


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class   lowerCamelCase						(	nn.Linear  ):

       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"]
        snake_case :			int							=   pruning_method

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

       def 					UpperCAmelCase					(      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 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






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










a					:				Tuple       =      [0.4_85, 0.4_56, 0.4_06]
a					:				Optional[int]       =      [0.2_29, 0.2_24, 0.2_25]
a					:				List[Any]       =      [0.5, 0.5, 0.5]
a					:				Tuple       =      [0.5, 0.5, 0.5]
a					:				Optional[Any]       =      [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
a					:				int       =      [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]






