





from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers



class  SCREAMING_SNAKE_CASE_  :


  """simple docstring"""





  def __init__(  self	,  A						)		->   None:





       '''simple docstring'''







       __magic_name__				=       vocab
       __magic_name__				=       normalizers.BertNormalizer(
           clean_text=A	,  handle_chinese_chars=A	,  strip_accents=A	,  lowercase=A	,  )
       try:
            import rjieba
       except ImportError:
            raise ImportError(
                '''You need to install rjieba to use RoFormerTokenizer. '''
                '''See https://pypi.org/project/rjieba/ for installation.'''						)
       __magic_name__				=       rjieba





  def 			__A      (  self	,  A	,  A						)		->   List[NormalizedString]:





       '''simple docstring'''







       __magic_name__				=       []

       # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
       for token, start, end in self.jieba.tokenize(str(A						)	,  hmm=A						):
            if token in self.vocab:
                 splits.append(normalized_string[start:end]						)
            else:
                 __magic_name__				=       self.normalizers.normalize_str(A						).split()
                 for token in token_list:
                      if token:
                           __magic_name__				=       start + len(A						)
                           splits.append(normalized_string[start:end]						)
                           __magic_name__				=       end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

       return splits







  def 			__A      (  self	,  A						)		->   List[str]:





       '''simple docstring'''







       pretok.split(self.jieba_split						)






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)