""" 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 from pathlib import Path from typing import Dict, List, Optional from transformers import PreTrainedTokenizer class ChessTokenizer(PreTrainedTokenizer): """ A custom tokenizer for chess moves using extended UCI notation. This tokenizer maps each possible chess move to a unique token ID. The vocabulary is built from the training dataset to ensure all moves encountered during training have a corresponding token. Example: >>> tokenizer = ChessTokenizer() >>> tokenizer.encode("WPe2e4 BPe7e5") [1, 42, 87, 2] # [BOS, e2e4, e7e5, EOS] """ 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 the chess tokenizer. Args: vocab_file: Path to a JSON file containing the vocabulary mapping. vocab: Dictionary mapping tokens to IDs (alternative to vocab_file). **kwargs: Additional arguments passed to PreTrainedTokenizer. """ # 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 any duplicate special-token entries passed through kwargs # to avoid "multiple values for keyword" errors when loading from disk. 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: # Create a minimal vocabulary with just special tokens # The full vocabulary should be built from the dataset self._vocab = self._create_default_vocab() # Create reverse mapping self._ids_to_tokens = {v: k for k, v in self._vocab.items()} # Call parent init AFTER setting up vocab 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 a fixed structured vocabulary (no dataset-dependent move tokens). Tokens: - Special: [PAD], [BOS], [EOS], [UNK] - Color: [W], [B] - Pieces: [P], [N], [BISHOP], [R], [Q], [K] - Squares: [a1]..[h8] - Suffixes: [x], [+], [#] - Castling: [O-O], [O-O-O] - Promotions: [prom_Q], [prom_R], [prom_B], [prom_N] - Move separator: [MOVE_END] """ special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN] colors = ["[W]", "[B]"] pieces = ["[P]", "[N]", "[BISHOP]", "[R]", "[Q]", "[K]"] files = "abcdefgh" ranks = "12345678" squares = [f"[{f}{r}]" for r in ranks for f in files] # a1..h8 suffixes = ["[x]", "[+]", "[#]"] castling = ["[O-O]", "[O-O-O]"] promotions = ["[prom_Q]", "[prom_R]", "[prom_B]", "[prom_N]"] move_end = ["[MOVE_END]"] tokens = special + colors + pieces + squares + suffixes + castling + promotions + move_end return {tok: i for i, tok in enumerate(tokens)} @classmethod def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1) -> "ChessTokenizer": # Structured tokenizer uses a fixed vocab; iterator is unused. return cls(vocab=cls().get_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 = 500, # max_samples: Optional[int] = 100000, # ) -> "ChessTokenizer": # """ # Build a tokenizer vocabulary from a Hugging Face dataset. # Args: # dataset_name: Name of the dataset on Hugging Face Hub. # split: Dataset split to use. # column: Column containing the game strings. # min_frequency: Minimum frequency for a token to be included (default: 500). # max_samples: Maximum number of samples to process (default: 100k). # Returns: # A ChessTokenizer with the built vocabulary. # """ # from datasets import load_dataset # dataset = load_dataset(dataset_name, split=split) # if max_samples is not None: # dataset = dataset.select(range(min(max_samples, len(dataset)))) # def game_iterator(): # for example in dataset: # yield example[column] # return cls.build_vocab_from_iterator(game_iterator(), min_frequency=min_frequency) @classmethod def build_vocab_from_dataset(cls,dataset_name: str = "dlouapre/lichess_2025-01_1M",split: str = "train",column: str = "text",min_frequency: int = 500,max_samples: Optional[int] = 100000,) -> "ChessTokenizer": # Structured tokenizer uses a fixed vocab; dataset params are unused. return cls(vocab=cls().get_vocab()) @property def vocab_size(self) -> int: """Return the size of the vocabulary.""" return len(self._vocab) def get_vocab(self) -> Dict[str, int]: """Return the vocabulary as a dictionary.""" return dict(self._vocab) def _move_to_tokens(self, move: str) -> List[str]: """ Convert one extended-UCI move string to structured tokens. Examples: "WPe2e4" -> ["[W]","[P]","[e2]","[e4]"] "WBb5c6(x+)" -> ["[W]","[BISHOP]","[b5]","[c6]","[x]","[+]"] "BKe8g8(o)" -> ["[B]","[O-O]"] "WPa7a8(Q)" -> ["[W]","[P]","[a7]","[a8]","[prom_Q]"] """ toks: List[str] = [] if not move: return [self.UNK_TOKEN] # Color color = move[0] toks.append("[W]" if color == "W" else "[B]") # Basic fields # move[1] is piece letter in dataset (P,N,B,R,Q,K) piece_char = move[1] if len(move) > 1 else "" piece_map = {"P": "[P]", "N": "[N]", "B": "[BISHOP]", "R": "[R]", "Q": "[Q]", "K": "[K]"} toks.append(piece_map.get(piece_char, self.UNK_TOKEN)) # Source and destination squares assumed at positions 2:4 and 4:6 # e.g. WPe2e4 -> from=e2 to=e4 if len(move) >= 6: from_sq = move[2:4] to_sq = move[4:6] toks.append(f"[{from_sq}]") toks.append(f"[{to_sq}]") else: # malformed toks.append(self.UNK_TOKEN) toks.append(self.UNK_TOKEN) # --- Castling --- # Dataset mentions (o)/(O)=castling, sometimes attached to king moves. # We'll map based on king destination: if "(o)" in move or "(O)" in move: # King ends on g-file => O-O ; on c-file => O-O-O if len(move) >= 6: to_sq = move[4:6] if to_sq[0] == "g": return [toks[0], "[O-O]"] if to_sq[0] == "c": return [toks[0], "[O-O-O]"] # --- Promotion --- if "(Q)" in move: toks.append("[prom_Q]") elif "(R)" in move: toks.append("[prom_R]") elif "(B)" in move: toks.append("[prom_B]") elif "(N)" in move: toks.append("[prom_N]") # --- Capture / check / mate --- # Capture patterns: "(x)" "(x+)" "(x+*)" etc. if "(x" in move: toks.append("[x]") # Checkmate sometimes written (+*) or similar if "(+*)" in move: toks.append("[#]") elif "(+)" in move or "(x+)" in move: toks.append("[+]") return toks def _tokenize(self, text: str) -> List[str]: """ Tokenize a game string into structured tokens. Each move becomes: [W]/[B], [PIECE], [from], [to], optional flags, then [MOVE_END] """ moves = text.strip().split() out: List[str] = [] for mv in moves: out.extend(self._move_to_tokens(mv)) out.append("[MOVE_END]") return out 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: 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 and t != "[MOVE_END]")) def save_vocabulary( self, save_directory: str, filename_prefix: Optional[str] = None, ) -> tuple: """ Save the vocabulary to a JSON file. Args: save_directory: Directory to save the vocabulary. filename_prefix: Optional prefix for the filename. Returns: Tuple containing the path to the saved vocabulary 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,) def count_vocab_from_dataset( dataset_name: str = "dlouapre/lichess_2025-01_1M", split: str = "train", column: str = "text", max_samples: Optional[int] = 10000, ) -> Dict[str, int]: """ Count token frequencies in a dataset (useful for vocabulary analysis). Args: dataset_name: Name of the dataset on Hugging Face Hub. split: Dataset split to use. column: Column containing the game strings. max_samples: Maximum number of samples to process. Returns: Dictionary mapping tokens to their frequencies. """ from collections import Counter from datasets import load_dataset 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() for example in dataset: moves = example[column].strip().split() token_counts.update(moves) return dict(token_counts)