507 lines
19 KiB
Python
507 lines
19 KiB
Python
# tractor: structured concurrent "actors".
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# Copyright 2018-eternity Tyler Goodlet.
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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``trio`` inspired apis and helpers
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"""
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from functools import partial
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import inspect
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from typing import (
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Optional,
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TYPE_CHECKING,
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)
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import typing
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import warnings
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import trio
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from async_generator import asynccontextmanager
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from ._debug import maybe_wait_for_debugger
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from ._state import current_actor, is_main_process
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from .log import get_logger, get_loglevel
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from ._runtime import Actor
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from ._portal import Portal
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from ._exceptions import is_multi_cancelled
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from ._root import open_root_actor
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from . import _state
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from . import _spawn
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if TYPE_CHECKING:
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import multiprocessing as mp
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log = get_logger(__name__)
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_default_bind_addr: tuple[str, int] = ('127.0.0.1', 0)
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class ActorNursery:
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'''
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The fundamental actor supervision construct: spawn and manage
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explicit lifetime and capability restricted, bootstrapped,
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``trio.run()`` scheduled sub-processes.
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Though the concept of a "process nursery" is different in complexity
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and slightly different in semantics then a tradtional single
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threaded task nursery, much of the interface is the same. New
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processes each require a top level "parent" or "root" task which is
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itself no different then any task started by a tradtional
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``trio.Nursery``. The main difference is that each "actor" (a
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process + ``trio.run()``) contains a full, paralell executing
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``trio``-task-tree. The following super powers ensue:
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- starting tasks in a child actor are completely independent of
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tasks started in the current process. They execute in *parallel*
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relative to tasks in the current process and are scheduled by their
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own actor's ``trio`` run loop.
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- tasks scheduled in a remote process still maintain an SC protocol
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across memory boundaries using a so called "structured concurrency
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dialogue protocol" which ensures task-hierarchy-lifetimes are linked.
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- remote tasks (in another actor) can fail and relay failure back to
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the caller task (in some other actor) via a seralized
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``RemoteActorError`` which means no zombie process or RPC
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initiated task can ever go off on its own.
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'''
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def __init__(
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self,
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actor: Actor,
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ria_nursery: trio.Nursery,
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da_nursery: trio.Nursery,
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errors: dict[tuple[str, str], Exception],
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) -> None:
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# self.supervisor = supervisor # TODO
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self._actor: Actor = actor
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self._ria_nursery = ria_nursery
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self._da_nursery = da_nursery
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self._children: dict[
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tuple[str, str],
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tuple[Actor, mp.Process, Optional[Portal]]
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] = {}
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# portals spawned with ``run_in_actor()`` are
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# cancelled when their "main" result arrives
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self._cancel_after_result_on_exit: set = set()
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self.cancelled: bool = False
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self._join_procs = trio.Event()
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self._at_least_one_child_in_debug: bool = False
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self.errors = errors
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self.exited = trio.Event()
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async def start_actor(
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self,
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name: str,
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*,
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bind_addr: tuple[str, int] = _default_bind_addr,
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rpc_module_paths: list[str] = None,
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enable_modules: list[str] = None,
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loglevel: str = None, # set log level per subactor
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nursery: trio.Nursery = None,
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debug_mode: Optional[bool] = None,
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infect_asyncio: bool = False,
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) -> Portal:
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'''
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Start a (daemon) actor: an process that has no designated
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"main task" besides the runtime.
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'''
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loglevel = loglevel or self._actor.loglevel or get_loglevel()
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# configure and pass runtime state
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_rtv = _state._runtime_vars.copy()
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_rtv['_is_root'] = False
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# allow setting debug policy per actor
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if debug_mode is not None:
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_rtv['_debug_mode'] = debug_mode
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self._at_least_one_child_in_debug = True
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enable_modules = enable_modules or []
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if rpc_module_paths:
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warnings.warn(
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"`rpc_module_paths` is now deprecated, use "
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" `enable_modules` instead.",
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DeprecationWarning,
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stacklevel=2,
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)
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enable_modules.extend(rpc_module_paths)
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subactor = Actor(
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name,
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# modules allowed to invoked funcs from
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enable_modules=enable_modules,
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loglevel=loglevel,
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arbiter_addr=current_actor()._arb_addr,
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)
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parent_addr = self._actor.accept_addr
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assert parent_addr
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# start a task to spawn a process
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# blocks until process has been started and a portal setup
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nursery = nursery or self._da_nursery
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# XXX: the type ignore is actually due to a `mypy` bug
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return await nursery.start( # type: ignore
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partial(
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_spawn.new_proc,
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name,
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self,
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subactor,
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self.errors,
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bind_addr,
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parent_addr,
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_rtv, # run time vars
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infect_asyncio=infect_asyncio,
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)
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)
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async def run_in_actor(
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self,
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fn: typing.Callable,
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*,
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name: Optional[str] = None,
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bind_addr: tuple[str, int] = _default_bind_addr,
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rpc_module_paths: Optional[list[str]] = None,
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enable_modules: list[str] = None,
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loglevel: str = None, # set log level per subactor
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infect_asyncio: bool = False,
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**kwargs, # explicit args to ``fn``
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) -> Portal:
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"""Spawn a new actor, run a lone task, then terminate the actor and
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return its result.
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Actors spawned using this method are kept alive at nursery teardown
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until the task spawned by executing ``fn`` completes at which point
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the actor is terminated.
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"""
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mod_path = fn.__module__
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if name is None:
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# use the explicit function name if not provided
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name = fn.__name__
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portal = await self.start_actor(
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name,
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enable_modules=[mod_path] + (
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enable_modules or rpc_module_paths or []
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),
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bind_addr=bind_addr,
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loglevel=loglevel,
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# use the run_in_actor nursery
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nursery=self._ria_nursery,
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infect_asyncio=infect_asyncio,
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)
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# XXX: don't allow stream funcs
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if not (
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inspect.iscoroutinefunction(fn) and
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not getattr(fn, '_tractor_stream_function', False)
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):
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raise TypeError(f'{fn} must be an async function!')
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# this marks the actor to be cancelled after its portal result
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# is retreived, see logic in `open_nursery()` below.
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self._cancel_after_result_on_exit.add(portal)
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await portal._submit_for_result(
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mod_path,
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fn.__name__,
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**kwargs
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)
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return portal
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async def cancel(self, hard_kill: bool = False) -> None:
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"""Cancel this nursery by instructing each subactor to cancel
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itself and wait for all subactors to terminate.
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If ``hard_killl`` is set to ``True`` then kill the processes
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directly without any far end graceful ``trio`` cancellation.
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"""
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self.cancelled = True
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log.cancel(f"Cancelling nursery in {self._actor.uid}")
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with trio.move_on_after(3) as cs:
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async with trio.open_nursery() as nursery:
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for subactor, proc, portal in self._children.values():
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# TODO: are we ever even going to use this or
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# is the spawning backend responsible for such
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# things? I'm thinking latter.
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if hard_kill:
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proc.terminate()
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else:
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if portal is None: # actor hasn't fully spawned yet
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event = self._actor._peer_connected[subactor.uid]
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log.warning(
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f"{subactor.uid} wasn't finished spawning?")
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await event.wait()
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# channel/portal should now be up
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_, _, portal = self._children[subactor.uid]
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# XXX should be impossible to get here
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# unless method was called from within
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# shielded cancel scope.
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if portal is None:
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# cancelled while waiting on the event
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# to arrive
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chan = self._actor._peers[subactor.uid][-1]
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if chan:
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portal = Portal(chan)
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else: # there's no other choice left
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proc.terminate()
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# spawn cancel tasks for each sub-actor
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assert portal
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if portal.channel.connected():
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nursery.start_soon(portal.cancel_actor)
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# if we cancelled the cancel (we hung cancelling remote actors)
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# then hard kill all sub-processes
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if cs.cancelled_caught:
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log.error(
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f"Failed to cancel {self}\nHard killing process tree!")
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for subactor, proc, portal in self._children.values():
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log.warning(f"Hard killing process {proc}")
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proc.terminate()
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# mark ourselves as having (tried to have) cancelled all subactors
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self._join_procs.set()
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@asynccontextmanager
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async def _open_and_supervise_one_cancels_all_nursery(
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actor: Actor,
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) -> typing.AsyncGenerator[ActorNursery, None]:
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# the collection of errors retreived from spawned sub-actors
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errors: dict[tuple[str, str], Exception] = {}
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# This is the outermost level "deamon actor" nursery. It is awaited
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# **after** the below inner "run in actor nursery". This allows for
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# handling errors that are generated by the inner nursery in
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# a supervisor strategy **before** blocking indefinitely to wait for
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# actors spawned in "daemon mode" (aka started using
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# ``ActorNursery.start_actor()``).
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# errors from this daemon actor nursery bubble up to caller
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async with trio.open_nursery() as da_nursery:
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try:
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# This is the inner level "run in actor" nursery. It is
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# awaited first since actors spawned in this way (using
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# ``ActorNusery.run_in_actor()``) are expected to only
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# return a single result and then complete (i.e. be canclled
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# gracefully). Errors collected from these actors are
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# immediately raised for handling by a supervisor strategy.
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# As such if the strategy propagates any error(s) upwards
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# the above "daemon actor" nursery will be notified.
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async with trio.open_nursery() as ria_nursery:
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anursery = ActorNursery(
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actor,
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ria_nursery,
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da_nursery,
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errors
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)
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try:
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# spawning of actors happens in the caller's scope
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# after we yield upwards
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yield anursery
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log.runtime(
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f"Waiting on subactors {anursery._children} "
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"to complete"
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)
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# Last bit before first nursery block ends in the case
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# where we didn't error in the caller's scope
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# signal all process monitor tasks to conduct
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# hard join phase.
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anursery._join_procs.set()
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except BaseException as err:
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# If we error in the root but the debugger is
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# engaged we don't want to prematurely kill (and
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# thus clobber access to) the local tty since it
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# will make the pdb repl unusable.
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# Instead try to wait for pdb to be released before
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# tearing down.
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await maybe_wait_for_debugger(
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child_in_debug=anursery._at_least_one_child_in_debug
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)
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# if the caller's scope errored then we activate our
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# one-cancels-all supervisor strategy (don't
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# worry more are coming).
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anursery._join_procs.set()
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try:
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# XXX: hypothetically an error could be
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# raised and then a cancel signal shows up
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# slightly after in which case the `else:`
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# block here might not complete? For now,
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# shield both.
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with trio.CancelScope(shield=True):
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etype = type(err)
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if etype in (
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trio.Cancelled,
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KeyboardInterrupt
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) or (
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is_multi_cancelled(err)
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):
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log.cancel(
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f"Nursery for {current_actor().uid} "
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f"was cancelled with {etype}")
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else:
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log.exception(
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f"Nursery for {current_actor().uid} "
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f"errored with {err}, ")
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# cancel all subactors
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await anursery.cancel()
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except trio.MultiError as merr:
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# If we receive additional errors while waiting on
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# remaining subactors that were cancelled,
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# aggregate those errors with the original error
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# that triggered this teardown.
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if err not in merr.exceptions:
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raise trio.MultiError(merr.exceptions + [err])
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else:
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raise
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# ria_nursery scope end
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# XXX: do we need a `trio.Cancelled` catch here as well?
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# this is the catch around the ``.run_in_actor()`` nursery
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except (
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Exception,
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trio.MultiError,
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trio.Cancelled
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) as err:
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# XXX: yet another guard before allowing the cancel
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# sequence in case a (single) child is in debug.
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await maybe_wait_for_debugger(
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child_in_debug=anursery._at_least_one_child_in_debug
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)
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# If actor-local error was raised while waiting on
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# ".run_in_actor()" actors then we also want to cancel all
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# remaining sub-actors (due to our lone strategy:
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# one-cancels-all).
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log.cancel(f"Nursery cancelling due to {err}")
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if anursery._children:
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with trio.CancelScope(shield=True):
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await anursery.cancel()
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raise
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finally:
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# No errors were raised while awaiting ".run_in_actor()"
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# actors but those actors may have returned remote errors as
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# results (meaning they errored remotely and have relayed
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# those errors back to this parent actor). The errors are
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# collected in ``errors`` so cancel all actors, summarize
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# all errors and re-raise.
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if errors:
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if anursery._children:
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with trio.CancelScope(shield=True):
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await anursery.cancel()
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# use `MultiError` as needed
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if len(errors) > 1:
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raise trio.MultiError(tuple(errors.values()))
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else:
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raise list(errors.values())[0]
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# ria_nursery scope end - nursery checkpoint
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# after nursery exit
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@asynccontextmanager
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async def open_nursery(
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**kwargs,
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) -> typing.AsyncGenerator[ActorNursery, None]:
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'''
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Create and yield a new ``ActorNursery`` to be used for spawning
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structured concurrent subactors.
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When an actor is spawned a new trio task is started which
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invokes one of the process spawning backends to create and start
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a new subprocess. These tasks are started by one of two nurseries
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detailed below. The reason for spawning processes from within
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a new task is because ``trio_run_in_process`` itself creates a new
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internal nursery and the same task that opens a nursery **must**
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close it. It turns out this approach is probably more correct
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anyway since it is more clear from the following nested nurseries
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which cancellation scopes correspond to each spawned subactor set.
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'''
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implicit_runtime = False
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actor = current_actor(err_on_no_runtime=False)
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try:
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if actor is None and is_main_process():
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# if we are the parent process start the
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# actor runtime implicitly
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log.info("Starting actor runtime!")
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# mark us for teardown on exit
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implicit_runtime = True
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async with open_root_actor(**kwargs) as actor:
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assert actor is current_actor()
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try:
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async with _open_and_supervise_one_cancels_all_nursery(
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actor
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) as anursery:
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yield anursery
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finally:
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anursery.exited.set()
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else: # sub-nursery case
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try:
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async with _open_and_supervise_one_cancels_all_nursery(
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actor
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) as anursery:
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yield anursery
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finally:
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anursery.exited.set()
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finally:
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log.debug("Nursery teardown complete")
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# shutdown runtime if it was started
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if implicit_runtime:
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log.info("Shutting down actor tree")
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