# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # 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 LayoutXLM. """ import warnings 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 LayoutXLMProcessor(ProcessorMixin): r""" Constructs a LayoutXLM processor which combines a LayoutXLM image processor and a LayoutXLM tokenizer into a single processor. [`LayoutXLMProcessor`] offers all the functionalities you need to prepare data for the model. It first uses [`LayoutLMv2ImageProcessor`] to resize document images to a fixed size, and optionally applies OCR to get words and normalized bounding boxes. These are then provided to [`LayoutXLMTokenizer`] or [`LayoutXLMTokenizerFast`], which turns the words and bounding boxes into token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`. Optionally, one can provide integer `word_labels`, which are turned into token-level `labels` for token classification tasks (such as FUNSD, CORD). Args: image_processor (`LayoutLMv2ImageProcessor`, *optional*): An instance of [`LayoutLMv2ImageProcessor`]. The image processor is a required input. tokenizer (`LayoutXLMTokenizer` or `LayoutXLMTokenizerFast`, *optional*): An instance of [`LayoutXLMTokenizer`] or [`LayoutXLMTokenizerFast`]. The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "LayoutLMv2ImageProcessor" tokenizer_class = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast") def __init__(self, image_processor=None, tokenizer=None, **kwargs): if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead.", FutureWarning, ) feature_extractor = kwargs.pop("feature_extractor") image_processor = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`.") if tokenizer is None: raise ValueError("You need to specify a `tokenizer`.") super().__init__(image_processor, tokenizer) def __call__( self, images, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None, boxes: Union[List[List[int]], List[List[List[int]]]] = None, word_labels: Optional[Union[List[int], List[List[int]]]] = 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 first forwards the `images` argument to [`~LayoutLMv2ImagePrpcessor.__call__`]. In case [`LayoutLMv2ImagePrpcessor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and bounding boxes along with the additional arguments to [`~LayoutXLMTokenizer.__call__`] and returns the output, together with resized `images`. In case [`LayoutLMv2ImagePrpcessor`] was initialized with `apply_ocr` set to `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user along with the additional arguments to [`~LayoutXLMTokenizer.__call__`] and returns the output, together with resized `images``. Please refer to the docstring of the above two methods for more information. """ # verify input if self.image_processor.apply_ocr and (boxes is not None): raise ValueError( "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True." ) if self.image_processor.apply_ocr and (word_labels is not None): raise ValueError( "You cannot provide word labels if you initialized the image processor with apply_ocr set to True." ) if return_overflowing_tokens is True and return_offsets_mapping is False: raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.") # first, apply the image processor features = self.image_processor(images=images, return_tensors=return_tensors) # second, apply the tokenizer if text is not None and self.image_processor.apply_ocr and text_pair is None: if isinstance(text, str): text = [text] # add batch dimension (as the image processor always adds a batch dimension) text_pair = features["words"] encoded_inputs = self.tokenizer( text=text if text is not None else features["words"], text_pair=text_pair if text_pair is not None else None, boxes=boxes if boxes is not None else features["boxes"], word_labels=word_labels, 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 images = features.pop("pixel_values") if return_overflowing_tokens is True: images = self.get_overflowing_images(images, encoded_inputs["overflow_to_sample_mapping"]) encoded_inputs["image"] = images return encoded_inputs def get_overflowing_images(self, images, overflow_to_sample_mapping): # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image images_with_overflow = [] for sample_idx in overflow_to_sample_mapping: images_with_overflow.append(images[sample_idx]) if len(images_with_overflow) != len(overflow_to_sample_mapping): raise ValueError( "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got" f" {len(images_with_overflow)} and {len(overflow_to_sample_mapping)}" ) return images_with_overflow def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to PreTrainedTokenizer'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 PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) @property def model_input_names(self): return ["input_ids", "bbox", "attention_mask", "image"] @property def feature_extractor_class(self): warnings.warn( "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.", FutureWarning, ) return self.image_processor_class @property def feature_extractor(self): warnings.warn( "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.", FutureWarning, ) return self.image_processor __all__ = ["LayoutXLMProcessor"]