tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer uses a STRUCTURED approach to tokenize chess moves, breaking down
each move into its components to help the model learn legal chess patterns.

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

Instead of treating each move as a single token (which creates thousands of tokens),
we tokenize the COMPONENTS:
- Color tokens: W, B
- Piece tokens: P, N, B, R, Q, K  
- Square tokens: a1, a2, ..., h8 (64 squares)
- Suffix tokens: (x), (+), (+*), (o), (O), =Q, =R, =B, =N

This gives ~80 tokens total, helping the model learn:
1. Valid squares on the board
2. Which pieces can make which types of moves
3. The structure of legal chess moves
"""

from __future__ import annotations

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

from transformers import PreTrainedTokenizer


class ChessTokenizer(PreTrainedTokenizer):
    """
    A structured tokenizer for chess moves using component-based tokenization.
    
    Instead of treating each move as a single token, this tokenizer breaks moves
    into their structural components (color, piece, from-square, to-square, suffix).
    This smaller vocabulary helps the model learn valid chess patterns.
    
    Vocabulary (~80 tokens):
    - Special: [PAD], [BOS], [EOS], [UNK]
    - Colors: W, B
    - Pieces: P, N, B, R, Q, K
    - Squares: a1-h8 (64 tokens)
    - Suffixes: (x), (+), (+*), (o), (O), =Q, =R, =B, =N
    
    Example:
        >>> tokenizer = ChessTokenizer()
        >>> tokens = tokenizer.tokenize("WPe2e4 BPe7e5")
        >>> print(tokens)
        ['W', 'P', 'e2', 'e4', 'B', 'P', 'e7', 'e5']
    """
    
    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]"
    
    # Chess components
    COLORS = ["W", "B"]
    PIECES = ["P", "N", "B", "R", "Q", "K"]
    FILES = ["a", "b", "c", "d", "e", "f", "g", "h"]
    RANKS = ["1", "2", "3", "4", "5", "6", "7", "8"]
    SQUARES = [f + r for f in ["a", "b", "c", "d", "e", "f", "g", "h"] 
               for r in ["1", "2", "3", "4", "5", "6", "7", "8"]]  # a1, a2, ..., h8
    SUFFIXES = ["(x)", "(+)", "(+*)", "(o)", "(O)", "=Q", "=R", "=B", "=N"]
    
    # Regex pattern to parse extended UCI moves
    # Format: [W|B][Piece][from_sq][to_sq][optional: =PromoPiece][optional: suffix]
    MOVE_PATTERN = re.compile(
        r'^([WB])([PNBRQK])([a-h][1-8])([a-h][1-8])(=[QRBN])?(\([xo+*O]+\))?$'
    )
    
    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 the structured vocabulary
            self._vocab = self._create_structured_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_structured_vocab(self) -> Dict[str, int]:
        """
        Create the structured vocabulary with all chess components.
        
        This creates a fixed vocabulary of ~85 tokens covering all possible
        chess move components.
        """
        tokens = []
        
        # Special tokens first
        tokens.extend([self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])
        
        # Colors
        tokens.extend(self.COLORS)
        
        # Pieces
        tokens.extend(self.PIECES)
        
        # Squares (64 tokens)
        tokens.extend(self.SQUARES)
        
        # Suffixes
        tokens.extend(self.SUFFIXES)
        
        # Build vocabulary
        vocab = {token: idx for idx, token in enumerate(tokens)}
        return vocab
    
    def _create_default_vocab(self) -> Dict[str, int]:
        """Alias for _create_structured_vocab for compatibility."""
        return self._create_structured_vocab()
    
    def _parse_move(self, move: str) -> List[str]:
        """
        Parse a single move into its component tokens.
        
        Args:
            move: A move in extended UCI format (e.g., "WPe2e4", "BNg8f6(x)").
        
        Returns:
            List of component tokens.
        """
        move = move.strip()
        if not move:
            return []
        
        # Handle special tokens
        if move in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]:
            return [move]
        
        # Try to match the move pattern
        match = self.MOVE_PATTERN.match(move)
        if match:
            color, piece, from_sq, to_sq, promotion, suffix = match.groups()
            tokens = [color, piece, from_sq, to_sq]
            if promotion:
                tokens.append(promotion)
            if suffix:
                tokens.append(suffix)
            return tokens
        
        # If pattern doesn't match, try to extract what we can
        # This handles edge cases and malformed moves gracefully
        tokens = []
        i = 0
        
        # Color (W or B)
        if i < len(move) and move[i] in self.COLORS:
            tokens.append(move[i])
            i += 1
        
        # Piece (P, N, B, R, Q, K)
        if i < len(move) and move[i] in self.PIECES:
            tokens.append(move[i])
            i += 1
        
        # From square (e.g., e2)
        if i + 1 < len(move) and move[i:i+2] in self.SQUARES:
            tokens.append(move[i:i+2])
            i += 2
        
        # To square (e.g., e4)
        if i + 1 < len(move) and move[i:i+2] in self.SQUARES:
            tokens.append(move[i:i+2])
            i += 2
        
        # Promotion (e.g., =Q)
        if i + 1 < len(move) and move[i:i+2] in self.SUFFIXES:
            tokens.append(move[i:i+2])
            i += 2
        
        # Suffix (e.g., (x), (+), (+*), (o), (O))
        remaining = move[i:]
        if remaining in self.SUFFIXES:
            tokens.append(remaining)
        elif remaining:
            # Try to find a matching suffix
            for suffix in self.SUFFIXES:
                if remaining.startswith(suffix):
                    tokens.append(suffix)
                    break
        
        # If we couldn't parse anything, return UNK
        if not tokens:
            return [self.UNK_TOKEN]
        
        return tokens
    
    @classmethod
    def build_vocab_from_iterator(
        cls,
        iterator,
        min_frequency: int = 1,
    ) -> "ChessTokenizer":
        """
        Build a tokenizer (for compatibility - vocab is fixed).
        
        The structured tokenizer has a fixed vocabulary, so this method
        simply returns a new tokenizer instance.
        
        Args:
            iterator: An iterator yielding game strings (ignored for structured vocab).
            min_frequency: Minimum frequency (ignored for structured vocab).
        
        Returns:
            A ChessTokenizer with the structured vocabulary.
        """
        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 (for compatibility - vocab is fixed).
        
        The structured tokenizer has a fixed vocabulary covering all valid
        chess move components, so no dataset scanning is needed.
        
        Args:
            dataset_name: Name of the dataset (ignored).
            split: Dataset split (ignored).
            column: Column name (ignored).
            min_frequency: Minimum frequency (ignored).
            max_samples: Maximum samples (ignored).
        
        Returns:
            A ChessTokenizer with the structured vocabulary.
        """
        return cls()
    
    @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 _tokenize(self, text: str) -> List[str]:
        """
        Tokenize a string of moves into component tokens.
        
        Args:
            text: A string of space-separated moves.
        
        Returns:
            List of component tokens.
        """
        tokens = []
        moves = text.strip().split()
        
        for move in moves:
            move_tokens = self._parse_move(move)
            tokens.extend(move_tokens)
        
        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 tokens back to a move string.
        
        Reconstructs moves from component tokens by grouping them appropriately.
        """
        # Filter out special tokens
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        tokens = [t for t in tokens if t not in special]
        
        if not tokens:
            return ""
        
        # Reconstruct moves from components
        result = []
        current_move = []
        
        for token in tokens:
            # Start of a new move (color token)
            if token in self.COLORS:
                if current_move:
                    result.append("".join(current_move))
                current_move = [token]
            else:
                current_move.append(token)
        
        # Don't forget the last move
        if current_move:
            result.append("".join(current_move))
        
        return " ".join(result)
    
    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).
    
    With the structured tokenizer, this counts component frequencies.
    
    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
    
    tokenizer = ChessTokenizer()
    
    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:
        tokens = tokenizer._tokenize(example[column])
        token_counts.update(tokens)
    
    return dict(token_counts)