



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class        __snake_case		(  UpperCamelCase_		):




							def __init__( self  : Optional[Any]       ,       A_  : Tuple       ,       A_  : str):
									super().__init__()
									self.register_modules(unet=A_       ,       scheduler=A_)







							@torch.no_grad()
							def __call__( self  : Any       ,       A_  : int = 1       ,       A_  : Optional[torch.Generator] = None       ,       A_  : int = 5_0       ,       A_  : Optional[str] = "pil"       ,       A_  : bool = True       ,       **A_  : Dict       ,       ):
									lowerCAmelCase_    :  Optional[int]             =				torch.randn(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,       generator=A_       ,       )
									lowerCAmelCase_    :  int             =				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_    :  Tuple             =				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_    :  Any             =				self.scheduler.step(A_       ,       A_       ,       A_).prev_sample

									lowerCAmelCase_    :  Optional[Any]             =				(image / 2 + 0.5).clamp(0       ,       1)
									lowerCAmelCase_    :  List[Any]             =				image.cpu().permute(0       ,       2       ,       3       ,       1).numpy()
									if output_type == "pil":
											lowerCAmelCase_    :  List[str]             =				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_detr import DetrImageProcessor


A__						:						str          =      logging.get_logger(__name__)



class        __snake_case		(  UpperCamelCase_		):




							def __init__( self  : Union[str, Any]       ,       *A_  : int       ,       **A_  : List[str]):
									warnings.warn(
									    '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
									    ''' Please use DetrImageProcessor instead.'''       ,       A_       ,       )
									super().__init__(*A_       ,       **A_)





