'''simple docstring'''






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					(UnCLIPScheduler,)







  def    lowercase_  (							self   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= {
        """num_train_timesteps""": 1000,
        """variance_type""": """fixed_small_log""",
        """clip_sample""": True,
        """clip_sample_range""": 1.0,
        """prediction_type""": """epsilon""",
    }

    config.update(**snake_case_					)
    return config







  def    lowercase_  (							self					):

    '''simple docstring'''







    for timesteps in [1, 5, 100, 1000]:
      self.check_over_configs(num_train_timesteps=snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    for variance in ["fixed_small_log", "learned_range"]:
      self.check_over_configs(variance_type=snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







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







  def    lowercase_  (							self					):

    '''simple docstring'''







    for clip_sample_range in [1, 5, 10, 20]:
      self.check_over_configs(clip_sample_range=snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    for prediction_type in ["epsilon", "sample"]:
      self.check_over_configs(prediction_type=snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    for time_step in [0, 500, 999]:
      for prev_timestep in [None, 5, 100, 250, 500, 750]:
        if prev_timestep is not None and prev_timestep >= time_step:
          continue

        self.check_over_forward(time_step=snake_case_   ,    prev_timestep=snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= self.scheduler_classes[0]
    __UpperCAmelCase:					Dict		= self.get_scheduler_config(variance_type="""fixed_small_log"""					)
    __UpperCAmelCase:					Tuple		= scheduler_class(**snake_case_					)

    assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_0_0_0e-1_0					)					) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_5_4_9_6_2_5					)					) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_9_9_4_9_8_7					)					) < 1e-5







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.scheduler_classes[0]
    __UpperCAmelCase:					Any		= self.get_scheduler_config(variance_type="""learned_range"""					)
    __UpperCAmelCase:					List[str]		= scheduler_class(**snake_case_					)

    __UpperCAmelCase:					List[str]		= 0.5

    assert scheduler._get_variance(1   ,    predicted_variance=snake_case_					) - -1_0.1_7_1_2_7_9_0 < 1e-5
    assert scheduler._get_variance(487   ,    predicted_variance=snake_case_					) - -5.7_9_9_8_0_5_2 < 1e-5
    assert scheduler._get_variance(999   ,    predicted_variance=snake_case_					) - -0.0_0_1_0_0_1_1 < 1e-5







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self.scheduler_classes[0]
    __UpperCAmelCase:					List[Any]		= self.get_scheduler_config()
    __UpperCAmelCase:					Any		= scheduler_class(**snake_case_					)

    __UpperCAmelCase:					List[str]		= scheduler.timesteps

    __UpperCAmelCase:					List[str]		= self.dummy_model()
    __UpperCAmelCase:					str		= self.dummy_sample_deter
    __UpperCAmelCase:					Optional[Any]		= torch.manual_seed(0					)

    for i, t in enumerate(snake_case_					):
      # 1. predict noise residual
      __UpperCAmelCase:					Optional[Any]		= model(snake_case_   ,    snake_case_					)

      # 2. predict previous mean of sample x_t-1
      __UpperCAmelCase:					int		= scheduler.step(snake_case_   ,    snake_case_   ,    snake_case_   ,    generator=snake_case_					).prev_sample

      __UpperCAmelCase:					int		= pred_prev_sample

    __UpperCAmelCase:					List[Any]		= torch.sum(torch.abs(snake_case_					)					)
    __UpperCAmelCase:					Union[str, Any]		= torch.mean(torch.abs(snake_case_					)					)

    assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5					) < 1e-2
    assert abs(result_mean.item() - 0.3_2_8_4_7_4_3					) < 1e-3







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.scheduler_classes[0]
    __UpperCAmelCase:					Tuple		= self.get_scheduler_config()
    __UpperCAmelCase:					str		= scheduler_class(**snake_case_					)

    scheduler.set_timesteps(25					)

    __UpperCAmelCase:					str		= scheduler.timesteps

    __UpperCAmelCase:					str		= self.dummy_model()
    __UpperCAmelCase:					Optional[int]		= self.dummy_sample_deter
    __UpperCAmelCase:					Any		= torch.manual_seed(0					)

    for i, t in enumerate(snake_case_					):
      # 1. predict noise residual
      __UpperCAmelCase:					Dict		= model(snake_case_   ,    snake_case_					)

      if i + 1 == timesteps.shape[0]:
        __UpperCAmelCase:					Dict		= None
      else:
        __UpperCAmelCase:					List[str]		= timesteps[i + 1]

      # 2. predict previous mean of sample x_t-1
      __UpperCAmelCase:					Dict		= scheduler.step(
          snake_case_   ,    snake_case_   ,    snake_case_   ,    prev_timestep=snake_case_   ,    generator=snake_case_					).prev_sample

      __UpperCAmelCase:					Union[str, Any]		= pred_prev_sample

    __UpperCAmelCase:					List[Any]		= torch.sum(torch.abs(snake_case_					)					)
    __UpperCAmelCase:					Union[str, Any]		= torch.mean(torch.abs(snake_case_					)					)

    assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3					) < 1e-2
    assert abs(result_mean.item() - 0.3_3_6_2_0_3_8					) < 1e-3







  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self					):

    '''simple docstring'''







    pass
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				int     )							->	int:

  if not isinstance(_lowercase   ,    _lowercase     ):
    __UpperCAmelCase:					List[str]		= F'''Input value of [number={number}] must be an integer'''
    raise TypeError(_lowercase     )

  if number < 1:
    __UpperCAmelCase:					Dict		= F'''Input value of [number={number}] must be > 0'''
    raise ValueError(_lowercase     )

  __UpperCAmelCase:					int		= 1

  for i in range(1   ,    _lowercase     ):
    current_number *= 4 * i - 2
    current_number //= i + 1

  return current_number


if __name__ == "__main__":
    import doctest

    doctest.testmod()