





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							List[Any]:
										super().__init__()
										self.register_modules(vqvae=lowerCAmelCase_				,						unet=lowerCAmelCase_				,						scheduler=lowerCAmelCase_  )






					@torch.no_grad()
					def __call__(		self				,						lowerCAmelCase_ = 1				,						lowerCAmelCase_ = None				,						lowerCAmelCase_ = 0.0				,						lowerCAmelCase_ = 50				,						lowerCAmelCase_ = "pil"				,						lowerCAmelCase_ = True				,						**lowerCAmelCase_				,						)		->							Union[Tuple, ImagePipelineOutput]:
										_snake_case									=				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,						generator=lowerCAmelCase_				,						)
										_snake_case									=				latents.to(self.device  )

										# scale the initial noise by the standard deviation required by the scheduler
										_snake_case									=				latents * self.scheduler.init_noise_sigma

										self.scheduler.set_timesteps(lowerCAmelCase_  )

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										_snake_case									=				'eta' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )

										_snake_case									=				{}
										if accepts_eta:
															_snake_case									=				eta

										for t in self.progress_bar(self.scheduler.timesteps  ):
															_snake_case									=				self.scheduler.scale_model_input(lowerCAmelCase_				,						lowerCAmelCase_  )
															# predict the noise residual
															_snake_case									=				self.unet(lowerCAmelCase_				,						lowerCAmelCase_  ).sample
															# compute the previous noisy sample x_t -> x_t-1
															_snake_case									=				self.scheduler.step(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						**lowerCAmelCase_  ).prev_sample

										# decode the image latents with the VAE
										_snake_case									=				self.vqvae.decode(lowerCAmelCase_  ).sample

										_snake_case									=				(image / 2 + 0.5).clamp(0				,						1  )
										_snake_case									=				image.cpu().permute(0				,						2				,						3				,						1  ).numpy()
										if output_type == "pil":
															_snake_case									=				self.numpy_to_pil(lowerCAmelCase_  )

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase_  )










from cva import destroyAllWindows, imread, imshow, waitKey





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]						)			-> Optional[int]:



					'''simple docstring'''




					_snake_case							,     _snake_case									=				img.shape[0], img.shape[1]

					# converting each pixel's color to its negative
					for i in range(UpperCamelCase__						):
										for j in range(UpperCamelCase__						):
															_snake_case									=				[255, 255, 255] - img[i][j]

					return img


if __name__ == "__main__":
					# read original image
					UpperCAmelCase_      							=     imread("""image_data/lena.jpg""", 1)

					# convert to its negative
					UpperCAmelCase_      							=     convert_to_negative(img)

					# show result image
					imshow("""negative of original image""", img)
					waitKey(0)
					destroyAllWindows()



