






'''simple docstring'''

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class        a_		(    lowerCamelCase       ):
  lowercase  	=   (DDIMParallelScheduler,)
  lowercase  	=   (("""eta""", 0.0), ("""num_inference_steps""", 50))

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0_0_0_1,
            """beta_end""": 0.0_2,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**_SCREAMING_SNAKE_CASE   )
        return config

  def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

        UpperCamelCase						,UpperCamelCase       						=      10, 0.0

        UpperCamelCase       						=      self.dummy_model()
        UpperCamelCase       						=      self.dummy_sample_deter

        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )

        for t in scheduler.timesteps:
              UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      scheduler.step(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ).prev_sample

        return sample

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



        """simple docstring"""






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

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






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

        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config(steps_offset=1   )
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )
        scheduler.set_timesteps(5   )
        assert torch.equal(scheduler.timesteps    ,	torch.LongTensor([801, 601, 401, 201, 1]   )   )

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,	[0.0_0_2, 0.0_2, 0.2, 2]   ):
              self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE    ,	beta_end=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






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

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






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

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






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

  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






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

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



        """simple docstring"""






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

  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=_SCREAMING_SNAKE_CASE    ,	num_inference_steps=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






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

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config()
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

        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_4_7_7_1   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980    ,	960   ) - 0.3_2_4_6_0   )   ) < 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_0_9_7_9   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999    ,	998   ) - 0.0_2   )   ) < 1e-5

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.scheduler_classes[0]
        UpperCamelCase       						=      self.get_scheduler_config()
        UpperCamelCase       						=      scheduler_class(**_SCREAMING_SNAKE_CASE   )

        UpperCamelCase						,UpperCamelCase       						=      10, 0.0
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )

        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(_SCREAMING_SNAKE_CASE   )[0:3, None].repeat(1    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      model(samples.flatten(0    ,	1   )    ,	timesteps.flatten(0    ,	1   )   )
        UpperCamelCase       						=      scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE    ,	timesteps.flatten(0    ,	1   )    ,	samples.flatten(0    ,	1   )    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 1147.7904   ) < 1e-2
        assert abs(result_mean.item() - 0.4_9_8_2   ) < 1e-3

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



        """simple docstring"""






        UpperCamelCase       						=      self.full_loop()

        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 172.0067   ) < 1e-2
        assert abs(result_mean.item() - 0.2_2_3_9_6_7   ) < 1e-3

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      self.full_loop(prediction_type="""v_prediction"""   )

        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 52.5302   ) < 1e-2
        assert abs(result_mean.item() - 0.0_6_8_4   ) < 1e-3

  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,	beta_start=0.0_1   )
        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 149.8295   ) < 1e-2
        assert abs(result_mean.item() - 0.1_9_5_1   ) < 1e-3


  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,	beta_start=0.0_1   )
        UpperCamelCase       						=      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE   )   )
        UpperCamelCase       						=      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE   )   )

        assert abs(result_sum.item() - 149.0784   ) < 1e-2
        assert abs(result_mean.item() - 0.1_9_4_1   ) < 1e-3








'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				str:
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					):
            raise ValueError("""iterations must be defined as integers"""					)
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					) or not number >= 1:
            raise ValueError(
                """starting number must be
                         and integer and be more than 0"""					)
      if not iterations >= 1:
            raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz"""					)

      UpperCamelCase       						=      """"""
      while number <= iterations:
            if number % 3 == 0:
                  out += "Fizz"
            if number % 5 == 0:
                  out += "Buzz"
            if 0 not in (number % 3, number % 5):
                  out += str(__UpperCamelCase					)

            # print(out)
            number += 1
            out += " "
      return out


if __name__ == "__main__":
     import doctest

     doctest.testmod()
