How to use is_valid_core method in hypothesis

Best Python code snippet using hypothesis

dfas.py

Source:dfas.py

...98    assert v == prefix + v_core + suffix, (list(u), list(v))99    better = runner.cached_test_function(u)100    worse = runner.cached_test_function(v)101    allow_discards = worse.has_discards or better.has_discards102    def is_valid_core(s):103        if not (len(u_core) <= len(s) <= len(v_core)):104            return False105        buf = prefix + s + suffix106        result = runner.cached_test_function(buf)107        return (108            predicate(result)109            # Because we're often using this to learn strategies110            # rather than entire complex test functions, it's111            # important that our replacements are precise and112            # don't leave the rest of the test case in a weird113            # state.114            and result.buffer == buf115            # Because the shrinker is good at removing discarded116            # data, unless we need discards to allow one or both117            # of u and v to result in valid shrinks, we don't118            # count attempts that have them as valid. This will119            # cause us to match fewer strings, which will make120            # the resulting shrink pass more efficient when run121            # on test functions it wasn't really intended for.122            and (allow_discards or not result.has_discards)123        )124    assert sort_key(u_core) < sort_key(v_core)125    assert is_valid_core(u_core)126    assert is_valid_core(v_core)127    learner = LStar(is_valid_core)128    prev = -1129    while learner.generation != prev:130        prev = learner.generation131        learner.learn(u_core)132        learner.learn(v_core)133        # L* has a tendency to learn DFAs which wrap around to134        # the beginning. We don't want to it to do that unless135        # it's accurate, so we use these as examples to show136        # check going around the DFA twice.137        learner.learn(u_core * 2)138        learner.learn(v_core * 2)139        if learner.dfa.max_length(learner.dfa.start) > len(v_core):140            # The language we learn is finite and bounded above141            # by the length of v_core. This is important in order142            # to keep our shrink passes reasonably efficient -143            # otherwise they can match far too much. So whenever144            # we learn a DFA that could match a string longer145            # than len(v_core) we fix it by finding the first146            # string longer than v_core and learning that as147            # a correction.148            x = next(learner.dfa.all_matching_strings(min_length=len(v_core) + 1))149            assert not is_valid_core(x)150            learner.learn(x)151            assert not learner.dfa.matches(x)152            assert learner.generation != prev153        else:154            # We mostly care about getting the right answer on the155            # minimal test case, but because we're doing this offline156            # anyway we might as well spend a little more time trying157            # small examples to make sure the learner gets them right.158            for x in islice(learner.dfa.all_matching_strings(), 100):159                if not is_valid_core(x):160                    learner.learn(x)161                    assert learner.generation != prev162                    break163    # We've now successfully learned a DFA that works for shrinking164    # our failed normalisation further. Canonicalise it into a concrete165    # DFA so we can save it for later.166    new_dfa = learner.dfa.canonicalise()167    assert math.isfinite(new_dfa.max_length(new_dfa.start))168    shrinker = runner.new_shrinker(runner.cached_test_function(v), predicate)169    assert (len(prefix), len(v) - len(suffix)) in shrinker.matching_regions(new_dfa)170    name = "tmp-dfa-" + repr(new_dfa)171    shrinker.extra_dfas[name] = new_dfa172    shrinker.fixate_shrink_passes([dfa_replacement(name)])173    assert sort_key(shrinker.buffer) < sort_key(v)...

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