





from __future__ import annotations

def      __snake_case						(					_lowerCAmelCase : list[list[int]]				,				_lowerCAmelCase : list[list[int]]	) ->						list[int]:
   assert len(_lowerCAmelCase	) == len(_lowerCAmelCase	)

   A_					:				Optional[int]          =			len(_lowerCAmelCase	)
   A_					:				Dict          =			list(range(_lowerCAmelCase	)	)
   A_					:				List[Any]          =			[-1] * n  # who the donor has donated to
   A_					:				Union[str, Any]          =			[-1] * n  # who the recipient has received from
   A_					:				Dict          =			[0] * n

   while unmatched_donors:
      A_					:				List[str]          =			unmatched_donors[0]
      A_					:				Dict          =			donor_pref[donor]
      A_					:				str          =			donor_preference[num_donations[donor]]
      num_donations[donor] += 1
      A_					:				Optional[Any]          =			recipient_pref[recipient]
      A_					:				Dict          =			rec_record[recipient]

      if prev_donor != -1:
         if rec_preference.index(_lowerCAmelCase	) > rec_preference.index(_lowerCAmelCase	):
            A_					:				Dict          =			donor
            A_					:				Union[str, Any]          =			recipient
            unmatched_donors.append(_lowerCAmelCase	)
            unmatched_donors.remove(_lowerCAmelCase	)
      else:
         A_					:				Dict          =			donor
         A_					:				Optional[int]          =			recipient
         unmatched_donors.remove(_lowerCAmelCase	)
   return donor_record







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
