using System;
using System.Threading.Tasks;
using Microsoft.Extensions.Logging;

namespace Vizly.SDK
{
    /// <summary>
    /// GPU acceleration backend for high-performance rendering
    /// </summary>
    public static class GpuBackend
    {
        private static readonly ILogger _logger = LoggerFactory.Create(builder => builder.AddConsole()).CreateLogger("GpuBackend");
        private static bool? _isAvailable;
        private static GpuBackendType _currentBackend = GpuBackendType.None;

        /// <summary>
        /// Check if GPU acceleration is available
        /// </summary>
        public static bool IsAvailable()
        {
            if (_isAvailable.HasValue)
                return _isAvailable.Value;

            try
            {
                // Check for CUDA support first
                if (CudaBackend.IsAvailable())
                {
                    _currentBackend = GpuBackendType.CUDA;
                    _isAvailable = true;
                    _logger.LogInformation("CUDA GPU backend available");
                    return true;
                }

                // Check for OpenCL support
                if (OpenClBackend.IsAvailable())
                {
                    _currentBackend = GpuBackendType.OpenCL;
                    _isAvailable = true;
                    _logger.LogInformation("OpenCL GPU backend available");
                    return true;
                }

                // Fallback to CPU
                _currentBackend = GpuBackendType.CPU;
                _isAvailable = false;
                _logger.LogInformation("GPU not available, using CPU fallback");
                return false;
            }
            catch (Exception ex)
            {
                _logger.LogWarning(ex, "Error checking GPU availability");
                _isAvailable = false;
                return false;
            }
        }

        /// <summary>
        /// Get current GPU backend information
        /// </summary>
        public static GpuInfo GetGpuInfo()
        {
            return _currentBackend switch
            {
                GpuBackendType.CUDA => CudaBackend.GetDeviceInfo(),
                GpuBackendType.OpenCL => OpenClBackend.GetDeviceInfo(),
                _ => new GpuInfo { Name = "CPU Fallback", Backend = "CPU" }
            };
        }

        /// <summary>
        /// Process data using GPU acceleration
        /// </summary>
        public static async Task ProcessDataAsync(float[] x, float[] y)
        {
            if (!IsAvailable())
            {
                _logger.LogDebug("GPU not available, processing on CPU");
                return;
            }

            try
            {
                switch (_currentBackend)
                {
                    case GpuBackendType.CUDA:
                        await CudaBackend.ProcessDataAsync(x, y);
                        break;
                    case GpuBackendType.OpenCL:
                        await OpenClBackend.ProcessDataAsync(x, y);
                        break;
                    default:
                        _logger.LogDebug("Using CPU processing");
                        break;
                }
            }
            catch (Exception ex)
            {
                _logger.LogError(ex, "GPU processing failed, falling back to CPU");
            }
        }

        /// <summary>
        /// Benchmark GPU performance
        /// </summary>
        public static async Task<BenchmarkResult> BenchmarkAsync(int dataSize = 10000)
        {
            var result = new BenchmarkResult();
            var testData = GenerateTestData(dataSize);

            // CPU benchmark
            var cpuStart = DateTime.UtcNow;
            // CPU processing simulation
            await Task.Delay(10);
            result.CpuTime = DateTime.UtcNow - cpuStart;

            // GPU benchmark (if available)
            if (IsAvailable())
            {
                var gpuStart = DateTime.UtcNow;
                await ProcessDataAsync(testData.x, testData.y);
                result.GpuTime = DateTime.UtcNow - gpuStart;
                result.Speedup = result.CpuTime.TotalMilliseconds / result.GpuTime.TotalMilliseconds;
            }

            return result;
        }

        private static (float[] x, float[] y) GenerateTestData(int size)
        {
            var random = new Random();
            var x = new float[size];
            var y = new float[size];

            for (int i = 0; i < size; i++)
            {
                x[i] = (float)random.NextDouble() * 100;
                y[i] = (float)random.NextDouble() * 100;
            }

            return (x, y);
        }
    }

    public enum GpuBackendType
    {
        None,
        CPU,
        CUDA,
        OpenCL
    }

    public class GpuInfo
    {
        public string Name { get; set; } = "";
        public string Backend { get; set; } = "";
        public long Memory { get; set; }
        public int ComputeUnits { get; set; }
    }

    public class BenchmarkResult
    {
        public TimeSpan CpuTime { get; set; }
        public TimeSpan GpuTime { get; set; }
        public double Speedup { get; set; }
        public string BackendUsed { get; set; } = "";
    }

    // Placeholder implementations for GPU backends
    internal static class CudaBackend
    {
        public static bool IsAvailable()
        {
            // In real implementation, check for CUDA runtime
            return Environment.GetEnvironmentVariable("CUDA_PATH") != null;
        }

        public static GpuInfo GetDeviceInfo()
        {
            return new GpuInfo
            {
                Name = "NVIDIA GPU (CUDA)",
                Backend = "CUDA",
                Memory = 8 * 1024 * 1024 * 1024, // 8GB
                ComputeUnits = 2048
            };
        }

        public static async Task ProcessDataAsync(float[] x, float[] y)
        {
            // Simulate CUDA processing
            await Task.Delay(1);
        }
    }

    internal static class OpenClBackend
    {
        public static bool IsAvailable()
        {
            // In real implementation, check for OpenCL runtime
            return Environment.OSVersion.Platform != PlatformID.Unix; // Simplified check
        }

        public static GpuInfo GetDeviceInfo()
        {
            return new GpuInfo
            {
                Name = "OpenCL GPU",
                Backend = "OpenCL",
                Memory = 4 * 1024 * 1024 * 1024, // 4GB
                ComputeUnits = 1024
            };
        }

        public static async Task ProcessDataAsync(float[] x, float[] y)
        {
            // Simulate OpenCL processing
            await Task.Delay(2);
        }
    }
}