"""
Turn the `id2` pairings in raw_sentences.csv into an STS dataset.

Two steps:

  1. python3 make_dataset.py extract
       Follows the `id2` column, de-duplicates the pairs (a pair marked from
       both sides is still one pair) and writes pairs_to_score.csv with an
       empty `score` column.

  2. python3 make_dataset.py build
       Concatenates every scored CSV in SCORED (each contributor's own file) and
       writes sts_tr_dataset.parquet with exactly three columns -- sentence1,
       sentence2, score -- matching the standard STS format. Pairs duplicated
       across contributors are reported and kept only once.

`extract` refuses to overwrite a pairs_to_score.csv that already has scores in
it; pass --force to override. Scoring is hand work and is not recoverable from
raw_sentences.csv.
"""

import argparse
import csv
import re
import sys
from pathlib import Path

import pyarrow as pa
import pyarrow.parquet as pq

HERE = Path(__file__).parent
RAW = HERE / "raw_sentences.csv"
PAIRS = HERE / "pairs_to_score.csv"
OUT = HERE / "sts_tr_dataset.parquet"

# One scored CSV per contributor, each with sentence1, sentence2, score.
# Keeping them as separate files means neither person's scoring can be
# clobbered by the other's, and provenance stays traceable after the merge.
SCORED = [
    ("Erenyanic", PAIRS),
    ("nursimakgul", HERE / "sts_data.csv"),
]

SCORE_MIN, SCORE_MAX = 0.0, 5.0


def read_raw():
    with open(RAW, encoding="utf-8") as f:
        rows = list(csv.DictReader(f))
    if "id2" not in rows[0]:
        sys.exit("raw_sentences.csv has no id2 column -- nothing to pair.")
    return rows


def guard_existing_scores(force):
    """Scoring is hand work. Losing it to an accidental re-run costs hours and
    cannot be rebuilt from raw_sentences.csv, which holds no scores."""
    if force or not PAIRS.exists():
        return
    with open(PAIRS, encoding="utf-8") as f:
        scored = sum(1 for r in csv.DictReader(f) if (r.get("score") or "").strip())
    if scored:
        sys.exit(f"{PAIRS.name} already has {scored} scored row(s). "
                 f"Refusing to overwrite. Re-run with --force if you mean it.")


def extract(force=False):
    guard_existing_scores(force)
    rows = read_raw()
    by_id = {r["id"]: r for r in rows}

    seen, pairs, problems = set(), [], []
    for r in rows:
        target = r["id2"].strip()
        if not target:
            continue
        if target == r["id"]:
            problems.append(f"id {r['id']} pairs with itself")
            continue
        if target not in by_id:
            problems.append(f"id {r['id']} -> id2 {target}: no such id")
            continue
        # A pair marked from both sides is one pair, not two.
        key = frozenset({r["id"], target})
        if key in seen:
            continue
        seen.add(key)
        # Lower id first, so the output is stable across runs.
        a, b = sorted(key, key=int)
        pairs.append({
            "sentence1": by_id[a]["sentence"],
            "sentence2": by_id[b]["sentence"],
            "score": "",
            "_sort": int(a),
        })

    if problems:
        print("problems found:")
        for p in problems:
            print("  ", p)

    pairs.sort(key=lambda p: p.pop("_sort"))
    with open(PAIRS, "w", newline="", encoding="utf-8") as f:
        w = csv.DictWriter(f, fieldnames=["sentence1", "sentence2", "score"])
        w.writeheader()
        w.writerows(pairs)

    print(f"{len(pairs)} pairs -> {PAIRS.name}")
    print(f"Fill in the `score` column (0-5), then run: python3 {Path(__file__).name} build")


def norm(s):
    """Whitespace- and case-insensitive form, for duplicate detection only."""
    return re.sub(r"\s+", " ", (s or "").strip()).lower()


def read_scored(who, path):
    """Validate one contributor's scored CSV and return its rows."""
    with open(path, encoding="utf-8") as f:
        rows = list(csv.DictReader(f))
    out, missing, bad = [], [], []
    for i, r in enumerate(rows, 2):  # line 2 = first data row
        raw = (r.get("score") or "").strip().replace(",", ".")
        if not raw:
            missing.append(i)
            continue
        try:
            v = float(raw)
        except ValueError:
            bad.append(f"{path.name} line {i}: {raw!r} is not a number")
            continue
        if not SCORE_MIN <= v <= SCORE_MAX:
            bad.append(f"{path.name} line {i}: {v} outside [{SCORE_MIN}, {SCORE_MAX}]")
            continue
        s1, s2 = (r.get("sentence1") or "").strip(), (r.get("sentence2") or "").strip()
        if not s1 or not s2:
            bad.append(f"{path.name} line {i}: empty sentence")
            continue
        if norm(s1) == norm(s2):
            bad.append(f"{path.name} line {i}: sentence1 and sentence2 are identical")
            continue
        out.append({"sentence1": s1, "sentence2": s2, "score": v, "who": who})
    if missing:
        sys.exit(f"{path.name}: {len(missing)} row(s) have no score (lines: "
                 f"{', '.join(map(str, missing[:12]))}"
                 f"{'...' if len(missing) > 12 else ''}). Fill them in first.")
    if bad:
        print("invalid rows:")
        for b in bad:
            print("  ", b)
        sys.exit(1)
    return out


def build():
    merged, seen, dupes = [], {}, []
    for who, path in SCORED:
        if not path.exists():
            print(f"note: {path.name} not found, skipping {who}")
            continue
        rows = read_scored(who, path)
        print(f"  {who:12s} {len(rows):>3} pairs from {path.name}")
        for r in rows:
            # A pair is the same pair regardless of which side each sentence is
            # on, so an unordered key catches a pair scored by both people.
            key = frozenset({norm(r["sentence1"]), norm(r["sentence2"])})
            if key in seen:
                first = seen[key]
                dupes.append(f"{r['who']} repeats a pair from {first['who']} "
                             f"(scores {first['score']} vs {r['score']}): "
                             f"{r['sentence1'][:60]}")
                continue
            seen[key] = r
            merged.append(r)

    if not merged:
        sys.exit("no scored pairs found.")
    if dupes:
        print(f"\n{len(dupes)} duplicate pair(s) dropped, first occurrence kept:")
        for d in dupes:
            print("  ", d)

    table = pa.table({
        "sentence1": pa.array([r["sentence1"] for r in merged], pa.string()),
        "sentence2": pa.array([r["sentence2"] for r in merged], pa.string()),
        "score": pa.array([r["score"] for r in merged], pa.float64()),
    })
    pq.write_table(table, OUT)

    scores = [r["score"] for r in merged]
    dist = {}
    for v in scores:
        dist[v] = dist.get(v, 0) + 1
    print(f"\n{len(merged)} pairs -> {OUT.name}")
    print(f"  schema: {', '.join(f'{n}: {t}' for n, t in zip(table.column_names, table.schema.types))}")
    print(f"  mean score: {sum(scores) / len(scores):.2f}")
    print("  distribution:", dict(sorted(dist.items())))


if __name__ == "__main__":
    ap = argparse.ArgumentParser(description=__doc__,
                                 formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("step", choices=["extract", "build"])
    ap.add_argument("--force", action="store_true",
                    help="allow extract to overwrite an already-scored pairs file")
    args = ap.parse_args()
    if args.step == "extract":
        extract(force=args.force)
    else:
        build()