


"""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()

_lowerCAmelCase							:    Any					   =	logging.get_logger(__name__)


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







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     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:
      _lowerCamelCase   :				Tuple							=     TOKENIZER_CLASSES
   else:
      _lowerCamelCase   :				Optional[int]							=     {tokenizer_name: getattr(_lowerCamelCase			,		tokenizer_name + "Fast"       )}

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

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

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

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

         _lowerCamelCase   :				List[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__":
  _lowerCAmelCase							:    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.''',
  )
  _lowerCAmelCase							:    List[Any]					   =	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_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SEWForCTC""",
        """SEWForSequenceClassification""",
        """SEWModel""",
        """SEWPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

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

