





'''simple docstring'''







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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =['image_processor', 'tokenizer']
				__a            ='LayoutLMv3ImageProcessor'
				__a            =('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')




				def __init__(  self     :					Tuple					,				__a     :					int=None					,				__a     :					Union[str, Any]=None					,				**__a     :					Optional[Any]					):
								_a								= None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."					,				__a					,				)
												_a								= kwargs.pop("feature_extractor"					)

								_a								= 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__(__a					,				__a					)




				def __call__(  self     :					Any					,				__a     :					List[str]					,				__a     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,				__a     :					Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,				__a     :					Union[List[List[int]], List[List[List[int]]]] = None					,				__a     :					Optional[Union[List[int], List[List[int]]]] = None					,				__a     :					bool = True					,				__a     :					Union[bool, str, PaddingStrategy] = False					,				__a     :					Union[bool, str, TruncationStrategy] = None					,				__a     :					Optional[int] = None					,				__a     :					int = 0					,				__a     :					Optional[int] = None					,				__a     :					Optional[bool] = None					,				__a     :					Optional[bool] = None					,				__a     :					bool = False					,				__a     :					bool = False					,				__a     :					bool = False					,				__a     :					bool = False					,				__a     :					bool = True					,				__a     :					Optional[Union[str, TensorType]] = None					,				**__a     :					Dict					,				):
								# 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."					)

								# first, apply the image processor
								_a								= self.image_processor(images=__a					,				return_tensors=__a					)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
												if isinstance(__a					,				__a					):
																_a								= [text]  # add batch dimension (as the image processor always adds a batch dimension)
												_a								= features["words"]

								_a								= 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=__a					,				add_special_tokens=__a					,				padding=__a					,				truncation=__a					,				max_length=__a					,				stride=__a					,				pad_to_multiple_of=__a					,				return_token_type_ids=__a					,				return_attention_mask=__a					,				return_overflowing_tokens=__a					,				return_special_tokens_mask=__a					,				return_offsets_mapping=__a					,				return_length=__a					,				verbose=__a					,				return_tensors=__a					,				**__a					,				)

								# add pixel values
								_a								= features.pop("pixel_values"					)
								if return_overflowing_tokens is True:
												_a								= self.get_overflowing_images(__a					,				encoded_inputs["overflow_to_sample_mapping"]					)
								_a								= images

								return encoded_inputs




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					List[Any]					):
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								_a								= []
								for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]					)

								if len(__a					) != len(__a					):
												raise ValueError(
												    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
												    f' {len(__a					)} and {len(__a					)}'					)

								return images_with_overflow




				def   UpperCamelCase__   (  self     :					int					,				*__a     :					str					,				**__a     :					Tuple					):
								return self.tokenizer.batch_decode(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				*__a     :					List[Any]					,				**__a     :					List[str]					):
								return self.tokenizer.decode(*__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Tuple					):
								return ["input_ids", "bbox", "attention_mask", "pixel_values"]




				@property
				def   UpperCamelCase__   (  self     :					int					):
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,				__a					,				)
								return self.image_processor_class




				@property
				def   UpperCamelCase__   (  self     :					List[str]					):
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,				__a					,				)
								return self.image_processor


import numpy as np






def        a_		(       __lowercase   :	np.array ) ->  np.array:
			return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
			import doctest

			doctest.testmod()