






"""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 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  Union[str, Any]						,	lowercase_     :  VQModel						,	lowercase_     :  UNetaDModel						,	lowercase_     :  DDIMScheduler):




          '''simple docstring'''


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



      @torch.no_grad()
      def __call__(						self     :  int						,	lowercase_     :  int = 1						,	lowercase_     :  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,	lowercase_     :  float = 0.0						,	lowercase_     :  int = 50						,	lowercase_     :  Optional[str] = "pil"						,	lowercase_     :  bool = True						,	**lowercase_     :  int						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		randn_tensor(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=lowercase_						,	)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		latents.to(self.device)

          # scale the initial noise by the standard deviation required by the scheduler
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		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
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())

          SCREAMING_SNAKE_CASE_      :  str    						=		{}
          if accepts_eta:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		eta

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

          # decode the image latents with the VAE
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.vqvae.decode(lowercase_).sample

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

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowercase_)










"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


UpperCAmelCase_   : Any   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

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




          '''simple docstring'''


          warnings.warn(
              '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
              ''' Please use SegformerImageProcessor instead.'''						,	lowercase_						,	)
          super().__init__(*lowercase_						,	**lowercase_)


