





from typing import TYPE_CHECKING

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


UpperCAmelCase_       : Union[str, Any]							=    {
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[Any]							=    ["""DebertaV2TokenizerFast"""]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[Any]							=    [
          """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFDebertaV2ForMaskedLM""",
          """TFDebertaV2ForQuestionAnswering""",
          """TFDebertaV2ForSequenceClassification""",
          """TFDebertaV2ForTokenClassification""",
          """TFDebertaV2Model""",
          """TFDebertaV2PreTrainedModel""",
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : str							=    [
          """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DebertaV2ForMaskedLM""",
          """DebertaV2ForMultipleChoice""",
          """DebertaV2ForQuestionAnswering""",
          """DebertaV2ForSequenceClassification""",
          """DebertaV2ForTokenClassification""",
          """DebertaV2Model""",
          """DebertaV2PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_deberta_va import (
          DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DebertaVaConfig,
          DebertaVaOnnxConfig,
      )
      from .tokenization_deberta_va import DebertaVaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_deberta_va import (
                TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDebertaVaForMaskedLM,
                TFDebertaVaForQuestionAnswering,
                TFDebertaVaForSequenceClassification,
                TFDebertaVaForTokenClassification,
                TFDebertaVaModel,
                TFDebertaVaPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deberta_va import (
                DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                DebertaVaForMaskedLM,
                DebertaVaForMultipleChoice,
                DebertaVaForQuestionAnswering,
                DebertaVaForSequenceClassification,
                DebertaVaForTokenClassification,
                DebertaVaModel,
                DebertaVaPreTrainedModel,
            )

else:
      import sys

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





def 						_lowerCAmelCase    (					__lowerCamelCase :  list  ):



 """simple docstring"""





 if len(__lowerCamelCase  ) <= 1:
  return data
 else:
  return [
      *quick_sort([e for e in data[1:] if e <= data[0]]  ),
      data[0],
      *quick_sort([e for e in data[1:] if e > data[0]]  ),
  ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()






