|
|
| from transformers import PreTrainedTokenizer |
| import json |
| import os |
|
|
| class ChessTokenizer(PreTrainedTokenizer): |
| model_input_names = ["input_ids", "attention_mask"] |
| def __init__(self, vocab_file="vocab.json", **kwargs): |
| if os.path.exists(vocab_file): |
| with open(vocab_file, 'r') as f: data = json.load(f) |
| self.token_to_id = data["token_to_id"] |
| self.id_to_token = {int(k): v for k, v in data["id_to_token"].items()} |
| else: |
| raise ValueError(f"CRITIQUE: {vocab_file} introuvable.") |
| self.unk_token = "[UNK]" |
| self.pad_token = "[PAD]" |
| self.bos_token = "[BOS]" |
| self.eos_token = "[EOS]" |
| super().__init__(pad_token="[PAD]", bos_token="[BOS]", eos_token="[EOS]", unk_token="[UNK]", **kwargs) |
| |
| @property |
| def vocab_size(self): return len(self.token_to_id) |
| def get_vocab(self): return self.token_to_id |
| def _convert_token_to_id(self, token): return self.token_to_id.get(token, self.token_to_id.get("[UNK]", 0)) |
| def _convert_id_to_token(self, index): return self.id_to_token.get(index, "[UNK]") |
| def __call__(self, text, **kwargs): |
| if isinstance(text, list): return {"input_ids": [[0]] * len(text)} |
| moves = text.split() |
| ids = [self.token_to_id.get(m, self.token_to_id.get("[UNK]", 0)) for m in moves] |
| max_len = kwargs.get('max_length', 256) |
| ids = ids[:max_len] |
| return {"input_ids": ids} |
| def save_pretrained(self, save_directory, **kwargs): |
| with open(os.path.join(save_directory, "vocab.json"), "w") as f: |
| json.dump({"token_to_id": self.token_to_id, "id_to_token": self.id_to_token}, f) |
| with open(os.path.join(save_directory, "tokenizer_config.json"), "w") as f: |
| json.dump({"model_type": "chess_transformer"}, f) |
| @classmethod |
| def from_pretrained(cls, path, **kwargs): |
| vocab_path = os.path.join(path, "vocab.json") |
| if os.path.exists(vocab_path): return cls(vocab_file=vocab_path, **kwargs) |
| return cls(**kwargs) |
|
|
