






'''simple docstring'''







from __future__ import annotations

from typing import Generic, TypeVar

UpperCamelCase_					=				TypeVar("T")




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      pass




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      pass




class 					__UpperCAmelCase (		Generic[T]	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase = 10						):
             UpperCAmelCase__	:   list[T]												=	[]
             UpperCAmelCase__	:   Optional[int]												=	limit




      def __bool__(					self						):
             return bool(self.stack						)




      def __str__(					self						):
             return str(self.stack						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             if len(self.stack						) >= self.limit:
                    raise StackOverflowError
             self.stack.append(_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             if not self.stack:
                    raise StackUnderflowError
             return self.stack.pop()




      def 							lowerCamelCase     (					self						):
             if not self.stack:
                    raise StackUnderflowError
             return self.stack[-1]




      def 							lowerCamelCase     (					self						):
             return not bool(self.stack						)




      def 							lowerCamelCase     (					self						):
             return self.size() == self.limit




      def 							lowerCamelCase     (					self						):
             return len(self.stack						)




      def __contains__(					self  ,		_UpperCAmelCase						):
             return item in self.stack






def  lowerCAmelCase__		(					)   -> None:
       UpperCAmelCase__	:   Stack[int]												=	Stack(1_0			)
       assert bool(a_			) is False
       assert stack.is_empty() is True
       assert stack.is_full() is False
       assert str(a_			) == "[]"

       try:
              UpperCAmelCase__	:   Dict												=	stack.pop()
              raise AssertionError  # This should not happen
       except StackUnderflowError:
              assert True  # This should happen

       try:
              UpperCAmelCase__	:   Optional[Any]												=	stack.peek()
              raise AssertionError  # This should not happen
       except StackUnderflowError:
              assert True  # This should happen

       for i in range(1_0			):
              assert stack.size() == i
              stack.push(a_			)

       assert bool(a_			)
       assert not stack.is_empty()
       assert stack.is_full()
       assert str(a_			) == str(list(range(1_0			)			)			)
       assert stack.pop() == 9
       assert stack.peek() == 8

       stack.push(1_0_0			)
       assert str(a_			) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 1_0_0]			)

       try:
              stack.push(2_0_0			)
              raise AssertionError  # This should not happen
       except StackOverflowError:
              assert True  # This should happen

       assert not stack.is_empty()
       assert stack.size() == 1_0

       assert 5 in stack
       assert 5_5 not in stack


if __name__ == "__main__":
  test_stack()







'''simple docstring'''







import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 					__UpperCAmelCase (		nn.Linear	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = True  ,		_UpperCAmelCase = "constant"  ,		_UpperCAmelCase = 0.0  ,		_UpperCAmelCase = "topK"  ,		):
             super(_UpperCAmelCase  ,		self						).__init__(in_features=_UpperCAmelCase  ,		out_features=_UpperCAmelCase  ,		bias=_UpperCAmelCase						)
             assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
             UpperCAmelCase__	:   Union[str, Any]												=	pruning_method

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




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