





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case           =			{
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			["""ConvBertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConvBertForMaskedLM""",
      """ConvBertForMultipleChoice""",
      """ConvBertForQuestionAnswering""",
      """ConvBertForSequenceClassification""",
      """ConvBertForTokenClassification""",
      """ConvBertLayer""",
      """ConvBertModel""",
      """ConvBertPreTrainedModel""",
      """load_tf_weights_in_convbert""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFConvBertForMaskedLM""",
      """TFConvBertForMultipleChoice""",
      """TFConvBertForQuestionAnswering""",
      """TFConvBertForSequenceClassification""",
      """TFConvBertForTokenClassification""",
      """TFConvBertLayer""",
      """TFConvBertModel""",
      """TFConvBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
  from .tokenization_convbert import ConvBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_convbert_fast import ConvBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convbert import (
        CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvBertForMaskedLM,
        ConvBertForMultipleChoice,
        ConvBertForQuestionAnswering,
        ConvBertForSequenceClassification,
        ConvBertForTokenClassification,
        ConvBertLayer,
        ConvBertModel,
        ConvBertPreTrainedModel,
        load_tf_weights_in_convbert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_convbert import (
        TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertLayer,
        TFConvBertModel,
        TFConvBertPreTrainedModel,
    )


else:
  import sys

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













from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def _A       (   ):
   lowercase__			       =  HfArgumentParser(__magic_name__	)
   lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   lowercase__			       =  TensorFlowBenchmark(args=__magic_name__	)
   try:
      lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      lowercase__			       =  "Arg --no_{0} is no longer used, please use --no-{0} instead."
      lowercase__			       =  " ".join(str(__magic_name__	).split(" "	)[:-1]	)
      lowercase__			       =  ""
      lowercase__			       =  eval(str(__magic_name__	).split(" "	)[-1]	)
      lowercase__			       =  []
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]	)
         else:
            wrong_args.append(__magic_name__	)
      if len(__magic_name__	) > 0:
         lowercase__			       =  full_error_msg + begin_error_msg + str(__magic_name__	)
      raise ValueError(__magic_name__	)
   benchmark.run()


if __name__ == "__main__":
  main()






