




"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :UNetaDModel
   _lowerCamelCase    :ScoreSdeVeScheduler






   def __init__(		self       :		Dict   ,     UpperCamelCase       :		UNetaDModel   ,     UpperCamelCase       :		ScoreSdeVeScheduler   )			->						Optional[int]:

         """simple docstring"""
         super().__init__()
         self.register_modules(unet=UpperCamelCase   ,     scheduler=UpperCamelCase   )






   @torch.no_grad()
   def __call__(		self       :		Optional[int]   ,     UpperCamelCase       :		int = 1   ,     UpperCamelCase       :		int = 20_00   ,     UpperCamelCase       :		Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,     UpperCamelCase       :		Optional[str] = "pil"   ,     UpperCamelCase       :		bool = True   ,     **UpperCamelCase       :		int   ,     )			->						Union[ImagePipelineOutput, Tuple]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.unet.config.sample_size
         lowerCAmelCase__      :			Any   =	(batch_size, 3, img_size, img_size)

         lowerCAmelCase__      :			List[str]   =	self.unet

         lowerCAmelCase__      :			int   =	randn_tensor(UpperCamelCase   ,     generator=UpperCamelCase   ) * self.scheduler.init_noise_sigma
         lowerCAmelCase__      :			List[Any]   =	sample.to(self.device   )

         self.scheduler.set_timesteps(UpperCamelCase   )
         self.scheduler.set_sigmas(UpperCamelCase   )

         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
               lowerCAmelCase__      :			Any   =	self.scheduler.sigmas[i] * torch.ones(shape[0]   ,     device=self.device   )

               # correction step
               for _ in range(self.scheduler.config.correct_steps   ):
                     lowerCAmelCase__      :			str   =	self.unet(UpperCamelCase   ,     UpperCamelCase   ).sample
                     lowerCAmelCase__      :			List[Any]   =	self.scheduler.step_correct(UpperCamelCase   ,     UpperCamelCase   ,     generator=UpperCamelCase   ).prev_sample

               # prediction step
               lowerCAmelCase__      :			str   =	model(UpperCamelCase   ,     UpperCamelCase   ).sample
               lowerCAmelCase__      :			int   =	self.scheduler.step_pred(UpperCamelCase   ,     UpperCamelCase   ,     UpperCamelCase   ,     generator=UpperCamelCase   )

               lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	output.prev_sample, output.prev_sample_mean

         lowerCAmelCase__      :			Optional[int]   =	sample_mean.clamp(0   ,     1   )
         lowerCAmelCase__      :			Union[str, Any]   =	sample.cpu().permute(0   ,     2   ,     3   ,     1   ).numpy()
         if output_type == "pil":
               lowerCAmelCase__      :			Optional[int]   =	self.numpy_to_pil(UpperCamelCase   )

         if not return_dict:
               return (sample,)

         return ImagePipelineOutput(images=UpperCamelCase   )











"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_A													=      logging.get_logger(__name__)


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Union[str, Any]   ,     *UpperCamelCase       :		int   ,     **UpperCamelCase       :		List[Any]   )			->						None:

         """simple docstring"""
         warnings.warn(
             """The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use CLIPImageProcessor instead."""   ,     UpperCamelCase   ,     )
         super().__init__(*UpperCamelCase   ,     **UpperCamelCase   )





