

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def  snake_case_(_UpperCamelCase	)				->      List[Any]:



  """simple docstring"""






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

  _snake_case		=				torch.load(os.path.join(_UpperCamelCase					,			'''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(_UpperCamelCase					,			_UpperCamelCase	).sum().item()
      elif pruning_method == "sigmoied_threshold":
        _snake_case		=				ThresholdBinarizer.apply(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	).sum().item()
      elif pruning_method == "l0":
        _snake_case,	_snake_case		=				-0.1, 1.1
        _snake_case		=				torch.sigmoid(_UpperCamelCase	)
        _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(_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__":
 __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  snake_case_(_UpperCamelCase	)				->      int:



  """simple docstring"""






  _snake_case		=				len(_UpperCamelCase	)
  _snake_case		=				[0] * length
  _snake_case		=				[[False for i in range(_UpperCamelCase	)] for j in range(_UpperCamelCase	)]
  for i, c in enumerate(_UpperCamelCase	):
    _snake_case		=				i
    for j in range(i + 1	):
      if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
        _snake_case		=				True
        _snake_case		=				min(_UpperCamelCase					,			0 if j == 0 else (cut[j - 1] + 1)	)
    _snake_case		=				mincut
  return cut[length - 1]


if __name__ == "__main__":
 __A     						=      input('''Enter the string: ''').strip()
 __A     						=      find_minimum_partitions(s)
 print(f'''Minimum number of partitions required for the \'{s}\' is {ans}''')

