



'''simple docstring'''


def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







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






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if not isinstance(lowercase__							,						lowercase__							):
          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()

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







'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

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

     print("name".ljust(6_0							,						" "							)							,						"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":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
  lowercase											=      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''',
  )

  lowercase											=      parser.parse_args()

  main(args)


