

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

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


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

def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: int				,__UpperCamelCase	: List[str]				,__UpperCamelCase	: 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:
            A_						  =				TOKENIZER_CLASSES
      else:
            A_						  =				{tokenizer_name: getattr(__UpperCamelCase				,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(__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:
                        A_  ,     A_						  =				checkpoint.split("/"    )
                        A_						  =				os.path.join(__UpperCamelCase				,__UpperCamelCase    )
                  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(__UpperCamelCase    )[-1][0]
                        if next_char == "/":
                              A_						  =				os.path.join(__UpperCamelCase				,__UpperCamelCase    )
                              A_						  =				None

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

                  A_						  =				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__":
      __a    :Optional[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.',
      )
      __a    :List[str]      =			parser.parse_args()

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



from __future__ import annotations






def 						a__			(				A__,      A__ = None,      A__ = None				):
			if start is None:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					0

			if end is None:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					len(A__				) - 1

			if start >= end:
						return

			SCREAMING_SNAKE_CASE_		:     Tuple					=					(start + end) // 2

			slowsort(A__,      A__,      A__				)
			slowsort(A__,      mid + 1,      A__				)

			if sequence[end] < sequence[mid]:
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					sequence[mid], sequence[end]

			slowsort(A__,      A__,      end - 1				)


if __name__ == "__main__":
				from doctest import testmod

				testmod()





