






"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_								=	{
    'configuration_esm': ['ESM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'EsmConfig'],
    'tokenization_esm': ['EsmTokenizer'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'ESM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'EsmForMaskedLM',
           'EsmForSequenceClassification',
           'EsmForTokenClassification',
           'EsmModel',
           'EsmPreTrainedModel',
       ]
       lowercase_								=	['EsmForProteinFolding', 'EsmFoldPreTrainedModel']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_								=	[
           'TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFEsmForMaskedLM',
           'TFEsmForSequenceClassification',
           'TFEsmForTokenClassification',
           'TFEsmModel',
           'TFEsmPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
       from .tokenization_esm import EsmTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_esm import (
                  ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  EsmForMaskedLM,
                  EsmForSequenceClassification,
                  EsmForTokenClassification,
                  EsmModel,
                  EsmPreTrainedModel,
              )
              from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_esm import (
                  TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFEsmForMaskedLM,
                  TFEsmForSequenceClassification,
                  TFEsmForTokenClassification,
                  TFEsmModel,
                  TFEsmPreTrainedModel,
              )


else:
       import sys

       lowercase_								=	_LazyModule(__name__, globals()['__file__'], _import_structure)








'''simple docstring'''



import math
from decimal import Decimal, getcontext




def       _a      (		_lowercase   :      int = 4000000    ):





     '''simple docstring'''






     try:
          __UpperCAmelCase      :					List[str]    				=     int(_lowercase    )
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''    )
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''    )
     __UpperCAmelCase      :					Tuple    				=     100
     __UpperCAmelCase      :					Optional[Any]    				=     (Decimal(5    ) ** Decimal(0.5    ) + 1) / Decimal(2    )

     __UpperCAmelCase      :					Tuple    				=     (math.floor(math.log(n * (phi + 2)     ,   _lowercase    ) - 1    ) // 3) * 3 + 2
     __UpperCAmelCase      :					List[Any]    				=     Decimal(round(phi ** Decimal(index + 1    )    )    ) / (phi + 2)
     __UpperCAmelCase      :					List[str]    				=     num // 2
     return int(_lowercase    )


if __name__ == "__main__":
      print(f"""{solution() = }""")