


"""simple docstring"""







import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def _A		(		lowercase						):







    """simple docstring"""
    a											=args.serialization_dir
    a											=args.pruning_method
    a											=args.threshold

    a											=torch.load(os.path.join(lowerCAmelCase_			,       '''pytorch_model.bin'''						)			,       map_location='''cpu'''						)

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

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

       main(args)






'''simple docstring'''

from __future__ import annotations




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[str]         =  sorted(lowerCAmelCase_						)
      _snake_case		:						Any         =  len(lowerCAmelCase_						)
      _snake_case		:						List[str]         =  length >> 1
      return (
          (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
          if length % 2 == 0
          else sorted_list[mid_index]
      )




def  _a     (				):





      """simple docstring"""



      import doctest

      doctest.testmod()


if __name__ == "__main__":
   main()


