test_rpython.py 34.4 KB
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from rpython.rtyper.lltypesystem import rffi, lltype
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from rpython.rlib import rmu_fast as rmu
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from util import fncptr_from_rpy_func, fncptr_from_py_script, may_spawn_proc
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import ctypes
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# -------------------
# helper functions
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def rand_list_of(n):
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    # 32 extend to 64-bit integers (to avoid overflow in summation
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    from random import randrange, getstate
    init_state = getstate()
    return [rffi.r_longlong(randrange(-(1 << 31), (1 << 31) - 1)) for _ in range(n)], init_state
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# --------------------------
# tests
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@may_spawn_proc
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def test_add():
    def add(a, b):
        return a + b

    fn, _ = fncptr_from_rpy_func(add, [rffi.LONGLONG, rffi.LONGLONG], rffi.LONGLONG)

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    assert fn(1, 2) == 3
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@may_spawn_proc
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def test_vec3prod():
    def prod(v1, v2):
        a = v1[0] * v2[0]
        b = v1[1] * v2[1]
        c = v1[2] * v2[2]
        return a + b + c

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    fnc, (db, bdlgen) = fncptr_from_rpy_func(prod, [rffi.CArrayPtr(rffi.LONGLONG), rffi.CArrayPtr(rffi.LONGLONG)], rffi.LONGLONG)
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    bdlgen.mu.current_thread_as_mu_thread(rmu.null(rmu.MuCPtr))
    with lltype.scoped_alloc(rffi.CArray(rffi.LONGLONG), 3) as vec1:
        vec1[0] = 1
        vec1[1] = 2
        vec1[2] = 3
        with lltype.scoped_alloc(rffi.CArray(rffi.LONGLONG), 3) as vec2:
            vec2[0] = 4
            vec2[1] = 5
            vec2[2] = 6
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            assert fnc(vec1, vec2) == 32
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@may_spawn_proc
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def test_find_min():
    def find_min(xs, sz):
        m = xs[0]
        for i in range(1, sz):
            x = xs[i]
            if x < m:
                m = x
        return m

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    fnc, (db, bdlgen) = fncptr_from_rpy_func(find_min, [rffi.CArrayPtr(rffi.LONGLONG), rffi.INTPTR_T], rffi.LONGLONG)
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    bdlgen.mu.current_thread_as_mu_thread(rmu.null(rmu.MuCPtr))
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    with lltype.scoped_alloc(rffi.CArray(rffi.LONGLONG), 5) as arr:
        lst = [23, 100, 0, 78, -5]
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        for i, k in enumerate(lst):
            arr[i] = k
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        fnc(arr, 5) == -5
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@may_spawn_proc
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def test_arraysum():
    from rpython.rlib.jit import JitDriver
    d = JitDriver(greens=[], reds='auto')
    def arraysum(arr, sz):
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        sum = rffi.r_longlong(0)
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        for i in range(sz):
            d.jit_merge_point()
            sum += arr[i]
        return sum

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    fnc, (db, bdlgen) = fncptr_from_rpy_func(arraysum, [rffi.CArrayPtr(rffi.LONGLONG), rffi.SIZE_T], rffi.LONGLONG)
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    bdlgen.mu.current_thread_as_mu_thread(rmu.null(rmu.MuCPtr))
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    n = 100
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    lst, _ = rand_list_of(n)
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    with lltype.scoped_alloc(rffi.CArray(rffi.LONGLONG), n) as arr:
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        for i, k in enumerate(lst):
            arr[i] = k
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        assert fnc(arr, rffi.cast(rffi.SIZE_T, n)) == arraysum(arr, rffi.cast(rffi.SIZE_T, n))
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@may_spawn_proc
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def test_quicksort():
    # algorithm taken from Wikipedia
    def swap(arr, i, j):
        t = arr[i]
        arr[i] = arr[j]
        arr[j] = t

    def partition(arr, idx_low, idx_high):
        pivot = arr[idx_high]
        i = idx_low
        for j in range(idx_low, idx_high):
            if arr[j] < pivot:
                swap(arr, i, j)
                i += 1
        swap(arr, i, idx_high)
        return i

    def quicksort(arr, start, end):
        if start < end:
            p = partition(arr, start, end)
            quicksort(arr, start, p - 1)
            quicksort(arr, p + 1, end)
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    fnc, (db, bdlgen) = fncptr_from_rpy_func(quicksort, [rffi.CArrayPtr(rffi.LONGLONG), lltype.Signed, lltype.Signed], lltype.Void)
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    bdlgen.mu.current_thread_as_mu_thread(rmu.null(rmu.MuCPtr))
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    # fnc = quicksort
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    n = 100
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    from random import setstate
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    init_state = (3, (
    2147483648L, 3430835514L, 2928424416L, 3147699060L, 2823572732L, 2905216632L, 1887281517L, 14272356L, 1356039141L,
    2741361235L, 1824725388L, 2228169284L, 2679861265L, 3150239284L, 657657570L, 1407124159L, 517316568L, 653526369L,
    139268705L, 3784719953L, 2212355490L, 3452491289L, 1232629882L, 1791207424L, 2898278956L, 1147783320L, 1824413680L,
    1993303973L, 2568444883L, 4228847642L, 4163974668L, 385627078L, 3663560714L, 320542554L, 1565882322L, 3416481154L,
    4219229298L, 315071254L, 778331393L, 3961037651L, 2951403614L, 3355970261L, 102946340L, 2509883952L, 215897963L,
    3361072826L, 689991350L, 3348092598L, 1763608447L, 2140226443L, 3813151178L, 2619956936L, 51244592L, 2130725065L,
    3867113849L, 1980820881L, 2600246771L, 3207535572L, 257556968L, 2223367443L, 3706150033L, 1711074250L, 4252385224L,
    3197142331L, 4139558716L, 748471849L, 2281163369L, 2596250092L, 2804492653L, 484240110L, 3726117536L, 2483815933L,
    2173995598L, 3765136999L, 3178931194L, 1237068319L, 3427263384L, 3958412830L, 2268556676L, 360704423L, 4113430429L,
    3758882140L, 3743971788L, 1685454939L, 488386L, 3511218911L, 3020688912L, 2168345327L, 3149651862L, 1472484695L,
    2011779229L, 1112533726L, 1873931730L, 2196153055L, 3806225492L, 1515074892L, 251489714L, 1958141723L, 2081062631L,
    3703490262L, 3211541213L, 1436109217L, 2664448365L, 2350764370L, 1285829042L, 3496997759L, 2306637687L, 1571644344L,
    1020052455L, 3114491401L, 2994766034L, 1518527036L, 994512437L, 1732585804L, 2089330296L, 2592371643L, 2377347339L,
    2617648350L, 1478066246L, 389918052L, 1126787130L, 2728695369L, 2921719205L, 3193658789L, 2101782606L, 4284039483L,
    2704867468L, 3843423543L, 119359906L, 1882384901L, 832276556L, 1862974878L, 1943541262L, 1823624942L, 2146680272L,
    333006125L, 929197835L, 639017219L, 1640196300L, 1424826762L, 2119569013L, 4259272802L, 2089277168L, 2030198981L,
    2950559216L, 621654826L, 3452546704L, 4085446289L, 3038316311L, 527272378L, 1679817853L, 450787204L, 3525043861L,
    3838351358L, 1558592021L, 3649888848L, 3328370698L, 3247166155L, 3855970537L, 1183088418L, 2778702834L, 2820277014L,
    1530905121L, 1434023607L, 3942716950L, 41643359L, 310637634L, 1537174663L, 4265200088L, 3126624846L, 2837665903L,
    446994733L, 85970060L, 643115053L, 1751804182L, 1480207958L, 2977093071L, 544778713L, 738954842L, 3370733859L,
    3242319053L, 2707786138L, 4041098196L, 1671493839L, 3420415077L, 2473516599L, 3949211965L, 3686186772L, 753757988L,
    220738063L, 772481263L, 974568026L, 3190407677L, 480257177L, 3620733162L, 2616878358L, 665763320L, 2808607644L,
    3851308236L, 3633157256L, 4240746864L, 1261222691L, 268963935L, 1449514350L, 4229662564L, 1342533852L, 1913674460L,
    1761163533L, 1974260074L, 739184472L, 3811507072L, 2880992381L, 3998389163L, 2673626426L, 2212222504L, 231447607L,
    2608719702L, 3509764733L, 2403318909L, 635983093L, 4233939991L, 2894463467L, 177171270L, 2962364044L, 1191007101L,
    882222586L, 1004217833L, 717897978L, 2125381922L, 626199402L, 3694698943L, 1373935523L, 762314613L, 2291077454L,
    2111081024L, 3758576304L, 2812129656L, 4067461097L, 3700761868L, 2281420733L, 197217625L, 460620692L, 506837624L,
    1532931238L, 3872395078L, 3629107738L, 2273221134L, 2086345980L, 1240615886L, 958420495L, 4059583254L, 3119201875L,
    3742950862L, 891360845L, 2974235885L, 87814219L, 4067521161L, 615939803L, 1881195074L, 2225917026L, 2775128741L,
    2996201447L, 1590546624L, 3960431955L, 1417477945L, 913935155L, 1610033170L, 3212701447L, 2545374014L, 2887105562L,
    2991635417L, 3194532260L, 1565555757L, 2142474733L, 621483430L, 2268177481L, 919992760L, 2022043644L, 2756890220L,
    881105937L, 2621060794L, 4262292201L, 480112895L, 2557060162L, 2367031748L, 2172434102L, 296539623L, 3043643256L,
    59166373L, 2947638193L, 1312917612L, 1798724013L, 75864164L, 339661149L, 289536004L, 422147716L, 1134944052L,
    1095534216L, 1231984277L, 239787072L, 923053211L, 1015393503L, 2558889580L, 4194512643L, 448088150L, 707905706L,
    2649061310L, 3081089715L, 3432955562L, 2217740069L, 1965789353L, 3320360228L, 3625802364L, 2420747908L, 3116949010L,
    442654625L, 2157578112L, 3603825090L, 3111995525L, 1124579902L, 101836896L, 3297125816L, 136981134L, 4253748197L,
    3809600572L, 1668193778L, 4146759785L, 3712590372L, 2998653463L, 3032597504L, 1046471011L, 2843821193L, 802959497L,
    3307715534L, 3226042258L, 1014478160L, 3105844949L, 3209150965L, 610876993L, 2563947590L, 2482526324L, 3913970138L,
    2812702315L, 4281779167L, 1026357391L, 2579486306L, 402208L, 3457975059L, 1714004950L, 2543595755L, 2421499458L,
    478932497L, 3117588180L, 1565800974L, 1757724858L, 1483685124L, 2262270397L, 3794544469L, 3986696110L, 2914756339L,
    1952061826L, 2672480198L, 3793151752L, 309930721L, 1861137379L, 94571340L, 1162935802L, 3681554226L, 4027302061L,
    21079572L, 446709644L, 1587253187L, 1845056582L, 3080553052L, 3575272255L, 2526224735L, 3569822959L, 2685900491L,
    918305237L, 1399881227L, 1554912161L, 703181091L, 738501299L, 269937670L, 1078548118L, 2313670525L, 3495159622L,
    2659487842L, 11394628L, 1222454456L, 3392065094L, 3426833642L, 1153231613L, 1234517654L, 3144547626L, 2148039080L,
    3790136587L, 684648337L, 3956093475L, 1384378197L, 2042781475L, 759764431L, 222267088L, 3187778457L, 3795259108L,
    2817237549L, 3494781277L, 3762880618L, 892345749L, 2153484401L, 721588894L, 779278769L, 3306398772L, 4221452913L,
    1981375723L, 379087895L, 1604791625L, 1426046977L, 4231163093L, 1344994557L, 1341041093L, 1072537134L, 1829925137L,
    3791772627L, 3176876700L, 2553745117L, 664821113L, 473469583L, 1076256869L, 2406012795L, 3141453822L, 4123012649L,
    3058620143L, 1785080140L, 1181483189L, 3587874749L, 1453504375L, 707249496L, 2022787257L, 2436320047L, 602521701L,
    483826957L, 821599664L, 3333871672L, 3024431570L, 3814441382L, 416508285L, 1217138244L, 3975201118L, 3077724941L,
    180118569L, 3754556886L, 4121534265L, 3495283397L, 700504668L, 3113972067L, 719371171L, 910731026L, 619936911L,
    2937105529L, 2039892965L, 3853404454L, 3783801801L, 783321997L, 1135195902L, 326690505L, 1774036419L, 3476057413L,
    1518029608L, 1248626026L, 427510490L, 3443223611L, 4087014505L, 2858955517L, 1918675812L, 3921514056L, 3929126528L,
    4048889933L, 1583842117L, 3742539544L, 602292017L, 3393759050L, 3929818519L, 3119818281L, 3472644693L, 1993924627L,
    4163228821L, 2943877721L, 3143487730L, 4087113198L, 1149082355L, 1713272081L, 1243627655L, 3511633996L, 3358757220L,
    3812981394L, 650044449L, 2143650644L, 3869591312L, 3719322297L, 386030648L, 2633538573L, 672966554L, 3498396042L,
    3907556L, 2308686209L, 2878779858L, 1475925955L, 2701537395L, 1448018484L, 2962578755L, 1383479284L, 3731453464L,
    3659512663L, 1521189121L, 843749206L, 2243090279L, 572717972L, 3400421356L, 3440777300L, 1393518699L, 1681924551L,
    466257295L, 568413244L, 3288530316L, 2951425105L, 2624424893L, 2410788864L, 2243174464L, 1385949609L, 2454100663L,
    1113953725L, 2127471443L, 1775715557L, 3874125135L, 1901707926L, 3152599339L, 2277843623L, 1941785089L, 3171888228L,
    802596998L, 3397391306L, 1743834429L, 395463904L, 2099329462L, 3761809163L, 262702111L, 1868879810L, 2887406426L,
    1160032302L, 4164116477L, 2287740849L, 3312176050L, 747117003L, 4048006270L, 3955419375L, 2724452926L, 3141695820L,
    791246424L, 524525849L, 1794277132L, 295485241L, 4125127474L, 825108028L, 1582794137L, 1259992755L, 2938829230L,
    912029932L, 1534496985L, 3075283272L, 4052041116L, 1125808104L, 2032938837L, 4008676545L, 1638361535L, 1649316497L,
    1302633381L, 4221627277L, 1206130263L, 3114681993L, 3409690900L, 3373263243L, 2922903613L, 349048087L, 4049532385L,
    3458779287L, 1737687814L, 287275672L, 645786941L, 1492233180L, 3925845678L, 3344829077L, 1669219217L, 665224162L,
    2679234088L, 1986576411L, 50610077L, 1080114376L, 1881648396L, 3818465156L, 1486861008L, 3824208930L, 1782008170L,
    4115911912L, 656413265L, 771498619L, 2709443211L, 1919820065L, 451888753L, 1449812173L, 2001941180L, 2997921765L,
    753032713L, 3011517640L, 2386888602L, 3181040472L, 1280522185L, 1036471598L, 1243809973L, 2985144032L, 2238294821L,
    557934351L, 347132246L, 1797956016L, 624L), None)
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    setstate(init_state)
    lst, init_state = rand_list_of(n)

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    print "original list:"
    print lst

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    with lltype.scoped_alloc(rffi.CArray(rffi.LONGLONG), n) as arr:
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        for i, k in enumerate(lst):
            arr[i] = k
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        fnc(arr, 0, n - 1)  # inplace sort
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        lst_s = sorted(lst)
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	print "expected list:"
	print lst_s

	print "result:"
	for i in range(n):
	    print arr[i],
        else:
	    print

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        for i in range(n):
            assert lst_s[i] == arr[i], "%d != %d" % (lst_s[i], arr[i])
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@may_spawn_proc
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def test_partition_in_quicksort():
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    # algorithm taken from Wikipedia
    def swap(arr, i, j):
        t = arr[i]
        arr[i] = arr[j]
        arr[j] = t

    def partition(arr, idx_low, idx_high):
        pivot = arr[idx_high]
        i = idx_low
        for j in range(idx_low, idx_high):
            if arr[j] < pivot:
                swap(arr, i, j)
                i += 1
        swap(arr, i, idx_high)
        return i

    fnc, (db, bdlgen) = fncptr_from_rpy_func(partition, [rffi.CArrayPtr(rffi.LONGLONG), lltype.Signed, lltype.Signed],
                                             lltype.Signed)
    bdlgen.mu.current_thread_as_mu_thread(rmu.null(rmu.MuCPtr))
    # fnc = partition

    n = 100
    from random import setstate
    init_state = (3, (
    2147483648L, 3430835514L, 2928424416L, 3147699060L, 2823572732L, 2905216632L, 1887281517L, 14272356L, 1356039141L,
    2741361235L, 1824725388L, 2228169284L, 2679861265L, 3150239284L, 657657570L, 1407124159L, 517316568L, 653526369L,
    139268705L, 3784719953L, 2212355490L, 3452491289L, 1232629882L, 1791207424L, 2898278956L, 1147783320L, 1824413680L,
    1993303973L, 2568444883L, 4228847642L, 4163974668L, 385627078L, 3663560714L, 320542554L, 1565882322L, 3416481154L,
    4219229298L, 315071254L, 778331393L, 3961037651L, 2951403614L, 3355970261L, 102946340L, 2509883952L, 215897963L,
    3361072826L, 689991350L, 3348092598L, 1763608447L, 2140226443L, 3813151178L, 2619956936L, 51244592L, 2130725065L,
    3867113849L, 1980820881L, 2600246771L, 3207535572L, 257556968L, 2223367443L, 3706150033L, 1711074250L, 4252385224L,
    3197142331L, 4139558716L, 748471849L, 2281163369L, 2596250092L, 2804492653L, 484240110L, 3726117536L, 2483815933L,
    2173995598L, 3765136999L, 3178931194L, 1237068319L, 3427263384L, 3958412830L, 2268556676L, 360704423L, 4113430429L,
    3758882140L, 3743971788L, 1685454939L, 488386L, 3511218911L, 3020688912L, 2168345327L, 3149651862L, 1472484695L,
    2011779229L, 1112533726L, 1873931730L, 2196153055L, 3806225492L, 1515074892L, 251489714L, 1958141723L, 2081062631L,
    3703490262L, 3211541213L, 1436109217L, 2664448365L, 2350764370L, 1285829042L, 3496997759L, 2306637687L, 1571644344L,
    1020052455L, 3114491401L, 2994766034L, 1518527036L, 994512437L, 1732585804L, 2089330296L, 2592371643L, 2377347339L,
    2617648350L, 1478066246L, 389918052L, 1126787130L, 2728695369L, 2921719205L, 3193658789L, 2101782606L, 4284039483L,
    2704867468L, 3843423543L, 119359906L, 1882384901L, 832276556L, 1862974878L, 1943541262L, 1823624942L, 2146680272L,
    333006125L, 929197835L, 639017219L, 1640196300L, 1424826762L, 2119569013L, 4259272802L, 2089277168L, 2030198981L,
    2950559216L, 621654826L, 3452546704L, 4085446289L, 3038316311L, 527272378L, 1679817853L, 450787204L, 3525043861L,
    3838351358L, 1558592021L, 3649888848L, 3328370698L, 3247166155L, 3855970537L, 1183088418L, 2778702834L, 2820277014L,
    1530905121L, 1434023607L, 3942716950L, 41643359L, 310637634L, 1537174663L, 4265200088L, 3126624846L, 2837665903L,
    446994733L, 85970060L, 643115053L, 1751804182L, 1480207958L, 2977093071L, 544778713L, 738954842L, 3370733859L,
    3242319053L, 2707786138L, 4041098196L, 1671493839L, 3420415077L, 2473516599L, 3949211965L, 3686186772L, 753757988L,
    220738063L, 772481263L, 974568026L, 3190407677L, 480257177L, 3620733162L, 2616878358L, 665763320L, 2808607644L,
    3851308236L, 3633157256L, 4240746864L, 1261222691L, 268963935L, 1449514350L, 4229662564L, 1342533852L, 1913674460L,
    1761163533L, 1974260074L, 739184472L, 3811507072L, 2880992381L, 3998389163L, 2673626426L, 2212222504L, 231447607L,
    2608719702L, 3509764733L, 2403318909L, 635983093L, 4233939991L, 2894463467L, 177171270L, 2962364044L, 1191007101L,
    882222586L, 1004217833L, 717897978L, 2125381922L, 626199402L, 3694698943L, 1373935523L, 762314613L, 2291077454L,
    2111081024L, 3758576304L, 2812129656L, 4067461097L, 3700761868L, 2281420733L, 197217625L, 460620692L, 506837624L,
    1532931238L, 3872395078L, 3629107738L, 2273221134L, 2086345980L, 1240615886L, 958420495L, 4059583254L, 3119201875L,
    3742950862L, 891360845L, 2974235885L, 87814219L, 4067521161L, 615939803L, 1881195074L, 2225917026L, 2775128741L,
    2996201447L, 1590546624L, 3960431955L, 1417477945L, 913935155L, 1610033170L, 3212701447L, 2545374014L, 2887105562L,
    2991635417L, 3194532260L, 1565555757L, 2142474733L, 621483430L, 2268177481L, 919992760L, 2022043644L, 2756890220L,
    881105937L, 2621060794L, 4262292201L, 480112895L, 2557060162L, 2367031748L, 2172434102L, 296539623L, 3043643256L,
    59166373L, 2947638193L, 1312917612L, 1798724013L, 75864164L, 339661149L, 289536004L, 422147716L, 1134944052L,
    1095534216L, 1231984277L, 239787072L, 923053211L, 1015393503L, 2558889580L, 4194512643L, 448088150L, 707905706L,
    2649061310L, 3081089715L, 3432955562L, 2217740069L, 1965789353L, 3320360228L, 3625802364L, 2420747908L, 3116949010L,
    442654625L, 2157578112L, 3603825090L, 3111995525L, 1124579902L, 101836896L, 3297125816L, 136981134L, 4253748197L,
    3809600572L, 1668193778L, 4146759785L, 3712590372L, 2998653463L, 3032597504L, 1046471011L, 2843821193L, 802959497L,
    3307715534L, 3226042258L, 1014478160L, 3105844949L, 3209150965L, 610876993L, 2563947590L, 2482526324L, 3913970138L,
    2812702315L, 4281779167L, 1026357391L, 2579486306L, 402208L, 3457975059L, 1714004950L, 2543595755L, 2421499458L,
    478932497L, 3117588180L, 1565800974L, 1757724858L, 1483685124L, 2262270397L, 3794544469L, 3986696110L, 2914756339L,
    1952061826L, 2672480198L, 3793151752L, 309930721L, 1861137379L, 94571340L, 1162935802L, 3681554226L, 4027302061L,
    21079572L, 446709644L, 1587253187L, 1845056582L, 3080553052L, 3575272255L, 2526224735L, 3569822959L, 2685900491L,
    918305237L, 1399881227L, 1554912161L, 703181091L, 738501299L, 269937670L, 1078548118L, 2313670525L, 3495159622L,
    2659487842L, 11394628L, 1222454456L, 3392065094L, 3426833642L, 1153231613L, 1234517654L, 3144547626L, 2148039080L,
    3790136587L, 684648337L, 3956093475L, 1384378197L, 2042781475L, 759764431L, 222267088L, 3187778457L, 3795259108L,
    2817237549L, 3494781277L, 3762880618L, 892345749L, 2153484401L, 721588894L, 779278769L, 3306398772L, 4221452913L,
    1981375723L, 379087895L, 1604791625L, 1426046977L, 4231163093L, 1344994557L, 1341041093L, 1072537134L, 1829925137L,
    3791772627L, 3176876700L, 2553745117L, 664821113L, 473469583L, 1076256869L, 2406012795L, 3141453822L, 4123012649L,
    3058620143L, 1785080140L, 1181483189L, 3587874749L, 1453504375L, 707249496L, 2022787257L, 2436320047L, 602521701L,
    483826957L, 821599664L, 3333871672L, 3024431570L, 3814441382L, 416508285L, 1217138244L, 3975201118L, 3077724941L,
    180118569L, 3754556886L, 4121534265L, 3495283397L, 700504668L, 3113972067L, 719371171L, 910731026L, 619936911L,
    2937105529L, 2039892965L, 3853404454L, 3783801801L, 783321997L, 1135195902L, 326690505L, 1774036419L, 3476057413L,
    1518029608L, 1248626026L, 427510490L, 3443223611L, 4087014505L, 2858955517L, 1918675812L, 3921514056L, 3929126528L,
    4048889933L, 1583842117L, 3742539544L, 602292017L, 3393759050L, 3929818519L, 3119818281L, 3472644693L, 1993924627L,
    4163228821L, 2943877721L, 3143487730L, 4087113198L, 1149082355L, 1713272081L, 1243627655L, 3511633996L, 3358757220L,
    3812981394L, 650044449L, 2143650644L, 3869591312L, 3719322297L, 386030648L, 2633538573L, 672966554L, 3498396042L,
    3907556L, 2308686209L, 2878779858L, 1475925955L, 2701537395L, 1448018484L, 2962578755L, 1383479284L, 3731453464L,
    3659512663L, 1521189121L, 843749206L, 2243090279L, 572717972L, 3400421356L, 3440777300L, 1393518699L, 1681924551L,
    466257295L, 568413244L, 3288530316L, 2951425105L, 2624424893L, 2410788864L, 2243174464L, 1385949609L, 2454100663L,
    1113953725L, 2127471443L, 1775715557L, 3874125135L, 1901707926L, 3152599339L, 2277843623L, 1941785089L, 3171888228L,
    802596998L, 3397391306L, 1743834429L, 395463904L, 2099329462L, 3761809163L, 262702111L, 1868879810L, 2887406426L,
    1160032302L, 4164116477L, 2287740849L, 3312176050L, 747117003L, 4048006270L, 3955419375L, 2724452926L, 3141695820L,
    791246424L, 524525849L, 1794277132L, 295485241L, 4125127474L, 825108028L, 1582794137L, 1259992755L, 2938829230L,
    912029932L, 1534496985L, 3075283272L, 4052041116L, 1125808104L, 2032938837L, 4008676545L, 1638361535L, 1649316497L,
    1302633381L, 4221627277L, 1206130263L, 3114681993L, 3409690900L, 3373263243L, 2922903613L, 349048087L, 4049532385L,
    3458779287L, 1737687814L, 287275672L, 645786941L, 1492233180L, 3925845678L, 3344829077L, 1669219217L, 665224162L,
    2679234088L, 1986576411L, 50610077L, 1080114376L, 1881648396L, 3818465156L, 1486861008L, 3824208930L, 1782008170L,
    4115911912L, 656413265L, 771498619L, 2709443211L, 1919820065L, 451888753L, 1449812173L, 2001941180L, 2997921765L,
    753032713L, 3011517640L, 2386888602L, 3181040472L, 1280522185L, 1036471598L, 1243809973L, 2985144032L, 2238294821L,
    557934351L, 347132246L, 1797956016L, 624L), None)
    setstate(init_state)
    lst, init_state = rand_list_of(n)

    with lltype.scoped_alloc(rffi.CArray(rffi.LONGLONG), n) as arr:
        for i, k in enumerate(lst):
            arr[i] = k

        idx = fnc(arr, 0, n - 1)

        first_partition = [
            -562164038,
            -2071388465,
            -663526532,
            77489857,
            -343649111,
            -1660130362,
            -1364581753,
            -2038184925,
            -1165174475,
            -1849978230,
            -1236284585,
            -347764193,
            -415184763,
            -864996653,
            -1431147879,
            -254259567,
            -948603419,
            -777817366,
            -762104870,
            118960100,
            -982992600,
            -291431596,
            -1300455919,
            98312853,
            -451757010,
            -127589060,
            -1770428162,
            -1836098229,
            -918293874,
            -337375506,
            -1787719536,
            -2086483893,
            -730620516,
            -365703180,
            -1528919012,
            -1666015908,
            75036665,
            -1068382947,
            -2097740676,
            -158140475,
            181349155,
            1134943658,
            926214681,
            1436898456,
            1896535137,
            654725403,
            964722898,
            829972680,
            777329866,
            726385788,
            1050914914,
            1280292061,
            727975360,
            1023937016,
            640384790,
            637969418,
            1884043455,
            1925731670,
            1057772537,
            1322685888,
            1351410892,
            945183403,
            2014860171,
            1918531212,
            955471993,
            1075682797,
            238111242,
            1508508491,
            828291293,
            1789417882,
            1102829861,
            1435471727,
            1980476539,
            1344494232,
            1771547746,
            784699465,
            478704353,
            1664007571,
            511675340,
            1174338681,
            835473661,
            1039011592,
            1901271880,
            1983373831,
            782060246,
            1847820592,
            1751300194,
            558677750,
            1338238899,
            1313544470,
            232877310,
            599055646,
            873066597,
            1433425901,
            1192634012,
            1322616334,
            2026877877,
            1070749459,
            1899988061,
            632945766,
        ]

        assert idx == 40
        for i in range(n):
            assert arr[i] == first_partition[i], "%d != %d" % (arr[i], first_partition[i])


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def test_linkedlist_reversal():
    def reverse_linkedlist(head):
        h = head
        nxt = head.nxt
        while nxt:
            n = nxt.nxt
            nxt.nxt = h
            h = nxt
            nxt = n
        head.nxt = nxt
        return h

    Node = lltype.ForwardReference()
    NodePtr = lltype.Ptr(Node)
    Node.become(lltype.Struct("Node", ('val', rffi.CHAR), ('nxt', NodePtr)))

John Zhang's avatar
John Zhang committed
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    fnc, (db, bdlgen) = fncptr_from_rpy_func(reverse_linkedlist, [NodePtr], NodePtr)
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    bdlgen.mu.current_thread_as_mu_thread(rmu.null(rmu.MuCPtr))
    # fnc = reverse_linkedlist
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    # linked list: a -> b -> c -> d
    with lltype.scoped_alloc(Node) as a:
        a.val = 'a'
        with lltype.scoped_alloc(Node) as b:
            a.nxt = b
            b.val = 'b'
            with lltype.scoped_alloc(Node) as c:
                b.nxt = c
                c.val = 'c'
                with lltype.scoped_alloc(Node) as d:
                    c.nxt = d
                    d.val = 'd'
                    d.nxt = lltype.nullptr(Node)

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                    h = fnc(a)
                    print '%s -> %s -> %s -> %s' % (h.val, h.nxt.val, h.nxt.nxt.val, h.nxt.nxt.nxt.val)
                    assert h.val == 'd'
                    assert h.nxt.val == 'c'
                    assert h.nxt.nxt.val == 'b'
                    assert h.nxt.nxt.nxt.val == 'a'
                    assert h.nxt.nxt.nxt.nxt == lltype.nullptr(Node)
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@may_spawn_proc
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def test_threadtran_fib():
    def build_test_bundle(bldr, rmu):
        """
        Builds the following test bundle.
            .typedef @i64 = int<64>
            .const @0_i64 <@i64> = 0
            .const @1_i64 <@i64> = 1
            .const @2_i64 <@i64> = 2
            .funcsig @sig_i64_i64 = (@i64) -> (@i64)
            .funcdef @fib VERSION @fib_v1 <@sig_i64_i64> {
                @fib_v1.blk0(<@i64> @fib_v1.blk0.k):
                    SWITCH <@i64> @fib_v1.blk0.k @fib_v1.blk2 (@fib_v1.blk0.k) {
                        @0_i64 @fib_v1.blk1 (@0_i64)
                        @1_i64 @fib_v1.blk1 (@1_i64)
                    }
                @fib_v1.blk1(<@i64> @fib_v1.blk1.rtn):
                    RET @fib_v1.blk1.rtn
                @fib_v1.blk2(<@i64> @fib_v1.blk1.k):
                    @fib_v1.blk2.k_1 = SUB <@i64> @fib_v1.blk2.k @1_i64
                    @fib_v1.blk2.res1 = CALL <@sig_i64_i64> @fib (@fib_v1.blk2.k_1)
                    @fib_v1.blk2.k_2 = SUB <@i64> @fib_v1.blk2.k @2_i64
                    @fib_v1.blk2.res2 = CALL <@sig_i64_i64> @fib (@fib_v1.blk2.k_2)
                    @fib_v1.blk2.res = ADD <@i64> @fib_v1.blk2.res1 @fib_v1.blk2.res2
                    RET @fib_v1.blk2.res2
            }
        :type bldr: rpython.rlib.rmu.MuIRBuilder
        :type rmu: rpython.rlib.rmu_fast
        :return: (rmu.MuVM(), rmu.MuCtx, rmu.MuIRBuilder, MuID, MuID)
        """
        i64 = bldr.gen_sym("@i64")
        bldr.new_type_int(i64, 64)

        c_0_i64 = bldr.gen_sym("@0_i64")
        bldr.new_const_int(c_0_i64, i64, 0)
        c_1_i64 = bldr.gen_sym("@1_i64")
        bldr.new_const_int(c_1_i64, i64, 1)
        c_2_i64 = bldr.gen_sym("@2_i64")
        bldr.new_const_int(c_2_i64, i64, 2)

        sig_i64_i64 = bldr.gen_sym("@sig_i64_i64")
        bldr.new_funcsig(sig_i64_i64, [i64], [i64])

        fib = bldr.gen_sym("@fib")
        bldr.new_func(fib, sig_i64_i64)

        # function body
        v1 = bldr.gen_sym("@fib_v1")
        blk0 = bldr.gen_sym("@fib_v1.blk0")
        blk1 = bldr.gen_sym("@fib_v1.blk1")
        blk2 = bldr.gen_sym("@fib_v1.blk2")

        # blk0
        blk0_k = bldr.gen_sym("@fib_v1.blk0.k")
        dest_defl = bldr.gen_sym()
        dest_0 = bldr.gen_sym()
        dest_1 = bldr.gen_sym()
        bldr.new_dest_clause(dest_defl, blk2, [blk0_k])
        bldr.new_dest_clause(dest_0, blk1, [c_0_i64])
        bldr.new_dest_clause(dest_1, blk1, [c_1_i64])
        op_switch = bldr.gen_sym()
        bldr.new_switch(op_switch, i64, blk0_k, dest_defl, [c_0_i64, c_1_i64], [dest_0, dest_1])
        bldr.new_bb(blk0, [blk0_k], [i64], rmu.MU_NO_ID, [op_switch])

        # blk1
        blk1_rtn = bldr.gen_sym("@fig_v1.blk1.rtn")
        blk1_op_ret = bldr.gen_sym()
        bldr.new_ret(blk1_op_ret, [blk1_rtn])
        bldr.new_bb(blk1, [blk1_rtn], [i64], rmu.MU_NO_ID, [blk1_op_ret])

        # blk2
        blk2_k = bldr.gen_sym("@fig_v1.blk2.k")
        blk2_k_1 = bldr.gen_sym("@fig_v1.blk2.k_1")
        blk2_k_2 = bldr.gen_sym("@fig_v1.blk2.k_2")
        blk2_res = bldr.gen_sym("@fig_v1.blk2.res")
        blk2_res1 = bldr.gen_sym("@fig_v1.blk2.res1")
        blk2_res2 = bldr.gen_sym("@fig_v1.blk2.res2")
        op_sub_1 = bldr.gen_sym()
        bldr.new_binop(op_sub_1, blk2_k_1, rmu.MuBinOptr.SUB, i64, blk2_k, c_1_i64)
        op_call_1 = bldr.gen_sym()
        bldr.new_call(op_call_1, [blk2_res1], sig_i64_i64, fib, [blk2_k_1])
        op_sub_2 = bldr.gen_sym()
        bldr.new_binop(op_sub_2, blk2_k_2, rmu.MuBinOptr.SUB, i64, blk2_k, c_2_i64)
        op_call_2 = bldr.gen_sym()
        bldr.new_call(op_call_2, [blk2_res2], sig_i64_i64, fib, [blk2_k_2])
        op_add = bldr.gen_sym()
        bldr.new_binop(op_add, blk2_res, rmu.MuBinOptr.ADD, i64, blk2_res1, blk2_res2)
        blk2_op_ret = bldr.gen_sym()
        bldr.new_ret(blk2_op_ret, [blk2_res])
        bldr.new_bb(blk2, [blk2_k], [i64], rmu.MU_NO_ID,
                    [op_sub_1, op_call_1, op_sub_2, op_call_2, op_add, blk2_op_ret])
        bldr.new_func_ver(v1, fib, [blk0, blk1, blk2])

        return {
            "@i64": i64,
            "test_fnc_sig": sig_i64_i64,
            "test_fnc": fib,
            "result_type": i64
        }

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    (fnp, _), (mu, ctx, bldr) = fncptr_from_py_script(build_test_bundle, None, 'fib', [ctypes.c_longlong])
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    mu.current_thread_as_mu_thread(rmu.null(rmu.MuCPtr))
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    assert fnp(20) == 6765
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@may_spawn_proc
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def test_new():
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    def build_test_bundle(bldr, rmu):
        """
        Builds the following test bundle.
            .typedef @i64 = int<64>
            .typedef @refi64 = ref<@i64>
            .const @1_i64 <@i64> = 1
            .const @NULL_refi64 <@refi64> = NULL
            .funcsig @sig__i64 = () -> (@i64)
            .funcdef @test_fnc VERSION @test_fnc.v1 <@sig__i64> {
                %blk0():
                    %r = NEW <@i64>
                    %ir = GETIREF <@refi64> %r
                    STORE <@i64> %ir @1_i64
                    %res = LOAD <@i64> %ir
                    RET %res
            }
        :type bldr: rpython.rlib.rmu.MuIRBuilder
        :type rmu: rpython.rlib.rmu_fast
        :return: (rmu.MuVM(), rmu.MuCtx, rmu.MuIRBuilder, MuID, MuID)
        """
        i1 = bldr.gen_sym("@i1")
        bldr.new_type_int(i1, 1)
        i64 = bldr.gen_sym("@i64")
        bldr.new_type_int(i64, 64)
        refi64 = bldr.gen_sym("@refi64")
        bldr.new_type_ref(refi64, i64)

        c_1_i64 = bldr.gen_sym("@1_64")
        bldr.new_const_int(c_1_i64, i64, 1)

        sig__i64 = bldr.gen_sym("@sig__i64")
        bldr.new_funcsig(sig__i64, [], [i64])

        test_fnc = bldr.gen_sym("@test_fnc")
        bldr.new_func(test_fnc, sig__i64)

        test_fnc_v1 = bldr.gen_sym("@test_fnc.v1")
        blk0 = bldr.gen_sym("@test_fnc.v1.blk0")
        r = bldr.gen_sym("@test_fnc.v1.blk0.r")
        ir = bldr.gen_sym("@test_fnc.v1.blk0.ir")
        res = bldr.gen_sym("@test_fnc.v1.blk0.res")
        op_new = bldr.gen_sym()
        bldr.new_new(op_new, r, i64)
        op_getiref = bldr.gen_sym()
        bldr.new_getiref(op_getiref, ir, refi64, r)
        op_store = bldr.gen_sym()
        bldr.new_store(op_store, False, rmu.MuMemOrd.NOT_ATOMIC, i64, ir, c_1_i64)
        op_load = bldr.gen_sym()
        bldr.new_load(op_load, res, False, rmu.MuMemOrd.NOT_ATOMIC, i64, ir)
        op_ret = bldr.gen_sym()
        bldr.new_ret(op_ret, [res])
        bldr.new_bb(blk0, [], [], rmu.MU_NO_ID, [op_new, op_getiref, op_store, op_load, op_ret])

        bldr.new_func_ver(test_fnc_v1, test_fnc, [blk0])

        return {
            "test_fnc": test_fnc,
            "test_fnc_sig": sig__i64,
            "result_type": i64,
            "@i64": i64
        }

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    (fnp, _), (mu, ctx, bldr) = fncptr_from_py_script(build_test_bundle, None, 'test_fnc')
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    mu.current_thread_as_mu_thread(rmu.null(rmu.MuCPtr))
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    assert fnp() == 1
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@may_spawn_proc
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def test_new_cmpeq():
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    def build_test_bundle(bldr, rmu):
        """
        Builds the following test bundle.
            .typedef @i64 = int<64>
            .typedef @refi64 = ref<@i64>
            .const @NULL_refi64 <@refi64> = NULL
            .funcsig @sig__i64 = () -> (@i64)
            .funcdef @test_fnc VERSION @test_fnc.v1 <@sig__i64> {
                @test_fnc.v1.blk0():
                    @test_fnc.v1.blk0.r = NEW <@i64>
                    @test_fnc.v1.blk0.cmpres = EQ <@refi64> @test_fnc.v1.blk0.r @NULL_refi64
                    @@test_fnc.v1.blk0.res = ZEXT <@i1 @i64> @test_fnc.v1.blk0.cmpres
                    RET @test_fnc.v1.blk0.res
            }
        :type bldr: rpython.rlib.rmu.MuIRBuilder
        :type rmu: rpython.rlib.rmu_fast
        :return: (rmu.MuVM(), rmu.MuCtx, rmu.MuIRBuilder, MuID, MuID)
        """
        i1 = bldr.gen_sym("@i1")
        bldr.new_type_int(i1, 1)
        i64 = bldr.gen_sym("@i64")
        bldr.new_type_int(i64, 64)
        refi64 = bldr.gen_sym("@refi64")
        bldr.new_type_ref(refi64, i64)

        NULL_refi64 = bldr.gen_sym("@NULL_refi64")
        bldr.new_const_null(NULL_refi64, refi64)

        sig__i64 = bldr.gen_sym("@sig__i64")
        bldr.new_funcsig(sig__i64, [], [i64])

        test_fnc = bldr.gen_sym("@test_fnc")
        bldr.new_func(test_fnc, sig__i64)

        test_fnc_v1 = bldr.gen_sym("@test_fnc.v1")
        blk0 = bldr.gen_sym("@test_fnc.v1.blk0")
        r = bldr.gen_sym("@test_fnc.v1.blk0.r")
        cmpres = bldr.gen_sym("@test_fnc.v1.blk0.cmpres")
        res = bldr.gen_sym("@test_fnc.v1.blk0.res")
        op_new = bldr.gen_sym()
        bldr.new_new(op_new, r, i64)
        op_eq = bldr.gen_sym()
        bldr.new_cmp(op_eq, cmpres, rmu.MuCmpOptr.EQ, refi64, r, NULL_refi64)
        op_zext = bldr.gen_sym()
        bldr.new_conv(op_zext, res, rmu.MuConvOptr.ZEXT, i1, i64, cmpres)
        op_ret = bldr.gen_sym()
        bldr.new_ret(op_ret, [res])
        bldr.new_bb(blk0, [], [], rmu.MU_NO_ID, [op_new, op_eq, op_zext, op_ret])

        bldr.new_func_ver(test_fnc_v1, test_fnc, [blk0])

        return {
            "test_fnc": test_fnc,
            "test_fnc_sig": sig__i64,
            "result_type": i64,
            "@i64": i64
        }

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    (fnp, _), (mu, ctx, bldr) = fncptr_from_py_script(build_test_bundle, None, 'test_fnc')
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    mu.current_thread_as_mu_thread(rmu.null(rmu.MuCPtr))
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    assert fnp() == 0

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if __name__ == '__main__':
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('testfnc', help="Test function name")
    opts = parser.parse_args()

    globals()[opts.testfnc]()