw0T1kEwmK7c5BxfWEZHzYEBMdnf08k28teUUAGD2oLbo2qSBxCMiIqkYN+dg6zUiciZWBcRLlixBeHg41Go1oqKicODAgUrPLSkpweuvv46IiAio1Wp07twZSUlJJueEh4dDJpNVuM2YMUM8p2/fvhXunzZtmjXDJwfKLSrB9DWHUKITMLB9MGLvD5d6SEQkIS9u30xETsjigHjdunWIj4/HvHnzcOjQIXTu3BkxMTHIysoye/6cOXPw6aef4uOPP8bJkycxbdo0DB8+HIcPHxbP+fPPP3Ht2jXxtnXrVgDAY489ZnKtKVOmmJz33nvvWTp8ciBBEPDC+qO4cvMWmjR0x3uPdeKqcqJ6TuxFzAwxETkRiwPixMRETJkyBbGxsWjXrh2WL18Od3d3rFixwuz5q1evxiuvvILBgwejefPmePrppzF48GB88MEH4jkBAQEIDg4Wb7/88gsiIiLQp08fk2u5u7ubnOft7W3p8MmBPt91AdtOZUKpkGPp+HvEX4REVH95sxcxETkhiwJirVaLlJQUREdHl11ALkd0dDT27t1r9jEajQZqtdrkmJubG3bv3l3pc3z99dd44oknKmQT16xZA39/f3To0AGzZ89GUVFRpWPVaDTIy8szuZHjpFy6gXeTTgMA5g5thw6hPhKPiIicQflexEREzsKitms5OTnQ6XTiLkhGQUFBOH36tNnHxMTEIDExEb1790ZERASSk5OxYcMG6HQ6s+dv3LgRN2/exKRJk0yOjxs3Dk2bNkWjRo1w7NgxvPTSS0hNTcWGDRvMXichIQELFiyw5OWRjdwo1CJu7WGU6gUM7dwI46OaSD0kInISLJkgImdk9z7EixcvxpQpU9CmTRvIZDJEREQgNja20hKLL774AoMGDUKjRo1Mjk+dOlX8/44dOyIkJAT9+/fHuXPnEBERUeE6s2fPRnx8vPh1Xl4ewsLCbPSqqDJ6vYCZ647gWm4xmvt7IGFER9YNE5FI7DLBRXVE5EQsKpnw9/eHQqFAZmamyfHMzEwEBwebfUxAQAA2btyIwsJCXLp0CadPn4anpyeaN29e4dxLly5h27ZtmDx58l3HEhUVBQA4e/as2ftVKhW8vb1NbmR/y3aew84z2VC5yLFk/D3wVHHvFyIqI3aZYIaYiJyIRQGxUqlEZGQkkpOTxWN6vR7Jycno2bNnlY9Vq9UIDQ1FaWkpfvjhBzzyyCMVzlm5ciUCAwMxZMiQu47lyJEjAICQkBBLXgLZ0b7z1/HBf1MBAG880gFtQ/hHCBGZEksmWENMRE7E4vRdfHw8Jk6ciG7duqFHjx5YtGgRCgsLERsbCwCYMGECQkNDkZCQAADYv38/rly5gi5duuDKlSuYP38+9Ho9XnzxRZPr6vV6rFy5EhMnToSLi+mwzp07h7Vr12Lw4MHw8/PDsWPHMHPmTPTu3RudOnWy9rWTDWXna/DsN4ehF4AR94TisW6NpR4SETkhY8lEPrtMEJETsTggHj16NLKzszF37lxkZGSgS5cuSEpKEhfapaenQy4vSzwXFxdjzpw5OH/+PDw9PTF48GCsXr0avr6+Jtfdtm0b0tPT8cQTT1R4TqVSiW3btonBd1hYGEaOHIk5c+ZYOnyyA51ewPPrDiMrX4OWgZ54c1gH1g0TkVneLJkgIickEwRBkHoQjpCXlwcfHx/k5uayntjGFm07g0Xb0uDmqsDPz9yPFoFeUg+JSBJ1fZ6xxes7dS0Pgxbvgr+nCgfnRN/9AURUr0g1j1q1dTOR0e60HCxOTgMAvD2iA4NhIqqS2GWCGWIiciIMiMlqmXnFeH7dYQgCMLZHGIZ3Zd0wEVXN2GVCW6pHcYn5fvRERI7GgJisUqrT45lvDiOnQIu2Id6YN7S91EMiolrAU+kC4xIDZomJyFkwICarJG49gwMXbsBT5YIl47pC7aqQekhEVAvI5TJ43e5Pzk4TROQsGBCTxbanZmHpjnMAgHdGdkTzAE+JR0REtUnZbnXMEBORc2BATBa5evMWZq47AgCY0LMpHu7UqOoHEBHdQdycgxliInISDIip2kp0esStPYSbRSXoGOqDV4e0lXpIRFQLebvd7kXMDDEROQkGxFRt7yWdxqH0m/BSu2DJuHugcmHdMBFZzkvN1mtE5FwYEFO1/PevDHy26wIA4P1HO6OJn7vEIyKi2kosmbjFkgkicg4MiOmuLt8owqz1RwEATz7QDAM7BEs8IiKqzYwlE/nMEBORk2BATFXSlOowY+0h5BWXomsTX7w0sI3UQyKiWs6bJRNE5GQYEFOVEracxrG/c+Hr7opPxt0DpQu/ZYioZsrarrFkgoicA6MbqtTmY9ew6o+LAIDEUZ0R6usm7YCIqE4wbt/MDDEROQsGxGTWxZxCvPTDMQDAtD4R6NcmSOIREVFdUbaojgExETkHBsRUQXGJDtPXHEKBphQ9whti1oBWUg+JiOoQsQ8xN+YgIifBgJgqeP2Xkzh5LQ9+Hkp8NLYrXBT8NiEi2zFmiNllgoicBSMdMvHTkStYuz8dMhnw4eguCPZRSz0kIqpjfLiojoicDANiEp3NKsDsDccBAM882AK9WwVIPCIiqouMGeJbJTpoS/USj4aIiAEx3XZLq8P0NSko0urQs7kfnotm3TAR2Yfn7S4TAMsmiMg5MCAmAMBrP53AmcwC+HuqsHhsFyjkMqmHRER1lEIug6eKC+uIyHkwICZ8d/Ayvk/5G3IZ8NHYLgj0Yt0wEdmXt7EXMVuvEZETYEBcz6Vm5GPuTycAAPEPtcJ9Ef4Sj4iI6gPjbnX5zBATkRNwufspVFfkFZfgbFYB0jLzkZZZgLSsAhy5fBPFJXr0bhWA6X1bSD1EIqonxM05WENMRE6AAXEdlHurBGez8nEms+B24GsIgDPyis2e39TPHR+O6gw564aJyEHEzTlYMkFEToABcS12s0iLtCxD0HsmMx9nswz/zcrXVPqYIG8VWgV5oUWgJ1oGeqFlkCc6hvpA7apw4MiJqL7zYoaYiJwIA+Ja4Eah1lDmYCx3yDKUO2RXEfiG+KjRMsgLLQM9DbfbQbCxIT4RkZTKFtWxhpiIpMeA2IkIgoCUS//g1LU8Q7lDliHrm1OgrfQxob5uaBl0O+i9nfFtEegpZl+IiJyRcVEdM8RE5AwYEDuRr/ZewrxNf5m9L6yhmyHgDTQEvK2CvBAR6Cn28iQiqk2Mi+rYZYKInAGjKSeSmpkPAGgd5IUH2wSiVZAh6xsR6AF3Jf+piKju4KI6InImjLKciPEXw6juYXjygWYSj4aIyH7Ydo2InAk35nAixi1MjYtNiIjqKrHLBBfVEZETYEDsRIwZYm92giCiOk4smWCGmIicAANiJ5J/+xeDNztEEFEdJ5ZMsIaYiJwAA2InIpZMuLFkgojqNuMnYYVaHUp1eolHQ0T1HQNiJyKWTDBDTER1nFe5tRIFGtYRE5G0GBA7ieISHTSlhiwJa4iJqK5zVcjhrjRsGc+FdUQkNQbETsLYnF4mA7y42QYR1QPGLDEX1hGR1BgQOwnjLwRPlQvkcpnEoyEisj8urCMiZ8GA2EmwfpiI6htjeVget28mIokxIHYSZR0mGBATUf3gzZIJInISDIidRFmGmPXDRFQ/iBlilkwQkcQYEDsJY4aEGWIiqi/EGmKWTBCRxBgQOwlj2yHWEBNRfSF2mWCGmIgkxoDYSZRliFkyQUT1Q9miOgbERCQtBsROgl0miKi+Mc53+SyZICKJMSB2EuwyQUT1jfETMZZMEJHUGBA7CXaZIKL6hovqiMhZMCB2EuwyQUT1DduuEZGzYEDsJFhDTET1jRc35iAiJ8GA2EmU1RCzZIKI6gdjAqBAUwq9XpB4NERUnzEgdhLMEBNRfWPMEAsCUKBlHTERSYcBsRMoLtFBU6oHwICYiOoPtasCKhfDryHWERORlBgQO4HyPTg92WWCiOqRsoV1zBATkXQYEDsB44ISL5ULFHKZxKMhInIcby6sIyInwIDYCYj1w2y5RkT1jJeardeISHoMiJ2AscOEF8sliKieEUsmuDkHEUmIAbETYIaYiOorY8lEPksmiEhCDIidgLhLHTtMEFE9w0V1ROQMrAqIlyxZgvDwcKjVakRFReHAgQOVnltSUoLXX38dERERUKvV6Ny5M5KSkkzOmT9/PmQymcmtTZs2JucUFxdjxowZ8PPzg6enJ0aOHInMzExrhu90jL8IuCkHEdU3xkQAF9URkZQsDojXrVuH+Ph4zJs3D4cOHULnzp0RExODrKwss+fPmTMHn376KT7++GOcPHkS06ZNw/Dhw3H48GGT89q3b49r166Jt927d5vcP3PmTPz8889Yv349du7ciatXr2LEiBGWDt8pMUNMRPWVMRHARXVEJCWLA+LExERMmTIFsbGxaNeuHZYvXw53d3esWLHC7PmrV6/GK6+8gsGDB6N58+Z4+umnMXjwYHzwwQcm57m4uCA4OFi8+fv7i/fl5ubiiy++QGJiIvr164fIyEisXLkSf/zxB/bt22fpS3A6xto51hATUX3jxQwxETkBiwJirVaLlJQUREdHl11ALkd0dDT27t1r9jEajQZqtdrkmJubW4UMcFpaGho1aoTmzZtj/PjxSE9PF+9LSUlBSUmJyfO2adMGTZo0qfJ58/LyTG7OSiyZYJcJIqpnxD7ErCEmIglZFBDn5ORAp9MhKCjI5HhQUBAyMjLMPiYmJgaJiYlIS0uDXq/H1q1bsWHDBly7dk08JyoqCqtWrUJSUhKWLVuGCxcuoFevXsjPzwcAZGRkQKlUwtfXt9rPm5CQAB8fH/EWFhZmyUt1qDxmiImonjLOe/kaZoiJSDp27zKxePFitGzZEm3atIFSqURcXBxiY2Mhl5c99aBBg/DYY4+hU6dOiImJwZYtW3Dz5k189913Vj/v7NmzkZubK94uX75si5djF2LbNdYQE1E9Iy6qY4aYiCRkUUDs7+8PhUJRobtDZmYmgoODzT4mICAAGzduRGFhIS5duoTTp0/D09MTzZs3r/R5fH190apVK5w9exYAEBwcDK1Wi5s3b1b7eVUqFby9vU1uzsrYkJ5dJoiovvFx49bNRCQ9iwJipVKJyMhIJCcni8f0ej2Sk5PRs2fPKh+rVqsRGhqK0tJS/PDDD3jkkUcqPbegoADnzp1DSEgIACAyMhKurq4mz5uamor09PS7Pm9twAwxEdVX3uW2bhYEQeLREFF9ZXFKMj4+HhMnTkS3bt3Qo0cPLFq0CIWFhYiNjQUATJgwAaGhoUhISAAA7N+/H1euXEGXLl1w5coVzJ8/H3q9Hi+++KJ4zVmzZmHo0KFo2rQprl69innz5kGhUGDs2LEAAB8fHzz55JOIj49Hw4YN4e3tjWeeeQY9e/bEvffea4v3QVLGzIgPa4iJqJ4xdpnQC0ChVgdPFT8pIyLHs3jmGT16NLKzszF37lxkZGSgS5cuSEpKEhfapaenm9QHFxcXY86cOTh//jw8PT0xePBgrF692mSB3N9//42xY8fi+vXrCAgIwAMPPIB9+/YhICBAPOfDDz+EXC7HyJEjodFoEBMTg6VLl9bgpTsHTakOxSV6AMwQE1H9o3aVw1UhQ4lOQN6tEgbERCQJmVBPPqPKy8uDj48PcnNznaqeOKdAg25vbgMAnHt7MBRymcQjIiJrOes8Yyv2en2Rb2zF9UItfnu+N1oHe9nsukRU+0g1j9q9ywRVzVg/7KVyYTBMRPWSsfUaF9YRkVQYEEusrMMEyyWIqH4q25yDATERSYMBscTEDDF3qSOieooZYiKSGgNiiXGXOiKq77y4fTMRSYwBscSMvwDYYYKI6qvyvYiJiKTAgFhiZRlilkwQUf1k/IQsX8MMMRFJgwGxxLhLHRHVd1xUR0RSY0AsMdYQE1F9x0V1RCQ1BsQSK6shZskEEdVPXFRHRFJjQCwxZoiJqL4TF9UxQ0xEEmFALDHWEBNRfSeWTLCGmIgkwoBYYuJOdSyZIKJ6ypgQyC9myQQRSYMBscTEDDFLJoionjK2ncwrLoEgCBKPhojqIwbEEhNriFkyQUT1lHH+K9EJKC7RSzwaIqqPGBBLSFOqEyd/bsxBRPWVu1IBhVwGgAvriEgaDIglVL5ezlPFgJiI6ieZTFau9RoDYiJyPAbEEjJO/J4qF7go+E9BRPUXW68RkZQYhUmIHSaIiAzKFtax0wQROR4DYgmxwwQRkYGYIWbJBBFJgAGxhPLFDDEDYiKq38pKJpghJiLHY0AsobJtm1kyQUT1GxfVEZGUGBBLiNs2ExEZiNs3c1EdEUmAAbGEyjLEDIiJqH4rqyFmyQQROR4DYgkZJ352mSCi+s5YOpbPDDERSYABsYSYISYiMuCiOiKSEgNiCbGGmIjIQKwh5qI6IpIAA2IJiRtzsMsEEdVzYpcJlkwQkQQYEEuIGWIiIgMuqiMiKTEglhBriImIDMq2bmaGmIgcjwGxhMq6TDAgJqL6zZgY0JbqUVyik3g0RFTfMCCWiLZUj1u3J33WEBNRfeepdIFMZvj/fHaaICIHY0AskfK9Nj1VDIiJqH6Ty2XwUrFsgoikwYBYIsYOE54qF7go+M9AROSlZus1IpIGIzGJlHWYYHaYiAgo14uYJRNE5GAMiCXCDhNERKaMCQJmiInI0RgQS4QdJoiITBkTBFxUR0SOxoBYImUZYpZMEBEB5Tbn4KI6InIwBsQS4S51RESmxM05WDJBRA7GgFgirCEmIjLlxQwxEUmEAbFEymqIWTJBRASUX1THGmIiciwGxBIxZkC8WDJBRDWwZMkShIeHQ61WIyoqCgcOHKj03A0bNqBbt27w9fWFh4cHunTpgtWrVztwtFUra7vGDDERORYDYomINcRcVEdEVlq3bh3i4+Mxb948HDp0CJ07d0ZMTAyysrLMnt+wYUO8+uqr2Lt3L44dO4bY2FjExsbit99+c/DIzTOuqWCXCSJyNAbEEjE2nueiOiKyVmJiIqZMmYLY2Fi0a9cOy5cvh7u7O1asWGH2/L59+2L48OFo27YtIiIi8Nxzz6FTp07YvXu3g0duHhfVEZFUGBBLpCxDzICYiCyn1WqRkpKC6Oho8ZhcLkd0dDT27t1718cLgoDk5GSkpqaid+/eZs/RaDTIy8szudkT264RkVQYEEtE7DLBDDERWSEnJwc6nQ5BQUEmx4OCgpCRkVHp43Jzc+Hp6QmlUokhQ4bg448/xkMPPWT23ISEBPj4+Ii3sLAwm76GO4kBMRfVEZGDMSCWiNhlgjXERORAXl5eOHLkCP7880+89dZbiI+Px44dO8yeO3v2bOTm5oq3y5cv23VsxvnwVokO2lK9XZ+LiKg8RmMS0JbqcatEB4AZYiKyjr+/PxQKBTIzM02OZ2ZmIjg4uNLHyeVytGjRAgDQpUsXnDp1CgkJCejbt2+Fc1UqFVQqlU3HXRVPVdmvpPziEvh5Ou65iah+Y4ZYAvnl6uO82IeYiKygVCoRGRmJ5ORk8Zher0dycjJ69uxZ7evo9XpoNBp7DNFiLgq5GBSz0wQRORKjMQkYJ3oPpQIuCv5NQkTWiY+Px8SJE9GtWzf06NEDixYtQmFhIWJjYwEAEyZMQGhoKBISEgAYaoK7deuGiIgIaDQabNmyBatXr8ayZcukfBkmvNUuKNCUcmEdETkUA2IJcNtmIrKF0aNHIzs7G3PnzkVGRga6dOmCpKQkcaFdeno65PKyP7oLCwsxffp0/P3333Bzc0ObNm3w9ddfY/To0VK9hAq83VxxNbeYC+uIyKEYEEugbNtmBsREVDNxcXGIi4sze9+di+XefPNNvPnmmw4YlfWMZWTMEBORI/HzegmUZYj59wgRUXllrdcYEBOR4zAgloC4KQczxEREJoylZMwQE5EjMSCWAGuIiYjM81azywQROR4DYgmU1RCzZIKIqDwxQ8ySCSJyIAbEEmCGmIjIPLGGmBliInIgBsQSYA0xEZF5YpcJZoiJyIEYEEvAmPlglwkiIlNcVEdEUrAqIF6yZAnCw8OhVqsRFRWFAwcOVHpuSUkJXn/9dURERECtVqNz585ISkoyOSchIQHdu3eHl5cXAgMDMWzYMKSmppqc07dvX8hkMpPbtGnTrBm+5JghJiIyr6ztGksmiMhxLA6I161bh/j4eMybNw+HDh1C586dERMTg6ysLLPnz5kzB59++ik+/vhjnDx5EtOmTcPw4cNx+PBh8ZydO3dixowZ2LdvH7Zu3YqSkhIMGDAAhYWFJteaMmUKrl27Jt7ee+89S4fvFFhDTERknvGTs3xmiInIgSwOiBMTEzFlyhTExsaiXbt2WL58Odzd3bFixQqz569evRqvvPIKBg8ejObNm+Ppp5/G4MGD8cEHH4jnJCUlYdKkSWjfvj06d+6MVatWIT09HSkpKSbXcnd3R3BwsHjz9va2dPhOgTvVERGZx0V1RCQFiwJirVaLlJQUREdHl11ALkd0dDT27t1r9jEajQZqtdrkmJubG3bv3l3p8+Tm5gIAGjZsaHJ8zZo18Pf3R4cOHTB79mwUFRVVeg2NRoO8vDyTm7PgTnVEROYZPzkr0JSiVKeXeDREVF9YFJHl5ORAp9MhKCjI5HhQUBBOnz5t9jExMTFITExE7969ERERgeTkZGzYsAE6nc7s+Xq9Hs8//zzuv/9+dOjQQTw+btw4NG3aFI0aNcKxY8fw0ksvITU1FRs2bDB7nYSEBCxYsMCSl+cQJTo9irSG184MMRGRKa9y/dkLNKXwdVdKOBoiqi/snqJcvHgxpkyZgjZt2kAmkyEiIgKxsbGVlljMmDEDJ06cqJBBnjp1qvj/HTt2REhICPr3749z584hIiKiwnVmz56N+Ph48eu8vDyEhYXZ6FVZr/zuS17cmIOIyISrQg43VwVuleiQd4sBMRE5hkUlE/7+/lAoFMjMzDQ5npmZieDgYLOPCQgIwMaNG1FYWIhLly7h9OnT8PT0RPPmzSucGxcXh19++QXbt29H48aNqxxLVFQUAODs2bNm71epVPD29ja5OQNjhwkPpQIuCna9IyK6k7GcjK3XiMhRLIrIlEolIiMjkZycLB7T6/VITk5Gz549q3ysWq1GaGgoSktL8cMPP+CRRx4R7xMEAXFxcfjxxx/x+++/o1mzZncdy5EjRwAAISEhlrwEybHDBBFR1coW1jEgJiLHsPgz+/j4eEycOBHdunVDjx49sGjRIhQWFiI2NhYAMGHCBISGhiIhIQEAsH//fly5cgVdunTBlStXMH/+fOj1erz44oviNWfMmIG1a9fip59+gpeXFzIyMgAAPj4+cHNzw7lz57B27VoMHjwYfn5+OHbsGGbOnInevXujU6dOtngfHIYdJoiIqiZuzsFexETkIBYHxKNHj0Z2djbmzp2LjIwMdOnSBUlJSeJCu/T0dMjlZYnn4uJizJkzB+fPn4enpycGDx6M1atXw9fXVzxn2bJlAAybb5S3cuVKTJo0CUqlEtu2bROD77CwMIwcORJz5syx4iVLix0miIiq5q1myQQROZZVUVlcXBzi4uLM3rdjxw6Tr/v06YOTJ09WeT1BEKq8PywsDDt37rRojM7KWEPsxQwxEZFZXuJudQyIicgxuKrLwcQMMTtMEBGZVbaojiUTROQYDIgdTKwh5qI6IiKzvJkhJiIHY0DsYGUZYgbERETmGBMG+cwQE5GDMCB2MGPGg4vqiIjMY9s1InI0BsQOZqyJY4aYiMg84y6eLJkgIkdhQOxgZRliBsREROaIfYhZMkFEDsKA2MFYQ0xEVDVvZoiJyMEYEDuYcZEIa4iJiMwryxAzICYix2BA7GBiyQQzxEREZhnnxwJNKfT6qjduIiKyBQbEDlSq06NQqwPAGmIiosoYF9UJAlCgZR0xEdkfA2IHKt9T04s71RERmaV2VUDpYvj1xDpiInIEBsQOZKyHc1cq4KrgW09EVJmy3eqYISYi+2NU5kDits2sHyYiqpJx4TEX1hGRIzAgdiCx5Ro7TBARVaksQ8yAmIjsjwGxA7HDBBFR9RgXHudzcw4icgAGxA5UliFmQExEVBVxcw6WTBCRAzAgdqCyGmKWTBARVcWLi+qIyIEYEDsQM8RERNXDRXVE5EgMiB2INcRERNXDRXVE5EgMiB0o7/biEHaZICKqmvGTNGaIicgRGBA7EDPERETVY1xrwS4TROQIDIgdiDXERETVwwwxETkSA2IH4k51RETVI7ZdY5cJInIABsQOxJ3qiIiqR1xUxwwxETkAA2IHYg0xEVH1iCUTt0ogCILEoyGiuo4BsYOU6vQo1OoAsIaYiOhujIkDvQBx7iQishcGxA5SfqW0F3eqIyKqktpVDleFDACQz7IJIrIzBsQOYqyDc1cq4Krg205EVBWZTFZucw4urCMi+2Jk5iDGCZ3ZYSKi6jHOl1xYR0T2xoDYQcQOE1xQR0RULeUX1hER2RMDYgcRO0xwQR0RUbWw9RoROQoDYgcpyxCzZIKIqDqMPdtZQ0xE9saA2EHEXeqYISYiqhZjhphdJojI3hgQOwhriImILCPWEBczQ0xE9sWA2EHKaohZMkFEVB1eKmPJBDPERGRfDIgdxLgxBzPERETVU5YhZkBMRPbFgNhBxJIJ1hATEVULF9URkaMwIHYQcVEdM8RERNXCtmtE5CgMiB2kLEPMGmIiouowfqKWz0V1RGRnDIgdRFxUxwwxEVG1iBliLqojIjtjQOwgxrZBrCEmIqoer9sbGeUVl0AQBIlHQ0R1GQNiByjV6VGgMdYQs2SCiKg6jAmEEp2A4hK9xKMhorqMAbEDGINhAPBiyQQRUbV4KBWQywz/z4V1RGRPDIgdwNhhws1VAaUL33IiouqQyWRlvYhZR0xEdsTozAHYYYKIyDplrdfYaYKI7IcBsQOwwwQRkXXEzTlYMkFEdsSA2AG4Sx0RkXW8VCyZICL7Y0DsAGW71LFkgojIEmUZYpZMEJH9MCB2AGaIiYisw805iMgRGBA7AGuIiYisI3aZYA0xEdkRA2IHKNuljiUTRESWMCYS8lkyQUR2xIDYAZghJiKyjlhDzJIJIrIjBsQOwBpiIiLreLEPMRE5AANiByjrMsGAmIjIEsbuPMwQE5E9MSB2AO5UR0RkHS6qIyJHYEDsAKwhJiKyTlnbNZZMEJH9MCB2gLIuEwyIiYgsYfxkLZ8ZYiKyIwbEdlaq06NAYwiIvbhTHRGRRYyJBE2pHsUlOolHQ0R1lVUB8ZIlSxAeHg61Wo2oqCgcOHCg0nNLSkrw+uuvIyIiAmq1Gp07d0ZSUpLF1ywuLsaMGTPg5+cHT09PjBw5EpmZmdYM36GMwTDAgJiIyFKeShfIZIb/Zy9iIrIXiwPidevWIT4+HvPmzcOhQ4fQuXNnxMTEICsry+z5c+bMwaeffoqPP/4YJ0+exLRp0zB8+HAcPnzYomvOnDkTP//8M9avX4+dO3fi6tWrGDFihBUv2bGMdW9qVzlULgqJR0NEVLvI5TJ4qm53mmDZBBHZicUBcWJiIqZMmYLY2Fi0a9cOy5cvh7u7O1asWGH2/NWrV+OVV17B4MGD0bx5czz99NMYPHgwPvjgg2pfMzc3F1988QUSExPRr18/REZGYuXKlfjjjz+wb98+K1+6Y4gdJrigjojIKmUL6xgQE5F9WBQQa7VapKSkIDo6uuwCcjmio6Oxd+9es4/RaDRQq9Umx9zc3LB79+5qXzMlJQUlJSUm57Rp0wZNmjSp8nnz8vJMblIQO0xwQR0RkVXKWq+xZIKI7MOigDgnJwc6nQ5BQUEmx4OCgpCRkWH2MTExMUhMTERaWhr0ej22bt2KDRs24Nq1a9W+ZkZGBpRKJXx9fav9vAkJCfDx8RFvYWFhlrxUmynLELN+mIjIGsb5k50miMhe7N5lYvHixWjZsiXatGkDpVKJuLg4xMbGQi6371PPnj0bubm54u3y5ct2fb7KsOUaEVHNeLEXMRHZmUVRqb+/PxQKRYXuDpmZmQgODjb7mICAAGzcuBGFhYW4dOkSTp8+DU9PTzRv3rza1wwODoZWq8XNmzer/bwqlQre3t4mNylwUw4iopox9iLmojoisheLAmKlUonIyEgkJyeLx/R6PZKTk9GzZ88qH6tWqxEaGorS0lL88MMPeOSRR6p9zcjISLi6upqck5qaivT09Ls+r9TKMsQsmSAisgYX1RGRvVkcpcXHx2PixIno1q0bevTogUWLFqGwsBCxsbEAgAkTJiA0NBQJCQkAgP379+PKlSvo0qULrly5gvnz50Ov1+PFF1+s9jV9fHzw5JNPIj4+Hg0bNoS3tzeeeeYZ9OzZE/fee68t3ge7YYaYiKhmyhbVMSAmIvuwOCAePXo0srOzMXfuXGRkZKBLly5ISkoSF8Wlp6eb1AcXFxdjzpw5OH/+PDw9PTF48GCsXr3aZIHc3a4JAB9++CHkcjlGjhwJjUaDmJgYLF26tAYv3THERXWsISYiskrZojrWEBORfcgEQRCkHoQj5OXlwcfHB7m5uQ6tJ5785UFsO5WJt4d3xLioJg57XiJyPKnmGUeR6vV9d/AyXvz+GB5sHYCVsT0c9rxE5HhSzTN27zJR35VliFlDTERkDWOGmH2IicheGBDbGWuIiYhqhovqiMjeGBDbWT77EBMR1QgX1RGRvTEgtrOyDDFLJoiIrOHNjTmIyM4YENuRTi8gX8MMMRFRTRjXYNwq0aFEp5d4NERUFzEgtqOCcgtAvJghJiKyiqeqbP5k6zUisgcGxHZkrHdTu8qhclFIPBoiotrJRSGHh9Iwh3JhHRHZAwNiO8plhwkiIpvgwjoisicGxHbEXeqIiGyDC+uIyJ4YENuRceJmhwkiopoxLqxjhpiI7IEBsR0xQ0xEZBvGDHE+A2IisgMGxHbEXeqIiGxDrCFmyQQR2QEDYjvKE3epY8kEEVFNGFtXsmSCiOyBAbEdMUNMRGQbZYvqGBATke0xILYj1hATEdlG2aI6lkwQke0xILajsi4TDIiJiGqCGWIisicGxHZkzBBz22YiopoxftLGrZuJyB4YENuRWEPMkgkiohoRM8RcVEdEdsCA2I6MmQxuzEFEVDNilwmWTBCRHTAgtiNmiImIbEPsQ8ySCSKyAwbEdqLTC8jXcFEdEZEtGD9pK9CUolSnl3g0RFTXMCC2k4JyWQwuqiMiqhmvcomFAg2zxERkWwyI7cS48EPlIofaVSHxaIiIajelixxut+dSdpogIltjQGwn3JSDiMi2jJtz5HJhHRHZGANiOynblIPlEkREtuDF1mtEZCcMiO2EGWIiItvyFluvsWSCiGyLAbGdiC3X2GGCiMgmylqvMUNMRLbFgNhOjL0ymSEmIrINcbc61hATkY0xILaTsgwxa4iJiGzBuKiOXSaIyNYYENsJa4iJiGzLm4vqiMhOGBDbSVmXCQbERES2IHaZ4KI6IrIxBsR2UpYhZskEEZEtGOdTZoiJyNYYENsJu0wQEdkWF9URkb0wILYTdpkgIrKtsrZrLJkgIttiQGwn7DJBRGRbxvk0nyUTRGRjDIjthF0miIhsS8wQs2SCiGyMAbEd6PUCCjTsMkFEZEtexgyxphR6vSDxaIioLmFAbAf5mlIIt+dqL5ZMEJEdLVmyBOHh4VCr1YiKisKBAwcqPfezzz5Dr1690KBBAzRo0ADR0dFVnu9sjAkGQQAKtKwjJiLbYUBsB8aP81QucqhdFRKPhojqqnXr1iE+Ph7z5s3DoUOH0LlzZ8TExCArK8vs+Tt27MDYsWOxfft27N27F2FhYRgwYACuXLni4JFbR+2qgNLF8GuLZRNEZEsMiO2A9cNE5AiJiYmYMmUKYmNj0a5dOyxfvhzu7u5YsWKF2fPXrFmD6dOno0uXLmjTpg0+//xz6PV6JCcnO3jk1vPm5hxEZAcMiO2gbJc6lksQkX1otVqkpKQgOjpaPCaXyxEdHY29e/dW6xpFRUUoKSlBw4YNzd6v0WiQl5dncpOacXMOdpogIltiQGwHzBATkb3l5ORAp9MhKCjI5HhQUBAyMjKqdY2XXnoJjRo1Mgmqy0tISICPj494CwsLq/G4a0rMELMXMRHZEANiO+AudUTk7N555x18++23+PHHH6FWq82eM3v2bOTm5oq3y5cvO3iUFRkXKrOGmIhsiZ/p2wF3qSMie/P394dCoUBmZqbJ8czMTAQHB1f52IULF+Kdd97Btm3b0KlTp0rPU6lUUKlUNhmvrZTtVseAmIhshxliO+AudURkb0qlEpGRkSYL4owL5Hr27Fnp49577z288cYbSEpKQrdu3RwxVJviojoisgdGbHZgzFx4sWSCiOwoPj4eEydORLdu3dCjRw8sWrQIhYWFiI2NBQBMmDABoaGhSEhIAAC8++67mDt3LtauXYvw8HCx1tjT0xOenp6SvQ5LGBfVMUNMRLbEgNgOxC4Tbnx7ich+Ro8ejezsbMydOxcZGRno0qULkpKSxIV26enpkMvLPghctmwZtFotHn30UZPrzJs3D/Pnz3fk0K1mzBCzywQR2RIjNjsQu0wwQ0xEdhYXF4e4uDiz9+3YscPk64sXL9p/QHYm1hCzZIKIbIg1xHYg1hBzUR0RkU0Z12awZIKIbIkBsR2IXSa4qI6IyKbK+hAzICYi22FAbAfMEBMR2Ye4qI4lE0RkQwyI7SCfNcRERHbBDDER2QMDYhvT6wXka9hlgojIHoyfvOUXl0IQBIlHQ0R1BQNiGyvQlsI4RzNDTERkW8Z5VacXUKTVSTwaIqorGBDbmLF+WOkih9pVIfFoiIjqFrWrHC5yGQCWTRCR7TAgtjFxUw5mh4mIbE4mk7EXMRHZHANiGxM35WD9MBGRXbAXMRHZGgNiGxNbrjFDTERkF2UZYgbERGQbVgXES5YsQXh4ONRqNaKionDgwIEqz1+0aBFat24NNzc3hIWFYebMmSguLhbvDw8Ph0wmq3CbMWOGeE7fvn0r3D9t2jRrhm9X4qYc7EFMRGQXxoRDfjFLJojINiz+XH/dunWIj4/H8uXLERUVhUWLFiEmJgapqakIDAyscP7atWvx8ssvY8WKFbjvvvtw5swZTJo0CTKZDImJiQCAP//8Ezpd2WrhEydO4KGHHsJjjz1mcq0pU6bg9ddfF792d3e3dPh2V5YhZskEEZE9eLFkgohszOKoLTExEVOmTEFsbCwAYPny5di8eTNWrFiBl19+ucL5f/zxB+6//36MGzcOgCEbPHbsWOzfv188JyAgwOQx77zzDiIiItCnTx+T4+7u7ggODrZ0yA5VVkPMDDERkT2Im3OwZIKIbMSikgmtVouUlBRER0eXXUAuR3R0NPbu3Wv2Mffddx9SUlLEsorz589jy5YtGDx4cKXP8fXXX+OJJ56ATCYzuW/NmjXw9/dHhw4dMHv2bBQVFVU6Vo1Gg7y8PJObI7DLBBGRfYnbN7NkgohsxKIMcU5ODnQ6HYKCgkyOBwUF4fTp02YfM27cOOTk5OCBBx6AIAgoLS3FtGnT8Morr5g9f+PGjbh58yYmTZpU4TpNmzZFo0aNcOzYMbz00ktITU3Fhg0bzF4nISEBCxYssOTl2QS7TBAR2RczxERka3aP2nbs2IG3334bS5cuRVRUFM6ePYvnnnsOb7zxBl577bUK53/xxRcYNGgQGjVqZHJ86tSp4v937NgRISEh6N+/P86dO4eIiIgK15k9ezbi4+PFr/Py8hAWFmbDV2Yeu0wQEdmX2GWCNcREZCMWBcT+/v5QKBTIzMw0OZ6ZmVlpbe9rr72Gxx9/HJMnTwZgCGYLCwsxdepUvPrqq5DLy6o2Ll26hG3btlWa9S0vKioKAHD27FmzAbFKpYJKpar2a7MV1hATEdmX8RM4dpkgIluxqIZYqVQiMjISycnJ4jG9Xo/k5GT07NnT7GOKiopMgl4AUCgMWxoLgmByfOXKlQgMDMSQIUPuOpYjR44AAEJCQix5CXZXVkPMkgkiInvwUrFkgohsy+KoLT4+HhMnTkS3bt3Qo0cPLFq0CIWFhWLXiQkTJiA0NBQJCQkAgKFDhyIxMRFdu3YVSyZee+01DB06VAyMAUNgvXLlSkycOBEuLqbDOnfuHNauXYvBgwfDz88Px44dw8yZM9G7d2906tSpJq/f5pghJiKyr7KSCWaIicg2LA6IR48ejezsbMydOxcZGRno0qULkpKSxIV26enpJhnhOXPmQCaTYc6cObhy5QoCAgIwdOhQvPXWWybX3bZtG9LT0/HEE09UeE6lUolt27aJwXdYWBhGjhyJOXPmWDp8u2MNMRGRfYldJpghJiIbkQl31i3UUXl5efDx8UFubi68vb3t8hx6vYCIV7dAEIADr/ZHoJfaLs9DRM7JEfOMlJzl9V29eQv3vfM7XBUynHlzUIUWnURUe0k1z1i1dTOZV6AthfHPC2aIiYjsw1gyUaITUFyil3g0RFQXMCC2IePHd0oXOdSuirucTURE1vBQKiC/nRTOZ+s1IrIBBsQ2xF3qiIjsTyaTwUvNXsREZDsMiG2Iu9QRETmGcZ7NvcVOE0RUcwyIbYgdJoiIHMObGWIisiEGxDZk7InpxU05iIjsSgyI2XqNiGyAAbENiRlibspBRGRXYi9ibs5BRDbAgNiGxBpilkwQEdmVcZ5llwkisgUGxDYkdpngojoiIrsSu0xwUR0R2QADYhtihpiIyDHKSiaYISaimmNAbEP5xawhJiJyBC6qIyJbYkBsQ2Ubc7BkgojInoyJBy6qIyJbYEBsQ3nMEBMROYQx8cAMMRHZAgNiG2INMRGRYxgTD+wyQUS2wIDYhowlEz7sMkFEZFfGDZBYMkFEtsCA2Eb0eqFsUR0zxEREdsVFdURkSwyIbaRQWwq9YPh/1hATEdmXcZ7VlOpRXKKTeDREVNsxILYR48d2SoUcKhe+rURE9uSlcoFMZvj/fJZNEFENMXKzEePHdt5uLpAZZ2kiIrILuVwGTxU35yAi22BAbCNiQMz6YSIihzDOt8wQE1FNMSC2EWPJhBfrh4mIHMKLvYiJyEYYENtIWYaYLdeIiByhbLc6BsREVDMMiG2Eu9QRETlWWes1lkwQUc0wILYR44TMGmIiIsfwduOiOiKyDQbENlKWIWbJBBGRI3BzDiKyFQbENsIuE0REjmUsUWOXCSKqKQbENsIaYiIixzIuYmbJBBHVFANiGymrIWbJBBGRI7BkgohshQGxjTBDTETkWGWL6lgyQUQ1w4DYRsSAmDXEREQOwQwxEdkKA2IbMZZM+LDLBBGRQ3BjDiKyFQbENqDXC8hnhpiIyKGM8y27TBBRTTEgtoFCbSn0guH/WUNMROQYXrcXMRdpdSjR6SUeDRHVZgyIbcC4oMNVIYPKhW8pEZEjeJXr6sMsMRHVBKM3Gyi/KYdMJpN4NERE9YOLQg4PpQIAF9YRUc0wILYBMSBmuQQRkUNxYR0R2QIDYhswlkxwUw4iIscqa73Gkgkish4DYhtghpiISBrGzTnymSEmohpgQGwDbLlGRCQNLzVLJoio5hgQ24BYMsFNOYiIHMpYqsaSCSKqCQbENlC+ywQRETkOF9URkS0wILYB40TMGmIiIscqW1THgJiIrMeA2AaMH9WxywQRkWMZS9XyuDEHEdUAA2IbYIaYiEgaxgwxu0wQUU0wILaBPHaZICKShBf7EBORDTAgtgGxZIJdJoiIHKqsZIIZYiKyHgNiG2CGmIhIGlxUR0S2wIC4hgRB4E51REQSKWu7xpIJIrIeA+IaKtTqoBcM/88MMRGRYxm7+xRoSqEzTsZERBZiQFxDxuywq0IGtSvfTiIiR/Iql4goYJaYiKzECK6GytcPy2QyiUdDRFS/KF3kYjKCC+uIyFoMiGuorMMEyyWIiKRgLFfL5cI6IrISA+IaEhfUcZc6IiJJlC2sY0BMRNZhQFxD3KWOiEhaxoQEN+cgImsxIK6hsgwxA2IiIikwQ0xENcWAuIaMvS+5Sx0RkTSMnSby2WWCiKzEgLiGmCEmIpJWWckEM8REZB0GxDXEGmIiImmxZIKIasqqgHjJkiUIDw+HWq1GVFQUDhw4UOX5ixYtQuvWreHm5oawsDDMnDkTxcXF4v3z58+HTCYzubVp08bkGsXFxZgxYwb8/Pzg6emJkSNHIjMz05rh25TYdo1dJoiIJGH8hI6L6ojIWhYHxOvWrUN8fDzmzZuHQ4cOoXPnzoiJiUFWVpbZ89euXYuXX34Z8+bNw6lTp/DFF19g3bp1eOWVV0zOa9++Pa5duybedu/ebXL/zJkz8fPPP2P9+vXYuXMnrl69ihEjRlg6fJtjhpiISFrGNRzMEBORtSxOayYmJmLKlCmIjY0FACxfvhybN2/GihUr8PLLL1c4/48//sD999+PcePGAQDCw8MxduxY7N+/33QgLi4IDg42+5y5ubn44osvsHbtWvTr1w8AsHLlSrRt2xb79u3Dvffea+nLsJnyO9UREZHjlWWIGRATkXUsyhBrtVqkpKQgOjq67AJyOaKjo7F3716zj7nvvvuQkpIillWcP38eW7ZsweDBg03OS0tLQ6NGjdC8eXOMHz8e6enp4n0pKSkoKSkxed42bdqgSZMmlT6vRqNBXl6eyc0eynaqY8kEEZEUvG6XrLHLBBFZy6IoLicnBzqdDkFBQSbHg4KCcPr0abOPGTduHHJycvDAAw9AEASUlpZi2rRpJiUTUVFRWLVqFVq3bo1r165hwYIF6NWrF06cOAEvLy9kZGRAqVTC19e3wvNmZGSYfd6EhAQsWLDAkpdnFWOG2IsZYiIiSXBRHRHVlN27TOzYsQNvv/02li5dikOHDmHDhg3YvHkz3njjDfGcQYMG4bHHHkOnTp0QExODLVu24ObNm/juu++sft7Zs2cjNzdXvF2+fNkWL8eEIAhsu0ZEJDGWTBBRTVmUIfb394dCoajQ3SEzM7PS+t/XXnsNjz/+OCZPngwA6NixIwoLCzF16lS8+uqrkMsrxuS+vr5o1aoVzp49CwAIDg6GVqvFzZs3TbLEVT2vSqWCSqWy5OVZrFCrg14w/D9LJoiIpGGcf/M1pdDrBcjlMolHRES1jUUZYqVSicjISCQnJ4vH9Ho9kpOT0bNnT7OPKSoqqhD0KhQKAIYMqzkFBQU4d+4cQkJCAACRkZFwdXU1ed7U1FSkp6dX+ryOYMxGuMhlcHNVSDYOIqL6zJghFgSgQMs6YiKynMVpzfj4eEycOBHdunVDjx49sGjRIhQWFopdJyZMmIDQ0FAkJCQAAIYOHYrExER07doVUVFROHv2LF577TUMHTpUDIxnzZqFoUOHomnTprh69SrmzZsHhUKBsWPHAgB8fHzw5JNPIj4+Hg0bNoS3tzeeeeYZ9OzZ0zk6TLi5QiZjRoKISApqVwWULnJoS/XIu1XCEjYispjFAfHo0aORnZ2NuXPnIiMjA126dEFSUpK40C49Pd0kIzxnzhzIZDLMmTMHV65cQUBAAIYOHYq33npLPOfvv//G2LFjcf36dQQEBOCBBx7Avn37EBAQIJ7z4YcfQi6XY+TIkdBoNIiJicHSpUtr8tprzLiimZtyEBFJy1vtgpwCLTtNEJFVZEJldQt1TF5eHnx8fJCbmwtvb2+bXDP5VCae/PIgOjX2waa4B2xyTSKqvewxzzgTZ359/RbuwPmcQqybei+imvtJPRwispJU84zdu0zUZdyUg4jIOXiJrdeYISYiyzEgrgFuykFE5ByMpWtsvUZE1mBAXAPsQUxE5By4OQcR1QQD4hoo32WCiIikU7Y5B0smiMhyDIhrQCyZYJcJIiJJGefhfGaIicgKDIhrgBliIiLnwJIJIqoJBsQ1wC4TRETOoWxRHUsmiMhyDIhrgF0miIicAzPERFQTDIhrgBliIiLnIC6qY0BMRFZgQFwDYts11hATEUnK+EkdSyaIyBoMiK0kCIK4IxIzxERE0vK6PQ+zywQRWYMBsZWKtDro9AIA1hATEUmtrGSiFIIgSDwaIqptGBBbyVin5iKXwc1VIfFoiIjqN2NiQqcXUKTVSTwaIqptGBBbqazDhCtkMpnEoyEiqt/cXBVwkRvmYi6sIyJLMSC2UlmHCZZLEBFJTSaTlbVe48I6IrIQA2IrscMEEZFzETfnYIaYiCzEgNhK7EFMRORc2GmCiKzFgNhK3KWOiMi5sBcxEVmLAbGVxJIJZoiJiJwCd6sjImsxILaSWDLBGmIiIqcgBsS3GBATkWUYEFtJLJlglwkiIqcglkwUs2SCiCzDgNhKzBATETkXby6qIyIrMSC2kjEg9mKGmIjIKRjnYy6qIyJLMSC2UlnJBDPERETOQNyYgxliIrIQA2IrsWSCiMi5cFEdEVmLAbGV2HaNiMi5lGWIWTJBRJZhQGwFQRCQX8yNOYiInEnZxhzMEBORZRgQW+FWiQ6legEAM8RERM6irMtEKQRBkHg0RFSbMCC2gnFBnUIug7tSIfFoiIgIKOsyodXpoSnVSzwaIqpNGBBbQVxQp3aBTCaTeDRERAQAHkoXyG9PySybICJLMCC2grigjh0miIichlwug5eardeIyHIMiK1QliFmQExE5EyMC+tyuTkHEVmAAbEVxE052GGCiMipeDNDTERWYEBsBWaIiYicU/lOE0RE1cWA2ArclIOIyDkZO01wUR0RWYIBsRXyuCkHETmJJUuWIDw8HGq1GlFRUThw4ECl5/71118YOXIkwsPDIZPJsGjRIscN1EHKdqtjQExE1ceA2ArMEBORM1i3bh3i4+Mxb948HDp0CJ07d0ZMTAyysrLMnl9UVITmzZvjnXfeQXBwsINH6xhiDTEX1RGRBRgQW0GsIWbbNSKSUGJiIqZMmYLY2Fi0a9cOy5cvh7u7O1asWGH2/O7du+P999/HmDFjoFKpHDxaxxC3b2aGmIgswIDYCuwyQURS02q1SElJQXR0tHhMLpcjOjoae/futclzaDQa5OXlmdycXVmGmAExEVUfA2IrsMsEEUktJycHOp0OQUFBJseDgoKQkZFhk+dISEiAj4+PeAsLC7PJde2pgYdhXj72dy6KtCybIKLqYUBsBe5UR0T1wezZs5GbmyveLl++LPWQ7qpf6yAEeauQfqMIb/xySurhEFEtwYDYCmKXCWaIiUgi/v7+UCgUyMzMNDmemZlpswVzKpUK3t7eJjdn5+PuisRRXSCTAd8cSEfSCdtky4mobmNAbCFBEMpliFlDTETSUCqViIyMRHJysnhMr9cjOTkZPXv2lHBk0ru/hT+m9m4OAHh5wzFcy70l8YiIyNkxILbQrRIdSvUCAGaIiUha8fHx+Oyzz/Dll1/i1KlTePrpp1FYWIjY2FgAwIQJEzB79mzxfK1WiyNHjuDIkSPQarW4cuUKjhw5grNnz0r1EuzmhYdao2OoD24WlSB+3VHobs/bRETmMCC2kLHDhEIug7tSIfFoiKg+Gz16NBYuXIi5c+eiS5cuOHLkCJKSksSFdunp6bh27Zp4/tWrV9G1a1d07doV165dw8KFC9G1a1dMnjxZqpdgN0oXORaP6QJ3pQJ7z1/Hp/87J/WQiMiJ8TN/C5V1mHCBTCaTeDREVN/FxcUhLi7O7H07duww+To8PByCUH8ypc0DPDH/X+3x4vfHkPjfM7g/wh+dw3ylHhYROSFmiC3EDhNERLXHY5GNMaRjCEr1Ap779jAKNGzFRkQVMSC2EHsQExHVHjKZDG8P74hGPmpcvF6E+Zv+knpIROSEGBBbiLvUERHVLj7urlg0pivkMuD7lL+x6ehVqYdERE6GAbGFmCEmIqp9ejRriLgHWwAAXv3xOP7+p0jiERGRM2FAbCFjDbGXmhliIqLa5Nn+LXFPE1/kF5fi+W+PoFSnl3pIROQkGBBbiLvUERHVTi4KORaP6QpPlQsOXvoHS7azFRsRGTAgthC7TBAR1V5hDd3x5rAOAIDFyWdw8OINiUdERM6AAbGFyvchJiKi2mdY11AM7xoKvQA89+0RcV4novqLAbGF8o0lE8wQExHVWq8/0h5hDd1w5eYtzPnxRL3asISIKmJAbCGxZII1xEREtZaX2hWLx3SFQi7DpqNX8ePhK1IPiYgkxIDYQnnMEBMR1Qn3NGmA5/u3BAC8tvEELl0vlHhERCQVBsQWKltUxxpiIqLabvqDLdAjvCEKtTo8++0RlLAVG1G9ZFVAvGTJEoSHh0OtViMqKgoHDhyo8vxFixahdevWcHNzQ1hYGGbOnIni4mLx/oSEBHTv3h1eXl4IDAzEsGHDkJqaanKNvn37QiaTmdymTZtmzfCtJggCN+YgIqpDFHIZPhzTBd5qFxy9fBOLtp2RekhEJAGLA+J169YhPj4e8+bNw6FDh9C5c2fExMQgKyvL7Plr167Fyy+/jHnz5uHUqVP44osvsG7dOrzyyiviOTt37sSMGTOwb98+bN26FSUlJRgwYAAKC00/vpoyZQquXbsm3t577z1Lh18jxSV6lOgMCy9YMkFEVDeE+rrhnZGdAABLd5zDvvPXJR4RETmaxQFxYmIipkyZgtjYWLRr1w7Lly+Hu7s7VqxYYfb8P/74A/fffz/GjRuH8PBwDBgwAGPHjjXJKiclJWHSpElo3749OnfujFWrViE9PR0pKSkm13J3d0dwcLB48/b2tnT4NWLMDstlgIdS4dDnJiIi+xncMQSjujWGIAAz1x3BzSKt1EMiIgeyKCDWarVISUlBdHR02QXkckRHR2Pv3r1mH3PfffchJSVFDIDPnz+PLVu2YPDgwZU+T25uLgCgYcOGJsfXrFkDf39/dOjQAbNnz0ZRUeV70Ws0GuTl5Zncaqr8phwymazG1yMiIucxb2h7NPP3wLXcYrz8w3G2YiOqRyxaGZaTkwOdToegoCCT40FBQTh9+rTZx4wbNw45OTl44IEHIAgCSktLMW3aNJOSifL0ej2ef/553H///ejQoYPJdZo2bYpGjRrh2LFjeOmll5CamooNGzaYvU5CQgIWLFhgycu7K9YPExHVXR4qF3w0pitGLNuDpL8ysO7PyxjTo4nUwyIiB7B7l4kdO3bg7bffxtKlS3Ho0CFs2LABmzdvxhtvvGH2/BkzZuDEiRP49ttvTY5PnToVMTEx6NixI8aPH4+vvvoKP/74I86dM78X/ezZs5GbmyveLl++XOPXknfL2HKNHSaIiOqijo19MGtAawDAgp9P4lx2gcQjIiJHsCgg9vf3h0KhQGZmpsnxzMxMBAcHm33Ma6+9hscffxyTJ09Gx44dMXz4cLz99ttISEiAXm/a3iYuLg6//PILtm/fjsaNG1c5lqioKADA2bNnzd6vUqng7e1tcqspZoiJiOq+Kb2a4/4WfrhVosOz3xyGplQn9ZCIyM4sCoiVSiUiIyORnJwsHtPr9UhOTkbPnj3NPqaoqAhyuenTKBSGBWnG+ixBEBAXF4cff/wRv//+O/6/vXsPi6ra+wD+3TMww/1+RxC8x0UlVETwliaW0UVPx9TS7PZamBdOeTQ1T6+RaWqklpanrF41rZOdUktFvJApYRimgoBCauCAoDDInZn9/gEMkSiDDm6Y+X6eh4cc92x+C235ZT2/vZa/v3+rtaSlpQEAPD092zKEO8JT6oiIjJ9MJmD13/vD0cocZ/LVWLWPW7ERGbs2t0zExsZi48aN+Oyzz5CRkYEXX3wR5eXlmD59OgBg6tSpWLBgge766OhorF+/Htu2bUNubi4SEhKwePFiREdH64JxTEwMNm/ejK1bt8LW1hYqlQoqlQqVlZUAgPPnz2Pp0qVITU3F77//ju+++w5Tp07FsGHD0LdvX0N8H/TSdEodWyaIiIyZu50FljdsxfZRUg5+zL4icUVE1J7anOwmTpyIK1eu4PXXX4dKpUL//v2xZ88e3YN2Fy9ebLYivGjRIgiCgEWLFiEvLw+urq6Ijo5GXFyc7pr169cDqD984882bdqEp59+GgqFAvv370d8fDzKy8vh4+ODCRMmYNGiRbcz5tvGFWIiItMxJtADTw72xebki4j98iT2zB4KZxul1GURUTsQRBPZV0atVsPe3h6lpaW33U+8YMdv+CLlEmLv74VZo3oauEIi6uwMMc90ZMY+vpZU1mjw8LojyC68jtH3uGHj1AHcdpOoHUk1z7T7LhPGRLfLhAVbJoiITIGlQo41k0KgMJNhf0YhNidfkLokImoHDMRtoNtlgsc2ExGZjHs87TB/bB8AwJu7M5CpKpO4IiIyNAbiNmAPMRGRaZoe4YcRvV1RXafFrC9+RVUtt2IjMiYMxG3QtMsEAzERkSkRBAHv/K0fXGwUyCwow9s/tHw6KxF1TgzEbaBbIea2a0REJsfVVomVj/cDAHx69HccOFvQyjuIqLNgINaTKIo8qY6IyMSN6O2GZyLqD4969avfUFhWJXFFRGQIDMR6qqrVolZTv0MdWyaIiEzXvLG90cfDFsXlNfjHlyeh1ZrE7qVERo2BWE+Nq8MyAbBWyCWuhoiIpGJhLsfaSSFQmsnwY3YRPvkpV+qSiOgOMRDrqal/2JybshMRmbie7rZY/FAAAGDFnkycyS+VuCIiuhMMxHpi/zAREf3ZlDBf3B/gjhqNFi9v/ZX7ExN1YgzEemo8pc6Wp9QRERHqt2JbPqEv3O2UyCkqx4NrfsTr357GtfIaqUsjojZiINYTV4iJiOivnKwV+M+MIRgb6AGNVsTnxy5gxMpD+PSnXNRqtFKXR0R6YiDWE/cgJiKilvg4WWHDU6HY+nwY+njYorSyFv/amY4H3vsRh7OuSF0eEemBgVhPulPquEJMREQtGNLdBbtnDUXcY0FwtDLHucLrmPZJCp799DhyrlyXujwiugUGYj3pWia4BzEREd2EXCZgSlhXHHplJJ6J8IeZTEDi2UJExSchbne67t8SIupYGIj11PhQHVeIiYioNfZW5ng9OgB75gzDyN6uqNWI2PhjLka+cwhfpFyEhod5EHUoDMR6alohZg8xERHpp4ebDTZNH4RN0weim6s1istrsGDHKUSvPYKfc4qlLo+IGjAQ60n3UB1XiImIqI1G9nbD3jnDsPihANhamCH9shoTP0rGS1tScelqhdTlEZk8BmI96R6qYw8xERHdBnO5DM9G+uPQKyMwJcwXMgH4/pQKo1Yfxsq9mSivrpO6RCKTxUCspzLdCjFbJoiI6PY52ygR91gwds8aivBuzqip02LdwXO4b9UhfPPrH9Cyv5jormMg1hN3mSAiIkO6x9MOW58Pw4YnQ+HjZIkCdTXmbj+J8euP4teL16Quj8ikMBDrQRTFpl0mGIiJiMhABEHA2CAPJMwdjnlje8NaIUfapRI89sFRxG5Pg6q0SuoSiUwCA7Eequu0qGk4gpMtE0REZGgW5nK8NKIHDr4yAn8L7QIA2PFrHkauPIR1B7JRVauRuEIi48ZArIfGHSZkAmCtYCAmIqL24WZngZWP98O3MREI7eqIyloNVu7LwqhVh/H9qcsQRfYXE7UHBmI9NPYP21qYQyYTJK6GiIiMXT8fB/xnRjjee6I/PO0tkFdSiZe2nMDEj5JxJr9U6vKIjA4DsR5Kdf3DXB0mIqK7QxAEPNLfG4n/GI7Zo3pCaSZDSu5VPLT2CBbs+A1XyqqlLpHIaDAQ60G3wwQP5SAiorvMSmGGuff3woFXRiC6nxdEEfgi5RKGrTiI5XvOoqSiRuoSiTo9BmI98JQ6IiKSmreDJdZOCsFXM8LRz8cBlbUarD90HkNXHMTaxGxc58EeRLeNgVgPTafUsWWCiIikNdDPCf99aQg2Th2APh62KKuqw6qELAxbcRD//jGHO1IQ3QYGYj1whZiIiDoSQRBwf4A7vp81FGsmhcDfxRpXy2vw5u4MjHjnELb8fAG1DduFElHrGIj1wFPqiIioI5LJBDzczwsJc4dh+YRgeNlbQKWuwsJvTmPUqsPYceIPaHgUNFGrGIj1oDuljivERETUAZnJZZg40BcHXx2Bf0UHwMVGiYtXKxD75UmMjU/CD9zDmOiWGIj10LRCzB5iIiLquJRmcjwd4Y+keSMwb2xv2FuaI7vwOl7ccgIPr/sJhzILGYyJWsBArAf2EBMRUWdipTDDSyN6IGneSMy6rwesFXKcyivF05uO4+8fHkNK7lWpSyTqUBiI9dC0ywQDMRERdR72luaIHdMbSfNG4rlIfyjMZDj++zX8/cNjmPpJCn77o0TqEok6BAZiPZTpVojZMkFERJ2Ps40Six4KQNKrIzElzBdmMgFJWVfw8Lqf8D//9wuyCsqkLpFIUgzEeuAuE0REZAw87C0Q91gwDvxjBMaHeEMQgL1nChAVn4S529Nwobhc6hKJJMFA3ApRFJt2mWAgJiIiI+DrbIXVE/tj35xheCDIA6IIfPNrHkatOowFO07hcmml1CUS3VUMxK2ortOipmFzc7ZMEBGRMenpbov1T4Zi58xIDO/lijqtiC9SLmL4O4ewdFc6iq5XS10i0V3BQNyKxh0mBAGwVjAQExGR8QnuYo/PnhmEr2aEY5C/E2rqtPj4SC6GrTiIlXszUdrwbyGRsWIgbkVj/7Ct0gwymSBxNURERO1noJ8Ttr8wGJ8/Mwh9u9ijokaDdQfPYejyA3j/4Dlcr66TukSidsFA3IpS9g8TEZEJEQQBw3q54tuYCGx4MhS93G2grqrDO3szEdkQjMuquGJMxoWBuBWN/9PzUA4iIjIlgiBgbJAHfpg9DPET+6ObizVKKmrxzt5MDF1xEOsOZDMYk9FgIG5F06Ec7B8mIiLTI5cJeDTEGwmxw+uDsWt9MF65LwuRyw9ibSKDMXV+DMSt4LHNREREfwrGc4fjvSf6o7urNUora7EqoSkYqxmMqZNiIG4FD+UgIiJqIpcJeKS/N/Y1BOMebjZNwfjtA1jDYEydEANxK3SHcnCFmIiISKcxGO+dMwxrJoWgh1v9w3erG4Lxe/uzuV0bdRoMxK1oWiFmDzEREdFfyWUCHu7nhb1zhmHtpBD0bAjG7+7PQuTyA4jfn8VgTB0eA3Er2ENMRETUOrlMQHRDMF43uT4Yl1XVIX5/NiKXH8C7CQzG1HExELeiaZcJBmIiIqLWyGQCHupbH4zfn3wvernXB+P3ErMR+fYBrE7IQmkFgzF1LAzErWhaIWbLBBERkb5kMgHj+npiz+z6YNzb3RZl1XVYk1i/Yrx6XyaDMXUYTHmt4C4TREREt68xGD8Q5IE9Z1RYk5iNs6oyrDlwDpt++h1PR/jh2Uh/OFgp7ko9Gq2IK2XVyCupRF5JJfL/9NHF0Qov39cDzjbKu1ILdRwMxK3gLhNERER3TiYT8GCwJ8YGemDvGRXeawjGaxuD8RA/PDf0zoNxZY1GF3R1n681/HdpJVSlVajViDd9/7dpeVg4LgAT7vWGIAh3VAt1HgzEreAuE0RERIYjkwl4INgTUYEe2JeuQvz++mC87uA5fHq0Phg/G+kPR+sbg7Eoiii6XtM87DYE3vzSSuSXVOFqeU2rNZjJBHjYW8DLwRLeDR+utkpsO34JGZfVeOWrk9hx4g/EPRYMfxfr9vg2UAfDlHcLVbUa1NRpAbBlgoiIyJBkMgFjgzwxJsAD+9IL8F5iNjIuq7Hu4Dls+ikXUwZ3hZVC/qfwW4W8kkrdv8u3Yqs0g7ejJbwcLOHlYAFvB6uGz5bwdrSEm60F5LIbV38nh/ni4yO5iN+fhaPnixEVn4TZo3ri+aHdoDDjY1fGjIH4FhpXhwUBsFHwW0VERGRo9cHYA2MC3JGQUYD4/fXB+KOknBavFwTA3daiPuA61gfdLg6N4bc+8N5um6O5XIYZw7vjwSBPLPzvKfyYXYR39mbiu7R8vDU+GKFdHe9kqNSBMeXdQmP/sK3SDLIWfpIkIiIiw5DJBEQFeuD+e+qD8c6T+bC1MIOXfVPQ9XawhLudRbuv1vo6W+HzZwbh27R8/O+udGQWlOFvG47iybCueHVsbz5XZIQYiG+BO0wQERHdXY3BOCrQQ9I6BEHAoyHeGN7LFXHfZ+A/qX/g/5IvYF+6Cm88HISxQdLWR4Z1Wz9ivf/++/Dz84OFhQXCwsKQkpJyy+vj4+PRu3dvWFpawsfHB3PnzkVVVVWb7llVVYWYmBg4OzvDxsYGEyZMQEFBwe2UrzeeUkdERGTaHK0VWPl4P2x9Lgx+zlYoUFdjxuZUvPD5L7hcWil1eWQgbQ7E27dvR2xsLJYsWYITJ06gX79+iIqKQmFhYYvXb926FfPnz8eSJUuQkZGBjz/+GNu3b8drr73WpnvOnTsXO3fuxFdffYXDhw8jPz8f48ePv40h66/plDoupBMREZmyIT1csGfOMMwc2QNmMgH70gtw/+okfH7sd2i0N9/GjTqHNgfi1atX4/nnn8f06dMREBCADRs2wMrKCp988kmL1x89ehQRERGYPHky/Pz8MGbMGEyaNKnZCnBr9ywtLcXHH3+M1atX47777kNoaCg2bdqEo0ePIjk5+TaH3jquEBMREVEjC3M5Xonqjd2zhiLE1wHXq+vw+rdnMGH9UZxVqaUuj+5AmwJxTU0NUlNTMXr06KYbyGQYPXo0jh071uJ7hgwZgtTUVF0AzsnJwffff48HH3xQ73umpqaitra22TV9+vSBr6/vTb9udXU11Gp1s4+2Yg8xERER/VVvD1t8PWMIlj4SCBulGdIuleChNUewYs9ZVNVqpC6PbkObegGKioqg0Wjg7u7e7HV3d3ecPXu2xfdMnjwZRUVFiIyMhCiKqKurw4wZM3QtE/rcU6VSQaFQwMHB4YZrVCpVi1932bJleOONN9oyvBvwlDoiIiJqiUwm4KlwP9wf4IF/fXcGe86o8MGh89h96jLeeiwYET1cpC4RAJBfUonknGIcO1+MlN+vAgACvewQ6GWv++xqy6Oq27059tChQ3jrrbfwwQcfICwsDOfOncPs2bOxdOlSLF68uN2+7oIFCxAbG6v7tVqtho+PT5vuwVPqiIiI6FY87C2w4alQ7D2jwpJvz+BCcQWm/PtnjL/XG4vGBcCphRP32lOBugrHztcH4OTcYlworrjhmgvFFfj+VNOCorudsllADvSyQxdHS5M6urpNSc/FxQVyufyG3R0KCgrg4dHy9iOLFy/GU089heeeew4AEBwcjPLycrzwwgtYuHChXvf08PBATU0NSkpKmq0S3+rrKpVKKJV39hMPe4iJiIhIH1GBHhjS3Rkr92bi8+QL2HEiDwfPFmLxQwF4LMS73cJloboKx3KKkZxzFck5xcgtKm/2+zIBCPa2x+DuzhjczRnmMhnO5JfiTL4ap/NLkVtUjgJ1NQrUhThwtmkzA3tL84aAXB+Sg7zt4O9i0+IJf8agTYFYoVAgNDQUiYmJePTRRwEAWq0WiYmJmDlzZovvqaiogEzWvFVZLpcDqD+TXJ97hoaGwtzcHImJiZgwYQIAIDMzExcvXkR4eHhbhtAmTbtMMBATERHRrdlamOONR4LwSIg3Fnx9CpkFZYj98iR2nMhD3GNB6Opsfcdf40pZNX7ObVgBzinG+Ss3BuBAL3uEd3fG4G5OGOjnBNu/LOxF9mxq5yivrsNZlRqn89S6oJxVUIbSylocPV+Mo+eLdddamsvRx9MWQX9aTe7lYQOlmfyOxyW1NvcCxMbGYtq0aRgwYAAGDRqE+Ph4lJeXY/r06QCAqVOnwtvbG8uWLQMAREdHY/Xq1QgJCdG1TCxevBjR0dG6YNzaPe3t7fHss88iNjYWTk5OsLOzw8svv4zw8HAMHjzYUN+LGzStELNlgoiIiPRzr68jds2KxMYfc/De/mwcOVeEMe8mYc7oXnhuqD/M5frvaVB8vRo/517V9QFnF15v9vuCAAR42mFwN2eEd3PGQH8n2LdhIc9aaYbQrk4I7eqke62mTousgjJdQD6Tr0Z6vhqVtRr8erEEv14s0V1rJhPQ090WgV52CPKyQ6C3Pe7xtIONsnNlpzZXO3HiRFy5cgWvv/46VCoV+vfvjz179ugeirt48WKzFeFFixZBEAQsWrQIeXl5cHV1RXR0NOLi4vS+JwC8++67kMlkmDBhAqqrqxEVFYUPPvjgTsbeqsYe4r/+ZEVERER0K+ZyGV4a0QMPBnli4X9P4adzxVi+5yy+TcvDsvHBCPF1bPF918prmgXgzIKyG67p42GL8O71AXiQvxMcrAzbp6wwkyHI2x5B3va61zRaEblF5X8KyfWfSypqkXFZjYzLavwntf5aQQD8na0R0LCKPKyXCwK97G/y1ToGQRRFk9hNWq1Ww97eHqWlpbCzs9PrPQPe3I+i69XYPSuyw/9BEpH0bmee6UyMfXxE7UUURew4kYc3d6fjWkUtBAGYOrgrXonqDa0W9S0QDX3AZ1Vq/DWZ9Xa31bVAhPk7w/EuP6h3M6IoIq+kUreKfCavPiSr1M1PI469vxdmjeqp1z2lmmc613r2XabbZYIrxERERHSbBEHAhNAuGNnHDW/uTseOE3n47Fj9g3fXa+puCMA93WzqWyC6OyPM3wnONh1zWzRBENDF0QpdHK0QFdi0yUHR9epmq8gD/ZxucZeOgYH4JqpqNaip0wLgQ3VERER055ysFVj99/4YH9IFC/97SrclWjdXa4R3q98FYnA3506/L7CLjRLDe7lieC9XqUvRGwPxTchlAjY9PRDqqlrYdrLGcCIiIuq4Inu6YO+cYfjtj1L4OVvBzc5C6pJMHpPeTZjLZRjZx03qMoiIiMgIWZjLMci/47cSmAr99/0gIiIiIjJCDMREREREZNIYiImIiIjIpDEQExEREZFJYyAmIiIiIpPGQExEREREJo2BmIiIiIhMGgMxEREREZk0BmIiIiIiMmkMxERERERk0hiIiYiIiMikMRATERERkUljICYiIiIik8ZATEREREQmjYGYiIiIiEwaAzERERERmTQGYiIiIiIyaQzERERERGTSGIiJiIiIyKQxEBMRERGRSWMgJiIiIiKTxkBMRERERCaNgZiIiIiITBoDMRERERGZNDOpC7hbRFEEAKjVaokrISJj1Ti/NM43xobzKBG1N6nmUZMJxGVlZQAAHx8fiSshImNXVlYGe3t7qcswOM6jRHS33O15VBCNdSnjL7RaLfLz82FrawtBEPR6j1qtho+PDy5dugQ7O7t2rlAaHKNx4Bg7BlEUUVZWBi8vL8hkxteRxnm0ZRyjceAYOwap5lGTWSGWyWTo0qXLbb3Xzs6uw/7FMRSO0ThwjNIzxpXhRpxHb41jNA4co/SkmEeNbwmDiIiIiKgNGIiJiIiIyKQxEN+CUqnEkiVLoFQqpS6l3XCMxoFjpI7KFP7cOEbjwDGaNpN5qI6IiIiIqCVcISYiIiIik8ZATEREREQmjYGYiIiIiEwaAzERERERmTQGYiIiIiIyaQzEt/D+++/Dz88PFhYWCAsLQ0pKitQlGcyyZcswcOBA2Nraws3NDY8++igyMzOlLqvdvP322xAEAXPmzJG6FIPKy8vDk08+CWdnZ1haWiI4OBi//PKL1GUZjEajweLFi+Hv7w9LS0t0794dS5cuBTfH6Tw4jxoPY51HAc6lxEB8U9u3b0dsbCyWLFmCEydOoF+/foiKikJhYaHUpRnE4cOHERMTg+TkZCQkJKC2thZjxoxBeXm51KUZ3PHjx/Hhhx+ib9++UpdiUNeuXUNERATMzc3xww8/ID09HatWrYKjo6PUpRnM8uXLsX79eqxbtw4ZGRlYvnw5VqxYgbVr10pdGumB86jxMNZ5FOBcSg1EatGgQYPEmJgY3a81Go3o5eUlLlu2TMKq2k9hYaEIQDx8+LDUpRhUWVmZ2LNnTzEhIUEcPny4OHv2bKlLMph//vOfYmRkpNRltKtx48aJzzzzTLPXxo8fL06ZMkWiiqgtOI8aB2OeR0WRcynV4wpxC2pqapCamorRo0frXpPJZBg9ejSOHTsmYWXtp7S0FADg5OQkcSWGFRMTg3HjxjX7szQW3333HQYMGIDHH38cbm5uCAkJwcaNG6Uuy6CGDBmCxMREZGVlAQBOnjyJI0eO4IEHHpC4MmoN51HjYczzKMC5lOqZSV1AR1RUVASNRgN3d/dmr7u7u+Ps2bMSVdV+tFot5syZg4iICAQFBUldjsFs27YNJ06cwPHjx6UupV3k5ORg/fr1iI2NxWuvvYbjx49j1qxZUCgUmDZtmtTlGcT8+fOhVqvRp08fyOVyaDQaxMXFYcqUKVKXRq3gPGocjH0eBTiXUj0GYkJMTAxOnz6NI0eOSF2KwVy6dAmzZ89GQkICLCwspC6nXWi1WgwYMABvvfUWACAkJASnT5/Ghg0bjGYS//LLL7FlyxZs3boVgYGBSEtLw5w5c+Dl5WU0YyTjwHm08+JcahxjvFMMxC1wcXGBXC5HQUFBs9cLCgrg4eEhUVXtY+bMmdi1axeSkpLQpUsXqcsxmNTUVBQWFuLee+/VvabRaJCUlIR169ahuroacrlcwgrvnKenJwICApq9ds899+Drr7+WqCLDe/XVVzF//nw88cQTAIDg4GBcuHABy5Yt4yTewXEe7fxMYR4FOJdyLq3HHuIWKBQKhIaGIjExUfeaVqtFYmIiwsPDJazMcERRxMyZM/HNN9/gwIED8Pf3l7okgxo1ahROnTqFtLQ03ceAAQMwZcoUpKWlGcUkHhERccMWT1lZWejatatEFRleRUUFZLLm05RcLodWq5WoItIX59HOzxTmUYBzKTWQ+qm+jmrbtm2iUqkUP/30UzE9PV184YUXRAcHB1GlUkldmkG8+OKLor29vXjo0CHx8uXLuo+KigqpS2s3xvZ0dEpKimhmZibGxcWJ2dnZ4pYtW0QrKytx8+bNUpdmMNOmTRO9vb3FXbt2ibm5ueKOHTtEFxcXcd68eVKXRnrgPGp8jG0eFUXOpVSPgfgW1q5dK/r6+ooKhUIcNGiQmJycLHVJBgOgxY9NmzZJXVq7McaJfOfOnWJQUJCoVCrFPn36iB999JHUJRmUWq0WZ8+eLfr6+ooWFhZit27dxIULF4rV1dVSl0Z64jxqXIxxHhVFzqUkioIo8pgSIiIiIjJd7CEmIiIiIpPGQExEREREJo2BmIiIiIhMGgMxEREREZk0BmIiIiIiMmkMxERERERk0hiIiYiIiMikMRATERERkUljICYiIiIik8ZATEREREQmjYGYiIiIiEza/wO1LaWOYxlq0wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from io import BytesIO\n",
        "import cv2\n",
        "\n",
        "def categorizacion(url):\n",
        "  respuesta = requests.get(url)\n",
        "  img = Image.open(BytesIO(respuesta.content))\n",
        "  img = np.array(img).astype(float)/255\n",
        "\n",
        "  img = cv2.resize(img , (224,224))\n",
        "  prediccion= modelo.predict(img.reshape(-1 , 224 , 224 , 3))\n",
        "  return np.argmax(prediccion[0], axis=-1)"
      ],
      "metadata": {
        "id": "zuGs-kHJjc-s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "while True:\n",
        "  try:\n",
        "    url = input(\"Introduce aqui el link de la Imagen : \")\n",
        "    prediccion= categorizacion(url)\n",
        "    if prediccion == 1:\n",
        "      print(\"La imagen Corresponde a un Colectivo\")\n",
        "    elif prediccion == 2:\n",
        "      print(\"La Imagen Corresponde a un Ventilador\")\n",
        "\n",
        "  except :\n",
        "     print(\"No se pudo encontrar la direccion de la Imagen\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jjiOWIzqkng",
        "outputId": "3fcd2b1c-575e-4e89-d14c-660a5f0f9810"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No se pudo encontrar la direccion de la Imagen\n",
            "Introduce aqui el link de la Imagen : https://th.bing.com/th/id/OIP.q4uy1CnD7dD9jt-rVx9aHgHaCv?w=332&h=129&c=7&r=0&o=5&pid=1.7\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "La imagen Corresponde a un Colectivo\n",
            "Introduce aqui el link de la Imagen : https://th.bing.com/th/id/OIP.TpRiILM9_eQL2Ii9LGZ-yAHaGY?w=251&h=216&c=7&r=0&o=5&pid=1.7\n",
            "1/1 [==============================] - 0s 108ms/step\n",
            "La Imagen Corresponde a un Ventilador\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cuWe26m8q7xl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}