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
_DPT_REPO_ROOT = _THIS_DIR / "DPT"
if _DPT_REPO_ROOT.is_dir():
    dpt_path = str(_DPT_REPO_ROOT)
    if dpt_path not in sys.path:
        sys.path.insert(0, dpt_path)

from dpt.models import DPTDepthModel  # type: ignore[import]
from dpt.transforms import Resize, NormalizeImage, PrepareForNet  # type: ignore[import]
from torchvision.transforms import Compose
import cv2


DPTModelType = Literal["dpt_large", "dpt_hybrid"]


@dataclass
class DPTConfig:
    model_type: DPTModelType = "dpt_large"
    device: str = "cuda"


_DPT_WEIGHTS_URLS = {
    # 官方 DPT 模型权重托管在：
    # https://github.com/isl-org/DPT#models
    "dpt_large": "https://github.com/isl-org/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
    "dpt_hybrid": "https://github.com/isl-org/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
}


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

    url = _DPT_WEIGHTS_URLS.get(model_type)
    if not url:
        raise FileNotFoundError(
            f"找不到 DPT 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 DPT 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("\nDPT 权重下载完成。")


def load_dpt_from_config(cfg: DPTConfig) -> Tuple[DPTDepthModel, DPTConfig, Compose]:
    """
    加载 DPT 模型与对应的预处理 transform。
    """
    ckpt_name = {
        "dpt_large": "dpt_large-midas-2f21e586.pt",
        "dpt_hybrid": "dpt_hybrid-midas-501f0c75.pt",
    }[cfg.model_type]

    ckpt_path = _DPT_REPO_ROOT / "weights" / ckpt_name
    _download_if_missing(cfg.model_type, ckpt_path)

    if cfg.model_type == "dpt_large":
        net_w = net_h = 384
        model = DPTDepthModel(
            path=str(ckpt_path),
            backbone="vitl16_384",
            non_negative=True,
            enable_attention_hooks=False,
        )
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    else:
        net_w = net_h = 384
        model = DPTDepthModel(
            path=str(ckpt_path),
            backbone="vitb_rn50_384",
            non_negative=True,
            enable_attention_hooks=False,
        )
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])

    device = "cuda" if cfg.device.startswith("cuda") and torch.cuda.is_available() else "cpu"
    model.to(device).eval()

    transform = Compose(
        [
            Resize(
                net_w,
                net_h,
                resize_target=None,
                keep_aspect_ratio=True,
                ensure_multiple_of=32,
                resize_method="minimal",
                image_interpolation_method=cv2.INTER_CUBIC,
            ),
            normalization,
            PrepareForNet(),
        ]
    )

    cfg = DPTConfig(model_type=cfg.model_type, device=device)
    return model, cfg, transform


def infer_dpt(
    model: DPTDepthModel,
    transform: Compose,
    image_bgr: np.ndarray,
    device: str,
) -> np.ndarray:
    """
    对单张 BGR 图像做深度推理，返回 float32 深度图（未归一化）。
    """
    img = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
    sample = transform({"image": img})["image"]

    input_batch = torch.from_numpy(sample).to(device).unsqueeze(0)
    with torch.no_grad():
        prediction = model(input_batch)
        prediction = (
            torch.nn.functional.interpolate(
                prediction.unsqueeze(1),
                size=img.shape[:2],
                mode="bicubic",
                align_corners=False,
            )
            .squeeze()
            .cpu()
            .numpy()
        )

    return prediction.astype("float32")