




'''simple docstring'''







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			_a  (	lowercase__ ):
       __a				:     Any   				=					(DDIMParallelScheduler,)
       __a				:     Dict   				=					(('''eta''', 0.0), ('''num_inference_steps''', 50))
       def     A      (	self						:	List[str]   ,				**lowercase						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		{
               'num_train_timesteps': 1_000,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
               'clip_sample': True,
           }

           config.update(**_a				)
           return config
       def     A      (	self						:	Union[str, Any]   ,				**lowercase						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.scheduler_classes[0]
           UpperCAmelCase			       =		self.get_scheduler_config(**_a				)
           UpperCAmelCase			       =		scheduler_class(**_a				)

           UpperCAmelCase			       =		10, 0.0

           UpperCAmelCase			       =		self.dummy_model()
           UpperCAmelCase			       =		self.dummy_sample_deter

           scheduler.set_timesteps(_a				)

           for t in scheduler.timesteps:
               UpperCAmelCase			       =		model(_a   ,				_a				)
               UpperCAmelCase			       =		scheduler.step(_a   ,				_a   ,				_a   ,				_a				).prev_sample

           return sample
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           for timesteps in [100, 500, 1_000]:
               self.check_over_configs(num_train_timesteps=_a				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

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

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



           '''simple docstring'''

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



           '''simple docstring'''

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



           '''simple docstring'''

           for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=_a				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

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



           '''simple docstring'''

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



           '''simple docstring'''

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



           '''simple docstring'''

           UpperCAmelCase			       =		self.scheduler_classes[0]
           UpperCAmelCase			       =		self.get_scheduler_config()
           UpperCAmelCase			       =		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     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.scheduler_classes[0]
           UpperCAmelCase			       =		self.get_scheduler_config()
           UpperCAmelCase			       =		scheduler_class(**_a				)

           UpperCAmelCase			       =		10, 0.0
           scheduler.set_timesteps(_a				)

           UpperCAmelCase			       =		self.dummy_model()
           UpperCAmelCase			       =		self.dummy_sample_deter
           UpperCAmelCase			       =		self.dummy_sample_deter + 0.1
           UpperCAmelCase			       =		self.dummy_sample_deter - 0.1

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

           UpperCAmelCase			       =		model(samples.flatten(0   ,				1				)   ,				timesteps.flatten(0   ,				1				)				)
           UpperCAmelCase			       =		scheduler.batch_step_no_noise(_a   ,				timesteps.flatten(0   ,				1				)   ,				samples.flatten(0   ,				1				)   ,				_a				)

           UpperCAmelCase			       =		torch.sum(torch.abs(_a				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(_a				)				)

           assert abs(result_sum.item() - 1147.7904				) < 1E-2
           assert abs(result_mean.item() - 0.4982				) < 1E-3
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop()

           UpperCAmelCase			       =		torch.sum(torch.abs(_a				)				)
           UpperCAmelCase			       =		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     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(prediction_type='''v_prediction'''				)

           UpperCAmelCase			       =		torch.sum(torch.abs(_a				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(_a				)				)

           assert abs(result_sum.item() - 52.5302				) < 1E-2
           assert abs(result_mean.item() - 0.0684				) < 1E-3
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(set_alpha_to_one=_a   ,				beta_start=0.01				)
           UpperCAmelCase			       =		torch.sum(torch.abs(_a				)				)
           UpperCAmelCase			       =		torch.mean(torch.abs(_a				)				)

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







       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.full_loop(set_alpha_to_one=_a   ,				beta_start=0.01				)
           UpperCAmelCase			       =		torch.sum(torch.abs(_a				)				)
           UpperCAmelCase			       =		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()



