



'''simple docstring'''





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




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




  def 						_lowercase     ( self      :	str			,	**_a      :	Optional[int]   )					->					Any:
    __lowerCamelCase       :       Tuple           = {
        '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      :	int			,	**_a      :	Dict   )					->					Dict:
    __lowerCamelCase       :       List[str]           = self.scheduler_classes[0]
    __lowerCamelCase       :       Tuple           = self.get_scheduler_config(**_a   )
    __lowerCamelCase       :       Optional[int]           = scheduler_class(**_a   )

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

    __lowerCamelCase       :       Optional[int]           = self.dummy_model()
    __lowerCamelCase       :       Optional[Any]           = self.dummy_sample_deter

    scheduler.set_timesteps(_a   )

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

    return sample




  def 						_lowercase     ( self      :	List[Any]   )					->					Tuple:
    for timesteps in [100, 500, 1000]:
      self.check_over_configs(num_train_timesteps=_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Optional[Any]:
    for steps_offset in [0, 1]:
      self.check_over_configs(steps_offset=_a   )

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




  def 						_lowercase     ( self      :	Any   )					->					List[str]:
    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      :	int   )					->					List[Any]:
    for schedule in ["linear", "squaredcos_cap_v2"]:
      self.check_over_configs(beta_schedule=_a   )




  def 						_lowercase     ( self      :	int   )					->					List[Any]:
    for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=_a   )




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




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					List[Any]:
    for timestep_spacing in ["trailing", "leading"]:
      self.check_over_configs(timestep_spacing=_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Dict:
    for rescale_betas_zero_snr in [True, False]:
      self.check_over_configs(rescale_betas_zero_snr=_a   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Tuple:
    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      :	Dict   )					->					List[str]:
    for t in [1, 10, 49]:
      self.check_over_forward(time_step=_a   )




  def 						_lowercase     ( self      :	Optional[int]   )					->					Any:
    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]   )					->					List[Any]:
    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      :	Dict   )					->					Optional[Any]:
    __lowerCamelCase       :       List[Any]           = self.scheduler_classes[0]
    __lowerCamelCase       :       Optional[Any]           = self.get_scheduler_config()
    __lowerCamelCase       :       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   ) - 0.14771   )   ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(980			,	960   ) - 0.32460   )   ) < 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   ) - 0.00979   )   ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(999			,	998   ) - 0.02   )   ) < 1e-5




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Optional[int]:
    __lowerCamelCase       :       Any           = self.scheduler_classes[0]
    __lowerCamelCase       :       Optional[int]           = self.get_scheduler_config()
    __lowerCamelCase       :       str           = scheduler_class(**_a   )

    __lowerCamelCase       ,__lowerCamelCase       :       str           = 10, 0.0
    scheduler.set_timesteps(_a   )

    __lowerCamelCase       :       Optional[int]           = self.dummy_model()
    __lowerCamelCase       :       Optional[int]           = self.dummy_sample_deter
    __lowerCamelCase       :       List[str]           = self.dummy_sample_deter + 0.1
    __lowerCamelCase       :       Any           = self.dummy_sample_deter - 0.1

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

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

    __lowerCamelCase       :       List[str]           = torch.sum(torch.abs(_a   )   )
    __lowerCamelCase       :       List[str]           = 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      :	Optional[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Optional[int]           = self.full_loop()

    __lowerCamelCase       :       List[Any]           = torch.sum(torch.abs(_a   )   )
    __lowerCamelCase       :       Optional[int]           = torch.mean(torch.abs(_a   )   )

    assert abs(result_sum.item() - 172.0067   ) < 1e-2
    assert abs(result_mean.item() - 0.223967   ) < 1e-3




  def 						_lowercase     ( self      :	List[Any]   )					->					Dict:
    __lowerCamelCase       :       Any           = self.full_loop(prediction_type='v_prediction'   )

    __lowerCamelCase       :       str           = torch.sum(torch.abs(_a   )   )
    __lowerCamelCase       :       int           = 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]   )					->					Optional[Any]:
    # We specify different beta, so that the first alpha is 0.99
    __lowerCamelCase       :       str           = self.full_loop(set_alpha_to_one=_a			,	beta_start=0.01   )
    __lowerCamelCase       :       int           = torch.sum(torch.abs(_a   )   )
    __lowerCamelCase       :       str           = 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      :	int   )					->					Dict:
    # We specify different beta, so that the first alpha is 0.99
    __lowerCamelCase       :       Union[str, Any]           = self.full_loop(set_alpha_to_one=_a			,	beta_start=0.01   )
    __lowerCamelCase       :       Optional[Any]           = torch.sum(torch.abs(_a   )   )
    __lowerCamelCase       :       Optional[Any]           = 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 collections.abc import Sequence

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


if __name__ == "__main__":
   _UpperCamelCase											=						(0.0, 0.0, 5.0, 9.3, 7.0)
   _UpperCamelCase											=						1_0.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))






