






'''simple docstring'''


import torch
from torch import autograd
class  _SCREAMING_SNAKE_CASE				(		autograd.Function						):


			'''simple docstring'''





			@staticmethod
			def 					A   ( lowercase				:    int			,   lowercase				:    torch.tensor			,   lowercase				:    float			,   lowercase				:    bool      )				->     Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__       =			inputs.numel()
									UpperCamelCase__       =			int(0.0_0_5 * nb_elems      ) + 1
									if sigmoid:
															UpperCamelCase__       =			(torch.sigmoid(lowercase      ) > threshold).type(inputs.type()      )
									else:
															UpperCamelCase__       =			(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__       =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,   1      )      ).values
															UpperCamelCase__       =			(inputs > k_threshold).type(inputs.type()      )
									return mask







			@staticmethod
			def 					A   ( lowercase				:    List[str]			,   lowercase				:    Union[str, Any]      )				->     str:

									'''simple docstring'''







									return gradOutput, None, None
class  _SCREAMING_SNAKE_CASE				(		autograd.Function						):


			'''simple docstring'''





			@staticmethod
			def 					A   ( lowercase				:    Any			,   lowercase				:    torch.tensor			,   lowercase				:    float      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			inputs.clone()
									UpperCamelCase__     , UpperCamelCase__       =			inputs.flatten().sort(descending=lowercase      )
									UpperCamelCase__       =			int(threshold * inputs.numel()      )

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







			@staticmethod
			def 					A   ( lowercase				:    Dict			,   lowercase				:    Optional[int]      )				->     Any:

									'''simple docstring'''







									return gradOutput, None
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			@staticmethod
			def 					A   ( lowercase				:    torch.tensor			,   lowercase				:    float      )				->     int:

									'''simple docstring'''







									UpperCamelCase__       =			inputs.clone()
									UpperCamelCase__     , UpperCamelCase__       =			inputs.abs().flatten().sort(descending=lowercase      )
									UpperCamelCase__       =			int(threshold * inputs.numel()      )

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








'''simple docstring'''


lowerCamelCase_					:			List[Any]     						=				{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

lowerCamelCase_					:			Tuple     						=				{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}







def 						__magic_name__(			_A    ,   _A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			True
						UpperCamelCase__       =			[]

						for neighbour in graph[vert]:
												if not visited[neighbour]:
																		connected_verts += dfs(_A    ,   _A    ,   _A       )

						return [vert, *connected_verts]







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			len(_A       )
						UpperCamelCase__       =			graph_size * [False]
						UpperCamelCase__       =			[]

						for i in range(_A       ):
												if not visited[i]:
																		UpperCamelCase__       =			dfs(_A    ,   _A    ,   _A       )
																		components_list.append(_A       )

						return components_list


if __name__ == "__main__":
					import doctest

					doctest.testmod()
