
"""simple docstring"""
import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def 				a__   (   a   :				Any		):

		"""simple docstring"""







		_snake_case     :						str						=   args.pruning_method
		_snake_case     :						Any						=   args.threshold

		_snake_case     :						Union[str, Any]						=   args.model_name_or_path.rstrip("/"		)
		_snake_case     :						Dict						=   args.target_model_path

		print(f'Load fine-pruned model from {model_name_or_path}'		)
		_snake_case     :						Dict						=   torch.load(os.path.join(a       ,     "pytorch_model.bin"		)		)
		_snake_case     :						Union[str, Any]						=   {}

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

		if target_model_path is None:
				_snake_case     :						Optional[Any]						=   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__":
				_a      :			Optional[int]					=							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""",
				)

				_a      :			Optional[int]					=							parser.parse_args()

				main(args)



"""simple docstring"""
def 				a__   (   a   :				list       ,     a   :				int       ,     a   :				int = 0       ,     a   :				int = 0		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   right or len(a		) - 1
  if left > right:
    return -1
  elif list_data[left] == key:
    return left
  elif list_data[right] == key:
    return right
  else:
    return search(a       ,     a       ,     left + 1       ,     right - 1		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

