





"""simple docstring"""





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   =  {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DebertaForMaskedLM",
           "DebertaForQuestionAnswering",
           "DebertaForSequenceClassification",
           "DebertaForTokenClassification",
           "DebertaModel",
           "DebertaPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__   =  [
           "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFDebertaForMaskedLM",
           "TFDebertaForQuestionAnswering",
           "TFDebertaForSequenceClassification",
           "TFDebertaForTokenClassification",
           "TFDebertaModel",
           "TFDebertaPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

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












'''simple docstring'''






from typing import Any






class        lowercase_      :





     def __init__(     self     :					Dict     ,							__lowerCamelCase     :					Any			):
           snake_case__       :   str	      =    data
           snake_case__       :   Dict	      =    None






class        lowercase_      :





     def __init__(     self     :					int			):
           snake_case__       :   List[Any]	      =    None





     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Any	      =    self.head
           while temp is not None:
                 print(temp.data     ,							end=' '			)
                 snake_case__       :   int	      =    temp.next
           print()





     def 			_lowerCAmelCase      (     self     :					Optional[Any]     ,							__lowerCamelCase     :					Any			):
           snake_case__       :   Union[str, Any]	      =    Node(__lowerCamelCase			)
           snake_case__       :   List[str]	      =    self.head
           snake_case__       :   Any	      =    new_node





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					Tuple			):
           if node_data_a == node_data_a:
                 return
           else:
                 snake_case__       :   List[Any]	      =    self.head
                 while node_a is not None and node_a.data != node_data_a:
                       snake_case__       :   List[str]	      =    node_a.next

                 snake_case__       :   Union[str, Any]	      =    self.head
                 while node_a is not None and node_a.data != node_data_a:
                       snake_case__       :   str	      =    node_a.next

                 if node_a is None or node_a is None:
                       return

                 snake_case__      ,							snake_case__       :   Dict	      =    node_a.data, node_a.data


if __name__ == "__main__":
      A_			      =		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()
