





'''simple docstring'''

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class lowerCamelCase						(a__					):
  _lowercase  :	Union[str, Any]       					=				(DDPMParallelScheduler,)







  def 				UpperCAmelCase_				(		self     ,     **lowercase__		)		->						Optional[int]:






        """simple docstring"""





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

        config.update(**lowercase__		)
        return config







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






        """simple docstring"""





        for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """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=lowercase__     ,     beta_end=lowercase__		)







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






        """simple docstring"""





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







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






        """simple docstring"""





        for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase__		)







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






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowercase__		)







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






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     self.scheduler_classes[0]
        _snake_case		:	Any					     =     self.get_scheduler_config()
        _snake_case		:	Any					     =     scheduler_class(**lowercase__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00_979		)		) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5







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






        """simple docstring"""





        _snake_case		:	Dict					     =     self.scheduler_classes[0]
        _snake_case		:	Tuple					     =     self.get_scheduler_config()
        _snake_case		:	Optional[Any]					     =     scheduler_class(**lowercase__		)

        _snake_case		:	Optional[int]					     =     len(lowercase__		)

        _snake_case		:	Dict					     =     self.dummy_model()
        _snake_case		:	Dict					     =     self.dummy_sample_deter
        _snake_case		:	List[Any]					     =     self.dummy_sample_deter + 0.1
        _snake_case		:	Dict					     =     self.dummy_sample_deter - 0.1

        _snake_case		:	str					     =     samplea.shape[0]
        _snake_case		:	Optional[int]					     =     torch.stack([samplea, samplea, samplea]     ,     dim=0		)
        _snake_case		:	List[str]					     =     torch.arange(lowercase__		)[0:3, None].repeat(1     ,     lowercase__		)

        _snake_case		:	int					     =     model(samples.flatten(0     ,     1		)     ,     timesteps.flatten(0     ,     1		)		)
        _snake_case		:	str					     =     scheduler.batch_step_no_noise(lowercase__     ,     timesteps.flatten(0     ,     1		)     ,     samples.flatten(0     ,     1		)		)

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

        assert abs(result_sum.item() - 1_153.1_833		) < 1E-2
        assert abs(result_mean.item() - 0.5_005		) < 1E-3







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






        """simple docstring"""





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

        _snake_case		:	List[str]					     =     len(lowercase__		)

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

        for t in reversed(range(lowercase__		)		):
              # 1. predict noise residual
              _snake_case		:	Union[str, Any]					     =     model(lowercase__     ,     lowercase__		)

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

              _snake_case		:	List[str]					     =     pred_prev_sample

        _snake_case		:	int					     =     torch.sum(torch.abs(lowercase__		)		)
        _snake_case		:	Tuple					     =     torch.mean(torch.abs(lowercase__		)		)

        assert abs(result_sum.item() - 258.9_606		) < 1E-2
        assert abs(result_mean.item() - 0.3_372		) < 1E-3







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






        """simple docstring"""





        _snake_case		:	Tuple					     =     self.scheduler_classes[0]
        _snake_case		:	str					     =     self.get_scheduler_config(prediction_type='''v_prediction'''		)
        _snake_case		:	Dict					     =     scheduler_class(**lowercase__		)

        _snake_case		:	Dict					     =     len(lowercase__		)

        _snake_case		:	Union[str, Any]					     =     self.dummy_model()
        _snake_case		:	Optional[int]					     =     self.dummy_sample_deter
        _snake_case		:	List[Any]					     =     torch.manual_seed(0		)

        for t in reversed(range(lowercase__		)		):
              # 1. predict noise residual
              _snake_case		:	List[str]					     =     model(lowercase__     ,     lowercase__		)

              # 2. predict previous mean of sample x_t-1
              _snake_case		:	Union[str, Any]					     =     scheduler.step(lowercase__     ,     lowercase__     ,     lowercase__     ,     generator=lowercase__		).prev_sample

              _snake_case		:	Optional[Any]					     =     pred_prev_sample

        _snake_case		:	List[Any]					     =     torch.sum(torch.abs(lowercase__		)		)
        _snake_case		:	str					     =     torch.mean(torch.abs(lowercase__		)		)

        assert abs(result_sum.item() - 202.0_296		) < 1E-2
        assert abs(result_mean.item() - 0.2_631		) < 1E-3







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	Dict					     =     self.scheduler_classes[0]
        _snake_case		:	Optional[Any]					     =     self.get_scheduler_config()
        _snake_case		:	Union[str, Any]					     =     scheduler_class(**lowercase__		)

        _snake_case		:	str					     =     [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=lowercase__		)

        _snake_case		:	List[str]					     =     scheduler.timesteps

        for i, timestep in enumerate(lowercase__		):
              if i == len(lowercase__		) - 1:
                    _snake_case		:	int					     =     -1
              else:
                    _snake_case		:	Optional[Any]					     =     timesteps[i + 1]

              _snake_case		:	str					     =     scheduler.previous_timestep(lowercase__		)
              _snake_case		:	Tuple					     =     prev_t.item()

              self.assertEqual(lowercase__     ,     lowercase__		)







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






        """simple docstring"""





        _snake_case		:	str					     =     self.scheduler_classes[0]
        _snake_case		:	int					     =     self.get_scheduler_config()
        _snake_case		:	int					     =     scheduler_class(**lowercase__		)

        _snake_case		:	Dict					     =     [100, 87, 50, 51, 0]

        with self.assertRaises(lowercase__     ,     msg='''`custom_timesteps` must be in descending order.'''		):
              scheduler.set_timesteps(timesteps=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





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

        _snake_case		:	Tuple					     =     [100, 87, 50, 1, 0]
        _snake_case		:	List[Any]					     =     len(lowercase__		)

        with self.assertRaises(lowercase__     ,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''		):
              scheduler.set_timesteps(num_inference_steps=lowercase__     ,     timesteps=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	int					     =     self.scheduler_classes[0]
        _snake_case		:	int					     =     self.get_scheduler_config()
        _snake_case		:	Union[str, Any]					     =     scheduler_class(**lowercase__		)

        _snake_case		:	Union[str, Any]					     =     [scheduler.config.num_train_timesteps]

        with self.assertRaises(
            lowercase__     ,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,     ):
              scheduler.set_timesteps(timesteps=lowercase__		)









'''simple docstring'''

def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      if n == 1 or not isinstance(lowerCAmelCase_			,      lowerCAmelCase_	):
            return 0
      elif n == 2:
            return 1
      else:
            _snake_case		:	Union[str, Any]					     =     [0, 1]
            for i in range(2			,      n + 1	):
                  sequence.append(sequence[i - 1] + sequence[i - 2]	)

            return sequence[n]




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[int]					     =     0
      _snake_case		:	int					     =     2

      while digits < n:
            index += 1
            _snake_case		:	Tuple					     =     len(str(fibonacci(lowerCAmelCase_	)	)	)

      return index




def    _a    (							lowerCAmelCase_ = 1_000	):





      """simple docstring"""



      return fibonacci_digits_index(lowerCAmelCase_	)


if __name__ == "__main__":
   print(solution(int(str(input()).strip())))


