"""Build the Siemens offline smoke dataset from a completed dataset_build run.

Must be run AFTER `python main.py --dataset-build-config
scenarios/siemens_build/siemens-pdf-build.yaml` has completed successfully.

It uses the stable `latest/` alias so you don't need to know the run_id.

Usage:
    python scripts/build_siemens_offline_smoke.py

Output:
    datasets/normalized/siemens_pdf_offline_smoke.csv
    (referenced by scenarios/offline/siemens-pdf-offline-smoke.yaml)
"""

from __future__ import annotations

from pathlib import Path

# ---------------------------------------------------------------------------
# Paths — all relative to the siemens_ragas/ repository root
# ---------------------------------------------------------------------------
REPO_ROOT = Path(__file__).resolve().parents[1]

DRAFT_DATASET_PATH = (
    REPO_ROOT / "outputs" / "dataset-builds" / "siemens-pdf-question-bank"
    / "latest" / "dataset_draft.csv"
)
SOURCE_CHUNKS_PATH = (
    REPO_ROOT / "outputs" / "dataset-builds" / "siemens-pdf-question-bank"
    / "latest" / "source_chunks.jsonl"
)
OUTPUT_PATH = REPO_ROOT / "datasets" / "normalized" / "siemens_pdf_offline_smoke.csv"


def main() -> None:
    """Convert the Siemens build artefacts into an offline-evaluable dataset."""
    if not DRAFT_DATASET_PATH.exists():
        raise FileNotFoundError(
            f"Draft dataset not found: {DRAFT_DATASET_PATH}\n"
            "Run the dataset build first:\n"
            "  python main.py --dataset-build-config "
            "scenarios/siemens_build/siemens-pdf-build.yaml"
        )
    if not SOURCE_CHUNKS_PATH.exists():
        raise FileNotFoundError(
            f"Source chunks not found: {SOURCE_CHUNKS_PATH}\n"
            "Run the dataset build first."
        )

    # Import here so the script is importable even before rag_eval is fully set up.
    from rag_eval.dataset_builder.offline_converter import build_offline_smoke_dataset

    output = build_offline_smoke_dataset(
        draft_dataset_path=DRAFT_DATASET_PATH,
        source_chunks_path=SOURCE_CHUNKS_PATH,
        output_path=OUTPUT_PATH,
    )

    import pandas as pd
    frame = pd.read_csv(output)
    print(f"Offline smoke dataset written to: {output}")
    print(f"Total rows: {len(frame)}")
    if len(frame) > 0:
        lang_counts = frame["language"].value_counts().to_dict() if "language" in frame.columns else {}
        diff_counts = frame["difficulty"].value_counts().to_dict() if "difficulty" in frame.columns else {}
        print(f"Language distribution: {lang_counts}")
        print(f"Difficulty distribution: {diff_counts}")


if __name__ == "__main__":
    main()