






"""simple docstring"""


# 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	:					str				,		A	:					Dict				,		A	:					List[str]			):


      '''simple docstring'''




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





    @torch.no_grad()
    def __call__(							self	:					str				,		A	:					int = 1				,		A	:					Optional[torch.Generator] = None				,		A	:					int = 5_0				,		A	:					Optional[str] = "pil"				,		A	:					bool = True				,		**A	:					Optional[int]				,		):


      '''simple docstring'''




      a     :			Optional[int]				=   torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,		generator=A				,		)
      a     :			List[Any]				=   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     :			Optional[Any]				=   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     :			List[str]				=   self.scheduler.step(A				,		A				,		A			).prev_sample

      a     :			Optional[int]				=   (image / 2 + 0.5).clamp(0				,		1			)
      a     :			Dict				=   image.cpu().permute(0				,		2				,		3				,		1			).numpy()
      if output_type == "pil":
        a     :			int				=   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"











from typing import List, Optional, Tuple, Union

import torch

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





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







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






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

     lowercase__    =  (image / 2 + 0.5).clamp(0    ,							1					)
     lowercase__    =  image.cpu().permute(0    ,							2    ,							3    ,							1					).numpy()
     if output_type == "pil":
        lowercase__    =  self.numpy_to_pil(_lowercase					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






