


'''simple docstring'''
def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):

							if not root or root not in binary_tree_mirror_dictionary:
														return
							A_ 						=		binary_tree_mirror_dictionary[root][:2]
							A_ 						=		[right_child, left_child]
							binary_tree_mirror_dict(UpperCamelCase_	,     UpperCamelCase_   )
							binary_tree_mirror_dict(UpperCamelCase_	,     UpperCamelCase_   )







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_ = 1   ):

							if not binary_tree:
														raise ValueError("binary tree cannot be empty"   )
							if root not in binary_tree:
														A_ 						=		f"root {root} is not present in the binary_tree"
														raise ValueError(UpperCamelCase_   )
							A_ 						=		dict(UpperCamelCase_   )
							binary_tree_mirror_dict(UpperCamelCase_	,     UpperCamelCase_   )
							return binary_tree_mirror_dictionary


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE				:							int		       =		{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
							print(F"Binary tree: {binary_tree}")
							__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		binary_tree_mirror(binary_tree, 5)
							print(F"Binary tree mirror: {binary_tree_mirror_dictionary}")



'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					int      	=["image_processor", "tokenizer"]
						_UpperCAmelCase		:					Dict      	="EfficientNetImageProcessor"
						_UpperCAmelCase		:					Union[str, Any]      	=("BertTokenizer", "BertTokenizerFast")






						def __init__(     self       :			List[Any]   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			int  ):
													super().__init__(lowerCAmelCase   ,     lowerCAmelCase  )






						def __call__(     self       :			str   ,     lowerCAmelCase       :			Tuple=None   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			Optional[int]="max_length"   ,     lowerCAmelCase       :			Tuple=64   ,     lowerCAmelCase       :			Dict=None   ,     **lowerCAmelCase       :			Any  ):
													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:
																				A_ 						=		self.tokenizer(
																				    lowerCAmelCase   ,     padding=lowerCAmelCase   ,     max_length=lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if images is not None:
																				A_ 						=		self.image_processor(lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if text is not None and images is not None:
																				A_ 						=		image_features.pixel_values
																				return encoding
													elif text is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**lowerCAmelCase  )   ,     tensor_type=lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     *lowerCAmelCase       :			Optional[int]   ,     **lowerCAmelCase       :			str  ):
													return self.tokenizer.batch_decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str   ,     *lowerCAmelCase       :			Any   ,     **lowerCAmelCase       :			Dict  ):
													return self.tokenizer.decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						@property
						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		self.tokenizer.model_input_names
													A_ 						=		self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





