ai-content-maker/.venv/Lib/site-packages/numba/tests/test_extending.py

import inspect
import math
import operator
import sys
import pickle
import multiprocessing
import ctypes
import warnings
import re

import numpy as np
from llvmlite import ir

import numba
from numba import njit, jit, vectorize, guvectorize, objmode
from numba.core import types, errors, typing, compiler, cgutils
from numba.core.typed_passes import type_inference_stage
from numba.core.registry import cpu_target
from numba.core.imputils import lower_constant
from numba.tests.support import (
    TestCase,
    captured_stdout,
    temp_directory,
    override_config,
    run_in_new_process_in_cache_dir,
    skip_if_typeguard,
)
from numba.core.errors import LoweringError
import unittest

from numba.extending import (
    typeof_impl,
    type_callable,
    lower_builtin,
    lower_cast,
    overload,
    overload_attribute,
    overload_method,
    models,
    register_model,
    box,
    unbox,
    NativeValue,
    intrinsic,
    _Intrinsic,
    register_jitable,
    get_cython_function_address,
    is_jitted,
    overload_classmethod,
)
from numba.core.typing.templates import (
    ConcreteTemplate,
    signature,
    infer,
    infer_global,
    AbstractTemplate,
)


# Pandas-like API implementation
from .pdlike_usecase import Index, Series


try:
    import scipy.special.cython_special as sc
except ImportError:
    sc = None


# -----------------------------------------------------------------------
# Define a custom type and an implicit cast on it


class MyDummy(object):
    pass


class MyDummyType(types.Opaque):
    def can_convert_to(self, context, toty):
        if isinstance(toty, types.Number):
            from numba.core.typeconv import Conversion

            return Conversion.safe


mydummy_type = MyDummyType("mydummy")
mydummy = MyDummy()


@typeof_impl.register(MyDummy)
def typeof_mydummy(val, c):
    return mydummy_type


@lower_cast(MyDummyType, types.Number)
def mydummy_to_number(context, builder, fromty, toty, val):
    """
    Implicit conversion from MyDummy to int.
    """
    return context.get_constant(toty, 42)


def get_dummy():
    return mydummy


register_model(MyDummyType)(models.OpaqueModel)


@unbox(MyDummyType)
def unbox_index(typ, obj, c):
    return NativeValue(c.context.get_dummy_value())


# -----------------------------------------------------------------------
# Define a second custom type but w/o implicit cast to Number


def base_dummy_type_factory(name):
    class DynType(object):
        pass

    class DynTypeType(types.Opaque):
        pass

    dyn_type_type = DynTypeType(name)

    @typeof_impl.register(DynType)
    def typeof_mydummy(val, c):
        return dyn_type_type

    register_model(DynTypeType)(models.OpaqueModel)
    return DynTypeType, DynType, dyn_type_type


MyDummyType2, MyDummy2, mydummy_type_2 = base_dummy_type_factory("mydummy2")


@unbox(MyDummyType2)
def unbox_index2(typ, obj, c):
    return NativeValue(c.context.get_dummy_value())


# -----------------------------------------------------------------------
# Define a function's typing and implementation using the classical
# two-step API


def func1(x=None):
    raise NotImplementedError


def type_func1_(context):
    def typer(x=None):
        if x in (None, types.none):
            # 0-arg or 1-arg with None
            return types.int32
        elif isinstance(x, types.Float):
            # 1-arg with float
            return x

    return typer


type_func1 = type_callable(func1)(type_func1_)


@lower_builtin(func1)
@lower_builtin(func1, types.none)
def func1_nullary(context, builder, sig, args):
    return context.get_constant(sig.return_type, 42)


@lower_builtin(func1, types.Float)
def func1_unary(context, builder, sig, args):
    def func1_impl(x):
        return math.sqrt(2 * x)

    return context.compile_internal(builder, func1_impl, sig, args)


# We can do the same for a known internal operation, here "print_item"
# which we extend to support MyDummyType.


@infer
class PrintDummy(ConcreteTemplate):
    key = "print_item"
    cases = [signature(types.none, mydummy_type)]


@lower_builtin("print_item", MyDummyType)
def print_dummy(context, builder, sig, args):
    [x] = args
    pyapi = context.get_python_api(builder)
    strobj = pyapi.unserialize(pyapi.serialize_object("hello!"))
    pyapi.print_object(strobj)
    pyapi.decref(strobj)
    return context.get_dummy_value()


# -----------------------------------------------------------------------
# Define an overloaded function (combined API)


def where(cond, x, y):
    raise NotImplementedError


def np_where(cond, x, y):
    """
    Wrap np.where() to allow for keyword arguments
    """
    return np.where(cond, x, y)


def call_where(cond, x, y):
    return where(cond, y=y, x=x)


@overload(where)
def overload_where_arrays(cond, x, y):
    """
    Implement where() for arrays.
    """
    # Choose implementation based on argument types.
    if isinstance(cond, types.Array):
        if x.dtype != y.dtype:
            raise errors.TypingError("x and y should have the same dtype")

        # Array where() => return an array of the same shape
        if all(ty.layout == "C" for ty in (cond, x, y)):

            def where_impl(cond, x, y):
                """
                Fast implementation for C-contiguous arrays
                """
                shape = cond.shape
                if x.shape != shape or y.shape != shape:
                    raise ValueError("all inputs should have the same shape")
                res = np.empty_like(x)
                cf = cond.flat
                xf = x.flat
                yf = y.flat
                rf = res.flat
                for i in range(cond.size):
                    rf[i] = xf[i] if cf[i] else yf[i]
                return res

        else:

            def where_impl(cond, x, y):
                """
                Generic implementation for other arrays
                """
                shape = cond.shape
                if x.shape != shape or y.shape != shape:
                    raise ValueError("all inputs should have the same shape")
                res = np.empty_like(x)
                for idx, c in np.ndenumerate(cond):
                    res[idx] = x[idx] if c else y[idx]
                return res

        return where_impl


# We can define another overload function for the same function, they
# will be tried in turn until one succeeds.


@overload(where)
def overload_where_scalars(cond, x, y):
    """
    Implement where() for scalars.
    """
    if not isinstance(cond, types.Array):
        if x != y:
            raise errors.TypingError("x and y should have the same type")

        def where_impl(cond, x, y):
            """
            Scalar where() => return a 0-dim array
            """
            scal = x if cond else y
            # Can't use full_like() on Numpy < 1.8
            arr = np.empty_like(scal)
            arr[()] = scal
            return arr

        return where_impl


# -----------------------------------------------------------------------
# Overload an already defined built-in function, extending it for new types.


@overload(len)
def overload_len_dummy(arg):
    if isinstance(arg, MyDummyType):

        def len_impl(arg):
            return 13

        return len_impl


@overload(operator.add)
def overload_add_dummy(arg1, arg2):
    if isinstance(arg1, (MyDummyType, MyDummyType2)) and isinstance(
        arg2, (MyDummyType, MyDummyType2)
    ):

        def dummy_add_impl(arg1, arg2):
            return 42

        return dummy_add_impl


@overload(operator.delitem)
def overload_dummy_delitem(obj, idx):
    if isinstance(obj, MyDummyType) and isinstance(idx, types.Integer):

        def dummy_delitem_impl(obj, idx):
            print("del", obj, idx)

        return dummy_delitem_impl


@overload(operator.getitem)
def overload_dummy_getitem(obj, idx):
    if isinstance(obj, MyDummyType) and isinstance(idx, types.Integer):

        def dummy_getitem_impl(obj, idx):
            return idx + 123

        return dummy_getitem_impl


@overload(operator.setitem)
def overload_dummy_setitem(obj, idx, val):
    if all(
        [
            isinstance(obj, MyDummyType),
            isinstance(idx, types.Integer),
            isinstance(val, types.Integer),
        ]
    ):

        def dummy_setitem_impl(obj, idx, val):
            print(idx, val)

        return dummy_setitem_impl


def call_add_operator(arg1, arg2):
    return operator.add(arg1, arg2)


def call_add_binop(arg1, arg2):
    return arg1 + arg2


@overload(operator.iadd)
def overload_iadd_dummy(arg1, arg2):
    if isinstance(arg1, (MyDummyType, MyDummyType2)) and isinstance(
        arg2, (MyDummyType, MyDummyType2)
    ):

        def dummy_iadd_impl(arg1, arg2):
            return 42

        return dummy_iadd_impl


def call_iadd_operator(arg1, arg2):
    return operator.add(arg1, arg2)


def call_iadd_binop(arg1, arg2):
    arg1 += arg2

    return arg1


def call_delitem(obj, idx):
    del obj[idx]


def call_getitem(obj, idx):
    return obj[idx]


def call_setitem(obj, idx, val):
    obj[idx] = val


@overload_method(MyDummyType, "length")
def overload_method_length(arg):
    def imp(arg):
        return len(arg)

    return imp


def cache_overload_method_usecase(x):
    return x.length()


def call_func1_nullary():
    return func1()


def call_func1_unary(x):
    return func1(x)


def len_usecase(x):
    return len(x)


def print_usecase(x):
    print(x)


def getitem_usecase(x, key):
    return x[key]


def npyufunc_usecase(x):
    return np.cos(np.sin(x))


def get_data_usecase(x):
    return x._data


def get_index_usecase(x):
    return x._index


def is_monotonic_usecase(x):
    return x.is_monotonic_increasing


def make_series_usecase(data, index):
    return Series(data, index)


def clip_usecase(x, lo, hi):
    return x.clip(lo, hi)


# -----------------------------------------------------------------------


def return_non_boxable():
    return np


@overload(return_non_boxable)
def overload_return_non_boxable():
    def imp():
        return np

    return imp


def non_boxable_ok_usecase(sz):
    mod = return_non_boxable()
    return mod.arange(sz)


def non_boxable_bad_usecase():
    return return_non_boxable()


def mk_func_input(f):
    pass


@infer_global(mk_func_input)
class MkFuncTyping(AbstractTemplate):
    def generic(self, args, kws):
        assert isinstance(args[0], types.MakeFunctionLiteral)
        return signature(types.none, *args)


def mk_func_test_impl():
    mk_func_input(lambda a: a)


# -----------------------------------------------------------------------
# Define a types derived from types.Callable and overloads for them


class MyClass(object):
    pass


class CallableTypeRef(types.Callable):

    def __init__(self, instance_type):
        self.instance_type = instance_type
        self.sig_to_impl_key = {}
        self.compiled_templates = []
        super(CallableTypeRef, self).__init__('callable_type_ref'
                                              '[{}]'.format(self.instance_type))

    def get_call_type(self, context, args, kws):

        res_sig = None
        for template in context._functions[type(self)]:
            try:
                res_sig = template.apply(args, kws)
            except Exception:
                pass  # for simplicity assume args must match exactly
            else:
                compiled_ovlds = getattr(template, '_compiled_overloads', {})
                if args in compiled_ovlds:
                    self.sig_to_impl_key[res_sig] = compiled_ovlds[args]
                    self.compiled_templates.append(template)
                    break

        return res_sig

    def get_call_signatures(self):
        sigs = list(self.sig_to_impl_key.keys())
        return sigs, True

    def get_impl_key(self, sig):
        return self.sig_to_impl_key[sig]


@register_model(CallableTypeRef)
class CallableTypeModel(models.OpaqueModel):

    def __init__(self, dmm, fe_type):

        models.OpaqueModel.__init__(self, dmm, fe_type)


infer_global(MyClass, CallableTypeRef(MyClass))


@lower_constant(CallableTypeRef)
def constant_callable_typeref(context, builder, ty, pyval):
    return context.get_dummy_value()


# -----------------------------------------------------------------------


@overload(np.exp)
def overload_np_exp(obj):
    if isinstance(obj, MyDummyType):

        def imp(obj):
            # Returns a constant if a MyDummyType is seen
            return 0xDEADBEEF

        return imp


class TestLowLevelExtending(TestCase):
    """
    Test the low-level two-tier extension API.
    """

    # Check with `@jit` from within the test process and also in a new test
    # process so as to check the registration mechanism.

    def test_func1(self):
        pyfunc = call_func1_nullary
        cfunc = jit(nopython=True)(pyfunc)
        self.assertPreciseEqual(cfunc(), 42)
        pyfunc = call_func1_unary
        cfunc = jit(nopython=True)(pyfunc)
        self.assertPreciseEqual(cfunc(None), 42)
        self.assertPreciseEqual(cfunc(18.0), 6.0)

    @TestCase.run_test_in_subprocess
    def test_func1_isolated(self):
        self.test_func1()

    def test_type_callable_keeps_function(self):
        self.assertIs(type_func1, type_func1_)
        self.assertIsNotNone(type_func1)

    @TestCase.run_test_in_subprocess
    def test_cast_mydummy(self):
        pyfunc = get_dummy
        cfunc = njit(types.float64(),)(pyfunc)
        self.assertPreciseEqual(cfunc(), 42.0)

    def test_mk_func_literal(self):
        """make sure make_function is passed to typer class as a literal
        """
        test_ir = compiler.run_frontend(mk_func_test_impl)
        typingctx = cpu_target.typing_context
        targetctx = cpu_target.target_context
        typingctx.refresh()
        targetctx.refresh()
        typing_res = type_inference_stage(typingctx, targetctx, test_ir, (),
                                          None)
        self.assertTrue(
            any(
                isinstance(a, types.MakeFunctionLiteral)
                for a in typing_res.typemap.values()
            )
        )


class TestPandasLike(TestCase):
    """
    Test implementing a pandas-like Index object.
    Also stresses most of the high-level API.
    """

    def test_index_len(self):
        i = Index(np.arange(3))
        cfunc = jit(nopython=True)(len_usecase)
        self.assertPreciseEqual(cfunc(i), 3)

    def test_index_getitem(self):
        i = Index(np.int32([42, 8, -5]))
        cfunc = jit(nopython=True)(getitem_usecase)
        self.assertPreciseEqual(cfunc(i, 1), 8)
        ii = cfunc(i, slice(1, None))
        self.assertIsInstance(ii, Index)
        self.assertEqual(list(ii), [8, -5])

    def test_index_ufunc(self):
        """
        Check Numpy ufunc on an Index object.
        """
        i = Index(np.int32([42, 8, -5]))
        cfunc = jit(nopython=True)(npyufunc_usecase)
        ii = cfunc(i)
        self.assertIsInstance(ii, Index)
        self.assertPreciseEqual(ii._data, np.cos(np.sin(i._data)))

    def test_index_get_data(self):
        # The _data attribute is exposed with make_attribute_wrapper()
        i = Index(np.int32([42, 8, -5]))
        cfunc = jit(nopython=True)(get_data_usecase)
        data = cfunc(i)
        self.assertIs(data, i._data)

    def test_index_is_monotonic(self):
        # The is_monotonic_increasing attribute is exposed with
        # overload_attribute()
        cfunc = jit(nopython=True)(is_monotonic_usecase)
        for values, expected in [
            ([8, 42, 5], False),
            ([5, 8, 42], True),
            ([], True),
        ]:
            i = Index(np.int32(values))
            got = cfunc(i)
            self.assertEqual(got, expected)

    def test_series_len(self):
        i = Index(np.int32([2, 4, 3]))
        s = Series(np.float64([1.5, 4.0, 2.5]), i)
        cfunc = jit(nopython=True)(len_usecase)
        self.assertPreciseEqual(cfunc(s), 3)

    def test_series_get_index(self):
        i = Index(np.int32([2, 4, 3]))
        s = Series(np.float64([1.5, 4.0, 2.5]), i)
        cfunc = jit(nopython=True)(get_index_usecase)
        got = cfunc(s)
        self.assertIsInstance(got, Index)
        self.assertIs(got._data, i._data)

    def test_series_ufunc(self):
        """
        Check Numpy ufunc on an Series object.
        """
        i = Index(np.int32([42, 8, -5]))
        s = Series(np.int64([1, 2, 3]), i)
        cfunc = jit(nopython=True)(npyufunc_usecase)
        ss = cfunc(s)
        self.assertIsInstance(ss, Series)
        self.assertIsInstance(ss._index, Index)
        self.assertIs(ss._index._data, i._data)
        self.assertPreciseEqual(ss._values, np.cos(np.sin(s._values)))

    def test_series_constructor(self):
        i = Index(np.int32([42, 8, -5]))
        d = np.float64([1.5, 4.0, 2.5])
        cfunc = jit(nopython=True)(make_series_usecase)
        got = cfunc(d, i)
        self.assertIsInstance(got, Series)
        self.assertIsInstance(got._index, Index)
        self.assertIs(got._index._data, i._data)
        self.assertIs(got._values, d)

    def test_series_clip(self):
        i = Index(np.int32([42, 8, -5]))
        s = Series(np.float64([1.5, 4.0, 2.5]), i)
        cfunc = jit(nopython=True)(clip_usecase)
        ss = cfunc(s, 1.6, 3.0)
        self.assertIsInstance(ss, Series)
        self.assertIsInstance(ss._index, Index)
        self.assertIs(ss._index._data, i._data)
        self.assertPreciseEqual(ss._values, np.float64([1.6, 3.0, 2.5]))


class TestHighLevelExtending(TestCase):
    """
    Test the high-level combined API.
    """

    def test_where(self):
        """
        Test implementing a function with @overload.
        """
        pyfunc = call_where
        cfunc = jit(nopython=True)(pyfunc)

        def check(*args, **kwargs):
            expected = np_where(*args, **kwargs)
            got = cfunc(*args, **kwargs)
            self.assertPreciseEqual(expected, got)

        check(x=3, cond=True, y=8)
        check(True, 3, 8)
        check(
            np.bool_([True, False, True]),
            np.int32([1, 2, 3]),
            np.int32([4, 5, 5]),
        )

        # The typing error is propagated
        with self.assertRaises(errors.TypingError) as raises:
            cfunc(np.bool_([]), np.int32([]), np.int64([]))
        self.assertIn(
            "x and y should have the same dtype", str(raises.exception)
        )

    def test_len(self):
        """
        Test re-implementing len() for a custom type with @overload.
        """
        cfunc = jit(nopython=True)(len_usecase)
        self.assertPreciseEqual(cfunc(MyDummy()), 13)
        self.assertPreciseEqual(cfunc([4, 5]), 2)

    def test_print(self):
        """
        Test re-implementing print() for a custom type with @overload.
        """
        cfunc = jit(nopython=True)(print_usecase)
        with captured_stdout():
            cfunc(MyDummy())
            self.assertEqual(sys.stdout.getvalue(), "hello!\n")

    def test_add_operator(self):
        """
        Test re-implementing operator.add() for a custom type with @overload.
        """
        pyfunc = call_add_operator
        cfunc = jit(nopython=True)(pyfunc)

        self.assertPreciseEqual(cfunc(1, 2), 3)
        self.assertPreciseEqual(cfunc(MyDummy2(), MyDummy2()), 42)

        # this will call add(Number, Number) as MyDummy implicitly casts to
        # Number
        self.assertPreciseEqual(cfunc(MyDummy(), MyDummy()), 84)

    def test_add_binop(self):
        """
        Test re-implementing '+' for a custom type via @overload(operator.add).
        """
        pyfunc = call_add_binop
        cfunc = jit(nopython=True)(pyfunc)

        self.assertPreciseEqual(cfunc(1, 2), 3)
        self.assertPreciseEqual(cfunc(MyDummy2(), MyDummy2()), 42)

        # this will call add(Number, Number) as MyDummy implicitly casts to
        # Number
        self.assertPreciseEqual(cfunc(MyDummy(), MyDummy()), 84)

    def test_iadd_operator(self):
        """
        Test re-implementing operator.add() for a custom type with @overload.
        """
        pyfunc = call_iadd_operator
        cfunc = jit(nopython=True)(pyfunc)

        self.assertPreciseEqual(cfunc(1, 2), 3)
        self.assertPreciseEqual(cfunc(MyDummy2(), MyDummy2()), 42)

        # this will call add(Number, Number) as MyDummy implicitly casts to
        # Number
        self.assertPreciseEqual(cfunc(MyDummy(), MyDummy()), 84)

    def test_iadd_binop(self):
        """
        Test re-implementing '+' for a custom type via @overload(operator.add).
        """
        pyfunc = call_iadd_binop
        cfunc = jit(nopython=True)(pyfunc)

        self.assertPreciseEqual(cfunc(1, 2), 3)
        self.assertPreciseEqual(cfunc(MyDummy2(), MyDummy2()), 42)

        # this will call add(Number, Number) as MyDummy implicitly casts to
        # Number
        self.assertPreciseEqual(cfunc(MyDummy(), MyDummy()), 84)

    def test_delitem(self):
        pyfunc = call_delitem
        cfunc = jit(nopython=True)(pyfunc)
        obj = MyDummy()
        e = None

        with captured_stdout() as out:
            try:
                cfunc(obj, 321)
            except Exception as exc:
                e = exc

        if e is not None:
            raise e
        self.assertEqual(out.getvalue(), "del hello! 321\n")

    def test_getitem(self):
        pyfunc = call_getitem
        cfunc = jit(nopython=True)(pyfunc)
        self.assertPreciseEqual(cfunc(MyDummy(), 321), 321 + 123)

    def test_setitem(self):
        pyfunc = call_setitem
        cfunc = jit(nopython=True)(pyfunc)
        obj = MyDummy()
        e = None

        with captured_stdout() as out:
            try:
                cfunc(obj, 321, 123)
            except Exception as exc:
                e = exc

        if e is not None:
            raise e
        self.assertEqual(out.getvalue(), "321 123\n")

    def test_no_cpython_wrapper(self):
        """
        Test overloading whose return value cannot be represented in CPython.
        """
        # Test passing Module type from a @overload implementation to ensure
        # that the *no_cpython_wrapper* flag works
        ok_cfunc = jit(nopython=True)(non_boxable_ok_usecase)
        n = 10
        got = ok_cfunc(n)
        expect = non_boxable_ok_usecase(n)
        np.testing.assert_equal(expect, got)
        # Verify that the Module type cannot be returned to CPython
        bad_cfunc = jit(nopython=True)(non_boxable_bad_usecase)
        with self.assertRaises(TypeError) as raises:
            bad_cfunc()
        errmsg = str(raises.exception)
        expectmsg = "cannot convert native Module"
        self.assertIn(expectmsg, errmsg)

    def test_typing_vs_impl_signature_mismatch_handling(self):
        """
        Tests that an overload which has a differing typing and implementing
        signature raises an exception.
        """

        def gen_ol(impl=None):
            def myoverload(a, b, c, kw=None):
                pass

            @overload(myoverload)
            def _myoverload_impl(a, b, c, kw=None):
                return impl

            @jit(nopython=True)
            def foo(a, b, c, d):
                myoverload(a, b, c, kw=d)

            return foo

        sentinel = "Typing and implementation arguments differ in"

        # kwarg value is different
        def impl1(a, b, c, kw=12):
            if a > 10:
                return 1
            else:
                return -1

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(impl1)(1, 2, 3, 4)
        msg = str(e.exception)
        self.assertIn(sentinel, msg)
        self.assertIn("keyword argument default values", msg)
        self.assertIn('<Parameter "kw=12">', msg)
        self.assertIn('<Parameter "kw=None">', msg)

        # kwarg name is different
        def impl2(a, b, c, kwarg=None):
            if a > 10:
                return 1
            else:
                return -1

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(impl2)(1, 2, 3, 4)
        msg = str(e.exception)
        self.assertIn(sentinel, msg)
        self.assertIn("keyword argument names", msg)
        self.assertIn('<Parameter "kwarg=None">', msg)
        self.assertIn('<Parameter "kw=None">', msg)

        # arg name is different
        def impl3(z, b, c, kw=None):
            if a > 10:      # noqa: F821
                return 1
            else:
                return -1

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(impl3)(1, 2, 3, 4)
        msg = str(e.exception)
        self.assertIn(sentinel, msg)
        self.assertIn("argument names", msg)
        self.assertFalse("keyword" in msg)
        self.assertIn('<Parameter "a">', msg)
        self.assertIn('<Parameter "z">', msg)

        from .overload_usecases import impl4, impl5

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(impl4)(1, 2, 3, 4)
        msg = str(e.exception)
        self.assertIn(sentinel, msg)
        self.assertIn("argument names", msg)
        self.assertFalse("keyword" in msg)
        self.assertIn("First difference: 'z'", msg)

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(impl5)(1, 2, 3, 4)
        msg = str(e.exception)
        self.assertIn(sentinel, msg)
        self.assertIn("argument names", msg)
        self.assertFalse("keyword" in msg)
        self.assertIn('<Parameter "a">', msg)
        self.assertIn('<Parameter "z">', msg)

        # too many args
        def impl6(a, b, c, d, e, kw=None):
            if a > 10:
                return 1
            else:
                return -1

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(impl6)(1, 2, 3, 4)
        msg = str(e.exception)
        self.assertIn(sentinel, msg)
        self.assertIn("argument names", msg)
        self.assertFalse("keyword" in msg)
        self.assertIn('<Parameter "d">', msg)
        self.assertIn('<Parameter "e">', msg)

        # too few args
        def impl7(a, b, kw=None):
            if a > 10:
                return 1
            else:
                return -1

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(impl7)(1, 2, 3, 4)
        msg = str(e.exception)
        self.assertIn(sentinel, msg)
        self.assertIn("argument names", msg)
        self.assertFalse("keyword" in msg)
        self.assertIn('<Parameter "c">', msg)

        # too many kwargs
        def impl8(a, b, c, kw=None, extra_kwarg=None):
            if a > 10:
                return 1
            else:
                return -1

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(impl8)(1, 2, 3, 4)
        msg = str(e.exception)
        self.assertIn(sentinel, msg)
        self.assertIn("keyword argument names", msg)
        self.assertIn('<Parameter "extra_kwarg=None">', msg)

        # too few kwargs
        def impl9(a, b, c):
            if a > 10:
                return 1
            else:
                return -1

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(impl9)(1, 2, 3, 4)
        msg = str(e.exception)
        self.assertIn(sentinel, msg)
        self.assertIn("keyword argument names", msg)
        self.assertIn('<Parameter "kw=None">', msg)

    def test_typing_vs_impl_signature_mismatch_handling_var_positional(self):
        """
        Tests that an overload which has a differing typing and implementing
        signature raises an exception and uses VAR_POSITIONAL (*args) in typing
        """

        def myoverload(a, kw=None):
            pass

        from .overload_usecases import var_positional_impl

        overload(myoverload)(var_positional_impl)

        @jit(nopython=True)
        def foo(a, b):
            return myoverload(a, b, 9, kw=11)

        with self.assertRaises(errors.TypingError) as e:
            foo(1, 5)
        msg = str(e.exception)
        self.assertIn("VAR_POSITIONAL (e.g. *args) argument kind", msg)
        self.assertIn("offending argument name is '*star_args_token'", msg)

    def test_typing_vs_impl_signature_mismatch_handling_var_keyword(self):
        """
        Tests that an overload which uses **kwargs (VAR_KEYWORD)
        """

        def gen_ol(impl, strict=True):
            def myoverload(a, kw=None):
                pass

            overload(myoverload, strict=strict)(impl)

            @jit(nopython=True)
            def foo(a, b):
                return myoverload(a, kw=11)

            return foo

        # **kwargs in typing
        def ol1(a, **kws):
            def impl(a, kw=10):
                return a

            return impl

        gen_ol(ol1, False)(1, 2)  # no error if strictness not enforced
        with self.assertRaises(errors.TypingError) as e:
            gen_ol(ol1)(1, 2)
        msg = str(e.exception)
        self.assertIn("use of VAR_KEYWORD (e.g. **kwargs) is unsupported", msg)
        self.assertIn("offending argument name is '**kws'", msg)

        # **kwargs in implementation
        def ol2(a, kw=0):
            def impl(a, **kws):
                return a

            return impl

        with self.assertRaises(errors.TypingError) as e:
            gen_ol(ol2)(1, 2)
        msg = str(e.exception)
        self.assertIn("use of VAR_KEYWORD (e.g. **kwargs) is unsupported", msg)
        self.assertIn("offending argument name is '**kws'", msg)

    def test_overload_method_kwargs(self):
        # Issue #3489
        @overload_method(types.Array, "foo")
        def fooimpl(arr, a_kwarg=10):
            def impl(arr, a_kwarg=10):
                return a_kwarg

            return impl

        @njit
        def bar(A):
            return A.foo(), A.foo(20), A.foo(a_kwarg=30)

        Z = np.arange(5)

        self.assertEqual(bar(Z), (10, 20, 30))

    def test_overload_method_literal_unpack(self):
        # Issue #3683
        @overload_method(types.Array, "litfoo")
        def litfoo(arr, val):
            # Must be an integer
            if isinstance(val, types.Integer):
                # Must not be literal
                if not isinstance(val, types.Literal):

                    def impl(arr, val):
                        return val

                    return impl

        @njit
        def bar(A):
            return A.litfoo(0xCAFE)

        A = np.zeros(1)
        bar(A)
        self.assertEqual(bar(A), 0xCAFE)

    def test_overload_ufunc(self):
        # Issue #4133.
        # Use an extended type (MyDummyType) to use with a customized
        # ufunc (np.exp).
        @njit
        def test():
            return np.exp(mydummy)

        self.assertEqual(test(), 0xDEADBEEF)

    def test_overload_method_stararg(self):
        @overload_method(MyDummyType, "method_stararg")
        def _ov_method_stararg(obj, val, val2, *args):
            def get(obj, val, val2, *args):
                return (val, val2, args)

            return get

        @njit
        def foo(obj, *args):
            # Test with expanding stararg
            return obj.method_stararg(*args)

        obj = MyDummy()
        self.assertEqual(foo(obj, 1, 2), (1, 2, ()))
        self.assertEqual(foo(obj, 1, 2, 3), (1, 2, (3,)))
        self.assertEqual(foo(obj, 1, 2, 3, 4), (1, 2, (3, 4)))

        @njit
        def bar(obj):
            # Test with explicit argument
            return (
                obj.method_stararg(1, 2),
                obj.method_stararg(1, 2, 3),
                obj.method_stararg(1, 2, 3, 4),
            )

        self.assertEqual(
            bar(obj), ((1, 2, ()), (1, 2, (3,)), (1, 2, (3, 4))),
        )

        # Check cases that put tuple type into stararg
        # NOTE: the expected result has an extra tuple because of stararg.
        self.assertEqual(
            foo(obj, 1, 2, (3,)), (1, 2, ((3,),)),
        )
        self.assertEqual(
            foo(obj, 1, 2, (3, 4)), (1, 2, ((3, 4),)),
        )
        self.assertEqual(
            foo(obj, 1, 2, (3, (4, 5))), (1, 2, ((3, (4, 5)),)),
        )

    def test_overload_classmethod(self):
        # Add classmethod to a subclass of Array
        class MyArray(types.Array):
            pass

        @overload_classmethod(MyArray, "array_alloc")
        def ol_array_alloc(cls, nitems):
            def impl(cls, nitems):
                arr = np.arange(nitems)
                return arr
            return impl

        @njit
        def foo(nitems):
            return MyArray.array_alloc(nitems)

        nitems = 13
        self.assertPreciseEqual(foo(nitems), np.arange(nitems))

        # Check that the base type doesn't get the classmethod

        @njit
        def no_classmethod_in_base(nitems):
            return types.Array.array_alloc(nitems)

        with self.assertRaises(errors.TypingError) as raises:
            no_classmethod_in_base(nitems)
        self.assertIn(
            "Unknown attribute 'array_alloc' of",
            str(raises.exception),
        )

    def test_overload_callable_typeref(self):

        @overload(CallableTypeRef)
        def callable_type_call_ovld1(x):
            if isinstance(x, types.Integer):
                def impl(x):
                    return 42.5 + x
                return impl

        @overload(CallableTypeRef)
        def callable_type_call_ovld2(x):
            if isinstance(x, types.UnicodeType):
                def impl(x):
                    return '42.5' + x

                return impl

        @njit
        def foo(a, b):
            return MyClass(a), MyClass(b)

        args = (4, '4')
        expected = (42.5 + args[0], '42.5' + args[1])
        self.assertPreciseEqual(foo(*args), expected)


def _assert_cache_stats(cfunc, expect_hit, expect_misses):
    hit = cfunc._cache_hits[cfunc.signatures[0]]
    if hit != expect_hit:
        raise AssertionError("cache not used")
    miss = cfunc._cache_misses[cfunc.signatures[0]]
    if miss != expect_misses:
        raise AssertionError("cache not used")


@skip_if_typeguard
class TestOverloadMethodCaching(TestCase):
    # Nested multiprocessing.Pool raises AssertionError:
    # "daemonic processes are not allowed to have children"
    _numba_parallel_test_ = False

    def test_caching_overload_method(self):
        self._cache_dir = temp_directory(self.__class__.__name__)
        with override_config("CACHE_DIR", self._cache_dir):
            self.run_caching_overload_method()

    def run_caching_overload_method(self):
        cfunc = jit(nopython=True, cache=True)(cache_overload_method_usecase)
        self.assertPreciseEqual(cfunc(MyDummy()), 13)
        _assert_cache_stats(cfunc, 0, 1)
        llvmir = cfunc.inspect_llvm((mydummy_type,))
        # Ensure the inner method is not a declaration
        decls = [
            ln
            for ln in llvmir.splitlines()
            if ln.startswith("declare") and "overload_method_length" in ln
        ]
        self.assertEqual(len(decls), 0)
        # Test in a separate process
        try:
            ctx = multiprocessing.get_context("spawn")
        except AttributeError:
            ctx = multiprocessing
        q = ctx.Queue()
        p = ctx.Process(
            target=run_caching_overload_method, args=(q, self._cache_dir)
        )
        p.start()
        q.put(MyDummy())
        p.join()
        # Ensure subprocess exited normally
        self.assertEqual(p.exitcode, 0)
        res = q.get(timeout=1)
        self.assertEqual(res, 13)


def run_caching_overload_method(q, cache_dir):
    """
    Used by TestOverloadMethodCaching.test_caching_overload_method
    """
    with override_config("CACHE_DIR", cache_dir):
        arg = q.get()
        cfunc = jit(nopython=True, cache=True)(cache_overload_method_usecase)
        res = cfunc(arg)
        q.put(res)
        # Check cache stat
        _assert_cache_stats(cfunc, 1, 0)


class TestIntrinsic(TestCase):
    def test_void_return(self):
        """
        Verify that returning a None from codegen function is handled
        automatically for void functions, otherwise raise exception.
        """

        @intrinsic
        def void_func(typingctx, a):
            sig = types.void(types.int32)

            def codegen(context, builder, signature, args):
                pass  # do nothing, return None, should be turned into
                # dummy value

            return sig, codegen

        @intrinsic
        def non_void_func(typingctx, a):
            sig = types.int32(types.int32)

            def codegen(context, builder, signature, args):
                pass  # oops, should be returning a value here, raise exception

            return sig, codegen

        @jit(nopython=True)
        def call_void_func():
            void_func(1)
            return 0

        @jit(nopython=True)
        def call_non_void_func():
            non_void_func(1)
            return 0

        # void func should work
        self.assertEqual(call_void_func(), 0)
        # not void function should raise exception
        with self.assertRaises(LoweringError) as e:
            call_non_void_func()
        self.assertIn("non-void function returns None", e.exception.msg)

    def test_ll_pointer_cast(self):
        """
        Usecase test: custom reinterpret cast to turn int values to pointers
        """
        from ctypes import CFUNCTYPE, POINTER, c_float, c_int

        # Use intrinsic to make a reinterpret_cast operation
        def unsafe_caster(result_type):
            assert isinstance(result_type, types.CPointer)

            @intrinsic
            def unsafe_cast(typingctx, src):
                self.assertIsInstance(typingctx, typing.Context)
                if isinstance(src, types.Integer):
                    sig = result_type(types.uintp)

                    # defines the custom code generation
                    def codegen(context, builder, signature, args):
                        [src] = args
                        rtype = signature.return_type
                        llrtype = context.get_value_type(rtype)
                        return builder.inttoptr(src, llrtype)

                    return sig, codegen

            return unsafe_cast

        # make a nopython function to use our cast op.
        # this is not usable from cpython due to the returning of a pointer.
        def unsafe_get_ctypes_pointer(src):
            raise NotImplementedError("not callable from python")

        @overload(unsafe_get_ctypes_pointer, strict=False)
        def array_impl_unsafe_get_ctypes_pointer(arrtype):
            if isinstance(arrtype, types.Array):
                unsafe_cast = unsafe_caster(types.CPointer(arrtype.dtype))

                def array_impl(arr):
                    return unsafe_cast(src=arr.ctypes.data)

                return array_impl

        # the ctype wrapped function for use in nopython mode
        def my_c_fun_raw(ptr, n):
            for i in range(n):
                print(ptr[i])

        prototype = CFUNCTYPE(None, POINTER(c_float), c_int)
        my_c_fun = prototype(my_c_fun_raw)

        # Call our pointer-cast in a @jit compiled function and use
        # the pointer in a ctypes function
        @jit(nopython=True)
        def foo(arr):
            ptr = unsafe_get_ctypes_pointer(arr)
            my_c_fun(ptr, arr.size)

        # Test
        arr = np.arange(10, dtype=np.float32)
        with captured_stdout() as buf:
            foo(arr)
            got = buf.getvalue().splitlines()
        buf.close()
        expect = list(map(str, arr))
        self.assertEqual(expect, got)

    def test_serialization(self):
        """
        Test serialization of intrinsic objects
        """
        # define a intrinsic
        @intrinsic
        def identity(context, x):
            def codegen(context, builder, signature, args):
                return args[0]

            sig = x(x)
            return sig, codegen

        # use in a jit function
        @jit(nopython=True)
        def foo(x):
            return identity(x)

        self.assertEqual(foo(1), 1)

        # get serialization memo
        memo = _Intrinsic._memo
        memo_size = len(memo)

        # pickle foo and check memo size
        serialized_foo = pickle.dumps(foo)
        # increases the memo size
        memo_size += 1
        self.assertEqual(memo_size, len(memo))
        # unpickle
        foo_rebuilt = pickle.loads(serialized_foo)
        self.assertEqual(memo_size, len(memo))
        # check rebuilt foo
        self.assertEqual(foo(1), foo_rebuilt(1))

        # pickle identity directly
        serialized_identity = pickle.dumps(identity)
        # memo size unchanged
        self.assertEqual(memo_size, len(memo))
        # unpickle
        identity_rebuilt = pickle.loads(serialized_identity)
        # must be the same object
        self.assertIs(identity, identity_rebuilt)
        # memo size unchanged
        self.assertEqual(memo_size, len(memo))

    def test_deserialization(self):
        """
        Test deserialization of intrinsic
        """

        def defn(context, x):
            def codegen(context, builder, signature, args):
                return args[0]

            return x(x), codegen

        memo = _Intrinsic._memo
        memo_size = len(memo)
        # invoke _Intrinsic indirectly to avoid registration which keeps an
        # internal reference inside the compiler
        original = _Intrinsic("foo", defn)
        self.assertIs(original._defn, defn)
        pickled = pickle.dumps(original)
        # by pickling, a new memo entry is created
        memo_size += 1
        self.assertEqual(memo_size, len(memo))
        del original  # remove original before unpickling

        # by deleting, the memo entry is NOT removed due to recent
        # function queue
        self.assertEqual(memo_size, len(memo))

        # Manually force clear of _recent queue
        _Intrinsic._recent.clear()
        memo_size -= 1
        self.assertEqual(memo_size, len(memo))

        rebuilt = pickle.loads(pickled)
        # verify that the rebuilt object is different
        self.assertIsNot(rebuilt._defn, defn)

        # the second rebuilt object is the same as the first
        second = pickle.loads(pickled)
        self.assertIs(rebuilt._defn, second._defn)

    def test_docstring(self):

        @intrinsic
        def void_func(typingctx, a: int):
            """void_func docstring"""
            sig = types.void(types.int32)

            def codegen(context, builder, signature, args):
                pass  # do nothing, return None, should be turned into
                # dummy value

            return sig, codegen

        self.assertEqual("numba.tests.test_extending", void_func.__module__)
        self.assertEqual("void_func", void_func.__name__)
        self.assertEqual("TestIntrinsic.test_docstring.<locals>.void_func",
                         void_func.__qualname__)
        self.assertDictEqual({'a': int}, void_func.__annotations__)
        self.assertEqual("void_func docstring", void_func.__doc__)


class TestRegisterJitable(unittest.TestCase):
    def test_no_flags(self):
        @register_jitable
        def foo(x, y):
            return x + y

        def bar(x, y):
            return foo(x, y)

        cbar = jit(nopython=True)(bar)

        expect = bar(1, 2)
        got = cbar(1, 2)
        self.assertEqual(expect, got)

    def test_flags_no_nrt(self):
        @register_jitable(_nrt=False)
        def foo(n):
            return np.arange(n)

        def bar(n):
            return foo(n)

        self.assertEqual(bar(3).tolist(), [0, 1, 2])

        cbar = jit(nopython=True)(bar)
        with self.assertRaises(errors.TypingError) as raises:
            cbar(2)
        msg = (
            "Only accept returning of array passed into the function as "
            "argument"
        )
        self.assertIn(msg, str(raises.exception))


class TestImportCythonFunction(unittest.TestCase):
    @unittest.skipIf(sc is None, "Only run if SciPy >= 0.19 is installed")
    def test_getting_function(self):
        addr = get_cython_function_address(
            "scipy.special.cython_special", "j0"
        )
        functype = ctypes.CFUNCTYPE(ctypes.c_double, ctypes.c_double)
        _j0 = functype(addr)
        j0 = jit(nopython=True)(lambda x: _j0(x))
        self.assertEqual(j0(0), 1)

    def test_missing_module(self):
        with self.assertRaises(ImportError) as raises:
            get_cython_function_address("fakemodule", "fakefunction")
        # The quotes are not there in Python 2
        msg = "No module named '?fakemodule'?"
        match = re.match(msg, str(raises.exception))
        self.assertIsNotNone(match)

    @unittest.skipIf(sc is None, "Only run if SciPy >= 0.19 is installed")
    def test_missing_function(self):
        with self.assertRaises(ValueError) as raises:
            get_cython_function_address(
                "scipy.special.cython_special", "foo"
            )
        msg = (
            "No function 'foo' found in __pyx_capi__ of "
            "'scipy.special.cython_special'"
        )
        self.assertEqual(msg, str(raises.exception))


@overload_method(
    MyDummyType, "method_jit_option_check_nrt", jit_options={"_nrt": True}
)
def ov_method_jit_option_check_nrt(obj):
    def imp(obj):
        return np.arange(10)

    return imp


@overload_method(
    MyDummyType, "method_jit_option_check_no_nrt", jit_options={"_nrt": False}
)
def ov_method_jit_option_check_no_nrt(obj):
    def imp(obj):
        return np.arange(10)

    return imp


@overload_attribute(
    MyDummyType, "attr_jit_option_check_nrt", jit_options={"_nrt": True}
)
def ov_attr_jit_option_check_nrt(obj):
    def imp(obj):
        return np.arange(10)

    return imp


@overload_attribute(
    MyDummyType, "attr_jit_option_check_no_nrt", jit_options={"_nrt": False}
)
def ov_attr_jit_option_check_no_nrt(obj):
    def imp(obj):
        return np.arange(10)

    return imp


class TestJitOptionsNoNRT(TestCase):
    # Test overload*(jit_options={...}) by turning off _nrt

    def check_error_no_nrt(self, func, *args, **kwargs):
        # Check that the compilation fails with a complaint about dynamic array
        msg = (
            "Only accept returning of array passed into "
            "the function as argument"
        )
        with self.assertRaises(errors.TypingError) as raises:
            func(*args, **kwargs)
        self.assertIn(msg, str(raises.exception))

    def no_nrt_overload_check(self, flag):
        def dummy():
            return np.arange(10)

        @overload(dummy, jit_options={"_nrt": flag})
        def ov_dummy():
            def dummy():
                return np.arange(10)

            return dummy

        @njit
        def foo():
            return dummy()

        if flag:
            self.assertPreciseEqual(foo(), np.arange(10))
        else:
            self.check_error_no_nrt(foo)

    def test_overload_no_nrt(self):
        self.no_nrt_overload_check(True)
        self.no_nrt_overload_check(False)

    def test_overload_method_no_nrt(self):
        @njit
        def udt(x):
            return x.method_jit_option_check_nrt()

        self.assertPreciseEqual(udt(mydummy), np.arange(10))

        @njit
        def udt(x):
            return x.method_jit_option_check_no_nrt()

        self.check_error_no_nrt(udt, mydummy)

    def test_overload_attribute_no_nrt(self):
        @njit
        def udt(x):
            return x.attr_jit_option_check_nrt

        self.assertPreciseEqual(udt(mydummy), np.arange(10))

        @njit
        def udt(x):
            return x.attr_jit_option_check_no_nrt

        self.check_error_no_nrt(udt, mydummy)


class TestBoxingCallingJIT(TestCase):
    def setUp(self):
        super().setUp()
        many = base_dummy_type_factory("mydummy2")
        self.DynTypeType, self.DynType, self.dyn_type_type = many
        self.dyn_type = self.DynType()

    def test_unboxer_basic(self):
        # Implements an unboxer on DynType that calls an intrinsic into the
        # unboxer code.
        magic_token = 0xCAFE
        magic_offset = 123

        @intrinsic
        def my_intrinsic(typingctx, val):
            # An intrinsic that returns `val + magic_offset`
            def impl(context, builder, sig, args):
                [val] = args
                return builder.add(val, val.type(magic_offset))

            sig = signature(val, val)
            return sig, impl

        @unbox(self.DynTypeType)
        def unboxer(typ, obj, c):
            # The unboxer that calls some jitcode
            def bridge(x):
                # proof that this is a jit'ed context by calling jit only
                # intrinsic
                return my_intrinsic(x)

            args = [c.context.get_constant(types.intp, magic_token)]
            sig = signature(types.voidptr, types.intp)
            is_error, res = c.pyapi.call_jit_code(bridge, sig, args)
            return NativeValue(res, is_error=is_error)

        @box(self.DynTypeType)
        def boxer(typ, val, c):
            # The boxer that returns an integer representation
            res = c.builder.ptrtoint(val, cgutils.intp_t)
            return c.pyapi.long_from_ssize_t(res)

        @njit
        def passthru(x):
            return x

        out = passthru(self.dyn_type)
        self.assertEqual(out, magic_token + magic_offset)

    def test_unboxer_raise(self):
        # Testing exception raising in jitcode called from unboxing.
        @unbox(self.DynTypeType)
        def unboxer(typ, obj, c):
            # The unboxer that calls some jitcode
            def bridge(x):
                if x > 0:
                    raise ValueError("cannot be x > 0")
                return x

            args = [c.context.get_constant(types.intp, 1)]
            sig = signature(types.voidptr, types.intp)
            is_error, res = c.pyapi.call_jit_code(bridge, sig, args)
            return NativeValue(res, is_error=is_error)

        @box(self.DynTypeType)
        def boxer(typ, val, c):
            # The boxer that returns an integer representation
            res = c.builder.ptrtoint(val, cgutils.intp_t)
            return c.pyapi.long_from_ssize_t(res)

        @njit
        def passthru(x):
            return x

        with self.assertRaises(ValueError) as raises:
            passthru(self.dyn_type)
        self.assertIn(
            "cannot be x > 0", str(raises.exception),
        )

    def test_boxer(self):
        # Call jitcode inside the boxer
        magic_token = 0xCAFE
        magic_offset = 312

        @intrinsic
        def my_intrinsic(typingctx, val):
            # An intrinsic that returns `val + magic_offset`
            def impl(context, builder, sig, args):
                [val] = args
                return builder.add(val, val.type(magic_offset))

            sig = signature(val, val)
            return sig, impl

        @unbox(self.DynTypeType)
        def unboxer(typ, obj, c):
            return NativeValue(c.context.get_dummy_value())

        @box(self.DynTypeType)
        def boxer(typ, val, c):
            # Note: this doesn't do proper error handling
            def bridge(x):
                return my_intrinsic(x)

            args = [c.context.get_constant(types.intp, magic_token)]
            sig = signature(types.intp, types.intp)
            is_error, res = c.pyapi.call_jit_code(bridge, sig, args)
            return c.pyapi.long_from_ssize_t(res)

        @njit
        def passthru(x):
            return x

        r = passthru(self.dyn_type)
        self.assertEqual(r, magic_token + magic_offset)

    def test_boxer_raise(self):
        # Call jitcode inside the boxer
        @unbox(self.DynTypeType)
        def unboxer(typ, obj, c):
            return NativeValue(c.context.get_dummy_value())

        @box(self.DynTypeType)
        def boxer(typ, val, c):
            def bridge(x):
                if x > 0:
                    raise ValueError("cannot do x > 0")
                return x

            args = [c.context.get_constant(types.intp, 1)]
            sig = signature(types.intp, types.intp)
            is_error, res = c.pyapi.call_jit_code(bridge, sig, args)
            # The error handling
            retval = cgutils.alloca_once(c.builder, c.pyapi.pyobj, zfill=True)
            with c.builder.if_then(c.builder.not_(is_error)):
                obj = c.pyapi.long_from_ssize_t(res)
                c.builder.store(obj, retval)
            return c.builder.load(retval)

        @njit
        def passthru(x):
            return x

        with self.assertRaises(ValueError) as raises:
            passthru(self.dyn_type)
        self.assertIn(
            "cannot do x > 0", str(raises.exception),
        )


def with_objmode_cache_ov_example(x):
    # This is the function stub for overloading inside
    # TestCachingOverloadObjmode.test_caching_overload_objmode
    pass


@skip_if_typeguard
class TestCachingOverloadObjmode(TestCase):
    """Test caching of the use of overload implementations that use
    `with objmode`
    """
    _numba_parallel_test_ = False

    def setUp(self):
        warnings.simplefilter("error", errors.NumbaWarning)

    def tearDown(self):
        warnings.resetwarnings()

    def test_caching_overload_objmode(self):
        cache_dir = temp_directory(self.__class__.__name__)
        with override_config("CACHE_DIR", cache_dir):

            def realwork(x):
                # uses numpy code
                arr = np.arange(x) / x
                return np.linalg.norm(arr)

            def python_code(x):
                # create indirections
                return realwork(x)

            @overload(with_objmode_cache_ov_example)
            def _ov_with_objmode_cache_ov_example(x):
                def impl(x):
                    with objmode(y="float64"):
                        y = python_code(x)
                    return y

                return impl

            @njit(cache=True)
            def testcase(x):
                return with_objmode_cache_ov_example(x)

            expect = realwork(123)
            got = testcase(123)
            self.assertEqual(got, expect)

            testcase_cached = njit(cache=True)(testcase.py_func)
            got = testcase_cached(123)
            self.assertEqual(got, expect)

    @classmethod
    def check_objmode_cache_ndarray(cls):
        def do_this(a, b):
            return np.sum(a + b)

        def do_something(a, b):
            return np.sum(a + b)

        @overload(do_something)
        def overload_do_something(a, b):
            def _do_something_impl(a, b):
                with objmode(y='float64'):
                    y = do_this(a, b)
                return y
            return _do_something_impl

        @njit(cache=True)
        def test_caching():
            a = np.arange(20)
            b = np.arange(20)
            return do_something(a, b)

        got = test_caching()
        expect = test_caching.py_func()

        # Check result
        if got != expect:
            raise AssertionError("incorrect result")
        return test_caching

    @classmethod
    def populate_objmode_cache_ndarray_check_cache(cls):
        cls.check_objmode_cache_ndarray()

    @classmethod
    def check_objmode_cache_ndarray_check_cache(cls):
        disp = cls.check_objmode_cache_ndarray()
        if len(disp.stats.cache_misses) != 0:
            raise AssertionError('unexpected cache miss')
        if len(disp.stats.cache_hits) <= 0:
            raise AssertionError("unexpected missing cache hit")

    def test_check_objmode_cache_ndarray(self):
        # See issue #6130.
        # Env is missing after cache load.
        cache_dir = temp_directory(self.__class__.__name__)
        with override_config("CACHE_DIR", cache_dir):
            # Run in new process to populate the cache
            run_in_new_process_in_cache_dir(
                self.populate_objmode_cache_ndarray_check_cache, cache_dir
            )
            # Run in new process to use the cache in a fresh process.
            res = run_in_new_process_in_cache_dir(
                self.check_objmode_cache_ndarray_check_cache, cache_dir
            )
        self.assertEqual(res['exitcode'], 0)


class TestMisc(TestCase):
    def test_is_jitted(self):
        def foo(x):
            pass

        self.assertFalse(is_jitted(foo))
        self.assertTrue(is_jitted(njit(foo)))
        self.assertFalse(is_jitted(vectorize(foo)))
        self.assertFalse(is_jitted(vectorize(parallel=True)(foo)))
        self.assertFalse(
            is_jitted(guvectorize("void(float64[:])", "(m)")(foo))
        )

    def test_overload_arg_binding(self):
        # See issue #7982, checks that calling a function with named args works
        # correctly irrespective of the order in which the names are supplied.
        @njit
        def standard_order():
            return np.full(shape=123, fill_value=456).shape

        @njit
        def reversed_order():
            return np.full(fill_value=456, shape=123).shape

        self.assertPreciseEqual(standard_order(), standard_order.py_func())
        self.assertPreciseEqual(reversed_order(), reversed_order.py_func())


class TestOverloadPreferLiteral(TestCase):
    def test_overload(self):
        def prefer_lit(x):
            pass

        def non_lit(x):
            pass

        def ov(x):
            if isinstance(x, types.IntegerLiteral):
                # With prefer_literal=False, this branch will not be reached.
                if x.literal_value == 1:
                    def impl(x):
                        return 0xcafe
                    return impl
                else:
                    raise errors.TypingError('literal value')
            else:
                def impl(x):
                    return x * 100
                return impl

        overload(prefer_lit, prefer_literal=True)(ov)
        overload(non_lit)(ov)

        @njit
        def check_prefer_lit(x):
            return prefer_lit(1), prefer_lit(2), prefer_lit(x)

        a, b, c = check_prefer_lit(3)
        self.assertEqual(a, 0xcafe)
        self.assertEqual(b, 200)
        self.assertEqual(c, 300)

        @njit
        def check_non_lit(x):
            return non_lit(1), non_lit(2), non_lit(x)

        a, b, c = check_non_lit(3)
        self.assertEqual(a, 100)
        self.assertEqual(b, 200)
        self.assertEqual(c, 300)

    def test_overload_method(self):
        def ov(self, x):
            if isinstance(x, types.IntegerLiteral):
                # With prefer_literal=False, this branch will not be reached.
                if x.literal_value == 1:
                    def impl(self, x):
                        return 0xcafe
                    return impl
                else:
                    raise errors.TypingError('literal value')
            else:
                def impl(self, x):
                    return x * 100
                return impl

        overload_method(
            MyDummyType, "method_prefer_literal",
            prefer_literal=True,
        )(ov)

        overload_method(
            MyDummyType, "method_non_literal",
            prefer_literal=False,
        )(ov)

        @njit
        def check_prefer_lit(dummy, x):
            return (
                dummy.method_prefer_literal(1),
                dummy.method_prefer_literal(2),
                dummy.method_prefer_literal(x),
            )

        a, b, c = check_prefer_lit(MyDummy(), 3)
        self.assertEqual(a, 0xcafe)
        self.assertEqual(b, 200)
        self.assertEqual(c, 300)

        @njit
        def check_non_lit(dummy, x):
            return (
                dummy.method_non_literal(1),
                dummy.method_non_literal(2),
                dummy.method_non_literal(x),
            )

        a, b, c = check_non_lit(MyDummy(), 3)
        self.assertEqual(a, 100)
        self.assertEqual(b, 200)
        self.assertEqual(c, 300)


class TestIntrinsicPreferLiteral(TestCase):
    def test_intrinsic(self):
        def intrin(context, x):
            # This intrinsic will return 0xcafe if `x` is a literal `1`.
            sig = signature(types.intp, x)
            if isinstance(x, types.IntegerLiteral):
                # With prefer_literal=False, this branch will not be reached
                if x.literal_value == 1:
                    def codegen(context, builder, signature, args):
                        atype = signature.args[0]
                        llrtype = context.get_value_type(atype)
                        return ir.Constant(llrtype, 0xcafe)
                    return sig, codegen
                else:
                    raise errors.TypingError('literal value')
            else:
                def codegen(context, builder, signature, args):
                    atype = signature.return_type
                    llrtype = context.get_value_type(atype)
                    int_100 = ir.Constant(llrtype, 100)
                    return builder.mul(args[0], int_100)
                return sig, codegen

        prefer_lit = intrinsic(prefer_literal=True)(intrin)
        non_lit = intrinsic(prefer_literal=False)(intrin)

        @njit
        def check_prefer_lit(x):
            return prefer_lit(1), prefer_lit(2), prefer_lit(x)

        a, b, c = check_prefer_lit(3)
        self.assertEqual(a, 0xcafe)
        self.assertEqual(b, 200)
        self.assertEqual(c, 300)

        @njit
        def check_non_lit(x):
            return non_lit(1), non_lit(2), non_lit(x)

        a, b, c = check_non_lit(3)
        self.assertEqual(a, 100)
        self.assertEqual(b, 200)
        self.assertEqual(c, 300)


class TestNumbaInternalOverloads(TestCase):

    def test_signatures_match_overloaded_api(self):
        # This is a "best-effort" test to try and ensure that Numba's internal
        # overload declarations have signatures with argument names that match
        # the API they are overloading. The purpose of ensuring there is a
        # match is so that users can use call-by-name for positional arguments.

        # Set this to:
        # 0 to make violations raise a ValueError (default).
        # 1 to get violations reported to STDOUT
        # 2 to get a verbose output of everything that was checked and its state
        #   reported to STDOUT.
        DEBUG = 0

        # np.random.* does not have a signature exposed to `inspect`... so
        # custom parse the docstrings.
        def sig_from_np_random(x):
            if not x.startswith('_'):
                thing = getattr(np.random, x)
                if inspect.isbuiltin(thing):
                    docstr = thing.__doc__.splitlines()
                    for l in docstr:
                        if l:
                            sl = l.strip()
                            if sl.startswith(x): # its the signature
                                # special case np.random.seed, it has `self` in
                                # the signature whereas all the other functions
                                # do not!?
                                if x == "seed":
                                    sl = "seed(seed)"

                                fake_impl = f"def {sl}:\n\tpass"
                                l = {}
                                try:
                                    exec(fake_impl, {}, l)
                                except SyntaxError:
                                    # likely elipsis, e.g. rand(d0, d1, ..., dn)
                                    if DEBUG == 2:
                                        print("... skipped as cannot parse "
                                              "signature")
                                    return None
                                else:
                                    fn = l.get(x)
                                    return inspect.signature(fn)

        def checker(func, overload_func):
            if DEBUG == 2:
                print(f"Checking: {func}")

            def create_message(func, overload_func, func_sig, ol_sig):
                msg = []
                s = (f"{func} from module '{getattr(func, '__module__')}' "
                     "has mismatched sig.")
                msg.append(s)
                msg.append(f"    - expected: {func_sig}")
                msg.append(f"    -      got: {ol_sig}")
                lineno = inspect.getsourcelines(overload_func)[1]
                tmpsrcfile = inspect.getfile(overload_func)
                srcfile = tmpsrcfile.replace(numba.__path__[0], '')
                msg.append(f"from {srcfile}:{lineno}")
                msgstr = '\n' + '\n'.join(msg)
                return msgstr

            func_sig = None
            try:
                func_sig = inspect.signature(func)
            except ValueError:
                # probably a built-in/C code, see if it's a np.random function
                if fname := getattr(func, '__name__', False):
                    if maybe_func := getattr(np.random, fname, False):
                        if maybe_func == func:
                            # it's a built-in from np.random
                            func_sig = sig_from_np_random(fname)

            if func_sig is not None:
                ol_sig = inspect.signature(overload_func)
                x = list(func_sig.parameters.keys())
                y = list(ol_sig.parameters.keys())
                for a, b in zip(x[:len(y)], y):
                    if a != b:
                        p = func_sig.parameters[a]
                        if p.kind == p.POSITIONAL_ONLY:
                            # probably a built-in/C code
                            if DEBUG == 2:
                                print("... skipped as positional only "
                                      "arguments found")
                            break
                        elif '*' in str(p): # probably *args or similar
                            if DEBUG == 2:
                                print("... skipped as contains *args")
                            break
                        else:
                            # Only error/report on functions that have a module
                            # or are from somewhere other than Numba.
                            if (not func.__module__ or
                                    not func.__module__.startswith("numba")):
                                msgstr = create_message(func, overload_func,
                                                        func_sig, ol_sig)
                                if DEBUG != 0:
                                    if DEBUG == 2:
                                        print("... INVALID")
                                    if msgstr:
                                        print(msgstr)
                                    break
                                else:
                                    raise ValueError(msgstr)
                            else:
                                if DEBUG == 2:
                                    if not func.__module__:
                                        print("... skipped as no __module__ "
                                              "present")
                                    else:
                                        print("... skipped as Numba internal")
                                break
                else:
                    if DEBUG == 2:
                        print("... OK")

        # Compile something to make sure that the typing context registries
        # are populated with everything from the CPU target.
        njit(lambda : None).compile(())
        tyctx = numba.core.typing.context.Context()
        tyctx.refresh()

        # Walk the registries and check each function that is an overload
        regs = tyctx._registries
        for k, v in regs.items():
            for item in k.functions:
                if getattr(item, '_overload_func', False):
                    checker(item.key, item._overload_func)


if __name__ == "__main__":
    unittest.main()