File size: 5,754 Bytes
d62f3eb | 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 | """
Custom Chess Tokenizer for the Chess Challenge.
"""
from __future__ import annotations
import re
import json
import os
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer
class ChessTokenizer(PreTrainedTokenizer):
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):
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]:
special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
return {token: idx for idx, token in enumerate(special_tokens)}
@classmethod
def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1):
from collections import Counter
token_counts = Counter()
for game in iterator:
# 1. Nettoyage : on enlève les suffixes
game = re.sub(r'\(.*?\)', '', game)
moves = game.strip().split()
for i, move in enumerate(moves):
# 2. Logique Square-Aware : Cases (e2) ou Lettres (W)
tokens = re.findall(r'[a-h][1-8]|.', move)
token_counts.update(tokens)
# 3. Ajout explicite de l'espace
if i < len(moves) - 1:
token_counts.update([" "])
tokens = sorted([t for t, c in token_counts.items() if c >= min_frequency])
special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
vocab = {token: idx for idx, token in enumerate(special_tokens + 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 = 1, max_samples: Optional[int] = 50000):
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]:
# 1. Nettoyage
text = re.sub(r'\(.*?\)', '', text)
moves = text.strip().split()
all_tokens = []
for i, move in enumerate(moves):
# 2. Regex
tokens = re.findall(r'[a-h][1-8]|.', move)
all_tokens.extend(tokens)
# 3. Espace
if i < len(moves) - 1:
all_tokens.append(" ")
return all_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}
filtered_tokens = [t for t in tokens if t not in special]
# On joint avec "" car l'espace " " est déjà un token dans la liste
return "".join(filtered_tokens)
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,)
# Fonction utilitaire inchangée pour compter les tokens
def count_vocab_from_dataset(dataset_name="dlouapre/lichess_2025-01_1M", split="train", column="text", max_samples=10000):
from collections import Counter
from datasets import load_dataset
dataset = load_dataset(dataset_name, split=split)
if max_samples: dataset = dataset.select(range(min(max_samples, len(dataset))))
token_counts = Counter()
for example in dataset:
text = re.sub(r'\(.*?\)', '', example[column])
moves = text.strip().split()
for move in moves:
tokens = re.findall(r'[a-h][1-8]|.', move)
token_counts.update(tokens)
token_counts.update([" "])
return dict(token_counts) |