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774b9b6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | from __future__ import annotations
import json
import os
from typing import Dict, List, Optional
import re
from transformers import PreTrainedTokenizer
class ChessTokenizer(PreTrainedTokenizer):
"""
Chess tokenizer with structured move tokens:
Each move is split into: [side][piece][from][to][suffixes].
Example:
"WPe2e4 BNg8xf6+" -> [W][P][e2][e4] [B][N][g8][f6][x][+]
"""
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]"
MOVE_RE = re.compile(
r"^(?P<side>[WB])"
r"(?P<piece>[PNBRQK])"
r"(?P<src>[a-h][1-8])"
r"(?P<dst>[a-h][1-8])"
r"(?P<suffix>.*)$"
)
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
# 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 vocab
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()
# 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]:
special = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
sides = ["[W]", "[B]"]
pieces = ["[P]", "[N]", "[B]", "[R]", "[Q]", "[K]"]
squares = [f"[{f}{r}]" for f in "abcdefgh" for r in "12345678"]
suffixes = ["[x]", "[+]", "[#]", "[O-O]", "[O-O-O]",
"[prom_Q]", "[prom_R]", "[prom_B]", "[prom_N]"]
vocab_list = special + sides + pieces + squares + suffixes
return {tok: i for i, tok in enumerate(vocab_list)}
@classmethod
def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1) -> "ChessTokenizer":
from collections import Counter
token_counts = Counter()
tokenizer = cls()
for game in iterator:
tokens = tokenizer._tokenize(game)
token_counts.update(tokens)
# Keep tokens meeting frequency threshold
tokens = [t for t, c in token_counts.items() if c >= min_frequency]
tokens = sorted(tokens)
special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
vocab = {tok: i for i, tok in enumerate(special + tokens)}
return cls(vocab=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":
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)
@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]:
tokens: List[str] = []
moves = text.strip().split()
for move in moves:
# Castling
if "O-O-O" in move:
tokens.append("[W]" if move.startswith("W") else "[B]")
tokens.append("[O-O-O]")
continue
if "O-O" in move:
tokens.append("[W]" if move.startswith("W") else "[B]")
tokens.append("[O-O]")
continue
m = self.MOVE_RE.match(move)
if not m:
tokens.append(self.UNK_TOKEN)
continue
tokens.append(f"[{m.group('side')}]")
tokens.append(f"[{m.group('piece')}]")
tokens.append(f"[{m.group('src')}]")
tokens.append(f"[{m.group('dst')}]")
suffix = m.group("suffix")
if "x" in suffix:
tokens.append("[x]")
if "+" in suffix:
tokens.append("[+]")
if "*" in suffix:
tokens.append("[#]")
if "=" in suffix:
promo = suffix.split("=")[-1].upper()
tokens.append(f"[prom_{promo}]")
return tokens
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:
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,)
|