diff --git a/.gitattributes b/.gitattributes
index c2ebc938e789f24ab3a27f829b3b04b56e59e7f5..997937d8997007229dcd494ab9c724a5bb866392 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -94,3 +94,4 @@ lib/python3.10/site-packages/av/video/stream.cpython-310-x86_64-linux-gnu.so fil
 lib/python3.10/site-packages/av/video/codeccontext.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 lib/python3.10/site-packages/av/video/reformatter.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 lib/python3.10/site-packages/av/video/frame.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+lib/python3.10/site-packages/av/sidedata/sidedata.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
diff --git a/lib/python3.10/site-packages/av/sidedata/sidedata.cpython-310-x86_64-linux-gnu.so b/lib/python3.10/site-packages/av/sidedata/sidedata.cpython-310-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..a525a0eb029d1c450105758f419e101bb7eeb946
--- /dev/null
+++ b/lib/python3.10/site-packages/av/sidedata/sidedata.cpython-310-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1808806ad22b1e8cca102941bf36917bcb2ebb81801a051088e7c0dd2577a31e
+size 720649
diff --git a/lib/python3.10/site-packages/numba/cpython/cmathimpl.py b/lib/python3.10/site-packages/numba/cpython/cmathimpl.py
new file mode 100644
index 0000000000000000000000000000000000000000..4305e9d45dde68b5f32b56e462bd5b064bfaed18
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cpython/cmathimpl.py
@@ -0,0 +1,542 @@
+"""
+Implement the cmath module functions.
+"""
+
+
+import cmath
+import math
+
+from numba.core.imputils import Registry, impl_ret_untracked
+from numba.core import types, cgutils
+from numba.core.typing import signature
+from numba.cpython import builtins, mathimpl
+from numba.core.extending import overload
+
+registry = Registry('cmathimpl')
+lower = registry.lower
+
+
+def is_nan(builder, z):
+    return builder.fcmp_unordered('uno', z.real, z.imag)
+
+def is_inf(builder, z):
+    return builder.or_(mathimpl.is_inf(builder, z.real),
+                       mathimpl.is_inf(builder, z.imag))
+
+def is_finite(builder, z):
+    return builder.and_(mathimpl.is_finite(builder, z.real),
+                        mathimpl.is_finite(builder, z.imag))
+
+
+@lower(cmath.isnan, types.Complex)
+def isnan_float_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [value] = args
+    z = context.make_complex(builder, typ, value=value)
+    res = is_nan(builder, z)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+@lower(cmath.isinf, types.Complex)
+def isinf_float_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [value] = args
+    z = context.make_complex(builder, typ, value=value)
+    res = is_inf(builder, z)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower(cmath.isfinite, types.Complex)
+def isfinite_float_impl(context, builder, sig, args):
+    [typ] = sig.args
+    [value] = args
+    z = context.make_complex(builder, typ, value=value)
+    res = is_finite(builder, z)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@overload(cmath.rect)
+def impl_cmath_rect(r, phi):
+    if all([isinstance(typ, types.Float) for typ in [r, phi]]):
+        def impl(r, phi):
+            if not math.isfinite(phi):
+                if not r:
+                    # cmath.rect(0, phi={inf, nan}) = 0
+                    return abs(r)
+                if math.isinf(r):
+                    # cmath.rect(inf, phi={inf, nan}) = inf + j phi
+                    return complex(r, phi)
+            real = math.cos(phi)
+            imag = math.sin(phi)
+            if real == 0. and math.isinf(r):
+                # 0 * inf would return NaN, we want to keep 0 but xor the sign
+                real /= r
+            else:
+                real *= r
+            if imag == 0. and math.isinf(r):
+                # ditto
+                imag /= r
+            else:
+                imag *= r
+            return complex(real, imag)
+        return impl
+
+
+def intrinsic_complex_unary(inner_func):
+    def wrapper(context, builder, sig, args):
+        [typ] = sig.args
+        [value] = args
+        z = context.make_complex(builder, typ, value=value)
+        x = z.real
+        y = z.imag
+        # Same as above: math.isfinite() is unavailable on 2.x so we precompute
+        # its value and pass it to the pure Python implementation.
+        x_is_finite = mathimpl.is_finite(builder, x)
+        y_is_finite = mathimpl.is_finite(builder, y)
+        inner_sig = signature(sig.return_type,
+                              *(typ.underlying_float,) * 2 + (types.boolean,) * 2)
+        res = context.compile_internal(builder, inner_func, inner_sig,
+                                        (x, y, x_is_finite, y_is_finite))
+        return impl_ret_untracked(context, builder, sig, res)
+    return wrapper
+
+
+NAN = float('nan')
+INF = float('inf')
+
+@lower(cmath.exp, types.Complex)
+@intrinsic_complex_unary
+def exp_impl(x, y, x_is_finite, y_is_finite):
+    """cmath.exp(x + y j)"""
+    if x_is_finite:
+        if y_is_finite:
+            c = math.cos(y)
+            s = math.sin(y)
+            r = math.exp(x)
+            return complex(r * c, r * s)
+        else:
+            return complex(NAN, NAN)
+    elif math.isnan(x):
+        if y:
+            return complex(x, x)  # nan + j nan
+        else:
+            return complex(x, y)  # nan + 0j
+    elif x > 0.0:
+        # x == +inf
+        if y_is_finite:
+            real = math.cos(y)
+            imag = math.sin(y)
+            # Avoid NaNs if math.cos(y) or math.sin(y) == 0
+            # (e.g. cmath.exp(inf + 0j) == inf + 0j)
+            if real != 0:
+                real *= x
+            if imag != 0:
+                imag *= x
+            return complex(real, imag)
+        else:
+            return complex(x, NAN)
+    else:
+        # x == -inf
+        if y_is_finite:
+            r = math.exp(x)
+            c = math.cos(y)
+            s = math.sin(y)
+            return complex(r * c, r * s)
+        else:
+            r = 0
+            return complex(r, r)
+
+@lower(cmath.log, types.Complex)
+@intrinsic_complex_unary
+def log_impl(x, y, x_is_finite, y_is_finite):
+    """cmath.log(x + y j)"""
+    a = math.log(math.hypot(x, y))
+    b = math.atan2(y, x)
+    return complex(a, b)
+
+
+@lower(cmath.log, types.Complex, types.Complex)
+def log_base_impl(context, builder, sig, args):
+    """cmath.log(z, base)"""
+    [z, base] = args
+
+    def log_base(z, base):
+        return cmath.log(z) / cmath.log(base)
+
+    res = context.compile_internal(builder, log_base, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+
+@overload(cmath.log10)
+def impl_cmath_log10(z):
+    if not isinstance(z, types.Complex):
+        return
+
+    LN_10 = 2.302585092994045684
+
+    def log10_impl(z):
+        """cmath.log10(z)"""
+        z = cmath.log(z)
+        # This formula gives better results on +/-inf than cmath.log(z, 10)
+        # See http://bugs.python.org/issue22544
+        return complex(z.real / LN_10, z.imag / LN_10)
+
+    return log10_impl
+
+
+@overload(cmath.phase)
+def phase_impl(x):
+    """cmath.phase(x + y j)"""
+
+    if not isinstance(x, types.Complex):
+        return
+
+    def impl(x):
+        return math.atan2(x.imag, x.real)
+    return impl
+
+
+@overload(cmath.polar)
+def polar_impl(x):
+    if not isinstance(x, types.Complex):
+        return
+
+    def impl(x):
+        r, i = x.real, x.imag
+        return math.hypot(r, i), math.atan2(i, r)
+    return impl
+
+
+@lower(cmath.sqrt, types.Complex)
+def sqrt_impl(context, builder, sig, args):
+    # We risk spurious overflow for components >= FLT_MAX / (1 + sqrt(2)).
+
+    SQRT2 = 1.414213562373095048801688724209698079E0
+    ONE_PLUS_SQRT2 = (1. + SQRT2)
+    theargflt = sig.args[0].underlying_float
+    # Get a type specific maximum value so scaling for overflow is based on that
+    MAX = mathimpl.DBL_MAX if theargflt.bitwidth == 64 else mathimpl.FLT_MAX
+    # THRES will be double precision, should not impact typing as it's just
+    # used for comparison, there *may* be a few values near THRES which
+    # deviate from e.g. NumPy due to rounding that occurs in the computation
+    # of this value in the case of a 32bit argument.
+    THRES = MAX / ONE_PLUS_SQRT2
+
+    def sqrt_impl(z):
+        """cmath.sqrt(z)"""
+        # This is NumPy's algorithm, see npy_csqrt() in npy_math_complex.c.src
+        a = z.real
+        b = z.imag
+        if a == 0.0 and b == 0.0:
+            return complex(abs(b), b)
+        if math.isinf(b):
+            return complex(abs(b), b)
+        if math.isnan(a):
+            return complex(a, a)
+        if math.isinf(a):
+            if a < 0.0:
+                return complex(abs(b - b), math.copysign(a, b))
+            else:
+                return complex(a, math.copysign(b - b, b))
+
+        # The remaining special case (b is NaN) is handled just fine by
+        # the normal code path below.
+
+        # Scale to avoid overflow
+        if abs(a) >= THRES or abs(b) >= THRES:
+            a *= 0.25
+            b *= 0.25
+            scale = True
+        else:
+            scale = False
+        # Algorithm 312, CACM vol 10, Oct 1967
+        if a >= 0:
+            t = math.sqrt((a + math.hypot(a, b)) * 0.5)
+            real = t
+            imag = b / (2 * t)
+        else:
+            t = math.sqrt((-a + math.hypot(a, b)) * 0.5)
+            real = abs(b) / (2 * t)
+            imag = math.copysign(t, b)
+        # Rescale
+        if scale:
+            return complex(real * 2, imag)
+        else:
+            return complex(real, imag)
+
+    res = context.compile_internal(builder, sqrt_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+
+@lower(cmath.cos, types.Complex)
+def cos_impl(context, builder, sig, args):
+    def cos_impl(z):
+        """cmath.cos(z) = cmath.cosh(z j)"""
+        return cmath.cosh(complex(-z.imag, z.real))
+
+    res = context.compile_internal(builder, cos_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@overload(cmath.cosh)
+def impl_cmath_cosh(z):
+    if not isinstance(z, types.Complex):
+        return
+
+    def cosh_impl(z):
+        """cmath.cosh(z)"""
+        x = z.real
+        y = z.imag
+        if math.isinf(x):
+            if math.isnan(y):
+                # x = +inf, y = NaN => cmath.cosh(x + y j) = inf + Nan * j
+                real = abs(x)
+                imag = y
+            elif y == 0.0:
+                # x = +inf, y = 0 => cmath.cosh(x + y j) = inf + 0j
+                real = abs(x)
+                imag = y
+            else:
+                real = math.copysign(x, math.cos(y))
+                imag = math.copysign(x, math.sin(y))
+            if x < 0.0:
+                # x = -inf => negate imaginary part of result
+                imag = -imag
+            return complex(real, imag)
+        return complex(math.cos(y) * math.cosh(x),
+                    math.sin(y) * math.sinh(x))
+    return cosh_impl
+
+
+@lower(cmath.sin, types.Complex)
+def sin_impl(context, builder, sig, args):
+    def sin_impl(z):
+        """cmath.sin(z) = -j * cmath.sinh(z j)"""
+        r = cmath.sinh(complex(-z.imag, z.real))
+        return complex(r.imag, -r.real)
+
+    res = context.compile_internal(builder, sin_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@overload(cmath.sinh)
+def impl_cmath_sinh(z):
+    if not isinstance(z, types.Complex):
+        return
+
+    def sinh_impl(z):
+        """cmath.sinh(z)"""
+        x = z.real
+        y = z.imag
+        if math.isinf(x):
+            if math.isnan(y):
+                # x = +/-inf, y = NaN => cmath.sinh(x + y j) = x + NaN * j
+                real = x
+                imag = y
+            else:
+                real = math.cos(y)
+                imag = math.sin(y)
+                if real != 0.:
+                    real *= x
+                if imag != 0.:
+                    imag *= abs(x)
+            return complex(real, imag)
+        return complex(math.cos(y) * math.sinh(x),
+                       math.sin(y) * math.cosh(x))
+    return sinh_impl
+
+
+@lower(cmath.tan, types.Complex)
+def tan_impl(context, builder, sig, args):
+    def tan_impl(z):
+        """cmath.tan(z) = -j * cmath.tanh(z j)"""
+        r = cmath.tanh(complex(-z.imag, z.real))
+        return complex(r.imag, -r.real)
+
+    res = context.compile_internal(builder, tan_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+
+@overload(cmath.tanh)
+def impl_cmath_tanh(z):
+    if not isinstance(z, types.Complex):
+        return
+
+    def tanh_impl(z):
+        """cmath.tanh(z)"""
+        x = z.real
+        y = z.imag
+        if math.isinf(x):
+            real = math.copysign(1., x)
+            if math.isinf(y):
+                imag = 0.
+            else:
+                imag = math.copysign(0., math.sin(2. * y))
+            return complex(real, imag)
+        # This is CPython's algorithm (see c_tanh() in cmathmodule.c).
+        # XXX how to force float constants into single precision?
+        tx = math.tanh(x)
+        ty = math.tan(y)
+        cx = 1. / math.cosh(x)
+        txty = tx * ty
+        denom = 1. + txty * txty
+        return complex(
+            tx * (1. + ty * ty) / denom,
+            ((ty / denom) * cx) * cx)
+
+    return tanh_impl
+
+
+@lower(cmath.acos, types.Complex)
+def acos_impl(context, builder, sig, args):
+    LN_4 = math.log(4)
+    THRES = mathimpl.FLT_MAX / 4
+
+    def acos_impl(z):
+        """cmath.acos(z)"""
+        # CPython's algorithm (see c_acos() in cmathmodule.c)
+        if abs(z.real) > THRES or abs(z.imag) > THRES:
+            # Avoid unnecessary overflow for large arguments
+            # (also handles infinities gracefully)
+            real = math.atan2(abs(z.imag), z.real)
+            imag = math.copysign(
+                math.log(math.hypot(z.real * 0.5, z.imag * 0.5)) + LN_4,
+                -z.imag)
+            return complex(real, imag)
+        else:
+            s1 = cmath.sqrt(complex(1. - z.real, -z.imag))
+            s2 = cmath.sqrt(complex(1. + z.real, z.imag))
+            real = 2. * math.atan2(s1.real, s2.real)
+            imag = math.asinh(s2.real * s1.imag - s2.imag * s1.real)
+            return complex(real, imag)
+
+    res = context.compile_internal(builder, acos_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@overload(cmath.acosh)
+def impl_cmath_acosh(z):
+    if not isinstance(z, types.Complex):
+        return
+
+    LN_4 = math.log(4)
+    THRES = mathimpl.FLT_MAX / 4
+
+    def acosh_impl(z):
+        """cmath.acosh(z)"""
+        # CPython's algorithm (see c_acosh() in cmathmodule.c)
+        if abs(z.real) > THRES or abs(z.imag) > THRES:
+            # Avoid unnecessary overflow for large arguments
+            # (also handles infinities gracefully)
+            real = math.log(math.hypot(z.real * 0.5, z.imag * 0.5)) + LN_4
+            imag = math.atan2(z.imag, z.real)
+            return complex(real, imag)
+        else:
+            s1 = cmath.sqrt(complex(z.real - 1., z.imag))
+            s2 = cmath.sqrt(complex(z.real + 1., z.imag))
+            real = math.asinh(s1.real * s2.real + s1.imag * s2.imag)
+            imag = 2. * math.atan2(s1.imag, s2.real)
+            return complex(real, imag)
+        # Condensed formula (NumPy)
+        #return cmath.log(z + cmath.sqrt(z + 1.) * cmath.sqrt(z - 1.))
+
+    return acosh_impl
+
+
+@lower(cmath.asinh, types.Complex)
+def asinh_impl(context, builder, sig, args):
+    LN_4 = math.log(4)
+    THRES = mathimpl.FLT_MAX / 4
+
+    def asinh_impl(z):
+        """cmath.asinh(z)"""
+        # CPython's algorithm (see c_asinh() in cmathmodule.c)
+        if abs(z.real) > THRES or abs(z.imag) > THRES:
+            real = math.copysign(
+                math.log(math.hypot(z.real * 0.5, z.imag * 0.5)) + LN_4,
+                z.real)
+            imag = math.atan2(z.imag, abs(z.real))
+            return complex(real, imag)
+        else:
+            s1 = cmath.sqrt(complex(1. + z.imag, -z.real))
+            s2 = cmath.sqrt(complex(1. - z.imag, z.real))
+            real = math.asinh(s1.real * s2.imag - s2.real * s1.imag)
+            imag = math.atan2(z.imag, s1.real * s2.real - s1.imag * s2.imag)
+            return complex(real, imag)
+
+    res = context.compile_internal(builder, asinh_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.asin, types.Complex)
+def asin_impl(context, builder, sig, args):
+    def asin_impl(z):
+        """cmath.asin(z) = -j * cmath.asinh(z j)"""
+        r = cmath.asinh(complex(-z.imag, z.real))
+        return complex(r.imag, -r.real)
+
+    res = context.compile_internal(builder, asin_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.atan, types.Complex)
+def atan_impl(context, builder, sig, args):
+    def atan_impl(z):
+        """cmath.atan(z) = -j * cmath.atanh(z j)"""
+        r = cmath.atanh(complex(-z.imag, z.real))
+        if math.isinf(z.real) and math.isnan(z.imag):
+            # XXX this is odd but necessary
+            return complex(r.imag, r.real)
+        else:
+            return complex(r.imag, -r.real)
+
+    res = context.compile_internal(builder, atan_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
+
+@lower(cmath.atanh, types.Complex)
+def atanh_impl(context, builder, sig, args):
+    LN_4 = math.log(4)
+    THRES_LARGE = math.sqrt(mathimpl.FLT_MAX / 4)
+    THRES_SMALL = math.sqrt(mathimpl.FLT_MIN)
+    PI_12 = math.pi / 2
+
+    def atanh_impl(z):
+        """cmath.atanh(z)"""
+        # CPython's algorithm (see c_atanh() in cmathmodule.c)
+        if z.real < 0.:
+            # Reduce to case where z.real >= 0., using atanh(z) = -atanh(-z).
+            negate = True
+            z = -z
+        else:
+            negate = False
+
+        ay = abs(z.imag)
+        if math.isnan(z.real) or z.real > THRES_LARGE or ay > THRES_LARGE:
+            if math.isinf(z.imag):
+                real = math.copysign(0., z.real)
+            elif math.isinf(z.real):
+                real = 0.
+            else:
+                # may be safe from overflow, depending on hypot's implementation...
+                h = math.hypot(z.real * 0.5, z.imag * 0.5)
+                real = z.real/4./h/h
+            imag = -math.copysign(PI_12, -z.imag)
+        elif z.real == 1. and ay < THRES_SMALL:
+            # C99 standard says:  atanh(1+/-0.) should be inf +/- 0j
+            if ay == 0.:
+                real = INF
+                imag = z.imag
+            else:
+                real = -math.log(math.sqrt(ay) /
+                                 math.sqrt(math.hypot(ay, 2.)))
+                imag = math.copysign(math.atan2(2., -ay) / 2, z.imag)
+        else:
+            sqay = ay * ay
+            zr1 = 1 - z.real
+            real = math.log1p(4. * z.real / (zr1 * zr1 + sqay)) * 0.25
+            imag = -math.atan2(-2. * z.imag,
+                               zr1 * (1 + z.real) - sqay) * 0.5
+
+        if math.isnan(z.imag):
+            imag = NAN
+        if negate:
+            return complex(-real, -imag)
+        else:
+            return complex(real, imag)
+
+    res = context.compile_internal(builder, atanh_impl, sig, args)
+    return impl_ret_untracked(context, builder, sig, res)
diff --git a/lib/python3.10/site-packages/numba/cpython/hashing.py b/lib/python3.10/site-packages/numba/cpython/hashing.py
new file mode 100644
index 0000000000000000000000000000000000000000..410f9be325d778887d340da9c016cd9eb9174980
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cpython/hashing.py
@@ -0,0 +1,10 @@
+import sys
+from numba.core.utils import _RedirectSubpackage
+from numba.core import config
+
+if config.USE_LEGACY_TYPE_SYSTEM:
+    sys.modules[__name__] = _RedirectSubpackage(locals(),
+                                                "numba.cpython.old_hashing")
+else:
+    sys.modules[__name__] = _RedirectSubpackage(locals(),
+                                                "numba.cpython.new_hashing")
diff --git a/lib/python3.10/site-packages/numba/cpython/listobj.py b/lib/python3.10/site-packages/numba/cpython/listobj.py
new file mode 100644
index 0000000000000000000000000000000000000000..08c23dea4835862d38ed2c9f11812d7764006f96
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cpython/listobj.py
@@ -0,0 +1,1260 @@
+"""
+Support for native homogeneous lists.
+"""
+
+
+import math
+import operator
+from functools import cached_property
+
+from llvmlite import ir
+from numba.core import types, typing, errors, cgutils, config
+from numba.core.imputils import (lower_builtin, lower_cast,
+                                    iternext_impl, impl_ret_borrowed,
+                                    impl_ret_new_ref, impl_ret_untracked,
+                                    RefType)
+from numba.core.extending import overload_method, overload
+from numba.misc import quicksort
+from numba.cpython import slicing
+from numba import literal_unroll
+
+
+def get_list_payload(context, builder, list_type, value):
+    """
+    Given a list value and type, get its payload structure (as a
+    reference, so that mutations are seen by all).
+    """
+    payload_type = types.ListPayload(list_type)
+    payload = context.nrt.meminfo_data(builder, value.meminfo)
+    ptrty = context.get_data_type(payload_type).as_pointer()
+    payload = builder.bitcast(payload, ptrty)
+    return context.make_data_helper(builder, payload_type, ref=payload)
+
+
+def get_itemsize(context, list_type):
+    """
+    Return the item size for the given list type.
+    """
+    llty = context.get_data_type(list_type.dtype)
+    return context.get_abi_sizeof(llty)
+
+
+class _ListPayloadMixin(object):
+
+    @property
+    def size(self):
+        return self._payload.size
+
+    @size.setter
+    def size(self, value):
+        self._payload.size = value
+
+    @property
+    def dirty(self):
+        return self._payload.dirty
+
+    @property
+    def data(self):
+        return self._payload._get_ptr_by_name('data')
+
+    def _gep(self, idx):
+        return cgutils.gep(self._builder, self.data, idx)
+
+    def getitem(self, idx):
+        ptr = self._gep(idx)
+        data_item = self._builder.load(ptr)
+        return self._datamodel.from_data(self._builder, data_item)
+
+    def fix_index(self, idx):
+        """
+        Fix negative indices by adding the size to them.  Positive
+        indices are left untouched.
+        """
+        is_negative = self._builder.icmp_signed('<', idx,
+                                                ir.Constant(idx.type, 0))
+        wrapped_index = self._builder.add(idx, self.size)
+        return self._builder.select(is_negative, wrapped_index, idx)
+
+    def is_out_of_bounds(self, idx):
+        """
+        Return whether the index is out of bounds.
+        """
+        underflow = self._builder.icmp_signed('<', idx,
+                                              ir.Constant(idx.type, 0))
+        overflow = self._builder.icmp_signed('>=', idx, self.size)
+        return self._builder.or_(underflow, overflow)
+
+    def clamp_index(self, idx):
+        """
+        Clamp the index in [0, size].
+        """
+        builder = self._builder
+        idxptr = cgutils.alloca_once_value(builder, idx)
+
+        zero = ir.Constant(idx.type, 0)
+        size = self.size
+
+        underflow = self._builder.icmp_signed('<', idx, zero)
+        with builder.if_then(underflow, likely=False):
+            builder.store(zero, idxptr)
+        overflow = self._builder.icmp_signed('>=', idx, size)
+        with builder.if_then(overflow, likely=False):
+            builder.store(size, idxptr)
+
+        return builder.load(idxptr)
+
+    def guard_index(self, idx, msg):
+        """
+        Raise an error if the index is out of bounds.
+        """
+        with self._builder.if_then(self.is_out_of_bounds(idx), likely=False):
+            self._context.call_conv.return_user_exc(self._builder,
+                                                    IndexError, (msg,))
+
+    def fix_slice(self, slice):
+        """
+        Fix slice start and stop to be valid (inclusive and exclusive, resp)
+        indexing bounds.
+        """
+        return slicing.fix_slice(self._builder, slice, self.size)
+
+    def incref_value(self, val):
+        "Incref an element value"
+        self._context.nrt.incref(self._builder, self.dtype, val)
+
+    def decref_value(self, val):
+        "Decref an element value"
+        self._context.nrt.decref(self._builder, self.dtype, val)
+
+
+class ListPayloadAccessor(_ListPayloadMixin):
+    """
+    A helper object to access the list attributes given the pointer to the
+    payload type.
+    """
+    def __init__(self, context, builder, list_type, payload_ptr):
+        self._context = context
+        self._builder = builder
+        self._ty = list_type
+        self._datamodel = context.data_model_manager[list_type.dtype]
+        payload_type = types.ListPayload(list_type)
+        ptrty = context.get_data_type(payload_type).as_pointer()
+        payload_ptr = builder.bitcast(payload_ptr, ptrty)
+        payload = context.make_data_helper(builder, payload_type,
+                                           ref=payload_ptr)
+        self._payload = payload
+
+
+class ListInstance(_ListPayloadMixin):
+
+    def __init__(self, context, builder, list_type, list_val):
+        self._context = context
+        self._builder = builder
+        self._ty = list_type
+        self._list = context.make_helper(builder, list_type, list_val)
+        self._itemsize = get_itemsize(context, list_type)
+        self._datamodel = context.data_model_manager[list_type.dtype]
+
+    @property
+    def dtype(self):
+        return self._ty.dtype
+
+    @property
+    def _payload(self):
+        # This cannot be cached as it can be reallocated
+        return get_list_payload(self._context, self._builder, self._ty, self._list)
+
+    @property
+    def parent(self):
+        return self._list.parent
+
+    @parent.setter
+    def parent(self, value):
+        self._list.parent = value
+
+    @property
+    def value(self):
+        return self._list._getvalue()
+
+    @property
+    def meminfo(self):
+        return self._list.meminfo
+
+    def set_dirty(self, val):
+        if self._ty.reflected:
+            self._payload.dirty = cgutils.true_bit if val else cgutils.false_bit
+
+    def clear_value(self, idx):
+        """Remove the value at the location
+        """
+        self.decref_value(self.getitem(idx))
+        # it's necessary for the dtor which just decref every slot on it.
+        self.zfill(idx, self._builder.add(idx, idx.type(1)))
+
+    def setitem(self, idx, val, incref, decref_old_value=True):
+        # Decref old data
+        if decref_old_value:
+            self.decref_value(self.getitem(idx))
+
+        ptr = self._gep(idx)
+        data_item = self._datamodel.as_data(self._builder, val)
+        self._builder.store(data_item, ptr)
+        self.set_dirty(True)
+        if incref:
+            # Incref the underlying data
+            self.incref_value(val)
+
+    def inititem(self, idx, val, incref=True):
+        ptr = self._gep(idx)
+        data_item = self._datamodel.as_data(self._builder, val)
+        self._builder.store(data_item, ptr)
+        if incref:
+            self.incref_value(val)
+
+    def zfill(self, start, stop):
+        """Zero-fill the memory at index *start* to *stop*
+
+        *stop* MUST not be smaller than *start*.
+        """
+        builder = self._builder
+        base = self._gep(start)
+        end = self._gep(stop)
+        intaddr_t = self._context.get_value_type(types.intp)
+        size = builder.sub(builder.ptrtoint(end, intaddr_t),
+                           builder.ptrtoint(base, intaddr_t))
+        cgutils.memset(builder, base, size, ir.IntType(8)(0))
+
+    @classmethod
+    def allocate_ex(cls, context, builder, list_type, nitems):
+        """
+        Allocate a ListInstance with its storage.
+        Return a (ok, instance) tuple where *ok* is a LLVM boolean and
+        *instance* is a ListInstance object (the object's contents are
+        only valid when *ok* is true).
+        """
+        intp_t = context.get_value_type(types.intp)
+
+        if isinstance(nitems, int):
+            nitems = ir.Constant(intp_t, nitems)
+
+        payload_type = context.get_data_type(types.ListPayload(list_type))
+        payload_size = context.get_abi_sizeof(payload_type)
+
+        itemsize = get_itemsize(context, list_type)
+        # Account for the fact that the payload struct contains one entry
+        payload_size -= itemsize
+
+        ok = cgutils.alloca_once_value(builder, cgutils.true_bit)
+        self = cls(context, builder, list_type, None)
+
+        # Total allocation size = <payload header size> + nitems * itemsize
+        allocsize, ovf = cgutils.muladd_with_overflow(builder, nitems,
+                                                      ir.Constant(intp_t, itemsize),
+                                                      ir.Constant(intp_t, payload_size))
+        with builder.if_then(ovf, likely=False):
+            builder.store(cgutils.false_bit, ok)
+
+        with builder.if_then(builder.load(ok), likely=True):
+            meminfo = context.nrt.meminfo_new_varsize_dtor_unchecked(
+                builder, size=allocsize, dtor=self.get_dtor())
+            with builder.if_else(cgutils.is_null(builder, meminfo),
+                                 likely=False) as (if_error, if_ok):
+                with if_error:
+                    builder.store(cgutils.false_bit, ok)
+                with if_ok:
+                    self._list.meminfo = meminfo
+                    self._list.parent = context.get_constant_null(types.pyobject)
+                    self._payload.allocated = nitems
+                    self._payload.size = ir.Constant(intp_t, 0)  # for safety
+                    self._payload.dirty = cgutils.false_bit
+                    # Zero the allocated region
+                    self.zfill(self.size.type(0), nitems)
+
+        return builder.load(ok), self
+
+    def define_dtor(self):
+        "Define the destructor if not already defined"
+        context = self._context
+        builder = self._builder
+        mod = builder.module
+        # Declare dtor
+        fnty = ir.FunctionType(ir.VoidType(), [cgutils.voidptr_t])
+        fn = cgutils.get_or_insert_function(mod, fnty,
+                                            '.dtor.list.{}'.format(self.dtype))
+        if not fn.is_declaration:
+            # End early if the dtor is already defined
+            return fn
+        fn.linkage = 'linkonce_odr'
+        # Populate the dtor
+        builder = ir.IRBuilder(fn.append_basic_block())
+        base_ptr = fn.args[0]  # void*
+
+        # get payload
+        payload = ListPayloadAccessor(context, builder, self._ty, base_ptr)
+
+        # Loop over all data to decref
+        intp = payload.size.type
+        with cgutils.for_range_slice(
+                builder, start=intp(0), stop=payload.size, step=intp(1),
+                intp=intp) as (idx, _):
+            val = payload.getitem(idx)
+            context.nrt.decref(builder, self.dtype, val)
+        builder.ret_void()
+        return fn
+
+    def get_dtor(self):
+        """"Get the element dtor function pointer as void pointer.
+
+        It's safe to be called multiple times.
+        """
+        # Define and set the Dtor
+        dtor = self.define_dtor()
+        dtor_fnptr = self._builder.bitcast(dtor, cgutils.voidptr_t)
+        return dtor_fnptr
+
+    @classmethod
+    def allocate(cls, context, builder, list_type, nitems):
+        """
+        Allocate a ListInstance with its storage.  Same as allocate_ex(),
+        but return an initialized *instance*.  If allocation failed,
+        control is transferred to the caller using the target's current
+        call convention.
+        """
+        ok, self = cls.allocate_ex(context, builder, list_type, nitems)
+        with builder.if_then(builder.not_(ok), likely=False):
+            context.call_conv.return_user_exc(builder, MemoryError,
+                                              ("cannot allocate list",))
+        return self
+
+    @classmethod
+    def from_meminfo(cls, context, builder, list_type, meminfo):
+        """
+        Allocate a new list instance pointing to an existing payload
+        (a meminfo pointer).
+        Note the parent field has to be filled by the caller.
+        """
+        self = cls(context, builder, list_type, None)
+        self._list.meminfo = meminfo
+        self._list.parent = context.get_constant_null(types.pyobject)
+        context.nrt.incref(builder, list_type, self.value)
+        # Payload is part of the meminfo, no need to touch it
+        return self
+
+    def resize(self, new_size):
+        """
+        Ensure the list is properly sized for the new size.
+        """
+        def _payload_realloc(new_allocated):
+            payload_type = context.get_data_type(types.ListPayload(self._ty))
+            payload_size = context.get_abi_sizeof(payload_type)
+            # Account for the fact that the payload struct contains one entry
+            payload_size -= itemsize
+
+            allocsize, ovf = cgutils.muladd_with_overflow(
+                builder, new_allocated,
+                ir.Constant(intp_t, itemsize),
+                ir.Constant(intp_t, payload_size))
+            with builder.if_then(ovf, likely=False):
+                context.call_conv.return_user_exc(builder, MemoryError,
+                                                  ("cannot resize list",))
+
+            ptr = context.nrt.meminfo_varsize_realloc_unchecked(builder,
+                                                                self._list.meminfo,
+                                                                size=allocsize)
+            cgutils.guard_memory_error(context, builder, ptr,
+                                       "cannot resize list")
+            self._payload.allocated = new_allocated
+
+        context = self._context
+        builder = self._builder
+        intp_t = new_size.type
+
+        itemsize = get_itemsize(context, self._ty)
+        allocated = self._payload.allocated
+
+        two = ir.Constant(intp_t, 2)
+        eight = ir.Constant(intp_t, 8)
+
+        # allocated < new_size
+        is_too_small = builder.icmp_signed('<', allocated, new_size)
+        # (allocated >> 2) > new_size
+        is_too_large = builder.icmp_signed('>', builder.ashr(allocated, two), new_size)
+
+        with builder.if_then(is_too_large, likely=False):
+            # Exact downsize to requested size
+            # NOTE: is_too_large must be aggressive enough to avoid repeated
+            # upsizes and downsizes when growing a list.
+            _payload_realloc(new_size)
+
+        with builder.if_then(is_too_small, likely=False):
+            # Upsize with moderate over-allocation (size + size >> 2 + 8)
+            new_allocated = builder.add(eight,
+                                        builder.add(new_size,
+                                                    builder.ashr(new_size, two)))
+            _payload_realloc(new_allocated)
+            self.zfill(self.size, new_allocated)
+
+        self._payload.size = new_size
+        self.set_dirty(True)
+
+    def move(self, dest_idx, src_idx, count):
+        """
+        Move `count` elements from `src_idx` to `dest_idx`.
+        """
+        dest_ptr = self._gep(dest_idx)
+        src_ptr = self._gep(src_idx)
+        cgutils.raw_memmove(self._builder, dest_ptr, src_ptr,
+                            count, itemsize=self._itemsize)
+
+        self.set_dirty(True)
+
+class ListIterInstance(_ListPayloadMixin):
+
+    def __init__(self, context, builder, iter_type, iter_val):
+        self._context = context
+        self._builder = builder
+        self._ty = iter_type
+        self._iter = context.make_helper(builder, iter_type, iter_val)
+        self._datamodel = context.data_model_manager[iter_type.yield_type]
+
+    @classmethod
+    def from_list(cls, context, builder, iter_type, list_val):
+        list_inst = ListInstance(context, builder, iter_type.container, list_val)
+        self = cls(context, builder, iter_type, None)
+        index = context.get_constant(types.intp, 0)
+        self._iter.index = cgutils.alloca_once_value(builder, index)
+        self._iter.meminfo = list_inst.meminfo
+        return self
+
+    @property
+    def _payload(self):
+        # This cannot be cached as it can be reallocated
+        return get_list_payload(self._context, self._builder,
+                                self._ty.container, self._iter)
+
+    @property
+    def value(self):
+        return self._iter._getvalue()
+
+    @property
+    def index(self):
+        return self._builder.load(self._iter.index)
+
+    @index.setter
+    def index(self, value):
+        self._builder.store(value, self._iter.index)
+
+
+#-------------------------------------------------------------------------------
+# Constructors
+
+def build_list(context, builder, list_type, items):
+    """
+    Build a list of the given type, containing the given items.
+    """
+    nitems = len(items)
+    inst = ListInstance.allocate(context, builder, list_type, nitems)
+    # Populate list
+    inst.size = context.get_constant(types.intp, nitems)
+    for i, val in enumerate(items):
+        inst.setitem(context.get_constant(types.intp, i), val, incref=True)
+
+    return impl_ret_new_ref(context, builder, list_type, inst.value)
+
+
+@lower_builtin(list, types.IterableType)
+def list_constructor(context, builder, sig, args):
+
+    def list_impl(iterable):
+        res = []
+        res.extend(iterable)
+        return res
+
+    return context.compile_internal(builder, list_impl, sig, args)
+
+@lower_builtin(list)
+def list_constructor(context, builder, sig, args):
+    list_type = sig.return_type
+    list_len = 0
+    inst = ListInstance.allocate(context, builder, list_type, list_len)
+    return impl_ret_new_ref(context, builder, list_type, inst.value)
+
+#-------------------------------------------------------------------------------
+# Various operations
+
+@lower_builtin(len, types.List)
+def list_len(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    return inst.size
+
+@lower_builtin('getiter', types.List)
+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)
+
+@lower_builtin('iternext', types.ListIter)
+@iternext_impl(RefType.BORROWED)
+def iternext_listiter(context, builder, sig, args, result):
+    inst = ListIterInstance(context, builder, sig.args[0], args[0])
+
+    index = inst.index
+    nitems = inst.size
+    is_valid = builder.icmp_signed('<', index, nitems)
+    result.set_valid(is_valid)
+
+    with builder.if_then(is_valid):
+        result.yield_(inst.getitem(index))
+        inst.index = builder.add(index, context.get_constant(types.intp, 1))
+
+
+@lower_builtin(operator.getitem, types.List, types.Integer)
+def getitem_list(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    index = args[1]
+
+    index = inst.fix_index(index)
+    inst.guard_index(index, msg="getitem out of range")
+    result = inst.getitem(index)
+
+    return impl_ret_borrowed(context, builder, sig.return_type, result)
+
+@lower_builtin(operator.setitem, types.List, types.Integer, types.Any)
+def setitem_list(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    index = args[1]
+    value = args[2]
+
+    index = inst.fix_index(index)
+    inst.guard_index(index, msg="setitem out of range")
+    inst.setitem(index, value, incref=True)
+    return context.get_dummy_value()
+
+
+@lower_builtin(operator.getitem, types.List, types.SliceType)
+def getslice_list(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    slice = context.make_helper(builder, sig.args[1], args[1])
+    slicing.guard_invalid_slice(context, builder, sig.args[1], slice)
+    inst.fix_slice(slice)
+
+    # Allocate result and populate it
+    result_size = slicing.get_slice_length(builder, slice)
+    result = ListInstance.allocate(context, builder, sig.return_type,
+                                   result_size)
+    result.size = result_size
+    with cgutils.for_range_slice_generic(builder, slice.start, slice.stop,
+                                         slice.step) as (pos_range, neg_range):
+        with pos_range as (idx, count):
+            value = inst.getitem(idx)
+            result.inititem(count, value, incref=True)
+        with neg_range as (idx, count):
+            value = inst.getitem(idx)
+            result.inititem(count, value, incref=True)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, result.value)
+
+@lower_builtin(operator.setitem, types.List, types.SliceType, types.Any)
+def setitem_list(context, builder, sig, args):
+    dest = ListInstance(context, builder, sig.args[0], args[0])
+    src = ListInstance(context, builder, sig.args[2], args[2])
+
+    slice = context.make_helper(builder, sig.args[1], args[1])
+    slicing.guard_invalid_slice(context, builder, sig.args[1], slice)
+    dest.fix_slice(slice)
+
+    src_size = src.size
+    avail_size = slicing.get_slice_length(builder, slice)
+    size_delta = builder.sub(src.size, avail_size)
+
+    zero = ir.Constant(size_delta.type, 0)
+    one = ir.Constant(size_delta.type, 1)
+
+    with builder.if_else(builder.icmp_signed('==', slice.step, one)) as (then, otherwise):
+        with then:
+            # Slice step == 1 => we can resize
+
+            # Compute the real stop, e.g. for dest[2:0] = [...]
+            real_stop = builder.add(slice.start, avail_size)
+            # Size of the list tail, after the end of slice
+            tail_size = builder.sub(dest.size, real_stop)
+
+            with builder.if_then(builder.icmp_signed('>', size_delta, zero)):
+                # Grow list then move list tail
+                dest.resize(builder.add(dest.size, size_delta))
+                dest.move(builder.add(real_stop, size_delta), real_stop,
+                          tail_size)
+
+            with builder.if_then(builder.icmp_signed('<', size_delta, zero)):
+                # Move list tail then shrink list
+                dest.move(builder.add(real_stop, size_delta), real_stop,
+                          tail_size)
+                dest.resize(builder.add(dest.size, size_delta))
+
+            dest_offset = slice.start
+
+            with cgutils.for_range(builder, src_size) as loop:
+                value = src.getitem(loop.index)
+                dest.setitem(builder.add(loop.index, dest_offset), value, incref=True)
+
+        with otherwise:
+            with builder.if_then(builder.icmp_signed('!=', size_delta, zero)):
+                msg = "cannot resize extended list slice with step != 1"
+                context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+            with cgutils.for_range_slice_generic(
+                builder, slice.start, slice.stop, slice.step) as (pos_range, neg_range):
+                with pos_range as (index, count):
+                    value = src.getitem(count)
+                    dest.setitem(index, value, incref=True)
+                with neg_range as (index, count):
+                    value = src.getitem(count)
+                    dest.setitem(index, value, incref=True)
+
+    return context.get_dummy_value()
+
+
+
+@lower_builtin(operator.delitem, types.List, types.Integer)
+def delitem_list_index(context, builder, sig, args):
+
+    def list_delitem_impl(lst, i):
+        lst.pop(i)
+
+    return context.compile_internal(builder, list_delitem_impl, sig, args)
+
+
+@lower_builtin(operator.delitem, types.List, types.SliceType)
+def delitem_list(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    slice = context.make_helper(builder, sig.args[1], args[1])
+
+    slicing.guard_invalid_slice(context, builder, sig.args[1], slice)
+    inst.fix_slice(slice)
+
+    slice_len = slicing.get_slice_length(builder, slice)
+
+    one = ir.Constant(slice_len.type, 1)
+
+    with builder.if_then(builder.icmp_signed('!=', slice.step, one), likely=False):
+        msg = "unsupported del list[start:stop:step] with step != 1"
+        context.call_conv.return_user_exc(builder, NotImplementedError, (msg,))
+
+    # Compute the real stop, e.g. for dest[2:0]
+    start = slice.start
+    real_stop = builder.add(start, slice_len)
+    # Decref the removed range
+    with cgutils.for_range_slice(
+            builder, start, real_stop, start.type(1)
+            ) as (idx, _):
+        inst.decref_value(inst.getitem(idx))
+
+    # Size of the list tail, after the end of slice
+    tail_size = builder.sub(inst.size, real_stop)
+    inst.move(start, real_stop, tail_size)
+    inst.resize(builder.sub(inst.size, slice_len))
+
+    return context.get_dummy_value()
+
+
+# XXX should there be a specific module for Sequence or collection base classes?
+
+@lower_builtin(operator.contains, types.Sequence, types.Any)
+def in_seq(context, builder, sig, args):
+    def seq_contains_impl(lst, value):
+        for elem in lst:
+            if elem == value:
+                return True
+        return False
+
+    return context.compile_internal(builder, seq_contains_impl, sig, args)
+
+@lower_builtin(bool, types.Sequence)
+def sequence_bool(context, builder, sig, args):
+    def sequence_bool_impl(seq):
+        return len(seq) != 0
+
+    return context.compile_internal(builder, sequence_bool_impl, sig, args)
+
+
+@overload(operator.truth)
+def sequence_truth(seq):
+    if isinstance(seq, types.Sequence):
+        def impl(seq):
+            return len(seq) != 0
+        return impl
+
+
+@lower_builtin(operator.add, types.List, types.List)
+def list_add(context, builder, sig, args):
+    a = ListInstance(context, builder, sig.args[0], args[0])
+    b = ListInstance(context, builder, sig.args[1], args[1])
+
+    a_size = a.size
+    b_size = b.size
+    nitems = builder.add(a_size, b_size)
+    dest = ListInstance.allocate(context, builder, sig.return_type, nitems)
+    dest.size = nitems
+
+    with cgutils.for_range(builder, a_size) as loop:
+        value = a.getitem(loop.index)
+        value = context.cast(builder, value, a.dtype, dest.dtype)
+        dest.setitem(loop.index, value, incref=True)
+    with cgutils.for_range(builder, b_size) as loop:
+        value = b.getitem(loop.index)
+        value = context.cast(builder, value, b.dtype, dest.dtype)
+        dest.setitem(builder.add(loop.index, a_size), value, incref=True)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, dest.value)
+
+@lower_builtin(operator.iadd, types.List, types.List)
+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)
+
+
+@lower_builtin(operator.mul, types.List, types.Integer)
+@lower_builtin(operator.mul, types.Integer, types.List)
+def list_mul(context, builder, sig, args):
+    if isinstance(sig.args[0], types.List):
+        list_idx, int_idx = 0, 1
+    else:
+        list_idx, int_idx = 1, 0
+    src = ListInstance(context, builder, sig.args[list_idx], args[list_idx])
+    src_size = src.size
+
+    mult = args[int_idx]
+    zero = ir.Constant(mult.type, 0)
+    mult = builder.select(cgutils.is_neg_int(builder, mult), zero, mult)
+    nitems = builder.mul(mult, src_size)
+
+    dest = ListInstance.allocate(context, builder, sig.return_type, nitems)
+    dest.size = nitems
+
+    with cgutils.for_range_slice(builder, zero, nitems, src_size, inc=True) as (dest_offset, _):
+        with cgutils.for_range(builder, src_size) as loop:
+            value = src.getitem(loop.index)
+            dest.setitem(builder.add(loop.index, dest_offset), value, incref=True)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, dest.value)
+
+@lower_builtin(operator.imul, types.List, types.Integer)
+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, incref=True)
+
+    return impl_ret_borrowed(context, builder, sig.return_type, inst.value)
+
+
+#-------------------------------------------------------------------------------
+# Comparisons
+
+@lower_builtin(operator.is_, types.List, types.List)
+def list_is(context, builder, sig, args):
+    a = ListInstance(context, builder, sig.args[0], args[0])
+    b = ListInstance(context, builder, sig.args[1], args[1])
+    ma = builder.ptrtoint(a.meminfo, cgutils.intp_t)
+    mb = builder.ptrtoint(b.meminfo, cgutils.intp_t)
+    return builder.icmp_signed('==', ma, mb)
+
+@lower_builtin(operator.eq, types.List, types.List)
+def list_eq(context, builder, sig, args):
+    aty, bty = sig.args
+    a = ListInstance(context, builder, aty, args[0])
+    b = ListInstance(context, builder, bty, args[1])
+
+    a_size = a.size
+    same_size = builder.icmp_signed('==', a_size, b.size)
+
+    res = cgutils.alloca_once_value(builder, same_size)
+
+    with builder.if_then(same_size):
+        with cgutils.for_range(builder, a_size) as loop:
+            v = a.getitem(loop.index)
+            w = b.getitem(loop.index)
+            itemres = context.generic_compare(builder, operator.eq,
+                                              (aty.dtype, bty.dtype), (v, w))
+            with builder.if_then(builder.not_(itemres)):
+                # Exit early
+                builder.store(cgutils.false_bit, res)
+                loop.do_break()
+
+    return builder.load(res)
+
+
+def all_list(*args):
+    return all([isinstance(typ, types.List) for typ in args])
+
+@overload(operator.ne)
+def impl_list_ne(a, b):
+    if not all_list(a, b):
+        return
+
+    def list_ne_impl(a, b):
+        return not (a == b)
+
+    return list_ne_impl
+
+@overload(operator.le)
+def impl_list_le(a, b):
+    if not all_list(a, b):
+        return
+
+    def list_le_impl(a, b):
+        m = len(a)
+        n = len(b)
+        for i in range(min(m, n)):
+            if a[i] < b[i]:
+                return True
+            elif a[i] > b[i]:
+                return False
+        return m <= n
+
+    return list_le_impl
+
+@overload(operator.lt)
+def impl_list_lt(a, b):
+    if not all_list(a, b):
+        return
+
+    def list_lt_impl(a, b):
+        m = len(a)
+        n = len(b)
+        for i in range(min(m, n)):
+            if a[i] < b[i]:
+                return True
+            elif a[i] > b[i]:
+                return False
+        return m < n
+
+    return list_lt_impl
+
+@overload(operator.ge)
+def impl_list_ge(a, b):
+    if not all_list(a, b):
+        return
+
+    def list_ge_impl(a, b):
+        return b <= a
+
+    return list_ge_impl
+
+@overload(operator.gt)
+def impl_list_gt(a, b):
+    if not all_list(a, b):
+        return
+
+    def list_gt_impl(a, b):
+        return b < a
+
+    return list_gt_impl
+
+#-------------------------------------------------------------------------------
+# Methods
+
+@lower_builtin("list.append", types.List, types.Any)
+def list_append(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    item = args[1]
+
+    n = inst.size
+    new_size = builder.add(n, ir.Constant(n.type, 1))
+    inst.resize(new_size)
+    inst.setitem(n, item, incref=True)
+
+    return context.get_dummy_value()
+
+@lower_builtin("list.clear", types.List)
+def list_clear(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    inst.resize(context.get_constant(types.intp, 0))
+
+    return context.get_dummy_value()
+
+
+@overload_method(types.List, "copy")
+def list_copy(lst):
+    def list_copy_impl(lst):
+        return list(lst)
+
+    return list_copy_impl
+
+
+@overload_method(types.List, "count")
+def list_count(lst, value):
+
+    def list_count_impl(lst, value):
+        res = 0
+        for elem in lst:
+            if elem == value:
+                res += 1
+        return res
+
+    return list_count_impl
+
+
+def _list_extend_list(context, builder, sig, args):
+    src = ListInstance(context, builder, sig.args[1], args[1])
+    dest = ListInstance(context, builder, sig.args[0], args[0])
+
+    src_size = src.size
+    dest_size = dest.size
+    nitems = builder.add(src_size, dest_size)
+    dest.resize(nitems)
+    dest.size = nitems
+
+    with cgutils.for_range(builder, src_size) as loop:
+        value = src.getitem(loop.index)
+        value = context.cast(builder, value, src.dtype, dest.dtype)
+        dest.setitem(builder.add(loop.index, dest_size), value, incref=True)
+
+    return dest
+
+@lower_builtin("list.extend", types.List, types.IterableType)
+def list_extend(context, builder, sig, args):
+    if isinstance(sig.args[1], types.List):
+        # Specialize for list operands, for speed.
+        _list_extend_list(context, builder, sig, args)
+        return context.get_dummy_value()
+
+    def list_extend(lst, iterable):
+        # Speed hack to avoid NRT refcount operations inside the loop
+        meth = lst.append
+        for v in iterable:
+            meth(v)
+
+    return context.compile_internal(builder, list_extend, sig, args)
+
+
+if config.USE_LEGACY_TYPE_SYSTEM:
+    intp_max = types.intp.maxval
+else:
+    intp_max = types.py_int.maxval
+
+
+@overload_method(types.List, "index")
+def list_index(lst, value, start=0, stop=intp_max):
+
+    if not isinstance(start, (int, types.Integer, types.Omitted)):
+        raise errors.TypingError(f'arg "start" must be an Integer. Got {start}')
+    if not isinstance(stop, (int, types.Integer, types.Omitted)):
+        raise errors.TypingError(f'arg "stop" must be an Integer. Got {stop}')
+
+    def list_index_impl(lst, value, start=0, stop=intp_max):
+        n = len(lst)
+        if start < 0:
+            start += n
+            if start < 0:
+                start = 0
+        if stop < 0:
+            stop += n
+        if stop > n:
+            stop = n
+        for i in range(start, stop):
+            if lst[i] == value:
+                return i
+        # XXX references are leaked when raising
+        raise ValueError("value not in list")
+    return list_index_impl
+
+
+@lower_builtin("list.insert", types.List, types.Integer,
+           types.Any)
+def list_insert(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    index = inst.fix_index(args[1])
+    index = inst.clamp_index(index)
+    value = args[2]
+
+    n = inst.size
+    one = ir.Constant(n.type, 1)
+    new_size = builder.add(n, one)
+    inst.resize(new_size)
+    inst.move(builder.add(index, one), index, builder.sub(n, index))
+    inst.setitem(index, value, incref=True, decref_old_value=False)
+
+    return context.get_dummy_value()
+
+@lower_builtin("list.pop", types.List)
+def list_pop(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+
+    n = inst.size
+    cgutils.guard_zero(context, builder, n,
+                       (IndexError, "pop from empty list"))
+    n = builder.sub(n, ir.Constant(n.type, 1))
+    res = inst.getitem(n)
+    inst.incref_value(res)  # incref the pop'ed element
+    inst.clear_value(n)     # clear the storage space
+    inst.resize(n)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+@lower_builtin("list.pop", types.List, types.Integer)
+def list_pop(context, builder, sig, args):
+    inst = ListInstance(context, builder, sig.args[0], args[0])
+    idx = inst.fix_index(args[1])
+
+    n = inst.size
+    cgutils.guard_zero(context, builder, n,
+                       (IndexError, "pop from empty list"))
+    inst.guard_index(idx, "pop index out of range")
+
+    res = inst.getitem(idx)
+
+    one = ir.Constant(n.type, 1)
+    n = builder.sub(n, ir.Constant(n.type, 1))
+    inst.move(idx, builder.add(idx, one), builder.sub(n, idx))
+    inst.resize(n)
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+@overload_method(types.List, "remove")
+def list_remove(lst, value):
+
+    def list_remove_impl(lst, value):
+        for i in range(len(lst)):
+            if lst[i] == value:
+                lst.pop(i)
+                return
+        # XXX references are leaked when raising
+        raise ValueError("list.remove(x): x not in list")
+
+    return list_remove_impl
+
+@overload_method(types.List, "reverse")
+def list_reverse(lst):
+
+    def list_reverse_impl(lst):
+        for a in range(0, len(lst) // 2):
+            b = -a - 1
+            lst[a], lst[b] = lst[b], lst[a]
+
+    return list_reverse_impl
+
+# -----------------------------------------------------------------------------
+# Sorting
+
+def gt(a, b):
+    return a > b
+
+sort_forwards = quicksort.make_jit_quicksort().run_quicksort
+sort_backwards = quicksort.make_jit_quicksort(lt=gt).run_quicksort
+
+arg_sort_forwards = quicksort.make_jit_quicksort(is_argsort=True,
+                                                 is_list=True).run_quicksort
+arg_sort_backwards = quicksort.make_jit_quicksort(is_argsort=True, lt=gt,
+                                                  is_list=True).run_quicksort
+
+
+def _sort_check_reverse(reverse):
+    if isinstance(reverse, types.Omitted):
+        rty = reverse.value
+    elif isinstance(reverse, types.Optional):
+        rty = reverse.type
+    else:
+        rty = reverse
+    if not isinstance(rty, (types.Boolean, types.Integer, int, bool)):
+        msg = "an integer is required for 'reverse' (got type %s)" % reverse
+        raise errors.TypingError(msg)
+    return rty
+
+
+def _sort_check_key(key):
+    if isinstance(key, types.Optional):
+        msg = ("Key must concretely be None or a Numba JIT compiled function, "
+               "an Optional (union of None and a value) was found")
+        raise errors.TypingError(msg)
+    if not (cgutils.is_nonelike(key) or isinstance(key, types.Dispatcher)):
+        msg = "Key must be None or a Numba JIT compiled function"
+        raise errors.TypingError(msg)
+
+
+@overload_method(types.List, "sort")
+def ol_list_sort(lst, key=None, reverse=False):
+
+    _sort_check_key(key)
+    _sort_check_reverse(reverse)
+
+    if cgutils.is_nonelike(key):
+        KEY = False
+        sort_f = sort_forwards
+        sort_b = sort_backwards
+    elif isinstance(key, types.Dispatcher):
+        KEY = True
+        sort_f = arg_sort_forwards
+        sort_b = arg_sort_backwards
+
+    def impl(lst, key=None, reverse=False):
+        if KEY is True:
+            _lst = [key(x) for x in lst]
+        else:
+            _lst = lst
+        if reverse is False or reverse == 0:
+            tmp = sort_f(_lst)
+        else:
+            tmp = sort_b(_lst)
+        if KEY is True:
+            lst[:] = [lst[i] for i in tmp]
+    return impl
+
+
+@overload(sorted)
+def ol_sorted(iterable, key=None, reverse=False):
+
+    if not isinstance(iterable, types.IterableType):
+        return False
+
+    _sort_check_key(key)
+    _sort_check_reverse(reverse)
+
+    def impl(iterable, key=None, reverse=False):
+        lst = list(iterable)
+        lst.sort(key=key, reverse=reverse)
+        return lst
+    return impl
+
+# -----------------------------------------------------------------------------
+# Implicit casting
+
+@lower_cast(types.List, types.List)
+def list_to_list(context, builder, fromty, toty, val):
+    # Casting from non-reflected to reflected
+    assert fromty.dtype == toty.dtype
+    return val
+
+# -----------------------------------------------------------------------------
+# Implementations for types.LiteralList
+# -----------------------------------------------------------------------------
+
+_banned_error = errors.TypingError("Cannot mutate a literal list")
+
+
+# Things that mutate literal lists are banned
+@overload_method(types.LiteralList, 'append')
+def literal_list_banned_append(lst, obj):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'extend')
+def literal_list_banned_extend(lst, iterable):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'insert')
+def literal_list_banned_insert(lst, index, obj):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'remove')
+def literal_list_banned_remove(lst, value):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'pop')
+def literal_list_banned_pop(lst, index=-1):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'clear')
+def literal_list_banned_clear(lst):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'sort')
+def literal_list_banned_sort(lst, key=None, reverse=False):
+    raise _banned_error
+
+
+@overload_method(types.LiteralList, 'reverse')
+def literal_list_banned_reverse(lst):
+    raise _banned_error
+
+if config.USE_LEGACY_TYPE_SYSTEM:
+    _index_end = types.intp.maxval
+else:
+    _index_end = types.py_int.maxval
+
+@overload_method(types.LiteralList, 'index')
+def literal_list_index(lst, x, start=0, end=_index_end):
+    # TODO: To make this work, need consts as slice for start/end so as to
+    # be able to statically analyse the bounds, then its a just loop body
+    # versioning based iteration along with enumerate to find the item
+    if isinstance(lst, types.LiteralList):
+        msg = "list.index is unsupported for literal lists"
+        raise errors.TypingError(msg)
+
+@overload_method(types.LiteralList, 'count')
+def literal_list_count(lst, x):
+    if isinstance(lst, types.LiteralList):
+        def impl(lst, x):
+            count = 0
+            for val in literal_unroll(lst):
+                if val == x:
+                    count += 1
+            return count
+        return impl
+
+@overload_method(types.LiteralList, 'copy')
+def literal_list_count(lst):
+    if isinstance(lst, types.LiteralList):
+        def impl(lst):
+            return lst # tuples are immutable, as is this, so just return it
+        return impl
+
+@overload(operator.delitem)
+def literal_list_delitem(lst, index):
+    if isinstance(lst, types.LiteralList):
+        raise _banned_error
+
+@overload(operator.setitem)
+def literal_list_setitem(lst, index, value):
+    if isinstance(lst, types.LiteralList):
+        raise errors.TypingError("Cannot mutate a literal list")
+
+@overload(operator.getitem)
+def literal_list_getitem(lst, *args):
+    if not isinstance(lst, types.LiteralList):
+        return
+    msg = ("Cannot __getitem__ on a literal list, return type cannot be "
+           "statically determined.")
+    raise errors.TypingError(msg)
+
+@overload(len)
+def literal_list_len(lst):
+    if not isinstance(lst, types.LiteralList):
+        return
+    l = lst.count
+    return lambda lst: l
+
+@overload(operator.contains)
+def literal_list_contains(lst, item):
+    if isinstance(lst, types.LiteralList):
+        def impl(lst, item):
+            for val in literal_unroll(lst):
+                if val == item:
+                    return True
+            return False
+        return impl
+
+@lower_cast(types.LiteralList, types.LiteralList)
+def literallist_to_literallist(context, builder, fromty, toty, val):
+    if len(fromty) != len(toty):
+        # Disallowed by typing layer
+        raise NotImplementedError
+
+    olditems = cgutils.unpack_tuple(builder, val, len(fromty))
+    items = [context.cast(builder, v, f, t)
+             for v, f, t in zip(olditems, fromty, toty)]
+    return context.make_tuple(builder, toty, items)
diff --git a/lib/python3.10/site-packages/numba/cpython/old_hashing.py b/lib/python3.10/site-packages/numba/cpython/old_hashing.py
new file mode 100644
index 0000000000000000000000000000000000000000..836d1616a2a49e291321fce0823492cc48e33273
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cpython/old_hashing.py
@@ -0,0 +1,743 @@
+"""
+Hash implementations for Numba types
+"""
+
+import math
+import numpy as np
+import sys
+import ctypes
+import warnings
+from collections import namedtuple
+
+import llvmlite.binding as ll
+from llvmlite import ir
+
+from numba import literal_unroll
+from numba.core.extending import (
+    overload, overload_method, intrinsic, register_jitable)
+from numba.core import errors
+from numba.core import types
+from numba.core.unsafe.bytes import grab_byte, grab_uint64_t
+from numba.cpython.randomimpl import (const_int, get_next_int, get_next_int32,
+                                      get_state_ptr)
+
+# This is Py_hash_t, which is a Py_ssize_t, which has sizeof(size_t):
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Include/pyport.h#L91-L96    # noqa: E501
+_hash_width = sys.hash_info.width
+_Py_hash_t = getattr(types, 'int%s' % _hash_width)
+_Py_uhash_t = getattr(types, 'uint%s' % _hash_width)
+
+# Constants from CPython source, obtained by various means:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Include/pyhash.h    # noqa: E501
+_PyHASH_INF = sys.hash_info.inf
+_PyHASH_NAN = sys.hash_info.nan
+_PyHASH_MODULUS = _Py_uhash_t(sys.hash_info.modulus)
+_PyHASH_BITS = 31 if types.intp.bitwidth == 32 else 61  # mersenne primes
+_PyHASH_MULTIPLIER = 0xf4243  # 1000003UL
+_PyHASH_IMAG = _PyHASH_MULTIPLIER
+_PyLong_SHIFT = sys.int_info.bits_per_digit
+_Py_HASH_CUTOFF = sys.hash_info.cutoff
+_Py_hashfunc_name = sys.hash_info.algorithm
+
+
+# This stub/overload pair are used to force branch pruning to remove the dead
+# branch based on the potential `None` type of the hash_func which works better
+# if the predicate for the prune in an ir.Arg. The obj is an arg to allow for
+# a custom error message.
+def _defer_hash(hash_func):
+    pass
+
+
+@overload(_defer_hash)
+def ol_defer_hash(obj, hash_func):
+    err_msg = f"unhashable type: '{obj}'"
+
+    def impl(obj, hash_func):
+        if hash_func is None:
+            raise TypeError(err_msg)
+        else:
+            return hash_func()
+    return impl
+
+
+# hash(obj) is implemented by calling obj.__hash__()
+@overload(hash)
+def hash_overload(obj):
+    attempt_generic_msg = ("No __hash__ is defined for object of type "
+                           f"'{obj}' and a generic hash() cannot be "
+                           "performed as there is no suitable object "
+                           "represention in Numba compiled code!")
+
+    def impl(obj):
+        if hasattr(obj, '__hash__'):
+            return _defer_hash(obj, getattr(obj, '__hash__'))
+        else:
+            raise TypeError(attempt_generic_msg)
+    return impl
+
+
+@register_jitable
+def process_return(val):
+    asint = _Py_hash_t(val)
+    if (asint == int(-1)):
+        asint = int(-2)
+    return asint
+
+
+# This is a translation of CPython's _Py_HashDouble:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Python/pyhash.c#L34-L129   # noqa: E501
+# NOTE: In Python 3.10 hash of nan is now hash of the pointer to the PyObject
+# containing said nan. Numba cannot replicate this as there is no object, so it
+# elects to replicate the behaviour i.e. hash of nan is something "unique" which
+# satisfies https://bugs.python.org/issue43475.
+
+@register_jitable(locals={'x': _Py_uhash_t,
+                          'y': _Py_uhash_t,
+                          'm': types.double,
+                          'e': types.intc,
+                          'sign': types.intc,
+                          '_PyHASH_MODULUS': _Py_uhash_t,
+                          '_PyHASH_BITS': types.intc})
+def _Py_HashDouble(v):
+    if not np.isfinite(v):
+        if (np.isinf(v)):
+            if (v > 0):
+                return _PyHASH_INF
+            else:
+                return -_PyHASH_INF
+        else:
+            # Python 3.10 does not use `_PyHASH_NAN`.
+            # https://github.com/python/cpython/blob/2c4792264f9218692a1bd87398a60591f756b171/Python/pyhash.c#L102   # noqa: E501
+            # Numba returns a pseudo-random number to reflect the spirit of the
+            # change.
+            x = _prng_random_hash()
+            return process_return(x)
+
+    m, e = math.frexp(v)
+
+    sign = 1
+    if (m < 0):
+        sign = -1
+        m = -m
+
+    # process 28 bits at a time;  this should work well both for binary
+    #  and hexadecimal floating point.
+    x = 0
+    while (m):
+        x = ((x << 28) & _PyHASH_MODULUS) | x >> (_PyHASH_BITS - 28)
+        m *= 268435456.0  # /* 2**28 */
+        e -= 28
+        y = int(m)  # /* pull out integer part */
+        m -= y
+        x += y
+        if x >= _PyHASH_MODULUS:
+            x -= _PyHASH_MODULUS
+    # /* adjust for the exponent;  first reduce it modulo _PyHASH_BITS */
+    if e >= 0:
+        e = e % _PyHASH_BITS
+    else:
+        e = _PyHASH_BITS - 1 - ((-1 - e) % _PyHASH_BITS)
+
+    x = ((x << e) & _PyHASH_MODULUS) | x >> (_PyHASH_BITS - e)
+
+    x = x * sign
+    return process_return(x)
+
+
+@intrinsic
+def _fpext(tyctx, val):
+    def impl(cgctx, builder, signature, args):
+        val = args[0]
+        return builder.fpext(val, ir.DoubleType())
+    sig = types.float64(types.float32)
+    return sig, impl
+
+
+@intrinsic
+def _prng_random_hash(tyctx):
+
+    def impl(cgctx, builder, signature, args):
+        state_ptr = get_state_ptr(cgctx, builder, "internal")
+        bits = const_int(_hash_width)
+
+        # Why not just use get_next_int() with the correct bitwidth?
+        # get_next_int() always returns an i64, because the bitwidth it is
+        # passed may not be a compile-time constant, so it needs to allocate
+        # the largest unit of storage that may be required. Therefore, if the
+        # hash width is 32, then we need to use get_next_int32() to ensure we
+        # don't return a wider-than-expected hash, even if everything above
+        # the low 32 bits would have been zero.
+        if _hash_width == 32:
+            value = get_next_int32(cgctx, builder, state_ptr)
+        else:
+            value = get_next_int(cgctx, builder, state_ptr, bits, False)
+
+        return value
+
+    sig = _Py_hash_t()
+    return sig, impl
+
+
+# This is a translation of CPython's long_hash, but restricted to the numerical
+# domain reachable by int64/uint64 (i.e. no BigInt like support):
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/longobject.c#L2934-L2989    # noqa: E501
+# obdigit is a uint32_t which is typedef'd to digit
+# int32_t is typedef'd to sdigit
+
+
+@register_jitable(locals={'x': _Py_uhash_t,
+                          'p1': _Py_uhash_t,
+                          'p2': _Py_uhash_t,
+                          'p3': _Py_uhash_t,
+                          'p4': _Py_uhash_t,
+                          '_PyHASH_MODULUS': _Py_uhash_t,
+                          '_PyHASH_BITS': types.int32,
+                          '_PyLong_SHIFT': types.int32,})
+def _long_impl(val):
+    # This function assumes val came from a long int repr with val being a
+    # uint64_t this means having to split the input into PyLong_SHIFT size
+    # chunks in an unsigned hash wide type, max numba can handle is a 64bit int
+
+    # mask to select low _PyLong_SHIFT bits
+    _tmp_shift = 32 - _PyLong_SHIFT
+    mask_shift = (~types.uint32(0x0)) >> _tmp_shift
+
+    # a 64bit wide max means Numba only needs 3 x 30 bit values max,
+    # or 5 x 15 bit values max on 32bit platforms
+    i = (64 // _PyLong_SHIFT) + 1
+
+    # alg as per hash_long
+    x = 0
+    p3 = (_PyHASH_BITS - _PyLong_SHIFT)
+    for idx in range(i - 1, -1, -1):
+        p1 = x << _PyLong_SHIFT
+        p2 = p1 & _PyHASH_MODULUS
+        p4 = x >> p3
+        x = p2 | p4
+        # the shift and mask splits out the `ob_digit` parts of a Long repr
+        x += types.uint32((val >> idx * _PyLong_SHIFT) & mask_shift)
+        if x >= _PyHASH_MODULUS:
+            x -= _PyHASH_MODULUS
+    return _Py_hash_t(x)
+
+
+# This has no CPython equivalent, CPython uses long_hash.
+@overload_method(types.Integer, '__hash__')
+@overload_method(types.Boolean, '__hash__')
+def int_hash(val):
+
+    _HASH_I64_MIN = -2 if sys.maxsize <= 2 ** 32 else -4
+    _SIGNED_MIN = types.int64(-0x8000000000000000)
+
+    # Find a suitable type to hold a "big" value, i.e. iinfo(ty).min/max
+    # this is to ensure e.g. int32.min is handled ok as it's abs() is its value
+    _BIG = types.int64 if getattr(val, 'signed', False) else types.uint64
+
+    # this is a bit involved due to the CPython repr of ints
+    def impl(val):
+        # If the magnitude is under PyHASH_MODULUS, just return the
+        # value val as the hash, couple of special cases if val == val:
+        # 1. it's 0, in which case return 0
+        # 2. it's signed int minimum value, return the value CPython computes
+        # but Numba cannot as there's no type wide enough to hold the shifts.
+        #
+        # If the magnitude is greater than PyHASH_MODULUS then... if the value
+        # is negative then negate it switch the sign on the hash once computed
+        # and use the standard wide unsigned hash implementation
+        val = _BIG(val)
+        mag = abs(val)
+        if mag < _PyHASH_MODULUS:
+            if val == 0:
+                ret = 0
+            elif val == _SIGNED_MIN:  # e.g. int64 min, -0x8000000000000000
+                ret = _Py_hash_t(_HASH_I64_MIN)
+            else:
+                ret = _Py_hash_t(val)
+        else:
+            needs_negate = False
+            if val < 0:
+                val = -val
+                needs_negate = True
+            ret = _long_impl(val)
+            if needs_negate:
+                ret = -ret
+        return process_return(ret)
+    return impl
+
+# This is a translation of CPython's float_hash:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/floatobject.c#L528-L532    # noqa: E501
+
+
+@overload_method(types.Float, '__hash__')
+def float_hash(val):
+    if val.bitwidth == 64:
+        def impl(val):
+            hashed = _Py_HashDouble(val)
+            return hashed
+    else:
+        def impl(val):
+            # widen the 32bit float to 64bit
+            fpextended = np.float64(_fpext(val))
+            hashed = _Py_HashDouble(fpextended)
+            return hashed
+    return impl
+
+# This is a translation of CPython's complex_hash:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/complexobject.c#L408-L428    # noqa: E501
+
+
+@overload_method(types.Complex, '__hash__')
+def complex_hash(val):
+    def impl(val):
+        hashreal = hash(val.real)
+        hashimag = hash(val.imag)
+        # Note:  if the imaginary part is 0, hashimag is 0 now,
+        # so the following returns hashreal unchanged.  This is
+        # important because numbers of different types that
+        # compare equal must have the same hash value, so that
+        # hash(x + 0*j) must equal hash(x).
+        combined = hashreal + _PyHASH_IMAG * hashimag
+        return process_return(combined)
+    return impl
+
+
+# Python 3.8 strengthened its hash alg for tuples.
+# This is a translation of CPython's tuplehash for Python >=3.8
+# https://github.com/python/cpython/blob/b738237d6792acba85b1f6e6c8993a812c7fd815/Objects/tupleobject.c#L338-L391    # noqa: E501
+
+# These consts are needed for this alg variant, they are from:
+# https://github.com/python/cpython/blob/b738237d6792acba85b1f6e6c8993a812c7fd815/Objects/tupleobject.c#L353-L363    # noqa: E501
+if _Py_uhash_t.bitwidth // 8 > 4:
+    _PyHASH_XXPRIME_1 = _Py_uhash_t(11400714785074694791)
+    _PyHASH_XXPRIME_2 = _Py_uhash_t(14029467366897019727)
+    _PyHASH_XXPRIME_5 = _Py_uhash_t(2870177450012600261)
+
+    @register_jitable(locals={'x': types.uint64})
+    def _PyHASH_XXROTATE(x):
+        # Rotate left 31 bits
+        return ((x << types.uint64(31)) | (x >> types.uint64(33)))
+else:
+    _PyHASH_XXPRIME_1 = _Py_uhash_t(2654435761)
+    _PyHASH_XXPRIME_2 = _Py_uhash_t(2246822519)
+    _PyHASH_XXPRIME_5 = _Py_uhash_t(374761393)
+
+    @register_jitable(locals={'x': types.uint64})
+    def _PyHASH_XXROTATE(x):
+        # Rotate left 13 bits
+        return ((x << types.uint64(13)) | (x >> types.uint64(19)))
+
+
+@register_jitable(locals={'acc': _Py_uhash_t, 'lane': _Py_uhash_t,
+                          '_PyHASH_XXPRIME_5': _Py_uhash_t,
+                          '_PyHASH_XXPRIME_1': _Py_uhash_t,
+                          'tl': _Py_uhash_t})
+def _tuple_hash(tup):
+    tl = len(tup)
+    acc = _PyHASH_XXPRIME_5
+    for x in literal_unroll(tup):
+        lane = hash(x)
+        if lane == _Py_uhash_t(-1):
+            return -1
+        acc += lane * _PyHASH_XXPRIME_2
+        acc = _PyHASH_XXROTATE(acc)
+        acc *= _PyHASH_XXPRIME_1
+
+    acc += tl ^ (_PyHASH_XXPRIME_5 ^ _Py_uhash_t(3527539))
+
+    if acc == _Py_uhash_t(-1):
+        return process_return(1546275796)
+
+    return process_return(acc)
+
+
+@overload_method(types.BaseTuple, '__hash__')
+def tuple_hash(val):
+    def impl(val):
+        return _tuple_hash(val)
+    return impl
+
+
+# ------------------------------------------------------------------------------
+# String/bytes hashing needs hashseed info, this is from:
+# https://stackoverflow.com/a/41088757
+# with thanks to Martijn Pieters
+#
+# Developer note:
+# CPython makes use of an internal "hashsecret" which is essentially a struct
+# containing some state that is set on CPython initialization and contains magic
+# numbers used particularly in unicode/string hashing. This code binds to the
+# Python runtime libraries in use by the current process and reads the
+# "hashsecret" state so that it can be used by Numba. As this is done at runtime
+# the behaviour and influence of the PYTHONHASHSEED environment variable is
+# accommodated.
+
+from ctypes import (  # noqa
+    c_size_t,
+    c_ubyte,
+    c_uint64,
+    pythonapi,
+    Structure,
+    Union,
+)  # noqa
+
+
+class FNV(Structure):
+    _fields_ = [
+        ('prefix', c_size_t),
+        ('suffix', c_size_t)
+    ]
+
+
+class SIPHASH(Structure):
+    _fields_ = [
+        ('k0', c_uint64),
+        ('k1', c_uint64),
+    ]
+
+
+class DJBX33A(Structure):
+    _fields_ = [
+        ('padding', c_ubyte * 16),
+        ('suffix', c_size_t),
+    ]
+
+
+class EXPAT(Structure):
+    _fields_ = [
+        ('padding', c_ubyte * 16),
+        ('hashsalt', c_size_t),
+    ]
+
+
+class _Py_HashSecret_t(Union):
+    _fields_ = [
+        # ensure 24 bytes
+        ('uc', c_ubyte * 24),
+        # two Py_hash_t for FNV
+        ('fnv', FNV),
+        # two uint64 for SipHash24
+        ('siphash', SIPHASH),
+        # a different (!) Py_hash_t for small string optimization
+        ('djbx33a', DJBX33A),
+        ('expat', EXPAT),
+    ]
+
+
+_hashsecret_entry = namedtuple('_hashsecret_entry', ['symbol', 'value'])
+
+
+# Only a few members are needed at present
+def _build_hashsecret():
+    """Read hash secret from the Python process
+
+    Returns
+    -------
+    info : dict
+        - keys are "djbx33a_suffix", "siphash_k0", siphash_k1".
+        - values are the namedtuple[symbol:str, value:int]
+    """
+    # Read hashsecret and inject it into the LLVM symbol map under the
+    # prefix `_numba_hashsecret_`.
+    pyhashsecret = _Py_HashSecret_t.in_dll(pythonapi, '_Py_HashSecret')
+    info = {}
+
+    def inject(name, val):
+        symbol_name = "_numba_hashsecret_{}".format(name)
+        val = ctypes.c_uint64(val)
+        addr = ctypes.addressof(val)
+        ll.add_symbol(symbol_name, addr)
+        info[name] = _hashsecret_entry(symbol=symbol_name, value=val)
+
+    inject('djbx33a_suffix', pyhashsecret.djbx33a.suffix)
+    inject('siphash_k0', pyhashsecret.siphash.k0)
+    inject('siphash_k1', pyhashsecret.siphash.k1)
+    return info
+
+
+_hashsecret = _build_hashsecret()
+
+
+# ------------------------------------------------------------------------------
+
+
+if _Py_hashfunc_name in ('siphash13', 'siphash24', 'fnv'):
+
+    # Check for use of the FNV hashing alg, warn users that it's not implemented
+    # and functionality relying of properties derived from hashing will be fine
+    # but hash values themselves are likely to be different.
+    if _Py_hashfunc_name == 'fnv':
+        msg = ("FNV hashing is not implemented in Numba. See PEP 456 "
+               "https://www.python.org/dev/peps/pep-0456/ "
+               "for rationale over not using FNV. Numba will continue to work, "
+               "but hashes for built in types will be computed using "
+               "siphash24. This will permit e.g. dictionaries to continue to "
+               "behave as expected, however anything relying on the value of "
+               "the hash opposed to hash as a derived property is likely to "
+               "not work as expected.")
+        warnings.warn(msg)
+
+    # This is a translation of CPython's siphash24 function:
+    # https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Python/pyhash.c#L287-L413    # noqa: E501
+    # and also, since Py 3.11, a translation of CPython's siphash13 function:
+    # https://github.com/python/cpython/blob/9dda9020abcf0d51d59b283a89c58c8e1fb0f574/Python/pyhash.c#L376-L424
+    # the only differences are in the use of SINGLE_ROUND in siphash13 vs.
+    # DOUBLE_ROUND in siphash24, and that siphash13 has an extra "ROUND" applied
+    # just before the final XORing of components to create the return value.
+
+    # /* *********************************************************************
+    # <MIT License>
+    # Copyright (c) 2013  Marek Majkowski <marek@popcount.org>
+
+    # Permission is hereby granted, free of charge, to any person obtaining a
+    # copy of this software and associated documentation files (the "Software"),
+    # to deal in the Software without restriction, including without limitation
+    # the rights to use, copy, modify, merge, publish, distribute, sublicense,
+    # and/or sell copies of the Software, and to permit persons to whom the
+    # Software is furnished to do so, subject to the following conditions:
+
+    # The above copyright notice and this permission notice shall be included in
+    # all copies or substantial portions of the Software.
+
+    # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+    # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+    # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    # DEALINGS IN THE SOFTWARE.
+    # </MIT License>
+
+    # Original location:
+    # https://github.com/majek/csiphash/
+
+    # Solution inspired by code from:
+    # Samuel Neves (supercop/crypto_auth/siphash24/little)
+    #djb (supercop/crypto_auth/siphash24/little2)
+    # Jean-Philippe Aumasson (https://131002.net/siphash/siphash24.c)
+
+    # Modified for Python by Christian Heimes:
+    # - C89 / MSVC compatibility
+    # - _rotl64() on Windows
+    # - letoh64() fallback
+    # */
+
+    @register_jitable(locals={'x': types.uint64,
+                              'b': types.uint64, })
+    def _ROTATE(x, b):
+        return types.uint64(((x) << (b)) | ((x) >> (types.uint64(64) - (b))))
+
+    @register_jitable(locals={'a': types.uint64,
+                              'b': types.uint64,
+                              'c': types.uint64,
+                              'd': types.uint64,
+                              's': types.uint64,
+                              't': types.uint64, })
+    def _HALF_ROUND(a, b, c, d, s, t):
+        a += b
+        c += d
+        b = _ROTATE(b, s) ^ a
+        d = _ROTATE(d, t) ^ c
+        a = _ROTATE(a, 32)
+        return a, b, c, d
+
+    @register_jitable(locals={'v0': types.uint64,
+                              'v1': types.uint64,
+                              'v2': types.uint64,
+                              'v3': types.uint64, })
+    def _SINGLE_ROUND(v0, v1, v2, v3):
+        v0, v1, v2, v3 = _HALF_ROUND(v0, v1, v2, v3, 13, 16)
+        v2, v1, v0, v3 = _HALF_ROUND(v2, v1, v0, v3, 17, 21)
+        return v0, v1, v2, v3
+
+    @register_jitable(locals={'v0': types.uint64,
+                              'v1': types.uint64,
+                              'v2': types.uint64,
+                              'v3': types.uint64, })
+    def _DOUBLE_ROUND(v0, v1, v2, v3):
+        v0, v1, v2, v3 = _SINGLE_ROUND(v0, v1, v2, v3)
+        v0, v1, v2, v3 = _SINGLE_ROUND(v0, v1, v2, v3)
+        return v0, v1, v2, v3
+
+    def _gen_siphash(alg):
+        if alg == 'siphash13':
+            _ROUNDER = _SINGLE_ROUND
+            _EXTRA_ROUND = True
+        elif alg == 'siphash24':
+            _ROUNDER = _DOUBLE_ROUND
+            _EXTRA_ROUND = False
+        else:
+            assert 0, 'unreachable'
+
+        @register_jitable(locals={'v0': types.uint64,
+                                  'v1': types.uint64,
+                                  'v2': types.uint64,
+                                  'v3': types.uint64,
+                                  'b': types.uint64,
+                                  'mi': types.uint64,
+                                  't': types.uint64,
+                                  'mask': types.uint64,
+                                  'jmp': types.uint64,
+                                  'ohexefef': types.uint64})
+        def _siphash(k0, k1, src, src_sz):
+            b = types.uint64(src_sz) << 56
+            v0 = k0 ^ types.uint64(0x736f6d6570736575)
+            v1 = k1 ^ types.uint64(0x646f72616e646f6d)
+            v2 = k0 ^ types.uint64(0x6c7967656e657261)
+            v3 = k1 ^ types.uint64(0x7465646279746573)
+
+            idx = 0
+            while (src_sz >= 8):
+                mi = grab_uint64_t(src, idx)
+                idx += 1
+                src_sz -= 8
+                v3 ^= mi
+                v0, v1, v2, v3 = _ROUNDER(v0, v1, v2, v3)
+                v0 ^= mi
+
+            # this is the switch fallthrough:
+            # https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Python/pyhash.c#L390-L400    # noqa: E501
+            t = types.uint64(0x0)
+            boffset = idx * 8
+            ohexefef = types.uint64(0xff)
+            if src_sz >= 7:
+                jmp = (6 * 8)
+                mask = ~types.uint64(ohexefef << jmp)
+                t = (t & mask) | (types.uint64(grab_byte(src, boffset + 6))
+                                  << jmp)
+            if src_sz >= 6:
+                jmp = (5 * 8)
+                mask = ~types.uint64(ohexefef << jmp)
+                t = (t & mask) | (types.uint64(grab_byte(src, boffset + 5))
+                                  << jmp)
+            if src_sz >= 5:
+                jmp = (4 * 8)
+                mask = ~types.uint64(ohexefef << jmp)
+                t = (t & mask) | (types.uint64(grab_byte(src, boffset + 4))
+                                  << jmp)
+            if src_sz >= 4:
+                t &= types.uint64(0xffffffff00000000)
+                for i in range(4):
+                    jmp = i * 8
+                    mask = ~types.uint64(ohexefef << jmp)
+                    t = (t & mask) | (types.uint64(grab_byte(src, boffset + i))
+                                      << jmp)
+            if src_sz >= 3:
+                jmp = (2 * 8)
+                mask = ~types.uint64(ohexefef << jmp)
+                t = (t & mask) | (types.uint64(grab_byte(src, boffset + 2))
+                                  << jmp)
+            if src_sz >= 2:
+                jmp = (1 * 8)
+                mask = ~types.uint64(ohexefef << jmp)
+                t = (t & mask) | (types.uint64(grab_byte(src, boffset + 1))
+                                  << jmp)
+            if src_sz >= 1:
+                mask = ~(ohexefef)
+                t = (t & mask) | (types.uint64(grab_byte(src, boffset + 0)))
+
+            b |= t
+            v3 ^= b
+            v0, v1, v2, v3 = _ROUNDER(v0, v1, v2, v3)
+            v0 ^= b
+            v2 ^= ohexefef
+            v0, v1, v2, v3 = _ROUNDER(v0, v1, v2, v3)
+            v0, v1, v2, v3 = _ROUNDER(v0, v1, v2, v3)
+            if _EXTRA_ROUND:
+                v0, v1, v2, v3 = _ROUNDER(v0, v1, v2, v3)
+            t = (v0 ^ v1) ^ (v2 ^ v3)
+            return t
+
+        return _siphash
+
+    _siphash13 = _gen_siphash('siphash13')
+    _siphash24 = _gen_siphash('siphash24')
+
+    _siphasher = _siphash13 if _Py_hashfunc_name == 'siphash13' else _siphash24
+
+else:
+    msg = "Unsupported hashing algorithm in use %s" % _Py_hashfunc_name
+    raise ValueError(msg)
+
+
+@intrinsic
+def _inject_hashsecret_read(tyctx, name):
+    """Emit code to load the hashsecret.
+    """
+    if not isinstance(name, types.StringLiteral):
+        raise errors.TypingError("requires literal string")
+
+    sym = _hashsecret[name.literal_value].symbol
+    resty = types.uint64
+    sig = resty(name)
+
+    def impl(cgctx, builder, sig, args):
+        mod = builder.module
+        try:
+            # Search for existing global
+            gv = mod.get_global(sym)
+        except KeyError:
+            # Inject the symbol if not already exist.
+            gv = ir.GlobalVariable(mod, ir.IntType(64), name=sym)
+        v = builder.load(gv)
+        return v
+
+    return sig, impl
+
+
+def _load_hashsecret(name):
+    return _hashsecret[name].value
+
+
+@overload(_load_hashsecret)
+def _impl_load_hashsecret(name):
+    def imp(name):
+        return _inject_hashsecret_read(name)
+    return imp
+
+
+# This is a translation of CPythons's _Py_HashBytes:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Python/pyhash.c#L145-L191    # noqa: E501
+
+
+@register_jitable(locals={'_hash': _Py_uhash_t})
+def _Py_HashBytes(val, _len):
+    if (_len == 0):
+        return process_return(0)
+
+    if (_len < _Py_HASH_CUTOFF):
+        # TODO: this branch needs testing, needs a CPython setup for it!
+        # /* Optimize hashing of very small strings with inline DJBX33A. */
+        _hash = _Py_uhash_t(5381)  # /* DJBX33A starts with 5381 */
+        for idx in range(_len):
+            _hash = ((_hash << 5) + _hash) + np.uint8(grab_byte(val, idx))
+
+        _hash ^= _len
+        _hash ^= _load_hashsecret('djbx33a_suffix')
+    else:
+        tmp = _siphasher(types.uint64(_load_hashsecret('siphash_k0')),
+                         types.uint64(_load_hashsecret('siphash_k1')),
+                         val, _len)
+        _hash = process_return(tmp)
+    return process_return(_hash)
+
+# This is an approximate translation of CPython's unicode_hash:
+# https://github.com/python/cpython/blob/d1dd6be613381b996b9071443ef081de8e5f3aff/Objects/unicodeobject.c#L11635-L11663    # noqa: E501
+
+
+@overload_method(types.UnicodeType, '__hash__')
+def unicode_hash(val):
+    from numba.cpython.unicode import _kind_to_byte_width
+
+    def impl(val):
+        kindwidth = _kind_to_byte_width(val._kind)
+        _len = len(val)
+        # use the cache if possible
+        current_hash = val._hash
+        if current_hash != -1:
+            return current_hash
+        else:
+            # cannot write hash value to cache in the unicode struct due to
+            # pass by value on the struct making the struct member immutable
+            return _Py_HashBytes(val._data, kindwidth * _len)
+
+    return impl
diff --git a/lib/python3.10/site-packages/numba/cpython/setobj.py b/lib/python3.10/site-packages/numba/cpython/setobj.py
new file mode 100644
index 0000000000000000000000000000000000000000..441067ef365c12e71e4414de2eff05a10421f078
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cpython/setobj.py
@@ -0,0 +1,1711 @@
+"""
+Support for native homogeneous sets.
+"""
+
+
+import collections
+import contextlib
+import math
+import operator
+from functools import cached_property
+
+from llvmlite import ir
+from numba.core import types, typing, cgutils
+from numba.core.imputils import (lower_builtin, lower_cast,
+                                    iternext_impl, impl_ret_borrowed,
+                                    impl_ret_new_ref, impl_ret_untracked,
+                                    for_iter, call_len, RefType)
+from numba.misc import quicksort
+from numba.cpython import slicing
+from numba.core.errors import NumbaValueError, TypingError
+from numba.core.extending import overload, overload_method, intrinsic
+
+
+def get_payload_struct(context, builder, set_type, ptr):
+    """
+    Given a set value and type, get its payload structure (as a
+    reference, so that mutations are seen by all).
+    """
+    payload_type = types.SetPayload(set_type)
+    ptrty = context.get_data_type(payload_type).as_pointer()
+    payload = builder.bitcast(ptr, ptrty)
+    return context.make_data_helper(builder, payload_type, ref=payload)
+
+
+def get_entry_size(context, set_type):
+    """
+    Return the entry size for the given set type.
+    """
+    llty = context.get_data_type(types.SetEntry(set_type))
+    return context.get_abi_sizeof(llty)
+
+
+# Note these values are special:
+# - EMPTY is obtained by issuing memset(..., 0xFF)
+# - (unsigned) EMPTY > (unsigned) DELETED > any other hash value
+EMPTY = -1
+DELETED = -2
+FALLBACK = -43
+
+# Minimal size of entries table.  Must be a power of 2!
+MINSIZE = 16
+
+# Number of cache-friendly linear probes before switching to non-linear probing
+LINEAR_PROBES = 3
+
+DEBUG_ALLOCS = False
+
+
+def get_hash_value(context, builder, typ, value):
+    """
+    Compute the hash of the given value.
+    """
+    typingctx = context.typing_context
+    fnty = typingctx.resolve_value_type(hash)
+    sig = fnty.get_call_type(typingctx, (typ,), {})
+    fn = context.get_function(fnty, sig)
+    h = fn(builder, (value,))
+    # Fixup reserved values
+    is_ok = is_hash_used(context, builder, h)
+    fallback = ir.Constant(h.type, FALLBACK)
+    return builder.select(is_ok, h, fallback)
+
+
+@intrinsic
+def _get_hash_value_intrinsic(typingctx, value):
+    def impl(context, builder, typ, args):
+        return get_hash_value(context, builder, value, args[0])
+    fnty = typingctx.resolve_value_type(hash)
+    sig = fnty.get_call_type(typingctx, (value,), {})
+    return sig, impl
+
+
+def is_hash_empty(context, builder, h):
+    """
+    Whether the hash value denotes an empty entry.
+    """
+    empty = ir.Constant(h.type, EMPTY)
+    return builder.icmp_unsigned('==', h, empty)
+
+def is_hash_deleted(context, builder, h):
+    """
+    Whether the hash value denotes a deleted entry.
+    """
+    deleted = ir.Constant(h.type, DELETED)
+    return builder.icmp_unsigned('==', h, deleted)
+
+def is_hash_used(context, builder, h):
+    """
+    Whether the hash value denotes an active entry.
+    """
+    # Everything below DELETED is an used entry
+    deleted = ir.Constant(h.type, DELETED)
+    return builder.icmp_unsigned('<', h, deleted)
+
+
+def check_all_set(*args):
+    if not all([isinstance(typ, types.Set) for typ in args]):
+        raise TypingError(f"All arguments must be Sets, got {args}")
+
+    if not all([args[0].dtype == s.dtype for s in args]):
+        raise TypingError(f"All Sets must be of the same type, got {args}")
+
+
+SetLoop = collections.namedtuple('SetLoop', ('index', 'entry', 'do_break'))
+
+
+class _SetPayload(object):
+
+    def __init__(self, context, builder, set_type, ptr):
+        payload = get_payload_struct(context, builder, set_type, ptr)
+        self._context = context
+        self._builder = builder
+        self._ty = set_type
+        self._payload = payload
+        self._entries = payload._get_ptr_by_name('entries')
+        self._ptr = ptr
+
+    @property
+    def mask(self):
+        return self._payload.mask
+
+    @mask.setter
+    def mask(self, value):
+        # CAUTION: mask must be a power of 2 minus 1
+        self._payload.mask = value
+
+    @property
+    def used(self):
+        return self._payload.used
+
+    @used.setter
+    def used(self, value):
+        self._payload.used = value
+
+    @property
+    def fill(self):
+        return self._payload.fill
+
+    @fill.setter
+    def fill(self, value):
+        self._payload.fill = value
+
+    @property
+    def finger(self):
+        return self._payload.finger
+
+    @finger.setter
+    def finger(self, value):
+        self._payload.finger = value
+
+    @property
+    def dirty(self):
+        return self._payload.dirty
+
+    @dirty.setter
+    def dirty(self, value):
+        self._payload.dirty = value
+
+    @property
+    def entries(self):
+        """
+        A pointer to the start of the entries array.
+        """
+        return self._entries
+
+    @property
+    def ptr(self):
+        """
+        A pointer to the start of the NRT-allocated area.
+        """
+        return self._ptr
+
+    def get_entry(self, idx):
+        """
+        Get entry number *idx*.
+        """
+        entry_ptr = cgutils.gep(self._builder, self._entries, idx)
+        entry = self._context.make_data_helper(self._builder,
+                                               types.SetEntry(self._ty),
+                                               ref=entry_ptr)
+        return entry
+
+    def _lookup(self, item, h, for_insert=False):
+        """
+        Lookup the *item* with the given hash values in the entries.
+
+        Return a (found, entry index) tuple:
+        - If found is true, <entry index> points to the entry containing
+          the item.
+        - If found is false, <entry index> points to the empty entry that
+          the item can be written to (only if *for_insert* is true)
+        """
+        context = self._context
+        builder = self._builder
+
+        intp_t = h.type
+
+        mask = self.mask
+        dtype = self._ty.dtype
+        tyctx = context.typing_context
+        fnty = tyctx.resolve_value_type(operator.eq)
+        sig = fnty.get_call_type(tyctx, (dtype, dtype), {})
+        eqfn = context.get_function(fnty, sig)
+
+        one = ir.Constant(intp_t, 1)
+        five = ir.Constant(intp_t, 5)
+
+        # The perturbation value for probing
+        perturb = cgutils.alloca_once_value(builder, h)
+        # The index of the entry being considered: start with (hash & mask)
+        index = cgutils.alloca_once_value(builder,
+                                          builder.and_(h, mask))
+        if for_insert:
+            # The index of the first deleted entry in the lookup chain
+            free_index_sentinel = mask.type(-1)  # highest unsigned index
+            free_index = cgutils.alloca_once_value(builder, free_index_sentinel)
+
+        bb_body = builder.append_basic_block("lookup.body")
+        bb_found = builder.append_basic_block("lookup.found")
+        bb_not_found = builder.append_basic_block("lookup.not_found")
+        bb_end = builder.append_basic_block("lookup.end")
+
+        def check_entry(i):
+            """
+            Check entry *i* against the value being searched for.
+            """
+            entry = self.get_entry(i)
+            entry_hash = entry.hash
+
+            with builder.if_then(builder.icmp_unsigned('==', h, entry_hash)):
+                # Hashes are equal, compare values
+                # (note this also ensures the entry is used)
+                eq = eqfn(builder, (item, entry.key))
+                with builder.if_then(eq):
+                    builder.branch(bb_found)
+
+            with builder.if_then(is_hash_empty(context, builder, entry_hash)):
+                builder.branch(bb_not_found)
+
+            if for_insert:
+                # Memorize the index of the first deleted entry
+                with builder.if_then(is_hash_deleted(context, builder, entry_hash)):
+                    j = builder.load(free_index)
+                    j = builder.select(builder.icmp_unsigned('==', j, free_index_sentinel),
+                                       i, j)
+                    builder.store(j, free_index)
+
+        # First linear probing.  When the number of collisions is small,
+        # the lineary probing loop achieves better cache locality and
+        # is also slightly cheaper computationally.
+        with cgutils.for_range(builder, ir.Constant(intp_t, LINEAR_PROBES)):
+            i = builder.load(index)
+            check_entry(i)
+            i = builder.add(i, one)
+            i = builder.and_(i, mask)
+            builder.store(i, index)
+
+        # If not found after linear probing, switch to a non-linear
+        # perturbation keyed on the unmasked hash value.
+        # XXX how to tell LLVM this branch is unlikely?
+        builder.branch(bb_body)
+        with builder.goto_block(bb_body):
+            i = builder.load(index)
+            check_entry(i)
+
+            # Perturb to go to next entry:
+            #   perturb >>= 5
+            #   i = (i * 5 + 1 + perturb) & mask
+            p = builder.load(perturb)
+            p = builder.lshr(p, five)
+            i = builder.add(one, builder.mul(i, five))
+            i = builder.and_(mask, builder.add(i, p))
+            builder.store(i, index)
+            builder.store(p, perturb)
+            # Loop
+            builder.branch(bb_body)
+
+        with builder.goto_block(bb_not_found):
+            if for_insert:
+                # Not found => for insertion, return the index of the first
+                # deleted entry (if any), to avoid creating an infinite
+                # lookup chain (issue #1913).
+                i = builder.load(index)
+                j = builder.load(free_index)
+                i = builder.select(builder.icmp_unsigned('==', j, free_index_sentinel),
+                                   i, j)
+                builder.store(i, index)
+            builder.branch(bb_end)
+
+        with builder.goto_block(bb_found):
+            builder.branch(bb_end)
+
+        builder.position_at_end(bb_end)
+
+        found = builder.phi(ir.IntType(1), 'found')
+        found.add_incoming(cgutils.true_bit, bb_found)
+        found.add_incoming(cgutils.false_bit, bb_not_found)
+
+        return found, builder.load(index)
+
+    @contextlib.contextmanager
+    def _iterate(self, start=None):
+        """
+        Iterate over the payload's entries.  Yield a SetLoop.
+        """
+        context = self._context
+        builder = self._builder
+
+        intp_t = context.get_value_type(types.intp)
+        one = ir.Constant(intp_t, 1)
+        size = builder.add(self.mask, one)
+
+        with cgutils.for_range(builder, size, start=start) as range_loop:
+            entry = self.get_entry(range_loop.index)
+            is_used = is_hash_used(context, builder, entry.hash)
+            with builder.if_then(is_used):
+                loop = SetLoop(index=range_loop.index, entry=entry,
+                               do_break=range_loop.do_break)
+                yield loop
+
+    @contextlib.contextmanager
+    def _next_entry(self):
+        """
+        Yield a random entry from the payload.  Caller must ensure the
+        set isn't empty, otherwise the function won't end.
+        """
+        context = self._context
+        builder = self._builder
+
+        intp_t = context.get_value_type(types.intp)
+        zero = ir.Constant(intp_t, 0)
+        one = ir.Constant(intp_t, 1)
+        mask = self.mask
+
+        # Start walking the entries from the stored "search finger" and
+        # break as soon as we find a used entry.
+
+        bb_body = builder.append_basic_block('next_entry_body')
+        bb_end = builder.append_basic_block('next_entry_end')
+
+        index = cgutils.alloca_once_value(builder, self.finger)
+        builder.branch(bb_body)
+
+        with builder.goto_block(bb_body):
+            i = builder.load(index)
+            # ANDing with mask ensures we stay inside the table boundaries
+            i = builder.and_(mask, builder.add(i, one))
+            builder.store(i, index)
+            entry = self.get_entry(i)
+            is_used = is_hash_used(context, builder, entry.hash)
+            builder.cbranch(is_used, bb_end, bb_body)
+
+        builder.position_at_end(bb_end)
+
+        # Update the search finger with the next position.  This avoids
+        # O(n**2) behaviour when pop() is called in a loop.
+        i = builder.load(index)
+        self.finger = i
+        yield self.get_entry(i)
+
+
+class SetInstance(object):
+
+    def __init__(self, context, builder, set_type, set_val):
+        self._context = context
+        self._builder = builder
+        self._ty = set_type
+        self._entrysize = get_entry_size(context, set_type)
+        self._set = context.make_helper(builder, set_type, set_val)
+
+    @property
+    def dtype(self):
+        return self._ty.dtype
+
+    @property
+    def payload(self):
+        """
+        The _SetPayload for this set.
+        """
+        # This cannot be cached as the pointer can move around!
+        context = self._context
+        builder = self._builder
+
+        ptr = self._context.nrt.meminfo_data(builder, self.meminfo)
+        return _SetPayload(context, builder, self._ty, ptr)
+
+    @property
+    def value(self):
+        return self._set._getvalue()
+
+    @property
+    def meminfo(self):
+        return self._set.meminfo
+
+    @property
+    def parent(self):
+        return self._set.parent
+
+    @parent.setter
+    def parent(self, value):
+        self._set.parent = value
+
+    def get_size(self):
+        """
+        Return the number of elements in the size.
+        """
+        return self.payload.used
+
+    def set_dirty(self, val):
+        if self._ty.reflected:
+            self.payload.dirty = cgutils.true_bit if val else cgutils.false_bit
+
+    def _add_entry(self, payload, entry, item, h, do_resize=True):
+        context = self._context
+        builder = self._builder
+
+        old_hash = entry.hash
+        entry.hash = h
+        self.incref_value(item)
+        entry.key = item
+        # used++
+        used = payload.used
+        one = ir.Constant(used.type, 1)
+        used = payload.used = builder.add(used, one)
+        # fill++ if entry wasn't a deleted one
+        with builder.if_then(is_hash_empty(context, builder, old_hash),
+                             likely=True):
+            payload.fill = builder.add(payload.fill, one)
+        # Grow table if necessary
+        if do_resize:
+            self.upsize(used)
+        self.set_dirty(True)
+
+    def _add_key(self, payload, item, h, do_resize=True, do_incref=True):
+        context = self._context
+        builder = self._builder
+
+        found, i = payload._lookup(item, h, for_insert=True)
+        not_found = builder.not_(found)
+
+        with builder.if_then(not_found):
+            # Not found => add it
+            entry = payload.get_entry(i)
+            old_hash = entry.hash
+            entry.hash = h
+            if do_incref:
+                self.incref_value(item)
+            entry.key = item
+            # used++
+            used = payload.used
+            one = ir.Constant(used.type, 1)
+            used = payload.used = builder.add(used, one)
+            # fill++ if entry wasn't a deleted one
+            with builder.if_then(is_hash_empty(context, builder, old_hash),
+                                 likely=True):
+                payload.fill = builder.add(payload.fill, one)
+            # Grow table if necessary
+            if do_resize:
+                self.upsize(used)
+            self.set_dirty(True)
+
+    def _remove_entry(self, payload, entry, do_resize=True, do_decref=True):
+        # Mark entry deleted
+        entry.hash = ir.Constant(entry.hash.type, DELETED)
+        if do_decref:
+            self.decref_value(entry.key)
+        # used--
+        used = payload.used
+        one = ir.Constant(used.type, 1)
+        used = payload.used = self._builder.sub(used, one)
+        # Shrink table if necessary
+        if do_resize:
+            self.downsize(used)
+        self.set_dirty(True)
+
+    def _remove_key(self, payload, item, h, do_resize=True):
+        context = self._context
+        builder = self._builder
+
+        found, i = payload._lookup(item, h)
+
+        with builder.if_then(found):
+            entry = payload.get_entry(i)
+            self._remove_entry(payload, entry, do_resize)
+
+        return found
+
+    def add(self, item, do_resize=True):
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        h = get_hash_value(context, builder, self._ty.dtype, item)
+        self._add_key(payload, item, h, do_resize)
+
+    def add_pyapi(self, pyapi, item, do_resize=True):
+        """A version of .add for use inside functions following Python calling
+        convention.
+        """
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        h = self._pyapi_get_hash_value(pyapi, context, builder, item)
+        self._add_key(payload, item, h, do_resize)
+
+    def _pyapi_get_hash_value(self, pyapi, context, builder, item):
+        """Python API compatible version of `get_hash_value()`.
+        """
+        argtypes = [self._ty.dtype]
+        resty = types.intp
+
+        def wrapper(val):
+            return _get_hash_value_intrinsic(val)
+
+        args = [item]
+        sig = typing.signature(resty, *argtypes)
+        is_error, retval = pyapi.call_jit_code(wrapper, sig, args)
+        # Handle return status
+        with builder.if_then(is_error, likely=False):
+            # Raise nopython exception as a Python exception
+            builder.ret(pyapi.get_null_object())
+        return retval
+
+    def contains(self, item):
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        h = get_hash_value(context, builder, self._ty.dtype, item)
+        found, i = payload._lookup(item, h)
+        return found
+
+    def discard(self, item):
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        h = get_hash_value(context, builder, self._ty.dtype, item)
+        found = self._remove_key(payload, item, h)
+        return found
+
+    def pop(self):
+        context = self._context
+        builder = self._builder
+
+        lty = context.get_value_type(self._ty.dtype)
+        key = cgutils.alloca_once(builder, lty)
+
+        payload = self.payload
+        with payload._next_entry() as entry:
+            builder.store(entry.key, key)
+            # since the value is returned don't decref in _remove_entry()
+            self._remove_entry(payload, entry, do_decref=False)
+
+        return builder.load(key)
+
+    def clear(self):
+        context = self._context
+        builder = self._builder
+
+        intp_t = context.get_value_type(types.intp)
+        minsize = ir.Constant(intp_t, MINSIZE)
+        self._replace_payload(minsize)
+        self.set_dirty(True)
+
+    def copy(self):
+        """
+        Return a copy of this set.
+        """
+        context = self._context
+        builder = self._builder
+
+        payload = self.payload
+        used = payload.used
+        fill = payload.fill
+
+        other = type(self)(context, builder, self._ty, None)
+
+        no_deleted_entries = builder.icmp_unsigned('==', used, fill)
+        with builder.if_else(no_deleted_entries, likely=True) \
+            as (if_no_deleted, if_deleted):
+            with if_no_deleted:
+                # No deleted entries => raw copy the payload
+                ok = other._copy_payload(payload)
+                with builder.if_then(builder.not_(ok), likely=False):
+                    context.call_conv.return_user_exc(builder, MemoryError,
+                                                      ("cannot copy set",))
+
+            with if_deleted:
+                # Deleted entries => re-insert entries one by one
+                nentries = self.choose_alloc_size(context, builder, used)
+                ok = other._allocate_payload(nentries)
+                with builder.if_then(builder.not_(ok), likely=False):
+                    context.call_conv.return_user_exc(builder, MemoryError,
+                                                      ("cannot copy set",))
+
+                other_payload = other.payload
+                with payload._iterate() as loop:
+                    entry = loop.entry
+                    other._add_key(other_payload, entry.key, entry.hash,
+                                   do_resize=False)
+
+        return other
+
+    def intersect(self, other):
+        """
+        In-place intersection with *other* set.
+        """
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        with payload._iterate() as loop:
+            entry = loop.entry
+            found, _ = other_payload._lookup(entry.key, entry.hash)
+            with builder.if_then(builder.not_(found)):
+                self._remove_entry(payload, entry, do_resize=False)
+
+        # Final downsize
+        self.downsize(payload.used)
+
+    def difference(self, other):
+        """
+        In-place difference with *other* set.
+        """
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        with other_payload._iterate() as loop:
+            entry = loop.entry
+            self._remove_key(payload, entry.key, entry.hash, do_resize=False)
+
+        # Final downsize
+        self.downsize(payload.used)
+
+    def symmetric_difference(self, other):
+        """
+        In-place symmetric difference with *other* set.
+        """
+        context = self._context
+        builder = self._builder
+        other_payload = other.payload
+
+        with other_payload._iterate() as loop:
+            key = loop.entry.key
+            h = loop.entry.hash
+            # We must reload our payload as it may be resized during the loop
+            payload = self.payload
+            found, i = payload._lookup(key, h, for_insert=True)
+            entry = payload.get_entry(i)
+            with builder.if_else(found) as (if_common, if_not_common):
+                with if_common:
+                    self._remove_entry(payload, entry, do_resize=False)
+                with if_not_common:
+                    self._add_entry(payload, entry, key, h)
+
+        # Final downsize
+        self.downsize(self.payload.used)
+
+    def issubset(self, other, strict=False):
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        cmp_op = '<' if strict else '<='
+
+        res = cgutils.alloca_once_value(builder, cgutils.true_bit)
+        with builder.if_else(
+            builder.icmp_unsigned(cmp_op, payload.used, other_payload.used)
+            ) as (if_smaller, if_larger):
+            with if_larger:
+                # self larger than other => self cannot possibly a subset
+                builder.store(cgutils.false_bit, res)
+            with if_smaller:
+                # check whether each key of self is in other
+                with payload._iterate() as loop:
+                    entry = loop.entry
+                    found, _ = other_payload._lookup(entry.key, entry.hash)
+                    with builder.if_then(builder.not_(found)):
+                        builder.store(cgutils.false_bit, res)
+                        loop.do_break()
+
+        return builder.load(res)
+
+    def isdisjoint(self, other):
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        res = cgutils.alloca_once_value(builder, cgutils.true_bit)
+
+        def check(smaller, larger):
+            # Loop over the smaller of the two, and search in the larger
+            with smaller._iterate() as loop:
+                entry = loop.entry
+                found, _ = larger._lookup(entry.key, entry.hash)
+                with builder.if_then(found):
+                    builder.store(cgutils.false_bit, res)
+                    loop.do_break()
+
+        with builder.if_else(
+            builder.icmp_unsigned('>', payload.used, other_payload.used)
+            ) as (if_larger, otherwise):
+
+            with if_larger:
+                # len(self) > len(other)
+                check(other_payload, payload)
+
+            with otherwise:
+                # len(self) <= len(other)
+                check(payload, other_payload)
+
+        return builder.load(res)
+
+    def equals(self, other):
+        context = self._context
+        builder = self._builder
+        payload = self.payload
+        other_payload = other.payload
+
+        res = cgutils.alloca_once_value(builder, cgutils.true_bit)
+        with builder.if_else(
+            builder.icmp_unsigned('==', payload.used, other_payload.used)
+            ) as (if_same_size, otherwise):
+            with if_same_size:
+                # same sizes => check whether each key of self is in other
+                with payload._iterate() as loop:
+                    entry = loop.entry
+                    found, _ = other_payload._lookup(entry.key, entry.hash)
+                    with builder.if_then(builder.not_(found)):
+                        builder.store(cgutils.false_bit, res)
+                        loop.do_break()
+            with otherwise:
+                # different sizes => cannot possibly be equal
+                builder.store(cgutils.false_bit, res)
+
+        return builder.load(res)
+
+    @classmethod
+    def allocate_ex(cls, context, builder, set_type, nitems=None):
+        """
+        Allocate a SetInstance with its storage.
+        Return a (ok, instance) tuple where *ok* is a LLVM boolean and
+        *instance* is a SetInstance object (the object's contents are
+        only valid when *ok* is true).
+        """
+        intp_t = context.get_value_type(types.intp)
+
+        if nitems is None:
+            nentries = ir.Constant(intp_t, MINSIZE)
+        else:
+            if isinstance(nitems, int):
+                nitems = ir.Constant(intp_t, nitems)
+            nentries = cls.choose_alloc_size(context, builder, nitems)
+
+        self = cls(context, builder, set_type, None)
+        ok = self._allocate_payload(nentries)
+        return ok, self
+
+    @classmethod
+    def allocate(cls, context, builder, set_type, nitems=None):
+        """
+        Allocate a SetInstance with its storage.  Same as allocate_ex(),
+        but return an initialized *instance*.  If allocation failed,
+        control is transferred to the caller using the target's current
+        call convention.
+        """
+        ok, self = cls.allocate_ex(context, builder, set_type, nitems)
+        with builder.if_then(builder.not_(ok), likely=False):
+            context.call_conv.return_user_exc(builder, MemoryError,
+                                              ("cannot allocate set",))
+        return self
+
+    @classmethod
+    def from_meminfo(cls, context, builder, set_type, meminfo):
+        """
+        Allocate a new set instance pointing to an existing payload
+        (a meminfo pointer).
+        Note the parent field has to be filled by the caller.
+        """
+        self = cls(context, builder, set_type, None)
+        self._set.meminfo = meminfo
+        self._set.parent = context.get_constant_null(types.pyobject)
+        context.nrt.incref(builder, set_type, self.value)
+        # Payload is part of the meminfo, no need to touch it
+        return self
+
+    @classmethod
+    def choose_alloc_size(cls, context, builder, nitems):
+        """
+        Choose a suitable number of entries for the given number of items.
+        """
+        intp_t = nitems.type
+        one = ir.Constant(intp_t, 1)
+        minsize = ir.Constant(intp_t, MINSIZE)
+
+        # Ensure number of entries >= 2 * used
+        min_entries = builder.shl(nitems, one)
+        # Find out first suitable power of 2, starting from MINSIZE
+        size_p = cgutils.alloca_once_value(builder, minsize)
+
+        bb_body = builder.append_basic_block("calcsize.body")
+        bb_end = builder.append_basic_block("calcsize.end")
+
+        builder.branch(bb_body)
+
+        with builder.goto_block(bb_body):
+            size = builder.load(size_p)
+            is_large_enough = builder.icmp_unsigned('>=', size, min_entries)
+            with builder.if_then(is_large_enough, likely=False):
+                builder.branch(bb_end)
+            next_size = builder.shl(size, one)
+            builder.store(next_size, size_p)
+            builder.branch(bb_body)
+
+        builder.position_at_end(bb_end)
+        return builder.load(size_p)
+
+    def upsize(self, nitems):
+        """
+        When adding to the set, ensure it is properly sized for the given
+        number of used entries.
+        """
+        context = self._context
+        builder = self._builder
+        intp_t = nitems.type
+
+        one = ir.Constant(intp_t, 1)
+        two = ir.Constant(intp_t, 2)
+
+        payload = self.payload
+
+        # Ensure number of entries >= 2 * used
+        min_entries = builder.shl(nitems, one)
+        size = builder.add(payload.mask, one)
+        need_resize = builder.icmp_unsigned('>=', min_entries, size)
+
+        with builder.if_then(need_resize, likely=False):
+            # Find out next suitable size
+            new_size_p = cgutils.alloca_once_value(builder, size)
+
+            bb_body = builder.append_basic_block("calcsize.body")
+            bb_end = builder.append_basic_block("calcsize.end")
+
+            builder.branch(bb_body)
+
+            with builder.goto_block(bb_body):
+                # Multiply by 4 (ensuring size remains a power of two)
+                new_size = builder.load(new_size_p)
+                new_size = builder.shl(new_size, two)
+                builder.store(new_size, new_size_p)
+                is_too_small = builder.icmp_unsigned('>=', min_entries, new_size)
+                builder.cbranch(is_too_small, bb_body, bb_end)
+
+            builder.position_at_end(bb_end)
+
+            new_size = builder.load(new_size_p)
+            if DEBUG_ALLOCS:
+                context.printf(builder,
+                               "upsize to %zd items: current size = %zd, "
+                               "min entries = %zd, new size = %zd\n",
+                               nitems, size, min_entries, new_size)
+            self._resize(payload, new_size, "cannot grow set")
+
+    def downsize(self, nitems):
+        """
+        When removing from the set, ensure it is properly sized for the given
+        number of used entries.
+        """
+        context = self._context
+        builder = self._builder
+        intp_t = nitems.type
+
+        one = ir.Constant(intp_t, 1)
+        two = ir.Constant(intp_t, 2)
+        minsize = ir.Constant(intp_t, MINSIZE)
+
+        payload = self.payload
+
+        # Ensure entries >= max(2 * used, MINSIZE)
+        min_entries = builder.shl(nitems, one)
+        min_entries = builder.select(builder.icmp_unsigned('>=', min_entries, minsize),
+                                     min_entries, minsize)
+        # Shrink only if size >= 4 * min_entries && size > MINSIZE
+        max_size = builder.shl(min_entries, two)
+        size = builder.add(payload.mask, one)
+        need_resize = builder.and_(
+            builder.icmp_unsigned('<=', max_size, size),
+            builder.icmp_unsigned('<', minsize, size))
+
+        with builder.if_then(need_resize, likely=False):
+            # Find out next suitable size
+            new_size_p = cgutils.alloca_once_value(builder, size)
+
+            bb_body = builder.append_basic_block("calcsize.body")
+            bb_end = builder.append_basic_block("calcsize.end")
+
+            builder.branch(bb_body)
+
+            with builder.goto_block(bb_body):
+                # Divide by 2 (ensuring size remains a power of two)
+                new_size = builder.load(new_size_p)
+                new_size = builder.lshr(new_size, one)
+                # Keep current size if new size would be < min_entries
+                is_too_small = builder.icmp_unsigned('>', min_entries, new_size)
+                with builder.if_then(is_too_small):
+                    builder.branch(bb_end)
+                builder.store(new_size, new_size_p)
+                builder.branch(bb_body)
+
+            builder.position_at_end(bb_end)
+
+            # Ensure new_size >= MINSIZE
+            new_size = builder.load(new_size_p)
+            # At this point, new_size should be < size if the factors
+            # above were chosen carefully!
+
+            if DEBUG_ALLOCS:
+                context.printf(builder,
+                               "downsize to %zd items: current size = %zd, "
+                               "min entries = %zd, new size = %zd\n",
+                               nitems, size, min_entries, new_size)
+            self._resize(payload, new_size, "cannot shrink set")
+
+    def _resize(self, payload, nentries, errmsg):
+        """
+        Resize the payload to the given number of entries.
+
+        CAUTION: *nentries* must be a power of 2!
+        """
+        context = self._context
+        builder = self._builder
+
+        # Allocate new entries
+        old_payload = payload
+
+        ok = self._allocate_payload(nentries, realloc=True)
+        with builder.if_then(builder.not_(ok), likely=False):
+            context.call_conv.return_user_exc(builder, MemoryError,
+                                              (errmsg,))
+
+        # Re-insert old entries
+        # No incref since they already were the first time they were inserted
+        payload = self.payload
+        with old_payload._iterate() as loop:
+            entry = loop.entry
+            self._add_key(payload, entry.key, entry.hash,
+                          do_resize=False, do_incref=False)
+
+        self._free_payload(old_payload.ptr)
+
+    def _replace_payload(self, nentries):
+        """
+        Replace the payload with a new empty payload with the given number
+        of entries.
+
+        CAUTION: *nentries* must be a power of 2!
+        """
+        context = self._context
+        builder = self._builder
+
+        # decref all of the previous entries
+        with self.payload._iterate() as loop:
+            entry = loop.entry
+            self.decref_value(entry.key)
+
+        # Free old payload
+        self._free_payload(self.payload.ptr)
+
+        ok = self._allocate_payload(nentries, realloc=True)
+        with builder.if_then(builder.not_(ok), likely=False):
+            context.call_conv.return_user_exc(builder, MemoryError,
+                                              ("cannot reallocate set",))
+
+    def _allocate_payload(self, nentries, realloc=False):
+        """
+        Allocate and initialize payload for the given number of entries.
+        If *realloc* is True, the existing meminfo is reused.
+
+        CAUTION: *nentries* must be a power of 2!
+        """
+        context = self._context
+        builder = self._builder
+
+        ok = cgutils.alloca_once_value(builder, cgutils.true_bit)
+
+        intp_t = context.get_value_type(types.intp)
+        zero = ir.Constant(intp_t, 0)
+        one = ir.Constant(intp_t, 1)
+
+        payload_type = context.get_data_type(types.SetPayload(self._ty))
+        payload_size = context.get_abi_sizeof(payload_type)
+        entry_size = self._entrysize
+        # Account for the fact that the payload struct already contains an entry
+        payload_size -= entry_size
+
+        # Total allocation size = <payload header size> + nentries * entry_size
+        allocsize, ovf = cgutils.muladd_with_overflow(builder, nentries,
+                                                      ir.Constant(intp_t, entry_size),
+                                                      ir.Constant(intp_t, payload_size))
+        with builder.if_then(ovf, likely=False):
+            builder.store(cgutils.false_bit, ok)
+
+        with builder.if_then(builder.load(ok), likely=True):
+            if realloc:
+                meminfo = self._set.meminfo
+                ptr = context.nrt.meminfo_varsize_alloc_unchecked(builder,
+                                                                  meminfo,
+                                                        size=allocsize)
+                alloc_ok = cgutils.is_null(builder, ptr)
+            else:
+                # create destructor to be called upon set destruction
+                dtor = self._imp_dtor(context, builder.module)
+                meminfo = context.nrt.meminfo_new_varsize_dtor_unchecked(
+                    builder, allocsize, builder.bitcast(dtor, cgutils.voidptr_t))
+                alloc_ok = cgutils.is_null(builder, meminfo)
+
+            with builder.if_else(alloc_ok,
+                                 likely=False) as (if_error, if_ok):
+                with if_error:
+                    builder.store(cgutils.false_bit, ok)
+                with if_ok:
+                    if not realloc:
+                        self._set.meminfo = meminfo
+                        self._set.parent = context.get_constant_null(types.pyobject)
+                    payload = self.payload
+                    # Initialize entries to 0xff (EMPTY)
+                    cgutils.memset(builder, payload.ptr, allocsize, 0xFF)
+                    payload.used = zero
+                    payload.fill = zero
+                    payload.finger = zero
+                    new_mask = builder.sub(nentries, one)
+                    payload.mask = new_mask
+
+                    if DEBUG_ALLOCS:
+                        context.printf(builder,
+                                       "allocated %zd bytes for set at %p: mask = %zd\n",
+                                       allocsize, payload.ptr, new_mask)
+
+        return builder.load(ok)
+
+    def _free_payload(self, ptr):
+        """
+        Free an allocated old payload at *ptr*.
+        """
+        self._context.nrt.meminfo_varsize_free(self._builder, self.meminfo, ptr)
+
+    def _copy_payload(self, src_payload):
+        """
+        Raw-copy the given payload into self.
+        """
+        context = self._context
+        builder = self._builder
+
+        ok = cgutils.alloca_once_value(builder, cgutils.true_bit)
+
+        intp_t = context.get_value_type(types.intp)
+        zero = ir.Constant(intp_t, 0)
+        one = ir.Constant(intp_t, 1)
+
+        payload_type = context.get_data_type(types.SetPayload(self._ty))
+        payload_size = context.get_abi_sizeof(payload_type)
+        entry_size = self._entrysize
+        # Account for the fact that the payload struct already contains an entry
+        payload_size -= entry_size
+
+        mask = src_payload.mask
+        nentries = builder.add(one, mask)
+
+        # Total allocation size = <payload header size> + nentries * entry_size
+        # (note there can't be any overflow since we're reusing an existing
+        #  payload's parameters)
+        allocsize = builder.add(ir.Constant(intp_t, payload_size),
+                                builder.mul(ir.Constant(intp_t, entry_size),
+                                            nentries))
+
+        with builder.if_then(builder.load(ok), likely=True):
+            # create destructor for new meminfo
+            dtor = self._imp_dtor(context, builder.module)
+            meminfo = context.nrt.meminfo_new_varsize_dtor_unchecked(
+                builder, allocsize, builder.bitcast(dtor, cgutils.voidptr_t))
+            alloc_ok = cgutils.is_null(builder, meminfo)
+
+            with builder.if_else(alloc_ok, likely=False) as (if_error, if_ok):
+                with if_error:
+                    builder.store(cgutils.false_bit, ok)
+                with if_ok:
+                    self._set.meminfo = meminfo
+                    payload = self.payload
+                    payload.used = src_payload.used
+                    payload.fill = src_payload.fill
+                    payload.finger = zero
+                    payload.mask = mask
+
+                    # instead of using `_add_key` for every entry, since the
+                    # size of the new set is the same, we can just copy the
+                    # data directly without having to re-compute the hash
+                    cgutils.raw_memcpy(builder, payload.entries,
+                                       src_payload.entries, nentries,
+                                       entry_size)
+                    # increment the refcounts to simulate `_add_key` for each
+                    # element
+                    with src_payload._iterate() as loop:
+                        self.incref_value(loop.entry.key)
+
+                    if DEBUG_ALLOCS:
+                        context.printf(builder,
+                                       "allocated %zd bytes for set at %p: mask = %zd\n",
+                                       allocsize, payload.ptr, mask)
+
+        return builder.load(ok)
+
+    def _imp_dtor(self, context, module):
+        """Define the dtor for set
+        """
+        llvoidptr = cgutils.voidptr_t
+        llsize_t= context.get_value_type(types.size_t)
+        # create a dtor function that takes (void* set, size_t size, void* dtor_info)
+        fnty = ir.FunctionType(
+            ir.VoidType(),
+            [llvoidptr, llsize_t, llvoidptr],
+        )
+        # create type-specific name
+        fname = f".dtor.set.{self._ty.dtype}"
+
+        fn = cgutils.get_or_insert_function(module, fnty, name=fname)
+
+        if fn.is_declaration:
+            # Set linkage
+            fn.linkage = 'linkonce_odr'
+            # Define
+            builder = ir.IRBuilder(fn.append_basic_block())
+            payload = _SetPayload(context, builder, self._ty, fn.args[0])
+            with payload._iterate() as loop:
+                entry = loop.entry
+                context.nrt.decref(builder, self._ty.dtype, entry.key)
+            builder.ret_void()
+
+        return fn
+
+    def incref_value(self, val):
+        """Incref an element value
+        """
+        self._context.nrt.incref(self._builder, self._ty.dtype, val)
+
+    def decref_value(self, val):
+        """Decref an element value
+        """
+        self._context.nrt.decref(self._builder, self._ty.dtype, val)
+
+
+class SetIterInstance(object):
+
+    def __init__(self, context, builder, iter_type, iter_val):
+        self._context = context
+        self._builder = builder
+        self._ty = iter_type
+        self._iter = context.make_helper(builder, iter_type, iter_val)
+        ptr = self._context.nrt.meminfo_data(builder, self.meminfo)
+        self._payload = _SetPayload(context, builder, self._ty.container, ptr)
+
+    @classmethod
+    def from_set(cls, context, builder, iter_type, set_val):
+        set_inst = SetInstance(context, builder, iter_type.container, set_val)
+        self = cls(context, builder, iter_type, None)
+        index = context.get_constant(types.intp, 0)
+        self._iter.index = cgutils.alloca_once_value(builder, index)
+        self._iter.meminfo = set_inst.meminfo
+        return self
+
+    @property
+    def value(self):
+        return self._iter._getvalue()
+
+    @property
+    def meminfo(self):
+        return self._iter.meminfo
+
+    @property
+    def index(self):
+        return self._builder.load(self._iter.index)
+
+    @index.setter
+    def index(self, value):
+        self._builder.store(value, self._iter.index)
+
+    def iternext(self, result):
+        index = self.index
+        payload = self._payload
+        one = ir.Constant(index.type, 1)
+
+        result.set_exhausted()
+
+        with payload._iterate(start=index) as loop:
+            # An entry was found
+            entry = loop.entry
+            result.set_valid()
+            result.yield_(entry.key)
+            self.index = self._builder.add(loop.index, one)
+            loop.do_break()
+
+
+#-------------------------------------------------------------------------------
+# Constructors
+
+def build_set(context, builder, set_type, items):
+    """
+    Build a set of the given type, containing the given items.
+    """
+    nitems = len(items)
+    inst = SetInstance.allocate(context, builder, set_type, nitems)
+
+    if nitems > 0:
+
+        # Populate set.  Inlining the insertion code for each item would be very
+        # costly, instead we create a LLVM array and iterate over it.
+        array = cgutils.pack_array(builder, items)
+        array_ptr = cgutils.alloca_once_value(builder, array)
+
+        count = context.get_constant(types.intp, nitems)
+        with cgutils.for_range(builder, count) as loop:
+            item = builder.load(cgutils.gep(builder, array_ptr, 0, loop.index))
+            inst.add(item)
+
+    return impl_ret_new_ref(context, builder, set_type, inst.value)
+
+
+@lower_builtin(set)
+def set_empty_constructor(context, builder, sig, args):
+    set_type = sig.return_type
+    inst = SetInstance.allocate(context, builder, set_type)
+    return impl_ret_new_ref(context, builder, set_type, inst.value)
+
+@lower_builtin(set, types.IterableType)
+def set_constructor(context, builder, sig, args):
+    set_type = sig.return_type
+    items_type, = sig.args
+    items, = args
+
+    # If the argument has a len(), preallocate the set so as to
+    # avoid resizes.
+    # `for_iter` increfs each item in the set, so a `decref` is required each
+    # iteration to balance. Because the `incref` from `.add` is dependent on
+    # the item not already existing in the set, just removing its incref is not
+    # enough to guarantee all memory is freed
+    n = call_len(context, builder, items_type, items)
+    inst = SetInstance.allocate(context, builder, set_type, n)
+    with for_iter(context, builder, items_type, items) as loop:
+        inst.add(loop.value)
+        context.nrt.decref(builder, set_type.dtype, loop.value)
+
+    return impl_ret_new_ref(context, builder, set_type, inst.value)
+
+
+#-------------------------------------------------------------------------------
+# Various operations
+
+@lower_builtin(len, types.Set)
+def set_len(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    return inst.get_size()
+
+@lower_builtin(operator.contains, types.Set, types.Any)
+def in_set(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    return inst.contains(args[1])
+
+@lower_builtin('getiter', types.Set)
+def getiter_set(context, builder, sig, args):
+    inst = SetIterInstance.from_set(context, builder, sig.return_type, args[0])
+    return impl_ret_borrowed(context, builder, sig.return_type, inst.value)
+
+@lower_builtin('iternext', types.SetIter)
+@iternext_impl(RefType.BORROWED)
+def iternext_listiter(context, builder, sig, args, result):
+    inst = SetIterInstance(context, builder, sig.args[0], args[0])
+    inst.iternext(result)
+
+
+#-------------------------------------------------------------------------------
+# Methods
+
+# One-item-at-a-time operations
+
+@lower_builtin("set.add", types.Set, types.Any)
+def set_add(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    item = args[1]
+    inst.add(item)
+
+    return context.get_dummy_value()
+
+
+@intrinsic
+def _set_discard(typingctx, s, item):
+    sig = types.none(s, item)
+
+    def set_discard(context, builder, sig, args):
+        inst = SetInstance(context, builder, sig.args[0], args[0])
+        item = args[1]
+        inst.discard(item)
+
+        return context.get_dummy_value()
+
+    return sig, set_discard
+
+
+@overload_method(types.Set, "discard")
+def ol_set_discard(s, item):
+    return lambda s, item: _set_discard(s, item)
+
+
+@intrinsic
+def _set_pop(typingctx, s):
+    sig = s.dtype(s)
+
+    def set_pop(context, builder, sig, args):
+        inst = SetInstance(context, builder, sig.args[0], args[0])
+        used = inst.payload.used
+        with builder.if_then(cgutils.is_null(builder, used), likely=False):
+            context.call_conv.return_user_exc(builder, KeyError,
+                                            ("set.pop(): empty set",))
+
+        return inst.pop()
+
+    return sig, set_pop
+
+
+@overload_method(types.Set, "pop")
+def ol_set_pop(s):
+    return lambda s: _set_pop(s)
+
+
+@intrinsic
+def _set_remove(typingctx, s, item):
+    sig = types.none(s, item)
+
+    def set_remove(context, builder, sig, args):
+        inst = SetInstance(context, builder, sig.args[0], args[0])
+        item = args[1]
+        found = inst.discard(item)
+        with builder.if_then(builder.not_(found), likely=False):
+            context.call_conv.return_user_exc(builder, KeyError,
+                                            ("set.remove(): key not in set",))
+
+        return context.get_dummy_value()
+
+    return sig, set_remove
+
+
+@overload_method(types.Set, "remove")
+def ol_set_remove(s, item):
+    if s.dtype == item:
+        return lambda s, item: _set_remove(s, item)
+
+
+# Mutating set operations
+
+@intrinsic
+def _set_clear(typingctx, s):
+    sig = types.none(s)
+
+    def set_clear(context, builder, sig, args):
+        inst = SetInstance(context, builder, sig.args[0], args[0])
+        inst.clear()
+        return context.get_dummy_value()
+
+    return sig, set_clear
+
+
+@overload_method(types.Set, "clear")
+def ol_set_clear(s):
+    return lambda s: _set_clear(s)
+
+
+@intrinsic
+def _set_copy(typingctx, s):
+    sig = s(s)
+
+    def set_copy(context, builder, sig, args):
+        inst = SetInstance(context, builder, sig.args[0], args[0])
+        other = inst.copy()
+        return impl_ret_new_ref(context, builder, sig.return_type, other.value)
+
+    return sig, set_copy
+
+
+@overload_method(types.Set, "copy")
+def ol_set_copy(s):
+    return lambda s: _set_copy(s)
+
+
+def set_difference_update(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+
+    inst.difference(other)
+
+    return context.get_dummy_value()
+
+
+@intrinsic
+def _set_difference_update(typingctx, a, b):
+    sig = types.none(a, b)
+    return sig, set_difference_update
+
+
+@overload_method(types.Set, "difference_update")
+def set_difference_update_impl(a, b):
+    check_all_set(a, b)
+    return lambda a, b: _set_difference_update(a, b)
+
+
+def set_intersection_update(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+    inst.intersect(other)
+    return context.get_dummy_value()
+
+
+@intrinsic
+def _set_intersection_update(typingctx, a, b):
+    sig = types.none(a, b)
+    return sig, set_intersection_update
+
+
+@overload_method(types.Set, "intersection_update")
+def set_intersection_update_impl(a, b):
+    check_all_set(a, b)
+    return lambda a, b: _set_intersection_update(a, b)
+
+
+def set_symmetric_difference_update(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    other = SetInstance(context, builder, sig.args[1], args[1])
+    inst.symmetric_difference(other)
+    return context.get_dummy_value()
+
+
+@intrinsic
+def _set_symmetric_difference_update(typingctx, a, b):
+    sig = types.none(a, b)
+    return sig, set_symmetric_difference_update
+
+
+@overload_method(types.Set, "symmetric_difference_update")
+def set_symmetric_difference_update_impl(a, b):
+    check_all_set(a, b)
+    return lambda a, b: _set_symmetric_difference_update(a, b)
+
+
+@lower_builtin("set.update", types.Set, types.IterableType)
+def set_update(context, builder, sig, args):
+    inst = SetInstance(context, builder, sig.args[0], args[0])
+    items_type = sig.args[1]
+    items = args[1]
+
+    # If the argument has a len(), assume there are few collisions and
+    # presize to len(set) + len(items)
+    n = call_len(context, builder, items_type, items)
+    if n is not None:
+        new_size = builder.add(inst.payload.used, n)
+        inst.upsize(new_size)
+
+    with for_iter(context, builder, items_type, items) as loop:
+        # make sure that the items being added are of the same dtype as the
+        # set instance
+        casted = context.cast(builder, loop.value, items_type.dtype, inst.dtype)
+        inst.add(casted)
+        # decref each item to counter balance the incref from `for_iter`
+        # `.add` will conditionally incref when the item does not already exist
+        # in the set, therefore removing its incref is not enough to guarantee
+        # all memory is freed
+        context.nrt.decref(builder, items_type.dtype, loop.value)
+
+    if n is not None:
+        # If we pre-grew the set, downsize in case there were many collisions
+        inst.downsize(inst.payload.used)
+
+    return context.get_dummy_value()
+
+def gen_operator_impl(op, impl):
+    @intrinsic
+    def _set_operator_intr(typingctx, a, b):
+        sig = a(a, b)
+        def codegen(context, builder, sig, args):
+            assert sig.return_type == sig.args[0]
+            impl(context, builder, sig, args)
+            return impl_ret_borrowed(context, builder, sig.args[0], args[0])
+        return sig, codegen
+
+    @overload(op)
+    def _ol_set_operator(a, b):
+        check_all_set(a, b)
+        return lambda a, b: _set_operator_intr(a, b)
+
+
+for op_, op_impl in [
+    (operator.iand, set_intersection_update),
+    (operator.ior, set_update),
+    (operator.isub, set_difference_update),
+    (operator.ixor, set_symmetric_difference_update),
+    ]:
+    gen_operator_impl(op_, op_impl)
+
+
+# Set operations creating a new set
+
+@overload(operator.sub)
+@overload_method(types.Set, "difference")
+def impl_set_difference(a, b):
+    check_all_set(a, b)
+
+    def difference_impl(a, b):
+        s = a.copy()
+        s.difference_update(b)
+        return s
+
+    return difference_impl
+
+@overload(operator.and_)
+@overload_method(types.Set, "intersection")
+def set_intersection(a, b):
+    check_all_set(a, b)
+
+    def intersection_impl(a, b):
+        if len(a) < len(b):
+            s = a.copy()
+            s.intersection_update(b)
+            return s
+        else:
+            s = b.copy()
+            s.intersection_update(a)
+            return s
+
+    return intersection_impl
+
+@overload(operator.xor)
+@overload_method(types.Set, "symmetric_difference")
+def set_symmetric_difference(a, b):
+    check_all_set(a, b)
+
+    def symmetric_difference_impl(a, b):
+        if len(a) > len(b):
+            s = a.copy()
+            s.symmetric_difference_update(b)
+            return s
+        else:
+            s = b.copy()
+            s.symmetric_difference_update(a)
+            return s
+
+    return symmetric_difference_impl
+
+@overload(operator.or_)
+@overload_method(types.Set, "union")
+def set_union(a, b):
+    check_all_set(a, b)
+
+    def union_impl(a, b):
+        if len(a) > len(b):
+            s = a.copy()
+            s.update(b)
+            return s
+        else:
+            s = b.copy()
+            s.update(a)
+            return s
+
+    return union_impl
+
+
+# Predicates
+
+@intrinsic
+def _set_isdisjoint(typingctx, a, b):
+    sig = types.boolean(a, b)
+
+    def codegen(context, builder, sig, args):
+        inst = SetInstance(context, builder, sig.args[0], args[0])
+        other = SetInstance(context, builder, sig.args[1], args[1])
+
+        return inst.isdisjoint(other)
+
+    return sig, codegen
+
+
+@overload_method(types.Set, "isdisjoint")
+def set_isdisjoint(a, b):
+    check_all_set(a, b)
+
+    return lambda a, b: _set_isdisjoint(a, b)
+
+
+@intrinsic
+def _set_issubset(typingctx, a, b):
+    sig = types.boolean(a, b)
+
+    def codegen(context, builder, sig, args):
+        inst = SetInstance(context, builder, sig.args[0], args[0])
+        other = SetInstance(context, builder, sig.args[1], args[1])
+
+        return inst.issubset(other)
+
+    return sig, codegen
+
+@overload(operator.le)
+@overload_method(types.Set, "issubset")
+def set_issubset(a, b):
+    check_all_set(a, b)
+
+    return lambda a, b: _set_issubset(a, b)
+
+
+@overload(operator.ge)
+@overload_method(types.Set, "issuperset")
+def set_issuperset(a, b):
+    check_all_set(a, b)
+
+    def superset_impl(a, b):
+        return b.issubset(a)
+
+    return superset_impl
+
+@intrinsic
+def _set_eq(typingctx, a, b):
+    sig = types.boolean(a, b)
+
+    def codegen(context, builder, sig, args):
+        inst = SetInstance(context, builder, sig.args[0], args[0])
+        other = SetInstance(context, builder, sig.args[1], args[1])
+
+        return inst.equals(other)
+
+    return sig, codegen
+
+@overload(operator.eq)
+def set_eq(a, b):
+    check_all_set(a, b)
+
+    return lambda a, b: _set_eq(a, b)
+
+@overload(operator.ne)
+def set_ne(a, b):
+    check_all_set(a, b)
+
+    def ne_impl(a, b):
+        return not a == b
+
+    return ne_impl
+
+@intrinsic
+def _set_lt(typingctx, a, b):
+    sig = types.boolean(a, b)
+
+    def codegen(context, builder, sig, args):
+        inst = SetInstance(context, builder, sig.args[0], args[0])
+        other = SetInstance(context, builder, sig.args[1], args[1])
+
+        return inst.issubset(other, strict=True)
+
+    return sig, codegen
+
+@overload(operator.lt)
+def set_lt(a, b):
+    check_all_set(a, b)
+
+    return lambda a, b: _set_lt(a, b)
+
+@overload(operator.gt)
+def set_gt(a, b):
+    check_all_set(a, b)
+
+    def gt_impl(a, b):
+        return b < a
+
+    return gt_impl
+
+@lower_builtin(operator.is_, types.Set, types.Set)
+def set_is(context, builder, sig, args):
+    a = SetInstance(context, builder, sig.args[0], args[0])
+    b = SetInstance(context, builder, sig.args[1], args[1])
+    ma = builder.ptrtoint(a.meminfo, cgutils.intp_t)
+    mb = builder.ptrtoint(b.meminfo, cgutils.intp_t)
+    return builder.icmp_signed('==', ma, mb)
+
+
+# -----------------------------------------------------------------------------
+# Implicit casting
+
+@lower_cast(types.Set, types.Set)
+def set_to_set(context, builder, fromty, toty, val):
+    # Casting from non-reflected to reflected
+    assert fromty.dtype == toty.dtype
+    return val
diff --git a/lib/python3.10/site-packages/numba/cpython/unicode_support.py b/lib/python3.10/site-packages/numba/cpython/unicode_support.py
new file mode 100644
index 0000000000000000000000000000000000000000..b42b7e0e6152a215f2329ec345fdebed0f711063
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cpython/unicode_support.py
@@ -0,0 +1,768 @@
+"""
+This module contains support functions for more advanced unicode operations.
+This is not a public API and is for Numba internal use only. Most of the
+functions are relatively straightforward translations of the functions with the
+same name in CPython.
+"""
+from collections import namedtuple
+from enum import IntEnum
+
+import llvmlite.ir
+import numpy as np
+
+from numba.core import types, cgutils, config
+from numba.core.imputils import (impl_ret_untracked)
+
+from numba.core.extending import overload, intrinsic, register_jitable
+from numba.core.errors import TypingError
+
+# This is equivalent to the struct `_PyUnicode_TypeRecord defined in CPython's
+# Objects/unicodectype.c
+typerecord = namedtuple('typerecord',
+                        'upper lower title decimal digit flags')
+
+# The Py_UCS4 type from CPython:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/unicodeobject.h#L112    # noqa: E501
+if config.USE_LEGACY_TYPE_SYSTEM:
+    _Py_UCS4 = types.uint32
+else:
+    _Py_UCS4 = types.c_uint32
+
+# ------------------------------------------------------------------------------
+# Start code related to/from CPython's unicodectype impl
+#
+# NOTE: the original source at:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c   # noqa: E501
+# contains this statement:
+#
+# /*
+#   Unicode character type helpers.
+#
+#   Written by Marc-Andre Lemburg (mal@lemburg.com).
+#   Modified for Python 2.0 by Fredrik Lundh (fredrik@pythonware.com)
+#
+#   Copyright (c) Corporation for National Research Initiatives.
+#
+# */
+
+
+# This enum contains the values defined in CPython's Objects/unicodectype.c that
+# provide masks for use against the various members of the typerecord
+#
+# See: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L13-L27    # noqa: E501
+#
+
+
+_Py_TAB = 0x9
+_Py_LINEFEED = 0xa
+_Py_CARRIAGE_RETURN = 0xd
+_Py_SPACE = 0x20
+
+
+class _PyUnicode_TyperecordMasks(IntEnum):
+    ALPHA_MASK = 0x01
+    DECIMAL_MASK = 0x02
+    DIGIT_MASK = 0x04
+    LOWER_MASK = 0x08
+    LINEBREAK_MASK = 0x10
+    SPACE_MASK = 0x20
+    TITLE_MASK = 0x40
+    UPPER_MASK = 0x80
+    XID_START_MASK = 0x100
+    XID_CONTINUE_MASK = 0x200
+    PRINTABLE_MASK = 0x400
+    NUMERIC_MASK = 0x800
+    CASE_IGNORABLE_MASK = 0x1000
+    CASED_MASK = 0x2000
+    EXTENDED_CASE_MASK = 0x4000
+
+
+def _PyUnicode_gettyperecord(a):
+    raise RuntimeError("Calling the Python definition is invalid")
+
+
+@intrinsic
+def _gettyperecord_impl(typingctx, codepoint):
+    """
+    Provides the binding to numba_gettyperecord, returns a `typerecord`
+    namedtuple of properties from the codepoint.
+    """
+    if not isinstance(codepoint, types.Integer):
+        raise TypingError("codepoint must be an integer")
+
+    def details(context, builder, signature, args):
+        ll_void = context.get_value_type(types.void)
+        ll_Py_UCS4 = context.get_value_type(_Py_UCS4)
+        ll_intc = context.get_value_type(types.intc)
+        ll_intc_ptr = ll_intc.as_pointer()
+        ll_uchar = context.get_value_type(types.uchar)
+        ll_uchar_ptr = ll_uchar.as_pointer()
+        ll_ushort = context.get_value_type(types.ushort)
+        ll_ushort_ptr = ll_ushort.as_pointer()
+        fnty = llvmlite.ir.FunctionType(ll_void, [
+            ll_Py_UCS4,    # code
+            ll_intc_ptr,   # upper
+            ll_intc_ptr,   # lower
+            ll_intc_ptr,   # title
+            ll_uchar_ptr,  # decimal
+            ll_uchar_ptr,  # digit
+            ll_ushort_ptr, # flags
+        ])
+        fn = cgutils.get_or_insert_function(
+            builder.module,
+            fnty, name="numba_gettyperecord")
+        upper = cgutils.alloca_once(builder, ll_intc, name='upper')
+        lower = cgutils.alloca_once(builder, ll_intc, name='lower')
+        title = cgutils.alloca_once(builder, ll_intc, name='title')
+        decimal = cgutils.alloca_once(builder, ll_uchar, name='decimal')
+        digit = cgutils.alloca_once(builder, ll_uchar, name='digit')
+        flags = cgutils.alloca_once(builder, ll_ushort, name='flags')
+
+        byref = [ upper, lower, title, decimal, digit, flags]
+        builder.call(fn, [args[0]] + byref)
+        buf = []
+        for x in byref:
+            buf.append(builder.load(x))
+
+        res = context.make_tuple(builder, signature.return_type, tuple(buf))
+        return impl_ret_untracked(context, builder, signature.return_type, res)
+
+    tupty = types.NamedTuple([types.intc, types.intc, types.intc, types.uchar,
+                              types.uchar, types.ushort], typerecord)
+    sig = tupty(_Py_UCS4)
+    return sig, details
+
+
+@overload(_PyUnicode_gettyperecord)
+def gettyperecord_impl(a):
+    """
+    Provides a _PyUnicode_gettyperecord binding, for convenience it will accept
+    single character strings and code points.
+    """
+    if isinstance(a, types.UnicodeType):
+        from numba.cpython.unicode import _get_code_point
+
+        def impl(a):
+            if len(a) > 1:
+                msg = "gettyperecord takes a single unicode character"
+                raise ValueError(msg)
+            code_point = _get_code_point(a, 0)
+            data = _gettyperecord_impl(_Py_UCS4(code_point))
+            return data
+        return impl
+    if isinstance(a, types.Integer):
+        return lambda a: _gettyperecord_impl(_Py_UCS4(a))
+
+
+# whilst it's possible to grab the _PyUnicode_ExtendedCase symbol as it's global
+# it is safer to use a defined api:
+@intrinsic
+def _PyUnicode_ExtendedCase(typingctx, index):
+    """
+    Accessor function for the _PyUnicode_ExtendedCase array, binds to
+    numba_get_PyUnicode_ExtendedCase which wraps the array and does the lookup
+    """
+    if not isinstance(index, types.Integer):
+        raise TypingError("Expected an index")
+
+    def details(context, builder, signature, args):
+        ll_Py_UCS4 = context.get_value_type(_Py_UCS4)
+        ll_intc = context.get_value_type(types.intc)
+        fnty = llvmlite.ir.FunctionType(ll_Py_UCS4, [ll_intc])
+        fn = cgutils.get_or_insert_function(
+            builder.module,
+            fnty, name="numba_get_PyUnicode_ExtendedCase")
+        return builder.call(fn, [args[0]])
+
+    sig = _Py_UCS4(types.intc)
+    return sig, details
+
+# The following functions are replications of the functions with the same name
+# in CPython's Objects/unicodectype.c
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L64-L71    # noqa: E501
+@register_jitable
+def _PyUnicode_ToTitlecase(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if (ctype.flags & _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK):
+        return _PyUnicode_ExtendedCase(ctype.title & 0xFFFF)
+    return ch + ctype.title
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L76-L81    # noqa: E501
+@register_jitable
+def _PyUnicode_IsTitlecase(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.TITLE_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L86-L91    # noqa: E501
+@register_jitable
+def _PyUnicode_IsXidStart(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.XID_START_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L96-L101    # noqa: E501
+@register_jitable
+def _PyUnicode_IsXidContinue(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.XID_CONTINUE_MASK != 0
+
+
+@register_jitable
+def _PyUnicode_ToDecimalDigit(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if ctype.flags & _PyUnicode_TyperecordMasks.DECIMAL_MASK:
+        return ctype.decimal
+    return -1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L123-L1128  # noqa: E501
+@register_jitable
+def _PyUnicode_ToDigit(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if ctype.flags & _PyUnicode_TyperecordMasks.DIGIT_MASK:
+        return ctype.digit
+    return -1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L140-L145    # noqa: E501
+@register_jitable
+def _PyUnicode_IsNumeric(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.NUMERIC_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L160-L165    # noqa: E501
+@register_jitable
+def _PyUnicode_IsPrintable(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.PRINTABLE_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L170-L175    # noqa: E501
+@register_jitable
+def _PyUnicode_IsLowercase(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.LOWER_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L180-L185    # noqa: E501
+@register_jitable
+def _PyUnicode_IsUppercase(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.UPPER_MASK != 0
+
+
+@register_jitable
+def _PyUnicode_IsLineBreak(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.LINEBREAK_MASK != 0
+
+
+@register_jitable
+def _PyUnicode_ToUppercase(ch):
+    raise NotImplementedError
+
+
+@register_jitable
+def _PyUnicode_ToLowercase(ch):
+    raise NotImplementedError
+
+
+# From: https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodectype.c#L211-L225    # noqa: E501
+@register_jitable
+def _PyUnicode_ToLowerFull(ch, res):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if (ctype.flags & _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK):
+        index = ctype.lower & 0xFFFF
+        n = ctype.lower >> 24
+        for i in range(n):
+            res[i] = _PyUnicode_ExtendedCase(index + i)
+        return n
+    res[0] = ch + ctype.lower
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/201c8f79450628241574fba940e08107178dc3a5/Objects/unicodectype.c#L227-L241    # noqa: E501
+@register_jitable
+def _PyUnicode_ToTitleFull(ch, res):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if (ctype.flags & _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK):
+        index = ctype.title & 0xFFFF
+        n = ctype.title >> 24
+        for i in range(n):
+            res[i] = _PyUnicode_ExtendedCase(index + i)
+        return n
+    res[0] = ch + ctype.title
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L243-L257    # noqa: E501
+@register_jitable
+def _PyUnicode_ToUpperFull(ch, res):
+    ctype = _PyUnicode_gettyperecord(ch)
+    if (ctype.flags & _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK):
+        index = ctype.upper & 0xFFFF
+        n = ctype.upper >> 24
+        for i in range(n):
+            # Perhaps needed to use unicode._set_code_point() here
+            res[i] = _PyUnicode_ExtendedCase(index + i)
+        return n
+    res[0] = ch + ctype.upper
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L259-L272    # noqa: E501
+@register_jitable
+def _PyUnicode_ToFoldedFull(ch, res):
+    ctype = _PyUnicode_gettyperecord(ch)
+    extended_case_mask = _PyUnicode_TyperecordMasks.EXTENDED_CASE_MASK
+    if ctype.flags & extended_case_mask and (ctype.lower >> 20) & 7:
+        index = (ctype.lower & 0xFFFF) + (ctype.lower >> 24)
+        n = (ctype.lower >> 20) & 7
+        for i in range(n):
+            res[i] = _PyUnicode_ExtendedCase(index + i)
+        return n
+    return _PyUnicode_ToLowerFull(ch, res)
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L274-L279    # noqa: E501
+@register_jitable
+def _PyUnicode_IsCased(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.CASED_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L281-L286    # noqa: E501
+@register_jitable
+def _PyUnicode_IsCaseIgnorable(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.CASE_IGNORABLE_MASK != 0
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L123-L135    # noqa: E501
+@register_jitable
+def _PyUnicode_IsDigit(ch):
+    if _PyUnicode_ToDigit(ch) < 0:
+        return 0
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L106-L118    # noqa: E501
+@register_jitable
+def _PyUnicode_IsDecimalDigit(ch):
+    if _PyUnicode_ToDecimalDigit(ch) < 0:
+        return 0
+    return 1
+
+
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Objects/unicodectype.c#L291-L296    # noqa: E501
+@register_jitable
+def _PyUnicode_IsSpace(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.SPACE_MASK != 0
+
+
+@register_jitable
+def _PyUnicode_IsAlpha(ch):
+    ctype = _PyUnicode_gettyperecord(ch)
+    return ctype.flags & _PyUnicode_TyperecordMasks.ALPHA_MASK != 0
+
+
+# End code related to/from CPython's unicodectype impl
+# ------------------------------------------------------------------------------
+
+
+# ------------------------------------------------------------------------------
+# Start code related to/from CPython's pyctype
+
+# From the definition in CPython's Include/pyctype.h
+# From: https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L5-L11    # noqa: E501
+class _PY_CTF(IntEnum):
+    LOWER = 0x01
+    UPPER = 0x02
+    ALPHA = 0x01 | 0x02
+    DIGIT = 0x04
+    ALNUM = 0x01 | 0x02 | 0x04
+    SPACE = 0x08
+    XDIGIT = 0x10
+
+
+# From the definition in CPython's Python/pyctype.c
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Python/pyctype.c#L5    # noqa: E501
+_Py_ctype_table = np.array([
+    0,  # 0x0 '\x00'
+    0,  # 0x1 '\x01'
+    0,  # 0x2 '\x02'
+    0,  # 0x3 '\x03'
+    0,  # 0x4 '\x04'
+    0,  # 0x5 '\x05'
+    0,  # 0x6 '\x06'
+    0,  # 0x7 '\x07'
+    0,  # 0x8 '\x08'
+    _PY_CTF.SPACE,  # 0x9 '\t'
+    _PY_CTF.SPACE,  # 0xa '\n'
+    _PY_CTF.SPACE,  # 0xb '\v'
+    _PY_CTF.SPACE,  # 0xc '\f'
+    _PY_CTF.SPACE,  # 0xd '\r'
+    0,  # 0xe '\x0e'
+    0,  # 0xf '\x0f'
+    0,  # 0x10 '\x10'
+    0,  # 0x11 '\x11'
+    0,  # 0x12 '\x12'
+    0,  # 0x13 '\x13'
+    0,  # 0x14 '\x14'
+    0,  # 0x15 '\x15'
+    0,  # 0x16 '\x16'
+    0,  # 0x17 '\x17'
+    0,  # 0x18 '\x18'
+    0,  # 0x19 '\x19'
+    0,  # 0x1a '\x1a'
+    0,  # 0x1b '\x1b'
+    0,  # 0x1c '\x1c'
+    0,  # 0x1d '\x1d'
+    0,  # 0x1e '\x1e'
+    0,  # 0x1f '\x1f'
+    _PY_CTF.SPACE,  # 0x20 ' '
+    0,  # 0x21 '!'
+    0,  # 0x22 '"'
+    0,  # 0x23 '#'
+    0,  # 0x24 '$'
+    0,  # 0x25 '%'
+    0,  # 0x26 '&'
+    0,  # 0x27 "'"
+    0,  # 0x28 '('
+    0,  # 0x29 ')'
+    0,  # 0x2a '*'
+    0,  # 0x2b '+'
+    0,  # 0x2c ','
+    0,  # 0x2d '-'
+    0,  # 0x2e '.'
+    0,  # 0x2f '/'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x30 '0'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x31 '1'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x32 '2'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x33 '3'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x34 '4'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x35 '5'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x36 '6'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x37 '7'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x38 '8'
+    _PY_CTF.DIGIT | _PY_CTF.XDIGIT,  # 0x39 '9'
+    0,  # 0x3a ':'
+    0,  # 0x3b ';'
+    0,  # 0x3c '<'
+    0,  # 0x3d '='
+    0,  # 0x3e '>'
+    0,  # 0x3f '?'
+    0,  # 0x40 '@'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x41 'A'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x42 'B'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x43 'C'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x44 'D'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x45 'E'
+    _PY_CTF.UPPER | _PY_CTF.XDIGIT,  # 0x46 'F'
+    _PY_CTF.UPPER,  # 0x47 'G'
+    _PY_CTF.UPPER,  # 0x48 'H'
+    _PY_CTF.UPPER,  # 0x49 'I'
+    _PY_CTF.UPPER,  # 0x4a 'J'
+    _PY_CTF.UPPER,  # 0x4b 'K'
+    _PY_CTF.UPPER,  # 0x4c 'L'
+    _PY_CTF.UPPER,  # 0x4d 'M'
+    _PY_CTF.UPPER,  # 0x4e 'N'
+    _PY_CTF.UPPER,  # 0x4f 'O'
+    _PY_CTF.UPPER,  # 0x50 'P'
+    _PY_CTF.UPPER,  # 0x51 'Q'
+    _PY_CTF.UPPER,  # 0x52 'R'
+    _PY_CTF.UPPER,  # 0x53 'S'
+    _PY_CTF.UPPER,  # 0x54 'T'
+    _PY_CTF.UPPER,  # 0x55 'U'
+    _PY_CTF.UPPER,  # 0x56 'V'
+    _PY_CTF.UPPER,  # 0x57 'W'
+    _PY_CTF.UPPER,  # 0x58 'X'
+    _PY_CTF.UPPER,  # 0x59 'Y'
+    _PY_CTF.UPPER,  # 0x5a 'Z'
+    0,  # 0x5b '['
+    0,  # 0x5c '\\'
+    0,  # 0x5d ']'
+    0,  # 0x5e '^'
+    0,  # 0x5f '_'
+    0,  # 0x60 '`'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x61 'a'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x62 'b'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x63 'c'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x64 'd'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x65 'e'
+    _PY_CTF.LOWER | _PY_CTF.XDIGIT,  # 0x66 'f'
+    _PY_CTF.LOWER,  # 0x67 'g'
+    _PY_CTF.LOWER,  # 0x68 'h'
+    _PY_CTF.LOWER,  # 0x69 'i'
+    _PY_CTF.LOWER,  # 0x6a 'j'
+    _PY_CTF.LOWER,  # 0x6b 'k'
+    _PY_CTF.LOWER,  # 0x6c 'l'
+    _PY_CTF.LOWER,  # 0x6d 'm'
+    _PY_CTF.LOWER,  # 0x6e 'n'
+    _PY_CTF.LOWER,  # 0x6f 'o'
+    _PY_CTF.LOWER,  # 0x70 'p'
+    _PY_CTF.LOWER,  # 0x71 'q'
+    _PY_CTF.LOWER,  # 0x72 'r'
+    _PY_CTF.LOWER,  # 0x73 's'
+    _PY_CTF.LOWER,  # 0x74 't'
+    _PY_CTF.LOWER,  # 0x75 'u'
+    _PY_CTF.LOWER,  # 0x76 'v'
+    _PY_CTF.LOWER,  # 0x77 'w'
+    _PY_CTF.LOWER,  # 0x78 'x'
+    _PY_CTF.LOWER,  # 0x79 'y'
+    _PY_CTF.LOWER,  # 0x7a 'z'
+    0,  # 0x7b '{'
+    0,  # 0x7c '|'
+    0,  # 0x7d '}'
+    0,  # 0x7e '~'
+    0,  # 0x7f '\x7f'
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+], dtype=np.intc)
+
+
+# From the definition in CPython's Python/pyctype.c
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Python/pyctype.c#L145    # noqa: E501
+_Py_ctype_tolower = np.array([
+    0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07,
+    0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f,
+    0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16, 0x17,
+    0x18, 0x19, 0x1a, 0x1b, 0x1c, 0x1d, 0x1e, 0x1f,
+    0x20, 0x21, 0x22, 0x23, 0x24, 0x25, 0x26, 0x27,
+    0x28, 0x29, 0x2a, 0x2b, 0x2c, 0x2d, 0x2e, 0x2f,
+    0x30, 0x31, 0x32, 0x33, 0x34, 0x35, 0x36, 0x37,
+    0x38, 0x39, 0x3a, 0x3b, 0x3c, 0x3d, 0x3e, 0x3f,
+    0x40, 0x61, 0x62, 0x63, 0x64, 0x65, 0x66, 0x67,
+    0x68, 0x69, 0x6a, 0x6b, 0x6c, 0x6d, 0x6e, 0x6f,
+    0x70, 0x71, 0x72, 0x73, 0x74, 0x75, 0x76, 0x77,
+    0x78, 0x79, 0x7a, 0x5b, 0x5c, 0x5d, 0x5e, 0x5f,
+    0x60, 0x61, 0x62, 0x63, 0x64, 0x65, 0x66, 0x67,
+    0x68, 0x69, 0x6a, 0x6b, 0x6c, 0x6d, 0x6e, 0x6f,
+    0x70, 0x71, 0x72, 0x73, 0x74, 0x75, 0x76, 0x77,
+    0x78, 0x79, 0x7a, 0x7b, 0x7c, 0x7d, 0x7e, 0x7f,
+    0x80, 0x81, 0x82, 0x83, 0x84, 0x85, 0x86, 0x87,
+    0x88, 0x89, 0x8a, 0x8b, 0x8c, 0x8d, 0x8e, 0x8f,
+    0x90, 0x91, 0x92, 0x93, 0x94, 0x95, 0x96, 0x97,
+    0x98, 0x99, 0x9a, 0x9b, 0x9c, 0x9d, 0x9e, 0x9f,
+    0xa0, 0xa1, 0xa2, 0xa3, 0xa4, 0xa5, 0xa6, 0xa7,
+    0xa8, 0xa9, 0xaa, 0xab, 0xac, 0xad, 0xae, 0xaf,
+    0xb0, 0xb1, 0xb2, 0xb3, 0xb4, 0xb5, 0xb6, 0xb7,
+    0xb8, 0xb9, 0xba, 0xbb, 0xbc, 0xbd, 0xbe, 0xbf,
+    0xc0, 0xc1, 0xc2, 0xc3, 0xc4, 0xc5, 0xc6, 0xc7,
+    0xc8, 0xc9, 0xca, 0xcb, 0xcc, 0xcd, 0xce, 0xcf,
+    0xd0, 0xd1, 0xd2, 0xd3, 0xd4, 0xd5, 0xd6, 0xd7,
+    0xd8, 0xd9, 0xda, 0xdb, 0xdc, 0xdd, 0xde, 0xdf,
+    0xe0, 0xe1, 0xe2, 0xe3, 0xe4, 0xe5, 0xe6, 0xe7,
+    0xe8, 0xe9, 0xea, 0xeb, 0xec, 0xed, 0xee, 0xef,
+    0xf0, 0xf1, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7,
+    0xf8, 0xf9, 0xfa, 0xfb, 0xfc, 0xfd, 0xfe, 0xff,
+], dtype=np.uint8)
+
+
+# From the definition in CPython's Python/pyctype.c
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Python/pyctype.c#L180
+_Py_ctype_toupper = np.array([
+    0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07,
+    0x08, 0x09, 0x0a, 0x0b, 0x0c, 0x0d, 0x0e, 0x0f,
+    0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16, 0x17,
+    0x18, 0x19, 0x1a, 0x1b, 0x1c, 0x1d, 0x1e, 0x1f,
+    0x20, 0x21, 0x22, 0x23, 0x24, 0x25, 0x26, 0x27,
+    0x28, 0x29, 0x2a, 0x2b, 0x2c, 0x2d, 0x2e, 0x2f,
+    0x30, 0x31, 0x32, 0x33, 0x34, 0x35, 0x36, 0x37,
+    0x38, 0x39, 0x3a, 0x3b, 0x3c, 0x3d, 0x3e, 0x3f,
+    0x40, 0x41, 0x42, 0x43, 0x44, 0x45, 0x46, 0x47,
+    0x48, 0x49, 0x4a, 0x4b, 0x4c, 0x4d, 0x4e, 0x4f,
+    0x50, 0x51, 0x52, 0x53, 0x54, 0x55, 0x56, 0x57,
+    0x58, 0x59, 0x5a, 0x5b, 0x5c, 0x5d, 0x5e, 0x5f,
+    0x60, 0x41, 0x42, 0x43, 0x44, 0x45, 0x46, 0x47,
+    0x48, 0x49, 0x4a, 0x4b, 0x4c, 0x4d, 0x4e, 0x4f,
+    0x50, 0x51, 0x52, 0x53, 0x54, 0x55, 0x56, 0x57,
+    0x58, 0x59, 0x5a, 0x7b, 0x7c, 0x7d, 0x7e, 0x7f,
+    0x80, 0x81, 0x82, 0x83, 0x84, 0x85, 0x86, 0x87,
+    0x88, 0x89, 0x8a, 0x8b, 0x8c, 0x8d, 0x8e, 0x8f,
+    0x90, 0x91, 0x92, 0x93, 0x94, 0x95, 0x96, 0x97,
+    0x98, 0x99, 0x9a, 0x9b, 0x9c, 0x9d, 0x9e, 0x9f,
+    0xa0, 0xa1, 0xa2, 0xa3, 0xa4, 0xa5, 0xa6, 0xa7,
+    0xa8, 0xa9, 0xaa, 0xab, 0xac, 0xad, 0xae, 0xaf,
+    0xb0, 0xb1, 0xb2, 0xb3, 0xb4, 0xb5, 0xb6, 0xb7,
+    0xb8, 0xb9, 0xba, 0xbb, 0xbc, 0xbd, 0xbe, 0xbf,
+    0xc0, 0xc1, 0xc2, 0xc3, 0xc4, 0xc5, 0xc6, 0xc7,
+    0xc8, 0xc9, 0xca, 0xcb, 0xcc, 0xcd, 0xce, 0xcf,
+    0xd0, 0xd1, 0xd2, 0xd3, 0xd4, 0xd5, 0xd6, 0xd7,
+    0xd8, 0xd9, 0xda, 0xdb, 0xdc, 0xdd, 0xde, 0xdf,
+    0xe0, 0xe1, 0xe2, 0xe3, 0xe4, 0xe5, 0xe6, 0xe7,
+    0xe8, 0xe9, 0xea, 0xeb, 0xec, 0xed, 0xee, 0xef,
+    0xf0, 0xf1, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7,
+    0xf8, 0xf9, 0xfa, 0xfb, 0xfc, 0xfd, 0xfe, 0xff,
+], dtype=np.uint8)
+
+
+class _PY_CTF_LB(IntEnum):
+    LINE_BREAK = 0x01
+    LINE_FEED = 0x02
+    CARRIAGE_RETURN = 0x04
+
+
+_Py_ctype_islinebreak = np.array([
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    _PY_CTF_LB.LINE_BREAK | _PY_CTF_LB.LINE_FEED,  # 0xa '\n'
+    _PY_CTF_LB.LINE_BREAK,  # 0xb '\v'
+    _PY_CTF_LB.LINE_BREAK,  # 0xc '\f'
+    _PY_CTF_LB.LINE_BREAK | _PY_CTF_LB.CARRIAGE_RETURN,  # 0xd '\r'
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    _PY_CTF_LB.LINE_BREAK,  # 0x1c '\x1c'
+    _PY_CTF_LB.LINE_BREAK,  # 0x1d '\x1d'
+    _PY_CTF_LB.LINE_BREAK,  # 0x1e '\x1e'
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    _PY_CTF_LB.LINE_BREAK,  # 0x85 '\x85'
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0,
+], dtype=np.intc)
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pymacro.h#L25    # noqa: E501
+@register_jitable
+def _Py_CHARMASK(ch):
+    """
+    Equivalent to the CPython macro `Py_CHARMASK()`, masks off all but the
+    lowest 256 bits of ch.
+    """
+    return types.uint8(ch) & types.uint8(0xff)
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L30    # noqa: E501
+@register_jitable
+def _Py_TOUPPER(ch):
+    """
+    Equivalent to the CPython macro `Py_TOUPPER()` converts an ASCII range
+    code point to the upper equivalent
+    """
+    return _Py_ctype_toupper[_Py_CHARMASK(ch)]
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L29    # noqa: E501
+@register_jitable
+def _Py_TOLOWER(ch):
+    """
+    Equivalent to the CPython macro `Py_TOLOWER()` converts an ASCII range
+    code point to the lower equivalent
+    """
+    return _Py_ctype_tolower[_Py_CHARMASK(ch)]
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L18    # noqa: E501
+@register_jitable
+def _Py_ISLOWER(ch):
+    """
+    Equivalent to the CPython macro `Py_ISLOWER()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.LOWER
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L19    # noqa: E501
+@register_jitable
+def _Py_ISUPPER(ch):
+    """
+    Equivalent to the CPython macro `Py_ISUPPER()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.UPPER
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L20    # noqa: E501
+@register_jitable
+def _Py_ISALPHA(ch):
+    """
+    Equivalent to the CPython macro `Py_ISALPHA()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.ALPHA
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L21    # noqa: E501
+@register_jitable
+def _Py_ISDIGIT(ch):
+    """
+    Equivalent to the CPython macro `Py_ISDIGIT()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.DIGIT
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L22    # noqa: E501
+@register_jitable
+def _Py_ISXDIGIT(ch):
+    """
+    Equivalent to the CPython macro `Py_ISXDIGIT()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.XDIGIT
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L23    # noqa: E501
+@register_jitable
+def _Py_ISALNUM(ch):
+    """
+    Equivalent to the CPython macro `Py_ISALNUM()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.ALNUM
+
+
+# Translation of:
+# https://github.com/python/cpython/blob/1d4b6ba19466aba0eb91c4ba01ba509acf18c723/Include/pyctype.h#L24    # noqa: E501
+@register_jitable
+def _Py_ISSPACE(ch):
+    """
+    Equivalent to the CPython macro `Py_ISSPACE()`
+    """
+    return _Py_ctype_table[_Py_CHARMASK(ch)] & _PY_CTF.SPACE
+
+
+@register_jitable
+def _Py_ISLINEBREAK(ch):
+    """Check if character is ASCII line break"""
+    return _Py_ctype_islinebreak[_Py_CHARMASK(ch)] & _PY_CTF_LB.LINE_BREAK
+
+
+@register_jitable
+def _Py_ISLINEFEED(ch):
+    """Check if character is line feed `\n`"""
+    return _Py_ctype_islinebreak[_Py_CHARMASK(ch)] & _PY_CTF_LB.LINE_FEED
+
+
+@register_jitable
+def _Py_ISCARRIAGERETURN(ch):
+    """Check if character is carriage return `\r`"""
+    return _Py_ctype_islinebreak[_Py_CHARMASK(ch)] & _PY_CTF_LB.CARRIAGE_RETURN
+
+
+# End code related to/from CPython's pyctype
+# ------------------------------------------------------------------------------
diff --git a/lib/python3.10/site-packages/numba/cuda/cudadrv/devices.py b/lib/python3.10/site-packages/numba/cuda/cudadrv/devices.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cc9e2e393fc07276f01c5c4c14952bd81f04b50
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/cudadrv/devices.py
@@ -0,0 +1,248 @@
+"""
+Expose each GPU devices directly.
+
+This module implements a API that is like the "CUDA runtime" context manager
+for managing CUDA context stack and clean up.  It relies on thread-local globals
+to separate the context stack management of each thread. Contexts are also
+shareable among threads.  Only the main thread can destroy Contexts.
+
+Note:
+- This module must be imported by the main-thread.
+
+"""
+import functools
+import threading
+from contextlib import contextmanager
+
+from .driver import driver, USE_NV_BINDING
+
+
+class _DeviceList(object):
+    def __getattr__(self, attr):
+        # First time looking at "lst" attribute.
+        if attr == "lst":
+            # Device list is not initialized.
+            # Query all CUDA devices.
+            numdev = driver.get_device_count()
+            gpus = [_DeviceContextManager(driver.get_device(devid))
+                    for devid in range(numdev)]
+            # Define "lst" to avoid re-initialization
+            self.lst = gpus
+            return gpus
+
+        # Other attributes
+        return super(_DeviceList, self).__getattr__(attr)
+
+    def __getitem__(self, devnum):
+        '''
+        Returns the context manager for device *devnum*.
+        '''
+        return self.lst[devnum]
+
+    def __str__(self):
+        return ', '.join([str(d) for d in self.lst])
+
+    def __iter__(self):
+        return iter(self.lst)
+
+    def __len__(self):
+        return len(self.lst)
+
+    @property
+    def current(self):
+        """Returns the active device or None if there's no active device
+        """
+        with driver.get_active_context() as ac:
+            devnum = ac.devnum
+            if devnum is not None:
+                return self[devnum]
+
+
+class _DeviceContextManager(object):
+    """
+    Provides a context manager for executing in the context of the chosen
+    device. The normal use of instances of this type is from
+    ``numba.cuda.gpus``. For example, to execute on device 2::
+
+       with numba.cuda.gpus[2]:
+           d_a = numba.cuda.to_device(a)
+
+    to copy the array *a* onto device 2, referred to by *d_a*.
+    """
+
+    def __init__(self, device):
+        self._device = device
+
+    def __getattr__(self, item):
+        return getattr(self._device, item)
+
+    def __enter__(self):
+        _runtime.get_or_create_context(self._device.id)
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        # this will verify that we are popping the right device context.
+        self._device.get_primary_context().pop()
+
+    def __str__(self):
+        return "<Managed Device {self.id}>".format(self=self)
+
+
+class _Runtime(object):
+    """Emulate the CUDA runtime context management.
+
+    It owns all Devices and Contexts.
+    Keeps at most one Context per Device
+    """
+
+    def __init__(self):
+        self.gpus = _DeviceList()
+
+        # For caching the attached CUDA Context
+        self._tls = threading.local()
+
+        # Remember the main thread
+        # Only the main thread can *actually* destroy
+        self._mainthread = threading.current_thread()
+
+        # Avoid mutation of runtime state in multithreaded programs
+        self._lock = threading.RLock()
+
+    @contextmanager
+    def ensure_context(self):
+        """Ensure a CUDA context is available inside the context.
+
+        On entrance, queries the CUDA driver for an active CUDA context and
+        attaches it in TLS for subsequent calls so they do not need to query
+        the CUDA driver again.  On exit, detach the CUDA context from the TLS.
+
+        This will allow us to pickup thirdparty activated CUDA context in
+        any top-level Numba CUDA API.
+        """
+        with driver.get_active_context():
+            oldctx = self._get_attached_context()
+            newctx = self.get_or_create_context(None)
+            self._set_attached_context(newctx)
+            try:
+                yield
+            finally:
+                self._set_attached_context(oldctx)
+
+    def get_or_create_context(self, devnum):
+        """Returns the primary context and push+create it if needed
+        for *devnum*.  If *devnum* is None, use the active CUDA context (must
+        be primary) or create a new one with ``devnum=0``.
+        """
+        if devnum is None:
+            attached_ctx = self._get_attached_context()
+            if attached_ctx is None:
+                return self._get_or_create_context_uncached(devnum)
+            else:
+                return attached_ctx
+        else:
+            if USE_NV_BINDING:
+                devnum = int(devnum)
+            return self._activate_context_for(devnum)
+
+    def _get_or_create_context_uncached(self, devnum):
+        """See also ``get_or_create_context(devnum)``.
+        This version does not read the cache.
+        """
+        with self._lock:
+            # Try to get the active context in the CUDA stack or
+            # activate GPU-0 with the primary context
+            with driver.get_active_context() as ac:
+                if not ac:
+                    return self._activate_context_for(0)
+                else:
+                    # Get primary context for the active device
+                    ctx = self.gpus[ac.devnum].get_primary_context()
+                    # Is active context the primary context?
+                    if USE_NV_BINDING:
+                        ctx_handle = int(ctx.handle)
+                        ac_ctx_handle = int(ac.context_handle)
+                    else:
+                        ctx_handle = ctx.handle.value
+                        ac_ctx_handle = ac.context_handle.value
+                    if ctx_handle != ac_ctx_handle:
+                        msg = ('Numba cannot operate on non-primary'
+                               ' CUDA context {:x}')
+                        raise RuntimeError(msg.format(ac_ctx_handle))
+                    # Ensure the context is ready
+                    ctx.prepare_for_use()
+                return ctx
+
+    def _activate_context_for(self, devnum):
+        with self._lock:
+            gpu = self.gpus[devnum]
+            newctx = gpu.get_primary_context()
+            # Detect unexpected context switch
+            cached_ctx = self._get_attached_context()
+            if cached_ctx is not None and cached_ctx is not newctx:
+                raise RuntimeError('Cannot switch CUDA-context.')
+            newctx.push()
+            return newctx
+
+    def _get_attached_context(self):
+        return getattr(self._tls, 'attached_context', None)
+
+    def _set_attached_context(self, ctx):
+        self._tls.attached_context = ctx
+
+    def reset(self):
+        """Clear all contexts in the thread.  Destroy the context if and only
+        if we are in the main thread.
+        """
+        # Pop all active context.
+        while driver.pop_active_context() is not None:
+            pass
+
+        # If it is the main thread
+        if threading.current_thread() == self._mainthread:
+            self._destroy_all_contexts()
+
+    def _destroy_all_contexts(self):
+        # Reset all devices
+        for gpu in self.gpus:
+            gpu.reset()
+
+
+_runtime = _Runtime()
+
+# ================================ PUBLIC API ================================
+
+gpus = _runtime.gpus
+
+
+def get_context(devnum=None):
+    """Get the current device or use a device by device number, and
+    return the CUDA context.
+    """
+    return _runtime.get_or_create_context(devnum)
+
+
+def require_context(fn):
+    """
+    A decorator that ensures a CUDA context is available when *fn* is executed.
+
+    Note: The function *fn* cannot switch CUDA-context.
+    """
+    @functools.wraps(fn)
+    def _require_cuda_context(*args, **kws):
+        with _runtime.ensure_context():
+            return fn(*args, **kws)
+
+    return _require_cuda_context
+
+
+def reset():
+    """Reset the CUDA subsystem for the current thread.
+
+    In the main thread:
+    This removes all CUDA contexts.  Only use this at shutdown or for
+    cleaning up between tests.
+
+    In non-main threads:
+    This clear the CUDA context stack only.
+
+    """
+    _runtime.reset()
diff --git a/lib/python3.10/site-packages/numba/cuda/cudadrv/libs.py b/lib/python3.10/site-packages/numba/cuda/cudadrv/libs.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce3ed9c96e4057b9742f1a12021657fc40ea2580
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/cudadrv/libs.py
@@ -0,0 +1,176 @@
+"""CUDA Toolkit libraries lookup utilities.
+
+CUDA Toolkit libraries can be available via either:
+
+- the `cuda-nvcc` and `cuda-nvrtc` conda packages for CUDA 12,
+- the `cudatoolkit` conda package for CUDA 11,
+- a user supplied location from CUDA_HOME,
+- a system wide location,
+- package-specific locations (e.g. the Debian NVIDIA packages),
+- or can be discovered by the system loader.
+"""
+
+import os
+import sys
+import ctypes
+
+from numba.misc.findlib import find_lib
+from numba.cuda.cuda_paths import get_cuda_paths
+from numba.cuda.cudadrv.driver import locate_driver_and_loader, load_driver
+from numba.cuda.cudadrv.error import CudaSupportError
+
+
+if sys.platform == 'win32':
+    _dllnamepattern = '%s.dll'
+    _staticnamepattern = '%s.lib'
+elif sys.platform == 'darwin':
+    _dllnamepattern = 'lib%s.dylib'
+    _staticnamepattern = 'lib%s.a'
+else:
+    _dllnamepattern = 'lib%s.so'
+    _staticnamepattern = 'lib%s.a'
+
+
+def get_libdevice():
+    d = get_cuda_paths()
+    paths = d['libdevice'].info
+    return paths
+
+
+def open_libdevice():
+    with open(get_libdevice(), 'rb') as bcfile:
+        return bcfile.read()
+
+
+def get_cudalib(lib, static=False):
+    """
+    Find the path of a CUDA library based on a search of known locations. If
+    the search fails, return a generic filename for the library (e.g.
+    'libnvvm.so' for 'nvvm') so that we may attempt to load it using the system
+    loader's search mechanism.
+    """
+    if lib == 'nvvm':
+        return get_cuda_paths()['nvvm'].info or _dllnamepattern % 'nvvm'
+    else:
+        dir_type = 'static_cudalib_dir' if static else 'cudalib_dir'
+        libdir = get_cuda_paths()[dir_type].info
+
+    candidates = find_lib(lib, libdir, static=static)
+    namepattern = _staticnamepattern if static else _dllnamepattern
+    return max(candidates) if candidates else namepattern % lib
+
+
+def open_cudalib(lib):
+    path = get_cudalib(lib)
+    return ctypes.CDLL(path)
+
+
+def check_static_lib(path):
+    if not os.path.isfile(path):
+        raise FileNotFoundError(f'{path} not found')
+
+
+def _get_source_variable(lib, static=False):
+    if lib == 'nvvm':
+        return get_cuda_paths()['nvvm'].by
+    elif lib == 'libdevice':
+        return get_cuda_paths()['libdevice'].by
+    else:
+        dir_type = 'static_cudalib_dir' if static else 'cudalib_dir'
+        return get_cuda_paths()[dir_type].by
+
+
+def test():
+    """Test library lookup.  Path info is printed to stdout.
+    """
+    failed = False
+
+    # Check for the driver
+    try:
+        dlloader, candidates = locate_driver_and_loader()
+        print('Finding driver from candidates:')
+        for location in candidates:
+            print(f'\t{location}')
+        print(f'Using loader {dlloader}')
+        print('\tTrying to load driver', end='...')
+        dll, path = load_driver(dlloader, candidates)
+        print('\tok')
+        print(f'\t\tLoaded from {path}')
+    except CudaSupportError as e:
+        print(f'\tERROR: failed to open driver: {e}')
+        failed = True
+
+    # Find the absolute location of the driver on Linux. Various driver-related
+    # issues have been reported by WSL2 users, and it is almost always due to a
+    # Linux (i.e. not- WSL2) driver being installed in a WSL2 system.
+    # Providing the absolute location of the driver indicates its version
+    # number in the soname (e.g. "libcuda.so.530.30.02"), which can be used to
+    # look up whether the driver was intended for "native" Linux.
+    if sys.platform == 'linux' and not failed:
+        pid = os.getpid()
+        mapsfile = os.path.join(os.path.sep, 'proc', f'{pid}', 'maps')
+        try:
+            with open(mapsfile) as f:
+                maps = f.read()
+        # It's difficult to predict all that might go wrong reading the maps
+        # file - in case various error conditions ensue (the file is not found,
+        # not readable, etc.) we use OSError to hopefully catch any of them.
+        except OSError:
+            # It's helpful to report that this went wrong to the user, but we
+            # don't set failed to True because this doesn't have any connection
+            # to actual CUDA functionality.
+            print(f'\tERROR: Could not open {mapsfile} to determine absolute '
+                  'path to libcuda.so')
+        else:
+            # In this case we could read the maps, so we can report the
+            # relevant ones to the user
+            locations = set(s for s in maps.split() if 'libcuda.so' in s)
+            print('\tMapped libcuda.so paths:')
+            for location in locations:
+                print(f'\t\t{location}')
+
+    # Checks for dynamic libraries
+    libs = 'nvvm nvrtc cudart'.split()
+    for lib in libs:
+        path = get_cudalib(lib)
+        print('Finding {} from {}'.format(lib, _get_source_variable(lib)))
+        print('\tLocated at', path)
+
+        try:
+            print('\tTrying to open library', end='...')
+            open_cudalib(lib)
+            print('\tok')
+        except OSError as e:
+            print('\tERROR: failed to open %s:\n%s' % (lib, e))
+            failed = True
+
+    # Check for cudadevrt (the only static library)
+    lib = 'cudadevrt'
+    path = get_cudalib(lib, static=True)
+    print('Finding {} from {}'.format(lib, _get_source_variable(lib,
+                                                                static=True)))
+    print('\tLocated at', path)
+
+    try:
+        print('\tChecking library', end='...')
+        check_static_lib(path)
+        print('\tok')
+    except FileNotFoundError as e:
+        print('\tERROR: failed to find %s:\n%s' % (lib, e))
+        failed = True
+
+    # Check for libdevice
+    where = _get_source_variable('libdevice')
+    print(f'Finding libdevice from {where}')
+    path = get_libdevice()
+    print('\tLocated at', path)
+
+    try:
+        print('\tChecking library', end='...')
+        check_static_lib(path)
+        print('\tok')
+    except FileNotFoundError as e:
+        print('\tERROR: failed to find %s:\n%s' % (lib, e))
+        failed = True
+
+    return not failed
diff --git a/lib/python3.10/site-packages/numba/cuda/cudadrv/ndarray.py b/lib/python3.10/site-packages/numba/cuda/cudadrv/ndarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..bca40dfd977dc3c657835d93fd45142d16fe46f7
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/cudadrv/ndarray.py
@@ -0,0 +1,20 @@
+from numba.cuda.cudadrv import devices, driver
+from numba.core.registry import cpu_target
+
+
+def _calc_array_sizeof(ndim):
+    """
+    Use the ABI size in the CPU target
+    """
+    ctx = cpu_target.target_context
+    return ctx.calc_array_sizeof(ndim)
+
+
+def ndarray_device_allocate_data(ary):
+    """
+    Allocate gpu data buffer
+    """
+    datasize = driver.host_memory_size(ary)
+    # allocate
+    gpu_data = devices.get_context().memalloc(datasize)
+    return gpu_data
diff --git a/lib/python3.10/site-packages/numba/cuda/cudadrv/nvvm.py b/lib/python3.10/site-packages/numba/cuda/cudadrv/nvvm.py
new file mode 100644
index 0000000000000000000000000000000000000000..1da13a325cae089f7bb60aaadffd0d48217e17fe
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/cudadrv/nvvm.py
@@ -0,0 +1,707 @@
+"""
+This is a direct translation of nvvm.h
+"""
+import logging
+import re
+import sys
+import warnings
+from ctypes import (c_void_p, c_int, POINTER, c_char_p, c_size_t, byref,
+                    c_char)
+
+import threading
+
+from llvmlite import ir
+
+from .error import NvvmError, NvvmSupportError, NvvmWarning
+from .libs import get_libdevice, open_libdevice, open_cudalib
+from numba.core import cgutils, config
+
+
+logger = logging.getLogger(__name__)
+
+ADDRSPACE_GENERIC = 0
+ADDRSPACE_GLOBAL = 1
+ADDRSPACE_SHARED = 3
+ADDRSPACE_CONSTANT = 4
+ADDRSPACE_LOCAL = 5
+
+# Opaque handle for compilation unit
+nvvm_program = c_void_p
+
+# Result code
+nvvm_result = c_int
+
+RESULT_CODE_NAMES = '''
+NVVM_SUCCESS
+NVVM_ERROR_OUT_OF_MEMORY
+NVVM_ERROR_PROGRAM_CREATION_FAILURE
+NVVM_ERROR_IR_VERSION_MISMATCH
+NVVM_ERROR_INVALID_INPUT
+NVVM_ERROR_INVALID_PROGRAM
+NVVM_ERROR_INVALID_IR
+NVVM_ERROR_INVALID_OPTION
+NVVM_ERROR_NO_MODULE_IN_PROGRAM
+NVVM_ERROR_COMPILATION
+'''.split()
+
+for i, k in enumerate(RESULT_CODE_NAMES):
+    setattr(sys.modules[__name__], k, i)
+
+# Data layouts. NVVM IR 1.8 (CUDA 11.6) introduced 128-bit integer support.
+
+_datalayout_original = ('e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-'
+                        'i64:64:64-f32:32:32-f64:64:64-v16:16:16-v32:32:32-'
+                        'v64:64:64-v128:128:128-n16:32:64')
+_datalayout_i128 = ('e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-'
+                    'i128:128:128-f32:32:32-f64:64:64-v16:16:16-v32:32:32-'
+                    'v64:64:64-v128:128:128-n16:32:64')
+
+
+def is_available():
+    """
+    Return if libNVVM is available
+    """
+    try:
+        NVVM()
+    except NvvmSupportError:
+        return False
+    else:
+        return True
+
+
+_nvvm_lock = threading.Lock()
+
+
+class NVVM(object):
+    '''Process-wide singleton.
+    '''
+    _PROTOTYPES = {
+
+        # nvvmResult nvvmVersion(int *major, int *minor)
+        'nvvmVersion': (nvvm_result, POINTER(c_int), POINTER(c_int)),
+
+        # nvvmResult nvvmCreateProgram(nvvmProgram *cu)
+        'nvvmCreateProgram': (nvvm_result, POINTER(nvvm_program)),
+
+        # nvvmResult nvvmDestroyProgram(nvvmProgram *cu)
+        'nvvmDestroyProgram': (nvvm_result, POINTER(nvvm_program)),
+
+        # nvvmResult nvvmAddModuleToProgram(nvvmProgram cu, const char *buffer,
+        #                                   size_t size, const char *name)
+        'nvvmAddModuleToProgram': (
+            nvvm_result, nvvm_program, c_char_p, c_size_t, c_char_p),
+
+        # nvvmResult nvvmLazyAddModuleToProgram(nvvmProgram cu,
+        #                                       const char* buffer,
+        #                                       size_t size,
+        #                                       const char *name)
+        'nvvmLazyAddModuleToProgram': (
+            nvvm_result, nvvm_program, c_char_p, c_size_t, c_char_p),
+
+        # nvvmResult nvvmCompileProgram(nvvmProgram cu, int numOptions,
+        #                          const char **options)
+        'nvvmCompileProgram': (
+            nvvm_result, nvvm_program, c_int, POINTER(c_char_p)),
+
+        # nvvmResult nvvmGetCompiledResultSize(nvvmProgram cu,
+        #                                      size_t *bufferSizeRet)
+        'nvvmGetCompiledResultSize': (
+            nvvm_result, nvvm_program, POINTER(c_size_t)),
+
+        # nvvmResult nvvmGetCompiledResult(nvvmProgram cu, char *buffer)
+        'nvvmGetCompiledResult': (nvvm_result, nvvm_program, c_char_p),
+
+        # nvvmResult nvvmGetProgramLogSize(nvvmProgram cu,
+        #                                      size_t *bufferSizeRet)
+        'nvvmGetProgramLogSize': (nvvm_result, nvvm_program, POINTER(c_size_t)),
+
+        # nvvmResult nvvmGetProgramLog(nvvmProgram cu, char *buffer)
+        'nvvmGetProgramLog': (nvvm_result, nvvm_program, c_char_p),
+
+        # nvvmResult nvvmIRVersion (int* majorIR, int* minorIR, int* majorDbg,
+        #                           int* minorDbg )
+        'nvvmIRVersion': (nvvm_result, POINTER(c_int), POINTER(c_int),
+                          POINTER(c_int), POINTER(c_int)),
+        # nvvmResult nvvmVerifyProgram (nvvmProgram prog, int numOptions,
+        #                               const char** options)
+        'nvvmVerifyProgram': (nvvm_result, nvvm_program, c_int,
+                              POINTER(c_char_p))
+    }
+
+    # Singleton reference
+    __INSTANCE = None
+
+    def __new__(cls):
+        with _nvvm_lock:
+            if cls.__INSTANCE is None:
+                cls.__INSTANCE = inst = object.__new__(cls)
+                try:
+                    inst.driver = open_cudalib('nvvm')
+                except OSError as e:
+                    cls.__INSTANCE = None
+                    errmsg = ("libNVVM cannot be found. Do `conda install "
+                              "cudatoolkit`:\n%s")
+                    raise NvvmSupportError(errmsg % e)
+
+                # Find & populate functions
+                for name, proto in inst._PROTOTYPES.items():
+                    func = getattr(inst.driver, name)
+                    func.restype = proto[0]
+                    func.argtypes = proto[1:]
+                    setattr(inst, name, func)
+
+        return cls.__INSTANCE
+
+    def __init__(self):
+        ir_versions = self.get_ir_version()
+        self._majorIR = ir_versions[0]
+        self._minorIR = ir_versions[1]
+        self._majorDbg = ir_versions[2]
+        self._minorDbg = ir_versions[3]
+        self._supported_ccs = get_supported_ccs()
+
+    @property
+    def data_layout(self):
+        if (self._majorIR, self._minorIR) < (1, 8):
+            return _datalayout_original
+        else:
+            return _datalayout_i128
+
+    @property
+    def supported_ccs(self):
+        return self._supported_ccs
+
+    def get_version(self):
+        major = c_int()
+        minor = c_int()
+        err = self.nvvmVersion(byref(major), byref(minor))
+        self.check_error(err, 'Failed to get version.')
+        return major.value, minor.value
+
+    def get_ir_version(self):
+        majorIR = c_int()
+        minorIR = c_int()
+        majorDbg = c_int()
+        minorDbg = c_int()
+        err = self.nvvmIRVersion(byref(majorIR), byref(minorIR),
+                                 byref(majorDbg), byref(minorDbg))
+        self.check_error(err, 'Failed to get IR version.')
+        return majorIR.value, minorIR.value, majorDbg.value, minorDbg.value
+
+    def check_error(self, error, msg, exit=False):
+        if error:
+            exc = NvvmError(msg, RESULT_CODE_NAMES[error])
+            if exit:
+                print(exc)
+                sys.exit(1)
+            else:
+                raise exc
+
+
+class CompilationUnit(object):
+    def __init__(self):
+        self.driver = NVVM()
+        self._handle = nvvm_program()
+        err = self.driver.nvvmCreateProgram(byref(self._handle))
+        self.driver.check_error(err, 'Failed to create CU')
+
+    def __del__(self):
+        driver = NVVM()
+        err = driver.nvvmDestroyProgram(byref(self._handle))
+        driver.check_error(err, 'Failed to destroy CU', exit=True)
+
+    def add_module(self, buffer):
+        """
+         Add a module level NVVM IR to a compilation unit.
+         - The buffer should contain an NVVM module IR either in the bitcode
+           representation (LLVM3.0) or in the text representation.
+        """
+        err = self.driver.nvvmAddModuleToProgram(self._handle, buffer,
+                                                 len(buffer), None)
+        self.driver.check_error(err, 'Failed to add module')
+
+    def lazy_add_module(self, buffer):
+        """
+        Lazily add an NVVM IR module to a compilation unit.
+        The buffer should contain NVVM module IR either in the bitcode
+        representation or in the text representation.
+        """
+        err = self.driver.nvvmLazyAddModuleToProgram(self._handle, buffer,
+                                                     len(buffer), None)
+        self.driver.check_error(err, 'Failed to add module')
+
+    def compile(self, **options):
+        """Perform Compilation.
+
+        Compilation options are accepted as keyword arguments, with the
+        following considerations:
+
+        - Underscores (`_`) in option names are converted to dashes (`-`), to
+          match NVVM's option name format.
+        - Options that take a value will be emitted in the form
+          "-<name>=<value>".
+        - Booleans passed as option values will be converted to integers.
+        - Options which take no value (such as `-gen-lto`) should have a value
+          of `None` passed in and will be emitted in the form "-<name>".
+
+        For documentation on NVVM compilation options, see the CUDA Toolkit
+        Documentation:
+
+        https://docs.nvidia.com/cuda/libnvvm-api/index.html#_CPPv418nvvmCompileProgram11nvvmProgramiPPKc
+        """
+
+        def stringify_option(k, v):
+            k = k.replace('_', '-')
+
+            if v is None:
+                return f'-{k}'
+
+            if isinstance(v, bool):
+                v = int(v)
+
+            return f'-{k}={v}'
+
+        options = [stringify_option(k, v) for k, v in options.items()]
+
+        c_opts = (c_char_p * len(options))(*[c_char_p(x.encode('utf8'))
+                                             for x in options])
+        # verify
+        err = self.driver.nvvmVerifyProgram(self._handle, len(options), c_opts)
+        self._try_error(err, 'Failed to verify\n')
+
+        # compile
+        err = self.driver.nvvmCompileProgram(self._handle, len(options), c_opts)
+        self._try_error(err, 'Failed to compile\n')
+
+        # get result
+        reslen = c_size_t()
+        err = self.driver.nvvmGetCompiledResultSize(self._handle, byref(reslen))
+
+        self._try_error(err, 'Failed to get size of compiled result.')
+
+        output_buffer = (c_char * reslen.value)()
+        err = self.driver.nvvmGetCompiledResult(self._handle, output_buffer)
+        self._try_error(err, 'Failed to get compiled result.')
+
+        # get log
+        self.log = self.get_log()
+        if self.log:
+            warnings.warn(self.log, category=NvvmWarning)
+
+        return output_buffer[:]
+
+    def _try_error(self, err, msg):
+        self.driver.check_error(err, "%s\n%s" % (msg, self.get_log()))
+
+    def get_log(self):
+        reslen = c_size_t()
+        err = self.driver.nvvmGetProgramLogSize(self._handle, byref(reslen))
+        self.driver.check_error(err, 'Failed to get compilation log size.')
+
+        if reslen.value > 1:
+            logbuf = (c_char * reslen.value)()
+            err = self.driver.nvvmGetProgramLog(self._handle, logbuf)
+            self.driver.check_error(err, 'Failed to get compilation log.')
+
+            return logbuf.value.decode('utf8')  # populate log attribute
+
+        return ''
+
+
+COMPUTE_CAPABILITIES = (
+    (3, 5), (3, 7),
+    (5, 0), (5, 2), (5, 3),
+    (6, 0), (6, 1), (6, 2),
+    (7, 0), (7, 2), (7, 5),
+    (8, 0), (8, 6), (8, 7), (8, 9),
+    (9, 0)
+)
+
+# Maps CTK version -> (min supported cc, max supported cc) inclusive
+CTK_SUPPORTED = {
+    (11, 2): ((3, 5), (8, 6)),
+    (11, 3): ((3, 5), (8, 6)),
+    (11, 4): ((3, 5), (8, 7)),
+    (11, 5): ((3, 5), (8, 7)),
+    (11, 6): ((3, 5), (8, 7)),
+    (11, 7): ((3, 5), (8, 7)),
+    (11, 8): ((3, 5), (9, 0)),
+    (12, 0): ((5, 0), (9, 0)),
+    (12, 1): ((5, 0), (9, 0)),
+    (12, 2): ((5, 0), (9, 0)),
+    (12, 3): ((5, 0), (9, 0)),
+    (12, 4): ((5, 0), (9, 0)),
+}
+
+
+def ccs_supported_by_ctk(ctk_version):
+    try:
+        # For supported versions, we look up the range of supported CCs
+        min_cc, max_cc = CTK_SUPPORTED[ctk_version]
+        return tuple([cc for cc in COMPUTE_CAPABILITIES
+                      if min_cc <= cc <= max_cc])
+    except KeyError:
+        # For unsupported CUDA toolkit versions, all we can do is assume all
+        # non-deprecated versions we are aware of are supported.
+        return tuple([cc for cc in COMPUTE_CAPABILITIES
+                      if cc >= config.CUDA_DEFAULT_PTX_CC])
+
+
+def get_supported_ccs():
+    try:
+        from numba.cuda.cudadrv.runtime import runtime
+        cudart_version = runtime.get_version()
+    except: # noqa: E722
+        # We can't support anything if there's an error getting the runtime
+        # version (e.g. if it's not present or there's another issue)
+        _supported_cc = ()
+        return _supported_cc
+
+    # Ensure the minimum CTK version requirement is met
+    min_cudart = min(CTK_SUPPORTED)
+    if cudart_version < min_cudart:
+        _supported_cc = ()
+        ctk_ver = f"{cudart_version[0]}.{cudart_version[1]}"
+        unsupported_ver = (f"CUDA Toolkit {ctk_ver} is unsupported by Numba - "
+                           f"{min_cudart[0]}.{min_cudart[1]} is the minimum "
+                           "required version.")
+        warnings.warn(unsupported_ver)
+        return _supported_cc
+
+    _supported_cc = ccs_supported_by_ctk(cudart_version)
+    return _supported_cc
+
+
+def find_closest_arch(mycc):
+    """
+    Given a compute capability, return the closest compute capability supported
+    by the CUDA toolkit.
+
+    :param mycc: Compute capability as a tuple ``(MAJOR, MINOR)``
+    :return: Closest supported CC as a tuple ``(MAJOR, MINOR)``
+    """
+    supported_ccs = NVVM().supported_ccs
+
+    if not supported_ccs:
+        msg = "No supported GPU compute capabilities found. " \
+              "Please check your cudatoolkit version matches your CUDA version."
+        raise NvvmSupportError(msg)
+
+    for i, cc in enumerate(supported_ccs):
+        if cc == mycc:
+            # Matches
+            return cc
+        elif cc > mycc:
+            # Exceeded
+            if i == 0:
+                # CC lower than supported
+                msg = "GPU compute capability %d.%d is not supported" \
+                      "(requires >=%d.%d)" % (mycc + cc)
+                raise NvvmSupportError(msg)
+            else:
+                # return the previous CC
+                return supported_ccs[i - 1]
+
+    # CC higher than supported
+    return supported_ccs[-1]  # Choose the highest
+
+
+def get_arch_option(major, minor):
+    """Matches with the closest architecture option
+    """
+    if config.FORCE_CUDA_CC:
+        arch = config.FORCE_CUDA_CC
+    else:
+        arch = find_closest_arch((major, minor))
+    return 'compute_%d%d' % arch
+
+
+MISSING_LIBDEVICE_FILE_MSG = '''Missing libdevice file.
+Please ensure you have a CUDA Toolkit 11.2 or higher.
+For CUDA 12, ``cuda-nvcc`` and ``cuda-nvrtc`` are required:
+
+    $ conda install -c conda-forge cuda-nvcc cuda-nvrtc "cuda-version>=12.0"
+
+For CUDA 11, ``cudatoolkit`` is required:
+
+    $ conda install -c conda-forge cudatoolkit "cuda-version>=11.2,<12.0"
+'''
+
+
+class LibDevice(object):
+    _cache_ = None
+
+    def __init__(self):
+        if self._cache_ is None:
+            if get_libdevice() is None:
+                raise RuntimeError(MISSING_LIBDEVICE_FILE_MSG)
+            self._cache_ = open_libdevice()
+
+        self.bc = self._cache_
+
+    def get(self):
+        return self.bc
+
+
+cas_nvvm = """
+    %cas_success = cmpxchg volatile {Ti}* %iptr, {Ti} %old, {Ti} %new monotonic monotonic
+    %cas = extractvalue {{ {Ti}, i1 }} %cas_success, 0
+""" # noqa: E501
+
+
+# Translation of code from CUDA Programming Guide v6.5, section B.12
+ir_numba_atomic_binary_template = """
+define internal {T} @___numba_atomic_{T}_{FUNC}({T}* %ptr, {T} %val) alwaysinline {{
+entry:
+    %iptr = bitcast {T}* %ptr to {Ti}*
+    %old2 = load volatile {Ti}, {Ti}* %iptr
+    br label %attempt
+
+attempt:
+    %old = phi {Ti} [ %old2, %entry ], [ %cas, %attempt ]
+    %dold = bitcast {Ti} %old to {T}
+    %dnew = {OP} {T} %dold, %val
+    %new = bitcast {T} %dnew to {Ti}
+    {CAS}
+    %repeat = icmp ne {Ti} %cas, %old
+    br i1 %repeat, label %attempt, label %done
+
+done:
+    %result = bitcast {Ti} %old to {T}
+    ret {T} %result
+}}
+""" # noqa: E501
+
+ir_numba_atomic_inc_template = """
+define internal {T} @___numba_atomic_{Tu}_inc({T}* %iptr, {T} %val) alwaysinline {{
+entry:
+    %old2 = load volatile {T}, {T}* %iptr
+    br label %attempt
+
+attempt:
+    %old = phi {T} [ %old2, %entry ], [ %cas, %attempt ]
+    %bndchk = icmp ult {T} %old, %val
+    %inc = add {T} %old, 1
+    %new = select i1 %bndchk, {T} %inc, {T} 0
+    {CAS}
+    %repeat = icmp ne {T} %cas, %old
+    br i1 %repeat, label %attempt, label %done
+
+done:
+    ret {T} %old
+}}
+""" # noqa: E501
+
+ir_numba_atomic_dec_template = """
+define internal {T} @___numba_atomic_{Tu}_dec({T}* %iptr, {T} %val) alwaysinline {{
+entry:
+    %old2 = load volatile {T}, {T}* %iptr
+    br label %attempt
+
+attempt:
+    %old = phi {T} [ %old2, %entry ], [ %cas, %attempt ]
+    %dec = add {T} %old, -1
+    %bndchk = icmp ult {T} %dec, %val
+    %new = select i1 %bndchk, {T} %dec, {T} %val
+    {CAS}
+    %repeat = icmp ne {T} %cas, %old
+    br i1 %repeat, label %attempt, label %done
+
+done:
+    ret {T} %old
+}}
+""" # noqa: E501
+
+ir_numba_atomic_minmax_template = """
+define internal {T} @___numba_atomic_{T}_{NAN}{FUNC}({T}* %ptr, {T} %val) alwaysinline {{
+entry:
+    %ptrval = load volatile {T}, {T}* %ptr
+    ; Return early when:
+    ; - For nanmin / nanmax when val is a NaN
+    ; - For min / max when val or ptr is a NaN
+    %early_return = fcmp uno {T} %val, %{PTR_OR_VAL}val
+    br i1 %early_return, label %done, label %lt_check
+
+lt_check:
+    %dold = phi {T} [ %ptrval, %entry ], [ %dcas, %attempt ]
+    ; Continue attempts if dold less or greater than val (depending on whether min or max)
+    ; or if dold is NaN (for nanmin / nanmax)
+    %cmp = fcmp {OP} {T} %dold, %val
+    br i1 %cmp, label %attempt, label %done
+
+attempt:
+    ; Attempt to swap in the value
+    %old = bitcast {T} %dold to {Ti}
+    %iptr = bitcast {T}* %ptr to {Ti}*
+    %new = bitcast {T} %val to {Ti}
+    {CAS}
+    %dcas = bitcast {Ti} %cas to {T}
+    br label %lt_check
+
+done:
+    ret {T} %ptrval
+}}
+""" # noqa: E501
+
+
+def ir_cas(Ti):
+    return cas_nvvm.format(Ti=Ti)
+
+
+def ir_numba_atomic_binary(T, Ti, OP, FUNC):
+    params = dict(T=T, Ti=Ti, OP=OP, FUNC=FUNC, CAS=ir_cas(Ti))
+    return ir_numba_atomic_binary_template.format(**params)
+
+
+def ir_numba_atomic_minmax(T, Ti, NAN, OP, PTR_OR_VAL, FUNC):
+    params = dict(T=T, Ti=Ti, NAN=NAN, OP=OP, PTR_OR_VAL=PTR_OR_VAL,
+                  FUNC=FUNC, CAS=ir_cas(Ti))
+
+    return ir_numba_atomic_minmax_template.format(**params)
+
+
+def ir_numba_atomic_inc(T, Tu):
+    return ir_numba_atomic_inc_template.format(T=T, Tu=Tu, CAS=ir_cas(T))
+
+
+def ir_numba_atomic_dec(T, Tu):
+    return ir_numba_atomic_dec_template.format(T=T, Tu=Tu, CAS=ir_cas(T))
+
+
+def llvm_replace(llvmir):
+    replacements = [
+        ('declare double @"___numba_atomic_double_add"(double* %".1", double %".2")',     # noqa: E501
+         ir_numba_atomic_binary(T='double', Ti='i64', OP='fadd', FUNC='add')),
+        ('declare float @"___numba_atomic_float_sub"(float* %".1", float %".2")',         # noqa: E501
+         ir_numba_atomic_binary(T='float', Ti='i32', OP='fsub', FUNC='sub')),
+        ('declare double @"___numba_atomic_double_sub"(double* %".1", double %".2")',     # noqa: E501
+         ir_numba_atomic_binary(T='double', Ti='i64', OP='fsub', FUNC='sub')),
+        ('declare i64 @"___numba_atomic_u64_inc"(i64* %".1", i64 %".2")',
+         ir_numba_atomic_inc(T='i64', Tu='u64')),
+        ('declare i64 @"___numba_atomic_u64_dec"(i64* %".1", i64 %".2")',
+         ir_numba_atomic_dec(T='i64', Tu='u64')),
+        ('declare float @"___numba_atomic_float_max"(float* %".1", float %".2")',         # noqa: E501
+         ir_numba_atomic_minmax(T='float', Ti='i32', NAN='', OP='nnan olt',
+                                PTR_OR_VAL='ptr', FUNC='max')),
+        ('declare double @"___numba_atomic_double_max"(double* %".1", double %".2")',     # noqa: E501
+         ir_numba_atomic_minmax(T='double', Ti='i64', NAN='', OP='nnan olt',
+                                PTR_OR_VAL='ptr', FUNC='max')),
+        ('declare float @"___numba_atomic_float_min"(float* %".1", float %".2")',         # noqa: E501
+         ir_numba_atomic_minmax(T='float', Ti='i32', NAN='', OP='nnan ogt',
+                                PTR_OR_VAL='ptr', FUNC='min')),
+        ('declare double @"___numba_atomic_double_min"(double* %".1", double %".2")',     # noqa: E501
+         ir_numba_atomic_minmax(T='double', Ti='i64', NAN='', OP='nnan ogt',
+                                PTR_OR_VAL='ptr', FUNC='min')),
+        ('declare float @"___numba_atomic_float_nanmax"(float* %".1", float %".2")',      # noqa: E501
+         ir_numba_atomic_minmax(T='float', Ti='i32', NAN='nan', OP='ult',
+                                PTR_OR_VAL='', FUNC='max')),
+        ('declare double @"___numba_atomic_double_nanmax"(double* %".1", double %".2")',  # noqa: E501
+         ir_numba_atomic_minmax(T='double', Ti='i64', NAN='nan', OP='ult',
+                                PTR_OR_VAL='', FUNC='max')),
+        ('declare float @"___numba_atomic_float_nanmin"(float* %".1", float %".2")',      # noqa: E501
+         ir_numba_atomic_minmax(T='float', Ti='i32', NAN='nan', OP='ugt',
+                                PTR_OR_VAL='', FUNC='min')),
+        ('declare double @"___numba_atomic_double_nanmin"(double* %".1", double %".2")',  # noqa: E501
+         ir_numba_atomic_minmax(T='double', Ti='i64', NAN='nan', OP='ugt',
+                                PTR_OR_VAL='', FUNC='min')),
+        ('immarg', '')
+    ]
+
+    for decl, fn in replacements:
+        llvmir = llvmir.replace(decl, fn)
+
+    llvmir = llvm140_to_70_ir(llvmir)
+
+    return llvmir
+
+
+def compile_ir(llvmir, **opts):
+    if isinstance(llvmir, str):
+        llvmir = [llvmir]
+
+    if opts.pop('fastmath', False):
+        opts.update({
+            'ftz': True,
+            'fma': True,
+            'prec_div': False,
+            'prec_sqrt': False,
+        })
+
+    cu = CompilationUnit()
+    libdevice = LibDevice()
+
+    for mod in llvmir:
+        mod = llvm_replace(mod)
+        cu.add_module(mod.encode('utf8'))
+    cu.lazy_add_module(libdevice.get())
+
+    return cu.compile(**opts)
+
+
+re_attributes_def = re.compile(r"^attributes #\d+ = \{ ([\w\s]+)\ }")
+
+
+def llvm140_to_70_ir(ir):
+    """
+    Convert LLVM 14.0 IR for LLVM 7.0.
+    """
+    buf = []
+    for line in ir.splitlines():
+        if line.startswith('attributes #'):
+            # Remove function attributes unsupported by LLVM 7.0
+            m = re_attributes_def.match(line)
+            attrs = m.group(1).split()
+            attrs = ' '.join(a for a in attrs if a != 'willreturn')
+            line = line.replace(m.group(1), attrs)
+
+        buf.append(line)
+
+    return '\n'.join(buf)
+
+
+def set_cuda_kernel(function):
+    """
+    Mark a function as a CUDA kernel. Kernels have the following requirements:
+
+    - Metadata that marks them as a kernel.
+    - Addition to the @llvm.used list, so that they will not be discarded.
+    - The noinline attribute is not permitted, because this causes NVVM to emit
+      a warning, which counts as failing IR verification.
+
+    Presently it is assumed that there is one kernel per module, which holds
+    for Numba-jitted functions. If this changes in future or this function is
+    to be used externally, this function may need modification to add to the
+    @llvm.used list rather than creating it.
+    """
+    module = function.module
+
+    # Add kernel metadata
+    mdstr = ir.MetaDataString(module, "kernel")
+    mdvalue = ir.Constant(ir.IntType(32), 1)
+    md = module.add_metadata((function, mdstr, mdvalue))
+
+    nmd = cgutils.get_or_insert_named_metadata(module, 'nvvm.annotations')
+    nmd.add(md)
+
+    # Create the used list
+    ptrty = ir.IntType(8).as_pointer()
+    usedty = ir.ArrayType(ptrty, 1)
+
+    fnptr = function.bitcast(ptrty)
+
+    llvm_used = ir.GlobalVariable(module, usedty, 'llvm.used')
+    llvm_used.linkage = 'appending'
+    llvm_used.section = 'llvm.metadata'
+    llvm_used.initializer = ir.Constant(usedty, [fnptr])
+
+    # Remove 'noinline' if it is present.
+    function.attributes.discard('noinline')
+
+
+def add_ir_version(mod):
+    """Add NVVM IR version to module"""
+    # We specify the IR version to match the current NVVM's IR version
+    i32 = ir.IntType(32)
+    ir_versions = [i32(v) for v in NVVM().get_ir_version()]
+    md_ver = mod.add_metadata(ir_versions)
+    mod.add_named_metadata('nvvmir.version', md_ver)
diff --git a/lib/python3.10/site-packages/numba/cuda/cudadrv/rtapi.py b/lib/python3.10/site-packages/numba/cuda/cudadrv/rtapi.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a88457f9cb5a1e0cb134eb4dcb59267d1cf3f54
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/cudadrv/rtapi.py
@@ -0,0 +1,10 @@
+"""
+Declarations of the Runtime API functions.
+"""
+
+from ctypes import c_int, POINTER
+
+API_PROTOTYPES = {
+    # cudaError_t cudaRuntimeGetVersion ( int* runtimeVersion )
+    'cudaRuntimeGetVersion': (c_int, POINTER(c_int)),
+}
diff --git a/lib/python3.10/site-packages/numba/cuda/kernels/__init__.py b/lib/python3.10/site-packages/numba/cuda/kernels/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/lib/python3.10/site-packages/numba/cuda/kernels/reduction.py b/lib/python3.10/site-packages/numba/cuda/kernels/reduction.py
new file mode 100644
index 0000000000000000000000000000000000000000..f733935b6223c301bbf13251c4a9f50ffb38b622
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/kernels/reduction.py
@@ -0,0 +1,262 @@
+"""
+A library written in CUDA Python for generating reduction kernels
+"""
+
+from numba.np.numpy_support import from_dtype
+
+
+_WARPSIZE = 32
+_NUMWARPS = 4
+
+
+def _gpu_reduce_factory(fn, nbtype):
+    from numba import cuda
+
+    reduce_op = cuda.jit(device=True)(fn)
+    inner_sm_size = _WARPSIZE + 1   # plus one to avoid SM collision
+    max_blocksize = _NUMWARPS * _WARPSIZE
+
+    @cuda.jit(device=True)
+    def inner_warp_reduction(sm_partials, init):
+        """
+        Compute reduction within a single warp
+        """
+        tid = cuda.threadIdx.x
+        warpid = tid // _WARPSIZE
+        laneid = tid % _WARPSIZE
+
+        sm_this = sm_partials[warpid, :]
+        sm_this[laneid] = init
+        cuda.syncwarp()
+
+        width = _WARPSIZE // 2
+        while width:
+            if laneid < width:
+                old = sm_this[laneid]
+                sm_this[laneid] = reduce_op(old, sm_this[laneid + width])
+            cuda.syncwarp()
+            width //= 2
+
+    @cuda.jit(device=True)
+    def device_reduce_full_block(arr, partials, sm_partials):
+        """
+        Partially reduce `arr` into `partials` using `sm_partials` as working
+        space.  The algorithm goes like:
+
+            array chunks of 128:  |   0 | 128 | 256 | 384 | 512 |
+                        block-0:  |   x |     |     |   x |     |
+                        block-1:  |     |   x |     |     |   x |
+                        block-2:  |     |     |   x |     |     |
+
+        The array is divided into chunks of 128 (size of a threadblock).
+        The threadblocks consumes the chunks in roundrobin scheduling.
+        First, a threadblock loads a chunk into temp memory.  Then, all
+        subsequent chunks are combined into the temp memory.
+
+        Once all chunks are processed.  Inner-block reduction is performed
+        on the temp memory.  So that, there will just be one scalar result
+        per block.  The result from each block is stored to `partials` at
+        the dedicated slot.
+        """
+        tid = cuda.threadIdx.x
+        blkid = cuda.blockIdx.x
+        blksz = cuda.blockDim.x
+        gridsz = cuda.gridDim.x
+
+        # block strided loop to compute the reduction
+        start = tid + blksz * blkid
+        stop = arr.size
+        step = blksz * gridsz
+
+        # load first value
+        tmp = arr[start]
+        # loop over all values in block-stride
+        for i in range(start + step, stop, step):
+            tmp = reduce_op(tmp, arr[i])
+
+        cuda.syncthreads()
+        # inner-warp reduction
+        inner_warp_reduction(sm_partials, tmp)
+
+        cuda.syncthreads()
+        # at this point, only the first slot for each warp in tsm_partials
+        # is valid.
+
+        # finish up block reduction
+        # warning: this is assuming 4 warps.
+        # assert numwarps == 4
+        if tid < 2:
+            sm_partials[tid, 0] = reduce_op(sm_partials[tid, 0],
+                                            sm_partials[tid + 2, 0])
+            cuda.syncwarp()
+        if tid == 0:
+            partials[blkid] = reduce_op(sm_partials[0, 0], sm_partials[1, 0])
+
+    @cuda.jit(device=True)
+    def device_reduce_partial_block(arr, partials, sm_partials):
+        """
+        This computes reduction on `arr`.
+        This device function must be used by 1 threadblock only.
+        The blocksize must match `arr.size` and must not be greater than 128.
+        """
+        tid = cuda.threadIdx.x
+        blkid = cuda.blockIdx.x
+        blksz = cuda.blockDim.x
+        warpid = tid // _WARPSIZE
+        laneid = tid % _WARPSIZE
+
+        size = arr.size
+        # load first value
+        tid = cuda.threadIdx.x
+        value = arr[tid]
+        sm_partials[warpid, laneid] = value
+
+        cuda.syncthreads()
+
+        if (warpid + 1) * _WARPSIZE < size:
+            # fully populated warps
+            inner_warp_reduction(sm_partials, value)
+        else:
+            # partially populated warps
+            # NOTE: this uses a very inefficient sequential algorithm
+            if laneid == 0:
+                sm_this = sm_partials[warpid, :]
+                base = warpid * _WARPSIZE
+                for i in range(1, size - base):
+                    sm_this[0] = reduce_op(sm_this[0], sm_this[i])
+
+        cuda.syncthreads()
+        # finish up
+        if tid == 0:
+            num_active_warps = (blksz + _WARPSIZE - 1) // _WARPSIZE
+
+            result = sm_partials[0, 0]
+            for i in range(1, num_active_warps):
+                result = reduce_op(result, sm_partials[i, 0])
+
+            partials[blkid] = result
+
+    def gpu_reduce_block_strided(arr, partials, init, use_init):
+        """
+        Perform reductions on *arr* and writing out partial reduction result
+        into *partials*.  The length of *partials* is determined by the
+        number of threadblocks. The initial value is set with *init*.
+
+        Launch config:
+
+        Blocksize must be multiple of warpsize and it is limited to 4 warps.
+        """
+        tid = cuda.threadIdx.x
+
+        sm_partials = cuda.shared.array((_NUMWARPS, inner_sm_size),
+                                        dtype=nbtype)
+        if cuda.blockDim.x == max_blocksize:
+            device_reduce_full_block(arr, partials, sm_partials)
+        else:
+            device_reduce_partial_block(arr, partials, sm_partials)
+        # deal with the initializer
+        if use_init and tid == 0 and cuda.blockIdx.x == 0:
+            partials[0] = reduce_op(partials[0], init)
+
+    return cuda.jit(gpu_reduce_block_strided)
+
+
+class Reduce(object):
+    """Create a reduction object that reduces values using a given binary
+    function. The binary function is compiled once and cached inside this
+    object. Keeping this object alive will prevent re-compilation.
+    """
+
+    _cache = {}
+
+    def __init__(self, functor):
+        """
+        :param functor: A function implementing a binary operation for
+                        reduction. It will be compiled as a CUDA device
+                        function using ``cuda.jit(device=True)``.
+        """
+        self._functor = functor
+
+    def _compile(self, dtype):
+        key = self._functor, dtype
+        if key in self._cache:
+            kernel = self._cache[key]
+        else:
+            kernel = _gpu_reduce_factory(self._functor, from_dtype(dtype))
+            self._cache[key] = kernel
+        return kernel
+
+    def __call__(self, arr, size=None, res=None, init=0, stream=0):
+        """Performs a full reduction.
+
+        :param arr: A host or device array.
+        :param size: Optional integer specifying the number of elements in
+                    ``arr`` to reduce. If this parameter is not specified, the
+                    entire array is reduced.
+        :param res: Optional device array into which to write the reduction
+                    result to. The result is written into the first element of
+                    this array. If this parameter is specified, then no
+                    communication of the reduction output takes place from the
+                    device to the host.
+        :param init: Optional initial value for the reduction, the type of which
+                    must match ``arr.dtype``.
+        :param stream: Optional CUDA stream in which to perform the reduction.
+                    If no stream is specified, the default stream of 0 is
+                    used.
+        :return: If ``res`` is specified, ``None`` is returned. Otherwise, the
+                result of the reduction is returned.
+        """
+        from numba import cuda
+
+        # ensure 1d array
+        if arr.ndim != 1:
+            raise TypeError("only support 1D array")
+
+        # adjust array size
+        if size is not None:
+            arr = arr[:size]
+
+        init = arr.dtype.type(init)  # ensure the right type
+
+        # return `init` if `arr` is empty
+        if arr.size < 1:
+            return init
+
+        kernel = self._compile(arr.dtype)
+
+        # Perform the reduction on the GPU
+        blocksize = _NUMWARPS * _WARPSIZE
+        size_full = (arr.size // blocksize) * blocksize
+        size_partial = arr.size - size_full
+        full_blockct = min(size_full // blocksize, _WARPSIZE * 2)
+
+        # allocate size of partials array
+        partials_size = full_blockct
+        if size_partial:
+            partials_size += 1
+        partials = cuda.device_array(shape=partials_size, dtype=arr.dtype)
+
+        if size_full:
+            # kernel for the fully populated threadblocks
+            kernel[full_blockct, blocksize, stream](arr[:size_full],
+                                                    partials[:full_blockct],
+                                                    init,
+                                                    True)
+
+        if size_partial:
+            # kernel for partially populated threadblocks
+            kernel[1, size_partial, stream](arr[size_full:],
+                                            partials[full_blockct:],
+                                            init,
+                                            not full_blockct)
+
+        if partials.size > 1:
+            # finish up
+            kernel[1, partials_size, stream](partials, partials, init, False)
+
+        # handle return value
+        if res is not None:
+            res[:1].copy_to_device(partials[:1], stream=stream)
+            return
+        else:
+            return partials[0]
diff --git a/lib/python3.10/site-packages/numba/cuda/kernels/transpose.py b/lib/python3.10/site-packages/numba/cuda/kernels/transpose.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1df36e048891119b08fa67b452df637c85db9df
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/kernels/transpose.py
@@ -0,0 +1,65 @@
+from numba import cuda
+from numba.cuda.cudadrv.driver import driver
+import math
+from numba.np import numpy_support as nps
+
+
+def transpose(a, b=None):
+    """Compute the transpose of 'a' and store it into 'b', if given,
+    and return it. If 'b' is not given, allocate a new array
+    and return that.
+
+    This implements the algorithm documented in
+    http://devblogs.nvidia.com/parallelforall/efficient-matrix-transpose-cuda-cc/
+
+    :param a: an `np.ndarray` or a `DeviceNDArrayBase` subclass. If already on
+        the device its stream will be used to perform the transpose (and to copy
+        `b` to the device if necessary).
+    """
+
+    # prefer `a`'s stream if
+    stream = getattr(a, 'stream', 0)
+
+    if not b:
+        cols, rows = a.shape
+        strides = a.dtype.itemsize * cols, a.dtype.itemsize
+        b = cuda.cudadrv.devicearray.DeviceNDArray(
+            (rows, cols),
+            strides,
+            dtype=a.dtype,
+            stream=stream)
+
+    dt = nps.from_dtype(a.dtype)
+
+    tpb = driver.get_device().MAX_THREADS_PER_BLOCK
+    # we need to factor available threads into x and y axis
+    tile_width = int(math.pow(2, math.log(tpb, 2) / 2))
+    tile_height = int(tpb / tile_width)
+
+    tile_shape = (tile_height, tile_width + 1)
+
+    @cuda.jit
+    def kernel(input, output):
+
+        tile = cuda.shared.array(shape=tile_shape, dtype=dt)
+
+        tx = cuda.threadIdx.x
+        ty = cuda.threadIdx.y
+        bx = cuda.blockIdx.x * cuda.blockDim.x
+        by = cuda.blockIdx.y * cuda.blockDim.y
+        x = by + tx
+        y = bx + ty
+
+        if by + ty < input.shape[0] and bx + tx < input.shape[1]:
+            tile[ty, tx] = input[by + ty, bx + tx]
+        cuda.syncthreads()
+        if y < output.shape[0] and x < output.shape[1]:
+            output[y, x] = tile[tx, ty]
+
+    # one block per tile, plus one for remainders
+    blocks = int(b.shape[0] / tile_height + 1), int(b.shape[1] / tile_width + 1)
+    # one thread per tile element
+    threads = tile_height, tile_width
+    kernel[blocks, threads, stream](a, b)
+
+    return b
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/__init__.py b/lib/python3.10/site-packages/numba/cuda/simulator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d24aa6e7df0f941e2bf781c681122530ceb93e68
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/__init__.py
@@ -0,0 +1,38 @@
+import sys
+
+from .api import *
+from .vector_types import vector_types
+from .reduction import Reduce
+from .cudadrv.devicearray import (device_array, device_array_like, pinned,
+                                  pinned_array, pinned_array_like,
+                                  mapped_array, to_device, auto_device)
+from .cudadrv import devicearray
+from .cudadrv.devices import require_context, gpus
+from .cudadrv.devices import get_context as current_context
+from .cudadrv.runtime import runtime
+from numba.core import config
+reduce = Reduce
+
+# Register simulated vector types as module level variables
+for name, svty in vector_types.items():
+    setattr(sys.modules[__name__], name, svty)
+    for alias in svty.aliases:
+        setattr(sys.modules[__name__], alias, svty)
+del vector_types, name, svty, alias
+
+# Ensure that any user code attempting to import cudadrv etc. gets the
+# simulator's version and not the real version if the simulator is enabled.
+if config.ENABLE_CUDASIM:
+    import sys
+    from numba.cuda.simulator import cudadrv
+    sys.modules['numba.cuda.cudadrv'] = cudadrv
+    sys.modules['numba.cuda.cudadrv.devicearray'] = cudadrv.devicearray
+    sys.modules['numba.cuda.cudadrv.devices'] = cudadrv.devices
+    sys.modules['numba.cuda.cudadrv.driver'] = cudadrv.driver
+    sys.modules['numba.cuda.cudadrv.runtime'] = cudadrv.runtime
+    sys.modules['numba.cuda.cudadrv.drvapi'] = cudadrv.drvapi
+    sys.modules['numba.cuda.cudadrv.error'] = cudadrv.error
+    sys.modules['numba.cuda.cudadrv.nvvm'] = cudadrv.nvvm
+
+    from . import compiler
+    sys.modules['numba.cuda.compiler'] = compiler
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/api.py b/lib/python3.10/site-packages/numba/cuda/simulator/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b3c5bfb5331794b1881132b1f47eb7417e6382b
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/api.py
@@ -0,0 +1,110 @@
+'''
+Contains CUDA API functions
+'''
+
+# Imports here bring together parts of the API from other modules, so some of
+# them appear unused.
+from contextlib import contextmanager
+
+from .cudadrv.devices import require_context, reset, gpus  # noqa: F401
+from .kernel import FakeCUDAKernel
+from numba.core.sigutils import is_signature
+from warnings import warn
+from ..args import In, Out, InOut  # noqa: F401
+
+
+def select_device(dev=0):
+    assert dev == 0, 'Only a single device supported by the simulator'
+
+
+def is_float16_supported():
+    return True
+
+
+class stream(object):
+    '''
+    The stream API is supported in the simulator - however, all execution
+    occurs synchronously, so synchronization requires no operation.
+    '''
+    @contextmanager
+    def auto_synchronize(self):
+        yield
+
+    def synchronize(self):
+        pass
+
+
+def synchronize():
+    pass
+
+
+def close():
+    gpus.closed = True
+
+
+def declare_device(*args, **kwargs):
+    pass
+
+
+def detect():
+    print('Found 1 CUDA devices')
+    print('id %d    %20s %40s' % (0, 'SIMULATOR', '[SUPPORTED]'))
+    print('%40s: 5.0' % 'compute capability')
+
+
+def list_devices():
+    return gpus
+
+
+# Events
+
+class Event(object):
+    '''
+    The simulator supports the event API, but they do not record timing info,
+    and all simulation is synchronous. Execution time is not recorded.
+    '''
+    def record(self, stream=0):
+        pass
+
+    def wait(self, stream=0):
+        pass
+
+    def synchronize(self):
+        pass
+
+    def elapsed_time(self, event):
+        warn('Simulator timings are bogus')
+        return 0.0
+
+
+event = Event
+
+
+def jit(func_or_sig=None, device=False, debug=False, argtypes=None,
+        inline=False, restype=None, fastmath=False, link=None,
+        boundscheck=None, opt=True, cache=None
+        ):
+    # Here for API compatibility
+    if boundscheck:
+        raise NotImplementedError("bounds checking is not supported for CUDA")
+
+    if link is not None:
+        raise NotImplementedError('Cannot link PTX in the simulator')
+
+    # Check for first argument specifying types - in that case the
+    # decorator is not being passed a function
+    if (func_or_sig is None or is_signature(func_or_sig)
+            or isinstance(func_or_sig, list)):
+        def jitwrapper(fn):
+            return FakeCUDAKernel(fn,
+                                  device=device,
+                                  fastmath=fastmath,
+                                  debug=debug)
+        return jitwrapper
+    return FakeCUDAKernel(func_or_sig, device=device, debug=debug)
+
+
+@contextmanager
+def defer_cleanup():
+    # No effect for simulator
+    yield
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/compiler.py b/lib/python3.10/site-packages/numba/cuda/simulator/compiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..7db28d41ac65a8669f9ae2f6ed231304091df940
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/compiler.py
@@ -0,0 +1,9 @@
+'''
+The compiler is not implemented in the simulator. This module provides a stub
+to allow tests to import successfully.
+'''
+
+compile = None
+compile_for_current_device = None
+compile_ptx = None
+compile_ptx_for_current_device = None
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/__init__.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dde9362d44669831843a33ed2d944c3c64ed91fa
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/__init__.py
@@ -0,0 +1,2 @@
+from numba.cuda.simulator.cudadrv import (devicearray, devices, driver, drvapi,
+                                          error, nvvm)
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/devicearray.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/devicearray.py
new file mode 100644
index 0000000000000000000000000000000000000000..785f7cdc1748e496f894c0ad1f84c1b48abeba90
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/devicearray.py
@@ -0,0 +1,436 @@
+'''
+The Device Array API is not implemented in the simulator. This module provides
+stubs to allow tests to import correctly.
+'''
+from contextlib import contextmanager
+from numba.np.numpy_support import numpy_version
+
+import numpy as np
+
+
+DeviceRecord = None
+from_record_like = None
+
+
+errmsg_contiguous_buffer = ("Array contains non-contiguous buffer and cannot "
+                            "be transferred as a single memory region. Please "
+                            "ensure contiguous buffer with numpy "
+                            ".ascontiguousarray()")
+
+
+class FakeShape(tuple):
+    '''
+    The FakeShape class is used to provide a shape which does not allow negative
+    indexing, similar to the shape in CUDA Python. (Numpy shape arrays allow
+    negative indexing)
+    '''
+
+    def __getitem__(self, k):
+        if isinstance(k, int) and k < 0:
+            raise IndexError('tuple index out of range')
+        return super(FakeShape, self).__getitem__(k)
+
+
+class FakeWithinKernelCUDAArray(object):
+    '''
+    Created to emulate the behavior of arrays within kernels, where either
+    array.item or array['item'] is valid (that is, give all structured
+    arrays `numpy.recarray`-like semantics). This behaviour does not follow
+    the semantics of Python and NumPy with non-jitted code, and will be
+    deprecated and removed.
+    '''
+
+    def __init__(self, item):
+        assert isinstance(item, FakeCUDAArray)
+        self.__dict__['_item'] = item
+
+    def __wrap_if_fake(self, item):
+        if isinstance(item, FakeCUDAArray):
+            return FakeWithinKernelCUDAArray(item)
+        else:
+            return item
+
+    def __getattr__(self, attrname):
+        try:
+            if attrname in dir(self._item._ary):  # For e.g. array size.
+                return self.__wrap_if_fake(getattr(self._item._ary, attrname))
+            else:
+                return self.__wrap_if_fake(self._item.__getitem__(attrname))
+        except Exception as e:
+            if not isinstance(e, AttributeError):
+                raise AttributeError(attrname) from e
+
+    def __setattr__(self, nm, val):
+        self._item.__setitem__(nm, val)
+
+    def __getitem__(self, idx):
+        return self.__wrap_if_fake(self._item.__getitem__(idx))
+
+    def __setitem__(self, idx, val):
+        self._item.__setitem__(idx, val)
+
+    def __len__(self):
+        return len(self._item)
+
+    def __array_ufunc__(self, ufunc, method, *args, **kwargs):
+        # ufuncs can only be called directly on instances of numpy.ndarray (not
+        # things that implement its interfaces, like the FakeCUDAArray or
+        # FakeWithinKernelCUDAArray). For other objects, __array_ufunc__ is
+        # called when they are arguments to ufuncs, to provide an opportunity
+        # to somehow implement the ufunc. Since the FakeWithinKernelCUDAArray
+        # is just a thin wrapper over an ndarray, we can implement all ufuncs
+        # by passing the underlying ndarrays to a call to the intended ufunc.
+        call = getattr(ufunc, method)
+
+        def convert_fakes(obj):
+            if isinstance(obj, FakeWithinKernelCUDAArray):
+                obj = obj._item._ary
+
+            return obj
+
+        out = kwargs.get('out')
+        if out:
+            kwargs['out'] = tuple(convert_fakes(o) for o in out)
+        args = tuple(convert_fakes(a) for a in args)
+        return call(*args, **kwargs)
+
+
+class FakeCUDAArray(object):
+    '''
+    Implements the interface of a DeviceArray/DeviceRecord, but mostly just
+    wraps a NumPy array.
+    '''
+
+    __cuda_ndarray__ = True  # There must be gpu_data attribute
+
+    def __init__(self, ary, stream=0):
+        self._ary = ary
+        self.stream = stream
+
+    @property
+    def alloc_size(self):
+        return self._ary.nbytes
+
+    @property
+    def nbytes(self):
+        # return nbytes -- FakeCUDAArray is a wrapper around NumPy
+        return self._ary.nbytes
+
+    def __getattr__(self, attrname):
+        try:
+            attr = getattr(self._ary, attrname)
+            return attr
+        except AttributeError as e:
+            msg = "Wrapped array has no attribute '%s'" % attrname
+            raise AttributeError(msg) from e
+
+    def bind(self, stream=0):
+        return FakeCUDAArray(self._ary, stream)
+
+    @property
+    def T(self):
+        return self.transpose()
+
+    def transpose(self, axes=None):
+        return FakeCUDAArray(np.transpose(self._ary, axes=axes))
+
+    def __getitem__(self, idx):
+        ret = self._ary.__getitem__(idx)
+        if type(ret) not in [np.ndarray, np.void]:
+            return ret
+        else:
+            return FakeCUDAArray(ret, stream=self.stream)
+
+    def __setitem__(self, idx, val):
+        return self._ary.__setitem__(idx, val)
+
+    def copy_to_host(self, ary=None, stream=0):
+        if ary is None:
+            ary = np.empty_like(self._ary)
+        else:
+            check_array_compatibility(self, ary)
+        np.copyto(ary, self._ary)
+        return ary
+
+    def copy_to_device(self, ary, stream=0):
+        '''
+        Copy from the provided array into this array.
+
+        This may be less forgiving than the CUDA Python implementation, which
+        will copy data up to the length of the smallest of the two arrays,
+        whereas this expects the size of the arrays to be equal.
+        '''
+        sentry_contiguous(self)
+        self_core, ary_core = array_core(self), array_core(ary)
+        if isinstance(ary, FakeCUDAArray):
+            sentry_contiguous(ary)
+            check_array_compatibility(self_core, ary_core)
+        else:
+            ary_core = np.array(
+                ary_core,
+                order='C' if self_core.flags['C_CONTIGUOUS'] else 'F',
+                subok=True,
+                copy=False if numpy_version < (2, 0) else None)
+            check_array_compatibility(self_core, ary_core)
+        np.copyto(self_core._ary, ary_core)
+
+    @property
+    def shape(self):
+        return FakeShape(self._ary.shape)
+
+    def ravel(self, *args, **kwargs):
+        return FakeCUDAArray(self._ary.ravel(*args, **kwargs))
+
+    def reshape(self, *args, **kwargs):
+        return FakeCUDAArray(self._ary.reshape(*args, **kwargs))
+
+    def view(self, *args, **kwargs):
+        return FakeCUDAArray(self._ary.view(*args, **kwargs))
+
+    def is_c_contiguous(self):
+        return self._ary.flags.c_contiguous
+
+    def is_f_contiguous(self):
+        return self._ary.flags.f_contiguous
+
+    def __str__(self):
+        return str(self._ary)
+
+    def __repr__(self):
+        return repr(self._ary)
+
+    def __len__(self):
+        return len(self._ary)
+
+    # TODO: Add inplace, bitwise, unary magic methods
+    #  (or maybe inherit this class from numpy)?
+    def __eq__(self, other):
+        return FakeCUDAArray(self._ary == other)
+
+    def __ne__(self, other):
+        return FakeCUDAArray(self._ary != other)
+
+    def __lt__(self, other):
+        return FakeCUDAArray(self._ary < other)
+
+    def __le__(self, other):
+        return FakeCUDAArray(self._ary <= other)
+
+    def __gt__(self, other):
+        return FakeCUDAArray(self._ary > other)
+
+    def __ge__(self, other):
+        return FakeCUDAArray(self._ary >= other)
+
+    def __add__(self, other):
+        return FakeCUDAArray(self._ary + other)
+
+    def __sub__(self, other):
+        return FakeCUDAArray(self._ary - other)
+
+    def __mul__(self, other):
+        return FakeCUDAArray(self._ary * other)
+
+    def __floordiv__(self, other):
+        return FakeCUDAArray(self._ary // other)
+
+    def __truediv__(self, other):
+        return FakeCUDAArray(self._ary / other)
+
+    def __mod__(self, other):
+        return FakeCUDAArray(self._ary % other)
+
+    def __pow__(self, other):
+        return FakeCUDAArray(self._ary ** other)
+
+    def split(self, section, stream=0):
+        return [
+            FakeCUDAArray(a)
+            for a in np.split(self._ary, range(section, len(self), section))
+        ]
+
+
+def array_core(ary):
+    """
+    Extract the repeated core of a broadcast array.
+
+    Broadcast arrays are by definition non-contiguous due to repeated
+    dimensions, i.e., dimensions with stride 0. In order to ascertain memory
+    contiguity and copy the underlying data from such arrays, we must create
+    a view without the repeated dimensions.
+
+    """
+    if not ary.strides or not ary.size:
+        return ary
+    core_index = []
+    for stride in ary.strides:
+        core_index.append(0 if stride == 0 else slice(None))
+    return ary[tuple(core_index)]
+
+
+def is_contiguous(ary):
+    """
+    Returns True iff `ary` is C-style contiguous while ignoring
+    broadcasted and 1-sized dimensions.
+    As opposed to array_core(), it does not call require_context(),
+    which can be quite expensive.
+    """
+    size = ary.dtype.itemsize
+    for shape, stride in zip(reversed(ary.shape), reversed(ary.strides)):
+        if shape > 1 and stride != 0:
+            if size != stride:
+                return False
+            size *= shape
+    return True
+
+
+def sentry_contiguous(ary):
+    core = array_core(ary)
+    if not core.flags['C_CONTIGUOUS'] and not core.flags['F_CONTIGUOUS']:
+        raise ValueError(errmsg_contiguous_buffer)
+
+
+def check_array_compatibility(ary1, ary2):
+    ary1sq, ary2sq = ary1.squeeze(), ary2.squeeze()
+    if ary1.dtype != ary2.dtype:
+        raise TypeError('incompatible dtype: %s vs. %s' %
+                        (ary1.dtype, ary2.dtype))
+    if ary1sq.shape != ary2sq.shape:
+        raise ValueError('incompatible shape: %s vs. %s' %
+                         (ary1.shape, ary2.shape))
+    if ary1sq.strides != ary2sq.strides:
+        raise ValueError('incompatible strides: %s vs. %s' %
+                         (ary1.strides, ary2.strides))
+
+
+def to_device(ary, stream=0, copy=True, to=None):
+    ary = np.array(ary,
+                   copy=False if numpy_version < (2, 0) else None,
+                   subok=True)
+    sentry_contiguous(ary)
+    if to is None:
+        buffer_dtype = np.int64 if ary.dtype.char in 'Mm' else ary.dtype
+        return FakeCUDAArray(
+            np.ndarray(
+                buffer=np.copy(array_core(ary)).view(buffer_dtype),
+                dtype=ary.dtype,
+                shape=ary.shape,
+                strides=ary.strides,
+            ).view(type=type(ary)),
+        )
+    else:
+        to.copy_to_device(ary, stream=stream)
+
+
+@contextmanager
+def pinned(arg):
+    yield
+
+
+def mapped_array(*args, **kwargs):
+    for unused_arg in ('portable', 'wc'):
+        if unused_arg in kwargs:
+            kwargs.pop(unused_arg)
+    return device_array(*args, **kwargs)
+
+
+def pinned_array(shape, dtype=np.float64, strides=None, order='C'):
+    return np.ndarray(shape=shape, strides=strides, dtype=dtype, order=order)
+
+
+def managed_array(shape, dtype=np.float64, strides=None, order='C'):
+    return np.ndarray(shape=shape, strides=strides, dtype=dtype, order=order)
+
+
+def device_array(*args, **kwargs):
+    stream = kwargs.pop('stream') if 'stream' in kwargs else 0
+    return FakeCUDAArray(np.ndarray(*args, **kwargs), stream=stream)
+
+
+def _contiguous_strides_like_array(ary):
+    """
+    Given an array, compute strides for a new contiguous array of the same
+    shape.
+    """
+    # Don't recompute strides if the default strides will be sufficient to
+    # create a contiguous array.
+    if ary.flags['C_CONTIGUOUS'] or ary.flags['F_CONTIGUOUS'] or ary.ndim <= 1:
+        return None
+
+    # Otherwise, we need to compute new strides using an algorithm adapted from
+    # NumPy v1.17.4's PyArray_NewLikeArrayWithShape in
+    # core/src/multiarray/ctors.c. We permute the strides in ascending order
+    # then compute the stride for the dimensions with the same permutation.
+
+    # Stride permutation. E.g. a stride array (4, -2, 12) becomes
+    # [(1, -2), (0, 4), (2, 12)]
+    strideperm = [ x for x in enumerate(ary.strides) ]
+    strideperm.sort(key=lambda x: x[1])
+
+    # Compute new strides using permutation
+    strides = [0] * len(ary.strides)
+    stride = ary.dtype.itemsize
+    for i_perm, _ in strideperm:
+        strides[i_perm] = stride
+        stride *= ary.shape[i_perm]
+    return tuple(strides)
+
+
+def _order_like_array(ary):
+    if ary.flags['F_CONTIGUOUS'] and not ary.flags['C_CONTIGUOUS']:
+        return 'F'
+    else:
+        return 'C'
+
+
+def device_array_like(ary, stream=0):
+    strides = _contiguous_strides_like_array(ary)
+    order = _order_like_array(ary)
+    return device_array(shape=ary.shape, dtype=ary.dtype, strides=strides,
+                        order=order)
+
+
+def pinned_array_like(ary):
+    strides = _contiguous_strides_like_array(ary)
+    order = _order_like_array(ary)
+    return pinned_array(shape=ary.shape, dtype=ary.dtype, strides=strides,
+                        order=order)
+
+
+def auto_device(ary, stream=0, copy=True):
+    if isinstance(ary, FakeCUDAArray):
+        return ary, False
+
+    if not isinstance(ary, np.void):
+        ary = np.array(
+            ary,
+            copy=False if numpy_version < (2, 0) else None,
+            subok=True)
+    return to_device(ary, stream, copy), True
+
+
+def is_cuda_ndarray(obj):
+    "Check if an object is a CUDA ndarray"
+    return getattr(obj, '__cuda_ndarray__', False)
+
+
+def verify_cuda_ndarray_interface(obj):
+    "Verify the CUDA ndarray interface for an obj"
+    require_cuda_ndarray(obj)
+
+    def requires_attr(attr, typ):
+        if not hasattr(obj, attr):
+            raise AttributeError(attr)
+        if not isinstance(getattr(obj, attr), typ):
+            raise AttributeError('%s must be of type %s' % (attr, typ))
+
+    requires_attr('shape', tuple)
+    requires_attr('strides', tuple)
+    requires_attr('dtype', np.dtype)
+    requires_attr('size', int)
+
+
+def require_cuda_ndarray(obj):
+    "Raises ValueError is is_cuda_ndarray(obj) evaluates False"
+    if not is_cuda_ndarray(obj):
+        raise ValueError('require an cuda ndarray object')
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/devices.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/devices.py
new file mode 100644
index 0000000000000000000000000000000000000000..3237fb2c6adea223bf079665319d3ef7b3c8489e
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/devices.py
@@ -0,0 +1,117 @@
+import numpy as np
+from collections import namedtuple
+
+_MemoryInfo = namedtuple("_MemoryInfo", "free,total")
+
+_SIMULATOR_CC = (5, 2)
+
+
+class FakeCUDADevice:
+    def __init__(self):
+        self.uuid = 'GPU-00000000-0000-0000-0000-000000000000'
+
+    @property
+    def compute_capability(self):
+        return _SIMULATOR_CC
+
+
+class FakeCUDAContext:
+    '''
+    This stub implements functionality only for simulating a single GPU
+    at the moment.
+    '''
+    def __init__(self, device_id):
+        self._device_id = device_id
+        self._device = FakeCUDADevice()
+
+    def __enter__(self):
+        pass
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        pass
+
+    def __str__(self):
+        return "<Managed Device {self.id}>".format(self=self)
+
+    @property
+    def id(self):
+        return self._device_id
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def compute_capability(self):
+        return _SIMULATOR_CC
+
+    def reset(self):
+        pass
+
+    def get_memory_info(self):
+        """
+        Cross-platform free / total host memory is hard without external
+        dependencies, e.g. `psutil` - so return infinite memory to maintain API
+        type compatibility
+        """
+        return _MemoryInfo(float('inf'), float('inf'))
+
+    def memalloc(self, sz):
+        """
+        Allocates memory on the simulated device
+        At present, there is no division between simulated
+        host memory and simulated device memory.
+        """
+        return np.ndarray(sz, dtype='u1')
+
+    def memhostalloc(self, sz, mapped=False, portable=False, wc=False):
+        '''Allocates memory on the host'''
+        return self.memalloc(sz)
+
+
+class FakeDeviceList:
+    '''
+    This stub implements a device list containing a single GPU. It also
+    keeps track of the GPU status, i.e. whether the context is closed or not,
+    which may have been set by the user calling reset()
+    '''
+    def __init__(self):
+        self.lst = (FakeCUDAContext(0),)
+        self.closed = False
+
+    def __getitem__(self, devnum):
+        self.closed = False
+        return self.lst[devnum]
+
+    def __str__(self):
+        return ', '.join([str(d) for d in self.lst])
+
+    def __iter__(self):
+        return iter(self.lst)
+
+    def __len__(self):
+        return len(self.lst)
+
+    @property
+    def current(self):
+        if self.closed:
+            return None
+        return self.lst[0]
+
+
+gpus = FakeDeviceList()
+
+
+def reset():
+    gpus[0].closed = True
+
+
+def get_context(devnum=0):
+    return FakeCUDAContext(devnum)
+
+
+def require_context(func):
+    '''
+    In the simulator, a context is always "available", so this is a no-op.
+    '''
+    return func
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/driver.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/driver.py
new file mode 100644
index 0000000000000000000000000000000000000000..09de5b729af1da79db35f9c73bed08dfabffff48
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/driver.py
@@ -0,0 +1,62 @@
+'''
+Most of the driver API is unsupported in the simulator, but some stubs are
+provided to allow tests to import correctly.
+'''
+
+
+def device_memset(dst, val, size, stream=0):
+    dst.view('u1')[:size].fill(bytes([val])[0])
+
+
+def host_to_device(dst, src, size, stream=0):
+    dst.view('u1')[:size] = src.view('u1')[:size]
+
+
+def device_to_host(dst, src, size, stream=0):
+    host_to_device(dst, src, size)
+
+
+def device_memory_size(obj):
+    return obj.itemsize * obj.size
+
+
+def device_to_device(dst, src, size, stream=0):
+    host_to_device(dst, src, size)
+
+
+class FakeDriver(object):
+    def get_device_count(self):
+        return 1
+
+
+driver = FakeDriver()
+
+
+class Linker:
+    @classmethod
+    def new(cls, max_registers=0, lineinfo=False, cc=None):
+        return Linker()
+
+    @property
+    def lto(self):
+        return False
+
+
+class LinkerError(RuntimeError):
+    pass
+
+
+class NvrtcError(RuntimeError):
+    pass
+
+
+class CudaAPIError(RuntimeError):
+    pass
+
+
+def launch_kernel(*args, **kwargs):
+    msg = 'Launching kernels directly is not supported in the simulator'
+    raise RuntimeError(msg)
+
+
+USE_NV_BINDING = False
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/drvapi.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/drvapi.py
new file mode 100644
index 0000000000000000000000000000000000000000..44c697f37debb3a6a80d7516063f4524cbc3a152
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/drvapi.py
@@ -0,0 +1,4 @@
+'''
+drvapi is not implemented in the simulator, but this module exists to allow
+tests to import correctly.
+'''
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/dummyarray.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/dummyarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..adabaa7828c24856a0a52d45c29f27bbdd544831
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/dummyarray.py
@@ -0,0 +1,4 @@
+# Dummy arrays are not implemented in the simulator. This file allows the dummy
+# array tests to be imported, but they are skipped on the simulator.
+
+Array = None
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/error.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/error.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaaa2884a0d2380015b6a4f11177e2fcaaa7f51d
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/error.py
@@ -0,0 +1,6 @@
+class CudaSupportError(RuntimeError):
+    pass
+
+
+class NvrtcError(Exception):
+    pass
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/libs.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/libs.py
new file mode 100644
index 0000000000000000000000000000000000000000..347b936c5d9ae465b3d8644dc63529d363add4ed
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/libs.py
@@ -0,0 +1,2 @@
+def check_static_lib(lib):
+    raise FileNotFoundError('Linking libraries not supported by cudasim')
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/nvvm.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/nvvm.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a011a77a4002e655085d2da67a7f06f4b1f0519
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/nvvm.py
@@ -0,0 +1,29 @@
+'''
+NVVM is not supported in the simulator, but stubs are provided to allow tests
+to import correctly.
+'''
+
+
+class NvvmSupportError(ImportError):
+    pass
+
+
+class NVVM(object):
+    def __init__(self):
+        raise NvvmSupportError('NVVM not supported in the simulator')
+
+
+CompilationUnit = None
+compile_ir = None
+set_cuda_kernel = None
+get_arch_option = None
+LibDevice = None
+NvvmError = None
+
+
+def is_available():
+    return False
+
+
+def get_supported_ccs():
+    return ()
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/runtime.py b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..308d19e7683b27754c68dca334f22805e50821d2
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/cudadrv/runtime.py
@@ -0,0 +1,19 @@
+'''
+The runtime API is unsupported in the simulator, but some stubs are
+provided to allow tests to import correctly.
+'''
+
+
+class FakeRuntime(object):
+    def get_version(self):
+        return (-1, -1)
+
+    def is_supported_version(self):
+        return True
+
+    @property
+    def supported_versions(self):
+        return (-1, -1),
+
+
+runtime = FakeRuntime()
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/kernel.py b/lib/python3.10/site-packages/numba/cuda/simulator/kernel.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3ca2259938b99f0beefffe10b199715dd5e6707
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/kernel.py
@@ -0,0 +1,308 @@
+from contextlib import contextmanager
+import functools
+import sys
+import threading
+
+import numpy as np
+
+from .cudadrv.devicearray import FakeCUDAArray, FakeWithinKernelCUDAArray
+from .kernelapi import Dim3, FakeCUDAModule, swapped_cuda_module
+from ..errors import normalize_kernel_dimensions
+from ..args import wrap_arg, ArgHint
+
+
+"""
+Global variable to keep track of the current "kernel context", i.e the
+FakeCUDAModule.  We only support one kernel launch at a time.
+No support for concurrent kernel launch.
+"""
+_kernel_context = None
+
+
+@contextmanager
+def _push_kernel_context(mod):
+    """
+    Push the current kernel context.
+    """
+    global _kernel_context
+    assert _kernel_context is None, "concurrent simulated kernel not supported"
+    _kernel_context = mod
+    try:
+        yield
+    finally:
+        _kernel_context = None
+
+
+def _get_kernel_context():
+    """
+    Get the current kernel context. This is usually done by a device function.
+    """
+    return _kernel_context
+
+
+class FakeOverload:
+    '''
+    Used only to provide the max_cooperative_grid_blocks method
+    '''
+    def max_cooperative_grid_blocks(self, blockdim):
+        # We can only run one block in a cooperative grid because we have no
+        # mechanism for synchronization between different blocks
+        return 1
+
+
+class FakeOverloadDict(dict):
+    def __getitem__(self, key):
+        # Always return a fake overload for any signature, as we don't keep
+        # track of overloads in the simulator.
+        return FakeOverload()
+
+
+class FakeCUDAKernel(object):
+    '''
+    Wraps a @cuda.jit-ed function.
+    '''
+
+    def __init__(self, fn, device, fastmath=False, extensions=[], debug=False):
+        self.fn = fn
+        self._device = device
+        self._fastmath = fastmath
+        self._debug = debug
+        self.extensions = list(extensions) # defensive copy
+        # Initial configuration: grid unconfigured, stream 0, no dynamic shared
+        # memory.
+        self.grid_dim = None
+        self.block_dim = None
+        self.stream = 0
+        self.dynshared_size = 0
+        functools.update_wrapper(self, fn)
+
+    def __call__(self, *args):
+        if self._device:
+            with swapped_cuda_module(self.fn, _get_kernel_context()):
+                return self.fn(*args)
+
+        # Ensure we've been given a valid grid configuration
+        grid_dim, block_dim = normalize_kernel_dimensions(self.grid_dim,
+                                                          self.block_dim)
+
+        fake_cuda_module = FakeCUDAModule(grid_dim, block_dim,
+                                          self.dynshared_size)
+        with _push_kernel_context(fake_cuda_module):
+            # fake_args substitutes all numpy arrays for FakeCUDAArrays
+            # because they implement some semantics differently
+            retr = []
+
+            def fake_arg(arg):
+                # map the arguments using any extension you've registered
+                _, arg = functools.reduce(
+                    lambda ty_val, extension: extension.prepare_args(
+                        *ty_val,
+                        stream=0,
+                        retr=retr),
+                    self.extensions,
+                    (None, arg)
+                )
+
+                if isinstance(arg, np.ndarray) and arg.ndim > 0:
+                    ret = wrap_arg(arg).to_device(retr)
+                elif isinstance(arg, ArgHint):
+                    ret = arg.to_device(retr)
+                elif isinstance(arg, np.void):
+                    ret = FakeCUDAArray(arg)  # In case a np record comes in.
+                else:
+                    ret = arg
+                if isinstance(ret, FakeCUDAArray):
+                    return FakeWithinKernelCUDAArray(ret)
+                return ret
+
+            fake_args = [fake_arg(arg) for arg in args]
+            with swapped_cuda_module(self.fn, fake_cuda_module):
+                # Execute one block at a time
+                for grid_point in np.ndindex(*grid_dim):
+                    bm = BlockManager(self.fn, grid_dim, block_dim, self._debug)
+                    bm.run(grid_point, *fake_args)
+
+            for wb in retr:
+                wb()
+
+    def __getitem__(self, configuration):
+        self.grid_dim, self.block_dim = \
+            normalize_kernel_dimensions(*configuration[:2])
+
+        if len(configuration) == 4:
+            self.dynshared_size = configuration[3]
+
+        return self
+
+    def bind(self):
+        pass
+
+    def specialize(self, *args):
+        return self
+
+    def forall(self, ntasks, tpb=0, stream=0, sharedmem=0):
+        if ntasks < 0:
+            raise ValueError("Can't create ForAll with negative task count: %s"
+                             % ntasks)
+        return self[ntasks, 1, stream, sharedmem]
+
+    @property
+    def overloads(self):
+        return FakeOverloadDict()
+
+    @property
+    def py_func(self):
+        return self.fn
+
+
+# Thread emulation
+
+class BlockThread(threading.Thread):
+    '''
+    Manages the execution of a function for a single CUDA thread.
+    '''
+    def __init__(self, f, manager, blockIdx, threadIdx, debug):
+        if debug:
+            def debug_wrapper(*args, **kwargs):
+                np.seterr(divide='raise')
+                f(*args, **kwargs)
+            target = debug_wrapper
+        else:
+            target = f
+
+        super(BlockThread, self).__init__(target=target)
+        self.syncthreads_event = threading.Event()
+        self.syncthreads_blocked = False
+        self._manager = manager
+        self.blockIdx = Dim3(*blockIdx)
+        self.threadIdx = Dim3(*threadIdx)
+        self.exception = None
+        self.daemon = True
+        self.abort = False
+        self.debug = debug
+        blockDim = Dim3(*self._manager._block_dim)
+        self.thread_id = self.threadIdx.x + (blockDim.x * (self.threadIdx.y +
+                                                           blockDim.y *
+                                                           self.threadIdx.z))
+
+    def run(self):
+        try:
+            super(BlockThread, self).run()
+        except Exception as e:
+            tid = 'tid=%s' % list(self.threadIdx)
+            ctaid = 'ctaid=%s' % list(self.blockIdx)
+            if str(e) == '':
+                msg = '%s %s' % (tid, ctaid)
+            else:
+                msg = '%s %s: %s' % (tid, ctaid, e)
+            tb = sys.exc_info()[2]
+            # Using `with_traceback` here would cause it to be mutated by
+            # future raise statements, which may or may not matter.
+            self.exception = (type(e)(msg), tb)
+
+    def syncthreads(self):
+
+        if self.abort:
+            raise RuntimeError("abort flag set on syncthreads call")
+
+        self.syncthreads_blocked = True
+        self.syncthreads_event.wait()
+        self.syncthreads_event.clear()
+
+        if self.abort:
+            raise RuntimeError("abort flag set on syncthreads clear")
+
+    def syncthreads_count(self, value):
+        idx = self.threadIdx.x, self.threadIdx.y, self.threadIdx.z
+        self._manager.block_state[idx] = value
+        self.syncthreads()
+        count = np.count_nonzero(self._manager.block_state)
+        self.syncthreads()
+        return count
+
+    def syncthreads_and(self, value):
+        idx = self.threadIdx.x, self.threadIdx.y, self.threadIdx.z
+        self._manager.block_state[idx] = value
+        self.syncthreads()
+        test = np.all(self._manager.block_state)
+        self.syncthreads()
+        return 1 if test else 0
+
+    def syncthreads_or(self, value):
+        idx = self.threadIdx.x, self.threadIdx.y, self.threadIdx.z
+        self._manager.block_state[idx] = value
+        self.syncthreads()
+        test = np.any(self._manager.block_state)
+        self.syncthreads()
+        return 1 if test else 0
+
+    def __str__(self):
+        return 'Thread <<<%s, %s>>>' % (self.blockIdx, self.threadIdx)
+
+
+class BlockManager(object):
+    '''
+    Manages the execution of a thread block.
+
+    When run() is called, all threads are started. Each thread executes until it
+    hits syncthreads(), at which point it sets its own syncthreads_blocked to
+    True so that the BlockManager knows it is blocked. It then waits on its
+    syncthreads_event.
+
+    The BlockManager polls threads to determine if they are blocked in
+    syncthreads(). If it finds a blocked thread, it adds it to the set of
+    blocked threads. When all threads are blocked, it unblocks all the threads.
+    The thread are unblocked by setting their syncthreads_blocked back to False
+    and setting their syncthreads_event.
+
+    The polling continues until no threads are alive, when execution is
+    complete.
+    '''
+    def __init__(self, f, grid_dim, block_dim, debug):
+        self._grid_dim = grid_dim
+        self._block_dim = block_dim
+        self._f = f
+        self._debug = debug
+        self.block_state = np.zeros(block_dim, dtype=np.bool_)
+
+    def run(self, grid_point, *args):
+        # Create all threads
+        threads = set()
+        livethreads = set()
+        blockedthreads = set()
+        for block_point in np.ndindex(*self._block_dim):
+            def target():
+                self._f(*args)
+            t = BlockThread(target, self, grid_point, block_point, self._debug)
+            t.start()
+            threads.add(t)
+            livethreads.add(t)
+
+        # Potential optimisations:
+        # 1. Continue the while loop immediately after finding a blocked thread
+        # 2. Don't poll already-blocked threads
+        while livethreads:
+            for t in livethreads:
+                if t.syncthreads_blocked:
+                    blockedthreads.add(t)
+                elif t.exception:
+
+                    # Abort all other simulator threads on exception,
+                    # do *not* join immediately to facilitate debugging.
+                    for t_other in threads:
+                        t_other.abort = True
+                        t_other.syncthreads_blocked = False
+                        t_other.syncthreads_event.set()
+
+                    raise t.exception[0].with_traceback(t.exception[1])
+            if livethreads == blockedthreads:
+                for t in blockedthreads:
+                    t.syncthreads_blocked = False
+                    t.syncthreads_event.set()
+                blockedthreads = set()
+            livethreads = set([ t for t in livethreads if t.is_alive() ])
+        # Final check for exceptions in case any were set prior to thread
+        # finishing, before we could check it
+        for t in threads:
+            if t.exception:
+                raise t.exception[0].with_traceback(t.exception[1])
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/kernelapi.py b/lib/python3.10/site-packages/numba/cuda/simulator/kernelapi.py
new file mode 100644
index 0000000000000000000000000000000000000000..64793df054cc2e2baeb00d059338fa570e53718a
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/kernelapi.py
@@ -0,0 +1,495 @@
+'''
+Implements the cuda module as called from within an executing kernel
+(@cuda.jit-decorated function).
+'''
+
+from contextlib import contextmanager
+import sys
+import threading
+import traceback
+from numba.core import types
+import numpy as np
+
+from numba.np import numpy_support
+
+from .vector_types import vector_types
+
+
+class Dim3(object):
+    '''
+    Used to implement thread/block indices/dimensions
+    '''
+    def __init__(self, x, y, z):
+        self.x = x
+        self.y = y
+        self.z = z
+
+    def __str__(self):
+        return '(%s, %s, %s)' % (self.x, self.y, self.z)
+
+    def __repr__(self):
+        return 'Dim3(%s, %s, %s)' % (self.x, self.y, self.z)
+
+    def __iter__(self):
+        yield self.x
+        yield self.y
+        yield self.z
+
+
+class GridGroup:
+    '''
+    Used to implement the grid group.
+    '''
+
+    def sync(self):
+        # Synchronization of the grid group is equivalent to synchronization of
+        # the thread block, because we only support cooperative grids with one
+        # block.
+        threading.current_thread().syncthreads()
+
+
+class FakeCUDACg:
+    '''
+    CUDA Cooperative Groups
+    '''
+    def this_grid(self):
+        return GridGroup()
+
+
+class FakeCUDALocal(object):
+    '''
+    CUDA Local arrays
+    '''
+    def array(self, shape, dtype):
+        if isinstance(dtype, types.Type):
+            dtype = numpy_support.as_dtype(dtype)
+        return np.empty(shape, dtype)
+
+
+class FakeCUDAConst(object):
+    '''
+    CUDA Const arrays
+    '''
+    def array_like(self, ary):
+        return ary
+
+
+class FakeCUDAShared(object):
+    '''
+    CUDA Shared arrays.
+
+    Limitations: assumes that only one call to cuda.shared.array is on a line,
+    and that that line is only executed once per thread. i.e.::
+
+        a = cuda.shared.array(...); b = cuda.shared.array(...)
+
+    will erroneously alias a and b, and::
+
+        for i in range(10):
+            sharedarrs[i] = cuda.shared.array(...)
+
+    will alias all arrays created at that point (though it is not certain that
+    this would be supported by Numba anyway).
+    '''
+
+    def __init__(self, dynshared_size):
+        self._allocations = {}
+        self._dynshared_size = dynshared_size
+        self._dynshared = np.zeros(dynshared_size, dtype=np.byte)
+
+    def array(self, shape, dtype):
+        if isinstance(dtype, types.Type):
+            dtype = numpy_support.as_dtype(dtype)
+        # Dynamic shared memory is requested with size 0 - this all shares the
+        # same underlying memory
+        if shape == 0:
+            # Count must be the maximum number of whole elements that fit in the
+            # buffer (Numpy complains if the buffer is not a multiple of the
+            # element size)
+            count = self._dynshared_size // dtype.itemsize
+            return np.frombuffer(self._dynshared.data, dtype=dtype, count=count)
+
+        # Otherwise, identify allocations by source file and line number
+        # We pass the reference frame explicitly to work around
+        # http://bugs.python.org/issue25108
+        stack = traceback.extract_stack(sys._getframe())
+        caller = stack[-2][0:2]
+        res = self._allocations.get(caller)
+        if res is None:
+            res = np.empty(shape, dtype)
+            self._allocations[caller] = res
+        return res
+
+
+addlock = threading.Lock()
+sublock = threading.Lock()
+andlock = threading.Lock()
+orlock = threading.Lock()
+xorlock = threading.Lock()
+maxlock = threading.Lock()
+minlock = threading.Lock()
+compare_and_swaplock = threading.Lock()
+caslock = threading.Lock()
+inclock = threading.Lock()
+declock = threading.Lock()
+exchlock = threading.Lock()
+
+
+class FakeCUDAAtomic(object):
+    def add(self, array, index, val):
+        with addlock:
+            old = array[index]
+            array[index] += val
+        return old
+
+    def sub(self, array, index, val):
+        with sublock:
+            old = array[index]
+            array[index] -= val
+        return old
+
+    def and_(self, array, index, val):
+        with andlock:
+            old = array[index]
+            array[index] &= val
+        return old
+
+    def or_(self, array, index, val):
+        with orlock:
+            old = array[index]
+            array[index] |= val
+        return old
+
+    def xor(self, array, index, val):
+        with xorlock:
+            old = array[index]
+            array[index] ^= val
+        return old
+
+    def inc(self, array, index, val):
+        with inclock:
+            old = array[index]
+            if old >= val:
+                array[index] = 0
+            else:
+                array[index] += 1
+        return old
+
+    def dec(self, array, index, val):
+        with declock:
+            old = array[index]
+            if (old == 0) or (old > val):
+                array[index] = val
+            else:
+                array[index] -= 1
+        return old
+
+    def exch(self, array, index, val):
+        with exchlock:
+            old = array[index]
+            array[index] = val
+        return old
+
+    def max(self, array, index, val):
+        with maxlock:
+            old = array[index]
+            array[index] = max(old, val)
+        return old
+
+    def min(self, array, index, val):
+        with minlock:
+            old = array[index]
+            array[index] = min(old, val)
+        return old
+
+    def nanmax(self, array, index, val):
+        with maxlock:
+            old = array[index]
+            array[index] = np.nanmax([array[index], val])
+        return old
+
+    def nanmin(self, array, index, val):
+        with minlock:
+            old = array[index]
+            array[index] = np.nanmin([array[index], val])
+        return old
+
+    def compare_and_swap(self, array, old, val):
+        with compare_and_swaplock:
+            index = (0,) * array.ndim
+            loaded = array[index]
+            if loaded == old:
+                array[index] = val
+            return loaded
+
+    def cas(self, array, index, old, val):
+        with caslock:
+            loaded = array[index]
+            if loaded == old:
+                array[index] = val
+            return loaded
+
+
+class FakeCUDAFp16(object):
+    def hadd(self, a, b):
+        return a + b
+
+    def hsub(self, a, b):
+        return a - b
+
+    def hmul(self, a, b):
+        return a * b
+
+    def hdiv(self, a, b):
+        return a / b
+
+    def hfma(self, a, b, c):
+        return a * b + c
+
+    def hneg(self, a):
+        return -a
+
+    def habs(self, a):
+        return abs(a)
+
+    def hsin(self, x):
+        return np.sin(x, dtype=np.float16)
+
+    def hcos(self, x):
+        return np.cos(x, dtype=np.float16)
+
+    def hlog(self, x):
+        return np.log(x, dtype=np.float16)
+
+    def hlog2(self, x):
+        return np.log2(x, dtype=np.float16)
+
+    def hlog10(self, x):
+        return np.log10(x, dtype=np.float16)
+
+    def hexp(self, x):
+        return np.exp(x, dtype=np.float16)
+
+    def hexp2(self, x):
+        return np.exp2(x, dtype=np.float16)
+
+    def hexp10(self, x):
+        return np.float16(10 ** x)
+
+    def hsqrt(self, x):
+        return np.sqrt(x, dtype=np.float16)
+
+    def hrsqrt(self, x):
+        return np.float16(x ** -0.5)
+
+    def hceil(self, x):
+        return np.ceil(x, dtype=np.float16)
+
+    def hfloor(self, x):
+        return np.ceil(x, dtype=np.float16)
+
+    def hrcp(self, x):
+        return np.reciprocal(x, dtype=np.float16)
+
+    def htrunc(self, x):
+        return np.trunc(x, dtype=np.float16)
+
+    def hrint(self, x):
+        return np.rint(x, dtype=np.float16)
+
+    def heq(self, a, b):
+        return a == b
+
+    def hne(self, a, b):
+        return a != b
+
+    def hge(self, a, b):
+        return a >= b
+
+    def hgt(self, a, b):
+        return a > b
+
+    def hle(self, a, b):
+        return a <= b
+
+    def hlt(self, a, b):
+        return a < b
+
+    def hmax(self, a, b):
+        return max(a, b)
+
+    def hmin(self, a, b):
+        return min(a, b)
+
+
+class FakeCUDAModule(object):
+    '''
+    An instance of this class will be injected into the __globals__ for an
+    executing function in order to implement calls to cuda.*. This will fail to
+    work correctly if the user code does::
+
+        from numba import cuda as something_else
+
+    In other words, the CUDA module must be called cuda.
+    '''
+
+    def __init__(self, grid_dim, block_dim, dynshared_size):
+        self.gridDim = Dim3(*grid_dim)
+        self.blockDim = Dim3(*block_dim)
+        self._cg = FakeCUDACg()
+        self._local = FakeCUDALocal()
+        self._shared = FakeCUDAShared(dynshared_size)
+        self._const = FakeCUDAConst()
+        self._atomic = FakeCUDAAtomic()
+        self._fp16 = FakeCUDAFp16()
+        # Insert the vector types into the kernel context
+        # Note that we need to do this in addition to exposing them as module
+        # variables in `simulator.__init__.py`, because the test cases need
+        # to access the actual cuda module as well as the fake cuda module
+        # for vector types.
+        for name, svty in vector_types.items():
+            setattr(self, name, svty)
+            for alias in svty.aliases:
+                setattr(self, alias, svty)
+
+    @property
+    def cg(self):
+        return self._cg
+
+    @property
+    def local(self):
+        return self._local
+
+    @property
+    def shared(self):
+        return self._shared
+
+    @property
+    def const(self):
+        return self._const
+
+    @property
+    def atomic(self):
+        return self._atomic
+
+    @property
+    def fp16(self):
+        return self._fp16
+
+    @property
+    def threadIdx(self):
+        return threading.current_thread().threadIdx
+
+    @property
+    def blockIdx(self):
+        return threading.current_thread().blockIdx
+
+    @property
+    def warpsize(self):
+        return 32
+
+    @property
+    def laneid(self):
+        return threading.current_thread().thread_id % 32
+
+    def syncthreads(self):
+        threading.current_thread().syncthreads()
+
+    def threadfence(self):
+        # No-op
+        pass
+
+    def threadfence_block(self):
+        # No-op
+        pass
+
+    def threadfence_system(self):
+        # No-op
+        pass
+
+    def syncthreads_count(self, val):
+        return threading.current_thread().syncthreads_count(val)
+
+    def syncthreads_and(self, val):
+        return threading.current_thread().syncthreads_and(val)
+
+    def syncthreads_or(self, val):
+        return threading.current_thread().syncthreads_or(val)
+
+    def popc(self, val):
+        return bin(val).count("1")
+
+    def fma(self, a, b, c):
+        return a * b + c
+
+    def cbrt(self, a):
+        return a ** (1 / 3)
+
+    def brev(self, val):
+        return int('{:032b}'.format(val)[::-1], 2)
+
+    def clz(self, val):
+        s = '{:032b}'.format(val)
+        return len(s) - len(s.lstrip('0'))
+
+    def ffs(self, val):
+        # The algorithm is:
+        # 1. Count the number of trailing zeros.
+        # 2. Add 1, because the LSB is numbered 1 rather than 0, and so on.
+        # 3. If we've counted 32 zeros (resulting in 33), there were no bits
+        #    set so we need to return zero.
+        s = '{:032b}'.format(val)
+        r = (len(s) - len(s.rstrip('0')) + 1) % 33
+        return r
+
+    def selp(self, a, b, c):
+        return b if a else c
+
+    def grid(self, n):
+        bdim = self.blockDim
+        bid = self.blockIdx
+        tid = self.threadIdx
+        x = bid.x * bdim.x + tid.x
+        if n == 1:
+            return x
+        y = bid.y * bdim.y + tid.y
+        if n == 2:
+            return (x, y)
+        z = bid.z * bdim.z + tid.z
+        if n == 3:
+            return (x, y, z)
+
+        raise RuntimeError("Global ID has 1-3 dimensions. %d requested" % n)
+
+    def gridsize(self, n):
+        bdim = self.blockDim
+        gdim = self.gridDim
+        x = bdim.x * gdim.x
+        if n == 1:
+            return x
+        y = bdim.y * gdim.y
+        if n == 2:
+            return (x, y)
+        z = bdim.z * gdim.z
+        if n == 3:
+            return (x, y, z)
+
+        raise RuntimeError("Global grid has 1-3 dimensions. %d requested" % n)
+
+
+@contextmanager
+def swapped_cuda_module(fn, fake_cuda_module):
+    from numba import cuda
+
+    fn_globs = fn.__globals__
+    # get all globals that is the "cuda" module
+    orig = dict((k, v) for k, v in fn_globs.items() if v is cuda)
+    # build replacement dict
+    repl = dict((k, fake_cuda_module) for k, v in orig.items())
+    # replace
+    fn_globs.update(repl)
+    try:
+        yield
+    finally:
+        # revert
+        fn_globs.update(orig)
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/reduction.py b/lib/python3.10/site-packages/numba/cuda/simulator/reduction.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b819c043549c936fc9a73271fe846f02eb05001
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/reduction.py
@@ -0,0 +1,15 @@
+from functools import reduce as pyreduce
+
+
+def Reduce(func):
+    def reduce_wrapper(seq, res=None, init=0):
+        r = pyreduce(func, seq, init)
+        if res is not None:
+            res[0] = r
+            return None
+        else:
+            return r
+    return reduce_wrapper
+
+
+reduce = Reduce
diff --git a/lib/python3.10/site-packages/numba/cuda/simulator/vector_types.py b/lib/python3.10/site-packages/numba/cuda/simulator/vector_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..de82ab35e1085b816e934f09f87c457b9c6e2f45
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/simulator/vector_types.py
@@ -0,0 +1,63 @@
+from numba import types, config
+from numba.cuda.stubs import _vector_type_stubs
+
+
+class SimulatedVectorType:
+    attributes = ['x', 'y', 'z', 'w']
+
+    def __init__(self, *args):
+        args_flattened = []
+        for arg in args:
+            if isinstance(arg, SimulatedVectorType):
+                args_flattened += arg.as_list()
+            else:
+                args_flattened.append(arg)
+        self._attrs = self.attributes[:len(args_flattened)]
+        if not self.num_elements == len(args_flattened):
+            raise TypeError(
+                f"{self.name} expects {self.num_elements}"
+                f" elements, got {len(args_flattened)}"
+            )
+
+        for arg, attr in zip(args_flattened, self._attrs):
+            setattr(self, attr, arg)
+
+    @property
+    def name(self):
+        raise NotImplementedError()
+
+    @property
+    def num_elements(self):
+        raise NotImplementedError()
+
+    def as_list(self):
+        return [getattr(self, attr) for attr in self._attrs]
+
+
+def make_simulated_vector_type(num_elements, name):
+    if config.USE_LEGACY_TYPE_SYSTEM:
+        base_type = types.float32
+    else:
+        base_type = types.np_float32
+
+    obj = type(name, (SimulatedVectorType,), {
+        "num_elements": num_elements,
+        "base_type": base_type,
+        "name": name
+    })
+    obj.user_facing_object = obj
+    return obj
+
+
+def _initialize():
+    _simulated_vector_types = {}
+    for stub in _vector_type_stubs:
+        num_elements = int(stub.__name__[-1])
+        _simulated_vector_types[stub.__name__] = (
+            make_simulated_vector_type(num_elements, stub.__name__)
+        )
+        _simulated_vector_types[stub.__name__].aliases = stub.aliases
+    return _simulated_vector_types
+
+
+vector_types = _initialize()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/__init__.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e7d31af3b99e121a9ae04bc855a6c80cc4594d
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/__init__.py
@@ -0,0 +1,8 @@
+from numba.cuda.testing import ensure_supported_ccs_initialized
+from numba.testing import load_testsuite
+import os
+
+
+def load_tests(loader, tests, pattern):
+    ensure_supported_ccs_initialized()
+    return load_testsuite(loader, os.path.dirname(__file__))
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_array_attr.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_array_attr.py
new file mode 100644
index 0000000000000000000000000000000000000000..32f75c855cc5657ad81a15e805503e6ace650c45
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_array_attr.py
@@ -0,0 +1,145 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+
+
+class TestArrayAttr(CUDATestCase):
+
+    def test_contigous_2d(self):
+        ary = np.arange(10)
+        cary = ary.reshape(2, 5)
+        fary = np.asfortranarray(cary)
+
+        dcary = cuda.to_device(cary)
+        dfary = cuda.to_device(fary)
+        self.assertTrue(dcary.is_c_contiguous())
+        self.assertTrue(not dfary.is_c_contiguous())
+        self.assertTrue(not dcary.is_f_contiguous())
+        self.assertTrue(dfary.is_f_contiguous())
+
+    def test_contigous_3d(self):
+        ary = np.arange(20)
+        cary = ary.reshape(2, 5, 2)
+        fary = np.asfortranarray(cary)
+
+        dcary = cuda.to_device(cary)
+        dfary = cuda.to_device(fary)
+        self.assertTrue(dcary.is_c_contiguous())
+        self.assertTrue(not dfary.is_c_contiguous())
+        self.assertTrue(not dcary.is_f_contiguous())
+        self.assertTrue(dfary.is_f_contiguous())
+
+    def test_contigous_4d(self):
+        ary = np.arange(60)
+        cary = ary.reshape(2, 5, 2, 3)
+        fary = np.asfortranarray(cary)
+
+        dcary = cuda.to_device(cary)
+        dfary = cuda.to_device(fary)
+        self.assertTrue(dcary.is_c_contiguous())
+        self.assertTrue(not dfary.is_c_contiguous())
+        self.assertTrue(not dcary.is_f_contiguous())
+        self.assertTrue(dfary.is_f_contiguous())
+
+    def test_ravel_1d(self):
+        ary = np.arange(60)
+        dary = cuda.to_device(ary)
+        for order in 'CFA':
+            expect = ary.ravel(order=order)
+            dflat = dary.ravel(order=order)
+            flat = dflat.copy_to_host()
+            self.assertTrue(dary is not dflat)  # ravel returns new array
+            self.assertEqual(flat.ndim, 1)
+            self.assertPreciseEqual(expect, flat)
+
+    @skip_on_cudasim('CUDA Array Interface is not supported in the simulator')
+    def test_ravel_stride_1d(self):
+        ary = np.arange(60)
+        dary = cuda.to_device(ary)
+        # No-copy stride device array
+        darystride = dary[::2]
+        dary_data = dary.__cuda_array_interface__['data'][0]
+        ddarystride_data = darystride.__cuda_array_interface__['data'][0]
+        self.assertEqual(dary_data, ddarystride_data)
+        # Fail on ravel on non-contiguous array
+        with self.assertRaises(NotImplementedError):
+            darystride.ravel()
+
+    def test_ravel_c(self):
+        ary = np.arange(60)
+        reshaped = ary.reshape(2, 5, 2, 3)
+
+        expect = reshaped.ravel(order='C')
+        dary = cuda.to_device(reshaped)
+        dflat = dary.ravel()
+        flat = dflat.copy_to_host()
+        self.assertTrue(dary is not dflat)
+        self.assertEqual(flat.ndim, 1)
+        self.assertPreciseEqual(expect, flat)
+
+        # explicit order kwarg
+        for order in 'CA':
+            expect = reshaped.ravel(order=order)
+            dary = cuda.to_device(reshaped)
+            dflat = dary.ravel(order=order)
+            flat = dflat.copy_to_host()
+            self.assertTrue(dary is not dflat)
+            self.assertEqual(flat.ndim, 1)
+            self.assertPreciseEqual(expect, flat)
+
+    @skip_on_cudasim('CUDA Array Interface is not supported in the simulator')
+    def test_ravel_stride_c(self):
+        ary = np.arange(60)
+        reshaped = ary.reshape(2, 5, 2, 3)
+
+        dary = cuda.to_device(reshaped)
+        darystride = dary[::2, ::2, ::2, ::2]
+        dary_data = dary.__cuda_array_interface__['data'][0]
+        ddarystride_data = darystride.__cuda_array_interface__['data'][0]
+        self.assertEqual(dary_data, ddarystride_data)
+        with self.assertRaises(NotImplementedError):
+            darystride.ravel()
+
+    def test_ravel_f(self):
+        ary = np.arange(60)
+        reshaped = np.asfortranarray(ary.reshape(2, 5, 2, 3))
+        for order in 'FA':
+            expect = reshaped.ravel(order=order)
+            dary = cuda.to_device(reshaped)
+            dflat = dary.ravel(order=order)
+            flat = dflat.copy_to_host()
+            self.assertTrue(dary is not dflat)
+            self.assertEqual(flat.ndim, 1)
+            self.assertPreciseEqual(expect, flat)
+
+    @skip_on_cudasim('CUDA Array Interface is not supported in the simulator')
+    def test_ravel_stride_f(self):
+        ary = np.arange(60)
+        reshaped = np.asfortranarray(ary.reshape(2, 5, 2, 3))
+        dary = cuda.to_device(reshaped)
+        darystride = dary[::2, ::2, ::2, ::2]
+        dary_data = dary.__cuda_array_interface__['data'][0]
+        ddarystride_data = darystride.__cuda_array_interface__['data'][0]
+        self.assertEqual(dary_data, ddarystride_data)
+        with self.assertRaises(NotImplementedError):
+            darystride.ravel()
+
+    def test_reshape_c(self):
+        ary = np.arange(10)
+        expect = ary.reshape(2, 5)
+        dary = cuda.to_device(ary)
+        dary_reshaped = dary.reshape(2, 5)
+        got = dary_reshaped.copy_to_host()
+        self.assertPreciseEqual(expect, got)
+
+    def test_reshape_f(self):
+        ary = np.arange(10)
+        expect = ary.reshape(2, 5, order='F')
+        dary = cuda.to_device(ary)
+        dary_reshaped = dary.reshape(2, 5, order='F')
+        got = dary_reshaped.copy_to_host()
+        self.assertPreciseEqual(expect, got)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_context_stack.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_context_stack.py
new file mode 100644
index 0000000000000000000000000000000000000000..030052507358b4c2e0f1d0c48599cd1db3fc6b4b
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_context_stack.py
@@ -0,0 +1,145 @@
+import numbers
+from ctypes import byref
+import weakref
+
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from numba.cuda.cudadrv import driver
+
+
+class TestContextStack(CUDATestCase):
+    def setUp(self):
+        super().setUp()
+        # Reset before testing
+        cuda.close()
+
+    def test_gpus_current(self):
+        self.assertIs(cuda.gpus.current, None)
+        with cuda.gpus[0]:
+            self.assertEqual(int(cuda.gpus.current.id), 0)
+
+    def test_gpus_len(self):
+        self.assertGreater(len(cuda.gpus), 0)
+
+    def test_gpus_iter(self):
+        gpulist = list(cuda.gpus)
+        self.assertGreater(len(gpulist), 0)
+
+
+class TestContextAPI(CUDATestCase):
+
+    def tearDown(self):
+        super().tearDown()
+        cuda.close()
+
+    def test_context_memory(self):
+        try:
+            mem = cuda.current_context().get_memory_info()
+        except NotImplementedError:
+            self.skipTest('EMM Plugin does not implement get_memory_info()')
+
+        self.assertIsInstance(mem.free, numbers.Number)
+        self.assertEqual(mem.free, mem[0])
+
+        self.assertIsInstance(mem.total, numbers.Number)
+        self.assertEqual(mem.total, mem[1])
+
+        self.assertLessEqual(mem.free, mem.total)
+
+    @unittest.skipIf(len(cuda.gpus) < 2, "need more than 1 gpus")
+    @skip_on_cudasim('CUDA HW required')
+    def test_forbidden_context_switch(self):
+        # Cannot switch context inside a `cuda.require_context`
+        @cuda.require_context
+        def switch_gpu():
+            with cuda.gpus[1]:
+                pass
+
+        with cuda.gpus[0]:
+            with self.assertRaises(RuntimeError) as raises:
+                switch_gpu()
+
+            self.assertIn("Cannot switch CUDA-context.", str(raises.exception))
+
+    @unittest.skipIf(len(cuda.gpus) < 2, "need more than 1 gpus")
+    def test_accepted_context_switch(self):
+        def switch_gpu():
+            with cuda.gpus[1]:
+                return cuda.current_context().device.id
+
+        with cuda.gpus[0]:
+            devid = switch_gpu()
+        self.assertEqual(int(devid), 1)
+
+
+@skip_on_cudasim('CUDA HW required')
+class Test3rdPartyContext(CUDATestCase):
+    def tearDown(self):
+        super().tearDown()
+        cuda.close()
+
+    def test_attached_primary(self, extra_work=lambda: None):
+        # Emulate primary context creation by 3rd party
+        the_driver = driver.driver
+        if driver.USE_NV_BINDING:
+            dev = driver.binding.CUdevice(0)
+            hctx = the_driver.cuDevicePrimaryCtxRetain(dev)
+        else:
+            dev = 0
+            hctx = driver.drvapi.cu_context()
+            the_driver.cuDevicePrimaryCtxRetain(byref(hctx), dev)
+        try:
+            ctx = driver.Context(weakref.proxy(self), hctx)
+            ctx.push()
+            # Check that the context from numba matches the created primary
+            # context.
+            my_ctx = cuda.current_context()
+            if driver.USE_NV_BINDING:
+                self.assertEqual(int(my_ctx.handle), int(ctx.handle))
+            else:
+                self.assertEqual(my_ctx.handle.value, ctx.handle.value)
+
+            extra_work()
+        finally:
+            ctx.pop()
+            the_driver.cuDevicePrimaryCtxRelease(dev)
+
+    def test_attached_non_primary(self):
+        # Emulate non-primary context creation by 3rd party
+        the_driver = driver.driver
+        if driver.USE_NV_BINDING:
+            flags = 0
+            dev = driver.binding.CUdevice(0)
+            hctx = the_driver.cuCtxCreate(flags, dev)
+        else:
+            hctx = driver.drvapi.cu_context()
+            the_driver.cuCtxCreate(byref(hctx), 0, 0)
+        try:
+            cuda.current_context()
+        except RuntimeError as e:
+            # Expecting an error about non-primary CUDA context
+            self.assertIn("Numba cannot operate on non-primary CUDA context ",
+                          str(e))
+        else:
+            self.fail("No RuntimeError raised")
+        finally:
+            the_driver.cuCtxDestroy(hctx)
+
+    def test_cudajit_in_attached_primary_context(self):
+        def do():
+            from numba import cuda
+
+            @cuda.jit
+            def foo(a):
+                for i in range(a.size):
+                    a[i] = i
+
+            a = cuda.device_array(10)
+            foo[1, 1](a)
+            self.assertEqual(list(a.copy_to_host()), list(range(10)))
+
+        self.test_attached_primary(do)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_array_slicing.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_array_slicing.py
new file mode 100644
index 0000000000000000000000000000000000000000..5033a115fa1fcf407f9db8f3ff75435e5bdaf3aa
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_array_slicing.py
@@ -0,0 +1,375 @@
+from itertools import product
+
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from unittest.mock import patch
+
+
+class CudaArrayIndexing(CUDATestCase):
+    def test_index_1d(self):
+        arr = np.arange(10)
+        darr = cuda.to_device(arr)
+        x, = arr.shape
+        for i in range(-x, x):
+            self.assertEqual(arr[i], darr[i])
+        with self.assertRaises(IndexError):
+            darr[-x - 1]
+        with self.assertRaises(IndexError):
+            darr[x]
+
+    def test_index_2d(self):
+        arr = np.arange(3 * 4).reshape(3, 4)
+        darr = cuda.to_device(arr)
+        x, y = arr.shape
+        for i in range(-x, x):
+            for j in range(-y, y):
+                self.assertEqual(arr[i, j], darr[i, j])
+        with self.assertRaises(IndexError):
+            darr[-x - 1, 0]
+        with self.assertRaises(IndexError):
+            darr[x, 0]
+        with self.assertRaises(IndexError):
+            darr[0, -y - 1]
+        with self.assertRaises(IndexError):
+            darr[0, y]
+
+    def test_index_3d(self):
+        arr = np.arange(3 * 4 * 5).reshape(3, 4, 5)
+        darr = cuda.to_device(arr)
+        x, y, z = arr.shape
+        for i in range(-x, x):
+            for j in range(-y, y):
+                for k in range(-z, z):
+                    self.assertEqual(arr[i, j, k], darr[i, j, k])
+        with self.assertRaises(IndexError):
+            darr[-x - 1, 0, 0]
+        with self.assertRaises(IndexError):
+            darr[x, 0, 0]
+        with self.assertRaises(IndexError):
+            darr[0, -y - 1, 0]
+        with self.assertRaises(IndexError):
+            darr[0, y, 0]
+        with self.assertRaises(IndexError):
+            darr[0, 0, -z - 1]
+        with self.assertRaises(IndexError):
+            darr[0, 0, z]
+
+
+class CudaArrayStridedSlice(CUDATestCase):
+
+    def test_strided_index_1d(self):
+        arr = np.arange(10)
+        darr = cuda.to_device(arr)
+        for i in range(arr.size):
+            np.testing.assert_equal(arr[i::2], darr[i::2].copy_to_host())
+
+    def test_strided_index_2d(self):
+        arr = np.arange(6 * 7).reshape(6, 7)
+        darr = cuda.to_device(arr)
+
+        for i in range(arr.shape[0]):
+            for j in range(arr.shape[1]):
+                np.testing.assert_equal(arr[i::2, j::2],
+                                        darr[i::2, j::2].copy_to_host())
+
+    def test_strided_index_3d(self):
+        arr = np.arange(6 * 7 * 8).reshape(6, 7, 8)
+        darr = cuda.to_device(arr)
+
+        for i in range(arr.shape[0]):
+            for j in range(arr.shape[1]):
+                for k in range(arr.shape[2]):
+                    np.testing.assert_equal(
+                        arr[i::2, j::2, k::2],
+                        darr[i::2, j::2, k::2].copy_to_host())
+
+
+class CudaArraySlicing(CUDATestCase):
+    def test_prefix_1d(self):
+        arr = np.arange(5)
+        darr = cuda.to_device(arr)
+        for i in range(arr.size):
+            expect = arr[i:]
+            got = darr[i:].copy_to_host()
+            self.assertTrue(np.all(expect == got))
+
+    def test_prefix_2d(self):
+        arr = np.arange(3 ** 2).reshape(3, 3)
+        darr = cuda.to_device(arr)
+        for i in range(arr.shape[0]):
+            for j in range(arr.shape[1]):
+                expect = arr[i:, j:]
+                sliced = darr[i:, j:]
+                self.assertEqual(expect.shape, sliced.shape)
+                self.assertEqual(expect.strides, sliced.strides)
+                got = sliced.copy_to_host()
+                self.assertTrue(np.all(expect == got))
+
+    def test_select_3d_first_two_dim(self):
+        arr = np.arange(3 * 4 * 5).reshape(3, 4, 5)
+        darr = cuda.to_device(arr)
+        # Select first dimension
+        for i in range(arr.shape[0]):
+            expect = arr[i]
+            sliced = darr[i]
+            self.assertEqual(expect.shape, sliced.shape)
+            self.assertEqual(expect.strides, sliced.strides)
+            got = sliced.copy_to_host()
+            self.assertTrue(np.all(expect == got))
+        # Select second dimension
+        for i in range(arr.shape[0]):
+            for j in range(arr.shape[1]):
+                expect = arr[i, j]
+                sliced = darr[i, j]
+                self.assertEqual(expect.shape, sliced.shape)
+                self.assertEqual(expect.strides, sliced.strides)
+                got = sliced.copy_to_host()
+                self.assertTrue(np.all(expect == got))
+
+    def test_select_f(self):
+        a = np.arange(5 * 6 * 7).reshape(5, 6, 7, order='F')
+        da = cuda.to_device(a)
+
+        for i in range(a.shape[0]):
+            for j in range(a.shape[1]):
+                self.assertTrue(np.array_equal(da[i, j, :].copy_to_host(),
+                                               a[i, j, :]))
+            for j in range(a.shape[2]):
+                self.assertTrue(np.array_equal(da[i, :, j].copy_to_host(),
+                                               a[i, :, j]))
+        for i in range(a.shape[1]):
+            for j in range(a.shape[2]):
+                self.assertTrue(np.array_equal(da[:, i, j].copy_to_host(),
+                                               a[:, i, j]))
+
+    def test_select_c(self):
+        a = np.arange(5 * 6 * 7).reshape(5, 6, 7, order='C')
+        da = cuda.to_device(a)
+
+        for i in range(a.shape[0]):
+            for j in range(a.shape[1]):
+                self.assertTrue(np.array_equal(da[i, j, :].copy_to_host(),
+                                               a[i, j, :]))
+            for j in range(a.shape[2]):
+                self.assertTrue(np.array_equal(da[i, :, j].copy_to_host(),
+                                               a[i, :, j]))
+        for i in range(a.shape[1]):
+            for j in range(a.shape[2]):
+                self.assertTrue(np.array_equal(da[:, i, j].copy_to_host(),
+                                               a[:, i, j]))
+
+    def test_prefix_select(self):
+        arr = np.arange(5 * 7).reshape(5, 7, order='F')
+
+        darr = cuda.to_device(arr)
+        self.assertTrue(np.all(darr[:1, 1].copy_to_host() == arr[:1, 1]))
+
+    def test_negative_slicing_1d(self):
+        arr = np.arange(10)
+        darr = cuda.to_device(arr)
+        for i, j in product(range(-10, 10), repeat=2):
+            np.testing.assert_array_equal(arr[i:j],
+                                          darr[i:j].copy_to_host())
+
+    def test_negative_slicing_2d(self):
+        arr = np.arange(12).reshape(3, 4)
+        darr = cuda.to_device(arr)
+        for x, y, w, s in product(range(-4, 4), repeat=4):
+            np.testing.assert_array_equal(arr[x:y, w:s],
+                                          darr[x:y, w:s].copy_to_host())
+
+    def test_empty_slice_1d(self):
+        arr = np.arange(5)
+        darr = cuda.to_device(arr)
+        for i in range(darr.shape[0]):
+            np.testing.assert_array_equal(darr[i:i].copy_to_host(), arr[i:i])
+        # empty slice of empty slice
+        self.assertFalse(darr[:0][:0].copy_to_host())
+        # out-of-bound slice just produces empty slices
+        np.testing.assert_array_equal(darr[:0][:1].copy_to_host(),
+                                      arr[:0][:1])
+        np.testing.assert_array_equal(darr[:0][-1:].copy_to_host(),
+                                      arr[:0][-1:])
+
+    def test_empty_slice_2d(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        np.testing.assert_array_equal(darr[:0].copy_to_host(), arr[:0])
+        np.testing.assert_array_equal(darr[3, :0].copy_to_host(), arr[3, :0])
+        # empty slice of empty slice
+        self.assertFalse(darr[:0][:0].copy_to_host())
+        # out-of-bound slice just produces empty slices
+        np.testing.assert_array_equal(darr[:0][:1].copy_to_host(), arr[:0][:1])
+        np.testing.assert_array_equal(darr[:0][-1:].copy_to_host(),
+                                      arr[:0][-1:])
+
+
+class CudaArraySetting(CUDATestCase):
+    """
+    Most of the slicing logic is tested in the cases above, so these
+    tests focus on the setting logic.
+    """
+
+    def test_scalar(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        arr[2, 2] = 500
+        darr[2, 2] = 500
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_rank(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        arr[2] = 500
+        darr[2] = 500
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_broadcast(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        arr[:, 2] = 500
+        darr[:, 2] = 500
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_column(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=7, fill_value=400)
+        arr[2] = _400
+        darr[2] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_row(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=5, fill_value=400)
+        arr[:, 2] = _400
+        darr[:, 2] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_subarray(self):
+        arr = np.arange(5 * 6 * 7).reshape(5, 6, 7)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=(6, 7), fill_value=400)
+        arr[2] = _400
+        darr[2] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_deep_subarray(self):
+        arr = np.arange(5 * 6 * 7 * 8).reshape(5, 6, 7, 8)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=(5, 6, 8), fill_value=400)
+        arr[:, :, 2] = _400
+        darr[:, :, 2] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_array_assign_all(self):
+        arr = np.arange(5 * 7).reshape(5, 7)
+        darr = cuda.to_device(arr)
+        _400 = np.full(shape=(5, 7), fill_value=400)
+        arr[:] = _400
+        darr[:] = _400
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_strides(self):
+        arr = np.ones(20)
+        darr = cuda.to_device(arr)
+        arr[::2] = 500
+        darr[::2] = 500
+        np.testing.assert_array_equal(darr.copy_to_host(), arr)
+
+    def test_incompatible_highdim(self):
+        darr = cuda.to_device(np.arange(5 * 7))
+
+        with self.assertRaises(ValueError) as e:
+            darr[:] = np.ones(shape=(1, 2, 3))
+
+        self.assertIn(
+            member=str(e.exception),
+            container=[
+                "Can't assign 3-D array to 1-D self",  # device
+                "could not broadcast input array from shape (2,3) "
+                "into shape (35,)",  # simulator, NP >= 1.20
+            ])
+
+    def test_incompatible_shape(self):
+        darr = cuda.to_device(np.arange(5))
+
+        with self.assertRaises(ValueError) as e:
+            darr[:] = [1, 3]
+
+        self.assertIn(
+            member=str(e.exception),
+            container=[
+                "Can't copy sequence with size 2 to array axis 0 with "
+                "dimension 5",  # device
+                "could not broadcast input array from shape (2,) into "
+                "shape (5,)",   # simulator, NP >= 1.20
+            ])
+
+    @skip_on_cudasim('cudasim does not use streams and operates synchronously')
+    def test_sync(self):
+        # There should be a synchronization when no stream is supplied
+        darr = cuda.to_device(np.arange(5))
+
+        with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                          return_value=None) as mock_sync:
+            darr[0] = 10
+
+        mock_sync.assert_called_once()
+
+    @skip_on_cudasim('cudasim does not use streams and operates synchronously')
+    def test_no_sync_default_stream(self):
+        # There should not be a synchronization when the array has a default
+        # stream, whether it is the default stream, the legacy default stream,
+        # the per-thread default stream, or another stream.
+        streams = (cuda.stream(), cuda.default_stream(),
+                   cuda.legacy_default_stream(),
+                   cuda.per_thread_default_stream())
+
+        for stream in streams:
+            darr = cuda.to_device(np.arange(5), stream=stream)
+
+            with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                              return_value=None) as mock_sync:
+                darr[0] = 10
+
+            mock_sync.assert_not_called()
+
+    @skip_on_cudasim('cudasim does not use streams and operates synchronously')
+    def test_no_sync_supplied_stream(self):
+        # There should not be a synchronization when a stream is supplied for
+        # the setitem call, whether it is the default stream, the legacy default
+        # stream, the per-thread default stream, or another stream.
+        streams = (cuda.stream(), cuda.default_stream(),
+                   cuda.legacy_default_stream(),
+                   cuda.per_thread_default_stream())
+
+        for stream in streams:
+            darr = cuda.to_device(np.arange(5))
+
+            with patch.object(cuda.cudadrv.driver.Stream, 'synchronize',
+                              return_value=None) as mock_sync:
+                darr.setitem(0, 10, stream=stream)
+
+            mock_sync.assert_not_called()
+
+    @unittest.skip('Requires PR #6367')
+    def test_issue_6505(self):
+        # On Windows, the writes to ary_v would not be visible prior to the
+        # assertion, due to the assignment being done with a kernel launch that
+        # returns asynchronously - there should now be a sync after the kernel
+        # launch to ensure that the writes are always visible.
+        ary = cuda.mapped_array(2, dtype=np.int32)
+        ary[:] = 0
+
+        ary_v = ary.view('u1')
+        ary_v[1] = 1
+        ary_v[5] = 1
+        self.assertEqual(sum(ary), 512)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_auto_context.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_auto_context.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a4d59310dd34b36a1d8bd473a8f5e5d7eda5d93
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_auto_context.py
@@ -0,0 +1,21 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaAutoContext(CUDATestCase):
+    def test_auto_context(self):
+        """A problem was revealed by a customer that the use cuda.to_device
+        does not create a CUDA context.
+        This tests the problem
+        """
+        A = np.arange(10, dtype=np.float32)
+        newA = np.empty_like(A)
+        dA = cuda.to_device(A)
+
+        dA.copy_to_host(newA)
+        self.assertTrue(np.allclose(A, newA))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_devicerecord.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_devicerecord.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2acd34d7eca1dcc1efe48b38089c50bbeade0e7
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_devicerecord.py
@@ -0,0 +1,179 @@
+import numpy as np
+import ctypes
+from numba.cuda.cudadrv.devicearray import (DeviceRecord, from_record_like,
+                                            auto_device)
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import skip_on_cudasim
+from numba.np import numpy_support
+from numba import cuda
+
+N_CHARS = 5
+
+recordtype = np.dtype(
+    [
+        ('a', np.float64),
+        ('b', np.int32),
+        ('c', np.complex64),
+        ('d', (np.str_, N_CHARS))
+    ],
+    align=True
+)
+
+recordwitharray = np.dtype(
+    [
+        ('g', np.int32),
+        ('h', np.float32, 2)
+    ],
+    align=True
+)
+
+recwithmat = np.dtype([('i', np.int32),
+                       ('j', np.float32, (3, 3))])
+
+recwithrecwithmat = np.dtype([('x', np.int32), ('y', recwithmat)])
+
+
+@skip_on_cudasim('Device Record API unsupported in the simulator')
+class TestCudaDeviceRecord(CUDATestCase):
+    """
+    Tests the DeviceRecord class with np.void host types.
+    """
+    def setUp(self):
+        super().setUp()
+        self._create_data(np.zeros)
+
+    def _create_data(self, array_ctor):
+        self.dtype = np.dtype([('a', np.int32), ('b', np.float32)], align=True)
+        self.hostz = array_ctor(1, self.dtype)[0]
+        self.hostnz = array_ctor(1, self.dtype)[0]
+        self.hostnz['a'] = 10
+        self.hostnz['b'] = 11.0
+
+    def _check_device_record(self, reference, rec):
+        self.assertEqual(rec.shape, tuple())
+        self.assertEqual(rec.strides, tuple())
+        self.assertEqual(rec.dtype, reference.dtype)
+        self.assertEqual(rec.alloc_size, reference.dtype.itemsize)
+        self.assertIsNotNone(rec.gpu_data)
+        self.assertNotEqual(rec.device_ctypes_pointer, ctypes.c_void_p(0))
+
+        numba_type = numpy_support.from_dtype(reference.dtype)
+        self.assertEqual(rec._numba_type_, numba_type)
+
+    def test_device_record_interface(self):
+        hostrec = self.hostz.copy()
+        devrec = DeviceRecord(self.dtype)
+        self._check_device_record(hostrec, devrec)
+
+    def test_device_record_copy(self):
+        hostrec = self.hostz.copy()
+        devrec = DeviceRecord(self.dtype)
+        devrec.copy_to_device(hostrec)
+
+        # Copy back and check values are all zeros
+        hostrec2 = self.hostnz.copy()
+        devrec.copy_to_host(hostrec2)
+        np.testing.assert_equal(self.hostz, hostrec2)
+
+        # Copy non-zero values to GPU and back and check values
+        hostrec3 = self.hostnz.copy()
+        devrec.copy_to_device(hostrec3)
+
+        hostrec4 = self.hostz.copy()
+        devrec.copy_to_host(hostrec4)
+        np.testing.assert_equal(hostrec4, self.hostnz)
+
+    def test_from_record_like(self):
+        # Create record from host record
+        hostrec = self.hostz.copy()
+        devrec = from_record_like(hostrec)
+        self._check_device_record(hostrec, devrec)
+
+        # Create record from device record and check for distinct data
+        devrec2 = from_record_like(devrec)
+        self._check_device_record(devrec, devrec2)
+        self.assertNotEqual(devrec.gpu_data, devrec2.gpu_data)
+
+    def test_auto_device(self):
+        # Create record from host record
+        hostrec = self.hostnz.copy()
+        devrec, new_gpu_obj = auto_device(hostrec)
+        self._check_device_record(hostrec, devrec)
+        self.assertTrue(new_gpu_obj)
+
+        # Copy data back and check it is equal to auto_device arg
+        hostrec2 = self.hostz.copy()
+        devrec.copy_to_host(hostrec2)
+        np.testing.assert_equal(hostrec2, hostrec)
+
+
+class TestCudaDeviceRecordWithRecord(TestCudaDeviceRecord):
+    """
+    Tests the DeviceRecord class with np.record host types
+    """
+    def setUp(self):
+        CUDATestCase.setUp(self)
+        self._create_data(np.recarray)
+
+
+@skip_on_cudasim('Structured array attr access not supported in simulator')
+class TestRecordDtypeWithStructArrays(CUDATestCase):
+    '''
+    Test operation of device arrays on structured arrays.
+    '''
+
+    def _createSampleArrays(self):
+        self.sample1d = cuda.device_array(3, dtype=recordtype)
+        self.samplerec1darr = cuda.device_array(1, dtype=recordwitharray)[0]
+        self.samplerecmat = cuda.device_array(1,dtype=recwithmat)[0]
+
+    def setUp(self):
+        super().setUp()
+        self._createSampleArrays()
+
+        ary = self.sample1d
+        for i in range(ary.size):
+            x = i + 1
+            ary[i]['a'] = x / 2
+            ary[i]['b'] = x
+            ary[i]['c'] = x * 1j
+            ary[i]['d'] = str(x) * N_CHARS
+
+    def test_structured_array1(self):
+        ary = self.sample1d
+        for i in range(self.sample1d.size):
+            x = i + 1
+            self.assertEqual(ary[i]['a'], x / 2)
+            self.assertEqual(ary[i]['b'], x)
+            self.assertEqual(ary[i]['c'], x * 1j)
+            self.assertEqual(ary[i]['d'], str(x) * N_CHARS)
+
+    def test_structured_array2(self):
+        ary = self.samplerec1darr
+        ary['g'] = 2
+        ary['h'][0] = 3.0
+        ary['h'][1] = 4.0
+        self.assertEqual(ary['g'], 2)
+        self.assertEqual(ary['h'][0], 3.0)
+        self.assertEqual(ary['h'][1], 4.0)
+
+    def test_structured_array3(self):
+        ary = self.samplerecmat
+        mat = np.array([[5.0, 10.0, 15.0],
+                       [20.0, 25.0, 30.0],
+                       [35.0, 40.0, 45.0]],
+                       dtype=np.float32).reshape(3,3)
+        ary['j'][:] = mat
+        np.testing.assert_equal(ary['j'], mat)
+
+    def test_structured_array4(self):
+        arr = np.zeros(1, dtype=recwithrecwithmat)
+        d_arr = cuda.to_device(arr)
+        d_arr[0]['y']['i'] = 1
+        self.assertEqual(d_arr[0]['y']['i'], 1)
+        d_arr[0]['y']['j'][0, 0] = 2.0
+        self.assertEqual(d_arr[0]['y']['j'][0, 0], 2.0)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_driver.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_driver.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea9d72fa89cefa739be556061f19e26f680cbba7
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_driver.py
@@ -0,0 +1,235 @@
+from ctypes import byref, c_int, c_void_p, sizeof
+
+from numba.cuda.cudadrv.driver import (host_to_device, device_to_host, driver,
+                                       launch_kernel)
+from numba.cuda.cudadrv import devices, drvapi, driver as _driver
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import skip_on_cudasim
+
+
+ptx1 = '''
+    .version 1.4
+    .target sm_10, map_f64_to_f32
+
+    .entry _Z10helloworldPi (
+    .param .u64 __cudaparm__Z10helloworldPi_A)
+    {
+    .reg .u32 %r<3>;
+    .reg .u64 %rd<6>;
+    .loc	14	4	0
+$LDWbegin__Z10helloworldPi:
+    .loc	14	6	0
+    cvt.s32.u16 	%r1, %tid.x;
+    ld.param.u64 	%rd1, [__cudaparm__Z10helloworldPi_A];
+    cvt.u64.u16 	%rd2, %tid.x;
+    mul.lo.u64 	%rd3, %rd2, 4;
+    add.u64 	%rd4, %rd1, %rd3;
+    st.global.s32 	[%rd4+0], %r1;
+    .loc	14	7	0
+    exit;
+$LDWend__Z10helloworldPi:
+    } // _Z10helloworldPi
+'''
+
+ptx2 = '''
+.version 3.0
+.target sm_20
+.address_size 64
+
+    .file	1 "/tmp/tmpxft_000012c7_00000000-9_testcuda.cpp3.i"
+    .file	2 "testcuda.cu"
+
+.entry _Z10helloworldPi(
+    .param .u64 _Z10helloworldPi_param_0
+)
+{
+    .reg .s32 	%r<3>;
+    .reg .s64 	%rl<5>;
+
+
+    ld.param.u64 	%rl1, [_Z10helloworldPi_param_0];
+    cvta.to.global.u64 	%rl2, %rl1;
+    .loc 2 6 1
+    mov.u32 	%r1, %tid.x;
+    mul.wide.u32 	%rl3, %r1, 4;
+    add.s64 	%rl4, %rl2, %rl3;
+    st.global.u32 	[%rl4], %r1;
+    .loc 2 7 2
+    ret;
+}
+'''
+
+
+@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
+class TestCudaDriver(CUDATestCase):
+    def setUp(self):
+        super().setUp()
+        self.assertTrue(len(devices.gpus) > 0)
+        self.context = devices.get_context()
+        device = self.context.device
+        ccmajor, _ = device.compute_capability
+        if ccmajor >= 2:
+            self.ptx = ptx2
+        else:
+            self.ptx = ptx1
+
+    def tearDown(self):
+        super().tearDown()
+        del self.context
+
+    def test_cuda_driver_basic(self):
+        module = self.context.create_module_ptx(self.ptx)
+        function = module.get_function('_Z10helloworldPi')
+
+        array = (c_int * 100)()
+
+        memory = self.context.memalloc(sizeof(array))
+        host_to_device(memory, array, sizeof(array))
+
+        ptr = memory.device_ctypes_pointer
+        stream = 0
+
+        if _driver.USE_NV_BINDING:
+            ptr = c_void_p(int(ptr))
+            stream = _driver.binding.CUstream(stream)
+
+        launch_kernel(function.handle,  # Kernel
+                      1,   1, 1,        # gx, gy, gz
+                      100, 1, 1,        # bx, by, bz
+                      0,                # dynamic shared mem
+                      stream,           # stream
+                      [ptr])            # arguments
+
+        device_to_host(array, memory, sizeof(array))
+        for i, v in enumerate(array):
+            self.assertEqual(i, v)
+
+        module.unload()
+
+    def test_cuda_driver_stream_operations(self):
+        module = self.context.create_module_ptx(self.ptx)
+        function = module.get_function('_Z10helloworldPi')
+
+        array = (c_int * 100)()
+
+        stream = self.context.create_stream()
+
+        with stream.auto_synchronize():
+            memory = self.context.memalloc(sizeof(array))
+            host_to_device(memory, array, sizeof(array), stream=stream)
+
+            ptr = memory.device_ctypes_pointer
+            if _driver.USE_NV_BINDING:
+                ptr = c_void_p(int(ptr))
+
+            launch_kernel(function.handle,  # Kernel
+                          1,   1, 1,        # gx, gy, gz
+                          100, 1, 1,        # bx, by, bz
+                          0,                # dynamic shared mem
+                          stream.handle,    # stream
+                          [ptr])            # arguments
+
+        device_to_host(array, memory, sizeof(array), stream=stream)
+
+        for i, v in enumerate(array):
+            self.assertEqual(i, v)
+
+    def test_cuda_driver_default_stream(self):
+        # Test properties of the default stream
+        ds = self.context.get_default_stream()
+        self.assertIn("Default CUDA stream", repr(ds))
+        self.assertEqual(0, int(ds))
+        # bool(stream) is the check that is done in memcpy to decide if async
+        # version should be used. So the default (0) stream should be true-ish
+        # even though 0 is usually false-ish in Python.
+        self.assertTrue(ds)
+        self.assertFalse(ds.external)
+
+    def test_cuda_driver_legacy_default_stream(self):
+        # Test properties of the legacy default stream
+        ds = self.context.get_legacy_default_stream()
+        self.assertIn("Legacy default CUDA stream", repr(ds))
+        self.assertEqual(1, int(ds))
+        self.assertTrue(ds)
+        self.assertFalse(ds.external)
+
+    def test_cuda_driver_per_thread_default_stream(self):
+        # Test properties of the per-thread default stream
+        ds = self.context.get_per_thread_default_stream()
+        self.assertIn("Per-thread default CUDA stream", repr(ds))
+        self.assertEqual(2, int(ds))
+        self.assertTrue(ds)
+        self.assertFalse(ds.external)
+
+    def test_cuda_driver_stream(self):
+        # Test properties of non-default streams
+        s = self.context.create_stream()
+        self.assertIn("CUDA stream", repr(s))
+        self.assertNotIn("Default", repr(s))
+        self.assertNotIn("External", repr(s))
+        self.assertNotEqual(0, int(s))
+        self.assertTrue(s)
+        self.assertFalse(s.external)
+
+    def test_cuda_driver_external_stream(self):
+        # Test properties of a stream created from an external stream object.
+        # We use the driver API directly to create a stream, to emulate an
+        # external library creating a stream
+        if _driver.USE_NV_BINDING:
+            handle = driver.cuStreamCreate(0)
+            ptr = int(handle)
+        else:
+            handle = drvapi.cu_stream()
+            driver.cuStreamCreate(byref(handle), 0)
+            ptr = handle.value
+        s = self.context.create_external_stream(ptr)
+
+        self.assertIn("External CUDA stream", repr(s))
+        # Ensure neither "Default" nor "default"
+        self.assertNotIn("efault", repr(s))
+        self.assertEqual(ptr, int(s))
+        self.assertTrue(s)
+        self.assertTrue(s.external)
+
+    def test_cuda_driver_occupancy(self):
+        module = self.context.create_module_ptx(self.ptx)
+        function = module.get_function('_Z10helloworldPi')
+
+        value = self.context.get_active_blocks_per_multiprocessor(function,
+                                                                  128, 128)
+        self.assertTrue(value > 0)
+
+        def b2d(bs):
+            return bs
+
+        grid, block = self.context.get_max_potential_block_size(function, b2d,
+                                                                128, 128)
+        self.assertTrue(grid > 0)
+        self.assertTrue(block > 0)
+
+
+class TestDevice(CUDATestCase):
+    def test_device_get_uuid(self):
+        # A device UUID looks like:
+        #
+        #     GPU-e6489c45-5b68-3b03-bab7-0e7c8e809643
+        #
+        # To test, we construct an RE that matches this form and verify that
+        # the returned UUID matches.
+        #
+        # Device UUIDs may not conform to parts of the UUID specification (RFC
+        # 4122) pertaining to versions and variants, so we do not extract and
+        # validate the values of these bits.
+
+        h = '[0-9a-f]{%d}'
+        h4 = h % 4
+        h8 = h % 8
+        h12 = h % 12
+        uuid_format = f'^GPU-{h8}-{h4}-{h4}-{h4}-{h12}$'
+
+        dev = devices.get_context().device
+        self.assertRegex(dev.uuid, uuid_format)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_libraries.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_libraries.py
new file mode 100644
index 0000000000000000000000000000000000000000..890bf68293565a24d0f36a56a613ea7a126d202e
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_libraries.py
@@ -0,0 +1,22 @@
+from numba.cuda.testing import unittest
+from numba.cuda.testing import skip_on_cudasim, skip_unless_conda_cudatoolkit
+from numba.misc.findlib import find_lib
+
+
+@skip_on_cudasim('Library detection unsupported in the simulator')
+@skip_unless_conda_cudatoolkit
+class TestLibraryDetection(unittest.TestCase):
+    def test_detect(self):
+        """
+        This test is solely present to ensure that shipped cudatoolkits have
+        additional core libraries in locations that Numba scans by default.
+        PyCulib (and potentially others) rely on Numba's library finding
+        capacity to find and subsequently load these libraries.
+        """
+        core_libs = ['nvvm']
+        for l in core_libs:
+            self.assertNotEqual(find_lib(l), [])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_memory.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..6402f77730cc841f3d622974caf2db9f7db61a7e
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_memory.py
@@ -0,0 +1,193 @@
+import ctypes
+
+import numpy as np
+
+from numba.cuda.cudadrv import driver, drvapi, devices
+from numba.cuda.testing import unittest, ContextResettingTestCase
+from numba.cuda.testing import skip_on_cudasim
+
+
+@skip_on_cudasim('CUDA Memory API unsupported in the simulator')
+class TestCudaMemory(ContextResettingTestCase):
+    def setUp(self):
+        super().setUp()
+        self.context = devices.get_context()
+
+    def tearDown(self):
+        del self.context
+        super(TestCudaMemory, self).tearDown()
+
+    def _template(self, obj):
+        self.assertTrue(driver.is_device_memory(obj))
+        driver.require_device_memory(obj)
+        if driver.USE_NV_BINDING:
+            expected_class = driver.binding.CUdeviceptr
+        else:
+            expected_class = drvapi.cu_device_ptr
+        self.assertTrue(isinstance(obj.device_ctypes_pointer,
+                                   expected_class))
+
+    def test_device_memory(self):
+        devmem = self.context.memalloc(1024)
+        self._template(devmem)
+
+    def test_device_view(self):
+        devmem = self.context.memalloc(1024)
+        self._template(devmem.view(10))
+
+    def test_host_alloc(self):
+        devmem = self.context.memhostalloc(1024, mapped=True)
+        self._template(devmem)
+
+    def test_pinned_memory(self):
+        ary = np.arange(10)
+        devmem = self.context.mempin(ary, ary.ctypes.data,
+                                     ary.size * ary.dtype.itemsize,
+                                     mapped=True)
+        self._template(devmem)
+
+    def test_managed_memory(self):
+        devmem = self.context.memallocmanaged(1024)
+        self._template(devmem)
+
+    def test_derived_pointer(self):
+        # Use MemoryPointer.view to create derived pointer
+
+        def handle_val(mem):
+            if driver.USE_NV_BINDING:
+                return int(mem.handle)
+            else:
+                return mem.handle.value
+
+        def check(m, offset):
+            # create view
+            v1 = m.view(offset)
+            self.assertEqual(handle_val(v1.owner), handle_val(m))
+            self.assertEqual(m.refct, 2)
+            self.assertEqual(handle_val(v1) - offset, handle_val(v1.owner))
+            # create a view
+            v2 = v1.view(offset)
+            self.assertEqual(handle_val(v2.owner), handle_val(m))
+            self.assertEqual(handle_val(v2.owner), handle_val(m))
+            self.assertEqual(handle_val(v2) - offset * 2,
+                             handle_val(v2.owner))
+            self.assertEqual(m.refct, 3)
+            del v2
+            self.assertEqual(m.refct, 2)
+            del v1
+            self.assertEqual(m.refct, 1)
+
+        m = self.context.memalloc(1024)
+        check(m=m, offset=0)
+        check(m=m, offset=1)
+
+    def test_user_extension(self):
+        # User can use MemoryPointer to wrap externally defined pointers.
+        # This test checks if the finalizer is invokded at correct time
+        fake_ptr = ctypes.c_void_p(0xdeadbeef)
+        dtor_invoked = [0]
+
+        def dtor():
+            dtor_invoked[0] += 1
+
+        # Ensure finalizer is called when pointer is deleted
+        ptr = driver.MemoryPointer(context=self.context, pointer=fake_ptr,
+                                   size=40, finalizer=dtor)
+        self.assertEqual(dtor_invoked[0], 0)
+        del ptr
+        self.assertEqual(dtor_invoked[0], 1)
+
+        # Ensure removing derived pointer doesn't call finalizer
+        ptr = driver.MemoryPointer(context=self.context, pointer=fake_ptr,
+                                   size=40, finalizer=dtor)
+        owned = ptr.own()
+        del owned
+        self.assertEqual(dtor_invoked[0], 1)
+        del ptr
+        self.assertEqual(dtor_invoked[0], 2)
+
+
+class TestCudaMemoryFunctions(ContextResettingTestCase):
+    def setUp(self):
+        super().setUp()
+        self.context = devices.get_context()
+
+    def tearDown(self):
+        del self.context
+        super(TestCudaMemoryFunctions, self).tearDown()
+
+    def test_memcpy(self):
+        hstary = np.arange(100, dtype=np.uint32)
+        hstary2 = np.arange(100, dtype=np.uint32)
+        sz = hstary.size * hstary.dtype.itemsize
+        devary = self.context.memalloc(sz)
+
+        driver.host_to_device(devary, hstary, sz)
+        driver.device_to_host(hstary2, devary, sz)
+
+        self.assertTrue(np.all(hstary == hstary2))
+
+    def test_memset(self):
+        dtype = np.dtype('uint32')
+        n = 10
+        sz = dtype.itemsize * 10
+        devary = self.context.memalloc(sz)
+        driver.device_memset(devary, 0xab, sz)
+
+        hstary = np.empty(n, dtype=dtype)
+        driver.device_to_host(hstary, devary, sz)
+
+        hstary2 = np.array([0xabababab] * n, dtype=np.dtype('uint32'))
+        self.assertTrue(np.all(hstary == hstary2))
+
+    def test_d2d(self):
+        hst = np.arange(100, dtype=np.uint32)
+        hst2 = np.empty_like(hst)
+        sz = hst.size * hst.dtype.itemsize
+        dev1 = self.context.memalloc(sz)
+        dev2 = self.context.memalloc(sz)
+        driver.host_to_device(dev1, hst, sz)
+        driver.device_to_device(dev2, dev1, sz)
+        driver.device_to_host(hst2, dev2, sz)
+        self.assertTrue(np.all(hst == hst2))
+
+
+@skip_on_cudasim('CUDA Memory API unsupported in the simulator')
+class TestMVExtent(ContextResettingTestCase):
+    def test_c_contiguous_array(self):
+        ary = np.arange(100)
+        arysz = ary.dtype.itemsize * ary.size
+        s, e = driver.host_memory_extents(ary)
+        self.assertTrue(ary.ctypes.data == s)
+        self.assertTrue(arysz == driver.host_memory_size(ary))
+
+    def test_f_contiguous_array(self):
+        ary = np.asfortranarray(np.arange(100).reshape(2, 50))
+        arysz = ary.dtype.itemsize * np.prod(ary.shape)
+        s, e = driver.host_memory_extents(ary)
+        self.assertTrue(ary.ctypes.data == s)
+        self.assertTrue(arysz == driver.host_memory_size(ary))
+
+    def test_single_element_array(self):
+        ary = np.asarray(np.uint32(1234))
+        arysz = ary.dtype.itemsize
+        s, e = driver.host_memory_extents(ary)
+        self.assertTrue(ary.ctypes.data == s)
+        self.assertTrue(arysz == driver.host_memory_size(ary))
+
+    def test_ctypes_struct(self):
+        class mystruct(ctypes.Structure):
+            _fields_ = [('x', ctypes.c_int), ('y', ctypes.c_int)]
+
+        data = mystruct(x=123, y=432)
+        sz = driver.host_memory_size(data)
+        self.assertTrue(ctypes.sizeof(data) == sz)
+
+    def test_ctypes_double(self):
+        data = ctypes.c_double(1.234)
+        sz = driver.host_memory_size(data)
+        self.assertTrue(ctypes.sizeof(data) == sz)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_ndarray.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_ndarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c9c9195eb4b63eaa6b6764657f2f127be8b9b88
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_cuda_ndarray.py
@@ -0,0 +1,547 @@
+import itertools
+import numpy as np
+from numba.cuda.cudadrv import devicearray
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import skip_on_cudasim
+
+
+class TestCudaNDArray(CUDATestCase):
+    def test_device_array_interface(self):
+        dary = cuda.device_array(shape=100)
+        devicearray.verify_cuda_ndarray_interface(dary)
+
+        ary = np.empty(100)
+        dary = cuda.to_device(ary)
+        devicearray.verify_cuda_ndarray_interface(dary)
+
+        ary = np.asarray(1.234)
+        dary = cuda.to_device(ary)
+        self.assertEqual(dary.ndim, 0)
+        devicearray.verify_cuda_ndarray_interface(dary)
+
+    def test_device_array_from_readonly(self):
+        ary = np.arange(100, dtype=np.float32)
+        # Make the array readonly
+        ary.flags.writeable = False
+        self.assertFalse(ary.flags.writeable)
+        # Ensure that we can copy the readonly array
+        dary = cuda.to_device(ary)
+        retr = dary.copy_to_host()
+        np.testing.assert_array_equal(retr, ary)
+
+    def test_devicearray_dtype(self):
+        dary = cuda.device_array(shape=(100,), dtype="f4")
+        self.assertEqual(dary.dtype, np.dtype("f4"))
+
+    def test_devicearray_no_copy(self):
+        array = np.arange(100, dtype=np.float32)
+        cuda.to_device(array, copy=False)
+
+    def test_devicearray_shape(self):
+        ary = np.arange(2 * 3 * 4).reshape(2, 3, 4)
+        dary = cuda.to_device(ary)
+        self.assertEqual(ary.shape, dary.shape)
+        self.assertEqual(ary.shape[1:], dary.shape[1:])
+
+    def test_devicearray(self):
+        array = np.arange(100, dtype=np.int32)
+        original = array.copy()
+        gpumem = cuda.to_device(array)
+        array[:] = 0
+        gpumem.copy_to_host(array)
+
+        np.testing.assert_array_equal(array, original)
+
+    def test_stream_bind(self):
+        stream = cuda.stream()
+        with stream.auto_synchronize():
+            arr = cuda.device_array(
+                (3, 3),
+                dtype=np.float64,
+                stream=stream)
+            self.assertEqual(arr.bind(stream).stream, stream)
+            self.assertEqual(arr.stream, stream)
+
+    def test_len_1d(self):
+        ary = np.empty((3,))
+        dary = cuda.device_array(3)
+        self.assertEqual(len(ary), len(dary))
+
+    def test_len_2d(self):
+        ary = np.empty((3, 5))
+        dary = cuda.device_array((3, 5))
+        self.assertEqual(len(ary), len(dary))
+
+    def test_len_3d(self):
+        ary = np.empty((3, 5, 7))
+        dary = cuda.device_array((3, 5, 7))
+        self.assertEqual(len(ary), len(dary))
+
+    def test_devicearray_partition(self):
+        N = 100
+        array = np.arange(N, dtype=np.int32)
+        original = array.copy()
+        gpumem = cuda.to_device(array)
+        left, right = gpumem.split(N // 2)
+
+        array[:] = 0
+
+        self.assertTrue(np.all(array == 0))
+
+        right.copy_to_host(array[N // 2:])
+        left.copy_to_host(array[:N // 2])
+
+        self.assertTrue(np.all(array == original))
+
+    def test_devicearray_replace(self):
+        N = 100
+        array = np.arange(N, dtype=np.int32)
+        original = array.copy()
+        gpumem = cuda.to_device(array)
+        cuda.to_device(array * 2, to=gpumem)
+        gpumem.copy_to_host(array)
+        np.testing.assert_array_equal(array, original * 2)
+
+    @skip_on_cudasim('This works in the simulator')
+    def test_devicearray_transpose_wrongdim(self):
+        gpumem = cuda.to_device(np.array(np.arange(12)).reshape(3, 4, 1))
+
+        with self.assertRaises(NotImplementedError) as e:
+            np.transpose(gpumem)
+
+        self.assertEqual(
+            "transposing a non-2D DeviceNDArray isn't supported",
+            str(e.exception))
+
+    def test_devicearray_transpose_identity(self):
+        # any-shape identities should work
+        original = np.array(np.arange(24)).reshape(3, 4, 2)
+        array = np.transpose(cuda.to_device(original),
+                             axes=(0, 1, 2)).copy_to_host()
+        self.assertTrue(np.all(array == original))
+
+    def test_devicearray_transpose_duplicatedaxis(self):
+        gpumem = cuda.to_device(np.array(np.arange(12)).reshape(3, 4))
+
+        with self.assertRaises(ValueError) as e:
+            np.transpose(gpumem, axes=(0, 0))
+
+        self.assertIn(
+            str(e.exception),
+            container=[
+                'invalid axes list (0, 0)',  # GPU
+                'repeated axis in transpose',  # sim
+            ])
+
+    def test_devicearray_transpose_wrongaxis(self):
+        gpumem = cuda.to_device(np.array(np.arange(12)).reshape(3, 4))
+
+        with self.assertRaises(ValueError) as e:
+            np.transpose(gpumem, axes=(0, 2))
+
+        self.assertIn(
+            str(e.exception),
+            container=[
+                'invalid axes list (0, 2)',  # GPU
+                'invalid axis for this array',
+                'axis 2 is out of bounds for array of dimension 2',  # sim
+            ])
+
+    def test_devicearray_view_ok(self):
+        original = np.array(np.arange(12), dtype="i2").reshape(3, 4)
+        array = cuda.to_device(original)
+        for dtype in ("i4", "u4", "i8", "f8"):
+            with self.subTest(dtype=dtype):
+                np.testing.assert_array_equal(
+                    array.view(dtype).copy_to_host(),
+                    original.view(dtype)
+                )
+
+    def test_devicearray_view_ok_not_c_contig(self):
+        original = np.array(np.arange(32), dtype="i2").reshape(4, 8)
+        array = cuda.to_device(original)[:, ::2]
+        original = original[:, ::2]
+        np.testing.assert_array_equal(
+            array.view("u2").copy_to_host(),
+            original.view("u2")
+        )
+
+    def test_devicearray_view_bad_not_c_contig(self):
+        original = np.array(np.arange(32), dtype="i2").reshape(4, 8)
+        array = cuda.to_device(original)[:, ::2]
+        with self.assertRaises(ValueError) as e:
+            array.view("i4")
+
+        msg = str(e.exception)
+        self.assertIn('To change to a dtype of a different size,', msg)
+
+        contiguous_pre_np123 = 'the array must be C-contiguous' in msg
+        contiguous_post_np123 = 'the last axis must be contiguous' in msg
+        self.assertTrue(contiguous_pre_np123 or contiguous_post_np123,
+                        'Expected message to mention contiguity')
+
+    def test_devicearray_view_bad_itemsize(self):
+        original = np.array(np.arange(12), dtype="i2").reshape(4, 3)
+        array = cuda.to_device(original)
+        with self.assertRaises(ValueError) as e:
+            array.view("i4")
+        self.assertEqual(
+            "When changing to a larger dtype,"
+            " its size must be a divisor of the total size in bytes"
+            " of the last axis of the array.",
+            str(e.exception))
+
+    def test_devicearray_transpose_ok(self):
+        original = np.array(np.arange(12)).reshape(3, 4)
+        array = np.transpose(cuda.to_device(original)).copy_to_host()
+        self.assertTrue(np.all(array == original.T))
+
+    def test_devicearray_transpose_T(self):
+        original = np.array(np.arange(12)).reshape(3, 4)
+        array = cuda.to_device(original).T.copy_to_host()
+        self.assertTrue(np.all(array == original.T))
+
+    def test_devicearray_contiguous_slice(self):
+        # memcpys are dumb ranges of bytes, so trying to
+        # copy to a non-contiguous range shouldn't work!
+        a = np.arange(25).reshape(5, 5, order='F')
+        s = np.full(fill_value=5, shape=(5,))
+
+        d = cuda.to_device(a)
+        a[2] = s
+
+        # d is in F-order (not C-order), so d[2] is not contiguous
+        # (40-byte strides). This means we can't memcpy to it!
+        with self.assertRaises(ValueError) as e:
+            d[2].copy_to_device(s)
+        self.assertEqual(
+            devicearray.errmsg_contiguous_buffer,
+            str(e.exception))
+
+        # if d[2].copy_to_device(s), then this would pass:
+        # self.assertTrue((a == d.copy_to_host()).all())
+
+    def _test_devicearray_contiguous_host_copy(self, a_c, a_f):
+        """
+        Checks host->device memcpys
+        """
+        self.assertTrue(a_c.flags.c_contiguous)
+        self.assertTrue(a_f.flags.f_contiguous)
+
+        for original, copy in [
+            (a_f, a_f),
+            (a_f, a_c),
+            (a_c, a_f),
+            (a_c, a_c),
+        ]:
+            msg = '%s => %s' % (
+                'C' if original.flags.c_contiguous else 'F',
+                'C' if copy.flags.c_contiguous else 'F',
+            )
+
+            d = cuda.to_device(original)
+            d.copy_to_device(copy)
+            self.assertTrue(np.all(d.copy_to_host() == a_c), msg=msg)
+            self.assertTrue(np.all(d.copy_to_host() == a_f), msg=msg)
+
+    def test_devicearray_contiguous_copy_host_3d(self):
+        a_c = np.arange(5 * 5 * 5).reshape(5, 5, 5)
+        a_f = np.array(a_c, order='F')
+        self._test_devicearray_contiguous_host_copy(a_c, a_f)
+
+    def test_devicearray_contiguous_copy_host_1d(self):
+        a_c = np.arange(5)
+        a_f = np.array(a_c, order='F')
+        self._test_devicearray_contiguous_host_copy(a_c, a_f)
+
+    def test_devicearray_contiguous_copy_device(self):
+        a_c = np.arange(5 * 5 * 5).reshape(5, 5, 5)
+        a_f = np.array(a_c, order='F')
+        self.assertTrue(a_c.flags.c_contiguous)
+        self.assertTrue(a_f.flags.f_contiguous)
+
+        d = cuda.to_device(a_c)
+
+        with self.assertRaises(ValueError) as e:
+            d.copy_to_device(cuda.to_device(a_f))
+        self.assertEqual(
+            "incompatible strides: {} vs. {}".format(a_c.strides, a_f.strides),
+            str(e.exception))
+
+        d.copy_to_device(cuda.to_device(a_c))
+        self.assertTrue(np.all(d.copy_to_host() == a_c))
+
+        d = cuda.to_device(a_f)
+
+        with self.assertRaises(ValueError) as e:
+            d.copy_to_device(cuda.to_device(a_c))
+        self.assertEqual(
+            "incompatible strides: {} vs. {}".format(a_f.strides, a_c.strides),
+            str(e.exception))
+
+        d.copy_to_device(cuda.to_device(a_f))
+        self.assertTrue(np.all(d.copy_to_host() == a_f))
+
+    def test_devicearray_broadcast_host_copy(self):
+        broadsize = 4
+        coreshape = (2, 3)
+        coresize = np.prod(coreshape)
+        core_c = np.arange(coresize).reshape(coreshape, order='C')
+        core_f = np.arange(coresize).reshape(coreshape, order='F')
+        for dim in range(len(coreshape)):
+            newindex = (slice(None),) * dim + (np.newaxis,)
+            broadshape = coreshape[:dim] + (broadsize,) + coreshape[dim:]
+            broad_c = np.broadcast_to(core_c[newindex], broadshape)
+            broad_f = np.broadcast_to(core_f[newindex], broadshape)
+            dbroad_c = cuda.to_device(broad_c)
+            dbroad_f = cuda.to_device(broad_f)
+            np.testing.assert_array_equal(dbroad_c.copy_to_host(), broad_c)
+            np.testing.assert_array_equal(dbroad_f.copy_to_host(), broad_f)
+            # Also test copying across different core orderings
+            dbroad_c.copy_to_device(broad_f)
+            dbroad_f.copy_to_device(broad_c)
+            np.testing.assert_array_equal(dbroad_c.copy_to_host(), broad_f)
+            np.testing.assert_array_equal(dbroad_f.copy_to_host(), broad_c)
+
+    def test_devicearray_contiguous_host_strided(self):
+        a_c = np.arange(10)
+        d = cuda.to_device(a_c)
+        arr = np.arange(20)[::2]
+        d.copy_to_device(arr)
+        np.testing.assert_array_equal(d.copy_to_host(), arr)
+
+    def test_devicearray_contiguous_device_strided(self):
+        d = cuda.to_device(np.arange(20))
+        arr = np.arange(20)
+
+        with self.assertRaises(ValueError) as e:
+            d.copy_to_device(cuda.to_device(arr)[::2])
+        self.assertEqual(
+            devicearray.errmsg_contiguous_buffer,
+            str(e.exception))
+
+    @skip_on_cudasim('DeviceNDArray class not present in simulator')
+    def test_devicearray_relaxed_strides(self):
+        # From the reproducer in Issue #6824.
+
+        # Construct a device array that is contiguous even though
+        # the strides for the first axis (800) are not equal to
+        # the strides * size (10 * 8 = 80) for the previous axis,
+        # because the first axis size is 1.
+        arr = devicearray.DeviceNDArray((1, 10), (800, 8), np.float64)
+
+        # Ensure we still believe the array to be contiguous because
+        # strides checking is relaxed.
+        self.assertTrue(arr.flags['C_CONTIGUOUS'])
+        self.assertTrue(arr.flags['F_CONTIGUOUS'])
+
+    def test_c_f_contiguity_matches_numpy(self):
+        # From the reproducer in Issue #4943.
+
+        shapes = ((1, 4), (4, 1))
+        orders = ('C', 'F')
+
+        for shape, order in itertools.product(shapes, orders):
+            arr = np.ndarray(shape, order=order)
+            d_arr = cuda.to_device(arr)
+            self.assertEqual(arr.flags['C_CONTIGUOUS'],
+                             d_arr.flags['C_CONTIGUOUS'])
+            self.assertEqual(arr.flags['F_CONTIGUOUS'],
+                             d_arr.flags['F_CONTIGUOUS'])
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_simple_c(self):
+        # C-order 1D array
+        a = np.zeros(10, order='C')
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_simple_f(self):
+        # F-order array that is also C layout.
+        a = np.zeros(10, order='F')
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_2d_c(self):
+        # C-order 2D array
+        a = np.zeros((2, 10), order='C')
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_2d_f(self):
+        # F-order array that can only be F layout
+        a = np.zeros((2, 10), order='F')
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'F')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_noncontig_slice_c(self):
+        # Non-contiguous slice of C-order array
+        a = np.zeros((5, 5), order='C')
+        d = cuda.to_device(a)[:,2]
+        self.assertEqual(d._numba_type_.layout, 'A')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_noncontig_slice_f(self):
+        # Non-contiguous slice of F-order array
+        a = np.zeros((5, 5), order='F')
+        d = cuda.to_device(a)[2,:]
+        self.assertEqual(d._numba_type_.layout, 'A')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_contig_slice_c(self):
+        # Contiguous slice of C-order array
+        a = np.zeros((5, 5), order='C')
+        d = cuda.to_device(a)[2,:]
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_contig_slice_f(self):
+        # Contiguous slice of F-order array - is both C- and F-contiguous, so
+        # types as 'C' layout
+        a = np.zeros((5, 5), order='F')
+        d = cuda.to_device(a)[:,2]
+        self.assertEqual(d._numba_type_.layout, 'C')
+
+    @skip_on_cudasim('Typing not done in the simulator')
+    def test_devicearray_typing_order_broadcasted(self):
+        # Broadcasted array, similar to that used for passing scalars to ufuncs
+        a = np.broadcast_to(np.array([1]), (10,))
+        d = cuda.to_device(a)
+        self.assertEqual(d._numba_type_.layout, 'A')
+
+    def test_bug6697(self):
+        ary = np.arange(10, dtype=np.int16)
+        dary = cuda.to_device(ary)
+        got = np.asarray(dary)
+        self.assertEqual(got.dtype, dary.dtype)
+
+    @skip_on_cudasim('DeviceNDArray class not present in simulator')
+    def test_issue_8477(self):
+        # Ensure that we can copy a zero-length device array to a zero-length
+        # host array when the strides of the device and host arrays differ -
+        # this should be possible because the strides are irrelevant when the
+        # length is zero. For more info see
+        # https://github.com/numba/numba/issues/8477.
+
+        # Create a device array with shape (0,) and strides (8,)
+        dev_array = devicearray.DeviceNDArray(shape=(0,), strides=(8,),
+                                              dtype=np.int8)
+
+        # Create a host array with shape (0,) and strides (0,)
+        host_array = np.ndarray(shape=(0,), strides=(0,), dtype=np.int8)
+
+        # Sanity check for this test - ensure our destination has the strides
+        # we expect, because strides can be ignored in some cases by the
+        # ndarray constructor - checking here ensures that we haven't failed to
+        # account for unexpected behaviour across different versions of NumPy
+        self.assertEqual(host_array.strides, (0,))
+
+        # Ensure that the copy succeeds in both directions
+        dev_array.copy_to_host(host_array)
+        dev_array.copy_to_device(host_array)
+
+        # Ensure that a device-to-device copy also succeeds when the strides
+        # differ - one way of doing this is to copy the host array across and
+        # use that for copies in both directions.
+        dev_array_from_host = cuda.to_device(host_array)
+        self.assertEqual(dev_array_from_host.shape, (0,))
+        self.assertEqual(dev_array_from_host.strides, (0,))
+
+        dev_array.copy_to_device(dev_array_from_host)
+        dev_array_from_host.copy_to_device(dev_array)
+
+
+class TestRecarray(CUDATestCase):
+    def test_recarray(self):
+        # From issue #4111
+        a = np.recarray((16,), dtype=[
+            ("value1", np.int64),
+            ("value2", np.float64),
+        ])
+        a.value1 = np.arange(a.size, dtype=np.int64)
+        a.value2 = np.arange(a.size, dtype=np.float64) / 100
+
+        expect1 = a.value1
+        expect2 = a.value2
+
+        def test(x, out1, out2):
+            i = cuda.grid(1)
+            if i < x.size:
+                out1[i] = x.value1[i]
+                out2[i] = x.value2[i]
+
+        got1 = np.zeros_like(expect1)
+        got2 = np.zeros_like(expect2)
+        cuda.jit(test)[1, a.size](a, got1, got2)
+
+        np.testing.assert_array_equal(expect1, got1)
+        np.testing.assert_array_equal(expect2, got2)
+
+
+class TestCoreContiguous(CUDATestCase):
+    def _test_against_array_core(self, view):
+        self.assertEqual(
+            devicearray.is_contiguous(view),
+            devicearray.array_core(view).flags['C_CONTIGUOUS']
+        )
+
+    def test_device_array_like_1d(self):
+        d_a = cuda.device_array(10, order='C')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_2d(self):
+        d_a = cuda.device_array((10, 12), order='C')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_2d_transpose(self):
+        d_a = cuda.device_array((10, 12), order='C')
+        self._test_against_array_core(d_a.T)
+
+    def test_device_array_like_3d(self):
+        d_a = cuda.device_array((10, 12, 14), order='C')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_1d_f(self):
+        d_a = cuda.device_array(10, order='F')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_2d_f(self):
+        d_a = cuda.device_array((10, 12), order='F')
+        self._test_against_array_core(d_a)
+
+    def test_device_array_like_2d_f_transpose(self):
+        d_a = cuda.device_array((10, 12), order='F')
+        self._test_against_array_core(d_a.T)
+
+    def test_device_array_like_3d_f(self):
+        d_a = cuda.device_array((10, 12, 14), order='F')
+        self._test_against_array_core(d_a)
+
+    def test_1d_view(self):
+        shape = 10
+        view = np.zeros(shape)[::2]
+        self._test_against_array_core(view)
+
+    def test_1d_view_f(self):
+        shape = 10
+        view = np.zeros(shape, order='F')[::2]
+        self._test_against_array_core(view)
+
+    def test_2d_view(self):
+        shape = (10, 12)
+        view = np.zeros(shape)[::2, ::2]
+        self._test_against_array_core(view)
+
+    def test_2d_view_f(self):
+        shape = (10, 12)
+        view = np.zeros(shape, order='F')[::2, ::2]
+        self._test_against_array_core(view)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_deallocations.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_deallocations.py
new file mode 100644
index 0000000000000000000000000000000000000000..66fbbc372e9a1347dded4cff3e699175b5d9c80d
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_deallocations.py
@@ -0,0 +1,249 @@
+from contextlib import contextmanager
+
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import (unittest, skip_on_cudasim,
+                                skip_if_external_memmgr, CUDATestCase)
+from numba.tests.support import captured_stderr
+from numba.core import config
+
+
+@skip_on_cudasim('not supported on CUDASIM')
+@skip_if_external_memmgr('Deallocation specific to Numba memory management')
+class TestDeallocation(CUDATestCase):
+    def test_max_pending_count(self):
+        # get deallocation manager and flush it
+        deallocs = cuda.current_context().memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+        # deallocate to maximum count
+        for i in range(config.CUDA_DEALLOCS_COUNT):
+            cuda.to_device(np.arange(1))
+            self.assertEqual(len(deallocs), i + 1)
+        # one more to trigger .clear()
+        cuda.to_device(np.arange(1))
+        self.assertEqual(len(deallocs), 0)
+
+    def test_max_pending_bytes(self):
+        # get deallocation manager and flush it
+        ctx = cuda.current_context()
+        deallocs = ctx.memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+        mi = ctx.get_memory_info()
+
+        max_pending = 10**6  # 1MB
+        old_ratio = config.CUDA_DEALLOCS_RATIO
+        try:
+            # change to a smaller ratio
+            config.CUDA_DEALLOCS_RATIO = max_pending / mi.total
+            # due to round off error (floor is used in calculating
+            # _max_pending_bytes) it can be off by 1.
+            self.assertAlmostEqual(deallocs._max_pending_bytes, max_pending,
+                                   delta=1)
+
+            # allocate half the max size
+            # this will not trigger deallocation
+            cuda.to_device(np.ones(max_pending // 2, dtype=np.int8))
+            self.assertEqual(len(deallocs), 1)
+
+            # allocate another remaining
+            # this will not trigger deallocation
+            cuda.to_device(np.ones(deallocs._max_pending_bytes -
+                                   deallocs._size, dtype=np.int8))
+            self.assertEqual(len(deallocs), 2)
+
+            # another byte to trigger .clear()
+            cuda.to_device(np.ones(1, dtype=np.int8))
+            self.assertEqual(len(deallocs), 0)
+        finally:
+            # restore old ratio
+            config.CUDA_DEALLOCS_RATIO = old_ratio
+
+
+@skip_on_cudasim("defer_cleanup has no effect in CUDASIM")
+@skip_if_external_memmgr('Deallocation specific to Numba memory management')
+class TestDeferCleanup(CUDATestCase):
+    def test_basic(self):
+        harr = np.arange(5)
+        darr1 = cuda.to_device(harr)
+        deallocs = cuda.current_context().memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+        with cuda.defer_cleanup():
+            darr2 = cuda.to_device(harr)
+            del darr1
+            self.assertEqual(len(deallocs), 1)
+            del darr2
+            self.assertEqual(len(deallocs), 2)
+            deallocs.clear()
+            self.assertEqual(len(deallocs), 2)
+
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+    def test_nested(self):
+        harr = np.arange(5)
+        darr1 = cuda.to_device(harr)
+        deallocs = cuda.current_context().memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+        with cuda.defer_cleanup():
+            with cuda.defer_cleanup():
+                darr2 = cuda.to_device(harr)
+                del darr1
+                self.assertEqual(len(deallocs), 1)
+                del darr2
+                self.assertEqual(len(deallocs), 2)
+                deallocs.clear()
+                self.assertEqual(len(deallocs), 2)
+            deallocs.clear()
+            self.assertEqual(len(deallocs), 2)
+
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+    def test_exception(self):
+        harr = np.arange(5)
+        darr1 = cuda.to_device(harr)
+        deallocs = cuda.current_context().memory_manager.deallocations
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+        class CustomError(Exception):
+            pass
+
+        with self.assertRaises(CustomError):
+            with cuda.defer_cleanup():
+                darr2 = cuda.to_device(harr)
+                del darr2
+                self.assertEqual(len(deallocs), 1)
+                deallocs.clear()
+                self.assertEqual(len(deallocs), 1)
+                raise CustomError
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+        del darr1
+        self.assertEqual(len(deallocs), 1)
+        deallocs.clear()
+        self.assertEqual(len(deallocs), 0)
+
+
+class TestDeferCleanupAvail(CUDATestCase):
+    def test_context_manager(self):
+        # just make sure the API is available
+        with cuda.defer_cleanup():
+            pass
+
+
+@skip_on_cudasim('not supported on CUDASIM')
+class TestDel(CUDATestCase):
+    """
+    Ensure resources are deleted properly without ignored exception.
+    """
+    @contextmanager
+    def check_ignored_exception(self, ctx):
+        with captured_stderr() as cap:
+            yield
+            ctx.deallocations.clear()
+        self.assertFalse(cap.getvalue())
+
+    def test_stream(self):
+        ctx = cuda.current_context()
+        stream = ctx.create_stream()
+        with self.check_ignored_exception(ctx):
+            del stream
+
+    def test_event(self):
+        ctx = cuda.current_context()
+        event = ctx.create_event()
+        with self.check_ignored_exception(ctx):
+            del event
+
+    def test_pinned_memory(self):
+        ctx = cuda.current_context()
+        mem = ctx.memhostalloc(32)
+        with self.check_ignored_exception(ctx):
+            del mem
+
+    def test_mapped_memory(self):
+        ctx = cuda.current_context()
+        mem = ctx.memhostalloc(32, mapped=True)
+        with self.check_ignored_exception(ctx):
+            del mem
+
+    def test_device_memory(self):
+        ctx = cuda.current_context()
+        mem = ctx.memalloc(32)
+        with self.check_ignored_exception(ctx):
+            del mem
+
+    def test_managed_memory(self):
+        ctx = cuda.current_context()
+        mem = ctx.memallocmanaged(32)
+        with self.check_ignored_exception(ctx):
+            del mem
+
+    def test_pinned_contextmanager(self):
+        # Check that temporarily pinned memory is unregistered immediately,
+        # such that it can be re-pinned at any time
+        class PinnedException(Exception):
+            pass
+
+        arr = np.zeros(1)
+        ctx = cuda.current_context()
+        ctx.deallocations.clear()
+        with self.check_ignored_exception(ctx):
+            with cuda.pinned(arr):
+                pass
+            with cuda.pinned(arr):
+                pass
+            # Should also work inside a `defer_cleanup` block
+            with cuda.defer_cleanup():
+                with cuda.pinned(arr):
+                    pass
+                with cuda.pinned(arr):
+                    pass
+            # Should also work when breaking out of the block due to an
+            # exception
+            try:
+                with cuda.pinned(arr):
+                    raise PinnedException
+            except PinnedException:
+                with cuda.pinned(arr):
+                    pass
+
+    def test_mapped_contextmanager(self):
+        # Check that temporarily mapped memory is unregistered immediately,
+        # such that it can be re-mapped at any time
+        class MappedException(Exception):
+            pass
+
+        arr = np.zeros(1)
+        ctx = cuda.current_context()
+        ctx.deallocations.clear()
+        with self.check_ignored_exception(ctx):
+            with cuda.mapped(arr):
+                pass
+            with cuda.mapped(arr):
+                pass
+            # Should also work inside a `defer_cleanup` block
+            with cuda.defer_cleanup():
+                with cuda.mapped(arr):
+                    pass
+                with cuda.mapped(arr):
+                    pass
+            # Should also work when breaking out of the block due to an
+            # exception
+            try:
+                with cuda.mapped(arr):
+                    raise MappedException
+            except MappedException:
+                with cuda.mapped(arr):
+                    pass
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_detect.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_detect.py
new file mode 100644
index 0000000000000000000000000000000000000000..528e11bf848893026a9b7885c2a959190d455222
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_detect.py
@@ -0,0 +1,81 @@
+import os
+import sys
+import subprocess
+import threading
+from numba import cuda
+from numba.cuda.testing import (unittest, CUDATestCase, skip_on_cudasim,
+                                skip_under_cuda_memcheck)
+from numba.tests.support import captured_stdout
+
+
+class TestCudaDetect(CUDATestCase):
+    def test_cuda_detect(self):
+        # exercise the code path
+        with captured_stdout() as out:
+            cuda.detect()
+        output = out.getvalue()
+        self.assertIn('Found', output)
+        self.assertIn('CUDA devices', output)
+
+
+@skip_under_cuda_memcheck('Hangs cuda-memcheck')
+class TestCUDAFindLibs(CUDATestCase):
+
+    def run_cmd(self, cmdline, env):
+        popen = subprocess.Popen(cmdline,
+                                 stdout=subprocess.PIPE,
+                                 stderr=subprocess.PIPE,
+                                 env=env)
+
+        # finish in 5 minutes or kill it
+        timeout = threading.Timer(5 * 60., popen.kill)
+        try:
+            timeout.start()
+            out, err = popen.communicate()
+            # the process should exit with an error
+            return out.decode(), err.decode()
+        finally:
+            timeout.cancel()
+        return None, None
+
+    def run_test_in_separate_process(self, envvar, envvar_value):
+        env_copy = os.environ.copy()
+        env_copy[envvar] = str(envvar_value)
+        code = """if 1:
+            from numba import cuda
+            @cuda.jit('(int64,)')
+            def kernel(x):
+                pass
+            kernel(1,)
+            """
+        cmdline = [sys.executable, "-c", code]
+        return self.run_cmd(cmdline, env_copy)
+
+    @skip_on_cudasim('Simulator does not hit device library search code path')
+    @unittest.skipIf(not sys.platform.startswith('linux'), "linux only")
+    def test_cuda_find_lib_errors(self):
+        """
+        This tests that the find_libs works as expected in the case of an
+        environment variable being used to set the path.
+        """
+        # one of these is likely to exist on linux, it's also unlikely that
+        # someone has extracted the contents of libdevice into here!
+        locs = ['lib', 'lib64']
+
+        looking_for = None
+        for l in locs:
+            looking_for = os.path.join(os.path.sep, l)
+            if os.path.exists(looking_for):
+                break
+
+        # This is the testing part, the test will only run if there's a valid
+        # path in which to look
+        if looking_for is not None:
+            out, err = self.run_test_in_separate_process("NUMBA_CUDA_DRIVER",
+                                                         looking_for)
+            self.assertTrue(out is not None)
+            self.assertTrue(err is not None)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_emm_plugins.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_emm_plugins.py
new file mode 100644
index 0000000000000000000000000000000000000000..209355ed69935920c4dbbe1fdc7ad84b3f9c1a11
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_emm_plugins.py
@@ -0,0 +1,192 @@
+import ctypes
+import numpy as np
+import weakref
+
+from numba import cuda
+from numba.core import config
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+from numba.tests.support import linux_only
+
+if not config.ENABLE_CUDASIM:
+    class DeviceOnlyEMMPlugin(cuda.HostOnlyCUDAMemoryManager):
+        """
+        Dummy EMM Plugin implementation for testing. It memorises which plugin
+        API methods have been called so that the tests can check that Numba
+        called into the plugin as expected.
+        """
+
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+
+            # For tracking our dummy allocations
+            self.allocations = {}
+            self.count = 0
+
+            # For tracking which methods have been called
+            self.initialized = False
+            self.memalloc_called = False
+            self.reset_called = False
+            self.get_memory_info_called = False
+            self.get_ipc_handle_called = False
+
+        def memalloc(self, size):
+            # We maintain a list of allocations and keep track of them, so that
+            # we can test that the finalizers of objects returned by memalloc
+            # get called.
+
+            # Numba should have initialized the memory manager when preparing
+            # the context for use, prior to any memalloc call.
+            if not self.initialized:
+                raise RuntimeError("memalloc called before initialize")
+            self.memalloc_called = True
+
+            # Create an allocation and record it
+            self.count += 1
+            alloc_count = self.count
+            self.allocations[alloc_count] = size
+
+            # The finalizer deletes the record from our internal dict of
+            # allocations.
+            finalizer_allocs = self.allocations
+
+            def finalizer():
+                del finalizer_allocs[alloc_count]
+
+            # We use an AutoFreePointer so that the finalizer will be run when
+            # the reference count drops to zero.
+            ctx = weakref.proxy(self.context)
+            ptr = ctypes.c_void_p(alloc_count)
+            return cuda.cudadrv.driver.AutoFreePointer(ctx, ptr, size,
+                                                       finalizer=finalizer)
+
+        def initialize(self):
+            # No special initialization needed.
+            self.initialized = True
+
+        def reset(self):
+            # We remove all allocations on reset, just as a real EMM Plugin
+            # would do. Note that our finalizers in memalloc don't check
+            # whether the allocations are still alive, so running them after
+            # reset will detect any allocations that are floating around at
+            # exit time; however, the atexit finalizer for weakref will only
+            # print a traceback, not terminate the interpreter abnormally.
+            self.reset_called = True
+
+        def get_memory_info(self):
+            # Return some dummy memory information
+            self.get_memory_info_called = True
+            return cuda.MemoryInfo(free=32, total=64)
+
+        def get_ipc_handle(self, memory):
+            # The dummy IPC handle is only a string, so it is important that
+            # the tests don't try to do too much with it (e.g. open / close
+            # it).
+            self.get_ipc_handle_called = True
+            return "Dummy IPC handle for alloc %s" % memory.device_pointer.value
+
+        @property
+        def interface_version(self):
+            # The expected version for an EMM Plugin.
+            return 1
+
+    class BadVersionEMMPlugin(DeviceOnlyEMMPlugin):
+        """A plugin that claims to implement a different interface version"""
+
+        @property
+        def interface_version(self):
+            return 2
+
+
+@skip_on_cudasim('EMM Plugins not supported on CUDA simulator')
+class TestDeviceOnlyEMMPlugin(CUDATestCase):
+    """
+    Tests that the API of an EMM Plugin that implements device allocations
+    only is used correctly by Numba.
+    """
+
+    def setUp(self):
+        super().setUp()
+        # Always start afresh with a new context and memory manager
+        cuda.close()
+        cuda.set_memory_manager(DeviceOnlyEMMPlugin)
+
+    def tearDown(self):
+        super().tearDown()
+        # Unset the memory manager for subsequent tests
+        cuda.close()
+        cuda.cudadrv.driver._memory_manager = None
+
+    def test_memalloc(self):
+        mgr = cuda.current_context().memory_manager
+
+        # Allocate an array and check that memalloc was called with the correct
+        # size.
+        arr_1 = np.arange(10)
+        d_arr_1 = cuda.device_array_like(arr_1)
+        self.assertTrue(mgr.memalloc_called)
+        self.assertEqual(mgr.count, 1)
+        self.assertEqual(mgr.allocations[1], arr_1.nbytes)
+
+        # Allocate again, with a different size, and check that it is also
+        # correct.
+        arr_2 = np.arange(5)
+        d_arr_2 = cuda.device_array_like(arr_2)
+        self.assertEqual(mgr.count, 2)
+        self.assertEqual(mgr.allocations[2], arr_2.nbytes)
+
+        # Remove the first array, and check that our finalizer was called for
+        # the first array only.
+        del d_arr_1
+        self.assertNotIn(1, mgr.allocations)
+        self.assertIn(2, mgr.allocations)
+
+        # Remove the second array and check that its finalizer was also
+        # called.
+        del d_arr_2
+        self.assertNotIn(2, mgr.allocations)
+
+    def test_initialized_in_context(self):
+        # If we have a CUDA context, it should already have initialized its
+        # memory manager.
+        self.assertTrue(cuda.current_context().memory_manager.initialized)
+
+    def test_reset(self):
+        ctx = cuda.current_context()
+        ctx.reset()
+        self.assertTrue(ctx.memory_manager.reset_called)
+
+    def test_get_memory_info(self):
+        ctx = cuda.current_context()
+        meminfo = ctx.get_memory_info()
+        self.assertTrue(ctx.memory_manager.get_memory_info_called)
+        self.assertEqual(meminfo.free, 32)
+        self.assertEqual(meminfo.total, 64)
+
+    @linux_only
+    def test_get_ipc_handle(self):
+        # We don't attempt to close the IPC handle in this test because Numba
+        # will be expecting a real IpcHandle object to have been returned from
+        # get_ipc_handle, and it would cause problems to do so.
+        arr = np.arange(2)
+        d_arr = cuda.device_array_like(arr)
+        ipch = d_arr.get_ipc_handle()
+        ctx = cuda.current_context()
+        self.assertTrue(ctx.memory_manager.get_ipc_handle_called)
+        self.assertIn("Dummy IPC handle for alloc 1", ipch._ipc_handle)
+
+
+@skip_on_cudasim('EMM Plugins not supported on CUDA simulator')
+class TestBadEMMPluginVersion(CUDATestCase):
+    """
+    Ensure that Numba rejects EMM Plugins with incompatible version
+    numbers.
+    """
+
+    def test_bad_plugin_version(self):
+        with self.assertRaises(RuntimeError) as raises:
+            cuda.set_memory_manager(BadVersionEMMPlugin)
+        self.assertIn('version 1 required', str(raises.exception))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_events.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_events.py
new file mode 100644
index 0000000000000000000000000000000000000000..b611a4a75fddfec427cd61f5d4db515066690548
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_events.py
@@ -0,0 +1,38 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaEvent(CUDATestCase):
+    def test_event_elapsed(self):
+        N = 32
+        dary = cuda.device_array(N, dtype=np.double)
+        evtstart = cuda.event()
+        evtend = cuda.event()
+
+        evtstart.record()
+        cuda.to_device(np.arange(N, dtype=np.double), to=dary)
+        evtend.record()
+        evtend.wait()
+        evtend.synchronize()
+        # Exercise the code path
+        evtstart.elapsed_time(evtend)
+
+    def test_event_elapsed_stream(self):
+        N = 32
+        stream = cuda.stream()
+        dary = cuda.device_array(N, dtype=np.double)
+        evtstart = cuda.event()
+        evtend = cuda.event()
+
+        evtstart.record(stream=stream)
+        cuda.to_device(np.arange(N, dtype=np.double), to=dary, stream=stream)
+        evtend.record(stream=stream)
+        evtend.wait(stream=stream)
+        evtend.synchronize()
+        # Exercise the code path
+        evtstart.elapsed_time(evtend)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_host_alloc.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_host_alloc.py
new file mode 100644
index 0000000000000000000000000000000000000000..62c4ecafe6c04a85ff52c022f1417bfe5fef9c68
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_host_alloc.py
@@ -0,0 +1,65 @@
+import numpy as np
+from numba.cuda.cudadrv import driver
+from numba import cuda
+from numba.cuda.testing import unittest, ContextResettingTestCase
+
+
+class TestHostAlloc(ContextResettingTestCase):
+    def test_host_alloc_driver(self):
+        n = 32
+        mem = cuda.current_context().memhostalloc(n, mapped=True)
+
+        dtype = np.dtype(np.uint8)
+        ary = np.ndarray(shape=n // dtype.itemsize, dtype=dtype,
+                         buffer=mem)
+
+        magic = 0xab
+        driver.device_memset(mem, magic, n)
+
+        self.assertTrue(np.all(ary == magic))
+
+        ary.fill(n)
+
+        recv = np.empty_like(ary)
+
+        driver.device_to_host(recv, mem, ary.size)
+
+        self.assertTrue(np.all(ary == recv))
+        self.assertTrue(np.all(recv == n))
+
+    def test_host_alloc_pinned(self):
+        ary = cuda.pinned_array(10, dtype=np.uint32)
+        ary.fill(123)
+        self.assertTrue(all(ary == 123))
+        devary = cuda.to_device(ary)
+        driver.device_memset(devary, 0, driver.device_memory_size(devary))
+        self.assertTrue(all(ary == 123))
+        devary.copy_to_host(ary)
+        self.assertTrue(all(ary == 0))
+
+    def test_host_alloc_mapped(self):
+        ary = cuda.mapped_array(10, dtype=np.uint32)
+        ary.fill(123)
+        self.assertTrue(all(ary == 123))
+        driver.device_memset(ary, 0, driver.device_memory_size(ary))
+        self.assertTrue(all(ary == 0))
+        self.assertTrue(sum(ary != 0) == 0)
+
+    def test_host_operators(self):
+        for ary in [cuda.mapped_array(10, dtype=np.uint32),
+                    cuda.pinned_array(10, dtype=np.uint32)]:
+            ary[:] = range(10)
+            self.assertTrue(sum(ary + 1) == 55)
+            self.assertTrue(sum((ary + 1) * 2 - 1) == 100)
+            self.assertTrue(sum(ary < 5) == 5)
+            self.assertTrue(sum(ary <= 5) == 6)
+            self.assertTrue(sum(ary > 6) == 3)
+            self.assertTrue(sum(ary >= 6) == 4)
+            self.assertTrue(sum(ary ** 2) == 285)
+            self.assertTrue(sum(ary // 2) == 20)
+            self.assertTrue(sum(ary / 2.0) == 22.5)
+            self.assertTrue(sum(ary % 2) == 5)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_init.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..600687fd52aa515b5adca075f1510726d914d9b4
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_init.py
@@ -0,0 +1,139 @@
+import multiprocessing as mp
+import os
+
+from numba import cuda
+from numba.cuda.cudadrv.driver import CudaAPIError, driver
+from numba.cuda.cudadrv.error import CudaSupportError
+from numba.cuda.testing import skip_on_cudasim, unittest, CUDATestCase
+
+
+# A mock of cuInit that always raises a CudaAPIError
+def cuInit_raising(arg):
+    raise CudaAPIError(999, 'CUDA_ERROR_UNKNOWN')
+
+
+# Test code to run in a child that patches driver.cuInit to a variant that
+# always raises. We can't use mock.patch.object here because driver.cuInit is
+# not assigned until we attempt to initialize - mock.patch.object cannot locate
+# the non-existent original method, and so fails. Instead we patch
+# driver.cuInit with our raising version prior to any attempt to initialize.
+def cuInit_raising_test(result_queue):
+    driver.cuInit = cuInit_raising
+
+    success = False
+    msg = None
+
+    try:
+        # A CUDA operation that forces initialization of the device
+        cuda.device_array(1)
+    except CudaSupportError as e:
+        success = True
+        msg = e.msg
+
+    result_queue.put((success, msg))
+
+
+# Similar to cuInit_raising_test above, but for testing that the string
+# returned by cuda_error() is as expected.
+def initialization_error_test(result_queue):
+    driver.cuInit = cuInit_raising
+
+    success = False
+    msg = None
+
+    try:
+        # A CUDA operation that forces initialization of the device
+        cuda.device_array(1)
+    except CudaSupportError:
+        success = True
+
+    msg = cuda.cuda_error()
+    result_queue.put((success, msg))
+
+
+# For testing the path where Driver.__init__() catches a CudaSupportError
+def cuda_disabled_test(result_queue):
+    success = False
+    msg = None
+
+    try:
+        # A CUDA operation that forces initialization of the device
+        cuda.device_array(1)
+    except CudaSupportError as e:
+        success = True
+        msg = e.msg
+
+    result_queue.put((success, msg))
+
+
+# Similar to cuda_disabled_test, but checks cuda.cuda_error() instead of the
+# exception raised on initialization
+def cuda_disabled_error_test(result_queue):
+    success = False
+    msg = None
+
+    try:
+        # A CUDA operation that forces initialization of the device
+        cuda.device_array(1)
+    except CudaSupportError:
+        success = True
+
+    msg = cuda.cuda_error()
+    result_queue.put((success, msg))
+
+
+@skip_on_cudasim('CUDA Simulator does not initialize driver')
+class TestInit(CUDATestCase):
+    def _test_init_failure(self, target, expected):
+        # Run the initialization failure test in a separate subprocess
+        ctx = mp.get_context('spawn')
+        result_queue = ctx.Queue()
+        proc = ctx.Process(target=target, args=(result_queue,))
+        proc.start()
+        proc.join(30) # should complete within 30s
+        success, msg = result_queue.get()
+
+        # Ensure the child process raised an exception during initialization
+        # before checking the message
+        if not success:
+            self.fail('CudaSupportError not raised')
+
+        self.assertIn(expected, msg)
+
+    def test_init_failure_raising(self):
+        expected = 'Error at driver init: CUDA_ERROR_UNKNOWN (999)'
+        self._test_init_failure(cuInit_raising_test, expected)
+
+    def test_init_failure_error(self):
+        expected = 'CUDA_ERROR_UNKNOWN (999)'
+        self._test_init_failure(initialization_error_test, expected)
+
+    def _test_cuda_disabled(self, target):
+        # Uses _test_init_failure to launch the test in a separate subprocess
+        # with CUDA disabled.
+        cuda_disabled = os.environ.get('NUMBA_DISABLE_CUDA')
+        os.environ['NUMBA_DISABLE_CUDA'] = "1"
+        try:
+            expected = 'CUDA is disabled due to setting NUMBA_DISABLE_CUDA=1'
+            self._test_init_failure(cuda_disabled_test, expected)
+        finally:
+            if cuda_disabled is not None:
+                os.environ['NUMBA_DISABLE_CUDA'] = cuda_disabled
+            else:
+                os.environ.pop('NUMBA_DISABLE_CUDA')
+
+    def test_cuda_disabled_raising(self):
+        self._test_cuda_disabled(cuda_disabled_test)
+
+    def test_cuda_disabled_error(self):
+        self._test_cuda_disabled(cuda_disabled_error_test)
+
+    def test_init_success(self):
+        # Here we assume that initialization is successful (because many bad
+        # things will happen with the test suite if it is not) and check that
+        # there is no error recorded.
+        self.assertIsNone(cuda.cuda_error())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_inline_ptx.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_inline_ptx.py
new file mode 100644
index 0000000000000000000000000000000000000000..40a6fa599e2bc7f7813a9d2c3f2ce6d89397bb39
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_inline_ptx.py
@@ -0,0 +1,37 @@
+from llvmlite import ir
+
+from numba.cuda.cudadrv import nvvm
+from numba.cuda.testing import unittest, ContextResettingTestCase
+from numba.cuda.testing import skip_on_cudasim
+
+
+@skip_on_cudasim('Inline PTX cannot be used in the simulator')
+class TestCudaInlineAsm(ContextResettingTestCase):
+    def test_inline_rsqrt(self):
+        mod = ir.Module(__name__)
+        mod.triple = 'nvptx64-nvidia-cuda'
+        nvvm.add_ir_version(mod)
+        fnty = ir.FunctionType(ir.VoidType(), [ir.PointerType(ir.FloatType())])
+        fn = ir.Function(mod, fnty, 'cu_rsqrt')
+        bldr = ir.IRBuilder(fn.append_basic_block('entry'))
+
+        rsqrt_approx_fnty = ir.FunctionType(ir.FloatType(), [ir.FloatType()])
+        inlineasm = ir.InlineAsm(rsqrt_approx_fnty,
+                                 'rsqrt.approx.f32 $0, $1;',
+                                 '=f,f', side_effect=True)
+        val = bldr.load(fn.args[0])
+        res = bldr.call(inlineasm, [val])
+
+        bldr.store(res, fn.args[0])
+        bldr.ret_void()
+
+        # generate ptx
+        mod.data_layout = nvvm.NVVM().data_layout
+        nvvm.set_cuda_kernel(fn)
+        nvvmir = str(mod)
+        ptx = nvvm.compile_ir(nvvmir)
+        self.assertTrue('rsqrt.approx.f32' in str(ptx))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_is_fp16.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_is_fp16.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcc73fa155f2ee73dd39ab363df56782656f8798
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_is_fp16.py
@@ -0,0 +1,12 @@
+from numba import cuda
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim, skip_unless_cc_53
+
+
+class TestIsFP16Supported(CUDATestCase):
+    def test_is_fp16_supported(self):
+        self.assertTrue(cuda.is_float16_supported())
+
+    @skip_on_cudasim
+    @skip_unless_cc_53
+    def test_device_supports_float16(self):
+        self.assertTrue(cuda.get_current_device().supports_float16)
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_linker.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_linker.py
new file mode 100644
index 0000000000000000000000000000000000000000..22e2ee8375c8c742e0636b8aa4760674e3c8f01b
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_linker.py
@@ -0,0 +1,317 @@
+import numpy as np
+import warnings
+from numba.cuda.testing import unittest
+from numba.cuda.testing import (skip_on_cudasim, skip_if_cuda_includes_missing)
+from numba.cuda.testing import CUDATestCase, test_data_dir
+from numba.cuda.cudadrv.driver import (CudaAPIError, Linker,
+                                       LinkerError)
+from numba.cuda.cudadrv.error import NvrtcError
+from numba.cuda import require_context
+from numba.tests.support import ignore_internal_warnings
+from numba import cuda, void, float64, int64, int32, typeof, float32
+
+
+CONST1D = np.arange(10, dtype=np.float64)
+
+
+def simple_const_mem(A):
+    C = cuda.const.array_like(CONST1D)
+    i = cuda.grid(1)
+
+    A[i] = C[i] + 1.0
+
+
+def func_with_lots_of_registers(x, a, b, c, d, e, f):
+    a1 = 1.0
+    a2 = 1.0
+    a3 = 1.0
+    a4 = 1.0
+    a5 = 1.0
+    b1 = 1.0
+    b2 = 1.0
+    b3 = 1.0
+    b4 = 1.0
+    b5 = 1.0
+    c1 = 1.0
+    c2 = 1.0
+    c3 = 1.0
+    c4 = 1.0
+    c5 = 1.0
+    d1 = 10
+    d2 = 10
+    d3 = 10
+    d4 = 10
+    d5 = 10
+    for i in range(a):
+        a1 += b
+        a2 += c
+        a3 += d
+        a4 += e
+        a5 += f
+        b1 *= b
+        b2 *= c
+        b3 *= d
+        b4 *= e
+        b5 *= f
+        c1 /= b
+        c2 /= c
+        c3 /= d
+        c4 /= e
+        c5 /= f
+        d1 <<= b
+        d2 <<= c
+        d3 <<= d
+        d4 <<= e
+        d5 <<= f
+    x[cuda.grid(1)] = a1 + a2 + a3 + a4 + a5
+    x[cuda.grid(1)] += b1 + b2 + b3 + b4 + b5
+    x[cuda.grid(1)] += c1 + c2 + c3 + c4 + c5
+    x[cuda.grid(1)] += d1 + d2 + d3 + d4 + d5
+
+
+def simple_smem(ary, dty):
+    sm = cuda.shared.array(100, dty)
+    i = cuda.grid(1)
+    if i == 0:
+        for j in range(100):
+            sm[j] = j
+    cuda.syncthreads()
+    ary[i] = sm[i]
+
+
+def coop_smem2d(ary):
+    i, j = cuda.grid(2)
+    sm = cuda.shared.array((10, 20), float32)
+    sm[i, j] = (i + 1) / (j + 1)
+    cuda.syncthreads()
+    ary[i, j] = sm[i, j]
+
+
+def simple_maxthreads(ary):
+    i = cuda.grid(1)
+    ary[i] = i
+
+
+LMEM_SIZE = 1000
+
+
+def simple_lmem(A, B, dty):
+    C = cuda.local.array(LMEM_SIZE, dty)
+    for i in range(C.shape[0]):
+        C[i] = A[i]
+    for i in range(C.shape[0]):
+        B[i] = C[i]
+
+
+@skip_on_cudasim('Linking unsupported in the simulator')
+class TestLinker(CUDATestCase):
+    _NUMBA_NVIDIA_BINDING_0_ENV = {'NUMBA_CUDA_USE_NVIDIA_BINDING': '0'}
+
+    @require_context
+    def test_linker_basic(self):
+        '''Simply go through the constructor and destructor
+        '''
+        linker = Linker.new(cc=(5, 3))
+        del linker
+
+    def _test_linking(self, eager):
+        global bar  # must be a global; other it is recognized as a freevar
+        bar = cuda.declare_device('bar', 'int32(int32)')
+
+        link = str(test_data_dir / 'jitlink.ptx')
+
+        if eager:
+            args = ['void(int32[:], int32[:])']
+        else:
+            args = []
+
+        @cuda.jit(*args, link=[link])
+        def foo(x, y):
+            i = cuda.grid(1)
+            x[i] += bar(y[i])
+
+        A = np.array([123], dtype=np.int32)
+        B = np.array([321], dtype=np.int32)
+
+        foo[1, 1](A, B)
+
+        self.assertTrue(A[0] == 123 + 2 * 321)
+
+    def test_linking_lazy_compile(self):
+        self._test_linking(eager=False)
+
+    def test_linking_eager_compile(self):
+        self._test_linking(eager=True)
+
+    def test_linking_cu(self):
+        bar = cuda.declare_device('bar', 'int32(int32)')
+
+        link = str(test_data_dir / 'jitlink.cu')
+
+        @cuda.jit(link=[link])
+        def kernel(r, x):
+            i = cuda.grid(1)
+
+            if i < len(r):
+                r[i] = bar(x[i])
+
+        x = np.arange(10, dtype=np.int32)
+        r = np.zeros_like(x)
+
+        kernel[1, 32](r, x)
+
+        # Matches the operation of bar() in jitlink.cu
+        expected = x * 2
+        np.testing.assert_array_equal(r, expected)
+
+    def test_linking_cu_log_warning(self):
+        bar = cuda.declare_device('bar', 'int32(int32)')
+
+        link = str(test_data_dir / 'warn.cu')
+
+        with warnings.catch_warnings(record=True) as w:
+            ignore_internal_warnings()
+
+            @cuda.jit('void(int32)', link=[link])
+            def kernel(x):
+                bar(x)
+
+        self.assertEqual(len(w), 1, 'Expected warnings from NVRTC')
+        # Check the warning refers to the log messages
+        self.assertIn('NVRTC log messages', str(w[0].message))
+        # Check the message pertaining to the unused variable is provided
+        self.assertIn('declared but never referenced', str(w[0].message))
+
+    def test_linking_cu_error(self):
+        bar = cuda.declare_device('bar', 'int32(int32)')
+
+        link = str(test_data_dir / 'error.cu')
+
+        with self.assertRaises(NvrtcError) as e:
+            @cuda.jit('void(int32)', link=[link])
+            def kernel(x):
+                bar(x)
+
+        msg = e.exception.args[0]
+        # Check the error message refers to the NVRTC compile
+        self.assertIn('NVRTC Compilation failure', msg)
+        # Check the expected error in the CUDA source is reported
+        self.assertIn('identifier "SYNTAX" is undefined', msg)
+        # Check the filename is reported correctly
+        self.assertIn('in the compilation of "error.cu"', msg)
+
+    def test_linking_unknown_filetype_error(self):
+        expected_err = "Don't know how to link file with extension .cuh"
+        with self.assertRaisesRegex(RuntimeError, expected_err):
+            @cuda.jit('void()', link=['header.cuh'])
+            def kernel():
+                pass
+
+    def test_linking_file_with_no_extension_error(self):
+        expected_err = "Don't know how to link file with no extension"
+        with self.assertRaisesRegex(RuntimeError, expected_err):
+            @cuda.jit('void()', link=['data'])
+            def kernel():
+                pass
+
+    @skip_if_cuda_includes_missing
+    def test_linking_cu_cuda_include(self):
+        link = str(test_data_dir / 'cuda_include.cu')
+
+        # An exception will be raised when linking this kernel due to the
+        # compile failure if CUDA includes cannot be found by Nvrtc.
+        @cuda.jit('void()', link=[link])
+        def kernel():
+            pass
+
+    def test_try_to_link_nonexistent(self):
+        with self.assertRaises(LinkerError) as e:
+            @cuda.jit('void(int32[::1])', link=['nonexistent.a'])
+            def f(x):
+                x[0] = 0
+        self.assertIn('nonexistent.a not found', e.exception.args)
+
+    def test_set_registers_no_max(self):
+        """Ensure that the jitted kernel used in the test_set_registers_* tests
+        uses more than 57 registers - this ensures that test_set_registers_*
+        are really checking that they reduced the number of registers used from
+        something greater than the maximum."""
+        compiled = cuda.jit(func_with_lots_of_registers)
+        compiled = compiled.specialize(np.empty(32), *range(6))
+        self.assertGreater(compiled.get_regs_per_thread(), 57)
+
+    def test_set_registers_57(self):
+        compiled = cuda.jit(max_registers=57)(func_with_lots_of_registers)
+        compiled = compiled.specialize(np.empty(32), *range(6))
+        self.assertLessEqual(compiled.get_regs_per_thread(), 57)
+
+    def test_set_registers_38(self):
+        compiled = cuda.jit(max_registers=38)(func_with_lots_of_registers)
+        compiled = compiled.specialize(np.empty(32), *range(6))
+        self.assertLessEqual(compiled.get_regs_per_thread(), 38)
+
+    def test_set_registers_eager(self):
+        sig = void(float64[::1], int64, int64, int64, int64, int64, int64)
+        compiled = cuda.jit(sig, max_registers=38)(func_with_lots_of_registers)
+        self.assertLessEqual(compiled.get_regs_per_thread(), 38)
+
+    def test_get_const_mem_size(self):
+        sig = void(float64[::1])
+        compiled = cuda.jit(sig)(simple_const_mem)
+        const_mem_size = compiled.get_const_mem_size()
+        self.assertGreaterEqual(const_mem_size, CONST1D.nbytes)
+
+    def test_get_no_shared_memory(self):
+        compiled = cuda.jit(func_with_lots_of_registers)
+        compiled = compiled.specialize(np.empty(32), *range(6))
+        shared_mem_size = compiled.get_shared_mem_per_block()
+        self.assertEqual(shared_mem_size, 0)
+
+    def test_get_shared_mem_per_block(self):
+        sig = void(int32[::1], typeof(np.int32))
+        compiled = cuda.jit(sig)(simple_smem)
+        shared_mem_size = compiled.get_shared_mem_per_block()
+        self.assertEqual(shared_mem_size, 400)
+
+    def test_get_shared_mem_per_specialized(self):
+        compiled = cuda.jit(simple_smem)
+        compiled_specialized = compiled.specialize(
+            np.zeros(100, dtype=np.int32), np.float64)
+        shared_mem_size = compiled_specialized.get_shared_mem_per_block()
+        self.assertEqual(shared_mem_size, 800)
+
+    def test_get_max_threads_per_block(self):
+        compiled = cuda.jit("void(float32[:,::1])")(coop_smem2d)
+        max_threads = compiled.get_max_threads_per_block()
+        self.assertGreater(max_threads, 0)
+
+    def test_max_threads_exceeded(self):
+        compiled = cuda.jit("void(int32[::1])")(simple_maxthreads)
+        max_threads = compiled.get_max_threads_per_block()
+        nelem = max_threads + 1
+        ary = np.empty(nelem, dtype=np.int32)
+        try:
+            compiled[1, nelem](ary)
+        except CudaAPIError as e:
+            self.assertIn("cuLaunchKernel", e.msg)
+
+    def test_get_local_mem_per_thread(self):
+        sig = void(int32[::1], int32[::1], typeof(np.int32))
+        compiled = cuda.jit(sig)(simple_lmem)
+        local_mem_size = compiled.get_local_mem_per_thread()
+        calc_size = np.dtype(np.int32).itemsize * LMEM_SIZE
+        self.assertGreaterEqual(local_mem_size, calc_size)
+
+    def test_get_local_mem_per_specialized(self):
+        compiled = cuda.jit(simple_lmem)
+        compiled_specialized = compiled.specialize(
+            np.zeros(LMEM_SIZE, dtype=np.int32),
+            np.zeros(LMEM_SIZE, dtype=np.int32),
+            np.float64)
+        local_mem_size = compiled_specialized.get_local_mem_per_thread()
+        calc_size = np.dtype(np.float64).itemsize * LMEM_SIZE
+        self.assertGreaterEqual(local_mem_size, calc_size)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_managed_alloc.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_managed_alloc.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9cc37ca84ad31274b226fc8841cca2093703194
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_managed_alloc.py
@@ -0,0 +1,127 @@
+import numpy as np
+from ctypes import byref, c_size_t
+from numba.cuda.cudadrv.driver import device_memset, driver, USE_NV_BINDING
+from numba import cuda
+from numba.cuda.testing import unittest, ContextResettingTestCase
+from numba.cuda.testing import skip_on_cudasim, skip_on_arm
+from numba.tests.support import linux_only
+
+
+@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
+@linux_only
+@skip_on_arm('Managed Alloc support is experimental/untested on ARM')
+class TestManagedAlloc(ContextResettingTestCase):
+
+    def get_total_gpu_memory(self):
+        # We use a driver function to directly get the total GPU memory because
+        # an EMM plugin may report something different (or not implement
+        # get_memory_info at all).
+        if USE_NV_BINDING:
+            free, total = driver.cuMemGetInfo()
+            return total
+        else:
+            free = c_size_t()
+            total = c_size_t()
+            driver.cuMemGetInfo(byref(free), byref(total))
+            return total.value
+
+    def skip_if_cc_major_lt(self, min_required, reason):
+        """
+        Skip the current test if the compute capability of the device is
+        less than `min_required`.
+        """
+        ctx = cuda.current_context()
+        cc_major = ctx.device.compute_capability[0]
+        if cc_major < min_required:
+            self.skipTest(reason)
+
+    # CUDA Unified Memory comes in two flavors. For GPUs in the Kepler and
+    # Maxwell generations, managed memory allocations work as opaque,
+    # contiguous segments that can either be on the device or the host. For
+    # GPUs in the Pascal or later generations, managed memory operates on a
+    # per-page basis, so we can have arrays larger than GPU memory, where only
+    # part of them is resident on the device at one time. To ensure that this
+    # test works correctly on all supported GPUs, we'll select the size of our
+    # memory such that we only oversubscribe the GPU memory if we're on a
+    # Pascal or newer GPU (compute capability at least 6.0).
+
+    def test_managed_alloc_driver_undersubscribe(self):
+        msg = "Managed memory unsupported prior to CC 3.0"
+        self.skip_if_cc_major_lt(3, msg)
+        self._test_managed_alloc_driver(0.5)
+
+    # This test is skipped by default because it is easy to hang the machine
+    # for a very long time or get OOM killed if the GPU memory size is >50% of
+    # the system memory size. Even if the system does have more than 2x the RAM
+    # of the GPU, this test runs for a very long time (in comparison to the
+    # rest of the tests in the suite).
+    #
+    # However, it is left in here for manual testing as required.
+
+    @unittest.skip
+    def test_managed_alloc_driver_oversubscribe(self):
+        msg = "Oversubscription of managed memory unsupported prior to CC 6.0"
+        self.skip_if_cc_major_lt(6, msg)
+        self._test_managed_alloc_driver(2.0)
+
+    def test_managed_alloc_driver_host_attach(self):
+        msg = "Host attached managed memory is not accessible prior to CC 6.0"
+        self.skip_if_cc_major_lt(6, msg)
+        # Only test with a small array (0.01 * memory size) to keep the test
+        # quick.
+        self._test_managed_alloc_driver(0.01, attach_global=False)
+
+    def _test_managed_alloc_driver(self, memory_factor, attach_global=True):
+        # Verify that we can allocate and operate on managed
+        # memory through the CUDA driver interface.
+
+        total_mem_size = self.get_total_gpu_memory()
+        n_bytes = int(memory_factor * total_mem_size)
+
+        ctx = cuda.current_context()
+        mem = ctx.memallocmanaged(n_bytes, attach_global=attach_global)
+
+        dtype = np.dtype(np.uint8)
+        n_elems = n_bytes // dtype.itemsize
+        ary = np.ndarray(shape=n_elems, dtype=dtype, buffer=mem)
+
+        magic = 0xab
+        device_memset(mem, magic, n_bytes)
+        ctx.synchronize()
+
+        # Note that this assertion operates on the CPU, so this
+        # test effectively drives both the CPU and the GPU on
+        # managed memory.
+
+        self.assertTrue(np.all(ary == magic))
+
+    def _test_managed_array(self, attach_global=True):
+        # Check the managed_array interface on both host and device.
+
+        ary = cuda.managed_array(100, dtype=np.double)
+        ary.fill(123.456)
+        self.assertTrue(all(ary == 123.456))
+
+        @cuda.jit('void(double[:])')
+        def kernel(x):
+            i = cuda.grid(1)
+            if i < x.shape[0]:
+                x[i] = 1.0
+
+        kernel[10, 10](ary)
+        cuda.current_context().synchronize()
+
+        self.assertTrue(all(ary == 1.0))
+
+    def test_managed_array_attach_global(self):
+        self._test_managed_array()
+
+    def test_managed_array_attach_host(self):
+        self._test_managed_array()
+        msg = "Host attached managed memory is not accessible prior to CC 6.0"
+        self.skip_if_cc_major_lt(6, msg)
+        self._test_managed_array(attach_global=False)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_mvc.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_mvc.py
new file mode 100644
index 0000000000000000000000000000000000000000..c25bc5ae2d1f46b3f873e921e92c7e53ede33fa6
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_mvc.py
@@ -0,0 +1,54 @@
+import multiprocessing as mp
+import traceback
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import (skip_on_cudasim, skip_under_cuda_memcheck,
+                                skip_if_mvc_libraries_unavailable)
+from numba.tests.support import linux_only
+
+
+def child_test():
+    from numba import config, cuda
+
+    # Change the MVC config after importing numba.cuda
+    config.CUDA_ENABLE_MINOR_VERSION_COMPATIBILITY = 1
+
+    @cuda.jit
+    def f():
+        pass
+
+    f[1, 1]()
+
+
+def child_test_wrapper(result_queue):
+    try:
+        output = child_test()
+        success = True
+    # Catch anything raised so it can be propagated
+    except: # noqa: E722
+        output = traceback.format_exc()
+        success = False
+
+    result_queue.put((success, output))
+
+
+@linux_only
+@skip_under_cuda_memcheck('May hang CUDA memcheck')
+@skip_on_cudasim('Simulator does not require or implement MVC')
+@skip_if_mvc_libraries_unavailable
+class TestMinorVersionCompatibility(CUDATestCase):
+    def test_mvc(self):
+        # Run test with Minor Version Compatibility enabled in a child process
+        ctx = mp.get_context('spawn')
+        result_queue = ctx.Queue()
+        proc = ctx.Process(target=child_test_wrapper, args=(result_queue,))
+        proc.start()
+        proc.join()
+        success, output = result_queue.get()
+
+        # Ensure the child process ran to completion before checking its output
+        if not success:
+            self.fail(output)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_nvvm_driver.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_nvvm_driver.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e560764c50566579f83c84c6f23d9043b1bab37
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_nvvm_driver.py
@@ -0,0 +1,199 @@
+import warnings
+
+from llvmlite import ir
+from numba.cuda.cudadrv import nvvm, runtime
+from numba.cuda.testing import unittest
+from numba.cuda.cudadrv.nvvm import LibDevice, NvvmError, NVVM
+from numba.cuda.testing import skip_on_cudasim
+
+
+@skip_on_cudasim('NVVM Driver unsupported in the simulator')
+class TestNvvmDriver(unittest.TestCase):
+    def get_nvvmir(self):
+        versions = NVVM().get_ir_version()
+        data_layout = NVVM().data_layout
+        return nvvmir_generic.format(data_layout=data_layout, v=versions)
+
+    def test_nvvm_compile_simple(self):
+        nvvmir = self.get_nvvmir()
+        ptx = nvvm.compile_ir(nvvmir).decode('utf8')
+        self.assertTrue('simple' in ptx)
+        self.assertTrue('ave' in ptx)
+
+    def test_nvvm_compile_nullary_option(self):
+        # Tests compilation with an option that doesn't take an argument
+        # ("-gen-lto") - all other NVVM options are of the form
+        # "-<name>=<value>"
+
+        # -gen-lto is not available prior to CUDA 11.5
+        if runtime.get_version() < (11, 5):
+            self.skipTest("-gen-lto unavailable in this toolkit version")
+
+        nvvmir = self.get_nvvmir()
+        ltoir = nvvm.compile_ir(nvvmir, opt=3, gen_lto=None, arch="compute_52")
+
+        # Verify we correctly passed the option by checking if we got LTOIR
+        # from NVVM (by looking for the expected magic number for LTOIR)
+        self.assertEqual(ltoir[:4], b'\xed\x43\x4e\x7f')
+
+    def test_nvvm_bad_option(self):
+        # Ensure that unsupported / non-existent options are reported as such
+        # to the user / caller
+        msg = "-made-up-option=2 is an unsupported option"
+        with self.assertRaisesRegex(NvvmError, msg):
+            nvvm.compile_ir("", made_up_option=2)
+
+    def test_nvvm_from_llvm(self):
+        m = ir.Module("test_nvvm_from_llvm")
+        m.triple = 'nvptx64-nvidia-cuda'
+        nvvm.add_ir_version(m)
+        fty = ir.FunctionType(ir.VoidType(), [ir.IntType(32)])
+        kernel = ir.Function(m, fty, name='mycudakernel')
+        bldr = ir.IRBuilder(kernel.append_basic_block('entry'))
+        bldr.ret_void()
+        nvvm.set_cuda_kernel(kernel)
+
+        m.data_layout = NVVM().data_layout
+        ptx = nvvm.compile_ir(str(m)).decode('utf8')
+        self.assertTrue('mycudakernel' in ptx)
+        self.assertTrue('.address_size 64' in ptx)
+
+    def test_used_list(self):
+        # Construct a module
+        m = ir.Module("test_used_list")
+        m.triple = 'nvptx64-nvidia-cuda'
+        m.data_layout = NVVM().data_layout
+        nvvm.add_ir_version(m)
+
+        # Add a function and mark it as a kernel
+        fty = ir.FunctionType(ir.VoidType(), [ir.IntType(32)])
+        kernel = ir.Function(m, fty, name='mycudakernel')
+        bldr = ir.IRBuilder(kernel.append_basic_block('entry'))
+        bldr.ret_void()
+        nvvm.set_cuda_kernel(kernel)
+
+        # Verify that the used list was correctly constructed
+        used_lines = [line for line in str(m).splitlines()
+                      if 'llvm.used' in line]
+        msg = 'Expected exactly one @"llvm.used" array'
+        self.assertEqual(len(used_lines), 1, msg)
+
+        used_line = used_lines[0]
+        # Kernel should be referenced in the used list
+        self.assertIn("mycudakernel", used_line)
+        # Check linkage of the used list
+        self.assertIn("appending global", used_line)
+        # Ensure used list is in the metadata section
+        self.assertIn('section "llvm.metadata"', used_line)
+
+    def test_nvvm_ir_verify_fail(self):
+        m = ir.Module("test_bad_ir")
+        m.triple = "unknown-unknown-unknown"
+        m.data_layout = NVVM().data_layout
+        nvvm.add_ir_version(m)
+        with self.assertRaisesRegex(NvvmError, 'Invalid target triple'):
+            nvvm.compile_ir(str(m))
+
+    def _test_nvvm_support(self, arch):
+        compute_xx = 'compute_{0}{1}'.format(*arch)
+        nvvmir = self.get_nvvmir()
+        ptx = nvvm.compile_ir(nvvmir, arch=compute_xx, ftz=1, prec_sqrt=0,
+                              prec_div=0).decode('utf8')
+        self.assertIn(".target sm_{0}{1}".format(*arch), ptx)
+        self.assertIn('simple', ptx)
+        self.assertIn('ave', ptx)
+
+    def test_nvvm_support(self):
+        """Test supported CC by NVVM
+        """
+        for arch in nvvm.get_supported_ccs():
+            self._test_nvvm_support(arch=arch)
+
+    def test_nvvm_warning(self):
+        m = ir.Module("test_nvvm_warning")
+        m.triple = 'nvptx64-nvidia-cuda'
+        m.data_layout = NVVM().data_layout
+        nvvm.add_ir_version(m)
+
+        fty = ir.FunctionType(ir.VoidType(), [])
+        kernel = ir.Function(m, fty, name='inlinekernel')
+        builder = ir.IRBuilder(kernel.append_basic_block('entry'))
+        builder.ret_void()
+        nvvm.set_cuda_kernel(kernel)
+
+        # Add the noinline attribute to trigger NVVM to generate a warning
+        kernel.attributes.add('noinline')
+
+        with warnings.catch_warnings(record=True) as w:
+            nvvm.compile_ir(str(m))
+
+        self.assertEqual(len(w), 1)
+        self.assertIn('overriding noinline attribute', str(w[0]))
+
+
+@skip_on_cudasim('NVVM Driver unsupported in the simulator')
+class TestArchOption(unittest.TestCase):
+    def test_get_arch_option(self):
+        # Test returning the nearest lowest arch.
+        self.assertEqual(nvvm.get_arch_option(5, 3), 'compute_53')
+        self.assertEqual(nvvm.get_arch_option(7, 5), 'compute_75')
+        self.assertEqual(nvvm.get_arch_option(7, 7), 'compute_75')
+        # Test known arch.
+        supported_cc = nvvm.get_supported_ccs()
+        for arch in supported_cc:
+            self.assertEqual(nvvm.get_arch_option(*arch), 'compute_%d%d' % arch)
+        self.assertEqual(nvvm.get_arch_option(1000, 0),
+                         'compute_%d%d' % supported_cc[-1])
+
+
+@skip_on_cudasim('NVVM Driver unsupported in the simulator')
+class TestLibDevice(unittest.TestCase):
+    def test_libdevice_load(self):
+        # Test that constructing LibDevice gives a bitcode file
+        libdevice = LibDevice()
+        self.assertEqual(libdevice.bc[:4], b'BC\xc0\xde')
+
+
+nvvmir_generic = '''\
+target triple="nvptx64-nvidia-cuda"
+target datalayout = "{data_layout}"
+
+define i32 @ave(i32 %a, i32 %b) {{
+entry:
+%add = add nsw i32 %a, %b
+%div = sdiv i32 %add, 2
+ret i32 %div
+}}
+
+define void @simple(i32* %data) {{
+entry:
+%0 = call i32 @llvm.nvvm.read.ptx.sreg.ctaid.x()
+%1 = call i32 @llvm.nvvm.read.ptx.sreg.ntid.x()
+%mul = mul i32 %0, %1
+%2 = call i32 @llvm.nvvm.read.ptx.sreg.tid.x()
+%add = add i32 %mul, %2
+%call = call i32 @ave(i32 %add, i32 %add)
+%idxprom = sext i32 %add to i64
+%arrayidx = getelementptr inbounds i32, i32* %data, i64 %idxprom
+store i32 %call, i32* %arrayidx, align 4
+ret void
+}}
+
+declare i32 @llvm.nvvm.read.ptx.sreg.ctaid.x() nounwind readnone
+
+declare i32 @llvm.nvvm.read.ptx.sreg.ntid.x() nounwind readnone
+
+declare i32 @llvm.nvvm.read.ptx.sreg.tid.x() nounwind readnone
+
+!nvvmir.version = !{{!1}}
+!1 = !{{i32 {v[0]}, i32 {v[1]}, i32 {v[2]}, i32 {v[3]}}}
+
+!nvvm.annotations = !{{!2}}
+!2 = !{{void (i32*)* @simple, !"kernel", i32 1}}
+
+@"llvm.used" = appending global [1 x i8*] [i8* bitcast (void (i32*)* @simple to i8*)], section "llvm.metadata"
+'''  # noqa: E501
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_pinned.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_pinned.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef727c5a89cbcd84ef813a7227b0654ab3070f68
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_pinned.py
@@ -0,0 +1,37 @@
+import numpy as np
+import platform
+
+from numba import cuda
+from numba.cuda.testing import unittest, ContextResettingTestCase
+
+
+class TestPinned(ContextResettingTestCase):
+
+    def _run_copies(self, A):
+        A0 = np.copy(A)
+
+        stream = cuda.stream()
+        ptr = cuda.to_device(A, copy=False, stream=stream)
+        ptr.copy_to_device(A, stream=stream)
+        ptr.copy_to_host(A, stream=stream)
+        stream.synchronize()
+
+        self.assertTrue(np.allclose(A, A0))
+
+    def test_pinned(self):
+        machine = platform.machine()
+        if machine.startswith('arm') or machine.startswith('aarch64'):
+            count = 262144   # 2MB
+        else:
+            count = 2097152  # 16MB
+        A = np.arange(count)
+        with cuda.pinned(A):
+            self._run_copies(A)
+
+    def test_unpinned(self):
+        A = np.arange(2 * 1024 * 1024) # 16 MB
+        self._run_copies(A)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_profiler.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..1660d4d42fc1de142d4762a0ed7859278f99f2fb
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_profiler.py
@@ -0,0 +1,20 @@
+import unittest
+from numba.cuda.testing import ContextResettingTestCase
+from numba import cuda
+from numba.cuda.testing import skip_on_cudasim
+
+
+@skip_on_cudasim('CUDA Profiler unsupported in the simulator')
+class TestProfiler(ContextResettingTestCase):
+    def test_profiling(self):
+        with cuda.profiling():
+            a = cuda.device_array(10)
+            del a
+
+        with cuda.profiling():
+            a = cuda.device_array(100)
+            del a
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_ptds.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_ptds.py
new file mode 100644
index 0000000000000000000000000000000000000000..b03fd3647aeac35c0375c5947b072aaefa56bf1c
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_ptds.py
@@ -0,0 +1,149 @@
+import multiprocessing as mp
+import logging
+import traceback
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.cuda.testing import (skip_on_cudasim, skip_with_cuda_python,
+                                skip_under_cuda_memcheck)
+from numba.tests.support import linux_only
+
+
+def child_test():
+    from numba import cuda, int32, void
+    from numba.core import config
+    import io
+    import numpy as np
+    import threading
+
+    # Enable PTDS before we make any CUDA driver calls.  Enabling it first
+    # ensures that PTDS APIs are used because the CUDA driver looks up API
+    # functions on first use and memoizes them.
+    config.CUDA_PER_THREAD_DEFAULT_STREAM = 1
+
+    # Set up log capture for the Driver API so we can see what API calls were
+    # used.
+    logbuf = io.StringIO()
+    handler = logging.StreamHandler(logbuf)
+    cudadrv_logger = logging.getLogger('numba.cuda.cudadrv.driver')
+    cudadrv_logger.addHandler(handler)
+    cudadrv_logger.setLevel(logging.DEBUG)
+
+    # Set up data for our test, and copy over to the device
+    N = 2 ** 16
+    N_THREADS = 10
+    N_ADDITIONS = 4096
+
+    # Seed the RNG for repeatability
+    np.random.seed(1)
+    x = np.random.randint(low=0, high=1000, size=N, dtype=np.int32)
+    r = np.zeros_like(x)
+
+    # One input and output array for each thread
+    xs = [cuda.to_device(x) for _ in range(N_THREADS)]
+    rs = [cuda.to_device(r) for _ in range(N_THREADS)]
+
+    # Compute the grid size and get the [per-thread] default stream
+    n_threads = 256
+    n_blocks = N // n_threads
+    stream = cuda.default_stream()
+
+    # A simple multiplication-by-addition kernel. What it does exactly is not
+    # too important; only that we have a kernel that does something.
+    @cuda.jit(void(int32[::1], int32[::1]))
+    def f(r, x):
+        i = cuda.grid(1)
+
+        if i > len(r):
+            return
+
+        # Accumulate x into r
+        for j in range(N_ADDITIONS):
+            r[i] += x[i]
+
+    # This function will be used to launch the kernel from each thread on its
+    # own unique data.
+    def kernel_thread(n):
+        f[n_blocks, n_threads, stream](rs[n], xs[n])
+
+    # Create threads
+    threads = [threading.Thread(target=kernel_thread, args=(i,))
+               for i in range(N_THREADS)]
+
+    # Start all threads
+    for thread in threads:
+        thread.start()
+
+    # Wait for all threads to finish, to ensure that we don't synchronize with
+    # the device until all kernels are scheduled.
+    for thread in threads:
+        thread.join()
+
+    # Synchronize with the device
+    cuda.synchronize()
+
+    # Check output is as expected
+    expected = x * N_ADDITIONS
+    for i in range(N_THREADS):
+        np.testing.assert_equal(rs[i].copy_to_host(), expected)
+
+    # Return the driver log output to the calling process for checking
+    handler.flush()
+    return logbuf.getvalue()
+
+
+def child_test_wrapper(result_queue):
+    try:
+        output = child_test()
+        success = True
+    # Catch anything raised so it can be propagated
+    except: # noqa: E722
+        output = traceback.format_exc()
+        success = False
+
+    result_queue.put((success, output))
+
+
+# Run on Linux only until the reason for test hangs on Windows (Issue #8635,
+# https://github.com/numba/numba/issues/8635) is diagnosed
+@linux_only
+@skip_under_cuda_memcheck('Hangs cuda-memcheck')
+@skip_on_cudasim('Streams not supported on the simulator')
+class TestPTDS(CUDATestCase):
+    @skip_with_cuda_python('Function names unchanged for PTDS with NV Binding')
+    def test_ptds(self):
+        # Run a test with PTDS enabled in a child process
+        ctx = mp.get_context('spawn')
+        result_queue = ctx.Queue()
+        proc = ctx.Process(target=child_test_wrapper, args=(result_queue,))
+        proc.start()
+        proc.join()
+        success, output = result_queue.get()
+
+        # Ensure the child process ran to completion before checking its output
+        if not success:
+            self.fail(output)
+
+        # Functions with a per-thread default stream variant that we expect to
+        # see in the output
+        ptds_functions = ('cuMemcpyHtoD_v2_ptds', 'cuLaunchKernel_ptsz',
+                          'cuMemcpyDtoH_v2_ptds')
+
+        for fn in ptds_functions:
+            with self.subTest(fn=fn, expected=True):
+                self.assertIn(fn, output)
+
+        # Non-PTDS versions of the functions that we should not see in the
+        # output:
+        legacy_functions = ('cuMemcpyHtoD_v2', 'cuLaunchKernel',
+                            'cuMemcpyDtoH_v2')
+
+        for fn in legacy_functions:
+            with self.subTest(fn=fn, expected=False):
+                # Ensure we only spot these function names appearing without a
+                # _ptds or _ptsz suffix by checking including the end of the
+                # line in the log
+                fn_at_end = f'{fn}\n'
+                self.assertNotIn(fn_at_end, output)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_reset_device.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_reset_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2e0b6d108dda4cdf58ec340ede7a429b556cefc
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_reset_device.py
@@ -0,0 +1,36 @@
+import threading
+from numba import cuda
+from numba.cuda.cudadrv.driver import driver
+from numba.cuda.testing import unittest, ContextResettingTestCase
+from queue import Queue
+
+
+class TestResetDevice(ContextResettingTestCase):
+    def test_reset_device(self):
+
+        def newthread(exception_queue):
+            try:
+                devices = range(driver.get_device_count())
+                for _ in range(2):
+                    for d in devices:
+                        cuda.select_device(d)
+                        cuda.close()
+            except Exception as e:
+                exception_queue.put(e)
+
+        # Do test on a separate thread so that we don't affect
+        # the current context in the main thread.
+
+        exception_queue = Queue()
+        t = threading.Thread(target=newthread, args=(exception_queue,))
+        t.start()
+        t.join()
+
+        exceptions = []
+        while not exception_queue.empty():
+            exceptions.append(exception_queue.get())
+        self.assertEqual(exceptions, [])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_runtime.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..51e0722eca4eb2f3868ebdff47e1b85ac224efcc
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_runtime.py
@@ -0,0 +1,85 @@
+import multiprocessing
+import os
+from numba.core import config
+from numba.cuda.cudadrv.runtime import runtime
+from numba.cuda.testing import unittest, SerialMixin, skip_on_cudasim
+from unittest.mock import patch
+
+
+def set_visible_devices_and_check(q):
+    try:
+        from numba import cuda
+        import os
+
+        os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+        q.put(len(cuda.gpus.lst))
+    except: # noqa: E722
+        # Sentinel value for error executing test code
+        q.put(-1)
+
+
+if config.ENABLE_CUDASIM:
+    SUPPORTED_VERSIONS = (-1, -1),
+else:
+    SUPPORTED_VERSIONS = ((11, 0), (11, 1), (11, 2), (11, 3), (11, 4), (11, 5),
+                          (11, 6), (11, 7))
+
+
+class TestRuntime(unittest.TestCase):
+    def test_is_supported_version_true(self):
+        for v in SUPPORTED_VERSIONS:
+            with patch.object(runtime, 'get_version', return_value=v):
+                self.assertTrue(runtime.is_supported_version())
+
+    @skip_on_cudasim('The simulator always simulates a supported runtime')
+    def test_is_supported_version_false(self):
+        # Check with an old unsupported version and some potential future
+        # versions
+        for v in ((10, 2), (11, 8), (12, 0)):
+            with patch.object(runtime, 'get_version', return_value=v):
+                self.assertFalse(runtime.is_supported_version())
+
+    def test_supported_versions(self):
+        self.assertEqual(SUPPORTED_VERSIONS, runtime.supported_versions)
+
+
+class TestVisibleDevices(unittest.TestCase, SerialMixin):
+    def test_visible_devices_set_after_import(self):
+        # See Issue #6149. This test checks that we can set
+        # CUDA_VISIBLE_DEVICES after importing Numba and have the value
+        # reflected in the available list of GPUs. Prior to the fix for this
+        # issue, Numba made a call to runtime.get_version() on import that
+        # initialized the driver and froze the list of available devices before
+        # CUDA_VISIBLE_DEVICES could be set by the user.
+
+        # Avoid importing cuda at the top level so that
+        # set_visible_devices_and_check gets to import it first in its process
+        from numba import cuda
+
+        if len(cuda.gpus.lst) in (0, 1):
+            self.skipTest('This test requires multiple GPUs')
+
+        if os.environ.get('CUDA_VISIBLE_DEVICES'):
+            msg = 'Cannot test when CUDA_VISIBLE_DEVICES already set'
+            self.skipTest(msg)
+
+        ctx = multiprocessing.get_context('spawn')
+        q = ctx.Queue()
+        p = ctx.Process(target=set_visible_devices_and_check, args=(q,))
+        p.start()
+        try:
+            visible_gpu_count = q.get()
+        finally:
+            p.join()
+
+        # Make an obvious distinction between an error running the test code
+        # and an incorrect number of GPUs in the list
+        msg = 'Error running set_visible_devices_and_check'
+        self.assertNotEqual(visible_gpu_count, -1, msg=msg)
+
+        # The actual check that we see only one GPU
+        self.assertEqual(visible_gpu_count, 1)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_select_device.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_select_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..aca78d94bff59d41966b24ceca026284692c8a51
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_select_device.py
@@ -0,0 +1,41 @@
+#
+# Test does not work on some cards.
+#
+import threading
+from queue import Queue
+
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, ContextResettingTestCase
+
+
+def newthread(exception_queue):
+    try:
+        cuda.select_device(0)
+        stream = cuda.stream()
+        A = np.arange(100)
+        dA = cuda.to_device(A, stream=stream)
+        stream.synchronize()
+        del dA
+        del stream
+        cuda.close()
+    except Exception as e:
+        exception_queue.put(e)
+
+
+class TestSelectDevice(ContextResettingTestCase):
+    def test_select_device(self):
+        exception_queue = Queue()
+        for i in range(10):
+            t = threading.Thread(target=newthread, args=(exception_queue,))
+            t.start()
+            t.join()
+
+        exceptions = []
+        while not exception_queue.empty():
+            exceptions.append(exception_queue.get())
+        self.assertEqual(exceptions, [])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_streams.py b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_streams.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4fbec19f7a7c64bd09b820a854af07f180ca1b3
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudadrv/test_streams.py
@@ -0,0 +1,122 @@
+import asyncio
+import functools
+import threading
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+
+
+def with_asyncio_loop(f):
+    @functools.wraps(f)
+    def runner(*args, **kwds):
+        loop = asyncio.new_event_loop()
+        loop.set_debug(True)
+        try:
+            return loop.run_until_complete(f(*args, **kwds))
+        finally:
+            loop.close()
+    return runner
+
+
+@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
+class TestCudaStream(CUDATestCase):
+    def test_add_callback(self):
+        def callback(stream, status, event):
+            event.set()
+
+        stream = cuda.stream()
+        callback_event = threading.Event()
+        stream.add_callback(callback, callback_event)
+        self.assertTrue(callback_event.wait(1.0))
+
+    def test_add_callback_with_default_arg(self):
+        callback_event = threading.Event()
+
+        def callback(stream, status, arg):
+            self.assertIsNone(arg)
+            callback_event.set()
+
+        stream = cuda.stream()
+        stream.add_callback(callback)
+        self.assertTrue(callback_event.wait(1.0))
+
+    @with_asyncio_loop
+    async def test_async_done(self):
+        stream = cuda.stream()
+        await stream.async_done()
+
+    @with_asyncio_loop
+    async def test_parallel_tasks(self):
+        async def async_cuda_fn(value_in: float) -> float:
+            stream = cuda.stream()
+            h_src, h_dst = cuda.pinned_array(8), cuda.pinned_array(8)
+            h_src[:] = value_in
+            d_ary = cuda.to_device(h_src, stream=stream)
+            d_ary.copy_to_host(h_dst, stream=stream)
+            done_result = await stream.async_done()
+            self.assertEqual(done_result, stream)
+            return h_dst.mean()
+
+        values_in = [1, 2, 3, 4]
+        tasks = [asyncio.create_task(async_cuda_fn(v)) for v in values_in]
+        values_out = await asyncio.gather(*tasks)
+        self.assertTrue(np.allclose(values_in, values_out))
+
+    @with_asyncio_loop
+    async def test_multiple_async_done(self):
+        stream = cuda.stream()
+        done_aws = [stream.async_done() for _ in range(4)]
+        done = await asyncio.gather(*done_aws)
+        for d in done:
+            self.assertEqual(d, stream)
+
+    @with_asyncio_loop
+    async def test_multiple_async_done_multiple_streams(self):
+        streams = [cuda.stream() for _ in range(4)]
+        done_aws = [stream.async_done() for stream in streams]
+        done = await asyncio.gather(*done_aws)
+
+        # Ensure we got the four original streams in done
+        self.assertSetEqual(set(done), set(streams))
+
+    @with_asyncio_loop
+    async def test_cancelled_future(self):
+        stream = cuda.stream()
+        done1, done2 = stream.async_done(), stream.async_done()
+        done1.cancel()
+        await done2
+        self.assertTrue(done1.cancelled())
+        self.assertTrue(done2.done())
+
+
+@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
+class TestFailingStream(CUDATestCase):
+    # This test can only be run in isolation because it corrupts the CUDA
+    # context, which cannot be recovered from within the same process. It is
+    # left here so that it can be run manually for debugging / testing purposes
+    # - or may be re-enabled if in future there is infrastructure added for
+    # running tests in a separate process (a subprocess cannot be used because
+    # CUDA will have been initialized before the fork, so it cannot be used in
+    # the child process).
+    @unittest.skip
+    @with_asyncio_loop
+    async def test_failed_stream(self):
+        ctx = cuda.current_context()
+        module = ctx.create_module_ptx("""
+            .version 6.5
+            .target sm_30
+            .address_size 64
+            .visible .entry failing_kernel() { trap; }
+        """)
+        failing_kernel = module.get_function("failing_kernel")
+
+        stream = cuda.stream()
+        failing_kernel.configure((1,), (1,), stream=stream).__call__()
+        done = stream.async_done()
+        with self.assertRaises(Exception):
+            await done
+        self.assertIsNotNone(done.exception())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_array_methods.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_array_methods.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f129b5df03121032c8f7252926eace3eab5c5d8
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_array_methods.py
@@ -0,0 +1,35 @@
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import CUDATestCase
+import unittest
+
+
+def reinterpret_array_type(byte_arr, start, stop, output):
+    # Tested with just one thread
+    val = byte_arr[start:stop].view(np.int32)[0]
+    output[0] = val
+
+
+class TestCudaArrayMethods(CUDATestCase):
+    def test_reinterpret_array_type(self):
+        """
+        Reinterpret byte array as int32 in the GPU.
+        """
+        pyfunc = reinterpret_array_type
+        kernel = cuda.jit(pyfunc)
+
+        byte_arr = np.arange(256, dtype=np.uint8)
+        itemsize = np.dtype(np.int32).itemsize
+        for start in range(0, 256, itemsize):
+            stop = start + itemsize
+            expect = byte_arr[start:stop].view(np.int32)[0]
+
+            output = np.zeros(1, dtype=np.int32)
+            kernel[1, 1](byte_arr, start, stop, output)
+
+            got = output[0]
+            self.assertEqual(expect, got)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_blackscholes.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_blackscholes.py
new file mode 100644
index 0000000000000000000000000000000000000000..1375162d9e1015009ea2a1c7ce098340231074f3
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_blackscholes.py
@@ -0,0 +1,120 @@
+import numpy as np
+import math
+from numba import cuda, double, void
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+RISKFREE = 0.02
+VOLATILITY = 0.30
+
+A1 = 0.31938153
+A2 = -0.356563782
+A3 = 1.781477937
+A4 = -1.821255978
+A5 = 1.330274429
+RSQRT2PI = 0.39894228040143267793994605993438
+
+
+def cnd(d):
+    K = 1.0 / (1.0 + 0.2316419 * np.abs(d))
+    ret_val = (RSQRT2PI * np.exp(-0.5 * d * d) *
+               (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))))
+    return np.where(d > 0, 1.0 - ret_val, ret_val)
+
+
+def black_scholes(callResult, putResult, stockPrice, optionStrike, optionYears,
+                  Riskfree, Volatility):
+    S = stockPrice
+    X = optionStrike
+    T = optionYears
+    R = Riskfree
+    V = Volatility
+    sqrtT = np.sqrt(T)
+    d1 = (np.log(S / X) + (R + 0.5 * V * V) * T) / (V * sqrtT)
+    d2 = d1 - V * sqrtT
+    cndd1 = cnd(d1)
+    cndd2 = cnd(d2)
+
+    expRT = np.exp(- R * T)
+    callResult[:] = (S * cndd1 - X * expRT * cndd2)
+    putResult[:] = (X * expRT * (1.0 - cndd2) - S * (1.0 - cndd1))
+
+
+def randfloat(rand_var, low, high):
+    return (1.0 - rand_var) * low + rand_var * high
+
+
+class TestBlackScholes(CUDATestCase):
+    def test_blackscholes(self):
+        OPT_N = 400
+        iterations = 2
+
+        stockPrice = randfloat(np.random.random(OPT_N), 5.0, 30.0)
+        optionStrike = randfloat(np.random.random(OPT_N), 1.0, 100.0)
+        optionYears = randfloat(np.random.random(OPT_N), 0.25, 10.0)
+
+        callResultNumpy = np.zeros(OPT_N)
+        putResultNumpy = -np.ones(OPT_N)
+
+        callResultNumba = np.zeros(OPT_N)
+        putResultNumba = -np.ones(OPT_N)
+
+        # numpy
+        for i in range(iterations):
+            black_scholes(callResultNumpy, putResultNumpy, stockPrice,
+                          optionStrike, optionYears, RISKFREE, VOLATILITY)
+
+        @cuda.jit(double(double), device=True, inline=True)
+        def cnd_cuda(d):
+            K = 1.0 / (1.0 + 0.2316419 * math.fabs(d))
+            ret_val = (RSQRT2PI * math.exp(-0.5 * d * d) *
+                       (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))))
+            if d > 0:
+                ret_val = 1.0 - ret_val
+            return ret_val
+
+        @cuda.jit(void(double[:], double[:], double[:], double[:], double[:],
+                       double, double))
+        def black_scholes_cuda(callResult, putResult, S, X, T, R, V):
+            i = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x
+            if i >= S.shape[0]:
+                return
+            sqrtT = math.sqrt(T[i])
+            d1 = ((math.log(S[i] / X[i]) + (R + 0.5 * V * V) * T[i])
+                  / (V * sqrtT))
+            d2 = d1 - V * sqrtT
+            cndd1 = cnd_cuda(d1)
+            cndd2 = cnd_cuda(d2)
+
+            expRT = math.exp((-1. * R) * T[i])
+            callResult[i] = (S[i] * cndd1 - X[i] * expRT * cndd2)
+            putResult[i] = (X[i] * expRT * (1.0 - cndd2) - S[i] * (1.0 - cndd1))
+
+        # numba
+        blockdim = 512, 1
+        griddim = int(math.ceil(float(OPT_N) / blockdim[0])), 1
+        stream = cuda.stream()
+        d_callResult = cuda.to_device(callResultNumba, stream)
+        d_putResult = cuda.to_device(putResultNumba, stream)
+        d_stockPrice = cuda.to_device(stockPrice, stream)
+        d_optionStrike = cuda.to_device(optionStrike, stream)
+        d_optionYears = cuda.to_device(optionYears, stream)
+
+        for i in range(iterations):
+            black_scholes_cuda[griddim, blockdim, stream](
+                d_callResult, d_putResult, d_stockPrice, d_optionStrike,
+                d_optionYears, RISKFREE, VOLATILITY)
+        d_callResult.copy_to_host(callResultNumba, stream)
+        d_putResult.copy_to_host(putResultNumba, stream)
+        stream.synchronize()
+
+        delta = np.abs(callResultNumpy - callResultNumba)
+        L1norm = delta.sum() / np.abs(callResultNumpy).sum()
+
+        max_abs_err = delta.max()
+        self.assertTrue(L1norm < 1e-13)
+        self.assertTrue(max_abs_err < 1e-13)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_const_string.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_const_string.py
new file mode 100644
index 0000000000000000000000000000000000000000..173319cb223c11bd0cb1866926d50b63dee9a36e
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_const_string.py
@@ -0,0 +1,129 @@
+import re
+import numpy as np
+from numba import cuda
+from numba.cuda.testing import unittest, skip_on_cudasim, CUDATestCase
+from llvmlite import ir
+
+
+@skip_on_cudasim("This is testing CUDA backend code generation")
+class TestConstStringCodegen(unittest.TestCase):
+    def test_const_string(self):
+        # These imports are incompatible with CUDASIM
+        from numba.cuda.descriptor import cuda_target
+        from numba.cuda.cudadrv.nvvm import compile_ir
+
+        targetctx = cuda_target.target_context
+        mod = targetctx.create_module("")
+        textstring = 'A Little Brown Fox'
+        gv0 = targetctx.insert_const_string(mod, textstring)
+        # Insert the same const string a second time - the first should be
+        # reused.
+        targetctx.insert_const_string(mod, textstring)
+
+        res = re.findall(r"@\"__conststring__.*internal.*constant.*\["
+                         r"19\s+x\s+i8\]", str(mod))
+        # Ensure that the const string was only inserted once
+        self.assertEqual(len(res), 1)
+
+        fnty = ir.FunctionType(ir.IntType(8).as_pointer(), [])
+
+        # Using insert_const_string
+        fn = ir.Function(mod, fnty, "test_insert_const_string")
+        builder = ir.IRBuilder(fn.append_basic_block())
+        res = builder.addrspacecast(gv0, ir.PointerType(ir.IntType(8)),
+                                    'generic')
+        builder.ret(res)
+
+        matches = re.findall(r"@\"__conststring__.*internal.*constant.*\["
+                             r"19\s+x\s+i8\]", str(mod))
+        self.assertEqual(len(matches), 1)
+
+        # Using insert_string_const_addrspace
+        fn = ir.Function(mod, fnty, "test_insert_string_const_addrspace")
+        builder = ir.IRBuilder(fn.append_basic_block())
+        res = targetctx.insert_string_const_addrspace(builder, textstring)
+        builder.ret(res)
+
+        matches = re.findall(r"@\"__conststring__.*internal.*constant.*\["
+                             r"19\s+x\s+i8\]", str(mod))
+        self.assertEqual(len(matches), 1)
+
+        ptx = compile_ir(str(mod)).decode('ascii')
+        matches = list(re.findall(r"\.const.*__conststring__", ptx))
+
+        self.assertEqual(len(matches), 1)
+
+
+# Inspired by the reproducer from Issue #7041.
+class TestConstString(CUDATestCase):
+    def test_assign_const_unicode_string(self):
+        @cuda.jit
+        def str_assign(arr):
+            i = cuda.grid(1)
+            if i < len(arr):
+                arr[i] = "XYZ"
+
+        n_strings = 8
+        arr = np.zeros(n_strings + 1, dtype="<U12")
+        str_assign[1, n_strings](arr)
+
+        # Expected result, e.g.:
+        #     ['XYZ' 'XYZ' 'XYZ' 'XYZ' 'XYZ' 'XYZ' 'XYZ' 'XYZ' '']
+        expected = np.zeros_like(arr)
+        expected[:-1] = 'XYZ'
+        expected[-1] = ''
+        np.testing.assert_equal(arr, expected)
+
+    def test_assign_const_byte_string(self):
+        @cuda.jit
+        def bytes_assign(arr):
+            i = cuda.grid(1)
+            if i < len(arr):
+                arr[i] = b"XYZ"
+
+        n_strings = 8
+        arr = np.zeros(n_strings + 1, dtype="S12")
+        bytes_assign[1, n_strings](arr)
+
+        # Expected result, e.g.:
+        #     [b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'XYZ' b'']
+        expected = np.zeros_like(arr)
+        expected[:-1] = b'XYZ'
+        expected[-1] = b''
+        np.testing.assert_equal(arr, expected)
+
+    def test_assign_const_string_in_record(self):
+        @cuda.jit
+        def f(a):
+            a[0]['x'] = 1
+            a[0]['y'] = 'ABC'
+            a[1]['x'] = 2
+            a[1]['y'] = 'XYZ'
+
+        dt = np.dtype([('x', np.int32), ('y', np.dtype('<U12'))])
+        a = np.zeros(2, dt)
+
+        f[1, 1](a)
+
+        reference = np.asarray([(1, 'ABC'), (2, 'XYZ')], dtype=dt)
+        np.testing.assert_array_equal(reference, a)
+
+    def test_assign_const_bytes_in_record(self):
+        @cuda.jit
+        def f(a):
+            a[0]['x'] = 1
+            a[0]['y'] = b'ABC'
+            a[1]['x'] = 2
+            a[1]['y'] = b'XYZ'
+
+        dt = np.dtype([('x', np.float32), ('y', np.dtype('S12'))])
+        a = np.zeros(2, dt)
+
+        f[1, 1](a)
+
+        reference = np.asarray([(1, b'ABC'), (2, b'XYZ')], dtype=dt)
+        np.testing.assert_array_equal(reference, a)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_cooperative_groups.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_cooperative_groups.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a9d0040406a29c2e38ebc9618d7c01ecaf95bdb
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_cooperative_groups.py
@@ -0,0 +1,147 @@
+from __future__ import print_function
+
+import numpy as np
+
+from numba import config, cuda, int32
+from numba.cuda.testing import (unittest, CUDATestCase, skip_on_cudasim,
+                                skip_unless_cc_60, skip_if_cudadevrt_missing,
+                                skip_if_mvc_enabled)
+
+
+@cuda.jit
+def this_grid(A):
+    cuda.cg.this_grid()
+    A[0] = 1.0
+
+
+@cuda.jit
+def sync_group(A):
+    g = cuda.cg.this_grid()
+    g.sync()
+    A[0] = 1.0
+
+
+@cuda.jit
+def no_sync(A):
+    A[0] = cuda.grid(1)
+
+
+def sequential_rows(M):
+    # The grid writes rows one at a time. Each thread reads an element from
+    # the previous row written by its "opposite" thread.
+    #
+    # A failure to sync the grid at each row would result in an incorrect
+    # result as some threads could run ahead of threads in other blocks, or
+    # fail to see the update to the previous row from their opposite thread.
+
+    col = cuda.grid(1)
+    g = cuda.cg.this_grid()
+
+    rows = M.shape[0]
+    cols = M.shape[1]
+
+    for row in range(1, rows):
+        opposite = cols - col - 1
+        M[row, col] = M[row - 1, opposite] + 1
+        g.sync()
+
+
+@skip_if_cudadevrt_missing
+@skip_if_mvc_enabled('CG not supported with MVC')
+class TestCudaCooperativeGroups(CUDATestCase):
+    @skip_unless_cc_60
+    def test_this_grid(self):
+        A = np.full(1, fill_value=np.nan)
+        this_grid[1, 1](A)
+
+        # Ensure the kernel executed beyond the call to cuda.this_grid()
+        self.assertFalse(np.isnan(A[0]), 'Value was not set')
+
+    @skip_unless_cc_60
+    @skip_on_cudasim("Simulator doesn't differentiate between normal and "
+                     "cooperative kernels")
+    def test_this_grid_is_cooperative(self):
+        A = np.full(1, fill_value=np.nan)
+        this_grid[1, 1](A)
+
+        # this_grid should have been determined to be cooperative
+        for key, overload in this_grid.overloads.items():
+            self.assertTrue(overload.cooperative)
+
+    @skip_unless_cc_60
+    def test_sync_group(self):
+        A = np.full(1, fill_value=np.nan)
+        sync_group[1, 1](A)
+
+        # Ensure the kernel executed beyond the call to cuda.sync_group()
+        self.assertFalse(np.isnan(A[0]), 'Value was not set')
+
+    @skip_unless_cc_60
+    @skip_on_cudasim("Simulator doesn't differentiate between normal and "
+                     "cooperative kernels")
+    def test_sync_group_is_cooperative(self):
+        A = np.full(1, fill_value=np.nan)
+        sync_group[1, 1](A)
+        # sync_group should have been determined to be cooperative
+        for key, overload in sync_group.overloads.items():
+            self.assertTrue(overload.cooperative)
+
+    @skip_on_cudasim("Simulator does not implement linking")
+    def test_false_cooperative_doesnt_link_cudadevrt(self):
+        """
+        We should only mark a kernel as cooperative and link cudadevrt if the
+        kernel uses grid sync. Here we ensure that one that doesn't use grid
+        synsync isn't marked as such.
+        """
+        A = np.full(1, fill_value=np.nan)
+        no_sync[1, 1](A)
+
+        for key, overload in no_sync.overloads.items():
+            self.assertFalse(overload.cooperative)
+            for link in overload._codelibrary._linking_files:
+                self.assertNotIn('cudadevrt', link)
+
+    @skip_unless_cc_60
+    def test_sync_at_matrix_row(self):
+        if config.ENABLE_CUDASIM:
+            # Use a small matrix to compute using a single block in a
+            # reasonable amount of time
+            shape = (32, 32)
+        else:
+            shape = (1024, 1024)
+        A = np.zeros(shape, dtype=np.int32)
+        blockdim = 32
+        griddim = A.shape[1] // blockdim
+
+        sig = (int32[:,::1],)
+        c_sequential_rows = cuda.jit(sig)(sequential_rows)
+
+        overload = c_sequential_rows.overloads[sig]
+        mb = overload.max_cooperative_grid_blocks(blockdim)
+        if griddim > mb:
+            unittest.skip("GPU cannot support enough cooperative grid blocks")
+
+        c_sequential_rows[griddim, blockdim](A)
+
+        reference = np.tile(np.arange(shape[0]), (shape[1], 1)).T
+        np.testing.assert_equal(A, reference)
+
+    @skip_unless_cc_60
+    def test_max_cooperative_grid_blocks(self):
+        # The maximum number of blocks will vary based on the device so we
+        # can't test for an expected value, but we can check that the function
+        # doesn't error, and that varying the number of dimensions of the block
+        # whilst keeping the total number of threads constant doesn't change
+        # the maximum to validate some of the logic.
+        sig = (int32[:,::1],)
+        c_sequential_rows = cuda.jit(sig)(sequential_rows)
+        overload = c_sequential_rows.overloads[sig]
+        blocks1d = overload.max_cooperative_grid_blocks(256)
+        blocks2d = overload.max_cooperative_grid_blocks((16, 16))
+        blocks3d = overload.max_cooperative_grid_blocks((16, 4, 4))
+        self.assertEqual(blocks1d, blocks2d)
+        self.assertEqual(blocks1d, blocks3d)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_errors.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..c20fb8dccdf844ead9e4b46a3651b54a40b9efde
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_errors.py
@@ -0,0 +1,79 @@
+from numba import cuda
+from numba.core.errors import TypingError
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+
+
+def noop(x):
+    pass
+
+
+class TestJitErrors(CUDATestCase):
+    """
+    Test compile-time errors with @jit.
+    """
+
+    def test_too_many_dims(self):
+        kernfunc = cuda.jit(noop)
+
+        with self.assertRaises(ValueError) as raises:
+            kernfunc[(1, 2, 3, 4), (5, 6)]
+        self.assertIn("griddim must be a sequence of 1, 2 or 3 integers, "
+                      "got [1, 2, 3, 4]",
+                      str(raises.exception))
+
+        with self.assertRaises(ValueError) as raises:
+            kernfunc[(1, 2,), (3, 4, 5, 6)]
+        self.assertIn("blockdim must be a sequence of 1, 2 or 3 integers, "
+                      "got [3, 4, 5, 6]",
+                      str(raises.exception))
+
+    def test_non_integral_dims(self):
+        kernfunc = cuda.jit(noop)
+
+        with self.assertRaises(TypeError) as raises:
+            kernfunc[2.0, 3]
+        self.assertIn("griddim must be a sequence of integers, got [2.0]",
+                      str(raises.exception))
+
+        with self.assertRaises(TypeError) as raises:
+            kernfunc[2, 3.0]
+        self.assertIn("blockdim must be a sequence of integers, got [3.0]",
+                      str(raises.exception))
+
+    def _test_unconfigured(self, kernfunc):
+        with self.assertRaises(ValueError) as raises:
+            kernfunc(0)
+        self.assertIn("launch configuration was not specified",
+                      str(raises.exception))
+
+    def test_unconfigured_typed_cudakernel(self):
+        kernfunc = cuda.jit("void(int32)")(noop)
+        self._test_unconfigured(kernfunc)
+
+    def test_unconfigured_untyped_cudakernel(self):
+        kernfunc = cuda.jit(noop)
+        self._test_unconfigured(kernfunc)
+
+    @skip_on_cudasim('TypingError does not occur on simulator')
+    def test_typing_error(self):
+        # see #5860, this is present to catch changes to error reporting
+        # accidentally breaking the CUDA target
+
+        @cuda.jit(device=True)
+        def dev_func(x):
+            # floor is deliberately not imported for the purpose of this test.
+            return floor(x)  # noqa: F821
+
+        @cuda.jit
+        def kernel_func():
+            dev_func(1.5)
+
+        with self.assertRaises(TypingError) as raises:
+            kernel_func[1, 1]()
+        excstr = str(raises.exception)
+        self.assertIn("resolving callee type: type(CUDADispatcher", excstr)
+        self.assertIn("NameError: name 'floor' is not defined", excstr)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_idiv.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_idiv.py
new file mode 100644
index 0000000000000000000000000000000000000000..44b770f422deb7adefa7192982be9925e1ed291a
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_idiv.py
@@ -0,0 +1,37 @@
+import numpy as np
+from numba import cuda, float32, float64, int32, void
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestCudaIDiv(CUDATestCase):
+    def test_inplace_div(self):
+
+        @cuda.jit(void(float32[:, :], int32, int32))
+        def div(grid, l_x, l_y):
+            for x in range(l_x):
+                for y in range(l_y):
+                    grid[x, y] /= 2.0
+
+        x = np.ones((2, 2), dtype=np.float32)
+        grid = cuda.to_device(x)
+        div[1, 1](grid, 2, 2)
+        y = grid.copy_to_host()
+        self.assertTrue(np.all(y == 0.5))
+
+    def test_inplace_div_double(self):
+
+        @cuda.jit(void(float64[:, :], int32, int32))
+        def div_double(grid, l_x, l_y):
+            for x in range(l_x):
+                for y in range(l_y):
+                    grid[x, y] /= 2.0
+
+        x = np.ones((2, 2), dtype=np.float64)
+        grid = cuda.to_device(x)
+        div_double[1, 1](grid, 2, 2)
+        y = grid.copy_to_host()
+        self.assertTrue(np.all(y == 0.5))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_lang.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_lang.py
new file mode 100644
index 0000000000000000000000000000000000000000..0241c1e408ce2715dffebbf27d06d7a3083da136
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_lang.py
@@ -0,0 +1,64 @@
+"""
+Test basic language features
+
+"""
+
+import numpy as np
+from numba import cuda, float64
+from numba.cuda.testing import unittest, CUDATestCase
+
+
+class TestLang(CUDATestCase):
+    def test_enumerate(self):
+        tup = (1., 2.5, 3.)
+
+        @cuda.jit("void(float64[:])")
+        def foo(a):
+            for i, v in enumerate(tup):
+                a[i] = v
+
+        a = np.zeros(len(tup))
+        foo[1, 1](a)
+        self.assertTrue(np.all(a == tup))
+
+    def test_zip(self):
+        t1 = (1, 2, 3)
+        t2 = (4.5, 5.6, 6.7)
+
+        @cuda.jit("void(float64[:])")
+        def foo(a):
+            c = 0
+            for i, j in zip(t1, t2):
+                c += i + j
+            a[0] = c
+
+        a = np.zeros(1)
+        foo[1, 1](a)
+        b = np.array(t1)
+        c = np.array(t2)
+        self.assertTrue(np.all(a == (b + c).sum()))
+
+    def test_issue_872(self):
+        '''
+        Ensure that typing and lowering of CUDA kernel API primitives works in
+        more than one block. Was originally to ensure that macro expansion works
+        for more than one block (issue #872), but macro expansion has been
+        replaced by a "proper" implementation of all kernel API functions.
+        '''
+
+        @cuda.jit("void(float64[:,:])")
+        def cuda_kernel_api_in_multiple_blocks(ary):
+            for i in range(2):
+                tx = cuda.threadIdx.x
+            for j in range(3):
+                ty = cuda.threadIdx.y
+            sm = cuda.shared.array((2, 3), float64)
+            sm[tx, ty] = 1.0
+            ary[tx, ty] = sm[tx, ty]
+
+        a = np.zeros((2, 3))
+        cuda_kernel_api_in_multiple_blocks[1, (2, 3)](a)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_matmul.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_matmul.py
new file mode 100644
index 0000000000000000000000000000000000000000..51f1181a3a8ad46c38708eb215dfa26570354ab1
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_matmul.py
@@ -0,0 +1,74 @@
+import numpy as np
+
+from numba import cuda, float32, void
+from numba.cuda.testing import unittest, CUDATestCase
+from numba.core import config
+
+# Ensure the test takes a reasonable amount of time in the simulator
+if config.ENABLE_CUDASIM:
+    bpg, tpb = 2, 8
+else:
+    bpg, tpb = 50, 32
+
+n = bpg * tpb
+SM_SIZE = (tpb, tpb)
+
+
+class TestCudaMatMul(CUDATestCase):
+
+    def test_func(self):
+
+        @cuda.jit(void(float32[:, ::1], float32[:, ::1], float32[:, ::1]))
+        def cu_square_matrix_mul(A, B, C):
+            sA = cuda.shared.array(shape=SM_SIZE, dtype=float32)
+            sB = cuda.shared.array(shape=(tpb, tpb), dtype=float32)
+
+            tx = cuda.threadIdx.x
+            ty = cuda.threadIdx.y
+            bx = cuda.blockIdx.x
+            by = cuda.blockIdx.y
+            bw = cuda.blockDim.x
+            bh = cuda.blockDim.y
+
+            x = tx + bx * bw
+            y = ty + by * bh
+
+            acc = float32(0)  # forces all the math to be f32
+            for i in range(bpg):
+                if x < n and y < n:
+                    sA[ty, tx] = A[y, tx + i * tpb]
+                    sB[ty, tx] = B[ty + i * tpb, x]
+
+                cuda.syncthreads()
+
+                if x < n and y < n:
+                    for j in range(tpb):
+                        acc += sA[ty, j] * sB[j, tx]
+
+                cuda.syncthreads()
+
+            if x < n and y < n:
+                C[y, x] = acc
+
+        np.random.seed(42)
+        A = np.array(np.random.random((n, n)), dtype=np.float32)
+        B = np.array(np.random.random((n, n)), dtype=np.float32)
+        C = np.empty_like(A)
+
+        stream = cuda.stream()
+        with stream.auto_synchronize():
+            dA = cuda.to_device(A, stream)
+            dB = cuda.to_device(B, stream)
+            dC = cuda.to_device(C, stream)
+            cu_square_matrix_mul[(bpg, bpg), (tpb, tpb), stream](dA, dB, dC)
+            dC.copy_to_host(C, stream)
+
+        # Host compute
+        Cans = np.dot(A, B)
+
+        # Check result
+        np.testing.assert_allclose(C, Cans, rtol=1e-5)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_minmax.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_minmax.py
new file mode 100644
index 0000000000000000000000000000000000000000..aee97fd63e0c7a2dfb7b4bf2280d86ea39d6e260
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_minmax.py
@@ -0,0 +1,113 @@
+import numpy as np
+
+from numba import cuda, float64
+from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
+
+
+def builtin_max(A, B, C):
+    i = cuda.grid(1)
+
+    if i >= len(C):
+        return
+
+    C[i] = float64(max(A[i], B[i]))
+
+
+def builtin_min(A, B, C):
+    i = cuda.grid(1)
+
+    if i >= len(C):
+        return
+
+    C[i] = float64(min(A[i], B[i]))
+
+
+@skip_on_cudasim('Tests PTX emission')
+class TestCudaMinMax(CUDATestCase):
+    def _run(
+            self,
+            kernel,
+            numpy_equivalent,
+            ptx_instruction,
+            dtype_left,
+            dtype_right,
+            n=5):
+        kernel = cuda.jit(kernel)
+
+        c = np.zeros(n, dtype=np.float64)
+        a = np.arange(n, dtype=dtype_left) + .5
+        b = np.full(n, fill_value=2, dtype=dtype_right)
+
+        kernel[1, c.shape](a, b, c)
+        np.testing.assert_allclose(c, numpy_equivalent(a, b))
+
+        ptx = next(p for p in kernel.inspect_asm().values())
+        self.assertIn(ptx_instruction, ptx)
+
+    def test_max_f8f8(self):
+        self._run(
+            builtin_max,
+            np.maximum,
+            'max.f64',
+            np.float64,
+            np.float64)
+
+    def test_max_f4f8(self):
+        self._run(
+            builtin_max,
+            np.maximum,
+            'max.f64',
+            np.float32,
+            np.float64)
+
+    def test_max_f8f4(self):
+        self._run(
+            builtin_max,
+            np.maximum,
+            'max.f64',
+            np.float64,
+            np.float32)
+
+    def test_max_f4f4(self):
+        self._run(
+            builtin_max,
+            np.maximum,
+            'max.f32',
+            np.float32,
+            np.float32)
+
+    def test_min_f8f8(self):
+        self._run(
+            builtin_min,
+            np.minimum,
+            'min.f64',
+            np.float64,
+            np.float64)
+
+    def test_min_f4f8(self):
+        self._run(
+            builtin_min,
+            np.minimum,
+            'min.f64',
+            np.float32,
+            np.float64)
+
+    def test_min_f8f4(self):
+        self._run(
+            builtin_min,
+            np.minimum,
+            'min.f64',
+            np.float64,
+            np.float32)
+
+    def test_min_f4f4(self):
+        self._run(
+            builtin_min,
+            np.minimum,
+            'min.f32',
+            np.float32,
+            np.float32)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_sync.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_sync.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4d9326f0357e1c299d4bd9c5781e5e2a22b7002
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_sync.py
@@ -0,0 +1,271 @@
+import numpy as np
+from numba import cuda, int32, float32
+from numba.cuda.testing import skip_on_cudasim, unittest, CUDATestCase
+from numba.core.config import ENABLE_CUDASIM
+
+
+def useless_syncthreads(ary):
+    i = cuda.grid(1)
+    cuda.syncthreads()
+    ary[i] = i
+
+
+def useless_syncwarp(ary):
+    i = cuda.grid(1)
+    cuda.syncwarp()
+    ary[i] = i
+
+
+def useless_syncwarp_with_mask(ary):
+    i = cuda.grid(1)
+    cuda.syncwarp(0xFFFF)
+    ary[i] = i
+
+
+def coop_syncwarp(res):
+    sm = cuda.shared.array(32, int32)
+    i = cuda.grid(1)
+
+    sm[i] = i
+    cuda.syncwarp()
+
+    if i < 16:
+        sm[i] = sm[i] + sm[i + 16]
+        cuda.syncwarp(0xFFFF)
+
+    if i < 8:
+        sm[i] = sm[i] + sm[i + 8]
+        cuda.syncwarp(0xFF)
+
+    if i < 4:
+        sm[i] = sm[i] + sm[i + 4]
+        cuda.syncwarp(0xF)
+
+    if i < 2:
+        sm[i] = sm[i] + sm[i + 2]
+        cuda.syncwarp(0x3)
+
+    if i == 0:
+        res[0] = sm[0] + sm[1]
+
+
+def simple_smem(ary):
+    N = 100
+    sm = cuda.shared.array(N, int32)
+    i = cuda.grid(1)
+    if i == 0:
+        for j in range(N):
+            sm[j] = j
+    cuda.syncthreads()
+    ary[i] = sm[i]
+
+
+def coop_smem2d(ary):
+    i, j = cuda.grid(2)
+    sm = cuda.shared.array((10, 20), float32)
+    sm[i, j] = (i + 1) / (j + 1)
+    cuda.syncthreads()
+    ary[i, j] = sm[i, j]
+
+
+def dyn_shared_memory(ary):
+    i = cuda.grid(1)
+    sm = cuda.shared.array(0, float32)
+    sm[i] = i * 2
+    cuda.syncthreads()
+    ary[i] = sm[i]
+
+
+def use_threadfence(ary):
+    ary[0] += 123
+    cuda.threadfence()
+    ary[0] += 321
+
+
+def use_threadfence_block(ary):
+    ary[0] += 123
+    cuda.threadfence_block()
+    ary[0] += 321
+
+
+def use_threadfence_system(ary):
+    ary[0] += 123
+    cuda.threadfence_system()
+    ary[0] += 321
+
+
+def use_syncthreads_count(ary_in, ary_out):
+    i = cuda.grid(1)
+    ary_out[i] = cuda.syncthreads_count(ary_in[i])
+
+
+def use_syncthreads_and(ary_in, ary_out):
+    i = cuda.grid(1)
+    ary_out[i] = cuda.syncthreads_and(ary_in[i])
+
+
+def use_syncthreads_or(ary_in, ary_out):
+    i = cuda.grid(1)
+    ary_out[i] = cuda.syncthreads_or(ary_in[i])
+
+
+def _safe_cc_check(cc):
+    if ENABLE_CUDASIM:
+        return True
+    else:
+        return cuda.get_current_device().compute_capability >= cc
+
+
+class TestCudaSync(CUDATestCase):
+    def _test_useless(self, kernel):
+        compiled = cuda.jit("void(int32[::1])")(kernel)
+        nelem = 10
+        ary = np.empty(nelem, dtype=np.int32)
+        exp = np.arange(nelem, dtype=np.int32)
+        compiled[1, nelem](ary)
+        np.testing.assert_equal(ary, exp)
+
+    def test_useless_syncthreads(self):
+        self._test_useless(useless_syncthreads)
+
+    @skip_on_cudasim("syncwarp not implemented on cudasim")
+    def test_useless_syncwarp(self):
+        self._test_useless(useless_syncwarp)
+
+    @skip_on_cudasim("syncwarp not implemented on cudasim")
+    @unittest.skipUnless(_safe_cc_check((7, 0)),
+                         "Partial masks require CC 7.0 or greater")
+    def test_useless_syncwarp_with_mask(self):
+        self._test_useless(useless_syncwarp_with_mask)
+
+    @skip_on_cudasim("syncwarp not implemented on cudasim")
+    @unittest.skipUnless(_safe_cc_check((7, 0)),
+                         "Partial masks require CC 7.0 or greater")
+    def test_coop_syncwarp(self):
+        # coop_syncwarp computes the sum of all integers from 0 to 31 (496)
+        # using a single warp
+        expected = 496
+        nthreads = 32
+        nblocks = 1
+
+        compiled = cuda.jit("void(int32[::1])")(coop_syncwarp)
+        res = np.zeros(1, dtype=np.int32)
+        compiled[nblocks, nthreads](res)
+        np.testing.assert_equal(expected, res[0])
+
+    def test_simple_smem(self):
+        compiled = cuda.jit("void(int32[::1])")(simple_smem)
+        nelem = 100
+        ary = np.empty(nelem, dtype=np.int32)
+        compiled[1, nelem](ary)
+        self.assertTrue(np.all(ary == np.arange(nelem, dtype=np.int32)))
+
+    def test_coop_smem2d(self):
+        compiled = cuda.jit("void(float32[:,::1])")(coop_smem2d)
+        shape = 10, 20
+        ary = np.empty(shape, dtype=np.float32)
+        compiled[1, shape](ary)
+        exp = np.empty_like(ary)
+        for i in range(ary.shape[0]):
+            for j in range(ary.shape[1]):
+                exp[i, j] = (i + 1) / (j + 1)
+        self.assertTrue(np.allclose(ary, exp))
+
+    def test_dyn_shared_memory(self):
+        compiled = cuda.jit("void(float32[::1])")(dyn_shared_memory)
+        shape = 50
+        ary = np.empty(shape, dtype=np.float32)
+        compiled[1, shape, 0, ary.size * 4](ary)
+        self.assertTrue(np.all(ary == 2 * np.arange(ary.size, dtype=np.int32)))
+
+    def test_threadfence_codegen(self):
+        # Does not test runtime behavior, just the code generation.
+        sig = (int32[:],)
+        compiled = cuda.jit(sig)(use_threadfence)
+        ary = np.zeros(10, dtype=np.int32)
+        compiled[1, 1](ary)
+        self.assertEqual(123 + 321, ary[0])
+        if not ENABLE_CUDASIM:
+            self.assertIn("membar.gl;", compiled.inspect_asm(sig))
+
+    def test_threadfence_block_codegen(self):
+        # Does not test runtime behavior, just the code generation.
+        sig = (int32[:],)
+        compiled = cuda.jit(sig)(use_threadfence_block)
+        ary = np.zeros(10, dtype=np.int32)
+        compiled[1, 1](ary)
+        self.assertEqual(123 + 321, ary[0])
+        if not ENABLE_CUDASIM:
+            self.assertIn("membar.cta;", compiled.inspect_asm(sig))
+
+    def test_threadfence_system_codegen(self):
+        # Does not test runtime behavior, just the code generation.
+        sig = (int32[:],)
+        compiled = cuda.jit(sig)(use_threadfence_system)
+        ary = np.zeros(10, dtype=np.int32)
+        compiled[1, 1](ary)
+        self.assertEqual(123 + 321, ary[0])
+        if not ENABLE_CUDASIM:
+            self.assertIn("membar.sys;", compiled.inspect_asm(sig))
+
+    def _test_syncthreads_count(self, in_dtype):
+        compiled = cuda.jit(use_syncthreads_count)
+        ary_in = np.ones(72, dtype=in_dtype)
+        ary_out = np.zeros(72, dtype=np.int32)
+        ary_in[31] = 0
+        ary_in[42] = 0
+        compiled[1, 72](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 70))
+
+    def test_syncthreads_count(self):
+        self._test_syncthreads_count(np.int32)
+
+    def test_syncthreads_count_upcast(self):
+        self._test_syncthreads_count(np.int16)
+
+    def test_syncthreads_count_downcast(self):
+        self._test_syncthreads_count(np.int64)
+
+    def _test_syncthreads_and(self, in_dtype):
+        compiled = cuda.jit(use_syncthreads_and)
+        nelem = 100
+        ary_in = np.ones(nelem, dtype=in_dtype)
+        ary_out = np.zeros(nelem, dtype=np.int32)
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 1))
+        ary_in[31] = 0
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 0))
+
+    def test_syncthreads_and(self):
+        self._test_syncthreads_and(np.int32)
+
+    def test_syncthreads_and_upcast(self):
+        self._test_syncthreads_and(np.int16)
+
+    def test_syncthreads_and_downcast(self):
+        self._test_syncthreads_and(np.int64)
+
+    def _test_syncthreads_or(self, in_dtype):
+        compiled = cuda.jit(use_syncthreads_or)
+        nelem = 100
+        ary_in = np.zeros(nelem, dtype=in_dtype)
+        ary_out = np.zeros(nelem, dtype=np.int32)
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 0))
+        ary_in[31] = 1
+        compiled[1, nelem](ary_in, ary_out)
+        self.assertTrue(np.all(ary_out == 1))
+
+    def test_syncthreads_or(self):
+        self._test_syncthreads_or(np.int32)
+
+    def test_syncthreads_or_upcast(self):
+        self._test_syncthreads_or(np.int16)
+
+    def test_syncthreads_or_downcast(self):
+        self._test_syncthreads_or(np.int64)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_ufuncs.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_ufuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a98abde74fe68c1788d6ee9876777dc12b06330
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_ufuncs.py
@@ -0,0 +1,277 @@
+import functools
+import numpy as np
+import unittest
+
+from numba import config, cuda, types
+from numba.tests.support import TestCase
+from numba.tests.test_ufuncs import BasicUFuncTest
+
+
+def _make_ufunc_usecase(ufunc):
+    ldict = {}
+    arg_str = ','.join(['a{0}'.format(i) for i in range(ufunc.nargs)])
+    func_str = f'def fn({arg_str}):\n    np.{ufunc.__name__}({arg_str})'
+    exec(func_str, globals(), ldict)
+    fn = ldict['fn']
+    fn.__name__ = '{0}_usecase'.format(ufunc.__name__)
+    return fn
+
+
+# This test would also be a CUDATestCase, but to avoid a confusing and
+# potentially dangerous inheritance diamond with setUp methods that modify
+# global state, we implement the necessary parts of CUDATestCase within this
+# class instead. These are:
+#
+# - Disable parallel testing with _numba_parallel_test_.
+# - Disabling CUDA performance warnings for the duration of tests.
+class TestUFuncs(BasicUFuncTest, TestCase):
+    _numba_parallel_test_ = False
+
+    def setUp(self):
+        BasicUFuncTest.setUp(self)
+
+        # The basic ufunc test does not set up complex inputs, so we'll add
+        # some here for testing with CUDA.
+        self.inputs.extend([
+            (np.complex64(-0.5 - 0.5j), types.complex64),
+            (np.complex64(0.0), types.complex64),
+            (np.complex64(0.5 + 0.5j), types.complex64),
+
+            (np.complex128(-0.5 - 0.5j), types.complex128),
+            (np.complex128(0.0), types.complex128),
+            (np.complex128(0.5 + 0.5j), types.complex128),
+
+            (np.array([-0.5 - 0.5j, 0.0, 0.5 + 0.5j], dtype='c8'),
+             types.Array(types.complex64, 1, 'C')),
+            (np.array([-0.5 - 0.5j, 0.0, 0.5 + 0.5j], dtype='c16'),
+             types.Array(types.complex128, 1, 'C')),
+        ])
+
+        # Test with multiple dimensions
+        self.inputs.extend([
+            # Basic 2D and 3D arrays
+            (np.linspace(0, 1).reshape((5, -1)),
+             types.Array(types.float64, 2, 'C')),
+            (np.linspace(0, 1).reshape((2, 5, -1)),
+             types.Array(types.float64, 3, 'C')),
+            # Complex data (i.e. interleaved)
+            (np.linspace(0, 1 + 1j).reshape(5, -1),
+             types.Array(types.complex128, 2, 'C')),
+            # F-ordered
+            (np.asfortranarray(np.linspace(0, 1).reshape((5, -1))),
+             types.Array(types.float64, 2, 'F')),
+        ])
+
+        # Add tests for other integer types
+        self.inputs.extend([
+            (np.uint8(0), types.uint8),
+            (np.uint8(1), types.uint8),
+            (np.int8(-1), types.int8),
+            (np.int8(0), types.int8),
+
+            (np.uint16(0), types.uint16),
+            (np.uint16(1), types.uint16),
+            (np.int16(-1), types.int16),
+            (np.int16(0), types.int16),
+
+            (np.ulonglong(0), types.ulonglong),
+            (np.ulonglong(1), types.ulonglong),
+            (np.longlong(-1), types.longlong),
+            (np.longlong(0), types.longlong),
+
+            (np.array([0,1], dtype=np.ulonglong),
+             types.Array(types.ulonglong, 1, 'C')),
+            (np.array([0,1], dtype=np.longlong),
+             types.Array(types.longlong, 1, 'C')),
+        ])
+
+        self._low_occupancy_warnings = config.CUDA_LOW_OCCUPANCY_WARNINGS
+        self._warn_on_implicit_copy = config.CUDA_WARN_ON_IMPLICIT_COPY
+
+        # Disable warnings about low gpu utilization in the test suite
+        config.CUDA_LOW_OCCUPANCY_WARNINGS = 0
+        # Disable warnings about host arrays in the test suite
+        config.CUDA_WARN_ON_IMPLICIT_COPY = 0
+
+    def tearDown(self):
+        # Restore original warning settings
+        config.CUDA_LOW_OCCUPANCY_WARNINGS = self._low_occupancy_warnings
+        config.CUDA_WARN_ON_IMPLICIT_COPY = self._warn_on_implicit_copy
+
+    def _make_ufunc_usecase(self, ufunc):
+        return _make_ufunc_usecase(ufunc)
+
+    @functools.lru_cache(maxsize=None)
+    def _compile(self, pyfunc, args):
+        # We return an already-configured kernel so that basic_ufunc_test can
+        # call it just like it does for a CPU function
+        return cuda.jit(args)(pyfunc)[1, 1]
+
+    def basic_int_ufunc_test(self, name=None):
+        skip_inputs = [
+            types.float32,
+            types.float64,
+            types.Array(types.float32, 1, 'C'),
+            types.Array(types.float32, 2, 'C'),
+            types.Array(types.float64, 1, 'C'),
+            types.Array(types.float64, 2, 'C'),
+            types.Array(types.float64, 3, 'C'),
+            types.Array(types.float64, 2, 'F'),
+            types.complex64,
+            types.complex128,
+            types.Array(types.complex64, 1, 'C'),
+            types.Array(types.complex64, 2, 'C'),
+            types.Array(types.complex128, 1, 'C'),
+            types.Array(types.complex128, 2, 'C'),
+        ]
+        self.basic_ufunc_test(name, skip_inputs=skip_inputs)
+
+    ############################################################################
+    # Trigonometric Functions
+
+    def test_sin_ufunc(self):
+        self.basic_ufunc_test(np.sin, kinds='cf')
+
+    def test_cos_ufunc(self):
+        self.basic_ufunc_test(np.cos, kinds='cf')
+
+    def test_tan_ufunc(self):
+        self.basic_ufunc_test(np.tan, kinds='cf')
+
+    def test_arcsin_ufunc(self):
+        self.basic_ufunc_test(np.arcsin, kinds='cf')
+
+    def test_arccos_ufunc(self):
+        self.basic_ufunc_test(np.arccos, kinds='cf')
+
+    def test_arctan_ufunc(self):
+        self.basic_ufunc_test(np.arctan, kinds='cf')
+
+    def test_arctan2_ufunc(self):
+        self.basic_ufunc_test(np.arctan2, kinds='f')
+
+    def test_hypot_ufunc(self):
+        self.basic_ufunc_test(np.hypot, kinds='f')
+
+    def test_sinh_ufunc(self):
+        self.basic_ufunc_test(np.sinh, kinds='cf')
+
+    def test_cosh_ufunc(self):
+        self.basic_ufunc_test(np.cosh, kinds='cf')
+
+    def test_tanh_ufunc(self):
+        self.basic_ufunc_test(np.tanh, kinds='cf')
+
+    def test_arcsinh_ufunc(self):
+        self.basic_ufunc_test(np.arcsinh, kinds='cf')
+
+    def test_arccosh_ufunc(self):
+        self.basic_ufunc_test(np.arccosh, kinds='cf')
+
+    def test_arctanh_ufunc(self):
+        # arctanh is only valid is only finite in the range ]-1, 1[
+        # This means that for any of the integer types it will produce
+        # conversion from infinity/-infinity to integer. That's undefined
+        # behavior in C, so the results may vary from implementation to
+        # implementation. This means that the result from the compiler
+        # used to compile NumPy may differ from the result generated by
+        # llvm. Skipping the integer types in this test avoids failed
+        # tests because of this.
+        to_skip = [types.Array(types.uint32, 1, 'C'), types.uint32,
+                   types.Array(types.int32, 1, 'C'), types.int32,
+                   types.Array(types.uint64, 1, 'C'), types.uint64,
+                   types.Array(types.int64, 1, 'C'), types.int64]
+
+        self.basic_ufunc_test(np.arctanh, skip_inputs=to_skip, kinds='cf')
+
+    def test_deg2rad_ufunc(self):
+        self.basic_ufunc_test(np.deg2rad, kinds='f')
+
+    def test_rad2deg_ufunc(self):
+        self.basic_ufunc_test(np.rad2deg, kinds='f')
+
+    def test_degrees_ufunc(self):
+        self.basic_ufunc_test(np.degrees, kinds='f')
+
+    def test_radians_ufunc(self):
+        self.basic_ufunc_test(np.radians, kinds='f')
+
+    ############################################################################
+    # Comparison functions
+    def test_greater_ufunc(self):
+        self.signed_unsigned_cmp_test(np.greater)
+
+    def test_greater_equal_ufunc(self):
+        self.signed_unsigned_cmp_test(np.greater_equal)
+
+    def test_less_ufunc(self):
+        self.signed_unsigned_cmp_test(np.less)
+
+    def test_less_equal_ufunc(self):
+        self.signed_unsigned_cmp_test(np.less_equal)
+
+    def test_not_equal_ufunc(self):
+        self.signed_unsigned_cmp_test(np.not_equal)
+
+    def test_equal_ufunc(self):
+        self.signed_unsigned_cmp_test(np.equal)
+
+    def test_logical_and_ufunc(self):
+        self.basic_ufunc_test(np.logical_and)
+
+    def test_logical_or_ufunc(self):
+        self.basic_ufunc_test(np.logical_or)
+
+    def test_logical_xor_ufunc(self):
+        self.basic_ufunc_test(np.logical_xor)
+
+    def test_logical_not_ufunc(self):
+        self.basic_ufunc_test(np.logical_not)
+
+    def test_maximum_ufunc(self):
+        self.basic_ufunc_test(np.maximum)
+
+    def test_minimum_ufunc(self):
+        self.basic_ufunc_test(np.minimum)
+
+    def test_fmax_ufunc(self):
+        self.basic_ufunc_test(np.fmax)
+
+    def test_fmin_ufunc(self):
+        self.basic_ufunc_test(np.fmin)
+
+    def test_bitwise_and_ufunc(self):
+        self.basic_int_ufunc_test(np.bitwise_and)
+
+    def test_bitwise_or_ufunc(self):
+        self.basic_int_ufunc_test(np.bitwise_or)
+
+    def test_bitwise_xor_ufunc(self):
+        self.basic_int_ufunc_test(np.bitwise_xor)
+
+    def test_invert_ufunc(self):
+        self.basic_int_ufunc_test(np.invert)
+
+    def test_bitwise_not_ufunc(self):
+        self.basic_int_ufunc_test(np.bitwise_not)
+
+    # Note: there is no entry for np.left_shift and np.right_shift
+    # because their implementations in NumPy have undefined behavior
+    # when the second argument is a negative. See the comment in
+    # numba/tests/test_ufuncs.py for more details.
+
+    ############################################################################
+    # Mathematical Functions
+
+    def test_log_ufunc(self):
+        self.basic_ufunc_test(np.log, kinds='cf')
+
+    def test_log2_ufunc(self):
+        self.basic_ufunc_test(np.log2, kinds='cf')
+
+    def test_log10_ufunc(self):
+        self.basic_ufunc_test(np.log10, kinds='cf')
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_vectorize_device.py b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_vectorize_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..e33598d8b7bccc3ced0ef7f9eb27fe93a6a84dce
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudapy/test_vectorize_device.py
@@ -0,0 +1,36 @@
+from numba import vectorize
+from numba import cuda, float32
+import numpy as np
+from numba.cuda.testing import skip_on_cudasim, CUDATestCase
+import unittest
+
+
+@skip_on_cudasim('ufunc API unsupported in the simulator')
+class TestCudaVectorizeDeviceCall(CUDATestCase):
+    def test_cuda_vectorize_device_call(self):
+
+        @cuda.jit(float32(float32, float32, float32), device=True)
+        def cu_device_fn(x, y, z):
+            return x ** y / z
+
+        def cu_ufunc(x, y, z):
+            return cu_device_fn(x, y, z)
+
+        ufunc = vectorize([float32(float32, float32, float32)], target='cuda')(
+            cu_ufunc)
+
+        N = 100
+
+        X = np.array(np.random.sample(N), dtype=np.float32)
+        Y = np.array(np.random.sample(N), dtype=np.float32)
+        Z = np.array(np.random.sample(N), dtype=np.float32) + 0.1
+
+        out = ufunc(X, Y, Z)
+
+        gold = (X ** Y) / Z
+
+        self.assertTrue(np.allclose(out, gold))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudasim/__init__.py b/lib/python3.10/site-packages/numba/cuda/tests/cudasim/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0465337eb70062fc004a0973c45d0be07803812a
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudasim/__init__.py
@@ -0,0 +1,6 @@
+from numba.testing import load_testsuite
+import os
+
+
+def load_tests(loader, tests, pattern):
+    return load_testsuite(loader, os.path.dirname(__file__))
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudasim/support.py b/lib/python3.10/site-packages/numba/cuda/tests/cudasim/support.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fca39cadd70bb9201ebef9716f52ca8aa22e7fd
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudasim/support.py
@@ -0,0 +1,6 @@
+from numba import cuda
+
+
+@cuda.jit(device=True)
+def cuda_module_in_device_function():
+    return cuda.threadIdx.x
diff --git a/lib/python3.10/site-packages/numba/cuda/tests/cudasim/test_cudasim_issues.py b/lib/python3.10/site-packages/numba/cuda/tests/cudasim/test_cudasim_issues.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f544821ab8066351a04a82b1c5c84bba5389c59
--- /dev/null
+++ b/lib/python3.10/site-packages/numba/cuda/tests/cudasim/test_cudasim_issues.py
@@ -0,0 +1,102 @@
+import threading
+
+import numpy as np
+
+from numba import cuda
+from numba.cuda.testing import CUDATestCase, skip_unless_cudasim
+import numba.cuda.simulator as simulator
+import unittest
+
+
+class TestCudaSimIssues(CUDATestCase):
+    def test_record_access(self):
+        backyard_type = [('statue', np.float64),
+                         ('newspaper', np.float64, (6,))]
+
+        goose_type = [('garden', np.float64, (12,)),
+                      ('town', np.float64, (42,)),
+                      ('backyard', backyard_type)]
+
+        goose_np_type = np.dtype(goose_type, align=True)
+
+        @cuda.jit
+        def simple_kernel(f):
+            f.garden[0] = 45.0
+            f.backyard.newspaper[3] = 2.0
+            f.backyard.newspaper[3] = f.backyard.newspaper[3] + 3.0
+
+        item = np.recarray(1, dtype=goose_np_type)
+        simple_kernel[1, 1](item[0])
+        np.testing.assert_equal(item[0]['garden'][0], 45)
+        np.testing.assert_equal(item[0]['backyard']['newspaper'][3], 5)
+
+    def test_recarray_setting(self):
+        recordwith2darray = np.dtype([('i', np.int32),
+                                      ('j', np.float32, (3, 2))])
+        rec = np.recarray(2, dtype=recordwith2darray)
+        rec[0]['i'] = 45
+
+        @cuda.jit
+        def simple_kernel(f):
+            f[1] = f[0]
+        simple_kernel[1, 1](rec)
+        np.testing.assert_equal(rec[0]['i'], rec[1]['i'])
+
+    def test_cuda_module_in_device_function(self):
+        """
+        Discovered in https://github.com/numba/numba/issues/1837.
+        When the `cuda` module is referenced in a device function,
+        it does not have the kernel API (e.g. cuda.threadIdx, cuda.shared)
+        """
+        from numba.cuda.tests.cudasim import support
+
+        inner = support.cuda_module_in_device_function
+
+        @cuda.jit
+        def outer(out):
+            tid = inner()
+            if tid < out.size:
+                out[tid] = tid
+
+        arr = np.zeros(10, dtype=np.int32)
+        outer[1, 11](arr)
+        expected = np.arange(arr.size, dtype=np.int32)
+        np.testing.assert_equal(expected, arr)
+
+    @skip_unless_cudasim('Only works on CUDASIM')
+    def test_deadlock_on_exception(self):
+        def assert_no_blockthreads():
+            blockthreads = []
+            for t in threading.enumerate():
+                if not isinstance(t, simulator.kernel.BlockThread):
+                    continue
+
+                # join blockthreads with a short timeout to allow aborted
+                # threads to exit
+                t.join(1)
+                if t.is_alive():
+                    self.fail("Blocked kernel thread: %s" % t)
+
+            self.assertListEqual(blockthreads, [])
+
+        @simulator.jit
+        def assign_with_sync(x, y):
+            i = cuda.grid(1)
+            y[i] = x[i]
+
+            cuda.syncthreads()
+            cuda.syncthreads()
+
+        x = np.arange(3)
+        y = np.empty(3)
+        assign_with_sync[1, 3](x, y)
+        np.testing.assert_array_equal(x, y)
+        assert_no_blockthreads()
+
+        with self.assertRaises(IndexError):
+            assign_with_sync[1, 6](x, y)
+        assert_no_blockthreads()
+
+
+if __name__ == '__main__':
+    unittest.main()