Mon May 13 11:41:55 UTC 2024  I: starting to build python-array-api-compat/unstable/arm64 on jenkins on '2024-05-13 11:41'
Mon May 13 11:41:55 UTC 2024  I: The jenkins build log is/was available at https://jenkins.debian.net/userContent/reproducible/debian/build_service/arm64_13/17801/console.log
Mon May 13 11:41:55 UTC 2024  I: Downloading source for unstable/python-array-api-compat=1.4-1
--2024-05-13 11:41:56--  http://deb.debian.org/debian/pool/main/p/python-array-api-compat/python-array-api-compat_1.4-1.dsc
Connecting to 46.16.76.132:3128... connected.
Proxy request sent, awaiting response... 200 OK
Length: 2217 (2.2K) [text/prs.lines.tag]
Saving to: ‘python-array-api-compat_1.4-1.dsc’

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2024-05-13 11:41:56 (305 MB/s) - ‘python-array-api-compat_1.4-1.dsc’ saved [2217/2217]

Mon May 13 11:41:56 UTC 2024  I: python-array-api-compat_1.4-1.dsc
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Hash: SHA512

Format: 3.0 (quilt)
Source: python-array-api-compat
Binary: python3-array-api-compat
Architecture: all
Version: 1.4-1
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders: Andreas Tille <tille@debian.org>
Homepage: https://data-apis.org/array-api-compat/
Standards-Version: 4.6.2
Vcs-Browser: https://salsa.debian.org/python-team/packages/python-array-api-compat
Vcs-Git: https://salsa.debian.org/python-team/packages/python-array-api-compat.git
Build-Depends: debhelper-compat (= 13), dh-python, python3-all, python3-pytest <!nocheck>, python3-numpy <!nocheck>, python3-torch <!nocheck>
Package-List:
 python3-array-api-compat deb python optional arch=all
Checksums-Sha1:
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 2e9a27a7a82108cd139507c80d63d779725fa97e45cdfef3ff1441366d7c0d1e 2388 python-array-api-compat_1.4-1.debian.tar.xz
Files:
 8161f70e9813bb703a6815a411f1ded7 33757 python-array-api-compat_1.4.orig.tar.gz
 a2e1adc310cf6ef85c683c98a28853a8 2388 python-array-api-compat_1.4-1.debian.tar.xz

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Mon May 13 11:41:56 UTC 2024  I: Checking whether the package is not for us
Mon May 13 11:41:56 UTC 2024  I: Starting 1st build on remote node codethink01-arm64.debian.net.
Mon May 13 11:41:56 UTC 2024  I: Preparing to do remote build '1' on codethink01-arm64.debian.net.
Mon May 13 11:45:49 UTC 2024  I: Deleting $TMPDIR on codethink01-arm64.debian.net.
I: pbuilder: network access will be disabled during build
I: Current time: Sun Jun 15 06:04:58 -12 2025
I: pbuilder-time-stamp: 1750010698
I: Building the build Environment
I: extracting base tarball [/var/cache/pbuilder/unstable-reproducible-base.tgz]
I: copying local configuration
W: --override-config is not set; not updating apt.conf Read the manpage for details.
I: mounting /proc filesystem
I: mounting /sys filesystem
I: creating /{dev,run}/shm
I: mounting /dev/pts filesystem
I: redirecting /dev/ptmx to /dev/pts/ptmx
I: policy-rc.d already exists
I: Copying source file
I: copying [python-array-api-compat_1.4-1.dsc]
I: copying [./python-array-api-compat_1.4.orig.tar.gz]
I: copying [./python-array-api-compat_1.4-1.debian.tar.xz]
I: Extracting source
gpgv: Signature made Wed Nov 15 17:10:03 2023 
gpgv:                using RSA key F1F007320A035541F0A663CA578A0494D1C646D1
gpgv:                issuer "tille@debian.org"
gpgv: Can't check signature: No public key
dpkg-source: warning: cannot verify inline signature for ./python-array-api-compat_1.4-1.dsc: no acceptable signature found
dpkg-source: info: extracting python-array-api-compat in python-array-api-compat-1.4
dpkg-source: info: unpacking python-array-api-compat_1.4.orig.tar.gz
dpkg-source: info: unpacking python-array-api-compat_1.4-1.debian.tar.xz
I: Not using root during the build.
I: Installing the build-deps
I: user script /srv/workspace/pbuilder/1278330/tmp/hooks/D02_print_environment starting
I: set
  BUILDDIR='/build/reproducible-path'
  BUILDUSERGECOS='first user,first room,first work-phone,first home-phone,first other'
  BUILDUSERNAME='pbuilder1'
  BUILD_ARCH='arm64'
  DEBIAN_FRONTEND='noninteractive'
  DEB_BUILD_OPTIONS='buildinfo=+all reproducible=+all parallel=12 '
  DISTRIBUTION='unstable'
  HOME='/root'
  HOST_ARCH='arm64'
  IFS=' 	
  '
  INVOCATION_ID='c7baef8b2a344ae3b6bdfa4017c659f4'
  LANG='C'
  LANGUAGE='en_US:en'
  LC_ALL='C'
  MAIL='/var/mail/root'
  OPTIND='1'
  PATH='/usr/sbin:/usr/bin:/sbin:/bin:/usr/games'
  PBCURRENTCOMMANDLINEOPERATION='build'
  PBUILDER_OPERATION='build'
  PBUILDER_PKGDATADIR='/usr/share/pbuilder'
  PBUILDER_PKGLIBDIR='/usr/lib/pbuilder'
  PBUILDER_SYSCONFDIR='/etc'
  PPID='1278330'
  PS1='# '
  PS2='> '
  PS4='+ '
  PWD='/'
  SHELL='/bin/bash'
  SHLVL='2'
  SUDO_COMMAND='/usr/bin/timeout -k 18.1h 18h /usr/bin/ionice -c 3 /usr/bin/nice /usr/sbin/pbuilder --build --configfile /srv/reproducible-results/rbuild-debian/r-b-build.lFBCVyf3/pbuilderrc_VoS9 --distribution unstable --hookdir /etc/pbuilder/first-build-hooks --debbuildopts -b --basetgz /var/cache/pbuilder/unstable-reproducible-base.tgz --buildresult /srv/reproducible-results/rbuild-debian/r-b-build.lFBCVyf3/b1 --logfile b1/build.log python-array-api-compat_1.4-1.dsc'
  SUDO_GID='109'
  SUDO_UID='104'
  SUDO_USER='jenkins'
  TERM='unknown'
  TZ='/usr/share/zoneinfo/Etc/GMT+12'
  USER='root'
  _='/usr/bin/systemd-run'
  http_proxy='http://192.168.101.4:3128'
I: uname -a
  Linux codethink01-arm64 6.1.0-21-cloud-arm64 #1 SMP Debian 6.1.90-1 (2024-05-03) aarch64 GNU/Linux
I: ls -l /bin
  lrwxrwxrwx 1 root root 7 Jun 15 14:05 /bin -> usr/bin
I: user script /srv/workspace/pbuilder/1278330/tmp/hooks/D02_print_environment finished
 -> Attempting to satisfy build-dependencies
 -> Creating pbuilder-satisfydepends-dummy package
Package: pbuilder-satisfydepends-dummy
Version: 0.invalid.0
Architecture: arm64
Maintainer: Debian Pbuilder Team <pbuilder-maint@lists.alioth.debian.org>
Description: Dummy package to satisfy dependencies with aptitude - created by pbuilder
 This package was created automatically by pbuilder to satisfy the
 build-dependencies of the package being currently built.
Depends: debhelper-compat (= 13), dh-python, python3-all, python3-pytest, python3-numpy, python3-torch
dpkg-deb: building package 'pbuilder-satisfydepends-dummy' in '/tmp/satisfydepends-aptitude/pbuilder-satisfydepends-dummy.deb'.
Selecting previously unselected package pbuilder-satisfydepends-dummy.
(Reading database ... 19744 files and directories currently installed.)
Preparing to unpack .../pbuilder-satisfydepends-dummy.deb ...
Unpacking pbuilder-satisfydepends-dummy (0.invalid.0) ...
dpkg: pbuilder-satisfydepends-dummy: dependency problems, but configuring anyway as you requested:
 pbuilder-satisfydepends-dummy depends on debhelper-compat (= 13); however:
  Package debhelper-compat is not installed.
 pbuilder-satisfydepends-dummy depends on dh-python; however:
  Package dh-python is not installed.
 pbuilder-satisfydepends-dummy depends on python3-all; however:
  Package python3-all is not installed.
 pbuilder-satisfydepends-dummy depends on python3-pytest; however:
  Package python3-pytest is not installed.
 pbuilder-satisfydepends-dummy depends on python3-numpy; however:
  Package python3-numpy is not installed.
 pbuilder-satisfydepends-dummy depends on python3-torch; however:
  Package python3-torch is not installed.

Setting up pbuilder-satisfydepends-dummy (0.invalid.0) ...
Reading package lists...
Building dependency tree...
Reading state information...
Initializing package states...
Writing extended state information...
Building tag database...
pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0)
pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0)
The following NEW packages will be installed:
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libdouble-conversion3{a} libdrm-amdgpu1{a} libdrm-common{a} libdrm-nouveau2{a} libdrm-radeon1{a} libdrm2{a} libdw1t64{a} libedit2{a} libegl-mesa0{a} libegl1{a} libelf1t64{a} libevdev2{a} libevent-core-2.1-7t64{a} libevent-pthreads-2.1-7t64{a} libexif12{a} libexpat1{a} libfabric1{a} libfile-stripnondeterminism-perl{a} libfmt9{a} libfontconfig1{a} libfreetype6{a} libfreexl1{a} libfribidi0{a} libfyba0t64{a} libgav1-1{a} libgbm1{a} libgd3{a} libgdal34t64{a} libgdcm3.0t64{a} libgdk-pixbuf-2.0-0{a} libgdk-pixbuf2.0-common{a} libgeos-c1t64{a} libgeos3.12.1t64{a} libgeotiff5{a} libgfortran5{a} libgif7{a} libgl1{a} libgl1-mesa-dri{a} libglapi-mesa{a} libglib2.0-0t64{a} libgloo0{a} libglvnd0{a} libglx-mesa0{a} libglx0{a} libgme0{a} libgphoto2-6t64{a} libgphoto2-port12t64{a} libgraphite2-3{a} libgsm1{a} libgssapi-krb5-2{a} libgstreamer-plugins-base1.0-0{a} libgstreamer1.0-0{a} libgudev-1.0-0{a} libharfbuzz0b{a} libhdf4-0-alt{a} libhdf5-103-1t64{a} libhdf5-hl-100t64{a} libheif-plugin-dav1d{a} libheif-plugin-libde265{a} libheif1{a} libhiredis1.1.0{a} libhsa-runtime64-1{a} libhsakmt1{a} libhwloc-plugins{a} libhwloc15{a} libhwy1t64{a} libibverbs1{a} libice6{a} libicu72{a} libimath-3-1-29t64{a} libinput-bin{a} libinput10{a} libjbig0{a} libjpeg62-turbo{a} libjson-c5{a} libjxl0.7{a} libk5crypto3{a} libkeyutils1{a} libkmlbase1t64{a} libkmldom1t64{a} libkmlengine1t64{a} libkrb5-3{a} libkrb5support0{a} liblapack3{a} liblcms2-2{a} libldap-2.5-0{a} liblept5{a} liblerc4{a} libleveldb1d{a} libllvm17t64{a} liblmdb0{a} libltdl7{a} libmagic-mgc{a} libmagic1t64{a} libmariadb3{a} libmbedcrypto7t64{a} libmd4c0{a} libminizip1t64{a} libmp3lame0{a} libmpg123-0t64{a} libmtdev1t64{a} libmunge2{a} libnetcdf19t64{a} libnghttp2-14{a} libnl-3-200{a} libnl-route-3-200{a} libnorm1t64{a} libnspr4{a} libnss3{a} libnuma1{a} libodbc2{a} libodbcinst2{a} libogdi4.1{a} libogg0{a} libonnx1t64{a} libopencv-calib3d406t64{a} libopencv-contrib406t64{a} libopencv-core406t64{a} libopencv-dnn406t64{a} libopencv-features2d406t64{a} libopencv-flann406t64{a} libopencv-highgui406t64{a} libopencv-imgcodecs406t64{a} libopencv-imgproc406t64{a} libopencv-ml406t64{a} libopencv-objdetect406t64{a} libopencv-video406t64{a} libopencv-videoio406t64{a} libopenexr-3-1-30{a} libopengl0{a} libopenjp2-7{a} libopenmpi3t64{a} libopenmpt0t64{a} libopus0{a} liborc-0.4-0t64{a} libpango-1.0-0{a} libpangocairo-1.0-0{a} libpangoft2-1.0-0{a} libpciaccess0{a} libpcre2-16-0{a} libpgm-5.3-0t64{a} libpipeline1{a} libpixman-1-0{a} libpmix2t64{a} libpng16-16t64{a} libpoppler126t64{a} libpq5{a} libproj25{a} libprotobuf32t64{a} libpsl5t64{a} libpthreadpool0{a} libpython3-stdlib{a} libpython3.11-minimal{a} libpython3.11-stdlib{a} libpython3.12-minimal{a} libpython3.12-stdlib{a} libqhull-r8.0{a} libqt5core5t64{a} libqt5dbus5t64{a} libqt5gui5t64{a} libqt5network5t64{a} libqt5opengl5t64{a} libqt5test5t64{a} libqt5widgets5t64{a} librabbitmq4{a} librav1e0{a} libraw1394-11{a} librdmacm1t64{a} libreadline8t64{a} librist4{a} librsvg2-2{a} librtmp1{a} librttopo1{a} libsasl2-2{a} libsasl2-modules-db{a} libsensors-config{a} libsensors5{a} libsharpyuv0{a} libshine3{a} libsleef3{a} libsm6{a} libsnappy1v5{a} libsocket++1{a} libsodium23{a} libsoxr0{a} libspatialite8t64{a} libspeex1{a} libsrt1.5-gnutls{a} libssh-gcrypt-4{a} libssh2-1t64{a} libsub-override-perl{a} libsuperlu6{a} libsvtav1enc1d1{a} libswresample4{a} libswscale7{a} libsz2{a} libtbb12{a} libtbbbind-2-5{a} libtbbmalloc2{a} libtensorpipe0{a} libtesseract5{a} libthai-data{a} libthai0{a} libtheora0{a} libtiff6{a} libtirpc-common{a} libtirpc3t64{a} libtool{a} libtorch-test{a} libtorch2.1{a} libtwolame0{a} libuchardet0{a} libucx0{a} libudfread0{a} libunwind8{a} liburiparser1{a} libusb-1.0-0{a} libuv1t64{a} libva-drm2{a} libva-x11-2{a} libva2{a} libvdpau1{a} libvorbis0a{a} libvorbisenc2{a} libvorbisfile3{a} libvpx9{a} libvulkan1{a} libwacom-common{a} libwacom9{a} libwayland-client0{a} libwayland-server0{a} libwebp7{a} libwebpmux3{a} libx11-6{a} libx11-data{a} libx11-xcb1{a} libx264-164{a} libx265-199{a} libxau6{a} libxcb-dri2-0{a} libxcb-dri3-0{a} libxcb-glx0{a} libxcb-icccm4{a} libxcb-image0{a} libxcb-keysyms1{a} libxcb-present0{a} libxcb-randr0{a} libxcb-render-util0{a} libxcb-render0{a} libxcb-shape0{a} libxcb-shm0{a} libxcb-sync1{a} libxcb-util1{a} libxcb-xfixes0{a} libxcb-xinerama0{a} libxcb-xinput0{a} libxcb-xkb1{a} libxcb1{a} libxdmcp6{a} libxerces-c3.2t64{a} libxext6{a} libxfixes3{a} libxkbcommon-x11-0{a} libxkbcommon0{a} libxml2{a} libxnnpack0{a} libxnvctrl0{a} libxpm4{a} libxrender1{a} libxshmfence1{a} libxvidcore4{a} libxxf86vm1{a} libyaml-0-2{a} libyuv0{a} libz3-4{a} libzmq5{a} libzvbi-common{a} libzvbi0t64{a} m4{a} man-db{a} mariadb-common{a} media-types{a} mysql-common{a} netbase{a} ocl-icd-libopencl1{a} openssl{a} po-debconf{a} proj-data{a} python3{a} python3-all{a} python3-astunparse{a} python3-attr{a} python3-certifi{a} python3-chardet{a} python3-charset-normalizer{a} python3-dateutil{a} python3-distutils{a} python3-expecttest{a} python3-filelock{a} python3-fsspec{a} python3-hypothesis{a} python3-idna{a} python3-iniconfig{a} python3-jinja2{a} python3-lib2to3{a} python3-markupsafe{a} python3-minimal{a} python3-mpmath{a} python3-networkx{a} python3-numpy{a} python3-packaging{a} python3-pkg-resources{a} python3-pluggy{a} python3-psutil{a} python3-pytest{a} python3-requests{a} python3-setuptools{a} python3-six{a} python3-sortedcontainers{a} python3-sympy{a} python3-torch{a} python3-typing-extensions{a} python3-urllib3{a} python3-yaml{a} python3.11{a} python3.11-minimal{a} python3.12{a} python3.12-minimal{a} readline-common{a} sensible-utils{a} shared-mime-info{a} tzdata{a} unixodbc-common{a} x11-common{a} xkb-data{a} 
The following packages are RECOMMENDED but will NOT be installed:
  curl dbus git gstreamer1.0-plugins-base isympy-common krb5-locales libaacs0 libarchive-cpio-perl libgdk-pixbuf2.0-bin libglib2.0-data libgphoto2-l10n libheif-plugin-aomenc libheif-plugin-x265 libldap-common libltdl-dev libmail-sendmail-perl libpam-cap libqt5svg5 librsvg2-common libsasl2-modules libtorch-dev libvdpau-va-gl1 lynx mesa-va-drivers mesa-vdpau-drivers mesa-vulkan-drivers ninja-build poppler-data proj-bin publicsuffix pybind11-dev python3-babel python3-click python3-dask python3-distributed python3-dropbox python3-fusepy python3-gdal python3-libarchive-c python3-matplotlib python3-paramiko python3-pil python3-pydot python3-pygit2 python3-pygments python3-pygraphviz python3-scipy qt5-gtk-platformtheme qttranslations5-l10n qtwayland5 va-driver-all vdpau-driver-all wget xdg-user-dirs 
0 packages upgraded, 398 newly installed, 0 to remove and 0 not upgraded.
Need to get 288 MB of archives. After unpacking 1322 MB will be used.
Writing extended state information...
Get: 1 http://deb.debian.org/debian unstable/main arm64 libpython3.11-minimal arm64 3.11.9-1 [813 kB]
Get: 2 http://deb.debian.org/debian unstable/main arm64 libexpat1 arm64 2.6.2-1 [88.7 kB]
Get: 3 http://deb.debian.org/debian unstable/main arm64 python3.11-minimal arm64 3.11.9-1 [1767 kB]
Get: 4 http://deb.debian.org/debian unstable/main arm64 python3-minimal arm64 3.11.8-1 [26.3 kB]
Get: 5 http://deb.debian.org/debian unstable/main arm64 media-types all 10.1.0 [26.9 kB]
Get: 6 http://deb.debian.org/debian unstable/main arm64 netbase all 6.4 [12.8 kB]
Get: 7 http://deb.debian.org/debian unstable/main arm64 tzdata all 2024a-4 [255 kB]
Get: 8 http://deb.debian.org/debian unstable/main arm64 readline-common all 8.2-4 [69.3 kB]
Get: 9 http://deb.debian.org/debian unstable/main arm64 libreadline8t64 arm64 8.2-4 [157 kB]
Get: 10 http://deb.debian.org/debian unstable/main arm64 libpython3.11-stdlib arm64 3.11.9-1 [1775 kB]
Get: 11 http://deb.debian.org/debian unstable/main arm64 python3.11 arm64 3.11.9-1 [602 kB]
Get: 12 http://deb.debian.org/debian unstable/main arm64 libpython3-stdlib arm64 3.11.8-1 [9332 B]
Get: 13 http://deb.debian.org/debian unstable/main arm64 python3 arm64 3.11.8-1 [27.4 kB]
Get: 14 http://deb.debian.org/debian unstable/main arm64 libpython3.12-minimal arm64 3.12.3-1 [802 kB]
Get: 15 http://deb.debian.org/debian unstable/main arm64 python3.12-minimal arm64 3.12.3-1 [1931 kB]
Get: 16 http://deb.debian.org/debian unstable/main arm64 sensible-utils all 0.0.22 [22.4 kB]
Get: 17 http://deb.debian.org/debian unstable/main arm64 openssl arm64 3.2.1-3 [1324 kB]
Get: 18 http://deb.debian.org/debian unstable/main arm64 ca-certificates all 20240203 [158 kB]
Get: 19 http://deb.debian.org/debian unstable/main arm64 libmagic-mgc arm64 1:5.45-3 [314 kB]
Get: 20 http://deb.debian.org/debian unstable/main arm64 libmagic1t64 arm64 1:5.45-3 [100 kB]
Get: 21 http://deb.debian.org/debian unstable/main arm64 file arm64 1:5.45-3 [43.0 kB]
Get: 22 http://deb.debian.org/debian unstable/main arm64 gettext-base arm64 0.21-14+b1 [160 kB]
Get: 23 http://deb.debian.org/debian unstable/main arm64 libuchardet0 arm64 0.0.8-1+b1 [69.0 kB]
Get: 24 http://deb.debian.org/debian unstable/main arm64 groff-base arm64 1.23.0-4 [1130 kB]
Get: 25 http://deb.debian.org/debian unstable/main arm64 bsdextrautils arm64 2.40-8 [93.0 kB]
Get: 26 http://deb.debian.org/debian unstable/main arm64 libpipeline1 arm64 1.5.7-2 [36.5 kB]
Get: 27 http://deb.debian.org/debian unstable/main arm64 man-db arm64 2.12.1-1 [1394 kB]
Get: 28 http://deb.debian.org/debian unstable/main arm64 m4 arm64 1.4.19-4 [277 kB]
Get: 29 http://deb.debian.org/debian unstable/main arm64 autoconf all 2.71-3 [332 kB]
Get: 30 http://deb.debian.org/debian unstable/main arm64 autotools-dev all 20220109.1 [51.6 kB]
Get: 31 http://deb.debian.org/debian unstable/main arm64 automake all 1:1.16.5-1.3 [823 kB]
Get: 32 http://deb.debian.org/debian unstable/main arm64 autopoint all 0.21-14 [496 kB]
Get: 33 http://deb.debian.org/debian unstable/main arm64 libdebhelper-perl all 13.15.3 [88.0 kB]
Get: 34 http://deb.debian.org/debian unstable/main arm64 libtool all 2.4.7-7 [517 kB]
Get: 35 http://deb.debian.org/debian unstable/main arm64 dh-autoreconf all 20 [17.1 kB]
Get: 36 http://deb.debian.org/debian unstable/main arm64 libarchive-zip-perl all 1.68-1 [104 kB]
Get: 37 http://deb.debian.org/debian unstable/main arm64 libsub-override-perl all 0.10-1 [10.6 kB]
Get: 38 http://deb.debian.org/debian unstable/main arm64 libfile-stripnondeterminism-perl all 1.13.1-1 [19.4 kB]
Get: 39 http://deb.debian.org/debian unstable/main arm64 dh-strip-nondeterminism all 1.13.1-1 [8620 B]
Get: 40 http://deb.debian.org/debian unstable/main arm64 libelf1t64 arm64 0.191-1+b1 [187 kB]
Get: 41 http://deb.debian.org/debian unstable/main arm64 dwz arm64 0.15-1+b1 [102 kB]
Get: 42 http://deb.debian.org/debian unstable/main arm64 libicu72 arm64 72.1-4+b1 [9224 kB]
Get: 43 http://deb.debian.org/debian unstable/main arm64 libxml2 arm64 2.9.14+dfsg-1.3+b3 [624 kB]
Get: 44 http://deb.debian.org/debian unstable/main arm64 gettext arm64 0.21-14+b1 [1249 kB]
Get: 45 http://deb.debian.org/debian unstable/main arm64 intltool-debian all 0.35.0+20060710.6 [22.9 kB]
Get: 46 http://deb.debian.org/debian unstable/main arm64 po-debconf all 1.0.21+nmu1 [248 kB]
Get: 47 http://deb.debian.org/debian unstable/main arm64 debhelper all 13.15.3 [901 kB]
Get: 48 http://deb.debian.org/debian unstable/main arm64 python3-pkg-resources all 68.1.2-2 [241 kB]
Get: 49 http://deb.debian.org/debian unstable/main arm64 python3-lib2to3 all 3.12.3-1 [77.6 kB]
Get: 50 http://deb.debian.org/debian unstable/main arm64 python3-distutils all 3.12.3-1 [131 kB]
Get: 51 http://deb.debian.org/debian unstable/main arm64 python3-setuptools all 68.1.2-2 [468 kB]
Get: 52 http://deb.debian.org/debian unstable/main arm64 dh-python all 6.20240422 [107 kB]
Get: 53 http://deb.debian.org/debian unstable/main arm64 libbrotli1 arm64 1.1.0-2+b3 [295 kB]
Get: 54 http://deb.debian.org/debian unstable/main arm64 libpng16-16t64 arm64 1.6.43-5 [272 kB]
Get: 55 http://deb.debian.org/debian unstable/main arm64 libfreetype6 arm64 2.13.2+dfsg-1+b4 [408 kB]
Get: 56 http://deb.debian.org/debian unstable/main arm64 fonts-dejavu-mono all 2.37-8 [489 kB]
Get: 57 http://deb.debian.org/debian unstable/main arm64 fonts-dejavu-core all 2.37-8 [840 kB]
Get: 58 http://deb.debian.org/debian unstable/main arm64 fontconfig-config arm64 2.15.0-1.1 [317 kB]
Get: 59 http://deb.debian.org/debian unstable/main arm64 libfontconfig1 arm64 2.15.0-1.1 [385 kB]
Get: 60 http://deb.debian.org/debian unstable/main arm64 fontconfig arm64 2.15.0-1.1 [462 kB]
Get: 61 http://deb.debian.org/debian unstable/main arm64 gdal-data all 3.8.5+dfsg-1 [547 kB]
Get: 62 http://deb.debian.org/debian unstable/main arm64 gdal-plugins arm64 3.8.5+dfsg-1 [331 kB]
Get: 63 http://deb.debian.org/debian unstable/main arm64 libnl-3-200 arm64 3.7.0-0.3 [56.5 kB]
Get: 64 http://deb.debian.org/debian unstable/main arm64 libnl-route-3-200 arm64 3.7.0-0.3 [165 kB]
Get: 65 http://deb.debian.org/debian unstable/main arm64 libibverbs1 arm64 50.0-2+b1 [58.2 kB]
Get: 66 http://deb.debian.org/debian unstable/main arm64 ibverbs-providers arm64 50.0-2+b1 [315 kB]
Get: 67 http://deb.debian.org/debian unstable/main arm64 iso-codes all 4.16.0-1 [2936 kB]
Get: 68 http://deb.debian.org/debian unstable/main arm64 libabsl20230802 arm64 20230802.1-4 [432 kB]
Get: 69 http://deb.debian.org/debian unstable/main arm64 libaec0 arm64 1.1.3-1 [22.1 kB]
Get: 70 http://deb.debian.org/debian unstable/main arm64 libbsd0 arm64 0.12.2-1 [129 kB]
Get: 71 http://deb.debian.org/debian unstable/main arm64 libedit2 arm64 3.1-20230828-1+b1 [89.1 kB]
Get: 72 http://deb.debian.org/debian unstable/main arm64 libz3-4 arm64 4.8.12-3.1+b2 [6508 kB]
Get: 73 http://deb.debian.org/debian unstable/main arm64 libllvm17t64 arm64 1:17.0.6-12 [21.3 MB]
Get: 74 http://deb.debian.org/debian unstable/main arm64 libamd-comgr2 arm64 6.0+git20231212.4510c28+dfsg-3+b1 [12.1 MB]
Get: 75 http://deb.debian.org/debian unstable/main arm64 libdrm-common all 2.4.120-2 [7688 B]
Get: 76 http://deb.debian.org/debian unstable/main arm64 libdrm2 arm64 2.4.120-2 [37.5 kB]
Get: 77 http://deb.debian.org/debian unstable/main arm64 libdrm-amdgpu1 arm64 2.4.120-2 [21.0 kB]
Get: 78 http://deb.debian.org/debian unstable/main arm64 libnuma1 arm64 2.0.18-1 [21.4 kB]
Get: 79 http://deb.debian.org/debian unstable/main arm64 libhsakmt1 arm64 5.7.0-1 [57.6 kB]
Get: 80 http://deb.debian.org/debian unstable/main arm64 libhsa-runtime64-1 arm64 5.7.1-2 [276 kB]
Get: 81 http://deb.debian.org/debian unstable/main arm64 libamdhip64-5 arm64 5.7.1-3 [8698 kB]
Get: 82 http://deb.debian.org/debian unstable/main arm64 libaom3 arm64 3.8.2-2 [1528 kB]
Get: 83 http://deb.debian.org/debian unstable/main arm64 libarchive13t64 arm64 3.7.2-2 [321 kB]
Get: 84 http://deb.debian.org/debian unstable/main arm64 libblas3 arm64 3.12.0-3 [91.7 kB]
Get: 85 http://deb.debian.org/debian unstable/main arm64 libgfortran5 arm64 14-20240429-1 [361 kB]
Get: 86 http://deb.debian.org/debian unstable/main arm64 liblapack3 arm64 3.12.0-3 [1757 kB]
Get: 87 http://deb.debian.org/debian unstable/main arm64 libarpack2t64 arm64 3.9.1-1.1+b1 [81.4 kB]
Get: 88 http://deb.debian.org/debian unstable/main arm64 libsuperlu6 arm64 6.0.1+dfsg1-1+b1 [141 kB]
Get: 89 http://deb.debian.org/debian unstable/main arm64 libarmadillo12 arm64 1:12.8.2+dfsg-1 [102 kB]
Get: 90 http://deb.debian.org/debian unstable/main arm64 libva2 arm64 2.21.0-1 [68.5 kB]
Get: 91 http://deb.debian.org/debian unstable/main arm64 libva-drm2 arm64 2.21.0-1 [18.3 kB]
Get: 92 http://deb.debian.org/debian unstable/main arm64 libxau6 arm64 1:1.0.9-1+b1 [18.1 kB]
Get: 93 http://deb.debian.org/debian unstable/main arm64 libxdmcp6 arm64 1:1.1.2-3+b1 [24.3 kB]
Get: 94 http://deb.debian.org/debian unstable/main arm64 libxcb1 arm64 1.17.0-1 [143 kB]
Get: 95 http://deb.debian.org/debian unstable/main arm64 libx11-data all 2:1.8.7-1 [328 kB]
Get: 96 http://deb.debian.org/debian unstable/main arm64 libx11-6 arm64 2:1.8.7-1+b1 [775 kB]
Get: 97 http://deb.debian.org/debian unstable/main arm64 libx11-xcb1 arm64 2:1.8.7-1+b1 [232 kB]
Get: 98 http://deb.debian.org/debian unstable/main arm64 libxcb-dri3-0 arm64 1.17.0-1 [106 kB]
Get: 99 http://deb.debian.org/debian unstable/main arm64 libxext6 arm64 2:1.3.4-1+b1 [51.7 kB]
Get: 100 http://deb.debian.org/debian unstable/main arm64 libxfixes3 arm64 1:6.0.0-2+b1 [20.5 kB]
Get: 101 http://deb.debian.org/debian unstable/main arm64 libva-x11-2 arm64 2.21.0-1 [22.8 kB]
Get: 102 http://deb.debian.org/debian unstable/main arm64 libvdpau1 arm64 1.5-2+b1 [24.2 kB]
Get: 103 http://deb.debian.org/debian unstable/main arm64 ocl-icd-libopencl1 arm64 2.3.2-1+b1 [39.3 kB]
Get: 104 http://deb.debian.org/debian unstable/main arm64 libavutil58 arm64 7:6.1.1-4+b1 [361 kB]
Get: 105 http://deb.debian.org/debian unstable/main arm64 libpixman-1-0 arm64 0.42.2-1+b1 [477 kB]
Get: 106 http://deb.debian.org/debian unstable/main arm64 libxcb-render0 arm64 1.17.0-1 [114 kB]
Get: 107 http://deb.debian.org/debian unstable/main arm64 libxcb-shm0 arm64 1.17.0-1 [105 kB]
Get: 108 http://deb.debian.org/debian unstable/main arm64 libxrender1 arm64 1:0.9.10-1.1+b1 [27.0 kB]
Get: 109 http://deb.debian.org/debian unstable/main arm64 libcairo2 arm64 1.18.0-3+b1 [478 kB]
Get: 110 http://deb.debian.org/debian unstable/main arm64 libcodec2-1.2 arm64 1.2.0-2+b1 [8168 kB]
Get: 111 http://deb.debian.org/debian unstable/main arm64 libdav1d7 arm64 1.4.1-1 [251 kB]
Get: 112 http://deb.debian.org/debian unstable/main arm64 libglib2.0-0t64 arm64 2.80.2-1 [1393 kB]
Get: 113 http://deb.debian.org/debian unstable/main arm64 libgsm1 arm64 1.0.22-1+b1 [26.9 kB]
Get: 114 http://deb.debian.org/debian unstable/main arm64 libhwy1t64 arm64 1.0.7-8.1 [356 kB]
Get: 115 http://deb.debian.org/debian unstable/main arm64 liblcms2-2 arm64 2.14-2+b1 [144 kB]
Get: 116 http://deb.debian.org/debian unstable/main arm64 libjxl0.7 arm64 0.7.0-10.2+b3 [683 kB]
Get: 117 http://deb.debian.org/debian unstable/main arm64 libmp3lame0 arm64 3.100-6+b2 [352 kB]
Get: 118 http://deb.debian.org/debian unstable/main arm64 libopenjp2-7 arm64 2.5.0-2+b3 [177 kB]
Get: 119 http://deb.debian.org/debian unstable/main arm64 libopus0 arm64 1.4-1+b1 [180 kB]
Get: 120 http://deb.debian.org/debian unstable/main arm64 librav1e0 arm64 0.7.1-2 [630 kB]
Get: 121 http://deb.debian.org/debian unstable/main arm64 libcairo-gobject2 arm64 1.18.0-3+b1 [129 kB]
Get: 122 http://deb.debian.org/debian unstable/main arm64 libgdk-pixbuf2.0-common all 2.42.10+dfsg-3 [307 kB]
Get: 123 http://deb.debian.org/debian unstable/main arm64 shared-mime-info arm64 2.4-4 [755 kB]
Get: 124 http://deb.debian.org/debian unstable/main arm64 libjpeg62-turbo arm64 1:2.1.5-3 [172 kB]
Get: 125 http://deb.debian.org/debian unstable/main arm64 libdeflate0 arm64 1.20-1 [41.5 kB]
Get: 126 http://deb.debian.org/debian unstable/main arm64 libjbig0 arm64 2.1-6.1+b1 [30.4 kB]
Get: 127 http://deb.debian.org/debian unstable/main arm64 liblerc4 arm64 4.0.0+ds-4+b1 [142 kB]
Get: 128 http://deb.debian.org/debian unstable/main arm64 libsharpyuv0 arm64 1.4.0-0.1 [111 kB]
Get: 129 http://deb.debian.org/debian unstable/main arm64 libwebp7 arm64 1.4.0-0.1 [267 kB]
Get: 130 http://deb.debian.org/debian unstable/main arm64 libtiff6 arm64 4.5.1+git230720-4 [307 kB]
Get: 131 http://deb.debian.org/debian unstable/main arm64 libgdk-pixbuf-2.0-0 arm64 2.42.10+dfsg-3+b3 [130 kB]
Get: 132 http://deb.debian.org/debian unstable/main arm64 libfribidi0 arm64 1.0.13-3+b1 [71.3 kB]
Get: 133 http://deb.debian.org/debian unstable/main arm64 libgraphite2-3 arm64 1.3.14-2 [69.2 kB]
Get: 134 http://deb.debian.org/debian unstable/main arm64 libharfbuzz0b arm64 8.3.0-2+b1 [2178 kB]
Get: 135 http://deb.debian.org/debian unstable/main arm64 libthai-data all 0.1.29-2 [168 kB]
Get: 136 http://deb.debian.org/debian unstable/main arm64 libdatrie1 arm64 0.2.13-3 [37.2 kB]
Get: 137 http://deb.debian.org/debian unstable/main arm64 libthai0 arm64 0.1.29-2 [48.0 kB]
Get: 138 http://deb.debian.org/debian unstable/main arm64 libpango-1.0-0 arm64 1.52.2+ds-1 [206 kB]
Get: 139 http://deb.debian.org/debian unstable/main arm64 libpangoft2-1.0-0 arm64 1.52.2+ds-1 [45.5 kB]
Get: 140 http://deb.debian.org/debian unstable/main arm64 libpangocairo-1.0-0 arm64 1.52.2+ds-1 [33.0 kB]
Get: 141 http://deb.debian.org/debian unstable/main arm64 librsvg2-2 arm64 2.58.0+dfsg-1 [1735 kB]
Get: 142 http://deb.debian.org/debian unstable/main arm64 libshine3 arm64 3.1.1-2+b1 [24.3 kB]
Get: 143 http://deb.debian.org/debian unstable/main arm64 libsnappy1v5 arm64 1.2.0-2 [28.3 kB]
Get: 144 http://deb.debian.org/debian unstable/main arm64 libspeex1 arm64 1.2.1-2+b1 [49.5 kB]
Get: 145 http://deb.debian.org/debian unstable/main arm64 libsvtav1enc1d1 arm64 1.7.0+dfsg-2+b1 [1083 kB]
Get: 146 http://deb.debian.org/debian unstable/main arm64 libsoxr0 arm64 0.1.3-4+b1 [53.3 kB]
Get: 147 http://deb.debian.org/debian unstable/main arm64 libswresample4 arm64 7:6.1.1-4+b1 [84.7 kB]
Get: 148 http://deb.debian.org/debian unstable/main arm64 libogg0 arm64 1.3.5-3+b1 [23.5 kB]
Get: 149 http://deb.debian.org/debian unstable/main arm64 libtheora0 arm64 1.1.1+dfsg.1-16.1+b2 [147 kB]
Get: 150 http://deb.debian.org/debian unstable/main arm64 libtwolame0 arm64 0.4.0-2+b1 [48.8 kB]
Get: 151 http://deb.debian.org/debian unstable/main arm64 libvorbis0a arm64 1.3.7-2 [82.3 kB]
Get: 152 http://deb.debian.org/debian unstable/main arm64 libvorbisenc2 arm64 1.3.7-2 [75.5 kB]
Get: 153 http://deb.debian.org/debian unstable/main arm64 libvpx9 arm64 1.14.0-2 [1059 kB]
Get: 154 http://deb.debian.org/debian unstable/main arm64 libwebpmux3 arm64 1.4.0-0.1 [122 kB]
Get: 155 http://deb.debian.org/debian unstable/main arm64 libx264-164 arm64 2:0.164.3108+git31e19f9-1 [442 kB]
Get: 156 http://deb.debian.org/debian unstable/main arm64 libx265-199 arm64 3.5-2+b1 [603 kB]
Get: 157 http://deb.debian.org/debian unstable/main arm64 libxvidcore4 arm64 2:1.3.7-1+b1 [201 kB]
Get: 158 http://deb.debian.org/debian unstable/main arm64 libzvbi-common all 0.2.42-2 [70.4 kB]
Get: 159 http://deb.debian.org/debian unstable/main arm64 libzvbi0t64 arm64 0.2.42-2 [259 kB]
Get: 160 http://deb.debian.org/debian unstable/main arm64 libavcodec60 arm64 7:6.1.1-4+b1 [4998 kB]
Get: 161 http://deb.debian.org/debian unstable/main arm64 libudfread0 arm64 1.1.2-1+b1 [16.2 kB]
Get: 162 http://deb.debian.org/debian unstable/main arm64 libbluray2 arm64 1:1.3.4-1+b1 [124 kB]
Get: 163 http://deb.debian.org/debian unstable/main arm64 libchromaprint1 arm64 1.5.1-5 [38.8 kB]
Get: 164 http://deb.debian.org/debian unstable/main arm64 libgme0 arm64 0.6.3-7+b1 [120 kB]
Get: 165 http://deb.debian.org/debian unstable/main arm64 libmpg123-0t64 arm64 1.32.6-3 [141 kB]
Get: 166 http://deb.debian.org/debian unstable/main arm64 libvorbisfile3 arm64 1.3.7-2 [20.5 kB]
Get: 167 http://deb.debian.org/debian unstable/main arm64 libopenmpt0t64 arm64 0.7.6-1+b1 [715 kB]
Get: 168 http://deb.debian.org/debian unstable/main arm64 librabbitmq4 arm64 0.11.0-1+b2 [40.6 kB]
Get: 169 http://deb.debian.org/debian unstable/main arm64 libcjson1 arm64 1.7.17-2 [28.2 kB]
Get: 170 http://deb.debian.org/debian unstable/main arm64 libmbedcrypto7t64 arm64 2.28.8-1 [269 kB]
Get: 171 http://deb.debian.org/debian unstable/main arm64 librist4 arm64 0.2.10+dfsg-2 [68.1 kB]
Get: 172 http://deb.debian.org/debian unstable/main arm64 libsrt1.5-gnutls arm64 1.5.3-1+b2 [287 kB]
Get: 173 http://deb.debian.org/debian unstable/main arm64 libkrb5support0 arm64 1.20.1-6+b1 [33.0 kB]
Get: 174 http://deb.debian.org/debian unstable/main arm64 libcom-err2 arm64 1.47.1~rc2-1 [22.5 kB]
Get: 175 http://deb.debian.org/debian unstable/main arm64 libk5crypto3 arm64 1.20.1-6+b1 [80.5 kB]
Get: 176 http://deb.debian.org/debian unstable/main arm64 libkeyutils1 arm64 1.6.3-3 [9112 B]
Get: 177 http://deb.debian.org/debian unstable/main arm64 libkrb5-3 arm64 1.20.1-6+b1 [315 kB]
Get: 178 http://deb.debian.org/debian unstable/main arm64 libgssapi-krb5-2 arm64 1.20.1-6+b1 [124 kB]
Get: 179 http://deb.debian.org/debian unstable/main arm64 libssh-gcrypt-4 arm64 0.10.6-2+b1 [207 kB]
Get: 180 http://deb.debian.org/debian unstable/main arm64 libnorm1t64 arm64 1.5.9+dfsg-3.1 [204 kB]
Get: 181 http://deb.debian.org/debian unstable/main arm64 libpgm-5.3-0t64 arm64 5.3.128~dfsg-2.1 [151 kB]
Get: 182 http://deb.debian.org/debian unstable/main arm64 libsodium23 arm64 1.0.18-1+b1 [118 kB]
Get: 183 http://deb.debian.org/debian unstable/main arm64 libzmq5 arm64 4.3.5-1+b2 [251 kB]
Get: 184 http://deb.debian.org/debian unstable/main arm64 libavformat60 arm64 7:6.1.1-4+b1 [1068 kB]
Get: 185 http://deb.debian.org/debian unstable/main arm64 libgav1-1 arm64 0.19.0-2+b1 [481 kB]
Get: 186 http://deb.debian.org/debian unstable/main arm64 libyuv0 arm64 0.0~git202401110.af6ac82-1 [123 kB]
Get: 187 http://deb.debian.org/debian unstable/main arm64 libavif16 arm64 1.0.4-3 [99.4 kB]
Get: 188 http://deb.debian.org/debian unstable/main arm64 libbenchmark1.8.3 arm64 1.8.3-3 [111 kB]
Get: 189 http://deb.debian.org/debian unstable/main arm64 libblosc1 arm64 1.21.5+ds-1+b1 [39.0 kB]
Get: 190 http://deb.debian.org/debian unstable/main arm64 libcap2-bin arm64 1:2.66-5 [34.0 kB]
Get: 191 http://deb.debian.org/debian unstable/main arm64 libsasl2-modules-db arm64 2.1.28+dfsg1-6 [20.1 kB]
Get: 192 http://deb.debian.org/debian unstable/main arm64 libsasl2-2 arm64 2.1.28+dfsg1-6 [55.3 kB]
Get: 193 http://deb.debian.org/debian unstable/main arm64 libldap-2.5-0 arm64 2.5.17+dfsg-1 [173 kB]
Get: 194 http://deb.debian.org/debian unstable/main arm64 libnghttp2-14 arm64 1.61.0-1+b1 [71.0 kB]
Get: 195 http://deb.debian.org/debian unstable/main arm64 libpsl5t64 arm64 0.21.2-1.1 [56.8 kB]
Get: 196 http://deb.debian.org/debian unstable/main arm64 librtmp1 arm64 2.4+20151223.gitfa8646d.1-2+b4 [56.7 kB]
Get: 197 http://deb.debian.org/debian unstable/main arm64 libssh2-1t64 arm64 1.11.0-4.1+b2 [207 kB]
Get: 198 http://deb.debian.org/debian unstable/main arm64 libcurl3t64-gnutls arm64 8.7.1-5 [405 kB]
Get: 199 http://deb.debian.org/debian unstable/main arm64 libcfitsio10t64 arm64 4.3.1-1.1+b1 [514 kB]
Get: 200 http://deb.debian.org/debian unstable/main arm64 libcharls2 arm64 2.4.2-2+b1 [78.4 kB]
Get: 201 http://deb.debian.org/debian unstable/main arm64 libcpuinfo0 arm64 0.0~git20230113.6481e8b-2 [24.7 kB]
Get: 202 http://deb.debian.org/debian unstable/main arm64 libcurl4t64 arm64 8.7.1-5 [412 kB]
Get: 203 http://deb.debian.org/debian unstable/main arm64 libdbus-1-3 arm64 1.14.10-4+b1 [195 kB]
Get: 204 http://deb.debian.org/debian unstable/main arm64 libraw1394-11 arm64 2.1.2-2+b1 [38.1 kB]
Get: 205 http://deb.debian.org/debian unstable/main arm64 libusb-1.0-0 arm64 2:1.0.27-1 [55.2 kB]
Get: 206 http://deb.debian.org/debian unstable/main arm64 libdc1394-25 arm64 2.2.6-4+b1 [105 kB]
Get: 207 http://deb.debian.org/debian unstable/main arm64 libde265-0 arm64 1.0.15-1+b1 [151 kB]
Get: 208 http://deb.debian.org/debian unstable/main arm64 libdnnl3 arm64 3.1.1-2 [2630 kB]
Get: 209 http://deb.debian.org/debian unstable/main arm64 libdouble-conversion3 arm64 3.3.0-1+b1 [37.0 kB]
Get: 210 http://deb.debian.org/debian unstable/main arm64 libdrm-nouveau2 arm64 2.4.120-2 [18.7 kB]
Get: 211 http://deb.debian.org/debian unstable/main arm64 libdrm-radeon1 arm64 2.4.120-2 [21.1 kB]
Get: 212 http://deb.debian.org/debian unstable/main arm64 libdw1t64 arm64 0.191-1+b1 [230 kB]
Get: 213 http://deb.debian.org/debian unstable/main arm64 libwayland-server0 arm64 1.22.0-2.1+b1 [32.3 kB]
Get: 214 http://deb.debian.org/debian unstable/main arm64 libxcb-randr0 arm64 1.17.0-1 [116 kB]
Get: 215 http://deb.debian.org/debian unstable/main arm64 libgbm1 arm64 24.0.7-1 [40.3 kB]
Get: 216 http://deb.debian.org/debian unstable/main arm64 libglapi-mesa arm64 24.0.7-1 [46.0 kB]
Get: 217 http://deb.debian.org/debian unstable/main arm64 libwayland-client0 arm64 1.22.0-2.1+b1 [24.8 kB]
Get: 218 http://deb.debian.org/debian unstable/main arm64 libxcb-dri2-0 arm64 1.17.0-1 [106 kB]
Get: 219 http://deb.debian.org/debian unstable/main arm64 libxcb-present0 arm64 1.17.0-1 [105 kB]
Get: 220 http://deb.debian.org/debian unstable/main arm64 libxcb-sync1 arm64 1.17.0-1 [108 kB]
Get: 221 http://deb.debian.org/debian unstable/main arm64 libxcb-xfixes0 arm64 1.17.0-1 [109 kB]
Get: 222 http://deb.debian.org/debian unstable/main arm64 libxshmfence1 arm64 1.3-1+b1 [9080 B]
Get: 223 http://deb.debian.org/debian unstable/main arm64 libegl-mesa0 arm64 24.0.7-1 [112 kB]
Get: 224 http://deb.debian.org/debian unstable/main arm64 libevdev2 arm64 1.13.1+dfsg-1+b1 [31.0 kB]
Get: 225 http://deb.debian.org/debian unstable/main arm64 libevent-core-2.1-7t64 arm64 2.1.12-stable-8.1+b3 [125 kB]
Get: 226 http://deb.debian.org/debian unstable/main arm64 libevent-pthreads-2.1-7t64 arm64 2.1.12-stable-8.1+b3 [54.3 kB]
Get: 227 http://deb.debian.org/debian unstable/main arm64 libexif12 arm64 0.6.24-1+b1 [396 kB]
Get: 228 http://deb.debian.org/debian unstable/main arm64 librdmacm1t64 arm64 50.0-2+b1 [66.6 kB]
Get: 229 http://deb.debian.org/debian unstable/main arm64 libfabric1 arm64 1.17.0-3+b1 [479 kB]
Get: 230 http://deb.debian.org/debian unstable/main arm64 libfmt9 arm64 9.1.0+ds1-2 [107 kB]
Get: 231 http://deb.debian.org/debian unstable/main arm64 libminizip1t64 arm64 1:1.3.dfsg+really1.3.1-1 [52.0 kB]
Get: 232 http://deb.debian.org/debian unstable/main arm64 libfreexl1 arm64 2.0.0-1+b2 [38.1 kB]
Get: 233 http://deb.debian.org/debian unstable/main arm64 libfyba0t64 arm64 4.1.1-11 [106 kB]
Get: 234 http://deb.debian.org/debian unstable/main arm64 libheif-plugin-dav1d arm64 1.17.6-1+b2 [9888 B]
Get: 235 http://deb.debian.org/debian unstable/main arm64 libheif-plugin-libde265 arm64 1.17.6-1+b2 [13.0 kB]
Get: 236 http://deb.debian.org/debian unstable/main arm64 libheif1 arm64 1.17.6-1+b2 [247 kB]
Get: 237 http://deb.debian.org/debian unstable/main arm64 libxpm4 arm64 1:3.5.17-1+b1 [52.9 kB]
Get: 238 http://deb.debian.org/debian unstable/main arm64 libgd3 arm64 2.3.3-9+b3 [114 kB]
Get: 239 http://deb.debian.org/debian unstable/main arm64 libgeos3.12.1t64 arm64 3.12.1-3 [800 kB]
Get: 240 http://deb.debian.org/debian unstable/main arm64 libgeos-c1t64 arm64 3.12.1-3 [87.9 kB]
Get: 241 http://deb.debian.org/debian unstable/main arm64 proj-data all 9.4.0-1 [6258 kB]
Get: 242 http://deb.debian.org/debian unstable/main arm64 libproj25 arm64 9.4.0-1+b1 [1178 kB]
Get: 243 http://deb.debian.org/debian unstable/main arm64 libgeotiff5 arm64 1.7.1-5+b1 [66.1 kB]
Get: 244 http://deb.debian.org/debian unstable/main arm64 libgif7 arm64 5.2.2-1 [43.7 kB]
Get: 245 http://deb.debian.org/debian unstable/main arm64 libsz2 arm64 1.1.3-1 [7664 B]
Get: 246 http://deb.debian.org/debian unstable/main arm64 libhdf4-0-alt arm64 4.3.0-1 [236 kB]
Get: 247 http://deb.debian.org/debian unstable/main arm64 libhdf5-103-1t64 arm64 1.10.10+repack-3.3 [1035 kB]
Get: 248 http://deb.debian.org/debian unstable/main arm64 libjson-c5 arm64 0.17-1+b1 [43.5 kB]
Get: 249 http://deb.debian.org/debian unstable/main arm64 liburiparser1 arm64 0.9.7+dfsg-2+b1 [41.1 kB]
Get: 250 http://deb.debian.org/debian unstable/main arm64 libkmlbase1t64 arm64 1.3.0-12 [44.8 kB]
Get: 251 http://deb.debian.org/debian unstable/main arm64 libkmldom1t64 arm64 1.3.0-12 [141 kB]
Get: 252 http://deb.debian.org/debian unstable/main arm64 libkmlengine1t64 arm64 1.3.0-12 [70.6 kB]
Get: 253 http://deb.debian.org/debian unstable/main arm64 mysql-common all 5.8+1.1.0 [6636 B]
Get: 254 http://deb.debian.org/debian unstable/main arm64 mariadb-common all 1:10.11.7-4 [25.9 kB]
Get: 255 http://deb.debian.org/debian unstable/main arm64 libmariadb3 arm64 1:10.11.7-4 [165 kB]
Get: 256 http://deb.debian.org/debian unstable/main arm64 libhdf5-hl-100t64 arm64 1.10.10+repack-3.3 [60.1 kB]
Get: 257 http://deb.debian.org/debian unstable/main arm64 libnetcdf19t64 arm64 1:4.9.2-6 [441 kB]
Get: 258 http://deb.debian.org/debian unstable/main arm64 libltdl7 arm64 2.4.7-7+b1 [393 kB]
Get: 259 http://deb.debian.org/debian unstable/main arm64 libodbc2 arm64 2.3.12-1+b2 [133 kB]
Get: 260 http://deb.debian.org/debian unstable/main arm64 unixodbc-common all 2.3.12-1 [8496 B]
Get: 261 http://deb.debian.org/debian unstable/main arm64 libodbcinst2 arm64 2.3.12-1+b2 [36.3 kB]
Get: 262 http://deb.debian.org/debian unstable/main arm64 libtirpc-common all 1.3.4+ds-1.3 [10.9 kB]
Get: 263 http://deb.debian.org/debian unstable/main arm64 libtirpc3t64 arm64 1.3.4+ds-1.3 [78.4 kB]
Get: 264 http://deb.debian.org/debian unstable/main arm64 libogdi4.1 arm64 4.1.1+ds-4 [196 kB]
Get: 265 http://deb.debian.org/debian unstable/main arm64 libnspr4 arm64 2:4.35-1.1+b1 [101 kB]
Get: 266 http://deb.debian.org/debian unstable/main arm64 libnss3 arm64 2:3.99-1 [1293 kB]
Get: 267 http://deb.debian.org/debian unstable/main arm64 libpoppler126t64 arm64 22.12.0-2.2+b1 [1762 kB]
Get: 268 http://deb.debian.org/debian unstable/main arm64 libpq5 arm64 16.3-1 [209 kB]
Get: 269 http://deb.debian.org/debian unstable/main arm64 libqhull-r8.0 arm64 2020.2-6+b1 [226 kB]
Get: 270 http://deb.debian.org/debian unstable/main arm64 librttopo1 arm64 1.1.0-3+b1 [164 kB]
Get: 271 http://deb.debian.org/debian unstable/main arm64 libspatialite8t64 arm64 5.1.0-3+b1 [1595 kB]
Get: 272 http://deb.debian.org/debian unstable/main arm64 libxerces-c3.2t64 arm64 3.2.4+debian-1.3 [781 kB]
Get: 273 http://deb.debian.org/debian unstable/main arm64 libgdal34t64 arm64 3.8.5+dfsg-1 [7416 kB]
Get: 274 http://deb.debian.org/debian unstable/main arm64 libsocket++1 arm64 1.12.13+git20131030.5d039ba-1+b1 [72.6 kB]
Get: 275 http://deb.debian.org/debian unstable/main arm64 libgdcm3.0t64 arm64 3.0.24-1 [2035 kB]
Get: 276 http://deb.debian.org/debian unstable/main arm64 libglvnd0 arm64 1.7.0-1+b1 [41.7 kB]
Get: 277 http://deb.debian.org/debian unstable/main arm64 libxcb-glx0 arm64 1.17.0-1 [122 kB]
Get: 278 http://deb.debian.org/debian unstable/main arm64 libxxf86vm1 arm64 1:1.1.4-1+b2 [20.1 kB]
Get: 279 http://deb.debian.org/debian unstable/main arm64 libvulkan1 arm64 1.3.280.0-1 [119 kB]
Get: 280 http://deb.debian.org/debian unstable/main arm64 libsensors-config all 1:3.6.0-9 [14.6 kB]
Get: 281 http://deb.debian.org/debian unstable/main arm64 libsensors5 arm64 1:3.6.0-9 [33.9 kB]
Get: 282 http://deb.debian.org/debian unstable/main arm64 libgl1-mesa-dri arm64 24.0.7-1 [7018 kB]
Get: 283 http://deb.debian.org/debian unstable/main arm64 libglx-mesa0 arm64 24.0.7-1 [150 kB]
Get: 284 http://deb.debian.org/debian unstable/main arm64 libglx0 arm64 1.7.0-1+b1 [31.0 kB]
Get: 285 http://deb.debian.org/debian unstable/main arm64 libgl1 arm64 1.7.0-1+b1 [90.9 kB]
Get: 286 http://deb.debian.org/debian unstable/main arm64 libhiredis1.1.0 arm64 1.2.0-6+b2 [48.3 kB]
Get: 287 http://deb.debian.org/debian unstable/main arm64 libhwloc15 arm64 2.10.0-1+b1 [140 kB]
Get: 288 http://deb.debian.org/debian unstable/main arm64 libmunge2 arm64 0.5.15-4 [19.2 kB]
Get: 289 http://deb.debian.org/debian unstable/main arm64 libpciaccess0 arm64 0.17-3+b1 [51.1 kB]
Get: 290 http://deb.debian.org/debian unstable/main arm64 libxnvctrl0 arm64 535.171.04-1 [13.8 kB]
Get: 291 http://deb.debian.org/debian unstable/main arm64 libhwloc-plugins arm64 2.10.0-1+b1 [18.0 kB]
Get: 292 http://deb.debian.org/debian unstable/main arm64 libpmix2t64 arm64 5.0.2-3 [580 kB]
Get: 293 http://deb.debian.org/debian unstable/main arm64 libucx0 arm64 1.16.0+ds-5 [938 kB]
Get: 294 http://deb.debian.org/debian unstable/main arm64 libopenmpi3t64 arm64 4.1.6-13.3 [2170 kB]
Get: 295 http://deb.debian.org/debian unstable/main arm64 libgloo0 arm64 0.0~git20230519.597accf-2+b2 [177 kB]
Get: 296 http://deb.debian.org/debian unstable/main arm64 libgphoto2-port12t64 arm64 2.5.31-2.1+b1 [135 kB]
Get: 297 http://deb.debian.org/debian unstable/main arm64 libgphoto2-6t64 arm64 2.5.31-2.1+b1 [718 kB]
Get: 298 http://deb.debian.org/debian unstable/main arm64 libunwind8 arm64 1.6.2-3+b1 [47.9 kB]
Get: 299 http://deb.debian.org/debian unstable/main arm64 libgstreamer1.0-0 arm64 1.24.3-1 [1245 kB]
Get: 300 http://deb.debian.org/debian unstable/main arm64 liborc-0.4-0t64 arm64 1:0.4.38-1 [172 kB]
Get: 301 http://deb.debian.org/debian unstable/main arm64 libgstreamer-plugins-base1.0-0 arm64 1.24.3-1 [883 kB]
Get: 302 http://deb.debian.org/debian unstable/main arm64 libgudev-1.0-0 arm64 238-5 [13.6 kB]
Get: 303 http://deb.debian.org/debian unstable/main arm64 x11-common all 1:7.7+23 [252 kB]
Get: 304 http://deb.debian.org/debian unstable/main arm64 libice6 arm64 2:1.0.10-1+b1 [53.7 kB]
Get: 305 http://deb.debian.org/debian unstable/main arm64 libimath-3-1-29t64 arm64 3.1.9-3.1+b1 [35.0 kB]
Get: 306 http://deb.debian.org/debian unstable/main arm64 libwacom-common all 2.10.0-2 [62.5 kB]
Get: 307 http://deb.debian.org/debian unstable/main arm64 libwacom9 arm64 2.10.0-2 [21.5 kB]
Get: 308 http://deb.debian.org/debian unstable/main arm64 libinput-bin arm64 1.25.0-1+b2 [24.8 kB]
Get: 309 http://deb.debian.org/debian unstable/main arm64 libmtdev1t64 arm64 1.1.6-1.2 [21.9 kB]
Get: 310 http://deb.debian.org/debian unstable/main arm64 libinput10 arm64 1.25.0-1+b2 [116 kB]
Get: 311 http://deb.debian.org/debian unstable/main arm64 liblept5 arm64 1.82.0-3+b4 [977 kB]
Get: 312 http://deb.debian.org/debian unstable/main arm64 libleveldb1d arm64 1.23-5+b1 [122 kB]
Get: 313 http://deb.debian.org/debian unstable/main arm64 liblmdb0 arm64 0.9.31-1+b1 [43.0 kB]
Get: 314 http://deb.debian.org/debian unstable/main arm64 libmd4c0 arm64 0.4.8-1+b1 [42.2 kB]
Get: 315 http://deb.debian.org/debian unstable/main arm64 libprotobuf32t64 arm64 3.21.12-8.2 [851 kB]
Get: 316 http://deb.debian.org/debian unstable/main arm64 libonnx1t64 arm64 1.14.1-2.1+b1 [923 kB]
Get: 317 http://deb.debian.org/debian unstable/main arm64 libtbbmalloc2 arm64 2021.11.0-2 [35.0 kB]
Get: 318 http://deb.debian.org/debian unstable/main arm64 libtbbbind-2-5 arm64 2021.11.0-2 [13.7 kB]
Get: 319 http://deb.debian.org/debian unstable/main arm64 libtbb12 arm64 2021.11.0-2 [63.1 kB]
Get: 320 http://deb.debian.org/debian unstable/main arm64 libopencv-core406t64 arm64 4.6.0+dfsg-13.1+b2 [875 kB]
Get: 321 http://deb.debian.org/debian unstable/main arm64 libopencv-flann406t64 arm64 4.6.0+dfsg-13.1+b2 [114 kB]
Get: 322 http://deb.debian.org/debian unstable/main arm64 libopencv-imgproc406t64 arm64 4.6.0+dfsg-13.1+b2 [911 kB]
Get: 323 http://deb.debian.org/debian unstable/main arm64 libopencv-features2d406t64 arm64 4.6.0+dfsg-13.1+b2 [209 kB]
Get: 324 http://deb.debian.org/debian unstable/main arm64 libopencv-calib3d406t64 arm64 4.6.0+dfsg-13.1+b2 [591 kB]
Get: 325 http://deb.debian.org/debian unstable/main arm64 libopencv-dnn406t64 arm64 4.6.0+dfsg-13.1+b2 [995 kB]
Get: 326 http://deb.debian.org/debian unstable/main arm64 libopengl0 arm64 1.7.0-1+b1 [32.1 kB]
Get: 327 http://deb.debian.org/debian unstable/main arm64 libpcre2-16-0 arm64 10.42-4+b1 [217 kB]
Get: 328 http://deb.debian.org/debian unstable/main arm64 libqt5core5t64 arm64 5.15.10+dfsg-7.2+b1 [1700 kB]
Get: 329 http://deb.debian.org/debian unstable/main arm64 libegl1 arm64 1.7.0-1+b1 [33.9 kB]
Get: 330 http://deb.debian.org/debian unstable/main arm64 libqt5dbus5t64 arm64 5.15.10+dfsg-7.2+b1 [205 kB]
Get: 331 http://deb.debian.org/debian unstable/main arm64 libqt5network5t64 arm64 5.15.10+dfsg-7.2+b1 [653 kB]
Get: 332 http://deb.debian.org/debian unstable/main arm64 libsm6 arm64 2:1.2.3-1+b1 [32.7 kB]
Get: 333 http://deb.debian.org/debian unstable/main arm64 libxcb-icccm4 arm64 0.4.1-1.1+b1 [26.5 kB]
Get: 334 http://deb.debian.org/debian unstable/main arm64 libxcb-util1 arm64 0.4.0-1+b1 [23.2 kB]
Get: 335 http://deb.debian.org/debian unstable/main arm64 libxcb-image0 arm64 0.4.0-2+b1 [22.0 kB]
Get: 336 http://deb.debian.org/debian unstable/main arm64 libxcb-keysyms1 arm64 0.4.0-1+b2 [16.0 kB]
Get: 337 http://deb.debian.org/debian unstable/main arm64 libxcb-render-util0 arm64 0.3.9-1+b1 [17.5 kB]
Get: 338 http://deb.debian.org/debian unstable/main arm64 libxcb-shape0 arm64 1.17.0-1 [105 kB]
Get: 339 http://deb.debian.org/debian unstable/main arm64 libxcb-xinerama0 arm64 1.17.0-1 [105 kB]
Get: 340 http://deb.debian.org/debian unstable/main arm64 libxcb-xinput0 arm64 1.17.0-1 [129 kB]
Get: 341 http://deb.debian.org/debian unstable/main arm64 libxcb-xkb1 arm64 1.17.0-1 [128 kB]
Get: 342 http://deb.debian.org/debian unstable/main arm64 xkb-data all 2.41-2 [795 kB]
Get: 343 http://deb.debian.org/debian unstable/main arm64 libxkbcommon0 arm64 1.6.0-1+b1 [103 kB]
Get: 344 http://deb.debian.org/debian unstable/main arm64 libxkbcommon-x11-0 arm64 1.6.0-1+b1 [15.4 kB]
Get: 345 http://deb.debian.org/debian unstable/main arm64 libqt5gui5t64 arm64 5.15.10+dfsg-7.2+b1 [3134 kB]
Get: 346 http://deb.debian.org/debian unstable/main arm64 libqt5widgets5t64 arm64 5.15.10+dfsg-7.2+b1 [2263 kB]
Get: 347 http://deb.debian.org/debian unstable/main arm64 libqt5opengl5t64 arm64 5.15.10+dfsg-7.2+b1 [142 kB]
Get: 348 http://deb.debian.org/debian unstable/main arm64 libqt5test5t64 arm64 5.15.10+dfsg-7.2+b1 [138 kB]
Get: 349 http://deb.debian.org/debian unstable/main arm64 libopencv-highgui406t64 arm64 4.6.0+dfsg-13.1+b2 [113 kB]
Get: 350 http://deb.debian.org/debian unstable/main arm64 libopenexr-3-1-30 arm64 3.1.5-5.1+b2 [854 kB]
Get: 351 http://deb.debian.org/debian unstable/main arm64 libopencv-imgcodecs406t64 arm64 4.6.0+dfsg-13.1+b2 [113 kB]
Get: 352 http://deb.debian.org/debian unstable/main arm64 libopencv-ml406t64 arm64 4.6.0+dfsg-13.1+b2 [180 kB]
Get: 353 http://deb.debian.org/debian unstable/main arm64 libopencv-objdetect406t64 arm64 4.6.0+dfsg-13.1+b2 [198 kB]
Get: 354 http://deb.debian.org/debian unstable/main arm64 libopencv-video406t64 arm64 4.6.0+dfsg-13.1+b2 [156 kB]
Get: 355 http://deb.debian.org/debian unstable/main arm64 libtesseract5 arm64 5.3.4-1.1+b1 [1155 kB]
Get: 356 http://deb.debian.org/debian unstable/main arm64 libopencv-contrib406t64 arm64 4.6.0+dfsg-13.1+b2 [3470 kB]
Get: 357 http://deb.debian.org/debian unstable/main arm64 libswscale7 arm64 7:6.1.1-4+b1 [182 kB]
Get: 358 http://deb.debian.org/debian unstable/main arm64 libopencv-videoio406t64 arm64 4.6.0+dfsg-13.1+b2 [193 kB]
Get: 359 http://deb.debian.org/debian unstable/main arm64 libpthreadpool0 arm64 0.0~git20210507.1787867-2+b1 [21.4 kB]
Get: 360 http://deb.debian.org/debian unstable/main arm64 libpython3.12-stdlib arm64 3.12.3-1 [1889 kB]
Get: 361 http://deb.debian.org/debian unstable/main arm64 libsleef3 arm64 3.5.1-3 [406 kB]
Get: 362 http://deb.debian.org/debian unstable/main arm64 libuv1t64 arm64 1.48.0-3 [142 kB]
Get: 363 http://deb.debian.org/debian unstable/main arm64 libtensorpipe0 arm64 0.0~git20220513.bb1473a-5+b2 [483 kB]
Get: 364 http://deb.debian.org/debian unstable/main arm64 libxnnpack0 arm64 0.0~git20221221.51a9875-1 [363 kB]
Get: 365 http://deb.debian.org/debian unstable/main arm64 libtorch2.1 arm64 2.1.2+dfsg-4 [39.3 MB]
Get: 366 http://deb.debian.org/debian unstable/main arm64 libtorch-test arm64 2.1.2+dfsg-4 [6161 kB]
Get: 367 http://deb.debian.org/debian unstable/main arm64 libyaml-0-2 arm64 0.2.5-1+b1 [49.0 kB]
Get: 368 http://deb.debian.org/debian unstable/main arm64 python3.12 arm64 3.12.3-1 [659 kB]
Get: 369 http://deb.debian.org/debian unstable/main arm64 python3-all arm64 3.11.8-1 [1056 B]
Get: 370 http://deb.debian.org/debian unstable/main arm64 python3-six all 1.16.0-6 [16.3 kB]
Get: 371 http://deb.debian.org/debian unstable/main arm64 python3-astunparse all 1.6.3-2 [13.8 kB]
Get: 372 http://deb.debian.org/debian unstable/main arm64 python3-attr all 23.2.0-2 [65.5 kB]
Get: 373 http://deb.debian.org/debian unstable/main arm64 python3-certifi all 2023.11.17-1 [155 kB]
Get: 374 http://deb.debian.org/debian unstable/main arm64 python3-chardet all 5.2.0+dfsg-1 [107 kB]
Get: 375 http://deb.debian.org/debian unstable/main arm64 python3-charset-normalizer all 3.3.2-1 [51.6 kB]
Get: 376 http://deb.debian.org/debian unstable/main arm64 python3-dateutil all 2.9.0-2 [79.4 kB]
Get: 377 http://deb.debian.org/debian unstable/main arm64 python3-sortedcontainers all 2.4.0-2 [31.9 kB]
Get: 378 http://deb.debian.org/debian unstable/main arm64 python3-hypothesis all 6.100.7-1 [315 kB]
Get: 379 http://deb.debian.org/debian unstable/main arm64 python3-expecttest all 0.1.5-1 [7492 B]
Get: 380 http://deb.debian.org/debian unstable/main arm64 python3-filelock all 3.14.0-1 [10.9 kB]
Get: 381 http://deb.debian.org/debian unstable/main arm64 python3-fsspec all 2024.3.1-1 [115 kB]
Get: 382 http://deb.debian.org/debian unstable/main arm64 python3-idna all 3.6-2 [37.0 kB]
Get: 383 http://deb.debian.org/debian unstable/main arm64 python3-iniconfig all 1.1.1-2 [6396 B]
Get: 384 http://deb.debian.org/debian unstable/main arm64 python3-markupsafe arm64 2.1.5-1 [14.6 kB]
Get: 385 http://deb.debian.org/debian unstable/main arm64 python3-jinja2 all 3.1.3-1 [119 kB]
Get: 386 http://deb.debian.org/debian unstable/main arm64 python3-mpmath all 1.3.0-1 [419 kB]
Get: 387 http://deb.debian.org/debian unstable/main arm64 python3-networkx all 3.2.1-4 [14.2 MB]
Get: 388 http://deb.debian.org/debian unstable/main arm64 python3-numpy arm64 1:1.26.4+ds-8 [4219 kB]
Get: 389 http://deb.debian.org/debian unstable/main arm64 python3-packaging all 24.0-1 [45.5 kB]
Get: 390 http://deb.debian.org/debian unstable/main arm64 python3-pluggy all 1.5.0-1 [26.9 kB]
Get: 391 http://deb.debian.org/debian unstable/main arm64 python3-psutil arm64 5.9.8-2 [225 kB]
Get: 392 http://deb.debian.org/debian unstable/main arm64 python3-pytest all 8.1.2-1 [246 kB]
Get: 393 http://deb.debian.org/debian unstable/main arm64 python3-urllib3 all 1.26.18-2 [116 kB]
Get: 394 http://deb.debian.org/debian unstable/main arm64 python3-requests all 2.31.0+dfsg-1 [68.6 kB]
Get: 395 http://deb.debian.org/debian unstable/main arm64 python3-sympy all 1.12-7 [3869 kB]
Get: 396 http://deb.debian.org/debian unstable/main arm64 python3-typing-extensions all 4.10.0-1 [66.2 kB]
Get: 397 http://deb.debian.org/debian unstable/main arm64 python3-yaml arm64 6.0.1-2 [153 kB]
Get: 398 http://deb.debian.org/debian unstable/main arm64 python3-torch arm64 2.1.2+dfsg-4 [9932 kB]
Fetched 288 MB in 4s (75.1 MB/s)
debconf: delaying package configuration, since apt-utils is not installed
Selecting previously unselected package libpython3.11-minimal:arm64.
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Local time is now:      Sun Jun 15 18:06:48 UTC 2025.
Universal Time is now:  Sun Jun 15 18:06:48 UTC 2025.
Run 'dpkg-reconfigure tzdata' if you wish to change it.

Setting up libcap2-bin (1:2.66-5) ...
Setting up mariadb-common (1:10.11.7-4) ...
update-alternatives: using /etc/mysql/mariadb.cnf to provide /etc/mysql/my.cnf (my.cnf) in auto mode
Setting up libpgm-5.3-0t64:arm64 (5.3.128~dfsg-2.1) ...
Setting up autotools-dev (20220109.1) ...
Setting up libz3-4:arm64 (4.8.12-3.1+b2) ...
Setting up libglib2.0-0t64:arm64 (2.80.2-1) ...
No schema files found: doing nothing.
Setting up libblas3:arm64 (3.12.0-3) ...
update-alternatives: using /usr/lib/aarch64-linux-gnu/blas/libblas.so.3 to provide /usr/lib/aarch64-linux-gnu/libblas.so.3 (libblas.so.3-aarch64-linux-gnu) in auto mode
Setting up libmunge2:arm64 (0.5.15-4) ...
Setting up libjpeg62-turbo:arm64 (1:2.1.5-3) ...
Setting up libva2:arm64 (2.21.0-1) ...
Setting up libx11-data (2:1.8.7-1) ...
Setting up libnspr4:arm64 (2:4.35-1.1+b1) ...
Setting up librtmp1:arm64 (2.4+20151223.gitfa8646d.1-2+b4) ...
Setting up libtensorpipe0:arm64 (0.0~git20220513.bb1473a-5+b2) ...
Setting up libdbus-1-3:arm64 (1.14.10-4+b1) ...
Setting up libfribidi0:arm64 (1.0.13-3+b1) ...
Setting up libopus0:arm64 (1.4-1+b1) ...
Setting up libexif12:arm64 (0.6.24-1+b1) ...
Setting up fonts-dejavu-mono (2.37-8) ...
Setting up libpng16-16t64:arm64 (1.6.43-5) ...
Setting up libhwloc15:arm64 (2.10.0-1+b1) ...
Setting up libimath-3-1-29t64:arm64 (3.1.9-3.1+b1) ...
Setting up libvorbis0a:arm64 (1.3.7-2) ...
Setting up autopoint (0.21-14) ...
Setting up libmariadb3:arm64 (1:10.11.7-4) ...
Setting up fonts-dejavu-core (2.37-8) ...
Setting up unixodbc-common (2.3.12-1) ...
Setting up libsocket++1:arm64 (1.12.13+git20131030.5d039ba-1+b1) ...
Setting up libsensors5:arm64 (1:3.6.0-9) ...
Setting up libjxl0.7:arm64 (0.7.0-10.2+b3) ...
Setting up libk5crypto3:arm64 (1.20.1-6+b1) ...
Setting up libltdl7:arm64 (2.4.7-7+b1) ...
Setting up libglapi-mesa:arm64 (24.0.7-1) ...
Setting up libsasl2-2:arm64 (2.1.28+dfsg1-6) ...
Setting up libgfortran5:arm64 (14-20240429-1) ...
Setting up libvulkan1:arm64 (1.3.280.0-1) ...
Setting up autoconf (2.71-3) ...
Setting up libcpuinfo0:arm64 (0.0~git20230113.6481e8b-2) ...
Setting up libwebp7:arm64 (1.4.0-0.1) ...
Setting up libfmt9:arm64 (9.1.0+ds1-2) ...
Setting up libgif7:arm64 (5.2.2-1) ...
Setting up libodbc2:arm64 (2.3.12-1+b2) ...
Setting up liburiparser1:arm64 (0.9.7+dfsg-2+b1) ...
Setting up libnuma1:arm64 (2.0.18-1) ...
Setting up libfyba0t64:arm64 (4.1.1-11) ...
Setting up libvpx9:arm64 (1.14.0-2) ...
Setting up dwz (0.15-1+b1) ...
Setting up librttopo1:arm64 (1.1.0-3+b1) ...
Setting up libsrt1.5-gnutls:arm64 (1.5.3-1+b2) ...
Setting up libdav1d7:arm64 (1.4.1-1) ...
Setting up libxnnpack0 (0.0~git20221221.51a9875-1) ...
Setting up libmtdev1t64:arm64 (1.1.6-1.2) ...
Setting up sensible-utils (0.0.22) ...
Setting up libminizip1t64:arm64 (1:1.3.dfsg+really1.3.1-1) ...
Setting up ocl-icd-libopencl1:arm64 (2.3.2-1+b1) ...
Setting up libxshmfence1:arm64 (1.3-1+b1) ...
Setting up libtiff6:arm64 (4.5.1+git230720-4) ...
Setting up libuchardet0:arm64 (0.0.8-1+b1) ...
Setting up libblosc1:arm64 (1.21.5+ds-1+b1) ...
Setting up libnl-3-200:arm64 (3.7.0-0.3) ...
Setting up libmd4c0:arm64 (0.4.8-1+b1) ...
Setting up libopenjp2-7:arm64 (2.5.0-2+b3) ...
Setting up libsub-override-perl (0.10-1) ...
Setting up libthai-data (0.1.29-2) ...
Setting up libonnx1t64:arm64 (1.14.1-2.1+b1) ...
Setting up netbase (6.4) ...
Setting up libabsl20230802:arm64 (20230802.1-4) ...
Setting up libkrb5-3:arm64 (1.20.1-6+b1) ...
Setting up libevent-core-2.1-7t64:arm64 (2.1.12-stable-8.1+b3) ...
Setting up libusb-1.0-0:arm64 (2:1.0.27-1) ...
Setting up libssh2-1t64:arm64 (1.11.0-4.1+b2) ...
Setting up libde265-0:arm64 (1.0.15-1+b1) ...
Setting up libkmlbase1t64:arm64 (1.3.0-12) ...
Setting up openssl (3.2.1-3) ...
Setting up libwebpmux3:arm64 (1.4.0-0.1) ...
Setting up libbsd0:arm64 (0.12.2-1) ...
Setting up libdnnl3:arm64 (3.1.1-2) ...
Setting up libdrm-common (2.4.120-2) ...
Setting up libyuv0:arm64 (0.0~git202401110.af6ac82-1) ...
Setting up libhiredis1.1.0:arm64 (1.2.0-6+b2) ...
Setting up libjson-c5:arm64 (0.17-1+b1) ...
Setting up libevdev2:arm64 (1.13.1+dfsg-1+b1) ...
Setting up readline-common (8.2-4) ...
Setting up libxml2:arm64 (2.9.14+dfsg-1.3+b3) ...
Setting up iso-codes (4.16.0-1) ...
Setting up libgudev-1.0-0:arm64 (238-5) ...
Setting up libzvbi-common (0.2.42-2) ...
Setting up libgstreamer1.0-0:arm64 (1.24.3-1) ...
Setcap worked! gst-ptp-helper is not suid!
Setting up libmp3lame0:arm64 (3.100-6+b2) ...
Setting up libsz2:arm64 (1.1.3-1) ...
Setting up libvorbisenc2:arm64 (1.3.7-2) ...
Setting up gdal-plugins:arm64 (3.8.5+dfsg-1) ...
Setting up libwacom-common (2.10.0-2) ...
Setting up libxkbcommon0:arm64 (1.6.0-1+b1) ...
Setting up libwayland-client0:arm64 (1.22.0-2.1+b1) ...
Setting up automake (1:1.16.5-1.3) ...
update-alternatives: using /usr/bin/automake-1.16 to provide /usr/bin/automake (automake) in auto mode
Setting up libfile-stripnondeterminism-perl (1.13.1-1) ...
Setting up libgdcm3.0t64:arm64 (3.0.24-1) ...
Setting up python3.12-minimal (3.12.3-1) ...
Setting up libleveldb1d:arm64 (1.23-5+b1) ...
Setting up libice6:arm64 (2:1.0.10-1+b1) ...
Setting up libodbcinst2:arm64 (2.3.12-1+b2) ...
Setting up libxdmcp6:arm64 (1:1.1.2-3+b1) ...
Setting up liblapack3:arm64 (3.12.0-3) ...
update-alternatives: using /usr/lib/aarch64-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/aarch64-linux-gnu/liblapack.so.3 (liblapack.so.3-aarch64-linux-gnu) in auto mode
Setting up libxcb1:arm64 (1.17.0-1) ...
Setting up gettext (0.21-14+b1) ...
Setting up libarpack2t64:arm64 (3.9.1-1.1+b1) ...
Setting up libxcb-xfixes0:arm64 (1.17.0-1) ...
Setting up libzvbi0t64:arm64 (0.2.42-2) ...
Setting up libxcb-xinput0:arm64 (1.17.0-1) ...
Setting up libtool (2.4.7-7) ...
Setting up libxcb-render0:arm64 (1.17.0-1) ...
Setting up libwacom9:arm64 (2.10.0-2) ...
Setting up libevent-pthreads-2.1-7t64:arm64 (2.1.12-stable-8.1+b3) ...
Setting up fontconfig-config (2.15.0-1.1) ...
Setting up libxcb-glx0:arm64 (1.17.0-1) ...
Setting up libedit2:arm64 (3.1-20230828-1+b1) ...
Setting up libxcb-keysyms1:arm64 (0.4.0-1+b2) ...
Setting up libxcb-shape0:arm64 (1.17.0-1) ...
Setting up libxcb-render-util0:arm64 (0.3.9-1+b1) ...
Setting up libnss3:arm64 (2:3.99-1) ...
Setting up libxcb-shm0:arm64 (1.17.0-1) ...
Setting up libxcb-icccm4:arm64 (0.4.1-1.1+b1) ...
Setting up libsuperlu6:arm64 (6.0.1+dfsg1-1+b1) ...
Setting up libldap-2.5-0:arm64 (2.5.17+dfsg-1) ...
Setting up liblept5:arm64 (1.82.0-3+b4) ...
Setting up libxcb-util1:arm64 (0.4.0-1+b1) ...
Setting up libinput-bin (1.25.0-1+b2) ...
Setting up libxcb-xkb1:arm64 (1.17.0-1) ...
Setting up libxcb-image0:arm64 (0.4.0-2+b1) ...
Setting up libtbbbind-2-5:arm64 (2021.11.0-2) ...
Setting up intltool-debian (0.35.0+20060710.6) ...
Setting up libnl-route-3-200:arm64 (3.7.0-0.3) ...
Setting up libxcb-present0:arm64 (1.17.0-1) ...
Setting up libkmldom1t64:arm64 (1.3.0-12) ...
Setting up dh-autoreconf (20) ...
Setting up libthai0:arm64 (0.1.29-2) ...
Setting up ca-certificates (20240203) ...
Updating certificates in /etc/ssl/certs...
146 added, 0 removed; done.
Setting up libvorbisfile3:arm64 (1.3.7-2) ...
Setting up libxcb-xinerama0:arm64 (1.17.0-1) ...
Setting up libllvm17t64:arm64 (1:17.0.6-12) ...
Setting up libfreetype6:arm64 (2.13.2+dfsg-1+b4) ...
Setting up libxcb-sync1:arm64 (1.17.0-1) ...
Setting up libopenexr-3-1-30:arm64 (3.1.5-5.1+b2) ...
Setting up shared-mime-info (2.4-4) ...
Setting up libxkbcommon-x11-0:arm64 (1.6.0-1+b1) ...
Setting up libdc1394-25:arm64 (2.2.6-4+b1) ...
Setting up libgssapi-krb5-2:arm64 (1.20.1-6+b1) ...
Setting up libgav1-1:arm64 (0.19.0-2+b1) ...
Setting up libhdf4-0-alt:arm64 (4.3.0-1) ...
Setting up libx265-199:arm64 (3.5-2+b1) ...
Setting up libreadline8t64:arm64 (8.2-4) ...
Setting up libxcb-dri2-0:arm64 (1.17.0-1) ...
Setting up dh-strip-nondeterminism (1.13.1-1) ...
Setting up libdrm2:arm64 (2.4.120-2) ...
Setting up libfreexl1:arm64 (2.0.0-1+b2) ...
Setting up libva-drm2:arm64 (2.21.0-1) ...
Setting up groff-base (1.23.0-4) ...
Setting up libxcb-randr0:arm64 (1.17.0-1) ...
Setting up libx11-6:arm64 (2:1.8.7-1+b1) ...
Setting up libharfbuzz0b:arm64 (8.3.0-2+b1) ...
Setting up libgdk-pixbuf-2.0-0:arm64 (2.42.10+dfsg-3+b3) ...
Setting up libgphoto2-port12t64:arm64 (2.5.31-2.1+b1) ...
Setting up libamd-comgr2:arm64 (6.0+git20231212.4510c28+dfsg-3+b1) ...
Setting up libfontconfig1:arm64 (2.15.0-1.1) ...
Setting up libbluray2:arm64 (1:1.3.4-1+b1) ...
Setting up libsm6:arm64 (2:1.2.3-1+b1) ...
Setting up libarchive13t64:arm64 (3.7.2-2) ...
Setting up libopenmpt0t64:arm64 (0.7.6-1+b1) ...
Setting up libarmadillo12 (1:12.8.2+dfsg-1) ...
Setting up libdrm-amdgpu1:arm64 (2.4.120-2) ...
Setting up libssh-gcrypt-4:arm64 (0.10.6-2+b1) ...
Setting up libxcb-dri3-0:arm64 (1.17.0-1) ...
Setting up libinput10:arm64 (1.25.0-1+b2) ...
Setting up libx11-xcb1:arm64 (2:1.8.7-1+b1) ...
Setting up libibverbs1:arm64 (50.0-2+b1) ...
Setting up fontconfig (2.15.0-1.1) ...
Regenerating fonts cache... done.
Setting up libavif16:arm64 (1.0.4-3) ...
Setting up libdrm-nouveau2:arm64 (2.4.120-2) ...
Setting up libzmq5:arm64 (4.3.5-1+b2) ...
Setting up libpython3.12-stdlib:arm64 (3.12.3-1) ...
Setting up libxpm4:arm64 (1:3.5.17-1+b1) ...
Setting up libxrender1:arm64 (1:0.9.10-1.1+b1) ...
Setting up libcurl4t64:arm64 (8.7.1-5) ...
Setting up libtirpc3t64:arm64 (1.3.4+ds-1.3) ...
Setting up libgbm1:arm64 (24.0.7-1) ...
Setting up ibverbs-providers:arm64 (50.0-2+b1) ...
Setting up libogdi4.1:arm64 (4.1.1+ds-4) ...
Setting up libdrm-radeon1:arm64 (2.4.120-2) ...
Setting up po-debconf (1.0.21+nmu1) ...
Setting up libpython3.11-stdlib:arm64 (3.11.9-1) ...
Setting up python3.12 (3.12.3-1) ...
Setting up libpango-1.0-0:arm64 (1.52.2+ds-1) ...
Setting up libgl1-mesa-dri:arm64 (24.0.7-1) ...
Setting up libpq5:arm64 (16.3-1) ...
Setting up libcurl3t64-gnutls:arm64 (8.7.1-5) ...
Setting up libxext6:arm64 (2:1.3.4-1+b1) ...
Setting up libgstreamer-plugins-base1.0-0:arm64 (1.24.3-1) ...
Setting up libtbb12:arm64 (2021.11.0-2) ...
Setting up libkmlengine1t64:arm64 (1.3.0-12) ...
Setting up libxerces-c3.2t64:arm64 (3.2.4+debian-1.3) ...
Setting up man-db (2.12.1-1) ...
Not building database; man-db/auto-update is not 'true'.
Setting up libcairo2:arm64 (1.18.0-3+b1) ...
Setting up libxxf86vm1:arm64 (1:1.1.4-1+b2) ...
Setting up libxnvctrl0:arm64 (535.171.04-1) ...
Setting up libpoppler126t64:arm64 (22.12.0-2.2+b1) ...
Setting up libqt5core5t64:arm64 (5.15.10+dfsg-7.2+b1) ...
Setting up libegl-mesa0:arm64 (24.0.7-1) ...
Setting up libcfitsio10t64:arm64 (4.3.1-1.1+b1) ...
Setting up libxfixes3:arm64 (1:6.0.0-2+b1) ...
Setting up libtesseract5:arm64 (5.3.4-1.1+b1) ...
Setting up libvdpau1:arm64 (1.5-2+b1) ...
Setting up libtheora0:arm64 (1.1.1+dfsg.1-16.1+b2) ...
Setting up libegl1:arm64 (1.7.0-1+b1) ...
Setting up librdmacm1t64:arm64 (50.0-2+b1) ...
Setting up libcairo-gobject2:arm64 (1.18.0-3+b1) ...
Setting up libpangoft2-1.0-0:arm64 (1.52.2+ds-1) ...
Setting up libhdf5-103-1t64:arm64 (1.10.10+repack-3.3) ...
Setting up libva-x11-2:arm64 (2.21.0-1) ...
Setting up libhsakmt1:arm64 (5.7.0-1) ...
Setting up libpangocairo-1.0-0:arm64 (1.52.2+ds-1) ...
Setting up libpython3-stdlib:arm64 (3.11.8-1) ...
Setting up libfabric1:arm64 (1.17.0-3+b1) ...
Setting up libhdf5-hl-100t64:arm64 (1.10.10+repack-3.3) ...
Setting up python3.11 (3.11.9-1) ...
Setting up libnetcdf19t64:arm64 (1:4.9.2-6) ...
Setting up libqt5dbus5t64:arm64 (5.15.10+dfsg-7.2+b1) ...
Setting up libglx-mesa0:arm64 (24.0.7-1) ...
Setting up libglx0:arm64 (1.7.0-1+b1) ...
Setting up libproj25:arm64 (9.4.0-1+b1) ...
Setting up libqt5network5t64:arm64 (5.15.10+dfsg-7.2+b1) ...
Setting up debhelper (13.15.3) ...
Setting up libavutil58:arm64 (7:6.1.1-4+b1) ...
Setting up python3 (3.11.8-1) ...
Setting up python3-sortedcontainers (2.4.0-2) ...
Setting up libqt5test5t64:arm64 (5.15.10+dfsg-7.2+b1) ...
Setting up libhwloc-plugins:arm64 (2.10.0-1+b1) ...
Setting up python3-markupsafe (2.1.5-1) ...
Setting up python3-psutil (5.9.8-2) ...
Setting up libgeotiff5:arm64 (1.7.1-5+b1) ...
Setting up librsvg2-2:arm64 (2.58.0+dfsg-1) ...
Setting up python3-six (1.16.0-6) ...
Setting up python3-jinja2 (3.1.3-1) ...
Setting up python3-packaging (24.0-1) ...
Setting up libgl1:arm64 (1.7.0-1+b1) ...
Setting up python3-certifi (2023.11.17-1) ...
Setting up libswresample4:arm64 (7:6.1.1-4+b1) ...
Setting up python3-idna (3.6-2) ...
Setting up python3-typing-extensions (4.10.0-1) ...
Setting up libpmix2t64:arm64 (5.0.2-3) ...
Setting up python3-urllib3 (1.26.18-2) ...
Setting up python3-pluggy (1.5.0-1) ...
Setting up libavcodec60:arm64 (7:6.1.1-4+b1) ...
Setting up libhsa-runtime64-1 (5.7.1-2) ...
Setting up python3-dateutil (2.9.0-2) ...
Setting up python3-mpmath (1.3.0-1) ...
Setting up python3-lib2to3 (3.12.3-1) ...
Setting up libopencv-core406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libswscale7:arm64 (7:6.1.1-4+b1) ...
Setting up libspatialite8t64:arm64 (5.1.0-3+b1) ...
Setting up python3-pkg-resources (68.1.2-2) ...
Setting up python3-distutils (3.12.3-1) ...
python3.12: can't get files for byte-compilation
Setting up python3-iniconfig (1.1.1-2) ...
Setting up python3-sympy (1.12-7) ...
Setting up python3-attr (23.2.0-2) ...
Setting up python3-filelock (3.14.0-1) ...
Setting up python3-astunparse (1.6.3-2) ...
Setting up python3-setuptools (68.1.2-2) ...
Setting up python3-charset-normalizer (3.3.2-1) ...
Setting up python3-pytest (8.1.2-1) ...
Setting up python3-hypothesis (6.100.7-1) ...
Setting up python3-fsspec (2024.3.1-1) ...
Setting up python3-all (3.11.8-1) ...
Setting up python3-yaml (6.0.1-2) ...
Setting up libopencv-flann406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libavformat60:arm64 (7:6.1.1-4+b1) ...
Setting up python3-networkx (3.2.1-4) ...
Setting up libqt5gui5t64:arm64 (5.15.10+dfsg-7.2+b1) ...
Setting up libopencv-ml406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up python3-chardet (5.2.0+dfsg-1) ...
Setting up libamdhip64-5 (5.7.1-3) ...
Setting up python3-requests (2.31.0+dfsg-1) ...
Setting up libopencv-imgproc406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up python3-numpy (1:1.26.4+ds-8) ...
Setting up python3-expecttest (0.1.5-1) ...
Setting up dh-python (6.20240422) ...
Setting up libucx0:arm64 (1.16.0+ds-5) ...
Setting up libopencv-dnn406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libqt5widgets5t64:arm64 (5.15.10+dfsg-7.2+b1) ...
Setting up libopencv-features2d406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libopenmpi3t64:arm64 (4.1.6-13.3) ...
Setting up libqt5opengl5t64:arm64 (5.15.10+dfsg-7.2+b1) ...
Setting up libopencv-calib3d406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libopencv-video406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libgloo0 (0.0~git20230519.597accf-2+b2) ...
Setting up libopencv-highgui406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libopencv-objdetect406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libheif1:arm64 (1.17.6-1+b2) ...
Setting up libgdal34t64:arm64 (3.8.5+dfsg-1) ...
Setting up libgd3:arm64 (2.3.3-9+b3) ...
Setting up libheif-plugin-dav1d:arm64 (1.17.6-1+b2) ...
Setting up libheif-plugin-libde265:arm64 (1.17.6-1+b2) ...
Setting up libopencv-imgcodecs406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libopencv-contrib406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libgphoto2-6t64:arm64 (2.5.31-2.1+b1) ...
No diversion 'diversion of /lib/udev/hwdb.d/20-libgphoto2-6.hwdb to /lib/udev/hwdb.d/20-libgphoto2-6.hwdb.usr-is-merged by usr-is-merged', none removed.
No diversion 'diversion of /lib/udev/rules.d/60-libgphoto2-6.rules to /lib/udev/rules.d/60-libgphoto2-6.rules.usr-is-merged by usr-is-merged', none removed.
Setting up libopencv-videoio406t64:arm64 (4.6.0+dfsg-13.1+b2) ...
Setting up libtorch2.1:arm64 (2.1.2+dfsg-4) ...
Setting up libtorch-test (2.1.2+dfsg-4) ...
Setting up python3-torch (2.1.2+dfsg-4) ...
Processing triggers for libc-bin (2.38-11) ...
Processing triggers for ca-certificates (20240203) ...
Updating certificates in /etc/ssl/certs...
0 added, 0 removed; done.
Running hooks in /etc/ca-certificates/update.d...
done.
Reading package lists...
Building dependency tree...
Reading state information...
Reading extended state information...
Initializing package states...
Writing extended state information...
Building tag database...
 -> Finished parsing the build-deps
I: Building the package
I: Running cd /build/reproducible-path/python-array-api-compat-1.4/ && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-buildpackage -us -uc -b && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-genchanges -S  > ../python-array-api-compat_1.4-1_source.changes
dpkg-buildpackage: info: source package python-array-api-compat
dpkg-buildpackage: info: source version 1.4-1
dpkg-buildpackage: info: source distribution unstable
dpkg-buildpackage: info: source changed by Andreas Tille <tille@debian.org>
 dpkg-source --before-build .
dpkg-buildpackage: info: host architecture arm64
 debian/rules clean
dh clean --with python3 --buildsystem=pybuild
   dh_auto_clean -O--buildsystem=pybuild
I: pybuild base:311: python3.12 setup.py clean 
running clean
removing '/build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build' (and everything under it)
'build/bdist.linux-aarch64' does not exist -- can't clean it
'build/scripts-3.12' does not exist -- can't clean it
I: pybuild base:311: python3.11 setup.py clean 
running clean
removing '/build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build' (and everything under it)
'build/bdist.linux-aarch64' does not exist -- can't clean it
'build/scripts-3.11' does not exist -- can't clean it
   dh_autoreconf_clean -O--buildsystem=pybuild
   dh_clean -O--buildsystem=pybuild
 debian/rules binary
dh binary --with python3 --buildsystem=pybuild
   dh_update_autotools_config -O--buildsystem=pybuild
   dh_autoreconf -O--buildsystem=pybuild
   dh_auto_configure -O--buildsystem=pybuild
I: pybuild base:311: python3.12 setup.py config 
running config
I: pybuild base:311: python3.11 setup.py config 
running config
   dh_auto_build -O--buildsystem=pybuild
I: pybuild base:311: /usr/bin/python3.12 setup.py build 
running build
running build_py
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat
copying array_api_compat/_internal.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat
copying array_api_compat/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/torch
copying array_api_compat/torch/linalg.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/torch
copying array_api_compat/torch/_aliases.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/torch
copying array_api_compat/torch/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/torch
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/numpy
copying array_api_compat/numpy/linalg.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/numpy
copying array_api_compat/numpy/_typing.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/numpy
copying array_api_compat/numpy/_aliases.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/numpy
copying array_api_compat/numpy/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/numpy
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/cupy
copying array_api_compat/cupy/linalg.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/cupy
copying array_api_compat/cupy/_typing.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/cupy
copying array_api_compat/cupy/_aliases.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/cupy
copying array_api_compat/cupy/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/cupy
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/_typing.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/_linalg.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/_helpers.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/_aliases.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build/array_api_compat/common
I: pybuild base:311: /usr/bin/python3 setup.py build 
running build
running build_py
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat
copying array_api_compat/_internal.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat
copying array_api_compat/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/torch
copying array_api_compat/torch/linalg.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/torch
copying array_api_compat/torch/_aliases.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/torch
copying array_api_compat/torch/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/torch
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/numpy
copying array_api_compat/numpy/linalg.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/numpy
copying array_api_compat/numpy/_typing.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/numpy
copying array_api_compat/numpy/_aliases.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/numpy
copying array_api_compat/numpy/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/numpy
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/cupy
copying array_api_compat/cupy/linalg.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/cupy
copying array_api_compat/cupy/_typing.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/cupy
copying array_api_compat/cupy/_aliases.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/cupy
copying array_api_compat/cupy/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/cupy
creating /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/_typing.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/_linalg.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/_helpers.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/_aliases.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/common
copying array_api_compat/common/__init__.py -> /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build/array_api_compat/common
   dh_auto_test -O--buildsystem=pybuild
I: pybuild base:311: cd /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build; python3.12 -m pytest --ignore tests/test_vendoring.py
============================= test session starts ==============================
platform linux -- Python 3.12.3, pytest-8.1.2, pluggy-1.5.0
rootdir: /build/reproducible-path/python-array-api-compat-1.4
plugins: hypothesis-6.100.7
collected 27 items

tests/test_array_namespace.py s.Fs.F.F.                                  [ 33%]
tests/test_common.py s.F                                                 [ 44%]
tests/test_isdtype.py s.Fs.Fs.Fs.Fs.F                                    [100%]

=================================== FAILURES ===================================
_______________________ test_array_namespace[None-torch] _______________________

library = 'torch', api_version = None

    @pytest.mark.parametrize("library", ["cupy", "numpy", "torch"])
    @pytest.mark.parametrize("api_version", [None, '2021.12'])
    def test_array_namespace(library, api_version):
>       lib = import_(library)

tests/test_array_namespace.py:12: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/_helpers.py:8: in import_
    return import_module(library)
/usr/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1387: in _gcd_import
    ???
<frozen importlib._bootstrap>:1360: in _find_and_load
    ???
<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked
    ???
<frozen importlib._bootstrap>:935: in _load_unlocked
    ???
<frozen importlib._bootstrap_external>:995: in exec_module
    ???
<frozen importlib._bootstrap>:488: in _call_with_frames_removed
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    
    r"""
    The torch package contains data structures for multi-dimensional
    tensors and defines mathematical operations over these tensors.
    Additionally, it provides many utilities for efficient serialization of
    Tensors and arbitrary types, and other useful utilities.
    
    It has a CUDA counterpart, that enables you to run your tensor computations
    on an NVIDIA GPU with compute capability >= 3.0.
    """
    
    import math
    import os
    import sys
    import platform
    import textwrap
    import ctypes
    import inspect
    
    # multipy/deploy is setting this import before importing torch, this is the most
    # reliable way we have to detect if we're running within deploy.
    # https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
    def _running_with_deploy():
        return sys.modules.get("torch._meta_registrations", None) is object
    
    from ._utils import _import_dotted_name, classproperty
    from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
        USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
    
    # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
    if _running_with_deploy():
        __version__ = "torch-deploy-1.8"
    else:
        from .torch_version import __version__ as __version__
    
    from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
    import builtins
    
    __all__ = [
        'typename', 'is_tensor', 'is_storage',
        'set_default_tensor_type', 'set_default_device',
        'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
        'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
        'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
        'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
        'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
        'TypedStorage', 'UntypedStorage',
        'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
        'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
        'lobpcg', 'use_deterministic_algorithms',
        'are_deterministic_algorithms_enabled',
        'is_deterministic_algorithms_warn_only_enabled',
        'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
        'set_float32_matmul_precision', 'get_float32_matmul_precision',
        'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
        'SymBool', 'sym_not',
        'sym_int', 'sym_float', 'sym_max', 'sym_min', 'compile', 'vmap',
        'export',
    ]
    
    ################################################################################
    # Load the extension module
    ################################################################################
    
    if sys.platform == 'win32':
        pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
        py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
        th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
    
        # When users create a virtualenv that inherits the base environment,
        # we will need to add the corresponding library directory into
        # DLL search directories. Otherwise, it will rely on `PATH` which
        # is dependent on user settings.
        if sys.exec_prefix != sys.base_exec_prefix:
            base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
        else:
            base_py_dll_path = ''
    
        dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
    
        if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
            nvtoolsext_dll_path = os.path.join(
                os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
        else:
            nvtoolsext_dll_path = ''
    
        from .version import cuda as cuda_version
        import glob
        if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
            cuda_version_1 = cuda_version.replace('.', '_')
            cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
            default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
            cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
        else:
            cuda_path = ''
    
        dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
    
        kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
        with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
        prev_error_mode = kernel32.SetErrorMode(0x0001)
    
        kernel32.LoadLibraryW.restype = ctypes.c_void_p
        if with_load_library_flags:
            kernel32.LoadLibraryExW.restype = ctypes.c_void_p
    
        for dll_path in dll_paths:
            os.add_dll_directory(dll_path)
    
        try:
            ctypes.CDLL('vcruntime140.dll')
            ctypes.CDLL('msvcp140.dll')
            ctypes.CDLL('vcruntime140_1.dll')
        except OSError:
            print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
                     It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
    
        dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
        path_patched = False
        for dll in dlls:
            is_loaded = False
            if with_load_library_flags:
                res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
                last_error = ctypes.get_last_error()
                if res is None and last_error != 126:
                    err = ctypes.WinError(last_error)
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
                elif res is not None:
                    is_loaded = True
            if not is_loaded:
                if not path_patched:
                    os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
                    path_patched = True
                res = kernel32.LoadLibraryW(dll)
                if res is None:
                    err = ctypes.WinError(ctypes.get_last_error())
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
    
        kernel32.SetErrorMode(prev_error_mode)
    
    
    def _preload_cuda_deps(lib_folder, lib_name):
        """Preloads cuda deps if they could not be found otherwise."""
        # Should only be called on Linux if default path resolution have failed
        assert platform.system() == 'Linux', 'Should only be called on Linux'
        import glob
        lib_path = None
        for path in sys.path:
            nvidia_path = os.path.join(path, 'nvidia')
            if not os.path.exists(nvidia_path):
                continue
            candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
            if candidate_lib_paths and not lib_path:
                lib_path = candidate_lib_paths[0]
            if lib_path:
                break
        if not lib_path:
            raise ValueError(f"{lib_name} not found in the system path {sys.path}")
        ctypes.CDLL(lib_path)
    
    
    # See Note [Global dependencies]
    def _load_global_deps() -> None:
        if _running_with_deploy() or platform.system() == 'Windows':
            return
    
        lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
        here = os.path.abspath(__file__)
        lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
    
        try:
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
        except OSError as err:
            # Can only happen for wheel with cuda libs as PYPI deps
            # As PyTorch is not purelib, but nvidia-*-cu12 is
            cuda_libs: Dict[str, str] = {
                'cublas': 'libcublas.so.*[0-9]',
                'cudnn': 'libcudnn.so.*[0-9]',
                'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
                'cuda_runtime': 'libcudart.so.*[0-9]',
                'cuda_cupti': 'libcupti.so.*[0-9]',
                'cufft': 'libcufft.so.*[0-9]',
                'curand': 'libcurand.so.*[0-9]',
                'cusolver': 'libcusolver.so.*[0-9]',
                'cusparse': 'libcusparse.so.*[0-9]',
                'nccl': 'libnccl.so.*[0-9]',
                'nvtx': 'libnvToolsExt.so.*[0-9]',
            }
            is_cuda_lib_err = [lib for lib in cuda_libs.values() if(lib.split('.')[0] in err.args[0])]
            if not is_cuda_lib_err:
                raise err
            for lib_folder, lib_name in cuda_libs.items():
                _preload_cuda_deps(lib_folder, lib_name)
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
    
    
    if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
            (_running_with_deploy() or platform.system() != 'Windows'):
        # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
        # few circumstances:
        #
        #   1. You're in a build environment (e.g., fbcode) where
        #      libtorch_global_deps is not available, but you still need
        #      to get mkl to link in with RTLD_GLOBAL or it will just
        #      not work.
        #
        #   2. You're trying to run PyTorch under UBSAN and you need
        #      to ensure that only one copy of libtorch is loaded, so
        #      vptr checks work properly
        #
        # If you're using this setting, you must verify that all the libraries
        # you load consistently use the same libstdc++, or you may have
        # mysterious segfaults.
        #
        old_flags = sys.getdlopenflags()
        sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
        from torch._C import *  # noqa: F403
        sys.setdlopenflags(old_flags)
        del old_flags
    
    else:
        # Easy way.  You want this most of the time, because it will prevent
        # C++ symbols from libtorch clobbering C++ symbols from other
        # libraries, leading to mysterious segfaults.
        #
        # If building in an environment where libtorch_global_deps isn't available
        # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
        # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
        #
        # See Note [Global dependencies]
        if USE_GLOBAL_DEPS:
            _load_global_deps()
        from torch._C import *  # noqa: F403
    
    # Appease the type checker; ordinarily this binding is inserted by the
    # torch._C module initialization code in C
    if TYPE_CHECKING:
        import torch._C as _C
    
    class SymInt:
        """
        Like an int (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return builtins.bool(self != 0)
    
        def __int__(self):
            return self.node.int_()
    
        def __index__(self):
            return self.node.int_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_float__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return str(self.node)
    
    class SymFloat:
        """
        Like an float (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_int__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    class SymBool:
        """
        Like an bool (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
    
        Unlike regular bools, regular boolean operators will force extra guards instead
        of symbolically evaluate.  Use the bitwise operators instead to handle this.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        def __int__(self):
            return builtins.int(self.node.bool_())
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
        def __and__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __or__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        # We very carefully define __sym_not__, and not a number of other
        # plausible alternatives:
        #
        #   - We do not override __not__ because this is not a real magic
        #     method; you cannot override the meaning of the not builtin in
        #     Python.  We use the name 'sym_not' to clarify that in user code you
        #     cannot use the builtin not or operator.not_ or operator.__not__ and
        #     hit this magic method; you must use our custom sym_not operator.
        #
        #   - We do not override the __invert__ method because SymBool is
        #     meant to be usable in situations where bool is expected.  However,
        #     bitwise negation ~a does the wrong thing with booleans (because
        #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
        #     This would be a giant footgun, so we get around it by defining
        #     our own operator.  Note that bitwise and/or do the right thing,
        #     so we reuse the conventional operators there for readability.
        #
        def __sym_not__(self) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    def sym_not(a):
        r""" SymInt-aware utility for logical negation.
    
        Args:
            a (SymBool or bool): Object to negate
        """
        if hasattr(a, '__sym_not__'):
            return a.__sym_not__()
        return not a
    
    def sym_float(a):
        r""" SymInt-aware utility for float casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymFloat):
            return a
        elif hasattr(a, '__sym_float__'):
            return a.__sym_float__()
        return py_float(a)  # type: ignore[operator]
    
    
    def sym_int(a):
        r""" SymInt-aware utility for int casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymInt):
            return a
        elif isinstance(a, SymFloat):
            return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
        return py_int(a)  # type: ignore[operator]
    
    def sym_max(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_max__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            # NB: If you actually care about preserving output type exactly
            # if you do something like max(0, 0.0), it is NOT sound to treat
            # min/max as commutative
            return b.__sym_max__(a)
        return builtins.max(a, b)  # type: ignore[operator]
    
    def sym_min(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_min__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            return b.__sym_min__(a)
        return builtins.min(a, b)  # type: ignore[operator]
    
    # Check to see if we can load C extensions, and if not provide some guidance
    # on what the problem might be.
    try:
        # _initExtension is chosen (arbitrarily) as a sentinel.
        from torch._C import _initExtension
    except ImportError:
        import torch._C as _C_for_compiled_check
    
        # The __file__ check only works for Python 3.7 and above.
        if _C_for_compiled_check.__file__ is None:
>           raise ImportError(textwrap.dedent('''
                Failed to load PyTorch C extensions:
                    It appears that PyTorch has loaded the `torch/_C` folder
                    of the PyTorch repository rather than the C extensions which
                    are expected in the `torch._C` namespace. This can occur when
                    using the `install` workflow. e.g.
                        $ python setup.py install && python -c "import torch"
    
                    This error can generally be solved using the `develop` workflow
                        $ python setup.py develop && python -c "import torch"  # This should succeed
                    or by running Python from a different directory.
                ''').strip()) from None
E           ImportError: Failed to load PyTorch C extensions:
E               It appears that PyTorch has loaded the `torch/_C` folder
E               of the PyTorch repository rather than the C extensions which
E               are expected in the `torch._C` namespace. This can occur when
E               using the `install` workflow. e.g.
E                   $ python setup.py install && python -c "import torch"
E           
E               This error can generally be solved using the `develop` workflow
E                   $ python setup.py develop && python -c "import torch"  # This should succeed
E               or by running Python from a different directory.

/usr/lib/python3/dist-packages/torch/__init__.py:451: ImportError
_____________________ test_array_namespace[2021.12-torch] ______________________

library = 'torch', api_version = '2021.12'

    @pytest.mark.parametrize("library", ["cupy", "numpy", "torch"])
    @pytest.mark.parametrize("api_version", [None, '2021.12'])
    def test_array_namespace(library, api_version):
>       lib = import_(library)

tests/test_array_namespace.py:12: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/_helpers.py:8: in import_
    return import_module(library)
/usr/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1387: in _gcd_import
    ???
<frozen importlib._bootstrap>:1360: in _find_and_load
    ???
<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked
    ???
<frozen importlib._bootstrap>:935: in _load_unlocked
    ???
<frozen importlib._bootstrap_external>:995: in exec_module
    ???
<frozen importlib._bootstrap>:488: in _call_with_frames_removed
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    
    r"""
    The torch package contains data structures for multi-dimensional
    tensors and defines mathematical operations over these tensors.
    Additionally, it provides many utilities for efficient serialization of
    Tensors and arbitrary types, and other useful utilities.
    
    It has a CUDA counterpart, that enables you to run your tensor computations
    on an NVIDIA GPU with compute capability >= 3.0.
    """
    
    import math
    import os
    import sys
    import platform
    import textwrap
    import ctypes
    import inspect
    
    # multipy/deploy is setting this import before importing torch, this is the most
    # reliable way we have to detect if we're running within deploy.
    # https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
    def _running_with_deploy():
        return sys.modules.get("torch._meta_registrations", None) is object
    
    from ._utils import _import_dotted_name, classproperty
    from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
        USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
    
    # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
    if _running_with_deploy():
        __version__ = "torch-deploy-1.8"
    else:
        from .torch_version import __version__ as __version__
    
    from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
    import builtins
    
    __all__ = [
        'typename', 'is_tensor', 'is_storage',
        'set_default_tensor_type', 'set_default_device',
        'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
        'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
        'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
        'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
        'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
        'TypedStorage', 'UntypedStorage',
        'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
        'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
        'lobpcg', 'use_deterministic_algorithms',
        'are_deterministic_algorithms_enabled',
        'is_deterministic_algorithms_warn_only_enabled',
        'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
        'set_float32_matmul_precision', 'get_float32_matmul_precision',
        'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
        'SymBool', 'sym_not',
        'sym_int', 'sym_float', 'sym_max', 'sym_min', 'compile', 'vmap',
        'export',
    ]
    
    ################################################################################
    # Load the extension module
    ################################################################################
    
    if sys.platform == 'win32':
        pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
        py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
        th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
    
        # When users create a virtualenv that inherits the base environment,
        # we will need to add the corresponding library directory into
        # DLL search directories. Otherwise, it will rely on `PATH` which
        # is dependent on user settings.
        if sys.exec_prefix != sys.base_exec_prefix:
            base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
        else:
            base_py_dll_path = ''
    
        dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
    
        if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
            nvtoolsext_dll_path = os.path.join(
                os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
        else:
            nvtoolsext_dll_path = ''
    
        from .version import cuda as cuda_version
        import glob
        if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
            cuda_version_1 = cuda_version.replace('.', '_')
            cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
            default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
            cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
        else:
            cuda_path = ''
    
        dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
    
        kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
        with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
        prev_error_mode = kernel32.SetErrorMode(0x0001)
    
        kernel32.LoadLibraryW.restype = ctypes.c_void_p
        if with_load_library_flags:
            kernel32.LoadLibraryExW.restype = ctypes.c_void_p
    
        for dll_path in dll_paths:
            os.add_dll_directory(dll_path)
    
        try:
            ctypes.CDLL('vcruntime140.dll')
            ctypes.CDLL('msvcp140.dll')
            ctypes.CDLL('vcruntime140_1.dll')
        except OSError:
            print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
                     It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
    
        dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
        path_patched = False
        for dll in dlls:
            is_loaded = False
            if with_load_library_flags:
                res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
                last_error = ctypes.get_last_error()
                if res is None and last_error != 126:
                    err = ctypes.WinError(last_error)
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
                elif res is not None:
                    is_loaded = True
            if not is_loaded:
                if not path_patched:
                    os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
                    path_patched = True
                res = kernel32.LoadLibraryW(dll)
                if res is None:
                    err = ctypes.WinError(ctypes.get_last_error())
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
    
        kernel32.SetErrorMode(prev_error_mode)
    
    
    def _preload_cuda_deps(lib_folder, lib_name):
        """Preloads cuda deps if they could not be found otherwise."""
        # Should only be called on Linux if default path resolution have failed
        assert platform.system() == 'Linux', 'Should only be called on Linux'
        import glob
        lib_path = None
        for path in sys.path:
            nvidia_path = os.path.join(path, 'nvidia')
            if not os.path.exists(nvidia_path):
                continue
            candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
            if candidate_lib_paths and not lib_path:
                lib_path = candidate_lib_paths[0]
            if lib_path:
                break
        if not lib_path:
            raise ValueError(f"{lib_name} not found in the system path {sys.path}")
        ctypes.CDLL(lib_path)
    
    
    # See Note [Global dependencies]
    def _load_global_deps() -> None:
        if _running_with_deploy() or platform.system() == 'Windows':
            return
    
        lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
        here = os.path.abspath(__file__)
        lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
    
        try:
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
        except OSError as err:
            # Can only happen for wheel with cuda libs as PYPI deps
            # As PyTorch is not purelib, but nvidia-*-cu12 is
            cuda_libs: Dict[str, str] = {
                'cublas': 'libcublas.so.*[0-9]',
                'cudnn': 'libcudnn.so.*[0-9]',
                'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
                'cuda_runtime': 'libcudart.so.*[0-9]',
                'cuda_cupti': 'libcupti.so.*[0-9]',
                'cufft': 'libcufft.so.*[0-9]',
                'curand': 'libcurand.so.*[0-9]',
                'cusolver': 'libcusolver.so.*[0-9]',
                'cusparse': 'libcusparse.so.*[0-9]',
                'nccl': 'libnccl.so.*[0-9]',
                'nvtx': 'libnvToolsExt.so.*[0-9]',
            }
            is_cuda_lib_err = [lib for lib in cuda_libs.values() if(lib.split('.')[0] in err.args[0])]
            if not is_cuda_lib_err:
                raise err
            for lib_folder, lib_name in cuda_libs.items():
                _preload_cuda_deps(lib_folder, lib_name)
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
    
    
    if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
            (_running_with_deploy() or platform.system() != 'Windows'):
        # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
        # few circumstances:
        #
        #   1. You're in a build environment (e.g., fbcode) where
        #      libtorch_global_deps is not available, but you still need
        #      to get mkl to link in with RTLD_GLOBAL or it will just
        #      not work.
        #
        #   2. You're trying to run PyTorch under UBSAN and you need
        #      to ensure that only one copy of libtorch is loaded, so
        #      vptr checks work properly
        #
        # If you're using this setting, you must verify that all the libraries
        # you load consistently use the same libstdc++, or you may have
        # mysterious segfaults.
        #
        old_flags = sys.getdlopenflags()
        sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
        from torch._C import *  # noqa: F403
        sys.setdlopenflags(old_flags)
        del old_flags
    
    else:
        # Easy way.  You want this most of the time, because it will prevent
        # C++ symbols from libtorch clobbering C++ symbols from other
        # libraries, leading to mysterious segfaults.
        #
        # If building in an environment where libtorch_global_deps isn't available
        # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
        # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
        #
        # See Note [Global dependencies]
        if USE_GLOBAL_DEPS:
            _load_global_deps()
        from torch._C import *  # noqa: F403
    
    # Appease the type checker; ordinarily this binding is inserted by the
    # torch._C module initialization code in C
    if TYPE_CHECKING:
        import torch._C as _C
    
    class SymInt:
        """
        Like an int (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return builtins.bool(self != 0)
    
        def __int__(self):
            return self.node.int_()
    
        def __index__(self):
            return self.node.int_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_float__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return str(self.node)
    
    class SymFloat:
        """
        Like an float (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_int__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    class SymBool:
        """
        Like an bool (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
    
        Unlike regular bools, regular boolean operators will force extra guards instead
        of symbolically evaluate.  Use the bitwise operators instead to handle this.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        def __int__(self):
            return builtins.int(self.node.bool_())
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
        def __and__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __or__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        # We very carefully define __sym_not__, and not a number of other
        # plausible alternatives:
        #
        #   - We do not override __not__ because this is not a real magic
        #     method; you cannot override the meaning of the not builtin in
        #     Python.  We use the name 'sym_not' to clarify that in user code you
        #     cannot use the builtin not or operator.not_ or operator.__not__ and
        #     hit this magic method; you must use our custom sym_not operator.
        #
        #   - We do not override the __invert__ method because SymBool is
        #     meant to be usable in situations where bool is expected.  However,
        #     bitwise negation ~a does the wrong thing with booleans (because
        #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
        #     This would be a giant footgun, so we get around it by defining
        #     our own operator.  Note that bitwise and/or do the right thing,
        #     so we reuse the conventional operators there for readability.
        #
        def __sym_not__(self) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    def sym_not(a):
        r""" SymInt-aware utility for logical negation.
    
        Args:
            a (SymBool or bool): Object to negate
        """
        if hasattr(a, '__sym_not__'):
            return a.__sym_not__()
        return not a
    
    def sym_float(a):
        r""" SymInt-aware utility for float casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymFloat):
            return a
        elif hasattr(a, '__sym_float__'):
            return a.__sym_float__()
        return py_float(a)  # type: ignore[operator]
    
    
    def sym_int(a):
        r""" SymInt-aware utility for int casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymInt):
            return a
        elif isinstance(a, SymFloat):
            return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
        return py_int(a)  # type: ignore[operator]
    
    def sym_max(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_max__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            # NB: If you actually care about preserving output type exactly
            # if you do something like max(0, 0.0), it is NOT sound to treat
            # min/max as commutative
            return b.__sym_max__(a)
        return builtins.max(a, b)  # type: ignore[operator]
    
    def sym_min(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_min__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            return b.__sym_min__(a)
        return builtins.min(a, b)  # type: ignore[operator]
    
    # Check to see if we can load C extensions, and if not provide some guidance
    # on what the problem might be.
    try:
        # _initExtension is chosen (arbitrarily) as a sentinel.
        from torch._C import _initExtension
    except ImportError:
        import torch._C as _C_for_compiled_check
    
        # The __file__ check only works for Python 3.7 and above.
        if _C_for_compiled_check.__file__ is None:
>           raise ImportError(textwrap.dedent('''
                Failed to load PyTorch C extensions:
                    It appears that PyTorch has loaded the `torch/_C` folder
                    of the PyTorch repository rather than the C extensions which
                    are expected in the `torch._C` namespace. This can occur when
                    using the `install` workflow. e.g.
                        $ python setup.py install && python -c "import torch"
    
                    This error can generally be solved using the `develop` workflow
                        $ python setup.py develop && python -c "import torch"  # This should succeed
                    or by running Python from a different directory.
                ''').strip()) from None
E           ImportError: Failed to load PyTorch C extensions:
E               It appears that PyTorch has loaded the `torch/_C` folder
E               of the PyTorch repository rather than the C extensions which
E               are expected in the `torch._C` namespace. This can occur when
E               using the `install` workflow. e.g.
E                   $ python setup.py install && python -c "import torch"
E           
E               This error can generally be solved using the `develop` workflow
E                   $ python setup.py develop && python -c "import torch"  # This should succeed
E               or by running Python from a different directory.

/usr/lib/python3/dist-packages/torch/__init__.py:451: ImportError
_________________________ test_array_namespace_errors __________________________

    def test_array_namespace_errors():
        pytest.raises(TypeError, lambda: array_namespace([1]))
        pytest.raises(TypeError, lambda: array_namespace())
    
        import numpy as np
>       import torch

tests/test_array_namespace.py:34: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    
    r"""
    The torch package contains data structures for multi-dimensional
    tensors and defines mathematical operations over these tensors.
    Additionally, it provides many utilities for efficient serialization of
    Tensors and arbitrary types, and other useful utilities.
    
    It has a CUDA counterpart, that enables you to run your tensor computations
    on an NVIDIA GPU with compute capability >= 3.0.
    """
    
    import math
    import os
    import sys
    import platform
    import textwrap
    import ctypes
    import inspect
    
    # multipy/deploy is setting this import before importing torch, this is the most
    # reliable way we have to detect if we're running within deploy.
    # https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
    def _running_with_deploy():
        return sys.modules.get("torch._meta_registrations", None) is object
    
    from ._utils import _import_dotted_name, classproperty
    from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
        USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
    
    # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
    if _running_with_deploy():
        __version__ = "torch-deploy-1.8"
    else:
        from .torch_version import __version__ as __version__
    
    from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
    import builtins
    
    __all__ = [
        'typename', 'is_tensor', 'is_storage',
        'set_default_tensor_type', 'set_default_device',
        'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
        'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
        'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
        'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
        'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
        'TypedStorage', 'UntypedStorage',
        'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
        'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
        'lobpcg', 'use_deterministic_algorithms',
        'are_deterministic_algorithms_enabled',
        'is_deterministic_algorithms_warn_only_enabled',
        'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
        'set_float32_matmul_precision', 'get_float32_matmul_precision',
        'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
        'SymBool', 'sym_not',
        'sym_int', 'sym_float', 'sym_max', 'sym_min', 'compile', 'vmap',
        'export',
    ]
    
    ################################################################################
    # Load the extension module
    ################################################################################
    
    if sys.platform == 'win32':
        pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
        py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
        th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
    
        # When users create a virtualenv that inherits the base environment,
        # we will need to add the corresponding library directory into
        # DLL search directories. Otherwise, it will rely on `PATH` which
        # is dependent on user settings.
        if sys.exec_prefix != sys.base_exec_prefix:
            base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
        else:
            base_py_dll_path = ''
    
        dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
    
        if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
            nvtoolsext_dll_path = os.path.join(
                os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
        else:
            nvtoolsext_dll_path = ''
    
        from .version import cuda as cuda_version
        import glob
        if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
            cuda_version_1 = cuda_version.replace('.', '_')
            cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
            default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
            cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
        else:
            cuda_path = ''
    
        dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
    
        kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
        with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
        prev_error_mode = kernel32.SetErrorMode(0x0001)
    
        kernel32.LoadLibraryW.restype = ctypes.c_void_p
        if with_load_library_flags:
            kernel32.LoadLibraryExW.restype = ctypes.c_void_p
    
        for dll_path in dll_paths:
            os.add_dll_directory(dll_path)
    
        try:
            ctypes.CDLL('vcruntime140.dll')
            ctypes.CDLL('msvcp140.dll')
            ctypes.CDLL('vcruntime140_1.dll')
        except OSError:
            print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
                     It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
    
        dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
        path_patched = False
        for dll in dlls:
            is_loaded = False
            if with_load_library_flags:
                res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
                last_error = ctypes.get_last_error()
                if res is None and last_error != 126:
                    err = ctypes.WinError(last_error)
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
                elif res is not None:
                    is_loaded = True
            if not is_loaded:
                if not path_patched:
                    os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
                    path_patched = True
                res = kernel32.LoadLibraryW(dll)
                if res is None:
                    err = ctypes.WinError(ctypes.get_last_error())
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
    
        kernel32.SetErrorMode(prev_error_mode)
    
    
    def _preload_cuda_deps(lib_folder, lib_name):
        """Preloads cuda deps if they could not be found otherwise."""
        # Should only be called on Linux if default path resolution have failed
        assert platform.system() == 'Linux', 'Should only be called on Linux'
        import glob
        lib_path = None
        for path in sys.path:
            nvidia_path = os.path.join(path, 'nvidia')
            if not os.path.exists(nvidia_path):
                continue
            candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
            if candidate_lib_paths and not lib_path:
                lib_path = candidate_lib_paths[0]
            if lib_path:
                break
        if not lib_path:
            raise ValueError(f"{lib_name} not found in the system path {sys.path}")
        ctypes.CDLL(lib_path)
    
    
    # See Note [Global dependencies]
    def _load_global_deps() -> None:
        if _running_with_deploy() or platform.system() == 'Windows':
            return
    
        lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
        here = os.path.abspath(__file__)
        lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
    
        try:
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
        except OSError as err:
            # Can only happen for wheel with cuda libs as PYPI deps
            # As PyTorch is not purelib, but nvidia-*-cu12 is
            cuda_libs: Dict[str, str] = {
                'cublas': 'libcublas.so.*[0-9]',
                'cudnn': 'libcudnn.so.*[0-9]',
                'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
                'cuda_runtime': 'libcudart.so.*[0-9]',
                'cuda_cupti': 'libcupti.so.*[0-9]',
                'cufft': 'libcufft.so.*[0-9]',
                'curand': 'libcurand.so.*[0-9]',
                'cusolver': 'libcusolver.so.*[0-9]',
                'cusparse': 'libcusparse.so.*[0-9]',
                'nccl': 'libnccl.so.*[0-9]',
                'nvtx': 'libnvToolsExt.so.*[0-9]',
            }
            is_cuda_lib_err = [lib for lib in cuda_libs.values() if(lib.split('.')[0] in err.args[0])]
            if not is_cuda_lib_err:
                raise err
            for lib_folder, lib_name in cuda_libs.items():
                _preload_cuda_deps(lib_folder, lib_name)
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
    
    
    if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
            (_running_with_deploy() or platform.system() != 'Windows'):
        # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
        # few circumstances:
        #
        #   1. You're in a build environment (e.g., fbcode) where
        #      libtorch_global_deps is not available, but you still need
        #      to get mkl to link in with RTLD_GLOBAL or it will just
        #      not work.
        #
        #   2. You're trying to run PyTorch under UBSAN and you need
        #      to ensure that only one copy of libtorch is loaded, so
        #      vptr checks work properly
        #
        # If you're using this setting, you must verify that all the libraries
        # you load consistently use the same libstdc++, or you may have
        # mysterious segfaults.
        #
        old_flags = sys.getdlopenflags()
        sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
        from torch._C import *  # noqa: F403
        sys.setdlopenflags(old_flags)
        del old_flags
    
    else:
        # Easy way.  You want this most of the time, because it will prevent
        # C++ symbols from libtorch clobbering C++ symbols from other
        # libraries, leading to mysterious segfaults.
        #
        # If building in an environment where libtorch_global_deps isn't available
        # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
        # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
        #
        # See Note [Global dependencies]
        if USE_GLOBAL_DEPS:
            _load_global_deps()
        from torch._C import *  # noqa: F403
    
    # Appease the type checker; ordinarily this binding is inserted by the
    # torch._C module initialization code in C
    if TYPE_CHECKING:
        import torch._C as _C
    
    class SymInt:
        """
        Like an int (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return builtins.bool(self != 0)
    
        def __int__(self):
            return self.node.int_()
    
        def __index__(self):
            return self.node.int_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_float__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return str(self.node)
    
    class SymFloat:
        """
        Like an float (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_int__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    class SymBool:
        """
        Like an bool (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
    
        Unlike regular bools, regular boolean operators will force extra guards instead
        of symbolically evaluate.  Use the bitwise operators instead to handle this.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        def __int__(self):
            return builtins.int(self.node.bool_())
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
        def __and__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __or__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        # We very carefully define __sym_not__, and not a number of other
        # plausible alternatives:
        #
        #   - We do not override __not__ because this is not a real magic
        #     method; you cannot override the meaning of the not builtin in
        #     Python.  We use the name 'sym_not' to clarify that in user code you
        #     cannot use the builtin not or operator.not_ or operator.__not__ and
        #     hit this magic method; you must use our custom sym_not operator.
        #
        #   - We do not override the __invert__ method because SymBool is
        #     meant to be usable in situations where bool is expected.  However,
        #     bitwise negation ~a does the wrong thing with booleans (because
        #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
        #     This would be a giant footgun, so we get around it by defining
        #     our own operator.  Note that bitwise and/or do the right thing,
        #     so we reuse the conventional operators there for readability.
        #
        def __sym_not__(self) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    def sym_not(a):
        r""" SymInt-aware utility for logical negation.
    
        Args:
            a (SymBool or bool): Object to negate
        """
        if hasattr(a, '__sym_not__'):
            return a.__sym_not__()
        return not a
    
    def sym_float(a):
        r""" SymInt-aware utility for float casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymFloat):
            return a
        elif hasattr(a, '__sym_float__'):
            return a.__sym_float__()
        return py_float(a)  # type: ignore[operator]
    
    
    def sym_int(a):
        r""" SymInt-aware utility for int casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymInt):
            return a
        elif isinstance(a, SymFloat):
            return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
        return py_int(a)  # type: ignore[operator]
    
    def sym_max(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_max__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            # NB: If you actually care about preserving output type exactly
            # if you do something like max(0, 0.0), it is NOT sound to treat
            # min/max as commutative
            return b.__sym_max__(a)
        return builtins.max(a, b)  # type: ignore[operator]
    
    def sym_min(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_min__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            return b.__sym_min__(a)
        return builtins.min(a, b)  # type: ignore[operator]
    
    # Check to see if we can load C extensions, and if not provide some guidance
    # on what the problem might be.
    try:
        # _initExtension is chosen (arbitrarily) as a sentinel.
        from torch._C import _initExtension
    except ImportError:
        import torch._C as _C_for_compiled_check
    
        # The __file__ check only works for Python 3.7 and above.
        if _C_for_compiled_check.__file__ is None:
>           raise ImportError(textwrap.dedent('''
                Failed to load PyTorch C extensions:
                    It appears that PyTorch has loaded the `torch/_C` folder
                    of the PyTorch repository rather than the C extensions which
                    are expected in the `torch._C` namespace. This can occur when
                    using the `install` workflow. e.g.
                        $ python setup.py install && python -c "import torch"
    
                    This error can generally be solved using the `develop` workflow
                        $ python setup.py develop && python -c "import torch"  # This should succeed
                    or by running Python from a different directory.
                ''').strip()) from None
E           ImportError: Failed to load PyTorch C extensions:
E               It appears that PyTorch has loaded the `torch/_C` folder
E               of the PyTorch repository rather than the C extensions which
E               are expected in the `torch._C` namespace. This can occur when
E               using the `install` workflow. e.g.
E                   $ python setup.py install && python -c "import torch"
E           
E               This error can generally be solved using the `develop` workflow
E                   $ python setup.py develop && python -c "import torch"  # This should succeed
E               or by running Python from a different directory.

/usr/lib/python3/dist-packages/torch/__init__.py:451: ImportError
__________________________ test_to_device_host[torch] __________________________

library = 'torch'

    @pytest.mark.parametrize("library", ["cupy", "numpy", "torch"])
    def test_to_device_host(library):
        # different libraries have different semantics
        # for DtoH transfers; ensure that we support a portable
        # shim for common array libs
        # see: https://github.com/scipy/scipy/issues/18286#issuecomment-1527552919
>       xp = import_('array_api_compat.' + library)

tests/test_common.py:14: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/_helpers.py:8: in import_
    return import_module(library)
/usr/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1387: in _gcd_import
    ???
<frozen importlib._bootstrap>:1360: in _find_and_load
    ???
<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked
    ???
<frozen importlib._bootstrap>:935: in _load_unlocked
    ???
<frozen importlib._bootstrap_external>:995: in exec_module
    ???
<frozen importlib._bootstrap>:488: in _call_with_frames_removed
    ???
array_api_compat/torch/__init__.py:1: in <module>
    from torch import *
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    
    r"""
    The torch package contains data structures for multi-dimensional
    tensors and defines mathematical operations over these tensors.
    Additionally, it provides many utilities for efficient serialization of
    Tensors and arbitrary types, and other useful utilities.
    
    It has a CUDA counterpart, that enables you to run your tensor computations
    on an NVIDIA GPU with compute capability >= 3.0.
    """
    
    import math
    import os
    import sys
    import platform
    import textwrap
    import ctypes
    import inspect
    
    # multipy/deploy is setting this import before importing torch, this is the most
    # reliable way we have to detect if we're running within deploy.
    # https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
    def _running_with_deploy():
        return sys.modules.get("torch._meta_registrations", None) is object
    
    from ._utils import _import_dotted_name, classproperty
    from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
        USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
    
    # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
    if _running_with_deploy():
        __version__ = "torch-deploy-1.8"
    else:
        from .torch_version import __version__ as __version__
    
    from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
    import builtins
    
    __all__ = [
        'typename', 'is_tensor', 'is_storage',
        'set_default_tensor_type', 'set_default_device',
        'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
        'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
        'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
        'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
        'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
        'TypedStorage', 'UntypedStorage',
        'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
        'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
        'lobpcg', 'use_deterministic_algorithms',
        'are_deterministic_algorithms_enabled',
        'is_deterministic_algorithms_warn_only_enabled',
        'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
        'set_float32_matmul_precision', 'get_float32_matmul_precision',
        'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
        'SymBool', 'sym_not',
        'sym_int', 'sym_float', 'sym_max', 'sym_min', 'compile', 'vmap',
        'export',
    ]
    
    ################################################################################
    # Load the extension module
    ################################################################################
    
    if sys.platform == 'win32':
        pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
        py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
        th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
    
        # When users create a virtualenv that inherits the base environment,
        # we will need to add the corresponding library directory into
        # DLL search directories. Otherwise, it will rely on `PATH` which
        # is dependent on user settings.
        if sys.exec_prefix != sys.base_exec_prefix:
            base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
        else:
            base_py_dll_path = ''
    
        dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
    
        if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
            nvtoolsext_dll_path = os.path.join(
                os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
        else:
            nvtoolsext_dll_path = ''
    
        from .version import cuda as cuda_version
        import glob
        if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
            cuda_version_1 = cuda_version.replace('.', '_')
            cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
            default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
            cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
        else:
            cuda_path = ''
    
        dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
    
        kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
        with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
        prev_error_mode = kernel32.SetErrorMode(0x0001)
    
        kernel32.LoadLibraryW.restype = ctypes.c_void_p
        if with_load_library_flags:
            kernel32.LoadLibraryExW.restype = ctypes.c_void_p
    
        for dll_path in dll_paths:
            os.add_dll_directory(dll_path)
    
        try:
            ctypes.CDLL('vcruntime140.dll')
            ctypes.CDLL('msvcp140.dll')
            ctypes.CDLL('vcruntime140_1.dll')
        except OSError:
            print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
                     It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
    
        dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
        path_patched = False
        for dll in dlls:
            is_loaded = False
            if with_load_library_flags:
                res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
                last_error = ctypes.get_last_error()
                if res is None and last_error != 126:
                    err = ctypes.WinError(last_error)
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
                elif res is not None:
                    is_loaded = True
            if not is_loaded:
                if not path_patched:
                    os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
                    path_patched = True
                res = kernel32.LoadLibraryW(dll)
                if res is None:
                    err = ctypes.WinError(ctypes.get_last_error())
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
    
        kernel32.SetErrorMode(prev_error_mode)
    
    
    def _preload_cuda_deps(lib_folder, lib_name):
        """Preloads cuda deps if they could not be found otherwise."""
        # Should only be called on Linux if default path resolution have failed
        assert platform.system() == 'Linux', 'Should only be called on Linux'
        import glob
        lib_path = None
        for path in sys.path:
            nvidia_path = os.path.join(path, 'nvidia')
            if not os.path.exists(nvidia_path):
                continue
            candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
            if candidate_lib_paths and not lib_path:
                lib_path = candidate_lib_paths[0]
            if lib_path:
                break
        if not lib_path:
            raise ValueError(f"{lib_name} not found in the system path {sys.path}")
        ctypes.CDLL(lib_path)
    
    
    # See Note [Global dependencies]
    def _load_global_deps() -> None:
        if _running_with_deploy() or platform.system() == 'Windows':
            return
    
        lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
        here = os.path.abspath(__file__)
        lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
    
        try:
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
        except OSError as err:
            # Can only happen for wheel with cuda libs as PYPI deps
            # As PyTorch is not purelib, but nvidia-*-cu12 is
            cuda_libs: Dict[str, str] = {
                'cublas': 'libcublas.so.*[0-9]',
                'cudnn': 'libcudnn.so.*[0-9]',
                'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
                'cuda_runtime': 'libcudart.so.*[0-9]',
                'cuda_cupti': 'libcupti.so.*[0-9]',
                'cufft': 'libcufft.so.*[0-9]',
                'curand': 'libcurand.so.*[0-9]',
                'cusolver': 'libcusolver.so.*[0-9]',
                'cusparse': 'libcusparse.so.*[0-9]',
                'nccl': 'libnccl.so.*[0-9]',
                'nvtx': 'libnvToolsExt.so.*[0-9]',
            }
            is_cuda_lib_err = [lib for lib in cuda_libs.values() if(lib.split('.')[0] in err.args[0])]
            if not is_cuda_lib_err:
                raise err
            for lib_folder, lib_name in cuda_libs.items():
                _preload_cuda_deps(lib_folder, lib_name)
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
    
    
    if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
            (_running_with_deploy() or platform.system() != 'Windows'):
        # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
        # few circumstances:
        #
        #   1. You're in a build environment (e.g., fbcode) where
        #      libtorch_global_deps is not available, but you still need
        #      to get mkl to link in with RTLD_GLOBAL or it will just
        #      not work.
        #
        #   2. You're trying to run PyTorch under UBSAN and you need
        #      to ensure that only one copy of libtorch is loaded, so
        #      vptr checks work properly
        #
        # If you're using this setting, you must verify that all the libraries
        # you load consistently use the same libstdc++, or you may have
        # mysterious segfaults.
        #
        old_flags = sys.getdlopenflags()
        sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
        from torch._C import *  # noqa: F403
        sys.setdlopenflags(old_flags)
        del old_flags
    
    else:
        # Easy way.  You want this most of the time, because it will prevent
        # C++ symbols from libtorch clobbering C++ symbols from other
        # libraries, leading to mysterious segfaults.
        #
        # If building in an environment where libtorch_global_deps isn't available
        # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
        # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
        #
        # See Note [Global dependencies]
        if USE_GLOBAL_DEPS:
            _load_global_deps()
        from torch._C import *  # noqa: F403
    
    # Appease the type checker; ordinarily this binding is inserted by the
    # torch._C module initialization code in C
    if TYPE_CHECKING:
        import torch._C as _C
    
    class SymInt:
        """
        Like an int (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return builtins.bool(self != 0)
    
        def __int__(self):
            return self.node.int_()
    
        def __index__(self):
            return self.node.int_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_float__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return str(self.node)
    
    class SymFloat:
        """
        Like an float (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_int__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    class SymBool:
        """
        Like an bool (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
    
        Unlike regular bools, regular boolean operators will force extra guards instead
        of symbolically evaluate.  Use the bitwise operators instead to handle this.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        def __int__(self):
            return builtins.int(self.node.bool_())
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
        def __and__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __or__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        # We very carefully define __sym_not__, and not a number of other
        # plausible alternatives:
        #
        #   - We do not override __not__ because this is not a real magic
        #     method; you cannot override the meaning of the not builtin in
        #     Python.  We use the name 'sym_not' to clarify that in user code you
        #     cannot use the builtin not or operator.not_ or operator.__not__ and
        #     hit this magic method; you must use our custom sym_not operator.
        #
        #   - We do not override the __invert__ method because SymBool is
        #     meant to be usable in situations where bool is expected.  However,
        #     bitwise negation ~a does the wrong thing with booleans (because
        #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
        #     This would be a giant footgun, so we get around it by defining
        #     our own operator.  Note that bitwise and/or do the right thing,
        #     so we reuse the conventional operators there for readability.
        #
        def __sym_not__(self) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    def sym_not(a):
        r""" SymInt-aware utility for logical negation.
    
        Args:
            a (SymBool or bool): Object to negate
        """
        if hasattr(a, '__sym_not__'):
            return a.__sym_not__()
        return not a
    
    def sym_float(a):
        r""" SymInt-aware utility for float casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymFloat):
            return a
        elif hasattr(a, '__sym_float__'):
            return a.__sym_float__()
        return py_float(a)  # type: ignore[operator]
    
    
    def sym_int(a):
        r""" SymInt-aware utility for int casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymInt):
            return a
        elif isinstance(a, SymFloat):
            return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
        return py_int(a)  # type: ignore[operator]
    
    def sym_max(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_max__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            # NB: If you actually care about preserving output type exactly
            # if you do something like max(0, 0.0), it is NOT sound to treat
            # min/max as commutative
            return b.__sym_max__(a)
        return builtins.max(a, b)  # type: ignore[operator]
    
    def sym_min(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_min__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            return b.__sym_min__(a)
        return builtins.min(a, b)  # type: ignore[operator]
    
    # Check to see if we can load C extensions, and if not provide some guidance
    # on what the problem might be.
    try:
        # _initExtension is chosen (arbitrarily) as a sentinel.
        from torch._C import _initExtension
    except ImportError:
        import torch._C as _C_for_compiled_check
    
        # The __file__ check only works for Python 3.7 and above.
        if _C_for_compiled_check.__file__ is None:
>           raise ImportError(textwrap.dedent('''
                Failed to load PyTorch C extensions:
                    It appears that PyTorch has loaded the `torch/_C` folder
                    of the PyTorch repository rather than the C extensions which
                    are expected in the `torch._C` namespace. This can occur when
                    using the `install` workflow. e.g.
                        $ python setup.py install && python -c "import torch"
    
                    This error can generally be solved using the `develop` workflow
                        $ python setup.py develop && python -c "import torch"  # This should succeed
                    or by running Python from a different directory.
                ''').strip()) from None
E           ImportError: Failed to load PyTorch C extensions:
E               It appears that PyTorch has loaded the `torch/_C` folder
E               of the PyTorch repository rather than the C extensions which
E               are expected in the `torch._C` namespace. This can occur when
E               using the `install` workflow. e.g.
E                   $ python setup.py install && python -c "import torch"
E           
E               This error can generally be solved using the `develop` workflow
E                   $ python setup.py develop && python -c "import torch"  # This should succeed
E               or by running Python from a different directory.

/usr/lib/python3/dist-packages/torch/__init__.py:451: ImportError
_______________________ test_isdtype_spec_dtypes[torch] ________________________

library = 'torch'

    @pytest.mark.parametrize("library", ["cupy", "numpy", "torch"])
    def test_isdtype_spec_dtypes(library):
>       xp = import_('array_api_compat.' + library)

tests/test_isdtype.py:69: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/_helpers.py:8: in import_
    return import_module(library)
/usr/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1387: in _gcd_import
    ???
<frozen importlib._bootstrap>:1360: in _find_and_load
    ???
<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked
    ???
<frozen importlib._bootstrap>:935: in _load_unlocked
    ???
<frozen importlib._bootstrap_external>:995: in exec_module
    ???
<frozen importlib._bootstrap>:488: in _call_with_frames_removed
    ???
array_api_compat/torch/__init__.py:1: in <module>
    from torch import *
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    
    r"""
    The torch package contains data structures for multi-dimensional
    tensors and defines mathematical operations over these tensors.
    Additionally, it provides many utilities for efficient serialization of
    Tensors and arbitrary types, and other useful utilities.
    
    It has a CUDA counterpart, that enables you to run your tensor computations
    on an NVIDIA GPU with compute capability >= 3.0.
    """
    
    import math
    import os
    import sys
    import platform
    import textwrap
    import ctypes
    import inspect
    
    # multipy/deploy is setting this import before importing torch, this is the most
    # reliable way we have to detect if we're running within deploy.
    # https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
    def _running_with_deploy():
        return sys.modules.get("torch._meta_registrations", None) is object
    
    from ._utils import _import_dotted_name, classproperty
    from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
        USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
    
    # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
    if _running_with_deploy():
        __version__ = "torch-deploy-1.8"
    else:
        from .torch_version import __version__ as __version__
    
    from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
    import builtins
    
    __all__ = [
        'typename', 'is_tensor', 'is_storage',
        'set_default_tensor_type', 'set_default_device',
        'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
        'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
        'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
        'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
        'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
        'TypedStorage', 'UntypedStorage',
        'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
        'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
        'lobpcg', 'use_deterministic_algorithms',
        'are_deterministic_algorithms_enabled',
        'is_deterministic_algorithms_warn_only_enabled',
        'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
        'set_float32_matmul_precision', 'get_float32_matmul_precision',
        'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
        'SymBool', 'sym_not',
        'sym_int', 'sym_float', 'sym_max', 'sym_min', 'compile', 'vmap',
        'export',
    ]
    
    ################################################################################
    # Load the extension module
    ################################################################################
    
    if sys.platform == 'win32':
        pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
        py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
        th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
    
        # When users create a virtualenv that inherits the base environment,
        # we will need to add the corresponding library directory into
        # DLL search directories. Otherwise, it will rely on `PATH` which
        # is dependent on user settings.
        if sys.exec_prefix != sys.base_exec_prefix:
            base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
        else:
            base_py_dll_path = ''
    
        dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
    
        if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
            nvtoolsext_dll_path = os.path.join(
                os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
        else:
            nvtoolsext_dll_path = ''
    
        from .version import cuda as cuda_version
        import glob
        if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
            cuda_version_1 = cuda_version.replace('.', '_')
            cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
            default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
            cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
        else:
            cuda_path = ''
    
        dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
    
        kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
        with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
        prev_error_mode = kernel32.SetErrorMode(0x0001)
    
        kernel32.LoadLibraryW.restype = ctypes.c_void_p
        if with_load_library_flags:
            kernel32.LoadLibraryExW.restype = ctypes.c_void_p
    
        for dll_path in dll_paths:
            os.add_dll_directory(dll_path)
    
        try:
            ctypes.CDLL('vcruntime140.dll')
            ctypes.CDLL('msvcp140.dll')
            ctypes.CDLL('vcruntime140_1.dll')
        except OSError:
            print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
                     It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
    
        dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
        path_patched = False
        for dll in dlls:
            is_loaded = False
            if with_load_library_flags:
                res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
                last_error = ctypes.get_last_error()
                if res is None and last_error != 126:
                    err = ctypes.WinError(last_error)
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
                elif res is not None:
                    is_loaded = True
            if not is_loaded:
                if not path_patched:
                    os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
                    path_patched = True
                res = kernel32.LoadLibraryW(dll)
                if res is None:
                    err = ctypes.WinError(ctypes.get_last_error())
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
    
        kernel32.SetErrorMode(prev_error_mode)
    
    
    def _preload_cuda_deps(lib_folder, lib_name):
        """Preloads cuda deps if they could not be found otherwise."""
        # Should only be called on Linux if default path resolution have failed
        assert platform.system() == 'Linux', 'Should only be called on Linux'
        import glob
        lib_path = None
        for path in sys.path:
            nvidia_path = os.path.join(path, 'nvidia')
            if not os.path.exists(nvidia_path):
                continue
            candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
            if candidate_lib_paths and not lib_path:
                lib_path = candidate_lib_paths[0]
            if lib_path:
                break
        if not lib_path:
            raise ValueError(f"{lib_name} not found in the system path {sys.path}")
        ctypes.CDLL(lib_path)
    
    
    # See Note [Global dependencies]
    def _load_global_deps() -> None:
        if _running_with_deploy() or platform.system() == 'Windows':
            return
    
        lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
        here = os.path.abspath(__file__)
        lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
    
        try:
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
        except OSError as err:
            # Can only happen for wheel with cuda libs as PYPI deps
            # As PyTorch is not purelib, but nvidia-*-cu12 is
            cuda_libs: Dict[str, str] = {
                'cublas': 'libcublas.so.*[0-9]',
                'cudnn': 'libcudnn.so.*[0-9]',
                'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
                'cuda_runtime': 'libcudart.so.*[0-9]',
                'cuda_cupti': 'libcupti.so.*[0-9]',
                'cufft': 'libcufft.so.*[0-9]',
                'curand': 'libcurand.so.*[0-9]',
                'cusolver': 'libcusolver.so.*[0-9]',
                'cusparse': 'libcusparse.so.*[0-9]',
                'nccl': 'libnccl.so.*[0-9]',
                'nvtx': 'libnvToolsExt.so.*[0-9]',
            }
            is_cuda_lib_err = [lib for lib in cuda_libs.values() if(lib.split('.')[0] in err.args[0])]
            if not is_cuda_lib_err:
                raise err
            for lib_folder, lib_name in cuda_libs.items():
                _preload_cuda_deps(lib_folder, lib_name)
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
    
    
    if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
            (_running_with_deploy() or platform.system() != 'Windows'):
        # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
        # few circumstances:
        #
        #   1. You're in a build environment (e.g., fbcode) where
        #      libtorch_global_deps is not available, but you still need
        #      to get mkl to link in with RTLD_GLOBAL or it will just
        #      not work.
        #
        #   2. You're trying to run PyTorch under UBSAN and you need
        #      to ensure that only one copy of libtorch is loaded, so
        #      vptr checks work properly
        #
        # If you're using this setting, you must verify that all the libraries
        # you load consistently use the same libstdc++, or you may have
        # mysterious segfaults.
        #
        old_flags = sys.getdlopenflags()
        sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
        from torch._C import *  # noqa: F403
        sys.setdlopenflags(old_flags)
        del old_flags
    
    else:
        # Easy way.  You want this most of the time, because it will prevent
        # C++ symbols from libtorch clobbering C++ symbols from other
        # libraries, leading to mysterious segfaults.
        #
        # If building in an environment where libtorch_global_deps isn't available
        # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
        # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
        #
        # See Note [Global dependencies]
        if USE_GLOBAL_DEPS:
            _load_global_deps()
        from torch._C import *  # noqa: F403
    
    # Appease the type checker; ordinarily this binding is inserted by the
    # torch._C module initialization code in C
    if TYPE_CHECKING:
        import torch._C as _C
    
    class SymInt:
        """
        Like an int (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return builtins.bool(self != 0)
    
        def __int__(self):
            return self.node.int_()
    
        def __index__(self):
            return self.node.int_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_float__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return str(self.node)
    
    class SymFloat:
        """
        Like an float (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_int__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    class SymBool:
        """
        Like an bool (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
    
        Unlike regular bools, regular boolean operators will force extra guards instead
        of symbolically evaluate.  Use the bitwise operators instead to handle this.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        def __int__(self):
            return builtins.int(self.node.bool_())
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
        def __and__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __or__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        # We very carefully define __sym_not__, and not a number of other
        # plausible alternatives:
        #
        #   - We do not override __not__ because this is not a real magic
        #     method; you cannot override the meaning of the not builtin in
        #     Python.  We use the name 'sym_not' to clarify that in user code you
        #     cannot use the builtin not or operator.not_ or operator.__not__ and
        #     hit this magic method; you must use our custom sym_not operator.
        #
        #   - We do not override the __invert__ method because SymBool is
        #     meant to be usable in situations where bool is expected.  However,
        #     bitwise negation ~a does the wrong thing with booleans (because
        #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
        #     This would be a giant footgun, so we get around it by defining
        #     our own operator.  Note that bitwise and/or do the right thing,
        #     so we reuse the conventional operators there for readability.
        #
        def __sym_not__(self) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    def sym_not(a):
        r""" SymInt-aware utility for logical negation.
    
        Args:
            a (SymBool or bool): Object to negate
        """
        if hasattr(a, '__sym_not__'):
            return a.__sym_not__()
        return not a
    
    def sym_float(a):
        r""" SymInt-aware utility for float casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymFloat):
            return a
        elif hasattr(a, '__sym_float__'):
            return a.__sym_float__()
        return py_float(a)  # type: ignore[operator]
    
    
    def sym_int(a):
        r""" SymInt-aware utility for int casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymInt):
            return a
        elif isinstance(a, SymFloat):
            return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
        return py_int(a)  # type: ignore[operator]
    
    def sym_max(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_max__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            # NB: If you actually care about preserving output type exactly
            # if you do something like max(0, 0.0), it is NOT sound to treat
            # min/max as commutative
            return b.__sym_max__(a)
        return builtins.max(a, b)  # type: ignore[operator]
    
    def sym_min(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_min__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            return b.__sym_min__(a)
        return builtins.min(a, b)  # type: ignore[operator]
    
    # Check to see if we can load C extensions, and if not provide some guidance
    # on what the problem might be.
    try:
        # _initExtension is chosen (arbitrarily) as a sentinel.
        from torch._C import _initExtension
    except ImportError:
        import torch._C as _C_for_compiled_check
    
        # The __file__ check only works for Python 3.7 and above.
        if _C_for_compiled_check.__file__ is None:
>           raise ImportError(textwrap.dedent('''
                Failed to load PyTorch C extensions:
                    It appears that PyTorch has loaded the `torch/_C` folder
                    of the PyTorch repository rather than the C extensions which
                    are expected in the `torch._C` namespace. This can occur when
                    using the `install` workflow. e.g.
                        $ python setup.py install && python -c "import torch"
    
                    This error can generally be solved using the `develop` workflow
                        $ python setup.py develop && python -c "import torch"  # This should succeed
                    or by running Python from a different directory.
                ''').strip()) from None
E           ImportError: Failed to load PyTorch C extensions:
E               It appears that PyTorch has loaded the `torch/_C` folder
E               of the PyTorch repository rather than the C extensions which
E               are expected in the `torch._C` namespace. This can occur when
E               using the `install` workflow. e.g.
E                   $ python setup.py install && python -c "import torch"
E           
E               This error can generally be solved using the `develop` workflow
E                   $ python setup.py develop && python -c "import torch"  # This should succeed
E               or by running Python from a different directory.

/usr/lib/python3/dist-packages/torch/__init__.py:451: ImportError
________________ test_isdtype_additional_dtypes[float16-torch] _________________

library = 'torch', dtype_ = 'float16'

    @pytest.mark.parametrize("library", ["cupy", "numpy", "torch"])
    @pytest.mark.parametrize("dtype_", additional_dtypes)
    def test_isdtype_additional_dtypes(library, dtype_):
>       xp = import_('array_api_compat.' + library)

tests/test_isdtype.py:104: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/_helpers.py:8: in import_
    return import_module(library)
/usr/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1387: in _gcd_import
    ???
<frozen importlib._bootstrap>:1360: in _find_and_load
    ???
<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked
    ???
<frozen importlib._bootstrap>:935: in _load_unlocked
    ???
<frozen importlib._bootstrap_external>:995: in exec_module
    ???
<frozen importlib._bootstrap>:488: in _call_with_frames_removed
    ???
array_api_compat/torch/__init__.py:1: in <module>
    from torch import *
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    
    r"""
    The torch package contains data structures for multi-dimensional
    tensors and defines mathematical operations over these tensors.
    Additionally, it provides many utilities for efficient serialization of
    Tensors and arbitrary types, and other useful utilities.
    
    It has a CUDA counterpart, that enables you to run your tensor computations
    on an NVIDIA GPU with compute capability >= 3.0.
    """
    
    import math
    import os
    import sys
    import platform
    import textwrap
    import ctypes
    import inspect
    
    # multipy/deploy is setting this import before importing torch, this is the most
    # reliable way we have to detect if we're running within deploy.
    # https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
    def _running_with_deploy():
        return sys.modules.get("torch._meta_registrations", None) is object
    
    from ._utils import _import_dotted_name, classproperty
    from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
        USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
    
    # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
    if _running_with_deploy():
        __version__ = "torch-deploy-1.8"
    else:
        from .torch_version import __version__ as __version__
    
    from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
    import builtins
    
    __all__ = [
        'typename', 'is_tensor', 'is_storage',
        'set_default_tensor_type', 'set_default_device',
        'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
        'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
        'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
        'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
        'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
        'TypedStorage', 'UntypedStorage',
        'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
        'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
        'lobpcg', 'use_deterministic_algorithms',
        'are_deterministic_algorithms_enabled',
        'is_deterministic_algorithms_warn_only_enabled',
        'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
        'set_float32_matmul_precision', 'get_float32_matmul_precision',
        'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
        'SymBool', 'sym_not',
        'sym_int', 'sym_float', 'sym_max', 'sym_min', 'compile', 'vmap',
        'export',
    ]
    
    ################################################################################
    # Load the extension module
    ################################################################################
    
    if sys.platform == 'win32':
        pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
        py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
        th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
    
        # When users create a virtualenv that inherits the base environment,
        # we will need to add the corresponding library directory into
        # DLL search directories. Otherwise, it will rely on `PATH` which
        # is dependent on user settings.
        if sys.exec_prefix != sys.base_exec_prefix:
            base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
        else:
            base_py_dll_path = ''
    
        dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
    
        if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
            nvtoolsext_dll_path = os.path.join(
                os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
        else:
            nvtoolsext_dll_path = ''
    
        from .version import cuda as cuda_version
        import glob
        if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
            cuda_version_1 = cuda_version.replace('.', '_')
            cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
            default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
            cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
        else:
            cuda_path = ''
    
        dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
    
        kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
        with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
        prev_error_mode = kernel32.SetErrorMode(0x0001)
    
        kernel32.LoadLibraryW.restype = ctypes.c_void_p
        if with_load_library_flags:
            kernel32.LoadLibraryExW.restype = ctypes.c_void_p
    
        for dll_path in dll_paths:
            os.add_dll_directory(dll_path)
    
        try:
            ctypes.CDLL('vcruntime140.dll')
            ctypes.CDLL('msvcp140.dll')
            ctypes.CDLL('vcruntime140_1.dll')
        except OSError:
            print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
                     It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
    
        dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
        path_patched = False
        for dll in dlls:
            is_loaded = False
            if with_load_library_flags:
                res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
                last_error = ctypes.get_last_error()
                if res is None and last_error != 126:
                    err = ctypes.WinError(last_error)
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
                elif res is not None:
                    is_loaded = True
            if not is_loaded:
                if not path_patched:
                    os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
                    path_patched = True
                res = kernel32.LoadLibraryW(dll)
                if res is None:
                    err = ctypes.WinError(ctypes.get_last_error())
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
    
        kernel32.SetErrorMode(prev_error_mode)
    
    
    def _preload_cuda_deps(lib_folder, lib_name):
        """Preloads cuda deps if they could not be found otherwise."""
        # Should only be called on Linux if default path resolution have failed
        assert platform.system() == 'Linux', 'Should only be called on Linux'
        import glob
        lib_path = None
        for path in sys.path:
            nvidia_path = os.path.join(path, 'nvidia')
            if not os.path.exists(nvidia_path):
                continue
            candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
            if candidate_lib_paths and not lib_path:
                lib_path = candidate_lib_paths[0]
            if lib_path:
                break
        if not lib_path:
            raise ValueError(f"{lib_name} not found in the system path {sys.path}")
        ctypes.CDLL(lib_path)
    
    
    # See Note [Global dependencies]
    def _load_global_deps() -> None:
        if _running_with_deploy() or platform.system() == 'Windows':
            return
    
        lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
        here = os.path.abspath(__file__)
        lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
    
        try:
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
        except OSError as err:
            # Can only happen for wheel with cuda libs as PYPI deps
            # As PyTorch is not purelib, but nvidia-*-cu12 is
            cuda_libs: Dict[str, str] = {
                'cublas': 'libcublas.so.*[0-9]',
                'cudnn': 'libcudnn.so.*[0-9]',
                'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
                'cuda_runtime': 'libcudart.so.*[0-9]',
                'cuda_cupti': 'libcupti.so.*[0-9]',
                'cufft': 'libcufft.so.*[0-9]',
                'curand': 'libcurand.so.*[0-9]',
                'cusolver': 'libcusolver.so.*[0-9]',
                'cusparse': 'libcusparse.so.*[0-9]',
                'nccl': 'libnccl.so.*[0-9]',
                'nvtx': 'libnvToolsExt.so.*[0-9]',
            }
            is_cuda_lib_err = [lib for lib in cuda_libs.values() if(lib.split('.')[0] in err.args[0])]
            if not is_cuda_lib_err:
                raise err
            for lib_folder, lib_name in cuda_libs.items():
                _preload_cuda_deps(lib_folder, lib_name)
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
    
    
    if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
            (_running_with_deploy() or platform.system() != 'Windows'):
        # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
        # few circumstances:
        #
        #   1. You're in a build environment (e.g., fbcode) where
        #      libtorch_global_deps is not available, but you still need
        #      to get mkl to link in with RTLD_GLOBAL or it will just
        #      not work.
        #
        #   2. You're trying to run PyTorch under UBSAN and you need
        #      to ensure that only one copy of libtorch is loaded, so
        #      vptr checks work properly
        #
        # If you're using this setting, you must verify that all the libraries
        # you load consistently use the same libstdc++, or you may have
        # mysterious segfaults.
        #
        old_flags = sys.getdlopenflags()
        sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
        from torch._C import *  # noqa: F403
        sys.setdlopenflags(old_flags)
        del old_flags
    
    else:
        # Easy way.  You want this most of the time, because it will prevent
        # C++ symbols from libtorch clobbering C++ symbols from other
        # libraries, leading to mysterious segfaults.
        #
        # If building in an environment where libtorch_global_deps isn't available
        # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
        # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
        #
        # See Note [Global dependencies]
        if USE_GLOBAL_DEPS:
            _load_global_deps()
        from torch._C import *  # noqa: F403
    
    # Appease the type checker; ordinarily this binding is inserted by the
    # torch._C module initialization code in C
    if TYPE_CHECKING:
        import torch._C as _C
    
    class SymInt:
        """
        Like an int (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return builtins.bool(self != 0)
    
        def __int__(self):
            return self.node.int_()
    
        def __index__(self):
            return self.node.int_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_float__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return str(self.node)
    
    class SymFloat:
        """
        Like an float (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_int__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    class SymBool:
        """
        Like an bool (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
    
        Unlike regular bools, regular boolean operators will force extra guards instead
        of symbolically evaluate.  Use the bitwise operators instead to handle this.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        def __int__(self):
            return builtins.int(self.node.bool_())
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
        def __and__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __or__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        # We very carefully define __sym_not__, and not a number of other
        # plausible alternatives:
        #
        #   - We do not override __not__ because this is not a real magic
        #     method; you cannot override the meaning of the not builtin in
        #     Python.  We use the name 'sym_not' to clarify that in user code you
        #     cannot use the builtin not or operator.not_ or operator.__not__ and
        #     hit this magic method; you must use our custom sym_not operator.
        #
        #   - We do not override the __invert__ method because SymBool is
        #     meant to be usable in situations where bool is expected.  However,
        #     bitwise negation ~a does the wrong thing with booleans (because
        #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
        #     This would be a giant footgun, so we get around it by defining
        #     our own operator.  Note that bitwise and/or do the right thing,
        #     so we reuse the conventional operators there for readability.
        #
        def __sym_not__(self) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    def sym_not(a):
        r""" SymInt-aware utility for logical negation.
    
        Args:
            a (SymBool or bool): Object to negate
        """
        if hasattr(a, '__sym_not__'):
            return a.__sym_not__()
        return not a
    
    def sym_float(a):
        r""" SymInt-aware utility for float casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymFloat):
            return a
        elif hasattr(a, '__sym_float__'):
            return a.__sym_float__()
        return py_float(a)  # type: ignore[operator]
    
    
    def sym_int(a):
        r""" SymInt-aware utility for int casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymInt):
            return a
        elif isinstance(a, SymFloat):
            return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
        return py_int(a)  # type: ignore[operator]
    
    def sym_max(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_max__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            # NB: If you actually care about preserving output type exactly
            # if you do something like max(0, 0.0), it is NOT sound to treat
            # min/max as commutative
            return b.__sym_max__(a)
        return builtins.max(a, b)  # type: ignore[operator]
    
    def sym_min(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_min__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            return b.__sym_min__(a)
        return builtins.min(a, b)  # type: ignore[operator]
    
    # Check to see if we can load C extensions, and if not provide some guidance
    # on what the problem might be.
    try:
        # _initExtension is chosen (arbitrarily) as a sentinel.
        from torch._C import _initExtension
    except ImportError:
        import torch._C as _C_for_compiled_check
    
        # The __file__ check only works for Python 3.7 and above.
        if _C_for_compiled_check.__file__ is None:
>           raise ImportError(textwrap.dedent('''
                Failed to load PyTorch C extensions:
                    It appears that PyTorch has loaded the `torch/_C` folder
                    of the PyTorch repository rather than the C extensions which
                    are expected in the `torch._C` namespace. This can occur when
                    using the `install` workflow. e.g.
                        $ python setup.py install && python -c "import torch"
    
                    This error can generally be solved using the `develop` workflow
                        $ python setup.py develop && python -c "import torch"  # This should succeed
                    or by running Python from a different directory.
                ''').strip()) from None
E           ImportError: Failed to load PyTorch C extensions:
E               It appears that PyTorch has loaded the `torch/_C` folder
E               of the PyTorch repository rather than the C extensions which
E               are expected in the `torch._C` namespace. This can occur when
E               using the `install` workflow. e.g.
E                   $ python setup.py install && python -c "import torch"
E           
E               This error can generally be solved using the `develop` workflow
E                   $ python setup.py develop && python -c "import torch"  # This should succeed
E               or by running Python from a different directory.

/usr/lib/python3/dist-packages/torch/__init__.py:451: ImportError
________________ test_isdtype_additional_dtypes[float128-torch] ________________

library = 'torch', dtype_ = 'float128'

    @pytest.mark.parametrize("library", ["cupy", "numpy", "torch"])
    @pytest.mark.parametrize("dtype_", additional_dtypes)
    def test_isdtype_additional_dtypes(library, dtype_):
>       xp = import_('array_api_compat.' + library)

tests/test_isdtype.py:104: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/_helpers.py:8: in import_
    return import_module(library)
/usr/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1387: in _gcd_import
    ???
<frozen importlib._bootstrap>:1360: in _find_and_load
    ???
<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked
    ???
<frozen importlib._bootstrap>:935: in _load_unlocked
    ???
<frozen importlib._bootstrap_external>:995: in exec_module
    ???
<frozen importlib._bootstrap>:488: in _call_with_frames_removed
    ???
array_api_compat/torch/__init__.py:1: in <module>
    from torch import *
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    
    r"""
    The torch package contains data structures for multi-dimensional
    tensors and defines mathematical operations over these tensors.
    Additionally, it provides many utilities for efficient serialization of
    Tensors and arbitrary types, and other useful utilities.
    
    It has a CUDA counterpart, that enables you to run your tensor computations
    on an NVIDIA GPU with compute capability >= 3.0.
    """
    
    import math
    import os
    import sys
    import platform
    import textwrap
    import ctypes
    import inspect
    
    # multipy/deploy is setting this import before importing torch, this is the most
    # reliable way we have to detect if we're running within deploy.
    # https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
    def _running_with_deploy():
        return sys.modules.get("torch._meta_registrations", None) is object
    
    from ._utils import _import_dotted_name, classproperty
    from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
        USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
    
    # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
    if _running_with_deploy():
        __version__ = "torch-deploy-1.8"
    else:
        from .torch_version import __version__ as __version__
    
    from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
    import builtins
    
    __all__ = [
        'typename', 'is_tensor', 'is_storage',
        'set_default_tensor_type', 'set_default_device',
        'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
        'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
        'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
        'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
        'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
        'TypedStorage', 'UntypedStorage',
        'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
        'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
        'lobpcg', 'use_deterministic_algorithms',
        'are_deterministic_algorithms_enabled',
        'is_deterministic_algorithms_warn_only_enabled',
        'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
        'set_float32_matmul_precision', 'get_float32_matmul_precision',
        'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
        'SymBool', 'sym_not',
        'sym_int', 'sym_float', 'sym_max', 'sym_min', 'compile', 'vmap',
        'export',
    ]
    
    ################################################################################
    # Load the extension module
    ################################################################################
    
    if sys.platform == 'win32':
        pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
        py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
        th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
    
        # When users create a virtualenv that inherits the base environment,
        # we will need to add the corresponding library directory into
        # DLL search directories. Otherwise, it will rely on `PATH` which
        # is dependent on user settings.
        if sys.exec_prefix != sys.base_exec_prefix:
            base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
        else:
            base_py_dll_path = ''
    
        dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
    
        if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
            nvtoolsext_dll_path = os.path.join(
                os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
        else:
            nvtoolsext_dll_path = ''
    
        from .version import cuda as cuda_version
        import glob
        if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
            cuda_version_1 = cuda_version.replace('.', '_')
            cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
            default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
            cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
        else:
            cuda_path = ''
    
        dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
    
        kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
        with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
        prev_error_mode = kernel32.SetErrorMode(0x0001)
    
        kernel32.LoadLibraryW.restype = ctypes.c_void_p
        if with_load_library_flags:
            kernel32.LoadLibraryExW.restype = ctypes.c_void_p
    
        for dll_path in dll_paths:
            os.add_dll_directory(dll_path)
    
        try:
            ctypes.CDLL('vcruntime140.dll')
            ctypes.CDLL('msvcp140.dll')
            ctypes.CDLL('vcruntime140_1.dll')
        except OSError:
            print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
                     It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
    
        dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
        path_patched = False
        for dll in dlls:
            is_loaded = False
            if with_load_library_flags:
                res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
                last_error = ctypes.get_last_error()
                if res is None and last_error != 126:
                    err = ctypes.WinError(last_error)
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
                elif res is not None:
                    is_loaded = True
            if not is_loaded:
                if not path_patched:
                    os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
                    path_patched = True
                res = kernel32.LoadLibraryW(dll)
                if res is None:
                    err = ctypes.WinError(ctypes.get_last_error())
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
    
        kernel32.SetErrorMode(prev_error_mode)
    
    
    def _preload_cuda_deps(lib_folder, lib_name):
        """Preloads cuda deps if they could not be found otherwise."""
        # Should only be called on Linux if default path resolution have failed
        assert platform.system() == 'Linux', 'Should only be called on Linux'
        import glob
        lib_path = None
        for path in sys.path:
            nvidia_path = os.path.join(path, 'nvidia')
            if not os.path.exists(nvidia_path):
                continue
            candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
            if candidate_lib_paths and not lib_path:
                lib_path = candidate_lib_paths[0]
            if lib_path:
                break
        if not lib_path:
            raise ValueError(f"{lib_name} not found in the system path {sys.path}")
        ctypes.CDLL(lib_path)
    
    
    # See Note [Global dependencies]
    def _load_global_deps() -> None:
        if _running_with_deploy() or platform.system() == 'Windows':
            return
    
        lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
        here = os.path.abspath(__file__)
        lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
    
        try:
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
        except OSError as err:
            # Can only happen for wheel with cuda libs as PYPI deps
            # As PyTorch is not purelib, but nvidia-*-cu12 is
            cuda_libs: Dict[str, str] = {
                'cublas': 'libcublas.so.*[0-9]',
                'cudnn': 'libcudnn.so.*[0-9]',
                'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
                'cuda_runtime': 'libcudart.so.*[0-9]',
                'cuda_cupti': 'libcupti.so.*[0-9]',
                'cufft': 'libcufft.so.*[0-9]',
                'curand': 'libcurand.so.*[0-9]',
                'cusolver': 'libcusolver.so.*[0-9]',
                'cusparse': 'libcusparse.so.*[0-9]',
                'nccl': 'libnccl.so.*[0-9]',
                'nvtx': 'libnvToolsExt.so.*[0-9]',
            }
            is_cuda_lib_err = [lib for lib in cuda_libs.values() if(lib.split('.')[0] in err.args[0])]
            if not is_cuda_lib_err:
                raise err
            for lib_folder, lib_name in cuda_libs.items():
                _preload_cuda_deps(lib_folder, lib_name)
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
    
    
    if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
            (_running_with_deploy() or platform.system() != 'Windows'):
        # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
        # few circumstances:
        #
        #   1. You're in a build environment (e.g., fbcode) where
        #      libtorch_global_deps is not available, but you still need
        #      to get mkl to link in with RTLD_GLOBAL or it will just
        #      not work.
        #
        #   2. You're trying to run PyTorch under UBSAN and you need
        #      to ensure that only one copy of libtorch is loaded, so
        #      vptr checks work properly
        #
        # If you're using this setting, you must verify that all the libraries
        # you load consistently use the same libstdc++, or you may have
        # mysterious segfaults.
        #
        old_flags = sys.getdlopenflags()
        sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
        from torch._C import *  # noqa: F403
        sys.setdlopenflags(old_flags)
        del old_flags
    
    else:
        # Easy way.  You want this most of the time, because it will prevent
        # C++ symbols from libtorch clobbering C++ symbols from other
        # libraries, leading to mysterious segfaults.
        #
        # If building in an environment where libtorch_global_deps isn't available
        # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
        # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
        #
        # See Note [Global dependencies]
        if USE_GLOBAL_DEPS:
            _load_global_deps()
        from torch._C import *  # noqa: F403
    
    # Appease the type checker; ordinarily this binding is inserted by the
    # torch._C module initialization code in C
    if TYPE_CHECKING:
        import torch._C as _C
    
    class SymInt:
        """
        Like an int (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return builtins.bool(self != 0)
    
        def __int__(self):
            return self.node.int_()
    
        def __index__(self):
            return self.node.int_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_float__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return str(self.node)
    
    class SymFloat:
        """
        Like an float (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_int__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    class SymBool:
        """
        Like an bool (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
    
        Unlike regular bools, regular boolean operators will force extra guards instead
        of symbolically evaluate.  Use the bitwise operators instead to handle this.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        def __int__(self):
            return builtins.int(self.node.bool_())
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
        def __and__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __or__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        # We very carefully define __sym_not__, and not a number of other
        # plausible alternatives:
        #
        #   - We do not override __not__ because this is not a real magic
        #     method; you cannot override the meaning of the not builtin in
        #     Python.  We use the name 'sym_not' to clarify that in user code you
        #     cannot use the builtin not or operator.not_ or operator.__not__ and
        #     hit this magic method; you must use our custom sym_not operator.
        #
        #   - We do not override the __invert__ method because SymBool is
        #     meant to be usable in situations where bool is expected.  However,
        #     bitwise negation ~a does the wrong thing with booleans (because
        #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
        #     This would be a giant footgun, so we get around it by defining
        #     our own operator.  Note that bitwise and/or do the right thing,
        #     so we reuse the conventional operators there for readability.
        #
        def __sym_not__(self) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    def sym_not(a):
        r""" SymInt-aware utility for logical negation.
    
        Args:
            a (SymBool or bool): Object to negate
        """
        if hasattr(a, '__sym_not__'):
            return a.__sym_not__()
        return not a
    
    def sym_float(a):
        r""" SymInt-aware utility for float casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymFloat):
            return a
        elif hasattr(a, '__sym_float__'):
            return a.__sym_float__()
        return py_float(a)  # type: ignore[operator]
    
    
    def sym_int(a):
        r""" SymInt-aware utility for int casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymInt):
            return a
        elif isinstance(a, SymFloat):
            return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
        return py_int(a)  # type: ignore[operator]
    
    def sym_max(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_max__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            # NB: If you actually care about preserving output type exactly
            # if you do something like max(0, 0.0), it is NOT sound to treat
            # min/max as commutative
            return b.__sym_max__(a)
        return builtins.max(a, b)  # type: ignore[operator]
    
    def sym_min(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_min__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            return b.__sym_min__(a)
        return builtins.min(a, b)  # type: ignore[operator]
    
    # Check to see if we can load C extensions, and if not provide some guidance
    # on what the problem might be.
    try:
        # _initExtension is chosen (arbitrarily) as a sentinel.
        from torch._C import _initExtension
    except ImportError:
        import torch._C as _C_for_compiled_check
    
        # The __file__ check only works for Python 3.7 and above.
        if _C_for_compiled_check.__file__ is None:
>           raise ImportError(textwrap.dedent('''
                Failed to load PyTorch C extensions:
                    It appears that PyTorch has loaded the `torch/_C` folder
                    of the PyTorch repository rather than the C extensions which
                    are expected in the `torch._C` namespace. This can occur when
                    using the `install` workflow. e.g.
                        $ python setup.py install && python -c "import torch"
    
                    This error can generally be solved using the `develop` workflow
                        $ python setup.py develop && python -c "import torch"  # This should succeed
                    or by running Python from a different directory.
                ''').strip()) from None
E           ImportError: Failed to load PyTorch C extensions:
E               It appears that PyTorch has loaded the `torch/_C` folder
E               of the PyTorch repository rather than the C extensions which
E               are expected in the `torch._C` namespace. This can occur when
E               using the `install` workflow. e.g.
E                   $ python setup.py install && python -c "import torch"
E           
E               This error can generally be solved using the `develop` workflow
E                   $ python setup.py develop && python -c "import torch"  # This should succeed
E               or by running Python from a different directory.

/usr/lib/python3/dist-packages/torch/__init__.py:451: ImportError
_______________ test_isdtype_additional_dtypes[complex256-torch] _______________

library = 'torch', dtype_ = 'complex256'

    @pytest.mark.parametrize("library", ["cupy", "numpy", "torch"])
    @pytest.mark.parametrize("dtype_", additional_dtypes)
    def test_isdtype_additional_dtypes(library, dtype_):
>       xp = import_('array_api_compat.' + library)

tests/test_isdtype.py:104: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/_helpers.py:8: in import_
    return import_module(library)
/usr/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1387: in _gcd_import
    ???
<frozen importlib._bootstrap>:1360: in _find_and_load
    ???
<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked
    ???
<frozen importlib._bootstrap>:935: in _load_unlocked
    ???
<frozen importlib._bootstrap_external>:995: in exec_module
    ???
<frozen importlib._bootstrap>:488: in _call_with_frames_removed
    ???
array_api_compat/torch/__init__.py:1: in <module>
    from torch import *
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    
    r"""
    The torch package contains data structures for multi-dimensional
    tensors and defines mathematical operations over these tensors.
    Additionally, it provides many utilities for efficient serialization of
    Tensors and arbitrary types, and other useful utilities.
    
    It has a CUDA counterpart, that enables you to run your tensor computations
    on an NVIDIA GPU with compute capability >= 3.0.
    """
    
    import math
    import os
    import sys
    import platform
    import textwrap
    import ctypes
    import inspect
    
    # multipy/deploy is setting this import before importing torch, this is the most
    # reliable way we have to detect if we're running within deploy.
    # https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
    def _running_with_deploy():
        return sys.modules.get("torch._meta_registrations", None) is object
    
    from ._utils import _import_dotted_name, classproperty
    from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
        USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
    
    # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
    if _running_with_deploy():
        __version__ = "torch-deploy-1.8"
    else:
        from .torch_version import __version__ as __version__
    
    from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
    import builtins
    
    __all__ = [
        'typename', 'is_tensor', 'is_storage',
        'set_default_tensor_type', 'set_default_device',
        'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
        'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
        'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
        'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
        'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
        'TypedStorage', 'UntypedStorage',
        'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
        'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
        'lobpcg', 'use_deterministic_algorithms',
        'are_deterministic_algorithms_enabled',
        'is_deterministic_algorithms_warn_only_enabled',
        'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
        'set_float32_matmul_precision', 'get_float32_matmul_precision',
        'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
        'SymBool', 'sym_not',
        'sym_int', 'sym_float', 'sym_max', 'sym_min', 'compile', 'vmap',
        'export',
    ]
    
    ################################################################################
    # Load the extension module
    ################################################################################
    
    if sys.platform == 'win32':
        pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
        py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
        th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
    
        # When users create a virtualenv that inherits the base environment,
        # we will need to add the corresponding library directory into
        # DLL search directories. Otherwise, it will rely on `PATH` which
        # is dependent on user settings.
        if sys.exec_prefix != sys.base_exec_prefix:
            base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
        else:
            base_py_dll_path = ''
    
        dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
    
        if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
            nvtoolsext_dll_path = os.path.join(
                os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
        else:
            nvtoolsext_dll_path = ''
    
        from .version import cuda as cuda_version
        import glob
        if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
            cuda_version_1 = cuda_version.replace('.', '_')
            cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
            default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
            cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
        else:
            cuda_path = ''
    
        dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
    
        kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
        with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
        prev_error_mode = kernel32.SetErrorMode(0x0001)
    
        kernel32.LoadLibraryW.restype = ctypes.c_void_p
        if with_load_library_flags:
            kernel32.LoadLibraryExW.restype = ctypes.c_void_p
    
        for dll_path in dll_paths:
            os.add_dll_directory(dll_path)
    
        try:
            ctypes.CDLL('vcruntime140.dll')
            ctypes.CDLL('msvcp140.dll')
            ctypes.CDLL('vcruntime140_1.dll')
        except OSError:
            print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
                     It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
    
        dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
        path_patched = False
        for dll in dlls:
            is_loaded = False
            if with_load_library_flags:
                res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
                last_error = ctypes.get_last_error()
                if res is None and last_error != 126:
                    err = ctypes.WinError(last_error)
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
                elif res is not None:
                    is_loaded = True
            if not is_loaded:
                if not path_patched:
                    os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
                    path_patched = True
                res = kernel32.LoadLibraryW(dll)
                if res is None:
                    err = ctypes.WinError(ctypes.get_last_error())
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
    
        kernel32.SetErrorMode(prev_error_mode)
    
    
    def _preload_cuda_deps(lib_folder, lib_name):
        """Preloads cuda deps if they could not be found otherwise."""
        # Should only be called on Linux if default path resolution have failed
        assert platform.system() == 'Linux', 'Should only be called on Linux'
        import glob
        lib_path = None
        for path in sys.path:
            nvidia_path = os.path.join(path, 'nvidia')
            if not os.path.exists(nvidia_path):
                continue
            candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
            if candidate_lib_paths and not lib_path:
                lib_path = candidate_lib_paths[0]
            if lib_path:
                break
        if not lib_path:
            raise ValueError(f"{lib_name} not found in the system path {sys.path}")
        ctypes.CDLL(lib_path)
    
    
    # See Note [Global dependencies]
    def _load_global_deps() -> None:
        if _running_with_deploy() or platform.system() == 'Windows':
            return
    
        lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
        here = os.path.abspath(__file__)
        lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
    
        try:
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
        except OSError as err:
            # Can only happen for wheel with cuda libs as PYPI deps
            # As PyTorch is not purelib, but nvidia-*-cu12 is
            cuda_libs: Dict[str, str] = {
                'cublas': 'libcublas.so.*[0-9]',
                'cudnn': 'libcudnn.so.*[0-9]',
                'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
                'cuda_runtime': 'libcudart.so.*[0-9]',
                'cuda_cupti': 'libcupti.so.*[0-9]',
                'cufft': 'libcufft.so.*[0-9]',
                'curand': 'libcurand.so.*[0-9]',
                'cusolver': 'libcusolver.so.*[0-9]',
                'cusparse': 'libcusparse.so.*[0-9]',
                'nccl': 'libnccl.so.*[0-9]',
                'nvtx': 'libnvToolsExt.so.*[0-9]',
            }
            is_cuda_lib_err = [lib for lib in cuda_libs.values() if(lib.split('.')[0] in err.args[0])]
            if not is_cuda_lib_err:
                raise err
            for lib_folder, lib_name in cuda_libs.items():
                _preload_cuda_deps(lib_folder, lib_name)
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
    
    
    if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
            (_running_with_deploy() or platform.system() != 'Windows'):
        # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
        # few circumstances:
        #
        #   1. You're in a build environment (e.g., fbcode) where
        #      libtorch_global_deps is not available, but you still need
        #      to get mkl to link in with RTLD_GLOBAL or it will just
        #      not work.
        #
        #   2. You're trying to run PyTorch under UBSAN and you need
        #      to ensure that only one copy of libtorch is loaded, so
        #      vptr checks work properly
        #
        # If you're using this setting, you must verify that all the libraries
        # you load consistently use the same libstdc++, or you may have
        # mysterious segfaults.
        #
        old_flags = sys.getdlopenflags()
        sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
        from torch._C import *  # noqa: F403
        sys.setdlopenflags(old_flags)
        del old_flags
    
    else:
        # Easy way.  You want this most of the time, because it will prevent
        # C++ symbols from libtorch clobbering C++ symbols from other
        # libraries, leading to mysterious segfaults.
        #
        # If building in an environment where libtorch_global_deps isn't available
        # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
        # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
        #
        # See Note [Global dependencies]
        if USE_GLOBAL_DEPS:
            _load_global_deps()
        from torch._C import *  # noqa: F403
    
    # Appease the type checker; ordinarily this binding is inserted by the
    # torch._C module initialization code in C
    if TYPE_CHECKING:
        import torch._C as _C
    
    class SymInt:
        """
        Like an int (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return builtins.bool(self != 0)
    
        def __int__(self):
            return self.node.int_()
    
        def __index__(self):
            return self.node.int_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_float__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return str(self.node)
    
    class SymFloat:
        """
        Like an float (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_int__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    class SymBool:
        """
        Like an bool (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
    
        Unlike regular bools, regular boolean operators will force extra guards instead
        of symbolically evaluate.  Use the bitwise operators instead to handle this.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        def __int__(self):
            return builtins.int(self.node.bool_())
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
        def __and__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __or__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        # We very carefully define __sym_not__, and not a number of other
        # plausible alternatives:
        #
        #   - We do not override __not__ because this is not a real magic
        #     method; you cannot override the meaning of the not builtin in
        #     Python.  We use the name 'sym_not' to clarify that in user code you
        #     cannot use the builtin not or operator.not_ or operator.__not__ and
        #     hit this magic method; you must use our custom sym_not operator.
        #
        #   - We do not override the __invert__ method because SymBool is
        #     meant to be usable in situations where bool is expected.  However,
        #     bitwise negation ~a does the wrong thing with booleans (because
        #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
        #     This would be a giant footgun, so we get around it by defining
        #     our own operator.  Note that bitwise and/or do the right thing,
        #     so we reuse the conventional operators there for readability.
        #
        def __sym_not__(self) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    def sym_not(a):
        r""" SymInt-aware utility for logical negation.
    
        Args:
            a (SymBool or bool): Object to negate
        """
        if hasattr(a, '__sym_not__'):
            return a.__sym_not__()
        return not a
    
    def sym_float(a):
        r""" SymInt-aware utility for float casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymFloat):
            return a
        elif hasattr(a, '__sym_float__'):
            return a.__sym_float__()
        return py_float(a)  # type: ignore[operator]
    
    
    def sym_int(a):
        r""" SymInt-aware utility for int casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymInt):
            return a
        elif isinstance(a, SymFloat):
            return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
        return py_int(a)  # type: ignore[operator]
    
    def sym_max(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_max__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            # NB: If you actually care about preserving output type exactly
            # if you do something like max(0, 0.0), it is NOT sound to treat
            # min/max as commutative
            return b.__sym_max__(a)
        return builtins.max(a, b)  # type: ignore[operator]
    
    def sym_min(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_min__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            return b.__sym_min__(a)
        return builtins.min(a, b)  # type: ignore[operator]
    
    # Check to see if we can load C extensions, and if not provide some guidance
    # on what the problem might be.
    try:
        # _initExtension is chosen (arbitrarily) as a sentinel.
        from torch._C import _initExtension
    except ImportError:
        import torch._C as _C_for_compiled_check
    
        # The __file__ check only works for Python 3.7 and above.
        if _C_for_compiled_check.__file__ is None:
>           raise ImportError(textwrap.dedent('''
                Failed to load PyTorch C extensions:
                    It appears that PyTorch has loaded the `torch/_C` folder
                    of the PyTorch repository rather than the C extensions which
                    are expected in the `torch._C` namespace. This can occur when
                    using the `install` workflow. e.g.
                        $ python setup.py install && python -c "import torch"
    
                    This error can generally be solved using the `develop` workflow
                        $ python setup.py develop && python -c "import torch"  # This should succeed
                    or by running Python from a different directory.
                ''').strip()) from None
E           ImportError: Failed to load PyTorch C extensions:
E               It appears that PyTorch has loaded the `torch/_C` folder
E               of the PyTorch repository rather than the C extensions which
E               are expected in the `torch._C` namespace. This can occur when
E               using the `install` workflow. e.g.
E                   $ python setup.py install && python -c "import torch"
E           
E               This error can generally be solved using the `develop` workflow
E                   $ python setup.py develop && python -c "import torch"  # This should succeed
E               or by running Python from a different directory.

/usr/lib/python3/dist-packages/torch/__init__.py:451: ImportError
________________ test_isdtype_additional_dtypes[bfloat16-torch] ________________

library = 'torch', dtype_ = 'bfloat16'

    @pytest.mark.parametrize("library", ["cupy", "numpy", "torch"])
    @pytest.mark.parametrize("dtype_", additional_dtypes)
    def test_isdtype_additional_dtypes(library, dtype_):
>       xp = import_('array_api_compat.' + library)

tests/test_isdtype.py:104: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/_helpers.py:8: in import_
    return import_module(library)
/usr/lib/python3.12/importlib/__init__.py:90: in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1387: in _gcd_import
    ???
<frozen importlib._bootstrap>:1360: in _find_and_load
    ???
<frozen importlib._bootstrap>:1331: in _find_and_load_unlocked
    ???
<frozen importlib._bootstrap>:935: in _load_unlocked
    ???
<frozen importlib._bootstrap_external>:995: in exec_module
    ???
<frozen importlib._bootstrap>:488: in _call_with_frames_removed
    ???
array_api_compat/torch/__init__.py:1: in <module>
    from torch import *
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

    
    r"""
    The torch package contains data structures for multi-dimensional
    tensors and defines mathematical operations over these tensors.
    Additionally, it provides many utilities for efficient serialization of
    Tensors and arbitrary types, and other useful utilities.
    
    It has a CUDA counterpart, that enables you to run your tensor computations
    on an NVIDIA GPU with compute capability >= 3.0.
    """
    
    import math
    import os
    import sys
    import platform
    import textwrap
    import ctypes
    import inspect
    
    # multipy/deploy is setting this import before importing torch, this is the most
    # reliable way we have to detect if we're running within deploy.
    # https://github.com/pytorch/multipy/blob/d60f34ad38c371e441fe7ffdb77a3c3dda5a5d19/multipy/runtime/interpreter/interpreter_impl.cpp#L134-L137
    def _running_with_deploy():
        return sys.modules.get("torch._meta_registrations", None) is object
    
    from ._utils import _import_dotted_name, classproperty
    from ._utils_internal import get_file_path, prepare_multiprocessing_environment, \
        USE_RTLD_GLOBAL_WITH_LIBTORCH, USE_GLOBAL_DEPS
    
    # TODO(torch_deploy) figure out how to freeze version.py in fbcode build
    if _running_with_deploy():
        __version__ = "torch-deploy-1.8"
    else:
        from .torch_version import __version__ as __version__
    
    from typing import Any, Callable, Dict, Optional, Set, Tuple, Type, TYPE_CHECKING, Union, List
    import builtins
    
    __all__ = [
        'typename', 'is_tensor', 'is_storage',
        'set_default_tensor_type', 'set_default_device',
        'set_rng_state', 'get_rng_state', 'manual_seed', 'initial_seed', 'seed',
        'save', 'load', 'set_printoptions', 'chunk', 'split', 'stack', 'matmul',
        'no_grad', 'enable_grad', 'rand', 'randn', 'inference_mode',
        'DoubleStorage', 'FloatStorage', 'LongStorage', 'IntStorage',
        'ShortStorage', 'CharStorage', 'ByteStorage', 'BoolStorage',
        'TypedStorage', 'UntypedStorage',
        'DoubleTensor', 'FloatTensor', 'LongTensor', 'IntTensor',
        'ShortTensor', 'CharTensor', 'ByteTensor', 'BoolTensor', 'Tensor',
        'lobpcg', 'use_deterministic_algorithms',
        'are_deterministic_algorithms_enabled',
        'is_deterministic_algorithms_warn_only_enabled',
        'set_deterministic_debug_mode', 'get_deterministic_debug_mode',
        'set_float32_matmul_precision', 'get_float32_matmul_precision',
        'set_warn_always', 'is_warn_always_enabled', 'SymInt', 'SymFloat',
        'SymBool', 'sym_not',
        'sym_int', 'sym_float', 'sym_max', 'sym_min', 'compile', 'vmap',
        'export',
    ]
    
    ################################################################################
    # Load the extension module
    ################################################################################
    
    if sys.platform == 'win32':
        pfiles_path = os.getenv('ProgramFiles', 'C:\\Program Files')
        py_dll_path = os.path.join(sys.exec_prefix, 'Library', 'bin')
        th_dll_path = os.path.join(os.path.dirname(__file__), 'lib')
    
        # When users create a virtualenv that inherits the base environment,
        # we will need to add the corresponding library directory into
        # DLL search directories. Otherwise, it will rely on `PATH` which
        # is dependent on user settings.
        if sys.exec_prefix != sys.base_exec_prefix:
            base_py_dll_path = os.path.join(sys.base_exec_prefix, 'Library', 'bin')
        else:
            base_py_dll_path = ''
    
        dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
    
        if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
            nvtoolsext_dll_path = os.path.join(
                os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
        else:
            nvtoolsext_dll_path = ''
    
        from .version import cuda as cuda_version
        import glob
        if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
            cuda_version_1 = cuda_version.replace('.', '_')
            cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
            default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)
            cuda_path = os.path.join(os.getenv(cuda_path_var, default_path), 'bin')
        else:
            cuda_path = ''
    
        dll_paths.extend(filter(os.path.exists, [nvtoolsext_dll_path, cuda_path]))
    
        kernel32 = ctypes.WinDLL('kernel32.dll', use_last_error=True)
        with_load_library_flags = hasattr(kernel32, 'AddDllDirectory')
        prev_error_mode = kernel32.SetErrorMode(0x0001)
    
        kernel32.LoadLibraryW.restype = ctypes.c_void_p
        if with_load_library_flags:
            kernel32.LoadLibraryExW.restype = ctypes.c_void_p
    
        for dll_path in dll_paths:
            os.add_dll_directory(dll_path)
    
        try:
            ctypes.CDLL('vcruntime140.dll')
            ctypes.CDLL('msvcp140.dll')
            ctypes.CDLL('vcruntime140_1.dll')
        except OSError:
            print('''Microsoft Visual C++ Redistributable is not installed, this may lead to the DLL load failure.
                     It can be downloaded at https://aka.ms/vs/16/release/vc_redist.x64.exe''')
    
        dlls = glob.glob(os.path.join(th_dll_path, '*.dll'))
        path_patched = False
        for dll in dlls:
            is_loaded = False
            if with_load_library_flags:
                res = kernel32.LoadLibraryExW(dll, None, 0x00001100)
                last_error = ctypes.get_last_error()
                if res is None and last_error != 126:
                    err = ctypes.WinError(last_error)
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
                elif res is not None:
                    is_loaded = True
            if not is_loaded:
                if not path_patched:
                    os.environ['PATH'] = ';'.join(dll_paths + [os.environ['PATH']])
                    path_patched = True
                res = kernel32.LoadLibraryW(dll)
                if res is None:
                    err = ctypes.WinError(ctypes.get_last_error())
                    err.strerror += f' Error loading "{dll}" or one of its dependencies.'
                    raise err
    
        kernel32.SetErrorMode(prev_error_mode)
    
    
    def _preload_cuda_deps(lib_folder, lib_name):
        """Preloads cuda deps if they could not be found otherwise."""
        # Should only be called on Linux if default path resolution have failed
        assert platform.system() == 'Linux', 'Should only be called on Linux'
        import glob
        lib_path = None
        for path in sys.path:
            nvidia_path = os.path.join(path, 'nvidia')
            if not os.path.exists(nvidia_path):
                continue
            candidate_lib_paths = glob.glob(os.path.join(nvidia_path, lib_folder, 'lib', lib_name))
            if candidate_lib_paths and not lib_path:
                lib_path = candidate_lib_paths[0]
            if lib_path:
                break
        if not lib_path:
            raise ValueError(f"{lib_name} not found in the system path {sys.path}")
        ctypes.CDLL(lib_path)
    
    
    # See Note [Global dependencies]
    def _load_global_deps() -> None:
        if _running_with_deploy() or platform.system() == 'Windows':
            return
    
        lib_name = 'libtorch_global_deps' + ('.dylib' if platform.system() == 'Darwin' else '.so')
        here = os.path.abspath(__file__)
        lib_path = os.path.join(os.path.dirname(here), 'lib', lib_name)
    
        try:
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
        except OSError as err:
            # Can only happen for wheel with cuda libs as PYPI deps
            # As PyTorch is not purelib, but nvidia-*-cu12 is
            cuda_libs: Dict[str, str] = {
                'cublas': 'libcublas.so.*[0-9]',
                'cudnn': 'libcudnn.so.*[0-9]',
                'cuda_nvrtc': 'libnvrtc.so.*[0-9]',
                'cuda_runtime': 'libcudart.so.*[0-9]',
                'cuda_cupti': 'libcupti.so.*[0-9]',
                'cufft': 'libcufft.so.*[0-9]',
                'curand': 'libcurand.so.*[0-9]',
                'cusolver': 'libcusolver.so.*[0-9]',
                'cusparse': 'libcusparse.so.*[0-9]',
                'nccl': 'libnccl.so.*[0-9]',
                'nvtx': 'libnvToolsExt.so.*[0-9]',
            }
            is_cuda_lib_err = [lib for lib in cuda_libs.values() if(lib.split('.')[0] in err.args[0])]
            if not is_cuda_lib_err:
                raise err
            for lib_folder, lib_name in cuda_libs.items():
                _preload_cuda_deps(lib_folder, lib_name)
            ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
    
    
    if (USE_RTLD_GLOBAL_WITH_LIBTORCH or os.getenv('TORCH_USE_RTLD_GLOBAL')) and \
            (_running_with_deploy() or platform.system() != 'Windows'):
        # Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a
        # few circumstances:
        #
        #   1. You're in a build environment (e.g., fbcode) where
        #      libtorch_global_deps is not available, but you still need
        #      to get mkl to link in with RTLD_GLOBAL or it will just
        #      not work.
        #
        #   2. You're trying to run PyTorch under UBSAN and you need
        #      to ensure that only one copy of libtorch is loaded, so
        #      vptr checks work properly
        #
        # If you're using this setting, you must verify that all the libraries
        # you load consistently use the same libstdc++, or you may have
        # mysterious segfaults.
        #
        old_flags = sys.getdlopenflags()
        sys.setdlopenflags(os.RTLD_GLOBAL | os.RTLD_LAZY)
        from torch._C import *  # noqa: F403
        sys.setdlopenflags(old_flags)
        del old_flags
    
    else:
        # Easy way.  You want this most of the time, because it will prevent
        # C++ symbols from libtorch clobbering C++ symbols from other
        # libraries, leading to mysterious segfaults.
        #
        # If building in an environment where libtorch_global_deps isn't available
        # like parts of fbsource, but where RTLD_GLOBAL causes segfaults, you will
        # want USE_RTLD_GLOBAL_WITH_LIBTORCH = False and USE_GLOBAL_DEPS = False
        #
        # See Note [Global dependencies]
        if USE_GLOBAL_DEPS:
            _load_global_deps()
        from torch._C import *  # noqa: F403
    
    # Appease the type checker; ordinarily this binding is inserted by the
    # torch._C module initialization code in C
    if TYPE_CHECKING:
        import torch._C as _C
    
    class SymInt:
        """
        Like an int (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return builtins.bool(self != 0)
    
        def __int__(self):
            return self.node.int_()
    
        def __index__(self):
            return self.node.int_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_float__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return str(self.node)
    
    class SymFloat:
        """
        Like an float (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
    
        def __eq__(self, other: object) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __lt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __gt__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __le__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __ge__(self, other) -> builtins.bool:
            raise AssertionError("type stub not overridden")
    
        def __sym_max__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_min__(self, other):
            raise AssertionError("type stub not overridden")
    
        def __sym_int__(self):
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    class SymBool:
        """
        Like an bool (including magic methods), but redirects all operations on the
        wrapped node. This is used in particular to symbolically record operations
        in the symbolic shape workflow.
    
        Unlike regular bools, regular boolean operators will force extra guards instead
        of symbolically evaluate.  Use the bitwise operators instead to handle this.
        """
    
        def __init__(self, node):
            # This field MUST be named node; C++ binding code assumes that this
            # class has a field named node that stores SymNode
            self.node = node
    
        def __bool__(self):
            return self.node.bool_()
    
        def __int__(self):
            return builtins.int(self.node.bool_())
    
        # Magic methods installed by torch.fx.experimental.symbolic_shapes
        def __and__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __or__(self, other) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        # We very carefully define __sym_not__, and not a number of other
        # plausible alternatives:
        #
        #   - We do not override __not__ because this is not a real magic
        #     method; you cannot override the meaning of the not builtin in
        #     Python.  We use the name 'sym_not' to clarify that in user code you
        #     cannot use the builtin not or operator.not_ or operator.__not__ and
        #     hit this magic method; you must use our custom sym_not operator.
        #
        #   - We do not override the __invert__ method because SymBool is
        #     meant to be usable in situations where bool is expected.  However,
        #     bitwise negation ~a does the wrong thing with booleans (because
        #     bool is a subclass of int, so ~1 = -2 which is not falseish.)
        #     This would be a giant footgun, so we get around it by defining
        #     our own operator.  Note that bitwise and/or do the right thing,
        #     so we reuse the conventional operators there for readability.
        #
        def __sym_not__(self) -> "SymBool":
            raise AssertionError("type stub not overridden")
    
        def __repr__(self):
            return self.node.str()
    
    def sym_not(a):
        r""" SymInt-aware utility for logical negation.
    
        Args:
            a (SymBool or bool): Object to negate
        """
        if hasattr(a, '__sym_not__'):
            return a.__sym_not__()
        return not a
    
    def sym_float(a):
        r""" SymInt-aware utility for float casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymFloat):
            return a
        elif hasattr(a, '__sym_float__'):
            return a.__sym_float__()
        return py_float(a)  # type: ignore[operator]
    
    
    def sym_int(a):
        r""" SymInt-aware utility for int casting.
    
        Args:
            a (SymInt, SymFloat, or object): Object to cast
        """
        if isinstance(a, SymInt):
            return a
        elif isinstance(a, SymFloat):
            return math.floor(a) if a >= 0 else math.ceil(a)  # type: ignore[arg-type, call-overload]
        return py_int(a)  # type: ignore[operator]
    
    def sym_max(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_max__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            # NB: If you actually care about preserving output type exactly
            # if you do something like max(0, 0.0), it is NOT sound to treat
            # min/max as commutative
            return b.__sym_max__(a)
        return builtins.max(a, b)  # type: ignore[operator]
    
    def sym_min(a, b):
        """ SymInt-aware utility for max()."""
        if isinstance(a, (SymInt, SymFloat)):
            return a.__sym_min__(b)
        elif isinstance(b, (SymInt, SymFloat)):
            return b.__sym_min__(a)
        return builtins.min(a, b)  # type: ignore[operator]
    
    # Check to see if we can load C extensions, and if not provide some guidance
    # on what the problem might be.
    try:
        # _initExtension is chosen (arbitrarily) as a sentinel.
        from torch._C import _initExtension
    except ImportError:
        import torch._C as _C_for_compiled_check
    
        # The __file__ check only works for Python 3.7 and above.
        if _C_for_compiled_check.__file__ is None:
>           raise ImportError(textwrap.dedent('''
                Failed to load PyTorch C extensions:
                    It appears that PyTorch has loaded the `torch/_C` folder
                    of the PyTorch repository rather than the C extensions which
                    are expected in the `torch._C` namespace. This can occur when
                    using the `install` workflow. e.g.
                        $ python setup.py install && python -c "import torch"
    
                    This error can generally be solved using the `develop` workflow
                        $ python setup.py develop && python -c "import torch"  # This should succeed
                    or by running Python from a different directory.
                ''').strip()) from None
E           ImportError: Failed to load PyTorch C extensions:
E               It appears that PyTorch has loaded the `torch/_C` folder
E               of the PyTorch repository rather than the C extensions which
E               are expected in the `torch._C` namespace. This can occur when
E               using the `install` workflow. e.g.
E                   $ python setup.py install && python -c "import torch"
E           
E               This error can generally be solved using the `develop` workflow
E                   $ python setup.py develop && python -c "import torch"  # This should succeed
E               or by running Python from a different directory.

/usr/lib/python3/dist-packages/torch/__init__.py:451: ImportError
=========================== short test summary info ============================
FAILED tests/test_array_namespace.py::test_array_namespace[None-torch] - Impo...
FAILED tests/test_array_namespace.py::test_array_namespace[2021.12-torch] - I...
FAILED tests/test_array_namespace.py::test_array_namespace_errors - ImportErr...
FAILED tests/test_common.py::test_to_device_host[torch] - ImportError: Failed...
FAILED tests/test_isdtype.py::test_isdtype_spec_dtypes[torch] - ImportError: ...
FAILED tests/test_isdtype.py::test_isdtype_additional_dtypes[float16-torch]
FAILED tests/test_isdtype.py::test_isdtype_additional_dtypes[float128-torch]
FAILED tests/test_isdtype.py::test_isdtype_additional_dtypes[complex256-torch]
FAILED tests/test_isdtype.py::test_isdtype_additional_dtypes[bfloat16-torch]
=================== 9 failed, 10 passed, 8 skipped in 1.26s ====================
E: pybuild pybuild:389: test: plugin distutils failed with: exit code=1: cd /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.12_array_api_compat/build; python3.12 -m pytest --ignore tests/test_vendoring.py
I: pybuild base:311: cd /build/reproducible-path/python-array-api-compat-1.4/.pybuild/cpython3_3.11_array_api_compat/build; python3.11 -m pytest --ignore tests/test_vendoring.py
============================= test session starts ==============================
platform linux -- Python 3.11.9, pytest-8.1.2, pluggy-1.5.0
rootdir: /build/reproducible-path/python-array-api-compat-1.4
plugins: hypothesis-6.100.7
collected 27 items

tests/test_array_namespace.py s..s.....                                  [ 33%]
tests/test_common.py s..                                                 [ 44%]
tests/test_isdtype.py s..s..s..s..s..                                    [100%]

======================== 19 passed, 8 skipped in 2.95s =========================
dh_auto_test: error: pybuild --test --test-pytest -i python{version} -p "3.12 3.11" returned exit code 13
make: *** [debian/rules:9: binary] Error 25
dpkg-buildpackage: error: debian/rules binary subprocess returned exit status 2
I: copying local configuration
E: Failed autobuilding of package
I: unmounting dev/ptmx filesystem
I: unmounting dev/pts filesystem
I: unmounting dev/shm filesystem
I: unmounting proc filesystem
I: unmounting sys filesystem
I: cleaning the build env 
I: removing directory /srv/workspace/pbuilder/1278330 and its subdirectories
Mon May 13 11:45:50 UTC 2024  W: No second build log, what happened?