"""
TinyChess: a ~100K-parameter recurrent chess reasoning substrate.

Core ideas (see docs/RESEARCH_LOG.md):
  * 64 persistent per-square latent states, never collapsed to one vector.
  * ONE shared recurrent core applied n times: parameters are reused, depth is free.
  * Learned memory slots participate in the same attention as the squares.
  * A router mixes candidate operations per step.
  * ACT-style learned halting gives adaptive depth.
  * Compositional move embeddings scored against the *legal* candidate set only.

Everything is instrumented: `forward(..., trace=True)` returns h0..hn, memory,
router weights, halting probabilities and per-step move latents.
"""
from __future__ import annotations

import math
from dataclasses import dataclass, field, asdict
from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F

from .encoding import (N_PIECE_TOKENS, N_SQ_EXTRA, N_GLOBAL, MOVE_FIELD_ORDER,
                       MOVE_FIELD_SIZES)
from .quant import QuantPolicy, maybe_quant

OPS = ["attn", "local", "mlp", "mem"]


@dataclass
class TinyChessConfig:
    d_model: int = 64
    n_heads: int = 4
    d_ff: int = 128
    d_attn_enc: int = 48
    d_ff_enc: int = 64
    n_mem: int = 8
    d_move: int = 32
    max_steps: int = 12
    min_steps: int = 1
    halt_threshold: float = 0.95
    ponder_cost: float = 1e-2
    n_refine: int = 2
    use_router: bool = True
    router_temp: float = 1.0
    router_topk: int = 0          # 0 = dense mixture
    use_memory: bool = True
    use_local: bool = True
    use_attn: bool = True
    use_halting: bool = True
    use_refine: bool = True
    compositional_moves: bool = True
    n_value_bins: int = 1          # 1 => scalar tanh value
    dropout: float = 0.0
    # structural plasticity
    plastic: bool = False
    # thought channel
    thought: bool = False
    # arch family for ablations: 'recurrent' | 'mlp' | 'transformer'
    family: str = "recurrent"
    n_layers: int = 2              # only for 'transformer'/'mlp' baselines

    def to_dict(self):
        return asdict(self)


# ---------------------------------------------------------------------------
# helpers
# ---------------------------------------------------------------------------

class RMSNorm(nn.Module):
    """Cheaper than LayerNorm (no bias), keeps the parameter budget for compute."""

    def __init__(self, d):
        super().__init__()
        self.g = nn.Parameter(torch.ones(d))

    def forward(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + 1e-6) * self.g


def _neighbour_index():
    """[64, 8] index of the 8 king-neighbours of each square; self if off-board."""
    idx = torch.zeros(64, 8, dtype=torch.long)
    valid = torch.zeros(64, 8)
    dirs = [(1, 0), (-1, 0), (0, 1), (0, -1), (1, 1), (1, -1), (-1, 1), (-1, -1)]
    for s in range(64):
        r, f = divmod(s, 8)
        for k, (dr, df) in enumerate(dirs):
            rr, ff = r + dr, f + df
            if 0 <= rr < 8 and 0 <= ff < 8:
                idx[s, k] = rr * 8 + ff
                valid[s, k] = 1.0
            else:
                idx[s, k] = s
    return idx, valid


def _ray_index():
    """[64, 4, 7] sliding-ray neighbours (rank, file, diag, anti-diag), self-padded."""
    idx = torch.zeros(64, 4, 7, dtype=torch.long)
    valid = torch.zeros(64, 4, 7)
    axes = [((0, 1), (0, -1)), ((1, 0), (-1, 0)), ((1, 1), (-1, -1)), ((1, -1), (-1, 1))]
    for s in range(64):
        r, f = divmod(s, 8)
        for a, (d1, d2) in enumerate(axes):
            slot = 0
            for (dr, df) in (d1, d2):
                for step in range(1, 8):
                    rr, ff = r + dr * step, f + df * step
                    if not (0 <= rr < 8 and 0 <= ff < 8) or slot >= 7:
                        break
                    idx[s, a, slot] = rr * 8 + ff
                    valid[s, a, slot] = 1.0
                    slot += 1
            for j in range(slot, 7):
                idx[s, a, j] = s
    return idx, valid


# ---------------------------------------------------------------------------
# Board encoder (bidirectional / jointly contextualised)
# ---------------------------------------------------------------------------

class BoardEncoder(nn.Module):
    """Produces 64 persistent per-square latent states.

    'Bidirectional' = every square attends to every other square of the CURRENT
    position. No future information is used anywhere.
    """

    def __init__(self, cfg: TinyChessConfig):
        super().__init__()
        d = cfg.d_model
        self.cfg = cfg
        self.piece = nn.Embedding(N_PIECE_TOKENS, d)
        # factorised square identity: file x rank instead of a 64xd table
        self.file_emb = nn.Embedding(8, d)
        self.rank_emb = nn.Embedding(8, d)
        self.extra = nn.Linear(N_SQ_EXTRA, d, bias=False)
        self.glob = nn.Linear(N_GLOBAL, d)
        self.norm_in = RMSNorm(d)
        da = cfg.d_attn_enc
        self.qkv = nn.Linear(d, 3 * da, bias=False)
        self.proj = nn.Linear(da, d, bias=False)
        self.norm_a = RMSNorm(d)
        self.ff = nn.Sequential(nn.Linear(d, cfg.d_ff_enc), nn.GELU(),
                                nn.Linear(cfg.d_ff_enc, d, bias=False))
        self.norm_f = RMSNorm(d)
        self.register_buffer("sq_ids", torch.arange(64), persistent=False)
        self.register_buffer("file_ids", torch.arange(64) % 8, persistent=False)
        self.register_buffer("rank_ids", torch.arange(64) // 8, persistent=False)

    def forward(self, squares, extras, glob):
        # squares [B,64] long, extras [B,64,E] float, glob [B,G] float
        x = self.piece(squares) + (self.file_emb(self.file_ids) + self.rank_emb(self.rank_ids))[None]
        x = x + self.extra(extras)
        x = x + self.glob(glob)[:, None, :]
        x = self.norm_in(x)
        B = x.shape[0]
        h = self.cfg.n_heads
        hd = self.cfg.d_attn_enc // h
        q, k, v = self.qkv(x).chunk(3, -1)
        q = q.view(B, 64, h, hd).transpose(1, 2)
        k = k.view(B, 64, h, hd).transpose(1, 2)
        v = v.view(B, 64, h, hd).transpose(1, 2)
        a = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(B, 64, -1)
        a = self.proj(a)
        x = self.norm_a(x + a)
        x = self.norm_f(x + self.ff(x))
        return x


# ---------------------------------------------------------------------------
# The shared recurrent core
# ---------------------------------------------------------------------------

class RecurrentCore(nn.Module):
    """Applied n times with the SAME parameters.

    Operations available at every step (mixed by the router):
      attn  - global attention over 64 squares + memory slots
      local - structured chess mixing (king-neighbours + sliding rays)
      mlp   - pointwise nonlinearity
      mem   - explicit memory read + gated write
    """

    def __init__(self, cfg: TinyChessConfig):
        super().__init__()
        d, h = cfg.d_model, cfg.n_heads
        self.cfg = cfg
        self.d, self.h = d, h
        self.hd = d // h

        # -- global attention (squares + memory as tokens) --
        if cfg.use_attn:
            self.qkv = nn.Linear(d, 3 * d, bias=False)
            self.proj = nn.Linear(d, d, bias=False)
            self.tok_type = nn.Parameter(torch.zeros(2, d))  # square vs memory tag

        # -- local structured mixing --
        if cfg.use_local:
            nb, nbv = _neighbour_index()
            ry, ryv = _ray_index()
            self.register_buffer("nb_idx", nb, persistent=False)
            self.register_buffer("nb_valid", nbv, persistent=False)
            self.register_buffer("ray_idx", ry, persistent=False)
            self.register_buffer("ray_valid", ryv, persistent=False)
            # per-direction gates (cheap) + one shared mixing matrix
            self.nb_gate = nn.Parameter(torch.zeros(8, d))
            self.ray_gate = nn.Parameter(torch.zeros(4, d))
            self.ray_decay = nn.Parameter(torch.zeros(4, 7))
            self.local_proj = nn.Linear(2 * d, d, bias=False)
            # flattened gather indices: [64*8] and [64*28]
            self._p_nb_cache = None

        # -- pointwise MLP (plastic: hidden units can be masked) --
        self.ff1 = nn.Linear(d, cfg.d_ff)
        self.ff2 = nn.Linear(cfg.d_ff, d, bias=False)
        self.register_buffer("ff_mask", torch.ones(cfg.d_ff), persistent=True)

        # -- memory --
        if cfg.use_memory:
            self.mem0 = nn.Parameter(torch.randn(cfg.n_mem, d) * 0.02)
            self.mem_r = nn.Linear(d, d, bias=False)      # query from squares
            self.mem_w1 = nn.Linear(2 * d, 32, bias=False)   # factorised write
            self.mem_w2 = nn.Linear(32, 2 * d, bias=False)
            self.register_buffer("mem_mask", torch.ones(cfg.n_mem), persistent=True)

        # -- router --
        self.ops = [o for o in OPS
                    if (o != "attn" or cfg.use_attn)
                    and (o != "local" or cfg.use_local)
                    and (o != "mem" or cfg.use_memory)]
        if cfg.use_router:
            self.router = nn.Linear(2 * d, len(self.ops))
        # -- halting --
        if cfg.use_halting:
            self.halt = nn.Linear(2 * d, 1)

        self.norm1 = RMSNorm(d)
        self.norm2 = RMSNorm(d)
        self.step_emb = nn.Parameter(torch.zeros(cfg.max_steps + 1, d))

    # ---- individual operations -------------------------------------------------
    def _attn(self, x, mem):
        B, N, d = x.shape
        if mem is not None:
            toks = torch.cat([x + self.tok_type[0], mem + self.tok_type[1]], 1)
        else:
            toks = x + self.tok_type[0]
        T = toks.shape[1]
        q, k, v = self.qkv(toks).chunk(3, -1)
        q = q.view(B, T, self.h, self.hd).transpose(1, 2)
        k = k.view(B, T, self.h, self.hd).transpose(1, 2)
        v = v.view(B, T, self.h, self.hd).transpose(1, 2)
        o = F.scaled_dot_product_attention(q, k, v)
        o = o.transpose(1, 2).reshape(B, T, d)
        o = self.proj(o)
        return o[:, :N], (o[:, N:] if mem is not None else None)

    def _local(self, x):
        """Structured chess mixing: king-neighbours + sliding rays.

        Key identity: each direction d contributes  gate[d] * (P_d @ x), where
        P_d is a fixed 64x64 permutation-like matrix and gate[d] is a per-channel
        vector. Summing over directions is therefore

            sum_d  (P_d @ x) * gate_d

        For the rays the 7 distance slots collapse into P_a (a = 4 axes) because
        the decay weight depends only on (square, axis, slot), not on channels.
        We precompute P_nb [8,64,64] and P_ray [4,64,64] ONCE per forward from
        the current gates, then use two batched matmuls. This avoids the
        [B,64,4,7,d] intermediate entirely.
        """
        B, N, d = x.shape
        # --- king neighbours ---
        # P_nb[k] is a 0/1 matrix selecting neighbour k of each square
        nb = torch.einsum("knm,bmd->bknd", self._P_nb(), x)      # [B,8,64,d]
        nb = torch.einsum("bknd,kd->bnd", nb, torch.tanh(self.nb_gate))
        # --- sliding rays (decay folded into the matrix) ---
        ry = torch.einsum("anm,bmd->band", self._P_ray(), x)     # [B,4,64,d]
        ry = torch.einsum("band,ad->bnd", ry, torch.tanh(self.ray_gate))
        return self.local_proj(torch.cat([nb, ry], -1))

    def _P_nb(self):
        """[8,64,64] neighbour selection matrices (cached; no grad path)."""
        if getattr(self, "_p_nb_cache", None) is None:
            P = torch.zeros(8, 64, 64)
            for k in range(8):
                P[k, torch.arange(64), self.nb_idx[:, k]] = self.nb_valid[:, k]
            self._p_nb_cache = P
        return self._p_nb_cache

    def _P_ray(self):
        """[4,64,64] ray matrices with the learned decay folded in.

        Depends on self.ray_decay, so it is rebuilt every call (cheap: 4x64x7
        scatter) and keeps the gradient to ray_decay.
        """
        w = self.ray_valid * torch.sigmoid(self.ray_decay).unsqueeze(0)  # [64,4,7]
        P = torch.zeros(4, 64, 64, device=w.device, dtype=w.dtype)
        rows = torch.arange(64, device=w.device).view(64, 1).expand(64, 7)
        for a in range(4):
            P[a] = P[a].index_put((rows.reshape(-1), self.ray_idx[:, a, :].reshape(-1)),
                                  w[:, a, :].reshape(-1), accumulate=True)
        return P

    def _mlp(self, x):
        h = F.gelu(self.ff1(x)) * self.ff_mask
        return self.ff2(h)

    def _mem_read(self, x, mem):
        q = self.mem_r(x)                                      # [B,64,d]
        att = torch.einsum("bnd,bmd->bnm", q, mem) / math.sqrt(self.d)
        att = att + torch.log(self.mem_mask.clamp_min(1e-9))[None, None]
        w = att.softmax(-1)
        return torch.einsum("bnm,bmd->bnd", w, mem), w

    def _mem_write(self, x, mem):
        summ = x.mean(1, keepdim=True).expand(-1, mem.shape[1], -1)
        gz = self.mem_w2(F.gelu(self.mem_w1(torch.cat([mem, summ], -1))))
        gate, cand = gz.chunk(2, -1)
        gate = torch.sigmoid(gate)
        new = mem * (1 - gate) + torch.tanh(cand) * gate
        return new * self.mem_mask[None, :, None], gate

    # ---- one recurrent step ----------------------------------------------------
    def forward(self, x, mem, step: int, qp: Optional[QuantPolicy] = None,
                collect: Optional[dict] = None):
        d = self.d
        xn = self.norm1(x) + self.step_emb[min(step, self.cfg.max_steps)]
        summary = torch.cat([xn.mean(1), xn.amax(1)], -1)      # [B,2d]

        # router decides which operations matter this step
        if self.cfg.use_router:
            logits = self.router(summary) / self.cfg.router_temp
            if self.cfg.router_topk and self.cfg.router_topk < len(self.ops):
                k = self.cfg.router_topk
                thresh = logits.topk(k, -1).values[:, -1:]
                logits = logits.masked_fill(logits < thresh, float("-inf"))
            w = logits.softmax(-1)
        else:
            w = x.new_full((x.shape[0], len(self.ops)), 1.0 / len(self.ops))

        delta = torch.zeros_like(x)
        mem_out = mem
        mem_attn = None
        for i, op in enumerate(self.ops):
            wi = w[:, i][:, None, None]
            if op == "attn":
                o, mdelta = self._attn(xn, mem)
                o = maybe_quant(o, qp, "attn")
                delta = delta + wi * o
                if mdelta is not None and mem is not None:
                    mem_out = mem_out + w[:, i][:, None, None] * mdelta
            elif op == "local":
                delta = delta + wi * maybe_quant(self._local(xn), qp, "local")
            elif op == "mlp":
                delta = delta + wi * maybe_quant(self._mlp(self.norm2(x)), qp, "mlp")
            elif op == "mem" and mem is not None:
                r, mem_attn = self._mem_read(xn, mem_out)
                delta = delta + wi * maybe_quant(r, qp, "mem")
                mem_out, _ = self._mem_write(xn, mem_out)

        x_new = x + delta

        p_halt = None
        if self.cfg.use_halting:
            s2 = torch.cat([x_new.mean(1), x_new.amax(1)], -1)
            p_halt = torch.sigmoid(self.halt(s2)).squeeze(-1)

        if collect is not None:
            collect.setdefault("router", []).append(w.detach())
            if p_halt is not None:
                collect.setdefault("halt", []).append(p_halt.detach())
            if mem_attn is not None:
                collect.setdefault("mem_attn", []).append(mem_attn.detach())
        return x_new, mem_out, p_halt


# ---------------------------------------------------------------------------
# Compositional move embeddings
# ---------------------------------------------------------------------------

class MoveEmbedder(nn.Module):
    """Moves are built from structural components, not a flat 4096-way vocabulary."""

    def __init__(self, cfg: TinyChessConfig):
        super().__init__()
        self.cfg = cfg
        dm = cfg.d_move
        if cfg.compositional_moves:
            self.tables = nn.ModuleList([
                nn.Embedding(MOVE_FIELD_SIZES[f], dm) for f in MOVE_FIELD_ORDER])
            self.mix = nn.Linear(dm, dm, bias=False)
        else:
            # ablation: flat from*to vocabulary (deliberately bigger, for comparison)
            self.flat = nn.Embedding(64 * 64, dm)
        self.norm = RMSNorm(dm)

    def forward(self, fields):
        # fields [B,L,7] long
        if self.cfg.compositional_moves:
            e = 0
            for i, t in enumerate(self.tables):
                e = e + t(fields[..., i])
            e = e + self.mix(F.gelu(e))
        else:
            e = self.flat(fields[..., 0] * 64 + fields[..., 1])
        return self.norm(e)


# ---------------------------------------------------------------------------
# Full model
# ---------------------------------------------------------------------------

class TinyChess(nn.Module):
    def __init__(self, cfg: TinyChessConfig):
        super().__init__()
        self.cfg = cfg
        d = cfg.d_model
        self.encoder = BoardEncoder(cfg)
        if cfg.family == "recurrent":
            self.core = RecurrentCore(cfg)
        elif cfg.family == "transformer":
            self.blocks = nn.ModuleList([RecurrentCore(cfg) for _ in range(cfg.n_layers)])
        elif cfg.family == "mlp":
            self.blocks = nn.ModuleList([
                nn.Sequential(RMSNorm(d), nn.Linear(d, cfg.d_ff), nn.GELU(),
                              nn.Linear(cfg.d_ff, d)) for _ in range(cfg.n_layers)])
        else:
            raise ValueError(cfg.family)

        self.move_emb = MoveEmbedder(cfg)
        self.readout = nn.Linear(d, cfg.d_move, bias=False)        # h -> z_move
        self.sq_to_move = nn.Linear(d, cfg.d_move, bias=False)    # per-square -> move space
        if cfg.use_refine:
            self.refine = nn.GRUCell(cfg.d_move, cfg.d_move)
        self.move_scale = nn.Parameter(torch.tensor(1.0))
        self.value = nn.Sequential(nn.Linear(2 * d, 24), nn.GELU(), nn.Linear(24, cfg.n_value_bins))
        self.norm_out = RMSNorm(d)

    # ---- parameter accounting -------------------------------------------------
    def param_report(self) -> dict:
        groups = {}
        for name, p in self.named_parameters():
            top = name.split(".")[0]
            groups[top] = groups.get(top, 0) + p.numel()
        total = sum(groups.values())
        active = self.active_params()
        return {"groups": groups, "total": total, "active": active}

    def active_params(self) -> int:
        """Parameters that are actually live given plasticity masks."""
        total = sum(p.numel() for p in self.parameters())
        core = getattr(self, "core", None)
        if core is None:
            return total
        dead_ff = int((core.ff_mask == 0).sum())
        # each dead hidden unit removes: ff1 row (d+1) and ff2 column (d)
        total -= dead_ff * (self.cfg.d_model * 2 + 1)
        if self.cfg.use_memory:
            dead_m = int((core.mem_mask == 0).sum())
            total -= dead_m * self.cfg.d_model
        return total

    # ---- forward ---------------------------------------------------------------
    def forward(self, squares, extras, glob, cand_fields=None, cand_mask=None,
                steps: Optional[int] = None, adaptive: bool = False,
                trace: bool = False, qp: Optional[QuantPolicy] = None):
        """
        squares [B,64] long; extras [B,64,E]; glob [B,G]
        cand_fields [B,L,7] long; cand_mask [B,L] bool  (legal candidates only)
        Returns dict with logits, value, trajectory info.
        """
        B = squares.shape[0]
        x = self.encoder(squares, extras, glob)
        collect: dict = {} if trace else None
        traj = [x.detach()] if trace else None

        cfg = self.cfg
        mem = None
        if cfg.family == "recurrent":
            if cfg.use_memory:
                mem = self.core.mem0[None].expand(B, -1, -1).contiguous()
            n = steps if steps is not None else cfg.max_steps
            n = max(cfg.min_steps, min(n, cfg.max_steps))

            if adaptive and cfg.use_halting:
                x, mem, info = self._act_loop(x, mem, n, qp, collect, traj)
            else:
                halts = []
                for t in range(n):
                    x, mem, ph = self.core(x, mem, t, qp, collect)
                    if trace:
                        traj.append(x.detach())
                    if ph is not None:
                        halts.append(ph)
                info = {"n_steps": torch.full((B,), float(n), device=x.device),
                        "ponder": torch.zeros(B, device=x.device),
                        "halt_probs": torch.stack(halts, 1) if halts else None}
        else:
            for blk in self.blocks:
                if cfg.family == "transformer":
                    x, mem, _ = blk(x, None, 0, qp, collect)
                else:
                    x = x + blk(x)
                if trace:
                    traj.append(x.detach())
            info = {"n_steps": torch.full((B,), float(len(self.blocks)), device=x.device),
                    "ponder": torch.zeros(B, device=x.device), "halt_probs": None}

        x = self.norm_out(x)
        pooled = torch.cat([x.mean(1), x.amax(1)], -1)           # [B,2d]
        value = self.value(pooled)

        out = {"h": x, "pooled": pooled, "value": value, "memory": mem, **info}
        if trace:
            out["trajectory"] = traj
            out["collect"] = collect

        if cand_fields is None:
            return out

        z = self.readout(pooled[:, :self.cfg.d_model])            # [B,dm]
        cand = self.move_emb(cand_fields)                         # [B,L,dm]
        # squares contribute directly: a move's from/to squares index the board state
        sq_m = self.sq_to_move(x)                                 # [B,64,dm]
        idx_f = cand_fields[..., 0].clamp(0, 63)
        idx_t = cand_fields[..., 1].clamp(0, 63)
        cand = cand + torch.gather(sq_m, 1, idx_f[..., None].expand(-1, -1, sq_m.shape[-1]))
        cand = cand + torch.gather(sq_m, 1, idx_t[..., None].expand(-1, -1, sq_m.shape[-1]))

        zs = [z]
        if cfg.use_refine and cfg.n_refine > 0:
            ctx = z
            for _ in range(cfg.n_refine):
                z = self.refine(ctx, z)
                zs.append(z)

        logits = torch.einsum("bd,bld->bl", z, cand) * self.move_scale / math.sqrt(cfg.d_move)
        if cand_mask is not None:
            logits = logits.masked_fill(~cand_mask, float("-inf"))
        out["logits"] = logits
        out["z_move"] = z
        if trace:
            out["z_steps"] = [zz.detach() for zz in zs]
            out["refine_logits"] = [
                (torch.einsum("bd,bld->bl", zz, cand) * self.move_scale / math.sqrt(cfg.d_move)
                 ).masked_fill(~cand_mask, float("-inf")).detach() if cand_mask is not None else None
                for zz in zs]
        return out

    # ---- ACT / adaptive depth ---------------------------------------------------
    def _act_loop(self, x, mem, n_max, qp, collect, traj):
        """Graves-style ACT.

        Each batch element accumulates halting mass until it exceeds
        `halt_threshold`; the step at which that happens gets the *remainder*
        weight. The returned state is the halting-weighted mean of the visited
        states, so gradients flow into the halting unit. `ponder` is the
        expected number of steps and is what the compute penalty charges for.
        """
        B = x.shape[0]
        dev = x.device
        still = torch.ones(B, device=dev)          # 1 while the element is running
        acc = torch.zeros(B, device=dev)           # accumulated halting mass
        x_out = torch.zeros_like(x)
        mem_out = torch.zeros_like(mem) if mem is not None else None
        n_steps = torch.zeros(B, device=dev)       # discrete steps actually taken
        ponder = torch.zeros(B, device=dev)        # differentiable remainder term
        thr = self.cfg.halt_threshold
        halts = []
        for t in range(n_max):
            x, mem, ph = self.core(x, mem, t, qp, collect)
            if traj is not None:
                traj.append(x.detach())
            if ph is None:
                ph = torch.full((B,), 1.0 / n_max, device=dev)
            halts.append(ph)

            forced_min = t < (self.cfg.min_steps - 1)
            is_last = (t == n_max - 1)
            new_acc = acc + ph
            # an element finishes this step if it crosses the threshold, or time is up
            finish = ((new_acc > thr) | is_last) & (~torch.tensor(forced_min, device=dev))
            finish = finish & (still > 0)
            w = torch.where(finish, (1.0 - acc).clamp(min=0.0), ph) * still
            x_out = x_out + w[:, None, None] * x
            if mem_out is not None:
                mem_out = mem_out + w[:, None, None] * mem
            n_steps = n_steps + still
            ponder = ponder + still * (1.0 - acc).clamp(min=0.0)
            acc = torch.where(finish, acc, new_acc)
            still = torch.where(finish, torch.zeros_like(still), still)
            if float(still.max()) == 0.0:
                break

        info = {"n_steps": n_steps, "ponder": ponder,
                "halt_probs": torch.stack(halts, 1)}
        return x_out, (mem_out if mem_out is not None else mem), info


def build_model(cfg: TinyChessConfig) -> TinyChess:
    m = TinyChess(cfg)
    for p in m.parameters():
        if p.dim() > 1 and p.requires_grad:
            pass
    return m