import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = (EulerDiscreteScheduler,)
				lowerCamelCase_    = 1_0
				def   lowerCAmelCase_       (						self		,     **lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				{
						    'num_train_timesteps': 1_1_0_0,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**lowercase )
						return config
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""simple docstring"""





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



						"""simple docstring"""





						A_		:    List[Any]         =				self.scheduler_classes[0]
						A_		:    List[Any]         =				self.get_scheduler_config()
						A_		:    Dict         =				scheduler_class(**lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						A_		:    Dict         =				torch.manual_seed(0 )

						A_		:    Dict         =				self.dummy_model()
						A_		:    List[Any]         =				self.dummy_sample_deter * scheduler.init_noise_sigma
						A_		:    Union[str, Any]         =				sample.to(lowercase )

						for i, t in enumerate(scheduler.timesteps ):
								A_		:    Union[str, Any]         =				scheduler.scale_model_input(lowercase		,     lowercase )

								A_		:    List[Any]         =				model(lowercase		,     lowercase )

								A_		:    int         =				scheduler.step(lowercase		,     lowercase		,     lowercase		,     generator=lowercase )
								A_		:    Any         =				output.prev_sample

						A_		:    List[Any]         =				torch.sum(torch.abs(lowercase ) )
						A_		:    List[str]         =				torch.mean(torch.abs(lowercase ) )

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



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self.scheduler_classes[0]
						A_		:    Any         =				self.get_scheduler_config(prediction_type='v_prediction' )
						A_		:    Tuple         =				scheduler_class(**lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						A_		:    Tuple         =				torch.manual_seed(0 )

						A_		:    Optional[Any]         =				self.dummy_model()
						A_		:    List[Any]         =				self.dummy_sample_deter * scheduler.init_noise_sigma
						A_		:    Dict         =				sample.to(lowercase )

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

								A_		:    Optional[int]         =				model(lowercase		,     lowercase )

								A_		:    List[Any]         =				scheduler.step(lowercase		,     lowercase		,     lowercase		,     generator=lowercase )
								A_		:    Dict         =				output.prev_sample

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

						assert abs(result_sum.item() - 0.0002 ) < 1E-2
						assert abs(result_mean.item() - 2.26_76E-06 ) < 1E-3
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				self.scheduler_classes[0]
						A_		:    Optional[int]         =				self.get_scheduler_config()
						A_		:    str         =				scheduler_class(**lowercase )

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

						A_		:    Dict         =				torch.manual_seed(0 )

						A_		:    Dict         =				self.dummy_model()
						A_		:    Union[str, Any]         =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						A_		:    Union[str, Any]         =				sample.to(lowercase )

						for t in scheduler.timesteps:
								A_		:    Optional[int]         =				scheduler.scale_model_input(lowercase		,     lowercase )

								A_		:    List[Any]         =				model(lowercase		,     lowercase )

								A_		:    Tuple         =				scheduler.step(lowercase		,     lowercase		,     lowercase		,     generator=lowercase )
								A_		:    Optional[int]         =				output.prev_sample

						A_		:    str         =				torch.sum(torch.abs(lowercase ) )
						A_		:    List[Any]         =				torch.mean(torch.abs(lowercase ) )

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







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.scheduler_classes[0]
						A_		:    Optional[Any]         =				self.get_scheduler_config()
						A_		:    Any         =				scheduler_class(**lowercase		,     use_karras_sigmas=lowercase )

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

						A_		:    Tuple         =				torch.manual_seed(0 )

						A_		:    Tuple         =				self.dummy_model()
						A_		:    List[str]         =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						A_		:    List[str]         =				sample.to(lowercase )

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

								A_		:    int         =				model(lowercase		,     lowercase )

								A_		:    Union[str, Any]         =				scheduler.step(lowercase		,     lowercase		,     lowercase		,     generator=lowercase )
								A_		:    int         =				output.prev_sample

						A_		:    int         =				torch.sum(torch.abs(lowercase ) )
						A_		:    Tuple         =				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







from ..utils import DummyObject, requires_backends





class UpperCAmelCase						( metaclass=__A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''onnx''']
				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(self		,     ['onnx'] )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )






