207 lines
9.1 KiB
Python
207 lines
9.1 KiB
Python
# coding=utf-8
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# Copyright 2024 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 UDOP.
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"""
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from typing import Optional, Union
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from transformers import logging
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from ...image_processing_utils import BatchFeature
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from ...image_utils import ImageInput
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from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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logger = logging.get_logger(__name__)
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class UdopTextKwargs(TextKwargs, total=False):
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word_labels: Optional[Union[list[int], list[list[int]]]]
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boxes: Union[list[list[int]], list[list[list[int]]]]
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class UdopProcessorKwargs(ProcessingKwargs, total=False):
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text_kwargs: UdopTextKwargs
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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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"truncation": 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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}
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class UdopProcessor(ProcessorMixin):
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r"""
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Constructs a UDOP processor which combines a LayoutLMv3 image processor and a UDOP tokenizer into a single processor.
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[`UdopProcessor`] offers all the functionalities you need to prepare data for the model.
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It first uses [`LayoutLMv3ImageProcessor`] to resize, rescale and normalize document images, and optionally applies OCR
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to get words and normalized bounding boxes. These are then provided to [`UdopTokenizer`] or [`UdopTokenizerFast`],
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which turns the words and bounding boxes into token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`.
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Optionally, one can provide integer `word_labels`, which are turned into token-level `labels` for token
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classification tasks (such as FUNSD, CORD).
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Additionally, it also supports passing `text_target` and `text_pair_target` to the tokenizer, which can be used to
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prepare labels for language modeling tasks.
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Args:
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image_processor (`LayoutLMv3ImageProcessor`):
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An instance of [`LayoutLMv3ImageProcessor`]. The image processor is a required input.
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tokenizer (`UdopTokenizer` or `UdopTokenizerFast`):
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An instance of [`UdopTokenizer`] or [`UdopTokenizerFast`]. 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 = "LayoutLMv3ImageProcessor"
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tokenizer_class = ("UdopTokenizer", "UdopTokenizerFast")
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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: Optional[ImageInput] = 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[UdopProcessorKwargs],
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) -> BatchFeature:
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"""
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This method first forwards the `images` argument to [`~UdopImageProcessor.__call__`]. In case
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[`UdopImageProcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
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bounding boxes along with the additional arguments to [`~UdopTokenizer.__call__`] and returns the output,
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together with the prepared `pixel_values`. In case [`UdopImageProcessor`] was initialized with `apply_ocr` set
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to `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the
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additional arguments to [`~UdopTokenizer.__call__`] and returns the output, together with the prepared
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`pixel_values`.
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Alternatively, one can pass `text_target` and `text_pair_target` to prepare the targets of UDOP.
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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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# verify input
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output_kwargs = self._merge_kwargs(
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UdopProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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boxes = output_kwargs["text_kwargs"].pop("boxes", None)
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word_labels = output_kwargs["text_kwargs"].pop("word_labels", None)
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text_pair = output_kwargs["text_kwargs"].pop("text_pair", None)
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return_overflowing_tokens = output_kwargs["text_kwargs"].get("return_overflowing_tokens", False)
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return_offsets_mapping = output_kwargs["text_kwargs"].get("return_offsets_mapping", False)
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text_target = output_kwargs["text_kwargs"].get("text_target", None)
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if self.image_processor.apply_ocr and (boxes is not None):
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raise ValueError(
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"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
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)
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if self.image_processor.apply_ocr and (word_labels is not None):
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raise ValueError(
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"You cannot provide word labels if you initialized the image processor with apply_ocr set to True."
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)
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if return_overflowing_tokens and not return_offsets_mapping:
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raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")
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if text_target is not None:
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# use the processor to prepare the targets of UDOP
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return self.tokenizer(
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**output_kwargs["text_kwargs"],
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)
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else:
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# use the processor to prepare the inputs of UDOP
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# first, apply the image processor
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features = self.image_processor(images=images, **output_kwargs["images_kwargs"])
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features_words = features.pop("words", None)
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features_boxes = features.pop("boxes", None)
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output_kwargs["text_kwargs"].pop("text_target", None)
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output_kwargs["text_kwargs"].pop("text_pair_target", None)
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output_kwargs["text_kwargs"]["text_pair"] = text_pair
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output_kwargs["text_kwargs"]["boxes"] = boxes if boxes is not None else features_boxes
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output_kwargs["text_kwargs"]["word_labels"] = word_labels
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# second, apply the tokenizer
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if text is not None and self.image_processor.apply_ocr and text_pair is None:
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if isinstance(text, str):
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text = [text] # add batch dimension (as the image processor always adds a batch dimension)
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output_kwargs["text_kwargs"]["text_pair"] = features_words
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encoded_inputs = self.tokenizer(
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text=text if text is not None else features_words,
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**output_kwargs["text_kwargs"],
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)
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# add pixel values
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if return_overflowing_tokens is True:
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features["pixel_values"] = self.get_overflowing_images(
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features["pixel_values"], encoded_inputs["overflow_to_sample_mapping"]
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)
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features.update(encoded_inputs)
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return features
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# Copied from transformers.models.layoutlmv3.processing_layoutlmv3.LayoutLMv3Processor.get_overflowing_images
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def get_overflowing_images(self, images, overflow_to_sample_mapping):
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# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
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images_with_overflow = []
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for sample_idx in overflow_to_sample_mapping:
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images_with_overflow.append(images[sample_idx])
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if len(images_with_overflow) != len(overflow_to_sample_mapping):
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raise ValueError(
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"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
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f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}"
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)
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return images_with_overflow
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# Copied from transformers.models.layoutlmv3.processing_layoutlmv3.LayoutLMv3Processor.batch_decode
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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 PreTrainedTokenizer'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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# Copied from transformers.models.layoutlmv3.processing_layoutlmv3.LayoutLMv3Processor.decode
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def decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to PreTrainedTokenizer'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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return ["pixel_values", "input_ids", "bbox", "attention_mask"]
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__all__ = ["UdopProcessor"]
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