
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class 			__A( a			):
				snake_case_						   =    (DDPMScheduler,)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								__a										=    {
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''variance_type''': '''fixed_small''',
								    '''clip_sample''': True,
								}

								config.update(**_snake_case				)
								return config




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								for timesteps in [1, 5, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''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=_snake_case		,     beta_end=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								self.check_over_configs(thresholding=_snake_case				)
								for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=_snake_case		,     prediction_type=_snake_case		,     sample_max_value=_snake_case		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								for t in [0, 500, 999]:
												self.check_over_forward(time_step=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_0979				)				) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    len(_snake_case				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter
								__a										=    torch.manual_seed(0				)

								for t in reversed(range(_snake_case				)				):
												# 1. predict noise residual
												__a										=    model(_snake_case		,     _snake_case				)

												# 2. predict previous mean of sample x_t-1
												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case		,     generator=_snake_case				).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												__a										=    pred_prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 258.9606				) < 1E-2
								assert abs(result_mean.item() - 0.3372				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config(prediction_type='''v_prediction'''				)
								__a										=    scheduler_class(**_snake_case				)

								__a										=    len(_snake_case				)

								__a										=    self.dummy_model()
								__a										=    self.dummy_sample_deter
								__a										=    torch.manual_seed(0				)

								for t in reversed(range(_snake_case				)				):
												# 1. predict noise residual
												__a										=    model(_snake_case		,     _snake_case				)

												# 2. predict previous mean of sample x_t-1
												__a										=    scheduler.step(_snake_case		,     _snake_case		,     _snake_case		,     generator=_snake_case				).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												__a										=    pred_prev_sample

								__a										=    torch.sum(torch.abs(_snake_case				)				)
								__a										=    torch.mean(torch.abs(_snake_case				)				)

								assert abs(result_sum.item() - 202.0296				) < 1E-2
								assert abs(result_mean.item() - 0.2631				) < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [100, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=_snake_case				)

								__a										=    scheduler.timesteps

								for i, timestep in enumerate(_snake_case				):
												if i == len(_snake_case				) - 1:
																__a										=    -1
												else:
																__a										=    timesteps[i + 1]

												__a										=    scheduler.previous_timestep(_snake_case				)
												__a										=    prev_t.item()

												self.assertEqual(_snake_case		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [100, 87, 50, 51, 0]

								with self.assertRaises(_snake_case		,     msg='''`custom_timesteps` must be in descending order.'''				):
												scheduler.set_timesteps(timesteps=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [100, 87, 50, 1, 0]
								__a										=    len(_snake_case				)

								with self.assertRaises(_snake_case		,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''				):
												scheduler.set_timesteps(num_inference_steps=_snake_case		,     timesteps=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.scheduler_classes[0]
								__a										=    self.get_scheduler_config()
								__a										=    scheduler_class(**_snake_case				)

								__a										=    [scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    _snake_case		,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''		,     ):
												scheduler.set_timesteps(timesteps=_snake_case				)

def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  list:
				__a										=    len(a__  )
				__a										=    [[0] * n for i in range(a__  )]
				for i in range(a__  ):
								__a										=    y_points[i]

				for i in range(2						,    a__  ):
								for j in range(a__						,    a__  ):
												__a										=    (
												    (xa - x_points[j - i + 1]) * q[j][i - 1]
												    - (xa - x_points[j]) * q[j - 1][i - 1]
												) / (x_points[j] - x_points[j - i + 1])

				return [q[n - 1][n - 1], q]


if __name__ == "__main__":
					import doctest

					doctest.testmod()