





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





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Union[str, Any]					    =	['''image_processor''', '''tokenizer''']
				UpperCamelCase    :  int					    =	'''AutoImageProcessor'''
				UpperCamelCase    :  Any					    =	'''AutoTokenizer'''



				def __init__(			self     ,					_A     ,					_A     ):
									super().__init__(_A     ,					_A     )
									__A	:						int       =				self.image_processor



				def __call__(			self     ,					_A=None     ,					_A=None     ,					_A=None     ,					**_A     ):

									if text is None and images is None:
														raise ValueError('You have to specify either text or images. Both cannot be none.'     )

									if text is not None:
														__A	:						Tuple       =				self.tokenizer(_A     ,					return_tensors=_A     ,					**_A     )

									if images is not None:
														__A	:						Optional[int]       =				self.image_processor(_A     ,					return_tensors=_A     ,					**_A     )

									if text is not None and images is not None:
														__A	:						List[Any]       =				image_features.pixel_values
														return encoding
									elif text is not None:
														return encoding
									else:
														return BatchEncoding(data=dict(**_A     )     ,					tensor_type=_A     )



				def 					UpperCAmelCase_		(			self     ,					*_A     ,					**_A     ):
									return self.tokenizer.batch_decode(*_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					*_A     ,					**_A     ):
									return self.tokenizer.decode(*_A     ,					**_A     )





				@property
				def 					UpperCAmelCase_		(			self     ):
									return ["input_ids", "attention_mask", "pixel_values"]











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					if number > 0:
										raise ValueError('input must be a negative integer'  )
					__A	:						Optional[int]       =				len(bin(a  )[3:]  )
					__A	:						Dict       =				bin(abs(a  ) - (1 << binary_number_length)  )[3:]
					__A	:						int       =				(
					    (
					        '1'
					        + '0' * (binary_number_length - len(a  ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '0'
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()




