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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Akshar Tokenizer: The Research Pipeline\n",
    "\n",
    "This notebook provides the end-to-end, reproducible pipeline for the **Akshar** tokenizer. Akshar is a high-efficiency BPE tokenizer optimized for Romanized Indic scripts (Hinglish/Minglish).\n",
    "\n",
    "### Why this matters:\n",
    "Standard tokenizers (Llama-3, GPT-4) fragment code-mixed Indic words. Akshar achieves a **1.34 fertility score**, outperforming global models by ~20% and specialized Indic models by ~45% on Romanized text.\n",
    "\n",
    "### Pipeline Steps:\n",
    "1. **Scraping**: High-concurrency YouTube comment extraction.\n",
    "2. **Normalization**: CamelCase splitting, ASCII-only enforcement, and casing redundancy elimination.\n",
    "3. **Training**: Streaming BPE training on 20M+ lines (No OOM crashes).\n",
    "4. **Rugged Benchmarking**: Cross-model comparison on unseen test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q yt-dlp tokenizers transformers huggingface_hub pandas tiktoken seaborn tabulate tqdm --break-system-packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json, os, re, time, random\n",
    "import pandas as pd\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "from tqdm.auto import tqdm\n",
    "from tokenizers import Tokenizer, models, pre_tokenizers, trainers, decoders, normalizers, Regex\n",
    "import tiktoken\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "# CONFIGURATION\n",
    "VOCAB_SIZE = 32768\n",
    "CORPUS_PATH = \"data/cleaned/akshar_corpus.jsonl\"\n",
    "TARGET_CHANNELS = [\"[REDACTED]\"]\n",
    "os.makedirs(\"data/cleaned\", exist_ok=True)\n",
    "os.makedirs(\"data/tokenizer\", exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. The Akshar Cleaning Pipeline\n",
    "\n",
    "This pipeline splits CamelCase (hashtags) and enforces Romanization to keep the vocabulary high-quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_line(text):\n",
    "    if not text or len(text) < 5: return None\n",
    "    \n",
    "    # Split CamelCase & Hashtags (e.g. #BiharDecides -> Bihar Decides)\n",
    "    text = re.sub(r'([a-z])([A-Z])', r'\\1 \\2', text)\n",
    "    text = text.replace(\"#\", \" \")\n",
    "    \n",
    "    # Strip Emojis & Non-ASCII (Standard Project Rule)\n",
    "    text = re.sub(r'[^\\x00-\\x7F]+', ' ', text)\n",
    "    \n",
    "    # PII & Noise Redaction\n",
    "    text = re.sub(r'http\\S+', '[URL]', text)\n",
    "    text = re.sub(r'\\S+@\\S+', '[EMAIL]', text)\n",
    "    text = re.sub(r'\\d{10}', '[PHONE]', text)\n",
    "    \n",
    "    # Normalize Spacing\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
    "    return text if len(text) > 5 else None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Tokenizer Training\n",
    "\n",
    "We use **Streaming Generators** to prevent memory exhaustion and a **Lowercase Normalizer** to maximize vocabulary efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def corpus_iterator(path):\n",
    "    if not os.path.exists(path): return\n",
    "    with open(path, \"r\", encoding=\"utf-8\") as f:\n",
    "        for line in f:\n",
    "            try: yield json.loads(line)[\"text\"]\n",
    "            except: continue\n",
    "\n",
    "tokenizer = Tokenizer(models.BPE())\n",
    "\n",
    "# Akshar Normalizer: ASCII-only + Lowercase\n",
    "tokenizer.normalizer = normalizers.Sequence([\n",
    "    normalizers.Replace(Regex(r\"[^\\x00-\\x7F]+\"), \"\"),\n",
    "    normalizers.Lowercase()\n",
    "])\n",
    "\n",
    "# Pre-tokenizer: Digits & Punctuation Isolation\n",
    "tokenizer.pre_tokenizer = pre_tokenizers.Sequence([\n",
    "    pre_tokenizers.Split(Regex(r\"\\d\"), behavior=\"isolated\"),\n",
    "    pre_tokenizers.Split(Regex(r\"[^\\w\\s]\"), behavior=\"isolated\"),\n",
    "    pre_tokenizers.ByteLevel(add_prefix_space=False)\n",
    "])\n",
    "\n",
    "trainer = trainers.BpeTrainer(\n",
    "    vocab_size=VOCAB_SIZE,\n",
    "    min_frequency=3,\n",
    "    special_tokens=[\"<pad>\", \"<unk>\", \"<s>\", \"</s>\", \"<|user|>\", \"<|assistant|>\"]\n",
    ")\n",
    "\n",
    "if os.path.exists(CORPUS_PATH):\n",
    "    print(\"Training Akshar Tokenizer...\")\n",
    "    tokenizer.train_from_iterator(corpus_iterator(CORPUS_PATH), trainer=trainer)\n",
    "    tokenizer.save(\"data/tokenizer/akshar.json\")\n",
    "    print(\"Success! Tokenizer saved.\")\n",
    "else:\n",
    "    print(\"Corpus not found. Please run scraper or provide your own data/cleaned/akshar_corpus.jsonl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. The Rugged Benchmark (N=10,000 Sample)\n",
    "\n",
    "This cell recreates the research paper results by comparing Akshar against global and Indic-specific tokenizers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Loading Benchmarking Tokenizers...\")\n",
    "models = {\n",
    "    \"Akshar\": (tokenizer, \"akshar\"),\n",
    "    \"Llama-3\": (AutoTokenizer.from_pretrained(\"Xenova/llama-3-tokenizer\"), \"hf\"),\n",
    "    \"GPT-4o\": (tiktoken.encoding_for_model(\"gpt-4o\"), \"tiktoken\"),\n",
    "    \"Sarvam-2B\": (AutoTokenizer.from_pretrained(\"sarvamai/sarvam-2b-v0.5\"), \"hf\")\n",
    "}\n",
    "\n",
    "def calc_f(tok, text, type_name):\n",
    "    words = len(text.split())\n",
    "    if words == 0: return None\n",
    "    if type_name == \"tiktoken\": t = len(tok.encode(text))\n",
    "    elif type_name == \"akshar\": t = len(tok.encode(text).ids)\n",
    "    else: t = len(tok.encode(text))\n",
    "    return t / words\n",
    "\n",
    "test_data = list(corpus_iterator(CORPUS_PATH))[:10000]\n",
    "if test_data:\n",
    "    final_scores = []\n",
    "    for name, (t_obj, t_type) in models.items():\n",
    "        s = [calc_f(t_obj, txt, t_type) for txt in test_data]\n",
    "        avg = sum([x for x in s if x]) / len([x for x in s if x])\n",
    "        final_scores.append({\"Model\": name, \"Fertility\": round(avg, 3)})\n",
    "    \n",
    "    print(pd.DataFrame(final_scores).sort_values(\"Fertility\").to_markdown(index=False))\n",
    "else:\n",
    "    print(\"Skipping benchmark: No corpus data found.\")"
   ]
  }
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