

from itertools import combinations, product





def  _snake_case		(snake_case_					:     Dict    ,       snake_case_					:     str    ,       snake_case_					:     Optional[int]    ,       snake_case_					:     str  )							->		bool:
  if xa == ya == 0 or xa == ya == 0:
    return False
  __UpperCAmelCase	:  Tuple		=     xa * xa + ya * ya
  __UpperCAmelCase	:  Optional[int]		=     xa * xa + ya * ya
  __UpperCAmelCase	:  List[str]		=     (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
  return (
      a_square + b_square == c_square
      or a_square + c_square == b_square
      or b_square + c_square == a_square
  )





def  _snake_case		(snake_case_					:     Tuple = 50  )							->		int:
  return sum(
      1
      for pta, pta in combinations(product(range(limit + 1  )    ,       repeat=2  )    ,       2  )
      if is_right(*a_    ,       *a_  )  )


if __name__ == "__main__":
      print(F'''{solution() = }''')













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

# Load iris file
A_			:Union[str, Any]	    =		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
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_			:Optional[Any]	    =		[[1, 2, 1, 4], [2, 3, 4, 5]]

A_			:Union[str, Any]	    =		knn.predict(X_new)

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

