


from __future__ import annotations

from collections.abc import Iterator
from typing import Any







class      _lowercase					:


       """simple docstring"""




       def __init__(  self				:     Union[str, Any]   , __lowerCamelCase				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    data
             lowerCamelCase__						:							Node | None       =    None





       def __str__(  self				:     Optional[int]			):


             '''simple docstring'''



             return f"{self.data}"







class      _lowercase					:


       """simple docstring"""




       def __init__(  self				:     Optional[int]			):


             '''simple docstring'''



             lowerCamelCase__						:							Node | None       =    None
             lowerCamelCase__						:							Node | None       =    None





       def __iter__(  self				:     str			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    self.front
             while node:
                   yield node.data
                   lowerCamelCase__						:							str       =    node.next





       def __len__(  self				:     int			):


             '''simple docstring'''



             return len(tuple(iter(self			)			)			)





       def __str__(  self				:     Tuple			):


             '''simple docstring'''



             return " <- ".join(str(_lowerCAmelCase			) for item in self			)





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             return len(self			) == 0





       def 		lowerCAmelCase	(  self				:     List[Any]   , __lowerCamelCase				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    Node(_lowerCAmelCase			)
             if self.is_empty():
                   lowerCamelCase__						:							Tuple       =    node
             else:
                   assert isinstance(self.rear   , _lowerCAmelCase			)
                   lowerCamelCase__						:							Dict       =    node
                   lowerCamelCase__						:							List[str]       =    node





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             if self.is_empty():
                   raise IndexError("dequeue from empty queue"			)
             assert isinstance(self.front   , _lowerCAmelCase			)
             lowerCamelCase__						:							str       =    self.front
             lowerCamelCase__						:							Dict       =    self.front.next
             if self.front is None:
                   lowerCamelCase__						:							List[Any]       =    None
             return node.data





       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    None


if __name__ == "__main__":
    from doctest import testmod

    testmod()



import torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				Any 			=    (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
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				Optional[int] 			=    mask.flatten()
           __snake_case       :				Dict 			=    0
           __snake_case       :				Optional[Any] 			=    1
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				List[Any] 			=    mask.flatten()
           __snake_case       :				int 			=    0
           __snake_case       :				Optional[int] 			=    1
           return mask



