





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    if n == 0:
        return 1

    elif n % 2 == 1:
        return binary_exponentiation(_SCREAMING_SNAKE_CASE       ,    n - 1							) * a

    else:
        _A    =       binary_exponentiation(_SCREAMING_SNAKE_CASE       ,    n / 2							)
        return b * b


if __name__ == "__main__":
 try:
  __A       : Any						    = int(input("Enter Base : ").strip())
  __A       : Dict						    = int(input("Enter Power : ").strip())
 except ValueError:
  print("Invalid literal for integer")

 __A       : Optional[Any]						    = binary_exponentiation(BASE, POWER)
 print(f"{BASE}^({POWER}) : {RESULT}")












from typing import TYPE_CHECKING

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


__A       : Dict						    = {"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

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


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       : Tuple						    = [
     "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       : Dict						    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





