



from __future__ import annotations







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     Dict  ,lowerCamelCase_:     int = 0							)	->      None:
       UpperCAmelCase_     :       Any  		=					value
       UpperCAmelCase_     :       Node | None  		=					None
       UpperCAmelCase_     :       Node | None  		=					None







def       lowerCamelCase_  (    _a       :						Node | None   ,		_a       :						Node | None    ):



    '''simple docstring'''




    if treea is None:
        return treea
    if treea is None:
        return treea

    UpperCAmelCase_     :       int  		=					treea.value + treea.value
    UpperCAmelCase_     :       Union[str, Any]  		=					merge_two_binary_trees(treea.left   ,		treea.left    )
    UpperCAmelCase_     :       List[str]  		=					merge_two_binary_trees(treea.right   ,		treea.right    )
    return treea







def       lowerCamelCase_  (    _a       :						Node | None    ):



    '''simple docstring'''




    if root:
        print(root.value    )
        print_preorder(root.left    )
        print_preorder(root.right    )


if __name__ == "__main__":
      UpperCamelCase_									=   Node(1)
      UpperCamelCase_									=   Node(2)
      UpperCamelCase_									=   Node(3)
      UpperCamelCase_									=   Node(4)

      UpperCamelCase_									=   Node(2)
      UpperCamelCase_									=   Node(4)
      UpperCamelCase_									=   Node(6)
      UpperCamelCase_									=   Node(9)
      UpperCamelCase_									=   Node(5)

      print('''Tree1 is: ''')
      print_preorder(treea)
      print('''Tree2 is: ''')
      print_preorder(treea)
      UpperCamelCase_									=   merge_two_binary_trees(treea, treea)
      print('''Merged Tree is: ''')
      print_preorder(merged_tree)


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Any      =							['''image_processor''', '''tokenizer''']
							lowerCamelCase__:				List[Any]      =							'''EfficientNetImageProcessor'''
							lowerCamelCase__:				str      =							('''BertTokenizer''', '''BertTokenizerFast''')



							def __init__(     self	,				__snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								super().__init__(__snake_case	,				__snake_case							)



							def __call__(     self	,				__snake_case=None	,				__snake_case=None	,				__snake_case="max_length"	,				__snake_case=6_4	,				__snake_case=None	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none."							)

								if text is not None:
									UpperCAmelCase:					Dict 						=     self.tokenizer(
									    __snake_case	,				padding=__snake_case	,				max_length=__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if images is not None:
									UpperCAmelCase:					List[str] 						=     self.image_processor(__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if text is not None and images is not None:
									UpperCAmelCase:					Optional[int] 						=     image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**__snake_case							)	,				tensor_type=__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								return self.tokenizer.batch_decode(*__snake_case	,				**__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     List[str]:






								"""simple docstring"""



								return self.tokenizer.decode(*__snake_case	,				**__snake_case							)




							@property
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.tokenizer.model_input_names
								UpperCAmelCase:					int 						=     self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
