






def        lowerCamelCase__   (					__lowerCamelCase    :  Union[str, Any]						,	__lowerCamelCase    :  Optional[int]						,	__lowerCamelCase    :  List[str]	):
  assert b != 0, "This cannot accept modulo that is == 0"
  if n == 0:
    return 1

  if n % 2 == 1:
    return (bin_exp_mod(__lowerCamelCase						,	n - 1						,	__lowerCamelCase	) * a) % b

  __UpperCAmelCase							:			str     =						bin_exp_mod(__lowerCamelCase						,	n / 2						,	__lowerCamelCase	)
  return (r * r) % b


if __name__ == "__main__":
       try:
              a	: Dict      =				int(input("Enter Base : ").strip())
              a	: List[str]      =				int(input("Enter Power : ").strip())
              a	: List[Any]      =				int(input("Enter Modulo : ").strip())
       except ValueError:
              print("Invalid literal for integer")

       print(bin_exp_mod(BASE, POWER, MODULO))










from typing import TYPE_CHECKING

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


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: str      =				[
           "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "HubertForCTC",
           "HubertForSequenceClassification",
           "HubertModel",
           "HubertPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFHubertForCTC",
           "TFHubertModel",
           "TFHubertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_hubert import (
                  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  HubertForCTC,
                  HubertForSequenceClassification,
                  HubertModel,
                  HubertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_hubert import (
                  TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFHubertForCTC,
                  TFHubertModel,
                  TFHubertPreTrainedModel,
              )


else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


