{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9644db35",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!python learn_bpe.py -s 20000 -i dataset/output.txt -o dataset/codec.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68a4113a",
   "metadata": {},
   "outputs": [],
   "source": [
    "!apply_bpe.py -i ./dataset/output.txt -o ./dataset/output_dataset.txt -c ./dataset/codec.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "06254f0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size: 20217\n"
     ]
    }
   ],
   "source": [
    "def count_tokens(file_path):\n",
    "    try:\n",
    "        with open(file_path, 'r', encoding='utf-8') as file:\n",
    "            text = file.read()\n",
    "            # Split the text into tokens based on spaces\n",
    "            tokens = text.split()\n",
    "            # Count the vocabulary size (number of unique tokens)\n",
    "            vocabulary_size = len(set(tokens))\n",
    "            return vocabulary_size\n",
    "    except IOError:\n",
    "        print(f\"Error: Could not open or read the file '{file_path}'\")\n",
    "        return -1\n",
    "\n",
    "# Example usage\n",
    "file_path = './dataset/output_dataset.txt'  # Replace with the actual file path\n",
    "vocabulary_size = count_tokens(file_path)\n",
    "if vocabulary_size != -1:\n",
    "    print(f\"Vocabulary size: {vocabulary_size}\")\n"
   ]
  }
 ],
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