from __future__ import annotations

import torch
from torch import nn


def squash(vectors: torch.Tensor) -> torch.Tensor:
    squared_norm = vectors.square().sum(dim=-1, keepdim=True)
    scale = squared_norm / (1 + squared_norm)
    return scale * vectors / torch.sqrt(squared_norm + 1e-8)


class DynamicRoutingCapsuleNet(nn.Module):
    def __init__(self, routing_iterations: int = 3) -> None:
        super().__init__()
        self.routing_iterations = routing_iterations
        self.primary = nn.Linear(64, 28)
        self.transforms = nn.Parameter(torch.randn(7, 10, 4, 8) * 0.08)

    def forward(self, pixels: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        primary = squash(torch.tanh(self.primary(pixels)).reshape(-1, 7, 4))
        votes = torch.einsum("bpd,pcde->bpce", primary, self.transforms)
        routing_logits = torch.zeros(
            len(pixels),
            7,
            10,
            device=pixels.device,
        )
        digit_capsules = None
        for iteration in range(self.routing_iterations):
            coupling = torch.softmax(routing_logits, dim=2)
            digit_capsules = squash((coupling[..., None] * votes).sum(dim=1))
            if iteration + 1 < self.routing_iterations:
                agreement = (votes * digit_capsules[:, None]).sum(dim=-1)
                routing_logits = routing_logits + agreement
        assert digit_capsules is not None
        return digit_capsules, digit_capsules.norm(dim=-1)


class MatchedMLP(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.network = nn.Sequential(
            nn.Linear(64, 54),
            nn.GELU(),
            nn.Linear(54, 10),
        )

    def forward(self, pixels: torch.Tensor) -> torch.Tensor:
        return self.network(pixels)


def parameter_count(model: nn.Module) -> int:
    return sum(parameter.numel() for parameter in model.parameters())