



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







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     Tuple  ,lowerCamelCase_:     List[Any]  ,lowerCamelCase_:     int							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Optional[Any]  		=					0
       # max greyscale value for #FFFFFF
       UpperCAmelCase_     :       List[Any]  		=					int(self.get_greyscale(255  ,255  ,255							)							)

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

       UpperCAmelCase_     :       Optional[Any]  		=					input_img
       UpperCAmelCase_     :       Union[str, Any]  		=					threshold
       UpperCAmelCase_		, UpperCAmelCase_     :       Optional[int]  		=					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
       UpperCAmelCase_     :       Any  		=					[
           [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
       ]
       UpperCAmelCase_     :       Optional[int]  		=					np.ones((self.width, self.height, 3)  ,np.uinta							) * 255


   @classmethod
   def       A__    (    cls:     int  ,lowerCamelCase_:     int  ,lowerCamelCase_:     int  ,lowerCamelCase_:     int							)	->      float:
       return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red






   def       A__    (    self:     Optional[int]							)	->      None:
       for y in range(self.height							):
           for x in range(self.width							):
               UpperCAmelCase_     :       Any  		=					int(self.get_greyscale(*self.input_img[y][x]							)							)
               if self.threshold > greyscale + self.error_table[y][x]:
                   UpperCAmelCase_     :       str  		=					(0, 0, 0)
                   UpperCAmelCase_     :       Union[str, Any]  		=					greyscale + self.error_table[x][y]
               else:
                   UpperCAmelCase_     :       Union[str, Any]  		=					(255, 255, 255)
                   UpperCAmelCase_     :       Optional[int]  		=					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_									=   [
          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()





from typing import TYPE_CHECKING

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


UpperCAmelCase__  =			{
    "configuration_align": [
        "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AlignConfig",
        "AlignTextConfig",
        "AlignVisionConfig",
    ],
    "processing_align": ["AlignProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  =			[
							    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "AlignModel",
							    "AlignPreTrainedModel",
							    "AlignTextModel",
							    "AlignVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_align import (
							    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    AlignConfig,
							    AlignTextConfig,
							    AlignVisionConfig,
							)
							from .processing_align import AlignProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_align import (
														    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AlignModel,
														    AlignPreTrainedModel,
														    AlignTextModel,
														    AlignVisionModel,
														)

else:
							import sys

							UpperCAmelCase__  =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
