#!/usr/bin/env python3
"""
Neural Delta GCL Compressor (Optional Second Stage)

VAE-style neural compression layered on top of Delta GCL.
Per canonical lock-in: Delta GCL is lawful base codec, neural layer is learned transport compressor.

Canonical Field Equation:
q_θ(z | x) = N( μ_θ(x), diag(σ²_θ(x)) )
z = μ_θ(x) + σ_θ(x) ⊙ ε,    ε ~ N(0, I)
x̂ = g_φ(z)
L = D(x, x̂) + β · KL(q_θ(z | x) || N(0, I))
R_total = R_ΔGCL · R_neural

Architecture:
raw metadata m → DeltaGCL(m) = x → q_θ(z | x) → z → x̂ = g_φ(z) 
→ verify x̂ ≈ x → verify DeltaGCLDecode(x̂) preserves invariant → commit or refuse

Per AGENTS.md: This is a shim layer - logic in Lean, only JSON marshaling here.
"""

import sys
from pathlib import Path
# Add project root to Python path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))

import json
import hashlib
from typing import Dict, Any, Optional, Tuple
from dataclasses import dataclass
import numpy as np

from infra.delta_gcl_compression_service import DeltaGCLCompressionService, CompressionResult


@dataclass
class NeuralCompressionResult:
    """Neural compression result"""
    latent: np.ndarray  # 64-dimensional latent representation
    reconstructed_delta_gcl: str
    neural_ratio: float
    total_ratio: float
    verified: bool
    verification_error: Optional[str]


class NeuralDeltaGCLCompressor:
    """
    VAE-style neural compressor for Delta GCL sequences.
    
    This is an optional second stage that compresses Delta GCL output further.
    Per canonical lock-in: Delta GCL remains the lawful base codec.
    """
    
    def __init__(self, latent_dim: int = 64):
        self.latent_dim = latent_dim
        self.delta_gcl_service = DeltaGCLCompressionService()
        
        # Model parameters (placeholder - would be loaded from trained model)
        self.encoder_mean = None  # μ_θ
        self.encoder_std = None   # σ_θ
        self.decoder_weights = None  # g_φ
        self.beta = 1e-3  # KL regularization weight
        
        # Model state
        self.is_trained = False
        self.model_version = "0.1.0-placeholder"
    
    def encode(self, delta_gcl: str) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Encode Delta GCL sequence to latent representation.
        
        q_θ(z | x) = N( μ_θ(x), diag(σ²_θ(x)) )
        z = μ_θ(x) + σ_θ(x) ⊙ ε,    ε ~ N(0, I)
        
        Returns: (μ, σ, z)
        """
        if not self.is_trained:
            # Placeholder: use hash-based encoding until model is trained
            hash_bytes = hashlib.sha256(delta_gcl.encode()).digest()
            # Convert first 64 bytes to float array
            mu = np.frombuffer(hash_bytes[:self.latent_dim * 4], dtype=np.float32)
            if len(mu) < self.latent_dim:
                mu = np.pad(mu, (0, self.latent_dim - len(mu)))
            sigma = np.ones(self.latent_dim) * 0.1  # Fixed std for placeholder
        else:
            # Actual VAE encoder would be called here
            mu, sigma = self._encode_with_model(delta_gcl)
        
        # Reparameterization trick
        epsilon = np.random.randn(self.latent_dim)
        z = mu + sigma * epsilon
        
        return mu, sigma, z
    
    def _encode_with_model(self, delta_gcl: str) -> Tuple[np.ndarray, np.ndarray]:
        """Encode using actual trained VAE encoder (not implemented yet)"""
        # This would call the actual neural network
        # For now, return placeholder
        return np.zeros(self.latent_dim), np.ones(self.latent_dim)
    
    def decode(self, z: np.ndarray) -> str:
        """
        Decode latent representation back to Delta GCL sequence.
        
        x̂ = g_φ(z)
        """
        if not self.is_trained:
            # Placeholder: use deterministic hash-based decoding
            z_bytes = z.tobytes()[:32]
            hash_val = hashlib.sha256(z_bytes).hexdigest()
            # Reconstruct Delta GCL format (simplified)
            return f"F{hash_val[:8]}"
        else:
            # Actual VAE decoder would be called here
            return self._decode_with_model(z)
    
    def _decode_with_model(self, z: np.ndarray) -> str:
        """Decode using actual trained VAE decoder (not implemented yet)"""
        # This would call the actual neural network
        return ""
    
    def compress_with_neural(self, manifest: Dict[str, Any], 
                            manifest_id: str,
                            verify: bool = True) -> NeuralCompressionResult:
        """
        Compress manifest using two-stage process:
        1. Delta GCL (lawful base codec)
        2. Neural compression (learned transport compressor)
        
        Canonical stack:
        raw metadata m → DeltaGCL(m) = x → q_θ(z | x) → z → x̂ = g_φ(z) 
        → verify x̂ ≈ x → verify DeltaGCLDecode(x̂) preserves invariant → commit or refuse
        """
        # Stage 1: Delta GCL compression (lawful base codec)
        delta_gcl_result = self.delta_gcl_service.compress_manifest(
            manifest, manifest_id, use_delta=True, verify=True
        )
        
        if not delta_gcl_result.verified:
            return NeuralCompressionResult(
                latent=np.zeros(self.latent_dim),
                reconstructed_delta_gcl="",
                neural_ratio=1.0,
                total_ratio=delta_gcl_result.stats["compressed_size"] / delta_gcl_result.stats["original_size"],
                verified=False,
                verification_error=f"Delta GCL verification failed: {delta_gcl_result.verification_error}"
            )
        
        delta_gcl = delta_gcl_result.delta_gcl
        original_size = delta_gcl_result.stats["original_size"]
        delta_gcl_size = delta_gcl_result.stats["compressed_size"]
        
        # Stage 2: Neural compression (learned transport compressor)
        mu, sigma, z = self.encode(delta_gcl)
        
        # Calculate neural compression ratio
        latent_size = self.latent_dim * 4  # 4 bytes per float32
        neural_ratio = latent_size / delta_gcl_size
        
        # Decode latent back to Delta GCL
        reconstructed_delta_gcl = self.decode(z)
        
        # Verification: check x̂ ≈ x
        reconstruction_match = self._verify_reconstruction(delta_gcl, reconstructed_delta_gcl)
        
        # Verification: check DeltaGCLDecode(x̂) preserves invariant
        if verify and reconstruction_match:
            # Decode reconstructed Delta GCL and check invariants
            verified, error = self.delta_gcl_service.verify_compression(
                reconstructed_delta_gcl, manifest
            )
        else:
            verified = False
            error = "Reconstruction verification failed" if not reconstruction_match else None
        
        # Total compression ratio
        total_ratio = latent_size / original_size
        
        return NeuralCompressionResult(
            latent=z,
            reconstructed_delta_gcl=reconstructed_delta_gcl,
            neural_ratio=neural_ratio,
            total_ratio=total_ratio,
            verified=verified,
            verification_error=error
        )
    
    def _verify_reconstruction(self, original: str, reconstructed: str) -> bool:
        """Verify that reconstructed Delta GCL matches original (x̂ ≈ x)"""
        if not self.is_trained:
            # Placeholder: accept reconstruction for untrained model
            return True
        
        # For trained model, check if reconstructed is close to original
        # This would use a similarity metric (e.g., BLEU, edit distance)
        # For now, simple equality check
        return original == reconstructed
    
    def compute_loss(self, original: str, reconstructed: str, 
                    mu: np.ndarray, sigma: np.ndarray) -> float:
        """
        Compute VAE loss function.
        
        L = D(x, x̂) + β · KL(q_θ(z | x) || N(0, I))
        """
        # Reconstruction loss D(x, x̂)
        reconstruction_loss = self._reconstruction_loss(original, reconstructed)
        
        # KL divergence: KL(q_θ(z | x) || N(0, I))
        kl_divergence = self._kl_divergence(mu, sigma)
        
        # Total loss
        total_loss = reconstruction_loss + self.beta * kl_divergence
        
        return total_loss
    
    def _reconstruction_loss(self, original: str, reconstructed: str) -> float:
        """Compute reconstruction loss D(x, x̂)"""
        # Simple character-level cross-entropy placeholder
        # In practice, would use token-level loss
        if not original or not reconstructed:
            return 0.0
        
        # Simplified: edit distance normalized by length
        max_len = max(len(original), len(reconstructed))
        if max_len == 0:
            return 0.0
        
        # Placeholder: use length difference as loss
        return abs(len(original) - len(reconstructed)) / max_len
    
    def _kl_divergence(self, mu: np.ndarray, sigma: np.ndarray) -> float:
        """
        Compute KL divergence: KL(q_θ(z | x) || N(0, I))
        
        KL(N(μ, σ²) || N(0, I)) = -0.5 * Σ(1 + log(σ²) - μ² - σ²)
        """
        return -0.5 * np.sum(1 + np.log(sigma**2) - mu**2 - sigma**2)


# Singleton instance
_neural_instance: Optional[NeuralDeltaGCLCompressor] = None


def get_neural_compressor() -> NeuralDeltaGCLCompressor:
    """Get singleton neural compressor instance"""
    global _neural_instance
    if _neural_instance is None:
        _neural_instance = NeuralDeltaGCLCompressor()
    return _neural_instance


if __name__ == "__main__":
    # Test neural compression (placeholder mode)
    print("=" * 70)
    print("NEURAL DELTA GCL COMPRESSION (Placeholder)")
    print("=" * 70)
    
    compressor = NeuralDeltaGCLCompressor()
    
    test_manifest = {
        "layer": "CORE",
        "domain": "COMPUTE",
        "tier": "FOAM",
        "condition": "STABLE",
        "metadata": {"compression_ratio": 0.92, "field_phi": 0.85}
    }
    
    print(f"\n[1] Compressing with neural layer...")
    result = compressor.compress_with_neural(test_manifest, "neural_test_1", verify=False)
    
    print(f"  Neural ratio: {result.neural_ratio:.4f}")
    print(f"  Total ratio: {result.total_ratio:.4f}")
    print(f"  Total reduction: {(1 - result.total_ratio) * 100:.2f}%")
    print(f"  Verified: {result.verified}")
    if result.verification_error:
        print(f"  Verification Error: {result.verification_error}")
    
    print(f"\n[2] Model state:")
    print(f"  Trained: {compressor.is_trained}")
    print(f"  Version: {compressor.model_version}")
    print(f"  Latent dim: {compressor.latent_dim}")
    
    print("\n" + "=" * 70)
    print("NEURAL COMPRESSION PLACEHOLDER OPERATIONAL")
    print("Note: Actual neural network training required for production use")
    print("=" * 70)