


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


class       __lowerCAmelCase       (	nn.Linear   ):






  """simple docstring"""





  def __init__(       self    :    List[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    bool = True      ,			_lowerCAmelCase    :    str = "constant"      ,			_lowerCAmelCase    :    float = 0.0      ,			_lowerCAmelCase    :    str = "topK"      ,			)				->	Tuple:




     """simple docstring"""
     super(_lowerCAmelCase      ,			self					).__init__(in_features=_lowerCAmelCase      ,			out_features=_lowerCAmelCase      ,			bias=_lowerCAmelCase					)
     assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
     snake_case_         =   pruning_method

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



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	str:




     """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    lowerCAmelCase__					(       self    :    Optional[int]      ,			_lowerCAmelCase    :    torch.tensor      ,			_lowerCAmelCase    :    float					)				->	int:




     """simple docstring"""
     # Get the mask
     if self.pruning_method == "topK":
        snake_case_         =   TopKBinarizer.apply(self.mask_scores      ,			_lowerCAmelCase					)
     elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
        snake_case_         =   "sigmoied" in self.pruning_method
        snake_case_         =   ThresholdBinarizer.apply(self.mask_scores      ,			_lowerCAmelCase      ,			_lowerCAmelCase					)
     elif self.pruning_method == "magnitude":
        snake_case_         =   MagnitudeBinarizer.apply(self.weight      ,			_lowerCAmelCase					)
     elif self.pruning_method == "l0":
        snake_case_      ,      snake_case_      ,      snake_case_         =   -0.1, 1.1, 2 / 3
        if self.training:
           snake_case_         =   torch.zeros_like(self.mask_scores					).uniform_().clamp(0.0_001      ,			0.9_999					)
           snake_case_         =   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b					)
        else:
           snake_case_         =   torch.sigmoid(self.mask_scores					)
        snake_case_         =   s * (r - l) + l
        snake_case_         =   s_bar.clamp(min=0.0      ,			max=1.0					)
     # Mask weights with computed mask
     snake_case_         =   mask * self.weight
     # Compute output (linear layer) with masked weights
     return nn.functional.linear(_lowerCAmelCase      ,			_lowerCAmelCase      ,			self.bias					)








from __future__ import annotations



def      _lowerCAmelCase  (							lowerCAmelCase_							:int							)->list[int]:
   '''simple docstring'''






   assert (
       isinstance(lowerCAmelCase_						,  lowerCAmelCase_							) and mask > 0
   ), F'''mask needs to be positive integer, your input {mask}'''
   snake_case_         =   []
   snake_case_         =   mask

   while submask:
      all_submasks.append(lowerCAmelCase_							)
      snake_case_         =   (submask - 1) & mask

   return all_submasks


if __name__ == "__main__":
   import doctest

   doctest.testmod()




