Best_Model_of_ViT_For_UWB_Pose_Classification.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Version 1.2\n",
        "Contact: น้องซี"
      ],
      "metadata": {
        "id": "twiat-G5cehl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installing Requirements + Setup"
      ],
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        "id": "JcS94gid5ByM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9xR1Jlh9zUQ0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "630a8795-2f7f-42c1-bfec-ecfaaef70d01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.10.1-py3-none-any.whl (469 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 KB\u001b[0m \u001b[31m21.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting transformers\n",
            "  Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m64.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
            "Requirement already satisfied: albumentations in /usr/local/lib/python3.8/dist-packages (1.2.1)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (2.25.1)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (4.64.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from datasets) (1.22.4)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.4)\n",
            "Collecting multiprocess\n",
            "  Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting xxhash\n",
            "  Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (6.0)\n",
            "Collecting huggingface-hub<1.0.0,>=0.2.0\n",
            "  Downloading huggingface_hub-0.12.1-py3-none-any.whl (190 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
            "Collecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Collecting dill<0.3.7,>=0.3.0\n",
            "  Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 KB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2023.1.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from datasets) (23.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m87.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: opencv-python-headless>=4.1.1 in /usr/local/lib/python3.8/dist-packages (from albumentations) (4.7.0.72)\n",
            "Requirement already satisfied: scikit-image>=0.16.1 in /usr/local/lib/python3.8/dist-packages (from albumentations) (0.19.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from albumentations) (1.10.1)\n",
            "Requirement already satisfied: qudida>=0.0.4 in /usr/local/lib/python3.8/dist-packages (from albumentations) (0.0.4)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.4)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (3.0.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.2.0)\n",
            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.8/dist-packages (from qudida>=0.0.4->albumentations) (1.2.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (1.26.14)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (4.0.0)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-image>=0.16.1->albumentations) (1.4.1)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.8/dist-packages (from scikit-image>=0.16.1->albumentations) (2.9.0)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.8/dist-packages (from scikit-image>=0.16.1->albumentations) (3.0)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.8/dist-packages (from scikit-image>=0.16.1->albumentations) (2023.2.27)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-image>=0.16.1->albumentations) (8.4.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (3.1.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (1.2.0)\n",
            "Installing collected packages: tokenizers, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets\n",
            "Successfully installed datasets-2.10.1 dill-0.3.6 huggingface-hub-0.12.1 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.2 transformers-4.26.1 xxhash-3.2.0\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  zstd\n",
            "0 upgraded, 1 newly installed, 0 to remove and 22 not upgraded.\n",
            "Need to get 343 kB of archives.\n",
            "After this operation, 1,592 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 zstd amd64 1.4.4+dfsg-3ubuntu0.1 [343 kB]\n",
            "Fetched 343 kB in 2s (200 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package zstd.\n",
            "(Reading database ... 128215 files and directories currently installed.)\n",
            "Preparing to unpack .../zstd_1.4.4+dfsg-3ubuntu0.1_amd64.deb ...\n",
            "Unpacking zstd (1.4.4+dfsg-3ubuntu0.1) ...\n",
            "Setting up zstd (1.4.4+dfsg-3ubuntu0.1) ...\n",
            "Processing triggers for man-db (2.9.1-1) ...\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets transformers torch albumentations\n",
        "!sudo apt install zstd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfZC1Ah7WX29",
        "outputId": "bca9ca30-8e29-4cb3-bddb-6cff4bb6c252"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset"
      ],
      "metadata": {
        "id": "bc9wVhLXr-Y2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r /content/drive/MyDrive/signal ./"
      ],
      "metadata": {
        "id": "NXNsca5Q77g6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pillow==9.2.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "TWvbtt-68Zg9",
        "outputId": "4d4a47cc-42b9-4792-d67e-0c380bc068dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pillow==9.2.0\n",
            "  Downloading Pillow-9.2.0-cp38-cp38-manylinux_2_28_x86_64.whl (3.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m85.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pillow\n",
            "  Attempting uninstall: pillow\n",
            "    Found existing installation: Pillow 8.4.0\n",
            "    Uninstalling Pillow-8.4.0:\n",
            "      Successfully uninstalled Pillow-8.4.0\n",
            "Successfully installed pillow-9.2.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328,
          "referenced_widgets": [
            "2e8a0a90604e4a72a04e77ed11993ef5",
            "36b736df666c4e81bde6df7da6df3425",
            "492bf8713b674de4b63e72fa7f21f58d",
            "79b6e8cac0d6479e9940ffadcf57f62d",
            "1749e672639640208b778d22b6f47881",
            "56fbc519d31949d0bc7c595694fbc01a",
            "d74986f0a15d4fe8959a1b8669411e5d",
            "9b84e470482c453d9451042d01447390",
            "bdf13b378e154e0f884c0cf8ed38f8d1",
            "414664f01d8e4ea6b3be43f1f0721161",
            "39b105e5f9d44ea69a420348d051d43b",
            "5e545ff9ef724599ac44cffef4bbd2ff",
            "097eb59aba324df298bd23d08e1a1d06",
            "e8aa2a8f5ccf4308aa4dd362fc9f0a8a",
            "fc380fc01fbf41e4827ae0e9773d4f49",
            "025160f7c4e24478947608793fb0c245",
            "d0fc9bca8b914236b2fdc32a2be03a64"
          ]
        },
        "id": "A4olG1uYdhmb",
        "outputId": "505addcb-a206-494d-b605-5e942c0bcff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing Dataset + Augmentation\n"
      ],
      "metadata": {
        "collapsed": false,
        "id": "T89ES0oB5ByR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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XiXHUbYhGYpMnj4t4iQzuF/rgJbdG9DUauBXuGwfmCfpS+8+wLfNWnfpYk8l29Pf4wFCOegEGTFZrEdoKsffevZiVvn2zYPPc2p6KknC747c/cH3APWAjR1Q6s7hIPHYJycPACtGx5vlXD6rbvYSdegqpH3dcH7APnqeysvX3iEEnTF64uefzI3RkInEgfrAlXyQN5dFmRq94epYmrVJF7bpcH7ArbrGuzqXAbb3B0vNf+ofcjeqpCnqLDLob9L/XIVNH/aw2bny8zV1FcA87V1y/4PoNiMBjIZGvXN2YWbl1YUafFCsf/gMflBeWGO/m87q1WIn/CW7D9W/+n/7Ab3/D7Q9mnbBwYKoGimYnRdV4bAs0js78ljB5Oy8AQoKbcLd8g1vZscmGDHLTGwTg7eIx87FNgspGVJwZ94uPPyMYOOuRFgC3CCSEG8/Z7YH4N/w//6OPZrmBNLkx+eyVnlLPTGd8NzejRo5GDf9oXSqBQW29SS2wY9XqAJTWVRsKyOQsfBkKjYgRveO3f/W//TOufyw30kO2xRF6rLg9Fku4m3wgnfXpahEt+YPWuUYLS3AbXMRthXvAPlj0dBsCiroNqITgph2Tc0h8sItbvc0u0AkwYrXe4CbKD3liDgsq9yAYWHLMyuovgpThm7FSt72NPvyyHLGwDjQWHvmHnqLLsGjPXtG1Gpsb01hzqUqVLOJy16W+os6OpWLXsx0JAXAjP+JayfE2MUqwZmI/YHj6qWIKPVNWxDYfZE3t9hXuC66/+8+/w33BbVUh2Xl5baKjZb6/BxNglqiN8JlmJ2UWNsCMCETrg8nugIUIZIFNdXsr/D1/iH1niksEwN3hvh5KbB2v3CNLA/KWjQmoBbvBi5bqr/558FTd1TAdk8wnNjL+e1z0RsA9YL/43/7wn/8Oty4DX5qwco4J3iREOEktoMN/Mn694KJVzvwy+huBFRZwXzzFtN/+wPWb2AgmUWKEjlJY6x52BrCvMliEB67aZlav9UA3s3EKYUaYgSgBmlsJ3osqbJsj+S3w7gEKwFsAf3tf4ic4wL3j+kB45wSmkHiOX4E96712zLQ3j/FSc4alOnOAxxmEBe9CRrvmZL8Aqj7sV6ibJQCNZv8P3P6AewATbGoZkVslNih5RAkGwr7KVXq4VwI8icjYYSelwtkI9/AuW7niQjnEDfHjFo4JpwIH31cdGwfhio/+uQlz756NBN9CaF3y7kMRYh+twNGVNNu/82MN7zX755RM22uWFRpP4UE2VyHIf4VeNIhhE5AWbVc5AEM2GmizSGZ8K+dJlpOsr/nbg9wGAOAirnfuVoDEyAlHdmtpoc2n5jaXMHkKqGMz/uRtWkqO7IQAhFn0IXI7zQiGJYoSjDcBVeEnWRtU0ZylKwCeAgOskHZeZg+Eu3d2qTIIjlFqv+bjkaA5burJ9aOmPqpmNXW8DhrPVJPuzGLAxjz68++4/l2C6ybcL7jZ3BluKhiEi3cPha+QedkhRpkB/gQUkSUfkIhgv3kXwf9EaLDRUybx9R23P3D7Avd1odyjitym0g+bYGOmqVqcojOMYWHyQdh3VAuRKztkAc4I+ZxawsyPK2uFSwtJ6AC07MWkfRXTLwXer7g9UBHRFwlejx80l0Mh8vvgQ+j70dd9FL+pfdKP33H7SlwUwSBeECZEK8480VCjASAKxsa6GsdMmvIvQxBk3f1uu8KtiLoafIRNngxanBD3jYaDX8P6Sn6HKeQzikSVJU6o2wI8I+4MULqmcPAwwcbFGR/EVhpNSZ8/SgIWEZbuLwYB+f4qcXSDpVjgCPuAsyKGfqiS1o9g4bE01thQtW7AP5e2q/2ZA6BJ3FUQBw4zF+HeYZNHTuyhMF/jQSwo5jrDPsxLMN7OTD6KrJiWQW7PVNhv/elmAeBWTwqXe3j3DhgvBeI8a+7vHOrnVny+LyH7by4eaSH+bpscfhlKqZ6kyp6IzZLz5cUGdJ8YuYOB23CnUirJk1MqWOgKJQRtYGg+KUs5WPIz8SiUUOUJLi4czJoQ3n00C68H7FCX3pM0uCcfKWHrQBY/NJ9V54cabVatdCdfgFIRpLZ216/mprIQ6O74/LvHjOtD4rAM3i7+fsH1gjBzwk803gLxZ4QZDqASMmHy0eD6WOIqtc2aiVMpJTuDITbOkMABmqyLwa5sY7KUljnzJk7MhVM3fH19i72hIfo6w21LcY1OCJO3FDC67+UK0xCNMtkWCQGAUUw8iWVUOTDchhDFELEHH+KJH9V4ho+/TmKfwfdU0tSQgIRr9G4t1m5m5dTyjDgjrHATYBEzmSChc8r1YqUqySGozqbaWyhdcTLNbP58F+zMe5GAVZZMmSNRuBn3xLlUOdhR72nE71dDbwEkhNmz9xwIxt+2xUmcKENvbNuWAY4mEYP0W7byOq77ANgNv/2B+wW4IPwCN/vb1+Vuayv3EA60jdR+XfneAzAvHA8/Uzdrdi5lxgVgr0Redz+3A0CsHG6Fu+P6BfhVZpnYmgXAQSFhwz0ttwcA3D8BBnf4u8FvlPNjhBCYxc2eiGhQ8wKKz7T1dzMB34phiFoh9KIY5N/usI+F1Hm3co2QYIQLrMDEFspg4C4IK1zCXdS3OxGwLEF2hIqyMWEB45EQLcKE+w9YTMZEdyPODlYtEz7/vrhP/l8+A1R4zPj7jXKkXpYshziKHeLX4MExgUz7tyX1V5/cEypqljWGvaubeCDf/ec/cLsjXvD2M64ANrzdPCBxaoZF/GAQEtwnD3Ax4bBySBA1aMGGZw2l0NCk7KBHBsVsfkr1B1dI49s4TrkniDbHB5a7XnRzI1rbVJ7iklRZRrFdy/XQ95mfrbDRTNXGuhEzihV2lSjBDSDDBaDDQT66bc2HIXWdbK7qg9dMywNQ/Cr/ewUcpGLhg2vGFPde3TLDyKOmw3ccFie5GAUSgGUkg671N18AShslahrh3gED+2DRExfcV57FcEG8wD4QDOIMt+E+s+KcvZT5lfcEzLzC7gmY4BRqEl7eDdwn1rstx316gCl30Dh3SAwO5C0yhFF8ze2rJ7rOFR8Mbl8R/kD4q7IE62ef7tXSQI9M7ZR0+LcHUVHfV7C5nPFsmBy+qDEL5JPqNvcOe/e3hNt/w+3LQvq0TcAKzHBfED4BSUKFiBMm3FaWNcnGFB4LW7vnhSxT1iyAxwo3A2JrBzjeyEggY+uYyYYo7uKmYpMTgkGwcDl0wzD5tACsv6+IojlFA5vqcZHAET42kr9hEVbeh5O0de6M8RQCQlL5setoIIwSEm+V/YLNtCuv3duDFaa44rrhjQTuX7z7dQkd4fweOsqy4KHdZ+/qMKlDZ9X2W34Nn+pPVl6G7gAtljsKSnzg+g73DkD4DwonLBshM1ItfGxYmb4aVpUK3lNBpML3F20FYhbv1F/1AQBZvLZiStB4VuyrTLc9q/xzTdKJU4/+oTyrIg6NSNxGKfhGTEuKR/RQsPOZrlrJpom/lHkFRRo8SumBZ+EXz4CFjZedUv2lYbzIQSjJj7sGNFBuTwQ2iVxDPcKp+udSh/HZ/DlnZPCAqtlBcSQrVysqH8T/r7UcAMAsYWbbJwdiGg4RoL1c36x364KIaDxXiiOruBHEykpiYseP3ZY4eaAcIAuOW1XiMFx8xEKf6izrUhEeCbB5DvZlYgPUmUn0CgKunGbYk+QSQlqi8eRQdu+4AfdPuP2BcPH3W0mcz/62l6BXg47v2X22QUcA3VNPA0qeWg/2gPI9VmDD9eERYb9wUsfbr4AYH7HBrotL3qXFrojwNMUBuHH0nX+bEQhtLgtmYpLeJZnlDSEtYAa1RPLUrE2wyIh5OdIkyJykizsaCuJUBjChiHv1b6MEKUehc3EqT1Um0gmYPKX9F8rKL/Jh8mPDp36Xau1JFasKlpzxfE07xVv4RYfs2HR/hwffDR0V+AeBy0GSifUNt8E+6qWyiUIJXgY2W4HyySwJTByVFsTq59aF0XQjhUmcAolkUMCtvgRclsgjRmH7bTwQAbBUuCtvUKQIWGGgaVSCIgEkm26IEKvn5INZkCvVSFX5oCQYnY5cHKTC2blwvykCA0euDDxM3Dc7e5hS8T+S1fYB3Kh8M7OLAdU8ttU3klxD9p7qQx/Sfl6B73nK5/qEFLrg1iL2MGFKXECA1B12IG+IM1sGWCqThSopD544MLk6M0Yqq5xoRJzykaqIOKfw/RZxe+B+qSSD7NePFjA+jMqbsIdG9CGq/eliwU5IqXNrikWJdSabq8t6JNyx3FZBEZFuWY6O5G8EwCFO3MNsOJm5w2OgZKMoS4JVGbhvi0vePYA73DswUeLKwFyvsysF6Ue6TmuMfOYlP9B7dMR7E2Y/jHJ6ek95S9VPp0VJysxeYVdQRmS8ePJCQ6IaIs+d55qYic31YcY1t7kiTLAzogUolncTGgwOQEHOvFipU8nz+sid2yS/J4nRXk1wqY4+sU49KI8NIDGvZ9fRkOUB7oG3ubpEuxyxnCDVckKSn0093yxroibzQvDGqJnJ4YwQ2RKYaWTWFKlMkk1U8mTMTMc5Hv8+8OeqQT0I6iBnNGQ1SM0Ch4MZpotE/8hQHwBIXaOmbQr3cUZQv4GpuEANE+0VELJEAXXUxUI4JcwiXIAJbxcfDGARUjEJETZwtelZiiLVij9/OYAJLvlgF2IGtODuF7gZbxdPChn7IYyPVMfmgbtUbwxk85plvAh7Glo+7SyePEwGt4z9IuAyHX3w5jW3u492cdOOu1+G+2OgifEBhdsLzdwLgB++qH98l9zmgxJ9wTLlA+7hXcLtHTYhUmzujDDBkdtlBj5xoVZsyKE2weD+C3vcwyxBxgAM7r94l2AlF5kZ/STFEGS+DOcMJdjkmXauLHhxp2tVNM4eWIKFo5gjKRbCcQNGYSThqxqBnHePHJucy9kJhjGLF2uAy+6yhJiYMEOkIhJ6irjZE05TFLIeLJY7iptKFSTyJWyHYgsvnct0z9zYJGxkeGoEbQ6k/7U5fV+Rb7vxgtz5pEviuzFwq3cPzh7GtsB6miOCkqVpxBQ6KxJrOY6MgIhgAK6WiKiH4bpuYvauv8M9OMozi4YUSsceUsFlt4nyYRGMx4y7hPdlyU87KqFMpxRhEAwXsS5usSx3blke8DGJUmIKOoIEWYvI4hfzXBdV1Jy8Tn1cdyxSpgjB3mJhJN5KUbSG7rqHj3ZxvyDska62iEN713PYo44valFGndedH1qjXrCVlmwfsRAzhoiM2N7cMf08zlFKYDfDEg3C7MMse2nAh7TA+jAhWp+Zkvn8dx8M3LsU/LAS4zNTPB/CReKlLVFmVgiCJGCMXbkJoBLJMkb0iFs5vokIHmt5kdPnucocRCicqqG/k0QbWet3qgQzoBh6Za7yAzTV9NWImpXk5o1dHQ4cUHK7A/D/8tf/MrYTpdFM94j1SpJke89OUwdgdn4O+qOOezx+8AbVNnlQ9OcXoTUJuNRSzVY3OKszCeFnb7cFka3pUfhqmGEvCA9cjYj4xocZ0XramY0a/AkPwciVVVqXSzeh6wTEqJTdPBobTBWnXAUQaC4AQMTkgjFGxANTqSx6QPOqKDejY+sNdr4O+lv0wcq+JZczRnokGD77UfhHKz3Vu3Z+dp3nhK2MD6QAyJ272mEzfZTMg1bBjZbbqXJfdU8mmNtXhA3XB5tpnIGlEMmssuksJzrYhPnWHNOq29yG2wN3wptYT6EV+5lhm4CbVIS8LqVe6+xkY2PLgMij2VCANKLlT9Glpq/BKIlqQjC4RtgEWLL/+TBLDaa+5aFEAcVqm0fS4c0H8B1GTS3hHN+TsyoSh+fe3mFpy8kEu4mP5yOdiUZM91bIkwEmuBlhpqj7JcidDXL/9PkLeOOBnEP8LlbZd7Eo5TARIascJbWJohcVspJL4B3uC2gPWQrFyBAyCs7Ka28RLXABGQca2swPUpjphGAQOVFkf1A+RD6zcXcq/WHv/4bbO67vcNF/GDNM/XVNdNWQEmP87YOnvptUa0gjDEtKtU243nln3ttXIFVe+GGWtp4U7cPLiz97H+8shgLIXy1Kszz4k67OaBNYVoDg3KYMYKtw/6zfHYjwH5nLYD64Y8mPxG1kOMYDqKsr17N0q1a3P/Ku73P//HeB44q4HYX7GGS12FYiWYma6FLNDBKuoqMFgyvgHrhBvD4PrtpFdnsyrrpvCBbuImdmwOAOprugzV/eYR+Lu1RfW+wIyvRdVDwxz9q6gy1YMo5W0bJEzoOYkQFYLC0RHaFjWe6TkiimcgYrbivCA9cHbEQAwi8fSfUEd4nkn36n9efQMP3UHfw4KdUOpFW44gNI/vYV7r1ErMcJ4ee6IFS39bBrEHoDgDh7Oy0hFVmOnnp74PYJWD3F/hITy7MQDIxLS9go12S5RQ9CzQluXcIKl7wzQFooLtUC8eLtutgNb8a7COQNBclaGdn2RNHpb9m/j7rTqVb99LrMQu1oLim6PscilUfWYhkZ53jUg9jKBp1Jv7JORDje0Z4jRH8IGsTak1DxTGT88bc3mARhfYl3uqIzt4fqRqeJEjhBr3FcjgESBSyLUQ8AGedn3GfvsDgx7MNwrD4dG+4W7cK91VOVPylxxAoAPMSsGgHaGK6XpTJGKlJEqfEue+cnoJbJiroDNWdD+wj1vhDIpdom8BB61GytJI3FjvwCkVmKo/0UoSrFNX07gAbhlKFxfEPf/l6zr7wdhxqYnGduuYqqRKEhkA1wjW9yyIq5fjSwlfmdcFR7tsm99DN4iyK+c4Ew8DB5k0u/2gQ34/5JuGfyDgtWWIrLXKX8hmF1LJqFEtvZnEmwqWpHBsEiGkljnwHgDZIlJ8/mbyNm4VDS54dDHBQGy3r1YV3cqoozGsS5tYByblM/miIYVURUzFicO/DA9U5apw+XBZzOPzJ/DlXyPb7c8+597lEePEYyo34eoGz/ouTJzsiOTcrwIWJ2UZxNLeAg/Rmi6aAMpWiidJt9ByzuptrW1VFdMSxh9rHajLshewDA2AmVWkR3utQW6ig42hNU9UlWso4AiW4eQZUyv2+OydzW1gjXqFw6UaT1zqf2fKg7zP1PcBHRKDfbQezSD8KfolFpdH8NeK+zDaB6IdpKQ2BGDBYKd1F3noZoLQSIThb7N8QpmorELz330vZGNUwQJ1Zc7r8CETeANsy0lJ++gba9KX0CHBBJo/+VmXW4IFoE2syAVh5wp5MaAzISmApFXE9pUH95Elo4V7e5iw+Rt5/LQdDFkr8hNGJlDltRPemrXNNH3S/4bZW4nhX2m7/Nyz3jdFugUPX8ANU+ZgT4+DLYw8uefOT+UGpr3gg4r/JZ2S91UVzUzm0AXegFeYxYQlXcEoJsb5O3M8K8OFHBERGAm2C8cZT2vi1h9lQZ5u1XXN8RwDnj7LAWUyiyIVNwi+vJzLCr1OycAYuY8EYpkZZjoPTiC5nnCq6wlmMEb0b2uTKsWb6ZAOAO9i9ErWyhWPLJlR8aPp6JcecmrYTjC8JXuIi3G64P3B8A4OZOG3sa4vmBMk/PhcXBzR1SVhUZ6vMabJJlloDo3QPXd9EZVmHK4rUu9G+oxapLTgwslWlzhlu5TkKcPNsENk+RQJGQ9cE5my7qlA8iijkEUzke46Skt2wSmnxDULOrk4TO7Cjnk5J6VmrodQEE5TOG06CRZmKap+1ExfM+rPizD5V7bQiZ4iZhfCsGJsMD+AdVBj0EuzOYti+FmfOwwR7vcs9QEvs+e5mpdVkrpKHaQEvqKU0IFoY1HvjwiXewfEt4u5T8dCQ44+22cHTIjGAQL1SLpjhXWNG7AMDvf0WYODcyUvKnzi6PEkiqiRaJCiXgjePtD76WEwzEeBl/rjw9vAlnZj2jSGf6+zya0+D+CcitRdzefZgRLjQmw7APOWgk6Wame0mmP0iHyL2H0Gnnp7pfF1LN8RhYmcXfDRvyqn0B0B2b0XkNk/h68ueIfhZmHtgAluxdYiEtGK6BwM0TzSs1Pg1HmzvR0J1EaQSDOHOuXFBxWU5UpbLVwUPFuqvtm/inNvYKXpaE6IljqV6ybJOkOKvkaSMBAPv384d3cmfbE9TEm/gMZefoUMPDvg1fsXtyeM+xOGvUxB/fPHxQYuYpAMOKN/HAqFk18nGgSGdKCMnAMe+bBEEDboPJRDFcEB6ChTOQYKkJkm1Xf78sEOy8/wKAnTdurbKNOcbecngVs3v5sIIQqe4uEBKuhgMUsijZ4qipiUFOCQK/2mVjvh6OP4VviuHWbbwdLem5x0H73w9PKdPwvR9Cl/IKD8BGzxFtCSFKa9NzpD/YM67KFdHvNRJ4nwMyN54+CtS3yUcAWEy4gBDu919xWzn/IVz4Mar+GvndnhDufkG8AStjJx7iQp0YM+4G+AV30vjmOrUP0qd6WGlJxa8SRWWUCb0f1ixXyPojvZ6xMwFGLMz5vaPWKuZ+YJFJJUSc+ck73CdcH7UN+Adh2AEtIewrQ+XkAc07Xj8bZ8O5d9weUoYpm6gPnCA02ltNOGoSWzb0EEU+5loHyvnutDKwUfEc/xPxZYokuucY5PzBZGKYadNLBNLHSQGaES8Il0pTCVmgllC/RrkZg7rhpaiRjufywQ6//nHggZN5ovlD9C+6r/4E0PJ6DYP6U/3NB2sv6+85MGiTfy9CajFyCJUpXfdK698ZRG0y4VeEmbd2ffuEuApuzWyKD4rgUdRJuLDZCLLCQtkHtrLYswFf49BQeFeaeAPHelL17E7iZf5aV7+92+Sg7YxtBn2DNTW9XOEi7VC/lNsw6sZTAjZ8anjyuJ09TavpjEApqP3unQQG4RXF8QUgjCx/s0VSkbBiK6STm7jBpdvm7YIw435BuCzh4kMOu5T4D66WGFXTlPhxQQCV0uQScLrQI1lGMbO0UIkpSo+TrQTlkuZT667DkwTTVolJkqfRyKnC6Kl7LnFiSQUNYvX4pAQpLjtIvt+HBxVywYhypLoze6+rvwv79ssXoS9dPQZBGtqnxT3gJsnj1d0e6gCHgqkTsbJoYEkcMVKSm2OTDZdNoD5zmmgszr+fWHycpfABhQxjYaQWfUrneFTLyzA2sxrUOAmBymH9jBH0liBdBVhD+TmN7J1DueJF1t/YDsVGUbSuDQCsRKaxb7Bv4f+bUPXNk3XPrhI/396w//hr3zhm68cTUbtaDJVnZhvnJyAh0p4PtdcuAPjkObregHz5kK3bw7y45HMYnwNgaLchXzZ/2RSlzB+Z2JaZ+6RxlBZzNjzFrOhMReJuvoqDBJRntTIGCQEYM7KejtYHtO2JlQbdCnyBu5BXqcPRJtxJqzsYT1Kmmh8in/3N1U5RvY5P9cAo/4xqKT68uwPfYG3Fyp5YP552MlVhkFkC1FnzBYS00fQ5sckYrWJHu+j8obx3DLUezQLAwtMlW+oo0aZbhVLybNHJ5LNRrdkNkZZXUDom25vy1Z40yoc1wR9lL7NNAsOmUqG8AnOok+Jw3CPsRcknEaA6F7QfcyM59KUZjhv/cRg2nkcmLzMRtMhpdPvqEXGN4tXEyx64Gvur3HZUFcUsFk7RAQLh1TZ4C0smhAliOvyJiY1ZiFdyol2O48zoW/NuKqgUTcbOhgUv7Y6GDXNPoi2mHWswWsuA/tfccAxuXfZekTH1A7Vr1KbflARCFf8Lc+ibqvaUL+38+4FepeT8I6/mV9hvA4E7KKv27j8MnoKaoCCBv6AEsoxCdvw4gxHdemJ3vHn7tAASDQAAEhXfqYGkbUWilFk0zMdpLJtXJQCShG9pQVboa49wByiYgzXzzxJxt/EZ3jcnFmm9gPTzRdQk0s7uU/EOXB9Awv3GewXdZWGPC4T/WYF5BwaBQ8Ulc3ZWqB+4vnvasyVktqBjGIbqXYYdyzxBkL8uB2/MbA7icgcbFw5v2uRQYLoTCDN+IsO9lvyauN12OJozplJTdlUZAs3ft8HJPwVcs0j+dFMlKdrg9eYSXCoG0UEprIN2Xvn2PSPAx8fNNWbOvMOlhn0a2YAO/Wn+0YNOtVbyuZ/xvZi7AcDABOxgYd8hw4JL8cKP5r7dq1jPmdgU2nepxRqyl/LFCZBnY+YsUheEIvocmbH01nIahCDlBaO9dnvmQLdJIuLKxMBuvAdAvXoPSeaLVoUhXfwojiafFQAyk/GOwJvsUmwl2UGZk+0ete7k2hakQhiA+wUua+FP+6ljMAzCBFMKlcjlo4FTC6sEf+CZTzI3uMqkNpBEqNBoqkenH4iGimeNSkJr80+8l2SSDBzYqhvPkoz6lgGmasyYgMR1R7D6uJZi4ZUGMGQjL2LnAezVLdu9n+mLXeGiv92lslUSl6PIkTkDbs/bbHXJSzrTE508pzM7ogMr4i98muL1P9HvH4VhZCeDGrU9xlFj2yA6Mz84lDqanrTPfiRqcx+q2FPZHAfihnYqCAivaF09RUz7x5WKuf85+5e4k5Aw1lwJjAZzk/hxgdYAVEfO985V0omj+i7m6VNRngAhbbVY2EJW0JmCNuQTxYzCLSpLEzTh0c3lGzKop5jWgiloSEWN0G6uXGxyzDiaxkcalUtF/WTmPknCF8EmL6UuqdFEQ7Dr9jXm5cw+WIRfuGAlAPfw0SzhpkYm1+Ibwp5wuXe1XFIGrNZHUF+SG1xkxnV997c73ANXivXUMp9w7TY5J98gB7s1/5U2jBnhE7CyXSgbs0NuWUGvDdM2S9ZoCtqY0H8QGlHMLOVFx1BzwA/v/0J8xJTFOrbt1cEQVfGpZ+JdUxmqPPWATcXn9LGyyy8G5+sSjR1Dr50gVYMhp7+tUkgxqm9MapBf0GOKva8Jtm+OhYjGuaKmzT27MAEGplBUgl4Grc0NjbDIJzMt3EboTveoGM0cMMXbJOSfAAQDPoyXCcHArmJYmAFT4kRb+EGpRubGqvqStxX3h3/blu9p/PWN3YcHyaOMao5c8VzffcXtAfvNW7vc3ktBpXabngwf0L2UrjzBRriEN4p0s8AkghCAkR+7HHdv5JU/kxavwPWURssfDbvXZUtFyCj0f9r5VDWpbS2ezOtNIWZPgxFZ7zZAQpxZLX2zuFFrs5SBhqoc/Qr0gkT37NtcbA5UIfv3WGzRbpMSD3tI0AgVBzbOY6TZqTZKwUq3Bz7/3WNDWP3nd7h3rlfAEzQD6XutfgZIHMIbL0KkLeLPCBRLJPFf1SROrTrfVsvJRtlELP7HNcoe/ox4re+DLMKHbiD+saByqj5gDf0ovMCCC9DEr1L/LLWJX+P7Pwp7ovPT8e+V5q5L5snXHuDZiDK3MFcqVzieOWntufTWSe5VygFFf25K4tHEaW0ToItJz5QWGI43jc0NArASHrqJfJJYxmg1sOYT9E85+YPhdgCQeJcjt+L6d3+7AxP+5cJpZznPFpL6cwC9SsSBcxTVJbsMRysKF2UQzYUQ0oBUDrZmBrvRyDq0qe5LhzikQzbRHWeoxQAaC7t69ivosRA+/lF6U7aM2aqTTgk6YYY1YiJYFRaiE9XFvD+AYTpHEjG3fvCa8AYSBxfOreGh8JgqLjxIS+/OD5KxUP98isGSq3m74/qOMJdKIW9DH0QmEHTQx3OIrJnLxAJwD9y+IlyKoM9ZaDNXYSiopS2AajrQOUf0seTF/7ja3n+JUhILCuoXxe5k3Y28ie13Qm2dfSW9uAxfc4x60TdGKGq/wSHp/MccYz8OpButKHuzJlGM0k439s5nL4w2esgBJc5r+spfqpwa7Aj4ATCsiIBXACtlU/33AHrxNn+VXLLqZk5/rr82TDs4ujNwlbd9D/tRMCkPU7BcjKXcoHs+YjfMAeS2mLVDfVsSgiGl02Pi/XHcReQNlGCRPfY9Pj/Un8pwFXMSW5oBJDgqmf3u3QOf78ADoAx00Y7LU2L4bHZyY3I+9CTRnVRM7gEXEX7hKnHIBbO2/Tk17ZnWeyc7LEJ/tMvtEkwK1bDzsoZm1Pswc/YCaptcUvFNzasBNNLkh6CfXbEPVxUyNHIrme9YFm9Ld6tFW+QKHrEFucK/VSRk6pAsd7vp1d7ntA/yqLbDleCA6wr38FSCgQqe6Q6/ScZmThXqISefDI8hfikqskIxUCXl5vhDzA461T/pu6SlPYGjhjYoTkstkoSvwrk9AIulbHK/SVjrWhZAmIXXN+tYC0MEU30sHvZC+yXxpTjZDQBEq7bMyxGuP85zk8TXAdhkM1K1o6aNXI4FSWlLw3aag+ENQ2aylavUDa7u+e4ZL7PmYFR4w7AnI26pg/OLCz4VVhmoxLaVZnNIijbqqeX30XQ806TaVMeq3R5vimuRvkQwwK0eEJyzQPRUuZP2nbcPWJGdCU1LllYTRDKatipVg2JAE5DxLwE/lzGlihIQVYlDV1PNtRv44PBl+yvAeqvjheopggRGpdSt/gU5eLBlbbmEjnBGJuE3cRTRTpa//SsoP+caWQJ5m8WCIeaUQcIQKeb764QGrexSTh9+KWm9Id9gKnYKzWd6sXCfgdT7xeOF1awhL9+NZPN685qNJywX4cgyQN6CvGzG0P8DH/B2Irn8nZzJt+WXuly8pR7lpnLdnwmaWqMMd8wnde78vr1CidEvxYLotA3kkdko/4SrKEL+cseEAX6AhmkMa+TIDJOIUplUa+qWFayVZ/+jWq/hVtDFVu0ICnnZUQUYasBGKTcFFIZOyzTCRbhJZeuuTDudYgehszg6/Z3Sjew0z6ycy+/km6PsprDBRYQZERxOFnIcZwOytQpj1SsS8FRxj6DIAGf2GPmEFXgv9TNyqk0PbAqYCpcYyOJUfTgV1ETiPa7sN2Dzty8iGr4DFCc/A6mUSI5GsY4DBUgNDv1lWXNm1leCcvL2PfXYuiSRplotyQWy8yBoKj6iiaZgJ2oG12Bnh6+WuHlaogFos9ELHITpKD3drmV/MEbfCGfls+WrBv6uhg2pJVRFJFkxo2x8hncvFUty4Fxq2K0rmqfFO3M0ZxVmd84Yjo/OLMUswXguGWd8xJJX9di/Inq6XrSlh7XVEDl+itbhu8fMmxjdvsKC6u/hntPBLd4+FXtw3AlHqkoNyOvcxgs7j2fIqS8T0+P4c6mQQ9Xj+OYonwwqtyQLBhWj3+UqCcHgJyShbVupAHEMVrPyTfjOBrf5kPfXaiJ8MxzzF22AzGeePdLMdxObrFuL6m9/dQ/aBMARaAXIASV6w3CtzSzeDBKkGsjv0hRB/WPzqhKQKIcYlIAV6/aNcvkejmTU79WLoe4woSwkmomCQpDb7wYq5OChZ6YSDRkPDdtys6C2tveFhnIkSCbxwnvOXmToU5ernko7hRJA6ZKr3CknG9YWFO9jAjPx+WLjnAZqKW1cCz3u8jpbF7HPdhOrt+LbOpnnEMpAJSDiSo1Y8Fao7z5YvGEhm4PLYf97c9bgaBbdMgNZGR2jwe2BsOHGManyLFGciwp9p6Z02Jqekf5jk7K/EjWNYkImwpkLAUlxpJBTIfRXiA8pNlsJ7ID2Y7mEn2wC4DXtfEpECwJ1dTWeisCDRE2F0wPBq5nCSWK/P673vKQcTN3P/l9/Ww/C1/ila1EauEoA2LA/hqF2kvVCUgQT3OppK3WOR06VvZMSJ3Tsep+6/RL0+JAH3zCyojFma76nCERUP1+0dhtQ9HJihG2oYNF/B1XnWGDXfp0AiYpXegNVD83OGEgRpRxFwTnEc1ltkMLybGgkx0xGTTW+pQKjKQPHBcLnanQyRCgyCdEbNI3XZGYIgmQV78uXVh4KXJRpMMFGH+1C9pdBiOoQRBBk5MsbuBBZobosK3BRWEgc5rKTJKiAS7FSg2vNaqTSanZDBEg966mu3b7zigAVLEv4oySN1jXTmVycHPxEe46wPUK7jjCiUlNV9jxbN6DoE1cX1ymOgnMxV5U3EvAy4264hmMRQOnfzEZ1eoQXpWGPRclwleHIJVKoqXhBqMtPVsNXppnrUhFUxWcy2UsjKtKASIFIcA+4r0Ih5oWroz1wW6X9tcK8Aqn661Y44LZxfrCNlQDmZPt0+5DzJJZY4GdR26ddell9aarOFMhao54yg/ul4EYVbD5XyPM2IRqZgp+lHQGmuzxK7TTlPudgkc789iEucPhg6FebMlJoAbR6qmlzKku5fV33bKXTzOqpzKAV1a9A7S5gwfbUfMDn9XSqJe20dJQUzxENt9J1sEOeeyyJcBvsBqxSFASAKgXVaoSZKJju6g+C4tpPtcYMlPYU64lr9dr9x000C2Zfci/nUapUzd9zjFmYeKMGJKnXZRbeUdlwfBdmiXajpiyvtuz+pgNL0em6/mDpo1SkNkUqYMjTOUt/5BV80H98bUorzB3saMFoBwyCCkdNbecTkSZrYA4IDwTrnQEuwFfgAWc4ZMQCbxmBxPbEEZOG9/UjHm3f2XfqIlzy1iy01wXDyp9PLPj+CW+kkdgySi6bjQ3/rEyPaqizYbsa3lniYjt9APKlRcLW42XaOxk2toJToMJwCTnRugwYq1RD3eLITQjuLvRJwXqWirZCC4NZHBDgc0AKYU/eWKgk+JHoKYyb8VXeVbFdnSWiuydDpkuTRmFt2JQMLTeXgFHAvnefSeLjVqJpdos4pPYne+QJ6TdEw1ZerpAKLpcejMcE0AaT0k6Q9Cm3IWxwUbB/g/sKTEuwsvcNZVGv7PsoLcwIBjc1VkGC1DJpb9XfBKy1oUYNY0XwJKKgsnKgs9tAvkXp/tXwJiUZN6jZEQWa058w1Un4+6Sb5TbUK4PwW/UyDhPb85lGCx5ZzsYwbO0Ypvb43q+9baBOutGlvZz3KnSQrIbaB5bJs6Y3+WBj0Z8CO9gPTOJv9DZ6+40InnfJF1teP5f5oEGLbVQAZity567FxgwwtTr4wXQa3aX6i5pqPIaJIlGOkTyH2jYU80oCaalLBCwnwgJZwTQL2fApBBNSkKyo4UMp8Nj0ICtvoELWPW/k1xLLnVpLFrO/5kzTMW2AywKlgqboKVM1HXmuWWqEs0zgEXFdvXR4wcwCwPUP7yLCBe7hb1+lpiZwe+DefPim9FHCOYkUcxNskhIdyiRsxZJPYRIhe4BWIHU4oJXXJFtm1jSIFXyN60ZWWh9yXvuxShWTbrXQMJoy6/mzJ7GMzCOMmeR+g2gZL2Ou41O/oySvGJZjSneNEPwMo9U8Vi17EHHWinkhDEVvelZkvuuoJe6tAj2I1UD1fUvK1b7iLYlJRYqxkxSRxSFLutRGybQ+t0kbbsDCrbh+U53fRGzIUsGkQt1IoKQtIaU/fD4yklkpMMixV0mJYUlFKgoEVXZhbGbPOJqAiXd05zEcTeggPnroDyKYYERw5N/Vi7Obfg9LGnGwiIlS2xYyN6O5rI63KuP5u1PGqngFiOlU3hKyeZVutjygIbuMayWMU72az9THSR3IVPEZobWsCjSeG5B67t0m+/jQSp48KUm0r9r1gbCVms75gLtn4JKom4njY5B2VWxevZHpN0sUqi5NGRnD+1IPJs5U46Mh261fjTpXiLgnbBj9f+6fmxR21tB6ugWyctPgFt+/7oosRGKzVNRCM6n1Gb3rAHK4g6zgkFPmM/JNYq6jdnKqlyruUOV2ZV9rxj81IE7T2v4Akt+iavK4mB2Dnlg8p0wkuAfCJ7gJYcP1C/ArANzeEQBEjsbKI8DLTNFCALfIhUMYZbe6J9S3CArroWWQ74kqfFNXJA4WcVbcQxdS1KBfl2kqxrAr+DY4PQHAT0Ehfn95KBSOUe1FXYdAS3XfAXvY/BQaUVWkq6hvQP0tw/mgx585PG3CVe3AApSAIDpDZNVucDn+hipnSxQIUvUgMPr2LBbvEa2mCGgf3mbUh2deKg705kXVbepkb4dqbc/fBSa3Nagp0tyovoeHeFWvb0eHbKsePUHaRFLurR7qznJ1Dxdro3pzyama0WU11wpWmLioJ3F2DsazQGPkMnWt+24Ocsb3TeoCc6xqRBDRLVqp10VyKhkySV+h3Qsesv9aFgYImy/lQ2Bw3fBGZruIW6xiPlj7yZ7bzGFSLVjTyEwSlCgOuWBZVK2SCuvFmfcNJFs4f3iu7NKggeGxdUILskV2DCMkNqWhTNV6+rxHHRuftSkCkEW9SUgvZNCozSOScEwde6bfAfF6Zmqbwk7tgBWGxT005S8EyQDZNdSorKa6BS1tZ1dkDg1r0yoS3Mxho1xYQTIObg/e8q/KBQACeQs12ZtL5J7NqTUS4oRZxbwaeUWeXN0ZqQNPuBUnljurz9RCeSq7HJUP1yOA2pomhKwZZN2BHjIGh4KbHULsSg8Nf98+Qrp31Lonl15sWcDqdAs8a1ZPQL4zGzEOISpC5TLjlnxZJi2xmMrLg4KduYgXx46k2sShe0gIR0MdxfEB2dMWqp387SvcxDbBKhRjUkKkFRFWfdQwCom/TseRacjoW8OBGPo6lLc5abSgHb1yUqQ7FULNoM5ze5m56E4bRWUhgSm5lHiuqDuImdrp8j4dzdvRaht1dguVKTS1YKNbG0nkTNtiQZoKEsuOYWNGf0OJe3cbwsr2QpIrru+ACBIUFMJjEgGrdpqc8UYVQR7yllXEgyTJMyJLuAdgcTe4Zv+Qxe0hGhK97mfcN7z9zOhrE5c8rmKIsjpYE46gpCYesbT/U4/qR6hPZdTrk+b+zNiCF8HsHDd2qB8hsUNIKgBlGlUaawxMO7yijFjWabRQkco+NbeVIz8AuAfcow1uyq2Vzhg1x7G8wuX9GyIHg94EWdksuAFgp2v5LqI1l1KWG7b7rl49pzNWpMynNyth90eAMHVEhYZERZ/RPdiUoXRSwvgBPu0rm+NjfXK4TPN5053HmNoV0IZSw7bSXRjSzhp6LGfIvFhwi+60zW2bPlt665JglVG3yV+9SDgMD2oiUnkqzIWVVYhLnT/GLa3sH8zvd5CSWjKGCh4o1e0aUWnQUarenUZ0Zbi2lGVxoLjlQQGgtWwBCpuAuhomCTjSN+cVsoc6tfhRAl+0eYFQsyaHRbSHajyLLloXQSf5mGIzQoJdlxxf7BLwDXjgZnAXZR/ZR5AQP4n/icwdZCTegFU2ElFSrFvVOowSkSOicMzHaswbU7/ucDD1dij1kI7NbSN65NQaaNzILfRqTO3R6PLih/CKVyDDdEjbPySOZPegOlPd0Lh69w1Se/dU5HOUM94S1GcUFNgJpMjv3Sqa9zFL8GsTwWIcHR/YRwleYAsvwTHnaWCTfxBpRI1DI+gWCEpPb82icnsVhdXbtKpoS19dqu2aOe6myrFSSnEPLL31wUdZie6U37YDRh3vGJBLhs1ILb3mx2f2a7vEe8yxFz5rEmv7eNWTTQjMWsgV0+DERq6cp/s2wf2CAJ46yoKv0o8UE8/CA8XotCGUI1mIPDWkrWolsmI++ety4/JspWimgiRZ7W6nrP453EI3H0tPMztGB73FXt8v2t/4flNtJtsSfHRIYKq6Dx+DqR3QKlKmQ+Kqig469JX+NAJrVDkSWWthkw3Z4a3EYez1M8FNJUrDSRyZA0cqWdlwkVh/BGARKBxJ3Ao2sgk9rN2LNrgNwRafiE5h42ywkTx27BhjY379k48htnrUqD8kNB29BI6m+zUW/4+BD6cX/oO60dCYHrpBbyWNVPOHzgQbJ9+0E4xydM2qG00j+SeE7qpZ5/V2yKM5AsEwpu4um5HQyS1Mjby+VCXx5ViniB3LDK9jJwCzd/nIyvoieVNdZ4eK/k5tYNvZ1LWRH8bYbNjyQi64PpCntdr2lqyaujz/ugRQuRSpt8HEOJeREmuxznetYBNJXYsEFFgEuCR0egMmCa0HIInwtLkjWVVzt9n1NQOT+HsMsElZL3DyGsddZM7Q2/g622d9z6IP2Hpd7zQymMqnusGfTkFdUhNwDNVqQ3VM4/syEc1vbN9rOqTvFbWdrpIS1q7+F7oUVZukTRdDxFYJoCXtxNRasxyEWfKkUZwLRdGJwFYFi1nFTHO8ix7PSk+YeDTapO2nn9lNFtdf6HGajxdgoajfobSwiy2j8xnL1Rs60nKgLTWiqkYynqetxMLB1BOmF5lar+R84kpgx5Y2Daqr/NNUgYb6TpfD6nTjew7b4fxJvCNnZZlaXUDRJNgZnXfASWKDNByfcf+EaLnOGZnw6G+R9jpfDn1dnGCNWOANu9+4qoD4ysmtT18R0PKK8Qf2prqR/NAw6KqWvukyXgQGkRhobxvmKdQvH1H+psQAR3FDGKvZQSYJ1rIJFNYUZGrz36pP+X6FXhnvj6Kva6BJKR7TjYNhS0SjKOlBDWv52ByYI1WDXXObjE80wmGztpeNlAAMBweFHBw0SYVsCbZ4uwA5ozJyJGgJyIcyt+RJUYuZB80gTLgBd0qtsbL1oOUCBaXzGtVENsiQY1vZw4TdO/PbNaYebUtS+yCquLO6+PfQmN2x+Ne1luM7hxrca40PtZZwXLalJv9PAvJTF2G0tTccqbR5pnsHDKozJb9nVoxegHAxynIqJFOWaGwKS8nfYGANRyVHIwnfOQpOCeJluKYaWXMHcodzIYxJfWBLUBf96yg1vmdTH4XE/8wHmOkQhr3siTyN46QKEhFrnoq1DIDLjN7UXp+9V/eSvpwZ1EykV0jfcuY1PZ6tTrlLTnux1ZRD7rQy907CQJFtmTMCRdYZALgDt4y1s2pKTJ5cpFyPmGWEs4alAh2JFydgkkotkK0HwYSTuVzus/oKnT/Ysm81mFoDA0QlMqVEo9WpJlxFx+t2SsXWSR30JsuONKC75RAOcbfKIwOQeXHflYYupmr4nOLp5cO0o6wfx7r7rTmzZhZFCuxEuuIGFDRt82z6zR3lQ3Lp+5LTswkxy8Yjabxk7teiAkRIoOkPU9mkkDXoTbpR9asmyZYxOMyFnA9qekH1oefdfLzAAMn3ixNqwcfuJAWmvGQDefEkv7y3t/27gzZphalKEG2qJwAfEUI+BIIxrXFtFDPQ+z9bGk/U0dT31IENR5CtrUmVOuujEWtG3GMkNVWG96nFt1xdmtNjhk7kcyQ77kEbh5lfmoReNGpRISGpe0B7PvOlF/3yQtgzYahSp0092Dr81CAK1cw+mzz9us7Hbs2P/DmdFFvyeyY+A6Uw5QxMelxXJstTOxQ2BmVhwGeikXoya31PUoYkSNqg5Wq3peLcLGb2DKOPjVreJTSNtf1/rWJEDpqCqkpE0FZelpOoa962BD77fqXOgBPmEzdAxJU88lB74ujvLUrSS1EX/z4w7aqflYdtbyv59KT/bANX2mVlHHn92/cVqYiRqtH4EbayGF6BgXYoEgLfUHsj9T2D4yEMN2Z+HV4RCCFoWpcL1aCNNj/hWZCyO76hF0BHl74Pdm3CCnbV7cMKbHr7H22T07E/pdZNH+5w4DNsQIUjEs+gguL75u4nheN6NNWWqSDuopBl0/yKVzAv+adr+/WSBa+Ghh1uPMJPDBLlNND0TAMpuEXKbPQexgQcgLozZHawKQF8v6lesynQazw6GCVzvcyhjvdJaexTWy20pCJX5Az9rPVDlHFOFWz0SCIqc2HT2lO19znV+RHBzgfDZPaO1o63aNLKO8Ew/ojf2JiNN3VAPF3EqhKaTQpxlJMkogijf+bqbKIJh1iydQcdVDUOhldrGFQ6+XeROl5z3gJ40p9Km3lt8RQB9zjkose/UWsDz+0PK5ffUe5lbB9U0FYk6MAcNbQTxpfD2Mp0am+hCHNSi9k3Bl4NTly3x7nSUdeT3kTdm8rjg28SAtZHrEZ92whaS5NYEtiOrb1ftlBNiPG82ho0W4hMsU32zKoYqob4Zwr50fWtQt6B3BYTEieqz6pZwE1V0J00K0EedQjIHozdRZnoNgZBMTgGUUObbIX8Mxf2Khn09ac/R/MhVAFKUPxOv4B6z3932tlGTmcA9RrQo+OU9bTl751s12c1aI2ygp5DpUHHssun1EUCkAnMhEhlbLU+l/Gyk4jylATTDJ36BIkmzj4CiOkg6PaVFUkbd/mGqcK/4q7s/UMNkh1A3pdxrV7HxzJ6YYKzCFFZtZWq7oSOtiXyuiY/AnsBFhh7VA9gbL0bgnztritS68vHPdnTiIdinKns82OYKuSOe4PTUYjduILmtkneYhTumu4b6/7XwW/LkcdcQ31PVfK83COo2buCek1Ing1ThaN8MntnRiTjEJVGDqGKraMw9AyldAIkVkOvmzr0+oB4F/6oiah+tXzD+LiBGufYr9MhwSCQTw6CImnle9WYFq0IKsG8ti4Xk1DWFfKLUi1LNH2oHVGF3NongYLF0yGj14d6QJ3XoGwdA+Wp2gFixGpaGCFAlgTQ85CxkvSiHV5DjZ3NwVF3fwRMNx9aENx/F8usQ/qEHXTf0VqC7IwTTCvh7YWPvBSCnd+lF5Xt1pjO5R+2cxDvcgiv2JIK4RxIpd2ZrufNpT0TLwBTBbeCs07Lyh4RUejOjYZGrG/1stNbvmL04MESVL4+i51NhvIiPjAlNiOFHUlUscjcgRJmkPtfW/UDujUDdSfd00TQ9X0beU2zOgjVn+pbNHHNJFPXjzBEBRfs047mvE0DPt4GzO85GlJ3UP/sY4KDgZW8LqQcrP8KHZtqjBy+GED1SQvfMymk/C47Ua/dc8xor0/Q1Z7sCTQe//aqaB4DaZgmPpvo9ELqKG7IZrwZaGRrU2F8hry5R2kn1Ysk499I6NRCfFBGaExVzRJMY059AK6R30Zjzp3PZsTGTJ61ul4D7pJDKOoglzojMey7GO0HUG0pdNR8l/zQw1NZ589tah/jG3CySUPl2oFQ32ZvCU2GGxFiRFyrQCSBNq8h004ClYKMTptplmjGpAGB7G6r2uk629/cIvpHgD83B4WU9406WhwAeSgHo1kZ1YKWpodr8RAGhkz9uL5Eyqb80kFAu4+PunFkdMsITakaNULcUVk6B7p/bwcwRf0qZQ2n7i9q6gv0BiNk9pVRP9bGFr0kchZADa+mDemF1MegaIzsltxA69ev1olZprzqEHTnnpPAhSIAONqlLy3bgFGy6XfDh5Zmj5070sLeU02UaoXQtjvT3NOpC8XH1suU9LqMAZ0VvY23r5cro+Akl5p7eunQdGcybF07Dezh0WtTc+DA+++ZF3/CCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccMIJJ5xwwgknnHDCCSeccML/P+D/BXOvaPH13T6/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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au/0U/+uqsFXNq1x86ieDVmiXwMlyQ4iUhWmUY81D8JZuPjC+4fJaDLVNXI8SuVHSzLYLjBCXYwe4EoCtmTQBIG3VV2F0AxeHeJooOhDd5BdgmfxHGG/wJ0TgAkSyqNiBOgGBHfgiMeICOFzr80EBAGP6Vcm0fbTx8ylY5ze2Ubou6m+n/xnJXvHmlrOuVeLKtdRqsRuY5GKTqWoqH1WyZao66BP893C+AifgBK8uMwfQxvN70FgOwfkNlzd2JI2aRW/kspDjxBS5sLFC7FD3pelKyAn5C/wi/JUiZ7wwQkf1v8F/YfrDC9UZ5kXqAcc//cLOAb8EuMnfEBPmIeRhd/DtkBNN/ADlVZyPfq4ROsMKcehb9euOF09NuGu1tdSwxuSqIyq6ZLQGa0TfLfNMDQlIAQu+/Xv45V/h/yOul8nfAgaMLZUL+Fu4/AH/hm+/4/oVHsBbfjo7jGroLLi8AsB4K5TXUVrXCyamhMttWtk0MvmGQOOC11M434iDMrbn64SEeQnstHJkM01YcPkOHOA/EMU5AGA8UZnwZy2diu1ZAupT5zbb+08ND+2qDZH9w5hb3eaZ18mFBPjvON8Q3+GHcHnHNbFg7eyhBfwN/g3nG/yH0eqk9Uyd4J8zmJtGF5S58iY71Q2s1Z/kPBzL5lGOSeI7wrynHH1IzNeltokTTTQPYZEa1C5EQFfEPwSVv5kEZTjozhbj3J6hZjm2vLzP3bsKYmX9uEbB3bYC0TPdqvpto0mYn91p6KZCyFqwJzcZ8Df4FPx3XG7wN5yvAHB+gz8hutCy23gEgMsf+HZlMUqTfXEFKXuhAPr3rLKedNmEiODJhM9UNSEFL1Y8ndZEVGX1H7talIKJ8uAQT4gDLm/QlQBM7LfSgEIzqhx9XSbXzqulv4oaWjUxw7H0RKBp+El74hloa36mzoQRv874l779N/zLiF8Lpe2hXG6bY/950BZ1g2H1NRnOFWrjQAmX78EvGG8YryyLAfxyxfWE0dVrxie8ngDg2++4/AEA44IL2cIfOWKeGSG9tcAv00UNLE7DY27qZVGRlWO7mc8Itx4rg0y0yhItHof5BFzFPsPE9PfBvqpK1/cAyCJM06W24YlpuV/XZnpMv8KhmOMklHqXAsiTN2OFlSbzr1aOX3P59EMWmIUfXgNPivIE3eeg3bnceOYULksnMnSRWMuFeI95RMbvmIwH8Q6fgA94x/7Caj8x1/+OceHPq5KDqcANQqALgODxz1k6K8KuMQ35foiYQAl4A+BCdFPxzTcp3zmdwOXxfPxtBno9GbpPcB1akYXVJ5EkFCysd8Sv7GVtZFnBq8QTy76qlo2VDqwHPdkgoNZYsepXj31yo3bhuc1WErDg8vs/A9BcLb8EcvVdiVUou+pVYG9edKYTztfg34C7BG8S/A0eOL8BCf4dcIhH1giZQAEA367wb8BXABz7ptg6bJ6yTMF1wC8210n764BBZLGjroXrCZcEJFwHxFOYB5yXcAVEMUBcJg9cHS4LrkOgEOVZWGkc2PGZVz55Xo/5bEMcEV0OKNC/xK1dP0Dc+g7aGdKLisS3OZCS5ka1zqyNVkEB6iWuNx9q1C17qMD0t1YGKu1lCeTf9kOIB+COy4LxBiRcegtjG4hexwWX/0O2ZyQJWydcFvgbf3hzdsAJAPzCLnf/Atzhb+RxDNEJA75N0KXi+BUNfEdxgUVHmskUXYgAELLznJ4Ocn0K84B4ymM180VAwuxwlS9k84uqlRIIyWoau2ZJU1p+dIgueDdFw4adXwQnGdCVHc0BQOHYa09Y6MKG8+WvAqtgta08I6xdIy4sj7cpaiwow+WG8xsAzDL94xWXtxCHyQ8c8evspujiJqvCLzhfs/8yq2gJwDRSYOYkNgQbGZN/D8gMeyKnd3ZbIi9LtdDnAXC8F0AOUQp03FcEZj39awlxwDwgDiFSStSJbTKqUzMNZggRU3bfEf4kZtkQZBnkLrMNdMi7R3AnljxVI+RovXqI+xfwhwmU21KtALH8oTo+AHN8tS3pK51jQByQd0dszFmtAPUKpPUCHcOlbHFbIHRvWupf4K/Bf4TLG375e0BC/II4TEjh8gfOb5iHEAfEE2uTsOe8AdUqyryEtP8Fv/xvpZMIGBPmBVhwASVnBNVQyQyPb3IBIMGTjfwOQKKdiTHHu4j4MzmeAhJeT4gJs+PMUbyw0ycOmL8inoAj/4xfmH1GMJtnVvdF6Owl0yiA8RYAzCe+OUvWvX/HSLz8AHzgvCDe4RfEI9O0HTHnhS5zhpULtCsFCRB1uJ5FtQ0NVCUL3avK2X4SHkrkJyvBCgl+opKge0vIgTIurOqBROSd944xKagItk03+tKo3JdGTE9YYGVdzFbJYPcA0hSX4BNAKR33zKr5tAWYFQVAGKe3BQZgkF1QA9MfnKQ+mT8yw/3AaiL7uRI0MCZ5ehMk65lqIKKPTndVZGQokWUmgv6AT5NfwtoEMQfFYj7XTuKA1oGmCdYTBrje7pZ+IyRZplzbMD1Fed0ySrWWfFtd2YkQ7J5Z8CnS55oDgMt3nH8PAPxbGG8iak9sx1y+4+rggfEEf4O/wd9xfZFKJNas37UmW4EuyNl+eZsgTy+WyN6lEusG+oBfcHkTJnJn0cm+egNUErKLIzq8fhG34BFIuJ4L7hUHxBe+8AuuL0zB88DmlyAZ2K5CgJvImQo3zUPAAPwxeRL3H5hPYbwBbroO8N9x1r4ko0x/AB/AobBQ3fhGLtOQddDTRD0ZhzJd5Rmw1pW9CbYQaZQjwtom/8f1gyvpKAOpbtcvwb8FAJVL/PkvQml5gvMVv/w9YICnwPc7fMJvDuOCyyvObyJDE/w7vr0BN/iv/G6dQecwvrEEHK8A4CUISdLGuhXzscVmTdLUXqhyWgYf8HdOa5+HPBfkWKW24EJ0+LevgEg5j+n1PwT8PvlE29URXybatx4H+BviV0QXCJ/zjXuh9tPrV1p1YQbGG+YhxK+4HvCLk4NCP3A94dsNs8M4BFJCooM/SRrKMvkF8ynQKFFmDI258ywveD15cAc8prg8u9VfQfUM3shC1wO8Q5QDGiiEEF3pc35oTzwDPQuJaeseqra6ueWroDN9A6U1QINyi46yyNYEfxAn0TJhCXCYKVApIhuyoQeOLiaUp3iqn7LIAYV5qvetSIFwuGYofBKjGxMkIhVVNxCmNTuMyCsh7/AsK8y2kSR/UA48bmz9QCtxpjsIsvlk6ghn0Ruj4Qju8h24lUFVisC6EA+Ip5yk2KZ2t0Og21XH8pXZwTuAFt9H8MB1QfyKIioI80WIlh0qlDrNFiSOUAO8wwt2z82G2VSaKVCCcPjl7+Hb7+CM8Xecb8amWeAXzAm4CXW+TZfvuLoJCRfgOgTNVGffJFuofDQN7oKew+yYznQLW0YMBWKz8GMW2Qlk+kDqjwPOb7whPYr5ywXINTbg+qJnhOB1gL8Fik+SZikBJKFsB9C5IBTJfEEE8AX+O/5hmWYX4lf4t/zdnHiEf+d9dr/9R3z7HddToAAYjG090nKVztLAOtxwluRTnTxSRLxDHIIn9bGKF6ua3Owj621ImEZSyT8CaVpw8IeAheQIoH6Hh1ztCUabI7QJ/iOopZLXTCqo0zddKNpSuAMHjKRoJuDOVY1iMo/LRBseckYwqaFHzmG72Ez1D/gj8EGLdmIPvDJm1MvJix0z6lPqUcLoMJvwKYNDPDKBQr4G9hvyySJkREeHb7diSMmsYZvJmncl6C4iNariINTsuBW7BVmpgkLzWVdxos/A8AUjENx5kXxVxyYeu4gBOFy+k4s/6AxljKvjBpzoCVrmzovADwG3CbImaNAvwFWGwAMgU+ZSrBDAeKbIw0XSIfEQt5BDUCn/+QTy2pJ2wUjKKPjuQQat1ZUAh8sNowicMcGnKSJ4ozOMC9+EDDqTo8pTJThZLVHqrzcYKQgRWJOA6BIDZvLsEE+xH7wz75JBjQMb7/OAeOQMEvZuOkNqx0LQR2B2YcwGu5hK5B4+cOiVcj0zi3WIp3xwCGt0NyAhnvB6Ao6IHO7HPISYeEKjpkI7RAfn38xe6UTKIiC7klncG2Cn/1CQOVXHwy3ajPKqC53Lc0McKNc6wMG/AYuEJRI7vX8bQH6cK5lQjnZCikwErdTg9XR+B1RxUUNYfpGDX1LQzIm8FUZ81KOyEA3Y2kp0j+KCOODyBy5XVitJtfduupKBf0NM8LfpcpPdZHeMQPxg5AsOJ9zXhkgomD6KlHh18ML441DuNxf9bwbiCdcXxFOAm8iddFZ+5hAHXE/h8j6NA66nMJ+AhDjg+lXm6wTaRYkhAFQV4gnjzXBT8mIOuUUA45toogP8DZdXxAPiwApoJONpCAAH8a9fcEnwCa9fgz/DO2CZri8Yv4brV8NTj0CS74Ad4S7v2UKCcRoTadMyyuLGSfxNaFqTCHnEU45o0cTPJ3HUubyLgP0skhGIBH8LPuG3RfjNwG3xHn7Ju6ZVxSukVMjGfMSAhFKXvH5yernJ78pmjS2DLEDoWlOByP0J2vRo6184SUcpj19XHlyiSlkgGn+vz4ZQsQgDRhTmZCiSe+KXIaePX6bZVmhdm1qPy+ipMT5rsaZ8LU8c4kF0VkU4AQezipxEZzSq6fgaR85Y8Cn4AXATxQJyAZqdD+KgOmE6vh9iSWlKn0OkHJyBGfV4EqpdMA6ijDtc3ljJYK81cB0wvjG5e4f4NQBMxBciiyV4SqsGLt/p+J7wG1iFV9fMuNAxVMAyXb4H7xD/M1y/wi+Td/9EDHVMAQMdphpigr8FJKOKiMut2Aq84LIIrWii0IckPgL+PT+l8ur3NpQ9Aaw5UOqkF048q9gyoibKxBeUlDCLAqcEqnlG7F2XO/m71MD1hHgKAOYTi12K5ZAnm2OVL8wsr7KvdxbF0Q8ABWUGzCJV4PA6AKdAEabrC/wHn28zJlyPiOR8gOiEjgngehHpSpEn0euuEtYnM9paSH7B9WugmLlPuJ6Cp+17R8QBbrzxfgAi4VFQtzsJvRNyecF1wOWdxwUJlzdcT/CvwAHe4fIHrgk4Cc8fgAsuV1y+Iw6IR1zVP7cACL98F6J5A4Dxv8DlFvx3vJ4QHR9NwbP1hgtvRcW3fw/R4TfAu8nf/pkOT4tD8AsiRXpu8MDlXV6/C/NTOlP9NQE37uDlHVcHv+B8w+sgx8G9sZJzfpsicLkBH7jQAibl9UAc1FRoL5yU0ftLyZ+E/nziAI9CpsVBKkH9Vdk4IA6BFQM+QSR4GpBU5HkgTRS6pDAsFR4dXh1rdNcX+AHzCf4N8SjJdSdw5Jak340btQKBO0s+nC8c1Zwd4gWXm6xJjVQBGMQNIhrjfEJEiIBfJpwQE/wbokM8wSn71OMlxsT7AQBE2l2vOo3S1lH0VIDyXv2dScp/AA7+njeq+g8TR00YKcwFEZoJWHB5R3xh7uVvGG9M5RCJ5t9ZGR0X+O/wDnFBvJEFHbBMGMRUvMG/h+iAm2gX9k/a7RDTAi8rmyXpHSMytjorz8ZsRZHidw9y4VggAqV8d80njQ+GxE2OsL5FbEkTMVmVOnKFUazs14FzoJTVdcEmhsZUJHoyyVpwzbXVDeRFm5VxbVUITtIwuz70RQAInA/qKZ5EqpWDR4gnYKCIM52gEsaE6wGXD2allzdEcIYiR6Uv8G+TT/jtEvwNl+sUT3zKxfltGoeAr6wDUPKLJ+51g//OHNTfwuUN5ysulxAlrEJKJ+1kOC+Aw7d/5wQcpHD5Hf6O65cQT5N/C//6D9O3v+PyhjjAf2duSqf+MZC9pZ4gZYTvGG+IwDkZHTTlILAXRkuLMyfnilpMLJBGlmbCG8v0suDqcAHrD9cviAMud4DchAcALH+zguhkLQ2FTOcL2QBJHk01ic4O1wHxa6AOzg7xC/HmcD3jVRQwovVZa3OSEA1mXR5s3eOI+YSrw+Um+0kcKw9Q621APON64mskjJoVZbY748SrhbWpF1zJRHuBB7yDR3gd4BEgNrHTgIeXNGw4RPLBItDmJjilaAmcSNiJN7lSNs0psMfqhPOCy3dcMfmFz4iKUhiy6GdJubjcMC60qQ/nGy7f8dsbfML5jT38lGfpF1ze8foWLt/hb7j8B8QXXP5gHhyX4L/jt3f88hrOV7x+hX/DCD4jU3sBMQHzt6mNCSVbxSfaVwnItjLIea2aACF/7MolA24QtxFNnnDoOISYJgwhpolEDYnmSElxpGLe5aNYOSEDSFD/SwV6vpxSFTHO1wEjwmyyNHQVXYeMNoP5yaEgzelMgKQKMC2Kss6L8wh/F412YGUXKmwTosNo6J5epJJxYMOG9OPrieXwKNM0ClaOJ+ZuWLGxZG3wDcDlXbiLTDnA1KO2Oecc6JRLxpdP0+sQVMSQPj6KpQVMeAmUm0PtUg2jHl9BtjxJf9qy84bLiWMkc+LzWs9vOF/hr5iBs5Cmv4u9dQTAq4s1FkkFGpUupcu6BdGT/l5JQBeKTziA4zSeiFVLSkYFTTObpHR8qxq5mpp+ChDvJsQPj8RWwSiR5Fnmmzw+8cDqVnTAnR0gejqSLhuPgi6117PJMosuO0PgJPWzFN/qoq4cKVltSKUy4ExWAJg9Mdcnu+pUyn0H0DamBACOD5kgOShNclLMMiHh8s5x/ehw/nfOIfC3cH6b4EI84XKbiGRf6a1XprDzAgy4vgnSC65nHsfChHT89F9PYvsTWdw45jRLvoL/wPkKf8b5hl+u0+tr8A6//B3jDa8XxBf4d8RL+OVfcb7ht4UzOcCezgl8cprxfRZ+qMnTOmEWy0RWJ7YqdQpFMsd1uBIhSmibN06II53D0AMA9kdGByR2UNO/r19l2o6yxxfZ2KIVm9mzSUeHYepc7MirMfO/lA9hVF2WHPWWyGZkUaNCnBXfE64UznUYyRu64FVXy0nXc0GOryfEr/DX7M+KX4Ak2S2cpyxRtBMn+KlA+zlbKoZTepF67NyWp17OP+HjoPQMNDU7JBZnTJMJ4BTArLabcYdt2uAw2nb1aRLc7rK/7Aa/TJTqxqHFG/wbn2wx3rgA7UTzprN+mXRPRW7a2k/auukdrBZYpjt4YfbRsJzsXBxkevS0TscCeh5yATJHyN0dj2IbOfnpJESpxlNlczgOEelfLlaa3kWIv2sz2WpTZivsNK0KbEI85MLRIbqJQjZxmHIH5Sn/e1QR7zIR/KYp+CQs7nmA6GQp/wE5DZ8zpnQiowb7HQdIeNqUA31IFP4NMSGe+Egq1gFIMafAxh2jEy99YnGs2qG/8+YEOuuH9+Jgovzw8xXf3iZ/Q7wFvwDLBIrxOMQlC02WG8ovdY05+amTVK7bnAUHO3kBMD6dITDZieeYvJLXF+AWRicbJ1zmiDRc15diqUPiJp5sgA8z2QnQOLjBMKOtIQB5mhP2xBUzkutwMTXYBWYCzjH3NFNk4ZNvh8sxHZOLOp6ChhWiA17EqnGIxERPbIRQmOoqgWKzq1M4XO2CgTBhwL+xJLUFKPbDrmAzFtEVagolO17PuHxH/MAVXInnhKCgPm2fMDucE7f7SlqjZgcvnCrhl2m+4bLAvyPeEW8TgF/+CP4V5wXXrxOdn0bHquCOy226psD7sqW/mUDp5Eu1nAQ9oDhGKyeSSsIRn6xpTjGmiCX3fWBavBJffEdMiC/Frobo4A/sbuS0JqPPQY5mUC8Vz3RVTIe6ZGnWy0ab5mbjzfEf8LdsAVen1akyyngOgGw/Il/kPNTN2dfp3zhkMoiY2Mw6TXBMkZ5UEQA3Fi85XfBOR4CjXAQouDfJF6VgHTWYgBt0NfNbkz2COVuURowyR1wEGx1Es2C8zLc9TDCzt4Zz0M5aVUtsScnbMNTZMp4KbOLFobwjozSr0e3KMSwHEOUQ2TvluBmubHSJatkUyaAVQzFqyeMOSo+sTTPat9bFd2afqVespCKWEpZItJhmkxxYX6dRncWxzyJ+HkSrAABcj4L3nSOnRE9j222ydd4hObDhOkxEEOyBQ6DoRXzHmODf2VHv3+FP8N9xeeOkOx4aaejyHf92hh9wueL8Bj9MYwpwYVww55MLQqSoyZEn9fw2QY5VwUcWxzEBB/ZWRlU8lIjvZZymitY4Ho3oxLGi+UGW4IZs1pAqSfFSoOSmx/rAPdVZLwsn/EKkh7+L90AHx9hPF8ge/MWw5LshEcFHyyiPnNUjpmWE4KhFY+3JMEqZKE6DHDWwwn3IjfI4OFyHyRvODeSEKdJDyO1Kng1dXXHAz0UkAwCyKhadiWRgfUXK9NC1P8r0q4dWL4RxRjAhsuMX+VHmB+LGunzAL9O4BPR2hKFE0nLHitcy42n266AS6wDAyz2W3K5o1LE+l/t4MCWduZC/2bqsIUMkrxT6okUliQS4N9pXD/OCoJt6ii6gUWFNH5U6azDMPqqLSmsbTBlXDlQ1JlClaKLDe8k6xFEUdId5kK98sOPUIbrA7oAhY6C2lZqfcMAR1yHLuOyhGNihYLGfXbi+8HTOQ15kmoxSSPl3SVGtApWQ/WJmOqO4GDUnshaXOij3lTUmB7nTguRFP3RGWVcsu8mOxrjWqlxmkF4nT1OWdKjV0SNldNlY4R4PefCtLVwwjm6PGnWiUjwy0Rhqy2CJyUw6I2+WJYY84/nOkVMv4gC4aR6yY1HXqiVcMqSoX7xuj1yni0Pwy3R18EfEIXhMcLhKXFtNPx6mF/OyQzyFOGAE4puk7UAiKwPiy4QhYJmAcP0KPnSAuveSPVlRQhekwtP+Q8rYiF94Lr8lkZtqV8rxAfGE6ML8lV2b/o3zJApvnKWY1BPi2kdDSQy8HoLHhIWOfylMHJh1yIUPHAuIIrBybrlyLF2Q8hbQMDNjzue2XM6w1szLYiUc8nwVC1VIhMaZ4j1na6dbxq9t6YtOcoWFzwFiGBwzMvw6pZkDSOH1K1Mh52CIDOExGVhv5LfoKHTALyGeENEYSREBjaiCEXBM7+oMK5+q7szarhTwR84B21r3MGKo9SRUPwcNjQRIV2HR0MLP7d5sZZm6OSk9nkz1Gra7o2Vc3l9Wv9IydTPsyi9jw+H6CSuWwmynrDg2NVdqTAv8ljMHJzkAk6WNAmdTLYjm5PrVyuS1/pa9xhEODtdTAPHYE4hJ0MbOeZC6BtYU/01YOomkeJKGvwblUpy8l52uocMJkDlE5NaNlyTB3zE7dg8hO+SmPHCD4UCGHKOs9WJ6GpaZh7vErTPuFQir4+VhPFAM2WMg+9Ss03TgDQsFHx3yT8trKxUWC65fCgnGhpfoPBztTMYIU5kzZMHKCtgBcMxBZ9u0M9R/lGEB4CjFWHQ/TDHBvwSfEIlfmiPHZql2VJVPkjaLtSeO1VG0Nd+cpOTmLK04OxUncUme+M3XAd5JaoJ24AYM7PHnwB1Jtw8uMFJk2frnqIBMD3tPaOwG+Hs2mNiMvQEnNdomzmAi4huCXya7v3Z2fHxmTI0ZVPSYzVLaJFQI9EymYeN1BmPG9VVbacKyc0aS4uk3oLSHoh1ex+cpX5XyBikjaFjzAMqWjGY5lxUScedNxh94dbIXbTCDUF5EB7/gSmdtLAFuKvyaiy6wKS8MWZMzKWPkSzqECDmq5MN8ellNQ7s7Q2bq544fzlxkj5SR13aeivQWFDe12jqPsKxh7onXrAtyhaF6K/80d3KWQ9PKU4Le0Fl7ilXR6Ma7qXfTQB7GiozszVJQqrFiiaaeOBtodZwynGEwyt+R9cXCTtdqibkebCuTwWqqx7Zi8+X41D4iB/KRs/ZcTYrpmr7oQN5KCqOJu05FAx8W5bg/VuWfTS0YcnyFV38StkpQuXMdpwNHzTIun/LC0uV1QlxC1Zk4BN6EVERlgncTnydjxsineibqRg2eng+YmOhe3m+Exlkj/qzCs5NMf7vqjQEOz8hKjsIXOmNiTtKbpXI97pB/OtO0+jpc/oIbdJaBeMwRKTZ/Ib5bNyGFOEyyVCZNbCX0xnZdGYRL18HUGXPZs06aBiXXxUIiTSTHHHnw+a7LQ0YuFa+eDlV0SL88lLLpUKMIe6pbNdAf2cmi3I7JSNzgXNJKRnS4RcHaM5KB+l/w4GoosU5GyZTsXouH1Zd3itUIdNmnwmh7IU3oyrc/C6CfOkFlcJwvlkIHzcQqljW94hO8C5CJjsodh4CB2OSkUnQUPxFvNaZ4qcXNKr5mMGOxEWCyM2jHVk+pyJMI1kcdpzLQM2s6HMwYKTUczIg4442zksJxaDvnKR4yM2MLhrDX8IygWhyZIqOvCkMxqdoZlBc93qMJBpZkPXLlVa6kvBVaHubTJEcDlaaodTyV9bTA+w1LbGcUya/Z6LG1US+q+qsmbCzAlcLavBghbh1MQDDkaxheta2v4pooR77qOF9PncIWHPDBXGYsq6JuuusJQD4Tgi2SRXKLSvqLDrgb0SnBPQ0URWXGyXBiiOmqUUfZg8I6Q6pHmUf2yFudAPjE+8QBM3lJNGtBXs65DEpPbPHYD/qa/jPIU58mPeykpa3ZAUuYT8UeDK6t1XFNjIPHpGQeHFhSzrHkt4jIrhYHXbrg0ch6/6EklHIwmTW6vIRmYzWTYySmqVpgW3TpMkdrJSHQMzkMuykyxWjjxjEgwTdGDvlHnX1ZWWbmLobxMrEeOMkUrmBIhX8E9cr2dhHLhS+7nV1FZmgqh1nEpoVuRwTCBfWix8yKVzRxTkrWh/tJr7NMqFo0UHe2Rc/UqQh0kGy9Y0KX8Z4ZpHINOz5ZcDvmUtqvfNqm4dx6glUtT5IRNY+gXPllbQ2GAOggMd6j1yxjx7pFygPEglUdjTCOOipQaasQFl2phs6kxmjGKw2KpFRFZ05SsEaGnacEANd7wSqUiItFMphKLDgz9O3AJSCFPEM2uFIOLgVRIp29UXACUVdQE5/ynr4z3MpiGB5xREWvKkaKFzMvmLT+OIScImSovz6YZMn11LYXGnFcss/qzhbcA1zhElHerykfcCapwLRLq8jxxmRDT2QJan8oe0iJOIJ3wOGUs/ztAo12zZnVMIsDclansdTsS4PAp2Y6hYI9gHfxtuor5eEchXGmHrVUjmlFx0tekPn1o1iH0hBrNac+iy0kFMzou4a8KnrVUzdkBbIdnbvPNCcCYZITn0NtDjpAqcGSJgopDMhH6+i6koTmZideVfLU6q0OdMWCoZNsgVTGQNLWdLjlUTHupe6iWXmckVUJspSfVnirxeMr5uQQUbDbqCxWwbH7nWPB4uQnSVedBpMVnWqVu6wp5qNjDG4oWWD2NSoYl5DFLfexvemMpXIQVtH4/3RAxNM0WQr2mERPmMACfdJ/KxyqXs+umJGavFxBalsSvFzVsXezg8kj97MH7AGJ0eLgRAfNY1H5DloodxHQIRBqWqnOqmWyqWvvl0SsdyoPts1PJS/GnDCCtyhcXU6CZKtLWilGvGLGzfTwSWkvhclFL/KJVgbYE16ZyVqmZyfxiFW6B+VkwYyDGJdFjEOgjoCY9T+32kuFQPUvyuvyxYhMczVTJP6ylO8m81Zb3hp25VbPHDRqUFKq8Hxb3s+DYt2ildKzcBYMBeI10yyb/4rZUVJd7PkZkthG17rRnqd5EeMsWV5SupD0W7yK7anRlgzamcGkzqxkWtSOm73XNacE7+XVU1z83USruyTS6oJ2lAzO2dGbjFBeg3JeipVclWnFdNWjJqO+LcNorz2y95O5WJf7VXO+p4DS9ezg2CHvxPFJW7QOMgeulJ40NwNiYurM9qP6TTUZEUbzsyQiKd/R5n4747vRxSrX2SFl+g9IirjapJVb1BouPU1o1oFQC0+18IX3uxVyjRg27xafONZCj3oRXVq3pYJYoEHtZolR2dEbZGGfijbJqs4mV4uSN5PJ4pBtFKbXNg96nS7ZWJRKqH4hocl2oXbf6vjoXgarDYv38Kf/+r/5v6IZODOgExwnB8AFpMmTku4m3IIH4jBlYXfjqunsz0gpfQhYgGFi/sqfM8OFgpZONIqPQrBm/vohh2oc4N9zPBaQk7MppqXnlYqfodphY7mR1VDrFu0IDMCC+LWU2qwLTfqptZw91N2js8IsK9gyL9ZA56tta/0VCYJMQKH2EbRfleh+Z0JveiFHznw9mkJqvdmRb8RRNnyX6j7j5j+AxMkiE5ygThXRQS7oKSuasmByF6rcbLlfun+d+VkJGpdlJe9QU5krpw8wHE1JqUqduMyGjXGjjoU1Y6Jgb45riwNwks1DcroVD4vGvgc+F46Hoh0WOz5aOeFj/ooBsbNYIYb1f0v8/98Hmzj/9F/9t3/Lk5TqtVgZGd7h1fEhrnR4X+XsZOakWqxgwDI08YZ3Mnrk/JKQA1cGM067Mt4fRYbOf+S2iF7vRgkWVholc0BVMa+Kh3o0rK9RZA0l/Ef5mJP6qKNb32j7BGXUHsTqp9VP2trszYPJQ3XmRRTyum5dOahhUcr5lCMqWB4ZVSVIzIztyVaAnNNka1AOqvZxy0FhJF6mokBIUoU//eN//zfOUNSPKLRDpiDnMiDlNLxM1vKJp3loXrfjuEjOASlhdLTYqSBoJNBBDIxrQjxNuLGBcrmFwkOk1Kwjot4cvWgn3kjJ7PyymuWgRzYzqsqha98KGkLR+pv7NgplA/p0TEaU6y6tdL5mYcgxHqd8kn8qyuR33QQE+6H2gtoqaIlBO2vLpDoVgfqoB9TnWL/lIG1QMEE9uwDLeqfsrdO23inVrDq3yAijbs5oWwzW9lRpWL84QX1+uq/DVFU5WTUcD5Rb2k1DhcPcWHiyxKdI+/KkmwTW/9Xngt2bPYHLeZD6Mz+ZIn2poge9b45N0BxZhWT4lnm6+smyh5rr89CtyjU014Ne8DPDT//4P/6NGxiKE0TrlqpWUVBJndqzXYPuaNP1tNRLEHq2G8FSskCS0cbWUZB87FC2PlmDuhqO7J1oA7bolPzUJ+oqNB4U0LlUNmnZSfs6yUFdb8fJjk9RLVXFzDWQ/SFv8SaIHj4lM+6afam5L3IAwwQ2iKdYzS8A1NZqB3PioPZX0bC92RSrI11rTKVdW+rvlZo1tmSjCLNZBmNvaIqUFPX5sfIwtS76HK0uX2nlQ8dxaImgC8VEhj5hbehOEP5X1Nm05QzRVJpGMuZjq4SYRFsS6FnNsNuqrB5i769zuBqSuBoFHjMvarGN/yUAcBqNzXHLiqQU6bbWSt9qWzV5zYVSVYq/vJm4NBTUK6bh1tG2211aMjedABJqatObeZv2mlu722JqivH1ZH5OnTLbUNXQeQqgMS+6vMCCcuW0QnDm3cyY6SeALgFY8jUDwvp0Eip3tbVd1LA5Ms7OSrQztzHBvXr7gs9NOb/BvMv+VL0jNpP/CCBlPwX/kRmzqgSad1NZVApZmW46ujUrDrBuNbNQi36Vvr1ijdmEtxW9qqtglBCYNFvm2gU6FFtJ5x4yZTtgCZTJxim89gg0NKvUtiU3VQTHKvnIopRq8uVlc4ex2AIwVVvkVDfIua2u+NmEOit0LQm23Wikdvlzqn+6ioebAsq9UrmTRFfOIZsy/RZbku3Ck73YWNDPc8F1crS0u20fPNt6ZWsXxQLKyMVTwnqrwpV6qsF/+NZzvY6O9iRBTjmz7oamgQfGQTe539CiZfLWQ5QJ1MEvml07xSVrb3S0dlZCCCpuOgCpyXnrgeWy3fxc1sBEYys63g69sIF80fSXilV5OrA6d1IRHJCQP6pLVZFgKRoNxanta7R7l0365e6lnDuh3W9JRzWB6vhwmVlvCqig66oBHIOs9JYUxGXLcomQrDQBZwkrtp6LGibh2CgiWimURLnJxlp1RHN5zK5RL6afT8BRjifQ6TdapmYu+moQTYsWOqkYVlib1DjfXZYbapNOVctgePdPOdNq6xjvJn0t3bwbCjooCciuWG9DjroA9DxKwxGi40Nxs+dV3tKzx+itUUU8mpotgVa8w9Xj5u9lMqsgTKpq0YXS31KK+F5EoYSQ10HhMZ7qgn8eNrUIyzliS47bGkhP/eos/fWoTGFKo2yx0eEMhI2BElY9AaGiV9tuPgmxqjwT+oQEOPZAVUf8KWKitraBHMkf74KxzLIfMMk3tA0tPbM5ZAMU7XKWrO1mYlYsYuhOtdZbks0eFlaQ/R0RU+aOa2CZq2EVHODR7wd3+R+p88jDpOKmCtt0ayg8Vj3g41J4PoLPq8J0No+M3HSSbUP27B3xEGoaXQLU9UPjuZRejkyLAWmiT5rzt3EhHXf8FUZPpskHn+EP5M+pAZ29frWMXngDBX1OYyxJTdMjITswJc8jANO8ySa6eSdg3hwiJlUYKsr+uX6vWpqG2vy9ExOrD/+1IdpiiYcC+wp6lFGkgLAuOwH8CYHuoRR8odYV+KK7OaGTsfE80LHq90qMCKQgf09VVvEwj2roSid2MnykHbdUHiOa/wIWXWBl+Y42kq/n5tGoR/0vAQt306ee/0RpdNt60e60g2nOqA+QkSpU3e4EdHOx7KDwwAV2LtAryag47YsEK3Z61PPAbOhZ8wiNTbMBea9ft8VCbZr4Dq2r7j4NDTZWcgOAakouZN0r3w+xKmm1AvlXickXJS3zzphTVmXOBLUfSKlOlCBjFA2Zqvwpgb8EKchQ2oZ+YLIQiYZGc6YEtfhhNVSxjRIv8ihrstp/IZ1TjXVN3yrxrlQiAKAgspv4kSYi3JsOZ6Oq14Tlgk0BdjSmSRSdwGxVT8875MEqxESb+6MCtM/hJsoI8Qtr9DR8+fs91MRHqLumyH+YthbenpY/ryh3NJmysHssVl1K0mUmmgzjY0Z7vGFOGG98cht9WdDT15S1EmGHnBOTAMkWGvUYhWTuCyZyZGwoMJeRGeUDGJRr4Z3smLDjn8xIOh2cgF7E9efcPQOthvsJdx0zjGActn8dFB2YzL+lHDdqQGcjW1XhtkXVeWUCemKr1X90rVawAI1kL6C5ma3mVM9O6X4qZypBlatYCDd5aojMt6+3WGVtuFQZy6qKD1Bt9CvXEPrfVwF+rjXLUoGDSquNZvhm4PKpUAziYVOmo9IK1uuvg5BT/7p6RXxG+fSoI0vzeCiC11m+p948rXBc/5Htm+ypqWarvM4z+tFQqnnRQw7wt+/S9dIjJivlliLdrPCVKpdNOC/wZqNLbGi3SLHNx7GHfo+kGGVjjvIda8hHXbx0ytMdcF+QsiQR7TkoqgapBLgyd7/puf3Jap/9CRZhWTaR9D9OWj+XMT1f8+5a7ArHfg8rznfMTwNflAmUTIVHvslhKl2fXDgwV7b6t5P6HdvdRrUvWVrjFULlR1QV08rohi9miQkeMY9i23e3MNSsIdHMF9zfsbAuyMOAERPafaGpTG3jz/oExR/1WelBRP8Uxd6IQy8/2I5DZRKY8VQ8GyteMOj4sVYY24byWmzYW/cC/kmf2V8GKxYhIJwDq4PADOD+qC+tVG0LlKBfx+v8/ITelaGL4Vjt5dLCqTygwPJp9PS3FFrP61hWkrVbW5t8ycREmACl/ygnLeZ7dxME7+H3AHQDdU7nmQCTjq480or4NVmfuA/5Zs92lsKB/vd3yp/IIOpKKO6ISNXtDRr1aRSjwLZg24UOGibPv6InJ0xUaqhLAjBG8agHfyY2WWYUvlKaJv5EgTllaEx5iOKA2TJserQ22oqwQb4+nQUTSUibdRCdGX9yC0o92Q9lp1u9RqI/CIZcSc1Bt2x5POvCbCFuBCeeATtk5GLk+3LReHnWlO66zMFMcyGdQ6ua96vqqtRdqNYbUBNuxa5KqDlZy0R7i5y8H7WaKO6hwrWkK9/UGbNqGIrYAUDmV5sw7q0q2YayVBuxb0kH27Tlovralv8w11ZXaIiyflEYw+Pk8xXeswrssd8EyfHbzn0hiOsKRrtZp2XJDJYltMinMknAGiuGB3cOCOlxU4LRnPjV9QaQJsBfN6X9385ElRL8EnyiIw4mv4ToJvJxZgtPhbUVXw1u+Ru1qfhShTLCSGkVZXfQDgikHnpdQu4//cP/9Dck9TLWUkbb9hoPRMNOqmGV7Rn8QXAADnGY2tNO6sx2A0XqkxGFdmhqcO30h1xhFVkw529VrvJqt1q9ee2Qt+dnRthmQxLI/lUtX1Ghfh+aeacDymOq2OisZKuMBn/0QpFJRZ3nG/yNR8C/4noWXYJq+Mifslbp7On+Ua7tUPdEa87tOHIEq04DkibqTXMnE6MSE2pc8Pq1nMfsB9XShjoLA3ODq/Xgod3Tz6+2T3sLaxX6Ps4pV/jERqIf32wk0O/19tA1mt+GlK/LV/+2JZXP0V+SIAJxSs0mSeYC5vpu/rSPqZDjuaRU4h+R9WNw9tJ2ozqnVBMByV3nTCSqqUhHpEjEAigxvlBEqpy/3nYqW7ndlq64VWm/MGWy9w7o51655ibVfDd2EtVcnqKRYz8NrC1IjcEoX2zpbzSU8UBKlMJxXOQETFOJXxAdRgd/C/yhqSRs+K5CXwJaiaPHTLsAKOlRY28tAj2sfHnsnNdjrw3ONkOyYp+kaM09SnAoI79F/1M+MAiApqsU4l7hYJguwV1uthZSa/mmZm+1K/i68tRoCphOSJmSdo3dIDL9T0LrsGwKdP2U/b0oRnXx9mhSrSf1xsqWqfQ5wjCFSN4GUTrHhS0KIl+W6cR3NBKrnmkbHVhTi60LnGor+87B+jJ/ipUZV7zentlkoRHxqFlgH1qr9l7ydqqqTG4qsmxU6Kxp3yUatvLt6zq+UvwM4s4M3W5xDuUdXsz87Ie6N+yzxdx2MD0qtnGnfVFpsay58ClaHlampDzQkdZQrR5VT9fr1NOHOkGsdWu1C1I8wTuMesKH+DtGOeqtUl2LDtgChhHmFJUEHIIHIiZe3KXEZ0RK8i1cZYnje8pjCrpcclU+SQomairMsjuFfrb82mSY+zXmZZcz5lbsWhb4nFcODlgCfdq5mjlvQjg+IS7AqUY1i0SZtZlihB/GnOhOpVHzOt23HVc8bddEgkGzIA75S5BabWdbjhMB2HDojGa0ylCFxyZUOVc1EI0eZWt2+zpKD3D5bnVRc9xqrIWXa4pTcchg2ShfdGIhzWrsCdlxY/9Thfa2LFoB/nrY2rCU98e2oW6jjyb0czv4rIxua7YiW6PE5tFovgzYByZQWeUsH7saj1sdKbJIiMhYVbUNKGGRcDwE3o3KtkvwCXGZ7DGcWaERFL1jnockXV3KkzuXYrwoORKtml+lNQD+UDJCV5JUqklzlf2kZtAtGMfQKrE2Glgdvk8FEyru9+r0VbWOTYJY6vFVR7ZdK/kVK/SqGo5lu+tQ0El1YQbK1CEpg/qk8xGW0i1sYyFnNCfn2MydRVJxqUv3YMoELIZbpABMviC4oM6RSKmNd8Rl8vcA3ctRJRNVpIbetcs+F2mocWc2Y1czTkt5qZkw17tIvfstcQwAeTFLw479jjDDteRzLTnkI4jRRsLiZEzb7oawRjlcVRn7ISF7Rw5pyy0m5O/gaJysdyp0njVXjOSTOpHUYnpludGYOjUVc9yuNsulEEqHWSjCRXbuky0DSJDGMps64XylI7H8qbVtxJYURnMxVzfX5PLGSNvvrD2pWeksbGzzsumw9mTWh+OzgUP5es6c7xSdAEBPfVuHYi4ahuJ0MkbgdQ17V58gvJqGs6KLFCmPukRUAT3k1Jhsx0iSWLzjAmB7r8hhc6eOLdxwVl8+ZdevrWpF7tuDUbXR4syzqgY0w/uMitm0kr+Y44zATU27SYblwIKlk0u5Aa7XkS7OraSVxKD8HRL0ZLLKLjQjI3d+rvtflstfTqqmqlF9ZvNv1bxWZd1M3pj51n2T8zxsBnRZYUQOXRSZ865cPCt9tlCw+dR00N4xHZldfbMdkK2m5br4cJau28XcFCALl6LnHRFskKwjw2vhMddbBq65v6lN6tTUm+Lt5+Ack2bdWbuuxF82pnok+/mgFsbNFJusnKHpG0y3n+EcAFb8YVun54klrn7TrojvSu2aOlFedOFzOtE6VDzYtGtQnfJZD6RTdlqf9CqndVfcrjmu4y8H3WX/YKFWnK7EqhvL6BGocE1NfukzDAASIKH1UWu+pagqvlIMANmXy18IkUN4iuP+tZgzitRBBLH6iUxaZ98RUYHLBN0+wnZA3BVPaQQKca9+2dQbtB6tlH6iUJRc4WHeHhaCoqEorvJMrPaM6TUbSOtJveVadqSOuSusxUhtR9olBACYURwzSP/+nKtz9dCvop5WOtlyUIFYXawVq3ZudGvunOO6cl2WeWz6PCRrK5TTai/WEOjcaVUOS2fPcL6KfFn/mwAU+chuxbVUTegz8HCUqqFuFYbPgHDQUsday/Ivbur9boftu7Jw7XDnhNmO0j1ZKmzCsM0OQNuQQcNLFo993aPhnar9tF1DeS2oVt/4qj503tdzNn52GXl70xXjWdOuk3Mo5FF0gLVRNPOt5SPbZmWPSVc6urcGBnkGU1GMdtIVewBLaAUsXf9sef6osslElTryvYVWcDTj2w4ofzz4ALipPkHOvmhFvwbEtf6827B4q/bXoh7TostrK7ui+wpKXYI1nBVr0qcSge5f1W45pOpN1KevYn9kfPLfBD143yEOE5PmUT6ljJIaZJQ6eQ5diW9RlT/dVxR16ybdt/s8U9GFAg1V+eQCrsdBPwUPPtpXQVm49jj2ONCDDMuHCkl7be/8aK8fQ6s4bgxU/WjqDEXDWvKFM2vD/gEQPppr/gz8mGklfrpAf9VO4rElegIlytIz6urJtkIZQFczS2ZPVulasoGW0WTorYm/4sh3OuU/r61QnKZebicoNCqrtzViPXttSjWmuJb+Zrea7VEp42alCfCXohSIUIpzyuWin2/gSl5S9WudymeJ9Go4u/j8g61Bs/jos09pkiNIuZSOFXk/oquHtINz96cZpQvJPaqQaNQxWy2CTPSvjVrrWAmUVvznOYql1H4qboU9zLhzjib9nKp3iyizuktbsCJpM0Rp/7aYK4pXsOFoW5mq2udnL1Y0rQ45mp+dr460/NKZL1e5Xj3iQI3EoQ/Nl3DXmPRGx+1Fq6KYsWXpnwLtYx6XnC6n9XQ9jK6oWnFa02Q3DoGvNtPIHduUom72o01NiyHfb4Az3tsHdlHaCtf6si58CWc9LgHVh2m6HawmrNLdHVeiZdY+cwNX2Fs22cfmSHQ6sjZf1LR5hRS7nAwu575oArxvR6Zh8y3a9dOqZJJKumJEqNm78jDbqqZ5aPjE0MsXMbv4WgXUxpxq5V1zF4rNa039CbgL23eAhJvtNgxfDpz1YdVKbSqoqlpChTFeTYMT6WMVFfvUvNjfMWcJVDe2m2q7MC9l5VVtDZ55i46RsCi/qDbbPeki7mNivVA/7ZJT4stBqxFYQwyAzWVBXQ+Z9jEFpInK8CE5DvOAEZj1oLVVEf8c9BnAk/3RnvzpHWpb8EhXmdu4MBrKeIZXlSlw0URcY6VQovy8bPlKrXp2pe0aYg0a7dO5aRQA3AT5HikL/Uez9gysmlZ26NpPxq0YT0Vl2o2x/GkL1lzHFnDlHtkW0fZQ7d4+oSrVqOMrLZkTqsIAzDcOqaRuyOrna27DBq30oGDkRnTondVw7ppbA1t61xZYtmoFSIHDRIm5jIb0sUiasddrk/sQkjZX1pyYbOps2sQaaKexTqpzw8nR9QG5svOO7ZuVr00avOW0GYCzb2jDQL7TbauHPNWZvYYQwWFQ6tf2NAWsbvdJpgDtmVSCsDqxXWDPQw/D/hS0HTGuKGvX0kDx11D149BWcXL5e6c51q+tN18yqRrVow94p167q0IzYBKQMBrVjvLQf454IB3039gwM32XOr+a0rEBrWFO+a1AGU+antqQ2bI6o6jBDvpDoble+ePh6mKCAqW1RrvT0Wlx3fmwDd0k9D4cPvNzGxyY+6xpEZXXxfTOVf+v1F7OsQ60LDUVyl3WElFsNhcISMWnGhqkA9OoK8ukoFWp4ym7BVrN2AlDLTFcDc23uqYrLNDsOGzftQPt6hc70LTyVNbV9nLatrjXEOjyXZ2dI2B5pxqsyl/VS60fnqMjBVqcjeOJD0xIwdPB2TkJmCc96mdoYvVBu25KciuY7J3uiLh6QJ+yjfKBeF1pNVVHfurBCpZeYf2mVn/aMOa6Ot/zMiE1P11JqS2oElJtBEjN0FlCT70Cz2PoTIDXrKh8AM5ajw6GEFcgR0q75+c4IAVP32xJtDEwAPApRCZZOmc00DdhYL91XPEDy2kK2b0imPrwQ2LoU68/OI5vA37M7FgDZQztBobPEvpD+IGqGgb2gzUfyuuVTbOVqLFQL8XmafEzKUbD6rqMlkFWEqHq9saUr5pKlrEb3mmOV2jVg/pDDq53DN1h3ZfR3qlsFzQCuqs5rFTLsqhbpmLMG+TSzkUz2l2e4teIzDRduGvYeJiwhOIUMTQDYlsnqFza3eMEdX9ERixATsupj4hzcmKU9P1n7uqhmQZVNNeEYNv/Fv4Ei9Kz32H3gbT1r6HUlF9VWn4I1N/5FKwtDACNW62Ah/W7cqbEYFBjq/CzrolBtRYcdDtRzkatlhn9VI3fDsK2/tb0RfdUbUjgnzu4dhd3Km/apHE01+3PVZhkV//ErTMaU/5mJuovd+WPeLjeTdsXA82XwaSMpHjZfMquyz1frEwwLEG0bZkWLVVZ6olrpF9RoVvVu2zTtkxU51f1Sq65nAvpqUZHi8KHchCcobbBjKEDDsABkHw/46KpWQY/Lanc9YWI/qxoTqCvSax46TqJPPZzibnbU62h02puPkJqzfmef6DsQnstgqzQWHoQDXr9TQGbNejHtZ5ihKZY4TRt8def3Wo3OU4Um4weUXyb/o32+Ax7KqBDNBOReyTfkY86fQCOQZYH1xCHIKhMglsAD2OglGrvggzvRF8/U9gMda6zwFVP9UaxLmzpDNPDt/uNZtfVIx72J0R8UdXaoz9f1TYDLmHrC5HVHVMDJ8eUTCpzu2KFT9yQM0TZxUSEjPJL3VgWAZR7kdv68zfZHJ2wTND1knTBGYnfluk4hvjfCOAorFGXtYxIBWuHupRtBaDPXLmS0vEEIH/CUAus9fQRdyywbew2lJ6v9me3zuI8QLcyAi4LgWcPKCiNnmIntzRUbHoxF7XlpEatDrWqZ7TTQ7+Woai6Tl+ykeTgbzn2BsDfQhwmnwLc9Nwydyvd7hYrKa8x+qbiw7LrbcX2Q4Cdwg1p2mvj0i/5MT+Fm3APKE8TKXz+SdC2h0r0wLpj0RBidSLuBtQRBzvglTKWyvI/DNJQfUSKNKq8Q+LbE1COZxJxj0nPsitoxpUZVVSblMzZHdrQMGEAlgnuh7KZ/r8Jf6FPdIcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddthhhx122GGHHXbYYYcddtjh/2fwfwNuK7lY8MZfWwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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wofdG0IG9KJEO6cpGK5SC0Hto+/WAk+pLdbCuvtpVrPMMqlfTnroUziiaKPrcNAAeWheFz9Gss4gRPJyKhU4rAlFPdqm+fQ9DzX/aG778gs+/urnD+I7hzcNweFOSkqJYvSBcMI0Y3xCumD5hvLmpB4DpCrsg9IhBppjZFDpgBK6YgeEGXPHlqw/GwTjlxXfZQchPlqPzXLJPg5pe4XGBpizR9L0w1Gee6Nue+mki4Mko3tXsa/Izmn5/INnODovDgs9Rj2+xwwg9EP+UBiYuMvRCgwGi1IyR+ouDceHC3Nk79Jj56eaVlEnoYbSb0uRRgLVzTPtAPrGaQYiCamm3x3PPjPL+nPkwSXOF1CScuYot1TdCPb4UW3M2ao7PM0/6ULIeVKsl9PPGwJ6eWZ0FvsQk+QkwjM/3QI/pT5hGhA7ju/Kte9g7AISrewXsxU+Ds5sHXFgxfWIlPABXNxmEzY03WiICg2DcdAUM4ga2PnkwkudRIzXpjMO2D+XUTAzFo3va9viFNV3ppj2nC+x1b6sO1O2UQcK0m97xsM+/J8VelTj/htRz/SzN0dhT9y2K+NGPkxsnWrQpEyD0brpi7oErMPNJQwZo3Dg8rBjhwhWAm1ePBfOAGRiA1yuCQRiAO3DjCBH/hV6cpBr9yX4Wz+OzXKwKkMpIi8/vpR+VnGuMbI1t1V0qDg5ZTfggSdO6M9XPkrOLk4f0sFi8+rCqugNt2gPI7g6g+BBiPscKu9FHB0KHMLifPyH0GBbYBbiTq8Stc1UGIcKiN0xXYKGskXnlegxGxlDJBujx+qIZVLvSmprMtyqXvNsRiiJHH7oy1RsV8fDgBexpwOb5Ax9CdaA4KMoEKE5VtBexbOY6NZ6rAu2fCus/Dxt/A23AivEdoYe9wb47GIx/i8l1HlcXXjB/ws9/xr/8AwCMbwTdxwUbtH5j5eAOwaIYAfRukhUdG4WaQg+7+IG7Pb246cX//A8u9A+TRR76NPi9/IZvy734MH3oLSpWPuLmir5zRsi30UeR2hV2cZDFGzfE4DjJI4PQu3DFfGUYhO2uwDU0dvWOGv/KZkDHWKnhmpFcpVgyh0S1ZX1A2jEqzLiCmvZocRdXtZeB8bgzjyiZlarnMSNzF7hpNlFd/QbubDzdN4i6A26ra1sVEzykLWpqN767L39zdoS9YZxcuHIyRk91Ti8IBp9vFB8i7R8bmmkHKwI+JVt5wXRFAJWP4fvoSxFcxcDT6+hCD6z4IYuIFBDGgWgU1f8kNLPHqaCn3Y2FFgXW/TI5aSGXkVGXWAHpq497WNEH9PLvRzkCJdZIGrFVFStu1LTCwmGFfceP/4ovv7jxF/fjLxh/hX0HgNC5eMs0IvSIa5JI/q2YrhwQ2kQQAD2l2EUGCJ8QepUyMgN3DCvmuMXDgNBjGhGusOv/5z6F+LSwyXLXD6p6iB48J6Gf7dbT9LtYBQ+fZaN45rgAMZh5i+FN4N4I5cQV8R8jmecqzJu+T3QBLoAYBpvucg3XfUPM5oC0y/xRfA6q/Ed4tPma2+ePQccn9OOTLJVhAk+UPEq2N5XmEWdL1lo1qb4xanY4+w57c9bgy69u/CW6L7ATfpSPTl0BDmlCHCPQ170aW+5ELX+BvQMrxgXTDeMM+w78ncfqIotHvAmdyxK+Sgmq30EdznnSGVLR22c5u8mvewzxZLGcdg3Bmum/O7r5HIalz+iTjzVAU8w/bQsBLMw2ZzfYxcUFluM7vrxhfKe/qN+xUZQyvtzIcxRSArtHG8BZRHG3x2CUmNvIx/9891ic3bysiEcMe/aqV9vB4D0vNY89pG+jb8uEeJpC99cyF+kA5P89O/PdLNc9y3J9pK+2KPlcXPVACzlAuj4zvofMPdcyOC3B0M11uUIH7waqHRIuEHoXc0cSdUAH9SnEmlePg+DFyb0QUU0HmrqY97VdX2sQ/bMK84g231P3jUbl9if5smaFZ8qsuxq8wE0bHLzTUNueyWNLGeVw0jg5dH6cMN7cdMVw83aB3ZxYmeHKcnQlwRk4yBRTPGMmKF0VuF4MzTgTYovwlrdkIl/qBdgQRjY2YvbTNar451wB4IMJsELfXWlGqj3rgmUPqbka+DfRAXfWBm4VHfgYfRPg1Si5AquDfO1gc2PEPlde17amrfvrDcv3+kPbgNX581vKt8emrvIntepNisyHuW2PR4/zP5oKtH6jB3p275bifKXOCnhVy63HYvWQ6N69Jyp0606Fe3Pj2Sjax4lqXhiHJ4TSxbhRzAKJZmJE0acrZ3lylEigeIj43AglpY8lyD4O8tQRBJCsjxUhreBwVPnKTcTmLsASVXxOWSyu1qR4AtA+oIccpn/uK80jD7egwr2t1mEeIKZZDc/QvgP3uKsHFkJBj4rpqdiOGK+wq7M32JsjkXbHOLnQw74BFx8WZ1fOhev4jfMmSsIAMXOejNd4ZkPYYG/s1EfPaYVdPJU0bBjIs0h+reFvP8wIwOuAz/o7Sf8hwnHfi543Wo7XCh/i8/9haXfKgZ9oAxZgcaRn7xTPFGEf7Uvap0Plf2gqYVG5V3CrlX7afEsvgJm+UutpU8F4Sb/GMtLYnNDm0TtTVkWjEnFlfpuoKOLg30UbFuq+1v7H+9vUtene/hYb97hXeyeLGtI7XYDVje+w786+8W4zG8bJDRNC74fNYXYxuXj6E8fKJYwJxQAricOyPyvs5tOujh0za8e8bhDg4g5hoXdRiBJidcfrJ0w9LAixryJJteW7cjMFaxbG5XGQ8ICtn5BST+GCz9BHhKLd/nLAWHL1uNg3U5SCz+yxk05qZFQOTFbGRmB8cfYW12E63GBv+PyG8U726HhnXfwC8JYhmRAVXKm2+Fd2jzZvVwAeG+zi7YZgnIRJQ49wddFV105LjMjLAve5YYEKFW1v1ZlCWNb0/ZRj7XF/o1jaf9yHrLBX4CDX7mMx+uf9eo0MPjHISXxuzs7A3cXUJHtzMLC9s++8u0T8BA/71xm0yepYVhXTpX0K4I+eqZ6nPXV7heED4ZJ9Bjzw0jdTakmecLu5rseor9CBvIQa3+M6mwESBjizWx5x3h6XlNxwMNk+gmGJg4JcBdfdeAzTFtRUXKs6X4ytGJ0rxhvs5OyK8asbZwzvQIf5gs9vsBPsguEGu3FikeE4JxA6wjXjmYIyLGlLrzgx3Koy62KW0zUtw5xiV3vi3YF3AY/bNv2gh+kD9Azm9w30GxHT38+h+Q0d+272yYeoHorVxTUVNm4Vu7DUvNPWhwk5XwlsSlUVyzuPx/kgBhl9o6jBLzyqfanoUx5mc+A/NpWb2vaJ16msojzUcXxv8+q+HbynqXcl1hMi+bfQs4KcCz/oZKQ9obAqyHN1iGGhG8Z3hxm4s/9+ARb6HAd9lMNwxJxtyvQl3Ce+coQu7nvvaZlGB8DFpLtJ1rgbArAiVmAZqAdvl0cLQoqmHifaFOqjda+Oeeqct738t9BnXmpR4W/x0Gur8ft4M83w1b8bGvXRhhZgdZRT/Ibx3dl3DJEdV4zvmHhHkNBhnBCuwIqRE97oy5n7TJlAJZ31xtxGTvqWMM6Y5TmB93FYEa4IG9DBXrgnPRA/5LX9HvmgFWb2HwRh/V+cUPwcfedOctBcUM8MJNoqbY7cSahzPePuDN8wG4vc8KcVlCkCgHuBh2+gQkM9uTriYxGXw8DpHmpYHLT7VoO1x27fITXbakITx5U8pfG1B7nAvjt7h10cfVZrI98o7pCIBbiSrgdnElnmXdldllZyIn2H3LKTnig3PYNxFvQtBPqsAjtGaQMlGWHe6yHoq7rIEVscvBjtUFeUjeZzgAiYRz+WeZT35xuoMD+IXfDXD1XYXOr5Afc8ryeCoDXj1lZTWb8EwTfY1dkVdpHPFzlw8gd9+3WjlRjCglQDpyNFjoSEggw5+PZgtDkWE+AksMk7iPDgmCoQxffaFa/gZKh6h+UPqJhW/3bjMR+UwaUsfzgc+/V8rGEATUH+u5mYe0lVzeOHJwGlu1eHGbg5LAgRY4rh8gVTzzykU39MJrqKxCLCL1dyoQKXiUin7ZQZ2hGuZLvURPwoNyRAynsvpkC/SdEmar2LBQ+1ZHn146xQmxAHBXgs/pqFZ2qpWcRLi449fKLnQYamP9RsaN8RfGBLFLU10dZnkHkRnCsAZ2fYO+zs7A3oSNcDsBvsG/BHjAsmQ/hOkA8QXiA71DEiro5XtYlxRDE3Wj1M6L2BvSFcYRePi5s6XmF8R3jhVPktRadmnQFnMACB92Ke+TEPQ501fSvM+TvFAxMVYWKhOu61T4/XBzcb/Uidu6T7WU/IZuf1uhrFnXZzlMcZFfpCGtwKcrSl28MFgW3H+hvsjV01DQtaozjYpN1owVYBKXSTCmTM02HuVIUGAOYuk+hRhFfBzubPj5h3x++1ljHNkGCmZPfERtGrJwHUUnN9xEA85PIDW7x5qXxG3cqB3Z9CNS5vnfaHj7vH2HfHH3V16Ly9O6wYFpZMGtSM3smVeWilT79pk5Foo50XAN4YrOcUEPGoBJDqeWuGK+1mQ/Iyys5LI0PKGsxgbuYxMXs5O9mYKky+OW5NvFOoefLYeyi7sRdm/FbH6CFfHufba0/8SXTigx1UcHIRmLlzltCcnbZ3R8uF+QtD9uZoe5mN8uusZM7HUHtkNVDsUdZFkn3Z83cRpIn4X5fS8EAf5cC4AjOnlbC1APksYs8bkADzSmeiP6Q/GR8/hRhrkMjqv8e6+O+s3A+lpmQAPZMH9B2pzoM+LvCAtPpesz+7ZNxmV9gYE9LfGAbsnTeQ16FLxetB2DHyh27dVArHpNszZS2K3iiuBQJc0FUZltZarXf86XURmVkNZHL8oZHQtzOsDwXek/SMlG2ChYWntYsdfo/QzvNP9IHc/oPyogoW4O6y9N4ca7Q3Wjk+zvTVCvKjF4xvzs4AMP3Rf/nFgeOWMXgzvFMkc/yKn/8eX/4N058IDSX/WjKPeiBu635RTd/ZE+opdk/GZU/TY7xh+oRxwjRi/Irpz8CCcMXrSDvcCjgVImtWlpugqvpzHQ8k6MOkso+W/GaF2Ki/q6BWTR+CMHnp43d25poeT7OY7ozO2GB5KevQ42db425e8QPXcVfE8ebsOz5PsG+UlzRsGBQmCrA01ShmlMorb/q117eOF23yjSQvlQMUEQCwmIyGLAzlggQWmZN8fq5wkmINUhv/PZCgbdqzCPdpD3D+nppXYzEf6VuTjtm0iCY8SF3QdAzkbbA3RxkbYMAoKtaOkjbsDbggAF8mOm/fXbjC3jB+xecZocNP/4Af/0bNzezoDJwRMr5jesF4o52VAOBKYXFqq2cjVZbLXWBvGN8QDKYR9j3hU8LoBDC9I7xgnDD9CeMbfh7x+qeI0nttNgQOmabvI2rvTfyno40bHo5sLbH2fG3NiKZ1pnnXXtf23vpDXOaAqil3kN2ye/KZRmtoM9ICGNibs2/OzvzmIq6uhsIC4zvCAvSwbw4bbO8//4Kf/ojxHT/G7290mN/VjjTAcMMcnegedkEYgKhGZ6SPt6y0dA7gDT5jiwtr57hJEzjOKaDpxnlJ8aMIA2+wyPvSTy+enld9rjO2oqOa2e7yysH/zU7SQw7/Ji/72+l3aO5J4OxjJHkYW/YhU9qnWNaaQXb+YL1/c5LNqX2jZBVsGO45sLphAAZpWiPw0ofYujzUqlLsmivcUaneyHAXOhYkq/1G9tamiyOVX3mC6vDMHiR+fPJAzBTBkoctPlPnk7TmfLYCi8MKXDyFYfBPMD7LKNPHDEmKn2FRHmiKS8ttTwvM7Q2As7OPy4PIT+dNDClozixlN2B11njim8XZOz5HTb3wJgvKZx8Ae0v3xrQ6WSssqcHEmhusun1cgdi6bA8GinNOF7Y1dYodf7Cd3PMrXgsktUlNHjRHFx/Rx9HHY3VZVv7kyYI+1CXDrkO+FoxoI4wmwMF4LM4CYXXqiygehvhSlnvHGsaNNJdlbWW12tp475cVYSO2sDdnF8A4rLDvUXS5sHp7jd4xfWbd9j6k7xI5ewcuGG6wE0YAYBAU5EgFbm5c8TM/GYXjQXssyvcz0xqj6EUtdGa8IRjYK38ci0H4yJ0zMLC3FD+KQGcAdHjtGckHIGlKesy1sFR4lp7S/04K+LslC/82KpJXLP6ipKAK0gB4Y49hcFGBWgN0jj+r54LeuWVhHl2pZFgZUhHhdFfdiKmZWxTJbrwBG8Iq0UhHFqQGyVc+s7JVkJ4CYcnfpHAhlPquV2Ks+bQp/JWmWq/NAxVSn7uy/Af4a0fq0bmHEq50umv92yRRzQbBPLr34+BARoWFoEjnnaQVJsg3WFvSLll2xZdfnL3h57/HBD9Ozt4xXcjJiGLA9h5wWD1MupFqU7lkWVIZd2ycMC6YRjd1GBeMv/KHfjfYxQHeLo6Y4+Yww0bnZsPI+8aMscWF7AHatWZlZ2sGOvqqSyw2CjKwpRtpB5uV0uGSxbmwi8bWJwU2+XOa4cLggIbxQWWwAlfMFEPy6Zqg8VBytOXaWm2D7go29aZ1ZlcjDfGhxv82hHIHFtiLa6fOqJJB71mARtiQS7IjskSl7OLSiOmTtwPZhSNYHEYFDYcVNorSu0JbNnKKE9psytbHGCjvve1hb/g84fXFQT580TnEkDpcRDoBDCtC56gJfpxhzb0Z0OIe2YUG0tstvSbLO8Znw6VuCcUS86gfJIwpclfAdv1Ce378nj8tt0fampeflQwuK3iYwVkwq368b6cmL5bw9e7ypgPjIXEnqW+XZh3YF+YCMf0HPX0sdSB/xdkF4+SmztPyMcNLzNibsSspWQtA9tfkBeAQabcS8mLfMMwIL25cfDRAI7opFsKwIcRFmCKbo42xsp1HmyMAETDSqgCKae7qjKJQiHaN9WgbseY/MPYu0ctiY5yOdmWCWvOesd3Bmk9UXkGTw4tYYvmECgbXX2Rr07rDfEWzx52u6CDrvtFnzt6lZLOFO3B3dmbBCfGmEV5gZ3z+CnvDOLpp9ONX93kC4GJYLxhv312IEA986J1dEIxHj7C6cWbPw3i7urB52zmtSUOPH3+FvWEegIv7/IbxKzAicMeoV1cMb3QmpiANN2DF3GMQWOrOMUk9jHeW4hychKCVKyGXMJl5I5irhnsESU36IQ5+j9eLEqgmYymC7jtMvdfivM7lI2qeVLQrgtvciXxKHSv3Gi0qLh1A8c2Ii6k6ltMDP2xJypHMtZW//QOx/xA2H78NIPgihRYXzvyFs3cEcWioZhdYKlvxb7hFiV5a0Lu3K4aNd+0S10qedOW72C8RFk+0qWUYapSs1NAeIj7qnpYLe6+pAzqPzuXwuwcQ4CQT6gORyhazVo1HFbbtfOCxSc+gTpXophulF8+gqktWSfoKpYFdctbkjN3Q++j92BX25nRPKJx9AzZHFS4kuhCX8ghLAXbBcKcP+IUNw4awwC4YgFfQSQteahPhns3ZO4FHOiY+DTQ9YpDG8neGAOBCx6HDsMDeMUlIZsXnNyoQexV324JJMixhmQxHyO7x9o5wzfk7D+pEK5Py3JQ3Q14Rr6ejjxYP/CX2CwCV+ZHq9GkREhuspV1R96Ttxe8ZfztGa5uKSpr8WhjFe7dv+5NbF+vYspQDXSAq9A0W0ZJzFHQRdQNEVHz8n44MJv6AH+X8LpxdhsjrasN2iQSulNuW/Abuw7BgWNnGFS5ZgRvsH9MeHpKBYRfOWNswLsCAsKRBiExjF4QLeXKhx0hIbUpct/UoraTZ6zEkbDUeX5LlQ6vkWNiHC7AiDGwpdXi9IPSekpolIg/mRZ7/FPlE7ukfyDLFwYEDHLZRvBB1NaN0lZavqTi55bfsMZ+Wo3vyuz6fAEgXT5D8WGBXF+Je6CtlWtDTaYsKFYfB6ZVcGiCEic356FAP/CxDK+43GwxLEma0oasKHgIU1dQuczaF8k7aDTFglOSQwNpdxQdMlueGTImgH0da59hV5MtYreR00kdkpEKpoc6BasYwQbzbFKLCjqiGMQCmZJd98yUVMJXEQjVFFMJiV0dAdN4DCjTrah/aFfLA7ClLUDuxgnxkcsU00pJwGFoUEVMlJE8sWaWypGtDuPowOILxFgRe50ALHjr1Uld2YljqxE8GDqJ8l8ToJIa1Y75QuoYAQ3ZVzQnUcGXHqE/PPvUYeWcvsDyzzOVhS3wmmp3kk+YhkxQ65bnJIF9ZoXNiPFbMV17L0ee3K2oYncqXD7W3DUpslWHRlRg5VcghAOnb8fE4HVTMRGHAWNjDSD0eqyMht5ErkJHg3lBR3QMqDA9W7vbOpmTniSOJIVw08tJjajIAMGyY2agY4q4sHfEubZzOfY6AToCLMPUMjKjUVldKkcgfcVPWMXd0kkbjsGfsxnyl3WnimVJ3C1sY9b6LURKdsGUPnhLdpUJ+9hIVWuhZwpVDRJSh7HX00upqNcmgsQ1aosKtwvV5k17tmvZtCvHdbE6cUEKJgbDFwDRzBuRRXURq4nY8cZfyED9icncwHgbjzZFxzR21HOC2C9B7xEgJ+CBnRyuQivGBdc14c8kNXxFBH7KoNmc3D95PEIuz8GRT8hjFA+3ExBALfa5qdePiAc87r/oY7UR0m1ZgwbDAAj/1bLYObHR2lEBkV9o0K/ZqiELuTkZk861A4PQFgQEHXDB3GGYAmDvYCyb+PCHJszW1IhQNQSs1d/lSuIWNUY2rG66zpyRiylFaEXpMvWLleoYos4HSZaJU7njPZYkCaHx+pwYAxk7/e3zx4eLpZUelc/VR4CcOAGAoZWFU7gjNv6sf785OCFcfev4+bu/t7Owd4eJsh/Ed09VR53qPDeMNAQ6ICwaoIUtsqkS7/KQRdJbD02O0y+L0WP30AjtjfHP0wYoXjAtwc1gJs8TmrYF9V3kMbBgMC8Y75hvCnzF/YkBxSdtRA4hxSADg7eDi8i4btTDwGrPdwGjiCtyAK2W/B1b6dqYIpOyxEWSprjzsQqvXY80Er94QRlouHOMCkYGinw7wcqWNl/+CojtxYxkrue6cMyA+TdyaJu47J8XmHrhiSqvYPHG5gGJ97rcU/EcriT34GYlHC2y/YFOjD6MZ1CPcUwqCjetU7mRFWV5UGiTXP47g5gSIiaCxnREuzm4YZ0zRyLuTi0rgIr+AsDlyZXp2dZVgi5T2YzGszQ3vOLCye7QoKS7POSsDt7aqF+DOI1vIG/2TXyTgAi2idDTWhm9kacclgSvPcJBFS0CVVPvQEdT6bqEAUjw/RL2xUvaQdjgonlTrSkOxsTiGQZ/XHejZTenSvwBzWE+uWChM2BqQEUd+x2ALSN/4OiKuwYzvsAswYPwEe+csrCt+XmBvbgTvdxrphUJ/2OrP3zosGL86AGHw9hdn4Ue4L7+yhbrE7X094W1xdBYg7sry7mzvUypGtMDiLGf9aBc3jd7e8CXmU94wrfjyqxO9b28uXH1coxMDM+MEOzkAn2+Y4XDFSBsRugkYfyVwMUJLBIuunP8LfL7j9ZOzb0CHz1HbxmDpioFB2XEGVgxvanovbC28Md4Zn3QGXvK3FeVWD5nnyT0SIPbOyRwANoxvCB0wtqKIQNpwJirigfPeo1zsMZl0S/TZydpGrvpXtgeuwJXhd84BRWF0FgxXBJYUfXhbvA5mnJxdMK0IF9iZcxPhMSKmgdm7ow/gGUzRbYqNzRQEC2I0LPj8K2D8BHyegMFNvbeTG1ZMV9gFKWD44uP8GO8AML67YcI0wq4OgF183KclGl7EoLe4LZuzE8In2DhzOv954n1Uru7zO6Yb7OoGXtA4vrvPt3gAa/AK2MUNC+wbwif8+BV2QhhpWtoFdsbAoZ0BwAxc8HkmcM0umK74vAIbxjthhAJkkqLY0slx5i9ag1R8tFB1OCdadZbj13PHGCoIcg/RNugZO5sxXvCTAS4IM6whEHTMcXvZcjsMgEFYeFEbgD5F85MZgLR6iZjMAAZTz2qdUc8MdKsopchkYKXLCmnh2ik3zjTKGFoc2JMqZETa0fqsjaGQODqDAzyteLrDzi4MPo5+kOkuKTYLSxowPrASVhLRkyyfMgcIh5X6PYu9whD6ENGiDsOCaeGdgnvM9/Rx84gozeBuICmByH+UnxE5wPBdq2SbO8rGkLUW7NiS4l4I1UuKcoUFJjFq4+5wa+7jq6WP6RMC4GW+8tajxZm/0bnD2MKeGzq3ZzaNCyMvCFA+g6ENH+YOw0ZTArIgpGa+KJKej4t+LxIVb28YZt7pIYrPOzBjnPDjL5w5i2RyBTh0CC/evjs7AX92doOdgNWHEXbBMLmJl05D9pbenCyWpb0ovjp7i1/i8cONcGBG1B0nlsP2eI0jOGN8R5jI5BgG2DfYK6VChhUDMN7w483ZCYjfUvlEkiyACsfVj1h5+fZELjZ9broj/hgnXnqxYriS4PwysbDpED9YTX7DBfZGeOp8TQZJNEKAlFdB2jzq3xeEK8ILAMwjZmCYKIdyWDAb4Iowp3gjTAobEpjf4zNKJDIYTFdgQXihyRDlou0wGyX/DADeBscAoEFOJBGNPPKZJqQc5LybxfpBQwHJdDa8KKXgxeJADA8DM7DTR7XfSURFbWJvwJVTYFbMd3yOqGHv7IJhI/ByWBBmFz55yxI3Bq9TssVKPlC8JX4t7/M7pj9Ru/RgCzFBgooiTglacWtF1IEOUnbSosLrBnaBvaRN12OcUHRxUgvsWMhbj4+TMkUkTQTAHeMWd29LPlnokkMmSpCy8iI8ovRj5BgBWV+vEPuHvm7BENtsctvUKHuOmSDKzhlAR6s+Iu4TBEvvKIsvbElrY2WhKzzXJKPCmJU1CQn/7MUFn/GEKgqmPDazwRiFyltyTbDC3lzcFAAArsxeHPkIG4YF4w2vK2v/uBC2U7zF27DQJIuTG6Ss4zLtH7/i9ZMT6yJuphqhSsIQOjYHIx/faOLSJ0sWJwpOsn7Kb6LJM2tQCcRhhN2ASoaLKsPuf+gxg3HfBdNLhgzQFuvAdEXcUJhUOQtOCfNMn4ArydG4LyFiFEobbdJz3pKYPKQu/dv4MKFExQBcKOM4bh9HmymsCilrcmShxFngSWFZwdKmNReue0z/iOpIrwFjvOMvbrhhjpG6qJff8eUrpk/4uSfhNIKTq1fYG8YJ9s8AMN5ggeFGgo3gzBs+v7nYiL1hGilAYjfg3dkbvkwYb7B/L0KXnZsrc/nC4u0GGOAO+0a7/NPzvPHrF8MO7FKAhHcmA0zSQRE7mz7xhASmDmHgN9HxsooV0wu1Yjk5MpofUwcLvI7Mxy8In7x9c2HAxLMrfML0ArvhdcTcAxdMLz58cq8jhjfeRmYBgNlEg8/NrNZfX4ABw4a5Bwxer0CHL1cl/4w6MCQ1554WshEsGlntmlg/oVHiqhfxObCK73w2wzUncYg/yE/CsIE1KfTEbRENZW9Jb25zTD+IqW4XZUCwM5E8LOVXWnEgbkB0aFZAFsrIUoSN/AnquviY9yQsCQoAj53u9KZSyzhqpaE+6kkhTpQ0IonS1DUyarLqcmOgV2O9YOMPAGs0iYsme+4CGDbyBCiNr0ewd3a3478p1bcIqChvJs6ouVe9Re5H8zq10BEOH8vM7PpkNx5Qq0Dy0nY4SfDd7OCA7Qpffp+s4gQzDcCVFrDS5Y1zzxbYxcdcNbvQJ0sA/izzDMvfJiMI/d9Iidt3hhU7jO8IV4QbPr+n5kmhR9j1JTkWuMFe6NjKxFhg34ARAd7egD+m57Q3SoAfZ9KwiDr3osLEwkbsVoceYSB/IsaiYmAi9AifWBxyba8DGZ2hZ8flQlI8SIRmYAPg5rBhBuYRA8d45tiBhRR37IwkqlmxgEVsx9DRklgk7nocf8pj0lRhh33uyVhPul7brFoKrjnkucclzQi7VAjW6YXbJJckhUAaRZqHFi3jNXewIgP8ED758IIwME9wM1Znkm+0tC8xDYWIvF1g3+hhxl+JuceYahnXmr3B3jHe8OUrH7/jyxQxIz++eztxoobyS+gxBMFZ6Knsmhko442B6BvChvCJobsOuOQqBjRw0UacomFwJR8cvFfb3Cd9Sh73FeEFw0o+TZDUHoP56m0Hu/nXT/71yms+AQDhhZmgSz0fNsB48qx7PxsCwC2tiUumMD01uP/qCwdxvzh5C0GWBLHRiS6zHR+QsNQBuyBps+z8mp9cCZRsVMLAHFjZNppTZr2++gNWR0E/nerSJV+kNE3AD79lLggZH6DZQxqKHQtKMyPkX5LlXNlFfaAt9Og8KXdbD5AYAKkDQl01jxWuGbTbxGwxiEFmkqySNyEuObFv7N7itJMxSAS1q/oDaiudl8GUDTl4zKOzaCWrv5BJ/IAJDJKdtjvlSBXqW6xP/VMT229t2tRBgbeLiwNuQrGy5X+tXF0VUyr7NXH8BmwwdvGhx/juos6KiQ5zz5unXZ14x1TRNTUQeidAHe2Y2mN6wZdfaZ1A3EfFAvZKuwT+fAE2jO+Y/kgus109hZfYAY9+QxCUkZsOvbMrryl7oREZJ5UJD4BSnNRefjwQNsa1ZY4ZgkUpm71HGPB55oUWBhMhN1w/AIMwAsDrC4CUuDncMP0Zn78idB497M3ZPs0ocr2vGBa2QXvAYHpR/LEmJw89ZgWxAZhX2It6eX2ymIk7oTynRxZn6PO5UZCaaXtmqJ4nQYyBTiUhrCwsYnmGtK3YOTWeGnk0TlHV3A+AzorIZGTaEhfqktQVha5c5WiQRMwSgqOC1JZhKSmQaK0OunRGFk7EpbSBJ5xArWlOV2EPcqekdaaZnX2tWKVvpO6h6gfmOD8vAONEdBdPxaAzTgx1dS74hma1D3qUBKSUcDxIDsm+X0OFwmSk/fonHSNVslFtC/uUoUgya5+IlTdlHBbWgj6vW+G/H0IP3CipMfb4dWDt3CdfgaSFisbG9AIrqNOmDAOQnAhKJwocKBhbuCL0LhgHVmfQ2TTsxNC0YxAg2nPRbAis3YJm5UX5WPxQaXc/JbQoTV3519nImsQrcktgjRGbTonl8CKWEjTbsQ8ueb69MqN7oM8XshEeRFWRpdth5gGZ412dD5xkFC3OwK4SjYZ6kKBZVuv9je9FZjsJFEBk1F+L6ZNe3hSzLmyQLAzOKF7M9pVA5vOUen/BD3iBvfF20TGA8YlCs+GCn0eG0y4IL5iu+cNfgDuC4WTHK6uegTZIATC90LgnF5vR7OmFnImYg5N6zPI7bjNJUVAJG87ArPaujjDQVenxCObfVABaMUFScEhaRgxKq24JA8JLdm/MO0GP0Psw+GAwf1JZ950y4uMjfCKwPbxg+qTeakdLKGMfCNtaWfVfeQL0SmAb8vxe45neB+P5WTw6H/kvsFksqzXQ+UxT9dk8ROe1Rx9MTBtHtvMUWljepsThwsFCKLF6U4maHHiTzzESRqQF6saOisQUN2DFDwEZEKhtmqDH8RgqA91CneN6kuaqaxAxox9bE69yJLOdVbwtChc5jsU4InFh2fpaJhZptwwA4AEvwkaKRRmTHBpuejaqHjT0rPosh8ceiebNhbcW8EkAxwUC5auhyvXbCbKpdt5Q0Pwt9UsZ/eIqBqg9NrtzyYISzAFn1eYu4pDpVVM2V/fGMjwZb5teEK5+fHMCWGTs27PjzJp93BiyMcA1+6itFU/2CgDTFWPSd/wNsk8svS6w4F3aegoJ4ooQBae2ktmu1VEirOwAMVJmeetKK2g5uyDJuRbXpKe9wUKPEbnqvMKueL3j85X3E9x8bFNy8ucr0GG6UvrI6wuFQ2dSjr5YajddOWfCZMlH6GhxyHT1481NnR8HByAMlHQcPTlwVCwANi5LB4IECAALz5PTASwUCX10MrvIHhBe6QD4cIGFx+ZC/ozJvpfwdWV9ptmyxKZ9Eqj0pny06BIgWsNSChDABhP999TG1YcBmIixguLomLsV05QGBibDkkJq4YpwxSBtLOxw9MyUF4559Jh6zAYjf0dHJMTcwy6Y2PyN2U+0qh38tGL6yLcj5DzbQ7KYQcwyDYtKaC4ioKHHPCSPZ74kJRuMx83ZGOC+IFy8VSs6gvHoEN4RDOwLwurCxYcXhN4H46IxEHmXx8Fh9Rho8sRUEkFGYYArmaphiXvRe7s59AhXHwzGxYWLT29Lm5va9Fp5JdCiCpBo9IASn0mPeXkoiLmivWSj+GwBDO/VoznVyAyh5uzdhd7T20nSxNnVB3YKM2zbMMLPXPRDpoXB5nPTNdOo4cpqrkuPFJXyzPNbehz5z8pwsMAY1HH6U8M9Sw2FgjaNHmrzMdNHWyPFqx0jKfqgHpxi/R2wugwLVL2F8fH1l5aPqj8zUTbuqn5GmUWdp9vjNODj2J/S6akfpDjQfSj8Kijzhi9lb7CuttlW85E1xRfU0bM0MN2CNgAwAQh/BwCjoShzDJRTtFAkML9jyhi/JYE096TRLPA6wm6Y2TeKAbqf/0hr0GLKbfQb5itwRxhSpncCqPn7eXHUpheMFXZDD7koUKnLhH02vlvO4gXiHYXNBeHFR53IaW8JTwlXvAKImV/JivLYXFwkCAoFxe1uPCDmu7PGkyfU+9SBmKnIJn789gCUUUte1wWAh2ToGWqLBJWICX5HVhSxvDVxKowan14JSySnXkYswEfPL2M1tXRYlpGggxU91nECnqFJJfKediKJb231jeQ9/m5EEWL9AZ2nIe7UYws+pyxWQoIEIzDqIdmOpM87qEBLAKXxSsKsBqtTpA7KzwDQpzQICNxTwMWF2yj+oNY4e0GR3H+yENuDERxdDEDn5U3HhuwGxGXZAHTGSRFf0XtvaCRrxRgBF+OjkRoUfyS0qICNpEyXsybU2AIQczBaINq8kfclBmhuCocCUsSRYklXi5Pq8VEgrML0YpLJIl46r36u+EFno9HsVACeuA4AH3CiwzRwWkZPkevXAaH30xVhIKaUD4kKO1IX2XAJPC5zh7nj7c0lrAy2EPRfxwm5hqGGLr0zmLRMp+QPNS6ZxQYAGcilUxkos2ZzCQphFJaXx/CNPFXs6rS9oVFAq/4t0OldYhQlQzSFTHUmPlRHj1a77TITEiiWD0XWHNvliUSr8NXAsyXxkmJNu8LCa1PVIhcQ8HGEUxKTkjImyJa4Rt6NI3zxhmHldTbiNEgqRk87loQXH9e2hx7hBVg8gLC4sNCahHD10xh3bQBAaZHR9p0lcCpGZ756izh1ZfRUpjX7GfGrVrTknMNukitE3v2BrSZvV6k/AkRWD8Bujtb+cswzjTLSTCP+W+TYYZXYksPqienZ5MjehDgWcaXAxr3qlYSLzemeg6pKFjnvahbkO61bDleJA6SM1MbOC/pAhk57IOAexu2W+KFkZJJt0DFrAXZ1YkwTm/FPVbPL+pB9J0nkTefR+YYA7zGrjMbIOjMr96ToKXPWw/iZNZcVu7BLkzh0qv7aDVLnqRVlwot/FvMgxeRP4W+lmEqLR5hSZAnJCYc9qqWdWDv887HVr++FArcVi5SVdIc/9yn5r0LFUJvqQN/+ZDNP9ydSw9zaXPxXP3g6NjCRscLVz4ubemW2X/0IF7E3O2Na8PoJ0wsBTPMV0+Dj5guhx9R7+0K8Yul2ABFicGyV+unFvV4QXnz45ALLRawc145hQG2ti0OwYnqB7bMRiaApJdjLdsBx45qryk8TmcFOXlpFFAUzJ7PZ8sW40qBU8mlYk8VsN6BzYfWWxQzb6D5+woZm/kpSx3b8YuCTBaaMCm2l0YGwUfGOtZuIfOKpsFnJcIXVuGVngtyi5SXo5IgyRYugiT5fj6rs9aJmGGB1lEqxIsBRNgkc4IvwsqF/or148aEHNvWyjQ9XjJsLBmHw0wvszdmI518QVm9vLqLZsoN1kAG9emzMRkC44vUTNfT6wjZKT3wZd2jKnjxXLuGa4XYApTaLDaqdjIgtDAsL4LjEdsXcsx6Xmk3FlB1ZLNnI6mndsUAl1eYIrRSjQkagFMnE8QqddlKnli7JN9VMGeuU81JGbF+Ti3nNmrUOOaAuocs6TF/okABOINZU+1J7za1A5zJERWTT6sDa3yRnRbyN5DZ5ibXEFbGkxDsffSOrO2QyiwoSremVdd+nxUOUBdJ5RCtNJW5yeU9ipvfYMPcuJvWkgKFhngOw8SKHmHMUzQ8kOReXPsaT8cYBQMe1JbPM85jSQdiclXm/8PhsvOWiif0kM4MEp0A58IiBE+mwekAruOCG0pMVWvMRLqirDvg4aIXQZJoDUkzTxkAO+Fuzsi5Wz/bnevWH//J//HN4QTC8wljdFrMuwhXjO8bJ/fxnH64Yv8IaTAO9ZvvuwoufPsHeMC4pzSf6AQH48h53p4AFcIONXsg7YGDvaRVl0Ft1RhLhYagztGuXOglOTpON7yxLzSD7IJvylkz8yCDmH/WRcSAXvuMNpHgaxI/7RtMlSq/AzkpEZ8npjMmBF6DjfBfZ4IlrC7wKOUXFpA/sI6YxiT1n3M0uasAltvyJxJtcSmZAk7fKgKRS0J0SbBsp5VSskKCLevUyCcV0uSt8+o6oc+J6DW2lhAHg+C1S9SZtH9WwrDuEgdJewhVBmo9mlpTUcdWOej8J5LbCXtnxv8Ku9NqC4X3RVzUQUFtedUqmxkfdXOh8iAeRq6RLcOHqaaTgormSrMC4L7XJlmdko1NPa0PjFYHPdAvZG16PolxNEEx2yQE+3hIrbDSnad3hJ903mYTMf8n4Vv2hf59BtQraHvVBqClroZhYP6yJKQ3uYa0m0yDFlBXT2PASn449DMNirwmCyF0JGQCgvgvY8YpvpAJSDLzRcIJIeFV4vBpWT69/5WljpAmvPANv4ULcTFQc9s4BHgZkaIqLUCB/8hRIJ7mf3hLr+wQUFMOF/L3S+jgf31asM+RDl0BiAGLyNvV7IUGE/+QZsUMP2f2AnmfufJKUWxa3OhNMaYmKJvnDP/7Xf45moh4vy6qKhM2NvJyU28fvwMbFFYO3m4vF5GUny7prCHwiRmq0xR1MhmsCHClY0hmZrxr+jH1ObK0l+loKgxQVzF3gPakWE1bSEIHWxUNzpK6qUqxxsYoMHXRXZfwljrBmzKp5rv6ZnjcqK6XZa7OhMCuDqVKKimeRnxJl2FPxOmbBQ21nRmOgRiMe3GE3p+2BTFVuDp3/Ic2/ZH/4BDTI5I6aDpz2UVGpUNTPxivPZUBQB0limfxMQSpBqWxOtyJ/ohwMh/U6ZTF3eT+76g9p7iW1kCOydLXP1nOmS61HEMry+Xc00t69+tlL8Z+bGcXPxu0HtKfBH1Gog3lCptVo57MieTzXY3PJ0pJIRjJiPHo3yadVTBZFkOePngqj38Aq2bXOstgPso+UaFglfgLbGEFn2kcMQduOMgr6gL/DrgWSXfOYSr4wgwZrzRklG6ec9ZFuoYNCnSkZE+RTITKM8phtk1cdkCXT6pI8SO1ZdrnSrJiylqNZ3/Z98KNghK5Q7o2wmv5yacGRxofNofMgMMerSjwAE2RZjL6msR6jtp4CYDw2dmw7T/mn2p7LOsE4SzS/hIljhzpyO2Kejuz8nQbXAJ3H4kjKdh4mgmTioqo+c1UwCB1BVJZfVciHrCThhhrYy89QSEzxpa4kA7djecMOonjrO9akNj134SGl2bJuFxXSyLjq/hbxe9ldasyUuLPwe+pBg9LypiV9eT6EDeRorrCsuqm5FYhb3+TmvKf7O0/H0NypSmpXIHcIsmOpJFZeTlwvNlOQNER9S02HtnzIXYRQvNGiwyavrTnQ+z5TNixa8mnhlzfd5k51+549U3YVrfFnDdaopPPFicxA6g5Fo6aa1ZpsrSMIv40MOq9jMAAAD7g0ZCZ9RlJkdeiZt2IQxXhEER1NVcOyOs4eOabK87HePL/pWAlgOFmGO5BoL5gbm0hlnC7SVmfNCVZ5FUAFtXSERAaxaFVDZO9rZIcRDPBIZmJS96SIch3TljofWuwb4KOfodAxz/fSmWCUa1Jo4dx3LDlYD9FWHQDIU8hTknzNtSlR1ckKFrn3Bz07FSv4oJdJdFWf5FGNutR5dJ5YLRckDdGbqlLnjwWJ8KK0XlxauWnVw4eaq2kUFgKmfUvsiepMdnufNAOQfD6APT8ZeW3la7fMZH9F/ZAsGajxR2t8ip/NM5q2kjupiWOmbHJnAQUcE9evX1nhJHGITEsXKDEQy/BehLTqMgpFTb2qR9bH8LauxRRpay6oQYwHuRjLDFA5iF8x3NSr2pyGMLL4sq5KU+ehrGEhXT5bRoOj9x2VSehSanPtgugKdQ+rqqpLxgOuUbipW4tOxp9b6/ZmVU1T8pi2nIXwiEe1mbRE2Y8/jP/3P2UlmsFGfSy1rHkB3YNi/h3aIolTmX3T/h8mP6h1vcm7oUdE7hUFt0edp2Kd57sczTp2MDNDQk7W47NHK/dnc+WDFPU3GzqgwsJ55pb9eppJIZHo0rLPZOouilrvIM3yTRzNS3YBZeoYYMW4uHhggHygIzU1cjGBGKlpx9AL6lrdBQCVKLRnBuy9+/p812LlzgM5m8Yz5b1sGxhg9WVJfYuU3O9JKIw2rRZ0ilNRbfPMQ3ryludFYNNS/F70wZr/MP6Pf8pOrInzCtKbtFCgkotlK9o6jjoYJVwfTrt8UETTPdR9WtuWmrcWz9zJomRR+WOzFe1G5cbmmSw9WQQ21MlCJGuQVQZhzS81FQvSjQ0qXUAnh9kixEjyU6cR1p1EywZtStCtPU+KMbeZBNVEMbd8oucUCwS4lCmykNefbYWFShCu1Txu+h/Fvt35QXFjKI7zAnLGsmORHkE/UVFtxegHpEsWTB+M4suCOp/9rAcKGWs2SprqZz28D0krlj20sqYmHldTx/6W8CgHq4+CZDkZJF8H6DIxWVDo9mXhk6+zGM2CntcsdR/2OsB91nz8sMIUy/4NpKfWMyI5e+v70n33rmYTNSeJpa4FZ9M9r6id+SFnmpZbGTcCdgJjmZ6khjwMDMmS3gPA6kh87lGvJKsqFiNDu6ELo3qMQ26uYX/svAD1LpM4VFxlpcW85O4MrFGFneBTocdruyL15Lg/XEnNvkfGhubIYmQKT6uFllNvt/TzcUKdQpoyjaSOG4VljXwkxX8NWhOTxMLTneyfH7JyB6zZJD3jHwrRg1E4mJd1E3ttGcRl4PFPVr6iUuhP9aHVRFoX+w108G6eKXbwdguYYk80Pl/hHmlj5qDYgQyONSist80VXfpoTv4e9u3ONj2puQDIJmR7mmjvzN4lrQRTAMbpkzVfNsTnnorcsf3LCtf0byZHlfQSPmiGE8tlPYVHKKO3troaoRmoOMW+vygd2Gd0PtgzMdWN7dDoAc8dlC8ol03yyz3HbQ5QLlSUuIqtMw4oQLUmc+g+FZ7sgVmJBuPWQ5Y29dv21WJdldZ6B9ypFz+szEO6QlMdQP2sTcOidaM2jJBQyFrKywwlQDbmMbpYDkv1gjKXZW/Yd1zvI9qxHHYt8qJpDkcbkjSxrySKXCxj+dsXkB24iyb3JK40thecrcvLAyhLWd+VIKfiyTfFsnvzvgnE1CNY1/yQDhT0wRvN/fSSOxG5kIwwK8phAzq/5wI/ZU1KK/X0yNraHcxYYNxYuhcwWUHVAGZy/Tkf9A/j//inBE9Eh1ey3ThDO/WgV3yT69C2Bq/TAYUKTf3waYWMYtaCmmKvdbLUmKbsz9FEjyQeCR7Gb1yUZKHzDfkd7xW5KFLNpHhMUBuHp7UDhYJqwg57kxNUVaTC9cmWg8ro6Z0pDtix6kZjGDWr1HhO7qSaNGuX0h5Kqy6l0lxllCbdujNGq9I+uhNRTuiS5ZO46hQAv2tLCaspbkuvM5sDDgsQ831kd3TKfPXtyqXPncfmWMw7xDeqeXQDVofeW9oG1icLqnofduVKcqs3yQhO9slSUWuSt15NNrmlkZFUCc4SSNoA/QGnAxugReUin9xKRj7ZmnKHvfiNLZ6sSSfTNHNg64N9KpYQqRtdptFqXtRnMiXoygqL4zWrsNlz+spt3LBqU98hOX4iub2ocM2eIu54jc3xNyB5GFswXChk6qa6saMi7Z6Pknfyw1bjHn2DAdokGatVvUftRLbUr6Et2jpadU7L0yJHLggiMm8OPWKQOm4vA7M7ghmCuPJ69pihqC5ZAHCyn0xSdp0ve78lBYe1Sv8rJHEUb4UXb3hENo5HrDwtwbIz1RZzXkm+aiyaBN6SZA8KQHRzaXGfWvRNJTtuYqVlMPSY0k95KN5ROvAwUl5mnEvHPpzZYdNDO6rGaIk6+vTPLmmfUp5Xg6b30mQiYRmTcmIYsvNZJ7nwDyTMkFsG6ZEc6egt/xYRcnnTwoTLZ8j4iXiIhGj6RoQ7HAldYSzpkpgHyTAV8nbF+yBUaAPuyhvIjZCsNsBqa2/NHr/xjuMZ/makGJFW+ExEu+GfFdm65qYAE5vveVjnu9CaQ9HmmQ7El+KA5JZE0tZa4fzFcfiBmpSRyqMjsltBLE3qUr+nLa3utfym9Rc+sbmGjhA3UIyH1lZvkUQklwYNCU6/Py5eKV+uCvkQb1G0s05QPUwHPKYNxZo/AuR7P8i7qrQY/S2pNrk3CVrRFZpZVU9KoSV0HDFho1zSpYP85Hbl3wy0qr3+A0Mijx6V7Mv4UR2UsvWzAD9AbROcpZFHuid7MX1niXUoEL+A6Khbi8PikkySfxmTg/YGIivfs289CRMkAPwYjDDVG8ofD6k2p455pHgtWxIDvOSjsFy1EBWrIOHnG8tXUQt8I/275iVjmUW1sqo/Zo7IxPKXlWmOTC1NK8YqWJMY2iSOKZg1thW07JDpV/8VxOVtfPWqQJWO6OucmNiBPNTJkzKlpRnlqhfWDFMjABgV2cFUVvSYC2v6aHkcTfSY09AAF4uPWdWQhdyOjCmfXZvbrHnbvZQ4tUkPIwt4FM597n09boUpLSJ4Mn2pZa78AF4+EXovsfhk0df2kAHg7eLszQFKg3f09QWszvJXK2GUz9TpGhT+v2U7F8e5GHqPzqPg/iWrKrnGicU9Oh94k3L2ipwaLHrAwNuQh462kLbIDAlaQKwVvYyMhoFE+685A2nVAXWvHsaVvChRF5kVURi7+YsoZ0Ud1NUN8QGJzC4/f0i7Ic2uzU/6LlkyVa+zBfjbkIB8Mi8Ypc0YgP/BAuh8EvW6E8aXUkR6wB9mTRa9+FiVxVkh6sR8QL7Hu7S7v6BbGSFeTqZB7LKSu7SpAASqV1XgzHsvSYwQpf0TmUrEatLvVZtAWz6YqAZHWxFri8meTIbcc2v2Bu3YtH2CNNwR328woJfYMVPWNqspVPxJv42ez8M96aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTjrppJNOOumkk0466aSTTvr/E/2/5bJMG7O2tzwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import glob\n",
        "import random\n",
        "from PIL import Image, ImageOps\n",
        "import albumentations as A\n",
        "import numpy as np\n",
        "\n",
        "files = glob.glob(\"signal/**/*.png\", recursive=True)\n",
        "\n",
        "augment = A.Compose([\n",
        "    # A.HorizontalFlip(p=0.5),\n",
        "    # A.RGBShift(p=0.25),\n",
        "    # A.ColorJitter(p=0.25)\n",
        "])\n",
        "\n",
        "\n",
        "size = 224\n",
        "\n",
        "for _ in range(20):\n",
        "    # Test Transformations\n",
        "    choice = random.choice(files)\n",
        "    img = Image.open(choice)\n",
        "    img = ImageOps.contain(img, (size, size), Image.Resampling.LANCZOS).convert(\"RGB\")\n",
        "    img = ImageOps.autocontrast(img)\n",
        "    img = Image.fromarray(augment(image=np.array(img))[\"image\"])\n",
        "\n",
        "    img = ImageOps.pad(img, (size, size), centering=(0,0))\n",
        "\n",
        "    # img = ImageOps.grayscale(img)\n",
        "\n",
        "    # img = ImageOps.autocontrast(img)\n",
        "    display(img)\n"
      ],
      "metadata": {
        "id": "vlSxGpQ55ByR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5da0351e-43ea-46a7-8fe5-49d8be5856c6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Processed Dataset"
      ],
      "metadata": {
        "collapsed": false,
        "id": "_hm9T-905ByS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"imagefolder\", data_dir=\"/content/signal/with augment/signal_train_augment_timediff2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261,
          "referenced_widgets": [
            "ca4cf6e715d047258e2b73d6676b213f",
            "8112156ec2ad4fae8e212229ff96dc69",
            "c8f5a417061e4b1792800da12ecb31d9",
            "77bd891356804fd181f55322684eff5e",
            "d8468c06cd644a428dce76fe4927fc1a",
            "94bba7d1eb754bbca4ddbb200d4af828",
            "0f5913a9b346447c8980b9431e377fd0",
            "a4559a98a8684c3ba9fa5ad570d7df88",
            "5ebdcb2f58a0455aa7560f4dc008f045",
            "1e91ba5618164014aee7fe4e261b4e4a",
            "89b985760ca34ea7bcaedb9bfd1cd7a1",
            "7cdcd302209248f99a9553733e9f32c7",
            "b1ee0eb836eb4661a2403f260c9580fb",
            "3a865e87a56449cf9115ab17627d5e2d",
            "da5857d2641d473b92ec34cc8f7ce24e",
            "a983f159d5034802a36029576b712886",
            "a9b0ebaf25784647a41902dde1979aa9",
            "75881b9bcbd54d70980666bc89c0893c",
            "a801f19de352460aa03454a818a7530a",
            "dae5d0da28db43ffab10de149ee620e3",
            "af276b55454943fdba04ccd4d64b9405",
            "09b8ed7a8a5f426e93c33101245f14f2",
            "e79be70ed4f94d58bc81d72e4d7aaf27",
            "0fa19e34dad04317be6abe0e57d3a210",
            "b56eb00ee991481dae74b26591169c8e",
            "6bf166f23e3f4d888923151cb6663fe7",
            "01af67c649e142c69d7d6afe89515bcc",
            "6a6b9a652a8f41f6baed4cff979699fa",
            "4f61819e166646d3ac179bb925ed007a",
            "1c2878159e2d440a85119fe739bd1c8c",
            "a5e309761a96457caf019fbe5cbaac08",
            "6162013307d6408c866bc7403d5f83e5",
            "de8bd64155b048f18120713842daaba6",
            "ef4f5a4395ed49eeb228efeaa165ed41",
            "db1e8864eb8348dd994398bc8c17240f",
            "cdc70412b702481b8131acfc1e2e3015",
            "45b85a354c0e41d9bed9a6b148594f5f",
            "b5fbf1479dfc4b2481414b105fadf217",
            "5bc9a94147394738a5736211e99b0ddb",
            "e0d35d0bc4524639b5ba06cc248fc578",
            "da91db41549c43f3bae4e66834b60775",
            "4452fa4f15f54ce49f964c882fea99f4",
            "f0224d07241f4fd2bc38668d1298154e",
            "82a60bb5103d40839a95f0e52dda89aa",
            "f96fbc451b0b4d31a3bdfc32e0346018",
            "ff49b4d0e3a9440b928fc616661f43ce",
            "3027d39da4bb4c4ead4ed2895cc4d164",
            "74f7562c75a54c139b5b70cf4853ff6d",
            "15f8bf909fcf4eb09e13f89efe388fb4",
            "ff544b5ae6024157bfa6e4d86272ec76",
            "e75f32146b124e3c986798d30b1ea4f4",
            "6f5ed55f3bf64c94a77f243f592cdb0d",
            "a72eb513f9bb483cbab5e6a60578492b",
            "6bec15e24aae4bfca619ccacfcc03e66",
            "a0eaa4a30a484cd7b62eab7be59e3cce",
            "d26e2ca9da174e84bfc1c721940b3285",
            "0e31509c548a442e984e1ebe0a600b40",
            "b35f283beba24141a488f45083b115e7",
            "0bfe7d2e87c6445e8933784e7277e7b5",
            "bdba48e736714d3781fbc84b1df146a8",
            "646f701b83e9434aa964d9c7293c27e5",
            "004a5bd4b25f4110860bf560db91903a",
            "48642a6fe2334196b7b6c207ac2f333d",
            "1c2645c0462f4455b5ed80cef0e62891",
            "6d94b575a08e4ac18f78682fcd11c641",
            "eb7a1c9d271d42a08d789af96943749e"
          ]
        },
        "id": "-sYIxfzRXC63",
        "outputId": "8c5065f4-cf38-4a96-9586-d7c4879ca20f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Resolving data files:   0%|          | 0/3045 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca4cf6e715d047258e2b73d6676b213f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset imagefolder/default to /root/.cache/huggingface/datasets/imagefolder/default-ca3797a7cb0837e3/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3045 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7cdcd302209248f99a9553733e9f32c7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e79be70ed4f94d58bc81d72e4d7aaf27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef4f5a4395ed49eeb228efeaa165ed41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f96fbc451b0b4d31a3bdfc32e0346018"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset imagefolder downloaded and prepared to /root/.cache/huggingface/datasets/imagefolder/default-ca3797a7cb0837e3/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d26e2ca9da174e84bfc1c721940b3285"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset[\"train\"].train_test_split(test_size=0.2)"
      ],
      "metadata": {
        "id": "joaaAkLrX-AW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cFGDkSLkYotK",
        "outputId": "999dde69-a9f7-410d-8f3b-8a45dbfe0d85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['image', 'label'],\n",
              "        num_rows: 2436\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['image', 'label'],\n",
              "        num_rows: 609\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialize Training\n",
        "I chose ViT for this as it is one of the state-of-the art models at this time.\n",
        "The rationale behind choosing the variant finetuned on the imagenet as well was to perhaps harness the benefits of transfer learning. Unfortunately, I was not able to validate this hypothesis as there was not enough time."
      ],
      "metadata": {
        "collapsed": false,
        "id": "lanYpc8l5ByU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import ViTFeatureExtractor, ViTForImageClassification\n",
        "model_name_or_path = 'google/vit-base-patch16-224'\n",
        "feature_extractor = ViTFeatureExtractor.from_pretrained(model_name_or_path)\n",
        "\n",
        "labels = data['train'].features['label'].names\n",
        "\n",
        "model = ViTForImageClassification.from_pretrained(\n",
        "    model_name_or_path,\n",
        "    num_labels=len(labels),\n",
        "    id2label={str(i): c for i, c in enumerate(labels)},\n",
        "    label2id={c: str(i) for i, c in enumerate(labels)},\n",
        "    ignore_mismatched_sizes=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231,
          "referenced_widgets": [
            "0b909622263a4362b3a86472db535eec",
            "8f8451c15fb545ec9682048eb9ee6a42",
            "6fdf0ec1bc2c45e5958e02dca6c1cb20",
            "06aa2c1aad9a4d089bc4292c3e4f3ed1",
            "d389980b40b049aa8268f8276950c4da",
            "f81dd61beef24e2eaadd5142f67f2e0d",
            "d466640d940043618359ab1fe92a30c2",
            "1d27fe069cb14dd696a39b58f1f0710c",
            "1e26abd82f3b4d78999cfca812d134f2",
            "56554a2b87534d9b93ff66178b7d8396",
            "f492864f371641a58efe75357714212e",
            "9c4ab0bda97e468bb051a76e199fd94d",
            "4b55113c13e142769300edb337f0ff68",
            "06ff33f77b1b444c801f3b7f77da62d9",
            "4657529f938e4bc18e081416c697e746",
            "e96f7cd00841406a959aa6669dbf46d1",
            "2001d5f7f5dd48c980d11c4591714602",
            "b0d8a349787e4852b7546438e1b4ba6e",
            "660f606dae4346e198201e05abb01c01",
            "ac0489859ff947da85e94a39e4d189da",
            "17860b255e5e41e3bf822f60c5910db7",
            "4b1bf5aabf624f9884119f641f649dc2",
            "fdd0a204603e40b1b6afff15e2ece75e",
            "b815d5b46a354b16818393396cb6f5d6",
            "a4ed116eaa194195af4b9c829715fd28",
            "79fdb8ccc09b4fe3ae699ce456198211",
            "2d96a200837548b19bef6dd56892e163",
            "cd300d5b702540909f71c599ff766a37",
            "c8a2a213ff96404b9870c69739dedd63",
            "7162c72186ee4f809750b21c05617c8a",
            "b1dc57b2f5b244ee86084f72bb2bf25f",
            "5dd3dc440c1246be8da0b7d800792175",
            "b555ecbefb8e47efb2336d8f4ce31190"
          ]
        },
        "id": "PCBqg1TlZCdZ",
        "outputId": "e5606b65-497a-487d-b28e-9095655a3d59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b909622263a4362b3a86472db535eec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/69.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c4ab0bda97e468bb051a76e199fd94d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/346M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fdd0a204603e40b1b6afff15e2ece75e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224 and are newly initialized because the shapes did not match:\n",
            "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([7, 768]) in the model instantiated\n",
            "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([7]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfpqlCP7m2wF",
        "outputId": "4158ad09-7bd2-47ac-81c8-cb78f03c3bcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['0', '1', '2', '3', '4', '5', '6']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_extractor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBDu-jI8ZJB8",
        "outputId": "6cd6dae7-7484-4ef4-9a7e-1ca2477b43a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ViTFeatureExtractor {\n",
              "  \"do_normalize\": true,\n",
              "  \"do_rescale\": true,\n",
              "  \"do_resize\": true,\n",
              "  \"image_mean\": [\n",
              "    0.5,\n",
              "    0.5,\n",
              "    0.5\n",
              "  ],\n",
              "  \"image_processor_type\": \"ViTFeatureExtractor\",\n",
              "  \"image_std\": [\n",
              "    0.5,\n",
              "    0.5,\n",
              "    0.5\n",
              "  ],\n",
              "  \"resample\": 2,\n",
              "  \"rescale_factor\": 0.00392156862745098,\n",
              "  \"size\": {\n",
              "    \"height\": 224,\n",
              "    \"width\": 224\n",
              "  }\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def process_example(example):\n",
        "    example[\"image\"] = example[\"image\"]\n",
        "    inputs = feature_extractor(example['image'], return_tensors='pt')\n",
        "    inputs['label'] = example['label']\n",
        "    return inputs"
      ],
      "metadata": {
        "id": "TTbdAv9gZIha"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def _transform(example_batch):\n",
        "    IMG_SIZE = 224\n",
        "    new_imgs = []\n",
        "    for img in example_batch:\n",
        "        image = ImageOps.contain(img, (IMG_SIZE, IMG_SIZE), Image.Resampling.LANCZOS).convert(\"RGB\")\n",
        "        image = ImageOps.autocontrast(image)\n",
        "        image = ImageOps.pad(image, (IMG_SIZE, IMG_SIZE), centering=(0, 0))\n",
        "        new_imgs.append(image)\n",
        "    return new_imgs\n",
        "\n",
        "def transform(example_batch):\n",
        "    # Take a list of PIL images and turn them to pixel values\n",
        "    inputs = feature_extractor([x for x in _transform(example_batch['image'])], return_tensors='pt')\n",
        "\n",
        "    # Don't forget to include the labels!\n",
        "    inputs['labels'] = example_batch['label']\n",
        "    return inputs"
      ],
      "metadata": {
        "id": "ufPObLtHZPOT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_data = data.with_transform(transform)"
      ],
      "metadata": {
        "id": "dBtFo9bpcGoe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "\n",
        "def collate_fn(batch):\n",
        "    return {\n",
        "        'pixel_values': torch.stack([x['pixel_values'] for x in batch]),\n",
        "        'labels': torch.tensor([x['labels'] for x in batch])\n",
        "    }\n",
        "\n",
        "\n",
        "acc = load_metric(\"accuracy\")\n",
        "f1 = load_metric(\"f1\")\n",
        "\n",
        "def compute_metrics(p):\n",
        "    info = {}\n",
        "\n",
        "    preds = np.argmax(p.predictions, axis=1)\n",
        "    info[\"accuracy\"] = acc.compute(predictions=preds, references=p.label_ids)[\"accuracy\"]\n",
        "    info[\"f1\"] = f1.compute(predictions=preds, references=p.label_ids, average=\"macro\")[\"f1\"]\n",
        "\n",
        "    return info\n"
      ],
      "metadata": {
        "id": "MGZl47RgcSfE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134,
          "referenced_widgets": [
            "39fb6041757b4557831fa81e792a84f2",
            "391071e21a69415bb3fdd53938a1fa44",
            "223cf86970a7428f9757ad41313874ad",
            "eaeb5b60d60f460f82b60b86025f93d9",
            "39cb970f788947c7857e739e73a24904",
            "e33e0d350ffc473eab973ad8226c87d2",
            "8a67df2fa0234c0f9d9c200f66412837",
            "66d37e985f834b029ea67e7230414e54",
            "5acfa8545834456facdac817d44789fb",
            "645883cca8544538a0cf144977212ae1",
            "4b7d37e1a18c4fcab5286fb6c3799da4",
            "f3f948fae2c040659ab7adc0175b8044",
            "ca72122189b54eb383547da1f247f380",
            "56541a10e06e4eaea920c7da2b906fb0",
            "48b1ee3a7c61462c9b7842082e7509b4",
            "4376a1916d454f1182caf5052c8c4cc4",
            "b7ef0f54bcaf4ea1a7636effe205a6eb",
            "1a297473630043429954a249855e9857",
            "b5c3bac1bfc6442f9a2069966d1083ce",
            "71c54d7b33c24272a0c1a35f336be6b8",
            "7c9cc19002bf46d5ab1580fd7a1f44d2",
            "2b07882c060b40ef89c8a3a7e05cfdeb"
          ]
        },
        "outputId": "261cd350-4861-4b06-837c-c3cde4363622"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-65140206de28>:12: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  acc = load_metric(\"accuracy\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39fb6041757b4557831fa81e792a84f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3f948fae2c040659ab7adc0175b8044"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "  output_dir=\"./vit-signal-processing-noimgnet\",\n",
        "  per_device_train_batch_size=64,\n",
        "  evaluation_strategy=\"epoch\",\n",
        "  save_strategy=\"epoch\",\n",
        "  num_train_epochs=20,\n",
        "  fp16=True,\n",
        "  logging_steps=10,\n",
        "  learning_rate=2e-4,\n",
        "  save_total_limit=2,\n",
        "  warmup_ratio=0.05,\n",
        "  remove_unused_columns=False,\n",
        "  push_to_hub=False,\n",
        "  metric_for_best_model=\"f1\",\n",
        "  report_to='tensorboard',\n",
        "  load_best_model_at_end=True,\n",
        ")"
      ],
      "metadata": {
        "id": "yMGQFF1sdYUy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=collate_fn,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=prepared_data[\"train\"],\n",
        "    eval_dataset=prepared_data[\"test\"],\n",
        "    tokenizer=feature_extractor,\n",
        ")"
      ],
      "metadata": {
        "id": "OZJTmrg_eOqN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb4089d1-7a9e-486b-960c-dc716ed28e40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cuda_amp half precision backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_results = trainer.train()\n",
        "trainer.save_model()\n",
        "trainer.log_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_state()"
      ],
      "metadata": {
        "id": "ZHP0SMY6eVKU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3458ab72-f1ce-4e8e-e6df-66a27a308b2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 2436\n",
            "  Num Epochs = 20\n",
            "  Instantaneous batch size per device = 64\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 64\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 780\n",
            "  Number of trainable parameters = 85804039\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='780' max='780' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [780/780 1:02:32, Epoch 20/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.989200</td>\n",
              "      <td>0.923124</td>\n",
              "      <td>0.663383</td>\n",
              "      <td>0.647076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.466200</td>\n",
              "      <td>0.715477</td>\n",
              "      <td>0.776683</td>\n",
              "      <td>0.803707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.212000</td>\n",
              "      <td>0.346498</td>\n",
              "      <td>0.888342</td>\n",
              "      <td>0.906060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.095400</td>\n",
              "      <td>0.453226</td>\n",
              "      <td>0.889984</td>\n",
              "      <td>0.905911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.075900</td>\n",
              "      <td>0.325984</td>\n",
              "      <td>0.916256</td>\n",
              "      <td>0.923449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.039200</td>\n",
              "      <td>0.151685</td>\n",
              "      <td>0.967159</td>\n",
              "      <td>0.970403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.036800</td>\n",
              "      <td>0.185925</td>\n",
              "      <td>0.954023</td>\n",
              "      <td>0.954480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.016300</td>\n",
              "      <td>0.078482</td>\n",
              "      <td>0.980296</td>\n",
              "      <td>0.980905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.009100</td>\n",
              "      <td>0.081062</td>\n",
              "      <td>0.983580</td>\n",
              "      <td>0.985442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.025800</td>\n",
              "      <td>0.128656</td>\n",
              "      <td>0.970443</td>\n",
              "      <td>0.971137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.027100</td>\n",
              "      <td>0.169078</td>\n",
              "      <td>0.955665</td>\n",
              "      <td>0.959464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.001400</td>\n",
              "      <td>0.077953</td>\n",
              "      <td>0.981938</td>\n",
              "      <td>0.983908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.000300</td>\n",
              "      <td>0.074777</td>\n",
              "      <td>0.981938</td>\n",
              "      <td>0.983908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>0.074280</td>\n",
              "      <td>0.981938</td>\n",
              "      <td>0.983908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>0.073796</td>\n",
              "      <td>0.981938</td>\n",
              "      <td>0.983908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.073330</td>\n",
              "      <td>0.981938</td>\n",
              "      <td>0.983908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.073165</td>\n",
              "      <td>0.981938</td>\n",
              "      <td>0.983908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.073197</td>\n",
              "      <td>0.981938</td>\n",
              "      <td>0.983908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.073151</td>\n",
              "      <td>0.981938</td>\n",
              "      <td>0.983908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.073174</td>\n",
              "      <td>0.981938</td>\n",
              "      <td>0.983908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-39\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-39/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-39/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-39/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-78\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-78/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-78/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-78/preprocessor_config.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-117\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-117/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-117/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-117/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-39] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-156\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-156/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-156/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-156/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-78] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-195\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-195/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-195/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-195/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-117] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-234\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-234/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-234/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-234/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-156] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-273\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-273/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-273/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-273/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-195] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-312\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-312/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-312/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-312/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-234] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-351\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-351/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-351/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-351/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-273] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-390\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-390/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-390/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-390/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-312] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-429\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-429/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-429/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-429/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-390] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-468\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-468/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-468/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-468/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-429] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-507\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-507/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-507/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-507/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-468] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-546\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-546/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-546/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-546/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-507] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-585\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-585/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-585/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-585/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-546] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-624\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-624/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-624/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-624/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-585] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-663\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-663/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-663/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-663/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-624] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-702\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-702/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-702/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-702/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-663] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-741\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-741/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-741/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-741/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-702] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet/checkpoint-780\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/checkpoint-780/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/checkpoint-780/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/checkpoint-780/preprocessor_config.json\n",
            "Deleting older checkpoint [vit-signal-processing-noimgnet/checkpoint-741] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from ./vit-signal-processing-noimgnet/checkpoint-351 (score: 0.9854422191280513).\n",
            "Saving model checkpoint to ./vit-signal-processing-noimgnet\n",
            "Configuration saved in ./vit-signal-processing-noimgnet/config.json\n",
            "Model weights saved in ./vit-signal-processing-noimgnet/pytorch_model.bin\n",
            "Image processor saved in ./vit-signal-processing-noimgnet/preprocessor_config.json\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =         20.0\n",
            "  total_flos               = 3516281883GF\n",
            "  train_loss               =       0.1238\n",
            "  train_runtime            =   1:03:29.65\n",
            "  train_samples_per_second =       12.789\n",
            "  train_steps_per_second   =        0.205\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.log_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_state()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqPQ-_3I1UYs",
        "outputId": "47b79591-1d5a-4bc6-a14f-474890010095"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =         20.0\n",
            "  total_flos               = 3516281883GF\n",
            "  train_loss               =       0.1238\n",
            "  train_runtime            =   1:03:29.65\n",
            "  train_samples_per_second =       12.789\n",
            "  train_steps_per_second   =        0.205\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = trainer.evaluate(prepared_data[\"test\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "id": "uj-W4hWH1ufi",
        "outputId": "ca49a605-263b-4717-bc43-102b795c386c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 609\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='77' max='77' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [77/77 00:11]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.log_metrics(\"eval\", metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YBFG-fT16Ye",
        "outputId": "d6cdc2ca-856a-4e27-cd68-ad6aff72f9c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***** eval metrics *****\n",
            "  epoch                   =       20.0\n",
            "  eval_accuracy           =     0.9836\n",
            "  eval_f1                 =     0.9854\n",
            "  eval_loss               =     0.0811\n",
            "  eval_runtime            = 0:00:11.29\n",
            "  eval_samples_per_second =     53.934\n",
            "  eval_steps_per_second   =      6.819\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from sklearn import metrics\n",
        "import copy\n",
        " \n",
        "y_preds = []\n",
        "y_trues = []\n",
        "for index,val_item in enumerate(data[\"test\"]):\n",
        "     encoding = feature_extractor(_transform([val_item[\"image\"]])[0], return_tensors=\"pt\").to(\"cuda\")\n",
        "     outputs = model(**encoding)\n",
        "     y_pred = outputs.logits.argmax(-1)\n",
        "     y_true = val_item[\"label\"]\n",
        "     y_preds.append(y_pred)\n",
        "     y_trues.append(y_true)\n",
        "    #  print(f\"{index} out of {len(data['test'])}\")\n",
        "     "
      ],
      "metadata": {
        "id": "2oEv4QEGLFoQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "cm = metrics.confusion_matrix([int(x) for x in y_trues], [x.item() for x in y_preds], labels=[x for x in range(7)])\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "disp.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Mk_gby2deDvz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "2a6c984e-c91e-41a3-996a-883416e762b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get Results\n",
        "The test files can have many digits in one photo, so we first crop these according to the bounding boxes given."
      ],
      "metadata": {
        "collapsed": false,
        "id": "q9XliHwu5ByZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_images = glob.glob(\"/content/signal/signal_test/*\")\n",
        "test_images"
      ],
      "metadata": {
        "id": "dhmNQqHqWAYP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2dbce697-bba2-452b-c39f-68bd6f26d968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/signal/signal_test/8d1199bd-4e24-460e-9a37-dbdb039f97e0.png',\n",
              " '/content/signal/signal_test/995cdebc-3dc5-460e-82db-019b8afa4fad.png',\n",
              " '/content/signal/signal_test/357d769e-2148-404e-aa6d-a33b85d2ef9d.png',\n",
              " '/content/signal/signal_test/9da3f5bd-5cb2-40b8-b754-b6a331a40d11.png',\n",
              " '/content/signal/signal_test/3beacfb6-1686-422a-8606-f7c6d268c8e5.png',\n",
              " '/content/signal/signal_test/b080958e-7bcb-49bf-9c76-3a2a2ec30cc5.png',\n",
              " '/content/signal/signal_test/ea9f9f74-cad7-4fc8-83d7-147d2e1c0701.png',\n",
              " '/content/signal/signal_test/6175a44c-c394-43c1-b4d4-b328c8b051fc.png',\n",
              " '/content/signal/signal_test/04079ed6-01e6-42bc-b8a0-b30ea9446bf5.png',\n",
              " '/content/signal/signal_test/0d097254-9d9a-4348-bcda-73b534ff27c9.png',\n",
              " '/content/signal/signal_test/71def67c-3987-47f8-a337-a559cf0f0560.png',\n",
              " '/content/signal/signal_test/93b0f8ec-4c77-490f-af44-0ce78382ca92.png',\n",
              " '/content/signal/signal_test/55984454-e39e-43ff-b502-1bf28c469a5f.png',\n",
              " '/content/signal/signal_test/5d6553fd-c8c8-480c-92d6-13a008884dd7.png',\n",
              " '/content/signal/signal_test/f839b910-866f-4dad-9d5a-3aecdf9e7744.png',\n",
              " '/content/signal/signal_test/de5c1f75-ec1c-4ac5-beba-7895bf42d641.png',\n",
              " '/content/signal/signal_test/3bb8edbc-3367-4be0-8352-4500d55dc726.png',\n",
              " '/content/signal/signal_test/de58a816-7607-43af-a07d-ff04b34b0ab5.png',\n",
              " '/content/signal/signal_test/0bf17f5b-f52c-42ef-b771-0187588ad2de.png',\n",
              " '/content/signal/signal_test/a43d606d-d32a-4c3f-b771-a4c6914e5de0.png',\n",
              " '/content/signal/signal_test/6ea78645-9f90-4e97-8811-51554e468839.png',\n",
              " '/content/signal/signal_test/158922c9-ac47-415e-94c2-d9adcfbe509c.png',\n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image, ImageOps\n",
        "import torch\n",
        "\n",
        "answers_final = dict()\n",
        "\n",
        "with torch.no_grad():\n",
        "    for img_id, image_path in enumerate(test_images):\n",
        "        key = image_path.split(\"/\")[-1]\n",
        "\n",
        "        img = Image.open(image_path)\n",
        "        \n",
        "        # Transform those images\n",
        "        image = ImageOps.contain(img, (224, 224), Image.Resampling.LANCZOS).convert(\"RGB\")\n",
        "        image = ImageOps.autocontrast(image)\n",
        "        image = ImageOps.pad(image, (224, 224), centering=(0, 0))\n",
        "\n",
        "        encoding = feature_extractor(image, return_tensors=\"pt\").to(\"cuda\")\n",
        "        outputs = model(**encoding)\n",
        "        pred = outputs.logits.argmax(-1).item()\n",
        "        answers_final[f\"{key.split('.')[0]}\"] = model.config.id2label[str(pred)]\n",
        "\n",
        "answers_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXFbcHwWtZp4",
        "outputId": "ecb7acf3-819a-4e82-cd52-a11be9f8825c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'eb2bbddf-b648-4a40-a7d1-6e541aee6fe5': '1',\n",
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              " 'b8647e6c-c2a9-4d59-9753-647d551f2145': '1',\n",
              " '2ddaa36c-a210-4f78-a32d-c2bc3c04fe0a': '3',\n",
              " '900459c3-afec-4283-85e2-af92cbc41d17': '4',\n",
              " 'e0af9ced-c0fd-4cae-8664-d35acbd7f594': '1',\n",
              " '50d7fae0-e90d-41a6-a1c8-57b9c3e13977': '0',\n",
              " '1f8145e6-32bb-4dc9-8768-e6e9b1e4aac8': '1',\n",
              " 'fa145e7c-0395-47e7-aabc-28da8cb62be9': '5',\n",
              " 'a3791f32-5946-4daf-b5ab-1271af3d7945': '3',\n",
              " '81bbfa1c-e24a-44e8-8336-5c116ebdf2f4': '6',\n",
              " 'dd70c031-1dd9-4410-bd67-220996cb1788': '6',\n",
              " '5eaae10f-8a35-447c-9ea1-2f8c2e6dca9e': '6',\n",
              " '17445f56-bd54-43f0-9560-db427817b8e6': '5',\n",
              " '94033c62-3efb-430e-b4f4-a127c1a2ab81': '5',\n",
              " 'f9847eff-dc78-4d8e-8745-2f0c9eb2f942': '3',\n",
              " 'f3a000be-07c9-44ad-8ff0-b89a9a1070dd': '4',\n",
              " '84d3963b-cfed-450c-88ab-ca853273d15c': '1',\n",
              " '3dcecd85-2fa6-4e80-8ece-ae83af9017b9': '5',\n",
              " 'eb43382d-9425-4a65-846b-e40affccae18': '4',\n",
              " '3b92d557-bb77-43c8-8359-6270197d86a7': '5',\n",
              " 'a4e7ba9e-1da3-40ca-b1b2-8f9ef2fe8e95': '2',\n",
              " '70ecdc51-afe2-4811-8f13-b3b581d8cd6b': '1',\n",
              " 'e2a29402-5319-4338-a035-7058f76f1109': '3',\n",
              " 'f60c285b-2392-42aa-91e3-40bae8d4ab32': '6',\n",
              " '0b546103-eb69-4778-8ce7-38c1f69c9217': '3',\n",
              " '7b9f06e0-b2b6-4f65-945d-057ddf00117d': '3',\n",
              " '052d35a0-25d8-4eea-9827-2f8d18b6aa99': '6',\n",
              " '96dc5573-6184-409f-9981-5b480cfe9889': '5',\n",
              " 'a4f6b08c-32c7-454d-a6af-55f98dbee427': '3',\n",
              " '4cc3fc6b-11d5-41fa-b5e1-12a20aef633a': '6',\n",
              " '15e8312f-b7b8-4291-8258-1e388c71cd88': '4',\n",
              " 'cdacda39-01a2-426f-bdfd-d066c45409d1': '4',\n",
              " 'f92ae154-51c4-42d7-a267-c97eb2a7851c': '3',\n",
              " '5f28ce89-6b60-4da6-aa93-48e7e255db94': '1',\n",
              " '7c6e047d-5432-4d96-80a5-2e67244b3079': '3',\n",
              " '6d978f23-e46e-4063-b57e-f21932a1526f': '2',\n",
              " 'b98c22bf-593a-4c69-b214-504fc68232ba': '6',\n",
              " 'fea51ff7-842d-49c2-b779-d2d9dda7ddce': '4',\n",
              " '985ee990-3ab4-4703-a6d3-8feebf248b57': '4',\n",
              " 'd8d0ea90-b74c-481b-9f69-b01fe63e78f2': '0',\n",
              " '631f1b7a-3ea4-4dd3-9e2d-81dcce788dbe': '4',\n",
              " 'f7b09f36-fcb3-4aa0-89de-f8a96c762125': '3',\n",
              " '8f9e1a05-ca94-4309-9d93-0d91e4799f85': '1',\n",
              " 'f016e359-280a-4271-b9b7-81b991a910f2': '1',\n",
              " '64a1dc80-d435-43d2-abe2-21baa906c462': '4',\n",
              " '9774c8c8-f1ed-421b-ba46-bfe18ac13115': '6',\n",
              " '36cce7a8-a76d-4517-b459-9aec9bcf2541': '5',\n",
              " '964384cb-c075-4423-a7d5-4316c7e58394': '0',\n",
              " '245f7e3a-f32e-41ee-9a6d-c099f5f3f0a1': '4',\n",
              " '1a909767-4f53-4488-b926-9b9cd2dcb7ee': '0',\n",
              " '2471a7eb-af35-4ae7-870e-6650de9f0c2f': '4',\n",
              " 'e11d7c9a-c3dc-4be5-bfc1-5b2a04dee433': '6',\n",
              " '6ee7ddf7-aabe-4c07-a0d6-e65c1c1e9c5f': '6',\n",
              " '5fce955d-1068-40b3-afc3-111f8c22c9f9': '3',\n",
              " 'b44a46ba-15e0-4574-a49e-f8f5ae77f377': '5',\n",
              " '1589af85-8afe-43c3-a928-4c44b52164ff': '5',\n",
              " 'c11e1778-f8c5-484e-8813-fb9ebffc36bf': '6'}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"vit_augment_timediff_bypond-1.csv\", \"w\") as f:\n",
        "    f.write(\"id,class\\n\")\n",
        "    for name in answers_final:\n",
        "        f.write(f\"{name},{answers_final[name]}\\n\")"
      ],
      "metadata": {
        "id": "U3OnrWsmUxDo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U3SPxQFvXCu_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}