


import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= args.pruning_method
		lowercase__				= args.threshold

		lowercase__				= args.model_name_or_path.rstrip('''/'''	)
		lowercase__				= args.target_model_path

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

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

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

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

		torch.save(SCREAMING_SNAKE_CASE		,							os.path.join(SCREAMING_SNAKE_CASE		,							'''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)










'''simple docstring'''
UpperCamelCase__			=      {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 1000000,
    "gigajoule": 1000000000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 3600000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 4186800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1055.05585,
    "footpound": 1.35_58_18,
}




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
      lowercase_						:     Union[str, Any]							     =  (
          F"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
          F"""Valid values are: {", ".join(_UpperCamelCase				)}"""
      )
      raise ValueError(_UpperCamelCase				)
   return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




