

'''simple docstring'''

import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer





class 							_snake_case	(    nn.Linear  ):
   def __init__(	self     : Dict       ,SCREAMING_SNAKE_CASE__     : int       ,SCREAMING_SNAKE_CASE__     : int       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : str = "constant"       ,SCREAMING_SNAKE_CASE__     : float = 0.0       ,SCREAMING_SNAKE_CASE__     : str = "topK"       ,):
     super(SCREAMING_SNAKE_CASE__       ,self							).__init__(in_features=SCREAMING_SNAKE_CASE__       ,out_features=SCREAMING_SNAKE_CASE__       ,bias=SCREAMING_SNAKE_CASE__							)
     assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
     SCREAMING_SNAKE_CASE:List[Any]   =   pruning_method

     if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
       SCREAMING_SNAKE_CASE:Dict   =   mask_scale
       SCREAMING_SNAKE_CASE:Optional[int]   =   mask_init
       SCREAMING_SNAKE_CASE:Tuple   =   nn.Parameter(torch.empty(self.weight.size()							)							)
       self.init_mask()
   def       __UpperCamelCase  (	self     : List[Any]							):
     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       __UpperCamelCase  (	self     : List[Any]       ,SCREAMING_SNAKE_CASE__     : torch.tensor       ,SCREAMING_SNAKE_CASE__     : float							):
     # Get the mask
     if self.pruning_method == "topK":
       SCREAMING_SNAKE_CASE:str   =   TopKBinarizer.apply(self.mask_scores       ,SCREAMING_SNAKE_CASE__							)
     elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
       SCREAMING_SNAKE_CASE:List[str]   =   "sigmoied" in self.pruning_method
       SCREAMING_SNAKE_CASE:List[str]   =   ThresholdBinarizer.apply(self.mask_scores       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)
     elif self.pruning_method == "magnitude":
       SCREAMING_SNAKE_CASE:Any   =   MagnitudeBinarizer.apply(self.weight       ,SCREAMING_SNAKE_CASE__							)
     elif self.pruning_method == "l0":
       SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:str   =   -0.1, 1.1, 2 / 3
       if self.training:
         SCREAMING_SNAKE_CASE:int   =   torch.zeros_like(self.mask_scores							).uniform_().clamp(0.0_001       ,0.9_999							)
         SCREAMING_SNAKE_CASE:int   =   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b							)
       else:
         SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.sigmoid(self.mask_scores							)
       SCREAMING_SNAKE_CASE:List[str]   =   s * (r - l) + l
       SCREAMING_SNAKE_CASE:Any   =   s_bar.clamp(min=0.0       ,max=1.0							)
     # Mask weights with computed mask
     SCREAMING_SNAKE_CASE:Optional[int]   =   mask * self.weight
     # Compute output (linear layer) with masked weights
     return nn.functional.linear(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,self.bias							)









'''simple docstring'''

def 				A_					(	snake_case	):
  return "".join(chr(ord(snake_case	) + 32	) if "A" <= char <= "Z" else char for char in word	)


if __name__ == "__main__":
    from doctest import testmod

    testmod()






