"""
Template for benchmark tests.

This file provides a template for creating tests for new benchmarks.
When adding a new benchmark, copy this template and modify it as needed.
"""

import pytest
import numpy as np
from unittest.mock import patch, MagicMock

# Replace this with your benchmark's category
from ttsds.benchmarks.benchmark import BenchmarkCategory, BenchmarkDimension


# Create a mock implementation of your benchmark
class MockBenchmark:
    """Mock benchmark implementation for testing."""

    def __init__(self, name="MockBenchmark", **kwargs):
        self.name = name
        self.key = name.lower()
        self.category = (
            BenchmarkCategory.GENERIC
        )  # Replace with your benchmark's category
        self.dimension = (
            BenchmarkDimension.ONE_DIMENSIONAL
        )  # Replace with your benchmark's dimension
        self.description = "A mock benchmark for testing"
        self.device = "cpu"

        # Add any specific parameters your benchmark needs
        for k, v in kwargs.items():
            setattr(self, k, v)

    def _get_distribution(self, dataset):
        """Mock implementation of _get_distribution."""
        if self.dimension == BenchmarkDimension.ONE_DIMENSIONAL:
            # Return a 1D distribution
            return np.random.randn(len(dataset))
        else:
            # Return an N-dimensional distribution (mean, covariance)
            n_dim = 5  # Use appropriate dimension for your benchmark
            mean = np.random.randn(n_dim)
            A = np.random.randn(n_dim, n_dim)
            cov = A.T @ A  # Ensure positive semi-definite
            return mean, cov

    def compute_score(self, dataset, reference_datasets, noise_datasets):
        """Mock implementation of compute_score."""
        # Return a mock score and details
        score = 0.75
        details = (reference_datasets[0].name, noise_datasets[0].name)
        return score, details

    def _to_device(self, device):
        """Mock implementation of _to_device."""
        self.device = device


# Test benchmark initialization
def test_benchmark_initialization():
    """Test benchmark initialization with default parameters."""
    # Replace YourBenchmarkClass with your actual benchmark class
    with patch(
        "ttsds.benchmarks.your_category.your_benchmark.YourBenchmarkClass",
        MockBenchmark,
    ):
        benchmark = MockBenchmark()

        assert benchmark.name == "MockBenchmark"
        assert benchmark.key == "mockbenchmark"
        assert benchmark.category == BenchmarkCategory.GENERIC
        assert benchmark.dimension == BenchmarkDimension.ONE_DIMENSIONAL
        assert benchmark.description == "A mock benchmark for testing"
        assert benchmark.device == "cpu"


# Test getting distributions
def test_get_distribution(mock_dataset):
    """Test getting a distribution from the benchmark."""
    # Replace YourBenchmarkClass with your actual benchmark class
    with patch(
        "ttsds.benchmarks.your_category.your_benchmark.YourBenchmarkClass",
        MockBenchmark,
    ):
        benchmark = MockBenchmark()

        # Test getting a distribution
        distribution = benchmark._get_distribution(mock_dataset)

        # Add assertions specific to your benchmark's distribution
        if benchmark.dimension == BenchmarkDimension.ONE_DIMENSIONAL:
            assert isinstance(distribution, np.ndarray)
            assert len(distribution) == len(mock_dataset)
        else:
            mean, cov = distribution
            assert isinstance(mean, np.ndarray)
            assert isinstance(cov, np.ndarray)
            assert cov.shape[0] == cov.shape[1]  # Square matrix


# Test computing distance
def test_compute_distance(mock_dataset, mock_reference_dataset):
    """Test computing distance between distributions."""
    # Replace YourBenchmarkClass with your actual benchmark class
    with patch(
        "ttsds.benchmarks.your_category.your_benchmark.YourBenchmarkClass",
        MockBenchmark,
    ):
        benchmark = MockBenchmark()

        # Mock the get_distribution method if needed
        with patch.object(benchmark, "_get_distribution") as mock_get_dist:
            # Set up mock return values
            if benchmark.dimension == BenchmarkDimension.ONE_DIMENSIONAL:
                mock_get_dist.side_effect = [
                    np.random.randn(len(mock_dataset)),
                    np.random.randn(len(mock_reference_dataset)),
                ]
            else:
                n_dim = 5
                mock_get_dist.side_effect = [
                    (np.random.randn(n_dim), np.random.randn(n_dim, n_dim)),
                    (np.random.randn(n_dim), np.random.randn(n_dim, n_dim)),
                ]

            # Test computing distance
            # Note: You'll need to adapt this to how your benchmark computes distances
            # Replace with the actual method your benchmark uses
            with patch(
                "ttsds.benchmarks.benchmark.Benchmark.compute_distance"
            ) as mock_compute:
                mock_compute.return_value = 0.5

                distance = mock_compute(mock_dataset, mock_reference_dataset)

                assert isinstance(distance, float)
                assert 0 <= distance


# Test compute_score
def test_compute_score(mock_dataset, mock_reference_dataset, mock_noise_dataset):
    """Test computing benchmark score."""
    # Replace YourBenchmarkClass with your actual benchmark class
    with patch(
        "ttsds.benchmarks.your_category.your_benchmark.YourBenchmarkClass",
        MockBenchmark,
    ):
        benchmark = MockBenchmark()

        # Test computing score
        score, details = benchmark.compute_score(
            mock_dataset, [mock_reference_dataset], [mock_noise_dataset]
        )

        # Check the score and details
        assert isinstance(score, float)
        assert 0 <= score <= 1
        assert isinstance(details, tuple)
        assert len(details) == 2
        assert details[0] == mock_reference_dataset.name
        assert details[1] == mock_noise_dataset.name


# Add more tests specific to your benchmark's unique features
def test_benchmark_specific_feature():
    """Test a feature specific to this benchmark."""
    # Replace YourBenchmarkClass with your actual benchmark class
    with patch(
        "ttsds.benchmarks.your_category.your_benchmark.YourBenchmarkClass",
        MockBenchmark,
    ):
        # Add a specific parameter for your benchmark
        benchmark = MockBenchmark(special_param=42)

        # Test the specific feature
        assert benchmark.special_param == 42

        # Add more specific tests here
