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_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)