tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer uses 64 square tokens (a1-h8), representing each square on the board.
Each move is tokenized as 2 tokens: source square + destination square.

The dataset format uses extended UCI notation (e.g., WPe2e4, BNg8f6):
- W/B prefix for White/Black (ignored during tokenization)
- Piece letter: P=Pawn, N=Knight, B=Bishop, R=Rook, Q=Queen, K=King (ignored)
- Source and destination squares (e.g., e2e4) - these are extracted as tokens
- Special suffixes: (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling (ignored)
"""

from __future__ import annotations

import json
import os
from pathlib import Path
from typing import Dict, List, Optional

from transformers import PreTrainedTokenizer


class ChessTokenizer(PreTrainedTokenizer):
    """
    A custom tokenizer for chess moves using square tokens.
    
    This tokenizer uses 64 square tokens (a1-h8) to represent moves.
    Each move is tokenized as 2 tokens: source square + destination square.
    W/B prefixes, piece letters, and special suffixes are removed.
    
    Example:
        >>> tokenizer = ChessTokenizer()
        >>> tokenizer.encode("WPe2e4 BPe7e5")
        [1, 36, 40, 50, 54, 2]  # [BOS, e2, e4, e7, e5, EOS]
    """
    
    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 the chess tokenizer.
        
        Args:
            vocab_file: Path to a JSON file containing the vocabulary mapping.
            vocab: Dictionary mapping tokens to IDs (alternative to vocab_file).
            **kwargs: Additional arguments passed to PreTrainedTokenizer.
        """
        # 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

        # Remove any duplicate special-token entries passed through kwargs
        # to avoid "multiple values for keyword" errors when loading from disk.
        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:
            # Create a minimal vocabulary with just special tokens
            # The full vocabulary should be built from the dataset
            self._vocab = self._create_default_vocab()
        
        # Create reverse mapping
        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
        
        # Call parent init AFTER setting up vocab
        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]:
        """
        Create default vocabulary with 64 square tokens plus special tokens.
        
        The vocabulary consists of:
        - 4 special tokens: [PAD], [BOS], [EOS], [UNK]
        - 64 square tokens: a1, a2, ..., a8, b1, ..., h8
        """
        # Special tokens first
        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        
        # Generate all 64 squares (a1-h8)
        squares = []
        for file in 'abcdefgh':
            for rank in '12345678':
                squares.append(f"{file}{rank}")
        
        # Combine and create vocab
        all_tokens = special_tokens + squares
        vocab = {token: idx for idx, token in enumerate(all_tokens)}
        return vocab
    
    @classmethod
    def build_vocab_from_iterator(
        cls,
        iterator,
        min_frequency: int = 1,
    ) -> "ChessTokenizer":
        """
        Build a tokenizer vocabulary from an iterator of game strings.
        
        Args:
            iterator: An iterator yielding game strings (space-separated moves).
            min_frequency: Minimum frequency for a token to be included (not used for squares).
        
        Returns:
            A ChessTokenizer with the built vocabulary (64 squares + special tokens).
        """
        # The vocabulary is fixed: 64 squares + special tokens
        # No need to count from iterator since we always use all 64 squares
        return cls()
    
    @classmethod
    def build_vocab_from_dataset(
        cls,
        dataset_name: str = "dlouapre/lichess_2025-01_1M",
        split: str = "train",
        column: str = "text",
        min_frequency: int = 500,
        max_samples: Optional[int] = 100000,
    ) -> "ChessTokenizer":
        """
        Build a tokenizer vocabulary from a Hugging Face dataset.
        
        Args:
            dataset_name: Name of the dataset on Hugging Face Hub.
            split: Dataset split to use.
            column: Column containing the game strings.
            min_frequency: Minimum frequency for a token to be included (default: 500).
            max_samples: Maximum number of samples to process (default: 100k).
        
        Returns:
            A ChessTokenizer with the built vocabulary.
        """
        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 the size of the vocabulary."""
        return len(self._vocab)
    
    def get_vocab(self) -> Dict[str, int]:
        """Return the vocabulary as a dictionary."""
        return dict(self._vocab)
    
    def _extract_squares_from_move(self, move: str) -> tuple[str, str]:
        """
        Extract source and destination squares from a move string.
        
        Moves are in extended UCI format: [W|B][Piece][from_sq][to_sq][suffix]
        Examples: "WPe2e4" -> ("e2", "e4"), "BNg8f6(x)" -> ("g8", "f6")
        
        Args:
            move: Move string in extended UCI format.
        
        Returns:
            Tuple of (source_square, destination_square).
        """
        # Remove special suffixes like (x), (+), (+*), (o), (O)
        import re
        move_clean = re.sub(r'\([^)]*\)', '', move)
        
        # Extract source and destination squares
        # Format: [W|B][Piece][from_sq][to_sq][optional_promotion]
        if len(move_clean) >= 6:
            # Standard move: WPe2e4 or BNg8f6
            from_sq = move_clean[2:4]  # positions 2-3
            to_sq = move_clean[4:6]    # positions 4-5
        elif len(move_clean) >= 4:
            # Fallback for shorter moves (shouldn't happen, but be safe)
            # Assume first 2 chars are square, next 2 are square
            from_sq = move_clean[0:2]
            to_sq = move_clean[2:4]
        else:
            # Invalid move, return unknown
            return ("[UNK]", "[UNK]")
        
        return (from_sq, to_sq)
    
    def _tokenize(self, text: str) -> List[str]:
        """
        Tokenize a string of moves into a list of square tokens.
        
        Each move is split into 2 tokens: source square + destination square.
        
        Args:
            text: A string of space-separated moves in extended UCI format.
        
        Returns:
            List of square tokens (each move becomes 2 tokens).
        """
        moves = text.strip().split()
        tokens = []
        
        for move in moves:
            from_sq, to_sq = self._extract_squares_from_move(move)
            tokens.append(from_sq)
            tokens.append(to_sq)
        
        return tokens
    
    def _convert_token_to_id(self, token: str) -> int:
        """Convert a token to its ID."""
        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
    
    def _convert_id_to_token(self, index: int) -> str:
        """Convert an ID to its token."""
        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
    
    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """
        Convert a list of square tokens back to a string of moves.
        
        Pairs of square tokens are combined into UCI-format moves (e.g., "e2e4").
        Special tokens are filtered out.
        
        Args:
            tokens: List of square tokens (pairs represent moves).
        
        Returns:
            String of space-separated moves in UCI format.
        """
        # Filter out special tokens
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        square_tokens = [t for t in tokens if t not in special]
        
        # Combine pairs of square tokens into moves
        moves = []
        for i in range(0, len(square_tokens), 2):
            if i + 1 < len(square_tokens):
                moves.append(f"{square_tokens[i]}{square_tokens[i+1]}")
            else:
                # Odd number of tokens, just add the last one
                moves.append(square_tokens[i])
        
        return " ".join(moves)
    
    def save_vocabulary(
        self,
        save_directory: str,
        filename_prefix: Optional[str] = None,
    ) -> tuple:
        """
        Save the vocabulary to a JSON file.
        
        Args:
            save_directory: Directory to save the vocabulary.
            filename_prefix: Optional prefix for the filename.
        
        Returns:
            Tuple containing the path to the saved vocabulary file.
        """
        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]:
    """
    Count token frequencies in a dataset (useful for vocabulary analysis).
    
    Args:
        dataset_name: Name of the dataset on Hugging Face Hub.
        split: Dataset split to use.
        column: Column containing the game strings.
        max_samples: Maximum number of samples to process.
    
    Returns:
        Dictionary mapping tokens to their frequencies.
    """
    from collections import Counter
    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))))
    
    token_counts = Counter()
    
    for example in dataset:
        moves = example[column].strip().split()
        token_counts.update(moves)
    
    return dict(token_counts)