


'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    						={
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case    						=[
           """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XmodForCausalLM""",
           """XmodForMaskedLM""",
           """XmodForMultipleChoice""",
           """XmodForQuestionAnswering""",
           """XmodForSequenceClassification""",
           """XmodForTokenClassification""",
           """XmodModel""",
           """XmodPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xmod import (
                  XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XmodForCausalLM,
                  XmodForMaskedLM,
                  XmodForMultipleChoice,
                  XmodForQuestionAnswering,
                  XmodForSequenceClassification,
                  XmodForTokenClassification,
                  XmodModel,
                  XmodPreTrainedModel,
              )

else:
       import sys

       __snake_case    						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











def __UpperCamelCase			(       _A = 1000000  ):
						lowerCAmelCase_      			=  1
						lowerCAmelCase_      			=  1
						lowerCAmelCase_      			=  {1: 1}

						for inputa in range(2 ,  _A  ):
												lowerCAmelCase_      			=  0
												lowerCAmelCase_      			=  inputa

												while True:
																		if number in counters:
																								counter += counters[number]
																								break
																		if number % 2 == 0:
																								number //= 2
																								counter += 1
																		else:
																								lowerCAmelCase_      			=  (3 * number) + 1
																								counter += 1

												if inputa not in counters:
																		lowerCAmelCase_      			=  counter

												if counter > pre_counter:
																		lowerCAmelCase_      			=  inputa
																		lowerCAmelCase_      			=  counter
						return largest_number


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




