LayoutLM2.ipynb

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            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e8e11daae2b14e588b2ff77c709fa821": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "This is a script that we are using layoutLM2 on docvqa dataset"
      ],
      "metadata": {
        "id": "51njgAklNs1x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
      "metadata": {
        "id": "_tEc77jjNxjX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oKxlTHXzNqXg",
        "outputId": "8abf685b-cf00-4220-ef5c-4fbc87dfa395"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hCollecting git+https://github.com/facebookresearch/detectron2.git\n",
            "  Cloning https://github.com/facebookresearch/detectron2.git to /tmp/pip-req-build-lysaf1zd\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/facebookresearch/detectron2.git /tmp/pip-req-build-lysaf1zd\n",
            "  Resolved https://github.com/facebookresearch/detectron2.git to commit 898507047cf441a1e4be7a729270961c401c4354\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Downloading iopath-0.1.9-py3-none-any.whl (27 kB)\n",
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            "\u001b[?25hCollecting hydra-core>=1.1 (from detectron2==0.6)\n",
            "  Downloading hydra_core-1.3.2-py3-none-any.whl (154 kB)\n",
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            "\u001b[?25hCollecting black (from detectron2==0.6)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting portalocker (from iopath<0.1.10,>=0.1.7->detectron2==0.6)\n",
            "  Downloading portalocker-2.8.2-py3-none-any.whl (17 kB)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard->detectron2==0.6) (3.2.2)\n",
            "Building wheels for collected packages: detectron2, fvcore, antlr4-python3-runtime\n",
            "  Building wheel for detectron2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for detectron2: filename=detectron2-0.6-cp310-cp310-linux_x86_64.whl size=6118668 sha256=0dd51912ffc07b3369cc09b258427f19fc2c48db6c89a1cb1207ecc84ae1ea72\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-18r7arwb/wheels/47/e5/15/94c80df2ba85500c5d76599cc307c0a7079d0e221bb6fc4375\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fvcore: filename=fvcore-0.1.5.post20221221-py3-none-any.whl size=61400 sha256=ea158adb57a02ac1b7371809ccdc2e0beadef56490a3a98b6dedc94656039cce\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/c0/af/77c1cf53a1be9e42a52b48e5af2169d40ec2e89f7362489dd0\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=be47dab93f04db5f504834ca291ee031155b12881400b3f0448b6663825fbf34\n",
            "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
            "Successfully built detectron2 fvcore antlr4-python3-runtime\n",
            "Installing collected packages: antlr4-python3-runtime, yacs, portalocker, pathspec, omegaconf, mypy-extensions, iopath, hydra-core, black, fvcore, detectron2\n",
            "Successfully installed antlr4-python3-runtime-4.9.3 black-23.10.1 detectron2-0.6 fvcore-0.1.5.post20221221 hydra-core-1.3.2 iopath-0.1.9 mypy-extensions-1.0.0 omegaconf-2.3.0 pathspec-0.11.2 portalocker-2.8.2 yacs-0.1.8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pydevd_plugins"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.1.0->torchvision) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.1.0->torchvision) (1.3.0)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\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 19 not upgraded.\n",
            "Need to get 4,816 kB of archives.\n",
            "After this operation, 15.6 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-eng all 1:4.00~git30-7274cfa-1.1 [1,591 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-osd all 1:4.00~git30-7274cfa-1.1 [2,990 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr amd64 4.1.1-2.1build1 [236 kB]\n",
            "Fetched 4,816 kB in 1s (5,099 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 78, <> 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 ... 120874 files and directories currently installed.)\n",
            "Preparing to unpack .../tesseract-ocr-eng_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
            "Unpacking tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n",
            "Selecting previously unselected package tesseract-ocr-osd.\n",
            "Preparing to unpack .../tesseract-ocr-osd_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
            "Unpacking tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n",
            "Selecting previously unselected package tesseract-ocr.\n",
            "Preparing to unpack .../tesseract-ocr_4.1.1-2.1build1_amd64.deb ...\n",
            "Unpacking tesseract-ocr (4.1.1-2.1build1) ...\n",
            "Setting up tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n",
            "Setting up tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n",
            "Setting up tesseract-ocr (4.1.1-2.1build1) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n"
          ]
        }
      ],
      "source": [
        "!pip3 install -q transformers datasets\n",
        "!pip3 install 'git+https://github.com/facebookresearch/detectron2.git'\n",
        "!pip3 install torchvision\n",
        "!sudo apt install tesseract-ocr\n",
        "!pip3 install -q pytesseract"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt install tesseract-ocr\n",
        "!pip install -q pytesseract"
      ],
      "metadata": {
        "id": "pSJl8itQRCT8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f785b73-5c71-4c20-aa69-bd4f4ccf9e10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "tesseract-ocr is already the newest version (4.1.1-2.1build1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 19 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import cv2\n",
        "from PIL import Image\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "from torch.utils.data import Dataset,DataLoader\n",
        "\n",
        "import transformers\n",
        "from transformers import LayoutLMv2FeatureExtractor,AutoTokenizer\n",
        "from transformers import AutoModelForQuestionAnswering\n",
        "from transformers import AdamW\n",
        "# import accelerate\n",
        "import datasets\n",
        "from datasets import Dataset\n",
        "from datasets import Features, Sequence, Value, Array2D, Array3D"
      ],
      "metadata": {
        "id": "oR8_aKCGN4J1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
      ],
      "metadata": {
        "id": "5adppglj4Lmr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Model\n",
        "For the model purposes we are using layoutLM2 which we are loading from huggingface\n"
      ],
      "metadata": {
        "id": "_ZGje3UNSKqY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"microsoft/layoutlmv2-base-uncased\"\n",
        "batch_size = 4"
      ],
      "metadata": {
        "id": "-IexiafcRvEj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analysis of Dataset\n",
        "Due to constarints of google colab we are only using the validation split of DocVQA"
      ],
      "metadata": {
        "id": "RsCO1QQsSl82"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the validation Data"
      ],
      "metadata": {
        "id": "8Xy8BM_jX06M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('/content/drive/MyDrive/val/val_v1.0.json') as f:\n",
        "  data = json.load(f)"
      ],
      "metadata": {
        "id": "viGsrACiShos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.keys()"
      ],
      "metadata": {
        "id": "n35RSyNBTNXV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c79a27de-d9e4-42d7-ee23-5162a2f585ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['dataset_name', 'dataset_version', 'dataset_split', 'data'])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Dataset name:\", data['dataset_name'])\n",
        "print(\"Dataset split:\", data['dataset_split'])"
      ],
      "metadata": {
        "id": "hba9pIinTPQE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19544c3f-f9e9-4f2f-ae22-37f40cbfc310"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset name: docvqa\n",
            "Dataset split: val\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "doc_df = pd.DataFrame(data['data'])\n",
        "doc_df.head()"
      ],
      "metadata": {
        "id": "acRztwopTPs9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "outputId": "11fef7e7-c221-42e6-a3c7-ab64925b2be3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   questionId                                           question  \\\n",
              "0       49153  What is the ‘actual’ value per 1000, during th...   \n",
              "1       24580                        What is name of university?   \n",
              "2       57349                   What is the name of the company?   \n",
              "3       24581                  Where is the university located ?   \n",
              "4       24582                      To whom is the document sent?   \n",
              "\n",
              "                       image  docId ucsf_document_id ucsf_document_page_no  \\\n",
              "0  documents/pybv0228_81.png  14465         pybv0228                    81   \n",
              "1   documents/nkbl0226_1.png   7027         nkbl0226                     1   \n",
              "2  documents/snbx0223_22.png   4733         snbx0223                    22   \n",
              "3   documents/nkbl0226_1.png   7027         nkbl0226                     1   \n",
              "4   documents/nkbl0226_1.png   7027         nkbl0226                     1   \n",
              "\n",
              "                                             answers data_split  \n",
              "0                                             [0.28]        val  \n",
              "1  [university of california, University of Calif...        val  \n",
              "2                         [itc limited, ITC Limited]        val  \n",
              "3                             [san diego, San Diego]        val  \n",
              "4                                             [Paul]        val  "
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              "    async function quickchart(key) {\n",
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              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-16e56711-adaa-4946-9bce-61075187a274 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are eight columns in our dataset which includes\n",
        "1. questionId- The ID of that particular question\n",
        "2. question- The question that is beinf asked for particular image\n",
        "3. image- Image name that we are going to use for reterving from image folder\n",
        "4. docId- the id of document form which we are asking question\n",
        "5. ucsf_document_id- The document id with respect to uscf\n",
        "6. ucsf_document_page_no\t- the page number of document from the particular ucsf dataset\n",
        "7. answer- the answer of the particular question asked\n",
        "8. data_split= the particular split of our dataset which can be train test or val"
      ],
      "metadata": {
        "id": "7KO4LryxWfqA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualising a Random example"
      ],
      "metadata": {
        "id": "qQC-x3Y_X7f8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example = data['data'][20\n",
        "                      ]\n",
        "for k,v in example.items():\n",
        "  print(k + \":\", v)"
      ],
      "metadata": {
        "id": "MUSOaTVVWfCr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f810976d-8796-41b4-fecd-7dbf9c580c23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "questionId: 57374\n",
            "question: What is the name of the company?\n",
            "image: documents/ylvj0223_21.png\n",
            "docId: 4752\n",
            "ucsf_document_id: ylvj0223\n",
            "ucsf_document_page_no: 21\n",
            "answers: ['cigfil limited', 'CIGFIL LIMITED']\n",
            "data_split: val\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "root_dir = '/content/drive/MyDrive/val/'\n",
        "image = Image.open(root_dir + example['image'])\n",
        "image"
      ],
      "metadata": {
        "id": "0txYpsP0YAzF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ec02e5af-db30-4de7-c867-543943f40574"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=L size=1679x2340>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The image is an analysis report for the current year and previous year analysis reports"
      ],
      "metadata": {
        "id": "9wkSZh4k2s1H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting Corresponding text\n",
        "In this section we are using an OCR technique that helps in processing the output\n",
        "The OCR results are stored in data folder itself under the name of OCR results so lets see the text output for the corresponding image\n"
      ],
      "metadata": {
        "id": "iGdBj8ScZOgy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ocr_root_dir = root_dir + \"ocr_results/\"\n",
        "\n",
        "with open(ocr_root_dir + example['ucsf_document_id'] + \"_\" + example['ucsf_document_page_no'] + \".json\") as f:\n",
        "  ocr = json.load(f)\n",
        "words = \"\"\n",
        "for item in ocr['recognitionResults'][0]['lines']:\n",
        "  words += item['text']\n",
        "\n",
        "print(words)"
      ],
      "metadata": {
        "id": "emzR_E7SZZjw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d24ac40-007b-471e-aec7-fc085e2300f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\"CIGFIL LIMITED, CHENNAISchedules annexed to and forming part of Balance Sheet as at 31.03.20043) Acetate Filament Yarn/ FibreKgsRs. In lakhsCurrent Year25,47137.55Previous year(12,431)(27.67)4) Acetate TowKgsRs. In lakhsCurrent Year661,3281,262.58Previous year725,722)(1519.97)5) Poly PropeleneMTsRs. In lakhsCurrent Year102.55046.88Previous year235.949)(106.84)D) Opening and Closing Stock of goods produced :1) Opening Stock :a) Cigarette Filter Rodsin MNRRs. In lakhsCurrent Year64.23251.46Previous year18.7652)(13.56)b) Poly Propelene Woven Fabrics/ Sacksin KgsRs. In lakhsCurrent YearNILNILPrevious yearNILNIL2) Closing Stock :a) Cigarette Filter Rodsn MNRRs. In lakhsCurrent Year31.559423.97Previous year(64.232)51.46)b) Poly Propelene Woven Fabrics/ Sacks.in KgsRs. In lakhsCurrent Year32203.23Previous year2863.52.87E) Value of imported/indigenous raw materials, stores & spare parts and componentsconsumed :1) Raw Materials :PercentageRs. In lakhsImported :Current Year79.23%1,304.95Previous year82.85%)(1596.85)Indigenous :Current Year20.77%342.04Previous year(17.15%)(330.44)Source: https://www.industrydocuments.ucsf.edu/docs/ylvj0223\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applying OCR on entire Dataset\n",
        "We are going to use LayoutLMv2FeatureExtractor from huggingface to prepare our model for Training\n",
        "The Datasets from huggingface is being used for loading this text data, because of compatilbilty with huggingface transformers, and faster processing technqiues"
      ],
      "metadata": {
        "id": "OAQdY8tRczHT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = Dataset.from_pandas(doc_df.iloc[:50])# creating the document in format of huggingface dataset"
      ],
      "metadata": {
        "id": "tBY6lix0dS8X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_extractor = LayoutLMv2FeatureExtractor()# instance of our layout feature extractor"
      ],
      "metadata": {
        "id": "-dLCRPTZdflD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9de14798-0745-4737-bf4e-edb18f7d96d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py:30: FutureWarning: The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use LayoutLMv2ImageProcessor instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_ocr_words_and_boxes(examples):\n",
        "  '''Each image has an image features, it will contains text in format of words and certains boxes which may contains any graphs,tables'''\n",
        "\n",
        "  # get a batch of document images\n",
        "  images = [Image.open(root_dir + image_file).convert(\"RGB\") for image_file in examples['image']]\n",
        "\n",
        "  # resize every image to 224x224 + apply tesseract to get words + normalized boxes\n",
        "  encoded_inputs = feature_extractor(images)\n",
        "\n",
        "  examples['image'] = encoded_inputs.pixel_values\n",
        "  examples['words'] = encoded_inputs.words\n",
        "  examples['boxes'] = encoded_inputs.boxes\n",
        "\n",
        "  return examples"
      ],
      "metadata": {
        "id": "JCrb_HmReaNf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from multiprocess import set_start_method\n",
        "# import torch\n",
        "# import os\n",
        "# set_start_method(\"spawn\")"
      ],
      "metadata": {
        "id": "PTj_n-Ek7Uq7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#mapping this function to each image in our dataset using map functionality of datasets\n",
        "dataset_with_ocr = dataset.map(get_ocr_words_and_boxes, batched=True, batch_size=4)"
      ],
      "metadata": {
        "id": "Pgp8GloVetj9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ad2083e2898f43b4a041c0215c1fe294",
            "e59e9962baac4963965dbbdffb192976",
            "d423e784663d4b39bac8ce7c28e70fda",
            "8d2a3c8c85f84fedab667f59a64f662a",
            "e43f7551fbe64d4daeb1b680180ae136",
            "c5ff639e4ee24ce08abba3d54e3032ba",
            "e4e990a947d6469d88b4802f5f84a210",
            "10a8ad19fa7143f3bb9c3e4f8e8b0529",
            "2abb57f407794ca49c841a4fcfa9bc60",
            "483430e60744458ca6d1b5b39826fe1f",
            "02e9998eeff54e229610ac013cb30f77"
          ]
        },
        "outputId": "17250afc-cf5f-41b3-d223-5fe927511ec4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/50 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad2083e2898f43b4a041c0215c1fe294"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_with_ocr[0].keys()"
      ],
      "metadata": {
        "id": "vpQdp57Pe411",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aed0a2dd-09cf-47ce-8569-6bff7f6ad068"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['questionId', 'question', 'image', 'docId', 'ucsf_document_id', 'ucsf_document_page_no', 'answers', 'data_split', 'words', 'boxes'])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Encoding the Dataset\n",
        "We need to encode our dataset as the text for each image would be of variable length then for token prediction we only have imited heads that will predict our token<br>\n",
        "so for these purposes we would be using start positions and end positions<br>\n",
        "As we are doing the input of text and boxes we need to convert them to certains tokens that can be fed into our model with \\\n",
        "1. input_ids= The particular ID of the token\n",
        "2. attention_mask= The attention that is being applied to our text\n",
        "3. token_type_ids= There can be many type of token according to the context that is being applied\n",
        "4. bbox= the encoded input for the bounding box co-ordinates"
      ],
      "metadata": {
        "id": "_OrSuUvZfViz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the tokenizer"
      ],
      "metadata": {
        "id": "S8RE0kkAjNhu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)#loading the pretrained tokenizer for the particular model"
      ],
      "metadata": {
        "id": "Lvw_ixxehQwo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sub Finder\n",
        "We are using SubFinder lets see in the below example the question has some corresponding words which may contain our answer so we can see that the answer is being created using 3 strings of words to create the final answer so subfinder does that work"
      ],
      "metadata": {
        "id": "dWcE1h3QiMpw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"where is it located?\"\n",
        "words = [\"this\", \"is\", \"located\", \"in\", \"the\", \"university\", \"of\", \"california\", \"in\", \"the\", \"US\"]\n",
        "boxes = [[1000,1000,1000,1000] for _ in range(len(words))]\n",
        "answer = \"university of california\""
      ],
      "metadata": {
        "id": "KLBToloOhpMo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def subfinder(words_list, answer_list):\n",
        "    matches = []\n",
        "    start_indices = []\n",
        "    end_indices = []\n",
        "\n",
        "    for idx, word in enumerate(words_list):\n",
        "        try:\n",
        "            if word == answer_list[0] and words_list[idx:idx+len(answer_list)] == answer_list:\n",
        "                matches.append(answer_list)\n",
        "                start_indices.append(idx)\n",
        "                end_indices.append(idx + len(answer_list) - 1)\n",
        "        except IndexError as e:\n",
        "            print(f\"Error in subfinder: {e}\")\n",
        "            print(f\"Index: {idx}, Length of words_list: {len(words_list)}\")\n",
        "            print(f\"words_list: {words_list}\")\n",
        "            print(f\"answer_list: {answer_list}\")\n",
        "\n",
        "    if matches:\n",
        "        return matches[0], start_indices[0], end_indices[0]\n",
        "    else:\n",
        "        return None, 0, 0"
      ],
      "metadata": {
        "id": "wNlASBCiipFa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example Encoding"
      ],
      "metadata": {
        "id": "rgTwDysRhnFr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoding = tokenizer(question, words, boxes=boxes)"
      ],
      "metadata": {
        "id": "WCpIwUOXhsxa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(encoding.input_ids)"
      ],
      "metadata": {
        "id": "JaCL8SPHh0Mi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "d56acfd8-60dc-4f9b-8467-4e3e2d9aea83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[CLS] where is it located? [SEP] this is located in the university of california in the us [SEP]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(encoding.word_ids())"
      ],
      "metadata": {
        "id": "Oa7VHUJCh1vR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a54055dc-e1ed-4965-af1a-5ba47caf4cd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[None, 0, 1, 2, 3, 3, None, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(encoding.sequence_ids())"
      ],
      "metadata": {
        "id": "gc4E6thdh4ip",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8de4dc9e-762d-4a36-b346-59c1f4d43f6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[None, 0, 0, 0, 0, 0, None, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, None]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Combining all\n",
        "This is a function that wil combine all the operations which includes subfinding of our answer list and the encoding using tokenizer"
      ],
      "metadata": {
        "id": "aFWVgkWjjR6u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def encode_dataset(examples, max_length=512):\n",
        "  '''A function that takes examples as our input and then do the following\n",
        "  1. Extract out the question, words and boxes from the examples dictinary we created above\n",
        "  2. Encode using the autotokenizer of layoutlm2\n",
        "  3. adding the start and end positions to indicate the start and end of our sequence\n",
        "  4. Encoding using input IDs and tge token type IDs\n",
        "  5. Then finding the start and end index for the specific words in our list\n",
        "  6. If the particular text is being matched then appending the start and end'''\n",
        "  # take a batch\n",
        "  try:\n",
        "    if not examples:\n",
        "      print(\"Empty batch. Skipping...\")\n",
        "      return None\n",
        "\n",
        "    questions = examples['question']\n",
        "    words = examples['words']\n",
        "    boxes = examples['boxes']\n",
        "\n",
        "\n",
        "\n",
        "    # encode it\n",
        "    encoding = tokenizer(questions, words, boxes, max_length=max_length, padding=\"max_length\", truncation=True)\n",
        "\n",
        "    # next, add start_positions and end_positions\n",
        "    start_positions = []\n",
        "    end_positions = []\n",
        "    answers = examples['answers']\n",
        "    # for every example in the batch:\n",
        "    for batch_index in range(len(answers)):\n",
        "      print(\"Batch index:\", batch_index)\n",
        "      cls_index = encoding.input_ids[batch_index].index(tokenizer.cls_token_id)\n",
        "      # try to find one of the answers in the context, return first match\n",
        "      words_example = [word.lower() for word in words[batch_index]]\n",
        "      for answer in answers[batch_index]:\n",
        "        match, word_idx_start, word_idx_end = subfinder(words_example, answer.lower().split())\n",
        "        if match:\n",
        "          break\n",
        "      # EXPERIMENT (to account for when OCR context and answer don't perfectly match):\n",
        "      if not match:\n",
        "        for answer in answers[batch_index]:\n",
        "          for i in range(len(answer)):\n",
        "            # drop the ith character from the answer\n",
        "            answer_i = answer[:i] + answer[i+1:]\n",
        "            # check if we can find this one in the context\n",
        "            match, word_idx_start, word_idx_end = subfinder(words_example, answer_i.lower().split())\n",
        "            if match:\n",
        "              break\n",
        "      # END OF EXPERIMENT\n",
        "\n",
        "      if match:\n",
        "        sequence_ids = encoding.sequence_ids(batch_index)\n",
        "        # Start token index of the current span in the text.\n",
        "        token_start_index = 0\n",
        "        while sequence_ids[token_start_index] != 1:\n",
        "            token_start_index += 1\n",
        "\n",
        "        # End token index of the current span in the text.\n",
        "        token_end_index = len(encoding.input_ids[batch_index]) - 1\n",
        "        while sequence_ids[token_end_index] != 1:\n",
        "            token_end_index -= 1\n",
        "\n",
        "        word_ids = encoding.word_ids(batch_index)[token_start_index:token_end_index+1]\n",
        "        for id in word_ids:\n",
        "          if id == word_idx_start:\n",
        "            start_positions.append(token_start_index)\n",
        "            break\n",
        "          else:\n",
        "            token_start_index += 1\n",
        "\n",
        "        for id in word_ids[::-1]:\n",
        "          if id == word_idx_end:\n",
        "            end_positions.append(token_end_index)\n",
        "            break\n",
        "          else:\n",
        "            token_end_index -= 1\n",
        "\n",
        "        print(\"Verifying start position and end position:\")\n",
        "        print(\"True answer:\", answer)\n",
        "        start_position = start_positions[batch_index]\n",
        "        end_position = end_positions[batch_index]\n",
        "        reconstructed_answer = tokenizer.decode(encoding.input_ids[batch_index][start_position:end_position+1])\n",
        "        print(\"Reconstructed answer:\", reconstructed_answer)\n",
        "        print(\"-----------\")\n",
        "\n",
        "      else:\n",
        "        print(\"Answer not found in context\")\n",
        "        print(\"-----------\")\n",
        "        start_positions.append(cls_index)\n",
        "        end_positions.append(cls_index)\n",
        "\n",
        "    encoding['image'] = examples['image']\n",
        "    encoding['start_positions'] = start_positions\n",
        "    encoding['end_positions'] = end_positions\n",
        "\n",
        "  except IndexError as index_error:\n",
        "        print(f\"IndexError: {index_error}\")\n",
        "        # Print additional information to debug the issue\n",
        "        print(\"Printing example for debugging:\")\n",
        "        print(examples[0])  # Print the example at batch index 0\n",
        "        print(\"Printing variables for debugging:\")\n",
        "        print(\"questions:\", questions)\n",
        "        print(\"words:\", words)\n",
        "        print(\"boxes:\", boxes)\n",
        "        print(\"answers:\", answers)\n",
        "        print(\"batch_index:\", batch_index)\n",
        "        print(\"cls_index:\", cls_index)\n",
        "        print(\"words_example:\", words_example)\n",
        "        print(\"answer:\", answer)\n",
        "        print(\"match, word_idx_start, word_idx_end:\", match, word_idx_start, word_idx_end)\n",
        "        pass\n",
        "  except ValueError as value_error:\n",
        "      print(f\"ValueError: {value_error}\")\n",
        "      # Print additional information to debug the issue\n",
        "      print(\"Printing example for debugging:\")\n",
        "      print(examples[0])  # Print the example at batch index 0\n",
        "      pass\n",
        "  except Exception as e:\n",
        "      print(f\"Error processing example: {e}\")\n",
        "      # You can choose to handle the error in a specific way or simply skip the problematic example\n",
        "      pass\n",
        "\n",
        "\n",
        "  return encoding"
      ],
      "metadata": {
        "id": "16jHT1G6jejR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Performing Encoding"
      ],
      "metadata": {
        "id": "ktVMwHX30Wtu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = Features({\n",
        "    'input_ids': Sequence(feature=Value(dtype='int64')),\n",
        "    'bbox': Array2D(dtype=\"int64\", shape=(512, 4)),\n",
        "    'attention_mask': Sequence(Value(dtype='int64')),\n",
        "    'token_type_ids': Sequence(Value(dtype='int64')),\n",
        "    'image': Array3D(dtype=\"int64\", shape=(3, 224, 224)),\n",
        "    'start_positions': Value(dtype='int64'),\n",
        "    'end_positions': Value(dtype='int64'),\n",
        "})\n",
        "\n",
        "encoded_dataset = dataset_with_ocr.map(encode_dataset, batched=True, batch_size=2,\n",
        "                                       remove_columns=dataset_with_ocr.column_names,\n",
        "                                       features=features)"
      ],
      "metadata": {
        "id": "e1hwSq0o0H20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "36fa4c89f2034955b65663a29cbcf85b",
            "a42feeba6f1b4d4581915a575b590f10",
            "64166750beb142caa1e0015d26cf28e6",
            "10914227e7dc4bbeaf6c81b794fc6c72",
            "b69c7d7792a3460488e8d6f1f032cfbc",
            "9cb10a32ef4f493fabb32f633d0a3b35",
            "2cd5d02493ff4669930e3e217c61b916",
            "3eac43cd7ee3434491a8fdc30a20afc0",
            "58729c80473c4048b9796fc69b84e74a",
            "896b0515a6404914b5e1efb8a4a2cf5f",
            "f0d71d00c37e41c5a0aab05a9867f9d7"
          ]
        },
        "outputId": "70a867ea-be4c-4e39-db53-7f93541eedbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/50 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36fa4c89f2034955b65663a29cbcf85b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Batch index: 0\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 1\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: itc limited\n",
            "Reconstructed answer: itc limited\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: san diego\n",
            "Reconstructed answer: san diego\n",
            "-----------\n",
            "Batch index: 0\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 1128 sixteenth st., N. W., washington, D. C. 20036\n",
            "Reconstructed answer: 1128 sixteenth st., n. w., washington, d. c. 20036\n",
            "-----------\n",
            "Batch index: 0\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: The Robert A. Welch Foundation\n",
            "Reconstructed answer: the robert a. welch foundation\n",
            "-----------\n",
            "Batch index: 0\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 1\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: TRRF Vice President\n",
            "Reconstructed answer: trrf vice president\n",
            "-----------\n",
            "Batch index: 1\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 0\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 2\n",
            "Reconstructed answer: 2\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: 12:25 to 12:58 p.m.\n",
            "Reconstructed answer: 12 : 25 to 12 : 58 p. m.\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 2\n",
            "Reconstructed answer: 2\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: 6\n",
            "Reconstructed answer: 6\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 6\n",
            "Reconstructed answer: 6\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: trrf scientific advisory council meeting\n",
            "Reconstructed answer: trrf scientific advisory council meeting\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 3\n",
            "Reconstructed answer: 3\n",
            "-----------\n",
            "Batch index: 0\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: chennai\n",
            "Reconstructed answer: chennai\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: 79.23%\n",
            "Reconstructed answer: 79. 23 %\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 11:39 to 11:44 a.m.\n",
            "Reconstructed answer: 11 : 39 to 11 : 44 a. m.\n",
            "-----------\n",
            "Batch index: 0\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: (82.85%)\n",
            "Reconstructed answer: ( 82. 85 % )\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: 20.77%\n",
            "Reconstructed answer: 20. 77 %\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: (17.15%)\n",
            "Reconstructed answer: ( 17. 15 % )\n",
            "-----------\n",
            "Batch index: 0\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 31\n",
            "Reconstructed answer: 31\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: ITC Limited\n",
            "Reconstructed answer: itc limited\n",
            "-----------\n",
            "Batch index: 1\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: 2\n",
            "Reconstructed answer: 2\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 22\n",
            "Reconstructed answer: 2\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: y. c. deveshwar\n",
            "Reconstructed answer: y. c. deveshwar\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 10,000\n",
            "Reconstructed answer: 10, 00\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: 24,26,435\n",
            "Reconstructed answer: 24, 26, 435\n",
            "-----------\n",
            "Batch index: 1\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: 14,000\n",
            "Reconstructed answer: 14, 00\n",
            "-----------\n",
            "Batch index: 1\n",
            "Answer not found in context\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: ITC LIMITED\n",
            "Reconstructed answer: itc limited\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: The Environment\n",
            "Reconstructed answer: the environment\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: 2013\n",
            "Reconstructed answer: 2013\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: 50\n",
            "Reconstructed answer: 50\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: Essenza Di Wills, Fiama Di Wills, Vivel and Superia\n",
            "Reconstructed answer: essenza di wills, fiama di wills, vivel and superia\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: Classmate and Paperkraft\n",
            "Reconstructed answer: classmate and paperkraft\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: Wills Lifestyle and John Players\n",
            "Reconstructed answer: wills lifestyle and john players\n",
            "-----------\n",
            "Batch index: 1\n",
            "Verifying start position and end position:\n",
            "True answer: Mangaldeep\n",
            "Reconstructed answer: mangaldeep\n",
            "-----------\n",
            "Batch index: 0\n",
            "Verifying start position and end position:\n",
            "True answer: Aim\n",
            "Reconstructed answer: aim\n",
            "-----------\n",
            "Batch index: 1\n",
            "Answer not found in context\n",
            "-----------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_dataset"
      ],
      "metadata": {
        "id": "xGoFNOZo0fln",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "730a39c8-96f8-4cc6-ad19-619b38f3e8dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'image', 'start_positions', 'end_positions'],\n",
              "    num_rows: 50\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Examples"
      ],
      "metadata": {
        "id": "uhuUz6jC0pmF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "idx = 39\n",
        "\n",
        "tokenizer.decode(encoded_dataset['input_ids'][idx])"
      ],
      "metadata": {
        "id": "R95CFIpE0rcw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "13ac60c9-0025-4d0f-ec62-ec67e1319b53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[CLS] what percentage of non - smokers feel the need to restore romance and mystery to modern life? [SEP] emotional enhancement smokers show above average interest in romance and excitement feel the need to find more excitement and sensation in life feel the need to restore romance and mystery to modern life but are more materialistic than non - smokers doing enjoyable things means more to me than having a lot of prized possessions there should be less emphasis on money in our society the only meaningful measure of success is money rya712 / monitor / mg / pt 7886 9eets yankelovich monitor 1990 non - smokers smokers % % [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image = Image.open('/content/drive/MyDrive/val/' + dataset['image'][idx])\n",
        "image"
      ],
      "metadata": {
        "id": "NalQU7Ui-C_B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 891
        },
        "outputId": "4629bc50-67e4-41c8-a611-b14189d49496"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=L size=2159x1695>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for the answer by getting the start and end positions of the answer to serach in our encoded dataset. The encoded dataset will have tokens which are decoding using tokenizer.decode if the answer is not there then printing answer not found in context"
      ],
      "metadata": {
        "id": "ZOCTC3kDh8Rr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_position = encoded_dataset['start_positions'][idx]\n",
        "end_position = encoded_dataset['end_positions'][idx]\n",
        "if start_position != 0:\n",
        "  print(tokenizer.decode(encoded_dataset['input_ids'][idx][start_position: end_position+1]))\n",
        "else:\n",
        "  print(\"Answer not found in context\")"
      ],
      "metadata": {
        "id": "_KGtzOcU-GSG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2597951-1a1f-4f36-eb82-e30878740bf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer not found in context\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(encoded_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Scfm9dSNlAof",
        "outputId": "4bbc3ffa-12f1-49b5-ae9d-fe78113c9bab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_dataset.features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "poXgDxQJlCK_",
        "outputId": "766bc3c9-09a7-492c-a612-3467ebbc0ada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None),\n",
              " 'bbox': Array2D(shape=(512, 4), dtype='int64', id=None),\n",
              " 'attention_mask': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None),\n",
              " 'token_type_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None),\n",
              " 'image': Array3D(shape=(3, 224, 224), dtype='int64', id=None),\n",
              " 'start_positions': Value(dtype='int64', id=None),\n",
              " 'end_positions': Value(dtype='int64', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating DataLoaders\n",
        "The encoded dataset is in format of huggingface dataset so for training purposes we need to convert it to pytorch tensor format"
      ],
      "metadata": {
        "id": "bGtblX1plonn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_dataset.set_format(type=\"torch\")\n",
        "dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=4)\n",
        "batch = next(iter(dataloader))\n",
        "\n",
        "for k,v in batch.items():\n",
        "  print(k, v.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_a1b-3jdlq_5",
        "outputId": "a5404706-50b0-48ac-84f2-2ac77846d571"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_ids torch.Size([4, 512])\n",
            "bbox torch.Size([4, 512, 4])\n",
            "attention_mask torch.Size([4, 512])\n",
            "token_type_ids torch.Size([4, 512])\n",
            "image torch.Size([4, 3, 224, 224])\n",
            "start_positions torch.Size([4])\n",
            "end_positions torch.Size([4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = 2\n",
        "\n",
        "tokenizer.decode(batch['input_ids'][2])#decoding the tensor input for the batch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "3hfMWHI3mQcP",
        "outputId": "87d50fbd-89cc-4db5-e475-11ea3b81bdc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"[CLS] what is the name of the company? [SEP] 7 itc limited report and accounts 2013 itc ’ s brands : an asset for the nation the consumer needs and aspirations they fulfil, the benefit they generate for millions across itc ’ s value chains, the future - ready capabilities that support them, and the value that they create for the country, have made itc ’ s brands national assets, adding to india ’ s competitiveness. it is itc ’ s aspiration to be the no 1 fmcg player in the country, driven by its new fmcg businesses. a recent nielsen report has highlighted that itc's new fmcg businesses are the fastest growing among the top consumer goods companies operating in india. itc takes justifiable pride that, along with generating economic value, these celebrated indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. di wills * ; love delightfully soft skin? aia ans source : https : / / www. industrydocuments. ucsf. edu / docs / snbx0223 [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_position = batch['start_positions'][idx].item()\n",
        "end_position = batch['end_positions'][idx].item()\n",
        "\n",
        "tokenizer.decode(batch['input_ids'][idx][start_position:end_position+1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "LZO46J7YmW2i",
        "outputId": "018c669e-4f49-4b23-c6b4-f5b2e0a08898"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'itc limited'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training our Model\n",
        "Here we are going to fine tune our layoutmv2 on the encoded format of our dataset"
      ],
      "metadata": {
        "id": "HHh8q-uwmkFJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Model"
      ],
      "metadata": {
        "id": "sP6XRqKBm8zO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "7091c6ea1f814899877c551fd2d4197a",
            "831f4b6004434981b03ac48fdf7b3188",
            "374cb1b2c93f4ef1938528e050ab608b",
            "6b514c61ff854145a7c33b2b70607338",
            "6036f14e10f44f5e806748c4469d8ad2",
            "df67680ce13b44d1812925b2c559e3bd",
            "092e8bca4b4e4d8ca80a371dfc33c495",
            "08ca29b0f93c4a6aa7b9590774554207",
            "4ae19fcced2e4f2082e98f0e7deed45e",
            "6042e3c898e44874afcd8ced238451bb",
            "440f43553f51424190175fa6a3dc87cd"
          ]
        },
        "id": "uyX2ueCZmtKj",
        "outputId": "99416cad-b63d-4a85-fb71-30bed3d75e1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/802M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7091c6ea1f814899877c551fd2d4197a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of LayoutLMv2ForQuestionAnswering were not initialized from the model checkpoint at microsoft/layoutlmv2-base-uncased and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight', 'layoutlmv2.visual_segment_embedding']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Loop"
      ],
      "metadata": {
        "id": "Zmo6_LZTm-vT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are using AdamW for optimization purposes"
      ],
      "metadata": {
        "id": "hA4HLK8EnHW0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-RsnaK4nCpn",
        "outputId": "019d97c7-2152-44b5-c663-10cbe7edca17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train(model,epochs):\n",
        "  model.to(device)\n",
        "  model.train()\n",
        "\n",
        "  for epoch in range(20):  # loop over the dataset multiple times\n",
        "   for idx, batch in enumerate(dataloader):\n",
        "    # get the inputs;\n",
        "    input_ids = batch[\"input_ids\"].to(device)\n",
        "    attention_mask = batch[\"attention_mask\"].to(device)\n",
        "    token_type_ids = batch[\"token_type_ids\"].to(device)\n",
        "    bbox = batch[\"bbox\"].to(device)\n",
        "    image = batch[\"image\"].to(device)\n",
        "    start_positions = batch[\"start_positions\"].to(device)\n",
        "    end_positions = batch[\"end_positions\"].to(device)\n",
        "\n",
        "    # zero the parameter gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # forward + backward + optimize\n",
        "    outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids,\n",
        "                    bbox=bbox, image=image, start_positions=start_positions, end_positions=end_positions)\n",
        "    loss = outputs.loss\n",
        "    print(\"Loss:\", loss.item())\n",
        "    loss.backward()\n",
        "    optimizer.step()\n"
      ],
      "metadata": {
        "id": "ZE2NV8ERnABT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train(model,epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ekLskUNnl_N",
        "outputId": "87d841a0-3449-4bcd-c1f3-e66ed8f21f4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 2.9742963314056396\n",
            "Loss: 3.228206157684326\n",
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            "Loss: 1.9718997478485107\n",
            "Loss: 2.582052230834961\n",
            "Loss: 2.635157823562622\n",
            "Loss: 2.5672786235809326\n",
            "Loss: 1.9267942905426025\n",
            "Loss: 1.5168859958648682\n",
            "Loss: 1.8945560455322266\n",
            "Loss: 1.559384822845459\n",
            "Loss: 2.0213656425476074\n",
            "Loss: 4.022928237915039\n",
            "Loss: 2.018477439880371\n",
            "Loss: 1.4034615755081177\n",
            "Loss: 1.4870572090148926\n",
            "Loss: 1.9144827127456665\n",
            "Loss: 2.181875228881836\n",
            "Loss: 2.338700771331787\n",
            "Loss: 2.310318946838379\n",
            "Loss: 1.824385166168213\n",
            "Loss: 1.3690271377563477\n",
            "Loss: 1.7496044635772705\n",
            "Loss: 1.56382417678833\n",
            "Loss: 1.9072144031524658\n",
            "Loss: 2.0336227416992188\n",
            "Loss: 2.1484081745147705\n",
            "Loss: 1.298945665359497\n",
            "Loss: 1.331430196762085\n",
            "Loss: 1.754461646080017\n",
            "Loss: 3.1770684719085693\n",
            "Loss: 3.0502514839172363\n",
            "Loss: 3.051088333129883\n",
            "Loss: 1.834599256515503\n",
            "Loss: 1.4057790040969849\n",
            "Loss: 2.0283725261688232\n",
            "Loss: 1.7419264316558838\n",
            "Loss: 2.033545732498169\n",
            "Loss: 2.1877756118774414\n",
            "Loss: 2.1328237056732178\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inferencing\n",
        "1. The LayoutLMv2 processor is being used for processing both text and image\n",
        "2. After Passing through model it will return the logits for start and end positions which can be used to get start_idx and end_idx by getting the argmax of the probablities\n",
        "3. Then decoding it in text format by slicing through the encoded ids from start to end index"
      ],
      "metadata": {
        "id": "XY9xPZj0sIjo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example = data['data'][20]\n",
        "root_dir = '/content/drive/MyDrive/val/'\n",
        "question = example['question']\n",
        "image = Image.open(root_dir + example['image']).convert(\"RGB\")\n",
        "print(question)\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lJELdGCssJv6",
        "outputId": "35a8c63e-159d-4850-e554-ace2624b31f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What is the name of the company?\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1679x2340>"
            ],
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\n"
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import LayoutLMv2Processor\n",
        "\n",
        "processor = LayoutLMv2Processor.from_pretrained(\"microsoft/layoutlmv2-base-uncased\")\n",
        "\n",
        "# prepare for the model\n",
        "encoding = processor(image, question, return_tensors=\"pt\")\n",
        "print(encoding.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "fbce740ff0a64a23b2bb1240e47a75a4",
            "f8c1e766d56e427aa370ecf179963ab9",
            "26aaba9f68bc4a39bf6c14eea694d1bb",
            "9cbdb633d35440dc9c66e76901ada0f1",
            "ad00bb495e524af5907a7f63a650db2d",
            "06f42786aca84ce588db51f97fa2aec5",
            "d81efa89d65d468393d1b41a397be88b",
            "7d28e92c7d9445e2af998b7d312864e8",
            "b757943e21cf4e818f487442d19e5568",
            "7acb86570dc6472fba872723a338dc29",
            "e8e11daae2b14e588b2ff77c709fa821"
          ]
        },
        "id": "yRsPzWhytWCl",
        "outputId": "78a4baca-07fe-49e6-aa9e-d3e83496dd24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/135 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbce740ff0a64a23b2bb1240e47a75a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'bbox', 'image'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(processor.tokenizer.decode(encoding.input_ids.squeeze()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VE1nUQ5IuF1C",
        "outputId": "003afd58-99ac-49da-e530-3135feb47d10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] what is the name of the company? [SEP]. “ cigfil limited, chennai schedules annexed to and forming part of balance sheet as at 31. 03. 2004 3 ) acetate filament yarn / fibre current year previous year 4 ) acetate tow current year previous year 5 ) poly propelene current year previous year d ) opening and closing stock of goods produced : 1 ) opening stock : a ) cigarette filter rods current year previous year b ) poly propelene woven fabrics / sacks current year previous year 2 ) closing stock : a ) cigarette filter rods current year previous year b ) poly propelene woven fabrics / sacks. current year previous year kgs rs. in lakhs 25, 471 | 37. 35 ( 12, 431 ) ( 27. 67 ) kgs rs. in lakhs 661, 328 | 1, 262. 58 ( 725, 722 ) ( 1519. 97 ) mts rs. inlakhs 102. 550 | 46. 88 ( 235. 949 ) ( 106. 84 ) inmnr _ _ _ rs. in lakhs 64. 232 ( 18. 7652 ) ( 13. 56 ) in kgs rs. in lakhs. nil nil nil inmnr _ _ rs. in lakhs 31. 5594 23. 97 ( 64. 232 ) ( 51. 46 ) in kgs _ _ rs. in lakhs | 3220 s | 3. 23 2863. 5 [ | 2. 87, e ) value of imported / indigenous raw materials, stores & spare parts and components consumed : 1 ) raw materials : imported : current year previous year indigenous : current year previous year percentage 79. 23 % | ( 82. 85 % ) 20. 77 % ( 17. 15 % ) rs. in lakhs 1, 304. 95 ( 1596. 85 ) 342. 04 ( 330. 44 ) [SEP]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for k,v in encoding.items():\n",
        "  encoding[k] = v.to(model.device)\n",
        "\n",
        "outputs = model(**encoding)"
      ],
      "metadata": {
        "id": "t9tlit0muGc4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# step 3: get start_logits and end_logits\n",
        "start_logits = outputs.start_logits\n",
        "end_logits = outputs.end_logits\n",
        "\n",
        "# step 4: get largest logit for both\n",
        "predicted_start_idx = start_logits.argmax(-1).item()\n",
        "predicted_end_idx = end_logits.argmax(-1).item()\n",
        "print(\"Predicted start idx:\", predicted_start_idx)\n",
        "print(\"Predicted end idx:\", predicted_end_idx)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQY_F3cKuLT3",
        "outputId": "82af8e5f-a3f0-42f1-9f54-686fbf0e5bee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted start idx: 353\n",
            "Predicted end idx: 356\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# step 5: decode the predicted answer\n",
        "processor.tokenizer.decode(encoding.input_ids.squeeze()[predicted_start_idx:predicted_end_idx+1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "_S7itMyWuO-0",
        "outputId": "9f0386e8-148a-41a7-e92e-87edb19e4d89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'79. 23 %'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see that our model is giving decent outputs even when trained only on a subset of a chunk of validation data with whole training data we can improve our performance much"
      ],
      "metadata": {
        "id": "iY9pUxEuvVgp"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CeRnVO40vP8U"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}