"""Model-dependent defaults for the quantization form."""

import logging

import gradio as gr

from src.tools.model_size import get_model_size_b

logger = logging.getLogger(__name__)


def default_quant_method(model, oauth_token: gr.OAuthToken | None = None):
    """Use total parameters (including MoE experts), with Tuning as fallback."""
    size = None
    if model and model.strip():
        try:
            size = get_model_size_b(model.strip(), token=oauth_token.token if oauth_token else None)
        except Exception:
            logger.warning("Could not determine model size for the quantization default")
    return gr.update(value=(
        "RTN (Round-To-Nearest)" if size is not None and size > 100
        else "Tuning (AutoRound, recommended)"
    ))