'''simple docstring'''

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						(HeunDiscreteScheduler,)
   __UpperCAmelCase           =						10



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :List[str]       ={
            """num_train_timesteps""": 1_100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**lowerCAmelCase__     )
        return config



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]					,      [0.0002, 0.002, 0.02]     ):
             self.check_over_configs(beta_start=lowerCAmelCase__					,      beta_end=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





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



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        for clip_sample_range in [1.0, 2.0, 3.0]:
             self.check_over_configs(clip_sample_range=lowerCAmelCase__					,      clip_sample=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





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



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =self.scheduler_classes[0]
        _UpperCamelCase  :List[Any]       =self.get_scheduler_config()
        _UpperCamelCase  :Tuple       =scheduler_class(**lowerCAmelCase__     )

        scheduler.set_timesteps(self.num_inference_steps     )

        _UpperCamelCase  :Any       =self.dummy_model()
        _UpperCamelCase  :Dict       =self.dummy_sample_deter * scheduler.init_noise_sigma
        _UpperCamelCase  :List[Any]       =sample.to(lowerCAmelCase__     )

        for i, t in enumerate(scheduler.timesteps     ):
             _UpperCamelCase  :List[Any]       =scheduler.scale_model_input(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Union[str, Any]       =model(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :str       =scheduler.step(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
             _UpperCamelCase  :str       =output.prev_sample

        _UpperCamelCase  :Any       =torch.sum(torch.abs(lowerCAmelCase__     )     )
        _UpperCamelCase  :int       =torch.mean(torch.abs(lowerCAmelCase__     )     )

        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  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =self.scheduler_classes[0]
        _UpperCamelCase  :Any       =self.get_scheduler_config(prediction_type="""v_prediction"""     )
        _UpperCamelCase  :Union[str, Any]       =scheduler_class(**lowerCAmelCase__     )

        scheduler.set_timesteps(self.num_inference_steps     )

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

        for i, t in enumerate(scheduler.timesteps     ):
             _UpperCamelCase  :Optional[int]       =scheduler.scale_model_input(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[Any]       =model(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Dict       =scheduler.step(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
             _UpperCamelCase  :str       =output.prev_sample

        _UpperCamelCase  :Dict       =torch.sum(torch.abs(lowerCAmelCase__     )     )
        _UpperCamelCase  :int       =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() - 0.0002     ) < 1e-3



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =self.scheduler_classes[0]
        _UpperCamelCase  :Optional[Any]       =self.get_scheduler_config()
        _UpperCamelCase  :str       =scheduler_class(**lowerCAmelCase__     )

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

        _UpperCamelCase  :int       =self.dummy_model()
        _UpperCamelCase  :int       =self.dummy_sample_deter.to(lowerCAmelCase__     ) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
             _UpperCamelCase  :Tuple       =scheduler.scale_model_input(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[int]       =model(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[int]       =scheduler.step(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
             _UpperCamelCase  :List[str]       =output.prev_sample

        _UpperCamelCase  :Optional[int]       =torch.sum(torch.abs(lowerCAmelCase__     )     )
        _UpperCamelCase  :Any       =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() - 0.1233     ) < 1e-2
             assert abs(result_mean.item() - 0.0002     ) < 1e-3
        elif str(lowerCAmelCase__     ).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  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :int       =self.scheduler_classes[0]
        _UpperCamelCase  :Optional[Any]       =self.get_scheduler_config()
        _UpperCamelCase  :Optional[int]       =scheduler_class(**lowerCAmelCase__					,      use_karras_sigmas=lowerCAmelCase__     )

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

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

        for t in scheduler.timesteps:
             _UpperCamelCase  :int       =scheduler.scale_model_input(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :Optional[Any]       =model(lowerCAmelCase__					,      lowerCAmelCase__     )

             _UpperCamelCase  :int       =scheduler.step(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
             _UpperCamelCase  :Optional[Any]       =output.prev_sample

        _UpperCamelCase  :Tuple       =torch.sum(torch.abs(lowerCAmelCase__     )     )
        _UpperCamelCase  :Optional[Any]       =torch.mean(torch.abs(lowerCAmelCase__     )     )

        assert abs(result_sum.item() - 0.0_0015     ) < 1e-2
        assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95e-07     ) < 1e-2




import numpy as np
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Tuple					)->Union[str, Any]:
      _lowerCAmelCase      , _lowerCAmelCase			      =	a.shape
      _lowerCAmelCase			      =	min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	np.eye(_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	a.copy()

      for k in range(t - 1					):
            # select a column of modified matrix A':
            _lowerCAmelCase			      =	r[k:, [k]]
            # construct first basis vector
            _lowerCAmelCase			      =	np.zeros_like(_SCREAMING_SNAKE_CASE					)
            _lowerCAmelCase			      =	1.0
            # determine scaling factor
            _lowerCAmelCase			      =	np.linalg.norm(_SCREAMING_SNAKE_CASE					)
            # construct vector v for Householder reflection
            _lowerCAmelCase			      =	x + np.sign(x[0]					) * alpha * ea
            v /= np.linalg.norm(_SCREAMING_SNAKE_CASE					)

            # construct the Householder matrix
            _lowerCAmelCase			      =	np.eye(m - k					) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            _lowerCAmelCase			      =	np.block([[np.eye(_SCREAMING_SNAKE_CASE					), np.zeros((k, m - k)					)], [np.zeros((m - k, k)					), q_k]]					)

            _lowerCAmelCase			      =	q @ q_k.T
            _lowerCAmelCase			      =	q_k @ r

      return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()