# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Most of this work is copyright (C) 2013-2020 David R. MacIver
# (david@drmaciver.com), but it contains contributions by others. See
# CONTRIBUTING.rst for a full list of people who may hold copyright, and
# consult the git log if you need to determine who owns an individual
# contribution.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.
#
# END HEADER

"""This module provides support for a stateful style of testing, where tests
attempt to find a sequence of operations that cause a breakage rather than just
a single value.

Notably, the set of steps available at any point may depend on the
execution to date.
"""

import inspect
from collections.abc import Iterable
from copy import copy
from functools import lru_cache
from io import StringIO
from typing import Any, Dict, List
from unittest import TestCase

import attr

import hypothesis.internal.conjecture.utils as cu
import hypothesis.strategies as st
from hypothesis._settings import HealthCheck, Verbosity, settings as Settings
from hypothesis.control import current_build_context
from hypothesis.core import given
from hypothesis.errors import InvalidArgument, InvalidDefinition
from hypothesis.internal.reflection import (
    deprecated_posargs,
    function_digest,
    nicerepr,
    proxies,
    qualname,
)
from hypothesis.internal.validation import check_type
from hypothesis.reporting import current_verbosity, report
from hypothesis.strategies._internal.featureflags import FeatureStrategy
from hypothesis.strategies._internal.strategies import OneOfStrategy, SearchStrategy
from hypothesis.vendor.pretty import RepresentationPrinter

STATE_MACHINE_RUN_LABEL = cu.calc_label_from_name("another state machine step")
SHOULD_CONTINUE_LABEL = cu.calc_label_from_name("should we continue drawing")


class TestCaseProperty:  # pragma: no cover
    def __get__(self, obj, typ=None):
        if obj is not None:
            typ = type(obj)
        return typ._to_test_case()

    def __set__(self, obj, value):
        raise AttributeError("Cannot set TestCase")

    def __delete__(self, obj):
        raise AttributeError("Cannot delete TestCase")


@deprecated_posargs
def run_state_machine_as_test(state_machine_factory, *, settings=None):
    """Run a state machine definition as a test, either silently doing nothing
    or printing a minimal breaking program and raising an exception.

    state_machine_factory is anything which returns an instance of
    RuleBasedStateMachine when called with no arguments - it can be a class or a
    function. settings will be used to control the execution of the test.
    """
    if settings is None:
        try:
            settings = state_machine_factory.TestCase.settings
            check_type(Settings, settings, "state_machine_factory.TestCase.settings")
        except AttributeError:
            settings = Settings(deadline=None, suppress_health_check=HealthCheck.all())
    check_type(Settings, settings, "settings")

    @settings
    @given(st.data())
    def run_state_machine(factory, data):
        cd = data.conjecture_data
        machine = factory()
        check_type(RuleBasedStateMachine, machine, "state_machine_factory()")
        cd.hypothesis_runner = machine

        print_steps = (
            current_build_context().is_final or current_verbosity() >= Verbosity.debug
        )
        try:
            if print_steps:
                report("state = %s()" % (machine.__class__.__name__,))
            machine.check_invariants()
            max_steps = settings.stateful_step_count
            steps_run = 0

            while True:
                # We basically always want to run the maximum number of steps,
                # but need to leave a small probability of terminating early
                # in order to allow for reducing the number of steps once we
                # find a failing test case, so we stop with probability of
                # 2 ** -16 during normal operation but force a stop when we've
                # generated enough steps.
                cd.start_example(STATE_MACHINE_RUN_LABEL)
                if steps_run == 0:
                    cd.draw_bits(16, forced=1)
                elif steps_run >= max_steps:
                    cd.draw_bits(16, forced=0)
                    break
                else:
                    # All we really care about is whether this value is zero
                    # or non-zero, so if it's > 1 we discard it and insert a
                    # replacement value after
                    cd.start_example(SHOULD_CONTINUE_LABEL)
                    should_continue_value = cd.draw_bits(16)
                    if should_continue_value > 1:
                        cd.stop_example(discard=True)
                        cd.draw_bits(16, forced=int(bool(should_continue_value)))
                    else:
                        cd.stop_example()
                        if should_continue_value == 0:
                            break
                steps_run += 1

                # Choose a rule to run, preferring an initialize rule if there are
                # any which have not been run yet.
                if machine._initialize_rules_to_run:
                    init_rules = [
                        st.tuples(st.just(rule), st.fixed_dictionaries(rule.arguments))
                        for rule in machine._initialize_rules_to_run
                    ]
                    rule, data = cd.draw(st.one_of(init_rules))
                    machine._initialize_rules_to_run.remove(rule)
                else:
                    rule, data = cd.draw(machine._rules_strategy)
                data_to_print = dict(data)

                # Assign 'result' here in case executing the rule fails below
                result = multiple()
                try:
                    data = dict(data)
                    for k, v in list(data.items()):
                        if isinstance(v, VarReference):
                            data[k] = machine.names_to_values[v.name]
                    result = rule.function(machine, **data)
                    if rule.targets:
                        if isinstance(result, MultipleResults):
                            for single_result in result.values:
                                machine._add_result_to_targets(
                                    rule.targets, single_result
                                )
                        else:
                            machine._add_result_to_targets(rule.targets, result)
                finally:
                    if print_steps:
                        # 'result' is only used if the step has target bundles.
                        # If it does, and the result is a 'MultipleResult',
                        # then 'print_step' prints a multi-variable assignment.
                        machine._print_step(rule, data_to_print, result)
                machine.check_invariants()
                cd.stop_example()
        finally:
            if print_steps:
                report("state.teardown()")
            machine.teardown()

    # Use a machine digest to identify stateful tests in the example database
    run_state_machine.hypothesis.inner_test._hypothesis_internal_add_digest = function_digest(
        state_machine_factory
    )
    # Copy some attributes so @seed and @reproduce_failure "just work"
    run_state_machine._hypothesis_internal_use_seed = getattr(
        state_machine_factory, "_hypothesis_internal_use_seed", None
    )
    run_state_machine._hypothesis_internal_use_reproduce_failure = getattr(
        state_machine_factory, "_hypothesis_internal_use_reproduce_failure", None
    )
    run_state_machine._hypothesis_internal_print_given_args = False

    run_state_machine(state_machine_factory)


class StateMachineMeta(type):
    def __setattr__(self, name, value):
        if name == "settings" and isinstance(value, Settings):
            raise AttributeError(
                (
                    "Assigning {cls}.settings = {value} does nothing. Assign "
                    "to {cls}.TestCase.settings, or use @{value} as a decorator "
                    "on the {cls} class."
                ).format(cls=self.__name__, value=value)
            )
        return type.__setattr__(self, name, value)


class RuleBasedStateMachine(metaclass=StateMachineMeta):
    """A RuleBasedStateMachine gives you a structured way to define state machines.

    The idea is that a state machine carries a bunch of types of data
    divided into Bundles, and has a set of rules which may read data
    from bundles (or just from normal strategies) and push data onto
    bundles. At any given point a random applicable rule will be
    executed.
    """

    _rules_per_class = {}  # type: Dict[type, List[classmethod]]
    _invariants_per_class = {}  # type: Dict[type, List[classmethod]]
    _base_rules_per_class = {}  # type: Dict[type, List[classmethod]]
    _initializers_per_class = {}  # type: Dict[type, List[classmethod]]
    _base_initializers_per_class = {}  # type: Dict[type, List[classmethod]]

    def __init__(self):
        if not self.rules():
            raise InvalidDefinition("Type %s defines no rules" % (type(self).__name__,))
        self.bundles = {}  # type: Dict[str, list]
        self.name_counter = 1
        self.names_to_values = {}  # type: Dict[str, Any]
        self.__stream = StringIO()
        self.__printer = RepresentationPrinter(self.__stream)
        self._initialize_rules_to_run = copy(self.initialize_rules())
        self._rules_strategy = RuleStrategy(self)

    def __pretty(self, value):
        if isinstance(value, VarReference):
            return value.name
        self.__stream.seek(0)
        self.__stream.truncate(0)
        self.__printer.output_width = 0
        self.__printer.buffer_width = 0
        self.__printer.buffer.clear()
        self.__printer.pretty(value)
        self.__printer.flush()
        return self.__stream.getvalue()

    def __repr__(self):
        return "%s(%s)" % (type(self).__name__, nicerepr(self.bundles))

    def _new_name(self):
        result = "v%d" % (self.name_counter,)
        self.name_counter += 1
        return result

    def _last_names(self, n):
        assert self.name_counter > n
        count = self.name_counter
        return ["v%d" % (i,) for i in range(count - n, count)]

    def bundle(self, name):
        return self.bundles.setdefault(name, [])

    @classmethod
    def initialize_rules(cls):
        try:
            return cls._initializers_per_class[cls]
        except KeyError:
            pass

        for _, v in inspect.getmembers(cls):
            r = getattr(v, INITIALIZE_RULE_MARKER, None)
            if r is not None:
                cls.define_initialize_rule(
                    r.targets, r.function, r.arguments, r.precondition
                )
        cls._initializers_per_class[cls] = cls._base_initializers_per_class.pop(cls, [])
        return cls._initializers_per_class[cls]

    @classmethod
    def rules(cls):
        try:
            return cls._rules_per_class[cls]
        except KeyError:
            pass

        for _, v in inspect.getmembers(cls):
            r = getattr(v, RULE_MARKER, None)
            if r is not None:
                cls.define_rule(r.targets, r.function, r.arguments, r.precondition)
        cls._rules_per_class[cls] = cls._base_rules_per_class.pop(cls, [])
        return cls._rules_per_class[cls]

    @classmethod
    def invariants(cls):
        try:
            return cls._invariants_per_class[cls]
        except KeyError:
            pass

        target = []
        for _, v in inspect.getmembers(cls):
            i = getattr(v, INVARIANT_MARKER, None)
            if i is not None:
                target.append(i)
        cls._invariants_per_class[cls] = target
        return cls._invariants_per_class[cls]

    @classmethod
    def define_initialize_rule(cls, targets, function, arguments, precondition=None):
        converted_arguments = {}
        for k, v in arguments.items():
            converted_arguments[k] = v
        if cls in cls._initializers_per_class:
            target = cls._initializers_per_class[cls]
        else:
            target = cls._base_initializers_per_class.setdefault(cls, [])

        return target.append(Rule(targets, function, converted_arguments, precondition))

    @classmethod
    def define_rule(cls, targets, function, arguments, precondition=None):
        converted_arguments = {}
        for k, v in arguments.items():
            converted_arguments[k] = v
        if cls in cls._rules_per_class:
            target = cls._rules_per_class[cls]
        else:
            target = cls._base_rules_per_class.setdefault(cls, [])

        return target.append(Rule(targets, function, converted_arguments, precondition))

    def _print_step(self, rule, data, result):
        data_repr = {}
        for k, v in data.items():
            data_repr[k] = self.__pretty(v)
        self.step_count = getattr(self, "step_count", 0) + 1
        # If the step has target bundles, and the result is a MultipleResults
        # then we want to assign to multiple variables.
        if isinstance(result, MultipleResults):
            n_output_vars = len(result.values)
        else:
            n_output_vars = 1
        output_assignment = (
            "%s = " % (", ".join(self._last_names(n_output_vars)),)
            if rule.targets and n_output_vars >= 1
            else ""
        )
        report(
            "%sstate.%s(%s)"
            % (
                output_assignment,
                rule.function.__name__,
                ", ".join("%s=%s" % kv for kv in data_repr.items()),
            )
        )

    def _add_result_to_targets(self, targets, result):
        name = self._new_name()
        self.__printer.singleton_pprinters.setdefault(
            id(result), lambda obj, p, cycle: p.text(name)
        )
        self.names_to_values[name] = result
        for target in targets:
            self.bundles.setdefault(target, []).append(VarReference(name))

    def check_invariants(self):
        for invar in self.invariants():
            if invar.precondition and not invar.precondition(self):
                continue
            invar.function(self)

    def teardown(self):
        """Called after a run has finished executing to clean up any necessary
        state.

        Does nothing by default.
        """

    TestCase = TestCaseProperty()

    @classmethod
    @lru_cache()
    def _to_test_case(state_machine_class):
        class StateMachineTestCase(TestCase):
            settings = Settings(deadline=None, suppress_health_check=HealthCheck.all())

            def runTest(self):
                run_state_machine_as_test(state_machine_class)

            runTest.is_hypothesis_test = True

        StateMachineTestCase.__name__ = state_machine_class.__name__ + ".TestCase"
        StateMachineTestCase.__qualname__ = qualname(state_machine_class) + ".TestCase"
        return StateMachineTestCase


@attr.s()
class Rule:
    targets = attr.ib()
    function = attr.ib(repr=qualname)
    arguments = attr.ib()
    precondition = attr.ib()
    bundles = attr.ib(init=False)

    def __attrs_post_init__(self):
        arguments = {}
        bundles = []
        for k, v in sorted(self.arguments.items()):
            assert not isinstance(v, BundleReferenceStrategy)
            if isinstance(v, Bundle):
                bundles.append(v)
                consume = isinstance(v, BundleConsumer)
                arguments[k] = BundleReferenceStrategy(v.name, consume)
            else:
                arguments[k] = v
        self.bundles = tuple(bundles)
        self.arguments_strategy = st.fixed_dictionaries(arguments)


self_strategy = st.runner()


class BundleReferenceStrategy(SearchStrategy):
    def __init__(self, name, consume=False):
        self.name = name
        self.consume = consume

    def do_draw(self, data):
        machine = data.draw(self_strategy)
        bundle = machine.bundle(self.name)
        if not bundle:
            data.mark_invalid()
        # Shrink towards the right rather than the left. This makes it easier
        # to delete data generated earlier, as when the error is towards the
        # end there can be a lot of hard to remove padding.
        position = cu.integer_range(data, 0, len(bundle) - 1, center=len(bundle))
        if self.consume:
            return bundle.pop(position)
        else:
            return bundle[position]


class Bundle(SearchStrategy):
    def __init__(self, name, consume=False):
        self.name = name
        self.__reference_strategy = BundleReferenceStrategy(name, consume)

    def do_draw(self, data):
        machine = data.draw(self_strategy)
        reference = data.draw(self.__reference_strategy)
        return machine.names_to_values[reference.name]

    def __repr__(self):
        consume = self.__reference_strategy.consume
        if consume is False:
            return "Bundle(name=%r)" % (self.name,)
        return "Bundle(name=%r, consume=%r)" % (self.name, consume)

    def calc_is_empty(self, recur):
        # We assume that a bundle will grow over time
        return False

    def available(self, data):
        # ``self_strategy`` is an instance of the ``st.runner()`` strategy.
        # Hence drawing from it only returns the current state machine without
        # modifying the underlying buffer.
        machine = data.draw(self_strategy)
        return bool(machine.bundle(self.name))


class BundleConsumer(Bundle):
    def __init__(self, bundle):
        super().__init__(bundle.name, consume=True)


def consumes(bundle):
    """When introducing a rule in a RuleBasedStateMachine, this function can
    be used to mark bundles from which each value used in a step with the
    given rule should be removed. This function returns a strategy object
    that can be manipulated and combined like any other.

    For example, a rule declared with

    ``@rule(value1=b1, value2=consumes(b2), value3=lists(consumes(b3)))``

    will consume a value from Bundle ``b2`` and several values from Bundle
    ``b3`` to populate ``value2`` and ``value3`` each time it is executed.
    """
    if not isinstance(bundle, Bundle):
        raise TypeError("Argument to be consumed must be a bundle.")
    return BundleConsumer(bundle)


@attr.s()
class MultipleResults(Iterable):
    values = attr.ib()

    def __iter__(self):
        return iter(self.values)


def multiple(*args):
    """This function can be used to pass multiple results to the target(s) of
    a rule. Just use ``return multiple(result1, result2, ...)`` in your rule.

    It is also possible to use ``return multiple()`` with no arguments in
    order to end a rule without passing any result.
    """
    return MultipleResults(args)


def _convert_targets(targets, target):
    """Single validator and convertor for target arguments."""
    if target is not None:
        if targets:
            raise InvalidArgument(
                "Passing both targets=%r and target=%r is redundant - pass "
                "targets=%r instead." % (targets, target, tuple(targets) + (target,))
            )
        targets = (target,)

    converted_targets = []
    for t in targets:
        if not isinstance(t, Bundle):
            msg = "Got invalid target %r of type %r, but all targets must be Bundles."
            if isinstance(t, OneOfStrategy):
                msg += (
                    "\nIt looks like you passed `one_of(a, b)` or `a | b` as "
                    "a target.  You should instead pass `targets=(a, b)` to "
                    "add the return value of this rule to both the `a` and "
                    "`b` bundles, or define a rule for each target if it "
                    "should be added to exactly one."
                )
            raise InvalidArgument(msg % (t, type(t)))
        while isinstance(t, Bundle):
            t = t.name
        converted_targets.append(t)
    return tuple(converted_targets)


RULE_MARKER = "hypothesis_stateful_rule"
INITIALIZE_RULE_MARKER = "hypothesis_stateful_initialize_rule"
PRECONDITION_MARKER = "hypothesis_stateful_precondition"
INVARIANT_MARKER = "hypothesis_stateful_invariant"


@deprecated_posargs
def rule(*, targets=(), target=None, **kwargs):
    """Decorator for RuleBasedStateMachine. Any name present in target or
    targets will define where the end result of this function should go. If
    both are empty then the end result will be discarded.

    ``target`` must be a Bundle, or if the result should go to multiple
    bundles you can pass a tuple of them as the ``targets`` argument.
    It is invalid to use both arguments for a single rule.  If the result
    should go to exactly one of several bundles, define a separate rule for
    each case.

    kwargs then define the arguments that will be passed to the function
    invocation. If their value is a Bundle, or if it is ``consumes(b)``
    where ``b`` is a Bundle, then values that have previously been produced
    for that bundle will be provided. If ``consumes`` is used, the value
    will also be removed from the bundle.

    Any other kwargs should be strategies and values from them will be
    provided.
    """
    converted_targets = _convert_targets(targets, target)
    for k, v in kwargs.items():
        check_type(SearchStrategy, v, k)

    def accept(f):
        existing_rule = getattr(f, RULE_MARKER, None)
        existing_initialize_rule = getattr(f, INITIALIZE_RULE_MARKER, None)
        if existing_rule is not None or existing_initialize_rule is not None:
            raise InvalidDefinition(
                "A function cannot be used for two distinct rules. ", Settings.default
            )
        precondition = getattr(f, PRECONDITION_MARKER, None)
        rule = Rule(
            targets=converted_targets,
            arguments=kwargs,
            function=f,
            precondition=precondition,
        )

        @proxies(f)
        def rule_wrapper(*args, **kwargs):
            return f(*args, **kwargs)

        setattr(rule_wrapper, RULE_MARKER, rule)
        return rule_wrapper

    return accept


@deprecated_posargs
def initialize(*, targets=(), target=None, **kwargs):
    """Decorator for RuleBasedStateMachine.

    An initialize decorator behaves like a rule, but the decorated
    method is called at most once in a run. All initialize decorated
    methods will be called before any rule decorated methods, in an
    arbitrary order.
    """
    converted_targets = _convert_targets(targets, target)
    for k, v in kwargs.items():
        check_type(SearchStrategy, v, k)

    def accept(f):
        existing_rule = getattr(f, RULE_MARKER, None)
        existing_initialize_rule = getattr(f, INITIALIZE_RULE_MARKER, None)
        if existing_rule is not None or existing_initialize_rule is not None:
            raise InvalidDefinition(
                "A function cannot be used for two distinct rules. ", Settings.default
            )
        precondition = getattr(f, PRECONDITION_MARKER, None)
        if precondition:
            raise InvalidDefinition(
                "An initialization rule cannot have a precondition. ", Settings.default
            )
        rule = Rule(
            targets=converted_targets,
            arguments=kwargs,
            function=f,
            precondition=precondition,
        )

        @proxies(f)
        def rule_wrapper(*args, **kwargs):
            return f(*args, **kwargs)

        setattr(rule_wrapper, INITIALIZE_RULE_MARKER, rule)
        return rule_wrapper

    return accept


@attr.s()
class VarReference:
    name = attr.ib()


def precondition(precond):
    """Decorator to apply a precondition for rules in a RuleBasedStateMachine.
    Specifies a precondition for a rule to be considered as a valid step in the
    state machine. The given function will be called with the instance of
    RuleBasedStateMachine and should return True or False. Usually it will need
    to look at attributes on that instance.

    For example::

        class MyTestMachine(RuleBasedStateMachine):
            state = 1

            @precondition(lambda self: self.state != 0)
            @rule(numerator=integers())
            def divide_with(self, numerator):
                self.state = numerator / self.state

    This is better than using assume in your rule since more valid rules
    should be able to be run.
    """

    def decorator(f):
        @proxies(f)
        def precondition_wrapper(*args, **kwargs):
            return f(*args, **kwargs)

        existing_initialize_rule = getattr(f, INITIALIZE_RULE_MARKER, None)
        if existing_initialize_rule is not None:
            raise InvalidDefinition(
                "An initialization rule cannot have a precondition. ", Settings.default
            )

        rule = getattr(f, RULE_MARKER, None)
        if rule is None:
            setattr(precondition_wrapper, PRECONDITION_MARKER, precond)
        else:
            new_rule = Rule(
                targets=rule.targets,
                arguments=rule.arguments,
                function=rule.function,
                precondition=precond,
            )
            setattr(precondition_wrapper, RULE_MARKER, new_rule)

        invariant = getattr(f, INVARIANT_MARKER, None)
        if invariant is not None:
            new_invariant = Invariant(function=invariant.function, precondition=precond)
            setattr(precondition_wrapper, INVARIANT_MARKER, new_invariant)

        return precondition_wrapper

    return decorator


@attr.s()
class Invariant:
    function = attr.ib()
    precondition = attr.ib()


def invariant():
    """Decorator to apply an invariant for rules in a RuleBasedStateMachine.
    The decorated function will be run after every rule and can raise an
    exception to indicate failed invariants.

    For example::

        class MyTestMachine(RuleBasedStateMachine):
            state = 1

            @invariant()
            def is_nonzero(self):
                assert self.state != 0
    """

    def accept(f):
        existing_invariant = getattr(f, INVARIANT_MARKER, None)
        if existing_invariant is not None:
            raise InvalidDefinition(
                "A function cannot be used for two distinct invariants.",
                Settings.default,
            )
        precondition = getattr(f, PRECONDITION_MARKER, None)
        rule = Invariant(function=f, precondition=precondition)

        @proxies(f)
        def invariant_wrapper(*args, **kwargs):
            return f(*args, **kwargs)

        setattr(invariant_wrapper, INVARIANT_MARKER, rule)
        return invariant_wrapper

    return accept


LOOP_LABEL = cu.calc_label_from_name("RuleStrategy loop iteration")


class RuleStrategy(SearchStrategy):
    def __init__(self, machine):
        SearchStrategy.__init__(self)
        self.machine = machine
        self.rules = list(machine.rules())

        self.enabled_rules_strategy = st.shared(
            FeatureStrategy(), key=("enabled rules", machine),
        )

        # The order is a bit arbitrary. Primarily we're trying to group rules
        # that write to the same location together, and to put rules with no
        # target first as they have less effect on the structure. We order from
        # fewer to more arguments on grounds that it will plausibly need less
        # data. This probably won't work especially well and we could be
        # smarter about it, but it's better than just doing it in definition
        # order.
        self.rules.sort(
            key=lambda rule: (
                sorted(rule.targets),
                len(rule.arguments),
                rule.function.__name__,
            )
        )

    def __repr__(self):
        return "%s(machine=%s({...}))" % (
            self.__class__.__name__,
            self.machine.__class__.__name__,
        )

    def do_draw(self, data):
        if not any(self.is_valid(rule) for rule in self.rules):
            msg = "No progress can be made from state %r" % (self.machine,)
            raise InvalidDefinition(msg) from None

        feature_flags = data.draw(self.enabled_rules_strategy)

        # Note: The order of the filters here is actually quite important,
        # because checking is_enabled makes choices, so increases the size of
        # the choice sequence. This means that if we are in a case where many
        # rules are invalid we will make a lot more choices if we ask if they
        # are enabled before we ask if they are valid, so our test cases will
        # be artificially large.
        rule = data.draw(
            st.sampled_from(self.rules)
            .filter(self.is_valid)
            .filter(lambda r: feature_flags.is_enabled(r.function.__name__))
        )

        return (rule, data.draw(rule.arguments_strategy))

    def is_valid(self, rule):
        if rule.precondition and not rule.precondition(self.machine):
            return False

        for b in rule.bundles:
            bundle = self.machine.bundle(b.name)
            if not bundle:
                return False
        return True
