




import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







def lowerCamelCase__   (			_lowercase ,						_lowercase      ):

     '''simple docstring'''


     UpperCAmelCase_			:							str   =			CanineConfig()
     UpperCAmelCase_			:							Optional[Any]   =			CanineModel(lowercase_      )
     model.eval()

     print(f'''Building PyTorch model from configuration: {config}'''      )

     # Load weights from tf checkpoint
     load_tf_weights_in_canine(lowercase_ ,						lowercase_ ,						lowercase_      )

     # Save pytorch-model (weights and configuration)
     print(f'''Save PyTorch model to {pytorch_dump_path}'''      )
     model.save_pretrained(lowercase_      )

     # Save tokenizer files
     UpperCAmelCase_			:							int   =			CanineTokenizer()
     print(f'''Save tokenizer files to {pytorch_dump_path}'''      )
     tokenizer.save_pretrained(lowercase_      )


if __name__ == "__main__":
     __a							= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     __a							= parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)





'''simple docstring'''







from __future__ import annotations

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






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		_snake_case	       =       n // 2 if n % 2 == 0 else 3 * n + 1
		_snake_case	       =       collatz_sequence_length(lowercase_  ) + 1
		_snake_case	       =       sequence_length
		return sequence_length






def 		_lowercase		(	lowercase_ = 1000000  ):



		'''simple docstring'''







		_snake_case	       =       max((collatz_sequence_length(lowercase_  ), i) for i in range(1						,							lowercase_  )  )
		return result[1]


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





