





"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			__UpperCamelCase							(		snake_case_		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Union[str, Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								a :						Any      =							DDIMScheduler.from_config(scheduler.config )

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






							@torch.no_grad()
							def __call__(  self ,   lowerCAmelCase__ = 1 ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 0.0 ,   lowerCAmelCase__ = 50 ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = "pil" ,   lowerCAmelCase__ = True ,   )      ->					Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size ,   __snake_case ):
									a :						List[Any]      =							(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
								else:
									a :						int      =							(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(__snake_case ,   __snake_case ) and len(__snake_case ) != batch_size:
									raise ValueError(
									    f"""You have passed a list of generators of length {len(__snake_case )}, but requested an effective batch"""
									    f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

								a :						Dict      =							randn_tensor(__snake_case ,   generator=__snake_case ,   device=self.device ,   dtype=self.unet.dtype )

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

								for t in self.progress_bar(self.scheduler.timesteps ):
									# 1. predict noise model_output
									a :						List[Any]      =							self.unet(__snake_case ,   __snake_case ).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 :						int      =							self.scheduler.step(
									    __snake_case ,   __snake_case ,   __snake_case ,   eta=__snake_case ,   use_clipped_model_output=__snake_case ,   generator=__snake_case ).prev_sample

								a :						int      =							(image / 2 + 0.5).clamp(0 ,   1 )
								a :						List[str]      =							image.cpu().permute(0 ,   2 ,   3 ,   1 ).numpy()
								if output_type == "pil":
									a :						int      =							self.numpy_to_pil(__snake_case )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=__snake_case )









'''simple docstring'''






def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				""""""
      for word_or_phrase in separated:
            if not isinstance(lowerCAmelCase				,      lowerCAmelCase    ):
                  raise Exception("""join() accepts only strings to be joined"""    )
            joined += word_or_phrase + separator
      return joined.strip(lowerCAmelCase    )


if __name__ == "__main__":
      from doctest import testmod

      testmod()






