


from typing import Any







class      _lowercase					:


       """simple docstring"""




       def __init__(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    []
             lowerCamelCase__						:							int       =    0





       def __str__(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    """<""" + str(self.stack			)[1:-1] + """>"""
             return printed





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


             '''simple docstring'''



             self.stack.append(_lowerCAmelCase			)
             lowerCamelCase__						:							str       =    self.length + 1





       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             self.rotate(1			)
             lowerCamelCase__						:							List[str]       =    self.stack[self.length - 1]
             lowerCamelCase__						:							List[Any]       =    self.stack[:-1]
             self.rotate(self.length - 1			)
             lowerCamelCase__						:							Union[str, Any]       =    self.length - 1
             return dequeued





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


             '''simple docstring'''



             for _ in range(_lowerCAmelCase			):
                   lowerCamelCase__						:							List[str]       =    self.stack[0]
                   lowerCamelCase__						:							List[Any]       =    self.stack[1:]
                   self.put(_lowerCAmelCase			)
                   lowerCamelCase__						:							Tuple       =    self.length - 1





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


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    self.get()
             self.put(_lowerCAmelCase			)
             self.rotate(self.length - 1			)
             return front





       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             return self.length



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



