



'''simple docstring'''






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

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







    def     lowercase_ (   self				:						Optional[int]		,    **__snake_case				:						Optional[Any]			):
      a		:      Optional[Any]       =  {
          'num_train_timesteps': 2_01,
          'sigma_min': 0.002,
          'sigma_max': 80.0,
      }

      config.update(**__snake_case			)
      return config







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      str       =  10

      a		:      List[str]       =  self.get_scheduler_config()
      a		:      Optional[Any]       =  self.scheduler_classes[0](**__snake_case			)

      scheduler.set_timesteps(__snake_case			)

      a		:      List[str]       =  scheduler.timesteps[0]
      a		:      Dict       =  scheduler.timesteps[1]

      a		:      Optional[int]       =  self.dummy_sample
      a		:      Tuple       =  0.1 * sample

      a		:      Any       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case			).prev_sample
      a		:      int       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case			).prev_sample

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







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







    def     lowercase_ (   self				:						Any			):
      for clip_denoised in [True, False]:
        self.check_over_configs(clip_denoised=__snake_case			)







    def     lowercase_ (   self				:						Dict			):
      a		:      int       =  self.scheduler_classes[0]
      a		:      Optional[int]       =  self.get_scheduler_config()
      a		:      Optional[int]       =  scheduler_class(**__snake_case			)

      a		:      Union[str, Any]       =  1
      scheduler.set_timesteps(__snake_case			)
      a		:      Dict       =  scheduler.timesteps

      a		:      Dict       =  torch.manual_seed(0			)

      a		:      Union[str, Any]       =  self.dummy_model()
      a		:      List[str]       =  self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(__snake_case			):
        # 1. scale model input
        a		:      Tuple       =  scheduler.scale_model_input(__snake_case		,    __snake_case			)

        # 2. predict noise residual
        a		:      Optional[int]       =  model(__snake_case		,    __snake_case			)

        # 3. predict previous sample x_t-1
        a		:      Any       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case		,    generator=__snake_case			).prev_sample

        a		:      Dict       =  pred_prev_sample

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

      assert abs(result_sum.item() - 192.7614			) < 1e-2
      assert abs(result_mean.item() - 0.2510			) < 1e-3







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

      a		:      int       =  [1_06, 0]
      scheduler.set_timesteps(timesteps=__snake_case			)
      a		:      Optional[Any]       =  scheduler.timesteps

      a		:      str       =  torch.manual_seed(0			)

      a		:      Union[str, Any]       =  self.dummy_model()
      a		:      str       =  self.dummy_sample_deter * scheduler.init_noise_sigma

      for t in timesteps:
        # 1. scale model input
        a		:      str       =  scheduler.scale_model_input(__snake_case		,    __snake_case			)

        # 2. predict noise residual
        a		:      Dict       =  model(__snake_case		,    __snake_case			)

        # 3. predict previous sample x_t-1
        a		:      str       =  scheduler.step(__snake_case		,    __snake_case		,    __snake_case		,    generator=__snake_case			).prev_sample

        a		:      Tuple       =  pred_prev_sample

      a		:      str       =  torch.sum(torch.abs(__snake_case			)			)
      a		:      Tuple       =  torch.mean(torch.abs(__snake_case			)			)

      assert abs(result_sum.item() - 347.6357			) < 1e-2
      assert abs(result_mean.item() - 0.4527			) < 1e-3







    def     lowercase_ (   self				:						List[Any]			):
      a		:      List[str]       =  self.scheduler_classes[0]
      a		:      Optional[int]       =  self.get_scheduler_config()
      a		:      Any       =  scheduler_class(**__snake_case			)

      a		:      Optional[int]       =  [39, 30, 12, 15, 0]

      with self.assertRaises(__snake_case		,    msg='`timesteps` must be in descending order.'			):
        scheduler.set_timesteps(timesteps=__snake_case			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      List[Any]       =  self.scheduler_classes[0]
      a		:      List[str]       =  self.get_scheduler_config()
      a		:      Optional[Any]       =  scheduler_class(**__snake_case			)

      a		:      Union[str, Any]       =  [39, 30, 12, 1, 0]
      a		:      Optional[Any]       =  len(__snake_case			)

      with self.assertRaises(__snake_case		,    msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
        scheduler.set_timesteps(num_inference_steps=__snake_case		,    timesteps=__snake_case			)







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

      a		:      Dict       =  [scheduler.config.num_train_timesteps]

      with self.assertRaises(
          __snake_case		,    msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'		,    ):
        scheduler.set_timesteps(timesteps=__snake_case			)




'''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()