






'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      UNetaDModel
	SCREAMING_SNAKE_CASE_					:      KarrasVeScheduler
	def __init__(							self  ,      _lowercase  ,      _lowercase       )						->  Any:
							super().__init__()
							self.register_modules(unet=_lowercase  ,      scheduler=_lowercase       )







	@torch.no_grad()
	def __call__(							self  ,      _lowercase = 1  ,      _lowercase = 50  ,      _lowercase = None  ,      _lowercase = "pil"  ,      _lowercase = True  ,      **_lowercase  ,      )						->  Union[Tuple, ImagePipelineOutput]:
							lowercase_      :   int        =			self.unet.config.sample_size
							lowercase_      :   Optional[Any]        =			(batch_size, 3, img_size, img_size)

							lowercase_      :   Union[str, Any]        =			self.unet

							# sample x_0 ~ N(0, sigma_0^2 * I)
							lowercase_      :   Dict        =			randn_tensor(_lowercase  ,      generator=_lowercase  ,      device=self.device       ) * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(_lowercase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
													# here sigma_t == t_i from the paper
													lowercase_      :   Any        =			self.scheduler.schedule[t]
													lowercase_      :   Any        =			self.scheduler.schedule[t - 1] if t > 0 else 0

													# 1. Select temporarily increased noise level sigma_hat
													# 2. Add new noise to move from sample_i to sample_hat
													lowercase_  ,      lowercase_      :   Union[str, Any]        =			self.scheduler.add_noise_to_input(_lowercase  ,      _lowercase  ,      generator=_lowercase       )

													# 3. Predict the noise residual given the noise magnitude `sigma_hat`
													# The model inputs and output are adjusted by following eq. (213) in [1].
													lowercase_      :   Optional[int]        =			(sigma_hat / 2) * model((sample_hat + 1) / 2  ,      sigma_hat / 2       ).sample

													# 4. Evaluate dx/dt at sigma_hat
													# 5. Take Euler step from sigma to sigma_prev
													lowercase_      :   Optional[Any]        =			self.scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )

													if sigma_prev != 0:
																			# 6. Apply 2nd order correction
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			lowercase_      :   Dict        =			(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,      sigma_prev / 2       ).sample
																			lowercase_      :   Tuple        =			self.scheduler.step_correct(
																			    _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      step_output.prev_sample  ,      step_output['derivative']  ,      )
													lowercase_      :   Dict        =			step_output.prev_sample

							lowercase_      :   Tuple        =			(sample / 2 + 0.5).clamp(0  ,      1       )
							lowercase_      :   Tuple        =			sample.cpu().permute(0  ,      2  ,      3  ,      1       ).numpy()
							if output_type == "pil":
													lowercase_      :   Optional[int]        =			self.numpy_to_pil(_lowercase       )

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=_lowercase       )








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	list							)			->			list:





						"""simple docstring"""
						for i in range(len(a							) - 1						,			0						,			-1							):
												lowercase_      :   Any        =			False

												for j in range(a						,			0						,			-1							):
																		if unsorted[j] < unsorted[j - 1]:
																								lowercase_  ,      lowercase_      :   Any        =			unsorted[j - 1], unsorted[j]
																								lowercase_      :   int        =			True

												for j in range(a							):
																		if unsorted[j] > unsorted[j + 1]:
																								lowercase_  ,      lowercase_      :   Union[str, Any]        =			unsorted[j + 1], unsorted[j]
																								lowercase_      :   Optional[Any]        =			True

												if not swapped:
																		break
						return unsorted


if __name__ == "__main__":
						import doctest

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