

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     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A							):
												super().__init__()
												self.register_modules(vqvae=_A					,						unet=_A					,						scheduler=_A							)




							@torch.no_grad()
							def __call__( self					,						_A = 1					,						_A = None					,						_A = 0.0					,						_A = 5_0					,						_A = "pil"					,						_A = True					,						**_A					,						):
												__lowerCAmelCase					=							randn_tensor(
												    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,						generator=_A					,						)
												__lowerCAmelCase					=							latents.to(self.device							)

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

												self.scheduler.set_timesteps(_A							)

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

												__lowerCAmelCase					=							{}
												if accepts_eta:
																	__lowerCAmelCase					=							eta

												for t in self.progress_bar(self.scheduler.timesteps							):
																	__lowerCAmelCase					=							self.scheduler.scale_model_input(_A					,						_A							)
																	# predict the noise residual
																	__lowerCAmelCase					=							self.unet(_A					,						_A							).sample
																	# compute the previous noisy sample x_t -> x_t-1
																	__lowerCAmelCase					=							self.scheduler.step(_A					,						_A					,						_A					,						**_A							).prev_sample

												# decode the image latents with the VAE
												__lowerCAmelCase					=							self.vqvae.decode(_A							).sample

												__lowerCAmelCase					=							(image / 2 + 0.5).clamp(0					,						1							)
												__lowerCAmelCase					=							image.cpu().permute(0					,						2					,						3					,						1							).numpy()
												if output_type == "pil":
																	__lowerCAmelCase					=							self.numpy_to_pil(_A							)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=_A							)









import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						*_A					,						**_A							):
												warnings.warn(
												    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
												    ' Please use PoolFormerImageProcessor instead.'					,						_A					,						)
												super().__init__(*_A					,						**_A							)






