






import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



@slow
@require_torch_gpu
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    def _a							(				self      :     List[str]      )      ->							int:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    def _a							(				self      :     List[str]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""      )
           __UpperCAmelCase     				=sd_pipe.to(lowerCamelCase__      )
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase__      )

           sd_pipe.set_scheduler("""sample_euler"""      )

           __UpperCAmelCase     				="""A painting of a squirrel eating a burger"""
           __UpperCAmelCase     				=torch.manual_seed(0      )
           __UpperCAmelCase     				=sd_pipe([prompt] ,     generator=lowerCamelCase__ ,     guidance_scale=9.0 ,     num_inference_steps=20 ,     output_type="""np"""      )

           __UpperCAmelCase     				=output.images

           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           __UpperCAmelCase     				=np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2



    def _a							(				self      :     Tuple      )      ->							int:
           __UpperCAmelCase     				=StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""      )
           __UpperCAmelCase     				=sd_pipe.to(lowerCamelCase__      )
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase__      )

           sd_pipe.set_scheduler("""sample_euler"""      )

           __UpperCAmelCase     				="""A painting of a squirrel eating a burger"""
           __UpperCAmelCase     				=torch.manual_seed(0      )
           __UpperCAmelCase     				=sd_pipe([prompt] ,     generator=lowerCamelCase__ ,     guidance_scale=9.0 ,     num_inference_steps=20 ,     output_type="""np"""      )

           __UpperCAmelCase     				=output.images

           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           __UpperCAmelCase     				=np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-1



    def _a							(				self      :     str      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""      )
           __UpperCAmelCase     				=sd_pipe.to(lowerCamelCase__      )
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase__      )

           sd_pipe.set_scheduler("""sample_dpmpp_2m"""      )

           __UpperCAmelCase     				="""A painting of a squirrel eating a burger"""
           __UpperCAmelCase     				=torch.manual_seed(0      )
           __UpperCAmelCase     				=sd_pipe(
               [prompt] ,     generator=lowerCamelCase__ ,     guidance_scale=7.5 ,     num_inference_steps=15 ,     output_type="""np""" ,     use_karras_sigmas=lowerCamelCase__ ,     )

           __UpperCAmelCase     				=output.images

           __UpperCAmelCase     				=image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           __UpperCAmelCase     				=np.array(
               [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]      )

           assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      if len(SCREAMING_SNAKE_CASE   ) < 2:
            return collection

      def circle_sort_util(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ) -> bool:
            A_							       =		False

            if low == high:
                  return swapped

            A_							       =		low
            A_							       =		high

            while left < right:
                  if collection[left] > collection[right]:
                        A_       ,A_							       =		(
                            collection[right],
                            collection[left],
                        )
                        A_							       =		True

                  left += 1
                  right -= 1

            if left == right and collection[left] > collection[right + 1]:
                  A_       ,A_							       =		(
                      collection[right + 1],
                      collection[left],
                  )

                  A_							       =		True

            A_							       =		low + int((high - low) / 2   )
            A_							       =		circle_sort_util(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
            A_							       =		circle_sort_util(SCREAMING_SNAKE_CASE		,				mid + 1		,				SCREAMING_SNAKE_CASE   )

            return swapped or left_swap or right_swap

      A_							       =		True

      while is_not_sorted is True:
            A_							       =		circle_sort_util(SCREAMING_SNAKE_CASE		,				0		,				len(SCREAMING_SNAKE_CASE   ) - 1   )

      return collection


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





