




'''simple docstring'''





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





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

     """simple docstring"""




     _snake_case							=      args.serialization_dir
     _snake_case							=      args.pruning_method
     _snake_case							=      args.threshold

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

     _snake_case							=      0  # Number of remaining (not pruned) params in the encoder
     _snake_case							=      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":
                    _snake_case							=      TopKBinarizer.apply(lowerCAmelCase_					,						lowerCAmelCase_    ).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    _snake_case							=      ThresholdBinarizer.apply(lowerCAmelCase_					,						lowerCAmelCase_					,						lowerCAmelCase_    ).sum().item()
               elif pruning_method == "l0":
                    _snake_case							,					_snake_case							=      -0.1, 1.1
                    _snake_case							=      torch.sigmoid(lowerCAmelCase_    )
                    _snake_case							=      s * (r - l) + l
                    _snake_case							=      s_bar.clamp(min=0.0					,						max=1.0    )
                    _snake_case							=      (mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"    )
               remaining_count += mask_ones
               print(name.ljust(60					,						" "    )					,						str(round(100 * 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) %: "					,						100 * remaining_count / encoder_count    )


if __name__ == "__main__":
   UpperCamelCase_						:			int  	=						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''',
   )

   UpperCamelCase_						:			Any  	=						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)}')




