tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer treats each move as a single token using the extended UCI notation
from the Lichess dataset (e.g., WPe2e4, BNg8f6).

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

from transformers import PreTrainedTokenizer


class ChessTokenizer(PreTrainedTokenizer):
    """
    A custom tokenizer for chess moves using extended UCI notation.
    
    This tokenizer maps each possible chess move to a unique token ID.
    The vocabulary is built from the training dataset to ensure all moves
    encountered during training have a corresponding token.
    
    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]"
    EOM_TOKEN = "[EOM]"  # End of Move
    
    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
        self._eom_token = self.EOM_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)
        kwargs.pop("eom_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()}
        self.is_nlp_basic = True
        
        # 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 all possible tokens.
        Tokens either represent player, piece, square, or special move notation. 
        We impose no additional structure to the token set.
        """
        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN, self.EOM_TOKEN]
        normal_tokens = [ a+b for a in 'BW' for b in 'PNBRQK']  # Player + Piece
        normal_tokens += [a+b for a in 'abcdefgh' for b in '12345678'] # starting or ending square
        normal_tokens += ['(x)', '(+)', '(+*)', '(o)', '(O)', 'EV_NONE']   # special suffixes

        special_tokens += normal_tokens
        vocab = {token: idx for idx, token in enumerate(special_tokens)}
        return vocab
    
    @classmethod
    def build_vocab_from_iterator(
        cls,
        iterator,
        min_frequency: int = 1,
    ) -> "ChessTokenizer":
        """
        DOESN'T WORK YET ---
        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.
        """
        from collections import Counter
        
        token_counts = Counter()
        tokenizer = cls()
        for game in iterator:
            moves = game.strip().split()
            tokens = tokenizer._split_move(moves)
            token_counts.update(tokens)
        
        # Filter by frequency
        tokens = [
            token for token, count in token_counts.items()
            if count >= min_frequency
        ]
        
        # Sort for reproducibility
        tokens = sorted(tokens)
        
        # Build vocabulary
        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,
        max_samples: Optional[int] = 100000,
    ) -> "ChessTokenizer":
        """
        DOESN'T WORK YET ---
        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: 1).
            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 _split_move(self, move: str) -> tuple[str, str, str, str]:
        if len(move) < 6:
            return self.UNK_TOKEN, self.UNK_TOKEN, self.UNK_TOKEN, "EV_NONE"

        color = move[0]
        piece = move[1]
        from_sq = move[2:4]
        to_sq = move[4:6]
        suffix = move[6:]

        piece_tok = f"P_{color}{piece}"
        from_tok = f"F_{from_sq}"
        to_tok = f"T_{to_sq}"

        if "(o)" in suffix:
            event_suffix = "(o)"
        elif "(O)" in suffix:
            event_suffix = "(O)"
        else:
            promotion = ""
            promo_match = re.search(r"=([NBRQ])", suffix)
            if promo_match:
                promotion = f"={promo_match.group(1)}"

            capture = "(x" in suffix
            checkmate = "+*" in suffix
            check = "(+)" in suffix or "(x+)" in suffix

            if capture and checkmate:
                suffix_event = "(x+*)"
            elif capture and check:
                suffix_event = "(x+)"
            elif capture:
                suffix_event = "(x)"
            elif checkmate:
                suffix_event = "(+*)"
            elif check:
                suffix_event = "(+)"
            else:
                suffix_event = ""

            event_suffix = promotion + suffix_event

        event_tok = "EV_NONE" if event_suffix == "" else f"EV_{event_suffix}"
        return piece_tok, from_tok, to_tok, event_tok   
    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.
        """
        moves_list = text.strip().split()
        tokens = []

        for move in moves_list:
            if len(move) < 6:
                tokens.append(self.UNK_TOKEN)
                continue
            separation = [ move[0:2], move[2:4], move[4:6], move[6:] ]
            for part in separation:
                if  part and part in self._vocab:
                    tokens.append(part)
                elif part and part not in self._vocab:
                    tokens.append(self.UNK_TOKEN)
            tokens.append(self.EOM_TOKEN)
        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 string."""
        # Filter out special tokens for cleaner output
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        moves = []
        for token in tokens:
            if token == self.EOM_TOKEN:
                moves.append(" ")
            elif token not in special:
                moves.append(token)
        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",
    chess_tokenizer: ChessTokenizer = ChessTokenizer(),
    split: str = "train",
    column: str = "text",
    max_samples: Optional[int] = 10000,
) -> Dict[str, int]:
    """
    Count token frequencies in a dataset (useful for vocabulary analysis).
    ATTENTION: Only counts number of moves, not actual tokens
    
    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()
        moves = chess_tokenizer._tokenize(moves)
        token_counts.update(moves)
    
    return dict(token_counts)