


'''simple docstring'''

from __future__ import annotations



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int )		->       int | None:





   '''simple docstring'''







   for r in range(1     ,		_UpperCamelCase ):
      UpperCamelCase__			    =    []
      for x in range(n - 1 ):
         UpperCamelCase__			    =    pow(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
         if val in li:
            break
         li.append(_UpperCamelCase )
      else:
         return r
   return None


if __name__ == "__main__":
       __lowercase:		Optional[Any]    						=  int(input("Enter a prime number q: "))
       __lowercase:		List[Any]    						=  find_primitive(q)
       if a is None:
              print(F"""Cannot find the primitive for the value: {a!r}""")
       else:
              __lowercase:		List[Any]    						=  int(input("Enter private key of A: "))
              __lowercase:		List[str]    						=  pow(a, a_private, q)
              __lowercase:		Union[str, Any]    						=  int(input("Enter private key of B: "))
              __lowercase:		str    						=  pow(a, b_private, q)

              __lowercase:		List[Any]    						=  pow(b_public, a_private, q)
              __lowercase:		List[Any]    						=  pow(a_public, b_private, q)

              print("The key value generated by A is: ", a_secret)
              print("The key value generated by B is: ", b_secret)


from sklearn.neural_network import MLPClassifier

__A	:   Tuple	=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
__A	:   Optional[int]	=					[0, 1, 0, 0]


__A	:   int	=					MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


__A	:   str	=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
__A	:   str	=					clf.predict(test)




def        __UpperCamelCase     (      _A					:							int		)				->Union[str, Any]:



							"""simple docstring"""
							return list(_A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
