



import math


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					return sum(int(snake_case     ) for digit in str(snake_case     )     )


def 						_UpperCAmelCase							( snake_case = 30     ):





					"""simple docstring"""

					_lowerCAmelCase         =       []
					for digit in range(2      ,		1_00     ):
										for power in range(2      ,		1_00     ):
															_lowerCAmelCase         =       int(math.pow(snake_case      ,		snake_case     )     )
															if digit == digit_sum(snake_case     ):
																				digit_to_powers.append(snake_case     )

					digit_to_powers.sort()
					return digit_to_powers[n - 1]


if __name__ == "__main__":
	print(solution())




import argparse

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


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					CanineModel(__UpperCAmelCase		)
      model.eval()

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

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

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

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


if __name__ == "__main__":
     snake_case__						      =			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.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)