# coding=utf-8
#
# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Most of this work is copyright (C) 2013-2019 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.
"""
from __future__ import absolute_import, division, print_function
import inspect
from copy import copy
from unittest import TestCase
import attr
import hypothesis.internal.conjecture.utils as cu
import hypothesis.strategies as st
from hypothesis._settings import (
HealthCheck,
Verbosity,
note_deprecation,
settings as Settings,
)
from hypothesis.control import current_build_context
from hypothesis.core import given
from hypothesis.errors import InvalidArgument, InvalidDefinition
from hypothesis.internal.compat import hrange, quiet_raise, string_types
from hypothesis.internal.reflection import 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 CUnicodeIO, 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")
if False:
from typing import Any, Dict, List, Text # noqa
class TestCaseProperty(object): # 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(u"Cannot set TestCase")
def __delete__(self, obj):
raise AttributeError(u"Cannot delete TestCase")
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
GenericStateMachine 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):
machine = factory()
if isinstance(machine, GenericStateMachine) and not isinstance(
machine, RuleBasedStateMachine
):
note_deprecation(
"%s inherits from GenericStateMachine, which is deprecated. Use a "
"RuleBasedStateMachine, or a test function with st.data(), instead."
% (type(machine).__name__,),
since="2019-05-29",
)
else:
check_type(RuleBasedStateMachine, machine, "state_machine_factory()")
data.conjecture_data.hypothesis_runner = machine
print_steps = (
current_build_context().is_final or current_verbosity() >= Verbosity.debug
)
try:
if print_steps:
machine.print_start()
machine.check_invariants()
max_steps = settings.stateful_step_count
steps_run = 0
cd = data.conjecture_data
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
value = data.conjecture_data.draw(machine.steps())
# Assign 'result' here in case 'execute_step' fails below
result = multiple()
try:
result = machine.execute_step(value)
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(value, result)
machine.check_invariants()
data.conjecture_data.stop_example()
finally:
if print_steps:
machine.print_end()
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 GenericStateMachineMeta(type):
def __init__(self, *args, **kwargs):
super(GenericStateMachineMeta, self).__init__(*args, **kwargs)
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)
[docs]class GenericStateMachine(
GenericStateMachineMeta("GenericStateMachine", (object,), {}) # type: ignore
):
"""A GenericStateMachine is a deprecated approach to stateful testing.
In earlier versions of Hypothesis, you would define ``steps``,
``execute_step``, ``teardown``, and ``check_invariants`` methods;
and the engine would then run something like the following::
@given(st.data())
def test_the_stateful_thing(data):
x = MyStatemachineSubclass()
x.check_invariants()
try:
for _ in range(50):
step = data.draw(x.steps())
x.execute_step(step)
x.check_invariants()
finally:
x.teardown()
We now recommend using rule-based stateful testing instead wherever
possible. If your test is better expressed in the above format than
as a rule-based state machine, we suggest "unrolling" your method
definitions into a simple test function with the above control flow.
"""
def steps(self):
"""Return a SearchStrategy instance the defines the available next
steps."""
raise NotImplementedError(u"%r.steps()" % (self,))
def execute_step(self, step):
"""Execute a step that has been previously drawn from self.steps()
Returns the result of the step execution.
"""
raise NotImplementedError(u"%r.execute_step()" % (self,))
def print_start(self):
"""Called right at the start of printing.
By default does nothing.
"""
def print_end(self):
"""Called right at the end of printing.
By default does nothing.
"""
def print_step(self, step, result):
"""Print a step to the current reporter.
This is called right after a step is executed.
"""
self.step_count = getattr(self, u"step_count", 0) + 1
report(u"Step #%d: %s" % (self.step_count, nicerepr(step)))
def teardown(self):
"""Called after a run has finished executing to clean up any necessary
state.
Does nothing by default.
"""
def check_invariants(self):
"""Called after initializing and after executing each step."""
_test_case_cache = {} # type: dict
TestCase = TestCaseProperty()
@classmethod
def _to_test_case(state_machine_class):
try:
return state_machine_class._test_case_cache[state_machine_class]
except KeyError:
pass
class StateMachineTestCase(TestCase):
settings = Settings(deadline=None, suppress_health_check=HealthCheck.all())
# We define this outside of the class and assign it because you can't
# assign attributes to instance method values in Python 2
def runTest(self):
run_state_machine_as_test(state_machine_class)
runTest.is_hypothesis_test = True
StateMachineTestCase.runTest = runTest
base_name = state_machine_class.__name__
StateMachineTestCase.__name__ = str(base_name + u".TestCase")
StateMachineTestCase.__qualname__ = str(
getattr(state_machine_class, u"__qualname__", base_name) + u".TestCase"
)
state_machine_class._test_case_cache[state_machine_class] = StateMachineTestCase
return StateMachineTestCase
@attr.s()
class Rule(object):
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(BundleConsumer, self).__init__(bundle.name, consume=True)
[docs]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(object):
values = attr.ib()
[docs]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:
note_deprecation(
"Passing both targets=%r and target=%r is redundant, and "
"will become an error in a future version of Hypothesis. "
"Pass targets=%r instead."
% (targets, target, tuple(targets) + (target,)),
since="2018-08-18",
)
targets = tuple(targets) + (target,)
converted_targets = []
for t in targets:
if isinstance(t, string_types):
note_deprecation(
"Got %r as a target, but passing the name of a Bundle is "
"deprecated - please pass the Bundle directly." % (t,),
since="2018-08-18",
)
elif not isinstance(t, Bundle):
msg = (
"Got invalid target %r of type %r, but all targets must "
"be either a Bundle or the name of a Bundle."
)
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 = u"hypothesis_stateful_rule"
INITIALIZE_RULE_MARKER = u"hypothesis_stateful_initialize_rule"
PRECONDITION_MARKER = u"hypothesis_stateful_precondition"
INVARIANT_MARKER = u"hypothesis_stateful_invariant"
[docs]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
[docs]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(object):
name = attr.ib()
[docs]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(object):
function = attr.ib()
precondition = attr.ib()
[docs]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 = u"No progress can be made from state %r" % (self.machine,)
quiet_raise(InvalidDefinition(msg))
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
[docs]class RuleBasedStateMachine(GenericStateMachine):
"""A RuleBasedStateMachine gives you a more 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(
u"Type %s defines no rules" % (type(self).__name__,)
)
self.bundles = {} # type: Dict[Text, list]
self.name_counter = 1
self.names_to_values = {} # type: Dict[Text, Any]
self.__stream = CUnicodeIO()
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 u"%s(%s)" % (type(self).__name__, nicerepr(self.bundles))
def upcoming_name(self):
return u"v%d" % (self.name_counter,)
def last_names(self, n):
assert self.name_counter > n
count = self.name_counter
return [u"v%d" % (i,) for i in hrange(count - n, count)]
def new_name(self):
result = self.upcoming_name()
self.name_counter += 1
return result
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 steps(self):
# Pick initialize rules first
if self._initialize_rules_to_run:
return st.one_of(
[
st.tuples(st.just(rule), st.fixed_dictionaries(rule.arguments))
for rule in self._initialize_rules_to_run
]
)
return self.__rules_strategy
def print_start(self):
report(u"state = %s()" % (self.__class__.__name__,))
def print_end(self):
report(u"state.teardown()")
def print_step(self, step, result):
rule, data = step
data_repr = {}
for k, v in data.items():
data_repr[k] = self.__pretty(v)
self.step_count = getattr(self, u"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 = (
u"%s = " % (", ".join(self.last_names(n_output_vars)),)
if rule.targets and n_output_vars >= 1
else u""
)
report(
u"%sstate.%s(%s)"
% (
output_assignment,
rule.function.__name__,
u", ".join(u"%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.bundle(target).append(VarReference(name))
def execute_step(self, step):
rule, data = step
data = dict(data)
for k, v in list(data.items()):
if isinstance(v, VarReference):
data[k] = self.names_to_values[v.name]
result = rule.function(self, **data)
if rule.targets:
if isinstance(result, MultipleResults):
for single_result in result.values:
self._add_result_to_targets(rule.targets, single_result)
else:
self._add_result_to_targets(rule.targets, result)
if self._initialize_rules_to_run:
self._initialize_rules_to_run.remove(rule)
return result
def check_invariants(self):
for invar in self.invariants():
if invar.precondition and not invar.precondition(self):
continue
invar.function(self)