

from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any







@dataclass
class 						__magic_name__			:

   '''simple docstring'''




   lowerCamelCase__ :	int
   lowerCamelCase__ :	Node | None    =   None
   lowerCamelCase__ :	Node | None    =   None





def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  =Node(1							)
  a__  =Node(2							)
  a__  =Node(3							)
  a__  =Node(4							)
  a__  =Node(5							)
  return tree




def 			UpperCAmelCase__				(			_A      : Node | None							):





  '''simple docstring'''







  return [root.data, *preorder(root.left							), *preorder(root.right							)] if root else []




def 			UpperCAmelCase__				(			_A      : Node | None							):





  '''simple docstring'''







  return postorder(root.left							) + postorder(root.right							) + [root.data] if root else []




def 			UpperCAmelCase__				(			_A      : Node | None							):





  '''simple docstring'''







  return [*inorder(root.left							), root.data, *inorder(root.right							)] if root else []




def 			UpperCAmelCase__				(			_A      : Node | None							):





  '''simple docstring'''







  return (max(height(root.left							)	,      height(root.right							)							) + 1) if root else 0




def 			UpperCAmelCase__				(			_A      : Node | None							):





  '''simple docstring'''







  a__  =[]

  if root is None:
    return output

  a__  =deque([root]							)

  while process_queue:
    a__  =process_queue.popleft()
    output.append(node.data							)

    if node.left:
      process_queue.append(node.left							)
    if node.right:
      process_queue.append(node.right							)
  return output




def 			UpperCAmelCase__				(			_A      : Node | None	,      _A      : int							):





  '''simple docstring'''







  a__  =[]

  def populate_output(_A      : Node | None	,      _A      : int							) -> None:
    if not root:
      return
    if level == 1:
      output.append(root.data							)
    elif level > 1:
      populate_output(root.left	,      level - 1							)
      populate_output(root.right	,      level - 1							)

  populate_output(_A	,      _A							)
  return output




def 			UpperCAmelCase__				(			_A      : Node | None	,      _A      : int							):





  '''simple docstring'''







  a__  =[]

  def populate_output(_A      : Node | None	,      _A      : int							) -> None:
    if root is None:
      return
    if level == 1:
      output.append(root.data							)
    elif level > 1:
      populate_output(root.right	,      level - 1							)
      populate_output(root.left	,      level - 1							)

  populate_output(_A	,      _A							)
  return output




def 			UpperCAmelCase__				(			_A      : Node | None							):





  '''simple docstring'''







  if root is None:
    return []

  a__  =[]

  a__  =0
  a__  =height(_A							)

  for h in range(1	,      height_tree + 1							):
    if not flag:
      output.append(get_nodes_from_left_to_right(_A	,      _A							)							)
      a__  =1
    else:
      output.append(get_nodes_from_right_to_left(_A	,      _A							)							)
      a__  =0

  return output




def 			UpperCAmelCase__				(			):  # Main function for testing.





  '''simple docstring'''







  a__  =make_tree()
  print(F"""In-order Traversal: {inorder(_A							)}"""							)
  print(F"""Pre-order Traversal: {preorder(_A							)}"""							)
  print(F"""Post-order Traversal: {postorder(_A							)}"""	,      '''\n'''							)

  print(F"""Height of Tree: {height(_A							)}"""	,      '''\n'''							)

  print('''Complete Level Order Traversal: '''							)
  print(level_order(_A							)	,      '''\n'''							)

  print('''Level-wise order Traversal: '''							)

  for level in range(1	,      height(_A							) + 1							):
    print(F"""Level {level}:"""	,      get_nodes_from_left_to_right(_A	,      level=_A							)							)

  print('''\nZigZag order Traversal: '''							)
  print(zigzag(_A							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()



from typing import TYPE_CHECKING

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


lowerCamelCase           =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

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

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

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''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

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