# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..bert.tokenizer import BertTokenizer
__all__ = [
"ArtistTokenizer",
]
[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": "https://bj.bcebos.com/paddlenlp/models/transformers/artist/pai-painter-base-zh/vocab.txt",
"pai-painter-painting-base-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/artist/pai-painter-painting-base-zh/vocab.txt",
"pai-painter-scenery-base-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/artist/pai-painter-scenery-base-zh/vocab.txt",
"pai-painter-commercial-base-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/artist/pai-painter-commercial-base-zh/vocab.txt",
"pai-painter-large-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/artist/pai-painter-large-zh/vocab.txt",
}
}
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 - self.image_vocab_size in self.added_tokens_decoder:
return self.added_tokens_decoder[ids - self.image_vocab_size]
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 - self.image_vocab_size in self.added_tokens_decoder:
tokens.append(self.added_tokens_decoder[index - self.image_vocab_size])
else:
# note: process image_vocab_size offset
tokens.append(self._convert_id_to_token(index - self.image_vocab_size))
return tokens
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,
)
