{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ce2519f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
    "import random\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"text_recognizer\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')\n",
    "\n",
    "from text_recognizer.data.iam_paragraphs import IAMParagraphs\n",
    "from text_recognizer.data.iam_synthetic_paragraphs import IAMSyntheticParagraphs\n",
    "from text_recognizer.data.iam_extended_paragraphs import IAMExtendedParagraphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "726ac25b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _plot(image, figsize=(12,12), title='', vmin=0, vmax=255):\n",
    "    plt.figure(figsize=figsize)\n",
    "    if title:\n",
    "        plt.title(title)\n",
    "    plt.imshow(image, cmap='gray', vmin=vmin, vmax=vmax)\n",
    "\n",
    "def convert_y_label_to_string(y, mapping, padding_index=3):\n",
    "    return ''.join([mapping[i] for i in y if i != padding_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6188bce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dataset = IAMExtendedParagraphs(batch_size=1, word_pieces=True)\n",
    "dataset.prepare_data()\n",
    "dataset.setup()\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55b26b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(dataset.mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42501428",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = IAMParagraphs()\n",
    "dataset.prepare_data()\n",
    "dataset.setup()\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6e8c05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = next(iter(dataset.test_dataloader()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bed2170",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cf22683",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = dataset.data_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8541e6ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40447ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "016e8c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7aa8c021",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ef93252",
   "metadata": {},
   "outputs": [],
   "source": [
    "_plot(x[0], vmax=1, title=dataset.mapping.get_text(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c62572f",
   "metadata": {},
   "outputs": [],
   "source": [
    "_plot(x[0, 0], vmax=1, title=convert_y_label_to_string(y[0], dataset.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7778ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training\n",
    "\n",
    "for _ in range(5):\n",
    "    i = random.randint(0, len(dataset.data_train))\n",
    "    x, y = dataset.data_train[i]\n",
    "    _plot(x[0], vmax=1, title=convert_y_label_to_string(y, dataset.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbf845a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from einops import rearrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe4bfb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = dataset.data_train[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0ba4dec",
   "metadata": {},
   "outputs": [],
   "source": [
    "_plot(x[0], vmax=1, title=convert_y_label_to_string(y, dataset.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34348d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = 32\n",
    "patches = rearrange(x.unsqueeze(0), 'b c (h p1) (w p2) -> b c (h w) p1 p2', p1 = p, p2 = p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77bded74",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(20, 20))\n",
    "for i in range(15):\n",
    "    ax = fig.add_subplot(1, 15, i + 1)\n",
    "    ax.imshow(patches[0, 0, i + 160, :, :].squeeze(0), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d11ca56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing\n",
    "\n",
    "for _ in range(5):\n",
    "    i = random.randint(0, len(dataset.data_test))\n",
    "    x, y = dataset.data_test[i]\n",
    "    _plot(x[0], vmax=1, title=convert_y_label_to_string(y, dataset.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "548d10da",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = IAMSyntheticParagraphs()\n",
    "dataset.prepare_data()\n",
    "dataset.setup()\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "627730b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training\n",
    "\n",
    "for _ in range(5):\n",
    "    i = random.randint(0, len(dataset.data_train))\n",
    "    x, y = dataset.data_train[i]\n",
    "    _plot(x[0], vmax=1, title=convert_y_label_to_string(y, dataset.mapping))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4150722e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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