


'''simple docstring'''


from __future__ import annotations





class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case=None  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      data
											_SCREAMING_SNAKE_CASE :       int     		=      None






							def __repr__(    self  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      []
											_SCREAMING_SNAKE_CASE :       Dict     		=      self
											while temp:
															string_rep.append(f"""{temp.data}"""  )
															_SCREAMING_SNAKE_CASE :       int     		=      temp.next
											return "->".join(__snake_case  )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				if not elements_list:
								raise Exception("""The Elements List is empty"""     )

				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      Node(elements_list[0]     )
				for i in range(1							,      len(SCREAMING_SNAKE_CASE__     )     ):
								_SCREAMING_SNAKE_CASE :       Dict     		=      Node(elements_list[i]     )
								_SCREAMING_SNAKE_CASE :       Tuple     		=      current.next
				return head







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				if head_node is not None and isinstance(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):
								print_reverse(head_node.next     )
								print(head_node.data     )







def 			snake_case_   (  ):







				"""simple docstring"""





				from doctest import testmod

				testmod()

				_SCREAMING_SNAKE_CASE :       List[str]     		=      make_linked_list([14, 52, 14, 12, 43]     )
				print("""Linked List:"""     )
				print(SCREAMING_SNAKE_CASE__     )
				print("""Elements in Reverse:"""     )
				print_reverse(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
				main()








'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




