# 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 torch

from pytorch_lightning import LightningDataModule, LightningModule, Trainer
from pytorch_lightning.metrics.functional import accuracy
from pytorch_lightning.utilities import DistributedType
from tests.helpers import BoringModel
from tests.helpers.utils import get_default_logger, load_model_from_checkpoint, reset_seed


def run_model_test_without_loggers(
    trainer_options: dict, model: LightningModule, data: LightningDataModule = None, min_acc: float = 0.50
):
    reset_seed()

    # fit model
    trainer = Trainer(**trainer_options)
    trainer.fit(model, datamodule=data)

    # correct result and ok accuracy
    assert trainer.state.finished, f"Training failed with {trainer.state}"

    model2 = load_model_from_checkpoint(trainer.logger, trainer.checkpoint_callback.best_model_path, type(model))

    # test new model accuracy
    test_loaders = model2.test_dataloader() if not data else data.test_dataloader()
    if not isinstance(test_loaders, list):
        test_loaders = [test_loaders]

    if not isinstance(model2, BoringModel):
        for dataloader in test_loaders:
            run_prediction_eval_model_template(model2, dataloader, min_acc=min_acc)


def run_model_test(
    trainer_options,
    model: LightningModule,
    data: LightningDataModule = None,
    on_gpu: bool = True,
    version=None,
    with_hpc: bool = True,
    min_acc: float = 0.25
):
    reset_seed()
    save_dir = trainer_options['default_root_dir']

    # logger file to get meta
    logger = get_default_logger(save_dir, version=version)
    trainer_options.update(logger=logger)
    trainer = Trainer(**trainer_options)
    initial_values = torch.tensor([torch.sum(torch.abs(x)) for x in model.parameters()])
    trainer.fit(model, datamodule=data)
    post_train_values = torch.tensor([torch.sum(torch.abs(x)) for x in model.parameters()])

    assert trainer.state.finished, f"Training failed with {trainer.state}"
    # Check that the model is actually changed post-training
    change_ratio = torch.norm(initial_values - post_train_values)
    assert change_ratio > 0.1, f"the model is changed of {change_ratio}"

    # test model loading
    pretrained_model = load_model_from_checkpoint(logger, trainer.checkpoint_callback.best_model_path, type(model))

    # test new model accuracy
    test_loaders = model.test_dataloader() if not data else data.test_dataloader()
    if not isinstance(test_loaders, list):
        test_loaders = [test_loaders]

    if not isinstance(model, BoringModel):
        for dataloader in test_loaders:
            run_prediction_eval_model_template(model, dataloader, min_acc=min_acc)

    if with_hpc:
        if trainer._distrib_type in (DistributedType.DDP, DistributedType.DDP_SPAWN, DistributedType.DDP2):
            # on hpc this would work fine... but need to hack it for the purpose of the test
            trainer.optimizers, trainer.lr_schedulers, trainer.optimizer_frequencies = \
                trainer.init_optimizers(pretrained_model)

        # test HPC saving
        trainer.checkpoint_connector.hpc_save(save_dir, logger)
        # test HPC loading
        checkpoint_path = trainer.checkpoint_connector.get_max_ckpt_path_from_folder(save_dir)
        trainer.checkpoint_connector.hpc_load(checkpoint_path, on_gpu=on_gpu)


@torch.no_grad()
def run_prediction_eval_model_template(trained_model, dataloader, min_acc=0.50):
    # run prediction on 1 batch
    trained_model.cpu()
    trained_model.eval()

    batch = next(iter(dataloader))
    x, y = batch
    x = x.flatten(1)

    y_hat = trained_model(x)
    acc = accuracy(y_hat.cpu(), y.cpu(), top_k=2).item()

    assert acc >= min_acc, f"This model is expected to get > {min_acc} in test set (it got {acc})"

torch.abs torch.norm torch.no_grad