

'''simple docstring'''

from __future__ import annotations

lowerCAmelCase_						   =			{1: 1}

def     __magic_name__       ( A							)			->		int:
	if n in COLLATZ_SEQUENCE_LENGTHS:
		return COLLATZ_SEQUENCE_LENGTHS[n]
	snake_case							 =				n // 2 if n % 2 == 0 else 3 * n + 1
	snake_case							 =				collatz_sequence_length(A							) + 1
	snake_case							 =				sequence_length
	return sequence_length

def     __magic_name__       ( A = 1_0_0_0_0_0_0							)			->		int:
	snake_case							 =				max((collatz_sequence_length(A							), i) for i in range(1					,   A							)							)
	return result[1]


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








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def     __magic_name__       ( A							)			->		Dict:
	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)





