





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_lowerCAmelCase								=  logging.get_logger(__name__)


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



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase  ):
   '''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_  :					str         =						TOKENIZER_CLASSES
   else:
      A_  :					List[str]         =						{tokenizer_name: getattr(A__	,tokenizer_name + """Fast"""  )}

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

   for tokenizer_name in tokenizer_names:
      A_  :					Tuple         =						TOKENIZER_CLASSES[tokenizer_name]

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

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

         A_  :					Union[str, Any]         =						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__":
      _lowerCAmelCase								=  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								=  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_tokenizers_available, is_torch_available


__A   :    str											= {
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    Optional[int]											= [
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

  __A   :    Optional[int]											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



