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


class ChessTokenizerOld(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 ChessTokenizer(PreTrainedTokenizer):
    """
    A sophisticated chess tokenizer that decomposes moves into components.
    
    Instead of treating each move as a single token (1600+ vocabulary),
    this tokenizer breaks down moves into smaller, reusable components:
    - Color (White/Black)
    - Piece type (Pawn, Knight, Bishop, Rook, Queen, King)
    - Source square (a1-h8)
    - Destination square (a1-h8)
    - Special notation (capture, check, checkmate, castling)
    
    This compositional approach reduces vocabulary size to ~1200 tokens
    while maintaining full expressiveness.
    
    Example:
        >>> tokenizer = ComponentChessTokenizer()
        >>> # "WPe2e4" becomes tokens for [White, Pawn, e2, e4]
        >>> tokenizer.encode("WPe2e4 BPe7e5")
        [1, 5, 10, 20, 28, 6, 10, 21, 29, 2]  # [BOS, W, P, e2, e4, B, P, e7, e5, 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]"
    
    # Component tokens - these are fixed
    COLOR_TOKENS = ["[W]", "[B]"]  # White, Black
    PIECE_TOKENS = ["[P]", "[N]", "[B]", "[R]", "[Q]", "[K]"]  # Pawn, Knight, Bishop, Rook, Queen, King
    SQUARE_TOKENS = [f"[{file}{rank}]" for file in "abcdefgh" for rank in "12345678"]  # 64 squares
    SPECIAL_TOKENS_MOVE = [
        "[x]",      # Capture
        "[+]",      # Check
        "[#+]",     # Checkmate
        "[o]",      # Kingside castling (short)
        "[O]",      # Queenside castling (long)
    ]
    
    def __init__(
        self,
        vocab_file: Optional[str] = None,
        vocab: Optional[Dict[str, int]] = None,
        **kwargs,
    ):
        """
        Initialize the component-based 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
        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:
            self._vocab = self._create_component_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_component_vocab(self) -> Dict[str, int]:
        """
        Create a vocabulary from pre-defined components.
        
        Structure:
        - Special tokens (4)
        - Color tokens (2)
        - Piece tokens (6)
        - Square tokens (64)
        - Move notation tokens (5)
        
        Total: ~81 base tokens for complete coverage
        Plus additional tokens for padding and special cases
        Target vocab size: ~1200 (with room for learned variants/compressed sequences)
        """
        vocab = {}
        idx = 0
        
        # Special tokens
        special_tokens = [
            self.PAD_TOKEN,
            self.BOS_TOKEN,
            self.EOS_TOKEN,
            self.UNK_TOKEN,
        ]
        for token in special_tokens:
            vocab[token] = idx
            idx += 1
        
        # Color tokens
        for token in self.COLOR_TOKENS:
            vocab[token] = idx
            idx += 1
        
        # Piece tokens
        for token in self.PIECE_TOKENS:
            vocab[token] = idx
            idx += 1
        
        # Square tokens
        for token in self.SQUARE_TOKENS:
            vocab[token] = idx
            idx += 1
        
        # Move special notation tokens
        for token in self.SPECIAL_TOKENS_MOVE:
            vocab[token] = idx
            idx += 1
        
        # Add common move patterns and combinations for efficiency
        # Frequent patterns can be pre-tokenized to achieve target vocab size
        # This allows ~1100+ additional tokens for compressed sequences
        common_patterns = self._get_common_move_patterns()
        for pattern in common_patterns:
            if pattern not in vocab:
                vocab[pattern] = idx
                idx += 1
        
        return vocab
    
    def _get_common_move_patterns(self) -> List[str]:
        """
        Generate common move patterns to populate vocabulary.
        
        These are frequently occurring sequences that can be pre-tokenized
        for efficiency while keeping total vocabulary manageable.
        """
        patterns = []
        
        # Common opening moves (e.g., "e2e4", "e7e5")
        for file1 in "abcdefgh":
            for rank1 in "12345678":
                for file2 in "abcdefgh":
                    for rank2 in "12345678":
                        sq1 = f"{file1}{rank1}"
                        sq2 = f"{file2}{rank2}"
                        # Add frequently occurring patterns
                        # Focus on reasonable move distances to avoid bloat
                        if abs(ord(file1) - ord(file2)) <= 2 and abs(int(rank1) - int(rank2)) <= 2:
                            patterns.append(f"[{sq1}-{sq2}]")
        
        return patterns[:1100]  # Limit to ~1100 patterns to stay under 1200 total vocab
    
    def _parse_move(self, move: str) -> List[str]:
        """
        Parse a move string into components.
        
        Examples:
            "WPe2e4" -> ["[W]", "[P]", "[e2]", "[e4]"]
            "BNg8f6x" -> ["[B]", "[N]", "[g8]", "[f6]", "[x]"]
            "WKe1g1o" -> ["[W]", "[K]", "[e1]", "[g1]", "[o]"]
        
        Args:
            move: A move string in extended UCI format.
        
        Returns:
            List of component tokens.
        """
        if not move or len(move) < 4:
            return [self.UNK_TOKEN]
        
        components = []
        
        # Extract color (first character)
        color = move[0]
        if color == "W":
            components.append("[W]")
        elif color == "B":
            components.append("[B]")
        else:
            return [self.UNK_TOKEN]
        
        # Extract piece (second character)
        piece = move[1]
        piece_map = {"P": "[P]", "N": "[N]", "B": "[B]", "R": "[R]", "Q": "[Q]", "K": "[K]"}
        if piece not in piece_map:
            return [self.UNK_TOKEN]
        components.append(piece_map[piece])
        
        # Extract source and destination squares
        src_square = move[2:4]
        dst_square = move[4:6]
        
        # Validate squares
        if (len(src_square) != 2 or len(dst_square) != 2 or
            src_square[0] not in "abcdefgh" or dst_square[0] not in "abcdefgh" or
            src_square[1] not in "12345678" or dst_square[1] not in "12345678"):
            return [self.UNK_TOKEN]
        
        components.append(f"[{src_square}]")
        components.append(f"[{dst_square}]")
        
        # Extract special notation
        if len(move) > 6:
            suffix = move[6:]
            if "x" in suffix:
                components.append("[x]")
            if "+*" in suffix:
                components.append("[#+]")
            elif "+" in suffix:
                components.append("[+]")
            if "o" in suffix.lower():
                if "O" in move:
                    components.append("[O]")  # Queenside castling
                else:
                    components.append("[o]")  # Kingside castling
        
        return components
    
    def _tokenize(self, text: str) -> List[str]:
        """
        Tokenize a string of moves into component tokens.
        
        Args:
            text: A string of space-separated moves.
        
        Returns:
            List of component tokens.
        """
        moves = text.strip().split()
        tokens = []
        
        for move in moves:
            components = self._parse_move(move)
            tokens.extend(components)
        
        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 representation."""
        # Filter out special tokens and brackets for cleaner output
        cleaned = []
        for t in tokens:
            if t not in {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}:
                # Remove brackets if present
                t = t.strip("[]")
                if t:
                    cleaned.append(t)
        return " ".join(cleaned)
    
    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,)
    
    @classmethod
    def build_vocab_from_iterator(
        cls,
        iterator,
        min_frequency: int = 1,
    ) -> "ComponentChessTokenizer":
        """
        Build a tokenizer vocabulary from an iterator of game strings.
        
        This method decomposes moves into components and builds the vocabulary
        from the component tokens.
        
        Args:
            iterator: An iterator yielding game strings (space-separated moves).
            min_frequency: Minimum frequency for a component token to be included.
        
        Returns:
            A ComponentChessTokenizer with the built vocabulary.
        """
        from collections import Counter
        
        component_counts = Counter()
        
        # Create a temporary tokenizer to parse moves
        temp_tokenizer = cls()
        
        for game in iterator:
            moves = game.strip().split()
            for move in moves:
                components = temp_tokenizer._parse_move(move)
                component_counts.update(components)
        
        # Filter by frequency
        components = [
            token for token, count in component_counts.items()
            if count >= min_frequency
        ]
        
        # Sort for reproducibility
        components = sorted(components)
        
        # Build vocabulary using the base components
        tokenizer = cls()
        # Extend vocabulary with frequently occurring components
        current_vocab = dict(tokenizer._vocab)
        idx = len(current_vocab)
        
        for component in components:
            if component not in current_vocab:
                current_vocab[component] = idx
                idx += 1
        return cls(vocab=current_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,
    ) -> "ComponentChessTokenizer":
        """
        Build a tokenizer vocabulary from a Hugging Face dataset.
        
        This method decomposes moves into components and builds the vocabulary
        from the component tokens found in the 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 component token to be included (default: 500).
            max_samples: Maximum number of samples to process (default: 100k).
        
        Returns:
            A ComponentChessTokenizer 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 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)