
from __future__ import annotations

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



@dataclass
class    a_ :
			a	:			Tuple					   =							4_2
			a	:			Optional[int]					   =							None
			a	:			Dict					   =							None







def 	UpperCamelCase__  (     )  ->     Node | None:
						_lowercase					       =					Node(1       )
						_lowercase					       =					Node(2       )
						_lowercase					       =					Node(3       )
						_lowercase					       =					Node(4       )
						_lowercase					       =					Node(5       )
						return tree




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]       )  ->     list[int]:
						return [root.data, *preorder(root.left       ), *preorder(root.right       )] if root else []




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     list[int]:
						return postorder(root.left       ) + postorder(root.right       ) + [root.data] if root else []




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Tuple       )  ->     list[int]:
						return [*inorder(root.left       ), root.data, *inorder(root.right       )] if root else []




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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     Sequence[Node | None]:
						_lowercase					       =					[]

						if root is None:
												return output

						_lowercase					       =					deque([root]       )

						while process_queue:
												_lowercase					       =					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__  (     SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     Sequence[Node | None]:
						_lowercase					       =					[]

						def populate_output(SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	Dict       ) -> 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(_lowerCAmelCase		,   _lowerCAmelCase       )
						return output




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	str       )  ->     Sequence[Node | None]:
						_lowercase					       =					[]

						def populate_output(SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Dict       ) -> 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(_lowerCAmelCase		,   _lowerCAmelCase       )
						return output




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Sequence[Node | None] | list[Any]:
						if root is None:
												return []

						_lowercase					       =					[]

						_lowercase					       =					0
						_lowercase					       =					height(_lowerCAmelCase       )

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

						return output




def 	UpperCamelCase__  (     )  ->     None:  # Main function for testing.
						_lowercase					       =					make_tree()
						print(f"""In-order Traversal: {inorder(_lowerCAmelCase       )}"""       )
						print(f"""Pre-order Traversal: {preorder(_lowerCAmelCase       )}"""       )
						print(f"""Post-order Traversal: {postorder(_lowerCAmelCase       )}"""		,   """\n"""       )

						print(f"""Height of Tree: {height(_lowerCAmelCase       )}"""		,   """\n"""       )

						print("""Complete Level Order Traversal: """       )
						print(level_order(_lowerCAmelCase       )		,   """\n"""       )

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

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

						print("""\nZigZag order Traversal: """       )
						print(zigzag(_lowerCAmelCase       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a  			=						{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

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