
'''simple docstring'''







def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					int				):



						"""simple docstring"""





						return (input_a, input_a).count(0				) % 2
def    snake_case_						(							):



						"""simple docstring"""





						assert xor_gate(0 , 0				) == 0
						assert xor_gate(0 , 1				) == 1
						assert xor_gate(1 , 0				) == 1
						assert xor_gate(1 , 1				) == 0


if __name__ == "__main__":
							print(xor_gate(0, 0))
							print(xor_gate(0, 1))





'''simple docstring'''







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






class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	['''image_processor''', '''tokenizer''']
					lowerCAmelCase_	  =	'''EfficientNetImageProcessor'''
					lowerCAmelCase_	  =	('''BertTokenizer''', '''BertTokenizerFast''')






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											super().__init__(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					def __call__(  self							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE="max_length"							,				__SCREAMING_SNAKE_CASE=64							,				__SCREAMING_SNAKE_CASE=None							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											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:
																	lowercase_      :	str						=			self.tokenizer(
																	    __SCREAMING_SNAKE_CASE							,				padding=__SCREAMING_SNAKE_CASE							,				max_length=__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

											if images is not None:
																	lowercase_      :	int						=			self.image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

											if text is not None and images is not None:
																	lowercase_      :	Any						=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE						)							,				tensor_type=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





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






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





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






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Dict						=			self.tokenizer.model_input_names
											lowercase_      :	Optional[int]						=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)



