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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sid-betalol/Document-Classification-Models/blob/main/create_rvlcdip_subset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zV5fyoz7TI7C"
      },
      "outputs": [],
      "source": [
        "!pip install transformers[torch] -U\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoFeatureExtractor\n",
        "from PIL import Image\n",
        "import io\n",
        "from tqdm.auto import tqdm"
      ],
      "metadata": {
        "id": "MExv-42_TTEL"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k78QEivxTfIu",
        "outputId": "6cbb9227-ef97-4506-aac0-253b628191f9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e36oZrzZUDNF",
        "outputId": "f779da6f-57e6-4a07-c88e-3904408f356e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7a40d83523d0>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "task = 'rvl-cdip'\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(\"microsoft/dit-base\")\n",
        "num_labels = 16\n",
        "samples_per_class_train = 100\n",
        "samples_per_class_eval = 10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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        {
          "output_type": "display_data",
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            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/302 [00:00<?, ?B/s]"
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          "output_type": "stream",
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/beit/feature_extraction_beit.py:28: FutureWarning: The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use BeitImageProcessor instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/models/beit/image_processing_beit.py:110: FutureWarning: The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use `do_reduce_labels` instead.\n",
            "  warnings.warn(\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_and_save(dataset_name, split, feature_extractor, samples_per_class, num_labels, save_dir):\n",
        "    os.makedirs(save_dir, exist_ok=True)\n",
        "    # Adjusted to include trust_remote_code=True based on the warning\n",
        "    dataset_stream = load_dataset(dataset_name, split=split, streaming=True, trust_remote_code=True)\n",
        "    samples_collected = {label: 0 for label in range(num_labels)}\n",
        "\n",
        "    for example in tqdm(dataset_stream):\n",
        "        label = example['label']\n",
        "        if samples_collected[label] < samples_per_class:\n",
        "            samples_collected[label] += 1\n",
        "            # Check if the image is a bytes object or already an image object\n",
        "            if isinstance(example['image'], bytes):\n",
        "                # If it's bytes, convert it to an image\n",
        "                image = Image.open(io.BytesIO(example['image']))\n",
        "            else:\n",
        "                # If it's already an image, use it directly\n",
        "                image = example['image']\n",
        "\n",
        "            if image.mode != 'RGB':\n",
        "                image = image.convert('RGB')\n",
        "            pixel_values = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.squeeze()\n",
        "            torch.save((pixel_values, label), os.path.join(save_dir, f\"{split}_{label}_{samples_collected[label]}.pt\"))\n",
        "\n",
        "        if all(value == samples_per_class for value in samples_collected.values()):\n",
        "            break"
      ],
      "metadata": {
        "id": "1Kuo5HugTYwl"
      },
      "execution_count": 6,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "preprocess_and_save(\"rvl_cdip\", 'train', feature_extractor, samples_per_class_train, num_labels, f\"/content/drive/MyDrive/rvl-cdip-pp/DiT/{task}/train_data\")\n",
        "preprocess_and_save(\"rvl_cdip\", 'test', feature_extractor, samples_per_class_eval, num_labels, f\"/content/drive/MyDrive/rvl-cdip-pp/DiT/{task}/eval_data\")"
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