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            "PYTHONPATH=/usr/local/lib/python3.6  \\\n",
            "\t./python -E -Wi -O /usr/local/lib/python3.6/compileall.py \\\n",
            "\t-d /usr/local/lib/python3.6 -f \\\n",
            "\t-x 'bad_coding|badsyntax|site-packages|lib2to3/tests/data' \\\n",
            "\t/usr/local/lib/python3.6\n",
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            "Compiling '/usr/local/lib/python3.6/zipfile.py'...\n",
            "PYTHONPATH=/usr/local/lib/python3.6  \\\n",
            "\t./python -E -Wi -OO /usr/local/lib/python3.6/compileall.py \\\n",
            "\t-d /usr/local/lib/python3.6 -f \\\n",
            "\t-x 'bad_coding|badsyntax|site-packages|lib2to3/tests/data' \\\n",
            "\t/usr/local/lib/python3.6\n",
            "Listing '/usr/local/lib/python3.6'...\n",
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            "Compiling '/usr/local/lib/python3.6/zipfile.py'...\n",
            "PYTHONPATH=/usr/local/lib/python3.6  \\\n",
            "\t./python -E -Wi /usr/local/lib/python3.6/compileall.py \\\n",
            "\t-d /usr/local/lib/python3.6/site-packages -f \\\n",
            "\t-x badsyntax /usr/local/lib/python3.6/site-packages\n",
            "Listing '/usr/local/lib/python3.6/site-packages'...\n",
            "PYTHONPATH=/usr/local/lib/python3.6  \\\n",
            "\t./python -E -Wi -O /usr/local/lib/python3.6/compileall.py \\\n",
            "\t-d /usr/local/lib/python3.6/site-packages -f \\\n",
            "\t-x badsyntax /usr/local/lib/python3.6/site-packages\n",
            "Listing '/usr/local/lib/python3.6/site-packages'...\n",
            "PYTHONPATH=/usr/local/lib/python3.6  \\\n",
            "\t./python -E -Wi -OO /usr/local/lib/python3.6/compileall.py \\\n",
            "\t-d /usr/local/lib/python3.6/site-packages -f \\\n",
            "\t-x badsyntax /usr/local/lib/python3.6/site-packages\n",
            "Listing '/usr/local/lib/python3.6/site-packages'...\n",
            "PYTHONPATH=/usr/local/lib/python3.6  \\\n",
            "\t./python -E -m lib2to3.pgen2.driver /usr/local/lib/python3.6/lib2to3/Grammar.txt\n",
            "Generating grammar tables from /usr/local/lib/python3.6/lib2to3/Grammar.txt\n",
            "Writing grammar tables to /usr/local/lib/python3.6/lib2to3/Grammar3.6.9.final.0.pickle\n",
            "PYTHONPATH=/usr/local/lib/python3.6  \\\n",
            "\t./python -E -m lib2to3.pgen2.driver /usr/local/lib/python3.6/lib2to3/PatternGrammar.txt\n",
            "Generating grammar tables from /usr/local/lib/python3.6/lib2to3/PatternGrammar.txt\n",
            "Writing grammar tables to /usr/local/lib/python3.6/lib2to3/PatternGrammar3.6.9.final.0.pickle\n",
            "Creating directory /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/Python-ast.h /usr/local/include/python3.6m\n",
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            "/usr/bin/install -c -m 644 Python-3.6.9/Include/graminit.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/grammar.h /usr/local/include/python3.6m\n",
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            "/usr/bin/install -c -m 644 Python-3.6.9/Include/opcode.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/osdefs.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/osmodule.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/parsetok.h /usr/local/include/python3.6m\n",
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            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pgen.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pgenheaders.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/py_curses.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pyarena.h /usr/local/include/python3.6m\n",
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            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pycapsule.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pyctype.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pydebug.h /usr/local/include/python3.6m\n",
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            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pyerrors.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pyexpat.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pyfpe.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pygetopt.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pyhash.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pylifecycle.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pymacconfig.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pymacro.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pymath.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pymem.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pyport.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pystate.h /usr/local/include/python3.6m\n",
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            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pystrhex.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pystrtod.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pythonrun.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pythread.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/pytime.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/rangeobject.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/setobject.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/sliceobject.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/structmember.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/structseq.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/symtable.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/sysmodule.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/token.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/traceback.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/tupleobject.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/typeslots.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/ucnhash.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/unicodeobject.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/warnings.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Include/weakrefobject.h /usr/local/include/python3.6m\n",
            "/usr/bin/install -c -m 644 pyconfig.h /usr/local/include/python3.6m/pyconfig.h\n",
            "Creating directory /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu\n",
            "/usr/bin/install -c -m 644 Modules/config.c /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/config.c\n",
            "/usr/bin/install -c -m 644 Programs/python.o /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/python.o\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Modules/config.c.in /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/config.c.in\n",
            "/usr/bin/install -c -m 644 Makefile /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/Makefile\n",
            "/usr/bin/install -c -m 644 Modules/Setup /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/Setup\n",
            "/usr/bin/install -c -m 644 Modules/Setup.local /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/Setup.local\n",
            "/usr/bin/install -c -m 644 Modules/Setup.config /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/Setup.config\n",
            "/usr/bin/install -c -m 644 Misc/python.pc /usr/local/lib/pkgconfig/python-3.6.pc\n",
            "/usr/bin/install -c Python-3.6.9/Modules/makesetup /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/makesetup\n",
            "/usr/bin/install -c Python-3.6.9/install-sh /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/install-sh\n",
            "/usr/bin/install -c python-config.py /usr/local/lib/python3.6/config-3.6m-x86_64-linux-gnu/python-config.py\n",
            "/usr/bin/install -c python-config /usr/local/bin/python3.6m-config\n",
            "./python -E Python-3.6.9/setup.py install \\\n",
            "   \t--prefix=/usr/local \\\n",
            "\t--install-scripts=/usr/local/bin \\\n",
            "\t--install-platlib=/usr/local/lib/python3.6/lib-dynload \\\n",
            "\t--root=/\n",
            "running install\n",
            "running build\n",
            "running build_ext\n",
            "warning: openssl 0x00000000 is too old for _hashlib\n",
            "warning: building with the bundled copy of libffi is deprecated on this platform.  It will not be distributed with Python 3.7\n",
            "\n",
            "Python build finished successfully!\n",
            "The necessary bits to build these optional modules were not found:\n",
            "_dbm                  _gdbm                 _hashlib           \n",
            "To find the necessary bits, look in setup.py in detect_modules() for the module's name.\n",
            "\n",
            "The following modules found by detect_modules() in setup.py, have been\n",
            "built by the Makefile instead, as configured by the Setup files:\n",
            "atexit                pwd                   time               \n",
            "running build_scripts\n",
            "copying and adjusting /content/Python-3.6.9/Tools/scripts/pydoc3 -> build/scripts-3.6\n",
            "copying and adjusting /content/Python-3.6.9/Tools/scripts/idle3 -> build/scripts-3.6\n",
            "copying and adjusting /content/Python-3.6.9/Tools/scripts/2to3 -> build/scripts-3.6\n",
            "copying and adjusting /content/Python-3.6.9/Tools/scripts/pyvenv -> build/scripts-3.6\n",
            "changing mode of build/scripts-3.6/pydoc3 from 644 to 755\n",
            "changing mode of build/scripts-3.6/idle3 from 644 to 755\n",
            "changing mode of build/scripts-3.6/2to3 from 644 to 755\n",
            "changing mode of build/scripts-3.6/pyvenv from 644 to 755\n",
            "renaming build/scripts-3.6/pydoc3 to build/scripts-3.6/pydoc3.6\n",
            "renaming build/scripts-3.6/idle3 to build/scripts-3.6/idle3.6\n",
            "renaming build/scripts-3.6/2to3 to build/scripts-3.6/2to3-3.6\n",
            "renaming build/scripts-3.6/pyvenv to build/scripts-3.6/pyvenv-3.6\n",
            "running install_lib\n",
            "creating /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_codecs_jp.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_random.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_codecs_hk.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/spwd.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/array.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/termios.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_ssl.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/mmap.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_datetime.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_codecs_tw.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/unicodedata.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_socket.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/select.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/nis.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_codecs_cn.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_sysconfigdata_m_linux_x86_64-linux-gnu.py -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_lzma.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_csv.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_crypt.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_asyncio.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_curses.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_ctypes.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "creating /usr/local/lib/python3.6/lib-dynload/__pycache__\n",
            "copying build/lib.linux-x86_64-3.6/__pycache__/_sysconfigdata_m_linux_x86_64-linux-gnu.cpython-36.opt-2.pyc -> /usr/local/lib/python3.6/lib-dynload/__pycache__\n",
            "copying build/lib.linux-x86_64-3.6/__pycache__/_sysconfigdata_m_linux_x86_64-linux-gnu.cpython-36.opt-1.pyc -> /usr/local/lib/python3.6/lib-dynload/__pycache__\n",
            "copying build/lib.linux-x86_64-3.6/__pycache__/_sysconfigdata_m_linux_x86_64-linux-gnu.cpython-36.pyc -> /usr/local/lib/python3.6/lib-dynload/__pycache__\n",
            "copying build/lib.linux-x86_64-3.6/_bz2.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_bisect.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_sha512.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_curses_panel.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_tkinter.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/pyexpat.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_heapq.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_decimal.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_codecs_kr.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_blake2.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/zlib.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_sha1.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_sha3.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_posixsubprocess.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/readline.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_testbuffer.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_testimportmultiple.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/cmath.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_sqlite3.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_testmultiphase.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/ossaudiodev.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_sha256.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_lsprof.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_testcapi.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/audioop.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_multiprocessing.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_opcode.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_codecs_iso2022.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_struct.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/fcntl.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_ctypes_test.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/parser.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_multibytecodec.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/binascii.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_json.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/math.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/syslog.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/grp.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_pickle.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_elementtree.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/resource.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/_md5.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "copying build/lib.linux-x86_64-3.6/xxlimited.cpython-36m-x86_64-linux-gnu.so -> /usr/local/lib/python3.6/lib-dynload\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_codecs_jp.cpython-36m-x86_64-linux-gnu.so to 755\n",
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            "changing mode of /usr/local/lib/python3.6/lib-dynload/_blake2.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/zlib.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_sha1.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_sha3.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_posixsubprocess.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/readline.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_testbuffer.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_testimportmultiple.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/cmath.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_sqlite3.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_testmultiphase.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/ossaudiodev.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_sha256.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_lsprof.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_testcapi.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/audioop.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_multiprocessing.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_opcode.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_codecs_iso2022.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_struct.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/fcntl.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_ctypes_test.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/parser.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_multibytecodec.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/binascii.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_json.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/math.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/syslog.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/grp.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_pickle.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_elementtree.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/resource.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/_md5.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/xxlimited.cpython-36m-x86_64-linux-gnu.so to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/ to 755\n",
            "changing mode of /usr/local/lib/python3.6/lib-dynload/__pycache__ to 755\n",
            "running install_scripts\n",
            "copying build/scripts-3.6/idle3.6 -> /usr/local/bin\n",
            "copying build/scripts-3.6/pydoc3.6 -> /usr/local/bin\n",
            "copying build/scripts-3.6/2to3-3.6 -> /usr/local/bin\n",
            "copying build/scripts-3.6/pyvenv-3.6 -> /usr/local/bin\n",
            "changing mode of /usr/local/bin/idle3.6 to 755\n",
            "changing mode of /usr/local/bin/pydoc3.6 to 755\n",
            "changing mode of /usr/local/bin/2to3-3.6 to 755\n",
            "changing mode of /usr/local/bin/pyvenv-3.6 to 755\n",
            "rm /usr/local/lib/python3.6/lib-dynload/_sysconfigdata_m_linux_x86_64-linux-gnu.py\n",
            "rm -r /usr/local/lib/python3.6/lib-dynload/__pycache__\n",
            "/usr/bin/install -c -m 644 Python-3.6.9/Misc/python.man \\\n",
            "\t/usr/local/share/man/man1/python3.6.1\n",
            "if test ! -d /usr/local/lib/pkgconfig; then \\\n",
            "\techo \"Creating directory /usr/local/lib/pkgconfig\"; \\\n",
            "\t/usr/bin/install -c -d -m 755 /usr/local/lib/pkgconfig; \\\n",
            "fi\n",
            "if test -f /usr/local/bin/python3 -o -h /usr/local/bin/python3; \\\n",
            "then rm -f /usr/local/bin/python3; \\\n",
            "else true; \\\n",
            "fi\n",
            "(cd /usr/local/bin; ln -s python3.6 python3)\n",
            "if test \"3.6\" != \"3.6m\"; then \\\n",
            "\trm -f /usr/local/bin/python3.6-config; \\\n",
            "\t(cd /usr/local/bin; ln -s python3.6m-config python3.6-config); \\\n",
            "\trm -f /usr/local/lib/pkgconfig/python-3.6m.pc; \\\n",
            "\t(cd /usr/local/lib/pkgconfig; ln -s python-3.6.pc python-3.6m.pc); \\\n",
            "fi\n",
            "rm -f /usr/local/bin/python3-config\n",
            "(cd /usr/local/bin; ln -s python3.6-config python3-config)\n",
            "rm -f /usr/local/lib/pkgconfig/python3.pc\n",
            "(cd /usr/local/lib/pkgconfig; ln -s python-3.6.pc python3.pc)\n",
            "rm -f /usr/local/bin/idle3\n",
            "(cd /usr/local/bin; ln -s idle3.6 idle3)\n",
            "rm -f /usr/local/bin/pydoc3\n",
            "(cd /usr/local/bin; ln -s pydoc3.6 pydoc3)\n",
            "rm -f /usr/local/bin/2to3\n",
            "(cd /usr/local/bin; ln -s 2to3-3.6 2to3)\n",
            "rm -f /usr/local/bin/pyvenv\n",
            "(cd /usr/local/bin; ln -s pyvenv-3.6 pyvenv)\n",
            "if test \"x\" != \"x\" ; then \\\n",
            "\trm -f /usr/local/bin/python3-32; \\\n",
            "\t(cd /usr/local/bin; ln -s python3.6-32 python3-32) \\\n",
            "fi\n",
            "rm -f /usr/local/share/man/man1/python3.1\n",
            "(cd /usr/local/share/man/man1; ln -s python3.6.1 python3.1)\n",
            "if test \"xupgrade\" != \"xno\"  ; then \\\n",
            "\tcase upgrade in \\\n",
            "\t\tupgrade) ensurepip=\"--upgrade\" ;; \\\n",
            "\t\tinstall|*) ensurepip=\"\" ;; \\\n",
            "\tesac; \\\n",
            "\t ./python -E -m ensurepip \\\n",
            "\t\t$ensurepip --root=/ ; \\\n",
            "fi\n",
            "Segmentation fault (core dumped)\n",
            "make: *** [Makefile:1102: install] Error 139\n"
          ]
        }
      ],
      "source": [
        "!wget https://www.python.org/ftp/python/3.6.9/Python-3.6.9.tgz\n",
        "!tar xvfz Python-3.6.9.tgz\n",
        "!Python-3.6.9/configure\n",
        "!make\n",
        "!sudo make install"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mxnet\n",
        "!pip install gluonnlp==0.8.0\n",
        "!pip install tqdm pandas\n",
        "!pip install sentencepiece == 0.1.96\n",
        "!pip install transformers\n",
        "!pip install torch>=1.8.1\n",
        "!pip install numpy==1.23.1\n",
        "!pip install 'git+https://github.com/SKTBrain/KoBERT.git#egg=kobert_tokenizer&subdirectory=kobert_hf'\n",
        "\n",
        "\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZT0kSXyLV-VI",
        "outputId": "7ffe181e-82be-4704-dd83-96709d72db36"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting mxnet\n",
            "  Downloading mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl.metadata (3.4 kB)\n",
            "Requirement already satisfied: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.10/dist-packages (from mxnet) (1.26.4)\n",
            "Requirement already satisfied: requests<3,>=2.20.0 in /usr/local/lib/python3.10/dist-packages (from mxnet) (2.32.3)\n",
            "Collecting graphviz<0.9.0,>=0.8.1 (from mxnet)\n",
            "  Downloading graphviz-0.8.4-py2.py3-none-any.whl.metadata (6.4 kB)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.20.0->mxnet) (2024.8.30)\n",
            "Downloading mxnet-1.9.1-py3-none-manylinux2014_x86_64.whl (49.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 MB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading graphviz-0.8.4-py2.py3-none-any.whl (16 kB)\n",
            "Installing collected packages: graphviz, mxnet\n",
            "  Attempting uninstall: graphviz\n",
            "    Found existing installation: graphviz 0.20.3\n",
            "    Uninstalling graphviz-0.20.3:\n",
            "      Successfully uninstalled graphviz-0.20.3\n",
            "Successfully installed graphviz-0.8.4 mxnet-1.9.1\n",
            "Collecting gluonnlp==0.8.0\n",
            "  Downloading gluonnlp-0.8.0.tar.gz (235 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m235.5/235.5 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from gluonnlp==0.8.0) (1.26.4)\n",
            "Building wheels for collected packages: gluonnlp\n",
            "  Building wheel for gluonnlp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gluonnlp: filename=gluonnlp-0.8.0-py3-none-any.whl size=292694 sha256=09bfcde5f4c41d7c2564581461519af29d92a5489ba17456b7cbb25718aa2df0\n",
            "  Stored in directory: /root/.cache/pip/wheels/2d/cc/dc/7ec84dced25f738b8be400101abb67e4b50c905090a51017e4\n",
            "Successfully built gluonnlp\n",
            "Installing collected packages: gluonnlp\n",
            "Successfully installed gluonnlp-0.8.0\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.66.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "\u001b[31mERROR: Invalid requirement: '==': Expected package name at the start of dependency specifier\n",
            "    ==\n",
            "    ^\u001b[0m\u001b[31m\n",
            "\u001b[0mRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n",
            "Collecting numpy==1.23.1\n",
            "  Downloading numpy-1.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.2 kB)\n",
            "Downloading numpy-1.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.0 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.0/17.0 MB\u001b[0m \u001b[31m51.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: numpy\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.26.4\n",
            "    Uninstalling numpy-1.26.4:\n",
            "      Successfully uninstalled numpy-1.26.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albucore 0.0.16 requires numpy>=1.24, but you have numpy 1.23.1 which is incompatible.\n",
            "albumentations 1.4.15 requires numpy>=1.24.4, but you have numpy 1.23.1 which is incompatible.\n",
            "bigframes 1.21.0 requires numpy>=1.24.0, but you have numpy 1.23.1 which is incompatible.\n",
            "chex 0.1.87 requires numpy>=1.24.1, but you have numpy 1.23.1 which is incompatible.\n",
            "ibis-framework 9.2.0 requires numpy<3,>=1.23.2, but you have numpy 1.23.1 which is incompatible.\n",
            "jax 0.4.33 requires numpy>=1.24, but you have numpy 1.23.1 which is incompatible.\n",
            "jaxlib 0.4.33 requires numpy>=1.24, but you have numpy 1.23.1 which is incompatible.\n",
            "pandas-stubs 2.2.2.240909 requires numpy>=1.23.5, but you have numpy 1.23.1 which is incompatible.\n",
            "tensorflow 2.17.0 requires numpy<2.0.0,>=1.23.5; python_version <= \"3.11\", but you have numpy 1.23.1 which is incompatible.\n",
            "xarray 2024.9.0 requires numpy>=1.24, but you have numpy 1.23.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed numpy-1.23.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              },
              "id": "3612ed89fec448dda6a10860ce6109d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting kobert_tokenizer\n",
            "  Cloning https://github.com/SKTBrain/KoBERT.git to /tmp/pip-install-ftmbyn8n/kobert-tokenizer_15c1166c02414c0ca03e94408612c5da\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/SKTBrain/KoBERT.git /tmp/pip-install-ftmbyn8n/kobert-tokenizer_15c1166c02414c0ca03e94408612c5da\n",
            "  Resolved https://github.com/SKTBrain/KoBERT.git to commit 5c46b1c68e4755b54879431bd302db621f4d2f47\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: kobert_tokenizer\n",
            "  Building wheel for kobert_tokenizer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert_tokenizer: filename=kobert_tokenizer-0.1-py3-none-any.whl size=4632 sha256=3ea14a45363a4c284c9fea40012bf04d7a3594241bdafc027f6828bc847fe379\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-i2ik0lq0/wheels/e9/1a/3f/a864970e8a169c176befa3c4a1e07aa612f69195907a4045fe\n",
            "Successfully built kobert_tokenizer\n",
            "Installing collected packages: kobert_tokenizer\n",
            "Successfully installed kobert_tokenizer-0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoTokenizer\n",
        "\n",
        "kobert_tokenizer = AutoTokenizer.from_pretrained(\"skt/kobert-base-v1\", use_fast=False)\n",
        "kobert = AutoModel.from_pretrained(\"skt/kobert-base-v1\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398,
          "referenced_widgets": [
            "d85b7a445cf84fac876918c4b305d960",
            "2c45f59252f544738743211e262da265",
            "3d6f9191fd6743cea6a2f5b370e1b7c9",
            "fb28194909ca4b018ecb4048d7ea7503",
            "7f13d3b00bff4052a8bad792c51ae75d",
            "7ea2a770447944bf8dbc1a181a1cbe36",
            "684e37117ed44c7290e533b091e162ab",
            "c84e45bd0da54540a3212058cd0ead77",
            "4769c4ab9f14441e8c9127d988822593",
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            "5bd80e8f31124404b980b0ca00160002",
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          ]
        },
        "id": "6fiIx6GuY67Y",
        "outputId": "425ffac3-60b4-4454-8180-c9f21145c661"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/432 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d85b7a445cf84fac876918c4b305d960"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spiece.model:   0%|          | 0.00/371k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f80a082cbe55433a9920474b51b84eb0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/244 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "14ea0bbc4ecb4a299909af545cc4d335"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/535 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9177affb64e443928f28e4fd4df2d009"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/369M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f05a2d76f7c84f8e91ed2755f10af131"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['▁네이버', '페', '이', '_', '배', '달', '의', '민', '족']\n",
            "1654\n",
            "[[1470, 3, 2], [4829, 3, 2], [3647, 3, 2], [517, 365, 3, 2], [2287, 3, 2], [1597, 3, 2], [517, 7095, 3, 2], [2169, 3, 2], [517, 7263, 3, 2]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = kobert_tokenizer.tokenize(\"네이버페이_배달의민족 \")\n",
        "print(result)\n",
        "kobert_vocab = kobert_tokenizer.get_vocab()\n",
        "print(kobert_vocab.get('▁네이버'))\n",
        "print([kobert_tokenizer.encode(token) for token in result])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5AhdrVZZY9h",
        "outputId": "5d2b1c54-fd61-4f95-d282-063937baea1a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['▁네이버', '페', '이', '_', '배', '달', '의', '민', '족']\n",
            "1470\n",
            "[[1470, 3, 2], [4829, 3, 2], [3647, 3, 2], [517, 365, 3, 2], [2287, 3, 2], [1597, 3, 2], [517, 7095, 3, 2], [2169, 3, 2], [517, 7263, 3, 2]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import argparse\n",
        "from tokenizers import BertWordPieceTokenizer\n",
        "\n",
        "#parser = argparse.ArgumentParser()\n",
        "\n",
        "#parser.add_argument(\"--corpus_file\", type=str)\n",
        "#parser.add_argument(\"--vocab_size\", type=int, default=32000)\n",
        "#parser.add_argument(\"--limit_alphabet\", type=int, default=6000)\n",
        "\n",
        "#args = parser.parse_args()\n",
        "\n",
        "tokenizer = BertWordPieceTokenizer(\n",
        "    kobert_vocab,\n",
        "    clean_text=True,\n",
        "    handle_chinese_chars=True,\n",
        "    strip_accents=False, # Must be False if cased model\n",
        "    lowercase=False,\n",
        "    wordpieces_prefix=\"##\"\n",
        ")\n"
      ],
      "metadata": {
        "id": "-bIwL6wSZtRE"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kobert_tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lfg0nyAxboZw",
        "outputId": "f1b51854-5022-496b-c1c0-02ab5c7caf3b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XLNetTokenizer(name_or_path='skt/kobert-base-v1', vocab_size=8002, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='left', truncation_side='right', special_tokens={'bos_token': '[CLS]', 'eos_token': '[SEP]', 'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
              "\t0: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t1: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t2: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t3: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t4: AddedToken(\"[MASK]\", rstrip=False, lstrip=True, single_word=False, normalized=True, special=True),\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCapNnhUbr6q",
        "outputId": "e55713f7-77f8-4b51-99fb-9c7c650d41d9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tokenizer(vocabulary_size=8002, model=BertWordPiece, unk_token=[UNK], sep_token=[SEP], cls_token=[CLS], pad_token=[PAD], mask_token=[MASK], clean_text=True, handle_chinese_chars=True, strip_accents=False, lowercase=False, wordpieces_prefix=##)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# KoBERT 토크나이저 불러오기\n",
        "tokenizer = kobert_tokenizer\n",
        "\n",
        "# 새로 추가하려는 토큰 목록\n",
        "new_tokens = ['코로나', '백신', '확진자', '마스크', '방역']\n",
        "\n"
      ],
      "metadata": {
        "id": "COr_pQ4ydLWj"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. 새로운 토큰 중에서 이미 토크나이저에 있는 토큰 필터링\n",
        "existing_tokens = set(kobert_vocab.keys())\n",
        "tokens_to_add = [token for token in new_tokens if token not in existing_tokens]"
      ],
      "metadata": {
        "id": "PPgjzWaAdalx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokens_to_add"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJJzitP6e13i",
        "outputId": "e23de01c-0691-432c-d93f-63a176e7284c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['코로나', '백신', '확진자', '마스크', '방역']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. 추가할 토큰 출력\n",
        "if tokens_to_add:\n",
        "    print(f\"{len(tokens_to_add)}개의 새로운 토큰을 추가합니다: {tokens_to_add}\")\n",
        "else:\n",
        "    print(\"추가할 새로운 토큰이 없습니다.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8oDZjSYe4M6",
        "outputId": "eaf80b91-9573-47e2-cb04-66258e5a10af"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5개의 새로운 토큰을 추가합니다: ['코로나', '백신', '확진자', '마스크', '방역']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. 토크나이저에 새로운 단어 추가 (필요한 경우에만)\n",
        "if tokens_to_add:\n",
        "    num_added_tokens = tokenizer.add_tokens(tokens_to_add)\n",
        "    print(f\"{num_added_tokens}개의 토큰이 토크나이저에 추가되었습니다.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-FC4Olle6LX",
        "outputId": "0839dde7-3a48-4460-bef1-f3c605bce642"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5개의 토큰이 토크나이저에 추가되었습니다.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. 모델의 임베딩 레이어 크기 확장\n",
        "kobert.resize_token_embeddings(len(kobert_tokenizer))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSiLhpKBfATl",
        "outputId": "e549be0b-bf07-4851-f772-69782bf3bce6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embedding(8007, 768, padding_idx=1)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# 6. 추가된 토큰으로 테스트\n",
        "text = \"안녕하세요, 새로운단어1를 추가한 KoBERT 모델입니다.\"\n",
        "encoded_input = kobert_tokenizer(text, return_tensors='pt')"
      ],
      "metadata": {
        "id": "JZgMzOHZfCPE"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_input.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9o_exEpfIg2",
        "outputId": "3cbeb8ce-da9e-4dab-ed5b-ac1d440b5044"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_input['input_ids'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vc4M4KdThFLW",
        "outputId": "15a03181-856c-4629-f958-135ffa2f20d5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(8002)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 출력 확인\n",
        "output = kobert(**encoded_input)\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "KTu1hfl-fEDE",
        "outputId": "be2a92d0-9b98-47b5-854d-c2daf7e79d50"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "index out of range in self",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-0775a027e08f>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 모델 출력 확인\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkobert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mencoded_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1075\u001b[0m                 \u001b[0mtoken_type_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1076\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1077\u001b[0;31m         embedding_output = self.embeddings(\n\u001b[0m\u001b[1;32m   1078\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1079\u001b[0m             \u001b[0mposition_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mposition_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, token_type_ids, position_ids, inputs_embeds, past_key_values_length)\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minputs_embeds\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0minputs_embeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_embeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0mtoken_type_embeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoken_type_embeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken_type_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0membeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs_embeds\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtoken_type_embeddings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1560\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1561\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/sparse.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m         return F.embedding(\n\u001b[0m\u001b[1;32m    165\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m             self.norm_type, self.scale_grad_by_freq, self.sparse)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36membedding\u001b[0;34m(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)\u001b[0m\n\u001b[1;32m   2265\u001b[0m         \u001b[0;31m# remove once script supports set_grad_enabled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2266\u001b[0m         \u001b[0m_no_grad_embedding_renorm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2267\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale_grad_by_freq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: index out of range in self"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kobert = AutoModel.from_pretrained(\"skt/kobert-base-v1\")"
      ],
      "metadata": {
        "id": "UdPN_FZWgDXT"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kobert_tokenizer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGcZ_DWehXwG",
        "outputId": "61ca5d2d-d06f-4521-811a-20edb5af3d95"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XLNetTokenizer(name_or_path='skt/kobert-base-v1', vocab_size=8002, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='left', truncation_side='right', special_tokens={'bos_token': '[CLS]', 'eos_token': '[SEP]', 'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
              "\t0: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t1: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t2: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t3: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t4: AddedToken(\"[MASK]\", rstrip=False, lstrip=True, single_word=False, normalized=True, special=True),\n",
              "\t8002: AddedToken(\"새로운단어1\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),\n",
              "\t8003: AddedToken(\"새로운단어2\", rstrip=False, lstrip=False, single_word=False, normalized=True, special=False),\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kobert_tokenizer.encode(token)"
      ],
      "metadata": {
        "id": "DVqk7RHUhvoW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoTokenizer\n",
        "\n",
        "# 1. KoBERT 토크나이저와 모델 불러오기\n",
        "kobert_tokenizer = AutoTokenizer.from_pretrained(\"skt/kobert-base-v1\", use_fast=False)\n",
        "kobert = AutoModel.from_pretrained(\"skt/kobert-base-v1\")\n",
        "\n",
        "# 2. 추가할 새로운 토큰 정의\n",
        "new_tokens = ['새로운단어1', '새로운단어2']\n",
        "\n",
        "# 3. 기존 토크나이저에 없는 토큰만 추가 (중복 방지)\n",
        "existing_tokens = set(kobert_tokenizer.get_vocab().keys())\n",
        "tokens_to_add = [token for token in new_tokens if token not in existing_tokens]\n",
        "\n",
        "# 4. 새로운 토큰 추가\n",
        "if tokens_to_add:\n",
        "    kobert_tokenizer.add_tokens(tokens_to_add)\n",
        "    # print(f\"새로운 토큰 {}개가 추가되었습니다: \"{tokens_to_add}\")\n",
        "\n",
        "    # 모델의 임베딩 레이어 크기 확장 (중요)\n",
        "    kobert.resize_token_embeddings(len(kobert_tokenizer))\n",
        "\n",
        "# 5. 추가된 토큰으로 테스트\n",
        "text = \"안녕하세요, 새로운단어1를 추가한 KoBERT 모델입니다.\"\n",
        "\n",
        "encoded_input = kobert_tokenizer.tokenize(text, return_tensors='pt')\n",
        "\n",
        "# 6. 인코딩된 input의 최대 인덱스가 모델의 임베딩 크기를 초과하지 않는지 확인\n",
        "vocab_size = kobert.config.vocab_size\n",
        "print(f\"모델의 vocab 크기: {vocab_size}\")\n",
        "print(f\"입력된 텐서 최대 인덱스: {encoded_input['input_ids'].max()}\")\n",
        "\n",
        "# 7. 모델 출력 확인\n",
        "output = kobert(**encoded_input)\n",
        "print(output)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "v4KCAGvCebsS",
        "outputId": "1189cf24-7d6c-41ae-fc60-73b74ba059df"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "Keyword arguments {'return_tensors': 'pt'} not recognized.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "모델의 vocab 크기: 8004\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "list indices must be integers or slices, not str",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-780c27a70eec>\u001b[0m in \u001b[0;36m<cell line: 30>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0mvocab_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkobert\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"모델의 vocab 크기: {vocab_size}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"입력된 텐서 최대 인덱스: {encoded_input['input_ids'].max()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;31m# 7. 모델 출력 확인\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_input"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2kIdUByiBv4",
        "outputId": "d0f738cb-e4a9-4c9e-b787-e2f6d899fe11"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['▁안',\n",
              " '녕',\n",
              " '하세요',\n",
              " ',',\n",
              " '새로운단어1',\n",
              " '▁',\n",
              " '를',\n",
              " '▁추가',\n",
              " '한',\n",
              " '▁K',\n",
              " 'o',\n",
              " 'B',\n",
              " 'ER',\n",
              " 'T',\n",
              " '▁모델',\n",
              " '입니다',\n",
              " '.']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = kobert_tokenizer.tokenize(\"네이버페이_배달의민족 \")\n",
        "print(result)\n",
        "kobert_vocab = kobert_tokenizer.get_vocab()\n",
        "print(kobert_vocab.get('▁네이버'))\n",
        "print([kobert_tokenizer.encode(token) for token in result])"
      ],
      "metadata": {
        "id": "7gJkj6QGiBNl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer\n",
        "\n",
        "# KoBERT 토크나이저 불러오기\n",
        "tokenizer = BertTokenizer.from_pretrained('skt/kobert-base-v1', use_fast=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "0HJaaxLJdiN9",
        "outputId": "2685018a-ccb9-49ab-cf46-b06aeec3c1f1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'XLNetTokenizer'. \n",
            "The class this function is called from is 'BertTokenizer'.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "stat: path should be string, bytes, os.PathLike or integer, not NoneType",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-d281ea9cd2f9>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# KoBERT 토크나이저 불러오기\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBertTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'skt/kobert-base-v1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_fast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, cache_dir, force_download, local_files_only, token, revision, trust_remote_code, *init_inputs, **kwargs)\u001b[0m\n\u001b[1;32m   2269\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"loading file {file_path} from cache at {resolved_vocab_files[file_id]}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2271\u001b[0;31m         return cls._from_pretrained(\n\u001b[0m\u001b[1;32m   2272\u001b[0m             \u001b[0mresolved_vocab_files\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2273\u001b[0m             \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36m_from_pretrained\u001b[0;34m(cls, resolved_vocab_files, pretrained_model_name_or_path, init_configuration, token, cache_dir, local_files_only, _commit_hash, _is_local, trust_remote_code, *init_inputs, **kwargs)\u001b[0m\n\u001b[1;32m   2503\u001b[0m         \u001b[0;31m# Instantiate the tokenizer.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2504\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2505\u001b[0;31m             \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minit_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minit_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2506\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2507\u001b[0m             raise OSError(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/tokenization_bert.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, vocab_file, do_lower_case, do_basic_tokenize, never_split, unk_token, sep_token, pad_token, cls_token, mask_token, tokenize_chinese_chars, strip_accents, **kwargs)\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m     ):\n\u001b[0;32m--> 110\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvocab_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m             raise ValueError(\n\u001b[1;32m    112\u001b[0m                 \u001b[0;34mf\"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/genericpath.py\u001b[0m in \u001b[0;36misfile\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;34m\"\"\"Test whether a path is a regular file\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: stat: path should be string, bytes, os.PathLike or integer, not NoneType"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "phisFfiddjDJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ㅁfrom transformers import BertTokenizer\n",
        "\n",
        "# KoBERT 토크나이저 불러오기\n",
        "tokenizer = BertTokenizer.from_pretrained('skt/kobert-base-v1')\n",
        "\n",
        "# 새로 추가하려는 토큰 목록\n",
        "new_tokens = ['코로나', '백신', '확진자', '마스크', '방역']\n",
        "\n",
        "# 1. 새로운 토큰 중에서 이미 토크나이저에 있는 토큰 필터링\n",
        "existing_tokens = set(tokenizer.vocab.keys())\n",
        "tokens_to_add = [token for token in new_tokens if token not in existing_tokens]\n",
        "\n",
        "# 2. 추가할 토큰 출력\n",
        "if tokens_to_add:\n",
        "    print(f\"{len(tokens_to_add)}개의 새로운 토큰을 추가합니다: {tokens_to_add}\")\n",
        "else:\n",
        "    print(\"추가할 새로운 토큰이 없습니다.\")\n",
        "\n",
        "# 3. 토크나이저에 새로운 단어 추가 (필요한 경우에만)\n",
        "if tokens_to_add:\n",
        "    num_added_tokens = tokenizer.add_tokens(tokens_to_add)\n",
        "    print(f\"{num_added_tokens}개의 토큰이 토크나이저에 추가되었습니다.\")\n"
      ],
      "metadata": {
        "id": "jbS179ZRdeTo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertWordPieceTokenizer(strip_accents=False, lowercase=False)\n",
        "\n",
        "corpus_file   = [path]  # data path\n",
        "vocab_size    = 32000   #vocab의 크기. 보통 32,000이 좋다고 알려짐.\n",
        "limit_alphabet= 6000    #merge 수행 전 initial tokens이 유지되는 숫자 제한\n",
        "output_path   = 'hugging_%d'%(vocab_size)\n",
        "min_frequency = 5  # 단어의 최소 발생 빈도\n",
        "\n",
        "tokenizer.train(files=corpus_file,\n",
        "               vocab_size=vocab_size,\n",
        "               min_frequency=min_frequency,\n",
        "               limit_alphabet=limit_alphabet,\n",
        "               show_progress=True)\n",
        "\n",
        "tokenizer.save_model(hf_model_path)"
      ],
      "metadata": {
        "id": "AMlib5d9bnla"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}