1#@!#!123s

: / opt / imh-python / lib / python3.9 / site-packages / automat /
Filename : _methodical.py
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# -*- test-case-name: automat._test.test_methodical -*-
from __future__ import annotations

import collections
import sys
from dataclasses import dataclass, field
from functools import wraps
from inspect import getfullargspec as getArgsSpec
from itertools import count
from typing import Any, Callable, Hashable, Iterable, TypeVar

if sys.version_info < (3, 10):
    from typing_extensions import TypeAlias
else:
    from typing import TypeAlias

from ._core import Automaton, OutputTracer, Tracer, Transitioner
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,
        defaults=spec.defaults if spec.defaults else (),
        kwonlyargs=tuple(spec.kwonlyargs),
        kwonlydefaults=(
            tuple(spec.kwonlydefaults.items()) if spec.kwonlydefaults else ()
        ),
        annotations=tuple(spec.annotations.items()),
    )


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".

    Return type annotations are omitted, since we don't constrain input methods
    to have the same return type as output methods, nor output methods to have
    the same output type.

    :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 ())
        + tuple(a for a in spec.annotations if a[0] != "return")
    )


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


@dataclass(frozen=True)
class MethodicalState(object):
    """
    A state for a L{MethodicalMachine}.
    """

    machine: MethodicalMachine = field(repr=False)
    method: Callable[..., Any] = field()
    serialized: bool = field(repr=False)

    def upon(
        self,
        input: MethodicalInput,
        enter: MethodicalState | None = None,
        outputs: Iterable[MethodicalOutput] | None = None,
        collector: Callable[[Iterable[T]], object] = list,
    ) -> None:
        """
        Declare a state transition within the L{MethodicalMachine} associated
        with this L{MethodicalState}: upon the receipt of the `input`, enter
        the `state`, emitting each output in `outputs`.

        @param input: The input triggering a state transition.

        @param enter: The resulting state.

        @param outputs: The outputs to be triggered as a result of the declared
            state transition.

        @param 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.
        """
        if enter is None:
            enter = self
        if outputs is None:
            outputs = []
        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) -> str:
        return self.method.__name__


def _transitionerFromInstance(
    oself: object,
    symbol: str,
    automaton: Automaton[MethodicalState, MethodicalInput, MethodicalOutput],
) -> Transitioner[MethodicalState, MethodicalInput, MethodicalOutput]:
    """
    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(f: Callable[..., Any]) -> None:
    # 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


T = TypeVar("T")
R = TypeVar("R")


@dataclass(eq=False)
class MethodicalInput(object):
    """
    An input for a L{MethodicalMachine}.
    """

    automaton: Automaton[MethodicalState, MethodicalInput, MethodicalOutput] = field(
        repr=False
    )
    method: Callable[..., Any] = field()
    symbol: str = field(repr=False)
    collectors: dict[MethodicalState, Callable[[Iterable[T]], R]] = field(
        default_factory=dict, repr=False
    )

    argSpec: ArgSpec = field(init=False, repr=False)

    def __post_init__(self) -> None:
        self.argSpec = _getArgSpec(self.method)
        assertNoCode(self.method)

    def __get__(self, oself: object, type: None = None) -> object:
        """
        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: object, **kwargs: object) -> object:
            self.method(oself, *args, **kwargs)
            previousState = transitioner._state
            (outputs, outTracer) = transitioner.transition(self)
            collector = self.collectors[previousState]
            values = []
            for output in outputs:
                if outTracer is not None:
                    outTracer(output)
                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) -> str:
        return self.method.__name__


@dataclass(frozen=True)
class MethodicalOutput(object):
    """
    An output for a L{MethodicalMachine}.
    """

    machine: MethodicalMachine = field(repr=False)
    method: Callable[..., Any]
    argSpec: ArgSpec = field(init=False, repr=False, compare=False)

    def __post_init__(self) -> None:
        self.__dict__["argSpec"] = _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) -> str:
        return self.method.__name__


StringOutputTracer = Callable[[str], None]
StringTracer: TypeAlias = "Callable[[str, str, str], StringOutputTracer | None]"


def wrapTracer(
    wrapped: StringTracer | None,
) -> Tracer[MethodicalState, MethodicalInput, MethodicalOutput] | None:
    if wrapped is None:
        return None

    def tracer(
        state: MethodicalState,
        input: MethodicalInput,
        output: MethodicalState,
    ) -> OutputTracer[MethodicalOutput] | None:
        result = wrapped(state._name(), input._name(), output._name())
        if result is not None:
            return lambda out: result(out._name())
        return None

    return tracer


@dataclass(eq=False)
class MethodicalTracer(object):
    automaton: Automaton[MethodicalState, MethodicalInput, MethodicalOutput] = field(
        repr=False
    )
    symbol: str = field(repr=False)

    def __get__(
        self, oself: object, type: object = None
    ) -> Callable[[StringTracer], None]:
        transitioner = _transitionerFromInstance(oself, self.symbol, self.automaton)

        def setTrace(tracer: StringTracer | None) -> None:
            transitioner.setTrace(wrapTracer(tracer))

        return setTrace


counter = count()


def gensym():
    """
    Create a unique Python identifier.
    """
    return "_symbol_" + str(next(counter))


class MethodicalMachine(object):
    """
    A L{MethodicalMachine} is an interface to an L{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: bool = False, terminal: bool = False, serialized: Hashable = 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 initial: is this state the initial state?  Only one state on
            this L{automat.MethodicalMachine} may be an initial state; more
            than one is an error.

        @param 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 serialized: a serializable value to be used to represent this
            state to external systems.  This value should be hashable; L{str}
            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) -> MethodicalTracer:
        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__,
        )