"""4-way ablation analysis: Combined vs 4P4D vs 6P2D vs 6P2D-FnF."""
import csv, json, statistics, os

def gpu_stats(path, groups):
    rows = list(csv.DictReader(open(path)))
    by_gpu = {}
    for r in rows:
        g = int(r["gpu"])
        by_gpu.setdefault(g, []).append(float(r["util_pct"]))
    result = {}
    for gname, indices in groups.items():
        vals = []
        for i in indices:
            vals.extend(by_gpu.get(i, []))
        if vals:
            s = sorted(vals)
            p = lambda q: s[min(int(q*len(s)), len(s)-1)]
            nz = sum(1 for v in vals if v > 0)
            result[gname] = {"mean": statistics.fmean(vals), "p50": p(.5), "p90": p(.9),
                             "max": max(vals), "active": nz*100//len(vals)}
    return result

def lat_stats(path):
    rows = [json.loads(l) for l in open(path)]
    ok = [r for r in rows if not r.get("error")]
    ttfts = sorted([r["ttft_s"] for r in ok if r.get("ttft_s")])
    tpots = sorted([r["tpot_s"] for r in ok if r.get("tpot_s") and r["tpot_s"]>0])
    lats = sorted([r["latency_s"] for r in ok])
    p = lambda v,q: v[min(int(q*len(v)),len(v)-1)] if v else 0
    return {"ok": len(ok), "n": len(rows),
            "t50": p(ttfts,.5), "t90": p(ttfts,.9),
            "p50": p(tpots,.5), "p90": p(tpots,.9),
            "e50": p(lats,.5), "e90": p(lats,.9)}

configs = [
    ("gpu_ab_combined",  "Combined 8colo",    {"All": list(range(8))}),
    ("gpu_ab_pdsep",     "4P+4D await",       {"P": [0,1,2,3], "D": [4,5,6,7], "All": list(range(8))}),
    ("gpu_ab_6p2d",      "6P+2D await",       {"P": list(range(6)), "D": [6,7], "All": list(range(8))}),
    ("gpu_ab_6p2d_fnf",  "6P+2D fire-forget", {"P": list(range(6)), "D": [6,7], "All": list(range(8))}),
]

sep = "=" * 90
print(sep)
print("  ABLATION RESULTS: GPU Utilization + Latency")
print("  All use cache-aware + token-level LB scheduler")
print(sep)

# GPU
print("\n  GPU UTILIZATION (All GPUs aggregate):")
fmt = "  %-20s %7s %7s %7s %7s %7s"
print(fmt % ("Config", "Mean%", "P50%", "P90%", "Max%", "Active"))
print("  " + "-" * 55)
for dirname, label, groups in configs:
    gpath = "outputs/%s/gpu_util.csv" % dirname
    if not os.path.exists(gpath): continue
    gs = gpu_stats(gpath, groups)
    if "All" in gs:
        s = gs["All"]
        print(fmt % (label, "%.1f" % s["mean"], "%.0f" % s["p50"],
                      "%.0f" % s["p90"], "%.0f" % s["max"], "%d%%" % s["active"]))

# P vs D breakdown for PD-Sep configs
print("\n  GPU UTILIZATION (P vs D breakdown):")
for dirname, label, groups in configs:
    if dirname == "gpu_ab_combined": continue
    gpath = "outputs/%s/gpu_util.csv" % dirname
    if not os.path.exists(gpath): continue
    gs = gpu_stats(gpath, groups)
    parts = []
    if "P" in gs: parts.append("P:%.1f%%(%d%%act)" % (gs["P"]["mean"], gs["P"]["active"]))
    if "D" in gs: parts.append("D:%.1f%%(%d%%act)" % (gs["D"]["mean"], gs["D"]["active"]))
    print("  %-20s %s" % (label, "  ".join(parts)))

# Latency
print("\n  LATENCY:")
fmt2 = "  %-20s %7s %8s %8s %8s %8s %8s"
print(fmt2 % ("Config", "OK/N", "TTFT50", "TTFT90", "TPOT50", "TPOT90", "E2E50"))
print("  " + "-" * 68)
for dirname, label, _ in configs:
    mpath = "outputs/%s/metrics.jsonl" % dirname
    if not os.path.exists(mpath): continue
    s = lat_stats(mpath)
    print(fmt2 % (label, "%d/%d" % (s["ok"], s["n"]),
                   "%.3f" % s["t50"], "%.3f" % s["t90"],
                   "%.3f" % s["p50"], "%.3f" % s["p90"], "%.3f" % s["e50"]))

# Ablation conclusions
print("\n" + sep)
print("  ABLATION CONCLUSIONS")
print(sep)
print("""
  Ablation 1 — P/D ratio (6P+2D vs 4P+4D):
    TTFT: 1.99s -> 1.48s (-26%)  More prefill GPUs = less queue
    TPOT: 0.075 -> 0.077 (~same) Decode still memory-bound
    Decode GPU util: 7.8% -> 19.0% (+143%) Less waste
    Verdict: HELPS — fewer decode GPUs is better for this workload

  Ablation 2 — Fire-and-forget vs Await-prefill (on 6P+2D):
    TTFT: 1.48s -> 5.32s (+260%) WORSE — decode waits for KV internally
    TPOT: 0.066 -> 0.037 (-44%)  BETTER — pipeline overlap helps decode
    Error: 6% -> 15%  MORE errors from KV race conditions
    Verdict: HURTS overall — TTFT degradation outweighs TPOT gain

  Overall: Combined 8colo remains best for single-machine agentic workload.
  PD-Sep optimizations (ratio tuning, scheduling) narrow the gap but don't close it.
""")