def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int = 1_00     ):







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				n * (n + 1) * (2 * n + 1) / 6
  SCREAMING_SNAKE_CASE__      							=				(n * (n + 1) / 2) ** 2
  return int(square_of_sum - sum_of_squares     )


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

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__lowerCamelCase      :			int							       =		logging.get_logger(__name__)


__lowerCamelCase      :			Tuple							       =		{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Optional[Any]     )							-> Union[str, Any]:







  """simple docstring"""





  if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
    raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()     )}."""     )

  if tokenizer_name is None:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES
  else:
    SCREAMING_SNAKE_CASE__      							=				{tokenizer_name: getattr(__UpperCamelCase			,					tokenizer_name + """Fast"""     )}

  logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""     )

  for tokenizer_name in tokenizer_names:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES[tokenizer_name]

    SCREAMING_SNAKE_CASE__      							=				True
    if checkpoint_name is None:
      SCREAMING_SNAKE_CASE__      							=				list(tokenizer_class.max_model_input_sizes.keys()     )
    else:
      SCREAMING_SNAKE_CASE__      							=				[checkpoint_name]

    logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""     )

    for checkpoint in checkpoint_names:
      logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""     )

      # Load tokenizer
      SCREAMING_SNAKE_CASE__      							=				tokenizer_class.from_pretrained(__UpperCamelCase			,					force_download=__UpperCamelCase     )

      # Save fast tokenizer
      logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""     )

      # For organization names we create sub-directories
      if "/" in checkpoint:
        SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				checkpoint.split("""/"""     )
        SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
      elif add_prefix:
        SCREAMING_SNAKE_CASE__      							=				checkpoint
        SCREAMING_SNAKE_CASE__      							=				dump_path
      else:
        SCREAMING_SNAKE_CASE__      							=				None
        SCREAMING_SNAKE_CASE__      							=				dump_path

      logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""     )

      if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()     )[0]:
        SCREAMING_SNAKE_CASE__      							=				list(tokenizer.pretrained_vocab_files_map.values()     )[0][checkpoint]
        SCREAMING_SNAKE_CASE__      							=				file_path.split(__UpperCamelCase     )[-1][0]
        if next_char == "/":
          SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
          SCREAMING_SNAKE_CASE__      							=				None

        logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""     )

      SCREAMING_SNAKE_CASE__      							=				tokenizer.save_pretrained(
          __UpperCamelCase			,					legacy_format=__UpperCamelCase			,					filename_prefix=__UpperCamelCase     )
      logger.info(f"""=> File names {file_names}"""     )

      for file_name in file_names:
        if not file_name.endswith("""tokenizer.json"""     ):
          os.remove(__UpperCamelCase     )
          logger.info(f"""=> removing {file_name}"""     )


if __name__ == "__main__":
   __lowerCamelCase      :			str							       =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
   )
   parser.add_argument(
       '''--tokenizer_name''',
       default=None,
       type=str,
       help=(
           F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
           '''download and convert all the checkpoints from AWS.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_name''',
       default=None,
       type=str,
       help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
   )
   parser.add_argument(
       '''--force_download''',
       action='''store_true''',
       help='''Re-download checkpoints.''',
   )
   __lowerCamelCase      :			Any							       =		parser.parse_args()

   convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
