// neural_network_framework.c - Advanced neural network framework implementation
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <time.h>
#include <pthread.h>
#include <immintrin.h>
#include <cblas.h>
#include <cuda_runtime.h>
#include <cudnn.h>
#include <cublas_v2.h>
#include <sys/mman.h>
#include <fcntl.h>
#include <unistd.h>

#define MAX_LAYERS 100
#define MAX_BATCH_SIZE 1024
#define MAX_NEURONS 4096
#define CACHE_LINE_SIZE 64
#define NUM_THREADS 8
#define GPU_MEMORY_POOL_SIZE (1024 * 1024 * 1024) // 1GB

// Activation function types
typedef enum {
    ACTIVATION_RELU,
    ACTIVATION_SIGMOID,
    ACTIVATION_TANH,
    ACTIVATION_LEAKY_RELU,
    ACTIVATION_ELU,
    ACTIVATION_SWISH,
    ACTIVATION_GELU,
    ACTIVATION_SOFTMAX
} activation_type_t;

// Layer types
typedef enum {
    LAYER_DENSE,
    LAYER_CONV2D,
    LAYER_MAXPOOL2D,
    LAYER_AVGPOOL2D,
    LAYER_BATCHNORM,
    LAYER_DROPOUT,
    LAYER_LSTM,
    LAYER_GRU,
    LAYER_ATTENTION,
    LAYER_EMBEDDING
} layer_type_t;

// Optimizer types
typedef enum {
    OPTIMIZER_SGD,
    OPTIMIZER_MOMENTUM,
    OPTIMIZER_ADAM,
    OPTIMIZER_ADAMW,
    OPTIMIZER_RMSPROP,
    OPTIMIZER_ADAGRAD,
    OPTIMIZER_LAMB
} optimizer_type_t;

// Tensor structure
typedef struct {
    float *data;
    float *grad;
    int *shape;
    int num_dims;
    size_t size;
    bool requires_grad;
    bool is_cuda;
    cudnnTensorDescriptor_t cudnn_descriptor;
} tensor_t;

// Layer structure
typedef struct layer {
    layer_type_t type;
    char name[64];
    
    // Layer parameters
    tensor_t *weights;
    tensor_t *bias;
    tensor_t *output;
    tensor_t *grad_output;
    
    // Layer configuration
    int input_size;
    int output_size;
    activation_type_t activation;
    float dropout_rate;
    
    // Convolution parameters
    int kernel_size;
    int stride;
    int padding;
    int filters;
    
    // RNN parameters
    int hidden_size;
    int num_layers;
    bool bidirectional;
    
    // Batch normalization
    tensor_t *running_mean;
    tensor_t *running_var;
    float momentum;
    float epsilon;
    
    // Forward and backward functions
    int (*forward)(struct layer *layer, tensor_t *input, tensor_t *output);
    int (*backward)(struct layer *layer, tensor_t *grad_output, tensor_t *grad_input);
    
    // Optimization state
    tensor_t *weight_momentum;
    tensor_t *bias_momentum;
    tensor_t *weight_velocity;
    tensor_t *bias_velocity;
    
    struct layer *next;
    struct layer *prev;
} layer_t;

// Neural network model
typedef struct {
    layer_t *layers;
    int num_layers;
    
    // Training configuration
    optimizer_type_t optimizer;
    float learning_rate;
    float weight_decay;
    float momentum;
    float beta1;
    float beta2;
    float epsilon;
    int batch_size;
    
    // GPU resources
    cublasHandle_t cublas_handle;
    cudnnHandle_t cudnn_handle;
    void *gpu_memory_pool;
    size_t gpu_memory_used;
    
    // Training statistics
    float *loss_history;
    float *accuracy_history;
    int epoch;
    int iteration;
    
    // Thread pool for CPU parallelism
    pthread_t threads[NUM_THREADS];
    pthread_mutex_t mutex;
    pthread_cond_t cond;
    bool thread_pool_active;
    
} neural_network_t;

// Training data structure
typedef struct {
    tensor_t *inputs;
    tensor_t *targets;
    int num_samples;
    int current_batch;
    bool shuffle;
} dataset_t;

// Model checkpoint
typedef struct {
    char model_name[256];
    int epoch;
    float loss;
    float accuracy;
    time_t timestamp;
    size_t model_size;
} checkpoint_t;

// Function prototypes
int nn_init(neural_network_t *nn);
int nn_add_layer(neural_network_t *nn, layer_t *layer);
int nn_forward(neural_network_t *nn, tensor_t *input, tensor_t *output);
int nn_backward(neural_network_t *nn, tensor_t *loss_grad);
int nn_update_weights(neural_network_t *nn);
int nn_train(neural_network_t *nn, dataset_t *train_data, dataset_t *val_data, int epochs);
int nn_predict(neural_network_t *nn, tensor_t *input, tensor_t *output);
int nn_save_model(neural_network_t *nn, const char *filename);
int nn_load_model(neural_network_t *nn, const char *filename);
void nn_cleanup(neural_network_t *nn);

// Tensor operations
tensor_t *tensor_create(int *shape, int num_dims, bool requires_grad);
int tensor_zeros(tensor_t *tensor);
int tensor_ones(tensor_t *tensor);
int tensor_randn(tensor_t *tensor, float mean, float std);
int tensor_copy(tensor_t *dst, tensor_t *src);
int tensor_add(tensor_t *a, tensor_t *b, tensor_t *result);
int tensor_multiply(tensor_t *a, tensor_t *b, tensor_t *result);
int tensor_matmul(tensor_t *a, tensor_t *b, tensor_t *result);
int tensor_transpose(tensor_t *tensor);
void tensor_free(tensor_t *tensor);

// Layer implementations
layer_t *dense_layer_create(int input_size, int output_size, activation_type_t activation);
layer_t *conv2d_layer_create(int in_channels, int out_channels, int kernel_size, int stride, int padding);
layer_t *lstm_layer_create(int input_size, int hidden_size, int num_layers);
layer_t *attention_layer_create(int embed_dim, int num_heads);

// Activation functions
int activation_relu(tensor_t *input, tensor_t *output);
int activation_relu_backward(tensor_t *grad_output, tensor_t *input, tensor_t *grad_input);
int activation_sigmoid(tensor_t *input, tensor_t *output);
int activation_sigmoid_backward(tensor_t *grad_output, tensor_t *output, tensor_t *grad_input);
int activation_softmax(tensor_t *input, tensor_t *output);

// Loss functions
float loss_cross_entropy(tensor_t *predictions, tensor_t *targets);
int loss_cross_entropy_backward(tensor_t *predictions, tensor_t *targets, tensor_t *grad);
float loss_mse(tensor_t *predictions, tensor_t *targets);
int loss_mse_backward(tensor_t *predictions, tensor_t *targets, tensor_t *grad);

// Optimizers
int optimizer_sgd_update(neural_network_t *nn, layer_t *layer);
int optimizer_adam_update(neural_network_t *nn, layer_t *layer);
int optimizer_lamb_update(neural_network_t *nn, layer_t *layer);

// GPU operations
int gpu_init(neural_network_t *nn);
int tensor_to_gpu(tensor_t *tensor);
int tensor_to_cpu(tensor_t *tensor);
int gpu_matmul(tensor_t *a, tensor_t *b, tensor_t *result);
int gpu_conv2d_forward(layer_t *layer, tensor_t *input, tensor_t *output);
int gpu_conv2d_backward(layer_t *layer, tensor_t *grad_output, tensor_t *grad_input);
void gpu_cleanup(neural_network_t *nn);

// Distributed training
int distributed_init(neural_network_t *nn, int world_size, int rank);
int distributed_all_reduce(tensor_t *tensor);
int distributed_broadcast(tensor_t *tensor, int root);
void distributed_cleanup(void);

// Data augmentation
int augment_random_crop(tensor_t *image, int crop_size);
int augment_random_flip(tensor_t *image);
int augment_color_jitter(tensor_t *image, float brightness, float contrast, float saturation);
int augment_mixup(tensor_t *image1, tensor_t *image2, tensor_t *label1, tensor_t *label2, float alpha);

// Performance optimization
int optimize_graph(neural_network_t *nn);
int fuse_operations(neural_network_t *nn);
int quantize_model(neural_network_t *nn, int bits);
int profile_model(neural_network_t *nn, tensor_t *input);

// Example: Image classification model
int build_resnet50(neural_network_t *nn, int num_classes);
int build_efficientnet(neural_network_t *nn, int num_classes);
int build_vision_transformer(neural_network_t *nn, int num_classes, int patch_size);

// Global variables
static bool g_cuda_initialized = false;
static int g_num_gpus = 0;

int main(int argc, char *argv[]) {
    neural_network_t nn;
    int result;
    
    // Initialize neural network
    result = nn_init(&nn);
    if (result != 0) {
        fprintf(stderr, "Failed to initialize neural network\n");
        return 1;
    }
    
    printf("Neural Network Framework initialized\n");
    printf("CUDA available: %s\n", g_cuda_initialized ? "Yes" : "No");
    printf("Number of GPUs: %d\n", g_num_gpus);
    
    // Build a simple CNN model
    printf("\n=== Building CNN Model ===\n");
    
    // Input layer: 3x224x224 (RGB image)
    layer_t *conv1 = conv2d_layer_create(3, 64, 7, 2, 3);
    nn_add_layer(&nn, conv1);
    
    // Additional convolutional layers
    layer_t *conv2 = conv2d_layer_create(64, 128, 3, 2, 1);
    nn_add_layer(&nn, conv2);
    
    layer_t *conv3 = conv2d_layer_create(128, 256, 3, 2, 1);
    nn_add_layer(&nn, conv3);
    
    // Global average pooling (simplified as dense layer)
    layer_t *fc1 = dense_layer_create(256 * 28 * 28, 512, ACTIVATION_RELU);
    nn_add_layer(&nn, fc1);
    
    // Output layer
    layer_t *fc2 = dense_layer_create(512, 10, ACTIVATION_SOFTMAX);
    nn_add_layer(&nn, fc2);
    
    printf("Model architecture created with %d layers\n", nn.num_layers);
    
    // Create dummy training data
    printf("\n=== Creating Training Data ===\n");
    
    int batch_size = 32;
    int input_shape[] = {batch_size, 3, 224, 224};
    int label_shape[] = {batch_size, 10};
    
    tensor_t *input = tensor_create(input_shape, 4, false);
    tensor_t *labels = tensor_create(label_shape, 2, false);
    
    // Initialize with random data
    tensor_randn(input, 0.0f, 1.0f);
    tensor_zeros(labels);
    
    // Set some random labels
    for (int i = 0; i < batch_size; i++) {
        int label = rand() % 10;
        labels->data[i * 10 + label] = 1.0f;
    }
    
    // Forward pass
    printf("\n=== Forward Pass ===\n");
    
    tensor_t *output = tensor_create(label_shape, 2, false);
    result = nn_forward(&nn, input, output);
    if (result != 0) {
        fprintf(stderr, "Forward pass failed\n");
        goto cleanup;
    }
    
    // Calculate loss
    float loss = loss_cross_entropy(output, labels);
    printf("Initial loss: %.4f\n", loss);
    
    // Backward pass
    printf("\n=== Backward Pass ===\n");
    
    tensor_t *loss_grad = tensor_create(label_shape, 2, true);
    loss_cross_entropy_backward(output, labels, loss_grad);
    
    result = nn_backward(&nn, loss_grad);
    if (result != 0) {
        fprintf(stderr, "Backward pass failed\n");
        goto cleanup;
    }
    
    // Update weights
    nn.learning_rate = 0.001f;
    nn.optimizer = OPTIMIZER_ADAM;
    result = nn_update_weights(&nn);
    if (result != 0) {
        fprintf(stderr, "Weight update failed\n");
        goto cleanup;
    }
    
    printf("Weights updated successfully\n");
    
    // Performance profiling
    printf("\n=== Performance Profiling ===\n");
    
    clock_t start = clock();
    int num_iterations = 100;
    
    for (int i = 0; i < num_iterations; i++) {
        nn_forward(&nn, input, output);
        if (g_cuda_initialized) {
            cudaDeviceSynchronize();
        }
    }
    
    clock_t end = clock();
    double elapsed = ((double)(end - start)) / CLOCKS_PER_SEC;
    double throughput = (num_iterations * batch_size) / elapsed;
    
    printf("Forward pass performance:\n");
    printf("  Total time: %.3f seconds\n", elapsed);
    printf("  Throughput: %.1f images/second\n", throughput);
    printf("  Latency: %.3f ms/batch\n", (elapsed / num_iterations) * 1000);
    
    // Save model
    printf("\n=== Saving Model ===\n");
    result = nn_save_model(&nn, "model.bin");
    if (result == 0) {
        printf("Model saved successfully\n");
    }
    
cleanup:
    // Cleanup resources
    tensor_free(input);
    tensor_free(labels);
    tensor_free(output);
    tensor_free(loss_grad);
    nn_cleanup(&nn);
    
    printf("\nNeural network framework cleanup completed\n");
    return 0;
}

int nn_init(neural_network_t *nn) {
    if (!nn) return -1;
    
    memset(nn, 0, sizeof(neural_network_t));
    
    // Initialize mutex and condition variable
    pthread_mutex_init(&nn->mutex, NULL);
    pthread_cond_init(&nn->cond, NULL);
    
    // Initialize GPU if available
    if (gpu_init(nn) == 0) {
        g_cuda_initialized = true;
        cudaGetDeviceCount(&g_num_gpus);
    }
    
    // Set default training parameters
    nn->learning_rate = 0.001f;
    nn->weight_decay = 0.0001f;
    nn->momentum = 0.9f;
    nn->beta1 = 0.9f;
    nn->beta2 = 0.999f;
    nn->epsilon = 1e-8f;
    nn->batch_size = 32;
    
    return 0;
}

tensor_t *tensor_create(int *shape, int num_dims, bool requires_grad) {
    tensor_t *tensor = (tensor_t *)malloc(sizeof(tensor_t));
    if (!tensor) return NULL;
    
    memset(tensor, 0, sizeof(tensor_t));
    
    // Copy shape
    tensor->shape = (int *)malloc(num_dims * sizeof(int));
    if (!tensor->shape) {
        free(tensor);
        return NULL;
    }
    memcpy(tensor->shape, shape, num_dims * sizeof(int));
    tensor->num_dims = num_dims;
    
    // Calculate total size
    tensor->size = 1;
    for (int i = 0; i < num_dims; i++) {
        tensor->size *= shape[i];
    }
    
    // Allocate data
    tensor->data = (float *)aligned_alloc(CACHE_LINE_SIZE, tensor->size * sizeof(float));
    if (!tensor->data) {
        free(tensor->shape);
        free(tensor);
        return NULL;
    }
    
    // Allocate gradient if needed
    if (requires_grad) {
        tensor->grad = (float *)aligned_alloc(CACHE_LINE_SIZE, tensor->size * sizeof(float));
        if (!tensor->grad) {
            free(tensor->data);
            free(tensor->shape);
            free(tensor);
            return NULL;
        }
        memset(tensor->grad, 0, tensor->size * sizeof(float));
    }
    
    tensor->requires_grad = requires_grad;
    tensor->is_cuda = false;
    
    return tensor;
}

int tensor_randn(tensor_t *tensor, float mean, float std) {
    if (!tensor || !tensor->data) return -1;
    
    // Box-Muller transform for normal distribution
    for (size_t i = 0; i < tensor->size; i += 2) {
        float u1 = (float)rand() / RAND_MAX;
        float u2 = (float)rand() / RAND_MAX;
        
        float z0 = sqrtf(-2.0f * logf(u1)) * cosf(2.0f * M_PI * u2);
        float z1 = sqrtf(-2.0f * logf(u1)) * sinf(2.0f * M_PI * u2);
        
        tensor->data[i] = z0 * std + mean;
        if (i + 1 < tensor->size) {
            tensor->data[i + 1] = z1 * std + mean;
        }
    }
    
    return 0;
}

layer_t *dense_layer_create(int input_size, int output_size, activation_type_t activation) {
    layer_t *layer = (layer_t *)malloc(sizeof(layer_t));
    if (!layer) return NULL;
    
    memset(layer, 0, sizeof(layer_t));
    
    layer->type = LAYER_DENSE;
    snprintf(layer->name, sizeof(layer->name), "dense_%d_%d", input_size, output_size);
    layer->input_size = input_size;
    layer->output_size = output_size;
    layer->activation = activation;
    
    // Create weight tensor
    int weight_shape[] = {output_size, input_size};
    layer->weights = tensor_create(weight_shape, 2, true);
    if (!layer->weights) {
        free(layer);
        return NULL;
    }
    
    // Xavier initialization
    float scale = sqrtf(2.0f / (input_size + output_size));
    tensor_randn(layer->weights, 0.0f, scale);
    
    // Create bias tensor
    int bias_shape[] = {output_size};
    layer->bias = tensor_create(bias_shape, 1, true);
    if (!layer->bias) {
        tensor_free(layer->weights);
        free(layer);
        return NULL;
    }
    tensor_zeros(layer->bias);
    
    // Set forward and backward functions
    layer->forward = dense_forward;
    layer->backward = dense_backward;
    
    return layer;
}

int dense_forward(layer_t *layer, tensor_t *input, tensor_t *output) {
    if (!layer || !input || !output) return -1;
    
    int batch_size = input->shape[0];
    int input_size = layer->input_size;
    int output_size = layer->output_size;
    
    // Perform matrix multiplication: output = input @ weights.T + bias
    if (g_cuda_initialized && input->is_cuda) {
        // GPU implementation
        gpu_matmul(input, layer->weights, output);
    } else {
        // CPU implementation using BLAS
        cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
                    batch_size, output_size, input_size,
                    1.0f, input->data, input_size,
                    layer->weights->data, input_size,
                    0.0f, output->data, output_size);
    }
    
    // Add bias
    for (int i = 0; i < batch_size; i++) {
        for (int j = 0; j < output_size; j++) {
            output->data[i * output_size + j] += layer->bias->data[j];
        }
    }
    
    // Apply activation function
    switch (layer->activation) {
        case ACTIVATION_RELU:
            activation_relu(output, output);
            break;
        case ACTIVATION_SIGMOID:
            activation_sigmoid(output, output);
            break;
        case ACTIVATION_SOFTMAX:
            activation_softmax(output, output);
            break;
        default:
            break;
    }
    
    // Store output for backward pass
    layer->output = output;
    
    return 0;
}

int activation_relu(tensor_t *input, tensor_t *output) {
    if (!input || !output) return -1;
    
    // ReLU: f(x) = max(0, x)
    #pragma omp parallel for
    for (size_t i = 0; i < input->size; i++) {
        output->data[i] = fmaxf(0.0f, input->data[i]);
    }
    
    return 0;
}

int activation_softmax(tensor_t *input, tensor_t *output) {
    if (!input || !output) return -1;
    
    int batch_size = input->shape[0];
    int num_classes = input->shape[1];
    
    #pragma omp parallel for
    for (int b = 0; b < batch_size; b++) {
        float *in_ptr = input->data + b * num_classes;
        float *out_ptr = output->data + b * num_classes;
        
        // Find max for numerical stability
        float max_val = in_ptr[0];
        for (int i = 1; i < num_classes; i++) {
            max_val = fmaxf(max_val, in_ptr[i]);
        }
        
        // Compute exp and sum
        float sum = 0.0f;
        for (int i = 0; i < num_classes; i++) {
            out_ptr[i] = expf(in_ptr[i] - max_val);
            sum += out_ptr[i];
        }
        
        // Normalize
        float inv_sum = 1.0f / sum;
        for (int i = 0; i < num_classes; i++) {
            out_ptr[i] *= inv_sum;
        }
    }
    
    return 0;
}

float loss_cross_entropy(tensor_t *predictions, tensor_t *targets) {
    if (!predictions || !targets) return -1.0f;
    
    int batch_size = predictions->shape[0];
    int num_classes = predictions->shape[1];
    float total_loss = 0.0f;
    
    #pragma omp parallel for reduction(+:total_loss)
    for (int b = 0; b < batch_size; b++) {
        for (int c = 0; c < num_classes; c++) {
            int idx = b * num_classes + c;
            if (targets->data[idx] > 0) {
                total_loss += -targets->data[idx] * logf(predictions->data[idx] + 1e-7f);
            }
        }
    }
    
    return total_loss / batch_size;
}

int nn_forward(neural_network_t *nn, tensor_t *input, tensor_t *output) {
    if (!nn || !input || !output) return -1;
    
    tensor_t *current_input = input;
    tensor_t *current_output = NULL;
    
    // Forward pass through all layers
    layer_t *layer = nn->layers;
    while (layer) {
        // Allocate output tensor for this layer
        int output_shape[4];
        output_shape[0] = current_input->shape[0]; // batch size
        
        if (layer->type == LAYER_DENSE) {
            output_shape[1] = layer->output_size;
            current_output = tensor_create(output_shape, 2, true);
        } else if (layer->type == LAYER_CONV2D) {
            // Calculate output dimensions for convolution
            int h_out = (current_input->shape[2] + 2 * layer->padding - layer->kernel_size) / layer->stride + 1;
            int w_out = (current_input->shape[3] + 2 * layer->padding - layer->kernel_size) / layer->stride + 1;
            output_shape[1] = layer->filters;
            output_shape[2] = h_out;
            output_shape[3] = w_out;
            current_output = tensor_create(output_shape, 4, true);
        }
        
        // Perform forward pass
        int result = layer->forward(layer, current_input, current_output);
        if (result != 0) {
            return -1;
        }
        
        // Move to next layer
        if (layer != nn->layers) {
            tensor_free(current_input); // Free intermediate tensors
        }
        current_input = current_output;
        layer = layer->next;
    }
    
    // Copy final output
    tensor_copy(output, current_output);
    if (current_output != output) {
        tensor_free(current_output);
    }
    
    return 0;
}

int gpu_init(neural_network_t *nn) {
    if (!nn) return -1;
    
    // Check CUDA availability
    int device_count;
    cudaError_t cuda_err = cudaGetDeviceCount(&device_count);
    if (cuda_err != cudaSuccess || device_count == 0) {
        return -1;
    }
    
    // Select best GPU
    int best_device = 0;
    size_t max_memory = 0;
    
    for (int i = 0; i < device_count; i++) {
        cudaDeviceProp prop;
        cudaGetDeviceProperties(&prop, i);
        
        if (prop.totalGlobalMem > max_memory) {
            max_memory = prop.totalGlobalMem;
            best_device = i;
        }
        
        printf("GPU %d: %s, %zu MB, Compute %d.%d\n", 
               i, prop.name, prop.totalGlobalMem / (1024 * 1024),
               prop.major, prop.minor);
    }
    
    cudaSetDevice(best_device);
    
    // Initialize cuBLAS
    cublasCreate(&nn->cublas_handle);
    
    // Initialize cuDNN
    cudnnCreate(&nn->cudnn_handle);
    
    // Allocate GPU memory pool
    cudaMalloc(&nn->gpu_memory_pool, GPU_MEMORY_POOL_SIZE);
    nn->gpu_memory_used = 0;
    
    return 0;
}

int nn_save_model(neural_network_t *nn, const char *filename) {
    if (!nn || !filename) return -1;
    
    FILE *file = fopen(filename, "wb");
    if (!file) return -1;
    
    // Write model header
    checkpoint_t checkpoint;
    strncpy(checkpoint.model_name, "neural_network_v1", sizeof(checkpoint.model_name));
    checkpoint.epoch = nn->epoch;
    checkpoint.timestamp = time(NULL);
    
    fwrite(&checkpoint, sizeof(checkpoint_t), 1, file);
    
    // Write model architecture
    fwrite(&nn->num_layers, sizeof(int), 1, file);
    
    // Write each layer
    layer_t *layer = nn->layers;
    while (layer) {
        // Write layer metadata
        fwrite(&layer->type, sizeof(layer_type_t), 1, file);
        fwrite(&layer->input_size, sizeof(int), 1, file);
        fwrite(&layer->output_size, sizeof(int), 1, file);
        fwrite(&layer->activation, sizeof(activation_type_t), 1, file);
        
        // Write weights
        if (layer->weights) {
            fwrite(&layer->weights->size, sizeof(size_t), 1, file);
            fwrite(layer->weights->data, sizeof(float), layer->weights->size, file);
        }
        
        // Write bias
        if (layer->bias) {
            fwrite(&layer->bias->size, sizeof(size_t), 1, file);
            fwrite(layer->bias->data, sizeof(float), layer->bias->size, file);
        }
        
        layer = layer->next;
    }
    
    fclose(file);
    return 0;
}

void nn_cleanup(neural_network_t *nn) {
    if (!nn) return;
    
    // Free all layers
    layer_t *layer = nn->layers;
    while (layer) {
        layer_t *next = layer->next;
        
        tensor_free(layer->weights);
        tensor_free(layer->bias);
        tensor_free(layer->output);
        tensor_free(layer->grad_output);
        tensor_free(layer->weight_momentum);
        tensor_free(layer->bias_momentum);
        tensor_free(layer->weight_velocity);
        tensor_free(layer->bias_velocity);
        
        free(layer);
        layer = next;
    }
    
    // Cleanup GPU resources
    if (g_cuda_initialized) {
        gpu_cleanup(nn);
    }
    
    // Cleanup threading resources
    pthread_mutex_destroy(&nn->mutex);
    pthread_cond_destroy(&nn->cond);
    
    free(nn->loss_history);
    free(nn->accuracy_history);
}

void tensor_free(tensor_t *tensor) {
    if (!tensor) return;
    
    if (tensor->is_cuda && tensor->data) {
        cudaFree(tensor->data);
        if (tensor->grad) cudaFree(tensor->grad);
    } else {
        free(tensor->data);
        free(tensor->grad);
    }
    
    free(tensor->shape);
    
    if (tensor->cudnn_descriptor) {
        cudnnDestroyTensorDescriptor(tensor->cudnn_descriptor);
    }
    
    free(tensor);
}

void gpu_cleanup(neural_network_t *nn) {
    if (!nn) return;
    
    if (nn->cublas_handle) {
        cublasDestroy(nn->cublas_handle);
    }
    
    if (nn->cudnn_handle) {
        cudnnDestroy(nn->cudnn_handle);
    }
    
    if (nn->gpu_memory_pool) {
        cudaFree(nn->gpu_memory_pool);
    }
}

int tensor_zeros(tensor_t *tensor) {
    if (!tensor || !tensor->data) return -1;
    
    memset(tensor->data, 0, tensor->size * sizeof(float));
    return 0;
}

int tensor_copy(tensor_t *dst, tensor_t *src) {
    if (!dst || !src || dst->size != src->size) return -1;
    
    memcpy(dst->data, src->data, src->size * sizeof(float));
    
    if (src->requires_grad && dst->grad && src->grad) {
        memcpy(dst->grad, src->grad, src->size * sizeof(float));
    }
    
    return 0;
}

// ml_inference_engine.c - High-performance ML inference engine
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <pthread.h>
#include <semaphore.h>
#include <time.h>
#include <sys/time.h>
#include <immintrin.h>
#include <omp.h>

#define MAX_BATCH_SIZE 256
#define MAX_MODELS 16
#define MAX_REQUESTS 10000
#define THREAD_POOL_SIZE 16
#define CACHE_SIZE_MB 64

// Model optimization levels
typedef enum {
    OPT_LEVEL_NONE,
    OPT_LEVEL_BASIC,      // Basic optimizations
    OPT_LEVEL_AGGRESSIVE, // Aggressive optimizations
    OPT_LEVEL_EXTREME     // Maximum performance
} optimization_level_t;

// Model format types
typedef enum {
    MODEL_FORMAT_ONNX,
    MODEL_FORMAT_TENSORFLOW,
    MODEL_FORMAT_PYTORCH,
    MODEL_FORMAT_CUSTOM
} model_format_t;

// Quantization types
typedef enum {
    QUANT_NONE,
    QUANT_INT8,
    QUANT_INT4,
    QUANT_DYNAMIC
} quantization_type_t;

// Inference request
typedef struct {
    int request_id;
    void *input_data;
    size_t input_size;
    void *output_data;
    size_t output_size;
    struct timespec submit_time;
    struct timespec complete_time;
    void (*callback)(int request_id, void *output, size_t size);
    void *user_data;
} inference_request_t;

// Model instance
typedef struct {
    int model_id;
    char model_name[256];
    model_format_t format;
    void *model_data;
    size_t model_size;
    
    // Optimization settings
    optimization_level_t opt_level;
    quantization_type_t quantization;
    bool use_gpu;
    int gpu_device_id;
    
    // Model metadata
    int input_dims[4];
    int output_dims[4];
    int num_parameters;
    
    // Performance metrics
    float avg_latency_ms;
    float p99_latency_ms;
    int requests_processed;
    
    // Model-specific functions
    int (*preprocess)(void *input, void *processed, size_t size);
    int (*inference)(void *model, void *input, void *output);
    int (*postprocess)(void *raw_output, void *output, size_t size);
    
} model_instance_t;

// Inference engine
typedef struct {
    model_instance_t models[MAX_MODELS];
    int num_models;
    
    // Request queue
    inference_request_t *request_queue;
    int queue_head;
    int queue_tail;
    int queue_size;
    pthread_mutex_t queue_mutex;
    sem_t queue_sem;
    
    // Thread pool
    pthread_t worker_threads[THREAD_POOL_SIZE];
    int num_workers;
    bool running;
    
    // Batching
    int max_batch_size;
    int batch_timeout_ms;
    
    // Performance monitoring
    struct {
        uint64_t total_requests;
        uint64_t successful_requests;
        uint64_t failed_requests;
        double total_latency_ms;
        double max_latency_ms;
        double min_latency_ms;
    } stats;
    
    // Memory pool for zero-copy
    void *memory_pool;
    size_t pool_size;
    size_t pool_used;
    pthread_mutex_t pool_mutex;
    
} inference_engine_t;

// Function prototypes
int engine_init(inference_engine_t *engine, int num_workers);
int engine_load_model(inference_engine_t *engine, const char *model_path, 
                     model_format_t format, optimization_level_t opt_level);
int engine_submit_request(inference_engine_t *engine, int model_id, 
                         void *input, size_t input_size, 
                         void (*callback)(int, void*, size_t), void *user_data);
int engine_wait_completion(inference_engine_t *engine, int request_id, int timeout_ms);
void engine_print_stats(inference_engine_t *engine);
void engine_shutdown(inference_engine_t *engine);

// Worker thread function
void *inference_worker(void *arg);

// Optimization functions
int optimize_model_graph(model_instance_t *model);
int apply_operator_fusion(model_instance_t *model);
int apply_quantization(model_instance_t *model, quantization_type_t type);
int optimize_memory_layout(model_instance_t *model);

// SIMD optimized operations
void simd_relu(float *data, int size);
void simd_batch_norm(float *data, float *mean, float *var, int size);
void simd_conv2d_3x3(float *input, float *kernel, float *output, 
                     int height, int width, int channels);

// Model-specific implementations
int onnx_load_model(model_instance_t *model, const char *path);
int tensorflow_load_model(model_instance_t *model, const char *path);
int pytorch_load_model(model_instance_t *model, const char *path);

// Profiling and monitoring
void profile_model_performance(model_instance_t *model, void *test_input);
void monitor_system_resources(inference_engine_t *engine);

// Global engine instance
static inference_engine_t g_engine;

int main(int argc, char *argv[]) {
    int result;
    
    // Initialize inference engine
    result = engine_init(&g_engine, THREAD_POOL_SIZE);
    if (result != 0) {
        fprintf(stderr, "Failed to initialize inference engine\n");
        return 1;
    }
    
    printf("ML Inference Engine initialized with %d workers\n", g_engine.num_workers);
    
    // Load a model
    printf("\n=== Loading Model ===\n");
    
    result = engine_load_model(&g_engine, "model.onnx", MODEL_FORMAT_ONNX, OPT_LEVEL_AGGRESSIVE);
    if (result < 0) {
        fprintf(stderr, "Failed to load model\n");
        engine_shutdown(&g_engine);
        return 1;
    }
    
    int model_id = result;
    printf("Model loaded with ID: %d\n", model_id);
    
    // Prepare test input
    float test_input[3 * 224 * 224];
    for (int i = 0; i < 3 * 224 * 224; i++) {
        test_input[i] = (float)rand() / RAND_MAX;
    }
    
    // Benchmark single inference
    printf("\n=== Single Inference Benchmark ===\n");
    
    struct timespec start, end;
    clock_gettime(CLOCK_MONOTONIC, &start);
    
    int request_id = engine_submit_request(&g_engine, model_id, test_input, 
                                         sizeof(test_input), NULL, NULL);
    engine_wait_completion(&g_engine, request_id, 1000);
    
    clock_gettime(CLOCK_MONOTONIC, &end);
    double latency = (end.tv_sec - start.tv_sec) * 1000.0 + 
                    (end.tv_nsec - start.tv_nsec) / 1000000.0;
    
    printf("Single inference latency: %.3f ms\n", latency);
    
    // Benchmark batch inference
    printf("\n=== Batch Inference Benchmark ===\n");
    
    int num_requests = 1000;
    clock_gettime(CLOCK_MONOTONIC, &start);
    
    // Submit multiple requests
    for (int i = 0; i < num_requests; i++) {
        engine_submit_request(&g_engine, model_id, test_input, 
                            sizeof(test_input), NULL, NULL);
    }
    
    // Wait for all to complete
    usleep(500000); // 500ms
    
    clock_gettime(CLOCK_MONOTONIC, &end);
    double total_time = (end.tv_sec - start.tv_sec) * 1000.0 + 
                       (end.tv_nsec - start.tv_nsec) / 1000000.0;
    double throughput = num_requests / (total_time / 1000.0);
    
    printf("Batch inference results:\n");
    printf("  Total time: %.3f ms\n", total_time);
    printf("  Throughput: %.1f requests/second\n", throughput);
    
    // Print engine statistics
    printf("\n=== Engine Statistics ===\n");
    engine_print_stats(&g_engine);
    
    // Test SIMD optimizations
    printf("\n=== SIMD Optimization Test ===\n");
    
    float *data = aligned_alloc(64, 1024 * 1024 * sizeof(float));
    for (int i = 0; i < 1024 * 1024; i++) {
        data[i] = (float)rand() / RAND_MAX - 0.5f;
    }
    
    clock_gettime(CLOCK_MONOTONIC, &start);
    simd_relu(data, 1024 * 1024);
    clock_gettime(CLOCK_MONOTONIC, &end);
    
    double simd_time = (end.tv_sec - start.tv_sec) * 1000000.0 + 
                      (end.tv_nsec - start.tv_nsec) / 1000.0;
    printf("SIMD ReLU on 1M elements: %.3f μs\n", simd_time);
    
    free(data);
    
    // Shutdown engine
    engine_shutdown(&g_engine);
    
    printf("\nInference engine shutdown completed\n");
    return 0;
}

int engine_init(inference_engine_t *engine, int num_workers) {
    if (!engine || num_workers <= 0) return -1;
    
    memset(engine, 0, sizeof(inference_engine_t));
    
    // Initialize request queue
    engine->queue_size = MAX_REQUESTS;
    engine->request_queue = calloc(engine->queue_size, sizeof(inference_request_t));
    if (!engine->request_queue) return -1;
    
    pthread_mutex_init(&engine->queue_mutex, NULL);
    sem_init(&engine->queue_sem, 0, 0);
    
    // Initialize memory pool
    engine->pool_size = CACHE_SIZE_MB * 1024 * 1024;
    engine->memory_pool = aligned_alloc(4096, engine->pool_size);
    if (!engine->memory_pool) {
        free(engine->request_queue);
        return -1;
    }
    pthread_mutex_init(&engine->pool_mutex, NULL);
    
    // Start worker threads
    engine->num_workers = num_workers;
    engine->running = true;
    
    for (int i = 0; i < num_workers; i++) {
        if (pthread_create(&engine->worker_threads[i], NULL, inference_worker, engine) != 0) {
            engine->running = false;
            return -1;
        }
    }
    
    // Set thread affinity for better performance
    #ifdef __linux__
    for (int i = 0; i < num_workers; i++) {
        cpu_set_t cpuset;
        CPU_ZERO(&cpuset);
        CPU_SET(i % sysconf(_SC_NPROCESSORS_ONLN), &cpuset);
        pthread_setaffinity_np(engine->worker_threads[i], sizeof(cpu_set_t), &cpuset);
    }
    #endif
    
    return 0;
}

void *inference_worker(void *arg) {
    inference_engine_t *engine = (inference_engine_t *)arg;
    
    while (engine->running) {
        // Wait for request
        sem_wait(&engine->queue_sem);
        
        if (!engine->running) break;
        
        // Get request from queue
        pthread_mutex_lock(&engine->queue_mutex);
        
        if (engine->queue_head == engine->queue_tail) {
            pthread_mutex_unlock(&engine->queue_mutex);
            continue;
        }
        
        inference_request_t request = engine->request_queue[engine->queue_head];
        engine->queue_head = (engine->queue_head + 1) % engine->queue_size;
        
        pthread_mutex_unlock(&engine->queue_mutex);
        
        // Process request
        struct timespec process_start;
        clock_gettime(CLOCK_MONOTONIC, &process_start);
        
        // Find model
        model_instance_t *model = NULL;
        for (int i = 0; i < engine->num_models; i++) {
            if (engine->models[i].model_id == request.request_id) {
                model = &engine->models[i];
                break;
            }
        }
        
        if (model) {
            // Preprocess if needed
            void *processed_input = request.input_data;
            if (model->preprocess) {
                processed_input = malloc(request.input_size);
                model->preprocess(request.input_data, processed_input, request.input_size);
            }
            
            // Run inference
            void *raw_output = malloc(request.output_size);
            model->inference(model->model_data, processed_input, raw_output);
            
            // Postprocess if needed
            if (model->postprocess) {
                model->postprocess(raw_output, request.output_data, request.output_size);
            } else {
                memcpy(request.output_data, raw_output, request.output_size);
            }
            
            // Cleanup
            if (processed_input != request.input_data) {
                free(processed_input);
            }
            free(raw_output);
            
            // Update metrics
            model->requests_processed++;
            clock_gettime(CLOCK_MONOTONIC, &request.complete_time);
            
            double latency = (request.complete_time.tv_sec - process_start.tv_sec) * 1000.0 +
                           (request.complete_time.tv_nsec - process_start.tv_nsec) / 1000000.0;
            
            model->avg_latency_ms = (model->avg_latency_ms * (model->requests_processed - 1) + 
                                   latency) / model->requests_processed;
            
            engine->stats.successful_requests++;
        } else {
            engine->stats.failed_requests++;
        }
        
        // Call callback if provided
        if (request.callback) {
            request.callback(request.request_id, request.output_data, request.output_size);
        }
        
        // Update global stats
        engine->stats.total_requests++;
    }
    
    return NULL;
}

void simd_relu(float *data, int size) {
    const __m256 zero = _mm256_setzero_ps();
    
    // Process 8 elements at a time
    int simd_size = size - (size % 8);
    
    #pragma omp parallel for
    for (int i = 0; i < simd_size; i += 8) {
        __m256 vals = _mm256_load_ps(&data[i]);
        __m256 result = _mm256_max_ps(vals, zero);
        _mm256_store_ps(&data[i], result);
    }
    
    // Handle remaining elements
    for (int i = simd_size; i < size; i++) {
        data[i] = fmaxf(0.0f, data[i]);
    }
}

void simd_batch_norm(float *data, float *mean, float *var, int size) {
    const __m256 epsilon = _mm256_set1_ps(1e-5f);
    
    #pragma omp parallel for
    for (int i = 0; i < size - 7; i += 8) {
        __m256 x = _mm256_load_ps(&data[i]);
        __m256 m = _mm256_load_ps(&mean[i]);
        __m256 v = _mm256_load_ps(&var[i]);
        
        // Compute (x - mean) / sqrt(var + epsilon)
        __m256 diff = _mm256_sub_ps(x, m);
        __m256 std = _mm256_sqrt_ps(_mm256_add_ps(v, epsilon));
        __m256 result = _mm256_div_ps(diff, std);
        
        _mm256_store_ps(&data[i], result);
    }
}

void engine_print_stats(inference_engine_t *engine) {
    if (!engine) return;
    
    printf("Engine Statistics:\n");
    printf("  Total requests: %lu\n", engine->stats.total_requests);
    printf("  Successful: %lu\n", engine->stats.successful_requests);
    printf("  Failed: %lu\n", engine->stats.failed_requests);
    
    if (engine->stats.successful_requests > 0) {
        double avg_latency = engine->stats.total_latency_ms / engine->stats.successful_requests;
        printf("  Average latency: %.3f ms\n", avg_latency);
        printf("  Min latency: %.3f ms\n", engine->stats.min_latency_ms);
        printf("  Max latency: %.3f ms\n", engine->stats.max_latency_ms);
    }
    
    printf("\nModel Statistics:\n");
    for (int i = 0; i < engine->num_models; i++) {
        model_instance_t *model = &engine->models[i];
        printf("  Model %d (%s):\n", model->model_id, model->model_name);
        printf("    Requests processed: %d\n", model->requests_processed);
        printf("    Average latency: %.3f ms\n", model->avg_latency_ms);
        printf("    P99 latency: %.3f ms\n", model->p99_latency_ms);
    }
}

void engine_shutdown(inference_engine_t *engine) {
    if (!engine) return;
    
    // Stop accepting new requests
    engine->running = false;
    
    // Wake up all workers
    for (int i = 0; i < engine->num_workers; i++) {
        sem_post(&engine->queue_sem);
    }
    
    // Wait for workers to finish
    for (int i = 0; i < engine->num_workers; i++) {
        pthread_join(engine->worker_threads[i], NULL);
    }
    
    // Cleanup resources
    free(engine->request_queue);
    free(engine->memory_pool);
    
    pthread_mutex_destroy(&engine->queue_mutex);
    pthread_mutex_destroy(&engine->pool_mutex);
    sem_destroy(&engine->queue_sem);
    
    // Free model resources
    for (int i = 0; i < engine->num_models; i++) {
        free(engine->models[i].model_data);
    }
}