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, os, re
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer

_MOVE_RE = re.compile(r"^(?P<side>[WB])(?P<piece>[PNBRQK])(?P<src>[a-h][1-8])(?P<dst>[a-h][1-8])(?P<suffix>.*)$")
_PROMO_RE = re.compile(r"=([QRBNqrbn])")

def _parse_suffix(suffix: str):
    s = (suffix or "").strip()
    is_capture = "x" in s
    is_check = "+" in s
    is_mate = "*" in s
    castle = "O-O-O" if "(O)" in s else ("O-O" if "(o)" in s else None)
    promo = None
    m = _PROMO_RE.search(s)
    if m:
        promo = m.group(1).lower()
    return is_capture, is_check, is_mate, castle, promo

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"}

    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

        kwargs.pop("pad_token", None)
        kwargs.pop("bos_token", None)
        kwargs.pop("eos_token", None)
        kwargs.pop("unk_token", None)

        if vocab is not None:
            self._vocab = vocab
        elif vocab_file 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_default_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,
        )

    @property
    def vocab_size(self) -> int:
        return len(self._vocab)

    def get_vocab(self) -> Dict[str, int]:
        return dict(self._vocab)

    def _convert_token_to_id(self, token: str) -> int:
        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))

    def _convert_id_to_token(self, index: int) -> str:
        return self._ids_to_tokens.get(index, self.UNK_TOKEN)

    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.
        """
        tokens: List[str] = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        tokens += [f"[W{p}]" for p in "PNBRQK"]
        tokens += [f"[B{p}]" for p in "PNBRQK"]
        tokens += [f"[{f}{r}]" for f in "abcdefgh" for r in "12345678"]
        tokens += ["[x]", "[+]", "[#]", "[O-O]", "[O-O-O]"]
        tokens += [f"[={p}]" for p in "qrbn"]
        return {tok: i for i, tok in enumerate(tokens)}

    def _tokenize(self, text: str) -> List[str]:
        out: List[str] = []
        for move in (text or "").strip().split():
            # Raw UCI like e2e4 / e7e8q (no side/piece available)
            if re.fullmatch(r"[a-h][1-8][a-h][1-8][qrbn]?", move):
                src, dst = move[:2], move[2:4]
                out += [f"[{src}]", f"[{dst}]"]
                if len(move) == 5:
                    out += [f"[={move[4]}]"]
                continue

            m = _MOVE_RE.match(move)
            if not m:
                out.append(self.UNK_TOKEN)
                continue

            side = m.group("side")   # "W" or "B"
            piece = m.group("piece") # P/N/B/R/Q/K
            src = f"[{m.group('src')}]"
            dst = f"[{m.group('dst')}]"
            is_cap, is_chk, is_mate, castle, promo = _parse_suffix(m.group("suffix") or "")

            out += [f"[{side}{piece}]", src, dst]
            if castle:
                out.append(f"[{castle}]")
            if is_cap:
                out.append("[x]")
            if is_mate:
                out.append("[#]")
            elif is_chk:
                out.append("[+]")
            if promo:
                out.append(f"[={promo}]")
        return out

    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)

    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,)