



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   snake_case_   :	Optional[Any]       =						(DDIMScheduler,)
   snake_case_   :	Optional[int]       =						(("""eta""", 0.0), ("""num_inference_steps""", 50))

   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						**lowerCAmelCase							:     str)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   {
        """num_train_timesteps""": 1000,
        """beta_start""": 0.0_001,
        """beta_end""": 0.02,
        """beta_schedule""": """linear""",
        """clip_sample""": True,
    }

    config.update(**lowerCAmelCase)
    return config

   def        UpperCamelCase_					(   self							:     List[Any]			,						**lowerCAmelCase							:     str)			->			Optional[int]:

    """simple docstring"""







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

    _snake_case		,      _snake_case      :					Dict					=   10, 0.0

    _snake_case      :					Any					=   self.dummy_model()
    _snake_case      :					str					=   self.dummy_sample_deter

    scheduler.set_timesteps(lowerCAmelCase)

    for t in scheduler.timesteps:
     _snake_case      :					List[str]					=   model(lowerCAmelCase			,						lowerCAmelCase)
     _snake_case      :					Optional[Any]					=   scheduler.step(lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase).prev_sample

    return sample

   def        UpperCamelCase_					(   self							:     Any)			->			Any:

    """simple docstring"""







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

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

    """simple docstring"""







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

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

   def        UpperCamelCase_					(   self							:     List[Any])			->			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=lowerCAmelCase			,						beta_end=lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     int)			->			int:

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[Any]:

    """simple docstring"""







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

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

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     Dict)			->			Dict:

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     List[str])			->			Optional[Any]:

    """simple docstring"""







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

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

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     Optional[int])			->			Tuple:

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     int)			->			Optional[Any]:

    """simple docstring"""







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

   def        UpperCamelCase_					(   self							:     str)			->			Dict:

    """simple docstring"""







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

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

    """simple docstring"""







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

    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							:     str)			->			List[str]:

    """simple docstring"""







    _snake_case      :					Dict					=   self.full_loop()

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

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

   def        UpperCamelCase_					(   self							:     Optional[Any])			->			str:

    """simple docstring"""







    _snake_case      :					int					=   self.full_loop(prediction_type="""v_prediction""")

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

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

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

    """simple docstring"""







    _snake_case      :					int					=   self.full_loop(set_alpha_to_one=lowerCAmelCase			,						beta_start=0.01)
    _snake_case      :					Tuple					=   torch.sum(torch.abs(lowerCAmelCase))
    _snake_case      :					Dict					=   torch.mean(torch.abs(lowerCAmelCase))

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


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

    """simple docstring"""







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

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





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			)					->       Optional[int]:
 _snake_case      :					Any					=   [701, 301, 132, 57, 23, 10, 4, 1]
 for gap in gaps:
  for i in range(SCREAMING_SNAKE_CASE__			,    len(SCREAMING_SNAKE_CASE__			)			):
   _snake_case      :					List[Any]					=   collection[i]
   _snake_case      :					List[str]					=   i
   while j >= gap and collection[j - gap] > insert_value:
    _snake_case      :					int					=   collection[j - gap]
    j -= gap
   if j != i:
    _snake_case      :					Tuple					=   insert_value
 return collection


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		input("""Enter numbers separated by a comma:\n""").strip()
  a__      =		[int(item) for item in user_input.split(""",""")]
  print(shell_sort(unsorted))
