"""Q-Learning 与 DQN 共用的迷宫环境、展示与绘图工具。"""
from collections import deque

import numpy as np


START, END, WALL, ROAD = 0, 1, 2, 3                 # 分别是起点 终点 障碍 道路
ACTION_DELTAS = ((-1, 0), (1, 0), (0, -1), (0, 1))  # 动作: 0上 1下 2左 3右
ARROWS = "↑↓←→"
CHARS = {START: "S", END: "G", WALL: "#", ROAD: "."}


def generate_maze(rows, cols, seed=0, braid=0.0):
    """按 seed 随机生成可解迷宫（递归回溯法），行列须为不小于 3 的奇数。

    保证起点 (0,0) 到终点 (rows-1, cols-1) 连通。
    braid: 0~1, 生成后按该概率拆掉剩余内墙形成环路。
    0 = 完美迷宫（唯一通路, 长走廊多）；越大环路越多、可选路径越多。
    """
    if rows < 3 or cols < 3 or rows % 2 == 0 or cols % 2 == 0:
        raise ValueError("行列必须为不小于 3 的奇数")
    rng = np.random.default_rng(seed)
    maze = np.full((rows, cols), WALL, dtype=np.int8)
    maze[0, 0] = ROAD
    stack = [(0, 0)]
    while stack:
        r, c = stack[-1]
        neighbors = [
            (r + dr, c + dc, r + dr // 2, c + dc // 2)
            for dr, dc in ((-2, 0), (2, 0), (0, -2), (0, 2))
            if 0 <= r + dr < rows and 0 <= c + dc < cols
            and maze[r + dr, c + dc] == WALL
        ]
        if neighbors:
            nr, nc, mr, mc = neighbors[int(rng.integers(len(neighbors)))]
            maze[mr, mc] = maze[nr, nc] = ROAD
            stack.append((nr, nc))
        else:
            stack.pop()
    if braid > 0:
        for r in range(1, rows - 1):
            for c in range(1, cols - 1):
                if maze[r, c] == WALL and rng.random() < braid and (
                    (maze[r - 1, c] != WALL and maze[r + 1, c] != WALL)
                    or (maze[r, c - 1] != WALL and maze[r, c + 1] != WALL)
                ):
                    maze[r, c] = ROAD   # 拆掉两侧都是路的墙, 形成环路
    maze[0, 0], maze[rows - 1, cols - 1] = START, END
    return maze


class MazeEnv:
    """Gymnasium 风格的迷宫环境（不依赖 gymnasium, 仅遵循其 API 约定）。

    状态 obs 为格子坐标 (r, c)；动作: 0上 1下 2左 3右。
    奖励: 每步 step_reward；撞墙/越界 wall_penalty（原地不动）；
    到达终点 goal_reward 且 terminated。
    step() 返回 (obs, reward, terminated, truncated, info)：
      terminated = 到达终点（真正的终止状态）；
      truncated  = 达到 max_steps 步数上限（超时截断, 非终止状态）。
    """

    def __init__(self, maze, start_pos=None,
                 step_reward=-1.0, wall_penalty=-10.0, goal_reward=100.0, max_steps=200):
        self.maze = np.asarray(maze)
        self.shape = self.maze.shape
        self.n_actions = len(ACTION_DELTAS)
        self.start_pos = self._find(START) if start_pos is None else tuple(start_pos)
        self.goal = self._find(END)

        r, c = self.start_pos
        if not (0 <= r < self.shape[0] and 0 <= c < self.shape[1]) or self.maze[r, c] == WALL:
            raise ValueError(f"非法起点: {self.start_pos}")
        if self.start_pos == self.goal:
            raise ValueError("起点不能与终点重合")

        self.step_reward = step_reward
        self.wall_penalty = wall_penalty
        self.goal_reward = goal_reward
        self.max_steps = max_steps
        self.steps = 0
        self._pos = self.start_pos

    def _find(self, cell):
        hits = np.argwhere(self.maze == cell)
        if len(hits) == 0:
            raise ValueError(f"迷宫中找不到格子类型 {cell}")
        return tuple(int(v) for v in hits[0])

    def reset(self, *, seed=None, options=None):
        """重置到起点, 返回 (obs, info)。seed/options 仅为 API 兼容保留。"""
        self._pos = self.start_pos
        self.steps = 0
        return self._pos, {}

    def step(self, action):
        """执行动作, 返回 (obs, reward, terminated, truncated, info)。"""
        if not 0 <= action < self.n_actions:
            raise ValueError(f"非法动作: {action}")

        dr, dc = ACTION_DELTAS[action]
        nr, nc = self._pos[0] + dr, self._pos[1] + dc
        self.steps += 1

        terminated = False
        if (
            not (0 <= nr < self.shape[0] and 0 <= nc < self.shape[1])
            or self.maze[nr][nc] == WALL
        ):
            # 遇到边界或者撞墙: 原地不动
            reward = self.wall_penalty
        else:
            self._pos = (nr, nc)
            terminated = self._pos == self.goal
            reward = self.goal_reward if terminated else self.step_reward

        truncated = not terminated and self.steps >= self.max_steps
        return self._pos, reward, terminated, truncated, {}


def shortest_path_len(env):
    """BFS 求起点到终点的最短步数（不通返回 -1）。"""
    dist = {env.start_pos: 0}
    queue = deque([env.start_pos])
    while queue:
        pos = queue.popleft()
        if pos == env.goal:
            return dist[pos]
        for dr, dc in ACTION_DELTAS:
            nr, nc = pos[0] + dr, pos[1] + dc
            nxt = (nr, nc)
            if (0 <= nr < env.shape[0] and 0 <= nc < env.shape[1]
                    and env.maze[nr, nc] != WALL and nxt not in dist):
                dist[nxt] = dist[pos] + 1
                queue.append(nxt)
    return -1


def evaluate(env, act, episodes=100):
    """纯贪心策略（无探索）评测, 返回 (成功率, 成功局平均步数)。

    act(pos) -> 动作编号, 由各算法提供（查表或前向推理）。
    """
    wins, steps_list = 0, []
    for _ in range(episodes):
        obs, _ = env.reset()
        terminated = truncated = False
        while not (terminated or truncated):
            obs, _, terminated, truncated, _ = env.step(int(act(obs)))
        if terminated:
            wins += 1
            steps_list.append(env.steps)
    avg_steps = float(np.mean(steps_list)) if steps_list else float("nan")
    return wins / episodes, avg_steps


def show_maze(env):
    print(f"迷宫 {env.shape[0]}x{env.shape[1]}（S起点 G终点 #墙）:")
    for r in range(env.shape[0]):
        print("".join(CHARS[env.maze[r][c]] for c in range(env.shape[1])))


def show_policy(env, act):
    print("学到的策略（每格最优动作）:")
    for r in range(env.shape[0]):
        row = ""
        for c in range(env.shape[1]):
            if env.maze[r][c] == WALL:
                row += " # "
            elif (r, c) == env.goal:
                row += " G "
            else:
                row += f" {ARROWS[int(act((r, c)))]} "
        print(row)


def show_path(env, act):
    print("\n贪心策略走出的一条路径:")
    obs, path, terminated = env.reset()[0], [env.start_pos], False
    truncated = False
    while not (terminated or truncated):
        obs, _, terminated, truncated, _ = env.step(int(act(obs)))
        path.append(obs)
    grid = [[CHARS[env.maze[r][c]] for c in range(env.shape[1])]
            for r in range(env.shape[0])]
    for i, (r, c) in enumerate(path):
        if (r, c) not in (env.start_pos, env.goal):
            grid[r][c] = str(i % 10)   # 若重复经过同一格，显示最后一步序号
    print("\n".join(" ".join(row) for row in grid))
    print(f"到达终点: {'是' if terminated else '否'}，共 {len(path) - 1} 步")


# ─────────────────── 训练过程可视化（需 matplotlib） ───────────────────
def _setup_plt():
    import matplotlib

    matplotlib.use("Agg")               # 不弹窗，直接保存图片
    import matplotlib.pyplot as plt
    plt.rcParams["font.sans-serif"] = ["Microsoft YaHei", "SimHei"]  # 中文字体
    plt.rcParams["axes.unicode_minus"] = False                       # 正常显示负号
    return plt


def moving_avg(x, k=20):
    x = np.asarray(x, dtype=float)
    if len(x) < k:
        return np.array([])
    return np.convolve(x, np.ones(k) / k, mode="valid")


def _plot_ma(ax, x, window, color):
    x = np.asarray(x, dtype=float)
    ax.plot(x, color="gray", alpha=0.35, label="逐局值")
    ma = moving_avg(x, window)
    if len(ma):
        ax.plot(np.arange(window - 1, window - 1 + len(ma)), ma,
                color=color, lw=2, label=f"滑动平均(窗口{window})")
    ax.set_xlabel("训练局数 Episode")
    ax.legend()


def plot_training(env, rewards, successes, ep_steps, extra,
                  window=20, out="training.png", title="走迷宫训练过程"):
    """四联图: 回报曲线 / 滑动成功率 / 单局步数 / 第4格由 extra 提供。

    extra: (数值序列, y轴标签, 子图标题, 是否对数y轴)。
    """
    plt = _setup_plt()
    fig, axes = plt.subplots(2, 2, figsize=(12, 8))
    fig.suptitle(title, fontsize=15)

    ax = axes[0][0]
    _plot_ma(ax, rewards, window, "C0")
    ax.set_ylabel("单局回报")
    ax.set_title("逐局回报（越高越好）")

    ax = axes[0][1]
    _plot_ma(ax, successes, window, "C1")
    ax.set_ylim(-0.05, 1.05)
    ax.set_ylabel("成功率")
    ax.set_title(f"滑动成功率（窗口{window}）")

    ax = axes[1][0]
    _plot_ma(ax, ep_steps, window, "C2")
    sp = shortest_path_len(env)
    if sp >= 0:
        ax.axhline(sp, color="red", ls="--", lw=1.5, label=f"最短路 {sp} 步")
    ax.set_ylabel("单局步数")
    ax.set_title("单局步数（降到红线 = 学会最短路）")
    ax.legend()

    values, ylabel, subtitle, log_scale = extra
    ax = axes[1][1]
    ax.plot(np.asarray(values, dtype=float), color="C3", lw=1.2 if log_scale else 2)
    if log_scale:
        ax.set_yscale("log")
    ax.set_xlabel("训练局数 Episode")
    ax.set_ylabel(ylabel)
    ax.set_title(subtitle)

    fig.tight_layout(rect=[0, 0, 1, 0.96])
    fig.savefig(out, dpi=150)
    print(f"\n图片已保存: {out}")


def plot_snapshots(env, snapshots, v_final, out="snapshots.png",
                   title="V(s)=max Q 的学习过程（价值从 G 回传）"):
    """各训练阶段的价值热力图。

    snapshots/v_final: (标签, V二维数组)，V 为该阶段每格的 max Q，墙格可为 nan。
    """
    plt = _setup_plt()
    panels = list(snapshots) + [("最终", v_final)]
    vals = [np.asarray(V, dtype=float)[~np.isnan(V)] for _, V in panels]
    vmin = min(v.min() for v in vals)
    vmax = max(v.max() for v in vals)
    if vmin == vmax:
        vmin, vmax = vmin - 1, vmax + 1

    n = len(panels)
    fig, axes = plt.subplots(1, n, figsize=(3 * n, 3.4))
    axes = np.atleast_1d(axes)
    masked_wall = env.maze == WALL
    for ax, (tag, V) in zip(axes, panels):
        V = np.asarray(V, dtype=float)
        Vm = np.ma.masked_where(masked_wall, V)
        im = ax.imshow(Vm, cmap="viridis", vmin=vmin, vmax=vmax, origin="upper")
        fs = 8 if max(env.shape) <= 7 else 5
        for r in range(env.shape[0]):
            for c in range(env.shape[1]):
                if masked_wall[r, c]:
                    ax.text(c, r, "█", ha="center", va="center",
                            color="black", fontsize=fs)
                else:
                    ax.text(c, r, f"{V[r, c]:.0f}", ha="center", va="center",
                            color="white", fontsize=fs)
        ax.set_xticks([])
        ax.set_yticks([])
        ax.set_title(f"第 {tag} 局后" if tag != "最终" else "训练结束")
    fig.suptitle(title, fontsize=13)
    fig.colorbar(im, ax=axes, fraction=0.03, pad=0.02)
    fig.savefig(out, dpi=150, bbox_inches="tight")
    print(f"图片已保存: {out}")