




'''simple docstring'''



from __future__ import annotations

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






@dataclass
class 							_a				:





       '''simple docstring'''






       A						:				int
       A						:				Node | None        =						None
       A						:				Node | None        =						None




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						Node(1    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						Node(2    )
      SCREAMING_SNAKE_CASE   :      int    					=						Node(3    )
      SCREAMING_SNAKE_CASE   :      int    					=						Node(4    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						Node(5    )
      return tree




def   lowercase__(			__UpperCamelCase:					Node | None    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Node | None    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Node | None    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Node | None    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Node | None    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      list[Any]    					=						[]

      if root is None:
            return output

      SCREAMING_SNAKE_CASE   :      Dict    					=						deque([root]    )

      while process_queue:
            SCREAMING_SNAKE_CASE   :      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   lowercase__(			__UpperCamelCase:					Node | None      ,__UpperCamelCase:					int    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      list[Any]    					=						[]

      def populate_output(__UpperCamelCase:					Node | None      ,__UpperCamelCase:					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(__UpperCamelCase      ,__UpperCamelCase    )
      return output




def   lowercase__(			__UpperCamelCase:					Node | None      ,__UpperCamelCase:					int    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      list[Any]    					=						[]

      def populate_output(__UpperCamelCase:					Node | None      ,__UpperCamelCase:					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(__UpperCamelCase      ,__UpperCamelCase    )
      return output




def   lowercase__(			__UpperCamelCase:					Node | None    ):

      """simple docstring"""






      if root is None:
            return []

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

      SCREAMING_SNAKE_CASE   :      List[str]    					=						0
      SCREAMING_SNAKE_CASE   :      str    					=						height(__UpperCamelCase    )

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

      return output




def   lowercase__(			):  # Main function for testing.

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						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()








'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCamelCase_    	=				{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RoFormerForCausalLM",
          "RoFormerForMaskedLM",
          "RoFormerForMultipleChoice",
          "RoFormerForQuestionAnswering",
          "RoFormerForSequenceClassification",
          "RoFormerForTokenClassification",
          "RoFormerLayer",
          "RoFormerModel",
          "RoFormerPreTrainedModel",
          "load_tf_weights_in_roformer",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRoFormerForCausalLM",
          "TFRoFormerForMaskedLM",
          "TFRoFormerForMultipleChoice",
          "TFRoFormerForQuestionAnswering",
          "TFRoFormerForSequenceClassification",
          "TFRoFormerForTokenClassification",
          "TFRoFormerLayer",
          "TFRoFormerModel",
          "TFRoFormerPreTrainedModel",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxRoFormerForMaskedLM",
          "FlaxRoFormerForMultipleChoice",
          "FlaxRoFormerForQuestionAnswering",
          "FlaxRoFormerForSequenceClassification",
          "FlaxRoFormerForTokenClassification",
          "FlaxRoFormerModel",
          "FlaxRoFormerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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


