"""simple docstring"""
from __future__ import annotations

import math
from itertools import permutations







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5						,	int(math.sqrt(_SCREAMING_SNAKE_CASE  ) + 1  )						,	6  ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def 		__a  (      _SCREAMING_SNAKE_CASE = 7  )      ->int:
  a__:	Union[str, Any]     				=      ''.join(str(_SCREAMING_SNAKE_CASE  ) for i in range(1						,	n + 1  )  )
  a__:	Dict     				=      [int(''.join(_SCREAMING_SNAKE_CASE  )  ) for i in permutations(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )]
  a__:	Dict     				=      [num for num in perm_list if is_prime(_SCREAMING_SNAKE_CASE  )]
  return max(_SCREAMING_SNAKE_CASE  ) if pandigitals else 0


if __name__ == "__main__":
 print(f"{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
lowercase__   			=      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
lowercase__			,   lowercase__			,   lowercase__			,   lowercase__   			=      train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

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

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

lowercase__   			=      knn.predict(X_new)

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


