





"""simple docstring"""






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class 				__lowercase			(			_UpperCAmelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  (DDPMScheduler,)







					def  _lowerCamelCase		(						self	,			**_UpperCAmelCase     ):
									__a :						str			       =  {
									    '''num_train_timesteps''': 1000,
									    '''beta_start''': 0.0_0_0_1,
									    '''beta_end''': 0.0_2,
									    '''beta_schedule''': '''linear''',
									    '''variance_type''': '''fixed_small''',
									    '''clip_sample''': True,
									}

									config.update(**__UpperCamelCase     )
									return config







					def  _lowerCamelCase		(						self     ):






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







					def  _lowerCamelCase		(						self     ):






									for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]	,			[0.0_0_2, 0.0_2, 0.2, 2]     ):
													self.check_over_configs(beta_start=__UpperCamelCase	,			beta_end=__UpperCamelCase     )







					def  _lowerCamelCase		(						self     ):






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







					def  _lowerCamelCase		(						self     ):






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







					def  _lowerCamelCase		(						self     ):






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







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







					def  _lowerCamelCase		(						self     ):






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







					def  _lowerCamelCase		(						self     ):






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







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  self.scheduler_classes[0]
									__a :						Dict			       =  self.get_scheduler_config()
									__a :						Tuple			       =  scheduler_class(**__UpperCamelCase     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.0_0_9_7_9     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.0_2     )     ) < 1e-5







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  self.scheduler_classes[0]
									__a :						int			       =  self.get_scheduler_config()
									__a :						Any			       =  scheduler_class(**__UpperCamelCase     )

									__a :						Any			       =  len(__UpperCamelCase     )

									__a :						Any			       =  self.dummy_model()
									__a :						str			       =  self.dummy_sample_deter
									__a :						Tuple			       =  torch.manual_seed(0     )

									for t in reversed(range(__UpperCamelCase     )     ):
													# 1. predict noise residual
													__a :						List[Any]			       =  model(__UpperCamelCase	,			__UpperCamelCase     )

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

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

									__a :						Optional[int]			       =  torch.sum(torch.abs(__UpperCamelCase     )     )
									__a :						str			       =  torch.mean(torch.abs(__UpperCamelCase     )     )

									assert abs(result_sum.item() - 258.9606     ) < 1e-2
									assert abs(result_mean.item() - 0.3_3_7_2     ) < 1e-3







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.scheduler_classes[0]
									__a :						List[Any]			       =  self.get_scheduler_config(prediction_type='''v_prediction'''     )
									__a :						Optional[Any]			       =  scheduler_class(**__UpperCamelCase     )

									__a :						Dict			       =  len(__UpperCamelCase     )

									__a :						List[Any]			       =  self.dummy_model()
									__a :						List[str]			       =  self.dummy_sample_deter
									__a :						Dict			       =  torch.manual_seed(0     )

									for t in reversed(range(__UpperCamelCase     )     ):
													# 1. predict noise residual
													__a :						List[Any]			       =  model(__UpperCamelCase	,			__UpperCamelCase     )

													# 2. predict previous mean of sample x_t-1
													__a :						List[str]			       =  scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			generator=__UpperCamelCase     ).prev_sample

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

									__a :						Any			       =  torch.sum(torch.abs(__UpperCamelCase     )     )
									__a :						List[str]			       =  torch.mean(torch.abs(__UpperCamelCase     )     )

									assert abs(result_sum.item() - 202.0296     ) < 1e-2
									assert abs(result_mean.item() - 0.2_6_3_1     ) < 1e-3







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  self.scheduler_classes[0]
									__a :						Optional[int]			       =  self.get_scheduler_config()
									__a :						Tuple			       =  scheduler_class(**__UpperCamelCase     )

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

									scheduler.set_timesteps(timesteps=__UpperCamelCase     )

									__a :						Optional[Any]			       =  scheduler.timesteps

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

													__a :						List[Any]			       =  scheduler.previous_timestep(__UpperCamelCase     )
													__a :						int			       =  prev_t.item()

													self.assertEqual(__UpperCamelCase	,			__UpperCamelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.scheduler_classes[0]
									__a :						List[str]			       =  self.get_scheduler_config()
									__a :						Tuple			       =  scheduler_class(**__UpperCamelCase     )

									__a :						Optional[int]			       =  [100, 87, 50, 51, 0]

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







					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  self.scheduler_classes[0]
									__a :						Optional[int]			       =  self.get_scheduler_config()
									__a :						Union[str, Any]			       =  scheduler_class(**__UpperCamelCase     )

									__a :						Optional[int]			       =  [100, 87, 50, 1, 0]
									__a :						Optional[int]			       =  len(__UpperCamelCase     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  self.scheduler_classes[0]
									__a :						Dict			       =  self.get_scheduler_config()
									__a :						List[Any]			       =  scheduler_class(**__UpperCamelCase     )

									__a :						List[str]			       =  [scheduler.config.num_train_timesteps]

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






"""simple docstring"""






def     __A      (			a_						:int   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float(moles / volume) * nfactor)

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((moles * 0.0_8_2_1 * temperature) / (volume)))

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)))

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float)		->		float:
				return round(float((pressure * volume) / (0.0_8_2_1 * moles)))


if __name__ == "__main__":
		import doctest

		doctest.testmod()