"""Deep profile: why fire-and-forget TTFT is 5x worse than await."""
import json, statistics

await_rows = [json.loads(l) for l in open("outputs/gpu_ab_6p2d/metrics.jsonl")]
fnf_rows = [json.loads(l) for l in open("outputs/gpu_ab_6p2d_fnf/metrics.jsonl")]

await_ok = [r for r in await_rows if not r.get("error")]
fnf_ok = [r for r in fnf_rows if not r.get("error")]

# Match by request_id
await_by_id = {r["request_id"]: r for r in await_ok}
fnf_by_id = {r["request_id"]: r for r in fnf_ok}
common = set(await_by_id.keys()) & set(fnf_by_id.keys())

print("=" * 75)
print("  PROFILE: Fire-and-Forget vs Await-Prefill (same 6P+2D instances)")
print("=" * 75)
print(f"  Common requests: {len(common)}")

# Per-request comparison
diffs = []
for rid in common:
    a = await_by_id[rid]
    f = fnf_by_id[rid]
    if a.get("ttft_s") and f.get("ttft_s") and a["ttft_s"] > 0:
        diffs.append({
            "id": rid, "input": a["input_length"],
            "a_ttft": a["ttft_s"], "f_ttft": f["ttft_s"],
            "ratio": f["ttft_s"] / a["ttft_s"],
            "a_e2e": a["latency_s"], "f_e2e": f["latency_s"],
            "a_tpot": a.get("tpot_s", 0), "f_tpot": f.get("tpot_s", 0),
            "a_out": a.get("actual_output_tokens", 0) or 0,
            "f_out": f.get("actual_output_tokens", 0) or 0,
        })

diffs.sort(key=lambda x: x["input"])

print("\n  Per-request (sorted by input_length):")
hdr = "%8s %10s %10s %7s %10s %10s %8s %8s" % (
    "input", "await_TTFT", "fnf_TTFT", "ratio", "await_E2E", "fnf_E2E", "a_TPOT", "f_TPOT")
print("  " + hdr)
print("  " + "-" * len(hdr))
for d in diffs[:25]:
    print("  %8d %10.3f %10.3f %6.1fx %10.3f %10.3f %8.4f %8.4f" % (
        d["input"], d["a_ttft"], d["f_ttft"], d["ratio"],
        d["a_e2e"], d["f_e2e"], d["a_tpot"], d["f_tpot"]))

# Statistics
if diffs:
    ratios = [d["ratio"] for d in diffs]
    ratios.sort()
    p = lambda v, q: v[min(int(q*len(v)), len(v)-1)]
    print("\n  TTFT ratio (FnF / Await):")
    print("    p10=%.2fx  p50=%.2fx  p90=%.2fx  mean=%.2fx" % (
        p(ratios,.1), p(ratios,.5), p(ratios,.9), statistics.fmean(ratios)))

    faster = sum(1 for r in ratios if r < 1.0)
    print("    FnF faster: %d/%d (%.0f%%)" % (faster, len(ratios), faster*100/len(ratios)))

    # Bucket by input size
    print("\n  TTFT ratio by input size bucket:")
    buckets = [(0, 5000, "<5k"), (5000, 20000, "5-20k"), (20000, 50000, "20-50k"), (50000, 999999, ">50k")]
    for lo, hi, label in buckets:
        subset = [d for d in diffs if lo <= d["input"] < hi]
        if subset:
            rs = [d["ratio"] for d in subset]
            a_ttfts = [d["a_ttft"] for d in subset]
            f_ttfts = [d["f_ttft"] for d in subset]
            print("    %6s: n=%3d  await_TTFT=%.3f  fnf_TTFT=%.3f  ratio=%.2fx" % (
                label, len(subset), statistics.fmean(a_ttfts), statistics.fmean(f_ttfts),
                statistics.fmean(rs)))

    # TPOT comparison
    a_tpots = [d["a_tpot"] for d in diffs if d["a_tpot"] > 0]
    f_tpots = [d["f_tpot"] for d in diffs if d["f_tpot"] > 0]
    if a_tpots and f_tpots:
        print("\n  TPOT comparison:")
        print("    Await: mean=%.4f  p50=%.4f" % (statistics.fmean(a_tpots), sorted(a_tpots)[len(a_tpots)//2]))
        print("    FnF:   mean=%.4f  p50=%.4f" % (statistics.fmean(f_tpots), sorted(f_tpots)[len(f_tpots)//2]))

# Also look at non-common requests (FnF only failures)
fnf_err = [r for r in fnf_rows if r.get("error")]
await_err_ids = {r["request_id"] for r in await_rows if r.get("error")}
fnf_only_err = [r for r in fnf_err if r["request_id"] not in await_err_ids]
print("\n  Errors unique to FnF: %d" % len(fnf_only_err))
for r in fnf_only_err[:5]:
    print("    input=%d err=%s" % (r["input_length"], r["error"][:60]))