



from typing import List, Optional, Tuple, Union

import torch

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



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

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




	@torch.no_grad()
	def __call__(self						,			lowerCamelCase_ = 1						,			lowerCamelCase_ = None						,			lowerCamelCase_ = 1000						,			lowerCamelCase_ = "pil"						,			lowerCamelCase_ = True						,			):
						"""simple docstring"""

						if isinstance(self.unet.config.sample_size						,			lowerCamelCase_						):
											a   	=  (
											    batch_size,
											    self.unet.config.in_channels,
											    self.unet.config.sample_size,
											    self.unet.config.sample_size,
											)
						else:
											a   	=  (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

						if self.device.type == "mps":
											# randn does not work reproducibly on mps
											a   	=  randn_tensor(lowerCamelCase_						,			generator=lowerCamelCase_						)
											a   	=  image.to(self.device						)
						else:
											a   	=  randn_tensor(lowerCamelCase_						,			generator=lowerCamelCase_						,			device=self.device						)

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

						for t in self.progress_bar(self.scheduler.timesteps						):
											# 1. predict noise model_output
											a   	=  self.unet(lowerCamelCase_						,			lowerCamelCase_						).sample

											# 2. compute previous image: x_t -> x_t-1
											a   	=  self.scheduler.step(lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						,			generator=lowerCamelCase_						).prev_sample

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

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=lowerCamelCase_						)





def        a( A			:       int					,	A			:       int					,	A			:       int			)						->			float:
					"""simple docstring"""





					if common_ratio == 1:
										# Formula for sum if common ratio is 1
										return num_of_terms * first_term

					# Formula for finding sum of n terms of a GeometricProgression
					return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)
