import pytest
import numpy as np
from NeuroFlex import NeuroFlex
from NeuroFlex.utils.config import Config

@pytest.fixture
def neuroflex_instance():
    config = Config.get_config()
    return NeuroFlex(config)

def test_neuroflex_initialization(neuroflex_instance):
    assert isinstance(neuroflex_instance, NeuroFlex)
    assert neuroflex_instance.config is not None
    assert neuroflex_instance.logger is not None
    assert hasattr(neuroflex_instance, 'core_model')
    assert hasattr(neuroflex_instance, 'quantum_model')
    assert hasattr(neuroflex_instance, 'ethical_framework')
    assert hasattr(neuroflex_instance, 'explainable_ai')
    assert hasattr(neuroflex_instance, 'bci_processor')
    assert hasattr(neuroflex_instance, 'consciousness_sim')
    assert hasattr(neuroflex_instance, 'alphafold')
    assert hasattr(neuroflex_instance, 'math_solver')
    assert hasattr(neuroflex_instance, 'edge_optimizer')

def test_setup_core_model(neuroflex_instance):
    neuroflex_instance._setup_core_model()
    assert neuroflex_instance.core_model is not None
    assert neuroflex_instance.backend in ['pytorch', 'tensorflow']
    if neuroflex_instance.backend == 'pytorch':
        from NeuroFlex.core_neural_networks.jax.pytorch_module_converted import PyTorchModel
        assert isinstance(neuroflex_instance.core_model, PyTorchModel)
    elif neuroflex_instance.backend == 'tensorflow':
        from NeuroFlex.core_neural_networks.tensorflow.tensorflow_module import TensorFlowModel
        assert isinstance(neuroflex_instance.core_model, TensorFlowModel)

def test_setup_quantum_model(neuroflex_instance):
    neuroflex_instance._setup_quantum_model()
    if neuroflex_instance.config['USE_QUANTUM']:
        assert neuroflex_instance.quantum_model is not None
    else:
        assert neuroflex_instance.quantum_model is None

def test_setup_ethical_framework(neuroflex_instance):
    neuroflex_instance._setup_ethical_framework()
    assert neuroflex_instance.ethical_framework is not None

def test_setup_explainable_ai(neuroflex_instance):
    neuroflex_instance._setup_explainable_ai()
    assert neuroflex_instance.explainable_ai is not None

def test_setup_bci_processor(neuroflex_instance):
    neuroflex_instance._setup_bci_processor()
    if neuroflex_instance.config['USE_BCI']:
        assert neuroflex_instance.bci_processor is not None
    else:
        assert neuroflex_instance.bci_processor is None

def test_setup_consciousness_sim(neuroflex_instance):
    neuroflex_instance._setup_consciousness_sim()
    if neuroflex_instance.config['USE_CONSCIOUSNESS_SIM']:
        assert neuroflex_instance.consciousness_sim is not None
    else:
        assert neuroflex_instance.consciousness_sim is None

def test_setup_alphafold(neuroflex_instance):
    neuroflex_instance._setup_alphafold()
    if neuroflex_instance.config['USE_ALPHAFOLD']:
        assert neuroflex_instance.alphafold is not None
    else:
        assert neuroflex_instance.alphafold is None

def test_setup_math_solver(neuroflex_instance):
    neuroflex_instance._setup_math_solver()
    assert neuroflex_instance.math_solver is not None

def test_setup_edge_optimizer(neuroflex_instance):
    neuroflex_instance._setup_edge_optimizer()
    if neuroflex_instance.config['USE_EDGE_OPTIMIZATION']:
        assert neuroflex_instance.edge_optimizer is not None
    else:
        assert neuroflex_instance.edge_optimizer is None

def test_evaluate_ethics(neuroflex_instance):
    neuroflex_instance._setup_ethical_framework()
    action = {"type": "recommendation", "user_id": 123, "item_id": 456}
    result = neuroflex_instance.evaluate_ethics(action)
    assert isinstance(result, bool)

def test_explain_prediction(neuroflex_instance):
    neuroflex_instance._setup_explainable_ai()

    # Create a mock model with a predict method
    class MockModel:
        def predict(self, data):
            return np.random.rand(1)  # Return a random prediction

    mock_model = MockModel()
    neuroflex_instance.explainable_ai.set_model(mock_model)

    data = np.random.rand(10, 28, 28, 1)
    explanation = neuroflex_instance.explain_prediction(data)
    assert explanation is not None
    assert "Explanation for prediction" in explanation

def test_process_bci_data(neuroflex_instance):
    neuroflex_instance._setup_bci_processor()
    if neuroflex_instance.config['USE_BCI']:
        raw_data = np.random.rand(neuroflex_instance.config['BCI_NUM_CHANNELS'], 1000)
        processed_data = neuroflex_instance.process_bci_data(raw_data)
        assert processed_data is not None
        assert processed_data.shape == raw_data.shape
    else:
        raw_data = np.random.rand(10, 1000)
        processed_data = neuroflex_instance.process_bci_data(raw_data)
        assert np.array_equal(processed_data, raw_data)

def test_simulate_consciousness(neuroflex_instance):
    neuroflex_instance._setup_consciousness_sim()
    if neuroflex_instance.config['USE_CONSCIOUSNESS_SIM']:
        input_data = np.random.rand(64)
        result = neuroflex_instance.simulate_consciousness(input_data)
        assert result is not None
    else:
        input_data = np.random.rand(64)
        result = neuroflex_instance.simulate_consciousness(input_data)
        assert result is None

def test_predict_protein_structure(neuroflex_instance):
    neuroflex_instance._setup_alphafold()
    if neuroflex_instance.config['USE_ALPHAFOLD']:
        sequence = "MKWVTFISLLLLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAKTCVADESAENCDKS"
        structure = neuroflex_instance.predict_protein_structure(sequence)
        assert structure is not None
    else:
        sequence = "MKWVTFISLLLLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAKTCVADESAENCDKS"
        structure = neuroflex_instance.predict_protein_structure(sequence)
        assert structure is None

def test_solve_math_problem(neuroflex_instance):
    neuroflex_instance._setup_math_solver()
    problem = "Solve the equation: 2x + 5 = 15"
    solution = neuroflex_instance.solve_math_problem(problem)
    assert solution is not None

def test_optimize_for_edge(neuroflex_instance):
    neuroflex_instance._setup_edge_optimizer()
    neuroflex_instance._setup_core_model()
    if neuroflex_instance.config['USE_EDGE_OPTIMIZATION']:
        optimized_model = neuroflex_instance.optimize_for_edge(neuroflex_instance.core_model)
        assert optimized_model is not None
        assert optimized_model != neuroflex_instance.core_model
    else:
        optimized_model = neuroflex_instance.optimize_for_edge(neuroflex_instance.core_model)
        assert optimized_model == neuroflex_instance.core_model
