tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer treats each move as a sequence of structured tokens derived from 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
import re
from typing import Dict, List, Optional, Sequence, Union

from transformers import PreTrainedTokenizer


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


class ChessTokenizer(PreTrainedTokenizer):
    """
    A structured tokenizer for chess moves.

    Each move is decomposed into:
      SIDE_(W/B), PIECE_(P/N/B/R/Q/K), SQ_<src>, SQ_<dst>,
      and optional flags: CAPTURE, CHECK, MATE, CASTLE, PROMO_(Q/R/B/N).

    This avoids UNK explosions when using a move-as-token vocabulary.
    """

    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]"

    # Fixed token set
    SIDE_W = "SIDE_W"
    SIDE_B = "SIDE_B"

    PIECES = ["P", "N", "B", "R", "Q", "K"]

    PROMO_PREFIX = "PROMO_"
    CAPTURE = "CAPTURE"
    CHECK = "CHECK"
    MATE = "MATE"
    CASTLE = "CASTLE"

    def __init__(
        self,
        vocab_file: Optional[str] = None,
        vocab: Optional[Dict[str, int]] = None,
        **kwargs,
    ):
        kwargs.pop("pad_token", None)
        kwargs.pop("bos_token", None)
        kwargs.pop("eos_token", None)
        kwargs.pop("unk_token", None)

        self._pad_token = self.PAD_TOKEN
        self._bos_token = self.BOS_TOKEN
        self._eos_token = self.EOS_TOKEN
        self._unk_token = self.UNK_TOKEN

        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._build_fixed_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 _build_fixed_vocab(self) -> Dict[str, int]:
        special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        sides = [self.SIDE_W, self.SIDE_B]
        pieces = [f"PIECE_{p}" for p in self.PIECES]
        squares = [f"SQ_{file}{rank}" for file in "abcdefgh" for rank in "12345678"]
        promos = [f"{self.PROMO_PREFIX}{p}" for p in ["Q", "R", "B", "N"]]
        flags = [self.CAPTURE, self.CHECK, self.MATE, self.CASTLE]
        tokens = special + sides + pieces + squares + promos + flags
        return {tok: i for i, tok in enumerate(tokens)}

    @classmethod
    def build_vocab_from_dataset(cls, *args, **kwargs) -> "ChessTokenizer":
        """
        Kept for API compatibility with the template training script.
        This tokenizer uses a fixed vocabulary (no dataset-dependent pruning).
        """
        return cls()

    @classmethod
    def build_vocab_from_iterator(cls, *args, **kwargs) -> "ChessTokenizer":
        """
        Kept for API compatibility. This tokenizer uses a fixed vocabulary.
        """
        return cls()

    @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]:
        tokens: List[str] = []
        moves = text.strip().split()
        for mv in moves:
            tokens.extend(self._tokenize_move(mv))
        return tokens

    def _tokenize_move(self, move: str) -> List[str]:
        m = _MOVE_RE.match(move)
        if not m:
            return [self.UNK_TOKEN]

        side = m.group("side")
        piece = m.group("piece")
        src = m.group("src")
        dst = m.group("dst")
        rest = m.group("rest") or ""

        out: List[str] = []
        out.append(self.SIDE_W if side == "W" else self.SIDE_B)
        out.append(f"PIECE_{piece}")
        out.append(f"SQ_{src}")
        out.append(f"SQ_{dst}")

        promo = self._parse_promotion(rest)
        if promo is not None:
            out.append(f"{self.PROMO_PREFIX}{promo}")

        if "(x)" in rest or "x" in rest:
            out.append(self.CAPTURE)

        if "(+*)" in rest or "++" in rest or "#" in rest:
            out.append(self.MATE)
        elif "(+)" in rest or "+" in rest:
            out.append(self.CHECK)

        if "(o)" in rest or "(O)" in rest or "O-O" in rest:
            out.append(self.CASTLE)

        return out

    def _parse_promotion(self, rest: str) -> Optional[str]:
        m = re.search(r"=([QRBNqrbn])", rest)
        if m:
            return m.group(1).upper()
        return None

    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:
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        out: List[str] = []
        for t in tokens:
            if t in special:
                continue
            out.append(t)
        return " ".join(out)

    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,)

    def decode(self, token_ids: Union[int, Sequence[int]], skip_special_tokens: bool = False, **kwargs) -> str:
        if isinstance(token_ids, int):
            ids = [token_ids]
        elif "torch" in str(type(token_ids)):
            ids = token_ids.detach().cpu().flatten().tolist()
        else:
            ids = list(token_ids)

        toks = [self._convert_id_to_token(i) for i in ids]
        if skip_special_tokens:
            special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
            toks = [t for t in toks if t not in special]
        return self.convert_tokens_to_string(toks)