






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					,						__lowerCAmelCase      )->    Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     CanineConfig()
						_UpperCAmelCase							   =     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
						_UpperCAmelCase							   =     CanineTokenizer()
						print(F"""Save tokenizer files to {pytorch_dump_path}"""      )
						tokenizer.save_pretrained(__lowerCAmelCase      )


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








def __A       ( __lowerCAmelCase = 100					,						__lowerCAmelCase = 100      )->    int:





						"""simple docstring"""



						return max(
						    sum(int(__lowerCAmelCase      ) for x in str(base**power      )      )
						    for base in range(__lowerCAmelCase      )
						    for power in range(__lowerCAmelCase      )      )


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()
