import argparse
import datetime
import math
import os
from collections import defaultdict
from glob import glob

import numpy as np
import pandas as pd
import plotly.express as px
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
from utils import load_model_answers


def compute_mle_elo(df, SCALE=400, BASE=10, INIT_RATING=1000):
    models = pd.concat([df["model_a"], df["model_b"]]).unique()
    models = pd.Series(np.arange(len(models)), index=models)

    # duplicate battles
    df = pd.concat([df, df], ignore_index=True)
    p = len(models.index)
    n = df.shape[0]

    X = np.zeros([n, p])
    X[np.arange(n), models[df["model_a"]]] = +math.log(BASE)
    X[np.arange(n), models[df["model_b"]]] = -math.log(BASE)

    # one A win => two A win
    Y = np.zeros(n)
    Y[df["winner"] == "model_a"] = 1.0

    # one tie => one A win + one B win
    # find tie + tie (both bad) index
    tie_idx = (df["winner"] == "tie") | (df["winner"] == "tie (bothbad)")
    tie_idx[len(tie_idx) // 2 :] = False
    Y[tie_idx] = 1.0

    lr = LogisticRegression(fit_intercept=False, penalty=None, tol=1e-8)
    lr.fit(X, Y)

    elo_scores = SCALE * lr.coef_[0] + INIT_RATING

    # set anchor as gpt-3.5-turbo-0125 = 1000
    if "gpt-3.5-turbo-0125" in models.index:
        elo_scores += 1000 - elo_scores[models["gpt-3.5-turbo-0125"]]
    return pd.Series(elo_scores, index=models.index).sort_values(ascending=False)


def get_bootstrap_result(battles, func_compute_elo, num_round):
    rows = []
    for i in tqdm(range(num_round), desc="bootstrap"):
        rows.append(func_compute_elo(battles.sample(frac=1.0, replace=True)))
    df = pd.DataFrame(rows)
    return df[df.median().sort_values(ascending=False).index]


def preety_print_two_ratings(ratings_1, ratings_2, column_names):
    df = (
        pd.DataFrame(
            [[n, ratings_1[n], ratings_2[n]] for n in ratings_1.keys()],
            columns=["Model", column_names[0], column_names[1]],
        )
        .sort_values(column_names[0], ascending=False)
        .reset_index(drop=True)
    )
    df[column_names[0]] = (df[column_names[0]] + 0.5).astype(int)
    df[column_names[1]] = (df[column_names[1]] + 0.5).astype(int)
    df.index = df.index + 1
    return df


def visualize_bootstrap_scores(df, title):
    bars = (
        pd.DataFrame(dict(lower=df.quantile(0.025), rating=df.quantile(0.5), upper=df.quantile(0.975)))
        .reset_index(names="model")
        .sort_values("rating", ascending=False)
    )
    bars["error_y"] = bars["upper"] - bars["rating"]
    bars["error_y_minus"] = bars["rating"] - bars["lower"]
    bars["rating_rounded"] = np.round(bars["rating"], 2)
    fig = px.scatter(
        bars,
        x="model",
        y="rating",
        error_y="error_y",
        error_y_minus="error_y_minus",
        text="rating_rounded",
        title=title,
    )
    fig.update_layout(xaxis_title="Model", yaxis_title="Rating", height=600)
    return fig


def predict_win_rate(elo_ratings, SCALE=400, BASE=10, INIT_RATING=1000):
    names = sorted(list(elo_ratings.keys()))
    wins = defaultdict(lambda: defaultdict(lambda: 0))
    for a in names:
        for b in names:
            ea = 1 / (1 + BASE ** ((elo_ratings[b] - elo_ratings[a]) / SCALE))
            wins[a][b] = ea
            wins[b][a] = 1 - ea

    data = {a: [wins[a][b] if a != b else np.NAN for b in names] for a in names}

    df = pd.DataFrame(data, index=names)
    df.index.name = "model_a"
    df.columns.name = "model_b"
    return df.T


def get_win_rate_column(df, column, baseline="gpt-3.5-turbo-0125"):
    to_dict = df[["model", column]].set_index("model").to_dict()[column]
    win_rate_table = predict_win_rate(to_dict)
    return win_rate_table[baseline].fillna(0.5).apply(lambda x: round(x * 100, 2))


def get_battles_from_judgment(judge_name, first_game_only=False, WEIGHT=3):
    arena_hard_battles = pd.DataFrame()

    print("Turning judgment results into battles...")

    directory = f"data/arena-hard-v0.1/model_judgement/{judge_name}"
    assert os.path.exists(directory)
    for file in tqdm(glob(f"{directory}/*jsonl")):
        df = pd.read_json(file, lines=True)

        for _, row in df.iterrows():
            # game 1
            output = {"question_id": row["question_id"], "model_a": "gpt-3.5-turbo-0125", "model_b": row["model"]}

            game = row["games"][0]

            weight = 1
            if game["score"] == "A=B":
                output["winner"] = "tie"
            elif game["score"] == "A>B":
                output["winner"] = "model_a"
            elif game["score"] == "A>>B":
                output["winner"] = "model_a"
                weight = WEIGHT
            elif game["score"] == "B>A":
                output["winner"] = "model_b"
            elif game["score"] == "B>>A":
                output["winner"] = "model_b"
                weight = WEIGHT
            else:
                weight = 0

            if weight:
                arena_hard_battles = pd.concat([arena_hard_battles, pd.DataFrame([output] * weight)])

            if not first_game_only:
                # game 2
                output = {"question_id": row["question_id"], "model_a": "gpt-3.5-turbo-0125", "model_b": row["model"]}

                game = row["games"][1]

                weight = 1
                if game["score"] == "A=B":
                    output["winner"] = "tie"
                elif game["score"] == "A>B":
                    output["winner"] = "model_b"
                elif game["score"] == "A>>B":
                    output["winner"] = "model_b"
                    weight = WEIGHT
                elif game["score"] == "B>A":
                    output["winner"] = "model_a"
                elif game["score"] == "B>>A":
                    output["winner"] = "model_a"
                    weight = WEIGHT
                else:
                    weight = 0

                if weight:
                    arena_hard_battles = pd.concat([arena_hard_battles, pd.DataFrame([output] * weight)])
    arena_hard_battles.to_json("data/arena_hard_battles.jsonl", lines=True, orient="records")
    return arena_hard_battles


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--bench-name", type=str, default="arena-hard-v0.1")
    parser.add_argument("--judge-name", type=str, default="gpt-4-1106-preview")
    parser.add_argument("--baseline", type=str, default="gpt-3.5-turbo-0125")
    parser.add_argument("--load-battles", action="store_true")
    parser.add_argument("--load-bootstrap", action="store_true")
    parser.add_argument("--show-elo", action="store_true")
    parser.add_argument("--weight", type=int, default=3)
    parser.add_argument("--num-rounds", type=int, default=100)
    parser.add_argument("--output", action="store_true")
    parser.add_argument("--first-game-only", action="store_true")
    args = parser.parse_args()
    print(args)
    assert not args.load_bootstrap or (
        args.load_battles and args.load_bootstrap
    ), "If loading prexisting bootstrapping data, you must also load preexisting battles."

    answer_dir = os.path.join("data", args.bench_name, "model_answer/external")
    model_answers = load_model_answers(answer_dir)

    if args.load_battles:
        assert os.path.exists("data/arena_hard_battles.jsonl")
        battles = pd.read_json("data/arena_hard_battles.jsonl", lines=True)
    else:
        battles = get_battles_from_judgment(args.judge_name, args.first_game_only, args.weight)

    bootstrap_online_elo = compute_mle_elo(battles)

    if args.load_bootstrap:
        bootstrap_elo_lu = pd.read_json("data/bootstrapping_results.jsonl", lines=True)
    else:
        np.random.seed(42)
        bootstrap_elo_lu = get_bootstrap_result(battles, compute_mle_elo, args.num_rounds)
        bootstrap_elo_lu.to_json("data/bootstrapping_results.jsonl", lines=True, orient="records")

    stats = pd.DataFrame()
    stats["results"] = None
    stats["results"] = stats["results"].astype("object")

    for i, model in enumerate(bootstrap_online_elo.index):
        assert model in bootstrap_elo_lu.columns

        stats.at[i, "model"] = model
        stats.at[i, "score"] = bootstrap_online_elo[model]
        stats.at[i, "lower"] = np.percentile(bootstrap_elo_lu[model], 2.5)
        stats.at[i, "upper"] = np.percentile(bootstrap_elo_lu[model], 97.5)

        length = 0
        if model in model_answers:
            for _, row in model_answers[model].items():
                turn = row["choices"][0]["turns"][0]
                length += turn["token_len"]
            length /= len(model_answers[model])

        stats.at[i, "avg_tokens"] = int(length)
        stats.at[i, "results"] = bootstrap_elo_lu[model].tolist()

    if not args.show_elo:
        stats.sort_values(by="model", inplace=True)
        stats["score"] = get_win_rate_column(stats, "score", args.baseline).tolist()
        stats["lower"] = get_win_rate_column(stats, "lower", args.baseline).tolist()
        stats["upper"] = get_win_rate_column(stats, "upper", args.baseline).tolist()
        decimal = 1
    else:
        decimal = 0
        stats = stats.astype({"score": int, "lower": int, "upper": int})

    stats.sort_values(by="score", ascending=False, inplace=True)
    for _, row in stats.iterrows():
        interval = str((round(row["lower"] - row["score"], decimal), round(row["upper"] - row["score"], decimal)))
        print(
            f"{row['model'] : <30} | score: {round(row['score'], decimal) : ^5} | 95% CI: {interval : ^12} | average #tokens: {int(row['avg_tokens'])}"
        )

    if args.output:
        cur_date = datetime.datetime.now()
        date_str = cur_date.strftime("%Y%m%d")
        json_file_name = f"arena_hard_leaderboard_{date_str}.json"
        stats.to_json(json_file_name, orient="records", indent=4)
        import huggingface_hub

        huggingface_hub.HfApi().upload_file(
            path_or_fileobj=json_file_name,
            path_in_repo="data/leaderboard.json",
            repo_id="Vikhrmodels/leaderboard",
            repo_type="space",
        )

        huggingface_hub.HfApi().upload_file(
            path_or_fileobj=json_file_name,
            path_in_repo=f"leaderboard_logs/{json_file_name}",
            repo_id="Vikhrmodels/openbench-eval",
            repo_type="dataset",
        )