






"""simple docstring"""





import argparse
import os
import shutil

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



def   a    (     __UpperCAmelCase      :    List[Any]  )    ->							str:
						__magic_name__:	Union[str, Any]							=       args.pruning_method
						__magic_name__:	Tuple							=       args.threshold

						__magic_name__:	Dict							=       args.model_name_or_path.rstrip("""/"""  )
						__magic_name__:	str							=       args.target_model_path

						print(f'Load fine-pruned model from {model_name_or_path}'  )
						__magic_name__:	Optional[Any]							=       torch.load(os.path.join(__UpperCAmelCase      ,   """pytorch_model.bin"""  )  )
						__magic_name__:	int							=       {}

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

						if target_model_path is None:
												__magic_name__:	List[str]							=       os.path.join(
												    os.path.dirname(__UpperCAmelCase  )      ,   f'bertarized_{os.path.basename(__UpperCAmelCase  )}'  )

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

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


if __name__ == "__main__":
							__lowerCamelCase							=  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',
							)

							__lowerCamelCase							=  parser.parse_args()

							main(args)






from __future__ import annotations







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None   ):
      '''simple docstring'''







      if start is None:
            A_							       =		0

      if end is None:
            A_							       =		len(SCREAMING_SNAKE_CASE   ) - 1

      if start >= end:
            return

      A_							       =		(start + end) // 2

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
      slowsort(SCREAMING_SNAKE_CASE		,				mid + 1		,				SCREAMING_SNAKE_CASE   )

      if sequence[end] < sequence[mid]:
            A_       ,A_							       =		sequence[mid], sequence[end]

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				end - 1   )


if __name__ == "__main__":
      from doctest import testmod

      testmod()





