"""$project_name: A Flower / FlowerTune app."""

from $import_name.client import set_parameters
from $import_name.models import get_model


# Get function that will be executed by the strategy's evaluate() method
# Here we use it to save global model checkpoints
def get_evaluate_fn(model_cfg, save_every_round, total_round, save_path):
    """Return an evaluation function for saving global model."""

    def evaluate(server_round: int, parameters, config):
        # Save model
        if server_round != 0 and (
            server_round == total_round or server_round % save_every_round == 0
        ):
            # Init model
            model = get_model(model_cfg)
            set_parameters(model, parameters)

            model.save_pretrained(f"{save_path}/peft_{server_round}")

        return 0.0, {}

    return evaluate


def get_on_fit_config():
    """
    Return a function that will be used to construct the config
    that the client's fit() method will receive.
    """

    def fit_config_fn(server_round: int):
        fit_config = {"current_round": server_round}
        return fit_config

    return fit_config_fn


def fit_weighted_average(metrics):
    """Aggregate (federated) evaluation metrics."""
    # Multiply accuracy of each client by number of examples used
    losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
    examples = [num_examples for num_examples, _ in metrics]

    # Aggregate and return custom metric (weighted average)
    return {"train_loss": sum(losses) / sum(examples)}
