



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


a	:   Dict       			=       logging.get_logger(__name__)

a	:   Optional[int]       			=       {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}







class 							_a							(					lowercase__      ,						lowercase__ ):
			A							=     '''convnextv2'''

			def __init__(self,					SCREAMING_SNAKE_CASE_=3,					SCREAMING_SNAKE_CASE_=4,					SCREAMING_SNAKE_CASE_=4,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_="gelu",					SCREAMING_SNAKE_CASE_=0.0_2,					SCREAMING_SNAKE_CASE_=1E-12,					SCREAMING_SNAKE_CASE_=0.0,					SCREAMING_SNAKE_CASE_=224,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=None,					**SCREAMING_SNAKE_CASE_,					)    ->					List[str]:
						super().__init__(**__lowerCamelCase )

						UpperCAmelCase_:						Tuple					=			num_channels
						UpperCAmelCase_:						Optional[int]					=			patch_size
						UpperCAmelCase_:						List[Any]					=			num_stages
						UpperCAmelCase_:						List[Any]					=			[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
						UpperCAmelCase_:						List[Any]					=			[3, 3, 9, 3] if depths is None else depths
						UpperCAmelCase_:						List[str]					=			hidden_act
						UpperCAmelCase_:						Dict					=			initializer_range
						UpperCAmelCase_:						Tuple					=			layer_norm_eps
						UpperCAmelCase_:						Tuple					=			drop_path_rate
						UpperCAmelCase_:						str					=			image_size
						UpperCAmelCase_:						List[str]					=			["stem"] + [f'stage{idx}' for idx in range(1,					len(self.depths ) + 1 )]
						UpperCAmelCase_:						Optional[Any]					=			get_aligned_output_features_output_indices(
						    out_features=__lowerCamelCase,					out_indices=__lowerCamelCase,					stage_names=self.stage_names )










from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


if __name__ == "__main__":
    A      :    int		=					mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


