
# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 		UpperCamelCase__  (       __lowercase    ):
   def __init__(self :					Any					,				snake_case_ :					int					,				snake_case_ :					Optional[int]   ):
    super().__init__()
    self.register_modules(unet=A_					,				scheduler=A_   )







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

    __a :  Optional[Any]						=  (image / 2 + 0.5).clamp(0					,				1   )
    __a :  Optional[int]						=  image.cpu().permute(0					,				2					,				3					,				1   ).numpy()
    if output_type == "pil":
     __a :  List[Any]						=  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_glpn import GLPNImageProcessor


lowerCamelCase				:		Union[str, Any]     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


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

       """simple docstring"""






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



