
'''simple docstring'''





from typing import TYPE_CHECKING

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


a__		:   List[Any]            =       {
    "configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
    "tokenization_gpt2": ["GPT2Tokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   int            =       ["GPT2TokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Optional[int]            =       [
     "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPT2DoubleHeadsModel",
     "GPT2ForQuestionAnswering",
     "GPT2ForSequenceClassification",
     "GPT2ForTokenClassification",
     "GPT2LMHeadModel",
     "GPT2Model",
     "GPT2PreTrainedModel",
     "load_tf_weights_in_gpt2",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   int            =       [
     "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFGPT2DoubleHeadsModel",
     "TFGPT2ForSequenceClassification",
     "TFGPT2LMHeadModel",
     "TFGPT2MainLayer",
     "TFGPT2Model",
     "TFGPT2PreTrainedModel",
 ]

try:
 if not is_keras_nlp_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Dict            =       ["TFGPT2Tokenizer"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Dict            =       ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
 from .tokenization_gpta import GPTaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_fast import GPTaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpta import (
      GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTaDoubleHeadsModel,
      GPTaForQuestionAnswering,
      GPTaForSequenceClassification,
      GPTaForTokenClassification,
      GPTaLMHeadModel,
      GPTaModel,
      GPTaPreTrainedModel,
      load_tf_weights_in_gpta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gpta import (
      TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGPTaDoubleHeadsModel,
      TFGPTaForSequenceClassification,
      TFGPTaLMHeadModel,
      TFGPTaMainLayer,
      TFGPTaModel,
      TFGPTaPreTrainedModel,
  )

 try:
  if not is_keras_nlp_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_tf import TFGPTaTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
 import sys

 a__		:   Tuple            =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			str:







      """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(UpperCAmelCase      ,  n - 1      ,  UpperCAmelCase	) * a) % b

      __A       	=			bin_exp_mod(UpperCAmelCase      ,  n / 2      ,  UpperCAmelCase	)
      return (r * r) % b


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

 print(bin_exp_mod(BASE, POWER, MODULO))






