"""
TN scaffolds for efficient sequence modeling:
 - TensorTrainBottleneck: compress feature dimension via TT (factorized linear layer)
 - TTNBlock: shallow tree that aggregates local neighborhoods
 - MERALite: small multiscale block (coarse/fine) with residual

These are prototypes focusing on shape correctness and basic functionality.
"""

import numpy as np

try:
    import torch
    import torch.nn as nn

    HAVE_TORCH = True
except Exception:
    HAVE_TORCH = False

if HAVE_TORCH:

    class TensorTrainBottleneck(nn.Module):
        """
        Factorized linear layer using TT cores. For simplicity, we split input dim d into m factors.
        This is not a full TT library but demonstrates the idea.
        """

        def __init__(self, d_in=128, d_out=128, m=4, rank=8):
            super().__init__()
            self.m = m
            dims = self._factorize(d_in, m)
            outs = self._factorize(d_out, m)
            self.dims = dims
            self.outs = outs
            self.cores = nn.ParameterList()
            prev_r = 1
            for i in range(m):
                core = nn.Parameter(torch.randn(prev_r, dims[i], outs[i], rank) * 0.02)
                self.cores.append(core)
                prev_r = rank
            self.final_r = nn.Parameter(torch.randn(rank, 1) * 0.02)

        def _factorize(self, n, m):
            # simple near-equal factorization
            f = int(round(n ** (1.0 / m)))
            dims = [f] * (m - 1)
            last = int(np.ceil(n / (f ** (m - 1))))
            dims.append(last)
            assert np.prod(dims) >= n
            return dims

        def forward(self, x):
            # x: [B, T, d_in]
            B, T, D = x.shape
            # pad D to product(dims) if needed
            prod = int(np.prod(self.dims))
            if D < prod:
                pad = prod - D
                x = torch.cat([x, torch.zeros(B, T, pad, device=x.device, dtype=x.dtype)], dim=-1)
            x = x.reshape(B * T, prod)
            # reshape into m modes
            X = x
            r_prev = 1
            for i, core in enumerate(self.cores):
                # reshape X to [B*T, r_prev, dims[i], -1] with trailing grouped dims
                di = int(self.dims[i])
                outi = int(self.outs[i])
                X = X.reshape(B * T, r_prev, di, -1)
                # contract with core over (r_prev, di) -> new rank
                # core: [r_prev, di, outi, r]
                X = torch.einsum("bqdn,qdor->bron", X, core)  # [B*T, r, outi, new_trailing]
                r_prev = core.shape[-1]
                # fold outi into trailing dimension for next step
                X = X.reshape(B * T, r_prev, -1)
            # final collapse
            X = torch.einsum("brn,rq->bqn", X, self.final_r)  # [B*T, 1, trailing]
            Y = X.reshape(B, T, -1)
            return Y[..., : sum(self.outs)]  # trim to d_out-ish

    class TTNBlock(nn.Module):
        """Tree Tensor Network block for local aggregation"""

        def __init__(self, d=128, width=3):
            super().__init__()
            self.d = d
            self.width = width
            self.proj = None
            self.act = nn.ReLU()

        def _ensure_proj(self, in_dim):
            if self.proj is None or getattr(self.proj, "in_features", None) != in_dim:
                self.proj = nn.Linear(in_dim, self.d)

        def forward(self, x):
            # x: [B, T, d], aggregate in windows of 'width'
            B, T, d = x.shape
            pad = (self.width - (T % self.width)) % self.width
            if pad:
                x = torch.cat([x, torch.zeros(B, pad, d, device=x.device, dtype=x.dtype)], dim=1)
            X = x.reshape(B, -1, self.width * d)
            self._ensure_proj(X.size(-1))
            y = self.proj(X)
            return self.act(y)

    class MERALite(nn.Module):
        """Lightweight MERA-inspired multiscale block"""

        def __init__(self, d=128):
            super().__init__()
            self.coarse = nn.GRU(d, d, batch_first=True)
            self.fine = nn.GRU(d, d, batch_first=True)
            self.mix = nn.Linear(2 * d, d)

        def forward(self, x):
            # x: [B, T, d]
            y_fine, _ = self.fine(x)
            # downsample by 2 for "coarse"
            x2 = x[:, ::2, :]
            y_coarse, _ = self.coarse(x2)
            # upsample coarse
            up = torch.repeat_interleave(y_coarse, repeats=2, dim=1)[:, : x.shape[1], :]
            y = torch.cat([y_fine, up], dim=-1)
            return self.mix(y)
