| | """ |
| | Custom Chess Tokenizer V3 for the Chess Challenge. |
| | |
| | Enhanced version with additional chess-specific tokens for: |
| | - Castling moves (O-O, O-O-O) |
| | - Check/checkmate indicators (+, #) |
| | - Capture indicator (x) |
| | - Turn indicators ([WHITE], [BLACK]) |
| | |
| | This provides richer context while keeping vocabulary minimal (81 tokens total). |
| | """ |
| |
|
| | from __future__ import annotations |
| |
|
| | import json |
| | import os |
| | from pathlib import Path |
| | from typing import Dict, List, Optional |
| | import re |
| |
|
| | from transformers import PreTrainedTokenizer |
| |
|
| |
|
| | class ChessTokenizer(PreTrainedTokenizer): |
| | """ |
| | Enhanced chess tokenizer with special chess notation tokens. |
| | |
| | Vocabulary (79 tokens): |
| | - 4 special tokens: [PAD], [BOS], [EOS], [UNK] |
| | - 64 square tokens: a1-h8 |
| | - 4 promotion tokens: q, r, b, n |
| | - 2 castling tokens: O-O, O-O-O |
| | - 3 modifier tokens: +, #, x (check, checkmate, capture) |
| | - 2 turn tokens: [WHITE], [BLACK] |
| | """ |
| | |
| | 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]" |
| | WHITE_TOKEN = "[WHITE]" |
| | BLACK_TOKEN = "[BLACK]" |
| | |
| | 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) |
| | |
| | |
| | |
| | self.token_pattern = re.compile( |
| | r'O-O-O|O-O|' |
| | r'\[WHITE\]|\[BLACK\]|' |
| | r'[a-h][1-8]|' |
| | r'[qrbn]|' |
| | r'[+#x]' |
| | ) |
| |
|
| | 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]: |
| | """ |
| | Create the complete vocabulary with all chess-specific tokens. |
| | |
| | Total: 79 tokens |
| | """ |
| | vocab = {} |
| | idx = 0 |
| | |
| | |
| | for token in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]: |
| | vocab[token] = idx |
| | idx += 1 |
| | |
| | |
| | for f in 'abcdefgh': |
| | for r in '12345678': |
| | vocab[f"{f}{r}"] = idx |
| | idx += 1 |
| | |
| | |
| | for p in ['q', 'r', 'b', 'n']: |
| | vocab[p] = idx |
| | idx += 1 |
| | |
| | |
| | vocab["O-O"] = idx |
| | idx += 1 |
| | vocab["O-O-O"] = idx |
| | idx += 1 |
| | |
| | |
| | vocab["+"] = idx |
| | idx += 1 |
| | vocab["#"] = idx |
| | idx += 1 |
| | vocab["x"] = idx |
| | idx += 1 |
| | |
| | |
| | vocab[self.WHITE_TOKEN] = idx |
| | idx += 1 |
| | vocab[self.BLACK_TOKEN] = idx |
| | idx += 1 |
| | |
| | return vocab |
| | |
| | def _tokenize(self, text: str) -> List[str]: |
| | """ |
| | Enhanced tokenization with preprocessing for common chess notation variants. |
| | |
| | Handles: |
| | - Lichess format: (Q) → q, (x) → x, (+) → +, (#) → # |
| | - Standard notation: keeps O-O, O-O-O, +, #, x as-is |
| | - Extracts squares, promotions, castling, and modifiers |
| | """ |
| | |
| | text = (text.replace("(Q)", "q") |
| | .replace("(R)", "r") |
| | .replace("(B)", "b") |
| | .replace("(N)", "n") |
| | .replace("(x)", "x") |
| | .replace("(+)", "+") |
| | .replace("(#)", "#") |
| | .replace("(+*)", "#") |
| | .replace("(o)", "O-O") |
| | .replace("(O)", "O-O-O")) |
| | |
| | |
| | return self.token_pattern.findall(text) |
| | |
| | def _convert_token_to_id(self, token: str) -> int: |
| | """Convert a token to its ID.""" |
| | return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0)) |
| | |
| | def _convert_id_to_token(self, index: int) -> str: |
| | """Convert an ID to its token.""" |
| | return self._ids_to_tokens.get(index, self.UNK_TOKEN) |
| | |
| | def convert_tokens_to_string(self, tokens: List[str]) -> str: |
| | """ |
| | Reconstructs chess moves in standard UCI format with modifiers. |
| | |
| | Intelligently groups tokens: |
| | - Combines squares into moves: e2, e4 → e2e4 |
| | - Attaches promotions: a7, a8, q → a7a8q |
| | - Keeps modifiers separate: e2e4, x, + → e2e4x+ |
| | - Preserves castling and turn indicators |
| | """ |
| | special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN} |
| | clean_tokens = [t for t in tokens if t not in special] |
| | |
| | output = [] |
| | modifiers = {'+', '#', 'x'} |
| | promotions = {'q', 'r', 'b', 'n'} |
| | |
| | for token in clean_tokens: |
| | |
| | if token in ["O-O", "O-O-O", self.WHITE_TOKEN, self.BLACK_TOKEN]: |
| | output.append(token) |
| | |
| | elif token in promotions and output and len(output[-1]) == 4: |
| | output[-1] += token |
| | |
| | elif token in modifiers and output: |
| | output[-1] += token |
| | |
| | elif len(token) == 2 and token[0] in 'abcdefgh': |
| | if output and len(output[-1]) == 2 and output[-1][0] in 'abcdefgh': |
| | |
| | output[-1] += token |
| | else: |
| | |
| | output.append(token) |
| | else: |
| | output.append(token) |
| | |
| | return " ".join(output) |
| | |
| | def add_turn_indicators(self, text: str, add_white_indicator: bool = True) -> str: |
| | """ |
| | Add turn indicators to help the model understand whose turn it is. |
| | |
| | Args: |
| | text: Game string (space-separated moves) |
| | add_white_indicator: If True, add [WHITE] at start (white moves first) |
| | |
| | Returns: |
| | Game string with turn indicators |
| | """ |
| | moves = text.strip().split() |
| | result = [] |
| | |
| | |
| | is_white = add_white_indicator |
| | |
| | for move in moves: |
| | turn_token = self.WHITE_TOKEN if is_white else self.BLACK_TOKEN |
| | result.append(turn_token) |
| | result.append(move) |
| | is_white = not is_white |
| | |
| | return " ".join(result) |
| | |
| | def save_vocabulary( |
| | self, |
| | save_directory: str, |
| | filename_prefix: Optional[str] = None, |
| | ) -> tuple: |
| | """Save the vocabulary to a JSON file.""" |
| | 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,) |
| | |
| | @classmethod |
| | def build_vocab_from_iterator(cls, iterator, min_frequency=1): |
| | """Returns tokenizer with fixed vocabulary (doesn't depend on data).""" |
| | return cls() |
| | |
| | @classmethod |
| | def build_vocab_from_dataset(cls, **kwargs): |
| | """Returns tokenizer with fixed vocabulary (doesn't depend on data).""" |
| | return cls() |
| | |
| | @property |
| | def vocab_size(self) -> int: |
| | """Return the size of the vocabulary (79 tokens).""" |
| | return len(self._vocab) |
| | |
| | def get_vocab(self) -> Dict[str, int]: |
| | """Return the vocabulary as a dictionary.""" |
| | return dict(self._vocab) |
| |
|