"""semif-serve acceptance on the real card. Contract § Acceptance.

Needs SemIf's authored144 rows and their committed torch predictions (same model revision,
torch 2.10.0+cu128) from a SemIf checkout at the pinned commit:
    SEMIF_DIR=<checkout> SEMIF_URL=http://10.251.50.54:8032 SEMIF_TOKEN=... \
      uv run --with httpx python accept.py out.json

1 parity      our /decide vs their direct-authored144 predictions: top choice, max |Δp|, prompt hash
2 noise floor the same 144 rows again (A-vs-A)
3 negative    option descriptions rotated one place: agreement with the reference MUST drop
4 shared      one state, many criteria through /decide/shared vs /decide on the same rows
5 speed       21 binary criteria over one state, shared vs 21 sequential /decide, 3 runs, after warm-up
"""
import json
import os
import statistics
import sys
import time
from pathlib import Path

import httpx

URL, TOKEN, SEMIF = os.environ["SEMIF_URL"], os.environ["SEMIF_TOKEN"], Path(os.environ["SEMIF_DIR"])
H = {"Authorization": f"Bearer {TOKEN}"}
client = httpx.Client(timeout=300)


def row(r):
    return {k: r[k] for k in ("id", "state", "question", "options")}


def decide(r):
    resp = client.post(f"{URL}/decide", json=row(r), headers=H)
    resp.raise_for_status()
    return resp.json()


def argmax(p):
    return max(range(len(p)), key=p.__getitem__)


def compare(ours, ref):
    agree = sum(argmax(ours[i]["probabilities"]) == argmax(ref[i]["probabilities"]) for i in ref)
    gap = max(max(abs(a - b) for a, b in zip(ours[i]["probabilities"], ref[i]["probabilities"])) for i in ref)
    return {"rows": len(ref), "top_choice_agree": agree, "max_abs_prob_gap": gap}


rows = [json.loads(l) for l in (SEMIF / "benchmarks/data/authored144.jsonl").read_text().splitlines() if l.strip()]
ref = {p["id"]: p for p in map(json.loads, (SEMIF / "results/raw/predictions/direct-authored144.jsonl").read_text().splitlines()) if p}
report = {"url": URL, "health": client.get(f"{URL}/health").json()}

run_a = {r["id"]: decide(r) for r in rows}
run_b = {r["id"]: decide(r) for r in rows}
report["1_parity_vs_upstream"] = compare(run_a, ref)
report["1_prompt_sha256_equal"] = sum(run_a[i]["prompt_sha256"] == ref[i]["prompt_sha256"] for i in ref)
report["2_noise_floor_a_vs_b"] = compare(run_a, run_b)

rotated = []
for r in rows:
    descs = [o["description"] for o in r["options"]]
    descs = descs[1:] + descs[:1]
    rotated.append({**r, "options": [{**o, "description": d} for o, d in zip(r["options"], descs)]})
report["3_negative_rotated_options"] = compare({r["id"]: decide(r) for r in rotated}, ref)

# 4: group authored144 by identical state; score every multi-row group both ways.
groups = {}
for r in rows:
    groups.setdefault(json.dumps(r["state"], sort_keys=True), []).append(r)
multi = [g for g in groups.values() if len(g) > 1]
shared_out = {}
for g in multi:
    resp = client.post(f"{URL}/decide/shared", headers=H, json={
        "state": g[0]["state"], "decisions": [{k: r[k] for k in ("id", "question", "options")} for r in g]})
    resp.raise_for_status()
    shared_out.update({res["id"]: res for res in resp.json()["results"]})
report["4_shared_vs_direct"] = {"groups": len(multi), **compare(shared_out, {i: run_a[i] for i in shared_out})}

# 5: 21 binary criteria over one state
state = rows[0]["state"]
crit = [{"id": f"c{i}", "question": f"Does the evidence mention item number {i}?",
         "options": [{"id": "yes", "description": "Yes"}, {"id": "no", "description": "No"}]} for i in range(21)]
for _ in range(2):   # warm-up
    client.post(f"{URL}/decide/shared", headers=H, json={"state": state, "decisions": crit}).raise_for_status()
shared_s, seq_s, prefix = [], [], None
for _ in range(3):
    t = time.perf_counter()
    resp = client.post(f"{URL}/decide/shared", headers=H, json={"state": state, "decisions": crit})
    resp.raise_for_status()
    shared_s.append(time.perf_counter() - t)
    prefix = resp.json()["timing"]["prefix_tokens"]
    t = time.perf_counter()
    for c in crit:
        decide({**c, "state": state})
    seq_s.append(time.perf_counter() - t)
report["5_speed_21_binary"] = {
    "prefix_tokens": prefix,
    "shared_s": {"runs": shared_s, "median": statistics.median(shared_s)},
    "sequential_decide_s": {"runs": seq_s, "median": statistics.median(seq_s)},
}
json.dump(report, open(sys.argv[1], "w"), indent=1)
print(json.dumps({k: v for k, v in report.items() if k != "health"}, indent=1))