"""
HD Disentangled Regime Monitor
A concrete architecture for:
1. Numba-optimized HD feature extraction.
2. Disentangled Temporal VAE for regime encoding.
3. FLINT-backed 512-bit Latent Optimizer for "Most Likely Future" projection.
"""

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from numba import jit, float64
import math

# --- High Precision Imports ---
# We wrap this in a try-except block to allow the module to load for viewing
# even if python-flint isn't installed, though it won't run without it.
try:
    from flint import arb, acb_mat, ctx
    FLINT_ENABLED = True
except ImportError:
    FLINT_ENABLED = False
    print("Warning: python-flint not found. High-precision optimizer will run in mock mode.")

# =============================================================================
# SECTION 1: NUMBA-OPTIMIZED FEATURE ENGINEERING
# =============================================================================

@jit(nopython=True, fastmath=True, cache=True)
def construct_hd_features(prices, volumes, window_size):
    """
    Constructs High-Dimensional feature tensors from raw market data.
    Optimized with Numba to handle high-throughput data streams.
    
    Args:
        prices (np.array): 1D array of price points.
        volumes (np.array): 1D array of volume points.
        window_size (int): Lookback window size.
        
    Returns:
        np.array: Feature tensor of shape (n_samples, window_size, n_features).
    """
    n_samples = len(prices) - window_size
    # Features: LogRet, Volatility, RelVolume, Acceleration
    n_features = 4 
    features = np.empty((n_samples, window_size, n_features), dtype=np.float64)
    
    for i in range(n_samples):
        window_p = prices[i : i + window_size]
        window_v = volumes[i : i + window_size]
        
        # 1. Log Returns
        log_p = np.log(window_p)
        rets = np.diff(log_p)
        features[i, 1:, 0] = rets
        features[i, 0, 0] = 0.0
        
        # 2. Rolling Volatility (Standard Deviation of returns over mini-windows)
        # Simplified for numba: rolling std dev
        std_val = np.std(rets)
        features[i, :, 1] = std_val
        
        # 3. Relative Volume
        mean_vol = np.mean(window_v)
        if mean_vol > 0:
            features[i, :, 2] = window_v / mean_vol
        else:
            features[i, :, 2] = 0.0
            
        # 4. Acceleration (Second derivative of price)
        if window_size > 2:
            acc = np.diff(log_p, n=2)
            features[i, 2:, 3] = acc
            features[i, :2, 3] = 0.0
            
    return features

# =============================================================================
# SECTION 2: DISENTANGLED TEMPORAL VAE (PyTorch)
# =============================================================================

class BetaTCVAE(nn.Module):
    """
    Temporal VAE with Total Correlation disentanglement.
    Uses a Bidirectional GRU encoder and a Generative GRU decoder.
    Forces latent space to separate "Knobs" (Trend, Vol, etc).
    """
    def __init__(self, input_dim, hidden_dim, latent_dim, num_layers=1, beta=5.0):
        super(BetaTCVAE, self).__init__()
        self.latent_dim = latent_dim
        self.beta = beta
        
        # Encoder: Bidirectional GRU
        self.encoder_rnn = nn.GRU(input_dim, hidden_dim, num_layers, 
                                  batch_first=True, bidirectional=True)
        self.fc_mu = nn.Linear(hidden_dim * 2, latent_dim)
        self.fc_var = nn.Linear(hidden_dim * 2, latent_dim)
        
        # Decoder: Generative GRU
        # Input to decoder is latent vector z repeated across time steps
        self.decoder_rnn = nn.GRU(latent_dim + input_dim, hidden_dim, num_layers, batch_first=True)
        self.decoder_out = nn.Linear(hidden_dim, input_dim)

    def encode(self, x):
        # x: (batch, seq, feat)
        _, h = self.encoder_rnn(x)
        # Concatenate forward and backward final hidden states
        h_cat = torch.cat((h[0], h[1]), dim=1) 
        return self.fc_mu(h_cat), self.fc_var(h_cat)

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

    def decode(self, z, seq_len):
        # Repeat z for seq_len steps to seed the generator
        z_rep = z.unsqueeze(1).repeat(1, seq_len, 1)
        # Auto-regressive logic simplified: we feed z and noise/placeholder
        # In a real system, this would be teacher-forcing or iterative sampling
        placeholder = torch.zeros(z_rep.shape[0], seq_len, z_rep.shape[2]).to(z.device)
        dec_input = torch.cat((z_rep, placeholder), dim=-1)
        
        out, _ = self.decoder_rnn(dec_input)
        return self.decoder_out(out)

    def forward(self, x):
        mu, logvar = self.encode(x)
        z = self.reparameterize(mu, logvar)
        recon_x = self.decode(z, x.shape[1])
        return recon_x, mu, logvar, z

    def loss_function(self, recon_x, x, mu, logvar, z):
        # Reconstruction Loss
        BCE = F.mse_loss(recon_x, x, reduction='sum')
        
        # KL Divergence
        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
        KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
        
        # Total Correlation (TC) Minimization (Simplified for Beta-VAE style)
        # In a full TC-VAE, we would calculate minibatch TC. Here we use 
        # a high Beta to force independence statistically.
        
        return BCE + self.beta * KLD

# =============================================================================
# SECTION 3: FLINT 512-BIT LATENT OPTIMIZER
# =============================================================================

class FlintLatentOptimizer:
    """
    Navigates the latent space using FLINT 512-bit precision.
    Solves for the 'Most Likely Future' by descending the potential energy landscape.
    """
    def __init__(self, precision=512):
        if not FLINT_ENABLED:
            raise RuntimeError("FLINT library not installed.")
        
        self.precision = precision
        ctx.precision = precision
        
        # In a real scenario, these dynamics W are learned.
        # Here we initialize a random transition matrix for demonstration.
        # We assume z_t+1 = W * tanh(z_t) + noise
        self.W_dim = 10 # Example latent dim
        self.W_np = np.eye(self.W_dim) * 0.9 + np.random.randn(self.W_dim, self.W_dim) * 0.1
        
        # Convert W to FLINT matrix
        self.W_flint = self._to_flint_matrix(self.W_np)

    def _to_flint_matrix(self, arr):
        rows, cols = arr.shape
        mat = acb_mat(rows, cols)
        for r in range(rows):
            for c in range(cols):
                # Convert float to string for exact arb initialization
                mat[r, c] = arb(str(arr[r, c]))
        return mat

    def _to_flint_vec(self, arr):
        vec = acb_mat(len(arr), 1)
        for i in range(len(arr)):
            vec[i, 0] = arb(str(arr[i]))
        return vec

    def solve_most_likely_future(self, z_current_numpy, steps=10):
        """
        Projects the current latent state forward to find the stable attractor.
        Uses 512-bit arithmetic to avoid "noise" hallucination.
        """
        # 1. Convert numpy latent vector to FLINT arb vector
        z_state = self._to_flint_vec(z_current_numpy)
        
        # 2. Iterate Dynamics
        # We look for a stable state (Fixed Point).
        # z_{t+1} = W @ z_t
        # We stop when ||z_{t+1} - z_t|| < epsilon (512-bit epsilon)
        
        for _ in range(steps):
            # Matrix Multiply in High Precision
            z_next = self.W_flint * z_state
            
            # Convergence Check (Simplified)
            # In a real app, we compute delta norm here using arb functions
            z_state = z_next
            
        # 3. Return result as string (to preserve precision) and numpy array
        # We extract the real part (midpoint of the ball)
        result_np = np.zeros(self.W_dim)
        for i in range(self.W_dim):
            # arb to string to float (lossy, but for the sizing logic)
            result_np[i] = float(str(z_state[i, 0].real()))
            
        return result_np

# =============================================================================
# SECTION 4: MAIN MONITOR CLASS
# =============================================================================

class AdaptiveRegimeMonitor:
    """
    The main controller integrating Feature Engineering, VAE, and High-Precision Optimization.
    """
    def __init__(self, input_dim=4, hidden_dim=32, latent_dim=10, precision=512):
        self.vae = BetaTCVAE(input_dim, hidden_dim, latent_dim)
        self.latent_optimizer = None
        
        if FLINT_ENABLED:
            try:
                self.latent_optimizer = FlintLatentOptimizer(precision=precision)
                print(f"Initialized FLINT Optimizer at {precision}-bit precision.")
            except Exception as e:
                print(f"Failed to init FLINT: {e}. Using fallback.")
        
        # Latent Mapping (Mock semantic labels for "Knobs")
        self.regime_knobs = {
            'trend_strength': 0, # Index in latent vector
            'volatility_regime': 1,
            'fragility': 2
        }

    def train_vae(self, data_loader, epochs=5):
        """Trains the VAE to learn the market 'physics'."""
        optimizer = torch.optim.Adam(self.vae.parameters(), lr=1e-3)
        self.vae.train()
        
        print("Training VAE...")
        for epoch in range(epochs):
            total_loss = 0
            for batch in data_loader:
                x = batch[0] # assuming dataloader returns (x, y)
                optimizer.zero_grad()
                recon_x, mu, logvar, z = self.vae(x)
                loss = self.vae.loss_function(recon_x, x, mu, logvar, z)
                loss.backward()
                optimizer.step()
                total_loss += loss.item()
            print(f"Epoch {epoch+1}, Loss: {total_loss:.4f}")
        print("Training Complete.")

    def compute_regime_score(self, raw_window, raw_volume):
        """
        Full pipeline execution:
        1. Features (Numba)
        2. Latent State (VAE)
        3. Future Projection (FLINT)
        4. Sizing Output
        """
        # 1. Features
        # Note: Numba returns (N, Win, Feat). We take the last sample for live trading
        # or simulate a batch. Here we expand dims to simulate batch=1
        features = construct_hd_features(raw_window, raw_volume, len(raw_window))
        last_feature_window = features[-1:] # Shape (1, Window, Feat)
        
        # 2. Encode
        self.vae.eval()
        with torch.no_grad():
            x_tensor = torch.tensor(last_feature_window, dtype=torch.float32)
            _, mu, _, z = self.vae(x_tensor)
        
        # Current Latent State (Numpy)
        z_current = mu.squeeze().numpy()
        
        # 3. Optimize Future (FLINT)
        if self.latent_optimizer:
            z_future = self.latent_optimizer.solve_most_likely_future(z_current)
        else:
            # Fallback if FLINT missing
            z_future = z_current * 0.95 # Dummy decay

        # 4. Disentangled Interpretation ("Knobs")
        # We apply sigmoid to normalize the score between 0 and 1
        trend_score = torch.sigmoid(torch.tensor(z_future[self.regime_knobs['trend_strength']]))
        fragility_score = torch.sigmoid(torch.tensor(z_future[self.regime_knobs['fragility']]))
        
        # Regime Quality Indicator
        # High Quality = High Trend + Low Fragility
        regime_quality = (trend_score * (1 - fragility_score)).item()
        
        return regime_quality, z_future

# =============================================================================
# SECTION 5: BUILT-IN TESTS
# =============================================================================

def run_tests():
    print("-" * 40)
    print("RUNNING MODULE TESTS")
    print("-" * 40)
    
    # 1. Test Numba Features
    print("\n[1/3] Testing Numba Feature Engineering...")
    try:
        dummy_prices = np.random.rand(1000) * 100 + 50
        dummy_vols = np.random.rand(1000) * 1000
        feats = construct_hd_features(dummy_prices, dummy_vols, 64)
        assert feats.shape == (936, 64, 4) # 1000 - 64 = 936 samples
        print("   SUCCESS: Feature shape correct.")
    except Exception as e:
        print(f"   FAILED: {e}")

    # 2. Test VAE Structure
    print("\n[2/3] Testing VAE Architecture...")
    try:
        vae = BetaTCVAE(input_dim=4, hidden_dim=16, latent_dim=6)
        dummy_input = torch.randn(10, 64, 4) # Batch 10, Seq 64, Feat 4
        recon, mu, logvar, z = vae(dummy_input)
        loss = vae.loss_function(recon, dummy_input, mu, logvar, z)
        assert z.shape == (10, 6)
        print(f"   SUCCESS: VAE Forward pass OK, Latent Dim: 6.")
    except Exception as e:
        print(f"   FAILED: {e}")

    # 3. Test Full Pipeline (with Mock Data)
    print("\n[3/3] Testing Full Pipeline (Mock Market Data)...")
    try:
        # Generate synthetic data
        prices = np.cumsum(np.random.randn(200)) + 100
        vols = np.abs(np.random.randn(200)) * 10
            
        # Initialize Monitor
        monitor = AdaptiveRegimeMonitor(input_dim=4, hidden_dim=16, latent_dim=10)
        
        # We skip training for the test and just run inference
        
        # Run compute
        score, future_z = monitor.compute_regime_score(prices, vols)
        
        print(f"   Regime Quality Score: {score:.5f}")
        print(f"   Future Latent Vector (First 3): {future_z[:3]}")
        
        assert 0.0 <= score <= 1.0
        print("   SUCCESS: Full pipeline integration OK.")
        
    except Exception as e:
        print(f"   FAILED: {e}")

    print("\n" + "="*40)
    print("TESTS COMPLETE")
    print("="*40)

if __name__ == "__main__":
    run_tests()