



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__    :     int			  =   {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Optional[Any]			  =   ['MPNetTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     Union[str, Any]			  =   [
      'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MPNetForMaskedLM',
      'MPNetForMultipleChoice',
      'MPNetForQuestionAnswering',
      'MPNetForSequenceClassification',
      'MPNetForTokenClassification',
      'MPNetLayer',
      'MPNetModel',
      'MPNetPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    :     int			  =   [
      'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFMPNetEmbeddings',
      'TFMPNetForMaskedLM',
      'TFMPNetForMultipleChoice',
      'TFMPNetForQuestionAnswering',
      'TFMPNetForSequenceClassification',
      'TFMPNetForTokenClassification',
      'TFMPNetMainLayer',
      'TFMPNetModel',
      'TFMPNetPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
  from .tokenization_mpnet import MPNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mpnet_fast import MPNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mpnet import (
        MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        MPNetForMaskedLM,
        MPNetForMultipleChoice,
        MPNetForQuestionAnswering,
        MPNetForSequenceClassification,
        MPNetForTokenClassification,
        MPNetLayer,
        MPNetModel,
        MPNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mpnet import (
        TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMPNetEmbeddings,
        TFMPNetForMaskedLM,
        TFMPNetForMultipleChoice,
        TFMPNetForQuestionAnswering,
        TFMPNetForSequenceClassification,
        TFMPNetForTokenClassification,
        TFMPNetMainLayer,
        TFMPNetModel,
        TFMPNetPreTrainedModel,
    )

else:
  import sys

  UpperCamelCase__    :     int			  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





'''simple docstring'''





def     UpperCAmelCase     (    a_ = 9_9_8_0_0_1		)   ->       int:



   """simple docstring"""






   for number in range(n - 1      ,		9_9_9_9      ,		-1		):
      A_  :		Optional[Any]						=					str(a_		)

      # checks whether 'str_number' is a palindrome.
      if str_number == str_number[::-1]:
         A_  :		int						=					9_9_9

         # if 'number' is a product of two 3-digit numbers
         # then number is the answer otherwise fetch next number.
         while divisor != 9_9:
            if (number % divisor == 0) and (len(str(number // divisor		)		) == 3.0):
               return number
            divisor -= 1
   raise ValueError("""That number is larger than our acceptable range."""		)


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