





"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase       ,						_lowercase   ):
						assert len(_lowercase   ) == len(_lowercase   )

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

						while unmatched_donors:
												_UpperCamelCase			:					Tuple     =    unmatched_donors[0]
												_UpperCamelCase			:					Union[str, Any]     =    donor_pref[donor]
												_UpperCamelCase			:					List[str]     =    donor_preference[num_donations[donor]]
												num_donations[donor] += 1
												_UpperCamelCase			:					Dict     =    recipient_pref[recipient]
												_UpperCamelCase			:					int     =    rec_record[recipient]

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










"""simple docstring"""




import torch
from torch import autograd




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float, lowerCAmelCase__		:    bool							)	->      List[Any]:




										'''simple docstring'''



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






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[int]							)	->      Union[str, Any]:




										'''simple docstring'''



										return gradOutput, None, None




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    inputs.flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					Tuple     =    mask.flatten()
										_UpperCamelCase			:					int     =    0
										_UpperCamelCase			:					Tuple     =    1
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return gradOutput, None




class  _a						(      _lowerCAmelCase				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Optional[int]     =    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					List[Any]     =    int(threshold * inputs.numel()							)

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



