# 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 pytest
import torch

from pytorch_lightning import Trainer
from pytorch_lightning.loops.optimization.manual_loop import ManualResult
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests.helpers import BoringModel


def test_manual_result():
    training_step_output = {"loss": torch.tensor(25.0, requires_grad=True), "something": "jiraffe"}
    result = ManualResult.from_training_step_output(training_step_output, normalize=5)
    asdict = result.asdict()
    assert not asdict["loss"].requires_grad
    assert asdict["loss"] == 5
    assert result.extra == asdict


def test_warning_invalid_trainstep_output(tmpdir):
    class InvalidTrainStepModel(BoringModel):
        def __init__(self):
            super().__init__()
            self.automatic_optimization = False

        def training_step(self, batch, batch_idx):
            return 5

    model = InvalidTrainStepModel()
    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1)

    with pytest.raises(MisconfigurationException, match="return a Tensor, a dict with extras .* or have no return"):
        trainer.fit(model)