





'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[Any]         =  args.serialization_dir
      _snake_case		:						Union[str, Any]         =  args.pruning_method
      _snake_case		:						Optional[int]         =  args.threshold

      _snake_case		:						List[Any]         =  torch.load(os.path.join(lowerCAmelCase_      ,   '''pytorch_model.bin'''						)      ,   map_location='''cpu'''						)

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

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

   main(args)









'''simple docstring'''

from __future__ import annotations




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      if counter == 0 or capacity == 0:
            return 0

      # If weight of the nth item is more than Knapsack of capacity,
      #   then this item cannot be included in the optimal solution,
      # else return the maximum of two cases:
      #   (1) nth item included
      #   (2) not included
      if weights[counter - 1] > capacity:
            return knapsack(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
      else:
            _snake_case		:						List[str]         =  capacity - weights[counter - 1]
            _snake_case		:						Tuple         =  values[counter - 1] + knapsack(
                lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            _snake_case		:						Any         =  knapsack(lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_      ,   counter - 1						)
            return max(lowerCAmelCase_      ,   lowerCAmelCase_						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


