# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os

import pytest
import torch

from tests.backends import ddp_model
from tests.backends import DDPLauncher
from tests.utilities.distributed import call_training_script


@pytest.mark.parametrize('cli_args', [
    pytest.param('--max_epochs 1 --gpus 2 --accelerator 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 --accelerator 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 --accelerator 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


# START: test_cli ddp test
@pytest.mark.skipif(os.getenv("PL_IN_LAUNCHER", '0') == '1', reason="test runs only in DDPLauncher")
def internal_test_cli(tmpdir, args=None):
    """
    This test verify we can call function using test_cli name
    """

    return 1


@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
def test_cli(tmpdir):
    DDPLauncher.run_from_cmd_line("--max_epochs 1 --gpus 2 --accelerator ddp", internal_test_cli, 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'
    assert str(result['result']) == '1'
# END: test_cli ddp test


@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
@DDPLauncher.run("--max_epochs [max_epochs] --gpus 2 --accelerator [accelerator]",
                 max_epochs=["1"],
                 accelerator=["ddp", "ddp_spawn"])
def test_cli_to_pass(tmpdir, args=None):
    """
    This test verify we can call function using test_cli name
    """
    return '1'