# Source code for distributed.queues

    from __future__ import annotations

    import asyncio
    import logging
    import uuid
    from collections import defaultdict

    from dask.utils import parse_timedelta

    from distributed.client import Future
    from distributed.utils import Deadline, wait_for
    from distributed.worker import get_client

    logger = logging.getLogger(__name__)

    class QueueExtension:
        """An extension for the scheduler to manage queues

        This adds the following routes to the scheduler

        *  queue_create
        *  queue_release
        *  queue_put
        *  queue_get
        *  queue_size
        """

        def __init__(self, scheduler):
            self.scheduler = scheduler
            self.queues = dict()
            self.client_refcount = dict()
            self.future_refcount = defaultdict(int)

            self.scheduler.handlers.update(
                {
                    "queue_create": self.create,
                    "queue_put": self.put,
                    "queue_get": self.get,
                    "queue_qsize": self.qsize,
                }
            )

            self.scheduler.stream_handlers.update(
                {"queue-future-release": self.future_release, "queue_release": self.release}
            )

        def create(self, name=None, client=None, maxsize=0):
            logger.debug(f"Queue name: {name}")
            if name not in self.queues:
                self.queues[name] = asyncio.Queue(maxsize=maxsize)
                self.client_refcount[name] = 1
            else:
                self.client_refcount[name] += 1

        def release(self, name=None, client=None):
            if name not in self.queues:
                return

            self.client_refcount[name] -= 1
            if self.client_refcount[name] == 0:
                del self.client_refcount[name]
                futures = self.queues[name]._queue
                del self.queues[name]
                keys = [d["value"] for d in futures if d["type"] == "Future"]
                if keys:
                    self.scheduler.client_releases_keys(keys=keys, client="queue-%s" % name)

        async def put(self, name=None, key=None, data=None, client=None, timeout=None):
            deadline = Deadline.after(timeout)
            if key is not None:
                while key not in self.scheduler.tasks:
                    await asyncio.sleep(0.01)
                    if deadline.expired:
                        raise TimeoutError(f"Task {key} unknown to scheduler.")

                record = {"type": "Future", "value": key}
                self.future_refcount[name, key] += 1
                self.scheduler.client_desires_keys(keys=[key], client="queue-%s" % name)
            else:
                record = {"type": "msgpack", "value": data}
            await wait_for(self.queues[name].put(record), timeout=deadline.remaining)

        def future_release(self, name=None, key=None, client=None):
            self.scheduler.client_desires_keys(keys=[key], client=client)
            self.future_refcount[name, key] -= 1
            if self.future_refcount[name, key] == 0:
                self.scheduler.client_releases_keys(keys=[key], client="queue-%s" % name)
                del self.future_refcount[name, key]

        async def get(self, name=None, client=None, timeout=None, batch=False):
            def process(record):
                """Add task status if known"""
                if record["type"] == "Future":
                    record = record.copy()
                    key = record["value"]
                    ts = self.scheduler.tasks.get(key)
                    state = ts.state if ts is not None else "lost"

                    record["state"] = state
                    if state == "erred":
                        record["exception"] = ts.exception_blame.exception
                        record["traceback"] = ts.exception_blame.traceback

                return record

            if batch:
                q = self.queues[name]
                out = []
                if batch is True:
                    while not q.empty():
                        record = await q.get()
                        out.append(record)
                else:
                    if timeout is not None:
                        msg = (
                            "Dask queues don't support simultaneous use of "
                            "integer batch sizes and timeouts"
                        )
                        raise NotImplementedError(msg)
                    for _ in range(batch):
                        record = await q.get()
                        out.append(record)
                out = [process(o) for o in out]
                return out
            else:
                record = await wait_for(self.queues[name].get(), timeout=timeout)
                record = process(record)
                return record

        def qsize(self, name=None, client=None):
            return self.queues[name].qsize()

    class Queue:
        """Distributed Queue

        This allows multiple clients to share futures or small bits of data between
        each other with a multi-producer/multi-consumer queue.  All metadata is
        sequentialized through the scheduler.

        Elements of the Queue must be either Futures or msgpack-encodable data
        (ints, strings, lists, dicts).  All data is sent through the scheduler so
        it is wise not to send large objects.  To share large objects scatter the
        data and share the future instead.

        .. warning::

           This object is experimental

        Parameters
        ----------
        name: string (optional)
            Name used by other clients and the scheduler to identify the queue. If
            not given, a random name will be generated.
        client: Client (optional)
            Client used for communication with the scheduler.
            If not given, the default global client will be used.
        maxsize: int (optional)
            Number of items allowed in the queue. If 0 (the default), the queue
            size is unbounded.

        Examples
        --------
        >>> from dask.distributed import Client, Queue  # doctest: +SKIP
        >>> client = Client()  # doctest: +SKIP
        >>> queue = Queue('x')  # doctest: +SKIP
        >>> future = client.submit(f, x)  # doctest: +SKIP
        >>> queue.put(future)  # doctest: +SKIP

        See Also
        --------
        Variable: shared variable between clients
        """

        def __init__(self, name=None, client=None, maxsize=0):
            self._client = client
            self.name = name or "queue-" + uuid.uuid4().hex
            self.maxsize = maxsize
            self._maybe_start()

        def _maybe_start(self):
            if self.client:
                if self.client.asynchronous:
                    self._started = asyncio.ensure_future(self._start())
                else:
                    self.client.sync(self._start)

        @property
        def client(self):
            if not self._client:
                try:
                    self._client = get_client()
                except ValueError:
                    pass
            return self._client

        def _verify_running(self):
            if not self.client:
                raise RuntimeError(
                    f"{type(self)} object not properly initialized. This can happen"
                    " if the object is being deserialized outside of the context of"
                    " a Client or Worker."
                )

        async def _start(self):
            await self.client.scheduler.queue_create(name=self.name, maxsize=self.maxsize)
            return self

        def __await__(self):
            self._maybe_start()
            if hasattr(self, "_started"):
                return self._started.__await__()
            else:

                async def _():
                    return self

                return _().__await__()

        async def _put(self, value, timeout=None):
            if isinstance(value, Future):
                await self.client.scheduler.queue_put(
                    key=value.key, timeout=timeout, name=self.name
                )
            else:
                await self.client.scheduler.queue_put(
                    data=value, timeout=timeout, name=self.name
                )

        def put(self, value, timeout=None, **kwargs):
            """Put data into the queue

            Parameters
            ----------
            timeout : number or string or timedelta, optional
                Time in seconds to wait before timing out.
                Instead of number of seconds, it is also possible to specify
                a timedelta in string format, e.g. "200ms".
            """
            self._verify_running()
            timeout = parse_timedelta(timeout)
            return self.client.sync(self._put, value, timeout=timeout, **kwargs)

        def get(self, timeout=None, batch=False, **kwargs):
            """Get data from the queue

            Parameters
            ----------
            timeout : number or string or timedelta, optional
                Time in seconds to wait before timing out.
                Instead of number of seconds, it is also possible to specify
                a timedelta in string format, e.g. "200ms".
            batch : boolean, int (optional)
                If True then return all elements currently waiting in the queue.
                If an integer than return that many elements from the queue
                If False (default) then return one item at a time
            """
            self._verify_running()
            timeout = parse_timedelta(timeout)
            return self.client.sync(self._get, timeout=timeout, batch=batch, **kwargs)

        def qsize(self, **kwargs):
            """Current number of elements in the queue"""
            self._verify_running()
            return self.client.sync(self._qsize, **kwargs)

        async def _get(self, timeout=None, batch=False):
            resp = await self.client.scheduler.queue_get(
                timeout=timeout, name=self.name, batch=batch
            )

            def process(d):
                if d["type"] == "Future":
                    value = Future(d["value"], self.client, state=d["state"])
                    if d["state"] == "erred":
                        value._state.set_error(d["exception"], d["traceback"])
                    self.client._send_to_scheduler(
                        {"op": "queue-future-release", "name": self.name, "key": d["value"]}
                    )
                else:
                    value = d["value"]

                return value

            if batch is False:
                result = process(resp)
            else:
                result = list(map(process, resp))

            return result

        async def _qsize(self):
            result = await self.client.scheduler.queue_qsize(name=self.name)
            return result

        def close(self):
            self._verify_running()
            if self.client.status == "running":  # TODO: can leave zombie futures
                self.client._send_to_scheduler({"op": "queue_release", "name": self.name})

        def __reduce__(self):
            return type(self), (self.name, None, self.maxsize)