
"""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   _lowercase						(       UpperCamelCase__				):


     '''simple docstring'''





     _A								=							42
     _A								=							42



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Any:
          super().__init__()
          self.register_modules(unet=_a ,			scheduler=_a  )



     @torch.no_grad()
     def __call__(      self ,			__UpperCamelCase = 1 ,			__UpperCamelCase = 20_00 ,			__UpperCamelCase = None ,			__UpperCamelCase = "pil" ,			__UpperCamelCase = True ,			**__UpperCamelCase ,			)->					Union[ImagePipelineOutput, Tuple]:
          UpperCAmelCase__							: List[Any] 							=						self.unet.config.sample_size
          UpperCAmelCase__							: Any 							=						(batch_size, 3, img_size, img_size)

          UpperCAmelCase__							: Dict 							=						self.unet

          UpperCAmelCase__							: Any 							=						randn_tensor(_a ,			generator=_a  ) * self.scheduler.init_noise_sigma
          UpperCAmelCase__							: List[str] 							=						sample.to(self.device  )

          self.scheduler.set_timesteps(_a  )
          self.scheduler.set_sigmas(_a  )

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

               # correction step
               for _ in range(self.scheduler.config.correct_steps  ):
                    UpperCAmelCase__							: Tuple 							=						self.unet(_a ,			_a  ).sample
                    UpperCAmelCase__							: int 							=						self.scheduler.step_correct(_a ,			_a ,			generator=_a  ).prev_sample

               # prediction step
               UpperCAmelCase__							: Optional[int] 							=						model(_a ,			_a  ).sample
               UpperCAmelCase__							: Optional[Any] 							=						self.scheduler.step_pred(_a ,			_a ,			_a ,			generator=_a  )

               UpperCAmelCase__							: Tuple 							=						output.prev_sample, output.prev_sample_mean

          UpperCAmelCase__							: Tuple 							=						sample_mean.clamp(0 ,			1  )
          UpperCAmelCase__							: Dict 							=						sample.cpu().permute(0 ,			2 ,			3 ,			1  ).numpy()
          if output_type == "pil":
               UpperCAmelCase__							: Any 							=						self.numpy_to_pil(_a  )

          if not return_dict:
               return (sample,)

          return ImagePipelineOutput(images=_a  )





"""simple docstring"""




from typing import TYPE_CHECKING

from ....utils import _LazyModule


A__      :	List[str]          =    {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_tapex import TapexTokenizer


else:
    import sys

    A__      :	Any          =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)



