




'''simple docstring'''



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						(IPNDMScheduler,)
   lowerCamelCase_      =						(('''num_inference_steps''', 50),)



   def 							lowerCAmelCase_      (   self :     Dict  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{"num_train_timesteps": 1_0_0_0}
      config.update(**__lowerCamelCase   )
      return config



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Tuple=0  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				dict(self.forward_default_kwargs   )
      _SCREAMING_SNAKE_CASE	=				kwargs.pop("num_inference_steps"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.dummy_sample
      _SCREAMING_SNAKE_CASE	=				0.1 * sample
      _SCREAMING_SNAKE_CASE	=				[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

      for scheduler_class in self.scheduler_classes:
         _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config(**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				scheduler_class(**__lowerCamelCase   )
         scheduler.set_timesteps(__lowerCamelCase   )
         # copy over dummy past residuals
         _SCREAMING_SNAKE_CASE	=				dummy_past_residuals[:]

         if time_step is None:
            _SCREAMING_SNAKE_CASE	=				scheduler.timesteps[len(scheduler.timesteps   ) // 2]

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				scheduler_class.from_pretrained(__lowerCamelCase   )
            new_scheduler.set_timesteps(__lowerCamelCase   )
            # copy over dummy past residuals
            _SCREAMING_SNAKE_CASE	=				dummy_past_residuals[:]

         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample
         _SCREAMING_SNAKE_CASE	=				new_scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample

         assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample
         _SCREAMING_SNAKE_CASE	=				new_scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample

         assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      pass



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[int]=0  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				dict(self.forward_default_kwargs   )
      _SCREAMING_SNAKE_CASE	=				kwargs.pop("num_inference_steps"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.dummy_sample
      _SCREAMING_SNAKE_CASE	=				0.1 * sample
      _SCREAMING_SNAKE_CASE	=				[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

      for scheduler_class in self.scheduler_classes:
         _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config()
         _SCREAMING_SNAKE_CASE	=				scheduler_class(**__lowerCamelCase   )
         scheduler.set_timesteps(__lowerCamelCase   )

         # copy over dummy past residuals (must be after setting timesteps)
         _SCREAMING_SNAKE_CASE	=				dummy_past_residuals[:]

         if time_step is None:
            _SCREAMING_SNAKE_CASE	=				scheduler.timesteps[len(scheduler.timesteps   ) // 2]

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				scheduler_class.from_pretrained(__lowerCamelCase   )
            # copy over dummy past residuals
            new_scheduler.set_timesteps(__lowerCamelCase   )

            # copy over dummy past residual (must be after setting timesteps)
            _SCREAMING_SNAKE_CASE	=				dummy_past_residuals[:]

         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample
         _SCREAMING_SNAKE_CASE	=				new_scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample

         assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample
         _SCREAMING_SNAKE_CASE	=				new_scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample

         assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				**__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.scheduler_classes[0]
      _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				scheduler_class(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				1_0
      _SCREAMING_SNAKE_CASE	=				self.dummy_model()
      _SCREAMING_SNAKE_CASE	=				self.dummy_sample_deter
      scheduler.set_timesteps(__lowerCamelCase   )

      for i, t in enumerate(scheduler.timesteps   ):
         _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   ).prev_sample

      for i, t in enumerate(scheduler.timesteps   ):
         _SCREAMING_SNAKE_CASE	=				model(__lowerCamelCase  ,				__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   ).prev_sample

      return sample



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				dict(self.forward_default_kwargs   )

      _SCREAMING_SNAKE_CASE	=				kwargs.pop("num_inference_steps"  ,				__lowerCamelCase   )

      for scheduler_class in self.scheduler_classes:
         _SCREAMING_SNAKE_CASE	=				self.get_scheduler_config()
         _SCREAMING_SNAKE_CASE	=				scheduler_class(**__lowerCamelCase   )

         _SCREAMING_SNAKE_CASE	=				self.dummy_sample
         _SCREAMING_SNAKE_CASE	=				0.1 * sample

         if num_inference_steps is not None and hasattr(__lowerCamelCase  ,				"set_timesteps"   ):
            scheduler.set_timesteps(__lowerCamelCase   )
         elif num_inference_steps is not None and not hasattr(__lowerCamelCase  ,				"set_timesteps"   ):
            _SCREAMING_SNAKE_CASE	=				num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         _SCREAMING_SNAKE_CASE	=				[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         _SCREAMING_SNAKE_CASE	=				dummy_past_residuals[:]

         _SCREAMING_SNAKE_CASE	=				scheduler.timesteps[5]
         _SCREAMING_SNAKE_CASE	=				scheduler.timesteps[6]

         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample
         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample

         self.assertEqual(output_a.shape  ,				sample.shape   )
         self.assertEqual(output_a.shape  ,				output_a.shape   )

         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample
         _SCREAMING_SNAKE_CASE	=				scheduler.step(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   ).prev_sample

         self.assertEqual(output_a.shape  ,				sample.shape   )
         self.assertEqual(output_a.shape  ,				output_a.shape   )



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      for timesteps in [1_0_0, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=__lowerCamelCase  ,				time_step=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for t, num_inference_steps in zip([1, 5, 1_0]  ,				[1_0, 5_0, 1_0_0]   ):
         self.check_over_forward(num_inference_steps=__lowerCamelCase  ,				time_step=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.full_loop()
      _SCREAMING_SNAKE_CASE	=				torch.mean(torch.abs(__lowerCamelCase   )   )

      assert abs(result_mean.item() - 2_5_4_0_5_2_9   ) < 1_0







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			int:
   if n == 1 or not isinstance(__A	,					__A  ):
      return 0
   elif n == 2:
      return 1
   else:
      _SCREAMING_SNAKE_CASE	=				[0, 1]
      for i in range(2	,					n + 1  ):
         sequence.append(sequence[i - 1] + sequence[i - 2]  )

      return sequence[n]
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int  )			->			int:
   _SCREAMING_SNAKE_CASE	=				0
   _SCREAMING_SNAKE_CASE	=				2

   while digits < n:
      index += 1
      _SCREAMING_SNAKE_CASE	=				len(str(fibonacci(__A  )  )  )

   return index
def 			SCREAMING_SNAKE_CASE_			(							__A    :			int = 10_00  )			->			int:
   return fibonacci_digits_index(__A  )


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

