import pytest
import torch
import os
from tests.backends import ddp_model
from tests.utilities.dist import call_training_script


@pytest.mark.parametrize('cli_args', [
    pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
])
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_multi_gpu_model_ddp_fit_only(tmpdir, cli_args):
    # call the script
    std, err = call_training_script(ddp_model, cli_args, 'fit', tmpdir, timeout=120)

    # load the results of the script
    result_path = os.path.join(tmpdir, 'ddp.result')
    result = torch.load(result_path)

    # verify the file wrote the expected outputs
    assert result['status'] == 'complete'


@pytest.mark.parametrize('cli_args', [
    pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
])
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_multi_gpu_model_ddp_test_only(tmpdir, cli_args):
    # call the script
    call_training_script(ddp_model, cli_args, 'test', tmpdir)

    # load the results of the script
    result_path = os.path.join(tmpdir, 'ddp.result')
    result = torch.load(result_path)

    # verify the file wrote the expected outputs
    assert result['status'] == 'complete'


@pytest.mark.parametrize('cli_args', [
    pytest.param('--max_epochs 1 --gpus 2 --distributed_backend ddp'),
])
@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_multi_gpu_model_ddp_fit_test(tmpdir, cli_args):
    # call the script
    call_training_script(ddp_model, cli_args, 'fit_test', tmpdir, timeout=20)

    # load the results of the script
    result_path = os.path.join(tmpdir, 'ddp.result')
    result = torch.load(result_path)

    # verify the file wrote the expected outputs
    assert result['status'] == 'complete'

    model_outs = result['result']
    for out in model_outs:
        assert out['test_acc'] > 0.90