
"""simple docstring"""
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			List[Any]	=       (HeunDiscreteScheduler,)
    __lowercase				:			Optional[Any]	=       1_0
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Union[str, Any]            = {
          "num_train_timesteps": 11_00,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
      }

      config.update(**snake_case_  )
      return config
    def        lowerCamelCase__						(     self  ):
      for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=snake_case_  )
    def        lowerCamelCase__						(     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=snake_case_    ,     beta_end=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for schedule in ["linear", "scaled_linear", "exp"]:
        self.check_over_configs(beta_schedule=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for clip_sample_range in [1.0, 2.0, 3.0]:
        self.check_over_configs(clip_sample_range=snake_case_    ,     clip_sample=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for prediction_type in ["epsilon", "v_prediction", "sample"]:
        self.check_over_configs(prediction_type=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.scheduler_classes[0]
      _snake_case  :    str            = self.get_scheduler_config()
      _snake_case  :    Dict            = scheduler_class(**snake_case_  )

      scheduler.set_timesteps(self.num_inference_steps  )

      _snake_case  :    List[str]            = self.dummy_model()
      _snake_case  :    str            = self.dummy_sample_deter * scheduler.init_noise_sigma
      _snake_case  :    Dict            = sample.to(snake_case_  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    List[str]            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Optional[Any]            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    Dict            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    str            = output.prev_sample

      _snake_case  :    Optional[Any]            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Tuple            = torch.mean(torch.abs(snake_case_  )  )

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
      else:
        # CUDA
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.scheduler_classes[0]
      _snake_case  :    Dict            = self.get_scheduler_config(prediction_type="v_prediction"  )
      _snake_case  :    int            = scheduler_class(**snake_case_  )

      scheduler.set_timesteps(self.num_inference_steps  )

      _snake_case  :    List[str]            = self.dummy_model()
      _snake_case  :    Tuple            = self.dummy_sample_deter * scheduler.init_noise_sigma
      _snake_case  :    Tuple            = sample.to(snake_case_  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    Tuple            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Union[str, Any]            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    Optional[Any]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    Any            = output.prev_sample

      _snake_case  :    int            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Dict            = torch.mean(torch.abs(snake_case_  )  )

      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() - 0.0002  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.scheduler_classes[0]
      _snake_case  :    int            = self.get_scheduler_config()
      _snake_case  :    Tuple            = scheduler_class(**snake_case_  )

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

      _snake_case  :    Dict            = self.dummy_model()
      _snake_case  :    Dict            = self.dummy_sample_deter.to(snake_case_  ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
        _snake_case  :    Optional[Any]            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Tuple            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    Dict            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    Union[str, Any]            = output.prev_sample

      _snake_case  :    Tuple            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    str            = torch.mean(torch.abs(snake_case_  )  )

      if str(snake_case_  ).startswith("cpu"  ):
        # The following sum varies between 148 and 156 on mps. Why?
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
      elif str(snake_case_  ).startswith("mps"  ):
        # Larger tolerance on mps
        assert abs(result_mean.item() - 0.0002  ) < 1E-2
      else:
        # CUDA
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = self.scheduler_classes[0]
      _snake_case  :    int            = self.get_scheduler_config()
      _snake_case  :    int            = scheduler_class(**snake_case_    ,     use_karras_sigmas=snake_case_  )

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

      _snake_case  :    int            = self.dummy_model()
      _snake_case  :    int            = self.dummy_sample_deter.to(snake_case_  ) * scheduler.init_noise_sigma
      _snake_case  :    Tuple            = sample.to(snake_case_  )

      for t in scheduler.timesteps:
        _snake_case  :    Any            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Union[str, Any]            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    List[str]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    List[str]            = output.prev_sample

      _snake_case  :    Dict            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    int            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 0.00015  ) < 1E-2
      assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07  ) < 1E-2





def 	_lowerCamelCase  (		A_  :	float							,  A_  :	float							,  A_  :	int      )			->			float:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=[1 / first_term]
   UpperCamelCase__ :			Tuple 						=1 / first_term
   for _ in range(number_of_terms - 1      ):
      first_term += common_difference
      arithmetic_progression.append(A_      )
   UpperCamelCase__ :			List[str] 						=[1 / step for step in arithmetic_progression]
   return sum(A_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))

