import argparse
import glob
import importlib
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import gym
import stable_baselines3 as sb3  # noqa: F401
import torch as th  # noqa: F401
import yaml
from sb3_contrib import QRDQN, TQC
from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.sb2_compat.rmsprop_tf_like import RMSpropTFLike  # noqa: F401
from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv, VecEnv, VecFrameStack, VecNormalize
from utils.retro_utils import make_retro_env

# For custom activation fn
from torch import nn as nn  # noqa: F401 pylint: disable=unused-import

ALGOS = {
    "a2c": A2C,
    "ddpg": DDPG,
    "dqn": DQN,
    "ppo": PPO,
    "sac": SAC,
    "td3": TD3,
    # SB3 Contrib,
    "qrdqn": QRDQN,
    "tqc": TQC,
}


def flatten_dict_observations(env: gym.Env) -> gym.Env:
    assert isinstance(env.observation_space, gym.spaces.Dict)
    try:
        return gym.wrappers.FlattenObservation(env)
    except AttributeError:
        keys = env.observation_space.spaces.keys()
        return gym.wrappers.FlattenDictWrapper(env, dict_keys=list(keys))


def get_wrapper_class(hyperparams: Dict[str, Any]) -> Optional[Callable[[gym.Env], gym.Env]]:
    """
    Get one or more Gym environment wrapper class specified as a hyper parameter
    "env_wrapper".
    e.g.
    env_wrapper: gym_minigrid.wrappers.FlatObsWrapper

    for multiple, specify a list:

    env_wrapper:
        - utils.wrappers.PlotActionWrapper
        - utils.wrappers.TimeFeatureWrapper


    :param hyperparams:
    :return: maybe a callable to wrap the environment
        with one or multiple gym.Wrapper
    """

    def get_module_name(wrapper_name):
        return ".".join(wrapper_name.split(".")[:-1])

    def get_class_name(wrapper_name):
        return wrapper_name.split(".")[-1]

    if "env_wrapper" in hyperparams.keys():
        wrapper_name = hyperparams.get("env_wrapper")

        if wrapper_name is None:
            return None

        if not isinstance(wrapper_name, list):
            wrapper_names = [wrapper_name]
        else:
            wrapper_names = wrapper_name

        wrapper_classes = []
        wrapper_kwargs = []
        # Handle multiple wrappers
        for wrapper_name in wrapper_names:
            # Handle keyword arguments
            if isinstance(wrapper_name, dict):
                assert len(wrapper_name) == 1, (
                    "You have an error in the formatting "
                    f"of your YAML file near {wrapper_name}. "
                    "You should check the indentation."
                )
                wrapper_dict = wrapper_name
                wrapper_name = list(wrapper_dict.keys())[0]
                kwargs = wrapper_dict[wrapper_name]
            else:
                kwargs = {}
            wrapper_module = importlib.import_module(get_module_name(wrapper_name))
            wrapper_class = getattr(wrapper_module, get_class_name(wrapper_name))
            wrapper_classes.append(wrapper_class)
            wrapper_kwargs.append(kwargs)

        def wrap_env(env: gym.Env) -> gym.Env:
            """
            :param env:
            :return:
            """
            for wrapper_class, kwargs in zip(wrapper_classes, wrapper_kwargs):
                env = wrapper_class(env, **kwargs)
            return env

        return wrap_env
    else:
        return None


def get_callback_list(hyperparams: Dict[str, Any]) -> List[BaseCallback]:
    """
    Get one or more Callback class specified as a hyper-parameter
    "callback".
    e.g.
    callback: stable_baselines3.common.callbacks.CheckpointCallback

    for multiple, specify a list:

    callback:
        - utils.callbacks.PlotActionWrapper
        - stable_baselines3.common.callbacks.CheckpointCallback

    :param hyperparams:
    :return:
    """

    def get_module_name(callback_name):
        return ".".join(callback_name.split(".")[:-1])

    def get_class_name(callback_name):
        return callback_name.split(".")[-1]

    callbacks = []

    if "callback" in hyperparams.keys():
        callback_name = hyperparams.get("callback")

        if callback_name is None:
            return callbacks

        if not isinstance(callback_name, list):
            callback_names = [callback_name]
        else:
            callback_names = callback_name

        # Handle multiple wrappers
        for callback_name in callback_names:
            # Handle keyword arguments
            if isinstance(callback_name, dict):
                assert len(callback_name) == 1, (
                    "You have an error in the formatting "
                    f"of your YAML file near {callback_name}. "
                    "You should check the indentation."
                )
                callback_dict = callback_name
                callback_name = list(callback_dict.keys())[0]
                kwargs = callback_dict[callback_name]
            else:
                kwargs = {}
            callback_module = importlib.import_module(get_module_name(callback_name))
            callback_class = getattr(callback_module, get_class_name(callback_name))
            callbacks.append(callback_class(**kwargs))

    return callbacks


def create_test_env(
    env_id: str,
    n_envs: int = 1,
    stats_path: Optional[str] = None,
    seed: int = 0,
    log_dir: Optional[str] = None,
    should_render: bool = True,
    hyperparams: Optional[Dict[str, Any]] = None,
    env_kwargs: Optional[Dict[str, Any]] = None,
) -> VecEnv:
    """
    Create environment for testing a trained agent

    :param env_id:
    :param n_envs: number of processes
    :param stats_path: path to folder containing saved running averaged
    :param seed: Seed for random number generator
    :param log_dir: Where to log rewards
    :param should_render: For Pybullet env, display the GUI
    :param hyperparams: Additional hyperparams (ex: n_stack)
    :param env_kwargs: Optional keyword argument to pass to the env constructor
    :return:
    """
    # Avoid circular import
    from zoo_utils.exp_manager import ExperimentManager

    # Create the environment and wrap it if necessary
    env_wrapper = get_wrapper_class(hyperparams)

    hyperparams = {} if hyperparams is None else hyperparams

    if "env_wrapper" in hyperparams.keys():
        del hyperparams["env_wrapper"]

    vec_env_kwargs = {}
    vec_env_cls = DummyVecEnv
    if n_envs > 1 or (ExperimentManager.is_bullet(env_id) and should_render):
        # HACK: force SubprocVecEnv for Bullet env
        # as Pybullet envs does not follow gym.render() interface
        vec_env_cls = SubprocVecEnv
        # start_method = 'spawn' for thread safe

    from zoo_utils.exp_manager import RETRO_ENVS

    if env_id in RETRO_ENVS:
        env = make_retro_env(
            env_id,
            n_envs=n_envs,
            monitor_dir=log_dir,
            seed=seed,
            # wrapper_class=env_wrapper,
            env_kwargs=env_kwargs,
            vec_env_cls=vec_env_cls,
            vec_env_kwargs=vec_env_kwargs,
        )
    else:
        env = make_vec_env(
            env_id,
            n_envs=n_envs,
            monitor_dir=log_dir,
            seed=seed,
            wrapper_class=env_wrapper,
            env_kwargs=env_kwargs,
            vec_env_cls=vec_env_cls,
            vec_env_kwargs=vec_env_kwargs,
        )

    # Load saved stats for normalizing input and rewards
    # And optionally stack frames
    if stats_path is not None:
        if hyperparams["normalize"]:
            print("Loading running average")
            print(f"with params: {hyperparams['normalize_kwargs']}")
            path_ = os.path.join(stats_path, "vecnormalize.pkl")
            if os.path.exists(path_):
                env = VecNormalize.load(path_, env)
                # Deactivate training and reward normalization
                env.training = False
                env.norm_reward = False
            else:
                raise ValueError(f"VecNormalize stats {path_} not found")

        n_stack = hyperparams.get("frame_stack", 0)
        if n_stack > 0:
            print(f"Stacking {n_stack} frames")
            env = VecFrameStack(env, n_stack)
    return env


def linear_schedule(initial_value: Union[float, str]) -> Callable[[float], float]:
    """
    Linear learning rate schedule.

    :param initial_value: (float or str)
    :return: (function)
    """
    if isinstance(initial_value, str):
        initial_value = float(initial_value)

    def func(progress_remaining: float) -> float:
        """
        Progress will decrease from 1 (beginning) to 0
        :param progress_remaining: (float)
        :return: (float)
        """
        return progress_remaining * initial_value

    return func


def get_trained_models(log_folder: str) -> Dict[str, Tuple[str, str]]:
    """
    :param log_folder: Root log folder
    :return: Dict representing the trained agents
    """
    trained_models = {}
    for algo in os.listdir(log_folder):
        if not os.path.isdir(os.path.join(log_folder, algo)):
            continue
        for env_id in os.listdir(os.path.join(log_folder, algo)):
            # Retrieve env name
            env_id = env_id.split("_")[0]
            trained_models[f"{algo}-{env_id}"] = (algo, env_id)
    return trained_models


def get_latest_run_id(log_path: str, env_id: str) -> int:
    """
    Returns the latest run number for the given log name and log path,
    by finding the greatest number in the directories.

    :param log_path: path to log folder
    :param env_id:
    :return: latest run number
    """
    max_run_id = 0
    for path in glob.glob(os.path.join(log_path, env_id + "_[0-9]*")):
        file_name = os.path.basename(path)
        ext = file_name.split("_")[-1]
        if env_id == "_".join(file_name.split("_")[:-1]) and ext.isdigit() and int(ext) > max_run_id:
            max_run_id = int(ext)
    return max_run_id


def get_saved_hyperparams(
    stats_path: str,
    norm_reward: bool = False,
    test_mode: bool = False,
) -> Tuple[Dict[str, Any], str]:
    """
    :param stats_path:
    :param norm_reward:
    :param test_mode:
    :return:
    """
    hyperparams = {}
    if not os.path.isdir(stats_path):
        stats_path = None
    else:
        config_file = os.path.join(stats_path, "config.yml")
        if os.path.isfile(config_file):
            # Load saved hyperparameters
            with open(os.path.join(stats_path, "config.yml"), "r") as f:
                hyperparams = yaml.load(f, Loader=yaml.UnsafeLoader)  # pytype: disable=module-attr
            hyperparams["normalize"] = hyperparams.get("normalize", False)
        else:
            obs_rms_path = os.path.join(stats_path, "obs_rms.pkl")
            hyperparams["normalize"] = os.path.isfile(obs_rms_path)

        # Load normalization params
        if hyperparams["normalize"]:
            if isinstance(hyperparams["normalize"], str):
                normalize_kwargs = eval(hyperparams["normalize"])
                if test_mode:
                    normalize_kwargs["norm_reward"] = norm_reward
            else:
                normalize_kwargs = {"norm_obs": hyperparams["normalize"], "norm_reward": norm_reward}
            hyperparams["normalize_kwargs"] = normalize_kwargs
    return hyperparams, stats_path


class StoreDict(argparse.Action):
    """
    Custom argparse action for storing dict.

    In: args1:0.0 args2:"dict(a=1)"
    Out: {'args1': 0.0, arg2: dict(a=1)}
    """

    def __init__(self, option_strings, dest, nargs=None, **kwargs):
        self._nargs = nargs
        super(StoreDict, self).__init__(option_strings, dest, nargs=nargs, **kwargs)

    def __call__(self, parser, namespace, values, option_string=None):
        arg_dict = {}
        for arguments in values:
            key = arguments.split(":")[0]
            value = ":".join(arguments.split(":")[1:])
            # Evaluate the string as python code
            arg_dict[key] = eval(value)
        setattr(namespace, self.dest, arg_dict)
