tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer uses a compact hybrid scheme optimized for small models:
- Frequent moves are single tokens (e.g., WPe2e4).
- Rare moves fall back to two tokens: piece+from (e.g., WPe2) and to-square (e.g., e4).
- Promotions add a third token (q/r/b/n).

The dataset format uses:
- 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
"""

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 custom tokenizer for chess moves using extended UCI notation.
    
    This tokenizer uses a compact base vocabulary (piece+from, to-square,
    promotion tokens) and optionally adds frequent full-move tokens for
    shorter sequences and better sample efficiency.
    
    Example:
        >>> tokenizer = ChessTokenizer()
        >>> tokenizer.encode("WPe2e4 BPe7e5")
        [1, 42, 87, 2]  # [BOS, e2e4, e7e5, 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]"

    _MOVE_RE = re.compile(
        r"^(?P<color>[WB])(?P<piece>[PNBRQK])(?P<from>[a-h][1-8])(?P<to>[a-h][1-8])(?P<rest>.*)$"
    )
    _PROMO_RE = re.compile(r"=([NBRQnrbq])")
    
    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 compact default vocabulary that can tokenize any move
            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 a compact default vocabulary with full move coverage.
        
        For better compression, use `build_vocab_from_dataset()` to add
        frequent full-move tokens.
        """
        tokens = self._create_base_vocab_tokens()
        return {token: idx for idx, token in enumerate(tokens)}

    @classmethod
    def _create_base_vocab_tokens(cls) -> List[str]:
        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
        pieces = ["P", "N", "B", "R", "Q", "K"]
        colors = ["W", "B"]
        files = "abcdefgh"
        ranks = "12345678"
        squares = [f"{f}{r}" for f in files for r in ranks]
        piece_from_tokens = [f"{c}{p}{sq}" for c in colors for p in pieces for sq in squares]
        to_tokens = squares
        promo_tokens = ["q", "r", "b", "n"]
        return special_tokens + piece_from_tokens + to_tokens + promo_tokens

    @classmethod
    def _parse_move(cls, token: str) -> Optional[Tuple[str, str, str, str, Optional[str]]]:
        match = cls._MOVE_RE.match(token)
        if not match:
            return None
        color = match.group("color")
        piece = match.group("piece")
        from_sq = match.group("from")
        to_sq = match.group("to")
        rest = match.group("rest")
        promo_match = cls._PROMO_RE.search(rest)
        promo = promo_match.group(1).upper() if promo_match else None
        return color, piece, from_sq, to_sq, promo
    
    @classmethod
    def build_vocab_from_iterator(
        cls,
        iterator,
        min_frequency: int = 1,
        max_full_move_tokens: Optional[int] = 1200,
    ) -> "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.
            max_full_move_tokens: Maximum number of full-move tokens to keep.
        
        Returns:
            A ChessTokenizer with the built vocabulary.
        """
        from collections import Counter
        
        token_counts = Counter()
        
        for game in iterator:
            moves = game.strip().split()
            for move in moves:
                parsed = cls._parse_move(move)
                if not parsed:
                    continue
                color, piece, from_sq, to_sq, promo = parsed
                if promo:
                    continue
                token_counts[f"{color}{piece}{from_sq}{to_sq}"] += 1
        
        # Filter by frequency
        tokens = [
            token for token, count in token_counts.items()
            if count >= min_frequency
        ]

        # Sort by frequency, then lexicographically for reproducibility
        tokens.sort(key=lambda t: (-token_counts[t], t))

        if max_full_move_tokens is not None:
            tokens = tokens[:max_full_move_tokens]

        base_tokens = cls._create_base_vocab_tokens()
        vocab = {token: idx for idx, token in enumerate(base_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 = 500,
        max_samples: Optional[int] = 100000,
        max_full_move_tokens: Optional[int] = 1200,
    ) -> "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).
            max_full_move_tokens: Maximum number of full-move tokens to keep.
        
        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,
            max_full_move_tokens=max_full_move_tokens,
        )
    
    @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 a list of tokens.
        
        Args:
            text: A string of space-separated moves.
        
        Returns:
            List of move tokens.
        """
        raw = text.strip()
        if not raw:
            return []

        parts = raw.split()
        out: List[str] = []
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}

        for part in parts:
            if part in special:
                out.append(part)
                continue

            parsed = self._parse_move(part)
            if not parsed:
                out.append(self.UNK_TOKEN)
                continue

            color, piece, from_sq, to_sq, promo = parsed
            full_move = f"{color}{piece}{from_sq}{to_sq}"
            if promo is None and full_move in self._vocab:
                out.append(full_move)
                continue

            piece_from = f"{color}{piece}{from_sq}"
            to_token = f"{to_sq}"
            out.append(piece_from if piece_from in self._vocab else self.UNK_TOKEN)
            out.append(to_token if to_token in self._vocab else self.UNK_TOKEN)

            if promo:
                promo_token = promo.lower()
                out.append(promo_token if promo_token in self._vocab else self.UNK_TOKEN)

        return out
    
    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 string."""
        # Filter out special tokens for cleaner output
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        return " ".join(t for t in tokens if t not in special)
    
    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 normalized move 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 normalized full-move 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()
        for move in moves:
            parsed = ChessTokenizer._parse_move(move)
            if not parsed:
                continue
            color, piece, from_sq, to_sq, promo = parsed
            if promo:
                continue
            token_counts[f"{color}{piece}{from_sq}{to_sq}"] += 1
    
    return dict(token_counts)