






"""simple docstring"""







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Dict												=		(DDPMScheduler,)






						def      __snake_case      (		self  :Dict			, **__magic_name__  :List[str]				)      ->Optional[int]:
										lowercase    :      Dict       = {
										    """num_train_timesteps""": 1_000,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										    """variance_type""": """fixed_small""",
										    """clip_sample""": True,
										}

										config.update(**__magic_name__				)
										return config






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										for timesteps in [1, 5, 100, 1_000]:
														self.check_over_configs(num_train_timesteps=__magic_name__				)






						def      __snake_case      (		self  :int				)      ->int:
										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=__magic_name__			, beta_end=__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]				)      ->Any:
										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=__magic_name__				)






						def      __snake_case      (		self  :Tuple				)      ->List[str]:
										for variance in ["fixed_small", "fixed_large", "other"]:
														self.check_over_configs(variance_type=__magic_name__				)






						def      __snake_case      (		self  :Dict				)      ->Optional[Any]:
										for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=__magic_name__				)






						def      __snake_case      (		self  :Optional[int]				)      ->Optional[Any]:
										self.check_over_configs(thresholding=__magic_name__				)
										for threshold in [0.5, 1.0, 2.0]:
														for prediction_type in ["epsilon", "sample", "v_prediction"]:
																		self.check_over_configs(
																		    thresholding=__magic_name__			, prediction_type=__magic_name__			, sample_max_value=__magic_name__			, )






						def      __snake_case      (		self  :Union[str, Any]				)      ->Tuple:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(prediction_type=__magic_name__				)






						def      __snake_case      (		self  :Tuple				)      ->Union[str, Any]:
										for t in [0, 500, 999]:
														self.check_over_forward(time_step=__magic_name__				)






						def      __snake_case      (		self  :List[str]				)      ->Tuple:
										lowercase    :      int       = self.scheduler_classes[0]
										lowercase    :      Tuple       = self.get_scheduler_config()
										lowercase    :      List[str]       = scheduler_class(**__magic_name__				)

										assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_0979				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5






						def      __snake_case      (		self  :Dict				)      ->Tuple:
										lowercase    :      Union[str, Any]       = self.scheduler_classes[0]
										lowercase    :      Union[str, Any]       = self.get_scheduler_config()
										lowercase    :      Optional[Any]       = scheduler_class(**__magic_name__				)

										lowercase    :      Any       = len(__magic_name__				)

										lowercase    :      Optional[Any]       = self.dummy_model()
										lowercase    :      Any       = self.dummy_sample_deter
										lowercase    :      Dict       = torch.manual_seed(0				)

										for t in reversed(range(__magic_name__				)				):
														# 1. predict noise residual
														lowercase    :      Tuple       = model(__magic_name__			, __magic_name__				)

														# 2. predict previous mean of sample x_t-1
														lowercase    :      Optional[int]       = scheduler.step(__magic_name__			, __magic_name__			, __magic_name__			, generator=__magic_name__				).prev_sample

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

										lowercase    :      Any       = torch.sum(torch.abs(__magic_name__				)				)
										lowercase    :      Union[str, Any]       = torch.mean(torch.abs(__magic_name__				)				)

										assert abs(result_sum.item() - 258.9606				) < 1E-2
										assert abs(result_mean.item() - 0.3372				) < 1E-3






						def      __snake_case      (		self  :Tuple				)      ->Dict:
										lowercase    :      Any       = self.scheduler_classes[0]
										lowercase    :      Any       = self.get_scheduler_config(prediction_type="""v_prediction"""				)
										lowercase    :      Tuple       = scheduler_class(**__magic_name__				)

										lowercase    :      Union[str, Any]       = len(__magic_name__				)

										lowercase    :      int       = self.dummy_model()
										lowercase    :      List[Any]       = self.dummy_sample_deter
										lowercase    :      Union[str, Any]       = torch.manual_seed(0				)

										for t in reversed(range(__magic_name__				)				):
														# 1. predict noise residual
														lowercase    :      Tuple       = model(__magic_name__			, __magic_name__				)

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

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

										lowercase    :      Union[str, Any]       = torch.sum(torch.abs(__magic_name__				)				)
										lowercase    :      Tuple       = torch.mean(torch.abs(__magic_name__				)				)

										assert abs(result_sum.item() - 202.0296				) < 1E-2
										assert abs(result_mean.item() - 0.2631				) < 1E-3






						def      __snake_case      (		self  :List[Any]				)      ->Union[str, Any]:
										lowercase    :      Tuple       = self.scheduler_classes[0]
										lowercase    :      Tuple       = self.get_scheduler_config()
										lowercase    :      Tuple       = scheduler_class(**__magic_name__				)

										lowercase    :      List[Any]       = [100, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=__magic_name__				)

										lowercase    :      Union[str, Any]       = scheduler.timesteps

										for i, timestep in enumerate(__magic_name__				):
														if i == len(__magic_name__				) - 1:
																		lowercase    :      int       = -1
														else:
																		lowercase    :      Dict       = timesteps[i + 1]

														lowercase    :      str       = scheduler.previous_timestep(__magic_name__				)
														lowercase    :      Tuple       = prev_t.item()

														self.assertEqual(__magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Optional[Any]				)      ->List[Any]:
										lowercase    :      int       = self.scheduler_classes[0]
										lowercase    :      List[Any]       = self.get_scheduler_config()
										lowercase    :      Optional[Any]       = scheduler_class(**__magic_name__				)

										lowercase    :      Union[str, Any]       = [100, 87, 50, 51, 0]

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






						def      __snake_case      (		self  :str				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = self.scheduler_classes[0]
										lowercase    :      str       = self.get_scheduler_config()
										lowercase    :      List[Any]       = scheduler_class(**__magic_name__				)

										lowercase    :      str       = [100, 87, 50, 1, 0]
										lowercase    :      Optional[int]       = len(__magic_name__				)

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






						def      __snake_case      (		self  :str				)      ->Optional[int]:
										lowercase    :      int       = self.scheduler_classes[0]
										lowercase    :      Dict       = self.get_scheduler_config()
										lowercase    :      Optional[int]       = scheduler_class(**__magic_name__				)

										lowercase    :      Optional[int]       = [scheduler.config.num_train_timesteps]

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








"""simple docstring"""







def      UpperCamelCase       ( _A )     ->       int:
				lowercase    :      Dict       = 0
				while num > 0:
								digit_sum += num % 10
								num //= 10
				return digit_sum


def      UpperCamelCase       ( _A = 100 )     ->       int:
				lowercase    :      Union[str, Any]       = 1
				lowercase    :      Tuple       = 2

				for i in range(2  ,				max_n + 1 ):
								lowercase    :      Any       = pre_numerator
								lowercase    :      Dict       = 2 * i // 3 if i % 3 == 0 else 1
								lowercase    :      Optional[Any]       = cur_numerator
								lowercase    :      str       = e_cont * pre_numerator + temp

				return sum_digits(_A )


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