


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      ):
							__snake_case					,     __snake_case						:     List[Any]									=  image.size
							__snake_case					,     __snake_case						:     Tuple									=  (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
							__snake_case						:     str									=  image.resize((w, h)				,      resample=PIL_INTERPOLATION["lanczos"]      )
							__snake_case						:     int									=  np.array(__lowerCamelCase      ).astype(np.floataa      ) / 2_5_5.0
							__snake_case						:     Union[str, Any]									=  image[None].transpose(0				,      3				,      1				,      2      )
							__snake_case						:     Union[str, Any]									=  torch.from_numpy(__lowerCamelCase      )
							return 2.0 * image - 1.0

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							def __init__(				self			:    Tuple       ,      lowerCamelCase			:    VQModel       ,      lowerCamelCase			:    UNetaDModel       ,      lowerCamelCase			:    Union[
							        DDIMScheduler,
							        PNDMScheduler,
							        LMSDiscreteScheduler,
							        EulerDiscreteScheduler,
							        EulerAncestralDiscreteScheduler,
							        DPMSolverMultistepScheduler,
							    ]       ,      )      ->       Union[str, Any]:
														super().__init__()
														self.register_modules(vqvae=lowerCamelCase       ,      unet=lowerCamelCase       ,      scheduler=lowerCamelCase							)






							@torch.no_grad()
							def __call__(				self			:    List[str]       ,      lowerCamelCase			:    Union[torch.Tensor, PIL.Image.Image] = None       ,      lowerCamelCase			:    Optional[int] = 1       ,      lowerCamelCase			:    Optional[int] = 100       ,      lowerCamelCase			:    Optional[float] = 0.0       ,      lowerCamelCase			:    Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      lowerCamelCase			:    Optional[str] = "pil"       ,      lowerCamelCase			:    bool = True       ,      )      ->       Union[Tuple, ImagePipelineOutput]:
														if isinstance(lowerCamelCase       ,      PIL.Image.Image							):
																					__snake_case						:     Any									=  1
														elif isinstance(lowerCamelCase       ,      torch.Tensor							):
																					__snake_case						:     Any									=  image.shape[0]
														else:
																					raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase							)}'							)

														if isinstance(lowerCamelCase       ,      PIL.Image.Image							):
																					__snake_case						:     List[Any]									=  preprocess(lowerCamelCase							)

														__snake_case					,     __snake_case						:     int									=  image.shape[-2:]

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

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

														__snake_case						:     List[Any]									=  image.to(device=self.device       ,      dtype=lowerCamelCase							)

														# set timesteps and move to the correct device
														self.scheduler.set_timesteps(lowerCamelCase       ,      device=self.device							)
														__snake_case						:     str									=  self.scheduler.timesteps

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

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

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

														if output_type == "pil":
																					__snake_case						:     Tuple									=  self.numpy_to_pil(lowerCamelCase							)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowerCamelCase							)





import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)

