"""Capture real per-token expert routing traces from OLMoE and derive the
statistics that drive the expert-cache model: activation-frequency skew,
temporal reuse, and cross-layer predictability.
"""
import json, os, sys, time
import numpy as np
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

sys.path.insert(0, os.path.dirname(__file__))
import data

MODEL = "allenai/OLMoE-1B-7B-0924"
DEV = "cuda"
RES = os.path.join(os.path.dirname(__file__), "..", "results")


@torch.no_grad()
def trace(nseq=24, seqlen=2048):
    tok = AutoTokenizer.from_pretrained(MODEL)
    model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16,
                                                 low_cpu_mem_usage=True)
    model.eval(); model.config.use_cache = False
    L = model.config.num_hidden_layers
    E = model.config.num_experts
    K = model.config.num_experts_per_tok

    picks = {l: [] for l in range(L)}
    hooks = []

    def mk(l):
        def fn(mod, inp, out):
            logits = out[0] if isinstance(out, tuple) else out
            top = logits.float().reshape(-1, E).topk(K, dim=-1).indices
            picks[l].append(top.to(torch.int16).cpu())
        return fn

    for l, layer in enumerate(model.model.layers):
        hooks.append(layer.mlp.gate.register_forward_hook(mk(l)))

    tests = data.test_tokens(tok, seqlen)[:nseq]
    model.model.embed_tokens.to(DEV); model.model.rotary_emb.to(DEV)
    model.model.norm.to(DEV)
    for i, b in enumerate(tests):
        b = b.to(DEV)
        hs = model.model.embed_tokens(b)
        pos = torch.arange(seqlen, device=DEV).unsqueeze(0)
        pe = model.model.rotary_emb(hs, pos)
        for layer in model.model.layers:
            layer.to(DEV)
            hs = layer(hs, attention_mask=None, position_ids=pos,
                       position_embeddings=pe)
            hs = hs[0] if isinstance(hs, tuple) else hs
            layer.to("cpu")
            torch.cuda.empty_cache()
        print(f"  seq {i+1}/{len(tests)}", flush=True)
    for h in hooks:
        h.remove()

    T = torch.stack([torch.cat(picks[l]) for l in range(L)])   # [L, tokens, K]
    np.save(os.path.join(RES, "routing_trace.npy"), T.numpy().astype(np.int16))
    print("trace shape", tuple(T.shape))
    return T.numpy().astype(np.int64), L, E, K


def analyse(T, L, E, K):
    ntok = T.shape[1]
    freq = np.zeros((L, E))
    for l in range(L):
        c = np.bincount(T[l].reshape(-1), minlength=E)
        freq[l] = c / c.sum()
    json.dump({str(l): freq[l].tolist() for l in range(L)},
              open(os.path.join(RES, "routing_freq.json"), "w"))

    srt = np.sort(freq, axis=1)[:, ::-1]
    cum = np.cumsum(srt, axis=1)
    out = {
        "tokens": int(ntok), "layers": L, "experts": E, "topk": K,
        "gini": [float(gini(freq[l])) for l in range(L)],
        "mass_top25pct": float(cum[:, E // 4 - 1].mean()),
        "mass_top50pct": float(cum[:, E // 2 - 1].mean()),
        "cum_mean": cum.mean(0).tolist(),
    }

    # temporal reuse: probability an expert used at token t was also used at t-1
    reuse = []
    for l in range(L):
        a = T[l][:-1]; b = T[l][1:]
        m = np.zeros((len(a), E), dtype=bool)
        m[np.arange(len(a))[:, None], a] = True
        hit = m[np.arange(len(b))[:, None], b].sum(1) / K
        reuse.append(float(hit.mean()))
    out["reuse_prev_token"] = reuse

    # working set: distinct experts over a window of W tokens
    ws = {}
    for W in [1, 4, 16, 64, 256, 1024]:
        vals = []
        for l in range(L):
            n = min(len(T[l]) // W, 64)
            for i in range(n):
                vals.append(len(np.unique(T[l][i * W:(i + 1) * W])))
        ws[W] = float(np.mean(vals))
    out["working_set"] = ws
    json.dump(out, open(os.path.join(RES, "routing_stats.json"), "w"), indent=2)
    return out


def gini(p):
    x = np.sort(p)
    n = len(x)
    return float((2 * np.arange(1, n + 1) - n - 1).dot(x) / (n * x.sum()))


def simulate_cache(T, L, E, K, expert_bytes, cache_bytes, freq=None,
                   policy="lru", pin_frac=0.0):
    """Byte-accurate expert cache simulation over the real trace.

    Returns fraction of expert activations served from cache (hit rate) and
    bytes fetched from storage per token.
    """
    cap = int(cache_bytes // expert_bytes)
    if cap <= 0:
        return 0.0, K * L * expert_bytes
    npin = int(cap * pin_frac)
    pinned = set()
    if npin and freq is not None:
        flat = [(freq[l][e], (l, e)) for l in range(L) for e in range(E)]
        flat.sort(reverse=True)
        pinned = {k for _, k in flat[:npin]}
    from collections import OrderedDict
    cache = OrderedDict((k, True) for k in pinned)
    hits = tot = 0
    ntok = T.shape[1]
    for t in range(ntok):
        for l in range(L):
            for e in T[l, t]:
                key = (l, int(e))
                tot += 1
                if key in cache:
                    hits += 1
                    if key not in pinned:
                        cache.move_to_end(key)
                else:
                    cache[key] = True
                    while len(cache) > cap:
                        k0, _ = next(iter(cache.items()))
                        if k0 in pinned:
                            cache.move_to_end(k0)
                            continue
                        cache.popitem(last=False)
    hr = hits / tot
    return hr, (1 - hr) * K * L * expert_bytes


if __name__ == "__main__":
    nseq = int(sys.argv[1]) if len(sys.argv) > 1 else 24
    p = os.path.join(RES, "routing_trace.npy")
    if os.path.exists(p):
        T = np.load(p).astype(np.int64)
        L, E, K = T.shape[0], 64, T.shape[2]
    else:
        T, L, E, K = trace(nseq)
    st = analyse(T, L, E, K)
    print(json.dumps({k: v for k, v in st.items() if k != "cum_mean"}, indent=2))