


from typing import Any




class A  :






      def __init__(					self						:     Any			,   lowercase_						:     Any )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		str		=data
             _lowerCamelCase       :		List[str]		=None




class A  :






      def __init__(					self						:     Tuple )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=None






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.head
             while temp is not None:
                    print(temp.data			,   end=' ' )
                    _lowerCamelCase       :		Tuple		=temp.next
             print()






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     Any )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=Node(lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.head
             _lowerCamelCase       :		Union[str, Any]		=new_node






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     str )					->						Optional[int]:
             """simple docstring"""




             if node_data_a == node_data_a:
                    return
             else:
                    _lowerCamelCase       :		Optional[Any]		=self.head
                    while node_a is not None and node_a.data != node_data_a:
                           _lowerCamelCase       :		str		=node_a.next

                    _lowerCamelCase       :		Dict		=self.head
                    while node_a is not None and node_a.data != node_data_a:
                           _lowerCamelCase       :		Any		=node_a.next

                    if node_a is None or node_a is None:
                           return

                    _lowerCamelCase       :		Optional[int]		=node_a.data, node_a.data


if __name__ == "__main__":
 lowerCamelCase											=       LinkedList()
 for i in range(5, 0, -1):
  ll.push(i)

 ll.print_list()

 ll.swap_nodes(1, 4)
 print('After swapping')
 ll.print_list()



from typing import TYPE_CHECKING

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


UpperCamelCase  							=						{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						['''ElectraTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ElectraForCausalLM''',
          '''ElectraForMaskedLM''',
          '''ElectraForMultipleChoice''',
          '''ElectraForPreTraining''',
          '''ElectraForQuestionAnswering''',
          '''ElectraForSequenceClassification''',
          '''ElectraForTokenClassification''',
          '''ElectraModel''',
          '''ElectraPreTrainedModel''',
          '''load_tf_weights_in_electra''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFElectraForMaskedLM''',
          '''TFElectraForMultipleChoice''',
          '''TFElectraForPreTraining''',
          '''TFElectraForQuestionAnswering''',
          '''TFElectraForSequenceClassification''',
          '''TFElectraForTokenClassification''',
          '''TFElectraModel''',
          '''TFElectraPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						[
          '''FlaxElectraForCausalLM''',
          '''FlaxElectraForMaskedLM''',
          '''FlaxElectraForMultipleChoice''',
          '''FlaxElectraForPreTraining''',
          '''FlaxElectraForQuestionAnswering''',
          '''FlaxElectraForSequenceClassification''',
          '''FlaxElectraForTokenClassification''',
          '''FlaxElectraModel''',
          '''FlaxElectraPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
      from .tokenization_electra import ElectraTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_electra_fast import ElectraTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_electra import (
                ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                ElectraForCausalLM,
                ElectraForMaskedLM,
                ElectraForMultipleChoice,
                ElectraForPreTraining,
                ElectraForQuestionAnswering,
                ElectraForSequenceClassification,
                ElectraForTokenClassification,
                ElectraModel,
                ElectraPreTrainedModel,
                load_tf_weights_in_electra,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_electra import (
                TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFElectraForMaskedLM,
                TFElectraForMultipleChoice,
                TFElectraForPreTraining,
                TFElectraForQuestionAnswering,
                TFElectraForSequenceClassification,
                TFElectraForTokenClassification,
                TFElectraModel,
                TFElectraPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_electra import (
                FlaxElectraForCausalLM,
                FlaxElectraForMaskedLM,
                FlaxElectraForMultipleChoice,
                FlaxElectraForPreTraining,
                FlaxElectraForQuestionAnswering,
                FlaxElectraForSequenceClassification,
                FlaxElectraForTokenClassification,
                FlaxElectraModel,
                FlaxElectraPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase  							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



