




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class        __snake_case  (_a		):
				lowerCAmelCase__				=     (UnCLIPScheduler,)



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	**_UpperCAmelCase      :		Optional[int]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      {
								    """num_train_timesteps""": 1000,
								    """variance_type""": """fixed_small_log""",
								    """clip_sample""": True,
								    """clip_sample_range""": 1.0,
								    """prediction_type""": """epsilon""",
								}

								config.update(**_UpperCAmelCase      )
								return config



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								for timesteps in [1, 5, 100, 1000]:
												self.check_over_configs(num_train_timesteps=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Tuple:





								'''simple docstring'''




								for variance in ["fixed_small_log", "learned_range"]:
												self.check_over_configs(variance_type=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[str]:





								'''simple docstring'''




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



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





								'''simple docstring'''




								for clip_sample_range in [1, 5, 10, 20]:
												self.check_over_configs(clip_sample_range=_UpperCAmelCase      )



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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    str:





								'''simple docstring'''




								for time_step in [0, 500, 999]:
												for prev_timestep in [None, 5, 100, 250, 500, 750]:
																if prev_timestep is not None and prev_timestep >= time_step:
																				continue

																self.check_over_forward(time_step=_UpperCAmelCase		,	prev_timestep=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    Any										=      self.get_scheduler_config(variance_type="""fixed_small_log"""      )
								_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )

								assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 1.0_000E-10      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0549625      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.9994987      )      ) < 1E-5



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    str										=      self.get_scheduler_config(variance_type="""learned_range"""      )
								_lowerCAmelCase    :    List[str]										=      scheduler_class(**_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      0.5

								assert scheduler._get_variance(1		,	predicted_variance=_UpperCAmelCase      ) - -10.1712790 < 1E-5
								assert scheduler._get_variance(487		,	predicted_variance=_UpperCAmelCase      ) - -5.7998052 < 1E-5
								assert scheduler._get_variance(999		,	predicted_variance=_UpperCAmelCase      ) - -0.0010011 < 1E-5



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    str										=      self.get_scheduler_config()
								_lowerCAmelCase    :    Optional[int]										=      scheduler_class(**_UpperCAmelCase      )

								_lowerCAmelCase    :    Any										=      scheduler.timesteps

								_lowerCAmelCase    :    Optional[int]										=      self.dummy_model()
								_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample_deter
								_lowerCAmelCase    :    int										=      torch.manual_seed(0      )

								for i, t in enumerate(_UpperCAmelCase      ):
												# 1. predict noise residual
												_lowerCAmelCase    :    str										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )

												# 2. predict previous mean of sample x_t-1
												_lowerCAmelCase    :    Any										=      scheduler.step(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	generator=_UpperCAmelCase      ).prev_sample

												_lowerCAmelCase    :    Tuple										=      pred_prev_sample

								_lowerCAmelCase    :    Union[str, Any]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 252.2682495      ) < 1E-2
								assert abs(result_mean.item() - 0.3284743      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    Optional[int]										=      self.get_scheduler_config()
								_lowerCAmelCase    :    Optional[int]										=      scheduler_class(**_UpperCAmelCase      )

								scheduler.set_timesteps(25      )

								_lowerCAmelCase    :    int										=      scheduler.timesteps

								_lowerCAmelCase    :    str										=      self.dummy_model()
								_lowerCAmelCase    :    Optional[int]										=      self.dummy_sample_deter
								_lowerCAmelCase    :    List[Any]										=      torch.manual_seed(0      )

								for i, t in enumerate(_UpperCAmelCase      ):
												# 1. predict noise residual
												_lowerCAmelCase    :    int										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )

												if i + 1 == timesteps.shape[0]:
																_lowerCAmelCase    :    int										=      None
												else:
																_lowerCAmelCase    :    Any										=      timesteps[i + 1]

												# 2. predict previous mean of sample x_t-1
												_lowerCAmelCase    :    List[Any]										=      scheduler.step(
												    _UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	prev_timestep=_UpperCAmelCase		,	generator=_UpperCAmelCase      ).prev_sample

												_lowerCAmelCase    :    List[str]										=      pred_prev_sample

								_lowerCAmelCase    :    Any										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Dict										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 258.2044983      ) < 1E-2
								assert abs(result_mean.item() - 0.3362038      ) < 1E-3



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[Any]:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    str:





								'''simple docstring'''




								pass







import numpy as np




def 							_UpperCAmelCase  (UpperCamelCase_ : np.array       ):
				'''simple docstring'''
				return (2 / (1 + np.exp(-2 * vector       ))) - 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()

