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# coding=utf-8 | |
# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved. | |
# | |
# 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. | |
""" | |
Processor class for BridgeTower. | |
""" | |
from typing import List, Optional, Union | |
from ...processing_utils import ProcessorMixin | |
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy | |
from ...utils import TensorType | |
class BridgeTowerProcessor(ProcessorMixin): | |
r""" | |
Constructs a BridgeTower processor which wraps a Roberta tokenizer and BridgeTower image processor into a single | |
processor. | |
[`BridgeTowerProcessor`] offers all the functionalities of [`BridgeTowerImageProcessor`] and | |
[`RobertaTokenizerFast`]. See the docstring of [`~BridgeTowerProcessor.__call__`] and | |
[`~BridgeTowerProcessor.decode`] for more information. | |
Args: | |
image_processor (`BridgeTowerImageProcessor`): | |
An instance of [`BridgeTowerImageProcessor`]. The image processor is a required input. | |
tokenizer (`RobertaTokenizerFast`): | |
An instance of ['RobertaTokenizerFast`]. The tokenizer is a required input. | |
""" | |
attributes = ["image_processor", "tokenizer"] | |
image_processor_class = "BridgeTowerImageProcessor" | |
tokenizer_class = ("RobertaTokenizer", "RobertaTokenizerFast") | |
def __init__(self, image_processor, tokenizer): | |
super().__init__(image_processor, tokenizer) | |
def __call__( | |
self, | |
images, | |
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, | |
add_special_tokens: bool = True, | |
padding: Union[bool, str, PaddingStrategy] = False, | |
truncation: Union[bool, str, TruncationStrategy] = None, | |
max_length: Optional[int] = None, | |
stride: int = 0, | |
pad_to_multiple_of: Optional[int] = None, | |
return_token_type_ids: Optional[bool] = None, | |
return_attention_mask: Optional[bool] = None, | |
return_overflowing_tokens: bool = False, | |
return_special_tokens_mask: bool = False, | |
return_offsets_mapping: bool = False, | |
return_length: bool = False, | |
verbose: bool = True, | |
return_tensors: Optional[Union[str, TensorType]] = None, | |
**kwargs, | |
) -> BatchEncoding: | |
""" | |
This method uses [`BridgeTowerImageProcessor.__call__`] method to prepare image(s) for the model, and | |
[`RobertaTokenizerFast.__call__`] to prepare text for the model. | |
Please refer to the docstring of the above two methods for more information. | |
""" | |
encoding = self.tokenizer( | |
text=text, | |
add_special_tokens=add_special_tokens, | |
padding=padding, | |
truncation=truncation, | |
max_length=max_length, | |
stride=stride, | |
pad_to_multiple_of=pad_to_multiple_of, | |
return_token_type_ids=return_token_type_ids, | |
return_attention_mask=return_attention_mask, | |
return_overflowing_tokens=return_overflowing_tokens, | |
return_special_tokens_mask=return_special_tokens_mask, | |
return_offsets_mapping=return_offsets_mapping, | |
return_length=return_length, | |
verbose=verbose, | |
return_tensors=return_tensors, | |
**kwargs, | |
) | |
# add pixel_values + pixel_mask | |
encoding_image_processor = self.image_processor( | |
images, return_tensors=return_tensors, do_normalize=True, do_center_crop=True, **kwargs | |
) | |
encoding.update(encoding_image_processor) | |
return encoding | |
def batch_decode(self, *args, **kwargs): | |
""" | |
This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please | |
refer to the docstring of this method for more information. | |
""" | |
return self.tokenizer.batch_decode(*args, **kwargs) | |
def decode(self, *args, **kwargs): | |
""" | |
This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer | |
to the docstring of this method for more information. | |
""" | |
return self.tokenizer.decode(*args, **kwargs) | |
def model_input_names(self): | |
tokenizer_input_names = self.tokenizer.model_input_names | |
image_processor_input_names = self.image_processor.model_input_names | |
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) | |