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How to use _hypothesis_do_random method in hypothesis

Best Python code snippet using hypothesis

random.py

Source:random.py

...52                ),53                result,54            )55        )56    def _hypothesis_do_random(self, method, kwargs):57        raise NotImplementedError()58RANDOM_METHODS = [59    name60    for name in [61        "_randbelow",62        "betavariate",63        "choice",64        "choices",65        "expovariate",66        "gammavariate",67        "gauss",68        "getrandbits",69        "lognormvariate",70        "normalvariate",71        "paretovariate",72        "randint",73        "random",74        "randrange",75        "sample",76        "shuffle",77        "triangular",78        "uniform",79        "vonmisesvariate",80        "weibullvariate",81        "randbytes",82    ]83    if hasattr(Random, name)84]85# Fake shims to get a good signature86def getrandbits(self, n: int) -> int:87    raise NotImplementedError()88def random(self) -> float:89    raise NotImplementedError()90def _randbelow(self, n: int) -> int:91    raise NotImplementedError()92STUBS = {f.__name__: f for f in [getrandbits, random, _randbelow]}93SIGNATURES = {}94def sig_of(name):95    try:96        return SIGNATURES[name]97    except KeyError:98        pass99    target = getattr(Random, name)100    result = inspect.signature(STUBS.get(name, target))101    SIGNATURES[name] = result102    return result103def define_copy_method(name):104    target = getattr(Random, name)105    def implementation(self, **kwargs):106        result = self._hypothesis_do_random(name, kwargs)107        self._hypothesis_log_random(name, kwargs, result)108        return result109    spec = inspect.getfullargspec(STUBS.get(name, target))110    result = define_function_signature(target.__name__, target.__doc__, spec)(111        implementation112    )113    result.__module__ = __name__114    result.__qualname__ = "HypothesisRandom." + result.__name__115    setattr(HypothesisRandom, name, result)116for r in RANDOM_METHODS:117    define_copy_method(r)118@attr.s(slots=True)119class RandomState:120    next_states = attr.ib(default=attr.Factory(dict))121    state_id = attr.ib(default=None)122def state_for_seed(data, seed):123    try:124        seeds_to_states = data.seeds_to_states125    except AttributeError:126        seeds_to_states = {}127        data.seeds_to_states = seeds_to_states128    try:129        state = seeds_to_states[seed]130    except KeyError:131        state = RandomState()132        seeds_to_states[seed] = state133    return state134UNIFORM = st.floats(0, 1)135def normalize_zero(f):136    if f == 0.0:137        return 0.0138    else:139        return f140class ArtificialRandom(HypothesisRandom):141    VERSION = 10 ** 6142    def __init__(self, note_method_calls, data):143        HypothesisRandom.__init__(self, note_method_calls=note_method_calls)144        self.__data = data145        self.__state = RandomState()146    def __repr__(self):147        return "HypothesisRandom(generated data)"148    def __copy__(self):149        result = ArtificialRandom(150            note_method_calls=self._HypothesisRandom__note_method_calls,151            data=self.__data,152        )153        result.setstate(self.getstate())154        return result155    def __convert_result(self, method, kwargs, result):156        if method == "choice":157            return kwargs.get("seq")[result]158        if method in ("choices", "sample"):159            seq = kwargs["population"]160            return [seq[i] for i in result]161        if method == "shuffle":162            seq = kwargs["x"]163            original = list(seq)164            for i, i2 in enumerate(result):165                seq[i] = original[i2]166            return167        return result168    def _hypothesis_do_random(self, method, kwargs):169        if method == "choices":170            key = (method, len(kwargs["population"]), kwargs.get("k"))171        elif method == "choice":172            key = (method, len(kwargs["seq"]))173        elif method == "shuffle":174            key = (method, len(kwargs["x"]))175        else:176            key = (method,) + tuple(sorted(kwargs))177        try:178            result, self.__state = self.__state.next_states[key]179        except KeyError:180            pass181        else:182            return self.__convert_result(method, kwargs, result)183        if method == "_randbelow":184            result = cu.integer_range(self.__data, 0, kwargs["n"] - 1)185        elif method in ("betavariate", "random"):186            result = self.__data.draw(UNIFORM)187        elif method == "uniform":188            a = normalize_zero(kwargs["a"])189            b = normalize_zero(kwargs["b"])190            result = self.__data.draw(st.floats(a, b))191        elif method in ("weibullvariate", "gammavariate"):192            result = self.__data.draw(st.floats(min_value=0.0, allow_infinity=False))193        elif method in ("gauss", "normalvariate"):194            mu = kwargs["mu"]195            result = mu + self.__data.draw(196                st.floats(allow_nan=False, allow_infinity=False)197            )198        elif method == "vonmisesvariate":199            result = self.__data.draw(st.floats(0, 2 * math.pi))200        elif method == "randrange":201            if kwargs["stop"] is None:202                stop = kwargs["start"]203                start = 0204            else:205                start = kwargs["start"]206                stop = kwargs["stop"]207            step = kwargs["step"]208            if start == stop:209                raise ValueError(210                    "empty range for randrange(%d, %d, %d)" % (start, stop, step)211                )212            if step != 1:213                endpoint = (stop - start) // step214                if (start - stop) % step == 0:215                    endpoint -= 1216                i = cu.integer_range(self.__data, 0, endpoint)217                result = start + i * step218            else:219                result = cu.integer_range(self.__data, start, stop - 1)220        elif method == "randint":221            result = cu.integer_range(self.__data, kwargs["a"], kwargs["b"])222        elif method == "choice":223            seq = kwargs["seq"]224            result = cu.integer_range(self.__data, 0, len(seq) - 1)225        elif method == "choices":226            k = kwargs["k"]227            result = self.__data.draw(228                st.lists(229                    st.integers(0, len(kwargs["population"]) - 1),230                    min_size=k,231                    max_size=k,232                )233            )234        elif method == "sample":235            k = kwargs["k"]236            seq = kwargs["population"]237            if k > len(seq) or k < 0:238                raise ValueError(239                    "Sample size %d not in expected range 0 <= k <= %d" % (k, len(seq))240                )241            result = self.__data.draw(242                st.lists(243                    st.sampled_from(range(len(seq))),244                    min_size=k,245                    max_size=k,246                    unique=True,247                )248            )249        elif method == "getrandbits":250            result = self.__data.draw_bits(kwargs["n"])251        elif method == "triangular":252            low = normalize_zero(kwargs["low"])253            high = normalize_zero(kwargs["high"])254            mode = normalize_zero(kwargs["mode"])255            if mode is None:256                result = self.__data.draw(st.floats(low, high))257            elif self.__data.draw_bits(1):258                result = self.__data.draw(st.floats(mode, high))259            else:260                result = self.__data.draw(st.floats(low, mode))261        elif method in ("paretovariate", "expovariate", "lognormvariate"):262            result = self.__data.draw(st.floats(min_value=0.0))263        elif method == "shuffle":264            result = self.__data.draw(st.permutations(range(len(kwargs["x"]))))265        # This is tested for but only appears in 3.9 so doesn't appear in coverage.266        elif method == "randbytes":  # pragma: no cover267            n = kwargs["n"]268            result = self.__data.draw(st.binary(min_size=n, max_size=n))269        else:270            raise NotImplementedError(method)271        new_state = RandomState()272        self.__state.next_states[key] = (result, new_state)273        self.__state = new_state274        return self.__convert_result(method, kwargs, result)275    def seed(self, seed):276        self.__state = state_for_seed(self.__data, seed)277    def getstate(self):278        if self.__state.state_id is not None:279            return self.__state.state_id280        try:281            states_for_ids = self.__data.states_for_ids282        except AttributeError:283            states_for_ids = {}284            self.__data.states_for_ids = states_for_ids285        self.__state.state_id = len(states_for_ids)286        states_for_ids[self.__state.state_id] = self.__state287        return self.__state.state_id288    def setstate(self, state):289        self.__state = self.__data.states_for_ids[state]290DUMMY_RANDOM = Random(0)291def convert_kwargs(name, kwargs):292    kwargs = dict(kwargs)293    signature = sig_of(name)294    bound = signature.bind(DUMMY_RANDOM, **kwargs)295    bound.apply_defaults()296    for k in list(kwargs):297        if (298            kwargs[k] is signature.parameters[k].default299            or signature.parameters[k].kind != inspect.Parameter.KEYWORD_ONLY300        ):301            kwargs.pop(k)302    arg_names = list(signature.parameters)[1:]303    args = []304    for a in arg_names:305        if signature.parameters[a].kind == inspect.Parameter.KEYWORD_ONLY:306            break307        args.append(bound.arguments[a])308        kwargs.pop(a, None)309    while args:310        name = arg_names[len(args) - 1]311        if args[-1] is signature.parameters[name].default:312            args.pop()313        else:314            break  # pragma: no cover  # Only on Python < 3.8315    return (args, kwargs)316class TrueRandom(HypothesisRandom):317    def __init__(self, seed, note_method_calls):318        HypothesisRandom.__init__(self, note_method_calls)319        self.__seed = seed320        self.__random = Random(seed)321    def _hypothesis_do_random(self, method, kwargs):322        args, kwargs = convert_kwargs(method, kwargs)323        return getattr(self.__random, method)(*args, **kwargs)324    def __copy__(self):325        result = TrueRandom(326            seed=self.__seed,327            note_method_calls=self._HypothesisRandom__note_method_calls,328        )329        result.setstate(self.getstate())330        return result331    def __repr__(self):332        return "Random(%r)" % (self.__seed,)333    def seed(self, seed):334        self.__random.seed(seed)335        self.__seed = seed...

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