






import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 						lowerCamelCase_				(      _a ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__()
								self.register_modules(vqvae=_A       ,					unet=_A       ,					scheduler=_A     )







				@torch.no_grad()
				def __call__(						self       ,					__lowerCAmelCase = 1       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = 0.0       ,					__lowerCAmelCase = 5_0       ,					__lowerCAmelCase = "pil"       ,					__lowerCAmelCase = True       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:Any        =      randn_tensor(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,					generator=_A       ,					)
								__magic_name__		:List[str]        =      latents.to(self.device     )

								# scale the initial noise by the standard deviation required by the scheduler
								__magic_name__		:Union[str, Any]        =      latents * self.scheduler.init_noise_sigma

								self.scheduler.set_timesteps(_A     )

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								__magic_name__		:Optional[int]        =      '''eta''' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )

								__magic_name__		:Tuple        =      {}
								if accepts_eta:
												__magic_name__		:Dict        =      eta

								for t in self.progress_bar(self.scheduler.timesteps     ):
												__magic_name__		:Optional[int]        =      self.scheduler.scale_model_input(_A       ,					_A     )
												# predict the noise residual
												__magic_name__		:Dict        =      self.unet(_A       ,					_A     ).sample
												# compute the previous noisy sample x_t -> x_t-1
												__magic_name__		:Dict        =      self.scheduler.step(_A       ,					_A       ,					_A       ,					**_A     ).prev_sample

								# decode the image latents with the VAE
								__magic_name__		:Union[str, Any]        =      self.vqvae.decode(_A     ).sample

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

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=_A     )














from __future__ import annotations

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      0
				__magic_name__		:Tuple        =      len(snake_case ) - 1

				while i < j:
								if nums[i] + nums[j] == target:
												return [i, j]
								elif nums[i] + nums[j] < target:
												__magic_name__		:str        =      i + 1
								else:
												__magic_name__		:List[Any]        =      j - 1

				return []


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(f"{two_pointer([2, 7, 11, 15], 9) = }")






