tokenizer.py

"""
Custom Chess Tokenizer for the Chess1MChallenge.

Goal: maximize legal-move rate in the evaluation.

Key idea:
- The evaluator only needs to recover the UCI move (e.g. "e2e4") from the model output.
  It extracts squares like [a-h][1-8] and builds a move from the first 2 squares.
- So we normalize dataset tokens like "WPe2e4(x+)" to plain UCI "e2e4" (plus promotion suffix "q/r/b/n").
- We use a FIXED UCI vocabulary so there is (almost) no OOV -> far fewer [UNK] -> higher legal-move rate.

Vocabulary:
- All from-to square pairs: "a1a2", ..., excluding from==to.
- All promotion moves: e7e8[qrbn], a2a1[qrbn], including capture-promotions (still covered by from-to).
"""

from __future__ import annotations

import json
import os
import re
from typing import Dict, List, Optional

from transformers import PreTrainedTokenizer


_SQUARE_RE = re.compile(r"[a-h][1-8]")
_PROMO_RE = re.compile(r"=([QRBN])")  # dataset often uses "=Q"


class ChessTokenizer(PreTrainedTokenizer):
    """
    Tokenizer that maps each chess move to a single token.

    It is compatible with Hugging Face `AutoTokenizer(..., trust_remote_code=True)`.

    Notes:
    - Input text may contain "extended UCI" tokens from the Lichess dataset
      (e.g. "WPe2e4", "BKe8g8(O)", "WPe7e8=Q(+)" ...).
    - We normalize those tokens to plain UCI: "e2e4", "e8g8", "e7e8q", ...
    """

    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 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

        # Avoid duplicate special-token args 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:
            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,
        )

    def _create_default_vocab(self) -> Dict[str, int]:
        special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        return {tok: i for i, tok in enumerate(special)}

    @staticmethod
    def _normalize_one_token(tok: str) -> str:
        """
        Convert an extended token to plain UCI.

        Examples:
          "WPe2e4"         -> "e2e4"
          "BKe8g8(O)"      -> "e8g8"
          "WPe7e8=Q(+)"    -> "e7e8q"
          "WPe5d6(x)"      -> "e5d6"
        """
        squares = _SQUARE_RE.findall(tok)
        if len(squares) < 2:
            return ChessTokenizer.UNK_TOKEN

        uci = squares[0] + squares[1]

        m = _PROMO_RE.search(tok)
        if m:
            uci += m.group(1).lower()  # Q->q etc.

        return uci

    @classmethod
    def build_fixed_uci_vocab(cls) -> "ChessTokenizer":
        """
        Build a FIXED vocabulary of (almost) all possible UCI moves.

        This dramatically reduces OOV compared to building vocab from the dataset
        with a high min_frequency.
        """
        files = "abcdefgh"
        ranks = "12345678"

        tokens: List[str] = []

        # All from-to square pairs (excluding from==to)
        for f1 in files:
            for r1 in ranks:
                for f2 in files:
                    for r2 in ranks:
                        if f1 == f2 and r1 == r2:
                            continue
                        tokens.append(f"{f1}{r1}{f2}{r2}")

        # Promotions: white (7->8) and black (2->1), with q/r/b/n
        promos = "qrbn"

        # White promotions
        for f in files:
            fr = f + "7"
            for df in (-1, 0, 1):
                j = files.index(f) + df
                if 0 <= j < 8:
                    to = files[j] + "8"
                    base = fr + to
                    for p in promos:
                        tokens.append(base + p)

        # Black promotions
        for f in files:
            fr = f + "2"
            for df in (-1, 0, 1):
                j = files.index(f) + df
                if 0 <= j < 8:
                    to = files[j] + "1"
                    base = fr + to
                    for p in promos:
                        tokens.append(base + p)

        tokens = sorted(set(tokens))

        special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
        vocab = {tok: i for i, tok in enumerate(special + tokens)}
        return cls(vocab=vocab)

    @classmethod
    def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1) -> "ChessTokenizer":
        """
        Optional: build vocabulary from an iterator of strings.

        We normalize tokens to UCI before counting.
        """
        from collections import Counter

        counts = Counter()
        for game in iterator:
            raw = game.strip().split()
            norm = [cls._normalize_one_token(t) for t in raw]
            counts.update(norm)

        tokens = [t for t, c in counts.items() if c >= min_frequency]
        tokens = sorted(set(tokens))

        special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
        vocab = {tok: i for i, tok in enumerate(special + tokens)}
        return cls(vocab=vocab)

    @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]:
        raw = text.strip().split()
        return [self._normalize_one_token(t) for t in raw]

    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 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,)