


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 	__a   (       UpperCAmelCase    ):
		_a			:						Dict    			=			(KDPMaAncestralDiscreteScheduler,)
		_a			:						Tuple    			=			10
		def 			UpperCAmelCase__       (    self    ,					**_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'num_train_timesteps': 1100,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									}

									config.update(**_SCREAMING_SNAKE_CASE		)
									return config
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									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=_SCREAMING_SNAKE_CASE    ,					beta_end=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





									if torch_device == "mps":
																return
									_UpperCAmelCase					=    self.scheduler_classes[0]
									_UpperCAmelCase					=    self.get_scheduler_config()
									_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

									scheduler.set_timesteps(self.num_inference_steps		)

									_UpperCAmelCase					=    torch.manual_seed(0		)

									_UpperCAmelCase					=    self.dummy_model()
									_UpperCAmelCase					=    self.dummy_sample_deter * scheduler.init_noise_sigma
									_UpperCAmelCase					=    sample.to(_SCREAMING_SNAKE_CASE		)

									for i, t in enumerate(scheduler.timesteps		):
																_UpperCAmelCase					=    scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    output.prev_sample

									_UpperCAmelCase					=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_sum.item() - 13849.3877		) < 1e-2
									assert abs(result_mean.item() - 18.0331		) < 5e-3
		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									if torch_device == "mps":
																return
									_UpperCAmelCase					=    self.scheduler_classes[0]
									_UpperCAmelCase					=    self.get_scheduler_config(prediction_type='v_prediction'		)
									_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

									scheduler.set_timesteps(self.num_inference_steps		)

									_UpperCAmelCase					=    self.dummy_model()
									_UpperCAmelCase					=    self.dummy_sample_deter * scheduler.init_noise_sigma
									_UpperCAmelCase					=    sample.to(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    torch.manual_seed(0		)

									for i, t in enumerate(scheduler.timesteps		):
																_UpperCAmelCase					=    scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    output.prev_sample

									_UpperCAmelCase					=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_sum.item() - 328.9970		) < 1e-2
									assert abs(result_mean.item() - 0.4284		) < 1e-3

		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									if torch_device == "mps":
																return
									_UpperCAmelCase					=    self.scheduler_classes[0]
									_UpperCAmelCase					=    self.get_scheduler_config()
									_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

									scheduler.set_timesteps(self.num_inference_steps    ,					device=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    torch.manual_seed(0		)

									_UpperCAmelCase					=    self.dummy_model()
									_UpperCAmelCase					=    self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE		) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
																_UpperCAmelCase					=    scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    output.prev_sample

									_UpperCAmelCase					=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert abs(result_sum.item() - 13849.3818		) < 1e-1
									assert abs(result_mean.item() - 18.0331		) < 1e-3




from __future__ import annotations

def  lowerCAmelCase__				(    a__:	list[int]    ,						a__:	int )						->	list[int]:


							'''simple docstring'''





							_UpperCAmelCase					=    {}
							for index, val in enumerate(a__ ):
														_UpperCAmelCase					=    target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														_UpperCAmelCase					=    index
							return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'''{two_sum([2, 7, 1_1, 1_5], 9) = }''')
