



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 			lowerCamelCase__     (				_lowerCamelCase):




		'''simple docstring'''



		def __init__(self						,__lowerCamelCase						,__lowerCamelCase						,__lowerCamelCase					)	->     Any:

						"""simple docstring"""






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



		@torch.no_grad()
		def __call__(self						,__lowerCamelCase = 1						,__lowerCamelCase = None						,__lowerCamelCase = 0.0						,__lowerCamelCase = 50						,__lowerCamelCase = "pil"						,__lowerCamelCase = True						,**__lowerCamelCase						,)	->     Union[Tuple, ImagePipelineOutput]:

						"""simple docstring"""






						lowerCAmelCase__	:							int				=	randn_tensor(
						    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,generator=__UpperCamelCase						,)
						lowerCAmelCase__	:							Tuple				=	latents.to(self.device					)

						# scale the initial noise by the standard deviation required by the scheduler
						lowerCAmelCase__	:							Optional[int]				=	latents * self.scheduler.init_noise_sigma

						self.scheduler.set_timesteps(__UpperCamelCase					)

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

						lowerCAmelCase__	:							str				=	{}
						if accepts_eta:
										lowerCAmelCase__	:							str				=	eta

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

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

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

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=__UpperCamelCase					)






import re






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							str:

							'''simple docstring'''






							if len(re.findall('[ATCG]'			,	lowerCAmelCase__				)				) != len(lowerCAmelCase__				):
														raise ValueError('Invalid Strand'				)

							return dna.translate(dna.maketrans('ATCG'			,	'TAGC'				)				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()