


'''simple docstring'''


from __future__ import annotations

from dataclasses import dataclass



@dataclass
class 	__lowerCAmelCase		:


  '''simple docstring'''







  a_  					=							42
  a_  					=							None
  a_  					=							None







def      lowerCamelCase					(      lowerCamelCase  :      TreeNode | None):

      # Validation
      def is_valid_tree(lowerCamelCase  :      TreeNode | None) -> bool:
            if node is None:
                  return True

            if not isinstance(lowerCamelCase							,						lowerCamelCase):
                  return False

            try:
                  float(node.data)
            except (TypeError, ValueError):
                  return False

            return is_valid_tree(node.left) and is_valid_tree(node.right)

      if not is_valid_tree(lowerCamelCase):
            raise ValueError(
                """Each node should be type of TreeNode and data should be float.""")

      def is_binary_search_tree_recursive_check(
          lowerCamelCase  :      TreeNode | None							,						lowerCamelCase  :      float							,						lowerCamelCase  :      float) -> bool:

            if node is None:
                  return True

            return (
                left_bound < node.data < right_bound
                and is_binary_search_tree_recursive_check(node.left							,						lowerCamelCase							,						node.data)
                and is_binary_search_tree_recursive_check(
                    node.right							,						node.data							,						lowerCamelCase)
            )

      return is_binary_search_tree_recursive_check(lowerCamelCase							,						-float("""inf""")							,						float("""inf"""))


if __name__ == "__main__":
     import doctest

     doctest.testmod()




'''simple docstring'''


from typing import TYPE_CHECKING

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


__magic_name__			=      {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['ConvNextFeatureExtractor']
     __magic_name__			=      ['ConvNextImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ConvNextForImageClassification',
         'ConvNextModel',
         'ConvNextPreTrainedModel',
         'ConvNextBackbone',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'TFConvNextForImageClassification',
         'TFConvNextModel',
         'TFConvNextPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_convnext import ConvNextFeatureExtractor
          from .image_processing_convnext import ConvNextImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnext import (
              CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextBackbone,
              ConvNextForImageClassification,
              ConvNextModel,
              ConvNextPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure)
