"""Llama-shaped decoder with NVFP4 weights, shipped pre-quantised.

NVFP4 is the Blackwell 4-bit format: values are **e2m1** (1 sign, 2 exponent, 1 mantissa bit), grouped
in blocks of 16 along the input dimension, each block carrying an **e4m3** scale, and the whole tensor
carrying one fp32 global scale. Every 2-D weight arrives as a triple:

    packed : (out, in // 2) uint8            -- two e2m1 codes per byte, LOW nibble first
    bscale : (out, in // 16) float8_e4m3fn   -- per-block scale
    gscale : () float32                      -- per-tensor scale

and its value is `E2M1[code] * bscale.float() * gscale`.

The e2m1 magnitude ladder is exactly `[0, .5, 1, 1.5, 2, 3, 4, 6]`; the nibble is `sign << 3 | mag`.
That ladder is the whole reason the format is interesting: it is not uniform, so a dequantisation is a
7-entry table lookup rather than a multiply-add, and the natural implementation is a small LUT held in
registers or shared memory while the packed bytes stream past.

The weights are quantised ONCE, here, and the reference dequantises exactly these bytes -- the agent is
graded on its kernel, not on its rounding policy.
"""
from model import HELPERS_CORE

QUANT = r'''
# e2m1: 3 magnitude bits -> this ladder; bit 3 is the sign.
_E2M1 = [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0]


def _e2m1_lut(device):
    """16-entry signed lookup: index = nibble, value = the represented number."""
    v = torch.tensor(_E2M1, device=device, dtype=torch.float32)
    return torch.cat([v, -v])


def _quantise(w, dt, block=16):
    """fp32 -> NVFP4 (packed e2m1 nibbles, per-block e4m3 scale, per-tensor fp32 scale)."""
    if dt == "bf16":
        return w.to(torch.bfloat16)
    if dt != "nvfp4":
        raise ValueError(dt)
    out, inn = w.shape
    g = w.view(out, inn // block, block)
    bamax = g.abs().amax(dim=-1, keepdim=True)                       # (out, nb, 1)
    gscale = (w.abs().amax() / (6.0 * 448.0)).clamp(min=1e-12)
    bs = (bamax / 6.0 / gscale).clamp(min=1e-6, max=448.0).to(torch.float8_e4m3fn)
    eff = bs.float() * gscale                                        # the scale actually stored
    n = (g / eff.clamp(min=1e-12)).clamp(-6.0, 6.0)
    ladder = torch.tensor(_E2M1, device=w.device, dtype=torch.float32)
    mag = torch.argmin((n.abs().unsqueeze(-1) - ladder).abs(), dim=-1).to(torch.uint8)
    code = (mag | ((n < 0).to(torch.uint8) << 3)).view(out, inn)
    packed = (code[:, 0::2] | (code[:, 1::2] << 4)).contiguous()
    return (packed, bs.squeeze(-1), gscale)


def _deq(w, block=16):
    """(packed, bscale, gscale) -> bf16. Plain bf16 weights pass through."""
    if not isinstance(w, tuple):
        return w
    packed, bs, gs = w
    out = packed.shape[0]
    lo = (packed & 0xF).to(torch.int64)
    hi = (packed >> 4).to(torch.int64)
    code = torch.stack([lo, hi], dim=-1).view(out, -1)
    inn = code.shape[1]
    v = _e2m1_lut(packed.device)[code].view(out, inn // block, block)
    v = v * (bs.float() * gs).unsqueeze(-1)
    return v.view(out, inn).to(torch.bfloat16)
'''

BODY = r'''
def make_weights(cfg, seed=0, device="cuda"):
    """Deterministic 1/sqrt(fan_in)-scaled weights, shipped ALREADY NVFP4-quantised."""
    g = torch.Generator(device=device).manual_seed(seed)
    d, ffn, n_q, n_kv, hd = cfg["d"], cfg["ffn"], cfg["n_q"], cfg["n_kv"], cfg["hd"]
    dt, blk = cfg["wdtype"], cfg["block"]

    def rnd(*shape, fan_in):
        w = torch.randn(*shape, device=device, dtype=torch.float32, generator=g) / (fan_in ** 0.5)
        return _quantise(w, dt, blk)

    ones = lambda: torch.ones(d, device=device, dtype=torch.bfloat16)
    W = {"embed": rnd(cfg["vocab"], d, fan_in=d), "final_norm": ones(), "layers": []}
    for _ in range(cfg["layers"]):
        W["layers"].append(dict(
            in_norm=ones(), post_norm=ones(),
            q=rnd(n_q * hd, d, fan_in=d), k=rnd(n_kv * hd, d, fan_in=d),
            v=rnd(n_kv * hd, d, fan_in=d), o=rnd(d, n_q * hd, fan_in=n_q * hd),
            gate=rnd(ffn, d, fan_in=d), up=rnd(ffn, d, fan_in=d), down=rnd(d, ffn, fan_in=ffn)))
    return W


def make_kv(cfg, batch, prefill_len, max_seq, seed=0, device="cuda"):
    """KV cache already holding `prefill_len` tokens. Decode starts at pos = prefill_len."""
    g = torch.Generator(device=device).manual_seed(seed + 777)
    kv = []
    for _ in range(cfg["layers"]):
        k = torch.zeros(batch, cfg["n_kv"], max_seq, cfg["hd"], device=device, dtype=torch.bfloat16)
        v = torch.zeros_like(k)
        k[:, :, :prefill_len] = torch.randn(batch, cfg["n_kv"], prefill_len, cfg["hd"], device=device,
                                            dtype=torch.float32, generator=g).to(torch.bfloat16) * 0.5
        v[:, :, :prefill_len] = torch.randn(batch, cfg["n_kv"], prefill_len, cfg["hd"], device=device,
                                            dtype=torch.float32, generator=g).to(torch.bfloat16) * 0.5
        kv.append((k, v))
    return kv


def build_model(weights, kv_cache, cfg, max_seq_len):
    """UNTIMED setup. Dequantises ONCE here rather than per step -- dequantising inside every step
    allocates GBs per call, which perturbs the caching allocator enough that cuBLAS picks different
    GEMV algorithms run-to-run and two bit-identical implementations drift apart."""
    cos, sin = _rope_cache(cfg, max_seq_len, weights["final_norm"].device)
    blk = cfg["block"]
    W = {"embed": _deq(weights["embed"], blk), "final_norm": weights["final_norm"],
         "layers": [{k: (v if k.endswith("norm") else _deq(v, blk)) for k, v in L.items()}
                    for L in weights["layers"]]}
    return {"W": W, "kv": kv_cache, "cfg": cfg, "cos": cos, "sin": sin}


@torch.no_grad()
def decode_step(handle, token_ids, pos):
    """One decode step for every sequence in the batch. Appends this position's K/V into the cache.

    token_ids: (B,) int64      pos: int, the absolute position being written
    returns:   (B, vocab) logits
    """
    W, kv, cfg = handle["W"], handle["kv"], handle["cfg"]
    cos, sin = handle["cos"], handle["sin"]
    B = token_ids.shape[0]
    n_q, n_kv, hd = cfg["n_q"], cfg["n_kv"], cfg["hd"]
    rep = n_q // n_kv

    x = W["embed"][token_ids]
    for li, L in enumerate(W["layers"]):
        h = _rms_norm(x, L["in_norm"], cfg["eps"])
        q = (h @ L["q"].T).view(B, n_q, 1, hd)
        k = (h @ L["k"].T).view(B, n_kv, 1, hd)
        v = (h @ L["v"].T).view(B, n_kv, 1, hd)
        q = _apply_rope(q, cos, sin, pos)
        k = _apply_rope(k, cos, sin, pos)
        kc, vc = kv[li]
        kc[:, :, pos:pos + 1] = k
        vc[:, :, pos:pos + 1] = v
        kk = kc[:, :, :pos + 1].repeat_interleave(rep, dim=1)
        vv = vc[:, :, :pos + 1].repeat_interleave(rep, dim=1)
        att = F.scaled_dot_product_attention(q, kk, vv)
        x = x + (att.reshape(B, n_q * hd) @ L["o"].T)
        h = _rms_norm(x, L["post_norm"], cfg["eps"])
        x = x + ((F.silu(h @ L["gate"].T) * (h @ L["up"].T)) @ L["down"].T)
    x = _rms_norm(x, W["final_norm"], cfg["eps"])
    return x @ W["embed"].T                      # tied lm_head
'''

MODEL_SRC = HELPERS_CORE + QUANT + BODY