





import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property



class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  @cached_property
  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''						)





  @cached_property
  def 			__A      (  self						)		->   int:





       '''simple docstring'''







       return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''						)





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.tokenizer_ab
       __magic_name__				=       ''' I am a small frog.'''
       __magic_name__				=       tok([src_text]	,  padding=A	,  truncation=A						)['''input_ids''']
       __magic_name__				=       tok.batch_decode(A	,  skip_special_tokens=A	,  clean_up_tokenization_spaces=A						)[0]
       assert src_text == decoded





  def 			__A      (  self						)		->   str:





       '''simple docstring'''







       __magic_name__				=       self.rust_tokenizer_ab
       __magic_name__				=       ''' I am a small frog.'''
       __magic_name__				=       tok([src_text]	,  padding=A	,  truncation=A						)['''input_ids''']
       __magic_name__				=       tok.batch_decode(A	,  skip_special_tokens=A	,  clean_up_tokenization_spaces=A						)[0]
       assert src_text == decoded





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





       '''simple docstring'''







       assert self.tokenizer_ab.add_prefix_space
       assert self.tokenizer_ab([''' Sam''', '''Sam''']						).input_ids == [[55_02, 2], [55_02, 2]]







  def 			__A      (  self						)		->   Union[str, Any]:





       '''simple docstring'''







       assert self.rust_tokenizer_ab.add_prefix_space
       assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']						).input_ids == [[55_02, 2], [55_02, 2]]






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)