
'''simple docstring'''






import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		(DPMSolverSDEScheduler,)
    lowerCAmelCase_				    =		10



    def   lowerCamelCase_  (	self      :       str,**__A      :       List[Any]     ):
          _lowerCamelCase				:   Optional[int]								=   {
              "num_train_timesteps": 1_1_0_0,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "noise_sampler_seed": 0,
          }

          config.update(**__A     )
          return config



    def   lowerCamelCase_  (	self      :       List[str]     ):
          for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__A     )



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          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   lowerCamelCase_  (	self      :       Tuple     ):
          for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=__A     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   str								=   self.scheduler_classes[0]
          _lowerCamelCase				:   str								=   self.get_scheduler_config()
          _lowerCamelCase				:   List[Any]								=   scheduler_class(**__A     )

          scheduler.set_timesteps(self.num_inference_steps     )

          _lowerCamelCase				:   Tuple								=   self.dummy_model()
          _lowerCamelCase				:   List[str]								=   self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase				:   Dict								=   sample.to(__A     )

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

                _lowerCamelCase				:   Tuple								=   model(__A,__A     )

                _lowerCamelCase				:   Tuple								=   scheduler.step(__A,__A,__A     )
                _lowerCamelCase				:   List[str]								=   output.prev_sample

          _lowerCamelCase				:   List[Any]								=   torch.sum(torch.abs(__A     )     )
          _lowerCamelCase				:   str								=   torch.mean(torch.abs(__A     )     )

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 167.47821044921875     ) < 1e-2
                assert abs(result_mean.item() - 0.2178705964565277     ) < 1e-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 171.59352111816406     ) < 1e-2
                assert abs(result_mean.item() - 0.22342906892299652     ) < 1e-3
          else:
                assert abs(result_sum.item() - 162.52383422851562     ) < 1e-2
                assert abs(result_mean.item() - 0.211619570851326     ) < 1e-3



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Any								=   self.scheduler_classes[0]
          _lowerCamelCase				:   Tuple								=   self.get_scheduler_config(prediction_type="v_prediction"     )
          _lowerCamelCase				:   Union[str, Any]								=   scheduler_class(**__A     )

          scheduler.set_timesteps(self.num_inference_steps     )

          _lowerCamelCase				:   Optional[int]								=   self.dummy_model()
          _lowerCamelCase				:   Union[str, Any]								=   self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase				:   Tuple								=   sample.to(__A     )

          for i, t in enumerate(scheduler.timesteps     ):
                _lowerCamelCase				:   str								=   scheduler.scale_model_input(__A,__A     )

                _lowerCamelCase				:   Dict								=   model(__A,__A     )

                _lowerCamelCase				:   Dict								=   scheduler.step(__A,__A,__A     )
                _lowerCamelCase				:   Optional[Any]								=   output.prev_sample

          _lowerCamelCase				:   Dict								=   torch.sum(torch.abs(__A     )     )
          _lowerCamelCase				:   List[Any]								=   torch.mean(torch.abs(__A     )     )

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 124.77149200439453     ) < 1e-2
                assert abs(result_mean.item() - 0.16226289014816284     ) < 1e-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 128.1663360595703     ) < 1e-2
                assert abs(result_mean.item() - 0.16688326001167297     ) < 1e-3
          else:
                assert abs(result_sum.item() - 119.8487548828125     ) < 1e-2
                assert abs(result_mean.item() - 0.1560530662536621     ) < 1e-3



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Optional[Any]								=   self.scheduler_classes[0]
          _lowerCamelCase				:   Union[str, Any]								=   self.get_scheduler_config()
          _lowerCamelCase				:   Any								=   scheduler_class(**__A     )

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

          _lowerCamelCase				:   Any								=   self.dummy_model()
          _lowerCamelCase				:   int								=   self.dummy_sample_deter.to(__A     ) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                _lowerCamelCase				:   Dict								=   scheduler.scale_model_input(__A,__A     )

                _lowerCamelCase				:   Dict								=   model(__A,__A     )

                _lowerCamelCase				:   int								=   scheduler.step(__A,__A,__A     )
                _lowerCamelCase				:   str								=   output.prev_sample

          _lowerCamelCase				:   int								=   torch.sum(torch.abs(__A     )     )
          _lowerCamelCase				:   str								=   torch.mean(torch.abs(__A     )     )

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 167.46957397460938     ) < 1e-2
                assert abs(result_mean.item() - 0.21805934607982635     ) < 1e-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 171.59353637695312     ) < 1e-2
                assert abs(result_mean.item() - 0.22342908382415771     ) < 1e-3
          else:
                assert abs(result_sum.item() - 162.52383422851562     ) < 1e-2
                assert abs(result_mean.item() - 0.211619570851326     ) < 1e-3



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   Dict								=   self.scheduler_classes[0]
          _lowerCamelCase				:   str								=   self.get_scheduler_config()
          _lowerCamelCase				:   List[str]								=   scheduler_class(**__A,use_karras_sigmas=__A     )

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

          _lowerCamelCase				:   Any								=   self.dummy_model()
          _lowerCamelCase				:   Optional[Any]								=   self.dummy_sample_deter.to(__A     ) * scheduler.init_noise_sigma
          _lowerCamelCase				:   Optional[Any]								=   sample.to(__A     )

          for t in scheduler.timesteps:
                _lowerCamelCase				:   str								=   scheduler.scale_model_input(__A,__A     )

                _lowerCamelCase				:   Tuple								=   model(__A,__A     )

                _lowerCamelCase				:   Union[str, Any]								=   scheduler.step(__A,__A,__A     )
                _lowerCamelCase				:   Tuple								=   output.prev_sample

          _lowerCamelCase				:   Optional[Any]								=   torch.sum(torch.abs(__A     )     )
          _lowerCamelCase				:   List[str]								=   torch.mean(torch.abs(__A     )     )

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 176.66974135742188     ) < 1e-2
                assert abs(result_mean.item() - 0.23003872730981811     ) < 1e-2
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 177.63653564453125     ) < 1e-2
                assert abs(result_mean.item() - 0.23003872730981811     ) < 1e-2
          else:
                assert abs(result_sum.item() - 170.3135223388672     ) < 1e-2
                assert abs(result_mean.item() - 0.23003872730981811     ) < 1e-2

'''simple docstring'''






def 		A_       (	_lowerCAmelCase   :    float							):



      """simple docstring"""




      return 10 - x * x







def 		A_       (	_lowerCAmelCase   :    float				,	_lowerCAmelCase   :    float							):



      """simple docstring"""




      if equation(_lowerCAmelCase							) * equation(_lowerCAmelCase							) >= 0:
            raise ValueError("Wrong space!"							)

      _lowerCamelCase				:   List[str]								=   a
      while (b - a) >= 0.0_1:
            # Find middle point
            _lowerCamelCase				:   Union[str, Any]								=   (a + b) / 2
            # Check if middle point is root
            if equation(_lowerCAmelCase							) == 0.0:
                  break
            # Decide the side to repeat the steps
            if equation(_lowerCAmelCase							) * equation(_lowerCAmelCase							) < 0:
                  _lowerCamelCase				:   Union[str, Any]								=   c
            else:
                  _lowerCamelCase				:   Any								=   c
      return c


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 print(bisection(-2, 5))
 print(bisection(0, 6))