




"""simple docstring"""


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					(DDIMParallelScheduler,)
 lowercase__ 					=					(("eta", 0.0), ("num_inference_steps", 50))


 def  UpperCamelCase_   (				self:		str,      **a_:		int		):




   '''simple docstring'''



   _snake_case							:		int								=  {
       """num_train_timesteps""": 1_000,
       """beta_start""": 0.0_001,
       """beta_end""": 0.02,
       """beta_schedule""": """linear""",
       """clip_sample""": True,
   }

   config.update(**a_		)
   return config


 def  UpperCamelCase_   (				self:		int,      **a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  self.scheduler_classes[0]
   _snake_case							:		List[str]								=  self.get_scheduler_config(**a_		)
   _snake_case							:		List[Any]								=  scheduler_class(**a_		)

   _snake_case		,					_snake_case							:		Union[str, Any]								=  10, 0.0

   _snake_case							:		List[Any]								=  self.dummy_model()
   _snake_case							:		Tuple								=  self.dummy_sample_deter

   scheduler.set_timesteps(a_		)

   for t in scheduler.timesteps:
     _snake_case							:		Optional[Any]								=  model(a_,      a_		)
     _snake_case							:		Optional[int]								=  scheduler.step(a_,      a_,      a_,      a_		).prev_sample

   return sample


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   for timesteps in [100, 500, 1_000]:
     self.check_over_configs(num_train_timesteps=a_		)


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



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

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


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




   '''simple docstring'''



   for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1],      [0.002, 0.02, 0.2, 2]		):
     self.check_over_configs(beta_start=a_,      beta_end=a_		)


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




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		str		):




   '''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  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		int		):




   '''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  UpperCamelCase_   (				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  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  self.scheduler_classes[0]
   _snake_case							:		List[str]								=  self.get_scheduler_config()
   _snake_case							:		Tuple								=  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		) - 0.14_771		)		) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(980,      960		) - 0.32_460		)		) < 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		) - 0.00_979		)		) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(999,      998		) - 0.02		)		) < 1E-5


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




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.scheduler_classes[0]
   _snake_case							:		Any								=  self.get_scheduler_config()
   _snake_case							:		List[Any]								=  scheduler_class(**a_		)

   _snake_case		,					_snake_case							:		List[Any]								=  10, 0.0
   scheduler.set_timesteps(a_		)

   _snake_case							:		Optional[Any]								=  self.dummy_model()
   _snake_case							:		Union[str, Any]								=  self.dummy_sample_deter
   _snake_case							:		Dict								=  self.dummy_sample_deter + 0.1
   _snake_case							:		Union[str, Any]								=  self.dummy_sample_deter - 0.1

   _snake_case							:		Any								=  samplea.shape[0]
   _snake_case							:		Optional[Any]								=  torch.stack([samplea, samplea, samplea],      dim=0		)
   _snake_case							:		Dict								=  torch.arange(a_		)[0:3, None].repeat(1,      a_		)

   _snake_case							:		List[Any]								=  model(samples.flatten(0,      1		),      timesteps.flatten(0,      1		)		)
   _snake_case							:		List[str]								=  scheduler.batch_step_no_noise(a_,      timesteps.flatten(0,      1		),      samples.flatten(0,      1		),      a_		)

   _snake_case							:		Tuple								=  torch.sum(torch.abs(a_		)		)
   _snake_case							:		str								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_sum.item() - 1_147.7_904		) < 1E-2
   assert abs(result_mean.item() - 0.4_982		) < 1E-3


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self.full_loop()

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

   assert abs(result_sum.item() - 172.0_067		) < 1E-2
   assert abs(result_mean.item() - 0.223_967		) < 1E-3


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.full_loop(prediction_type="""v_prediction"""		)

   _snake_case							:		Optional[int]								=  torch.sum(torch.abs(a_		)		)
   _snake_case							:		List[Any]								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_sum.item() - 52.5_302		) < 1E-2
   assert abs(result_mean.item() - 0.0_684		) < 1E-3


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.full_loop(set_alpha_to_one=a_,      beta_start=0.01		)
   _snake_case							:		Dict								=  torch.sum(torch.abs(a_		)		)
   _snake_case							:		List[str]								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_sum.item() - 149.8_295		) < 1E-2
   assert abs(result_mean.item() - 0.1_951		) < 1E-3


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.full_loop(set_alpha_to_one=a_,      beta_start=0.01		)
   _snake_case							:		List[Any]								=  torch.sum(torch.abs(a_		)		)
   _snake_case							:		Optional[int]								=  torch.mean(torch.abs(a_		)		)

   assert abs(result_sum.item() - 149.0_784		) < 1E-2
   assert abs(result_mean.item() - 0.1_941		) < 1E-3








"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					list    ):
  """simple docstring"""

  if len(snake_case__    ) <= 1:
    return [tuple(snake_case__    )]

  _snake_case							:		List[Any]								=  []

  def generate(snake_case__							:					int		,   snake_case__							:					list    ):
    if k == 1:
      res.append(tuple(arr[:]    )    )
      return

    generate(k - 1		,   snake_case__    )

    for i in range(k - 1    ):
      if k % 2 == 0:  # k is even
        _snake_case		,					_snake_case							:		Optional[Any]								=  arr[k - 1], arr[i]
      else:  # k is odd
        _snake_case		,					_snake_case							:		List[str]								=  arr[k - 1], arr[0]
      generate(k - 1		,   snake_case__    )

  generate(len(snake_case__    )		,   snake_case__    )
  return res


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(heaps(arr))


