




import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

snake_case_ : Union[str, Any]    		=       logging.get_logger(__name__)


snake_case_ : Optional[int]    		=       {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}


def    A						(__A   :    str   ,				__A   :    Optional[int]   ,				__A   :    Optional[Any]   ,				__A   :    Union[str, Any]       )   ->      Optional[int]:







     """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:
          UpperCAmelCase_          =							TOKENIZER_CLASSES
     else:
          UpperCAmelCase_          =							{tokenizer_name: getattr(__A   ,				tokenizer_name + '''Fast'''       )}

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

     for tokenizer_name in tokenizer_names:
          UpperCAmelCase_          =							TOKENIZER_CLASSES[tokenizer_name]

          UpperCAmelCase_          =							True
          if checkpoint_name is None:
               UpperCAmelCase_          =							list(tokenizer_class.max_model_input_sizes.keys()       )
          else:
               UpperCAmelCase_          =							[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
               UpperCAmelCase_          =							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:
                    UpperCAmelCase_     ,       UpperCAmelCase_          =							checkpoint.split('''/'''       )
                    UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
               elif add_prefix:
                    UpperCAmelCase_          =							checkpoint
                    UpperCAmelCase_          =							dump_path
               else:
                    UpperCAmelCase_          =							None
                    UpperCAmelCase_          =							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]:
                    UpperCAmelCase_          =							list(tokenizer.pretrained_vocab_files_map.values()       )[0][checkpoint]
                    UpperCAmelCase_          =							file_path.split(__A       )[-1][0]
                    if next_char == "/":
                         UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
                         UpperCAmelCase_          =							None

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

               UpperCAmelCase_          =							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__":
    snake_case_ : 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.",
    )
    snake_case_ : Union[str, Any]    		=       parser.parse_args()

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












from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class __snake_case			:
   pass






