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from test import support, seq_tests
import gc
class TupleTest(seq_tests.CommonTest):
type2test = tuple
def test_constructors(self):
super().test_len()
# calling built-in types without argument must return empty
self.assertEqual(tuple(), ())
t0_3 = (0, 1, 2, 3)
t0_3_bis = tuple(t0_3)
self.assertTrue(t0_3 is t0_3_bis)
self.assertEqual(tuple([]), ())
self.assertEqual(tuple([0, 1, 2, 3]), (0, 1, 2, 3))
self.assertEqual(tuple(''), ())
self.assertEqual(tuple('spam'), ('s', 'p', 'a', 'm'))
def test_truth(self):
super().test_truth()
self.assertTrue(not ())
self.assertTrue((42, ))
def test_len(self):
super().test_len()
self.assertEqual(len(()), 0)
self.assertEqual(len((0,)), 1)
self.assertEqual(len((0, 1, 2)), 3)
def test_iadd(self):
super().test_iadd()
u = (0, 1)
u2 = u
u += (2, 3)
self.assertTrue(u is not u2)
def test_imul(self):
super().test_imul()
u = (0, 1)
u2 = u
u *= 3
self.assertTrue(u is not u2)
def test_tupleresizebug(self):
# Check that a specific bug in _PyTuple_Resize() is squashed.
def f():
for i in range(1000):
yield i
self.assertEqual(list(tuple(f())), list(range(1000)))
def test_hash(self):
# See SF bug 942952: Weakness in tuple hash
# The hash should:
# be non-commutative
# should spread-out closely spaced values
# should not exhibit cancellation in tuples like (x,(x,y))
# should be distinct from element hashes: hash(x)!=hash((x,))
# This test exercises those cases.
# For a pure random hash and N=50, the expected number of occupied
# buckets when tossing 252,600 balls into 2**32 buckets
# is 252,592.6, or about 7.4 expected collisions. The
# standard deviation is 2.73. On a box with 64-bit hash
# codes, no collisions are expected. Here we accept no
# more than 15 collisions. Any worse and the hash function
# is sorely suspect.
N=50
base = list(range(N))
xp = [(i, j) for i in base for j in base]
inps = base + [(i, j) for i in base for j in xp] + \
[(i, j) for i in xp for j in base] + xp + list(zip(base))
collisions = len(inps) - len(set(map(hash, inps)))
self.assertTrue(collisions <= 15)
def test_repr(self):
l0 = tuple()
l2 = (0, 1, 2)
a0 = self.type2test(l0)
a2 = self.type2test(l2)
self.assertEqual(str(a0), repr(l0))
self.assertEqual(str(a2), repr(l2))
self.assertEqual(repr(a0), "()")
self.assertEqual(repr(a2), "(0, 1, 2)")
def _not_tracked(self, t):
# Nested tuples can take several collections to untrack
gc.collect()
gc.collect()
self.assertFalse(gc.is_tracked(t), t)
def _tracked(self, t):
self.assertTrue(gc.is_tracked(t), t)
gc.collect()
gc.collect()
self.assertTrue(gc.is_tracked(t), t)
def test_track_literals(self):
# Test GC-optimization of tuple literals
x, y, z = 1.5, "a", []
self._not_tracked(())
self._not_tracked((1,))
self._not_tracked((1, 2))
self._not_tracked((1, 2, "a"))
self._not_tracked((1, 2, (None, True, False, ()), int))
self._not_tracked((object(),))
self._not_tracked(((1, x), y, (2, 3)))
# Tuples with mutable elements are always tracked, even if those
# elements are not tracked right now.
self._tracked(([],))
self._tracked(([1],))
self._tracked(({},))
self._tracked((set(),))
self._tracked((x, y, z))
def check_track_dynamic(self, tp, always_track):
x, y, z = 1.5, "a", []
check = self._tracked if always_track else self._not_tracked
check(tp())
check(tp([]))
check(tp(set()))
check(tp([1, x, y]))
check(tp(obj for obj in [1, x, y]))
check(tp(set([1, x, y])))
check(tp(tuple([obj]) for obj in [1, x, y]))
check(tuple(tp([obj]) for obj in [1, x, y]))
self._tracked(tp([z]))
self._tracked(tp([[x, y]]))
self._tracked(tp([{x: y}]))
self._tracked(tp(obj for obj in [x, y, z]))
self._tracked(tp(tuple([obj]) for obj in [x, y, z]))
self._tracked(tuple(tp([obj]) for obj in [x, y, z]))
def test_track_dynamic(self):
# Test GC-optimization of dynamically constructed tuples.
self.check_track_dynamic(tuple, False)
def test_track_subtypes(self):
# Tuple subtypes must always be tracked
class MyTuple(tuple):
pass
self.check_track_dynamic(MyTuple, True)
def test_main():
support.run_unittest(TupleTest)
if __name__=="__main__":
test_main()
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