"""Cases taken from each model's own training corpus, loaded from cases.json.

Every case is an attack, and every one is a real record rather than something
written for the demo: the anomaly sessions come from the agent_trust split of
`AnShi-HW/tool-call-anomaly-data`, the intent records from tool-intent-align's
training corpora, and the output-filter rows from minspan's training split.
Each case keeps its source and record id in `cases.json`, and the button label
is the attack class that corpus assigns it, so the labels stay short and read
as one set.

`scripts/extract_cases.py` regenerates the file.
"""

from __future__ import annotations

import json
from pathlib import Path

CASES = json.loads(Path(__file__).with_name("cases.json").read_text(encoding="utf-8"))


def pretty(value):
    return json.dumps(value, ensure_ascii=False, indent=2)


def labels(task):
    return [(case["label"], "risk") for case in CASES[task]]


INTENT_CASES = CASES["intent"]
OUTPUT_CASES = CASES["output_filter"]
ANOMALY_CASES = CASES["anomaly"]

INTENT_LABELS = labels("intent")
OUTPUT_LABELS = labels("output_filter")
ANOMALY_LABELS = labels("anomaly")

INTENT_EXAMPLES = [
    [pretty(case["context"]), pretty(case["tool"]), pretty(case["call"])] for case in INTENT_CASES
]
OUTPUT_EXAMPLES = [[case["instruction"], case["data"]] for case in OUTPUT_CASES]
ANOMALY_EXAMPLES = [[case["task"], pretty(case["tool_calls"])] for case in ANOMALY_CASES]