"""Test the reduce API."""
import pytest
import numpy as np
import ray
from ray.util.collective.types import Backend, ReduceOp

from ray.util.collective.tests.cpu_util import create_collective_workers


@pytest.mark.parametrize("backend", [Backend.GLOO])
@pytest.mark.parametrize("group_name", ["default", "test", "123?34!"])
@pytest.mark.parametrize("dst_rank", [0, 1])
def test_reduce_different_name(ray_start_single_node, group_name, dst_rank,
                               backend):
    world_size = 2
    actors, _ = create_collective_workers(
        num_workers=world_size, group_name=group_name, backend=backend)
    results = ray.get(
        [a.do_reduce.remote(group_name, dst_rank) for a in actors])
    for i in range(world_size):
        if i == dst_rank:
            assert (results[i] == np.ones(
                (10, ), dtype=np.float32) * world_size).all()
        else:
            assert (results[i] == np.ones((10, ), dtype=np.float32)).all()


@pytest.mark.parametrize("backend", [Backend.GLOO])
@pytest.mark.parametrize("array_size", [2, 2**5, 2**10, 2**15, 2**20])
@pytest.mark.parametrize("dst_rank", [0, 1])
def test_reduce_different_array_size(ray_start_single_node, array_size,
                                     dst_rank, backend):
    world_size = 2
    actors, _ = create_collective_workers(world_size, backend=backend)
    ray.wait([
        a.set_buffer.remote(np.ones(array_size, dtype=np.float32))
        for a in actors
    ])
    results = ray.get([a.do_reduce.remote(dst_rank=dst_rank) for a in actors])
    for i in range(world_size):
        if i == dst_rank:
            assert (results[i] == np.ones(
                (array_size, ), dtype=np.float32) * world_size).all()
        else:
            assert (results[i] == np.ones((array_size, ),
                                          dtype=np.float32)).all()


@pytest.mark.parametrize("backend", [Backend.GLOO])
@pytest.mark.parametrize("dst_rank", [0, 1])
def test_reduce_multiple_group(ray_start_single_node,
                               dst_rank,
                               backend,
                               num_groups=5):
    world_size = 2
    actors, _ = create_collective_workers(world_size, backend=backend)
    for group_name in range(1, num_groups):
        ray.get([
            actor.init_group.remote(world_size, i, backend, str(group_name))
            for i, actor in enumerate(actors)
        ])
    for i in range(num_groups):
        group_name = "default" if i == 0 else str(i)
        results = ray.get([
            a.do_reduce.remote(dst_rank=dst_rank, group_name=group_name)
            for a in actors
        ])
        for j in range(world_size):
            if j == dst_rank:
                assert (results[j] == np.ones(
                    (10, ), dtype=np.float32) * (i + 2)).all()
            else:
                assert (results[j] == np.ones((10, ), dtype=np.float32)).all()


@pytest.mark.parametrize("backend", [Backend.GLOO])
@pytest.mark.parametrize("dst_rank", [0, 1])
def test_reduce_different_op(ray_start_single_node, dst_rank, backend):
    world_size = 2
    actors, _ = create_collective_workers(world_size, backend=backend)

    # check product
    ray.wait([
        a.set_buffer.remote(np.ones(10, dtype=np.float32) * (i + 2))
        for i, a in enumerate(actors)
    ])
    results = ray.get([
        a.do_reduce.remote(dst_rank=dst_rank, op=ReduceOp.PRODUCT)
        for a in actors
    ])
    for i in range(world_size):
        if i == dst_rank:
            assert (results[i] == np.ones((10, ), dtype=np.float32) * 6).all()
        else:
            assert (results[i] == np.ones(
                (10, ), dtype=np.float32) * (i + 2)).all()

    # check min
    ray.wait([
        a.set_buffer.remote(np.ones(10, dtype=np.float32) * (i + 2))
        for i, a in enumerate(actors)
    ])
    results = ray.get([
        a.do_reduce.remote(dst_rank=dst_rank, op=ReduceOp.MIN) for a in actors
    ])
    for i in range(world_size):
        if i == dst_rank:
            assert (results[i] == np.ones((10, ), dtype=np.float32) * 2).all()
        else:
            assert (results[i] == np.ones(
                (10, ), dtype=np.float32) * (i + 2)).all()

    # check max
    ray.wait([
        a.set_buffer.remote(np.ones(10, dtype=np.float32) * (i + 2))
        for i, a in enumerate(actors)
    ])
    results = ray.get([
        a.do_reduce.remote(dst_rank=dst_rank, op=ReduceOp.MAX) for a in actors
    ])
    for i in range(world_size):
        if i == dst_rank:
            assert (results[i] == np.ones((10, ), dtype=np.float32) * 3).all()
        else:
            assert (results[i] == np.ones(
                (10, ), dtype=np.float32) * (i + 2)).all()


@pytest.mark.parametrize("backend", [Backend.GLOO])
@pytest.mark.parametrize("dst_rank", [0, 1])
def test_reduce_torch_numpy(ray_start_single_node, dst_rank, backend):
    import torch
    world_size = 2
    actors, _ = create_collective_workers(world_size, backend=backend)
    ray.wait([actors[1].set_buffer.remote(torch.ones(10, ))])
    results = ray.get([a.do_reduce.remote(dst_rank=dst_rank) for a in actors])
    if dst_rank == 0:
        assert (results[0] == np.ones((10, )) * world_size).all()
        assert (results[1] == torch.ones((10, ))).all()
    else:
        assert (results[0] == np.ones((10, ))).all()
        assert (results[1] == torch.ones((10, )) * world_size).all()


@pytest.mark.parametrize("backend", [Backend.GLOO])
def test_reduce_invalid_rank(ray_start_single_node, backend, dst_rank=3):
    world_size = 2
    actors, _ = create_collective_workers(world_size, backend=backend)
    with pytest.raises(ValueError):
        ray.get([a.do_reduce.remote(dst_rank=dst_rank) for a in actors])


if __name__ == "__main__":
    import pytest
    import sys
    sys.exit(pytest.main(["-v", "-x", __file__]))
