code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  def 					A_			(		snake_case_     :       Optional[Any]						,snake_case_     :       List[str]						,snake_case_     :       Tuple    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      # Initialise PyTorch model
      UpperCamelCase				:   Union[str, Any]          =							TaConfig.from_json_file(snake_case_    )
      print(f'Building PyTorch model from configuration: {config}'    )
      UpperCamelCase				:   List[Any]          =							TaForConditionalGeneration(snake_case_    )
  
      # Load weights from tf checkpoint
      load_tf_weights_in_ta(snake_case_						,snake_case_						,snake_case_    )
  
      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'    )
      model.save_pretrained(snake_case_    )
  
  
  if __name__ == "__main__":
    __A :		List[str]      							=    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __A :		Any      							=    parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]						,snake_case_     :       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(snake_case_						,n - 1						,snake_case_    ) * a) % b
  
      UpperCamelCase				:   List[Any]          =							bin_exp_mod(snake_case_						,n / 2						,snake_case_    )
      return (r * r) % b
  
  
  if __name__ == "__main__":
    try:
      __A :		Any      							=    int(input('''Enter Base : ''').strip())
      __A :		Any      							=    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))
  
  
  
  
  