




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						(EulerDiscreteScheduler,)
 UpperCamelCase						       =						10

 def     a__			(    self :  List[Any]						,							**A_ :  Optional[Any]  )       ->     Any:

       """simple docstring"""






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

       config.update(**A_  )
       return config

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

       """simple docstring"""






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

 def     a__			(    self :  int  )       ->     Any:

       """simple docstring"""






       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=A_						,							beta_end=A_  )

 def     a__			(    self :  Any  )       ->     List[Any]:

       """simple docstring"""






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

 def     a__			(    self :  Any  )       ->     str:

       """simple docstring"""






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

 def     a__			(    self :  Tuple  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config()
       lowerCamelCase_							=   scheduler_class(**A_  )

       scheduler.set_timesteps(self.num_inference_steps  )

       lowerCamelCase_							=   torch.manual_seed(0  )

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

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

             lowerCamelCase_							=   model(A_						,							A_  )

             lowerCamelCase_							=   scheduler.step(A_						,							A_						,							A_						,							generator=A_  )
             lowerCamelCase_							=   output.prev_sample

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

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

 def     a__			(    self :  List[Any]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config(prediction_type='v_prediction'  )
       lowerCamelCase_							=   scheduler_class(**A_  )

       scheduler.set_timesteps(self.num_inference_steps  )

       lowerCamelCase_							=   torch.manual_seed(0  )

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

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

             lowerCamelCase_							=   model(A_						,							A_  )

             lowerCamelCase_							=   scheduler.step(A_						,							A_						,							A_						,							generator=A_  )
             lowerCamelCase_							=   output.prev_sample

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

       assert abs(result_sum.item() - 0.0002  ) < 1E-2
       assert abs(result_mean.item() - 2.2_676E-06  ) < 1E-3

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

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config()
       lowerCamelCase_							=   scheduler_class(**A_  )

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

       lowerCamelCase_							=   torch.manual_seed(0  )

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

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

             lowerCamelCase_							=   model(A_						,							A_  )

             lowerCamelCase_							=   scheduler.step(A_						,							A_						,							A_						,							generator=A_  )
             lowerCamelCase_							=   output.prev_sample

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

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





 def     a__			(    self :  Tuple  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config()
       lowerCamelCase_							=   scheduler_class(**A_						,							use_karras_sigmas=A_  )

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

       lowerCamelCase_							=   torch.manual_seed(0  )

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

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

             lowerCamelCase_							=   model(A_						,							A_  )

             lowerCamelCase_							=   scheduler.step(A_						,							A_						,							A_						,							generator=A_  )
             lowerCamelCase_							=   output.prev_sample

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

       assert abs(result_sum.item() - 124.52299499511719  ) < 1E-2
       assert abs(result_mean.item() - 0.16213932633399963  ) < 1E-3









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
 from .autoencoder_kl import AutoencoderKL
 from .controlnet import ControlNetModel
 from .dual_transformer_ad import DualTransformeraDModel
 from .modeling_utils import ModelMixin
 from .prior_transformer import PriorTransformer
 from .ta_film_transformer import TaFilmDecoder
 from .transformer_ad import TransformeraDModel
 from .unet_ad import UNetaDModel
 from .unet_ad import UNetaDModel
 from .unet_ad_condition import UNetaDConditionModel
 from .unet_ad_condition import UNetaDConditionModel
 from .vq_model import VQModel

if is_flax_available():
 from .controlnet_flax import FlaxControlNetModel
 from .unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .vae_flax import FlaxAutoencoderKL



