

"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					@property
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						torch.manual_seed(0		)
						lowerCAmelCase__	:						List[str]  				=	UNetaDModel(
						    block_out_channels=(3_2, 6_4)   ,layers_per_block=2   ,sample_size=3_2   ,in_channels=3   ,out_channels=3   ,down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')   ,up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')   ,)
						return model







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						str  				=	self.dummy_uncond_unet
						lowerCAmelCase__	:						Dict  				=	ScoreSdeVeScheduler()

						lowerCAmelCase__	:						Optional[Any]  				=	ScoreSdeVePipeline(unet=lowercase_   ,scheduler=lowercase_		)
						sde_ve.to(lowercase_		)
						sde_ve.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	torch.manual_seed(0		)
						lowerCAmelCase__	:						int  				=	sde_ve(num_inference_steps=2   ,output_type='''numpy'''   ,generator=lowercase_		).images

						lowerCAmelCase__	:						Optional[int]  				=	torch.manual_seed(0		)
						lowerCAmelCase__	:						Any  				=	sde_ve(num_inference_steps=2   ,output_type='''numpy'''   ,generator=lowercase_   ,return_dict=lowercase_		)[
						    0
						]

						lowerCAmelCase__	:						int  				=	image[0, -3:, -3:, -1]
						lowerCAmelCase__	:						Optional[Any]  				=	image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						lowerCAmelCase__	:						Optional[int]  				=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

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







@slow
@require_torch
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__	:						Tuple  				=	'''google/ncsnpp-church-256'''
						lowerCAmelCase__	:						int  				=	UNetaDModel.from_pretrained(lowercase_		)

						lowerCAmelCase__	:						str  				=	ScoreSdeVeScheduler.from_pretrained(lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	ScoreSdeVePipeline(unet=lowercase_   ,scheduler=lowercase_		)
						sde_ve.to(lowercase_		)
						sde_ve.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						List[str]  				=	torch.manual_seed(0		)
						lowerCAmelCase__	:						Dict  				=	sde_ve(num_inference_steps=1_0   ,output_type='''numpy'''   ,generator=lowercase_		).images

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

						assert image.shape == (1, 2_5_6, 2_5_6, 3)

						lowerCAmelCase__	:						int  				=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]		)

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







"""simple docstring"""





import random

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	[], [], []
	for element in data:
		if element < pivot:
			less.append(A_ )
		elif element > pivot:
			greater.append(A_ )
		else:
			equal.append(A_ )
	return less, equal, greater





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	# index = len(items) // 2 when trying to find the median
	#   (value of index when items is sorted)

	# invalid input
	if index >= len(A_ ) or index < 0:
		return None

	lowerCAmelCase__	:						str  				=	items[random.randint(0						,     len(A_ ) - 1 )]
	lowerCAmelCase__	:						Optional[Any]  				=	0
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	_partition(A_						,     A_ )
	lowerCAmelCase__	:						str  				=	len(A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	len(A_ )

	# index is the pivot
	if m <= index < m + count:
		return pivot
	# must be in smaller
	elif m > index:
		return quick_select(A_						,     A_ )
	# must be in larger
	else:
		return quick_select(A_						,     index - (m + count) )




