



"""simple docstring"""


from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def  _a	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    k_size // 2
				_UpperCAmelCase		,						_UpperCAmelCase					=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				_UpperCAmelCase					=    1 / (2 * pi * sigma) * exp(-(square(_SCREAMING_SNAKE_CASE				) + square(_SCREAMING_SNAKE_CASE				)) / (2 * square(_SCREAMING_SNAKE_CASE				))				)
				return g







def  _a	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				_UpperCAmelCase		,						_UpperCAmelCase					=    image.shape[0], image.shape[1]
				# dst image height and width
				_UpperCAmelCase					=    height - k_size + 1
				_UpperCAmelCase					=    width - k_size + 1

				# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
				_UpperCAmelCase					=    zeros((dst_height * dst_width, k_size * k_size)				)
				_UpperCAmelCase					=    0
				for i, j in product(range(_SCREAMING_SNAKE_CASE				)					,     range(_SCREAMING_SNAKE_CASE				)				):
								_UpperCAmelCase					=    ravel(image[i : i + k_size, j : j + k_size]				)
								_UpperCAmelCase					=    window
								row += 1

				#  turn the kernel into shape(k*k, 1)
				_UpperCAmelCase					=    gen_gaussian_kernel(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    ravel(_SCREAMING_SNAKE_CASE				)

				# reshape and get the dst image
				_UpperCAmelCase					=    dot(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).reshape(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).astype(_SCREAMING_SNAKE_CASE				)

				return dst


if __name__ == "__main__":
					# read original image
					__A   :							Tuple										=   imread(r"../image_data/lena.jpg")
					# turn image in gray scale value
					__A   :							List[str]										=   cvtColor(img, COLOR_BGR2GRAY)

					# get values with two different mask size
					__A   :							str										=   gaussian_filter(gray, 3, sigma=1)
					__A   :							Union[str, Any]										=   gaussian_filter(gray, 5, sigma=0.8)

					# show result images
					imshow("gaussian filter with 3x3 mask", gaussianaxa)
					imshow("gaussian filter with 5x5 mask", gaussianaxa)
					waitKey()








"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple   )->int:
							_UpperCAmelCase					=    inspect.getfile(accelerate.test_utils   )
							_UpperCAmelCase					=    os.path.sep.join(
							    mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']   )

							from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

							_UpperCAmelCase					=    test_metrics



			@require_cpu
			def  lowercase__						(		self     :    Any   )->int:
							debug_launcher(self.test_metrics.main				,   num_processes=1   )



			@require_cpu
			def  lowercase__						(		self     :    List[str]   )->List[str]:
							debug_launcher(self.test_metrics.main   )



			@require_single_gpu
			def  lowercase__						(		self     :    List[Any]   )->Dict:
							self.test_metrics.main()



			@require_multi_gpu
			def  lowercase__						(		self     :    str   )->int:
							print(F'Found {torch.cuda.device_count()} devices.'   )
							_UpperCAmelCase					=    ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
							with patch_environment(omp_num_threads=1   ):
											execute_subprocess_async(__UpperCamelCase				,   env=os.environ.copy()   )



