

'''simple docstring'''
class 							A__   :





  def __init__(		self				:     str							, _a				:     Optional[Any]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =val
       _SCREAMING_SNAKE_CASE							   =None
       _SCREAMING_SNAKE_CASE							   =None





  def 						A  (		self				:     List[Any]							, _a				:     int			)     ->   Dict:





       '''simple docstring'''






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


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					Optional[Any]    )				->      Tuple:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					Tuple    )				->      int:
     """simple docstring"""




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


if __name__ == "__main__":
     print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))



'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
