tokenizer.py

"""
Chess Move Tokenizer - Component-based approach.

This tokenizer decomposes chess moves into atomic components for efficient
representation. Each move is broken down into: color, piece type, source square,
destination square, and optional annotations (capture, check, promotion, etc.).

The vocabulary is built from atomic components rather than full moves, which
allows for better generalization and a smaller vocabulary size.
"""

from __future__ import annotations

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

import re
from transformers import PreTrainedTokenizer


# Regular expression to parse extended UCI move notation
# Format: [W|B][Piece][from_square][to_square][optional_suffixes]
MOVE_PATTERN = re.compile(
    r"^(?P<side>[WB])"
    r"(?P<piece>[PNBRQK])"
    r"(?P<src>[a-h][1-8])"
    r"(?P<dst>[a-h][1-8])"
    r"(?P<suffix>.*)$"
)


class ChessTokenizer(PreTrainedTokenizer):
    """
    Component-based chess move tokenizer.
    
    Instead of treating each complete move as a single token, this tokenizer
    breaks down moves into atomic components (color, piece, squares, annotations).
    This approach results in a much smaller vocabulary while maintaining
    the ability to represent all possible chess moves.
    
    Example usage:
        >>> tokenizer = ChessTokenizer()
        >>> tokens = tokenizer._tokenize("WPe2e4 BPe7e5")
        >>> # Returns: ['[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]"
    
    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.
        """
        # Set up special token strings
        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 prevent conflicts with special tokens
        # This avoids errors when loading saved tokenizers
        for token_key in ["pad_token", "bos_token", "eos_token", "unk_token"]:
            kwargs.pop(token_key, None)
        
        # Initialize vocabulary from provided source or create default
        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:
            # Fallback: create minimal vocabulary with component tokens
            self._vocab = self._create_default_vocab()
        
        # Build reverse lookup: token_id -> token_string
        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]:
        """
        Construct the default component-based vocabulary.
        
        Creates a vocabulary from atomic chess move components:
        - Special tokens (padding, start, end, unknown)
        - Color indicators (White/Black)
        - Piece types (Pawn, Knight, Bishop, Rook, Queen, King)
        - Board squares (64 squares: a1-h8)
        - Move annotations (capture, check, checkmate, castling, promotions)
        """
        # Core special tokens
        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]

        # Player color indicators
        color_tokens = ["[W]", "[B]"]

        # Chess piece types (note: Bishop uses [BISHOP] to avoid conflict with [B])
        piece_tokens = ["[P]", "[N]", "[BISHOP]", "[R]", "[Q]", "[K]"]

        # All 64 chess board squares
        square_tokens = [f"[{file}{rank}]" for rank in "12345678" for file in "abcdefgh"]

        # Move annotations: capture, check, checkmate, castling, promotions
        annotation_tokens = ["[x]", "[+]", "[#]", "[O-O]", "[O-O-O]", 
                            "[prom_Q]", "[prom_R]", "[prom_B]", "[prom_N]"]

        # Combine all components into vocabulary
        all_tokens = special_tokens + color_tokens + piece_tokens + square_tokens + annotation_tokens
        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.
        
        Returns:
            A ChessTokenizer with the built 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 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.
        """
        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]:
        """
        Decompose chess moves into component tokens.
        
        Parses each move and breaks it down into atomic components:
        color, piece, source square, destination square, and annotations.
        
        Args:
            text: Space-separated sequence of moves in extended UCI format.
        
        Returns:
            List of component tokens representing the moves.
        """
        token_list: List[str] = []
        move_sequence = text.strip().split()

        for move_str in move_sequence:
            # Handle queenside castling (long castling)
            if "O-O-O" in move_str:
                player_color = "[W]" if move_str.startswith("W") else "[B]"
                token_list.append(player_color)
                token_list.append("[O-O-O]")
                continue

            # Handle kingside castling (short castling)
            if "O-O" in move_str:
                player_color = "[W]" if move_str.startswith("W") else "[B]"
                token_list.append(player_color)
                token_list.append("[O-O]")
                continue

            # Parse standard moves using regex
            match = MOVE_PATTERN.match(move_str)
            if not match:
                token_list.append(self.UNK_TOKEN)
                continue

            # Extract move components
            player_color = "[W]" if match.group("side") == "W" else "[B]"
            piece_type = match.group("piece")
            from_square = match.group("src")
            to_square = match.group("dst")
            move_annotations = match.group("suffix") or ""

            # Add color and piece
            token_list.append(player_color)
            
            # Handle Bishop separately (B conflicts with Black)
            if piece_type == "B":
                token_list.append("[BISHOP]")
            else:
                token_list.append(f"[{piece_type}]")

            # Add squares
            token_list.append(f"[{from_square}]")
            token_list.append(f"[{to_square}]")

            # Process annotations
            if "x" in move_annotations:
                token_list.append("[x]")  # Capture

            # Check/checkmate (checkmate takes priority)
            if "*" in move_annotations:
                token_list.append("[#]")  # Checkmate
            elif "+" in move_annotations:
                token_list.append("[+]")  # Check

            # Promotion
            if "=" in move_annotations:
                promo_idx = move_annotations.find("=")
                if promo_idx != -1 and promo_idx + 1 < len(move_annotations):
                    promoted_piece = move_annotations[promo_idx + 1].upper()
                    if promoted_piece in ("Q", "R", "B", "N"):
                        token_list.append(f"[prom_{promoted_piece}]")

        return token_list
    
    def _convert_token_to_id(self, token: str) -> int:
        """Map token string to its vocabulary ID."""
        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
    
    def _convert_id_to_token(self, index: int) -> str:
        """Map vocabulary ID back to token string."""
        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
    
    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """Reconstruct string from token list, filtering special tokens."""
        special_token_set = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        return " ".join(t for t in tokens if t not in special_token_set)
    
    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]:
    """
    Analyze token frequency distribution in the dataset.
    
    Useful for understanding which components appear most frequently
    and for vocabulary size planning.
    
    Args:
        dataset_name: HuggingFace dataset identifier.
        split: Which dataset split to analyze.
        column: Column name containing the game sequences.
        max_samples: Limit number of samples for faster analysis.
    
    Returns:
        Frequency dictionary: token -> count.
    """
    from collections import Counter
    from datasets import load_dataset
    
    # Load dataset
    dataset = load_dataset(dataset_name, split=split)
    
    # Limit samples if requested
    if max_samples is not None:
        dataset = dataset.select(range(min(max_samples, len(dataset))))
    
    # Count component frequencies
    tokenizer = ChessTokenizer()
    frequency_counter = Counter()
    
    for sample in dataset:
        component_tokens = tokenizer._tokenize(sample[column])
        frequency_counter.update(component_tokens)
    
    return dict(frequency_counter)