




"""simple docstring"""







from __future__ import annotations

SCREAMING_SNAKE_CASE_      =					{1: 1}





def     __snake_case   (							_lowercase						):


    """simple docstring"""







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





def     __snake_case   (							_lowercase = 100_0000						):


    """simple docstring"""







    UpperCamelCase										=  max((collatz_sequence_length(_lowercase						), i) for i in range(1 ,_lowercase						)						)
    return result[1]


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





"""simple docstring"""







import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  BertConfig.from_pretrained(
        '''bert-large-cased''' ,vocab_size=_lowercase ,max_position_embeddings=512 ,is_decoder=_lowercase ,add_cross_attention=_lowercase ,)
    UpperCamelCase										=  bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase										=  BertGenerationConfig(**_lowercase						)
    if is_encoder:
        UpperCamelCase										=  BertGenerationEncoder(_lowercase						)
    else:
        UpperCamelCase										=  BertGenerationDecoder(_lowercase						)
    print(f'Building PyTorch model from configuration: {config}'						)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _lowercase ,_lowercase ,model_class='''bert''' ,is_encoder_named_decoder=_lowercase ,is_encoder=_lowercase ,)

    # Save pytorch-model
    print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
    model.save_pretrained(_lowercase						)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_      =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--is_encoder_named_decoder',
      action='store_true',
      help='If decoder has to be renamed to encoder in PyTorch model.',
  )
  parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
  parser.add_argument('--vocab_size', default=50358, type=int, help='Vocab size of model')
  SCREAMING_SNAKE_CASE_      =					parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )