import fnmatch
import os
import random
import time

import pybullet_envs_gymnasium  # noqa: F401 pylint: disable=unused-import
from datasets import load_dataset
from huggingface_hub import HfApi

from src.evaluation import evaluate
from src.logging import setup_logger

logger = setup_logger(__name__)

API = HfApi(token=os.environ.get("TOKEN"))
RESULTS_REPO = "open-rl-leaderboard/results_v2"

ALL_ENV_IDS = [
    "AdventureNoFrameskip-v4",
    "AirRaidNoFrameskip-v4",
    "AlienNoFrameskip-v4",
    "AmidarNoFrameskip-v4",
    "AssaultNoFrameskip-v4",
    "AsterixNoFrameskip-v4",
    "AsteroidsNoFrameskip-v4",
    "AtlantisNoFrameskip-v4",
    "BankHeistNoFrameskip-v4",
    "BattleZoneNoFrameskip-v4",
    "BeamRiderNoFrameskip-v4",
    "BerzerkNoFrameskip-v4",
    "BowlingNoFrameskip-v4",
    "BoxingNoFrameskip-v4",
    "BreakoutNoFrameskip-v4",
    "CarnivalNoFrameskip-v4",
    "CentipedeNoFrameskip-v4",
    "ChopperCommandNoFrameskip-v4",
    "CrazyClimberNoFrameskip-v4",
    "DefenderNoFrameskip-v4",
    "DemonAttackNoFrameskip-v4",
    "DoubleDunkNoFrameskip-v4",
    "ElevatorActionNoFrameskip-v4",
    "EnduroNoFrameskip-v4",
    "FishingDerbyNoFrameskip-v4",
    "FreewayNoFrameskip-v4",
    "FrostbiteNoFrameskip-v4",
    "GopherNoFrameskip-v4",
    "GravitarNoFrameskip-v4",
    "HeroNoFrameskip-v4",
    "IceHockeyNoFrameskip-v4",
    "JamesbondNoFrameskip-v4",
    "JourneyEscapeNoFrameskip-v4",
    "KangarooNoFrameskip-v4",
    "KrullNoFrameskip-v4",
    "KungFuMasterNoFrameskip-v4",
    "MontezumaRevengeNoFrameskip-v4",
    "MsPacmanNoFrameskip-v4",
    "NameThisGameNoFrameskip-v4",
    "PhoenixNoFrameskip-v4",
    "PitfallNoFrameskip-v4",
    "PongNoFrameskip-v4",
    "PooyanNoFrameskip-v4",
    "PrivateEyeNoFrameskip-v4",
    "QbertNoFrameskip-v4",
    "RiverraidNoFrameskip-v4",
    "RoadRunnerNoFrameskip-v4",
    "RobotankNoFrameskip-v4",
    "SeaquestNoFrameskip-v4",
    "SkiingNoFrameskip-v4",
    "SolarisNoFrameskip-v4",
    "SpaceInvadersNoFrameskip-v4",
    "StarGunnerNoFrameskip-v4",
    "TennisNoFrameskip-v4",
    "TimePilotNoFrameskip-v4",
    "TutankhamNoFrameskip-v4",
    "UpNDownNoFrameskip-v4",
    "VentureNoFrameskip-v4",
    "VideoPinballNoFrameskip-v4",
    "WizardOfWorNoFrameskip-v4",
    "YarsRevengeNoFrameskip-v4",
    "ZaxxonNoFrameskip-v4",
    # Box2D
    "BipedalWalker-v3",
    "BipedalWalkerHardcore-v3",
    "CarRacing-v2",
    "LunarLander-v2",
    "LunarLanderContinuous-v2",
    # Toy text
    "Blackjack-v1",
    "CliffWalking-v0",
    "FrozenLake-v1",
    "FrozenLake8x8-v1",
    # Classic control
    "Acrobot-v1",
    "CartPole-v1",
    "MountainCar-v0",
    "MountainCarContinuous-v0",
    "Pendulum-v1",
    # MuJoCo
    "Ant-v4",
    "HalfCheetah-v4",
    "Hopper-v4",
    "Humanoid-v4",
    "HumanoidStandup-v4",
    "InvertedDoublePendulum-v4",
    "InvertedPendulum-v4",
    "Pusher-v4",
    "Reacher-v4",
    "Swimmer-v4",
    "Walker2d-v4",
    # PyBullet
    "AntBulletEnv-v0",
    "HalfCheetahBulletEnv-v0",
    "HopperBulletEnv-v0",
    "HumanoidBulletEnv-v0",
    "InvertedDoublePendulumBulletEnv-v0",
    "InvertedPendulumSwingupBulletEnv-v0",
    "MinitaurBulletEnv-v0",
    "ReacherBulletEnv-v0",
    "Walker2DBulletEnv-v0",
]


def pattern_match(patterns, source_list):
    if isinstance(patterns, str):
        patterns = [patterns]

    env_ids = set()
    for pattern in patterns:
        for matching in fnmatch.filter(source_list, pattern):
            env_ids.add(matching)
    return sorted(list(env_ids))


def _backend_routine():
    # List only the text classification models
    rl_models = list(API.list_models(filter=["reinforcement-learning"]))
    logger.info(f"Found {len(rl_models)} RL models")

    compatible_models = []
    for model in rl_models:
        filenames = [sib.rfilename for sib in model.siblings]
        if "agent.pt" in filenames:
            compatible_models.append((model.modelId, model.sha))

    logger.info(f"Found {len(compatible_models)} compatible models")

    dataset = load_dataset(RESULTS_REPO, split="train", download_mode="force_redownload", verification_mode="no_checks")
    evaluated_models = [("/".join([x["user_id"], x["model_id"]]), x["sha"]) for x in dataset]
    pending_models = list(set(compatible_models) - set(evaluated_models))
    logger.info(f"Found {len(pending_models)} pending models")

    if len(pending_models) == 0:
        return None

    # Shuffle the dataset
    random.shuffle(pending_models)

    # Select a random model
    repo_id, sha = pending_models.pop()
    user_id, model_id = repo_id.split("/")
    row = {"model_id": model_id, "user_id": user_id, "sha": sha}

    # Run an evaluation on the models
    model_info = API.model_info(repo_id, revision=sha)

    # Extract the environment IDs from the tags (usually only one)
    env_ids = pattern_match(model_info.tags, ALL_ENV_IDS)
    if len(env_ids) > 0:
        env_id = env_ids[0]
        logger.info(f"Running evaluation on {user_id}/{model_id}")

        try:
            episodic_returns = evaluate(repo_id, sha, env_id)
            row["status"] = "DONE"
            row["env_id"] = env_id
            row["episodic_returns"] = episodic_returns
        except Exception as e:
            logger.error(f"Error evaluating {repo_id}: {e}")
            logger.exception(e)
            row["status"] = "FAILED"

    else:
        logger.error(f"No environment found for {model_id}")
        row["status"] = "FAILED"

    # load the last version of the dataset
    dataset = load_dataset(
        RESULTS_REPO, split="train", download_mode="force_redownload", verification_mode="no_checks"
    )
    dataset.add_item(row)
    dataset.push_to_hub(RESULTS_REPO, split="train", token=API.token)
    time.sleep(60)  # Sleep for 1 minute to avoid rate limiting


def backend_routine():
    try:
        _backend_routine()
    except Exception as e:
        logger.error(f"{e.__class__.__name__}: {str(e)}")
        logger.exception(e)


if __name__ == "__main__":
    backend_routine()