from __future__ import annotations

import argparse
import os
from typing import Tuple

import numpy as np
import torch
from PIL import Image

from common.image_io import fetch_url_bytes, bytes_to_pil, ImageLoadError

try:
    import open_clip
except Exception:
    open_clip = None

try:
    from transformers import CLIPProcessor, CLIPModel
except Exception:
    CLIPModel = None
    CLIPProcessor = None


def load_openclip(model_name: str = "ViT-B-32", pretrained: str = "openai") -> Tuple:
    device = "cuda" if torch.cuda.is_available() else "cpu"
    if open_clip is None:
        raise RuntimeError("open_clip is not installed")
    model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained=pretrained)
    model = model.to(device).eval()
    return model, preprocess, device


def embed_openclip(model, preprocess, device, pil_image: Image.Image) -> np.ndarray:
    image_input = preprocess(pil_image).unsqueeze(0).to(device)
    with torch.no_grad():
        image_features = model.encode_image(image_input)
        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
    return image_features.cpu().numpy()[0]


def load_hf_clip(model_name: str = "openai/clip-vit-base-patch32") -> Tuple:
    device = "cuda" if torch.cuda.is_available() else "cpu"
    if CLIPModel is None or CLIPProcessor is None:
        raise RuntimeError("transformers (CLIP) is not installed")
    model = CLIPModel.from_pretrained(model_name).to(device).eval()
    processor = CLIPProcessor.from_pretrained(model_name)
    return model, processor, device


def embed_hf_clip(model, processor, device, pil_image: Image.Image) -> np.ndarray:
    inputs = processor(images=pil_image, return_tensors="pt")
    inputs = {k: v.to(device) for k, v in inputs.items()}
    with torch.no_grad():
        feats = model.get_image_features(**inputs)
        feats = feats / feats.norm(dim=-1, keepdim=True)
    return feats.cpu().numpy()[0]


def load_image(path_or_url: str) -> Image.Image:
    if path_or_url.startswith("http://") or path_or_url.startswith("https://"):
        data = fetch_url_bytes(path_or_url)
        return bytes_to_pil(data)
    else:
        return Image.open(path_or_url).convert("RGB")


def main() -> None:
    parser = argparse.ArgumentParser(description="Vectorize an image using CLIP (open_clip or HuggingFace)")
    parser.add_argument("input", help="Path to image file or URL")
    parser.add_argument("--backend", choices=("openclip", "hf"), default="openclip")
    parser.add_argument("--model", default=None, help="Model name (backend-specific)")
    parser.add_argument("--pretrained", default="openai", help="open_clip pretrained source (openclip backend)")
    parser.add_argument("--out", default=None, help="Output .npy path (defaults to stdout)")
    args = parser.parse_args()

    try:
        img = load_image(args.input)
    except ImageLoadError as e:
        raise SystemExit(f"Failed to load image: {e}")

    if args.backend == "openclip":
        model_name = args.model or os.getenv("MODEL_NAME", "ViT-B-32")
        pretrained = args.pretrained
        model, preprocess, device = load_openclip(model_name, pretrained=pretrained)
        vec = embed_openclip(model, preprocess, device, img)
    else:
        model_name = args.model or "openai/clip-vit-base-patch32"
        model, processor, device = load_hf_clip(model_name)
        vec = embed_hf_clip(model, processor, device, img)

    vec = np.asarray(vec, dtype=np.float32)

    if args.out:
        np.save(args.out, vec)
        print(f"Saved vector shape={vec.shape} to {args.out}")
    else:
        # Print a short summary and the vector length. Full vector to stdout can be large.
        print(f"vector_shape={vec.shape}")
        print(np.array2string(vec, precision=6, separator=", "))


if __name__ == "__main__":
    main()