

'''simple docstring'''







class 					A__      :



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      name
           A_						=      value
           A_						=      weight



    def __repr__(					self       )						->      Dict:





           '''simple docstring'''
           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           return self.value



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           return self.name



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           return self.weight







    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           return self.value / self.weight

def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Tuple:
       A_						=      []
       for i in range(len(UpperCAmelCase__				)				):
              menu.append(Things(name[i],       value[i],       weight[i]				)				)
       return menu
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[Any]:
       A_						=      sorted(UpperCAmelCase__,       key=UpperCAmelCase__,       reverse=UpperCAmelCase__				)
       A_						=      []
       A_					,			A_						=      0.0, 0.0
       for i in range(len(UpperCAmelCase__				)				):
              if (total_cost + items_copy[i].get_weight()) <= max_cost:
                     result.append(items_copy[i]				)
                     total_cost += items_copy[i].get_weight()
                     total_value += items_copy[i].get_value()
       return (result, total_value)
def       UpperCAmelCase__    (      )			->			Optional[int]:
       pass


if __name__ == "__main__":
      import doctest

      doctest.testmod()



'''simple docstring'''







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 					A__      (      _snake_case			):
    lowercase						=		(IPNDMScheduler,)
    lowercase						=		(("num_inference_steps", 50),)



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      {"""num_train_timesteps""": 1000}
           config.update(**UpperCamelCase__       )
           return config



    def 					snake_case_			(					self		,				UpperCamelCase__=0		,				**UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      dict(self.forward_default_kwargs       )
           A_						=      kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__       )
           A_						=      self.dummy_sample
           A_						=      0.1 * sample
           A_						=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  A_						=      self.get_scheduler_config(**UpperCamelCase__       )
                  A_						=      scheduler_class(**UpperCamelCase__       )
                  scheduler.set_timesteps(UpperCamelCase__       )
                  # copy over dummy past residuals
                  A_						=      dummy_past_residuals[:]

                  if time_step is None:
                         A_						=      scheduler.timesteps[len(scheduler.timesteps       ) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase__       )
                         A_						=      scheduler_class.from_pretrained(UpperCamelCase__       )
                         new_scheduler.set_timesteps(UpperCamelCase__       )
                         # copy over dummy past residuals
                         A_						=      dummy_past_residuals[:]

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

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

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

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



    def 					snake_case_			(					self       )						->      Optional[int]:





           '''simple docstring'''
           pass



    def 					snake_case_			(					self		,				UpperCamelCase__=0		,				**UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      dict(self.forward_default_kwargs       )
           A_						=      kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__       )
           A_						=      self.dummy_sample
           A_						=      0.1 * sample
           A_						=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                  A_						=      self.get_scheduler_config()
                  A_						=      scheduler_class(**UpperCamelCase__       )
                  scheduler.set_timesteps(UpperCamelCase__       )

                  # copy over dummy past residuals (must be after setting timesteps)
                  A_						=      dummy_past_residuals[:]

                  if time_step is None:
                         A_						=      scheduler.timesteps[len(scheduler.timesteps       ) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(UpperCamelCase__       )
                         A_						=      scheduler_class.from_pretrained(UpperCamelCase__       )
                         # copy over dummy past residuals
                         new_scheduler.set_timesteps(UpperCamelCase__       )

                         # copy over dummy past residual (must be after setting timesteps)
                         A_						=      dummy_past_residuals[:]

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

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

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      new_scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

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



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      Tuple:





           '''simple docstring'''
           A_						=      self.scheduler_classes[0]
           A_						=      self.get_scheduler_config(**UpperCamelCase__       )
           A_						=      scheduler_class(**UpperCamelCase__       )

           A_						=      10
           A_						=      self.dummy_model()
           A_						=      self.dummy_sample_deter
           scheduler.set_timesteps(UpperCamelCase__       )

           for i, t in enumerate(scheduler.timesteps       ):
                  A_						=      model(UpperCamelCase__		,				UpperCamelCase__       )
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ).prev_sample

           for i, t in enumerate(scheduler.timesteps       ):
                  A_						=      model(UpperCamelCase__		,				UpperCamelCase__       )
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       ).prev_sample

           return sample



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





           '''simple docstring'''
           A_						=      dict(self.forward_default_kwargs       )

           A_						=      kwargs.pop("""num_inference_steps"""		,				UpperCamelCase__       )

           for scheduler_class in self.scheduler_classes:
                  A_						=      self.get_scheduler_config()
                  A_						=      scheduler_class(**UpperCamelCase__       )

                  A_						=      self.dummy_sample
                  A_						=      0.1 * sample

                  if num_inference_steps is not None and hasattr(UpperCamelCase__		,				"""set_timesteps"""       ):
                         scheduler.set_timesteps(UpperCamelCase__       )
                  elif num_inference_steps is not None and not hasattr(UpperCamelCase__		,				"""set_timesteps"""       ):
                         A_						=      num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  A_						=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                  A_						=      dummy_past_residuals[:]

                  A_						=      scheduler.timesteps[5]
                  A_						=      scheduler.timesteps[6]

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

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

                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample
                  A_						=      scheduler.step(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       ).prev_sample

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



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           for timesteps in [100, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCamelCase__		,				time_step=UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           for t, num_inference_steps in zip([1, 5, 10]		,				[10, 50, 100]       ):
                  self.check_over_forward(num_inference_steps=UpperCamelCase__		,				time_step=UpperCamelCase__       )







    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      self.full_loop()
           A_						=      torch.mean(torch.abs(UpperCamelCase__       )       )

           assert abs(result_mean.item() - 2540529       ) < 10
