#include "utils.hpp"

#include <cstdlib>

#include "ggml-qnn.h"

#include "qnn-types.hpp"

#ifdef __linux__
#include <unistd.h>
#endif

namespace qnn {

qnn_dimension_array_t get_internal_dimension(const ggml_dimension_array_t &dims, uint32_t rank) {
    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS should be 4");
    GGML_ASSERT(rank <= GGML_MAX_DIMS && rank > 0);

    qnn_dimension_array_t internal_dims = {};
    /*
     * Both the ggml and qnn tensor in memory are stored as row-major format.
     * But the dimensions of the tensor are stored in different order.
     * For example, a 2x3 matrix:
     *   [
     *     [1, 2, 3],
     *     [4, 5, 6],
     *   ]
     * The ggml tensor will have dimensions [3, 2], while the qnn tensor will have dimensions [2, 3].
     */
    for (uint32_t i = 0; i < rank; i++) {
        internal_dims[i] = std::max<uint32_t>(dims[rank - 1 - i], 1);
    }

    return internal_dims;
}

// TODO: mapping more ggml data type to QNN data type
// ref:explanation of k-quants, https://github.com/ggerganov/llama.cpp/pull/1684
Qnn_DataType_t qnn_datatype_from_ggml_datatype(ggml_type ggml_type) {
    switch (ggml_type) {
        case GGML_TYPE_F32:
            return QNN_DATATYPE_FLOAT_32;
        case GGML_TYPE_F16:
            return QNN_DATATYPE_FLOAT_16;
        case GGML_TYPE_I32:
            return QNN_DATATYPE_INT_32;
        case GGML_TYPE_I16:
            return QNN_DATATYPE_INT_16;
        case GGML_TYPE_I8:
            return QNN_DATATYPE_INT_8;
        case GGML_TYPE_Q8_0:
            return QNN_DATATYPE_SFIXED_POINT_8;
        case GGML_TYPE_Q4_0:
            return QNN_DATATYPE_SFIXED_POINT_4;
        default:
            break;
    }
    return QNN_DATATYPE_UNDEFINED;
}

ggml_type ggml_datatype_from_qnn_datatype(Qnn_DataType_t qnn_type) {
    switch (qnn_type) {
        case QNN_DATATYPE_FLOAT_32:
            return GGML_TYPE_F32;
        case QNN_DATATYPE_FLOAT_16:
            return GGML_TYPE_F16;
        case QNN_DATATYPE_UINT_32:
        case QNN_DATATYPE_INT_32:
            return GGML_TYPE_I32;
        case QNN_DATATYPE_INT_16:
            return GGML_TYPE_I16;
        case QNN_DATATYPE_INT_8:
            return GGML_TYPE_I8;
        case QNN_DATATYPE_SFIXED_POINT_8:
            return GGML_TYPE_Q8_0;
        case QNN_DATATYPE_SFIXED_POINT_4:
            return GGML_TYPE_Q4_0;
        default:
            break;
    }
    return GGML_TYPE_COUNT;
}

size_t qnn_datatype_size(Qnn_DataType_t qnn_type) {
    switch (qnn_type) {
        case QNN_DATATYPE_FLOAT_32:
            return sizeof(float);
        case QNN_DATATYPE_FLOAT_16:
            return sizeof(uint16_t);
        case QNN_DATATYPE_UINT_32:
        case QNN_DATATYPE_INT_32:
            return sizeof(int32_t);
        case QNN_DATATYPE_INT_16:
            return sizeof(int16_t);
        case QNN_DATATYPE_INT_8:
            return sizeof(int8_t);
        case QNN_DATATYPE_SFIXED_POINT_8:
            return sizeof(int8_t);
        case QNN_DATATYPE_SFIXED_POINT_4:
            return sizeof(int8_t);
        default:
            break;
    }
    return 0;
}

const char *qnn_datatype_to_string(Qnn_DataType_t qnn_type) {
    switch (qnn_type) {
        case QNN_DATATYPE_FLOAT_32:
            return "QNN_DATATYPE_FLOAT_32";
        case QNN_DATATYPE_FLOAT_16:
            return "QNN_DATATYPE_FLOAT_16";
        case QNN_DATATYPE_UINT_32:
            return "QNN_DATATYPE_UINT_32";
        case QNN_DATATYPE_INT_32:
            return "QNN_DATATYPE_INT_32";
        case QNN_DATATYPE_INT_16:
            return "QNN_DATATYPE_INT_16";
        case QNN_DATATYPE_INT_8:
            return "QNN_DATATYPE_INT_8";
        case QNN_DATATYPE_SFIXED_POINT_8:
            return "QNN_DATATYPE_SFIXED_POINT_8";
        case QNN_DATATYPE_SFIXED_POINT_4:
            return "QNN_DATATYPE_SFIXED_POINT_4";
        default:
            break;
    }

    return "QNN_DATATYPE_UNDEFINED";
}

uint32_t get_ggml_tensor_rank(const ggml_tensor *tensor) {
    uint32_t rank = 0;
    for (int i = 0; i < GGML_MAX_DIMS; i++) {
        if ((0 != tensor->ne[i]) && (1 != tensor->ne[i])) {
            rank++;
        }
    }
    return rank;
}

const char *get_ggml_type_name(ggml_type type) {
    const auto *traits = ggml_get_type_traits(type);
    return traits->type_name;
}

const char *get_backend_name(QNNBackend device_index) {
    switch (device_index) {
        case QNN_BACKEND_CPU:
            return "QNN-CPU";
        case QNN_BACKEND_GPU:
            return "QNN-GPU";
        case QNN_BACKEND_NPU:
            return "QNN-NPU";
        case QNN_BACKEND_COUNT:
        default:
            return "unknown";
    }
}

const char *get_chipset_desc(uint32_t chipset_id) {
    switch (chipset_id) {
        case SM8450:
            return "SM8450";
        case SM8475:
            return "SM8475";
        case SM8550:
            return "SM8550";
        case SM8650:
            return "SM8650";
        default:
            return "unknown";
    }
}

const char *get_htparch_desc(size_t htp_arch) {
    switch (htp_arch) {
        case V68:
            return "QCOM_HTP_V68";
        case V69:
            return "QCOM_HTP_V69";
        case V73:
            return "QCOM_HTP_V73";
        case V75:
            return "QCOM_HTP_V75";
        default:
            return "unknown";
    }
}

intptr_t align_to(size_t alignment, intptr_t offset) {
    return offset % alignment == 0
               ? offset
               : offset + (static_cast<intptr_t>(alignment) - (offset % static_cast<intptr_t>(alignment)));
}

uint32_t get_ggml_tensor_data_size(const ggml_tensor *tensor) {
    /*
    size_t data_size = ggml_row_size(tensor->type, tensor->ne[0]);
    size_t n_dims = qnn_get_ggml_tensor_rank(tensor);
    for (int i = 1; i < n_dims; i++) {
        data_size *= tensor->ne[i];
    }

    return data_size;
    */
    return ggml_nbytes(tensor);
}

void *align_alloc(size_t alignment, size_t size) {
    size_t size_aligned = size;
    if ((size_aligned % alignment) != 0) {
        size_aligned += (alignment - (size_aligned % alignment));
    }

    void *data = std::aligned_alloc(alignment, size_aligned);
    if (!data) {
        QNN_LOG_WARN("aligned_alloc failed\n");
        return nullptr;
    }

    return data;
}

void align_free(void *ptr) { std::free(ptr); }

// =================================================================================================
//
//  QNN backend internal helper functions
//
// =================================================================================================
// TODO: only support GGML_OP_ADD/GGML_OP_MUL/GGML_OP_MUL_MAT
const char *opname_from_ggmlop(enum ggml_op ggmlop) {
    switch (ggmlop) {
        case GGML_OP_ADD:
            return QNN_OP_ELEMENT_WISE_ADD;
        case GGML_OP_MUL:
            return QNN_OP_ELEMENT_WISE_MULTIPLY;
        case GGML_OP_MUL_MAT:
            return QNN_OP_MAT_MUL;
        default:
            break;
    }
    return nullptr;
}

const char *get_qnn_error_string(Qnn_ErrorHandle_t error) {
    // A complete list of error codes can be found at here:
    // https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/api_error_codes.html
    switch (error) {
        case QNN_SUCCESS:
            return "QNN_SUCCESS";
        case QNN_COMMON_ERROR_GENERAL:
            return "QNN_COMMON_ERROR_GENERAL";

        // QnnGraph_Error_t
        case QNN_GRAPH_ERROR_UNSUPPORTED_FEATURE:
            return "QNN_GRAPH_ERROR_UNSUPPORTED_FEATURE";
        case QNN_GRAPH_ERROR_MEM_ALLOC:
            return "QNN_GRAPH_ERROR_MEM_ALLOC";
        case QNN_GRAPH_ERROR_INVALID_ARGUMENT:
            return "QNN_GRAPH_ERROR_INVALID_ARGUMENT";
        case QNN_GRAPH_ERROR_INVALID_HANDLE:
            return "QNN_GRAPH_ERROR_INVALID_HANDLE";
        case QNN_GRAPH_ERROR_GRAPH_DOES_NOT_EXIST:
            return "QNN_GRAPH_ERROR_GRAPH_DOES_NOT_EXIST";
        case QNN_GRAPH_ERROR_INVALID_NAME:
            return "QNN_GRAPH_ERROR_INVALID_NAME";
        case QNN_GRAPH_ERROR_INVALID_TENSOR:
            return "QNN_GRAPH_ERROR_INVALID_TENSOR";
        case QNN_GRAPH_ERROR_INVALID_OP_CONFIG:
            return "QNN_GRAPH_ERROR_INVALID_OP_CONFIG";
        case QNN_GRAPH_ERROR_SET_PROFILE:
            return "QNN_GRAPH_ERROR_SET_PROFILE";
        case QNN_GRAPH_ERROR_UNCONNECTED_NODE:
            return "QNN_GRAPH_ERROR_UNCONNECTED_NODE";
        case QNN_GRAPH_ERROR_CREATE_FAILED:
            return "QNN_GRAPH_ERROR_CREATE_FAILED";

        // QnnOpPackage_Error_t
        case QNN_OP_PACKAGE_ERROR_LIBRARY_ALREADY_INITIALIZED:
            return "QNN_OP_PACKAGE_ERROR_LIBRARY_ALREADY_INITIALIZED";
        case QNN_OP_PACKAGE_ERROR_LIBRARY_NOT_INITIALIZED:
            return "QNN_OP_PACKAGE_ERROR_LIBRARY_NOT_INITIALIZED";
        case QNN_OP_PACKAGE_ERROR_INVALID_HANDLE:
            return "QNN_OP_PACKAGE_ERROR_INVALID_HANDLE";
        case QNN_OP_PACKAGE_ERROR_INVALID_INFRASTRUCTURE:
            return "QNN_OP_PACKAGE_ERROR_INVALID_INFRASTRUCTURE";
        case QNN_OP_PACKAGE_ERROR_INVALID_INFO:
            return "QNN_OP_PACKAGE_ERROR_INVALID_INFO";
        case QNN_OP_PACKAGE_ERROR_VALIDATION_FAILURE:
            return "QNN_OP_PACKAGE_ERROR_VALIDATION_FAILURE";
        case QNN_OP_PACKAGE_ERROR_INVALID_ARGUMENT:
            return "QNN_OP_PACKAGE_ERROR_INVALID_ARGUMENT";
        default:
            return nullptr;
    }
}

#ifdef __linux__

size_t get_system_total_memory_in_bytes() {
    auto pages = (size_t)sysconf(_SC_PHYS_PAGES);
    auto page_size = (size_t)sysconf(_SC_PAGE_SIZE);
    return pages * page_size;
}

size_t get_system_free_memory_in_bytes() {
    auto avail_pages = (size_t)sysconf(_SC_AVPHYS_PAGES);
    auto page_size = (size_t)sysconf(_SC_PAGE_SIZE);
    return avail_pages * page_size;
}

#endif

} // namespace qnn