#!/usr/bin/env python3
"""Create and optionally publish a resized HF dataset for the HyperView Space."""

from __future__ import annotations

import argparse
import json
import os
from datetime import datetime, timezone
from pathlib import Path

import numpy as np
import pandas as pd
from PIL import Image
from datasets import Dataset, Image as HFImage

PROJECT_ROOT = Path(__file__).resolve().parents[2]
DEFAULT_DATASET_ROOT = PROJECT_ROOT / "kaggle_jaguar_dataset_v2"
DEFAULT_CORESET_CSV = PROJECT_ROOT / "data/validation_coreset.csv"
DEFAULT_OUTPUT_DIR = PROJECT_ROOT / "HyperViewDemoHuggingFaceSpace/dataset_build"
DEFAULT_REPO_ID = os.environ.get("HF_DATASET_REPO", "hyper3labs/jaguar-hyperview-demo")


def utc_now() -> str:
    return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Build resized train+validation demo dataset with split tags for HyperView."
    )
    parser.add_argument("--dataset_root", type=Path, default=DEFAULT_DATASET_ROOT)
    parser.add_argument("--coreset_csv", type=Path, default=DEFAULT_CORESET_CSV)
    parser.add_argument("--output_dir", type=Path, default=DEFAULT_OUTPUT_DIR)
    parser.add_argument("--repo_id", type=str, default=DEFAULT_REPO_ID)
    parser.add_argument("--config_name", type=str, default="default")
    parser.add_argument("--image_size", type=int, default=384)
    parser.add_argument("--jpeg_quality", type=int, default=90)
    parser.add_argument(
        "--image_variant",
        type=str,
        default="foreground_only",
        choices=["foreground_only", "full_rgb"],
    )
    parser.add_argument("--max_samples", type=int, default=None)
    parser.add_argument("--private", action="store_true")
    parser.add_argument("--hf_token_env", type=str, default="HF_TOKEN")
    parser.add_argument("--no_push", action="store_true")
    return parser.parse_args()


def load_rows(dataset_root: Path, coreset_csv: Path, max_samples: int | None) -> pd.DataFrame:
    train_csv = dataset_root / "train.csv"
    if not train_csv.exists():
        raise FileNotFoundError(f"Missing train.csv at {train_csv}")

    train_df = pd.read_csv(train_csv)
    coreset_df = pd.read_csv(coreset_csv)
    coreset_filenames = set(coreset_df["filename"].astype(str).tolist())

    train_df = train_df.copy()
    train_df["filename"] = train_df["filename"].astype(str)
    train_df["label"] = train_df["ground_truth"].astype(str)
    train_df["split_tag"] = np.where(train_df["filename"].isin(coreset_filenames), "validation", "train")
    train_df["sample_id"] = train_df["filename"]

    if max_samples is not None:
        train_df = train_df.iloc[: int(max_samples)].copy()

    return train_df[["filename", "label", "split_tag", "sample_id"]]


def load_variant_image(image_path: Path, image_variant: str) -> Image.Image:
    if image_variant == "foreground_only":
        rgba = Image.open(image_path).convert("RGBA")
        rgba_np = np.array(rgba, dtype=np.uint8)
        rgb = rgba_np[:, :, :3]
        alpha = rgba_np[:, :, 3]
        mask = (alpha > 0).astype(np.uint8)
        cutout_rgb = (rgb * mask[:, :, np.newaxis]).astype(np.uint8)
        return Image.fromarray(cutout_rgb, mode="RGB")
    return Image.open(image_path).convert("RGB")


def build_resized_images(
    rows_df: pd.DataFrame,
    dataset_root: Path,
    output_images_dir: Path,
    image_size: int,
    jpeg_quality: int,
    image_variant: str,
) -> pd.DataFrame:
    source_images_dir = dataset_root / "train"
    if not source_images_dir.exists():
        raise FileNotFoundError(f"Missing image directory: {source_images_dir}")

    output_images_dir.mkdir(parents=True, exist_ok=True)

    records: list[dict[str, str]] = []
    for _, row in rows_df.iterrows():
        filename = str(row["filename"])
        src = source_images_dir / filename
        if not src.exists():
            raise FileNotFoundError(f"Missing source image: {src}")

        image = load_variant_image(src, image_variant=image_variant)
        image = image.resize((int(image_size), int(image_size)), Image.Resampling.BICUBIC)

        dst_name = f"{Path(filename).stem}.jpg"
        dst = output_images_dir / dst_name
        image.save(dst, format="JPEG", quality=int(jpeg_quality), optimize=True)

        records.append(
            {
                "image": str(dst),
                "label": str(row["label"]),
                "filename": filename,
                "split_tag": str(row["split_tag"]),
                "sample_id": str(row["sample_id"]),
            }
        )

    return pd.DataFrame(records)


def build_hf_dataset(records_df: pd.DataFrame) -> Dataset:
    payload = {
        "image": records_df["image"].tolist(),
        "label": records_df["label"].tolist(),
        "filename": records_df["filename"].tolist(),
        "split_tag": records_df["split_tag"].tolist(),
        "sample_id": records_df["sample_id"].tolist(),
    }
    dataset = Dataset.from_dict(payload)
    dataset = dataset.cast_column("image", HFImage())
    return dataset


def maybe_push_to_hub(
    dataset: Dataset,
    repo_id: str,
    config_name: str,
    private: bool,
    hf_token_env: str,
    no_push: bool,
) -> str:
    if no_push:
        return "skipped (--no_push)"

    token = os.environ.get(hf_token_env)
    if not token:
        raise RuntimeError(
            f"Missing Hugging Face token in environment variable {hf_token_env}."
        )

    dataset.push_to_hub(
        repo_id=repo_id,
        config_name=config_name,
        token=token,
        private=bool(private),
    )
    return f"pushed:{repo_id}:{config_name}"


def main() -> int:
    args = parse_args()

    output_dir = args.output_dir.resolve()
    images_out = output_dir / "images"
    dataset_out = output_dir / "hf_dataset"
    output_dir.mkdir(parents=True, exist_ok=True)

    rows_df = load_rows(
        dataset_root=args.dataset_root.resolve(),
        coreset_csv=args.coreset_csv.resolve(),
        max_samples=args.max_samples,
    )
    if rows_df.empty:
        raise RuntimeError("No dataset rows found for publish pipeline.")

    records_df = build_resized_images(
        rows_df=rows_df,
        dataset_root=args.dataset_root.resolve(),
        output_images_dir=images_out,
        image_size=int(args.image_size),
        jpeg_quality=int(args.jpeg_quality),
        image_variant=args.image_variant,
    )

    dataset = build_hf_dataset(records_df)
    dataset.save_to_disk(str(dataset_out))

    publish_status = maybe_push_to_hub(
        dataset=dataset,
        repo_id=args.repo_id,
        config_name=args.config_name,
        private=args.private,
        hf_token_env=args.hf_token_env,
        no_push=args.no_push,
    )

    metadata = {
        "generated_at_utc": utc_now(),
        "dataset_root": str(args.dataset_root.resolve()),
        "coreset_csv": str(args.coreset_csv.resolve()),
        "output_dir": str(output_dir),
        "repo_id": args.repo_id,
        "config_name": args.config_name,
        "image_size": int(args.image_size),
        "jpeg_quality": int(args.jpeg_quality),
        "image_variant": args.image_variant,
        "num_rows": int(len(records_df)),
        "split_counts": records_df["split_tag"].value_counts().to_dict(),
        "push_status": publish_status,
    }

    metadata_path = output_dir / "publish_metadata.json"
    metadata_path.write_text(json.dumps(metadata, indent=2), encoding="utf-8")

    print("=== HyperView demo dataset pipeline complete ===")
    print(f"Rows: {len(records_df)}")
    print(f"HF dataset saved to: {dataset_out}")
    print(f"Push status: {publish_status}")
    print(f"Metadata: {metadata_path}")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())