






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 				_UpperCAmelCase (	A__     ):

				"""simple docstring"""




				lowercase__    							=	(LMSDiscreteScheduler,)
				lowercase__    							=	10




				def     lowercase__       (					self						:   List[str],  **lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      {
								    '''num_train_timesteps''': 1_100,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**lowerCamelCase  )
								return config




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								for timesteps in [10, 50, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								for beta_start, beta_end in zip([0.00001, 0.0001, 0.001],  [0.0002, 0.002, 0.02]  ):
												self.check_over_configs(beta_start=lowerCamelCase,  beta_end=lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=lowerCamelCase  )




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								for t in [0, 500, 800]:
												self.check_over_forward(time_step=lowerCamelCase  )




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.scheduler_classes[0]
								lowercase__       			=      self.get_scheduler_config()
								lowercase__       			=      scheduler_class(**lowerCamelCase  )

								scheduler.set_timesteps(self.num_inference_steps  )

								lowercase__       			=      self.dummy_model()
								lowercase__       			=      self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps  ):
												lowercase__       			=      scheduler.scale_model_input(lowerCamelCase,  lowerCamelCase  )

												lowercase__       			=      model(lowerCamelCase,  lowerCamelCase  )

												lowercase__       			=      scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
												lowercase__       			=      output.prev_sample

								lowercase__       			=      torch.sum(torch.abs(lowerCamelCase  )  )
								lowercase__       			=      torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 1006.388  ) < 1E-2
								assert abs(result_mean.item() - 1.31  ) < 1E-3




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      self.scheduler_classes[0]
								lowercase__       			=      self.get_scheduler_config(prediction_type='''v_prediction'''  )
								lowercase__       			=      scheduler_class(**lowerCamelCase  )

								scheduler.set_timesteps(self.num_inference_steps  )

								lowercase__       			=      self.dummy_model()
								lowercase__       			=      self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps  ):
												lowercase__       			=      scheduler.scale_model_input(lowerCamelCase,  lowerCamelCase  )

												lowercase__       			=      model(lowerCamelCase,  lowerCamelCase  )

												lowercase__       			=      scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
												lowercase__       			=      output.prev_sample

								lowercase__       			=      torch.sum(torch.abs(lowerCamelCase  )  )
								lowercase__       			=      torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 0.0017  ) < 1E-2
								assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.scheduler_classes[0]
								lowercase__       			=      self.get_scheduler_config()
								lowercase__       			=      scheduler_class(**lowerCamelCase  )

								scheduler.set_timesteps(self.num_inference_steps,  device=lowerCamelCase  )

								lowercase__       			=      self.dummy_model()
								lowercase__       			=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								lowercase__       			=      sample.to(lowerCamelCase  )

								for i, t in enumerate(scheduler.timesteps  ):
												lowercase__       			=      scheduler.scale_model_input(lowerCamelCase,  lowerCamelCase  )

												lowercase__       			=      model(lowerCamelCase,  lowerCamelCase  )

												lowercase__       			=      scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
												lowercase__       			=      output.prev_sample

								lowercase__       			=      torch.sum(torch.abs(lowerCamelCase  )  )
								lowercase__       			=      torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 1006.388  ) < 1E-2
								assert abs(result_mean.item() - 1.31  ) < 1E-3




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      self.scheduler_classes[0]
								lowercase__       			=      self.get_scheduler_config()
								lowercase__       			=      scheduler_class(**lowerCamelCase,  use_karras_sigmas=lowerCamelCase  )

								scheduler.set_timesteps(self.num_inference_steps,  device=lowerCamelCase  )

								lowercase__       			=      self.dummy_model()
								lowercase__       			=      self.dummy_sample_deter.to(lowerCamelCase  ) * scheduler.init_noise_sigma
								lowercase__       			=      sample.to(lowerCamelCase  )

								for t in scheduler.timesteps:
												lowercase__       			=      scheduler.scale_model_input(lowerCamelCase,  lowerCamelCase  )

												lowercase__       			=      model(lowerCamelCase,  lowerCamelCase  )

												lowercase__       			=      scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
												lowercase__       			=      output.prev_sample

								lowercase__       			=      torch.sum(torch.abs(lowerCamelCase  )  )
								lowercase__       			=      torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 3812.9927  ) < 2E-2
								assert abs(result_mean.item() - 4.9648  ) < 1E-3












from collections import defaultdict






def   a      (						lowerCamelCase_ = 5    ):






				'''simple docstring'''
				lowercase__       			=      defaultdict(lowerCamelCase_    )
				lowercase__       			=      0

				while True:
								lowercase__       			=      get_digits(lowerCamelCase_    )
								freqs[digits].append(lowerCamelCase_    )

								if len(freqs[digits]    ) == max_base:
												lowercase__       			=      freqs[digits][0] ** 3
												return base

								num += 1






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return "".join(sorted(str(num**3    )    )    )


if __name__ == "__main__":
							print(F"{solution() = }")




