


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 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







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





class 	lowerCamelCase_       (				_UpperCamelCase				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       )   ->			List[Any]:







								'''simple docstring'''

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






					@torch.no_grad()
					def __call__(				self ,       snake_case_ = None ,       snake_case_ = 1 ,       snake_case_ = 1_0_0 ,       snake_case_ = 0.0 ,       snake_case_ = None ,       snake_case_ = "pil" ,       snake_case_ = True ,       )   ->			List[str]:







								'''simple docstring'''

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

								if isinstance(snake_case_ ,       PIL.Image.Image						):
											__lowercase 					=   preprocess(snake_case_						)

								__lowercase 					=   image.shape[-2:]

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

								__lowercase 					=   randn_tensor(snake_case_ ,       generator=snake_case_ ,       device=self.device ,       dtype=snake_case_						)

								__lowercase 					=   image.to(device=self.device ,       dtype=snake_case_						)

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

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

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

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

								if output_type == "pil":
											__lowercase 					=   self.numpy_to_pil(snake_case_						)

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=snake_case_						)






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)
@dataclass
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				42


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .pipeline_if import IFPipeline
	from .pipeline_if_imgaimg import IFImgaImgPipeline
	from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
	from .pipeline_if_inpainting import IFInpaintingPipeline
	from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
	from .pipeline_if_superresolution import IFSuperResolutionPipeline
	from .safety_checker import IFSafetyChecker
	from .watermark import IFWatermarker


