tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer breaks down moves into 5 components:
Color, Piece, Source, Destination, Suffix.
"""

from __future__ import annotations

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

from transformers import PreTrainedTokenizer


class ChessTokenizer(PreTrainedTokenizer):
    """
    A component-based tokenizer for chess moves.
    
    Each move is split into 5 tokens:
    [Color, Piece, Source, Destination, Suffix]
    
    Vocabulary is fixed and deterministic.
    """
    
    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]"
    
    # Component definitions
    COLORS = ["W", "B"]
    PIECES = ["P", "N", "B", "R", "Q", "K"]
    FILES = ["a", "b", "c", "d", "e", "f", "g", "h"]
    RANKS = ["1", "2", "3", "4", "5", "6", "7", "8"]
    SUFFIXES = ["", "(x)", "(+)", "(+*)", "(o)", "(O)"]
    
    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_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]:
        tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        
        # Add all possible components
        tokens.extend(self.COLORS)
        tokens.extend(self.PIECES)
        
        # Squares
        squares = [f"{f}{r}" for f in self.FILES for r in self.RANKS]
        tokens.extend(squares)
        
        # Suffixes (ensure empty string is handled explicitly if needed, but usually empty splitting result needs a token)
        # We will map "no suffix" to a specific token, e.g., "_" or just use PAD? 
        # Using a dedicated empty token is safer for the 5-component structure.
        # Let's use "[None]" for empty suffix to be explicit, or just "" if valid key.
        # JSON keys must be strings. "" is valid.
        
        # Add suffixes
        for s in self.SUFFIXES:
            if s == "":
                tokens.append("[None]") # Representation for empty suffix
            else:
                tokens.append(s)
                
        # Unique tokens only (order matters for ID stability)
        seen = set()
        unique_tokens = []
        for t in tokens:
            if t not in seen:
                unique_tokens.append(t)
                seen.add(t)
                
        return {t: i for i, t in enumerate(unique_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]:
        # Text is space-separated moves
        moves = text.strip().split()
        tokens = []
        
        for move in moves:
            # Handle special tokens directly if they appear in text (rare in raw data but good for safety)
            if move in [self.BOS_TOKEN, self.EOS_TOKEN, self.PAD_TOKEN, self.UNK_TOKEN]:
                # Expand special tokens to 5-tuples for consistency?
                # Or keep as single tokens?
                # If we want the model to reshape (..., 5), we MUST have multiple of 5.
                # Let's repeat them 5 times.
                tokens.extend([move] * 5)
                continue
                
            # Parse Move: e.g. WPe2e4(x)
            # Regex to capture: (Color)(Piece)(Src)(Dst)(Suffix)
            # Suffix is optional.
            # However some moves might be castling? 
            # Note: Dataset says "(o)/(O)=castling". 
            # If the move is literally "(o)", it lacks Color/Piece.
            # But the example `WPe2e4` implies standard algebraic.
            # `(o)` usually appears as `WKe1g1(o)`?
            # Let's assume the string format is always full or identifiable.
            
            # Simple parsing:
            # Color: 1 char
            # Piece: 1 char
            # Src: 2 chars
            # Dst: 2 chars
            # Suffix: Remainder
            
            if len(move) < 6: # Shortest move WPe2e4 is 6 chars.
                # Maybe castling? "0-0"? No, "extended UCI". 
                # If invalid, emit UNK x 5
                tokens.extend([self.UNK_TOKEN] * 5)
                continue
                
            c = move[0]
            p = move[1]
            src = move[2:4]
            dst = move[4:6]
            suf = move[6:]
            
            if suf == "":
                suf_tok = "[None]"
            else:
                suf_tok = suf
                
            # Validation (optional, but good for safety)
            raw_components = [c, p, src, dst, suf_tok]
            
            # Check if all are in vocab, else UNK
            final_components = []
            for comp in raw_components:
                if comp in self._vocab:
                    final_components.append(comp)
                else:
                    final_components.append(self.UNK_TOKEN)
            
            tokens.extend(final_components)
            
        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:
        # Reconstruct moves
        # tokens is list of components
        output = []
        # Process in chunks of 5
        for i in range(0, len(tokens), 5):
            chunk = tokens[i:i+5]
            if len(chunk) < 5:
                break
                
            # Check if special
            if chunk[0] in [self.BOS_TOKEN, self.EOS_TOKEN, self.PAD_TOKEN]:
                continue # Skip specials for string output
                
            c, p, src, dst, suf = chunk
            if suf == "[None]":
                suf = ""
            
            output.append(f"{c}{p}{src}{dst}{suf}")
            
        return " ".join(output)

    @classmethod
    def build_vocab_from_dataset(cls, *args, **kwargs):
        # We use a fixed vocab, so just return an instance
        return cls()
        
    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,)