


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'''
def 	_A  (       A							)  ->				list:

	if len(A							) <= 1:
		return [tuple(A							)]

	lowercase						:		Dict   					=		[]

	def generate(A							,A							):
		lowercase						:		List[Any]   					=		[0] * n
		res.append(tuple(A							)							)

		lowercase						:		Tuple   					=		0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					lowercase					,		lowercase						:		List[Any]   					=		arr[i], arr[0]
				else:
					lowercase					,		lowercase						:		Optional[Any]   					=		arr[i], arr[c[i]]
				res.append(tuple(A							)							)
				c[i] += 1
				lowercase						:		Optional[Any]   					=		0
			else:
				lowercase						:		Any   					=		0
				i += 1

	generate(len(A							)							,A							)
	return res


if __name__ == "__main__":
					lowerCAmelCase   : Any             =				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase   : List[str]             =				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))


