



from itertools import permutations

def 	a_					(							_lowerCAmelCase = 5	)				->				int:
  if gon_side < 3 or gon_side > 5:
    raise ValueError('gon_side must be in the range [3, 5]'	)

  # Since it's 16, we know 10 is on the outer ring
  # Put the big numbers at the end so that they are never the first number
  __lowerCamelCase	:	int   	=			list(range(gon_side + 1	,0	,-1	)	)
  __lowerCamelCase	:	Dict   	=			list(range(gon_side + 2	,gon_side * 2 + 1	)	)

  for perm in permutations(small_numbers + big_numbers	):
    __lowerCamelCase	:	Union[str, Any]   	=			generate_gon_ring(_lowerCAmelCase	,list(_lowerCAmelCase	)	)
    if is_magic_gon(_lowerCAmelCase	):
      return int(''.join(str(_lowerCAmelCase	) for n in numbers	)	)

  __lowerCamelCase	:	Any   	=			F'Magic {gon_side}-gon ring is impossible'
  raise ValueError(_lowerCAmelCase	)

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				list[int]:
  __lowerCamelCase	:	str   	=			[0] * (gon_side * 3)
  __lowerCamelCase	:	Union[str, Any]   	=			perm[0:3]
  perm.append(perm[1]	)

  __lowerCamelCase	:	List[Any]   	=			1 if gon_side < 5 else 2

  for i in range(1	,len(_lowerCAmelCase	) // 3 + magic_number	):
    __lowerCamelCase	:	List[str]   	=			perm[2 * i + 1]
    __lowerCamelCase	:	List[str]   	=			result[3 * i - 1]
    __lowerCamelCase	:	Optional[Any]   	=			perm[2 * i + 2]

  return result

def 	a_					(							_lowerCAmelCase	)				->				bool:
  if len(_lowerCAmelCase	) % 3 != 0:
    raise ValueError('a gon ring should have a length that is a multiple of 3'	)

  if min(numbers[::3]	) != numbers[0]:
    return False

  __lowerCamelCase	:	Any   	=			sum(numbers[:3]	)

  return all(sum(numbers[i : i + 3]	) == total for i in range(3	,len(_lowerCAmelCase	)	,3	)	)


if __name__ == "__main__":
   print(solution())











'''simple docstring'''





from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_UpperCamelCase							    =						load_iris()
iris.keys()


print(f'''Target names: \n {iris.target_names} ''')
print(f'''\n Features: \n {iris.feature_names}''')

# Train set e Test set
_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase							    =						train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCamelCase							    =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_UpperCamelCase							    =						[[1, 2, 1, 4], [2, 3, 4, 5]]

_UpperCamelCase							    =						knn.predict(X_new)

print(
    f'''\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'''
    f''' {iris["target_names"][prediction]}'''
)






