




from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowercase_      :							Dict    							=					TypeVar('T', bound=bool)






class        __lowerCAmelCase					(			Generic[T]					):







			def __init__(			self       :     Dict       ,       snake_case__       :     List[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	value
									_UpperCAmelCase						=	None
									_UpperCAmelCase						=	None







			@property
			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									return self._value







			@staticmethod
			def UpperCamelCase     (			snake_case__       :     Union[str, Any]       ,       snake_case__       :     int ):




									"""simple docstring"""


									if not roota:
															return roota

									if not roota:
															return roota

									if roota.value > roota.value:
															_UpperCAmelCase						=	roota, roota

									if random.choice([True, False] ):
															_UpperCAmelCase						=	roota.right, roota.left

									_UpperCAmelCase						=	RandomizedHeapNode.merge(roota.left       ,       __snake_case )

									return roota






class        __lowerCAmelCase					(			Generic[T]					):







			def __init__(			self       :     List[str]       ,       snake_case__       :     Tuple = () ):




									"""simple docstring"""


									_UpperCAmelCase						=	None

									if data:
															for item in data:
																					self.insert(__snake_case )







			def UpperCamelCase     (			self       :     int       ,       snake_case__       :     List[Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	RandomizedHeapNode.merge(self._root       ,       RandomizedHeapNode(__snake_case ) )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase						=	self.top()

									if self._root is None:
															return None

									_UpperCAmelCase						=	RandomizedHeapNode.merge(self._root.left       ,       self._root.right )

									return result







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									if not self._root:
															raise IndexError("Can't get top element for the empty heap." )
									return self._root.value







			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	None







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									_UpperCAmelCase						=	[]
									while self:
															result.append(self.pop() )

									return result







			def __bool__(			self       :     Optional[Any] ):




									"""simple docstring"""


									return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()






import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[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
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

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