






import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class        snake_case_				(							nn.Linear   ):







   """simple docstring"""



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


       """simple docstring"""



       super(snake_case__ ,							self						).__init__(in_features=snake_case__ ,							out_features=snake_case__ ,							bias=snake_case__						)
       assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
       _lowercase     			=						pruning_method

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

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Optional[Any]:


       """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       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						torch.tensor ,							snake_case__:						float						)	->		Tuple:


       """simple docstring"""



       if self.pruning_method == "topK":
           _lowercase     			=						TopKBinarizer.apply(self.mask_scores ,							snake_case__						)
       elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
           _lowercase     			=						'sigmoied' in self.pruning_method
           _lowercase     			=						ThresholdBinarizer.apply(self.mask_scores ,							snake_case__ ,							snake_case__						)
       elif self.pruning_method == "magnitude":
           _lowercase     			=						MagnitudeBinarizer.apply(self.weight ,							snake_case__						)
       elif self.pruning_method == "l0":
           _lowercase			,     _lowercase			,     _lowercase     			=						-0.1, 1.1, 2 / 3
           if self.training:
               _lowercase     			=						torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01 ,							0.99_99						)
               _lowercase     			=						torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
           else:
               _lowercase     			=						torch.sigmoid(self.mask_scores						)
           _lowercase     			=						s * (r - l) + l
           _lowercase     			=						s_bar.clamp(min=0.0 ,							max=1.0						)
       # Mask weights with computed mask
       _lowercase     			=						mask * self.weight
       # Compute output (linear layer) with masked weights
       return nn.functional.linear(snake_case__ ,							snake_case__ ,							self.bias						)








from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowerCamelCase					    =	TypeVar('''T''', bound='''Enum''')

@unique
class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    80
   __lowerCAmelCase  =    70
   __lowerCAmelCase  =    60
   __lowerCAmelCase  =    50
   __lowerCAmelCase  =    40
   __lowerCAmelCase  =    30
   __lowerCAmelCase  =    20
   __lowerCAmelCase  =    10






@unique
class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    24
   __lowerCAmelCase  =    21
   __lowerCAmelCase  =    18
   __lowerCAmelCase  =    15
   __lowerCAmelCase  =    12
   __lowerCAmelCase  =    9
   __lowerCAmelCase  =    6
   __lowerCAmelCase  =    3
   __lowerCAmelCase  =    2
   __lowerCAmelCase  =    1
   __lowerCAmelCase  =    -1
   __lowerCAmelCase  =    -2
   __lowerCAmelCase  =    -3
   __lowerCAmelCase  =    -6
   __lowerCAmelCase  =    -9
   __lowerCAmelCase  =    -12
   __lowerCAmelCase  =    -15
   __lowerCAmelCase  =    -18
   __lowerCAmelCase  =    -21
   __lowerCAmelCase  =    -24

   @classmethod
   def       SCREAMING_SNAKE_CASE__					(     cls:						type[T]						)	->		dict:


       """simple docstring"""



       return {unit.name: unit.value for unit in cls if unit.value > 0}

   @classmethod
   def       SCREAMING_SNAKE_CASE__					(     cls:						type[T]						)	->		dict:


       """simple docstring"""



       return {unit.name: unit.value for unit in cls if unit.value < 0}






def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
    for name_prefix, value_prefix in prefixes.items():
        _lowercase     			=						value / (10**value_prefix)
        if numerical_part > 1:
            return f'''{numerical_part!s} {name_prefix}'''
    return str(_lowercase   )
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    for prefix in BinaryUnit:
        _lowercase     			=						value / (2**prefix.value)
        if numerical_part > 1:
            return f'''{numerical_part!s} {prefix.name}'''
    return str(_lowercase   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
