


"""simple docstring"""
import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()






def 						lowercase				(	_snake_case		: Optional[Any]	,  _snake_case		: Tuple	,  _snake_case		: List[Any]	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		Optional[Any]         =							ConvBertConfig.from_json_file(_snake_case	)
	__snake_case      :		List[Any]         =							ConvBertModel(_snake_case	)

	__snake_case      :		Optional[Any]         =							load_tf_weights_in_convbert(_snake_case	,  _snake_case	,  _snake_case	)
	model.save_pretrained(_snake_case	)

	__snake_case      :		str         =							TFConvBertModel.from_pretrained(_snake_case	,  from_pt=_snake_case	)
	tf_model.save_pretrained(_snake_case	)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       str							   =							argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
			)
			parser.add_argument(
			    """--convbert_config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained ConvBERT model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			SCREAMING_SNAKE_CASE					:       str							   =							parser.parse_args()
			convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)












'''simple docstring'''

from __future__ import annotations

lowerCAmelCase__      :   Tuple      	=			{1: 1}


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		__UpperCAmelCase	:							int     =     n // 2 if n % 2 == 0 else 3 * n + 1
		__UpperCAmelCase	:							str     =     collatz_sequence_length(_UpperCAmelCase  ) + 1
		__UpperCAmelCase	:							Union[str, Any]     =     sequence_length
		return sequence_length


def 						__UpperCamelCase						(			_UpperCAmelCase = 1000000  ):
		__UpperCAmelCase	:							Union[str, Any]     =     max((collatz_sequence_length(_UpperCAmelCase  ), i) for i in range(1,       _UpperCAmelCase  )  )
		return result[1]


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




