training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic libraries\n",
    "import os\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import numpy as np\n",
    "\n",
    "# HuggingFace libraries\n",
    "from PIL import Image\n",
    "from transformers import TrOCRProcessor, VisionEncoderDecoderModel, ViTFeatureExtractor, RobertaTokenizer, AdamW\n",
    "from datasets import load_metric\n",
    "import requests\n",
    "\n",
    "# Plotting and visualization + progress bar + Levenshtein distance\n",
    "import Levenshtein as lv  # You might need to install the 'python-Levenshtein' package\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Chess libraries\n",
    "import chess\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a regular PyTorch dataset. We first create a Pandas dataframe with 2 columns. Each row consists of the file name of an image, and the corresponding text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>file_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e4</td>\n",
       "      <td>en_0002_easy_26_b00.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c5</td>\n",
       "      <td>en_0002_easy_26_b01.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nf3</td>\n",
       "      <td>en_0002_easy_26_b02.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nc6</td>\n",
       "      <td>en_0002_easy_26_b03.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bg2</td>\n",
       "      <td>en_0002_easy_26_b04.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  text                file_name\n",
       "0   e4  en_0002_easy_26_b00.png\n",
       "1   c5  en_0002_easy_26_b01.png\n",
       "2  Nf3  en_0002_easy_26_b02.png\n",
       "3  Nc6  en_0002_easy_26_b03.png\n",
       "4  Bg2  en_0002_easy_26_b04.png"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data/data.csv')\n",
    "df = df[['corrected','box']]\n",
    "df.rename(columns = {\"corrected\": \"text\",\"box\": \"file_name\"}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split up the data into training + testing, using sklearn's train_test_split function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(df, test_size=0.2)\n",
    "train_df, val_df = train_test_split(train_df, test_size = 0.125) # 0.125 x 0.8 = 0.1 for validation size\n",
    "\n",
    "# we reset the indices to start from zero\n",
    "train_df.reset_index(drop=True, inplace=True)\n",
    "test_df.reset_index(drop=True, inplace=True)\n",
    "val_df.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each element of the dataset should return 2 things:\n",
    "\n",
    "pixel_values, which serve as input to the model.\n",
    "labels, which are the input_ids of the corresponding text in the image.\n",
    "We use TrOCRProcessor to prepare the data for the model. TrOCRProcessor is actually just a wrapper around a ViTFeatureExtractor (which can be used to resize + normalize images) and a RobertaTokenizer (which can be used to encode and decode text into/from input_ids)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ChessDataset(Dataset):\n",
    "    def __init__(self, root_dir, df, processor, max_target_length=128):\n",
    "        self.root_dir = root_dir\n",
    "        self.df = df\n",
    "        self.processor = processor\n",
    "        self.max_target_length = max_target_length\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        # get file name + text\n",
    "        file_name = self.df['file_name'][idx]\n",
    "        text = self.df['text'][idx]\n",
    "        # prepare image (i.e. resize + normalize)\n",
    "        image = Image.open(self.root_dir + file_name).convert(\"RGB\")\n",
    "        pixel_values = self.processor(image, return_tensors=\"pt\").pixel_values\n",
    "        # add labels (input_ids) by encoding the text\n",
    "        labels = self.processor.tokenizer(text,\n",
    "                                          padding=\"max_length\",\n",
    "                                          max_length=self.max_target_length).input_ids\n",
    "        # important: make sure that PAD tokens are ignored by the loss function\n",
    "        labels = [label if label != self.processor.tokenizer.pad_token_id else -100 for label in labels]\n",
    "\n",
    "        encoding = {\"pixel_values\": pixel_values.squeeze(), \"labels\": torch.tensor(labels)}\n",
    "        return encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's initialize the training, validation and evaluation datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n"
     ]
    }
   ],
   "source": [
    "processor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-base-handwritten\")\n",
    "train_dataset = ChessDataset(root_dir='Data/boxes/',\n",
    "                           df=train_df,\n",
    "                           processor=processor)\n",
    "test_dataset = ChessDataset(root_dir='Data/boxes/',\n",
    "                           df=test_df,\n",
    "                           processor=processor)\n",
    "validation_dataset = ChessDataset(root_dir='Data/boxes/',\n",
    "                           df=val_df,\n",
    "                           processor=processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 1869\n",
      "Number of test examples: 535\n",
      "Number of validation examples: 267\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of training examples:\", len(train_dataset))\n",
    "print(\"Number of test examples:\", len(test_dataset))\n",
    "print(\"Number of validation examples:\", len(validation_dataset))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's verify an example from the training dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pixel_values torch.Size([3, 384, 384])\n",
      "labels torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "encoding = train_dataset[0]\n",
    "for k,v in encoding.items():\n",
    "  print(k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also check the original image and decode the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=221x87>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = Image.open(train_dataset.root_dir + train_df['file_name'][0]).convert(\"RGB\")\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qd2\n"
     ]
    }
   ],
   "source": [
    "labels = encoding['labels']\n",
    "labels[labels == -100] = processor.tokenizer.pad_token_id\n",
    "label_str = processor.decode(labels, skip_special_tokens=True)\n",
    "print(label_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create corresponding dataloaders:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=4)\n",
    "validation_dataloader = DataLoader(validation_dataset, batch_size=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we initialize the TrOCR model from its pretrained weights. Note that the weights of the language modeling head are already initialized from pre-training, as the model was already trained to generate text during its pre-training stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_handwritten_model():\n",
    "    model = VisionEncoderDecoderModel.from_pretrained(\"microsoft/trocr-base-handwritten\")\n",
    "    model.to(device)\n",
    "    # set special tokens used for creating the decoder_input_ids from the labels\n",
    "    model.config.decoder_start_token_id = processor.tokenizer.cls_token_id\n",
    "    model.config.pad_token_id = processor.tokenizer.pad_token_id\n",
    "    # make sure vocab size is set correctly\n",
    "    model.config.vocab_size = model.config.decoder.vocab_size\n",
    "\n",
    "    # set beam search parameters\n",
    "    model.config.eos_token_id = processor.tokenizer.sep_token_id\n",
    "    model.config.max_length = 64\n",
    "    model.config.early_stopping = True\n",
    "    model.config.no_repeat_ngram_size = 3\n",
    "    model.config.length_penalty = 2.0\n",
    "    model.config.num_beams = 4\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will evaluate the model on the Character Error Rate (CER), which is available in HuggingFace Datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Character error rate (CER) is a common metric of the performance of an automatic speech recognition (ASR) system. CER is similar to Word Error Rate (WER), but operates on character instead of word.\n",
    "\n",
    "Character error rate can be computed as:\n",
    "\n",
    "CER = (S + D + I) / N = (S + D + I) / (S + D + C)\n",
    "\n",
    "where\n",
    "\n",
    "S is the number of substitutions,\n",
    "\n",
    "D is the number of deletions,\n",
    "\n",
    "I is the number of insertions,\n",
    "\n",
    "C is the number of correct characters,\n",
    "\n",
    "N is the number of characters in the reference (N=S+D+C)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CER’s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the performance of the ASR system with a CER of 0 being a perfect score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marcp\\AppData\\Local\\Temp\\ipykernel_13152\\4287149634.py:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
      "  cer_metric = load_metric(\"cer\")\n"
     ]
    }
   ],
   "source": [
    "cer_metric = load_metric(\"cer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cer(pred_ids, label_ids):\n",
    "    pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
    "    label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
    "    label_str = processor.batch_decode(label_ids, skip_special_tokens=True)\n",
    "\n",
    "    cer = cer_metric.compute(predictions=pred_str, references=label_str)\n",
    "\n",
    "    return cer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Warning\n",
    "\n",
    "This code is very long and not necessary to evaluate our model you can skip it if you'de like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we evaluate the base model performances on our test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\models\\vit\\feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a039f6ad707044328349129d76d03d25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/4.17k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\huggingface_hub\\file_download.py:147: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\marcp\\.cache\\huggingface\\hub. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df11c3021dd845269d413beec2432cda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/1.33G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-handwritten and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84db959b7a0e4f8db5f04b017d0c5859",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ed5c8207be3423db01c20d4774d149b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing images:   0%|          | 0/535 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\generation\\utils.py:1473: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decoded: qolip\n",
      "actual: Qd4\n",
      "decoded: cb.\n",
      "actual: c6\n",
      "decoded: cxds.\n",
      "actual: cxd5\n",
      "decoded: rxets\n",
      "actual: Rxe5\n",
      "decoded: bags.\n",
      "actual: Bg5\n",
      "decoded: 0-20\n",
      "actual: O-O\n",
      "decoded: inch.\n",
      "actual: Nc6\n",
      "decoded: laxily\n",
      "actual: Qxd4\n",
      "decoded: nell.\n",
      "actual: Nc6\n",
      "decoded: qc.\n",
      "actual: Qc7\n",
      "decoded: 1375\n",
      "actual: Bf5\n",
      "decoded: lo-o\n",
      "actual: O-O\n",
      "decoded: get\n",
      "actual: Qc7\n",
      "decoded: gxt4\n",
      "actual: gxf4+\n",
      "decoded: bold\n",
      "actual: Bd6\n",
      "decoded: 0-00,\n",
      "actual: O-O\n",
      "decoded: t key.\n",
      "actual: fxe4\n",
      "decoded: bd2\n",
      "actual: Bd2\n",
      "decoded: kkg 7\n",
      "actual: Kxg7\n",
      "decoded: qez\n",
      "actual: Qe2\n",
      "decoded: ke 3.\n",
      "actual: Kc3\n",
      "decoded: ac-\n",
      "actual: Qc7\n",
      "decoded: al 4.\n",
      "actual: d4\n",
      "decoded: 94.\n",
      "actual: g4\n",
      "decoded: kkg2\n",
      "actual: Kxg2\n",
      "decoded: NG 3\n",
      "actual: Nf3\n",
      "decoded: neg\n",
      "actual: Nf6\n",
      "decoded: seq.\n",
      "actual: e4\n",
      "decoded: bcf.\n",
      "actual: Bc4\n",
      "decoded: dbt.\n",
      "actual: d4\n",
      "decoded: ez\n",
      "actual: e3\n",
      "decoded: nf3\n",
      "actual: Nf3\n",
      "decoded: pyfy\n",
      "actual: Nf3\n",
      "decoded: plyyly\n",
      "actual: Ng4\n",
      "decoded: tx 94th\n",
      "actual: fxg4+\n",
      "decoded: bbogs.\n",
      "actual: Bg6\n",
      "decoded: neptil\n",
      "actual: Nf6\n",
      "decoded: \" 26\n",
      "actual: d6\n",
      "decoded: riff\n",
      "actual: Bf4\n",
      "decoded: nxf2\n",
      "actual: Nxf2\n",
      "decoded: c.\n",
      "actual: c5\n",
      "decoded: gets.\n",
      "actual: gxf5\n",
      "decoded: bey.\n",
      "actual: Be4\n",
      "decoded: nfb.\n",
      "actual: Nf6\n",
      "decoded: exm.\n",
      "actual: exd5\n",
      "decoded: ncez\n",
      "actual: Nce2\n",
      "decoded: daybox.\n",
      "actual: a6\n",
      "decoded: bxc3\n",
      "actual: Bxc3\n",
      "decoded: hobt.\n",
      "actual: h6+\n",
      "decoded: dxcts\n",
      "actual: dxc5\n",
      "decoded: 1kip\n",
      "actual: Kf8\n",
      "decoded: bbt.\n",
      "actual: Bb7\n",
      "decoded: by 2\n",
      "actual: Bg2\n",
      "decoded: 1 Feb.\n",
      "actual: Be6\n",
      "decoded: usedly\n",
      "actual: Nxd4\n",
      "decoded: rabb.\n",
      "actual: Rd6\n",
      "decoded: 1.bf5.\n",
      "actual: Bf5\n",
      "decoded: rxack.\n",
      "actual: Rxa8\n",
      "decoded: bb7\n",
      "actual: Bb7\n",
      "decoded: 0-30\n",
      "actual: O-O\n",
      "decoded: bd?\n",
      "actual: Bd3\n",
      "decoded: rolb.\n",
      "actual: Rd6\n",
      "decoded: felt.\n",
      "actual: c4\n",
      "decoded: pxxful\n",
      "actual: Kxf1\n",
      "decoded: reg\n",
      "actual: Re8\n",
      "decoded: nxxly\n",
      "actual: Nxd4\n",
      "decoded: hs.\n",
      "actual: h5\n",
      "decoded: bwdly\n",
      "actual: Bxd4\n",
      "decoded: nfl\n",
      "actual: Nf1\n",
      "decoded: qez\n",
      "actual: Qe7\n",
      "decoded: bbt.\n",
      "actual: Bb5\n",
      "decoded: reb.\n",
      "actual: Re8\n",
      "decoded: qez\n",
      "actual: Qc7\n",
      "decoded: fy-\n",
      "actual: f4\n",
      "decoded: bfi\n",
      "actual: Bf4\n",
      "decoded: axis\n",
      "actual: Qxd5\n",
      "decoded: boz\n",
      "actual: Bc2\n",
      "decoded: 02h2\n",
      "actual: Qh2\n",
      "decoded: roi 7\n",
      "actual: Ra7\n",
      "decoded: qa1\n",
      "actual: Qa1\n",
      "decoded: expect.\n",
      "actual: c4\n",
      "decoded: rbit.\n",
      "actual: Rb1\n",
      "decoded: bbs.\n",
      "actual: Bb5\n",
      "decoded: fy-\n",
      "actual: b4\n",
      "decoded: pegla\n",
      "actual: h6\n",
      "decoded: nf3\n",
      "actual: Nf3\n",
      "decoded: f3.\n",
      "actual: f3\n",
      "decoded: oxfb.\n",
      "actual: Qxf6\n",
      "decoded: qqt.\n",
      "actual: Qg4\n",
      "decoded: r.68\n",
      "actual: Rb8\n",
      "decoded: lobb.\n",
      "actual: d6\n",
      "decoded: raa\n",
      "actual: Ra1\n",
      "decoded: bf 1.\n",
      "actual: Bf1\n",
      "decoded: bebec.\n",
      "actual: Be6\n",
      "decoded: bcy.\n",
      "actual: Bc4\n",
      "decoded: nexally\n",
      "actual: Nxd4\n",
      "decoded: gift\n",
      "actual: Qf6\n",
      "decoded: boust\n",
      "actual: Bxc3+\n",
      "decoded: c4 (\n",
      "actual: c4\n",
      "decoded: eq\n",
      "actual: c4\n",
      "decoded: nfc.\n",
      "actual: Nf6\n",
      "decoded: kf2\n",
      "actual: Kf2\n",
      "decoded: ney\n",
      "actual: Ne4\n",
      "decoded: ragg\n",
      "actual: Raa8\n",
      "decoded: cf.\n",
      "actual: c6\n",
      "decoded: 641\n",
      "actual: b4\n",
      "decoded: qi 7\n",
      "actual: Qc7\n",
      "decoded: 0xgz\n",
      "actual: Qxg7\n",
      "decoded: rxco.\n",
      "actual: Rxc8\n",
      "decoded: pkg\n",
      "actual: Bf3\n",
      "decoded: bc. 7\n",
      "actual: Bc7\n",
      "decoded: bb 7\n",
      "actual: Bb7\n",
      "decoded: nebec.\n",
      "actual: Ne6\n",
      "decoded: bg2\n",
      "actual: Bg2\n",
      "decoded: ojt\n",
      "actual: a5\n",
      "decoded: NCb.\n",
      "actual: Nc6\n",
      "decoded: sign 7.\n",
      "actual: Bg7\n",
      "decoded: 63\n",
      "actual: b3\n",
      "decoded: eb\n",
      "actual: e6\n",
      "decoded: exols.\n",
      "actual: cxd5\n",
      "decoded: desy.\n",
      "actual: Qe8+\n",
      "decoded: 96.\n",
      "actual: g6\n",
      "decoded: nyxfig.\n",
      "actual: Nxf6\n",
      "decoded: nez\n",
      "actual: Nc3\n",
      "decoded: 1206\n",
      "actual: Qe6\n",
      "decoded: fxes\n",
      "actual: fxe5\n",
      "decoded: git.\n",
      "actual: exf5+\n",
      "decoded: Alex 3\n",
      "actual: Nf3\n",
      "decoded: bixes.\n",
      "actual: Bxe8\n",
      "decoded: bxd4\n",
      "actual: Bxd4\n",
      "decoded: roly\n",
      "actual: Rd1\n",
      "decoded: dixes.\n",
      "actual: dxe5\n",
      "decoded: db.\n",
      "actual: d5\n",
      "decoded: Iraq 3\n",
      "actual: Ra3\n",
      "decoded: prot\n",
      "actual: Be7\n",
      "decoded: 64\n",
      "actual: b4\n",
      "decoded: nxi 3\n",
      "actual: Nxc3\n",
      "decoded: ncb.\n",
      "actual: Nc6\n",
      "decoded: ndo\n",
      "actual: Nxe5\n",
      "decoded: qxbilt.\n",
      "actual: Qxb1+\n",
      "decoded: well.\n",
      "actual: Nc4\n",
      "decoded: khg\n",
      "actual: Kh8\n",
      "decoded: fb.2\n",
      "actual: Bb7\n",
      "decoded: kg2\n",
      "actual: Kg2\n",
      "decoded: 13 9 7\n",
      "actual: Bg7\n",
      "decoded: nf3t.\n",
      "actual: Nf3\n",
      "decoded: culls.\n",
      "actual: c4\n",
      "decoded: nixon.\n",
      "actual: Nxa1\n",
      "decoded: rel 2\n",
      "actual: Rd7\n",
      "decoded: gb.\n",
      "actual: g6\n",
      "decoded: 00\n",
      "actual: O-O\n",
      "decoded: e 4.\n",
      "actual: e4\n",
      "decoded: 2d.\n",
      "actual: Qd6\n",
      "decoded: 93.\n",
      "actual: g3\n",
      "decoded: alfs.\n",
      "actual: Nf6\n",
      "decoded: 13c8.\n",
      "actual: Bc8\n",
      "decoded: bez\n",
      "actual: Be7\n",
      "decoded: nics.\n",
      "actual: Nc3\n",
      "decoded: 21' 2\n",
      "actual: Qc2\n",
      "decoded: bdz\n",
      "actual: Qd2\n",
      "decoded: rxack\n",
      "actual: Rxa4\n",
      "decoded: publy.\n",
      "actual: Nb4\n",
      "decoded: rxat.\n",
      "actual: Rxa7\n",
      "decoded: ray.\n",
      "actual: Ra4\n",
      "decoded: kagor\n",
      "actual: Kg8\n",
      "decoded: ref\n",
      "actual: Re8\n",
      "decoded: 0x68.\n",
      "actual: Qxb8\n",
      "decoded: inhalt\n",
      "actual: Nbd7\n",
      "decoded: nxxily\n",
      "actual: Nxd4\n",
      "decoded: xxxif 2\n",
      "actual: Rxf2\n",
      "decoded: nf3\n",
      "actual: Nf3\n",
      "decoded: nebec.\n",
      "actual: Nc6\n",
      "decoded: rxa1\n",
      "actual: Rxa1\n",
      "decoded: except\n",
      "actual: Qxe8+\n",
      "decoded: qy4\n",
      "actual: Qa4\n",
      "decoded: except\n",
      "actual: Qxe8+\n",
      "decoded: axe7\n",
      "actual: Qxe7\n",
      "decoded: axe 4.\n",
      "actual: Qxe4\n",
      "decoded: qa7\n",
      "actual: Qa7\n",
      "decoded: nxxily\n",
      "actual: Nxd4\n",
      "decoded: raz\n",
      "actual: Ra7\n",
      "decoded: six off.\n",
      "actual: gxf8\n",
      "decoded: qcrg.\n",
      "actual: Qc7\n",
      "decoded: dut.\n",
      "actual: d4\n",
      "decoded: 05.\n",
      "actual: c5\n",
      "decoded: rxbg.\n",
      "actual: Rxb4\n",
      "decoded: 1375\n",
      "actual: Bf5\n",
      "decoded: logles.\n",
      "actual: b6\n",
      "decoded: nxtab.\n",
      "actual: Nxa6\n",
      "decoded: nf.\n",
      "actual: Nf3\n",
      "decoded: nct.\n",
      "actual: Rb8\n",
      "decoded: be7\n",
      "actual: Be7\n",
      "decoded: extends\n",
      "actual: d5\n",
      "decoded: old.\n",
      "actual: d4\n",
      "decoded: nf3\n",
      "actual: Nf3\n",
      "decoded: 65.\n",
      "actual: b5\n",
      "decoded: als\n",
      "actual: d5\n",
      "decoded: bf 4.\n",
      "actual: h4\n",
      "decoded: kgt.\n",
      "actual: Kh5\n",
      "decoded: except\n",
      "actual: Kxg4\n",
      "decoded: qids\n",
      "actual: Qd8\n",
      "decoded: bg7\n",
      "actual: Bg7\n",
      "decoded: did it\n",
      "actual: d4\n",
      "decoded: 13gt\n",
      "actual: Bg7\n",
      "decoded: rxfm.\n",
      "actual: Rxf1\n",
      "decoded: alf\n",
      "actual: a4\n",
      "decoded: nh.\n",
      "actual: Nh6\n",
      "decoded: 45\n",
      "actual: e5\n",
      "decoded: 65\n",
      "actual: b5\n",
      "decoded: 2x.p.\n",
      "actual: Qxb7\n",
      "decoded: 0-o\n",
      "actual: O-O\n",
      "decoded: 94.\n",
      "actual: g4\n",
      "decoded: old 3.\n",
      "actual: Bd3\n",
      "decoded: 0d7\n",
      "actual: Qd7\n",
      "decoded: 65.\n",
      "actual: b5\n",
      "decoded: kr. 7\n",
      "actual: Rc7\n",
      "decoded: inch.\n",
      "actual: Nc4\n",
      "decoded: xxxably\n",
      "actual: Bxf4\n",
      "decoded: flip\n",
      "actual: f4\n",
      "decoded: 95\n",
      "actual: e5\n",
      "decoded: to-o.\n",
      "actual: O-O\n",
      "decoded: old\n",
      "actual: d4\n",
      "decoded: . Bgz\n",
      "actual: Bg7\n",
      "decoded: s3-\n",
      "actual: b3\n",
      "decoded: nxts.\n",
      "actual: Nxf5\n",
      "decoded: des.\n",
      "actual: Qe8\n",
      "decoded: ex-25\n",
      "actual: exf5\n",
      "decoded: 65\n",
      "actual: b5\n",
      "decoded: a b\n",
      "actual: e6\n",
      "decoded: 68-a\n",
      "actual: b8=Q\n",
      "decoded: boys.\n",
      "actual: Bg5\n",
      "decoded: Bxe 7\n",
      "actual: Bxe7\n",
      "decoded: bfg.\n",
      "actual: Bf8\n",
      "decoded: nyy\n",
      "actual: Nd7\n",
      "decoded: ncq\n",
      "actual: Nc4\n",
      "decoded: qcrf\n",
      "actual: Qc7\n",
      "decoded: 95.\n",
      "actual: g5\n",
      "decoded: rxbl2\n",
      "actual: Rxb2\n",
      "decoded: excl 5\n",
      "actual: exd5\n",
      "decoded: taxy.\n",
      "actual: Qxc4\n",
      "decoded: axbs.\n",
      "actual: axb5\n",
      "decoded: ke 1\n",
      "actual: Kc1\n",
      "decoded: qez\n",
      "actual: Qe3\n",
      "decoded: bbf\n",
      "actual: Bb7\n",
      "decoded: jcd8\n",
      "actual: Kd8\n",
      "decoded: axa1\n",
      "actual: Qxa1\n",
      "decoded: to-by 3.\n",
      "actual: b3\n",
      "decoded: nce?\n",
      "actual: Nce7\n",
      "decoded: c3.\n",
      "actual: c3\n",
      "decoded: gxf5\n",
      "actual: gxf5\n",
      "decoded: bw.2\n",
      "actual: Bxb7\n",
      "decoded: tryf?\n",
      "actual: Rxf7\n",
      "decoded: e g\n",
      "actual: e4\n",
      "decoded: ela\n",
      "actual: e4\n",
      "decoded: rdf.\n",
      "actual: Rd7\n",
      "decoded: 10-00\n",
      "actual: O-O\n",
      "decoded: qby\n",
      "actual: Qb4\n",
      "decoded: nxob\n",
      "actual: Nxc6\n",
      "decoded: nez\n",
      "actual: Nc2\n",
      "decoded: bgz.\n",
      "actual: Bg2\n",
      "decoded: NCG.\n",
      "actual: Nc6\n",
      "decoded: quick.\n",
      "actual: Qxd8+\n",
      "decoded: burden.\n",
      "actual: Bxd2+\n",
      "decoded: f 3.\n",
      "actual: f3\n",
      "decoded: elyly\n",
      "actual: e4\n",
      "decoded: boxfit.\n",
      "actual: Bxf4\n",
      "decoded: aczo\n",
      "actual: Qc2\n",
      "decoded: bgz\n",
      "actual: Bg2\n",
      "decoded: qosh\n",
      "actual: Qc5+\n",
      "decoded: unify\n",
      "actual: Nf6\n",
      "decoded: ro1.\n",
      "actual: Rb1\n",
      "decoded: qc8\n",
      "actual: Qc8\n",
      "decoded: hxg.\n",
      "actual: hxg6\n",
      "decoded: e4.\n",
      "actual: e4\n",
      "decoded: QB b.\n",
      "actual: Qb6\n",
      "decoded: berg\n",
      "actual: Be5\n",
      "decoded: exa.\n",
      "actual: Qxa7\n",
      "decoded: rrb.\n",
      "actual: Rb7\n",
      "decoded: f3.\n",
      "actual: f3\n",
      "decoded: hz\n",
      "actual: h3\n",
      "decoded: 0-0\n",
      "actual: O-O\n",
      "decoded: db.\n",
      "actual: d6\n",
      "decoded: kg 7\n",
      "actual: Kg7\n",
      "decoded: Neg 3.\n",
      "actual: Ng3\n",
      "decoded: rxcl\n",
      "actual: Rxc1\n",
      "decoded: kg 6.\n",
      "actual: Kg6\n",
      "decoded: oldle\n",
      "actual: d4\n",
      "decoded: nacket.\n",
      "actual: Nxd4\n",
      "decoded: 13, 3\n",
      "actual: Bc5\n",
      "decoded: qgt\n",
      "actual: Qg4\n",
      "decoded: ref\n",
      "actual: Re8\n",
      "decoded: qcz\n",
      "actual: Qc2\n",
      "decoded: reaps.\n",
      "actual: Rea8\n",
      "decoded: 735\n",
      "actual: e5\n",
      "decoded: bf 3\n",
      "actual: Bf3\n",
      "decoded: 13a.b\n",
      "actual: Ba6\n",
      "decoded: at.\n",
      "actual: a4\n",
      "decoded: fp.\n",
      "actual: f4\n",
      "decoded: jibffi\n",
      "actual: Bf6\n",
      "decoded: es\n",
      "actual: e5\n",
      "decoded: tabo\n",
      "actual: Qb6\n",
      "decoded: fg.\n",
      "actual: b4\n",
      "decoded: free?\n",
      "actual: fxe3\n",
      "decoded: his.\n",
      "actual: Bf2\n",
      "decoded: 0xc5.\n",
      "actual: Qxc5\n",
      "decoded: rxolly\n",
      "actual: Rxd4\n",
      "decoded: offly\n",
      "actual: Qf4\n",
      "decoded: bdz\n",
      "actual: Bd3\n",
      "decoded: 0-20\n",
      "actual: O-O\n",
      "decoded: qfb.\n",
      "actual: Qf6\n",
      "decoded: ire8\n",
      "actual: Re8\n",
      "decoded: ncf.\n",
      "actual: Nc6\n",
      "decoded: eb\n",
      "actual: e6\n",
      "decoded: cyb.\n",
      "actual: g3\n",
      "decoded: wells\n",
      "actual: Nd5\n",
      "decoded: rabi\n",
      "actual: Rab1\n",
      "decoded: ingch.\n",
      "actual: Ng4\n",
      "decoded: axe 3.\n",
      "actual: Qxc3\n",
      "decoded: belf.\n",
      "actual: Bc4\n",
      "decoded: racket\n",
      "actual: Rxd8+\n",
      "decoded: nb2\n",
      "actual: Nb2\n",
      "decoded: 0-\n",
      "actual: O-O\n",
      "decoded: calf-\n",
      "actual: d4\n",
      "decoded: refy.\n",
      "actual: Rb1\n",
      "decoded: bxcq\n",
      "actual: bxc4\n",
      "decoded: qez\n",
      "actual: Qc7\n",
      "decoded: n.h.\n",
      "actual: Nf6\n",
      "decoded: qb.\n",
      "actual: Qb7\n",
      "decoded: rcz\n",
      "actual: Rc2\n",
      "decoded: 13xhz\n",
      "actual: Bxh3\n",
      "decoded: nxa1\n",
      "actual: Nxa1\n",
      "decoded: nget.\n",
      "actual: Ng4\n",
      "decoded: qh.\n",
      "actual: Qh6\n",
      "decoded: boat.\n",
      "actual: Ba4\n",
      "decoded: bxby.\n",
      "actual: Bxb4\n",
      "decoded: bXY5\n",
      "actual: Nxf7\n",
      "decoded: rec1\n",
      "actual: Rec1\n",
      "decoded: publy.\n",
      "actual: Bb4\n",
      "decoded: typely\n",
      "actual: fxe4\n",
      "decoded: ng 3.\n",
      "actual: Ng3\n",
      "decoded: nez\n",
      "actual: Nc3\n",
      "decoded: nf3.\n",
      "actual: Nf3\n",
      "decoded: nxxols\n",
      "actual: Nxd5\n",
      "decoded: fixety\n",
      "actual: fxe5\n",
      "decoded: bd 2\n",
      "actual: Bd2\n",
      "decoded: qcilt.\n",
      "actual: Qc1+\n",
      "decoded: nd?\n",
      "actual: Nd7\n",
      "decoded: play. 1\n",
      "actual: Rd1\n",
      "decoded: 2 Ref.\n",
      "actual: Re5\n",
      "decoded: nf3\n",
      "actual: Nf3\n",
      "decoded: al 5\n",
      "actual: a5\n",
      "decoded: r62\n",
      "actual: Rb2\n",
      "decoded: ngst\n",
      "actual: Ng5+\n",
      "decoded: a 4.\n",
      "actual: a4\n",
      "decoded: Qf3\n",
      "actual: Qf3\n",
      "decoded: 93.\n",
      "actual: g3\n",
      "decoded: eb\n",
      "actual: e6\n",
      "decoded: keds.\n",
      "actual: Red8\n",
      "decoded: pubi-t\n",
      "actual: Nb5\n",
      "decoded: 0-0\n",
      "actual: O-O\n",
      "decoded: kbil.\n",
      "actual: Kb1\n",
      "decoded: 2nd century\n",
      "actual: d6\n",
      "decoded: Bxg3\n",
      "actual: Bxg3\n",
      "decoded: ribs\n",
      "actual: Ne3\n",
      "decoded: ab\n",
      "actual: g6\n",
      "decoded: nbeltz\n",
      "actual: Nbd7\n",
      "decoded: 0/5\n",
      "actual: d5\n",
      "decoded: exd 5\n",
      "actual: exd5\n",
      "decoded: nfb.\n",
      "actual: Nf6\n",
      "decoded: nb3\n",
      "actual: Nb3\n",
      "decoded: syrezh.\n",
      "actual: Bxe3+\n",
      "decoded: cb.\n",
      "actual: c6\n",
      "decoded: qez\n",
      "actual: Qe3\n",
      "decoded: foxout.\n",
      "actual: bxd4\n",
      "decoded: exact\n",
      "actual: Rxa1\n",
      "decoded: bf 3.\n",
      "actual: Bf3\n",
      "decoded: exhibits\n",
      "actual: Qxb6\n",
      "decoded: 96.\n",
      "actual: g6\n",
      "decoded: bd 7\n",
      "actual: Bd7\n",
      "decoded: boats.\n",
      "actual: Ba5\n",
      "decoded: rxers.\n",
      "actual: Rxe5\n",
      "decoded: bi 1.\n",
      "actual: Bc1\n",
      "decoded: edd 7\n",
      "actual: Rdd7\n",
      "decoded: nixels.\n",
      "actual: Nxe6\n",
      "decoded: discz.\n",
      "actual: dxc3\n",
      "decoded: qaly\n",
      "actual: Qa4\n",
      "decoded: nxit.\n",
      "actual: Nxc6\n",
      "decoded: ively\n",
      "actual: Nxg4\n",
      "decoded: at 5 1.\n",
      "actual: a6\n",
      "decoded: rbs.\n",
      "actual: Rb5\n",
      "decoded: bhz\n",
      "actual: Bh3\n",
      "decoded: NF 3.\n",
      "actual: Nf3\n",
      "decoded: phis.\n",
      "actual: h5\n",
      "decoded: laxfi\n",
      "actual: Qxf8#\n",
      "decoded: 66.\n",
      "actual: b6\n",
      "decoded: rello\n",
      "actual: Rd6\n",
      "decoded: bcb.\n",
      "actual: Bc6\n",
      "decoded: nxd5\n",
      "actual: Nxd5\n",
      "decoded: off\n",
      "actual: Bf8\n",
      "decoded: axd 3\n",
      "actual: Qxd3\n",
      "decoded: py 7.\n",
      "actual: Rd7\n",
      "decoded: 94-\n",
      "actual: g4\n",
      "decoded: eq\n",
      "actual: e4\n",
      "decoded: bbb.\n",
      "actual: Bd6\n",
      "decoded: bxsacks\n",
      "actual: Bxa3\n",
      "decoded: gigsburg\n",
      "actual: Qxb6\n",
      "decoded: nbd 7\n",
      "actual: Nbd7\n",
      "decoded: lazy\n",
      "actual: a3\n",
      "decoded: nids.\n",
      "actual: Nd5\n",
      "decoded: reg\n",
      "actual: Re8\n",
      "decoded: QL 4.\n",
      "actual: Qf4\n",
      "decoded: eb\n",
      "actual: e6\n",
      "decoded: cb.\n",
      "actual: c6\n",
      "decoded: od 5\n",
      "actual: d5\n",
      "decoded: bezzles\n",
      "actual: Kh4\n",
      "decoded: rhyt.\n",
      "actual: Bb4+\n",
      "decoded: by.\n",
      "actual: b4\n",
      "decoded: exactly.\n",
      "actual: cxd4\n",
      "decoded: xxbb.\n",
      "actual: cxb6\n",
      "decoded: cis.\n",
      "actual: c5\n",
      "decoded: rxcts.\n",
      "actual: Rxc5\n",
      "decoded: e 4.\n",
      "actual: e4\n",
      "decoded: fxey.\n",
      "actual: fxe4\n",
      "decoded: qc2\n",
      "actual: Na3\n",
      "decoded: bbcs.\n",
      "actual: Bc5\n",
      "decoded: cb.\n",
      "actual: c6\n",
      "decoded: e 3.\n",
      "actual: e3\n",
      "decoded: nag\n",
      "actual: Nc5\n",
      "decoded: n3-\n",
      "actual: h3\n",
      "decoded: ypi-\n",
      "actual: Kf1\n",
      "decoded: res.\n",
      "actual: Rc5\n",
      "decoded: nd 2\n",
      "actual: Nd2\n",
      "decoded: nfty.\n",
      "actual: Nf4\n",
      "decoded: erb.\n",
      "actual: e6\n",
      "decoded: ngly\n",
      "actual: Ng4\n",
      "decoded: 1394 (\n",
      "actual: Bg4\n",
      "decoded: bj8\n",
      "actual: Bf8\n",
      "decoded: by-\n",
      "actual: b4\n",
      "decoded: bdz\n",
      "actual: Bd3\n",
      "decoded: res\n",
      "actual: Re8\n",
      "decoded: 4xxx 2\n",
      "actual: Kxf2\n",
      "decoded: navy.\n",
      "actual: Nf4\n",
      "decoded: qol 7\n",
      "actual: Qd7\n",
      "decoded: khf\n",
      "actual: Kh7\n",
      "decoded: ne 3.\n",
      "actual: Nc3\n",
      "decoded: olfs.\n",
      "actual: Qf6\n",
      "decoded: wgyly\n",
      "actual: Qg4\n",
      "decoded: cb.\n",
      "actual: c6\n",
      "decoded: By,\n",
      "actual: Bg2\n",
      "decoded: 2nd century\n",
      "actual: d6\n",
      "decoded: nc.\n",
      "actual: Nc3\n",
      "decoded: L 4.\n",
      "actual: c4\n",
      "decoded: 0d7\n",
      "actual: Qd7\n",
      "decoded: reg\n",
      "actual: Re8\n",
      "decoded: ritt.\n",
      "actual: Rc1+\n",
      "decoded: bhz\n",
      "actual: Bh3\n",
      "decoded: rbs.\n",
      "actual: Rb5\n",
      "decoded: cxd5\n",
      "actual: cxd5\n",
      "decoded: 0 067\n",
      "actual: Qf7\n",
      "decoded: riff\n",
      "actual: Rf8\n",
      "decoded: oxd8\n",
      "actual: Qxd8\n",
      "decoded: rezhily\n",
      "actual: Kxc4\n",
      "decoded: 0x62\n",
      "actual: Qxb2\n",
      "decoded: 05.\n",
      "actual: c5\n",
      "decoded: e 4.\n",
      "actual: e4\n",
      "decoded: nitz.\n",
      "actual: Nf3\n",
      "decoded: qfs\n",
      "actual: Qf5\n",
      "decoded: rho'd\n",
      "actual: Rhe8\n",
      "decoded: nc.\n",
      "actual: Nc3\n",
      "decoded: n65.\n",
      "actual: Nb6\n",
      "decoded: 65.\n",
      "actual: b5\n",
      "decoded: qez\n",
      "actual: Qe3\n",
      "decoded: bc4\n",
      "actual: Bc4\n",
      "decoded: ne 7\n",
      "actual: Ne7\n",
      "decoded: nkay.\n",
      "actual: Nxe4\n",
      "decoded: bp 3.\n",
      "actual: Be3\n",
      "decoded: 23\n",
      "actual: a3\n",
      "decoded: lucky.\n",
      "actual: Bxd4\n",
      "decoded: qaz\n",
      "actual: Qc7\n",
      "decoded: 1965\n",
      "actual: Nb5\n",
      "decoded: kho\n",
      "actual: Kh8\n",
      "decoded: dxxly\n",
      "actual: dxc4\n",
      "decoded: kh8\n",
      "actual: Kh8\n",
      "decoded: old 4 1\n",
      "actual: d4\n",
      "decoded: fb.\n",
      "actual: f6\n",
      "decoded: dxe5.\n",
      "actual: dxe5\n",
      "decoded: QB\n",
      "actual: g3\n",
      "decoded: nbb.\n",
      "actual: Nb6\n",
      "decoded: reix,\n",
      "actual: Kc3\n",
      "decoded: infolds\n",
      "actual: Nf6\n",
      "decoded: axals\n",
      "actual: Qxd5\n",
      "decoded: bebec.\n",
      "actual: Be6\n",
      "decoded: briefs.\n",
      "actual: Bf5\n",
      "decoded: AVJ2\n",
      "actual: Nd2\n",
      "decoded: be 1.\n",
      "actual: Ke2\n",
      "decoded: 9-010\n",
      "actual: O-O-O\n",
      "decoded: liftly\n",
      "actual: Bf4\n",
      "decoded: reil.\n",
      "actual: Re1\n",
      "decoded: drew.\n",
      "actual: Rc1\n",
      "decoded: acst\n",
      "actual: Qc5\n",
      "decoded: by-t7n.f.\n",
      "actual: Bxf7+\n",
      "decoded: nxe5\n",
      "actual: Nc4\n",
      "decoded: rcz.\n",
      "actual: Rc3\n",
      "decoded: qbb.\n",
      "actual: Qb6\n",
      "decoded: rold?\n",
      "actual: Rdd7\n",
      "decoded: a b.\n",
      "actual: e6\n",
      "decoded: 15.\n",
      "actual: Be1\n",
      "decoded: ely\n",
      "actual: e4\n",
      "decoded: 2a4\n",
      "actual: Qa4\n",
      "decoded: 93.\n",
      "actual: g3\n",
      "decoded: 0e7\n",
      "actual: Qe7\n",
      "decoded: rydds\n",
      "actual: Rxd8\n",
      "decoded: qd2\n",
      "actual: Qd2\n",
      "decoded: 6x65\n",
      "actual: bxc5\n",
      "decoded: 0-20\n",
      "actual: O-O\n",
      "decoded: deb.\n",
      "actual: Qe6\n",
      "decoded: els\n",
      "actual: d5\n",
      "decoded: rxolts.\n",
      "actual: Rxd5\n",
      "decoded: qct.\n",
      "actual: Qc4\n",
      "decoded: c5-\n",
      "actual: c5\n",
      "decoded: rxf2\n",
      "actual: Rxf2\n",
      "decoded: ngb.\n",
      "actual: Ng6\n",
      "decoded: hxeq\n",
      "actual: hxg4\n",
      "decoded: INC 7\n",
      "actual: Nc7\n",
      "decoded: nxe5\n",
      "actual: Nxe5\n",
      "decoded: d 4.\n",
      "actual: d4\n",
      "decoded: kg 7\n",
      "actual: Kg7\n",
      "decoded: 0-30\n",
      "actual: O-O\n",
      "decoded: nxxols\n",
      "actual: Nxd5\n",
      "decoded: bcq.\n",
      "actual: Bc4\n",
      "decoded: c4-\n",
      "actual: c4\n",
      "decoded: 0-00\n",
      "actual: O-O\n",
      "decoded: xxed 6.\n",
      "actual: Rxd6\n",
      "decoded: cbs.\n",
      "actual: c5\n",
      "decoded: bd?\n",
      "actual: Bd7\n",
      "Average CER: 0.6969291041253668\n",
      "Global Accuracy: 0.29024922118380053\n",
      "Character Accuracies: {'Q': 0.03614457831325301, 'd': 0.3939393939393939, '4': 0.11504424778761062, 'c': 0.47572815533980584, '6': 0.03571428571428571, 'x': 0.6693548387096774, '5': 0.2222222222222222, 'R': 0.0, 'e': 0.7, 'B': 0.03225806451612903, 'g': 0.42857142857142855, 'O': 0.0, '-': 0.7058823529411765, 'N': 0.04950495049504951, '7': 0.125, 'f': 0.45454545454545453, '2': 0.3235294117647059, 'K': 0.0, '3': 0.29577464788732394, '+': 0.0, 'a': 0.6153846153846154, 'h': 0.6363636363636364, '8': 0.14285714285714285, 'b': 0.43283582089552236, '1': 0.20689655172413793, '=': 0.0, '#': 0.0}\n"
     ]
    }
   ],
   "source": [
    "# Load the model\n",
    "feature_extractor = ViTFeatureExtractor.from_pretrained('microsoft/trocr-base-handwritten')\n",
    "tokenizer = RobertaTokenizer.from_pretrained('microsoft/trocr-base-handwritten')\n",
    "model = load_handwritten_model()\n",
    "# Initialize variables for CER and accuracies\n",
    "total_cer = 0\n",
    "total_accuracy = 0\n",
    "char_accuracies = {}\n",
    "total_chars = {}\n",
    "\n",
    "for idx, row in tqdm(test_df.iterrows(), total=len(test_df), desc=\"Processing images\"):\n",
    "    # Load and process the image\n",
    "    image_path = f'Data/boxes/{row[\"file_name\"]}'\n",
    "    image = Image.open(image_path)  # Replace with your image path\n",
    "    pixel_values = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.to(device)\n",
    "\n",
    "    # Generate the prediction\n",
    "    output_ids = model.generate(pixel_values)\n",
    "\n",
    "    # Decode the prediction\n",
    "    decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
    "\n",
    "    # Compute CER\n",
    "    cer = lv.distance(decoded_output, row['text']) / max(len(decoded_output), len(row['text']))\n",
    "    total_cer += cer\n",
    "\n",
    "    # Compute accuracy for the length of the shorter sequence\n",
    "    min_length = min(len(row['text']), len(decoded_output))\n",
    "    if min_length > 0:\n",
    "        accuracy = accuracy_score(list(row['text'])[:min_length], list(decoded_output)[:min_length])\n",
    "    else:\n",
    "        accuracy = 0\n",
    "\n",
    "    total_accuracy += accuracy\n",
    "\n",
    "    # Update character accuracies\n",
    "    for actual_char, predicted_char in zip(row['text'], decoded_output):\n",
    "        if actual_char not in char_accuracies:\n",
    "            char_accuracies[actual_char] = []\n",
    "            total_chars[actual_char] = 0\n",
    "        char_accuracies[actual_char].append(actual_char == predicted_char)\n",
    "        total_chars[actual_char] += 1\n",
    "\n",
    "\n",
    "\n",
    "    tqdm.write(f\"decoded: {decoded_output}\")\n",
    "    tqdm.write(f\"actual: {row['text']}\")\n",
    "\n",
    "# Compute average CER and accuracies\n",
    "average_cer = total_cer / len(test_df)\n",
    "global_accuracy = total_accuracy / len(test_df)\n",
    "\n",
    "# Compute character-specific accuracies\n",
    "for char in char_accuracies:\n",
    "    char_accuracies[char] = sum(char_accuracies[char]) / total_chars[char]\n",
    "\n",
    "print(\"Average CER:\", average_cer)\n",
    "print(\"Global Accuracy:\", global_accuracy)\n",
    "print(\"Character Accuracies:\", char_accuracies)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Warning\n",
    "\n",
    "This code is very long and not necessary to evaluate our model you can skip it if you'de like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we train our base model on our training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training with learning rate: 1e-05\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[20], line 12\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining with learning rate: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Reset the model to its pre-trained state\u001b[39;00m\n\u001b[1;32m---> 12\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mload_handwritten_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;66;03m# Initialize the optimizer with the current learning rate\u001b[39;00m\n\u001b[0;32m     15\u001b[0m optimizer \u001b[38;5;241m=\u001b[39m AdamW(model\u001b[38;5;241m.\u001b[39mparameters(), lr\u001b[38;5;241m=\u001b[39mlr)\n",
      "Cell \u001b[1;32mIn[14], line 2\u001b[0m, in \u001b[0;36mload_handwritten_model\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_handwritten_model\u001b[39m():\n\u001b[1;32m----> 2\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[43mVisionEncoderDecoderModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmicrosoft/trocr-base-handwritten\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m     model\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;66;03m# set special tokens used for creating the decoder_input_ids from the labels\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\models\\vision_encoder_decoder\\modeling_vision_encoder_decoder.py:358\u001b[0m, in \u001b[0;36mVisionEncoderDecoderModel.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m    352\u001b[0m     logger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[0;32m    353\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFast initialization is currently not supported for VisionEncoderDecoderModel. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    354\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFalling back to slow initialization...\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    355\u001b[0m     )\n\u001b[0;32m    356\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_fast_init\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m--> 358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mfrom_pretrained(pretrained_model_name_or_path, \u001b[38;5;241m*\u001b[39mmodel_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\modeling_utils.py:3236\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m   3233\u001b[0m     config \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_check_and_enable_flash_attn_2(config, torch_dtype\u001b[38;5;241m=\u001b[39mtorch_dtype, device_map\u001b[38;5;241m=\u001b[39mdevice_map)\n\u001b[0;32m   3235\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ContextManagers(init_contexts):\n\u001b[1;32m-> 3236\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(config, \u001b[38;5;241m*\u001b[39mmodel_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs)\n\u001b[0;32m   3238\u001b[0m \u001b[38;5;66;03m# make sure we use the model's config since the __init__ call might have copied it\u001b[39;00m\n\u001b[0;32m   3239\u001b[0m config \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\models\\vision_encoder_decoder\\modeling_vision_encoder_decoder.py:195\u001b[0m, in \u001b[0;36mVisionEncoderDecoderModel.__init__\u001b[1;34m(self, config, encoder, decoder)\u001b[0m\n\u001b[0;32m    192\u001b[0m     encoder \u001b[38;5;241m=\u001b[39m AutoModel\u001b[38;5;241m.\u001b[39mfrom_config(config\u001b[38;5;241m.\u001b[39mencoder)\n\u001b[0;32m    194\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m decoder \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 195\u001b[0m     decoder \u001b[38;5;241m=\u001b[39m \u001b[43mAutoModelForCausalLM\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecoder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    197\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mencoder \u001b[38;5;241m=\u001b[39m encoder\n\u001b[0;32m    198\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecoder \u001b[38;5;241m=\u001b[39m decoder\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\models\\auto\\auto_factory.py:441\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_config\u001b[1;34m(cls, config, **kwargs)\u001b[0m\n\u001b[0;32m    439\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m    440\u001b[0m     model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n\u001b[1;32m--> 441\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m model_class\u001b[38;5;241m.\u001b[39m_from_config(config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    443\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    444\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    445\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    446\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\modeling_utils.py:1192\u001b[0m, in \u001b[0;36mPreTrainedModel._from_config\u001b[1;34m(cls, config, **kwargs)\u001b[0m\n\u001b[0;32m   1190\u001b[0m         model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1191\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1192\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(config, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1194\u001b[0m \u001b[38;5;66;03m# restore default dtype if it was modified\u001b[39;00m\n\u001b[0;32m   1195\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_orig \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\models\\trocr\\modeling_trocr.py:737\u001b[0m, in \u001b[0;36mTrOCRForCausalLM.__init__\u001b[1;34m(self, config)\u001b[0m\n\u001b[0;32m    735\u001b[0m config\u001b[38;5;241m.\u001b[39mis_encoder_decoder \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    736\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(config)\n\u001b[1;32m--> 737\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[43mTrOCRDecoderWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    739\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_projection \u001b[38;5;241m=\u001b[39m nn\u001b[38;5;241m.\u001b[39mLinear(config\u001b[38;5;241m.\u001b[39mhidden_size, config\u001b[38;5;241m.\u001b[39mvocab_size, bias\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m    741\u001b[0m \u001b[38;5;66;03m# Initialize weights and apply final processing\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\models\\trocr\\modeling_trocr.py:718\u001b[0m, in \u001b[0;36mTrOCRDecoderWrapper.__init__\u001b[1;34m(self, config)\u001b[0m\n\u001b[0;32m    716\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, config):\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(config)\n\u001b[1;32m--> 718\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecoder \u001b[38;5;241m=\u001b[39m \u001b[43mTrOCRDecoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\transformers\\models\\trocr\\modeling_trocr.py:458\u001b[0m, in \u001b[0;36mTrOCRDecoder.__init__\u001b[1;34m(self, config)\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_idx \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mpad_token_id\n\u001b[0;32m    456\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed_scale \u001b[38;5;241m=\u001b[39m math\u001b[38;5;241m.\u001b[39msqrt(config\u001b[38;5;241m.\u001b[39mhidden_size) \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39mscale_embedding \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m1.0\u001b[39m\n\u001b[1;32m--> 458\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed_tokens \u001b[38;5;241m=\u001b[39m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEmbedding\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvocab_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhidden_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding_idx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    460\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39muse_learned_position_embeddings:\n\u001b[0;32m    461\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed_positions \u001b[38;5;241m=\u001b[39m TrOCRLearnedPositionalEmbedding(config\u001b[38;5;241m.\u001b[39mmax_position_embeddings, config\u001b[38;5;241m.\u001b[39mhidden_size)\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\torch\\nn\\modules\\sparse.py:144\u001b[0m, in \u001b[0;36mEmbedding.__init__\u001b[1;34m(self, num_embeddings, embedding_dim, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse, _weight, _freeze, device, dtype)\u001b[0m\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    142\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweight \u001b[38;5;241m=\u001b[39m Parameter(torch\u001b[38;5;241m.\u001b[39mempty((num_embeddings, embedding_dim), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfactory_kwargs),\n\u001b[0;32m    143\u001b[0m                             requires_grad\u001b[38;5;241m=\u001b[39m\u001b[38;5;129;01mnot\u001b[39;00m _freeze)\n\u001b[1;32m--> 144\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreset_parameters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    145\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    146\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(_weight\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m [num_embeddings, embedding_dim], \\\n\u001b[0;32m    147\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mShape of weight does not match num_embeddings and embedding_dim\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\torch\\nn\\modules\\sparse.py:153\u001b[0m, in \u001b[0;36mEmbedding.reset_parameters\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    152\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreset_parameters\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 153\u001b[0m     \u001b[43minit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormal_\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    154\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fill_padding_idx_with_zero()\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\torch\\nn\\init.py:155\u001b[0m, in \u001b[0;36mnormal_\u001b[1;34m(tensor, mean, std)\u001b[0m\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39moverrides\u001b[38;5;241m.\u001b[39mhas_torch_function_variadic(tensor):\n\u001b[0;32m    154\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39moverrides\u001b[38;5;241m.\u001b[39mhandle_torch_function(normal_, (tensor,), tensor\u001b[38;5;241m=\u001b[39mtensor, mean\u001b[38;5;241m=\u001b[39mmean, std\u001b[38;5;241m=\u001b[39mstd)\n\u001b[1;32m--> 155\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_no_grad_normal_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\marcp\\miniconda3\\envs\\ML2\\lib\\site-packages\\torch\\nn\\init.py:19\u001b[0m, in \u001b[0;36m_no_grad_normal_\u001b[1;34m(tensor, mean, std)\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_no_grad_normal_\u001b[39m(tensor, mean, std):\n\u001b[0;32m     18\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m---> 19\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtensor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormal_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstd\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# We define a range of learning rates we want to test\n",
    "learning_rates = [1e-5, 8e-6, 4e-6, 1e-6]\n",
    "\n",
    "best_lr = None # To store the best learning rate\n",
    "best_average_cer = float('inf') # To store the best character error rate\n",
    "best_model = None  # To store the best model\n",
    "\n",
    "for lr in learning_rates:\n",
    "    print(f\"Training with learning rate: {lr}\")\n",
    "\n",
    "    # Reset the model to its pre-trained state\n",
    "    model = load_handwritten_model()\n",
    "\n",
    "    # Initialize the optimizer with the current learning rate\n",
    "    optimizer = AdamW(model.parameters(), lr=lr)\n",
    "\n",
    "    for epoch in range(10): # loop over the dataset multiple times\n",
    "        # train\n",
    "        model.train()\n",
    "        train_loss = 0.0\n",
    "        for batch in tqdm(train_dataloader):\n",
    "            # get the inputs\n",
    "            for k,v in batch.items():\n",
    "                batch[k] = v.to(device)\n",
    "            # forward + backward + optimize\n",
    "            outputs = model(**batch)\n",
    "            loss = outputs.loss\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            train_loss += loss.item()\n",
    "        print(f\"Loss after epoch {epoch}:\", train_loss/len(train_dataloader))\n",
    "        # evaluate on the validation set\n",
    "\n",
    "        model.eval()\n",
    "        valid_cer = 0.0\n",
    "        with torch.no_grad():\n",
    "            for batch in tqdm(validation_dataloader):\n",
    "                # run batch generation\n",
    "                outputs = model.generate(batch[\"pixel_values\"].to(device))\n",
    "                # compute metrics\n",
    "                cer = compute_cer(pred_ids=outputs, label_ids=batch[\"labels\"])\n",
    "                valid_cer += cer\n",
    "        average_cer = valid_cer / len(validation_dataloader)\n",
    "        print(f\"Validation Metric for LR {lr} after epoch {epoch}: {average_cer}\")\n",
    "        # Save the best learning rate based on validation performance\n",
    "        if average_cer < best_average_cer:\n",
    "            best_average_cer = average_cer\n",
    "            best_lr = lr\n",
    "            best_model = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we save the weights of our trained model to avoid having to retrain it for future uses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'bool' object has no attribute 'save_pretrained'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Saving the best model\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m best_model \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m----> 3\u001b[0m     \u001b[43mbest_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave_pretrained\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/content/drive/MyDrive/ML_project/chessreader_project_2/best_model_handwritten_lower\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBest model saved with learning rate: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbest_lr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'bool' object has no attribute 'save_pretrained'"
     ]
    }
   ],
   "source": [
    "# Saving the best model\n",
    "if best_model is not None:\n",
    "    best_model.save_pretrained(\"/content/drive/MyDrive/ML_project/chessreader_project_2/best_model_handwritten_lower\")\n",
    "    print(f\"Best model saved with learning rate: {best_lr}\")\n",
    "else:\n",
    "    print(\"No model was saved.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then define a function to load the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_best_handwritten():\n",
    "  # Path to the saved model\n",
    "  model_path = \"best_model_handwritten_lower\"\n",
    "\n",
    "  # Load the model\n",
    "  model = VisionEncoderDecoderModel.from_pretrained(model_path)\n",
    "\n",
    "  # Make sure to also move the model to the correct device (e.g., GPU or CPU)\n",
    "  model.to(device)\n",
    "  # set special tokens used for creating the decoder_input_ids from the labels\n",
    "  model.config.decoder_start_token_id = processor.tokenizer.cls_token_id\n",
    "  model.config.pad_token_id = processor.tokenizer.pad_token_id\n",
    "  # make sure vocab size is set correctly\n",
    "  model.config.vocab_size = model.config.decoder.vocab_size\n",
    "\n",
    "  # set beam search parameters\n",
    "  model.config.eos_token_id = processor.tokenizer.sep_token_id\n",
    "  model.config.max_length = 64\n",
    "  model.config.early_stopping = True\n",
    "  model.config.no_repeat_ngram_size = 3\n",
    "  model.config.length_penalty = 2.0\n",
    "  model.config.num_beams = 4\n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we evaluate the performances of the trained model on useen data, the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "actual: Rb8\n",
      "decoded: a6\n",
      "actual: a6\n",
      "decoded: Be2\n",
      "actual: Be2\n",
      "decoded: Nd5\n",
      "actual: Nd5\n",
      "decoded: cxb6\n",
      "actual: cxb6\n",
      "decoded: e6\n",
      "actual: e6\n",
      "decoded: b8=Q\n",
      "actual: b8=Q\n",
      "decoded: Bd6\n",
      "actual: Bd6\n",
      "decoded: Nxd4\n",
      "actual: Nxd4\n",
      "decoded: b3\n",
      "actual: b3\n",
      "decoded: Bxb2\n",
      "actual: Bxb2\n",
      "decoded: Ngf5\n",
      "actual: Ngf5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Qxe3\n",
      "actual: Qxe3\n",
      "decoded: Rc1\n",
      "actual: Rc1\n",
      "decoded: Nc6\n",
      "actual: Nc6\n",
      "decoded: e6\n",
      "actual: e6\n",
      "decoded: a6\n",
      "actual: a6\n",
      "decoded: Bf8\n",
      "actual: Bf8\n",
      "decoded: Ne2\n",
      "actual: Ne2\n",
      "decoded: a3\n",
      "actual: a3\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: h5\n",
      "actual: h5\n",
      "decoded: Qg6\n",
      "actual: Qg6\n",
      "decoded: a4\n",
      "actual: a4\n",
      "decoded: Bxe7\n",
      "actual: Bxe7\n",
      "decoded: Qf6\n",
      "actual: Qf6\n",
      "decoded: Qb4+\n",
      "actual: Qb4+\n",
      "decoded: Bxg7\n",
      "actual: Bxg7\n",
      "decoded: Nd5\n",
      "actual: Nd5\n",
      "decoded: Na3\n",
      "actual: Nc4\n",
      "decoded: dxe5\n",
      "actual: dxe5\n",
      "decoded: Bd8\n",
      "actual: Bd8\n",
      "decoded: Rxc1\n",
      "actual: Rxc1\n",
      "decoded: Ra7\n",
      "actual: Ra7\n",
      "decoded: Bc2\n",
      "actual: Bc2\n",
      "decoded: Ba2\n",
      "actual: Ba2\n",
      "decoded: Nb8\n",
      "actual: Nb8\n",
      "decoded: Qe3\n",
      "actual: Qe3\n",
      "decoded: Qa4\n",
      "actual: Qa4\n",
      "decoded: Qe8+\n",
      "actual: Qe8+\n",
      "decoded: Rd2\n",
      "actual: Kd2\n",
      "decoded: cxd5\n",
      "actual: cxd5\n",
      "decoded: e4\n",
      "actual: e4\n",
      "decoded: Fxe1\n",
      "actual: Ke2\n",
      "decoded: d5\n",
      "actual: d5\n",
      "decoded: Ne3\n",
      "actual: Nxg4\n",
      "decoded: Bg7\n",
      "actual: Bg7\n",
      "decoded: b6\n",
      "actual: b6\n",
      "decoded: f3\n",
      "actual: f3\n",
      "decoded: Qg4\n",
      "actual: Qg4\n",
      "decoded: Bg5\n",
      "actual: Bg5\n",
      "decoded: bxa3\n",
      "actual: bxa3\n",
      "decoded: Qd4\n",
      "actual: Qd4\n",
      "decoded: Nxc5\n",
      "actual: Nxc5\n",
      "decoded: Qh4\n",
      "actual: Qh4\n",
      "decoded: f5\n",
      "actual: f5\n",
      "decoded: Nb5\n",
      "actual: Nb5\n",
      "decoded: e6\n",
      "actual: e6\n",
      "decoded: Bf4\n",
      "actual: Bf4\n",
      "decoded: Qe5+\n",
      "actual: Qe5+\n",
      "decoded: Qxd4\n",
      "actual: Qxd4\n",
      "decoded: Nd3+\n",
      "actual: Nd3+\n",
      "decoded: g3\n",
      "actual: g3\n",
      "decoded: Qxc5\n",
      "actual: Qxc5\n",
      "decoded: Bc6\n",
      "actual: Bc6\n",
      "decoded: Bc8\n",
      "actual: Bc8\n",
      "decoded: Bh4\n",
      "actual: Bh4\n",
      "decoded: Rxa4\n",
      "actual: Rxa4\n",
      "decoded: bxc5\n",
      "actual: bxc5\n",
      "decoded: Rff2\n",
      "actual: Rff2\n",
      "decoded: Nf4\n",
      "actual: Nf4\n",
      "decoded: a5+\n",
      "actual: a6\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Rxd6\n",
      "actual: Rxd6\n",
      "decoded: Qe2\n",
      "actual: Qe2\n",
      "decoded: Qa4\n",
      "actual: Qa4\n",
      "decoded: b6\n",
      "actual: b6\n",
      "decoded: Rxa3\n",
      "actual: Rxa3\n",
      "decoded: Rad8\n",
      "actual: Rad8\n",
      "decoded: Qb7\n",
      "actual: Qb7\n",
      "decoded: Bf4\n",
      "actual: Bf4\n",
      "decoded: Rc6\n",
      "actual: Rc6\n",
      "decoded: c4\n",
      "actual: c4\n",
      "decoded: Kf7\n",
      "actual: Kf7\n",
      "decoded: Nf6+\n",
      "actual: Nf6+\n",
      "decoded: h6\n",
      "actual: h6\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: e4\n",
      "actual: e4\n",
      "decoded: cxd6\n",
      "actual: cxd6\n",
      "decoded: Bxb3\n",
      "actual: Bxb3\n",
      "decoded: Rac1\n",
      "actual: Rac1\n",
      "decoded: a5\n",
      "actual: a5\n",
      "decoded: Qd5\n",
      "actual: Qd5\n",
      "decoded: f6\n",
      "actual: f6\n",
      "decoded: d5\n",
      "actual: d5\n",
      "decoded: a6\n",
      "actual: a6\n",
      "decoded: c6\n",
      "actual: c6\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: Qf5\n",
      "actual: Qf5\n",
      "decoded: Nxe5\n",
      "actual: Nxe5\n",
      "decoded: c4\n",
      "actual: c4\n",
      "decoded: Qf3\n",
      "actual: Qf3\n",
      "decoded: Bc5\n",
      "actual: Bc5\n",
      "decoded: Bf4\n",
      "actual: Bf4\n",
      "decoded: Nge2\n",
      "actual: Nge2\n",
      "decoded: Bd6\n",
      "actual: Bd6\n",
      "decoded: Kf6\n",
      "actual: Kf6\n",
      "decoded: f3\n",
      "actual: f3\n",
      "decoded: Rf7\n",
      "actual: Rf7\n",
      "decoded: h6\n",
      "actual: h6\n",
      "decoded: cxd4\n",
      "actual: cxd4\n",
      "decoded: Rxc8\n",
      "actual: Rxc8\n",
      "decoded: h6\n",
      "actual: h6\n",
      "decoded: c3\n",
      "actual: c3\n",
      "decoded: Kxc4\n",
      "actual: Kc5\n",
      "decoded: Qb3\n",
      "actual: Qb3\n",
      "decoded: Qg4\n",
      "actual: Qg4\n",
      "decoded: Bf6\n",
      "actual: Bf6\n",
      "decoded: Na4\n",
      "actual: Na4\n",
      "decoded: Bd8\n",
      "actual: Kc3\n",
      "decoded: g5\n",
      "actual: g5\n",
      "decoded: Bg2\n",
      "actual: Bg2\n",
      "decoded: Qe8+\n",
      "actual: Qe8+\n",
      "decoded: exd5\n",
      "actual: exd5\n",
      "decoded: Bc4\n",
      "actual: Bc4\n",
      "decoded: Be1\n",
      "actual: Be1\n",
      "decoded: Qd8\n",
      "actual: Qd8\n",
      "decoded: Rxc5\n",
      "actual: Rxc5\n",
      "decoded: Bf8\n",
      "actual: Bf8\n",
      "decoded: Nxe6\n",
      "actual: Nxe6\n",
      "decoded: Re1\n",
      "actual: Re1\n",
      "decoded: Qxd5\n",
      "actual: Qxd5\n",
      "decoded: Nd7\n",
      "actual: Nd7\n",
      "decoded: f4\n",
      "actual: f4\n",
      "decoded: Bh5\n",
      "actual: Bh5\n",
      "decoded: cxd6\n",
      "actual: cxd6\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: Qc7\n",
      "actual: Qc7\n",
      "decoded: Bf3\n",
      "actual: Bf3\n",
      "decoded: Qxc6\n",
      "actual: Qxc6\n",
      "decoded: Rxe1+\n",
      "actual: Rxe1+\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: Ba3\n",
      "actual: Ba3\n",
      "decoded: Red2\n",
      "actual: Red2\n",
      "decoded: d5\n",
      "actual: d5\n",
      "decoded: Bh3\n",
      "actual: Bh3\n",
      "decoded: Ba4\n",
      "actual: Ba4\n",
      "decoded: Rac1\n",
      "actual: Rac1\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: f5\n",
      "actual: f5\n",
      "decoded: h4\n",
      "actual: h4\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: Nxe5\n",
      "actual: Nxe5\n",
      "decoded: fxe5\n",
      "actual: fxe5\n",
      "decoded: b4\n",
      "actual: b4\n",
      "decoded: f6\n",
      "actual: f6\n",
      "decoded: a3\n",
      "actual: a3\n",
      "decoded: Nh6\n",
      "actual: Nh6\n",
      "decoded: Qxa1\n",
      "actual: Qxa1\n",
      "decoded: e3\n",
      "actual: e3\n",
      "decoded: Rd8\n",
      "actual: Rd8\n",
      "decoded: Qb3\n",
      "actual: Qb3\n",
      "decoded: Nf5\n",
      "actual: Nf5\n",
      "decoded: Nxc6\n",
      "actual: Nxc6\n",
      "decoded: b4\n",
      "actual: b4\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: Kg8\n",
      "actual: Kg8\n",
      "decoded: Nc5\n",
      "actual: Nc5\n",
      "decoded: Nxd4\n",
      "actual: Nxd4\n",
      "decoded: Nxb6\n",
      "actual: Nxb6\n",
      "decoded: Bxe5\n",
      "actual: Bxe5\n",
      "decoded: b6\n",
      "actual: b6\n",
      "decoded: d5\n",
      "actual: d5\n",
      "decoded: Nxf1\n",
      "actual: Nxf1\n",
      "decoded: Re8\n",
      "actual: Re8\n",
      "decoded: Nc5\n",
      "actual: Nc5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Be3\n",
      "actual: Be3\n",
      "decoded: Rxb7\n",
      "actual: Rxb7\n",
      "decoded: Re8\n",
      "actual: Re8\n",
      "decoded: Nf5\n",
      "actual: Nf5\n",
      "decoded: Qc7\n",
      "actual: Qc7\n",
      "decoded: e4\n",
      "actual: e4\n",
      "decoded: Bg6\n",
      "actual: Bg6\n",
      "decoded: Nc6\n",
      "actual: Nc6\n",
      "decoded: Rxg5\n",
      "actual: Rxg5\n",
      "decoded: Rd1\n",
      "actual: Rd1\n",
      "decoded: Qh3\n",
      "actual: Qh3\n",
      "decoded: Bxb7\n",
      "actual: Bxb7\n",
      "decoded: Ba6\n",
      "actual: Ba6\n",
      "decoded: cxd4\n",
      "actual: cxd4\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: Bxf6\n",
      "actual: Bxf6\n",
      "decoded: Nxd5\n",
      "actual: Nxd5\n",
      "decoded: a4\n",
      "actual: a4\n",
      "decoded: Kd7\n",
      "actual: Kd7\n",
      "decoded: Ra2\n",
      "actual: Ra2\n",
      "decoded: Qa4\n",
      "actual: Qa4\n",
      "decoded: cxd4\n",
      "actual: cxd4\n",
      "decoded: Bf5\n",
      "actual: Bf5\n",
      "decoded: Bxf3\n",
      "actual: Bxf3\n",
      "decoded: Qc8\n",
      "actual: Qc8\n",
      "decoded: Nxe5\n",
      "actual: Nxe5\n",
      "decoded: Nc6\n",
      "actual: Nc6\n",
      "decoded: Bd2\n",
      "actual: Bd2\n",
      "decoded: e5\n",
      "actual: e5\n",
      "decoded: Nf1\n",
      "actual: Nf1\n",
      "decoded: Rac1\n",
      "actual: Rac1\n",
      "decoded: Bxf1\n",
      "actual: Bxf1\n",
      "decoded: Rb1\n",
      "actual: Rb1\n",
      "decoded: a5\n",
      "actual: a5\n",
      "decoded: e3\n",
      "actual: e3\n",
      "decoded: exf5\n",
      "actual: exf5\n",
      "decoded: Bd3\n",
      "actual: Bd3\n",
      "decoded: fxg6\n",
      "actual: fxg6\n",
      "decoded: Bb6\n",
      "actual: Bb6\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: e6\n",
      "actual: e6\n",
      "decoded: Nxf5\n",
      "actual: Nxf5\n",
      "decoded: d5\n",
      "actual: d5\n",
      "decoded: Rd7\n",
      "actual: Rd7\n",
      "decoded: B3\n",
      "actual: b3\n",
      "decoded: Nh6\n",
      "actual: Nh6\n",
      "decoded: exd4\n",
      "actual: exd4\n",
      "decoded: Kf2\n",
      "actual: Kf2\n",
      "decoded: Be6\n",
      "actual: Be6\n",
      "decoded: Rb8\n",
      "actual: Rb8\n",
      "decoded: Rd7\n",
      "actual: Rd7\n",
      "decoded: Qxb7\n",
      "actual: Qxb7\n",
      "decoded: Rb8\n",
      "actual: Rb8\n",
      "decoded: Bxd6\n",
      "actual: Bxd6\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Nxd4\n",
      "actual: Nxd4\n",
      "decoded: Nf6\n",
      "actual: Nf6\n",
      "decoded: g4\n",
      "actual: g4\n",
      "decoded: Qb4\n",
      "actual: Qb4\n",
      "decoded: Rb8\n",
      "actual: Rb8\n",
      "decoded: Rxa3\n",
      "actual: Rxa3\n",
      "decoded: axb5\n",
      "actual: axb5\n",
      "decoded: Qg3\n",
      "actual: Qg3\n",
      "decoded: a3\n",
      "actual: a3\n",
      "decoded: Bxf6\n",
      "actual: Bxf6\n",
      "decoded: Bc2\n",
      "actual: Bc2\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: h5\n",
      "actual: h5\n",
      "decoded: Ndxe5\n",
      "actual: Ndxe5\n",
      "decoded: Ng8\n",
      "actual: Ng8\n",
      "decoded: Nxd5\n",
      "actual: Nxd5\n",
      "decoded: d6\n",
      "actual: d6\n",
      "decoded: Qf6\n",
      "actual: Qf6\n",
      "decoded: g6\n",
      "actual: g6\n",
      "decoded: Qxc4\n",
      "actual: Qxc4\n",
      "decoded: d6\n",
      "actual: d6\n",
      "decoded: Nh4\n",
      "actual: Nh4\n",
      "decoded: f6\n",
      "actual: f6\n",
      "decoded: b5\n",
      "actual: b5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Rxa7\n",
      "actual: Rxa7\n",
      "decoded: h5\n",
      "actual: b6\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: Rh7\n",
      "actual: Rh7\n",
      "decoded: c4\n",
      "actual: c4\n",
      "decoded: Rfd1\n",
      "actual: Rfd1\n",
      "decoded: Kg2\n",
      "actual: Kg2\n",
      "decoded: Rxb4\n",
      "actual: Rxb4\n",
      "decoded: Rc8\n",
      "actual: Rc8\n",
      "decoded: fxe4\n",
      "actual: fxe4\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: Ne4\n",
      "actual: Ne4\n",
      "decoded: Be2\n",
      "actual: Be2\n",
      "decoded: Rxd5\n",
      "actual: Rxd5\n",
      "decoded: Nb5\n",
      "actual: Nb5\n",
      "decoded: Qh4+\n",
      "actual: Qh4+\n",
      "decoded: Be6\n",
      "actual: Be6\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: g3\n",
      "actual: g3\n",
      "decoded: Bg4\n",
      "actual: Bg4\n",
      "decoded: Rxa3\n",
      "actual: Rxa3\n",
      "decoded: f3\n",
      "actual: f3\n",
      "decoded: e5\n",
      "actual: e5\n",
      "decoded: h3\n",
      "actual: h3\n",
      "decoded: Bf2\n",
      "actual: Bf2\n",
      "decoded: Qxd8\n",
      "actual: Qxd8\n",
      "decoded: Ne3\n",
      "actual: Ne3\n",
      "decoded: Nd2\n",
      "actual: Nd2\n",
      "decoded: e3\n",
      "actual: e3\n",
      "decoded: Qg4\n",
      "actual: Qg4\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: Nc6\n",
      "actual: Nc6\n",
      "decoded: Nd2\n",
      "actual: Nd2\n",
      "decoded: Nc5\n",
      "actual: Nc5\n",
      "decoded: Nbc6\n",
      "actual: Nbc6\n",
      "decoded: Qxb6\n",
      "actual: Qxb6\n",
      "decoded: Bxd5\n",
      "actual: Bxd5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Rad1\n",
      "actual: Rad1\n",
      "decoded: Nxe5\n",
      "actual: Nxe5\n",
      "decoded: Qxg7\n",
      "actual: Qxg7\n",
      "decoded: Qc2\n",
      "actual: Qc2\n",
      "decoded: Kf8\n",
      "actual: Kf8\n",
      "decoded: Rxg6+\n",
      "actual: Rxg6+\n",
      "decoded: Bd2\n",
      "actual: Bd2\n",
      "decoded: exd5\n",
      "actual: exd5\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: f6\n",
      "actual: f6\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Bxe7\n",
      "actual: Bxe7\n",
      "decoded: e6\n",
      "actual: e6\n",
      "decoded: Qe7\n",
      "actual: Qe7\n",
      "decoded: Bb7\n",
      "actual: Bb7\n",
      "decoded: Qxe8+\n",
      "actual: Qxe8+\n",
      "decoded: Rb7\n",
      "actual: Rb7\n",
      "decoded: Nc4\n",
      "actual: Nc4\n",
      "decoded: dxe4\n",
      "actual: dxe4\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Bf4\n",
      "actual: Bf4\n",
      "decoded: Nd5\n",
      "actual: Nd5\n",
      "decoded: Rc3\n",
      "actual: Rc3\n",
      "decoded: Bd2\n",
      "actual: Bd2\n",
      "decoded: Be3\n",
      "actual: Qc2\n",
      "decoded: Qxc5\n",
      "actual: Qxc5\n",
      "decoded: Kxa6\n",
      "actual: Kxa6\n",
      "decoded: Qxg6+\n",
      "actual: Qxg6+\n",
      "decoded: Rb6\n",
      "actual: Rb6\n",
      "decoded: Raxb1\n",
      "actual: Raxb1\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: g6\n",
      "actual: g6\n",
      "decoded: hxg6\n",
      "actual: hxg6\n",
      "decoded: Qa4\n",
      "actual: Qa4\n",
      "decoded: dxc4\n",
      "actual: dxc4\n",
      "decoded: Ne7\n",
      "actual: Ne7\n",
      "decoded: d6\n",
      "actual: d6\n",
      "decoded: Qxa1\n",
      "actual: Qxa1\n",
      "decoded: Rc5\n",
      "actual: Rc5\n",
      "decoded: Qxc5\n",
      "actual: Qxc5\n",
      "decoded: Nd1\n",
      "actual: Nd1\n",
      "decoded: Rea8\n",
      "actual: Rea8\n",
      "decoded: Nf4\n",
      "actual: Nf4\n",
      "decoded: Qh3\n",
      "actual: Qh3\n",
      "decoded: Nf4\n",
      "actual: Nf4\n",
      "decoded: g5\n",
      "actual: g5\n",
      "decoded: Rb8\n",
      "actual: Rb8\n",
      "decoded: f4\n",
      "actual: f4\n",
      "decoded: Ke3\n",
      "actual: Ke3\n",
      "decoded: gxf4\n",
      "actual: gxf4\n",
      "decoded: Bd7\n",
      "actual: Bd7\n",
      "decoded: cxd5\n",
      "actual: cxd5\n",
      "decoded: Kxg2\n",
      "actual: Kxg2\n",
      "decoded: h5\n",
      "actual: h5\n",
      "decoded: Bd7\n",
      "actual: Bd7\n",
      "decoded: Rab8\n",
      "actual: Rab8\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Nc6\n",
      "actual: Nc6\n",
      "decoded: cxd5\n",
      "actual: cxd5\n",
      "decoded: Rc3\n",
      "actual: Rc3\n",
      "decoded: Qxf5\n",
      "actual: Qxf5\n",
      "decoded: Ne4\n",
      "actual: Ne4\n",
      "decoded: Qe2\n",
      "actual: Qe2\n",
      "decoded: Qxe8+\n",
      "actual: Qxe8+\n",
      "decoded: Rd1\n",
      "actual: Rd1\n",
      "decoded: Rxc5\n",
      "actual: Rxc5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Bb7\n",
      "actual: Bb7\n",
      "decoded: Rb5\n",
      "actual: Rb5\n",
      "decoded: e5\n",
      "actual: e5\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: Qd1\n",
      "actual: Qd1\n",
      "decoded: g3\n",
      "actual: g3\n",
      "decoded: Ng5\n",
      "actual: Ng5\n",
      "decoded: Rfd1\n",
      "actual: Rfd1\n",
      "decoded: d6\n",
      "actual: d6\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: g6\n",
      "actual: g6\n",
      "decoded: Qe2\n",
      "actual: Qe2\n",
      "decoded: g6\n",
      "actual: g6\n",
      "decoded: c6\n",
      "actual: c6\n",
      "decoded: Qc2\n",
      "actual: Qc2\n",
      "decoded: fxe4\n",
      "actual: fxe4\n",
      "decoded: Bg5\n",
      "actual: Bg5\n",
      "decoded: Bc3\n",
      "actual: Bc3\n",
      "decoded: Qxf6\n",
      "actual: Qxf6\n",
      "decoded: fxg5\n",
      "actual: fxg5\n",
      "decoded: h4\n",
      "actual: h4\n",
      "decoded: Qxb3\n",
      "actual: Qxb3\n",
      "decoded: Kg8\n",
      "actual: Kg8\n",
      "decoded: e4\n",
      "actual: e4\n",
      "decoded: Bf5\n",
      "actual: Bf5\n",
      "decoded: Qxb6\n",
      "actual: Qxb6\n",
      "decoded: bxa6\n",
      "actual: bxa6\n",
      "decoded: a5\n",
      "actual: a6\n",
      "decoded: Qxd5\n",
      "actual: Qxd5\n",
      "decoded: Qd5\n",
      "actual: Qd5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Nh3+\n",
      "actual: Nh3+\n",
      "decoded: b6\n",
      "actual: b6\n",
      "decoded: Bf4\n",
      "actual: Bf4\n",
      "decoded: Qf5\n",
      "actual: Qf5\n",
      "decoded: exf6\n",
      "actual: exf6\n",
      "Average CER: 0.021246105919003114\n",
      "Global Accuracy: 0.9784423676012461\n",
      "Character Accuracies: {'N': 1.0, 'b': 0.9692307692307692, '6': 0.9719626168224299, 'R': 1.0, 'c': 0.956989247311828, '8': 0.9743589743589743, 'd': 1.0, '7': 0.9761904761904762, 'B': 0.9891304347826086, 'x': 0.9923076923076923, 'e': 0.9662921348314607, 'h': 0.9393939393939394, 'a': 1.0, '1': 1.0, 'g': 0.9818181818181818, '3': 0.9868421052631579, 'K': 0.8846153846153846, 'f': 0.9875, '2': 0.9459459459459459, '5': 0.9696969696969697, '4': 0.9888888888888889, 'Q': 0.9866666666666667, 'O': 1.0, '-': 1.0, '+': 1.0, '=': 1.0}\n"
     ]
    }
   ],
   "source": [
    "# Load the model\n",
    "feature_extractor = ViTFeatureExtractor.from_pretrained('microsoft/trocr-base-handwritten')\n",
    "tokenizer = RobertaTokenizer.from_pretrained('microsoft/trocr-base-handwritten')\n",
    "best_model = load_best_handwritten()\n",
    "# Initialize variables for CER and accuracies\n",
    "total_cer = 0\n",
    "total_accuracy = 0\n",
    "char_accuracies = {}\n",
    "total_chars = {}\n",
    "\n",
    "for idx, row in tqdm(test_df.iterrows(), total=len(test_df), desc=\"Processing images\"):\n",
    "    # Load and process the image\n",
    "    image_path = f'Data/boxes/{row[\"file_name\"]}'\n",
    "    image = Image.open(image_path)  # Replace with your image path\n",
    "    pixel_values = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.to(device)\n",
    "\n",
    "    # Generate the prediction\n",
    "    output_ids = best_model.generate(pixel_values)\n",
    "\n",
    "    # Decode the prediction\n",
    "    decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
    "\n",
    "    # Compute CER\n",
    "    cer = lv.distance(decoded_output, row['text']) / max(len(decoded_output), len(row['text']))\n",
    "    total_cer += cer\n",
    "\n",
    "    # Compute accuracy for the length of the shorter sequence\n",
    "    min_length = min(len(row['text']), len(decoded_output))\n",
    "    if min_length > 0:\n",
    "        accuracy = accuracy_score(list(row['text'])[:min_length], list(decoded_output)[:min_length])\n",
    "    else:\n",
    "        accuracy = 0\n",
    "\n",
    "    total_accuracy += accuracy\n",
    "\n",
    "    # Update character accuracies\n",
    "    for actual_char, predicted_char in zip(row['text'], decoded_output):\n",
    "        if actual_char not in char_accuracies:\n",
    "            char_accuracies[actual_char] = []\n",
    "            total_chars[actual_char] = 0\n",
    "        char_accuracies[actual_char].append(actual_char == predicted_char)\n",
    "        total_chars[actual_char] += 1\n",
    "\n",
    "    tqdm.write(f\"decoded: {decoded_output}\")\n",
    "    tqdm.write(f\"actual: {row['text']}\")\n",
    "\n",
    "# Compute average CER and accuracies\n",
    "average_cer = total_cer / len(test_df)\n",
    "global_accuracy = total_accuracy / len(test_df)\n",
    "\n",
    "# Compute character-specific accuracies\n",
    "for char in char_accuracies:\n",
    "    char_accuracies[char] = sum(char_accuracies[char]) / total_chars[char]\n",
    "\n",
    "print(\"Average CER:\", average_cer)\n",
    "print(\"Global Accuracy:\", global_accuracy)\n",
    "print(\"Character Accuracies:\", char_accuracies)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the character-level accuracies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sorting the dictionary by values (accuracy)\n",
    "sorted_dict = dict(sorted(char_accuracies.items(), key=lambda item: item[1], reverse=True))\n",
    "\n",
    "# Extracting characters and their accuracies for plotting\n",
    "characters = list(sorted_dict.keys())\n",
    "accuracies = list(sorted_dict.values())\n",
    "\n",
    "# Creating the bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(characters, accuracies, color='skyblue')\n",
    "plt.xlabel('Characters')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Character Accuracies')\n",
    "plt.ylim(0, 1)  # Assuming accuracy values are between 0 and 1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 1 model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we do the same with the TrOCR-base-stage1 model to see if we can get some performance boosting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5ceb8470724480e8f99ce366c2c01d5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "preprocessor_config.json:   0%|          | 0.00/228 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "981702a9a682472d8e557b05fb6c6293",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/1.12k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05bb3c13df304019869460f9a7b50abb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70647da8b4d84162b6c83f9bd73f1306",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e198d7fc1b444e8a3a0963cba235e4e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-base-stage1\")\n",
    "train_dataset = ChessDataset(root_dir='Data/boxes/',\n",
    "                           df=train_df,\n",
    "                           processor=processor)\n",
    "test_dataset = ChessDataset(root_dir='Data/boxes/',\n",
    "                           df=test_df,\n",
    "                           processor=processor)\n",
    "validation_dataset = ChessDataset(root_dir='Data/boxes/',\n",
    "                           df=val_df,\n",
    "                           processor=processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=4)\n",
    "validation_dataloader = DataLoader(validation_dataset, batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_stage1_model():\n",
    "    model = VisionEncoderDecoderModel.from_pretrained(\"microsoft/trocr-base-stage1\")\n",
    "    model.to(device)\n",
    "    # set special tokens used for creating the decoder_input_ids from the labels\n",
    "    model.config.decoder_start_token_id = processor.tokenizer.cls_token_id\n",
    "    model.config.pad_token_id = processor.tokenizer.pad_token_id\n",
    "    # make sure vocab size is set correctly\n",
    "    model.config.vocab_size = model.config.decoder.vocab_size\n",
    "\n",
    "    # set beam search parameters\n",
    "    model.config.eos_token_id = processor.tokenizer.sep_token_id\n",
    "    model.config.max_length = 64\n",
    "    model.config.early_stopping = True\n",
    "    model.config.no_repeat_ngram_size = 3\n",
    "    model.config.length_penalty = 2.0\n",
    "    model.config.num_beams = 4\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Warning\n",
    "\n",
    "This code is very long and not necessary to evaluate our model you can skip it if you'de like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We evaluate the base model on the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "392d28bd26694360bfc409dab7387d45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/4.21k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb01f87613304fe2b06f9c899a2e3117",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/1.54G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-stage1 and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45fe01cac0f04b269fe8cad2167f3a3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1473: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "decoded: 9\n",
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      "decoded: 1.\n",
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      "decoded: (2 ]\n",
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      "actual: Qh4+\n",
      "decoded: !\n",
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      "actual: d4\n",
      "decoded:  -\n",
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      "actual: Kxa6\n",
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      "actual: Qxg6+\n",
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      "actual: Raxb1\n",
      "decoded: .\n",
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      "actual: Ke3\n",
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      "actual: Bd7\n",
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      "actual: Kxg2\n",
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      "actual: cxd5\n",
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      "actual: Rc3\n",
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      "decoded: .\n",
      "actual: Ne4\n",
      "decoded: .\n",
      "actual: Qe2\n",
      "decoded: \n",
      "actual: Qxe8+\n",
      "decoded: \n",
      "actual: Rd1\n",
      "decoded: \n",
      "actual: Rxc5\n",
      "decoded: \n",
      "actual: O-O\n",
      "decoded: .\n",
      "actual: Bb7\n",
      "decoded: \n",
      "actual: Rb5\n",
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      "decoded: \n",
      "actual: Nc3\n",
      "decoded: and\n",
      "actual: Qd1\n",
      "decoded: .\n",
      "actual: g3\n",
      "decoded: .\n",
      "actual: Ng5\n",
      "decoded: \n",
      "actual: Rfd1\n",
      "decoded: \n",
      "actual: d6\n",
      "decoded: .\n",
      "actual: Nc3\n",
      "decoded: .\n",
      "actual: g6\n",
      "decoded: \n",
      "actual: Qe2\n",
      "decoded: .\n",
      "actual: g6\n",
      "decoded: \n",
      "actual: c6\n",
      "decoded: .\n",
      "actual: Qc2\n",
      "decoded: \n",
      "actual: fxe4\n",
      "decoded: .\n",
      "actual: Bg5\n",
      "decoded: .\n",
      "actual: Bc3\n",
      "decoded: \n",
      "actual: Qxf6\n",
      "decoded: \n",
      "actual: fxg5\n",
      "decoded: .\n",
      "actual: h4\n",
      "decoded: . 3\n",
      "actual: Qxb3\n",
      "decoded: \n",
      "actual: Kg8\n",
      "decoded: \n",
      "actual: e4\n",
      "decoded: \n",
      "actual: Bf5\n",
      "decoded: \n",
      "actual: Qxb6\n",
      "decoded: \n",
      "actual: bxa6\n",
      "decoded: on\n",
      "actual: a6\n",
      "decoded: \n",
      "actual: Qxd5\n",
      "decoded: \n",
      "actual: Qd5\n",
      "decoded: \n",
      "actual: O-O\n",
      "decoded: .\n",
      "actual: Nh3+\n",
      "decoded: \n",
      "actual: b6\n",
      "decoded: \n",
      "actual: Bf4\n",
      "decoded: \n",
      "actual: Qf5\n",
      "decoded: \n",
      "actual: exf6\n",
      "Average CER: 0.9914330218068538\n",
      "Global Accuracy: 0.0037383177570093455\n",
      "Character Accuracies: {'N': 0.0, 'R': 0.0, 'h': 0.07142857142857142, 'g': 0.0, '6': 0.0, 'B': 0.0, 'd': 0.0, 'K': 0.0, 'b': 0.0, 'a': 0.0, 'Q': 0.0, 'f': 0.0, 'e': 0.0, '5': 0.0, 'c': 0.0, '4': 0.16666666666666666, '7': 0.0, '2': 0.6666666666666666, 'O': 0.0, '3': 0.3333333333333333, '1': 0.0, 'x': 0.0}\n"
     ]
    }
   ],
   "source": [
    "# Load the model\n",
    "feature_extractor = ViTFeatureExtractor.from_pretrained('microsoft/trocr-base-stage1')\n",
    "tokenizer = RobertaTokenizer.from_pretrained('microsoft/trocr-base-stage1')\n",
    "model = load_stage1_model()\n",
    "# Initialize variables for CER and accuracies\n",
    "total_cer = 0\n",
    "total_accuracy = 0\n",
    "char_accuracies = {}\n",
    "total_chars = {}\n",
    "\n",
    "for idx, row in test_df.iterrows():\n",
    "    # Load and process the image\n",
    "    image_path = f'Data/boxes/{row[\"file_name\"]}'\n",
    "    image = Image.open(image_path)  # Replace with your image path\n",
    "    pixel_values = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.to(device)\n",
    "\n",
    "    # Generate the prediction\n",
    "    output_ids = model.generate(pixel_values)\n",
    "\n",
    "    # Decode the prediction\n",
    "    decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
    "\n",
    "    # Compute CER\n",
    "    cer = lv.distance(decoded_output, row['text']) / max(len(decoded_output), len(row['text']))\n",
    "    total_cer += cer\n",
    "\n",
    "    # Compute accuracy for the length of the shorter sequence\n",
    "    min_length = min(len(row['text']), len(decoded_output))\n",
    "    if min_length > 0:\n",
    "        accuracy = accuracy_score(list(row['text'])[:min_length], list(decoded_output)[:min_length])\n",
    "    else:\n",
    "        accuracy = 0\n",
    "\n",
    "    total_accuracy += accuracy\n",
    "\n",
    "    # Update character accuracies\n",
    "    for actual_char, predicted_char in zip(row['text'], decoded_output):\n",
    "        if actual_char not in char_accuracies:\n",
    "            char_accuracies[actual_char] = []\n",
    "            total_chars[actual_char] = 0\n",
    "        char_accuracies[actual_char].append(actual_char == predicted_char)\n",
    "        total_chars[actual_char] += 1\n",
    "\n",
    "    print(\"decoded:\", decoded_output)\n",
    "    print(\"actual:\", row['text'])\n",
    "\n",
    "# Compute average CER and accuracies\n",
    "average_cer = total_cer / len(test_df)\n",
    "global_accuracy = total_accuracy / len(test_df)\n",
    "\n",
    "# Compute character-specific accuracies\n",
    "for char in char_accuracies:\n",
    "    char_accuracies[char] = sum(char_accuracies[char]) / total_chars[char]\n",
    "\n",
    "print(\"Average CER:\", average_cer)\n",
    "print(\"Global Accuracy:\", global_accuracy)\n",
    "print(\"Character Accuracies:\", char_accuracies)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Warning\n",
    "\n",
    "This code is very long and not necessary to evaluate our model you can skip it if you'de like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we train the base model on the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training with learning rate: 1e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-stage1 and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss after epoch 0: 0.7632064052626618\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c40b62875d0c4cdfa3b0a399da991dea",
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Metric for LR 1e-05 after epoch 0: 0.07045852523244972\n"
     ]
    },
    {
     "data": {
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       "model_id": "f61ced3849294926bc2f4d2fcd064c28",
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss after epoch 1: 0.5115728190439379\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f84e77fa6d214fbf8d45a3ecd3ef3cc8",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Metric for LR 1e-05 after epoch 1: 0.0648152403639673\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d69e6debfe614e7aadc2ac4af5ef5f70",
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss after epoch 2: 0.4424571893536128\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "Validation Metric for LR 8e-06 after epoch 5: 0.06063189078992415\n"
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     "text": [
      "Validation Metric for LR 8e-06 after epoch 6: 0.06272300034372026\n"
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     "text": [
      "Validation Metric for LR 8e-06 after epoch 9: 0.053056615611488306\n",
      "Training with learning rate: 4e-06\n"
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     "text": [
      "Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-base-stage1 and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']\n",
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     "text": [
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     "text": [
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      "Training with learning rate: 1e-06\n"
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     "output_type": "stream",
     "text": [
      "Validation Metric for LR 1e-06 after epoch 9: 0.05316692379905725\n"
     ]
    }
   ],
   "source": [
    "# We define a range of learning rates we want to test\n",
    "learning_rates = [1e-5, 8e-6, 4e-6, 1e-6]\n",
    "\n",
    "best_lr = None # To store the best learning rate\n",
    "best_average_cer = float('inf') # To store the best character error rate\n",
    "best_model = None  # To store the best model\n",
    "\n",
    "for lr in learning_rates:\n",
    "    print(f\"Training with learning rate: {lr}\")\n",
    "\n",
    "    # Reset the model to its pre-trained state\n",
    "    model = load_stage1_model()\n",
    "\n",
    "    # Initialize the optimizer with the current learning rate\n",
    "    optimizer = AdamW(model.parameters(), lr=lr)\n",
    "\n",
    "    for epoch in range(10): # loop over the dataset multiple times\n",
    "        # train\n",
    "        model.train()\n",
    "        train_loss = 0.0\n",
    "        for batch in tqdm(train_dataloader):\n",
    "            # get the inputs\n",
    "            for k,v in batch.items():\n",
    "                batch[k] = v.to(device)\n",
    "            # forward + backward + optimize\n",
    "            outputs = model(**batch)\n",
    "            loss = outputs.loss\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            train_loss += loss.item()\n",
    "        print(f\"Loss after epoch {epoch}:\", train_loss/len(train_dataloader))\n",
    "        # evaluate on the validation set\n",
    "\n",
    "        model.eval()\n",
    "        valid_cer = 0.0\n",
    "        with torch.no_grad():\n",
    "            for batch in tqdm(validation_dataloader):\n",
    "                # run batch generation\n",
    "                outputs = model.generate(batch[\"pixel_values\"].to(device))\n",
    "                # compute metrics\n",
    "                cer = compute_cer(pred_ids=outputs, label_ids=batch[\"labels\"])\n",
    "                valid_cer += cer\n",
    "        average_cer = valid_cer / len(validation_dataloader)\n",
    "        print(f\"Validation Metric for LR {lr} after epoch {epoch}: {average_cer}\")\n",
    "        # Save the best learning rate based on validation performance\n",
    "        if average_cer < best_average_cer:\n",
    "            best_average_cer = average_cer\n",
    "            best_lr = lr\n",
    "            best_model = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we save the trained model for future uses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model saved with learning rate: 8e-06\n"
     ]
    }
   ],
   "source": [
    "# Saving the best model\n",
    "if best_model is not None:\n",
    "    best_model.save_pretrained(\"/content/drive/MyDrive/ML_project/chessreader_project_2/best_model_stage1_lower\")\n",
    "    print(f\"Best model saved with learning rate: {best_lr}\")\n",
    "else:\n",
    "    print(\"No model was saved.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we define a function to load the trained model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_best_stage1():\n",
    "  # Path to the saved model\n",
    "  model_path = \"best_model_stage1_lower\"\n",
    "\n",
    "  # Load the model\n",
    "  model = VisionEncoderDecoderModel.from_pretrained(model_path)\n",
    "\n",
    "  # Make sure to also move the model to the correct device (e.g., GPU or CPU)\n",
    "  model.to(device)\n",
    "  # set special tokens used for creating the decoder_input_ids from the labels\n",
    "  model.config.decoder_start_token_id = processor.tokenizer.cls_token_id\n",
    "  model.config.pad_token_id = processor.tokenizer.pad_token_id\n",
    "  # make sure vocab size is set correctly\n",
    "  model.config.vocab_size = model.config.decoder.vocab_size\n",
    "\n",
    "  # set beam search parameters\n",
    "  model.config.eos_token_id = processor.tokenizer.sep_token_id\n",
    "  model.config.max_length = 64\n",
    "  model.config.early_stopping = True\n",
    "  model.config.no_repeat_ngram_size = 3\n",
    "  model.config.length_penalty = 2.0\n",
    "  model.config.num_beams = 4\n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we evaluate the model performances on the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decoded: Nb5\n",
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      "decoded: Bg2\n",
      "actual: Bg2\n",
      "decoded: Qe8+\n",
      "actual: Qe8+\n",
      "decoded: exd5\n",
      "actual: exd5\n",
      "decoded: Bc4\n",
      "actual: Bc4\n",
      "decoded: g6\n",
      "actual: Be1\n",
      "decoded: Qd8\n",
      "actual: Qd8\n",
      "decoded: Rxc5\n",
      "actual: Rxc5\n",
      "decoded: Bf8\n",
      "actual: Bf8\n",
      "decoded: Nxe6\n",
      "actual: Nxe6\n",
      "decoded: Re1\n",
      "actual: Re1\n",
      "decoded: Qxd5\n",
      "actual: Qxd5\n",
      "decoded: Nd7\n",
      "actual: Nd7\n",
      "decoded: f4\n",
      "actual: f4\n",
      "decoded: Bh5\n",
      "actual: Bh5\n",
      "decoded: cxd6\n",
      "actual: cxd6\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: Qc7\n",
      "actual: Qc7\n",
      "decoded: Bf3\n",
      "actual: Bf3\n",
      "decoded: Qxc6\n",
      "actual: Qxc6\n",
      "decoded: Rxe1+\n",
      "actual: Rxe1+\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: Ba3\n",
      "actual: Ba3\n",
      "decoded: Red2\n",
      "actual: Red2\n",
      "decoded: d5\n",
      "actual: d5\n",
      "decoded: Bh3\n",
      "actual: Bh3\n",
      "decoded: Bd4\n",
      "actual: Ba4\n",
      "decoded: Rxc1\n",
      "actual: Rac1\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: f5\n",
      "actual: f5\n",
      "decoded: h4\n",
      "actual: h4\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: Nxe5\n",
      "actual: Nxe5\n",
      "decoded: fxe5\n",
      "actual: fxe5\n",
      "decoded: b4\n",
      "actual: b4\n",
      "decoded: f6\n",
      "actual: f6\n",
      "decoded: a3\n",
      "actual: a3\n",
      "decoded: Nh6\n",
      "actual: Nh6\n",
      "decoded: Qxa1\n",
      "actual: Qxa1\n",
      "decoded: e3\n",
      "actual: e3\n",
      "decoded: Rd8\n",
      "actual: Rd8\n",
      "decoded: Qb3\n",
      "actual: Qb3\n",
      "decoded: Nf5\n",
      "actual: Nf5\n",
      "decoded: Nxc6\n",
      "actual: Nxc6\n",
      "decoded: b4\n",
      "actual: b4\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: Kg8\n",
      "actual: Kg8\n",
      "decoded: Nc5\n",
      "actual: Nc5\n",
      "decoded: Nxd4\n",
      "actual: Nxd4\n",
      "decoded: Nxb6\n",
      "actual: Nxb6\n",
      "decoded: Bxe5\n",
      "actual: Bxe5\n",
      "decoded: b6\n",
      "actual: b6\n",
      "decoded: d5\n",
      "actual: d5\n",
      "decoded: Nxf1\n",
      "actual: Nxf1\n",
      "decoded: Re8\n",
      "actual: Re8\n",
      "decoded: Nc5\n",
      "actual: Nc5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Be3\n",
      "actual: Be3\n",
      "decoded: Rxb7\n",
      "actual: Rxb7\n",
      "decoded: Re8\n",
      "actual: Re8\n",
      "decoded: Nf5\n",
      "actual: Nf5\n",
      "decoded: Qc7\n",
      "actual: Qc7\n",
      "decoded: e4\n",
      "actual: e4\n",
      "decoded: Bg6\n",
      "actual: Bg6\n",
      "decoded: Nc6\n",
      "actual: Nc6\n",
      "decoded: fxg5\n",
      "actual: Rxg5\n",
      "decoded: Rd1\n",
      "actual: Rd1\n",
      "decoded: Qh3\n",
      "actual: Qh3\n",
      "decoded: Bxb7\n",
      "actual: Bxb7\n",
      "decoded: Ba6\n",
      "actual: Ba6\n",
      "decoded: cxd4\n",
      "actual: cxd4\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: Bxf6\n",
      "actual: Bxf6\n",
      "decoded: Nxd5\n",
      "actual: Nxd5\n",
      "decoded: a4\n",
      "actual: a4\n",
      "decoded: Kd7\n",
      "actual: Kd7\n",
      "decoded: Ra2\n",
      "actual: Ra2\n",
      "decoded: Qa4\n",
      "actual: Qa4\n",
      "decoded: cxd4\n",
      "actual: cxd4\n",
      "decoded: f5\n",
      "actual: Bf5\n",
      "decoded: Bxf3\n",
      "actual: Bxf3\n",
      "decoded: Qc8\n",
      "actual: Qc8\n",
      "decoded: Nxe5\n",
      "actual: Nxe5\n",
      "decoded: d6\n",
      "actual: Nc6\n",
      "decoded: Bd2\n",
      "actual: Bd2\n",
      "decoded: e5\n",
      "actual: e5\n",
      "decoded: Nf1\n",
      "actual: Nf1\n",
      "decoded: Rxc1\n",
      "actual: Rac1\n",
      "decoded: Bxf1\n",
      "actual: Bxf1\n",
      "decoded: Rb1\n",
      "actual: Rb1\n",
      "decoded: a5\n",
      "actual: a5\n",
      "decoded: e3\n",
      "actual: e3\n",
      "decoded: exf5\n",
      "actual: exf5\n",
      "decoded: Bd3\n",
      "actual: Bd3\n",
      "decoded: fxg6\n",
      "actual: fxg6\n",
      "decoded: Bb6\n",
      "actual: Bb6\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: e6\n",
      "actual: e6\n",
      "decoded: Nxf5\n",
      "actual: Nxf5\n",
      "decoded: d5\n",
      "actual: d5\n",
      "decoded: Rd7\n",
      "actual: Rd7\n",
      "decoded: b3\n",
      "actual: b3\n",
      "decoded: Nxg4\n",
      "actual: Nh6\n",
      "decoded: exd4\n",
      "actual: exd4\n",
      "decoded: Kf2\n",
      "actual: Kf2\n",
      "decoded: Be6\n",
      "actual: Be6\n",
      "decoded: Rb8\n",
      "actual: Rb8\n",
      "decoded: Rd7\n",
      "actual: Rd7\n",
      "decoded: Qxb7\n",
      "actual: Qxb7\n",
      "decoded: Rb8\n",
      "actual: Rb8\n",
      "decoded: Bxd6\n",
      "actual: Bxd6\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Nxd4\n",
      "actual: Nxd4\n",
      "decoded: Nf6\n",
      "actual: Nf6\n",
      "decoded: g4\n",
      "actual: g4\n",
      "decoded: Qb4\n",
      "actual: Qb4\n",
      "decoded: Rb8\n",
      "actual: Rb8\n",
      "decoded: Rxg3\n",
      "actual: Rxa3\n",
      "decoded: axb5\n",
      "actual: axb5\n",
      "decoded: Qg3\n",
      "actual: Qg3\n",
      "decoded: a3\n",
      "actual: a3\n",
      "decoded: Bxf6\n",
      "actual: Bxf6\n",
      "decoded: Bc2\n",
      "actual: Bc2\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: h5\n",
      "actual: h5\n",
      "decoded: Ndxe5\n",
      "actual: Ndxe5\n",
      "decoded: Ng8\n",
      "actual: Ng8\n",
      "decoded: Nxd5\n",
      "actual: Nxd5\n",
      "decoded: d6\n",
      "actual: d6\n",
      "decoded: Qf6\n",
      "actual: Qf6\n",
      "decoded: g6\n",
      "actual: g6\n",
      "decoded: Qxc4\n",
      "actual: Qxc4\n",
      "decoded: d6\n",
      "actual: d6\n",
      "decoded: Nh4\n",
      "actual: Nh4\n",
      "decoded: f6\n",
      "actual: f6\n",
      "decoded: b5\n",
      "actual: b5\n",
      "decoded: c8\n",
      "actual: O-O\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Rxa7\n",
      "actual: Rxa7\n",
      "decoded: h5\n",
      "actual: b6\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: Rh7\n",
      "actual: Rh7\n",
      "decoded: c4\n",
      "actual: c4\n",
      "decoded: Rfd1\n",
      "actual: Rfd1\n",
      "decoded: Kg2\n",
      "actual: Kg2\n",
      "decoded: Rxb4\n",
      "actual: Rxb4\n",
      "decoded: Rc8\n",
      "actual: Rc8\n",
      "decoded: fxe4\n",
      "actual: fxe4\n",
      "decoded: Nf3\n",
      "actual: Nf3\n",
      "decoded: Ne4\n",
      "actual: Ne4\n",
      "decoded: Be2\n",
      "actual: Be2\n",
      "decoded: Rxd5\n",
      "actual: Rxd5\n",
      "decoded: Nb5\n",
      "actual: Nb5\n",
      "decoded: Qb4+\n",
      "actual: Qh4+\n",
      "decoded: Be6\n",
      "actual: Be6\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: g3g2\n",
      "actual: g3\n",
      "decoded: Bg4\n",
      "actual: Bg4\n",
      "decoded: Rxa3\n",
      "actual: Rxa3\n",
      "decoded: f3\n",
      "actual: f3\n",
      "decoded: e5\n",
      "actual: e5\n",
      "decoded: h3\n",
      "actual: h3\n",
      "decoded: Bf2\n",
      "actual: Bf2\n",
      "decoded: Qxd8\n",
      "actual: Qxd8\n",
      "decoded: Ne3\n",
      "actual: Ne3\n",
      "decoded: Nd2\n",
      "actual: Nd2\n",
      "decoded: e3\n",
      "actual: e3\n",
      "decoded: Qg4\n",
      "actual: Qg4\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: Nc6\n",
      "actual: Nc6\n",
      "decoded: Nd2\n",
      "actual: Nd2\n",
      "decoded: Nc5\n",
      "actual: Nc5\n",
      "decoded: Nbc6\n",
      "actual: Nbc6\n",
      "decoded: Qxb6\n",
      "actual: Qxb6\n",
      "decoded: Bxd5\n",
      "actual: Bxd5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Rad1\n",
      "actual: Rad1\n",
      "decoded: Nxe5\n",
      "actual: Nxe5\n",
      "decoded: Qxg7\n",
      "actual: Qxg7\n",
      "decoded: Qc2\n",
      "actual: Qc2\n",
      "decoded: Kf8\n",
      "actual: Kf8\n",
      "decoded: Rxg6+\n",
      "actual: Rxg6+\n",
      "decoded: Bd2\n",
      "actual: Bd2\n",
      "decoded: exd5\n",
      "actual: exd5\n",
      "decoded: d4\n",
      "actual: d4\n",
      "decoded: f6\n",
      "actual: f6\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Bxe7\n",
      "actual: Bxe7\n",
      "decoded: e6\n",
      "actual: e6\n",
      "decoded: Qe7\n",
      "actual: Qe7\n",
      "decoded: Bb7\n",
      "actual: Bb7\n",
      "decoded: Qxe8+\n",
      "actual: Qxe8+\n",
      "decoded: Rb7\n",
      "actual: Rb7\n",
      "decoded: Nc4\n",
      "actual: Nc4\n",
      "decoded: dxe4\n",
      "actual: dxe4\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Bf4\n",
      "actual: Bf4\n",
      "decoded: Nd5\n",
      "actual: Nd5\n",
      "decoded: Rc3\n",
      "actual: Rc3\n",
      "decoded: Bd2\n",
      "actual: Bd2\n",
      "decoded: Be3\n",
      "actual: Qc2\n",
      "decoded: Qc5+\n",
      "actual: Qxc5\n",
      "decoded: a7\n",
      "actual: Kxa6\n",
      "decoded: Qxg6+\n",
      "actual: Qxg6+\n",
      "decoded: Rb6\n",
      "actual: Rb6\n",
      "decoded: Rxf5\n",
      "actual: Raxb1\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: g6\n",
      "actual: g6\n",
      "decoded: Kg6\n",
      "actual: hxg6\n",
      "decoded: Qa4\n",
      "actual: Qa4\n",
      "decoded: dxc4\n",
      "actual: dxc4\n",
      "decoded: Ne7\n",
      "actual: Ne7\n",
      "decoded: d6\n",
      "actual: d6\n",
      "decoded: Qxa1\n",
      "actual: Qxa1\n",
      "decoded: Rc5\n",
      "actual: Rc5\n",
      "decoded: Qxc5\n",
      "actual: Qxc5\n",
      "decoded: Nd1\n",
      "actual: Nd1\n",
      "decoded: Reg8\n",
      "actual: Rea8\n",
      "decoded: Nf4\n",
      "actual: Nf4\n",
      "decoded: Qh3\n",
      "actual: Qh3\n",
      "decoded: Nf2\n",
      "actual: Nf4\n",
      "decoded: g5\n",
      "actual: g5\n",
      "decoded: Rb8\n",
      "actual: Rb8\n",
      "decoded: f4\n",
      "actual: f4\n",
      "decoded: Ke3\n",
      "actual: Ke3\n",
      "decoded: gxf4\n",
      "actual: gxf4\n",
      "decoded: Bd7\n",
      "actual: Bd7\n",
      "decoded: c5\n",
      "actual: cxd5\n",
      "decoded: Kxg2\n",
      "actual: Kxg2\n",
      "decoded: h5\n",
      "actual: h5\n",
      "decoded: Bd7\n",
      "actual: Bd7\n",
      "decoded: Rab8\n",
      "actual: Rab8\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Nc6\n",
      "actual: Nc6\n",
      "decoded: cxd5\n",
      "actual: cxd5\n",
      "decoded: Rc3\n",
      "actual: Rc3\n",
      "decoded: Qxf5\n",
      "actual: Qxf5\n",
      "decoded: Ne4\n",
      "actual: Ne4\n",
      "decoded: Qe2\n",
      "actual: Qe2\n",
      "decoded: Qxe8+\n",
      "actual: Qxe8+\n",
      "decoded: Rd1\n",
      "actual: Rd1\n",
      "decoded: Rxc5\n",
      "actual: Rxc5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Bb7\n",
      "actual: Bb7\n",
      "decoded: Rb5\n",
      "actual: Rb5\n",
      "decoded: e5\n",
      "actual: e5\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: Qd1\n",
      "actual: Qd1\n",
      "decoded: g3\n",
      "actual: g3\n",
      "decoded: Ng5\n",
      "actual: Ng5\n",
      "decoded: Rfd1\n",
      "actual: Rfd1\n",
      "decoded: d6\n",
      "actual: d6\n",
      "decoded: Nc3\n",
      "actual: Nc3\n",
      "decoded: g6\n",
      "actual: g6\n",
      "decoded: Qe2\n",
      "actual: Qe2\n",
      "decoded: g6\n",
      "actual: g6\n",
      "decoded: c6\n",
      "actual: c6\n",
      "decoded: Qc2\n",
      "actual: Qc2\n",
      "decoded: fxe4\n",
      "actual: fxe4\n",
      "decoded: Bg5\n",
      "actual: Bg5\n",
      "decoded: Bc3\n",
      "actual: Bc3\n",
      "decoded: Qxf6\n",
      "actual: Qxf6\n",
      "decoded: fxg5\n",
      "actual: fxg5\n",
      "decoded: h4\n",
      "actual: h4\n",
      "decoded: Qxb3\n",
      "actual: Qxb3\n",
      "decoded: Kg8\n",
      "actual: Kg8\n",
      "decoded: e4\n",
      "actual: e4\n",
      "decoded: Bf5\n",
      "actual: Bf5\n",
      "decoded: Qxb6\n",
      "actual: Qxb6\n",
      "decoded: hxa6\n",
      "actual: bxa6\n",
      "decoded: a6\n",
      "actual: a6\n",
      "decoded: Qxd5\n",
      "actual: Qxd5\n",
      "decoded: Qd5\n",
      "actual: Qd5\n",
      "decoded: O-O\n",
      "actual: O-O\n",
      "decoded: Nh3+\n",
      "actual: Nh3+\n",
      "decoded: b6\n",
      "actual: b6\n",
      "decoded: Bf4\n",
      "actual: Bf4\n",
      "decoded: Qf5\n",
      "actual: Qf5\n",
      "decoded: exf6\n",
      "actual: exf6\n",
      "Average CER: 0.054267912772585664\n",
      "Global Accuracy: 0.9424610591900311\n",
      "Character Accuracies: {'N': 0.979381443298969, 'b': 0.9384615384615385, '6': 0.9519230769230769, 'R': 0.971830985915493, 'c': 0.9354838709677419, '8': 0.9473684210526315, 'd': 0.9813084112149533, '7': 1.0, 'B': 0.9565217391304348, 'x': 0.9538461538461539, 'e': 0.9550561797752809, 'h': 0.8181818181818182, 'a': 0.8076923076923077, '1': 1.0, 'g': 0.9636363636363636, '3': 0.9605263157894737, 'K': 0.7307692307692307, 'f': 0.9375, '2': 0.9166666666666666, '5': 0.9587628865979382, '4': 0.9777777777777777, 'Q': 0.9866666666666667, 'O': 0.967741935483871, '-': 0.9375, '+': 1.0, '=': 1.0}\n"
     ]
    }
   ],
   "source": [
    "# Load the model\n",
    "feature_extractor = ViTFeatureExtractor.from_pretrained('microsoft/trocr-base-stage1')\n",
    "tokenizer = RobertaTokenizer.from_pretrained('microsoft/trocr-base-stage1')\n",
    "best_model = load_best_stage1()\n",
    "# Initialize variables for CER and accuracies\n",
    "total_cer = 0\n",
    "total_accuracy = 0\n",
    "char_accuracies = {}\n",
    "total_chars = {}\n",
    "\n",
    "for idx, row in test_df.iterrows():\n",
    "    # Load and process the image\n",
    "    image_path = f'Data/boxes/{row[\"file_name\"]}'\n",
    "    image = Image.open(image_path)  # Replace with your image path\n",
    "    pixel_values = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.to(device)\n",
    "\n",
    "    # Generate the prediction\n",
    "    output_ids = best_model.generate(pixel_values)\n",
    "\n",
    "    # Decode the prediction\n",
    "    decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
    "\n",
    "    # Compute CER\n",
    "    cer = lv.distance(decoded_output, row['text']) / max(len(decoded_output), len(row['text']))\n",
    "    total_cer += cer\n",
    "\n",
    "    # Compute accuracy for the length of the shorter sequence\n",
    "    min_length = min(len(row['text']), len(decoded_output))\n",
    "    if min_length > 0:\n",
    "        accuracy = accuracy_score(list(row['text'])[:min_length], list(decoded_output)[:min_length])\n",
    "    else:\n",
    "        accuracy = 0\n",
    "\n",
    "    total_accuracy += accuracy\n",
    "\n",
    "    # Update character accuracies\n",
    "    for actual_char, predicted_char in zip(row['text'], decoded_output):\n",
    "        if actual_char not in char_accuracies:\n",
    "            char_accuracies[actual_char] = []\n",
    "            total_chars[actual_char] = 0\n",
    "        char_accuracies[actual_char].append(actual_char == predicted_char)\n",
    "        total_chars[actual_char] += 1\n",
    "\n",
    "    print(\"decoded:\", decoded_output)\n",
    "    print(\"actual:\", row['text'])\n",
    "\n",
    "# Compute average CER and accuracies\n",
    "average_cer = total_cer / len(test_df)\n",
    "global_accuracy = total_accuracy / len(test_df)\n",
    "\n",
    "# Compute character-specific accuracies\n",
    "for char in char_accuracies:\n",
    "    char_accuracies[char] = sum(char_accuracies[char]) / total_chars[char]\n",
    "\n",
    "print(\"Average CER:\", average_cer)\n",
    "print(\"Global Accuracy:\", global_accuracy)\n",
    "print(\"Character Accuracies:\", char_accuracies)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Sorting the dictionary by values (accuracy)\n",
    "sorted_dict = dict(sorted(char_accuracies.items(), key=lambda item: item[1], reverse=True))\n",
    "\n",
    "# Extracting characters and their accuracies for plotting\n",
    "characters = list(sorted_dict.keys())\n",
    "accuracies = list(sorted_dict.values())\n",
    "\n",
    "# Creating the bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(characters, accuracies, color='skyblue')\n",
    "plt.xlabel('Characters')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Character Accuracies')\n",
    "plt.ylim(0, 1)  # Assuming accuracy values are between 0 and 1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A chess game\n",
    "\n",
    "Here we do a simulation of a chess game where we can try to limit the output to possible chess game for as long as possible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>file_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e4</td>\n",
       "      <td>00.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e6</td>\n",
       "      <td>01.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d4</td>\n",
       "      <td>02.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d5</td>\n",
       "      <td>03.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nd2</td>\n",
       "      <td>04.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  text file_name\n",
       "0   e4    00.png\n",
       "1   e6    01.png\n",
       "2   d4    02.png\n",
       "3   d5    03.png\n",
       "4  Nd2    04.png"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('game1/data.csv')\n",
    "df = df[['corrected','box']]\n",
    "df.rename(columns = {\"corrected\": \"text\",\"box\": \"file_name\"}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8515459c8544647ae8019e93fd8090e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing images:   0%|          | 0/37 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0. decoded : e4\n",
      "0. actual : e4\n",
      "True\n",
      "1. decoded : e6\n",
      "1. actual : e6\n",
      "True\n",
      "2. decoded : d4\n",
      "2. actual : d4\n",
      "True\n",
      "3. decoded : d5\n",
      "3. actual : d5\n",
      "True\n",
      "4. decoded : Nd2\n",
      "4. actual : Nd2\n",
      "True\n",
      "5. decoded : Nf6\n",
      "5. actual : Nf6\n",
      "True\n",
      "6. decoded : e5\n",
      "6. actual : e5\n",
      "True\n",
      "7. decoded : Nfd7\n",
      "7. actual : Nfd7\n",
      "True\n",
      "8. decoded : c3\n",
      "8. actual : c3\n",
      "True\n",
      "9. decoded : c5\n",
      "9. actual : c5\n",
      "True\n",
      "10. decoded : f4\n",
      "10. actual : f4\n",
      "True\n",
      "11. decoded : Nc6\n",
      "11. actual : Nc6\n",
      "True\n",
      "12. decoded : Ndf3\n",
      "12. actual : Ndf3\n",
      "True\n",
      "13. decoded : Qb6\n",
      "13. actual : Qb6\n",
      "True\n",
      "14. decoded : g3\n",
      "14. actual : g3\n",
      "True\n",
      "15. decoded : Be7\n",
      "15. actual : Be7\n",
      "True\n",
      "16. decoded : Bh3\n",
      "16. actual : Bh3\n",
      "True\n",
      "17. decoded : cxd4\n",
      "17. actual : cxd4\n",
      "True\n",
      "18. decoded : cxd4\n",
      "18. actual : cxd4\n",
      "True\n",
      "19. decoded : f6\n",
      "19. actual : f6\n",
      "True\n",
      "20. decoded : Kf1\n",
      "20. actual : Kf1\n",
      "True\n",
      "21. decoded : O-O\n",
      "21. actual : O-O\n",
      "True\n",
      "22. decoded : Kg2\n",
      "22. actual : Kg2\n",
      "True\n",
      "23. decoded : fxe5\n",
      "23. actual : fxe5\n",
      "True\n",
      "24. decoded : fxe5\n",
      "24. actual : fxe5\n",
      "True\n",
      "25. decoded : Ndxe5\n",
      "25. actual : Ndxe5\n",
      "True\n",
      "26. decoded : dxe5\n",
      "26. actual : dxe5\n",
      "True\n",
      "27. decoded : Nxe5\n",
      "27. actual : Nxe5\n",
      "True\n",
      "28. decoded : Qe2\n",
      "28. actual : Qe2\n",
      "True\n",
      "29. decoded : Nc4\n",
      "29. actual : Nc4\n",
      "True\n",
      "30. decoded : b3\n",
      "30. actual : b3\n",
      "True\n",
      "31. decoded : Bf6\n",
      "31. actual : Bf6\n",
      "True\n",
      "32. decoded : Rb1\n",
      "32. actual : Rb1\n",
      "True\n",
      "33. decoded : Kh8\n",
      "33. actual : Kh8\n",
      "True\n",
      "34. decoded : Qf2\n",
      "34. actual : Qf2\n",
      "True\n",
      "35. decoded : d4\n",
      "35. actual : d4\n",
      "True\n",
      "36. decoded : Qc2\n",
      "36. actual : Qc2\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "import chess\n",
    "\n",
    "# Load the model\n",
    "feature_extractor = ViTFeatureExtractor.from_pretrained('microsoft/trocr-base-handwritten')\n",
    "tokenizer = RobertaTokenizer.from_pretrained('microsoft/trocr-base-handwritten')\n",
    "\n",
    "game1_prediction = []\n",
    "game1_labels = []\n",
    "\n",
    "best_model = load_best_handwritten()\n",
    "\n",
    "board = chess.Board()\n",
    "\n",
    "# Define a list of previous moves (in standard algebraic notation)\n",
    "previous_moves = []\n",
    "\n",
    "legal_game = True\n",
    "\n",
    "for idx, row in  tqdm(df.iterrows(), total=len(df), desc=\"Processing images\"):\n",
    "    # Load and process the image\n",
    "    image = Image.open('game1/boxes/' + row['file_name'])  # Replace with your image path\n",
    "    pixel_values = feature_extractor(images=image, return_tensors=\"pt\").pixel_values.to(device)\n",
    "\n",
    "    # Generate the prediction\n",
    "    output_ids = best_model.generate(pixel_values, num_return_sequences=4)\n",
    "\n",
    "    legal_moves = list(board.legal_moves)\n",
    "    legal_moves = [board.san(chess.Move.from_uci(move.uci())) for move in legal_moves]\n",
    "\n",
    "\n",
    "    if legal_game:\n",
    "      for i in range(len(output_ids)):\n",
    "        if tokenizer.decode(output_ids[i], skip_special_tokens=True) in legal_moves:\n",
    "          # Decode the prediction\n",
    "          game1_prediction.append(tokenizer.decode(output_ids[i], skip_special_tokens=True))\n",
    "          board.push_san(tokenizer.decode(output_ids[i], skip_special_tokens=True))\n",
    "          break\n",
    "        elif i == 1:\n",
    "          legal_game = False\n",
    "          game1_prediction.append(tokenizer.decode(output_ids[0], skip_special_tokens=True))\n",
    "    else:\n",
    "        game1_prediction.append(tokenizer.decode(output_ids[0], skip_special_tokens=True))\n",
    "\n",
    "    game1_labels.append(row['text'])\n",
    "\n",
    "    tqdm.write(f\"{idx}. decoded : {game1_prediction[idx]}\")\n",
    "    tqdm.write(f\"{idx}. actual : {row['text']}\")\n",
    "    tqdm.write(str(legal_game))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we do a simple series of test to evaluate how well we did on this single game "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CER: 0.0\n",
      "Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "# computes the CER for the test_prediction and test_labels\n",
    "# cer = compute_cer(test_prediction, test_labels)\n",
    "cer_best = cer_metric.compute(predictions=game1_prediction, references=game1_labels)\n",
    "\n",
    "# compute percentage of correct predictions from test_prediction and test_labels\n",
    "correct_best = np.sum(np.array(game1_prediction) == np.array(game1_labels))\n",
    "accuracy_best = correct_best / len(game1_prediction)\n",
    "\n",
    "\n",
    "print(\"CER:\", cer_best)\n",
    "print(\"Accuracy: \", accuracy_best)"
   ]
  }
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