import os
import json
import requests

import pandas as pd
from huggingface_hub import HfApi, hf_hub_download, snapshot_download
from huggingface_hub.repocard import metadata_load
from tqdm.contrib.concurrent import thread_map
from apscheduler.schedulers.background import BackgroundScheduler

from tqdm.contrib.concurrent import thread_map

DATASET_REPO_URL = "https://huggingface.co/datasets/pkalkman/drlc-leaderboard-data"
DATASET_REPO_ID = "pkalkman/drlc-leaderboard-data"
HF_TOKEN = os.environ.get("HF_TOKEN")

api = HfApi(token=HF_TOKEN)


# Read the environments from the JSON file
with open('envs.json', 'r') as f:
    rl_envs = json.load(f)


def download_leaderboard_dataset():
    # Download the dataset from the Hugging Face Hub
    path = snapshot_download(repo_id=DATASET_REPO_ID, repo_type="dataset")
    return path


def get_metadata(model_id):
    try:
        readme_path = hf_hub_download(model_id, filename="README.md", etag_timeout=180)
        return metadata_load(readme_path)
    except requests.exceptions.HTTPError:
        # 404 README.md not found
        return None


def parse_metrics_accuracy(meta):
    if "model-index" not in meta:
        return None
    result = meta["model-index"][0]["results"]
    metrics = result[0]["metrics"]
    accuracy = metrics[0]["value"]
    return accuracy


# We keep the worst case episode
def parse_rewards(accuracy):
    default_std = -1000
    default_reward = -1000
    if accuracy is not None:
        accuracy = str(accuracy)
        parsed = accuracy.split('+/-')
        if len(parsed) > 1:
            mean_reward = float(parsed[0].strip())
            std_reward = float(parsed[1].strip())
        elif len(parsed) == 1:  # only mean reward
            mean_reward = float(parsed[0].strip())
            std_reward = float(0)
        else:
            mean_reward = float(default_std)
            std_reward = float(default_reward)

    else:
        mean_reward = float(default_std)
        std_reward = float(default_reward)
    return mean_reward, std_reward


def get_model_ids(rl_env):
    api = HfApi()
    models = api.list_models(filter=rl_env)
    model_ids = [x.modelId for x in models]
    return model_ids


# Parralelized version
def update_leaderboard_dataset_parallel(rl_env, path):
    # Get model ids associated with rl_env
    model_ids = get_model_ids(rl_env)

    def process_model(model_id):
        meta = get_metadata(model_id)
        # LOADED_MODEL_METADATA[model_id] = meta if meta is not None else ''
        if meta is None:
            return None
        user_id = model_id.split('/')[0]
        row = {}
        row["User"] = user_id
        row["Model"] = model_id
        accuracy = parse_metrics_accuracy(meta)
        mean_reward, std_reward = parse_rewards(accuracy)
        mean_reward = mean_reward if not pd.isna(mean_reward) else 0
        std_reward = std_reward if not pd.isna(std_reward) else 0
        row["Results"] = mean_reward - std_reward
        row["Mean Reward"] = mean_reward
        row["Std Reward"] = std_reward
        return row

    data = list(thread_map(process_model, model_ids, desc="Processing models"))

    # Filter out None results (models with no metadata)
    data = [row for row in data if row is not None]

    ranked_dataframe = rank_dataframe(pd.DataFrame.from_records(data))
    new_history = ranked_dataframe
    file_path = path + "/" + rl_env + ".csv"
    new_history.to_csv(file_path, index=False)

    return ranked_dataframe


def update_leaderboard_dataset(rl_env, path):
    # Get model ids associated with rl_env
    model_ids = get_model_ids(rl_env)
    data = []
    for model_id in model_ids:
        """
        readme_path = hf_hub_download(model_id, filename="README.md")
        meta = metadata_load(readme_path)
        """
        meta = get_metadata(model_id)
        # LOADED_MODEL_METADATA[model_id] = meta if meta is not None else ''
        if meta is None:
            continue
        user_id = model_id.split('/')[0]
        row = {}
        row["User"] = user_id
        row["Model"] = model_id
        accuracy = parse_metrics_accuracy(meta)
        mean_reward, std_reward = parse_rewards(accuracy)
        mean_reward = mean_reward if not pd.isna(mean_reward) else 0
        std_reward = std_reward if not pd.isna(std_reward) else 0
        row["Results"] = mean_reward - std_reward
        row["Mean Reward"] = mean_reward
        row["Std Reward"] = std_reward
        data.append(row)

    ranked_dataframe = rank_dataframe(pd.DataFrame.from_records(data))
    new_history = ranked_dataframe
    file_path = path + "/" + rl_env + ".csv"
    new_history.to_csv(file_path, index=False)

    return ranked_dataframe


def get_data_no_html(rl_env, path) -> pd.DataFrame:
    """
    Get data from rl_env
    :return: data as a pandas DataFrame
    """
    csv_path = path + "/" + rl_env + ".csv"
    data = pd.read_csv(csv_path)

    return data


def rank_dataframe(dataframe):
    dataframe = dataframe.sort_values(by=['Results', 'User', 'Model'], ascending=False)
    if 'Ranking' not in dataframe.columns:
        dataframe.insert(0, 'Ranking', [i for i in range(1, len(dataframe) + 1)])
    else:
        dataframe['Ranking'] = [i for i in range(1, len(dataframe) + 1)]
    return dataframe


def run_update_dataset():
    path_ = download_leaderboard_dataset()
    for i in range(0, len(rl_envs)):
        rl_env = rl_envs[i]
        update_leaderboard_dataset_parallel(rl_env["rl_env"], path_)

    api.upload_folder(
        folder_path=path_,
        repo_id="pkalkman/drlc-leaderboard-data",
        repo_type="dataset",
        commit_message="Update dataset")


scheduler = BackgroundScheduler()
scheduler.add_job(run_update_dataset, 'interval', seconds=10800)
scheduler.start()