114 lines
4.3 KiB
Python
114 lines
4.3 KiB
Python
# coding=utf-8
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# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Processor class for BridgeTower.
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"""
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from typing import Union
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from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
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class BridgeTowerProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {
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"text_kwargs": {
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"add_special_tokens": True,
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"padding": False,
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"stride": 0,
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"return_overflowing_tokens": False,
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"return_special_tokens_mask": False,
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"return_offsets_mapping": False,
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"return_length": False,
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"verbose": True,
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},
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"images_kwargs": {
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"do_normalize": True,
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"do_center_crop": True,
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},
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}
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class BridgeTowerProcessor(ProcessorMixin):
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r"""
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Constructs a BridgeTower processor which wraps a Roberta tokenizer and BridgeTower image processor into a single
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processor.
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[`BridgeTowerProcessor`] offers all the functionalities of [`BridgeTowerImageProcessor`] and
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[`RobertaTokenizerFast`]. See the docstring of [`~BridgeTowerProcessor.__call__`] and
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[`~BridgeTowerProcessor.decode`] for more information.
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Args:
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image_processor (`BridgeTowerImageProcessor`):
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An instance of [`BridgeTowerImageProcessor`]. The image processor is a required input.
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tokenizer (`RobertaTokenizerFast`):
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An instance of ['RobertaTokenizerFast`]. The tokenizer is a required input.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "BridgeTowerImageProcessor"
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tokenizer_class = ("RobertaTokenizer", "RobertaTokenizerFast")
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def __init__(self, image_processor, tokenizer):
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super().__init__(image_processor, tokenizer)
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def __call__(
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self,
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images,
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text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
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audio=None,
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videos=None,
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**kwargs: Unpack[BridgeTowerProcessorKwargs],
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) -> BatchEncoding:
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"""
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This method uses [`BridgeTowerImageProcessor.__call__`] method to prepare image(s) for the model, and
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[`RobertaTokenizerFast.__call__`] to prepare text for the model.
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Please refer to the docstring of the above two methods for more information.
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"""
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output_kwargs = self._merge_kwargs(
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BridgeTowerProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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encoding = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
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# add pixel_values + pixel_mask
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encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
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encoding.update(encoding_image_processor)
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return encoding
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer
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to the docstring of this method for more information.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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image_processor_input_names = self.image_processor.model_input_names
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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__all__ = ["BridgeTowerProcessor"]
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