

"""simple docstring"""




import argparse
import os
import shutil

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





def       lowercase__						(       snake_case_   :Tuple    ):
	__UpperCAmelCase      		= args.pruning_method
	__UpperCAmelCase      		= args.threshold

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

	print(F'''Load fine-pruned model from {model_name_or_path}'''    )
	__UpperCAmelCase      		= torch.load(os.path.join(snake_case_			,		'''pytorch_model.bin'''    )    )
	__UpperCAmelCase      		= {}

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

	if target_model_path is None:
		__UpperCAmelCase      		= os.path.join(
		    os.path.dirname(snake_case_    )			,		F'''bertarized_{os.path.basename(snake_case_    )}'''    )

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

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


if __name__ == "__main__":
				_lowercase   :	Tuple												=						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',
				)

				_lowercase   :	str												=						parser.parse_args()

				main(args)


def   lowerCamelCase__							(							__lowerCAmelCase    :    list							):




			"""simple docstring"""


			for i in range(len(__lowerCAmelCase							) - 1       ,				0       ,				-1							):
						lowerCAmelCase_         =      False

						for j in range(__lowerCAmelCase       ,				0       ,				-1							):
									if unsorted[j] < unsorted[j - 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j - 1], unsorted[j]
												lowerCAmelCase_         =      True

						for j in range(__lowerCAmelCase							):
									if unsorted[j] > unsorted[j + 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j + 1], unsorted[j]
												lowerCAmelCase_         =      True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			_A  =	input("Enter numbers separated by a comma:\n").strip()
			_A  =	[int(item) for item in user_input.split(",")]
			print(f"""{cocktail_shaker_sort(unsorted) = }""")



