




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			val
								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			None






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								if self.val:
														if val < self.val:
																				if self.left is None:
																										_UpperCAmelCase      =			Node(_SCREAMING_SNAKE_CASE			)
																				else:
																										self.left.insert(_SCREAMING_SNAKE_CASE			)
														elif val > self.val:
																				if self.right is None:
																										_UpperCAmelCase      =			Node(_SCREAMING_SNAKE_CASE			)
																				else:
																										self.right.insert(_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			val




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				List[Any]:
						# Recursive traversal
						if root:
												inorder(root.left			,     snake_case       )
												res.append(root.val       )
												inorder(root.right			,     snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Optional[Any]:
						# Build BST
						if len(snake_case       ) == 0:
												return arr
						_UpperCAmelCase      =			Node(arr[0]       )
						for i in range(1			,     len(snake_case       )       ):
												root.insert(arr[i]       )
						# Traverse BST in order.
						_UpperCAmelCase      =			[]
						inorder(snake_case			,     snake_case       )
						return res


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





from typing import Dict

from .base import GenericTensor, Pipeline




class       _A				(		__lowercase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								if tokenize_kwargs is None:
														_UpperCAmelCase      =			{}

								if truncation is not None:
														if "truncation" in tokenize_kwargs:
																				raise ValueError(
																				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
														_UpperCAmelCase      =			truncation

								_UpperCAmelCase      =			tokenize_kwargs

								_UpperCAmelCase      =			{}
								if return_tensors is not None:
														_UpperCAmelCase      =			return_tensors

								return preprocess_params, {}, postprocess_params






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.framework
								_UpperCAmelCase      =			self.tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								return model_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)
								return model_outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
														return model_outputs[0]
								if self.framework == "pt":
														return model_outputs[0].tolist()
								elif self.framework == "tf":
														return model_outputs[0].numpy().tolist()






		def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)