def v1_to_v2()

in pytext/config/config_adapter.py [0:0]

• 60 lines of code
• 20 McCabe index (conditional complexity)

def v1_to_v2(json_config):
    # migrate optimizer params
    [task] = json_config["task"].values()
    if (
        "scheduler" not in task
        or task["scheduler"] is None
        or task["scheduler"].get("type") is None
    ):
        return json_config
    op_type = task["scheduler"].get("type")
    if op_type == "step_lr":
        op_config = {"StepLR": {}}
        for key in ["step_size", "gamma"]:
            if key in task["scheduler"]:
                op_config["StepLR"][key] = task["scheduler"][key]
        task["scheduler"] = op_config
    elif op_type == "lm_fine_tuning":
        op_config = {"LmFineTuning": {}}
        for key in [
            "cut_frac",
            "ratio",
            "non_pretrained_param_groups",
            "lm_lr_multiplier",
            "lm_use_per_layer_lr",
            "lm_gradual_unfreezing",
            "last_epoch",
        ]:
            if key in task["scheduler"]:
                op_config["LmFineTuning"][key] = task["scheduler"][key]
        task["scheduler"] = op_config
    elif op_type == "reduce_lr_on_plateau":
        op_config = {"ReduceLROnPlateau": {}}
        for key in [
            "lower_is_better",
            "factor",
            "patience",
            "min_lr",
            "threshold",
            "threshold_is_absolute",
            "cooldown",
        ]:
            if key in task["scheduler"]:
                op_config["ReduceLROnPlateau"][key] = task["scheduler"][key]
        task["scheduler"] = op_config
    elif op_type == "cosine_annealing_lr":
        op_config = {"CosineAnnealingLR": {}}
        for key in ["t_max", "eta_min"]:
            if key in task["scheduler"]:
                op_config["CosineAnnealingLR"][key] = task["scheduler"][key]
        task["scheduler"] = op_config
    elif op_type == "exponential_lr":
        op_config = {"ExponentialLR": {}}
        for key in ["gamma"]:
            if key in task["scheduler"]:
                op_config["ExponentialLR"][key] = task["scheduler"][key]
        task["scheduler"] = op_config
    elif op_type == "none":
        del task["scheduler"]
    else:
        raise ValueError("Migration for your scheduler %s not supported." % op_type)
    return json_config