Optical Character Recognition (OCR)/Tesseract MICR (CMC7 Dataset)/TrOCR_cmc7_tesseractMICR.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQOWa6KScahE"
      },
      "source": [
        "## CMC7 Tesseract MICR: OCR\n",
        "\n",
        "Dataset Source: https://github.com/DoubangoTelecom/tesseractMICR/tree/master/datasets/cmc7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJE2FSXeth8A"
      },
      "source": [
        "#### Install Necessary & Missing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f25zxiK4thyr",
        "outputId": "f3384c5d-4306-4486-b864-705525f84dd6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "%pip install transformers torch torchvision\n",
        "%pip install evaluate datasets jiwer\n",
        "%pip install accelerate -U\n",
        "%pip install scikit-learn -U"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qeaiaS5-cahJ"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfC4GNAgcahK",
        "outputId": "72055cec-bb38-40eb-8b0d-bc176aa17a8e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "import os, sys, itertools, glob\n",
        "os.environ['TOKENIZERS_PARALLELISM']='false'\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from PIL import Image\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "import datasets\n",
        "from datasets import load_dataset\n",
        "\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import transformers\n",
        "from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "from transformers import VisionEncoderDecoderModel, TrOCRProcessor, default_data_collator\n",
        "\n",
        "import evaluate\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnQ-Uy7EtWBo"
      },
      "source": [
        "#### Login For Access to HuggingFace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_812CZStV3H",
        "outputId": "25163457-ccf4-457c-cff6-6692b9d7bb17"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRCbLlMvtVtK"
      },
      "source": [
        "#### Mount Google Drive to Notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eylwAylQtRts",
        "outputId": "35afd57f-c5bf-473a-d3f7-d34f2de20318"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QDsD4ipFcahL"
      },
      "source": [
        "#### Display Versions of Relevant Software & Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FcZFZPezcahL",
        "outputId": "35ba863d-412f-4a50-831b-f6ba68f9af60"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        Python: 3.10.6\n",
            "        Pandas: 1.5.3\n",
            "      Datasets: 2.13.1\n",
            "         Torch: 2.0.1+cu118\n",
            "  Transformers: 4.31.0\n",
            "      Evaluate: 0.4.0\n",
            "       Sklearn: 1.3.0\n"
          ]
        }
      ],
      "source": [
        "print(\"Python:\".rjust(15), sys.version[0:6])\n",
        "print(\"Pandas:\".rjust(15), pd.__version__)\n",
        "print(\"Datasets:\".rjust(15), datasets.__version__)\n",
        "print(\"Torch:\".rjust(15), torch.__version__)\n",
        "print(\"Transformers:\".rjust(15), transformers.__version__)\n",
        "print(\"Evaluate:\".rjust(15), evaluate.__version__)\n",
        "print(\"Sklearn:\".rjust(15), sklearn.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PaCjvjkqcahM"
      },
      "source": [
        "#### Ingest & Preprocess Training Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MQSyuZ1p8MTU",
        "outputId": "771581de-809e-46d0-a2de-64119c387b99"
      },
      "outputs": [
        {
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            "text/plain": [
              "                                            file_name  \\\n",
              "0     cmc7/tesseract_cmc7_font_generator_00001868.tif   \n",
              "1     cmc7/tesseract_cmc7_font_generator_00000088.tif   \n",
              "2     cmc7/tesseract_cmc7_font_generator_00000608.tif   \n",
              "3     cmc7/tesseract_cmc7_font_generator_00001197.tif   \n",
              "4     cmc7/tesseract_cmc7_font_generator_00001567.tif   \n",
              "...                                               ...   \n",
              "3001  cmc7/tesseract_cmc7_font_generator_00002548.tif   \n",
              "3002  cmc7/tesseract_cmc7_font_generator_00001128.tif   \n",
              "3003  cmc7/tesseract_cmc7_font_generator_00001469.tif   \n",
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              "3005  cmc7/tesseract_cmc7_font_generator_00001294.tif   \n",
              "\n",
              "                                              text_file  \n",
              "0     /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "1     /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "2     /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "3     /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "4     /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "...                                                 ...  \n",
              "3001  /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "3002  /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "3003  /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "3004  /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "3005  /content/drive/MyDrive/projects/cmc7/cmc7/tess...  \n",
              "\n",
              "[3006 rows x 2 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parent_dir = \"/content/drive/MyDrive/projects/cmc7/cmc7/\"\n",
        "tif_files = glob.glob(parent_dir + \"*.tif\")\n",
        "txt_files = glob.glob(parent_dir + \"*.txt\")\n",
        "\n",
        "# Create dataframe of each list\n",
        "tif_df = pd.DataFrame(tif_files, columns=['file_name'])\n",
        "txt_df = pd.DataFrame(txt_files, columns=['text_file'])\n",
        "\n",
        "# Then create new column with the integer value at the value\n",
        "tif_df['sample_id'] = tif_df['file_name'].apply(lambda x: x.split(\".\")[0].split(\"_\")[-1])\n",
        "txt_df['sample_id'] = txt_df['text_file'].apply(lambda x: x.split(\".\")[0].split(\"_\")[-1])\n",
        "\n",
        "# Make all paths relative (to this directory)\n",
        "tif_df['file_name'] = tif_df['file_name'].apply(lambda x: x.split(\"/cmc7/\")[-1])\n",
        "\n",
        "# set index for concatenation\n",
        "tif_df = tif_df.set_index(keys='sample_id')\n",
        "txt_df = txt_df.set_index(keys='sample_id')\n",
        "\n",
        "# concatenate the two dataframes based on that column\n",
        "df = pd.concat([tif_df, txt_df], axis=1, join='inner')\n",
        "\n",
        "df = df.dropna()\n",
        "\n",
        "df = df.drop_duplicates()\n",
        "\n",
        "df = df.reset_index(drop=True)\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_bMgLb08MTU"
      },
      "source": [
        "#### Convert Text File Names to Text Contained In Files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wEI_qVjm8MTV",
        "outputId": "b8a4fef1-6f08-4517-e957-675a29cdf098"
      },
      "outputs": [
        {
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>cmc7/tesseract_cmc7_font_generator_00001567.tif</td>\n",
              "      <td>H9693054 H926034426112I  517843777942F   37057G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3001</th>\n",
              "      <td>cmc7/tesseract_cmc7_font_generator_00002548.tif</td>\n",
              "      <td>J39H9388541J322226245828J</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3002</th>\n",
              "      <td>cmc7/tesseract_cmc7_font_generator_00001128.tif</td>\n",
              "      <td>H6926013H  770160775918I   441072377704F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3003</th>\n",
              "      <td>cmc7/tesseract_cmc7_font_generator_00001469.tif</td>\n",
              "      <td>H2840031H     010005224772I    337828919144F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3004</th>\n",
              "      <td>cmc7/tesseract_cmc7_font_generator_00001123.tif</td>\n",
              "      <td>H4579999H 976212805167I     066140678390F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3005</th>\n",
              "      <td>cmc7/tesseract_cmc7_font_generator_00001294.tif</td>\n",
              "      <td>H3009953H  428573004463I  165008635205F</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3006 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08efcd36-2e38-4c06-8a9f-b6b46caea8e4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
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              "\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-07589ff8-58e2-4b92-bb9b-f32d7975cc3d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08efcd36-2e38-4c06-8a9f-b6b46caea8e4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-08efcd36-2e38-4c06-8a9f-b6b46caea8e4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                            file_name  \\\n",
              "0     cmc7/tesseract_cmc7_font_generator_00001868.tif   \n",
              "1     cmc7/tesseract_cmc7_font_generator_00000088.tif   \n",
              "2     cmc7/tesseract_cmc7_font_generator_00000608.tif   \n",
              "3     cmc7/tesseract_cmc7_font_generator_00001197.tif   \n",
              "4     cmc7/tesseract_cmc7_font_generator_00001567.tif   \n",
              "...                                               ...   \n",
              "3001  cmc7/tesseract_cmc7_font_generator_00002548.tif   \n",
              "3002  cmc7/tesseract_cmc7_font_generator_00001128.tif   \n",
              "3003  cmc7/tesseract_cmc7_font_generator_00001469.tif   \n",
              "3004  cmc7/tesseract_cmc7_font_generator_00001123.tif   \n",
              "3005  cmc7/tesseract_cmc7_font_generator_00001294.tif   \n",
              "\n",
              "                                                   text  \n",
              "0     H5398391  H301796346367I 496090255170F     05199G  \n",
              "1             H8110582   H882817108149I   202976112724F  \n",
              "2                             H7858877   H685315008146I  \n",
              "3            H9986574H     441349536028I  049986175477F  \n",
              "4       H9693054 H926034426112I  517843777942F   37057G  \n",
              "...                                                 ...  \n",
              "3001                          J39H9388541J322226245828J  \n",
              "3002           H6926013H  770160775918I   441072377704F  \n",
              "3003       H2840031H     010005224772I    337828919144F  \n",
              "3004          H4579999H 976212805167I     066140678390F  \n",
              "3005            H3009953H  428573004463I  165008635205F  \n",
              "\n",
              "[3006 rows x 2 columns]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def replace_file_name_with_contents(file_path):\n",
        "    with open(file_path, 'r') as file:\n",
        "        return file.read()\n",
        "\n",
        "df['text'] = df['text_file'].apply(replace_file_name_with_contents)\n",
        "\n",
        "df = df.drop(columns=['text_file'])\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcLLQgkZ8MTV"
      },
      "source": [
        "#### Clean Up Text/Labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wjjmlTNj8MTV",
        "outputId": "78e0647c-5dba-40fa-b2ba-30a0b5601308"
      },
      "outputs": [
        {
          "data": {
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>cmc7/tesseract_cmc7_font_generator_00000088.tif</td>\n",
              "      <td>8110582 882817108149 202976112724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>7858877 685315008146</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "\n",
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              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-18efcb12-30d8-4a0c-b5bd-adbed75636bf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-18efcb12-30d8-4a0c-b5bd-adbed75636bf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                         file_name  \\\n",
              "0  cmc7/tesseract_cmc7_font_generator_00001868.tif   \n",
              "1  cmc7/tesseract_cmc7_font_generator_00000088.tif   \n",
              "2  cmc7/tesseract_cmc7_font_generator_00000608.tif   \n",
              "3  cmc7/tesseract_cmc7_font_generator_00001197.tif   \n",
              "4  cmc7/tesseract_cmc7_font_generator_00001567.tif   \n",
              "\n",
              "                                      text  \n",
              "0  5398391 301796346367 496090255170 05199  \n",
              "1        8110582 882817108149 202976112724  \n",
              "2                     7858877 685315008146  \n",
              "3        9986574 441349536028 049986175477  \n",
              "4  9693054 926034426112 517843777942 37057  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Replace all letters with spaces\n",
        "df['text'] = df['text'].str.replace('[a-zA-Z]', ' ', regex=True)\n",
        "\n",
        "# Then replace all instances of multiple\n",
        "# consecutive spaces with single space\n",
        "df['text'] = df['text'].apply(lambda x: ' '.join(x.split()))\n",
        "\n",
        "# Remove all leading & trailing whitespace\n",
        "df['text'] = df['text'].apply(lambda x: x.strip())\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZwzT1kfcahM"
      },
      "source": [
        "#### Visualize: Histogram of Text Lengths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "t40q3Ta9cahN",
        "outputId": "9dcaeb54-865c-43c7-b83a-56cb090c607a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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liIqK4urVq+Ztu3fvtmhTs2ZNTp06RaFChShdurTFK1++fOZ2NWrUYOTIkezYsYPKlSuzcOFCAFxcXMyzNvxThmFgGAYpKSkW200mE8HBwbi7u/P1119TtGhRatasmSPntAabjgFu0aIFLVq0uOs+wzCYMWMGb7zxhvlvFl988QUBAQGsXLmSLl26cOzYMdatW8fu3bupXbs2AO+//z4tW7bknXfeITg4mAULFpCamsrnn3+Oi4sLlSpV4sCBA7z77rsWQVlEREQEIC4ujgMHDlhs8/f3p1+/fsyYMYOBAwcyYMAATpw4wZgxYxg2bNh9//N/06ZNKVWqFN27d2fq1KkkJCTwxhtvAJiHVnTt2pW3336btm3bMn78eIoUKcL58+dZvnw5r732GmlpacyePZunn36a4OBgTpw4walTp+jWrRsAxYsX5+zZsxw4cIAiRYrg7e2Nq6srI0eO5NKlS/ccBvHrr7+yePFimjVrRsGCBbl48SJvvfUW7u7u5mEOAG+//TbNmzfHwcGB5cuX89Zbb7FkyRIcHR0f9FLbTJ4drHH27Fmio6Np0qSJeVu+fPkIDQ0lMjISgMjISHx9fc3hF6BJkyY4ODiwc+dOc5snnnjCPHgb7gzgPnHiBDdv3rzruVNSUoiPj7d4iYiI7ZhMf+8l9uuf3DMRERHUqFHD4jVu3DgKFy7MmjVr2LVrF9WqVePll1+mV69e5gB7PxwdHVm5ciWJiYnUqVOHf//73+ZZILJmV/Dw8OCnn36iWLFidOjQgQoVKtCrVy+Sk5Px8fHBw8OD48eP07FjR8qWLUufPn3o378/L730EgAdO3akefPmPPnkkxQsWJCvv/4auPOQ2oULF+5Zm5ubG1u3bqVly5aULl2azp074+3tzY4dOyhUqJC53dq1a2nQoAG1a9fm+++/59tvv33oFg/Js7NAREdHAxAQEGCxPSAgwLwvOjra4jcEwMnJCT8/P4s2JUqUyHaMrH358+fPdu7Jkyczbty4nPkiIiIi8tCYN2/en86b27BhQ3bt2nXP/REREdm2/fEZp/Lly7Nt2zbz++3btwNQunRp87bAwEDmz59/13P4+PhYjMn9I1dXV5YtW5Zt+1/NBxwcHMyaNWv+tA3Ajz/++Jdt8ro82wNsSyNHjiQuLs78ioqKsnVJIiIi8ohYsWIFGzZs4Ny5c2zcuJE+ffpQr14985RmkvvybA9w1vQgV69eJSgoyLz96tWr5vWtAwMDzdOKZElPTycmJsb8+cDAQIuB5lnH+P05/sjV1RVXV9cc+R4iIiIiv5eQkMDw4cO5cOECBQoUoEmTJkybNs3WZdmVPNsDXKJECQIDA9m0aZN5W3x8PDt37iQsLAyAsLAwYmNjLVYe+fHHH8nMzCQ0NNTc5qeffiItLc3cZsOGDZQrV+6uwx9EREREclO3bt04efIkycnJXLx4kXnz5uHv72/rsuyKTQNwYmKieW47wPzE4oULFzCZTAwZMoQJEyawatUqDh8+TLdu3QgODjYPtK5QoQLNmzend+/e7Nq1i+3btzNgwAC6dOlCcHAwAM8//zwuLi706tWLX375hcWLF/Pee+8xbNgwG31rEREREbElmw6B2LNnD08++aT5fVYo7d69O/PmzTMvw9enTx9iY2OpX78+69ats1iDesGCBQwYMIDGjRvj4OBAx44dmTlzpnl/vnz5WL9+Pf3796dWrVoUKFCA0aNHawo0ERERETtl0wDcqFEji8mk/8hkMjF+/HjzGtN34+fnZ574+V6qVq3K1q1b/3adIiIiIvLoyLNjgEVEREREcoMCsIiIiIjYFQVgEREREbErCsAiIiIiYlcUgEVERETErigAi4iIiIhdUQAWEREREbuiACwiIiIidkUBWERERETsigKwiIiIiNgVBWARERERsSsKwCIiIiJiVxSARURERMSuKACLiIiIiF1RABYRERERu+Jk6wJERETkjvPnz3PmzBnOnj1LcnIyPj4+lC9fnqpVq+Lq6mrr8kQeGQrAIiIiecC6desYMWIEv/76K25ubmRmZpKSksKtW7cICQlh4sSJPPfccxiGgclk62pFHm4aAiEiImJj+/bt47XXXqN8+fIcPHiQa9eucf36dRISEvj1119p3bo1w4cPZ+XKlZhMJgzDsHXJIg819QCLiIjYyJ3eXBP79u3Dzc2NRYsWAZCeno6Dw50+qpCQEKZPn05aWhpLliyhXbt26gUW+YfUAywiImJjmZmZpKWlcf36dQCcnJxwcHAwh2BHR0ccHR1JS0uzZZkijwwFYBERERsx/X83blhYGB4eHgwaNIijR49y8eJFrl+/TlxcHCkpKcydO5dNmzbRtGlTG1cs8mjQEAgREREbMgyDKlWqMHr0aAYNGsQTTzxBSEgI7u7upKenc+bMGeLi4hgzZgx9+vQBMPcMi8jfowAsIiJiQ1kPtYWHh3Ps2DF+/PFHDh8+TGxsLE5OTpQsWZKnnnqKoKAgW5cq8shQABYREbGxrKEQDg4ONGnShKeeegqTyWTeLiI5SwFYREQkD4iJiWHz5s3s3LmTa9euYRgGvr6+VK9enZYtWxIQEGDrEkUeGRpEJCIiYmO//fYb/fr1o0ePHqxfv57r169z8+ZNdu/ezZAhQyhXrhzLli2zdZkijwz1AIuIiNhIZmYmDg4OTJ48mTNnzrBx40ZCQ0OztXvllVcYP348pUqVokaNGjaoVOTRoh5gERERG8la0W3Hjh288MILhIaGkpmZSXp6OhkZGWRkZAAwadIkUlNTOXfunMXnROTvUQAWERGxkayH3IoUKcK+ffu4du0aDg4OODk5mRe/AIiOjiYtLQ1vb2+Lz4nI36MALCIiYiNZ8/n279+fn3/+mcGDB7N27VpOnDjBxYsXiYqKYuPGjbRv357SpUtTsWJFG1cs8mjQGGAREREbMgyDJ598kunTp/Pmm2/y7LPPkp6ejmEY5iEQbdq0YebMmQQHB9u4WpFHgwKwiIiIDZlMJjIzM2nZsiUtW7bkypUrnD17ltu3b+Pt7U3lypXx8PCwdZkijxQFYBERERtzcHDAMAwMwyAoKEirvonkMo0BFhERyQNMJpN5TPAf7dmzhx9//NE8JEJE/hkFYBERkTwqa7qzgQMH0qRJE27fvm3jikQeDRoCISIikgcYhpFterOs99999x2JiYl4eXnZojSRR44CsIiISB5wt7l9s0JxgQIFKFCggA2qEnk0KQCLiIjYWFpaGjt27CAqKoq6detStmxZzp07x3fffYe7uzstW7bUFGgiOUgBWERExIYMw+Crr77ipZdeIn/+/Pj7+zNnzhw6duxIZmYmKSkp/Pe//2XTpk1UrlzZJjUmJiaSkpKCg4MDLi4uuLm5mVepE3kYKQCLiIjYUGxsLJMmTWLKlCm8/PLLvPXWWzz99NO8+OKLTJkyhbS0NJ577jnGjh3LsmXLrFrbjRs3WL16NT/99BPXrl0jJSUFLy8vypQpQ4cOHQgNDbVqPSI5RbNAiIiI2FB8fDyxsbEMHDgQd3d3XnrpJRITExk2bBiGYeDs7MxLL73Enj17AMjMzLRKXdevX2fo0KH07duXCxcuEBISQoUKFciXLx/r1q0jLCyMkSNHWqUWkZymHmAREREbSk1NxcXFhfPnz1OqVClOnToFQFJSkvnBuKSkJJycrPNHdmZmJg4ODnz++eccPHiQbdu2UbNmzWztli1bxqhRo6hRowadOnW66ywWInmVeoBFRERsKH/+/NSuXZvBgwfz9ddf8+6771KtWjXeffddAM6cOcNnn31GWFiYVes6evQooaGh5vCbkZGBYRjmHuhnnnmGYsWKcezYMavWJZITFIBFRERsqECBAowYMYJz587Rr18/ChUqxFdffcWWLVsoWLAgNWrU4Ny5c4wePRrgnqvF5bQSJUpw6tQp9u/fD4Cjo6PFanVRUVHEx8drejZ5KGkIhIiIiI2FhYVx5MgR4uPj8fHxASAyMpJly5bh4+NDw4YN8ff3t0otWQH3hRdeICIigj59+vDCCy9QoUIFfH19ycjI4OzZs7z11lv4+/vTtGlT4O7zGIvkVQrAIiIieURW+AXw8vKiR48eNqnDMAxKlSrFnDlzmDRpEm+//TaxsbGkpaVhGAbu7u506NCBcePGUaxYMZvUKPJPKACLiIjYWEpKCkeOHCEpKYkiRYoQEhKCo6Oj+cGyuLg4UlJSKFSokFXqMZlMpKenU7JkST777DMArly5wq1bt/D29rZaHSK5RWOARUREbCgtLY3Zs2fTokULWrduTfPmzfnkk0+A/w0reOedd+jWrRtw52G03GYYhnnWiayH34KCgihevDheXl6kpKTkeg0iuUkBWERExAYMwwBg8+bNfPbZZwwYMIBjx47x4osv8uabbzJ27Fhz2+TkZNzc3ADrjLU1mUxs27aNGzdumB9+O3HiBGPGjOG5556jZ8+erF+/3mpzEovkNAVgERERG9q9ezfFixdn9OjRFC5cmBEjRjBv3jzmzp3LpEmTAEhISMDT09Mq9WSF2qZNmxIREQHAyZMnadasGQsXLqRAgQJcvXqV5s2bs2TJEqvUJJLTNAZYRETEhhITE829u4ZhkJ6eTnh4ODNnzqRv374UKVIER0dH3N3drVqXr68v3t7eAIwaNYoqVaqwZMkSPDw8APjvf//LzJkzefLJJwkICLBqbSL/lHqARUREbCgoKIibN29y8uRJTCYTzs7OpKen07ZtWyZPnszUqVP56KOPKF68OGC96cZu375tDrZHjx6ld+/e5vALMGDAAKKiokhNTbVKPSI5SQFYRETEBrKCbIMGDQgJCSEqKgq4MwTBycmJjIwMunfvzpAhQ4A706JZs66kpCR27NjB8ePHuXnzZralmD08PIiJicHFxcUqdYnkJA2BEBERsaEaNWowY8YMc8DMWojC0dERgH//+9/UqVMHPz8/IPd7gLOOX61aNcaOHWtegS4qKorMzExzfUeOHMHf39+iV1jkYaEALCIiYmN/9YBbtWrVrFTJ/8yePZuMjAwyMjJITk7Gz8/PYhnmbdu20a5dO6s9nCeSkxSARUREbCyrZ3XHjh389ttvNGrUiHz58gFw7do1YmNjCQkJwdXV1Wo11axZ80/3Dx8+3EqViOQ8jQEWERGxsaw5gd955x3at2/P6dOnzfsmTJhA+fLl2bp1q0Vba9X0d/eL5GUKwCIiIjaWNd73o48+4tSpU1SuXNm8b9iwYWzcuJE6deoA1psF4s/Ok56ezpAhQ+jXr59VahHJaRoCISIiYkOGYZjDZkBAQLY5dYsXL26eAi2vSEtL49KlS6SlpQGW30HkYaAALCIiYkNZwfHmzZvExMSQmZmJi4sLnp6eeHp64ubmlufCpbu7O8uWLTO/z2v1ifwVBWAREREb2r9/Pz///DO7d+/m2LFjXLlyhaSkJLy9valYsSJPPfUU3bp1o0CBAlav7datW+YZIAAyMjK4desWrq6umv9XHmoKwCIiIjYybdo0fvjhB65cuULt2rXp1q0bgYGBuLi4cP36dQ4cOMDcuXMZM2YM/fv3Z9SoUVabduzYsWPMnDmThIQEunTpwpNPPsmHH37IkiVLKFSoEC+++CIdO3a0Si0iOS1PPwSXkZHBqFGjKFGiBO7u7pQqVYo333zT4slTwzAYPXo0QUFBuLu706RJE06dOmVxnJiYGLp27YqPjw++vr706tWLxMREa38dERERC0eOHGHQoEEcPnyYuXPn0rdvX9q3b0+rVq3o3r0706dP5/Dhw0RGRnLx4kUmTZpkldkXrly5wvDhw/nhhx9ITExk1KhRjBs3jrfffpsOHTrg4eFBv379WLt2ba7XIpIb8nQP8JQpU/joo4+YP38+lSpVYs+ePfTs2ZN8+fIxaNAgAKZOncrMmTOZP38+JUqUYNSoUYSHh3P06FHc3NwA6Nq1K1euXGHDhg2kpaXRs2dP+vTpw8KFC2359URExM7NnTsXwGKFtSxZQddkMlG5cmW+/PJLbt26ZZXxtseOHeP48eNERkYSEBDArFmzeP3119m2bRtVqlQhOTmZESNG8Pnnn9OiRYu71i+Sl+Xpu3XHjh20bduWVq1aUbx4cZ555hmaNWvGrl27gDs/HGbMmMEbb7xB27ZtqVq1Kl988QWXL19m5cqVwJ3/idetW8dnn31GaGgo9evX5/3332fRokVcvnzZht9ORETsXWZmJoZh4ODgwLp16zh58iSxsbHAneD7+7BrMplyffhDVuiOiYnB3d3dPCNFcHAwBQsWpGLFigC4ublRokQJ85+jmhNYHjZ5OgA//vjjbNq0iZMnTwJw8OBBtm3bRosWLQA4e/Ys0dHRNGnSxPyZfPnyERoaSmRkJACRkZH4+vpSu3Ztc5smTZrg4ODAzp0773relJQU4uPjLV4iIiI5zcHBwRxyu3fvzvPPP8+wYcP49NNPOX36NElJSWRmZlq9Li8vL0wmE1u3biUpKYldu3aRmprKsWPHALh9+zYXLlygcOHCgGaBkIdPnh4CMWLECOLj4ylfvjyOjo5kZGQwceJEunbtCkB0dDRAtjkTAwICzPuio6MpVKiQxX4nJyf8/PzMbf5o8uTJjBs3Lqe/joiIyD3t27ePiIgINm3axKRJk3j11VepUKEC9evXJzw83KKzJ7dkBdkaNWpQpUoV+vfvT5EiRTCZTDRq1IihQ4cyYsQI1q9fz6pVqxgzZozF50QeFnm6B3jJkiUsWLCAhQsXsm/fPubPn88777zD/Pnzc/W8I0eOJC4uzvyKiorK1fOJiIgULlyYrl278vnnn3P8+HGWLl1K4cKFmTZtGs8++yxgnaEGhmEQEBDAhAkTaNiwIT4+PowaNYrZs2cTGBhI06ZNmTt3Li+99BIvvPACoAAsD5883QP86quvMmLECLp06QJAlSpVOH/+PJMnT6Z79+4EBgYCcPXqVYKCgsyfu3r1KtWrVwcgMDCQa9euWRw3PT2dmJgY8+f/yNXVFVdX11z4RiIiItllZGSwZ88eoqKiOHz4MHv27OHAgQO4urrSrVs3GjZsCFgnaJpMJgzDICQkhPfff99i35dffsmXX36Z6zWI5LY8HYBv3bqV7alSR0dH83ioEiVKEBgYyKZNm8yBNz4+np07d9K3b18AwsLCiI2NZe/evdSqVQuAH3/8kczMTEJDQ633ZURERO7h8uXLhIWFAdC+fXs6d+7M3Llzsw3hs5asoJ2enk5ycjJeXl6AljyWR0eeDsBt2rRh4sSJFCtWjEqVKrF//37effddXnzxReDO/6BDhgxhwoQJlClTxjwNWnBwMO3atQOgQoUKNG/enN69e/Pxxx+TlpbGgAED6NKlC8HBwTb8diIiIne4u7vTrl07oqKiSEtL4/jx4/j6+hIWFkb+/PlxcrL+H9e//PILK1as4LfffuPJJ58kPDwcd3d38/7Fixfj5+dH06ZNrV6byD+VpwPw+++/z6hRo+jXrx/Xrl0jODiYl156idGjR5vbvPbaayQlJdGnTx9iY2OpX78+69atM88BDLBgwQIGDBhA48aNcXBwoGPHjsycOdMWX0lERCSbAgUKsHz5cgC2bt3K2rVrGTlyJKdPn6ZOnTo8//zz9OvXL9d7YLOOf+rUKfr168fJkycpXrw4n3/+OS+88AJTpkzBx8cHgPnz51O7dm2aNm2qnmF56OTpAOzt7c2MGTOYMWPGPduYTCbGjx/P+PHj79nGz89Pi16IiEieFhsbS0JCAkWLFqVt27YULlyYNWvWmFdbs2YAnj17Nm5ubhw6dIiCBQuyZ88enn/+ef7zn/8wY8YMPD09uXXrltWWZRbJaXk6AIuIiNiDM2fOMGnSJC5dusShQ4e4efMmZcuWpWHDhixZssT8zEpur7aWNcvE8ePHadCgAQULFiQlJYXatWuzdu1aWrduzauvvsqHH35ISkqKeWywyMNGAVhERMRGsnpco6OjOXToEM899xxjx44lNDTUJkMKss7p4uJCUlIShmHg6upKeno6pUqVYtmyZbRu3ZqJEycSFxdnHg4h8rBRABYREbGRrCnH6tWrx+7du4E7U6LdunULR0dHi+dZrFUPQK1atdi/fz9RUVEUK1YMJycnMjIyqFSpEgsXLqRPnz4cP35cD5PLQ0sBWERExIayQufNmzeJiIjgp59+4vbt2/j5+dGsWTMaNWpk9VqeeeYZ0tPTLZZhzpqGNCwsjBkzZtCrVy88PDwsPifysMjTK8GJiIjYg/j4eEaNGkWXLl3YsWMHly9fZs2aNTRu3JiBAwdaBFFrKFu2LKNHj6Z48eIW27PGIDdu3Jhz586Z5y4WedgoAIuIiNhIRkYGcGdO3U2bNrF69Wp27tzJqlWrOHDgACtWrGDDhg3m1dessRRyFmuHbhFrUgAWERGxscOHD1OzZk2aNm1KRkYGaWlpGIbB008/TeXKlTl8+DBg3QCc2zNOiNiS7m4REREb8/b25tq1a0RHR+Po6IizszMmk4n4+HiL2RY01lYkZygAi4iI2EhWL2vbtm25ceMGQ4YMYffu3Vy8eJFjx47Rt29frl69SuPGjW1cqcijRbNAiIiI2FBGRgZ169ZlwoQJjBw5knr16uHg4EBqaioVKlRg5syZ1KtXD1APsEhOUQAWERGxEZPJhKOjIwAtW7akZcuWnDt3jqtXrxIQEJBtFgZbWL58OX5+fhbTsZ09exaAEiVK2KgqkX9GAVhERMQGzp8/T1JSEm5ubjg7O+Pg4ICzszP+/v4EBgZiMplITU3FycnJpg+k9ejRgzJlyrB3717zthYtWnD69GnS09NtVpfIP6EALCIiYgMzZ85kx44d5M+fHwcHB5ycnHBycjI/BOfs7Iy7uzsJCQmMGTOG0qVLW71GwzA4dOgQTk6WcWHx4sUkJiZavR6RnKIALCIiYgOVKlXCZDJhMplITk4mJSWFlJQUUlNTSU5OJj4+HsMwuHz5MqmpqVatzTAMc213G4ZRrVo1q9YjktMUgEVERKwsMzOTF1980dZl3FPWw3ZXrlwhISEBBwcHXF1d8fLywsPDA1dXVxtXKPLPKACLiIhYmYODAzt37iQtLQ13d3dcXV1xcXEx/zfrlTUUIutBOWtITU1ly5Yt7N69m71793LmzBl+++03UlNTCQgIoEaNGrRp04aOHTtatS6RnKQALCIiYgOTJk0iKioKNzc34E4ozno5Ojri6OiIi4sLSUlJLF26lAIFCuR6TTExMYwaNYr9+/eTkZFBo0aNaNu2LX5+fhiGQVRUFD///DNDhw7l1VdfZeLEibzwwgu5XpdITlMAFhERsYHnn3+emzdvkpGRQUpKCsnJyeZlkJOTk0lNTSUjI4PY2FirDTmIi4sjNjaWr776ipIlS961Tb9+/QBYsWIFX3zxBX5+frRs2dIq9YnkFAVgERERG+jcubOtS8imRIkSLFiwALizQAdwz2EO7du3p02bNuZ2Ig8TBWAREREbyMzMNP/aZDKxf/9+Nm/ezNWrV/nPf/5DQEAAv/76K/nz5yd//vxWrev3C3SkpqaSlJRknqc4a6o2R0dH89RtIg8b3bUiIiI28PvFLSIiIujatSuenp6cPn2af//73wQEBDBz5kwSEhKYM2eOTerat28fixYt4uzZs6SlpeHk5ISLiwu3b9+mUaNGDB482DxlmsjDxHZLy4iIiAgZGRkMGzaMvn37cvLkSXx8fMwPxnXo0IGffvrJqvUYhgHAgQMH6NmzJ4sXLyZfvnwEBATg5eWFk5MTKSkpWgVOHmrqARYREbGh27dvc/r0aYYOHUpsbCwZGRnmYQV+fn5ER0dbtZ7MzEwcHR3Ztm0bjo6OnDx58k8fwlPvrzyM1AMsIiJiQykpKXh6enL+/Hnc3NxwcHDAy8sLgGPHjuHv7w/8r2c2t2UFWldXV0qXLq1FL+SRpAAsIiJiQ76+vrRr145x48Zx4MABMjIyuHnzJvv27WPixIl06dLFqvVkBeBatWrh7OzMZ599xu3bt4mLiyM5OZm0tDSLB/hEHkYaAiEiImID169f54MPPuD1119n0qRJdOjQga5du+Lq6kqbNm349ddfadiwIRMnTgSsN9Qg66G2S5cusXXrVr777jsWLlxIkSJFcHR0xM3NjcTERF544QXCw8P1EJw8lBSARUREbCA+Pp4PPviAS5cuMW7cODZv3sy6des4ePAgKSkp1K9fn6eeesrqdWWF2fT0dJ544gny5ctHQkKCeXGOzMxMLl68SGxsrNVrE8kpCsAiIiI2ULJkSdasWUO/fv1o164dkyZNonnz5jRv3tymdWUF4Pbt29O+ffv7bi/yMNEYYBERERupW7cu27Zto1atWrz88svMmDGDqKgooqKiiImJ4datWzYdb3v9+nU2btzIzJkz2bt3L4C5J1jkYaYALCIiYiMZGRm4ubkxa9YsGjduzLBhw6hWrRqVKlWiVKlSeHl50bFjRwCrB+GbN2/yyiuv0L17d4YMGcIPP/wAwPz58+nYsSO//PKLVesRyUkaAiEiImJlWQ+OOTo68vPPPzNr1iy2bdvGiBEjaNWqFbGxsWRmZpKQkECRIkUA6w01yMjIwNHRkSlTpnDy5ElOnDhB165dzUsjt23blvnz5xMVFUWlSpX0EJw8lBSARURErMxkMhETE8OcOXP49NNPCQoKYsWKFVSvXv1PP2NNW7ZsoVu3bnh5eXH16lU8PDwACAwM5MaNG1oJTh5qGgIhIiJiRVkLWqxdu5a33nqLwYMHs2XLFqpXr05GRoaNq7OUmJgIQFpamnlxjt9++420tDR8fHwAPQQnDycFYBERESvKCowVK1Zk9erV9O/f3zy+N2uYgS05ONyJBp07d2bNmjWcPXuW27dvm3t8x48fT9GiRSlevLgNqxT5ZzQEQkRExAZq1KgB/G/MbV6RFdAHDRrEtm3beP7557l06RJffPEF77//PufOnePbb7+lWLFiNq5U5O9TABYREbGBrKEQeSn8/p6DgwPLli1j3bp1REZGcu7cOYoXL85LL71EcHCwrcsT+UcUgEVERGzgYRg7m5mZSVhYGI8//rh5zG9eG6cs8ndoDLCIiIhkk5KSwieffMILL7xAuXLlOHToEAALFy5k/fr15h5skYeRArCIiEgekJCQwPXr17l58yaJiYmkpKTYdKqxjz76iFGjRhEaGkpMTIz54bi0tDSmTJmiadDkoaYhECIiIjaUkpLC8uXL2bBhAzdu3ADujAt2c3Pj5s2bTJgwgVq1alm9rmnTprFkyRKeeuop3n77bVxdXQEIDQ1lxIgRODkpQsjDS3eviIiIDWRmZuLg4MAnn3zChAkTqFChAlWqVCEjI4OUlBQMwzC3sbb09HRu3LhBpUqVSEtLIzMzExcXFwCcnJxISEh4KMYwi9yLArCIiIgNZI2h/fLLLxk4cCCjRo2ycUX/k56eTrly5fjhhx/o1KkTDg4OeHp6kpaWxpo1ayhfvrytSxT5RxSARUREbCCrBzUwMJAiRYrYuBpLrq6uDB48mHfffZf4+HiSk5PZsWMHBw4c4P3332fGjBm2LlHkH1EAFhERsYGsANyxY0e+//57ypcvT8WKFTGZTDg5OeHk5ISjo6NN5gk2mUz06NGDpKQkpk6dSqFChejUqRMFCxZk8uTJdO3a1eo1ieQkBWAREREbOnToEGvWrGHHjh3UrFmTfPny4eTkhKurK7dv32b69On4+/tbtaZr166xYMEChg4dSv/+/bly5QoZGRl5rqda5O9SABYREbGy38+h6+HhQc+ePcnIyDBPf5aUlERGRgbXr1+3el0mk4kLFy7w2muv0bdvX1xdXQkKCrLYL/KwUwAWERGxst+HyAkTJtiwEktZdQUEBNC1a1e2bdtGkyZNsu0XedgpAIuIiFjZli1bCA0Nxc3NjcOHD5uHPLi4uFi8nJ2dcXZ2tnp9Hh4eAPTq1Ytx48ZRtmxZfHx8cHd3x9XVFR8fH/PSyCIPowcOwFFRUZhMJvM4oF27drFw4UIqVqxInz59crxAERGRR82IESNYuXIlbm5u9O/fn/T0dBwdHXFwcDA/+Obk5IRhGHz//fdWexAua4jDkSNHWLhwISVKlGDQoEGkpqaSnp6Og4MD6enpNG3alB9++IGMjAybPKQn8k89cAB+/vnn6dOnD//617+Ijo6madOmVKpUiQULFhAdHc3o0aNzo04REZFHxssvv2zuQe3QoQOpqakWr7S0NPN/rRkws4Y4PPbYY5w5c8Y8VjktLc38SkpKwtfXF0DhVx5aDxyAjxw5Qt26dQFYsmQJlStXZvv27axfv56XX35ZAVhEROQvdO/eHbjT4zpkyBDbFnMXrq6uFC1aFMBmq9GJ5KYHDsBpaWnm9cA3btzI008/DUD58uW5cuVKzlYnIiLyiElPT2ft2rW0adMm20NleWGWhT179pCZmUndunU5fPgwK1aswMvLCzc3NxwcHKhVqxZ16tSxaY0i/9QDB+BKlSrx8ccf06pVKzZs2MCbb74JwOXLl60+T6GIiMjD5vbt27z33nvs2bOHRo0aUbZsWQoWLIiLi4s5/KamppKZmcnVq1eZO3cu5cuXp0uXLrle24kTJxg3bhx169albt26rFu3jrFjxxIUFERqaio3btygQYMGzJ8/n+LFi+d6PSK55YED8JQpU2jfvj1vv/023bt3p1q1agCsWrXKPDRCRERE7s7Ly4vRo0czefJkvv/+e0qXLk1ISAhFihTBx8eHpKQkLly4wK5du9i9ezfdunVj0KBBuVpTVs/zxo0byZ8/Py+99BIA0dHRdOnShYULFwJw4cIFBg0axIYNG+jdu3ee6LEW+TseOAA3atSI69evEx8fT/78+c3b+/TpY542RURERO7OZDLxxBNP8MQTT7B161aWLFnCxo0biY6OJjk5GWdnZ6pUqULbtm1ZvHgxBQsWzPWasoLsr7/+iouLC4UKFQLA19eX9PR04M5Y4GLFiuHi4sKFCxdyvSaR3PS35gE2DIO9e/dy5swZnn/+eby9vXFxcVEAFhERuQ9Zsys0aNCABg0a2Lia/7l586bFA28DBw40/zpre2Jionp95aH3wAH4/PnzNG/enAsXLpCSkkLTpk3x9vZmypQppKSk8PHHH+dGnSIiIo+MPwbIzMxMEhISgDtB08nJyfyyZtgsWLAgx48fJzY2Fl9fX/N0Z1k9xFevXiU2Nta8NLLIw+qBA/DgwYOpXbs2Bw8etHjorX379vTu3TtHixMREXnUnTp1ilWrVnHixAkMw8DZ2RlXV1fS09OpXr06vXr1yvWxtlnHbtGiBStXrmT+/PkMHjw42/7Zs2eTmppKjRo1LLaLPGweOABv3bqVHTt24OLiYrG9ePHiXLp0KccKExEReVRlBdqzZ8/St29fdu3aRVhYGM7OzqSkpJCRkcFvv/1mHnZgrQDcqFEjmjdvzujRozl9+jQtW7Ykf/783L59m8WLF/Ppp58yefJkHnvssVyrRcQaHjgAZ2ZmkpGRkW37xYsX8fb2zpGiREREHmVZgXbHjh2cOXOGEydO/OmwAmsuRDFp0iTc3Nz48ssv+fjjj8nIyMDBwQFPT0+mTJlC//79rVaLSG554ADcrFkzZsyYwezZs4E7f2tMTExkzJgxtGzZMscLFBEReVRlZGRQoUKFPDWmNivoDhs2jH379hEfH4+fnx/169fH3d3d1uWJ5IgHDsDTpk0jPDycihUrkpyczPPPP8+pU6coUKAAX3/9dW7UKCIi8kjJGnJQrVo1Nm/ezJdffkmnTp1IT0/HyckJR0dHTCYTjo6ONqkvMzOTgIAAWrRoYZPzi+S2Bw7ARYoU4eDBgyxatIhDhw6RmJhIr1696Nq1a678zfDSpUsMHz6ctWvXcuvWLUqXLs3cuXOpXbs2cOefkcaMGcOnn35KbGws9erV46OPPqJMmTLmY8TExDBw4EC+++47HBwc6NixI++99x5eXl45Xq+IiMhfyRoCERsbS0REBMuWLWPVqlUUKVIEZ2dnPDw8SE5OJjw8nCeffNLqC078fuwx6GE3efT8rXmAnZyceOGFF3K6lmxu3rxJvXr1ePLJJ1m7di0FCxbk1KlTFgtwTJ06lZkzZzJ//nxKlCjBqFGjCA8P5+jRo7i5uQHQtWtXrly5woYNG0hLS6Nnz5706dPHvLKNiIiINWUFyt9++43SpUtTt25drl+/zqVLl8zLIF+4cIHAwECbBOA/1inyqLmvALxq1ar7PuDTTz/9t4v5oylTplC0aFHmzp1r3laiRAnzrw3DYMaMGbzxxhu0bdsWgC+++IKAgABWrlxJly5dOHbsGOvWrWP37t3mXuP333+fli1b8s477xAcHJxj9YqIiNwPk8mEYRg888wzPPPMM3/Z3poPwWXRMsfyKLuvANyuXbv7OpjJZLrrDBF/16pVqwgPD+fZZ59ly5YtFC5cmH79+pnnGz579izR0dE0adLE/Jl8+fIRGhpKZGQkXbp0ITIyEl9fX3P4BWjSpAkODg7s3LmT9u3bZztvSkoKKSkp5vfx8fE59p1ERETgfyHYZDKRnJxsXmHN2dkZZ2dnHBwccHV1tWl9Io+q+wrAmZmZuV3HXf3666989NFHDBs2jNdff53du3czaNAgXFxc6N69O9HR0QAEBARYfC4gIMC8Lzo62rymeRYnJyf8/PzMbf5o8uTJjBs3Lhe+kYiIyP+YTCZ27drFmjVrOH36NBkZGRiGgaenJ0lJSQwZMsQmc+5mZmZy4MABjh8/TkJCAi4uLri5ueHu7o6zszMtWrSwSa+0SE75W2OArSUzM5PatWszadIkAGrUqMGRI0f4+OOP6d69e66dd+TIkQwbNsz8Pj4+nqJFi+ba+URExL5k9fz+/PPPvPjii+TPn58zZ87g5+dHvnz5OHr0KL6+vgwYMMCivbUsWLCAIUOG4ODggIODA2lpaaSlpZGRkUFycjIpKSkKwPJQ+1t376ZNm2jdujWlSpWiVKlStG7dmo0bN+Z0bQQFBVGxYkWLbRUqVODChQsABAYGAnD16lWLNlevXjXvCwwM5Nq1axb709PTiYmJMbf5I1dXV3x8fCxeIiIiOSXrX1anT59OjRo12L59O48//jgvvvgikZGRjB49mlatWlGqVCnA+sMR/vOf/zBgwADOnz/P1atXiYmJISEhgaSkJG7fvo2zs7NV6xHJaQ8cgD/88EOaN2+Ot7c3gwcPZvDgwfj4+NCyZUtmzZqVo8XVq1ePEydOWGw7efIkISEhwJ0H4gIDA9m0aZN5f3x8PDt37iQsLAyAsLAwYmNj2bt3r7nNjz/+SGZmJqGhoTlar4iIyIO4cOEC9erVA+5M2Zn1HM1//vMffv75Zw4ePGj1mlJTU0lNTWXQoEF4eHhgGIbFdGi2HJcsklMeeAjEpEmTmD59uvmfZQAGDRpEvXr1mDRpUo4ukTh06FAef/xxJk2aRKdOndi1axezZ8+2WIVuyJAhTJgwgTJlypinQQsODjY/uFehQgWaN29O7969+fjjj0lLS2PAgAF06dJFM0CIiIhNeXt7ExsbC0CxYsW4cOECN27cIDMzk2vXruHi4mKTuv71r3/x7bff8q9//Uu9vfJIeuAAHBsbS/PmzbNtb9asGcOHD8+RorLUqVOHFStWMHLkSMaPH0+JEiWYMWMGXbt2Nbd57bXXSEpKok+fPsTGxlK/fn3WrVtnngMY7oxlGjBgAI0bNzYvhDFz5swcrVVEROR+ZY2fffrpp4mPjyc1NZXnnnuOQYMGcfv2bU6ePEnBggUtpv60lqze3oEDB3Lq1CkqVaqEp6cnHh4euLi44OPjQ61ataxel0hOeuAA/PTTT7NixQpeffVVi+3ffvstrVu3zrHCsrRu3fpPj2symRg/fjzjx4+/Zxs/Pz8teiEiInlCZmamOQD36tWLa9eukZmZSYsWLRg5ciQLFiygQIECfPjhh1YNwL9fnW7ZsmWUL1+eL7/8klu3blk8AFe0aFHOnz9v8T1EHjYPHIArVqzIxIkTiYiIMI+z/fnnn9m+fTv/+c9/LHpWBw0alHOVioiIPOQMw7AIje7u7ubnWgBefPFFXnzxRVuUZn7QLiAggCtXrpi3Z2RkkJ6eTlpaGqmpqeYeYoVfeZg9cACeM2cO+fPn5+jRoxw9etS83dfXlzlz5pjfm0wmBWAREZHfMZlMzJ49mw4dOlCgQAE2btyIyWTCy8sLDw8PXF1dzfPturu74+XlZZW6DMMgOTkZd3d3APM0ZyaTybw4h4uLi9XqEcltDxyAz549mxt1iIiI2IVZs2bRsGFDChQowKBBg7h69SoODg6kp6eTmZlp7iWOj4/n5s2b5MuXL9drOnToEDNmzGDu3Ln8+uuvjB07luDgYFxcXMxh3N3dHQcHB4oXL07Tpk1zvSaR3JSnF8IQERF51CxdutQ87GHhwoWkpqaahxhkvdLT07l9+zbe3t5WqSkjI8M8pCE2NpaIiAjy589PQkICKSkppKSkkJGRQVxcHE899RRNmzYlIyMDR0dHq9QnktMeOAAbhsGyZcvYvHmzeeD+7y1fvjzHihMREXnUlC1b1vzr6tWr266Q36lZsyZz5swhNTWVmjVrmhec+jMKv/Iwe+AAPGTIED755BOefPJJAgICrL46jYiIyMNs9OjR+Pj44Onpibu7OwULFsTJyQlXV1fc3d3x9PTE09MTZ2dnihQpYpWaEhMTMQzD3OOcmpoKYB4DnPXSg2/yqHjgAPzll1+yfPlyWrZsmRv1iIiIPLJSU1NZtmwZAMnJySQnJxMdHQ2Aq6sr6enp5tXg3N3dSUpKskpd+/fv5+uvv6Z79+5UqlTpTx92S0hI4NSpUwQHBxMYGGiV+kRy2gMH4Hz58lGyZMncqEVEROSR5uLiws6dO4E7My0sXryYpUuXMnDgQMqXL09ycjIbN25k3rx5TJ482Wp1+fn5cf36dUaMGEHVqlWpXbs25cqVw9vb27yw1OXLl9m6dSvz5s0jJCSERYsWWa0+kZz2wAF47NixjBs3js8//9w8XYqIiIjcn6xhBikpKbz33nusWrWK8uXLm/fXqlWLQoUK8cEHH9CuXTvzAhW5qVKlSixZsoTvv/+ezz77jG+++YbMzEw8PDzIyMjgxo0bpKenExYWxowZMwgPD9cQSHmoPXAA7tSpE19//TWFChWiePHi2dYI37dvX44VJyIi8qjJWkEtNjaWixcv3nWas4IFC3LkyBEAqwTgrHO0atWKVq1acevWLfbt20d0dDSGYVCsWDFq1qyZ7c98kYfVAwfg7t27s3fvXl544QU9BCciIvKAsh4k8/LyomrVqrzyyitMnjwZFxcXPDw8uHz5Mh9++CE1a9a0Wk1Zf5ZnzUPs4eFB/fr1rXZ+EWt74AD8/fff88MPP+h/DBERkX8gMDCQcePG0adPH8LDwylXrhzJycns27ePoKAgli5dClh3yeE/nssavc8itvDAAbho0aL4+PjkRi0iIiJ2JTw8nCNHjrBw4UJOnjxJRkYGvXr14tlnn7V1aQAKv/LIeuAAPG3aNF577TU+/vhjihcvngsliYiI2A8nJyfatWuHr68vrq6uwJ3p0lxcXGxcmcij64ED8AsvvMCtW7coVaoUHh4e2QbEx8TE5FhxIiIij7KDBw8yZ84c1qxZQ+XKlVm5ciXnzp1j9erVNG7cmAoVKti6RJFH0gMH4BkzZuRCGSIiIvYha1ztr7/+yogRI8jIyOCxxx7j/PnzALi5ubFnzx6uXbvG+PHjbTIONy0tjWvXrpGWloaLi4v55ezsrClQ5ZHwt2aBEBERkb8nK9Bu3LiRuLg4duzYwRdffMGnn34K3Hk4zs/Pj7Nnz1q0t5aYmBhmzZrF1q1biYuLIyMjAwcHB1xcXLh+/TrLli2jcuXKVqtHJDc8cAD+veTkZPN64Vn0gJyIiMi9GYYBwG+//WZecvjixYt4eHiY29y6dcvczlqy5iceO3Ysy5cvp02bNpQvX560tDRSU1PJyMjg+vXr+Pn5WbUukdzwwAE4KSmJ4cOHs2TJEm7cuJFtf9Ya5iIiIpJdVm9u0aJFSUpK4saNGzg4OJgD8NGjRzl37hxPPPGEVevKCtwrV67knXfeoUuXLlY9v4g1PfDkgq+99ho//vgjH330Ea6urnz22WeMGzeO4OBgvvjii9yoUURE5JGRNdduixYtKFmyJK+88goRERHcuHGD7777jpdeeon09HQ6d+4MWG8qsqy6ateubZXzidjSAwfg7777jg8//JCOHTvi5OREgwYNeOONN5g0aRILFizIjRpFREQeKenp6RQsWJApU6ZgGAbHjx/n9OnTdOrUCT8/P2bNmkWpUqWsOv436zwtWrRg9uzZREREcOnSJa5evcrNmzdJSkoiJSXFKrWI5LYHHgIRExNDyZIlgTvjfbOmPatfvz59+/bN2epEREQeQU5Od/74DQ4OZt68eQBcu3aNAgUKWKzGZouFKNasWcPhw4dp164d5cqVw8PDAwcHB5ydnUlJSWH16tV4enpavS6RnPTAAbhkyZKcPXuWYsWKUb58eZYsWULdunX57rvv8PX1zYUSRUREHh3nz5/n+vXruLi44OTkhJOTE+7u7ri5uREbG4uTk5NNpxtr2rQpDRs2JD09ncTERFJTU0lLSyMtLY2kpCQt0CGPhAcOwD179uTgwYM0bNiQESNG0KZNGz744APS0tJ49913c6NGERGRh17WLAtvv/0269evp2LFiqSnp5uHFWQFXzc3N2JiYpg4cSKhoaFWr7Nfv35WP6eItT1wAB46dKj5102aNOHYsWPs27eP0qVLU7Vq1RwtTkRE5FGTkJDA6dOn8fDwoHr16hQrVgyTyURCQgJJSUlkZmZy69Ytm/a0JiYmEh0djaOjI46Ojri7u+Pq6oqTk5PFdG0iD6t/NA8wQPHixSlevHgOlCIiIvLoyhrbO3jwYPz9/Tl69Ciurq5UrVqVsLAwChcubOMK77h48SLvvPMOBw8e5PTp07i5uZGeng6Av78/e/bssXGFIv/cfc8CERkZyerVqy22ffHFF5QoUYJChQrRp08fPR0qIiLyF2rWrMm7777LnDlzKFq0KG+88QYdO3Zk4sSJHDlyxBw2bSElJYXXX3+ddevW0bp1a3777Td69uxJ7dq1SU5O1tzA8si47wA8fvx4fvnlF/P7w4cP06tXL5o0acKIESP47rvvmDx5cq4UKSIi8qjIyMggJSWFwoUL88Ybb3D8+HFeffVVvvrqK6pWrcru3bttVtvNmzdZt24d69evp2/fvri7u/Pqq6+ydOlSevXqRXR0tM1qE8lJ9x2ADxw4QOPGjc3vFy1aRGhoKJ9++inDhg1j5syZLFmyJFeKFBEReVQ4Ojri6upKUlIS+/btY9GiRezbt4/AwEDKlStn0xmVssYgFytWjCtXruDh4WEOvY0aNTL/OZ+ZmWmzGkVywn2PAb558yYBAQHm91u2bKFFixbm93Xq1CEqKipnqxMRkVxXvwFsf4D2/79irvxNJ0+e5OjRo+zfv5+DBw9y5swZ6tevz6RJkwgLC7NpbYZhEBAQwJEjRyhQoADBwcGsXr2arl27EhkZiY+Pj03rE9t50Cmp6wHbcqWSnHHfATggIICzZ89StGhRUlNT2bdvH+PGjTPvT0hIwNnZOVeKFBERedhlrer28ssvExERQZkyZXj++eeZPn06BQoUICUlhWvXruHm5mazeYD9/f154YUXOHXqFJUrV6Z9+/aMHDmSOXPmcOHCBf773/8CtlmgQyQn3XcAbtmyJSNGjGDKlCmsXLkSDw8PGjRoYN5/6NAhSpUqlStFioiIPOyyQuOpU6coV64c5cuXZ9u2baxbtw6TyWReFMPJyYnU1FTWrl1r9RCcP39+Ro4caX7/+uuvU758eY4cOUKDBg148sknLb6LyMPqvgPwm2++SYcOHWjYsCFeXl7Mnz/fYo7Czz//nGbNmuVKkSIiIo+KGTNmEBcXR1JSEmlpaaSnp5Oammp+paWlcfv2bZvOA5yUlMSlS5fw8fGhQ4cOdOjQQeN+5ZFy3wG4QIEC/PTTT8TFxeHl5YWjo6PF/qVLl+Ll5ZXjBYqIiDxKOnbsaOsS/tTBgwf5/PPPWbduHZUrV+abb77h7NmzfP/99zRu3JgKFSrYukSRf+y+Z4HIki9fvmzhF8DPz0/rg4uIiDyEjP9/svHXX39lxIgRHDt2jDp16nDt2jUA3N3d2bNnD19//bVFe5GH1QMHYBEREXm0ZAXajRs3EhcXx/r16y2GNQYGBuLn58fZs2ct2os8rBSARURE7FxWoP3tt9/MwxkvXryIh4eHuc2tW7cUfOWRoQAsIiJi57JmdShatChJSUncuHEDBwcHcwA+evQo586do2LFirYsUyTH3FcArlmzJjdv3gTuLIl869atXC1KRERErMfB4U4caNGiBSVLluSVV14hIiKCGzdu8N133/HSSy+Rnp5O586dAU2DJg+/+wrAx44dIykpCYBx48aRmJiYq0WJiIiIdaWnp1OwYEGmTJmCYRgcP36c06dP06lTJ/z8/Jg1axalSpUyL+gh8jC7r2nQqlevTs+ePalfvz6GYfDOO+/cc8qz0aNH52iBIiIikvucnO5EguDgYObNmwfAtWvXKFCggLmHGNT7K4+G+wrA8+bNY8yYMaxevRqTycTatWvN/6P8nslkUgAWERF5yJw/f57r16/j4uJiXo3O3d0dNzc3YmNjcXJystnyzCK54b4CcLly5Vi0aBFwZ5zQpk2bKFSoUK4WJiIiIrkrMzMTBwcH3n77bdavX0/FihVJT08nJSUFwBx83dzciImJYeLEiYSGhtq4apF/7r5XgsuipRBFREQeLQkJCZw+fRoPDw+qV69OsWLFMJlMJCQkkJSURGZmJrdu3dKCV/LIeOAADHDmzBlmzJjBsWPHAKhYsSKDBw+mVKlSOVqciIiI5J6ssb2DBw/G39+fo0eP4urqStWqVQkLC6Nw4cI2rlAkdzxwAP7hhx94+umnqV69OvXq1QNg+/btVKpUie+++46mTZvmeJEiIiKSe2rWrEnNmjW5dOkSc+fO5Y033sDX15c2bdrQtm1bypcvf9dnf0QeVg+8EMaIESMYOnQoO3fu5N133+Xdd99l586dDBkyhOHDh+dGjSIiIpKLMjIySElJoXDhwrzxxhscP36cV199la+++oqqVauye/duW5cokqMe+K9zx44dY8mSJdm2v/jii8yYMSMnahIRERErcnR0xNHRkaSkJE6cOMHJkyc5fPgwgYGBAPj6+tq2QJEc9sABuGDBghw4cIAyZcpYbD9w4IBmhhAREXkInTx5kqNHj7J//34OHjzImTNnqF+/PpMmTSIsLMzW5YnkuAcOwL1796ZPnz78+uuvPP7448CdMcBTpkxh2LBhOV6giIiI5I6sVd1efvllIiIiKFOmDM8//zzTp0+nQIECpKSkcO3aNdzc3DQPsDxSHjgAjxo1Cm9vb6ZNm8bIkSOBO6vGjB07lkGDBuV4gSIiIpI7slZ1O3XqFOXKlaN8+fJs27aNdevWYTKZzItiODk5kZqaytq1axWC5ZHwwAHYZDIxdOhQhg4dSkJCAgDe3t45XpiIiIhYx4wZM4iLiyMpKYm0tDTS09NJTU01v9LS0rh9+7bmAZZHxj+a00TBV0RE5OHXsWNHW5cgYlUPPA2aiIiIiMjDTAFYREREROyKArCIiIiI2BUFYBERERGxK38rAA8YMICYmJicrkVEREREJNfddwC+ePGi+dcLFy4kMTERgCpVqhAVFZXzlYmIiIiI5IL7ngatfPny+Pv7U69ePZKTk4mKiqJYsWKcO3eOtLS03KxRRERERCTH3HcPcGxsLEuXLqVWrVpkZmbSsmVLypYtS0pKCj/88ANXr17NzTpFRERERHLEfQfgtLQ06taty3/+8x/c3d3Zv38/c+fOxdHRkc8//5wSJUpQrly53KxVREREROQfu+8A7OvrS2hoKMOGDSM1NZXbt29Tr149nJycWLx4MTdv3mTOnDm5WStvvfUWJpOJIUOGmLclJyfTv39//P398fLyomPHjtl6oy9cuECrVq3w8PCgUKFCvPrqq6Snp+dqrSIiIiKSN913AL506RJvvPEGrq6upKenU6tWLRo0aEBqair79u3DZDJRv379XCt09+7dfPLJJ1StWtVi+9ChQ/nuu+9YunQpW7Zs4fLly3To0MG8PyMjg1atWpGamsqOHTuYP38+8+bNY/To0blWq4iIiIjkXfcdgAsUKECbNm2YPHkyHh4e7N69m4EDB2IymXjllVfIly8fDRs2zJUiExMT6dq1K59++in58+c3b4+Li2POnDm8++67PPXUU9SqVYu5c+eyY8cOfv75ZwDWr1/P0aNH+eqrr6hevTotWrTgzTffZNasWaSmpuZKvSIiIiKSd/3thTDy5ctHp06dcHZ25scff+Ts2bP069cvJ2sz69+/P61ataJJkyYW2/fu3UtaWprF9vLly1OsWDEiIyMBiIyMpEqVKgQEBJjbhIeHEx8fzy+//HLX86WkpBAfH2/xEhEREZFHw31Pg/Z7hw4donDhwgCEhITg7OxMYGAgnTt3ztHiABYtWsS+ffvYvXt3tn3R0dG4uLjg6+trsT0gIIDo6Ghzm9+H36z9WfvuZvLkyYwbNy4HqhcRERGRvOZv9QAXLVoUB4c7Hz1y5AhFixbN0aKyREVFMXjwYBYsWICbm1uunONuRo4cSVxcnPmlhT5EREREHh1/ewiENezdu5dr165Rs2ZNnJyccHJyYsuWLcycORMnJycCAgJITU0lNjbW4nNXr14lMDAQgMDAwGyzQmS9z2rzR66urvj4+Fi8REREROTRkKcDcOPGjTl8+DAHDhwwv2rXrk3Xrl3Nv3Z2dmbTpk3mz5w4cYILFy4QFhYGQFhYGIcPH+batWvmNhs2bMDHx4eKFSta/TuJiIiIiG39rTHA1uLt7U3lypUttnl6euLv72/e3qtXL4YNG4afnx8+Pj4MHDiQsLAwHnvsMQCaNWtGxYoV+de//sXUqVOJjo7mjTfeoH///ri6ulr9O4mIiIiIbeXpAHw/pk+fjoODAx07diQlJYXw8HA+/PBD835HR0dWr15N3759CQsLw9PTk+7duzN+/HgbVi0iIiIitvLQBeCIiAiL925ubsyaNYtZs2bd8zMhISGsWbMmlysTERERkYdBnh4DLCIiIiKS0xSARURERMSuKACLiIiIiF1RABYRERERu6IALCIiIiJ2RQFYREREROyKArCIiIiI2BUFYBERERGxKwrAIiIiImJXFIBFRERExK4oAIuIiIiIXVEAFhERERG7ogAsIiIiInZFAVhERERE7IoCsIiIiIjYFQVgEREREbErCsAiIiIiYlcUgEVERETErigAi4iIiIhdUQAWEREREbuiACwiIiIidkUBWERERETsigKwiIiIiNgVBWARERERsSsKwCIiIiJiVxSARURERMSuKACLiIiIiF1RABYRERERu6IALCIiIiJ2RQFYREREROyKArCIiIiI2BUFYBERERGxKwrAIiIiImJXFIBFRERExK4oAIuIiIiIXVEAFhERERG7ogAsIiIiInZFAVhERERE7IoCsIiIiIjYFQVgEREREbErCsAiIiIiYlcUgEVERETErigAi4iIiIhdUQAWEREREbuiACwiIiIidkUBWERERETsigKwiIiIiNgVBWARERERsSsKwCIiIiJiVxSARURERMSuKACLiIiIiF1RABYRERERu6IALCIiIiJ2RQFYREREROyKArCIiIiI2BUFYBERERGxKwrAIvJQMZlMrFy50tZliIjIQ0wBWETyjN9++42+fftSrFgxXF1dCQwMJDw8nO3bt1vl/LkRrosXL86MGTMe+HMRERG0bduWoKAgPD09qV69OgsWLMjWbunSpZQvXx43NzeqVKnCmjVrcqBqEZFHmwKwiOQZHTt2ZP/+/cyfP5+TJ0+yatUqGjVqxI0bN3L1vKmpqbl6/L9jx44dVK1alW+++YZDhw7Rs2dPunXrxurVqy3aPPfcc/Tq1Yv9+/fTrl072rVrx5EjR2xYuYjIQ8CQvxQXF2cARlxcnK1LEXlk3bx50wCMiIiIP20HGJ9++qnRrl07w93d3ShdurTx7bffWrSJiIgw6tSpY7i4uBiBgYHG8OHDjbS0NPP+hg0bGv379zcGDx5s+Pv7G40aNTJCQkIMwPwKCQkxt1+5cqVRo0YNw9XV1ShRooQxduxY8/EyMzONMWPGGEWLFjVcXFyMoKAgY+DAgebz/P6Y//RHbsuWLY2ePXua33fq1Mlo1aqVRZvQ0FDjpZde+vMDbd1qGGB+1WPr79/+5csWHqQ+W9dqHVsNw+B3r63/23N+q8FYzK+t57fe4xiPNt0zOetBr2M9LH/OGFtz/z58kLymHmARyRO8vLzw8vJi5cqVpKSk/GnbcePG0alTJw4dOkTLli3p2rUrMTExAFy6dImWLVtSp04dDh48yEcffcScOXOYMGGCxTHmz5+Pi4sL27dv5+OPP2b37t0AzJ07lytXrpjfb926lW7dujF48GCOHj3KJ598wrx585g4cSIA33zzDdOnT+eTTz7h1KlTrFy5kipVqgCwfPlyihQpwvjx47ly5QpXrlwxn99kMjFv3rwHukZxcXH4+fmZ30dGRtKkSROLNuHh4URGRj7QcUVE7I0CsIjkCU5OTsybN4/58+fj6+tLvXr1eP311zl06FC2tj169OC5556jdOnSTJo0icTERHbt2gXAhx9+SNGiRfnggw8oX7487dq1Y9y4cUybNo3MzEzzMcqUKcPUqVMpV64c5cqVo2DBggD4+voSGBhofj9u3DhGjBhB9+7dKVmyJE2bNuXNN9/kk08+AeDChQsEBgbSpEkTihUrRt26denduzcAfn5+ODo64u3tTWBgIIGBgebzlytXjnz58t339VmyZAm7d++mZ8+e5m3R0dEEBARYtAsICCA6Ovq+jysiYo8UgEUkz+jYsSOXL19m1apVNG/enIiICGrWrJmtp7Rq1armX3t6euLj48O1a9cAOHbsGGFhYZhMJnObevXqkZiYyMWLF83batWqdV81HTx4kPHjx5t7qL28vOjduzdXrlzh1q1bPPvss9y+fZuSJUvSu3dvVqxYQXp6+l8e9/jx47Rv3/6+ati8eTM9e/bk008/pVKlSvf1GRERubc8HYAnT55MnTp18Pb2plChQrRr144TJ05YtElOTqZ///74+/vj5eVFx44duXr1qkWbCxcu0KpVKzw8PChUqBCvvvrqff0BJSLW5+bmRtOmTRk1ahQ7duygR48ejBkzxqKNs7OzxXuTyWTRu3s/PD0976tdYmIi48aN48CBA+bX4cOHOXXqFG5ubhQtWpQTJ07w4Ycf4u7uTr9+/XjiiSdIS0t7oHruZcuWLbRp04bp06fTrVs3i32BgYHZft5dvXrVoqdZRESyy9MBeMuWLfTv35+ff/6ZDRs2kJaWRrNmzUhKSjK3GTp0KN999x1Lly5ly5YtXL58mQ4dOpj3Z2Rk0KpVK1JTU9mxYwfz589n3rx5jB492hZfSUQeUMWKFS3+n/8rFSpUIDIyEsMwzNu2b9+Ot7c3RYoU+dPPOjs7k5GRYbGtZs2anDhxgtKlS2d7OTjc+RHq7u5OmzZtmDlzJhEREURGRnL48GEAXFxcsh3zfkVERNCqVSumTJlCnz59su0PCwtj06ZNFts2bNhAWFjY3zqfiIi9cLJ1AX9m3bp1Fu/nzZtHoUKF2Lt3L0888QRxcXHMmTOHhQsX8tRTTwF3HmCpUKECP//8M4899hjr16/n6NGjbNy4kYCAAKpXr86bb77J8OHDGTt2LC4uLrb4aiLyBzdu3ODZZ5/lxRdfpGrVqnh7e7Nnzx6mTp1K27Zt7/s4/fr1Y8aMGQwcOJABAwZw4sQJxowZw7Bhw8yB9V6KFy/Opk2bqFevHq6uruTPn5/Ro0fTunVrihUrxjPPPIODgwMHDx7kyJEjTJgwgXnz5pGRkUFoaCgeHh589dVXuLu7ExISYj7mTz/9RJcuXXB1daVAgQIAlC9fnsmTJ99zGMTmzZtp3bo1gwcPpmPHjuZxvS4uLuYH4QYPHkzDhg2ZNm0arVq1YtGiRezZs4fZs2ff9/USEbFHeboH+I/i4uIAzD/89+7dS1pamsVT0OXLl6dYsWLmp6AjIyOpUqWKxYMi4eHhxMfH88svv9z1PCkpKcTHx1u8RCR3eXl5ERoayvTp03niiSeoXLkyo0aNonfv3nzwwQf3fZzChQuzZs0adu3aRbVq1Xj55Zfp1asXb7zxxl9+dtq0aWzYsIGiRYtSo0YN4M7Pi9WrV7N+/Xrq1KnDY489xvTp080B19fXl08//ZR69epRtWpVNm7cyHfffYe/vz8A48eP59y5c5QqVcr8YB3AiRMnzD/T7mb+/PncunWLyZMnExQUZH79/l+4Hn/8cRYuXMjs2bOpVq0ay5YtY+XKlVSuXPm+r5eIiD0yGb//d8I8LDMzk6effprY2Fi2bdsGwMKFC+nZs2e2KZPq1q3Lk08+af5nw/Pnz/PDDz+Y99+6dQtPT0/WrFlDixYtsp1r7NixjBs3Ltv2uLg4fHx8cvibiYhY2bZt0KCB+W19trKd+vf9cVv8qfG7ZxofyMPxJ9zfsQ1o8Lv3W+H/fw+3XdhGg7n/27e151bqF7v/399Hhe6ZnPWg17Me29j2+3t061aon7v3YXx8PPny5buvvPbQ9AD379+fI0eOsGjRolw/18iRI4mLizO/oqKicv2cIiIiImIdeXoMcJYBAwawevVqfvrpJ4uHWAIDA0lNTSU2NhZfX1/z9t8/BR0YGGieH/T3+7P23Y2rqyuurq45/C1EREREJC/I0z3AhmEwYMAAVqxYwY8//kiJEiUs9teqVQtnZ2eLp6BPnDjBhQsXzE9Bh4WFcfjwYfMcoXDnKWkfHx8qVqxonS8iIiIiInlGnu4B7t+/PwsXLuTbb7/F29vb/BR0vnz5cHd3J1++fPTq1Ythw4bh5+eHj48PAwcOJCwsjMceewyAZs2aUbFiRf71r38xdepUoqOjeeONN+jfv796eUVERETsUJ4OwB999BEAjRo1stg+d+5cevToAcD06dNxcHCgY8eOpKSkEB4ezocffmhu6+joyOrVq+nbty9hYWF4enrSvXt3xo8fb62vISIiIiJ5SJ4OwPczQYWbmxuzZs1i1qxZ92wTEhLCmjVrcrI0EREREXlI5ekxwCIiIiIiOU0BWERERETsigKwiIiIiNgVBWARERERsSsKwCIiIiJiVxSARURERMSuKACLiIiIiF1RABYRERERu6IALCIiIiJ2RQFYREREROyKArCIiIiI2BUFYBERERGxKwrAIiIiImJXFIBFRERExK4oAIuIiIiIXVEAFhERERG7ogAsIiIiInZFAVhERERE7IoCsIiIiIjYFQVgEREREbErCsAiIiIiYlcUgEVERETErigAi4iIiIhdUQAWEREREbuiACwiIiIidkUBWERERETsigKwiIiIiNgVBWARERERsSsKwCIiIiJiVxSARURERMSuKACLiIiIiF1RABYRERERu6IALCIiIiJ2RQFYREREROyKArCIiIiI2BUFYBERERGxKwrAIiIiImJXFIBFRERExK4oAIuIiIiIXVEAFhERERG7ogAsIiIiInZFAVhERERE7IoCsIiIiIjYFQVgEREREbErCsAiIiIiYlcUgEVERETErigAi4iIiIhdUQAWEREREbuiACwiIiIidkUBWERERETsigKwiIiIiNgVBWARERERsSsKwCIiIiJiVxSARURERMSuKACLiIiIiF1RABYRERERu6IALCIiIiJ2RQFYREREROyKArCIiIiI2BUFYBERERGxKwrAIiIiImJXFIBFRERExK7YVQCeNWsWxYsXx83NjdDQUHbt2mXrkkRERETEyuwmAC9evJhhw4YxZswY9u3bR7Vq1QgPD+fatWu2Lk1ERERErMhuAvC7775L79696dmzJxUrVuTjjz/Gw8ODzz//3NaliYiIiIgVOdm6AGtITU1l7969jBw50rzNwcGBJk2aEBkZma19SkoKKSkp5vdxcXEAxMfH536xIiK5LSnJ4m06ScD9/3x7mH4UPky1Ppiku7y/82WTEpIg+Xd7EpL059cD0KXKGekkWf5USUrK9YubdZ8bhvGXbe0iAF+/fp2MjAwCAgIstgcEBHD8+PFs7SdPnsy4ceOybS9atGiu1SgiYjvNH6h1vny5VEYueJhq/Wfu/XvY/K0H+/21d/Zzz+SunYDFpWxuvfswISGBfH/xG2kXAfhBjRw5kmHDhpnfZ2ZmEhMTg7+/PyaTySo1xMfHU7RoUaKiovDx8bHKOR8Gui53p+tyb7o2d6frcm+6Nnen63JvujZ3Z+3rYhgGCQkJBAcH/2VbuwjABQoUwNHRkatXr1psv3r1KoGBgdnau7q64urqarHN19c3N0u8Jx8fH/3PdBe6Lnen63JvujZ3p+tyb7o2d6frcm+6NndnzevyVz2/WeziITgXFxdq1arFpk2bzNsyMzPZtGkTYWFhNqxMRERERKzNLnqAAYYNG0b37t2pXbs2devWZcaMGSQlJdGzZ09blyYiIiIiVmQ3Abhz58789ttvjB49mujoaKpXr866deuyPRiXV7i6ujJmzJhsQzHsna7L3em63Juuzd3putybrs3d6brcm67N3eXl62Iy7meuCBERERGRR4RdjAEWEREREcmiACwiIiIidkUBWERERETsigKwiIiIiNgVBeA8aNasWRQvXhw3NzdCQ0PZtWuXrUuyubFjx2IymSxe5cuXt3VZVvfTTz/Rpk0bgoODMZlMrFy50mK/YRiMHj2aoKAg3N3dadKkCadOnbJNsVb2V9emR48e2e6h5lZcmtNWJk+eTJ06dfD29qZQoUK0a9eOEydOWLRJTk6mf//++Pv74+XlRceOHbMtHPSouZ/r0qhRo2z3zMsvv2yjiq3no48+omrVqubFC8LCwli7dq15vz3eL/DX18Ve75c/euuttzCZTAwZMsS8LS/eMwrAeczixYsZNmwYY8aMYd++fVSrVo3w8HCuXbtm69JsrlKlSly5csX82rZtm61LsrqkpCSqVavGrFmz7rp/6tSpzJw5k48//pidO3fi6elJeHg4ycnJVq7U+v7q2gA0b97c4h76+uuvrVihbWzZsoX+/fvz888/s2HDBtLS0mjWrBlJSUnmNkOHDuW7775j6dKlbNmyhcuXL9OhQwcbVp377ue6APTu3dvinpk6daqNKraeIkWK8NZbb7F371727NnDU089Rdu2bfnll18A+7xf4K+vC9jn/fJ7u3fv5pNPPqFq1aoW2/PkPWNInlK3bl2jf//+5vcZGRlGcHCwMXnyZBtWZXtjxowxqlWrZusy8hTAWLFihfl9ZmamERgYaLz99tvmbbGxsYarq6vx9ddf26BC2/njtTEMw+jevbvRtm1bm9STl1y7ds0AjC1bthiGcececXZ2NpYuXWpuc+zYMQMwIiMjbVWm1f3xuhiGYTRs2NAYPHiw7YrKQ/Lnz2989tlnul/+IOu6GIbul4SEBKNMmTLGhg0bLK5FXr1n1AOch6SmprJ3716aNGli3ubg4ECTJk2IjIy0YWV5w6lTpwgODqZkyZJ07dqVCxcu2LqkPOXs2bNER0db3D/58uUjNDRU98//i4iIoFChQpQrV46+ffty48YNW5dkdXFxcQD4+fkBsHfvXtLS0izum/Lly1OsWDG7um/+eF2yLFiwgAIFClC5cmVGjhzJrVu3bFGezWRkZLBo0SKSkpIICwvT/fL//nhdstjz/dK/f39atWplcW9A3v0ZYzcrwT0Mrl+/TkZGRrbV6QICAjh+/LiNqsobQkNDmTdvHuXKlePKlSuMGzeOBg0acOTIEby9vW1dXp4QHR0NcNf7J2ufPWvevDkdOnSgRIkSnDlzhtdff50WLVoQGRmJo6OjrcuziszMTIYMGUK9evWoXLkycOe+cXFxwdfX16KtPd03d7suAM8//zwhISEEBwdz6NAhhg8fzokTJ1i+fLkNq7WOw4cPExYWRnJyMl5eXqxYsYKKFSty4MABu75f7nVdwL7vl0WLFrFv3z52796dbV9e/RmjACwPhRYtWph/XbVqVUJDQwkJCWHJkiX06tXLhpXJw6JLly7mX1epUoWqVatSqlQpIiIiaNy4sQ0rs57+/ftz5MgRuxw//2fudV369Olj/nWVKlUICgqicePGnDlzhlKlSlm7TKsqV64cBw4cIC4ujmXLltG9e3e2bNli67Js7l7XpWLFinZ7v0RFRTF48GA2bNiAm5ubrcu5bxoCkYcUKFAAR0fHbE9GXr16lcDAQBtVlTf5+vpStmxZTp8+betS8oyse0T3z/0pWbIkBQoUsJt7aMCAAaxevZrNmzdTpEgR8/bAwEBSU1OJjY21aG8v9829rsvdhIaGAtjFPePi4kLp0qWpVasWkydPplq1arz33nt2f7/c67rcjb3cL3v37uXatWvUrFkTJycnnJyc2LJlCzNnzsTJyYmAgIA8ec8oAOchLi4u1KpVi02bNpm3ZWZmsmnTJosxRgKJiYmcOXOGoKAgW5eSZ5QoUYLAwECL+yc+Pp6dO3fq/rmLixcvcuPGjUf+HjIMgwEDBrBixQp+/PFHSpQoYbG/Vq1aODs7W9w3J06c4MKFC4/0ffNX1+VuDhw4APDI3zN3k5mZSUpKit3eL/eSdV3uxl7ul8aNG3P48GEOHDhgftWuXZuuXbuaf50X7xkNgchjhg0bRvfu3alduzZ169ZlxowZJCUl0bNnT1uXZlOvvPIKbdq0ISQkhMuXLzNmzBgcHR157rnnbF2aVSUmJlr0Jpw9e5YDBw7g5+dHsWLFGDJkCBMmTKBMmTKUKFGCUaNGERwcTLt27WxXtJX82bXx8/Nj3LhxdOzYkcDAQM6cOcNrr71G6dKlCQ8Pt2HVua9///4sXLiQb7/9Fm9vb/OYu3z58uHu7k6+fPno1asXw4YNw8/PDx8fHwYOHEhYWBiPPfaYjavPPX91Xc6cOcPChQtp2bIl/v7+HDp0iKFDh/LEE09km+LpUTNy5EhatGhBsWLFSEhIYOHChURERPDDDz/Y7f0Cf35d7Pl+8fb2thg7D+Dp6Ym/v795e568Z2w2/4Tc0/vvv28UK1bMcHFxMerWrWv8/PPPti7J5jp37mwEBQUZLi4uRuHChY3OnTsbp0+ftnVZVrd582YDyPbq3r27YRh3pkIbNWqUERAQYLi6uhqNGzc2Tpw4YduireTPrs2tW7eMZs2aGQULFjScnZ2NkJAQo3fv3kZ0dLSty851d7smgDF37lxzm9u3bxv9+vUz8ufPb3h4eBjt27c3rly5YruireCvrsuFCxeMJ554wvDz8zNcXV2N0qVLG6+++qoRFxdn28Kt4MUXXzRCQkIMFxcXo2DBgkbjxo2N9evXm/fb4/1iGH9+Xez5frmbP04JlxfvGZNhGIY1A7eIiIiIiC1pDLCIiIiI2BUFYBERERGxKwrAIiIiImJXFIBFRERExK4oAIuIiIiIXVEAFhERERG7ogAsIiIiInZFAVhERERE7IoCsIg8dObNm4evr+8/Po7JZGLlypX/+Di20KNHj7+9xPUTTzzBwoULze+tdR0iIiIwmUzExsbm+rnystTUVIoXL86ePXtsXYqI3VIAFhGr+yfhzZryQp3nzp3DZDJx4MCBHDneqlWruHr1Kl26dDFvu3LlCi1atPjHx96/fz/PPvssAQEBuLm5UaZMGXr37s3Jkyf/8bGtoXjx4syYMSPXz+Pi4sIrr7zC8OHDc/1cInJ3CsAiInZk5syZ9OzZEweH//34DwwMxNXV9R8dd/Xq1Tz22GOkpKSwYMECjh07xldffUW+fPkYNWrUPy37T6Wmpubq8R/U/dTTtWtXtm3bxi+//GKFikTkjxSARSTPeffdd6lSpQqenp4ULVqUfv36kZiYmK3dypUrKVOmDG5uboSHhxMVFWWx/9tvv6VmzZq4ublRsmRJxo0bR3p6eo7VeeTIEVq0aIGXlxcBAQH861//4vr16+b9jRo1YtCgQbz22mv4+fkRGBjI2LFjLY5x/Phx6tevj5ubGxUrVmTjxo0WQxJKlCgBQI0aNTCZTDRq1Mji8++88w5BQUH4+/vTv39/0tLS7lnvb7/9xo8//kibNm0stv/+fFk9zsuXL+fJJ5/Ew8ODatWqERkZec/j3rp1i549e9KyZUtWrVpFkyZNKFGiBKGhobzzzjt88sknFu337t1L7dq18fDw4PHHH+fEiRPmfWfOnKFt27YEBATg5eVFnTp12Lhxo8Xnixcvzptvvkm3bt3w8fGhT58+AAwfPpyyZcvi4eFByZIlGTVqVLbr8d1331GnTh3c3NwoUKAA7du3B+78Xp0/f56hQ4diMpkwmUzmz2zbto0GDRrg7u5O0aJFGTRoEElJSX9aT2pqKgMGDCAoKAg3NzdCQkKYPHmy+TP58+enXr16LFq06J7XVURyjwKwiOQ5Dg4OzJw5k19++YX58+fz448/8tprr1m0uXXrFhMnTuSLL75g+/btxMbGWvyz/tatW+nWrRuDBw/m6NGjfPLJJ8ybN4+JEyfmSI2xsbE89dRT1KhRgz179rBu3TquXr1Kp06dLNrNnz8fT09Pdu7cydSpUxk/fjwbNmwAICMjg3bt2uHh4cHOnTuZPXs2//3vfy0+v2vXLgA2btzIlStXWL58uXnf5s2bOXPmDJs3b2b+/PnMmzePefPm3bPmbdu24eHhQYUKFf7y+/33v//llVde4cCBA5QtW5bnnnvunn95+OGHH7h+/Xq236Msfxyv/d///pdp06axZ88enJycePHFF837EhMTadmyJZs2bWL//v00b96cNm3acOHCBYtjvPPOO1SrVo39+/ebe5i9vb2ZN28eR48e5b333uPTTz9l+vTp5s98//33tG/fnpYtW7J//342bdpE3bp1AVi+fDlFihRh/PjxXLlyhStXrgB3Annz5s3p2LEjhw4dYvHixWzbto0BAwb8aT0zZ85k1apVLFmyhBMnTrBgwQKKFy9u8Zm6deuydevWe/wOiEiuMkRErKx79+5G27Zt77v90qVLDX9/f/P7uXPnGoDx888/m7cdO3bMAIydO3cahmEYjRs3NiZNmmRxnC+//NIICgoyvweMFStW/K0633zzTaNZs2YW26KiogzAOHHihGEYhtGwYUOjfv36Fm3q1KljDB8+3DAMw1i7dq3h5ORkXLlyxbx/w4YNFnWdPXvWAIz9+/dnqy0kJMRIT083b3v22WeNzp073/P7TJ8+3ShZsmS27Xc732effWbe/8svvxiAcezYsbsed8qUKQZgxMTE3PPchmEYmzdvNgBj48aN5m3ff/+9ARi3b9++5+cqVapkvP/+++b3ISEhRrt27f70XIZhGG+//bZRq1Yt8/uwsDCja9eu92wfEhJiTJ8+3WJbr169jD59+lhs27p1q+Hg4GCu+W71DBw40HjqqaeMzMzMe57vvffeM4oXL/6X30NEcp56gEUkz9m4cSONGzemcOHCeHt7869//YsbN25w69YtcxsnJyfq1Kljfl++fHl8fX05duwYAAcPHmT8+PF4eXmZX7179+bKlSsWx/m7Dh48yObNmy2OX758eeBOr2GWqlWrWnwuKCiIa9euAXDixAmKFi1KYGCgeX9Wj+T9qFSpEo6Ojnc99t3cvn0bNze3+zr27+sOCgoCuOexDcO4r2Pez7ETExN55ZVXqFChAr6+vnh5eXHs2LFsPcC1a9fOdtzFixdTr149AgMD8fLy4o033rD43IEDB2jcuPED1Xrw4EHmzZtn8fscHh5OZmYmZ8+evWc9PXr04MCBA5QrV45Bgwaxfv36bMd2d3fPkXtRRB6ck60LEBH5vXPnztG6dWv69u3LxIkT8fPzY9u2bfTq1YvU1FQ8PDzu6ziJiYmMGzeODh06ZNt3vyHwr47fpk0bpkyZkm1fVqgDcHZ2tthnMpnIzMz8x+f/O8cuUKAAN2/efOBjZ42Hvdexy5YtC9wZzxwWFvaPjv3KK6+wYcMG3nnnHUqXLo27uzvPPPNMtgfLPD09Ld5HRkbStWtXxo0bR3h4OPny5WPRokVMmzbN3Mbd3f0va/ujxMREXnrpJQYNGpRtX7Fixe5ZT82aNTl79ixr165l48aNdOrUiSZNmrBs2TJzm5iYGAoWLPjANYnIP6cALCJ5yt69e8nMzGTatGnmmQqWLFmSrV16ejp79uwx95ieOHGC2NhY8/jWmjVrcuLECUqXLp0rddasWZNvvvmG4sWL4+T0936UlitXjqioKK5evUpAQAAAu3fvtmjj4uIC3Bkv/E/VqFGD6Ohobt68Sf78+f/x8bI0a9aMAgUKMHXqVFasWJFtf2xs7H3P27x9+3Z69OhhfjgtMTGRc+fO/eXnduzYQUhIiMUY6vPnz1u0qVq1Kps2baJnz553PYaLi0u261yzZk2OHj36t+4jHx8fOnfuTOfOnXnmmWdo3rw5MTEx+Pn5AXceoqxRo8YDH1dE/jkFYBGxibi4uGxz2/r7+1O6dGnS0tJ4//33adOmDdu3b+fjjz/O9nlnZ2cGDhzIzJkzcXJyYsCAATz22GPmQDx69Ghat25NsWLFeOaZZ3BwcODgwYMcOXKECRMm/OM6+/fvz6effspzzz1nnuXh9OnTLFq0iM8++8xiaMK9NG3alFKlStG9e3emTp1KQkICb7zxBvC/ntFChQrh7u7OunXrKFKkCG5ubuTLl+++6/+9GjVqUKBAAbZv307r1q3/1jHuxtPTk88++4xnn32Wp59+mkGDBlG6dGmuX7/OkiVLuHDhwn3PdlCmTBmWL19OmzZtMJlMjBo16r56zMuUKWM+T506dfj++++zhfExY8bQuHFjSpUqRZcuXUhPT2fNmjXm+XiLFy/OTz/9RJcuXXB1daVAgQIMHz6cxx57jAEDBvDvf/8bT09Pjh49yoYNG/jggw/uWc+7775LUFAQNWrUwMHBgaVLlxIYGGjxF4GtW7fy5ptv3td1EZGcpTHAImITERER1KhRw+I1btw4qlWrxrvvvsuUKVOoXLkyCxYssJg+KouHhwfDhw/n+eefp169enh5ebF48WLz/vDwcFavXs369eupU6cOjz32GNOnTyckJCRH6gwODmb79u1kZGTQrFkzqlSpwpAhQ/D19bWYY/fPODo6snLlShITE6lTpw7//ve/zT2YWcM0nJycmDlzJp988gnBwcG0bdv2ger/4/l69uzJggUL/vYx7qVt27bs2LEDZ2dnnn/+ecqXL89zzz1HXFzcA/2F49133yV//vw8/vjjtGnThvDwcGrWrPmXn3v66acZOnQoAwYMoHr16uzYsSPb/MONGjVi6dKlrFq1iurVq/PUU0+ZZ9kAGD9+POfOnaNUqVLmoQlVq1Zly5YtnDx5kgYNGlCjRg1Gjx5NcHDwn9bj7e3N1KlTqV27NnXq1OHcuXOsWbPGfG9ERkYSFxfHM888c9/XRkRyjsl40KcXREQk12zfvp369etz+vRpSpUqlePHj46OplKlSuzbt++B/zIgOadz585Uq1aN119/3daliNglDYEQEbGhFStW4OXlRZkyZTh9+jSDBw+mXr16uRJ+4c6qb3PmzOHChQsKwDaSmppKlSpVGDp0qK1LEbFb6gEWEbGhL774ggkTJnDhwgUKFChAkyZNmDZtGv7+/rYuTUTkkaUALCIiIiJ2RQ/BiYiIiIhdUQAWEREREbuiACwiIiIidkUBWERERETsigKwiIiIiNgVBWARERERsSsKwCIiIiJiVxSARURERMSu/B+BrHEi48W+mwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The string values range from 20 to 39 characters.\n"
          ]
        }
      ],
      "source": [
        "df['label_len'] = df['text'].str.len()\n",
        "\n",
        "MIN_LENGTH_VALUE = min(df['label_len'])\n",
        "MAX_LENGTH_VALUE = max(df['label_len'])\n",
        "LENGTH_VALUE_MEAN = df['label_len'].mean()\n",
        "LENGTH_VALUE_MEDIAN = df['label_len'].median()\n",
        "\n",
        "graph_y_max = 1000\n",
        "\n",
        "df.hist(\"label_len\",\n",
        "        grid=False,\n",
        "        color=\"Blue\",\n",
        "        figsize=(8,5),\n",
        "        bins=MAX_LENGTH_VALUE + 1,\n",
        "        range=(0, MAX_LENGTH_VALUE + 1)\n",
        "        )\n",
        "\n",
        "plt.suptitle('')\n",
        "plt.xlabel('Label Length (in Characters)')\n",
        "plt.ylabel('# of Samples')\n",
        "plt.title('Label Length')\n",
        "\n",
        "# Display & label vertical line @ minimum value\n",
        "plt.axvline(MIN_LENGTH_VALUE, color='red', linestyle='solid', linewidth=2.5)\n",
        "plt.text(MIN_LENGTH_VALUE * 0.67, graph_y_max * 0.20, 'Shortest: {:.0f}'.format(MIN_LENGTH_VALUE))\n",
        "\n",
        "# Display & label vertical line @ maximum value\n",
        "plt.axvline(MAX_LENGTH_VALUE + 1, color='red', linestyle='solid', linewidth=2.5)\n",
        "plt.text(MAX_LENGTH_VALUE * 0.87, graph_y_max * 1.00, 'Longest: {:.0f}'.format(MAX_LENGTH_VALUE))\n",
        "\n",
        "# Display & label vertical line @ median value\n",
        "plt.axvline(LENGTH_VALUE_MEDIAN,\n",
        "            color='yellow',\n",
        "            linestyle='solid',\n",
        "            linewidth=2.5)\n",
        "\n",
        "plt.text(LENGTH_VALUE_MEDIAN * 0.95,\n",
        "         graph_y_max * 0.50,\n",
        "         'Median(in Yellow):{:.2f}'.format(LENGTH_VALUE_MEDIAN),\n",
        "         rotation=88)\n",
        "\n",
        "# Display & label vertical line @ mean value\n",
        "plt.axvline(LENGTH_VALUE_MEAN,\n",
        "            color='green',\n",
        "            linestyle='solid',\n",
        "            linewidth=2.5)\n",
        "\n",
        "plt.text(LENGTH_VALUE_MEAN * 1.025,\n",
        "         graph_y_max * 0.45,\n",
        "         'Mean (in Green): {:.2f}'.format(LENGTH_VALUE_MEAN),\n",
        "         rotation=88)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(f\"The string values range from {MIN_LENGTH_VALUE} to {MAX_LENGTH_VALUE} characters.\")\n",
        "\n",
        "df = df.drop(columns=['label_len'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxPDX845cahN"
      },
      "source": [
        "#### Split Dataset into Training & Testing Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uUp5fnQcahN",
        "outputId": "88383f45-622d-46ee-c57a-6d71b035a8e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Dataset Shape: (2494, 2)\n",
            "Testing Dataset Shape: (512, 2)\n"
          ]
        }
      ],
      "source": [
        "train_df, test_df = train_test_split(df,\n",
        "                                     test_size=0.17,\n",
        "                                     random_state=42)\n",
        "\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "test_df = test_df.reset_index(drop=True)\n",
        "\n",
        "print(f\"Training Dataset Shape: {train_df.shape}\")\n",
        "print(f\"Testing Dataset Shape: {test_df.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9qLLL2FlcahO"
      },
      "source": [
        "#### Create Dataset Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "JAMKko57cahO"
      },
      "outputs": [],
      "source": [
        "class CMC7_Tesseract_MICR_OCR_Dataset(Dataset):\n",
        "\n",
        "    def __init__(self,\n",
        "                 root_dir,\n",
        "                 df,\n",
        "                 processor,\n",
        "                 max_target_length=40):\n",
        "\n",
        "        self.root_dir = root_dir\n",
        "        self.df = df\n",
        "        self.processor = processor\n",
        "        self.max_target_length = max_target_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.df)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # get file name + text\n",
        "        file_name = self.df['file_name'][idx]\n",
        "        text = self.df['text'][idx]\n",
        "        # prepare image (i.e. resize + normalize)\n",
        "        image = Image.open(self.root_dir + file_name).convert(\"RGB\")\n",
        "        pixel_values = self.processor(image, return_tensors=\"pt\").pixel_values\n",
        "        # add labels (input_ids) by encoding the text\n",
        "        labels = self.processor.tokenizer(text,\n",
        "                                          padding=\"max_length\",\n",
        "                                          max_length=self.max_target_length\n",
        "                                          ).input_ids\n",
        "        # important: make sure that PAD tokens are ignored by loss function\n",
        "        labels = [label if label != self.processor.tokenizer.pad_token_id\n",
        "                  else -100 for label in labels]\n",
        "\n",
        "        encoding = {\"pixel_values\" : pixel_values.squeeze(), \"labels\" : torch.tensor(labels)}\n",
        "        return encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y2B_FW17cahP"
      },
      "source": [
        "##### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6wrwFU4cahP",
        "outputId": "4d9f0c38-6285-4a19-9f2b-00d948141f65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cuda\n"
          ]
        }
      ],
      "source": [
        "MODEL_CKPT = \"microsoft/trocr-large-printed\"\n",
        "MODEL_NAME =  MODEL_CKPT.split(\"/\")[-1] + \"-cmc7_tesseract_MICR_ocr\"\n",
        "\n",
        "NUM_OF_EPOCHS = 5\n",
        "BATCH_SIZE = 8\n",
        "\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "STRATEGY = \"epoch\"\n",
        "\n",
        "DEVICE = torch.device(\"cuda\")\n",
        "print(DEVICE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsHp1-QXcahP"
      },
      "source": [
        "##### Instantiate Processor, Create Training, & Testing Dataset Instances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jO78A4xcahP",
        "outputId": "4c61e511-cbe8-4888-b0c2-5a8d0343bfd2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n"
          ]
        }
      ],
      "source": [
        "parent_dir = \"/content/drive/MyDrive/projects/cmc7/\"\n",
        "\n",
        "image_processor = TrOCRProcessor.from_pretrained(MODEL_CKPT)\n",
        "\n",
        "train_ds = CMC7_Tesseract_MICR_OCR_Dataset(root_dir=parent_dir,\n",
        "                                           df=train_df,\n",
        "                                           processor=image_processor)\n",
        "\n",
        "test_ds = CMC7_Tesseract_MICR_OCR_Dataset(root_dir=parent_dir,\n",
        "                                          df=test_df,\n",
        "                                          processor=image_processor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D9UyYG4HcahP"
      },
      "source": [
        "##### Print Length of Training & Testing Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsKs3XGLcahP",
        "outputId": "d5993b1b-2ed2-4fbb-9572-01450607b583"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The training dataset has 2494 samples in it.\n",
            "The testing dataset has 512 samples in it.\n"
          ]
        }
      ],
      "source": [
        "print(f\"The training dataset has {len(train_ds)} samples in it.\")\n",
        "print(f\"The testing dataset has {len(test_ds)} samples in it.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xV3ogKxccahP"
      },
      "source": [
        "##### Example of Input Data Shapes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_rRfCZVcahP",
        "outputId": "4a24fc71-eda6-449e-c9c4-17cfdcead360"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pixel_values : torch.Size([3, 384, 384])\n",
            "      labels : torch.Size([40])\n"
          ]
        }
      ],
      "source": [
        "encoding = train_ds[5]\n",
        "\n",
        "for k,v in encoding.items():\n",
        "    print(k.rjust(12, \" \"), \":\", v.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pc0BLrkvcahQ"
      },
      "source": [
        "##### Show Example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "AdevC9XlcahQ",
        "outputId": "23439a60-06b4-46b7-f6b2-b05af068a291"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/projects/cmc7/cmc7/tesseract_cmc7_font_generator_00001537.tif\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=2287x68 at 0x79880F72CD90>"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(train_ds.root_dir + train_df['file_name'][0])\n",
        "\n",
        "image = Image.open(train_ds.root_dir + train_df['file_name'][0]).convert(\"RGB\")\n",
        "\n",
        "image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ChXdCuTjcahQ"
      },
      "source": [
        "##### Show Label for Above Example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hk51sGtNcahQ",
        "outputId": "cae9a0d1-119f-4afb-8ab6-7896f7204ffd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5541587 048218278578 333619595084 88009\n"
          ]
        }
      ],
      "source": [
        "labels = encoding['labels']\n",
        "labels[labels == -100] = image_processor.tokenizer.pad_token_id\n",
        "label_str = image_processor.decode(labels, skip_special_tokens=True)\n",
        "print(label_str)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLp2DwUKcahQ"
      },
      "source": [
        "#### Instantiate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
            "138f444f45424ef7a1e3a088b54bd342",
            "f99f6d724ae44406b1fae47f4a7fe738",
            "e45c95daffb64e348d6215dd4de85df5",
            "0eaf2e4a5c874aef99f7dda678a501f9",
            "1e75c3739ffb4a6185a10a2a0c9ce8f1",
            "aef5876398e049888c5b859a658ff517",
            "ef8c7df0140042f1a8733522ad9afb79",
            "2e7600cf05014fc7b4532526e6b2da31",
            "05601c4e846842f48507793176b09ce1",
            "f7f9e609699044718d2be72ec6c1c89d",
            "ac647026ded94889a3758fc499036216",
            "83bc2f1f14cd40549931d178ef887f94",
            "0c1ffe12ad7c4652b749f0d69acc3a3d",
            "2ce3653e94de47a9a11ca380ed061b8d",
            "a8c0168be5d349b4b9b262ec0f406b62",
            "cd53600dd027400786d08943d76170c5",
            "cf28cd81ec83469680339f83f1815d90",
            "e4768abde9694364a0ff6ad98635d4f8",
            "54bbf1be10514f488a30816b71d28760",
            "0d6d616a95be456295c1697bb3a6272c",
            "8b4a6bfc763b4a9ba17108c37025c37f",
            "987621922c974b44bd0c4d9852e4d02b",
            "ddabce80819d483ab184ca6f930d9f46",
            "7378964a167640baa79bfdfdf9529f57",
            "4d9074021c97416ab763062ec96f32dd",
            "10e1002686874e63aed022e8ceaba727",
            "d04c58c0d3514f4db7eae71f9db81a4b",
            "05524bdfec8f49a59d4f9ff5d4832383",
            "4caf22a774094effacd94f19b0f356db",
            "06902f64ab95478886295d3f22bafc29",
            "7f062b50954e408c85b7c6c248db4956",
            "fc33b88607a942469d0c95c10c2f07f5",
            "6c5567c855b54908bec48af93662f1e4"
          ]
        },
        "id": "wqz68bUicahQ",
        "outputId": "56d3908b-51e8-41aa-f02d-64615356fa81"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "138f444f45424ef7a1e3a088b54bd342",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/4.21k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83bc2f1f14cd40549931d178ef887f94",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.43G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-large-printed and are newly initialized: ['encoder.pooler.dense.bias', 'encoder.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ddabce80819d483ab184ca6f930d9f46",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = (\n",
        "    VisionEncoderDecoderModel.from_pretrained(MODEL_CKPT)\n",
        "    ).to(DEVICE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVoXEDlScahQ"
      },
      "source": [
        "##### Model Configuration Modifications"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "mevn37otcahQ"
      },
      "outputs": [],
      "source": [
        "model.config.decoder_start_token_id = image_processor.tokenizer.cls_token_id\n",
        "model.config.pad_token_id = image_processor.tokenizer.pad_token_id\n",
        "\n",
        "model.config.vocab_size = model.config.decoder.vocab_size\n",
        "\n",
        "model.config.eos_token_id = image_processor.tokenizer.sep_token_id\n",
        "model.config.max_length = MAX_LENGTH_VALUE\n",
        "model.config.early_stopping = True\n",
        "model.config.no_repeat_ngram_size = 3\n",
        "model.config.length_penalty = -1\n",
        "model.config.num_beams = 8"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnwbIec-cahQ"
      },
      "source": [
        "##### Define Metrics Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "3c5aac0da7694f76896b91e5bdc0005a",
            "e875c795c14f428694c142f68a26757d",
            "aaf083fa8a944938acd509fd1a9b196f",
            "2813d79c3a2e44848892974dee60aae4",
            "8e70522009004f01b0216051d57e3e71",
            "0a778f235bc6490da22e15c5cf4fc813",
            "c6e1f0609ad144d0a82be22c58212c37",
            "1a67e9d0f3e44895a313fce336c65bec",
            "359245eafe6442aea12c7a1328cd26c8",
            "53ebb0c5d9984caa865be1dc9b31b608",
            "186913617b3d4bcab4fe59c7e5aa2006"
          ]
        },
        "id": "gAi4u3ZBcahR",
        "outputId": "1ab27155-2890-4647-afbb-d8c299fe942e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3c5aac0da7694f76896b91e5bdc0005a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.60k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cer_metric = evaluate.load(\"cer\")\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    label_ids = pred.label_ids\n",
        "    pred_ids = pred.predictions\n",
        "\n",
        "    pred_str = image_processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
        "    label_ids[label_ids == -100] = image_processor.tokenizer.pad_token_id\n",
        "    label_str = image_processor.batch_decode(label_ids, skip_special_tokens=True)\n",
        "\n",
        "    cer = cer_metric.compute(predictions=pred_str, references=label_str)\n",
        "\n",
        "    return {\"cer\" : cer}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VkiAYDyFcahR"
      },
      "source": [
        "#### Define Training Arguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "Ihg96Rw8cahR"
      },
      "outputs": [],
      "source": [
        "args = Seq2SeqTrainingArguments(\n",
        "    output_dir = MODEL_NAME,\n",
        "    num_train_epochs=NUM_OF_EPOCHS,\n",
        "    predict_with_generate=True,\n",
        "    evaluation_strategy=\"no\",\n",
        "    save_strategy=STRATEGY,\n",
        "    logging_strategy=\"steps\",\n",
        "    logging_steps=31,\n",
        "    logging_first_step=True,\n",
        "    report_to=REPORTS_TO,\n",
        "    auto_find_batch_size=True,\n",
        "    hub_private_repo=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ESE8qeQKcahR"
      },
      "source": [
        "##### Define Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2k8srmFNcahR",
        "outputId": "f53e96b8-2ea1-4edd-b91c-c65598662a94"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/trocr/processing_trocr.py:135: FutureWarning: `feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.\n",
            "  warnings.warn(\n",
            "Cloning https://huggingface.co/DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    tokenizer=image_processor.feature_extractor,\n",
        "    args=args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=test_ds,\n",
        "    data_collator=default_data_collator\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Usv-jxfWcahR"
      },
      "source": [
        "##### Fit/Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "76AkuFxlcahR",
        "outputId": "c42c3b7a-03a3-403b-80e0-c7a00fb13976"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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"
          ]
        },
        {
          "data": {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2' max='3120' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [   2/3120 : < :, Epoch 0.00/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6235' max='6235' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6235/6235 2:24:20, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>9.160900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>6.118100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>5.501200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>4.629400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>124</td>\n",
              "      <td>4.384200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>155</td>\n",
              "      <td>4.113000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>186</td>\n",
              "      <td>4.490700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>217</td>\n",
              "      <td>4.059000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>248</td>\n",
              "      <td>3.947500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>279</td>\n",
              "      <td>3.617200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>310</td>\n",
              "      <td>3.409800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>341</td>\n",
              "      <td>3.831800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>372</td>\n",
              "      <td>3.513400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>403</td>\n",
              "      <td>3.038900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>434</td>\n",
              "      <td>3.259800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>465</td>\n",
              "      <td>2.900600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>496</td>\n",
              "      <td>2.667300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>527</td>\n",
              "      <td>3.184600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>558</td>\n",
              "      <td>2.832700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>589</td>\n",
              "      <td>2.772300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>620</td>\n",
              "      <td>2.519000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>651</td>\n",
              "      <td>2.456200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>682</td>\n",
              "      <td>2.465800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>713</td>\n",
              "      <td>2.437400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>744</td>\n",
              "      <td>2.056900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>775</td>\n",
              "      <td>2.457700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>806</td>\n",
              "      <td>2.493600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>837</td>\n",
              "      <td>2.107000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>868</td>\n",
              "      <td>2.116100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>899</td>\n",
              "      <td>2.000100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>930</td>\n",
              "      <td>2.030900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>961</td>\n",
              "      <td>2.374700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>992</td>\n",
              "      <td>2.243300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1023</td>\n",
              "      <td>1.815600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1054</td>\n",
              "      <td>2.164600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1085</td>\n",
              "      <td>1.893500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1116</td>\n",
              "      <td>1.681000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1147</td>\n",
              "      <td>1.839200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1178</td>\n",
              "      <td>1.767400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1209</td>\n",
              "      <td>1.681000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1240</td>\n",
              "      <td>1.460400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1271</td>\n",
              "      <td>1.476900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1302</td>\n",
              "      <td>1.505800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1333</td>\n",
              "      <td>1.219700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1364</td>\n",
              "      <td>1.261500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1395</td>\n",
              "      <td>1.505800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1426</td>\n",
              "      <td>1.209100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1457</td>\n",
              "      <td>1.670400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1488</td>\n",
              "      <td>1.322800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1519</td>\n",
              "      <td>1.051800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1550</td>\n",
              "      <td>1.195200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1581</td>\n",
              "      <td>1.180000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1612</td>\n",
              "      <td>1.132000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1643</td>\n",
              "      <td>1.334600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1674</td>\n",
              "      <td>1.090500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1705</td>\n",
              "      <td>1.201300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1736</td>\n",
              "      <td>1.324000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1767</td>\n",
              "      <td>1.221400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1798</td>\n",
              "      <td>1.046700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1829</td>\n",
              "      <td>1.213000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1860</td>\n",
              "      <td>1.476500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1891</td>\n",
              "      <td>1.207500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1922</td>\n",
              "      <td>1.178200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1953</td>\n",
              "      <td>1.116900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1984</td>\n",
              "      <td>0.991700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2015</td>\n",
              "      <td>0.944800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2046</td>\n",
              "      <td>1.400400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2077</td>\n",
              "      <td>1.056400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2108</td>\n",
              "      <td>0.975500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2139</td>\n",
              "      <td>1.730700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2170</td>\n",
              "      <td>0.979700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2201</td>\n",
              "      <td>0.911600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2232</td>\n",
              "      <td>0.970900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2263</td>\n",
              "      <td>1.022800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2294</td>\n",
              "      <td>1.088100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2325</td>\n",
              "      <td>1.055300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2356</td>\n",
              "      <td>0.822200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2387</td>\n",
              "      <td>0.901200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2418</td>\n",
              "      <td>0.748800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2449</td>\n",
              "      <td>0.958900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2480</td>\n",
              "      <td>0.883900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2511</td>\n",
              "      <td>0.841000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2542</td>\n",
              "      <td>0.791000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2573</td>\n",
              "      <td>0.709700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2604</td>\n",
              "      <td>0.700900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2635</td>\n",
              "      <td>0.669000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2666</td>\n",
              "      <td>0.812500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2697</td>\n",
              "      <td>0.624500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2728</td>\n",
              "      <td>0.559900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2759</td>\n",
              "      <td>0.691900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2790</td>\n",
              "      <td>0.545400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2821</td>\n",
              "      <td>0.661300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2852</td>\n",
              "      <td>0.701300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2883</td>\n",
              "      <td>0.674500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2914</td>\n",
              "      <td>0.697600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2945</td>\n",
              "      <td>0.577400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2976</td>\n",
              "      <td>0.651600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3007</td>\n",
              "      <td>0.613600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3038</td>\n",
              "      <td>0.690500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3069</td>\n",
              "      <td>0.674800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3100</td>\n",
              "      <td>0.566900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3131</td>\n",
              "      <td>0.562000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3162</td>\n",
              "      <td>0.622600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3193</td>\n",
              "      <td>0.502900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3224</td>\n",
              "      <td>0.596300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3255</td>\n",
              "      <td>0.476300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3286</td>\n",
              "      <td>0.545100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3317</td>\n",
              "      <td>0.512400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3348</td>\n",
              "      <td>0.510700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3379</td>\n",
              "      <td>0.539600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3410</td>\n",
              "      <td>0.649500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3441</td>\n",
              "      <td>0.582100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3472</td>\n",
              "      <td>0.574500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3503</td>\n",
              "      <td>0.503200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3534</td>\n",
              "      <td>0.541100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3565</td>\n",
              "      <td>0.531700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3596</td>\n",
              "      <td>0.511400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3627</td>\n",
              "      <td>0.475600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3658</td>\n",
              "      <td>0.390100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3689</td>\n",
              "      <td>0.447000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3720</td>\n",
              "      <td>0.525700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3751</td>\n",
              "      <td>0.391500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3782</td>\n",
              "      <td>0.313000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3813</td>\n",
              "      <td>0.521200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3844</td>\n",
              "      <td>0.430800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3875</td>\n",
              "      <td>0.379000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3906</td>\n",
              "      <td>0.333000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3937</td>\n",
              "      <td>0.402900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3968</td>\n",
              "      <td>0.398500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3999</td>\n",
              "      <td>0.335300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4030</td>\n",
              "      <td>0.335600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4061</td>\n",
              "      <td>0.371000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4092</td>\n",
              "      <td>0.398400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4123</td>\n",
              "      <td>0.285600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4154</td>\n",
              "      <td>0.383500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4185</td>\n",
              "      <td>0.344300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4216</td>\n",
              "      <td>0.351700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4247</td>\n",
              "      <td>0.326800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4278</td>\n",
              "      <td>0.293500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4309</td>\n",
              "      <td>0.277500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4340</td>\n",
              "      <td>0.339300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4371</td>\n",
              "      <td>0.243400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4402</td>\n",
              "      <td>0.401900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4433</td>\n",
              "      <td>0.306500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4464</td>\n",
              "      <td>0.266400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4495</td>\n",
              "      <td>0.361000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4526</td>\n",
              "      <td>0.257200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4557</td>\n",
              "      <td>0.280800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4588</td>\n",
              "      <td>0.242900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4619</td>\n",
              "      <td>0.250300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4650</td>\n",
              "      <td>0.255300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4681</td>\n",
              "      <td>0.348500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4712</td>\n",
              "      <td>0.283400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4743</td>\n",
              "      <td>0.295900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4774</td>\n",
              "      <td>0.302300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4805</td>\n",
              "      <td>0.231500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4836</td>\n",
              "      <td>0.269800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4867</td>\n",
              "      <td>0.261400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4898</td>\n",
              "      <td>0.255600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4929</td>\n",
              "      <td>0.200800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4960</td>\n",
              "      <td>0.232300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4991</td>\n",
              "      <td>0.229300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5022</td>\n",
              "      <td>0.182000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5053</td>\n",
              "      <td>0.197100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5084</td>\n",
              "      <td>0.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5115</td>\n",
              "      <td>0.201200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5146</td>\n",
              "      <td>0.220300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5177</td>\n",
              "      <td>0.159800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5208</td>\n",
              "      <td>0.156700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5239</td>\n",
              "      <td>0.178400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5270</td>\n",
              "      <td>0.165300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5301</td>\n",
              "      <td>0.187800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5332</td>\n",
              "      <td>0.168000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5363</td>\n",
              "      <td>0.157700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5394</td>\n",
              "      <td>0.159800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5425</td>\n",
              "      <td>0.157500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5456</td>\n",
              "      <td>0.183900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5487</td>\n",
              "      <td>0.181100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5518</td>\n",
              "      <td>0.151100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5549</td>\n",
              "      <td>0.159000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5580</td>\n",
              "      <td>0.182000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5611</td>\n",
              "      <td>0.177100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5642</td>\n",
              "      <td>0.178100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5673</td>\n",
              "      <td>0.185800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5704</td>\n",
              "      <td>0.169500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5735</td>\n",
              "      <td>0.167300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5766</td>\n",
              "      <td>0.161300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5797</td>\n",
              "      <td>0.155100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5828</td>\n",
              "      <td>0.185900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5859</td>\n",
              "      <td>0.153400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5890</td>\n",
              "      <td>0.177600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5921</td>\n",
              "      <td>0.156900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5952</td>\n",
              "      <td>0.157400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5983</td>\n",
              "      <td>0.135100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6014</td>\n",
              "      <td>0.161800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6045</td>\n",
              "      <td>0.161200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6076</td>\n",
              "      <td>0.154100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6107</td>\n",
              "      <td>0.141300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6138</td>\n",
              "      <td>0.128100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6169</td>\n",
              "      <td>0.150900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6200</td>\n",
              "      <td>0.121600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6231</td>\n",
              "      <td>0.146700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "train_results = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a24Q7TCZcahR"
      },
      "source": [
        "##### Save Model & Training Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327,
          "referenced_widgets": [
            "fde55ce8d2f84cd89daa19912ca2efc9",
            "b3892f2ece8c4c9f95f36ce8c56b265a",
            "bbbb17827fd9446eb608be0897c4078a",
            "ae7eb49239bd485895e17e5dd0818735",
            "525f6b61fbf248a694f9c8a9798e53e2",
            "a8ccf6a33e144ff0b7f90167a8da4b65",
            "e99d2bb30f5045c083c06ca3e88a7ee9",
            "cfd8fbe15cb847b2b8c4c9f3fdb0a443",
            "3990afcf4dd942bf8294a339c23ad0c1",
            "acb5a546d2b547bf9dba0f9460211511",
            "8cb304a450bb4954ae982837c92b047b"
          ]
        },
        "id": "6wn7TJHmcahR",
        "outputId": "1ad738a3-7006-46b9-cb2e-40bb032b9c21"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fde55ce8d2f84cd89daa19912ca2efc9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Jul23_18-53-49_d4bbd6913ce1/events.out.tfevents.1690138435.d4bbd6913ce1.427.0:   0%|         …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr\n",
            "   02514a2..b6dc77e  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr\n",
            "   02514a2..b6dc77e  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr\n",
            "   b6dc77e..5815323  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr\n",
            "   b6dc77e..5815323  main -> main\n",
            "\n"
          ]
        }
      ],
      "source": [
        "trainer.save_model()\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Dgbau378MTa"
      },
      "source": [
        "#### Evaluate Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "u-Dg1x8G8MTb",
        "outputId": "f3c68261-2b97-43e7-ad52-8031ff459e52"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='64' max='64' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [64/64 15:55]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "metrics = trainer.evaluate()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFlXCE8ssKy8"
      },
      "source": [
        "#### Display Metric Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xz1quvt1sKnu",
        "outputId": "5850d742-1185-4861-fa85-9d5673dbeac1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The Character Error Rate (CER) for this model is 0.004970720413999727\n"
          ]
        }
      ],
      "source": [
        "print(f\"The Character Error Rate (CER) for this model is {metrics['eval_cer']}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32njjmnacahS"
      },
      "source": [
        "##### Push Model to Hub (My Profile!!!)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "2wRIHeadcahS"
      },
      "outputs": [],
      "source": [
        "trainer.push_to_hub(\"All Dunn!!!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOI2XFrprYEJ"
      },
      "source": [
        "#### Save & Push Evaluation Results to HuggingFace Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7drLuSjOrX8b",
        "outputId": "13255e6b-5d44-45bc-f5f9-24dc3d903abd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PosixPath('DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr/results/result-2023_07_23-21_45_48.json')"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cer_score_as_dict = {\"cer\": metrics}\n",
        "hyperparams = {\"model\": \"trocr-large-printed\"}\n",
        "\n",
        "evaluate.save(\"DunnBC22/trocr-large-printed-cmc7_tesseract_MICR_ocr/results/\", experiment=\"v1\", **cer_score_as_dict, **hyperparams)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tN5P8f06cahS"
      },
      "source": [
        "### Notes & Other Takeaways From This Project\n",
        "****\n",
        "- Given that the dataset was smaller than one would like for this type of project, the character error ate (CER) of 0.005 is pretty good.\n",
        "\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMzmMlyqcahS"
      },
      "source": [
        "### Citations\n",
        "\n",
        "- Model Checkpoint\n",
        "  > @misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL}\n",
        "}\n",
        "\n",
        "- Metric (Character Error Rate [CER])\n",
        "  > @inproceedings{morris2004,\n",
        "author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n",
        "year = {2004},\n",
        "month = {01},\n",
        "pages = {},\n",
        "title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n",
        "}"
      ]
    }
  ],
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