# Copyright (c) Facebook, Inc. and its affiliates.

import os
import subprocess
import random
from typing import Tuple

import gym
import numpy as np
import pkg_resources
from nle import _pynethack, nethack
from nle.nethack.nethack import SCREEN_DESCRIPTIONS_SHAPE, OBSERVATION_DESC
from nle.env.base import FULL_ACTIONS, NLE_SPACE_ITEMS
from nle.env.tasks import NetHackStaircase
from minihack.wiki import NetHackWiki
from minihack.tiles import GlyphMapper

PATH_DAT_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "dat")
LIB_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "lib")
PATCH_SCRIPT = os.path.join(
    pkg_resources.resource_filename("minihack", "scripts"),
    "mh_patch_nhdat.sh",
)
MH_FULL_ACTIONS = list(FULL_ACTIONS)
try:
    MH_FULL_ACTIONS.remove(nethack.MiscDirection.UP)
except ValueError:
    pass
MH_FULL_ACTIONS = tuple(MH_FULL_ACTIONS)
HACKDIR = pkg_resources.resource_filename("nle", "nethackdir")

RGB_MAX_VAL = 255
N_TILE_PIXEL = 16
agent_view_sizes = [9, 7, 5, 3]
#agent_view_sizes = [13, 9, 7, 5]

MH_NETHACKOPTIONS = (
    "color",  # Display color for different monsters, objects, etc
    "showexp",  # Display the experience points on the status line
    "nobones",  # Disallow saving and loading bones files
    "nolegacy",  # Not display an introductory message when starting the game
    "nocmdassist",  # No command assistance
    "disclose:+i +a +v +g +c +o",  # End of game prompt replies
    "runmode:teleport",  # Update the map after movement has finished
    "mention_walls",  # Give feedback when walking against a wall
    "nosparkle",  # Not display sparkly effect for resisted magical attacks
    "showscore",  # Shows approximate accumulated score on the bottom line
)
# Autopickup on by default (all items)
# Manually adding "!autopickup" basen of env flag

MINIHACK_SPACE_FUNCS = {
    "glyphs_crop": lambda x, y: gym.spaces.Box(
        low=0,
        high=nethack.MAX_GLYPH,
        shape=(x, y),
        dtype=OBSERVATION_DESC["glyphs"]["dtype"],
    ),
    "chars_crop": lambda x, y: gym.spaces.Box(
        low=0,
        high=255,
        shape=(x, y),
        dtype=OBSERVATION_DESC["chars"]["dtype"],
    ),
    "colors_crop": lambda x, y: gym.spaces.Box(
        low=0,
        high=15,
        shape=(x, y),
        dtype=OBSERVATION_DESC["colors"]["dtype"],
    ),
    "specials_crop": lambda x, y: gym.spaces.Box(
        low=0,
        high=255,
        shape=(x, y),
        dtype=OBSERVATION_DESC["specials"]["dtype"],
    ),
    "tty_chars_crop": lambda x, y: gym.spaces.Box(
        low=0,
        high=255,
        shape=(x, y),
        dtype=OBSERVATION_DESC["tty_chars"]["dtype"],
    ),
    "tty_colors_crop": lambda x, y: gym.spaces.Box(
        low=0,
        high=31,
        shape=(x, y),
        dtype=OBSERVATION_DESC["tty_colors"]["dtype"],
    ),
    "screen_descriptions_crop": lambda x, y: gym.spaces.Box(
        low=0,
        high=127,
        shape=(x, y, _pynethack.nethack.NLE_SCREEN_DESCRIPTION_LENGTH),
        dtype=OBSERVATION_DESC["screen_descriptions"]["dtype"],
    ),
    "pixel_crop": lambda x, y: gym.spaces.Box(
        low=0,
        high=RGB_MAX_VAL,
        shape=(x * N_TILE_PIXEL, y * N_TILE_PIXEL, 3),
        dtype=np.uint8,
    ),
}

MH_DEFAULT_OBS_KEYS = [
    "glyphs",
    "chars",
    "colors",
    "specials",
    "glyphs_crop",
    "chars_crop",
    "colors_crop",
    "specials_crop",
    "blstats",
    "message",
]


class MiniHack(NetHackStaircase):
    """MiniHack base class.

    All MiniHack environments are derived from this class, which itself is
    derived from NLE base class.

    Note that this class itself is not used for creating new environment
    instances. Instead, ``MiniHackNavigation`` and ``MiniHackSkill`` provide a
    more convenient interface for doing this, both of which are directly
    derived from MiniHack for specific types of environments.
    """

    def __init__(
        self,
        *args,
        des_file: str,
        reward_win=1,
        reward_lose=0,
        obs_crop_h=9,
        obs_crop_w=9,
        obs_crop_pad=0,
        reward_manager=None,
        use_wiki=False,
        autopickup=True,
        pet=False,
        observation_keys=MH_DEFAULT_OBS_KEYS,
        seeds=None,
        **kwargs,
    ):
        """Constructs a new MiniHack environment.

        Args:
            des_file (str):
                The description file for the environment.
            reward_win (float):
                The reward received upon successfully completing an episode.
                Defaults to 1.
            reward_lose (float):
                The reward received upon death or aborting. Defaults to 1.
            obs_crop_h (int):
                The height of agent-centred cropped observation. Defaults to 9.
            obs_crop_w (int):
                The width of agent-centred cropped observation. Defaults to 9.
            obs_crop_pad (int):
                The padding for agent-centred cropped observation.
                Defaults to 0.
            reward_manager (RewardManager or None):
                The reward manager that describes the custom reward function of
                the agent. If None, the goal of the agent is to reach the stair
                down. Defaults to None.
            use_wiki (bool):
                Whether to use the NetHack wiki. Defaults to False.
            autopickup (bool):
                Turning autopickup on or off. Defaults to True.
            pet (bool):
                Whether to include the pet. Defaults to False.
            observation_keys (list):
                The keys of observations returned after every timestep by the
                environment as a dictionary. Defaults to
                ``minihack.base.MH_DEFAULT_OBS_KEYS``.
            seeds (list or None):
                A list of random seeds for sampling episodes. If none, the
                entire level distribution is used. Defaults to None.
            penalty_mode (str):
                The name of the mode for calculating the time step penalty.
                Can be ``constant``, ``exp``, ``square``, ``linear``, or
                ``always``. Defaults to ``constant``. Inherited from
                `NetHackScore`.
            penalty_step (float):
                A constant applied to amount of frozen steps. Defaults to
                -0.01. Inherited from `NetHackScore`.
            penalty_time (float):
                A constant applied to amount of frozen steps. Defaults to -0.0.
                Inherited from `NetHackScore`.
            savedir (str or None): path to save ttyrecs (game recordings) into.
                Defaults to None, which doesn't save any data. Otherwise,
                interpreted as a path to a new or existing directory.
                If "" (empty string), NLE choses a unique directory name.
                Inherited from `NLE`.
            character (str):
                Name of character. Defaults to "mon-hum-neu-mal". Interited
                from `NLE`.
            max_episode_steps (int):
                maximum amount of steps allowed before the game is forcefully
                quit.  In such cases, ``info["end_status"]`` ill be equal to
                ``StepStatus.ABORTED``. Defaults to 5000. Inherited from `NLE`.
            actions (list):
                list of actions. If None, the full action space will
                be used, i.e. ``nle.nethack.ACTIONS``. Defaults to None.
                Inherited from `NLE`.
            wizard (bool):
                activate wizard mode. Defaults to False.
            allow_all_yn_questions (bool):
                If set to True, no y/n questions in step() are declined.
                If set to False, only elements of SKIP_EXCEPTIONS are not
                declined. Defaults to False. Inherited from `NLE`.
            allow_all_modes (bool):
                If set to True, do not decline menus, text input or auto
                'MORE'. If set to False, only skip click through 'MORE'
                on death. Inherited from `NLE`.
        """
        # NetHack options
        options: Tuple = MH_NETHACKOPTIONS
        if not autopickup:
            options += ("!autopickup",)
        if not pet:
            options += ("pettype:none",)
        kwargs["options"] = kwargs.pop("options", options)
        # Actions space - move only
        kwargs["actions"] = kwargs.pop("actions", MH_FULL_ACTIONS)

        # Enter Wizard mode - turned off by default
        kwargs["wizard"] = kwargs.pop("wizard", False)
        # Allowing one-letter menu questions
        kwargs["allow_all_yn_questions"] = kwargs.pop(
            "allow_all_yn_questions", True
        )
        # Episode limit
        kwargs["max_episode_steps"] = kwargs.pop("max_episode_steps", 200)
        # Not saving NLE data by detauls
        kwargs["savedir"] = kwargs.pop("savedir", None)
        # Not spawning random monsters
        kwargs["spawn_monsters"] = kwargs.pop("spawn_monsters", False)

        # MiniHack's observation keys are kept separate
        self._minihack_obs_keys = list(observation_keys)
        # Handle RGB pixel observations
        if any("pixel" in key for key in self._minihack_obs_keys):
            self._glyph_mapper = GlyphMapper()
            # Make sure glyphs_crop is there
            if (
                "pixel_crop" in self._minihack_obs_keys
                and "glyphs_crop" not in self._minihack_obs_keys
            ):
                self._minihack_obs_keys.append("glyphs_crop")

        self.reward_manager = reward_manager
        if self.reward_manager is not None:
            self.reward_manager.reset()

        self._level_seeds = seeds

        super().__init__(*args, **kwargs)

        # Patch the nhdat library by compling the given .des file
        self.update(des_file)

        self.obs_crop_h = obs_crop_h
        self.obs_crop_w = obs_crop_w
        self.obs_crop_pad = obs_crop_pad

        assert self.obs_crop_h % 2 == 1
        assert self.obs_crop_w % 2 == 1

        self.reward_win = reward_win
        self.reward_lose = reward_lose

        self._scr_descr_index = self._observation_keys.index(
            "screen_descriptions"
        )
        self.observation_space = gym.spaces.Dict(
            self._get_obs_space_dict(dict(NLE_SPACE_ITEMS))
        )

        self.use_wiki = use_wiki
        if self.use_wiki:
            self.wiki = NetHackWiki()

    def _get_obs_space_dict(self, space_dict):
        obs_space_dict = {}
        for key in self._minihack_obs_keys:
            if key in space_dict.keys():
                obs_space_dict[key] = space_dict[key]
            elif key in MINIHACK_SPACE_FUNCS.keys():
                space_func = MINIHACK_SPACE_FUNCS[key]
                obs_space_dict[key] = space_func(
                    self.obs_crop_h, self.obs_crop_w
                )
            else:
                if "pixel" in self._minihack_obs_keys:
                    d_shape = OBSERVATION_DESC["glyphs"]["shape"]
                    shape = (
                        d_shape[0] * N_TILE_PIXEL,
                        d_shape[1] * N_TILE_PIXEL,
                        3,
                    )
                    obs_space_dict["pixel"] = gym.spaces.Box(
                        low=0,
                        high=RGB_MAX_VAL,
                        shape=shape,
                        dtype=np.uint8,
                    )
                else:
                    raise ValueError(
                        f'Observation key "{key}" is not supported'
                    )

        return obs_space_dict

    def reset(self, *args, **kwargs):
        if self.reward_manager is not None:
            self.reward_manager.reset()
        if self._level_seeds is not None:
            seed = random.choice(self._level_seeds)
            self.seed(seed, seed, reseed=False)
        return super().reset(*args, **kwargs)

    def _reward_fn(self, last_observation, action, observation, end_status):
        """Use reward_manager to collect reward calculated in _is_episode_end,
        or revert to standard sparse reward.
        """
        del action  # Unused
        if self.reward_manager is not None:
            reward = self.reward_manager.collect_reward()
        else:
            if end_status == self.StepStatus.TASK_SUCCESSFUL:
                reward = self.reward_win
            elif end_status == self.StepStatus.RUNNING:
                reward = 0
            else:  # death or aborted
                reward = self.reward_lose
        return reward + self._get_time_penalty(last_observation, observation)

    def step(self, action: int):
        self._previous_obs = tuple(a.copy() for a in self.last_observation)
        self._previous_action = action
        # Within this call, _is_episode_end is called and then _reward_fn,
        # both using self.reward_manager
        return super().step(action)

    def _is_episode_end(self, observation):
        if self.reward_manager is not None:
            # This also calculates reward, to be collected in _reward_fn by
            # collect_reward
            result = self.reward_manager.check_episode_end_call(
                self, self._previous_obs, self._previous_action, observation
            )
            if result:
                return self.StepStatus.TASK_SUCCESSFUL

        # Revert to staircase check (so we always end if we reach it)
        return super()._is_episode_end(observation)

    def update(self, des_file):
        """Update the current environment by replacing its description file."""
        self._patch_nhdat(des_file)

    def _patch_nhdat(self, des_file):
        """Patch the nhdat library. This includes compiling the given
        description file and replacing the new nhdat file in the temporary
        hackdir directory of the environment.
        """
        if not des_file.endswith(".des"):
            fpath = os.path.join(self.env._vardir, "mylevel.des")
            # If the des-file is passed as a string
            with open(fpath, "w") as f:
                f.writelines(des_file)
            des_file = fpath

        # Use the .des file if exists, otherwise search in minihack directory
        des_path = os.path.abspath(des_file)
        if not os.path.exists(des_path):
            des_path = os.path.abspath(os.path.join(PATH_DAT_DIR, des_file))
        if not os.path.exists(des_path):
            print(
                "{} file doesn't exist. Please provide a path to a valid .des \
                    file".format(
                    des_path
                )
            )
        try:
            _ = subprocess.call(
                [
                    PATCH_SCRIPT,
                    self.env._vardir,
                    HACKDIR,
                    LIB_DIR,
                    des_path,
                ]
            )
        except subprocess.CalledProcessError as e:
            raise RuntimeError(f"Couldn't patch the nhdat file.\n{e}")

    def _get_observation(self, observation):
        # Filter out observations that we don't need
        observation = super()._get_observation(observation)
        obs_dict = {}
        obs_dict_arr = {}

        for key in self._minihack_obs_keys:
            if "pixel" in key:
                continue
            if key in self._observation_keys:
                msg_arr = []
                for i in range(len(agent_view_sizes) + 1):
                    msg_arr.append(observation[key])

                obs_dict_arr[key] = msg_arr
            elif key in MINIHACK_SPACE_FUNCS.keys():
                orig_key = key.replace("_crop", "")
                if "tty" in orig_key:
                    loc = observation["tty_cursor"][::-1]
                    #obs_dict[key] = self._crop_observation(
                    #    observation[orig_key], loc
                    #)
                    obs_arr = []
                    for i in range(len(agent_view_sizes)):
                        obs_crop = self._crop_observation(
                            observation[orig_key], loc, agent_view_sizes[i]
                        )

                        obs_arr.append(obs_crop)

                    obs_detail = self._get_detail_view(observation[orig_key], loc)
                    obs_arr.append(obs_detail)

                    obs_dict_arr[key] = obs_arr
                else:
                    loc = observation["blstats"][:2]

                    obs_arr = []
                    for i in range(len(agent_view_sizes)):
                        obs_crop = self._crop_observation(
                            observation[orig_key], loc, agent_view_sizes[i]
                        )
                        obs_arr.append(obs_crop)

                    obs_detail = self._get_detail_view(observation[orig_key], loc)
                    obs_arr.append(obs_detail)

                    obs_dict_arr[key] = obs_arr


        if "pixel" in self._minihack_obs_keys:
            obs_dict["pixel"] = self._glyph_mapper.to_rgb(
                observation["glyphs"]
            )

        if "pixel_crop" in self._minihack_obs_keys:
            px_arr = []
            for i in range(len(agent_view_sizes) + 1):
                #print(i)
                obs_crop = self._glyph_mapper.to_rgb(
                    obs_dict_arr["glyphs_crop"][i]
                )

                px_arr.append(obs_crop)

                obs_dict_arr["pixel_crop"] = px_arr

            return obs_dict_arr

        return obs_dict

    def _crop_observation(self, obs, loc, view_size):
        dh = view_size // 2
        dw = view_size // 2


        (x, y) = loc
        x += dw
        y += dh

        obs = np.pad(
            obs,
            pad_width=(dw, dh),
            mode="constant",
            constant_values=self.obs_crop_pad,
        )
        return obs[y - dh : y + dh + 1, x - dw : x + dw + 1]

    def _get_detail_view(self, obs, loc):
        dh = 2 // 2
        dw = 1 // 2


        (x, y) = loc
        x += dw
        y += dh

        obs = np.pad(
            obs,
            pad_width=(dw, dh),
            mode="constant",
            constant_values=self.obs_crop_pad,
        )
        return obs[y - dh : y + 1, x - dw : x + dw + 1]

    def key_in_inventory(self, name):
        """Returns key of the given object in the inventory.

        Args:
            name (str): Name of the object.
        Returns:
            str: the key of the first item in the inventory that includes the
            argument name as a substring. Returns None if not found.
        """
        assert "inv_strs" in self._observation_keys
        assert "inv_letters" in self._observation_keys

        inv_strs_index = self._observation_keys.index("inv_strs")
        inv_letters_index = self._observation_keys.index("inv_letters")

        inv_strs = self.last_observation[inv_strs_index]
        inv_letters = self.last_observation[inv_letters_index]

        for letter, line in zip(inv_letters, inv_strs):
            if np.all(line == 0):
                break
            if name in line.tobytes().decode("utf-8"):
                return letter.tobytes().decode("utf-8")

        return None

    def _index_to_dir_action(self, index):
        """Returns the ASCII code for direction corresponding to given
        index in reshaped vector of adjacent 9 tiles (None for agent's
        position).
        """
        assert 0 <= index < 9
        index_to_dir_dict = {
            0: ord("y"),
            1: ord("k"),
            2: ord("u"),
            3: ord("h"),
            4: None,
            5: ord("l"),
            6: ord("b"),
            7: ord("j"),
            8: ord("n"),
        }
        return index_to_dir_dict[index]

    def get_object_direction(self, name, observation=None):
        """Find the game direction of the (first) object in the neighboring
        nine tiles that contains the given name in its description.

        Args:
            name (str): Name of the object.
            observation (dict): Agent observation.

        Returns:
            int: The index of the direction. None if not found.
        """
        if observation is None:
            observation = self.last_observation

        neighbors = self.get_neighbor_descriptions(observation)
        for i, tile_description in enumerate(neighbors):
            if name in tile_description:
                return self._index_to_dir_action(i)
        return None

    def get_neighbor_descriptions(self, observation=None):
        """Returns the descriptions of nine neighboring grids around the
        agent.
        """
        if observation is None:
            observation = self.last_observation
        blstats = observation[self._blstats_index]
        x, y = blstats[:2]

        neighbors = [
            self.get_screen_description(i, j, observation)
            for j in range(y - 1, y + 2)
            for i in range(x - 1, x + 2)
        ]
        return neighbors

    def get_neighbor_wiki_pages(self, observation=None):
        """Returns the page contents of the neighboring objects from NetHack
        wiki.
        """
        if not self.use_wiki:
            raise NotImplementedError(
                "use_wiki is set to false - initialise your environment with"
                "use_wiki=True to use the wiki"
            )
        neighbors_descriptions = self.get_neighbor_descriptions(observation)
        neighbor_pages = [
            self.wiki.get_page_text(description)
            for description in neighbors_descriptions
        ]
        return neighbor_pages

    def get_screen_description(self, x, y, observation=None):
        """Returns the description of the screen on (x,y) coordinates."""
        if observation is None:
            observation = self.last_observation

        des_arr = observation[self._scr_descr_index][y, x]
        symb_len = np.where(des_arr == 0)[0][0]

        return des_arr[:symb_len].tobytes().decode("utf-8")

    def get_screen_wiki_page(self, x, y, observation=None):
        """Returns the wiki page matching the object on (x,y) coordinates."""
        if not self.use_wiki:
            raise NotImplementedError(
                "use_wiki is set to false - initialise your environment with"
                "use_wiki=True to use the wiki"
            )
        description = self.get_screen_description(x, y, observation)
        return self.wiki.get_page_text(description)

    def screen_contains(self, name, observation=None):
        """Whether an object with the given name is visible on the screen, i.e.
        included in the screen descriptions of the observation dictionary.

        Args:
            name (str): Name of the object or monster.
            observation (dict): Agent observation.

        Returns:
            bool: True if the name is contained on the screen, False otherwise.
        """
        if observation is None:
            observation = self.last_observation

        y, x = SCREEN_DESCRIPTIONS_SHAPE[0:2]
        for j in range(y):
            for i in range(x):
                des_arr = observation[self._scr_descr_index][j, i]
                symb_len = np.where(des_arr == 0)[0][0]
                if name in des_arr[:symb_len].tobytes().decode("utf-8"):
                    return True
        return False
