





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






class       UpperCamelCase__  (		lowercase_     ):







	def __init__(				self			:   Optional[int] , lowerCamelCase			:   VQModel , lowerCamelCase			:   UNetaDModel , lowerCamelCase			:   Union[
	        DDIMScheduler,
	        PNDMScheduler,
	        LMSDiscreteScheduler,
	        EulerDiscreteScheduler,
	        EulerAncestralDiscreteScheduler,
	        DPMSolverMultistepScheduler,
	    ] , ):




				'''simple docstring'''
				super().__init__()
				self.register_modules(vqvae=UpperCamelCase__ , unet=UpperCamelCase__ , scheduler=UpperCamelCase__		)







	@torch.no_grad()
	def __call__(				self			:   int , lowerCamelCase			:   Union[torch.Tensor, PIL.Image.Image] = None , lowerCamelCase			:   Optional[int] = 1 , lowerCamelCase			:   Optional[int] = 1_0_0 , lowerCamelCase			:   Optional[float] = 0.0 , lowerCamelCase			:   Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCamelCase			:   Optional[str] = "pil" , lowerCamelCase			:   bool = True , ):




				'''simple docstring'''







				if isinstance(UpperCamelCase__ , PIL.Image.Image		):
							a__     = 1
				elif isinstance(UpperCamelCase__ , torch.Tensor		):
							a__     = image.shape[0]
				else:
							raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__		)}'''		)

				if isinstance(UpperCamelCase__ , PIL.Image.Image		):
							a__     = preprocess(UpperCamelCase__		)

				a__   ,				a__     = image.shape[-2:]

				# in_channels should be 6: 3 for latents, 3 for low resolution image
				a__     = (batch_size, self.unet.config.in_channels // 2, height, width)
				a__     = next(self.unet.parameters()		).dtype

				a__     = randn_tensor(UpperCamelCase__ , generator=UpperCamelCase__ , device=self.device , dtype=UpperCamelCase__		)

				a__     = image.to(device=self.device , dtype=UpperCamelCase__		)

				# set timesteps and move to the correct device
				self.scheduler.set_timesteps(UpperCamelCase__ , device=self.device		)
				a__     = self.scheduler.timesteps

				# scale the initial noise by the standard deviation required by the scheduler
				a__     = 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]
				a__     = "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
				a__     = {}
				if accepts_eta:
							a__     = eta

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

				# decode the image latents with the VQVAE
				a__     = self.vqvae.decode(UpperCamelCase__		).sample
				a__     = torch.clamp(UpperCamelCase__ , -1.0 , 1.0		)
				a__     = image / 2 + 0.5
				a__     = image.cpu().permute(0 , 2 , 3 , 1		).numpy()

				if output_type == "pil":
							a__     = self.numpy_to_pil(UpperCamelCase__		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=UpperCamelCase__		)










'''simple docstring'''


import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script






@require_cpu
class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				debug_launcher(test_script.main		)







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				debug_launcher(test_ops.main		)



