from src.display.formatting import model_hyperlink
from src.display.utils import AutoEvalColumn

# Models which have been flagged by users as being problematic for a reason or another
# (Model name to forum discussion link)
FLAGGED_MODELS = {
    "merged": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/510",
    "TeamUNIVA/Komodo_7B_v0.1.0": "https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard/discussions/44",
}

# Models which have been requested by orgs to not be submitted on the leaderboard
DO_NOT_SUBMIT_MODELS = [
]


def flag_models(leaderboard_data: list[dict]):
    for model_data in leaderboard_data:
        # Merges are flagged automatically
        if model_data[AutoEvalColumn.flagged.name] == True:
            flag_key = "merged"
        else:
            flag_key = model_data["model_name_for_query"]

        if flag_key in FLAGGED_MODELS:
            issue_num = FLAGGED_MODELS[flag_key].split("/")[-1]
            issue_link = model_hyperlink(
                FLAGGED_MODELS[flag_key],
                f"See discussion #{issue_num}",
            )
            model_data[
                AutoEvalColumn.model.name
            ] = f"{model_data[AutoEvalColumn.model.name]} has been flagged! {issue_link}"
            model_data[AutoEvalColumn.flagged.name] = True
        else:
            model_data[AutoEvalColumn.flagged.name] = False


def remove_forbidden_models(leaderboard_data: list[dict]):
    indices_to_remove = []
    for ix, model in enumerate(leaderboard_data):
        if model["model_name_for_query"] in DO_NOT_SUBMIT_MODELS:
            indices_to_remove.append(ix)

    for ix in reversed(indices_to_remove):
        leaderboard_data.pop(ix)
    return leaderboard_data


def filter_models(leaderboard_data: list[dict]):
    leaderboard_data = remove_forbidden_models(leaderboard_data)
    flag_models(leaderboard_data)