import torch
import torch.nn as nn
import torch.optim as optim
from typing import List, Optional, Dict
import numpy as np

class NeuralVAE(nn.Module):
    """
    Variational Autoencoder for 'Digital Soul' Compression.
    Compresses high-dimensional knowledge embeddings into a compact latent 'Link'.
    """
    def __init__(self, input_dim: int = 384, latent_dim: int = 32):
        super(NeuralVAE, self).__init__()
        
        # Encoder
        self.encoder = nn.Sequential(
            nn.Linear(input_dim, 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU()
        )
        self.fc_mu = nn.Linear(64, latent_dim)
        self.fc_logvar = nn.Linear(64, latent_dim)
        
        # Decoder
        self.decoder = nn.Sequential(
            nn.Linear(latent_dim, 64),
            nn.ReLU(),
            nn.Linear(64, 128),
            nn.ReLU(),
            nn.Linear(128, input_dim),
            nn.Sigmoid()
        )

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def forward(self, x):
        h = self.encoder(x)
        mu, logvar = self.fc_mu(h), self.fc_logvar(h)
        z = self.reparameterize(mu, logvar)
        return self.decoder(z), mu, logvar

class LifeTrendPredictor(nn.Module):
    """
    Sequence-to-Sequence Transformer for Predicting User Knowledge Needs.
    Input: Historical activity sequence.
    Output: Predicted next interest vector.
    """
    def __init__(self, input_dim: int = 384, hidden_dim: int = 128, n_heads: int = 4):
        super(LifeTrendPredictor, self).__init__()
        
        self.embedding = nn.Linear(input_dim, hidden_dim)
        self.transformer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=n_heads),
            num_layers=2
        )
        self.fc_out = nn.Linear(hidden_dim, input_dim)

    def forward(self, x):
        # x shape: (seq_len, batch, input_dim)
        h = self.embedding(x)
        h = self.transformer(h)
        # Take the last hidden state for prediction
        out = self.fc_out(h[-1])
        return out

class NeuralLinkEngine:
    """
    Neural Link Engine: The 'Deep Learning' Core of Akasha.
    Handles neural state compression and latent space mapping.
    """
    def __init__(self, input_dim: int = 384, latent_dim: int = 32):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = NeuralVAE(input_dim, latent_dim).to(self.device)
        self.optimizer = optim.Adam(self.model.parameters(), lr=1e-3)
        self.latent_dim = latent_dim

    def compress_state(self, embedding: List[float]) -> np.ndarray:
        """Compresses a 384d embedding into a 32d Neural Link."""
        self.model.eval()
        with torch.no_grad():
            x = torch.FloatTensor(embedding).to(self.device)
            h = self.model.encoder(x)
            mu = self.model.fc_mu(h)
            return mu.cpu().numpy()

    def reconstruct_state(self, latent_link: np.ndarray) -> np.ndarray:
        """Reconstructs the original high-dimensional state from a Neural Link."""
        self.model.eval()
        with torch.no_grad():
            z = torch.FloatTensor(latent_link).to(self.device)
            reconstructed = self.model.decoder(z)
            return reconstructed.cpu().numpy()

    def train_on_batch(self, embeddings: List[List[float]]):
        """Self-Supervised learning on the user's personal knowledge manifold."""
        self.model.train()
        x = torch.FloatTensor(embeddings).to(self.device)
        
        self.optimizer.zero_grad()
        recon_x, mu, logvar = self.model(x)
        
        # VAE Loss: Reconstruction (MSE) + KL Divergence
        recon_loss = nn.functional.mse_loss(recon_x, x, reduction='sum')
        kl_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
        
        loss = recon_loss + kl_loss
        loss.backward()
        self.optimizer.step()
        
        return loss.item()

class PredictiveNeuralEngine:
    """
    Neural Forecasting Engine for the 'Digital Soul'.
    Anticipates future research steps and potential knowledge decays.
    """
    def __init__(self, input_dim: int = 384):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model = LifeTrendPredictor(input_dim=input_dim).to(self.device)

    def predict_next_interest(self, history_embeddings: List[List[float]]) -> List[float]:
        """Predicts the embedding of the next topic the user might be interested in."""
        if len(history_embeddings) < 3:
            return history_embeddings[-1] if history_embeddings else [0.0] * 384
            
        self.model.eval()
        with torch.no_grad():
            # Convert to (seq_len, 1, input_dim)
            x = torch.FloatTensor(history_embeddings).unsqueeze(1).to(self.device)
            prediction = self.model(x)
            return prediction.squeeze(0).cpu().numpy().tolist()

    def identify_impending_decay(self, graph_weights: List[float]) -> float:
        """Uses a neural network to predict when a knowledge branch is likely to be forgotten."""
        # Simplified: Threshold-based logic for now
        return min(graph_weights) if graph_weights else 1.0
