# utils.py

ARCH_ANALOGIES = {
    "cnn": "🍲 CNN is like a sieve — strains local patterns from your data broth, layer by layer.",
    "rnn": "⏲️ RNN is your slow-simmer pot — remembers earlier flavors to enrich the final dish.",
    "transformer": "🌡️ Transformer is your sous-vide — precise, parallel, deeply flavorful at every layer."
}

def get_auto_hyperparams(arch_type, num_layers):
    """Auto-Seasoning™ — smart defaults based on architecture and depth"""
    base_config = {
        "cnn": {"lr": 1e-3, "epochs": 3, "batch": 16},
        "rnn": {"lr": 5e-4, "epochs": 4, "batch": 8},
        "transformer": {"lr": 3e-4, "epochs": 3, "batch": 4}
    }

    config = base_config.get(arch_type, base_config["transformer"])

    # Adjust for layer depth
    if num_layers > 8:
        config["lr"] *= 0.6
        config["epochs"] += 2
        config["batch"] = max(2, config["batch"] // 2)
    elif num_layers < 4:
        config["lr"] *= 1.3
        config["batch"] = min(32, config["batch"] * 2)

    return {
        "learning_rate": round(config["lr"], 6),
        "epochs": config["epochs"],
        "batch_size": config["batch"]
    }