from dataclasses import dataclass
from typing import Literal, Tuple

import sys
from pathlib import Path

import numpy as np
import torch
import requests

_THIS_DIR = Path(__file__).resolve().parent
_MIDAS_REPO_ROOT = _THIS_DIR / "MiDaS"
if _MIDAS_REPO_ROOT.is_dir():
    midas_path = str(_MIDAS_REPO_ROOT)
    if midas_path not in sys.path:
        sys.path.insert(0, midas_path)

from midas.model_loader import load_model, default_models  # type: ignore[import]
import utils  # from MiDaS repo


MiDaSModelType = Literal[
    "dpt_beit_large_512",
    "dpt_swin2_large_384",
    "dpt_swin2_tiny_256",
    "dpt_levit_224",
]


@dataclass
class MiDaSConfig:
    model_type: MiDaSModelType = "dpt_beit_large_512"
    device: str = "cuda"


_MIDAS_WEIGHTS_URLS = {
    # 官方权重参见 MiDaS 仓库 README
    "dpt_beit_large_512": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt",
    "dpt_swin2_large_384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_large_384.pt",
    "dpt_swin2_tiny_256": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_tiny_256.pt",
    "dpt_levit_224": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_levit_224.pt",
}


def _download_if_missing(model_type: str, ckpt_path: Path) -> None:
    if ckpt_path.is_file():
        return

    url = _MIDAS_WEIGHTS_URLS.get(model_type)
    if not url:
        raise FileNotFoundError(
            f"找不到 MiDaS 权重文件: {ckpt_path}\n"
            f"且当前未为 model_type='{model_type}' 配置自动下载 URL，请手动下载到该路径。"
        )

    ckpt_path.parent.mkdir(parents=True, exist_ok=True)
    print(f"自动下载 MiDaS 权重 ({model_type}):\n  {url}\n  -> {ckpt_path}")

    resp = requests.get(url, stream=True)
    resp.raise_for_status()

    total = int(resp.headers.get("content-length", "0") or "0")
    downloaded = 0
    chunk_size = 1024 * 1024

    with ckpt_path.open("wb") as f:
        for chunk in resp.iter_content(chunk_size=chunk_size):
            if not chunk:
                continue
            f.write(chunk)
            downloaded += len(chunk)
            if total > 0:
                done = int(50 * downloaded / total)
                print("\r[{}{}] {:.1f}%".format("#" * done, "." * (50 - done), downloaded * 100 / total), end="")
    print("\nMiDaS 权重下载完成。")


def load_midas_from_config(
    cfg: MiDaSConfig,
) -> Tuple[torch.nn.Module, MiDaSConfig, callable, int, int]:
    """
    加载 MiDaS 模型与对应 transform。
    返回: model, cfg, transform, net_w, net_h
    """
    # default_models 中给了默认权重路径名
    model_info = default_models[cfg.model_type]
    ckpt_path = _MIDAS_REPO_ROOT / model_info.path
    _download_if_missing(cfg.model_type, ckpt_path)

    device = "cuda" if cfg.device.startswith("cuda") and torch.cuda.is_available() else "cpu"
    model, transform, net_w, net_h = load_model(
        device=torch.device(device),
        model_path=str(ckpt_path),
        model_type=cfg.model_type,
        optimize=False,
        height=None,
        square=False,
    )

    cfg = MiDaSConfig(model_type=cfg.model_type, device=device)
    return model, cfg, transform, net_w, net_h


def infer_midas(
    model: torch.nn.Module,
    transform: callable,
    image_rgb: np.ndarray,
    net_w: int,
    net_h: int,
    device: str,
) -> np.ndarray:
    """
    对单张 RGB 图像做深度推理，返回 float32 深度图（未归一化）。
    """
    image = transform({"image": image_rgb})["image"]
    prediction = utils.process(
        torch.device(device),
        model,
        model_type="dpt",  # 这里具体字符串对 utils.process 的逻辑影响不大，只要不包含 "openvino"
        image=image,
        input_size=(net_w, net_h),
        target_size=image_rgb.shape[1::-1],
        optimize=False,
        use_camera=False,
    )
    return np.asarray(prediction, dtype="float32").squeeze()