SberOCR_Test_VLM_v1.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/mwzkhalil/English-to-Urdu/blob/main/SberOCR_Test_VLM_v1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# UniChart fine-tuning on ChartQA dataset."
      ],
      "metadata": {
        "id": "_njgS5952rSO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction  \n",
        "\n",
        "This notebook demonstrates the process of fine-tuning the UniChart model on the ChartQA dataset.  \n",
        "\n",
        "**Objective:**  \n",
        "To solve the Chart Question Answering task using a Vision Language Model (VLM).  \n",
        "\n",
        "Links to the resources, as well as the authors of the model and dataset used in this work, will be provided at the end of the notebook.  "
      ],
      "metadata": {
        "id": "LC5OCBGI21xA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## VLM Structure  \n",
        "\n",
        "### **Vision Language Models (VLM) Architecture**  \n",
        "\n",
        "**1. Image Encoder:**  \n",
        "   - Encodes an image into a vector representation suitable for processing by a neural network. Typically, CNNs or transformer-based models are used.  \n",
        "\n",
        "**2. Text Encoder:**  \n",
        "   - Processes textual data and converts it into vector representations. This often involves transformer models like BERT or GPT.  \n",
        "\n",
        "**3. Multimodal Integrator:**  \n",
        "   - Combines the image and text representations into a unified vector space. This can be achieved through methods like concatenation, attention mechanisms, or other neural network strategies.  \n",
        "\n",
        "**4. Decoder:**  \n",
        "   - Generates text based on the multimodal representation. A transformer-based architecture is commonly used for decoding.  \n",
        "\n",
        "### **Training Vision Language Models**  \n",
        "\n",
        "**1. Pretraining:**  \n",
        "   - Models are pretrained on large datasets containing image-text pairs to learn fundamental representations. Examples include datasets with image captions.  \n",
        "\n",
        "**2. Fine-Tuning:**  \n",
        "   - Models are fine-tuned for specific tasks like image captioning, visual question answering (VQA), or text generation based on images. This step helps the model adapt to particular tasks and improve its performance.  \n",
        "\n",
        "### **Examples of Models**  \n",
        "\n",
        "**LLaVA:**  \n",
        "   - Uses CLIP as the image encoder and Vicuna as the text decoder. The model undergoes a question-answer generation phase using GPT-4 and is subsequently fine-tuned.  \n",
        "\n",
        "**KOSMOS-2:**  \n",
        "   - Trained end-to-end, integrating all components into a unified training process.  \n",
        "\n",
        "### **Key Training Steps**  \n",
        "\n",
        "1. **Data Collection:** Gather image-text pairs.  \n",
        "2. **Pretraining:** Train models on large-scale datasets.  \n",
        "3. **Fine-Tuning:** Fine-tune the model for specialized tasks.  "
      ],
      "metadata": {
        "id": "DpRR0spY-0Ln"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Description  \n",
        "\n",
        "**UniChart** is a Vision-Language Model (VLM) specifically trained for working with charts. Initially, it is capable of efficiently summarizing information from charts. However, after fine-tuning on the ChartQA dataset, it provides confident and accurate answers to straightforward questions about charts.  \n",
        "\n",
        "The model's architecture incorporates the following components:  \n",
        "- **Model:** `VisionEncoderDecoderModel`  \n",
        "- **Processor:** `DonutProcessor`  \n",
        "- **Config:** `BartConfig`  \n",
        "\n",
        "From this, the principle of how VLMs work becomes clear.  \n",
        "\n",
        "VLMs are models that can simultaneously learn from images and text to solve a wide range of tasks, from visual question answering to generating captions for images.  \n",
        "\n",
        "In a broad sense, VLMs (Vision-Language Models) are multimodal models capable of learning from both images and text. These are a type of generative models that take images and text as input and generate text as output.  \n",
        "\n",
        "Use cases include image-based chat discussions, image recognition via instructions, visual question answering, document understanding, image captioning, and more. These models can also output bounding boxes or segmentation masks when tasked with detecting or segmenting specific objects. Additionally, they can localize various objects or answer questions about their relative or absolute positions.\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "iIjew75Q5d9T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The pictures above show typical VLM tasks, as well as the typical structure of such a model."
      ],
      "metadata": {
        "id": "rTUT-us57qXK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model evaluation:\n",
        "\n",
        "Relevance: the model was developed specifically for this task and was trained on VQA datasets and parts/similars of ChartQA (as will be seen in the tests at the end)\n",
        "Size: 490Mb, 200M parameters\n",
        "\n",
        "**Ease of Fine-Tuning:** Given the straightforward data architecture, fine-tuning the model is relatively simple. The only significant drawback is that, despite its modest size of 200 million parameters by today's standards, the model utilized nearly all available RAM and 15 GB of GPU memory in Google Colab with just 100 training examples.  \n",
        "\n",
        "These limitations of Colab dictated the fine-tuning conditions in this notebook. However, I believe this is more than sufficient for understanding the pipeline and addressing the task at hand.\n",
        "\n",
        "P.S. Initially, an attempt was made to use the more lightweight ViLT, but UniChart is a more relevant model, and is also easier to train on ChartQA (since they are created for each other)\n"
      ],
      "metadata": {
        "id": "H16Ulbk578Ju"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset description\n",
        "\n",
        "The ChartQA dataset contains approximately 30,000 various types of charts with comprehensive annotations. In theory, this enables training much more complex models and ensembles, as it includes data on charts in .csv format, annotations with bounding boxes, and more.  \n",
        "\n",
        "However, for training UniChart, only question/answer pairs are required. Fine-tuning UniChart on the full dataset in Colab is both time-consuming and inefficient (an attempt was made, but it proved unsuccessful due to insufficient memory and resources).  \n",
        "\n",
        "Thus, a selection of 100 images was chosen for training, 10 for validation, and 100 for testing.\n",
        "\n",
        "The choice of dataset is obvious, since the model and dataset are ideal for each other, and it also contains unambiguous and simple answers to questions. In general, relevance does not need reinforcement."
      ],
      "metadata": {
        "id": "Du6V0rll5fz0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **A more detailed description of the model and dataset can be found on GitHub, links to repositories are listed at the end.**"
      ],
      "metadata": {
        "id": "T12aY4-V-Hmy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Solution"
      ],
      "metadata": {
        "id": "AQ92aA6F3VZw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Installing libraries"
      ],
      "metadata": {
        "id": "5F6Vmnn23Zfw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cjQXN0g9QpAG",
        "outputId": "39dfb4ba-ac58-47a0-8298-d0889c3ac1e7",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: tokenizers, xxhash, tensorboardX, lightning-utilities, fsspec, dill, multiprocess, transformers, torchmetrics, pytorch-lightning, datasets\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.20.3\n",
            "    Uninstalling tokenizers-0.20.3:\n",
            "      Successfully uninstalled tokenizers-0.20.3\n",
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            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.46.3\n",
            "    Uninstalling transformers-4.46.3:\n",
            "      Successfully uninstalled transformers-4.46.3\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",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n",
            "sentence-transformers 3.2.1 requires transformers<5.0.0,>=4.41.0, but you have transformers 4.28.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 lightning-utilities-0.11.9 multiprocess-0.70.16 pytorch-lightning-1.8.5 tensorboardX-2.6.2.2 tokenizers-0.13.3 torchmetrics-1.6.0 transformers-4.28.1 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers==4.28.1 datasets torch pytorch-lightning==1.8.5 sentencepiece"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Transferring the dataset to the Colab working environment"
      ],
      "metadata": {
        "id": "e4J7uPfw3dHP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "from IPython.display import clear_output\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "!cp /content/drive/MyDrive/ChartQA_Dataset.zip /content/\n",
        "!unzip /content/ChartQA_Dataset.zip\n",
        "# with zipfile.ZipFile('/content/ChartQA_Dataset.zip', 'r') as zip_ref:\n",
        "#     zip_ref.extractall('/content/')\n",
        "clear_output()"
      ],
      "metadata": {
        "id": "yx813_pDQ05M"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Initializing model components"
      ],
      "metadata": {
        "id": "Be1_kPdt3jNX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Dataset class"
      ],
      "metadata": {
        "id": "92f4CJCM3mFN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json, os\n",
        "import random\n",
        "from typing import Any, List, Tuple\n",
        "from PIL import Image\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from transformers import DonutProcessor\n",
        "from datasets import load_dataset, load_from_disk\n",
        "\n",
        "added_tokens = []\n",
        "\n",
        "class ChartQADataset(Dataset):\n",
        "    \"\"\"\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        dataset: str,\n",
        "        images_folder: str,\n",
        "        max_length: int,\n",
        "        processor : DonutProcessor = None,\n",
        "        split: str = \"train\",\n",
        "        ignore_id: int = -100,\n",
        "        prompt_end_token: str = None,\n",
        "        task_prefix: str = '<chartqa>',\n",
        "        sort_json_key: bool = True,\n",
        "    ):\n",
        "        super().__init__()\n",
        "\n",
        "        self.max_length = max_length\n",
        "        self.split = split\n",
        "        self.ignore_id = ignore_id\n",
        "\n",
        "        self.prompt_end_token = prompt_end_token\n",
        "        self.sort_json_key = sort_json_key\n",
        "        self.images_folder = images_folder\n",
        "\n",
        "\n",
        "        self.dataset = dataset\n",
        "        self.dataset_length = len(self.dataset)\n",
        "\n",
        "        self.processor = processor\n",
        "        self.prompt_end_token_id = self.processor.tokenizer.convert_tokens_to_ids(self.prompt_end_token)\n",
        "        self.task_prefix = task_prefix\n",
        "\n",
        "\n",
        "    def __len__(self) -> int:\n",
        "        return self.dataset_length\n",
        "\n",
        "    def __getitem__(self, idx: int):\n",
        "\n",
        "        sample = self.dataset[idx]\n",
        "\n",
        "        # input_tensor\n",
        "        img_path = os.path.join(self.images_folder, sample['imgname'])\n",
        "        img = Image.open(img_path)\n",
        "        pixel_values = self.processor(img.convert(\"RGB\"), random_padding=self.split == \"train\", return_tensors=\"pt\").pixel_values\n",
        "        input_tensor = pixel_values.squeeze()\n",
        "\n",
        "        # input_ids\n",
        "        processed_parse = self.task_prefix + \" \" + sample['query'] + \" \" + self.prompt_end_token + \" \" + sample['label'] + self.processor.tokenizer.eos_token\n",
        "        input_ids = self.processor.tokenizer(\n",
        "            processed_parse,\n",
        "            add_special_tokens=False,\n",
        "            max_length=self.max_length,\n",
        "            padding=\"max_length\",\n",
        "            truncation=True,\n",
        "            return_tensors=\"pt\",\n",
        "        )[\"input_ids\"].squeeze(0)\n",
        "\n",
        "        if self.split == \"train\":\n",
        "            labels = input_ids.clone()\n",
        "            labels[\n",
        "                labels == self.processor.tokenizer.pad_token_id\n",
        "            ] = self.ignore_id  # model doesn't need to predict pad token\n",
        "            labels[\n",
        "                : torch.nonzero(labels == self.prompt_end_token_id).sum() + 1\n",
        "            ] = self.ignore_id  # model doesn't need to predict prompt\n",
        "            return input_tensor, input_ids, labels\n",
        "        else:\n",
        "            prompt_end_index = torch.nonzero(\n",
        "                input_ids == self.prompt_end_token_id\n",
        "            ).sum()  # return prompt end index instead of target output labels\n",
        "            return input_tensor, input_ids, prompt_end_index, processed_parse"
      ],
      "metadata": {
        "id": "7Ae2jvFMQ4M-"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Model class"
      ],
      "metadata": {
        "id": "eS2cU4f_3qNx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "import re\n",
        "from nltk import edit_distance\n",
        "import numpy as np\n",
        "import math, os\n",
        "\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "from torch.optim.lr_scheduler import LambdaLR\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.utilities import rank_zero_only\n",
        "\n",
        "\n",
        "class ChartQAModule(pl.LightningModule):\n",
        "    def __init__(self, config, processor, model, args, train_dataset, val_dataset):\n",
        "        super().__init__()\n",
        "        self.config = config\n",
        "        self.processor = processor\n",
        "        self.model = model\n",
        "        self.train_dataset = train_dataset\n",
        "        self.val_dataset = val_dataset\n",
        "        self.args=args\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        pixel_values, decoder_input_ids, labels = batch\n",
        "\n",
        "        outputs = self.model(pixel_values,\n",
        "                             decoder_input_ids=decoder_input_ids[:, :-1],\n",
        "                             labels=labels[:, 1:])\n",
        "        loss = outputs.loss\n",
        "        self.log_dict({\"train_loss\": loss}, sync_dist=True)\n",
        "        return loss\n",
        "\n",
        "    def compute_metric(self, gt, pred):\n",
        "      try:\n",
        "        gt = float(gt)\n",
        "        pred = float(pred)\n",
        "        return abs(gt - pred) / abs(gt) <= 0.05\n",
        "      except:\n",
        "        return str(gt).lower() == str(pred).lower()\n",
        "\n",
        "    def validation_step(self, batch, batch_idx, dataset_idx=0):\n",
        "        pixel_values, decoder_input_ids, prompt_end_idxs, answers = batch\n",
        "        decoder_prompts = pad_sequence(\n",
        "            [input_id[: end_idx + 1] for input_id, end_idx in zip(decoder_input_ids, prompt_end_idxs)],\n",
        "            batch_first=True,\n",
        "        )\n",
        "\n",
        "        outputs = self.model.generate(pixel_values,\n",
        "                                   decoder_input_ids=decoder_prompts,\n",
        "                                   max_length=self.args.max_length,\n",
        "                                   early_stopping=True,\n",
        "                                   pad_token_id=self.processor.tokenizer.pad_token_id,\n",
        "                                   eos_token_id=self.processor.tokenizer.eos_token_id,\n",
        "                                   use_cache=True,\n",
        "                                   num_beams=4,\n",
        "                                   bad_words_ids=[[self.processor.tokenizer.unk_token_id]],\n",
        "                                   return_dict_in_generate=True,)\n",
        "\n",
        "        predictions = []\n",
        "        for seq in self.processor.tokenizer.batch_decode(outputs.sequences):\n",
        "            seq = seq.replace(self.processor.tokenizer.eos_token, \"\").replace(self.processor.tokenizer.pad_token, \"\")\n",
        "            predictions.append(seq)\n",
        "\n",
        "        scores = list()\n",
        "        for pred, answer in zip(predictions, answers):\n",
        "            pred = pred.split(\"<s_answer>\")[1]\n",
        "            pred = pred.replace(self.processor.tokenizer.eos_token, \"\").replace(\"<s>\", \"\").strip(' ')\n",
        "            answer = answer.split(\"<s_answer>\")[1]\n",
        "            answer = answer.replace(self.processor.tokenizer.eos_token, \"\").strip(' ')\n",
        "            if self.compute_metric(answer, pred):\n",
        "              scores.append(1)\n",
        "            else:\n",
        "              scores.append(0)\n",
        "\n",
        "        return scores\n",
        "\n",
        "    def validation_epoch_end(self, validation_step_outputs):\n",
        "        # I set this to 1 manually\n",
        "        # (previously set to len(self.config.dataset_name_or_paths))\n",
        "        num_of_loaders = 1\n",
        "        if num_of_loaders == 1:\n",
        "            validation_step_outputs = [validation_step_outputs]\n",
        "        assert len(validation_step_outputs) == num_of_loaders\n",
        "        cnt = [0] * num_of_loaders\n",
        "        total_metric = [0] * num_of_loaders\n",
        "        val_metric = [0] * num_of_loaders\n",
        "        for i, results in enumerate(validation_step_outputs):\n",
        "            for scores in results:\n",
        "                cnt[i] += len(scores)\n",
        "                total_metric[i] += np.sum(scores)\n",
        "            val_metric[i] = total_metric[i] / cnt[i]\n",
        "            val_metric_name = f\"val_metric_{i}th_dataset\"\n",
        "            self.log_dict({val_metric_name: val_metric[i]}, sync_dist=True)\n",
        "        self.log_dict({\"val_metric\": np.sum(total_metric) / np.sum(cnt)}, sync_dist=True)\n",
        "        print(\"Epoch:\", str(self.current_epoch), \"Step:\", str(self.global_step), \"Validation Metric:\", str(np.sum(total_metric) / np.sum(cnt)))\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "\n",
        "        max_iter = None\n",
        "\n",
        "        if int(self.config.get(\"max_epochs\", -1)) > 0:\n",
        "            assert len(self.config.get(\"train_batch_sizes\")) == 1, \"Set max_epochs only if the number of datasets is 1\"\n",
        "            max_iter = (self.config.get(\"max_epochs\") * self.config.get(\"num_training_samples_per_epoch\")) / (\n",
        "                self.config.get(\"train_batch_sizes\")[0] * torch.cuda.device_count() * self.config.get(\"num_nodes\", 1)\n",
        "            )\n",
        "\n",
        "        if int(self.config.get(\"max_steps\", -1)) > 0:\n",
        "            max_iter = min(self.config.get(\"max_steps\"), max_iter) if max_iter is not None else self.config.get(\"max_steps\")\n",
        "\n",
        "        assert max_iter is not None\n",
        "        optimizer = torch.optim.Adam(self.parameters(), lr=self.config.get(\"lr\"))\n",
        "        scheduler = {\n",
        "            \"scheduler\": self.cosine_scheduler(optimizer, max_iter, self.config.get(\"warmup_steps\")),\n",
        "            \"name\": \"learning_rate\",\n",
        "            \"interval\": \"step\",\n",
        "        }\n",
        "        return [optimizer], [scheduler]\n",
        "\n",
        "    @staticmethod\n",
        "    def cosine_scheduler(optimizer, training_steps, warmup_steps):\n",
        "        def lr_lambda(current_step):\n",
        "            if current_step < warmup_steps:\n",
        "                return current_step / max(1, warmup_steps)\n",
        "            progress = current_step - warmup_steps\n",
        "            progress /= max(1, training_steps - warmup_steps)\n",
        "            return max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))\n",
        "\n",
        "        return LambdaLR(optimizer, lr_lambda)\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader(self.train_dataset, batch_size=self.args.batch_size, shuffle=True, num_workers=self.args.num_workers)\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader(self.val_dataset, batch_size=self.args.valid_batch_size, shuffle=False, num_workers=self.args.num_workers)\n",
        "\n",
        "    @rank_zero_only\n",
        "    def on_save_checkpoint(self, checkpoint):\n",
        "        save_path = os.path.join(self.config['result_path'], 'chartqa-checkpoint-epoch='+str(self.current_epoch)+'-'+str(self.global_step))\n",
        "        self.model.save_pretrained(save_path)\n",
        "        self.processor.save_pretrained(save_path)"
      ],
      "metadata": {
        "id": "Rrp2zaIHQ6gi"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Obtaining data for additional training"
      ],
      "metadata": {
        "id": "0aZMbuRQ3vK-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path1 = \"/content/ChartQA Dataset/train/train_augmented.json\"\n",
        "path2 = \"/content/ChartQA Dataset/train/train_human.json\"\n",
        "path3 = \"/content/ChartQA Dataset/val/val_human.json\"\n",
        "\n",
        "with open(path1) as f:\n",
        "    info_train_augmented = json.load(f)\n",
        "\n",
        "with open(path2) as f:\n",
        "    info_train_human = json.load(f)\n",
        "\n",
        "with open(path3) as f:\n",
        "    info_val_human = json.load(f)\n",
        "\n",
        "print(f\"Number of augmented train question/answer files: {len(info_train_augmented)}\")\n",
        "print(f\"Number of human train question/answer files: {len(info_train_human)}\")\n",
        "print(f\"Number of human val question/answer files: {len(info_val_human)}\")\n"
      ],
      "metadata": {
        "id": "b-NGXtkiVj3V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "e7c50cd9-c810-41a3-fbf7-67c783764c1a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/content/ChartQA Dataset/train/train_augmented.json'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-0ec408f2d6d6>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpath3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/ChartQA Dataset/val/val_human.json\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0minfo_train_augmented\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/ChartQA Dataset/train/train_augmented.json'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Basic model/training code"
      ],
      "metadata": {
        "id": "mN6YfZob30No"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import VisionEncoderDecoderConfig\n",
        "from transformers import DonutProcessor, VisionEncoderDecoderModel, BartConfig\n",
        "import argparse\n",
        "from torch.utils.data import DataLoader\n",
        "from typing import List\n",
        "from datasets import load_dataset\n",
        "\n",
        "# from data.chartqa_data import ChartQADataset\n",
        "# from model.chartqa_model import ChartQAModule\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "\n",
        "#from pytorch_lightning.loggers import WandbLogger\n",
        "#from pytorch_lightning.callbacks import LearningRateMonitor\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint\n",
        "\n",
        "\n",
        "\n",
        "def main():\n",
        "  # Instantiate the parser\n",
        "  parser = argparse.ArgumentParser(description='Train Chart Transformer')\n",
        "  parser.add_argument('--data-path', type=str, default = \"ahmed-masry/chartqa_without_images\", help='Path to the data file')\n",
        "  parser.add_argument('--train-images', type=str, default='/content/ChartQA/ChartQA Dataset/train/png/', help='Path to the training images')\n",
        "  parser.add_argument('--valid-images', type=str, default='/content/ChartQA/ChartQA Dataset/val/png', help='Path to the validation images')\n",
        "\n",
        "  parser.add_argument('--output-dir', type=str, default=\"/content/output_data\", help='Path to the output directory for saving the checkpoints')\n",
        "  parser.add_argument('--max-steps', type=int, default = 1000, help='Max number of iterations')\n",
        "  parser.add_argument('--batch-size', type=int, default=2, help='Batch Size for the model')\n",
        "  parser.add_argument('--valid-batch-size', type=int, default=2, help='Valid Batch Size for the model')\n",
        "  parser.add_argument('--max-length', type=int, default=512, help='Max length for decoder generation')\n",
        "  parser.add_argument('--num-workers', type=int, default=2, help='Number of workers')\n",
        "  parser.add_argument('--lr', type=float, default=5e-5, help='learning rate')\n",
        "\n",
        "  parser.add_argument('--check-val-every-n-epoch', type=int, default=1, help='Ru validation every n epochs')\n",
        "  parser.add_argument('--log-every-n-steps', type=int, default=50, help='Log every n steps')\n",
        "  parser.add_argument('--warmup-steps', type=int, default=50, help='Warmup steps')\n",
        "  parser.add_argument('--checkpoint-steps', type=int, default=1000, help='Checkpoint steps')\n",
        "  parser.add_argument('--gradient-clip-val', type=float, default=1.0, help='gradient clip value')\n",
        "\n",
        "  parser.add_argument('--accumulate-grad-batches', type=int, default=1, help='accumulate grad batches')\n",
        "  parser.add_argument('--gpus-num', type=int, default=1, help='gpus num')\n",
        "  parser.add_argument('--nodes-num', type=int, default=1, help='nodes num')\n",
        "\n",
        "  parser.add_argument('--checkpoint-path', type=str, default = \"ahmed-masry/unichart-base-960\", help='Path to the checkpoint')\n",
        "\n",
        "  args = parser.parse_args()\n",
        "\n",
        "  processor = DonutProcessor.from_pretrained(args.checkpoint_path)\n",
        "  model = VisionEncoderDecoderModel.from_pretrained(args.checkpoint_path)\n",
        "\n",
        "#   dataset = load_dataset(args.data_path)\n",
        "\n",
        "  train = info_train_human[:100]\n",
        "  val = info_val_human[:10]\n",
        "\n",
        "  train_dataset = ChartQADataset(train, images_folder = args.train_images, processor = processor, max_length=args.max_length,\n",
        "                              split=\"train\", prompt_end_token=\"<s_answer>\", task_prefix = \"<chartqa>\"\n",
        "                              )\n",
        "\n",
        "  val_dataset = ChartQADataset(val, images_folder = args.valid_images, processor = processor, max_length=args.max_length,\n",
        "                              split=\"valid\", prompt_end_token=\"<s_answer>\", task_prefix = \"<chartqa>\"\n",
        "                              )\n",
        "\n",
        "\n",
        "  config = {\"max_steps\":args.max_steps,\n",
        "            \"check_val_every_n_epoch\":args.check_val_every_n_epoch,\n",
        "            \"log_every_n_steps\":args.log_every_n_steps,\n",
        "            \"gradient_clip_val\":args.gradient_clip_val,\n",
        "            \"num_training_samples_per_epoch\": len(train),\n",
        "            \"lr\":args.lr,\n",
        "            \"train_batch_sizes\": [args.batch_size],\n",
        "            \"val_batch_sizes\": [args.valid_batch_size],\n",
        "            \"num_nodes\": args.nodes_num,\n",
        "            \"warmup_steps\": args.warmup_steps,\n",
        "            \"result_path\": args.output_dir,\n",
        "            \"verbose\": True,\n",
        "          }\n",
        "\n",
        "  model_module = ChartQAModule(config, processor, model, args, train_dataset, val_dataset)\n",
        "\n",
        "  # wandb_logger = WandbLogger(project=\"UniChart-ChartQA\")\n",
        "  # lr_callback = LearningRateMonitor(logging_interval=\"step\")\n",
        "  checkpoint_callback = ModelCheckpoint(dirpath=args.output_dir, every_n_train_steps = args.checkpoint_steps, save_last = True, save_top_k = -1)\n",
        "\n",
        "  trainer = pl.Trainer(\n",
        "        accelerator=\"gpu\",\n",
        "        devices=1,\n",
        "        max_steps=args.max_steps,\n",
        "        check_val_every_n_epoch=args.check_val_every_n_epoch,\n",
        "        # val_check_interval=100,\n",
        "        log_every_n_steps=args.log_every_n_steps,\n",
        "        gradient_clip_val=args.gradient_clip_val,\n",
        "\n",
        "        num_nodes=args.nodes_num,\n",
        "        precision=16, # we'll use mixed precision\n",
        "        num_sanity_val_steps=0,\n",
        "        #enable_checkpointing=True,\n",
        "        default_root_dir=args.output_dir,\n",
        "        # logger=wandb_logger,\n",
        "        callbacks=[checkpoint_callback],\n",
        "  )\n",
        "\n",
        "  trainer.fit(model_module)"
      ],
      "metadata": {
        "id": "Vn8G9W5kQ-hY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Initialization of the additional training process"
      ],
      "metadata": {
        "id": "etmSZyUD34kg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Simulate command-line arguments in a Colab cell\n",
        "import sys\n",
        "args = [\n",
        "    '--data-path', 'ahmed-masry/chartqa_without_images',\n",
        "    '--train-images', '/content/ChartQA Dataset/train/png/',\n",
        "    '--valid-images', '/content/ChartQA Dataset/val/png',\n",
        "    '--max-steps', '1000',\n",
        "    '--batch-size', '2',\n",
        "    '--valid-batch-size', '2',\n",
        "    '--num-workers', '2',\n",
        "    '--lr', '5e-5',\n",
        "    '--check-val-every-n-epoch', '1',\n",
        "    '--warmup-steps', '50',\n",
        "    '--checkpoint-steps', '1000',\n",
        "    '--checkpoint-path', 'ahmed-masry/unichart-base-960'\n",
        "]\n",
        "\n",
        "# Set sys.argv to simulate command-line arguments\n",
        "sys.argv = [''] + args\n",
        "\n",
        "# Run the main function\n",
        "main()"
      ],
      "metadata": {
        "id": "rVQEVSCfT1tk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "outputId": "38ea0b9e-f146-432b-82fd-4033393ced2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:ModelCheckpoint(save_last=True, save_top_k=-1, monitor=None) will duplicate the last checkpoint saved.\n",
            "INFO:pytorch_lightning.utilities.rank_zero:Using 16bit native Automatic Mixed Precision (AMP)\n",
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:604: UserWarning: Checkpoint directory /content/output_data exists and is not empty.\n",
            "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name  | Type                      | Params\n",
            "----------------------------------------------------\n",
            "0 | model | VisionEncoderDecoderModel | 201 M \n",
            "----------------------------------------------------\n",
            "201 M     Trainable params\n",
            "0         Non-trainable params\n",
            "201 M     Total params\n",
            "403.716   Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ab3e79c70484da4bc97ebacdea9caa0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e28c201dc884eeea5aa05813440c231"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0 Step: 50 Validation Metric: 0.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "831f9af5256445c1a7acb025ddd3e85e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1 Step: 100 Validation Metric: 0.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa377066568d4a829de71ddad2925db3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 2 Step: 150 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9deee6972f524aacbf475ff578c479ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 3 Step: 200 Validation Metric: 0.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f81b6fe557142b1b06e2445288514e2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 4 Step: 250 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f39a081686d471a9a401d8a98076aab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 5 Step: 300 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "22c412b5e1d843eaac4ada6ae12517b1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 6 Step: 350 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Exception ignored in: Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7846e00e81f0>\n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7846e00e81f0>Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    \n",
            "self._shutdown_workers()\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "if w.is_alive():    \n",
            "self._shutdown_workers()  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "\n",
            "      File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "assert self._parent_pid == os.getpid(), 'can only test a child process'    \n",
            "if w.is_alive():AssertionError\n",
            ": can only test a child process  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7846e00e81f0>\n",
            "\n",
            "AssertionErrorTraceback (most recent call last):\n",
            ": can only test a child process  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "\n",
            "    Exception ignored in: self._shutdown_workers()\n",
            "<function _MultiProcessingDataLoaderIter.__del__ at 0x7846e00e81f0>\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    Traceback (most recent call last):\n",
            "if w.is_alive():  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "\n",
            "      File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "self._shutdown_workers()    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "\n",
            "    AssertionErrorif w.is_alive():\n",
            ": can only test a child process\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cdcdab06bf124e918b0f783b31432f4e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 7 Step: 400 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "48481879accd42f193172852eba1351f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 8 Step: 450 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2bb83f18273e4a73805ed9604ddf7e00"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 9 Step: 500 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0355534bcd0e451787847c197bbf3283"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 10 Step: 550 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf8f12dae9b14bcc91b5586b99d2e289"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 11 Step: 600 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91e87a8ede1c4560a3589d34d51929a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 12 Step: 650 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "828abf7ae7a544779e0519e92f130c7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 13 Step: 700 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3097bb05fa6d4578a90313f5300e7149"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 14 Step: 750 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ca0e7bdb5b24410fbd0dc6caa3f583d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 15 Step: 800 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "c4531a6a3f064830a1607deb86bd6322"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 16 Step: 850 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffe3035c80b04564aa5d88b1fafa56c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 17 Step: 900 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00e37256787d4c8288fad49d60e9e245"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 18 Step: 950 Validation Metric: 0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6121d92ffb344f08a1d4609921a81c99"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_steps=1000` reached.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 19 Step: 1000 Validation Metric: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r /content/output_data/chartqa-checkpoint-epoch=19-1000 /content/drive/MyDrive/\n"
      ],
      "metadata": {
        "id": "i97OT3GJUUIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r /content/output_data/epoch=19-step=1000.ckpt /content/drive/MyDrive/\n"
      ],
      "metadata": {
        "id": "6Y23-In4UUF3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## First works of beautiful models (in the box xD)"
      ],
      "metadata": {
        "id": "RUhokpIm4ImC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DonutProcessor, VisionEncoderDecoderModel\n",
        "from PIL import Image\n",
        "import torch, os, re\n",
        "\n",
        "torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/multi_col_1229.png', 'chart_example_1.png')\n",
        "\n",
        "model_name = \"ahmed-masry/unichart-chartqa-960\"\n",
        "image_path = \"/content/chart_example_1.png\"\n",
        "input_prompt = \"<chartqa> What is the lowest value in blue bar? <s_answer>\"\n",
        "\n",
        "model = VisionEncoderDecoderModel.from_pretrained(\"/content/output_data/chartqa-checkpoint-epoch=19-1000\")\n",
        "processor = DonutProcessor.from_pretrained(\"/content/output_data/chartqa-checkpoint-epoch=19-1000\")\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "image = Image.open(image_path).convert(\"RGB\")\n",
        "decoder_input_ids = processor.tokenizer(input_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
        "pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "outputs = model.generate(\n",
        "    pixel_values.to(device),\n",
        "    decoder_input_ids=decoder_input_ids.to(device),\n",
        "    max_length=model.decoder.config.max_position_embeddings,\n",
        "    early_stopping=True,\n",
        "    pad_token_id=processor.tokenizer.pad_token_id,\n",
        "    eos_token_id=processor.tokenizer.eos_token_id,\n",
        "    use_cache=True,\n",
        "    num_beams=4,\n",
        "    bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
        "    return_dict_in_generate=True,\n",
        ")\n",
        "sequence = processor.batch_decode(outputs.sequences)[0]\n",
        "sequence = sequence.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n",
        "sequence = sequence.split(\"<s_answer>\")[1].strip()\n",
        "print(sequence)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAi_WVOnqe6F",
        "outputId": "7b80ea0a-2afb-4a13-9825-c0eca7412995"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 34.2k/34.2k [00:00<00:00, 57.9MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "eoTVIzMUrPAw",
        "outputId": "b6c21ce6-d46f-4632-d378-948c3fdf2a71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=800x557>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, the model gave the correct answer (this is not surprising, because this image was most likely used to train its pretrain)"
      ],
      "metadata": {
        "id": "JZ5pt3tK4Rki"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Auxiliary model initializations"
      ],
      "metadata": {
        "id": "332_ovI94a3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Дообученная модель\n",
        "# model_name = \"/content/output_data/chartqa-checkpoint-epoch=19-1000\"\n",
        "# model = VisionEncoderDecoderModel.from_pretrained(model_name)\n",
        "# processor = DonutProcessor.from_pretrained(model_name)\n",
        "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "# model.to(device)\n",
        "\n",
        "# # Изначальная модель\n",
        "# model_name = model_name = \"ahmed-masry/unichart-chartqa-960\"\n",
        "# model = VisionEncoderDecoderModel.from_pretrained(model_name)\n",
        "# processor = DonutProcessor.from_pretrained(model_name)\n",
        "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "# model.to(device)"
      ],
      "metadata": {
        "id": "L5wLnQ7cqe0m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing different versions of the model: pretrain, additionally trained by me. and a trained developer on 8 GPUs and a full dataset."
      ],
      "metadata": {
        "id": "b724XB_P4fcV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### We create a test dataset to test the pre-trained and post-trained models. We get the metrics on the train from the same ones that we used for training."
      ],
      "metadata": {
        "id": "5DxyQ6fgrf7z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "test_dataset = []\n",
        "\n",
        "with open(\"/content/ChartQA Dataset/test/test_human.json\") as f:\n",
        "    test_human = json.load(f)\n",
        "\n",
        "with open(\"/content/ChartQA Dataset/test/test_augmented.json\") as f:\n",
        "    test_augmented = json.load(f)\n",
        "\n",
        "print(len(test_augmented))\n",
        "\n",
        "unique_set = set()\n",
        "\n",
        "for q in test_augmented:\n",
        "    if q[\"imgname\"] in unique_set:\n",
        "        pass\n",
        "    else:\n",
        "        unique_set.add(q[\"imgname\"])\n",
        "\n",
        "unique_list = list(unique_set)\n",
        "\n",
        "print(len(unique_list))\n",
        "\n",
        "print(unique_list[:5])\n",
        "\n",
        "for img in unique_list:\n",
        "    for i in test_augmented:\n",
        "        if img == i[\"imgname\"]:\n",
        "            test_dataset.append((i[\"imgname\"], i[\"query\"], i[\"label\"]))\n",
        "\n",
        "print(len(test_dataset))\n",
        "\n",
        "# for i in test_augmented[:100]:\n",
        "#     print(i[\"label\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DJ6QqcErfm-",
        "outputId": "ade75b3d-ac7d-46b7-e332-e676f1cd23fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1250\n",
            "987\n",
            "['two_col_83300.png', 'two_col_4671.png', 'multi_col_1272.png', 'two_col_42876.png', 'two_col_83331.png']\n",
            "1250\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Initial model\n",
        "\n",
        "#### Accuracy on the train dataset"
      ],
      "metadata": {
        "id": "sH-qnKXSzYf3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = model_name = \"ahmed-masry/unichart-base-960\"\n",
        "model = VisionEncoderDecoderModel.from_pretrained(model_name)\n",
        "processor = DonutProcessor.from_pretrained(model_name)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Sa6it1ET0Amx",
        "outputId": "3347f065-8c2e-466d-da51-685407d0804d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VisionEncoderDecoderModel(\n",
              "  (encoder): DonutSwinModel(\n",
              "    (embeddings): DonutSwinEmbeddings(\n",
              "      (patch_embeddings): DonutSwinPatchEmbeddings(\n",
              "        (projection): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))\n",
              "      )\n",
              "      (norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (encoder): DonutSwinEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-1): 2 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (key): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (value): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=128, out_features=512, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=512, out_features=128, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (downsample): DonutSwinPatchMerging(\n",
              "            (reduction): Linear(in_features=512, out_features=256, bias=False)\n",
              "            (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (1): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-1): 2 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (downsample): DonutSwinPatchMerging(\n",
              "            (reduction): Linear(in_features=1024, out_features=512, bias=False)\n",
              "            (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (2): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-13): 14 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (key): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (value): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=512, out_features=2048, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=2048, out_features=512, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (downsample): DonutSwinPatchMerging(\n",
              "            (reduction): Linear(in_features=2048, out_features=1024, bias=False)\n",
              "            (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (3): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-1): 2 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): AdaptiveAvgPool1d(output_size=1)\n",
              "  )\n",
              "  (decoder): MBartForCausalLM(\n",
              "    (model): MBartDecoderWrapper(\n",
              "      (decoder): MBartDecoder(\n",
              "        (embed_tokens): Embedding(57531, 1024, padding_idx=1)\n",
              "        (embed_positions): MBartLearnedPositionalEmbedding(1538, 1024)\n",
              "        (layers): ModuleList(\n",
              "          (0-3): 4 x MBartDecoderLayer(\n",
              "            (self_attn): MBartAttention(\n",
              "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            )\n",
              "            (activation_fn): GELUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MBartAttention(\n",
              "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "      )\n",
              "    )\n",
              "    (lm_head): Linear(in_features=1024, out_features=57531, bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "image_path = \"/content/ChartQA Dataset/train/png/\"\n",
        "\n",
        "accuracy = 0\n",
        "\n",
        "real_answers = []\n",
        "model_answers = []\n",
        "\n",
        "for i in tqdm(info_train_human[:100]):\n",
        "    image = i[\"imgname\"]\n",
        "    query = i[\"query\"]\n",
        "    answer = i[\"label\"]\n",
        "    input_prompt = \"<chartqa> \" + query + \" <s_answer>\"\n",
        "\n",
        "    image = Image.open(os.path.join(image_path, image)).convert(\"RGB\")\n",
        "    decoder_input_ids = processor.tokenizer(input_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
        "    pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "    outputs = model.generate(\n",
        "        pixel_values.to(device),\n",
        "        decoder_input_ids=decoder_input_ids.to(device),\n",
        "        max_length=model.decoder.config.max_position_embeddings,\n",
        "        early_stopping=True,\n",
        "        pad_token_id=processor.tokenizer.pad_token_id,\n",
        "        eos_token_id=processor.tokenizer.eos_token_id,\n",
        "        use_cache=True,\n",
        "        num_beams=4,\n",
        "        bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
        "        return_dict_in_generate=True,\n",
        "    )\n",
        "    sequence = processor.batch_decode(outputs.sequences)[0]\n",
        "    sequence = sequence.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n",
        "    sequence = sequence.split(\"<s_answer>\")[1].strip()\n",
        "    # print(f\"QUERY: {query}\")\n",
        "    # print(f\"REAL ANSWER: {answer}\\nMODEL ANSWER: {sequence}\\n\")\n",
        "\n",
        "    real_answers.append(answer)\n",
        "    model_answers.append(sequence)\n",
        "\n",
        "real_answers_str = list(map(str, real_answers))\n",
        "model_answers_str = list(map(str, model_answers))\n",
        "\n",
        "exact_match = [real == model for real, model in zip(real_answers_str, model_answers_str)]\n",
        "accuracy = sum(exact_match) / len(exact_match)\n",
        "print(f'\\nExact Match Accuracy: {accuracy:.2f}')\n",
        "\n",
        "text_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, str)]\n",
        "f1 = f1_score([real_answers_str[i] for i in text_indices], [model_answers_str[i] for i in text_indices], average='macro')\n",
        "print(f'F1 Score (text answers): {f1:.2f}')\n",
        "\n",
        "numerical_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, (int, float))]\n",
        "real_numerical = [real_answers[i] for i in numerical_indices]\n",
        "model_numerical = [model_answers[i] for i in numerical_indices]\n",
        "\n",
        "try:\n",
        "    mae = mean_absolute_error(real_numerical, model_numerical)\n",
        "    rmse = np.sqrt(mean_squared_error(real_numerical, model_numerical))\n",
        "    print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
        "    print(f'Root Mean Squared Error (RMSE): {rmse:.2f}')\n",
        "except Exception as e:\n",
        "    print(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iqNpMb7rzbGh",
        "outputId": "26e1b92a-c24c-41aa-9da6-af08e3432b19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:38<00:00,  2.60it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Exact Match Accuracy: 0.10\n",
            "F1 Score (text answers): 0.02\n",
            "Found array with 0 sample(s) (shape=(0,)) while a minimum of 1 is required.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Accuracy on test dataset"
      ],
      "metadata": {
        "id": "22D9J6Zm0Ckj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "image_path = \"/content/ChartQA Dataset/test/png/\"\n",
        "\n",
        "accuracy = 0\n",
        "\n",
        "real_answers = []\n",
        "model_answers = []\n",
        "\n",
        "for i in tqdm(test_dataset[:100]):\n",
        "    image, query, answer = i\n",
        "    input_prompt = \"<chartqa> \" + query + \" <s_answer>\"\n",
        "\n",
        "    image = Image.open(os.path.join(image_path, image)).convert(\"RGB\")\n",
        "    decoder_input_ids = processor.tokenizer(input_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
        "    pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "    outputs = model.generate(\n",
        "        pixel_values.to(device),\n",
        "        decoder_input_ids=decoder_input_ids.to(device),\n",
        "        max_length=model.decoder.config.max_position_embeddings,\n",
        "        early_stopping=True,\n",
        "        pad_token_id=processor.tokenizer.pad_token_id,\n",
        "        eos_token_id=processor.tokenizer.eos_token_id,\n",
        "        use_cache=True,\n",
        "        num_beams=4,\n",
        "        bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
        "        return_dict_in_generate=True,\n",
        "    )\n",
        "    sequence = processor.batch_decode(outputs.sequences)[0]\n",
        "    sequence = sequence.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n",
        "    sequence = sequence.split(\"<s_answer>\")[1].strip()\n",
        "    # print(f\"QUERY: {query}\")\n",
        "    # print(f\"REAL ANSWER: {answer}\\nMODEL ANSWER: {sequence}\\n\")\n",
        "\n",
        "    real_answers.append(answer)\n",
        "    model_answers.append(sequence)\n",
        "\n",
        "real_answers_str = list(map(str, real_answers))\n",
        "model_answers_str = list(map(str, model_answers))\n",
        "\n",
        "exact_match = [real == model for real, model in zip(real_answers_str, model_answers_str)]\n",
        "accuracy = sum(exact_match) / len(exact_match)\n",
        "print(f'\\nExact Match Accuracy: {accuracy:.2f}')\n",
        "\n",
        "text_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, str)]\n",
        "f1 = f1_score([real_answers_str[i] for i in text_indices], [model_answers_str[i] for i in text_indices], average='macro')\n",
        "print(f'F1 Score (text answers): {f1:.2f}')\n",
        "\n",
        "numerical_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, (int, float))]\n",
        "real_numerical = [real_answers[i] for i in numerical_indices]\n",
        "model_numerical = [model_answers[i] for i in numerical_indices]\n",
        "\n",
        "try:\n",
        "    mae = mean_absolute_error(real_numerical, model_numerical)\n",
        "    rmse = np.sqrt(mean_squared_error(real_numerical, model_numerical))\n",
        "    print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
        "    print(f'Root Mean Squared Error (RMSE): {rmse:.2f}')\n",
        "except Exception as e:\n",
        "    print(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r17vAG6q0Ee_",
        "outputId": "8f64e9d3-adad-4bb0-c12e-ab4bd173ff93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:45<00:00,  2.20it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Exact Match Accuracy: 0.20\n",
            "F1 Score (text answers): 0.13\n",
            "Found array with 0 sample(s) (shape=(0,)) while a minimum of 1 is required.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Retrained model\n",
        "\n",
        "#### Accuracy on the train dataset"
      ],
      "metadata": {
        "id": "AJ3avThu0I4t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"/content/output_data/chartqa-checkpoint-epoch=19-1000\"\n",
        "model = VisionEncoderDecoderModel.from_pretrained(model_name)\n",
        "processor = DonutProcessor.from_pretrained(model_name)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yWn4kRKM0YkW",
        "outputId": "4148b6d0-f9fb-473d-ead5-05f0964d1c9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VisionEncoderDecoderModel(\n",
              "  (encoder): DonutSwinModel(\n",
              "    (embeddings): DonutSwinEmbeddings(\n",
              "      (patch_embeddings): DonutSwinPatchEmbeddings(\n",
              "        (projection): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))\n",
              "      )\n",
              "      (norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (encoder): DonutSwinEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-1): 2 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (key): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (value): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=128, out_features=512, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=512, out_features=128, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (downsample): DonutSwinPatchMerging(\n",
              "            (reduction): Linear(in_features=512, out_features=256, bias=False)\n",
              "            (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (1): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-1): 2 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (downsample): DonutSwinPatchMerging(\n",
              "            (reduction): Linear(in_features=1024, out_features=512, bias=False)\n",
              "            (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (2): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-13): 14 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (key): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (value): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=512, out_features=2048, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=2048, out_features=512, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (downsample): DonutSwinPatchMerging(\n",
              "            (reduction): Linear(in_features=2048, out_features=1024, bias=False)\n",
              "            (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (3): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-1): 2 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): AdaptiveAvgPool1d(output_size=1)\n",
              "  )\n",
              "  (decoder): MBartForCausalLM(\n",
              "    (model): MBartDecoderWrapper(\n",
              "      (decoder): MBartDecoder(\n",
              "        (embed_tokens): Embedding(57531, 1024, padding_idx=1)\n",
              "        (embed_positions): MBartLearnedPositionalEmbedding(1538, 1024)\n",
              "        (layers): ModuleList(\n",
              "          (0-3): 4 x MBartDecoderLayer(\n",
              "            (self_attn): MBartAttention(\n",
              "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            )\n",
              "            (activation_fn): GELUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MBartAttention(\n",
              "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "      )\n",
              "    )\n",
              "    (lm_head): Linear(in_features=1024, out_features=57531, bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "image_path = \"/content/ChartQA Dataset/train/png/\"\n",
        "\n",
        "accuracy = 0\n",
        "\n",
        "real_answers = []\n",
        "model_answers = []\n",
        "\n",
        "for i in tqdm(info_train_human[:100]):\n",
        "    image = i[\"imgname\"]\n",
        "    query = i[\"query\"]\n",
        "    answer = i[\"label\"]\n",
        "    input_prompt = \"<chartqa> \" + query + \" <s_answer>\"\n",
        "\n",
        "    image = Image.open(os.path.join(image_path, image)).convert(\"RGB\")\n",
        "    decoder_input_ids = processor.tokenizer(input_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
        "    pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "    outputs = model.generate(\n",
        "        pixel_values.to(device),\n",
        "        decoder_input_ids=decoder_input_ids.to(device),\n",
        "        max_length=model.decoder.config.max_position_embeddings,\n",
        "        early_stopping=True,\n",
        "        pad_token_id=processor.tokenizer.pad_token_id,\n",
        "        eos_token_id=processor.tokenizer.eos_token_id,\n",
        "        use_cache=True,\n",
        "        num_beams=4,\n",
        "        bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
        "        return_dict_in_generate=True,\n",
        "    )\n",
        "    sequence = processor.batch_decode(outputs.sequences)[0]\n",
        "    sequence = sequence.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n",
        "    sequence = sequence.split(\"<s_answer>\")[1].strip()\n",
        "    # print(f\"QUERY: {query}\")\n",
        "    # print(f\"REAL ANSWER: {answer}\\nMODEL ANSWER: {sequence}\\n\")\n",
        "\n",
        "    real_answers.append(answer)\n",
        "    model_answers.append(sequence)\n",
        "\n",
        "real_answers_str = list(map(str, real_answers))\n",
        "model_answers_str = list(map(str, model_answers))\n",
        "\n",
        "exact_match = [real == model for real, model in zip(real_answers_str, model_answers_str)]\n",
        "accuracy = sum(exact_match) / len(exact_match)\n",
        "print(f'\\nExact Match Accuracy: {accuracy:.2f}')\n",
        "\n",
        "text_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, str)]\n",
        "f1 = f1_score([real_answers_str[i] for i in text_indices], [model_answers_str[i] for i in text_indices], average='macro')\n",
        "print(f'F1 Score (text answers): {f1:.2f}')\n",
        "\n",
        "numerical_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, (int, float))]\n",
        "real_numerical = [real_answers[i] for i in numerical_indices]\n",
        "model_numerical = [model_answers[i] for i in numerical_indices]\n",
        "\n",
        "try:\n",
        "    mae = mean_absolute_error(real_numerical, model_numerical)\n",
        "    rmse = np.sqrt(mean_squared_error(real_numerical, model_numerical))\n",
        "    print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
        "    print(f'Root Mean Squared Error (RMSE): {rmse:.2f}')\n",
        "except Exception as e:\n",
        "    print(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcIEtjTl0O9Q",
        "outputId": "a9f1955a-ef65-4f6a-bdb3-94e45d361560"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:39<00:00,  2.53it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Exact Match Accuracy: 0.99\n",
            "F1 Score (text answers): 0.97\n",
            "Found array with 0 sample(s) (shape=(0,)) while a minimum of 1 is required.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Accuracy on test dataset"
      ],
      "metadata": {
        "id": "CSROnOk70M8o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "image_path = \"/content/ChartQA Dataset/test/png/\"\n",
        "\n",
        "accuracy = 0\n",
        "\n",
        "real_answers = []\n",
        "model_answers = []\n",
        "\n",
        "for i in tqdm(test_dataset[:100]):\n",
        "    image, query, answer = i\n",
        "    input_prompt = \"<chartqa> \" + query + \" <s_answer>\"\n",
        "\n",
        "    image = Image.open(os.path.join(image_path, image)).convert(\"RGB\")\n",
        "    decoder_input_ids = processor.tokenizer(input_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
        "    pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "    outputs = model.generate(\n",
        "        pixel_values.to(device),\n",
        "        decoder_input_ids=decoder_input_ids.to(device),\n",
        "        max_length=model.decoder.config.max_position_embeddings,\n",
        "        early_stopping=True,\n",
        "        pad_token_id=processor.tokenizer.pad_token_id,\n",
        "        eos_token_id=processor.tokenizer.eos_token_id,\n",
        "        use_cache=True,\n",
        "        num_beams=4,\n",
        "        bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
        "        return_dict_in_generate=True,\n",
        "    )\n",
        "    sequence = processor.batch_decode(outputs.sequences)[0]\n",
        "    sequence = sequence.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n",
        "    sequence = sequence.split(\"<s_answer>\")[1].strip()\n",
        "    # print(f\"QUERY: {query}\")\n",
        "    # print(f\"REAL ANSWER: {answer}\\nMODEL ANSWER: {sequence}\\n\")\n",
        "\n",
        "    real_answers.append(answer)\n",
        "    model_answers.append(sequence)\n",
        "\n",
        "real_answers_str = list(map(str, real_answers))\n",
        "model_answers_str = list(map(str, model_answers))\n",
        "\n",
        "exact_match = [real == model for real, model in zip(real_answers_str, model_answers_str)]\n",
        "accuracy = sum(exact_match) / len(exact_match)\n",
        "print(f'\\nExact Match Accuracy: {accuracy:.2f}')\n",
        "\n",
        "text_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, str)]\n",
        "f1 = f1_score([real_answers_str[i] for i in text_indices], [model_answers_str[i] for i in text_indices], average='macro')\n",
        "print(f'F1 Score (text answers): {f1:.2f}')\n",
        "\n",
        "numerical_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, (int, float))]\n",
        "real_numerical = [real_answers[i] for i in numerical_indices]\n",
        "model_numerical = [model_answers[i] for i in numerical_indices]\n",
        "\n",
        "try:\n",
        "    mae = mean_absolute_error(real_numerical, model_numerical)\n",
        "    rmse = np.sqrt(mean_squared_error(real_numerical, model_numerical))\n",
        "    print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
        "    print(f'Root Mean Squared Error (RMSE): {rmse:.2f}')\n",
        "except Exception as e:\n",
        "    print(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scD8LYSy0PNr",
        "outputId": "089b1da6-8e11-49bf-90c5-c02ec08b833f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:41<00:00,  2.39it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Exact Match Accuracy: 0.16\n",
            "F1 Score (text answers): 0.10\n",
            "Found array with 0 sample(s) (shape=(0,)) while a minimum of 1 is required.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Model fully trained on ChartQA. Taken for comparison. Training for 40,000 iterations (8 GPUs)\n",
        "\n",
        "#### Accuracy on the train dataset"
      ],
      "metadata": {
        "id": "z2IE_ytZ1YlI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = model_name = \"ahmed-masry/unichart-chartqa-960\"\n",
        "model = VisionEncoderDecoderModel.from_pretrained(model_name)\n",
        "processor = DonutProcessor.from_pretrained(model_name)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctJy99So1Xz-",
        "outputId": "059aa627-1d65-4ac4-9446-f76efb3566c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VisionEncoderDecoderModel(\n",
              "  (encoder): DonutSwinModel(\n",
              "    (embeddings): DonutSwinEmbeddings(\n",
              "      (patch_embeddings): DonutSwinPatchEmbeddings(\n",
              "        (projection): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))\n",
              "      )\n",
              "      (norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (encoder): DonutSwinEncoder(\n",
              "      (layers): ModuleList(\n",
              "        (0): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-1): 2 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (key): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (value): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=128, out_features=128, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=128, out_features=512, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=512, out_features=128, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (downsample): DonutSwinPatchMerging(\n",
              "            (reduction): Linear(in_features=512, out_features=256, bias=False)\n",
              "            (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (1): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-1): 2 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (downsample): DonutSwinPatchMerging(\n",
              "            (reduction): Linear(in_features=1024, out_features=512, bias=False)\n",
              "            (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (2): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-13): 14 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (key): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (value): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=512, out_features=512, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=512, out_features=2048, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=2048, out_features=512, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (downsample): DonutSwinPatchMerging(\n",
              "            (reduction): Linear(in_features=2048, out_features=1024, bias=False)\n",
              "            (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (3): DonutSwinStage(\n",
              "          (blocks): ModuleList(\n",
              "            (0-1): 2 x DonutSwinLayer(\n",
              "              (layernorm_before): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "              (attention): DonutSwinAttention(\n",
              "                (self): DonutSwinSelfAttention(\n",
              "                  (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "                (output): DonutSwinSelfOutput(\n",
              "                  (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "                  (dropout): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (drop_path): DonutSwinDropPath(p=0.1)\n",
              "              (layernorm_after): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "              (intermediate): DonutSwinIntermediate(\n",
              "                (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "                (intermediate_act_fn): GELUActivation()\n",
              "              )\n",
              "              (output): DonutSwinOutput(\n",
              "                (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "                (dropout): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): AdaptiveAvgPool1d(output_size=1)\n",
              "  )\n",
              "  (decoder): MBartForCausalLM(\n",
              "    (model): MBartDecoderWrapper(\n",
              "      (decoder): MBartDecoder(\n",
              "        (embed_tokens): Embedding(57531, 1024, padding_idx=1)\n",
              "        (embed_positions): MBartLearnedPositionalEmbedding(1538, 1024)\n",
              "        (layers): ModuleList(\n",
              "          (0-3): 4 x MBartDecoderLayer(\n",
              "            (self_attn): MBartAttention(\n",
              "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            )\n",
              "            (activation_fn): GELUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MBartAttention(\n",
              "              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "            (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "        (layernorm_embedding): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "      )\n",
              "    )\n",
              "    (lm_head): Linear(in_features=1024, out_features=57531, bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "image_path = \"/content/ChartQA Dataset/train/png/\"\n",
        "\n",
        "accuracy = 0\n",
        "\n",
        "real_answers = []\n",
        "model_answers = []\n",
        "\n",
        "for i in tqdm(info_train_human[:100]):\n",
        "    image = i[\"imgname\"]\n",
        "    query = i[\"query\"]\n",
        "    answer = i[\"label\"]\n",
        "    input_prompt = \"<chartqa> \" + query + \" <s_answer>\"\n",
        "\n",
        "    image = Image.open(os.path.join(image_path, image)).convert(\"RGB\")\n",
        "    decoder_input_ids = processor.tokenizer(input_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
        "    pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "    outputs = model.generate(\n",
        "        pixel_values.to(device),\n",
        "        decoder_input_ids=decoder_input_ids.to(device),\n",
        "        max_length=model.decoder.config.max_position_embeddings,\n",
        "        early_stopping=True,\n",
        "        pad_token_id=processor.tokenizer.pad_token_id,\n",
        "        eos_token_id=processor.tokenizer.eos_token_id,\n",
        "        use_cache=True,\n",
        "        num_beams=4,\n",
        "        bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
        "        return_dict_in_generate=True,\n",
        "    )\n",
        "    sequence = processor.batch_decode(outputs.sequences)[0]\n",
        "    sequence = sequence.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n",
        "    sequence = sequence.split(\"<s_answer>\")[1].strip()\n",
        "    # print(f\"QUERY: {query}\")\n",
        "    # print(f\"REAL ANSWER: {answer}\\nMODEL ANSWER: {sequence}\\n\")\n",
        "\n",
        "    real_answers.append(answer)\n",
        "    model_answers.append(sequence)\n",
        "\n",
        "real_answers_str = list(map(str, real_answers))\n",
        "model_answers_str = list(map(str, model_answers))\n",
        "\n",
        "exact_match = [real == model for real, model in zip(real_answers_str, model_answers_str)]\n",
        "accuracy = sum(exact_match) / len(exact_match)\n",
        "print(f'\\nExact Match Accuracy: {accuracy:.2f}')\n",
        "\n",
        "text_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, str)]\n",
        "f1 = f1_score([real_answers_str[i] for i in text_indices], [model_answers_str[i] for i in text_indices], average='macro')\n",
        "print(f'F1 Score (text answers): {f1:.2f}')\n",
        "\n",
        "numerical_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, (int, float))]\n",
        "real_numerical = [real_answers[i] for i in numerical_indices]\n",
        "model_numerical = [model_answers[i] for i in numerical_indices]\n",
        "\n",
        "try:\n",
        "    mae = mean_absolute_error(real_numerical, model_numerical)\n",
        "    rmse = np.sqrt(mean_squared_error(real_numerical, model_numerical))\n",
        "    print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
        "    print(f'Root Mean Squared Error (RMSE): {rmse:.2f}')\n",
        "except Exception as e:\n",
        "    print(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F9orMHnC1Xwm",
        "outputId": "23e52754-6ced-4ba6-fa07-c6aae1ee5e9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:38<00:00,  2.57it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Exact Match Accuracy: 0.62\n",
            "F1 Score (text answers): 0.45\n",
            "Found array with 0 sample(s) (shape=(0,)) while a minimum of 1 is required.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Accuracy on test dataset"
      ],
      "metadata": {
        "id": "J5tQBcBg1qlY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, mean_squared_error\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "image_path = \"/content/ChartQA Dataset/test/png/\"\n",
        "\n",
        "accuracy = 0\n",
        "\n",
        "real_answers = []\n",
        "model_answers = []\n",
        "\n",
        "for i in tqdm(test_dataset[:100]):\n",
        "    image, query, answer = i\n",
        "    input_prompt = \"<chartqa> \" + query + \" <s_answer>\"\n",
        "\n",
        "    image = Image.open(os.path.join(image_path, image)).convert(\"RGB\")\n",
        "    decoder_input_ids = processor.tokenizer(input_prompt, add_special_tokens=False, return_tensors=\"pt\").input_ids\n",
        "    pixel_values = processor(image, return_tensors=\"pt\").pixel_values\n",
        "\n",
        "    outputs = model.generate(\n",
        "        pixel_values.to(device),\n",
        "        decoder_input_ids=decoder_input_ids.to(device),\n",
        "        max_length=model.decoder.config.max_position_embeddings,\n",
        "        early_stopping=True,\n",
        "        pad_token_id=processor.tokenizer.pad_token_id,\n",
        "        eos_token_id=processor.tokenizer.eos_token_id,\n",
        "        use_cache=True,\n",
        "        num_beams=4,\n",
        "        bad_words_ids=[[processor.tokenizer.unk_token_id]],\n",
        "        return_dict_in_generate=True,\n",
        "    )\n",
        "    sequence = processor.batch_decode(outputs.sequences)[0]\n",
        "    sequence = sequence.replace(processor.tokenizer.eos_token, \"\").replace(processor.tokenizer.pad_token, \"\")\n",
        "    sequence = sequence.split(\"<s_answer>\")[1].strip()\n",
        "    # print(f\"QUERY: {query}\")\n",
        "    # print(f\"REAL ANSWER: {answer}\\nMODEL ANSWER: {sequence}\\n\")\n",
        "\n",
        "    real_answers.append(answer)\n",
        "    model_answers.append(sequence)\n",
        "\n",
        "real_answers_str = list(map(str, real_answers))\n",
        "model_answers_str = list(map(str, model_answers))\n",
        "\n",
        "exact_match = [real == model for real, model in zip(real_answers_str, model_answers_str)]\n",
        "accuracy = sum(exact_match) / len(exact_match)\n",
        "print(f'\\nExact Match Accuracy: {accuracy:.2f}')\n",
        "\n",
        "text_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, str)]\n",
        "f1 = f1_score([real_answers_str[i] for i in text_indices], [model_answers_str[i] for i in text_indices], average='macro')\n",
        "print(f'F1 Score (text answers): {f1:.2f}')\n",
        "\n",
        "numerical_indices = [i for i, ans in enumerate(real_answers) if isinstance(ans, (int, float))]\n",
        "real_numerical = [real_answers[i] for i in numerical_indices]\n",
        "model_numerical = [model_answers[i] for i in numerical_indices]\n",
        "\n",
        "try:\n",
        "    mae = mean_absolute_error(real_numerical, model_numerical)\n",
        "    rmse = np.sqrt(mean_squared_error(real_numerical, model_numerical))\n",
        "    print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
        "    print(f'Root Mean Squared Error (RMSE): {rmse:.2f}')\n",
        "except Exception as e:\n",
        "    print(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oY1qCG61XoJ",
        "outputId": "c6b45617-9f72-4c47-8b56-2e3b72893850"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:37<00:00,  2.66it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Exact Match Accuracy: 0.75\n",
            "F1 Score (text answers): 0.59\n",
            "Found array with 0 sample(s) (shape=(0,)) while a minimum of 1 is required.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusions"
      ],
      "metadata": {
        "id": "P4Cj2fLW4-63"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The metrics chosen for evaluating accuracy were exact accuracy and F1-score, as the question-answering task is straightforward and requires precise matching. I deliberately did not account for deviations in numerical values from the true ones, as a detailed analysis revealed that, in most cases, the differences are too critical, and the accuracy metrics would be distorted due to sharp outliers. Overall, the goal is to achieve an exact answer, which was my primary focus.\n",
        "Based on the results of training and testing, the following conclusions can be drawn:\n",
        "\n",
        "1. The model has already been pretrained on very similar data, as evidenced by the pretrain results (Train: 0.10/0.02, Test: 0.20/0.13).\n",
        "So it is likely that some part of the test data was in the original training set.\n",
        "\n",
        "    Conclusion: even in pretraining, the model is able to effectively summarize information from the graph.\n",
        "\n",
        "    Limitation: the model does not receive the exact positions of objects and contents during training.\n",
        "\n",
        "2. The retrained model is expectedly quickly retrained on small amounts of data and 20 training epochs. We obtained enormous accuracy on the training set and extremely low accuracy on the test set (Train: 0.99/0.97, Test: 0.16/0.10).\n",
        "It is also noticeable that the test sample results are worse than those of the pretrain.\n",
        "\n",
        "    Conclusion: fewer epochs, larger training set.\n",
        "    \n",
        "    Limitation: colab resources.\n",
        "\n",
        "3. The fine-tuned model by the developers achieved the following metrics: (Train: 0.62/0.45, Test: 0.75/0.59). These are excellent metric values without signs of overfitting. It is quite likely that using more complex metrics would yield even higher accuracy indicators.\n",
        "Conclusion: With sufficient resources, the model can confidently be used in production, given its low inference time and relatively modest requirements, even during training.\n",
        "\n",
        "Limitations: Ensembles of even simpler models may perform better for specific tasks (e.g., MobileNet -> Unet -> LSTM).\n",
        "\n",
        "All of the above can be summarized as follows: this architecture and model effectively perform their task — providing specific answers based on images. However, it seems that using a pre-trained model either implies leveraging large models like LLaVa from the HuggingFace top-tier list or developing custom ensembles tailored to a specific task.  \n",
        "\n",
        "In this case, the model does not even utilize half of the dataset's data, which naturally reduces its final accuracy. Nevertheless, even in this form, with full fine-tuning on ChartQA, UniChart is capable of fulfilling its tasks and stands out among models with a similar number of parameters due to its high performance (see GitHub)."
      ],
      "metadata": {
        "id": "Iqt1ZSxS_awW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ссылки\n",
        "\n",
        "В ходе работы была использована информация из следующих источников:\n",
        "\n",
        "1. UniChart GitHub: https://github.com/vis-nlp/UniChart/tree/main\n",
        "2. ChartQA GitHub: https://github.com/vis-nlp/ChartQA\n",
        "3. ViLT GitHub: https://github.com/dandelin/ViLT/tree/master\n",
        "4. HuggingFace: https://huggingface.co/"
      ],
      "metadata": {
        "id": "muJPdx4z5ieG"
      }
    }
  ]
}