code: 
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  class _lowerCamelCase (		UpperCamelCase_							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       ["""image_processor""", """tokenizer"""]
  		A_			:    str   					=       """AutoImageProcessor"""
  		A_			:    List[Any]   					=       """AutoTokenizer"""
  
  
  
  
  
  
  
  		def __init__(      self	:		Any						,  _A	:		List[Any]=None						,  _A	:		Tuple=None						,  **_A	:		int    )						->				Optional[Any]:
  			__magic_name__ :					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.'						,  __UpperCamelCase						,  )
  				__magic_name__ :					Union[str, Any]  			=  kwargs.pop('feature_extractor'    )
  
  			__magic_name__ :					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__(__UpperCamelCase						,  __UpperCamelCase    )
  			__magic_name__ :					Union[str, Any]  			=  self.image_processor
  			__magic_name__ :					str  			=  False
  
  
  
  
  
  
  
  		def __call__(      self	:		List[str]						,  *_A	:		str						,  **_A	:		Union[str, Any]    )						->				Union[str, Any]:
  
  
  			if self._in_target_context_manager:
  				return self.current_processor(*__UpperCamelCase						,  **__UpperCamelCase    )
  
  			__magic_name__ :					Optional[Any]  			=  kwargs.pop('images'						,  __UpperCamelCase    )
  			__magic_name__ :					Optional[Any]  			=  kwargs.pop('text'						,  __UpperCamelCase    )
  			if len(__UpperCamelCase    ) > 0:
  				__magic_name__ :					List[Any]  			=  args[0]
  				__magic_name__ :					List[str]  			=  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:
  				__magic_name__ :					Union[str, Any]  			=  self.image_processor(__UpperCamelCase						,  *__UpperCamelCase						,  **__UpperCamelCase    )
  			if text is not None:
  				__magic_name__ :					Dict  			=  self.tokenizer(__UpperCamelCase						,  **__UpperCamelCase    )
  
  			if text is None:
  				return inputs
  			elif images is None:
  				return encodings
  			else:
  				__magic_name__ :					str  			=  encodings['''input_ids''']
  				return inputs
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str						,  *_A	:		Optional[Any]						,  **_A	:		str    )						->				List[str]:
  			return self.tokenizer.batch_decode(*__UpperCamelCase						,  **__UpperCamelCase    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]						,  *_A	:		Union[str, Any]						,  **_A	:		Optional[int]    )						->				List[Any]:
  			return self.tokenizer.decode(*__UpperCamelCase						,  **__UpperCamelCase    )
  
  
  
  
  
  
  
  		@contextmanager
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Tuple:
  			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.'    )
  			__magic_name__ :					Dict  			=  True
  			__magic_name__ :					List[Any]  			=  self.tokenizer
  			yield
  			__magic_name__ :					List[Any]  			=  self.image_processor
  			__magic_name__ :					List[str]  			=  False
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Tuple    )						->				List[str]:
  			warnings.warn(
  			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,  __UpperCamelCase						,  )
  			return self.image_processor_class
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Tuple    )						->				Tuple:
  			warnings.warn(
  			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,  __UpperCamelCase						,  )
  			return self.image_processor
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			list[int | float]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if len(lowerCAmelCase	) == 0:
  		raise ValueError('find_min() arg is an empty sequence'	)
  	if (
  	    left >= len(lowerCAmelCase	)
  	    or left < -len(lowerCAmelCase	)
  	    or right >= len(lowerCAmelCase	)
  	    or right < -len(lowerCAmelCase	)
  	):
  		raise IndexError('list index out of range'	)
  	if left == right:
  		return nums[left]
  	__magic_name__ :					Tuple  			=  (left + right) >> 1  # the middle
  	__magic_name__ :					str  			=  find_min(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)  # find min in range[left, mid]
  	__magic_name__ :					Optional[Any]  			=  find_min(lowerCAmelCase	,	mid + 1	,	lowerCAmelCase	)  # find min in range[mid + 1, right]
  
  	return left_min if left_min <= right_min else right_min
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod(verbose=True)