'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Any   							=       (LMSDiscreteScheduler,)
			__magic_name__ :				Union[str, Any]   							=       10



			def   lowerCamelCase (							self				,			**snake_case_					):
									snake_case :       Optional[Any]				=    {
									    "num_train_timesteps": 1_100,
									    "beta_start": 0.0_001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**snake_case_					)
									return config



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



			def   lowerCamelCase (							self					):
									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=snake_case_				,			beta_end=snake_case_					)



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



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



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



			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    self.scheduler_classes[0]
									snake_case :       Optional[Any]				=    self.get_scheduler_config()
									snake_case :       Optional[Any]				=    scheduler_class(**snake_case_					)

									scheduler.set_timesteps(self.num_inference_steps					)

									snake_case :       List[str]				=    self.dummy_model()
									snake_case :       List[Any]				=    self.dummy_sample_deter * scheduler.init_noise_sigma

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

															snake_case :       int				=    model(snake_case_				,			snake_case_					)

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

									snake_case :       List[Any]				=    torch.sum(torch.abs(snake_case_					)					)
									snake_case :       Union[str, Any]				=    torch.mean(torch.abs(snake_case_					)					)

									assert abs(result_sum.item() - 1_006.388					) < 1e-2
									assert abs(result_mean.item() - 1.31					) < 1e-3



			def   lowerCamelCase (							self					):
									snake_case :       str				=    self.scheduler_classes[0]
									snake_case :       Any				=    self.get_scheduler_config(prediction_type="v_prediction"					)
									snake_case :       Union[str, Any]				=    scheduler_class(**snake_case_					)

									scheduler.set_timesteps(self.num_inference_steps					)

									snake_case :       Optional[int]				=    self.dummy_model()
									snake_case :       Optional[Any]				=    self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(scheduler.timesteps					):
															snake_case :       str				=    scheduler.scale_model_input(snake_case_				,			snake_case_					)

															snake_case :       Optional[int]				=    model(snake_case_				,			snake_case_					)

															snake_case :       Optional[Any]				=    scheduler.step(snake_case_				,			snake_case_				,			snake_case_					)
															snake_case :       Optional[Any]				=    output.prev_sample

									snake_case :       Optional[Any]				=    torch.sum(torch.abs(snake_case_					)					)
									snake_case :       Optional[int]				=    torch.mean(torch.abs(snake_case_					)					)

									assert abs(result_sum.item() - 0.0_017					) < 1e-2
									assert abs(result_mean.item() - 2.26_76e-06					) < 1e-3



			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.scheduler_classes[0]
									snake_case :       List[Any]				=    self.get_scheduler_config()
									snake_case :       Optional[int]				=    scheduler_class(**snake_case_					)

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

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

									for i, t in enumerate(scheduler.timesteps					):
															snake_case :       Any				=    scheduler.scale_model_input(snake_case_				,			snake_case_					)

															snake_case :       Optional[int]				=    model(snake_case_				,			snake_case_					)

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

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

									assert abs(result_sum.item() - 1_006.388					) < 1e-2
									assert abs(result_mean.item() - 1.31					) < 1e-3






			def   lowerCamelCase (							self					):
									snake_case :       str				=    self.scheduler_classes[0]
									snake_case :       Any				=    self.get_scheduler_config()
									snake_case :       Tuple				=    scheduler_class(**snake_case_				,			use_karras_sigmas=snake_case_					)

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

									snake_case :       Dict				=    self.dummy_model()
									snake_case :       str				=    self.dummy_sample_deter.to(snake_case_					) * scheduler.init_noise_sigma
									snake_case :       Optional[Any]				=    sample.to(snake_case_					)

									for t in scheduler.timesteps:
															snake_case :       Tuple				=    scheduler.scale_model_input(snake_case_				,			snake_case_					)

															snake_case :       int				=    model(snake_case_				,			snake_case_					)

															snake_case :       int				=    scheduler.step(snake_case_				,			snake_case_				,			snake_case_					)
															snake_case :       Dict				=    output.prev_sample

									snake_case :       Union[str, Any]				=    torch.sum(torch.abs(snake_case_					)					)
									snake_case :       int				=    torch.mean(torch.abs(snake_case_					)					)

									assert abs(result_sum.item() - 3_812.9_927					) < 2e-2
									assert abs(result_mean.item() - 4.9_648					) < 1e-3






'''simple docstring'''

def        UpperCAmelCase__   (				_lowerCAmelCase    :			int = 400_0000)    ->		int:



						"""simple docstring"""

						snake_case :       List[Any]				=    1
						snake_case :       int				=    2
						snake_case :       List[str]				=    0
						while j <= n:
												if j % 2 == 0:
																		total += j
												snake_case				,					snake_case :       Any				=    j, i + j

						return total


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





