




"""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 lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



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

							sd_pipe.set_scheduler('sample_euler'  )

							_lowerCamelCase     :			Dict    =    'A painting of a squirrel eating a burger'
							_lowerCamelCase     :			List[Any]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			Dict    =    sd_pipe([prompt]  ,      generator=lowercase  ,      guidance_scale=9.0  ,      num_inference_steps=20  ,      output_type='np'  )

							_lowerCamelCase     :			Union[str, Any]    =    output.images

							_lowerCamelCase     :			Any    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_lowerCamelCase     :			Dict    =    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 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'  )
							_lowerCamelCase     :			Any    =    sd_pipe.to(lowercase  )
							sd_pipe.set_progress_bar_config(disable=lowercase  )

							sd_pipe.set_scheduler('sample_euler'  )

							_lowerCamelCase     :			Union[str, Any]    =    'A painting of a squirrel eating a burger'
							_lowerCamelCase     :			List[str]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			Tuple    =    sd_pipe([prompt]  ,      generator=lowercase  ,      guidance_scale=9.0  ,      num_inference_steps=20  ,      output_type='np'  )

							_lowerCamelCase     :			Optional[int]    =    output.images

							_lowerCamelCase     :			List[Any]    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_lowerCamelCase     :			List[str]    =    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 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'  )
							_lowerCamelCase     :			int    =    sd_pipe.to(lowercase  )
							sd_pipe.set_progress_bar_config(disable=lowercase  )

							sd_pipe.set_scheduler('sample_dpmpp_2m'  )

							_lowerCamelCase     :			int    =    'A painting of a squirrel eating a burger'
							_lowerCamelCase     :			List[str]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			Any    =    sd_pipe(
							    [prompt]  ,      generator=lowercase  ,      guidance_scale=7.5  ,      num_inference_steps=15  ,      output_type='np'  ,      use_karras_sigmas=lowercase  ,      )

							_lowerCamelCase     :			List[Any]    =    output.images

							_lowerCamelCase     :			Optional[int]    =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_lowerCamelCase     :			Any    =    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

'''simple docstring'''







from __future__ import annotations

__lowerCAmelCase         =    [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
__lowerCAmelCase         =    [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Optional[int]			=   []
   _a				:	int			=   len(__a )

   for i in range(__a ):
      _a				:	float			=   -1
      for j in range(i + 1 ,	__a ):
         if arr[i] < arr[j]:
            _a				:	Any			=   arr[j]
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Tuple			=   []
   for i, outer in enumerate(__a ):
      _a				:	float			=   -1
      for inner in arr[i + 1 :]:
         if outer < inner:
            _a				:	Dict			=   inner
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	int			=   len(__a )
   _a				:	list[float]			=   []
   _a				:	list[float]			=   [-1] * arr_size

   for index in reversed(range(__a ) ):
      if stack:
         while stack[-1] <= arr[index]:
            stack.pop()
            if not stack:
               break
      if stack:
         _a				:	Dict			=   stack[-1]
      stack.append(arr[index] )
   return result


if __name__ == "__main__":
  from doctest import testmod
  from timeit import timeit

  testmod()
  print(next_greatest_element_slow(arr))
  print(next_greatest_element_fast(arr))
  print(next_greatest_element(arr))

  __lowerCAmelCase         =    (
      """from __main__ import arr, next_greatest_element_slow, """
      """next_greatest_element_fast, next_greatest_element"""
  )
  print(
      """next_greatest_element_slow():""",
      timeit("""next_greatest_element_slow(arr)""", setup=setup),
  )
  print(
      """next_greatest_element_fast():""",
      timeit("""next_greatest_element_fast(arr)""", setup=setup),
  )
  print(
      """     next_greatest_element():""",
      timeit("""next_greatest_element(arr)""", setup=setup),
  )



