paddlenlp.transformers.artist.tokenizer 源代码

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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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from ..bert.tokenizer import BertTokenizer

__all__ = [

[文档]class ArtistTokenizer(BertTokenizer): """ Constructs an Artist tokenizer. `ArtistTokenizer` is almost identical to `BertTokenizer`. Args: vocab_file (str): The vocabulary file path (ends with '.txt') required to instantiate a `WordpieceTokenizer`. do_lower_case (bool, optional): Whether to lowercase the input when tokenizing. Defaults to `True`. image_vocab_size (int, optional): The vocabulary size of image. Defaults to `16384`. do_basic_tokenize (bool, optional): Whether to use a basic tokenizer before a WordPiece tokenizer. Defaults to `True`. never_split (Iterable, optional): Collection of tokens which will never be split during tokenization. Only has an effect when `do_basic_tokenize=True`. Defaults to `None`. unk_token (str, optional): A special token representing the *unknown (out-of-vocabulary)* token. An unknown token is set to be `unk_token` inorder to be converted to an ID. Defaults to "[UNK]". sep_token (str, optional): A special token separating two different sentences in the same input. Defaults to "[SEP]". pad_token (str, optional): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "[PAD]". cls_token (str, optional): A special token used for sequence classification. It is the last token of the sequence when built with special tokens. Defaults to "[CLS]". mask_token (str, optional): A special token representing a masked token. This is the token used in the masked language modeling task which the model tries to predict the original unmasked ones. Defaults to "[MASK]". tokenize_chinese_chars (bool, optional): Whether to tokenize Chinese characters. Defaults to `True`. strip_accents: (bool, optional): Whether to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT). Defaults to `None`. Examples: .. code-block:: from paddlenlp.transformers import ArtistTokenizer tokenizer = ArtistTokenizer.from_pretrained('pai-painter-painting-base-zh') inputs = tokenizer('风阁水帘今在眼,且来先看早梅红', return_token_type_ids=False) print(inputs) ''' {'input_ids': [23983, 23707, 20101, 18750, 17175, 18146, 21090, 24408, 17068, 19725, 17428, 21076, 19577, 19833, 21657]} ''' """ resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "pai-painter-base-zh": "", "pai-painter-painting-base-zh": "", "pai-painter-scenery-base-zh": "", "pai-painter-commercial-base-zh": "", "pai-painter-large-zh": "", } } pretrained_init_configuration = { "pai-painter-base-zh": { "do_lower_case": True, "image_vocab_size": 16384, }, "pai-painter-painting-base-zh": { "do_lower_case": True, "image_vocab_size": 16384, }, "pai-painter-scenery-base-zh": { "do_lower_case": True, "image_vocab_size": 16384, }, "pai-painter-commercial-base-zh": { "do_lower_case": True, "image_vocab_size": 16384, }, "pai-painter-large-zh": { "do_lower_case": True, "image_vocab_size": 16384, }, } max_model_input_sizes = { "pai-painter-base-zh": 32, "pai-painter-painting-base-zh": 32, "pai-painter-scenery-base-zh": 32, "pai-painter-commercial-base-zh": 32, "pai-painter-large-zh": 32, } def __init__( self, vocab_file, do_lower_case=True, image_vocab_size=16384, do_basic_tokenize=True, never_split=None, unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", tokenize_chinese_chars=True, strip_accents=None, **kwargs ): super().__init__( vocab_file, do_lower_case, do_basic_tokenize, never_split, unk_token, sep_token, pad_token, cls_token, mask_token, tokenize_chinese_chars, strip_accents, **kwargs, ) # we need add image_vocab_size offset # for example [523, 102, 0, 0] # => [523 + image_vocab_size, 102 + image_vocab_size, 0 + image_vocab_size, 0 + image_vocab_size] self.image_vocab_size = image_vocab_size def _convert_token_to_id_with_added_voc(self, token): if token is None: return None if token in self.added_tokens_encoder: # note: process image_vocab_size offset return self.added_tokens_encoder[token] + self.image_vocab_size # note: process image_vocab_size offset return self._convert_token_to_id(token) + self.image_vocab_size def convert_ids_to_tokens(self, ids, skip_special_tokens=False): if isinstance(ids, int): if ids in self.added_tokens_decoder: return self.added_tokens_decoder[ids] else: # note: process image_vocab_size offset return self._convert_id_to_token(ids - self.image_vocab_size) tokens = [] for index in ids: index = int(index) if skip_special_tokens and index in self.all_special_ids: continue if index in self.added_tokens_decoder: tokens.append(self.added_tokens_decoder[index]) else: # note: process image_vocab_size offset tokens.append(self._convert_id_to_token(index - self.image_vocab_size)) return tokens
[文档] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence (we don't add special tokens). An Artist sequence has the following format: - single sequence: ``X`` Args: token_ids_0 (List[int]): List of IDs to which the special tokens will be added. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. We do'nt use sequence pairs. Defaults to None. Returns: List[int]: List of input_id. """ return token_ids_0
def __call__( self, text, text_pair=None, max_length=32, # default stride=0, is_split_into_words=False, padding="max_length", # default truncation=True, # default return_position_ids=False, return_token_type_ids=False, # don't return token_type_ids return_attention_mask=False, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False, return_dict=True, return_offsets_mapping=False, add_special_tokens=True, pad_to_multiple_of=None, return_tensors=None, verbose: bool = True, **kwargs ): return super().__call__( text, text_pair, max_length, stride, is_split_into_words, padding, truncation, return_position_ids, return_token_type_ids, return_attention_mask, return_length, return_overflowing_tokens, return_special_tokens_mask, return_dict, return_offsets_mapping, add_special_tokens, pad_to_multiple_of, return_tensors, verbose, **kwargs, )