



import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 						snake_case__							(       __lowercase							)		->		Any:







						"""simple docstring"""

						A__						:				Dict         =			args.serialization_dir
						A__						:				str         =			args.pruning_method
						A__						:				str         =			args.threshold

						A__						:				str         =			torch.load(os.path.join(snake_case_				,  "pytorch_model.bin"							)				,  map_location="cpu"							)

						A__						:				Tuple         =			0  # Number of remaining (not pruned) params in the encoder
						A__						:				List[Any]         =			0  # Number of params in the encoder

						print("name".ljust(6_0				,  " "							)				,  "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__						:				Any         =			TopKBinarizer.apply(snake_case_				,  snake_case_							).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								A__						:				Optional[int]         =			ThresholdBinarizer.apply(snake_case_				,  snake_case_				,  snake_case_							).sum().item()
																		elif pruning_method == "l0":
																								A__     ,							A__						:				Optional[int]         =			-0.1, 1.1
																								A__						:				Tuple         =			torch.sigmoid(snake_case_							)
																								A__						:				str         =			s * (r - l) + l
																								A__						:				int         =			s_bar.clamp(min=0.0				,  max=1.0							)
																								A__						:				Optional[Any]         =			(mask > 0.0).sum().item()
																		else:
																								raise ValueError("Unknown pruning method"							)
																		remaining_count += mask_ones
																		print(name.ljust(6_0				,  " "							)				,  str(round(1_0_0 * mask_ones / param.numel()				,  3							)							).ljust(2_0				,  " "							)				,  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_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
		snake_case     :    Optional[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',
		)

		snake_case     :    List[Any]						=    parser.parse_args()

		main(args)






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     __magic_name__				=       args.serialization_dir
     __magic_name__				=       args.pruning_method
     __magic_name__				=       args.threshold

     __magic_name__				=       torch.load(os.path.join(snake_case_ ,     '''pytorch_model.bin'''						) ,     map_location='''cpu'''						)

     __magic_name__				=       0  # Number of remaining (not pruned) params in the encoder
     __magic_name__				=       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":
                    __magic_name__				=       TopKBinarizer.apply(snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __magic_name__				=       ThresholdBinarizer.apply(snake_case_ ,     snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "l0":
                    __magic_name__		,  __magic_name__				=       -0.1, 1.1
                    __magic_name__				=       torch.sigmoid(snake_case_						)
                    __magic_name__				=       s * (r - l) + l
                    __magic_name__				=       s_bar.clamp(min=0.0 ,     max=1.0						)
                    __magic_name__				=       (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(snake_case_						)						)
          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_    :				Optional[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',
    )

    a_    :				List[Any]					=			parser.parse_args()

    main(args)