




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 							A__   (snake_case      :		str							)    ->	Tuple:
     __UpperCamelCase				:  List[str]								=			args.serialization_dir
     __UpperCamelCase				:  Dict								=			args.pruning_method
     __UpperCamelCase				:  Any								=			args.threshold

     __UpperCamelCase				:  List[str]								=			torch.load(os.path.join(snake_case		,  """pytorch_model.bin"""							)		,  map_location="""cpu"""							)

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












import os





def 							A__   ()    ->	int:
					__UpperCamelCase				:  int								=			os.path.dirname(os.path.realpath(snake_case							)							)
					__UpperCamelCase				:  List[str]								=			os.path.join(snake_case		,  """triangle.txt"""							)

					with open(snake_case							) as in_file:
										__UpperCamelCase				:  Optional[Any]								=			[[int(snake_case							) for i in line.split()] for line in in_file]

					while len(snake_case							) != 1:
										__UpperCamelCase				:  Union[str, Any]								=			triangle.pop()
										__UpperCamelCase				:  List[str]								=			triangle[-1]
										for j in range(len(snake_case							) - 1							):
															curr_row[j] += max(last_row[j]		,  last_row[j + 1]							)
					return triangle[0][0]


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






