"""Load the SCM-SQL dataset and print a few pairs from each level.

Two loading paths shown:
  1. Native YAML — zero extra dependencies beyond PyYAML.
  2. Hugging Face `datasets` — canonical for ML training / eval scripts.

Run:
  python examples/load_dataset.py
"""
from __future__ import annotations

from collections import Counter
from pathlib import Path


def load_from_yaml() -> list[dict]:
    """Load directly from the shipped YAML file (no HF dependency)."""
    import yaml
    here = Path(__file__).resolve().parent.parent
    data = yaml.safe_load((here / "data" / "pilot_500.yaml").read_text(encoding="utf-8"))
    return data["pairs"]


def load_from_huggingface() -> list[dict]:
    """Load from the Hugging Face hub. Requires `pip install datasets`."""
    from datasets import load_dataset  # type: ignore
    ds = load_dataset("AniruddhaAI/scm-sql", split="test")
    return list(ds)


def summarise(pairs: list[dict]) -> None:
    by_level = Counter(p["level"] for p in pairs)
    print(f"Loaded {len(pairs)} pairs")
    print(f"By level: {dict(sorted(by_level.items()))}")
    n_multi = sum(1 for p in pairs if p.get("turns"))
    print(f"Multi-turn dialogues: {n_multi}")

    print("\nFirst pair at each level:")
    seen: set[int] = set()
    for p in pairs:
        lvl = p["level"]
        if lvl in seen:
            continue
        seen.add(lvl)
        print(f"\n--- L{lvl}  ·  {p['id']}  ·  domains={p['domains']} ---")
        if "turns" in p:
            for i, t in enumerate(p["turns"], 1):
                print(f"  Turn {i} NL : {t['nl']}")
                print(f"         SQL: {t['gold_sql'].strip()[:120]}...")
        else:
            print(f"  NL : {p['nl']}")
            print(f"  SQL: {p['gold_sql'].strip()[:120]}...")
        if len(seen) == 6:
            break


if __name__ == "__main__":
    pairs = load_from_yaml()
    summarise(pairs)