tokenizer.py

"""
Optimized Chess Tokenizer using pure UCI notation.

This achieves ~84 vocab size by:
1. Using only squares (a1-h8) and promotion pieces (q,r,b,n)
2. Decomposing moves into from_square, to_square, (optional) promotion
3. No piece types, no color, no annotations
"""

from __future__ import annotations

import json
import os
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]"
    
    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

        # Remove duplicates from kwargs
        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()
        
        # Create reverse mapping
        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]:
        """
        Create vocabulary with all possible squares and promotion pieces.
        This ensures deterministic vocab size of exactly 72 tokens.
        """
        tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        
        # All squares a1-h8
        for file in 'abcdefgh':
            for rank in '12345678':
                tokens.append(f"{file}{rank}")
        
        # Promotion pieces (lowercase for UCI)
        tokens.extend(['q', 'r', 'b', 'n'])
        
        vocab = {token: idx for idx, token in enumerate(tokens)}
        return vocab
    
    @classmethod
    def build_vocab_from_dataset(
        cls,
        dataset_name: str = "dlouapre/lichess_2025-01_1M",
        split: str = "train",
        column: str = "text",
        min_frequency: int = 1,
        max_samples: Optional[int] = 100000,
    ) -> "ChessTokenizer":
        """
        Build tokenizer from dataset by converting to UCI format.
        
        This will create a vocabulary of ~72-84 tokens.
        """
        from datasets import load_dataset
        from collections import Counter
        
        dataset = load_dataset(dataset_name, split=split)
        
        if max_samples is not None:
            dataset = dataset.select(range(min(max_samples, len(dataset))))
        
        token_counts = Counter()
        
        # Process games and extract UCI components
        for example in dataset:
            moves = example[column].strip().split()
            for move in moves:
                # Convert extended UCI to decomposed UCI
                uci_tokens = cls._extended_to_uci_tokens(move)
                token_counts.update(uci_tokens)
        
        # Filter by frequency
        tokens = [
            token for token, count in token_counts.items()
            if count >= min_frequency
        ]
        
        # Sort for reproducibility
        tokens = sorted(set(tokens))
        
        # Build vocabulary
        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
        vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}
        
        return cls(vocab=vocab)
    
    @staticmethod
    def _extended_to_uci_tokens(move: str) -> List[str]:
        """
        Convert extended UCI format to decomposed UCI tokens.
        
        Input: "WPe2e4" or "BQd8h4(x+)" or "WPe7e8=Q"
        Output: ["e2", "e4"] or ["d8", "h4"] or ["e7", "e8", "q"]
        """
        if len(move) < 6:
            return []
        
        # Extract squares (positions 2-6)
        from_sq = move[2:4]
        to_sq = move[4:6]
        
        tokens = [from_sq, to_sq]
        
        # Check for promotion
        if "=" in move:
            promo_idx = move.index("=")
            if promo_idx + 1 < len(move):
                promo = move[promo_idx + 1].lower()
                if promo in 'qrbn':
                    tokens.append(promo)
        
        return tokens
    
    @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]:
        """
        Tokenize a string of moves.
        
        Input can be either:
        - Extended UCI: "WPe2e4 BPe7e5"
        - Decomposed UCI: "e2 e4 e7 e5"
        """
        tokens = text.strip().split()
        
        # If tokens look like extended UCI (start with W/B and piece letter)
        # convert them to decomposed format
        result = []
        for token in tokens:
            if len(token) >= 6 and token[0] in 'WB' and token[1] in 'PNBRQK':
                # Extended format - decompose it
                result.extend(self._extended_to_uci_tokens(token))
            else:
                # Already in simple format or is a square/promotion
                result.append(token)
        
        return result
    
    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:
        """Convert tokens back to string (space-separated)."""
        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,)