



"""simple docstring"""

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		(EulerDiscreteScheduler,)
						_A		:							str							       =		10







						def 						__UpperCamelCase							(self   ,		**lowercase__       ):
											snake_case_					:		int 					=					{
											    """num_train_timesteps""": 11_00,
											    """beta_start""": 0.0001,
											    """beta_end""": 0.02,
											    """beta_schedule""": """linear""",
											}

											config.update(**A_       )
											return config







						def 						__UpperCamelCase							(self       ):
											for timesteps in [10, 50, 1_00, 10_00]:
																self.check_over_configs(num_train_timesteps=A_       )







						def 						__UpperCamelCase							(self       ):
											for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]   ,		[0.0002, 0.002, 0.02]       ):
																self.check_over_configs(beta_start=A_   ,		beta_end=A_       )







						def 						__UpperCamelCase							(self       ):
											for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=A_       )







						def 						__UpperCamelCase							(self       ):
											for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=A_       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					self.scheduler_classes[0]
											snake_case_					:		Tuple 					=					self.get_scheduler_config()
											snake_case_					:		Optional[int] 					=					scheduler_class(**A_       )

											scheduler.set_timesteps(self.num_inference_steps       )

											snake_case_					:		Optional[Any] 					=					torch.manual_seed(0       )

											snake_case_					:		Union[str, Any] 					=					self.dummy_model()
											snake_case_					:		List[str] 					=					self.dummy_sample_deter * scheduler.init_noise_sigma
											snake_case_					:		int 					=					sample.to(A_       )

											for i, t in enumerate(scheduler.timesteps       ):
																snake_case_					:		Any 					=					scheduler.scale_model_input(A_   ,		A_       )

																snake_case_					:		Optional[Any] 					=					model(A_   ,		A_       )

																snake_case_					:		List[str] 					=					scheduler.step(A_   ,		A_   ,		A_   ,		generator=A_       )
																snake_case_					:		Union[str, Any] 					=					output.prev_sample

											snake_case_					:		Union[str, Any] 					=					torch.sum(torch.abs(A_       )       )
											snake_case_					:		str 					=					torch.mean(torch.abs(A_       )       )

											assert abs(result_sum.item() - 10.0807       ) < 1e-2
											assert abs(result_mean.item() - 0.0131       ) < 1e-3







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.scheduler_classes[0]
											snake_case_					:		Union[str, Any] 					=					self.get_scheduler_config(prediction_type="""v_prediction"""       )
											snake_case_					:		List[str] 					=					scheduler_class(**A_       )

											scheduler.set_timesteps(self.num_inference_steps       )

											snake_case_					:		Optional[Any] 					=					torch.manual_seed(0       )

											snake_case_					:		List[str] 					=					self.dummy_model()
											snake_case_					:		Optional[Any] 					=					self.dummy_sample_deter * scheduler.init_noise_sigma
											snake_case_					:		Optional[int] 					=					sample.to(A_       )

											for i, t in enumerate(scheduler.timesteps       ):
																snake_case_					:		Optional[Any] 					=					scheduler.scale_model_input(A_   ,		A_       )

																snake_case_					:		int 					=					model(A_   ,		A_       )

																snake_case_					:		Optional[int] 					=					scheduler.step(A_   ,		A_   ,		A_   ,		generator=A_       )
																snake_case_					:		str 					=					output.prev_sample

											snake_case_					:		str 					=					torch.sum(torch.abs(A_       )       )
											snake_case_					:		Union[str, Any] 					=					torch.mean(torch.abs(A_       )       )

											assert abs(result_sum.item() - 0.0002       ) < 1e-2
											assert abs(result_mean.item() - 2.2_676e-06       ) < 1e-3







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.scheduler_classes[0]
											snake_case_					:		List[str] 					=					self.get_scheduler_config()
											snake_case_					:		Optional[Any] 					=					scheduler_class(**A_       )

											scheduler.set_timesteps(self.num_inference_steps   ,		device=A_       )

											snake_case_					:		Union[str, Any] 					=					torch.manual_seed(0       )

											snake_case_					:		str 					=					self.dummy_model()
											snake_case_					:		Optional[Any] 					=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											snake_case_					:		Any 					=					sample.to(A_       )

											for t in scheduler.timesteps:
																snake_case_					:		Dict 					=					scheduler.scale_model_input(A_   ,		A_       )

																snake_case_					:		int 					=					model(A_   ,		A_       )

																snake_case_					:		Union[str, Any] 					=					scheduler.step(A_   ,		A_   ,		A_   ,		generator=A_       )
																snake_case_					:		str 					=					output.prev_sample

											snake_case_					:		List[str] 					=					torch.sum(torch.abs(A_       )       )
											snake_case_					:		Optional[int] 					=					torch.mean(torch.abs(A_       )       )

											assert abs(result_sum.item() - 10.0807       ) < 1e-2
											assert abs(result_mean.item() - 0.0131       ) < 1e-3







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					self.scheduler_classes[0]
											snake_case_					:		int 					=					self.get_scheduler_config()
											snake_case_					:		List[Any] 					=					scheduler_class(**A_   ,		use_karras_sigmas=A_       )

											scheduler.set_timesteps(self.num_inference_steps   ,		device=A_       )

											snake_case_					:		int 					=					torch.manual_seed(0       )

											snake_case_					:		Union[str, Any] 					=					self.dummy_model()
											snake_case_					:		int 					=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											snake_case_					:		Optional[Any] 					=					sample.to(A_       )

											for t in scheduler.timesteps:
																snake_case_					:		Union[str, Any] 					=					scheduler.scale_model_input(A_   ,		A_       )

																snake_case_					:		Optional[Any] 					=					model(A_   ,		A_       )

																snake_case_					:		Dict 					=					scheduler.step(A_   ,		A_   ,		A_   ,		generator=A_       )
																snake_case_					:		int 					=					output.prev_sample

											snake_case_					:		Any 					=					torch.sum(torch.abs(A_       )       )
											snake_case_					:		Tuple 					=					torch.mean(torch.abs(A_       )       )

											assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19       ) < 1e-2
											assert abs(result_mean.item() - 0.16213932633399963       ) < 1e-3









"""simple docstring"""

from copy import deepcopy







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__ = None   ,		lowercase__ = None       ):

											if arr is None and size is not None:
																snake_case_					:		str 					=					size
																snake_case_					:		Optional[Any] 					=					[0] * size
											elif arr is not None:
																self.init(lowercase__       )
											else:
																raise ValueError("""Either arr or size must be specified"""       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		Optional[Any] 					=					len(lowercase__       )
											snake_case_					:		int 					=					deepcopy(lowercase__       )
											for i in range(1   ,		self.size       ):
																snake_case_					:		Optional[Any] 					=					self.next_(lowercase__       )
																if j < self.size:
																					self.tree[j] += self.tree[i]







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					self.tree[:]
											for i in range(self.size - 1   ,		0   ,		-1       ):
																snake_case_					:		Optional[int] 					=					self.next_(lowercase__       )
																if j < self.size:
																					arr[j] -= arr[i]
											return arr







						@staticmethod
						def 						__UpperCamelCase							(lowercase__       ):
											return index + (index & (-index))







						@staticmethod
						def 						__UpperCamelCase							(lowercase__       ):
											return index - (index & (-index))







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											if index == 0:
																self.tree[0] += value
																return
											while index < self.size:
																self.tree[index] += value
																snake_case_					:		Tuple 					=					self.next_(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											self.add(lowercase__   ,		value - self.get(lowercase__       )       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											if right == 0:
																return 0
											snake_case_					:		List[str] 					=					self.tree[0]
											right -= 1  # make right inclusive
											while right > 0:
																result += self.tree[right]
																snake_case_					:		Optional[int] 					=					self.prev(lowercase__       )
											return result







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											return self.prefix(lowercase__       ) - self.prefix(lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											return self.query(lowercase__   ,		index + 1       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											value -= self.tree[0]
											if value < 0:
																return -1

											snake_case_					:		Tuple 					=					1  # Largest power of 2 <= size
											while j * 2 < self.size:
																j *= 2

											snake_case_					:		Tuple 					=					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()




