
from typing import List, Optional, Tuple, Union

import torch

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







class  _lowerCamelCase			(UpperCamelCase__					):

     def __init__(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            super().__init__()
            self.register_modules(unet=SCREAMING_SNAKE_CASE_							,       scheduler=SCREAMING_SNAKE_CASE_    )

     @torch.no_grad()
     def __call__(  self							,       SCREAMING_SNAKE_CASE_ = 1							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 1_000							,       SCREAMING_SNAKE_CASE_ = "pil"							,       SCREAMING_SNAKE_CASE_ = True							,       ):
            # Sample gaussian noise to begin loop
            if isinstance(self.unet.config.sample_size							,       SCREAMING_SNAKE_CASE_    ):
                   __snake_case						       =   (
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size,
                       self.unet.config.sample_size,
                   )
            else:
                   __snake_case						       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

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

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

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_    )





'''simple docstring'''



from __future__ import annotations

from random import randrange



def 	__lowerCAmelCase       (				lowerCamelCase :    list       ):






	'''simple docstring'''




	if len(lowerCamelCase       ) < 2:
		return collection
	__lowerCAmelCase       				=	randrange(len(lowerCamelCase       )       )  # Use random element as pivot
	__lowerCAmelCase       				=	collection[pivot_index]
	__lowerCAmelCase       				=	[]  # All elements greater than pivot
	__lowerCAmelCase       				=	[]  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	return [*quick_sort(lowerCamelCase       ), pivot, *quick_sort(lowerCamelCase       )]


if __name__ == "__main__":
	lowerCAmelCase						:		List[Any]						      = input('''Enter numbers separated by a comma:\n''').strip()
	lowerCAmelCase						:		Any						      = [int(item) for item in user_input.split(''',''')]
	print(quick_sort(unsorted))