


'''simple docstring'''
def       a				(      __a       )  ->     List[str]:



					'''simple docstring'''






					if number > 0:
										raise ValueError('''input must be a negative integer'''       )
					UpperCamelCase__       :Tuple							 =			len(bin(__UpperCamelCase       )[3:]       )
					UpperCamelCase__       :Union[str, Any]							 =			bin(abs(__UpperCamelCase       ) - (1 << binary_number_length)       )[3:]
					UpperCamelCase__       :Optional[int]							 =			(
					    (
					        '''1'''
					        + '''0''' * (binary_number_length - len(__UpperCamelCase       ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '''0'''
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
					import doctest

					doctest.testmod()



'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    (DDIMParallelScheduler,)
		_a   			=    (('eta', 0.0), ('num_inference_steps', 50))




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			{
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCamelCase_							)
							return config




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Optional[Any]							 =			self.get_scheduler_config(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			10, 0.0

							UpperCamelCase__       :List[str]							 =			self.dummy_model()
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter

							scheduler.set_timesteps(UpperCamelCase_							)

							for t in scheduler.timesteps:
												UpperCamelCase__       :Optional[int]							 =			model(UpperCamelCase_   ,       UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			scheduler.step(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							).prev_sample

							return sample




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timesteps in [100, 500, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config(steps_offset=1							)
							UpperCamelCase__       :str							 =			scheduler_class(**UpperCamelCase_							)
							scheduler.set_timesteps(5							)
							assert torch.equal(scheduler.timesteps   ,       torch.LongTensor([801, 601, 401, 201, 1]							)							)




		def    lowerCAmelCase__    (		self							):

							'''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=UpperCamelCase_   ,       beta_end=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t in [1, 10, 49]:
												self.check_over_forward(time_step=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, num_inference_steps in zip([1, 10, 50]   ,       [10, 50, 500]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       num_inference_steps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, eta in zip([1, 10, 49]   ,       [0.0, 0.5, 1.0]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       eta=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config()
							UpperCamelCase__       :int							 =			scheduler_class(**UpperCamelCase_							)

							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420   ,       400							) - 0.14771							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980   ,       960							) - 0.32460							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ,       486							) - 0.00979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ,       998							) - 0.02							)							) < 1e-5




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Dict							 =			self.get_scheduler_config()
							UpperCamelCase__       :Any							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			10, 0.0
							scheduler.set_timesteps(UpperCamelCase_							)

							UpperCamelCase__       :Optional[int]							 =			self.dummy_model()
							UpperCamelCase__       :List[Any]							 =			self.dummy_sample_deter
							UpperCamelCase__       :int							 =			self.dummy_sample_deter + 0.1
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter - 0.1

							UpperCamelCase__       :str							 =			samplea.shape[0]
							UpperCamelCase__       :Optional[Any]							 =			torch.stack([samplea, samplea, samplea]   ,       dim=0							)
							UpperCamelCase__       :List[Any]							 =			torch.arange(UpperCamelCase_							)[0:3, None].repeat(1   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			model(samples.flatten(0   ,       1							)   ,       timesteps.flatten(0   ,       1							)							)
							UpperCamelCase__       :Any							 =			scheduler.batch_step_no_noise(UpperCamelCase_   ,       timesteps.flatten(0   ,       1							)   ,       samples.flatten(0   ,       1							)   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 1147.7904							) < 1e-2
							assert abs(result_mean.item() - 0.4982							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.full_loop()

							UpperCamelCase__       :List[str]							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 172.0067							) < 1e-2
							assert abs(result_mean.item() - 0.223967							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.full_loop(prediction_type='''v_prediction'''							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :str							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 52.5302							) < 1e-2
							assert abs(result_mean.item() - 0.0684							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :str							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :int							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.8295							) < 1e-2
							assert abs(result_mean.item() - 0.1951							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :List[str]							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.0784							) < 1e-2
							assert abs(result_mean.item() - 0.1941							) < 1e-3