






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A      :       Tuple      =	logging.get_logger(__name__)

__A      :       int      =	{
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}






class 					a__    (			lowerCamelCase ,						lowerCamelCase						):
     '''simple docstring'''







     _snake_case             =  'convnext'






     def __init__(self    :	Optional[Any]				,				lowercase__    :	Any=3				,				lowercase__    :	int=4				,				lowercase__    :	Dict=4				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Optional[int]=None				,				lowercase__    :	Dict="gelu"				,				lowercase__    :	Optional[Any]=0.02				,				lowercase__    :	Optional[int]=1e-12				,				lowercase__    :	Any=1e-6				,				lowercase__    :	Tuple=0.0				,				lowercase__    :	Any=224				,				lowercase__    :	Tuple=None				,				lowercase__    :	Union[str, Any]=None				,				**lowercase__    :	Union[str, Any]				,				)  ->       List[str]:
           super().__init__(**lowercase__					)

           __A		   =      num_channels
           __A		   =      patch_size
           __A		   =      num_stages
           __A		   =      [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
           __A		   =      [3, 3, 9, 3] if depths is None else depths
           __A		   =      hidden_act
           __A		   =      initializer_range
           __A		   =      layer_norm_eps
           __A		   =      layer_scale_init_value
           __A		   =      drop_path_rate
           __A		   =      image_size
           __A		   =      ["stem"] + [f"stage{idx}" for idx in range(1				,				len(self.depths					) + 1					)]
           __A   ,				__A		   =      get_aligned_output_features_output_indices(
               out_features=lowercase__				,				out_indices=lowercase__				,				stage_names=self.stage_names					)







class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     _snake_case             =  version.parse('1.11'						)






     @property
     def     _UpperCamelCase   (self    :	str					)  ->       Mapping[str, Mapping[int, str]]:
           return OrderedDict(
               [
                   ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
               ]					)






     @property
     def     _UpperCamelCase   (self    :	int					)  ->       float:
           return 1e-5










'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      __A   ,				__A		   =      img.shape[0], img.shape[1]

      def to_grayscale(snake_case__							,			snake_case__							,			snake_case__							):
            return 0.2126 * red + 0.587 * green + 0.114 * blue

      def normalize(snake_case__							):
            return min(snake_case__							,			255							)

      for i in range(snake_case__							):
            for j in range(snake_case__							):
                  __A		   =      int(to_grayscale(*img[i][j]							)							)
                  __A		   =      [
                      normalize(snake_case__							),
                      normalize(greyscale + factor							),
                      normalize(greyscale + 2 * factor							),
                  ]

      return img


if __name__ == "__main__":
       # read original image
       __A      :       Dict      =	{
           percentage: imread('image_data/lena.jpg', 1) for percentage in (1_0, 2_0, 3_0, 4_0)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F"""Original image with sepia (factor: {percentage})""", img)

       waitKey(0)
       destroyAllWindows()


