






"""simple docstring"""


import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




def     snake_case						(A_   :Union[str, Any]   ):







  '''simple docstring'''

  a  :    Optional[int]       =     args.pruning_method
  a  :    List[str]       =     args.threshold

  a  :    int       =     args.model_name_or_path.rstrip('/'   )
  a  :    Dict       =     args.target_model_path

  print(f'''Load fine-pruned model from {model_name_or_path}'''   )
  a  :    List[str]       =     torch.load(os.path.join(A_			,    'pytorch_model.bin'   )   )
  a  :    Union[str, Any]       =     {}

  for name, tensor in model.items():
    if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
      a  :    Optional[Any]       =     tensor
      print(f'''Copied layer {name}'''   )
    elif "classifier" in name or "qa_output" in name:
      a  :    Dict       =     tensor
      print(f'''Copied layer {name}'''   )
    elif "bias" in name:
      a  :    Any       =     tensor
      print(f'''Copied layer {name}'''   )
    else:
      if pruning_method == "magnitude":
        a  :    Tuple       =     MagnitudeBinarizer.apply(inputs=A_			,    threshold=A_   )
        a  :    Tuple       =     tensor * mask
        print(f'''Pruned layer {name}'''   )
      elif pruning_method == "topK":
        if "mask_scores" in name:
          continue
        a  :    Any       =     name[:-6]
        a  :    List[Any]       =     model[f'''{prefix_}mask_scores''']
        a  :    List[Any]       =     TopKBinarizer.apply(A_			,    A_   )
        a  :    Dict       =     tensor * mask
        print(f'''Pruned layer {name}'''   )
      elif pruning_method == "sigmoied_threshold":
        if "mask_scores" in name:
          continue
        a  :    Any       =     name[:-6]
        a  :    str       =     model[f'''{prefix_}mask_scores''']
        a  :    str       =     ThresholdBinarizer.apply(A_			,    A_			,    A_   )
        a  :    Union[str, Any]       =     tensor * mask
        print(f'''Pruned layer {name}'''   )
      elif pruning_method == "l0":
        if "mask_scores" in name:
          continue
        a  :    Any       =     name[:-6]
        a  :    Dict       =     model[f'''{prefix_}mask_scores''']
        a,  a  :    Union[str, Any]       =     -0.1, 1.1
        a  :    Optional[int]       =     torch.sigmoid(A_   )
        a  :    Optional[Any]       =     s * (r - l) + l
        a  :    Optional[int]       =     s_bar.clamp(min=0.0			,    max=1.0   )
        a  :    Optional[Any]       =     tensor * mask
        print(f'''Pruned layer {name}'''   )
      else:
        raise ValueError('Unknown pruning method'   )

  if target_model_path is None:
    a  :    Optional[Any]       =     os.path.join(
        os.path.dirname(A_   )			,    f'''bertarized_{os.path.basename(A_   )}'''   )

  if not os.path.isdir(A_   ):
    shutil.copytree(A_			,    A_   )
    print(f'''\nCreated folder {target_model_path}'''   )

  torch.save(A_			,    os.path.join(A_			,    'pytorch_model.bin'   )   )
  print('\nPruned model saved! See you later!'   )


if __name__ == "__main__":
   _UpperCamelCase       :					List[Any]									=		argparse.ArgumentParser()

   parser.add_argument(
       '--pruning_method',
       choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
       type=str,
       required=True,
       help=(
           'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
           ' sigmoied_threshold = Soft movement pruning)'
       ),
   )
   parser.add_argument(
       '--threshold',
       type=float,
       required=False,
       help=(
           'For `magnitude` and `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(
       '--model_name_or_path',
       type=str,
       required=True,
       help='Folder containing the model that was previously fine-pruned',
   )
   parser.add_argument(
       '--target_model_path',
       default=None,
       type=str,
       required=False,
       help='Folder containing the model that was previously fine-pruned',
   )

   _UpperCamelCase       :					Any									=		parser.parse_args()

   main(args)












"""simple docstring"""


def     snake_case						(A_   :int   ):







  '''simple docstring'''

  if upper_limit < 0:
    raise ValueError('Limit for the Catalan sequence must be ≥ 0'   )

  a  :    Any       =     [0] * (upper_limit + 1)

  # Base case: C(0) = C(1) = 1
  a  :    Optional[int]       =     1
  if upper_limit > 0:
    a  :    List[str]       =     1

  # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
  for i in range(2			,    upper_limit + 1   ):
    for j in range(A_   ):
      catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

  return catalan_list


if __name__ == "__main__":
   print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
   print('\n*** Enter -1 at any time to quit ***')
   print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
   try:
      while True:
         _UpperCamelCase       :					Union[str, Any]									=		int(input().strip())
         if N < 0:
            print('\n********* Goodbye!! ************')
            break
         else:
            print(f'''The Catalan numbers from 0 through {N} are:''')
            print(catalan_numbers(N))
            print('Try another upper limit for the sequence: ', end='')
   except (NameError, ValueError):
      print('\n********* Invalid input, goodbye! ************\n')

   import doctest

   doctest.testmod()




