

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class      lowerCAmelCase					(     __UpperCamelCase					):


       '''simple docstring'''







       snake_case     				=						(KDPMaDiscreteScheduler,)
       snake_case     				=						10

       def 	lowerCamelCase__    (						self       :	str    ,						**__snake_case       :	Optional[int]  ) ->			Union[str, Any]:







        '''simple docstring'''

        lowerCamelCase       =     {
            'num_train_timesteps': 1100,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**__UpperCamelCase  )
        return config

       def 	lowerCamelCase__    (						self       :	Optional[Any]  ) ->			str:







        '''simple docstring'''

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

       def 	lowerCamelCase__    (						self       :	Dict  ) ->			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=__UpperCamelCase    ,						beta_end=__UpperCamelCase  )

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







        '''simple docstring'''

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

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







        '''simple docstring'''

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

       def 	lowerCamelCase__    (						self       :	Optional[int]  ) ->			Tuple:







        '''simple docstring'''

        lowerCamelCase       =     self.scheduler_classes[0]
        lowerCamelCase       =     self.get_scheduler_config(prediction_type='v_prediction'  )
        lowerCamelCase       =     scheduler_class(**__UpperCamelCase  )

        scheduler.set_timesteps(self.num_inference_steps  )

        lowerCamelCase       =     self.dummy_model()
        lowerCamelCase       =     self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCamelCase       =     sample.to(__UpperCamelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
         lowerCamelCase       =     scheduler.scale_model_input(__UpperCamelCase    ,						__UpperCamelCase  )

         lowerCamelCase       =     model(__UpperCamelCase    ,						__UpperCamelCase  )

         lowerCamelCase       =     scheduler.step(__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase  )
         lowerCamelCase       =     output.prev_sample

        lowerCamelCase       =     torch.sum(torch.abs(__UpperCamelCase  )  )
        lowerCamelCase       =     torch.mean(torch.abs(__UpperCamelCase  )  )

        if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6934e-07  ) < 1e-2
         assert abs(result_mean.item() - 6.1112e-10  ) < 1e-3
        else:
         # CUDA
         assert abs(result_sum.item() - 4.693_4286_5017_0972e-07  ) < 1e-2
         assert abs(result_mean.item() - 0.0002  ) < 1e-3

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







        '''simple docstring'''

        if torch_device == "mps":
         return
        lowerCamelCase       =     self.scheduler_classes[0]
        lowerCamelCase       =     self.get_scheduler_config()
        lowerCamelCase       =     scheduler_class(**__UpperCamelCase  )

        scheduler.set_timesteps(self.num_inference_steps  )

        lowerCamelCase       =     self.dummy_model()
        lowerCamelCase       =     self.dummy_sample_deter * scheduler.init_noise_sigma
        lowerCamelCase       =     sample.to(__UpperCamelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
         lowerCamelCase       =     scheduler.scale_model_input(__UpperCamelCase    ,						__UpperCamelCase  )

         lowerCamelCase       =     model(__UpperCamelCase    ,						__UpperCamelCase  )

         lowerCamelCase       =     scheduler.step(__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase  )
         lowerCamelCase       =     output.prev_sample

        lowerCamelCase       =     torch.sum(torch.abs(__UpperCamelCase  )  )
        lowerCamelCase       =     torch.mean(torch.abs(__UpperCamelCase  )  )

        if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 20.4125  ) < 1e-2
         assert abs(result_mean.item() - 0.0266  ) < 1e-3
        else:
         # CUDA
         assert abs(result_sum.item() - 20.4125  ) < 1e-2
         assert abs(result_mean.item() - 0.0266  ) < 1e-3





       def 	lowerCamelCase__    (						self       :	Optional[int]  ) ->			Optional[int]:







        '''simple docstring'''

        if torch_device == "mps":
         return
        lowerCamelCase       =     self.scheduler_classes[0]
        lowerCamelCase       =     self.get_scheduler_config()
        lowerCamelCase       =     scheduler_class(**__UpperCamelCase  )

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

        lowerCamelCase       =     self.dummy_model()
        lowerCamelCase       =     self.dummy_sample_deter.to(__UpperCamelCase  ) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
         lowerCamelCase       =     scheduler.scale_model_input(__UpperCamelCase    ,						__UpperCamelCase  )

         lowerCamelCase       =     model(__UpperCamelCase    ,						__UpperCamelCase  )

         lowerCamelCase       =     scheduler.step(__UpperCamelCase    ,						__UpperCamelCase    ,						__UpperCamelCase  )
         lowerCamelCase       =     output.prev_sample

        lowerCamelCase       =     torch.sum(torch.abs(__UpperCamelCase  )  )
        lowerCamelCase       =     torch.mean(torch.abs(__UpperCamelCase  )  )

        if str(__UpperCamelCase  ).startswith('cpu'  ):
         # The following sum varies between 148 and 156 on mps. Why?
         assert abs(result_sum.item() - 20.4125  ) < 1e-2
         assert abs(result_mean.item() - 0.0266  ) < 1e-3
        else:
         # CUDA
         assert abs(result_sum.item() - 20.4125  ) < 1e-2
         assert abs(result_mean.item() - 0.0266  ) < 1e-3






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     (KDPMaDiscreteScheduler,)
					SCREAMING_SNAKE_CASE           =     1_0

					def  lowercase__   (						self       :  str ,       **__UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       {
												    """num_train_timesteps""": 1_1_0_0,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**__UpperCamelCase )
												return config

					def  lowercase__   (						self       :  Optional[Any] ):
												for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																			self.check_over_configs(num_train_timesteps=__UpperCamelCase )

					def  lowercase__   (						self       :  Dict ):
												for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] ,       [0.0002, 0.002, 0.02] ):
																			self.check_over_configs(beta_start=__UpperCamelCase ,       beta_end=__UpperCamelCase )

					def  lowercase__   (						self       :  Union[str, Any] ):
												for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=__UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=__UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config(prediction_type="""v_prediction""" )
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

												lowerCamelCase_					=       self.dummy_model()
												lowerCamelCase_					=       self.dummy_sample_deter * scheduler.init_noise_sigma
												lowerCamelCase_					=       sample.to(__UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																			lowerCamelCase_					=       scheduler.scale_model_input(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

												lowerCamelCase_					=       torch.sum(torch.abs(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.mean(torch.abs(__UpperCamelCase ) )

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
																			assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 4.693_4286_5017_0972E-07 ) < 1E-2
																			assert abs(result_mean.item() - 0.0002 ) < 1E-3

					def  lowercase__   (						self       :  List[Any] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

												scheduler.set_timesteps(self.num_inference_steps )

												lowerCamelCase_					=       self.dummy_model()
												lowerCamelCase_					=       self.dummy_sample_deter * scheduler.init_noise_sigma
												lowerCamelCase_					=       sample.to(__UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																			lowerCamelCase_					=       scheduler.scale_model_input(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

												lowerCamelCase_					=       torch.sum(torch.abs(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.mean(torch.abs(__UpperCamelCase ) )

												if torch_device in ["cpu", "mps"]:
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3



					def  lowercase__   (						self       :  Optional[int] ):
												if torch_device == "mps":
																			return
												lowerCamelCase_					=       self.scheduler_classes[0]
												lowerCamelCase_					=       self.get_scheduler_config()
												lowerCamelCase_					=       scheduler_class(**__UpperCamelCase )

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

												lowerCamelCase_					=       self.dummy_model()
												lowerCamelCase_					=       self.dummy_sample_deter.to(__UpperCamelCase ) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																			lowerCamelCase_					=       scheduler.scale_model_input(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       model(__UpperCamelCase ,       __UpperCamelCase )

																			lowerCamelCase_					=       scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       output.prev_sample

												lowerCamelCase_					=       torch.sum(torch.abs(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.mean(torch.abs(__UpperCamelCase ) )

												if str(__UpperCamelCase ).startswith("""cpu""" ):
																			# The following sum varies between 148 and 156 on mps. Why?
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3
												else:
																			# CUDA
																			assert abs(result_sum.item() - 20.4125 ) < 1E-2
																			assert abs(result_mean.item() - 0.0266 ) < 1E-3





