






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_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.'
				)
				# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
				parser.add_argument(
				    '--original_config_file',
				    default=None,
				    type=str,
				    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(
				    '--scheduler_type',
				    default='pndm',
				    type=str,
				    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
				)
				parser.add_argument(
				    '--pipeline_type',
				    default=None,
				    type=str,
				    help=(
				        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
				        '. If `None` pipeline will be automatically inferred.'
				    ),
				)
				parser.add_argument(
				    '--image_size',
				    default=None,
				    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(
				    '--prediction_type',
				    default=None,
				    type=str,
				    help=(
				        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
				        ' Diffusion v2 Base. Use \'v_prediction\' 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.)')
				parser.add_argument(
				    '--stable_unclip',
				    type=str,
				    default=None,
				    required=False,
				    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
				)
				parser.add_argument(
				    '--stable_unclip_prior',
				    type=str,
				    default=None,
				    required=False,
				    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
				)
				parser.add_argument(
				    '--clip_stats_path',
				    type=str,
				    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
				    required=False,
				)
				parser.add_argument(
				    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
				)
				parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
				parser.add_argument(
				    '--vae_path',
				    type=str,
				    default=None,
				    required=False,
				    help='Set to a path, hub id to an already converted vae to not convert it again.',
				)
				__A      =				parser.parse_args()

				__A      =				download_from_original_stable_diffusion_ckpt(
				    checkpoint_path=args.checkpoint_path,
				    original_config_file=args.original_config_file,
				    image_size=args.image_size,
				    prediction_type=args.prediction_type,
				    model_type=args.pipeline_type,
				    extract_ema=args.extract_ema,
				    scheduler_type=args.scheduler_type,
				    num_in_channels=args.num_in_channels,
				    upcast_attention=args.upcast_attention,
				    from_safetensors=args.from_safetensors,
				    device=args.device,
				    stable_unclip=args.stable_unclip,
				    stable_unclip_prior=args.stable_unclip_prior,
				    clip_stats_path=args.clip_stats_path,
				    controlnet=args.controlnet,
				    vae_path=args.vae_path,
				)

				if args.half:
								pipe.to(torch_dtype=torch.floataa)

				if args.controlnet:
								# only save the controlnet model
								pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
				else:
								pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)












def      __A					(     _lowercase = 1_00_00_00 ):

					'''simple docstring'''







					_A								= 1
					_A								= 1
					_A								= {1: 1}

					for inputa in range(2					,			_lowercase ):
										_A								= 0
										_A								= inputa

										while True:
															if number in counters:
																				counter += counters[number]
																				break
															if number % 2 == 0:
																				number //= 2
																				counter += 1
															else:
																				_A								= (3 * number) + 1
																				counter += 1

										if inputa not in counters:
															_A								= counter

										if counter > pre_counter:
															_A								= inputa
															_A								= counter
					return largest_number


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



