src/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
import re
from typing import Dict, List, Optional

from transformers import PreTrainedTokenizer

SQUARE_RE = re.compile(r"[a-h][1-8]")
UCI_PROMO_RE = re.compile(r"^[a-h][1-8][a-h][1-8]([qrbn])$", re.IGNORECASE)
EQ_PROMO_RE = re.compile(r"=([QRBNqrbn])")
PAREN_PROMO_RE = re.compile(r"\(([QRBNqrbn])\)")

PROMOS = {"q", "r", "b", "n"}


class ChessTokenizer(PreTrainedTokenizer):
    vocab_files_names = {"vocab_file": "vocab.json"}
    model_input_names = ["input_ids", "attention_mask"]

    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,
    ):
        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 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_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 _create_fixed_vocab(self) -> Dict[str, int]:
        specials = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        # IMPORTANT: deterministic ids matching a1,a2,...,a8,b1,... style
        squares = [f"{f}{r}" for f in "abcdefgh" for r in "12345678"]
        promos = ["q", "r", "b", "n"]
        tokens = specials + squares + promos
        return {tok: i for i, tok in enumerate(tokens)}

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

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

    def _extract_promo_anywhere(self, mv: str) -> Optional[str]:
        m = EQ_PROMO_RE.search(mv)
        if m:
            return m.group(1).lower()
        m = PAREN_PROMO_RE.search(mv)
        if m:
            return m.group(1).lower()
        m = UCI_PROMO_RE.match(mv)
        if m:
            return m.group(1).lower()
        return None

    def _tokenize(self, text: str) -> List[str]:
        """
        Robust tokenization:
        - keeps special tokens ([BOS], etc.) as-is (HF handles them)
        - accepts already-split squares: "e2 e4"
        - accepts uci concat: "e2e4" -> e2,e4 (+promo)
        - accepts verbose tokens containing squares: "WPe2e4(x+)" -> e2,e4 (+promo)
        """
        tokens: List[str] = []

        for chunk in text.strip().split():
            # already-split square?
            if re.fullmatch(r"[a-h][1-8]", chunk):
                tokens.append(chunk)
                continue

            # promo alone?
            if chunk in PROMOS:
                tokens.append(chunk)
                continue

            # otherwise: extract squares from inside
            squares = SQUARE_RE.findall(chunk)
            if len(squares) >= 2:
                tokens.append(squares[0])
                tokens.append(squares[1])

                promo = self._extract_promo_anywhere(chunk)
                if promo in PROMOS:
                    tokens.append(promo)
            else:
                # allow special tokens to pass through if present
                if chunk in {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}:
                    tokens.append(chunk)
                else:
                    tokens.append(self.UNK_TOKEN)

        return tokens

    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:
        """
        Reconstruct "e2e4 e7e8q ..."
        """
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        clean = [t for t in tokens if t not in special]

        moves: List[str] = []
        i = 0
        while i < len(clean):
            if re.fullmatch(r"[a-h][1-8]", clean[i]) and i + 1 < len(clean) and re.fullmatch(r"[a-h][1-8]", clean[i + 1]):
                mv = clean[i] + clean[i + 1]
                i += 2
                if i < len(clean) and clean[i] in PROMOS:
                    mv += clean[i]
                    i += 1
                moves.append(mv)
            else:
                moves.append(clean[i])
                i += 1

        return " ".join(moves)

    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,)


from transformers import AutoTokenizer
ChessTokenizer.register_for_auto_class("AutoTokenizer")