import unittest
from quantum_finance.backend.quantum_algorithms import ShorWrapper, GroversAlgorithm
from quantum_finance.backend.ml_framework import BayesianNN, TransformerModel
from quantum_finance.backend.data_pipeline import DataPipeline
from quantum_finance.backend.quantum_hybrid_engine import QuantumHybridEngine

logging.basicConfig(level=logging.DEBUG)

class IntegrationTests(unittest.TestCase):

    def setUp(self):
        self.shor = ShorWrapper()
        self.grover = GroversAlgorithm()
        self.bnn = BayesianNN()
        self.transformer = TransformerModel()
        self.data_pipeline = DataPipeline()
        self.engine = QuantumHybridEngine()

    def test_quantum_ai_integration(self):
        # Test Shor's algorithm with BNN
        quantum_result = self.shor.run(15)
        ai_prediction = self.bnn.predict(quantum_result)
        self.assertIsNotNone(ai_prediction)

        # Test Grover's algorithm with Transformer
        search_space = list(range(1000))
        target = 42
        quantum_result = self.grover.run(search_space, target)
        ai_analysis = self.transformer.analyze(quantum_result)
        self.assertEqual(ai_analysis['found_target'], True)

    def test_data_pipeline_integration(self):
        # Test data ingestion and processing
        test_data = [1, 2, 3, 4, 5]
        processed_data = self.data_pipeline.process(test_data)
        self.assertEqual(len(processed_data), len(test_data))

        # Test data pipeline with quantum algorithm
        quantum_result = self.shor.run(21)
        pipeline_result = self.data_pipeline.integrate_quantum_result(quantum_result)
        self.assertIsNotNone(pipeline_result)

    # Add more integration tests as needed

if __name__ == '__main__':
    unittest.main(verbosity=2)