
from __future__ import annotations

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




@dataclass
class 				_lowerCamelCase :





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  42
     SCREAMING_SNAKE_CASE_		   =  None
     SCREAMING_SNAKE_CASE_		   =  None


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Dict					=				Node(1		)
      UpperCamelCase__    :   int					=				Node(2		)
      UpperCamelCase__    :   Dict					=				Node(3		)
      UpperCamelCase__    :   Optional[Any]					=				Node(4		)
      UpperCamelCase__    :   Dict					=				Node(5		)
      return tree


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      return [root.data, *preorder(root.left		), *preorder(root.right		)] if root else []


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      return postorder(root.left		) + postorder(root.right		) + [root.data] if root else []


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      return [*inorder(root.left		), root.data, *inorder(root.right		)] if root else []


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      return (max(height(root.left		) ,		height(root.right		)		) + 1) if root else 0


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   list[Any]					=				[]

      if root is None:
            return output

      UpperCamelCase__    :   Any					=				deque([root]		)

      while process_queue:
            UpperCamelCase__    :   Union[str, Any]					=				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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   list[Any]					=				[]

      def populate_output(UpperCamelCase__ ,		UpperCamelCase__		) -> 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(UpperCamelCase__ ,		UpperCamelCase__		)
      return output


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   list[Any]					=				[]

      def populate_output(UpperCamelCase__ ,		UpperCamelCase__		) -> 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(UpperCamelCase__ ,		UpperCamelCase__		)
      return output


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if root is None:
            return []

      UpperCamelCase__    :   list[Sequence[Node | None]]					=				[]

      UpperCamelCase__    :   Dict					=				0
      UpperCamelCase__    :   int					=				height(UpperCamelCase__		)

      for h in range(1 ,		height_tree + 1		):
            if not flag:
                  output.append(get_nodes_from_left_to_right(UpperCamelCase__ ,		UpperCamelCase__		)		)
                  UpperCamelCase__    :   List[str]					=				1
            else:
                  output.append(get_nodes_from_right_to_left(UpperCamelCase__ ,		UpperCamelCase__		)		)
                  UpperCamelCase__    :   List[str]					=				0

      return output


def 							SCREAMING_SNAKE_CASE_      (			):  # Main function for testing.
      UpperCamelCase__    :   str					=				make_tree()
      print(f'''In-order Traversal: {inorder(UpperCamelCase__		)}'''		)
      print(f'''Pre-order Traversal: {preorder(UpperCamelCase__		)}'''		)
      print(f'''Post-order Traversal: {postorder(UpperCamelCase__		)}''' ,		'''\n'''		)

      print(f'''Height of Tree: {height(UpperCamelCase__		)}''' ,		'''\n'''		)

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

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

      for level in range(1 ,		height(UpperCamelCase__		) + 1		):
            print(f'''Level {level}:''' ,		get_nodes_from_left_to_right(UpperCamelCase__ ,		level=UpperCamelCase__		)		)

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


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase      ={"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SwinForImageClassification",
        "SwinForMaskedImageModeling",
        "SwinModel",
        "SwinPreTrainedModel",
        "SwinBackbone",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFSwinForImageClassification",
        "TFSwinForMaskedImageModeling",
        "TFSwinModel",
        "TFSwinPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swin import (
            SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
            SwinModel,
            SwinPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_swin import (
            TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
            TFSwinModel,
            TFSwinPreTrainedModel,
        )

else:
    import sys

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


