import asyncio
import shutil
import tempfile

import gradio as gr
import pandas as pd
import plotly.express as px

import src.constants as constants
from src.constants import TASKS
from src.hub import glob, load_json_file


def fetch_result_paths():
    path = f"{constants.RESULTS_DATASET_ID}/**/**/*.json"
    return glob(path)


def sort_result_paths_per_model(paths):
    from collections import defaultdict

    d = defaultdict(list)
    for path in paths:
        model_id, _ = path[len(constants.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


async 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]
    results = await asyncio.gather(*[load_json_file(path) for path in result_paths])
    data = {"results": {}, "configs": {}}
    for result in results:
        data["results"].update(result["results"])
        data["configs"].update(result["configs"])
        model_name = result.get("model_name", "Model")
    df = pd.json_normalize([data])
    # df.columns = df.columns.str.split(".")  # .split return a list instead of a tuple
    return df.set_index(pd.Index([model_name])).reset_index()


async def load_results_dataframes(*model_ids, result_paths_per_model=None):
    result = await asyncio.gather(
        *[load_results_dataframe(model_id, result_paths_per_model) for model_id in model_ids]
    )
    return result


def concat_results(dfs):
    dfs = [df.set_index("index") for df in dfs if "index" in df.columns]
    if dfs:
        return pd.concat(dfs)


def display_results(task, hide_std_errors, show_only_differences, *dfs):
    df = concat_results(dfs)
    if df is None:
        return None, None
    df = df.T.rename_axis(columns=None)
    return (
        display_tab("results", df, task, hide_std_errors=hide_std_errors),
        display_tab("configs", df, task, show_only_differences=show_only_differences),
    )


def display_tab(tab, df, task, hide_std_errors=True, show_only_differences=False):
    if show_only_differences:
        any_difference = df.ne(df.iloc[:, 0], axis=0).any(axis=1)
    df = df.style.format(escape="html", na_rep="")
    # Hide rows
    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 row.startswith(f"{tab}.leaderboard_arc_challenge")
                )
                # Hide std errors
                or (hide_std_errors and row.endswith("_stderr,none"))
                # Hide non-different rows
                or (show_only_differences and not any_difference[row])
            )
        ],
        axis="index",
    )
    # Color metric result cells
    idx = pd.IndexSlice
    colored_rows = idx[
        [
            row
            for row in df.index
            if row.endswith("acc,none") or row.endswith("acc_norm,none") or row.endswith("exact_match,none")
        ]
    ]  # Apply only on numeric cells, otherwise the background gradient will not work
    subset = idx[colored_rows, idx[:]]
    df.background_gradient(cmap="PiYG", vmin=0, vmax=1, subset=subset, axis=None)
    # Format index values: remove prefix and suffix
    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(constants.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(constants.TASKS.values()),
                label="Tasks",
                info="Evaluation tasks to be displayed",
                value="All",
                visible=False,
            ),
        )
        * 2,
    )


def display_loading_message_for_results():
    return ("<h3 style='text-align: center;'>Loading...</h3>",) * 2


def plot_results(task, *dfs):
    df = concat_results(dfs)
    if df is not None:
        df = df[
            [
                col
                for col in df.columns
                if col.startswith("results.")
                and (col.endswith("acc,none") or col.endswith("acc_norm,none") or col.endswith("exact_match,none"))
            ]
        ]
        tasks = {key: tupl[0] for key, tupl in TASKS.items()}
        tasks["leaderboard_math"] = tasks["leaderboard_math_hard"]
        subtasks = {tupl[1]: tupl[0] for tupl in constants.SUBTASKS.get(task, [])}
        if task == "All":
            df = df[[col for col in df.columns if col.split(".")[1] in tasks]]
            # - IFEval: Calculate average of both strict accuracies
            ifeval_mean = df[
                [
                    "results.leaderboard_ifeval.inst_level_strict_acc,none",
                    "results.leaderboard_ifeval.prompt_level_strict_acc,none",
                ]
            ].mean(axis=1)
            df = df.drop(columns=[col for col in df.columns if col.split(".")[1] == "leaderboard_ifeval"])
            loc = df.columns.get_loc("results.leaderboard_math_hard.exact_match,none")
            df.insert(loc - 1, "results.leaderboard_ifeval", ifeval_mean)
            # Rename
            df = df.rename(columns=lambda col: tasks[col.split(".")[1]])
        else:
            df = df[[col for col in df.columns if col.startswith(f"results.{task}")]]
            # - IFEval: Return 4 accuracies
            if task == "leaderboard_ifeval":
                df = df.rename(columns=lambda col: col.split(".")[2].removesuffix(",none"))
            else:
                df = df.rename(columns=lambda col: tasks.get(col.split(".")[1], subtasks.get(col.split(".")[1])))
        fig_1 = px.bar(
            df.T.rename_axis(columns="Model"),
            barmode="group",
            labels={"index": "Benchmark" if task == "All" else "Subtask", "value": "Score"},
            color_discrete_sequence=["#FF9D00", "#32343D"],
        )
        fig_1.update_yaxes(range=[0, 1])
        fig_2 = px.line_polar(
            df.melt(ignore_index=False, var_name="Benchmark", value_name="Score").reset_index(names="Model"),
            r="Score", theta="Benchmark", color="Model",
            line_close=True,
            range_r=[0, 1],
            color_discrete_sequence=["#FF9D00", "#32343D"],
        )
        # Avoid bug with radar:
        fig_2.update_layout(
            title_text="",
            title_font_size=1,
        )
        return fig_1, fig_2
    else:
        return None, None


tmpdirname = None


def download_results(results):
    global tmpdirname
    if results:
        if tmpdirname:
            shutil.rmtree(tmpdirname)
        tmpdirname = tempfile.mkdtemp()
        path = f"{tmpdirname}/results.html"
        with open(path, "w") as f:
            f.write(results)
        return gr.File(path, visible=True)


def clear_results_file():
    global tmpdirname
    if tmpdirname:
        shutil.rmtree(tmpdirname)
        tmpdirname = None
    return gr.File(visible=False)