


import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
class      UpperCAmelCase_ ( nn.Linear   ):

   '''simple docstring'''




   def __init__(		self						,    __A						,    __A						,    __A = True						,    __A = "constant"						,    __A = 0.0						,    __A = "topK"						,    ):






     """simple docstring"""





     super(__A						,    self			).__init__(in_features=__A						,    out_features=__A						,    bias=__A			)
     assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
     lowerCamelCase			: List[str]   =    pruning_method

     if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
       lowerCamelCase			: List[Any]   =    mask_scale
       lowerCamelCase			: Optional[Any]   =    mask_init
       lowerCamelCase			: str   =    nn.Parameter(torch.empty(self.weight.size()			)			)
       self.init_mask()






   def _snake_case   (		self			):






     """simple docstring"""





     if self.mask_init == "constant":
       init.constant_(self.mask_scores						,    val=self.mask_scale			)
     elif self.mask_init == "uniform":
       init.uniform_(self.mask_scores						,    a=-self.mask_scale						,    b=self.mask_scale			)
     elif self.mask_init == "kaiming":
       init.kaiming_uniform_(self.mask_scores						,    a=math.sqrt(5			)			)






   def _snake_case   (		self						,    __A						,    __A			):






     """simple docstring"""





     if self.pruning_method == "topK":
       lowerCamelCase			: Any   =    TopKBinarizer.apply(self.mask_scores						,    __A			)
     elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
       lowerCamelCase			: Optional[int]   =    "sigmoied" in self.pruning_method
       lowerCamelCase			: Dict   =    ThresholdBinarizer.apply(self.mask_scores						,    __A						,    __A			)
     elif self.pruning_method == "magnitude":
       lowerCamelCase			: List[Any]   =    MagnitudeBinarizer.apply(self.weight						,    __A			)
     elif self.pruning_method == "l0":
       lowerCamelCase			: List[Any]   =    -0.1, 1.1, 2 / 3
       if self.training:
         lowerCamelCase			: Optional[int]   =    torch.zeros_like(self.mask_scores			).uniform_().clamp(0.0001						,    0.9999			)
         lowerCamelCase			: List[str]   =    torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b			)
       else:
         lowerCamelCase			: Dict   =    torch.sigmoid(self.mask_scores			)
       lowerCamelCase			: List[Any]   =    s * (r - l) + l
       lowerCamelCase			: Optional[Any]   =    s_bar.clamp(min=0.0						,    max=1.0			)
     # Mask weights with computed mask
     lowerCamelCase			: Optional[Any]   =    mask * self.weight
     # Compute output (linear layer) with masked weights
     return nn.functional.linear(__A						,    __A						,    self.bias			)







def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if number != int(SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if number < 0:
    raise ValueError("factorial() not defined for negative values"     )
  lowerCamelCase			: Optional[Any]   =    1
  for i in range(1				,						number + 1     ):
    value *= i
  return value






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if not isinstance(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):
    raise ValueError("factorial() only accepts integral values"     )
  if n < 0:
    raise ValueError("factorial() not defined for negative values"     )
  return 1 if n == 0 or n == 1 else n * factorial(n - 1     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _snake_case					=					int(input('''Enter a positive integer: ''').strip() or 0)
   print(f'''factorial{n} is {factorial(n)}''')



