from __future__ import annotations

import asyncio
import math
from time import sleep

import pytest

import dask

from distributed import (
    Adaptive,
    Client,
    LocalCluster,
    Scheduler,
    SpecCluster,
    Worker,
    wait,
)
from distributed.compatibility import LINUX, MACOS, WINDOWS
from distributed.metrics import time
from distributed.utils_test import async_wait_for, gen_test, slowinc


def test_adaptive_local_cluster(loop):
    with LocalCluster(
        n_workers=0,
        silence_logs=False,
        dashboard_address=":0",
        loop=loop,
    ) as cluster:
        alc = cluster.adapt(interval="100 ms")
        with Client(cluster, loop=loop) as c:
            assert not cluster.scheduler.workers
            future = c.submit(lambda x: x + 1, 1)
            assert future.result() == 2
            assert cluster.scheduler.workers

            sleep(0.1)
            assert cluster.scheduler.workers

            del future

            start = time()
            while cluster.scheduler.workers:
                sleep(0.01)
                assert time() < start + 30

            assert not cluster.scheduler.workers


@gen_test()
async def test_adaptive_local_cluster_multi_workers():
    async with LocalCluster(
        n_workers=0,
        silence_logs=False,
        processes=False,
        dashboard_address=":0",
        asynchronous=True,
    ) as cluster:

        cluster.scheduler.allowed_failures = 1000
        adapt = cluster.adapt(interval="100 ms")
        async with Client(cluster, asynchronous=True) as c:
            futures = c.map(slowinc, range(100), delay=0.01)

            while not cluster.scheduler.workers:
                await asyncio.sleep(0.01)

            await c.gather(futures)
            del futures

            while cluster.scheduler.workers:
                await asyncio.sleep(0.01)

            # no workers for a while
            for _ in range(10):
                assert not cluster.scheduler.workers
                await asyncio.sleep(0.05)

            futures = c.map(slowinc, range(100), delay=0.01)
            await c.gather(futures)


@pytest.mark.xfail(reason="changed API")
@gen_test()
async def test_adaptive_scale_down_override():
    class TestAdaptive(Adaptive):
        def __init__(self, *args, **kwargs):
            self.min_size = kwargs.pop("min_size", 0)
            super().__init__(*args, **kwargs)

        async def workers_to_close(self, **kwargs):
            num_workers = len(self.cluster.workers)
            to_close = await self.scheduler.workers_to_close(**kwargs)
            if num_workers - len(to_close) < self.min_size:
                to_close = to_close[: num_workers - self.min_size]

            return to_close

    class TestCluster(LocalCluster):
        def scale_up(self, n, **kwargs):
            assert False

    async with TestCluster(
        n_workers=10, processes=False, asynchronous=True, dashboard_address=":0"
    ) as cluster:
        ta = cluster.adapt(
            min_size=2, interval=0.1, scale_factor=2, Adaptive=TestAdaptive
        )
        await asyncio.sleep(0.3)

        # Assert that adaptive cycle does not reduce cluster below minimum size
        # as determined via override.
        assert len(cluster.scheduler.workers) == 2


@gen_test()
async def test_min_max():
    async with LocalCluster(
        n_workers=0,
        silence_logs=False,
        processes=False,
        dashboard_address=":0",
        asynchronous=True,
        threads_per_worker=1,
    ) as cluster:
        adapt = cluster.adapt(minimum=1, maximum=2, interval="20 ms", wait_count=10)
        async with Client(cluster, asynchronous=True) as c:
            start = time()
            while not cluster.scheduler.workers:
                await asyncio.sleep(0.01)
                assert time() < start + 1

            await asyncio.sleep(0.2)
            assert len(cluster.scheduler.workers) == 1
            assert len(adapt.log) == 1 and adapt.log[-1][1] == {"status": "up", "n": 1}

            futures = c.map(slowinc, range(100), delay=0.1)

            start = time()
            while len(cluster.scheduler.workers) < 2:
                await asyncio.sleep(0.01)
                assert time() < start + 1

            assert len(cluster.scheduler.workers) == 2
            await asyncio.sleep(0.5)
            assert len(cluster.scheduler.workers) == 2
            assert len(cluster.workers) == 2
            assert len(adapt.log) == 2 and all(
                d["status"] == "up" for _, d in adapt.log
            )

            del futures

            start = time()
            while len(cluster.scheduler.workers) != 1:
                await asyncio.sleep(0.01)
                assert time() < start + 2
            assert adapt.log[-1][1]["status"] == "down"


@gen_test()
async def test_avoid_churn():
    """We want to avoid creating and deleting workers frequently

    Instead we want to wait a few beats before removing a worker in case the
    user is taking a brief pause between work
    """
    async with LocalCluster(
        n_workers=0,
        asynchronous=True,
        processes=False,
        silence_logs=False,
        dashboard_address=":0",
    ) as cluster:
        async with Client(cluster, asynchronous=True) as client:
            adapt = cluster.adapt(interval="20 ms", wait_count=5)

            for i in range(10):
                await client.submit(slowinc, i, delay=0.040)
                await asyncio.sleep(0.040)

            assert len(adapt.log) == 1


@gen_test()
async def test_adapt_quickly():
    """We want to avoid creating and deleting workers frequently

    Instead we want to wait a few beats before removing a worker in case the
    user is taking a brief pause between work
    """
    async with LocalCluster(
        n_workers=0,
        asynchronous=True,
        processes=False,
        silence_logs=False,
        dashboard_address=":0",
    ) as cluster, Client(cluster, asynchronous=True) as client:
        adapt = cluster.adapt(interval="20 ms", wait_count=5, maximum=10)
        future = client.submit(slowinc, 1, delay=0.100)
        await wait(future)
        assert len(adapt.log) == 1

        # Scale up when there is plenty of available work
        futures = client.map(slowinc, range(1000), delay=0.100)
        while len(adapt.log) == 1:
            await asyncio.sleep(0.01)
        assert len(adapt.log) == 2
        assert adapt.log[-1][1]["status"] == "up"
        d = [x for x in adapt.log[-1] if isinstance(x, dict)][0]
        assert 2 < d["n"] <= adapt.maximum

        while len(cluster.workers) < adapt.maximum:
            await asyncio.sleep(0.01)

        del futures

        while len(cluster.scheduler.tasks) > 1:
            await asyncio.sleep(0.01)

        await cluster

        while (
            len(cluster.scheduler.workers) > 1
            or len(cluster.worker_spec) > 1
            or len(cluster.workers) > 1
        ):
            await asyncio.sleep(0.01)

        # Don't scale up for large sequential computations
        x = await client.scatter(1)
        for _ in range(100):
            x = client.submit(slowinc, x)

        await asyncio.sleep(0.1)
        assert len(cluster.workers) == 1


@gen_test()
async def test_adapt_down():
    """Ensure that redefining adapt with a lower maximum removes workers"""
    async with LocalCluster(
        n_workers=0,
        asynchronous=True,
        processes=False,
        silence_logs=False,
        dashboard_address=":0",
    ) as cluster, Client(cluster, asynchronous=True) as client:
        cluster.adapt(interval="20ms", maximum=5)

        futures = client.map(slowinc, range(1000), delay=0.1)
        while len(cluster.scheduler.workers) < 5:
            await asyncio.sleep(0.1)

        cluster.adapt(maximum=2)

        start = time()
        while len(cluster.scheduler.workers) != 2:
            await asyncio.sleep(0.1)
            assert time() < start + 60


@gen_test()
async def test_no_more_workers_than_tasks():
    with dask.config.set(
        {"distributed.scheduler.default-task-durations": {"slowinc": 1000}}
    ):
        async with LocalCluster(
            n_workers=0,
            silence_logs=False,
            processes=False,
            dashboard_address=":0",
            asynchronous=True,
        ) as cluster:
            adapt = cluster.adapt(minimum=0, maximum=4, interval="10 ms")
            async with Client(cluster, asynchronous=True) as client:
                await client.submit(slowinc, 1, delay=0.100)
                assert len(cluster.scheduler.workers) <= 1


@pytest.mark.filterwarnings("ignore:There is no current event loop:DeprecationWarning")
@pytest.mark.filterwarnings("ignore:make_current is deprecated:DeprecationWarning")
def test_basic_no_loop(cleanup):
    loop = None
    try:
        with LocalCluster(
            n_workers=0, silence_logs=False, dashboard_address=":0", loop=None
        ) as cluster:
            with Client(cluster) as client:
                cluster.adapt()
                future = client.submit(lambda x: x + 1, 1)
                assert future.result() == 2
            loop = cluster.loop
    finally:
        if loop is not None:
            loop.add_callback(loop.stop)


@pytest.mark.flaky(condition=LINUX, reruns=10, reruns_delay=5)
@pytest.mark.xfail(condition=MACOS or WINDOWS, reason="extremely flaky")
@gen_test()
async def test_target_duration():
    with dask.config.set(
        {
            "distributed.scheduler.default-task-durations": {"slowinc": 1},
            # adaptive target for queued tasks doesn't yet consider default or learned task durations
            "distributed.scheduler.worker-saturation": float("inf"),
        }
    ):
        async with LocalCluster(
            n_workers=0,
            asynchronous=True,
            processes=False,
            silence_logs=False,
            dashboard_address=":0",
        ) as cluster:
            adapt = cluster.adapt(interval="20ms", minimum=2, target_duration="5s")
            async with Client(cluster, asynchronous=True) as client:
                await client.wait_for_workers(2)
                futures = client.map(slowinc, range(100), delay=0.3)
                await wait(futures)

            assert adapt.log[0][1] == {"status": "up", "n": 2}
            assert adapt.log[1][1] == {"status": "up", "n": 20}


@gen_test()
async def test_worker_keys():
    """Ensure that redefining adapt with a lower maximum removes workers"""
    async with SpecCluster(
        scheduler={"cls": Scheduler, "options": {"dashboard_address": ":0"}},
        workers={
            "a-1": {"cls": Worker},
            "a-2": {"cls": Worker},
            "b-1": {"cls": Worker},
            "b-2": {"cls": Worker},
        },
        asynchronous=True,
    ) as cluster:

        def key(ws):
            return ws.name.split("-")[0]

        cluster._adaptive_options = {"worker_key": key}

        adaptive = cluster.adapt(minimum=1)
        await adaptive.adapt()

        while len(cluster.scheduler.workers) == 4:
            await asyncio.sleep(0.01)

        names = {ws.name for ws in cluster.scheduler.workers.values()}
        assert names == {"a-1", "a-2"} or names == {"b-1", "b-2"}


@gen_test()
async def test_adapt_cores_memory():
    async with LocalCluster(
        n_workers=0,
        threads_per_worker=2,
        memory_limit="3 GB",
        silence_logs=False,
        processes=False,
        dashboard_address=":0",
        asynchronous=True,
    ) as cluster:
        adapt = cluster.adapt(minimum_cores=3, maximum_cores=9)
        assert adapt.minimum == 2
        assert adapt.maximum == 4

        adapt = cluster.adapt(minimum_memory="7GB", maximum_memory="20 GB")
        assert adapt.minimum == 3
        assert adapt.maximum == 6

        adapt = cluster.adapt(
            minimum_cores=1,
            minimum_memory="7GB",
            maximum_cores=10,
            maximum_memory="1 TB",
        )
        assert adapt.minimum == 3
        assert adapt.maximum == 5


@gen_test()
async def test_adaptive_config():
    with dask.config.set(
        {"distributed.adaptive.minimum": 10, "distributed.adaptive.wait-count": 8}
    ):
        try:
            adapt = Adaptive(interval="5s")
            assert adapt.minimum == 10
            assert adapt.maximum == math.inf
            assert adapt.interval == 5
            assert adapt.wait_count == 8
        finally:
            adapt.stop()


@gen_test()
async def test_update_adaptive():
    async with LocalCluster(
        n_workers=0,
        threads_per_worker=2,
        memory_limit="3 GB",
        silence_logs=False,
        processes=False,
        dashboard_address=":0",
        asynchronous=True,
    ) as cluster:
        first = cluster.adapt(maximum=1)
        second = cluster.adapt(maximum=2)
        await asyncio.sleep(0.2)
        assert first.periodic_callback is None
        assert second.periodic_callback.is_running()


@gen_test()
async def test_adaptive_no_memory_limit():
    """Test that adapt() does not keep creating workers when no memory limit is set"""
    async with LocalCluster(
        n_workers=0,
        threads_per_worker=1,
        memory_limit=0,
        asynchronous=True,
        dashboard_address=":0",
    ) as cluster:
        cluster.adapt(minimum=1, maximum=10, interval="1 ms")
        async with Client(cluster, asynchronous=True) as client:
            await client.gather(client.map(slowinc, range(5), delay=0.35))
        assert (
            sum(
                state[1]["n"]
                for state in cluster._adaptive.log
                if state[1]["status"] == "up"
            )
            <= 5
        )


@gen_test()
async def test_scale_needs_to_be_awaited():
    """
    This tests that the adaptive class works fine if the scale method uses the
    `sync` method to schedule its task instead of loop.add_callback
    """

    class RequiresAwaitCluster(LocalCluster):
        def scale(self, n):
            # super invocation in the nested function scope is messy
            method = super().scale

            async def _():
                return method(n)

            return self.sync(_)

    async with RequiresAwaitCluster(
        n_workers=0, asynchronous=True, dashboard_address=":0"
    ) as cluster:
        async with Client(cluster, asynchronous=True) as client:
            futures = client.map(slowinc, range(5), delay=0.05)
            assert len(cluster.workers) == 0
            cluster.adapt()

            await client.gather(futures)

            del futures
            await async_wait_for(lambda: not cluster.workers, 10)


@gen_test()
async def test_adaptive_stopped():
    """
    We should ensure that the adapt PC is actually stopped once the cluster
    stops.
    """
    async with LocalCluster(
        n_workers=0, asynchronous=True, dashboard_address=":0"
    ) as cluster:
        instance = cluster.adapt(interval="10ms")
        assert instance.periodic_callback is not None

        await async_wait_for(lambda: instance.periodic_callback.is_running(), timeout=5)

        pc = instance.periodic_callback

    await async_wait_for(lambda: not pc.is_running(), timeout=5)