import socket
from dataclasses import asdict

import numpy as np
import pytest
from loky import get_reusable_executor

import xgboost as xgb
from xgboost import RabitTracker, build_info, federated
from xgboost import testing as tm
from xgboost.collective import Config


def run_rabit_worker(rabit_env: dict, world_size: int) -> int:
    with xgb.collective.CommunicatorContext(**rabit_env):
        assert xgb.collective.get_world_size() == world_size
        assert xgb.collective.is_distributed()
        assert xgb.collective.get_processor_name() == socket.gethostname()
        ret = xgb.collective.broadcast("test1234", 0)
        assert str(ret) == "test1234"
        reduced = xgb.collective.allreduce(np.asarray([1, 2, 3]), xgb.collective.Op.SUM)
        assert np.array_equal(reduced, np.asarray([2, 4, 6]))
    return 0


@pytest.mark.skipif(**tm.no_loky())
def test_rabit_communicator() -> None:
    world_size = 2
    tracker = RabitTracker(host_ip="127.0.0.1", n_workers=world_size)
    tracker.start()
    workers = []
    with get_reusable_executor(max_workers=world_size) as pool:
        for _ in range(world_size):
            worker = pool.submit(
                run_rabit_worker, rabit_env=tracker.worker_args(), world_size=world_size
            )
            workers.append(worker)

        for worker in workers:
            assert worker.result() == 0


def run_federated_worker(port: int, world_size: int, rank: int) -> int:
    with xgb.collective.CommunicatorContext(
        dmlc_communicator="federated",
        federated_server_address=f"localhost:{port}",
        federated_world_size=world_size,
        federated_rank=rank,
    ):
        assert xgb.collective.get_world_size() == world_size
        assert xgb.collective.is_distributed()
        assert xgb.collective.get_processor_name() == f"rank:{rank}"
        bret = xgb.collective.broadcast("test1234", 0)
        assert str(bret) == "test1234"
        aret = xgb.collective.allreduce(np.asarray([1, 2, 3]), xgb.collective.Op.SUM)
        assert np.array_equal(aret, np.asarray([2, 4, 6]))
    return 0


@pytest.mark.skipif(**tm.skip_win())
@pytest.mark.skipif(**tm.no_loky())
def test_federated_communicator() -> None:
    if not build_info()["USE_FEDERATED"]:
        pytest.skip("XGBoost not built with federated learning enabled")

    port = 9091
    world_size = 2
    with get_reusable_executor(max_workers=world_size + 1) as pool:
        kwargs = {"port": port, "n_workers": world_size, "blocking": False}
        tracker = pool.submit(federated.run_federated_server, **kwargs)
        if not tracker.running():
            raise RuntimeError("Error starting Federated Learning server")

        workers = []
        for rank in range(world_size):
            worker = pool.submit(
                run_federated_worker, port=port, world_size=world_size, rank=rank
            )
            workers.append(worker)
        for worker in workers:
            assert worker.result() == 0


def test_config_serialization() -> None:
    cfg = Config(retry=1, timeout=2, tracker_host_ip="127.0.0.1", tracker_port=None)
    cfg1 = Config(**asdict(cfg))
    assert cfg == cfg1