




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Dict:





  """simple docstring"""





  _lowercase: List[str]						     =    args.serialization_dir
  _lowercase: Dict						     =    args.pruning_method
  _lowercase: Optional[Any]						     =    args.threshold

  _lowercase: List[Any]						     =    torch.load(os.path.join(UpperCamelCase					,     'pytorch_model.bin'							)					,     map_location='cpu'							)

  _lowercase: Optional[int]						     =    0  # Number of remaining (not pruned) params in the encoder
  _lowercase: Any						     =    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":
        _lowercase: int						     =    TopKBinarizer.apply(UpperCamelCase					,     UpperCamelCase							).sum().item()
      elif pruning_method == "sigmoied_threshold":
        _lowercase: Union[str, Any]						     =    ThresholdBinarizer.apply(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							).sum().item()
      elif pruning_method == "l0":
        _lowercase     ,					_lowercase: List[str]						     =    -0.1, 1.1
        _lowercase: Optional[Any]						     =    torch.sigmoid(UpperCamelCase							)
        _lowercase: Union[str, Any]						     =    s * (r - l) + l
        _lowercase: List[Any]						     =    s_bar.clamp(min=0.0					,     max=1.0							)
        _lowercase: Union[str, Any]						     =    (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(UpperCamelCase							)							)
    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__						:       Union[str, Any]							=    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__						:       Tuple							=    parser.parse_args()

   main(args)






import math


def 		_lowerCamelCase				(					UpperCamelCase							)			->		int:





  """simple docstring"""





  return sum(int(UpperCamelCase							) for digit in str(UpperCamelCase							)							)


def 		_lowerCamelCase				(					UpperCamelCase = 30							)			->		int:





  """simple docstring"""





  _lowercase: int						     =    []
  for digit in range(2					,     100							):
    for power in range(2					,     100							):
      _lowercase: int						     =    int(math.pow(UpperCamelCase					,     UpperCamelCase							)							)
      if digit == digit_sum(UpperCamelCase							):
        digit_to_powers.append(UpperCamelCase							)

  digit_to_powers.sort()
  return digit_to_powers[n - 1]


if __name__ == "__main__":
   print(solution())
