





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 				__UpperCamelCase  (		snake_case     )  ->						Optional[int]:


   '''simple docstring'''







   __A				 =       args.serialization_dir
   __A				 =       args.pruning_method
   __A				 =       args.threshold

   __A				 =       torch.load(os.path.join(snake_case    ,	'''pytorch_model.bin'''     )    ,	map_location='''cpu'''     )

   __A				 =       0  # Number of remaining (not pruned) params in the encoder
   __A				 =       0  # Number of params in the encoder

   print('''name'''.ljust(6_0    ,	''' '''     )    ,	'''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":
            __A				 =       TopKBinarizer.apply(snake_case    ,	snake_case     ).sum().item()
         elif pruning_method == "sigmoied_threshold":
            __A				 =       ThresholdBinarizer.apply(snake_case    ,	snake_case    ,	snake_case     ).sum().item()
         elif pruning_method == "l0":
            __A ,						__A				 =       -0.1, 1.1
            __A				 =       torch.sigmoid(snake_case     )
            __A				 =       s * (r - l) + l
            __A				 =       s_bar.clamp(min=0.0    ,	max=1.0     )
            __A				 =       (mask > 0.0).sum().item()
         else:
            raise ValueError('''Unknown pruning method'''     )
         remaining_count += mask_ones
         print(name.ljust(6_0    ,	''' '''     )    ,	str(round(1_0_0 * mask_ones / param.numel()    ,	3     )     ).ljust(2_0    ,	''' '''     )    ,	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) %: '''    ,	1_0_0 * remaining_count / encoder_count     )


if __name__ == "__main__":
    _UpperCamelCase   :							Tuple     =	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   :							Optional[Any]     =	parser.parse_args()

    main(args)








from __future__ import annotations
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						list:


   '''simple docstring'''







   __A				 =       [None] * (len(snake_case     ) + len(snake_case     ))

   __A				 =       0  # pointer to current index for left Half
   __A				 =       0  # pointer to current index for the right Half
   __A				 =       0  # pointer to current index for the sorted array Half

   while pointera < len(snake_case     ) and pointera < len(snake_case     ):
      if left_half[pointera] < right_half[pointera]:
         __A				 =       left_half[pointera]
         pointera += 1
         index += 1
      else:
         __A				 =       right_half[pointera]
         pointera += 1
         index += 1
   while pointera < len(snake_case     ):
      __A				 =       left_half[pointera]
      pointera += 1
      index += 1

   while pointera < len(snake_case     ):
      __A				 =       right_half[pointera]
      pointera += 1
      index += 1

   return sorted_array
def 				__UpperCamelCase  (		snake_case     )  ->						list:


   '''simple docstring'''







   if len(snake_case     ) <= 1:
      return array
   # the actual formula to calculate the middle element = left + (right - left) // 2
   # this avoids integer overflow in case of large N
   __A				 =       0 + (len(snake_case     ) - 0) // 2

   # Split the array into halves till the array length becomes equal to One
   # merge the arrays of single length returned by mergeSort function and
   # pass them into the merge arrays function which merges the array
   __A				 =       array[:middle]
   __A				 =       array[middle:]

   return merge(merge_sort(snake_case     )    ,	merge_sort(snake_case     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

