157 lines
6.2 KiB
Python
157 lines
6.2 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team.
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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 Pix2Struct.
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"""
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from typing import Optional, Union
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from ...feature_extraction_utils import BatchFeature
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from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
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from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
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from ...utils import logging
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class Pix2StructImagesKwargs(ImagesKwargs, total=False):
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max_patches: Optional[int]
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header_text: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]]
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class Pix2StructProcessorKwargs(ProcessingKwargs, total=False):
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images_kwargs: Pix2StructImagesKwargs
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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_token_type_ids": 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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"max_patches": 2048,
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},
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}
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logger = logging.get_logger(__name__)
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class Pix2StructProcessor(ProcessorMixin):
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r"""
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Constructs a PIX2STRUCT processor which wraps a BERT tokenizer and PIX2STRUCT image processor into a single
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processor.
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[`Pix2StructProcessor`] offers all the functionalities of [`Pix2StructImageProcessor`] and [`T5TokenizerFast`]. See
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the docstring of [`~Pix2StructProcessor.__call__`] and [`~Pix2StructProcessor.decode`] for more information.
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Args:
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image_processor (`Pix2StructImageProcessor`):
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An instance of [`Pix2StructImageProcessor`]. The image processor is a required input.
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tokenizer (Union[`T5TokenizerFast`, `T5Tokenizer`]):
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An instance of ['T5TokenizerFast`] or ['T5Tokenizer`]. 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 = "Pix2StructImageProcessor"
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tokenizer_class = ("T5Tokenizer", "T5TokenizerFast")
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def __init__(self, image_processor, tokenizer):
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tokenizer.return_token_type_ids = False
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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=None,
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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[Pix2StructProcessorKwargs],
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) -> Union[BatchEncoding, BatchFeature]:
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"""
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This method uses [`Pix2StructImageProcessor.preprocess`] method to prepare image(s) for the model, and
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[`T5TokenizerFast.__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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if images is None and text is None:
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raise ValueError("You have to specify either images or text.")
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output_kwargs = self._merge_kwargs(
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Pix2StructProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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add_special_tokens = output_kwargs["text_kwargs"].pop("add_special_tokens", None)
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# Get only text
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if images is None and not self.image_processor.is_vqa:
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output_kwargs["text_kwargs"]["add_special_tokens"] = (
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add_special_tokens if add_special_tokens is not None else True
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)
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self.current_processor = self.tokenizer
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text_encoding = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
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return text_encoding
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if not self.image_processor.is_vqa:
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# add pixel_values
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encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
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else:
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# add pixel_values and bbox
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output_kwargs["images_kwargs"].setdefault("header_text", text)
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encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
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if text is not None and not self.image_processor.is_vqa:
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output_kwargs["text_kwargs"]["add_special_tokens"] = (
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add_special_tokens if add_special_tokens is not None else False
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)
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text_encoding = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
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if "attention_mask" in text_encoding:
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text_encoding["decoder_attention_mask"] = text_encoding.pop("attention_mask")
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if "input_ids" in text_encoding:
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text_encoding["decoder_input_ids"] = text_encoding.pop("input_ids")
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else:
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text_encoding = None
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if text_encoding is not None:
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encoding_image_processor.update(text_encoding)
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return encoding_image_processor
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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 Pix2StructTokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
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Please 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 Pix2StructTokenizerFast's [`~PreTrainedTokenizer.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.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__ = ["Pix2StructProcessor"]
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