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110 lines
4.1 KiB
110 lines
4.1 KiB
# 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 ...feature_extraction_utils import BatchFeature
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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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from ...utils import auto_docstring, logging
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class Pix2StructProcessorKwargs(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_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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@auto_docstring
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class Pix2StructProcessor(ProcessorMixin):
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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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@auto_docstring
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def __call__(
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self,
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images=None,
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text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
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**kwargs: Unpack[Pix2StructProcessorKwargs],
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) -> BatchEncoding | BatchFeature:
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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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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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@property
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def model_input_names(self):
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image_processor_input_names = self.image_processor.model_input_names
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decoder_ids = ["decoder_attention_mask", "decoder_input_ids"]
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return image_processor_input_names + decoder_ids
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__all__ = ["Pix2StructProcessor"]
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