#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>

// Constants for model
#define NUM_LAYERS 4
#define EMBEDDING_SIZE 64
#define NUM_HEADS 4
#define SEQ_LENGTH 32
#define VOCAB_SIZE 1000

// I/O Interface with systolic array
void systolic_array_multiply(int16_t *matrix_a, int16_t *matrix_b, int32_t *result, int m, int n, int p);

// Embedding weights and layer weights (assumed to be quantized and stored in flash memory)
static int8_t embedding_weights[VOCAB_SIZE][EMBEDDING_SIZE];
static int8_t layer_weights[NUM_LAYERS][EMBEDDING_SIZE][EMBEDDING_SIZE];
static int8_t output_weights[EMBEDDING_SIZE][VOCAB_SIZE];

// Function prototypes
void initialize_model();
void embedding_lookup(int8_t *input_ids, int8_t *output);
void attention_layer(int8_t *input, int8_t *output);
void forward_pass(int8_t *input_ids, int8_t *output);

// Function to perform matrix multiplication using the systolic array
void systolic_array_multiply(int16_t *matrix_a, int16_t *matrix_b, int32_t *result, int m, int n, int p) {
    // Initialize the systolic array hardware (this may involve setting control registers)
    // Assuming the hardware handles multiplication as a blocking operation.
    
    // Pass matrix dimensions to the systolic array (this part depends on your hardware API)
    set_systolic_array_dimensions(m, n, p);

    // Send matrix_a and matrix_b data to systolic array memory (adjust if using burst mode or DMA)
    for (int i = 0; i < m * n; i++) {
        write_systolic_array_matrix_a(i, matrix_a[i]);
    }
    for (int i = 0; i < n * p; i++) {
        write_systolic_array_matrix_b(i, matrix_b[i]);
    }

    // Trigger computation
    start_systolic_array();

    // Wait for computation to complete (or poll the completion flag)
    while (!systolic_array_complete()) {}

    // Read the results back from systolic array memory into `result`
    for (int i = 0; i < m * p; i++) {
        result[i] = read_systolic_array_result(i);
    }
}

// Initialize model parameters
void initialize_model() {
    // Load quantized weights (stubbed here for demonstration)
    for (int i = 0; i < VOCAB_SIZE; i++) {
        for (int j = 0; j < EMBEDDING_SIZE; j++) {
            embedding_weights[i][j] = rand() % 127;
        }
    }
    for (int l = 0; l < NUM_LAYERS; l++) {
        for (int i = 0; i < EMBEDDING_SIZE; i++) {
            for (int j = 0; j < EMBEDDING_SIZE; j++) {
                layer_weights[l][i][j] = rand() % 127;
            }
        }
    }
    for (int i = 0; i < EMBEDDING_SIZE; i++) {
        for (int j = 0; j < VOCAB_SIZE; j++) {
            output_weights[i][j] = rand() % 127;
        }
    }
}

// Embedding lookup
void embedding_lookup(int8_t *input_ids, int8_t *output) {
    for (int i = 0; i < SEQ_LENGTH; i++) {
        int id = input_ids[i];
        if (id < VOCAB_SIZE) {
            for (int j = 0; j < EMBEDDING_SIZE; j++) {
                output[i * EMBEDDING_SIZE + j] = embedding_weights[id][j];
            }
        }
    }
}

// Attention layer (simplified for demonstration)
void attention_layer(int8_t *input, int8_t *output) {
    int16_t q[EMBEDDING_SIZE * SEQ_LENGTH];
    int16_t k[EMBEDDING_SIZE * SEQ_LENGTH];
    int16_t v[EMBEDDING_SIZE * SEQ_LENGTH];
    int32_t scores[SEQ_LENGTH * SEQ_LENGTH] = {0};

    // Populate queries, keys, and values from input (stubbed here)
    for (int i = 0; i < SEQ_LENGTH; i++) {
        for (int j = 0; j < EMBEDDING_SIZE; j++) {
            q[i * EMBEDDING_SIZE + j] = input[i * EMBEDDING_SIZE + j];
            k[i * EMBEDDING_SIZE + j] = input[i * EMBEDDING_SIZE + j];
            v[i * EMBEDDING_SIZE + j] = input[i * EMBEDDING_SIZE + j];
        }
    }

    // Use systolic array for QK^T
    systolic_array_multiply(q, k, scores, SEQ_LENGTH, EMBEDDING_SIZE, SEQ_LENGTH);

    // Now, use attention scores to create weighted output (simplified here)
    for (int i = 0; i < SEQ_LENGTH; i++) {
        for (int j = 0; j < EMBEDDING_SIZE; j++) {
            int32_t sum = 0;
            for (int k = 0; k < SEQ_LENGTH; k++) {
                sum += scores[i * SEQ_LENGTH + k] * v[k * EMBEDDING_SIZE + j];
            }
            output[i * EMBEDDING_SIZE + j] = sum >> 8;
        }
    }
}

// Forward pass
void forward_pass(int8_t *input_ids, int8_t *output) {
    int8_t embeddings[SEQ_LENGTH * EMBEDDING_SIZE];
    int8_t layer_output[SEQ_LENGTH * EMBEDDING_SIZE];

    embedding_lookup(input_ids, embeddings);

    for (int l = 0; l < NUM_LAYERS; l++) {
        attention_layer(embeddings, layer_output);

        // Use systolic array for dense layer multiplication
        int32_t dense_result[SEQ_LENGTH * EMBEDDING_SIZE];
        systolic_array_multiply(layer_output, (int16_t *)layer_weights[l], dense_result, SEQ_LENGTH, EMBEDDING_SIZE, EMBEDDING_SIZE);

        // Quantize and store back to embeddings
        for (int i = 0; i < SEQ_LENGTH * EMBEDDING_SIZE; i++) {
            embeddings[i] = dense_result[i] >> 8;
        }
    }

    // Final output layer multiplication with output weights
    int32_t final_result[SEQ_LENGTH * VOCAB_SIZE];
    systolic_array_multiply(embeddings, (int16_t *)output_weights, final_result, SEQ_LENGTH, EMBEDDING_SIZE, VOCAB_SIZE);

    // Quantize final output
    for (int i = 0; i < VOCAB_SIZE; i++) {
        output[i] = final_result[i] >> 8;
    }
}

int main() {
    int8_t input_ids[SEQ_LENGTH] = {0};  // Input tokens
    int8_t output[VOCAB_SIZE];           // Output logits

    initialize_model();
    forward_pass(input_ids, output);

    // Print output
    for (int i = 0; i < VOCAB_SIZE; i++) {
        printf("Output %d: %d\n", i, output[i]);
    }

    return 0;
}
