




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  Any						,							A_					:  int						,							A_					:  Optional[int]  )   ->	Tuple:

       """simple docstring"""






       super().__init__()
       self.register_modules(unet=A_						,							scheduler=A_  )





 @torch.no_grad()
 def __call__(				self					:  Optional[Any]						,							A_					:  int = 1						,							A_					:  Optional[torch.Generator] = None						,							A_					:  int = 50						,							A_					:  Optional[str] = "pil"						,							A_					:  bool = True						,							**A_					:  List[Any]						,							)   ->	Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_		     =    torch.randn(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,							generator=A_						,							)
       lowerCamelCase_		     =    image.to(self.device  )

       # set step values
       self.scheduler.set_timesteps(A_  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
             # 1. predict noise model_output
             lowerCamelCase_		     =    self.unet(A_						,							A_  ).sample

             # 2. predict previous mean of image x_t-1 and add variance depending on eta
             # eta corresponds to η in paper and should be between [0, 1]
             # do x_t -> x_t-1
             lowerCamelCase_		     =    self.scheduler.step(A_						,							A_						,							A_  ).prev_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,), "This is a local test"

       return ImagePipelineOutput(images=A_  ), "This is a local test"









import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


lowerCamelCase				:		Any     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  Dict						,							*A_					:  Optional[int]						,							**A_					:  int  )   ->	None:

       """simple docstring"""






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



