"""simple docstring"""



import math





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = 0,      snake_case = 0):
     __snake_case				=							end or len(snake_case)
     for i in range(snake_case,      snake_case):
          __snake_case				=							i
          __snake_case				=							array[i]
          while temp_index != start and temp_index_value < array[temp_index - 1]:
               __snake_case				=							array[temp_index - 1]
               temp_index -= 1
          __snake_case				=							temp_index_value
     return array





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):  # Max Heap
     __snake_case				=							index
     __snake_case				=							2 * index + 1  # Left Node
     __snake_case				=							2 * index + 2  # Right Node

     if left_index < heap_size and array[largest] < array[left_index]:
          __snake_case				=							left_index

     if right_index < heap_size and array[largest] < array[right_index]:
          __snake_case				=							right_index

     if largest != index:
          __snake_case		,	__snake_case				=							array[largest], array[index]
          heapify(snake_case,      snake_case,      snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							len(snake_case)

     for i in range(n // 2,      -1,      -1):
          heapify(snake_case,      snake_case,      snake_case)

     for i in range(n - 1,      0,      -1):
          __snake_case		,	__snake_case				=							array[0], array[i]
          heapify(snake_case,      0,      snake_case)

     return array





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case):
     if (array[first_index] > array[middle_index]) != (
         array[first_index] > array[last_index]
     ):
          return array[first_index]
     elif (array[middle_index] > array[first_index]) != (
         array[middle_index] > array[last_index]
     ):
          return array[middle_index]
     else:
          return array[last_index]





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case):
     __snake_case				=							low
     __snake_case				=							high
     while True:
          while array[i] < pivot:
               i += 1
          j -= 1
          while pivot < array[j]:
               j -= 1
          if i >= j:
               return i
          __snake_case		,	__snake_case				=							array[j], array[i]
          i += 1





def    SCREAMING_SNAKE_CASE       (     snake_case):
     if len(snake_case) == 0:
          return array
     __snake_case				=							2 * math.ceil(math.loga(len(snake_case)))
     __snake_case				=							16
     return intro_sort(snake_case,      0,      len(snake_case),      snake_case,      snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case):
     while end - start > size_threshold:
          if max_depth == 0:
               return heap_sort(snake_case)
          max_depth -= 1
          __snake_case				=							median_of_a(snake_case,      snake_case,      start + ((end - start) // 2) + 1,      end - 1)
          __snake_case				=							partition(snake_case,      snake_case,      snake_case,      snake_case)
          intro_sort(snake_case,      snake_case,      snake_case,      snake_case,      snake_case)
          __snake_case				=							p
     return insertion_sort(snake_case,      snake_case,      snake_case)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowercase		:		Union[str, Any]														=							input("Enter numbers separated by a comma : ").strip()
       __lowercase		:		int														=							[float(item) for item in user_input.split(",")]
       print(sort(unsorted))
"""simple docstring"""



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  lowercase					(		self							:     Tuple    )    ->       str:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def  lowercase					(		self							:     int    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

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

         __snake_case				=							output.images

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

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
    def  lowercase					(		self							:     Optional[Any]    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

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

         __snake_case				=							output.images

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

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12]    )

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







    def  lowercase					(		self							:     List[str]    )    ->       Optional[Any]:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_dpmpp_2m'''    )

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

         __snake_case				=							output.images

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

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array(
             [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48]    )

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