code: 
  
  
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   ['''image_processor''', '''tokenizer''']
  	A						       =   '''AutoImageProcessor'''
  	A						       =   '''AutoTokenizer'''
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    **SCREAMING_SNAKE_CASE			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	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_			=	kwargs.pop('feature_extractor'			)
  
  				SCREAMING_SNAKE_CASE_			=	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			)
  				SCREAMING_SNAKE_CASE_			=	self.image_processor
  				SCREAMING_SNAKE_CASE_			=	False
  
  
  
  
  
  	def __call__(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self._in_target_context_manager:
  							return self.current_processor(*SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	kwargs.pop('images'			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	kwargs.pop('text'			,    SCREAMING_SNAKE_CASE			)
  				if len(SCREAMING_SNAKE_CASE			) > 0:
  							SCREAMING_SNAKE_CASE_			=	args[0]
  							SCREAMING_SNAKE_CASE_			=	args[1:]
  
  				if images is None and text is None:
  							raise ValueError('You need to specify either an `images` or `text` input to process.'			)
  
  				if images is not None:
  							SCREAMING_SNAKE_CASE_			=	self.image_processor(SCREAMING_SNAKE_CASE			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  				if text is not None:
  							SCREAMING_SNAKE_CASE_			=	self.tokenizer(SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  				if text is None:
  							return inputs
  				elif images is None:
  							return encodings
  				else:
  							SCREAMING_SNAKE_CASE_			=	encodings['input_ids']
  							return inputs
  
  
  
  
  
  	def 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@contextmanager
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				warnings.warn(
  				    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
  				    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
  				    'your images inputs, or in a separate call.'			)
  				SCREAMING_SNAKE_CASE_			=	True
  				SCREAMING_SNAKE_CASE_			=	self.tokenizer
  				yield
  				SCREAMING_SNAKE_CASE_			=	self.image_processor
  				SCREAMING_SNAKE_CASE_			=	False
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
  				"""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 	A_(       self			)     ->				Any:
  
  
  
  
  				"""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
  
style_context: 
  
  
  
  
  from __future__ import annotations
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	m - 1, 0
  			while i >= 0 and j < n:
  						if key == mat[i][j]:
  									print(F'Key {key} found at row- {i + 1} column- {j + 1}'   )
  									return
  						if key < mat[i][j]:
  									i -= 1
  						else:
  									j += 1
  			print(F'Key {key} not found'   )
  
  def lowercase						(     ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  			SCREAMING_SNAKE_CASE_			=	int(input('Enter the element to be searched:'   )   )
  			print(SCREAMING_SNAKE_CASE   )
  			search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE					,						len(SCREAMING_SNAKE_CASE   )					,						len(mat[0]   )					,						SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  				main()
  