


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)






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) = }""")


