

import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer





def      snake_case_				(__A					:						Any					)      ->     str:
   __lowerCAmelCase    =						args.pruning_method
   __lowerCAmelCase    =						args.threshold

   __lowerCAmelCase    =						args.model_name_or_path.rstrip("""/"""					)
   __lowerCAmelCase    =						args.target_model_path

   print(f'''Load fine-pruned model from {model_name_or_path}'''					)
   __lowerCAmelCase    =						torch.load(os.path.join(__A ,       """pytorch_model.bin"""					)					)
   __lowerCAmelCase    =						{}

   for name, tensor in model.items():
      if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
         __lowerCAmelCase    =						tensor
         print(f'''Copied layer {name}'''					)
      elif "classifier" in name or "qa_output" in name:
         __lowerCAmelCase    =						tensor
         print(f'''Copied layer {name}'''					)
      elif "bias" in name:
         __lowerCAmelCase    =						tensor
         print(f'''Copied layer {name}'''					)
      else:
         if pruning_method == "magnitude":
            __lowerCAmelCase    =						MagnitudeBinarizer.apply(inputs=__A ,       threshold=__A					)
            __lowerCAmelCase    =						tensor * mask
            print(f'''Pruned layer {name}'''					)
         elif pruning_method == "topK":
            if "mask_scores" in name:
               continue
            __lowerCAmelCase    =						name[:-6]
            __lowerCAmelCase    =						model[f'''{prefix_}mask_scores''']
            __lowerCAmelCase    =						TopKBinarizer.apply(__A ,       __A					)
            __lowerCAmelCase    =						tensor * mask
            print(f'''Pruned layer {name}'''					)
         elif pruning_method == "sigmoied_threshold":
            if "mask_scores" in name:
               continue
            __lowerCAmelCase    =						name[:-6]
            __lowerCAmelCase    =						model[f'''{prefix_}mask_scores''']
            __lowerCAmelCase    =						ThresholdBinarizer.apply(__A ,       __A ,       __A					)
            __lowerCAmelCase    =						tensor * mask
            print(f'''Pruned layer {name}'''					)
         elif pruning_method == "l0":
            if "mask_scores" in name:
               continue
            __lowerCAmelCase    =						name[:-6]
            __lowerCAmelCase    =						model[f'''{prefix_}mask_scores''']
            __lowerCAmelCase    =						-0.1, 1.1
            __lowerCAmelCase    =						torch.sigmoid(__A					)
            __lowerCAmelCase    =						s * (r - l) + l
            __lowerCAmelCase    =						s_bar.clamp(min=0.0 ,       max=1.0					)
            __lowerCAmelCase    =						tensor * mask
            print(f'''Pruned layer {name}'''					)
         else:
            raise ValueError("""Unknown pruning method"""					)

   if target_model_path is None:
      __lowerCAmelCase    =						os.path.join(
          os.path.dirname(__A					) ,       f'''bertarized_{os.path.basename(__A					)}'''					)

   if not os.path.isdir(__A					):
      shutil.copytree(__A ,       __A					)
      print(f'''\nCreated folder {target_model_path}'''					)

   torch.save(__A ,       os.path.join(__A ,       """pytorch_model.bin"""					)					)
   print("""\nPruned model saved! See you later!"""					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()

   parser.add_argument(
       """--pruning_method""",
       choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
       type=str,
       required=True,
       help=(
           """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
           """ sigmoied_threshold = Soft movement pruning)"""
       ),
   )
   parser.add_argument(
       """--threshold""",
       type=float,
       required=False,
       help=(
           """For `magnitude` and `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(
       """--model_name_or_path""",
       type=str,
       required=True,
       help="""Folder containing the model that was previously fine-pruned""",
   )
   parser.add_argument(
       """--target_model_path""",
       default=None,
       type=str,
       required=False,
       help="""Folder containing the model that was previously fine-pruned""",
   )

   __UpperCAmelCase								=			parser.parse_args()

   main(args)





from __future__ import annotations





def      snake_case_				(__A					:						list[float] ,       __A					:						list[float]					)      ->     float:
   __lowerCAmelCase  :      Union[str, Any]    =						sorted(numsa + numsa					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						divmod(len(__A					) ,       2					)
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of first array: """).split()]
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')


