'''simple docstring'''




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def      _UpperCAmelCase    (							_UpperCamelCase    :	List[str]   )	->      Any:
    A_     =   args.serialization_dir
    A_     =   args.pruning_method
    A_     =   args.threshold

    A_     =   torch.load(os.path.join(_UpperCamelCase,   '''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(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":
                A_     =   TopKBinarizer.apply(_UpperCamelCase,   _UpperCamelCase   ).sum().item()
            elif pruning_method == "sigmoied_threshold":
                A_     =   ThresholdBinarizer.apply(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   ).sum().item()
            elif pruning_method == "l0":
                A_   ,A_     =   -0.1, 1.1
                A_     =   torch.sigmoid(_UpperCamelCase   )
                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(60,   ''' '''   ),   str(round(1_00 * mask_ones / param.numel(),   3   )   ).ljust(20,   ''' '''   ),   str(_UpperCamelCase   )   )
        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__":
  __snake_case   :      List[str]						    =  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',
  )

  __snake_case   :      Optional[Any]						    =  parser.parse_args()

  main(args)







'''simple docstring'''




import math





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      float:
    A_     =   math.log(2,   10   ) * number
    A_     =   round((log_number - int(_UpperCamelCase   )),   15   )

    return difference





def      _UpperCAmelCase    (							_UpperCamelCase    :	int = 67_89_10   )	->      int:
    A_     =   90
    A_     =   0

    A_     =   math.log(1.2_3,   10   )
    A_     =   math.log(1.2_4,   10   )
    A_     =   0

    while position < number:
        A_     =   log_difference(_UpperCamelCase   )

        if difference >= upper_limit:
            power_iterator += 93

        elif difference < lower_limit:
            power_iterator += 1_96

        else:
            A_     =   power_iterator
            power_iterator += 1_96
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")






