




"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def 						UpperCAmelCase				(			A__:    str     )  ->     Tuple:
			__lowerCamelCase       ,      __lowerCamelCase					:	List[str]	       =				image.size
			__lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
			__lowerCamelCase					:	str	       =				image.resize((w, h)							,				resample=PIL_INTERPOLATION['lanczos']     )
			__lowerCamelCase					:	int	       =				np.array(A__     ).astype(np.floataa     ) / 255.0
			__lowerCamelCase					:	Optional[Any]	       =				image[None].transpose(0							,				3							,				1							,				2     )
			__lowerCamelCase					:	Any	       =				torch.from_numpy(A__     )
			return 2.0 * image - 1.0






class __lowercase(      lowercase__				):




					'''simple docstring'''


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





					@torch.no_grad()
					def __call__(    self						,	__a = None						,	__a = 1						,	__a = 100						,	__a = 0.0						,	__a = None						,	__a = "pil"						,	__a = True						,	):
								if isinstance(__a						,	PIL.Image.Image     ):
											__lowerCamelCase					:	Dict	       =				1
								elif isinstance(__a						,	torch.Tensor     ):
											__lowerCamelCase					:	List[str]	       =				image.shape[0]
								else:
											raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__a     )}'''     )

								if isinstance(__a						,	PIL.Image.Image     ):
											__lowerCamelCase					:	int	       =				preprocess(__a     )

								__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								__lowerCamelCase					:	Union[str, Any]	       =				(batch_size, self.unet.config.in_channels // 2, height, width)
								__lowerCamelCase					:	List[Any]	       =				next(self.unet.parameters()     ).dtype

								__lowerCamelCase					:	str	       =				randn_tensor(__a						,	generator=__a						,	device=self.device						,	dtype=__a     )

								__lowerCamelCase					:	Optional[int]	       =				image.to(device=self.device						,	dtype=__a     )

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(__a						,	device=self.device     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								__lowerCamelCase					:	Optional[Any]	       =				latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								__lowerCamelCase					:	str	       =				'eta' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
								__lowerCamelCase					:	List[str]	       =				{}
								if accepts_eta:
											__lowerCamelCase					:	Tuple	       =				eta

								for t in self.progress_bar(__a     ):
											# concat latents and low resolution image in the channel dimension.
											__lowerCamelCase					:	str	       =				torch.cat([latents, image]						,	dim=1     )
											__lowerCamelCase					:	Optional[int]	       =				self.scheduler.scale_model_input(__a						,	__a     )
											# predict the noise residual
											__lowerCamelCase					:	Union[str, Any]	       =				self.unet(__a						,	__a     ).sample
											# compute the previous noisy sample x_t -> x_t-1
											__lowerCamelCase					:	Dict	       =				self.scheduler.step(__a						,	__a						,	__a						,	**__a     ).prev_sample

								# decode the image latents with the VQVAE
								__lowerCamelCase					:	int	       =				self.vqvae.decode(__a     ).sample
								__lowerCamelCase					:	int	       =				torch.clamp(__a						,	-1.0						,	1.0     )
								__lowerCamelCase					:	Tuple	       =				image / 2 + 0.5
								__lowerCamelCase					:	List[str]	       =				image.cpu().permute(0						,	2						,	3						,	1     ).numpy()

								if output_type == "pil":
											__lowerCamelCase					:	Union[str, Any]	       =				self.numpy_to_pil(__a     )

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=__a     )









"""simple docstring"""
import math





def 						UpperCAmelCase				(			A__:    int = 100     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				sum(i * i for i in range(1							,				n + 1     )     )
			__lowerCamelCase					:	Union[str, Any]	       =				int(math.pow(sum(range(1							,				n + 1     )     )							,				2     )     )
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
					print(F"""{solution() = }""")



