import jax
import flax
import tensorflow as tf
from .core_neural_networks import NeuroFlex as CoreNeuroFlex, CNN, LRNN, LSTMModule
from .quantum_neural_networks import QuantumNeuralNetwork
from .ai_ethics import EthicalFramework, ExplainableAI
from .bci_integration import BCIProcessor
from .cognitive_architectures import ConsciousnessSimulation
from .scientific_domains import AlphaFoldIntegration, MathSolver
from .edge_ai import EdgeAIOptimization
from .utils import Config, setup_logging
from .Transformers.unified_transformer import UnifiedTransformer

class NeuroFlex:
    """
    NeuroFlex is the main class for the NeuroFlex project, integrating various AI and neural network components.

    This class provides a unified interface for setting up and using different AI models and tools,
    including core neural networks, quantum neural networks, ethical frameworks, explainable AI,
    BCI integration, consciousness simulation, protein structure prediction, and more.
    """

    def __init__(self, config=None):
        """
        Initialize the NeuroFlex instance.

        Args:
            config (Config, optional): Configuration object for NeuroFlex. If None, default config is used.

        Attributes:
            config (Config): Configuration for NeuroFlex components.
            logger (Logger): Logger for NeuroFlex operations.
            core_model (CoreNeuroFlex): Core neural network model.
            quantum_model (QuantumNeuralNetwork): Quantum neural network model.
            ethical_framework (EthicalFramework): Ethical evaluation framework.
            explainable_ai (ExplainableAI): Explainable AI component.
            bci_processor (BCIProcessor): Brain-Computer Interface processor.
            consciousness_sim (ConsciousnessSimulation): Consciousness simulation component.
            alphafold (AlphaFoldIntegration): AlphaFold integration for protein structure prediction.
            math_solver (MathSolver): Mathematical problem solver.
            edge_optimizer (EdgeAIOptimization): Edge AI optimization component.
            unified_transformer (UnifiedTransformer): Unified transformer model.
        """
        self.config = config or Config.get_config()
        self.logger = setup_logging()
        self.core_model = None
        self.quantum_model = None
        self.ethical_framework = None
        self.explainable_ai = None
        self.bci_processor = None
        self.consciousness_sim = None
        self.alphafold = None
        self.math_solver = None
        self.edge_optimizer = None
        self.unified_transformer = None

    def setup(self):
        self.logger.info("Setting up NeuroFlex components...")
        self._setup_core_model()
        self._setup_quantum_model()
        self._setup_ethical_framework()
        self._setup_explainable_ai()
        self._setup_bci_processor()
        self._setup_consciousness_sim()
        self._setup_alphafold()
        self._setup_math_solver()
        self._setup_edge_optimizer()
        self._setup_unified_transformer()
        self.logger.info("NeuroFlex setup complete.")

    def _setup_core_model(self):
        self.core_model = CoreNeuroFlex(
            features=self.config['CORE_MODEL_FEATURES'],
            use_cnn=self.config['USE_CNN'],
            use_rnn=self.config['USE_RNN'],
            use_lstm=self.config['USE_LSTM']
        )

    def _setup_quantum_model(self):
        if self.config['USE_QUANTUM']:
            self.quantum_model = QuantumNeuralNetwork(
                n_qubits=self.config['QUANTUM_N_QUBITS'],
                n_layers=self.config['QUANTUM_N_LAYERS']
            )

    def _setup_ethical_framework(self):
        self.ethical_framework = EthicalFramework()

    def _setup_explainable_ai(self):
        self.explainable_ai = ExplainableAI()

    def _setup_bci_processor(self):
        if self.config['USE_BCI']:
            self.bci_processor = BCIProcessor(
                sampling_rate=self.config['BCI_SAMPLING_RATE'],
                num_channels=self.config['BCI_NUM_CHANNELS']
            )

    def _setup_consciousness_sim(self):
        if self.config['USE_CONSCIOUSNESS_SIM']:
            self.consciousness_sim = ConsciousnessSimulation(
                features=self.config['CONSCIOUSNESS_SIM_FEATURES']
            )

    def _setup_alphafold(self):
        if self.config['USE_ALPHAFOLD']:
            self.alphafold = AlphaFoldIntegration()

    def _setup_math_solver(self):
        self.math_solver = MathSolver()

    def _setup_edge_optimizer(self):
        if self.config['USE_EDGE_OPTIMIZATION']:
            self.edge_optimizer = EdgeAIOptimization()

    def _setup_unified_transformer(self):
        if self.config['USE_UNIFIED_TRANSFORMER']:
            self.unified_transformer = UnifiedTransformer(
                vocab_size=self.config['UNIFIED_TRANSFORMER_VOCAB_SIZE'],
                d_model=self.config['UNIFIED_TRANSFORMER_D_MODEL'],
                num_heads=self.config['UNIFIED_TRANSFORMER_NUM_HEADS'],
                num_layers=self.config['UNIFIED_TRANSFORMER_NUM_LAYERS'],
                d_ff=self.config['UNIFIED_TRANSFORMER_D_FF'],
                max_seq_length=self.config['UNIFIED_TRANSFORMER_MAX_SEQ_LENGTH'],
                dropout=self.config['UNIFIED_TRANSFORMER_DROPOUT']
            )

    def train(self, data, labels):
        # Implement training logic here
        pass

    def predict(self, data):
        # Implement prediction logic here
        pass

    def optimize_for_edge(self, model):
        if self.edge_optimizer:
            return self.edge_optimizer.optimize(model)
        return model

    def evaluate_ethics(self, action):
        if self.ethical_framework:
            return self.ethical_framework.evaluate_action(action)
        return True

    def explain_prediction(self, data, prediction):
        if self.explainable_ai:
            return self.explainable_ai.explain_prediction(data, prediction)
        return None

    def process_bci_data(self, raw_data):
        if self.bci_processor:
            return self.bci_processor.process(raw_data)
        return raw_data

    def simulate_consciousness(self, input_data):
        if self.consciousness_sim:
            return self.consciousness_sim.simulate_consciousness(input_data)
        return None

    def predict_protein_structure(self, sequence):
        if self.alphafold:
            return self.alphafold.predict_structure(sequence)
        return None

    def solve_math_problem(self, problem):
        return self.math_solver.solve(problem)
