"""simple docstring"""






from typing import TYPE_CHECKING

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


__lowerCAmelCase    :       Optional[Any]					     ={
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase    :       Tuple					     =[
           """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """RobertaPreLayerNormForCausalLM""",
           """RobertaPreLayerNormForMaskedLM""",
           """RobertaPreLayerNormForMultipleChoice""",
           """RobertaPreLayerNormForQuestionAnswering""",
           """RobertaPreLayerNormForSequenceClassification""",
           """RobertaPreLayerNormForTokenClassification""",
           """RobertaPreLayerNormModel""",
           """RobertaPreLayerNormPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase    :       List[str]					     =[
           """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFRobertaPreLayerNormForCausalLM""",
           """TFRobertaPreLayerNormForMaskedLM""",
           """TFRobertaPreLayerNormForMultipleChoice""",
           """TFRobertaPreLayerNormForQuestionAnswering""",
           """TFRobertaPreLayerNormForSequenceClassification""",
           """TFRobertaPreLayerNormForTokenClassification""",
           """TFRobertaPreLayerNormMainLayer""",
           """TFRobertaPreLayerNormModel""",
           """TFRobertaPreLayerNormPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase    :       List[str]					     =[
           """FlaxRobertaPreLayerNormForCausalLM""",
           """FlaxRobertaPreLayerNormForMaskedLM""",
           """FlaxRobertaPreLayerNormForMultipleChoice""",
           """FlaxRobertaPreLayerNormForQuestionAnswering""",
           """FlaxRobertaPreLayerNormForSequenceClassification""",
           """FlaxRobertaPreLayerNormForTokenClassification""",
           """FlaxRobertaPreLayerNormModel""",
           """FlaxRobertaPreLayerNormPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

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




"""simple docstring"""






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   str							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   List[Any]							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[Any]							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   int							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   List[str]							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[Any]							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)



