tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer decomposes moves into atomic tokens:
Piece -> Source Square -> Target Square -> Suffixes.
Example: "WPe2e4" -> ['P', 'e2', 'e4'] (Color is implicit to save context)
Example: "Bxb7+" -> ['B', 'c8', 'b7', '(x)', '(+)']
"""

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"}
    
    # Special tokens
    PAD_TOKEN = "[PAD]"
    BOS_TOKEN = "[BOS]"
    EOS_TOKEN = "[EOS]"
    UNK_TOKEN = "[UNK]"
    
    # Atomic components
    PIECES = ["P", "N", "B", "R", "Q", "K"]
    FILES = "abcdefgh"
    RANKS = "12345678"
    SUFFIXES = ["(x)", "(+)", "(+*)", "(o)", "(O)", "(=)"]

    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

        # Clean kwargs
        kwargs.pop("pad_token", None)
        kwargs.pop("bos_token", None)
        kwargs.pop("eos_token", None)
        kwargs.pop("unk_token", None)
        
        # Load or create FIXED vocabulary
        if vocab is not None:
            self._vocab = vocab
        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]:
        """Creates the fixed vocabulary of ~80 atomic tokens."""
        vocab = {}
        idx = 0
        
        # 1. Special Tokens
        for token in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]:
            vocab[token] = idx
            idx += 1
            
        # 2. Pieces
        for p in self.PIECES:
            vocab[p] = idx
            idx += 1
            
        # 3. Squares (a1...h8)
        # We treat squares as atomic tokens for better spatial learning
        for f in self.FILES:
            for r in self.RANKS:
                vocab[f"{f}{r}"] = idx
                idx += 1
                
        # 4. Suffixes
        for s in self.SUFFIXES:
            vocab[s] = idx
            idx += 1
            
        return vocab

    @classmethod
    def build_vocab_from_dataset(
        cls,
        dataset_name: str = "dlouapre/lichess_2025-01_1M",
        **kwargs
    ) -> "ChessTokenizer":
        """
        Override: Returns the tokenizer with the fixed vocabulary immediately.
        We do not need to scan the dataset anymore.
        """
        print("Initializing Fixed Vocabulary Tokenizer (Deconstructed Strategy)...")
        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]:
        """
        Decomposes move strings into atomic tokens.
        Input: "[BOS] WPe2e4 BNg8f6"
        Output: ['[BOS]', 'P', 'e2', 'e4', 'N', 'g8', 'f6']
        """
        tokens = []
        moves = text.strip().split()
        
        # Set of special tokens for quick lookup
        special_tokens = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}

        for move in moves:
            # Skip empty strings
            if not move: continue
            
            # 1. Handle Special Tokens (Important for data.py compatibility)
            if move in special_tokens:
                tokens.append(move)
                continue

            # 2. Regex to parse Lichess format: WPe2e4(x)
            # Group 1: Color (W/B) - Ignored
            # Group 2: Piece (P/N/B/R/Q/K)
            # Group 3: Source (e.g. e2)
            # Group 4: Target (e.g. e4)
            # Group 5: Suffix (optional)
            match = re.match(r"([WB])([PNBRQK])([a-h][1-8])([a-h][1-8])(.*)", move)
            
            if match:
                _, piece, src, dst, suffix = match.groups()
                tokens.extend([piece, src, dst])
                if suffix:
                    if suffix in self._vocab:
                        tokens.append(suffix)
            else:
                # Fallback for unexpected formats
                found_any = False
                
                # Check for piece
                for p in self.PIECES:
                    if p in move:
                        tokens.append(p)
                        found_any = True
                        break
                
                # Check for squares
                squares = re.findall(r"[a-h][1-8]", move)
                tokens.extend(squares)
                if squares: found_any = True

                # Check for suffixes
                for s in self.SUFFIXES:
                    if s in move:
                        tokens.append(s)
                        found_any = True
                
                if not found_any:
                    tokens.append(self.UNK_TOKEN)
                    
        return tokens

    def _convert_token_to_id(self, token: str) -> int:
        return self._vocab.get(token, self._vocab.get(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:
        # Reconstructs a readable string (space separated for clarity)
        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,)