





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 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     Any:
		lowerCamelCase   : Dict								=image.size
		lowerCamelCase   : Optional[Any]								=(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
		lowerCamelCase   : Optional[int]								=image.resize((w, h)							,       resample=PIL_INTERPOLATION['''lanczos''']		)
		lowerCamelCase   : Optional[Any]								=np.array(UpperCamelCase__		).astype(np.floataa		) / 2_5_5.0
		lowerCamelCase   : Optional[Any]								=image[None].transpose(0							,       3							,       1							,       2		)
		lowerCamelCase   : List[Any]								=torch.from_numpy(UpperCamelCase__		)
		return 2.0 * image - 1.0



class        snake_case_		(			__lowercase):







		def __init__(		self			,     __lowercase			,     __lowercase			,     __lowercase			,     )				->   Optional[int]:
				super().__init__()
				self.register_modules(vqvae=__a			,     unet=__a			,     scheduler=__a   )







		@torch.no_grad()
		def __call__(		self			,     __lowercase = None			,     __lowercase = 1			,     __lowercase = 1_0_0			,     __lowercase = 0.0			,     __lowercase = None			,     __lowercase = "pil"			,     __lowercase = True			,     )				->   Union[Tuple, ImagePipelineOutput]:
				if isinstance(__a			,     PIL.Image.Image   ):
						lowerCamelCase   : List[Any]								=1
				elif isinstance(__a			,     torch.Tensor   ):
						lowerCamelCase   : List[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   ):
						lowerCamelCase   : List[Any]								=preprocess(__a   )

				lowerCamelCase   : Union[str, Any]								=image.shape[-2:]

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

				lowerCamelCase   : List[Any]								=randn_tensor(__a			,     generator=__a			,     device=self.device			,     dtype=__a   )

				lowerCamelCase   : int								=image.to(device=self.device			,     dtype=__a   )

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

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

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

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

				if output_type == "pil":
						lowerCamelCase   : Optional[Any]								=self.numpy_to_pil(__a   )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=__a   )








from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class        snake_case_		(			_A						,					_A):







		@register_to_config
		def __init__(		self			,     __lowercase = 7_6_8			,     )				->   List[Any]:
				super().__init__()

				lowerCamelCase   : str								=nn.Parameter(torch.zeros(1			,     __lowercase   )   )
				lowerCamelCase   : Union[str, Any]								=nn.Parameter(torch.ones(1			,     __lowercase   )   )







		def 			__lowercase   (		self			,     __lowercase = None			,     __lowercase = None			,     )				->   List[str]:
				lowerCamelCase   : Tuple								=nn.Parameter(self.mean.to(__lowercase   ).to(__lowercase   )   )
				lowerCamelCase   : Dict								=nn.Parameter(self.std.to(__lowercase   ).to(__lowercase   )   )
				return self







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[Any]:
				lowerCamelCase   : Any								=(embeds - self.mean) * 1.0 / self.std
				return embeds







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[int]:
				lowerCamelCase   : Any								=(embeds * self.std) + self.mean
				return embeds

