





"""simple docstring"""






from copy import deepcopy



class 					__UpperCAmelCase							:
				def __init__(		self    :			List[Any]	, a_    :			list[int] | None = None	, a_    :			int | None = None    )	->	None:



							'''simple docstring'''



							if arr is None and size is not None:
										a__  :	Optional[int]		 =			size
										a__  :	Dict		 =			[0] * size
							elif arr is not None:
										self.init(a_    )
							else:
										raise ValueError("Either arr or size must be specified"    )
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			list[int]    )	->	None:



							'''simple docstring'''



							a__  :	Any		 =			len(a_    )
							a__  :	Union[str, Any]		 =			deepcopy(a_    )
							for i in range(1	, self.size    ):
										a__  :	List[str]		 =			self.next_(a_    )
										if j < self.size:
													self.tree[j] += self.tree[i]
				def       UpperCAmelCase      (		self    :			Tuple    )	->	list[int]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			self.tree[:]
							for i in range(self.size - 1	, 0	, -1    ):
										a__  :	Dict		 =			self.next_(a_    )
										if j < self.size:
													arr[j] -= arr[i]
							return arr
				@staticmethod
				def       UpperCAmelCase      (		a_    :			int    )	->	int:



							'''simple docstring'''



							return index + (index & (-index))
				@staticmethod
				def       UpperCAmelCase      (		a_    :			int    )	->	int:



							'''simple docstring'''



							return index - (index & (-index))
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			int	, a_    :			int    )	->	None:



							'''simple docstring'''



							if index == 0:
										self.tree[0] += value
										return
							while index < self.size:
										self.tree[index] += value
										a__  :	str		 =			self.next_(a_    )
				def       UpperCAmelCase      (		self    :			Any	, a_    :			int	, a_    :			int    )	->	None:



							'''simple docstring'''



							self.add(a_	, value - self.get(a_    )    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]	, a_    :			int    )	->	int:



							'''simple docstring'''



							if right == 0:
										return 0
							a__  :	int		 =			self.tree[0]
							right -= 1  # make right inclusive
							while right > 0:
										result += self.tree[right]
										a__  :	str		 =			self.prev(a_    )
							return result
				def       UpperCAmelCase      (		self    :			int	, a_    :			int	, a_    :			int    )	->	int:



							'''simple docstring'''



							return self.prefix(a_    ) - self.prefix(a_    )
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			int    )	->	int:



							'''simple docstring'''



							return self.query(a_	, index + 1    )






				def       UpperCAmelCase      (		self    :			int	, a_    :			int    )	->	int:



							'''simple docstring'''



							value -= self.tree[0]
							if value < 0:
										return -1

							a__  :	List[str]		 =			1  # Largest power of 2 <= size
							while j * 2 < self.size:
										j *= 2

							a__  :	Optional[Any]		 =			0

							while j > 0:
										if i + j < self.size and self.tree[i + j] <= value:
													value -= self.tree[i + j]
													i += j
										j //= 2
							return i


if __name__ == "__main__":
		import doctest

		doctest.testmod()






"""simple docstring"""






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						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

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

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

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

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

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