#!/usr/bin/env python3
"""Diversity proxy statistics + legal-subject word cloud (appendix material).

Case-type composition is not part of the LEGEX ground truth, so this script
summarizes sample diversity along the dimensions that ARE annotated: field
coverage, observed ISIC sectors, party structure, and dispute-value coverage.
It also renders a word cloud over the normalized free-text
``legal_subject_judgement`` labels (underscores stripped) for the appendix /
HF dataset card. Reads the published goldenset JSONL (``--gold-dir``).

Usage:
    uv run --with wordcloud python scripts/diversity_stats.py
Outputs:
    data/analysis/tables/diversity.tex
    data/analysis/figures/legal_subject_wordcloud.png
"""

import argparse
import re
import sys
from pathlib import Path

REPO_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(REPO_ROOT))

from legex import published  # noqa: E402
from legex.analysis.countries import COUNTRY_NAMES, RELEASE_COUNTRIES  # noqa: E402

COST_BLOCK = (
    "dispute_value_nominal",
    "plaintiff_loosing_share",
    "court_cost_awarded_nominal",
    "party_compensation_awarded_nominal",
)
ISIC_FIELDS = (
    "plaintiff_no1_ISIC1_industry_category",
    "defendant_no1_ISIC1_industry_category",
)
NON_SECTORS = {"", "none", "no_allocation_possible"}


def _stats(cc: str, gold_dir: Path) -> dict | None:
    if not published.gold_file(gold_dir, cc).exists():
        return None
    # Count judgments the same way as the paper: non-empty
    # legal_subject_judgement (a few released rows lack it).
    _, gold = published.load_gold_labels(gold_dir, cc)
    rows = {
        cid: f
        for cid, f in gold.items()
        if f.get("legal_subject_judgement", "").strip()
    }
    n = len(rows)
    if not n:
        return None
    sectors: set[str] = set()
    n_multi = n_dispute = 0
    cost_filled = cost_total = 0
    subjects: list[str] = []
    for fields in rows.values():
        for f in ISIC_FIELDS:
            v = fields.get(f, "").strip().lower()
            if v not in NON_SECTORS:
                sectors.add(v)
        try:
            multi = int(float(fields.get("plaintiffs_all_count") or 0)) > 1 or \
                    int(float(fields.get("defendants_all_count") or 0)) > 1
        except ValueError:
            multi = False
        n_multi += multi
        n_dispute += bool(fields.get("dispute_value_nominal", "").strip())
        for f in COST_BLOCK:
            cost_total += 1
            cost_filled += bool(fields.get(f, "").strip())
        subj = fields.get("legal_subject_judgement", "").strip()
        if subj:
            subjects.append(subj)
    return {
        "cc": cc,
        "n": n,
        "sectors": len(sectors),
        "pct_multi": 100.0 * n_multi / n,
        "pct_dispute": 100.0 * n_dispute / n,
        "pct_cost": 100.0 * cost_filled / cost_total,
        "subjects": subjects,
    }


def _normalise_subject(s: str) -> str:
    s = s.replace("_", " ").strip()
    s = re.sub(r"\s+", " ", s)
    return s.title()


def write_table(all_stats: list[dict], out: Path) -> None:
    lines = [
        "% Auto-generated by scripts/diversity_stats.py — do not edit by hand.",
        r"\begin{table}[t]",
        r"\caption{Sample diversity along the annotated dimensions."
        r" \emph{Sectors} counts the distinct ISIC top-level sectors observed"
        r" among plaintiffs and defendants (of 22 possible, A--V);"
        r" \emph{multi-party} is the share of judgments with more than one"
        r" plaintiff or defendant; the last two columns give the share of"
        r" judgments with a coded dispute value and the fill rate over the"
        r" four cost-block fields.}",
        r"\label{tab:diversity}",
        r"\vskip 0.05in",
        r"\centering\small",
        r"\begin{tabular}{@{}lrrrrr@{}}",
        r"\toprule",
        r"\textbf{Jurisdiction} & \textbf{$n$} & \textbf{Sectors}"
        r" & \textbf{Multi-party} & \textbf{Dispute value} & \textbf{Cost block} \\",
        r"\midrule",
    ]
    for s in all_stats:
        lines.append(
            f"{COUNTRY_NAMES[s['cc']]} & {s['n']} & {s['sectors']}"
            f" & {s['pct_multi']:.0f}\\% & {s['pct_dispute']:.0f}\\%"
            f" & {s['pct_cost']:.0f}\\% \\\\"
        )
    lines += [r"\bottomrule", r"\end{tabular}", r"\end{table}", ""]
    out.parent.mkdir(parents=True, exist_ok=True)
    out.write_text("\n".join(lines), encoding="utf-8")
    print(f"wrote {out}")


def write_wordcloud(subjects: list[str], out: Path) -> None:
    from wordcloud import STOPWORDS, WordCloud

    # Word-level cloud: full subject labels are long multi-word phrases and
    # render as unreadable sentences; individual (stopword-free) terms show
    # the topical spread instead.
    text = " ".join(_normalise_subject(s) for s in subjects)
    stopwords = STOPWORDS | {"Law", "Legal", "Case", "Proceedings", "Procedure"}
    wc = WordCloud(
        width=1600,
        height=900,
        background_color="white",
        colormap="cividis",
        max_words=100,
        prefer_horizontal=0.95,
        stopwords=stopwords,
        collocations=False,
        random_state=0,  # deterministic layout across runs
    ).generate(text)
    out.parent.mkdir(parents=True, exist_ok=True)
    wc.to_file(str(out))
    print(f"wrote {out} ({len(subjects)} labels)")


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__.splitlines()[0])
    ap.add_argument("--gold-dir", type=Path, default=None,
                    help="published goldenset data directory (default: "
                         "submission/goldensets/data, else ../goldensets/data)")
    args = ap.parse_args()
    gold_dir = args.gold_dir or published.default_gold_dir(REPO_ROOT)

    all_stats = []
    subjects: list[str] = []
    for cc in sorted(RELEASE_COUNTRIES, key=lambda c: COUNTRY_NAMES[c]):
        s = _stats(cc, gold_dir)
        if s is None:
            print(f"[{cc}] no goldenset — skipped", file=sys.stderr)
            continue
        subjects.extend(s.pop("subjects"))
        all_stats.append(s)
    write_table(all_stats, REPO_ROOT / "data/analysis/tables/diversity.tex")
    write_wordcloud(subjects, REPO_ROOT / "data/analysis/figures/legal_subject_wordcloud.png")


if __name__ == "__main__":
    main()