'''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    :     List[str]         =       logging.get_logger(__name__)


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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			Any				):
     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    		=  TOKENIZER_CLASSES
     else:
          _A    		=  {tokenizer_name: getattr(__snake_case   ,		tokenizer_name + 'Fast'				)}

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

     for tokenizer_name in tokenizer_names:
          _A    		=  TOKENIZER_CLASSES[tokenizer_name]

          _A    		=  True
          if checkpoint_name is None:
               _A    		=  list(tokenizer_class.max_model_input_sizes.keys()				)
          else:
               _A    		=  [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    		=  tokenizer_class.from_pretrained(__snake_case   ,		force_download=__snake_case				)

               # 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    		=  checkpoint.split('/'				)
                    _A    		=  os.path.join(__snake_case   ,		__snake_case				)
               elif add_prefix:
                    _A    		=  checkpoint
                    _A    		=  dump_path
               else:
                    _A    		=  None
                    _A    		=  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(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
                    _A    		=  file_path.split(__snake_case				)[-1][0]
                    if next_char == "/":
                         _A    		=  os.path.join(__snake_case   ,		__snake_case				)
                         _A    		=  None

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

               _A    		=  tokenizer.save_pretrained(
                   __snake_case   ,		legacy_format=__snake_case   ,		filename_prefix=__snake_case				)
               logger.info(F'=> File names {file_names}'				)

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


if __name__ == "__main__":
   _UpperCAmelCase    :     int         =       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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			str   ,		__snake_case							:			List[Any]=False				):

     if isinstance(__snake_case   ,		__snake_case				) and isinstance(__snake_case   ,		__snake_case				):
          _A    		=  len(set_a.intersection(__snake_case				)				)

          if alternative_union:
               _A    		=  len(__snake_case				) + len(__snake_case				)
          else:
               _A    		=  len(set_a.union(__snake_case				)				)

          return intersection / union

     if isinstance(__snake_case   ,		(list, tuple)				) and isinstance(__snake_case   ,		(list, tuple)				):
          _A    		=  [element for element in set_a if element in set_b]

          if alternative_union:
               _A    		=  len(__snake_case				) + len(__snake_case				)
               return len(__snake_case				) / union
          else:
               _A    		=  set_a + [element for element in set_b if element not in set_a]
               return len(__snake_case				) / len(__snake_case				)

          return len(__snake_case				) / len(__snake_case				)
     return None


if __name__ == "__main__":
   _UpperCAmelCase    :     Optional[Any]         =       {'''a''', '''b''', '''c''', '''d''', '''e'''}
   _UpperCAmelCase    :     str         =       {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
   print(jaccard_similarity(set_a, set_b))



