"""DignityCheck eval — solo clasificadores (rápido). Tasa de falsos positivos sobre frases no-odio."""
import json
import pandas as pd, torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
DEV = "mps" if torch.backends.mps.is_available() else "cpu"
df = pd.read_csv("ic/public/dignityai/dignitycheck/dignitycheck_v0.1.csv"); texts = df.texto.tolist()
@torch.no_grad()
def lg(tok, mod, bs=32):
    o = []
    for i in range(0, len(texts), bs):
        enc = tok(texts[i:i+bs], truncation=True, max_length=96, padding=True, return_tensors="pt").to(DEV)
        o.append(mod(**enc).logits.float().cpu())
    return torch.cat(o)
def load(r): return AutoTokenizer.from_pretrained(r), AutoModelForSequenceClassification.from_pretrained(r).to(DEV).eval()
tk, md = load("piuba-bigdata/beto-contextualized-hate-speech"); S = torch.sigmoid(lg(tk, md)).numpy(); df["uba"] = S[:, [1,2,3,4,6,7]].max(1)
tk, md = load("pysentimiento/robertuito-hate-speech"); df["robertuito"] = torch.sigmoid(lg(tk, md)[:, 0]).numpy()
tk, md = load("antonn-dromundo/SinOdio-BETO-HateSpeech-Detector-v3"); df["sinodio"] = torch.softmax(lg(tk, md), -1)[:, 1].numpy()
MOD = ["uba", "robertuito", "sinodio"]
res = {"n": len(df), "umbral": 0.5,
       "falsos_positivos_pct": {m: round(float((df[m] > 0.5).mean()) * 100, 1) for m in MOD},
       "por_grupo": {m: {g: round(float((s[m] > 0.5).mean()) * 100, 1) for g, s in df.groupby("grupo")} for m in MOD},
       "por_tipo": {m: {t: round(float((s[m] > 0.5).mean()) * 100, 1) for t, s in df.groupby("tipo")} for m in MOD}}
df.to_csv("dignitycheck_scores.csv", index=False)
json.dump(res, open("dignitycheck_eval.json", "w"), ensure_ascii=False, indent=2)
print(json.dumps(res, ensure_ascii=False, indent=1))