import json
import sys
from pathlib import Path

_GRADES = [(90, "A"), (75, "B"), (60, "C"), (40, "D"), (0, "F")]


def _score(r: dict) -> tuple[int, list[str]]:
    """
    Voto 0-100 sulla qualità del clean.md per vettorizzazione.

    Penalità struttura:
      livello 0 (assente)  → −40
      livello 1 (piatto)   → −15

    Penalità residui (degradano il retrieval):
      backtick             → −2/cad  (max −20)
      dot-leader           → −5/cad  (max −10)
      URL/watermark        → −5/cad  (max −15)
      immagini             → −5/cad  (max −10)
      <br> inline          → −2/cad  (max −15)
      simboli encoding     → −1/cad  (max −10)
      formule inline [N.M] → −1/cad  (max −8)
      footnote residui     → −1/cad  (max −8)
      caratteri PUA        → −2/cad  (max −20)

    Penalità anomalie:
      bare headers         → −3/cad  (max −15)
    """
    score     = 100
    detail    = []
    structure = r.get("structure", {})
    anomalie  = r.get("anomalie",  {})
    residui   = r.get("residui",   {})

    livello = structure.get("livello_struttura", 0)
    if livello == 0:
        score -= 40
        detail.append("struttura assente −40")
    elif livello == 1:
        score -= 15
        detail.append("struttura piatta −15")

    def _pen(key: str, per_item: int, cap: int, label: str) -> None:
        n = residui.get(key, 0)
        if n:
            p = min(cap, n * per_item)
            nonlocal score
            score -= p
            detail.append(f"{label} ×{n} −{p}")

    _pen("backtick",         2, 20, "backtick")
    _pen("dotleader",        5, 10, "dot-leader")
    _pen("url",              5, 15, "url")
    _pen("immagini",         5, 10, "immagini")
    _pen("br_inline",        2, 15, "<br> inline")
    _pen("simboli_encoding", 1, 10, "simboli encoding")
    _pen("formule_inline",   1,  8, "formule inline")
    _pen("footnote_markers", 1,  8, "footnote residui")
    _pen("pua_markers",      2, 20, "caratteri PUA font Symbol")
    _pen("formula_headers",  3, 15, "formula/esercizio come header")

    n_bare = anomalie.get("bare_headers", 0)
    if n_bare:
        p = min(15, n_bare * 3)
        score -= p
        detail.append(f"bare headers ×{n_bare} −{p}")

    return max(0, score), detail


def _grade(score: int) -> str:
    return next(g for threshold, g in _GRADES if score >= threshold)


def validate(stems: list[str], project_root: Path, detail: bool = False) -> None:
    conv_dir = project_root / "conversione"

    paths = (
        [conv_dir / s / "report.json" for s in stems]
        if stems
        else sorted(conv_dir.glob("*/report.json"))
    )

    if not paths:
        print("Nessun report.json trovato in conversione/*/")
        sys.exit(0)

    rows = [
        json.loads(p.read_text(encoding="utf-8")) if p.exists()
        else {"stem": p.parent.name, "_missing": True}
        for p in paths
    ]

    col    = max(len(r.get("stem", "stem")) for r in rows) + 2
    header = (
        f"{'stem':<{col}}"
        f"{'h2':>4}{'h3':>5}  "
        f"{'strategia':<18}"
        f"{'bare':>5}{'corte':>6}{'lunghe':>7}"
        f"{'btk':>5}{'br':>4}{'enc':>4}{'url':>4}{'fhdr':>5}"
        f"{'med':>6}"
        f"  {'voto':>4}  grade"
    )
    sep = "─" * len(header)
    print(f"\n{header}\n{sep}")

    scores = []
    for r in rows:
        if r.get("_missing"):
            print(f"{r['stem']:<{col}}  (report.json non trovato)")
            continue

        st   = r.get("structure",    {})
        an   = r.get("anomalie",     {})
        res  = r.get("residui",      {})
        dist = r.get("distribution", {})
        s, pen = _score(r)
        scores.append(s)

        print(
            f"{r['stem']:<{col}}"
            f"{st.get('n_h2',              0):>4}"
            f"{st.get('n_h3',              0):>5}  "
            f"{st.get('strategia_chunking','?'):<18}"
            f"{an.get('bare_headers',      0):>5}"
            f"{an.get('short_sections',    0):>6}"
            f"{an.get('long_sections',     0):>7}"
            f"{res.get('backtick',         0):>5}"
            f"{res.get('br_inline',        0):>4}"
            f"{res.get('simboli_encoding', 0):>4}"
            f"{res.get('url',              0):>4}"
            f"{res.get('formula_headers',  0):>5}"
            f"{dist.get('mediana',         0):>6}"
            f"  {s:>4}  {_grade(s)}"
        )
        if detail and pen:
            for p in pen:
                print(f"  {'':>{col}}  ↳ {p}")

    print(sep)
    if scores:
        media = sum(scores) / len(scores)
        print(
            f"Documenti: {len(scores)}   "
            f"Media: {media:.0f}/100 {_grade(int(media))}   "
            f"(A≥90  B≥75  C≥60  D≥40  F<40)"
        )
    print(
        "\nColonne: bare=header vuoti  corte=sez<150ch  lunghe=sez>1500ch  "
        "btk=backtick  br=<br>inline  enc=simboli encoding  fhdr=formula-header  med=mediana chars\n"
    )