#!/usr/bin/env python3
"""Reproduce the model's KNOWN FAILURE CASES (see KNOWN_LIMITATIONS.md).

All inputs are SYNTHETIC — fictional names, no real personal data. The point is
to show, honestly and reproducibly, where the model still misses or mislabels
PERSON names so you can decide where extra safeguards (post-pass, review) are
needed.  pip install onnxruntime transformers numpy

Run from the repo root:  python examples/known_limitations.py
"""
import json
from pathlib import Path

import numpy as np
import onnxruntime as ort
from transformers import AutoTokenizer

ROOT = Path(__file__).resolve().parent.parent
PER_THRESHOLD = 0.2

tok = AutoTokenizer.from_pretrained(str(ROOT))
cfg = json.load(open(ROOT / "config.json", encoding="utf-8"))
id2label = {int(k): v for k, v in cfg["id2label"].items()}
label2id = cfg["label2id"]
sess = ort.InferenceSession(str(ROOT / "onnx" / "model_quantized.onnx"))
in_names = {i.name for i in sess.get_inputs()}


def softmax(x):
    e = np.exp(x - x.max(-1, keepdims=True)); return e / e.sum(-1, keepdims=True)


def predict_per(text):
    enc = tok(text, return_offsets_mapping=True, return_tensors="np", truncation=True, max_length=512)
    offs = enc["offset_mapping"][0]
    feeds = {"input_ids": enc["input_ids"].astype(np.int64),
             "attention_mask": enc["attention_mask"].astype(np.int64)}
    if "token_type_ids" in in_names:
        feeds["token_type_ids"] = np.zeros_like(enc["input_ids"], dtype=np.int64)
    probs = softmax(sess.run(None, feeds)[0][0])
    ids = probs.argmax(-1)
    pb, pi = label2id["B-PER"], label2id["I-PER"]
    spans, cur = [], None
    for i, (s, e) in enumerate(offs):
        if s == e:
            if cur: spans.append(cur); cur = None
            continue
        lab = id2label[int(ids[i])]
        if lab == "O" and probs[i, pb] + probs[i, pi] >= PER_THRESHOLD:
            lab = "I-PER" if cur else "B-PER"
        if not lab.endswith("PER"):
            if cur: spans.append(cur); cur = None
            continue
        if lab == "B-PER" or cur is None:
            if cur: spans.append(cur)
            cur = [int(s), int(e)]
        else:
            cur[1] = int(e)
    if cur: spans.append(cur)
    return [text[a:b] for a, b in spans]


# (input, fictional clean name behind the garble, failure mode)
CASES = [
    # 1) Heavy OCR garble -> missed entirely
    ("Pozwana ote tobodaaka wniosła sprzeciw.", "Bożena Łobodzińska", "heavy OCR garble -> MISSED"),
    ("Powód go Hea stawił się osobiście.", "Igor Heliasz", "heavy OCR garble -> MISSED"),
    ("Decyzję doręczono: ist yi daziak.", "Justyna Idziak", "heavy OCR garble -> MISSED"),
    # 2) Partial detection -> residue leak
    ("Wniosek złożył wiar Żoją w dniu 5 maja.", "Wiktor Żołądek", "partial -> only part caught (residue)"),
    ("WIESEAW KUE stawił się na rozprawie.", "Wiesław Kuc", "ALL-CAPS garble -> fragmented (gap leaks)"),
]

if __name__ == "__main__":
    print(f"PER threshold = {PER_THRESHOLD}\n")
    for text, clean, mode in CASES:
        per = predict_per(text)
        print(f"# {mode}   (fictional: {clean})")
        print(f"  input : {text}")
        print(f"  model : PER = {per if per else '— (nothing)'}")
        print()