
"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCamelCase	:				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:
      UpperCamelCase	:				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:
      UpperCamelCase	:				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:
      UpperCamelCase	:				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

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




"""simple docstring"""






from __future__ import annotations

from typing import Any





class  _A						:

      def __init__(						self		,       __lowerCAmelCase = 6						):







            """simple docstring"""



            lowercase														=	None
            lowercase														=	None
            self.create_linked_list(__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	Node()
            lowercase														=	current_node
            lowercase														=	current_node
            lowercase														=	current_node
            for _ in range(1		,       __lowerCAmelCase						):
                  lowercase														=	Node()
                  lowercase														=	current_node
                  lowercase														=	previous_node
                  lowercase														=	current_node
            lowercase														=	self.front
            lowercase														=	previous_node

      def 				A__       (						self						):







            """simple docstring"""



            return (
                self.front == self.rear
                and self.front is not None
                and self.front.data is None
            )

      def 				A__       (						self						):







            """simple docstring"""



            self.check_can_perform_operation()
            return self.front.data if self.front else None

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if self.rear is None:
                  return

            self.check_is_full()
            if not self.is_empty():
                  lowercase														=	self.rear.next
            if self.rear:
                  lowercase														=	data

      def 				A__       (						self						):







            """simple docstring"""



            self.check_can_perform_operation()
            if self.rear is None or self.front is None:
                  return None
            if self.front == self.rear:
                  lowercase														=	self.front.data
                  lowercase														=	None
                  return data

            lowercase														=	self.front
            lowercase														=	old_front.next
            lowercase														=	old_front.data
            lowercase														=	None
            return data

      def 				A__       (						self						):







            """simple docstring"""



            if self.is_empty():
                  raise Exception("""Empty Queue"""						)







      def 				A__       (						self						):







            """simple docstring"""



            if self.rear and self.rear.next == self.front:
                  raise Exception("""Full Queue"""						)






class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	None
            lowercase														=	None
            lowercase														=	None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



