


'''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							(_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
					__a   ,    __a      :	Dict    =  image.size
					__a   ,    __a      :	Optional[int]    =  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
					__a      :	str    =  image.resize((w, h)						,				resample=PIL_INTERPOLATION['lanczos']       )
					__a      :	Optional[int]    =  np.array(_SCREAMING_SNAKE_CASE       ).astype(np.floataa       ) / 2_5_5.0
					__a      :	str    =  image[None].transpose(0						,				3						,				1						,				2       )
					__a      :	str    =  torch.from_numpy(_SCREAMING_SNAKE_CASE       )
					return 2.0 * image - 1.0





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a						,    __a						,    __a						,    ):

						'''simple docstring'''





						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						,    ):

						'''simple docstring'''





						if isinstance(__a						,    PIL.Image.Image							):
											__a      :	Union[str, Any]    =  1
						elif isinstance(__a						,    torch.Tensor							):
											__a      :	Any    =  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							):
											__a      :	List[str]    =  preprocess(__a							)

						__a   ,    __a      :	List[Any]    =  image.shape[-2:]

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

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

						__a      :	Union[str, Any]    =  image.to(device=self.device						,    dtype=__a							)

						# set timesteps and move to the correct device
						self.scheduler.set_timesteps(__a						,    device=self.device							)
						__a      :	Dict    =  self.scheduler.timesteps

						# scale the initial noise by the standard deviation required by the scheduler
						__a      :	Union[str, 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]
						__a      :	str    =  'eta' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
						__a      :	str    =  {}
						if accepts_eta:
											__a      :	Optional[Any]    =  eta

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

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

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

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=__a							)







'''simple docstring'''



from .imports import is_rich_available


if is_rich_available():
	from rich.traceback import install

	install(show_locals=False)

else:
	raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')



