"""One (or a few) complete transformer decoder layers, taking a hidden state in and out.

This is the megakernel problem with the whole-model scaffolding removed: no embedding, no LM head,
no 16-layer schedule to amortise anything over. Just RMSNorm -> QKV -> RoPE -> KV append -> GQA
attention -> output projection -> residual -> RMSNorm -> SwiGLU MLP -> residual, which is about 40
fusable operations, and it must come out of one launch.

`src(build_name, step_name)` renders the reference with task-specific entry-point names.
"""
from model import HELPERS_CORE

TEMPLATE = r'''
def make_weights(cfg, seed=0, device="cuda"):
    """Deterministic 1/sqrt(fan_in)-scaled weights. No checkpoint is shipped or downloaded."""
    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"]

    def rnd(*shape, fan_in):
        return (torch.randn(*shape, device=device, dtype=torch.float32, generator=g)
                / (fan_in ** 0.5)).to(torch.bfloat16)

    ones = lambda: torch.ones(d, device=device, dtype=torch.bfloat16)
    layers = []
    for _ in range(cfg["layers"]):
        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 {"layers": layers}


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 make_step_args(cfg, batch, base_pos, seed, n):
    """(x, pos) per call -- a fresh (B, d) bf16 hidden state and the position being appended."""
    g = torch.Generator(device="cuda").manual_seed(seed)
    return [(torch.randn(batch, cfg["d"], device="cuda", dtype=torch.float32,
                         generator=g).to(torch.bfloat16), base_pos + i) for i in range(n)]


def {BUILD}(weights, kv_cache, cfg, max_seq_len):
    """UNTIMED setup. Repack weights, build RoPE tables, allocate scratch, launch a daemon, ..."""
    dev = weights["layers"][0]["q"].device
    cos, sin = _rope_cache(cfg, max_seq_len, dev)
    return {"W": weights["layers"], "kv": kv_cache, "cfg": cfg, "cos": cos, "sin": sin}


@torch.no_grad()
def {STEP}(handle, x, pos):
    """Run the layer(s) on one hidden state; append this position's K/V into the cache.

    x       : (B, d) bf16    the incoming residual stream
    pos     : int            the absolute position being written
    returns : (B, d) fp32    the residual stream after the layer(s)
    """
    W, kv, cfg = handle["W"], handle["kv"], handle["cfg"]
    cos, sin = handle["cos"], handle["sin"]
    B = x.shape[0]
    n_q, n_kv, hd = cfg["n_q"], cfg["n_kv"], cfg["hd"]
    rep = n_q // n_kv

    for li, L in enumerate(W):
        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)
    return x.float()
'''


def src(build_name, step_name):
    return HELPERS_CORE + TEMPLATE.replace("{BUILD}", build_name).replace("{STEP}", step_name)