import numpy as np
import tensorflow as tf
import math
import logging
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional
from tensorflow.keras import layers, models, callbacks

# Set up logging for production monitoring
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

@dataclass
class Vec6:
    """A 6D vector for representing complex states, inspired by multi-dimensional metrics."""
    a: float = 0.0
    b: float = 0.0
    c: float = 0.0
    d: float = 0.0
    e: float = 0.0
    f: float = 0.0

    def norm(self) -> float:
        """Calculate the Euclidean norm of the vector."""
        return math.sqrt(self.a**2 + self.b**2 + self.c**2 + self.d**2 + self.e**2 + self.f**2)

    def to_array(self) -> np.ndarray:
        """Convert to a numpy array for easier computation."""
        return np.array([self.a, self.b, self.c, self.d, self.e, self.f])

class RomanSteeringEngine:
    """Enhanced steering engine for validating neural outputs based on field-curvature entanglement.
    This simulates a 'consciousness-like' check, ensuring predictions align with historical patterns.
    """
    def __init__(self):
        self.prev_state: List[float] = [0.0, 0.0, 0.0]  # Initial state for metrics (e.g., representation, entanglement, topology share)

    def get_fces_scalar(self, current_metrics: List[float]) -> float:
        """Compute the Field-Curvature Entanglement Scalar (FCES) for steering.
        This is refined to handle edge cases and provide more stable outputs.
        """
        try:
            f = np.array(current_metrics)
            c = np.array(current_metrics) - np.array(self.prev_state)
            fn = np.linalg.norm(f)
            cn = np.linalg.norm(c)
            
            if fn > 0 and cn > 0:
                cross = np.linalg.norm(np.cross(f, c))
                scalar = cross / (fn * cn + 1e-6)  # Added epsilon for numerical stability
                self.prev_state = current_metrics  # Update state
                return scalar
            return 0.0  # Default if norms are zero
        except Exception as e:
            logger.error(f"Error in FCES calculation: {e}")
            return 0.0  # Fallback to safe value

class MemoryResonanceLayer(layers.Layer):
    """Advanced custom layer for feature correlation and memory augmentation.
    Builds on the original EntanglementLayer by adding adaptive weighting based on temporal relevance.
    This mimics how human consciousness prioritizes recent memories while retaining long-term patterns.
    """
    def __init__(self, num_features: int, memory_decay: float = 0.9, **kwargs):
        super().__init__(**kwargs)
        self.num_features = num_features
        self.memory_decay = memory_decay  # Hyperparameter for weighting recent vs. past data
        self.attn = layers.Dense(num_features, activation='softmax')  # Attention mechanism

    def call(self, x: tf.Tensor) -> tf.Tensor:
        """Apply attention-based entanglement with memory resonance.
        x: Input tensor (e.g., from LSTM output)
        """
        weights = self.attn(x)  # Generate attention weights
        # Apply memory resonance: Decay older weights to emphasize recent patterns
        decayed_weights = weights * tf.math.exp(-self.memory_decay * tf.range(1, tf.shape(x)[1] + 1, dtype=tf.float32))
        return x * decayed_weights  # Entangled output

class RomanAI_Core:
    """Unified core for the Temporal Insight Engine.
    This handles model building, training, prediction, and steering for production-ready use.
    """
    def __init__(self, input_shape: tuple, memory_decay: float = 0.9):
        self.steering = RomanSteeringEngine()
        self.model = self._build_model(input_shape, memory_decay)
        self.history = None  # For storing training history

    def _build_model(self, input_shape: tuple, memory_decay: float) -> tf.keras.Model:
        """Construct the enhanced model with LSTM, MemoryResonanceLayer, and output layers."""
        inputs = layers.Input(shape=input_shape)
        
        # Initial Temporal Analysis: LSTM for sequence processing
        x = layers.LSTM(64, return_sequences=True, activation='tanh')(inputs)  # Tuned activation for stability
        
        # Memory Resonance Core: Enhanced entanglement with adaptive weighting
        x = MemoryResonanceLayer(num_features=64, memory_decay=memory_decay)(x)
        
        # Deep Logic Layer: Additional LSTM for refined temporal understanding
        x = layers.LSTM(32, return_sequences=False)(x)  # No sequences for final output
        outputs = layers.Dense(1, activation='linear')(x)  # Linear output for predictions
        
        # Compile with metrics for accuracy evaluation
        model = tf.keras.Model(inputs, outputs)
        model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae', 'mse'])  # Added MSE for detailed tracking
        return model

    def train(self, X_train: np.ndarray, y_train: np.ndarray, epochs: int = 50, validation_data: Optional[tuple] = None):
        """Train the model with callbacks for production use."""
        try:
            callbacks_list = [
                callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),
                callbacks.ModelCheckpoint('best_romanai_model.keras', save_best_only=True)
            ]
            self.history = self.model.fit(
                X_train, y_train,
                epochs=epochs,
                validation_data=validation_data,
                callbacks=callbacks_list
            )
            logger.info("Training completed successfully.")
        except Exception as e:
            logger.error(f"Training error: {e}")
            raise

    def predictive_step(self, data_packet: np.ndarray, current_metrics: List[float] = [0.12, 0.85, 0.45]) -> Dict[str, Any]:
        """Enhanced prediction with steering, uncertainty estimation, and output structuring.
        Returns a dictionary for better usability in production.
        """
        try:
            raw_pred = self.model.predict(data_packet)  # Raw neural prediction
            
            # Compute steering scalar
            fces = self.steering.get_fces_scalar(current_metrics)
            
            if fces > 0.7:  # Threshold for intervention
                logger.info(f"[RomanAI] High Entanglement Detected ({fces:.4f}). Applying Steering.")
                steered_pred = raw_pred * (1.0 - (fces * 0.1))  # Dampen prediction
                uncertainty = fces * 0.2  # Simple uncertainty estimate based on entanglement
            else:
                steered_pred = raw_pred
                uncertainty = 0.1  # Baseline uncertainty
            
            return {
                'prediction': steered_pred.flatten()[0],  # Simplified output
                'uncertainty': uncertainty,  # Added for confidence levels
                'fces_value': fces  # For debugging/monitoring
            }
        except Exception as e:
            logger.error(f"Prediction error: {e}")
            return {'prediction': 0.0, 'uncertainty': 1.0, 'fces_value': 0.0}  # Safe fallback

# --- Execution Example ---
if __name__ == "__main__":
    # Initialize and train the model
    input_shape = (10, 5)  # 10 time steps, 5 features
    core = RomanAI_Core(input_shape=input_shape, memory_decay=0.85)  # Tunable parameter
    
    # Sample data: In production, load from real sources
    X_train = np.random.rand(1000, 10, 5)  # Training features
    y_train = np.random.rand(1000, 1)     # Training targets
    
    core.train(X_train, y_train, epochs=20, validation_data=(np.random.rand(200, 10, 5), np.random.rand(200, 1)))
    
    # Make a prediction
    sample_input = np.random.rand(1, 10, 5)
    prediction_result = core.predictive_step(sample_input)
    
    logger.info(f"Final Cognitive Output: Prediction={prediction_result['prediction']}, Uncertainty={prediction_result['uncertainty']}")