

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							 =			{'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE							 =			['''LevitFeatureExtractor''']
      _SCREAMING_SNAKE_CASE							 =			['''LevitImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE							 =			[
          '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''LevitForImageClassification''',
          '''LevitForImageClassificationWithTeacher''',
          '''LevitModel''',
          '''LevitPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_levit import LevitFeatureExtractor
            from .image_processing_levit import LevitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_levit import (
                LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                LevitForImageClassification,
                LevitForImageClassificationWithTeacher,
                LevitModel,
                LevitPreTrainedModel,
            )
else:
      import sys

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













from __future__ import annotations




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__ = 0       ):




									"""simple docstring"""

									__UpperCamelCase :   str									=value
									__UpperCamelCase :   Node | None									=None
									__UpperCamelCase :   Node | None									=None






def 				A	(			a_				,a_   )	->		Node | None:
					if treea is None:
										return treea
					if treea is None:
										return treea

					__UpperCamelCase :   Dict									=treea.value + treea.value
					__UpperCamelCase :   Any									=merge_two_binary_trees(treea.left				,treea.left   )
					__UpperCamelCase :   Union[str, Any]									=merge_two_binary_trees(treea.right				,treea.right   )
					return treea






def 				A	(			a_   )	->		None:
					if root:
										print(root.value   )
										print_preorder(root.left   )
										print_preorder(root.right   )


if __name__ == "__main__":
							A_			:List[str]	    =		Node(1)
							A_			:Dict	    =		Node(2)
							A_			:str	    =		Node(3)
							A_			:Union[str, Any]	    =		Node(4)

							A_			:Any	    =		Node(2)
							A_			:Tuple	    =		Node(4)
							A_			:Optional[Any]	    =		Node(6)
							A_			:Optional[Any]	    =		Node(9)
							A_			:str	    =		Node(5)

							print('''Tree1 is: ''')
							print_preorder(treea)
							print('''Tree2 is: ''')
							print_preorder(treea)
							A_			:Optional[Any]	    =		merge_two_binary_trees(treea, treea)
							print('''Merged Tree is: ''')
							print_preorder(merged_tree)

