





"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCamelCase_									=False




class 			_a						(				unittest.TestCase    ):
				pass




@nightly
@require_torch_gpu
class 			_a						(				unittest.TestCase    ):






				def     snake_case     (			self		:   List[str]							)     ->				Tuple:




										'''simple docstring'''



										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def     snake_case     (			self		:   Optional[int]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''',			torch_dtype=torch.floataa							)
										pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : int         =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''							)

										_UpperCamelCase      : Dict         =   torch.manual_seed(0							)
										_UpperCamelCase      : List[str]         =   pipe.dual_guided(
										    prompt='''first prompt''',			image=lowerCAmelCase__,			text_to_image_strength=0.75,			generator=lowerCAmelCase__,			guidance_scale=7.5,			num_inference_steps=2,			output_type='''numpy''',			).images

										with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(lowerCAmelCase__							)
																_UpperCamelCase      : str         =   VersatileDiffusionPipeline.from_pretrained(lowerCAmelCase__,			torch_dtype=torch.floataa							)
										pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Union[str, Any]         =   generator.manual_seed(0							)
										_UpperCamelCase      : int         =   pipe.dual_guided(
										    prompt='''first prompt''',			image=lowerCAmelCase__,			text_to_image_strength=0.75,			generator=lowerCAmelCase__,			guidance_scale=7.5,			num_inference_steps=2,			output_type='''numpy''',			).images

										assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






				def     snake_case     (			self		:   Optional[Any]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''',			torch_dtype=torch.floataa							)
										pipe.to(lowerCAmelCase__							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   '''cyberpunk 2077'''
										_UpperCamelCase      : List[Any]         =   load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''							)
										_UpperCamelCase      : List[str]         =   torch.manual_seed(0							)
										_UpperCamelCase      : Any         =   pipe.dual_guided(
										    prompt=lowerCAmelCase__,			image=lowerCAmelCase__,			text_to_image_strength=0.75,			generator=lowerCAmelCase__,			guidance_scale=7.5,			num_inference_steps=5_0,			output_type='''numpy''',			).images

										_UpperCamelCase      : Optional[Any]         =   image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										_UpperCamelCase      : Optional[Any]         =   np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]							)

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

										_UpperCamelCase      : Any         =   '''A painting of a squirrel eating a burger '''
										_UpperCamelCase      : Dict         =   torch.manual_seed(0							)
										_UpperCamelCase      : Any         =   pipe.text_to_image(
										    prompt=lowerCAmelCase__,			generator=lowerCAmelCase__,			guidance_scale=7.5,			num_inference_steps=5_0,			output_type='''numpy'''							).images

										_UpperCamelCase      : Optional[int]         =   image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										_UpperCamelCase      : Tuple         =   np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

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

										_UpperCamelCase      : List[Any]         =   pipe.image_variation(lowerCAmelCase__,			generator=lowerCAmelCase__,			output_type='''numpy'''							).images

										_UpperCamelCase      : Optional[int]         =   image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										_UpperCamelCase      : List[str]         =   np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]							)

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










"""simple docstring"""




from __future__ import annotations

from random import choice





def 	a_    (							_lowercase       ):
						return choice(_lowercase       )





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   random_pivot(_lowercase       )

						# partition based on pivot
						# linear time
						_UpperCamelCase      : Union[str, Any]         =   [e for e in lst if e < pivot]
						_UpperCamelCase      : int         =   [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(_lowercase       ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(_lowercase       ) < k - 1:
												return kth_number(_lowercase	,						k - len(_lowercase       ) - 1       )
						# pivot is in elements smaller than k
						else:
												return kth_number(_lowercase	,						_lowercase       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



