"""Accuracy under thinking budgets (2k/4k/8k/16k tokens) with budget forcing, reusing the 64k eval samples.

  MODAL_PROFILE=aside modal deploy modal_app/eval_dory.py::APP
  MODAL_PROFILE=aside python3 scripts/spawn_budget.py

Uploads each result file to the beam-dory-data Volume (eval/budget_inputs/) and spawns one budget_force call per
configuration. Results land in eval/results/<model>/budget-*.jsonl.
"""
import json
import subprocess
from pathlib import Path

import modal

RES = Path(__file__).resolve().parent.parent / "data" / "eval" / "results"
CAPS = [2048, 4096, 8192, 16384]
# (local result file, model on the Volume, reasoning_effort, medium_effort)
CONFIGS = [
    ("base_loops2-64k", "base", "", False),
    ("base_loops2eff", "base", "", True),
    ("rE-t050_loops2-low-64k", "merges/rE-t050", "low", False),
    ("rE-t050_loops2-default-64k", "merges/rE-t050", "", False),
    ("rE-t050_loops2-high-64k", "merges/rE-t050", "high", False),
    ("mix030_loops2-64k", "merges/rA-t030", "", False),
]

fn = modal.Function.from_name("beam-dory-eval", "budget_force", environment_name="main")
for tag, model, effort, eff in CONFIGS:
    dest = f"eval/budget_inputs/{tag}.jsonl"
    subprocess.run(["modal", "volume", "put", "-f", "beam-dory-data", str(RES / f"{tag}.jsonl"), dest],
                   check=True, capture_output=True)
    call = fn.spawn(model, 2, dest, CAPS, effort, eff)
    print(json.dumps({"tag": tag, "model": model, "effort": effort, "efficient": eff, "call": call.object_id}), flush=True)