






from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase=3     )->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase     =      int(mask / 2     )
						# copy image size
						_UpperCAmelCase     =      zeros_like(__lowerCAmelCase     )
						for i in range(__lowerCAmelCase			,		gray_img.shape[0] - bd     ):
												for j in range(__lowerCAmelCase			,		gray_img.shape[1] - bd     ):
																		# get mask according with mask
																		_UpperCAmelCase     =      ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]     )
																		# calculate mask median
																		_UpperCAmelCase     =      sort(__lowerCAmelCase     )[inta(divide((multiply(__lowerCAmelCase			,		__lowerCAmelCase     ))			,		2     ) + 1     )]
																		_UpperCAmelCase     =      median
						return median_img


if __name__ == "__main__":
		# read original image
		_a				     =  imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		_a				     =  cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		_a				     =  median_filter(gray, 3)
		_a				     =  median_filter(gray, 5)

		# show result images
		imshow('''median filter with 3x3 mask''', medianaxa)
		imshow('''median filter with 5x5 mask''', medianaxa)
		waitKey(0)








import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference





@slow
@require_torch_gpu
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'runwayml/stable-diffusion-v1-5'
							_UpperCAmelCase     =      TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase	,   torch_dtype=torch.floataa   ).to('cuda'   )
							_UpperCAmelCase     =      DDIMScheduler.from_config(pipe.scheduler.config   )
							_UpperCAmelCase     =      torch.Generator(device='cuda'   ).manual_seed(0   )

							_UpperCAmelCase     =      'A bear is playing a guitar on Times Square'
							_UpperCAmelCase     =      pipe(prompt=UpperCAmelCase	,   generator=UpperCAmelCase   ).images

							_UpperCAmelCase     =      load_pt(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'   )

							assert_mean_pixel_difference(UpperCAmelCase	,   UpperCAmelCase   )
