__all__ = ["Monitor", "ResultsWriter", "get_monitor_files", "load_results"]

import csv
import json
import os
import time
from glob import glob
from typing import Dict, List, Optional, Tuple, Union

import gym
import numpy as np
import pandas

from stable_baselines3.common.type_aliases import GymObs, GymStepReturn


class Monitor(gym.Wrapper):
    """
    A monitor wrapper for Gym environments, it is used to know the episode reward, length, time and other data.

    :param env: The environment
    :param filename: the location to save a log file, can be None for no log
    :param allow_early_resets: allows the reset of the environment before it is done
    :param reset_keywords: extra keywords for the reset call,
        if extra parameters are needed at reset
    :param info_keywords: extra information to log, from the information return of env.step()
    :param override_existing: appends to file if ``filename`` exists, otherwise
        override existing files (default)
    """

    EXT = "monitor.csv"

    def __init__(
        self,
        env: gym.Env,
        filename: Optional[str] = None,
        allow_early_resets: bool = True,
        reset_keywords: Tuple[str, ...] = (),
        info_keywords: Tuple[str, ...] = (),
        override_existing: bool = True,
    ):
        super().__init__(env=env)
        self.t_start = time.time()
        if filename is not None:
            self.results_writer = ResultsWriter(
                filename,
                header={"t_start": self.t_start, "env_id": env.spec and env.spec.id},
                extra_keys=reset_keywords + info_keywords,
                override_existing=override_existing,
            )
        else:
            self.results_writer = None
        self.reset_keywords = reset_keywords
        self.info_keywords = info_keywords
        self.allow_early_resets = allow_early_resets
        self.rewards = None
        self.needs_reset = True
        self.episode_returns = []
        self.episode_lengths = []
        self.episode_times = []
        self.total_steps = 0
        self.current_reset_info = {}  # extra info about the current episode, that was passed in during reset()

    def reset(self, **kwargs) -> GymObs:
        """
        Calls the Gym environment reset. Can only be called if the environment is over, or if allow_early_resets is True

        :param kwargs: Extra keywords saved for the next episode. only if defined by reset_keywords
        :return: the first observation of the environment
        """
        if not self.allow_early_resets and not self.needs_reset:
            raise RuntimeError(
                "Tried to reset an environment before done. If you want to allow early resets, "
                "wrap your env with Monitor(env, path, allow_early_resets=True)"
            )
        self.rewards = []
        self.needs_reset = False
        for key in self.reset_keywords:
            value = kwargs.get(key)
            if value is None:
                raise ValueError(f"Expected you to pass keyword argument {key} into reset")
            self.current_reset_info[key] = value
        return self.env.reset(**kwargs)

    def step(self, action: Union[np.ndarray, int]) -> GymStepReturn:
        """
        Step the environment with the given action

        :param action: the action
        :return: observation, reward, done, information
        """
        if self.needs_reset:
            raise RuntimeError("Tried to step environment that needs reset")
        observation, reward, done, info = self.env.step(action)
        self.rewards.append(reward)
        if done:
            self.needs_reset = True
            ep_rew = sum(self.rewards)
            ep_len = len(self.rewards)
            ep_info = {"r": round(ep_rew, 6), "l": ep_len, "t": round(time.time() - self.t_start, 6)}
            for key in self.info_keywords:
                ep_info[key] = info[key]
            self.episode_returns.append(ep_rew)
            self.episode_lengths.append(ep_len)
            self.episode_times.append(time.time() - self.t_start)
            ep_info.update(self.current_reset_info)
            if self.results_writer:
                self.results_writer.write_row(ep_info)
            info["episode"] = ep_info
        self.total_steps += 1
        return observation, reward, done, info

    def close(self) -> None:
        """
        Closes the environment
        """
        super().close()
        if self.results_writer is not None:
            self.results_writer.close()

    def get_total_steps(self) -> int:
        """
        Returns the total number of timesteps

        :return:
        """
        return self.total_steps

    def get_episode_rewards(self) -> List[float]:
        """
        Returns the rewards of all the episodes

        :return:
        """
        return self.episode_returns

    def get_episode_lengths(self) -> List[int]:
        """
        Returns the number of timesteps of all the episodes

        :return:
        """
        return self.episode_lengths

    def get_episode_times(self) -> List[float]:
        """
        Returns the runtime in seconds of all the episodes

        :return:
        """
        return self.episode_times


class LoadMonitorResultsError(Exception):
    """
    Raised when loading the monitor log fails.
    """

    pass


class ResultsWriter:
    """
    A result writer that saves the data from the `Monitor` class

    :param filename: the location to save a log file, can be None for no log
    :param header: the header dictionary object of the saved csv
    :param reset_keywords: the extra information to log, typically is composed of
        ``reset_keywords`` and ``info_keywords``
    :param override_existing: appends to file if ``filename`` exists, otherwise
        override existing files (default)
    """

    def __init__(
        self,
        filename: str = "",
        header: Optional[Dict[str, Union[float, str]]] = None,
        extra_keys: Tuple[str, ...] = (),
        override_existing: bool = True,
    ):
        if header is None:
            header = {}
        if not filename.endswith(Monitor.EXT):
            if os.path.isdir(filename):
                filename = os.path.join(filename, Monitor.EXT)
            else:
                filename = filename + "." + Monitor.EXT
        # Append mode when not overridding existing file
        mode = "w" if override_existing else "a"
        # Prevent newline issue on Windows, see GH issue #692
        self.file_handler = open(filename, f"{mode}t", newline="\n")
        self.logger = csv.DictWriter(self.file_handler, fieldnames=("r", "l", "t") + extra_keys)
        if override_existing:
            self.file_handler.write(f"#{json.dumps(header)}\n")
            self.logger.writeheader()

        self.file_handler.flush()

    def write_row(self, epinfo: Dict[str, Union[float, int]]) -> None:
        """
        Close the file handler

        :param epinfo: the information on episodic return, length, and time
        """
        if self.logger:
            self.logger.writerow(epinfo)
            self.file_handler.flush()

    def close(self) -> None:
        """
        Close the file handler
        """
        self.file_handler.close()


def get_monitor_files(path: str) -> List[str]:
    """
    get all the monitor files in the given path

    :param path: the logging folder
    :return: the log files
    """
    return glob(os.path.join(path, "*" + Monitor.EXT))


def load_results(path: str) -> pandas.DataFrame:
    """
    Load all Monitor logs from a given directory path matching ``*monitor.csv``

    :param path: the directory path containing the log file(s)
    :return: the logged data
    """
    monitor_files = get_monitor_files(path)
    if len(monitor_files) == 0:
        raise LoadMonitorResultsError(f"No monitor files of the form *{Monitor.EXT} found in {path}")
    data_frames, headers = [], []
    for file_name in monitor_files:
        with open(file_name) as file_handler:
            first_line = file_handler.readline()
            assert first_line[0] == "#"
            header = json.loads(first_line[1:])
            data_frame = pandas.read_csv(file_handler, index_col=None)
            headers.append(header)
            data_frame["t"] += header["t_start"]
        data_frames.append(data_frame)
    data_frame = pandas.concat(data_frames)
    data_frame.sort_values("t", inplace=True)
    data_frame.reset_index(inplace=True)
    data_frame["t"] -= min(header["t_start"] for header in headers)
    return data_frame
