code: 
  
  
  import argparse
  import os
  
  import torch
  from emmental.modules import ThresholdBinarizer, TopKBinarizer
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   List[Any]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = args.serialization_dir
       __UpperCamelCase   = args.pruning_method
       __UpperCamelCase   = args.threshold
  
       __UpperCamelCase   = torch.load(os.path.join(lowercase_				,     'pytorch_model.bin'   )				,     map_location='cpu'   )
  
       __UpperCamelCase   = 0  # Number of remaining (not pruned) params in the encoder
       __UpperCamelCase   = 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":
                      __UpperCamelCase   = TopKBinarizer.apply(lowercase_				,     lowercase_   ).sum().item()
                 elif pruning_method == "sigmoied_threshold":
                      __UpperCamelCase   = ThresholdBinarizer.apply(lowercase_				,     lowercase_				,     lowercase_   ).sum().item()
                 elif pruning_method == "l0":
                      __UpperCamelCase,						__UpperCamelCase   = -0.1, 1.1
                      __UpperCamelCase   = torch.sigmoid(lowercase_   )
                      __UpperCamelCase   = s * (r - l) + l
                      __UpperCamelCase   = s_bar.clamp(min=0.0				,     max=1.0   )
                      __UpperCamelCase   = (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(lowercase_   )   )
            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__":
      __snake_case             =     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''',
      )
  
      __snake_case             =     parser.parse_args()
  
      main(args)
  
  
  
style_context: 
  
  
  from math import cos, sin
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = float(_lowercase   )
       return [[scaling_factor * int(x == y   ) for x in range(2   )] for y in range(2   )]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       return [[c, -s], [s, c]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[c * c, cs], [cs, s * s]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]
  
  
  print(f"""    {scaling(5) = }""")
  print(f"""  {rotation(4_5) = }""")
  print(f"""{projection(4_5) = }""")
  
  
  print(f"""{reflection(4_5) = }""")
  
  
  