


from pathlib import Path

import numpy as np
from PIL import Image




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     np.ndarray:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
						return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     np.ndarray:




						'''simple docstring'''







						return (gray > 127) & (gray <= 255)




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     np.ndarray:




						'''simple docstring'''







						__UpperCAmelCase			:							Union[str, Any]	    =	np.zeros_like(lowercase_      )
						__UpperCAmelCase			:							Union[str, Any]	    =	np.zeros(
						    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

						# Copy image to padded image
						__UpperCAmelCase			:							List[str]	    =	image

						# Iterate over image & apply kernel
						for x in range(image.shape[1]      ):
												for y in range(image.shape[0]      ):
																		__UpperCAmelCase			:							Any	    =	(
																		    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																		).sum()
																		__UpperCAmelCase			:							str	    =	int(summation > 0      )
						return output


if __name__ == "__main__":
				# read original image
				lowerCAmelCase       		=    Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
				lowerCAmelCase       		=    np.array(Image.open(lena_path))
				# kernel to be applied
				lowerCAmelCase       		=    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
				lowerCAmelCase       		=    dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
				# Save the output image
				lowerCAmelCase       		=    Image.fromarray(output).convert("""RGB""")
				pil_img.save("""result_dilation.png""")





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		UNetaDModel
	_lowerCAmelCase     :		ScoreSdeVeScheduler


	def __init__(    self				,     lowercase__				,     lowercase__):
							super().__init__()
							self.register_modules(unet=lowercase__				,     scheduler=lowercase__)


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = 1				,     lowercase__ = 2_0_0_0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							int	    =	self.unet.config.sample_size
							__UpperCAmelCase			:							Optional[int]	    =	(batch_size, 3, img_size, img_size)

							__UpperCAmelCase			:							int	    =	self.unet

							__UpperCAmelCase			:							Tuple	    =	randn_tensor(lowercase__				,     generator=lowercase__) * self.scheduler.init_noise_sigma
							__UpperCAmelCase			:							List[str]	    =	sample.to(self.device)

							self.scheduler.set_timesteps(lowercase__)
							self.scheduler.set_sigmas(lowercase__)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
													__UpperCAmelCase			:							List[str]	    =	self.scheduler.sigmas[i] * torch.ones(shape[0]				,     device=self.device)

													# correction step
													for _ in range(self.scheduler.config.correct_steps):
																			__UpperCAmelCase			:							int	    =	self.unet(lowercase__				,     lowercase__).sample
																			__UpperCAmelCase			:							str	    =	self.scheduler.step_correct(lowercase__				,     lowercase__				,     generator=lowercase__).prev_sample

													# prediction step
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__).sample
													__UpperCAmelCase			:							Dict	    =	self.scheduler.step_pred(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__)

													__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	output.prev_sample, output.prev_sample_mean

							__UpperCAmelCase			:							List[str]	    =	sample_mean.clamp(0				,     1)
							__UpperCAmelCase			:							Union[str, Any]	    =	sample.cpu().permute(0				,     2				,     3				,     1).numpy()
							if output_type == "pil":
													__UpperCAmelCase			:							List[Any]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=lowercase__)

