tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

Decomposed Chess Tokenizer (coverage, no UNKs in practice for well-formed moves).

Each dataset move like:
  WPe2e4
  WBb5c6(x+)
  WPe7e8=Q(+)
is tokenized into:
  ["WP", "e2_f", "e4_t"]                     # normal
  ["WB", "b5_f", "c6_t"]                     # capture/check ignored
  ["WP", "e7_f", "e8_t", "q"]                # promotion token appended
"""

from __future__ import annotations

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

from transformers import PreTrainedTokenizer


class ChessTokenizer(PreTrainedTokenizer):
    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]"  

    SQUARE_RE = re.compile(r"([a-h][1-8])([a-h][1-8])")
    PROMO_RE = re.compile(r"=([QRBN])", re.IGNORECASE)

    def __init__(
        self,
        vocab_file: Optional[str] = None,
        vocab: Optional[Dict[str, int]] = None,
        **kwargs,
    ):
        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 kwargs on load
        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 = dict(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]

        # color+piece tokens
        pieces = ["P", "N", "B", "R", "Q", "K"]
        cp = [f"W{p}" for p in pieces] + [f"B{p}" for p in pieces]

        # squares with role suffix
        files = "abcdefgh"
        ranks = "12345678"
        squares = [f"{f}{r}" for f in files for r in ranks]
        from_tokens = [f"{sq}_f" for sq in squares]
        to_tokens = [f"{sq}_t" for sq in squares]

        # promotions as separate token (lowercase)
        promo = ["q", "r", "b", "n"]

        tokens = special + cp + from_tokens + to_tokens + promo
        return {t: i for i, t in enumerate(tokens)}

    @classmethod
    def build_vocab_from_dataset(
        cls,
        dataset_name: str = "dlouapre/lichess_2025-01_1M",
        split: str = "train",
        column: str = "text",
        min_frequency: int = 0,
        max_samples: Optional[int] = None,
        save_dir: Optional[str] = None,
    ) -> "ChessTokenizer":
       
        tok = cls()
        if save_dir is not None:
            tok.save_pretrained(save_dir)
        return tok

    @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]:
        text = text.strip()
        if not text:
            return []

        raw = text.split()
        out: List[str] = []
        for tok in raw:
            # keep explicit BOS/EOS if they appear in text
            if tok in (self.BOS_TOKEN, self.EOS_TOKEN, self.PAD_TOKEN, self.UNK_TOKEN):
                out.append(tok)
                continue

            # Expect at least color+piece at positions 0,1
            if len(tok) < 6:
                out.append(self.UNK_TOKEN)
                continue

            color = tok[0]  # W/B
            piece = tok[1]  # P/N/B/R/Q/K
            cp = f"{color}{piece}"

            # Find squares anywhere in token (works even with suffixes like (x+), (o), etc.)
            m = self.SQUARE_RE.search(tok)
            if not m:
                out.append(self.UNK_TOKEN)
                continue

            from_sq, to_sq = m.group(1), m.group(2)
            out.extend([cp, f"{from_sq}_f", f"{to_sq}_t"])

            # Promotion
            pm = self.PROMO_RE.search(tok)
            if pm:
                out.append(pm.group(1).lower())

        return out

    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:
        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]:
    """
    With a fixed vocab tokenizer, "count vocab from dataset" is not very meaningful.
    Kept for API compatibility; returns the fixed vocab.
    """
    return ChessTokenizer().get_vocab()


from transformers import AutoTokenizer
AutoTokenizer.register("ChessTokenizer", ChessTokenizer)