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 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
        kwargs.pop("added_tokens_decoder", None)
        kwargs.pop("added_tokens_encoder", None)
        kwargs.pop("special_tokens_map", None)
        kwargs.pop("tokenizer_class", None)
        kwargs.pop("auto_map", None)

        extra = kwargs.pop("extra_special_tokens", None)
        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 fixed structured vocabulary (no dataset-dependent move tokens).

        Tokens:
        - Special: [PAD], [BOS], [EOS], [UNK]
        - Color: [W], [B]
        - Pieces: [P], [N], [BISHOP], [R], [Q], [K]
        - Squares: [a1]..[h8]
        - Suffixes: [x], [+], [#]
        - Castling: [O-O], [O-O-O]
        - Promotions: [prom_Q], [prom_R], [prom_B], [prom_N]
        - Move separator: [MOVE_END]
        """
        special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        colors = ["[W]", "[B]"]
        pieces = ["[P]", "[N]", "[BISHOP]", "[R]", "[Q]", "[K]"]

        files = "abcdefgh"
        ranks = "12345678"
        squares = [f"[{f}{r}]" for r in ranks for f in files]  # a1..h8

        suffixes = ["[x]", "[+]", "[#]"]
        castling = ["[O-O]", "[O-O-O]"]
        promotions = ["[prom_Q]", "[prom_R]", "[prom_B]", "[prom_N]"]
        move_end = ["[MOVE_END]"]

        tokens = special + colors + pieces + squares + suffixes + castling + promotions + move_end
        return {tok: i for i, tok in enumerate(tokens)}

    @classmethod
    def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1) -> "ChessTokenizer":
        # Structured tokenizer uses a fixed vocab; iterator is unused.
        return cls(vocab=cls().get_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)

    @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":
        # Structured tokenizer uses a fixed vocab; dataset params are unused.
        return cls(vocab=cls().get_vocab())
    
    @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 _move_to_tokens(self, move: str) -> List[str]:
        """
        Convert one extended-UCI move string to structured tokens.

        Examples:
        "WPe2e4" -> ["[W]","[P]","[e2]","[e4]"]
        "WBb5c6(x+)" -> ["[W]","[BISHOP]","[b5]","[c6]","[x]","[+]"]
        "BKe8g8(o)" -> ["[B]","[O-O]"]
        "WPa7a8(Q)" -> ["[W]","[P]","[a7]","[a8]","[prom_Q]"]
        """
        toks: List[str] = []

        if not move:
            return [self.UNK_TOKEN]

        # Color
        color = move[0]
        toks.append("[W]" if color == "W" else "[B]")

        # Basic fields
        # move[1] is piece letter in dataset (P,N,B,R,Q,K)
        piece_char = move[1] if len(move) > 1 else ""
        piece_map = {"P": "[P]", "N": "[N]", "B": "[BISHOP]", "R": "[R]", "Q": "[Q]", "K": "[K]"}
        toks.append(piece_map.get(piece_char, self.UNK_TOKEN))

        # Source and destination squares assumed at positions 2:4 and 4:6
        # e.g. WPe2e4 -> from=e2 to=e4
        if len(move) >= 6:
            from_sq = move[2:4]
            to_sq = move[4:6]
            toks.append(f"[{from_sq}]")
            toks.append(f"[{to_sq}]")
        else:
            # malformed
            toks.append(self.UNK_TOKEN)
            toks.append(self.UNK_TOKEN)

        # --- Castling ---
        # Dataset mentions (o)/(O)=castling, sometimes attached to king moves.
        # We'll map based on king destination:
        if "(o)" in move or "(O)" in move:
            # King ends on g-file => O-O ; on c-file => O-O-O
            if len(move) >= 6:
                to_sq = move[4:6]
                if to_sq[0] == "g":
                    return [toks[0], "[O-O]"]
                if to_sq[0] == "c":
                    return [toks[0], "[O-O-O]"]

        # --- Promotion ---
        if "(Q)" in move:
            toks.append("[prom_Q]")
        elif "(R)" in move:
            toks.append("[prom_R]")
        elif "(B)" in move:
            toks.append("[prom_B]")
        elif "(N)" in move:
            toks.append("[prom_N]")

        # --- Capture / check / mate ---
        # Capture patterns: "(x)" "(x+)" "(x+*)" etc.
        if "(x" in move:
            toks.append("[x]")

        # Checkmate sometimes written (+*) or similar
        if "(+*)" in move:
            toks.append("[#]")
        elif "(+)" in move or "(x+)" in move:
            toks.append("[+]")

        return toks

    def _tokenize(self, text: str) -> List[str]:
        """
        Tokenize a game string into structured tokens.

        Each move becomes:
        [W]/[B], [PIECE], [from], [to], optional flags, then [MOVE_END]
        """
        moves = text.strip().split()
        out: List[str] = []
        for mv in moves:
            out.extend(self._move_to_tokens(mv))
            out.append("[MOVE_END]")
        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:
        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 and t != "[MOVE_END]"))
    
    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)