



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       Union[List[PIL.Image.Image], np.ndarray]
       UpperCamelCase__    :       Optional[List[bool]]


if is_transformers_available() and is_torch_available():
 from .modeling_roberta_series import RobertaSeriesModelWithTransformation
 from .pipeline_alt_diffusion import AltDiffusionPipeline
 from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline










from __future__ import annotations

__snake_case		:Union[str, Any] 						=	{1: 1}





def 	__snake_case      (     _UpperCAmelCase							):
    if n in COLLATZ_SEQUENCE_LENGTHS:
        return COLLATZ_SEQUENCE_LENGTHS[n]
    __a					=							n // 2 if n % 2 == 0 else 3 * n + 1
    __a					=							collatz_sequence_length(_UpperCAmelCase							) + 1
    __a					=							sequence_length
    return sequence_length





def 	__snake_case      (     _UpperCAmelCase = 1000000							):
    __a					=							max((collatz_sequence_length(_UpperCAmelCase							), i) for i in range(1  ,					_UpperCAmelCase							)							)
    return result[1]


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





