





def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int						):
     if not isinstance(snake_case_ ,     snake_case_						):
          raise TypeError('''recursive_lucas_number accepts only integer arguments.'''						)
     if n_th_number == 0:
          return 2
     if n_th_number == 1:
          return 1

     return recursive_lucas_number(n_th_number - 1						) + recursive_lucas_number(
         n_th_number - 2						)




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int						):
     if not isinstance(snake_case_ ,     snake_case_						):
          raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''						)
     __magic_name__		,  __magic_name__				=       2, 1
     for _ in range(snake_case_						):
          __magic_name__		,  __magic_name__				=       b, a + b
     return a


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

    testmod()
    a_    :				List[str]					=			int(input('Enter the number of terms in lucas series:\n').strip())
    print('Using recursive function to calculate lucas series:')
    print(' '.join(str(recursive_lucas_number(i)) for i in range(n)))
    print('\nUsing dynamic function to calculate lucas series:')
    print(' '.join(str(dynamic_lucas_number(i)) for i in range(n)))






from ..utils import DummyObject, requires_backends



class  SCREAMING_SNAKE_CASE_  ( metaclass=SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							["""torch""", """transformers""", """k_diffusion"""]





  def __init__(  self	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(self	,  ['''torch''', '''transformers''', '''k_diffusion''']						)





  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Dict:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)







  @classmethod
  def 			__A      (  cls	,  *A	,  **A						)		->   Optional[Any]:





       '''simple docstring'''







       requires_backends(cls	,  ['''torch''', '''transformers''', '''k_diffusion''']						)