



"""simple docstring"""




import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class 	a__		:


   def __init__(				self   :				str,  lowerCAmelCase   :				str,  lowerCAmelCase   :				int      )     ->							str:
         lowercase	:   str											= 0
         # max greyscale value for #FFFFFF
         lowercase	:   List[str]											= int(self.get_greyscale(255,  255,  255      )      )

         if not self.min_threshold < threshold < self.max_threshold:
               lowercase	:   Optional[Any]											= f'''Factor value should be from 0 to {self.max_threshold}'''
               raise ValueError(lowerCAmelCase      )

         lowercase	:   Optional[int]											= input_img
         lowercase	:   Optional[Any]											= threshold
         lowercase						,     lowercase	:   str											= self.input_img.shape[1], self.input_img.shape[0]

         # error table size (+4 columns and +1 row) greater than input image because of
         # lack of if statements
         lowercase	:   int											= [
             [0 for _ in range(self.height + 4      )] for __ in range(self.width + 1      )
         ]
         lowercase	:   Optional[Any]											= np.ones((self.width, self.height, 3),  np.uinta      ) * 255


   @classmethod
   def 							lowercase					(				cls   :				Union[str, Any],  lowerCAmelCase   :				int,  lowerCAmelCase   :				int,  lowerCAmelCase   :				int      )     ->							float:
         return 0.114 * blue + 0.587 * green + 0.2126 * red






   def 							lowercase					(				self   :				str      )     ->							None:
         for y in range(self.height      ):
               for x in range(self.width      ):
                     lowercase	:   List[str]											= int(self.get_greyscale(*self.input_img[y][x]      )      )
                     if self.threshold > greyscale + self.error_table[y][x]:
                           lowercase	:   Dict											= (0, 0, 0)
                           lowercase	:   Optional[Any]											= greyscale + self.error_table[x][y]
                     else:
                           lowercase	:   str											= (255, 255, 255)
                           lowercase	:   List[Any]											= greyscale + self.error_table[x][y] - 255
                     self.error_table[y][x + 1] += int(8 / 32 * current_error      )
                     self.error_table[y][x + 2] += int(4 / 32 * current_error      )
                     self.error_table[y + 1][x] += int(8 / 32 * current_error      )
                     self.error_table[y + 1][x + 1] += int(4 / 32 * current_error      )
                     self.error_table[y + 1][x + 2] += int(2 / 32 * current_error      )
                     self.error_table[y + 1][x - 1] += int(4 / 32 * current_error      )
                     self.error_table[y + 1][x - 2] += int(2 / 32 * current_error      )


if __name__ == "__main__":
     # create Burke's instances with original images in greyscale
     _UpperCamelCase:					Tuple     				=	[
         Burkes(imread('image_data/lena.jpg', 1), threshold)
         for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
     ]

     for burkes in burkes_instances:
          burkes.process()

     for burkes in burkes_instances:
          imshow(
              f'''Original image with dithering threshold: {burkes.threshold}''',
              burkes.output_img,
          )

     waitKey(0)
     destroyAllWindows()





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase:					Tuple     				=	logging.get_logger(__name__)

_UpperCamelCase:					List[str]     				=	{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					'regnet'
   _lowerCamelCase       				=					['x', 'y']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				Union[str, Any]=3,  lowerCAmelCase   :				Tuple=32,  lowerCAmelCase   :				Optional[int]=[128, 192, 512, 1088],  lowerCAmelCase   :				Optional[int]=[2, 6, 12, 2],  lowerCAmelCase   :				str=64,  lowerCAmelCase   :				List[str]="y",  lowerCAmelCase   :				Optional[int]="relu",  **lowerCAmelCase   :				List[Any],  )     ->							Union[str, Any]:
         super().__init__(**lowerCAmelCase      )
         if layer_type not in self.layer_types:
               raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types      )}'''      )
         lowercase	:   str											= num_channels
         lowercase	:   str											= embedding_size
         lowercase	:   int											= hidden_sizes
         lowercase	:   Tuple											= depths
         lowercase	:   str											= groups_width
         lowercase	:   List[str]											= layer_type
         lowercase	:   Optional[int]											= hidden_act
         # always downsample in the first stage
         lowercase	:   Optional[Any]											= True
