from test import support
import random
import unittest
from functools import cmp_to_key
verbose = support.verbose
nerrors = 0
def check(tag, expected, raw, compare=None):
global nerrors
if verbose:
print(" checking", tag)
orig = raw[:] # save input in case of error
if compare:
raw.sort(key=cmp_to_key(compare))
else:
raw.sort()
if len(expected) != len(raw):
print("error in", tag)
print("length mismatch;", len(expected), len(raw))
print(expected)
print(orig)
print(raw)
nerrors += 1
return
for i, good in enumerate(expected):
maybe = raw[i]
if good is not maybe:
print("error in", tag)
print("out of order at index", i, good, maybe)
print(expected)
print(orig)
print(raw)
nerrors += 1
return
class TestBase(unittest.TestCase):
def testStressfully(self):
# Try a variety of sizes at and around powers of 2, and at powers of 10.
sizes = [0]
for power in range(1, 10):
n = 2 ** power
sizes.extend(range(n-1, n+2))
sizes.extend([10, 100, 1000])
class Complains(object):
maybe_complain = True
def __init__(self, i):
self.i = i
def __lt__(self, other):
if Complains.maybe_complain and random.random() < 0.001:
if verbose:
print(" complaining at", self, other)
raise RuntimeError
return self.i < other.i
def __repr__(self):
return "Complains(%d)" % self.i
class Stable(object):
def __init__(self, key, i):
self.key = key
self.index = i
def __lt__(self, other):
return self.key < other.key
def __repr__(self):
return "Stable(%d, %d)" % (self.key, self.index)
for n in sizes:
x = list(range(n))
if verbose:
print("Testing size", n)
s = x[:]
check("identity", x, s)
s = x[:]
s.reverse()
check("reversed", x, s)
s = x[:]
random.shuffle(s)
check("random permutation", x, s)
y = x[:]
y.reverse()
s = x[:]
check("reversed via function", y, s, lambda a, b: (b>a)-(b<a))
if verbose:
print(" Checking against an insane comparison function.")
print(" If the implementation isn't careful, this may segfault.")
s = x[:]
s.sort(key=cmp_to_key(lambda a, b: int(random.random() * 3) - 1))
check("an insane function left some permutation", x, s)
if len(x) >= 2:
def bad_key(x):
raise RuntimeError
s = x[:]
self.assertRaises(RuntimeError, s.sort, key=bad_key)
x = [Complains(i) for i in x]
s = x[:]
random.shuffle(s)
Complains.maybe_complain = True
it_complained = False
try:
s.sort()
except RuntimeError:
it_complained = True
if it_complained:
Complains.maybe_complain = False
check("exception during sort left some permutation", x, s)
s = [Stable(random.randrange(10), i) for i in range(n)]
augmented = [(e, e.index) for e in s]
augmented.sort() # forced stable because ties broken by index
x = [e for e, i in augmented] # a stable sort of s
check("stability", x, s)
def test_small_stability(self):
from itertools import product
from operator import itemgetter
# Exhaustively test stability across all lists of small lengths
# and only a few distinct elements.
# This can provoke edge cases that randomization is unlikely to find.
# But it can grow very expensive quickly, so don't overdo it.
NELTS = 3
MAXSIZE = 9
pick0 = itemgetter(0)
for length in range(MAXSIZE + 1):
# There are NELTS ** length distinct lists.
for t in product(range(NELTS), repeat=length):
xs = list(zip(t, range(length)))
# Stability forced by index in each element.
forced = sorted(xs)
# Use key= to hide the index from compares.
native = sorted(xs, key=pick0)
self.assertEqual(forced, native)
#==============================================================================
class TestBugs(unittest.TestCase):
def test_bug453523(self):
# bug 453523 -- list.sort() crasher.
# If this fails, the most likely outcome is a core dump.
# Mutations during a list sort should raise a ValueError.
class C:
def __lt__(self, other):
if L and random.random() < 0.75:
L.pop()
else:
L.append(3)
return random.random() < 0.5
L = [C() for i in range(50)]
self.assertRaises(ValueError, L.sort)
def test_undetected_mutation(self):
# Python 2.4a1 did not always detect mutation
memorywaster = []
for i in range(20):
def mutating_cmp(x, y):
L.append(3)
L.pop()
return (x > y) - (x < y)
L = [1,2]
self.assertRaises(ValueError, L.sort, key=cmp_to_key(mutating_cmp))
def mutating_cmp(x, y):
L.append(3)
del L[:]
return (x > y) - (x < y)
self.assertRaises(ValueError, L.sort, key=cmp_to_key(mutating_cmp))
memorywaster = [memorywaster]
#==============================================================================
class TestDecorateSortUndecorate(unittest.TestCase):
def test_decorated(self):
data = 'The quick Brown fox Jumped over The lazy Dog'.split()
copy = data[:]
random.shuffle(data)
data.sort(key=str.lower)
def my_cmp(x, y):
xlower, ylower = x.lower(), y.lower()
return (xlower > ylower) - (xlower < ylower)
copy.sort(key=cmp_to_key(my_cmp))
def test_baddecorator(self):
data = 'The quick Brown fox Jumped over The lazy Dog'.split()
self.assertRaises(TypeError, data.sort, key=lambda x,y: 0)
def test_stability(self):
data = [(random.randrange(100), i) for i in range(200)]
copy = data[:]
data.sort(key=lambda t: t[0]) # sort on the random first field
copy.sort() # sort using both fields
self.assertEqual(data, copy) # should get the same result
def test_key_with_exception(self):
# Verify that the wrapper has been removed
data = list(range(-2, 2))
dup = data[:]
self.assertRaises(ZeroDivisionError, data.sort, key=lambda x: 1/x)
self.assertEqual(data, dup)
def test_key_with_mutation(self):
data = list(range(10))
def k(x):
del data[:]
data[:] = range(20)
return x
self.assertRaises(ValueError, data.sort, key=k)
def test_key_with_mutating_del(self):
data = list(range(10))
class SortKiller(object):
def __init__(self, x):
pass
def __del__(self):
del data[:]
data[:] = range(20)
def __lt__(self, other):
return id(self) < id(other)
self.assertRaises(ValueError, data.sort, key=SortKiller)
def test_key_with_mutating_del_and_exception(self):
data = list(range(10))
## dup = data[:]
class SortKiller(object):
def __init__(self, x):
if x > 2:
raise RuntimeError
def __del__(self):
del data[:]
data[:] = list(range(20))
self.assertRaises(RuntimeError, data.sort, key=SortKiller)
## major honking subtlety: we *can't* do:
##
## self.assertEqual(data, dup)
##
## because there is a reference to a SortKiller in the
## traceback and by the time it dies we're outside the call to
## .sort() and so the list protection gimmicks are out of
## date (this cost some brain cells to figure out...).
def test_reverse(self):
data = list(range(100))
random.shuffle(data)
data.sort(reverse=True)
self.assertEqual(data, list(range(99,-1,-1)))
def test_reverse_stability(self):
data = [(random.randrange(100), i) for i in range(200)]
copy1 = data[:]
copy2 = data[:]
def my_cmp(x, y):
x0, y0 = x[0], y[0]
return (x0 > y0) - (x0 < y0)
def my_cmp_reversed(x, y):
x0, y0 = x[0], y[0]
return (y0 > x0) - (y0 < x0)
data.sort(key=cmp_to_key(my_cmp), reverse=True)
copy1.sort(key=cmp_to_key(my_cmp_reversed))
self.assertEqual(data, copy1)
copy2.sort(key=lambda x: x[0], reverse=True)
self.assertEqual(data, copy2)
#==============================================================================
def check_against_PyObject_RichCompareBool(self, L):
## The idea here is to exploit the fact that unsafe_tuple_compare uses
## PyObject_RichCompareBool for the second elements of tuples. So we have,
## for (most) L, sorted(L) == [y[1] for y in sorted([(0,x) for x in L])]
## This will work as long as __eq__ => not __lt__ for all the objects in L,
## which holds for all the types used below.
##
## Testing this way ensures that the optimized implementation remains consistent
## with the naive implementation, even if changes are made to any of the
## richcompares.
##
## This function tests sorting for three lists (it randomly shuffles each one):
## 1. L
## 2. [(x,) for x in L]
## 3. [((x,),) for x in L]
random.seed(0)
random.shuffle(L)
L_1 = L[:]
L_2 = [(x,) for x in L]
L_3 = [((x,),) for x in L]
for L in [L_1, L_2, L_3]:
optimized = sorted(L)
reference = [y[1] for y in sorted([(0,x) for x in L])]
for (opt, ref) in zip(optimized, reference):
self.assertIs(opt, ref)
#note: not assertEqual! We want to ensure *identical* behavior.
class TestOptimizedCompares(unittest.TestCase):
def test_safe_object_compare(self):
heterogeneous_lists = [[0, 'foo'],
[0.0, 'foo'],
[('foo',), 'foo']]
for L in heterogeneous_lists:
self.assertRaises(TypeError, L.sort)
self.assertRaises(TypeError, [(x,) for x in L].sort)
self.assertRaises(TypeError, [((x,),) for x in L].sort)
float_int_lists = [[1,1.1],
[1<<70,1.1],
[1.1,1],
[1.1,1<<70]]
for L in float_int_lists:
check_against_PyObject_RichCompareBool(self, L)
def test_unsafe_object_compare(self):
# This test is by ppperry. It ensures that unsafe_object_compare is
# verifying ms->key_richcompare == tp->richcompare before comparing.
class WackyComparator(int):
def __lt__(self, other):
elem.__class__ = WackyList2
return int.__lt__(self, other)
class WackyList1(list):
pass
class WackyList2(list):
def __lt__(self, other):
raise ValueError
L = [WackyList1([WackyComparator(i), i]) for i in range(10)]
elem = L[-1]
with self.assertRaises(ValueError):
L.sort()
L = [WackyList1([WackyComparator(i), i]) for i in range(10)]
elem = L[-1]
with self.assertRaises(ValueError):
[(x,) for x in L].sort()
# The following test is also by ppperry. It ensures that
# unsafe_object_compare handles Py_NotImplemented appropriately.
class PointlessComparator:
def __lt__(self, other):
return NotImplemented
L = [PointlessComparator(), PointlessComparator()]
self.assertRaises(TypeError, L.sort)
self.assertRaises(TypeError, [(x,) for x in L].sort)
# The following tests go through various types that would trigger
# ms->key_compare = unsafe_object_compare
lists = [list(range(100)) + [(1<<70)],
[str(x) for x in range(100)] + ['\uffff'],
[bytes(x) for x in range(100)],
[cmp_to_key(lambda x,y: x<y)(x) for x in range(100)]]
for L in lists:
check_against_PyObject_RichCompareBool(self, L)
def test_unsafe_latin_compare(self):
check_against_PyObject_RichCompareBool(self, [str(x) for
x in range(100)])
def test_unsafe_long_compare(self):
check_against_PyObject_RichCompareBool(self, [x for
x in range(100)])
def test_unsafe_float_compare(self):
check_against_PyObject_RichCompareBool(self, [float(x) for
x in range(100)])
def test_unsafe_tuple_compare(self):
# This test was suggested by Tim Peters. It verifies that the tuple
# comparison respects the current tuple compare semantics, which do not
# guarantee that x < x <=> (x,) < (x,)
#
# Note that we don't have to put anything in tuples here, because
# the check function does a tuple test automatically.
check_against_PyObject_RichCompareBool(self, [float('nan')]*100)
check_against_PyObject_RichCompareBool(self, [float('nan') for
_ in range(100)])
def test_not_all_tuples(self):
self.assertRaises(TypeError, [(1.0, 1.0), (False, "A"), 6].sort)
self.assertRaises(TypeError, [('a', 1), (1, 'a')].sort)
self.assertRaises(TypeError, [(1, 'a'), ('a', 1)].sort)
def test_none_in_tuples(self):
expected = [(None, 1), (None, 2)]
actual = sorted([(None, 2), (None, 1)])
self.assertEqual(actual, expected)
#==============================================================================
if __name__ == "__main__":
unittest.main()
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