| from test import support, seq_tests |
| import unittest |
| |
| import gc |
| import pickle |
| |
| class TupleTest(seq_tests.CommonTest): |
| type2test = tuple |
| |
| def test_getitem_error(self): |
| msg = "tuple indices must be integers or slices" |
| with self.assertRaisesRegex(TypeError, msg): |
| ()['a'] |
| |
| def test_constructors(self): |
| super().test_constructors() |
| # 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')) |
| self.assertEqual(tuple(x for x in range(10) if x % 2), |
| (1, 3, 5, 7, 9)) |
| |
| def test_keyword_args(self): |
| with self.assertRaisesRegex(TypeError, 'keyword argument'): |
| tuple(sequence=()) |
| |
| 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))) |
| |
| # Various tests for hashing of tuples to check that we get few collisions. |
| # |
| # Earlier versions of the tuple hash algorithm had collisions |
| # reported at: |
| # - https://bugs.python.org/issue942952 |
| # - https://bugs.python.org/issue34751 |
| # |
| # Notes: |
| # - The hash of tuples is deterministic: if the test passes once on a given |
| # system, it will always pass. So the probabilities mentioned in the |
| # test_hash functions below should be interpreted assuming that the |
| # hashes are random. |
| # - Due to the structure in the testsuite inputs, collisions are not |
| # independent. For example, if hash((a,b)) == hash((c,d)), then also |
| # hash((a,b,x)) == hash((c,d,x)). But the quoted probabilities assume |
| # independence anyway. |
| # - We limit the hash to 32 bits in the tests to have a good test on |
| # 64-bit systems too. Furthermore, this is also a sanity check that the |
| # lower 32 bits of a 64-bit hash are sufficiently random too. |
| def test_hash1(self): |
| # Check for hash collisions between small integers in range(50) and |
| # certain tuples and nested tuples of such integers. |
| 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)) |
| self.assertEqual(len(inps), 252600) |
| hashes = set(hash(x) % 2**32 for x in inps) |
| collisions = len(inps) - len(hashes) |
| |
| # For a pure random 32-bit hash and N = 252,600 test items, the |
| # expected number of collisions equals |
| # |
| # 2**(-32) * N(N-1)/2 = 7.4 |
| # |
| # We allow up to 15 collisions, which suffices to make the test |
| # pass with 99.5% confidence. |
| self.assertLessEqual(collisions, 15) |
| |
| def test_hash2(self): |
| # Check for hash collisions between small integers (positive and |
| # negative), tuples and nested tuples of such integers. |
| |
| # All numbers in the interval [-n, ..., n] except -1 because |
| # hash(-1) == hash(-2). |
| n = 5 |
| A = [x for x in range(-n, n+1) if x != -1] |
| |
| B = A + [(a,) for a in A] |
| |
| L2 = [(a,b) for a in A for b in A] |
| L3 = L2 + [(a,b,c) for a in A for b in A for c in A] |
| L4 = L3 + [(a,b,c,d) for a in A for b in A for c in A for d in A] |
| |
| # T = list of testcases. These consist of all (possibly nested |
| # at most 2 levels deep) tuples containing at most 4 items from |
| # the set A. |
| T = A |
| T += [(a,) for a in B + L4] |
| T += [(a,b) for a in L3 for b in B] |
| T += [(a,b) for a in L2 for b in L2] |
| T += [(a,b) for a in B for b in L3] |
| T += [(a,b,c) for a in B for b in B for c in L2] |
| T += [(a,b,c) for a in B for b in L2 for c in B] |
| T += [(a,b,c) for a in L2 for b in B for c in B] |
| T += [(a,b,c,d) for a in B for b in B for c in B for d in B] |
| self.assertEqual(len(T), 345130) |
| hashes = set(hash(x) % 2**32 for x in T) |
| collisions = len(T) - len(hashes) |
| |
| # For a pure random 32-bit hash and N = 345,130 test items, the |
| # expected number of collisions equals |
| # |
| # 2**(-32) * N(N-1)/2 = 13.9 |
| # |
| # We allow up to 20 collisions, which suffices to make the test |
| # pass with 95.5% confidence. |
| self.assertLessEqual(collisions, 20) |
| |
| def test_hash3(self): |
| # Check for hash collisions between tuples containing 0.0 and 0.5. |
| # The hashes of 0.0 and 0.5 itself differ only in one high bit. |
| # So this implicitly tests propagation of high bits to low bits. |
| from itertools import product |
| T = list(product([0.0, 0.5], repeat=18)) |
| self.assertEqual(len(T), 262144) |
| hashes = set(hash(x) % 2**32 for x in T) |
| collisions = len(T) - len(hashes) |
| |
| # For a pure random 32-bit hash and N = 262,144 test items, the |
| # expected number of collisions equals |
| # |
| # 2**(-32) * N(N-1)/2 = 8.0 |
| # |
| # We allow up to 15 collisions, which suffices to make the test |
| # pass with 99.1% confidence. |
| self.assertLessEqual(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) |
| |
| @support.cpython_only |
| 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])) |
| |
| @support.cpython_only |
| def test_track_dynamic(self): |
| # Test GC-optimization of dynamically constructed tuples. |
| self.check_track_dynamic(tuple, False) |
| |
| @support.cpython_only |
| def test_track_subtypes(self): |
| # Tuple subtypes must always be tracked |
| class MyTuple(tuple): |
| pass |
| self.check_track_dynamic(MyTuple, True) |
| |
| @support.cpython_only |
| def test_bug7466(self): |
| # Trying to untrack an unfinished tuple could crash Python |
| self._not_tracked(tuple(gc.collect() for i in range(101))) |
| |
| def test_repr_large(self): |
| # Check the repr of large list objects |
| def check(n): |
| l = (0,) * n |
| s = repr(l) |
| self.assertEqual(s, |
| '(' + ', '.join(['0'] * n) + ')') |
| check(10) # check our checking code |
| check(1000000) |
| |
| def test_iterator_pickle(self): |
| # Userlist iterators don't support pickling yet since |
| # they are based on generators. |
| data = self.type2test([4, 5, 6, 7]) |
| for proto in range(pickle.HIGHEST_PROTOCOL + 1): |
| itorg = iter(data) |
| d = pickle.dumps(itorg, proto) |
| it = pickle.loads(d) |
| self.assertEqual(type(itorg), type(it)) |
| self.assertEqual(self.type2test(it), self.type2test(data)) |
| |
| it = pickle.loads(d) |
| next(it) |
| d = pickle.dumps(it, proto) |
| self.assertEqual(self.type2test(it), self.type2test(data)[1:]) |
| |
| def test_reversed_pickle(self): |
| data = self.type2test([4, 5, 6, 7]) |
| for proto in range(pickle.HIGHEST_PROTOCOL + 1): |
| itorg = reversed(data) |
| d = pickle.dumps(itorg, proto) |
| it = pickle.loads(d) |
| self.assertEqual(type(itorg), type(it)) |
| self.assertEqual(self.type2test(it), self.type2test(reversed(data))) |
| |
| it = pickle.loads(d) |
| next(it) |
| d = pickle.dumps(it, proto) |
| self.assertEqual(self.type2test(it), self.type2test(reversed(data))[1:]) |
| |
| def test_no_comdat_folding(self): |
| # Issue 8847: In the PGO build, the MSVC linker's COMDAT folding |
| # optimization causes failures in code that relies on distinct |
| # function addresses. |
| class T(tuple): pass |
| with self.assertRaises(TypeError): |
| [3,] + T((1,2)) |
| |
| def test_lexicographic_ordering(self): |
| # Issue 21100 |
| a = self.type2test([1, 2]) |
| b = self.type2test([1, 2, 0]) |
| c = self.type2test([1, 3]) |
| self.assertLess(a, b) |
| self.assertLess(b, c) |
| |
| if __name__ == "__main__": |
| unittest.main() |