


'''simple docstring'''






import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__A   :    Optional[Any]											= logging.get_logger(__name__)


__A   :    List[Any]											= {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[str]				,       A__   :  List[str]   ):
  '''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:
    lowerCAmelCase_     : Optional[Any]     =  TOKENIZER_CLASSES
  else:
    lowerCAmelCase_     : Tuple     =  {tokenizer_name: getattr(__lowerCAmelCase				,       tokenizer_name + """Fast"""   )}

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

  for tokenizer_name in tokenizer_names:
    lowerCAmelCase_     : Any     =  TOKENIZER_CLASSES[tokenizer_name]

    lowerCAmelCase_     : Any     =  True
    if checkpoint_name is None:
      lowerCAmelCase_     : Optional[int]     =  list(tokenizer_class.max_model_input_sizes.keys()   )
    else:
      lowerCAmelCase_     : str     =  [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
      lowerCAmelCase_     : str     =  tokenizer_class.from_pretrained(__lowerCAmelCase				,       force_download=__lowerCAmelCase   )

      # 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:
        lowerCAmelCase_,				lowerCAmelCase_     : int     =  checkpoint.split("""/"""   )
        lowerCAmelCase_     : List[Any]     =  os.path.join(__lowerCAmelCase				,       __lowerCAmelCase   )
      elif add_prefix:
        lowerCAmelCase_     : int     =  checkpoint
        lowerCAmelCase_     : List[Any]     =  dump_path
      else:
        lowerCAmelCase_     : List[str]     =  None
        lowerCAmelCase_     : Optional[Any]     =  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]:
        lowerCAmelCase_     : Any     =  list(tokenizer.pretrained_vocab_files_map.values()   )[0][checkpoint]
        lowerCAmelCase_     : str     =  file_path.split(__lowerCAmelCase   )[-1][0]
        if next_char == "/":
          lowerCAmelCase_     : Dict     =  os.path.join(__lowerCAmelCase				,       __lowerCAmelCase   )
          lowerCAmelCase_     : Dict     =  None

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

      lowerCAmelCase_     : Tuple     =  tokenizer.save_pretrained(
          __lowerCAmelCase				,       legacy_format=__lowerCAmelCase				,       filename_prefix=__lowerCAmelCase   )
      logger.info(f'=> File names {file_names}'   )

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


if __name__ == "__main__":
  __A   :    Union[str, Any]											= 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.",
  )
  __A   :    str											= parser.parse_args()

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





import math

__magic_name__  							=	10
__magic_name__  							=	7
__magic_name__  							=	BALLS_PER_COLOUR * NUM_COLOURS





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase = 20				):
    snake_case__    =					math.comb(__lowerCAmelCase     ,		__lowerCAmelCase				)
    snake_case__    =					math.comb(NUM_BALLS - BALLS_PER_COLOUR     ,		__lowerCAmelCase				)

    snake_case__    =					NUM_COLOURS * (1 - missing_colour / total)

    return F"""{result:.9f}"""


if __name__ == "__main__":
     print(solution(20))



