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6cce1d7 470e7d0 6cce1d7 470e7d0 6cce1d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | """
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):
model_input_names = ["input_ids", "attention_mask"]
# Special tokens
PAD_TOKEN = "[PAD]"
BOS_TOKEN = "[BOS]" # Beginning of Sequence (Start of Game)
EOS_TOKEN = "[EOS]" # End of Sequence (End of Game)
UNK_TOKEN = "[UNK]"
vocab_files_names = {
"vocab_file": "vocab.json"
}
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
# Clean kwargs to avoid conflicts
kwargs.pop("pad_token", None)
kwargs.pop("bos_token", None)
kwargs.pop("eos_token", None)
kwargs.pop("unk_token", None)
self.vocab_file = vocab_file
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]:
"""Creates basic vocab. Use build_vocab_from_dataset for full vocab."""
# 4 Special + 12 Pieces + 64 Squares = 80 tokens total
special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
vocab = {t: i for i, t in enumerate(special)}
return vocab
@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]:
"""
Splits text "WPe2e4 BNg8f6" into ["WP", "e2", "e4", "BN", "g8", "f6"]
"""
tokens = []
# Split by space to get individual moves first
raw_moves = text.strip().split()
for move in raw_moves:
# Check length to ensure it's a valid move string
if len(move) >= 6:
# Part 1: Player + Piece (Indices 0-2, e.g., "WP")
tokens.append(move[:2])
# Part 2: Start Square (Indices 2-4, e.g., "e2")
tokens.append(move[2:4])
# Part 3: End Square (Indices 4-6, e.g., "e4")
tokens.append(move[4:])
# Note: Suffixes like (x) or promotions (=Q) are ignored
# in this strict 3-token split implementation.
else:
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 the move string.
Note: This simply joins them. You might need custom logic
if you want to strictly recreate 'WPe2e4' from ['WP','e2','e4'].
"""
return " ".join(t for t in tokens if t not in [
self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN
])
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,)
@classmethod
def build_vocab_from_dataset(cls, dataset_name="dlouapre/lichess_2025-01_1M", split="train", max_samples=10000):
"""Scans dataset to find all unique pieces and squares."""
from datasets import load_dataset
dataset = load_dataset(dataset_name, split=split, streaming=True)
pieces = set()
squares = set()
endings = set()
print("Building vocabulary...")
count = 0
for example in dataset:
moves = example["text"].split()
for move in moves:
if len(move) >= 6:
pieces.add(move[:2]) # WP, BN, etc.
squares.add(move[2:4]) # e2
squares.add(move[4:]) # e4
count += 1
if count >= max_samples:
break
# Combine into vocab structure
special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
all_tokens = special + sorted(list(pieces)) + sorted(list(endings)) + sorted(list(squares))
vocab = {token: idx for idx, token in enumerate(all_tokens)}
return cls(vocab=vocab) |