






"""simple docstring"""
class     UpperCamelCase_ :





	def __init__(   self      :				int			,			lowerCAmelCase_      :				Optional[Any]		)		->    Optional[int]:
				UpperCAmelCase_  :				Optional[int]      =      val
				UpperCAmelCase_  :				Union[str, Any]      =      None
				UpperCAmelCase_  :				Optional[int]      =      None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				Optional[Any]		)		->    List[str]:
				if self.val:
							if val < self.val:
										if self.left is None:
													UpperCAmelCase_  :				Any      =      Node(UpperCAmelCase__		)
										else:
													self.left.insert(UpperCAmelCase__		)
							elif val > self.val:
										if self.right is None:
													UpperCAmelCase_  :				List[str]      =      Node(UpperCAmelCase__		)
										else:
													self.right.insert(UpperCAmelCase__		)
				else:
							UpperCAmelCase_  :				int      =      val





def        snake_case   (     A__			,A__		):


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

def        snake_case   (     A__		):


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


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











"""simple docstring"""
from typing import TYPE_CHECKING

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


lowerCamelCase_       			=   {
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''FunnelTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FunnelBaseModel''',
			    '''FunnelForMaskedLM''',
			    '''FunnelForMultipleChoice''',
			    '''FunnelForPreTraining''',
			    '''FunnelForQuestionAnswering''',
			    '''FunnelForSequenceClassification''',
			    '''FunnelForTokenClassification''',
			    '''FunnelModel''',
			    '''FunnelPreTrainedModel''',
			    '''load_tf_weights_in_funnel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFFunnelBaseModel''',
			    '''TFFunnelForMaskedLM''',
			    '''TFFunnelForMultipleChoice''',
			    '''TFFunnelForPreTraining''',
			    '''TFFunnelForQuestionAnswering''',
			    '''TFFunnelForSequenceClassification''',
			    '''TFFunnelForTokenClassification''',
			    '''TFFunnelModel''',
			    '''TFFunnelPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

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



