





"""simple docstring"""


import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[Any]										=  (CMStochasticIterativeScheduler,)
			__lowerCAmelCase     :				Dict										=  10







			def   _a      (      self						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			{
									    """num_train_timesteps""": 2_0_1,
									    """sigma_min""": 0.0_02,
									    """sigma_max""": 80.0,
									}

									config.update(**_A    )
									return config







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			1_0

									UpperCamelCase				:		Union[str, Any]				   =			self.get_scheduler_config()
									UpperCamelCase				:		int				   =			self.scheduler_classes[0](**_A    )

									scheduler.set_timesteps(_A    )

									UpperCamelCase				:		Optional[int]				   =			scheduler.timesteps[0]
									UpperCamelCase				:		List[str]				   =			scheduler.timesteps[1]

									UpperCamelCase				:		str				   =			self.dummy_sample
									UpperCamelCase				:		int				   =			0.1 * sample

									UpperCamelCase				:		str				   =			scheduler.step(_A						,   _A						,   _A    ).prev_sample
									UpperCamelCase				:		Optional[Any]				   =			scheduler.step(_A						,   _A						,   _A    ).prev_sample

									self.assertEqual(output_a.shape						,   sample.shape    )
									self.assertEqual(output_a.shape						,   output_a.shape    )







			def   _a      (      self    ):






									'''simple docstring'''







									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.scheduler_classes[0]
									UpperCamelCase				:		List[str]				   =			self.get_scheduler_config()
									UpperCamelCase				:		List[str]				   =			scheduler_class(**_A    )

									UpperCamelCase				:		str				   =			1
									scheduler.set_timesteps(_A    )
									UpperCamelCase				:		Optional[int]				   =			scheduler.timesteps

									UpperCamelCase				:		Any				   =			torch.manual_seed(0    )

									UpperCamelCase				:		Union[str, Any]				   =			self.dummy_model()
									UpperCamelCase				:		Dict				   =			self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(_A    ):
															# 1. scale model input
															UpperCamelCase				:		Any				   =			scheduler.scale_model_input(_A						,   _A    )

															# 2. predict noise residual
															UpperCamelCase				:		List[str]				   =			model(_A						,   _A    )

															# 3. predict previous sample x_t-1
															UpperCamelCase				:		Optional[int]				   =			scheduler.step(_A						,   _A						,   _A						,   generator=_A    ).prev_sample

															UpperCamelCase				:		Tuple				   =			pred_prev_sample

									UpperCamelCase				:		Tuple				   =			torch.sum(torch.abs(_A    )    )
									UpperCamelCase				:		Optional[int]				   =			torch.mean(torch.abs(_A    )    )

									assert abs(result_sum.item() - 1_92.76_14    ) < 1e-2
									assert abs(result_mean.item() - 0.25_10    ) < 1e-3







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.scheduler_classes[0]
									UpperCamelCase				:		int				   =			self.get_scheduler_config()
									UpperCamelCase				:		List[str]				   =			scheduler_class(**_A    )

									UpperCamelCase				:		int				   =			[1_0_6, 0]
									scheduler.set_timesteps(timesteps=_A    )
									UpperCamelCase				:		int				   =			scheduler.timesteps

									UpperCamelCase				:		Optional[int]				   =			torch.manual_seed(0    )

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

									for t in timesteps:
															# 1. scale model input
															UpperCamelCase				:		int				   =			scheduler.scale_model_input(_A						,   _A    )

															# 2. predict noise residual
															UpperCamelCase				:		int				   =			model(_A						,   _A    )

															# 3. predict previous sample x_t-1
															UpperCamelCase				:		str				   =			scheduler.step(_A						,   _A						,   _A						,   generator=_A    ).prev_sample

															UpperCamelCase				:		int				   =			pred_prev_sample

									UpperCamelCase				:		Union[str, Any]				   =			torch.sum(torch.abs(_A    )    )
									UpperCamelCase				:		Dict				   =			torch.mean(torch.abs(_A    )    )

									assert abs(result_sum.item() - 3_47.63_57    ) < 1e-2
									assert abs(result_mean.item() - 0.45_27    ) < 1e-3







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			self.scheduler_classes[0]
									UpperCamelCase				:		Any				   =			self.get_scheduler_config()
									UpperCamelCase				:		Optional[int]				   =			scheduler_class(**_A    )

									UpperCamelCase				:		List[Any]				   =			[3_9, 3_0, 1_2, 1_5, 0]

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			self.scheduler_classes[0]
									UpperCamelCase				:		List[str]				   =			self.get_scheduler_config()
									UpperCamelCase				:		List[Any]				   =			scheduler_class(**_A    )

									UpperCamelCase				:		Any				   =			[3_9, 3_0, 1_2, 1, 0]
									UpperCamelCase				:		Union[str, Any]				   =			len(_A    )

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			self.scheduler_classes[0]
									UpperCamelCase				:		Any				   =			self.get_scheduler_config()
									UpperCamelCase				:		int				   =			scheduler_class(**_A    )

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

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










"""simple docstring"""


import os

def      UpperCamelCase							():
						UpperCamelCase				:		Union[str, Any]				   =			os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE     )							,     """num.txt"""     )
						with open(SCREAMING_SNAKE_CASE     ) as file_hand:
												return str(sum(int(SCREAMING_SNAKE_CASE     ) for line in file_hand     )     )[:10]


if __name__ == "__main__":
		print(solution())



