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How to use has_existing_examples method in hypothesis

Best Python code snippet using hypothesis
engine.py
Source:engine.py
...430    def database(self):431        if self.database_key is None:432            return None433        return self.settings.database434    def has_existing_examples(self):435        return self.database is not None and Phase.reuse in self.settings.phases436    def reuse_existing_examples(self):437        """If appropriate (we have a database and have been told to use it),438        try to reload existing examples from the database.439        If there are a lot we don't try all of them. We always try the440        smallest example in the database (which is guaranteed to be the441        last failure) and the largest (which is usually the seed example442        which the last failure came from but we don't enforce that). We443        then take a random sampling of the remainder and try those. Any444        examples that are no longer interesting are cleared out.445        """446        if self.has_existing_examples():447            self.debug("Reusing examples from database")448            # We have to do some careful juggling here. We have two database449            # corpora: The primary and secondary. The primary corpus is a450            # small set of minimized examples each of which has at one point451            # demonstrated a distinct bug. We want to retry all of these.452            # We also have a secondary corpus of examples that have at some453            # point demonstrated interestingness (currently only ones that454            # were previously non-minimal examples of a bug, but this will455            # likely expand in future). These are a good source of potentially456            # interesting examples, but there are a lot of them, so we down457            # sample the secondary corpus to a more manageable size.458            corpus = sorted(459                self.settings.database.fetch(self.database_key), key=sort_key460            )461            desired_size = max(2, ceil(0.1 * self.settings.max_examples))462            for extra_key in [self.secondary_key, self.covering_key]:463                if len(corpus) < desired_size:464                    extra_corpus = list(self.settings.database.fetch(extra_key))465                    shortfall = desired_size - len(corpus)466                    if len(extra_corpus) <= shortfall:467                        extra = extra_corpus468                    else:469                        extra = self.random.sample(extra_corpus, shortfall)470                    extra.sort(key=sort_key)471                    corpus.extend(extra)472            self.used_examples_from_database = len(corpus) > 0473            for existing in corpus:474                last_data = ConjectureData.for_buffer(475                    existing, observer=self.tree.new_observer()476                )477                try:478                    self.test_function(last_data)479                finally:480                    if last_data.status != Status.INTERESTING:481                        self.settings.database.delete(self.database_key, existing)482                        self.settings.database.delete(self.secondary_key, existing)483    def exit_with(self, reason):484        self.exit_reason = reason485        raise RunIsComplete()486    def generate_new_examples(self):487        if Phase.generate not in self.settings.phases:488            return489        zero_data = self.cached_test_function(hbytes(self.settings.buffer_size))490        if zero_data.status > Status.OVERRUN:491            self.__data_cache.pin(zero_data.buffer)492        if zero_data.status == Status.OVERRUN or (493            zero_data.status == Status.VALID494            and len(zero_data.buffer) * 2 > self.settings.buffer_size495        ):496            fail_health_check(497                self.settings,498                "The smallest natural example for your test is extremely "499                "large. This makes it difficult for Hypothesis to generate "500                "good examples, especially when trying to reduce failing ones "501                "at the end. Consider reducing the size of your data if it is "502                "of a fixed size. You could also fix this by improving how "503                "your data shrinks (see https://hypothesis.readthedocs.io/en/"504                "latest/data.html#shrinking for details), or by introducing "505                "default values inside your strategy. e.g. could you replace "506                "some arguments with their defaults by using "507                "one_of(none(), some_complex_strategy)?",508                HealthCheck.large_base_example,509            )510        if zero_data is not Overrun:511            # If the language starts with writes of length >= cap then there is512            # only one string in it: Everything after cap is forced to be zero (or513            # to be whatever value is written there). That means that once we've514            # tried the zero value, there's nothing left for us to do, so we515            # exit early here.516            has_non_forced = False517            # It's impossible to fall out of this loop normally because if we518            # did then that would mean that all blocks are writes, so we would519            # already have triggered the exhaustedness check on the tree and520            # finished running.521            for b in zero_data.blocks:  # pragma: no branch522                if b.start >= self.cap:523                    break524                if not b.forced:525                    has_non_forced = True526                    break527            if not has_non_forced:528                self.exit_with(ExitReason.finished)529        self.health_check_state = HealthCheckState()530        count = 0531        while not self.interesting_examples and (532            count < 10 or self.health_check_state is not None533        ):534            prefix = self.generate_novel_prefix()535            def draw_bytes(data, n):536                if data.index < len(prefix):537                    result = prefix[data.index : data.index + n]538                    # We always draw prefixes as a whole number of blocks539                    assert len(result) == n540                else:541                    result = uniform(self.random, n)542                return self.__zero_bound(data, result)543            last_data = self.new_conjecture_data(draw_bytes)544            self.test_function(last_data)545            last_data.freeze()546            count += 1547        mutations = 0548        mutator = self._new_mutator()549        zero_bound_queue = []550        while not self.interesting_examples:551            if zero_bound_queue:552                # Whenever we generated an example and it hits a bound553                # which forces zero blocks into it, this creates a weird554                # distortion effect by making certain parts of the data555                # stream (especially ones to the right) much more likely556                # to be zero. We fix this by redistributing the generated557                # data by shuffling it randomly. This results in the558                # zero data being spread evenly throughout the buffer.559                # Hopefully the shrinking this causes will cause us to560                # naturally fail to hit the bound.561                # If it doesn't then we will queue the new version up again562                # (now with more zeros) and try again.563                overdrawn = zero_bound_queue.pop()564                buffer = bytearray(overdrawn.buffer)565                # These will have values written to them that are different566                # from what's in them anyway, so the value there doesn't567                # really "count" for distributional purposes, and if we568                # leave them in then they can cause the fraction of non569                # zero bytes to increase on redraw instead of decrease.570                for i in overdrawn.forced_indices:571                    buffer[i] = 0572                self.random.shuffle(buffer)573                buffer = hbytes(buffer)574                def draw_bytes(data, n):575                    result = buffer[data.index : data.index + n]576                    if len(result) < n:577                        result += hbytes(n - len(result))578                    return self.__rewrite(data, result)579                data = self.new_conjecture_data(draw_bytes=draw_bytes)580                self.test_function(data)581                data.freeze()582            else:583                origin = self.target_selector.select()584                mutations += 1585                data = self.new_conjecture_data(draw_bytes=mutator(origin))586                self.test_function(data)587                data.freeze()588                if data.status > origin.status:589                    mutations = 0590                elif data.status < origin.status or mutations >= 10:591                    # Cap the variations of a single example and move on to592                    # an entirely fresh start.  Ten is an entirely arbitrary593                    # constant, but it's been working well for years.594                    mutations = 0595                    mutator = self._new_mutator()596            if getattr(data, "hit_zero_bound", False):597                zero_bound_queue.append(data)598            mutations += 1599    def _run(self):600        self.reuse_existing_examples()601        self.generate_new_examples()602        self.shrink_interesting_examples()603        self.exit_with(ExitReason.finished)604    def new_conjecture_data(self, draw_bytes):605        return ConjectureData(606            draw_bytes=draw_bytes,607            max_length=self.settings.buffer_size,608            observer=self.tree.new_observer(),609        )610    def new_conjecture_data_for_buffer(self, buffer):611        return ConjectureData.for_buffer(buffer, observer=self.tree.new_observer())612    def shrink_interesting_examples(self):613        """If we've found interesting examples, try to replace each of them614        with a minimal interesting example with the same interesting_origin.615        We may find one or more examples with a new interesting_origin616        during the shrink process. If so we shrink these too.617        """618        if Phase.shrink not in self.settings.phases or not self.interesting_examples:619            return620        for prev_data in sorted(621            self.interesting_examples.values(), key=lambda d: sort_key(d.buffer)622        ):623            assert prev_data.status == Status.INTERESTING624            data = self.new_conjecture_data_for_buffer(prev_data.buffer)625            self.test_function(data)626            if data.status != Status.INTERESTING:627                self.exit_with(ExitReason.flaky)628        self.clear_secondary_key()629        while len(self.shrunk_examples) < len(self.interesting_examples):630            target, example = min(631                [632                    (k, v)633                    for k, v in self.interesting_examples.items()634                    if k not in self.shrunk_examples635                ],636                key=lambda kv: (sort_key(kv[1].buffer), sort_key(repr(kv[0]))),637            )638            self.debug("Shrinking %r" % (target,))639            if not self.settings.report_multiple_bugs:640                # If multi-bug reporting is disabled, we shrink our currently-minimal641                # failure, allowing 'slips' to any bug with a smaller minimal example.642                self.shrink(example, lambda d: d.status == Status.INTERESTING)643                return644            def predicate(d):645                if d.status < Status.INTERESTING:646                    return False647                return d.interesting_origin == target648            self.shrink(example, predicate)649            self.shrunk_examples.add(target)650    def clear_secondary_key(self):651        if self.has_existing_examples():652            # If we have any smaller examples in the secondary corpus, now is653            # a good time to try them to see if they work as shrinks. They654            # probably won't, but it's worth a shot and gives us a good655            # opportunity to clear out the database.656            # It's not worth trying the primary corpus because we already657            # tried all of those in the initial phase.658            corpus = sorted(659                self.settings.database.fetch(self.secondary_key), key=sort_key660            )661            for c in corpus:662                primary = {v.buffer for v in self.interesting_examples.values()}663                cap = max(map(sort_key, primary))664                if sort_key(c) > cap:665                    break...
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