using System.Diagnostics;
using System.Runtime.CompilerServices;
using System.Security.Cryptography;

namespace StellaOps.AdvisoryAI.Inference;

/// <summary>
/// Local LLM runtime using llama.cpp bindings.
/// Sprint: SPRINT_20251226_019_AI_offline_inference
/// Task: OFFLINE-05
/// </summary>
public sealed class LlamaCppRuntime : ILocalLlmRuntime
{
    private LocalLlmConfig? _config;
    private bool _modelLoaded;
    private string? _computedDigest;

    public string RuntimeType => "llama.cpp";

    public Task LoadModelAsync(LocalLlmConfig config, CancellationToken cancellationToken = default)
    {
        _config = config;

        // Verify model file exists
        if (!File.Exists(config.ModelPath))
        {
            throw new FileNotFoundException($"Model file not found: {config.ModelPath}");
        }

        // In a real implementation, this would:
        // 1. Load the GGUF/GGML model file
        // 2. Initialize llama.cpp context with config settings
        // 3. Verify digest if required

        _modelLoaded = true;
        return Task.CompletedTask;
    }

    public Task UnloadModelAsync(CancellationToken cancellationToken = default)
    {
        _modelLoaded = false;
        _config = null;
        _computedDigest = null;
        return Task.CompletedTask;
    }

    public Task<LocalModelStatus> GetStatusAsync(CancellationToken cancellationToken = default)
    {
        return Task.FromResult(new LocalModelStatus
        {
            Loaded = _modelLoaded,
            ModelPath = _config?.ModelPath ?? string.Empty,
            DigestVerified = _computedDigest == _config?.WeightsDigest,
            MemoryBytes = _modelLoaded ? EstimateMemoryUsage() : 0,
            Device = _config?.Device.ToString() ?? "Unknown",
            ContextSize = _config?.ContextLength ?? 0
        });
    }

    public async Task<LocalInferenceResult> GenerateAsync(string prompt, CancellationToken cancellationToken = default)
    {
        if (!_modelLoaded || _config is null)
        {
            throw new InvalidOperationException("Model not loaded");
        }

        var stopwatch = Stopwatch.StartNew();
        var firstTokenTime = 0L;

        // In a real implementation, this would call llama.cpp inference
        // For now, return a placeholder response

        await Task.Delay(100, cancellationToken); // Simulate first token
        firstTokenTime = stopwatch.ElapsedMilliseconds;

        await Task.Delay(400, cancellationToken); // Simulate generation

        stopwatch.Stop();

        var generatedContent = GeneratePlaceholderResponse(prompt);
        var tokensGenerated = generatedContent.Split(' ').Length;

        return new LocalInferenceResult
        {
            Content = generatedContent,
            TokensGenerated = tokensGenerated,
            InferenceTimeMs = stopwatch.ElapsedMilliseconds,
            TimeToFirstTokenMs = firstTokenTime,
            ModelId = $"local:{Path.GetFileName(_config.ModelPath)}",
            Deterministic = _config.Temperature == 0,
            Seed = _config.Seed
        };
    }

    public async IAsyncEnumerable<string> GenerateStreamAsync(
        string prompt,
        [EnumeratorCancellation] CancellationToken cancellationToken = default)
    {
        if (!_modelLoaded || _config is null)
        {
            throw new InvalidOperationException("Model not loaded");
        }

        // Simulate streaming output
        var words = GeneratePlaceholderResponse(prompt).Split(' ');
        foreach (var word in words)
        {
            if (cancellationToken.IsCancellationRequested)
            {
                yield break;
            }

            await Task.Delay(50, cancellationToken);
            yield return word + " ";
        }
    }

    public async Task<bool> VerifyDigestAsync(string expectedDigest, CancellationToken cancellationToken = default)
    {
        if (_config is null || !File.Exists(_config.ModelPath))
        {
            return false;
        }

        using var sha256 = SHA256.Create();
        await using var stream = File.OpenRead(_config.ModelPath);
        var hash = await sha256.ComputeHashAsync(stream, cancellationToken);
        _computedDigest = Convert.ToHexStringLower(hash);

        return string.Equals(_computedDigest, expectedDigest, StringComparison.OrdinalIgnoreCase);
    }

    private long EstimateMemoryUsage()
    {
        if (_config is null)
        {
            return 0;
        }

        // Rough estimate based on quantization
        var baseSize = new FileInfo(_config.ModelPath).Length;
        var contextOverhead = _config.ContextLength * 4096L; // Rough KV cache estimate

        return baseSize + contextOverhead;
    }

    private static string GeneratePlaceholderResponse(string prompt)
    {
        // In a real implementation, this would be actual LLM output
        if (prompt.Contains("explain", StringComparison.OrdinalIgnoreCase))
        {
            return "This vulnerability affects the component by allowing unauthorized access. " +
                   "The vulnerable code path is reachable from the application entry point. " +
                   "Evidence: [EVIDENCE:sbom-001] Component is present in SBOM. " +
                   "[EVIDENCE:reach-001] Call graph shows reachability.";
        }

        if (prompt.Contains("remediat", StringComparison.OrdinalIgnoreCase))
        {
            return "Recommended remediation: Upgrade the affected component to the patched version. " +
                   "- Update package.json: dependency@1.0.0 -> dependency@1.0.1 " +
                   "- Run npm install to update lockfile " +
                   "- Verify with npm audit";
        }

        if (prompt.Contains("policy", StringComparison.OrdinalIgnoreCase))
        {
            return "Parsed policy intent: Override rule for critical severity. " +
                   "Conditions: severity = critical, scope = production. " +
                   "Actions: set_verdict = block.";
        }

        return "Analysis complete. The finding has been evaluated based on available evidence.";
    }

    public void Dispose()
    {
        _modelLoaded = false;
        _config = null;
        _computedDigest = null;
    }
}