



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     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  ['''image_processor''', '''tokenizer''']
			UpperCamelCase           =  '''LayoutLMv3ImageProcessor'''
			UpperCamelCase           =  ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')



			def __init__(		self     :						Union[str, Any]				,	A     :						List[str]=None				,	A     :						List[Any]=None				,	**A     :						Optional[Any])     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.'				,	_lowerCAmelCase				,	)
									_UpperCAmelCase	 =     kwargs.pop('feature_extractor')

						_UpperCAmelCase	 =     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__(_lowerCAmelCase				,	_lowerCAmelCase)



			def __call__(		self     :						List[str]				,	A     :						Dict				,	A     :						Any = None				,	A     :						int = None				,	A     :						Dict = None				,	A     :						Tuple = None				,	A     :						Tuple = True				,	A     :						Dict = False				,	A     :						int = None				,	A     :						Any = None				,	A     :						str = 0				,	A     :						int = None				,	A     :						List[str] = None				,	A     :						Optional[Any] = None				,	A     :						Optional[int] = False				,	A     :						List[Any] = False				,	A     :						Any = False				,	A     :						Any = False				,	A     :						int = True				,	A     :						List[str] = None				,	**A     :						Tuple				,	)     ->					Optional[int]:



						"""simple docstring"""






						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
						_UpperCAmelCase	 =     self.image_processor(images=_lowerCAmelCase				,	return_tensors=_lowerCAmelCase)

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

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

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

						return encoded_inputs



			def 					_lowerCamelCase (		self     :						List[Any]				,	A     :						Any				,	A     :						str)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     []
						for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx])

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

						return images_with_overflow



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	*A     :						Tuple				,	**A     :						List[str])     ->					List[str]:



						"""simple docstring"""






						return self.tokenizer.batch_decode(*_lowerCAmelCase				,	**_lowerCAmelCase)



			def 					_lowerCamelCase (		self     :						Optional[int]				,	*A     :						Any				,	**A     :						Union[str, Any])     ->					str:



						"""simple docstring"""






						return self.tokenizer.decode(*_lowerCAmelCase				,	**_lowerCAmelCase)



			@property
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Any:



						"""simple docstring"""






						return ["input_ids", "bbox", "attention_mask", "pixel_values"]



			@property
			def 					_lowerCamelCase (		self     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,	_lowerCAmelCase				,	)
						return self.image_processor_class



			@property
			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Optional[int]:



						"""simple docstring"""






						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,	_lowerCAmelCase				,	)
						return self.image_processor





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				str:







			'''simple docstring'''
			if a < 0 or b < 0:
						raise ValueError('the value of both inputs must be positive'					)

			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"
			_UpperCAmelCase	 =     str(bin(_UpperCAmelCase					)					)[2:]  # remove the leading "0b"

			_UpperCAmelCase	 =     max(len(_UpperCAmelCase					)							,     len(_UpperCAmelCase					)					)

			return "0b" + "".join(
			    str(int(char_a != char_b					)					)
			    for char_a, char_b in zip(a_binary.zfill(_UpperCAmelCase					)							,     b_binary.zfill(_UpperCAmelCase					)					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
