



'''simple docstring'''






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 							a__(      lowerCamelCase__			):
    lowercase__				=					(KDPMaDiscreteScheduler,)
    lowercase__				=					10







    def     lowercase_ (   self				:						int		,    **__snake_case				:						List[str]			):
      a		:      Any       =  {
          'num_train_timesteps': 11_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**__snake_case			)
      return config







    def     lowercase_ (   self				:						Dict			):
      for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=__snake_case			)







    def     lowercase_ (   self				:						str			):
      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     lowercase_ (   self				:						List[Any]			):
      for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=__snake_case			)







    def     lowercase_ (   self				:						str			):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__snake_case			)







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Tuple       =  self.scheduler_classes[0]
      a		:      List[str]       =  self.get_scheduler_config(prediction_type='v_prediction'			)
      a		:      Union[str, Any]       =  scheduler_class(**__snake_case			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a		:      List[Any]       =  self.dummy_model()
      a		:      str       =  self.dummy_sample_deter * scheduler.init_noise_sigma
      a		:      List[Any]       =  sample.to(__snake_case			)

      for i, t in enumerate(scheduler.timesteps			):
        a		:      int       =  scheduler.scale_model_input(__snake_case		,    __snake_case			)

        a		:      Dict       =  model(__snake_case		,    __snake_case			)

        a		:      List[Any]       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case			)
        a		:      Any       =  output.prev_sample

      a		:      int       =  torch.sum(torch.abs(__snake_case			)			)
      a		:      List[Any]       =  torch.mean(torch.abs(__snake_case			)			)

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 4.6_9_3_4e-0_7			) < 1e-2
        assert abs(result_mean.item() - 6.1_1_1_2e-1_0			) < 1e-3
      else:
        # CUDA
        assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7			) < 1e-2
        assert abs(result_mean.item() - 0.0002			) < 1e-3







    def     lowercase_ (   self				:						int			):
      if torch_device == "mps":
        return
      a		:      List[str]       =  self.scheduler_classes[0]
      a		:      Optional[Any]       =  self.get_scheduler_config()
      a		:      Union[str, Any]       =  scheduler_class(**__snake_case			)

      scheduler.set_timesteps(self.num_inference_steps			)

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

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

        a		:      str       =  model(__snake_case		,    __snake_case			)

        a		:      str       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case			)
        a		:      Dict       =  output.prev_sample

      a		:      Optional[Any]       =  torch.sum(torch.abs(__snake_case			)			)
      a		:      Optional[Any]       =  torch.mean(torch.abs(__snake_case			)			)

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 20.4125			) < 1e-2
        assert abs(result_mean.item() - 0.0266			) < 1e-3
      else:
        # CUDA
        assert abs(result_sum.item() - 20.4125			) < 1e-2
        assert abs(result_mean.item() - 0.0266			) < 1e-3







    def     lowercase_ (   self				:						Any			):
      if torch_device == "mps":
        return
      a		:      int       =  self.scheduler_classes[0]
      a		:      str       =  self.get_scheduler_config()
      a		:      str       =  scheduler_class(**__snake_case			)

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

      a		:      List[Any]       =  self.dummy_model()
      a		:      Tuple       =  self.dummy_sample_deter.to(__snake_case			) * scheduler.init_noise_sigma

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

        a		:      List[str]       =  model(__snake_case		,    __snake_case			)

        a		:      int       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case			)
        a		:      Optional[int]       =  output.prev_sample

      a		:      Tuple       =  torch.sum(torch.abs(__snake_case			)			)
      a		:      List[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() - 20.4125			) < 1e-2
        assert abs(result_mean.item() - 0.0266			) < 1e-3
      else:
        # CUDA
        assert abs(result_sum.item() - 20.4125			) < 1e-2
        assert abs(result_mean.item() - 0.0266			) < 1e-3




'''simple docstring'''






class 							a__:







    def __init__(   self				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						Any		,    __snake_case				:						Tuple			):
      a		:      List[str]       =  name
      a		:      Dict       =  value
      a		:      List[str]       =  weight







    def __repr__(   self				:						int			):
      return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""







    def     lowercase_ (   self				:						Optional[int]			):
      return self.value







    def     lowercase_ (   self				:						List[str]			):
      return self.name







    def     lowercase_ (   self				:						int			):
      return self.weight







    def     lowercase_ (   self				:						List[str]			):
      return self.value / self.weight
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[int]       =  []
  for i in range(len(_A       )       ):
    menu.append(Things(name[i]				,    value[i]				,    weight[i]       )       )
  return menu
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  sorted(_A				,    key=_A				,    reverse=_A       )
  a		:      Optional[int]       =  []
  a     ,			a		:      str       =  0.0, 0.0
  for i in range(len(_A       )       ):
    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    lowerCamelCase__ (		):
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()