
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple    ):






  """simple docstring"""




  _lowerCAmelCase						=		args.serialization_dir
  _lowerCAmelCase						=		args.pruning_method
  _lowerCAmelCase						=		args.threshold

  _lowerCAmelCase						=		torch.load(os.path.join(_a		,				'pytorch_model.bin'    )		,				map_location='cpu'    )

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

       main(args)


'''simple docstring'''





from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap



@total_ordering
class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			int     ) ->							None:
        snake_case_    							=				word
        snake_case_    							=				count

  def __eq__(self	:			List[Any]						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count == other.count


  def __lt__(self	:			Tuple						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count < other.count

def 	snake_case_							(				_a  :				list[str]			,       _a  :				int      ):
      snake_case_    							=				Heap()
      snake_case_    							=				Counter(_a      )
      heap.build_max_heap(
          [WordCount(_a			,       _a      ) for word, count in count_by_word.items()]      )
      return [heap.extract_max().word for _ in range(min(_a			,       len(_a      )      )      )]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

