






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  Union[str, Any]      =			(EulerDiscreteScheduler,)
			lowercase__      :  Any      =			10





			def 		snake_case__			(				self	:       Union[str, Any]			,     **lowercase	:       str   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				{
									    """num_train_timesteps""": 1_100,
									    """beta_start""": 0.0001,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**lowercase   )
									return config





			def 		snake_case__			(				self	:       Optional[int]   )						->					List[Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       str   )						->					int:
									"""simple docstring"""







									for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,     [0.0002, 0.002, 0.02]   ):
															self.check_over_configs(beta_start=lowercase			,     beta_end=lowercase   )





			def 		snake_case__			(				self	:       Any   )						->					List[Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Tuple   )						->					Optional[Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       int   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				self.scheduler_classes[0]
									__lowercase					=				self.get_scheduler_config()
									__lowercase					=				scheduler_class(**lowercase   )

									scheduler.set_timesteps(self.num_inference_steps   )

									__lowercase					=				torch.manual_seed(0   )

									__lowercase					=				self.dummy_model()
									__lowercase					=				self.dummy_sample_deter * scheduler.init_noise_sigma
									__lowercase					=				sample.to(lowercase   )

									for i, t in enumerate(scheduler.timesteps   ):
															__lowercase					=				scheduler.scale_model_input(lowercase			,     lowercase   )

															__lowercase					=				model(lowercase			,     lowercase   )

															__lowercase					=				scheduler.step(lowercase			,     lowercase			,     lowercase			,     generator=lowercase   )
															__lowercase					=				output.prev_sample

									__lowercase					=				torch.sum(torch.abs(lowercase   )   )
									__lowercase					=				torch.mean(torch.abs(lowercase   )   )

									assert abs(result_sum.item() - 10.0807   ) < 1E-2
									assert abs(result_mean.item() - 0.0131   ) < 1E-3





			def 		snake_case__			(				self	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				self.scheduler_classes[0]
									__lowercase					=				self.get_scheduler_config(prediction_type="""v_prediction"""   )
									__lowercase					=				scheduler_class(**lowercase   )

									scheduler.set_timesteps(self.num_inference_steps   )

									__lowercase					=				torch.manual_seed(0   )

									__lowercase					=				self.dummy_model()
									__lowercase					=				self.dummy_sample_deter * scheduler.init_noise_sigma
									__lowercase					=				sample.to(lowercase   )

									for i, t in enumerate(scheduler.timesteps   ):
															__lowercase					=				scheduler.scale_model_input(lowercase			,     lowercase   )

															__lowercase					=				model(lowercase			,     lowercase   )

															__lowercase					=				scheduler.step(lowercase			,     lowercase			,     lowercase			,     generator=lowercase   )
															__lowercase					=				output.prev_sample

									__lowercase					=				torch.sum(torch.abs(lowercase   )   )
									__lowercase					=				torch.mean(torch.abs(lowercase   )   )

									assert abs(result_sum.item() - 0.0002   ) < 1E-2
									assert abs(result_mean.item() - 2.2_6_7_6E-0_6   ) < 1E-3





			def 		snake_case__			(				self	:       Optional[Any]   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				self.scheduler_classes[0]
									__lowercase					=				self.get_scheduler_config()
									__lowercase					=				scheduler_class(**lowercase   )

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

									__lowercase					=				torch.manual_seed(0   )

									__lowercase					=				self.dummy_model()
									__lowercase					=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									__lowercase					=				sample.to(lowercase   )

									for t in scheduler.timesteps:
															__lowercase					=				scheduler.scale_model_input(lowercase			,     lowercase   )

															__lowercase					=				model(lowercase			,     lowercase   )

															__lowercase					=				scheduler.step(lowercase			,     lowercase			,     lowercase			,     generator=lowercase   )
															__lowercase					=				output.prev_sample

									__lowercase					=				torch.sum(torch.abs(lowercase   )   )
									__lowercase					=				torch.mean(torch.abs(lowercase   )   )

									assert abs(result_sum.item() - 10.0807   ) < 1E-2
									assert abs(result_mean.item() - 0.0131   ) < 1E-3







			def 		snake_case__			(				self	:       int   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				self.scheduler_classes[0]
									__lowercase					=				self.get_scheduler_config()
									__lowercase					=				scheduler_class(**lowercase			,     use_karras_sigmas=lowercase   )

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

									__lowercase					=				torch.manual_seed(0   )

									__lowercase					=				self.dummy_model()
									__lowercase					=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									__lowercase					=				sample.to(lowercase   )

									for t in scheduler.timesteps:
															__lowercase					=				scheduler.scale_model_input(lowercase			,     lowercase   )

															__lowercase					=				model(lowercase			,     lowercase   )

															__lowercase					=				scheduler.step(lowercase			,     lowercase			,     lowercase			,     generator=lowercase   )
															__lowercase					=				output.prev_sample

									__lowercase					=				torch.sum(torch.abs(lowercase   )   )
									__lowercase					=				torch.mean(torch.abs(lowercase   )   )

									assert abs(result_sum.item() - 124.52_2994_9951_1719   ) < 1E-2
									assert abs(result_mean.item() - 0.1_6213_9326_3339_9963   ) < 1E-3










UpperCamelCase__ =						{
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.355_818,
}






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
												__lowercase					=				(
												    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
												    F"Valid values are: {', '.join(lowercase__   )}"
												)
												raise ValueError(lowercase__   )
						return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





