text: import argparse
  import logging
  
  from rich.logging import RichHandler
  from transformers import AutoTokenizer
  
  from tftokenizers.database import save_tokenizer_attributes
  from tftokenizers.types import TokenizerAttributes
  from tftokenizers.utils import parse_args_to_keywords
  
  
  def parse_args():
      parser = argparse.ArgumentParser(
          description="Save a huggingface tokenizer for conversion to a Tensorflow Text tokenizer."
      )
      parser.add_argument(
          "--name_or_path",
          "-n",
          type=str,
          help="Name to pretrained model or tokenizer from huggingface.co/models.",
          default="bert-base-uncased",
          # required=True
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="saved_tokenizers",
          help="Where to store the Huffingface tokenizer configs.",
      )
      parser.add_argument(
          "--verbose",
          action="store_true",
          help="Display debug info if passed in as argument",
      )
      args = parser.parse_args()
      return args
  
  
  def main():
      args = parse_args()
  
      logging.basicConfig(
          format="%(message)s",
          datefmt="[%X]",
          level="NOTSET" if args.verbose else logging.INFO,
          handlers=[RichHandler(rich_tracebacks=True)],
      )
  
      log = logging.getLogger("rich")
      log.info("Converting tokenizer...")
  
      name, path = parse_args_to_keywords(args)
      tokenizer = AutoTokenizer.from_pretrained(name)
      tokenizer.save_pretrained(path)
      log.info(f"Saved tokenizer to: {path}")
  
      attributes = TokenizerAttributes(
          name_or_path=name,
          export_path=path,
      )
      save_tokenizer_attributes(attributes)
      log.info("Saving a download script")
  
  
  if __name__ == "__main__":
      main()
  