"""simple docstring"""




import numpy as np

from transformers import Pipeline





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   Dict:
  _lowerCAmelCase		: List[str]						    =  np.max(_lowerCamelCase    ,axis=-1    ,keepdims=_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  np.exp(outputs - maxes			)
  return shifted_exp / shifted_exp.sum(axis=-1    ,keepdims=_lowerCamelCase			)






class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def 					__A						(	self       ,  **a__						):
       _lowerCAmelCase		: int						    =  {}
       if "second_text" in kwargs:
         _lowerCAmelCase		: Optional[int]						    =  kwargs["""second_text"""]
       return preprocess_kwargs, {}, {}






     def 					__A						(	self       ,  a__       ,  a__=None						):
       return self.tokenizer(a__       ,  text_pair=a__       ,  return_tensors=self.framework						)






     def 					__A						(	self       ,  a__						):
       return self.model(**a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Any						    =  model_outputs.logits[0].numpy()
       _lowerCAmelCase		: List[Any]						    =  softmax(a__						)

       _lowerCAmelCase		: List[str]						    =  np.argmax(a__						)
       _lowerCAmelCase		: Optional[int]						    =  self.model.config.idalabel[best_class]
       _lowerCAmelCase		: Optional[Any]						    =  probabilities[best_class].item()
       _lowerCAmelCase		: Any						    =  logits.tolist()
       return {"label": label, "score": score, "logits": logits}





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int = 1000000			) ->   int:
  _lowerCAmelCase		: List[str]						    =  [i - 1 for i in range(limit + 1			)]

  for i in range(2    ,limit + 1			):
    if phi[i] == i - 1:
      for j in range(2 * i    ,limit + 1    ,_lowerCamelCase			):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]			)


if __name__ == "__main__":
     print(solution())




