How to use the emmental.Meta function in emmental

def test_meta(caplog):
    """Unit test of meta."""
    caplog.set_level(logging.INFO)

    dirpath = "temp_test_meta_log_folder"

    Meta.reset()
    emmental.init(dirpath)

    # Check the log folder is created correctly
    assert os.path.isdir(dirpath) is True
    assert Meta.log_path.startswith(dirpath) is True

    # Check the config is created
    assert isinstance(Meta.config, dict) is True
    assert Meta.config["meta_config"] == {
        "seed": None,
        "verbose": True,
        "log_path": "logs",
        "use_exact_log_path": False,
    }

    emmental.Meta.update_config(

lr_scheduler = "cosine_annealing"
    dirpath = "temp_test_scheduler"
    model = nn.Linear(1, 1)
    emmental_learner = EmmentalLearner()

    Meta.reset()
    emmental.init(dirpath)

    config = {
        "learner_config": {
            "n_epochs": 4,
            "optimizer_config": {"optimizer": "sgd", "lr": 10},
            "lr_scheduler_config": {"lr_scheduler": lr_scheduler},
        }
    }
    emmental.Meta.update_config(config)
    emmental_learner.n_batches_per_epoch = 1
    emmental_learner._set_optimizer(model)
    emmental_learner._set_lr_scheduler(model)

    assert emmental_learner.optimizer.param_groups[0]["lr"] == 10

    emmental_learner.optimizer.step()
    emmental_learner._update_lr_scheduler(model, 0, {})
    assert (
        abs(emmental_learner.optimizer.param_groups[0]["lr"] - 8.535533905932738) < 1e-5
    )

    emmental_learner.optimizer.step()
    emmental_learner._update_lr_scheduler(model, 1, {})
    assert abs(emmental_learner.optimizer.param_groups[0]["lr"] - 5) < 1e-5

def test_bert_adam_optimizer(caplog):
    """Unit test of BertAdam optimizer."""
    caplog.set_level(logging.INFO)

    optimizer = "bert_adam"
    dirpath = "temp_test_optimizer"
    model = nn.Linear(1, 1)
    emmental_learner = EmmentalLearner()

    Meta.reset()
    emmental.init(dirpath)

    # Test default BertAdam setting
    config = {"learner_config": {"optimizer_config": {"optimizer": optimizer}}}
    emmental.Meta.update_config(config)
    emmental_learner._set_optimizer(model)

    assert emmental_learner.optimizer.defaults == {
        "lr": 0.001,
        "betas": (0.9, 0.999),
        "eps": 1e-08,
        "weight_decay": 0.0,
    }

    # Test new BertAdam setting
    config = {
        "learner_config": {
            "optimizer_config": {
                "optimizer": optimizer,
                "lr": 0.02,
                "l2": 0.05,

def test_linear_scheduler(caplog):
    """Unit test of linear scheduler."""
    caplog.set_level(logging.INFO)

    lr_scheduler = "linear"
    dirpath = "temp_test_scheduler"
    model = nn.Linear(1, 1)
    emmental_learner = EmmentalLearner()

    Meta.reset()
    emmental.init(dirpath)

    # Test per batch
    config = {
        "learner_config": {
            "n_epochs": 4,
            "optimizer_config": {"optimizer": "sgd", "lr": 10},
            "lr_scheduler_config": {"lr_scheduler": lr_scheduler},
        }
    }
    emmental.Meta.update_config(config)
    emmental_learner.n_batches_per_epoch = 1
    emmental_learner._set_optimizer(model)
    emmental_learner._set_lr_scheduler(model)

    assert emmental_learner.optimizer.param_groups[0]["lr"] == 10

def test_log_writer(caplog):
    """Unit test of log_writer."""
    caplog.set_level(logging.INFO)

    emmental.Meta.reset()

    emmental.init()
    emmental.Meta.update_config(
        config={
            "logging_config": {
                "counter_unit": "sample",
                "evaluation_freq": 10,
                "checkpointing": True,
                "checkpointer_config": {"checkpoint_freq": 2},
            }
        }
    )

    log_writer = LogWriter()

    log_writer.add_config(emmental.Meta.config)

def test_adam_optimizer(caplog):
    """Unit test of Adam optimizer."""
    caplog.set_level(logging.INFO)

    optimizer = "adam"
    dirpath = "temp_test_optimizer"
    model = nn.Linear(1, 1)
    emmental_learner = EmmentalLearner()

    Meta.reset()
    emmental.init(dirpath)

    # Test default Adam setting
    config = {"learner_config": {"optimizer_config": {"optimizer": optimizer}}}
    emmental.Meta.update_config(config)
    emmental_learner._set_optimizer(model)

    assert emmental_learner.optimizer.defaults == {
        "lr": 0.001,
        "betas": (0.9, 0.999),
        "eps": 1e-08,
        "amsgrad": False,
        "weight_decay": 0,
    }

    # Test new Adam setting
    config = {
        "learner_config": {
            "optimizer_config": {
                "optimizer": optimizer,
                "lr": 0.02,

emmental.init(dirpath)

    # Check the log folder is created correctly
    assert os.path.isdir(dirpath) is True
    assert Meta.log_path.startswith(dirpath) is True

    # Check the config is created
    assert isinstance(Meta.config, dict) is True
    assert Meta.config["meta_config"] == {
        "seed": None,
        "verbose": True,
        "log_path": "logs",
        "use_exact_log_path": False,
    }

    emmental.Meta.update_config(
        path="tests/shared", filename="emmental-test-config.yaml"
    )
    assert Meta.config["meta_config"] == {
        "seed": 1,
        "verbose": False,
        "log_path": "tests",
        "use_exact_log_path": False,
    }

    # Test unable to find config file
    Meta.reset()
    emmental.init(dirpath)

    emmental.Meta.update_config(path=os.path.dirname(__file__))
    assert Meta.config["meta_config"] == {
        "seed": None,

assert model.task_names == set(["task_1", "task_2"])

    model.remove_task("task_1")
    assert model.task_names == set(["task_2"])

    model.save(f"{dirpath}/saved_model.pth")

    model.load(f"{dirpath}/saved_model.pth")

    # Test w/o dataparallel

    Meta.reset()
    emmental.init(dirpath)

    config = {"model_config": {"dataparallel": False}}
    emmental.Meta.update_config(config)

    model = EmmentalModel(name="test", tasks=task1)

    assert repr(model) == "EmmentalModel(name=test)"
    assert model.name == "test"
    assert model.task_names == set(["task_1"])
    assert model.module_pool["m1"].weight.data.size() == (10, 10)
    assert model.module_pool["m2"].weight.data.size() == (2, 10)

    model.update_task(new_task1)

    assert model.module_pool["m1"].weight.data.size() == (5, 10)
    assert model.module_pool["m2"].weight.data.size() == (2, 5)

    model.update_task(task2)

"optimizer_config": {
                "optimizer": optimizer,
                "lr": 0.02,
                "l2": 0.05,
                f"{optimizer}_config": {
                    "max_iter": 30,
                    "max_eval": 40,
                    "tolerance_grad": 1e-04,
                    "tolerance_change": 1e-05,
                    "history_size": 10,
                    "line_search_fn": "strong_wolfe",
                },
            }
        }
    }
    emmental.Meta.update_config(config)
    emmental_learner._set_optimizer(model)

    assert emmental_learner.optimizer.defaults == {
        "lr": 0.02,
        "max_iter": 30,
        "max_eval": 40,
        "tolerance_grad": 1e-04,
        "tolerance_change": 1e-05,
        "history_size": 10,
        "line_search_fn": "strong_wolfe",
    }

    shutil.rmtree(dirpath)