






'''simple docstring'''


def 	__magic_name__( _A						,      _A						,      _A=False					):
						'''simple docstring'''





						if isinstance(_UpperCAmelCase						,      _UpperCAmelCase					) and isinstance(_UpperCAmelCase						,      _UpperCAmelCase					):
												UpperCamelCase__         = len(set_a.intersection(_UpperCAmelCase					)					)

												if alternative_union:
																		UpperCamelCase__         = len(_UpperCAmelCase					) + len(_UpperCAmelCase					)
												else:
																		UpperCamelCase__         = len(set_a.union(_UpperCAmelCase					)					)

												return intersection / union

						if isinstance(_UpperCAmelCase						,      (list, tuple)					) and isinstance(_UpperCAmelCase						,      (list, tuple)					):
												UpperCamelCase__         = [element for element in set_a if element in set_b]

												if alternative_union:
																		UpperCamelCase__         = len(_UpperCAmelCase					) + len(_UpperCAmelCase					)
																		return len(_UpperCAmelCase					) / union
												else:
																		UpperCamelCase__         = set_a + [element for element in set_b if element not in set_a]
																		return len(_UpperCAmelCase					) / len(_UpperCAmelCase					)

												return len(_UpperCAmelCase					) / len(_UpperCAmelCase					)
						return None


if __name__ == "__main__":
					lowerCamelCase_				: Any    	=							{'''a''', '''b''', '''c''', '''d''', '''e'''}
					lowerCamelCase_				: Optional[int]    	=							{'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
					print(jaccard_similarity(set_a, set_b))








'''simple docstring'''


import numpy as np

from transformers import Pipeline







def 	__magic_name__( _A					):
						'''simple docstring'''





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







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def 				A      (   self							:						int ,      **lowercase							:						List[Any]			)							->    List[str]:

									'''simple docstring'''







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






			def 				A      (   self							:						Optional[int] ,      lowercase							:						Union[str, Any] ,      lowercase							:						Any=None			)							->    Optional[int]:

									'''simple docstring'''







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






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    Any:

									'''simple docstring'''







									return self.model(**lowercase			)







			def 				A      (   self							:						List[str] ,      lowercase							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = model_outputs.logits[0].numpy()
									UpperCamelCase__         = softmax(lowercase			)

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