






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class  _lowercase   (   _A						):
      _a							:						Optional[Any]										=							(EulerDiscreteScheduler,)
      _a							:						Tuple										=							10
      def 						lowercase__					(     self				,			**a   ):
       snake_case__  :    Dict							       ={
           """num_train_timesteps""": 1_1_0_0,
           """beta_start""": 0.0001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
       }

       config.update(**a   )
       return config
      def 						lowercase__					(     self   ):
       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=a   )
      def 						lowercase__					(     self   ):
       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 						lowercase__					(     self   ):
       for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=a   )
      def 						lowercase__					(     self   ):
       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.scheduler_classes[0]
       snake_case__  :    Tuple							       =self.get_scheduler_config()
       snake_case__  :    Optional[int]							       =scheduler_class(**a   )

       scheduler.set_timesteps(self.num_inference_steps   )

       snake_case__  :    Optional[Any]							       =torch.manual_seed(0   )

       snake_case__  :    Tuple							       =self.dummy_model()
       snake_case__  :    str							       =self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case__  :    List[Any]							       =sample.to(a   )

       for i, t in enumerate(scheduler.timesteps   ):
        snake_case__  :    Optional[Any]							       =scheduler.scale_model_input(a				,			a   )

        snake_case__  :    int							       =model(a				,			a   )

        snake_case__  :    Any							       =scheduler.step(a				,			a				,			a				,			generator=a   )
        snake_case__  :    int							       =output.prev_sample

       snake_case__  :    Any							       =torch.sum(torch.abs(a   )   )
       snake_case__  :    Any							       =torch.mean(torch.abs(a   )   )

       assert abs(result_sum.item() - 10.0807   ) < 1e-2
       assert abs(result_mean.item() - 0.0131   ) < 1e-3
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.scheduler_classes[0]
       snake_case__  :    Union[str, Any]							       =self.get_scheduler_config(prediction_type="""v_prediction"""   )
       snake_case__  :    int							       =scheduler_class(**a   )

       scheduler.set_timesteps(self.num_inference_steps   )

       snake_case__  :    List[str]							       =torch.manual_seed(0   )

       snake_case__  :    int							       =self.dummy_model()
       snake_case__  :    List[Any]							       =self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case__  :    int							       =sample.to(a   )

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

        snake_case__  :    Any							       =model(a				,			a   )

        snake_case__  :    Union[str, Any]							       =scheduler.step(a				,			a				,			a				,			generator=a   )
        snake_case__  :    Optional[Any]							       =output.prev_sample

       snake_case__  :    List[Any]							       =torch.sum(torch.abs(a   )   )
       snake_case__  :    Tuple							       =torch.mean(torch.abs(a   )   )

       assert abs(result_sum.item() - 0.0002   ) < 1e-2
       assert abs(result_mean.item() - 2.26_76e-06   ) < 1e-3
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.scheduler_classes[0]
       snake_case__  :    Optional[int]							       =self.get_scheduler_config()
       snake_case__  :    int							       =scheduler_class(**a   )

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

       snake_case__  :    Optional[int]							       =torch.manual_seed(0   )

       snake_case__  :    Tuple							       =self.dummy_model()
       snake_case__  :    str							       =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       snake_case__  :    Union[str, Any]							       =sample.to(a   )

       for t in scheduler.timesteps:
        snake_case__  :    int							       =scheduler.scale_model_input(a				,			a   )

        snake_case__  :    Any							       =model(a				,			a   )

        snake_case__  :    str							       =scheduler.step(a				,			a				,			a				,			generator=a   )
        snake_case__  :    Optional[Any]							       =output.prev_sample

       snake_case__  :    Dict							       =torch.sum(torch.abs(a   )   )
       snake_case__  :    Optional[int]							       =torch.mean(torch.abs(a   )   )

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



      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =self.scheduler_classes[0]
       snake_case__  :    Optional[int]							       =self.get_scheduler_config()
       snake_case__  :    List[str]							       =scheduler_class(**a				,			use_karras_sigmas=a   )

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

       snake_case__  :    Tuple							       =torch.manual_seed(0   )

       snake_case__  :    Dict							       =self.dummy_model()
       snake_case__  :    Optional[int]							       =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       snake_case__  :    List[str]							       =sample.to(a   )

       for t in scheduler.timesteps:
        snake_case__  :    List[str]							       =scheduler.scale_model_input(a				,			a   )

        snake_case__  :    Any							       =model(a				,			a   )

        snake_case__  :    str							       =scheduler.step(a				,			a				,			a				,			generator=a   )
        snake_case__  :    Optional[Any]							       =output.prev_sample

       snake_case__  :    Optional[int]							       =torch.sum(torch.abs(a   )   )
       snake_case__  :    Union[str, Any]							       =torch.mean(torch.abs(a   )   )

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













def 			A__							(      _a  :					Optional[Any]		,  _a  :					Tuple		,  _a  :					List[str]=False	):





 '''simple docstring'''







 if isinstance(_a		,  _a	) and isinstance(_a		,  _a	):
  snake_case__  :    int							       =len(set_a.intersection(_a	)	)

  if alternative_union:
   snake_case__  :    int							       =len(_a	) + len(_a	)
  else:
   snake_case__  :    Tuple							       =len(set_a.union(_a	)	)

  return intersection / union

 if isinstance(_a		,  (list, tuple)	) and isinstance(_a		,  (list, tuple)	):
  snake_case__  :    Optional[int]							       =[element for element in set_a if element in set_b]

  if alternative_union:
   snake_case__  :    Optional[Any]							       =len(_a	) + len(_a	)
   return len(_a	) / union
  else:
   snake_case__  :    Dict							       =set_a + [element for element in set_b if element not in set_a]
   return len(_a	) / len(_a	)

  return len(_a	) / len(_a	)
 return None


if __name__ == "__main__":
       __lowerCamelCase	:		List[Any]										=   {"""a""", """b""", """c""", """d""", """e"""}
       __lowerCamelCase	:		Optional[int]										=   {"""c""", """d""", """e""", """f""", """h""", """i"""}
       print(jaccard_similarity(set_a, set_b))





