
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Any    	=     (KDPMaDiscreteScheduler,)
      _A						:		Dict    	=     10
      def A_			(			self		,	**lowerCamelCase ):
          snake_case__						=				{
              "num_train_timesteps": 11_00,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
          }

          config.update(**lowerCamelCase )
          return config
      def A_			(			self ):
          for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCamelCase )
      def A_			(			self ):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,	[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
              self.check_over_configs(beta_start=lowerCamelCase		,	beta_end=lowerCamelCase )
      def A_			(			self ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=lowerCamelCase )
      def A_			(			self ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config(prediction_type="v_prediction" )
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

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

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

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

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

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

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
              assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
              assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3
      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

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

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

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

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

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

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 20.41_25 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 20.41_25 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



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

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

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

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

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

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

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

          if str(lowerCamelCase ).startswith("cpu" ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 20.41_25 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 20.41_25 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



