






"""simple docstring"""


def     snake_case						(A_   :int = 1_0   ):







  '''simple docstring'''

  if not isinstance(A_			,    A_   ) or n < 0:
    raise ValueError('Invalid input'   )
  a  :    int       =     1_0**n
  a  :    List[str]       =     2_8_4_3_3 * (pow(2			,    7_8_3_0_4_5_7			,    A_   )) + 1
  return str(number % modulus   )


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print(f'''{solution(10) = }''')












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase       :					int									=		{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					Optional[Any]									=		[
       'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TimeSeriesTransformerForPrediction',
       'TimeSeriesTransformerModel',
       'TimeSeriesTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_time_series_transformer import (
       TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TimeSeriesTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_time_series_transformer import (
          TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TimeSeriesTransformerForPrediction,
          TimeSeriesTransformerModel,
          TimeSeriesTransformerPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase       :					List[str]									=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




