


'''simple docstring'''






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class    __snake_case       (   __a):







    """simple docstring"""



    lowercase    =					(UniPCMultistepScheduler,)
    lowercase    =					(('num_inference_steps', 25),)

    def __lowercase   (   self    :  Optional[int]    ,						**lowerCamelCase    :  Dict		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      int			=   {
          '''num_train_timesteps''': 10_00,
          '''beta_start''': 0.0_001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
          '''solver_order''': 2,
          '''solver_type''': '''bh2''',
      }

      config.update(**_A		)
      return config

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  str=0    ,						**lowerCamelCase    :  Tuple		)   ->  str:
      lowerCAmelCase_			:      Dict			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      str			=   kwargs.pop("""num_inference_steps"""    ,						_A		)
      lowerCAmelCase_			:      List[str]			=   self.dummy_sample
      lowerCAmelCase_			:      Dict			=   0.1 * sample
      lowerCAmelCase_			:      Union[str, Any]			=   [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Any			=   self.get_scheduler_config(**_A		)
        lowerCAmelCase_			:      str			=   scheduler_class(**_A		)
        scheduler.set_timesteps(_A		)
        # copy over dummy past residuals
        lowerCAmelCase_			:      str			=   dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(_A		)
          lowerCAmelCase_			:      Optional[Any]			=   scheduler_class.from_pretrained(_A		)
          new_scheduler.set_timesteps(_A		)
          # copy over dummy past residuals
          lowerCAmelCase_			:      Union[str, Any]			=   dummy_past_residuals[: new_scheduler.config.solver_order]

        lowerCAmelCase_			:      Dict			=   sample, sample
        for t in range(_A    ,						time_step + scheduler.config.solver_order + 1		):
          lowerCAmelCase_			:      int			=   scheduler.step(_A    ,						_A    ,						_A    ,						**_A		).prev_sample
          lowerCAmelCase_			:      int			=   new_scheduler.step(_A    ,						_A    ,						_A    ,						**_A		).prev_sample

          assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  List[str]=0    ,						**lowerCamelCase    :  Optional[int]		)   ->  List[Any]:
      lowerCAmelCase_			:      List[str]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      str			=   kwargs.pop("""num_inference_steps"""    ,						_A		)
      lowerCAmelCase_			:      List[Any]			=   self.dummy_sample
      lowerCAmelCase_			:      List[Any]			=   0.1 * sample
      lowerCAmelCase_			:      str			=   [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      List[Any]			=   self.get_scheduler_config()
        lowerCAmelCase_			:      int			=   scheduler_class(**_A		)
        scheduler.set_timesteps(_A		)

        # copy over dummy past residuals (must be after setting timesteps)
        lowerCAmelCase_			:      List[str]			=   dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(_A		)
          lowerCAmelCase_			:      Optional[int]			=   scheduler_class.from_pretrained(_A		)
          # copy over dummy past residuals
          new_scheduler.set_timesteps(_A		)

          # copy over dummy past residual (must be after setting timesteps)
          lowerCAmelCase_			:      Optional[int]			=   dummy_past_residuals[: new_scheduler.config.solver_order]

        lowerCAmelCase_			:      str			=   scheduler.step(_A    ,						_A    ,						_A    ,						**_A		).prev_sample
        lowerCAmelCase_			:      Dict			=   new_scheduler.step(_A    ,						_A    ,						_A    ,						**_A		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Union[str, Any]=None    ,						**lowerCamelCase    :  Optional[Any]		)   ->  Optional[int]:


      if scheduler is None:
        lowerCAmelCase_			:      Optional[Any]			=   self.scheduler_classes[0]
        lowerCAmelCase_			:      int			=   self.get_scheduler_config(**_A		)
        lowerCAmelCase_			:      Any			=   scheduler_class(**_A		)

      lowerCAmelCase_			:      Any			=   self.scheduler_classes[0]
      lowerCAmelCase_			:      Optional[int]			=   self.get_scheduler_config(**_A		)
      lowerCAmelCase_			:      Tuple			=   scheduler_class(**_A		)

      lowerCAmelCase_			:      Optional[int]			=   10
      lowerCAmelCase_			:      str			=   self.dummy_model()
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample_deter
      scheduler.set_timesteps(_A		)

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      List[Any]			=   model(_A    ,						_A		)
        lowerCAmelCase_			:      List[str]			=   scheduler.step(_A    ,						_A    ,						_A		).prev_sample

      return sample

    def __lowercase   (   self    :  List[Any]		)   ->  Tuple:
      lowerCAmelCase_			:      Optional[Any]			=   dict(self.forward_default_kwargs		)

      lowerCAmelCase_			:      Any			=   kwargs.pop("""num_inference_steps"""    ,						_A		)

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Any			=   self.get_scheduler_config()
        lowerCAmelCase_			:      Any			=   scheduler_class(**_A		)

        lowerCAmelCase_			:      Tuple			=   self.dummy_sample
        lowerCAmelCase_			:      Dict			=   0.1 * sample

        if num_inference_steps is not None and hasattr(_A    ,						"""set_timesteps"""		):
          scheduler.set_timesteps(_A		)
        elif num_inference_steps is not None and not hasattr(_A    ,						"""set_timesteps"""		):
          lowerCAmelCase_			:      List[str]			=   num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        lowerCAmelCase_			:      Optional[Any]			=   [residual + 0.2, residual + 0.15, residual + 0.10]
        lowerCAmelCase_			:      List[str]			=   dummy_past_residuals[: scheduler.config.solver_order]

        lowerCAmelCase_			:      Dict			=   scheduler.timesteps[5]
        lowerCAmelCase_			:      List[Any]			=   scheduler.timesteps[6]

        lowerCAmelCase_			:      Optional[int]			=   scheduler.step(_A    ,						_A    ,						_A    ,						**_A		).prev_sample
        lowerCAmelCase_			:      str			=   scheduler.step(_A    ,						_A    ,						_A    ,						**_A		).prev_sample

        self.assertEqual(output_a.shape    ,						sample.shape		)
        self.assertEqual(output_a.shape    ,						output_a.shape		)

    def __lowercase   (   self    :  List[Any]		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      Optional[int]			=   UniPCMultistepScheduler(**self.get_scheduler_config()		)
      lowerCAmelCase_			:      Tuple			=   self.full_loop(scheduler=_A		)
      lowerCAmelCase_			:      str			=   torch.mean(torch.abs(_A		)		)

      assert abs(result_mean.item() - 0.2_464		) < 1E-3

      lowerCAmelCase_			:      Tuple			=   DPMSolverSinglestepScheduler.from_config(scheduler.config		)
      lowerCAmelCase_			:      List[Any]			=   DEISMultistepScheduler.from_config(scheduler.config		)
      lowerCAmelCase_			:      List[Any]			=   DPMSolverMultistepScheduler.from_config(scheduler.config		)
      lowerCAmelCase_			:      int			=   UniPCMultistepScheduler.from_config(scheduler.config		)

      lowerCAmelCase_			:      int			=   self.full_loop(scheduler=_A		)
      lowerCAmelCase_			:      str			=   torch.mean(torch.abs(_A		)		)

      assert abs(result_mean.item() - 0.2_464		) < 1E-3

    def __lowercase   (   self    :  Any		)   ->  Tuple:


      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
        self.check_over_configs(num_train_timesteps=_A		)

    def __lowercase   (   self    :  int		)   ->  Tuple:
      self.check_over_configs(thresholding=_A		)
      for order in [1, 2, 3]:
        for solver_type in ["bh1", "bh2"]:
          for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  thresholding=_A    ,						prediction_type=_A    ,						sample_max_value=_A    ,						solver_order=_A    ,						solver_type=_A    ,						)

    def __lowercase   (   self    :  Any		)   ->  str:


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

    def __lowercase   (   self    :  List[Any]		)   ->  Union[str, Any]:


      for solver_type in ["bh1", "bh2"]:
        for order in [1, 2, 3]:
          for prediction_type in ["epsilon", "sample"]:
            self.check_over_configs(
                solver_order=_A    ,						solver_type=_A    ,						prediction_type=_A    ,						)
            lowerCAmelCase_			:      str			=   self.full_loop(
                solver_order=_A    ,						solver_type=_A    ,						prediction_type=_A    ,						)
            assert not torch.isnan(_A		).any(), "Samples have nan numbers"

    def __lowercase   (   self    :  Tuple		)   ->  Union[str, Any]:
      self.check_over_configs(lower_order_final=_A		)
      self.check_over_configs(lower_order_final=_A		)

    def __lowercase   (   self    :  Union[str, Any]		)   ->  Any:


      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
        self.check_over_forward(num_inference_steps=_A    ,						time_step=0		)

    def __lowercase   (   self    :  Optional[Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      List[str]			=   self.full_loop()
      lowerCAmelCase_			:      str			=   torch.mean(torch.abs(_A		)		)

      assert abs(result_mean.item() - 0.2_464		) < 1E-3

    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      lowerCAmelCase_			:      List[Any]			=   self.full_loop(prediction_type="""v_prediction"""		)
      lowerCAmelCase_			:      List[str]			=   torch.mean(torch.abs(_A		)		)

      assert abs(result_mean.item() - 0.1_014		) < 1E-3

    def __lowercase   (   self    :  List[Any]		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      Optional[int]			=   self.scheduler_classes[0]
      lowerCAmelCase_			:      Optional[int]			=   self.get_scheduler_config(thresholding=_A    ,						dynamic_thresholding_ratio=0		)
      lowerCAmelCase_			:      int			=   scheduler_class(**_A		)

      lowerCAmelCase_			:      str			=   10
      lowerCAmelCase_			:      Dict			=   self.dummy_model()
      lowerCAmelCase_			:      Dict			=   self.dummy_sample_deter.half()
      scheduler.set_timesteps(_A		)

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      str			=   model(_A    ,						_A		)
        lowerCAmelCase_			:      str			=   scheduler.step(_A    ,						_A    ,						_A		).prev_sample

      assert sample.dtype == torch.floataa





    def __lowercase   (   self    :  Optional[int]    ,						**lowerCamelCase    :  int		)   ->  Optional[int]:


      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Optional[int]			=   self.get_scheduler_config(**_A		)
        lowerCAmelCase_			:      Any			=   scheduler_class(**_A		)

        scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
        assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps







'''simple docstring'''
def 					a__       (					lowerCAmelCase__						)    ->	int:
    if a < 0:
        raise ValueError('''Input value must be a positive integer'''						)
    elif isinstance(lowerCAmelCase__  ,					lowerCAmelCase__						):
        raise TypeError('''Input value must be a \'int\' type'''						)
    return bin(lowerCAmelCase__						).count('''1'''						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
