import argparse
import os
import pickle as pkl
import time
import warnings
from collections import OrderedDict
from pprint import pprint
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union

import gym
import numpy as np
import optuna
import yaml
from optuna.integration.skopt import SkoptSampler
from optuna.pruners import BasePruner, MedianPruner, SuccessiveHalvingPruner
from optuna.samplers import BaseSampler, RandomSampler, TPESampler
from optuna.visualization import plot_optimization_history, plot_param_importances

# For using HER with GoalEnv
from stable_baselines3 import HerReplayBuffer  # noqa: F401
from stable_baselines3.common.base_class import BaseAlgorithm
from stable_baselines3.common.callbacks import (  # StopTrainingOnMaxEpisodes,; StopTrainingOnRewardThreshold,
    BaseCallback,
    CheckpointCallback,
    EvalCallback,
)
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
from stable_baselines3.common.preprocessing import is_image_space, is_image_space_channels_first
from stable_baselines3.common.sb2_compat.rmsprop_tf_like import RMSpropTFLike  # noqa: F401
from stable_baselines3.common.utils import constant_fn
from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv, VecEnv, VecFrameStack, VecNormalize, VecTransposeImage

# For custom activation fn
from torch import nn as nn  # noqa: F401

# Register custom envs
import utils.import_envs  # noqa: F401 pytype: disable=import-error
from utils.callbacks import CustomEvalCallback, SaveVecNormalizeCallback, TrialEvalCallback
from utils.hyperparams_opt import HYPERPARAMS_SAMPLER
from utils.utils import ALGOS, get_callback_list, get_latest_run_id, get_wrapper_class, linear_schedule

# from stable_baselines3.common.env_util import make_vec_env


class ExperimentManager(object):
    """
    Experiment manager: read the hyperparameters,
    preprocess them, create the environment and the RL model.

    Please take a look at `train.py` to have the details for each argument.
    """

    def __init__(
        self,
        args: argparse.Namespace,
        algo: str,
        env_id: str,
        log_folder: str,
        tensorboard_log: str = "",
        n_timesteps: int = 0,
        eval_freq: int = 10000,
        n_eval_episodes: int = 5,
        save_freq: int = -1,
        hyperparams: Optional[Dict[str, Any]] = None,
        env_kwargs: Optional[Dict[str, Any]] = None,
        trained_agent: str = "",
        optimize_hyperparameters: bool = False,
        storage: Optional[str] = None,
        study_name: Optional[str] = None,
        n_trials: int = 1,
        n_jobs: int = 1,
        sampler: str = "tpe",
        pruner: str = "median",
        n_startup_trials: int = 0,
        n_evaluations: int = 1,
        truncate_last_trajectory: bool = False,
        uuid_str: str = "",
        seed: int = 0,
        log_interval: int = 0,
        save_replay_buffer: bool = False,
        verbose: int = 1,
        vec_env_type: str = "dummy",
        n_eval_envs: int = 1,
    ):
        super(ExperimentManager, self).__init__()
        self.algo = algo
        self.env_id = env_id
        # Custom params
        self.custom_hyperparams = hyperparams
        self.env_kwargs = {} if env_kwargs is None else env_kwargs
        self.n_timesteps = n_timesteps
        self.normalize = False
        self.normalize_kwargs = {}
        self.env_wrapper = None
        self.frame_stack = None
        self.seed = seed
        self.saved_hyperparams = {}
        self._use_continuity_cost = False

        self.vec_env_class = {"dummy": DummyVecEnv, "subproc": SubprocVecEnv}[vec_env_type]

        self.vec_env_kwargs = {}
        # self.vec_env_kwargs = {} if vec_env_type == "dummy" else {"start_method": "fork"}

        # Callbacks
        self.callbacks = []
        self.save_freq = save_freq
        self.eval_freq = eval_freq
        self.n_eval_episodes = n_eval_episodes
        self.n_eval_envs = n_eval_envs

        self.n_envs = 1  # it will be updated when reading hyperparams
        self.n_actions = None  # For DDPG/TD3 action noise objects
        self._hyperparams = {}

        self.trained_agent = trained_agent
        self.continue_training = trained_agent.endswith(".zip") and os.path.isfile(trained_agent)
        self.truncate_last_trajectory = truncate_last_trajectory

        self._is_atari = self.is_atari(env_id)
        # Hyperparameter optimization config
        self.optimize_hyperparameters = optimize_hyperparameters
        self.storage = storage
        self.study_name = study_name
        # maximum number of trials for finding the best hyperparams
        self.n_trials = n_trials
        # number of parallel jobs when doing hyperparameter search
        self.n_jobs = n_jobs
        self.sampler = sampler
        self.pruner = pruner
        self.n_startup_trials = n_startup_trials
        self.n_evaluations = n_evaluations
        self.deterministic_eval = not self.is_atari(self.env_id)

        # Logging
        self.log_folder = log_folder
        self.tensorboard_log = None if tensorboard_log == "" else os.path.join(tensorboard_log, env_id)
        self.verbose = verbose
        self.args = args
        self.log_interval = log_interval
        self.save_replay_buffer = save_replay_buffer

        self.log_path = f"{log_folder}/{self.algo}/"
        self.save_path = os.path.join(
            self.log_path, f"{self.env_id}_{get_latest_run_id(self.log_path, self.env_id) + 1}{uuid_str}"
        )
        self.params_path = f"{self.save_path}/{self.env_id}"

    def setup_experiment(self) -> Optional[BaseAlgorithm]:
        """
        Read hyperparameters, pre-process them (create schedules, wrappers, callbacks, action noise objects)
        create the environment and possibly the model.

        :return: the initialized RL model
        """
        hyperparams, saved_hyperparams = self.read_hyperparameters()
        self.saved_hyperparams = saved_hyperparams
        hyperparams, self.env_wrapper, self.callbacks = self._preprocess_hyperparams(hyperparams)

        self.create_log_folder()
        self.create_callbacks()

        # Create env to have access to action space for action noise
        env = self.create_envs(self.n_envs, no_log=False)

        self._hyperparams = self._preprocess_action_noise(hyperparams, saved_hyperparams, env)

        if self.continue_training:
            model = self._load_pretrained_agent(self._hyperparams, env)
        elif self.optimize_hyperparameters:
            return None
        else:
            # Train an agent from scratch
            model = ALGOS[self.algo](
                env=env,
                tensorboard_log=self.tensorboard_log,
                seed=self.seed,
                verbose=self.verbose,
                **self._hyperparams,
            )

        self._save_config(saved_hyperparams)
        return model

    def learn(self, model: BaseAlgorithm) -> None:
        """
        :param model: an initialized RL model
        """
        kwargs = {}
        if self.log_interval > -1:
            kwargs = {"log_interval": self.log_interval}

        if len(self.callbacks) > 0:
            kwargs["callback"] = self.callbacks

        try:
            model.learn(self.n_timesteps, **kwargs)
        except KeyboardInterrupt:
            # this allows to save the model when interrupting training
            pass
        finally:
            # Release resources
            try:
                model.env.close()
            except EOFError:
                pass

    def save_trained_model(self, model: BaseAlgorithm) -> None:
        """
        Save trained model optionally with its replay buffer
        and ``VecNormalize`` statistics

        :param model:
        """
        print(f"Saving to {self.save_path}")
        model.save(f"{self.save_path}/{self.env_id}")

        if hasattr(model, "save_replay_buffer") and self.save_replay_buffer:
            print("Saving replay buffer")
            model.save_replay_buffer(os.path.join(self.save_path, "replay_buffer.pkl"))

        if self.normalize:
            # Important: save the running average, for testing the agent we need that normalization
            model.get_vec_normalize_env().save(os.path.join(self.params_path, "vecnormalize.pkl"))

    def _save_config(self, saved_hyperparams: Dict[str, Any]) -> None:
        """
        Save unprocessed hyperparameters, this can be use later
        to reproduce an experiment.

        :param saved_hyperparams:
        """
        # Save hyperparams
        with open(os.path.join(self.params_path, "config.yml"), "w") as f:
            yaml.dump(saved_hyperparams, f)

        # save command line arguments
        with open(os.path.join(self.params_path, "args.yml"), "w") as f:
            ordered_args = OrderedDict([(key, vars(self.args)[key]) for key in sorted(vars(self.args).keys())])
            yaml.dump(ordered_args, f)

        print(f"Log path: {self.save_path}")

    def read_hyperparameters(self) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        # Load hyperparameters from yaml file
        with open(f"hyperparams/{self.algo}.yml", "r") as f:
            hyperparams_dict = yaml.safe_load(f)
            if self.env_id in list(hyperparams_dict.keys()):
                hyperparams = hyperparams_dict[self.env_id]
            elif self._is_atari:
                hyperparams = hyperparams_dict["atari"]
            else:
                raise ValueError(f"Hyperparameters not found for {self.algo}-{self.env_id}")

        if self.custom_hyperparams is not None:
            # Overwrite hyperparams if needed
            hyperparams.update(self.custom_hyperparams)
        # Sort hyperparams that will be saved
        saved_hyperparams = OrderedDict([(key, hyperparams[key]) for key in sorted(hyperparams.keys())])

        if self.verbose > 0:
            pprint(saved_hyperparams)

        return hyperparams, saved_hyperparams

    @staticmethod
    def _preprocess_schedules(hyperparams: Dict[str, Any]) -> Dict[str, Any]:
        # Create schedules
        for key in ["learning_rate", "clip_range", "clip_range_vf"]:
            if key not in hyperparams:
                continue
            if isinstance(hyperparams[key], str):
                schedule, initial_value = hyperparams[key].split("_")
                initial_value = float(initial_value)
                hyperparams[key] = linear_schedule(initial_value)
            elif isinstance(hyperparams[key], (float, int)):
                # Negative value: ignore (ex: for clipping)
                if hyperparams[key] < 0:
                    continue
                hyperparams[key] = constant_fn(float(hyperparams[key]))
            else:
                raise ValueError(f"Invalid value for {key}: {hyperparams[key]}")
        return hyperparams

    def _preprocess_normalization(self, hyperparams: Dict[str, Any]) -> Dict[str, Any]:
        if "normalize" in hyperparams.keys():
            self.normalize = hyperparams["normalize"]

            # Special case, instead of both normalizing
            # both observation and reward, we can normalize one of the two.
            # in that case `hyperparams["normalize"]` is a string
            # that can be evaluated as python,
            # ex: "dict(norm_obs=False, norm_reward=True)"
            if isinstance(self.normalize, str):
                self.normalize_kwargs = eval(self.normalize)
                self.normalize = True

            # Use the same discount factor as for the algorithm
            if "gamma" in hyperparams:
                self.normalize_kwargs["gamma"] = hyperparams["gamma"]

            del hyperparams["normalize"]
        return hyperparams

    def _preprocess_hyperparams(
        self, hyperparams: Dict[str, Any]
    ) -> Tuple[Dict[str, Any], Optional[Callable], List[BaseCallback]]:
        self.n_envs = hyperparams.get("n_envs", 1)

        if self.verbose > 0:
            print(f"Using {self.n_envs} environments")

        # Convert schedule strings to objects
        hyperparams = self._preprocess_schedules(hyperparams)

        # Pre-process train_freq
        if "train_freq" in hyperparams and isinstance(hyperparams["train_freq"], list):
            hyperparams["train_freq"] = tuple(hyperparams["train_freq"])

        # Should we overwrite the number of timesteps?
        if self.n_timesteps > 0:
            if self.verbose:
                print(f"Overwriting n_timesteps with n={self.n_timesteps}")
        else:
            self.n_timesteps = int(hyperparams["n_timesteps"])

        # Pre-process normalize config
        hyperparams = self._preprocess_normalization(hyperparams)

        # Pre-process policy/buffer keyword arguments
        # Convert to python object if needed
        for kwargs_key in {"policy_kwargs", "replay_buffer_class", "replay_buffer_kwargs"}:
            if kwargs_key in hyperparams.keys() and isinstance(hyperparams[kwargs_key], str):
                hyperparams[kwargs_key] = eval(hyperparams[kwargs_key])

        # Delete keys so the dict can be pass to the model constructor
        if "n_envs" in hyperparams.keys():
            del hyperparams["n_envs"]
        del hyperparams["n_timesteps"]

        if "frame_stack" in hyperparams.keys():
            self.frame_stack = hyperparams["frame_stack"]
            del hyperparams["frame_stack"]

        # obtain a class object from a wrapper name string in hyperparams
        # and delete the entry
        env_wrapper = get_wrapper_class(hyperparams)
        if "env_wrapper" in hyperparams.keys():
            del hyperparams["env_wrapper"]

        callbacks = get_callback_list(hyperparams)
        if "callback" in hyperparams.keys():
            del hyperparams["callback"]

        return hyperparams, env_wrapper, callbacks

    def _preprocess_action_noise(
        self, hyperparams: Dict[str, Any], saved_hyperparams: Dict[str, Any], env: VecEnv
    ) -> Dict[str, Any]:
        # Parse noise string
        # Note: only off-policy algorithms are supported
        if hyperparams.get("noise_type") is not None:
            noise_type = hyperparams["noise_type"].strip()
            noise_std = hyperparams["noise_std"]

            # Save for later (hyperparameter optimization)
            self.n_actions = env.action_space.shape[0]

            if "normal" in noise_type:
                hyperparams["action_noise"] = NormalActionNoise(
                    mean=np.zeros(self.n_actions),
                    sigma=noise_std * np.ones(self.n_actions),
                )
            elif "ornstein-uhlenbeck" in noise_type:
                hyperparams["action_noise"] = OrnsteinUhlenbeckActionNoise(
                    mean=np.zeros(self.n_actions),
                    sigma=noise_std * np.ones(self.n_actions),
                )
            else:
                raise RuntimeError(f'Unknown noise type "{noise_type}"')

            print(f"Applying {noise_type} noise with std {noise_std}")

            del hyperparams["noise_type"]
            del hyperparams["noise_std"]

        return hyperparams

    def create_log_folder(self):
        os.makedirs(self.params_path, exist_ok=True)

    def create_callbacks(self):

        if self.save_freq > 0:
            # Account for the number of parallel environments
            self.save_freq = max(self.save_freq // self.n_envs, 1)
            self.callbacks.append(
                CheckpointCallback(
                    save_freq=self.save_freq,
                    save_path=self.save_path,
                    name_prefix="rl_model",
                    verbose=1,
                )
            )

        # self.callbacks.append(StopTrainingOnMaxEpisodes(max_episodes=1500, verbose=1))

        # Create test env if needed, do not normalize reward
        if self.eval_freq > 0 and not self.optimize_hyperparameters:
            # Account for the number of parallel environments
            self.eval_freq = max(self.eval_freq // self.n_envs, 1)

            if self.verbose > 0:
                print("Creating test environment")

            save_vec_normalize = SaveVecNormalizeCallback(save_freq=1, save_path=self.params_path)
            # stop_cb = StopTrainingOnRewardThreshold(reward_threshold=-50, verbose=1)
            # on_best = stop_cb
            on_best = save_vec_normalize

            eval_cb_class = EvalCallback
            if "env_wrapper" in self.saved_hyperparams and "utils.wrappers.ContinuityCostWrapper" in [
                list(wrapper.keys())[0] for wrapper in self.saved_hyperparams["env_wrapper"]
            ]:
                eval_cb_class = CustomEvalCallback
                self._use_continuity_cost = True
                print("Using ContinuityCostWrapper")

            eval_callback = eval_cb_class(
                self.create_envs(self.n_eval_envs, eval_env=True),
                callback_on_new_best=on_best,
                best_model_save_path=self.save_path,
                n_eval_episodes=self.n_eval_episodes,
                log_path=self.save_path,
                eval_freq=self.eval_freq,
                deterministic=self.deterministic_eval,
            )

            self.callbacks.append(eval_callback)

    @staticmethod
    def is_atari(env_id: str) -> bool:
        return "AtariEnv" in gym.envs.registry.env_specs[env_id].entry_point

    @staticmethod
    def is_bullet(env_id: str) -> bool:
        return "pybullet_envs" in gym.envs.registry.env_specs[env_id].entry_point

    @staticmethod
    def is_robotics_env(env_id: str) -> bool:
        return "gym.envs.robotics" in gym.envs.registry.env_specs[env_id].entry_point

    def _maybe_normalize(self, env: VecEnv, eval_env: bool) -> VecEnv:
        """
        Wrap the env into a VecNormalize wrapper if needed
        and load saved statistics when present.

        :param env:
        :param eval_env:
        :return:
        """
        # Pretrained model, load normalization
        path_ = os.path.join(os.path.dirname(self.trained_agent), self.env_id)
        path_ = os.path.join(path_, "vecnormalize.pkl")

        if os.path.exists(path_):
            print("Loading saved VecNormalize stats")
            env = VecNormalize.load(path_, env)
            # Deactivate training and reward normalization
            if eval_env:
                env.training = False
                env.norm_reward = False

        elif self.normalize:
            # Copy to avoid changing default values by reference
            local_normalize_kwargs = self.normalize_kwargs.copy()
            # Do not normalize reward for env used for evaluation
            if eval_env:
                if len(local_normalize_kwargs) > 0:
                    local_normalize_kwargs["norm_reward"] = False
                else:
                    local_normalize_kwargs = {"norm_reward": False}

            if self.verbose > 0:
                if len(local_normalize_kwargs) > 0:
                    print(f"Normalization activated: {local_normalize_kwargs}")
                else:
                    print("Normalizing input and reward")
            env = VecNormalize(env, **local_normalize_kwargs)
        return env

    @staticmethod
    def make_vec_env(
        env_id: Union[str, Type[gym.Env]],
        n_envs: int = 1,
        seed: Optional[int] = None,
        start_index: int = 0,
        monitor_dir: Optional[str] = None,
        wrapper_class: Optional[Callable[[gym.Env], gym.Env]] = None,
        env_kwargs: Optional[Dict[str, Any]] = None,
        vec_env_cls: Optional[Type[Union[DummyVecEnv, SubprocVecEnv]]] = None,
        vec_env_kwargs: Optional[Dict[str, Any]] = None,
        monitor_kwargs: Optional[Dict[str, Any]] = None,
    ) -> VecEnv:
        """
        Create a wrapped, monitored ``VecEnv``.
        By default it uses a ``DummyVecEnv`` which is usually faster
        than a ``SubprocVecEnv``.

        :param env_id: the environment ID or the environment class
        :param n_envs: the number of environments you wish to have in parallel
        :param seed: the initial seed for the random number generator
        :param start_index: start rank index
        :param monitor_dir: Path to a folder where the monitor files will be saved.
            If None, no file will be written, however, the env will still be wrapped
            in a Monitor wrapper to provide additional information about training.
        :param wrapper_class: Additional wrapper to use on the environment.
            This can also be a function with single argument that wraps the environment in many things.
        :param env_kwargs: Optional keyword argument to pass to the env constructor
        :param vec_env_cls: A custom ``VecEnv`` class constructor. Default: None.
        :param vec_env_kwargs: Keyword arguments to pass to the ``VecEnv`` class constructor.
        :param monitor_kwargs: Keyword arguments to pass to the ``Monitor`` class constructor.
        :return: The wrapped environment
        """
        env_kwargs = {} if env_kwargs is None else env_kwargs
        vec_env_kwargs = {} if vec_env_kwargs is None else vec_env_kwargs
        monitor_kwargs = {} if monitor_kwargs is None else monitor_kwargs

        def make_env(rank):
            def _init():
                if isinstance(env_id, str):
                    env = gym.make(env_id, **env_kwargs)
                else:
                    env = env_id(**env_kwargs)
                if seed is not None:
                    env.seed(seed + rank)
                    env.action_space.seed(seed + rank)
                # Wrap the env in a Monitor wrapper
                # to have additional training information
                monitor_path = os.path.join(monitor_dir, str(rank)) if monitor_dir is not None else None
                # Create the monitor folder if needed
                if monitor_path is not None:
                    os.makedirs(monitor_dir, exist_ok=True)
                # Optionally, wrap the environment with the provided wrapper
                if wrapper_class is not None:
                    env = wrapper_class(env)
                # Wrap after to have the continuity score (this may change the reward)
                # we may need 2 wrapper if we want to log true reward when
                # weight_continuity > 0
                env = Monitor(env, filename=monitor_path, **monitor_kwargs)

                return env

            return _init

        # No custom VecEnv is passed
        if vec_env_cls is None:
            # Default: use a DummyVecEnv
            vec_env_cls = DummyVecEnv

        return vec_env_cls([make_env(i + start_index) for i in range(n_envs)], **vec_env_kwargs)

    def create_envs(self, n_envs: int, eval_env: bool = False, no_log: bool = False) -> VecEnv:
        """
        Create the environment and wrap it if necessary.

        :param n_envs:
        :param eval_env: Whether is it an environment used for evaluation or not
        :param no_log: Do not log training when doing hyperparameter optim
            (issue with writing the same file)
        :return: the vectorized environment, with appropriate wrappers
        """
        # Do not log eval env (issue with writing the same file)
        log_dir = None if eval_env or no_log else self.save_path
        env_kwargs = self.env_kwargs.copy()

        # if "CAPS" in self.env_id:
        #     env_kwargs["render"] = eval_env or env_kwargs.get("render", False)
        #     env_kwargs["render_freq"] = 5 if eval_env else env_kwargs.get("render_freq", 10)

        monitor_kwargs = {}
        # Special case for GoalEnvs: log success rate too
        if "Neck" in self.env_id or self.is_robotics_env(self.env_id) or "parking-v0" in self.env_id:
            monitor_kwargs = dict(info_keywords=("is_success",))

        # Log continuity too
        if self._use_continuity_cost:
            monitor_kwargs = dict(info_keywords=("continuity_score",))

        # On most env, SubprocVecEnv does not help and is quite memory hungry
        # therefore we use DummyVecEnv by default
        env = self.make_vec_env(
            env_id=self.env_id,
            n_envs=n_envs,
            seed=self.seed,
            env_kwargs=env_kwargs,
            monitor_dir=log_dir,
            wrapper_class=self.env_wrapper,
            vec_env_cls=self.vec_env_class,
            vec_env_kwargs=self.vec_env_kwargs,
            monitor_kwargs=monitor_kwargs,
        )

        # Wrap the env into a VecNormalize wrapper if needed
        # and load saved statistics when present
        env = self._maybe_normalize(env, eval_env)

        # Optional Frame-stacking
        if self.frame_stack is not None:
            n_stack = self.frame_stack
            env = VecFrameStack(env, n_stack)
            if self.verbose > 0:
                print(f"Stacking {n_stack} frames")

        # Wrap if needed to re-order channels
        # (switch from channel last to channel first convention)
        if is_image_space(env.observation_space) and not is_image_space_channels_first(env.observation_space):
            if self.verbose > 0:
                print("Wrapping into a VecTransposeImage")
            env = VecTransposeImage(env)

        return env

    def _load_pretrained_agent(self, hyperparams: Dict[str, Any], env: VecEnv) -> BaseAlgorithm:
        # Continue training
        print("Loading pretrained agent")
        # Policy should not be changed
        del hyperparams["policy"]

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

        model = ALGOS[self.algo].load(
            self.trained_agent,
            env=env,
            seed=self.seed,
            tensorboard_log=self.tensorboard_log,
            verbose=self.verbose,
            **hyperparams,
        )

        replay_buffer_path = os.path.join(os.path.dirname(self.trained_agent), "replay_buffer.pkl")

        if os.path.exists(replay_buffer_path):
            print("Loading replay buffer")
            # `truncate_last_traj` will be taken into account only if we use HER replay buffer
            model.load_replay_buffer(replay_buffer_path, truncate_last_traj=self.truncate_last_trajectory)
        return model

    def _create_sampler(self, sampler_method: str) -> BaseSampler:
        # n_warmup_steps: Disable pruner until the trial reaches the given number of step.
        if sampler_method == "random":
            sampler = RandomSampler(seed=self.seed)
        elif sampler_method == "tpe":
            # TODO: try with multivariate=True
            sampler = TPESampler(n_startup_trials=self.n_startup_trials, seed=self.seed)
        elif sampler_method == "skopt":
            # cf https://scikit-optimize.github.io/#skopt.Optimizer
            # GP: gaussian process
            # Gradient boosted regression: GBRT
            sampler = SkoptSampler(skopt_kwargs={"base_estimator": "GP", "acq_func": "gp_hedge"})
        else:
            raise ValueError(f"Unknown sampler: {sampler_method}")
        return sampler

    def _create_pruner(self, pruner_method: str) -> BasePruner:
        if pruner_method == "halving":
            pruner = SuccessiveHalvingPruner(min_resource=1, reduction_factor=4, min_early_stopping_rate=0)
        elif pruner_method == "median":
            pruner = MedianPruner(n_startup_trials=self.n_startup_trials, n_warmup_steps=self.n_evaluations // 3)
        elif pruner_method == "none":
            # Do not prune
            pruner = MedianPruner(n_startup_trials=self.n_trials, n_warmup_steps=self.n_evaluations)
        else:
            raise ValueError(f"Unknown pruner: {pruner_method}")
        return pruner

    def objective(self, trial: optuna.Trial) -> float:

        kwargs = self._hyperparams.copy()

        # Hack to use DDPG/TD3 noise sampler
        trial.n_actions = self.n_actions
        # Hack when using HerReplayBuffer
        trial.using_her_replay_buffer = kwargs.get("replay_buffer_class") == HerReplayBuffer
        if trial.using_her_replay_buffer:
            trial.her_kwargs = kwargs.get("replay_buffer_kwargs", {})
        # Sample candidate hyperparameters
        sampled_hyperparams = HYPERPARAMS_SAMPLER[self.algo](trial)
        kwargs.update(sampled_hyperparams)

        model = ALGOS[self.algo](
            env=self.create_envs(self.n_envs, no_log=True),
            tensorboard_log=None,
            # We do not seed the trial
            seed=None,
            verbose=0,
            **kwargs,
        )

        model.trial = trial

        eval_env = self.create_envs(n_envs=self.n_eval_envs, eval_env=True)

        eval_freq = int(self.n_timesteps / self.n_evaluations)
        # Account for parallel envs
        eval_freq_ = max(eval_freq // model.get_env().num_envs, 1)
        # Use non-deterministic eval for Atari
        eval_callback = TrialEvalCallback(
            eval_env,
            trial,
            n_eval_episodes=self.n_eval_episodes,
            eval_freq=eval_freq_,
            deterministic=self.deterministic_eval,
        )

        try:
            model.learn(self.n_timesteps, callback=eval_callback)
            # Free memory
            model.env.close()
            eval_env.close()
        except (AssertionError, ValueError) as e:
            # Sometimes, random hyperparams can generate NaN
            # Free memory
            model.env.close()
            eval_env.close()
            # Prune hyperparams that generate NaNs
            print(e)
            print("============")
            print("Sampled hyperparams:")
            pprint(sampled_hyperparams)
            raise optuna.exceptions.TrialPruned()
        is_pruned = eval_callback.is_pruned
        reward = eval_callback.last_mean_reward

        del model.env, eval_env
        del model

        if is_pruned:
            raise optuna.exceptions.TrialPruned()

        return reward

    def hyperparameters_optimization(self) -> None:

        if self.verbose > 0:
            print("Optimizing hyperparameters")

        if self.storage is not None and self.study_name is None:
            warnings.warn(
                f"You passed a remote storage: {self.storage} but no `--study-name`."
                "The study name will be generated by Optuna, make sure to re-use the same study name "
                "when you want to do distributed hyperparameter optimization."
            )

        if self.tensorboard_log is not None:
            warnings.warn("Tensorboard log is deactivated when running hyperparameter optimization")
            self.tensorboard_log = None

        # TODO: eval each hyperparams several times to account for noisy evaluation
        sampler = self._create_sampler(self.sampler)
        pruner = self._create_pruner(self.pruner)

        if self.verbose > 0:
            print(f"Sampler: {self.sampler} - Pruner: {self.pruner}")

        study = optuna.create_study(
            sampler=sampler,
            pruner=pruner,
            storage=self.storage,
            study_name=self.study_name,
            load_if_exists=True,
            direction="maximize",
        )

        try:
            study.optimize(self.objective, n_trials=self.n_trials, n_jobs=self.n_jobs)
        except KeyboardInterrupt:
            pass

        print("Number of finished trials: ", len(study.trials))

        print("Best trial:")
        trial = study.best_trial

        print("Value: ", trial.value)

        print("Params: ")
        for key, value in trial.params.items():
            print(f"    {key}: {value}")

        report_name = (
            f"report_{self.env_id}_{self.n_trials}-trials-{self.n_timesteps}"
            f"-{self.sampler}-{self.pruner}_{int(time.time())}"
        )

        log_path = os.path.join(self.log_folder, self.algo, report_name)

        if self.verbose:
            print(f"Writing report to {log_path}")

        # Write report
        os.makedirs(os.path.dirname(log_path), exist_ok=True)
        study.trials_dataframe().to_csv(f"{log_path}.csv")

        # Save python object to inspect/re-use it later
        with open(f"{log_path}.pkl", "wb+") as f:
            pkl.dump(study, f)

        # Plot optimization result
        try:
            fig1 = plot_optimization_history(study)
            fig2 = plot_param_importances(study)

            fig1.show()
            fig2.show()
        except (ValueError, ImportError, RuntimeError):
            pass
