import os
from unittest import mock

import pytest

from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.plugins import ApexMixedPrecisionPlugin
from pytorch_lightning.utilities import _APEX_AVAILABLE
from tests.helpers.boring_model import BoringModel


@pytest.mark.skipif(not _APEX_AVAILABLE, reason="test requires apex")
@mock.patch.dict(
    os.environ, {
        "CUDA_VISIBLE_DEVICES": "0,1",
        "SLURM_NTASKS": "2",
        "SLURM_JOB_NAME": "SOME_NAME",
        "SLURM_NODEID": "0",
        "LOCAL_RANK": "0",
        "SLURM_LOCALID": "0"
    }
)
@mock.patch('torch.cuda.device_count', return_value=2)
@pytest.mark.parametrize(
    ['ddp_backend', 'gpus', 'num_processes'],
    [('ddp_cpu', None, 2), ('ddp', 2, 0), ('ddp2', 2, 0), ('ddp_spawn', 2, 0)],
)
def test_amp_choice_default_ddp_cpu(tmpdir, ddp_backend, gpus, num_processes):

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert isinstance(trainer.precision_plugin, ApexMixedPrecisionPlugin)
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        fast_dev_run=True,
        precision=16,
        amp_backend='apex',
        gpus=gpus,
        num_processes=num_processes,
        accelerator=ddp_backend,
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)


@pytest.mark.skipif(not _APEX_AVAILABLE, reason="test requires apex")
@mock.patch.dict(
    os.environ, {
        "CUDA_VISIBLE_DEVICES": "0,1",
        "SLURM_NTASKS": "2",
        "SLURM_JOB_NAME": "SOME_NAME",
        "SLURM_NODEID": "0",
        "LOCAL_RANK": "0",
        "SLURM_LOCALID": "0"
    }
)
@mock.patch('torch.cuda.device_count', return_value=2)
@pytest.mark.parametrize(
    ['ddp_backend', 'gpus', 'num_processes'],
    [('ddp_cpu', None, 2), ('ddp', 2, 0), ('ddp2', 2, 0), ('ddp_spawn', 2, 0)],
)
def test_amp_choice_custom_ddp_cpu(tmpdir, ddp_backend, gpus, num_processes):

    class MyApexPlugin(ApexMixedPrecisionPlugin):
        pass

    class CB(Callback):

        def on_fit_start(self, trainer, pl_module):
            assert isinstance(trainer.precision_plugin, MyApexPlugin)
            raise SystemExit()

    model = BoringModel()
    trainer = Trainer(
        fast_dev_run=True,
        precision=16,
        amp_backend='apex',
        gpus=gpus,
        num_processes=num_processes,
        accelerator=ddp_backend,
        plugins=[MyApexPlugin(amp_level="O2")],
        callbacks=[CB()],
    )

    with pytest.raises(SystemExit):
        trainer.fit(model)