import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCamelCase	: Optional[Any]    		=  False





class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       List[Any]=32		):





												'''simple docstring'''





												set_seed(0		)
												SCREAMING_SNAKE_CASE							:					Any           =							UNetaDModel(sample_size=UpperCamelCase__					,	in_channels=3					,	out_channels=3		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.optim.SGD(model.parameters()					,	lr=0.0001		)
												return model, optimizer



						@slow
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							'''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												SCREAMING_SNAKE_CASE							:					int           =							DDPMScheduler(
												    num_train_timesteps=1000					,	beta_start=0.0001					,	beta_end=0.02					,	beta_schedule='''linear'''					,	clip_sample=UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							DDIMScheduler(
												    num_train_timesteps=1000					,	beta_start=0.0001					,	beta_end=0.02					,	beta_schedule='''linear'''					,	clip_sample=UpperCamelCase__					,	)

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							[torch.randn((4, 3, 32, 32)		).clip(-1					,	1		).to(UpperCamelCase__		) for _ in range(4		)]
												SCREAMING_SNAKE_CASE							:					int           =							[torch.randn((4, 3, 32, 32)		).to(UpperCamelCase__		) for _ in range(4		)]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[torch.randint(0					,	1000					,	(4,)		).long().to(UpperCamelCase__		) for _ in range(4		)]

												# train with a DDPM scheduler
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.get_model_optimizer(resolution=32		)
												model.train().to(UpperCamelCase__		)
												for i in range(4		):
																		optimizer.zero_grad()
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							ddpm_scheduler.add_noise(clean_images[i]					,	noise[i]					,	timesteps[i]		)
																		SCREAMING_SNAKE_CASE							:					Tuple           =							model(UpperCamelCase__					,	timesteps[i]		).sample
																		SCREAMING_SNAKE_CASE							:					str           =							torch.nn.functional.mse_loss(UpperCamelCase__					,	noise[i]		)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.get_model_optimizer(resolution=32		)
												model.train().to(UpperCamelCase__		)
												for i in range(4		):
																		optimizer.zero_grad()
																		SCREAMING_SNAKE_CASE							:					int           =							ddim_scheduler.add_noise(clean_images[i]					,	noise[i]					,	timesteps[i]		)
																		SCREAMING_SNAKE_CASE							:					Any           =							model(UpperCamelCase__					,	timesteps[i]		).sample
																		SCREAMING_SNAKE_CASE							:					Dict           =							torch.nn.functional.mse_loss(UpperCamelCase__					,	noise[i]		)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(UpperCamelCase__					,	UpperCamelCase__					,	atol=1E-5		)		)
												self.assertTrue(torch.allclose(UpperCamelCase__					,	UpperCamelCase__					,	atol=1E-5		)		)




import random
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Tuple           =							[], [], []
						for element in data:
												if element < pivot:
																		less.append(_lowercase		)
												elif element > pivot:
																		greater.append(_lowercase		)
												else:
																		equal.append(_lowercase		)
						return less, equal, greater



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

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

						SCREAMING_SNAKE_CASE							:					Dict           =							items[random.randint(0	,						len(_lowercase		) - 1		)]
						SCREAMING_SNAKE_CASE							:					int           =							0
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							_partition(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							len(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(_lowercase		)

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





