
"""simple docstring"""





class     _UpperCAmelCase:



		def __init__(					self				,		__a)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  val
							_UpperCamelCase							 =  None
							_UpperCamelCase							 =  None




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






							'''simple docstring'''







							if self.val:
												if val < self.val:
																	if self.left is None:
																						_UpperCamelCase							 =  Node(__a)
																	else:
																						self.left.insert(__a)
												elif val > self.val:
																	if self.right is None:
																						_UpperCamelCase							 =  Node(__a)
																	else:
																						self.right.insert(__a)
							else:
												_UpperCamelCase							 =  val





def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[Any]:


					"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case	)							->		List[str]:


					"""simple docstring"""





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


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














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a      	=  {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OPTForCausalLM''',
		    '''OPTModel''',
		    '''OPTPreTrainedModel''',
		    '''OPTForSequenceClassification''',
		    '''OPTForQuestionAnswering''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a      	=  [
		    '''FlaxOPTForCausalLM''',
		    '''FlaxOPTModel''',
		    '''FlaxOPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_opt import (
				    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OPTForCausalLM,
				    OPTForQuestionAnswering,
				    OPTForSequenceClassification,
				    OPTModel,
				    OPTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
		import sys

		_a      	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
