from collections import defaultdict
import datetime
import logging
import uuid

import tornado.queues
from tornado.locks import Event
from tornado import gen

from .client import Future, _get_global_client, Client
from .utils import tokey, sync, thread_state, TimeoutError
from .worker import get_client

logger = logging.getLogger(__name__)


class QueueExtension(object):
    """ 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(lambda: 0)

        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}
        )

        self.scheduler.extensions["queues"] = self

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

    def release(self, stream=None, 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, stream=None, name=None, key=None, data=None, client=None, timeout=None
    ):
        if key is not None:
            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}
        if timeout is not None:
            timeout = datetime.timedelta(seconds=timeout)
        try:
            await self.queues[name].put(record, timeout=timeout)
        except gen.TimeoutError:
            raise TimeoutError("Timed out waiting for Queue")

    def future_release(self, name=None, key=None, client=None):
        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, stream=None, 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 i in range(batch):
                    record = await q.get()
                    out.append(record)
            out = [process(o) for o in out]
            return out
        else:
            if timeout is not None:
                timeout = datetime.timedelta(seconds=timeout)
            try:
                record = await self.queues[name].get(timeout=timeout)
            except gen.TimeoutError:
                raise TimeoutError("Timed out waiting for Queue")
            record = process(record)
            return record

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


class Queue(object):
    """ 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 and has known issues in Python 2

    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 or _get_global_client()
        self.name = name or "queue-" + uuid.uuid4().hex
        self._event_started = Event()
        if self.client.asynchronous or getattr(
            thread_state, "on_event_loop_thread", False
        ):

            async def _create_queue():
                await self.client.scheduler.queue_create(
                    name=self.name, maxsize=maxsize
                )
                self._event_started.set()

            self.client.loop.add_callback(_create_queue)
        else:
            sync(
                self.client.loop,
                self.client.scheduler.queue_create,
                name=self.name,
                maxsize=maxsize,
            )
            self._event_started.set()

    def __await__(self):
        async def _():
            await self._event_started.wait()
            return self

        return _().__await__()

    async def _put(self, value, timeout=None):
        if isinstance(value, Future):
            await self.client.scheduler.queue_put(
                key=tokey(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 """
        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 (optional)
            Time in seconds to wait before timing out
        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
         """
        return self.client.sync(self._get, timeout=timeout, batch=batch, **kwargs)

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

    async def _get(self, timeout=None, batch=False):
        try:
            resp = await self.client.scheduler.queue_get(
                timeout=timeout, name=self.name, batch=batch
            )
        except gen.TimeoutError:
            raise TimeoutError("Timed out waiting for Queue")

        def process(d):
            if d["type"] == "Future":
                value = Future(d["value"], self.client, inform=True, 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):
        if self.client.status == "running":  # TODO: can leave zombie futures
            self.client._send_to_scheduler({"op": "queue_release", "name": self.name})

    def __getstate__(self):
        return (self.name, self.client.scheduler.address)

    def __setstate__(self, state):
        name, address = state
        try:
            client = get_client(address)
            assert client.scheduler.address == address
        except (AttributeError, AssertionError):
            client = Client(address, set_as_default=False)
        self.__init__(name=name, client=client)
