
def 		UpperCamelCase     (       _UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   List[Any] ,			_UpperCAmelCase  :   List[Any]=False  )   ->			List[Any]:






						'''simple docstring'''

						if isinstance(_snake_case ,			_snake_case  ) and isinstance(_snake_case ,			_snake_case  ):
												_lowercase       :	List[Any]           =   len(set_a.intersection(_snake_case  )  )

												if alternative_union:
																		_lowercase       :	List[str]           =   len(_snake_case  ) + len(_snake_case  )
												else:
																		_lowercase       :	Union[str, Any]           =   len(set_a.union(_snake_case  )  )

												return intersection / union

						if isinstance(_snake_case ,			(list, tuple)  ) and isinstance(_snake_case ,			(list, tuple)  ):
												_lowercase       :	List[str]           =   [element for element in set_a if element in set_b]

												if alternative_union:
																		_lowercase       :	Any           =   len(_snake_case  ) + len(_snake_case  )
																		return len(_snake_case  ) / union
												else:
																		_lowercase       :	int           =   set_a + [element for element in set_b if element not in set_a]
																		return len(_snake_case  ) / len(_snake_case  )

												return len(_snake_case  ) / len(_snake_case  )
						return None


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










"""simple docstring"""

from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      for param in module.parameters():
            _A     =  False






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      _A     =  'cuda' if torch.cuda.is_available() else 'cpu'
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            _A     =  'mps'
      if device == "mps":
            print(
                'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                ' with generations.'				)
      return device






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  plt.imshow(_snake_case				)
      fig.axes.get_xaxis().set_visible(_snake_case				)
      fig.axes.get_yaxis().set_visible(_snake_case				)
      plt.show()







def 		_snake_case	(   )       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  datetime.now()
      _A     =  current_time.strftime('%H:%M:%S'				)
      return timestamp

