




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 						__lowercase		(_a							):




						"""simple docstring"""





						_snake_case												=						(UnCLIPScheduler,)

						def      UpperCAmelCase				(				self    ,				**A     )		->    Dict:
									snake_case				:    Optional[Any]           =	{
									    """num_train_timesteps""": 1_0_0_0,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**__lowerCAmelCase     )
									return config

						def      UpperCAmelCase				(				self     )		->    str:
									for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__lowerCAmelCase     )

						def      UpperCAmelCase				(				self     )		->    int:
									for variance in ["fixed_small_log", "learned_range"]:
												self.check_over_configs(variance_type=__lowerCAmelCase     )

						def      UpperCAmelCase				(				self     )		->    Optional[Any]:
									for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__lowerCAmelCase     )

						def      UpperCAmelCase				(				self     )		->    str:
									for clip_sample_range in [1, 5, 1_0, 2_0]:
												self.check_over_configs(clip_sample_range=__lowerCAmelCase     )

						def      UpperCAmelCase				(				self     )		->    List[str]:
									for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(prediction_type=__lowerCAmelCase     )

						def      UpperCAmelCase				(				self     )		->    Tuple:
									for time_step in [0, 5_0_0, 9_9_9]:
												for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
															if prev_timestep is not None and prev_timestep >= time_step:
																		continue

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

						def      UpperCAmelCase				(				self     )		->    int:
									snake_case				:    str           =	self.scheduler_classes[0]
									snake_case				:    Optional[Any]           =	self.get_scheduler_config(variance_type="""fixed_small_log"""     )
									snake_case				:    str           =	scheduler_class(**__lowerCAmelCase     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_8_7     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_9_9     ) - 0.9_99_49_87     )     ) < 1e-5

						def      UpperCAmelCase				(				self     )		->    List[str]:
									snake_case				:    Tuple           =	self.scheduler_classes[0]
									snake_case				:    int           =	self.get_scheduler_config(variance_type="""learned_range"""     )
									snake_case				:    List[Any]           =	scheduler_class(**__lowerCAmelCase     )

									snake_case				:    Optional[Any]           =	0.5

									assert scheduler._get_variance(1    ,				predicted_variance=__lowerCAmelCase     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_8_7    ,				predicted_variance=__lowerCAmelCase     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_9_9    ,				predicted_variance=__lowerCAmelCase     ) - -0.0_01_00_11 < 1e-5

						def      UpperCAmelCase				(				self     )		->    List[Any]:
									snake_case				:    Union[str, Any]           =	self.scheduler_classes[0]
									snake_case				:    Any           =	self.get_scheduler_config()
									snake_case				:    int           =	scheduler_class(**__lowerCAmelCase     )

									snake_case				:    Optional[int]           =	scheduler.timesteps

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

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

												# 2. predict previous mean of sample x_t-1
												snake_case				:    Union[str, Any]           =	scheduler.step(__lowerCAmelCase    ,				__lowerCAmelCase    ,				__lowerCAmelCase    ,				generator=__lowerCAmelCase     ).prev_sample

												snake_case				:    List[str]           =	pred_prev_sample

									snake_case				:    str           =	torch.sum(torch.abs(__lowerCAmelCase     )     )
									snake_case				:    int           =	torch.mean(torch.abs(__lowerCAmelCase     )     )

									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     )		->    Optional[Any]:
									snake_case				:    Union[str, Any]           =	self.scheduler_classes[0]
									snake_case				:    List[Any]           =	self.get_scheduler_config()
									snake_case				:    str           =	scheduler_class(**__lowerCAmelCase     )

									scheduler.set_timesteps(2_5     )

									snake_case				:    Optional[int]           =	scheduler.timesteps

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

									for i, t in enumerate(__lowerCAmelCase     ):
												# 1. predict noise residual
												snake_case				:    Optional[Any]           =	model(__lowerCAmelCase    ,				__lowerCAmelCase     )

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

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

												snake_case				:    Optional[int]           =	pred_prev_sample

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

									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     )		->    int:
									pass

						def      UpperCAmelCase				(				self     )		->    List[str]:
									pass











def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:
   return int((input_a, input_a).count(1					) != 0					)




def 						SCREAMING_SNAKE_CASE__ (   )  ->       None:
   assert or_gate(0						,0					) == 0
   assert or_gate(0						,1					) == 1
   assert or_gate(1						,0					) == 1
   assert or_gate(1						,1					) == 1


if __name__ == "__main__":
       print(or_gate(0, 1))
       print(or_gate(1, 0))
       print(or_gate(0, 0))
       print(or_gate(1, 1))





