

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]				)							->				Tuple:





				'''simple docstring'''






				return int((input_a, input_a).count(0				) == 0				)
def 						_snake_case(   )							->				List[str]:





				'''simple docstring'''






				assert and_gate(0   ,      0				) == 0
				assert and_gate(0   ,      1				) == 0
				assert and_gate(1   ,      0				) == 0
				assert and_gate(1   ,      1				) == 1


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




import numpy as np

from transformers import Pipeline
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       np.max(SCREAMING_SNAKE_CASE__   ,      axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)
    A__   				=       np.exp(outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict,**lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       {}
        if "second_text" in kwargs:
            A__   				=       kwargs['second_text']
        return preprocess_kwargs, {}, {}


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Optional[int]=None		)->   List[str]:





        '''simple docstring'''







        return self.tokenizer(lowercase_,text_pair=lowercase_,return_tensors=self.framework		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       model_outputs.logits[0].numpy()
        A__   				=       softmax(lowercase_		)

        A__   				=       np.argmax(lowercase_		)
        A__   				=       self.model.config.idalabel[best_class]
        A__   				=       probabilities[best_class].item()
        A__   				=       logits.tolist()
        return {"label": label, "score": score, "logits": logits}

