
'''simple docstring'''







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Any   =					(DDIMParallelScheduler,)
  __UpperCAmelCase				:				Dict   =					(('''eta''', 0.0), ('''num_inference_steps''', 50))


  def   __lowercase		(		self      :     List[str]							,**_a      :     Tuple       ):


     '''simple docstring'''






     _a				:	Dict			=   {
         'num_train_timesteps': 1000,
         'beta_start': 0.0001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'clip_sample': True,
     }

     config.update(**_a       )
     return config


  def   __lowercase		(		self      :     Union[str, Any]							,**_a      :     int       ):


     '''simple docstring'''






     _a				:	Dict			=   self.scheduler_classes[0]
     _a				:	Union[str, Any]			=   self.get_scheduler_config(**_a       )
     _a				:	int			=   scheduler_class(**_a       )

     _a,  _a				:	List[Any]			=   10, 0.0

     _a				:	List[str]			=   self.dummy_model()
     _a				:	Optional[int]			=   self.dummy_sample_deter

     scheduler.set_timesteps(_a       )

     for t in scheduler.timesteps:
        _a				:	Tuple			=   model(_a							,_a       )
        _a				:	Optional[Any]			=   scheduler.step(_a							,_a							,_a							,_a       ).prev_sample

     return sample


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


     '''simple docstring'''






     for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=_a       )


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


     '''simple docstring'''






     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=_a       )

     _a				:	str			=   self.scheduler_classes[0]
     _a				:	List[str]			=   self.get_scheduler_config(steps_offset=1       )
     _a				:	int			=   scheduler_class(**_a       )
     scheduler.set_timesteps(5       )
     assert torch.equal(scheduler.timesteps							,torch.LongTensor([801, 601, 401, 201, 1]       )       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,[0.002, 0.02, 0.2, 2]       ):
        self.check_over_configs(beta_start=_a							,beta_end=_a       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=_a       )


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=_a       )


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


     '''simple docstring'''






     for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=_a       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=_a       )


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     self.check_over_configs(thresholding=_a       )
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=_a							,prediction_type=_a							,sample_max_value=_a							,)


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     for t in [1, 10, 49]:
        self.check_over_forward(time_step=_a       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     for t, num_inference_steps in zip([1, 10, 50]							,[10, 50, 500]       ):
        self.check_over_forward(time_step=_a							,num_inference_steps=_a       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     for t, eta in zip([1, 10, 49]							,[0.0, 0.5, 1.0]       ):
        self.check_over_forward(time_step=_a							,eta=_a       )


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






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

     assert torch.sum(torch.abs(scheduler._get_variance(0							,0       ) - 0.0       )       ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(420							,400       ) - 0.1_4771       )       ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(980							,960       ) - 0.3_2460       )       ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0							,0       ) - 0.0       )       ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487							,486       ) - 0.0_0979       )       ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999							,998       ) - 0.02       )       ) < 1E-5


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






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

     _a,  _a				:	int			=   10, 0.0
     scheduler.set_timesteps(_a       )

     _a				:	Union[str, Any]			=   self.dummy_model()
     _a				:	int			=   self.dummy_sample_deter
     _a				:	Tuple			=   self.dummy_sample_deter + 0.1
     _a				:	Any			=   self.dummy_sample_deter - 0.1

     _a				:	Optional[Any]			=   samplea.shape[0]
     _a				:	List[str]			=   torch.stack([samplea, samplea, samplea]							,dim=0       )
     _a				:	str			=   torch.arange(_a       )[0:3, None].repeat(1							,_a       )

     _a				:	Optional[Any]			=   model(samples.flatten(0							,1       )							,timesteps.flatten(0							,1       )       )
     _a				:	List[Any]			=   scheduler.batch_step_no_noise(_a							,timesteps.flatten(0							,1       )							,samples.flatten(0							,1       )							,_a       )

     _a				:	Union[str, Any]			=   torch.sum(torch.abs(_a       )       )
     _a				:	int			=   torch.mean(torch.abs(_a       )       )

     assert abs(result_sum.item() - 1147.7904       ) < 1E-2
     assert abs(result_mean.item() - 0.4982       ) < 1E-3


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	int			=   self.full_loop()

     _a				:	str			=   torch.sum(torch.abs(_a       )       )
     _a				:	List[str]			=   torch.mean(torch.abs(_a       )       )

     assert abs(result_sum.item() - 172.0067       ) < 1E-2
     assert abs(result_mean.item() - 0.22_3967       ) < 1E-3


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self.full_loop(prediction_type='v_prediction'       )

     _a				:	List[str]			=   torch.sum(torch.abs(_a       )       )
     _a				:	Any			=   torch.mean(torch.abs(_a       )       )

     assert abs(result_sum.item() - 52.5302       ) < 1E-2
     assert abs(result_mean.item() - 0.0684       ) < 1E-3


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   self.full_loop(set_alpha_to_one=_a							,beta_start=0.01       )
     _a				:	int			=   torch.sum(torch.abs(_a       )       )
     _a				:	List[Any]			=   torch.mean(torch.abs(_a       )       )

     assert abs(result_sum.item() - 149.8295       ) < 1E-2
     assert abs(result_mean.item() - 0.1951       ) < 1E-3


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Dict			=   self.full_loop(set_alpha_to_one=_a							,beta_start=0.01       )
     _a				:	List[Any]			=   torch.sum(torch.abs(_a       )       )
     _a				:	Tuple			=   torch.mean(torch.abs(_a       )       )

     assert abs(result_sum.item() - 149.0784       ) < 1E-2
     assert abs(result_mean.item() - 0.1941       ) < 1E-3





'''simple docstring'''







from __future__ import annotations

from random import choice





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   return choice(__a )





def 					UpperCAmelCase_		(__a      :    list[int] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Dict			=   random_pivot(__a )

   # partition based on pivot
   # linear time
   _a				:	Optional[int]			=   [e for e in lst if e < pivot]
   _a				:	List[str]			=   [e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__a ) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__a ) < k - 1:
      return kth_number(__a ,	k - len(__a ) - 1 )
   # pivot is in elements smaller than k
   else:
      return kth_number(__a ,	__a )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



