
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							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Tuple:
							super().__init__()
							self.register_modules(vqvae=_UpperCamelCase							,							unet=_UpperCamelCase							,							scheduler=_UpperCamelCase	)




				@torch.no_grad()
				def __call__(   self							,							_UpperCamelCase = 1							,							_UpperCamelCase = None							,							_UpperCamelCase = 0.0							,							_UpperCamelCase = 50							,							_UpperCamelCase = "pil"							,							_UpperCamelCase = True							,							**_UpperCamelCase							,							)							-> Union[Tuple, ImagePipelineOutput]:
							lowerCAmelCase_         =      randn_tensor(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,							generator=_UpperCamelCase							,							)
							lowerCAmelCase_         =      latents.to(self.device	)

							# scale the initial noise by the standard deviation required by the scheduler
							lowerCAmelCase_         =      latents * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(_UpperCamelCase	)

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							lowerCAmelCase_         =      "eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

							lowerCAmelCase_         =      {}
							if accepts_eta:
										lowerCAmelCase_         =      eta

							for t in self.progress_bar(self.scheduler.timesteps	):
										lowerCAmelCase_         =      self.scheduler.scale_model_input(_UpperCamelCase							,							_UpperCamelCase	)
										# predict the noise residual
										lowerCAmelCase_         =      self.unet(_UpperCamelCase							,							_UpperCamelCase	).sample
										# compute the previous noisy sample x_t -> x_t-1
										lowerCAmelCase_         =      self.scheduler.step(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							**_UpperCamelCase	).prev_sample

							# decode the image latents with the VAE
							lowerCAmelCase_         =      self.vqvae.decode(_UpperCamelCase	).sample

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

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase	)





def   lowerCamelCase__							(							__lowerCAmelCase    :    list							):




			"""simple docstring"""


			for i in range(len(__lowerCAmelCase							) - 1       ,				0       ,				-1							):
						lowerCAmelCase_         =      False

						for j in range(__lowerCAmelCase       ,				0       ,				-1							):
									if unsorted[j] < unsorted[j - 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j - 1], unsorted[j]
												lowerCAmelCase_         =      True

						for j in range(__lowerCAmelCase							):
									if unsorted[j] > unsorted[j + 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j + 1], unsorted[j]
												lowerCAmelCase_         =      True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			_A  =	input("Enter numbers separated by a comma:\n").strip()
			_A  =	[int(item) for item in user_input.split(",")]
			print(f"""{cocktail_shaker_sort(unsorted) = }""")



