






'''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 	__magic_name__( _A					):
						'''simple docstring'''





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







class    _SCREAMING_SNAKE_CASE							(		lowercase_	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						Any ,      lowercase							:						Optional[Any] ,      lowercase							:						str ,      )							->    Dict:

									'''simple docstring'''







									super().__init__()
									self.register_modules(vqvae=lowercase ,      unet=lowercase ,      scheduler=lowercase			)







			@torch.no_grad()
			def __call__(   self							:						Optional[int] ,      lowercase							:						List[str] = None ,      lowercase							:						Dict = 1 ,      lowercase							:						List[str] = 1_0_0 ,      lowercase							:						Optional[Any] = 0.0 ,      lowercase							:						Union[str, Any] = None ,      lowercase							:						List[str] = "pil" ,      lowercase							:						Union[str, Any] = True ,      )							->    str:

									'''simple docstring'''







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

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

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

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

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

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

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

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

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

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

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowercase			)








'''simple docstring'''


import inspect
import unittest
class    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						Tuple			)							->    Any:

									'''simple docstring'''







									try:
															import diffusers  # noqa: F401
									except ImportError:
															assert False







			def 				A      (   self							:						Optional[int]			)							->    Union[str, Any]:

									'''simple docstring'''







									import diffusers
									from diffusers.dependency_versions_table import deps

									UpperCamelCase__         = inspect.getmembers(lowercase ,      inspect.isclass			)

									for cls_name, cls_module in all_classes:
															if "dummy_" in cls_module.__module__:
																					for backend in cls_module._backends:
																											if backend == "k_diffusion":
																																	UpperCamelCase__         = """k-diffusion"""
																											elif backend == "invisible_watermark":
																																	UpperCamelCase__         = """invisible-watermark"""
																											assert backend in deps, f"{backend} is not in the deps table!"
