"""MiniHack environment wrapper with BFS oracle and shaped rewards.

Ported from minihack_reference/src/env.py. Provides dual-stream
observations (9x9 local crop + 21x79 global map), a multi-tier BFS
oracle, and reward shaping (win bonus, BFS progress, exploration, step
penalty).
"""

from __future__ import annotations

import collections
import logging
from types import SimpleNamespace

import gymnasium as gym
import minihack  # noqa: F401 — registers MiniHack envs
import numpy as np

logger = logging.getLogger(__name__)

# Suppress noisy NLE INFO spam ("Not saving any NLE data." on every env create)
logging.getLogger("nle.env.base").setLevel(logging.WARNING)


# ── Staircase detection ──────────────────────────────────────────────


def find_staircase_from_glyphs(global_obs: np.ndarray) -> np.ndarray:
    """Locate the staircase '>' in the global glyph map.

    Args:
        global_obs: Glyph map, shape ``[B, H, W]`` or ``[H, W]``.

    Returns:
        Normalised ``(row/H, col/W)`` coords, shape ``[B, 2]``
        (float32). ``(-1, -1)`` when not visible.
    """
    squeeze = global_obs.ndim == 2
    if squeeze:
        global_obs = global_obs[np.newaxis]
    B, H, W = global_obs.shape
    coords = np.full((B, 2), -1.0, dtype=np.float32)
    for b in range(B):
        is_stair = (
            (global_obs[b] == 62)
            | (global_obs[b] == 2310)
            | (global_obs[b] == 2368)
            | (global_obs[b] == 2383)
        )
        positions = np.argwhere(is_stair)
        if positions.shape[0] > 0:
            coords[b, 0] = positions[0, 0] / max(1, H - 1)
            coords[b, 1] = positions[0, 1] / max(1, W - 1)
    return coords


# ── Environment wrapper ──────────────────────────────────────────────


class AdvancedObservationEnv(gym.Env):
    """MiniHack wrapper with dual-stream obs, BFS oracle, shaped rewards.

    Observations are ``(local_crop, global_map)`` where
    ``local_crop`` is a ``[crop_size, crop_size]`` glyph window centred
    on the agent and ``global_map`` is the full ``[21, 79]`` glyph grid.

    Args:
        env_id: MiniHack registry ID.
        des_file: Optional ``.des`` file content (for custom levels).
        cfg: Configuration namespace with ``crop_size``, ``action_dim``,
            ``pad_token``, ``map_h``, ``map_w``.
    """

    _UNWALKABLE = frozenset({32, 45, 124, 125})  # space, -, |, }
    _CLOSED_DOOR = 43  # '+'
    _DIR_MAP = {(-1, 0): 0, (0, 1): 1, (1, 0): 2, (0, -1): 3}
    _CARDINAL = [(-1, 0), (0, 1), (1, 0), (0, -1)]

    def __init__(
        self,
        env_id: str,
        des_file: str | None,
        cfg: SimpleNamespace,
    ) -> None:
        super().__init__()
        self.env_id = env_id
        self._cfg = cfg
        self._crop_half = cfg.crop_size // 2

        obs_keys = ("glyphs", "chars", "pixel")
        if des_file is not None:
            self._inner = gym.make(
                "MiniHack-Navigation-Custom-v0",
                des_file=des_file,
                observation_keys=obs_keys,
            )
        else:
            self._inner = gym.make(
                env_id, observation_keys=obs_keys,
            )

        self.observation_space = gym.spaces.Box(
            low=0, high=6000,
            shape=(cfg.crop_size, cfg.crop_size),
            dtype=np.int16,
        )
        self.action_space: gym.spaces.Discrete = gym.spaces.Discrete(cfg.action_dim)

        self._visited: set[tuple[int, int]] = set()
        self._prev_bfs_dist: int | None = None
        self.last_raw_obs: dict | None = None

    # ── gym.Env interface ────────────────────────────────────────────

    def reset(
        self, seed: int | None = None, options: dict | None = None,
    ) -> tuple[tuple[np.ndarray, np.ndarray], dict]:
        """Reset environment and tracking state.

        Args:
            seed: Optional RNG seed.
            options: Passed through to the inner env.

        Returns:
            ``((local_crop, global_map), info)``
        """
        obs, info = self._inner.reset(seed=seed, options=options)
        self.last_raw_obs = obs
        self._prev_bfs_dist = self._get_bfs_distance(obs)
        self._visited = set()
        agent_pos = self._get_agent_pos(obs)
        if agent_pos is not None:
            self._visited.add(agent_pos)
        return self._get_obs(obs), info

    def step(
        self, action: int,
    ) -> tuple[tuple[np.ndarray, np.ndarray], float, bool, bool, dict]:
        """Execute one environment step with shaped reward.

        Reward shaping:
        - Win bonus: ``+20.0``
        - BFS progress toward staircase: ``+0.5 * (prev - curr)``
        - New-tile exploration: ``+0.05``
        - Step penalty: ``-0.01``

        Args:
            action: Integer action in ``[0, action_dim)``.

        Returns:
            ``(obs, shaped_reward, terminated, truncated, info)``
        """
        inner_n = self._inner.action_space.n
        if action >= inner_n:
            action = action % inner_n

        obs, raw_reward, terminated, truncated, info = self._inner.step(action)
        self.last_raw_obs = obs
        reward = float(raw_reward)

        # Win bonus
        if terminated and reward > 0:
            info["won"] = True
            reward += 20.0
        else:
            info["won"] = False

        # BFS shaping
        curr_dist = self._get_bfs_distance(obs)
        if curr_dist is not None and self._prev_bfs_dist is not None:
            reward += (self._prev_bfs_dist - curr_dist) * 0.5
            self._prev_bfs_dist = curr_dist

        # Exploration bonus
        agent_pos = self._get_agent_pos(obs)
        if agent_pos is not None and agent_pos not in self._visited:
            reward += 0.05
            self._visited.add(agent_pos)

        # Step penalty
        reward -= 0.01

        return self._get_obs(obs), reward, terminated, truncated, info

    @property
    def unwrapped(self):
        """Access the inner MiniHack env."""
        return self._inner.unwrapped

    def close(self) -> None:
        """Close the inner environment."""
        self._inner.close()

    # ── Observation helpers ──────────────────────────────────────────

    def _get_obs(
        self, obs: dict,
    ) -> tuple[np.ndarray, np.ndarray]:
        """Extract dual-stream observation.

        Args:
            obs: Raw NLE observation dict.

        Returns:
            ``(local_crop [crop,crop], global_map [H,W])`` as int16.
        """
        return self._get_crop(obs), obs["glyphs"].copy().astype(np.int16)

    def _get_crop(self, obs: dict) -> np.ndarray:
        """Crop local glyph window centred on agent.

        Args:
            obs: Raw NLE observation dict.

        Returns:
            ``[crop_size, crop_size]`` int16 array.
        """
        glyphs = obs["glyphs"]
        chars = obs["chars"]
        agent_pos = np.argwhere(chars == ord("@"))
        cs = self._cfg.crop_size
        if len(agent_pos) == 0:
            return np.full((cs, cs), self._cfg.pad_token, dtype=np.int16)
        y, x = agent_pos[0]
        h = self._crop_half
        padded = np.pad(
            glyphs, h, mode="constant",
            constant_values=self._cfg.pad_token,
        )
        return padded[y:y + cs, x:x + cs].astype(np.int16)

    def _get_agent_pos(self, obs: dict) -> tuple[int, int] | None:
        """Find agent '@' position in the chars grid.

        Args:
            obs: Raw NLE observation dict.

        Returns:
            ``(row, col)`` or ``None``.
        """
        chars = obs["chars"]
        pos = np.argwhere(chars == ord("@"))
        return tuple(pos[0]) if len(pos) > 0 else None

    def _get_bfs_distance(self, obs: dict) -> int | None:
        """BFS shortest-path distance from agent to staircase.

        Args:
            obs: Raw NLE observation dict.

        Returns:
            Integer distance or ``None`` if unreachable / not visible.
        """
        chars = obs["chars"]
        start = np.argwhere(chars == ord("@"))
        target = np.argwhere(chars == ord(">"))
        if len(start) == 0 or len(target) == 0:
            return None
        start = tuple(start[0])
        target = tuple(target[0])
        if start == target:
            return 0
        queue: collections.deque = collections.deque([(start, 0)])
        visited = {start}
        while queue:
            (r, c), dist = queue.popleft()
            if (r, c) == target:
                return dist
            for dr, dc in self._CARDINAL:
                nr, nc = r + dr, c + dc
                if (
                    0 <= nr < self._cfg.map_h
                    and 0 <= nc < self._cfg.map_w
                    and (nr, nc) not in visited
                    and chars[nr, nc] not in self._UNWALKABLE
                ):
                    visited.add((nr, nc))
                    queue.append(((nr, nc), dist + 1))
        return None

    # ── BFS Oracle ───────────────────────────────────────────────────

    def get_oracle_action(self, obs: dict) -> int:
        """5-tier BFS oracle action.

        Priority:
        1. Kick adjacent closed door.
        2. BFS to staircase '>'.
        3. BFS to frontier (adjacent to unexplored space).
        4. BFS to farthest reachable tile.
        5. Random cardinal direction.

        Args:
            obs: Raw NLE observation dict (needs ``'chars'`` key).

        Returns:
            Action index in ``[0, action_dim)``.
        """
        if obs is None:
            return 0
        chars = obs["chars"]
        start = np.argwhere(chars == ord("@"))
        if len(start) == 0:
            return np.random.randint(0, 4)
        start = tuple(start[0])
        target_list = np.argwhere(chars == ord(">"))

        # 1. Adjacent closed door → kick
        for dr, dc in self._CARDINAL:
            nr, nc = start[0] + dr, start[1] + dc
            if (
                0 <= nr < self._cfg.map_h
                and 0 <= nc < self._cfg.map_w
                and chars[nr, nc] == self._CLOSED_DOOR
            ):
                return 11  # KICK

        # BFS to gather reachable tiles + check staircase
        queue: collections.deque = collections.deque([(start, [])])
        visited = {start}
        reachable: list[tuple[tuple[int, int], list[tuple[int, int]]]] = []
        target_path: list[tuple[int, int]] | None = None

        while queue:
            (r, c), path = queue.popleft()
            reachable.append(((r, c), path))
            for t_r, t_c in target_list:
                if r == t_r and c == t_c:
                    target_path = path
                    break
            if target_path is not None:
                break
            for dr, dc in self._CARDINAL:
                nr, nc = r + dr, c + dc
                if (
                    0 <= nr < self._cfg.map_h
                    and 0 <= nc < self._cfg.map_w
                    and (nr, nc) not in visited
                ):
                    ch = chars[nr, nc]
                    if ch not in self._UNWALKABLE and ch != self._CLOSED_DOOR:
                        visited.add((nr, nc))
                        queue.append(((nr, nc), path + [(dr, dc)]))

        # 2. Path to staircase
        if target_path:
            return self._DIR_MAP.get(target_path[0], 0)

        # 3. Frontier exploration — tiles adjacent to unexplored space
        frontier: list[list[tuple[int, int]]] = []
        for (r, c), path in reachable:
            if not path:
                continue
            for dr, dc in self._CARDINAL:
                nr, nc = r + dr, c + dc
                if (
                    0 <= nr < self._cfg.map_h
                    and 0 <= nc < self._cfg.map_w
                    and chars[nr, nc] == 32
                ):
                    frontier.append(path)
                    break
        if frontier:
            frontier.sort(key=len)
            return self._DIR_MAP.get(frontier[0][0], 0)

        # 4. Farthest reachable tile
        if reachable:
            reachable.sort(key=lambda x: len(x[1]), reverse=True)
            farthest = reachable[0][1]
            if farthest:
                return self._DIR_MAP.get(farthest[0], 0)

        # 5. Random cardinal
        return np.random.randint(0, 4)


# ── Factory ──────────────────────────────────────────────────────────


def make_env(
    env_id: str,
    des_file: str | None,
    cfg: SimpleNamespace,
) -> AdvancedObservationEnv:
    """Create a wrapped MiniHack environment.

    Args:
        env_id: MiniHack registry ID.
        des_file: Optional ``.des`` file content.
        cfg: Configuration namespace.

    Returns:
        Wrapped environment.
    """
    return AdvancedObservationEnv(env_id, des_file, cfg)


def collect_oracle_trajectory(
    env_id: str,
    seed: int,
    cfg: SimpleNamespace,
    max_steps: int = 500,
) -> dict | None:
    """Roll out the BFS oracle on a single episode.

    Args:
        env_id: MiniHack registry ID.
        seed: RNG seed for the episode.
        cfg: Configuration namespace.
        max_steps: Maximum episode length.

    Returns:
        ``{"local": [T,9,9], "global": [T,21,79],
          "actions": [T], "env_id": str}`` on success,
        or ``None`` on failure.
    """
    env = make_env(env_id, None, cfg)
    try:
        (local, glb), _info = env.reset(seed=seed)
        locals_list = [local]
        globals_list = [glb]
        actions_list: list[int] = []

        for _ in range(max_steps):
            action = env.get_oracle_action(env.last_raw_obs)
            actions_list.append(action)
            (local, glb), _reward, terminated, truncated, _info = env.step(
                action
            )
            locals_list.append(local)
            globals_list.append(glb)
            if terminated or truncated:
                break

        # Trim trailing obs (one more obs than actions)
        locals_arr = np.stack(locals_list[:-1], axis=0).astype(np.int16)
        globals_arr = np.stack(globals_list[:-1], axis=0).astype(np.int16)
        actions_arr = np.array(actions_list, dtype=np.int64)

        return {
            "local": locals_arr,
            "global": globals_arr,
            "actions": actions_arr,
            "env_id": env_id,
        }
    except Exception:
        logger.error(
            f"Oracle trajectory failed for {env_id} seed={seed}",
            exc_info=True,
        )
        return None
    finally:
        env.close()