


'''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()

UpperCAmelCase_      :		Dict          =		logging.get_logger(__name__)


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


def    UpperCAmelCase_  (       A		,	A		,	A		,	A      ):
   '''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:
      _a					:  str						=  TOKENIZER_CLASSES
   else:
      _a					:  Union[str, Any]						=  {tokenizer_name: getattr(A		,	tokenizer_name + 'Fast'      )}

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

   for tokenizer_name in tokenizer_names:
      _a					:  Union[str, Any]						=  TOKENIZER_CLASSES[tokenizer_name]

      _a					:  str						=  True
      if checkpoint_name is None:
         _a					:  Tuple						=  list(tokenizer_class.max_model_input_sizes.keys()      )
      else:
         _a					:  Union[str, Any]						=  [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
         _a					:  Tuple						=  tokenizer_class.from_pretrained(A		,	force_download=A      )

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

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

         _a					:  Dict						=  tokenizer.save_pretrained(
             A		,	legacy_format=A		,	filename_prefix=A      )
         logger.info(f'''=> File names {file_names}'''      )

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


if __name__ == "__main__":
 UpperCAmelCase_      :		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.",
 )
 UpperCAmelCase_      :		int          =		parser.parse_args()

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






'''simple docstring'''




def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   return "\n".join(
       f'''{number} * {i} = {number * i}''' for i in range(1		,	number_of_terms + 1      )      )


if __name__ == "__main__":
 print(multiplication_table(number=5, number_of_terms=10))


