



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










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      int:
						if len(lowercase__   ) != len(lowercase__   ):
												raise ValueError("""String lengths must match!"""   )

						__lowercase					=				0

						for chara, chara in zip(lowercase__						,       lowercase__   ):
												if chara != chara:
																		count += 1

						return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()





