






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A      =				logging.get_logger(__name__)

__A      =				{
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"ibert"

							def __init__(  self:   Any		,     __A:   Tuple=3_05_22		,     __A:   List[Any]=7_68		,     __A:   Tuple=12		,     __A:   Dict=12		,     __A:   List[str]=30_72		,     __A:   str="gelu"		,     __A:   str=0.1		,     __A:   Tuple=0.1		,     __A:   Optional[Any]=5_12		,     __A:   List[str]=2		,     __A:   Any=0.02		,     __A:   int=1e-12		,     __A:   List[Any]=1		,     __A:   List[Any]=0		,     __A:   Any=2		,     __A:   Optional[Any]="absolute"		,     __A:   Tuple=False		,     __A:   List[Any]="none"		,     **__A:   Any		,     )    ->				List[str]:
												super().__init__(pad_token_id=__A		,     bos_token_id=__A		,     eos_token_id=__A		,     **__A   )

												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= hidden_act
												_A								= intermediate_size
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= initializer_range
												_A								= layer_norm_eps
												_A								= position_embedding_type
												_A								= quant_mode
												_A								= force_dequant





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@property
							def       __A (  self:   Optional[int]   )    ->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_A								= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_A								= {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ]   )











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())))



