

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE					:  int             =      {
    '''configuration_mobilebert''': [
        '''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileBertConfig''',
        '''MobileBertOnnxConfig''',
    ],
    '''tokenization_mobilebert''': ['''MobileBertTokenizer'''],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      [
           '''MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''MobileBertForMaskedLM''',
           '''MobileBertForMultipleChoice''',
           '''MobileBertForNextSentencePrediction''',
           '''MobileBertForPreTraining''',
           '''MobileBertForQuestionAnswering''',
           '''MobileBertForSequenceClassification''',
           '''MobileBertForTokenClassification''',
           '''MobileBertLayer''',
           '''MobileBertModel''',
           '''MobileBertPreTrainedModel''',
           '''load_tf_weights_in_mobilebert''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      [
           '''TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFMobileBertForMaskedLM''',
           '''TFMobileBertForMultipleChoice''',
           '''TFMobileBertForNextSentencePrediction''',
           '''TFMobileBertForPreTraining''',
           '''TFMobileBertForQuestionAnswering''',
           '''TFMobileBertForSequenceClassification''',
           '''TFMobileBertForTokenClassification''',
           '''TFMobileBertMainLayer''',
           '''TFMobileBertModel''',
           '''TFMobileBertPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_mobilebert import (
           MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileBertConfig,
           MobileBertOnnxConfig,
       )
       from .tokenization_mobilebert import MobileBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mobilebert_fast import MobileBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilebert import (
                  MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileBertForMaskedLM,
                  MobileBertForMultipleChoice,
                  MobileBertForNextSentencePrediction,
                  MobileBertForPreTraining,
                  MobileBertForQuestionAnswering,
                  MobileBertForSequenceClassification,
                  MobileBertForTokenClassification,
                  MobileBertLayer,
                  MobileBertModel,
                  MobileBertPreTrainedModel,
                  load_tf_weights_in_mobilebert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mobilebert import (
                  TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMobileBertForMaskedLM,
                  TFMobileBertForMultipleChoice,
                  TFMobileBertForNextSentencePrediction,
                  TFMobileBertForPreTraining,
                  TFMobileBertForQuestionAnswering,
                  TFMobileBertForSequenceClassification,
                  TFMobileBertForTokenClassification,
                  TFMobileBertMainLayer,
                  TFMobileBertModel,
                  TFMobileBertPreTrainedModel,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





from functools import reduce

_SCREAMING_SNAKE_CASE					:  Any             =      (
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)
def     UpperCAmelCase_       (						_A = N    ):





     '''simple docstring'''




     return max(
         # mypy cannot properly interpret reduce
         int(reduce(lambda _A				,		_A    : str(int(_A    ) * int(_A    )    )				,		n[i : i + 13]    )    )
         for i in range(len(_A    ) - 12    )    )


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


