import json

import gradio as gr
import numpy as np
import pandas as pd
from huggingface_hub import HfFileSystem

from src.constants import RESULTS_DATASET_ID, TASKS


def fetch_result_paths():
    fs = HfFileSystem()
    paths = fs.glob(f"{RESULTS_DATASET_ID}/**/**/*.json")
    return paths


def sort_result_paths_per_model(paths):
    from collections import defaultdict

    d = defaultdict(list)
    for path in paths:
        model_id, _ = path[len(RESULTS_DATASET_ID) + 1:].rsplit("/", 1)
        d[model_id].append(path)
    return {model_id: sorted(paths) for model_id, paths in d.items()}


def update_load_results_component():
    return (gr.Button("Load", interactive=True), ) * 2


def load_results_dataframe(model_id, result_paths_per_model=None):
    if not model_id or not result_paths_per_model:
        return
    result_paths = result_paths_per_model[model_id]
    fs = HfFileSystem()
    data = {"results": {}, "configs": {}}
    for path in result_paths:
        with fs.open(path, "r") as f:
            d = json.load(f)
        data["results"].update(d["results"])
        data["configs"].update(d["configs"])
        model_name = d.get("model_name", "Model")
    df = pd.json_normalize([{key: value for key, value in data.items()}])
    # df.columns = df.columns.str.split(".")  # .split return a list instead of a tuple
    return df.set_index(pd.Index([model_name])).reset_index()


def load_results_dataframes(*model_ids, result_paths_per_model=None):
    return [load_results_dataframe(model_id, result_paths_per_model=result_paths_per_model) for model_id in model_ids]


def display_results(task, *dfs):
    dfs = [df.set_index("index") for df in dfs if "index" in df.columns]
    if not dfs:
        return None, None
    df = pd.concat(dfs)
    df = df.T.rename_axis(columns=None)
    return display_tab("results", df, task), display_tab("configs", df, task)


def display_tab(tab, df, task):
    df = df.style.format(na_rep="")
    df.hide(
        [
            row
            for row in df.index
            if (
                not row.startswith(f"{tab}.")
                or row.startswith(f"{tab}.leaderboard.")
                or row.endswith(".alias")
                or (not row.startswith(f"{tab}.{task}") if task != "All" else False)
            )
        ],
        axis="index",
    )
    df.apply(highlight_min_max, axis=1)
    start = len(f"{tab}.leaderboard_") if task == "All" else len(f"{tab}.{task} ")
    df.format_index(lambda idx: idx[start:].removesuffix(",none"), axis="index")
    return df.to_html()


def update_tasks_component():
    return (
        gr.Radio(
            ["All"] + list(TASKS.values()),
            label="Tasks",
            info="Evaluation tasks to be displayed",
            value="All",
            visible=True,
        ),
    ) * 2


def clear_results():
    # model_id_1, model_id_2, dataframe_1, dataframe_2, load_results_btn, load_configs_btn, results_task, configs_task
    return (
        None, None, None, None,
        *(gr.Button("Load", interactive=False), ) * 2,
        *(
            gr.Radio(
                ["All"] + list(TASKS.values()),
                label="Tasks",
                info="Evaluation tasks to be displayed",
                value="All",
                visible=False,
            ),
        ) * 2,
    )


def highlight_min_max(s):
    if s.name.endswith("acc,none") or s.name.endswith("acc_norm,none"):
        return np.where(s == np.nanmax(s.values), "background-color:darkgreen", "background-color:darkred")
    else:
        return [""] * len(s)