




import math
import sys

import cva
import numpy as np





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			np.ndarray:
	# For applying gaussian function for each element in matrix.
	a									=				math.sqrt(__lowerCamelCase       )
	a									=				1 / (sigma * math.sqrt(2 * math.pi       ))
	return cons * np.exp(-((img / sigma) ** 2) * 0.5       )





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			np.ndarray:
	a									=				kernel_size // 2
	return img[x - half : x + half + 1, y - half : y + half + 1]





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			np.ndarray:
	# Creates a gaussian kernel of given dimension.
	a									=				np.zeros((kernel_size, kernel_size)       )
	for i in range(0				,     __lowerCamelCase       ):
		for j in range(0				,     __lowerCamelCase       ):
			a									=				math.sqrt(
			    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
	return vec_gaussian(__lowerCamelCase				,     __lowerCamelCase       )





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     ) ->			np.ndarray:
	a									=				np.zeros(img.shape       )
	a									=				get_gauss_kernel(__lowerCamelCase				,     __lowerCamelCase       )
	a							,					a									=				img.shape
	for i in range(kernel_size // 2				,     size_x - kernel_size // 2       ):
		for j in range(kernel_size // 2				,     size_y - kernel_size // 2       ):
			a									=				get_slice(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       )
			a									=				img_s - img_s[kernel_size // 2, kernel_size // 2]
			a									=				vec_gaussian(__lowerCamelCase				,     __lowerCamelCase       )
			a									=				np.multiply(__lowerCamelCase				,     __lowerCamelCase       )
			a									=				np.multiply(__lowerCamelCase				,     __lowerCamelCase       )
			a									=				np.sum(__lowerCamelCase       ) / np.sum(__lowerCamelCase       )
			a									=				val
	return imga





def 						__A						(      __lowerCamelCase       ) ->			tuple:
	a									=				args[1] if args[1:] else """../image_data/lena.jpg"""
	a									=				float(args[2]       ) if args[2:] else 1.0
	a									=				float(args[3]       ) if args[3:] else 1.0
	if args[4:]:
		a									=				int(args[4]       )
		a									=				kernel_size + abs(kernel_size % 2 - 1       )
	else:
		a									=				5
	return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
		__UpperCamelCase   ,    __UpperCamelCase   ,    __UpperCamelCase   ,    __UpperCamelCase   :			Optional[int]          = parse_args(sys.argv)
		__UpperCamelCase   :			Union[str, Any]          = cva.imread(filename, 0)
		cva.imshow("input image", img)

		__UpperCamelCase   :			Any          = img / 255
		__UpperCamelCase   :			Dict          = out.astype("float32")
		__UpperCamelCase   :			Union[str, Any]          = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
		__UpperCamelCase   :			Any          = out * 255
		__UpperCamelCase   :			Optional[int]          = np.uinta(out)
		cva.imshow("output image", out)
		cva.waitKey(0)
		cva.destroyAllWindows()







import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


__UpperCamelCase   :			Optional[int]          = logging.get_logger(__name__)

class 						__lowerCAmelCase      (      __magic_name__					):
							def __init__(		self		:Union[str, Any]						, *__magic_name__		:int						, **__magic_name__		:str							):







								'''simple docstring'''







								warnings.warn(
								    """The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
								    """ Please use LayoutLMv2ImageProcessor instead."""						, __magic_name__						, )
								super().__init__(*__magic_name__						, **__magic_name__							)

