




import torch
from torch import autograd


class        UpperCAmelCase__				(							autograd.Function				):



     '''simple docstring'''
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =inputs.numel()
            a			   =int(0.0_05 * nb_elems	) + 1
            if sigmoid:
                   a			   =(torch.sigmoid(_lowerCAmelCase	) > threshold).type(inputs.type()	)
            else:
                   a			   =(inputs > threshold).type(inputs.type()	)
            if mask.sum() < nb_min:
                   # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
                   a			   =inputs.flatten().kthvalue(max(nb_elems - nb_min ,    1	)	).values
                   a			   =(inputs > k_threshold).type(inputs.type()	)
            return mask






     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase ,    _lowerCAmelCase	):
            return gradOutput, None, None



class        UpperCAmelCase__				(							autograd.Function				):



     '''simple docstring'''
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =inputs.clone()
            a   ,      a			   =inputs.flatten().sort(descending=_lowerCAmelCase	)
            a			   =int(threshold * inputs.numel()	)

            # flat_out and mask access the same memory.
            a			   =mask.flatten()
            a			   =0
            a			   =1
            return mask






     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase ,    _lowerCAmelCase	):
            return gradOutput, None



class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =inputs.clone()
            a   ,      a			   =inputs.abs().flatten().sort(descending=_lowerCAmelCase	)
            a			   =int(threshold * inputs.numel()	)

            # flat_out and mask access the same memory.
            a			   =mask.flatten()
            a			   =0
            a			   =1
            return mask






from __future__ import annotations

from typing import Any


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):  # noqa: A002
            a			   =item
            a			   =next



class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self	):
            a			   =None
            a			   =0
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            a			   =Node(_lowerCAmelCase ,    self.head	)
            self.size += 1
     def        lowerCAmelCase__  (				self	):
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   a			   =self.head.item
                   a			   =self.head.next
                   self.size -= 1
                   return item
     def        lowerCAmelCase__  (				self	):
            return self.head is None
     def __str__(				self	):
            if self.is_empty():
                   return ""
            else:
                   a			   =self.head
                   a			   =""""""
                   a			   =[]
                   while iterate:
                          item_list.append(str(iterate.item	)	)
                          a			   =iterate.next

                   a			   =""" --> """.join(_lowerCAmelCase	)

                   return item_str






     def __len__(				self	):
            return self.size
