





"""simple docstring"""






import math
import sys

import cva
import numpy as np






def      lowercase__ (					lowerCAmelCase__   :			np.ndarray							,			lowerCAmelCase__   :			float			)    ->			np.ndarray:



			'''simple docstring'''
			# For applying gaussian function for each element in matrix.
			a__  :	str		 =			math.sqrt(lowerCAmelCase__			)
			a__  :	Union[str, Any]		 =			1 / (sigma * math.sqrt(2 * math.pi			))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5			)






def      lowercase__ (					lowerCAmelCase__   :			np.ndarray							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			np.ndarray:



			'''simple docstring'''
			a__  :	List[str]		 =			kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			float			)    ->			np.ndarray:



			'''simple docstring'''
			# Creates a gaussian kernel of given dimension.
			a__  :	Optional[int]		 =			np.zeros((kernel_size, kernel_size)			)
			for i in range(0							,			lowerCAmelCase__			):
						for j in range(0							,			lowerCAmelCase__			):
									a__  :	Union[str, Any]		 =			math.sqrt(
									    abs(i - kernel_size // 2			) ** 2 + abs(j - kernel_size // 2			) ** 2			)
			return vec_gaussian(lowerCAmelCase__							,			lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			np.ndarray							,			lowerCAmelCase__   :			float							,			lowerCAmelCase__   :			float							,			lowerCAmelCase__   :			int							,			)    ->			np.ndarray:



			'''simple docstring'''
			a__  :	Optional[Any]		 =			np.zeros(img.shape			)
			a__  :	Optional[int]		 =			get_gauss_kernel(lowerCAmelCase__							,			lowerCAmelCase__			)
			a__ ,	a__  :	Optional[int]		 =			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__  :	Union[str, Any]		 =			get_slice(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)
									a__  :	Optional[Any]		 =			img_s - img_s[kernel_size // 2, kernel_size // 2]
									a__  :	Tuple		 =			vec_gaussian(lowerCAmelCase__							,			lowerCAmelCase__			)
									a__  :	Union[str, Any]		 =			np.multiply(lowerCAmelCase__							,			lowerCAmelCase__			)
									a__  :	Tuple		 =			np.multiply(lowerCAmelCase__							,			lowerCAmelCase__			)
									a__  :	Optional[Any]		 =			np.sum(lowerCAmelCase__			) / np.sum(lowerCAmelCase__			)
									a__  :	Any		 =			val
			return imga






def      lowercase__ (					lowerCAmelCase__   :			list			)    ->			tuple:



			'''simple docstring'''
			a__  :	List[str]		 =			args[1] if args[1:] else "../image_data/lena.jpg"
			a__  :	Any		 =			float(args[2]			) if args[2:] else 1.0
			a__  :	Any		 =			float(args[3]			) if args[3:] else 1.0
			if args[4:]:
						a__  :	Dict		 =			int(args[4]			)
						a__  :	Any		 =			kernel_size + abs(kernel_size % 2 - 1			)
			else:
						a__  :	List[str]		 =			5
			return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
		__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase	,	__UpperCAmelCase						=			parse_args(sys.argv)
		__UpperCAmelCase						=			cva.imread(filename, 0)
		cva.imshow('''input image''', img)

		__UpperCAmelCase						=			img / 255
		__UpperCAmelCase						=			out.astype('''float32''')
		__UpperCAmelCase						=			bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
		__UpperCAmelCase						=			out * 255
		__UpperCAmelCase						=			np.uinta(out)
		cva.imshow('''output image''', out)
		cva.waitKey(0)
		cva.destroyAllWindows()






"""simple docstring"""






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 					__UpperCAmelCase							(						unittest.TestCase						,  _UpperCamelCase  ):
				def       UpperCAmelCase      (		self    :			Dict    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Any		 =			load_tool("text-classification"    )
							self.tool.setup()
							a__  :	List[Any]		 =			load_tool("text-classification"	, remote=a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	str		 =			self.tool("That's quite cool"	, ["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	List[str]		 =			self.remote_tool("That's quite cool"	, ["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Any:



							'''simple docstring'''



							a__  :	List[str]		 =			self.tool(text="That's quite cool"	, labels=["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )






				def       UpperCAmelCase      (		self    :			List[Any]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.remote_tool(text="That's quite cool"	, labels=["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )