


'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				if not numbers:
								return 0

				if not isinstance(SCREAMING_SNAKE_CASE__							,      (list, tuple)     ) or not all(
				    isinstance(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ) for number in numbers     ):
								raise ValueError("""numbers must be an iterable of integers"""     )

				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      numbers[0]

				for i in range(1							,      len(SCREAMING_SNAKE_CASE__     )     ):
								# update the maximum and minimum subarray products
								_SCREAMING_SNAKE_CASE :       List[str]     		=      numbers[i]
								if number < 0:
												_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      min_till_now, max_till_now
								_SCREAMING_SNAKE_CASE :       Any     		=      max(SCREAMING_SNAKE_CASE__							,      max_till_now * number     )
								_SCREAMING_SNAKE_CASE :       str     		=      min(SCREAMING_SNAKE_CASE__							,      min_till_now * number     )

								# update the maximum product found till now
								_SCREAMING_SNAKE_CASE :       int     		=      max(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )

				return max_prod








'''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  )




