
"""simple docstring"""


from __future__ import annotations

A_						       =				{1: 1}






def    lowercase	(  lowerCAmelCase__	):
			if n in COLLATZ_SEQUENCE_LENGTHS:
						return COLLATZ_SEQUENCE_LENGTHS[n]
			lowerCamelCase_ 		=     n // 2 if n % 2 == 0 else 3 * n + 1
			lowerCamelCase_ 		=     collatz_sequence_length(lowerCAmelCase__	) + 1
			lowerCamelCase_ 		=     sequence_length
			return sequence_length






def    lowercase	(  lowerCAmelCase__ = 1_000_000	):
			lowerCamelCase_ 		=     max((collatz_sequence_length(lowerCAmelCase__	), i) for i in range(1  ,lowerCAmelCase__	)	)
			return result[1]


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





"""simple docstring"""


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


A_						       =				logging.get_logger(__name__)




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			List[str]									=						'hybrid-clip'
				a__:			List[Any]									=						True




				def __init__(							self       ,	UpperCAmelCase=512       ,	**UpperCAmelCase      ):
							super().__init__(**UpperCAmelCase      )

							if "text_config" not in kwargs:
										raise ValueError('''`text_config` can not be `None`.'''      )

							if "vision_config" not in kwargs:
										raise ValueError('''`vision_config` can not be `None`.'''      )

							lowerCamelCase_ 		=     kwargs.pop('''text_config'''      )
							lowerCamelCase_ 		=     kwargs.pop('''vision_config'''      )

							lowerCamelCase_ 		=     text_config.pop('''model_type'''      )
							lowerCamelCase_ 		=     vision_config.pop('''model_type'''      )

							from transformers import AutoConfig

							lowerCamelCase_ 		=     AutoConfig.for_model(UpperCAmelCase       ,	**UpperCAmelCase      )

							if vision_model_type == "clip":
										lowerCamelCase_ 		=     AutoConfig.for_model(UpperCAmelCase       ,	**UpperCAmelCase      ).vision_config
							elif vision_model_type == "clip_vision_model":
										from transformers import CLIPVisionConfig

										lowerCamelCase_ 		=     CLIPVisionConfig(**UpperCAmelCase      )
							else:
										lowerCamelCase_ 		=     AutoConfig.for_model(UpperCAmelCase       ,	**UpperCAmelCase      )

							lowerCamelCase_ 		=     projection_dim
							lowerCamelCase_ 		=     1.0




				@classmethod
				def 					UpperCAmelCase__	(							cls       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	**UpperCAmelCase      ):
							return cls(text_config=text_config.to_dict()       ,	vision_config=vision_config.to_dict()       ,	**UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     copy.deepcopy(self.__dict__      )
							lowerCamelCase_ 		=     self.text_config.to_dict()
							lowerCamelCase_ 		=     self.vision_config.to_dict()
							lowerCamelCase_ 		=     self.__class__.model_type
							return output



