



import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def        __a     (    lowerCAmelCase_   :					int				)      -> Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=   args.serialization_dir
						UpperCAmelCase_=   args.pruning_method
						UpperCAmelCase_=   args.threshold

						UpperCAmelCase_=   torch.load(os.path.join(lowerCAmelCase_			,"""pytorch_model.bin"""				)			,map_location="""cpu"""				)

						UpperCAmelCase_=   0  # Number of remaining (not pruned) params in the encoder
						UpperCAmelCase_=   0  # Number of params in the encoder

						print("""name""".ljust(60			,""" """				)			,"""Remaining Weights %"""			,"""Remaining Weight"""				)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								UpperCAmelCase_=   TopKBinarizer.apply(lowerCAmelCase_			,lowerCAmelCase_				).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								UpperCAmelCase_=   ThresholdBinarizer.apply(lowerCAmelCase_			,lowerCAmelCase_			,lowerCAmelCase_				).sum().item()
																		elif pruning_method == "l0":
																								UpperCAmelCase_,   UpperCAmelCase_=   -0.1, 1.1
																								UpperCAmelCase_=   torch.sigmoid(lowerCAmelCase_				)
																								UpperCAmelCase_=   s * (r - l) + l
																								UpperCAmelCase_=   s_bar.clamp(min=0.0			,max=1.0				)
																								UpperCAmelCase_=   (mask > 0.0).sum().item()
																		else:
																								raise ValueError("""Unknown pruning method"""				)
																		remaining_count += mask_ones
																		print(name.ljust(60			,""" """				)			,str(round(1_00 * mask_ones / param.numel()			,3				)				).ljust(20			,""" """				)			,str(lowerCAmelCase_				)				)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""""""				)
						print("""Remaining Weights (global) %: """			,1_00 * remaining_count / encoder_count				)


if __name__ == "__main__":
	__A				    =						argparse.ArgumentParser()

	parser.add_argument(
	    '''--pruning_method''',
	    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
	    type=str,
	    required=True,
	    help=(
	        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
	        ''' pruning)'''
	    ),
	)
	parser.add_argument(
	    '''--threshold''',
	    type=float,
	    required=False,
	    help=(
	        '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
	        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
	        '''Not needed for `l0`'''
	    ),
	)
	parser.add_argument(
	    '''--serialization_dir''',
	    type=str,
	    required=True,
	    help='''Folder containing the model that was previously fine-pruned''',
	)

	__A				    =						parser.parse_args()

	main(args)









def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



						if number != int(lowerCAmelCase_				):
												raise ValueError("""factorial() only accepts integral values"""				)
						if number < 0:
												raise ValueError("""factorial() not defined for negative values"""				)
						UpperCAmelCase_=   1
						for i in range(1			,number + 1				):
												value *= i
						return value



def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""factorial() only accepts integral values"""				)
						if n < 0:
												raise ValueError("""factorial() not defined for negative values"""				)
						return 1 if n == 0 or n == 1 else n * factorial(n - 1				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__A				    =						int(input('''Enter a positive integer: ''').strip() or 0)
	print(f'factorial{n} is {factorial(n)}')




