"""
Tests for the quantum enhancer module.
"""

import shutil
import tempfile
import time
import unittest

import numpy as np
import pytest
import tensorflow as tf

from ..ml.deep_learning.quantum_enhancer import QuantumEnhancer


@pytest.fixture
def random_generator():
    """Create a numpy random generator."""
    return np.random.default_rng(42)


@pytest.fixture
def test_data(random_generator):
    """Create test data."""
    return {
        "features": random_generator.random((100, 10)),
        "labels": random_generator.integers(0, 2, 100),
    }


@pytest.fixture
def quantum_enhancer():
    """Create a quantum enhancer instance."""
    return QuantumEnhancer()


def test_quantum_enhancement(quantum_enhancer, test_data):
    """Test quantum enhancement of data."""
    start_time = time.time()
    enhanced_data = quantum_enhancer.enhance(test_data["features"])
    end_time = time.time()

    assert enhanced_data.shape == test_data["features"].shape
    assert end_time - start_time < 5.0  # Should complete within 5 seconds


def test_quantum_optimization(quantum_enhancer, test_data):
    """Test quantum optimization of model parameters."""
    start_time = time.time()
    optimized_params = quantum_enhancer.optimize(
        test_data["features"], test_data["labels"]
    )
    end_time = time.time()

    assert isinstance(optimized_params, dict)
    assert end_time - start_time < 10.0  # Should complete within 10 seconds


def test_quantum_feature_selection(quantum_enhancer, test_data):
    """Test quantum feature selection."""
    start_time = time.time()
    selected_features = quantum_enhancer.select_features(
        test_data["features"], test_data["labels"]
    )
    end_time = time.time()

    assert isinstance(selected_features, np.ndarray)
    assert selected_features.shape[1] <= test_data["features"].shape[1]
    assert end_time - start_time < 8.0  # Should complete within 8 seconds


class TestQuantumEnhancer(unittest.TestCase):
    def setUp(self):
        """Set up test environment before each test."""
        self.temp_dir = tempfile.mkdtemp()
        self.config = {
            "num_qubits": 4,
            "error_correction_enabled": True,
            "optimization_level": "medium",
            "storage_path": self.temp_dir,
        }
        self.enhancer = QuantumEnhancer(self.config)

    def tearDown(self):
        """Clean up after each test."""
        if hasattr(self, "enhancer"):
            self.enhancer.cleanup()
        shutil.rmtree(self.temp_dir)

    def test_initialization(self):
        """Test quantum enhancer initialization."""
        self.assertIsNotNone(self.enhancer)
        self.assertTrue(self.enhancer.is_initialized())

        # Test with different optimization levels
        for level in ["low", "medium", "high"]:
            config = self.config.copy()
            config["optimization_level"] = level
            enhancer = QuantumEnhancer(config)
            self.assertTrue(enhancer.is_initialized())
            enhancer.cleanup()

    def test_enhance_numerical_data(self):
        """Test enhancement of numerical data."""
        input_data = np.array([1, 2, 3, 4])
        enhanced = self.enhancer.enhance(input_data)

        self.assertIsInstance(enhanced, np.ndarray)
        self.assertEqual(enhanced.shape, input_data.shape)
        self.assertFalse(np.array_equal(enhanced, input_data))  # Verify enhancement

    def test_enhance_tensor_data(self):
        """Test enhancement of tensor data."""
        input_tensor = tf.constant([[1, 2], [3, 4]])
        enhanced = self.enhancer.enhance(input_tensor)

        self.assertIsInstance(enhanced, tf.Tensor)
        self.assertEqual(enhanced.shape, input_tensor.shape)
        self.assertFalse(tf.reduce_all(tf.equal(enhanced, input_tensor)))

    def test_data_type_consistency(self):
        """Test that data type consistency is maintained."""
        inputs = [
            np.array([1, 2, 3, 4]),
            tf.constant([1, 2, 3, 4]),
            np.array([1, 2, 3, 4], dtype=np.float32),
        ]

        for input_data in inputs:
            enhanced = self.enhancer.enhance(input_data)
            self.assertEqual(type(enhanced), type(input_data))

    def test_performance(self):
        """Test performance with large datasets."""
        large_input = np.random.rand(10000)
        start_time = time.time()

        enhanced = self.enhancer.enhance(large_input)

        processing_time = time.time() - start_time
        self.assertLess(processing_time, 1.0)  # Should process within 1 second
        self.assertEqual(enhanced.shape, large_input.shape)

    def test_concurrent_enhancement(self):
        """Test handling of concurrent enhancement requests."""
        inputs = [np.random.rand(1000) for _ in range(10)]
        start_time = time.time()

        results = [self.enhancer.enhance(input_data) for input_data in inputs]

        processing_time = time.time() - start_time
        self.assertEqual(len(results), len(inputs))
        self.assertLess(processing_time, 2.0)  # Should process within 2 seconds

    def test_error_handling(self):
        """Test error handling for various scenarios."""
        # Test out-of-memory scenario
        huge_input = np.random.rand(1000000)
        with self.assertRaises(MemoryError):
            self.enhancer.enhance(huge_input)

        # Test invalid data types
        invalid_inputs = ["string", {"object": "invalid"}, None, np.nan]

        for invalid_input in invalid_inputs:
            with self.assertRaises(ValueError):
                self.enhancer.enhance(invalid_input)

    def test_cleanup(self):
        """Test proper cleanup of resources."""
        self.enhancer.cleanup()

        # Try to enhance after cleanup - should reinitialize automatically
        input_data = np.array([1, 2, 3, 4])
        enhanced = self.enhancer.enhance(input_data)
        self.assertIsNotNone(enhanced)

    def test_tensorflow_integration(self):
        """Test integration with TensorFlow models."""
        # Create a simple model
        model = tf.keras.Sequential(
            [tf.keras.layers.Dense(4, input_shape=(4,)), tf.keras.layers.Dense(2)]
        )

        # Enhance model weights
        weights = model.get_weights()
        enhanced_weights = [self.enhancer.enhance(weight) for weight in weights]

        # Verify enhancement
        self.assertEqual(len(enhanced_weights), len(weights))
        for enhanced, original in zip(enhanced_weights, weights):
            self.assertEqual(enhanced.shape, original.shape)
            self.assertEqual(enhanced.dtype, original.dtype)

    def test_quantum_state_management(self):
        """Test quantum state management."""
        # Test state initialization
        state = self.enhancer.get_state()
        self.assertIsNotNone(state)
        self.assertEqual(len(state["qubits"]), self.config["num_qubits"])

        # Test state coherence
        coherence = self.enhancer.calculate_coherence()
        self.assertGreaterEqual(coherence, 0)
        self.assertLessEqual(coherence, 1)

    def test_error_correction(self):
        """Test quantum error correction."""
        # Test with error correction enabled
        input_data = np.array([1, 2, 3, 4])
        enhanced = self.enhancer.enhance(input_data)
        self.assertIsNotNone(enhanced)

        # Test with error correction disabled
        config_no_correction = self.config.copy()
        config_no_correction["error_correction_enabled"] = False
        enhancer_no_correction = QuantumEnhancer(config_no_correction)
        enhanced_no_correction = enhancer_no_correction.enhance(input_data)
        self.assertIsNotNone(enhanced_no_correction)
        enhancer_no_correction.cleanup()


if __name__ == "__main__":
    unittest.main()
