



'''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  			=						logging.get_logger(__name__)


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





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Optional[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:
            snake_case__    :       str       		= TOKENIZER_CLASSES
      else:
            snake_case__    :       List[str]       		= {tokenizer_name: getattr(_lowerCAmelCase	,  tokenizer_name + """Fast"""						)}

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

      for tokenizer_name in tokenizer_names:
            snake_case__    :       Optional[Any]       		= TOKENIZER_CLASSES[tokenizer_name]

            snake_case__    :       str       		= True
            if checkpoint_name is None:
                  snake_case__    :       Any       		= list(tokenizer_class.max_model_input_sizes.keys()						)
            else:
                  snake_case__    :       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
                  snake_case__    :       Optional[int]       		= 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:
                        snake_case__		,							snake_case__    :       List[str]       		= checkpoint.split("""/"""						)
                        snake_case__    :       Tuple       		= os.path.join(_lowerCAmelCase	,  _lowerCAmelCase						)
                  elif add_prefix:
                        snake_case__    :       str       		= checkpoint
                        snake_case__    :       Dict       		= dump_path
                  else:
                        snake_case__    :       str       		= None
                        snake_case__    :       Tuple       		= 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]:
                        snake_case__    :       Union[str, Any]       		= list(tokenizer.pretrained_vocab_files_map.values()						)[0][checkpoint]
                        snake_case__    :       Optional[Any]       		= file_path.split(_lowerCAmelCase						)[-1][0]
                        if next_char == "/":
                              snake_case__    :       List[str]       		= os.path.join(_lowerCAmelCase	,  _lowerCAmelCase						)
                              snake_case__    :       str       		= None

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

                  snake_case__    :       str       		= 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  			=						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  			=						parser.parse_args()

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





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a  			=						{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

       __a  			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
