
class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					int				,A   :					List[Any]					):
					__A       =					val
					__A       =					None
					__A       =					None







	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					Dict					):
					if self.val:
									if val < self.val:
													if self.left is None:
																	__A       =					Node(A					)
													else:
																	self.left.insert(A					)
									elif val > self.val:
													if self.right is None:
																	__A       =					Node(A					)
													else:
																	self.right.insert(A					)
					else:
									__A       =					val





def 		UpperCAmelCase     (      a_						,  a_		)						->      Optional[int]:







				"""simple docstring"""



				if root:
								inorder(root.left						,  a_		)
								res.append(root.val		)
								inorder(root.right						,  a_		)


def 		UpperCAmelCase     (      a_		)						->      Union[str, Any]:







				"""simple docstring"""



				if len(a_		) == 0:
								return arr
				__A       =					Node(arr[0]		)
				for i in range(1						,  len(a_		)		):
								root.insert(arr[i]		)
				# Traverse BST in order.
				__A       =					[]
				inorder(a_						,  a_		)
				return res


if __name__ == "__main__":
					print(tree_sort([10, 1, 3, 2, 9, 14, 13]))

'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			['''image_processor''', '''tokenizer''']
			UpperCAmelCase_     :	str						=			'''ViltImageProcessor'''
			UpperCAmelCase_     :	Any						=			('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""			,       __lowerCAmelCase			,       )
							lowerCAmelCase     =    kwargs.pop("""feature_extractor""")

					lowerCAmelCase     =    image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("""You need to specify an `image_processor`.""")
					if tokenizer is None:
							raise ValueError("""You need to specify a `tokenizer`.""")

					super().__init__(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    self.image_processor



			def __call__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer(
					    text=__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       max_length=__lowerCAmelCase			,       stride=__lowerCAmelCase			,       pad_to_multiple_of=__lowerCAmelCase			,       return_token_type_ids=__lowerCAmelCase			,       return_attention_mask=__lowerCAmelCase			,       return_overflowing_tokens=__lowerCAmelCase			,       return_special_tokens_mask=__lowerCAmelCase			,       return_offsets_mapping=__lowerCAmelCase			,       return_length=__lowerCAmelCase			,       verbose=__lowerCAmelCase			,       return_tensors=__lowerCAmelCase			,       **__lowerCAmelCase			,       )
					# add pixel_values + pixel_mask
					lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors=__lowerCAmelCase)
					encoding.update(__lowerCAmelCase)

					return encoding



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


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



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer.model_input_names
					lowerCAmelCase     =    self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def 				a_			(  self):




					"""simple docstring"""


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







			@property
			def 				a_			(  self):




					"""simple docstring"""


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




