import unittest import seq_tests from seq_tests import ALWAYS_EQ, NEVER_EQ """ Tests common to tuple, list and UserList.UserList From Cpython seq_tests.py """ # import unittest import sys # import pickle # from test import support # from test.support import ALWAYS_EQ, NEVER_EQ # Various iterables # This is used for checking the constructor (here and in test_deque.py) def iterfunc(seqn): 'Regular generator' for i in seqn: yield i class Sequence: 'Sequence using __getitem__' def __init__(self, seqn): self.seqn = seqn def __getitem__(self, i): return self.seqn[i] class IterFunc: 'Sequence using iterator protocol' def __init__(self, seqn): self.seqn = seqn self.i = 0 def __iter__(self): return self def __next__(self): if self.i >= len(self.seqn): raise StopIteration v = self.seqn[self.i] self.i += 1 return v class IterGen: 'Sequence using iterator protocol defined with a generator' def __init__(self, seqn): self.seqn = seqn self.i = 0 def __iter__(self): for val in self.seqn: yield val class IterNextOnly: 'Missing __getitem__ and __iter__' def __init__(self, seqn): self.seqn = seqn self.i = 0 def __next__(self): if self.i >= len(self.seqn): raise StopIteration v = self.seqn[self.i] self.i += 1 return v class IterNoNext: 'Iterator missing __next__()' def __init__(self, seqn): self.seqn = seqn self.i = 0 def __iter__(self): return self class IterGenExc: 'Test propagation of exceptions' def __init__(self, seqn): self.seqn = seqn self.i = 0 def __iter__(self): return self def __next__(self): 3 // 0 class IterFuncStop: 'Test immediate stop' def __init__(self, seqn): pass def __iter__(self): return self def __next__(self): raise StopIteration from itertools import chain def itermulti(seqn): 'Test multiple tiers of iterators' return chain(map(lambda x:x, iterfunc(IterGen(Sequence(seqn))))) class LyingTuple(tuple): def __iter__(self): yield 1 class LyingList(list): def __iter__(self): yield 1 class CommonTest: # The type to be tested type2test = None def test_constructors(self): l0 = [] l1 = [0] l2 = [0, 1] u = self.type2test() u0 = self.type2test(l0) u1 = self.type2test(l1) u2 = self.type2test(l2) uu = self.type2test(u) uu0 = self.type2test(u0) uu1 = self.type2test(u1) uu2 = self.type2test(u2) v = self.type2test(tuple(u)) class OtherSeq: def __init__(self, initseq): self.__data = initseq def __len__(self): return len(self.__data) def __getitem__(self, i): return self.__data[i] s = OtherSeq(u0) v0 = self.type2test(s) self.assertEqual(len(v0), len(s)) s = "this is also a sequence" vv = self.type2test(s) self.assertEqual(len(vv), len(s)) # Create from various iteratables for s in ("123", "", range(1000), ('do', 1.2), range(2000,2200,5)): for g in (Sequence, IterFunc, IterGen, itermulti, iterfunc): self.assertEqual(self.type2test(g(s)), self.type2test(s)) self.assertEqual(self.type2test(IterFuncStop(s)), self.type2test()) self.assertEqual(self.type2test(c for c in "123"), self.type2test("123")) self.assertRaises(TypeError, self.type2test, IterNextOnly(s)) self.assertRaises(TypeError, self.type2test, IterNoNext(s)) self.assertRaises(ZeroDivisionError, self.type2test, IterGenExc(s)) # Issue #23757 self.assertEqual(self.type2test(LyingTuple((2,))), self.type2test((1,))) self.assertEqual(self.type2test(LyingList([2])), self.type2test([1])) def test_truth(self): self.assertFalse(self.type2test()) self.assertTrue(self.type2test([42])) def test_getitem(self): u = self.type2test([0, 1, 2, 3, 4]) for i in range(len(u)): self.assertEqual(u[i], i) self.assertEqual(u[int(i)], i) for i in range(-len(u), -1): self.assertEqual(u[i], len(u)+i) self.assertEqual(u[int(i)], len(u)+i) self.assertRaises(IndexError, u.__getitem__, -len(u)-1) self.assertRaises(IndexError, u.__getitem__, len(u)) self.assertRaises(ValueError, u.__getitem__, slice(0,10,0)) u = self.type2test() self.assertRaises(IndexError, u.__getitem__, 0) self.assertRaises(IndexError, u.__getitem__, -1) self.assertRaises(TypeError, u.__getitem__) a = self.type2test([10, 11]) self.assertEqual(a[0], 10) self.assertEqual(a[1], 11) self.assertEqual(a[-2], 10) self.assertEqual(a[-1], 11) self.assertRaises(IndexError, a.__getitem__, -3) self.assertRaises(IndexError, a.__getitem__, 3) def test_getslice(self): l = [0, 1, 2, 3, 4] u = self.type2test(l) self.assertEqual(u[0:0], self.type2test()) self.assertEqual(u[1:2], self.type2test([1])) self.assertEqual(u[-2:-1], self.type2test([3])) self.assertEqual(u[-1000:1000], u) self.assertEqual(u[1000:-1000], self.type2test([])) self.assertEqual(u[:], u) self.assertEqual(u[1:None], self.type2test([1, 2, 3, 4])) self.assertEqual(u[None:3], self.type2test([0, 1, 2])) # Extended slices self.assertEqual(u[::], u) self.assertEqual(u[::2], self.type2test([0, 2, 4])) self.assertEqual(u[1::2], self.type2test([1, 3])) self.assertEqual(u[::-1], self.type2test([4, 3, 2, 1, 0])) self.assertEqual(u[::-2], self.type2test([4, 2, 0])) self.assertEqual(u[3::-2], self.type2test([3, 1])) self.assertEqual(u[3:3:-2], self.type2test([])) self.assertEqual(u[3:2:-2], self.type2test([3])) self.assertEqual(u[3:1:-2], self.type2test([3])) self.assertEqual(u[3:0:-2], self.type2test([3, 1])) self.assertEqual(u[::-100], self.type2test([4])) self.assertEqual(u[100:-100:], self.type2test([])) self.assertEqual(u[-100:100:], u) self.assertEqual(u[100:-100:-1], u[::-1]) self.assertEqual(u[-100:100:-1], self.type2test([])) self.assertEqual(u[-100:100:2], self.type2test([0, 2, 4])) # Test extreme cases with long ints a = self.type2test([0,1,2,3,4]) self.assertEqual(a[ -pow(2,128): 3 ], self.type2test([0,1,2])) self.assertEqual(a[ 3: pow(2,145) ], self.type2test([3,4])) self.assertEqual(a[3::sys.maxsize], self.type2test([3])) def test_contains(self): u = self.type2test([0, 1, 2]) for i in u: self.assertIn(i, u) for i in min(u)-1, max(u)+1: self.assertNotIn(i, u) self.assertRaises(TypeError, u.__contains__) def test_contains_fake(self): # Sequences must use rich comparison against each item # (unless "is" is true, or an earlier item answered) # So ALWAYS_EQ must be found in all non-empty sequences. self.assertNotIn(ALWAYS_EQ, self.type2test([])) self.assertIn(ALWAYS_EQ, self.type2test([1])) self.assertIn(1, self.type2test([ALWAYS_EQ])) self.assertNotIn(NEVER_EQ, self.type2test([])) self.assertNotIn(ALWAYS_EQ, self.type2test([NEVER_EQ])) self.assertIn(NEVER_EQ, self.type2test([ALWAYS_EQ])) def test_contains_order(self): # Sequences must test in-order. If a rich comparison has side # effects, these will be visible to tests against later members. # In this test, the "side effect" is a short-circuiting raise. class DoNotTestEq(Exception): pass class StopCompares: def __eq__(self, other): raise DoNotTestEq checkfirst = self.type2test([1, StopCompares()]) self.assertIn(1, checkfirst) checklast = self.type2test([StopCompares(), 1]) self.assertRaises(DoNotTestEq, checklast.__contains__, 1) def test_len(self): self.assertEqual(len(self.type2test()), 0) self.assertEqual(len(self.type2test([])), 0) self.assertEqual(len(self.type2test([0])), 1) self.assertEqual(len(self.type2test([0, 1, 2])), 3) def test_minmax(self): u = self.type2test([0, 1, 2]) self.assertEqual(min(u), 0) self.assertEqual(max(u), 2) def test_addmul(self): u1 = self.type2test([0]) u2 = self.type2test([0, 1]) self.assertEqual(u1, u1 + self.type2test()) self.assertEqual(u1, self.type2test() + u1) self.assertEqual(u1 + self.type2test([1]), u2) self.assertEqual(self.type2test([-1]) + u1, self.type2test([-1, 0])) self.assertEqual(self.type2test(), u2*0) self.assertEqual(self.type2test(), 0*u2) self.assertEqual(self.type2test(), u2*0) self.assertEqual(self.type2test(), 0*u2) self.assertEqual(u2, u2*1) self.assertEqual(u2, 1*u2) self.assertEqual(u2, u2*1) self.assertEqual(u2, 1*u2) self.assertEqual(u2+u2, u2*2) self.assertEqual(u2+u2, 2*u2) self.assertEqual(u2+u2, u2*2) self.assertEqual(u2+u2, 2*u2) self.assertEqual(u2+u2+u2, u2*3) self.assertEqual(u2+u2+u2, 3*u2) class subclass(self.type2test): pass u3 = subclass([0, 1]) self.assertEqual(u3, u3*1) self.assertIsNot(u3, u3*1) def test_iadd(self): u = self.type2test([0, 1]) u += self.type2test() self.assertEqual(u, self.type2test([0, 1])) u += self.type2test([2, 3]) self.assertEqual(u, self.type2test([0, 1, 2, 3])) u += self.type2test([4, 5]) self.assertEqual(u, self.type2test([0, 1, 2, 3, 4, 5])) u = self.type2test("spam") u += self.type2test("eggs") self.assertEqual(u, self.type2test("spameggs")) def test_imul(self): u = self.type2test([0, 1]) u *= 3 self.assertEqual(u, self.type2test([0, 1, 0, 1, 0, 1])) u *= 0 self.assertEqual(u, self.type2test([])) def test_getitemoverwriteiter(self): # Verify that __getitem__ overrides are not recognized by __iter__ class T(self.type2test): def __getitem__(self, key): return str(key) + '!!!' self.assertEqual(next(iter(T((1,2)))), 1) def test_repeat(self): for m in range(4): s = tuple(range(m)) for n in range(-3, 5): self.assertEqual(self.type2test(s*n), self.type2test(s)*n) self.assertEqual(self.type2test(s)*(-4), self.type2test([])) self.assertEqual(id(s), id(s*1)) def test_bigrepeat(self): if sys.maxsize <= 2147483647: x = self.type2test([0]) x *= 2**16 self.assertRaises(MemoryError, x.__mul__, 2**16) if hasattr(x, '__imul__'): self.assertRaises(MemoryError, x.__imul__, 2**16) def test_subscript(self): a = self.type2test([10, 11]) self.assertEqual(a.__getitem__(0), 10) self.assertEqual(a.__getitem__(1), 11) self.assertEqual(a.__getitem__(-2), 10) self.assertEqual(a.__getitem__(-1), 11) self.assertRaises(IndexError, a.__getitem__, -3) self.assertRaises(IndexError, a.__getitem__, 3) self.assertEqual(a.__getitem__(slice(0,1)), self.type2test([10])) self.assertEqual(a.__getitem__(slice(1,2)), self.type2test([11])) self.assertEqual(a.__getitem__(slice(0,2)), self.type2test([10, 11])) self.assertEqual(a.__getitem__(slice(0,3)), self.type2test([10, 11])) self.assertEqual(a.__getitem__(slice(3,5)), self.type2test([])) self.assertRaises(ValueError, a.__getitem__, slice(0, 10, 0)) self.assertRaises(TypeError, a.__getitem__, 'x') def test_count(self): a = self.type2test([0, 1, 2])*3 self.assertEqual(a.count(0), 3) self.assertEqual(a.count(1), 3) self.assertEqual(a.count(3), 0) self.assertEqual(a.count(ALWAYS_EQ), 9) self.assertEqual(self.type2test([ALWAYS_EQ, ALWAYS_EQ]).count(1), 2) self.assertEqual(self.type2test([ALWAYS_EQ, ALWAYS_EQ]).count(NEVER_EQ), 2) self.assertEqual(self.type2test([NEVER_EQ, NEVER_EQ]).count(ALWAYS_EQ), 0) self.assertRaises(TypeError, a.count) class BadExc(Exception): pass class BadCmp: def __eq__(self, other): if other == 2: raise BadExc() return False self.assertRaises(BadExc, a.count, BadCmp()) def test_index(self): u = self.type2test([0, 1]) self.assertEqual(u.index(0), 0) self.assertEqual(u.index(1), 1) self.assertRaises(ValueError, u.index, 2) u = self.type2test([-2, -1, 0, 0, 1, 2]) self.assertEqual(u.count(0), 2) self.assertEqual(u.index(0), 2) self.assertEqual(u.index(0, 2), 2) self.assertEqual(u.index(-2, -10), 0) self.assertEqual(u.index(0, 3), 3) self.assertEqual(u.index(0, 3, 4), 3) self.assertRaises(ValueError, u.index, 2, 0, -10) self.assertEqual(u.index(ALWAYS_EQ), 0) self.assertEqual(self.type2test([ALWAYS_EQ, ALWAYS_EQ]).index(1), 0) self.assertEqual(self.type2test([ALWAYS_EQ, ALWAYS_EQ]).index(NEVER_EQ), 0) self.assertRaises(ValueError, self.type2test([NEVER_EQ, NEVER_EQ]).index, ALWAYS_EQ) self.assertRaises(TypeError, u.index) class BadExc(Exception): pass class BadCmp: def __eq__(self, other): if other == 2: raise BadExc() return False a = self.type2test([0, 1, 2, 3]) self.assertRaises(BadExc, a.index, BadCmp()) a = self.type2test([-2, -1, 0, 0, 1, 2]) self.assertEqual(a.index(0), 2) self.assertEqual(a.index(0, 2), 2) self.assertEqual(a.index(0, -4), 2) self.assertEqual(a.index(-2, -10), 0) self.assertEqual(a.index(0, 3), 3) self.assertEqual(a.index(0, -3), 3) self.assertEqual(a.index(0, 3, 4), 3) self.assertEqual(a.index(0, -3, -2), 3) self.assertEqual(a.index(0, -4*sys.maxsize, 4*sys.maxsize), 2) self.assertRaises(ValueError, a.index, 0, 4*sys.maxsize,-4*sys.maxsize) self.assertRaises(ValueError, a.index, 2, 0, -10) # def test_pickle(self): # lst = self.type2test([4, 5, 6, 7]) # for proto in range(pickle.HIGHEST_PROTOCOL + 1): # lst2 = pickle.loads(pickle.dumps(lst, proto)) # self.assertEqual(lst2, lst) # self.assertNotEqual(id(lst2), id(lst)) # def test_free_after_iterating(self): # support.check_free_after_iterating(self, iter, self.type2test) # support.check_free_after_iterating(self, reversed, self.type2test) """ From Cpython test_tuple.py """ # from test import support, seq_tests import unittest # import gc # import pickle # For tuple hashes, we normally only run a test to ensure that we get # the same results across platforms in a handful of cases. If that's # so, there's no real point to running more. Set RUN_ALL_HASH_TESTS to # run more anyway. That's usually of real interest only when analyzing, # or changing, the hash algorithm. In which case it's usually also # most useful to set JUST_SHOW_HASH_RESULTS, to see all the results # instead of wrestling with test "failures". See the bottom of the # file for extensive notes on what we're testing here and why. RUN_ALL_HASH_TESTS = False JUST_SHOW_HASH_RESULTS = False # if RUN_ALL_HASH_TESTS, just display class TupleTest(seq_tests.CommonTest, unittest.TestCase): type2test = tuple def test_getitem_error(self): t = () msg = "tuple indices must be integers or slices" with self.assertRaises(TypeError) as c: t['a'] self.assertIn(msg, c.exception.args[0]) 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.assertRaises(TypeError) as c: tuple(sequence=()) self.assertIn('keyword argument', c.exception.args[0]) 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))) # We expect tuples whose base components have deterministic hashes to # have deterministic hashes too - and, indeed, the same hashes across # platforms with hash codes of the same bit width. # Skulpt @TODO imporove tuple hash function # def test_hash_exact(self): # def check_one_exact(t, e32, e64): # got = hash(t) # expected = e32 #if support.NHASHBITS == 32 else e64 # if got != expected: # msg = f"FAIL hash({t!r}) == {got} != {expected}" # self.fail(msg) # check_one_exact((), 750394483, 5740354900026072187) # check_one_exact((0,), 1214856301, -8753497827991233192) # check_one_exact((0, 0), -168982784, -8458139203682520985) # check_one_exact((0.5,), 2077348973, -408149959306781352) # check_one_exact((0.5, (), (-2, 3, (4, 6))), 714642271, # -1845940830829704396) # # Various tests for hashing of tuples to check that we get few collisions. # # Does something only if RUN_ALL_HASH_TESTS is true. # # # # Earlier versions of the tuple hash algorithm had massive collisions # # reported at: # # - https://bugs.python.org/issue942952 # # - https://bugs.python.org/issue34751 # def test_hash_optional(self): # from itertools import product # if not RUN_ALL_HASH_TESTS: # return # # If specified, `expected` is a 2-tuple of expected # # (number_of_collisions, pileup) values, and the test fails if # # those aren't the values we get. Also if specified, the test # # fails if z > `zlimit`. # def tryone_inner(tag, nbins, hashes, expected=None, zlimit=None): # from collections import Counter # nballs = len(hashes) # mean, sdev = support.collision_stats(nbins, nballs) # c = Counter(hashes) # collisions = nballs - len(c) # z = (collisions - mean) / sdev # pileup = max(c.values()) - 1 # del c # got = (collisions, pileup) # failed = False # prefix = "" # if zlimit is not None and z > zlimit: # failed = True # prefix = f"FAIL z > {zlimit}; " # if expected is not None and got != expected: # failed = True # prefix += f"FAIL {got} != {expected}; " # if failed or JUST_SHOW_HASH_RESULTS: # msg = f"{prefix}{tag}; pileup {pileup:,} mean {mean:.1f} " # msg += f"coll {collisions:,} z {z:+.1f}" # if JUST_SHOW_HASH_RESULTS: # import sys # print(msg, file=sys.__stdout__) # else: # self.fail(msg) # def tryone(tag, xs, # native32=None, native64=None, hi32=None, lo32=None, # zlimit=None): # NHASHBITS = support.NHASHBITS # hashes = list(map(hash, xs)) # tryone_inner(tag + f"; {NHASHBITS}-bit hash codes", # 1 << NHASHBITS, # hashes, # native32 if NHASHBITS == 32 else native64, # zlimit) # if NHASHBITS > 32: # shift = NHASHBITS - 32 # tryone_inner(tag + "; 32-bit upper hash codes", # 1 << 32, # [h >> shift for h in hashes], # hi32, # zlimit) # mask = (1 << 32) - 1 # tryone_inner(tag + "; 32-bit lower hash codes", # 1 << 32, # [h & mask for h in hashes], # lo32, # zlimit) # # Tuples of smallish positive integers are common - nice if we # # get "better than random" for these. # tryone("range(100) by 3", list(product(range(100), repeat=3)), # (0, 0), (0, 0), (4, 1), (0, 0)) # # A previous hash had systematic problems when mixing integers of # # similar magnitude but opposite sign, obscurely related to that # # j ^ -2 == -j when j is odd. # cands = list(range(-10, -1)) + list(range(9)) # # Note: -1 is omitted because hash(-1) == hash(-2) == -2, and # # there's nothing the tuple hash can do to avoid collisions # # inherited from collisions in the tuple components' hashes. # tryone("-10 .. 8 by 4", list(product(cands, repeat=4)), # (0, 0), (0, 0), (0, 0), (0, 0)) # del cands # # The hashes here are a weird mix of values where all the # # variation is in the lowest bits and across a single high-order # # bit - the middle bits are all zeroes. A decent hash has to # # both propagate low bits to the left and high bits to the # # right. This is also complicated a bit in that there are # # collisions among the hashes of the integers in L alone. # L = [n << 60 for n in range(100)] # tryone("0..99 << 60 by 3", list(product(L, repeat=3)), # (0, 0), (0, 0), (0, 0), (324, 1)) # del L # # Used to suffer a massive number of collisions. # tryone("[-3, 3] by 18", list(product([-3, 3], repeat=18)), # (7, 1), (0, 0), (7, 1), (6, 1)) # # And even worse. hash(0.5) has only a single bit set, at the # # high end. A decent hash needs to propagate high bits right. # tryone("[0, 0.5] by 18", list(product([0, 0.5], repeat=18)), # (5, 1), (0, 0), (9, 1), (12, 1)) # # Hashes of ints and floats are the same across platforms. # # String hashes vary even on a single platform across runs, due # # to hash randomization for strings. So we can't say exactly # # what this should do. Instead we insist that the # of # # collisions is no more than 4 sdevs above the theoretically # # random mean. Even if the tuple hash can't achieve that on its # # own, the string hash is trying to be decently pseudo-random # # (in all bit positions) on _its_ own. We can at least test # # that the tuple hash doesn't systematically ruin that. # tryone("4-char tuples", # list(product("abcdefghijklmnopqrstuvwxyz", repeat=4)), # zlimit=4.0) # # The "old tuple test". See https://bugs.python.org/issue942952. # # Ensures, for example, that the hash: # # is non-commutative # # spreads closely spaced values # # doesn't exhibit cancellation in tuples like (x,(x,y)) # N = 50 # base = list(range(N)) # xp = list(product(base, repeat=2)) # inps = base + list(product(base, xp)) + \ # list(product(xp, base)) + xp + list(zip(base)) # tryone("old tuple test", inps, # (2, 1), (0, 0), (52, 49), (7, 1)) # del base, xp, inps # # The "new tuple test". See https://bugs.python.org/issue34751. # # Even more tortured nesting, and a mix of signed ints of very # # small magnitude. # n = 5 # A = [x for x in range(-n, n+1) if x != -1] # B = A + [(a,) for a in A] # L2 = list(product(A, repeat=2)) # L3 = L2 + list(product(A, repeat=3)) # L4 = L3 + list(product(A, repeat=4)) # # 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 += product(L3, B) # T += product(L2, repeat=2) # T += product(B, L3) # T += product(B, B, L2) # T += product(B, L2, B) # T += product(L2, B, B) # T += product(B, repeat=4) # assert len(T) == 345130 # tryone("new tuple test", T, # (9, 1), (0, 0), (21, 5), (6, 1)) 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) # Notes on testing hash codes. The primary thing is that Python doesn't # care about "random" hash codes. To the contrary, we like them to be # very regular when possible, so that the low-order bits are as evenly # distributed as possible. For integers this is easy: hash(i) == i for # all not-huge i except i==-1. # # For tuples of mixed type there's really no hope of that, so we want # "randomish" here instead. But getting close to pseudo-random in all # bit positions is more expensive than we've been willing to pay for. # # We can tolerate large deviations from random - what we don't want is # catastrophic pileups on a relative handful of hash codes. The dict # and set lookup routines remain effective provided that full-width hash # codes for not-equal objects are distinct. # # So we compute various statistics here based on what a "truly random" # hash would do, but don't automate "pass or fail" based on those # results. Instead those are viewed as inputs to human judgment, and the # automated tests merely ensure we get the _same_ results across # platforms. In fact, we normally don't bother to run them at all - # set RUN_ALL_HASH_TESTS to force it. # # When global JUST_SHOW_HASH_RESULTS is True, the tuple hash statistics # are just displayed to stdout. A typical output line looks like: # # old tuple test; 32-bit upper hash codes; \ # pileup 49 mean 7.4 coll 52 z +16.4 # # "old tuple test" is just a string name for the test being run. # # "32-bit upper hash codes" means this was run under a 64-bit build and # we've shifted away the lower 32 bits of the hash codes. # # "pileup" is 0 if there were no collisions across those hash codes. # It's 1 less than the maximum number of times any single hash code was # seen. So in this case, there was (at least) one hash code that was # seen 50 times: that hash code "piled up" 49 more times than ideal. # # "mean" is the number of collisions a perfectly random hash function # would have yielded, on average. # # "coll" is the number of collisions actually seen. # # "z" is "coll - mean" divided by the standard deviation of the number # of collisions a perfectly random hash function would suffer. A # positive value is "worse than random", and negative value "better than # random". Anything of magnitude greater than 3 would be highly suspect # for a hash function that claimed to be random. It's essentially # impossible that a truly random function would deliver a result 16.4 # sdevs "worse than random". # # But we don't care here! That's why the test isn't coded to fail. # Knowing something about how the high-order hash code bits behave # provides insight, but is irrelevant to how the dict and set lookup # code performs. The low-order bits are much more important to that, # and on the same test those did "just like random": # # old tuple test; 32-bit lower hash codes; \ # pileup 1 mean 7.4 coll 7 z -0.2 # # So there are always tradeoffs to consider. For another: # # 0..99 << 60 by 3; 32-bit hash codes; \ # pileup 0 mean 116.4 coll 0 z -10.8 # # That was run under a 32-bit build, and is spectacularly "better than # random". On a 64-bit build the wider hash codes are fine too: # # 0..99 << 60 by 3; 64-bit hash codes; \ # pileup 0 mean 0.0 coll 0 z -0.0 # # but their lower 32 bits are poor: # # 0..99 << 60 by 3; 32-bit lower hash codes; \ # pileup 1 mean 116.4 coll 324 z +19.2 # # In a statistical sense that's waaaaay too many collisions, but (a) 324 # collisions out of a million hash codes isn't anywhere near being a # real problem; and, (b) the worst pileup on a single hash code is a measly # 1 extra. It's a relatively poor case for the tuple hash, but still # fine for practical use. # # This isn't, which is what Python 3.7.1 produced for the hashes of # itertools.product([0, 0.5], repeat=18). Even with a fat 64-bit # hashcode, the highest pileup was over 16,000 - making a dict/set # lookup on one of the colliding values thousands of times slower (on # average) than we expect. # # [0, 0.5] by 18; 64-bit hash codes; \ # pileup 16,383 mean 0.0 coll 262,128 z +6073641856.9 # [0, 0.5] by 18; 32-bit lower hash codes; \ # pileup 262,143 mean 8.0 coll 262,143 z +92683.6 if __name__ == "__main__": unittest.main()