

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 				a  (	__SCREAMING_SNAKE_CASE				):
						_lowercase    						=			(UnCLIPScheduler,)


						def        _UpperCAmelCase				(       self						,					**A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    {
										    "num_train_timesteps": 1000,
										    "variance_type": "fixed_small_log",
										    "clip_sample": True,
										    "clip_sample_range": 1.0,
										    "prediction_type": "epsilon",
										}

										config.update(**_a	)
										return config


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self	):







										'''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=_a						,					prev_timestep=_a	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.scheduler_classes[0]
										_UpperCAmelCase       :		str 		=    self.get_scheduler_config(variance_type="fixed_small_log"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    scheduler_class(**_a	)

										assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.0000e-10	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.0_54_96_25	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.9_99_49_87	)	) < 1e-5


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.scheduler_classes[0]
										_UpperCAmelCase       :		Optional[Any] 		=    self.get_scheduler_config(variance_type="learned_range"	)
										_UpperCAmelCase       :		str 		=    scheduler_class(**_a	)

										_UpperCAmelCase       :		Optional[Any] 		=    0.5

										assert scheduler._get_variance(1						,					predicted_variance=_a	) - -10.1_71_27_90 < 1e-5
										assert scheduler._get_variance(487						,					predicted_variance=_a	) - -5.7_99_80_52 < 1e-5
										assert scheduler._get_variance(999						,					predicted_variance=_a	) - -0.0_01_00_11 < 1e-5


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.scheduler_classes[0]
										_UpperCAmelCase       :		List[Any] 		=    self.get_scheduler_config()
										_UpperCAmelCase       :		Dict 		=    scheduler_class(**_a	)

										_UpperCAmelCase       :		str 		=    scheduler.timesteps

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

										for i, t in enumerate(_a	):
														# 1. predict noise residual
														_UpperCAmelCase       :		List[str] 		=    model(_a						,					_a	)

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

														_UpperCAmelCase       :		Optional[Any] 		=    pred_prev_sample

										_UpperCAmelCase       :		List[Any] 		=    torch.sum(torch.abs(_a	)	)
										_UpperCAmelCase       :		Optional[Any] 		=    torch.mean(torch.abs(_a	)	)

										assert abs(result_sum.item() - 2_52.2_68_24_95	) < 1e-2
										assert abs(result_mean.item() - 0.3_28_47_43	) < 1e-3


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.scheduler_classes[0]
										_UpperCAmelCase       :		Optional[Any] 		=    self.get_scheduler_config()
										_UpperCAmelCase       :		Optional[Any] 		=    scheduler_class(**_a	)

										scheduler.set_timesteps(25	)

										_UpperCAmelCase       :		Union[str, Any] 		=    scheduler.timesteps

										_UpperCAmelCase       :		Union[str, Any] 		=    self.dummy_model()
										_UpperCAmelCase       :		Optional[Any] 		=    self.dummy_sample_deter
										_UpperCAmelCase       :		Optional[int] 		=    torch.manual_seed(0	)

										for i, t in enumerate(_a	):
														# 1. predict noise residual
														_UpperCAmelCase       :		Union[str, Any] 		=    model(_a						,					_a	)

														if i + 1 == timesteps.shape[0]:
																		_UpperCAmelCase       :		str 		=    None
														else:
																		_UpperCAmelCase       :		Union[str, Any] 		=    timesteps[i + 1]

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

														_UpperCAmelCase       :		Optional[Any] 		=    pred_prev_sample

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

										assert abs(result_sum.item() - 2_58.2_04_49_83	) < 1e-2
										assert abs(result_mean.item() - 0.3_36_20_38	) < 1e-3


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass







from __future__ import annotations

import numpy as np

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[float] )   ->       Dict:
				return np.maximum(0  ,      lowerCAmelCase )


if __name__ == "__main__":
					print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


