/*
 * This module provides a BLAS optimized matrix multiply,
 * inner product and dot for numpy arrays
 */
#define NPY_NO_DEPRECATED_API NPY_API_VERSION
#define _MULTIARRAYMODULE

#define PY_SSIZE_T_CLEAN
#include <Python.h>

#include "numpy/arrayobject.h"
#include "npy_cblas.h"
#include "arraytypes.h"
#include "common.h"

#include <assert.h>


static const double oneD[2] = {1.0, 0.0}, zeroD[2] = {0.0, 0.0};
static const float oneF[2] = {1.0, 0.0}, zeroF[2] = {0.0, 0.0};


/*
 * Helper: dispatch to appropriate cblas_?gemm for typenum.
 */
static void
gemm(int typenum, enum CBLAS_ORDER order,
     enum CBLAS_TRANSPOSE transA, enum CBLAS_TRANSPOSE transB,
     npy_intp m, npy_intp n, npy_intp k,
     PyArrayObject *A, npy_intp lda, PyArrayObject *B, npy_intp ldb, PyArrayObject *R)
{
    const void *Adata = PyArray_DATA(A), *Bdata = PyArray_DATA(B);
    void *Rdata = PyArray_DATA(R);
    npy_intp ldc = PyArray_DIM(R, 1) > 1 ? PyArray_DIM(R, 1) : 1;

    switch (typenum) {
        case NPY_DOUBLE:
            CBLAS_FUNC(cblas_dgemm)(order, transA, transB, m, n, k, 1.,
                        Adata, lda, Bdata, ldb, 0., Rdata, ldc);
            break;
        case NPY_FLOAT:
            CBLAS_FUNC(cblas_sgemm)(order, transA, transB, m, n, k, 1.f,
                        Adata, lda, Bdata, ldb, 0.f, Rdata, ldc);
            break;
        case NPY_CDOUBLE:
            CBLAS_FUNC(cblas_zgemm)(order, transA, transB, m, n, k, oneD,
                        Adata, lda, Bdata, ldb, zeroD, Rdata, ldc);
            break;
        case NPY_CFLOAT:
            CBLAS_FUNC(cblas_cgemm)(order, transA, transB, m, n, k, oneF,
                        Adata, lda, Bdata, ldb, zeroF, Rdata, ldc);
            break;
    }
}


/*
 * Helper: dispatch to appropriate cblas_?gemv for typenum.
 */
static void
gemv(int typenum, enum CBLAS_ORDER order, enum CBLAS_TRANSPOSE trans,
     PyArrayObject *A, npy_intp lda, PyArrayObject *X, npy_intp incX,
     PyArrayObject *R)
{
    const void *Adata = PyArray_DATA(A), *Xdata = PyArray_DATA(X);
    void *Rdata = PyArray_DATA(R);

    npy_intp m = PyArray_DIM(A, 0), n = PyArray_DIM(A, 1);

    switch (typenum) {
        case NPY_DOUBLE:
            CBLAS_FUNC(cblas_dgemv)(order, trans, m, n, 1., Adata, lda, Xdata, incX,
                        0., Rdata, 1);
            break;
        case NPY_FLOAT:
            CBLAS_FUNC(cblas_sgemv)(order, trans, m, n, 1.f, Adata, lda, Xdata, incX,
                        0.f, Rdata, 1);
            break;
        case NPY_CDOUBLE:
            CBLAS_FUNC(cblas_zgemv)(order, trans, m, n, oneD, Adata, lda, Xdata, incX,
                        zeroD, Rdata, 1);
            break;
        case NPY_CFLOAT:
            CBLAS_FUNC(cblas_cgemv)(order, trans, m, n, oneF, Adata, lda, Xdata, incX,
                        zeroF, Rdata, 1);
            break;
    }
}


/*
 * Helper: dispatch to appropriate cblas_?syrk for typenum.
 */
static void
syrk(int typenum, enum CBLAS_ORDER order, enum CBLAS_TRANSPOSE trans,
     npy_intp n, npy_intp k,
     PyArrayObject *A, npy_intp lda, PyArrayObject *R)
{
    const void *Adata = PyArray_DATA(A);
    void *Rdata = PyArray_DATA(R);
    npy_intp ldc = PyArray_DIM(R, 1) > 1 ? PyArray_DIM(R, 1) : 1;

    npy_intp i;
    npy_intp j;

    switch (typenum) {
        case NPY_DOUBLE:
            CBLAS_FUNC(cblas_dsyrk)(order, CblasUpper, trans, n, k, 1.,
                        Adata, lda, 0., Rdata, ldc);

            for (i = 0; i < n; i++) {
                for (j = i + 1; j < n; j++) {
                    *((npy_double*)PyArray_GETPTR2(R, j, i)) =
                            *((npy_double*)PyArray_GETPTR2(R, i, j));
                }
            }
            break;
        case NPY_FLOAT:
            CBLAS_FUNC(cblas_ssyrk)(order, CblasUpper, trans, n, k, 1.f,
                        Adata, lda, 0.f, Rdata, ldc);

            for (i = 0; i < n; i++) {
                for (j = i + 1; j < n; j++) {
                    *((npy_float*)PyArray_GETPTR2(R, j, i)) =
                            *((npy_float*)PyArray_GETPTR2(R, i, j));
                }
            }
            break;
        case NPY_CDOUBLE:
            CBLAS_FUNC(cblas_zsyrk)(order, CblasUpper, trans, n, k, oneD,
                        Adata, lda, zeroD, Rdata, ldc);

            for (i = 0; i < n; i++) {
                for (j = i + 1; j < n; j++) {
                    *((npy_cdouble*)PyArray_GETPTR2(R, j, i)) =
                            *((npy_cdouble*)PyArray_GETPTR2(R, i, j));
                }
            }
            break;
        case NPY_CFLOAT:
            CBLAS_FUNC(cblas_csyrk)(order, CblasUpper, trans, n, k, oneF,
                        Adata, lda, zeroF, Rdata, ldc);

            for (i = 0; i < n; i++) {
                for (j = i + 1; j < n; j++) {
                    *((npy_cfloat*)PyArray_GETPTR2(R, j, i)) =
                            *((npy_cfloat*)PyArray_GETPTR2(R, i, j));
                }
            }
            break;
    }
}


typedef enum {_scalar, _column, _row, _matrix} MatrixShape;


static MatrixShape
_select_matrix_shape(PyArrayObject *array)
{
    switch (PyArray_NDIM(array)) {
        case 0:
            return _scalar;
        case 1:
            if (PyArray_DIM(array, 0) > 1)
                return _column;
            return _scalar;
        case 2:
            if (PyArray_DIM(array, 0) > 1) {
                if (PyArray_DIM(array, 1) == 1)
                    return _column;
                else
                    return _matrix;
            }
            if (PyArray_DIM(array, 1) == 1)
                return _scalar;
            return _row;
    }
    return _matrix;
}


/*
 * This also makes sure that the data segment is aligned with
 * an itemsize address as well by returning one if not true.
 */
NPY_NO_EXPORT int
_bad_strides(PyArrayObject *ap)
{
    int itemsize = PyArray_ITEMSIZE(ap);
    int i, N=PyArray_NDIM(ap);
    npy_intp *strides = PyArray_STRIDES(ap);
    npy_intp *dims = PyArray_DIMS(ap);

    if (((npy_intp)(PyArray_DATA(ap)) % itemsize) != 0) {
        return 1;
    }
    for (i = 0; i < N; i++) {
        if ((strides[i] < 0) || (strides[i] % itemsize) != 0) {
            return 1;
        }
        if ((strides[i] == 0 && dims[i] > 1)) {
            return 1;
        }
    }

    return 0;
}

/*
 * dot(a,b)
 * Returns the dot product of a and b for arrays of floating point types.
 * Like the generic numpy equivalent the product sum is over
 * the last dimension of a and the second-to-last dimension of b.
 * NB: The first argument is not conjugated.;
 *
 * This is for use by PyArray_MatrixProduct2. It is assumed on entry that
 * the arrays ap1 and ap2 have a common data type given by typenum that is
 * float, double, cfloat, or cdouble and have dimension <= 2. The
 * __array_ufunc__ nonsense is also assumed to have been taken care of.
 */
NPY_NO_EXPORT PyObject *
cblas_matrixproduct(int typenum, PyArrayObject *ap1, PyArrayObject *ap2,
                    PyArrayObject *out)
{
    PyArrayObject *result = NULL, *out_buf = NULL;
    npy_intp j, lda, ldb;
    npy_intp l;
    int nd;
    npy_intp ap1stride = 0;
    npy_intp dimensions[NPY_MAXDIMS];
    npy_intp numbytes;
    MatrixShape ap1shape, ap2shape;

    if (_bad_strides(ap1)) {
            PyObject *op1 = PyArray_NewCopy(ap1, NPY_ANYORDER);

            Py_DECREF(ap1);
            ap1 = (PyArrayObject *)op1;
            if (ap1 == NULL) {
                goto fail;
            }
    }
    if (_bad_strides(ap2)) {
            PyObject *op2 = PyArray_NewCopy(ap2, NPY_ANYORDER);

            Py_DECREF(ap2);
            ap2 = (PyArrayObject *)op2;
            if (ap2 == NULL) {
                goto fail;
            }
    }
    ap1shape = _select_matrix_shape(ap1);
    ap2shape = _select_matrix_shape(ap2);

    if (ap1shape == _scalar || ap2shape == _scalar) {
        PyArrayObject *oap1, *oap2;
        oap1 = ap1; oap2 = ap2;
        /* One of ap1 or ap2 is a scalar */
        if (ap1shape == _scalar) {
            /* Make ap2 the scalar */
            PyArrayObject *t = ap1;
            ap1 = ap2;
            ap2 = t;
            ap1shape = ap2shape;
            ap2shape = _scalar;
        }

        if (ap1shape == _row) {
            ap1stride = PyArray_STRIDE(ap1, 1);
        }
        else if (PyArray_NDIM(ap1) > 0) {
            ap1stride = PyArray_STRIDE(ap1, 0);
        }

        if (PyArray_NDIM(ap1) == 0 || PyArray_NDIM(ap2) == 0) {
            npy_intp *thisdims;
            if (PyArray_NDIM(ap1) == 0) {
                nd = PyArray_NDIM(ap2);
                thisdims = PyArray_DIMS(ap2);
            }
            else {
                nd = PyArray_NDIM(ap1);
                thisdims = PyArray_DIMS(ap1);
            }
            l = 1;
            for (j = 0; j < nd; j++) {
                dimensions[j] = thisdims[j];
                l *= dimensions[j];
            }
        }
        else {
            l = PyArray_DIM(oap1, PyArray_NDIM(oap1) - 1);

            if (PyArray_DIM(oap2, 0) != l) {
                dot_alignment_error(oap1, PyArray_NDIM(oap1) - 1, oap2, 0);
                goto fail;
            }
            nd = PyArray_NDIM(ap1) + PyArray_NDIM(ap2) - 2;
            /*
             * nd = 0 or 1 or 2. If nd == 0 do nothing ...
             */
            if (nd == 1) {
                /*
                 * Either PyArray_NDIM(ap1) is 1 dim or PyArray_NDIM(ap2) is
                 * 1 dim and the other is 2 dim
                 */
                dimensions[0] = (PyArray_NDIM(oap1) == 2) ?
                                PyArray_DIM(oap1, 0) : PyArray_DIM(oap2, 1);
                l = dimensions[0];
                /*
                 * Fix it so that dot(shape=(N,1), shape=(1,))
                 * and dot(shape=(1,), shape=(1,N)) both return
                 * an (N,) array (but use the fast scalar code)
                 */
            }
            else if (nd == 2) {
                dimensions[0] = PyArray_DIM(oap1, 0);
                dimensions[1] = PyArray_DIM(oap2, 1);
                /*
                 * We need to make sure that dot(shape=(1,1), shape=(1,N))
                 * and dot(shape=(N,1),shape=(1,1)) uses
                 * scalar multiplication appropriately
                 */
                if (ap1shape == _row) {
                    l = dimensions[1];
                }
                else {
                    l = dimensions[0];
                }
            }

            /* Check if the summation dimension is 0-sized */
            if (PyArray_DIM(oap1, PyArray_NDIM(oap1) - 1) == 0) {
                l = 0;
            }
        }
    }
    else {
        /*
         * (PyArray_NDIM(ap1) <= 2 && PyArray_NDIM(ap2) <= 2)
         * Both ap1 and ap2 are vectors or matrices
         */
        l = PyArray_DIM(ap1, PyArray_NDIM(ap1) - 1);

        if (PyArray_DIM(ap2, 0) != l) {
            dot_alignment_error(ap1, PyArray_NDIM(ap1) - 1, ap2, 0);
            goto fail;
        }
        nd = PyArray_NDIM(ap1) + PyArray_NDIM(ap2) - 2;

        if (nd == 1) {
            dimensions[0] = (PyArray_NDIM(ap1) == 2) ?
                            PyArray_DIM(ap1, 0) : PyArray_DIM(ap2, 1);
        }
        else if (nd == 2) {
            dimensions[0] = PyArray_DIM(ap1, 0);
            dimensions[1] = PyArray_DIM(ap2, 1);
        }
    }

    out_buf = new_array_for_sum(ap1, ap2, out, nd, dimensions, typenum, &result);
    if (out_buf == NULL) {
        goto fail;
    }

    numbytes = PyArray_NBYTES(out_buf);
    memset(PyArray_DATA(out_buf), 0, numbytes);
    if (numbytes == 0 || l == 0) {
            Py_DECREF(ap1);
            Py_DECREF(ap2);
            Py_DECREF(out_buf);
            return PyArray_Return(result);
    }

    if (ap2shape == _scalar) {
        /*
         * Multiplication by a scalar -- Level 1 BLAS
         * if ap1shape is a matrix and we are not contiguous, then we can't
         * just blast through the entire array using a single striding factor
         */
        NPY_BEGIN_ALLOW_THREADS;

        if (typenum == NPY_DOUBLE) {
            if (l == 1) {
                *((double *)PyArray_DATA(out_buf)) = *((double *)PyArray_DATA(ap2)) *
                                                 *((double *)PyArray_DATA(ap1));
            }
            else if (ap1shape != _matrix) {
                CBLAS_FUNC(cblas_daxpy)(l,
                            *((double *)PyArray_DATA(ap2)),
                            (double *)PyArray_DATA(ap1),
                            ap1stride/sizeof(double),
                            (double *)PyArray_DATA(out_buf), 1);
            }
            else {
                int maxind, oind;
                npy_intp i, a1s, outs;
                char *ptr, *optr;
                double val;

                maxind = (PyArray_DIM(ap1, 0) >= PyArray_DIM(ap1, 1) ? 0 : 1);
                oind = 1 - maxind;
                ptr = PyArray_DATA(ap1);
                optr = PyArray_DATA(out_buf);
                l = PyArray_DIM(ap1, maxind);
                val = *((double *)PyArray_DATA(ap2));
                a1s = PyArray_STRIDE(ap1, maxind) / sizeof(double);
                outs = PyArray_STRIDE(out_buf, maxind) / sizeof(double);
                for (i = 0; i < PyArray_DIM(ap1, oind); i++) {
                    CBLAS_FUNC(cblas_daxpy)(l, val, (double *)ptr, a1s,
                                (double *)optr, outs);
                    ptr += PyArray_STRIDE(ap1, oind);
                    optr += PyArray_STRIDE(out_buf, oind);
                }
            }
        }
        else if (typenum == NPY_CDOUBLE) {
            if (l == 1) {
                npy_cdouble *ptr1, *ptr2, *res;

                ptr1 = (npy_cdouble *)PyArray_DATA(ap2);
                ptr2 = (npy_cdouble *)PyArray_DATA(ap1);
                res = (npy_cdouble *)PyArray_DATA(out_buf);
                res->real = ptr1->real * ptr2->real - ptr1->imag * ptr2->imag;
                res->imag = ptr1->real * ptr2->imag + ptr1->imag * ptr2->real;
            }
            else if (ap1shape != _matrix) {
                CBLAS_FUNC(cblas_zaxpy)(l,
                            (double *)PyArray_DATA(ap2),
                            (double *)PyArray_DATA(ap1),
                            ap1stride/sizeof(npy_cdouble),
                            (double *)PyArray_DATA(out_buf), 1);
            }
            else {
                int maxind, oind;
                npy_intp i, a1s, outs;
                char *ptr, *optr;
                double *pval;

                maxind = (PyArray_DIM(ap1, 0) >= PyArray_DIM(ap1, 1) ? 0 : 1);
                oind = 1 - maxind;
                ptr = PyArray_DATA(ap1);
                optr = PyArray_DATA(out_buf);
                l = PyArray_DIM(ap1, maxind);
                pval = (double *)PyArray_DATA(ap2);
                a1s = PyArray_STRIDE(ap1, maxind) / sizeof(npy_cdouble);
                outs = PyArray_STRIDE(out_buf, maxind) / sizeof(npy_cdouble);
                for (i = 0; i < PyArray_DIM(ap1, oind); i++) {
                    CBLAS_FUNC(cblas_zaxpy)(l, pval, (double *)ptr, a1s,
                                (double *)optr, outs);
                    ptr += PyArray_STRIDE(ap1, oind);
                    optr += PyArray_STRIDE(out_buf, oind);
                }
            }
        }
        else if (typenum == NPY_FLOAT) {
            if (l == 1) {
                *((float *)PyArray_DATA(out_buf)) = *((float *)PyArray_DATA(ap2)) *
                    *((float *)PyArray_DATA(ap1));
            }
            else if (ap1shape != _matrix) {
                CBLAS_FUNC(cblas_saxpy)(l,
                            *((float *)PyArray_DATA(ap2)),
                            (float *)PyArray_DATA(ap1),
                            ap1stride/sizeof(float),
                            (float *)PyArray_DATA(out_buf), 1);
            }
            else {
                int maxind, oind;
                npy_intp i, a1s, outs;
                char *ptr, *optr;
                float val;

                maxind = (PyArray_DIM(ap1, 0) >= PyArray_DIM(ap1, 1) ? 0 : 1);
                oind = 1 - maxind;
                ptr = PyArray_DATA(ap1);
                optr = PyArray_DATA(out_buf);
                l = PyArray_DIM(ap1, maxind);
                val = *((float *)PyArray_DATA(ap2));
                a1s = PyArray_STRIDE(ap1, maxind) / sizeof(float);
                outs = PyArray_STRIDE(out_buf, maxind) / sizeof(float);
                for (i = 0; i < PyArray_DIM(ap1, oind); i++) {
                    CBLAS_FUNC(cblas_saxpy)(l, val, (float *)ptr, a1s,
                                (float *)optr, outs);
                    ptr += PyArray_STRIDE(ap1, oind);
                    optr += PyArray_STRIDE(out_buf, oind);
                }
            }
        }
        else if (typenum == NPY_CFLOAT) {
            if (l == 1) {
                npy_cfloat *ptr1, *ptr2, *res;

                ptr1 = (npy_cfloat *)PyArray_DATA(ap2);
                ptr2 = (npy_cfloat *)PyArray_DATA(ap1);
                res = (npy_cfloat *)PyArray_DATA(out_buf);
                res->real = ptr1->real * ptr2->real - ptr1->imag * ptr2->imag;
                res->imag = ptr1->real * ptr2->imag + ptr1->imag * ptr2->real;
            }
            else if (ap1shape != _matrix) {
                CBLAS_FUNC(cblas_caxpy)(l,
                            (float *)PyArray_DATA(ap2),
                            (float *)PyArray_DATA(ap1),
                            ap1stride/sizeof(npy_cfloat),
                            (float *)PyArray_DATA(out_buf), 1);
            }
            else {
                int maxind, oind;
                npy_intp i, a1s, outs;
                char *ptr, *optr;
                float *pval;

                maxind = (PyArray_DIM(ap1, 0) >= PyArray_DIM(ap1, 1) ? 0 : 1);
                oind = 1 - maxind;
                ptr = PyArray_DATA(ap1);
                optr = PyArray_DATA(out_buf);
                l = PyArray_DIM(ap1, maxind);
                pval = (float *)PyArray_DATA(ap2);
                a1s = PyArray_STRIDE(ap1, maxind) / sizeof(npy_cfloat);
                outs = PyArray_STRIDE(out_buf, maxind) / sizeof(npy_cfloat);
                for (i = 0; i < PyArray_DIM(ap1, oind); i++) {
                    CBLAS_FUNC(cblas_caxpy)(l, pval, (float *)ptr, a1s,
                                (float *)optr, outs);
                    ptr += PyArray_STRIDE(ap1, oind);
                    optr += PyArray_STRIDE(out_buf, oind);
                }
            }
        }
        NPY_END_ALLOW_THREADS;
    }
    else if ((ap2shape == _column) && (ap1shape != _matrix)) {
        NPY_BEGIN_ALLOW_THREADS;

        /* Dot product between two vectors -- Level 1 BLAS */
        PyArray_DESCR(out_buf)->f->dotfunc(
                 PyArray_DATA(ap1), PyArray_STRIDE(ap1, (ap1shape == _row)),
                 PyArray_DATA(ap2), PyArray_STRIDE(ap2, 0),
                 PyArray_DATA(out_buf), l, NULL);
        NPY_END_ALLOW_THREADS;
    }
    else if (ap1shape == _matrix && ap2shape != _matrix) {
        /* Matrix vector multiplication -- Level 2 BLAS */
        /* lda must be MAX(M,1) */
        enum CBLAS_ORDER Order;
        npy_intp ap2s;

        if (!PyArray_ISONESEGMENT(ap1)) {
            PyObject *new;
            new = PyArray_Copy(ap1);
            Py_DECREF(ap1);
            ap1 = (PyArrayObject *)new;
            if (new == NULL) {
                goto fail;
            }
        }
        NPY_BEGIN_ALLOW_THREADS
        if (PyArray_ISCONTIGUOUS(ap1)) {
            Order = CblasRowMajor;
            lda = (PyArray_DIM(ap1, 1) > 1 ? PyArray_DIM(ap1, 1) : 1);
        }
        else {
            Order = CblasColMajor;
            lda = (PyArray_DIM(ap1, 0) > 1 ? PyArray_DIM(ap1, 0) : 1);
        }
        ap2s = PyArray_STRIDE(ap2, 0) / PyArray_ITEMSIZE(ap2);
        gemv(typenum, Order, CblasNoTrans, ap1, lda, ap2, ap2s, out_buf);
        NPY_END_ALLOW_THREADS;
    }
    else if (ap1shape != _matrix && ap2shape == _matrix) {
        /* Vector matrix multiplication -- Level 2 BLAS */
        enum CBLAS_ORDER Order;
        npy_intp ap1s;

        if (!PyArray_ISONESEGMENT(ap2)) {
            PyObject *new;
            new = PyArray_Copy(ap2);
            Py_DECREF(ap2);
            ap2 = (PyArrayObject *)new;
            if (new == NULL) {
                goto fail;
            }
        }
        NPY_BEGIN_ALLOW_THREADS
        if (PyArray_ISCONTIGUOUS(ap2)) {
            Order = CblasRowMajor;
            lda = (PyArray_DIM(ap2, 1) > 1 ? PyArray_DIM(ap2, 1) : 1);
        }
        else {
            Order = CblasColMajor;
            lda = (PyArray_DIM(ap2, 0) > 1 ? PyArray_DIM(ap2, 0) : 1);
        }
        if (ap1shape == _row) {
            ap1s = PyArray_STRIDE(ap1, 1) / PyArray_ITEMSIZE(ap1);
        }
        else {
            ap1s = PyArray_STRIDE(ap1, 0) / PyArray_ITEMSIZE(ap1);
        }
        gemv(typenum, Order, CblasTrans, ap2, lda, ap1, ap1s, out_buf);
        NPY_END_ALLOW_THREADS;
    }
    else {
        /*
         * (PyArray_NDIM(ap1) == 2 && PyArray_NDIM(ap2) == 2)
         * Matrix matrix multiplication -- Level 3 BLAS
         *  L x M  multiplied by M x N
         */
        enum CBLAS_ORDER Order;
        enum CBLAS_TRANSPOSE Trans1, Trans2;
        npy_intp M, N, L;

        /* Optimization possible: */
        /*
         * We may be able to handle single-segment arrays here
         * using appropriate values of Order, Trans1, and Trans2.
         */
        if (!PyArray_IS_C_CONTIGUOUS(ap2) && !PyArray_IS_F_CONTIGUOUS(ap2)) {
            PyObject *new = PyArray_Copy(ap2);

            Py_DECREF(ap2);
            ap2 = (PyArrayObject *)new;
            if (new == NULL) {
                goto fail;
            }
        }
        if (!PyArray_IS_C_CONTIGUOUS(ap1) && !PyArray_IS_F_CONTIGUOUS(ap1)) {
            PyObject *new = PyArray_Copy(ap1);

            Py_DECREF(ap1);
            ap1 = (PyArrayObject *)new;
            if (new == NULL) {
                goto fail;
            }
        }

        NPY_BEGIN_ALLOW_THREADS;

        Order = CblasRowMajor;
        Trans1 = CblasNoTrans;
        Trans2 = CblasNoTrans;
        L = PyArray_DIM(ap1, 0);
        N = PyArray_DIM(ap2, 1);
        M = PyArray_DIM(ap2, 0);
        lda = (PyArray_DIM(ap1, 1) > 1 ? PyArray_DIM(ap1, 1) : 1);
        ldb = (PyArray_DIM(ap2, 1) > 1 ? PyArray_DIM(ap2, 1) : 1);

        /*
         * Avoid temporary copies for arrays in Fortran order
         */
        if (PyArray_IS_F_CONTIGUOUS(ap1)) {
            Trans1 = CblasTrans;
            lda = (PyArray_DIM(ap1, 0) > 1 ? PyArray_DIM(ap1, 0) : 1);
        }
        if (PyArray_IS_F_CONTIGUOUS(ap2)) {
            Trans2 = CblasTrans;
            ldb = (PyArray_DIM(ap2, 0) > 1 ? PyArray_DIM(ap2, 0) : 1);
        }

        /*
         * Use syrk if we have a case of a matrix times its transpose.
         * Otherwise, use gemm for all other cases.
         */
        if (
            (PyArray_BYTES(ap1) == PyArray_BYTES(ap2)) &&
            (PyArray_DIM(ap1, 0) == PyArray_DIM(ap2, 1)) &&
            (PyArray_DIM(ap1, 1) == PyArray_DIM(ap2, 0)) &&
            (PyArray_STRIDE(ap1, 0) == PyArray_STRIDE(ap2, 1)) &&
            (PyArray_STRIDE(ap1, 1) == PyArray_STRIDE(ap2, 0)) &&
            ((Trans1 == CblasTrans) ^ (Trans2 == CblasTrans)) &&
            ((Trans1 == CblasNoTrans) ^ (Trans2 == CblasNoTrans))
        ) {
            if (Trans1 == CblasNoTrans) {
                syrk(typenum, Order, Trans1, N, M, ap1, lda, out_buf);
            }
            else {
                syrk(typenum, Order, Trans1, N, M, ap2, ldb, out_buf);
            }
        }
        else {
            gemm(typenum, Order, Trans1, Trans2, L, N, M, ap1, lda, ap2, ldb,
                 out_buf);
        }
        NPY_END_ALLOW_THREADS;
    }


    Py_DECREF(ap1);
    Py_DECREF(ap2);

    /* Trigger possible copyback into `result` */
    PyArray_ResolveWritebackIfCopy(out_buf);
    Py_DECREF(out_buf);

    return PyArray_Return(result);

fail:
    Py_XDECREF(ap1);
    Py_XDECREF(ap2);
    Py_XDECREF(out_buf);
    Py_XDECREF(result);
    return NULL;
}
