

"""simple docstring"""





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[int]   ,lowercase_			:				VQModel   ,lowercase_			:				UNetaDModel   ,lowercase_			:				DDIMScheduler		):
						super().__init__()
						self.register_modules(vqvae=lowercase_   ,unet=lowercase_   ,scheduler=lowercase_		)







					@torch.no_grad()
					def __call__(       self			:				Tuple   ,lowercase_			:				int = 1   ,lowercase_			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,lowercase_			:				float = 0.0   ,lowercase_			:				int = 5_0   ,lowercase_			:				Optional[str] = "pil"   ,lowercase_			:				bool = True   ,**lowercase_			:				Tuple   ,):
						lowerCAmelCase__	:						Dict  				=	randn_tensor(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,generator=lowercase_   ,)
						lowerCAmelCase__	:						Optional[int]  				=	latents.to(self.device		)

						# scale the initial noise by the standard deviation required by the scheduler
						lowerCAmelCase__	:						Dict  				=	latents * self.scheduler.init_noise_sigma

						self.scheduler.set_timesteps(lowercase_		)

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						lowerCAmelCase__	:						Union[str, Any]  				=	'''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)

						lowerCAmelCase__	:						Optional[Any]  				=	{}
						if accepts_eta:
							lowerCAmelCase__	:						str  				=	eta

						for t in self.progress_bar(self.scheduler.timesteps		):
							lowerCAmelCase__	:						Dict  				=	self.scheduler.scale_model_input(lowercase_   ,lowercase_		)
							# predict the noise residual
							lowerCAmelCase__	:						Optional[int]  				=	self.unet(lowercase_   ,lowercase_		).sample
							# compute the previous noisy sample x_t -> x_t-1
							lowerCAmelCase__	:						Tuple  				=	self.scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		).prev_sample

						# decode the image latents with the VAE
						lowerCAmelCase__	:						Any  				=	self.vqvae.decode(lowercase_		).sample

						lowerCAmelCase__	:						List[Any]  				=	(image / 2 + 0.5).clamp(0   ,1		)
						lowerCAmelCase__	:						str  				=	image.cpu().permute(0   ,2   ,3   ,1		).numpy()
						if output_type == "pil":
							lowerCAmelCase__	:						int  				=	self.numpy_to_pil(lowercase_		)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=lowercase_		)







"""simple docstring"""





from ..utils import DummyObject, requires_backends







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Tuple   ,*lowercase_			:				Tuple   ,**lowercase_			:				List[str]		):
						requires_backends(self   ,['''speech''']		)







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Union[str, Any]   ,*lowercase_			:				List[str]   ,**lowercase_			:				Any		):
						requires_backends(self   ,['''speech''']		)




