"""Sample docs per source and score corpus quality with cheap heuristics.

ponytail: heuristic eyeball score, not an LLM judge. Add LLM-judge pass if these
metrics look ambiguous and you need semantic quality, not just surface garble.
"""
import sys, re, random
import pyarrow.parquet as pq

SOURCES = ["eurlex", "parliamentary", "wikisource", "wikipedia",
           "dziennik_ustaw", "wolne_lektury"]
N = int(sys.argv[1]) if len(sys.argv) > 1 else 200  # docs sampled per source
SHOW = 2  # raw samples printed per source

PL_DIAC = set("ąćęłńóśźżĄĆĘŁŃÓŚŹŻ")
LETTER = re.compile(r"[^\W\d_]", re.UNICODE)

def sample_rows(path, n):
    """Grab n docs spread across scattered row groups (cheap pseudo-random)."""
    pf = pq.ParquetFile(path)
    ng = pf.num_row_groups
    groups = sorted(random.sample(range(ng), min(ng, 8)))
    out = []
    per = max(1, n // len(groups))
    for g in groups:
        tbl = pf.read_row_group(g, columns=["text"])
        texts = tbl.column("text").to_pylist()
        out += random.sample(texts, min(per, len(texts)))
        if len(out) >= n:
            break
    return out[:n]

def score(text):
    t = text or ""
    n = len(t)
    if n == 0:
        return None
    letters = sum(1 for c in t if LETTER.match(c))
    diac = sum(1 for c in t if c in PL_DIAC)
    repl = t.count("�")                      # OCR/decode garbage
    digits = sum(c.isdigit() for c in t)
    space = sum(c.isspace() for c in t)
    words = t.split()
    uniq = len(set(words)) / len(words) if words else 0
    return dict(
        chars=n,
        letter_ratio=letters / n,
        diac_per_kchar=1000 * diac / n,
        repl_per_kchar=1000 * repl / n,
        digit_ratio=digits / n,
        space_ratio=space / n,
        word_uniq=uniq,
    )

def avg(rows, k):
    v = [r[k] for r in rows if r]
    return sum(v) / len(v) if v else 0

print(f"sampling {N} docs/source\n")
print(f"{'source':<16} {'chars':>8} {'letter%':>8} {'diac/k':>7} "
      f"{'repl/k':>7} {'digit%':>7} {'uniq':>6} {'<200ch':>7}")
for s in SOURCES:
    path = f"data/{s}/{s}.parquet"
    docs = sample_rows(path, N)
    scored = [score(d) for d in docs]
    scored = [x for x in scored if x]
    short = sum(1 for x in scored if x["chars"] < 200) / len(scored)
    print(f"{s:<16} {avg(scored,'chars'):>8.0f} "
          f"{100*avg(scored,'letter_ratio'):>7.1f}% "
          f"{avg(scored,'diac_per_kchar'):>7.1f} "
          f"{avg(scored,'repl_per_kchar'):>7.2f} "
          f"{100*avg(scored,'digit_ratio'):>6.1f}% "
          f"{avg(scored,'word_uniq'):>6.2f} "
          f"{100*short:>6.1f}%")

print("\n=== raw samples ===")
for s in SOURCES:
    docs = sample_rows(f"data/{s}/{s}.parquet", SHOW)
    print(f"\n--- {s} ---")
    for d in docs:
        snippet = re.sub(r"\s+", " ", d)[:300]
        print(f"  [{len(d)} ch] {snippet}")