





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




class      UpperCamelCase_				:


							def __init__(							self			:Tuple							, __A			:Optional[int]							, __A			:int						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					0
									# max greyscale value for #FFFFFF
									SCREAMING_SNAKE_CASE__								=					int(self.get_greyscale(255							, 255							, 255						)						)

									if not self.min_threshold < threshold < self.max_threshold:
											SCREAMING_SNAKE_CASE__								=					f'''Factor value should be from 0 to {self.max_threshold}'''
											raise ValueError(__A						)

									SCREAMING_SNAKE_CASE__								=					input_img
									SCREAMING_SNAKE_CASE__								=					threshold
									SCREAMING_SNAKE_CASE__		,  SCREAMING_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
									SCREAMING_SNAKE_CASE__								=					[
									    [0 for _ in range(self.height + 4						)] for __ in range(self.width + 1						)
									]
									SCREAMING_SNAKE_CASE__								=					np.ones((self.width, self.height, 3)							, np.uinta						) * 255


							@classmethod
							def    _snake_case     (							cls			:str							, __A			:int							, __A			:int							, __A			:int						)      ->      float:



									"""simple docstring"""



									return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red


							def    _snake_case     (							self			:Union[str, Any]						)      ->      None:



									"""simple docstring"""



									for y in range(self.height						):
											for x in range(self.width						):
													SCREAMING_SNAKE_CASE__								=					int(self.get_greyscale(*self.input_img[y][x]						)						)
													if self.threshold > greyscale + self.error_table[y][x]:
															SCREAMING_SNAKE_CASE__								=					(0, 0, 0)
															SCREAMING_SNAKE_CASE__								=					greyscale + self.error_table[x][y]
													else:
															SCREAMING_SNAKE_CASE__								=					(255, 255, 255)
															SCREAMING_SNAKE_CASE__								=					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
	_lowerCamelCase       =	[
	    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()






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    ["image_processor", "tokenizer"]
							lowerCamelCase_							=    "AutoImageProcessor"
							lowerCamelCase_							=    "AutoTokenizer"


							def __init__(							self			:Any							, __A			:List[str]=None							, __A			:Dict=None							, **__A			:List[str]						)      ->      List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""							, __A							, )
											SCREAMING_SNAKE_CASE__								=					kwargs.pop("""feature_extractor"""						)

									SCREAMING_SNAKE_CASE__								=					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""						)
									if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""						)

									super().__init__(__A							, __A						)
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							def __call__(							self			:List[str]							, *__A			:int							, **__A			:Optional[int]						)      ->      Optional[Any]:



									"""simple docstring"""



									if self._in_target_context_manager:
											return self.current_processor(*__A							, **__A						)

									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""images"""							, __A						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""text"""							, __A						)
									if len(__A						) > 0:
											SCREAMING_SNAKE_CASE__								=					args[0]
											SCREAMING_SNAKE_CASE__								=					args[1:]

									if images is None and text is None:
											raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=					self.image_processor(__A							, *__A							, **__A						)
									if text is not None:
											SCREAMING_SNAKE_CASE__								=					self.tokenizer(__A							, **__A						)

									if text is None:
											return inputs
									elif images is None:
											return encodings
									else:
											SCREAMING_SNAKE_CASE__								=					encodings["""input_ids"""]
											return inputs


							def    _snake_case     (							self			:Any							, *__A			:Optional[Any]							, **__A			:int						)      ->      List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A							, **__A						)


							def    _snake_case     (							self			:str							, *__A			:Optional[int]							, **__A			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									return self.tokenizer.decode(*__A							, **__A						)


							@contextmanager
							def    _snake_case     (							self			:Dict						)      ->      List[str]:



									"""simple docstring"""



									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""						)
									SCREAMING_SNAKE_CASE__								=					True
									SCREAMING_SNAKE_CASE__								=					self.tokenizer
									yield
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							@property
							def    _snake_case     (							self			:List[str]						)      ->      int:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							, __A							, )
									return self.image_processor_class


							@property
							def    _snake_case     (							self			:str						)      ->      List[Any]:



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							, __A							, )
									return self.image_processor