
"""simple docstring"""




import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class SCREAMING_SNAKE_CASE       :






	'''simple docstring'''







	def __init__(  self :str       ,    __magic_name__ :List[str]       ,    __magic_name__ :int			)			->   List[str]:

			'''simple docstring'''






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

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

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


	@classmethod
	def    _UpperCamelCase							(  cls :Optional[Any]       ,    __magic_name__ :int       ,    __magic_name__ :int       ,    __magic_name__ :int			)			->   float:

			'''simple docstring'''






			return 0.114 * blue + 0.587 * green + 0.2_126 * red






	def    _UpperCamelCase							(  self :int			)			->   None:

			'''simple docstring'''






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

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		Optional[Any]         =		'MarkupLMFeatureExtractor'
	snake_case__							:		Tuple         =		('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
	snake_case__							:		Dict         =		True


	def __call__(  self :Any       ,    __magic_name__ :Dict=None       ,    __magic_name__ :Dict=None       ,    __magic_name__ :List[str]=None       ,    __magic_name__ :List[str]=None       ,    __magic_name__ :Tuple=None       ,    __magic_name__ :bool = True       ,    __magic_name__ :Union[bool, str, PaddingStrategy] = False       ,    __magic_name__ :Union[bool, str, TruncationStrategy] = None       ,    __magic_name__ :Optional[int] = None       ,    __magic_name__ :int = 0       ,    __magic_name__ :Optional[int] = None       ,    __magic_name__ :Optional[bool] = None       ,    __magic_name__ :Optional[bool] = None       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = False       ,    __magic_name__ :bool = True       ,    __magic_name__ :Optional[Union[str, TensorType]] = None       ,    **__magic_name__ :Optional[Any]       ,    )			->   BatchEncoding:

			'''simple docstring'''






			if self.parse_html:
					if html_strings is None:
							raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''			)

					if nodes is not None or xpaths is not None or node_labels is not None:
							raise ValueError(
							    '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''			)

					a__   		=     self.feature_extractor(__magic_name__			)
					a__   		=     features['''nodes''']
					a__   		=     features['''xpaths''']
			else:
					if html_strings is not None:
							raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''			)
					if nodes is None or xpaths is None:
							raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''			)

        # # second, apply the tokenizer
			if questions is not None and self.parse_html:
					if isinstance(__magic_name__       ,    __magic_name__			):
							a__   		=     [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

			a__   		=     self.tokenizer(
			    text=questions if questions is not None else nodes       ,    text_pair=nodes if questions is not None else None       ,    xpaths=__magic_name__       ,    node_labels=__magic_name__       ,    add_special_tokens=__magic_name__       ,    padding=__magic_name__       ,    truncation=__magic_name__       ,    max_length=__magic_name__       ,    stride=__magic_name__       ,    pad_to_multiple_of=__magic_name__       ,    return_token_type_ids=__magic_name__       ,    return_attention_mask=__magic_name__       ,    return_overflowing_tokens=__magic_name__       ,    return_special_tokens_mask=__magic_name__       ,    return_offsets_mapping=__magic_name__       ,    return_length=__magic_name__       ,    verbose=__magic_name__       ,    return_tensors=__magic_name__       ,    **__magic_name__       ,    )

			return encoded_inputs


	def    _UpperCamelCase							(  self :Tuple       ,    *__magic_name__ :Optional[int]       ,    **__magic_name__ :Any			)			->   int:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Tuple       ,    *__magic_name__ :Union[str, Any]       ,    **__magic_name__ :Union[str, Any]			)			->   Dict:

			'''simple docstring'''






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






	@property
	def    _UpperCamelCase							(  self :Optional[int]			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     self.tokenizer.model_input_names
			return tokenizer_input_names




