



import math
import sys

import cva
import numpy as np






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






def      __lowercase       (			a__							,       a__							,       a__							,       a__       )    ->			np.ndarray:
							__SCREAMING_SNAKE_CASE									= kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]






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






def      __lowercase       (			a__							,       a__							,       a__							,       a__							,       )    ->			np.ndarray:
							__SCREAMING_SNAKE_CASE									= np.zeros(img.shape       )
							__SCREAMING_SNAKE_CASE									= get_gauss_kernel(a__							,       a__       )
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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       ):
																					__SCREAMING_SNAKE_CASE									= get_slice(a__							,       a__							,       a__							,       a__       )
																					__SCREAMING_SNAKE_CASE									= img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__SCREAMING_SNAKE_CASE									= vec_gaussian(a__							,       a__       )
																					__SCREAMING_SNAKE_CASE									= np.multiply(a__							,       a__       )
																					__SCREAMING_SNAKE_CASE									= np.multiply(a__							,       a__       )
																					__SCREAMING_SNAKE_CASE									= np.sum(a__       ) / np.sum(a__       )
																					__SCREAMING_SNAKE_CASE									= val
							return imga






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


if __name__ == "__main__":
			lowerCAmelCase__		:					str							=parse_args(sys.argv)
			lowerCAmelCase__		:					List[str]							=cva.imread(filename, 0)
			cva.imshow('''input image''', img)

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











import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def      __lowercase       (			a__       )    ->			List[Any]:  # picklable for multiprocessing
							return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def      __lowercase       (			)    ->			Any:
							with parallel_backend('spark'       ):
														assert ParallelBackendConfig.backend_name == "spark"

							__SCREAMING_SNAKE_CASE									= [1, 2, 3]
							with pytest.raises(a__       ):
														with parallel_backend('unsupported backend'       ):
																					map_nested(a__							,       a__							,       num_proc=2       )

							with pytest.raises(a__       ):
														with parallel_backend('unsupported backend'       ):
																					map_nested(a__							,       a__							,       num_proc=-1       )






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc'							,       [2, -1]       )
def      __lowercase       (			a__       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= [1, 2]
							__SCREAMING_SNAKE_CASE									= {'a': 1, 'b': 2}
							__SCREAMING_SNAKE_CASE									= {'a': [1, 2], 'b': [3, 4]}
							__SCREAMING_SNAKE_CASE									= {'a': {'1': 1}, 'b': 2}
							__SCREAMING_SNAKE_CASE									= {'a': 1, 'b': 2, 'c': 3, 'd': 4}
							__SCREAMING_SNAKE_CASE									= [2, 3]
							__SCREAMING_SNAKE_CASE									= {'a': 2, 'b': 3}
							__SCREAMING_SNAKE_CASE									= {'a': [2, 3], 'b': [4, 5]}
							__SCREAMING_SNAKE_CASE									= {'a': {'1': 2}, 'b': 3}
							__SCREAMING_SNAKE_CASE									= {'a': 2, 'b': 3, 'c': 4, 'd': 5}

							with parallel_backend('spark'       ):
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa
														assert map_nested(a__							,       a__							,       num_proc=a__       ) == expected_map_nested_sa






