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zmwv823
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from transformers.models.bert.tokenization_bert import *
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import os
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class CLIPTokenizerRoberta(PreTrainedTokenizer):
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r"""
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Construct a BERT tokenizer. Based on WordPiece.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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File containing the vocabulary.
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether or not to lowercase the input when tokenizing.
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do_basic_tokenize (`bool`, *optional*, defaults to `True`):
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Whether or not to do basic tokenization before WordPiece.
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never_split (`Iterable`, *optional*):
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Collection of tokens which will never be split during tokenization. Only has an effect when
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`do_basic_tokenize=True`
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unk_token (`str`, *optional*, defaults to `"[UNK]"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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pad_token (`str`, *optional*, defaults to `"[PAD]"`):
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The token used for padding, for example when batching sequences of different lengths.
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cls_token (`str`, *optional*, defaults to `"[CLS]"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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mask_token (`str`, *optional*, defaults to `"[MASK]"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
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Whether or not to tokenize Chinese characters.
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This should likely be deactivated for Japanese (see this
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[issue](https://github.com/huggingface/transformers/issues/328)).
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strip_accents (`bool`, *optional*):
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Whether or not to strip all accents. If this option is not specified, then it will be determined by the
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value for `lowercase` (as in the original BERT).
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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#pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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#pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
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#max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
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def __init__(
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self,
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vocab_file,
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do_lower_case=True,
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do_basic_tokenize=True,
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never_split=None,
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unk_token="[UNK]",
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sep_token="[SEP]",
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pad_token="[PAD]",
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cls_token="[CLS]",
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mask_token="[MASK]",
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tokenize_chinese_chars=True,
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strip_accents=None,
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**kwargs
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):
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if not os.path.isfile(vocab_file):
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raise ValueError(
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f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
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" model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
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)
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self.vocab = load_vocab(vocab_file)
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self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
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self.do_basic_tokenize = do_basic_tokenize
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if do_basic_tokenize:
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self.basic_tokenizer = BasicTokenizer(
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do_lower_case=do_lower_case,
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never_split=never_split,
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tokenize_chinese_chars=tokenize_chinese_chars,
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strip_accents=strip_accents,
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)
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self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
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super().__init__(
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do_lower_case=do_lower_case,
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do_basic_tokenize=do_basic_tokenize,
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never_split=never_split,
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unk_token=unk_token,
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sep_token=sep_token,
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pad_token=pad_token,
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cls_token=cls_token,
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mask_token=mask_token,
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tokenize_chinese_chars=tokenize_chinese_chars,
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strip_accents=strip_accents,
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**kwargs,
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)
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@property
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def do_lower_case(self):
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return self.basic_tokenizer.do_lower_case
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@property
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def vocab_size(self):
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return len(self.vocab)
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def get_vocab(self):
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return dict(self.vocab, **self.added_tokens_encoder)
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def _tokenize(self, text):
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split_tokens = []
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if self.do_basic_tokenize:
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for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
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# If the token is part of the never_split set
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if token in self.basic_tokenizer.never_split:
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split_tokens.append(token)
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else:
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split_tokens += self.wordpiece_tokenizer.tokenize(token)
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else:
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split_tokens = self.wordpiece_tokenizer.tokenize(text)
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return split_tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.vocab.get(token, self.vocab.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.ids_to_tokens.get(index, self.unk_token)
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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out_string = " ".join(tokens).replace(" ##", "").strip()
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return out_string
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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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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A BERT sequence has the following format:
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- single sequence: `[CLS] X [SEP]`
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- pair of sequences: `[CLS] A [SEP] B [SEP]`
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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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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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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sep = [49407]
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cls = [49406]
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if token_ids_1 is None:
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return cls + token_ids_0 + sep
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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 + token_ids_1 + sep
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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,
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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 not None:
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
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return [1] + ([0] * len(token_ids_0)) + [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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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
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pair mask has the following format:
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```
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
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| first sequence | second sequence |
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```
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If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
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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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Returns:
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`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
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"""
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# sep = [self.sep_token_id]
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# cls = [self.cls_token_id]
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sep = [49407]
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cls = [49406]
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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) * [0] + len(token_ids_1 + sep) * [1]
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
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index = 0
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if os.path.isdir(save_directory):
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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else:
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vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
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with open(vocab_file, "w", encoding="utf-8") as writer:
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for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
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if index != token_index:
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logger.warning(
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f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
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" Please check that the vocabulary is not corrupted!"
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)
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index = token_index
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writer.write(token + "\n")
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index += 1
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return (vocab_file,)
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@@ -0,0 +1,37 @@
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{
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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@@ -0,0 +1,64 @@
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"auto_map": {
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"AutoTokenizer": [
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"clip_tokenizer_roberta.CLIPTokenizerRoberta",
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null
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]
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 77,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "CLIPTokenizerRoberta",
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"unk_token": "[UNK]",
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"use_fast": true
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}
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