


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 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
						def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
													super().__init__()

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

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

						@torch.no_grad()
						def __call__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,							SCREAMING_SNAKE_CASE__       : float = 0.0  ,							SCREAMING_SNAKE_CASE__       : int = 5_0  ,							SCREAMING_SNAKE_CASE__       : Optional[bool] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = "pil"  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							)	->    Union[ImagePipelineOutput, Tuple]:

													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size  ,							SCREAMING_SNAKE_CASE__  ):
																				a_      :	Any							=						(
																				    batch_size,
																				    self.unet.config.in_channels,
																				    self.unet.config.sample_size,
																				    self.unet.config.sample_size,
																				)
													else:
																				a_      :	Optional[Any]							=						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

													a_      :	Tuple							=						randn_tensor(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=self.device  ,							dtype=self.unet.dtype  )

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

													for t in self.progress_bar(self.scheduler.timesteps  ):
																				# 1. predict noise model_output
																				a_      :	int							=						self.unet(SCREAMING_SNAKE_CASE__  ,							SCREAMING_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_      :	str							=						self.scheduler.step(
																				    SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							eta=SCREAMING_SNAKE_CASE__  ,							use_clipped_model_output=SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ).prev_sample

													a_      :	Any							=						(image / 2 + 0.5).clamp(0  ,							1  )
													a_      :	Union[str, Any]							=						image.cpu().permute(0  ,							2  ,							3  ,							1  ).numpy()
													if output_type == "pil":
																				a_      :	Optional[int]							=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__  )






def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int						,					__A				:    int       )			->	float:

       """simple docstring"""

       a_      :	Optional[int]							=						(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total







def  SCREAMING_SNAKE_CASE_		(    )			->	Optional[Any]:

       """simple docstring"""

       print(sum_of_series(1						,					1						,					10       )       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


