



"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.activations import gelu_new, gelu_python, get_activation




@require_torch
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
													__a		:		Optional[Any]									=       get_activation("""gelu"""							)
													self.assertTrue(torch.allclose(gelu_python(_lowercase							)						,     torch_builtin(_lowercase							)							)							)
													self.assertFalse(torch.allclose(gelu_python(_lowercase							)						,     gelu_new(_lowercase							)							)							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
													__a		:		Optional[Any]									=       get_activation("""gelu"""							)
													__a		:		Optional[int]									=       get_activation("""gelu_10"""							)

													__a		:		str									=       torch_builtin(_lowercase							)
													__a		:		int									=       geluaa(_lowercase							)

													__a		:		Optional[int]									=       torch.where(y_gelu_aa < 10.0						,     1						,     0							)

													self.assertTrue(torch.max(_lowercase							).item() == 10.0							)
													self.assertTrue(torch.allclose(y_gelu * clipped_mask						,     y_gelu_aa * clipped_mask							)							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													get_activation("""gelu"""							)
													get_activation("""gelu_10"""							)
													get_activation("""gelu_fast"""							)
													get_activation("""gelu_new"""							)
													get_activation("""gelu_python"""							)
													get_activation("""gelu_pytorch_tanh"""							)
													get_activation("""linear"""							)
													get_activation("""mish"""							)
													get_activation("""quick_gelu"""							)
													get_activation("""relu"""							)
													get_activation("""sigmoid"""							)
													get_activation("""silu"""							)
													get_activation("""swish"""							)
													get_activation("""tanh"""							)
													with self.assertRaises(_lowercase							):
																				get_activation("""bogus"""							)
													with self.assertRaises(_lowercase							):
																				get_activation(_lowercase							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       get_activation("""gelu"""							)
													__a		:		List[Any]									=       1
													__a		:		int									=       get_activation("""gelu"""							)
													self.assertEqual(acta.a						,     1							)
													with self.assertRaises(_lowercase							):
																				__a		:		List[str]									=       acta.a






"""simple docstring"""

import math
import sys

import cva
import numpy as np





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float					):
							# For applying gaussian function for each element in matrix.
							__a		:		int									=       math.sqrt(_lowerCamelCase					)
							__a		:		Any									=       1 / (sigma * math.sqrt(2 * math.pi					))
							return cons * np.exp(-((img / sigma) ** 2) * 0.5					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							__a		:		Any									=       kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float					):
							# Creates a gaussian kernel of given dimension.
							__a		:		int									=       np.zeros((kernel_size, kernel_size)					)
							for i in range(0  ,       _lowerCamelCase					):
														for j in range(0  ,       _lowerCamelCase					):
																					__a		:		Any									=       math.sqrt(
																					    abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
							return vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: int  ,       ):
							__a		:		Tuple									=       np.zeros(img.shape					)
							__a		:		Optional[int]									=       get_gauss_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a							,     __a		:		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		:		List[str]									=       get_slice(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__a		:		Optional[Any]									=       vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Optional[Any]									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Tuple									=       np.sum(_lowerCamelCase					) / np.sum(_lowerCamelCase					)
																					__a		:		Optional[Any]									=       val
							return imga





def       __magic_name__    (   _lowerCamelCase				: list					):
							__a		:		Optional[Any]									=       args[1] if args[1:] else """../image_data/lena.jpg"""
							__a		:		Union[str, Any]									=       float(args[2]					) if args[2:] else 1.0
							__a		:		Optional[int]									=       float(args[3]					) if args[3:] else 1.0
							if args[4:]:
														__a		:		Any									=       int(args[4]					)
														__a		:		Any									=       kernel_size + abs(kernel_size % 2 - 1					)
							else:
														__a		:		Optional[int]									=       5
							return filename, spatial_variance, intensity_variance, kernel_size


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

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

