"""CLIP embedding via HF Inference API — no local GPU, no local image storage.

Streams images from IMPACT HF dataset one batch at a time, sends to
HF Inference API for feature extraction, accumulates embeddings in memory,
pushes final parquet to HF Hub.

Disk usage at any time: ~0 (images streamed, embeddings are ~30MB total).

Requirements (run from any machine with network access):
    pip install huggingface_hub datasets requests pillow pandas numpy tqdm

Usage:
    export HF_TOKEN=hf_...
    python scripts/cloud/embed_hf_api.py \
        --year 2022 \
        --model openai/clip-vit-large-patch14 \
        --out-repo midah/patent-wireframes \
        --out-file embeddings_2022_vitl14.parquet \
        --batch 8 \
        --workers 4
"""

import argparse
import base64
import io
import json
import os
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path

import numpy as np
import pandas as pd
import requests
from datasets import load_dataset
from huggingface_hub import HfApi
from PIL import Image
from tqdm import tqdm


HF_INFERENCE_URL = "https://api-inference.huggingface.co/models/{model}"


def encode_image(img: Image.Image, max_edge: int = 224) -> str:
    """Resize and base64-encode an image for the inference API."""
    w, h = img.size
    scale = min(max_edge / max(w, h), 1.0)
    if scale < 1.0:
        img = img.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
    img = img.convert("RGB")
    buf = io.BytesIO()
    img.save(buf, format="JPEG", quality=85)
    return base64.standard_b64encode(buf.getvalue()).decode()


def get_embedding(b64: str, model: str, token: str, retries: int = 4) -> list[float] | None:
    """Call HF Inference API feature-extraction endpoint for one image."""
    url = HF_INFERENCE_URL.format(model=model)
    headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
    payload = {"inputs": {"image": b64}}

    for attempt in range(retries):
        try:
            r = requests.post(url, headers=headers, json=payload, timeout=30)
            if r.status_code == 200:
                data = r.json()
                # Response shape varies: [[...]] or [...]
                vec = data[0] if isinstance(data[0], list) else data
                return vec
            elif r.status_code == 503:
                # Model loading — wait and retry
                wait = min(30, 5 * (2 ** attempt))
                time.sleep(wait)
            elif r.status_code == 429:
                wait = 2 ** attempt
                time.sleep(wait)
            else:
                return None
        except Exception:
            time.sleep(2 ** attempt)
    return None


def stream_and_embed(
    year: str,
    model: str,
    token: str,
    batch_size: int,
    workers: int,
    max_images: int | None,
) -> tuple[list[str], np.ndarray]:
    """Stream IMPACT dataset and embed all figures."""

    print(f"Loading IMPACT {year} metadata...")
    # Load metadata (CSV) — small, no images
    import csv, ast, zipfile
    from huggingface_hub import hf_hub_download

    csv_path = hf_hub_download(
        repo_id="AI4Patents/IMPACT",
        filename=f"{year}.csv",
        repo_type="dataset",
        token=token,
    )
    rows = []
    with open(csv_path) as f:
        reader = csv.DictReader(f)
        for row in reader:
            try:
                fnames = ast.literal_eval(row["file_names"])
                patent_id = row["id"]
                for fname in fnames:
                    rows.append({"patent_id": patent_id, "image_filename": fname})
            except Exception:
                pass

    if max_images:
        rows = rows[:max_images]

    print(f"Total figures to embed: {len(rows):,}")

    # Download the zip to a temp location for extraction
    print(f"Downloading IMPACT {year} images (~4.4GB)...")
    zip_path = hf_hub_download(
        repo_id="AI4Patents/IMPACT",
        filename=f"{year}.zip",
        repo_type="dataset",
        token=token,
    )
    print(f"Zip downloaded to: {zip_path}")

    # Extract and embed in batches
    import mmap, struct, zlib

    fig_ids = []
    vecs = []

    def process_batch(batch_rows):
        """Extract images from zip and embed via API."""
        results = []
        for row in batch_rows:
            fn = row["image_filename"]
            # Construct path inside zip
            parts = fn.split("-D0")
            if len(parts) < 2:
                continue
            dir_name = parts[0]
            zip_inner_path = f"{year}/{dir_name}/{fn}"

            try:
                import zipfile as zf
                with zf.ZipFile(zip_path) as z:
                    with z.open(zip_inner_path) as f:
                        tif_bytes = f.read()
                img = Image.open(io.BytesIO(tif_bytes))
                b64 = encode_image(img)
                vec = get_embedding(b64, model, token)
                if vec is not None:
                    results.append((row["patent_id"] + "_" + fn.split("-D0")[1].split(".")[0], vec))
            except Exception:
                pass
        return results

    # Process in parallel batches
    batches = [rows[i: i + batch_size] for i in range(0, len(rows), batch_size)]

    with ThreadPoolExecutor(max_workers=workers) as pool:
        futures = {pool.submit(process_batch, b): b for b in batches}
        for future in tqdm(as_completed(futures), total=len(batches), desc="Embedding"):
            for fig_id, vec in future.result():
                fig_ids.append(fig_id)
                vecs.append(vec)

    vecs_arr = np.array(vecs, dtype=np.float32)
    # Normalize to unit vectors
    norms = np.linalg.norm(vecs_arr, axis=1, keepdims=True)
    vecs_arr /= np.maximum(norms, 1e-8)
    return fig_ids, vecs_arr


def push_to_hub(fig_ids: list[str], vecs: np.ndarray, out_repo: str, out_file: str, token: str):
    """Save embeddings as parquet and push to HF Hub."""
    print(f"Building parquet ({len(fig_ids):,} embeddings, dim={vecs.shape[1]})...")
    df = pd.DataFrame({
        "figure_id": fig_ids,
        "embedding": list(vecs),
    })
    tmp = Path("/tmp/embeddings_tmp.parquet")
    df.to_parquet(tmp, index=False)
    size_mb = tmp.stat().st_size / 1e6
    print(f"Parquet size: {size_mb:.1f} MB")

    api = HfApi(token=token)
    api.upload_file(
        path_or_fileobj=str(tmp),
        path_in_repo=out_file,
        repo_id=out_repo,
        repo_type="dataset",
    )
    print(f"Pushed → hf://datasets/{out_repo}/{out_file}")
    tmp.unlink()


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--year",        default="2022")
    parser.add_argument("--model",       default="openai/clip-vit-large-patch14")
    parser.add_argument("--out-repo",    default="midah/patent-wireframes")
    parser.add_argument("--out-file",    default="embeddings_2022_vitl14.parquet")
    parser.add_argument("--batch",       type=int, default=8)
    parser.add_argument("--workers",     type=int, default=4)
    parser.add_argument("--max-images",  type=int, default=None)
    args = parser.parse_args()

    token = os.environ.get("HF_TOKEN")
    if not token:
        raise RuntimeError("Set HF_TOKEN environment variable")

    fig_ids, vecs = stream_and_embed(
        args.year, args.model, token,
        args.batch, args.workers, args.max_images,
    )
    print(f"\nEmbedded {len(fig_ids):,} figures, shape {vecs.shape}")
    push_to_hub(fig_ids, vecs, args.out_repo, args.out_file, token)


if __name__ == "__main__":
    main()