



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			@property
			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     Tuple													=	UNetaDModel(
					    block_out_channels=(32, 64)       ,       layers_per_block=2       ,       sample_size=32       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,       )
					return model




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self.dummy_uncond_unet
					__UpperCAmelCase    :     List[Any]													=	PNDMScheduler()

					__UpperCAmelCase    :     Any													=	PNDMPipeline(unet=a_       ,       scheduler=a_ )
					pndm.to(a_ )
					pndm.set_progress_bar_config(disable=a_ )

					__UpperCAmelCase    :     List[str]													=	torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	pndm(generator=a_       ,       num_inference_steps=20       ,       output_type='''numpy''' ).images

					__UpperCAmelCase    :     List[str]													=	torch.manual_seed(0 )
					__UpperCAmelCase    :     int													=	pndm(generator=a_       ,       num_inference_steps=20       ,       output_type='''numpy'''       ,       return_dict=a_ )[0]

					__UpperCAmelCase    :     Dict													=	image[0, -3:, -3:, -1]
					__UpperCAmelCase    :     Optional[int]													=	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					__UpperCAmelCase    :     str													=	np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )

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

@slow
@require_torch
class 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	'''google/ddpm-cifar10-32'''

					__UpperCAmelCase    :     str													=	UNetaDModel.from_pretrained(a_ )
					__UpperCAmelCase    :     List[str]													=	PNDMScheduler()

					__UpperCAmelCase    :     Optional[Any]													=	PNDMPipeline(unet=a_       ,       scheduler=a_ )
					pndm.to(a_ )
					pndm.set_progress_bar_config(disable=a_ )
					__UpperCAmelCase    :     Optional[Any]													=	torch.manual_seed(0 )
					__UpperCAmelCase    :     List[str]													=	pndm(generator=a_       ,       output_type='''numpy''' ).images

					__UpperCAmelCase    :     Dict													=	image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					__UpperCAmelCase    :     List[str]													=	np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5] )

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










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						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





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

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	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    :     str													=	zeros((dst_height * dst_width, k_size * k_size)						)
		__UpperCAmelCase    :     Optional[Any]													=	0
		for i, j in product(range(_UpperCAmelCase						) ,   range(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     int													=	ravel(image[i : i + k_size, j : j + k_size]						)
				__UpperCAmelCase    :     Union[str, Any]													=	window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		__UpperCAmelCase    :     Tuple													=	gen_gaussian_kernel(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	ravel(_UpperCAmelCase						)

		# reshape and get the dst image
		__UpperCAmelCase    :     Optional[Any]													=	dot(_UpperCAmelCase ,   _UpperCAmelCase						).reshape(_UpperCAmelCase ,   _UpperCAmelCase						).astype(_UpperCAmelCase						)

		return dst


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

				# get values with two different mask size
				__A   			=gaussian_filter(gray, 3, sigma=1)
				__A   			=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()





