


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE		:str											=       {
    '''configuration_led''': ['''LED_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LEDConfig'''],
    '''tokenization_led''': ['''LEDTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE		:List[Any]											=       ['''LEDTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE		:int											=       [
       '''LED_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''LEDForConditionalGeneration''',
       '''LEDForQuestionAnswering''',
       '''LEDForSequenceClassification''',
       '''LEDModel''',
       '''LEDPreTrainedModel''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE		:Tuple											=       ['''TFLEDForConditionalGeneration''', '''TFLEDModel''', '''TFLEDPreTrainedModel''']


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

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








def      _lowerCAmelCase  (							lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Tuple							)->Optional[Any]:
   '''simple docstring'''






   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

   snake_case_         =   bin_exp_mod(lowerCAmelCase_						,  n / 2						,  lowerCAmelCase_							)
   return (r * r) % b


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

   print(bin_exp_mod(BASE, POWER, MODULO))




