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 token import OP
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]"
    
    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_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(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 _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.
        """
        return text.strip().split()
    
    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 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)



class CoordinateTokenizer(ChessTokenizer):
    def __init__(self, **kwargs):
        squares = [f"{f}{r}" for f in "abcdefgh" for r in "12345678"]
        promotions = ["q", "r", "b", "n"]
        control = ["[PAD]", "[BOS]", "[EOS]", "[UNK]"]
        vocab_list = control + squares + promotions
        self._vocab = {t: i for i, t in enumerate(vocab_list)}
        self._ids_to_token = {i: t for t, i in self._vocab.items()}

        super().__init__(
            vocab=self._vocab, 
            pad_token="[PAD]", 
            bos_token="[BOS]", 
            eos_token="[EOS]", 
            unk_token="[UNK]",
            truncation_side="left",
            **kwargs
        )

    def _tokenize(self, text: str) -> List[str]:
        raw_moves = text.strip().split()
        tokens = []
        for raw_move in raw_moves:
            squares = re.findall(r'[a-h][1-8]', raw_move)
            tokens.extend(squares)
            if "=" in raw_move:
                idx = raw_move.index("=")
                if idx + 1 < len(raw_move):
                    tokens.append(raw_move[idx+1].lower())
            elif "q" in raw_move[-2:].lower():
                tokens.append(raw_move[-1].lower())
        return tokens


class CoordinateChessTokenizer(PreTrainedTokenizer):
    """
    Tokenizer that decomposes chess moves into coordinate components.
    
    Example:
        WPe2e4 -> ['e2', 'e4']
        WPa7a8q -> ['a7', 'a8', 'q']  # pawn promotion
    
    Vocabulary size: 72 tokens
    - 64 squares (a1-h8)
    - 4 promotions (q, r, b, n)
    - 4 special tokens
    """
    
    model_input_names = ["input_ids", "attention_mask"]
    vocab_files_names = {"vocab_file": "vocab.json"}
    
    PAD_TOKEN = "[PAD]"
    BOS_TOKEN = "[BOS]"
    EOS_TOKEN = "[EOS]"
    UNK_TOKEN = "[UNK]"
    
    # Regex to extract from-square, to-square, and optional promotion
    MOVE_PATTERN = re.compile(r'([a-h][1-8])([a-h][1-8])([qrbn])?')
    
    def __init__(self, vocab_file: Optional[str] = None, **kwargs):
        # Remove duplicate special token kwargs
        kwargs.pop("pad_token", None)
        kwargs.pop("bos_token", None)
        kwargs.pop("eos_token", None)
        kwargs.pop("unk_token", None)
        
        # Build fixed vocabulary
        if 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:
            self._vocab = self._create_vocab()
        
        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
        
        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_vocab(self) -> Dict[str, int]:
        """Create fixed vocabulary of 72 tokens."""
        tokens = [
            self.PAD_TOKEN,
            self.BOS_TOKEN,
            self.EOS_TOKEN,
            self.UNK_TOKEN,
        ]
        
        # Add all 64 squares
        for file in 'abcdefgh':
            for rank in '12345678':
                tokens.append(f"{file}{rank}")
        
        # Add promotion pieces
        tokens.extend(['q', 'r', 'b', 'n'])
        
        return {token: idx for idx, token in enumerate(tokens)}
    
    @property
    def vocab_size(self) -> int:
        return len(self._vocab)
    
    def get_vocab(self) -> Dict[str, int]:
        return dict(self._vocab)
    
    def _tokenize(self, text: str) -> List[str]:
        """
        Tokenize move string into coordinate components.
        
        Args:
            text: Space-separated moves like "WPe2e4 BNg8f6"
        
        Returns:
            List of coordinate tokens: ['e2', 'e4', 'g8', 'f6']
        """
        tokens = []
        raw_moves = text.strip().split()
        
        for move in raw_moves:
            match = self.MOVE_PATTERN.search(move)
            if match:
                from_sq, to_sq, promotion = match.groups()
                tokens.append(from_sq)
                tokens.append(to_sq)
                if promotion:
                    tokens.append(promotion)
        
        return tokens
    
    def _convert_token_to_id(self, token: str) -> int:
        return self._vocab.get(token, self._vocab[self.UNK_TOKEN])
    
    def _convert_id_to_token(self, index: int) -> str:
        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
    
    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """Reconstruct moves from coordinate tokens."""
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        clean = [t for t in tokens if t not in special]
        
        # Group into moves (2 or 3 tokens per move)
        moves = []
        i = 0
        while i < len(clean):
            if i + 1 < len(clean):
                move = clean[i] + clean[i + 1]
                i += 2
                # Check for promotion
                if i < len(clean) and clean[i] in ['q', 'r', 'b', 'n']:
                    move += clean[i]
                    i += 1
                moves.append(move)
            else:
                i += 1
        
        return " ".join(moves)
    
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple:
        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,)


class EnhancedCoordinateTokenizer(CoordinateChessTokenizer):
    """
    Extended version that preserves piece information as optional metadata.
    Vocabulary: 76 tokens (adds W, B, P, N, B, R, Q, K but makes them optional)
    
    Use this if you want to preserve color/piece info with minimal vocab growth.
    """
    
    def _create_vocab(self) -> Dict[str, int]:
        vocab = super()._create_vocab()
        
        # Add optional color and piece tokens
        piece_tokens = ['W', 'B', 'P', 'N', 'R', 'Q', 'K']  # Note: B appears in both contexts
        
        next_id = len(vocab)
        for token in piece_tokens:
            if token not in vocab:
                vocab[token] = next_id
                next_id += 1
        
        return vocab
    
    def _tokenize(self, text: str) -> List[str]:
        """
        Optionally include piece info: WPe2e4 -> ['W', 'P', 'e2', 'e4']
        Or strip it for minimal version: WPe2e4 -> ['e2', 'e4']
        """
        tokens = []
        raw_moves = text.strip().split()
        
        for move in raw_moves:
            # Extract color and piece if present
            if len(move) >= 2 and move[0] in 'WB' and move[1] in 'PNBRQK':
                # Uncomment to include piece info (increases sequence length):
                # tokens.extend([move[0], move[1]])
                pass
            
            # Extract coordinates
            match = self.MOVE_PATTERN.search(move)
            if match:
                from_sq, to_sq, promotion = match.groups()
                tokens.append(from_sq)
                tokens.append(to_sq)
                if promotion:
                    tokens.append(promotion)
        
        return tokens
    


class SanitizedChessTokenizer(ChessTokenizer):
    
    # Strategy:
    # 1. Strip suffixes: (, ), x, +, *, o, O, E
    # 2. Strip prefixes: W or B followed by P, N, B, R, Q, K
    #    Regex: ^[WB][PNBRQK] matches the start of the string
    
    # We can use a single regex to find the "Pure Move" part.
    # We look for the square-to-square pattern (e.g., e2e4) and optional promotion (q,r,b,n)
    # This is safer than stripping because it ignores all noise around the move.
    MOVE_PATTERN = re.compile(r'([a-h][1-8][a-h][1-8][qrbn]?)')

    def _sanitize(self, text: str) -> str:
        # Extract just the move part (e.g., "WPe2e4(x)" -> "e2e4")
        match = self.MOVE_PATTERN.search(text)
        if match:
            return match.group(1)
        return self.unk_token # Fallback if no valid move found

    def _tokenize(self, text: str) -> List[str]:
        # Tokenize by splitting space, then extracting the move
        tokens = []
        for t in text.strip().split():
            clean = self._sanitize(t)
            if clean != self.unk_token:
                tokens.append(clean)
        return tokens

    @classmethod
    def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1) -> "SanitizedChessTokenizer":
        from collections import Counter
        
        token_counts = Counter()
        
        for game in iterator:
            moves = game.strip().split()
            # Extract only the Pure UCI part
            clean_moves = []
            for m in moves:
                match = cls.MOVE_PATTERN.search(m)
                if match:
                    clean_moves.append(match.group(1))
            
            token_counts.update(clean_moves)
        
        # Filter by frequency
        tokens = [
            token for token, count in token_counts.items()
            if count >= min_frequency
        ]
        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)