



class     _UpperCAmelCase   :

  """simple docstring"""


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






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						str:




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





def   a_   (			lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      List[str]     ):
   # Recursive traversal
   if root:
      inorder(root.left,     _lowerCAmelCase     )
      res.append(root.val     )
      inorder(root.right,     _lowerCAmelCase     )

def   a_   (			lowerCAmelCase_				:      Any     ):
   # Build BST
   if len(_lowerCAmelCase     ) == 0:
      return arr
   __lowerCAmelCase							     =     Node(arr[0]     )
   for i in range(1,     len(_lowerCAmelCase     )     ):
      root.insert(arr[i]     )
   # Traverse BST in order.
   __lowerCAmelCase							     =     []
   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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[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

     _snake_case  :   str						   =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

