






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCamelCase_				(      __lowercase ):
				a__					       =  (DDIMParallelScheduler,)
				a__					       =  (('eta', 0.0), ('num_inference_steps', 50))







				def 			A	(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**__A     )
								return config







				def 			A	(						self       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.scheduler_classes[0]
								__magic_name__		:Union[str, Any]        =      self.get_scheduler_config(**__A     )
								__magic_name__		:Tuple        =      scheduler_class(**__A     )

								__magic_name__     ,				__magic_name__		:str        =      1_0, 0.0

								__magic_name__		:Optional[int]        =      self.dummy_model()
								__magic_name__		:Union[str, Any]        =      self.dummy_sample_deter

								scheduler.set_timesteps(__A     )

								for t in scheduler.timesteps:
												__magic_name__		:str        =      model(__A       ,					__A     )
												__magic_name__		:List[str]        =      scheduler.step(__A       ,					__A       ,					__A       ,					__A     ).prev_sample

								return sample







				def 			A	(						self     ):
								"""simple docstring"""
								for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=__A     )

								__magic_name__		:Union[str, Any]        =      self.scheduler_classes[0]
								__magic_name__		:List[str]        =      self.get_scheduler_config(steps_offset=1     )
								__magic_name__		:Tuple        =      scheduler_class(**__A     )
								scheduler.set_timesteps(5     )
								assert torch.equal(scheduler.timesteps       ,					torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,					[0.002, 0.02, 0.2, 2]     ):
												self.check_over_configs(beta_start=__A       ,					beta_end=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.check_over_configs(thresholding=__A     )
								for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=__A       ,					prediction_type=__A       ,					sample_max_value=__A       ,					)







				def 			A	(						self     ):
								"""simple docstring"""
								for t in [1, 1_0, 4_9]:
												self.check_over_forward(time_step=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								for t, num_inference_steps in zip([1, 1_0, 5_0]       ,					[1_0, 5_0, 5_0_0]     ):
												self.check_over_forward(time_step=__A       ,					num_inference_steps=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								for t, eta in zip([1, 1_0, 4_9]       ,					[0.0, 0.5, 1.0]     ):
												self.check_over_forward(time_step=__A       ,					eta=__A     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.scheduler_classes[0]
								__magic_name__		:Optional[Any]        =      self.get_scheduler_config()
								__magic_name__		:Optional[Any]        =      scheduler_class(**__A     )

								assert torch.sum(torch.abs(scheduler._get_variance(0       ,					0     ) - 0.0     )     ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_2_0       ,					4_0_0     ) - 0.14771     )     ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_8_0       ,					9_6_0     ) - 0.32460     )     ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(0       ,					0     ) - 0.0     )     ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ,					4_8_6     ) - 0.00979     )     ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ,					9_9_8     ) - 0.02     )     ) < 1E-5







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.scheduler_classes[0]
								__magic_name__		:Optional[int]        =      self.get_scheduler_config()
								__magic_name__		:Tuple        =      scheduler_class(**__A     )

								__magic_name__     ,				__magic_name__		:Any        =      1_0, 0.0
								scheduler.set_timesteps(__A     )

								__magic_name__		:Any        =      self.dummy_model()
								__magic_name__		:List[Any]        =      self.dummy_sample_deter
								__magic_name__		:int        =      self.dummy_sample_deter + 0.1
								__magic_name__		:Optional[int]        =      self.dummy_sample_deter - 0.1

								__magic_name__		:List[Any]        =      samplea.shape[0]
								__magic_name__		:List[Any]        =      torch.stack([samplea, samplea, samplea]       ,					dim=0     )
								__magic_name__		:Dict        =      torch.arange(__A     )[0:3, None].repeat(1       ,					__A     )

								__magic_name__		:Dict        =      model(samples.flatten(0       ,					1     )       ,					timesteps.flatten(0       ,					1     )     )
								__magic_name__		:Any        =      scheduler.batch_step_no_noise(__A       ,					timesteps.flatten(0       ,					1     )       ,					samples.flatten(0       ,					1     )       ,					__A     )

								__magic_name__		:str        =      torch.sum(torch.abs(__A     )     )
								__magic_name__		:List[Any]        =      torch.mean(torch.abs(__A     )     )

								assert abs(result_sum.item() - 1_1_4_7.7_9_0_4     ) < 1E-2
								assert abs(result_mean.item() - 0.4982     ) < 1E-3







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.full_loop()

								__magic_name__		:List[Any]        =      torch.sum(torch.abs(__A     )     )
								__magic_name__		:int        =      torch.mean(torch.abs(__A     )     )

								assert abs(result_sum.item() - 172.0067     ) < 1E-2
								assert abs(result_mean.item() - 0.223967     ) < 1E-3







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.full_loop(prediction_type='''v_prediction'''     )

								__magic_name__		:Tuple        =      torch.sum(torch.abs(__A     )     )
								__magic_name__		:str        =      torch.mean(torch.abs(__A     )     )

								assert abs(result_sum.item() - 52.5302     ) < 1E-2
								assert abs(result_mean.item() - 0.0684     ) < 1E-3







				def 			A	(						self     ):
								"""simple docstring"""
								# We specify different beta, so that the first alpha is 0.99
								__magic_name__		:Tuple        =      self.full_loop(set_alpha_to_one=__A       ,					beta_start=0.01     )
								__magic_name__		:int        =      torch.sum(torch.abs(__A     )     )
								__magic_name__		:Dict        =      torch.mean(torch.abs(__A     )     )

								assert abs(result_sum.item() - 149.8295     ) < 1E-2
								assert abs(result_mean.item() - 0.1951     ) < 1E-3







				def 			A	(						self     ):
								"""simple docstring"""
								# We specify different beta, so that the first alpha is 0.99
								__magic_name__		:Any        =      self.full_loop(set_alpha_to_one=__A       ,					beta_start=0.01     )
								__magic_name__		:Optional[int]        =      torch.sum(torch.abs(__A     )     )
								__magic_name__		:int        =      torch.mean(torch.abs(__A     )     )

								assert abs(result_sum.item() - 149.0784     ) < 1E-2
								assert abs(result_mean.item() - 0.1941     ) < 1E-3














from __future__ import annotations

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      0
				__magic_name__		:Tuple        =      len(snake_case ) - 1

				while i < j:
								if nums[i] + nums[j] == target:
												return [i, j]
								elif nums[i] + nums[j] < target:
												__magic_name__		:str        =      i + 1
								else:
												__magic_name__		:List[Any]        =      j - 1

				return []


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(f"{two_pointer([2, 7, 11, 15], 9) = }")






