CasperSecurity
# -*- test-case-name: automat._test.test_methodical -*-
import collections
from functools import wraps
from itertools import count
try:
# Python 3
from inspect import getfullargspec as getArgsSpec
except ImportError:
# Python 2
from inspect import getargspec as getArgsSpec
import attr
import six
from ._core import Transitioner, Automaton
from ._introspection import preserveName
ArgSpec = collections.namedtuple('ArgSpec', ['args', 'varargs', 'varkw',
'defaults', 'kwonlyargs',
'kwonlydefaults', 'annotations'])
def _getArgSpec(func):
"""
Normalize inspect.ArgSpec across python versions
and convert mutable attributes to immutable types.
:param Callable func: A function.
:return: The function's ArgSpec.
:rtype: ArgSpec
"""
spec = getArgsSpec(func)
return ArgSpec(
args=tuple(spec.args),
varargs=spec.varargs,
varkw=spec.varkw if six.PY3 else spec.keywords,
defaults=spec.defaults if spec.defaults else (),
kwonlyargs=tuple(spec.kwonlyargs) if six.PY3 else (),
kwonlydefaults=(
tuple(spec.kwonlydefaults.items())
if spec.kwonlydefaults else ()
) if six.PY3 else (),
annotations=tuple(spec.annotations.items()) if six.PY3 else (),
)
def _getArgNames(spec):
"""
Get the name of all arguments defined in a function signature.
The name of * and ** arguments is normalized to "*args" and "**kwargs".
:param ArgSpec spec: A function to interrogate for a signature.
:return: The set of all argument names in `func`s signature.
:rtype: Set[str]
"""
return set(
spec.args
+ spec.kwonlyargs
+ (('*args',) if spec.varargs else ())
+ (('**kwargs',) if spec.varkw else ())
+ spec.annotations
)
def _keywords_only(f):
"""
Decorate a function so all its arguments must be passed by keyword.
A useful utility for decorators that take arguments so that they don't
accidentally get passed the thing they're decorating as their first
argument.
Only works for methods right now.
"""
@wraps(f)
def g(self, **kw):
return f(self, **kw)
return g
@attr.s(frozen=True)
class MethodicalState(object):
"""
A state for a L{MethodicalMachine}.
"""
machine = attr.ib(repr=False)
method = attr.ib()
serialized = attr.ib(repr=False)
def upon(self, input, enter, outputs, collector=list):
"""
Declare a state transition within the :class:`automat.MethodicalMachine`
associated with this :class:`automat.MethodicalState`:
upon the receipt of the `input`, enter the `state`,
emitting each output in `outputs`.
:param MethodicalInput input: The input triggering a state transition.
:param MethodicalState enter: The resulting state.
:param Iterable[MethodicalOutput] outputs: The outputs to be triggered
as a result of the declared state transition.
:param Callable collector: The function to be used when collecting
output return values.
:raises TypeError: if any of the `outputs` signatures do not match
the `inputs` signature.
:raises ValueError: if the state transition from `self` via `input`
has already been defined.
"""
inputArgs = _getArgNames(input.argSpec)
for output in outputs:
outputArgs = _getArgNames(output.argSpec)
if not outputArgs.issubset(inputArgs):
raise TypeError(
"method {input} signature {inputSignature} "
"does not match output {output} "
"signature {outputSignature}".format(
input=input.method.__name__,
output=output.method.__name__,
inputSignature=getArgsSpec(input.method),
outputSignature=getArgsSpec(output.method),
))
self.machine._oneTransition(self, input, enter, outputs, collector)
def _name(self):
return self.method.__name__
def _transitionerFromInstance(oself, symbol, automaton):
"""
Get a L{Transitioner}
"""
transitioner = getattr(oself, symbol, None)
if transitioner is None:
transitioner = Transitioner(
automaton,
automaton.initialState,
)
setattr(oself, symbol, transitioner)
return transitioner
def _empty():
pass
def _docstring():
"""docstring"""
def assertNoCode(inst, attribute, f):
# The function body must be empty, i.e. "pass" or "return None", which
# both yield the same bytecode: LOAD_CONST (None), RETURN_VALUE. We also
# accept functions with only a docstring, which yields slightly different
# bytecode, because the "None" is put in a different constant slot.
# Unfortunately, this does not catch function bodies that return a
# constant value, e.g. "return 1", because their code is identical to a
# "return None". They differ in the contents of their constant table, but
# checking that would require us to parse the bytecode, find the index
# being returned, then making sure the table has a None at that index.
if f.__code__.co_code not in (_empty.__code__.co_code,
_docstring.__code__.co_code):
raise ValueError("function body must be empty")
def _filterArgs(args, kwargs, inputSpec, outputSpec):
"""
Filter out arguments that were passed to input that output won't accept.
:param tuple args: The *args that input received.
:param dict kwargs: The **kwargs that input received.
:param ArgSpec inputSpec: The input's arg spec.
:param ArgSpec outputSpec: The output's arg spec.
:return: The args and kwargs that output will accept.
:rtype: Tuple[tuple, dict]
"""
named_args = tuple(zip(inputSpec.args[1:], args))
if outputSpec.varargs:
# Only return all args if the output accepts *args.
return_args = args
else:
# Filter out arguments that don't appear
# in the output's method signature.
return_args = [v for n, v in named_args if n in outputSpec.args]
# Get any of input's default arguments that were not passed.
passed_arg_names = tuple(kwargs)
for name, value in named_args:
passed_arg_names += (name, value)
defaults = zip(inputSpec.args[::-1], inputSpec.defaults[::-1])
full_kwargs = {n: v for n, v in defaults if n not in passed_arg_names}
full_kwargs.update(kwargs)
if outputSpec.varkw:
# Only pass all kwargs if the output method accepts **kwargs.
return_kwargs = full_kwargs
else:
# Filter out names that the output method does not accept.
all_accepted_names = outputSpec.args[1:] + outputSpec.kwonlyargs
return_kwargs = {n: v for n, v in full_kwargs.items()
if n in all_accepted_names}
return return_args, return_kwargs
@attr.s(eq=False, hash=False)
class MethodicalInput(object):
"""
An input for a L{MethodicalMachine}.
"""
automaton = attr.ib(repr=False)
method = attr.ib(validator=assertNoCode)
symbol = attr.ib(repr=False)
collectors = attr.ib(default=attr.Factory(dict), repr=False)
argSpec = attr.ib(init=False, repr=False)
@argSpec.default
def _buildArgSpec(self):
return _getArgSpec(self.method)
def __get__(self, oself, type=None):
"""
Return a function that takes no arguments and returns values returned
by output functions produced by the given L{MethodicalInput} in
C{oself}'s current state.
"""
transitioner = _transitionerFromInstance(oself, self.symbol,
self.automaton)
@preserveName(self.method)
@wraps(self.method)
def doInput(*args, **kwargs):
self.method(oself, *args, **kwargs)
previousState = transitioner._state
(outputs, outTracer) = transitioner.transition(self)
collector = self.collectors[previousState]
values = []
for output in outputs:
if outTracer:
outTracer(output._name())
a, k = _filterArgs(args, kwargs, self.argSpec, output.argSpec)
value = output(oself, *a, **k)
values.append(value)
return collector(values)
return doInput
def _name(self):
return self.method.__name__
@attr.s(frozen=True)
class MethodicalOutput(object):
"""
An output for a L{MethodicalMachine}.
"""
machine = attr.ib(repr=False)
method = attr.ib()
argSpec = attr.ib(init=False, repr=False)
@argSpec.default
def _buildArgSpec(self):
return _getArgSpec(self.method)
def __get__(self, oself, type=None):
"""
Outputs are private, so raise an exception when we attempt to get one.
"""
raise AttributeError(
"{cls}.{method} is a state-machine output method; "
"to produce this output, call an input method instead.".format(
cls=type.__name__,
method=self.method.__name__
)
)
def __call__(self, oself, *args, **kwargs):
"""
Call the underlying method.
"""
return self.method(oself, *args, **kwargs)
def _name(self):
return self.method.__name__
@attr.s(eq=False, hash=False)
class MethodicalTracer(object):
automaton = attr.ib(repr=False)
symbol = attr.ib(repr=False)
def __get__(self, oself, type=None):
transitioner = _transitionerFromInstance(oself, self.symbol,
self.automaton)
def setTrace(tracer):
transitioner.setTrace(tracer)
return setTrace
counter = count()
def gensym():
"""
Create a unique Python identifier.
"""
return "_symbol_" + str(next(counter))
class MethodicalMachine(object):
"""
A :class:`MethodicalMachine` is an interface to an `Automaton`
that uses methods on a class.
"""
def __init__(self):
self._automaton = Automaton()
self._reducers = {}
self._symbol = gensym()
def __get__(self, oself, type=None):
"""
L{MethodicalMachine} is an implementation detail for setting up
class-level state; applications should never need to access it on an
instance.
"""
if oself is not None:
raise AttributeError(
"MethodicalMachine is an implementation detail.")
return self
@_keywords_only
def state(self, initial=False, terminal=False,
serialized=None):
"""
Declare a state, possibly an initial state or a terminal state.
This is a decorator for methods, but it will modify the method so as
not to be callable any more.
:param bool initial: is this state the initial state?
Only one state on this :class:`automat.MethodicalMachine`
may be an initial state; more than one is an error.
:param bool terminal: Is this state a terminal state?
i.e. a state that the machine can end up in?
(This is purely informational at this point.)
:param Hashable serialized: a serializable value
to be used to represent this state to external systems.
This value should be hashable;
:py:func:`unicode` is a good type to use.
"""
def decorator(stateMethod):
state = MethodicalState(machine=self,
method=stateMethod,
serialized=serialized)
if initial:
self._automaton.initialState = state
return state
return decorator
@_keywords_only
def input(self):
"""
Declare an input.
This is a decorator for methods.
"""
def decorator(inputMethod):
return MethodicalInput(automaton=self._automaton,
method=inputMethod,
symbol=self._symbol)
return decorator
@_keywords_only
def output(self):
"""
Declare an output.
This is a decorator for methods.
This method will be called when the state machine transitions to this
state as specified in the decorated `output` method.
"""
def decorator(outputMethod):
return MethodicalOutput(machine=self, method=outputMethod)
return decorator
def _oneTransition(self, startState, inputToken, endState, outputTokens,
collector):
"""
See L{MethodicalState.upon}.
"""
# FIXME: tests for all of this (some of it is wrong)
# if not isinstance(startState, MethodicalState):
# raise NotImplementedError("start state {} isn't a state"
# .format(startState))
# if not isinstance(inputToken, MethodicalInput):
# raise NotImplementedError("start state {} isn't an input"
# .format(inputToken))
# if not isinstance(endState, MethodicalState):
# raise NotImplementedError("end state {} isn't a state"
# .format(startState))
# for output in outputTokens:
# if not isinstance(endState, MethodicalState):
# raise NotImplementedError("output state {} isn't a state"
# .format(endState))
self._automaton.addTransition(startState, inputToken, endState,
tuple(outputTokens))
inputToken.collectors[startState] = collector
@_keywords_only
def serializer(self):
"""
"""
def decorator(decoratee):
@wraps(decoratee)
def serialize(oself):
transitioner = _transitionerFromInstance(oself, self._symbol,
self._automaton)
return decoratee(oself, transitioner._state.serialized)
return serialize
return decorator
@_keywords_only
def unserializer(self):
"""
"""
def decorator(decoratee):
@wraps(decoratee)
def unserialize(oself, *args, **kwargs):
state = decoratee(oself, *args, **kwargs)
mapping = {}
for eachState in self._automaton.states():
mapping[eachState.serialized] = eachState
transitioner = _transitionerFromInstance(
oself, self._symbol, self._automaton)
transitioner._state = mapping[state]
return None # it's on purpose
return unserialize
return decorator
@property
def _setTrace(self):
return MethodicalTracer(self._automaton, self._symbol)
def asDigraph(self):
"""
Generate a L{graphviz.Digraph} that represents this machine's
states and transitions.
@return: L{graphviz.Digraph} object; for more information, please
see the documentation for
U{graphviz<https://graphviz.readthedocs.io/>}
"""
from ._visualize import makeDigraph
return makeDigraph(
self._automaton,
stateAsString=lambda state: state.method.__name__,
inputAsString=lambda input: input.method.__name__,
outputAsString=lambda output: output.method.__name__,
)