import os import shutil import sentencepiece as spm from transformers import PreTrainedTokenizer class SAITokenizer(PreTrainedTokenizer): vocab_files_names = {"vocab_file": "tokenizer.model"} model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, unk_token="[UNK]", bos_token="[BOS]", eos_token="[EOS]", pad_token="[UNK]", additional_special_tokens=None, **kwargs, ): self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor(model_file=vocab_file) if additional_special_tokens is None: additional_special_tokens = ["<|im_start|>", "<|im_end|>"] super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, additional_special_tokens=additional_special_tokens, **kwargs, ) @property def vocab_size(self): return self.sp_model.get_piece_size() def get_vocab(self): vocab = {self.sp_model.id_to_piece(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, text, **kwargs): return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): return self.sp_model.piece_to_id(token) def _convert_id_to_token(self, index): return self.sp_model.id_to_piece(int(index)) def convert_tokens_to_string(self, tokens): return self.sp_model.decode(tokens) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): result = [self.bos_token_id] + list(token_ids_0) if token_ids_1 is not None: result += list(token_ids_1) return result + [self.eos_token_id] def save_vocabulary(self, save_directory, filename_prefix=None): os.makedirs(save_directory, exist_ok=True) filename = "tokenizer.model" if filename_prefix: filename = f"{filename_prefix}-{filename}" destination = os.path.join(save_directory, filename) if os.path.abspath(self.vocab_file) != os.path.abspath(destination): shutil.copyfile(self.vocab_file, destination) return (destination,)