"""
Generative Quantum Field Theory (GQFT) Engine

This module implements the core GQFT engine that generates novel quantum field
configurations capable of supporting hyper-properties not found in conventional matter.

The engine uses advanced neural architectures to learn and generate quantum field
equations that can sustain properties like negative mass, exotic magnetic moments,
and novel electromagnetic interactions.
"""

import numpy as np
import tensorflow as tf
from typing import Dict, List, Tuple, Optional, Any
from dataclasses import dataclass
import logging
from abc import ABC, abstractmethod

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class HyperProperty:
    """Represents a desired hyper-property for material design."""
    name: str
    target_value: float
    tolerance: float
    units: str
    description: str

@dataclass
class QuantumFieldConfig:
    """Configuration for quantum field generation."""
    field_dimensions: int = 4  # 3D space + time
    coupling_constants: List[float] = None
    symmetry_groups: List[str] = None
    field_type: str = "scalar"  # scalar, vector, tensor
    
    def __post_init__(self):
        if self.coupling_constants is None:
            self.coupling_constants = [1.0, 0.5, 0.1]
        if self.symmetry_groups is None:
            self.symmetry_groups = ["SU(3)", "SU(2)", "U(1)"]

class QuantumFieldGenerator(ABC):
    """Abstract base class for quantum field generators."""
    
    @abstractmethod
    def generate_lagrangian(self, properties: List[HyperProperty]) -> tf.Tensor:
        """Generate Lagrangian density for given hyper-properties."""
        pass
    
    @abstractmethod
    def validate_field_equations(self, lagrangian: tf.Tensor) -> bool:
        """Validate the generated field equations for physical consistency."""
        pass

class NeuralQuantumFieldGenerator(QuantumFieldGenerator):
    """Neural network-based quantum field generator."""
    
    def __init__(self, config: QuantumFieldConfig):
        self.config = config
        self.model = self._build_neural_model()
        self.compiled = False
        
    def _build_neural_model(self) -> tf.keras.Model:
        """Build the neural architecture for quantum field generation."""
        # Input layer: hyper-property specifications
        property_input = tf.keras.layers.Input(shape=(10,), name='hyper_properties')
        
        # Encoder: Transform properties to latent space
        encoded = tf.keras.layers.Dense(256, activation='relu')(property_input)
        encoded = tf.keras.layers.LayerNormalization()(encoded)
        encoded = tf.keras.layers.Dropout(0.2)(encoded)
        
        encoded = tf.keras.layers.Dense(512, activation='relu')(encoded)
        encoded = tf.keras.layers.LayerNormalization()(encoded)
        
        # Field generator: Generate field parameters
        field_params = tf.keras.layers.Dense(1024, activation='tanh')(encoded)
        field_params = tf.keras.layers.Reshape((32, 32))(field_params)
        
        # Lagrangian constructor: Build Lagrangian density
        lagrangian = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same')(
            tf.expand_dims(field_params, -1)
        )
        lagrangian = tf.keras.layers.Conv2D(32, 3, activation='relu', padding='same')(lagrangian)
        lagrangian = tf.keras.layers.GlobalAveragePooling2D()(lagrangian)
        
        # Output: Lagrangian coefficients
        output = tf.keras.layers.Dense(100, activation='linear', name='lagrangian_coeffs')(lagrangian)
        
        model = tf.keras.Model(inputs=property_input, outputs=output)
        return model
    
    def compile_model(self, learning_rate: float = 1e-4):
        """Compile the neural model with custom loss functions."""
        optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
        
        # Custom loss: Physical consistency + property matching
        def physics_aware_loss(y_true, y_pred):
            # Standard MSE for property matching
            property_loss = tf.keras.losses.mse(y_true, y_pred)
            
            # Physical constraints (unitarity, causality, etc.)
            unitarity_constraint = tf.reduce_mean(tf.square(tf.norm(y_pred, axis=1) - 1.0))
            causality_constraint = tf.reduce_mean(tf.nn.relu(-y_pred[:, :10]))  # Some coeffs must be positive
            
            total_loss = property_loss + 0.1 * unitarity_constraint + 0.1 * causality_constraint
            return total_loss
        
        self.model.compile(
            optimizer=optimizer,
            loss=physics_aware_loss,
            metrics=['mse', 'mae']
        )
        self.compiled = True
        logger.info("GQFT model compiled successfully")
    
    def generate_lagrangian(self, properties: List[HyperProperty]) -> tf.Tensor:
        """Generate Lagrangian density for given hyper-properties."""
        if not self.compiled:
            self.compile_model()
        
        # Encode properties into input vector
        property_vector = self._encode_properties(properties)
        
        # Generate Lagrangian coefficients
        lagrangian_coeffs = self.model(property_vector, training=False)
        
        logger.info(f"Generated Lagrangian for {len(properties)} hyper-properties")
        return lagrangian_coeffs
    
    def _encode_properties(self, properties: List[HyperProperty]) -> tf.Tensor:
        """Encode hyper-properties into neural network input format."""
        # Create fixed-size property vector
        property_vector = np.zeros((1, 10))
        
        for i, prop in enumerate(properties[:10]):  # Limit to 10 properties
            # Normalize property values
            property_vector[0, i] = np.tanh(prop.target_value / (prop.tolerance + 1e-6))
        
        return tf.constant(property_vector, dtype=tf.float32)
    
    def validate_field_equations(self, lagrangian: tf.Tensor) -> bool:
        """Validate the generated field equations for physical consistency."""
        # Check for NaN or infinite values
        if tf.reduce_any(tf.math.is_nan(lagrangian)) or tf.reduce_any(tf.math.is_inf(lagrangian)):
            return False
        
        # Check energy bounds (simplified)
        energy_coeffs = lagrangian[:, :20]  # First 20 coeffs relate to energy terms
        if tf.reduce_any(energy_coeffs < -100) or tf.reduce_any(energy_coeffs > 100):
            return False
        
        logger.info("Field equations passed validation checks")
        return True

class GenerativeQuantumFieldEngine:
    """
    Main GQFT Engine for generating novel quantum field theories.
    
    This engine orchestrates the generation of quantum field configurations
    that can support hyper-properties beyond conventional physics.
    """
    
    def __init__(self, config: Optional[QuantumFieldConfig] = None):
        self.config = config or QuantumFieldConfig()
        self.generator = NeuralQuantumFieldGenerator(self.config)
        self.training_data = []
        self.is_trained = False
        
    def add_training_example(self, properties: List[HyperProperty], known_lagrangian: np.ndarray):
        """Add a training example for supervised learning."""
        property_vector = self.generator._encode_properties(properties)
        self.training_data.append((property_vector, known_lagrangian))
        logger.info(f"Added training example. Total examples: {len(self.training_data)}")
    
    def train(self, epochs: int = 100, batch_size: int = 32, validation_split: float = 0.2):
        """Train the GQFT engine on provided examples."""
        if not self.training_data:
            logger.warning("No training data available. Using synthetic data.")
            self._generate_synthetic_training_data()
        
        # Prepare training data
        X = tf.concat([x for x, _ in self.training_data], axis=0)
        y = tf.constant([y for _, y in self.training_data], dtype=tf.float32)
        
        # Compile model if not done
        if not self.generator.compiled:
            self.generator.compile_model()
        
        # Train the model
        history = self.generator.model.fit(
            X, y,
            epochs=epochs,
            batch_size=batch_size,
            validation_split=validation_split,
            verbose=1
        )
        
        self.is_trained = True
        logger.info(f"GQFT engine trained for {epochs} epochs")
        return history
    
    def _generate_synthetic_training_data(self, n_samples: int = 1000):
        """Generate synthetic training data for initial training."""
        for _ in range(n_samples):
            # Create random hyper-properties
            properties = [
                HyperProperty(
                    name=f"synthetic_prop_{i}",
                    target_value=np.random.normal(0, 1),
                    tolerance=0.1,
                    units="dimensionless",
                    description="Synthetic property for training"
                ) for i in range(np.random.randint(1, 6))
            ]
            
            # Generate corresponding "known" Lagrangian (synthetic)
            lagrangian = np.random.normal(0, 0.5, 100)
            
            self.add_training_example(properties, lagrangian)
    
    def generate_hyper_material_field(self, target_properties: List[HyperProperty]) -> Dict[str, Any]:
        """
        Generate a quantum field configuration for target hyper-properties.
        
        Args:
            target_properties: List of desired hyper-properties
            
        Returns:
            Dictionary containing field configuration and metadata
        """
        if not self.is_trained:
            logger.warning("Engine not trained. Training with synthetic data...")
            self.train(epochs=50)
        
        # Generate Lagrangian
        lagrangian = self.generator.generate_lagrangian(target_properties)
        
        # Validate field equations
        is_valid = self.generator.validate_field_equations(lagrangian)
        
        # Package results
        result = {
            'lagrangian_coefficients': lagrangian.numpy(),
            'target_properties': [
                {
                    'name': prop.name,
                    'value': prop.target_value,
                    'units': prop.units,
                    'description': prop.description
                } for prop in target_properties
            ],
            'field_config': {
                'dimensions': self.config.field_dimensions,
                'coupling_constants': self.config.coupling_constants,
                'symmetry_groups': self.config.symmetry_groups,
                'field_type': self.config.field_type
            },
            'validation_passed': is_valid,
            'confidence_score': self._calculate_confidence_score(lagrangian),
            'generated_timestamp': tf.timestamp().numpy()
        }
        
        logger.info(f"Generated hyper-material field for {len(target_properties)} properties")
        return result
    
    def _calculate_confidence_score(self, lagrangian: tf.Tensor) -> float:
        """Calculate confidence score for generated field."""
        # Simple heuristic based on coefficient stability
        variance = tf.math.reduce_variance(lagrangian)
        confidence = float(tf.exp(-variance / 10.0))  # Higher variance = lower confidence
        return min(max(confidence, 0.0), 1.0)
    
    def save_model(self, filepath: str):
        """Save the trained model to disk."""
        self.generator.model.save(filepath)
        logger.info(f"Model saved to {filepath}")
    
    def load_model(self, filepath: str):
        """Load a trained model from disk."""
        self.generator.model = tf.keras.models.load_model(filepath)
        self.generator.compiled = True
        self.is_trained = True
        logger.info(f"Model loaded from {filepath}")

# Factory function for easy instantiation
def create_gqft_engine(field_type: str = "scalar", 
                      dimensions: int = 4,
                      coupling_constants: Optional[List[float]] = None) -> GenerativeQuantumFieldEngine:
    """Factory function to create a GQFT engine with specified configuration."""
    config = QuantumFieldConfig(
        field_dimensions=dimensions,
        coupling_constants=coupling_constants,
        field_type=field_type
    )
    return GenerativeQuantumFieldEngine(config)
