





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		(CMStochasticIterativeScheduler,)
   a						:							List[Any]   		=		10




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      {
           '''num_train_timesteps''': 201,
           '''sigma_min''': 0.002,
           '''sigma_max''': 80.0,
       }

       config.update(**__magic_name__							)
       return config




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






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      10

       UpperCamelCase__							:				Union[str, Any]          =      self.get_scheduler_config()
       UpperCamelCase__							:				Any          =      self.scheduler_classes[0](**__magic_name__							)

       scheduler.set_timesteps(__magic_name__							)

       UpperCamelCase__							:				int          =      scheduler.timesteps[0]
       UpperCamelCase__							:				Any          =      scheduler.timesteps[1]

       UpperCamelCase__							:				str          =      self.dummy_sample
       UpperCamelCase__							:				Union[str, Any]          =      0.1 * sample

       UpperCamelCase__							:				str          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__							).prev_sample
       UpperCamelCase__							:				str          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__							).prev_sample

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




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=__magic_name__							)




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






       """simple docstring"""
       for clip_denoised in [True, False]:
           self.check_over_configs(clip_denoised=__magic_name__							)




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






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      self.scheduler_classes[0]
       UpperCamelCase__							:				Optional[int]          =      self.get_scheduler_config()
       UpperCamelCase__							:				List[Any]          =      scheduler_class(**__magic_name__							)

       UpperCamelCase__							:				int          =      1
       scheduler.set_timesteps(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      scheduler.timesteps

       UpperCamelCase__							:				Tuple          =      torch.manual_seed(0							)

       UpperCamelCase__							:				Dict          =      self.dummy_model()
       UpperCamelCase__							:				Any          =      self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(__magic_name__							):
           # 1. scale model input
           UpperCamelCase__							:				Any          =      scheduler.scale_model_input(__magic_name__,		__magic_name__							)

           # 2. predict noise residual
           UpperCamelCase__							:				int          =      model(__magic_name__,		__magic_name__							)

           # 3. predict previous sample x_t-1
           UpperCamelCase__							:				Union[str, Any]          =      scheduler.step(__magic_name__,		__magic_name__,		__magic_name__,		generator=__magic_name__							).prev_sample

           UpperCamelCase__							:				Dict          =      pred_prev_sample

       UpperCamelCase__							:				Optional[int]          =      torch.sum(torch.abs(__magic_name__							)							)
       UpperCamelCase__							:				Optional[int]          =      torch.mean(torch.abs(__magic_name__							)							)

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




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






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.scheduler_classes[0]
       UpperCamelCase__							:				int          =      self.get_scheduler_config()
       UpperCamelCase__							:				Union[str, Any]          =      scheduler_class(**__magic_name__							)

       UpperCamelCase__							:				List[Any]          =      [106, 0]
       scheduler.set_timesteps(timesteps=__magic_name__							)
       UpperCamelCase__							:				Dict          =      scheduler.timesteps

       UpperCamelCase__							:				int          =      torch.manual_seed(0							)

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

       for t in timesteps:
           # 1. scale model input
           UpperCamelCase__							:				int          =      scheduler.scale_model_input(__magic_name__,		__magic_name__							)

           # 2. predict noise residual
           UpperCamelCase__							:				List[Any]          =      model(__magic_name__,		__magic_name__							)

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

           UpperCamelCase__							:				Any          =      pred_prev_sample

       UpperCamelCase__							:				Dict          =      torch.sum(torch.abs(__magic_name__							)							)
       UpperCamelCase__							:				Tuple          =      torch.mean(torch.abs(__magic_name__							)							)

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




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      self.scheduler_classes[0]
       UpperCamelCase__							:				Optional[Any]          =      self.get_scheduler_config()
       UpperCamelCase__							:				Tuple          =      scheduler_class(**__magic_name__							)

       UpperCamelCase__							:				List[str]          =      [39, 30, 12, 15, 0]

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




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






       """simple docstring"""
       UpperCamelCase__							:				Any          =      self.scheduler_classes[0]
       UpperCamelCase__							:				Tuple          =      self.get_scheduler_config()
       UpperCamelCase__							:				List[Any]          =      scheduler_class(**__magic_name__							)

       UpperCamelCase__							:				int          =      [39, 30, 12, 1, 0]
       UpperCamelCase__							:				Optional[Any]          =      len(__magic_name__							)

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




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.scheduler_classes[0]
       UpperCamelCase__							:				Optional[int]          =      self.get_scheduler_config()
       UpperCamelCase__							:				Dict          =      scheduler_class(**__magic_name__							)

       UpperCamelCase__							:				str          =      [scheduler.config.num_train_timesteps]

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








import random




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			Optional[int]      )      ->	tuple:
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      [], [], []
    for element in data:
        if element < pivot:
            less.append(__UpperCAmelCase      )
        elif element > pivot:
            greater.append(__UpperCAmelCase      )
        else:
            equal.append(__UpperCAmelCase      )
    return less, equal, greater





def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			int      )      ->	List[str]:
    # index = len(items) // 2 when trying to find the median
    #   (value of index when items is sorted)

    # invalid input
    if index >= len(__UpperCAmelCase      ) or index < 0:
        return None

    UpperCamelCase__							:				List[str]          =      items[random.randint(0		,   len(__UpperCAmelCase      ) - 1      )]
    UpperCamelCase__							:				List[Any]          =      0
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				int          =      _partition(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      len(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      len(__UpperCAmelCase      )

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(__UpperCAmelCase		,   __UpperCAmelCase      )
    # must be in larger
    else:
        return quick_select(__UpperCAmelCase		,   index - (m + count)      )

