"""第6回演習 rollout 補助（robosuite 直接起動、robomimic 不使用）。

配布 checkpoint（ACT）を robosuite で実行して成功率と動画を返します。
方策の入力解像度（240×320 の本編 / 96×96 のミニ実験）は checkpoint から自動判別します。
"""

from __future__ import annotations

import os

# mujoco を import する前にレンダリングバックエンドを決める（import 後の変更は効かない）。
# GPU ランタイム(Step 6)は egl、CPU ランタイム(Step 5)は osmesa。既に設定済みなら尊重する。
if "MUJOCO_GL" not in os.environ:
    try:
        import torch

        os.environ["MUJOCO_GL"] = "egl" if torch.cuda.is_available() else "osmesa"
    except ImportError:
        os.environ["MUJOCO_GL"] = "osmesa"

from pathlib import Path

import numpy as np

ENV_NAME = {"lift": "Lift", "can": "PickPlaceCan"}
HORIZON = {"lift": 150, "can": 400}

# robomimic のデータ収集時と同じ OSC_POSE デルタ制御
_CONTROLLER = {
    "type": "BASIC",
    "body_parts": {
        "right": {
            "type": "OSC_POSE",
            "input_max": 1, "input_min": -1,
            "output_max": [0.05, 0.05, 0.05, 0.5, 0.5, 0.5],
            "output_min": [-0.05, -0.05, -0.05, -0.5, -0.5, -0.5],
            "kp": 150, "damping": 1, "impedance_mode": "fixed",
            "kp_limits": [0, 300], "damping_limits": [0, 10],
            "position_limits": None, "orientation_limits": None,
            "uncouple_pos_ori": True, "control_delta": True,
            "interpolation": None, "ramp_ratio": 0.2,
            "input_ref_frame": "world",
            "gripper": {"type": "GRIP"},
        }
    },
}


def _make_env(task: str):
    import robosuite as suite

    return suite.make(
        ENV_NAME[task],
        robots=["Panda"],
        controller_configs=_CONTROLLER,
        control_freq=20,
        has_renderer=False,
        has_offscreen_renderer=True,
        use_camera_obs=True,
        camera_names=["agentview", "robot0_eye_in_hand"],
        camera_heights=240,
        camera_widths=320,
        ignore_done=True,
        reward_shaping=False,
        use_object_obs=False,
    )


def rollout(ckpt_dir, task: str = "lift", n: int = 10, seed: int = 0, show_video: bool = True):
    """checkpoint を n エピソード実行して成功率を返します（最初の 1 本は動画表示）。"""
    import torch
    from PIL import Image
    from lerobot.configs.policies import PreTrainedConfig
    from lerobot.policies.act.modeling_act import ACTPolicy
    from lerobot.policies.factory import make_pre_post_processors

    ckpt_dir = Path(ckpt_dir)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    policy = ACTPolicy.from_pretrained(ckpt_dir)
    policy.eval().to(device)
    cfg = PreTrainedConfig.from_pretrained(ckpt_dir)
    # lerobot 0.5.1 は正規化を policy 外の processor が担う（生バッチ直呼びは不可）
    pre, post = make_pre_post_processors(cfg, pretrained_path=str(ckpt_dir))
    # 予測チャンクの先頭 1 秒だけ実行して再予測する（5 秒 open-loop のままだと
    # can で 20%→85% の差が出る。実測して決めた設定）
    policy.config.n_action_steps = min(20, policy.config.n_action_steps)
    img_hw = tuple(cfg.input_features["observation.images.agentview"].shape[1:3])

    def to_batch(obs):
        frames = {
            "agentview": np.flipud(obs["agentview_image"]).copy(),
            "wrist": np.flipud(obs["robot0_eye_in_hand_image"]).copy(),
        }
        if img_hw != (240, 320):
            frames = {
                k: np.asarray(Image.fromarray(v).resize(img_hw[::-1], Image.BILINEAR))
                for k, v in frames.items()
            }
        return {
            "observation.images.agentview": torch.from_numpy(frames["agentview"]).permute(2, 0, 1).unsqueeze(0).float().to(device) / 255.0,
            "observation.images.wrist": torch.from_numpy(frames["wrist"]).permute(2, 0, 1).unsqueeze(0).float().to(device) / 255.0,
            "observation.state": torch.from_numpy(obs["robot0_joint_pos"].astype(np.float32)).unsqueeze(0).to(device),
        }

    env = _make_env(task)
    rng = np.random.default_rng(seed)
    results = []
    video_frames = []
    for k in range(n):
        np.random.seed(int(rng.integers(0, 2**31 - 1)))
        env.reset()
        policy.reset()
        obs = env._get_observations(force_update=True)
        success = False
        for _ in range(HORIZON[task]):
            with torch.no_grad():
                action = post(policy.select_action(pre(to_batch(obs))))
            obs, _, _, _ = env.step(action.squeeze(0).cpu().numpy())
            if k == 0:
                video_frames.append(np.flipud(obs["agentview_image"]).copy())
            success = success or env._check_success()
            if success:
                break
        results.append(bool(success))
        print(f"rollout {k + 1}/{n}: {'成功' if success else '失敗'}")
    env.close()

    rate = float(np.mean(results))
    print(f"\n成功率: {sum(results)}/{n} = {rate:.0%}")
    if show_video and video_frames:
        from IPython.display import Video, display

        import utils  # Colab で確実に再生できる yuv420p mp4 を書く

        utils.save_mp4(video_frames, "rollout_first.mp4", fps=20)
        display(Video("rollout_first.mp4", embed=True, width=400))
    return rate


def rollout_once(ckpt_dir, horizon: int = 400, seed: int = 0):
    """後方互換: can で 1 エピソードだけ実行します。"""
    return rollout(ckpt_dir, task="can", n=1, seed=seed)