





import torch
from torch import autograd





class   lowercase__     (				autograd.Function	):







  @staticmethod
  def 						UpperCAmelCase__		(						_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							Optional[int]			 =		inputs.numel()
       lowerCAmelCase_       :							List[str]			 =		int(0.005 * nb_elems    ) + 1
       if sigmoid:
            lowerCAmelCase_       :							Any			 =		(torch.sigmoid(_lowerCAmelCase    ) > threshold).type(inputs.type()    )
       else:
            lowerCAmelCase_       :							Any			 =		(inputs > threshold).type(inputs.type()    )
       if mask.sum() < nb_min:
            # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
            lowerCAmelCase_       :							int			 =		inputs.flatten().kthvalue(max(nb_elems - nb_min     ,    1    )    ).values
            lowerCAmelCase_       :							Any			 =		(inputs > k_threshold).type(inputs.type()    )
       return mask







  @staticmethod
  def 						UpperCAmelCase__		(						_lowercase     ,    _lowercase    ):
       return gradOutput, None, None





class   lowercase__     (				autograd.Function	):







  @staticmethod
  def 						UpperCAmelCase__		(						_lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							List[str]			 =		inputs.clone()
       lowerCAmelCase_ ,	lowerCAmelCase_       :							Dict			 =		inputs.flatten().sort(descending=_lowerCAmelCase    )
       lowerCAmelCase_       :							Union[str, Any]			 =		int(threshold * inputs.numel()    )

       # flat_out and mask access the same memory.
       lowerCAmelCase_       :							List[Any]			 =		mask.flatten()
       lowerCAmelCase_       :							List[str]			 =		0
       lowerCAmelCase_       :							str			 =		1
       return mask







  @staticmethod
  def 						UpperCAmelCase__		(						_lowercase     ,    _lowercase    ):
       return gradOutput, None





class   lowercase__     (				UpperCAmelCase__	):







  @staticmethod
  def 						UpperCAmelCase__		(						_lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							List[Any]			 =		inputs.clone()
       lowerCAmelCase_ ,	lowerCAmelCase_       :							str			 =		inputs.abs().flatten().sort(descending=_lowerCAmelCase    )
       lowerCAmelCase_       :							List[str]			 =		int(threshold * inputs.numel()    )

       # flat_out and mask access the same memory.
       lowerCAmelCase_       :							Dict			 =		mask.flatten()
       lowerCAmelCase_       :							Optional[int]			 =		0
       lowerCAmelCase_       :							Dict			 =		1
       return mask






from __future__ import annotations





def 							lowerCamelCase    (							UpperCAmelCase_	:      list[int | float]      )->			int | float:

       """simple docstring"""




       if len(UpperCAmelCase_      ) == 0:
              raise ValueError("""find_min() arg is an empty sequence"""      )
       a			   =nums[0]
       for num in nums:
              a			   =min(UpperCAmelCase_   ,       UpperCAmelCase_      )
       return min_num


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
