


"""simple docstring"""
def 						lowercase				(	_snake_case		: int	)	->str:
	"""simple docstring"""





	if not isinstance(_snake_case	,  _snake_case	):
		__snake_case      :		Dict         =							(
		    '''operation can not be conducted on a object of type '''
		    f"""{type(_snake_case	).__name__}"""
		)
		raise TypeError(_snake_case	)
	__snake_case      :		List[Any]         =							''''''
	for _ in range(0	,  32	):
		bit_string += str(number % 2	)
		__snake_case      :		List[Any]         =							number >> 1
	return bit_string






def 						lowercase				(	_snake_case		: int	)	->str:
	"""simple docstring"""





	if number < 0:
		raise ValueError('''the value of input must be positive'''	)
	elif isinstance(_snake_case	,  _snake_case	):
		raise TypeError('''Input value must be a \'int\' type'''	)
	elif isinstance(_snake_case	,  _snake_case	):
		raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''	)
	__snake_case      :		Any         =							0
	# iterator over [1 to 32],since we are dealing with 32 bit integer
	for _ in range(1	,  33	):
		# left shift the bits by unity
		__snake_case      :		List[Any]         =							result << 1
		# get the end bit
		__snake_case      :		List[Any]         =							number % 2
		# right shift the bits by unity
		__snake_case      :		List[Any]         =							number >> 1
		# add that bit to our ans
		__snake_case      :		Union[str, Any]         =							result | end_bit
	return get_reverse_bit_string(_snake_case	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()













"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(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
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

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



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

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





