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):
type2test = tuple
def test_getitem_error(self):
t = ()
msg = "tuple indices must be integers or slices"
with self.assertRaisesRegex(TypeError, msg):
t['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_keywords_in_subclass(self):
class subclass(tuple):
pass
u = subclass([1, 2])
self.assertIs(type(u), subclass)
self.assertEqual(list(u), [1, 2])
with self.assertRaises(TypeError):
subclass(sequence=())
class subclass_with_init(tuple):
def __init__(self, arg, newarg=None):
self.newarg = newarg
u = subclass_with_init([1, 2], newarg=3)
self.assertIs(type(u), subclass_with_init)
self.assertEqual(list(u), [1, 2])
self.assertEqual(u.newarg, 3)
class subclass_with_new(tuple):
def __new__(cls, arg, newarg=None):
self = super().__new__(cls, arg)
self.newarg = newarg
return self
u = subclass_with_new([1, 2], newarg=3)
self.assertIs(type(u), subclass_with_new)
self.assertEqual(list(u), [1, 2])
self.assertEqual(u.newarg, 3)
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.
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()
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