


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_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		k_size // 2
       UpperCamelCase    :	Optional[int]   	=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase    :	Dict   	=		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_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		image.shape[0], image.shape[1]
       # dst image height and width
       UpperCamelCase    :	str   	=		height - k_size + 1
       UpperCamelCase    :	Optional[int]   	=		width - k_size + 1

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

       #  turn the kernel into shape(k*k, 1)
       UpperCamelCase    :	Optional[int]   	=		gen_gaussian_kernel(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		ravel(SCREAMING_SNAKE_CASE_       )

       # reshape and get the dst image
       UpperCamelCase    :	Optional[int]   	=		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
       __UpperCAmelCase : Union[str, Any] 			=				imread(r"../image_data/lena.jpg")
       # turn image in gray scale value
       __UpperCAmelCase : Optional[Any] 			=				cvtColor(img, COLOR_BGR2GRAY)

       # get values with two different mask size
       __UpperCAmelCase : Optional[int] 			=				gaussian_filter(gray, 3, sigma=1)
       __UpperCAmelCase : List[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()





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
              pipe.to(__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

              UpperCamelCase    :	List[Any]   	=		image[0, 253:256, 253:256, -1]

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

