def dot_3_vm(context, builder, sig, args):
    """
    np.dot(vector, matrix, out)
    np.dot(matrix, vector, out)
    """
    xty, yty, outty = sig.args
    assert outty == sig.return_type
    dtype = xty.dtype

    x = make_array(xty)(context, builder, args[0])
    y = make_array(yty)(context, builder, args[1])
    out = make_array(outty)(context, builder, args[2])
    x_shapes = cgutils.unpack_tuple(builder, x.shape)
    y_shapes = cgutils.unpack_tuple(builder, y.shape)
    out_shapes = cgutils.unpack_tuple(builder, out.shape)
    if xty.ndim < yty.ndim:
        # Vector * matrix
        # Asked for x * y, we will compute y.T * x
        mty = yty
        m_shapes = y_shapes
        do_trans = yty.layout == 'F'
        m_data, v_data = y.data, x.data

        def check_args(a, b, out):
            m, = a.shape
            _m, n = b.shape
            if m != _m:
                raise ValueError("incompatible array sizes for "
                                 "np.dot(a, b) (vector * matrix)")
            if out.shape != (n,):
                raise ValueError("incompatible output array size for "
                                 "np.dot(a, b, out) (vector * matrix)")
    else:
        # Matrix * vector
        # We will compute x * y
        mty = xty
        m_shapes = x_shapes
        do_trans = xty.layout == 'C'
        m_data, v_data = x.data, y.data

        def check_args(a, b, out):
            m, _n = a.shape
            n, = b.shape
            if n != _n:
                raise ValueError("incompatible array sizes for np.dot(a, b) "
                                 "(matrix * vector)")
            if out.shape != (m,):
                raise ValueError("incompatible output array size for "
                                 "np.dot(a, b, out) (matrix * vector)")

    context.compile_internal(builder, check_args,
                             signature(types.none, *sig.args), args)
    for val in m_shapes:
        check_c_int(context, builder, val)

    call_xxgemv(context, builder, do_trans, mty, m_shapes, m_data,
                v_data, out.data)

    return impl_ret_borrowed(context, builder, sig.return_type,
                             out._getvalue())

def array_min(context, builder, sig, args):
    ty = sig.args[0].dtype
    if isinstance(ty, (types.NPDatetime, types.NPTimedelta)):
        # NaT is smaller than every other value, but it is
        # ignored as far as min() is concerned.
        nat = ty("NaT")

        def array_min_impl(arry):
            min_value = nat
            it = arry.flat
            for v in it:
                if v != nat:
                    min_value = v
                    break

            for v in it:
                if v != nat and v < min_value:
                    min_value = v
            return min_value

    else:

        def array_min_impl(arry):
            for v in arry.flat:
                min_value = v
                break

            for v in arry.flat:
                if v < min_value:
                    min_value = v
            return min_value

    res = context.compile_internal(builder, array_min_impl, sig, args)
    return impl_ret_borrowed(context, builder, sig.return_type, res)

def get_attr_impl(context, builder, typ, value, attr):
    """
    Generic getattr() for @jitclass instances.
    """
    if attr in typ.struct:
        # It's a struct field
        inst = context.make_helper(builder, typ, value=value)
        data_pointer = inst.data
        data = context.make_data_helper(builder, typ.get_data_type(),
                                        ref=data_pointer)
        return imputils.impl_ret_borrowed(context, builder,
                                          typ.struct[attr],
                                          getattr(data, _mangle_attr(attr)))
    elif attr in typ.jitprops:
        # It's a jitted property
        getter = typ.jitprops[attr]['get']
        sig = templates.signature(None, typ)
        dispatcher = types.Dispatcher(getter)
        sig = dispatcher.get_call_type(context.typing_context, [typ], {})
        call = context.get_function(dispatcher, sig)
        out = call(builder, [value])
        _add_linking_libs(context, call)
        return imputils.impl_ret_new_ref(context, builder, sig.return_type, out)

    raise NotImplementedError('attribute {0!r} not implemented'.format(attr))

def impl_dict_getiter(context, builder, sig, args):
    """Implement iter(Dict).  Semantically equivalent to dict.keys()
    """
    [td] = sig.args
    [d] = args
    iterablety = types.DictKeysIterableType(td)
    it = context.make_helper(builder, iterablety.iterator_type)

    fnty = ir.FunctionType(
        ir.VoidType(),
        [ll_dictiter_type, ll_dict_type],
    )

    fn = builder.module.get_or_insert_function(fnty, name='numba_dict_iter')

    proto = ctypes.CFUNCTYPE(ctypes.c_size_t)
    dictiter_sizeof = proto(_helperlib.c_helpers['dict_iter_sizeof'])
    state_type = ir.ArrayType(ir.IntType(8), dictiter_sizeof())

    pstate = cgutils.alloca_once(builder, state_type, zfill=True)
    it.state = _as_bytes(builder, pstate)
    it.parent = d

    dp = _dict_get_data(context, builder, iterablety.parent, args[0])
    builder.call(fn, [it.state, dp])
    return impl_ret_borrowed(
        context,
        builder,
        sig.return_type,
        it._getvalue(),
    )

def getitem_list(context, builder, sig, args):
    inst = ListInstance(context, builder, sig.args[0], args[0])
    index = args[1]

    index = inst.fix_index(index)
    result = inst.getitem(index)

    return impl_ret_borrowed(context, builder, sig.return_type, result)

def array_prod(context, builder, sig, args):
    def array_prod_impl(arr):
        c = 1
        for v in arr.flat:
            c *= v
        return c

    res = context.compile_internal(builder, array_prod_impl, sig, args, locals=dict(c=sig.return_type))
    return impl_ret_borrowed(context, builder, sig.return_type, res)

def array_sum(context, builder, sig, args):
    zero = sig.return_type(0)

    def array_sum_impl(arr):
        c = zero
        for v in arr.flat:
            c += v
        return c

    res = context.compile_internal(builder, array_sum_impl, sig, args, locals=dict(c=sig.return_type))
    return impl_ret_borrowed(context, builder, sig.return_type, res)

 def codegen(context, builder, sig, args):
     [d] = args
     [td] = sig.args
     iterhelper = context.make_helper(builder, resty)
     iterhelper.parent = d
     iterhelper.state = iterhelper.state.type(None)
     return impl_ret_borrowed(
         context,
         builder,
         resty,
         iterhelper._getvalue(),
     )

def array_max(context, builder, sig, args):
    def array_max_impl(arry):
        for v in arry.flat:
            max_value = v
            break

        for v in arry.flat:
            if v > max_value:
                max_value = v
        return max_value
    res = context.compile_internal(builder, array_max_impl, sig, args)
    return impl_ret_borrowed(context, builder, sig.return_type, res)

def array_max(context, builder, sig, args):
    def array_max_impl(arry):
        it = np.nditer(arry)
        for view in it:
            max_value = view.item()
            break

        for view in it:
            v = view.item()
            if v > max_value:
                max_value = v
        return max_value
    res = context.compile_internal(builder, array_max_impl, sig, args)
    return impl_ret_borrowed(context, builder, sig.return_type, res)

def list_mul_inplace(context, builder, sig, args):
    inst = ListInstance(context, builder, sig.args[0], args[0])
    src_size = inst.size

    mult = args[1]
    zero = ir.Constant(mult.type, 0)
    mult = builder.select(cgutils.is_neg_int(builder, mult), zero, mult)
    nitems = builder.mul(mult, src_size)

    inst.resize(nitems)

    with cgutils.for_range_slice(builder, src_size, nitems, src_size, inc=True) as (dest_offset, _):
        with cgutils.for_range(builder, src_size) as loop:
            value = inst.getitem(loop.index)
            inst.setitem(builder.add(loop.index, dest_offset), value)

    return impl_ret_borrowed(context, builder, sig.return_type, inst.value)

                def imp(context, builder, typ, val):
                    ary = aryty(context, builder)
                    dtype = elemty.dtype
                    newshape = [self.get_constant(types.intp, s) for s in
                                elemty.shape]
                    newstrides = [self.get_constant(types.intp, s) for s in
                                  elemty.strides]
                    newdata = cgutils.get_record_member(builder, val, offset,
                                                    self.get_data_type(dtype))
                    arrayobj.populate_array(
                        ary,
                        data=newdata,
                        shape=cgutils.pack_array(builder, newshape),
                        strides=cgutils.pack_array(builder, newstrides),
                        itemsize=context.get_constant(types.intp, elemty.size),
                        meminfo=None,
                        parent=None,
                    )

                    res = ary._getvalue()
                    return impl_ret_borrowed(context, builder, typ, res)

def attr_impl(context, builder, typ, value, attr):
    if attr in typ.struct:
        inst_struct = cgutils.create_struct_proxy(typ)
        inst = inst_struct(context, builder, value=value)
        data_pointer = inst.data
        data_struct = cgutils.create_struct_proxy(typ.get_data_type(),
                                                  kind='data')
        data = data_struct(context, builder, ref=data_pointer)
        return imputils.impl_ret_borrowed(context, builder,
                                          typ.struct[attr],
                                          getattr(data, attr))
    elif attr in typ.jitprops:
        getter = typ.jitprops[attr]['get']
        sig = templates.signature(None, typ)
        dispatcher = types.Dispatcher(getter)
        sig = dispatcher.get_call_type(context.typing_context, [typ], {})
        call = context.get_function(dispatcher, sig)
        out = call(builder, [value])
        return imputils.impl_ret_new_ref(context, builder, sig.return_type, out)

    raise NotImplementedError('attribute {0!r} not implemented'.format(attr))

def index_wrap_array(context, builder, sig, args):
    dest = make_index(context, builder, sig.return_type)
    dest.data = args[1]
    return impl_ret_borrowed(context, builder, sig.return_type, dest._getvalue())

def list_add_inplace(context, builder, sig, args):
    assert sig.args[0].dtype == sig.return_type.dtype
    dest = _list_extend_list(context, builder, sig, args)

    return impl_ret_borrowed(context, builder, sig.return_type, dest.value)

def series_wrap_array(context, builder, sig, args):
    src = make_series(context, builder, sig.args[0], value=args[0])
    dest = make_series(context, builder, sig.return_type)
    dest.values = args[1]
    dest.index = src.index
    return impl_ret_borrowed(context, builder, sig.return_type, dest._getvalue())

def getiter_list(context, builder, sig, args):
    inst = ListIterInstance.from_list(context, builder, sig.return_type, args[0])
    return impl_ret_borrowed(context, builder, sig.return_type, inst.value)

def dot_3_vm(context, builder, sig, args):
    """
    np.dot(vector, matrix, out)
    np.dot(matrix, vector, out)
    """
    xty, yty, outty = sig.args
    assert outty == sig.return_type
    dtype = xty.dtype

    x = make_array(xty)(context, builder, args[0])
    y = make_array(yty)(context, builder, args[1])
    out = make_array(outty)(context, builder, args[2])
    x_shapes = cgutils.unpack_tuple(builder, x.shape)
    y_shapes = cgutils.unpack_tuple(builder, y.shape)
    out_shapes = cgutils.unpack_tuple(builder, out.shape)
    if xty.ndim < yty.ndim:
        # Vector * matrix
        # Asked for x * y, we will compute y.T * x
        mty = yty
        m, n = m_shapes = y_shapes
        do_trans = yty.layout == 'F'
        m_data, v_data = y.data, x.data

        def check_args(a, b, out):
            m, = a.shape
            _m, n = b.shape
            if m != _m:
                raise ValueError("incompatible array sizes for np.dot(a, b) "
                                 "(vector * matrix)")
            if out.shape != (n,):
                raise ValueError("incompatible output array size for np.dot(a, b, out) "
                                 "(vector * matrix)")
    else:
        # Matrix * vector
        # We will compute x * y
        mty = xty
        m, n = m_shapes = x_shapes
        do_trans = xty.layout == 'C'
        m_data, v_data = x.data, y.data

        def check_args(a, b, out):
            m, _n= a.shape
            n, = b.shape
            if n != _n:
                raise ValueError("incompatible array sizes for np.dot(a, b) "
                                 "(matrix * vector)")
            if out.shape != (m,):
                raise ValueError("incompatible output array size for np.dot(a, b, out) "
                                 "(matrix * vector)")

    context.compile_internal(builder, check_args,
                             signature(types.none, *sig.args), args)
    check_c_int(context, builder, m)
    check_c_int(context, builder, n)

    fnty = ir.FunctionType(ir.IntType(32),
                           [ll_char, ll_char_p,               # kind, trans
                            intp_t, intp_t,                   # m, n
                            ll_void_p, ll_void_p, intp_t,     # alpha, a, lda
                            ll_void_p, ll_void_p, ll_void_p,  # x, beta, y
                           ])
    fn = builder.module.get_or_insert_function(fnty, name="numba_xxgemv")

    alpha = make_constant_slot(context, builder, dtype, 1.0)
    beta = make_constant_slot(context, builder, dtype, 0.0)

    if mty.layout == 'F':
        lda = m_shapes[0]
    else:
        m, n = n, m
        lda = m_shapes[1]

    kind = get_blas_kind(dtype)
    kind_val = ir.Constant(ll_char, ord(kind))
    trans = context.insert_const_string(builder.module,
                                        "t" if do_trans else "n")

    res = builder.call(fn, (kind_val, trans, m, n,
                            builder.bitcast(alpha, ll_void_p),
                            builder.bitcast(m_data, ll_void_p), lda,
                            builder.bitcast(v_data, ll_void_p),
                            builder.bitcast(beta, ll_void_p),
                            builder.bitcast(out.data, ll_void_p)))
    check_blas_return(context, builder, res)

    return impl_ret_borrowed(context, builder, sig.return_type, out._getvalue())

 def imp(context, builder, typ, val):
     dptr = cgutils.get_record_member(builder, val, offset,
                                      context.get_data_type(elemty))
     align = None if typ.aligned else 1
     res = self.unpack_value(builder, elemty, dptr, align)
     return impl_ret_borrowed(context, builder, typ, res)

 def set_inplace(context, builder, sig, args, op_impl=op_impl):
     assert sig.return_type == sig.args[0]
     op_impl(context, builder, sig, args)
     return impl_ret_borrowed(context, builder, sig.args[0], args[0])