
"""simple docstring"""





import torch
from torch import autograd


class A_      (autograd.Function ):




  '''simple docstring'''







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       inputs.numel()
     UpperCAmelCase_   :   Optional[Any]     	=       int(0.0_05 * nb_elems    ) + 1
     if sigmoid:
        UpperCAmelCase_   :   int     	=       (torch.sigmoid(lowercase_    ) > threshold).type(inputs.type()    )
     else:
        UpperCAmelCase_   :   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
        UpperCAmelCase_   :   Tuple     	=       inputs.flatten().kthvalue(max(nb_elems - nb_min							,  1    )    ).values
        UpperCAmelCase_   :   Optional[int]     	=       (inputs > k_threshold).type(inputs.type()    )
     return mask







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_    ):

     """simple docstring"""
     return gradOutput, None, None




class A_      (autograd.Function ):




  '''simple docstring'''







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       inputs.clone()
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       inputs.flatten().sort(descending=lowercase_    )
     UpperCAmelCase_   :   Dict     	=       int(threshold * inputs.numel()    )

     # flat_out and mask access the same memory.
     UpperCAmelCase_   :   int     	=       mask.flatten()
     UpperCAmelCase_   :   Any     	=       0
     UpperCAmelCase_   :   Any     	=       1
     return mask







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_    ):

     """simple docstring"""
     return gradOutput, None




class A_      (lowercase__ ):




  '''simple docstring'''







  @staticmethod
  def 			UpperCamelCase__   (						lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       inputs.clone()
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[Any]     	=       inputs.abs().flatten().sort(descending=lowercase_    )
     UpperCAmelCase_   :   Dict     	=       int(threshold * inputs.numel()    )

     # flat_out and mask access the same memory.
     UpperCAmelCase_   :   Tuple     	=       mask.flatten()
     UpperCAmelCase_   :   Any     	=       0
     UpperCAmelCase_   :   str     	=       1
     return mask




"""simple docstring"""





import math
from timeit import timeit







def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       0
   UpperCAmelCase_   :   Any     	=       abs(__lowerCamelCase    )
   while True:
      UpperCAmelCase_   :   Optional[int]     	=       n // 10
      digits += 1
      if n == 0:
         break
   return digits







def       __a	(						__lowerCamelCase    ):
   return 1 if n == 0 else math.floor(math.log(abs(__lowerCamelCase    ),						10    ) + 1    )







def       __a	(						__lowerCamelCase    ):
   return len(str(abs(__lowerCamelCase    )    )    )







def       __a	(						):
   from collections.abc import Callable

   def benchmark_a_function(__lowerCamelCase,						__lowerCamelCase    ) -> None:
      UpperCAmelCase_   :   Optional[Any]     	=       f"""{func.__name__}({value})"""
      UpperCAmelCase_   :   Optional[int]     	=       timeit(f"""__main__.{call}""",						setup="import __main__"    )
      print(f"""{call}: {func(__lowerCamelCase    )} -- {timing} seconds"""    )

   for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
      for func in (num_digits, num_digits_fast, num_digits_faster):
         benchmark_a_function(__lowerCamelCase,						__lowerCamelCase    )
      print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()


