






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[Any]       						=  ['''image_processor''', '''tokenizer''']
		_snake_case    :    Optional[Any]       						=  '''ViTImageProcessor'''
		_snake_case    :    Optional[int]       						=  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')



		def __init__(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							List[str]:
							UpperCAmelCase_    :      str            =						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				,		)
												UpperCAmelCase_    :      List[str]            =						kwargs.pop('feature_extractor'   )

							UpperCAmelCase_    :      Union[str, Any]            =						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   )



		def __call__(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							Dict:
							if text is None and visual_prompt is None and images is None:
												raise ValueError('You have to specify either text, visual prompt or images.'   )

							if text is not None and visual_prompt is not None:
												raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'   )

							if text is not None:
												UpperCAmelCase_    :      List[str]            =						self.tokenizer(_UpperCamelCase				,		return_tensors=_UpperCamelCase				,		**_UpperCamelCase   )

							if visual_prompt is not None:
												UpperCAmelCase_    :      Optional[Any]            =						self.image_processor(_UpperCamelCase				,		return_tensors=_UpperCamelCase				,		**_UpperCamelCase   )

							if images is not None:
												UpperCAmelCase_    :      str            =						self.image_processor(_UpperCamelCase				,		return_tensors=_UpperCamelCase				,		**_UpperCamelCase   )

							if visual_prompt is not None and images is not None:
												UpperCAmelCase_    :      Tuple            =						{
												    'pixel_values': image_features.pixel_values,
												    'conditional_pixel_values': prompt_features.pixel_values,
												}
												return encoding
							elif text is not None and images is not None:
												UpperCAmelCase_    :      Any            =						image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							elif visual_prompt is not None:
												UpperCAmelCase_    :      List[Any]            =						{
												    'conditional_pixel_values': prompt_features.pixel_values,
												}
												return encoding
							else:
												return BatchEncoding(data=dict(**_UpperCamelCase   )				,		tensor_type=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							Dict:
							return self.tokenizer.batch_decode(*_UpperCamelCase				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							Tuple:
							return self.tokenizer.decode(*_UpperCamelCase				,		**_UpperCamelCase   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							Any:
							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 					__UpperCAmelCase			(							self   )	->							List[str]:
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,		_UpperCamelCase				,		)
							return self.image_processor














def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							int  ):


					'''simple docstring'''


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

					UpperCAmelCase_    :      Tuple            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"
					UpperCAmelCase_    :      Union[str, Any]            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"

					UpperCAmelCase_    :      List[Any]            =						max(len(__snake_case  )     ,       len(__snake_case  )  )

					return "0b" + "".join(
					    str(int(char_a == '1' and char_b == '1'  )  )
					    for char_a, char_b in zip(a_binary.zfill(__snake_case  )     ,       b_binary.zfill(__snake_case  )  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






