tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer treats chess moves using a 'Square-Aware' Character strategy.
Instead of full moves (e.g., WPe2e4), it splits them into meaningful atomic parts:
- Pieces/Colors: W, B, P, N, B, R, Q, K
- Full Squares: e2, e4, h8 (keeps coordinates together for geometric understanding)
- Separators: Space " "

Example: "WPe2e4" -> ["W", "P", "e2", "e4"]
"""

from __future__ import annotations
import re
import json
import os
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer

class ChessTokenizer(PreTrainedTokenizer):
    """
    A custom tokenizer for chess moves using 'Square-Aware' tokenization.
    
    It maps atomic chess components (squares like 'e4', pieces like 'P') to IDs.
    This creates a small, dense vocabulary (~80 tokens) allowing deeper models.
    """
    
    model_input_names = ["input_ids", "attention_mask"]
    vocab_files_names = {"vocab_file": "vocab.json"}
    
    # Special tokens
    PAD_TOKEN = "[PAD]"
    BOS_TOKEN = "[BOS]"
    EOS_TOKEN = "[EOS]"
    UNK_TOKEN = "[UNK]"
    
    def __init__(
        self,
        vocab_file: Optional[str] = None,
        vocab: Optional[Dict[str, int]] = None,
        **kwargs,
    ):
        # Initialize special tokens
        self._pad_token = self.PAD_TOKEN
        self._bos_token = self.BOS_TOKEN
        self._eos_token = self.EOS_TOKEN
        self._unk_token = self.UNK_TOKEN

        # Clean kwargs to avoid conflicts
        kwargs.pop("pad_token", None)
        kwargs.pop("bos_token", None)
        kwargs.pop("eos_token", None)
        kwargs.pop("unk_token", None)
        
        # Load or create vocabulary
        if vocab is not None:
            self._vocab = vocab
        elif vocab_file is not None and os.path.exists(vocab_file):
            with open(vocab_file, "r", encoding="utf-8") as f:
                self._vocab = json.load(f)
        else:
            # Minimal default vocab (placeholder)
            self._vocab = self._create_default_vocab()
        
        # Create reverse mapping (ID -> Token)
        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
        
        super().__init__(
            pad_token=self._pad_token,
            bos_token=self._bos_token,
            eos_token=self._eos_token,
            unk_token=self._unk_token,
            **kwargs,
        )
    
    def _create_default_vocab(self) -> Dict[str, int]:
        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        return {token: idx for idx, token in enumerate(special_tokens)}
    
    @classmethod
    def build_vocab_from_iterator(
        cls,
        iterator,
        min_frequency: int = 1,
    ) -> "ChessTokenizer":
        """
        Build vocabulary by scanning the dataset.
        Splits text into pieces (W, P) and full squares (e2, e4).
        """
        from collections import Counter
        token_counts = Counter()
        
        for game in iterator:
            # 1. Nettoyage : on enlève les suffixes (x), (+)
            game = re.sub(r'\(.*?\)', '', game)
            
            # 2. Découpage par coups pour gérer les espaces correctement
            moves = game.strip().split()
            
            for i, move in enumerate(moves):
                # Regex : Capture soit une case [a-h][1-8], soit n'importe quel autre char (.)
                tokens = re.findall(r'[a-h][1-8]|.', move)
                token_counts.update(tokens)
                
                # Ajout explicite de l'espace entre les coups (sauf après le dernier)
                if i < len(moves) - 1:
                    token_counts.update([" "])
        
        # Filter and sort tokens
        tokens = [t for t, count in token_counts.items() if count >= min_frequency]
        tokens = sorted(tokens)
        
        # Build final vocabulary dict
        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
        vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}
        
        return cls(vocab=vocab)

    @classmethod
    def build_vocab_from_dataset(
        cls,
        dataset_name: str = "dlouapre/lichess_2025-01_1M",
        split: str = "train",
        column: str = "text",
        min_frequency: int = 1,  # Keep at 1 to catch all squares/pieces
        max_samples: Optional[int] = 50000,
    ) -> "ChessTokenizer":
        from datasets import load_dataset
        dataset = load_dataset(dataset_name, split=split)
        
        if max_samples is not None:
            dataset = dataset.select(range(min(max_samples, len(dataset))))
        
        def game_iterator():
            for example in dataset:
                yield example[column]
        
        return cls.build_vocab_from_iterator(game_iterator(), min_frequency=min_frequency)
    
    @property
    def vocab_size(self) -> int:
        return len(self._vocab)
    
    def get_vocab(self) -> Dict[str, int]:
        return dict(self._vocab)

    def _tokenize(self, text: str) -> List[str]:
        """
        Tokenize input text using the Square-Aware logic.
        "WPe2e4" -> ["W", "P", "e2", "e4"]
        """
        # 1. Remove suffixes
        text = re.sub(r'\(.*?\)', '', text)
        
        # 2. Split into moves to manage spaces
        moves = text.strip().split()
        
        all_tokens = []
        for i, move in enumerate(moves):
            # Regex match: squares OR single chars
            tokens = re.findall(r'[a-h][1-8]|.', move)
            all_tokens.extend(tokens)
            
            # Re-insert space token between moves
            if i < len(moves) - 1:
                all_tokens.append(" ")
                
        return all_tokens
        
    def _convert_token_to_id(self, token: str) -> int:
        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
    
    def _convert_id_to_token(self, index: int) -> str:
        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
    
    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """
        Convert tokens back to string.
        IMPORTANT: Join with empty string "" because space " " is already a token.
        """
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        filtered_tokens = [t for t in tokens if t not in special]
        
        # Join with "" because the space character is treated as a token in our vocab
        return "".join(filtered_tokens)
    
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple:
        if not os.path.isdir(save_directory):
            os.makedirs(save_directory, exist_ok=True)
        
        vocab_file = os.path.join(
            save_directory,
            (filename_prefix + "-" if filename_prefix else "") + "vocab.json",
        )
        
        with open(vocab_file, "w", encoding="utf-8") as f:
            json.dump(self._vocab, f, ensure_ascii=False, indent=2)
        
        return (vocab_file,)

def count_vocab_from_dataset(
    dataset_name: str = "dlouapre/lichess_2025-01_1M",
    split: str = "train",
    column: str = "text",
    max_samples: Optional[int] = 10000,
) -> Dict[str, int]:
    # Utility function remains similar but should use the new regex logic if needed for analysis
    # For simple counting, split() is often enough approximation, but let's be precise:
    from collections import Counter
    from datasets import load_dataset
    import re
    
    dataset = load_dataset(dataset_name, split=split)
    if max_samples is not None:
        dataset = dataset.select(range(min(max_samples, len(dataset))))
    
    token_counts = Counter()
    for example in dataset:
        text = re.sub(r'\(.*?\)', '', example[column])
        moves = text.strip().split()
        for move in moves:
            tokens = re.findall(r'[a-h][1-8]|.', move)
            token_counts.update(tokens)
            token_counts.update([" "])
            
    return dict(token_counts)