



import argparse
import os
import shutil

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







def     UpperCAmelCase_		(					snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  args.pruning_method
				lowerCAmelCase__		  =  args.threshold

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

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

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

				if target_model_path is None:
								lowerCAmelCase__		  =  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__":
	_lowerCAmelCase	:   Union[str, Any]    =   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	:   Optional[int]    =   parser.parse_args()

	main(args)





def     UpperCAmelCase_		(					)	->     int:



				"""simple docstring"""





				return 1







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else two_pence(x - 2					) + one_pence()







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else five_pence(x - 5					) + two_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else ten_pence(x - 10					) + five_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else twenty_pence(x - 20					) + ten_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else fifty_pence(x - 50					) + twenty_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else one_pound(x - 100					) + fifty_pence(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				return 0 if x < 0 else two_pound(x - 200					) + one_pound(snake_case__					)







def     UpperCAmelCase_		(					snake_case__ = 200					)	->     int:



				"""simple docstring"""





				return two_pound(snake_case__					)


if __name__ == "__main__":
	print(solution(int(input().strip())))
