"""simple docstring"""



from collections import defaultdict





def   _lowerCamelCase ( UpperCAmelCase__ = 5					)							->     int:


   '''simple docstring'''


   a__     				=		defaultdict(UpperCAmelCase__					)
   a__     				=		0

   while True:
      a__     				=		get_digits(UpperCAmelCase__					)
      freqs[digits].append(UpperCAmelCase__					)

      if len(freqs[digits]					) == max_base:
         a__     				=		freqs[digits][0] ** 3
         return base

      num += 1





def   _lowerCamelCase ( UpperCAmelCase__					)							->     str:


   '''simple docstring'''


   return "".join(sorted(str(num**3					)					)					)


if __name__ == "__main__":
     print(F'''{solution() = }''')



"""simple docstring"""



import argparse

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


logging.set_verbosity_info()





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		CanineConfig()
   a__     				=		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
   a__     				=		CanineTokenizer()
   print(f'''Save tokenizer files to {pytorch_dump_path}'''					)
   tokenizer.save_pretrained(UpperCAmelCase__					)


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


