






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


__A      =				datasets.logging.get_logger(__name__)


__A      =				'\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

__A      =				'\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

__A      =				'\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n'

__A      =				{
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,	_KWARGS_DESCRIPTION					)
class 						SCREAMING_SNAKE_CASE				( datasets.Metric					):





							"""simple docstring"""

							def       __A (  self:   Dict   )    ->				Optional[Any]:
												return datasets.MetricInfo(
												    description=_DESCRIPTION		,     citation=_CITATION		,     homepage='''https://github.com/google-research/bleurt'''		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
												        {
												            '''predictions''': datasets.Value('''string'''		,     id='''sequence'''   ),
												            '''references''': datasets.Value('''string'''		,     id='''sequence'''   ),
												        }   )		,     codebase_urls=['''https://github.com/google-research/bleurt''']		,     reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']		,     )

							def       __A (  self:   int		,     __A:   int   )    ->				Union[str, Any]:



												if self.config_name == "default":
																	logger.warning(
																	    '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
																	    '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''   )
																	_A								= """bleurt-base-128"""

												if self.config_name.lower() in CHECKPOINT_URLS:
																	_A								= self.config_name.lower()

												elif self.config_name.upper() in CHECKPOINT_URLS:
																	_A								= self.config_name.upper()

												else:
																	raise KeyError(
																	    f"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""   )

												# download the model checkpoint specified by self.config_name and set up the scorer
												_A								= dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]   )
												_A								= score.BleurtScorer(os.path.join(lowerCamelCase_		,     lowerCamelCase_   )   )



							def       __A (  self:   Union[str, Any]		,     __A:   List[str]		,     __A:   Optional[int]   )    ->				Optional[int]:
												_A								= self.scorer.score(references=lowerCamelCase_		,     candidates=lowerCamelCase_   )
												return {"scores": scores}












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())))



