text: 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)
  
      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()
  