Name	Size	Mode	Actions
__pycache__/	-	0755	rm
processing_layoutxlm.py	6155	0644	editdlrm
tokenization_layoutxlm.py	50467	0644	editdlrm
tokenization_layoutxlm_fast.py	32627	0644	editdlrm
__init__.py	1763	0644	editdlrm

# 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. """ 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 feature extractor and a LayoutXLM tokenizer into a single processor. [`LayoutXLMProcessor`] offers all the functionalities you need to prepare data for the model. It first uses [`LayoutLMv2FeatureExtractor`] 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: feature_extractor (`LayoutLMv2FeatureExtractor`): An instance of [`LayoutLMv2FeatureExtractor`]. The feature extractor is a required input. tokenizer (`LayoutXLMTokenizer` or `LayoutXLMTokenizerFast`): An instance of [`LayoutXLMTokenizer`] or [`LayoutXLMTokenizerFast`]. The tokenizer is a required input. """ feature_extractor_class = "LayoutLMv2FeatureExtractor" tokenizer_class = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast") 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] = False, 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 [`~LayoutLMv2FeatureExtractor.__call__`]. In case [`LayoutLMv2FeatureExtractor`] 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 [`LayoutLMv2FeatureExtractor`] 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.feature_extractor.apply_ocr and (boxes is not None): raise ValueError( "You cannot provide bounding boxes " "if you initialized the feature extractor with apply_ocr set to True." ) if self.feature_extractor.apply_ocr and (word_labels is not None): raise ValueError( "You cannot provide word labels " "if you initialized the feature extractor with apply_ocr set to True." ) # first, apply the feature extractor features = self.feature_extractor(images=images, return_tensors=return_tensors) # second, apply the tokenizer if text is not None and self.feature_extractor.apply_ocr and text_pair is None: if isinstance(text, str): text = [text] # add batch dimension (as the feature extractor 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 encoded_inputs["image"] = features.pop("pixel_values") return encoded_inputs