



import warnings

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

class 						UpperCAmelCase__     (     __UpperCamelCase  ):




			'''simple docstring'''






			UpperCamelCase     		=			["""image_processor""", """tokenizer"""]
			UpperCamelCase     		=			"""AutoImageProcessor"""
			UpperCamelCase     		=			"""AutoTokenizer"""




			def __init__(  self				:					Optional[int]			,			a_				:					List[str]=None			,			a_				:					List[Any]=None			,			**a_				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
							    ''' instead.'''			,			a_			,			)
							__UpperCAmelCase       :	List[Any]						=      kwargs.pop('''feature_extractor'''					)

					__UpperCAmelCase       :	int						=      image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError('''You have to specify an image_processor.'''					)
					if tokenizer is None:
							raise ValueError('''You have to specify a tokenizer.'''					)

					super().__init__(a_			,			a_					)
					__UpperCAmelCase       :	Optional[int]						=      self.image_processor




			def __call__(  self				:					Tuple			,			a_				:					Optional[Any]=None			,			a_				:					str=None			,			a_				:					Any=None			,			**a_				:					Optional[int]					):
					'''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       :	Tuple						=      self.tokenizer(a_			,			return_tensors=a_			,			**a_					)

					if images is not None:
							__UpperCAmelCase       :	Union[str, Any]						=      self.image_processor(a_			,			return_tensors=a_			,			**a_					)

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




			def 					snake_case__	(  self				:					Optional[int]			,			*a_				:					Optional[Any]			,			**a_				:					Any					):
					'''simple docstring'''





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




			def 					snake_case__	(  self				:					str			,			*a_				:					int			,			**a_				:					Optional[Any]					):
					'''simple docstring'''





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




			@property
			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





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




			@property
			def 					snake_case__	(  self				:					Dict					):
					'''simple docstring'''





					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,			a_			,			)
					return self.image_processor_class




			@property
			def 					snake_case__	(  self				:					Any					):
					'''simple docstring'''





					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,			a_			,			)
					return self.image_processor










class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					List[Any]			,			a_				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      data
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None

def 			a		( _UpperCAmelCase				:   BinaryTreeNode | None					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		if node is None:
				__UpperCAmelCase       :	List[Any]						=      BinaryTreeNode(_UpperCAmelCase		)
				return node

		# binary search tree is not empty,
		# so we will insert it into the tree
		# if new_value is less than value of data in node,
		#  add it to left subtree and proceed recursively
		if new_value < node.data:
				__UpperCAmelCase       :	int						=      insert(node.left_child					,  _UpperCAmelCase		)
		else:
				# if new_value is greater than value of data in node,
				#  add it to right subtree and proceed recursively
				__UpperCAmelCase       :	str						=      insert(node.right_child					,  _UpperCAmelCase		)
		return node

def 			a		( _UpperCAmelCase				:   None | BinaryTreeNode		):  # if node is None,return







		'''simple docstring'''





		if node:
				__UpperCAmelCase       :	Optional[int]						=      inorder(node.left_child		)
				__UpperCAmelCase       :	int						=      [*inorder_array, node.data]
				__UpperCAmelCase       :	Optional[Any]						=      inorder_array + inorder(node.right_child		)
		else:
				__UpperCAmelCase       :	List[Any]						=      []
		return inorder_array

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Any						=      insert(_UpperCAmelCase					,  15		)
		insert(_UpperCAmelCase					,  10		)
		insert(_UpperCAmelCase					,  25		)
		insert(_UpperCAmelCase					,  6		)
		insert(_UpperCAmelCase					,  14		)
		insert(_UpperCAmelCase					,  20		)
		insert(_UpperCAmelCase					,  60		)
		return root

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      make_tree()
		print('''Printing values of binary search tree in Inorder Traversal.'''		)
		inorder(_UpperCAmelCase		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





