
import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class  lowercase__(				_snake_case							):



	"""simple docstring"""




	a			:List[Any]         =	(DDPMParallelScheduler,)
	def 						_lowercase  (       self  :			List[Any]       ,							**SCREAMING_SNAKE_CASE_  :			str       )       ->     List[Any]:
					lowercase_       =					{
					    '''num_train_timesteps''': 1_0_0_0,
					    '''beta_start''': 0.00_01,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					    '''variance_type''': '''fixed_small''',
					    '''clip_sample''': True,
					}

					config.update(**_lowerCamelCase       )
					return config
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Tuple:
					for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=_lowerCamelCase       )
	def 						_lowercase  (       self  :			Tuple       )       ->     Optional[int]:
					for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,							[0.0_02, 0.02, 0.2, 2]       ):
									self.check_over_configs(beta_start=_lowerCamelCase       ,							beta_end=_lowerCamelCase       )
	def 						_lowercase  (       self  :			str       )       ->     Optional[int]:
					for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=_lowerCamelCase       )
	def 						_lowercase  (       self  :			str       )       ->     str:
					for variance in ["fixed_small", "fixed_large", "other"]:
									self.check_over_configs(variance_type=_lowerCamelCase       )
	def 						_lowercase  (       self  :			int       )       ->     Optional[Any]:
					for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=_lowerCamelCase       )
	def 						_lowercase  (       self  :			int       )       ->     Optional[Any]:
					self.check_over_configs(thresholding=_lowerCamelCase       )
					for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=_lowerCamelCase       ,							prediction_type=_lowerCamelCase       ,							sample_max_value=_lowerCamelCase       ,							)
	def 						_lowercase  (       self  :			Optional[int]       )       ->     str:
					for prediction_type in ["epsilon", "sample", "v_prediction"]:
									self.check_over_configs(prediction_type=_lowerCamelCase       )
	def 						_lowercase  (       self  :			Any       )       ->     Any:
					for t in [0, 5_0_0, 9_9_9]:
									self.check_over_forward(time_step=_lowerCamelCase       )
	def 						_lowercase  (       self  :			Tuple       )       ->     List[str]:
					lowercase_       =					self.scheduler_classes[0]
					lowercase_       =					self.get_scheduler_config()
					lowercase_       =					scheduler_class(**_lowerCamelCase       )

					assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_09_79       )       ) < 1e-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1e-5
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Dict:
					lowercase_       =					self.scheduler_classes[0]
					lowercase_       =					self.get_scheduler_config()
					lowercase_       =					scheduler_class(**_lowerCamelCase       )

					lowercase_       =					len(_lowerCamelCase       )

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

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

					lowercase_       =					model(samples.flatten(0       ,							1       )       ,							timesteps.flatten(0       ,							1       )       )
					lowercase_       =					scheduler.batch_step_no_noise(_lowerCamelCase       ,							timesteps.flatten(0       ,							1       )       ,							samples.flatten(0       ,							1       )       )

					lowercase_       =					torch.sum(torch.abs(_lowerCamelCase       )       )
					lowercase_       =					torch.mean(torch.abs(_lowerCamelCase       )       )

					assert abs(result_sum.item() - 11_53.18_33       ) < 1e-2
					assert abs(result_mean.item() - 0.50_05       ) < 1e-3
	def 						_lowercase  (       self  :			str       )       ->     Union[str, Any]:
					lowercase_       =					self.scheduler_classes[0]
					lowercase_       =					self.get_scheduler_config()
					lowercase_       =					scheduler_class(**_lowerCamelCase       )

					lowercase_       =					len(_lowerCamelCase       )

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

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

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

									lowercase_       =					pred_prev_sample

					lowercase_       =					torch.sum(torch.abs(_lowerCamelCase       )       )
					lowercase_       =					torch.mean(torch.abs(_lowerCamelCase       )       )

					assert abs(result_sum.item() - 2_58.96_06       ) < 1e-2
					assert abs(result_mean.item() - 0.33_72       ) < 1e-3
	def 						_lowercase  (       self  :			Tuple       )       ->     str:
					lowercase_       =					self.scheduler_classes[0]
					lowercase_       =					self.get_scheduler_config(prediction_type='''v_prediction'''       )
					lowercase_       =					scheduler_class(**_lowerCamelCase       )

					lowercase_       =					len(_lowerCamelCase       )

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

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

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

									lowercase_       =					pred_prev_sample

					lowercase_       =					torch.sum(torch.abs(_lowerCamelCase       )       )
					lowercase_       =					torch.mean(torch.abs(_lowerCamelCase       )       )

					assert abs(result_sum.item() - 2_02.02_96       ) < 1e-2
					assert abs(result_mean.item() - 0.26_31       ) < 1e-3
	def 						_lowercase  (       self  :			int       )       ->     List[Any]:
					lowercase_       =					self.scheduler_classes[0]
					lowercase_       =					self.get_scheduler_config()
					lowercase_       =					scheduler_class(**_lowerCamelCase       )

					lowercase_       =					[1_0_0, 8_7, 5_0, 1, 0]

					scheduler.set_timesteps(timesteps=_lowerCamelCase       )

					lowercase_       =					scheduler.timesteps

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

									lowercase_       =					scheduler.previous_timestep(_lowerCamelCase       )
									lowercase_       =					prev_t.item()

									self.assertEqual(_lowerCamelCase       ,							_lowerCamelCase       )
	def 						_lowercase  (       self  :			Any       )       ->     Optional[int]:
					lowercase_       =					self.scheduler_classes[0]
					lowercase_       =					self.get_scheduler_config()
					lowercase_       =					scheduler_class(**_lowerCamelCase       )

					lowercase_       =					[1_0_0, 8_7, 5_0, 5_1, 0]

					with self.assertRaises(_lowerCamelCase       ,							msg='''`custom_timesteps` must be in descending order.'''       ):
									scheduler.set_timesteps(timesteps=_lowerCamelCase       )
	def 						_lowercase  (       self  :			Any       )       ->     List[str]:
					lowercase_       =					self.scheduler_classes[0]
					lowercase_       =					self.get_scheduler_config()
					lowercase_       =					scheduler_class(**_lowerCamelCase       )

					lowercase_       =					[1_0_0, 8_7, 5_0, 1, 0]
					lowercase_       =					len(_lowerCamelCase       )

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




	def 						_lowercase  (       self  :			List[Any]       )       ->     int:
					lowercase_       =					self.scheduler_classes[0]
					lowercase_       =					self.get_scheduler_config()
					lowercase_       =					scheduler_class(**_lowerCamelCase       )

					lowercase_       =					[scheduler.config.num_train_timesteps]

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








from maths.prime_factors import prime_factors
def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            a						:Dict 							=       F'''Input value of [number={number}] must be an integer'''
            raise TypeError(UpperCAmelCase_						)
      if number < 1:
            raise ValueError('''Input must be a positive integer'''						)
      return -1 if len(prime_factors(UpperCAmelCase_						)						) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





