mlx

#include <erl_nif.h>
#include <mlx/mlx.h>
#include <mlx/ops.h>
#include <mlx/array.h>
#include <mlx/device.h>
#include <mlx/dtype.h>
#include <mlx/stream.h>
#include <memory>
#include <vector>
#include <queue>
#include <mutex>
#include <condition_variable>
#include <thread>
#include <atomic>
#include <future>
#include <unordered_map>
#include <chrono>

using namespace mlx::core;
namespace mx = mlx::core;

// Advanced streaming and async resource types
static ErlNifResourceType* ASYNC_OPERATION_RESOURCE_TYPE;
static ErlNifResourceType* STREAM_PIPELINE_RESOURCE_TYPE;
static ErlNifResourceType* OPERATION_QUEUE_RESOURCE_TYPE;

// Async operation types
enum class AsyncOpType {
    MATMUL,
    ATTENTION,
    CONVOLUTION,
    FFN,
    CUSTOM_KERNEL
};

// Async operation wrapper
struct AsyncOperationResource {
    std::future<array> future_result;
    AsyncOpType op_type;
    std::string operation_id;
    std::chrono::steady_clock::time_point start_time;
    bool is_completed;
    
    AsyncOperationResource(std::future<array>&& fut, AsyncOpType type, const std::string& id)
        : future_result(std::move(fut)), op_type(type), operation_id(id), 
          start_time(std::chrono::steady_clock::now()), is_completed(false) {}
};

// Stream pipeline for real-time processing
struct StreamPipelineResource {
    Stream primary_stream;
    Stream secondary_stream;
    std::queue<std::function<array()>> operation_queue;
    std::mutex queue_mutex;
    std::condition_variable queue_cv;
    std::atomic<bool> is_running{true};
    std::thread worker_thread;
    
    StreamPipelineResource(const Stream& primary, const Stream& secondary)
        : primary_stream(primary), secondary_stream(secondary) {
        worker_thread = std::thread(&StreamPipelineResource::process_queue, this);
    }
    
    ~StreamPipelineResource() {
        is_running = false;
        queue_cv.notify_all();
        if (worker_thread.joinable()) {
            worker_thread.join();
        }
    }
    
    void process_queue() {
        while (is_running) {
            std::unique_lock<std::mutex> lock(queue_mutex);
            queue_cv.wait(lock, [this] { return !operation_queue.empty() || !is_running; });
            
            if (!is_running) break;
            
            if (!operation_queue.empty()) {
                auto operation = operation_queue.front();
                operation_queue.pop();
                lock.unlock();
                
                // Execute operation asynchronously
                try {
                    array result = operation();
                    eval(result);
                } catch (const std::exception& e) {
                    // Log error but continue processing
                }
            }
        }
    }
};

// Operation queue for batch processing
struct OperationQueueResource {
    std::vector<std::function<array()>> batched_operations;
    std::mutex operations_mutex;
    size_t max_batch_size;
    std::chrono::milliseconds max_wait_time;
    
    OperationQueueResource(size_t batch_size, std::chrono::milliseconds wait_time)
        : max_batch_size(batch_size), max_wait_time(wait_time) {}
};

// Global async operation manager
static std::unordered_map<std::string, std::shared_ptr<AsyncOperationResource>> async_operations;
static std::mutex async_ops_mutex;
static std::atomic<size_t> operation_counter{0};

// Helper functions
static ERL_NIF_TERM make_atom(ErlNifEnv* env, const char* name) {
    ERL_NIF_TERM ret;
    if (enif_make_existing_atom(env, name, &ret, ERL_NIF_LATIN1)) {
        return ret;
    }
    return enif_make_atom(env, name);
}

static ERL_NIF_TERM make_error(ErlNifEnv* env, const char* reason) {
    return enif_make_tuple2(env, make_atom(env, "error"), make_atom(env, reason));
}

static ERL_NIF_TERM make_ok(ErlNifEnv* env, ERL_NIF_TERM term) {
    return enif_make_tuple2(env, make_atom(env, "ok"), term);
}

// Create async operation for non-blocking execution
static ERL_NIF_TERM mlx_async_matmul(ErlNifEnv* env, int argc, const ERL_NIF_TERM argv[]) {
    if (argc != 2) return enif_make_badarg(env);

    // Extract array resources (implementation would parse these from argv)
    // For now, simulate with placeholder
    
    try {
        std::string op_id = "async_matmul_" + std::to_string(operation_counter++);
        
        // Create async operation
        auto async_op = std::async(std::launch::async, []() -> array {
            // Simulate async matrix multiplication
            array a = ones({1024, 1024}, float32);
            array b = ones({1024, 1024}, float32);
            array result = matmul(a, b);
            eval(result);
            return result;
        });
        
        auto resource = std::make_shared<AsyncOperationResource>(
            std::move(async_op), AsyncOpType::MATMUL, op_id);
        
        {
            std::lock_guard<std::mutex> lock(async_ops_mutex);
            async_operations[op_id] = resource;
        }
        
        // Return operation ID for later retrieval
        ERL_NIF_TERM op_id_term = enif_make_string(env, op_id.c_str(), ERL_NIF_LATIN1);
        return make_ok(env, op_id_term);
        
    } catch (const std::exception& e) {
        return make_error(env, "async_matmul_error");
    }
}

// Check status of async operation
static ERL_NIF_TERM mlx_async_status(ErlNifEnv* env, int argc, const ERL_NIF_TERM argv[]) {
    if (argc != 1) return enif_make_badarg(env);

    char op_id[128];
    if (!enif_get_string(env, argv[0], op_id, sizeof(op_id), ERL_NIF_LATIN1)) {
        return enif_make_badarg(env);
    }

    try {
        std::lock_guard<std::mutex> lock(async_ops_mutex);
        auto it = async_operations.find(std::string(op_id));
        
        if (it == async_operations.end()) {
            return make_error(env, "operation_not_found");
        }
        
        auto& resource = it->second;
        auto status = resource->future_result.wait_for(std::chrono::seconds(0));
        
        if (status == std::future_status::ready) {
            return make_ok(env, make_atom(env, "completed"));
        } else {
            auto elapsed = std::chrono::steady_clock::now() - resource->start_time;
            auto elapsed_ms = std::chrono::duration_cast<std::chrono::milliseconds>(elapsed).count();
            
            ERL_NIF_TERM status_tuple = enif_make_tuple2(env,
                make_atom(env, "running"),
                enif_make_long(env, elapsed_ms));
            return make_ok(env, status_tuple);
        }
        
    } catch (const std::exception& e) {
        return make_error(env, "async_status_error");
    }
}

// Get result of completed async operation
static ERL_NIF_TERM mlx_async_result(ErlNifEnv* env, int argc, const ERL_NIF_TERM argv[]) {
    if (argc != 1) return enif_make_badarg(env);

    char op_id[128];
    if (!enif_get_string(env, argv[0], op_id, sizeof(op_id), ERL_NIF_LATIN1)) {
        return enif_make_badarg(env);
    }

    try {
        std::lock_guard<std::mutex> lock(async_ops_mutex);
        auto it = async_operations.find(std::string(op_id));
        
        if (it == async_operations.end()) {
            return make_error(env, "operation_not_found");
        }
        
        auto& resource = it->second;
        
        if (resource->future_result.wait_for(std::chrono::seconds(0)) != std::future_status::ready) {
            return make_error(env, "operation_not_completed");
        }
        
        // Get the result and create array resource
        array result = resource->future_result.get();
        
        // Create array resource wrapper (simplified)
        ERL_NIF_TERM result_term = make_atom(env, "array_result");
        
        // Remove completed operation
        async_operations.erase(it);
        
        return make_ok(env, result_term);
        
    } catch (const std::exception& e) {
        return make_error(env, "async_result_error");
    }
}

// Create streaming pipeline for real-time processing
static ERL_NIF_TERM mlx_create_stream_pipeline(ErlNifEnv* env, int argc, const ERL_NIF_TERM argv[]) {
    if (argc != 2) return enif_make_badarg(env);

    char primary_device[32], secondary_device[32];
    if (!enif_get_atom(env, argv[0], primary_device, sizeof(primary_device), ERL_NIF_LATIN1) ||
        !enif_get_atom(env, argv[1], secondary_device, sizeof(secondary_device), ERL_NIF_LATIN1)) {
        return enif_make_badarg(env);
    }

    try {
        Device primary = (strcmp(primary_device, "gpu") == 0) ? Device::gpu : Device::cpu;
        Device secondary = (strcmp(secondary_device, "gpu") == 0) ? Device::gpu : Device::cpu;
        
        Stream primary_stream = new_stream(primary);
        Stream secondary_stream = new_stream(secondary);
        
        StreamPipelineResource* res = (StreamPipelineResource*)enif_alloc_resource(
            STREAM_PIPELINE_RESOURCE_TYPE, sizeof(StreamPipelineResource));
        new(res) StreamPipelineResource(primary_stream, secondary_stream);
        
        ERL_NIF_TERM term = enif_make_resource(env, res);
        enif_release_resource(res);
        
        return make_ok(env, term);
        
    } catch (const std::exception& e) {
        return make_error(env, "stream_pipeline_error");
    }
}

// Queue operation in streaming pipeline
static ERL_NIF_TERM mlx_queue_stream_operation(ErlNifEnv* env, int argc, const ERL_NIF_TERM argv[]) {
    if (argc != 3) return enif_make_badarg(env);

    StreamPipelineResource* pipeline_res;
    if (!enif_get_resource(env, argv[0], STREAM_PIPELINE_RESOURCE_TYPE, (void**)&pipeline_res)) {
        return enif_make_badarg(env);
    }

    char operation_type[64];
    if (!enif_get_atom(env, argv[1], operation_type, sizeof(operation_type), ERL_NIF_LATIN1)) {
        return enif_make_badarg(env);
    }

    try {
        std::function<array()> operation;
        
        if (strcmp(operation_type, "attention_step") == 0) {
            operation = []() -> array {
                // Simulate attention computation step
                array q = random::normal(0, 1, {1, 64, 512}, float32);
                array k = random::normal(0, 1, {1, 64, 512}, float32);
                array v = random::normal(0, 1, {1, 64, 512}, float32);
                
                array scores = matmul(q, transpose(k, {-2, -1}));
                array attn = softmax(scores / sqrt(array(512.0f)), -1);
                return matmul(attn, v);
            };
        } else if (strcmp(operation_type, "ffn_step") == 0) {
            operation = []() -> array {
                // Simulate feed-forward step
                array x = random::normal(0, 1, {1, 64, 512}, float32);
                array w1 = random::normal(0, 1, {512, 2048}, float32);
                array w2 = random::normal(0, 1, {2048, 512}, float32);
                
                array hidden = gelu(matmul(x, w1));
                return matmul(hidden, w2);
            };
        } else {
            return make_error(env, "unknown_operation_type");
        }
        
        // Queue the operation
        {
            std::lock_guard<std::mutex> lock(pipeline_res->queue_mutex);
            pipeline_res->operation_queue.push(operation);
        }
        pipeline_res->queue_cv.notify_one();
        
        return make_ok(env, make_atom(env, "operation_queued"));
        
    } catch (const std::exception& e) {
        return make_error(env, "queue_operation_error");
    }
}

// Batch operations for improved throughput
static ERL_NIF_TERM mlx_create_batch_queue(ErlNifEnv* env, int argc, const ERL_NIF_TERM argv[]) {
    if (argc != 2) return enif_make_badarg(env);

    int batch_size, wait_time_ms;
    if (!enif_get_int(env, argv[0], &batch_size) ||
        !enif_get_int(env, argv[1], &wait_time_ms)) {
        return enif_make_badarg(env);
    }

    try {
        OperationQueueResource* res = (OperationQueueResource*)enif_alloc_resource(
            OPERATION_QUEUE_RESOURCE_TYPE, sizeof(OperationQueueResource));
        new(res) OperationQueueResource(batch_size, std::chrono::milliseconds(wait_time_ms));
        
        ERL_NIF_TERM term = enif_make_resource(env, res);
        enif_release_resource(res);
        
        return make_ok(env, term);
        
    } catch (const std::exception& e) {
        return make_error(env, "batch_queue_error");
    }
}

// Execute batched operations with optimized scheduling
static ERL_NIF_TERM mlx_execute_batch(ErlNifEnv* env, int argc, const ERL_NIF_TERM argv[]) {
    if (argc != 1) return enif_make_badarg(env);

    OperationQueueResource* queue_res;
    if (!enif_get_resource(env, argv[0], OPERATION_QUEUE_RESOURCE_TYPE, (void**)&queue_res)) {
        return enif_make_badarg(env);
    }

    try {
        std::lock_guard<std::mutex> lock(queue_res->operations_mutex);
        
        if (queue_res->batched_operations.empty()) {
            return make_ok(env, make_atom(env, "no_operations"));
        }
        
        // Execute all batched operations in parallel
        std::vector<std::future<array>> futures;
        for (auto& operation : queue_res->batched_operations) {
            futures.push_back(std::async(std::launch::async, operation));
        }
        
        // Wait for all operations to complete
        std::vector<array> results;
        for (auto& future : futures) {
            results.push_back(future.get());
        }
        
        // Clear the batch
        queue_res->batched_operations.clear();
        
        return make_ok(env, enif_make_int(env, results.size()));
        
    } catch (const std::exception& e) {
        return make_error(env, "batch_execution_error");
    }
}

// Real-time inference pipeline
static ERL_NIF_TERM mlx_realtime_inference(ErlNifEnv* env, int argc, const ERL_NIF_TERM argv[]) {
    if (argc != 3) return enif_make_badarg(env);

    char model_type[64];
    int max_tokens, beam_width;
    
    if (!enif_get_atom(env, argv[0], model_type, sizeof(model_type), ERL_NIF_LATIN1) ||
        !enif_get_int(env, argv[1], &max_tokens) ||
        !enif_get_int(env, argv[2], &beam_width)) {
        return enif_make_badarg(env);
    }

    try {
        // Set up real-time inference pipeline optimized for Apple Silicon
        if (strcmp(model_type, "transformer") == 0) {
            // Create transformer inference pipeline with KV caching
            auto pipeline_id = "realtime_transformer_" + std::to_string(operation_counter++);
            
            // Simulate creating inference pipeline
            auto inference_future = std::async(std::launch::async, [max_tokens, beam_width]() -> array {
                // Simulate real-time transformer inference
                for (int token = 0; token < max_tokens; token++) {
                    // Simulate token generation with beam search
                    array logits = random::normal(0, 1, {beam_width, 32000}, float32);
                    array probs = softmax(logits, -1);
                    // Sample next token
                    std::this_thread::sleep_for(std::chrono::milliseconds(10)); // Simulate processing time
                }
                return ones({max_tokens}, int32);
            });
            
            ERL_NIF_TERM pipeline_id_term = enif_make_string(env, pipeline_id.c_str(), ERL_NIF_LATIN1);
            return make_ok(env, pipeline_id_term);
        }
        
        return make_error(env, "unsupported_model_type");
        
    } catch (const std::exception& e) {
        return make_error(env, "realtime_inference_error");
    }
}

// Resource destructors
static void async_operation_resource_destructor(ErlNifEnv* env, void* obj) {
    AsyncOperationResource* res = (AsyncOperationResource*)obj;
    res->~AsyncOperationResource();
}

static void stream_pipeline_resource_destructor(ErlNifEnv* env, void* obj) {
    StreamPipelineResource* res = (StreamPipelineResource*)obj;
    res->~StreamPipelineResource();
}

static void operation_queue_resource_destructor(ErlNifEnv* env, void* obj) {
    OperationQueueResource* res = (OperationQueueResource*)obj;
    res->~OperationQueueResource();
}

// Streaming NIF function table
static ErlNifFunc nif_funcs[] = {
    // Async operations
    {"async_matmul", 2, mlx_async_matmul, ERL_NIF_DIRTY_JOB_CPU_BOUND},
    {"async_status", 1, mlx_async_status, 0},
    {"async_result", 1, mlx_async_result, 0},
    
    // Streaming pipelines
    {"create_stream_pipeline", 2, mlx_create_stream_pipeline, 0},
    {"queue_stream_operation", 3, mlx_queue_stream_operation, 0},
    
    // Batch processing
    {"create_batch_queue", 2, mlx_create_batch_queue, 0},
    {"execute_batch", 1, mlx_execute_batch, ERL_NIF_DIRTY_JOB_CPU_BOUND},
    
    // Real-time inference
    {"realtime_inference", 3, mlx_realtime_inference, ERL_NIF_DIRTY_JOB_CPU_BOUND}
};

static int load(ErlNifEnv* env, void** priv_data, ERL_NIF_TERM load_info) {
    ErlNifResourceFlags flags = (ErlNifResourceFlags)(ERL_NIF_RT_CREATE | ERL_NIF_RT_TAKEOVER);
    ErlNifResourceFlags* tried = NULL;
    
    ASYNC_OPERATION_RESOURCE_TYPE = enif_open_resource_type(
        env, NULL, "mlx_async_operation", async_operation_resource_destructor, flags, tried);
        
    STREAM_PIPELINE_RESOURCE_TYPE = enif_open_resource_type(
        env, NULL, "mlx_stream_pipeline", stream_pipeline_resource_destructor, flags, tried);
        
    OPERATION_QUEUE_RESOURCE_TYPE = enif_open_resource_type(
        env, NULL, "mlx_operation_queue", operation_queue_resource_destructor, flags, tried);

    if (!ASYNC_OPERATION_RESOURCE_TYPE || !STREAM_PIPELINE_RESOURCE_TYPE || !OPERATION_QUEUE_RESOURCE_TYPE) {
        return 1;
    }
    
    return 0;
}

static int upgrade(ErlNifEnv* env, void** priv_data, void** old_priv_data, ERL_NIF_TERM load_info) {
    return load(env, priv_data, load_info);
}

static void unload(ErlNifEnv* env, void* priv_data) {
    // Clean up async operations
    std::lock_guard<std::mutex> lock(async_ops_mutex);
    async_operations.clear();
}

ERL_NIF_INIT(mlx_streaming_nif, nif_funcs, load, NULL, upgrade, unload)