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import os |
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from shutil import copyfile |
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from typing import List, Optional |
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from omegaconf import DictConfig |
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from transformers.tokenization_utils import PreTrainedTokenizer |
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from transformers.utils import logging |
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from .fairseq_dictionary import Dictionary |
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from .guoke_tokenizer import GuokeTokenizer |
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from .sentencepiece_bpe import SentencepieceBPE |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = { |
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"sp_path": "sp.model", |
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"dict_path": "dict.txt" |
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} |
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class FairseqT5Tokenizer(PreTrainedTokenizer): |
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vocab_files_names = VOCAB_FILES_NAMES |
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model_input_names = ["input_ids", "attention_mask"] |
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def __init__( |
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self, |
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sp_path, |
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dict_path, |
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lower, |
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n_sentinel_tokens=0, |
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bos_token="<s>", |
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eos_token="</s>", |
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unk_token="<unk>", |
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pad_token="<pad>", |
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**kwargs |
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) -> None: |
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self.sp_path = sp_path |
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self.dict_path = dict_path |
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self.lower = lower |
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self.fs_tokenizer = GuokeTokenizer( |
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DictConfig( |
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dict( |
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lower=lower |
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) |
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) |
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) |
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self.fs_bpe = SentencepieceBPE( |
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dict( |
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sentencepiece_model=sp_path, |
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) |
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) |
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self.fs_dict = Dictionary.load(dict_path) |
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for i in range(n_sentinel_tokens): |
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self.fs_dict.add_symbol(f'<sen{i:03d}>') |
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if "sep_token" in kwargs: |
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assert kwargs["sep_token"] == eos_token |
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kwargs.pop("sep_token") |
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if "cls_token" in kwargs: |
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assert kwargs["cls_token"] == bos_token |
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kwargs.pop("cls_token") |
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super().__init__( |
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bos_token=bos_token, |
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eos_token=eos_token, |
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unk_token=unk_token, |
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pad_token=pad_token, |
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sep_token=eos_token, |
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cls_token=bos_token, |
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lower=self.lower, |
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n_sentinel_tokens=n_sentinel_tokens, |
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**kwargs, |
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) |
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@property |
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def vocab_size(self): |
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return len(self.fs_dict) |
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def get_vocab(self): |
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return self.fs_dict.indices |
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def get_special_tokens_mask( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False |
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) -> List[int]: |
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""" |
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding |
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special tokens using the tokenizer `prepare_for_model` method. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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already_has_special_tokens (`bool`, *optional*, defaults to `False`): |
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Whether or not the token list is already formatted with special tokens for the model. |
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Returns: |
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. |
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""" |
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if already_has_special_tokens: |
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return super().get_special_tokens_mask( |
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True |
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) |
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if token_ids_1 is None: |
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return [1] + ([0] * len(token_ids_0)) + [1] |
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return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] |
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def create_token_type_ids_from_sequences( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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sep = [self.sep_token_id] |
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cls = [self.cls_token_id] |
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if token_ids_1 is None: |
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return len(cls + token_ids_0 + sep) * [0] |
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return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] |
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def build_inputs_with_special_tokens( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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if token_ids_1 is None: |
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] |
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cls = [self.cls_token_id] |
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sep = [self.sep_token_id] |
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return cls + token_ids_0 + sep + sep + token_ids_1 + sep |
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def _tokenize(self, text: str) -> List[str]: |
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return self.fs_bpe.encode(self.fs_tokenizer.encode(text)).split(" ") |
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def _convert_token_to_id(self, token): |
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return self.fs_dict.index(token) |
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def _convert_id_to_token(self, index): |
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return self.fs_dict[index] |
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def convert_tokens_to_string(self, tokens): |
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return self.fs_bpe.decode(" ".join(tokens)) |
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None): |
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if not os.path.isdir(save_directory): |
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logger.error(f"Vocabulary path ({save_directory}) should be a directory") |
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return |
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out_sp_path = os.path.join( |
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["sp_path"] |
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) |
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out_dict_path = os.path.join( |
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["dict_path"] |
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) |
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if os.path.abspath(self.sp_path) != os.path.abspath(out_sp_path): |
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copyfile(self.sp_path, out_sp_path) |
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logger.info(f"Copy from {self.sp_path} to {out_sp_path}") |
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if os.path.abspath(self.dict_path) != os.path.abspath(out_dict_path): |
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copyfile(self.dict_path, out_dict_path) |
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logger.info(f"Copy from {self.dict_path} to {out_dict_path}") |
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return out_sp_path, out_dict_path |
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