


import argparse
import os
import shutil

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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= args.pruning_method
		lowerCAmelCase__       :      Union[str, Any]   					= args.threshold

		lowerCAmelCase__       :      Tuple   					= args.model_name_or_path.rstrip('''/'''	)
		lowerCAmelCase__       :      Optional[int]   					= args.target_model_path

		print(f'Load fine-pruned model from {model_name_or_path}'	)
		lowerCAmelCase__       :      Dict   					= torch.load(os.path.join(_a     ,       '''pytorch_model.bin'''	)	)
		lowerCAmelCase__       :      List[Any]   					= {}

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

		if target_model_path is None:
				lowerCAmelCase__       :      Union[str, 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__":
		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)








def 					lowerCamelCase_   (	_a = 4_000_000	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= []
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[Any]   					= 0, 1
		while b <= n:
				if b % 2 == 0:
						even_fibs.append(_a	)
				lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= b, a + b
		return sum(_a	)


if __name__ == "__main__":
		print(f'''{solution() = }''')




