





"""simple docstring"""




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  __UpperCamelCase       (		__lowercase		):
							lowerCamelCase     :      List[str]         =(EulerDiscreteScheduler,)
							lowerCamelCase     :      Optional[Any]         =10






							def 					__a     (     self   ,      **lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	Optional[Any]			=       {
								    "num_train_timesteps": 1100,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**__A   )
								return config






							def 					__a     (     self   )			->				Any:
								for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__A   )






							def 					__a     (     self   )			->				List[Any]:
								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]   ,      [0.0_002, 0.002, 0.02]   ):
									self.check_over_configs(beta_start=__A   ,      beta_end=__A   )






							def 					__a     (     self   )			->				List[Any]:
								for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=__A   )






							def 					__a     (     self   )			->				str:
								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=__A   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Any			=       self.scheduler_classes[0]
								a   :	Union[str, Any]			=       self.get_scheduler_config()
								a   :	Union[str, Any]			=       scheduler_class(**__A   )

								scheduler.set_timesteps(self.num_inference_steps   )

								a   :	Any			=       torch.manual_seed(0   )

								a   :	str			=       self.dummy_model()
								a   :	Optional[Any]			=       self.dummy_sample_deter * scheduler.init_noise_sigma
								a   :	Tuple			=       sample.to(__A   )

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

									a   :	Any			=       model(__A   ,      __A   )

									a   :	Union[str, Any]			=       scheduler.step(__A   ,      __A   ,      __A   ,      generator=__A   )
									a   :	Optional[int]			=       output.prev_sample

								a   :	List[Any]			=       torch.sum(torch.abs(__A   )   )
								a   :	Optional[Any]			=       torch.mean(torch.abs(__A   )   )

								assert abs(result_sum.item() - 10.0_807   ) < 1E-2
								assert abs(result_mean.item() - 0.0_131   ) < 1E-3






							def 					__a     (     self   )			->				int:
								a   :	Dict			=       self.scheduler_classes[0]
								a   :	int			=       self.get_scheduler_config(prediction_type="v_prediction"   )
								a   :	Union[str, Any]			=       scheduler_class(**__A   )

								scheduler.set_timesteps(self.num_inference_steps   )

								a   :	List[Any]			=       torch.manual_seed(0   )

								a   :	List[Any]			=       self.dummy_model()
								a   :	Optional[int]			=       self.dummy_sample_deter * scheduler.init_noise_sigma
								a   :	Tuple			=       sample.to(__A   )

								for i, t in enumerate(scheduler.timesteps   ):
									a   :	Tuple			=       scheduler.scale_model_input(__A   ,      __A   )

									a   :	Tuple			=       model(__A   ,      __A   )

									a   :	Dict			=       scheduler.step(__A   ,      __A   ,      __A   ,      generator=__A   )
									a   :	Any			=       output.prev_sample

								a   :	Optional[Any]			=       torch.sum(torch.abs(__A   )   )
								a   :	Tuple			=       torch.mean(torch.abs(__A   )   )

								assert abs(result_sum.item() - 0.0_002   ) < 1E-2
								assert abs(result_mean.item() - 2.2_676E-06   ) < 1E-3






							def 					__a     (     self   )			->				List[str]:
								a   :	List[str]			=       self.scheduler_classes[0]
								a   :	int			=       self.get_scheduler_config()
								a   :	Union[str, Any]			=       scheduler_class(**__A   )

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

								a   :	Dict			=       torch.manual_seed(0   )

								a   :	List[Any]			=       self.dummy_model()
								a   :	Dict			=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								a   :	Union[str, Any]			=       sample.to(__A   )

								for t in scheduler.timesteps:
									a   :	List[Any]			=       scheduler.scale_model_input(__A   ,      __A   )

									a   :	Optional[Any]			=       model(__A   ,      __A   )

									a   :	Union[str, Any]			=       scheduler.step(__A   ,      __A   ,      __A   ,      generator=__A   )
									a   :	Optional[Any]			=       output.prev_sample

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

								assert abs(result_sum.item() - 10.0_807   ) < 1E-2
								assert abs(result_mean.item() - 0.0_131   ) < 1E-3






							def 					__a     (     self   )			->				str:
								a   :	List[Any]			=       self.scheduler_classes[0]
								a   :	str			=       self.get_scheduler_config()
								a   :	Optional[int]			=       scheduler_class(**__A   ,      use_karras_sigmas=__A   )

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

								a   :	List[str]			=       torch.manual_seed(0   )

								a   :	Optional[Any]			=       self.dummy_model()
								a   :	str			=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								a   :	Optional[Any]			=       sample.to(__A   )

								for t in scheduler.timesteps:
									a   :	List[Any]			=       scheduler.scale_model_input(__A   ,      __A   )

									a   :	Optional[Any]			=       model(__A   ,      __A   )

									a   :	Union[str, Any]			=       scheduler.step(__A   ,      __A   ,      __A   ,      generator=__A   )
									a   :	Tuple			=       output.prev_sample

								a   :	Optional[int]			=       torch.sum(torch.abs(__A   )   )
								a   :	Dict			=       torch.mean(torch.abs(__A   )   )

								assert abs(result_sum.item() - 124.52_299_499_511_719   ) < 1E-2
								assert abs(result_mean.item() - 0.16_213_932_633_399_963   ) < 1E-3









"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 200      )				->int:






	'''simple docstring'''






	a   :	Dict			=       [1, 2, 5, 10, 20, 50, 100, 200]
	a   :	Optional[Any]			=       [0] * (pence + 1)
	a   :	List[Any]			=       1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(_lowercase					, pence + 1					, 1      ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682


