





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class   UpperCamelCase_       (     snake_case_    ):
							lowerCamelCase_           =					(EulerDiscreteScheduler,)
							lowerCamelCase_           =					10


							def      _snake_case  (							self    :Optional[int]							,				**__A    :Dict		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      {
									    """num_train_timesteps""": 1100,
									    """beta_start""": 0.0_0_0_1,
									    """beta_end""": 0.0_2,
									    """beta_schedule""": """linear""",
									}

									config.update(**__A		)
									return config


							def      _snake_case  (							self    :Tuple		)   ->   str:



									"""simple docstring"""



									for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=__A		)


							def      _snake_case  (							self    :str		)   ->   Optional[int]:



									"""simple docstring"""



									for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]							,				[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
											self.check_over_configs(beta_start=__A							,				beta_end=__A		)


							def      _snake_case  (							self    :Tuple		)   ->   str:



									"""simple docstring"""



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


							def      _snake_case  (							self    :str		)   ->   Union[str, Any]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :str		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=      self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__			=      scheduler_class(**__A		)

									scheduler.set_timesteps(self.num_inference_steps		)

									SCREAMING_SNAKE_CASE__			=      torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=      self.dummy_model()
									SCREAMING_SNAKE_CASE__			=      self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE__			=      sample.to(__A		)

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

											SCREAMING_SNAKE_CASE__			=      model(__A							,				__A		)

											SCREAMING_SNAKE_CASE__			=      scheduler.step(__A							,				__A							,				__A							,				generator=__A		)
											SCREAMING_SNAKE_CASE__			=      output.prev_sample

									SCREAMING_SNAKE_CASE__			=      torch.sum(torch.abs(__A		)		)
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(__A		)		)

									assert abs(result_sum.item() - 1_0.0_8_0_7		) < 1E-2
									assert abs(result_mean.item() - 0.0_1_3_1		) < 1E-3


							def      _snake_case  (							self    :int		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=      self.get_scheduler_config(prediction_type="""v_prediction"""		)
									SCREAMING_SNAKE_CASE__			=      scheduler_class(**__A		)

									scheduler.set_timesteps(self.num_inference_steps		)

									SCREAMING_SNAKE_CASE__			=      torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=      self.dummy_model()
									SCREAMING_SNAKE_CASE__			=      self.dummy_sample_deter * scheduler.init_noise_sigma
									SCREAMING_SNAKE_CASE__			=      sample.to(__A		)

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

											SCREAMING_SNAKE_CASE__			=      model(__A							,				__A		)

											SCREAMING_SNAKE_CASE__			=      scheduler.step(__A							,				__A							,				__A							,				generator=__A		)
											SCREAMING_SNAKE_CASE__			=      output.prev_sample

									SCREAMING_SNAKE_CASE__			=      torch.sum(torch.abs(__A		)		)
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(__A		)		)

									assert abs(result_sum.item() - 0.0_0_0_2		) < 1E-2
									assert abs(result_mean.item() - 2.2_676E-06		) < 1E-3


							def      _snake_case  (							self    :Optional[Any]		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=      self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__			=      scheduler_class(**__A		)

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

									SCREAMING_SNAKE_CASE__			=      torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=      self.dummy_model()
									SCREAMING_SNAKE_CASE__			=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									SCREAMING_SNAKE_CASE__			=      sample.to(__A		)

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE__			=      scheduler.scale_model_input(__A							,				__A		)

											SCREAMING_SNAKE_CASE__			=      model(__A							,				__A		)

											SCREAMING_SNAKE_CASE__			=      scheduler.step(__A							,				__A							,				__A							,				generator=__A		)
											SCREAMING_SNAKE_CASE__			=      output.prev_sample

									SCREAMING_SNAKE_CASE__			=      torch.sum(torch.abs(__A		)		)
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(__A		)		)

									assert abs(result_sum.item() - 1_0.0_8_0_7		) < 1E-2
									assert abs(result_mean.item() - 0.0_1_3_1		) < 1E-3


							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__			=      self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__			=      scheduler_class(**__A							,				use_karras_sigmas=__A		)

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

									SCREAMING_SNAKE_CASE__			=      torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=      self.dummy_model()
									SCREAMING_SNAKE_CASE__			=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									SCREAMING_SNAKE_CASE__			=      sample.to(__A		)

									for t in scheduler.timesteps:
											SCREAMING_SNAKE_CASE__			=      scheduler.scale_model_input(__A							,				__A		)

											SCREAMING_SNAKE_CASE__			=      model(__A							,				__A		)

											SCREAMING_SNAKE_CASE__			=      scheduler.step(__A							,				__A							,				__A							,				generator=__A		)
											SCREAMING_SNAKE_CASE__			=      output.prev_sample

									SCREAMING_SNAKE_CASE__			=      torch.sum(torch.abs(__A		)		)
									SCREAMING_SNAKE_CASE__			=      torch.mean(torch.abs(__A		)		)

									assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9		) < 1E-2
									assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3		) < 1E-3











from math import pow, sqrt






def 				SCREAMING_SNAKE_CASE__							(       *UpperCamelCase__:							float    ):
		SCREAMING_SNAKE_CASE__			=      len(UpperCamelCase__    ) > 0 and all(value > 0.0 for value in values    )
		return result






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError("""Input Error: Molar mass values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(molar_mass / pow(effusion_rate_a / effusion_rate_a					,   2    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(pow(effusion_rate_a / effusion_rate_a					,   2    ) / molar_mass					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)