"""Auditoría y versión limpia (v1.1) de MachismoDetect y SinOdio-LATAM, con el mismo método que Racismo/Clasismo."""
import glob, json, os, re, unicodedata
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import GroupShuffleSplit

SEED = 42
ABBREV = {"xq": "porque", "pq": "porque", "xk": "porque", "pk": "porque", "q": "que", "ke": "que", "k": "que", "pa": "para",
          "tb": "tambien", "tmb": "tambien", "toy": "estoy", "vdd": "verdad", "td": "todo", "nd": "nada", "grax": "gracias"}
LAUGHS = {"jaja", "jajaja", "jeje", "lol", "jajaj", "xd", "v"}

def skeleton(t):
    t = unicodedata.normalize("NFKD", str(t).lower())
    t = "".join(c for c in t if not unicodedata.combining(c))
    t = re.sub(r"[^a-zñ0-9 ]+", " ", t); t = re.sub(r"(.)\1{1,}", r"\1", t)
    return " ".join(w for w in (ABBREV.get(w, w) for w in t.split()) if w not in LAUGHS)

def splits_of(prefix):
    return {os.path.basename(f).split("__")[1].replace(".parquet", ""): pd.read_parquet(f) for f in sorted(glob.glob(f"data/{prefix}__*.parquet"))}

def to_long(name, parts):
    out = []
    for sp, d in parts.items():
        if name == "machismodetect":
            meta = d[["nivel", "subcategoria", "pais", "contexto"]]
            out.append(pd.concat([meta.assign(texto=d.texto_machista, etiqueta=1), meta.assign(texto=d.texto_inclusivo, etiqueta=0)]).assign(split_original=sp))
        else:
            out.append(d.rename(columns={"text": "texto", "label": "etiqueta", "country": "pais", "scenario": "escenario"}).assign(split_original=sp))
    df = pd.concat(out, ignore_index=True)
    df["texto"] = df.texto.astype(str).str.strip()
    df["esqueleto"] = df.texto.map(skeleton)
    return df

def baseline(tr, te, col="etiqueta"):
    v = TfidfVectorizer(ngram_range=(1, 2), min_df=2, sublinear_tf=True)
    m = LogisticRegression(max_iter=3000, class_weight="balanced").fit(v.fit_transform(tr.texto), tr[col])
    return round(float(f1_score(te[col], m.predict(v.transform(te.texto)), average="macro")), 3)

def audit(name, group_col):
    parts = splits_of(name)
    df = to_long(name, parts)
    rep = {"dataset": name, "filas_originales_por_split": {k: len(v) for k, v in parts.items()},
           "textos_formato_largo": len(df), "por_etiqueta_original": df.etiqueta.value_counts().to_dict()}
    # fuga en splits originales
    tr0, te0 = df[df.split_original == "train"], df[df.split_original == "test"]
    rep["fuga_original_test_en_train_exacta_pct"] = round(100 * te0.texto.isin(set(tr0.texto)).mean(), 1)
    rep["fuga_original_test_en_train_esqueleto_pct"] = round(100 * te0.esqueleto.isin(set(tr0.esqueleto)).mean(), 1)
    rep["baseline_split_original_f1"] = baseline(tr0, te0)
    # limpieza
    exact = df.drop_duplicates(["texto", "etiqueta"])
    clean = exact.drop_duplicates(["esqueleto", "etiqueta"])
    conf = clean.groupby("esqueleto").etiqueta.nunique(); ck = set(conf[conf > 1].index)
    clean = clean[~clean.esqueleto.isin(ck)].reset_index(drop=True)
    rep.update({"tras_duplicados_exactos": len(exact), "tras_casi_duplicados": len(clean),
                "esqueletos_con_etiqueta_contradictoria": len(ck), "por_etiqueta_v1_1": clean.etiqueta.value_counts().to_dict(),
                "unicos_por_etiqueta_pct_conservado": {int(k): round(100 * clean.etiqueta.eq(k).sum() / df.etiqueta.eq(k).sum(), 1) for k in [0, 1]}})
    g1 = GroupShuffleSplit(1, test_size=0.3, random_state=SEED); a, b = next(g1.split(clean, groups=clean.esqueleto))
    rest = clean.iloc[b]; g2 = GroupShuffleSplit(1, test_size=0.5, random_state=SEED); c, d = next(g2.split(rest, groups=rest.esqueleto))
    sp = {"train": clean.iloc[a], "validation": rest.iloc[c], "test": rest.iloc[d]}
    rep["splits_v1_1"] = {k: len(v) for k, v in sp.items()}
    rep["baseline_split_v1_1_f1"] = baseline(sp["train"], sp["test"])
    gs = GroupShuffleSplit(1, test_size=0.25, random_state=SEED); x, y = next(gs.split(clean, groups=clean[group_col]))
    rep[f"baseline_{group_col}_no_vistas_f1"] = baseline(clean.iloc[x], clean.iloc[y])
    rep["longitud_mediana"] = clean.groupby("etiqueta").texto.apply(lambda s: int(s.str.len().median())).to_dict()
    os.makedirs(f"v11/{name}", exist_ok=True)
    cols = [c for c in clean.columns if c not in ("esqueleto", "split_original")]
    for k, v in sp.items():
        v[cols].to_csv(f"v11/{name}/{k}.csv", index=False)
    clean[cols].to_parquet(f"v11/{name}/completo.parquet", index=False)
    json.dump(rep, open(f"v11/{name}/reporte_v1_1.json", "w"), ensure_ascii=False, indent=2, default=int)
    print(json.dumps(rep, ensure_ascii=False, default=int))

if __name__ == "__main__":
    audit("machismodetect", "subcategoria")
    audit("sinodio_latam", "escenario")