from __future__ import annotations

import argparse
import json
import os
from pathlib import Path

from huggingface_hub import HfApi


CARD_TEMPLATE = """---
language:
- da
- en
license: other
task_categories:
- image-classification
- image-to-text
- zero-shot-image-classification
pretty_name: Royal Danish Library Open COP Images
configs:
- config_name: images
  data_files:
  - split: train
    path: viewer_embedded/*.parquet
- config_name: metadata
  data_files:
  - split: train
    path: metadata/*.parquet
- config_name: flora_danica
  data_files:
  - split: train
    path: viewer_embedded_flora_danica/*.parquet
- config_name: flora_danica_metadata
  data_files:
  - split: train
    path: metadata_flora_danica/*.parquet
- config_name: portraetsamlingen
  data_files:
  - split: train
    path: viewer_embedded_portraetsamlingen/*.parquet
- config_name: portraetsamlingen_metadata
  data_files:
  - split: train
    path: metadata_portraetsamlingen/*.parquet
---

# Royal Danish Library Open COP Images

This dataset is an independently harvested research dataset from the Royal Danish Library
(Det Kgl. Bibliotek) COP/Digital Collections API.

The export intentionally excludes:

- aerial photographs / `Danmark set fra luften`
- newspapers, including `Danske aviser 1666-1883`
- text-heavy COP editions such as books, letters, manuscripts, pamphlets, catalogues and printed matter

Included COP editions:

- `Billeder`
- `Kort og Atlas` as a separate `source_subset=maps` subset, so it can be filtered out if only
  photographic/drawing material is wanted.
- `Flora Danica` subject collection as `source_subset=flora_danica`.
- `Portraetsamlingen` subject collection as `source_subset=portraetsamlingen`.

Records were discovered using broad license-keyword searches and then filtered by the actual MODS
rights/accessCondition metadata. The accepted rights buckets are:

- `Public Domain`
- `No known rights`
- `CC BY`
- `CC BY-SA`

In the current COP MODS records, KBL commonly expresses public-domain/no-known-rights material as
`Materialet er fri af ophavsret`.

## Counts

{counts}

## Files

- `metadata/*.parquet`: normalized metadata-only table, including the raw MODS XML.
- `metadata_flora_danica/*.parquet`: Flora Danica metadata-only table.
- `metadata_portraetsamlingen/*.parquet`: Portraetsamlingen metadata-only table.
- `metadata/*.json`: selected/excluded source definitions, license-query list and summary.
- `viewer_embedded/*.parquet`: Hugging Face viewer-compatible table with embedded JPEG bytes and
  normalized metadata.
- `viewer_embedded_flora_danica/*.parquet`: Flora Danica viewer-compatible table with embedded JPEG bytes.
- `viewer_embedded_portraetsamlingen/*.parquet`: Portraetsamlingen viewer-compatible table with embedded JPEG bytes.
- `webdataset-jpg1600-open/*.tar`: training shards with `<sample_id>.jpg` and `<sample_id>.json`.
- `webdataset-jpg1600-flora_danica/*.tar`: Flora Danica training shards.
- `webdataset-jpg1600-portraetsamlingen/*.tar`: Portraetsamlingen training shards.
- `manifests/*.json`: per-shard image download manifests.
- `scripts/*.py`: scraper/uploader scripts used for this export.

## Source Notes

KBL's public API page says the API provides machine access to datasets containing aerial photos,
images and literary texts. KBL's API documentation states that API metadata is CC0, while content
has varying licenses. This dataset therefore treats every image record according to its own MODS
rights/accessCondition metadata.

Source/API references:

- KBL COP API page: https://api.kb.dk/data/cop
- KBL digital-object API documentation: https://github.com/kb-dk/access-digital-objects
- COP backend/search docs: https://github.com/kb-dk/access-digital-objects/blob/master/cop-backend.md
- IIIF image delivery docs: https://github.com/kb-dk/access-digital-objects/blob/master/image-delivery.md

For Danish CLIP training, `preferred_title`, `preferred_description`, and `preferred_text` are
Danish-first fields: Danish metadata is used whenever present, with English as fallback.
"""


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--repo-id", required=True)
    parser.add_argument("--metadata-summary", type=Path, default=Path("data/kbl_open_metadata/summary.json"))
    parser.add_argument("--flora-summary", type=Path, default=Path("data/kbl_flora_danica_metadata/summary.json"))
    parser.add_argument(
        "--portraits-summary",
        type=Path,
        default=Path("data/kbl_portraetsamlingen_metadata/summary.json"),
    )
    parser.add_argument("--out", type=Path, default=Path("README.md"))
    parser.add_argument("--upload", action="store_true")
    return parser.parse_args()


def load_summary(path: Path) -> dict:
    if not path.exists():
        return {}
    return json.loads(path.read_text(encoding="utf-8"))


def section_counts(label: str, summary: dict) -> list[str]:
    if not summary:
        return [f"- `{label}`: not exported yet"]
    lines = [
        f"- `{label}` image samples: {summary.get('rows', 0):,}",
        f"- `{label}` source records: {summary.get('records', 0):,}",
    ]
    for title, counts in [
        ("licenses", summary.get("licenses", {})),
        ("source_subsets", summary.get("source_subsets", {})),
    ]:
        if counts:
            rendered = ", ".join(f"`{key}` {value:,}" for key, value in counts.items())
            lines.append(f"- `{label}` {title}: {rendered}")
    for key in ("duplicate_rows_skipped", "rejected_records", "dropped_ambiguous_rights_rows"):
        value = summary.get(key)
        if value:
            lines.append(f"- `{label}` {key}: {value:,}")
    return lines


def counts_text(summary_path: Path, flora_path: Path, portraits_path: Path) -> str:
    if not summary_path.exists():
        return "Counts will be filled after the export finishes."

    base = load_summary(summary_path)
    flora = load_summary(flora_path)
    portraits = load_summary(portraits_path)
    total_rows = sum(summary.get("rows", 0) for summary in (base, flora, portraits))
    total_records = sum(summary.get("records", 0) for summary in (base, flora, portraits))
    lines = [
        f"- Total image samples across configs: {total_rows:,}",
        f"- Total accepted source records across configs: {total_records:,}",
    ]
    lines.extend(section_counts("images", base))
    lines.extend(section_counts("flora_danica", flora))
    lines.extend(section_counts("portraetsamlingen", portraits))
    return "\n".join(lines)


def main() -> None:
    args = parse_args()
    card = CARD_TEMPLATE.format(
        counts=counts_text(args.metadata_summary, args.flora_summary, args.portraits_summary)
    )
    args.out.write_text(card, encoding="utf-8")
    if args.upload:
        token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN")
        if not token:
            raise SystemExit("Set HF_TOKEN before uploading.")
        HfApi(token=token).upload_file(
            repo_id=args.repo_id,
            repo_type="dataset",
            path_or_fileobj=str(args.out),
            path_in_repo="README.md",
            commit_message="Update KBL dataset card",
        )
        print("uploaded README.md")


if __name__ == "__main__":
    main()