



'''simple docstring'''





from itertools import combinations, product

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				bool:
  if xa == ya == 0 or xa == ya == 0:
    return False
  __lowerCamelCase	:	int   	=			xa * xa + ya * ya
  __lowerCamelCase	:	Union[str, Any]   	=			xa * xa + ya * ya
  __lowerCamelCase	:	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 	a_					(							_lowerCAmelCase = 50	)				->				int:
  return sum(
      1
      for pta, pta in combinations(product(range(limit + 1	)	,repeat=2	)	,2	)
      if is_right(*_lowerCAmelCase	,*_lowerCAmelCase	)	)


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
_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]}'''
)






