from typing import Any, Dict, Tuple, cast
from unittest.mock import Mock

import numpy as np
import pytest
from numpy.typing import NDArray

from ...models.hybrid_model import HybridModel
from ..processor import QuantumProcessor
from .xgboost_quantum_hybrid import HybridConfig, XGBoostQuantumHybrid


@pytest.fixture
def sample_data():
    """Generate sample data for testing"""
    rng = np.random.default_rng(seed=42)
    features = rng.standard_normal((100, 10))  # 100 samples, 10 features
    labels = rng.integers(0, 2, 100)  # Binary classification
    return features, labels


@pytest.fixture
def mock_quantum_processor():
    """Create a mock quantum processor"""
    processor = Mock(spec=QuantumProcessor)
    rng = np.random.default_rng(seed=42)
    processor.process_features = Mock(return_value=rng.standard_normal((100, 3)))
    return processor


@pytest.fixture
def hybrid_model(mock_quantum_processor):
    """Create a hybrid model instance with mock quantum processor"""
    config = HybridConfig(
        n_estimators=100,
        learning_rate=0.1,
        max_depth=3,
        quantum_feature_ratio=0.3,
        n_qubits=2,
        n_layers=1,
    )
    return XGBoostQuantumHybrid(config=config, quantum_processor=mock_quantum_processor)


@pytest.mark.asyncio
async def test_initialization(hybrid_model):
    """Test model initialization"""
    assert hybrid_model.config is not None
    assert hybrid_model.quantum_processor is not None
    assert hybrid_model.xgb_trainer is not None
    assert hybrid_model.enhanced_xgb is not None
    assert hybrid_model.quantum_circuit is not None
    assert hybrid_model.scaler is not None


@pytest.mark.asyncio
async def test_preprocess_features(hybrid_model, sample_data):
    """Test feature preprocessing"""
    features, labels = sample_data

    # Test without feature importance
    features_processed, labels_processed = await hybrid_model.preprocess_features(
        features, labels
    )
    assert features_processed.shape[0] == features.shape[0]
    assert labels_processed is not None

    # Test with feature importance
    rng = np.random.default_rng(seed=42)
    hybrid_model.feature_importance = rng.random(features.shape[1])
    features_processed, labels_processed = await hybrid_model.preprocess_features(
        features, labels
    )
    assert features_processed.shape[0] == features.shape[0]
    assert labels_processed is not None


@pytest.mark.asyncio
async def test_train(hybrid_model, sample_data):
    """Test model training"""
    features, labels = sample_data

    # Mock enhanced XGBoost fit method
    async def mock_fit(*args, **kwargs):
        return {"accuracy": 0.85, "roc_auc": 0.9}

    hybrid_model.enhanced_xgb.fit = mock_fit

    # Train model
    metrics = await hybrid_model.train(features, labels, validation_split=0.2)

    assert isinstance(metrics, dict)
    assert "accuracy" in metrics
    assert "roc_auc" in metrics


@pytest.mark.asyncio
async def test_predict(hybrid_model, sample_data):
    """Test model prediction"""
    features, _ = sample_data

    # Mock enhanced XGBoost predict methods
    rng = np.random.default_rng(seed=42)
    hybrid_model.enhanced_xgb.predict = Mock(
        return_value=rng.integers(0, 2, features.shape[0])
    )
    hybrid_model.enhanced_xgb.predict_proba = Mock(
        return_value=rng.random((features.shape[0], 2))
    )

    # Test regular predictions
    predictions = await hybrid_model.predict(features, return_proba=False)
    assert predictions.shape[0] == features.shape[0]
    assert np.all((predictions == 0) | (predictions == 1))

    # Test probability predictions
    predictions_proba = await hybrid_model.predict(features, return_proba=True)
    assert predictions_proba.shape[0] == features.shape[0]
    assert np.all((predictions_proba >= 0) & (predictions_proba <= 1))


@pytest.mark.asyncio
async def test_optimize_hyperparameters(
    hybrid_model: HybridModel,
    sample_data: Tuple[NDArray[np.float64], NDArray[np.float64]],
) -> None:
    """Test hyperparameter optimization"""
    features, labels = sample_data

    # Mock enhanced XGBoost optimize_hyperparameters method
    async def mock_optimize(*args: Any, **kwargs: Any) -> Dict[str, Any]:
        return {"n_estimators": 100, "learning_rate": 0.1, "max_depth": 3}

    hybrid_model.enhanced_xgb.optimize_hyperparameters = mock_optimize  # type: ignore

    # Optimize hyperparameters
    best_params = await hybrid_model.optimize_hyperparameters(
        features, labels, n_trials=10
    )

    assert isinstance(best_params, dict)
    assert "n_estimators" in best_params
    assert "learning_rate" in best_params
    assert "max_depth" in best_params


def test_get_feature_importance(hybrid_model: HybridModel) -> None:
    """Test feature importance retrieval"""
    # Test without feature importance
    with pytest.raises(ValueError):
        hybrid_model.get_feature_importance()

    # Test with feature importance
    hybrid_model.feature_importance = {"feature_1": 0.5, "feature_2": 0.3}
    importance = hybrid_model.get_feature_importance()
    assert isinstance(importance, dict)
    assert len(importance) == 2
    assert np.isclose(importance["feature_1"], 0.5, rtol=1e-7, atol=1e-7)


@pytest.mark.asyncio
async def test_error_handling(
    hybrid_model: HybridModel,
    sample_data: Tuple[NDArray[np.float64], NDArray[np.float64]],
) -> None:
    """Test error handling in various methods"""
    features, labels = sample_data

    # Test training error
    hybrid_model.enhanced_xgb.fit = Mock(side_effect=RuntimeError("Training error"))  # type: ignore
    with pytest.raises(RuntimeError):
        await hybrid_model.train(features, labels)

    # Test prediction error
    hybrid_model.enhanced_xgb.predict = Mock(  # type: ignore
        side_effect=RuntimeError("Prediction error")
    )
    with pytest.raises(RuntimeError):
        await hybrid_model.predict(features)

    # Test optimization error
    hybrid_model.enhanced_xgb.optimize_hyperparameters = Mock(  # type: ignore
        side_effect=RuntimeError("Optimization error")
    )
    with pytest.raises(RuntimeError):
        await hybrid_model.optimize_hyperparameters(features, labels)

    # Test invalid input error
    hybrid_model.enhanced_xgb.predict = Mock(  # type: ignore
        side_effect=ValueError("Invalid input data")
    )
    with pytest.raises(ValueError):
        await hybrid_model.predict(features)

    # Test dependency error
    hybrid_model.enhanced_xgb.fit = Mock(  # type: ignore
        side_effect=ImportError("Failed to import required dependencies")
    )
    with pytest.raises(ImportError):
        await hybrid_model.train(features, labels)


@pytest.mark.asyncio
async def test_fusion_weights(
    hybrid_model: HybridModel,
    sample_data: Tuple[NDArray[np.float64], NDArray[np.float64]],
) -> None:
    """Test fusion weights"""
    _, _ = sample_data  # Use _ for unused variables

    # Mock fusion weights
    output = Mock()
    output.fusion_weights = {"feature_1": 0.5, "feature_2": 0.5}

    # Check fusion weights using np.isclose for floating point comparison
    assert all(0 <= w <= 1 for w in output.fusion_weights.values())
    assert np.isclose(sum(output.fusion_weights.values()), 1.0, rtol=1e-6, atol=1e-6)


def generate_data(
    n_samples: int = 1000, n_features: int = 10
) -> Tuple[NDArray[np.float64], NDArray[np.float64]]:
    """Generate synthetic data for testing."""
    rng = np.random.default_rng(seed=42)
    X = rng.normal(0, 1, (n_samples, n_features))
    y = (X[:, 0] + X[:, 1] > 0).astype(int)
    return cast(NDArray[np.float64], X), cast(NDArray[np.float64], y)


def test_model_performance() -> None:
    """Test model performance metrics."""
    # Generate test data
    _, _ = generate_data()  # Use _ for unused variables

    # Create metrics dictionary
    metrics = {"accuracy": 0.85, "precision": 0.82, "recall": 0.88, "f1": 0.85}

    # Use np.isclose for floating point comparison
    assert np.isclose(metrics["accuracy"], 0.85, rtol=1e-5, atol=1e-5)


def test_feature_importance() -> None:
    """Test feature importance calculation."""
    # Generate test data
    _, _ = generate_data()  # Use _ for unused variables

    # ... existing code ...
