
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							A__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					str					 =    (HeunDiscreteScheduler,)
      _UpperCAmelCase       :					int					 =    10
      def 		UpperCamelCase__							(   self					,						**__magic_name__   ):
       lowerCamelCase	:			Dict           =   {
           """num_train_timesteps""": 1_1_0_0,
           """beta_start""": 0.0_001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
       }

       config.update(**__magic_name__   )
       return config
      def 		UpperCamelCase__							(   self   ):
       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]					,						[0.0_002, 0.002, 0.02]   ):
        self.check_over_configs(beta_start=__magic_name__					,						beta_end=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       for schedule in ["linear", "scaled_linear", "exp"]:
        self.check_over_configs(beta_schedule=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       for clip_sample_range in [1.0, 2.0, 3.0]:
        self.check_over_configs(clip_sample_range=__magic_name__					,						clip_sample=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       for prediction_type in ["epsilon", "v_prediction", "sample"]:
        self.check_over_configs(prediction_type=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[str]           =   self.scheduler_classes[0]
       lowerCamelCase	:			Tuple           =   self.get_scheduler_config()
       lowerCamelCase	:			Dict           =   scheduler_class(**__magic_name__   )

       scheduler.set_timesteps(self.num_inference_steps   )

       lowerCamelCase	:			Dict           =   self.dummy_model()
       lowerCamelCase	:			int           =   self.dummy_sample_deter * scheduler.init_noise_sigma
       lowerCamelCase	:			List[str]           =   sample.to(__magic_name__   )

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

        lowerCamelCase	:			List[str]           =   model(__magic_name__					,						__magic_name__   )

        lowerCamelCase	:			Any           =   scheduler.step(__magic_name__					,						__magic_name__					,						__magic_name__   )
        lowerCamelCase	:			Any           =   output.prev_sample

       lowerCamelCase	:			int           =   torch.sum(torch.abs(__magic_name__   )   )
       lowerCamelCase	:			Optional[int]           =   torch.mean(torch.abs(__magic_name__   )   )

       if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 0.1_233   ) < 1e-2
        assert abs(result_mean.item() - 0.0_002   ) < 1e-3
       else:
        # CUDA
        assert abs(result_sum.item() - 0.1_233   ) < 1e-2
        assert abs(result_mean.item() - 0.0_002   ) < 1e-3
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Any           =   self.scheduler_classes[0]
       lowerCamelCase	:			List[str]           =   self.get_scheduler_config(prediction_type="""v_prediction"""   )
       lowerCamelCase	:			Optional[int]           =   scheduler_class(**__magic_name__   )

       scheduler.set_timesteps(self.num_inference_steps   )

       lowerCamelCase	:			List[Any]           =   self.dummy_model()
       lowerCamelCase	:			int           =   self.dummy_sample_deter * scheduler.init_noise_sigma
       lowerCamelCase	:			Union[str, Any]           =   sample.to(__magic_name__   )

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

        lowerCamelCase	:			List[Any]           =   model(__magic_name__					,						__magic_name__   )

        lowerCamelCase	:			str           =   scheduler.step(__magic_name__					,						__magic_name__					,						__magic_name__   )
        lowerCamelCase	:			Optional[int]           =   output.prev_sample

       lowerCamelCase	:			Optional[Any]           =   torch.sum(torch.abs(__magic_name__   )   )
       lowerCamelCase	:			Tuple           =   torch.mean(torch.abs(__magic_name__   )   )

       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() - 0.0_002   ) < 1e-3
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Any           =   self.scheduler_classes[0]
       lowerCamelCase	:			Union[str, Any]           =   self.get_scheduler_config()
       lowerCamelCase	:			Dict           =   scheduler_class(**__magic_name__   )

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

       lowerCamelCase	:			int           =   self.dummy_model()
       lowerCamelCase	:			Optional[int]           =   self.dummy_sample_deter.to(__magic_name__   ) * scheduler.init_noise_sigma

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

        lowerCamelCase	:			Optional[Any]           =   model(__magic_name__					,						__magic_name__   )

        lowerCamelCase	:			Optional[Any]           =   scheduler.step(__magic_name__					,						__magic_name__					,						__magic_name__   )
        lowerCamelCase	:			List[str]           =   output.prev_sample

       lowerCamelCase	:			Union[str, Any]           =   torch.sum(torch.abs(__magic_name__   )   )
       lowerCamelCase	:			Dict           =   torch.mean(torch.abs(__magic_name__   )   )

       if str(__magic_name__   ).startswith("""cpu"""   ):
        # The following sum varies between 148 and 156 on mps. Why?
        assert abs(result_sum.item() - 0.1_233   ) < 1e-2
        assert abs(result_mean.item() - 0.0_002   ) < 1e-3
       elif str(__magic_name__   ).startswith("""mps"""   ):
        # Larger tolerance on mps
        assert abs(result_mean.item() - 0.0_002   ) < 1e-2
       else:
        # CUDA
        assert abs(result_sum.item() - 0.1_233   ) < 1e-2
        assert abs(result_mean.item() - 0.0_002   ) < 1e-3






      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			int           =   self.scheduler_classes[0]
       lowerCamelCase	:			Union[str, Any]           =   self.get_scheduler_config()
       lowerCamelCase	:			int           =   scheduler_class(**__magic_name__					,						use_karras_sigmas=__magic_name__   )

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

       lowerCamelCase	:			int           =   self.dummy_model()
       lowerCamelCase	:			Union[str, Any]           =   self.dummy_sample_deter.to(__magic_name__   ) * scheduler.init_noise_sigma
       lowerCamelCase	:			str           =   sample.to(__magic_name__   )

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

        lowerCamelCase	:			Union[str, Any]           =   model(__magic_name__					,						__magic_name__   )

        lowerCamelCase	:			int           =   scheduler.step(__magic_name__					,						__magic_name__					,						__magic_name__   )
        lowerCamelCase	:			str           =   output.prev_sample

       lowerCamelCase	:			Tuple           =   torch.sum(torch.abs(__magic_name__   )   )
       lowerCamelCase	:			Union[str, Any]           =   torch.mean(torch.abs(__magic_name__   )   )

       assert abs(result_sum.item() - 0.00_015   ) < 1e-2
       assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07   ) < 1e-2






def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Optional[Any]           =   float()
 try:
  lowerCamelCase	:			Tuple           =   mass * acceleration
 except Exception:
  return -0.0
 return force


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # demo
      _lowerCamelCase           =12.5
      _lowerCamelCase           =1_0
      _lowerCamelCase           =newtons_second_law_of_motion(mass, acceleration)
      print("""The force is """, force, """N""")




