{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Обзор датасетов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "for warn in [UserWarning, FutureWarning]: warnings.filterwarnings(\"ignore\", category = warn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Примеры использования генератора датасетов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.data_utils.dataset_generator import DatasetGenerator\n",
    "from src.data_utils.config import DatasetConfig\n",
    "from src.data_utils.dataset_params import DatasetName\n",
    "\n",
    "generator = DatasetGenerator(DatasetName.IMDB)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Полный пайплайн получения тензоров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10000, 300])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X_train, y_train), (X_val, y_val), (X_test, y_test) = generator.generate_dataset()\n",
    "\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Получение словаря (после вызова `build_vocabulary` или `generate_dataset`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'means'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab, id2word = generator.get_vocabulary()\n",
    "id2word[123]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Настройка конфига"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((128, 3), (64, 3), (32, 3))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = DatasetConfig(\n",
    "    train_size=128,\n",
    "    val_size=64,\n",
    "    test_size=32,\n",
    ")\n",
    "\n",
    "custom_generator = DatasetGenerator(dataset_name=DatasetName.POLARITY, config=config)\n",
    "\n",
    "train_df, val_df, test_df = custom_generator.load_raw_data()\n",
    "train_df.shape, val_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Визуализация для датасета `stanfordnlp/imdb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from nltk.probability import FreqDist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = DatasetGenerator(DatasetName.IMDB)\n",
    "\n",
    "df_imdb_train, df_imdb_val, df_imdb_test = generator.load_raw_data()\n",
    "df_imdb_full = pd.concat([df_imdb_train, df_imdb_val, df_imdb_test])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Базовая информация о датасете"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Общее количество примеров: 20000\n",
      "Количество классов: 2\n",
      "\n",
      "Первые 5 примеров:\n"
     ]
    },
    {
     "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>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6868</th>\n",
       "      <td>Dumb is as dumb does, in this thoroughly unint...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24016</th>\n",
       "      <td>I dug out from my garage some old musicals and...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9668</th>\n",
       "      <td>After watching this movie I was honestly disap...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13640</th>\n",
       "      <td>This movie was nominated for best picture but ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14018</th>\n",
       "      <td>Just like Al Gore shook us up with his painful...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  label\n",
       "6868   Dumb is as dumb does, in this thoroughly unint...      0\n",
       "24016  I dug out from my garage some old musicals and...      1\n",
       "9668   After watching this movie I was honestly disap...      0\n",
       "13640  This movie was nominated for best picture but ...      1\n",
       "14018  Just like Al Gore shook us up with his painful...      1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Общее количество примеров: {len(df_imdb_full)}\")\n",
    "print(f\"Количество классов: {df_imdb_full[\"label\"].nunique()}\")\n",
    "print(\"\\nПервые 5 примеров:\")\n",
    "df_imdb_full.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Распределение классов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "1    10010\n",
       "0     9990\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imdb_full[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ длины текстов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Описательная статистика длины текстов:\n",
      "count    20000.00000\n",
      "mean       231.54310\n",
      "std        172.10072\n",
      "min          4.00000\n",
      "25%        127.00000\n",
      "50%        173.00000\n",
      "75%        280.00000\n",
      "max       2470.00000\n",
      "Name: text_length, dtype: float64\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_imdb_full[\"text_length\"] = df_imdb_full[\"text\"].apply(lambda x: len(x.split()))\n",
    "\n",
    "print(\"Описательная статистика длины текстов:\")\n",
    "print(df_imdb_full[\"text_length\"].describe())\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.histplot(df_imdb_full[\"text_length\"], bins=50)\n",
    "plt.title(\"Распределение длины текстов\")\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(x=df_imdb_full[\"text_length\"])\n",
    "plt.title(\"Boxplot длины текстов\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ словаря"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Общее количество уникальных слов: 100868\n",
      "20 самых частых слов:\n",
      "[('the', 266485), (',', 217172), ('.', 186495), ('and', 129950), ('a', 128903), ('of', 115424), ('to', 107088), ('is', 87081), ('/', 80891), ('>', 80860), ('<', 80789), ('br', 80703), ('it', 75362), ('in', 74001), ('i', 69505), ('this', 59897), ('that', 57380), (\"'s\", 49042), ('was', 40141), ('as', 36666)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text_processor = generator.get_text_processor()\n",
    "tokenized_texts = [text_processor.preprocess_text(text) for text in df_imdb_full[\"text\"].tolist()]\n",
    "all_tokens = [item for sublist in tokenized_texts for item in sublist]\n",
    "\n",
    "fdist = FreqDist(all_tokens)\n",
    "print(f\"Общее количество уникальных слов: {len(fdist)}\")\n",
    "print(\"20 самых частых слов:\")\n",
    "print(fdist.most_common(20))\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "fdist.plot(20, title=\"20 самых частых слов\")\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Длина текстов по классам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Распределение длины текстов по классам')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.boxplot(data=df_imdb_full, x=\"label\", y=\"text_length\")\n",
    "plt.title(\"Распределение длины текстов по классам\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Примеры текстов по классам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Класс 0:\n",
      "First let me say I'm not someone who usually cringe at the fact of having to think while seeing movies, I love Tarkowsky movies for example, Berghman is also a favorite.....but this? The positive points can be summed up easily: the photography is splendid, and the music is perfect....does it make a good movie? I used to think so, but this one is a perfect example of one that (for me) doesn't make it.... maybe it's because I'm too dumb? possible, but I don't think so.... Tarkwosky for example als...\n",
      "\n",
      "Класс 1:\n",
      "Both Robert Duvall and Glenn Close played their roles with such believability, I simply cried. Glenn Close's role as Ruth, showed her wanting to deal with the situation, but she was under the domination of her husband. \"Let him think about what he did,\" Robert Duvall's character, Joe, said staunchly. The story depicted a rural family dealing with an accidental death of a son by his brother, called \"The Stone Boy,\" meaning he was so distraught and overwhelmed by what he did, he became emotionally...\n"
     ]
    }
   ],
   "source": [
    "for label in sorted(df_imdb_full[\"label\"].unique()):\n",
    "    print(f\"\\nКласс {label}:\")\n",
    "    sample = df_imdb_full[df_imdb_full[\"label\"] == label][\"text\"].sample(1).values[0]\n",
    "    print(f\"{sample[:500]}...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Визуализация для датасета `fancyzhx/amazon_polarity`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = DatasetGenerator(DatasetName.POLARITY)\n",
    "\n",
    "df_polar_train, df_polar_val, df_polar_test = generator.load_raw_data()\n",
    "df_polar_full = pd.concat([df_polar_train, df_polar_val, df_polar_test])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Базовая информация о датасете"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Общее количество примеров: 20000\n",
      "Количество классов: 2\n",
      "\n",
      "Первые 5 примеров:\n"
     ]
    },
    {
     "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>label</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2079998</th>\n",
       "      <td>0</td>\n",
       "      <td>Expensive Junk</td>\n",
       "      <td>This product consists of a piece of thin flexi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443106</th>\n",
       "      <td>0</td>\n",
       "      <td>Toast too dark</td>\n",
       "      <td>Even on the lowest setting, the toast is too d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3463669</th>\n",
       "      <td>1</td>\n",
       "      <td>Excellent imagery...dumbed down story</td>\n",
       "      <td>I enjoyed this disc. The video is stunning. I ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2914699</th>\n",
       "      <td>0</td>\n",
       "      <td>Are we pretending everyone is married?</td>\n",
       "      <td>The authors pretend that parents neither die n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603231</th>\n",
       "      <td>0</td>\n",
       "      <td>Not worth your time</td>\n",
       "      <td>Might as well just use a knife, this product h...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         label                                   title  \\\n",
       "2079998      0                          Expensive Junk   \n",
       "1443106      0                          Toast too dark   \n",
       "3463669      1   Excellent imagery...dumbed down story   \n",
       "2914699      0  Are we pretending everyone is married?   \n",
       "1603231      0                     Not worth your time   \n",
       "\n",
       "                                                   content  \n",
       "2079998  This product consists of a piece of thin flexi...  \n",
       "1443106  Even on the lowest setting, the toast is too d...  \n",
       "3463669  I enjoyed this disc. The video is stunning. I ...  \n",
       "2914699  The authors pretend that parents neither die n...  \n",
       "1603231  Might as well just use a knife, this product h...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Общее количество примеров: {len(df_polar_full)}\")\n",
    "print(f\"Количество классов: {df_polar_full[\"label\"].nunique()}\")\n",
    "print(\"\\nПервые 5 примеров:\")\n",
    "df_polar_full.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Распределение классов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "1    10106\n",
       "0     9894\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_polar_full[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ длины текстов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Описательная статистика длины текстов:\n",
      "count    20000.000000\n",
      "mean        73.689050\n",
      "std         42.197284\n",
      "min          9.000000\n",
      "25%         38.000000\n",
      "50%         65.000000\n",
      "75%        102.000000\n",
      "max        202.000000\n",
      "Name: text_length, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_polar_full[\"text_length\"] = df_polar_full[\"content\"].apply(lambda x: len(x.split()))\n",
    "\n",
    "print(\"Описательная статистика длины текстов:\")\n",
    "print(df_polar_full[\"text_length\"].describe())\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.histplot(df_polar_full[\"text_length\"], bins=50)\n",
    "plt.title(\"Распределение длины текстов\")\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(x=df_polar_full[\"text_length\"])\n",
    "plt.title(\"Boxplot длины текстов\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ словаря"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Общее количество уникальных слов: 61120\n",
      "20 самых частых слов:\n",
      "[('.', 79235), ('the', 75553), (',', 54469), ('i', 43963), ('and', 41193), ('a', 37385), ('to', 37211), ('it', 34717), ('of', 30205), ('this', 27750), ('is', 27048), ('in', 17191), ('for', 16055), ('that', 15680), ('was', 13456), ('you', 12999), ('!', 12055), ('not', 11602), ('but', 10971), ('with', 10767)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text_processor = generator.get_text_processor()\n",
    "tokenized_texts = [text_processor.preprocess_text(text) for text in df_polar_full[\"content\"].tolist()]\n",
    "all_tokens = [item for sublist in tokenized_texts for item in sublist]\n",
    "\n",
    "fdist = FreqDist(all_tokens)\n",
    "print(f\"Общее количество уникальных слов: {len(fdist)}\")\n",
    "print(\"20 самых частых слов:\")\n",
    "print(fdist.most_common(20))\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "fdist.plot(20, title=\"20 самых частых слов\")\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Длина текстов по классам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Распределение длины текстов по классам')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.boxplot(data=df_polar_full, x=\"label\", y=\"text_length\")\n",
    "plt.title(\"Распределение длины текстов по классам\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Примеры текстов по классам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Класс 0:\n",
      "The item was of inferior quality and was difficult to get to work. I returned the item for a refund....\n",
      "\n",
      "Класс 1:\n",
      "I also bought the Sylvania DVC860D DVD & VCR combo as a replacement for a cheap \"Monkey Wards\" VCR I bought 8yrs. ago for a much greater price than this unit. What a great Christmas buy at $99.99! It works great in re-running VHS Tapes out of my library and has played all the DVD's I have burnt with my new Plextor 708A DVD Burner.Also, this unit even hooked up to my 14yr. old TV using RF cables: how about that! This product does it all that they say it will, and is a fantastic bargain. I truly r...\n"
     ]
    }
   ],
   "source": [
    "for label in sorted(df_polar_full[\"label\"].unique()):\n",
    "    print(f\"\\nКласс {label}:\")\n",
    "    sample = df_polar_full[df_polar_full[\"label\"] == label][\"content\"].sample(1).values[0]\n",
    "    print(f\"{sample[:500]}...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "monkey-coding-dl-project-OWiM8ypK-py3.12",
   "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",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}