
'''simple docstring'''

def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       ):
     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					:  Optional[Any]         =    bin_exp_mod(lowerCAmelCase_ ,       n / 2 ,       lowerCAmelCase_       )
     return (r * r) % b


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

   print(bin_exp_mod(BASE, POWER, MODULO))








import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

     print(F'Building PyTorch model from configuration: {config}'			)

     # Load weights from tf checkpoint
     load_tf_weights_in_canine(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_			)

     # Save pytorch-model (weights and configuration)
     print(F'Save PyTorch model to {pytorch_dump_path}'			)
     model.save_pretrained(lowerCAmelCase_			)

     # Save tokenizer files
     lowerCAmelCase__		= CanineTokenizer()
     print(F'Save tokenizer files to {pytorch_dump_path}'			)
     tokenizer.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
     UpperCamelCase								=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
