





import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE_    (       __a       ):
						__magic_name__:      Optional[int]			   =							(LMSDiscreteScheduler,)
						__magic_name__:      List[Any]			   =							10






						def UpperCAmelCase_					(   self   :   Optional[Any]		, **_A   :   Any						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      List[str]           =						{
							    'num_train_timesteps': 1100,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							}

							config.update(**UpperCamelCase__						)
							return config






						def UpperCAmelCase_					(   self   :   List[str]						)				->     str:







							"""simple docstring"""


							for timesteps in [10, 50, 100, 1000]:
								self.check_over_configs(num_train_timesteps=UpperCamelCase__						)






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







							"""simple docstring"""


							for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		, [0.0_0_0_2, 0.0_0_2, 0.0_2]						):
								self.check_over_configs(beta_start=UpperCamelCase__		, beta_end=UpperCamelCase__						)






						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     Any:







							"""simple docstring"""


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






						def UpperCAmelCase_					(   self   :   Optional[int]						)				->     int:







							"""simple docstring"""


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






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







							"""simple docstring"""


							for t in [0, 500, 800]:
								self.check_over_forward(time_step=UpperCamelCase__						)






						def UpperCAmelCase_					(   self   :   List[Any]						)				->     List[str]:







							"""simple docstring"""


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

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		:      str           =						self.dummy_model()
							snake_case_		:      Dict           =						self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		:      Optional[int]           =						scheduler.scale_model_input(UpperCamelCase__		, UpperCamelCase__						)

								snake_case_		:      Dict           =						model(UpperCamelCase__		, UpperCamelCase__						)

								snake_case_		:      Optional[int]           =						scheduler.step(UpperCamelCase__		, UpperCamelCase__		, UpperCamelCase__						)
								snake_case_		:      List[Any]           =						output.prev_sample

							snake_case_		:      Tuple           =						torch.sum(torch.abs(UpperCamelCase__						)						)
							snake_case_		:      str           =						torch.mean(torch.abs(UpperCamelCase__						)						)

							assert abs(result_sum.item() - 1006.388						) < 1E-2
							assert abs(result_mean.item() - 1.3_1						) < 1E-3






						def UpperCAmelCase_					(   self   :   str						)				->     Tuple:







							"""simple docstring"""


							snake_case_		:      int           =						self.scheduler_classes[0]
							snake_case_		:      Dict           =						self.get_scheduler_config(prediction_type='v_prediction'						)
							snake_case_		:      Optional[Any]           =						scheduler_class(**UpperCamelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		:      List[Any]           =						self.dummy_model()
							snake_case_		:      int           =						self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		:      Optional[int]           =						scheduler.scale_model_input(UpperCamelCase__		, UpperCamelCase__						)

								snake_case_		:      Optional[Any]           =						model(UpperCamelCase__		, UpperCamelCase__						)

								snake_case_		:      str           =						scheduler.step(UpperCamelCase__		, UpperCamelCase__		, UpperCamelCase__						)
								snake_case_		:      str           =						output.prev_sample

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

							assert abs(result_sum.item() - 0.0_0_1_7						) < 1E-2
							assert abs(result_mean.item() - 2.2676E-06						) < 1E-3






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







							"""simple docstring"""


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

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

							snake_case_		:      str           =						self.dummy_model()
							snake_case_		:      str           =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		:      List[str]           =						sample.to(UpperCamelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		:      List[Any]           =						scheduler.scale_model_input(UpperCamelCase__		, UpperCamelCase__						)

								snake_case_		:      Tuple           =						model(UpperCamelCase__		, UpperCamelCase__						)

								snake_case_		:      Any           =						scheduler.step(UpperCamelCase__		, UpperCamelCase__		, UpperCamelCase__						)
								snake_case_		:      int           =						output.prev_sample

							snake_case_		:      str           =						torch.sum(torch.abs(UpperCamelCase__						)						)
							snake_case_		:      Optional[int]           =						torch.mean(torch.abs(UpperCamelCase__						)						)

							assert abs(result_sum.item() - 1006.388						) < 1E-2
							assert abs(result_mean.item() - 1.3_1						) < 1E-3






						def UpperCAmelCase_					(   self   :   Dict						)				->     List[str]:







							"""simple docstring"""


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

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

							snake_case_		:      Dict           =						self.dummy_model()
							snake_case_		:      int           =						self.dummy_sample_deter.to(UpperCamelCase__						) * scheduler.init_noise_sigma
							snake_case_		:      int           =						sample.to(UpperCamelCase__						)

							for t in scheduler.timesteps:
								snake_case_		:      Union[str, Any]           =						scheduler.scale_model_input(UpperCamelCase__		, UpperCamelCase__						)

								snake_case_		:      Any           =						model(UpperCamelCase__		, UpperCamelCase__						)

								snake_case_		:      Tuple           =						scheduler.step(UpperCamelCase__		, UpperCamelCase__		, UpperCamelCase__						)
								snake_case_		:      List[str]           =						output.prev_sample

							snake_case_		:      Optional[int]           =						torch.sum(torch.abs(UpperCamelCase__						)						)
							snake_case_		:      List[str]           =						torch.mean(torch.abs(UpperCamelCase__						)						)

							assert abs(result_sum.item() - 3812.9927						) < 2E-2
							assert abs(result_mean.item() - 4.9_6_4_8						) < 1E-3


"""simple docstring"""

from collections import defaultdict


def    lowerCAmelCase			(__UpperCamelCase							:    int = 5			):
							"""simple docstring"""


							__UpperCamelCase							=defaultdict(__UpperCamelCase			)
							__UpperCamelCase							=0

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

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

														num += 1


def    lowerCAmelCase			(__UpperCamelCase							:    int			):
							"""simple docstring"""


							return "".join(sorted(str(num**3			)			)			)


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



