






"""simple docstring"""
import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



class 		a_      (	nn.Linear ):







      '''simple docstring'''

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

       '''simple docstring'''



       super(lowerCamelCase_,     self					).__init__(in_features=lowerCamelCase_,     out_features=lowerCamelCase_,     bias=lowerCamelCase_					)
       assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
       lowerCamelCase__     :			Union[str, Any]     				=						pruning_method

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




      def 			a__							(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 			a__							(self,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



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









"""simple docstring"""
class 		a_      :







      '''simple docstring'''

      class 		a_      :







            '''simple docstring'''

            lowerCamelCase__       :				List[Any] =	'_prev', '_data', '_next'




            def __init__(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

             '''simple docstring'''



             lowerCamelCase__     :			Optional[int]     				=						link_p
             lowerCamelCase__     :			Optional[Any]     				=						element
             lowerCamelCase__     :			List[Any]     				=						link_n





            def 			a__							(self					):

             '''simple docstring'''



             return (
                 f''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
             )




      def __init__(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Any     				=						self._Node(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					)
       lowerCamelCase__     :			Dict     				=						self._Node(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					)
       lowerCamelCase__     :			List[str]     				=						self._trailer
       lowerCamelCase__     :			Union[str, Any]     				=						self._header
       lowerCamelCase__     :			Any     				=						0




      def __len__(self					):

       '''simple docstring'''



       return self._size




      def 			a__							(self					):

       '''simple docstring'''



       return self.__len__() == 0




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						self._Node(lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_					)
       lowerCamelCase__     :			Optional[int]     				=						new_node
       lowerCamelCase__     :			List[Any]     				=						new_node
       self._size += 1
       return self





      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						node._prev
       lowerCamelCase__     :			List[str]     				=						node._next

       lowerCamelCase__     :			Tuple     				=						successor
       lowerCamelCase__     :			Tuple     				=						predecessor
       self._size -= 1
       lowerCamelCase__     :			int     				=						node._data
       lowerCamelCase__     :			Dict     				=						None
       del node
       return temp



class 		a_      (	_DoublyLinkedBase ):







      '''simple docstring'''

      def 			a__							(self					):

       '''simple docstring'''



       if self.is_empty():
        raise Exception('List is empty'					)
       return self._header._next._data




      def 			a__							(self					):

       '''simple docstring'''



       if self.is_empty():
        raise Exception('List is empty'					)
       return self._trailer._prev._data




      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       return self._insert(self._header,     lowerCamelCase_,     self._header._next					)




      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       return self._insert(self._trailer._prev,     lowerCamelCase_,     self._trailer					)




      def 			a__							(self					):

       '''simple docstring'''



       if self.is_empty():
        raise IndexError('remove_first from empty list'					)
       return self._delete(self._header._next					)





      def 			a__							(self					):

       '''simple docstring'''



       if self.is_empty():
        raise IndexError('remove_first from empty list'					)
       return self._delete(self._trailer._prev					)

