#!/usr/bin/env python3
"""Small neural models used by the evaluation suite."""

from __future__ import annotations

import torch
import torch.nn as nn


class MLPClassifier(nn.Module):
    def __init__(self, input_dim: int, hidden_dim: int = 256, dropout: float = 0.1):
        super().__init__()
        mid_dim = max(hidden_dim // 2, 32)
        self.net = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, mid_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(mid_dim, 1),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.net(x).squeeze(-1)


class MLPRegressor(nn.Module):
    def __init__(self, input_dim: int, output_dim: int, hidden_dim: int = 256, dropout: float = 0.1):
        super().__init__()
        mid_dim = max(hidden_dim // 2, 32)
        self.net = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, mid_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(mid_dim, output_dim),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.net(x)


class MLPAutoencoder(nn.Module):
    def __init__(self, input_dim: int, hidden_dim: int = 256, latent_dim: int = 64, dropout: float = 0.1):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, latent_dim),
            nn.GELU(),
        )
        self.decoder = nn.Sequential(
            nn.Linear(latent_dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, input_dim),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.decoder(self.encoder(x))