def _adjust_config()

in lm_eval/evaluator.py [0:0]

• 40 lines of code
• 14 McCabe index (conditional complexity)

    def _adjust_config(task_dict):
        adjusted_task_dict = {}
        for task_name, task_obj in task_dict.items():
            if isinstance(task_obj, dict):
                adjusted_task_dict = {
                    **adjusted_task_dict,
                    **{task_name: _adjust_config(task_obj)},
                }

            else:
                if task_obj.get_config("output_type") == "generate_until":
                    if gen_kwargs is not None:
                        task_obj.set_config(
                            key="generation_kwargs", value=gen_kwargs, update=True
                        )

                if predict_only:
                    eval_logger.info(
                        f"Processing {task_name} in output-only mode. Metrics will not be calculated!"
                    )
                    # we have to change the class properties post-hoc. This is pretty hacky.
                    task_obj.override_metric(metric_name="bypass")

                # override tasks' fewshot values to the provided num_fewshot arg value
                # except if tasks have it set to 0 manually in their configs--then we should never overwrite that
                if num_fewshot is not None:
                    if (default_num_fewshot := task_obj.get_config("num_fewshot")) == 0:
                        eval_logger.info(
                            f"num_fewshot has been set to 0 for {task_name} in its config. Manual configuration will be ignored."
                        )
                    else:
                        eval_logger.warning(
                            f"Overwriting default num_fewshot of {task_name} from {default_num_fewshot} to {num_fewshot}"
                        )
                        task_obj.set_config(key="num_fewshot", value=num_fewshot)
                else:
                    # if num_fewshot not provided, and the task does not define a default one, default to 0
                    if (
                        default_num_fewshot := task_obj.get_config("num_fewshot")
                    ) is None:
                        task_obj.set_config(key="num_fewshot", value=0)
                # fewshot_random_seed set for tasks, even with a default num_fewshot (e.g. in the YAML file)
                task_obj.set_fewshot_seed(seed=fewshot_random_seed)
                eval_logger.info(
                    f"Setting fewshot random generator seed to {fewshot_random_seed}"
                )

                adjusted_task_dict[task_name] = task_obj

        return adjusted_task_dict