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

from transformers import PreTrainedTokenizer

import re

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 token
    
    MOVE_REGEX = re.compile(
        r"""
        (?P<color>[WB])
        (?P<piece>[PNBRQK])
        (?P<from>[a-h][1-8])
        (?P<capture>x)?
        (?P<to>[a-h][1-8])
        (?P<promotion>[QRBN])?
        (?P<check>[+#])?
        """,
        re.VERBOSE,
    )
    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 a minimal default vocabulary with just special tokens.
        
        For the full vocabulary, use `build_vocab_from_dataset()`.
        This minimal vocab is just a placeholder - you should build from data.
        """
        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        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":
        """
        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()
        
        for game in iterator:
            moves = game.strip().split()
            token_counts.update(moves)
        
        # 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_iterator_bis(
        cls,
        iterator,
        min_frequency: int = 1,
    ) -> "ChessTokenizer":
        from collections import Counter
        import re

        MOVE_REGEX = re.compile(
            r"""
            (?P<color>[WB])
            (?P<piece>[PNBRQK])
            (?P<from>[a-h][1-8])
            (?P<capture>x)?
            (?P<to>[a-h][1-8])
            (?P<promotion>[QRBN])?
            (?P<check>[+#])?
            """,
            re.VERBOSE,
        )

        token_counts = Counter()

        for game in iterator:
            moves = game.strip().split()

            for move in moves:
                # --- Castling ---
                if move in ("WKe1g1", "BKe8g8"):
                    token_counts.update(["W", "K", "CASTLE_K"])
                    continue

                if move in ("WKe1c1", "BKe8c8"):
                    token_counts.update(["W", "K", "CASTLE_Q"])
                    continue

                m = MOVE_REGEX.fullmatch(move)
                if not m:
                    token_counts.update([cls.UNK_TOKEN])
                    continue

                # Color + piece
                token_counts.update([
                    m["color"],
                    m["piece"],
                    m['from'],
                ])

                if m["capture"]:
                    token_counts.update(["CAP"])

                # --- TO = FIN DE COUP ---
                to_tok = m['to']

                if m["promotion"]:
                    to_tok += f"_PROM_{m['promotion']}"

                if m["check"] == "+":
                    to_tok += "_CHECK"
                elif m["check"] == "#":
                    to_tok += "_MATE"

                

                token_counts.update([to_tok])

        tokens = [tok for tok, c in token_counts.items() if c >= min_frequency]
        tokens = tokens + ["EOM"]
        tokens = sorted(tokens)

        special_tokens = [
            cls.PAD_TOKEN,
            cls.BOS_TOKEN,
            cls.EOS_TOKEN,
            cls.UNK_TOKEN,
        ]

        vocab = {tok: i for i, tok 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 = 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_bis(game_iterator(), min_frequency=min_frequency)
    
    @property
    def vocab_size(self) -> int:
        """Return the size of the vocabulary."""
        return max(self._vocab.values()) + 1
    
    def get_vocab(self) -> Dict[str, int]:
        """Return the vocabulary as a dictionary."""
        return dict(self._vocab)

        
    def _tokenize(self, text: str) -> List[str]:
        """
        Convert a game string into subword tokens.
        Example:
        "WPe2e4 BNg8f6" ->
        ["W","P","FROM_e2","TO_e4_EOM","B","N","FROM_g8","TO_f6_EOM"]
        """
        tokens: List[str] = []

        MOVE_REGEX = re.compile(
            r"""
            (?P<color>[WB])
            (?P<piece>[PNBRQK])
            (?P<from>[a-h][1-8])
            (?P<capture>x)?
            (?P<to>[a-h][1-8])
            (?P<promotion>[QRBN])?
            (?P<check>[+#])?
            """,
            re.VERBOSE,
        )

        moves = text.split()

        for move in moves:
            # --- Castling ---
            if move in ("WKe1g1", "BKe8g8"):
                tokens.extend(["W", "K", "CASTLE_K", "EOM"])
                continue

            if move in ("WKe1c1", "BKe8c8"):
                tokens.extend(["W", "K", "CASTLE_Q", "EOM"])
                continue

            m = MOVE_REGEX.fullmatch(move)
            if not m:
                tokens.append(self.UNK_TOKEN)
                continue

            # Color & piece
            tokens.append(m["color"])
            tokens.append(m["piece"])
            tokens.append(f"{m['from']}")

            if m["capture"]:
                tokens.append("CAP")

            # --- TO + FLAGS + EOM ---
            to_tok = f"{m['to']}"

            if m["promotion"]:
                to_tok += f"_PROM_{m['promotion']}"

            if m["check"] == "+":
                to_tok += "_CHECK"
            elif m["check"] == "#":
                to_tok += "_MATE"

            

            tokens.append(to_tok)
            if to_tok:
                tokens.append("EOM")

        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:
        special = {
            self.PAD_TOKEN,
            self.BOS_TOKEN,
            self.EOS_TOKEN,
            self.UNK_TOKEN,
        }

        moves = []
        current = []

        for tok in tokens:
            if tok in special:
                continue

            if tok == "EOM":
                moves.append("".join(current) + " ")
                current = []
                continue

            if tok == "W" or tok == "B":
                current.append(tok)

            elif tok in {"P","N","B","R","Q","K"}:
                current.append(tok)

            elif tok == "CAP":
                current.append("x")
            
            elif tok.startswith("CASTLE"):
                if tok == "CASTLE_K":
                    moves.append("O-O ")
                elif tok == "CASTLE_Q":
                    moves.append("O-O-O ")
                current = []

            else :
                current.append(tok[:2])
                body = tok[2:]

                if not body:
                    continue
                # Split flags
                parts = body.split("_")
                square = parts[0]
                current.append(square)

                for p in parts[1:]:
                    if p == "PROM":
                        continue
                    elif p in {"Q","R","B","N"}:
                        current.append(p)
                    elif p == "CHECK":
                        current.append("+")
                    elif p == "MATE":
                        current.append("#")



            
        moves.append("".join(current))

        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)