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




class     _SCREAMING_SNAKE_CASE     :
      def __init__(	self						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = 0
          # max greyscale value for #FFFFFF
          snake_case__  = int(self.get_greyscale(2_55						,		2_55						,		2_55       )       )

          if not self.min_threshold < threshold < self.max_threshold:
              snake_case__  = F"""Factor value should be from 0 to {self.max_threshold}"""
              raise ValueError(lowerCamelCase_       )

          snake_case__  = input_img
          snake_case__  = threshold
          snake_case__  ,    snake_case__  = 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
          snake_case__  = [
              [0 for _ in range(self.height + 4       )] for __ in range(self.width + 1       )
          ]
          snake_case__  = np.ones((self.width, self.height, 3)						,		np.uinta       ) * 2_55
      @classmethod
      def A_	(	cls						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red



      def A_	(	self       ):
          for y in range(self.height       ):
              for x in range(self.width       ):
                  snake_case__  = int(self.get_greyscale(*self.input_img[y][x]       )       )
                  if self.threshold > greyscale + self.error_table[y][x]:
                      snake_case__  = (0, 0, 0)
                      snake_case__  = greyscale + self.error_table[x][y]
                  else:
                      snake_case__  = (2_55, 2_55, 2_55)
                      snake_case__  = greyscale + self.error_table[x][y] - 2_55
                  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
     __magic_name__   				=     [
         Burkes(imread('''image_data/lena.jpg''', 1), threshold)
         for threshold in (1, 126, 130, 140)
     ]

     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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=							{
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							[
		    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SwitchTransformersEncoderModel''',
		    '''SwitchTransformersForConditionalGeneration''',
		    '''SwitchTransformersModel''',
		    '''SwitchTransformersPreTrainedModel''',
		    '''SwitchTransformersTop1Router''',
		    '''SwitchTransformersSparseMLP''',
		]


if TYPE_CHECKING:
		from .configuration_switch_transformers import (
		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SwitchTransformersConfig,
		    SwitchTransformersOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwitchTransformersEncoderModel,
				    SwitchTransformersForConditionalGeneration,
				    SwitchTransformersModel,
				    SwitchTransformersPreTrainedModel,
				    SwitchTransformersSparseMLP,
				    SwitchTransformersTopaRouter,
				)


else:
		import sys

		__UpperCAmelCase    				=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)