"""simple docstring"""






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


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

						parser.add_argument(
						    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
						)
						parser.add_argument(
						    '--original_config_file',
						    type=str,
						    required=True,
						    help='The YAML config file corresponding to the original architecture.',
						)
						parser.add_argument(
						    '--num_in_channels',
						    default=None,
						    type=int,
						    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
						)
						parser.add_argument(
						    '--image_size',
						    default=5_1_2,
						    type=int,
						    help=(
						        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
						        ' Base. Use 768 for Stable Diffusion v2.'
						    ),
						)
						parser.add_argument(
						    '--extract_ema',
						    action='store_true',
						    help=(
						        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
						        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
						        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
						    ),
						)
						parser.add_argument(
						    '--upcast_attention',
						    action='store_true',
						    help=(
						        'Whether the attention computation should always be upcasted. This is necessary when running stable'
						        ' diffusion 2.1.'
						    ),
						)
						parser.add_argument(
						    '--from_safetensors',
						    action='store_true',
						    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
						)
						parser.add_argument(
						    '--to_safetensors',
						    action='store_true',
						    help='Whether to store pipeline in safetensors format or not.',
						)
						parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')




						def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> Any:



										if string == "True":
														return True
										elif string == "False":
														return False
										else:
														raise ValueError(F"""could not parse string as bool {string}"""							)

						parser.add_argument(
						    '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
						)

						parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

						_A        =     parser.parse_args()

						_A        =     download_controlnet_from_original_ckpt(
						    checkpoint_path=args.checkpoint_path,
						    original_config_file=args.original_config_file,
						    image_size=args.image_size,
						    extract_ema=args.extract_ema,
						    num_in_channels=args.num_in_channels,
						    upcast_attention=args.upcast_attention,
						    from_safetensors=args.from_safetensors,
						    device=args.device,
						    use_linear_projection=args.use_linear_projection,
						    cross_attention_dim=args.cross_attention_dim,
						)

						controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)











'''simple docstring'''




from math import sqrt



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			0
		for i in range(1			,			int(sqrt(lowerCamelCase_	) + 1	)	):
				if n % i == 0 and i != sqrt(lowerCamelCase_	):
						total += i + n // i
				elif i == sqrt(lowerCamelCase_	):
						total += i
		return total - n



def 		__A		(  lowerCamelCase_ = 1_00_00	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			sum(
		    i
		    for i in range(1			,			lowerCamelCase_	)
		    if sum_of_divisors(sum_of_divisors(lowerCamelCase_	)	) == i and sum_of_divisors(lowerCamelCase_	) != i	)
		return total


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



