





class 					__magic_name__  :





     """simple docstring"""



     def __init__(							self       :str			,      snake_case       :int  ):





        '''simple docstring'''




        A_					:				Dict          =			data
        A_					:				int
        A_					:				Node




def      __snake_case						(					_lowerCAmelCase : Node	) ->						None:
   A_					:				Dict          =			0
   A_					:				str          =			x

def      __snake_case						(					_lowerCAmelCase : Node				,				_lowerCAmelCase : Node	) ->						None:
   A_      ,						A_					:				Any          =			find_set(_lowerCAmelCase	), find_set(_lowerCAmelCase	)
   if x == y:
      return

   elif x.rank > y.rank:
      A_					:				int          =			x
   else:
      A_					:				Optional[Any]          =			y
      if x.rank == y.rank:
         y.rank += 1

def      __snake_case						(					_lowerCAmelCase : Node	) ->						Node:
   if x != x.parent:
      A_					:				Dict          =			find_set(x.parent	)
   return x.parent

def      __snake_case						(					_lowerCAmelCase : Node	) ->						set:
   A_					:				int          =			({0, 1, 2}, {3, 4, 5})
   for s in sets:
      if node.data in s:
         return s
   A_					:				List[str]          =			f"{node.data} is not in {sets}"
   raise ValueError(_lowerCAmelCase	)

def      __snake_case						(					) ->						None:
   A_					:				str          =			[Node(_lowerCAmelCase	) for i in range(6	)]
   for v in vertex:
      make_set(_lowerCAmelCase	)

   union_set(vertex[0]				,				vertex[1]	)
   union_set(vertex[1]				,				vertex[2]	)
   union_set(vertex[3]				,				vertex[4]	)
   union_set(vertex[3]				,				vertex[5]	)

   for nodea in vertex:
      for nodea in vertex:
         if find_python_set(_lowerCAmelCase	).isdisjoint(find_python_set(_lowerCAmelCase	)	):
            assert find_set(_lowerCAmelCase	) != find_set(_lowerCAmelCase	)
         else:
            assert find_set(_lowerCAmelCase	) == find_set(_lowerCAmelCase	)


if __name__ == "__main__":
 test_disjoint_set()







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[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
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

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





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

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