

'''simple docstring'''

def      __magic_name__							(			A				)      ->		List[str]:
	snake_case							=					[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
	for gap in gaps:
		for i in range(A       , len(A				)				):
			snake_case							=					collection[i]
			snake_case							=					i
			while j >= gap and collection[j - gap] > insert_value:
				snake_case							=					collection[j - gap]
				j -= gap
			if j != i:
				snake_case							=					insert_value
	return collection


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						lowerCAmelCase_     =      input("Enter numbers separated by a comma:\n").strip()
						lowerCAmelCase_     =      [int(item) for item in user_input.split(",")]
						print(shell_sort(unsorted))








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					args.serialization_dir
	snake_case							=					args.pruning_method
	snake_case							=					args.threshold

	snake_case							=					torch.load(os.path.join(A       , 'pytorch_model.bin'				)       , map_location='cpu'				)

	snake_case							=					0  # Number of remaining (not pruned) params in the encoder
	snake_case							=					0  # Number of params in the encoder

	print('name'.ljust(6_0       , ' '				)       , 'Remaining Weights %'       , 'Remaining Weight'				)
	for name, param in st.items():
		if "encoder" not in name:
			continue

		if "mask_scores" in name:
			if pruning_method == "topK":
				snake_case							=					TopKBinarizer.apply(A       , A				).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							=					ThresholdBinarizer.apply(A       , A       , A				).sum().item()
			elif pruning_method == "l0":
				snake_case			,			snake_case							=					-0.1, 1.1
				snake_case							=					torch.sigmoid(A				)
				snake_case							=					s * (r - l) + l
				snake_case							=					s_bar.clamp(min=0.0       , max=1.0				)
				snake_case							=					(mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'				)
			remaining_count += mask_ones
			print(name.ljust(6_0       , ' '				)       , str(round(1_0_0 * mask_ones / param.numel()       , 3				)				).ljust(2_0       , ' '				)       , str(A				)				)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''				)
	print('Remaining Weights (global) %: '       , 1_0_0 * remaining_count / encoder_count				)


if __name__ == "__main__":
						lowerCAmelCase_     =      argparse.ArgumentParser()

						parser.add_argument(
						    "--pruning_method",
						    choices=["l0", "topK", "sigmoied_threshold"],
						    type=str,
						    required=True,
						    help=(
						        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
						        " pruning)"
						    ),
						)
						parser.add_argument(
						    "--threshold",
						    type=float,
						    required=False,
						    help=(
						        "For `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(
						    "--serialization_dir",
						    type=str,
						    required=True,
						    help="Folder containing the model that was previously fine-pruned",
						)

						lowerCAmelCase_     =      parser.parse_args()

						main(args)





