"""tilelli.core.tilelli_block — heterogeneous-pathway block with a per-token
soft router.

Up to five structurally-different operations run in parallel on the same
input, mixed by a per-token softmax router. Optional Ternary Dispenser
(n_banks > 1) replicates each pathway across n_banks weight banks; the
router dispatches both pathway and bank per token. Compute per token stays
constant; parameter capacity multiplies by n_banks.
"""
from __future__ import annotations

import torch
from torch import Tensor, nn

from tilelli.core.sparse_attention import SparseCausalAttention
from tilelli.core.ssm import DiagonalSSM
from tilelli.core.ternary_conv import TernaryCausalConv1d
from tilelli.core.ternary_linear import TernaryLinear


PATHWAY_NAMES_3 = ("local", "state", "sparse")
PATHWAY_NAMES_5 = ("local", "wide", "state", "sparse", "dense")


class TernaryFFN(nn.Module):
    """Tiny feed-forward network with ternary weights: d → expand·d → d."""

    def __init__(
        self,
        d_model: int,
        expand: int = 2,
        quantize: bool = True,
        per_row: bool = False,
        hadamard: bool = False,
        lsq: bool = False,
    ) -> None:
        super().__init__()
        d_inner = d_model * expand
        self.up = TernaryLinear(
            d_model, d_inner,
            quantize=quantize, per_row=per_row, hadamard=hadamard, lsq=lsq,
        )
        self.down = TernaryLinear(
            d_inner, d_model,
            quantize=quantize, per_row=per_row, hadamard=hadamard, lsq=lsq,
        )

    def forward(self, x: Tensor) -> Tensor:
        return self.down(torch.nn.functional.gelu(self.up(x)))


def _make_pathway(
    kind: str,
    d_model: int,
    d_head: int,
    kernel_size: int,
    wide_kernel_size: int,
    top_k: int,
    quantize: bool,
    per_row: bool,
    hadamard: bool,
    lsq: bool,
    dense_expand: int,
    fp_attention: bool,
) -> nn.Module:
    """Build a single pathway module of the named kind.

    fp_attention=True forces the Sparse pathway's Q/K/V projections to FP32
    even when the global quantize is True. From the Spectrum spinoff insight:
    attention is the precision-critical operation where ternary hurts most.
    """
    if kind == "local":
        return TernaryCausalConv1d(
            d_model, kernel_size=kernel_size,
            quantize=quantize, per_row=per_row, lsq=lsq,
        )
    if kind == "wide":
        return TernaryCausalConv1d(
            d_model, kernel_size=wide_kernel_size,
            quantize=quantize, per_row=per_row, lsq=lsq,
        )
    if kind == "state":
        return DiagonalSSM(d_model)
    if kind == "sparse":
        attn_quantize = False if fp_attention else quantize
        return SparseCausalAttention(
            d_model, d_head=d_head, top_k=top_k, quantize=attn_quantize,
        )
    if kind == "dense":
        return TernaryFFN(
            d_model, expand=dense_expand,
            quantize=quantize, per_row=per_row, hadamard=hadamard, lsq=lsq,
        )
    raise ValueError(f"unknown pathway kind: {kind}")


class TilelliBlock(nn.Module):
    """One Tilelli block: parallel heterogeneous pathways mixed by a router.

    Parameters
    ----------
    n_banks : int, default 1
        Number of weight banks per pathway (Ternary Dispenser). 1 = original.
        >1 = MoE at the weight level: each pathway holds n_banks copies, the
        router argmax-picks one bank per token. Adds a load-balancing aux
        loss accessible via .aux_loss after each forward.
    per_row, hadamard, lsq : bool
        Ternary-quantization tricks forwarded to TernaryLinear / Conv. All
        default off so the existing aurora-ternary baseline stays identical.
    skip_threshold, skip_mode : as before — only used by .infer().
    """

    def __init__(
        self,
        d_model: int,
        d_head: int = 32,
        kernel_size: int = 5,
        wide_kernel_size: int = 21,
        top_k: int = 8,
        pathways: int = 5,
        n_banks: int = 1,
        skip_threshold: float = 0.05,
        skip_mode: str = "per_call",
        quantize: bool = True,
        per_row: bool = False,
        hadamard: bool = False,
        lsq: bool = False,
        dense_expand: int = 2,
        fp_attention: bool = False,
        top_k_routing: int = 0,
    ) -> None:
        super().__init__()
        if pathways not in (3, 5):
            raise ValueError(f"pathways must be 3 or 5, got {pathways}")
        if skip_mode not in ("per_call", "per_token"):
            raise ValueError(f"skip_mode must be 'per_call' or 'per_token', got {skip_mode!r}")
        if n_banks < 1:
            raise ValueError(f"n_banks must be >= 1, got {n_banks}")
        self.d_model = d_model
        self.pathways = pathways
        self.n_banks = n_banks
        self.skip_threshold = skip_threshold
        self.skip_mode = skip_mode
        self.quantize = quantize
        self.top_k_routing = top_k_routing
        self.pathway_names = PATHWAY_NAMES_5 if pathways == 5 else PATHWAY_NAMES_3

        self.norm = nn.LayerNorm(d_model)

        def _build(kind: str) -> nn.Module | nn.ModuleList:
            mk = lambda: _make_pathway(
                kind, d_model, d_head, kernel_size, wide_kernel_size,
                top_k, quantize, per_row, hadamard, lsq, dense_expand,
                fp_attention,
            )
            if n_banks <= 1:
                return mk()
            return nn.ModuleList([mk() for _ in range(n_banks)])

        self.local = _build("local")
        self.state = _build("state")
        self.sparse = _build("sparse")
        if pathways == 5:
            self.wide = _build("wide")
            self.dense = _build("dense")

        # Router: routes over (pathway × bank) when n_banks > 1, else pathways.
        n_router_outputs = pathways * n_banks
        self.router = TernaryLinear(
            d_model, n_router_outputs,
            quantize=quantize, per_row=per_row, hadamard=hadamard, lsq=lsq,
        )

        self._aux_loss = torch.tensor(0.0)

    def _pathway_modules(self) -> list[tuple[str, nn.Module | nn.ModuleList]]:
        if self.pathways == 5:
            return [
                ("local", self.local),
                ("wide", self.wide),
                ("state", self.state),
                ("sparse", self.sparse),
                ("dense", self.dense),
            ]
        return [
            ("local", self.local),
            ("state", self.state),
            ("sparse", self.sparse),
        ]

    def _compute_single_bank(self, h: Tensor, r: Tensor) -> Tensor:
        outputs = [mod(h) for _, mod in self._pathway_modules()]
        return sum(r[..., i:i + 1] * outputs[i] for i in range(len(outputs)))

    def _compute_multi_bank(self, h: Tensor, r: Tensor) -> Tensor:
        """Multi-bank dispenser: per-token top-1 bank selection per pathway.

        r shape: (B, L, n_pathways * n_banks)
        """
        B, L, _ = r.shape
        plist = self._pathway_modules()
        n_paths = len(plist)
        r_2d = r.view(B, L, n_paths, self.n_banks)

        pathway_weights = r_2d.sum(dim=-1)  # (B, L, n_paths)
        bank_idx = r_2d.argmax(dim=-1)      # (B, L, n_paths)

        # Load balance: each bank should be selected ~1/n_banks of the time.
        bank_probs = r_2d.mean(dim=(0, 1))  # (n_paths, n_banks)
        target = 1.0 / self.n_banks
        self._aux_loss = ((bank_probs - target) ** 2).mean() * 0.01

        mixed = torch.zeros(B, L, self.d_model, device=h.device, dtype=h.dtype)
        for p_idx, (_name, banks) in enumerate(plist):
            pw = pathway_weights[..., p_idx:p_idx + 1]  # (B, L, 1)
            bidx = bank_idx[..., p_idx]                 # (B, L)
            for b in range(self.n_banks):
                mask = (bidx == b)
                if not mask.any():
                    continue
                out = banks[b](h)
                mixed = mixed + pw * out * mask.unsqueeze(-1).to(out.dtype)
        return mixed

    def _maybe_topk_route(self, r: Tensor) -> Tensor:
        """Optionally restrict routing to the top-k pathways per token (Mixtral-style)."""
        if self.top_k_routing <= 0 or self.top_k_routing >= r.shape[-1]:
            return r
        top_vals, top_idx = r.topk(self.top_k_routing, dim=-1)
        mask = torch.zeros_like(r)
        mask.scatter_(-1, top_idx, top_vals)
        return mask / mask.sum(dim=-1, keepdim=True).clamp(min=1e-12)

    def forward(self, x: Tensor) -> Tensor:
        h = self.norm(x)
        r = torch.softmax(self.router(h), dim=-1)
        r = self._maybe_topk_route(r)
        if self.n_banks <= 1:
            mixed = self._compute_single_bank(h, r)
        else:
            mixed = self._compute_multi_bank(h, r)
        return x + mixed

    @property
    def aux_loss(self) -> Tensor:
        """Load-balancing loss for multi-bank. Add to main loss during training."""
        return self._aux_loss

    @torch.no_grad()
    def infer(self, x: Tensor) -> Tensor:
        h = self.norm(x)
        r = torch.softmax(self.router(h), dim=-1)
        if self.n_banks > 1:
            return x + self._compute_multi_bank(h, r)
        y = torch.zeros_like(x)
        if self.skip_mode == "per_call":
            r_max = r.amax(dim=(0, 1))
            for i, (_, mod) in enumerate(self._pathway_modules()):
                if r_max[i].item() >= self.skip_threshold:
                    step = mod.infer(h) if hasattr(mod, "infer") else mod(h)
                    y = y + r[..., i:i + 1] * step
            return x + y
        for i, (_, mod) in enumerate(self._pathway_modules()):
            step = mod.infer(h) if hasattr(mod, "infer") else mod(h)
            mask = (r[..., i:i + 1] >= self.skip_threshold).to(step.dtype)
            y = y + mask * r[..., i:i + 1] * step
        return x + y

    @torch.no_grad()
    def router_weights(self, x: Tensor) -> Tensor:
        """Per-token router distribution.

        For single-bank: shape (B, L, n_pathways).
        For multi-bank: pathway-level weights (banks summed). Shape (B, L, n_pathways).
        """
        r = torch.softmax(self.router(self.norm(x)), dim=-1)
        if self.n_banks > 1:
            B, L, _ = r.shape
            n_paths = len(self._pathway_modules())
            return r.view(B, L, n_paths, self.n_banks).sum(dim=-1)
        return r

    @torch.no_grad()
    def router_entropy(self, x: Tensor) -> Tensor:
        r = self.router_weights(x).clamp_min(1e-12)
        return -(r * r.log()).sum(dim=-1)