import logging
from typing import Any, Dict  # , Tuple

# import numpy.typing as npt
import torch as th
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.freqai.RL.BaseReinforcementLearningModel import (BaseReinforcementLearningModel,
                                                                make_env)
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen

from pathlib import Path

logger = logging.getLogger(__name__)


class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
    """
    User created Reinforcement Learning Model prediction model.
    """

    def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):

        train_df = data_dictionary["train_features"]
        total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)

        # model arch
        policy_kwargs = dict(activation_fn=th.nn.ReLU,
                             net_arch=[512, 512, 256])

        model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
                                tensorboard_log=Path(dk.full_path / "tensorboard"),
                                **self.freqai_info['model_training_parameters']
                                )

        model.learn(
            total_timesteps=int(total_timesteps),
            callback=self.eval_callback
        )

        if Path(dk.data_path / "best_model.zip").is_file():
            logger.info('Callback found a best model.')
            best_model = self.MODELCLASS.load(dk.data_path / "best_model")
            return best_model

        logger.info('Couldnt find best model, using final model instead.')

        return model

    def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test, dk):
        """
        If user has particular environment configuration needs, they can do that by
        overriding this function. In the present case, the user wants to setup training
        environments for multiple workers.
        """
        train_df = data_dictionary["train_features"]
        test_df = data_dictionary["test_features"]
        eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)

        # environments
        if not self.train_env:
            env_id = "train_env"
            num_cpu = int(self.freqai_info["rl_config"]["thread_count"] / 2)
            self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
                                            self.reward_params, self.CONV_WIDTH,
                                            config=self.config) for i
                                            in range(num_cpu)])

            eval_env_id = 'eval_env'
            self.eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test,
                                           self.reward_params, self.CONV_WIDTH, monitor=True,
                                           config=self.config) for i
                                           in range(num_cpu)])
            self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
                                              render=False, eval_freq=eval_freq,
                                              best_model_save_path=dk.data_path)
        else:
            self.train_env.env_method('reset')
            self.eval_env.env_method('reset')
            self.train_env.env_method('reset_env', train_df, prices_train,
                                      self.CONV_WIDTH, self.reward_params)
            self.eval_env.env_method('reset_env', train_df, prices_train,
                                     self.CONV_WIDTH, self.reward_params)
            self.eval_callback.__init__(self.eval_env, deterministic=True,
                                        render=False, eval_freq=eval_freq,
                                        best_model_save_path=dk.data_path)