


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a      (				snake_case_			):
		_a	:  List[Any]				=	(DDPMParallelScheduler,)
		def 	UpperCAmelCase__				(			self			,    **_SCREAMING_SNAKE_CASE						) ->       Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			{
									    'num_train_timesteps': 1000,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'variance_type': 'fixed_small',
									    'clip_sample': True,
									}

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




									"""simple docstring"""





									for timesteps in [1, 5, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			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=_SCREAMING_SNAKE_CASE			,    beta_end=_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       int:




									"""simple docstring"""





									for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Tuple:




									"""simple docstring"""





									for variance in ["fixed_small", "fixed_large", "other"]:
																self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Dict:




									"""simple docstring"""





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




									"""simple docstring"""





									self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE						)
									for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "sample", "v_prediction"]:
																							self.check_over_configs(
																							    thresholding=_SCREAMING_SNAKE_CASE			,    prediction_type=_SCREAMING_SNAKE_CASE			,    sample_max_value=_SCREAMING_SNAKE_CASE			,    )
		def 	UpperCAmelCase__				(			self						) ->       Dict:




									"""simple docstring"""





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




									"""simple docstring"""





									for t in [0, 500, 999]:
																self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Tuple:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.scheduler_classes[0]
									_UpperCAmelCase 		=			self.get_scheduler_config()
									_UpperCAmelCase 		=			scheduler_class(**_SCREAMING_SNAKE_CASE						)

									assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487						) - 0.00979						)						) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999						) - 0.02						)						) < 1e-5
		def 	UpperCAmelCase__				(			self						) ->       Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.scheduler_classes[0]
									_UpperCAmelCase 		=			self.get_scheduler_config()
									_UpperCAmelCase 		=			scheduler_class(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			len(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			self.dummy_model()
									_UpperCAmelCase 		=			self.dummy_sample_deter
									_UpperCAmelCase 		=			self.dummy_sample_deter + 0.1
									_UpperCAmelCase 		=			self.dummy_sample_deter - 0.1

									_UpperCAmelCase 		=			samplea.shape[0]
									_UpperCAmelCase 		=			torch.stack([samplea, samplea, samplea]			,    dim=0						)
									_UpperCAmelCase 		=			torch.arange(_SCREAMING_SNAKE_CASE						)[0:3, None].repeat(1			,    _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			model(samples.flatten(0			,    1						)			,    timesteps.flatten(0			,    1						)						)
									_UpperCAmelCase 		=			scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE			,    timesteps.flatten(0			,    1						)			,    samples.flatten(0			,    1						)						)

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

									assert abs(result_sum.item() - 1153.1833						) < 1e-2
									assert abs(result_mean.item() - 0.5005						) < 1e-3
		def 	UpperCAmelCase__				(			self						) ->       Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.scheduler_classes[0]
									_UpperCAmelCase 		=			self.get_scheduler_config()
									_UpperCAmelCase 		=			scheduler_class(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			len(_SCREAMING_SNAKE_CASE						)

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

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

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

																_UpperCAmelCase 		=			pred_prev_sample

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

									assert abs(result_sum.item() - 258.9606						) < 1e-2
									assert abs(result_mean.item() - 0.3372						) < 1e-3
		def 	UpperCAmelCase__				(			self						) ->       Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.scheduler_classes[0]
									_UpperCAmelCase 		=			self.get_scheduler_config(prediction_type='v_prediction'						)
									_UpperCAmelCase 		=			scheduler_class(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			len(_SCREAMING_SNAKE_CASE						)

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

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

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

																_UpperCAmelCase 		=			pred_prev_sample

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

									assert abs(result_sum.item() - 202.0296						) < 1e-2
									assert abs(result_mean.item() - 0.2631						) < 1e-3
		def 	UpperCAmelCase__				(			self						) ->       Dict:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.scheduler_classes[0]
									_UpperCAmelCase 		=			self.get_scheduler_config()
									_UpperCAmelCase 		=			scheduler_class(**_SCREAMING_SNAKE_CASE						)

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

									scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			scheduler.timesteps

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

																_UpperCAmelCase 		=			scheduler.previous_timestep(_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase 		=			prev_t.item()

																self.assertEqual(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.scheduler_classes[0]
									_UpperCAmelCase 		=			self.get_scheduler_config()
									_UpperCAmelCase 		=			scheduler_class(**_SCREAMING_SNAKE_CASE						)

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

									with self.assertRaises(_SCREAMING_SNAKE_CASE			,    msg='`custom_timesteps` must be in descending order.'						):
																scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.scheduler_classes[0]
									_UpperCAmelCase 		=			self.get_scheduler_config()
									_UpperCAmelCase 		=			scheduler_class(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			[100, 87, 50, 1, 0]
									_UpperCAmelCase 		=			len(_SCREAMING_SNAKE_CASE						)

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






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




									"""simple docstring"""





									_UpperCAmelCase 		=			self.scheduler_classes[0]
									_UpperCAmelCase 		=			self.get_scheduler_config()
									_UpperCAmelCase 		=			scheduler_class(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			[scheduler.config.num_train_timesteps]

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

from maths.prime_check import is_prime





def    __lowercase     (						lowerCamelCase					:     int	):
 if not isinstance(lowerCamelCase  ,  lowerCamelCase	):
  UpperCamelCase_					:						List[str] =					F"Input value of [number={number}] must be an integer"
  raise TypeError(lowerCamelCase	)
 if is_prime(lowerCamelCase	) and is_prime(number + 2	):
  return number + 2
 else:
  return -1


if __name__ == "__main__":
   import doctest

   doctest.testmod()



