Invoice_extraction_with_layoutLM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Invoice extraction with layoutLM.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTp0pIIqNs-M"
      },
      "source": [
        "**Mounting Google-Drive:** \n",
        "\n",
        "First of all we must mount our drive"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/LayoutLM_model_4Z89mdt.zip -d layoutlm_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YqpQ9sXhNkvF",
        "outputId": "daaad7d4-18f1-49e9-d7db-994b4d5a8656"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/LayoutLM_model_4Z89mdt.zip\n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\59d5e384-7a40-4a99-8ab5-2d4f98129abe_box.txt  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\59d5e384-7a40-4a99-8ab5-2d4f98129abe_image.txt  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\59d5e384-7a40-4a99-8ab5-2d4f98129abe.txt  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\59d5e384-7a40-4a99-8ab5-2d4f98129abe_labels.txt  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269962_Page1.png_2022-03-24_202441.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-01975_Page0.png_2022-03-24_202539.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-01889_Page0.png_2022-03-24_202526.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01188_Page0.png_2022-03-24_202725.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00558297_Page1.png_2022-03-24_202323.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00557867_Page1.png_2022-03-24_202309.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-02104_Page0.png_2022-03-24_202552.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269757_Page1.png_2022-03-24_202352.1_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00552807_Page2.png_2022-03-24_202303.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-01954_Page0.png_2022-03-24_202535.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00427545_Page1.png_2022-03-24_202343.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00366_Page0.png_2022-03-24_202603.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270098_Page1.png_2022-03-24_202502.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00551495_Page2.png_2022-03-24_202257.7_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01233_Page0.png_2022-03-24_202730.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00557809_Page1.png_2022-03-24_202306.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269530_Page1.png_2022-03-24_202805.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-02340_Page0.png_2022-03-24_202555.7_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269723_Page1.png_2022-03-24_202837.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269866_Page1.png_2022-03-24_202412.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269873_Page1.png_2022-03-24_202414.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270027_Page1.png_2022-03-24_202454.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00552182_Page2.png_2022-03-24_202301.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269964_Page1.png_2022-03-24_202444.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270176_Page1.png_2022-03-24_202513.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269435_Page1.png_2022-03-24_202749.4_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00656_Page0.png_2022-03-24_202625.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01263_Page0.png_2022-03-24_202739.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-01903_Page0.png_2022-03-24_202530.1_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00869_Page0.png_2022-03-24_202642.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-02049_Page0.png_2022-03-24_202545.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269940_Page1.png_2022-03-24_202438.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269746_Page1.png_2022-03-24_202842.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269469_Page2.png_2022-03-24_202754.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269865_Page2.png_2022-03-24_202409.4_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01172_Page0.png_2022-03-24_202717.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269768_Page2.png_2022-03-24_202401.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269759_Page1.png_2022-03-24_202354.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00728_Page0.png_2022-03-24_202631.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269747_Page2.png_2022-03-24_202349.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270118_Page1.png_2022-03-24_202507.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01166_Page0.png_2022-03-24_202713.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01138_Page0.png_2022-03-24_202707.4_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00427616_Page2.png_2022-03-24_202233.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01209_Page0.png_2022-03-24_202727.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00551471_Page1.png_2022-03-24_202255.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269402_Page1.png_2022-03-24_202746.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01078_Page0.png_2022-03-24_202656.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269597_Page1.png_2022-03-24_202819.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269539_Page1.png_2022-03-24_202808.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00329670_Page1.png_2022-03-24_202337.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00328778_Page2.png_2022-03-24_202330.4_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270030_Page2.png_2022-03-24_202457.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270010_Page1.png_2022-03-24_202450.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269590_Page1.png_2022-03-24_202816.7_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01091_Page0.png_2022-03-24_202658.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-02070_Page0.png_2022-03-24_202548.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269548_Page1.png_2022-03-24_202813.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00551434_Page2.png_2022-03-24_202251.4_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269541_Page1.png_2022-03-24_202810.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270049_Page1.png_2022-03-24_202459.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270159_Page1.png_2022-03-24_202510.4_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-02029_Page0.png_2022-03-24_202542.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270112_Page1.png_2022-03-24_202504.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01241_Page0.png_2022-03-24_202733.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00744_Page0.png_2022-03-24_202634.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-02370_Page0.png_2022-03-24_202558.7_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00558253_Page1.png_2022-03-24_202320.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00329673_Page1.png_2022-03-24_202339.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269726_Page1.png_2022-03-24_202840.4_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00843_Page0.png_2022-03-24_202638.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269502_Page1.png_2022-03-24_202803.7_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00341_Page0.png_2022-03-24_202601.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00551264_Page1.png_2022-03-24_202240.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269636_Page2.png_2022-03-24_202833.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01179_Page0.png_2022-03-24_202720.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269760_Page2.png_2022-03-24_202358.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269629_Page1.png_2022-03-24_202831.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269483_Page1.png_2022-03-24_202757.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270345_Page1.png_2022-03-24_202521.4_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00326616_Page1.png_2022-03-24_202327.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269804_Page2.png_2022-03-24_202403.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00551268_Page1.png_2022-03-24_202244.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00424_Page0.png_2022-03-24_202611.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269708_Page2.png_2022-03-24_202835.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00922_Page0.png_2022-03-24_202646.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269886_Page1.png_2022-03-24_202421.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269445_Page1.png_2022-03-24_202752.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00508_Page0.png_2022-03-24_202617.4_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00451_Page0.png_2022-03-24_202614.1_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01054_Page0.png_2022-03-24_202652.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269489_Page2.png_2022-03-24_202801.1_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270341_Page1.png_2022-03-24_202518.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00551319_Page1.png_2022-03-24_202247.7_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00693_Page0.png_2022-03-24_202628.2_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01107_Page0.png_2022-03-24_202701.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00402_Page0.png_2022-03-24_202606.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270333_Page2.png_2022-03-24_202515.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269911_Page2.png_2022-03-24_202430.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269896_Page1.png_2022-03-24_202424.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01159_Page0.png_2022-03-24_202710.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269626_Page1.png_2022-03-24_202829.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01259_Page0.png_2022-03-24_202736.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270351_Page1.png_2022-03-24_202523.7_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269910_Page1.png_2022-03-24_202427.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269618_Page1.png_2022-03-24_202821.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269835_Page1.png_2022-03-24_202406.7_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01126_Page0.png_2022-03-24_202704.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00329446_Page2.png_2022-03-24_202333.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01003_Page0.png_2022-03-24_202648.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00517_Page0.png_2022-03-24_202619.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-01324_Page0.png_2022-03-24_202743.0_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00557870_Page2.png_2022-03-24_202312.5_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269620_Page1.png_2022-03-24_202826.1_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269920_Page1.png_2022-03-24_202434.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20129-00625_Page0.png_2022-03-24_202622.8_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01269882_Page1.png_2022-03-24_202417.9_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2021_01270004_Page2.png_2022-03-24_202447.3_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\FLPINE-20128-01927_Page0.png_2022-03-24_202532.6_image_0.jpg  \n",
            " extracting: layoutlm_train/59d5e384-7a40-4a99-8ab5-2d4f98129abe\\CALOSA-2018_00557971_Page1.png_2022-03-24_202317.0_image_0.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/LAYOUT_LM_TEST.zip -d layoutlm_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KgVYsmkfP63U",
        "outputId": "236c125d-3ce2-454a-af75-61e717e8b0ea"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/LAYOUT_LM_TEST.zip\n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\767cb324-62f6-42ff-a18b-aa0fa7a7f683_image.txt  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\767cb324-62f6-42ff-a18b-aa0fa7a7f683_box.txt  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\767cb324-62f6-42ff-a18b-aa0fa7a7f683.txt  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\767cb324-62f6-42ff-a18b-aa0fa7a7f683_labels.txt  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\FLPINE-20107-00302_Page0.png_2022-03-26_103051.2_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\FLPINE-20128-02370_Page0.png_2022-03-26_103011.9_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\FLPINE-20128-02340_Page0.png_2022-03-26_103009.3_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\FLPINE-20107-00329_Page0.png_2022-03-26_103058.8_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\KNDL-2018_13111_Page0.png_2022-03-26_103020.4_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\CALOSA-2021_01288008_Page1.png_2022-03-26_103043.6_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\FLPINE-20128-02070_Page0.png_2022-03-26_103003.8_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\FLPINE-20128-02104_Page0.png_2022-03-26_103006.5_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\KNDL-2018_13107_Page0.png_2022-03-26_103017.2_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\CALOSA-2021_01288016_Page1.png_2022-03-26_103048.7_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\CALOSA-2021_01288002_Page2.png_2022-03-26_103041.5_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\CALOSA-2018_00680250_Page1.png_2022-03-26_103031.9_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\CALOSA-2021_01288013_Page1.png_2022-03-26_103046.2_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\CALOSA-2018_00680256_Page1.png_2022-03-26_103035.3_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\FLPINE-20107-00306_Page0.png_2022-03-26_103055.3_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\CALOSA-2018_00679272_Page2.png_2022-03-26_103026.0_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\CALOSA-2021_01288000_Page1.png_2022-03-26_103038.9_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\KNDL-2018_13112_Page0.png_2022-03-26_103023.1_image_0.jpg  \n",
            " extracting: layoutlm_test/767cb324-62f6-42ff-a18b-aa0fa7a7f683\\CALOSA-2018_00679274_Page2.png_2022-03-26_103029.2_image_0.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fpYcLygMlgI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a1f7045-38d4-48c2-f0d4-3437ba486ab2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6K4S2s33ebY0"
      },
      "source": [
        "**Install libraries:**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jUmnKBmNZ-t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d683e3c-8870-4dec-a306-3087713856fc"
      },
      "source": [
        "! rm -r unilm\n",
        "! git clone -b remove_torch_save https://github.com/NielsRogge/unilm.git\n",
        "! cd unilm/layoutlm\n",
        "! pip install unilm/layoutlm"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'unilm'...\n",
            "remote: Enumerating objects: 4248, done.\u001b[K\n",
            "remote: Counting objects: 100% (4248/4248), done.\u001b[K\n",
            "remote: Compressing objects: 100% (2202/2202), done.\u001b[K\n",
            "remote: Total 4248 (delta 2085), reused 3591 (delta 1889), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (4248/4248), 7.29 MiB | 14.92 MiB/s, done.\n",
            "Resolving deltas: 100% (2085/2085), done.\n",
            "Processing ./unilm/layoutlm\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "Requirement already satisfied: transformers==2.9.0 in /usr/local/lib/python3.7/dist-packages (from layoutlm==0.0) (2.9.0)\n",
            "Requirement already satisfied: tensorboardX==2.0 in /usr/local/lib/python3.7/dist-packages (from layoutlm==0.0) (2.0)\n",
            "Requirement already satisfied: lxml==4.5.1 in /usr/local/lib/python3.7/dist-packages (from layoutlm==0.0) (4.5.1)\n",
            "Requirement already satisfied: seqeval==0.0.12 in /usr/local/lib/python3.7/dist-packages (from layoutlm==0.0) (0.0.12)\n",
            "Requirement already satisfied: Pillow==7.1.2 in /usr/local/lib/python3.7/dist-packages (from layoutlm==0.0) (7.1.2)\n",
            "Requirement already satisfied: Keras>=2.2.4 in /usr/local/lib/python3.7/dist-packages (from seqeval==0.0.12->layoutlm==0.0) (2.8.0)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from seqeval==0.0.12->layoutlm==0.0) (1.21.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from tensorboardX==2.0->layoutlm==0.0) (1.15.0)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorboardX==2.0->layoutlm==0.0) (3.17.3)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from transformers==2.9.0->layoutlm==0.0) (0.1.96)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==2.9.0->layoutlm==0.0) (2019.12.20)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers==2.9.0->layoutlm==0.0) (0.0.49)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==2.9.0->layoutlm==0.0) (4.63.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==2.9.0->layoutlm==0.0) (2.23.0)\n",
            "Requirement already satisfied: tokenizers==0.7.0 in /usr/local/lib/python3.7/dist-packages (from transformers==2.9.0->layoutlm==0.0) (0.7.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==2.9.0->layoutlm==0.0) (3.6.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==2.9.0->layoutlm==0.0) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==2.9.0->layoutlm==0.0) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==2.9.0->layoutlm==0.0) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==2.9.0->layoutlm==0.0) (2.10)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==2.9.0->layoutlm==0.0) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==2.9.0->layoutlm==0.0) (1.1.0)\n",
            "Building wheels for collected packages: layoutlm\n",
            "  Building wheel for layoutlm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for layoutlm: filename=layoutlm-0.0-py3-none-any.whl size=11487 sha256=c87a312d121bc87eb5ac73eee7abe80465e691afb53a9ad6befa189c25d834f6\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-gcsic4v5/wheels/ce/83/11/541e5dad69eb41732abc7eaa2efde047a655aaae9c6df0fe15\n",
            "Successfully built layoutlm\n",
            "Installing collected packages: layoutlm\n",
            "  Attempting uninstall: layoutlm\n",
            "    Found existing installation: layoutlm 0.0\n",
            "    Uninstalling layoutlm-0.0:\n",
            "      Successfully uninstalled layoutlm-0.0\n",
            "Successfully installed layoutlm-0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oIjawmtEPY1N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da9d079b-7972-4366-cae8-54459988e0ba"
      },
      "source": [
        "! rm -r transformers\n",
        "! git clone https://github.com/huggingface/transformers.git\n",
        "! cd transformers\n",
        "! pip install ./transformers"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'transformers'...\n",
            "remote: Enumerating objects: 107416, done.\u001b[K\n",
            "remote: Total 107416 (delta 0), reused 0 (delta 0), pack-reused 107416\u001b[K\n",
            "Receiving objects: 100% (107416/107416), 93.71 MiB | 13.71 MiB/s, done.\n",
            "Resolving deltas: 100% (78251/78251), done.\n",
            "Processing ./transformers\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (6.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (4.11.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (1.21.5)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (0.0.49)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (4.63.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (3.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (2.23.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (0.4.0)\n",
            "Collecting tokenizers!=0.11.3,>=0.11.1\n",
            "  Using cached tokenizers-0.11.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.5 MB)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (21.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.18.0.dev0) (2019.12.20)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers==4.18.0.dev0) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers==4.18.0.dev0) (3.0.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers==4.18.0.dev0) (3.7.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.18.0.dev0) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.18.0.dev0) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.18.0.dev0) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.18.0.dev0) (2021.10.8)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.18.0.dev0) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.18.0.dev0) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.18.0.dev0) (1.15.0)\n",
            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.18.0.dev0-py3-none-any.whl size=3926384 sha256=01aca3637d96469009442c531ee7ce96f51ab5845b937a0e83da78e8653a771c\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-u50tra5y/wheels/49/62/f4/6730819eed4e6468662b1519bf3bf46419b2335990c77f8767\n",
            "Successfully built transformers\n",
            "Installing collected packages: tokenizers, transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.7.0\n",
            "    Uninstalling tokenizers-0.7.0:\n",
            "      Successfully uninstalled tokenizers-0.7.0\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 2.9.0\n",
            "    Uninstalling transformers-2.9.0:\n",
            "      Successfully uninstalled transformers-2.9.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "layoutlm 0.0 requires transformers==2.9.0, but you have transformers 4.18.0.dev0 which is incompatible.\u001b[0m\n",
            "Successfully installed tokenizers-0.11.6 transformers-4.18.0.dev0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIjINVaNRl8J"
      },
      "source": [
        "from torch.nn import CrossEntropyLoss\n",
        "\n",
        "def get_labels(path):\n",
        "    with open(path, \"r\") as f:\n",
        "        labels = f.read().splitlines()\n",
        "    if \"O\" not in labels:\n",
        "        labels = [\"O\"] + labels\n",
        "    return labels\n",
        "\n",
        "labels = get_labels(\"/content/train/59d5e384-7a40-4a99-8ab5-2d4f98129abe_labels.txt\")\n",
        "num_labels = len(labels)\n",
        "label_map = {i: label for i, label in enumerate(labels)}\n",
        "# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later\n",
        "pad_token_label_id = CrossEntropyLoss().ignore_index"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lMg7v4lS6Nx",
        "outputId": "5bdf5f44-ce6f-466b-d0f4-9a607919a343"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'B-ADDRESS_1',\n",
              " 1: 'B-ADDRESS_2',\n",
              " 2: 'B-ADDRESS_3',\n",
              " 3: 'B-PARTY_ID_CODE_1',\n",
              " 4: 'B-PARTY_ID_CODE_2',\n",
              " 5: 'B-PARTY_NAME_1',\n",
              " 6: 'B-PARTY_NAME_2',\n",
              " 7: 'B-PARTY_NAME_3',\n",
              " 8: 'B-ROLE_1',\n",
              " 9: 'E-ADDRESS_1',\n",
              " 10: 'E-ADDRESS_2',\n",
              " 11: 'E-ADDRESS_3',\n",
              " 12: 'E-PARTY_ID_CODE_1',\n",
              " 13: 'E-PARTY_ID_CODE_2',\n",
              " 14: 'E-PARTY_NAME_1',\n",
              " 15: 'E-PARTY_NAME_2',\n",
              " 16: 'E-PARTY_NAME_3',\n",
              " 17: 'E-ROLE_1',\n",
              " 18: 'I-ADDRESS_1',\n",
              " 19: 'I-ADDRESS_2',\n",
              " 20: 'I-ADDRESS_3',\n",
              " 21: 'I-PARTY_ID_CODE_1',\n",
              " 22: 'I-PARTY_ID_CODE_2',\n",
              " 23: 'I-PARTY_NAME_1',\n",
              " 24: 'I-PARTY_NAME_2',\n",
              " 25: 'I-PARTY_NAME_3',\n",
              " 26: 'O',\n",
              " 27: 'S-PARTY_ID_CODE_1',\n",
              " 28: 'S-PARTY_ID_CODE_2',\n",
              " 29: 'S-PARTY_ID_CODE_3',\n",
              " 30: 'S-ROLE_1',\n",
              " 31: 'S-ROLE_2',\n",
              " 32: 'S-ROLE_3'}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9cBBA_ws0rTZ"
      },
      "source": [
        "from transformers import LayoutLMTokenizer\n",
        "from layoutlm.data.funsd import FunsdDataset, InputFeatures\n",
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
        "\n",
        "args = {'local_rank': -1,\n",
        "        'overwrite_cache': True,\n",
        "        'data_dir': '/content/train',\n",
        "        'model_name_or_path':'microsoft/layoutlm-base-uncased',\n",
        "        'max_seq_length': 512,\n",
        "        'model_type': 'layoutlm',}\n",
        "\n",
        "# class to turn the keys of a dict into attributes (thanks Stackoverflow)\n",
        "class AttrDict(dict):\n",
        "    def __init__(self, *args, **kwargs):\n",
        "        super(AttrDict, self).__init__(*args, **kwargs)\n",
        "        self.__dict__ = self\n",
        "\n",
        "args = AttrDict(args)\n",
        "\n",
        "tokenizer = LayoutLMTokenizer.from_pretrained(\"microsoft/layoutlm-base-uncased\")\n",
        "\n",
        "# the LayoutLM authors already defined a specific FunsdDataset, so we are going to use this here\n",
        "train_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=\"train\")\n",
        "train_sampler = RandomSampler(train_dataset)\n",
        "train_dataloader = DataLoader(train_dataset,\n",
        "                              sampler=train_sampler,\n",
        "                              batch_size=8)\n",
        "\n",
        "eval_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=\"test\")\n",
        "eval_sampler = SequentialSampler(eval_dataset)\n",
        "eval_dataloader = DataLoader(eval_dataset,\n",
        "                             sampler=eval_sampler,\n",
        "                            batch_size=2)\n",
        "\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8IzfppTr5EBJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "5b462f24-4536-4322-a58f-2305ea699a0f"
      },
      "source": [
        "batch = next(iter(train_dataloader))\n",
        "input_ids = batch[0][0]\n",
        "tokenizer.decode(input_ids)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[CLS] i # : 2018224859 bk : 20129 pg : 402, 07 / 16 / 2018 at 02 : 04 pm, recording 18 pages $ 154. 50 m doc stamp collection $ 367. 50 intangible tax $ 210. 00 ken burke, clerk of court and comptroller pinellas county,, fl by deputy clerk : clk100781 3 ( space above this line for recording data ) loan number : 113483013 min : 100094221134830139 mortgage definitions. words used in multiple sections of this document are defined below and other words are defined in sections 3, 11, 13, 18, 20 and 21. certain rules regarding the usage of words used in this document are also provided in section 16. ( a ) \" security instrument \" means this document, which is dated july 10, 2018, together with all riders to this document. ( b ) \" borrower \" is cheryl bradbury, whose address is 2134 serpentine circle s, st petersburg, florida 33712, and eric bradbury, wife and husband, whose address is 2134 serpintine circle s, saint petersburg, florida 33712, who is / are the mortgagor ( s ) under this security instrument. ( c ) \" mers \" is the mortgage electronic registration systems, inc. mers is a separate corporation that is acting solely as a nominee for lender and lender\\'s successors and assigns. mers is the mortgagee under this security instrument. mers is organized and existing under the laws of delaware, and has an address and telephone number of po box 2026, flint, mi 48501 - 2026, tel. ( 888 ) 679 - mers. ( d ) \" lender \" is north american savings bank, fsb. lender is a corporation organized and existing under the laws of the state of missouri. lender\\'s address is 12498 south 71 highway, grandview, missouri 64030. ( e ) \" note \" means the promissory note signed by borrower and dated july 10, 2018. the note states that borrower owes lender one hundred five thousand and 00 / 100 dollars ( u. s. $ 105, 000. 00 ) plus interest. borrower has promised to pay this debt in regular periodic payments and to pay the debt in full not later than august 1, 2048. ( f ) \" property \" means the property that is described below under the heading \" transfer of rights [SEP]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GSOZ3OSp5Ndq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3595cb91-c587-47db-f1f9-3f77a2ea4450"
      },
      "source": [
        "from transformers import LayoutLMForTokenClassification\n",
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = LayoutLMForTokenClassification.from_pretrained(\"microsoft/layoutlm-base-uncased\", num_labels=num_labels)\n",
        "model.to(device)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at microsoft/layoutlm-base-uncased were not used when initializing LayoutLMForTokenClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias']\n",
            "- This IS expected if you are initializing LayoutLMForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing LayoutLMForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of LayoutLMForTokenClassification were not initialized from the model checkpoint at microsoft/layoutlm-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LayoutLMForTokenClassification(\n",
              "  (layoutlm): LayoutLMModel(\n",
              "    (embeddings): LayoutLMEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (x_position_embeddings): Embedding(1024, 768)\n",
              "      (y_position_embeddings): Embedding(1024, 768)\n",
              "      (h_position_embeddings): Embedding(1024, 768)\n",
              "      (w_position_embeddings): Embedding(1024, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): LayoutLMEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): LayoutLMPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=33, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kG_NGIu30MR"
      },
      "source": [
        "\n",
        "from transformers import LayoutLMTokenizer\n",
        "from layoutlm.data.funsd import FunsdDataset, InputFeatures\n",
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
        "\n",
        "\n",
        "tokenizer = LayoutLMTokenizer.from_pretrained(\"microsoft/layoutlm-base-uncased\")"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kij9G--Z6kdD"
      },
      "source": [
        "**Start Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZzU-3tG5oER",
        "outputId": "04d081da-9c22-440f-be8b-37935eec920d"
      },
      "source": [
        "from transformers import AdamW\n",
        "from tqdm import tqdm\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "\n",
        "global_step = 0\n",
        "num_train_epochs = 20\n",
        "t_total = len(train_dataloader) * num_train_epochs # total number of training steps \n",
        "\n",
        "#put the model in training mode\n",
        "model.train()\n",
        "for epoch in range(num_train_epochs):\n",
        "  for batch in tqdm(train_dataloader, desc=\"Training\"):\n",
        "      input_ids = batch[0].to(device)\n",
        "      bbox = batch[4].to(device)\n",
        "      attention_mask = batch[1].to(device)\n",
        "      token_type_ids = batch[2].to(device)\n",
        "      labels = batch[3].to(device)\n",
        "\n",
        "      # forward pass\n",
        "      outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,\n",
        "                      labels=labels)\n",
        "      loss = outputs.loss\n",
        "\n",
        "      # print loss every 100 steps\n",
        "      if global_step % 100 == 0:\n",
        "        print(f\"Loss after {global_step} steps: {loss.item()}\")\n",
        "\n",
        "      # backward pass to get the gradients \n",
        "      loss.backward()\n",
        "\n",
        "      #print(\"Gradients on classification head:\")\n",
        "      #print(model.classifier.weight.grad[6,:].sum())\n",
        "\n",
        "      # update\n",
        "      optimizer.step()\n",
        "      optimizer.zero_grad()\n",
        "      global_step += 1"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: 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",
            "  FutureWarning,\n",
            "Training:   0%|          | 0/15 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss after 0 steps: 0.026747064664959908\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.63s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.62s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training:  67%|██████▋   | 10/15 [00:16<00:08,  1.60s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss after 100 steps: 0.0035663871094584465\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.60s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training:  33%|███▎      | 5/15 [00:07<00:16,  1.60s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss after 200 steps: 0.0010829007951542735\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.60s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.60s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.60s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n",
            "Training: 100%|██████████| 15/15 [00:24<00:00,  1.61s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNsh67yn5XFb"
      },
      "source": [
        "## Evaluation\n",
        "\n",
        "Now let's evaluate on the test set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t6lRUS245sIi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51ca6df1-2b3c-434b-d79f-d27a37795f0e"
      },
      "source": [
        "import numpy as np\n",
        "from seqeval.metrics import (\n",
        "    classification_report,\n",
        "    f1_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        ")\n",
        "\n",
        "eval_loss = 0.0\n",
        "nb_eval_steps = 0\n",
        "preds = None\n",
        "out_label_ids = None\n",
        "\n",
        "# put model in evaluation mode\n",
        "model.eval()\n",
        "for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n",
        "    with torch.no_grad():\n",
        "        input_ids = batch[0].to(device)\n",
        "        bbox = batch[4].to(device)\n",
        "        attention_mask = batch[1].to(device)\n",
        "        token_type_ids = batch[2].to(device)\n",
        "        labels = batch[3].to(device)\n",
        "\n",
        "        # forward pass\n",
        "        outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,\n",
        "                        labels=labels)\n",
        "        # get the loss and logits\n",
        "        tmp_eval_loss = outputs.loss\n",
        "        logits = outputs.logits\n",
        "\n",
        "        eval_loss += tmp_eval_loss.item()\n",
        "        nb_eval_steps += 1\n",
        "\n",
        "        # compute the predictions\n",
        "        if preds is None:\n",
        "            preds = logits.detach().cpu().numpy()\n",
        "            out_label_ids = labels.detach().cpu().numpy()\n",
        "        else:\n",
        "            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)\n",
        "            out_label_ids = np.append(\n",
        "                out_label_ids, labels.detach().cpu().numpy(), axis=0\n",
        "            )\n",
        "\n",
        "# compute average evaluation loss\n",
        "eval_loss = eval_loss / nb_eval_steps\n",
        "preds = np.argmax(preds, axis=2)\n",
        "\n",
        "out_label_list = [[] for _ in range(out_label_ids.shape[0])]\n",
        "preds_list = [[] for _ in range(out_label_ids.shape[0])]\n",
        "\n",
        "for i in range(out_label_ids.shape[0]):\n",
        "    for j in range(out_label_ids.shape[1]):\n",
        "        if out_label_ids[i, j] != pad_token_label_id:\n",
        "            out_label_list[i].append(label_map[out_label_ids[i][j]])\n",
        "            preds_list[i].append(label_map[preds[i][j]])\n",
        "\n",
        "results = {\n",
        "    \"loss\": eval_loss,\n",
        "    \"precision\": precision_score(out_label_list, preds_list),\n",
        "    \"recall\": recall_score(out_label_list, preds_list),\n",
        "    \"f1\": f1_score(out_label_list, preds_list),\n",
        "}\n",
        "print(results)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evaluating: 100%|██████████| 10/10 [00:01<00:00,  6.69it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'loss': 0.08794586632866412, 'precision': 0.85, 'recall': 0.912751677852349, 'f1': 0.8802588996763754}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AboPKnHBcHew",
        "outputId": "0b2fa25b-c3d2-4ae6-8cd5-8f404daba977"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PATH='/content/drive/MyDrive/layoutlm.pt'\n",
        "torch.save(model.state_dict(), PATH)"
      ],
      "metadata": {
        "id": "8UGshS3mZz_8"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhZWZhc_ZHlv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3b45722a-fd53-4e54-9797-43038f30b06a"
      },
      "source": [
        "!sudo apt install tesseract-ocr\n",
        "!pip install pytesseract"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  tesseract-ocr-eng tesseract-ocr-osd\n",
            "The following NEW packages will be installed:\n",
            "  tesseract-ocr tesseract-ocr-eng tesseract-ocr-osd\n",
            "0 upgraded, 3 newly installed, 0 to remove and 39 not upgraded.\n",
            "Need to get 4,795 kB of archives.\n",
            "After this operation, 15.8 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tesseract-ocr-eng all 4.00~git24-0e00fe6-1.2 [1,588 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tesseract-ocr-osd all 4.00~git24-0e00fe6-1.2 [2,989 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tesseract-ocr amd64 4.00~git2288-10f4998a-2 [218 kB]\n",
            "Fetched 4,795 kB in 3s (1,653 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 3.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package tesseract-ocr-eng.\n",
            "(Reading database ... 156210 files and directories currently installed.)\n",
            "Preparing to unpack .../tesseract-ocr-eng_4.00~git24-0e00fe6-1.2_all.deb ...\n",
            "Unpacking tesseract-ocr-eng (4.00~git24-0e00fe6-1.2) ...\n",
            "Selecting previously unselected package tesseract-ocr-osd.\n",
            "Preparing to unpack .../tesseract-ocr-osd_4.00~git24-0e00fe6-1.2_all.deb ...\n",
            "Unpacking tesseract-ocr-osd (4.00~git24-0e00fe6-1.2) ...\n",
            "Selecting previously unselected package tesseract-ocr.\n",
            "Preparing to unpack .../tesseract-ocr_4.00~git2288-10f4998a-2_amd64.deb ...\n",
            "Unpacking tesseract-ocr (4.00~git2288-10f4998a-2) ...\n",
            "Setting up tesseract-ocr-osd (4.00~git24-0e00fe6-1.2) ...\n",
            "Setting up tesseract-ocr-eng (4.00~git24-0e00fe6-1.2) ...\n",
            "Setting up tesseract-ocr (4.00~git2288-10f4998a-2) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Collecting pytesseract\n",
            "  Downloading pytesseract-0.3.9-py2.py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.7/dist-packages (from pytesseract) (21.3)\n",
            "Collecting Pillow>=8.0.0\n",
            "  Downloading Pillow-9.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.3 MB 4.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=21.3->pytesseract) (3.0.7)\n",
            "Installing collected packages: Pillow, pytesseract\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 7.1.2\n",
            "    Uninstalling Pillow-7.1.2:\n",
            "      Successfully uninstalled Pillow-7.1.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "layoutlm 0.0 requires Pillow==7.1.2, but you have pillow 9.0.1 which is incompatible.\n",
            "layoutlm 0.0 requires transformers==2.9.0, but you have transformers 4.18.0.dev0 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed Pillow-9.0.1 pytesseract-0.3.9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/UBIAI/layout_lm_tutorial.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A2Vz1Q6ueQFU",
        "outputId": "5f10dfa8-c1c9-43da-8fc3-56d47c773c66"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'layout_lm_tutorial'...\n",
            "remote: Enumerating objects: 6, done.\u001b[K\n",
            "remote: Counting objects: 100% (6/6), done.\u001b[K\n",
            "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
            "remote: Total 6 (delta 0), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (6/6), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.insert(1, './drive/MyDrive/layoutlm')\n",
        "from layoutlm_preprocess import *\n",
        "image_path='/content/FLPINE-20128-02340_Page0.png_2022-03-26_103009.3_image_0.jpg'\n",
        "image, words, boxes, actual_boxes = preprocess(image_path)"
      ],
      "metadata": {
        "id": "KmlUKUrEZcsN"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_path='/content/drive/MyDrive/layoutlm.pt'\n",
        "model=model_load(model_path,num_labels)\n",
        "word_level_predictions, final_boxes=convert_to_features(image, words, boxes, actual_boxes, model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHzP0Lkqek6c",
        "outputId": "cd57cc51-8615-4a88-fae5-5b31d13dabf5"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at microsoft/layoutlm-base-uncased were not used when initializing LayoutLMForTokenClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias']\n",
            "- This IS expected if you are initializing LayoutLMForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing LayoutLMForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of LayoutLMForTokenClassification were not initialized from the model checkpoint at microsoft/layoutlm-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 30, 30, 26, 26, 26, 5, 23, 14, 14, 14, 23, 26, 5, 23, 14, 14, 14, 14, 26, 3, 21, 12, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 31, 26, 26, 26, 26, 6, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 15, 26, 26, 26, 26, 26, 26, 4, 22, 13, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 1, 19, 19, 19, 19, 19, 19, 19, 10, 10, 10, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "draw = ImageDraw.Draw(image)\n",
        "font = ImageFont.load_default()\n",
        "def iob_to_label(label):\n",
        "  if label != 'O':\n",
        "    return label[2:]\n",
        "  else:\n",
        "    return \"\"\n",
        "label2color = {'ADDRESS_1':'green','ADDRESS_2':'green','ADDRESS_3':'green','PARTY_ID_CODE_1':'blue','PARTY_ID_CODE_2':'blue','PARTY_ID_CODE_3':'blue','ROLE_1':'black','ROLE_2':'black','ROLE_3':'black','PARTY_NAME_1':'red','PARTY_NAME_2':'red','PARTY_NAME_3':'red','':'violet'}\n",
        "label2color =  {k.lower(): v for k, v in label2color.items()}\n",
        "for prediction, box in zip(word_level_predictions, final_boxes):\n",
        "    predicted_label = iob_to_label(label_map[prediction]).lower()\n",
        "    draw.rectangle(box, outline=label2color[predicted_label])\n",
        "    draw.text((box[0] + 10, box[1] - 10), text=predicted_label, fill=label2color[predicted_label], font=font)\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9EhqRMQLfDHF",
        "outputId": "5e2791ad-d985-43a6-93fd-8d7ca9eb9476"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=2550x4381 at 0x7FE9B7F484D0>"
            ],
            "image/png": 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zaniW4nnhwZQ93VriEcKRSorXXY98uWT8c1cjN1af9NsDwOmmeOu43+9Sw7kU5Otk8YoAAABG8MfZBfC5tr6n/3oD4u3/tpJeyjXY6f1FsN/EISfa2XoA8FZVwWJ2WxvhTlBwMkfl7dGy3YPZ2wGNtJIVkx/faSv3NVu17UbUbYfisAUD0n09fnX73bLBKWKJI5WtuMBXuz83I/zZgizeIgAAAIBiIl4aOP7+VNZ84q11NYM/jKSMxbeVW7Xz9XKk9bl4w6aCbhtq2kykd8Fp3LkRfqLl+q8CXFir4217rfYIvCNXdZPRCL58mnwd4ZLGOdjmb4wNG+/xvv3WfvFvDygA0uJfJ+KVUQIAAIA4ES8D2bqtUx+gtr25Fo+HC+opK3K5V7Cd5S3sSFOtbnw3iQR2M/iClHcZvVdGxfFKtg6n0z3iZ2vpfpe/yj1rc93dnjraEdz2ELx6G4Y6iffG29qk6tyW040X7x7Z4HhbaXRiENK/2j3AsvB7a7N4O1ndXUmrz06c7tMuXQAAAKjhWbyUu208rDGS1C7v4HzIjbbdw4zcXo/3dbE7ZfGUZfmT+qFYNvLWUaSLgkqavzQiNSxD7uUuW+8AidbuyYfRjix9ruND8drgsOMwcm0fouEfguLe67/bkfW6qL/qygouePHuNpj+1VZ3zx1fWwgOy1Yw/NpOfN/0V3/SraV3DO7+ObzTAgAAADVEvGSovAPb9kbqbmtN+jr47ltZdzWzxFo11dvBheV+fWHLViOJE931SLfmE/e7zoe9ohLKovqGgfdrU4lQPBho7c7Cbx5xcVXHx1FbF2ewkqzN6l8Fld1tfZOma6dlbVZ+CaDVgHey9S6aG3in37d3vwpQ0Hhi+2AjI5wU3zECAACAOBEv+wQAnQx4D+vtXD/+77l1pq+9fjcony0fcPFP8RI7/Uo4UauzM+xZLrsChz2cSqdf57vhTasK08HVwXNwK+1+laG30y+biN5/a3IneX9N8jayuzj214EHsltMpJLdV/3pr+hcDWecF6w5lF7iJW6iFwUAAACD8Cxeqhz2vLez7ndk9RufHzPyc/JuL76S9wHr+6p/XOXSbUOTBltVOEIZiXoSa62/aVvJCMac1RQULL54kdXLn/2RvQ54k/F/nSle3NrrxVAz3fatpLT4PNFl+4mrt9Mf5XEi1d6GKuYy3kY1ePFHMun07hOdzXS1Bcfy3H51x+UZWW4W73T1k+r447/1Sbug7Laf2FdbbtIaAADAsMziBYYQWbS2rVv+bJXnZg2jiH6700S/K3AuBuFEzQc/vkpwoutjLolWL8C3RtJv+4n5zfF4+DVEX25Tf1y75TX5C5Ke6h3cPVHPZd5glwfSZCJs8QndGtjxP1fkHmZ8s/iVtpuCR14X6Wt7t/Lcmo8RKbtmHvnbb7MGMPHfEQWtrRrtdAAAAHyZxUtz/dK4Hs22FSkyfU/ZvYOz7kjmjnzvmQFmHgwlOMtkwFN2+3cu/mvNA9ZJwjh/F3ancZ9Valm/y73eXho1h7Maoqe7C7b2lhwnuqg8Ha9nuexPc9t6Tpe7hkHbd9qt1mYf1aVWQWz99ummaj7zxysZ6u91PFAPJrj1TXXqt18LAAAAzYl4ydPvXtIn/Gfz1pybs8LdrXv0QUdWlXV5JG58J+5Zn3gi3nR6LYxwaCMYf+2+tmfq9UgHP/BBzPJKKa5za8f6A287Z7FhGV3Pae/X1P3f1SaWf6H6/VHuNGKzvLiWmleeOGXxc/p6bSx/m5jOuNXdASdo9StTu9+gWm1n9ecNj6LTgJyYTNdoVfa5h1P//TyfowAAgNFYqJlz3KvXr5viRmHw9tzbTwpuo9THolv1jGPrMN8873iu/nz5760Wnp3WlFQwnoken62t3rfd7Ss4gPPKPcDnMI42LJc/U1zG8Xe6y3o8+DV1yddv/ce2Zzv1jRws+FGt4DJbthP/OLfM+5t86mt1orvandN8b/fsj62m3npMbPNWQJN2DrZb9lZ0Gh/tsy68AUcbAAAgi4iXCXzUf36f/vX2EW6/xm/09L4llL57dYxJ78BOpDKqHzYq5ngfcjEM9f4THOoja37tK50P7Sq+kBJT6A67OJfjQCtdx3PGzxirA1KcxAeHN5KyN+lukA/nb5Zfndy9bNJfVqh8t1xWVdZapIUxzwgAAPCZRLy0VDmj8fFf0ZH/tD54Os5bDcs6j7wXFrz11rCkU05B8Z2y3TnNuxfY1o3C1Y1PEay5YJtn+8GJRNPd4epxBkcehJFru7bVP2ezO+YNcPVv7pGpZNu+KpeCaNXRU9s/FnGfEPcWTKttMs000eNra4N8gKmX9VHk9Lj6mOV8uooPeMG7d+Tw4y+KyGa5L4qhPv8DAAAseRYvx4ksRvf1+5MyD/4v6td+IzfmXkvd2ma3kaztd9vZLSne1JW8HtTu+PQegdtC1+521dxtP734N2X1pBfxHjONOCun+Vhll9YIL3CubZC/I1lmrHkKhnQ6n7aA/H0huFfvwrJqGKEeAACAJxEvZwouf/q2S+//tD54qmJB1Lc1w7Lg7l66qXNvwhZPJH0re/UQnj8M9rL1w7TlANaPZ6vamvR77n2u+Lnb/VXC1oD3i+oPntXHkhu4S1uXU8H9+uLea/463zce0z61Tq/x4u/K5F4D3qOKdf0b1NUy5DvmPSRRz2F9Lc9R/Kx1+vQ4Wr81V3JBYJz1MfL1Er3YnxIAAGBqIl6ytf3P2qzWRv4v6pFrS0tMp87dpbncDO9ts8Stot1DiNw8fWtkdySL768dn7g3T6aP9HbKKq+E4/W+vzlvNnCu3VGKvEs0VHzWWp3ufnlMzTt/osHn//ZOmHa7aNJdekBOScsKuv7w958B/wCd7oAxOfKSi/zhmOsvcvMTNOCBD1gSAADAFhEv5yv42vWR/T43Tmwf/E538SEsbzFE+upxm+zEu8av3gYkt6RgNNtQfcRYf5sp0cJWoDJ+OJquodXtubd2JrrltxyBtsVPd286Ljdl7JfvHjakXd9k2NVk9N6S47IrsGHOXd9O0KTX3rllH/9HPNLjpKdyfMec7vhn2lk+M0xUKgAA8Jn+PLsABpX+T/Tmoexzy353yRP9xjtdbl8QLkaStsh9hK3iC4YrMQ6VLR8mPbBvW9bsnmjkKzySu+2Mb1n5WTe/cs/d277xBtO9HHwqt77lsFr8XC/tJq/E1aaa/KF57Pu68Yy3fW+3W+R4h7owlrKmim5tHByKKYzztvw0YEnXMOlFe+2X2+DGGfwj3wR2PyJmfYZcfgAAAAA4nVm8jKV4GcOyvco6jfeyFSBt2eoi0VHNga82lXUsB9jqtyzb3h3JmpKyKtnarPi6qrxldtb5baKg+MoXS30B47Q/9anPkp6Ik/WWEpnW03BgC2L7+i5yd2lbUuXfsshmz9NXOUlr2d3Wd0SuOuX9K/Zog4ez3nCuNNpUer0Ie18YuR9Nt/7rYJy/1JUjNuArMXdsBzwEAADgk5nFCzue/yU/WsY5Y3dljccTzeeWxTOt75kTuwvaiTR135hV8Nxxa4Ma6ZqXNQwlWPzrxl1bm+UO4JhncxzFL7TmAztCprvaSI/3oh7eZrD1rrn3hLnxx3ycKYNscY5GEElwm5ymEc515Msfbd/cCj7IRXbx2gEAAMZhFi9E7U5POeaW64WnAWXZvR1WM615axZFk3aCTe3umPvb3H63WovvWDZoNdJ15p7KyMZbGxx/7K9dx891j96bt3mugiOqHISa3Rv+RQiWcdbfoHR5q7/d/bsZOeTVRhLdvf6joLstWz0uez/Gbj3pHzaxdcg9xn9M6c+H8eMd/IPl8kASBRccS78LY9ny+BdhboVlA37uR8c3uZ8VAQAAzmUWL7Rx4h2xre+SD3iT7vvn99klEPXrn1/Pf2eduOeOW3u9ttxPq9tzveO6+8skyHSC/pX5mOfIdJnmbrfbMef3SPHBb3vVnfgevhpFDPg3paFOL4qDB223u4Mnn91eHl2ZSNYLPhtsXZDjXKVtv2/x1tryPJZ116odyqy+HpenYOq4seDrOIN4rW15Ui75aQcAAJiRiBdKPO9anhKirNaTvhPxcO6dlB9//zixdzhY1nTh5lvmbly243PL15Q6N60pqzOdQyRCi2fa9PYGHp+xfdiMwK0/MZUB8O7uibnpBZ0W3wevDJWzdi++CJ87HtDd4BIjMFFqWFNqq/g8OPs28kH0zerGB6f+nQSz0q/Aqy84IA1Pd6uSDtO1niPfLmrS9LLvpgAAADQn4oUdo921HH/mCkBc2U3SR2r4um9i3vnyV7/++fX987tsqvpqYNnpVu/yDf81U3z9+et0ydV2dltO7J7e623fA/4YxZciP3ju9cFrI3fqLp6W1ZdUuVp15RcdThc/hILPfpdZmXlpK6v+CkwZ711b2m7K3nUl86ztmyzRfMA6zwAAAB9OxAv7Dr5rGWl8qKeLAZ+s5p6syf1LiXx6+du3zZa/3fr5suVE4+m93vZtuHbl449dZQr4/HfBo0lz46LiGdJlguUNGzzXj0bBjPYP+Sx0+cOc5fuO8TnBid+eezYTgXpig6Bj4u0tkSXQAQAABifihZDRHkOYW48bFsBD2zuYQ70xXoDM+03Da/WApctrOqosLzGN+/UDQ2Q+924j6YIPzrlXazus63q5xVdOTK9c5n1e45z6GT/Ap2uunDM95hPEAQAAZiHihajdr5kffBdm6nuawAW4E0o9jzNsIjFd++tlanVkPvduI+nNVjeO71im4Wrbz3+ng6XdzDsrFK98nm6kl0gjJ86nLNAk8z7FjB/gZ0ymIyyMBAAAzE7Eyzq3XBN2l6k8vZI3zibQmzueFDBrmWFFHk1duUHBlrkb7zayOv+7x4ri8Y3LosRBvn8ZFDnM0Sqvz9SDRx2Z17tbQz+jnRcAAODDiXhZ4ZYrAHHueAJH8t21VtLzvysduWh5173abva6ccEzwrPaT9t6FG7W/PWsThM58XPf3Ufktn16zoyTqgEAAJ5EvABAiceNUTc9gSP5JuIsJPG7stY/b+4tNy3Yq77fThssN0t8XEnPBfchBwAAGJk7swDMx43jkQlgAOAsPiMdw6cdAADgdCJeAPgg5t0CAAAAAMzOQs38xze+E3xNGwAAAAAAgBGIePlNIshcfTrRV8UDih4NTjGZLCv8lpRvEZMDAAAAAADUE/Fyjq3AOLHxFGHww1//+6tm99cjXR2owYdiWfP9fs9NviXl9OPbBgAAAAAATE3ES63cxzoWhLvM7na7/frnV+5eBUn54OE3I/DtAQDgw+X+FxwAAAADEvES0ipqjbcj3KXAdBO+GdlVw+Bf//y66qFhhjoAET4tAwAAXICIF+iuYKHmGoJeHuqvhEtOJTdx56ok9wAAAADwOf44uwCuwIzbXfFAZXdL2UyQy/LDnXUB3G43114xowcAAAAAEGEWL/vccG+iLJoV6L56HQ2XJVtGuDbMlC0wwombiGsMAAAAAD6ZiJds9/u97Eb8ibfvrdx7PW9nc/XqEoF8oHFiQpdf3DhnbQqGCwAAAAAQ8VIiN+VNbPz6q3Szb1tuNf781bKpSNC73Gv2kGZr3KaTLr74mweQ5XEdBi82Xy6J8MrNYrgAAAAAgC8RL7viScYpMcZqjrsbFR9cak13u5FzeoO338YDp93zPmBqtXreg6f7etE+Xa1eHrI3unKBAQAAAABPIl4KjTxpcrewqRdQ3S0+EnJ/bUeYw4b6kR4LLsut7QvmXya6bjJ3fLX9dOSffnpx8BqID0K/pHyQ6bCmkh/g9LMMAAAAADA+ES8pWYFNk8wvNyOpeTDwYUFC276azMH92qgqd/3tecOYtgvt1nyroGso+Ow38qziyrR7q4uyfVfLTq86nq5/a68su9d8w6nkDdPxt6YGf9lO/cYCAAAAAHAMES9HSIRMy1v5idT28av43f/Ec3kHMfuDcj8kjGk173m1kd5TP9ONpzPg1S1zu/gKB71Zgvn08Vo9qrzy2eGvv3r+O3gK0vVnlbRaT6cTt/t22ilEBwAAAAA43h9nF8C4mq+5uhpuxbd8/DBYw3P7ek0SiNuG+o4Sh3l/ESyyvpEBrc6q3No4PU89+MOsYk63vBRzd2++5Vd1VU1qWGr+Koik4wXjloiN0w1Guku3H+nx8X8jdb4F1avvn8G3063aVgtIl7T1Hh7ZcbfNYD3PFrK2BwAAAAAuQ8RLucplfvuJZ8mr+y61LnCz64K9Vsd8WXZZ43Ot7xq0dZW+fo2g/tsMY+biBTPgX9W/wAtCrHRrrZo6RVa+2LWSgl4qS2pyROnL6S0eLmsk0kuwhtefJPbaiqIT25fFwwAAAADA1ES8nKnHLenRQrWJvEaSU8eTBS2XfS1geQFHxq3hZZ/73YXdXXKT6cQ3DFpdQsGm4vP760sq6CUyqTSt7btlfT2VjjkR9dlncN+26biwFgAAAADY5Vm8rCu+xXxr8XDW1fghvtrwaNnkXK43eq9HtBrKxpua8eqqnLmbsJXvvv3f4jeT3KbuGw8VnuWUpS/UU1pLN1L8chjwjKweS25w2/C4ZnyrAQAAAACOJOIlw2E3nWtioTG9jdvj6LYSqdyWDxirEU7H8ZlH7tiulvfayCCZzepxrc5jzj3vuyNQ2dQs6qdprr45BF8CrYauoJ2sc514A8zqOh5mP7eseUNLNLI8QcXfSwAAAAAASLNQM3luvzu7nFktl0QuTmX6nQUnupUxn8v7ZtKzfHDZrWYkF2xQoyysLWhka3wSa3eXdbS18da+b12XHUtBIwnx94TEG8j9dzX1AAAAAABzEfGyojI1qQ9dJk2btowW3qSJddOuOjJH5kOnPI343O6Cs0vru4s39VbSbljYcMHt9Pa584CPsZWwvv1kOfF6da/cMQEAAAAAeGOhZuA/V80vGxoqjBntgZ2un4fl8rzpxYS3zuMnjOcBx1jwGvmEkQcAAAAApibihe5Gy+G2DP7MyFPGMPEoTSEQDQUvp8PeTFzeAAAAAAAjE/HybmthydXNtgKwyhwiPQdud9/ifmfUO4n5tPF8M3jQ9eFnZ1iDXzYJ81bem5EBAAAAAIYi4mVf+hmE507udNu9lWC0P7jlUfQ7hOUXEWaZrt1VegS8YEfm7AAAAAAAzELEy28ucIu/MmY7MiMc3MceeHP9LqrRQuXdYoaqdiiv35V5/fbAc8Sev+pUwAXe/AEAAAAAPscfZxfA6IoThazA4Ha7pbcXPxxvujGPFLy15PjBphtb4nZPbuJN9X6/P357/9fyVz1s1Xx/EWlHhA8AAAAAcAyzeDlZPOt63VKQcLzTU8nEjNVEQFXZY9sGh22nKzPjx5zcnO60rKTT3yX6+bSLFgAAAAAYnIiXNpbPJf0abxVZErbO4CnFbOlUz+oSuME5wenH8RYX3KqdriJPI16tvN87w+kDVVbA1oC8tRYZtGBTI/NXAwAAAABgl4gXqDJskL9V1WqY/VW3vm5WO0GRdgYc/EhJ/WreOrmtHDmVvPhLMz2uitWjG+3aO8WAr0EAAAAA4BN4Fi8pJ965dtP8eJcZ891HhxYcaSIwrm/qMiP/cHux/G3bgw0G4emScntc1aTZ1/9b2Vri/7YyV7NZgg/tfvww60q42IsdAAAAADiLWbz8pubuc2TfrPafGydWK403WND1CEnDwRKTILdOx2iCJzrrFO8Gxk3amVHurNnrjUBa7vGmBzOrtbYh8SlGmyA7xfLpAAAAAMCHEPHym++f32eXsOLXP79e/2/XIh99Pbp4/XeWhrFEwzVvi7PPtx0fqV46Iu29Xu6yu+Idm4Rq9cebbqHtdw7anppPTrjTEsdbcELbjt7qi7ph+1mVrC5J/fzHWZdN2ePJd6tte0Rvb8VvfysBAAAAgAsba4oMXMCYMfkIfvz9I77x98/vrO0HsUyAamZe7j72NbKc7GojbTfb2ji4Y009q1umK/n1z6/gpfX98/uv//0V2XK3pC2V6XhxLps7aMGmypbmDl45x1/buWMSbKFfPfFrGwAAAACYnVm80Jg77J+s8kszWTOtO7VQsNly49ypisWzpVd33Joj+/h5vy9hlD2YueHTWyOTgxNPld7d8dOWJs46ZF+YAwAAAACOZBYvMCKToeknPov34G9sJOZnJzZ+lTvnNavN3WbLZvFu7fi2+7Az1BP71sziXd2y1Qx1AAAAAGB2ZvECIxJU8IGOeYx3Yo515cO/a56K3WSbrM3ic80T06P7fU9ua4b6p82lBgAAAABW/XF2AQAAR8tNZ+9rWu0Sb3m1zUgxAAAAAMCViHgBYD4iPQAAAACAjyXiBQAAAAAAAJiGiBcAAAAAAABgGiJeAAAAAAAAgGmIeAEAAAAAAACmIeIFAAAAAAAAmMbtfr+fXQMADOf75/fZJUCeH3//OLsEAAAAAOAIIl4A+BS32+3r68uffgAAAACAqVmoGQAAAAAAAGAaf55dAGOxMOmryIqX/UbMeptAc/f7/TGRFwAAAACAeYl4efdIFoMZwO5qn63a2W020sJuMa+NxLPbdBa72ulutbnJsWx+HLJ5AAAAAACgKxEvK1rN8eo0V6ys2chet1vjp1Nvddq8o6+vr7/+99fr//WszVO8Zu1yd4biywcAAAAAAJch4uVdVoCaiCpbtRNpttVk4qxKYNdhoVrZvPYDDFvY6R4jc9ho+MIBAAAAAMCViHgJyX18Y8FE20i22m/+bm4lALmW70Wd3m2WHf3651duI1Jh2jKPHAAAAAAaEvGy75FAPP63JqJ43awgds3afnfHrcNpKN24IBnG93wVf+ar9W3597h5h8u88058YwAAAAAA2vrj7AKYTORmd2Qt5XNvmj97v9/vq5V0jX6B8d1uN+8DxYzeJ7stnF0RAAAAAFyQiJeh9bhBfEy6HKnZjW8Yk9dmE4aRB1cCAAAAADRnoWbe9UhAB1/oMvdJw8CF9Xs3+MC3GivSAwAAAAD0IOKlvZpn7l7VI9qZLuBJVJuObV53XG751uxWU+lGIsRLFOj6Ov3AC9LLEAAAAACgOQs1k2eZfKTv3W897La4r9wG7wuRjnq4WMgRX0D7bcvlXsumtn6S1eNj4+VTIXcbSWxf0Fq65dwWOEDuu9xHWb6jpt9d5+LF2MkFrg0AAAAAGI1ZvESdfu/7eY/49Ep2xSscdn5bPEwN1p81JjU9RjoqG/atlh8/r6kq0kK9RHK5VV6kpNV948cS3z03ea0s7K2pMV+no3mM0vhv0QAAAAAAszOLl5CtsKThDN20g/OVD49zcmemtuq0ckpr1u4Fs2+Lisro65RgLH1ou8l0IvY+K+dLX0iR36Z3fN1ga0J2YqJ24leJHlftDgUAAAAAAFdlFi8nWA0nlqnq8/m1xxfTtsG5AuPEgGw9o/SA07TbXcF5bFv2VmtZhR0/kpFtio/r+Bgy3mOnoU4XkFVecJu53l62pN92cveNPN57dcuyxzC/7RKcXH6NEwcAAAAAnEXEy77EtLaGq92uOiXf7drpMkI4ONUr86ywciHWtyPdaud1s5qYcLe73MFvtQ6t9Wx7yB3PYV96H3Vh7B5sQZLd6lsgkY4SP0zXsJv1Bv887R5XYui2wulIaD37l5kAAAAAYHYWaqZKQaay+vNxbg1XVnLJbGZ1gvXbTyIHHhzb5by63V3i88IjBewWtrVE+WqEvGwk3c4pl9D9X6u/jRzXa1MtKwsrG7djRvvtGxJprd5Uz5I1pzZrWvNQRxqp54CCg7O9g5Vsbbn8WlJkMwAAAACgK7N42ZeeUhmfCTdUvnvYzehx0uuIy9yj3xr24lnUc53HuCYL1b61szUXcGvHflddJEF/vQBOn12dfpMc/+XZdiXqYuMs7//29tL2au+0CHzNYuPjX6IAAAAAcBkiXvKs3qGO3DseP989McMbdsHYr3bDUjaFN2KiUGH16KZbu3uWAW87hmedkWV2fu745/aeO26tvlG02nKToTtxPfBWK9gXm+W1DwAAAAC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          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQ4HALkUjGtf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bf051008-d803-42ed-f94d-6d5d45bd505c"
      },
      "source": [
        "import pytesseract\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "\n",
        "#image = Image.open('/content/form_example.jpg')\n",
        "image = Image.open(\"/content/CALOSA-2021_01288002_Page2.png_2022-03-26_103041.5_image_0.jpg\")\n",
        "image = image.convert(\"RGB\")\n",
        "image"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1692x2202 at 0x7F5E12E46550>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Czq7m1Exji1r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "outputId": "e25b1878-5567-4e3b-ac09-128620b931e2"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "width, height = image.size\n",
        "w_scale = 1000/width\n",
        "h_scale = 1000/height\n",
        "\n",
        "ocr_df = pytesseract.image_to_data(image, output_type='data.frame') \\\n",
        "            \n",
        "ocr_df = ocr_df.dropna() \\\n",
        "               .assign(left_scaled = ocr_df.left*w_scale,\n",
        "                       width_scaled = ocr_df.width*w_scale,\n",
        "                       top_scaled = ocr_df.top*h_scale,\n",
        "                       height_scaled = ocr_df.height*h_scale,\n",
        "                       right_scaled = lambda x: x.left_scaled + x.width_scaled,\n",
        "                       bottom_scaled = lambda x: x.top_scaled + x.height_scaled)\n",
        "\n",
        "float_cols = ocr_df.select_dtypes('float').columns\n",
        "ocr_df[float_cols] = ocr_df[float_cols].round(0).astype(int)\n",
        "ocr_df = ocr_df.replace(r'^\\s*$', np.nan, regex=True)\n",
        "ocr_df = ocr_df.dropna().reset_index(drop=True)\n",
        "ocr_df[:20]"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    level  page_num  block_num  par_num  line_num  word_num  left   top  \\\n",
              "0       5         1          1        1         1         1     7    12   \n",
              "1       5         1          1        1         1         2   215    12   \n",
              "2       5         1          1        1         1         3   322    12   \n",
              "3       5         1          1        1         1         4   416    12   \n",
              "4       5         1          1        1         1         5   490    12   \n",
              "5       5         1          1        1         1         6   562    14   \n",
              "6       5         1          1        1         1         7   649    12   \n",
              "7       5         1          1        1         1         8   734    12   \n",
              "8       5         1          1        1         1         9   890    12   \n",
              "9       5         1          2        1         1         1    12  2002   \n",
              "10      5         1          2        1         1         2    12  1884   \n",
              "11      5         1          2        1         1         3    12  1796   \n",
              "12      5         1          2        1         1         4    12  1712   \n",
              "13      5         1          2        1         1         5    12  1641   \n",
              "14      5         1          2        1         1         6    14  1563   \n",
              "15      5         1          2        1         1         7    12  1479   \n",
              "16      5         1          2        1         1         8    12  1320   \n",
              "17      5         1          2        1         1         9    12  1135   \n",
              "18      5         1          3        1         1         1   158   177   \n",
              "19      5         1          3        1         1         2   297   177   \n",
              "\n",
              "    width  height  conf       text  left_scaled  width_scaled  top_scaled  \\\n",
              "0     194      24    96   RECORDER            4           115           5   \n",
              "1     102      24    94      MEMO:          127            60           5   \n",
              "2      82      24    95       This          190            48           5   \n",
              "3      72      24    96       COPY          246            43           5   \n",
              "4      69      24    95        has          290            41           5   \n",
              "5      75      22    95        not          332            44           6   \n",
              "6      72      24    96       been          384            43           5   \n",
              "7     145      25    96    QUALITY          434            86           5   \n",
              "8     174      24    96   ASSURED.          526           103           5   \n",
              "9      24     194    96   RECORDER            7            14         909   \n",
              "10     24     104    93      MEMO:            7            14         856   \n",
              "11     24      83    96       This            7            14         816   \n",
              "12     24      72    96       COPY            7            14         777   \n",
              "13     24      69    96        has            7            14         745   \n",
              "14     22      75    96        not            8            13         710   \n",
              "15     24      72    96       been            7            14         672   \n",
              "16     25     146    95    QUALITY            7            15         599   \n",
              "17     24     174    96   ASSURED.            7            14         515   \n",
              "18    127      26    96  Recording           93            75          80   \n",
              "19    132      26    96  Requested          176            78          80   \n",
              "\n",
              "    height_scaled  right_scaled  bottom_scaled  \n",
              "0              11           119             16  \n",
              "1              11           187             16  \n",
              "2              11           239             16  \n",
              "3              11           288             16  \n",
              "4              11           330             16  \n",
              "5              10           376             16  \n",
              "6              11           426             16  \n",
              "7              11           520             17  \n",
              "8              11           629             16  \n",
              "9              88            21            997  \n",
              "10             47            21            903  \n",
              "11             38            21            853  \n",
              "12             33            21            810  \n",
              "13             31            21            777  \n",
              "14             34            21            744  \n",
              "15             33            21            704  \n",
              "16             66            22            666  \n",
              "17             79            21            594  \n",
              "18             12           168             92  \n",
              "19             12           254             92  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-465f4aab-b51f-41ee-8e8c-dd239c6ead8e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>level</th>\n",
              "      <th>page_num</th>\n",
              "      <th>block_num</th>\n",
              "      <th>par_num</th>\n",
              "      <th>line_num</th>\n",
              "      <th>word_num</th>\n",
              "      <th>left</th>\n",
              "      <th>top</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>conf</th>\n",
              "      <th>text</th>\n",
              "      <th>left_scaled</th>\n",
              "      <th>width_scaled</th>\n",
              "      <th>top_scaled</th>\n",
              "      <th>height_scaled</th>\n",
              "      <th>right_scaled</th>\n",
              "      <th>bottom_scaled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>194</td>\n",
              "      <td>24</td>\n",
              "      <td>96</td>\n",
              "      <td>RECORDER</td>\n",
              "      <td>4</td>\n",
              "      <td>115</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>119</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>215</td>\n",
              "      <td>12</td>\n",
              "      <td>102</td>\n",
              "      <td>24</td>\n",
              "      <td>94</td>\n",
              "      <td>MEMO:</td>\n",
              "      <td>127</td>\n",
              "      <td>60</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>187</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>322</td>\n",
              "      <td>12</td>\n",
              "      <td>82</td>\n",
              "      <td>24</td>\n",
              "      <td>95</td>\n",
              "      <td>This</td>\n",
              "      <td>190</td>\n",
              "      <td>48</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>239</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>416</td>\n",
              "      <td>12</td>\n",
              "      <td>72</td>\n",
              "      <td>24</td>\n",
              "      <td>96</td>\n",
              "      <td>COPY</td>\n",
              "      <td>246</td>\n",
              "      <td>43</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>288</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>490</td>\n",
              "      <td>12</td>\n",
              "      <td>69</td>\n",
              "      <td>24</td>\n",
              "      <td>95</td>\n",
              "      <td>has</td>\n",
              "      <td>290</td>\n",
              "      <td>41</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>330</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>562</td>\n",
              "      <td>14</td>\n",
              "      <td>75</td>\n",
              "      <td>22</td>\n",
              "      <td>95</td>\n",
              "      <td>not</td>\n",
              "      <td>332</td>\n",
              "      <td>44</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>376</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>649</td>\n",
              "      <td>12</td>\n",
              "      <td>72</td>\n",
              "      <td>24</td>\n",
              "      <td>96</td>\n",
              "      <td>been</td>\n",
              "      <td>384</td>\n",
              "      <td>43</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>426</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>734</td>\n",
              "      <td>12</td>\n",
              "      <td>145</td>\n",
              "      <td>25</td>\n",
              "      <td>96</td>\n",
              "      <td>QUALITY</td>\n",
              "      <td>434</td>\n",
              "      <td>86</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>520</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>890</td>\n",
              "      <td>12</td>\n",
              "      <td>174</td>\n",
              "      <td>24</td>\n",
              "      <td>96</td>\n",
              "      <td>ASSURED.</td>\n",
              "      <td>526</td>\n",
              "      <td>103</td>\n",
              "      <td>5</td>\n",
              "      <td>11</td>\n",
              "      <td>629</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>2002</td>\n",
              "      <td>24</td>\n",
              "      <td>194</td>\n",
              "      <td>96</td>\n",
              "      <td>RECORDER</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>909</td>\n",
              "      <td>88</td>\n",
              "      <td>21</td>\n",
              "      <td>997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>1884</td>\n",
              "      <td>24</td>\n",
              "      <td>104</td>\n",
              "      <td>93</td>\n",
              "      <td>MEMO:</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>856</td>\n",
              "      <td>47</td>\n",
              "      <td>21</td>\n",
              "      <td>903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>1796</td>\n",
              "      <td>24</td>\n",
              "      <td>83</td>\n",
              "      <td>96</td>\n",
              "      <td>This</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>816</td>\n",
              "      <td>38</td>\n",
              "      <td>21</td>\n",
              "      <td>853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>1712</td>\n",
              "      <td>24</td>\n",
              "      <td>72</td>\n",
              "      <td>96</td>\n",
              "      <td>COPY</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>777</td>\n",
              "      <td>33</td>\n",
              "      <td>21</td>\n",
              "      <td>810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>1641</td>\n",
              "      <td>24</td>\n",
              "      <td>69</td>\n",
              "      <td>96</td>\n",
              "      <td>has</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>745</td>\n",
              "      <td>31</td>\n",
              "      <td>21</td>\n",
              "      <td>777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>1563</td>\n",
              "      <td>22</td>\n",
              "      <td>75</td>\n",
              "      <td>96</td>\n",
              "      <td>not</td>\n",
              "      <td>8</td>\n",
              "      <td>13</td>\n",
              "      <td>710</td>\n",
              "      <td>34</td>\n",
              "      <td>21</td>\n",
              "      <td>744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>1479</td>\n",
              "      <td>24</td>\n",
              "      <td>72</td>\n",
              "      <td>96</td>\n",
              "      <td>been</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>672</td>\n",
              "      <td>33</td>\n",
              "      <td>21</td>\n",
              "      <td>704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>1320</td>\n",
              "      <td>25</td>\n",
              "      <td>146</td>\n",
              "      <td>95</td>\n",
              "      <td>QUALITY</td>\n",
              "      <td>7</td>\n",
              "      <td>15</td>\n",
              "      <td>599</td>\n",
              "      <td>66</td>\n",
              "      <td>22</td>\n",
              "      <td>666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>1135</td>\n",
              "      <td>24</td>\n",
              "      <td>174</td>\n",
              "      <td>96</td>\n",
              "      <td>ASSURED.</td>\n",
              "      <td>7</td>\n",
              "      <td>14</td>\n",
              "      <td>515</td>\n",
              "      <td>79</td>\n",
              "      <td>21</td>\n",
              "      <td>594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>158</td>\n",
              "      <td>177</td>\n",
              "      <td>127</td>\n",
              "      <td>26</td>\n",
              "      <td>96</td>\n",
              "      <td>Recording</td>\n",
              "      <td>93</td>\n",
              "      <td>75</td>\n",
              "      <td>80</td>\n",
              "      <td>12</td>\n",
              "      <td>168</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>297</td>\n",
              "      <td>177</td>\n",
              "      <td>132</td>\n",
              "      <td>26</td>\n",
              "      <td>96</td>\n",
              "      <td>Requested</td>\n",
              "      <td>176</td>\n",
              "      <td>78</td>\n",
              "      <td>80</td>\n",
              "      <td>12</td>\n",
              "      <td>254</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-465f4aab-b51f-41ee-8e8c-dd239c6ead8e')\"\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",
              "      \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-465f4aab-b51f-41ee-8e8c-dd239c6ead8e 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-465f4aab-b51f-41ee-8e8c-dd239c6ead8e');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yH6AwLdEjnLu",
        "outputId": "ddef3915-e7b0-4a63-e378-76ae73b1ba15"
      },
      "source": [
        "len(ocr_df)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "240"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0wkz6sqhjwU7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "471048ed-1197-4199-b5bf-d70672856c5c"
      },
      "source": [
        "words = list(ocr_df.text)\n",
        "coordinates = ocr_df[['left', 'top', 'width', 'height']]\n",
        "actual_boxes = []\n",
        "for idx, row in coordinates.iterrows():\n",
        "  x, y, w, h = tuple(row) # the row comes in (left, top, width, height) format\n",
        "  actual_box = [x, y, x+w, y+h] # we turn it into (left, top, left+widght, top+height) to get the actual box \n",
        "  actual_boxes.append(actual_box)\n",
        "\n",
        "def normalize_box(box, width, height):\n",
        "    return [\n",
        "        int(1000 * (box[0] / width)),\n",
        "        int(1000 * (box[1] / height)),\n",
        "        int(1000 * (box[2] / width)),\n",
        "        int(1000 * (box[3] / height)),\n",
        "    ]\n",
        "\n",
        "boxes = []\n",
        "for box in actual_boxes:\n",
        "  boxes.append(normalize_box(box, width, height))\n",
        "boxes"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " [0, 0, 1000, 1000],\n",
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              " [984, 3, 998, 91]]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqyAeMI9j105"
      },
      "source": [
        "def convert_example_to_features(image, words, boxes, actual_boxes, tokenizer, args, cls_token_box=[0, 0, 0, 0],\n",
        "                                 sep_token_box=[1000, 1000, 1000, 1000],\n",
        "                                 pad_token_box=[0, 0, 0, 0]):\n",
        "      width, height = image.size\n",
        "\n",
        "      tokens = []\n",
        "      token_boxes = []\n",
        "      actual_bboxes = [] # we use an extra b because actual_boxes is already used\n",
        "      token_actual_boxes = []\n",
        "      for word, box, actual_bbox in zip(words, boxes, actual_boxes):\n",
        "          word_tokens = tokenizer.tokenize(word)\n",
        "          tokens.extend(word_tokens)\n",
        "          token_boxes.extend([box] * len(word_tokens))\n",
        "          actual_bboxes.extend([actual_bbox] * len(word_tokens))\n",
        "          token_actual_boxes.extend([actual_bbox] * len(word_tokens))\n",
        "\n",
        "      # Truncation: account for [CLS] and [SEP] with \"- 2\". \n",
        "      special_tokens_count = 2 \n",
        "      if len(tokens) > args.max_seq_length - special_tokens_count:\n",
        "          tokens = tokens[: (args.max_seq_length - special_tokens_count)]\n",
        "          token_boxes = token_boxes[: (args.max_seq_length - special_tokens_count)]\n",
        "          actual_bboxes = actual_bboxes[: (args.max_seq_length - special_tokens_count)]\n",
        "          token_actual_boxes = token_actual_boxes[: (args.max_seq_length - special_tokens_count)]\n",
        "\n",
        "      # add [SEP] token, with corresponding token boxes and actual boxes\n",
        "      tokens += [tokenizer.sep_token]\n",
        "      token_boxes += [sep_token_box]\n",
        "      actual_bboxes += [[0, 0, width, height]]\n",
        "      token_actual_boxes += [[0, 0, width, height]]\n",
        "      \n",
        "      segment_ids = [0] * len(tokens)\n",
        "\n",
        "      # next: [CLS] token\n",
        "      tokens = [tokenizer.cls_token] + tokens\n",
        "      token_boxes = [cls_token_box] + token_boxes\n",
        "      actual_bboxes = [[0, 0, width, height]] + actual_bboxes\n",
        "      token_actual_boxes = [[0, 0, width, height]] + token_actual_boxes\n",
        "      segment_ids = [1] + segment_ids\n",
        "\n",
        "      input_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "      # The mask has 1 for real tokens and 0 for padding tokens. Only real\n",
        "      # tokens are attended to.\n",
        "      input_mask = [1] * len(input_ids)\n",
        "\n",
        "      # Zero-pad up to the sequence length.\n",
        "      padding_length = args.max_seq_length - len(input_ids)\n",
        "      input_ids += [tokenizer.pad_token_id] * padding_length\n",
        "      input_mask += [0] * padding_length\n",
        "      segment_ids += [tokenizer.pad_token_id] * padding_length\n",
        "      token_boxes += [pad_token_box] * padding_length\n",
        "      token_actual_boxes += [pad_token_box] * padding_length\n",
        "\n",
        "      assert len(input_ids) == args.max_seq_length\n",
        "      assert len(input_mask) == args.max_seq_length\n",
        "      assert len(segment_ids) == args.max_seq_length\n",
        "      #assert len(label_ids) == args.max_seq_length\n",
        "      assert len(token_boxes) == args.max_seq_length\n",
        "      assert len(token_actual_boxes) == args.max_seq_length\n",
        "      \n",
        "      return input_ids, input_mask, segment_ids, token_boxes, token_actual_boxes"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJO3J9qWj8U0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "outputId": "84ee520f-6de6-41b4-99d9-cfa03aa6122c"
      },
      "source": [
        "input_ids, input_mask, segment_ids, token_boxes, token_actual_boxes = convert_example_to_features(image=image, words=words, boxes=boxes, actual_boxes=actual_boxes, tokenizer=tokenizer, args=args)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-420bbb658fcd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msegment_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken_boxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken_actual_boxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_example_to_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mboxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mboxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactual_boxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mactual_boxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'tokenizer' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        },
        "id": "QcvxUcBekACh",
        "outputId": "541e9476-ee0d-4a81-e723-1c4e11fd7e87"
      },
      "source": [
        "tokenizer.decode(input_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"[CLS] lonos : a mme. faten amamou 102 avenue des champs elysees 75008 paris france hebergement web pro 1 & 1 ionos sarl service comptable 7, place de la gare bp 70109 57201 sarreguemines cedex facture du : 22. 05. 2021 n° de facture : 202522025862 n° de contrat : 86662335 n° client : 507553341 centre d'assistance : ionos. fr / assistance my ionos : my. ionos. fr / invoices numero de telephone : 0970 808 911 adresse email : info - facturation @ ionos. fr horaires d'ouverture : 7j / 7, 24h / 24 pour une authentification sdre et rapide, nous vous prions de bien vouloir preparer votre pin telephonique afin de pouvoir nous le communiquer lors de votre appel. vous pouvez configurer et gerer ce dernier sur my. ionos. fr. pos. prestation tarif quantite prixht ( eur ) tva ( % ) 1 frais d'abonnement 8, 00 eur ht par mois 1 mois 8, 00 20, 00 21. 05. 2021 - 21. 06. 2021 2 offre speciale offre speciale - 7, 00 20, 00 reduction sur la position 1 valide de 21 / 02 / 2021 a 21 / 02 / 2022 somme intermediaire ( ht ) 1, 00 eur + tva ( 20, 00 % ) 0, 20 eur montant a payer ( ttc ) 1, 20 eur le montant total dd sera preleve sur votre carte de credit d'ici les 7 prochains jours a compter de la date de la presente facture. pour plus d'informations, veuillez - vous referer au centre d'assistance ou rendez - vous sur my. ionos. fr professionnels tout retard de paiement entraine de plein droit des penalites de retard a hauteur de 1, 5 fois le taux d ’ interet legal annuel, par mois de retard [SEP]\""
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JNDRJPokC1H",
        "outputId": "32cf9ef3-d057-49f9-b324-356d3d35035b"
      },
      "source": [
        "input_ids = torch.tensor(input_ids, device=device).unsqueeze(0)\n",
        "input_ids.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 512])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtxjFuU5kJDj",
        "outputId": "edb29309-43b2-485e-a0d0-0db4104a1f15"
      },
      "source": [
        "attention_mask = torch.tensor(input_mask, device=device).unsqueeze(0)\n",
        "attention_mask.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 512])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e26jBeIrkLd7",
        "outputId": "b7f48e9d-9b48-4078-95a2-dc22988f8b2c"
      },
      "source": [
        "token_type_ids = torch.tensor(segment_ids, device=device).unsqueeze(0)\n",
        "token_type_ids.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 512])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHJQ0c2UkOGK",
        "outputId": "fdda89b4-38c8-4379-ee88-8ad83836f957"
      },
      "source": [
        "bbox = torch.tensor(token_boxes, device=device).unsqueeze(0)\n",
        "bbox.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 512, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOiZNMopkQfV"
      },
      "source": [
        "outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)\n",
        "outputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yX9o99xEkT0p"
      },
      "source": [
        "outputs.logits.shape\n",
        "print(outputs.logits)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjKC7AaVkYVx"
      },
      "source": [
        "outputs.logits.argmax(-1)\n",
        "print(outputs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Yueph4HkcDJ"
      },
      "source": [
        "token_predictions = outputs.logits.argmax(-1).squeeze().tolist() # the predictions are at the token level\n",
        "print(token_predictions)\n",
        "\n",
        "word_level_predictions = [] # let's turn them into word level predictions\n",
        "final_boxes = []\n",
        "for id, token_pred, box in zip(input_ids.squeeze().tolist(), token_predictions, token_actual_boxes):\n",
        "  if (tokenizer.decode([id]).startswith(\"##\")) or (id in [tokenizer.cls_token_id, \n",
        "                                                           tokenizer.sep_token_id, \n",
        "                                                          tokenizer.pad_token_id]):\n",
        "    # skip prediction + bounding box\n",
        "\n",
        "    continue\n",
        "  else:\n",
        "    word_level_predictions.append(token_pred)\n",
        "    final_boxes.append(box)\n",
        "\n",
        "# for id, prediction in zip(input_ids.squeeze().tolist(), predictions):\n",
        "#   if id != 0:\n",
        "#     print(tokenizer.decode([id]), label_map[prediction])\n",
        "print(word_level_predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laYonDf6kg09"
      },
      "source": [
        "print(len(word_level_predictions))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQ--4-oQkl6n",
        "outputId": "119c20ea-bba0-42b3-874a-b1f7268b48b4"
      },
      "source": [
        "print(len(final_boxes))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "339\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WT69kxNSkobc"
      },
      "source": [
        "draw = ImageDraw.Draw(image)\n",
        "\n",
        "font = ImageFont.load_default()\n",
        "\n",
        "def iob_to_label(label):\n",
        "  if label != 'O':\n",
        "    return label[2:]\n",
        "  else:\n",
        "    return \"\"\n",
        "\n",
        "label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}\n",
        "\n",
        "for prediction, box in zip(word_level_predictions, final_boxes):\n",
        "    predicted_label = iob_to_label(label_map[prediction]).lower()\n",
        "    # print(word_level_predictions)\n",
        "    # print(label_map[prediction])\n",
        "    #print('equal to other')\n",
        "    draw.rectangle(box, outline='blue')\n",
        "    draw.text((box[0] + 10, box[1] - 10), text=predicted_label, fill='blue', font=font)\n",
        "\n",
        "image"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}