



from typing import TYPE_CHECKING

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


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

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

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''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

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





def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		len(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		[[0] * n for i in range(__snake_case    )]
      for i in range(__snake_case    ):
            UpperCAmelCase_  :	Optional[Any]				      =		y_points[i]

      for i in range(2					,  __snake_case    ):
            for j in range(__snake_case					,  __snake_case    ):
                  UpperCAmelCase_  :	int				      =		(
                      (xa - x_points[j - i + 1]) * q[j][i - 1]
                      - (xa - x_points[j]) * q[j - 1][i - 1]
                  ) / (x_points[j] - x_points[j - i + 1])

      return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
