



"""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     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= ['''image_processor''', '''tokenizer''']
	_SCREAMING_SNAKE_CASE	:		Dict  						= '''LayoutLMv3ImageProcessor'''
	_SCREAMING_SNAKE_CASE	:		List[str]  						= ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')





	def __init__(      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       int=None     ,       _SCREAMING_SNAKE_CASE				:       Any=None     ,       **_SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.'     ,       _SCREAMING_SNAKE_CASE     ,       )
									SCREAMING_SNAKE_CASE						:							List[str]							=     kwargs.pop('feature_extractor'    )

					SCREAMING_SNAKE_CASE						:							int							=     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__(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def __call__(      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,       _SCREAMING_SNAKE_CASE				:       Union[List[List[int]], List[List[List[int]]]] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[List[int], List[List[int]]]] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Union[bool, str, PaddingStrategy] = False     ,       _SCREAMING_SNAKE_CASE				:       Union[bool, str, TruncationStrategy] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       int = 0     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[str, TensorType]] = None     ,       **_SCREAMING_SNAKE_CASE				:       Dict     ,       )						->	BatchEncoding:





					"""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
					SCREAMING_SNAKE_CASE						:							Dict							=     self.image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE    )

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

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     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=_SCREAMING_SNAKE_CASE     ,       add_special_tokens=_SCREAMING_SNAKE_CASE     ,       padding=_SCREAMING_SNAKE_CASE     ,       truncation=_SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       stride=_SCREAMING_SNAKE_CASE     ,       pad_to_multiple_of=_SCREAMING_SNAKE_CASE     ,       return_token_type_ids=_SCREAMING_SNAKE_CASE     ,       return_attention_mask=_SCREAMING_SNAKE_CASE     ,       return_overflowing_tokens=_SCREAMING_SNAKE_CASE     ,       return_special_tokens_mask=_SCREAMING_SNAKE_CASE     ,       return_offsets_mapping=_SCREAMING_SNAKE_CASE     ,       return_length=_SCREAMING_SNAKE_CASE     ,       verbose=_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )

					# add pixel values
					SCREAMING_SNAKE_CASE						:							List[Any]							=     features.pop('pixel_values'    )
					if return_overflowing_tokens is True:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.get_overflowing_images(_SCREAMING_SNAKE_CASE     ,       encoded_inputs['overflow_to_sample_mapping']    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     images

					return encoded_inputs





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     []
					for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx]    )

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

					return images_with_overflow





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       *_SCREAMING_SNAKE_CASE				:       List[Any]     ,       **_SCREAMING_SNAKE_CASE				:       str    )						->	Any:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Any     ,       *_SCREAMING_SNAKE_CASE				:       str     ,       **_SCREAMING_SNAKE_CASE				:       Any    )						->	List[Any]:





					"""simple docstring"""


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





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





					"""simple docstring"""


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





	@property
	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	Tuple:





					"""simple docstring"""


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







	@property
	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	List[str]:





					"""simple docstring"""


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










"""simple docstring"""

def  __snake_case							(	__A      :						list							,		__A      :						int							,		__A      :						int = 0							,		__A      :						int = 0     )     ->       int:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Any							=     right or len(__A     ) - 1
				if left > right:
								return -1
				elif list_data[left] == key:
								return left
				elif list_data[right] == key:
								return right
				else:
								return search(__A							,		__A							,		left + 1							,		right - 1     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





