diff --git a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-040dea35-0b7f-4fef-a3fc-38bb9588423e1765983126987-2025_12_17-15.52.16.573/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-040dea35-0b7f-4fef-a3fc-38bb9588423e1765983126987-2025_12_17-15.52.16.573/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..a18756aab547360a0092bf588d8323d7e9adefba --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-040dea35-0b7f-4fef-a3fc-38bb9588423e1765983126987-2025_12_17-15.52.16.573/source.csv @@ -0,0 +1,641 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,125,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"3:52:16 PM [info] Activating crowd-code\n3:52:16 PM [info] Recording started\n3:52:16 PM [info] Initializing git provider using file system watchers...\n3:52:16 PM [info] Git repository found\n3:52:16 PM [info] Git provider initialized successfully\n",Log,tab +3,529,"extension-output-pdoom-org.crowd-code-#1-crowd-code",245,0,"3:52:16 PM [info] Initial git state: [object Object]\n",Log,content +4,8075,"TERMINAL",0,0,"",,terminal_focus +5,11591,"TERMINAL",0,0,"ls",,terminal_command +6,11611,"TERMINAL",0,0,"]633;C_build\t\t\tCHANGELOG.md\t\tnotebooks\t\tsrc\t\t\tuv.lock\r\n_static\t\t\tdocs\t\t\tpyproject.toml\t\ttest_flax_ensemble.py\tvenv_docs\r\n_templates\t\tLICENSE\t\t\tREADME.md\t\ttests\r\n% \r \r",,terminal_output +7,13400,"TERMINAL",0,0,"cd docs",,terminal_command +8,13401,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +9,15110,"TERMINAL",0,0,"make html",,terminal_command +10,15160,"TERMINAL",0,0,"]633;C",,terminal_output +11,15179,"TERMINAL",0,0,"/bin/sh: sphinx-build: command not found\r\nmake: *** [html] Error 127\r\n% \r \r",,terminal_output +12,22406,"TERMINAL",0,0,"ls",,terminal_command +13,22408,"TERMINAL",0,0,"]633;Cbuild\t\tmake.bat\tMakefile\tsource\r\n% \r \r",,terminal_output +14,27607,"TERMINAL",0,0,"cat Makefile",,terminal_command +15,27616,"TERMINAL",0,0,"]633;C# Minimal makefile for Sphinx documentation\r\n#\r\n\r\n# You can set these variables from the command line, and also\r\n# from the environment for the first two.\r\nSPHINXOPTS ?=\r\nSPHINXBUILD ?= sphinx-build\r\nSOURCEDIR = source\r\nBUILDDIR = build\r\n\r\n# Put it first so that ""make"" without argument is like ""make help"".\r\nhelp:\r\n\t@$(SPHINXBUILD) -M help ""$(SOURCEDIR)"" ""$(BUILDDIR)"" $(SPHINXOPTS) $(O)\r\n\r\n.PHONY: help Makefile\r\n\r\n# Catch-all target: route all unknown targets to Sphinx using the new\r\n# ""make mode"" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\r\n%: Makefile\r\n\t@$(SPHINXBUILD) -M $@ ""$(SOURCEDIR)"" ""$(BUILDDIR)"" $(SPHINXOPTS) $(O)\r\n% \r \r",,terminal_output +16,32353,"TERMINAL",0,0,"make",,terminal_command +17,32369,"TERMINAL",0,0,"]633;C/bin/sh: sphinx-build: command not found\r\nmake: *** [help] Error 127\r\n% \r \r",,terminal_output +18,53903,"TERMINAL",0,0,"cd ..",,terminal_command +19,53904,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +20,55120,"TERMINAL",0,0,"uv venv",,terminal_command +21,55170,"TERMINAL",0,0,"]633;C",,terminal_output +22,55501,"TERMINAL",0,0,"Using CPython 3.13.7 interpreter at: /opt/homebrew/opt/python@3.13/bin/python3.13\r\nCreating virtual environment at: .venv\r\n",,terminal_output +23,57011,"TERMINAL",0,0,"Activate with: source .venv/bin/activate\r\n% \r \r",,terminal_output +24,62272,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_command +25,62276,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +26,62858,"TERMINAL",0,0,"ls",,terminal_command +27,62859,"TERMINAL",0,0,"]633;C_build\t\t\tCHANGELOG.md\t\tnotebooks\t\tsrc\t\t\tuv.lock\r\n_static\t\t\tdocs\t\t\tpyproject.toml\t\ttest_flax_ensemble.py\tvenv_docs\r\n_templates\t\tLICENSE\t\t\tREADME.md\t\ttests\r\n% \r \r",,terminal_output +28,69059,"TERMINAL",0,0,"uv sync",,terminal_command +29,69108,"TERMINAL",0,0,"]633;C",,terminal_output +30,69382,"TERMINAL",0,0,"Resolved 208 packages in 1ms\r\n",,terminal_output +31,70242,"TERMINAL",0,0,"โ ‹ Preparing packages... 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(0/162)\r\nurllib3  ------------------------------ 16.00 KiB/125.36 KiB\r\nhumanize  ------------------------------ 16.00 KiB/129.00 KiB\r\njinja2  ------------------------------ 16.00 KiB/130.15 KiB\r\nipywidgets ------------------------------ 14.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 16.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 32.00 KiB/162.49 KiB\r\nfsspec  ------------------------------ 16.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 16.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 14.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 9.56 KiB/191.11 KiB\r\ncoverage  ------------------------------ 14.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 998 B/209.37 KiB\r\ncontourpy  ------------------------------ 16.00 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 16.00 KiB/252.47 KiB\r\njoblib  ------------------------------ 14.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 15.89 KiB/333.33 KiB\r\npytest  ------------------------------ 16.00 KiB/335.58 KiB\r\njupyter-server ------------------------------ 14.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 14.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 16.00 KiB/426.07 KiB\r\nflax  ------------------------------ 14.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 14.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 92.82 KiB/573.64 KiB\r\nipython  ------------------------------ 14.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 7.68 KiB/909.02 KiB\r\npyzmq  ------------------------------ 14.91 KiB/1.28 MiB\r\njedi  ------------------------------ 14.88 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 14.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 14.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 8.74 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 14.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 16.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 14.91 KiB/2.61 MiB\r\njax  ------------------------------ 14.85 KiB/2.77 MiB\r\npillow  ------------------------------ 14.91 KiB/2.96 MiB\r\nsphinx  ------------------------------ 14.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 14.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 14.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 14.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 16.00 KiB/8.19 MiB\r\nbabel  ------------------------------ 16.00 KiB/9.71 MiB\r\njupyterlab ------------------------------ 14.91 KiB/11.14 MiB\r\nruff  ------------------------------ 14.91 KiB/11.24 MiB\r\nmypy  ------------------------------ 14.91 KiB/11.36 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (0/162)\r\nurllib3  ------------------------------ 48.00 KiB/125.36 KiB\r\nhumanize  ------------------------------ 16.00 KiB/129.00 KiB\r\njinja2  ------------------------------ 16.00 KiB/130.15 KiB\r\nipywidgets ------------------------------ 14.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 16.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 48.00 KiB/162.49 KiB\r\nfsspec  ------------------------------ 32.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 32.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 30.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 9.56 KiB/191.11 KiB\r\ncoverage  ------------------------------ 14.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 14.87 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 16.00 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 16.00 KiB/252.47 KiB\r\njoblib  ------------------------------ 30.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 30.01 KiB/333.33 KiB\r\npytest  ------------------------------ 32.00 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 12.02 KiB/341.41 KiB\r\njupyter-server ------------------------------ 14.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 30.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 32.00 KiB/426.07 KiB\r\nflax  ------------------------------ 30.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 14.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 92.82 KiB/573.64 KiB\r\nipython  ------------------------------ 14.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 14.88 KiB/909.02 KiB\r\npyzmq  ------------------------------ 14.91 KiB/1.28 MiB\r\njedi  ------------------------------ 14.88 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 30.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 30.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 8.74 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 30.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 16.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 14.91 KiB/2.61 MiB\r\njax  ------------------------------ 14.85 KiB/2.77 MiB\r\npillow  ------------------------------ 30.91 KiB/2.96 MiB\r\nsphinx  ------------------------------ 30.78 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 14.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 14.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 14.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 21.42 KiB/8.19 MiB\r\nbabel  ------------------------------ 31.89 KiB/9.71 MiB\r\njupyterlab ------------------------------ 30.91 KiB/11.14 MiB\r\nruff  ------------------------------ 14.91 KiB/11.24 MiB",,terminal_output +34,70594,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (0/162)\r\nurllib3  ------------------------------ 62.00 KiB/125.36 KiB\r\nhumanize  ------------------------------ 32.00 KiB/129.00 KiB\r\njinja2  ------------------------------ 47.89 KiB/130.15 KiB\r\nipywidgets ------------------------------ 46.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 48.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 63.13 KiB/162.49 KiB\r\nfsspec  ------------------------------ 48.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 48.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 46.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 32.00 KiB/191.11 KiB\r\ncoverage  ------------------------------ 62.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 46.87 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 48.00 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 32.00 KiB/252.47 KiB\r\njoblib  ------------------------------ 62.81 KiB/294.74 KiB\r\nfuro  ------------------------------ 46.01 KiB/333.33 KiB\r\npytest  ------------------------------ 52.66 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 14.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 62.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 62.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 48.00 KiB/426.07 KiB\r\nflax  ------------------------------ 46.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 46.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 156.82 KiB/573.64 KiB\r\nipython  ------------------------------ 46.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 35.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 30.91 KiB/1.28 MiB\r\njedi  ------------------------------ 48.87 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 46.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 46.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 30.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 39.44 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 48.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 30.91 KiB/2.61 MiB\r\njax  ------------------------------ 30.85 KiB/2.77 MiB\r\npillow  ------------------------------ 62.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 46.78 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 14.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 30.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 46.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 37.42 KiB/8.19 MiB\r\nbabel  ------------------------------ 47.89 KiB/9.71 MiB\r\njupyterlab ------------------------------ 62.80 KiB/11.14 MiB\r\nruff  ------------------------------ 46.91 KiB/11.24 MiB",,terminal_output +35,70696,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (0/162)\r\nurllib3  ------------------------------ 78.00 KiB/125.36 KiB\r\nhumanize  ------------------------------ 48.00 KiB/129.00 KiB\r\njinja2  ------------------------------ 62.67 KiB/130.15 KiB\r\nipywidgets ------------------------------ 62.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 64.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 79.13 KiB/162.49 KiB\r\nfsspec  ------------------------------ 64.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 64.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 62.77 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 48.00 KiB/191.11 KiB\r\ncoverage  ------------------------------ 62.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 58.67 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 63.27 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 48.00 KiB/252.47 KiB\r\njoblib  ------------------------------ 78.81 KiB/294.74 KiB\r\nfuro  ------------------------------ 62.01 KiB/333.33 KiB\r\npytest  ------------------------------ 62.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 30.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 78.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 62.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 64.00 KiB/426.07 KiB\r\nflax  ------------------------------ 62.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 62.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 172.82 KiB/573.64 KiB\r\nipython  ------------------------------ 62.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 35.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 46.91 KiB/1.28 MiB\r\njedi  ------------------------------ 64.87 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 62.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 62.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 46.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 55.44 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 53.20 KiB/2.37 MiB\r\nfonttools  ------------------------------ 62.80 KiB/2.61 MiB\r\njax  ------------------------------ 30.85 KiB/2.77 MiB\r\npillow  ------------------------------ 78.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 62.78 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 14.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 62.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 62.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 41.50 KiB/8.19 MiB\r\nbabel  ------------------------------ 61.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 62.80 KiB/11.14 MiB\r\nruff  ------------------------------ 62.91 KiB/11.24 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (0/162)\r\nurllib3  ------------------------------ 94.00 KiB/125.36 KiB\r\nhumanize  ------------------------------ 62.64 KiB/129.00 KiB\r\njinja2  ------------------------------ 78.67 KiB/130.15 KiB\r\nipywidgets ------------------------------ 78.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 80.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 82.85 KiB/162.49 KiB\r\nfsspec  ------------------------------ 80.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 80.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 78.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 65.57 KiB/191.11 KiB\r\ncoverage  ------------------------------ 94.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 74.67 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 79.27 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 79.03 KiB/252.47 KiB\r\njoblib  ------------------------------ 78.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 67.10 KiB/333.33 KiB\r\npytest  ------------------------------ 78.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 46.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 94.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 78.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 80.00 KiB/426.07 KiB\r\nflax  ------------------------------ 94.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 94.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 188.82 KiB/573.64 KiB\r\nipython  ------------------------------ 94.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 61.38 KiB/909.02 KiB\r\npyzmq  ------------------------------ 78.91 KiB/1.28 MiB\r\njedi  ------------------------------ 76.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 78.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 78.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 78.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 78.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 64.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 78.91 KiB/2.61 MiB\r\njax  ------------------------------ 94.85 KiB/2.77 MiB\r\npillow  ------------------------------ 94.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 78.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 30.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 78.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 93.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 57.50 KiB/8.19 MiB\r\nbabel  ------------------------------ 77.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 78.91 KiB/11.14 MiB\r\nruff  ------------------------------ 78.91 KiB/11.24 MiB",,terminal_output +36,71253,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (0/162)\r\nurllib3  ------------------------------ 110.00 KiB/125.36 KiB\r\nhumanize  ------------------------------ 110.64 KiB/129.00 KiB\r\njinja2  ------------------------------ 94.67 KiB/130.15 KiB\r\nipywidgets ------------------------------ 110.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 96.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 98.85 KiB/162.49 KiB\r\nfsspec  ------------------------------ 96.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 96.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 78.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 65.57 KiB/191.11 KiB\r\ncoverage  ------------------------------ 110.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 78.87 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 95.27 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 95.03 KiB/252.47 KiB\r\njoblib  ------------------------------ 94.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 83.10 KiB/333.33 KiB\r\npytest  ------------------------------ 94.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 62.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 110.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 78.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 92.87 KiB/426.07 KiB\r\nflax  ------------------------------ 94.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 110.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 236.82 KiB/573.64 KiB\r\nipython  ------------------------------ 110.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 77.38 KiB/909.02 KiB\r\npyzmq  ------------------------------ 110.91 KiB/1.28 MiB\r\njedi  ------------------------------ 92.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 94.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 94.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 78.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 78.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 80.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 78.91 KiB/2.61 MiB\r\njax  ------------------------------ 110.85 KiB/2.77 MiB\r\npillow  ------------------------------ 107.18 KiB/2.96 MiB\r\nsphinx  ------------------------------ 78.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 78.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 94.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 93.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 60.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 93.28 KiB/9.71 MiB\r\njupyterlab ------------------------------ 94.91 KiB/11.14 MiB\r\nruff  ------------------------------ 78.91 KiB/11.24 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (0/162)\r\nhumanize  ------------------------------ 126.64 KiB/129.00 KiB\r\njinja2  ------------------------------ 110.67 KiB/130.15 KiB\r\nipywidgets ------------------------------ 110.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 112.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 114.85 KiB/162.49 KiB\r\nfsspec  ------------------------------ 112.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 112.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 94.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 81.57 KiB/191.11 KiB\r\ncoverage  ------------------------------ 110.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 83.84 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 111.27 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 111.03 KiB/252.47 KiB\r\njoblib  ------------------------------ 110.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 94.01 KiB/333.33 KiB\r\npytest  ------------------------------ 94.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 78.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 110.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 94.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 108.87 KiB/426.07 KiB\r\nflax  ------------------------------ 110.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 110.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 252.82 KiB/573.64 KiB\r\nipython  ------------------------------ 110.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 77.38 KiB/909.02 KiB\r\npyzmq  ------------------------------ 110.91 KiB/1.28 MiB\r\njedi  ------------------------------ 108.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 110.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 110.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 94.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 94.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 96.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 94.91 KiB/2.61 MiB\r\njax  ------------------------------ 110.85 KiB/2.77 MiB\r\npillow  ------------------------------ 107.18 KiB/2.96 MiB\r\nsphinx  ------------------------------ 94.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 94.77 KiB/4.10 MiB\r\nnumpy  ------------------------------ 110.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 109.82 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 60.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 109.28 KiB/9.71 MiB\r\njupyterlab ------------------------------ 94.91 KiB/11.14 MiB\r\nruff  ------------------------------ 94.91 KiB/11.24 MiB\r\nmypy  ------------------------------ 94.91 KiB/11.36 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (0/162)\r\nhumanize  ------------------------------ 126.64 KiB/129.00 KiB\r\njinja2  ------------------------------ 110.67 KiB/130.15 KiB\r\nipywidgets ------------------------------ 126.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 112.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 114.85 KiB/162.49 KiB\r\nfsspec  ------------------------------ 112.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 112.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 94.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 81.57 KiB/191.11 KiB\r\ncoverage  ------------------------------ 110.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 83.84 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 111.27 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 111.03 KiB/252.47 KiB\r\njoblib  ------------------------------ 110.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 94.01 KiB/333.33 KiB\r\npytest  ------------------------------ 110.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 78.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 126.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 94.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 108.87 KiB/426.07 KiB\r\nflax  ------------------------------ 110.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 110.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 252.82 KiB/573.64 KiB\r\nipython  ------------------------------ 110.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 93.38 KiB/909.02 KiB\r\npyzmq  ------------------------------ 110.91 KiB/1.28 MiB\r\njedi  ------------------------------ 108.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 110.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 110.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 94.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 94.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 96.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 94.91 KiB/2.61 MiB\r\njax  ------------------------------ 110.85 KiB/2.77 MiB\r\npillow  ------------------------------ 123.18 KiB/2.96 MiB\r\nsphinx  ------------------------------ 94.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 94.77 KiB/4.10 MiB\r\nnumpy  ------------------------------ 110.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 109.82 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 61.09 KiB/8.19 MiB\r\nbabel  ------------------------------ 109.28 KiB/9.71 MiB\r\njupyterlab ------------------------------ 94.91 KiB/11.14 MiB\r\nruff  ------------------------------ 94.91 KiB/11.24 MiB\r\nmypy  ------------------------------ 94.91 KiB/11.36 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (0/162)\r\nhumanize  ------------------------------ 126.64 KiB/129.00 KiB\r\njinja2  ------------------------------ 126.57 KiB/130.15 KiB\r\nipywidgets ------------------------------ 126.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 128.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 114.85 KiB/162.49 KiB\r\nfsspec  ------------------------------ 128.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 128.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 110.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 96.00 KiB/191.11 KiB\r\ncoverage  ------------------------------ 126.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 83.84 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 127.27 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 127.03 KiB/252.47 KiB\r\njoblib  ------------------------------ 110.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 94.01 KiB/333.33 KiB\r\npytest  ------------------------------ 110.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 78.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 126.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 110.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 108.87 KiB/426.07 KiB\r\nflax  ------------------------------ 126.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 126.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 252.82 KiB/573.64 KiB\r\nipython  ------------------------------ 126.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 93.38 KiB/909.02 KiB\r\npyzmq  ------------------------------ 126.91 KiB/1.28 MiB\r\njedi  ------------------------------ 108.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 126.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 126.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 110.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 110.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 112.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 110.91 KiB/2.61 MiB\r\njax  ------------------------------ 126.85 KiB/2.77 MiB\r\npillow  ------------------------------ 123.18 KiB/2.96 MiB\r\nsphinx  ------------------------------ 110.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 110.77 KiB/4.10 MiB\r\nnumpy  ------------------------------ 126.59 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 110.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 61.09 KiB/8.19 MiB\r\nbabel  ------------------------------ 125.28 KiB/9.71 MiB\r\njupyterlab ------------------------------ 110.91 KiB/11.14 MiB\r\nruff  ------------------------------ 110.91 KiB/11.24 MiB\r\nmypy  ------------------------------ 96.37 KiB/11.36 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (1/162)\r\njinja2  ------------------------------ 126.57 KiB/130.15 KiB\r\nipywidgets ------------------------------ 126.88 KiB/136.49 KiB\r\nbleach  ------------------------------ 128.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 127.13 KiB/162.49 KiB\r\nfsspec  ------------------------------ 128.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 128.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 110.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 96.00 KiB/191.11 KiB\r\ncoverage  ------------------------------ 126.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 99.84 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 127.27 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 127.03 KiB/252.47 KiB\r\njoblib  ------------------------------ 126.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 110.01 KiB/333.33 KiB\r\npytest  ------------------------------ 110.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 94.80 KiB/341.41 KiB\r\njupyter-server ------------------------------ 126.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 110.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 124.87 KiB/426.07 KiB\r\nflax  ------------------------------ 126.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 126.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 268.82 KiB/573.64 KiB\r\nipython  ------------------------------ 126.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 93.38 KiB/909.02 KiB\r\npyzmq  ------------------------------ 126.91 KiB/1.28 MiB\r\njedi  ------------------------------ 124.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 126.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 126.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 110.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 110.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 112.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 110.91 KiB/2.61 MiB\r\njax  ------------------------------ 126.85 KiB/2.77 MiB\r\npillow  ------------------------------ 126.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 110.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 110.77 KiB/4.10 MiB\r\nnumpy  ------------------------------ 126.59 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 110.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 77.09 KiB/8.19 MiB\r\nbabel  ------------------------------ 125.28 KiB/9.71 MiB\r\njupyterlab ------------------------------ 110.91 KiB/11.14 MiB\r\nruff  ------------------------------ 110.91 KiB/11.24 MiB\r\nmypy  ------------------------------ 96.37 KiB/11.36 MiB\r\nnotebook  ------------------------------ 110.70 KiB/12.53 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (1/162)\r\njinja2  ------------------------------ 126.57 KiB/130.15 KiB\r\nbleach  ------------------------------ 128.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 127.13 KiB/162.49 KiB\r\nfsspec  ------------------------------ 128.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 128.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 110.87 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 96.00 KiB/191.11 KiB\r\ncoverage  ------------------------------ 126.91 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 99.84 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 127.27 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 127.03 KiB/252.47 KiB\r\njoblib  ------------------------------ 126.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 110.01 KiB/333.33 KiB\r\npytest  ------------------------------ 126.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 94.80 KiB/341.41 KiB\r\njupyter-server ------------------------------ 142.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 110.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 124.87 KiB/426.07 KiB\r\nflax  ------------------------------ 126.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 126.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 268.82 KiB/573.64 KiB\r\nipython  ------------------------------ 126.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 97.83 KiB/909.02 KiB\r\npyzmq  ------------------------------ 126.91 KiB/1.28 MiB\r\njedi  ------------------------------ 124.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 126.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 126.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 110.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 110.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 112.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 110.91 KiB/2.61 MiB\r\njax  ------------------------------ 126.85 KiB/2.77 MiB\r\npillow  ------------------------------ 126.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 110.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 110.77 KiB/4.10 MiB\r\nnumpy  ------------------------------ 126.59 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 110.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 77.09 KiB/8.19 MiB\r\nbabel  ------------------------------ 125.28 KiB/9.71 MiB\r\njupyterlab ------------------------------ 126.91 KiB/11.14 MiB\r\nruff  ------------------------------ 110.91 KiB/11.24 MiB\r\nmypy  ------------------------------ 96.37 KiB/11.36 MiB\r\nnotebook  ------------------------------ 110.70 KiB/12.53 MiB\r\ntensorstore ------------------------------ 93.91 KiB/13.14 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (1/162)\r\njinja2  ------------------------------ 126.67 KiB/130.15 KiB\r\nbleach  ------------------------------ 144.00 KiB/159.58 KiB\r\ncertifi  ------------------------------ 127.13 KiB/162.49 KiB\r\nfsspec  ------------------------------ 144.00 KiB/173.40 KiB\r\ncffi  ------------------------------ 144.00 KiB/174.61 KiB\r\nbeautifulsoup4 ------------------------------ 126.77 KiB/181.66 KiB\r\ncharset-normalizer ------------------------------ 112.00 KiB/191.11 KiB\r\ncoverage  ------------------------------ 142.80 KiB/204.05 KiB\r\njupyterlab-widgets ------------------------------ 115.84 KiB/209.37 KiB\r\ncontourpy  ------------------------------ 143.27 KiB/249.59 KiB\r\nnbconvert  ------------------------------ 143.03 KiB/252.47 KiB\r\njoblib  ------------------------------ 142.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 126.01 KiB/333.33 KiB\r\npytest  ------------------------------ 126.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 110.80 KiB/341.41 KiB\r\njupyter-server ------------------------------ 142.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 126.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 128.00 KiB/426.07 KiB\r\nflax  ------------------------------ 142.88 KiB/455.35 KiB\r\npybtex  ------------------------------ 142.83 KiB/548.20 KiB\r\ndocutils  ------------------------------ 284.82 KiB/573.64 KiB\r\nipython  ------------------------------ 142.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 99.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 142.91 KiB/1.28 MiB\r\njedi  ------------------------------ 140.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 142.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 142.91 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 126.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 126.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 128.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 126.91 KiB/2.61 MiB\r\njax  ------------------------------ 142.85 KiB/2.77 MiB\r\npillow  ------------------------------ 126.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 126.88 KiB/3.42 MiB\r\nvirtualenv\n[... 77342 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +37,71348,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (13/162)\r\nchex  ------------------------------ 0 B/98.68 KiB\r\nparso  ------------------------------ 0 B/101.22 KiB\r\njupyter-client ------------------------------ 0 B/103.62 KiB\r\npyparsing  ------------------------------ 14.88 KiB/105.19 KiB\r\nipykernel  ------------------------------ 48.00 KiB/114.43 KiB\r\npycparser  ------------------------------ 48.00 KiB/114.80 KiB\r\nsphinxcontrib-applehelp ------------------------------ 64.00 KiB/116.50 KiB\r\njupyterlab-widgets ------------------------------ 206.87 KiB/209.37 KiB\r\njoblib  ------------------------------ 238.81 KiB/294.74 KiB\r\nfuro  ------------------------------ 220.86 KiB/333.33 KiB\r\npytest  ------------------------------ 242.83 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 190.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 270.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 238.66 KiB/378.73 KiB\r\ntornado  ------------------------------ 224.00 KiB/426.07 KiB\r\nflax  ------------------------------ 269.70 KiB/455.35 KiB\r\npybtex  ------------------------------ 257.30 KiB/548.20 KiB\r\ndocutils  ------------------------------ 380.61 KiB/573.64 KiB\r\nipython  ------------------------------ 270.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 211.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 270.70 KiB/1.28 MiB\r\njedi  ------------------------------ 252.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 238.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 250.70 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 238.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 222.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 223.94 KiB/2.37 MiB\r\nfonttools  ------------------------------ 238.23 KiB/2.61 MiB\r\njax  ------------------------------ 269.95 KiB/2.77 MiB\r\npillow  ------------------------------ 244.59 KiB/2.96 MiB\r\nsphinx  ------------------------------ 222.78 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 206.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 222.81 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 238.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 188.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 221.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 254.91 KiB/11.14 MiB\r\nruff  ------------------------------ 236.17 KiB/11.24 MiB\r\nmypy  ------------------------------ 222.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 235.58 KiB/12.53 MiB\r\ntensorstore ------------------------------ 189.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 240.86 KiB/21.34 MiB\r\njaxlib  ------------------------------ 189.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 237.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (13/162)\r\nchex  ------------------------------ 16.00 KiB/98.68 KiB\r\nparso  ------------------------------ 14.88 KiB/101.22 KiB\r\njupyter-client ------------------------------ 14.74 KiB/103.62 KiB\r\npyparsing  ------------------------------ 14.88 KiB/105.19 KiB\r\nipykernel  ------------------------------ 64.00 KiB/114.43 KiB\r\npycparser  ------------------------------ 64.00 KiB/114.80 KiB\r\nsphinxcontrib-applehelp ------------------------------ 80.00 KiB/116.50 KiB\r\njoblib  ------------------------------ 254.81 KiB/294.74 KiB\r\nfuro  ------------------------------ 222.01 KiB/333.33 KiB\r\npytest  ------------------------------ 242.83 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 206.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 270.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 238.66 KiB/378.73 KiB\r\ntornado  ------------------------------ 240.00 KiB/426.07 KiB\r\nflax  ------------------------------ 269.70 KiB/455.35 KiB\r\npybtex  ------------------------------ 257.30 KiB/548.20 KiB\r\ndocutils  ------------------------------ 380.82 KiB/573.64 KiB\r\nipython  ------------------------------ 270.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 211.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 270.70 KiB/1.28 MiB\r\njedi  ------------------------------ 252.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 254.59 KiB/1.62 MiB\r\ntorchvision ------------------------------ 265.10 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 238.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 238.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 239.94 KiB/2.37 MiB\r\nfonttools  ------------------------------ 254.23 KiB/2.61 MiB\r\njax  ------------------------------ 269.95 KiB/2.77 MiB\r\npillow  ------------------------------ 254.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 238.78 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 222.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 238.81 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 254.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 204.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 237.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 254.91 KiB/11.14 MiB\r\nruff  ------------------------------ 252.17 KiB/11.24 MiB\r\nmypy  ------------------------------ 222.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 235.58 KiB/12.53 MiB\r\ntensorstore ------------------------------ 205.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 240.86 KiB/21.34 MiB\r\njaxlib  ------------------------------ 205.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 237.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (13/162)\r\nchex  ------------------------------ 16.00 KiB/98.68 KiB\r\nparso  ------------------------------ 14.88 KiB/101.22 KiB\r\njupyter-client ------------------------------ 14.74 KiB/103.62 KiB\r\npyparsing  ------------------------------ 30.88 KiB/105.19 KiB\r\nipykernel  ------------------------------ 64.00 KiB/114.43 KiB\r\npycparser  ------------------------------ 64.00 KiB/114.80 KiB\r\nsphinxcontrib-applehelp ------------------------------ 80.00 KiB/116.50 KiB\r\njoblib  ------------------------------ 254.81 KiB/294.74 KiB\r\nfuro  ------------------------------ 222.01 KiB/333.33 KiB\r\npytest  ------------------------------ 254.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 206.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 286.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 238.66 KiB/378.73 KiB\r\ntornado  ------------------------------ 240.00 KiB/426.07 KiB\r\nflax  ------------------------------ 269.70 KiB/455.35 KiB\r\npybtex  ------------------------------ 268.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 380.82 KiB/573.64 KiB\r\nipython  ------------------------------ 270.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 227.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 286.70 KiB/1.28 MiB\r\njedi  ------------------------------ 257.67 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 254.59 KiB/1.62 MiB\r\ntorchvision ------------------------------ 265.10 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 238.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 238.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 239.94 KiB/2.37 MiB\r\nfonttools  ------------------------------ 254.23 KiB/2.61 MiB\r\njax  ------------------------------ 270.05 KiB/2.77 MiB\r\npillow  ------------------------------ 254.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 238.78 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 222.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 238.81 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 254.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 204.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 237.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 254.91 KiB/11.14 MiB\r\nruff  ------------------------------ 252.17 KiB/11.24 MiB\r\nmypy  ------------------------------ 222.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 251.58 KiB/12.53 MiB\r\ntensorstore ------------------------------ 205.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 256.86 KiB/21.34 MiB\r\njaxlib  ------------------------------ 205.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 253.34 KiB/71.03 MiB ",,terminal_output +38,71756,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (13/162)\r\nidentify  ------------------------------ 14.88 KiB/96.78 KiB\r\nchex  ------------------------------ 32.00 KiB/98.68 KiB\r\nparso  ------------------------------ 30.88 KiB/101.22 KiB\r\njupyter-client ------------------------------ 14.87 KiB/103.62 KiB\r\npyparsing  ------------------------------ 46.88 KiB/105.19 KiB\r\nipykernel  ------------------------------ 80.00 KiB/114.43 KiB\r\npycparser  ------------------------------ 80.00 KiB/114.80 KiB\r\nsphinxcontrib-applehelp ------------------------------ 80.86 KiB/116.50 KiB\r\njoblib  ------------------------------ 270.81 KiB/294.74 KiB\r\nfuro  ------------------------------ 238.01 KiB/333.33 KiB\r\npytest  ------------------------------ 270.38 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 222.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 302.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 270.66 KiB/378.73 KiB\r\ntornado  ------------------------------ 255.89 KiB/426.07 KiB\r\nflax  ------------------------------ 285.70 KiB/455.35 KiB\r\npybtex  ------------------------------ 272.60 KiB/548.20 KiB\r\ndocutils  ------------------------------ 396.82 KiB/573.64 KiB\r\nipython  ------------------------------ 286.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 243.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 302.70 KiB/1.28 MiB\r\njedi  ------------------------------ 273.67 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 270.59 KiB/1.62 MiB\r\ntorchvision ------------------------------ 270.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 263.79 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 254.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 255.94 KiB/2.37 MiB\r\nfonttools  ------------------------------ 270.23 KiB/2.61 MiB\r\njax  ------------------------------ 286.05 KiB/2.77 MiB\r\npillow  ------------------------------ 270.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 249.01 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 238.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 254.81 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 270.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 220.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 253.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 270.91 KiB/11.14 MiB\r\nruff  ------------------------------ 268.17 KiB/11.24 MiB\r\nmypy  ------------------------------ 238.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 267.48 KiB/12.53 MiB\r\ntensorstore ------------------------------ 221.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 268.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 221.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 269.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (13/162)\r\nidentify  ------------------------------ 30.88 KiB/96.78 KiB\r\nchex  ------------------------------ 48.00 KiB/98.68 KiB\r\nparso  ------------------------------ 46.88 KiB/101.22 KiB\r\njupyter-client ------------------------------ 30.87 KiB/103.62 KiB\r\npyparsing  ------------------------------ 62.88 KiB/105.19 KiB\r\nipykernel  ------------------------------ 96.00 KiB/114.43 KiB\r\npycparser  ------------------------------ 96.00 KiB/114.80 KiB\r\nsphinxcontrib-applehelp ------------------------------ 96.86 KiB/116.50 KiB\r\njoblib  ------------------------------ 270.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 254.01 KiB/333.33 KiB\r\npytest  ------------------------------ 278.56 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 238.91 KiB/341.41 KiB\r\njupyter-server ------------------------------ 318.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 270.66 KiB/378.73 KiB\r\ntornado  ------------------------------ 256.00 KiB/426.07 KiB\r\nflax  ------------------------------ 301.70 KiB/455.35 KiB\r\npybtex  ------------------------------ 288.60 KiB/548.20 KiB\r\ndocutils  ------------------------------ 412.82 KiB/573.64 KiB\r\nipython  ------------------------------ 302.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 259.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 318.70 KiB/1.28 MiB\r\njedi  ------------------------------ 289.45 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 270.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 286.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 270.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 270.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 256.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 285.91 KiB/2.61 MiB\r\njax  ------------------------------ 302.05 KiB/2.77 MiB\r\npillow  ------------------------------ 286.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 265.01 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 254.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 270.81 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 286.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 236.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 269.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 286.91 KiB/11.14 MiB\r\nruff  ------------------------------ 268.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 254.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 268.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 237.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 284.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 237.29 KiB/52.42 MiB\r\ntorch  ------------------------------ 285.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (14/162)\r\nsnowballstemmer ------------------------------ 0 B/90.82 KiB\r\nanyio  ------------------------------ 14.88 KiB/93.79 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 0 B/96.39 KiB\r\nidentify  ------------------------------ 46.88 KiB/96.78 KiB\r\nchex  ------------------------------ 56.63 KiB/98.68 KiB\r\nparso  ------------------------------ 47.88 KiB/101.22 KiB\r\njupyter-client ------------------------------ 46.87 KiB/103.62 KiB\r\npyparsing  ------------------------------ 78.88 KiB/105.19 KiB\r\nipykernel  ------------------------------ 112.00 KiB/114.43 KiB\r\npycparser  ------------------------------ 112.00 KiB/114.80 KiB\r\nsphinxcontrib-applehelp ------------------------------ 112.86 KiB/116.50 KiB\r\njoblib  ------------------------------ 286.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 270.01 KiB/333.33 KiB\r\npytest  ------------------------------ 294.56 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 253.87 KiB/341.41 KiB\r\njupyter-server ------------------------------ 334.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 286.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 272.00 KiB/426.07 KiB\r\nflax  ------------------------------ 333.37 KiB/455.35 KiB\r\npybtex  ------------------------------ 304.60 KiB/548.20 KiB\r\ndocutils  ------------------------------ 428.82 KiB/573.64 KiB\r\nipython  ------------------------------ 334.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 275.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 334.70 KiB/1.28 MiB\r\njedi  ------------------------------ 305.45 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 286.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 318.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 286.80 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 286.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 272.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 317.91 KiB/2.61 MiB\r\njax  ------------------------------ 318.05 KiB/2.77 MiB\r\npillow  ------------------------------ 302.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 270.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 270.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 270.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 302.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 246.47 KiB/8.19 MiB\r\nbabel  ------------------------------ 285.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 318.91 KiB/11.14 MiB\r\nruff  ------------------------------ 300.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 286.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 284.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 253.91 KiB/13.14 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (14/162)\r\nsnowballstemmer ------------------------------ 14.84 KiB/90.82 KiB\r\nanyio  ------------------------------ 14.88 KiB/93.79 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 15.26 KiB/96.39 KiB\r\nidentify  ------------------------------ 46.88 KiB/96.78 KiB\r\nchex  ------------------------------ 56.63 KiB/98.68 KiB\r\nparso  ------------------------------ 47.88 KiB/101.22 KiB\r\njupyter-client ------------------------------ 62.87 KiB/103.62 KiB\r\npyparsing  ------------------------------ 78.88 KiB/105.19 KiB\r\nipykernel  ------------------------------ 112.00 KiB/114.43 KiB\r\npycparser  ------------------------------ 112.00 KiB/114.80 KiB\r\njoblib  ------------------------------ 286.91 KiB/294.74 KiB\r\nfuro  ------------------------------ 286.01 KiB/333.33 KiB\r\npytest  ------------------------------ 294.56 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 269.87 KiB/341.41 KiB\r\njupyter-server ------------------------------ 334.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 286.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 288.00 KiB/426.07 KiB\r\nflax  ------------------------------ 333.37 KiB/455.35 KiB\r\npybtex  ------------------------------ 304.60 KiB/548.20 KiB\r\ndocutils  ------------------------------ 444.82 KiB/573.64 KiB\r\nipython  ------------------------------ 334.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 275.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 334.70 KiB/1.28 MiB\r\njedi  ------------------------------ 305.45 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 302.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 318.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 286.80 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 302.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 288.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 317.91 KiB/2.61 MiB\r\njax  ------------------------------ 318.05 KiB/2.77 MiB\r\npillow  ------------------------------ 302.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 286.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 286.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 286.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 311.18 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 252.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 301.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 318.91 KiB/11.14 MiB\r\nruff  ------------------------------ 300.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 286.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 284.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 269.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 300.78 KiB/21.34 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (14/162)\r\nsnowballstemmer ------------------------------ 14.84 KiB/90.82 KiB\r\nanyio  ------------------------------ 30.88 KiB/93.79 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 15.26 KiB/96.39 KiB\r\nidentify  ------------------------------ 62.88 KiB/96.78 KiB\r\nchex  ------------------------------ 56.63 KiB/98.68 KiB\r\nparso  ------------------------------ 57.97 KiB/101.22 KiB\r\njupyter-client ------------------------------ 62.87 KiB/103.62 KiB\r\npyparsing  ------------------------------ 94.88 KiB/105.19 KiB\r\nipykernel  ------------------------------ 112.00 KiB/114.43 KiB\r\npycparser  ------------------------------ 112.00 KiB/114.80 KiB\r\nfuro  ------------------------------ 286.01 KiB/333.33 KiB\r\npytest  ------------------------------ 294.56 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 269.87 KiB/341.41 KiB\r\njupyter-server ------------------------------ 334.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 286.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 288.00 KiB/426.07 KiB\r\nflax  ------------------------------ 333.37 KiB/455.35 KiB\r\npybtex  ------------------------------ 304.60 KiB/548.20 KiB\r\ndocutils  ------------------------------ 444.82 KiB/573.64 KiB\r\nipython  ------------------------------ 334.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 275.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 334.70 KiB/1.28 MiB\r\njedi  ------------------------------ 316.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 302.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 318.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 286.80 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 302.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 288.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 317.91 KiB/2.61 MiB\r\njax  ------------------------------ 318.05 KiB/2.77 MiB\r\npillow  ------------------------------ 302.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 286.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 286.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 286.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 311.18 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 252.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 301.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 318.91 KiB/11.14 MiB\r\nruff  ------------------------------ 300.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 286.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 284.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 269.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 300.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 253.91 KiB/52.42 MiB\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (14/162)\r\nsnowballstemmer ------------------------------ 14.84 KiB/90.82 KiB\r\nanyio  ------------------------------ 30.88 KiB/93.79 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 15.26 KiB/96.39 KiB\r\nidentify  ------------------------------ 62.88 KiB/96.78 KiB\r\nchex  ------------------------------ 72.63 KiB/98.68 KiB\r\nparso  ------------------------------ 57.97 KiB/101.22 KiB\r\njupyter-client ------------------------------ 62.87 KiB/103.62 KiB\r\npyparsing  ------------------------------ 94.88 KiB/105.19 KiB\r\npycparser  ------------------------------ 114.80 KiB/114.80 KiB\r\nfuro  ------------------------------ 286.01 KiB/333.33 KiB\r\npytest  ------------------------------ 310.56 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 269.87 KiB/341.41 KiB\r\njupyter-server ------------------------------ 334.88 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 286.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 288.00 KiB/426.07 KiB\r\nflax  ------------------------------ 333.37 KiB/455.35 KiB\r\npybtex  ------------------------------ 304.60 KiB/548.20 KiB\r\ndocutils  ------------------------------ 444.82 KiB/573.64 KiB\r\nipython  ------------------------------ 334.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 275.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 334.70 KiB/1.28 MiB\r\njedi  ------------------------------ 316.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 302.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 318.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 286.80 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 302.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 288.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 317.91 KiB/2.61 MiB\r\njax  ------------------------------ 318.05 KiB/2.77 MiB\r\npillow  ------------------------------ 318.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 286.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 286.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 286.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 311.18 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 252.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 301.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 318.91 KiB/11.14 MiB\r\nruff  ------------------------------ 300.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 286.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 284.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 269.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 300.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 253.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 301.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (14/162)\r\nsnowballstemmer ------------------------------ 14.84 KiB/90.82 KiB\r\nanyio  ------------------------------ 30.88 KiB/93.79 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 15.26 KiB/96.39 KiB\r\nidentify  ------------------------------ 62.88 KiB/96.78 KiB\r\nchex  ------------------------------ 72.63 KiB/98.68 KiB\r\nparso  ------------------------------ 57.97 KiB/101.22 KiB\r\njupyter-client ------------------------------ 62.87 KiB/103.62 KiB\r\npyparsing  ------------------------------ 94.88 KiB/105.19 KiB\r\nfuro  ------------------------------ 286.01 KiB/333.33 KiB\r\npytest  ------------------------------ 310.56 KiB/335.58 KiB\r\nrpds-py  ------------------------------ 269.87 KiB/341.41 KiB\r\njupyter-server ------------------------------ 345.53 KiB/376.78 KiB\r\nprompt-toolkit ------------------------------ 286.77 KiB/378.73 KiB\r\ntornado  ------------------------------ 288.00 KiB/426.07 KiB\r\nflax  ------------------------------ 333.37 KiB/455.35 KiB\r\npybtex  ------------------------------ 304.60 KiB/548.20 KiB\r\ndocutils  ------------------------------ 444.82 KiB/573.64 KiB\r\nipython  ------------------------------ 334.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 275.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 350.70 KiB/1.28 MiB\r\njedi  ------------------------------ 316.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 302.91 KiB/1.62 MiB\r\ntorchvision ------------------------------ 318.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 302.80 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 302.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 288.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 317.91 KiB/2.61 MiB\r\njax  ------------------------------ 334.05 KiB/2.77 MiB\r\npillow  ------------------------------ 318.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 286.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 286.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 286.91 KiB/4.84 MiB\r\nmatp\n[... 58011 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +39,71867,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‡ Preparing packages... (19/162)\r\njsonschema ------------------------------ 32.00 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 48.00 KiB/86.66 KiB\r\nsphinxcontrib-serializinghtml ------------------------------ 48.00 KiB/89.91 KiB\r\nsnowballstemmer ------------------------------ 76.76 KiB/90.82 KiB\r\nanyio  ------------------------------ 78.88 KiB/93.79 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 64.00 KiB/96.39 KiB\r\nrpds-py  ------------------------------ 318.91 KiB/341.41 KiB\r\nprompt-toolkit ------------------------------ 334.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 352.00 KiB/426.07 KiB\r\nflax  ------------------------------ 381.70 KiB/455.35 KiB\r\npybtex  ------------------------------ 364.14 KiB/548.20 KiB\r\ndocutils  ------------------------------ 508.82 KiB/573.64 KiB\r\nipython  ------------------------------ 398.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 339.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 398.70 KiB/1.28 MiB\r\njedi  ------------------------------ 380.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 344.66 KiB/1.62 MiB\r\ntorchvision ------------------------------ 382.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 334.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 366.90 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 352.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 358.62 KiB/2.61 MiB\r\njax  ------------------------------ 382.68 KiB/2.77 MiB\r\npillow  ------------------------------ 381.13 KiB/2.96 MiB\r\nsphinx  ------------------------------ 350.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 334.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 334.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 366.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 316.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 365.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 382.91 KiB/11.14 MiB\r\nruff  ------------------------------ 364.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 334.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 332.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 333.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 364.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 317.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 365.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‡ Preparing packages... (19/162)\r\njsonschema ------------------------------ 32.00 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 48.00 KiB/86.66 KiB\r\nsphinxcontrib-serializinghtml ------------------------------ 64.00 KiB/89.91 KiB\r\nsnowballstemmer ------------------------------ 76.76 KiB/90.82 KiB\r\nanyio  ------------------------------ 78.88 KiB/93.79 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 64.00 KiB/96.39 KiB\r\nrpds-py  ------------------------------ 318.91 KiB/341.41 KiB\r\nprompt-toolkit ------------------------------ 350.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 352.00 KiB/426.07 KiB\r\nflax  ------------------------------ 397.70 KiB/455.35 KiB\r\npybtex  ------------------------------ 380.14 KiB/548.20 KiB\r\ndocutils  ------------------------------ 508.82 KiB/573.64 KiB\r\nipython  ------------------------------ 398.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 355.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 398.70 KiB/1.28 MiB\r\njedi  ------------------------------ 380.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 344.66 KiB/1.62 MiB\r\ntorchvision ------------------------------ 398.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 350.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 373.47 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 352.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 374.62 KiB/2.61 MiB\r\njax  ------------------------------ 382.68 KiB/2.77 MiB\r\npillow  ------------------------------ 381.13 KiB/2.96 MiB\r\nsphinx  ------------------------------ 366.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 334.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 350.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 381.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 332.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 381.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 398.91 KiB/11.14 MiB\r\nruff  ------------------------------ 380.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 340.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 348.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 333.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 380.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 317.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 381.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 48.00 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 64.00 KiB/86.66 KiB\r\nsphinxcontrib-serializinghtml ------------------------------ 64.00 KiB/89.91 KiB\r\nsnowballstemmer ------------------------------ 78.84 KiB/90.82 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 80.00 KiB/96.39 KiB\r\nrpds-py  ------------------------------ 334.91 KiB/341.41 KiB\r\nprompt-toolkit ------------------------------ 366.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 368.00 KiB/426.07 KiB\r\nflax  ------------------------------ 397.70 KiB/455.35 KiB\r\npybtex  ------------------------------ 394.44 KiB/548.20 KiB\r\ndocutils  ------------------------------ 524.82 KiB/573.64 KiB\r\nipython  ------------------------------ 398.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 355.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 398.70 KiB/1.28 MiB\r\njedi  ------------------------------ 396.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 360.66 KiB/1.62 MiB\r\ntorchvision ------------------------------ 398.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 366.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 373.47 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 368.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 374.62 KiB/2.61 MiB\r\njax  ------------------------------ 398.05 KiB/2.77 MiB\r\npillow  ------------------------------ 382.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 366.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 350.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 350.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 381.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 332.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 381.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 398.91 KiB/11.14 MiB\r\nruff  ------------------------------ 380.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 356.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 364.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 349.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 380.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 333.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 381.34 KiB/71.03 MiB ",,terminal_output +40,72051,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 48.00 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 64.00 KiB/86.66 KiB\r\nsphinxcontrib-serializinghtml ------------------------------ 64.00 KiB/89.91 KiB\r\nsnowballstemmer ------------------------------ 78.84 KiB/90.82 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 80.00 KiB/96.39 KiB\r\nrpds-py  ------------------------------ 334.91 KiB/341.41 KiB\r\nprompt-toolkit ------------------------------ 366.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 368.00 KiB/426.07 KiB\r\nflax  ------------------------------ 397.70 KiB/455.35 KiB\r\npybtex  ------------------------------ 394.44 KiB/548.20 KiB\r\ndocutils  ------------------------------ 524.82 KiB/573.64 KiB\r\nipython  ------------------------------ 398.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 355.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 414.70 KiB/1.28 MiB\r\njedi  ------------------------------ 396.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 360.66 KiB/1.62 MiB\r\ntorchvision ------------------------------ 398.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 366.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 373.47 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 368.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 374.62 KiB/2.61 MiB\r\njax  ------------------------------ 398.05 KiB/2.77 MiB\r\npillow  ------------------------------ 382.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 366.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 350.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 350.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 381.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 332.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 381.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 398.91 KiB/11.14 MiB\r\nruff  ------------------------------ 380.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 356.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 364.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 349.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 380.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 333.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 381.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 64.00 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 64.00 KiB/86.66 KiB\r\nsphinxcontrib-serializinghtml ------------------------------ 64.00 KiB/89.91 KiB\r\nsnowballstemmer ------------------------------ 78.84 KiB/90.82 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 80.00 KiB/96.39 KiB\r\nrpds-py  ------------------------------ 334.91 KiB/341.41 KiB\r\nprompt-toolkit ------------------------------ 366.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 368.00 KiB/426.07 KiB\r\nflax  ------------------------------ 413.60 KiB/455.35 KiB\r\npybtex  ------------------------------ 394.44 KiB/548.20 KiB\r\ndocutils  ------------------------------ 524.82 KiB/573.64 KiB\r\nipython  ------------------------------ 414.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 371.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 414.70 KiB/1.28 MiB\r\njedi  ------------------------------ 396.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 376.66 KiB/1.62 MiB\r\ntorchvision ------------------------------ 414.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 366.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 389.47 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 368.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 390.62 KiB/2.61 MiB\r\njax  ------------------------------ 398.05 KiB/2.77 MiB\r\npillow  ------------------------------ 382.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 382.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 366.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 366.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 397.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 348.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 397.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 414.91 KiB/11.14 MiB\r\nruff  ------------------------------ 396.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 356.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 364.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 349.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 396.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 333.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 397.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 64.00 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 80.00 KiB/86.66 KiB\r\nsphinxcontrib-serializinghtml ------------------------------ 80.00 KiB/89.91 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 96.00 KiB/96.39 KiB\r\nrpds-py  ------------------------------ 336.60 KiB/341.41 KiB\r\nprompt-toolkit ------------------------------ 366.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 384.00 KiB/426.07 KiB\r\nflax  ------------------------------ 413.60 KiB/455.35 KiB\r\npybtex  ------------------------------ 396.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 540.71 KiB/573.64 KiB\r\nipython  ------------------------------ 414.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 371.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 414.70 KiB/1.28 MiB\r\njedi  ------------------------------ 412.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 376.66 KiB/1.62 MiB\r\ntorchvision ------------------------------ 414.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 366.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 389.47 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 384.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 390.62 KiB/2.61 MiB\r\njax  ------------------------------ 414.05 KiB/2.77 MiB\r\npillow  ------------------------------ 398.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 382.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 366.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 366.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 397.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 348.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 397.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 414.91 KiB/11.14 MiB\r\nruff  ------------------------------ 396.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 356.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 380.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 365.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 396.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 349.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 397.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 64.00 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 80.00 KiB/86.66 KiB\r\nsphinxcontrib-serializinghtml ------------------------------ 80.00 KiB/89.91 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 96.00 KiB/96.39 KiB\r\nrpds-py  ------------------------------ 336.60 KiB/341.41 KiB\r\nprompt-toolkit ------------------------------ 366.88 KiB/378.73 KiB\r\ntornado  ------------------------------ 384.00 KiB/426.07 KiB\r\nflax  ------------------------------ 413.60 KiB/455.35 KiB\r\npybtex  ------------------------------ 396.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 540.71 KiB/573.64 KiB\r\nipython  ------------------------------ 414.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 371.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 414.70 KiB/1.28 MiB\r\njedi  ------------------------------ 412.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 376.66 KiB/1.62 MiB\r\ntorchvision ------------------------------ 414.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 382.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 389.47 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 384.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 390.62 KiB/2.61 MiB\r\njax  ------------------------------ 414.05 KiB/2.77 MiB\r\npillow  ------------------------------ 398.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 382.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 366.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 366.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 397.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 348.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 397.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 414.91 KiB/11.14 MiB\r\nruff  ------------------------------ 396.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 356.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 380.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 365.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 396.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 349.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 397.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 64.00 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 80.00 KiB/86.66 KiB\r\nsphinxcontrib-serializinghtml ------------------------------ 80.00 KiB/89.91 KiB\r\nsphinxcontrib-htmlhelp ------------------------------ 96.00 KiB/96.39 KiB\r\nrpds-py  ------------------------------ 336.60 KiB/341.41 KiB\r\ntornado  ------------------------------ 384.00 KiB/426.07 KiB\r\nflax  ------------------------------ 413.60 KiB/455.35 KiB\r\npybtex  ------------------------------ 396.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 540.71 KiB/573.64 KiB\r\nipython  ------------------------------ 418.92 KiB/806.18 KiB\r\nsetuptools ------------------------------ 371.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 430.70 KiB/1.28 MiB\r\njedi  ------------------------------ 412.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 376.66 KiB/1.62 MiB\r\ntorchvision ------------------------------ 414.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 382.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 389.47 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 384.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 390.62 KiB/2.61 MiB\r\njax  ------------------------------ 414.05 KiB/2.77 MiB\r\npillow  ------------------------------ 398.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 382.88 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 366.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 366.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 397.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 348.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 397.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 414.91 KiB/11.14 MiB\r\nruff  ------------------------------ 396.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 372.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 380.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 365.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 396.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 349.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 397.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 76.90 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 86.66 KiB/86.66 KiB\r\nsphinxcontrib-serializinghtml ------------------------------ 89.91 KiB/89.91 KiB\r\nrpds-py  ------------------------------ 336.60 KiB/341.41 KiB\r\ntornado  ------------------------------ 384.00 KiB/426.07 KiB\r\nflax  ------------------------------ 429.60 KiB/455.35 KiB\r\npybtex  ------------------------------ 396.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 556.71 KiB/573.64 KiB\r\nipython  ------------------------------ 418.92 KiB/806.18 KiB\r\nsetuptools ------------------------------ 387.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 430.70 KiB/1.28 MiB\r\njedi  ------------------------------ 412.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 392.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 430.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 382.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 392.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 384.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 398.23 KiB/2.61 MiB\r\njax  ------------------------------ 414.05 KiB/2.77 MiB\r\npillow  ------------------------------ 398.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 392.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 382.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 382.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 413.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 364.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 413.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 430.91 KiB/11.14 MiB\r\nruff  ------------------------------ 412.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 372.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 380.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 365.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 412.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 365.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 411.94 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 76.90 KiB/86.39 KiB\r\nsphinxcontrib-qthelp ------------------------------ 86.66 KiB/86.66 KiB\r\nrpds-py  ------------------------------ 336.60 KiB/341.41 KiB\r\ntornado  ------------------------------ 384.00 KiB/426.07 KiB\r\nflax  ------------------------------ 429.60 KiB/455.35 KiB\r\npybtex  ------------------------------ 396.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 556.71 KiB/573.64 KiB\r\nipython  ------------------------------ 418.92 KiB/806.18 KiB\r\nsetuptools ------------------------------ 387.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 430.70 KiB/1.28 MiB\r\njedi  ------------------------------ 412.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 392.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 430.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 382.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 392.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 384.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 398.23 KiB/2.61 MiB\r\njax  ------------------------------ 414.05 KiB/2.77 MiB\r\npillow  ------------------------------ 398.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 392.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 382.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 382.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 413.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 364.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 413.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 430.91 KiB/11.14 MiB\r\nruff  ------------------------------ 412.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 372.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 380.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 365.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 412.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 365.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 411.94 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 76.90 KiB/86.39 KiB\r\nrpds-py  ------------------------------ 336.60 KiB/341.41 KiB\r\ntornado  ------------------------------ 384.00 KiB/426.07 KiB\r\nflax  ------------------------------ 429.60 KiB/455.35 KiB\r\npybtex  ------------------------------ 396.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 556.71 KiB/573.64 KiB\r\nipython  ------------------------------ 418.92 KiB/806.18 KiB\r\nsetuptools ------------------------------ 387.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 430.70 KiB/1.28 MiB\r\njedi  ------------------------------ 412.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 392.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 430.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 382.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 392.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 384.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 398.23 KiB/2.61 MiB\r\njax  ------------------------------ 414.05 KiB/2.77 MiB\r\npillow  ------------------------------ 398.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 392.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 382.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 382.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 413.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 364.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 413.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 430.91 KiB/11.14 MiB\r\nruff  ------------------------------ 412.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 372.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 380.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 365.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 412.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 365.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 411.94 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (26/162)\r\njsonschema ------------------------------ 76.90 KiB/86.39 KiB\r\nrpds-py  ------------------------------ 336.60 KiB/341.41 KiB\r\ntornado  ------------------------------ 384.00 KiB/426.07 KiB\r\nflax  ------------------------------ 429.60 KiB/455.35 KiB\r\npybtex  ------------------------------ 396.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 556.71 KiB/573.64 KiB\r\nipython  ------------------------------ 418.92 KiB/806.18 KiB\r\nsetuptools[\n[... 5551 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +41,72345,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (33/162)\r\nmyst-parser ------------------------------ 0 B/82.60 KiB\r\ntraitlets  ------------------------------ 0 B/83.36 KiB\r\njsonschema ------------------------------ 76.90 KiB/86.39 KiB\r\ntornado  ------------------------------ 400.00 KiB/426.07 KiB\r\nflax  ------------------------------ 444.89 KiB/455.35 KiB\r\npybtex  ------------------------------ 412.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 556.71 KiB/573.64 KiB\r\nipython  ------------------------------ 434.92 KiB/806.18 KiB\r\nsetuptools ------------------------------ 403.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 446.70 KiB/1.28 MiB\r\njedi  ------------------------------ 428.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 392.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 446.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 398.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 392.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 400.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 398.23 KiB/2.61 MiB\r\njax  ------------------------------ 430.05 KiB/2.77 MiB\r\npillow  ------------------------------ 408.45 KiB/2.96 MiB\r\nsphinx  ------------------------------ 408.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 382.88 KiB/4.10 MiB\r\nnumpy  ------------------------------ 382.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 429.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 364.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 429.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 446.91 KiB/11.14 MiB\r\nruff  ------------------------------ 412.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 388.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 396.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 381.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 412.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 365.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 427.94 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (33/162)\r\nsphinxcontrib-devhelp ------------------------------ 0 B/80.60 KiB\r\nmsgpack  ------------------------------ 0 B/82.34 KiB\r\nmyst-parser ------------------------------ 14.91 KiB/82.60 KiB\r\ntraitlets  ------------------------------ 16.00 KiB/83.36 KiB\r\ntornado  ------------------------------ 400.00 KiB/426.07 KiB\r\nflax  ------------------------------ 444.89 KiB/455.35 KiB\r\npybtex  ------------------------------ 412.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 572.71 KiB/573.64 KiB\r\nipython  ------------------------------ 434.92 KiB/806.18 KiB\r\nsetuptools ------------------------------ 403.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 446.70 KiB/1.28 MiB\r\njedi  ------------------------------ 428.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 408.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 446.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 398.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 405.46 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 400.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 414.23 KiB/2.61 MiB\r\njax  ------------------------------ 430.05 KiB/2.77 MiB\r\npillow  ------------------------------ 408.45 KiB/2.96 MiB\r\nsphinx  ------------------------------ 408.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 392.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 398.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 429.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 380.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 429.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 446.91 KiB/11.14 MiB\r\nruff  ------------------------------ 428.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 388.71 KiB/11.36 MiB\r\nnotebook  ------------------------------ 396.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 381.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 428.33 KiB/21.34 MiB\r\njaxlib  ------------------------------ 365.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 427.94 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... 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(33/162)\r\nsphinxcontrib-devhelp ------------------------------ 16.00 KiB/80.60 KiB\r\nmsgpack  ------------------------------ 16.00 KiB/82.34 KiB\r\nmyst-parser ------------------------------ 30.91 KiB/82.60 KiB\r\ntraitlets  ------------------------------ 32.00 KiB/83.36 KiB\r\ntornado  ------------------------------ 418.02 KiB/426.07 KiB\r\npybtex  ------------------------------ 444.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 572.82 KiB/573.64 KiB\r\nipython  ------------------------------ 462.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 419.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 462.70 KiB/1.28 MiB\r\njedi  ------------------------------ 460.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 424.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 462.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 414.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 421.46 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 432.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 430.01 KiB/2.61 MiB\r\njax  ------------------------------ 462.05 KiB/2.77 MiB\r\npillow  ------------------------------ 440.45 KiB/2.96 MiB\r\nsphinx  ------------------------------ 424.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 408.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 414.69 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 430.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 396.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 445.40 KiB/9.71 MiB\r\njupyterlab ------------------------------ 462.91 KiB/11.14 MiB\r\nruff  ------------------------------ 444.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 414.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 412.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 413.69 KiB/13.14 MiB\r\nscipy  ------------------------------ 444.33 KiB/21.34 MiB\r\njaxlib  ------------------------------ 381.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 443.94 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (33/162)\r\nsphinxcontrib-devhelp ------------------------------ 16.00 KiB/80.60 KiB\r\nmsgpack  ------------------------------ 16.00 KiB/82.34 KiB\r\nmyst-parser ------------------------------ 30.91 KiB/82.60 KiB\r\ntraitlets  ------------------------------ 32.00 KiB/83.36 KiB\r\ntornado  ------------------------------ 418.02 KiB/426.07 KiB\r\npybtex  ------------------------------ 444.64 KiB/548.20 KiB\r\ndocutils  ------------------------------ 572.82 KiB/573.64 KiB\r\nipython  ------------------------------ 462.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 435.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 478.70 KiB/1.28 MiB\r\njedi  ------------------------------ 460.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 424.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 462.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 430.06 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 421.46 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 432.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 430.01 KiB/2.61 MiB\r\njax  ------------------------------ 462.05 KiB/2.77 MiB\r\npillow  ------------------------------ 440.45 KiB/2.96 MiB\r\nsphinx  ------------------------------ 424.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 408.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 414.69 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 430.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 396.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 445.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 462.91 KiB/11.14 MiB\r\nruff  ------------------------------ 444.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 414.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 428.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 413.69 KiB/13.14 MiB\r\nscipy  ------------------------------ 444.33 KiB/21.34 MiB\r\njaxlib  ------------------------------ 381.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 443.94 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... 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(37/162)\r\narrow  ------------------------------ 0 B/64.86 KiB\r\njupyter-lsp ------------------------------ 0 B/67.53 KiB\r\nsimplejson ------------------------------ 0 B/73.89 KiB\r\nnbformat  ------------------------------ 0 B/76.62 KiB\r\nhttpcore  ------------------------------ 0 B/76.71 KiB\r\nmyst-nb  ------------------------------ 16.00 KiB/78.42 KiB\r\nsphinxcontrib-devhelp ------------------------------ 48.00 KiB/80.60 KiB\r\nmsgpack  ------------------------------ 48.00 KiB/82.34 KiB\r\nmyst-parser ------------------------------ 62.46 KiB/82.60 KiB\r\ntraitlets  ------------------------------ 64.00 KiB/83.36 KiB\r\npybtex  ------------------------------ 476.64 KiB/548.20 KiB\r\nipython  ------------------------------ 494.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 453.38 KiB/909.02 KiB\r\npyzmq  ------------------------------ 510.13 KiB/1.28 MiB\r\njedi  ------------------------------ 492.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 456.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 510.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 462.06 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 453.46 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 464.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 462.23 KiB/2.61 MiB\r\njax  ------------------------------ 494.05 KiB/2.77 MiB\r\npillow  ------------------------------ 462.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 472.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 439.07 KiB/4.10 MiB\r\nnumpy  ------------------------------ 446.69 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 477.70 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 428.56 KiB/8.19 MiB\r\nbabel \n[... 4879 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +42,72568,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (37/162)\r\npexpect  ------------------------------ 14.92 KiB/62.28 KiB\r\nkiwisolver ------------------------------ 16.00 KiB/63.55 KiB\r\narrow  ------------------------------ 14.93 KiB/64.86 KiB\r\njupyter-lsp ------------------------------ 32.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 980 B/73.89 KiB\r\nnbformat  ------------------------------ 32.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 29.91 KiB/76.71 KiB\r\nmyst-nb  ------------------------------ 48.00 KiB/78.42 KiB\r\nsphinxcontrib-devhelp ------------------------------ 64.00 KiB/80.60 KiB\r\nmsgpack  ------------------------------ 80.00 KiB/82.34 KiB\r\nmyst-parser ------------------------------ 78.91 KiB/82.60 KiB\r\ntraitlets  ------------------------------ 80.00 KiB/83.36 KiB\r\npybtex  ------------------------------ 508.64 KiB/548.20 KiB\r\nipython  ------------------------------ 526.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 483.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 526.70 KiB/1.28 MiB\r\njedi  ------------------------------ 509.77 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 486.26 KiB/1.62 MiB\r\ntorchvision ------------------------------ 526.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 478.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 472.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 496.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 494.23 KiB/2.61 MiB\r\njax  ------------------------------ 525.09 KiB/2.77 MiB\r\npillow  ------------------------------ 494.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 504.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 456.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 462.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 494.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 460.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 509.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 526.91 KiB/11.14 MiB\r\nruff  ------------------------------ 508.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 478.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 492.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 461.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 508.68 KiB/21.34 MiB\r\njaxlib  ------------------------------ 442.69 KiB/52.42 MiB\r\ntorch  ------------------------------ 509.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (37/162)\r\npexpect  ------------------------------ 14.92 KiB/62.28 KiB\r\nkiwisolver ------------------------------ 16.00 KiB/63.55 KiB\r\narrow  ------------------------------ 30.93 KiB/64.86 KiB\r\njupyter-lsp ------------------------------ 32.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 14.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 32.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 29.91 KiB/76.71 KiB\r\nmyst-nb  ------------------------------ 48.00 KiB/78.42 KiB\r\nsphinxcontrib-devhelp ------------------------------ 64.00 KiB/80.60 KiB\r\nmsgpack  ------------------------------ 80.00 KiB/82.34 KiB\r\nmyst-parser ------------------------------ 78.91 KiB/82.60 KiB\r\npybtex  ------------------------------ 508.64 KiB/548.20 KiB\r\nipython  ------------------------------ 526.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 483.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 526.70 KiB/1.28 MiB\r\njedi  ------------------------------ 509.77 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 486.26 KiB/1.62 MiB\r\ntorchvision ------------------------------ 526.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 478.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 472.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 496.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 494.23 KiB/2.61 MiB\r\njax  ------------------------------ 525.09 KiB/2.77 MiB\r\npillow  ------------------------------ 494.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 504.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 456.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 462.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 494.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 460.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 509.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 526.91 KiB/11.14 MiB\r\nruff  ------------------------------ 508.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 478.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 492.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 461.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 508.68 KiB/21.34 MiB\r\njaxlib  ------------------------------ 442.69 KiB/52.42 MiB\r\ntorch  ------------------------------ 509.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (37/162)\r\nattrs  ------------------------------ 0 B/61.67 KiB\r\npexpect  ------------------------------ 30.92 KiB/62.28 KiB\r\nkiwisolver ------------------------------ 32.00 KiB/63.55 KiB\r\narrow  ------------------------------ 30.93 KiB/64.86 KiB\r\njupyter-lsp ------------------------------ 32.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 14.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 32.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 45.91 KiB/76.71 KiB\r\nmyst-nb  ------------------------------ 64.00 KiB/78.42 KiB\r\nsphinxcontrib-devhelp ------------------------------ 80.00 KiB/80.60 KiB\r\nmsgpack  ------------------------------ 82.34 KiB/82.34 KiB\r\nmyst-parser ------------------------------ 78.91 KiB/82.60 KiB\r\npybtex  ------------------------------ 523.21 KiB/548.20 KiB\r\nipython  ------------------------------ 542.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 499.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 542.70 KiB/1.28 MiB\r\njedi  ------------------------------ 525.77 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 486.26 KiB/1.62 MiB\r\ntorchvision ------------------------------ 542.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 494.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 488.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 512.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 510.23 KiB/2.61 MiB\r\njax  ------------------------------ 526.05 KiB/2.77 MiB\r\npillow  ------------------------------ 510.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 520.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 456.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 462.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 509.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 476.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 509.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 542.91 KiB/11.14 MiB\r\nruff  ------------------------------ 524.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 493.83 KiB/11.36 MiB\r\nnotebook  ------------------------------ 508.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 477.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 508.68 KiB/21.34 MiB\r\njaxlib  ------------------------------ 445.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 525.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (37/162)\r\nattrs  ------------------------------ 0 B/61.67 KiB\r\npexpect  ------------------------------ 30.92 KiB/62.28 KiB\r\nkiwisolver ------------------------------ 32.00 KiB/63.55 KiB\r\narrow  ------------------------------ 30.93 KiB/64.86 KiB\r\njupyter-lsp ------------------------------ 32.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 14.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 32.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 45.91 KiB/76.71 KiB\r\nmyst-nb  ------------------------------ 64.00 KiB/78.42 KiB\r\nsphinxcontrib-devhelp ------------------------------ 80.00 KiB/80.60 KiB\r\nmyst-parser ------------------------------ 78.91 KiB/82.60 KiB\r\npybtex  ------------------------------ 523.21 KiB/548.20 KiB\r\nipython  ------------------------------ 542.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 499.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 542.70 KiB/1.28 MiB\r\njedi  ------------------------------ 525.77 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 495.98 KiB/1.62 MiB\r\ntorchvision ------------------------------ 542.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 494.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 488.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 512.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 510.23 KiB/2.61 MiB\r\njax  ------------------------------ 526.05 KiB/2.77 MiB\r\npillow  ------------------------------ 510.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 520.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 456.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 462.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 509.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 476.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 509.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 542.91 KiB/11.14 MiB\r\nruff  ------------------------------ 524.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 493.83 KiB/11.36 MiB\r\nnotebook  ------------------------------ 508.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 477.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 508.68 KiB/21.34 MiB\r\njaxlib  ------------------------------ 445.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 525.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (37/162)\r\nattrs  ------------------------------ 0 B/61.67 KiB\r\npexpect  ------------------------------ 30.92 KiB/62.28 KiB\r\nkiwisolver ------------------------------ 32.00 KiB/63.55 KiB\r\narrow  ------------------------------ 30.93 KiB/64.86 KiB\r\njupyter-lsp ------------------------------ 32.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 14.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 48.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 45.91 KiB/76.71 KiB\r\nmyst-nb  ------------------------------ 64.00 KiB/78.42 KiB\r\nsphinxcontrib-devhelp ------------------------------ 80.00 KiB/80.60 KiB\r\npybtex  ------------------------------ 523.21 KiB/548.20 KiB\r\nipython  ------------------------------ 542.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 499.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 542.70 KiB/1.28 MiB\r\njedi  ------------------------------ 525.77 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 495.98 KiB/1.62 MiB\r\ntorchvision ------------------------------ 542.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 494.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 488.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 512.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 510.23 KiB/2.61 MiB\r\njax  ------------------------------ 526.05 KiB/2.77 MiB\r\npillow  ------------------------------ 510.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 520.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 472.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 478.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 509.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 476.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 509.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 542.91 KiB/11.14 MiB\r\nruff  ------------------------------ 524.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 493.83 KiB/11.36 MiB\r\nnotebook  ------------------------------ 508.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 477.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 524.68 KiB/21.34 MiB\r\njaxlib  ------------------------------ 445.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 525.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\nattrs  ------------------------------ 14.88 KiB/61.67 KiB\r\npexpect  ------------------------------ 46.92 KiB/62.28 KiB\r\nkiwisolver ------------------------------ 48.00 KiB/63.55 KiB\r\narrow  ------------------------------ 46.93 KiB/64.86 KiB\r\njupyter-lsp ------------------------------ 48.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 30.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 48.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 45.91 KiB/76.71 KiB\r\nmyst-nb  ------------------------------ 77.32 KiB/78.42 KiB\r\npybtex  ------------------------------ 524.64 KiB/548.20 KiB\r\nipython  ------------------------------ 542.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 499.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 558.70 KiB/1.28 MiB\r\njedi  ------------------------------ 541.77 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 495.98 KiB/1.62 MiB\r\ntorchvision ------------------------------ 542.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 494.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 488.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 512.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 510.23 KiB/2.61 MiB\r\njax  ------------------------------ 526.05 KiB/2.77 MiB\r\npillow  ------------------------------ 510.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 520.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 472.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 478.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 509.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 476.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 525.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 542.91 KiB/11.14 MiB\r\nruff  ------------------------------ 524.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 493.83 KiB/11.36 MiB\r\nnotebook  ------------------------------ 508.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 477.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 524.68 KiB/21.34 MiB\r\njaxlib  ------------------------------ 445.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 525.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\npackaging  ------------------------------ 0 B/52.72 KiB\r\nprometheus-client ------------------------------ 0 B/53.40 KiB\r\nmdit-py-plugins ------------------------------ 0 B/54.02 KiB\r\ntoolz  ------------------------------ 0 B/56.73 KiB\r\nh11  ------------------------------ 0 B/56.89 KiB\r\nwebsocket-client ------------------------------ 0 B/57.45 KiB\r\njupyterlab-server ------------------------------ 0 B/58.30 KiB\r\nattrs  ------------------------------ 14.88 KiB/61.67 KiB\r\npexpect  ------------------------------ 46.92 KiB/62.28 KiB\r\nkiwisolver ------------------------------ 48.00 KiB/63.55 KiB\r\narrow  ------------------------------ 46.93 KiB/64.86 KiB\r\njupyter-lsp ------------------------------ 48.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 30.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 48.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 61.91 KiB/76.71 KiB\r\nmyst-nb  ------------------------------ 77.32 KiB/78.42 KiB\r\npybtex  ------------------------------ 524.64 KiB/548.20 KiB\r\nipython  ------------------------------ 558.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 515.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 558.70 KiB/1.28 MiB\r\njedi  ------------------------------ 541.77 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 495.98 KiB/1.62 MiB\r\ntorchvision ------------------------------ 558.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 501.73 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 488.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 512.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 526.23 KiB/2.61 MiB\r\njax  ------------------------------ 542.05 KiB/2.77 MiB\r\npillow  ------------------------------ 526.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 536.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 472.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 478.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 525.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 476.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 525.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 558.80 KiB/11.14 MiB\r\nruff  ------------------------------ 524.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 507.69 KiB/11.36 MiB\r\nnotebook  ------------------------------ 524.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 477.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 524.68 KiB/21.34 MiB\r\njaxlib  ------------------------------ 445.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 541.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\npackaging  ------------------------------ 16.00 KiB/52.72 KiB\r\nprometheus-client ------------------------------ 16.00 KiB/53.40 KiB\r\nmdit-py-plugins ------------------------------ 14.87 KiB/54.02 KiB\r\ntoolz  ------------------------------ 16.00 KiB/56.73 KiB\r\nh11  ------------------------------ 15.78 KiB/56.89 KiB\r\nwebsocket-client ------------------------------ 16.00 KiB/57.45 KiB\r\njupyterlab-server ------------------------------ 16.00 KiB/58.30 KiB\r\nattrs  ------------------------------ 14.88 KiB/61.67 KiB\r\npexpect  ------------------------------ 46.92 KiB/62.28 KiB\r\nkiwisolver ------------------------------ 48.00 KiB/63.55 KiB\r\narrow  ------------------------------ 62.93 KiB/64.86 KiB\r\njupyter-lsp ------------------------------ 64.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 30.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 64.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 61.91 KiB/76.71 KiB\r\npybtex  ------------------------------ 524.64 KiB/548.20 KiB\r\nipython  ------------------------------ 558.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 515.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 558.70 KiB/1.28 MiB\r\njedi  ------------------------------ 541.77 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 511.98 KiB/1.62 MiB\r\ntorchvision ------------------------------ 558.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 501.73 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------\n[... 14488 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +43,72743,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\npackaging  ------------------------------ 16.00 KiB/52.72 KiB\r\nprometheus-client ------------------------------ 16.00 KiB/53.40 KiB\r\nmdit-py-plugins ------------------------------ 14.87 KiB/54.02 KiB\r\ntoolz  ------------------------------ 16.00 KiB/56.73 KiB\r\nh11  ------------------------------ 15.78 KiB/56.89 KiB\r\nwebsocket-client ------------------------------ 16.00 KiB/57.45 KiB\r\njupyterlab-server ------------------------------ 16.00 KiB/58.30 KiB\r\nattrs  ------------------------------ 30.88 KiB/61.67 KiB\r\narrow  ------------------------------ 62.93 KiB/64.86 KiB\r\njupyter-lsp ------------------------------ 64.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 46.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 64.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 64.00 KiB/76.71 KiB\r\npybtex  ------------------------------ 540.64 KiB/548.20 KiB\r\nipython  ------------------------------ 574.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 531.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 574.70 KiB/1.28 MiB\r\njedi  ------------------------------ 557.68 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 520.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 572.17 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 502.72 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 520.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 527.89 KiB/2.37 MiB\r\nfonttools  ------------------------------ 542.23 KiB/2.61 MiB\r\njax  ------------------------------ 558.05 KiB/2.77 MiB\r\npillow  ------------------------------ 542.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 552.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 488.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 494.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 541.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 508.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 541.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 574.80 KiB/11.14 MiB\r\nruff  ------------------------------ 556.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 523.69 KiB/11.36 MiB\r\nnotebook  ------------------------------ 540.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 509.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 524.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 477.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 557.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\npackaging  ------------------------------ 16.00 KiB/52.72 KiB\r\nprometheus-client ------------------------------ 16.00 KiB/53.40 KiB\r\nmdit-py-plugins ------------------------------ 14.87 KiB/54.02 KiB\r\ntoolz  ------------------------------ 16.00 KiB/56.73 KiB\r\nh11  ------------------------------ 15.78 KiB/56.89 KiB\r\nwebsocket-client ------------------------------ 16.00 KiB/57.45 KiB\r\njupyterlab-server ------------------------------ 32.00 KiB/58.30 KiB\r\nattrs  ------------------------------ 30.88 KiB/61.67 KiB\r\njupyter-lsp ------------------------------ 64.00 KiB/67.53 KiB\r\nsimplejson ------------------------------ 46.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 64.00 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 64.00 KiB/76.71 KiB\r\npybtex  ------------------------------ 540.64 KiB/548.20 KiB\r\nipython  ------------------------------ 574.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 531.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 574.70 KiB/1.28 MiB\r\njedi  ------------------------------ 557.68 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 520.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 572.17 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 502.72 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 520.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 543.89 KiB/2.37 MiB\r\nfonttools  ------------------------------ 542.23 KiB/2.61 MiB\r\njax  ------------------------------ 558.05 KiB/2.77 MiB\r\npillow  ------------------------------ 542.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 552.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 495.86 KiB/4.10 MiB\r\nnumpy  ------------------------------ 510.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 541.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 508.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 541.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 574.80 KiB/11.14 MiB\r\nruff  ------------------------------ 556.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 523.69 KiB/11.36 MiB\r\nnotebook  ------------------------------ 540.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 509.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 524.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 477.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 557.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\npackaging  ------------------------------ 29.80 KiB/52.72 KiB\r\nprometheus-client ------------------------------ 32.00 KiB/53.40 KiB\r\nmdit-py-plugins ------------------------------ 14.87 KiB/54.02 KiB\r\ntoolz  ------------------------------ 32.00 KiB/56.73 KiB\r\nh11  ------------------------------ 31.78 KiB/56.89 KiB\r\nwebsocket-client ------------------------------ 32.00 KiB/57.45 KiB\r\njupyterlab-server ------------------------------ 32.00 KiB/58.30 KiB\r\nattrs  ------------------------------ 30.88 KiB/61.67 KiB\r\nsimplejson ------------------------------ 46.91 KiB/73.89 KiB\r\nnbformat  ------------------------------ 76.62 KiB/76.62 KiB\r\nhttpcore  ------------------------------ 64.00 KiB/76.71 KiB\r\npybtex  ------------------------------ 540.64 KiB/548.20 KiB\r\nipython  ------------------------------ 574.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 531.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 574.70 KiB/1.28 MiB\r\njedi  ------------------------------ 557.68 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 520.56 KiB/1.62 MiB\r\r\ntorchvision ------------------------------ 572.17 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 502.72 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 520.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 543.89 KiB/2.37 MiB\r\nfonttools  ------------------------------ 542.23 KiB/2.61 MiB\r\njax  ------------------------------ 558.05 KiB/2.77 MiB\r\npillow  ------------------------------ 542.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 552.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 495.86 KiB/4.10 MiB\r\nnumpy  ------------------------------ 510.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 541.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 508.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 541.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 574.80 KiB/11.14 MiB\r\nruff  ------------------------------ 556.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 523.69 KiB/11.36 MiB\r\nnotebook  ------------------------------ 540.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 509.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 540.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 477.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 557.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\npackaging  ------------------------------ 29.80 KiB/52.72 KiB\r\nprometheus-client ------------------------------ 32.00 KiB/53.40 KiB\r\nmdit-py-plugins ------------------------------ 30.87 KiB/54.02 KiB\r\ntoolz  ------------------------------ 32.00 KiB/56.73 KiB\r\nh11  ------------------------------ 31.78 KiB/56.89 KiB\r\nwebsocket-client ------------------------------ 32.00 KiB/57.45 KiB\r\njupyterlab-server ------------------------------ 32.00 KiB/58.30 KiB\r\nattrs  ------------------------------ 46.88 KiB/61.67 KiB\r\nsimplejson ------------------------------ 62.91 KiB/73.89 KiB\r\nhttpcore  ------------------------------ 64.00 KiB/76.71 KiB\r\npybtex  ------------------------------ 540.64 KiB/548.20 KiB\r\nipython  ------------------------------ 574.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 531.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 574.70 KiB/1.28 MiB\r\njedi  ------------------------------ 572.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 520.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 572.17 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 502.72 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 520.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 543.89 KiB/2.37 MiB\r\nfonttools  ------------------------------ 542.23 KiB/2.61 MiB\r\njax  ------------------------------ 558.05 KiB/2.77 MiB\r\npillow  ------------------------------ 542.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 552.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 495.86 KiB/4.10 MiB\r\nnumpy  ------------------------------ 510.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 541.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 508.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 557.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 574.80 KiB/11.14 MiB\r\nruff  ------------------------------ 556.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 523.69 KiB/11.36 MiB\r\nnotebook  ------------------------------ 540.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 509.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 540.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 477.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 557.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\npackaging  ------------------------------ 29.80 KiB/52.72 KiB\r\nprometheus-client ------------------------------ 32.00 KiB/53.40 KiB\r\nmdit-py-plugins ------------------------------ 30.87 KiB/54.02 KiB\r\ntoolz  ------------------------------ 32.00 KiB/56.73 KiB\r\nh11  ------------------------------ 31.78 KiB/56.89 KiB\r\nwebsocket-client ------------------------------ 32.00 KiB/57.45 KiB\r\njupyterlab-server ------------------------------ 32.00 KiB/58.30 KiB\r\nattrs  ------------------------------ 46.88 KiB/61.67 KiB\r\nsimplejson ------------------------------ 62.91 KiB/73.89 KiB\r\nhttpcore  ------------------------------ 64.00 KiB/76.71 KiB\r\nipython  ------------------------------ 574.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 547.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 590.70 KiB/1.28 MiB\r\njedi  ------------------------------ 572.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 520.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 572.17 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 502.72 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 520.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 543.89 KiB/2.37 MiB\r\nfonttools  ------------------------------ 542.23 KiB/2.61 MiB\r\njax  ------------------------------ 558.05 KiB/2.77 MiB\r\npillow  ------------------------------ 548.21 KiB/2.96 MiB\r\nsphinx  ------------------------------ 552.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 495.86 KiB/4.10 MiB\r\nnumpy  ------------------------------ 510.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 541.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 508.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 557.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 574.80 KiB/11.14 MiB\r\nruff  ------------------------------ 556.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 526.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 540.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 509.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 540.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 477.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 557.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\npackaging  ------------------------------ 29.80 KiB/52.72 KiB\r\nprometheus-client ------------------------------ 32.00 KiB/53.40 KiB\r\nmdit-py-plugins ------------------------------ 30.87 KiB/54.02 KiB\r\ntoolz  ------------------------------ 32.00 KiB/56.73 KiB\r\nh11  ------------------------------ 31.78 KiB/56.89 KiB\r\nwebsocket-client ------------------------------ 32.00 KiB/57.45 KiB\r\njupyterlab-server ------------------------------ 32.00 KiB/58.30 KiB\r\nattrs  ------------------------------ 46.88 KiB/61.67 KiB\r\nsimplejson ------------------------------ 62.91 KiB/73.89 KiB\r\nhttpcore  ------------------------------ 64.00 KiB/76.71 KiB\r\nipython  ------------------------------ 574.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 547.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 590.70 KiB/1.28 MiB\r\njedi  ------------------------------ 572.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 520.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 572.17 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 502.72 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 520.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 543.89 KiB/2.37 MiB\r\nfonttools  ------------------------------ 542.23 KiB/2.61 MiB\r\njax  ------------------------------ 574.05 KiB/2.77 MiB\r\npillow  ------------------------------ 548.21 KiB/2.96 MiB\r\nsphinx  ------------------------------ 552.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 495.86 KiB/4.10 MiB\r\nnumpy  ------------------------------ 510.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 541.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 508.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 557.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 574.80 KiB/11.14 MiB\r\nruff  ------------------------------ 556.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 526.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 540.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 509.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 540.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 477.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 557.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (40/162)\r\npackaging  ------------------------------ 29.80 KiB/52.72 KiB\r\nprometheus-client ------------------------------ 32.00 KiB/53.40 KiB\r\nmdit-py-plugins ------------------------------ 30.87 KiB/54.02 KiB\r\ntoolz  ------------------------------ 32.00 KiB/56.73 KiB\r\nh11  ------------------------------ 31.78 KiB/56.89 KiB\r\nwebsocket-client ------------------------------ 32.00 KiB/57.45 KiB\r\njupyterlab-server ------------------------------ 32.00 KiB/58.30 KiB\r\nattrs  ------------------------------ 46.88 KiB/61.67 KiB\r\nsimplejson ------------------------------ 62.91 KiB/73.89 KiB\r\nipython  ------------------------------ 590.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 547.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 590.70 KiB/1.28 MiB\r\njedi  ------------------------------ 572.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 520.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 588.17 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 518.72 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 520.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 543.89 KiB/2.37 MiB\r\nfonttools  ------------------------------ 558.23 KiB/2.61 MiB\r\njax  ------------------------------ 574.05 KiB/2.77 MiB\r\npillow  ------------------------------ 548.21 KiB/2.96 MiB\r\nsphinx  ------------------------------ 568.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 495.86 KiB/4.10 MiB\r\nnumpy  ------------------------------ 510.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 557.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 508.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 557.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 590.80 KiB/11.14 MiB\r\nruff  ------------------------------ 572.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 526.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 555.95 KiB/12.53 MiB\r\ntensorstore ------------------------------ 525.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 540.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 493.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 573.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (49/162)\r\npackaging  ------------------------------ 45.80 KiB/52.72 KiB\r\nprometheus-client ------------------------------ 48.00 KiB/53.40 KiB\r\nmdit-py-plugins ------------------------------ 46.87 KiB/54.02 KiB\r\ntoolz  ------------------------------ 47.89 KiB/56.73 KiB\r\nh11  ------------------------------ 47.78 KiB/56.89 KiB\r\nwebsocket-client ------------------------------ 48.00 KiB/57.45 KiB\r\njupyterlab-server ------------------------------ 48.00 KiB/58.30 KiB\r\nattrs  ------------------------------ 61.67 KiB/61.67 KiB\r\nipython  ------------------------------ 590.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 563.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 596.82 KiB/1.28 MiB\r\njedi  ------------------------------ 588.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 536.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 588.17 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 518.72 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 536.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 559.89 KiB/2.37 MiB\r\nfonttools  ------------------------------ 558.23 KiB/2.61 MiB\r\njax  ------------------------------ 574.05 KiB/2.77 MiB\r\npillow  ------------------------------ 564.21 KiB/2.96 MiB\r\nsphinx  ------------------------------ 568.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 499.04 KiB/4.10 MiB\r\nnumpy  ------------------------------ 526.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 557.92 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 524.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 573.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 590.80 KiB/11.14 MiB\r\nruff  ------------------------------ 572.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 526.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 555.95 KiB/12.53 MiB\r\ntensorstore ------------------------------ 525.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 556.78 KiB/21.34 MiB\r\njaxlib  -\n[... 34662 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +44,72952,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (49/162)\r\npytest-xdist ------------------------------ 32.00 KiB/45.03 KiB\r\nargon2-cffi-bindings ------------------------------ 32.00 KiB/51.86 KiB\r\nmistune  ------------------------------ 32.00 KiB/52.44 KiB\r\nipython  ------------------------------ 638.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 576.01 KiB/909.02 KiB\r\npyzmq  ------------------------------ 628.82 KiB/1.28 MiB\r\njedi  ------------------------------ 620.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 568.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 622.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 558.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 562.92 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 592.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 606.23 KiB/2.61 MiB\r\njax  ------------------------------ 622.05 KiB/2.77 MiB\r\npillow  ------------------------------ 590.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 605.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 520.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 574.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 590.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 556.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 605.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 622.91 KiB/11.14 MiB\r\nruff  ------------------------------ 620.26 KiB/11.24 MiB\r\nmypy  ------------------------------ 574.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 588.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 573.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 588.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 541.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 605.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (49/162)\r\nargon2-cffi-bindings ------------------------------ 32.00 KiB/51.86 KiB\r\nmistune  ------------------------------ 48.00 KiB/52.44 KiB\r\nipython  ------------------------------ 638.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 576.01 KiB/909.02 KiB\r\npyzmq  ------------------------------ 631.68 KiB/1.28 MiB\r\njedi  ------------------------------ 636.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 584.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 622.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 558.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 578.92 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 592.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 606.23 KiB/2.61 MiB\r\njax  ------------------------------ 622.05 KiB/2.77 MiB\r\npillow  ------------------------------ 590.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 605.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 536.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 574.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 590.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 572.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 621.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 622.91 KiB/11.14 MiB\r\nruff  ------------------------------ 620.26 KiB/11.24 MiB\r\nmypy  ------------------------------ 574.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 588.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 573.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 588.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 541.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 605.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (49/162)\r\nargon2-cffi-bindings ------------------------------ 48.00 KiB/51.86 KiB\r\nipython  ------------------------------ 654.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 592.01 KiB/909.02 KiB\r\npyzmq  ------------------------------ 631.68 KiB/1.28 MiB\r\njedi  ------------------------------ 636.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 584.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 638.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 574.80 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 584.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 608.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 622.23 KiB/2.61 MiB\r\njax  ------------------------------ 638.05 KiB/2.77 MiB\r\npillow  ------------------------------ 606.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 621.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 552.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 590.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 606.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 572.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 621.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 638.80 KiB/11.14 MiB\r\nruff  ------------------------------ 636.26 KiB/11.24 MiB\r\nmypy  ------------------------------ 590.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 604.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 589.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 604.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 557.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 621.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (49/162)\r\nargon2-cffi-bindings ------------------------------ 48.00 KiB/51.86 KiB\r\nipython  ------------------------------ 654.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 592.01 KiB/909.02 KiB\r\npyzmq  ------------------------------ 647.68 KiB/1.28 MiB\r\njedi  ------------------------------ 636.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 584.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 638.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 574.80 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 584.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 608.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 622.23 KiB/2.61 MiB\r\njax  ------------------------------ 638.05 KiB/2.77 MiB\r\npillow  ------------------------------ 606.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 621.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 552.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 590.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 606.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 572.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 621.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 638.80 KiB/11.14 MiB\r\nruff  ------------------------------ 636.26 KiB/11.24 MiB\r\nmypy  ------------------------------ 590.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 604.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 589.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 604.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 557.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 621.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (49/162)\r\nipython  ------------------------------ 654.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 606.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 647.68 KiB/1.28 MiB\r\njedi  ------------------------------ 652.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 600.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 638.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 574.80 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 584.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 608.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 622.23 KiB/2.61 MiB\r\njax  ------------------------------ 638.05 KiB/2.77 MiB\r\npillow  ------------------------------ 622.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 621.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 552.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 590.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 606.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 578.37 KiB/8.19 MiB\r\nbabel  ------------------------------ 637.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 638.80 KiB/11.14 MiB\r\nruff  ------------------------------ 636.26 KiB/11.24 MiB\r\nmypy  ------------------------------ 590.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 604.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 589.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 620.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 557.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 621.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (61/162)\r\ntyping-extensions ------------------------------ 0 B/36.56 KiB\r\nsphinxcontrib-bibtex ------------------------------ 0 B/39.39 KiB\r\nexecnet  ------------------------------ 0 B/39.66 KiB\r\nipython  ------------------------------ 654.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 606.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 647.68 KiB/1.28 MiB\r\njedi  ------------------------------ 652.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 600.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 638.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 588.15 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 584.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 624.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 638.23 KiB/2.61 MiB\r\njax  ------------------------------ 654.05 KiB/2.77 MiB\r\npillow  ------------------------------ 622.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 636.89 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 552.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 590.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 622.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 578.37 KiB/8.19 MiB\r\nbabel  ------------------------------ 637.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 654.80 KiB/11.14 MiB\r\nruff  ------------------------------ 636.26 KiB/11.24 MiB\r\nmypy  ------------------------------ 606.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 620.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 589.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 620.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 557.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 637.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (61/162)\r\nsoupsieve  ------------------------------ 14.88 KiB/35.34 KiB\r\ntyping-extensions ------------------------------ 14.87 KiB/36.56 KiB\r\nsphinxcontrib-bibtex ------------------------------ 14.25 KiB/39.39 KiB\r\nexecnet  ------------------------------ 14.88 KiB/39.66 KiB\r\nipython  ------------------------------ 670.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 622.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 654.70 KiB/1.28 MiB\r\njedi  ------------------------------ 661.22 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 616.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 654.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 590.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 600.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 624.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 638.23 KiB/2.61 MiB\r\njax  ------------------------------ 670.05 KiB/2.77 MiB\r\npillow  ------------------------------ 638.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 648.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 568.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 606.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 638.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 594.37 KiB/8.19 MiB\r\nbabel  ------------------------------ 653.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 654.80 KiB/11.14 MiB\r\nruff  ------------------------------ 652.26 KiB/11.24 MiB\r\nmypy  ------------------------------ 622.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 636.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 605.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 636.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 573.80 KiB/52.42 MiB\r\ntorch  ------------------------------ 653.34 KiB/71.03 MiB ",,terminal_output +45,73092,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (61/162)\r\nsoupsieve  ------------------------------ 14.88 KiB/35.34 KiB\r\ntyping-extensions ------------------------------ 14.87 KiB/36.56 KiB\r\nsphinxcontrib-bibtex ------------------------------ 14.90 KiB/39.39 KiB\r\nexecnet  ------------------------------ 30.88 KiB/39.66 KiB\r\nipython  ------------------------------ 677.23 KiB/806.18 KiB\r\nsetuptools ------------------------------ 638.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 670.70 KiB/1.28 MiB\r\njedi  ------------------------------ 677.22 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 632.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 670.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 606.14 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 616.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 640.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 654.23 KiB/2.61 MiB\r\njax  ------------------------------ 686.05 KiB/2.77 MiB\r\npillow  ------------------------------ 654.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 648.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 584.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 622.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 638.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 610.37 KiB/8.19 MiB\r\nbabel  ------------------------------ 669.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 654.91 KiB/11.14 MiB\r\nruff  ------------------------------ 652.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 638.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 636.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 621.58 KiB/13.14 MiB\r\nscipy  ------------------------------ 652.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 573.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 653.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (61/162)\r\nsoupsieve  ------------------------------ 30.88 KiB/35.34 KiB\r\ntyping-extensions ------------------------------ 30.87 KiB/36.56 KiB\r\nsphinxcontrib-bibtex ------------------------------ 14.90 KiB/39.39 KiB\r\nexecnet  ------------------------------ 30.88 KiB/39.66 KiB\r\nipython  ------------------------------ 693.23 KiB/806.18 KiB\r\nsetuptools ------------------------------ 638.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 686.70 KiB/1.28 MiB\r\njedi  ------------------------------ 693.22 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 648.46 KiB/1.62 MiB\r\ntorchvision ------------------------------ 686.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 606.14 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 632.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 656.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 670.23 KiB/2.61 MiB\r\njax  ------------------------------ 686.05 KiB/2.77 MiB\r\npillow  ------------------------------ 654.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 664.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 600.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 638.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 654.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 626.37 KiB/8.19 MiB\r\nbabel  ------------------------------ 669.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 670.91 KiB/11.14 MiB\r\nruff  ------------------------------ 668.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 638.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 652.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 637.58 KiB/13.14 MiB\r\nscipy  ------------------------------ 652.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 589.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 669.24 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (61/162)\r\nsoupsieve  ------------------------------ 35.34 KiB/35.34 KiB\r\nsphinxcontrib-bibtex ------------------------------ 30.90 KiB/39.39 KiB\r\nexecnet  ------------------------------ 39.66 KiB/39.66 KiB\r\nipython  ------------------------------ 700.34 KiB/806.18 KiB\r\nsetuptools ------------------------------ 654.58 KiB/909.02 KiB\r\npyzmq  ------------------------------ 686.70 KiB/1.28 MiB\r\njedi  ------------------------------ 693.22 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 648.46 KiB/1.62 MiB\r\ntorchvision ------------------------------ 686.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 622.14 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 632.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 656.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 670.23 KiB/2.61 MiB\r\njax  ------------------------------ 702.05 KiB/2.77 MiB\r\npillow  ------------------------------ 670.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 680.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 600.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 638.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 670.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 626.37 KiB/8.19 MiB\r\nbabel  ------------------------------ 685.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 670.91 KiB/11.14 MiB\r\nruff  ------------------------------ 668.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 654.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 652.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 637.58 KiB/13.14 MiB\r\nscipy  ------------------------------ 668.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 589.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 669.24 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (61/162)\r\nsphinxcontrib-bibtex ------------------------------ 30.90 KiB/39.39 KiB\r\nexecnet  ------------------------------ 39.66 KiB/39.66 KiB\r\nipython  ------------------------------ 700.34 KiB/806.18 KiB\r\nsetuptools ------------------------------ 654.58 KiB/909.02 KiB\r\npyzmq  ------------------------------ 686.70 KiB/1.28 MiB\r\njedi  ------------------------------ 693.22 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 648.46 KiB/1.62 MiB\r\ntorchvision ------------------------------ 686.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 622.14 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 632.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 656.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 670.23 KiB/2.61 MiB\r\njax  ------------------------------ 702.05 KiB/2.77 MiB\r\npillow  ------------------------------ 670.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 680.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 600.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 638.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 670.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 626.37 KiB/8.19 MiB\r\nbabel  ------------------------------ 685.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 670.91 KiB/11.14 MiB\r\nruff  ------------------------------ 668.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 654.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 668.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 637.58 KiB/13.14 MiB\r\nscipy  ------------------------------ 668.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 589.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 685.24 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (61/162)\r\nsphinxcontrib-bibtex ------------------------------ 30.90 KiB/39.39 KiB\r\nipython  ------------------------------ 700.34 KiB/806.18 KiB\r\nsetuptools ------------------------------ 654.58 KiB/909.02 KiB\r\npyzmq  ------------------------------ 686.70 KiB/1.28 MiB\r\njedi  ------------------------------ 693.22 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 648.46 KiB/1.62 MiB\r\ntorchvision ------------------------------ 686.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 622.14 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 632.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 672.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 686.23 KiB/2.61 MiB\r\njax  ------------------------------ 702.05 KiB/2.77 MiB\r\npillow  ------------------------------ 670.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 680.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 600.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 638.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 670.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 626.37 KiB/8.19 MiB\r\nbabel  ------------------------------ 685.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 670.91 KiB/11.14 MiB\r\nruff  ------------------------------ 668.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 654.91 KiB/11.36 MiB\r\nnotebook  ------------------------------ 668.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 637.58 KiB/13.14 MiB\r\nscipy  ------------------------------ 668.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 589.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 685.24 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... 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(64/162)\r\nipython  ------------------------------ 716.34 KiB/806.18 KiB\r\nsetuptools ------------------------------ 670.58 KiB/909.02 KiB\r\npyzmq  ------------------------------ 702.70 KiB/1.28 MiB\r\njedi  ------------------------------ 700.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 648.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 702.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 638.14 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 648.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 672.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 686.23 KiB/2.61 MiB\r\njax  ------------------------------ 718.05 KiB/2.77 MiB\r\npillow  ------------------------------ 686.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 696.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 616.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 654.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 686.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 632.09 KiB/8.19 MiB\r\nbabel  ------------------------------ 693.58 KiB/9.71 MiB\r\njupyterlab ------------------------------ 686.91 KiB/11.14 MiB\r\nruff  ------------------------------ 684.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 662.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 668.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 653.58 KiB/13.14 MiB\r\nscipy  ------------------------------ 684.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 605.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 701.24 KiB/71.03 MiB ",,terminal_output +46,73799,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 0 B/26.08 KiB\r\nreferencing ------------------------------ 16.00 KiB/26.15 KiB\r\nasttokens  ------------------------------ 16.00 KiB/26.29 KiB\r\nimportlib-metadata ------------------------------ 16.00 KiB/27.01 KiB\r\njupyter-core ------------------------------ 14.88 KiB/28.29 KiB\r\nmpltern  ------------------------------ 14.91 KiB/32.14 KiB\r\njupyter-cache ------------------------------ 16.00 KiB/33.11 KiB\r\njson5  ------------------------------ 13.34 KiB/33.25 KiB\r\ntabulate  ------------------------------ 16.00 KiB/34.43 KiB\r\nipython  ------------------------------ 718.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 670.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 734.70 KiB/1.28 MiB\r\njedi  ------------------------------ 732.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 664.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 734.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 654.14 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 664.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 690.10 KiB/2.37 MiB\r\nfonttools  ------------------------------ 718.23 KiB/2.61 MiB\r\njax  ------------------------------ 734.05 KiB/2.77 MiB\r\npillow  ------------------------------ 702.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 712.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 632.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 670.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 702.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 636.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 701.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 718.91 KiB/11.14 MiB\r\nruff  ------------------------------ 700.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 678.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 689.58 KiB/12.53 MiB\r\ntensorstore ------------------------------ 669.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 692.74 KiB/21.34 MiB\r\njaxlib  ------------------------------ 633.21 KiB/52.42 MiB\r\ntorch  ------------------------------ 717.24 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 14.88 KiB/26.08 KiB\r\nasttokens  ------------------------------ 20.76 KiB/26.29 KiB\r\nimportlib-metadata ------------------------------ 27.01 KiB/27.01 KiB\r\njupyter-core ------------------------------ 14.88 KiB/28.29 KiB\r\nmpltern  ------------------------------ 14.91 KiB/32.14 KiB\r\njupyter-cache ------------------------------ 32.00 KiB/33.11 KiB\r\njson5  ------------------------------ 29.34 KiB/33.25 KiB\r\ntabulate  ------------------------------ 32.00 KiB/34.43 KiB\r\nipython  ------------------------------ 734.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 686.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 734.70 KiB/1.28 MiB\r\njedi  ------------------------------ 732.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 680.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 734.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 654.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 680.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 690.10 KiB/2.37 MiB\r\nfonttools  ------------------------------ 718.23 KiB/2.61 MiB\r\njax  ------------------------------ 749.17 KiB/2.77 MiB\r\npillow  ------------------------------ 718.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 728.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 647.75 KiB/4.10 MiB\r\nnumpy  ------------------------------ 686.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 718.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 652.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 717.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 718.91 KiB/11.14 MiB\r\nruff  ------------------------------ 716.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 694.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 705.58 KiB/12.53 MiB\r\ntensorstore ------------------------------ 669.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 708.74 KiB/21.34 MiB\r\njaxlib  ------------------------------ 633.21 KiB/52.42 MiB\r\ntorch  ------------------------------ 717.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 14.88 KiB/26.08 KiB\r\nasttokens  ------------------------------ 20.76 KiB/26.29 KiB\r\njupyter-core ------------------------------ 14.88 KiB/28.29 KiB\r\nmpltern  ------------------------------ 14.91 KiB/32.14 KiB\r\njupyter-cache ------------------------------ 32.00 KiB/33.11 KiB\r\njson5  ------------------------------ 29.34 KiB/33.25 KiB\r\ntabulate  ------------------------------ 32.00 KiB/34.43 KiB\r\nipython  ------------------------------ 734.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 686.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 734.70 KiB/1.28 MiB\r\njedi  ------------------------------ 732.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 680.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 734.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 654.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 680.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 690.10 KiB/2.37 MiB\r\nfonttools  ------------------------------ 718.23 KiB/2.61 MiB\r\njax  ------------------------------ 749.17 KiB/2.77 MiB\r\npillow  ------------------------------ 718.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 728.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 647.75 KiB/4.10 MiB\r\nnumpy  ------------------------------ 686.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 718.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 652.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 717.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 718.91 KiB/11.14 MiB\r\nruff  ------------------------------ 716.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 694.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 705.58 KiB/12.53 MiB\r\ntensorstore ------------------------------ 669.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 708.74 KiB/21.34 MiB\r\njaxlib  ------------------------------ 633.21 KiB/52.42 MiB\r\ntorch  ------------------------------ 717.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 14.88 KiB/26.08 KiB\r\nasttokens  ------------------------------ 20.76 KiB/26.29 KiB\r\njupyter-core ------------------------------ 14.88 KiB/28.29 KiB\r\nmpltern  ------------------------------ 30.91 KiB/32.14 KiB\r\njupyter-cache ------------------------------ 32.00 KiB/33.11 KiB\r\njson5  ------------------------------ 29.34 KiB/33.25 KiB\r\ntabulate  ------------------------------ 32.00 KiB/34.43 KiB\r\nipython  ------------------------------ 734.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 686.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 734.70 KiB/1.28 MiB\r\njedi  ------------------------------ 732.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 680.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 734.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 654.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 680.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 704.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 718.23 KiB/2.61 MiB\r\njax  ------------------------------ 749.17 KiB/2.77 MiB\r\npillow  ------------------------------ 718.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 728.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 647.75 KiB/4.10 MiB\r\nnumpy  ------------------------------ 686.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 718.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 652.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 717.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 718.91 KiB/11.14 MiB\r\nruff  ------------------------------ 716.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 694.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 705.58 KiB/12.53 MiB\r\ntensorstore ------------------------------ 669.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 708.74 KiB/21.34 MiB\r\njaxlib  ------------------------------ 633.21 KiB/52.42 MiB\r\ntorch  ------------------------------ 717.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 14.88 KiB/26.08 KiB\r\nasttokens  ------------------------------ 20.76 KiB/26.29 KiB\r\nmpltern  ------------------------------ 30.91 KiB/32.14 KiB\r\njupyter-cache ------------------------------ 32.00 KiB/33.11 KiB\r\njson5  ------------------------------ 29.34 KiB/33.25 KiB\r\ntabulate  ------------------------------ 32.00 KiB/34.43 KiB\r\nipython  ------------------------------ 734.88 KiB/806.18 KiB\r\nsetuptools ------------------------------ 702.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 750.70 KiB/1.28 MiB\r\njedi  ------------------------------ 748.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 680.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 745.92 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 654.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 696.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 704.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 734.23 KiB/2.61 MiB\r\njax  ------------------------------ 749.17 KiB/2.77 MiB\r\npillow  ------------------------------ 718.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 728.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 648.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 686.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 718.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 668.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 717.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 734.91 KiB/11.14 MiB\r\nruff  ------------------------------ 732.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 694.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 705.58 KiB/12.53 MiB\r\ntensorstore ------------------------------ 685.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 708.74 KiB/21.34 MiB\r\njaxlib  ------------------------------ 637.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 717.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 14.88 KiB/26.08 KiB\r\nasttokens  ------------------------------ 26.29 KiB/26.29 KiB\r\nmpltern  ------------------------------ 32.14 KiB/32.14 KiB\r\njson5  ------------------------------ 33.25 KiB/33.25 KiB\r\ntabulate  ------------------------------ 34.43 KiB/34.43 KiB\r\nipython  ------------------------------ 741.90 KiB/806.18 KiB\r\nsetuptools ------------------------------ 702.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 750.70 KiB/1.28 MiB\r\njedi  ------------------------------ 748.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 690.92 KiB/1.62 MiB\r\ntorchvision ------------------------------ 745.92 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 670.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 696.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 704.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 734.23 KiB/2.61 MiB\r\njax  ------------------------------ 765.17 KiB/2.77 MiB\r\npillow  ------------------------------ 734.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 744.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 648.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 702.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 734.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 668.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 733.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 734.91 KiB/11.14 MiB\r\nruff  ------------------------------ 732.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 710.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 716.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 685.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 716.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 637.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 733.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 14.88 KiB/26.08 KiB\r\nasttokens  ------------------------------ 26.29 KiB/26.29 KiB\r\njson5  ------------------------------ 33.25 KiB/33.25 KiB\r\ntabulate  ------------------------------ 34.43 KiB/34.43 KiB\r\nipython  ------------------------------ 741.90 KiB/806.18 KiB\r\nsetuptools ------------------------------ 702.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 750.70 KiB/1.28 MiB\r\njedi  ------------------------------ 748.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 690.92 KiB/1.62 MiB\r\ntorchvision ------------------------------ 745.92 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 670.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 696.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 704.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 734.23 KiB/2.61 MiB\r\njax  ------------------------------ 765.17 KiB/2.77 MiB\r\npillow  ------------------------------ 734.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 744.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 648.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 702.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 734.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 668.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 733.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 734.91 KiB/11.14 MiB\r\nruff  ------------------------------ 732.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 710.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 716.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 685.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 716.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 637.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 733.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 14.88 KiB/26.08 KiB\r\nasttokens  ------------------------------ 26.29 KiB/26.29 KiB\r\njson5  ------------------------------ 33.25 KiB/33.25 KiB\r\nipython  ------------------------------ 741.90 KiB/806.18 KiB\r\nsetuptools ------------------------------ 702.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 750.70 KiB/1.28 MiB\r\njedi  ------------------------------ 748.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 690.92 KiB/1.62 MiB\r\ntorchvision ------------------------------ 745.92 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 670.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 696.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 704.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 750.23 KiB/2.61 MiB\r\njax  ------------------------------ 765.17 KiB/2.77 MiB\r\npillow  ------------------------------ 734.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 744.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 648.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 702.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 734.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 668.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 733.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 734.91 KiB/11.14 MiB\r\nruff  ------------------------------ 732.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 710.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 716.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 685.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 716.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 637.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 733.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 14.88 KiB/26.08 KiB\r\nasttokens  ------------------------------ 26.29 KiB/26.29 KiB\r\nipython  ------------------------------ 741.90 KiB/806.18 KiB\r\nsetuptools ------------------------------ 702.69 KiB/909.02 KiB\r\npyzmq  ------------------------------ 750.70 KiB/1.28 MiB\r\njedi  ------------------------------ 748.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 690.92 KiB/1.62 MiB\r\ntorchvision ------------------------------ 745.92 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 670.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 696.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 704.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 750.23 KiB/2.61 MiB\r\njax  ------------------------------ 765.17 KiB/2.77 MiB\r\npillow  ------------------------------ 734.36 KiB/2.96 MiB\r\nsphinx  ------------------------------ 744.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 648.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 702.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 734.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 668.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 733.50 KiB/9.71 MiB\r\njupyterlab ------------------------------ 734.91 KiB/11.14 MiB\r\nruff  ------------------------------ 732.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 710.30 KiB/11.36 MiB\r\nnotebook  ------------------------------ 716.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 685.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 716.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 637.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 733.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (64/162)\r\nexecuting  ------------------------------ 14.88 KiB/26.08 KiB\r\nipython  ------------------------------ 741.90 KiB/806.18 KiB\r\nsetuptools ------------------------------ 702.69 KiB/909.02 KiB\r\npyzmq  -------------------------\n[... 117903 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +47,73893,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (95/162)\r\nmarkupsafe ------------------------------ 0 B/12.06 KiB\r\nnotebook-shim ------------------------------ 0 B/13.00 KiB\r\nsphinx-copybutton ------------------------------ 0 B/13.03 KiB\r\njupyter-server-terminals ------------------------------ 0 B/13.34 KiB\r\nalabaster  ------------------------------ 0 B/13.60 KiB\r\nptyprocess ------------------------------ 0 B/13.67 KiB\r\nsetuptools ------------------------------ 909.02 KiB/909.02 KiB\r\npyzmq  ------------------------------ 974.70 KiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 904.34 KiB/1.62 MiB\r\ntorchvision ------------------------------ 958.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 862.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 888.34 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 880.49 KiB/2.37 MiB\r\nfonttools  ------------------------------ 958.23 KiB/2.61 MiB\r\njax  ------------------------------ 942.05 KiB/2.77 MiB\r\npillow  ------------------------------ 942.14 KiB/2.96 MiB\r\nsphinx  ------------------------------ 920.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 833.84 KiB/4.10 MiB\r\nnumpy  ------------------------------ 910.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 926.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 860.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 922.27 KiB/9.71 MiB\r\njupyterlab ------------------------------ 958.91 KiB/11.14 MiB\r\nruff  ------------------------------ 940.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 912.46 KiB/11.36 MiB\r\nnotebook  ------------------------------ 908.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 893.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 924.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 830.10 KiB/52.42 MiB\r\ntorch  ------------------------------ 925.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (95/162)\r\nmatplotlib-inline ------------------------------ 0 B/9.67 KiB\r\nuri-template ------------------------------ 0 B/10.88 KiB\r\nisoduration ------------------------------ 0 B/11.06 KiB\r\nfilelock  ------------------------------ 0 B/11.46 KiB\r\nwebencodings ------------------------------ 0 B/11.50 KiB\r\npure-eval  ------------------------------ 0 B/11.56 KiB\r\nmarkupsafe ------------------------------ 0 B/12.06 KiB\r\nnotebook-shim ------------------------------ 0 B/13.00 KiB\r\nsphinx-copybutton ------------------------------ 0 B/13.03 KiB\r\njupyter-server-terminals ------------------------------ 0 B/13.34 KiB\r\nalabaster  ------------------------------ 0 B/13.60 KiB\r\nptyprocess ------------------------------ 0 B/13.67 KiB\r\npyzmq  ------------------------------ 974.70 KiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 904.34 KiB/1.62 MiB\r\ntorchvision ------------------------------ 958.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 862.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 888.34 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 880.49 KiB/2.37 MiB\r\nfonttools  ------------------------------ 958.23 KiB/2.61 MiB\r\njax  ------------------------------ 942.05 KiB/2.77 MiB\r\npillow  ------------------------------ 942.14 KiB/2.96 MiB\r\nsphinx  ------------------------------ 920.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 833.84 KiB/4.10 MiB\r\nnumpy  ------------------------------ 910.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 926.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 860.56 KiB/8.19 MiB\r\nbabel  ------------------------------ 922.27 KiB/9.71 MiB\r\njupyterlab ------------------------------ 958.91 KiB/11.14 MiB\r\nruff  ------------------------------ 940.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 912.46 KiB/11.36 MiB\r\nnotebook  ------------------------------ 908.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 893.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 924.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 830.10 KiB/52.42 MiB\r\ntorch  ------------------------------ 925.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (95/162)\r\nmatplotlib-inline ------------------------------ 0 B/9.67 KiB\r\nuri-template ------------------------------ 0 B/10.88 KiB\r\nisoduration ------------------------------ 0 B/11.06 KiB\r\nfilelock  ------------------------------ 0 B/11.46 KiB\r\nwebencodings ------------------------------ 0 B/11.50 KiB\r\npure-eval  ------------------------------ 0 B/11.56 KiB\r\nmarkupsafe ------------------------------ 0 B/12.06 KiB\r\nnotebook-shim ------------------------------ 0 B/13.00 KiB\r\nsphinx-copybutton ------------------------------ 0 B/13.03 KiB\r\njupyter-server-terminals ------------------------------ 0 B/13.34 KiB\r\nalabaster  ------------------------------ 0 B/13.60 KiB\r\nptyprocess ------------------------------ 0 B/13.67 KiB\r\npyzmq  ------------------------------ 974.70 KiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 904.34 KiB/1.62 MiB\r\ntorchvision ------------------------------ 1022.37 KiB/1.80 MiB\r\nsqlalchemy ------------------------------ 862.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 904.34 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 880.49 KiB/2.37 MiB\r\nfonttools  ------------------------------ 1022.23 KiB/2.61 MiB\r\njax  ------------------------------ 942.05 KiB/2.77 MiB\r\npillow  ------------------------------ 942.14 KiB/2.96 MiB\r\nsphinx  ------------------------------ 936.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 904.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 965.17 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 926.03 KiB/7.67 MiB\r\nscikit-learn ------------------------------ 940.46 KiB/8.19 MiB\r\nbabel  ------------------------------ 922.27 KiB/9.71 MiB\r\njupyterlab ------------------------------ 1.01 MiB/11.14 MiB\r\nruff  ------------------------------ 940.37 KiB/11.24 MiB\r\nmypy  ------------------------------ 912.46 KiB/11.36 MiB\r\nnotebook  ------------------------------ 972.06 KiB/12.53 MiB\r\ntensorstore ------------------------------ 973.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 940.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 830.10 KiB/52.42 MiB\r\ntorch  ------------------------------ 941.34 KiB/71.03 MiB ",,terminal_output +48,74145,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (99/162)\r\npandocfilters ------------------------------ 8.46 KiB/8.46 KiB\r\nimagesize  ------------------------------ 8.56 KiB/8.56 KiB\r\ndecorator  ------------------------------ 8.86 KiB/8.86 KiB\r\nfqdn  ------------------------------ 8.91 KiB/8.91 KiB\r\nmatplotlib-inline ------------------------------ 9.67 KiB/9.67 KiB\r\nuri-template ------------------------------ 10.88 KiB/10.88 KiB\r\r\nisoduration ------------------------------ 11.06 KiB/11.06 KiB\r\nfilelock  ------------------------------ 11.46 KiB/11.46 KiB\r\nwebencodings ------------------------------ 11.50 KiB/11.50 KiB\r\npure-eval  ------------------------------ 11.56 KiB/11.56 KiB\r\nmarkupsafe ------------------------------ 12.06 KiB/12.06 KiB\r\nnotebook-shim ------------------------------ 13.00 KiB/13.00 KiB\r\nsphinx-copybutton ------------------------------ 13.03 KiB/13.03 KiB\r\njupyter-server-terminals ------------------------------ 13.34 KiB/13.34 KiB\r\nalabaster  ------------------------------ 13.60 KiB/13.60 KiB\r\nptyprocess ------------------------------ 13.67 KiB/13.67 KiB\r\npyzmq  ------------------------------ 1.06 MiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 920.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 1.05 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 878.94 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 979.56 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 896.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 1.05 MiB/2.61 MiB\r\njax  ------------------------------ 990.05 KiB/2.77 MiB\r\npillow  ------------------------------ 1.01 MiB/2.96 MiB\r\nsphinx  ------------------------------ 952.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 952.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 1006.91 KiB/4.84 MiB\r\nmatplotlib ------------------------------ 1.01 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 972.46 KiB/8.19 MiB\r\nbabel  ------------------------------ 988.76 KiB/9.71 MiB\r\njupyterlab ------------------------------ 1.03 MiB/11.14 MiB\r\nruff  ------------------------------ 1.01 MiB/11.24 MiB\r\nmypy  ------------------------------ 1022.72 KiB/11.36 MiB\r\nnotebook  ------------------------------ 1019.84 KiB/12.53 MiB\r\ntensorstore ------------------------------ 1021.91 KiB/13.14 MiB\r\nscipy  ------------------------------ 940.78 KiB/21.34 MiB\r\njaxlib  ------------------------------ 941.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 957.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (99/162)\r\nimagesize  ------------------------------ 8.56 KiB/8.56 KiB\r\ndecorator  ------------------------------ 8.86 KiB/8.86 KiB\r\nfqdn  ------------------------------ 8.91 KiB/8.91 KiB\r\nmatplotlib-inline ------------------------------ 9.67 KiB/9.67 KiB\r\nuri-template ------------------------------ 10.88 KiB/10.88 KiB\r\nisoduration ------------------------------ 11.06 KiB/11.06 KiB\r\nfilelock  ------------------------------ 11.46 KiB/11.46 KiB\r\nwebencodings ------------------------------ 11.50 KiB/11.50 KiB\r\npure-eval  ------------------------------ 11.56 KiB/11.56 KiB\r\nmarkupsafe ------------------------------ 12.06 KiB/12.06 KiB\r\nnotebook-shim ------------------------------ 13.00 KiB/13.00 KiB\r\nsphinx-copybutton ------------------------------ 13.03 KiB/13.03 KiB\r\njupyter-server-terminals ------------------------------ 13.34 KiB/13.34 KiB\r\nalabaster  ------------------------------ 13.60 KiB/13.60 KiB\r\nptyprocess ------------------------------ 13.67 KiB/13.67 KiB\r\npyzmq  ------------------------------ 1.08 MiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 920.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 1.08 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 942.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1000.45 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 912.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 1.06 MiB/2.61 MiB\r\njax  ------------------------------ 990.05 KiB/2.77 MiB\r\npillow  ------------------------------ 1.05 MiB/2.96 MiB\r\nsphinx  ------------------------------ 979.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 963.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 1.01 MiB/4.84 MiB\r\nmatplotlib ------------------------------ 1.04 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1004.01 KiB/8.19 MiB\r\nbabel  ------------------------------ 988.76 KiB/9.71 MiB\r\njupyterlab ------------------------------ 1.06 MiB/11.14 MiB\r\nruff  ------------------------------ 1.03 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.01 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.01 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.02 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.03 MiB/21.34 MiB\r\njaxlib  ------------------------------ 973.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 1005.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (99/162)\r\nimagesize  ------------------------------ 8.56 KiB/8.56 KiB\r\nfqdn  ------------------------------ 8.91 KiB/8.91 KiB\r\nmatplotlib-inline ------------------------------ 9.67 KiB/9.67 KiB\r\nuri-template ------------------------------ 10.88 KiB/10.88 KiB\r\nisoduration ------------------------------ 11.06 KiB/11.06 KiB\r\nfilelock  ------------------------------ 11.46 KiB/11.46 KiB\r\nwebencodings ------------------------------ 11.50 KiB/11.50 KiB\r\npure-eval  ------------------------------ 11.56 KiB/11.56 KiB\r\nmarkupsafe ------------------------------ 12.06 KiB/12.06 KiB\r\nnotebook-shim ------------------------------ 13.00 KiB/13.00 KiB\r\nsphinx-copybutton ------------------------------ 13.03 KiB/13.03 KiB\r\njupyter-server-terminals ------------------------------ 13.34 KiB/13.34 KiB\r\nalabaster  ------------------------------ 13.60 KiB/13.60 KiB\r\nptyprocess ------------------------------ 13.67 KiB/13.67 KiB\r\npyzmq  ------------------------------ 1.08 MiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 920.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 1.08 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 942.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1000.45 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 912.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 1.06 MiB/2.61 MiB\r\njax  ------------------------------ 990.05 KiB/2.77 MiB\r\npillow  ------------------------------ 1.05 MiB/2.96 MiB\r\nsphinx  ------------------------------ 979.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 963.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 1.01 MiB/4.84 MiB\r\nmatplotlib ------------------------------ 1.04 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1004.01 KiB/8.19 MiB\r\nbabel  ------------------------------ 988.76 KiB/9.71 MiB\r\njupyterlab ------------------------------ 1.06 MiB/11.14 MiB\r\nruff  ------------------------------ 1.03 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.01 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.01 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.02 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.03 MiB/21.34 MiB\r\njaxlib  ------------------------------ 973.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 1005.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (99/162)\r\nimagesize  ------------------------------ 8.56 KiB/8.56 KiB\r\nfqdn  ------------------------------ 8.91 KiB/8.91 KiB\r\nmatplotlib-inline ------------------------------ 9.67 KiB/9.67 KiB\r\nuri-template ------------------------------ 10.88 KiB/10.88 KiB\r\nisoduration ------------------------------ 11.06 KiB/11.06 KiB\r\nfilelock  ------------------------------ 11.46 KiB/11.46 KiB\r\nwebencodings ------------------------------ 11.50 KiB/11.50 KiB\r\npure-eval  ------------------------------ 11.56 KiB/11.56 KiB\r\nmarkupsafe ------------------------------ 12.06 KiB/12.06 KiB\r\nnotebook-shim ------------------------------ 13.00 KiB/13.00 KiB\r\nsphinx-copybutton ------------------------------ 13.03 KiB/13.03 KiB\r\njupyter-server-terminals ------------------------------ 13.34 KiB/13.34 KiB\r\nalabaster  ------------------------------ 13.60 KiB/13.60 KiB\r\npyzmq  ------------------------------ 1.08 MiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 920.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 1.08 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 958.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1000.45 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 912.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 1.06 MiB/2.61 MiB\r\njax  ------------------------------ 990.05 KiB/2.77 MiB\r\npillow  ------------------------------ 1.05 MiB/2.96 MiB\r\nsphinx  ------------------------------ 979.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 963.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 1.01 MiB/4.84 MiB\r\nmatplotlib ------------------------------ 1.04 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1004.01 KiB/8.19 MiB\r\nbabel  ------------------------------ 988.76 KiB/9.71 MiB\r\njupyterlab ------------------------------ 1.06 MiB/11.14 MiB\r\nruff  ------------------------------ 1.03 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.01 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.01 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.02 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.03 MiB/21.34 MiB\r\njaxlib  ------------------------------ 973.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 1005.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (99/162)\r\nimagesize  ------------------------------ 8.56 KiB/8.56 KiB\r\nfqdn  ------------------------------ 8.91 KiB/8.91 KiB\r\nuri-template ------------------------------ 10.88 KiB/10.88 KiB\r\nisoduration ------------------------------ 11.06 KiB/11.06 KiB\r\nfilelock  ------------------------------ 11.46 KiB/11.46 KiB\r\nwebencodings ------------------------------ 11.50 KiB/11.50 KiB\r\npure-eval  ------------------------------ 11.56 KiB/11.56 KiB\r\nmarkupsafe ------------------------------ 12.06 KiB/12.06 KiB\r\nnotebook-shim ------------------------------ 13.00 KiB/13.00 KiB\r\nsphinx-copybutton ------------------------------ 13.03 KiB/13.03 KiB\r\njupyter-server-terminals ------------------------------ 13.34 KiB/13.34 KiB\r\nalabaster  ------------------------------ 13.60 KiB/13.60 KiB\r\npyzmq  ------------------------------ 1.08 MiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 920.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 1.08 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 958.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1000.45 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 912.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 1.06 MiB/2.61 MiB\r\njax  ------------------------------ 990.05 KiB/2.77 MiB\r\npillow  ------------------------------ 1.05 MiB/2.96 MiB\r\nsphinx  ------------------------------ 979.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 963.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 1.01 MiB/4.84 MiB\r\nmatplotlib ------------------------------ 1.04 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1004.01 KiB/8.19 MiB\r\nbabel  ------------------------------ 988.76 KiB/9.71 MiB\r\njupyterlab ------------------------------ 1.06 MiB/11.14 MiB\r\nruff  ------------------------------ 1.03 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.01 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.01 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.02 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.03 MiB/21.34 MiB\r\njaxlib  ------------------------------ 973.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 1021.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (99/162)\r\nimagesize  ------------------------------ 8.56 KiB/8.56 KiB\r\nuri-template ------------------------------ 10.88 KiB/10.88 KiB\r\nisoduration ------------------------------ 11.06 KiB/11.06 KiB\r\nfilelock  ------------------------------ 11.46 KiB/11.46 KiB\r\nwebencodings ------------------------------ 11.50 KiB/11.50 KiB\r\npure-eval  ------------------------------ 11.56 KiB/11.56 KiB\r\nmarkupsafe ------------------------------ 12.06 KiB/12.06 KiB\r\nnotebook-shim ------------------------------ 13.00 KiB/13.00 KiB\r\nsphinx-copybutton ------------------------------ 13.03 KiB/13.03 KiB\r\njupyter-server-terminals ------------------------------ 13.34 KiB/13.34 KiB\r\nalabaster  ------------------------------ 13.60 KiB/13.60 KiB\r\npyzmq  ------------------------------ 1.08 MiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 920.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 1.08 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 974.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1000.45 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 912.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 1.06 MiB/2.61 MiB\r\njax  ------------------------------ 1006.05 KiB/2.77 MiB\r\npillow  ------------------------------ 1.05 MiB/2.96 MiB\r\nsphinx  ------------------------------ 995.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 963.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 1.01 MiB/4.84 MiB\r\nmatplotlib ------------------------------ 1.04 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1004.01 KiB/8.19 MiB\r\nbabel  ------------------------------ 988.76 KiB/9.71 MiB\r\njupyterlab ------------------------------ 1.06 MiB/11.14 MiB\r\nruff  ------------------------------ 1.03 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.01 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.01 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.02 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.03 MiB/21.34 MiB\r\njaxlib  ------------------------------ 973.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 1021.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (99/162)\r\nuri-template ------------------------------ 10.88 KiB/10.88 KiB\r\nisoduration ------------------------------ 11.06 KiB/11.06 KiB\r\nfilelock  ------------------------------ 11.46 KiB/11.46 KiB\r\nwebencodings ------------------------------ 11.50 KiB/11.50 KiB\r\npure-eval  ------------------------------ 11.56 KiB/11.56 KiB\r\nmarkupsafe ------------------------------ 12.06 KiB/12.06 KiB\r\nnotebook-shim ------------------------------ 13.00 KiB/13.00 KiB\r\nsphinx-copybutton ------------------------------ 13.03 KiB/13.03 KiB\r\njupyter-server-terminals ------------------------------ 13.34 KiB/13.34 KiB\r\nalabaster  ------------------------------ 13.60 KiB/13.60 KiB\r\npyzmq  ------------------------------ 1.08 MiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 936.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 1.08 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 974.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1000.45 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 928.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 1.06 MiB/2.61 MiB\r\njax  ------------------------------ 1006.05 KiB/2.77 MiB\r\npillow  ------------------------------ 1.05 MiB/2.96 MiB\r\nsphinx  ------------------------------ 995.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 963.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 1.01 MiB/4.84 MiB\r\nmatplotlib ------------------------------ 1.04 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1004.01 KiB/8.19 MiB\r\nbabel  ------------------------------ 1004.76 KiB/9.71 MiB\r\njupyterlab ------------------------------ 1.06 MiB/11.14 MiB\r\nruff  ------------------------------ 1.03 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.01 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.01 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.02 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.03 MiB/21.34 MiB\r\njaxlib  ------------------------------ 973.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 1021.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... (99/162)\r\nuri-template ------------------------------ 10.88 KiB/10.88 KiB\r\nisoduration ------------------------------ 11.06 KiB/11.06 KiB\r\nfilelock  ------------------------------ 11.46 KiB/11.46 KiB\r\npure-eval  ------------------------------ 11.56 KiB/11.56 KiB\r\nmarkupsafe ------------------------------ 12.06 KiB/12.06 KiB\r\nnotebook-shim ------------------------------ 13.00 KiB/13.00 KiB\r\nsphinx-copybutton ------------------------------ 13.03 KiB/13.03 KiB\r\njupyter-server-terminals ------------------------------ 13.34 KiB/13.34 KiB\r\nalabaster  ------------------------------ 13.60 KiB/13.60 KiB\r\npyzmq  ------------------------------ 1.08 MiB/1.28 MiB\r\njedi  ------------------------------ 940.92 KiB/1.50 MiB\r\nnetworkx  ------------------------------ 936.56 KiB/1.62 MiB\r\ntorchvision ------------------------------ 1.08 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 974.91 KiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1000.45 KiB/2.23 MiB\r\ndebugpy  ------------------------------ 928.00 KiB/2.37 MiB\r\nfonttools  ------------------------------ 1.06 MiB/2.61 MiB\r\njax  ------------------------------ 1006.05 KiB/2.77 MiB\r\npillow  ------------------------------ 1.05 MiB/2.96 MiB\r\nsphinx  ------------------------------ 995.56 KiB/3.42 MiB\r\nvirtualenv ------------------------------ 963.56 KiB/4.10 MiB\r\nnumpy  ------------------------------ 1.01 MiB/4.84 MiB\r\nmatplotlib ------------------------------ 1.04 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1004.01 KiB/8.19 MiB\r\nbabel  ------------------------------ 1004.76 KiB/9.71 MiB\r\njupyterlab ------------------------------ 1.06 MiB/11.14 MiB\r\nruff  ------------------------------ 1.03 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.01 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.01 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.02 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.03 MiB/21.34 MiB\r\njaxlib  ------------------------------ 973.91 KiB/52.42 MiB\r\ntorch  ------------------------------ 1021.34 KiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r[\n[... 72057 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +49,74206,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... 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(125/162)\r\npyzmq  ------------------------------ 1.17 MiB/1.28 MiB\r\njedi  ------------------------------ 1.18 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.10 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.20 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.08 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.10 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.11 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.19 MiB/2.61 MiB\r\njax  ------------------------------ 1.19 MiB/2.77 MiB\r\npillow  ------------------------------ 1.15 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.16 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.07 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.12 MiB/4.84 MiB\r\nmatplotlib ------------------------------ 1.15 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.07 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.14 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.19 MiB/11.14 MiB\r\nruff  ------------------------------ 1.15 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.14 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.10 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.13 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.17 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.08 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.15 MiB/71.03 MiB ",,terminal_output +51,74452,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... 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(125/162)\r\nclick  ------------------------------ 0 B/99.82 KiB\r\nmpmath  ------------------------------ 0 B/523.63 KiB\r\npygments  ------------------------------ 16.00 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.20 MiB/1.28 MiB\r\njedi  ------------------------------ 1.21 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.13 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.23 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.11 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.13 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.12 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.20 MiB/2.61 MiB\r\njax  ------------------------------ 1.20 MiB/2.77 MiB\r\npillow  ------------------------------ 1.19 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.19 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.08 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.16 MiB/4.84 MiB\r\nsympy  ------------------------------ 16.00 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.19 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.11 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.17 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.20 MiB/11.14 MiB\r\nruff  ------------------------------ 1.17 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.17 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.13 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.15 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.20 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.10 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.19 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (125/162)\r\nmarkdown-it-py ------------------------------ 0 B/85.48 KiB\r\nclick  ------------------------------ 14.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 0 B/233.92 KiB\r\nmpmath  ------------------------------ 14.85 KiB/523.63 KiB\r\npygments  ------------------------------ 16.00 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.20 MiB/1.28 MiB\r\njedi  ------------------------------ 1.23 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.14 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.25 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.12 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.14 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.14 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.22 MiB/2.61 MiB\r\njax  ------------------------------ 1.21 MiB/2.77 MiB\r\npillow  ------------------------------ 1.20 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.21 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.10 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.16 MiB/4.84 MiB\r\nsympy  ------------------------------ 16.00 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.20 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.12 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.18 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.22 MiB/11.14 MiB\r\nruff  ------------------------------ 1.18 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.18 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.14 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.17 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.22 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.11 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.20 MiB/71.03 MiB ",,terminal_output +52,74640,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (126/162)\r\ncycler  ------------------------------ 0 B/8.13 KiB\r\npathspec  ------------------------------ 0 B/30.46 KiB\r\nwcwidth  ------------------------------ 0 B/33.37 KiB\r\nrequests  ------------------------------ 0 B/63.41 KiB\r\nmarkdown-it-py ------------------------------ 16.00 KiB/85.48 KiB\r\nclick  ------------------------------ 30.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 16.00 KiB/233.92 KiB\r\nmpmath  ------------------------------ 30.85 KiB/523.63 KiB\r\npygments  ------------------------------ 32.00 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.22 MiB/1.28 MiB\r\njedi  ------------------------------ 1.25 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.16 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.26 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.14 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.16 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.16 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.23 MiB/2.61 MiB\r\njax  ------------------------------ 1.22 MiB/2.77 MiB\r\npillow  ------------------------------ 1.22 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.22 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.11 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.17 MiB/4.84 MiB\r\nsympy  ------------------------------ 32.00 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.22 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.14 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.20 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.23 MiB/11.14 MiB\r\nruff  ------------------------------ 1.20 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.20 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.16 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.18 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.22 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.12 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.21 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (126/162)\r\ncycler  ------------------------------ 2.33 KiB/8.13 KiB\r\npathspec  ------------------------------ 0 B/30.46 KiB\r\nwcwidth  ------------------------------ 14.88 KiB/33.37 KiB\r\nrequests  ------------------------------ 14.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 32.00 KiB/85.48 KiB\r\nclick  ------------------------------ 30.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 16.00 KiB/233.92 KiB\r\nmpmath  ------------------------------ 30.85 KiB/523.63 KiB\r\npygments  ------------------------------ 48.00 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.22 MiB/1.28 MiB\r\njedi  ------------------------------ 1.25 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.17 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.26 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.14 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.16 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.17 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.25 MiB/2.61 MiB\r\njax  ------------------------------ 1.22 MiB/2.77 MiB\r\npillow  ------------------------------ 1.22 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.24 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.13 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.19 MiB/4.84 MiB\r\nsympy  ------------------------------ 48.00 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.22 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.14 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.20 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.25 MiB/11.14 MiB\r\nruff  ------------------------------ 1.20 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.20 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.16 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.19 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.23 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.12 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.21 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (126/162)\r\npathspec  ------------------------------ 14.88 KiB/30.46 KiB\r\nwcwidth  ------------------------------ 14.88 KiB/33.37 KiB\r\nrequests  ------------------------------ 30.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 32.00 KiB/85.48 KiB\r\nclick  ------------------------------ 46.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 29.01 KiB/233.92 KiB\r\ndistlib  ------------------------------ 0 B/457.98 KiB\r\nmpmath  ------------------------------ 46.85 KiB/523.63 KiB\r\npygments  ------------------------------ 62.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.23 MiB/1.28 MiB\r\njedi  ------------------------------ 1.25 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.17 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.26 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.16 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.17 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.19 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.26 MiB/2.61 MiB\r\njax  ------------------------------ 1.24 MiB/2.77 MiB\r\npillow  ------------------------------ 1.23 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.24 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.14 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.20 MiB/4.84 MiB\r\nsympy  ------------------------------ 62.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.23 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.15 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.21 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.26 MiB/11.14 MiB\r\nruff  ------------------------------ 1.22 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.22 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.18 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.19 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.25 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.14 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.23 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (126/162)\r\npathspec  ------------------------------ 14.88 KiB/30.46 KiB\r\nwcwidth  ------------------------------ 14.88 KiB/33.37 KiB\r\nrequests  ------------------------------ 30.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 48.00 KiB/85.48 KiB\r\nclick  ------------------------------ 46.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 29.01 KiB/233.92 KiB\r\ndistlib  ------------------------------ 0 B/457.98 KiB\r\nmpmath  ------------------------------ 46.85 KiB/523.63 KiB\r\npygments  ------------------------------ 62.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.23 MiB/1.28 MiB\r\njedi  ------------------------------ 1.25 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.17 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.26 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.16 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.17 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.19 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.26 MiB/2.61 MiB\r\njax  ------------------------------ 1.24 MiB/2.77 MiB\r\npillow  ------------------------------ 1.23 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.24 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.14 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.20 MiB/4.84 MiB\r\nsympy  ------------------------------ 62.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.23 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.15 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.21 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.26 MiB/11.14 MiB\r\nruff  ------------------------------ 1.22 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.22 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.18 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.19 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.25 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.14 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.23 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (126/162)\r\nwcwidth  ------------------------------ 30.88 KiB/33.37 KiB\r\nrequests  ------------------------------ 30.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 48.00 KiB/85.48 KiB\r\nclick  ------------------------------ 62.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 45.01 KiB/233.92 KiB\r\ndistlib  ------------------------------ 16.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 62.85 KiB/523.63 KiB\r\npygments  ------------------------------ 62.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.25 MiB/1.28 MiB\r\njedi  ------------------------------ 1.26 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.17 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.26 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.16 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.17 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.19 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.26 MiB/2.61 MiB\r\njax  ------------------------------ 1.25 MiB/2.77 MiB\r\npillow  ------------------------------ 1.24 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.25 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.14 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.20 MiB/4.84 MiB\r\nsympy  ------------------------------ 62.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.23 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.17 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.23 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.26 MiB/11.14 MiB\r\nruff  ------------------------------ 1.23 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.23 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.18 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.21 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.26 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.15 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.25 MiB/71.03 MiB ",,terminal_output +53,74794,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (126/162)\r\nwcwidth  ------------------------------ 30.88 KiB/33.37 KiB\r\nrequests  ------------------------------ 30.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 48.00 KiB/85.48 KiB\r\nclick  ------------------------------ 62.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 45.01 KiB/233.92 KiB\r\ndistlib  ------------------------------ 16.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 62.85 KiB/523.63 KiB\r\npygments  ------------------------------ 62.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.25 MiB/1.28 MiB\r\njedi  ------------------------------ 1.26 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.17 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.28 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.16 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.17 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.19 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.26 MiB/2.61 MiB\r\njax  ------------------------------ 1.25 MiB/2.77 MiB\r\npillow  ------------------------------ 1.24 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.25 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.14 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.20 MiB/4.84 MiB\r\nsympy  ------------------------------ 62.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.23 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.17 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.23 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.26 MiB/11.14 MiB\r\nruff  ------------------------------ 1.23 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.23 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.18 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.21 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.26 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.15 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.25 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ผ Preparing packages... (126/162)\r\nwcwidth  ------------------------------ 30.88 KiB/33.37 KiB\r\nrequests  ------------------------------ 30.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 48.00 KiB/85.48 KiB\r\nclick  ------------------------------ 62.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 45.01 KiB/233.92 KiB\r\ndistlib  ------------------------------ 16.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 62.85 KiB/523.63 KiB\r\npygments  ------------------------------ 62.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.25 MiB/1.28 MiB\r\njedi  ------------------------------ 1.26 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.17 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.28 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.16 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.17 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.19 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.26 MiB/2.61 MiB\r\njax  ------------------------------ 1.25 MiB/2.77 MiB\r\npillow  ------------------------------ 1.24 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.25 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.14 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.20 MiB/4.84 MiB\r\nsympy  ------------------------------ 62.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.25 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.17 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.23 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.26 MiB/11.14 MiB\r\nruff  ------------------------------ 1.23 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.23 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.18 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.21 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.26 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.15 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.25 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (128/162)\r\nrequests  ------------------------------ 46.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 63.34 KiB/85.48 KiB\r\nclick  ------------------------------ 62.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 48.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 16.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 62.85 KiB/523.63 KiB\r\npygments  ------------------------------ 78.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.25 MiB/1.28 MiB\r\njedi  ------------------------------ 1.28 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.19 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.28 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.16 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.19 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.20 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.28 MiB/2.61 MiB\r\njax  ------------------------------ 1.25 MiB/2.77 MiB\r\npillow  ------------------------------ 1.24 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.27 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.15 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.20 MiB/4.84 MiB\r\nsympy  ------------------------------ 78.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.25 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.17 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.23 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.26 MiB/11.14 MiB\r\nruff  ------------------------------ 1.25 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.25 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.19 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.22 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.26 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.17 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.25 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (128/162)\r\nrequests  ------------------------------ 46.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 63.34 KiB/85.48 KiB\r\nclick  ------------------------------ 62.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 48.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 32.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 78.17 KiB/523.63 KiB\r\npygments  ------------------------------ 78.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.26 MiB/1.28 MiB\r\njedi  ------------------------------ 1.28 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.19 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.28 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.16 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.19 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.20 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.28 MiB/2.61 MiB\r\njax  ------------------------------ 1.25 MiB/2.77 MiB\r\npillow  ------------------------------ 1.24 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.27 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.15 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.20 MiB/4.84 MiB\r\nsympy  ------------------------------ 78.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.25 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.17 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.23 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.26 MiB/11.14 MiB\r\nruff  ------------------------------ 1.25 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.25 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.19 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.22 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.26 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.17 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.25 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (128/162)\r\nrequests  ------------------------------ 62.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 64.00 KiB/85.48 KiB\r\nclick  ------------------------------ 78.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 48.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 32.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 78.17 KiB/523.63 KiB\r\npygments  ------------------------------ 94.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.26 MiB/1.28 MiB\r\njedi  ------------------------------ 1.28 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.21 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.30 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.17 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.21 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.22 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.30 MiB/2.61 MiB\r\njax  ------------------------------ 1.26 MiB/2.77 MiB\r\npillow  ------------------------------ 1.25 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.27 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.17 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.22 MiB/4.84 MiB\r\nsympy  ------------------------------ 94.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.26 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.18 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.23 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.28 MiB/11.14 MiB\r\nruff  ------------------------------ 1.25 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.26 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.21 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.22 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.26 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.17 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.26 MiB/71.03 MiB ",,terminal_output +54,74952,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (128/162)\r\nrequests  ------------------------------ 62.88 KiB/63.41 KiB\r\nmarkdown-it-py ------------------------------ 64.00 KiB/85.48 KiB\r\nclick  ------------------------------ 94.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 64.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 48.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 81.23 KiB/523.63 KiB\r\npygments  ------------------------------ 94.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.28 MiB/1.28 MiB\r\njedi  ------------------------------ 1.30 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.21 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.31 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.19 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.21 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.22 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.30 MiB/2.61 MiB\r\njax  ------------------------------ 1.28 MiB/2.77 MiB\r\npillow  ------------------------------ 1.26 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.28 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.17 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.22 MiB/4.84 MiB\r\nsympy  ------------------------------ 94.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.28 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.20 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.25 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.28 MiB/11.14 MiB\r\nruff  ------------------------------ 1.26 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.26 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.22 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.23 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.28 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.19 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.28 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (128/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\nclick  ------------------------------ 94.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 64.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 48.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 81.23 KiB/523.63 KiB\r\npygments  ------------------------------ 110.17 KiB/1.17 MiB\r\npyzmq  ------------------------------ 1.28 MiB/1.28 MiB\r\njedi  ------------------------------ 1.30 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.22 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.31 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.19 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.22 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.23 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.31 MiB/2.61 MiB\r\njax  ------------------------------ 1.28 MiB/2.77 MiB\r\npillow  ------------------------------ 1.26 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.29 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.17 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.23 MiB/4.84 MiB\r\nsympy  ------------------------------ 110.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.28 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.20 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.25 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.30 MiB/11.14 MiB\r\nruff  ------------------------------ 1.26 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.28 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.22 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.23 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.28 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.19 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.28 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (128/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\nclick  ------------------------------ 94.88 KiB/99.82 KiB\r\npsutil  ------------------------------ 80.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 64.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 81.23 KiB/523.63 KiB\r\npygments  ------------------------------ 110.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.30 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.22 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.31 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.20 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.22 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.23 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.31 MiB/2.61 MiB\r\njax  ------------------------------ 1.28 MiB/2.77 MiB\r\npillow  ------------------------------ 1.26 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.29 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.17 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.23 MiB/4.84 MiB\r\nsympy  ------------------------------ 110.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.28 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.20 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.25 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.30 MiB/11.14 MiB\r\nruff  ------------------------------ 1.28 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.28 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.22 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.25 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.28 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.20 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.28 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ด Preparing packages... (128/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\nclick  ------------------------------ 99.82 KiB/99.82 KiB\r\npsutil  ------------------------------ 80.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 64.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 97.23 KiB/523.63 KiB\r\npygments  ------------------------------ 110.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.30 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.22 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.33 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.20 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.22 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.23 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.31 MiB/2.61 MiB\r\njax  ------------------------------ 1.29 MiB/2.77 MiB\r\npillow  ------------------------------ 1.28 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.29 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.17 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.23 MiB/4.84 MiB\r\nsympy  ------------------------------ 110.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.29 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.22 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.25 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.30 MiB/11.14 MiB\r\nruff  ------------------------------ 1.28 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.28 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.22 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.25 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.28 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.20 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.29 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (131/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\npsutil  ------------------------------ 85.61 KiB/233.92 KiB\r\ndistlib  ------------------------------ 80.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 113.23 KiB/523.63 KiB\r\npygments  ------------------------------ 142.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.31 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.25 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.34 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.20 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.24 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.25 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.34 MiB/2.61 MiB\r\njax  ------------------------------ 1.31 MiB/2.77 MiB\r\npillow  ------------------------------ 1.30 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.30 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.19 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.26 MiB/4.84 MiB\r\nsympy  ------------------------------ 110.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.29 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.23 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.26 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.33 MiB/11.14 MiB\r\nruff  ------------------------------ 1.29 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.29 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.25 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.26 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.30 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.22 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.31 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (131/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\npsutil  ------------------------------ 85.61 KiB/233.92 KiB\r\ndistlib  ------------------------------ 80.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 113.23 KiB/523.63 KiB\r\npygments  ------------------------------ 142.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.31 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.25 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.34 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.20 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.24 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.25 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.34 MiB/2.61 MiB\r\njax  ------------------------------ 1.31 MiB/2.77 MiB\r\npillow  ------------------------------ 1.30 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.30 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.21 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.26 MiB/4.84 MiB\r\nsympy  ------------------------------ 110.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.29 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.23 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.26 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.33 MiB/11.14 MiB\r\nruff  ------------------------------ 1.29 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.29 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.25 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.26 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.30 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.22 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.31 MiB/71.03 MiB ",,terminal_output +55,75065,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (131/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\npsutil  ------------------------------ 112.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 111.43 KiB/457.98 KiB\r\nmpmath  ------------------------------ 142.17 KiB/523.63 KiB\r\npygments  ------------------------------ 158.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.32 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.27 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.37 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.23 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.25 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.25 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.34 MiB/2.61 MiB\r\njax  ------------------------------ 1.34 MiB/2.77 MiB\r\npillow  ------------------------------ 1.33 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.33 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.22 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.26 MiB/4.84 MiB\r\nsympy  ------------------------------ 126.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.33 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.26 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.30 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.33 MiB/11.14 MiB\r\nruff  ------------------------------ 1.32 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.32 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.29 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.29 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.32 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.25 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.32 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... (131/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\npsutil  ------------------------------ 112.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 111.43 KiB/457.98 KiB\r\nmpmath  ------------------------------ 142.17 KiB/523.63 KiB\r\npygments  ------------------------------ 158.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.32 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.27 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.37 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.23 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.27 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.25 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.34 MiB/2.61 MiB\r\njax  ------------------------------ 1.34 MiB/2.77 MiB\r\npillow  ------------------------------ 1.33 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.33 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.22 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.26 MiB/4.84 MiB\r\nsympy  ------------------------------ 126.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.33 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.26 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.30 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.33 MiB/11.14 MiB\r\nruff  ------------------------------ 1.32 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.32 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.29 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.29 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.32 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.25 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.32 MiB/71.03 MiB ",,terminal_output +56,75212,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (132/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\npsutil  ------------------------------ 128.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 127.43 KiB/457.98 KiB\r\nmpmath  ------------------------------ 142.17 KiB/523.63 KiB\r\npygments  ------------------------------ 158.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.33 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.27 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.37 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.23 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.27 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.25 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.34 MiB/2.61 MiB\r\njax  ------------------------------ 1.34 MiB/2.77 MiB\r\npillow  ------------------------------ 1.33 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.33 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.24 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.28 MiB/4.84 MiB\r\nsympy  ------------------------------ 126.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.33 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.26 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.30 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.34 MiB/11.14 MiB\r\nruff  ------------------------------ 1.34 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.34 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.29 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.31 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.32 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.26 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.32 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (132/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\npsutil  ------------------------------ 128.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 127.43 KiB/457.98 KiB\r\nmpmath  ------------------------------ 142.17 KiB/523.63 KiB\r\npygments  ------------------------------ 174.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.33 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.27 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.37 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.23 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.27 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.25 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.34 MiB/2.61 MiB\r\njax  ------------------------------ 1.34 MiB/2.77 MiB\r\npillow  ------------------------------ 1.33 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.33 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.24 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.28 MiB/4.84 MiB\r\nsympy  ------------------------------ 126.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.33 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.26 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.30 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.34 MiB/11.14 MiB\r\nruff  ------------------------------ 1.34 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.34 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.29 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.31 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.32 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.26 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.32 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (132/162)\r\nmarkdown-it-py ------------------------------ 80.00 KiB/85.48 KiB\r\npsutil  ------------------------------ 128.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 127.43 KiB/457.98 KiB\r\nmpmath  ------------------------------ 142.17 KiB/523.63 KiB\r\npygments  ------------------------------ 174.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.33 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.27 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.37 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.23 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.27 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.25 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.36 MiB/2.61 MiB\r\njax  ------------------------------ 1.34 MiB/2.77 MiB\r\npillow  ------------------------------ 1.33 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.33 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.24 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.28 MiB/4.84 MiB\r\nsympy  ------------------------------ 126.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.33 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.26 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.30 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.34 MiB/11.14 MiB\r\nruff  ------------------------------ 1.34 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.34 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.29 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.31 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.32 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.26 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.32 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (132/162)\r\npsutil  ------------------------------ 128.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 128.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 142.17 KiB/523.63 KiB\r\npygments  ------------------------------ 190.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.35 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.27 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.38 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.26 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.28 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.27 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.36 MiB/2.61 MiB\r\njax  ------------------------------ 1.34 MiB/2.77 MiB\r\npillow  ------------------------------ 1.34 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.35 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.25 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.30 MiB/4.84 MiB\r\nsympy  ------------------------------ 142.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.36 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.27 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.31 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.34 MiB/11.14 MiB\r\nruff  ------------------------------ 1.34 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.36 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.30 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.31 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.34 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.26 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.34 MiB/71.03 MiB ",,terminal_output +57,75289,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (132/162)\r\npsutil  ------------------------------ 144.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 128.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 174.17 KiB/523.63 KiB\r\npygments  ------------------------------ 206.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.36 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.31 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.39 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.26 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.30 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.31 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.40 MiB/2.61 MiB\r\njax  ------------------------------ 1.37 MiB/2.77 MiB\r\npillow  ------------------------------ 1.36 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.36 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.25 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.31 MiB/4.84 MiB\r\nsympy  ------------------------------ 206.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.36 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.29 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.33 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.37 MiB/11.14 MiB\r\nruff  ------------------------------ 1.36 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.36 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.32 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.33 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.36 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.28 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.37 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... (132/162)\r\npsutil  ------------------------------ 144.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 128.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 174.17 KiB/523.63 KiB\r\npygments  ------------------------------ 206.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.36 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.31 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.40 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.26 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.30 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.31 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.40 MiB/2.61 MiB\r\njax  ------------------------------ 1.39 MiB/2.77 MiB\r\npillow  ------------------------------ 1.37 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.38 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.25 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.31 MiB/4.84 MiB\r\nsympy  ------------------------------ 206.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.37 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.29 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.33 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.37 MiB/11.14 MiB\r\nruff  ------------------------------ 1.36 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.37 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.32 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.33 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.36 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.28 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.38 MiB/71.03 MiB ",,terminal_output +58,75396,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‡ Preparing packages... (133/162)\r\npsutil  ------------------------------ 160.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 144.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 190.17 KiB/523.63 KiB\r\npygments  ------------------------------ 206.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.36 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.31 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.40 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.26 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.30 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.31 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.42 MiB/2.61 MiB\r\njax  ------------------------------ 1.39 MiB/2.77 MiB\r\npillow  ------------------------------ 1.37 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.38 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.27 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.31 MiB/4.84 MiB\r\nsympy  ------------------------------ 206.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.37 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.30 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.33 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.39 MiB/11.14 MiB\r\nruff  ------------------------------ 1.37 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.37 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.33 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.34 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.37 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.29 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.38 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‡ Preparing packages... (133/162)\r\npsutil  ------------------------------ 160.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 144.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 190.17 KiB/523.63 KiB\r\npygments  ------------------------------ 222.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.37 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.33 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.42 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.28 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.30 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.33 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.42 MiB/2.61 MiB\r\njax  ------------------------------ 1.39 MiB/2.77 MiB\r\npillow  ------------------------------ 1.37 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.38 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.27 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.33 MiB/4.84 MiB\r\nsympy  ------------------------------ 222.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.39 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.30 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.34 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.39 MiB/11.14 MiB\r\nruff  ------------------------------ 1.37 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.39 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.33 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.34 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.39 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.29 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.39 MiB/71.03 MiB ",,terminal_output +59,75452,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‡ Preparing packages... (133/162)\r\npsutil  ------------------------------ 176.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 176.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 206.17 KiB/523.63 KiB\r\npygments  ------------------------------ 238.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.39 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.35 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.44 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.29 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.31 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.34 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.44 MiB/2.61 MiB\r\njax  ------------------------------ 1.42 MiB/2.77 MiB\r\npillow  ------------------------------ 1.40 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.40 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.30 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.34 MiB/4.84 MiB\r\nsympy  ------------------------------ 238.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.40 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.31 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.36 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.41 MiB/11.14 MiB\r\nruff  ------------------------------ 1.39 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.39 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.35 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.34 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.39 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.31 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.40 MiB/71.03 MiB ",,terminal_output +60,75545,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‡ Preparing packages... (133/162)\r\npsutil  ------------------------------ 192.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 176.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 222.17 KiB/523.63 KiB\r\npygments  ------------------------------ 254.06 KiB/1.17 MiB\r\njedi  ------------------------------ 1.41 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.36 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.45 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.31 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.31 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.36 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.45 MiB/2.61 MiB\r\njax  ------------------------------ 1.42 MiB/2.77 MiB\r\npillow  ------------------------------ 1.40 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.41 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.31 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.36 MiB/4.84 MiB\r\nsympy  ------------------------------ 254.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.40 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.32 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.37 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.42 MiB/11.14 MiB\r\nruff  ------------------------------ 1.39 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.40 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.36 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.36 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.39 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.33 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.40 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‡ Preparing packages... (133/162)\r\npsutil  ------------------------------ 192.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 176.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 222.17 KiB/523.63 KiB\r\npygments  ------------------------------ 254.06 KiB/1.17 MiB\r\njedi  ------------------------------ 1.41 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.36 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.45 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.31 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.31 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.36 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.45 MiB/2.61 MiB\r\njax  ------------------------------ 1.42 MiB/2.77 MiB\r\npillow  ------------------------------ 1.40 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.41 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.31 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.36 MiB/4.84 MiB\r\nsympy  ------------------------------ 254.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.42 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.32 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.37 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.42 MiB/11.14 MiB\r\nruff  ------------------------------ 1.39 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.40 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.36 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.36 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.39 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.33 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.40 MiB/71.03 MiB ",,terminal_output +61,75695,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (133/162)\r\npsutil  ------------------------------ 224.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 208.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 254.17 KiB/523.63 KiB\r\npygments  ------------------------------ 254.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.42 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.38 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.45 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.33 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.34 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.38 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.47 MiB/2.61 MiB\r\njax  ------------------------------ 1.45 MiB/2.77 MiB\r\npillow  ------------------------------ 1.43 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.42 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.32 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.37 MiB/4.84 MiB\r\nsympy  ------------------------------ 254.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.44 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.36 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.39 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.44 MiB/11.14 MiB\r\nruff  ------------------------------ 1.42 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.42 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.39 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.39 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.42 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.36 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.43 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (133/162)\r\npsutil  ------------------------------ 224.00 KiB/233.92 KiB\r\ndistlib  ------------------------------ 208.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 254.17 KiB/523.63 KiB\r\npygments  ------------------------------ 270.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.44 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.39 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.47 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.34 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.34 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.38 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.47 MiB/2.61 MiB\r\njax  ------------------------------ 1.45 MiB/2.77 MiB\r\npillow  ------------------------------ 1.43 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.44 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.33 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.39 MiB/4.84 MiB\r\nsympy  ------------------------------ 270.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.44 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.36 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.41 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.45 MiB/11.14 MiB\r\nruff  ------------------------------ 1.42 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.43 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.39 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.39 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.42 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.36 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.45 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (133/162)\r\ndistlib  ------------------------------ 217.43 KiB/457.98 KiB\r\nmpmath  ------------------------------ 270.17 KiB/523.63 KiB\r\npygments  ------------------------------ 270.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.44 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.41 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.47 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.34 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.36 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.38 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.48 MiB/2.61 MiB\r\njax  ------------------------------ 1.45 MiB/2.77 MiB\r\npillow  ------------------------------ 1.44 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.44 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.33 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.39 MiB/4.84 MiB\r\nsympy  ------------------------------ 270.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.45 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.37 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.42 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.45 MiB/11.14 MiB\r\nruff  ------------------------------ 1.43 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.43 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.41 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.40 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.43 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.37 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.45 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ‹ Preparing packages... (133/162)\r\ndistlib  ------------------------------ 233.43 KiB/457.98 KiB\r\nmpmath  ------------------------------ 286.17 KiB/523.63 KiB\r\npygments  ------------------------------ 286.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.45 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.41 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.48 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.36 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.36 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.40 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.50 MiB/2.61 MiB\r\njax  ------------------------------ 1.47 MiB/2.77 MiB\r\npillow  ------------------------------ 1.45 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.46 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.35 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.39 MiB/4.84 MiB\r\nsympy  ------------------------------ 286.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.47 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.37 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.42 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.47 MiB/11.14 MiB\r\nruff  ------------------------------ 1.45 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.45 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.43 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.40 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.45 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.37 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.46 MiB/71.03 MiB ",,terminal_output +62,75845,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... 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(134/162)\r\ndistlib  ------------------------------ 256.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 318.17 KiB/523.63 KiB\r\npygments  ------------------------------ 318.17 KiB/1.17 MiB\r\njedi  ------------------------------ 1.48 MiB/1.50 MiB\r\nnetworkx  ------------------------------ 1.42 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.50 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.37 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.38 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.43 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.51 MiB/2.61 MiB\r\njax  ------------------------------ 1.50 MiB/2.77 MiB\r\npillow  ------------------------------ 1.48 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.49 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.38 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.42 MiB/4.84 MiB\r\nsympy  ------------------------------ 318.56 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.49 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.39 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.45 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.50 MiB/11.14 MiB\r\nruff  ------------------------------ 1.48 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.48 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.44 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.43 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.48 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.40 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.50 MiB/71.03 MiB ",,terminal_output +63,75995,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ™ Preparing packages... 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(134/162)\r\ndistlib  ------------------------------ 291.59 KiB/457.98 KiB\r\nmpmath  ------------------------------ 354.77 KiB/523.63 KiB\r\npygments  ------------------------------ 366.17 KiB/1.17 MiB\r\nnetworkx  ------------------------------ 1.46 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.55 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.42 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.42 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.45 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.56 MiB/2.61 MiB\r\njax  ------------------------------ 1.54 MiB/2.77 MiB\r\npillow  ------------------------------ 1.53 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.53 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.42 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.45 MiB/4.84 MiB\r\nsympy  ------------------------------ 350.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.54 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.43 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.50 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.55 MiB/11.14 MiB\r\nruff  ------------------------------ 1.53 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.53 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.49 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.47 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.53 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.45 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.53 MiB/71.03 MiB ",,terminal_output +64,76052,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (134/162)\r\ndistlib  ------------------------------ 291.59 KiB/457.98 KiB\r\nmpmath  ------------------------------ 354.77 KiB/523.63 KiB\r\npygments  ------------------------------ 366.17 KiB/1.17 MiB\r\nnetworkx  ------------------------------ 1.46 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.55 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.42 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.44 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.47 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.56 MiB/2.61 MiB\r\njax  ------------------------------ 1.54 MiB/2.77 MiB\r\npillow  ------------------------------ 1.53 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.53 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.42 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.45 MiB/4.84 MiB\r\nsympy  ------------------------------ 350.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.54 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.45 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.50 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.55 MiB/11.14 MiB\r\nruff  ------------------------------ 1.53 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.53 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.50 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.48 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.53 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.47 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.53 MiB/71.03 MiB ",,terminal_output +65,76110,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ น Preparing packages... (134/162)\r\ndistlib  ------------------------------ 307.59 KiB/457.98 KiB\r\nmpmath  ------------------------------ 370.77 KiB/523.63 KiB\r\npygments  ------------------------------ 382.17 KiB/1.17 MiB\r\nnetworkx  ------------------------------ 1.49 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.56 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.44 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.46 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.48 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.58 MiB/2.61 MiB\r\njax  ------------------------------ 1.56 MiB/2.77 MiB\r\npillow  ------------------------------ 1.55 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.55 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.43 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.48 MiB/4.84 MiB\r\nsympy  ------------------------------ 382.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.54 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.47 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.51 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.58 MiB/11.14 MiB\r\nruff  ------------------------------ 1.54 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.54 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.52 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.50 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.54 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.48 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.54 MiB/71.03 MiB ",,terminal_output +66,76244,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... (135/162)\r\ndistlib  ------------------------------ 320.00 KiB/457.98 KiB\r\nmpmath  ------------------------------ 382.17 KiB/523.63 KiB\r\npygments  ------------------------------ 398.17 KiB/1.17 MiB\r\nnetworkx  ------------------------------ 1.49 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.58 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.45 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.47 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.49 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.58 MiB/2.61 MiB\r\njax  ------------------------------ 1.58 MiB/2.77 MiB\r\npillow  ------------------------------ 1.56 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.55 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.45 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.50 MiB/4.84 MiB\r\nsympy  ------------------------------ 382.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.55 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.48 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.53 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.58 MiB/11.14 MiB\r\nruff  ------------------------------ 1.56 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.57 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.54 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.51 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.56 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.50 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.56 MiB/71.03 MiB \r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... 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(135/162)\r\ndistlib  ------------------------------ 351.55 KiB/457.98 KiB\r\nmpmath  ------------------------------ 414.17 KiB/523.63 KiB\r\npygments  ------------------------------ 430.17 KiB/1.17 MiB\r\nnetworkx  ------------------------------ 1.52 MiB/1.62 MiB\r\ntorchvision ------------------------------ 1.61 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.47 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.49 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.50 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.61 MiB/2.61 MiB\r\njax  ------------------------------ 1.59 MiB/2.77 MiB\r\npillow  ------------------------------ 1.59 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.58 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.48 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.51 MiB/4.84 MiB\r\nsympy  ------------------------------ 414.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.58 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.50 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.55 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.61 MiB/11.14 MiB\r\nruff  ------------------------------ 1.57 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.57 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.55 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.53 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.59 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.51 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.57 MiB/71.03 MiB ",,terminal_output +67,76304,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ธ Preparing packages... 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(135/162)\r\npygments  ------------------------------ 572.50 KiB/1.17 MiB\r\ntorchvision ------------------------------ 1.73 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.58 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.61 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.64 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.73 MiB/2.61 MiB\r\njax  ------------------------------ 1.73 MiB/2.77 MiB\r\npillow  ------------------------------ 1.70 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.72 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.58 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.62 MiB/4.84 MiB\r\nsympy  ------------------------------ 542.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.72 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.62 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.69 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.73 MiB/11.14 MiB\r\nruff  ------------------------------ 1.72 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.72 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.69 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.65 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.72 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.63 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.71 MiB/71.03 MiB ",,terminal_output +74,77000,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ฆ Preparing packages... 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(138/162)\r\npygments  ------------------------------ 574.17 KiB/1.17 MiB\r\ntorchvision ------------------------------ 1.76 MiB/1.80 MiB\r\nsqlalchemy ------------------------------ 1.61 MiB/2.01 MiB\r\nwidgetsnbextension ------------------------------ 1.64 MiB/2.23 MiB\r\ndebugpy  ------------------------------ 1.67 MiB/2.37 MiB\r\nfonttools  ------------------------------ 1.76 MiB/2.61 MiB\r\njax  ------------------------------ 1.76 MiB/2.77 MiB\r\npillow  ------------------------------ 1.73 MiB/2.96 MiB\r\nsphinx  ------------------------------ 1.73 MiB/3.42 MiB\r\nvirtualenv ------------------------------ 1.60 MiB/4.10 MiB\r\nnumpy  ------------------------------ 1.66 MiB/4.84 MiB\r\nsympy  ------------------------------ 574.67 KiB/6.01 MiB\r\nmatplotlib ------------------------------ 1.73 MiB/7.67 MiB\r\nscikit-learn ------------------------------ 1.65 MiB/8.19 MiB\r\nbabel  ------------------------------ 1.70 MiB/9.71 MiB\r\njupyterlab ------------------------------ 1.76 MiB/11.14 MiB\r\nruff  ------------------------------ 1.75 MiB/11.24 MiB\r\nmypy  ------------------------------ 1.75 MiB/11.36 MiB\r\nnotebook  ------------------------------ 1.72 MiB/12.53 MiB\r\ntensorstore ------------------------------ 1.67 MiB/13.14 MiB\r\nscipy  ------------------------------ 1.75 MiB/21.34 MiB\r\njaxlib  ------------------------------ 1.66 MiB/52.42 MiB\r\ntorch  ------------------------------ 1.75 MiB/71.03 MiB ",,terminal_output +75,77071,"TERMINAL",0,0,"\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r\r Building probly @ file:///Users/franzsrambical/Downloads/probly\r\nโ ง Preparing packages... 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",,terminal_output +330,106451,"TERMINAL",0,0,"\rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [0/185] pycparser==2.22 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [1/185] pycparser==2.22 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [1/185] sphinxcontrib-applehelp==2.0.0 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [2/185] sphinxcontrib-applehelp==2.0.0 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [2/185] nbformat==5.10.4 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [3/185] nbformat==5.10.4 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [3/185] joblib==1.4.2 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [4/185] joblib==1.4.2 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [4/185] jupyter-client==8.6.3 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [5/185] jupyter-client==8.6.3 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [5/185] urllib3==2.3.0 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [6/185] urllib3==2.3.0 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [6/185] pyparsing==3.2.1 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [7/185] pyparsing==3.2.1 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [7/185] ipykernel==6.29.5 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [8/185] ipykernel==6.29.5 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [8/185] latexcodec==3.0.0 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [9/185] latexcodec==3.0.0 \rโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [9/185] nbconvert==7.16.6 \rโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [10/185] nbconvert==7.16.6 \rโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [10/185] pybtex==0.24.0 ",,terminal_output +331,106555,"TERMINAL",0,0,"\rโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [14/185] terminado==0.18.1 \rโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [16/185] contourpy==1.3.1 ",,terminal_output +332,106609,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [26/185] attrs==25.1.0 ",,terminal_output +333,106667,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [35/185] torchvision==0.24.0 ",,terminal_output +334,106858,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [41/185] asttokens==3.0.0 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [45/185] roman-numerals-py==3.1.0 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [49/185] prompt-toolkit==3.0.50 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [53/185] threadpoolctl==3.6.0 ",,terminal_output +335,107052,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [64/185] sphinxcontrib-serializinghtml==2.0.0 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [68/185] pybtex-docutils==1.0.3 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [71/185] iniconfig==2.0.0 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [77/185] mdit-py-plugins==0.4.2 ",,terminal_output +336,107260,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [79/185] ptyprocess==0.7.0 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [80/185] ptyprocess==0.7.0 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [80/185] defusedxml==0.7.1 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [84/185] jsonpointer==3.0.0 ",,terminal_output +337,107352,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [87/185] matplotlib-inline==0.1.7 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [92/185] rfc3986-validator==0.1.1 ",,terminal_output +338,107405,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [99/185] sphinxcontrib-jsmath==1.0.1 ",,terminal_output +339,107505,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [106/185] cycler==0.12.1 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [111/185] execnet==2.1.1 ",,terminal_output +340,107602,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [115/185] sphinx-copybutton==0.5.2 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [122/185] filelock==3.13.1 ",,terminal_output +341,107657,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [127/185] markupsafe==3.0.2 ",,terminal_output +342,107709,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ [137/185] jupyter-server-terminals==0.5.3 ",,terminal_output +343,107803,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘ [139/185] psutil==7.0.0 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘ [142/185] jupyter-core==5.7.2 ",,terminal_output +344,107952,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘ [144/185] notebook-shim==0.2.4 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘โ–‘ [145/185] myst-parser==4.0.1 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘โ–‘ [155/185] nodeenv==1.9.1 ",,terminal_output +345,108012,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘ [159/185] pexpect==4.9.0 ",,terminal_output +346,108072,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘โ–‘ [166/185] arrow==1.3.0 ",,terminal_output +347,108160,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘ [171/185] absl-py==2.3.1 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘ [172/185] absl-py==2.3.1 ",,terminal_output +348,108259,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘โ–‘ [173/185] importlib-resources==6.5.2 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘ [177/185] httpx==0.28.1 ",,terminal_output +349,108339,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘ [183/185] notebook==7.3.3 ",,terminal_output +350,108442,"TERMINAL",0,0,"\rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‘ [184/185] jupyterlab==4.3.6 \rโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ [185/185] jupyterlab==4.3.6 \rInstalled 185 packages in 2.18s\r\n + absl-py==2.3.1\r\n + aiofiles==25.1.0\r\n + alabaster==1.0.0\r\n + anyio==4.8.0\r\n + appnope==0.1.4\r\n + argon2-cffi==23.1.0\r\n + argon2-cffi-bindings==21.2.0\r\n + arrow==1.3.0\r\n + asttokens==3.0.0\r\n + async-lru==2.0.4\r\n + attrs==25.1.0\r\n + babel==2.17.0\r\n + beautifulsoup4==4.13.3\r\n + bleach==6.2.0\r\n + certifi==2025.1.31\r\n + cffi==1.17.1\r\n + cfgv==3.4.0\r\n + charset-normalizer==3.4.1\r\n + chex==0.1.90\r\n + click==8.2.1\r\n + comm==0.2.2\r\n + contourpy==1.3.1\r\n + coverage==7.6.12\r\n + cycler==0.12.1\r\n + debugpy==1.8.12\r\n + decorator==5.1.1\r\n + defusedxml==0.7.1\r\n + distlib==0.3.9\r\n + docutils==0.21.2\r\n + etils==1.13.0\r\n + execnet==2.1.1\r\n + executing==2.2.0\r\n + fastjsonschema==2.21.1\r\n + filelock==3.13.1\r\n + flax==0.12.0\r\n + fonttools==4.56.0\r\n + fqdn==1.5.1\r\n + fsspec==2024.6.1\r\n + furo==2024.8.6\r\n + h11==0.14.0\r\n + httpcore==1.0.7\r\n + httpx==0.28.1\r\n + humanize==4.14.0\r\n + identify==2.6.9\r\n + idna==3.10\r\n + imagesize==1.4.1\r\n + importlib-metadata==8.7.0\r\n + importlib-resources==6.5.2\r\n + iniconfig==2.0.0\r\n + ipykernel==6.29.5\r\n + ipython==8.32.0\r\n + ipywidgets==8.1.5\r\n + isoduration==20.11.0\r\n + jax==0.8.0\r\n + jaxlib==0.8.0\r\n + jedi==0.19.2\r\n + jinja2==3.1.4\r\n + joblib==1.4.2\r\n + json5==0.10.0\r\n + jsonpointer==3.0.0\r\n + jsonschema==4.23.0\r\n + jsonschema-specifications==2024.10.1\r\n + jupyter-cache==1.0.1\r\n + jupyter-client==8.6.3\r\n + jupyter-core==5.7.2\r\n + jupyter-events==0.12.0\r\n + jupyter-lsp==2.2.5\r\n + jupyter-server==2.15.0\r\n + jupyter-server-terminals==0.5.3\r\n + jupyterlab==4.3.6\r\n + jupyterlab-pygments==0.3.0\r\n + jupyterlab-server==2.27.3\r\n + jupyterlab-widgets==3.0.13\r\n + kiwisolver==1.4.8\r\n + latexcodec==3.0.0\r\n + markdown-it-py==3.0.0\r\n + markupsafe==3.0.2\r\n + matplotlib==3.10.1\r\n + matplotlib-inline==0.1.7\r\n + mdit-py-plugins==0.4.2\r\n + mdurl==0.1.2\r\n + mistune==3.1.2\r\n + ml-dtypes==0.5.3\r\n + mpltern==1.0.4\r\n + mpmath==1.3.0\r\n + msgpack==1.1.2\r\n + mypy==1.18.2\r\n + mypy-extensions==1.1.0\r\n + myst-nb==1.2.0\r\n + myst-parser==4.0.1\r\n + nbclient==0.10.2\r\n + nbconvert==7.16.6\r\n + nbformat==5.10.4\r\n + nest-asyncio==1.6.0\r\n + networkx==3.3\r\n + nodeenv==1.9.1\r\n + notebook==7.3.3\r\n + notebook-shim==0.2.4\r\n + numpy==2.1.2\r\n + opt-einsum==3.4.0\r\n + optax==0.2.6\r\n + orbax-checkpoint==0.11.25\r\n + overrides==7.7.0\r\n + packaging==24.1\r\n + pandocfilters==1.5.1\r\n + parso==0.8.4\r\n + pathspec==0.12.1\r\n + pexpect==4.9.0\r\n + pillow==11.1.0\r\n + platformdirs==4.3.6\r\n + pluggy==1.5.0\r\n + pre-commit==4.3.0\r\n + probly==0.3.1 (from file:///Users/franzsrambical/Downloads/probly)\r\n + prometheus-client==0.21.1\r\n + prompt-toolkit==3.0.50\r\n + protobuf==6.33.0\r\n + psutil==7.0.0\r\n + ptyprocess==0.7.0\r\n + pure-eval==0.2.3\r\n + pybtex==0.24.0\r\n + pybtex-docutils==1.0.3\r\n + pycparser==2.22\r\n + pygments==2.19.1\r\n + pyparsing==3.2.1\r\n + pytest==8.3.5\r\n + pytest-cov==6.0.0\r\n + pytest-xdist==3.6.1\r\n + python-dateutil==2.9.0.post0\r\n + python-json-logger==3.3.0\r\n + pyyaml==6.0.2\r\n + pyzmq==26.2.1\r\n + referencing==0.36.2\r\n + requests==2.32.3\r\n + rfc3339-validator==0.1.4\r\n + rfc3986-validator==0.1.1\r\n + rich==14.2.0\r\n + roman-numerals-py==3.1.0\r\n + rpds-py==0.23.1\r\n + ruff==0.14.1\r\n + scikit-learn==1.7.2\r\n + scipy==1.15.2\r\n + send2trash==1.8.3\r\n + setuptools==70.2.0\r\n + simplejson==3.20.2\r\n + six==1.17.0\r\n + sniffio==1.3.1\r\n + snowballstemmer==2.2.0\r\n + soupsieve==2.6\r\n + sphinx==8.2.3\r\n + sphinx-autodoc-typehints==3.1.0\r\n + sphinx-basic-ng==1.0.0b2\r\n + sphinx-copybutton==0.5.2\r\n + sphinxcontrib-applehelp==2.0.0\r\n + sphinxcontrib-bibtex==2.6.3\r\n + sphinxcontrib-devhelp==2.0.0\r\n + sphinxcontrib-htmlhelp==2.1.0\r\n + sphinxcontrib-jsmath==1.0.1\r\n + sphinxcontrib-qthelp==2.0.0\r\n + sphinxcontrib-serializinghtml==2.0.0\r\n + sqlalchemy==2.0.41\r\n + stack-data==0.6.3\r\n + sympy==1.14.0\r\n + tabulate==0.9.0\r\n + tensorstore==0.1.78\r\n + terminado==0.18.1\r\n + threadpoolctl==3.6.0\r\n + tinycss2==1.4.0\r\n + toolz==1.1.0\r\n + torch==2.9.0\r\n + torchvision==0.24.0\r\n + tornado==6.4.2\r\n + tqdm==4.67.1\r\n + traitlets==5.14.3\r\n + treescope==0.1.10\r\n + types-python-dateutil==2.9.0.20241206\r\n + typing-extensions==4.12.2\r\n + uri-template==1.3.0\r\n + urllib3==2.3.0\r\n + virtualenv==20.29.3\r\n + wcwidth==0.2.13\r\n + webcolors==24.11.1\r\n + webencodings==0.5.1\r\n + websocket-client==1.8.0\r\n + widgetsnbextension==4.0.13\r\n + zipp==3.23.0\r\n% \r \r",,terminal_output +351,128314,"TERMINAL",0,0,"cd docs",,terminal_command +352,128315,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +353,129595,"TERMINAL",0,0,"make html",,terminal_command +354,129645,"TERMINAL",0,0,"]633;C",,terminal_output +355,131622,"TERMINAL",0,0,"Running Sphinx v8.2.3\r\n",,terminal_output +356,132727,"TERMINAL",0,0,"loading translations [en]... done\r\n",,terminal_output +357,144373,"TERMINAL",0,0,"checking bibtex cache... out of date\r\nparsing bibtex file /Users/franzsrambical/Downloads/probly/docs/source/references.bib... parsed 19 entries\r\n",,terminal_output +358,144672,"TERMINAL",0,0,"[autosummary] generating autosummary for: api.rst, api/probly.datasets.rst, api/probly.datasets.torch.rst, api/probly.evaluation.metrics.rst, api/probly.evaluation.rst, api/probly.evaluation.tasks.rst, api/probly.layers.flax.rst, api/probly.layers.rst, api/probly.layers.torch.rst, api/probly.plot.credal.rst, ..., api/probly.traverse_nn.rst, api/probly.traverse_nn.torch.rst, api/probly.utils.errors.rst, api/probly.utils.probabilities.rst, api/probly.utils.rst, api/probly.utils.sets.rst, api/probly.utils.torch.rst, index.rst, methods.md, references.rst\r\n",,terminal_output +359,152126,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.batchnorm.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.stubs.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.nnpack.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.categorical.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.operator_support.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.tools_common.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.instantiator.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.gds.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.source_matcher_utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.runtime_assert.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.half_cauchy.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.collect_env.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.optim.lr_scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.optim'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'optim'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.jit.annotations.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'jit'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.jit'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.algorithms.join.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.transformed_distribution.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.export.dynamic_shapes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.losses.\r\nPossible hints:\r\n* AttributeError: module 'probly.train' has no attribute 'losses'\r\n* ModuleNotFoundError: No module named 'probly.train.losses'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.file_structure_representation.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.linear_fused.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.config.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.linear_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.functions.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.studentT.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.mtia.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.mtia'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mtia'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.callable.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.normalization.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.templates.remote_module_template.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.api.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.pareto.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.independent.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.combinatorics.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.profiler.itt.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'profiler'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.profiler'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.poisson.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.backcompat.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.distributed.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.common.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.wrap.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_opset9.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.combinatorics.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.compiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'compiler'\r\nWARNING: Failed to import probly.train.bayesian.torch.functional.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'functional'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.grad_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.placement_types.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.uniform.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.autocast_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cpu'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.hooks.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.base_sparsifier.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.miopen.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.weibull.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.sharding.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.linear_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.profiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mps'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.mps'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.fishersnedecor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.nearly_diagonal_sparsifier.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.device_mesh.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.graph_settings.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.one_hot_categorical.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.conv_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.datapipes.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.common.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.backend_registry.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.quantization_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no \n[... 33355 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +360,161897,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.fx.traceback.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.pt2e.export_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.random.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.fully_sharded_data_parallel.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.chi2.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.graph_transform_observer.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.optim.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'optim'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.conv.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.split_utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.lambda_scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.remote_device.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n",,terminal_output +361,171965,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.ao.ns.fx.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.passthrough.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.constraints.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.c10d_logger.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.kumaraswamy.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.profiler.profiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'profiler'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.profiler'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.api.remote_module.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.linear.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.templates.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.dirichlet.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.proxy_tensor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.testing.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'testing'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.version.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'version'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.modules.fused.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cudnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.transforms.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.export.exported_program.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.throughput_benchmark.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.multiprocessing.reductions.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.multiprocessing'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'multiprocessing'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.beta.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.dataframe_wrapper.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mps'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.wishart.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.jiterator.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.fileopener.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.dynamic.modules.linear.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.laplace.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.graph_module.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.checkpoint.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.activation.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.dataloader.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.exp_family.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.streams.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.xpu'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.mixture_same_family.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.filelister.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.amp.autocast_mode.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'amp'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.amp'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.amp.grad_scaler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'amp'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.amp'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.gumbel.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.package.importer.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.observer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.dataframes.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.observer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.stubs.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.quantization_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.core.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.graph_manipulation.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.logging_handlers.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.autocast_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.conv.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.reinplace.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.mkldnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n",,terminal_output +362,174415,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.callable.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.streams.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler_util.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.autograd.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.inverse_gamma.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.opt_einsum.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.unary.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.algorithms.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.conv.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.cauchy.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.multinomial.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.interpreter.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.dropout.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.anomaly_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fft.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fft'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.jit.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'jit'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.return_types.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'return_types'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.linear.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.cpp_backtrace.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.sampler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.style.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.base_scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.signal.windows.windows.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'signal'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.signal'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.functional.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.relaxed_categorical.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.von_mises.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.xpu'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.multivariate_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.tunable.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.hub.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'hub'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.storage.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'storage'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.func.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'func'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.model_zoo.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.cubic_scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.profiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cusparselt.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.fuser_method_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n",,terminal_output +363,193486,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.dlpack.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.compiler.config.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'compiler'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.compiler'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.pt2e.graph_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.torch_version.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'torch_version'\r\nWARNING: Failed to import probly.train.bayesian.torch.sparse.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'sparse'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.pass_manager.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.random.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.xpu'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.jit.frontend.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'jit'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.jit'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.negative_binomial.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.symbolic_shapes.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.library.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'library'\r\nWARNING: Failed to import probly.train.bayesian.torch.mtia.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mtia'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.fake_quantize.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.monitor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'monitor'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.pass_base.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.errors.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.functional_modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.immutable_collections.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.graph_signature.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.grouping.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.fuser_method_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.kleidiai.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.grad_scaler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cpu'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.sparse.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.openmp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.decoder.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.quant_type.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.mkl.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.const_fold.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.special.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'special'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.graph.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.distribution.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.event.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mps'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.mps'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.fx.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.numa.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'numa'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.api.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.reductions.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.linear.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.half_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.common.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.kl.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.recording.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n",,terminal_output +364,198075,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.backends.quantized.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.combining.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.datapipe.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.fx.ns_types.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.optim.swa_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.optim'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'optim'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.continuous_bernoulli.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.node.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.sparse.semi_structured.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'sparse'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.sparse'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.random.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'random'\r\nWARNING: Failed to import probly.train.bayesian.torch.accelerator.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'accelerator'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.gamma.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.function.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.grouping.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.api.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.conv.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.bn_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_opset11.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.backend_registration.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.qconfig.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.glob_group.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.routeddecoder.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.loss.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.device_mesh.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.exponential.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.operator_schemas.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.creation.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.log_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.quant_type.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.generalized_pareto.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.split_module.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.sparse.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.net_min_base.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.overrides.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'overrides'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.splitter_base.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.fsdp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.activation.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.amp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'amp'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.analyze.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.accelerator.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.accelerator'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'accelerator'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.package_importer.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.find_file_dependencies.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.numa.binding.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.numa'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'numa'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.constants.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.lkj_cholesky.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.pt2e.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_opset10.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.binary.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n",,terminal_output +365,198520,"TERMINAL",0,0,"\r\nExtension error (sphinx.ext.autosummary)!\r\n\r\n",,terminal_output +366,198582,"TERMINAL",0,0,"Versions\r\n========\r\n\r\n* Platform: darwin; (macOS-15.6-arm64-arm-64bit-Mach-O)\r\n* Python version: 3.13.7 (CPython)\r\n* Sphinx version: 8.2.3\r\n* Docutils version: 0.21.2\r\n* Jinja2 version: 3.1.4\r\n* Pygments version: 2.19.1\r\n\r\nLast Messages\r\n=============\r\n\r\nNone.\r\n\r\nLoaded Extensions\r\n=================\r\n\r\nNone.\r\n\r\nTraceback\r\n=========\r\n\r\n File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/events.py"", line 415, in emit\r\n raise ExtensionError(\r\n ...<4 lines>...\r\n ) from exc\r\n sphinx.errors.ExtensionError: Handler for event 'builder-inited' threw an exception (exception: no module named probly.train.bayesian.torch.accelerator)\r\n\r\n\r\nThe full traceback has been saved in:\r\n/var/folders/nn/241fnlwx03d7k7qt2jg98txr0000gn/T/sphinx-err-f0le1vx3.log\r\n\r\nTo report this error to the developers, please open an issue at . Thanks!\r\nPlease also report this if it was a user error, so that a better error message can be provided next time.\r\n",,terminal_output +367,199314,"TERMINAL",0,0,"make: *** [html] Error 2\r\n% \r \r",,terminal_output +368,209843,"TERMINAL",0,0,"ls",,terminal_command +369,209844,"TERMINAL",0,0,"]633;Cbuild\t\tmake.bat\tMakefile\tsource\r\n% \r \r",,terminal_output +370,212536,"TERMINAL",0,0,"ls build",,terminal_command +371,212542,"TERMINAL",0,0,"]633;Cdoctrees\thtml\r\n% \r \r",,terminal_output +372,216130,"TERMINAL",0,0,"ls build/html",,terminal_command +373,216131,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +374,254762,"TERMINAL",0,0,"git checkout main",,terminal_command +375,254788,"TERMINAL",0,0,"]633;CSwitched to branch 'main'\r\nYour branch is behind 'origin/main' by 16 commits, and can be fast-forwarded.\r\n (use ""git pull"" to update your local branch)\r\n% \r \r",,terminal_output +376,255228,"",0,0,"Switched from branch 'clean-build' to 'main'",,git_branch_checkout +377,261615,"TERMINAL",0,0,"make html",,terminal_command +378,261665,"TERMINAL",0,0,"]633;C",,terminal_output +379,261896,"TERMINAL",0,0,"Running Sphinx v8.2.3\r\n",,terminal_output +380,261970,"TERMINAL",0,0,"loading translations [en]... done\r\n",,terminal_output +381,262870,"TERMINAL",0,0,"checking bibtex cache... out of date\r\nparsing bibtex file /Users/franzsrambical/Downloads/probly/docs/source/references.bib... parsed 19 entries\r\n",,terminal_output +382,262949,"TERMINAL",0,0,"[autosummary] generating autosummary for: api.rst, api/probly.datasets.rst, api/probly.datasets.torch.rst, api/probly.evaluation.metrics.rst, api/probly.evaluation.rst, api/probly.evaluation.tasks.rst, api/probly.layers.flax.rst, api/probly.layers.rst, api/probly.layers.torch.rst, api/probly.plot.credal.rst, ..., api/probly.traverse_nn.rst, api/probly.traverse_nn.torch.rst, api/probly.utils.errors.rst, api/probly.utils.probabilities.rst, api/probly.utils.rst, api/probly.utils.sets.rst, api/probly.utils.torch.rst, index.rst, methods.md, references.rst\r\n",,terminal_output +383,263652,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.random.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'random'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.graph.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.linalg.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'linalg'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.streams.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.xpu'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.device_mesh.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.rnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.structures.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.chi2.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.observer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.selecting.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.rnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.studentT.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.activation.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.half_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.config.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.constraints.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.sparse.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'sparse'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.combinatorics.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.operator_support.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.log_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.dataloader.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.accelerator.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.accelerator'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'accelerator'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.runtime_assert.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.binary.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.event.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mps'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.mps'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.collect_env.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.weibull.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.algorithms.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.pareto.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.one_hot_categorical.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.autocast_mode.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.signal.windows.windows.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.signal'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'signal'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.common.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.serialization.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'serialization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.pass_base.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.templates.remote_module_template.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.special.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'special'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.analyze.is_from_package.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.hooks.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.throughput_benchmark.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.constants.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.decoder.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n",,terminal_output +384,264326,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.combining.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.deterministic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.exponential.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.autocast_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cpu'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.quantized.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\n",,terminal_output +385,264988,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.backends.cuda.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.rnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.dirichlet.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.activation.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.transformed_distribution.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.transforms.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.creation.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.source_matcher_utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.instantiator.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.dlpack.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.dropout.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.interpreter.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.net_min_base.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.conv.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.graph_transform_observer.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.reinplace.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n",,terminal_output +386,265877,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.cuda.sparse.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.dynamic.modules.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.placement_types.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.dynamic_shapes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.geometric.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.normalization.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.functional.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'functional'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.grad_scaler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.quantization_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.wrap.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.relaxed_bernoulli.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.batchnorm.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.split_module.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.mixture_same_family.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.generalized_pareto.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.modules.fused.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.linear_relu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.c10d_logger.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.signal.windows.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.signal'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'signal'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.functional.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.recording.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.fuser_method_mappings.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.numa.binding.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.numa'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'numa'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.fsdp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.qconfig_mapping.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.weak.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.von_mises.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.accelerator.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'accelerator'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.function.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.sparse.semi_structured.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.sparse'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'sparse'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.ops.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\nWARNING: Failed to import probly.train.bayesian.torch.amp.grad_scaler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'amp'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.amp'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.lambda_scheduler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.constraint_registry.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.forward_ad.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.grad_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.internal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.observer.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.memory.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.xpu'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.split_utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.callable.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.operators.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.checkpoint.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.base_scheduler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.package_importer.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.categorical.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.graphs.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.logging_handlers.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.pass_manager.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.laplace.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.fuser_method_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\nWARNING: Failed to import probly.train.bayesian.torch.storage.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'storage'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.memory.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.return_types.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'return_types'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.jiterator.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.functional_modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.miopen.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.layers.flax.\r\nPossible hints:\r\n* AttributeError: module 'probly.layers' has no attribute 'flax'\r\n* ModuleNotFoundError: No module named 'probly.layers.flax'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.custom_ops.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.errors.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.api.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mps'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.common.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.combining.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.exp_family.\r\nPossible hints:\r\n* AttributeError: module 'p\n[... 49332 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +387,266237,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.distributed.constants.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.beta.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.conv_relu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.rnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.profiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.nearly_diagonal_sparsifier.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.backend_registry.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.jit.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'jit'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.options.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.decomp_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.symbolic_shapes.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.mkldnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.losses.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.losses'\r\n* AttributeError: module 'probly.train' has no attribute 'losses'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.const_fold.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.openmp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.package.importer.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_opset9.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.inverse_gamma.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.compiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'compiler'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.immutable_collections.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.multinomial.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.optim.lr_scheduler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'optim'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.optim'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.fully_sharded_data_parallel.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.subgraph_rewriter.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.compiler.config.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'compiler'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.compiler'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.backcompat.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.grouping.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.cpp_backtrace.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.qconfig.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.fx.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.random.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.xpu'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.gamma.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.graph_drawer.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.unary.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_helper.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\nWARNING: Failed to import probly.train.bayesian.torch.quasirandom.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quasirandom'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.base_sparsifier.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.sharding.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.cauchy.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.weight_norm_sparsifier.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.bn_relu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.mha.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.logistic_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.torch_version.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'torch_version'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\nWARNING: Failed to import probly.train.bayesian.torch.mtia.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mtia'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.api.remote_module.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.jit.annotations.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'jit'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.jit'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_opset11.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.qconfig.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.dynamic.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.analyze.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.dataframes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.quant_type.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.backend_registration.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.sparse.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.exported_program.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.kumaraswamy.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cpu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler_util.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.datapipes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.grad_scaler.\r\nPossible hints:\r\n* ModuleNotFou\n[... 14731 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +388,266313,"TERMINAL",0,0,"WARNING: [autosummary] failed to import probly.layers.flax.\r\nPossible hints:\r\n* ImportError: \r\n* AttributeError: module 'probly.layers' has no attribute 'flax'\r\n* ModuleNotFoundError: No module named 'probly.layers.flax'\r\n",,terminal_output +389,266610,"TERMINAL",0,0,"\r\nExtension error (sphinx.ext.autosummary)!\r\n\r\nVersions\r\n========\r\n\r\n* Platform: darwin; (macOS-15.6-arm64-arm-64bit-Mach-O)\r\n* Python version: 3.13.7 (CPython)\r\n* Sphinx version: 8.2.3\r\n* Docutils version: 0.21.2\r\n* Jinja2 version: 3.1.4\r\n* Pygments version: 2.19.1\r\n\r\nLast Messages\r\n=============\r\n\r\nNone.\r\n\r\nLoaded Extensions\r\n=================\r\n\r\nNone.\r\n\r\nTraceback\r\n=========\r\n\r\n File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/events.py"", line 415, in emit\r\n raise ExtensionError(\r\n ...<4 lines>...\r\n ) from exc\r\n sphinx.errors.ExtensionError: Handler for event 'builder-inited' threw an exception (exception: no module named probly.train.bayesian.torch.accelerator)\r\n\r\n\r\nThe full traceback has been saved in:\r\n/var/folders/nn/241fnlwx03d7k7qt2jg98txr0000gn/T/sphinx-err-n83nye4k.log\r\n\r\nTo report this error to the developers, please open an issue at . Thanks!\r\nPlease also report this if it was a user error, so that a better error message can be provided next time.\r\n",,terminal_output +390,267246,"TERMINAL",0,0,"make: *** [html] Error 2\r\n% \r \r",,terminal_output +391,336660,"TERMINAL",0,0,"git checkout user-guide",,terminal_command +392,336678,"TERMINAL",0,0,"]633;Cerror: pathspec 'user-guide' did not match any file(s) known to git\r\n% \r \r",,terminal_output +393,362619,"TERMINAL",0,0,"git branch",,terminal_command +394,362658,"TERMINAL",0,0,"]633;C[?1h=\r advanced_topics\r\n clean-build\r\n evidential-regression-test/juliagoihman\r\n examples_and_tutorials\r\n feature-nidhimuchakani\r\n feature/introduction\r\n final-PR\r\n* main\r\n main_components\r\n natalie/dropout-flax\r\n user_guide\r\n\r[?1l>% \r \r",,terminal_output +395,368747,"TERMINAL",0,0,"git checkout user_guide",,terminal_command +396,368798,"TERMINAL",0,0,"]633;C",,terminal_output +397,368804,"TERMINAL",0,0,"Switched to branch 'user_guide'\r\nYour branch is up to date with 'n-te-gruppe/user_guide'.\r\n% \r \r",,terminal_output +398,370316,"",0,0,"Switched from branch 'main' to 'user_guide'",,git_branch_checkout +399,371087,"TERMINAL",0,0,"make html",,terminal_command +400,371138,"TERMINAL",0,0,"]633;C",,terminal_output +401,371395,"TERMINAL",0,0,"Running Sphinx v8.2.3\r\n",,terminal_output +402,371500,"TERMINAL",0,0,"loading translations [en]... done\r\n",,terminal_output +403,372441,"TERMINAL",0,0,"checking bibtex cache... out of date\r\nparsing bibtex file /Users/franzsrambical/Downloads/probly/docs/source/references.bib... parsed 70 entries\r\n",,terminal_output +404,372526,"TERMINAL",0,0,"myst v4.0.1: MdParserConfig(commonmark_only=False, gfm_only=False, enable_extensions=set(), disable_syntax=[], all_links_external=False, links_external_new_tab=False, url_schemes=('http', 'https', 'mailto', 'ftp'), ref_domains=None, fence_as_directive=set(), number_code_blocks=[], title_to_header=False, heading_anchors=0, heading_slug_func=None, html_meta={}, footnote_sort=True, footnote_transition=True, words_per_minute=200, substitutions={}, linkify_fuzzy_links=True, dmath_allow_labels=True, dmath_allow_space=True, dmath_allow_digits=True, dmath_double_inline=False, update_mathjax=True, mathjax_classes='tex2jax_process|mathjax_process|math|output_area', enable_checkboxes=False, suppress_warnings=[], highlight_code_blocks=True)\r\nmyst-nb v1.2.0: NbParserConfig(custom_formats={}, metadata_key='mystnb', cell_metadata_key='mystnb', kernel_rgx_aliases={}, eval_name_regex='^[a-zA-Z_][a-zA-Z0-9_]*$', execution_mode='off', execution_cache_path='', execution_excludepatterns=(), execution_timeout=30, execution_in_temp=False, execution_allow_errors=False, execution_raise_on_error=False, execution_show_tb=False, merge_streams=False, render_plugin='default', remove_code_source=False, remove_code_outputs=False, code_prompt_show='Show code cell {type}', code_prompt_hide='Hide code cell {type}', number_source_lines=False, output_stderr='show', render_text_lexer='myst-ansi', render_error_lexer='ipythontb', render_image_options={}, render_figure_options={}, render_markdown_format='commonmark', output_folder='build', append_css=True, metadata_to_fm=False)\r\nUsing jupyter-cache at: /Users/franzsrambical/Downloads/probly/docs/build/.jupyter_cache\r\nloading intersphinx inventory 'python3' from https://docs.python.org/3/objects.inv ...\r\nloading intersphinx inventory 'numpy' from https://numpy.org/doc/stable/objects.inv ...\r\nloading intersphinx inventory 'scipy' from https://docs.scipy.org/doc/scipy/objects.inv ...\r\nloading intersphinx inventory 'matplotlib' from https://matplotlib.org/stable/objects.inv ...\r\nloading intersphinx inventory 'PIL' from https://pillow.readthedocs.io/en/stable/objects.inv ...\r\nloading intersphinx inventory 'torch' from https://pytorch.org/docs/stable/objects.inv ...\r\n",,terminal_output +405,372873,"TERMINAL",0,0,"intersphinx inventory has moved: https://pytorch.org/docs/stable/objects.inv -> https://docs.pytorch.org/docs/stable/objects.inv\r\n",,terminal_output +406,373537,"TERMINAL",0,0,"building [mo]: targets for 0 po files that are out of date\r\nwriting output... \r\nbuilding [html]: targets for 616 source files that are out of date\r\nupdating environment: [new config] 616 added, 0 changed, 0 removed\r\nreading sources... 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[ 45%] api/probly.train.bayesian.torch.functional\rreading sources... [ 45%] api/probly.train.bayesian.torch.futures\rreading sources... [ 45%] api/probly.train.bayesian.torch.fx\rreading sources... [ 45%] api/probly.train.bayesian.torch.fx.config\rreading sources... [ 46%] api/probly.train.bayesian.torch.fx.experimental\rreading sources... [ 46%] api/probly.train.bayesian.torch.fx.experimental.const_fold\rreading sources... [ 46%] api/probly.train.bayesian.torch.fx.experimental.proxy_tensor\rreading sources... [ 46%] api/probly.train.bayesian.torch.fx.experimental.recording\rreading sources... [ 46%] api/probly.train.bayesian.torch.fx.experimental.sym_node\rreading sources... [ 46%] api/probly.train.bayesian.torch.fx.experimental.symbolic_shapes\rreading sources... [ 47%] api/probly.train.bayesian.torch.fx.graph\rreading sources... [ 47%] api/probly.train.bayesian.torch.fx.graph_module\rreading sources... [ 47%] api/probly.train.bayesian.torch.fx.immutable_collections\rreading sources... [ 47%] api/probly.train.bayesian.torch.fx.interpreter\rreading sources... [ 47%] api/probly.train.bayesian.torch.fx.node\rre\n[... 5197 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +420,379839,"TERMINAL",0,0,"reading sources... [ 55%] api/probly.train.bayesian.torch.multiprocessing.reductions\rreading sources... [ 55%] api/probly.train.bayesian.torch.multiprocessing.spawn\rreading sources... [ 55%] api/probly.train.bayesian.torch.nested\rreading sources... [ 55%] api/probly.train.bayesian.torch.nn\r",,terminal_output +421,379954,"TERMINAL",0,0,"reading sources... [ 56%] api/probly.train.bayesian.torch.nn.attention\rreading sources... [ 56%] api/probly.train.bayesian.torch.nn.common_types\rreading sources... [ 56%] api/probly.train.bayesian.torch.nn.functional\r",,terminal_output +422,380651,"TERMINAL",0,0,":11: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:11: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:9: (ERROR/3) Unexpected indentation.\r\n:10: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:13: (ERROR/3) Unexpected indentation.\r\n:9: (ERROR/3) Unexpected indentation.\r\n:10: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:13: (ERROR/3) Unexpected indentation.\r\n:18: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:13: (ERROR/3) Unexpected indentation.\r\n:12: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:14: (ERROR/3) Unexpected indentation.\r\n:18: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:20: (ERROR/3) Unexpected indentation.\r\n:25: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:22: (ERROR/3) Unexpected indentation.\r\n:23: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:29: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:31: (ERROR/3) Unexpected indentation.\r\n:32: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:12: (ERROR/3) Unexpected indentation.\r\n:13: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:9: (ERROR/3) Unexpected indentation.\r\n:11: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:14: (ERROR/3) Unexpected indentation.\r\n:7: (ERROR/3) Unexpected indentation.\r\n:8: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:12: (ERROR/3) Unexpected indentation.\r\n:15: (ERROR/3) Unexpected indentation.\r\n:18: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:20: (ERROR/3) Unexpected indentation.\r\n:21: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:12: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:15: (ERROR/3) Unexpected indentation.\r\n:21: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:13: (ERROR/3) Unexpected indentation.\r\n:16: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:13: (ERROR/3) Unexpected indentation.\r\n:12: (ERROR/3) Unexpected indentation.\r\n:15: (ERROR/3) Unexpected indentation.\r\n:16: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:18: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:21: (ERROR/3) Unexpected indentation.\r\n:22: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:15: (ERROR/3) Unexpected indentation.\r\n:16: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:18: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:21: (ERROR/3) Unexpected indentation.\r\n:22: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:15: (ERROR/3) Unexpected indentation.\r\n:16: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:18: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:21: (ERROR/3) Unexpected indentation.\r\n:22: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:14: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:8: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:13: (ERROR/3) Unexpected indentation.\r\n:14: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:18: (ERROR/3) Unexpected indentation.\r\n:20: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:32: (ERROR/3) Unexpected indentation.\r\n:39: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:42: (ERROR/3) Unexpected indentation.\r\n:49: (ERROR/3) Unexpected indentation.\r\n:50: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:71: (ERROR/3) Unexpected indentation.\r\n:72: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:14: (ERROR/3) Unexpected indentation.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:14: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:16: (ERROR/3) Unexpected indentation.\r\n:18: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:20: (ERROR/3) Unexpected indentation.\r\n:17: (ERROR/3) Unexpected indentation.\r\n:36: (ERROR/3) Unexpected indentation.\r\n:37: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:39: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:9: (ERROR/3) Unexpected indentation.\r\n:11: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:14: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:16: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:19: (ERROR/3) Unexpected indentation.\r\n:14: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:16: (ERROR/3) Unexpected indentation.\r\n:10: (ERROR/3) Unexpected indentation.\r\n:11: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\nreading sources... [ 56%] api/probly.train.bayesian.torch.nn.grad\rreading sources... [ 56%] api/probly.train.bayesian.torch.nn.init\r:16: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:23: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:16: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:23: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:12: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:16: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:23: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:16: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:23: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:12: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n",,terminal_output +423,380802,"TERMINAL",0,0,"reading sources... [ 56%] api/probly.train.bayesian.torch.nn.intrinsic\r",,terminal_output +424,380942,"TERMINAL",0,0,"reading sources... [ 56%] api/probly.train.bayesian.torch.nn.intrinsic.modules\rreading sources... [ 57%] api/probly.train.bayesian.torch.nn.intrinsic.modules.fused\rreading sources... [ 57%] api/probly.train.bayesian.torch.nn.intrinsic.qat\rreading sources... [ 57%] api/probly.train.bayesian.torch.nn.intrinsic.qat.modules\r",,terminal_output +425,381308,"TERMINAL",0,0,"reading sources... [ 57%] api/probly.train.bayesian.torch.nn.intrinsic.qat.modules.conv_fused\r",,terminal_output +426,381445,"TERMINAL",0,0,"reading sources... [ 57%] api/probly.train.bayesian.torch.nn.intrinsic.qat.modules.linear_fused\rreading sources... [ 57%] api/probly.train.bayesian.torch.nn.intrinsic.qat.modules.linear_relu\rreading sources... [ 58%] api/probly.train.bayesian.torch.nn.intrinsic.quantized\r",,terminal_output +427,381601,"TERMINAL",0,0,"reading sources... [ 58%] api/probly.train.bayesian.torch.nn.intrinsic.quantized.dynamic\rreading sources... [ 58%] api/probly.train.bayesian.torch.nn.intrinsic.quantized.dynamic.modules\rreading sources... [ 58%] api/probly.train.bayesian.torch.nn.intrinsic.quantized.dynamic.modules.linear_relu\rreading sources... [ 58%] api/probly.train.bayesian.torch.nn.intrinsic.quantized.modules\r",,terminal_output +428,381725,"TERMINAL",0,0,"reading sources... [ 58%] api/probly.train.bayesian.torch.nn.intrinsic.quantized.modules.bn_relu\rreading sources... [ 59%] api/probly.train.bayesian.torch.nn.intrinsic.quantized.modules.conv_relu\rreading sources... [ 59%] api/probly.train.bayesian.torch.nn.intrinsic.quantized.modules.linear_relu\rreading sources... [ 59%] api/probly.train.bayesian.torch.nn.modules\r",,terminal_output +429,381944,"TERMINAL",0,0,":6: (ERROR/3) Unexpected indentation.\r\n",,terminal_output +430,382021,"TERMINAL",0,0,":8: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:6: (ERROR/3) Unexpected indentation.\r\n:7: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n",,terminal_output +431,382362,"TERMINAL",0,0,":8: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:6: (ERROR/3) Unexpected indentation.\r\n:8: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:6: (ERROR/3) Unexpected indentation.\r\n:8: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:23: (ERROR/3) Unexpected indentation.\r\n:31: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:26: (ERROR/3) Unexpected indentation.\r\n:34: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:34: (ERROR/3) Unexpected indentation.\r\n:25: (ERROR/3) Unexpected indentation.\r\n:42: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:44: (ERROR/3) Unexpected indentation.\r\n:51: (ERROR/3) Unexpected indentation.\r\n",,terminal_output +432,382416,"TERMINAL",0,0,":8: (ERROR/3) Unexpected indentation.\r\n:11: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n",,terminal_output +433,382648,"TERMINAL",0,0,":20: (ERROR/3) Unexpected indentation.\r\n:26: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:8: (ERROR/3) Unexpected indentation.\r\n:8: (ERROR/3) Unexpected indentation.\r\n",,terminal_output +434,384648,"TERMINAL",0,0,"reading sources... [ 59%] api/probly.train.bayesian.torch.nn.modules.activation\r",,terminal_output +435,384748,"TERMINAL",0,0,":8: (ERROR/3) Unexpected indentation.\r\n:11: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n",,terminal_output +436,385091,"TERMINAL",0,0,"reading sources... [ 59%] api/probly.train.bayesian.torch.nn.modules.adaptive\r",,terminal_output +437,385143,"TERMINAL",0,0,"reading sources... [ 59%] api/probly.train.bayesian.torch.nn.modules.batchnorm\r",,terminal_output +438,385248,"TERMINAL",0,0,"reading sources... [ 60%] api/probly.train.bayesian.torch.nn.modules.channelshuffle\rreading sources... [ 60%] api/probly.train.bayesian.torch.nn.modules.container\r",,terminal_output +439,385408,"TERMINAL",0,0,"reading sources... [ 60%] api/probly.train.bayesian.torch.nn.modules.conv\r:6: (ERROR/3) Unexpected indentation.\r\n",,terminal_output +440,385598,"TERMINAL",0,0,"reading sources... [ 60%] api/probly.train.bayesian.torch.nn.modules.distance\r",,terminal_output +441,385927,"TERMINAL",0,0,"reading sources... [ 60%] api/probly.train.bayesian.torch.nn.modules.dropout\r",,terminal_output +442,386041,"TERMINAL",0,0,"reading sources... [ 60%] api/probly.train.bayesian.torch.nn.modules.flatten\rreading sources... [ 61%] api/probly.train.bayesian.torch.nn.modules.fold\r",,terminal_output +443,386105,"TERMINAL",0,0,"reading sources... [ 61%] api/probly.train.bayesian.torch.nn.modules.instancenorm\r",,terminal_output +444,386189,"TERMINAL",0,0,"reading sources... [ 61%] api/probly.train.bayesian.torch.nn.modules.lazy\rreading sources... [ 61%] api/probly.train.bayesian.torch.nn.modules.linear\r",,terminal_output +445,386277,"TERMINAL",0,0,"reading sources... [ 61%] api/probly.train.bayesian.torch.nn.modules.loss\r",,terminal_output +446,386863,"TERMINAL",0,0,"reading sources... [ 61%] api/probly.train.bayesian.torch.nn.modules.module\r:8: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:6: (ERROR/3) Unexpected indentation.\r\n:8: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:6: (ERROR/3) Unexpected indentation.\r\n:8: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:23: (ERROR/3) Unexpected indentation.\r\n:31: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:26: (ERROR/3) Unexpected indentation.\r\n:34: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:34: (ERROR/3) Unexpected indentation.\r\n:25: (ERROR/3) Unexpected indentation.\r\n:42: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:44: (ERROR/3) Unexpected indentation.\r\n:51: (ERROR/3) Unexpected indentation.\r\n:21: (ERROR/3) Unexpected indentation.\r\n:21: (ERROR/3) Unexpected indentation.\r\n",,terminal_output +447,387045,"TERMINAL",0,0,"reading sources... [ 62%] api/probly.train.bayesian.torch.nn.modules.normalization\r",,terminal_output +448,387165,"TERMINAL",0,0,"reading sources... [ 62%] api/probly.train.bayesian.torch.nn.modules.padding\r",,terminal_output +449,387381,"TERMINAL",0,0,"reading sources... [ 62%] api/probly.train.bayesian.torch.nn.modules.pixelshuffle\rreading sources... [ 62%] api/probly.train.bayesian.torch.nn.modules.pooling\r",,terminal_output +450,387799,"TERMINAL",0,0,"reading sources... [ 62%] api/probly.train.bayesian.torch.nn.modules.rnn\r",,terminal_output +451,388552,"TERMINAL",0,0,"reading sources... [ 62%] api/probly.train.bayesian.torch.nn.modules.sparse\r:8: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:6: (ERROR/3) Unexpected indentation.\r\n:7: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n",,terminal_output +452,388654,"TERMINAL",0,0,"reading sources... [ 62%] api/probly.train.bayesian.torch.nn.modules.transformer\r:20: (ERROR/3) Unexpected indentation.\r\n:26: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:11: (ERROR/3) Unexpected indentation.\r\n:17: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:8: (ERROR/3) Unexpected indentation.\r\n:8: (ERROR/3) Unexpected indentation.\r\n",,terminal_output +453,388738,"TERMINAL",0,0,"reading sources... [ 63%] api/probly.train.bayesian.torch.nn.modules.upsampling\r",,terminal_output +454,388808,"TERMINAL",0,0,"reading sources... [ 63%] api/probly.train.bayesian.torch.nn.modules.utils\rreading sources... [ 63%] api/probly.train.bayesian.torch.nn.parallel\r",,terminal_output +455,388873,"TERMINAL",0,0,":10: (ERROR/3) Unexpected indentation.\r\n:10: (ERROR/3) Unexpected indentation.\r\n:11: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n",,terminal_output +456,388923,"TERMINAL",0,0,":10: (ERROR/3) Unexpected indentation.\r\n",,terminal_output +457,389014,"TERMINAL",0,0,"reading sources... [ 63%] api/probly.train.bayesian.torch.nn.parallel.comm\rreading sources... [ 63%] api/probly.train.bayesian.torch.nn.parallel.data_parallel\r:10: (ERROR/3) Unexpected indentation.\r\nreading sources... [ 63%] api/probly.train.bayesian.torch.nn.parallel.distributed\r",,terminal_output +458,389125,"TERMINAL",0,0,"reading sources... [ 64%] api/probly.train.bayesian.torch.nn.parallel.parallel_apply\rreading sources... [ 64%] api/probly.train.bayesian.torch.nn.parallel.replicate\rreading sources... [ 64%] api/probly.train.bayesian.torch.nn.parallel.scatter_gather\r:10: (ERROR/3) Unexpected indentation.\r\n:11: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:10: (ERROR/3) Unexpected indentation.\r\n:11: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\nreading sources... [ 64%] api/probly.train.bayesian.torch.nn.parameter\r",,terminal_output +459,389211,"TERMINAL",0,0,"reading sources... [ 64%] api/probly.train.bayesian.torch.nn.qat\r",,terminal_output +460,389297,"TERMINAL",0,0,"reading sources... [ 64%] api/probly.train.bayesian.torch.nn.qat.dynamic\rreading sources... [ 65%] api/probly.train.bayesian.torch.nn.qat.dynamic.modules\rreading sources... [ 65%] api/probly.train.bayesian.torch.nn.qat.dynamic.modules.linear\rreading sources... [ 65%] api/probly.train.bayesian.torch.nn.qat.modules\r",,terminal_output +461,389499,"TERMINAL",0,0,"reading sources... [ 65%] api/probly.train.bayesian.torch.nn.qat.modules.conv\rreading sources... [ 65%] api/probly.train.bayesian.torch.nn.qat.modules.embedding_ops\rreading sources... [ 65%] api/probly.train.bayesian.torch.nn.qat.modules.linear\rreading sources... [ 66%] api/probly.train.bayesian.torch.nn.quantizable\rreading sources... [ 66%] api/probly.train.bayesian.torch.nn.quantizable.modules\r:11: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:13: (ERROR/3) Unexpected indentation.\r\nreading sources... [ 66%] api/probly.train.bayesian.torch.nn.quantized\r",,terminal_output +462,389881,"TERMINAL",0,0,"reading sources... [ 66%] api/probly.train.bayesian.torch.nn.quantized.dynamic\rreading sources... [ 66%] api/probly.train.bayesian.torch.nn.quantized.dynamic.modules\r",,terminal_output +463,390071,"TERMINAL",0,0,"reading sources... [ 66%] api/probly.train.bayesian.torch.nn.quantized.dynamic.modules.conv\r",,terminal_output +464,390171,"TERMINAL",0,0,"reading sources... [ 67%] api/probly.train.bayesian.torch.nn.quantized.dynamic.modules.linear\rreading sources... [ 67%] api/probly.train.bayesian.torch.nn.quantized.dynamic.modules.rnn\r",,terminal_output +465,390347,"TERMINAL",0,0,"reading sources... [ 67%] api/probly.train.bayesian.torch.nn.quantized.functional\rreading sources... [ 67%] api/probly.train.bayesian.torch.nn.quantized.modules\r",,terminal_output +466,390714,"TERMINAL",0,0,"reading sources... [ 67%] api/probly.train.bayesian.torch.nn.quantized.modules.activation\r",,terminal_output +467,391238,"TERMINAL",0,0,"reading sources... [ 67%] api/probly.train.bayesian.torch.nn.quantized.modules.batchnorm\rreading sources... [ 68%] api/probly.train.bayesian.torch.nn.quantized.modules.conv\r",,terminal_output +468,391364,"TERMINAL",0,0,"reading sources... [ 68%] api/probly.train.bayesian.torch.nn.quantized.modules.dropout\rreading sources... [ 68%] api/probly.train.bayesian.torch.nn.quantized.modules.embedding_ops\r",,terminal_output +469,391419,"TERMINAL",0,0,"reading sources... [ 68%] api/probly.train.bayesian.torch.nn.quantized.modules.functional_modules\r",,terminal_output +470,391548,"TERMINAL",0,0,"reading sources... [ 68%] api/probly.train.bayesian.torch.nn.quantized.modules.linear\rreading sources... [ 68%] api/probly.train.bayesian.torch.nn.quantized.modules.normalization\r",,terminal_output +471,391682,"TERMINAL",0,0,"reading sources... [ 69%] api/probly.train.bayesian.torch.nn.quantized.modules.rnn\rreading sources... [ 69%] api/probly.train.bayesian.torch.nn.quantized.modules.utils\rreading sources... [ 69%] api/probly.train.bayesian.torch.nn.utils\r:13: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:15: (ERROR/3) Unexpected indentation.\r\n:16: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:19: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:22: (ERROR/3) Unexpected indentation.\r\n:23: (ERROR/3) Unexpected indentation.\r\n:24: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:6: (ERROR/3) Unexpected indentation.\r\n",,terminal_output +472,391735,"TERMINAL",0,0,":23: (ERROR/3) Unexpected indentation.\r\n:24: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n",,terminal_output +473,392176,"TERMINAL",0,0,"reading sources... [ 69%] api/probly.train.bayesian.torch.nn.utils.clip_grad\r:13: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:15: (ERROR/3) Unexpected indentation.\r\n:16: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:13: (WARNING/2) Definition list ends without a blank line; unexpected unindent.\r\n:15: (ERROR/3) Unexpected indentation.\r\n:16: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\nreading sources... [ 69%] api/probly.train.bayesian.torch.nn.utils.convert_parameters\r:6: (ERROR/3) Unexpected indentation.\r\nreading sources... [ 69%] api/probly.train.bayesian.torch.nn.utils.fusion\rreading sources... [ 69%] api/probly.train.bayesian.torch.nn.utils.init\rreading sources... [ 70%] api/probly.train.bayesian.torch.nn.utils.memory_format\rreading sources... [ 70%] api/probly.train.bayesian.torch.nn.utils.parametrizations\r:59: (ERROR/3) Unexpected indentation.\r\n:60: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:46: (ERROR/3) Unexpected indentation.\r\n:47: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:59: (ERROR/3) Unexpected indentation.\r\n:60: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:46: (ERROR/3) Unexpected indentation.\r\n:47: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\nreading sources... [ 70%] api/probly.train.bayesian.torch.nn.utils.parametrize\r:6: (ERROR/3) Unexpected indentation.\r\n:71: (ERROR/3) Unexpected indentation.\r\n:72: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:14: (ERROR/3) Unexpected indentation.\r\n:6: (ERROR/3) Unexpected indentation.\r\n:71: (ERROR/3) Unexpected indentation.\r\n:72: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:14: (ERROR/3) Unexpected indentation.\r\nreading sources... [ 70%] api/probly.train.bayesian.torch.nn.utils.rnn\r:25: (ERROR/3) Unexpected indentation.\r\n:26: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:24: (ERROR/3) Unexpected indentation.\r\n:37: (ERROR/3) Unexpected indentation.\r\n:38: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:40: (ERROR/3) Unexpected indentation.\r\n:27: (ERROR/3) Unexpected indentation.\r\n:28: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:30: (ERROR/3) Unexpected indentation.\r\n:25: (ERROR/3) Unexpected indentation.\r\n:26: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:24: (ERROR/3) Unexpected indentation.\r\n:37: (ERROR/3) Unexpected indentation.\r\n:38: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:40: (ERROR/3) Unexpected indentation.\r\n:27: (ERROR/3) Unexpected indentation.\r\n:28: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:30: (ERROR/3) Unexpected indentation.\r\n",,terminal_output +474,393435,"TERMINAL",0,0,"reading sources... [ 70%] api/probly.train.bayesian.torch.nn.utils.spectral_norm\r:23: (ERROR/3) Unexpected indentation.\r\n:24: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\nreading sources... [ 70%] api/probly.train.bayesian.torch.nn.utils.stateless\rreading sources... [ 71%] api/probly.train.bayesian.torch.nn.utils.weight_norm\rreading sources... [ 71%] api/probly.train.bayesian.torch.numa\rreading sources... [ 71%] api/probly.train.bayesian.torch.numa.binding\rreading sources... [ 71%] api/probly.train.bayesian.torch.onnx\rreading sources... [ 71%] api/probly.train.bayesian.torch.onnx.errors\rreading sources... [ 71%] api/probly.train.bayesian.torch.onnx.operators\rreading sources... [ 72%] api/probly.train.bayesian.torch.onnx.ops\rreading sources... [ 72%] api/probly.train.bayesian.torch.onnx.symbolic_helper\rreading sources... [ 72%] api/probly.train.bayesian.torch.onnx.symbolic_opset10\rreading sources... [ 72%] api/probly.train.bayesian.torch.onnx.symbolic_opset11\rreading sources... [ 72%] api/probly.train.bayesian.torch.onnx.symbolic_opset9\rreading sources... [ 72%] api/probly.train.bayesian.torch.onnx.utils\rreading sources... [ 73%] api/probly.train.bayesian.torch.optim\rreading sources... [ 73%] api/probly.train.bayesian.torch.optim.lr_scheduler\rreading sources... [ 73%] api/probly.train.bayesian.torch.optim.swa_utils\rreading sources... [ 73%] api/probly.train.bayesian.torch.overrides\rreading sources... [ 73%] api/probly.train.bayesian.torch.package\rreading sources... [ 73%] api/probly.train.bayesian.torch.package.analyze\rreading sources... [ 74%] api/probly.train.bayesian.torch.package.analyze.find_first_use_of_broken_modules\rreading sources... [ 74%] api/probly.train.bayesian.torch.package.analyze.is_from_package\rreading sources... [ 74%] api/probly.train.bayesian.torch.package.analyze.trace_dependencies\r:6: (ERROR/3) Unexpected indentation.\r\nreading sources... [ 74%] api/probly.train.bayesian.torch.package.file_structure_representation\rreading sources... [ 74%] api/probly.train.bayesian.torch.package.find_file_dependencies\rreading sources... [ 74%] api/probly.train.bayesian.torch.package.glob_group\rreading sources... [ 75%] api/probly.train.bayesian.torch.package.importer\rreading sources... [ 75%] api/probly.train.bayesian.torch.package.package_exporter\rreading sources... [ 75%] api/probly.train.bayesian.torch.package.package_importer\rreading sources... [ 75%] api/probly.train.bayesian.torch.profiler\rreading sources... [ 75%] api/probly.train.bayesian.torch.profiler.itt\rreading sources... [ 75%] api/probly.train.bayesian.torch.profiler.profiler\rreading sources... [ 75%] api/probly.train.bayesian.torch.quantization\rreading sources... [ 76%] api/probly.train.bayesian.torch.quantization.fake_quantize\rreading sources... [ 76%] api/probly.train.bayesian.torch.quantization.fuse_modules\rreading sources... [ 76%] api/probly.train.bayesian.torch.quantization.fuser_method_mappings\rreading sources... [ 76%] api/probly.train.bayesian.torch.quantization.observer\rreading sources... [ 76%] api/probly.train.bayesian.torch.quantization.qconfig\rreading sources... [ 76%] api/probly.train.bayesian.torch.quantization.quant_type\rreading sources... [ 77%] api/probly.train.bayesian.torch.quantization.quantization_mappings\rreading sources... [ 77%] api/probly.train.bayesian.torch.quantization.quantize\rreading sources... [ 77%] api/probly.train.bayesian.torch.quantization.quantize_jit\rreading sources... [ 77%] api/probly.train.bayesian.torch.quantization.stubs\rreading sources... [ 77%] api/probly.train.bayesian.torch.quasirandom\rreading sources... [ 77%] api/probly.train.bayesian.torch.random\rreading sources... [ 78%] api/probly.train.bayesian.torch.return_types\rreading sources... [ 78%] api/probly.train.bayesian.torch.serialization\rreading sources... [ 78%] api/probly.train.bayesian.torch.signal\rreading sources... [ 78%] api/probly.train.bayesian.torch.signal.windows\rreading sources... [ 78%] api/probly.train.bayesian.torch.signal.windows.windows\rreading sources... [ 78%] api/probly.train.bayesian.torch.sparse\rreading sources... [ 79%] api/probly.train.bayesian.torch.sparse.semi_structured\rreading sources... [ 79%] api/probly.train.bayesian.torch.special\rreading sources... [ 79%] api/probly.train.bayesian.torch.storage\rreading sources... [ 79%] api/probly.train.bayesian.torch.testing\rreading sources... [ 79%] api/probly.train.bayesian.torch.torch_version\rreading sources... [ 79%] api/probly.train.bayesian.torch.types\rreading sources... [ 80%] api/probly.train.bayesian.torch.utils\rreading sources... [ 80%] api/probly.train.bayesian.torch.utils.backcompat\rreading sources... [ 80%] api/probly.train.bayesian.torch.utils.backend_registration\rreading sources... [ 80%] api/probly.train.bayesian.torch.utils.checkpoint\rreading sources... [ 80%] api/probly.train.bayesian.torch.utils.collect_env\rreading sources... [ 80%] api/probly.train.bayesian.torch.utils.cpp_backtrace\rreading sources... [ 81%] api/probly.train.bayesian.torch.utils.data\rreading sources... [ 81%] api/probly.train.bayesian.torch.utils.data.dataloader\rreading sources... [ 81%] api/probly.train.bayesian.torch.utils.data.datapipes\rreading sources... [ 81%] api/probly.train.bayesian.torch.utils.data.datapipes.dataframe\rreading sources... [ 81%] api/probly.train.bayesian.torch.utils.data.datapipes.dataframe.dataframe_wrapper\rreading sources... [ 81%] api/probly.train.bayesian.torch.utils.data.datapipes.dataframe.dataframes\rreading sources... [ 81%] api/probly.train.bayesian.torch.utils.data.datapipes.dataframe.datapipes\rreading sources... [ 82%] api/probly.train.bayesian.torch.utils.data.datapipes.dataframe.structures\rreading sources... 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[100%] user_guide\r\r\n",,terminal_output +476,394056,"TERMINAL",0,0,"/Users/franzsrambical/Downloads/probly/docs/source/api.rst:4: WARNING: autosummary: stub file not found 'probly'. Check your autosummary_generate setting.\r\n/Users/franzsrambical/Downloads/probly/src/probly/representation/credal_set/__init__.py:docstring of probly.representation.credal_set.credal_set.CredalSet:1: WARNING: duplicate object description of probly.representation.credal_set.credal_set.CredalSet, other instance in api/probly.representation, use :no-index: for one of them\r\n/Users/franzsrambical/Downloads/probly/src/probly/representation/sampling/__init__.py:docstring of probly.representation.sampling.sample.Sample:1: WARNING: duplicate object description of probly.representation.sampling.sample.Sample, other instance in api/probly.representation, use :no-index: for one of them\r\n/Users/franzsrambical/Downloads/probly/src/probly/representation/sampling/__init__.py:docstring of probly.representation.sampling.sampler.Sampler:1: WARNING: duplicate object description of probly.representation.sampling.sampler.Sampler, other instance in api/probly.representation, use :no-index: for one of them\r\nWARNING: autodoc: failed to import module 'accelerator' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.accelerator'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.accelerator.rst:30: WARNING: autosummary: failed to import memory.\r\nPossible hints:\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'memory'\r\n* KeyError: 'memory'\r\nWARNING: autodoc: failed to import module 'accelerator.memory' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.accelerator'\n""] [autodoc.import_object]\r\nWARNING: autodoc: failed to import module 'amp' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.amp'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.amp.rst:9: WARNING: autosummary: failed to import autocast_mode.\r\nPossible hints:\r\n* KeyError: 'autocast_mode'\r\n* ModuleNotFoundError: No module named 'autocast_mode'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.amp.rst:9: WARNING: autosummary: failed to import grad_scaler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'grad_scaler'\r\n* KeyError: 'grad_scaler'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\nWARNING: autodoc: failed to import module 'amp.autocast_mode' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.amp'\n""] [autodoc.import_object]\r\nWARNING: autodoc: failed to import module 'amp.grad_scaler' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.amp'\n""] [autodoc.import_object]\r\nWARNING: autodoc: failed to import module 'ao' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.rst:9: WARNING: autosummary: failed to import nn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'nn'\r\n* KeyError: 'nn'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.rst:9: WARNING: autosummary: failed to import ns.\r\nPossible hints:\r\n* KeyError: 'ns'\r\n* ModuleNotFoundError: No module named 'ns'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.rst:9: WARNING: autosummary: failed to import pruning.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'pruning'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* KeyError: 'pruning'\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.rst:9: WARNING: autosummary: failed to import quantization.\r\nPossible hints:\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'quantization'\r\n* KeyError: 'quantization'\r\nWARNING: autodoc: failed to import module 'ao.nn' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.rst:9: WARNING: autosummary: failed to import intrinsic.\r\nPossible hints:\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'intrinsic'\r\n* KeyError: 'intrinsic'\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.rst:9: WARNING: autosummary: failed to import qat.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'qat'\r\n* KeyError: 'qat'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.rst:9: WARNING: autosummary: failed to import quantizable.\r\nPossible hints:\r\n* KeyError: 'quantizable'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'quantizable'\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.rst:9: WARNING: autosummary: failed to import quantized.\r\nPossible hints:\r\n* KeyError: 'quantized'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'quantized'\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.rst:9: WARNING: autosummary: failed to import sparse.\r\nPossible hints:\r\n* KeyError: 'sparse'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'sparse'\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.rst:9: WARNING: autosummary: failed to import modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'modules'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* KeyError: 'modules'\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.rst:9: WARNING: autosummary: failed to import qat.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'qat'\r\n* KeyError: 'qat'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.rst:9: WARNING: autosummary: failed to import quantized.\r\nPossible hints:\r\n* KeyError: 'quantized'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'quantized'\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.modules' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.modules.rst:9: WARNING: autosummary: failed to import fused.\r\nPossible hints:\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'fused'\r\n* KeyError: 'fused'\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.modules.fused' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.qat' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.qat.rst:9: WARNING: autosummary: failed to import modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'modules'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* KeyError: 'modules'\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.qat.modules' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.rst:9: WARNING: autosummary: failed to import conv_fused.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'conv_fused'\r\n* KeyError: 'conv_fused'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.rst:9: WARNING: autosummary: failed to import linear_fused.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'linear_fused'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* KeyError: 'linear_fused'\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.rst:9: WARNING: autosummary: failed to import linear_relu.\r\nPossible hints:\r\n* KeyError: 'linear_relu'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'linear_relu'\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.qat.modules.conv_fused' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.qat.modules.linear_fused' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.qat.modules.linear_relu' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.quantized' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.quantized.rst:9: WARNING: autosummary: failed to import dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'dynamic'\r\n* KeyError: 'dynamic'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.quantized.rst:9: WARNING: autosummary: failed to import modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'modules'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* KeyError: 'modules'\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.quantized.dynamic' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.rst:9: WARNING: autosummary: failed to import modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'modules'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* KeyError: 'modules'\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.quantized.dynamic.modules' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.modules.rst:9: WARNING: autosummary: failed to import linear_relu.\r\nPossible hints:\r\n* KeyError: 'linear_relu'\r\n* ValueError: not enough values to unpack (expected 2, got 1)\r\n* ModuleNotFoundError: No module named 'linear_relu'\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.quantized.dynamic.modules.linear_relu' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 169, in import_module\n spec = find_spec(modname)\n', ' File """", line 95, in find_spec\n', ""ModuleNotFoundError: __path__ attribute not found on 'probly.train.bayesian.torch' while trying to find 'probly.train.bayesian.torch.ao'\n""] [autodoc.import_object]\r\nWARNING: autodoc: failed to import module 'ao.nn.intrinsic.quantized.modules' from module 'probly.train.bayesian.torch'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File """", line 93, in find_spec\n', ""AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'. Did you mean: '__name__'?\n"", '\nThe above exception was the direct cause of the following exception:\n\n', 'Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/f\n[... 711467 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +477,394250,"TERMINAL",0,0,"done\r\nchecking consistency... /Users/franzsrambical/Downloads/probly/docs/source/api/probly.datasets.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.evaluation.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.layers.rst: WARNING: document isn't included in any toctree 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[toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.accelerator.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.accelerator.memory.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.amp.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.amp.autocast_mode.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.amp.grad_scaler.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.ao.rst: WARNING: document isn't 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[ 88%] ../build/jupyter_execute/fc53eecd8056576e8f540161515483e273252cb9671e9c8a6667c619449d3755.png\rcopying images... [ 94%] ../build/jupyter_execute/63a31c25d2a1a36d44298f7c82a52054f5dc6e9c75431e30feba0642688fa054.png\rcopying images... [100%] ../build/jupyter_execute/6dda1916944730f52c5b32e217d11be5b7c014b6c6f4dc769f8fc991187ef0a9.png\r\r\ndumping search index in English (code: en)... ",,terminal_output +534,414274,"TERMINAL",0,0,"done\r\ndumping object inventory... done\r\n\r\n====================== slowest reading durations =======================\r\n2.795 api/probly.train.bayesian.torch.nn.modules\r\n1.563 api/probly.datasets\r\n0.808 api/probly.evaluation\r\n0.750 api/probly.layers.flax\r\n0.701 api/probly.train.bayesian.torch.nn.modules.rnn\r\nbuild succeeded, 1881 warnings.\r\n\r\nThe HTML pages are in build/html.\r\n",,terminal_output +535,418276,"TERMINAL",0,0,"% \r \r",,terminal_output +536,483398,"TERMINAL",0,0,"ls build/html",,terminal_command +537,483402,"TERMINAL",0,0,"]633;C_images\t\t\t\tcontributing.html\t\tindex.html\t\t\tpy-modindex.html\r\n_modules\t\t\tcore_concepts.html\t\tinstallation.html\t\treferences.html\r\n_sources\t\t\texamples\t\t\tintroduction.html\t\tsearch.html\r\n_static\t\t\t\texamples_&_tutorials.html\tmain_components.html\t\tsearchindex.js\r\nadvanced_topics.html\t\tfaq.html\t\t\tmethods.html\t\t\tuser_guide.html\r\napi\t\t\t\tgenindex.html\t\t\tnotebooks\r\napi.html\t\t\thidden_index.html\t\tobjects.inv\r\n% \r \r",,terminal_output +538,1748387,"TERMINAL",0,0,"",,terminal_command +539,1748387,"TERMINAL",0,0,"]633;C",,terminal_output +540,1754925,"TERMINAL",0,0,"git branch",,terminal_command +541,1754975,"TERMINAL",0,0,"]633;C",,terminal_output +542,1755000,"TERMINAL",0,0,"[?1h=\r advanced_topics\r\n clean-build\r\n evidential-regression-test/juliagoihman\r\n examples_and_tutorials\r\n feature-nidhimuchakani\r\n feature/introduction\r\n final-PR\r\n main\r\n main_components\r\n natalie/dropout-flax\r\n* user_guide\r\n\r[?1l>% \r \r",,terminal_output +543,1759329,"TERMINAL",0,0,"git checkout clean-buil",,terminal_command +544,1759346,"TERMINAL",0,0,"]633;Cerror: pathspec 'clean-buil' did not match any file(s) known to git\r\n% \r \r",,terminal_output +545,1760622,"TERMINAL",0,0,"git checkout clean-build",,terminal_command +546,1760671,"TERMINAL",0,0,"]633;CSwitched to branch 'clean-build'\r\nYour branch is up to date with 'n-te-gruppe/clean-build'.\r\n% \r \r",,terminal_output +547,1761445,"",0,0,"Switched from branch 'user_guide' to 'clean-build'",,git_branch_checkout +548,1819680,"TERMINAL",0,0,"git checkout e2f73f8fe9c6f860f36e2278b49c311776309afd",,terminal_command +549,1819699,"TERMINAL",0,0,"]633;CNote: switching to 'e2f73f8fe9c6f860f36e2278b49c311776309afd'.\r\n\r\nYou are in 'detached HEAD' state. You can look around, make experimental\r\nchanges and commit them, and you can discard any commits you make in this\r\nstate without impacting any branches by switching back to a branch.\r\n\r\nIf you want to create a new branch to retain commits you create, you may\r\ndo so (now or later) by using -c with the switch command. Example:\r\n\r\n git switch -c \r\n\r\nOr undo this operation with:\r\n\r\n git switch -\r\n\r\nTurn off this advice by setting config variable advice.detachedHead to false\r\n\r\nHEAD is now at e2f73f8 Initialize evidential classification test package\r\n% \r \r",,terminal_output +550,1827524,"TERMINAL",0,0,"make html",,terminal_command +551,1827574,"TERMINAL",0,0,"]633;C",,terminal_output +552,1829579,"TERMINAL",0,0,"Running Sphinx v8.2.3\r\n",,terminal_output +553,1830430,"TERMINAL",0,0,"loading translations [en]... done\r\n",,terminal_output +554,1839451,"TERMINAL",0,0,"loading pickled environment... ",,terminal_output +555,1839964,"TERMINAL",0,0,"checking bibtex cache... out of date\r\nparsing bibtex file /Users/franzsrambical/Downloads/probly/docs/source/references.bib... parsed 19 entries\r\ndone\r\n",,terminal_output +556,1840161,"TERMINAL",0,0,"[autosummary] generating autosummary for: api.rst, api/probly.datasets.rst, api/probly.datasets.torch.rst, api/probly.evaluation.metrics.rst, api/probly.evaluation.rst, api/probly.evaluation.tasks.rst, api/probly.layers.flax.rst, api/probly.layers.rst, api/probly.layers.torch.rst, api/probly.plot.credal.rst, ..., api/probly.traverse_nn.rst, api/probly.traverse_nn.torch.rst, api/probly.utils.errors.rst, api/probly.utils.probabilities.rst, api/probly.utils.rst, api/probly.utils.sets.rst, api/probly.utils.torch.rst, index.rst, methods.md, references.rst\r\n",,terminal_output +557,1845428,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.sparse.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'sparse'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.style.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.relaxed_categorical.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.return_types.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'return_types'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.relaxed_bernoulli.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.optim.lr_scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.optim'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'optim'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.fake_quantize.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.beta.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.qconfig_mapping.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.qconfig.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.conv_fused.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.pt2e.export_utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.numa.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'numa'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.remote_device.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.cauchy.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.distributed.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.functional.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.futures.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'futures'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.immutable_collections.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.streams.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.multiprocessing.reductions.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'multiprocessing'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.multiprocessing'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.jiterator.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.compiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'compiler'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.mkldnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.logistic_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.collect_env.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.pass_manager.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.lkj_cholesky.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.independent.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.loss.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.multinomial.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.rnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.combining.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.dynamic.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.version.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'version'\r\nWARNING: Failed to import probly.train.bayesian.torch.testing.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'testing'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.amp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'amp'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.kl.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.constants.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.proxy.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.core.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.linear_relu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.selecting.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cuda.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.api.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.weibull.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.profiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mps'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.mps'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.c10d_logger.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.distribution.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.sharding.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n",,terminal_output +558,1848291,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.utils.data.dataloader.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.stubs.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.numa.binding.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'numa'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.numa'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.datapipe.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.sym_node.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.mha.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.package.glob_group.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.linear_relu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.unary.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.poisson.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_helper.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.functional.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.miopen.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.quantization_mappings.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.recording.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.profiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.signal.windows.windows.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.signal'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'signal'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.modules.linear_relu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.combining.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.binary.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.jit.frontend.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'jit'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.jit'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.observer.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quasirandom.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quasirandom'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.graph_drawer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.bernoulli.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.package.file_structure_representation.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.pt2e.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.grad_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.transformed_distribution.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.pareto.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.templates.remote_module_template.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.graph_manipulation.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.graph.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.storage.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'storage'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.device_mesh.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.normalization.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.forward_ad.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.grouping.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.streamreader.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.nvtx.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.amp.autocast_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.amp'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'amp'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.decomp_utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.reinplace.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.checkpoint.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.jit.annotations.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'jit'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.jit'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.quant_type.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.pt2e.graph_utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.multiprocessing.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'multiprocessing'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.random.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.xpu'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.hub.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'hub'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.grad_scaler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.split_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.datapipes.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.internal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.autocast_mode.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cpu'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.special.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'special'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.modules.fused.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.observer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.conv.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.tunable.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.wishart.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.quantization_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.rnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.gds.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.device_mesh.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.common.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.model_zoo.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bay\n[... 1953 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +559,1851204,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.package.importer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.graph_settings.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.net_min_base.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.amp.grad_scaler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.amp'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'amp'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.constants.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.library.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'library'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.const_fold.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.structures.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.logging_handlers.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.embedding_ops.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n",,terminal_output +560,1855812,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.backends.kleidiai.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.operator_support.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler_legacy.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.half_cauchy.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.operators.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.serialization.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'serialization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.studentT.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.activation.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.source_matcher_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.embedding_ops.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.activation.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.memory.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.mtia.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mtia'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.common.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.conv_add.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.dynamic.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.weight_norm_sparsifier.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.fuser_method_mappings.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.dirichlet.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.dlpack.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.server_process_global_profiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.fsdp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n",,terminal_output +561,1857101,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.wrap.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.dataframe_wrapper.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.qat.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.ops.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.binomial.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.sparse.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.anomaly_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.fx.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.runtime_assert.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.negative_binomial.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.split_module.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.function.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.placement_types.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.signal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'signal'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.api.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.api.remote_module.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.accelerator.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.accelerator'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'accelerator'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.custom_ops.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.types.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'types'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.accelerator.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'accelerator'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n",,terminal_output +562,1857480,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.utils.data.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.grad_scaler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cpu'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.linear_fused.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.algorithms.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.callable.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.bn_relu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.chi2.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.inverse_gamma.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.dynamic.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_opset9.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cusparselt.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.profiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'profiler'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler_util.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.graphs.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.errors.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.param_fetch.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.cubic_scheduler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.conv.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.batchnorm.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.mixture_same_family.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.exp_family.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.distributed_c10d.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.matcher_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.options.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.backcompat.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.functional.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.backend_registry.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.functional_modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.routeddecoder.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cpu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.profiler.itt.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'profiler'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.profiler'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.api.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.hooks.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\n",,terminal_output +563,1864334,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.ao.quantization.fake_quantize.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.overrides.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'overrides'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.dynamic.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.export.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.nested.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'nested'\r\nWARNING: Failed to import probly.train.bayesian.torch.monitor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'monitor'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.operator_schemas.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.templates.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.one_hot_categorical.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.graph_transform_observer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.combinatorics.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.graph.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.dynamic.modules.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.nnpack.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.graph_module.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.gamma.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.config.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.categorical.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.splitter_base.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.quantized.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.kumaraswamy.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.combinatorics.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.conv_relu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.continuous_bernoulli.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.functions.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.random.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.uniform.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cudnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.package.package_exporter.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.fx.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.grouping.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.conv.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.streams.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.xpu'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.decoder.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.creation.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.functional.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'functional'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.fuser_method_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.torch_version.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'torch_version'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.constraints.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.reductions.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.proxy_tensor.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.opt_einsum.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.tools_common.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.transforms.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.sparse.semi_structured.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'sparse'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.sparse'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cpu'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cpu'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.nearly_diagonal_sparsifier.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.multivariate_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.half_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.sampler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.fileopener.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.nccl.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.signal.windows.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.signal'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'signal'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.dropout.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.lambda_scheduler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.algorithms.join.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.rnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\n",,terminal_output +564,1866281,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.base_sparsifier.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.random.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'random'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.traceback.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_opset11.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'onnx'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.onnx'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.package.package_importer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.shape_prop.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.profiler.profiler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'profiler'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.profiler'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.export.dynamic_shapes.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mps'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fft.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fft'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.constraint_registry.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.api.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.rnn.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.losses.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.losses'\r\n* AttributeError: module 'probly.train' has no attribute 'losses'\r\nWARNING: Failed to import probly.train.bayesian.torch.optim.swa_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.optim'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'optim'\r\nWARNING: Failed to import probly.train.bayesian.torch.mtia.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.mtia'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mtia'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.autograd.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.xpu'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'xpu'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.dataset.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.export.graph_signature.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.sparse.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.linalg.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'linalg'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.graph.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.autograd'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.log_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.passthrough.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'masked'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.masked'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.subgraph_rewriter.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.common.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.utils.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.package.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.dataframes.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.package.find_file_dependencies.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.laplace.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.jit.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'jit'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.node.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.openmp.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'backends'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.backends'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.geometric.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.event.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'mps'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.mps'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.fx.ns_types.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.weak.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.generalized_pareto.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.autocast_mode.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'cuda'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.cuda'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.package.analyze.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.package'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'package'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.cpp_backtrace.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.qconfig.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.gumbel.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.filelister.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.linear.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.pass_base.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.exponential.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.instantiator.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.conv.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.stubs.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.quantization'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'quantization'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.interpreter.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.base_scheduler.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'autograd'\r\nWARNING: Failed to import probly.train.bayesian.torch.compiler.config.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'compiler'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.compiler'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.export.exported_program.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'export'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.export'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.fishersnedecor.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.callable.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.deterministic.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.quant_type.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'ao'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.ao'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.throughput_benchmark.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.von_mises.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.func.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'func'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.backend_registration.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'utils'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.utils'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.lowrank_multivariate_normal.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributions'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributions'; 'probly.train.bayesian.torch' is not a package\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.symbolic_shapes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.fx'; 'probly.train.bayesian.torch' is not a package\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'fx'\r\nWARNING: Failed to import probly.train.bayesian.torch.optim.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'optim'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.fully_sharded_data_parallel.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute 'distributed'\r\n* AttributeError: module 'probly.train.bayesian.torch' has no attribute '__path__'\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch.distributed'; 'probly.train.bayesian.torch' is not a package\r\n",,terminal_output +565,1866873,"TERMINAL",0,0,"\r\nExtension error (sphinx.ext.autosummary)!\r\n\r\n",,terminal_output +566,1866930,"TERMINAL",0,0,"Versions\r\n========\r\n\r\n* Platform: darwin; (macOS-15.6-arm64-arm-64bit-Mach-O)\r\n* Python version: 3.13.7 (CPython)\r\n* Sphinx version: 8.2.3\r\n* Docutils version: 0.21.2\r\n* Jinja2 version: 3.1.4\r\n* Pygments version: 2.19.1\r\n\r\nLast Messages\r\n=============\r\n\r\nNone.\r\n\r\nLoaded Extensions\r\n=================\r\n\r\nNone.\r\n\r\nTraceback\r\n=========\r\n\r\n File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/events.py"", line 415, in emit\r\n raise ExtensionError(\r\n ...<4 lines>...\r\n ) from exc\r\n sphinx.errors.ExtensionError: Handler for event 'builder-inited' threw an exception (exception: no module named probly.train.bayesian.torch.accelerator)\r\n\r\n\r\nThe full traceback has been saved in:\r\n/var/folders/nn/241fnlwx03d7k7qt2jg98txr0000gn/T/sphinx-err-voo0olh7.log\r\n\r\nTo report this error to the developers, please open an issue at . Thanks!\r\nPlease also report this if it was a user error, so that a better error message can be provided next time.\r\n",,terminal_output +567,1867806,"TERMINAL",0,0,"make: *** [html] Error 2\r\n% \r \r",,terminal_output +568,1921956,"README.md",0,0,"# probly: Uncertainty Representation and Quantification for Machine Learning\n
\n\n \n \n \n\n\n[![PyPI version](https://badge.fury.io/py/probly.svg)](https://badge.fury.io/py/probly)\n[![PyPI status](https://img.shields.io/pypi/status/probly.svg?color=blue)](https://pypi.org/project/probly)\n[![PePy](https://static.pepy.tech/badge/probly?style=flat-square)](https://pepy.tech/project/probly)\n[![codecov](https://codecov.io/gh/pwhofman/probly/branch/main/graph/badge.svg)](https://codecov.io/gh/pwhofman/probly)\n[![Contributions Welcome](https://img.shields.io/badge/contributions-welcome-brightgreen)](.github/CONTRIBUTING.md)\n[![License](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://opensource.org/licenses/MIT)\n
\n\n## ๐Ÿ› ๏ธ Install\n`probly` is intended to work with **Python 3.12 and above**. Installation can be done via `pip` and\nor `uv`:\n\n```sh\npip install probly\n```\n\n```sh\nuv add probly\n```\n\n## โญ Quickstart\n\n`probly` makes it very easy to make models uncertainty-aware and perform several downstream tasks:\n\n```python\nimport probly\nimport torch.nn.functional as F\n\nnet = ... # get neural network\nmodel = probly.transformation.dropout(net) # make neural network a Dropout model\ntrain(model) # train model as usual\n\ndata = ... # get data\ndata_ood = ... # get out of distribution data\nsampler = probly.representation.Sampler(model, num_samples=20)\nsample = sampler.predict(data) # predict an uncertainty representation\nsample_ood = sampler.predict(data_ood)\n\neu = probly.quantification.classification.mutual_information(sample) # quantify model's epistemic uncertainty\neu_ood = probly.quantification.classification.mutual_information(sample_ood)\n\nauroc = probly.evaluation.tasks.out_of_distribution_detection(eu, eu_ood) # evaluate model's uncertainty\n```\n\n## ๐Ÿ“œ License\nThis project is licensed under the [MIT License](https://github.com/pwhofman/probly/blob/main/LICENSE).\n\n---\nBuilt with โค๏ธ by the probly team.\n",markdown,tab +569,1951669,"README.md",0,0,"",markdown,tab +570,2251188,"TERMINAL",0,0,"make html",,terminal_command +571,2251236,"TERMINAL",0,0,"]633;C",,terminal_output +572,2251408,"TERMINAL",0,0,"Running Sphinx v8.2.3\r\n",,terminal_output +573,2251459,"TERMINAL",0,0,"loading translations [en]... done\r\n",,terminal_output +574,2252490,"TERMINAL",0,0,"loading pickled environment... ",,terminal_output +575,2252669,"TERMINAL",0,0,"checking bibtex cache... out of date\r\nparsing bibtex file /Users/franzsrambical/Downloads/probly/docs/source/references.bib... parsed 19 entries\r\ndone\r\n",,terminal_output +576,2252824,"TERMINAL",0,0,"[autosummary] generating autosummary for: api.rst, api/probly.datasets.rst, api/probly.datasets.torch.rst, api/probly.evaluation.metrics.rst, api/probly.evaluation.rst, api/probly.evaluation.tasks.rst, api/probly.layers.flax.rst, api/probly.layers.rst, api/probly.layers.torch.rst, api/probly.plot.credal.rst, ..., api/probly.traverse_nn.rst, api/probly.traverse_nn.torch.rst, api/probly.utils.errors.rst, api/probly.utils.probabilities.rst, api/probly.utils.rst, api/probly.utils.sets.rst, api/probly.utils.torch.rst, index.rst, methods.md, references.rst\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.reductions.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.autocast_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.accelerator.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.streamreader.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantized.dynamic.modules.conv.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.quant_type.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.algorithms.join.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.normal.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.constants.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_helper.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cudnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.lkj_cholesky.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.graph_drawer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.inverse_gamma.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.loss.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.activation.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.importer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cusparselt.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.distribution.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.transformed_distribution.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.exported_program.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.utils.convert_parameters.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.operators.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.remote_device.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.utils.fusion.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.callable.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.node.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.fx.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.evidential.torch.\r\nPossible hints:\r\n* AttributeError: module 'probly.train.evidential' has no attribute 'torch'\r\n* ModuleNotFoundError: No module named 'probly.train.evidential.torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.nvtx.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.parallel.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.api.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.mtia.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.sparse.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.combining.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.qconfig.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.profiler.itt.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.base_scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.jit.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.grad_scaler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.kl.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.forward_ad.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.embedding_ops.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.parallel.style.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.api.remote_module.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantized.functional.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.dataframe_wrapper.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.distributed_c10d.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.amp.autocast_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.find_file_dependencies.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.recording.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.pt2e.export_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantizable.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.wishart.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.qat.modules.embedding_ops.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.proxy_tensor.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.transformation.evidential.regression.torch.\r\nPossible hints:\r\n* AttributeError: module 'probly.transformation.evidential.regression' has no attribute 'torch'\r\n* ModuleNotFoundError: No module named 'probly.transformation.evidential.regression.torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.utils.parametrize.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.conv.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.qat.modules.linear_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.conv.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.mtia.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.combinatorics.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.fake_quantize.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.quantized.dynamic.modules.linear_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.decoder.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.layers.torch.\r\nPossible hints:\r\n* AttributeError: module 'probly.layers' has no attribute 'torch'\r\n* ModuleNotFoundError: No module named 'probly.layers.torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.param_fetch.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.normalization.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.filelister.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.passthrough.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.subgraph_rewriter.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.immutable_collections.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.interpreter.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.cubic_scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.transforms.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.operator_schemas.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.glob_group.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.mixture_same_family.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.gumbel.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.split_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler_util.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.upsampling.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.random.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.pass_manager.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.runtime_assert.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.qat.modules.conv.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.dropout.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantized.dynamic.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.c10d_logger.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.quantized.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.multivariate_normal.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.grad.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.matcher_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.cpp_backtrace.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.observer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.shape_prop.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.random.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.event.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\n",,terminal_output +577,2253617,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.transformation.evidential.classification.torch.\r\nPossible hints:\r\n* AttributeError: module 'probly.transformation.evidential.classification' has no attribute 'torch'\r\n* ModuleNotFoundError: No module named 'probly.transformation.evidential.classification.torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.functional.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.gds.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.lazy.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.special.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.anomaly_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.conv_fused.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.embedding_ops.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.operator_support.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.quantized.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.quantized.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.distance.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.fake_quantize.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.qat.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.errors.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.nccl.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.von_mises.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.optim.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.utils.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.logging_handlers.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\n",,terminal_output +578,2253913,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.nn.functional.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.utils.torch.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.utils.torch'\r\n* AttributeError: module 'probly.utils' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.module.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.combinatorics.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.graphs.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.utils.init.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.functional.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.dynamic.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.selecting.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_opset9.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.amp.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.monitor.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.parallel.scatter_gather.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.qat.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.container.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.qat.modules.linear_fused.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.mps.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.opt_einsum.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.qat.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.mps.profiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.amp.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.ns.fx.ns_types.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.datapipe.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.overrides.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.bn_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.server_process_global_profiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.generalized_pareto.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.attention.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.streams.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.experimental.symbolic_shapes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.base_sparsifier.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.datasets.torch.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.datasets.torch'\r\n* AttributeError: module 'probly.datasets' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.streams.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.xpu.random.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.quant_type.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.routeddecoder.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.losses.\r\nPossible hints:\r\n* AttributeError: module 'probly.train' has no attribute 'losses'\r\n* ModuleNotFoundError: No module named 'probly.train.losses'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.flatten.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\n",,terminal_output +579,2254870,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.ao.pruning.sparsifier.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.backcompat.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.split_module.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\n",,terminal_output +580,2256147,"TERMINAL",0,0,"WARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.fully_sharded_data_parallel.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.common.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.weak.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.combining.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.quantized.dynamic.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.batchnorm.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cpu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.qat.dynamic.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.sparse.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.sampler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.graph_transform_observer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.graph.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.channelshuffle.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.multinomial.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.types.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.quantized.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cpu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.utils.stateless.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.common.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.qat.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.numa.binding.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.testing.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.parallel.comm.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.infra.pass_base.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.fold.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.conv_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.studentT.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.structures.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.padding.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.negative_binomial.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.sparse.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.qat.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.uniform.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.jit.templates.remote_module_template.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.weibull.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.dynamic.modules.linear_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.fishersnedecor.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantized.modules.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.quantization_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.functional.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.proxy.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.quantized.modules.bn_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.package_importer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.options.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.bernoulli.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.net_min_base.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.batchnorm.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.datapipes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.quantized.modules.linear_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.conv_add.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.instancenorm.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.dataset.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.hub.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.callable.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.source_matcher_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.creation.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.profiler_legacy.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.onnx.symbolic_opset11.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.optim.lr_scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.storage.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.cuda.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.traverse_nn.torch.\r\nPossible hints:\r\n* AttributeError: module 'probly.traverse_nn' has no attribute 'torch'\r\n* ModuleNotFoundError: No module named 'probly.traverse_nn.torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantized.modules.batchnorm.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.geometric.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.functional.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.openmp.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.adaptive.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.linear.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.qat.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.autograd.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.parallel.distributed.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.exponential.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.decomp_utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.scheduler.lambda_scheduler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.version.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.profiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.dataframes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.map.grouping.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.backend_registration.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.grouping.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.kleidiai.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.dlpack.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.signal.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.api.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.linear.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.tensor.device_mesh.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.independent.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.futures.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.custom_ops.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.graph_signature.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantized.dynamic.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.qat.modules.conv_fused.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.pixelshuffle.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.observer.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.mha.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.profiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.intrinsic.quantized.modules.conv_relu.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.reinplace.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.compiler.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.reference.modules.linear.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.dynamic.linear.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.modules.linear.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.core.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.library.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantized.modules.embedding_ops.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.intrinsic.quantized.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.throughput_benchmark.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.laplace.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.function.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.passes.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.graph_module.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.quantization_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.functions.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.memory.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.sparse.quantized.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.utils.clip_grad.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.constraint_registry.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.nn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.dynamic.modules.conv.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.compiler.config.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.pruning.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantized.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.autograd.grad_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantized.dynamic.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.fuser_method_mappings.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.fileopener.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.rnn.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.masked.maskedtensor.binary.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.common_types.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.amp.autocast_mode.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.poisson.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.backends.miopen.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.device_mesh.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.quantizable.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.package_exporter.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.quantization.stubs.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.internal.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.package.analyze.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.dirichlet.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.fx.traceback.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.export.dynamic_shapes.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.rpc.constants.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantizable.modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.model_zoo.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.nn.utils.parametrizations.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributions.cauchy.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.fsdp.wrap.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.distributed.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.cuda.tunable.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.iter.utils.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.quantization.qconfig.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.ao.nn.quantized.modules.functional_modules.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.sparse.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\nWARNING: Failed to import probly.train.bayesian.torch.utils.data.datapipes.dataframe.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.t\n[... 34806 bytes truncated to respect terminal scrollback settings ...]\n",,terminal_output +581,2256994,"TERMINAL",0,0,"WARNING: [autosummary] failed to import probly.datasets.torch.\r\nPossible hints:\r\n* ImportError: \r\n* ModuleNotFoundError: No module named 'probly.datasets.torch'\r\n* AttributeError: module 'probly.datasets' has no attribute 'torch'\r\nWARNING: [autosummary] failed to import probly.layers.torch.\r\nPossible hints:\r\n* AttributeError: module 'probly.layers' has no attribute 'torch'\r\n* ImportError: \r\n* ModuleNotFoundError: No module named 'probly.layers.torch'\r\n",,terminal_output +582,2257094,"TERMINAL",0,0,"WARNING: [autosummary] failed to import probly.representation.credal_set.torch.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.representation.credal_set.torch'\r\n* ImportError: \r\n* AttributeError: module 'probly.representation.credal_set' has no attribute 'torch'\r\n",,terminal_output +583,2257227,"TERMINAL",0,0,"WARNING: [autosummary] failed to import probly.train.bayesian.torch.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.bayesian.torch'\r\n* ImportError: \r\n* AttributeError: module 'probly.train.bayesian' has no attribute 'torch'\r\n\r\nExtension error (sphinx.ext.autosummary)!\r\n\r\nVersions\r\n========\r\n\r\n* Platform: darwin; (macOS-15.6-arm64-arm-64bit-Mach-O)\r\n* Python version: 3.13.7 (CPython)\r\n* Sphinx version: 8.2.3\r\n* Docutils version: 0.21.2\r\n* Jinja2 version: 3.1.4\r\n* Pygments version: 2.19.1\r\n\r\nLast Messages\r\n=============\r\n\r\nNone.\r\n\r\nLoaded Extensions\r\n=================\r\n\r\nNone.\r\n\r\nTraceback\r\n=========\r\n\r\n File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/events.py"", line 415, in emit\r\n raise ExtensionError(\r\n ...<4 lines>...\r\n ) from exc\r\n sphinx.errors.ExtensionError: Handler for event 'builder-inited' threw an exception (exception: no module named probly.train.bayesian.torch.accelerator)\r\n\r\n\r\nThe full traceback has been saved in:\r\n/var/folders/nn/241fnlwx03d7k7qt2jg98txr0000gn/T/sphinx-err-hxsij5k6.log\r\n\r\nTo report this error to the developers, please open an issue at . Thanks!\r\nPlease also report this if it was a user error, so that a better error message can be provided next time.\r\n",,terminal_output +584,2258121,"TERMINAL",0,0,"make: *** [html] Error 2\r\n% \r \r",,terminal_output +585,3675279,"TERMINAL",0,0,"make html",,terminal_command +586,3675328,"TERMINAL",0,0,"]633;C",,terminal_output +587,3675681,"TERMINAL",0,0,"Running Sphinx v8.2.3\r\n",,terminal_output +588,3675758,"TERMINAL",0,0,"loading translations [en]... done\r\n",,terminal_output +589,3676895,"TERMINAL",0,0,"loading pickled environment... ",,terminal_output +590,3677039,"TERMINAL",0,0,"checking bibtex cache... up to date\r\nThe configuration has changed (3 options: 'html_css_files', 'html_static_path', 'pygments_dark_style')\r\ndone\r\n[autosummary] generating autosummary for: api.rst, api/probly.datasets._torch.rst, api/probly.datasets.rst, api/probly.evaluation.metrics.rst, api/probly.evaluation.rst, api/probly.evaluation.tasks.rst, api/probly.layers._torch.rst, api/probly.layers.flax.rst, api/probly.layers.rst, api/probly.plot.credal.rst, ..., api/probly.traverse_nn.flax.rst, api/probly.traverse_nn.rst, api/probly.utils._torch.rst, api/probly.utils.errors.rst, api/probly.utils.probabilities.rst, api/probly.utils.rst, api/probly.utils.sets.rst, index.rst, methods.md, references.rst\r\n",,terminal_output +591,3679305,"TERMINAL",0,0,"WARNING: Failed to import probly.train.losses.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.losses'\r\n* AttributeError: module 'probly.train' has no attribute 'losses'\r\n",,terminal_output +592,3680846,"TERMINAL",0,0,"myst v4.0.1: MdParserConfig(commonmark_only=False, gfm_only=False, enable_extensions=set(), disable_syntax=[], all_links_external=False, links_external_new_tab=False, url_schemes=('http', 'https', 'mailto', 'ftp'), ref_domains=None, fence_as_directive=set(), number_code_blocks=[], title_to_header=False, heading_anchors=0, heading_slug_func=None, html_meta={}, footnote_sort=True, footnote_transition=True, words_per_minute=200, substitutions={}, linkify_fuzzy_links=True, dmath_allow_labels=True, dmath_allow_space=True, dmath_allow_digits=True, dmath_double_inline=False, update_mathjax=True, mathjax_classes='tex2jax_process|mathjax_process|math|output_area', enable_checkboxes=False, suppress_warnings=[], highlight_code_blocks=True)\r\nmyst-nb v1.2.0: NbParserConfig(custom_formats={}, metadata_key='mystnb', cell_metadata_key='mystnb', kernel_rgx_aliases={}, eval_name_regex='^[a-zA-Z_][a-zA-Z0-9_]*$', execution_mode='off', execution_cache_path='', execution_excludepatterns=(), execution_timeout=30, execution_in_temp=False, execution_allow_errors=False, execution_raise_on_error=False, execution_show_tb=False, merge_streams=False, render_plugin='default', remove_code_source=False, remove_code_outputs=False, code_prompt_show='Show code cell {type}', code_prompt_hide='Hide code cell {type}', number_source_lines=False, output_stderr='show', render_text_lexer='myst-ansi', render_error_lexer='ipythontb', render_image_options={}, render_figure_options={}, render_markdown_format='commonmark', output_folder='build', append_css=True, metadata_to_fm=False)\r\nUsing jupyter-cache at: /Users/franzsrambical/Downloads/probly/docs/build/.jupyter_cache\r\nbuilding [mo]: targets for 0 po files that are out of date\r\nwriting output... \r\nbuilding [html]: targets for 0 source files that are out of date\r\nupdating environment: 0 added, 5 changed, 0 removed\r\nreading sources... [ 20%] api/probly.representation.sampling.torch_sampler\rreading sources... [ 40%] api/probly.train.bayesian._torch\r",,terminal_output +593,3680910,"TERMINAL",0,0,"reading sources... [ 60%] api/probly.train.losses\rreading sources... [ 80%] api/probly.transformation.evidential.regression._torch\rreading sources... [100%] index\r\r\n",,terminal_output +594,3680968,"TERMINAL",0,0,"WARNING: autodoc: failed to import module 'losses' from module 'probly.train'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 172, in import_module\n raise ModuleNotFoundError(msg, name=modname) # NoQA: TRY301\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ""ModuleNotFoundError: No module named 'probly.train.losses'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/fashionmnist_ood_ensemble' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/label_relaxation_calibration' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/sklearn_selective_prediction' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/synthetic_regression_dropout' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/temperature_scaling_calibration' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/train_bnn_classification' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/train_evidential_classification' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/train_evidential_regression' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:79: WARNING: toctree contains reference to nonexisting document 'contributing' [toc.not_readable]\r\nlooking for now-outdated files... none found\r\npickling environment... ",,terminal_output +595,3681066,"TERMINAL",0,0,"done\r\nchecking consistency... /Users/franzsrambical/Downloads/probly/docs/source/api/probly.datasets._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.layers._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.representation.credal_set._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.calibration._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.evidential._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.losses.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.classification._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.regression._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.traverse_nn._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.utils._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\ndone\r\npreparing documents... done\r\ncopying assets... \r\ncopying static files... \r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/basic.css\r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/language_data.js\r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/documentation_options.js\r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/copybutton.js\r\ncopying static files: done\r\ncopying extra files... \r\ncopying extra files: done\r\ncopying assets: done\r\nwriting output... [ 17%] api/probly.representation.sampling\r",,terminal_output +596,3681257,"TERMINAL",0,0,"writing output... [ 33%] api/probly.representation.sampling.torch_sampler\rwriting output... [ 50%] api/probly.train.bayesian._torch\rwriting output... [ 67%] api/probly.train.losses\rwriting output... [ 83%] api/probly.transformation.evidential.regression._torch\rwriting output... [100%] index\r",,terminal_output +597,3681318,"TERMINAL",0,0,"\r\ngenerating indices... genindex ",,terminal_output +598,3681569,"TERMINAL",0,0,"py-modindex done\r\nhighlighting module code... [ 3%] builtins\rhighlighting module code... [ 6%] probly.datasets._torch\rhighlighting module code... [ 8%] probly.evaluation.metrics\rhighlighting module code... [ 11%] probly.evaluation.tasks\rhighlighting module code... [ 14%] probly.layers._torch\rhighlighting module code... [ 17%] probly.layers.flax\rhighlighting module code... [ 19%] probly.plot.credal\rhighlighting module code... [ 22%] probly.predictor\rhighlighting module code... [ 25%] probly.quantification.classification\rhighlighting module code... [ 28%] probly.quantification.regression\rhighlighting module code... [ 31%] probly.representation.credal_set._torch\rhighlighting module code... [ 33%] probly.representation.credal_set.credal_set\rhighlighting module code... [ 36%] probly.representation.credal_set.jax\rhighlighting module code... [ 39%] probly.representation.representer\rhighlighting module code... [ 42%] probly.representation.sampling.flax_sampler\rhighlighting module code... [ 44%] probly.representation.sampling.jax_sample\rhighlighting module code... [ 47%] probly.representation.sampling.sample\rhighlighting module code... [ 50%] probly.representation.sampling.sampler\rhighlighting module code... [ 53%] probly.representation.sampling.torch_sample\rhighlighting module code... [ 56%] probly.representation.sampling.torch_sampler\rhighlighting module code... [ 58%] probly.train.bayesian._torch\rhighlighting module code... [ 61%] probly.train.calibration._torch\rhighlighting module code... [ 64%] probly.train.evidential._torch\rhighlighting module code... [ 67%] probly.transformation.bayesian.common\rhighlighting module code... [ 69%] probly.transformation.dropconnect.common\rhighlighting module code... [ 72%] probly.transformation.dropout.common\rhighlighting module code... [ 75%] probly.transformation.ensemble.common\rhighlighting module code... [ 78%] probly.transformation.evidential.classification._torch\rhighlighting module code... [ 81%] probly.transformation.evidential.classification.common\rhighlighting module code... [ 83%] probly.transformation.evidential.regression._torch\rhighlighting module code... [ 86%] probly.transformation.evidential.regression.common\rhighlighting module code... [ 89%] probly.traverse_nn.common\rhighlighting module code... [ 92%] probly.utils._torch\rhighlighting module code... [ 94%] probly.utils.errors\rhighlighting module code... [ 97%] probly.utils.probabilities\rhighlighting module code... [100%] probly.utils.sets\r\r\nwriting additional pages... search done\r\ndumping search index in English (code: en)... done\r\ndumping object inventory... done\r\n\r\n====================== slowest reading durations =======================\r\n0.050 api/probly.train.bayesian._torch\r\n0.016 api/probly.representation.sampling.torch_sampler\r\n0.007 api/probly.transformation.evidential.regression._torch\r\n0.003 index\r\n0.001 api/probly.train.losses\r\nbuild succeeded, 22 warnings.\r\n\r\nThe HTML pages are in build/html.\r\n",,terminal_output +599,3682312,"TERMINAL",0,0,"% \r \r",,terminal_output +600,3838388,"TERMINAL",0,0,"git stash",,terminal_command +601,3838437,"TERMINAL",0,0,"]633;C",,terminal_output +602,3838477,"TERMINAL",0,0,"Saved working directory and index state WIP on (no branch): e2f73f8 Initialize evidential classification test package\r\n% \r \r",,terminal_output +603,3841325,"TERMINAL",0,0,"make html",,terminal_command +604,3841374,"TERMINAL",0,0,"]633;C",,terminal_output +605,3841639,"TERMINAL",0,0,"Running Sphinx v8.2.3\r\n",,terminal_output +606,3841728,"TERMINAL",0,0,"loading translations [en]... done\r\n",,terminal_output +607,3842729,"TERMINAL",0,0,"loading pickled environment... ",,terminal_output +608,3842878,"TERMINAL",0,0,"checking bibtex cache... up to date\r\nThe configuration has changed (3 options: 'html_css_files', 'html_static_path', 'pygments_dark_style')\r\ndone\r\n[autosummary] generating autosummary for: api.rst, api/probly.datasets._torch.rst, api/probly.datasets.rst, api/probly.evaluation.metrics.rst, api/probly.evaluation.rst, api/probly.evaluation.tasks.rst, api/probly.layers._torch.rst, api/probly.layers.flax.rst, api/probly.layers.rst, api/probly.plot.credal.rst, ..., api/probly.traverse_nn.flax.rst, api/probly.traverse_nn.rst, api/probly.utils._torch.rst, api/probly.utils.errors.rst, api/probly.utils.probabilities.rst, api/probly.utils.rst, api/probly.utils.sets.rst, index.rst, methods.md, references.rst\r\n",,terminal_output +609,3846357,"TERMINAL",0,0,"WARNING: Failed to import probly.train.losses.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.losses'\r\n* AttributeError: module 'probly.train' has no attribute 'losses'\r\n",,terminal_output +610,3847099,"TERMINAL",0,0,"[autosummary] generating autosummary for: /Users/franzsrambical/Downloads/probly/docs/source/api/probly.datasets.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.layers.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.representation.credal_set.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.calibration.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.evidential.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.classification.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.regression.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.traverse_nn.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.utils.rst\r\n",,terminal_output +611,3847401,"TERMINAL",0,0,"[autosummary] generating autosummary for: /Users/franzsrambical/Downloads/probly/docs/source/api/probly.datasets.torch.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.layers.flax.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.layers.torch.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.representation.credal_set.torch.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian.torch.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.calibration.torch.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.evidential.torch.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.classification.torch.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.regression.torch.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.traverse_nn.torch.rst, /Users/franzsrambical/Downloads/probly/docs/source/api/probly.utils.torch.rst\r\nmyst v4.0.1: MdParserConfig(commonmark_only=False, gfm_only=False, enable_extensions=set(), disable_syntax=[], all_links_external=False, links_external_new_tab=False, url_schemes=('http', 'https', 'mailto', 'ftp'), ref_domains=None, fence_as_directive=set(), number_code_blocks=[], title_to_header=False, heading_anchors=0, heading_slug_func=None, html_meta={}, footnote_sort=True, footnote_transition=True, words_per_minute=200, substitutions={}, linkify_fuzzy_links=True, dmath_allow_labels=True, dmath_allow_space=True, dmath_allow_digits=True, dmath_double_inline=False, update_mathjax=True, mathjax_classes='tex2jax_process|mathjax_process|math|output_area', enable_checkboxes=False, suppress_warnings=[], highlight_code_blocks=True)\r\nmyst-nb v1.2.0: NbParserConfig(custom_formats={}, metadata_key='mystnb', cell_metadata_key='mystnb', kernel_rgx_aliases={}, eval_name_regex='^[a-zA-Z_][a-zA-Z0-9_]*$', execution_mode='off', execution_cache_path='', execution_excludepatterns=(), execution_timeout=30, execution_in_temp=False, execution_allow_errors=False, execution_raise_on_error=False, execution_show_tb=False, merge_streams=False, render_plugin='default', remove_code_source=False, remove_code_outputs=False, code_prompt_show='Show code cell {type}', code_prompt_hide='Hide code cell {type}', number_source_lines=False, output_stderr='show', render_text_lexer='myst-ansi', render_error_lexer='ipythontb', render_image_options={}, render_figure_options={}, render_markdown_format='commonmark', output_folder='build', append_css=True, metadata_to_fm=False)\r\nUsing jupyter-cache at: /Users/franzsrambical/Downloads/probly/docs/build/.jupyter_cache\r\nbuilding [mo]: targets for 0 po files that are out of date\r\nwriting output... \r\nbuilding [html]: targets for 11 source files that are out of date\r\nupdating environment: 10 added, 14 changed, 0 removed\r\nreading sources... [ 4%] api/probly.datasets\rreading sources... [ 8%] api/probly.datasets.torch\r",,terminal_output +612,3847611,"TERMINAL",0,0,"reading sources... [ 12%] api/probly.layers\rreading sources... [ 17%] api/probly.layers.flax\r:6: (ERROR/3) Unexpected indentation.\r\n:7: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\nreading sources... [ 21%] api/probly.layers.torch\r:3: (WARNING/2) Inline emphasis start-string without end-string.\r\n:3: (WARNING/2) Inline emphasis start-string without end-string.\r\n",,terminal_output +613,3847983,"TERMINAL",0,0,"reading sources... [ 25%] api/probly.representation.credal_set\rreading sources... [ 29%] api/probly.representation.credal_set.torch\rreading sources... [ 33%] api/probly.representation.sampling.torch_sampler\rreading sources... [ 38%] api/probly.train.bayesian\rreading sources... [ 42%] api/probly.train.bayesian.torch\rreading sources... [ 46%] api/probly.train.calibration\rreading sources... [ 50%] api/probly.train.calibration.torch\rreading sources... [ 54%] api/probly.train.evidential\rreading sources... [ 58%] api/probly.train.evidential.torch\rreading sources... [ 62%] api/probly.train.losses\rreading sources... [ 67%] api/probly.transformation.evidential.classification\rreading sources... [ 71%] api/probly.transformation.evidential.classification.torch\rreading sources... [ 75%] api/probly.transformation.evidential.regression\rreading sources... [ 79%] api/probly.transformation.evidential.regression.torch\rreading sources... [ 83%] api/probly.traverse_nn\rreading sources... [ 88%] api/probly.traverse_nn.torch\rreading sources... [ 92%] api/probly.utils\r:5: (ERROR/3) Unexpected indentation.\r\n:7: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\n:5: (ERROR/3) Unexpected indentation.\r\n:10: (WARNING/2) Block quote ends without a blank line; unexpected unindent.\r\nreading sources... [ 96%] api/probly.utils.torch\rreading sources... [100%] index\r\r\n",,terminal_output +614,3848040,"TERMINAL",0,0,"/Users/franzsrambical/Downloads/probly/src/probly/layers/torch.py:docstring of probly.layers.torch.BayesConv2d.reset_parameters:6: WARNING: Inline emphasis start-string without end-string. [docutils]\r\n/Users/franzsrambical/Downloads/probly/src/probly/layers/torch.py:docstring of probly.layers.torch.BayesLinear.reset_parameters:6: WARNING: Inline emphasis start-string without end-string. [docutils]\r\nWARNING: Cannot resolve forward reference in type annotations of ""probly.representation.credal_set.CredalSet.lower"": name 'T' is not defined\r\nWARNING: Cannot resolve forward reference in type annotations of ""probly.representation.credal_set.CredalSet.upper"": name 'T' is not defined\r\nWARNING: autodoc: failed to import module 'losses' from module 'probly.train'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 172, in import_module\n raise ModuleNotFoundError(msg, name=modname) # NoQA: TRY301\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ""ModuleNotFoundError: No module named 'probly.train.losses'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/fashionmnist_ood_ensemble' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/label_relaxation_calibration' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/sklearn_selective_prediction' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/synthetic_regression_dropout' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/temperature_scaling_calibration' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/train_bnn_classification' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/train_evidential_classification' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/train_evidential_regression' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:79: WARNING: toctree contains reference to nonexisting document 'contributing' [toc.not_readable]\r\nlooking for now-outdated files... none found\r\npickling environment... ",,terminal_output +615,3848147,"TERMINAL",0,0,"done\r\nchecking consistency... /Users/franzsrambical/Downloads/probly/docs/source/api/probly.datasets._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.layers._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.representation.credal_set._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.calibration._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.evidential._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.losses.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.classification._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.regression._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.traverse_nn._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.utils._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\ndone\r\npreparing documents... done\r\ncopying assets... \r\ncopying static files... \r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/basic.css\r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/language_data.js\r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/documentation_options.js\r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/copybutton.js\r\ncopying static files: done\r\ncopying extra files... \r\ncopying extra files: done\r\ncopying assets: done\r\nwriting output... [ 3%] api\r",,terminal_output +616,3848286,"TERMINAL",0,0,"writing output... [ 7%] api/probly.datasets\rwriting output... [ 10%] api/probly.datasets.torch\r",,terminal_output +617,3849159,"TERMINAL",0,0,"writing output... [ 14%] api/probly.layers\rwriting output... [ 17%] api/probly.layers.flax\rwriting output... [ 21%] api/probly.layers.torch\rwriting output... [ 24%] api/probly.representation\rwriting output... [ 28%] api/probly.representation.credal_set\rwriting output... [ 31%] api/probly.representation.credal_set.torch\rwriting output... [ 34%] api/probly.representation.sampling\rwriting output... [ 38%] api/probly.representation.sampling.torch_sampler\rwriting output... [ 41%] api/probly.train\rwriting output... [ 45%] api/probly.train.bayesian\rwriting output... [ 48%] api/probly.train.bayesian.torch\rwriting output... [ 52%] api/probly.train.calibration\rwriting output... [ 55%] api/probly.train.calibration.torch\rwriting output... [ 59%] api/probly.train.evidential\rwriting output... [ 62%] api/probly.train.evidential.torch\rwriting output... [ 66%] api/probly.train.losses\rwriting output... [ 69%] api/probly.transformation.evidential\rwriting output... [ 72%] api/probly.transformation.evidential.classification\rwriting output... [ 76%] api/probly.transformation.evidential.classification.torch\rwriting output... [ 79%] api/probly.transformation.evidential.regression\rwriting output... [ 83%] api/probly.transformation.evidential.regression.torch\rwriting output... [ 86%] api/probly.traverse_nn\rwriting output... [ 90%] api/probly.traverse_nn.torch\rwriting output... [ 93%] api/probly.utils\r",,terminal_output +618,3849253,"TERMINAL",0,0,"writing output... [ 97%] api/probly.utils.torch\rwriting output... [100%] index\r",,terminal_output +619,3849682,"TERMINAL",0,0,"\r\ngenerating indices... genindex py-modindex done\r\nhighlighting module code... [ 2%] builtins\rhighlighting module code... [ 4%] probly.datasets._torch\rhighlighting module code... [ 7%] probly.datasets.torch\rhighlighting module code... [ 9%] probly.evaluation.metrics\rhighlighting module code... [ 11%] probly.evaluation.tasks\rhighlighting module code... [ 13%] probly.layers._torch\rhighlighting module code... [ 16%] probly.layers.flax\rhighlighting module code... [ 18%] probly.layers.torch\rhighlighting module code... [ 20%] probly.plot.credal\rhighlighting module code... [ 22%] probly.predictor\rhighlighting module code... [ 24%] probly.quantification.classification\rhighlighting module code... [ 27%] probly.quantification.regression\rhighlighting module code... [ 29%] probly.representation.credal_set._torch\rhighlighting module code... [ 31%] probly.representation.credal_set.credal_set\rhighlighting module code... [ 33%] probly.representation.credal_set.jax\rhighlighting module code... [ 36%] probly.representation.credal_set.torch\rhighlighting module code... [ 38%] probly.representation.representer\rhighlighting module code... [ 40%] probly.representation.sampling.flax_sampler\rhighlighting module code... [ 42%] probly.representation.sampling.jax_sample\rhighlighting module code... [ 44%] probly.representation.sampling.sample\rhighlighting module code... [ 47%] probly.representation.sampling.sampler\rhighlighting module code... [ 49%] probly.representation.sampling.torch_sample\rhighlighting module code... [ 51%] probly.representation.sampling.torch_sampler\rhighlighting module code... [ 53%] probly.train.bayesian._torch\rhighlighting module code... [ 56%] probly.train.bayesian.torch\rhighlighting module code... [ 58%] probly.train.calibration._torch\rhighlighting module code... [ 60%] probly.train.calibration.torch\rhighlighting module code... [ 62%] probly.train.evidential._torch\rhighlighting module code... [ 64%] probly.train.evidential.torch\rhighlighting module code... [ 67%] probly.transformation.bayesian.common\rhighlighting module code... [ 69%] probly.transformation.dropconnect.common\rhighlighting module code... [ 71%] probly.transformation.dropout.common\rhighlighting module code... [ 73%] probly.transformation.ensemble.common\rhighlighting module code... [ 76%] probly.transformation.evidential.classification._torch\rhighlighting module code... [ 78%] probly.transformation.evidential.classification.common\rhighlighting module code... [ 80%] probly.transformation.evidential.classification.torch\rhighlighting module code... [ 82%] probly.transformation.evidential.regression._torch\rhighlighting module code... [ 84%] probly.transformation.evidential.regression.common\rhighlighting module code... [ 87%] probly.transformation.evidential.regression.torch\rhighlighting module code... [ 89%] probly.traverse_nn.common\rhighlighting module code... [ 91%] probly.utils._torch\rhighlighting module code... [ 93%] probly.utils.errors\rhighlighting module code... [ 96%] probly.utils.probabilities\rhighlighting module code... [ 98%] probly.utils.sets\rhighlighting module code... [100%] probly.utils.torch\r\r\nwriting additional pages... search done\r\ndumping search index in English (code: en)... done\r\ndumping object inventory... done\r\n\r\n====================== slowest reading durations =======================\r\n0.104 api/probly.datasets.torch\r\n0.083 api/probly.layers.torch\r\n0.039 api/probly.layers.flax\r\n0.035 api/probly.train.evidential.torch\r\n0.032 api/probly.utils\r\nbuild succeeded, 26 warnings.\r\n\r\nThe HTML pages are in build/html.\r\n",,terminal_output +620,3850403,"TERMINAL",0,0,"% \r \r",,terminal_output +621,3872716,"TERMINAL",0,0,"cd /Users/franzsrambical/Downloads/probly/docs/source/api && \find . -name ""*.torch.*.rst"" -type f -delete",,terminal_command +622,3872735,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +623,3882119,"TERMINAL",0,0,"make html",,terminal_command +624,3882131,"TERMINAL",0,0,"]633;Cmake: *** No rule to make target `html'. Stop.\r\n% \r \r",,terminal_output +625,3885000,"TERMINAL",0,0,"cd ..",,terminal_command +626,3885000,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +627,3886558,"TERMINAL",0,0,"cd ..",,terminal_command +628,3886559,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +629,3887742,"TERMINAL",0,0,"make html",,terminal_command +630,3887791,"TERMINAL",0,0,"]633;C",,terminal_output +631,3888002,"TERMINAL",0,0,"Running Sphinx v8.2.3\r\n",,terminal_output +632,3888067,"TERMINAL",0,0,"loading translations [en]... done\r\n",,terminal_output +633,3889020,"TERMINAL",0,0,"loading pickled environment... ",,terminal_output +634,3889158,"TERMINAL",0,0,"checking bibtex cache... up to date\r\nThe configuration has changed (3 options: 'html_css_files', 'html_static_path', 'pygments_dark_style')\r\ndone\r\n[autosummary] generating autosummary for: api.rst, api/probly.datasets._torch.rst, api/probly.datasets.rst, api/probly.datasets.torch.rst, api/probly.evaluation.metrics.rst, api/probly.evaluation.rst, api/probly.evaluation.tasks.rst, api/probly.layers._torch.rst, api/probly.layers.flax.rst, api/probly.layers.rst, ..., api/probly.traverse_nn.torch.rst, api/probly.utils._torch.rst, api/probly.utils.errors.rst, api/probly.utils.probabilities.rst, api/probly.utils.rst, api/probly.utils.sets.rst, api/probly.utils.torch.rst, index.rst, methods.md, references.rst\r\n",,terminal_output +635,3890968,"TERMINAL",0,0,"WARNING: Failed to import probly.train.losses.\r\nPossible hints:\r\n* ModuleNotFoundError: No module named 'probly.train.losses'\r\n* AttributeError: module 'probly.train' has no attribute 'losses'\r\n",,terminal_output +636,3892887,"TERMINAL",0,0,"myst v4.0.1: MdParserConfig(commonmark_only=False, gfm_only=False, enable_extensions=set(), disable_syntax=[], all_links_external=False, links_external_new_tab=False, url_schemes=('http', 'https', 'mailto', 'ftp'), ref_domains=None, fence_as_directive=set(), number_code_blocks=[], title_to_header=False, heading_anchors=0, heading_slug_func=None, html_meta={}, footnote_sort=True, footnote_transition=True, words_per_minute=200, substitutions={}, linkify_fuzzy_links=True, dmath_allow_labels=True, dmath_allow_space=True, dmath_allow_digits=True, dmath_double_inline=False, update_mathjax=True, mathjax_classes='tex2jax_process|mathjax_process|math|output_area', enable_checkboxes=False, suppress_warnings=[], highlight_code_blocks=True)\r\nmyst-nb v1.2.0: NbParserConfig(custom_formats={}, metadata_key='mystnb', cell_metadata_key='mystnb', kernel_rgx_aliases={}, eval_name_regex='^[a-zA-Z_][a-zA-Z0-9_]*$', execution_mode='off', execution_cache_path='', execution_excludepatterns=(), execution_timeout=30, execution_in_temp=False, execution_allow_errors=False, execution_raise_on_error=False, execution_show_tb=False, merge_streams=False, render_plugin='default', remove_code_source=False, remove_code_outputs=False, code_prompt_show='Show code cell {type}', code_prompt_hide='Hide code cell {type}', number_source_lines=False, output_stderr='show', render_text_lexer='myst-ansi', render_error_lexer='ipythontb', render_image_options={}, render_figure_options={}, render_markdown_format='commonmark', output_folder='build', append_css=True, metadata_to_fm=False)\r\nUsing jupyter-cache at: /Users/franzsrambical/Downloads/probly/docs/build/.jupyter_cache\r\nbuilding [mo]: targets for 0 po files that are out of date\r\nwriting output... \r\nbuilding [html]: targets for 0 source files that are out of date\r\nupdating environment: 0 added, 2 changed, 0 removed\r\nreading sources... [ 50%] api/probly.train.losses\rreading sources... [100%] index\r\r\n",,terminal_output +637,3892947,"TERMINAL",0,0,"WARNING: autodoc: failed to import module 'losses' from module 'probly.train'; the following exception was raised:\r\n['Traceback (most recent call last):\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 269, in import_object\n module = import_module(modname, try_reload=True)\n', ' File ""/Users/franzsrambical/Downloads/probly/.venv/lib/python3.13/site-packages/sphinx/ext/autodoc/importer.py"", line 172, in import_module\n raise ModuleNotFoundError(msg, name=modname) # NoQA: TRY301\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ""ModuleNotFoundError: No module named 'probly.train.losses'\n""] [autodoc.import_object]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/fashionmnist_ood_ensemble' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/label_relaxation_calibration' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/sklearn_selective_prediction' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/synthetic_regression_dropout' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/temperature_scaling_calibration' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/train_bnn_classification' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/train_evidential_classification' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:58: WARNING: toctree contains reference to nonexisting document 'examples/train_evidential_regression' [toc.not_readable]\r\n/Users/franzsrambical/Downloads/probly/docs/source/index.rst:79: WARNING: toctree contains reference to nonexisting document 'contributing' [toc.not_readable]\r\nlooking for now-outdated files... none found\r\npickling environment... ",,terminal_output +638,3893050,"TERMINAL",0,0,"done\r\nchecking consistency... /Users/franzsrambical/Downloads/probly/docs/source/api/probly.datasets._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.layers._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.representation.credal_set._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.bayesian._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.calibration._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.evidential._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.train.losses.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.classification._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.transformation.evidential.regression._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.traverse_nn._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\n/Users/franzsrambical/Downloads/probly/docs/source/api/probly.utils._torch.rst: WARNING: document isn't included in any toctree [toc.not_included]\r\ndone\r\npreparing documents... done\r\ncopying assets... \r\ncopying static files... \r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/basic.css\r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/language_data.js\r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/documentation_options.js\r\nWriting evaluated template result to /Users/franzsrambical/Downloads/probly/docs/build/html/_static/copybutton.js\r\ncopying static files: done\r\ncopying extra files... \r\ncopying extra files: done\r\ncopying assets: done\r\nwriting output... [ 50%] api/probly.train.losses\r",,terminal_output +639,3893159,"TERMINAL",0,0,"writing output... [100%] index\r",,terminal_output +640,3893336,"TERMINAL",0,0,"\r\ngenerating indices... genindex py-modindex done\r\nhighlighting module code... [ 2%] builtins\rhighlighting module code... [ 4%] probly.datasets._torch\rhighlighting module code... [ 7%] probly.datasets.torch\rhighlighting module code... [ 9%] probly.evaluation.metrics\rhighlighting module code... [ 11%] probly.evaluation.tasks\rhighlighting module code... [ 13%] probly.layers._torch\rhighlighting module code... [ 16%] probly.layers.flax\rhighlighting module code... [ 18%] probly.layers.torch\rhighlighting module code... [ 20%] probly.plot.credal\rhighlighting module code... [ 22%] probly.predictor\rhighlighting module code... [ 24%] probly.quantification.classification\rhighlighting module code... [ 27%] probly.quantification.regression\rhighlighting module code... [ 29%] probly.representation.credal_set._torch\rhighlighting module code... [ 31%] probly.representation.credal_set.credal_set\rhighlighting module code... [ 33%] probly.representation.credal_set.jax\rhighlighting module code... [ 36%] probly.representation.credal_set.torch\rhighlighting module code... [ 38%] probly.representation.representer\rhighlighting module code... [ 40%] probly.representation.sampling.flax_sampler\rhighlighting module code... [ 42%] probly.representation.sampling.jax_sample\rhighlighting module code... [ 44%] probly.representation.sampling.sample\rhighlighting module code... [ 47%] probly.representation.sampling.sampler\rhighlighting module code... [ 49%] probly.representation.sampling.torch_sample\rhighlighting module code... [ 51%] probly.representation.sampling.torch_sampler\rhighlighting module code... [ 53%] probly.train.bayesian._torch\rhighlighting module code... [ 56%] probly.train.bayesian.torch\rhighlighting module code... [ 58%] probly.train.calibration._torch\rhighlighting module code... [ 60%] probly.train.calibration.torch\rhighlighting module code... [ 62%] probly.train.evidential._torch\rhighlighting module code... [ 64%] probly.train.evidential.torch\rhighlighting module code... [ 67%] probly.transformation.bayesian.common\rhighlighting module code... [ 69%] probly.transformation.dropconnect.common\rhighlighting module code... [ 71%] probly.transformation.dropout.common\rhighlighting module code... [ 73%] probly.transformation.ensemble.common\rhighlighting module code... [ 76%] probly.transformation.evidential.classification._torch\rhighlighting module code... [ 78%] probly.transformation.evidential.classification.common\rhighlighting module code... [ 80%] probly.transformation.evidential.classification.torch\rhighlighting module code... [ 82%] probly.transformation.evidential.regression._torch\rhighlighting module code... [ 84%] probly.transformation.evidential.regression.common\rhighlighting module code... [ 87%] probly.transformation.evidential.regression.torch\rhighlighting module code... [ 89%] probly.traverse_nn.common\rhighlighting module code... [ 91%] probly.utils._torch\rhighlighting module code... [ 93%] probly.utils.errors\rhighlighting module code... [ 96%] probly.utils.probabilities\rhighlighting module code... [ 98%] probly.utils.sets\rhighlighting module code... [100%] probly.utils.torch\r\r\nwriting additional pages... search done\r\ndumping search index in English (code: en)... done\r\ndumping object inventory... done\r\n\r\n====================== slowest reading durations =======================\r\n0.009 api/probly.train.losses\r\n0.005 index\r\nbuild succeeded, 22 warnings.\r\n\r\nThe HTML pages are in build/html.\r\n",,terminal_output +641,3893973,"TERMINAL",0,0,"% \r \r",,terminal_output diff --git a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..871d6af2047b9fd9c083db8ea13511e35c060699 --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv @@ -0,0 +1,16232 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,1,"examples/crowd_code.html",0,0,"\n\n\n\n \n \n \n \n\n\n\n \n \n \n \n \n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

AN, MM and FS worked on ideation and implementation. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

\n \n \n \n \n\n \n\n\n",html,tab +2,45,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab +3,88,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"6:25:56 PM [info] Activating crowd-code\n6:25:56 PM [info] Recording started\n6:25:56 PM [info] Initializing git provider using file system watchers...\n6:25:56 PM [info] Git repository found\n6:25:56 PM [info] Git provider initialized successfully\n6:25:56 PM [info] Initial git state: [object Object]\n",Log,content +4,1399,"examples/crowd_code.html",0,0,"",html,tab +5,2286,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab +6,3350,"examples/crowd_code.html",0,0,"",html,tab +7,4489,"examples/crowd_code.html",2472,0,"",html,selection_command +8,4616,"examples/crowd_code.html",2502,0,"",html,selection_command +9,4701,"examples/crowd_code.html",2495,0,"",html,selection_command +10,5086,"examples/crowd_code.html",2392,0,"",html,selection_command +11,5236,"examples/crowd_code.html",2164,0,"",html,selection_command +12,20251,"examples/jasmine.html",0,0,"",html,tab +13,24520,"examples/crowd_code.html",0,0,"",html,tab +14,24803,"examples/crowd_code.html",0,6816,"\n\n\n\n \n \n \n \n\n\n\n \n \n \n \n \n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

AN, MM and FS worked on ideation and implementation. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

\n \n \n \n \n\n \n\n\n",html,selection_command +15,24969,"examples/crowd_code.html",6816,0,"",html,selection_command +16,25564,"examples/jasmine.html",0,0,"",html,tab +17,26056,"examples/jasmine.html",0,0,"\n\n\n\n \n \n \n \n\n\n\n \n \n \n \n \n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

AN, MM and FS worked on ideation and implementation. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

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Data Is The New Oil

",html,selection_command +670,345651,"examples/jasmine.html",2502,40,"

Data Is The New Oil

\n

",html,selection_command +671,345907,"examples/jasmine.html",2502,211,"

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.",html,selection_command +672,345939,"examples/jasmine.html",2502,379,"

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.",html,selection_command +673,345966,"examples/jasmine.html",2502,388,"

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

",html,selection_command +674,346000,"examples/jasmine.html",2502,396,"

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

",html,selection_command +675,346201,"examples/jasmine.html",2502,388,"

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

",html,selection_command +676,346399,"examples/jasmine.html",2502,379,"

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.",html,selection_command +677,346556,"examples/jasmine.html",2502,211,"

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.",html,selection_command +678,346668,"examples/jasmine.html",2543,0,"",html,selection_command +679,346839,"examples/jasmine.html",2714,0,"",html,selection_command +680,347105,"examples/jasmine.html",2543,0,"",html,selection_command +681,348422,"examples/jasmine.html",2542,344,"",html,content +682,348785,"examples/jasmine.html",2541,0,"",html,selection_command +683,349186,"examples/jasmine.html",2542,0,"",html,selection_command +684,349279,"examples/jasmine.html",2542,0,"\n \n ",html,content +685,349439,"examples/jasmine.html",2543,8,"",html,content +686,350039,"examples/jasmine.html",2544,0,"",html,selection_command +687,350189,"examples/jasmine.html",2553,0,"",html,selection_command +688,350611,"examples/jasmine.html",2553,7,"

",html,selection_command +689,350736,"examples/jasmine.html",2553,184,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,",html,selection_command +690,350994,"examples/jasmine.html",2553,361,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt",html,selection_command +691,351020,"examples/jasmine.html",2553,536,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping",html,selection_command +692,351049,"examples/jasmine.html",2553,649,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.",html,selection_command +693,351083,"examples/jasmine.html",2553,658,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

",html,selection_command +694,351118,"examples/jasmine.html",2553,732,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2",html,selection_command +695,351151,"examples/jasmine.html",2553,795,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

",html,selection_command +696,351185,"examples/jasmine.html",2553,803,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

",html,selection_command +697,351218,"examples/jasmine.html",2553,975,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the",html,selection_command +698,351251,"examples/jasmine.html",2553,1088,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.",html,selection_command +699,351286,"examples/jasmine.html",2553,1097,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

",html,selection_command +700,351320,"examples/jasmine.html",2553,1105,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

",html,selection_command +701,351353,"examples/jasmine.html",2553,1282,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.",html,selection_command +702,351386,"examples/jasmine.html",2553,1457,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is ",html,selection_command +703,351419,"examples/jasmine.html",2553,1524,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.",html,selection_command +704,351453,"examples/jasmine.html",2553,1533,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

",html,selection_command +705,351486,"examples/jasmine.html",2553,1661,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
",html,selection_command +707,351553,"examples/jasmine.html",2553,1950,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
",html,selection_command +708,351586,"examples/jasmine.html",2553,1958,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

",html,selection_command +709,351619,"examples/jasmine.html",2553,2136,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the",html,selection_command +710,351653,"examples/jasmine.html",2553,2209,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).",html,selection_command +711,351686,"examples/jasmine.html",2553,2379,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply",html,selection_command +712,351719,"examples/jasmine.html",2553,2555,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for",html,selection_command +713,351753,"examples/jasmine.html",2553,2733,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to",html,selection_command +714,351786,"examples/jasmine.html",2553,2792,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.",html,selection_command +715,351819,"examples/jasmine.html",2553,2801,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

",html,selection_command +716,351853,"examples/jasmine.html",2553,2809,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

",html,selection_command +717,351888,"examples/jasmine.html",2553,2985,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet ",html,selection_command +718,351922,"examples/jasmine.html",2553,3160,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data",html,selection_command +719,351953,"examples/jasmine.html",2553,3201,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.",html,selection_command +720,351986,"examples/jasmine.html",2553,3210,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

",html,selection_command +721,352019,"examples/jasmine.html",2553,3218,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

",html,selection_command +722,352053,"examples/jasmine.html",2553,3388,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.",html,selection_command +723,352209,"examples/jasmine.html",2553,3397,"

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function defintions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

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Neural networks are able to clone the behavior of peak human intellectual performance (cite Gemini/OAI IMO) given enough compute, data, and the right algorithms (R1). While an increasing amount of capital expenditure is allocated to compute clusters, and a well-working recipe of equipping models with the required priors and capacity to reason is publicly available, the path to human-level intelligence with the ability to automate large fractions of the economy will increasingly be shaped by paradigms that are able to find and efficiently use untouched data troves.\n\nWhile product-feedback-loops (cite cursor) constitute an adaptive data trove, many domains like robotics are not mature enough to yield a product with wide enough adoption to create a feedback-loop of sufficient magnitude, begging the question of alternatives.\n\nOne paradigm proposed by the research community to overcome the data scarcity in those domains is that of world models. While world models can help frontier model development in numerous ways, an ambitious goal of the community is to train a world model to act as a simulation of the world (cite Genie 1, Genie 2), in order to train an agent in that simulation, via an adaptive curriculum (cite Michael Dennis) or otherwise.\n",html,content +768,378614,"examples/jasmine.html",3428,0,"",html,selection_command +769,379004,"examples/jasmine.html",3427,0,"",html,selection_command +770,379178,"examples/jasmine.html",3166,0,"",html,selection_command +771,379352,"examples/jasmine.html",3165,0,"",html,selection_command +772,379820,"examples/jasmine.html",2536,0,"",html,selection_command +773,380016,"examples/jasmine.html",2545,0,"",html,selection_command +774,380263,"examples/jasmine.html",2549,0,"",html,selection_command +775,380294,"examples/jasmine.html",2552,0,"",html,selection_command +776,380328,"examples/jasmine.html",2556,0,"",html,selection_command +777,380360,"examples/jasmine.html",2561,0,"",html,selection_command +778,380393,"examples/jasmine.html",2564,0,"",html,selection_command +779,380427,"examples/jasmine.html",2567,0,"",html,selection_command +780,380460,"examples/jasmine.html",2580,0,"",html,selection_command +781,380494,"examples/jasmine.html",2591,0,"",html,selection_command +782,380527,"examples/jasmine.html",2592,0,"",html,selection_command +783,380560,"examples/jasmine.html",2599,0,"",html,selection_command +784,380594,"examples/jasmine.html",2608,0,"",html,selection_command +785,380830,"examples/jasmine.html",2759,0,"",html,selection_command +786,381789,"examples/jasmine.html",2760,0,"",html,selection_command +787,381881,"examples/jasmine.html",2760,0,"\n ",html,content +788,383114,"examples/jasmine.html",2765,4,"",html,content +789,383498,"examples/jasmine.html",2764,0,"",html,selection_command +790,384013,"examples/jasmine.html",2766,0,"",html,selection_command +791,384670,"examples/jasmine.html",3432,0,"

\n

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\n ",html,content +793,384670,"examples/jasmine.html",3170,0,"

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Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 382ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +1417,674240,"TERMINAL",0,0,"created dist/transforms.v2.js in 456ms\r\n\r\n[2025-08-05 18:37:10] waiting for changes...\r\n",,terminal_output +1418,728959,"examples/jasmine.html",2536,0,"",html,selection_command +1419,729208,"examples/jasmine.html",2510,0,"",html,selection_command +1420,729240,"examples/jasmine.html",2436,0,"",html,selection_command +1421,729278,"examples/jasmine.html",2422,0,"",html,selection_command +1422,729305,"examples/jasmine.html",2398,0,"",html,selection_command +1423,729330,"examples/jasmine.html",2385,0,"",html,selection_command +1424,729364,"examples/jasmine.html",2376,0,"",html,selection_command +1425,729398,"examples/jasmine.html",2309,0,"",html,selection_command +1426,766747,"examples/jasmine.html",861,0,"",html,selection_command +1427,767731,"examples/jasmine.html",880,0,"",html,selection_command +1428,767865,"examples/jasmine.html",937,0,"",html,selection_command +1429,769136,"examples/jasmine.html",1038,0,"",html,selection_command +1430,769371,"examples/jasmine.html",1037,0,"",html,selection_command +1431,769517,"examples/jasmine.html",1036,0,"",html,selection_command +1432,769649,"examples/jasmine.html",1035,0,"",html,selection_command +1433,769834,"examples/jasmine.html",1035,1,"",html,content +1434,770281,"examples/jasmine.html",1035,1,"",html,content +1435,770715,"examples/jasmine.html",1034,0,"",html,selection_command +1436,771165,"examples/jasmine.html",1034,1,"",html,content +1437,771384,"examples/jasmine.html",1032,0,"",html,selection_command +1438,771736,"examples/jasmine.html",1032,2,"",html,content +1439,772042,"examples/jasmine.html",1031,0,"",html,selection_command +1440,772536,"examples/jasmine.html",1031,1,"",html,content +1441,774686,"TERMINAL",0,0,"^Cโ ™npm notice\r\nnpm notice New major version of npm available! 10.9.2 -> 11.5.2\r\nnpm notice Changelog: https://github.com/npm/cli/releases/tag/v11.5.2\r\nnpm notice To update run: npm install -g npm@11.5.2\r\nnpm notice\r\nโ ™",,terminal_output +1442,774702,"TERMINAL",0,0,"% \r \r",,terminal_output +1443,774738,"TERMINAL",0,0,"",,terminal_command +1444,774738,"TERMINAL",0,0,"]633;C",,terminal_output +1445,775427,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +1446,775447,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +1447,775993,"TERMINAL",0,0,"npm run dev",,terminal_command +1448,776044,"TERMINAL",0,0,"]633;C",,terminal_output +1449,776177,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +1450,776367,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +1451,776780,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) 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A Study in Length\n Generalization},\n author = {Hattie Zhou and Arwen Bradley and Etai Littwin and Noam Razin and\n Omid Saremi and Joshua M. Susskind and Samy Bengio and Preetum\n Nakkiran},\n booktitle = {The Twelfth International Conference on Learning Representations},\n year = {2024},\n url = {https://openreview.net/forum?id=AssIuHnmHX},\n}\n\n@inproceedings{ding2024causallm,\n title = {Causal{LM} is not optimal for in-context learning},\n author = {Nan Ding and Tomer Levinboim and Jialin Wu and Sebastian Goodman and\n Radu Soricut},\n booktitle = {The Twelfth International Conference on Learning Representations},\n year = {2024},\n url = {https://openreview.net/forum?id=guRNebwZBb},\n}\n\n@article{williams1989learning,\n title = {A learning algorithm for continually running fully recurrent neural\n networks},\n author = {Williams, Ronald J and Zipser, David},\n journal = {Neural computation},\n volume = {1},\n number = {2},\n pages = {270--280},\n year = {1989},\n publisher = {MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA\n journals-info~โ€ฆ},\n}\n\n@article{tay2022ul2,\n title = {Ul2: Unifying language learning paradigms},\n author = {Tay, Yi and Dehghani, Mostafa and Tran, Vinh Q and Garcia, Xavier\n and Wei, Jason and Wang, Xuezhi and Chung, Hyung Won and Shakeri,\n Siamak and Bahri, Dara and Schuster, Tal and others},\n journal = {arXiv preprint arXiv:2205.05131},\n year = {2022},\n}\n\n@misc{pfau2023last,\n title = {Last I checked, it was still not possible for a neural network alone\n (i.e. no MCTS) to beat the world's best Go players...},\n author = {Pfau, David},\n year = {2023},\n url = {https://twitter.com/pfau/status/1732785418565796167},\n note = {Accessed: 2023-12-07},\n}\n\n@article{deepmind2023alphacode,\n title = {AlphaCode 2 Technical Report},\n author = {Team, AlphaCode and Deepmind, Google},\n year = {2023},\n journal = {Google Deepmind},\n url = {\n https://storage.googleapis.com/deepmind-media/AlphaCode2/AlphaCode2_Tech_Report.pdf\n },\n}\n\n@article{reuters2023sam,\n author = {Tong, Anna and Dastin, Jeffrey and Hu, Krystal},\n title = {Sam Altman's ouster from OpenAI was precipitated by letter to board\n about AI breakthrough},\n journal = {Reuters},\n year = {2023},\n url = {\n https://www.reuters.com/technology/sam-altmans-ouster-openai-was-precipitated-by-letter-board-about-ai-breakthrough-2023-11-22/\n },\n note = {Accessed: 2023-12-07},\n}\n\n@misc{imbue2023podcast,\n title = {Noam Brown, FAIR: On achieving human-level performance in poker and\n Diplomacy, and the power of spending compute at inference time},\n author = {Noam Brown},\n howpublished = {\n https://imbue.com/podcast/2023-02-09-podcast-episode-27-noam-brown/\n },\n year = {2023},\n note = {Podcast episode 27, February 9, 2023},\n}\n\n@misc{karpathy2023youtube,\n author = {Karpathy, Andrej},\n title = {[1hr Talk] Intro to Large Language Models},\n howpublished = {YouTube},\n year = {2023},\n note = {Accessed: 2023-12-07},\n url = {https://www.youtube.com/watch?v=zjkBMFhNj_g&t=2100s},\n}\n\n@article{brown2019superhuman,\n title = {Superhuman AI for multiplayer poker},\n author = {Brown, Noam and Sandholm, Tuomas},\n journal = {Science},\n volume = {365},\n number = {6456},\n pages = {885--890},\n year = {2019},\n publisher = {American Association for the Advancement of Science},\n}\n\n@article{silver2016mastering,\n title = {Mastering the game of Go with deep neural networks and tree search},\n author = {Silver, David and Huang, Aja and Maddison, Chris J and Guez, Arthur\n and Sifre, Laurent and Van Den Driessche, George and Schrittwieser,\n Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot,\n Marc and others},\n journal = {nature},\n volume = {529},\n number = {7587},\n pages = {484--489},\n year = {2016},\n publisher = {Nature Publishing Group},\n}\n\n@article{schrittwieser2020mastering,\n title = {Mastering atari, go, chess and shogi by planning with a learned model\n },\n author = {Schrittwieser, Julian and Antonoglou, Ioannis and Hubert, Thomas and\n Simonyan, Karen and Sifre, Laurent and Schmitt, Simon and Guez,\n Arthur and Lockhart, Edward and Hassabis, Demis and Graepel, Thore\n and others},\n journal = {Nature},\n volume = {588},\n number = {7839},\n pages = {604--609},\n year = {2020},\n publisher = {Nature Publishing Group UK London},\n}\n\n@article{wei2022chain,\n title = {Chain-of-thought prompting elicits reasoning in large language models\n },\n author = {Wei, Jason and Wang, Xuezhi and Schuurmans, Dale and Bosma, Maarten\n and Xia, Fei and Chi, Ed and Le, Quoc V and Zhou, Denny and others},\n journal = {Advances in neural information processing systems},\n volume = {35},\n pages = {24824--24837},\n year = {2022},\n}\n\n@article{yao2024tree,\n title = {Tree of thoughts: Deliberate problem solving with large language\n models},\n author = {Yao, Shunyu and Yu, Dian and Zhao, Jeffrey and Shafran, Izhak and\n Griffiths, Tom and Cao, Yuan and Narasimhan, Karthik},\n journal = {Advances in Neural Information Processing Systems},\n volume = {36},\n year = {2024},\n}\n\n@article{lecun2022path,\n title = {A path towards autonomous machine intelligence version 0.9. 2,\n 2022-06-27},\n author = {LeCun, Yann},\n journal = {Open Review},\n volume = {62},\n number = {1},\n year = {2022},\n}\n\n@article{hoffmann2022training,\n title = {Training compute-optimal large language models},\n author = {Hoffmann, Jordan and Borgeaud, Sebastian and Mensch, Arthur and\n Buchatskaya, Elena and Cai, Trevor and Rutherford, Eliza and Casas,\n Diego de Las and Hendricks, Lisa Anne and Welbl, Johannes and Clark,\n Aidan and others},\n journal = {arXiv preprint arXiv:2203.15556},\n year = {2022},\n}\n\n@article{meta2024introducing,\n title = {Introducing meta llama 3: The most capable openly available llm to\n date},\n author = {Meta, AI},\n journal = {Meta AI.},\n year = {2024},\n}\n\n@misc{riley2024it,\n title = {It's just not a very useful scaling law.},\n author = {@riley_stews},\n year = {2024},\n url = {https://x.com/riley_stews/status/1781019732122198288},\n note = {Accessed: 2023-04-20},\n}\n\n@article{shazeer2017outrageously,\n title = {Outrageously large neural networks: The sparsely-gated\n mixture-of-experts layer},\n author = {Shazeer, Noam and Mirhoseini, Azalia and Maziarz, Krzysztof and\n Davis, Andy and Le, Quoc and Hinton, Geoffrey and Dean, Jeff},\n journal = {arXiv preprint arXiv:1701.06538},\n year = {2017},\n}\n\n@article{fedus2022switch,\n title = {Switch transformers: Scaling to trillion parameter models with simple\n and efficient sparsity},\n author = {Fedus, William and Zoph, Barret and Shazeer, Noam},\n journal = {Journal of Machine Learning Research},\n volume = {23},\n number = {120},\n pages = {1--39},\n year = {2022},\n}\n\n@article{schulman2015high,\n title = {High-dimensional continuous control using generalized advantage\n estimation},\n author = {Schulman, John and Moritz, Philipp and Levine, Sergey and Jordan,\n Michael and Abbeel, Pieter},\n journal = {arXiv preprint arXiv:1506.02438},\n year = {2015},\n}\n\n@article{srambical2025ppo,\n author = {Srambical, Franz},\n title = {PPO Is Secretly Using Monte Carlo Advantage Estimation In LLM\n Post-Training},\n journal = {p(doom) blog},\n year = {2025},\n note = {https://pdoom.org/blog.html},\n}\n\n@article{williams1992simple,\n title = {Simple statistical gradient-following algorithms for connectionist\n reinforcement learning},\n author = {Williams, Ronald J},\n journal = {Machine learning},\n volume = {8},\n pages = {229--256},\n year = {1992},\n publisher = {Springer},\n}\n\n@software{deepmind2020jax,\n title = {The {D}eep{M}ind {JAX} {E}cosystem},\n author = {DeepMind and Babuschkin, Igor and Baumli, Kate and Bell, Alison and\n Bhupatiraju, Surya and Bruce, Jake and Buchlovsky, Peter and Budden,\n David and Cai, Trevor and Clark, Aidan and Danihelka, Ivo and Dedieu,\n Antoine and Fantacci, Claudio and Godwin, Jonathan and Jones, Chris\n and Hemsley, Ross and Hennigan, Tom and Hessel, Matteo and Hou,\n Shaobo and Kapturowski, Steven and Keck, Thomas and Kemaev, Iurii and\n King, Michael and Kunesch, Markus and Martens, Lena and Merzic, Hamza\n and Mikulik, Vladimir and Norman, Tamara and Papamakarios, George and\n Quan, John and Ring, Roman and Ruiz, Francisco and Sanchez, Alvaro\n and Sartran, Laurent and Schneider, Rosalia and Sezener, Eren and\n Spencer, Stephen and Srinivasan, Srivatsan and Stanojevi\'{c}, Milo\v\n {s} and Stokowiec, Wojciech and Wang, Luyu and Zhou, Guangyao and\n Viola, Fabio},\n url = {http://github.com/deepmind},\n year = {2020},\n}\n\n@misc{jax2025jit,\n title = {JAX: Just-in-time compilation},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/jit-compilation.html},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{jax2025callbacks,\n title = {JAX: External callbacks},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/external-callbacks.html},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{jax2025checkify,\n title = {JAX: The `checkify` transformation},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/debugging/checkify_guide.html},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{jax2025key,\n title = {JAX: Key concepts},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/key-concepts.html},\n note = {Accessed: 2025-03-26},\n}\n\n@software{deepmind2020chex,\n title = {Chex},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n url = {http://github.com/google-deepmind/chex},\n year = {2020},\n}\n\n@misc{jax2025control,\n title = {JAX: Control flow and logical operators with JIT},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/control-flow.html},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{xla2025conditional,\n title = {XLA:Operation Semantics:Conditional},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://openxla.org/xla/operation_semantics#conditional},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{ayaka76822025error,\n author = {ayaka7682},\n title = {Message on public Discord server: Try this:\n \n import jax from jax._src.error_check import set_error_if, raise_if_error\n \n \n import jax.numpy as jnp\n \n \n @jax.jit\n \n \n def f(x, y):\n \n \n set_error_if(x != 0, 'x must be 0')\n \n \n return jnp.multiply(x, y)\n \n \n f(0, 0)\n \n \n raise_if_error()\n },\n year = {2025},\n url = {\n https://discord.com/channels/1107832795377713302/1107832795688083561/1354171414596419854\n },\n note = {Accessed: 2025-03-26},\n}\n\n@book{sutton1998reinforcement,\n title={Reinforcement learning: An introduction},\n author={Sutton, Richard S and Barto, Andrew G and others},\n volume={1},\n number={1},\n year={1998},\n publisher={MIT press Cambridge}\n}\n\n@article{sutton1999policy,\n title={Policy gradient methods for reinforcement learning with function approximation},\n author={Sutton, Richard S and McAllester, David and Singh, Satinder and Mansour, Yishay},\n journal={Advances in neural information processing systems},\n volume={12},\n year={1999}\n}\n\n@article{degris2012off,\n title={Off-policy actor-critic},\n author={Degris, Thomas and White, Martha and Sutton, Richard S},\n journal={arXiv preprint arXiv:1205.4839},\n year={2012}\n}\n\n@article{schulman2017proximal,\n title={Proximal policy optimization algorithms},\n author={Schulman, John and Wolski, Filip and Dhariwal, Prafulla and Radford, Alec and Klimov, Oleg},\n journal={arXiv preprint arXiv:1707.06347},\n 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Although ubiquitously used in large-scale language modeling , the necessity of the causal mask is seldom questioned in the literature.\n ""The causal mask is needed to prevent information leakage from future tokens"" is a commonly encountered, almost dogmatically repeated phrase.\n However, among researchers and practitioners alike, there exists a certain confusion around what the causal mask is and why we actually need it.

\n \n \n \n 1\n

A primer on the causal mask

\n

The confusion about the causal mask already starts with its name. The original Transformer paper \n does not mention the term causal mask at all. While we cannot definitively pin-point the origin of the term, its\n first well-known appearance is in the T5 paper , where it is used to describe the\n triangular mask that is applied to the attention weights in the self-attention mechanism (Figure 1, centre). The mask being triangular\n has the effect of only allowing information from previous tokens to be used in the computation of the current token, which already\n leads us to the first common misconception: For causal LMs at inference time, even if $$n$$ tokens have already been generated, token $$k$$ with $$k < n$$\n cannot attend to token $$j$$ with $$k < j < n$$, even though token $$j$$ is already known. From an information-theoretic perspective,\n it is clear that this is suboptimal, and indeed recent work has investigated the algorithmic deficiency of causal language models .\n

\n \n
\n
Figure 1: A schematic of full attention, causal masking, and prefix masking. Figure from . Used under CC-BY 4.0 license.
\n

\n When solely looking at inference time, what we ideally want is full attention, i.e. every (generated) token can attend to every (generated) token (Figure 1, left).\n This is the no-mask regime, where the attention weights are not modified at all. Yet, all LLMs also use\n the causal mask at inference time since omitting the mask would lead to a distribution shift between training and inference, thus impairing performance.\n Since the causal mask is needed at inference time because it was used during training, a natural question to ask is: Why do we need the causal mask during training?\n\n

\n 2\n

On the necessity of the causal mask

\n

\n For brevity's sake, we will focus on the GPT-style pre-training regime , where the model is trained to predict the next token given the previous tokens.\n One of the key advantages of the Transformer architecture over classical RNNs is that we can predict all tokens of a sequence in parallel.\n Specifically, this is achieved using a technique called teacher-forcing , where instead of using the tokens generated by the model, we use the ground-truth previous tokens to predict each 'next token'.\n Clearly, during the model prediction of token $$k$$, its computation should not use information from token $$k$$ and beyond, which brings us to the second common misconception: The causal mask is not needed to prevent information leakage from future tokens during training.\n

\n

\n To illustrate my point, suppose we have a sequence of tokens $$x_1, x_2, x_3, x_4$$ and we want the model to use tokens $$x_1, x_2, x_3$$ to predict token $$x_4$$.\n We need to prohibit tokens from attending to token $$x_4$$ when predicting token $$x_4$$, but we do not need to prohibit token $$x_1$$ from attending to tokens $$\{x_2, x_3\}$$, or token $$x_2$$ from attending to token $$x_3$$, which is exactly what the causal mask does.\n Instead of a triangular mask, using a block-sparse mask would suffice to prevent information leakage from future tokens, while allowing for all tokens in the context to attend to each other.\n

\n

\n The illustration above only depicts the case of a single token prediction during training, raising the question of whether the point holds for parallel training as well.\n In fact, the causal mask is neither needed for parallel training, nor for teacher-forcing, and a block-sparse mask suffices for both.\n

\n 3\n

What about PrefixLMs?

\n

\n We can motivate the use of block-sparse masks by looking at PrefixLMs , which are language models originally designed to solve sequence-to-sequence tasks.\n In PrefixLMs, the model is trained to predict the next token given the previous tokens and a so-called prefix which is input to the model.\n During supervised training of PrefixLMs, the prefix could be a prompt, a question, or any other kind of context that is given to the model.\n The model is then trained to predict the answer or continuation of the prompt. Since the prefix is fixed and not predicted by the model, the tokens in the prefix are not masked at all, while the rest of the sequence is causally masked (Figure 1, right).\n

\n

\n This masking procedure is specifically tailored towards the regime of sequence-to-sequence tasks under explicit supervision.\n However, the great abundance of textual data available on the Internet is unlabeled and largely unstructured, in which case PrefixLM training involves\n randomly splitting text into prefixes and targets. In this case, causal language modeling leads to much denser supervision than prefix language modeling,\n since the prefix does not provide a direct supervisory signal to the model.\n

\n

\n This motivates a procedure that benefits from both dense supervision as well as full attention: Taking each token as the sole target\n and using all previous tokens as the prefix. This is exactly the block-sparse mask and nothing inherently prohibits parallelization in this regime.\n However, the block-sparse mask depends on the position of the token in the sequence, which means that a naรฏve implementation would require $$n$$ times as much memory\n and $$n$$ times as much computation, since everything after the first attention map computation is token-position dependent (where $$n$$ is the sequence length).\n Thus, the main challenge in moving beyond the causal mask is mitigating the memory and compute overhead that comes with that.\n

\n \n\n \n\n

Contributions

\n

FS worked on all aspects of this post, including research, analysis and writing. This blog post has benefited from various discussions with senior colleagues, among others Preetum Nakkiran and Thomas Scialom.

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Learning},\n pages = \t {17473--17498},\n year = \t {2022},\n editor = \t {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},\n volume = \t {162},\n series = \t {Proceedings of Machine Learning Research},\n month = \t {17--23 Jul},\n publisher = {PMLR},\n pdf = \t {https://proceedings.mlr.press/v162/parker-holder22a/parker-holder22a.pdf},\n url = \t {https://proceedings.mlr.press/v162/parker-holder22a.html},\n abstract = \t {Training generally-capable agents with reinforcement learning (RL) remains a significant challenge. A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agentโ€™s capabilities. These methods benefit from theoretical robustness guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces in practice. By contrast, evolutionary approaches incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. This work proposes harnessing the power of evolution in a principled, regret-based curriculum. 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/Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 398ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +11432,8439448,"TERMINAL",0,0,"created dist/transforms.v2.js in 442ms\r\n\r\n[2025-08-05 20:46:35] waiting for changes...\r\n",,terminal_output +11433,8457596,"examples/jasmine.html",7107,0,"",html,selection_command +11434,8457835,"examples/jasmine.html",6860,0,"",html,selection_command +11435,8457868,"examples/jasmine.html",6852,0,"",html,selection_command +11436,8457901,"examples/jasmine.html",6779,0,"",html,selection_command +11437,8457935,"examples/jasmine.html",6770,0,"",html,selection_command +11438,8457984,"examples/jasmine.html",6743,0,"",html,selection_command +11439,8458001,"examples/jasmine.html",6465,0,"",html,selection_command +11440,8458028,"examples/jasmine.html",6196,0,"",html,selection_command +11441,8458062,"examples/jasmine.html",5920,0,"",html,selection_command +11442,8458099,"examples/jasmine.html",5912,0,"",html,selection_command +11443,8458132,"examples/jasmine.html",5903,0,"",html,selection_command +11444,8458161,"examples/jasmine.html",5768,0,"",html,selection_command +11445,8458196,"examples/jasmine.html",5503,0,"",html,selection_command +11446,8458228,"examples/jasmine.html",5343,0,"",html,selection_command +11447,8458260,"examples/jasmine.html",5083,0,"",html,selection_command +11448,8458294,"examples/jasmine.html",4804,0,"",html,selection_command +11449,8458326,"examples/jasmine.html",4584,0,"",html,selection_command +11450,8458360,"examples/jasmine.html",4352,0,"",html,selection_command +11451,8458450,"examples/jasmine.html",4344,0,"",html,selection_command +11452,8458633,"examples/jasmine.html",4283,0,"",html,selection_command +11453,8458784,"examples/jasmine.html",4287,0,"",html,selection_command +11454,8458949,"examples/jasmine.html",4288,0,"",html,selection_command +11455,8459257,"examples/jasmine.html",4289,0,"",html,selection_command +11456,8459412,"examples/jasmine.html",4290,0,"",html,selection_command +11457,8459588,"examples/jasmine.html",4291,0,"",html,selection_command +11458,8459840,"examples/jasmine.html",4291,0,"D",html,content +11459,8459844,"examples/jasmine.html",4292,0,"",html,selection_keyboard +11460,8460001,"examples/jasmine.html",4292,0,"e",html,content +11461,8460004,"examples/jasmine.html",4293,0,"",html,selection_keyboard +11462,8460097,"examples/jasmine.html",4293,0,"r",html,content +11463,8460099,"examples/jasmine.html",4294,0,"",html,selection_keyboard +11464,8460203,"examples/jasmine.html",4294,0,"i",html,content +11465,8460205,"examples/jasmine.html",4295,0,"",html,selection_keyboard +11466,8460298,"examples/jasmine.html",4295,0,"v",html,content +11467,8460299,"examples/jasmine.html",4296,0,"",html,selection_keyboard +11468,8460364,"examples/jasmine.html",4296,0,"i",html,content +11469,8460367,"examples/jasmine.html",4297,0,"",html,selection_keyboard +11470,8460520,"examples/jasmine.html",4297,0,"n",html,content +11471,8460523,"examples/jasmine.html",4298,0,"",html,selection_keyboard +11472,8460625,"examples/jasmine.html",4298,0,"g",html,content +11473,8460627,"examples/jasmine.html",4299,0,"",html,selection_keyboard +11474,8460724,"examples/jasmine.html",4299,0," ",html,content +11475,8460726,"examples/jasmine.html",4300,0,"",html,selection_keyboard +11476,8460906,"examples/jasmine.html",4299,0,"",html,selection_command +11477,8462288,"TERMINAL",0,0,"^Cโ ™% \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004h",,terminal_output +11478,8462358,"TERMINAL",0,0,"",,terminal_command +11479,8462358,"TERMINAL",0,0,"[?2004l\r\r\n% \r \r]633;E;;b07e0e29-8b6e-4188-b95b-fd2ce17f4bb1",,terminal_output +11480,8462358,"TERMINAL",0,0,"]633;C",,terminal_output +11481,8462837,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +11482,8462853,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +11483,8463345,"TERMINAL",0,0,"npm run dev",,terminal_command +11484,8463397,"TERMINAL",0,0,"]633;C",,terminal_output +11485,8463510,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +11486,8463708,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +11487,8464117,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 409ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +11488,8464451,"TERMINAL",0,0,"created dist/transforms.v2.js in 335ms\r\n\r\n[2025-08-05 20:47:00] waiting for changes...\r\n",,terminal_output +11489,8489094,"examples/jasmine.html",7160,0,"",html,selection_command +11490,8489626,"examples/jasmine.html",7164,0,"",html,selection_command +11491,8489793,"examples/jasmine.html",7166,0,"",html,selection_command +11492,8489944,"examples/jasmine.html",7171,0,"",html,selection_command +11493,8490117,"examples/jasmine.html",7173,0,"",html,selection_command +11494,8490389,"examples/jasmine.html",7174,0,"",html,selection_command +11495,8490531,"examples/jasmine.html",7175,0,"",html,selection_command +11496,8490694,"examples/jasmine.html",7176,0,"",html,selection_command +11497,8491039,"examples/jasmine.html",7176,1,"",html,content +11498,8492064,"TERMINAL",0,0,"^Cโ ™% \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004hnpm run dev",,terminal_output +11499,8492200,"TERMINAL",0,0,"cp examples/* dist",,terminal_output +11500,8492641,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +11501,8492641,"TERMINAL",0,0,"[?2004l\r\r\n]633;E;cp examples/* dist;b07e0e29-8b6e-4188-b95b-fd2ce17f4bb1",,terminal_output +11502,8492656,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +11503,8493269,"TERMINAL",0,0,"npm run dev",,terminal_command +11504,8493321,"TERMINAL",0,0,"]633;C",,terminal_output +11505,8493462,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +11506,8493665,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +11507,8494046,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 380ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +11508,8494395,"TERMINAL",0,0,"created dist/transforms.v2.js in 349ms\r\n\r\n[2025-08-05 20:47:30] waiting for changes...\r\n",,terminal_output +11509,8539359,"examples/jasmine.html",7268,0,"",html,selection_command +11510,8539601,"examples/jasmine.html",7576,0,"",html,selection_command +11511,8539632,"examples/jasmine.html",7839,0,"",html,selection_command +11512,8539666,"examples/jasmine.html",8062,0,"",html,selection_command +11513,8539700,"examples/jasmine.html",8337,0,"",html,selection_command +11514,8539733,"examples/jasmine.html",8420,0,"",html,selection_command +11515,8539766,"examples/jasmine.html",8701,0,"",html,selection_command +11516,8539800,"examples/jasmine.html",8784,0,"",html,selection_command +11517,8539834,"examples/jasmine.html",8801,0,"",html,selection_command +11518,8539965,"examples/jasmine.html",8803,0,"",html,selection_command +11519,8540238,"examples/jasmine.html",8801,0,"",html,selection_command +11520,8551360,"examples/jasmine.html",8802,0,"\n ",html,content +11521,8551606,"examples/jasmine.html",8807,0,"\n ",html,content +11522,8551606,"examples/jasmine.html",8803,4,"",html,content +11523,8551998,"examples/jasmine.html",8804,4,"",html,content +11524,8552242,"examples/jasmine.html",8803,1,"",html,content +11525,8552443,"examples/jasmine.html",8802,1,"",html,content +11526,8552880,"examples/jasmine.html",8801,0,"",html,selection_command +11527,8554182,"examples/jasmine.html",8784,0,"",html,selection_command +11528,8554830,"examples/jasmine.html",8785,0,"\n ",html,content +11529,8555152,"examples/jasmine.html",8790,0,"<",html,content +11530,8555495,"examples/jasmine.html",8791,0,"h",html,content +11531,8555812,"examples/jasmine.html",8792,0,"2",html,content +11532,8555814,"examples/jasmine.html",8793,0,"",html,selection_keyboard +11533,8556229,"examples/jasmine.html",8793,0,">",html,content +11534,8556233,"examples/jasmine.html",8794,0,"",html,selection_keyboard +11535,8556334,"examples/jasmine.html",8794,0,"",html,content +11536,8556551,"examples/jasmine.html",8793,0,"",html,selection_command +11537,8558034,"examples/jasmine.html",8794,0,"",html,selection_command +11538,8558398,"examples/jasmine.html",8793,0,"",html,selection_command +11539,8558840,"examples/jasmine.html",8799,0,"\n ",html,content +11540,8559219,"examples/jasmine.html",8804,0,"<",html,content +11541,8559220,"examples/jasmine.html",8805,0,"",html,selection_keyboard +11542,8559493,"examples/jasmine.html",8805,0,"p",html,content +11543,8559497,"examples/jasmine.html",8806,0,"",html,selection_keyboard +11544,8559870,"examples/jasmine.html",8806,0,">",html,content +11545,8559874,"examples/jasmine.html",8807,0,"",html,selection_keyboard +11546,8559977,"examples/jasmine.html",8807,0,"

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+11603,8618009,"examples/jasmine.html",8984,0,"",html,selection_command +11604,8618137,"examples/jasmine.html",8983,0,"",html,selection_command +11605,8618281,"examples/jasmine.html",8982,0,"",html,selection_command +11606,8618433,"examples/jasmine.html",8983,0,"",html,selection_command +11607,8618561,"examples/jasmine.html",8983,0," ",html,content +11608,8618564,"examples/jasmine.html",8984,0,"",html,selection_keyboard +11609,8622858,"examples/jasmine.html",8984,0,"S",html,content +11610,8622861,"examples/jasmine.html",8985,0,"",html,selection_keyboard +11611,8622896,"examples/jasmine.html",8985,0,"B",html,content +11612,8622901,"examples/jasmine.html",8986,0,"",html,selection_keyboard +11613,8624627,"examples/jasmine.html",8986,0," provided feedback and guidance.",html,content +11614,8624836,"examples/jasmine.html",9017,0,"",html,selection_command +11615,8625025,"examples/jasmine.html",8889,0,"",html,selection_command +11616,8626660,"TERMINAL",0,0,"^Cโ ™% \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004hnpm run dev",,terminal_output +11617,8626797,"TERMINAL",0,0,"cp examples/* dist",,terminal_output +11618,8626979,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +11619,8626980,"TERMINAL",0,0,"[?2004l\r\r\n]633;E;cp examples/* dist;b07e0e29-8b6e-4188-b95b-fd2ce17f4bb1",,terminal_output +11620,8626996,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +11621,8627517,"TERMINAL",0,0,"npm run dev",,terminal_command +11622,8627569,"TERMINAL",0,0,"]633;C",,terminal_output +11623,8627756,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +11624,8628044,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +11625,8628470,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) 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Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 615ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +11842,9437964,"TERMINAL",0,0,"created dist/transforms.v2.js in 524ms\r\n\r\n[2025-08-05 21:03:14] waiting for changes...\r\n",,terminal_output +11843,13278839,"examples/jasmine.html",9226,0,"",html,selection_command +11844,13280614,"examples/jasmine.html",8776,0,"",html,selection_mouse +11845,13280620,"examples/jasmine.html",8775,0,"",html,selection_command 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Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 400ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +15515,15238928,"TERMINAL",0,0,"created dist/transforms.v2.js in 369ms\r\n\r\n[2025-08-05 22:39:55] waiting for changes...\r\n",,terminal_output +15516,15247763,"examples/jasmine.html",9562,0,"",html,selection_command +15517,15248299,"examples/jasmine.html",9562,3,"",html,content +15518,15249450,"examples/jasmine.html",9562,1,"",html,content +15519,15250655,"TERMINAL",0,0,"^Cโ ™% \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004h",,terminal_output +15520,15250732,"TERMINAL",0,0,"",,terminal_command +15521,15250733,"TERMINAL",0,0,"[?2004l\r\r\n% \r \r]633;E;;b07e0e29-8b6e-4188-b95b-fd2ce17f4bb1",,terminal_output +15522,15250733,"TERMINAL",0,0,"]633;C",,terminal_output +15523,15251196,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +15524,15251212,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +15525,15251696,"TERMINAL",0,0,"npm run dev",,terminal_command +15526,15251747,"TERMINAL",0,0,"]633;C",,terminal_output +15527,15251891,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +15528,15252067,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +15529,15252617,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 548ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +15530,15253007,"TERMINAL",0,0,"created dist/transforms.v2.js in 391ms\r\n\r\n[2025-08-05 22:40:09] waiting for changes...\r\n",,terminal_output +15531,15322278,"examples/jasmine.html",9467,0,"",html,selection_command +15532,15322915,"examples/jasmine.html",9267,0,"",html,selection_command +15533,15323112,"examples/jasmine.html",9275,0,"",html,selection_command 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\n We introduce Jasmine, a production-ready JAX-based codebase for world modeling from unlabeled videos.\n Scale from single hosts to hundreds of xPUs thanks to XLA.\n

\n \n \n \n 1\n

Introduction

\n

\n We are at the cusp of an intelligence revolution. Neural networks are able to clone the behaviour of peak human intellectual performance \n given enough compute, data, and the right algorithms . While an increasing amount of capital expenditure is allocated to compute clusters, and a well-working\n recipe of equipping models with the required priors and capacity to reason is publicly available, the path to human-level intelligence with the ability to automate\n large fractions of the economy will increasingly be shaped by paradigms that are able to find and efficiently use untouched data troves.\n

\n

\n While product-feedback-loops constitute an adaptive data trove, many domains like robotics are not mature enough to yield a product with wide enough\n adoption to create a feedback-loop of sufficient magnitude, prompting the search for alternatives.\n One paradigm proposed by the research community to overcome the data scarcity in those domains is that of world models. While world models can help frontier model\n development in numerous ways, an ambitious goal of the community is to train a world model to act as a simulation of the world , in order to\n train an agent in that simulation, via an adaptive curriculum or otherwise.\n

\n

Deriving Empirical Environment Complexity Scaling Trends

\n

\n While numerous previous works have investigated large-scale world modeling and its application to robotics , world modeling for agent training calls for a vastly different treatment.\n Such regime requires the compounding error of world models to be orders of magnitude smaller than when solely used for short-term look-ahead. The feasibility of such a world model in its truest sense is entirely\n understudied, and Jasmine, a world modeling codebase, is our first milestone towards studying the setting using rigorous evaluations. Specifically, we want to develop Empirical Environment Complexity Scaling Trends, where we train world models to full convergence\n in environments of increasing complexity (Atari , RetroGym , Craftax , Minecraft )\n and under the synthetic infinite-data regime. Subsequently, we want to evaluate those models two-fold: i) via a taxonomy of granular benchmarks probing\n specific world modeling capabilities (reconstruction quality, environment dynamics at the body/tail of the data distribution, long-horizon consistency) , and ii) by training reinforcement learning (RL) agents in both\n the world model and the corresponding ground-truth environment, and measuring the performance difference between those agents.\n

\n

\n Ultimately, such treatment permits us to derive empirical estimates of compute and data requirements to model environments of increasing complexity sufficiently well (as determined by our evaluation procedure). Only given such estimates can we try to draw conclusions\n about the feasibility of world modeling of environments as complex as the real world for agent training. If our empirical estimates show resource requirement trends that are feasible under the assumption of the continuation of Moore's Law and increased capital\n expenditure, that would manifest world modeling as a paradigm with high likelihood of success in overcoming the data-scarcity in domains as general as (humanoid) robotics. Otherwise, the world modeling research community must realign its direction with downstream goals\n that are feasible.\n

\n

A batteries-included foundation for world modeling research

\n

\n Jasmine, our first milestone towards deriving Empirical Environment Complexity Scaling Trends, is the result of weeks of infrastructure work to make large-scale world modeling research more accessible. What started off as a fork of\n Jafar grew into a full-fledged world\n modeling codebase amenable to large-scale training, implementing multiple dynamics model baselines, asynchronous checkpointing, process-parallel dataloading, checkpointing of model weights, optimizer and dataloader states, checkpointing policies, full reproducibility with identical\n training curves, mixed precision training, optimized FlashAttention (via cuDNN SDPA), activation checkpointing, DDP\n (with FSDP/HSDP requiring changing a singe LoC), WSD schedule, index-shuffling during dataloading, and native Treescope support. Jasmine implements the new\n flax.nnx API and strictly adheres to Noam Shazeer's shape suffix convention, thereby providing\n a didactic implementation of world modeling architectures. Jasmine solely depends\n on battle-tested libraries from the Google ecosystem (Flax, Optax, Orbax, Grain,\n PIX, ArrayRecord).\n

\n

Releasing a dataset of fine-grained research engineering

\n

\n We captured every step of the research engineering process behind Jasmine using crowd-code ,\n a VS Code/ Cursor extension that captures fine-grained IDE interactions (character-level edits, navigation, debugging patterns, terminal usage) and allows researchers to contribute their \n engineering process to a crowd-sourced dataset. Today, we release crowd-code-0.1, our first dataset of dense IDE interactions, which encompasses the entire development of Jasmine.\n crowd-code-0.1 is unfiltered, uncleaned, and uncurated, but only contains IDE interactions of the authors. We are actively working on cleaning and curating the full dataset,\n which will be released in the future.\n

\n \n\n \n\n

Contributions

\n

MM, AN and FS worked on research, ideation and implementation. FS wrote the manuscript. SB provided feedback and guidance.

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(346) [0]\r348/ 435 (347) [0]\r349/ 435 (348) [0]\r350/ 435 (349) [0]\r351/ 435 (350) [0]\r352/ 435 (351) [0]\r353/ 435 (352) [0]\r354/ 435 (353) [0]\r355/ 435 (354) [0]\r356/ 435 (355) [0]\r357/ 435 (356) [0]\r358/ 435 (357) [0]\r359/ 435 (358) [0]\r360/ 435 (359) [0]\r361/ 435 (360) [0]\r362/ 435 (361) [0]\r363/ 435 (362) [0]\r364/ 435 (363) [0]\r365/ 435 (364) [0]\r366/ 435 (365) [0]\r367/ 435 (366) [0]\r368/ 435 (367) [0]\r369/ 435 (368) [0]\r370/ 435 (369) [0]\r371/ 435 (370) [0]\r372/ 435 (371) [0]\r373/ 435 (372) [0]\r374/ 435 (373) [0]\r375/ 435 (374) [0]\r376/ 435 (375) [0]\r377/ 435 (376) [0]\r378/ 435 (377) [0]\r379/ 435 (378) [0]\r380/ 435 (379) [0]\r381/ 435 (380) [0]\r382/ 435 (381) [0]\r383/ 435 (382) [0]\r384/ 435 (383) [0]\r385/ 435 (384) [0]\r386/ 435 (385) [0]\r387/ 435 (386) [0]\r388/ 435 (387) [0]\r389/ 435 (388) [0]\r390/ 435 (389) [0]\r391/ 435 (390) [0]\r392/ 435 (391) [0]\r393/ 435 (392) [0]\r394/ 435 (393) [0]\r395/ 435 (394) [0]\r396/ 435 (395) [0]\r397/ 435 (396) [0]\r398/ 435 (397) [0]\r399/ 435 (398) [0]\r400/ 435 (399) [0]\r401/ 435 (400) [0]\r402/ 435 (401) [0]\r403/ 435 (402) [0]\r404/ 435 (403) [0]\r405/ 435 (404) [0]\r406/ 435 (405) [0]\r407/ 435 (406) [0]\r408/ 435 (407) [0]\r409/ 435 (408) [0]\r410/ 435 (409) [0]\r411/ 435 (410) [0]\r412/ 435 (411) [0]\r413/ 435 (412) [0]\r414/ 435 (413) [0]\r415/ 435 (414) [0]\r416/ 435 (415) [0]\r417/ 435 (416) [0]\r418/ 435 (417) [0]\r419/ 435 (418) [0]\r420/ 435 (419) [0]\r421/ 435 (420) [0]\r422/ 435 (421) [0]\r423/ 435 (422) [0]\r424/ 435 (423) [0]\r425/ 435 (424) [0]\r426/ 435 (425) [0]\r427/ 435 (426) [0]\r428/ 435 (427) [0]\r429/ 435 (428) [0]\r430/ 435 (429) [0]\r431/ 435 (430) [0]\r432/ 435 (431) [0]\r433/ 435 (432) [0]\r434/ 435 (433) [0]\r435/ 435 (434) [0]\r",,terminal_output +15880,15856874,"TERMINAL",0,0,"Enumerating objects: 6, done.\r\nCounting objects: 16% (1/6)\rCounting objects: 33% (2/6)\rCounting objects: 50% (3/6)\rCounting objects: 66% (4/6)\rCounting objects: 83% (5/6)\rCounting objects: 100% (6/6)\rCounting objects: 100% (6/6), done.\r\nDelta compression using up to 8 threads\r\nCompressing objects: 25% (1/4)\rCompressing objects: 50% (2/4)\rCompressing objects: 75% (3/4)\rCompressing objects: 100% (4/4)\rCompressing objects: 100% (4/4), done.\r\nWriting objects: 25% (1/4)\rWriting objects: 50% (2/4)\rWriting objects: 75% (3/4)\rWriting objects: 100% (4/4)\rWriting objects: 100% (4/4), 8.46 KiB | 8.46 MiB/s, done.\r\nTotal 4 (delta 2), reused 0 (delta 0), pack-reused 0\r\n",,terminal_output +15881,15857331,"TERMINAL",0,0,"remote: Resolving deltas: 0% (0/2)\rremote: Resolving deltas: 50% (1/2)\rremote: Resolving deltas: 100% (2/2)\rremote: Resolving deltas: 100% (2/2), completed with 2 local objects.\r\nTo https://github.com/emergenz/pdoom.org\r\n bd12df8..e1b52cc e1b52ccb8cb5ca5acc8e6b416c44fcf7e6b438f1 -> gh-pages\r\n% \r \r",,terminal_output +15882,16103009,"examples/blog.html",0,0,"\n\n\n\n \n \n \n \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n
\n \n \n \n \n \n \n \n
\n \n \n\n",html,tab +15883,16104375,"examples/blog.html",25705,0,"",html,selection_command +15884,16104628,"examples/blog.html",25603,0,"",html,selection_command +15885,16104655,"examples/blog.html",25546,0,"",html,selection_command +15886,16104687,"examples/blog.html",25449,0,"",html,selection_command +15887,16104844,"examples/blog.html",25392,0,"",html,selection_command +15888,16105339,"examples/blog.html",25353,40," ",html,selection_command +15889,16105505,"examples/blog.html",25353,150," \n
",html,selection_command +15890,16105755,"examples/blog.html",25353,194," \n
\n
",html,selection_command +15891,16105787,"examples/blog.html",25353,296," \n
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

",html,selection_command +15892,16105819,"examples/blog.html",25353,402," \n
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

",html,selection_command +15893,16105852,"examples/blog.html",25353,652," \n
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

",html,selection_command +15894,16105886,"examples/blog.html",25353,677," \n
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n
",html,selection_command +15895,16110307,"examples/blog.html",25353,696," \n
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n
\n ",html,selection_command +15896,16113805,"examples/blog.html",26048,0,"",html,selection_command +15897,16113968,"examples/blog.html",26065,0,"",html,selection_command +15898,16114592,"examples/blog.html",26050,16,"
",html,selection_command +15899,16114719,"examples/blog.html",26031,35," \n
",html,selection_command +15900,16114976,"examples/blog.html",26006,60,"
\n \n
",html,selection_command +15901,16115013,"examples/blog.html",25756,310,"

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n \n \n ",html,selection_command +15902,16115036,"examples/blog.html",25650,416,"

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n \n \n ",html,selection_command +15903,16115068,"examples/blog.html",25548,518,"

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n \n \n ",html,selection_command +15904,16115098,"examples/blog.html",25504,562,"
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n
\n \n ",html,selection_command +15905,16115135,"examples/blog.html",25394,672,"
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n
\n \n ",html,selection_command +15906,16115165,"examples/blog.html",25353,713," \n
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n
\n \n ",html,selection_command +15907,16115297,"examples/blog.html",25332,734," \n \n
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n
\n \n ",html,selection_command +15908,16115471,"examples/blog.html",25267,799,"
June 22, 2025
\n \n \n
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n
\n \n ",html,selection_command +15909,16115606,"examples/blog.html",25230,836,"
\n
June 22, 2025
\n
\n \n
\n
\n

Crowd-Sourcing A Dataset To Make Agents Code Like Humans

\n

Alfred Nguyen, Mihir Mahajan, Franz Srambical

\n

We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on. Install once, and forget about it.

\n
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Scale from single hosts to hundreds of xPUs thanks to XLA.",html,content +16184,16165723,"examples/blog.html",25978,0,"",html,selection_command +16185,16166774,"examples/blog.html",25777,0,"",html,selection_command +16186,16170781,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +16187,16170799,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +16188,16172392,"TERMINAL",0,0,"npm run dev",,terminal_command +16189,16172444,"TERMINAL",0,0,"]633;C",,terminal_output +16190,16172751,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +16191,16173041,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +16192,16173509,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 484ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +16193,16174050,"TERMINAL",0,0,"created dist/transforms.v2.js in 538ms\r\n\r\n[2025-08-05 22:55:30] waiting for changes...\r\n",,terminal_output +16194,16867470,"examples/blog.html",25671,0,"",html,selection_command +16195,16867631,"examples/blog.html",25543,0,"",html,selection_command +16196,16868051,"examples/blog.html",25499,0,"",html,selection_command +16197,16868559,"examples/blog.html",25392,0,"",html,selection_command +16198,16868701,"examples/blog.html",25410,0,"",html,selection_command +16199,16868953,"examples/blog.html",25411,0,"",html,selection_command +16200,16868983,"examples/blog.html",25415,0,"",html,selection_command +16201,16869020,"examples/blog.html",25420,0,"",html,selection_command +16202,16869048,"examples/blog.html",25422,0,"",html,selection_command +16203,16869081,"examples/blog.html",25431,0,"",html,selection_command +16204,16869115,"examples/blog.html",25434,0,"",html,selection_command +16205,16869149,"examples/blog.html",25438,0,"",html,selection_command +16206,16869182,"examples/blog.html",25441,0,"",html,selection_command +16207,16869327,"examples/blog.html",25443,0,"",html,selection_command +16208,16869612,"examples/blog.html",25458,0,"",html,selection_command +16209,16869769,"examples/blog.html",25459,0,"",html,selection_command +16210,16870126,"examples/blog.html",25459,3,"",html,content +16211,16870316,"examples/blog.html",25459,0,"g",html,content +16212,16870318,"examples/blog.html",25460,0,"",html,selection_keyboard +16213,16870665,"examples/blog.html",25460,0,"i",html,content +16214,16870667,"examples/blog.html",25461,0,"",html,selection_keyboard +16215,16871099,"examples/blog.html",25461,0,"f",html,content +16216,16871102,"examples/blog.html",25462,0,"",html,selection_keyboard +16217,16871336,"examples/blog.html",25461,0,"",html,selection_command +16218,16872138,"examples/blog.html",25392,0,"",html,selection_command +16219,16892108,"examples/blog.html",0,0,"",html,tab +16220,16893900,"TERMINAL",0,0,"^Cโ ™% \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004h",,terminal_output +16221,16894039,"TERMINAL",0,0,"npm run dev",,terminal_output +16222,16894169,"TERMINAL",0,0,"cp examples/* dist",,terminal_output +16223,16894348,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +16224,16894348,"TERMINAL",0,0,"[?2004l\r\r\n]633;E;cp examples/* dist;b07e0e29-8b6e-4188-b95b-fd2ce17f4bb1",,terminal_output +16225,16894367,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +16226,16894916,"TERMINAL",0,0,"npm run dev",,terminal_command +16227,16894967,"TERMINAL",0,0,"]633;C",,terminal_output +16228,16895137,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +16229,16895347,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +16230,16895791,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 444ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +16231,16896274,"TERMINAL",0,0,"created dist/transforms.v2.js in 482ms\r\n\r\n[2025-08-05 23:07:32] waiting for changes...\r\n",,terminal_output diff --git a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-1d1c9d40-4350-4512-82d6-f9fdf36759211756538623431-2025_08_30-08.23.49.356/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-1d1c9d40-4350-4512-82d6-f9fdf36759211756538623431-2025_08_30-08.23.49.356/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..84659cde465567f3c05b8742caeb2f60d84b8bd7 --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-1d1c9d40-4350-4512-82d6-f9fdf36759211756538623431-2025_08_30-08.23.49.356/source.csv @@ -0,0 +1,2272 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,3,"utils/train_utils.py",0,0,"import jax\nimport optax\nimport operator\n\n\ndef get_lr_schedule(\n lr_schedule: str,\n init_lr: float,\n max_lr: float,\n decay_end: float,\n total_steps: int,\n warmup_steps: int,\n wsd_decay_steps: int,\n) -> optax.Schedule:\n supported_schedules = [""wsd"", ""cos""]\n if lr_schedule == ""cos"":\n assert (\n warmup_steps <= total_steps\n ), ""Warmup steps can't be greater than total steps.""\n return optax.warmup_cosine_decay_schedule(\n init_value=init_lr,\n peak_value=max_lr,\n warmup_steps=warmup_steps,\n decay_steps=total_steps, # Note: decay_steps includes the warmup steps, so we need to pass total value\n end_value=decay_end,\n )\n elif lr_schedule == ""wsd"":\n assert (\n warmup_steps + wsd_decay_steps <= total_steps\n ), ""Warmup and decay period is longer than total steps.""\n schedules = [\n optax.linear_schedule(\n init_value=init_lr, end_value=max_lr, transition_steps=warmup_steps\n ),\n optax.constant_schedule(value=max_lr),\n optax.linear_schedule(\n init_value=max_lr, end_value=decay_end, transition_steps=wsd_decay_steps\n ),\n ]\n boundaries = [warmup_steps, total_steps - wsd_decay_steps]\n return optax.join_schedules(schedules, boundaries)\n else:\n raise ValueError(\n f""Learning rate schedule not supported. Please use one of {supported_schedules}""\n )\n\n\ndef _count_component(component_params):\n """"""Count total parameters in a component.""""""\n params_sizes = jax.tree.map(jax.numpy.size, component_params)\n total_parameters = jax.tree.reduce(operator.add, params_sizes)\n return total_parameters\n\n\ndef count_parameters_by_component(params):\n """"""Count parameters for each component of the model.\n\n Args:\n params: Model parameters from nnx.split(model, nnx.Param, ...)\n\n Returns:\n Dictionary with parameter counts for each component\n """"""\n component_names = list(params.keys())\n print(f""Counting all components: {component_names}"")\n\n counts = {}\n total_params = 0\n\n for name in component_names:\n component_params = params[name]\n count = _count_component(component_params)\n counts[name] = count\n total_params += count\n\n counts[""total""] = total_params\n return counts\n\n\ndef bytes_to_gb(num_bytes):\n return num_bytes / (1024**3)\n\n\ndef print_compiled_memory_stats(compiled_stats):\n """"""from: https://github.com/AI-Hypercomputer/maxtext/blob/b18829fbaa48aec7ac350a03e62248e24c6a76b2/MaxText/max_utils.py#L739""""""\n output_gb = bytes_to_gb(compiled_stats.output_size_in_bytes)\n temp_gb = bytes_to_gb(compiled_stats.temp_size_in_bytes)\n argument_gb = bytes_to_gb(compiled_stats.argument_size_in_bytes)\n alias_gb = bytes_to_gb(compiled_stats.alias_size_in_bytes)\n host_temp_gb = bytes_to_gb(compiled_stats.host_temp_size_in_bytes)\n total_gb = output_gb + temp_gb + argument_gb - alias_gb\n print(\n f""Total memory size: {total_gb:.1f} GB, Output size: {output_gb:.1f} GB, Temp size: {temp_gb:.1f} GB, ""\n f""Argument size: {argument_gb:.1f} GB, Host temp size: {host_temp_gb:.1f} GB.""\n )\n\n\ndef print_compiled_cost_analysis(cost_stats):\n flops = float(cost_stats.get(""flops"", 0.0))\n bytes_accessed = float(cost_stats.get(""bytes accessed"", 0.0))\n gb = bytes_to_gb(bytes_accessed) if bytes_accessed else 0.0\n intensity = (flops / bytes_accessed) if bytes_accessed else float(""nan"")\n print(\n f""FLOPs: {flops:.3e}, Bytes: {bytes_accessed:.3e} ({gb:.1f} GB), ""\n f""Intensity: {intensity:.1f} FLOPs/byte""\n )\n\n\ndef print_mem_stats(label: str):\n """"""from: https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L713""""""\n print(f""\nMemstats: {label}:"")\n try:\n for d in jax.local_devices():\n stats = d.memory_stats()\n used = round(stats[""bytes_in_use""] / 2**30, 2)\n limit = round(stats[""bytes_limit""] / 2**30, 2)\n print(f""\tUsing (GB) {used} / {limit} ({used/limit:%}) on {d}"")\n except (RuntimeError, KeyError, TypeError) as ex:\n print(f""\tMemstats unavailable, error: {ex}"")\n",python,tab +2,26,"tasks",0,0,"",Log,tab +3,28,"utils/train_utils.py",0,0,"",python,tab +4,83,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab +5,1926,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"8:23:49 AM [info] Activating crowd-code\n8:23:49 AM [info] Recording started\n8:23:49 AM [info] Initializing git provider using file system watchers...\n8:23:49 AM [info] Git repository found\n8:23:49 AM [info] Git provider initialized successfully\n8:23:49 AM [info] Initial git state: [object Object]\n",Log,content +6,262330383,"utils/train_utils.py",0,0,"",python,tab +7,262360459,"utils/train_utils.py",237,0,"",python,selection_mouse +8,262360468,"utils/train_utils.py",236,0,"",python,selection_command +9,262361541,"utils/train_utils.py",4306,0,"",python,selection_command +10,262363810,"utils/train_utils.py",4306,0,"\n",python,content +11,262364349,"utils/train_utils.py",4307,0,"d",python,content +12,262364351,"utils/train_utils.py",4308,0,"",python,selection_keyboard +13,262364519,"utils/train_utils.py",4308,0,"e",python,content +14,262364523,"utils/train_utils.py",4309,0,"",python,selection_keyboard +15,262364638,"utils/train_utils.py",4309,0,"f",python,content 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+128,262392169,"utils/train_utils.py",4307,0,"",python,selection_command +129,262395354,"train_tokenizer.py",0,0,"import os\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nfrom typing import cast, Optional\n\nimport einops\nimport itertools\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 300_000\n seed: int = 0\n seq_len: int = 16\n image_channels: int = 3\n image_height: int = 90\n image_width: int = 160\n data_dir: str = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n init_lr: float = 0.0\n max_lr: float = 3e-4\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 20000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n warmup_steps: int = 10000\n # Tokenizer\n model_dim: int = 512\n ffn_dim: int = 2048\n latent_dim: int = 32\n num_latents: int = 1024\n patch_size: int = 4\n num_blocks: int = 4\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.01\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_tokenizer""\n tags: list[str] = field(default_factory=lambda: [""tokenizer""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 10000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[TokenizerVQVAE, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n return (\n TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n ffn_dim=args.ffn_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n ),\n rng,\n )\n\n\ndef build_optimizer(\n model: TokenizerVQVAE, args: Args\n) -> tuple[nnx.Optimizer, optax.Schedule]:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.Optimizer(model, tx)\n return optimizer, lr_schedule\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n return mesh, replicated_sharding, videos_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.Optimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args) -> grain.DataLoaderIterator:\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n return grain_iterator\n\n\ndef build_checkpoint_manager(args: Args) -> ocp.CheckpointManager:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n\n\ndef restore_checkpoint_if_needed(\n args: Args,\n checkpoint_manager: ocp.CheckpointManager,\n optimizer: nnx.Optimizer,\n grain_iterator: grain.DataLoaderIterator,\n restore_step: Optional[int] = None,\n) -> tuple[int, nnx.Optimizer, grain.DataLoaderIterator]:\n step = 0\n if restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n restore_step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = restore_step or 0\n print(f""Restored dataloader and model state from step {step}"")\n return step, optimizer, grain_iterator\n\n\ndef main(args: Args) -> None:\n jax.distributed.initialize()\n num_devices = jax.device_count()\n if num_devices == 0:\n raise ValueError(""No JAX devices found."")\n print(f""Running on {num_devices} devices."")\n\n if args.batch_size % num_devices != 0:\n raise ValueError(\n f""Global batch size {args.batch_size} must be divisible by ""\n f""number of devices {num_devices}.""\n )\n\n rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n tokenizer, rng = build_model(args, rng)\n\n _, params, _ = nnx.split(tokenizer, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\n ""entity"": args.entity,\n ""project"": args.project,\n ""name"": args.name,\n ""tags"": args.tags,\n ""group"": ""debug"",\n ""config"": args,\n }\n\n if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer, lr_schedule = build_optimizer(tokenizer, args)\n del tokenizer\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n mesh, replicated_sharding, videos_sharding = build_mesh_and_sharding(num_devices)\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n grain_iterator = build_dataloader(args)\n\n # --- Restore checkpoint ---\n step, optimizer, grain_iterator = restore_checkpoint_if_needed(\n args, checkpoint_manager, optimizer, grain_iterator\n )\n\n # --- Define loss and train step (close over args) ---\n def tokenizer_loss_fn(\n model: TokenizerVQVAE, inputs: dict\n ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n mse = jnp.square(gt - outputs[""recon""]).mean()\n q_loss = jnp.square(jax.lax.stop_gradient(outputs[""emb""]) - outputs[""z""]).mean()\n commitment_loss = jnp.square(\n outputs[""emb""] - jax.lax.stop_gradient(outputs[""z""])\n ).mean()\n loss = mse + q_loss + args.vq_beta * commitment_loss\n\n gt_clipped = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_clipped, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_clipped, recon)).mean()\n _, index_counts = jnp.unique_counts(\n jnp.ravel(outputs[""indices""]), size=args.num_latents, fill_value=0\n )\n codebook_usage = (index_counts != 0).mean()\n metrics = dict(\n loss=loss,\n mse=mse,\n q_loss=q_loss,\n commitment_loss=commitment_loss,\n psnr=psnr,\n ssim=ssim,\n codebook_usage=codebook_usage,\n )\n return loss, (outputs[""recon""], metrics)\n\n @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.Optimizer, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: TokenizerVQVAE) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n return tokenizer_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n optimizer.model\n )\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""encoder_gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""encoder""]\n )\n metrics[""vq_gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""vq""]\n )\n metrics[""decoder_gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""decoder""]\n )\n return loss, recon, metrics\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n if jax.process_index() == 0:\n first_videos = next(dataloader)\n sample_inputs = dict(videos=first_videos)\n compiled = train_step.lower(optimizer, sample_inputs).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader = itertools.chain([first_videos], dataloader)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n inputs = dict(videos=videos)\n loss, recon, metrics = train_step(optimizer, inputs)\n if step == first_step:\n print_mem_stats(""After params initialized"")\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n wandb.log(\n {\n ""loss"": loss,\n ""step"": step,\n **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\n recon_seq = recon[0].clip(0, 1)\n comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)\n comparison_seq = einops.rearrange(\n comparison_seq * 255, ""t h w c -> h (t w) c""\n )\n # NOTE: Process-dependent control flow deliberately happens\n # after indexing operation since it must not contain code\n # sections that lead to cross-accelerator communication.\n if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[0])),\n recon=wandb.Image(np.asarray(recon_seq[0])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = 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+333,262514040,"utils/train_utils.py",4340,0,"",python,selection_command +334,262514703,"utils/train_utils.py",4344,0,"",python,selection_command +335,262514956,"utils/train_utils.py",4380,0,"",python,selection_command +336,262514988,"utils/train_utils.py",4381,0,"",python,selection_command +337,262515027,"utils/train_utils.py",4385,0,"",python,selection_command +338,262515060,"utils/train_utils.py",4387,0,"",python,selection_command +339,262515101,"utils/train_utils.py",4391,0,"",python,selection_command +340,262515424,"utils/train_utils.py",4387,0,"",python,selection_command +341,262515758,"train_dynamics.py",0,0,"import os\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nimport itertools\nfrom typing import cast, Optional\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_000\n seed: int = 0\n seq_len: int = 16\n image_channels: int = 3\n image_height: int = 90\n image_width: int = 160\n data_dir: str = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[Genie, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n del genie.lam.decoder\n return genie, rng\n\n\ndef build_optimizer(genie: Genie, args: Args) -> tuple[nnx.Optimizer, optax.Schedule]:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.Optimizer(genie, tx)\n return optimizer, lr_schedule\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n return mesh, replicated_sharding, videos_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.Optimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args) -> grain.DataLoaderIterator:\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n return grain_iterator\n\n\ndef build_checkpoint_manager(args: Args) -> ocp.CheckpointManager:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n\n\ndef restore_or_initialize_components(\n args: Args,\n checkpoint_manager: ocp.CheckpointManager,\n optimizer: nnx.Optimizer,\n grain_iterator: grain.DataLoaderIterator,\n rng: jax.Array,\n replicated_sharding: NamedSharding,\n restore_step: Optional[int] = None,\n) -> tuple[int, nnx.Optimizer, grain.DataLoaderIterator, jax.Array]:\n step = 0\n if restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n restore_step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = restore_step or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n rng, _rng = jax.random.split(rng)\n optimizer = restore_genie_components(optimizer, replicated_sharding, _rng, args)\n # NOTE: We have to remove the (unused) tokenizer vq dropout due flax.nnx lazily initializing modules.\n # Specifically, the first dynamics model checkpoint will contain the vq dropout module,\n # but the first full restore will fail due to nnx not initializing the module when\n # dropout is set to 0.0.\n del optimizer.model.tokenizer.vq.drop\n return step, optimizer, grain_iterator, rng\n\n\ndef main(args: Args) -> None:\n jax.distributed.initialize()\n num_devices = jax.device_count()\n if num_devices == 0:\n raise ValueError(""No JAX devices found."")\n print(f""Running on {num_devices} devices."")\n\n if args.batch_size % num_devices != 0:\n raise ValueError(\n f""Global batch size {args.batch_size} must be divisible by ""\n f""number of devices {num_devices}.""\n )\n\n rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n genie, rng = build_model(args, rng)\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\n ""entity"": args.entity,\n ""project"": args.project,\n ""name"": args.name,\n ""tags"": args.tags,\n ""group"": ""debug"",\n ""config"": args,\n }\n\n if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer, lr_schedule = build_optimizer(genie, args)\n del genie\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n mesh, replicated_sharding, videos_sharding = build_mesh_and_sharding(num_devices)\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n grain_iterator = build_dataloader(args)\n\n # --- Restore checkpoint ---\n step, optimizer, grain_iterator, rng = restore_or_initialize_components(\n args, checkpoint_manager, optimizer, grain_iterator, rng, replicated_sharding\n )\n\n # --- Define loss and train step (close over args) ---\n def dynamics_loss_fn(\n model: Genie, inputs: dict\n ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt_val = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_val, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_val, recon)).mean()\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]),\n size=args.num_latent_actions,\n fill_value=0,\n )\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]),\n size=args.num_patch_latents,\n fill_value=0,\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_lam=codebook_usage_lam,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.Optimizer, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n optimizer.model\n )\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return loss, recon, metrics\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n if jax.process_index() == 0:\n first_videos = next(dataloader)\n sample_inputs = dict(videos=first_videos, mask_rng=rng)\n compiled = train_step.lower(optimizer, sample_inputs).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader = itertools.chain([first_videos], dataloader)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n inputs = dict(videos=videos, mask_rng=_rng_mask)\n loss, recon, metrics = train_step(optimizer, inputs)\n if step == first_step:\n print_mem_stats(""After params initialized"")\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n wandb.log(\n {\n ""loss"": loss,\n ""step"": step,\n **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\n recon_seq = recon[0].clip(0, 1)\n comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)\n comparison_seq = einops.rearrange(\n comparison_seq * 255, ""t h w c -> h (t w) c""\n )\n if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n grain_iterator # type: ignore\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n\n\nif __name__ == ""__main__"":\n args = tyro.cli(Args)\n main(args)\n",python,tab +342,262515765,"train_dynamics.py",774,0,"",python,selection_command +343,262518294,"utils/train_utils.py",0,0,"",python,tab 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calculate_tflops_per_device_tokenizer(args):\n """"""Calculate training TFLOP for tokenizer.\n\n Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L473",python,selection_command +1176,268369423,"utils/train_utils.py",4748,237,"def calculate_tflops_per_device_tokenizer(args):\n """"""Calculate training TFLOP for tokenizer.\n\n Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L473\n """"""",python,selection_command +1177,268369455,"utils/train_utils.py",4748,253,"def calculate_tflops_per_device_tokenizer(args):\n """"""Calculate training TFLOP for tokenizer.\n\n Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L473\n """"""\n # MLP flops",python,selection_command +1178,268369489,"utils/train_utils.py",4748,254,"def calculate_tflops_per_device_tokenizer(args):\n """"""Calculate training TFLOP for tokenizer.\n\n Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L473\n """"""\n # MLP flops\n",python,selection_command +1179,268369526,"utils/train_utils.py",4748,276,"def calculate_tflops_per_device_tokenizer(args):\n """"""Calculate training TFLOP for tokenizer.\n\n Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L473\n """"""\n # MLP flops\n\n # Attention flops",python,selection_command +1180,268369559,"utils/train_utils.py",4748,277,"def calculate_tflops_per_device_tokenizer(args):\n """"""Calculate training TFLOP for tokenizer.\n\n Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L473\n """"""\n # MLP flops\n\n # Attention flops\n",python,selection_command +1181,268369593,"utils/train_utils.py",4748,299,"def calculate_tflops_per_device_tokenizer(args):\n """"""Calculate training TFLOP for tokenizer.\n\n Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L473\n """"""\n # MLP flops\n\n # Attention flops\n\n # Embedding flops",python,selection_command +1182,268369626,"utils/train_utils.py",4748,300,"def calculate_tflops_per_device_tokenizer(args):\n """"""Calculate training TFLOP for tokenizer.\n\n Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L473\n """"""\n # MLP flops\n\n # Attention flops\n\n # Embedding flops\n",python,selection_command +1183,268369830,"utils/train_utils.py",4748,342,"def calculate_tflops_per_device_tokenizer(args):\n """"""Calculate training TFLOP for tokenizer.\n\n Adapted from https://github.com/AI-Hypercomputer/maxtext/blob/7898576359bacde81be25cb3038e348aac1f943b/MaxText/max_utils.py#L473\n """"""\n # MLP flops\n\n # Attention flops\n\n # Embedding flops\n\n # Combine flops with number of blocks",python,selection_command +1184,268577360,"utils/train_utils.py",5049,0," return result\n",python,content +1185,268577360,"utils/train_utils.py",5026,21," to_tflops = 1.0 / 1e12\n result = {\n ""encoder_attention_tflops"": ((qkv_spatial + attn_spatial + proj_spatial + qkv_temporal + attn_temporal + proj_temporal) * num_blocks) * to_tflops * 3.0,\n ""encoder_ffn_tflops"": (ffn_block * num_blocks) * to_tflops * 3.0,\n ""encoder_io_tflops"": encoder_io * to_tflops * 3.0,\n ""decoder_attention_tflops"": ((qkv_spatial + attn_spatial + proj_spatial + qkv_temporal + attn_temporal + proj_temporal) * num_blocks) * to_tflops * 3.0,\n ""decoder_ffn_tflops"": (ffn_block * num_blocks) * to_tflops * 3.0,\n ""decoder_io_tflops"": decoder_io * to_tflops * 3.0,\n ""vq_tflops"": vq_flops * to_tflops,\n ""total_tflops"": total_training_flops * to_tflops,\n ""per_device_batch_size"": batch_per_device,\n ""num_patches_per_frame"": N,\n }",python,content +1186,268577360,"utils/train_utils.py",5003,21," # Training FLOPs: scale forward by ~3 for fwd+bwd+update, add VQ once\n total_training_flops = 3.0 * forward_flops + vq_flops",python,content +1187,268577360,"utils/train_utils.py",4986,15," # Per-device batch\n num_devices = max(1, jax.device_count())\n batch_per_device = args.batch_size // num_devices\n\n # Shapes\n T = int(args.seq_len)\n H = int(args.image_height)\n W = int(args.image_width)\n patch = int(args.patch_size)\n # Number of patches per frame (ceil division with padding as in patchify)\n hn = (H + patch - 1) // patch\n wn = (W + patch - 1) // patch\n N = int(hn * wn)\n\n # Model dims\n M = int(args.model_dim)\n D_ffn = int(args.ffn_dim)\n num_blocks = int(args.num_blocks)\n\n # Convenience aliases\n B = float(batch_per_device)\n T_f = float(T)\n N_f = float(N)\n M_f = float(M)\n D_ffn_f = float(D_ffn)\n\n # Token counts for projections\n tokens_spatial = B * T_f * N_f # spatial attention runs per frame over N tokens\n tokens_temporal = B * N_f * T_f # temporal attention runs per spatial position over T tokens\n\n # Per-block FLOPs\n # Spatial attention (non-causal)\n qkv_spatial = 3.0 * 2.0 * tokens_spatial * M_f * M_f\n attn_spatial = 4.0 * (B * T_f) * (N_f * N_f) * M_f\n proj_spatial = 2.0 * tokens_spatial * M_f * M_f\n\n # Temporal attention (causal mask halves the non-causal complexity)\n qkv_temporal = 3.0 * 2.0 * tokens_temporal * M_f * M_f\n attn_temporal = 2.0 * (B * N_f) * (T_f * T_f) * M_f # = 0.5 * (4 * B*N * T^2 * M)\n proj_temporal = 2.0 * tokens_temporal * M_f * M_f\n\n # FFN per block (2 matmuls)\n ffn_block = 4.0 * B * T_f * N_f * M_f * D_ffn_f\n\n block_total = (\n qkv_spatial\n + attn_spatial\n + proj_spatial\n + qkv_temporal\n + attn_temporal\n + proj_temporal\n + ffn_block\n )\n\n # Encoder/Decoder IO (input/output linears executed once per forward)\n P = float(args.image_channels * (patch ** 2)) # patch token dim\n L = float(args.latent_dim) # latent dim\n\n # Encoder: input_dim=P -> M, output_dim=M -> L\n encoder_io = 2.0 * B * T_f * N_f * (P * M_f + M_f * L)\n # Decoder: input_dim=L -> M, output_dim=M -> P\n decoder_io = 2.0 * B * T_f * N_f * (L * M_f + M_f * P)\n\n # VQ distance compute (forward only; gradients do not flow through distances with STE)\n K = float(args.num_latents)\n vq_flops = 2.0 * (B * T_f * N_f) * L * K\n\n # Forward FLOPs\n encoder_forward = num_blocks * block_total + encoder_io\n decoder_forward = num_blocks * block_total + decoder_io\n forward_flops = encoder_forward + decoder_forward",python,content +1188,268577396,"utils/train_utils.py",8446,42,"",python,content +1189,268588005,"utils/train_utils.py",5008,0,"",python,selection_mouse +1190,268588017,"utils/train_utils.py",5007,0,"",python,selection_command +1191,268588498,"utils/train_utils.py",4985,0,"",python,selection_mouse +1192,268588504,"utils/train_utils.py",4984,0,"",python,selection_command 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+2101,272607513,"utils/train_utils.py",5200,0,"",python,selection_command +2102,272607545,"utils/train_utils.py",5254,0,"",python,selection_command +2103,272607580,"utils/train_utils.py",5308,0,"",python,selection_command +2104,272607613,"utils/train_utils.py",5329,0,"",python,selection_command +2105,272607650,"utils/train_utils.py",5330,0,"",python,selection_command +2106,272607681,"utils/train_utils.py",5365,0,"",python,selection_command +2107,272610998,"utils/train_utils.py",5406,0,"",python,selection_command +2108,272611161,"utils/train_utils.py",5407,0,"",python,selection_command +2109,272611310,"utils/train_utils.py",5429,0,"",python,selection_command +2110,272611449,"utils/train_utils.py",5466,0,"",python,selection_command +2111,272643746,"utils/nn.py",0,0,"import math\nfrom typing import Tuple, Callable, List\n\nfrom flax import nnx\nimport jax\nimport jax.numpy as jnp\nimport einops\n\n\nclass SpatioTemporalPositionalEncoding(nnx.Module):\n """"""\n Applies separate sinusoidal positional encodings to the temporal and spatial dimensions.\n """"""\n\n def __init__(self, d_model: int, max_len: int = 5000):\n self.d_model = d_model\n self.max_len = max_len\n\n pe = jnp.zeros((self.max_len, self.d_model))\n position = jnp.arange(0, self.max_len, dtype=jnp.float32)[:, None]\n div_term = jnp.exp(\n jnp.arange(0, self.d_model, 2) * (-math.log(10000.0) / self.d_model)\n )\n pe = pe.at[:, 0::2].set(jnp.sin(position * div_term))\n pe = pe.at[:, 1::2].set(jnp.cos(position * div_term))\n self.pe = nnx.Variable(pe)\n\n def __call__(self, x: jax.Array) -> jax.Array:\n """"""\n Args:\n x: The input tensor of shape (Batch, Time, Space, Dimension).\n\n Returns:\n The input tensor with positional encodings added.\n """"""\n assert x.ndim == 4, f""Input must be 4-dimensional, but got shape {x.shape}""\n\n num_timesteps = x.shape[1]\n num_spatial_patches = x.shape[2]\n\n # Temporal positional encoding: (1, T, 1, D)\n temporal_pe = self.pe.value[None, :num_timesteps, None, :]\n x = x + temporal_pe\n\n # Spatial positional encoding: (1, 1, S, D)\n spatial_pe = self.pe.value[None, None, :num_spatial_patches, :]\n x = x + spatial_pe\n\n return x\n\n\nclass STBlock(nnx.Module):\n def __init__(\n self,\n dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n sow_weights: bool,\n sow_activations: bool,\n ):\n self.dim = dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=False\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(self, x_BTNM: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z_BTNM = self.spatial_norm(x_BTNM)\n z_BTNM = self.spatial_attention(z_BTNM, sow_weights=self.sow_weights)\n x_BTNM = x_BTNM + z_BTNM\n\n # --- Temporal attention ---\n x_BNTM = x_BTNM.swapaxes(1, 2)\n z_BNTM = self.temporal_norm(x_BNTM)\n z_BNTM = self.temporal_attention(z_BNTM, sow_weights=self.sow_weights)\n x_BNTM = x_BNTM + z_BNTM\n x_BTNM = x_BNTM.swapaxes(1, 2)\n\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n if self.sow_activations:\n self.sow(nnx.Intermediate, ""activations"", x_BTNM)\n return x_BTNM\n\n\nclass STTransformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n V: vocabulary size\n """"""\n\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n sow_weights: bool = False,\n sow_activations: bool = False,\n sow_logits: bool = False,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_logits = sow_logits\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n\n self.pos_enc = SpatioTemporalPositionalEncoding(self.model_dim, max_len=max_len)\n\n self.blocks = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n STBlock(\n dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n sow_weights=self.sow_weights,\n sow_activations=self.sow_activations,\n )\n )\n\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(self, x_BTNI: jax.Array) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM)\n\n x_BTNV = self.output_dense(x_BTNM)\n if self.sow_logits:\n self.sow(nnx.Intermediate, ""logits"", x_BTNV)\n return x_BTNV\n\n\nclass TransformerBlock(nnx.Module):\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n sow_weights: bool,\n sow_activations: bool,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.decode = decode\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(\n self, x_BTNM: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None\n ) -> jax.Array:\n # --- Spatial attention ---\n B, T, N, M = x_BTNM.shape\n z_FNM = einops.rearrange(x_BTNM, ""b t n m -> (b t) n m"")\n z_FNM = self.spatial_norm(z_FNM)\n z_FNM = self.spatial_attention(z_FNM, sow_weights=self.sow_weights)\n z_BTNM = einops.rearrange(z_FNM, ""(b t) n m -> b t n m"", t=T)\n x_BTNM = x_BTNM + z_BTNM\n # --- Temporal attention ---\n z_PTM = einops.rearrange(x_BTNM, ""b t n m -> (b n) t m"")\n z_PTM = self.temporal_norm(z_PTM)\n z_PTM = self.temporal_attention(z_PTM, sow_weights=self.sow_weights)\n z_BTNM = einops.rearrange(z_PTM, ""(b n) t m -> b t n m"", n=N)\n x_BTNM = x_BTNM + z_BTNM\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n if self.sow_activations:\n self.sow(nnx.Intermediate, ""activations"", x_BTNM)\n\n return x_BTNM\n\n\nclass Transformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n V: vocabulary size\n F: number of frames in batch\n P: number of patch positions in batch\n """"""\n\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n sow_logits: bool = False,\n sow_weights: bool = False,\n sow_activations: bool = False,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_logits = sow_logits\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n\n self.pos_enc = SpatioTemporalPositionalEncoding(self.model_dim, max_len=max_len)\n\n self.blocks: List[TransformerBlock] = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n TransformerBlock(\n model_dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n sow_weights=self.sow_weights,\n sow_activations=self.sow_activations,\n rngs=rngs,\n )\n )\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self, x_BTNI: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None\n ) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM, pos_index)\n\n x_BTNV = self.output_dense(x_BTNM)\n if self.sow_logits:\n self.sow(nnx.Intermediate, ""logits"", x_BTNV)\n return x_BTNV\n\n\ndef normalize(x: jax.Array) -> jax.Array:\n return x / (jnp.linalg.norm(x, ord=2, axis=-1, keepdims=True) + 1e-8)\n\n\nclass VectorQuantizer(nnx.Module):\n """"""\n Dimension keys:\n D: B * T * N\n K: number of latents\n L: latent dimension\n """"""\n\n def __init__(\n self,\n latent_dim: int,\n num_latents: int,\n dropout: float,\n dtype: jnp.dtype,\n rngs: nnx.Rngs,\n ):\n self.latent_dim = latent_dim\n self.num_latents = num_latents\n self.dropout = dropout\n self.dtype = dtype\n\n self.codebook = nnx.Param(\n normalize(\n nnx.initializers.lecun_uniform()(\n rngs.params(), (self.num_latents, self.latent_dim)\n )\n )\n )\n self.drop = nnx.Dropout(self.dropout, rngs=rngs)\n\n def __call__(\n self, x_DL: jax.Array, training: bool\n ) -> Tuple[jax.Array, jax.Array, jax.Array, jax.Array]:\n # --- Compute distances ---\n x_DL = x_DL.astype(self.dtype)\n codebook = self.codebook.value.astype(self.dtype)\n\n x_DL = normalize(x_DL)\n normalized_codebook_KL = normalize(codebook)\n distance_DK = -jnp.matmul(x_DL, normalized_codebook_KL.T)\n if training:\n distance_DK = self.drop(distance_DK)\n\n # --- Get indices and embeddings ---\n indices_D = jnp.argmin(distance_DK, axis=-1)\n z_DL = codebook[indices_D]\n\n # --- Straight through estimator ---\n z_q_DL = x_DL + jax.lax.stop_gradient(z_DL - x_DL)\n return z_q_DL, z_DL, x_DL, indices_D\n\n def get_codes(self, indices_E: jax.Array) -> jax.Array:\n return self.codebook[indices_E]\n\n\ndef _create_flash_attention_fn(use_flash_attention: bool, is_causal: bool) -> Callable:\n """"""\n Create an attention function that uses flash attention if enabled.\n\n flax.nnx.MultiHeadAttention provides tensors with shape (batch..., length, num_heads, head_dim),\n but jax.nn.dot_product_attention expects (batch, length, num_heads, head_dim). We reshape to\n ensure compatibility. cuDNN's flash attention additionally requires a sequence length that\n is a multiple of 4. We pad the sequence length to the nearest multiple of 4 and mask\n accordingly. Note that cuDNN requires the mask to be broadcast before calling the attention\n function due to strict shape checking.\n """"""\n\n def attention_fn(\n query_BTHD, key_BSHD, value_BSHD, bias=None, mask_B111=None, **kwargs\n ):\n implementation = ""cudnn"" if use_flash_attention else None\n\n def _merge_batch_dims(x):\n return einops.rearrange(x, ""... l h k -> (...) l h k"")\n\n def _pad(x, pad_size):\n return jnp.pad(x, ((0, 0), (0, pad_size), (0, 0), (0, 0)))\n\n original_shape = query_BTHD.shape\n T = query_BTHD.shape[-3]\n S = key_BSHD.shape[-3]\n\n # Pad to nearest multiple of 4\n Q = ((T + 3) // 4) * 4\n pad_size_Q = Q - T\n K = ((S + 3) // 4) * 4\n pad_size_K = K - S\n\n query_BQHD = _pad(_merge_batch_dims(query_BTHD), pad_size_Q)\n key_BKHD = _pad(_merge_batch_dims(key_BSHD), pad_size_K)\n value_BKHD = _pad(_merge_batch_dims(value_BSHD), pad_size_K)\n\n attention_mask = jnp.ones((Q, K), dtype=jnp.bool_)\n attention_mask = attention_mask.at[T:, :].set(False)\n attention_mask = attention_mask.at[:, S:].set(False)\n\n mask_11TS = attention_mask[jnp.newaxis, jnp.newaxis, :, :]\n\n bias_4d = (\n jnp.pad(\n _merge_batch_dims(bias),\n ((0, 0), (0, 0), (0, pad_size_Q), (0, pad_size_K)),\n )\n if bias is not None\n else None\n )\n\n # NOTE: jax.nn.dot_product_attention does not support dropout\n output_4d = jax.nn.dot_product_attention(\n query=query_BQHD,\n key=key_BKHD,\n value=value_BKHD,\n bias=bias_4d,\n mask=mask_11TS,\n implementation=implementation,\n is_causal=is_causal,\n )\n return output_4d[..., :T, :, :].reshape(original_shape)\n\n return attention_fn\n",python,tab +2112,272645163,"utils/nn.py",8717,0,"",python,selection_keyboard +2113,272645563,"utils/nn.py",8686,0,"",python,selection_command +2114,272645814,"utils/nn.py",8659,0,"",python,selection_command +2115,272645844,"utils/nn.py",8635,0,"",python,selection_command +2116,272645878,"utils/nn.py",8613,0,"",python,selection_command +2117,272645910,"utils/nn.py",8578,0,"",python,selection_command 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a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-2cea4f45-eb3d-4d39-8263-124201da2dc81763721698838-2025_11_21-11.41.41.686/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-2cea4f45-eb3d-4d39-8263-124201da2dc81763721698838-2025_11_21-11.41.41.686/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..c6d5db7d05621e1002688a9bb2033dd60355418b --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-2cea4f45-eb3d-4d39-8263-124201da2dc81763721698838-2025_11_21-11.41.41.686/source.csv @@ -0,0 +1,546 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,3,"nemo/collections/llm/recipes/finetune_default.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import TYPE_CHECKING, Any, Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\n\nimport nemo.lightning as nl\nfrom nemo.collections import llm\nfrom nemo.collections.llm.gpt.data.packed_sequence import PackedSequenceSpecs\nfrom nemo.collections.llm.peft import DoRA, LoRA\nfrom nemo.collections.llm.recipes.log.default import tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed\nfrom nemo.lightning.pytorch.callbacks import PEFT\nfrom nemo.utils.exp_manager import TimingCallback\n\nif TYPE_CHECKING:\n from lightning.pytorch.loggers import TensorBoardLogger, WandbLogger\n\nTokenizerType = Any\n\n\ndef default_finetune_recipe(\n model: run.Config[pl.LightningModule],\n resume_path: str,\n dir: Optional[str] = None,\n name: str = ""default"",\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n packed_sequence: bool = False, # once packing recipe is well tested, change this default to true\n tokenizer: Optional[TokenizerType] = ""model"",\n) -> run.Partial:\n """"""\n Create a default fine-tuning recipe for any model.\n\n This function sets up a template for a complete configuration for fine-tuning, including\n model, trainer, data, logging, optimization, and resumption settings.\n\n Args:\n model (run.Config[pl.LightningModule]): Configuration for a NeMo model.\n resume_path (str): Path to the Huggingface model or pretrained distributed checkpoint for resume\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the fine-tuning run.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n packed_sequence (bool): Whether to use packed sequence.\n tokenizer (Optional[TokenizerType]): Tokenizer setting to be applied. Can be 'data' or 'model'\n or an instance of TokenizerSpec.\n\n Returns:\n run.Partial: Partial configuration for fine-tuning.\n\n See usages of this recipe for further details.\n """"""\n if packed_sequence:\n datamodule = run.Config(\n llm.SquadDataModule,\n seq_length=2048,\n global_batch_size=8,\n micro_batch_size=1,\n packed_sequence_specs=PackedSequenceSpecs(packed_sequence_size=2048),\n )\n else:\n datamodule = run.Config(llm.SquadDataModule, seq_length=2048, global_batch_size=128, micro_batch_size=1)\n recipe = run.Partial(\n llm.finetune,\n model=model,\n trainer=default_finetune_trainer(\n num_nodes=num_nodes,\n num_gpus_per_node=num_gpus_per_node,\n ),\n data=datamodule,\n log=default_finetune_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n optim=distributed_fused_adam_with_cosine_annealing(max_lr=1e-4, min_lr=0, warmup_steps=50, adam_beta2=0.98),\n resume=nemo_resume(resume_path),\n tokenizer=tokenizer,\n )\n\n return recipe\n\n\ndef default_finetune_trainer(\n tensor_parallelism=1,\n pipeline_parallelism=1,\n pipeline_parallelism_type=torch.bfloat16,\n virtual_pipeline_parallelism=None,\n context_parallelism=1,\n sequence_parallelism=False,\n num_nodes=1,\n num_gpus_per_node=8,\n max_steps=1000,\n limit_test_batches=None,\n limit_val_batches=None,\n val_check_interval=30,\n):\n """"""\n Create a default fine-tuning trainer for any model.\n\n This function sets up a template for strategy and trainer.\n\n Args:\n See docstrings of MegatronStrategy and Trainer.\n\n Returns:\n run.Config: Config for a finetuning trainer.\n\n See usages of this in recipes for further details.\n """"""\n strategy = run.Config(\n nl.MegatronStrategy,\n tensor_model_parallel_size=tensor_parallelism,\n pipeline_model_parallel_size=pipeline_parallelism,\n pipeline_dtype=pipeline_parallelism_type,\n virtual_pipeline_model_parallel_size=virtual_pipeline_parallelism,\n context_parallel_size=context_parallelism,\n sequence_parallel=sequence_parallelism,\n gradient_as_bucket_view=True,\n ckpt_load_strictness=""log_all"",\n )\n\n trainer = run.Config(\n nl.Trainer,\n accelerator=""gpu"",\n accumulate_grad_batches=1,\n devices=num_gpus_per_node,\n limit_test_batches=limit_test_batches,\n limit_val_batches=limit_val_batches,\n log_every_n_steps=1,\n max_steps=max_steps,\n num_nodes=num_nodes,\n plugins=bf16_mixed(),\n strategy=strategy,\n use_distributed_sampler=False,\n val_check_interval=val_check_interval,\n callbacks=[run.Config(TimingCallback)],\n )\n\n return trainer\n\n\ndef default_finetune_log(\n dir: Optional[str] = None,\n name: str = ""default"",\n tensorboard_logger: Optional[run.Config['TensorBoardLogger']] = None,\n wandb_logger: Optional[run.Config['WandbLogger']] = None,\n) -> run.Config[nl.NeMoLogger]:\n """"""\n Create a default fine-tuning logger for any model.\n\n This function sets up a template for ModelCheckpoint and NeMoLogger.\n\n Args:\n See docstrings of ModelCheckpoint and NeMoLogger.\n\n Returns:\n run.Config: Config for a finetuning NeMoLogger.\n\n See usages of this in recipes for further details.\n """"""\n\n ckpt = run.Config(\n nl.ModelCheckpoint,\n save_last=""link"",\n save_top_k=2,\n every_n_train_steps=50,\n filename=""{model_name}--{val_loss:.2f}-{step}-{consumed_samples}"",\n )\n\n return run.Config(\n nl.NeMoLogger,\n ckpt=ckpt,\n name=name,\n tensorboard=tensorboard_logger,\n wandb=wandb_logger,\n log_dir=dir,\n )\n\n\ndef nemo_resume(model_id: str) -> run.Config[nl.AutoResume]:\n """"""\n Configure automatic resumption from a NeMo checkpoint converted from Huggingface for\n https://huggingface.co/{model_id}.\n\n This NeMo checkpoint should be converted from Huggingface beforehand, using nemo.collections.llm.import_ckpt.\n When converting the checkpoint, the NeMo checkpoint will be saved in NEMO_HOME (set to ~/.cache/nemo by default).\n\n This function sets up the configuration to resume training from path nemo://{model_id}.\n This translates to the full path {NEMO_HOME}/models/{model_id}.\n\n Args:\n model_id (str): Path to the Huggingface model or pretrained distributed checkpoint for resume\n\n Returns:\n run.Config[nl.AutoResume]: Configuration for resuming from NeMo checkpoint.\n """"""\n return run.Config(\n nl.AutoResume,\n restore_config=run.Config(nl.RestoreConfig, path=f""nemo://{model_id}""),\n )\n\n\n@run.cli.factory(name='lora')\ndef lora() -> run.Config[PEFT]:\n """"""\n Factory function to create a LoRA configuration.\n\n Returns:\n run.Config[PEFT]: Configuration for the LoRA class.\n\n Examples:\n CLI usage:\n $ nemo llm finetune -f llama3_8b peft=lora\n\n Python API usage:\n >>> lora_config = lora()\n >>> print(lora_config)\n """"""\n return run.Config(LoRA)\n\n\n@run.cli.factory(name='dora')\ndef dora() -> run.Config[PEFT]:\n """"""\n Factory function to create a DoRA configuration.\n\n Returns:\n run.Config[PEFT]: Configuration for the DoRA class.\n\n Examples:\n CLI usage:\n $ nemo llm 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clone_scatter_output_in_embedding: true\n config_logger_dir: ''\n context_parallel_size: 1\n cpu_offloading: false\n cpu_offloading_activations: true\n cpu_offloading_num_layers: 0\n cpu_offloading_weights: true\n cross_entropy_loss_fusion: true\n data_step_fn:\n _call_: false\n _target_: nemo.collections.llm.gpt.model.base.gpt_data_step\n deallocate_pipeline_outputs: false\n defer_embedding_wgrad_compute: false\n deterministic_mode: false\n disable_parameter_transpose_cache: false\n distribute_saved_activations: null\n enable_autocast: false\n enable_cuda_graph: false\n expert_model_parallel_size: 1\n external_cuda_graph: false\n ffn_hidden_size: 4096\n finalize_model_grads_func: null\n first_pipeline_num_layers: null\n forward_step_fn:\n _call_: false\n _target_: nemo.collections.llm.gpt.model.base.gpt_forward_step\n fp16: false\n fp16_lm_cross_entropy: false\n fp32_residual_connection: false\n fp8: null\n fp8_amax_compute_algo: most_recent\n fp8_amax_history_len: 1\n fp8_dot_product_attention: false\n fp8_interval: 1\n fp8_margin: 0\n fp8_multi_head_attention: false\n fp8_wgrad: true\n gated_linear_unit: false\n grad_scale_func: null\n grad_sync_func: null\n gradient_accumulation_fusion: true\n hidden_dropout: 0.1\n hidden_size: 1024\n init_method: null\n init_method_std: 0.02\n kv_channels: null\n last_pipeline_num_layers: null\n layernorm_epsilon: 1.0e-05\n layernorm_zero_centered_gamma: false\n make_vocab_size_divisible_by: 128\n masked_softmax_fusion: true\n memory_efficient_layer_norm: false\n moe_aux_loss_coeff: 0\n moe_expert_capacity_factor: null\n moe_extended_tp: false\n moe_grouped_gemm: false\n moe_input_jitter_eps: null\n moe_layer_recompute: false\n moe_pad_expert_input_to_capacity: false\n moe_per_layer_logging: false\n moe_router_load_balancing_type: aux_loss\n moe_router_pre_softmax: false\n moe_router_topk: 2\n moe_shared_expert_intermediate_size: null\n moe_shared_expert_overlap: false\n moe_token_dispatcher_type: allgather\n moe_token_drop_policy: probs\n moe_token_dropping: false\n moe_z_loss_coeff: null\n no_sync_func: null\n normalization: LayerNorm\n num_attention_heads: 8\n num_layers: 2\n num_microbatches_with_partial_activation_checkpoints: null\n num_moe_experts: null\n num_query_groups: null\n output_layer_init_method: null\n overlap_p2p_comm: false\n parallel_output: true\n param_sync_func: null\n params_dtype:\n _call_: false\n _target_: torch.float32\n perform_initialization: true\n persist_layer_norm: false\n pipeline_dtype: null\n pipeline_model_parallel_size: 1\n pipeline_model_parallel_split_rank: null\n position_embedding_type: learned_absolute\n qk_layernorm: false\n recompute_granularity: null\n recompute_method: null\n recompute_num_layers: null\n rotary_base: 10000\n rotary_interleaved: false\n rotary_percent: 1.0\n seq_len_interpolation_factor: null\n seq_length: 1024\n sequence_parallel: false\n share_embeddings_and_output_weights: true\n tensor_model_parallel_size: 1\n test_mode: false\n timers: null\n tp_comm_atomic_ag: false\n tp_comm_atomic_rs: false\n tp_comm_bulk_dgrad: true\n tp_comm_bulk_wgrad: true\n tp_comm_overlap: false\n tp_comm_overlap_ag: true\n tp_comm_overlap_disable_fc1: false\n tp_comm_overlap_disable_qkv: false\n tp_comm_overlap_rs: true\n tp_comm_overlap_rs_dgrad: false\n tp_comm_split_ag: true\n tp_comm_split_rs: true\n tp_only_amax_red: false\n transformer_layer_spec:\n _call_: false\n _target_: nemo.collections.llm.gpt.model.base.default_layer_spec\n use_cpu_initialization: false\n use_ring_exchange_p2p: false\n use_te_rng_tracker: false\n variable_seq_lengths: false\n virtual_pipeline_model_parallel_size: null\n wgrad_deferral_limit: 0\n window_size: null\nmodel_transform: null\noptim:\n _target_: nemo.lightning.pytorch.optim.megatron.MegatronOptimizerModule\n config:\n _target_: megatron.core.optimizer.optimizer_config.OptimizerConfig\n adam_beta1: 0.9\n adam_beta2: 0.999\n adam_eps: 1.0e-08\n barrier_with_L1_time: false\n bf16: false\n clip_grad: 1.0\n config_logger_dir: ''\n decoupled_lr: null\n decoupled_min_lr: null\n fp16: false\n hysteresis: 2\n initial_loss_scale: 4294967296\n log_num_zeros_in_grad: false\n loss_scale: null\n loss_scale_window: 1000\n lr: 0.0001\n min_loss_scale: 1.0\n min_lr: null\n optimizer: adam\n overlap_param_gather_with_optimizer_step: false\n params_dtype:\n _call_: false\n _target_: torch.float32\n sgd_momentum: 0.9\n timers: null\n use_distributed_optimizer: true\n weight_decay: 0.01\n lr_mult: 1.0\n lr_scheduler: null\n no_weight_decay_cond: null\n scale_lr_cond: null\ntokenizer:\n _target_: nemo.collections.common.tokenizers.huggingface.auto_tokenizer.AutoTokenizer\n bos_token: null\n cls_token: null\n eos_token: null\n mask_token: null\n merges_file: megatron-gpt-345m_merges\n pad_token: null\n pretrained_model_name: gpt2\n sep_token: null\n trust_remote_code: false\n unk_token: null\n use_fast: false\n vocab_file: megatron-gpt-345m_vocab\n",yaml,tab +41,59739,"tests/lightning/_io/artifacts/model.yaml",3367,0,"",yaml,selection_command +42,62820,"tests/lightning/_io/artifacts/model.yaml",4805,0,"",yaml,selection_command +43,63714,"tests/lightning/_io/artifacts/model.yaml",4832,0,"",yaml,selection_command +44,64537,"tests/lightning/_io/artifacts/model.yaml",4805,0,"",yaml,selection_command +45,65301,"tests/lightning/_io/artifacts/model.yaml",4832,0,"",yaml,selection_command +46,65946,"tests/lightning/_io/artifacts/model.yaml",4805,0,"",yaml,selection_command +47,66127,"tests/lightning/_io/artifacts/model.yaml",4832,0,"",yaml,selection_command +48,67070,"tests/lightning/_io/artifacts/model.yaml",4996,0,"",yaml,selection_command +49,69350,"nemo/collections/llm/recipes/finetune_default.py",0,0,"",python,tab +50,71812,"nemo/collections/llm/recipes/finetune_default.py",1022,0,"",python,selection_command +51,72748,"nemo/collections/llm/recipes/finetune_default.py",1948,0,"",python,selection_command +52,73153,"nemo/collections/llm/recipes/finetune_default.py",3505,0,"",python,selection_command +53,75039,"nemo/collections/llm/recipes/finetune_default.py",3510,0,"",python,selection_command +54,75283,"nemo/collections/llm/recipes/finetune_default.py",3511,0,"",python,selection_command +55,75327,"nemo/collections/llm/recipes/finetune_default.py",3555,0,"",python,selection_command +56,75353,"nemo/collections/llm/recipes/finetune_default.py",3556,0,"",python,selection_command +57,75379,"nemo/collections/llm/recipes/finetune_default.py",3562,0,"",python,selection_command +58,75419,"nemo/collections/llm/recipes/finetune_default.py",3563,0,"",python,selection_command +59,75448,"nemo/collections/llm/recipes/finetune_default.py",3565,0,"",python,selection_command +60,75480,"nemo/collections/llm/recipes/finetune_default.py",3566,0,"",python,selection_command +61,75912,"nemo/collections/llm/recipes/finetune_default.py",3565,0,"",python,selection_command +62,76073,"nemo/collections/llm/recipes/finetune_default.py",3563,0,"",python,selection_command +63,76875,"nemo/collections/llm/recipes/finetune_default.py",3562,0,"",python,selection_command +64,77033,"nemo/collections/llm/recipes/finetune_default.py",3556,0,"",python,selection_command +65,77273,"nemo/collections/llm/recipes/finetune_default.py",3562,0,"",python,selection_command +66,77806,"nemo/collections/llm/recipes/finetune_default.py",3556,0,"",python,selection_command +67,126985,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n """"""\n Factory function to create a Qwen3 30B-A3B model configuration.\n This is a MoE (Mixture of Experts) model with 128 experts.\n\n Returns:\n run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n Examples:\n CLI usage:\n $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n Python API usage:\n >>> model_config = model()\n >>> print(model_config)\n """"""\n return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n # General\n dir: Optional[str] = None,\n name: str = ""default"",\n # Trainer\n tensor_parallelism: int = 4, # Default for 30B-A3B model\n pipeline_parallelism: int = 2,\n pipeline_parallelism_type: Optional[torch.dtype] = None,\n virtual_pipeline_parallelism: Optional[int] = None,\n context_parallelism: int = 1,\n expert_parallelism: Optional[int] = 4,\n sequence_parallelism: bool = True,\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n max_steps: int = 300000,\n precision: str = ""bf16-mixed"",\n accumulate_grad_batches: int = 1,\n gradient_clip_val: float = 1.0,\n limit_test_batches: int = 32,\n limit_val_batches: int = 32,\n log_every_n_steps: int = 10,\n val_check_interval: int = 500,\n # Data\n global_batch_size=32,\n micro_batch_size=2,\n seq_length=4096,\n # Optimizer\n warmup_steps=500,\n constant_steps=0,\n min_lr=3e-5,\n max_lr=3e-4,\n # Training function\n fn=pretrain,\n) -> run.Partial:\n """"""\n Create a pre-training recipe for Qwen3 30B-A3B model.\n\n This function sets up a complete configuration for pre-training, including\n model, trainer, data, logging, optimization, and resumption settings.\n This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n Args:\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the pre-training run.\n tensor_parallelism (int): Degree of tensor model parallelism.\n pipeline_parallelism (int): Degree of pipeline model parallelism.\n pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n context_parallelism (int): Degree of context parallelism.\n sequence_parallelism (bool): Whether to use sequence parallelism.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n max_steps (int): Maximum number of training steps.\n precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n accumulate_grad_batches (int): Number of steps per gradient accumulation.\n gradient_clip_val (float): Value for gradient clipping.\n limit_test_batches (int): Limit the number of test batches.\n limit_val_batches (int): Limit the number of validation batches.\n log_every_n_steps (int): Log every n steps.\n val_check_interval (int): Run validation every N steps.\n global_batch_size (int): Global batch size.\n micro_batch_size (int): Micro batch size.\n seq_length (int): Sequence length.\n warmup_steps (int): Number of warmup steps.\n constant_steps (int): Number of constant steps.\n min_lr (float): Minimum learning rate.\n max_lr (float): Maximum learning rate.\n fn (Callable): The pre-training function to use.\n\n Returns:\n run.Partial: Partial configuration for pre-training.\n\n Examples:\n CLI usage:\n $ nemo llm pretrain --factory qwen3_30b_a3b\n $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n Python API usage:\n >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n >>> print(recipe)\n\n Note:\n This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n """"""\n recipe = run.Partial(\n fn,\n model=model(),\n trainer=qwen3_trainer(\n tensor_parallelism=tensor_parallelism,\n pipeline_parallelism=pipeline_parallelism,\n pipeline_parallelism_type=pipeline_parallelism_type,\n virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n context_parallelism=context_parallelism,\n sequence_parallelism=sequence_parallelism,\n expert_parallelism=expert_parallelism,\n num_nodes=num_nodes,\n num_gpus_per_node=num_gpus_per_node,\n max_steps=max_steps,\n precision=precision,\n accumulate_grad_batches=accumulate_grad_batches,\n limit_test_batches=limit_test_batches,\n limit_val_batches=limit_val_batches,\n log_every_n_steps=log_every_n_steps,\n val_check_interval=val_check_interval,\n callbacks=[run.Config(TimingCallback)],\n ),\n data=run.Config(\n MockDataModule,\n seq_length=seq_length,\n global_batch_size=global_batch_size,\n micro_batch_size=micro_batch_size,\n tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n ),\n log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n optim=distributed_fused_adam_with_cosine_annealing(\n precision=precision,\n warmup_steps=warmup_steps,\n constant_steps=constant_steps,\n min_lr=min_lr,\n max_lr=max_lr,\n clip_grad=gradient_clip_val,\n ),\n resume=default_resume(),\n )\n recipe.model.config.recompute_granularity = ""full""\n recipe.model.config.recompute_method = ""uniform""\n recipe.model.config.recompute_num_layers = 1\n return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n dir: Optional[str] = None,\n name: str = ""default"",\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n peft_scheme: Optional[str] = 'lora',\n packed_sequence: bool = False,\n) -> run.Partial:\n """"""\n Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n This function sets up a complete configuration for fine-tuning, including\n model, trainer, data, logging, optimization, and resumption settings.\n The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n Args:\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the fine-tuning run.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n Allowed values: 'lora'/'dora'/'none'/None.\n packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n efficiency. Default sequence length is 2048.\n\n Returns:\n run.Partial: Partial configuration for fine-tuning.\n\n Examples:\n CLI usage:\n $ nemo llm finetune --factory qwen3_30b_a3b\n\n Python API usage:\n >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n >>> print(recipe)\n\n Note:\n This recipe uses the SQuAD dataset for fine-tuning.\n """"""\n recipe = default_finetune_recipe(\n model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n )\n if peft_scheme is None or peft_scheme.lower() == 'none':\n recipe.trainer.strategy.tensor_model_parallel_size = 4\n recipe.trainer.strategy.expert_model_parallel_size = 4\n recipe.trainer.strategy.expert_tensor_parallel_size = 1\n recipe.trainer.strategy.pipeline_model_parallel_size = 2\n recipe.trainer.strategy.sequence_parallel = True\n recipe.optim.config.lr = 5e-6\n elif peft_scheme.lower() in ['lora', 'dora']:\n recipe.trainer.strategy.tensor_model_parallel_size = 4\n recipe.trainer.strategy.expert_model_parallel_size = 4\n recipe.trainer.strategy.expert_tensor_parallel_size = 1\n recipe.trainer.strategy.sequence_parallel = True\n recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n recipe.optim.config.lr = 1e-4\n else:\n raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n return recipe\n",python,tab +68,128778,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10041,0,"",python,selection_command +69,129707,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10023,0,"",python,selection_command +70,129956,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9954,0,"",python,selection_command +71,129988,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9944,0,"",python,selection_command +72,130024,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9906,0,"",python,selection_command +73,130056,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9839,0,"",python,selection_command +74,130090,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9771,0,"",python,selection_command +75,130124,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9714,0,"",python,selection_command +76,130155,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9650,0,"",python,selection_command +77,130192,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9587,0,"",python,selection_command +78,130221,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9524,0,"",python,selection_command +79,130254,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9474,0,"",python,selection_command +80,131114,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9524,0,"",python,selection_command +81,131364,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9587,0,"",python,selection_command +82,131396,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9650,0,"",python,selection_command +83,131429,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9714,0,"",python,selection_command +84,131461,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9771,0,"",python,selection_command +85,131637,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9839,0,"",python,selection_command +86,131781,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9906,0,"",python,selection_command +87,133094,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9914,0,"",python,selection_command +88,138742,"nemo/collections/llm/recipes/finetune_default.py",0,0,"",python,tab +89,142653,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"",python,tab +90,147612,"nemo/collections/llm/recipes/finetune_default.py",0,0,"",python,tab +91,148892,"nemo/collections/llm/gpt/model/base.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport contextlib\nimport inspect\nfrom dataclasses import dataclass\nfrom functools import partial\nfrom typing import TYPE_CHECKING, Any, Callable, Literal, Optional, Union\n\nimport lightning.pytorch as L\nimport torch\nimport torch.distributed\nfrom megatron.core.inference.model_inference_wrappers.gpt.gpt_inference_wrapper import GPTInferenceWrapper\nfrom megatron.core.inference.model_inference_wrappers.inference_wrapper_config import InferenceWrapperConfig\nfrom megatron.core.models.gpt.gpt_model import GPTModel as MCoreGPTModel\nfrom megatron.core.optimizer import OptimizerConfig\nfrom megatron.core.transformer.dot_product_attention import DotProductAttention as MCoreDotProductAttention\nfrom megatron.core.transformer.enums import AttnBackend\nfrom megatron.core.transformer.spec_utils import ModuleSpec\nfrom megatron.core.transformer.transformer_config import TransformerConfig\nfrom megatron.core.utils import get_batch_on_this_cp_rank\nfrom torch import nn\n\nfrom nemo.collections.llm import fn\nfrom nemo.lightning import get_vocab_size, io\nfrom nemo.lightning.megatron_parallel import MaskedTokenLossReduction\nfrom nemo.lightning.pytorch.optim import MegatronOptimizerModule, OptimizerModule\nfrom nemo.utils import logging\nfrom nemo.utils.import_utils import safe_import\n\n_, HAVE_TE = safe_import(""transformer_engine"")\n\n# Gradient accumulation fusion may be enabled if available, for more information see:\n# https://github.com/NVIDIA/Megatron-LM/blob/01945b98d1ea3a2acb5e8301e181a328104f4856/megatron/core/tensor_parallel/layers.py#L575\n# TODO: Clean this up with a getter and install instructions\n_grad_accum_fusion_available = True\ntry:\n import fused_weight_gradient_mlp_cuda # noqa: F401 # pylint: disable=unused-import\nexcept ImportError:\n _grad_accum_fusion_available = False\n\nif TYPE_CHECKING:\n from transformers import GenerationConfig\n\n from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec\n\n\ndef gpt_data_step(dataloader_iter, use_mtp=False) -> dict[str, torch.Tensor]:\n """"""Process a single batch of data from the dataloader iterator.\n\n This function handles the data loading step for GPT models, managing\n pipeline parallelism by distributing data appropriately across pipeline stages.\n\n Args:\n dataloader_iter: Iterator over the dataloader\n use_mtp: Whether the Multi-Token Prediction Module is used. Input needs to be passed\n into the last ppieline stage if mtp is used.\n\n Returns:\n dict[str, torch.Tensor]: Processed batch with required tensors moved to appropriate devices\n """"""\n from megatron.core import parallel_state\n\n # Based on: https://github.com/NVIDIA/Megatron-LM/blob/main/pretrain_gpt.py#L87\n # https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py#L828-L842\n\n batch = next(dataloader_iter)\n\n _batch: dict\n if isinstance(batch, tuple) and len(batch) == 3:\n _batch = batch[0]\n else:\n _batch = batch\n\n required_device_keys = set()\n required_host_keys = set()\n\n required_device_keys.add(""attention_mask"")\n if ""cu_seqlens"" in _batch:\n required_device_keys.add(""cu_seqlens"")\n required_host_keys.add(""cu_seqlens_argmin"")\n required_host_keys.add(""max_seqlen"")\n\n if parallel_state.is_pipeline_first_stage() or use_mtp:\n required_device_keys.update((""tokens"", ""position_ids""))\n if parallel_state.is_pipeline_last_stage():\n required_device_keys.update((""labels"", ""loss_mask""))\n\n _batch_required_keys = {}\n for key, val in _batch.items():\n if key in required_device_keys:\n _batch_required_keys[key] = val.cuda(non_blocking=True)\n elif key in required_host_keys:\n _batch_required_keys[key] = val.cpu()\n else:\n _batch_required_keys[key] = None\n\n # slice batch along sequence dimension for context parallelism\n output = get_batch_on_this_cp_rank(_batch_required_keys)\n\n return output\n\n\ndef gpt_forward_step(model, batch) -> torch.Tensor:\n """"""Execute a forward step for the GPT model.\n\n This function prepares the arguments needed for the model's forward pass\n and handles both normal and packed sequence processing.\n\n Args:\n model: The GPT model\n batch: The input batch containing tokens, positions, and other required inputs\n\n Returns:\n torch.Tensor: Output tensor from the model forward pass\n """"""\n forward_args = {\n ""input_ids"": batch[""tokens""],\n ""position_ids"": batch[""position_ids""],\n ""labels"": batch[""labels""],\n }\n\n if ""attention_mask"" not in batch:\n assert (\n HAVE_TE\n ), ""The dataloader did not provide an attention mask, however Transformer Engine was not detected. \\n This requires Transformer Engine's implementation of fused or flash attention.""\n else:\n forward_args[""attention_mask""] = batch[""attention_mask""]\n\n if ""cu_seqlens"" in batch:\n forward_args[""packed_seq_params""] = get_packed_seq_params(batch)\n\n return model(**forward_args)\n\n\ndef transformer_engine_layer_spec(config: ""GPTConfig"") -> ModuleSpec:\n """"""Create a Transformer Engine layer specification based on the provided config.\n\n Args:\n config: GPT configuration object\n\n Returns:\n ModuleSpec: Module specification for Transformer Engine based layers\n """"""\n from megatron.core.models.gpt import gpt_layer_specs\n\n kwargs = {\n ""num_experts"": config.num_moe_experts,\n ""moe_grouped_gemm"": config.moe_grouped_gemm,\n ""qk_layernorm"": config.qk_layernorm,\n ""fp8"": bool(config.num_moe_experts and (config.fp8 is not None)),\n }\n if getattr(config, ""use_transformer_engine_op_fuser"", None) is not None:\n kwargs[""use_te_op_fuser""] = config.use_transformer_engine_op_fuser\n return gpt_layer_specs.get_gpt_layer_with_transformer_engine_spec(**kwargs)\n\n\ndef transformer_engine_full_layer_spec(config: ""GPTConfig"", vp_stage: Optional[int] = None) -> ModuleSpec:\n """"""Create a full Transformer Engine layer specification with autocast support.\n\n Args:\n config: GPT configuration object\n\n Returns:\n ModuleSpec: Module specification for full TE layers\n """"""\n from nemo.collections.nlp.models.language_modeling.megatron.gpt_full_te_layer_autocast_spec import (\n get_gpt_full_te_layer_autocast_spec,\n )\n\n return get_gpt_full_te_layer_autocast_spec(transformer_config=config, vp_stage=vp_stage)\n\n\ndef local_layer_spec(config: ""GPTConfig"") -> ModuleSpec:\n """"""Create a local layer specification without Transformer Engine.\n\n Args:\n config: GPT configuration object\n\n Returns:\n ModuleSpec: Module specification for local implementation layers\n """"""\n from megatron.core.models.gpt import gpt_layer_specs\n\n return gpt_layer_specs.get_gpt_layer_local_spec(\n num_experts=config.num_moe_experts,\n moe_grouped_gemm=config.moe_grouped_gemm,\n qk_layernorm=config.qk_layernorm,\n normalization=config.normalization,\n )\n\n\ndef default_layer_spec(config: ""GPTConfig"", vp_stage: Optional[int] = None) -> ModuleSpec:\n """"""Determine the most appropriate layer specification based on availability.\n\n Uses Transformer Engine specs if available, otherwise falls back to local implementation.\n\n Args:\n config: GPT configuration object\n\n Returns:\n ModuleSpec: The selected module specification\n """"""\n if HAVE_TE:\n if config.use_transformer_engine_full_layer_spec:\n return transformer_engine_full_layer_spec(config, vp_stage=vp_stage)\n else:\n return transformer_engine_layer_spec(config)\n else:\n return local_layer_spec(config)\n\n\ndef mtp_block_spec(config: ""GPTConfig"", vp_stage: Optional[int] = None) -> Optional[ModuleSpec]:\n """"""Pass in the MTP block spec if model has MTP layers.\n\n Args:\n config: GPT configuration object\n\n Returns:\n ModuleSpec: The MTP module specification\n """"""\n if getattr(config, ""mtp_num_layers"", None):\n from megatron.core.models.gpt.gpt_layer_specs import get_gpt_mtp_block_spec\n\n if isinstance(config.transformer_layer_spec, Callable):\n if 'vp_stage' in inspect.signature(config.transformer_layer_spec).parameters:\n spec = config.transformer_layer_spec(config, vp_stage=vp_stage)\n else:\n spec = config.transformer_layer_spec(config)\n else:\n spec = config.transformer_layer_spec\n return get_gpt_mtp_block_spec(config, spec, use_transformer_engine=HAVE_TE, vp_stage=vp_stage)\n else:\n return None\n\n\ndef torch_dtype_from_mcore_config(config: TransformerConfig) -> torch.dtype:\n """"""Extract the appropriate torch dtype from a Megatron Core configuration.\n\n Args:\n config: Megatron Core Transformer configuration\n\n Returns:\n torch.dtype: The appropriate torch dtype (float16, bfloat16, or float32)\n """"""\n if config.fp16:\n return torch.float16\n elif config.bf16:\n return torch.bfloat16\n else:\n return torch.float\n\n\ndef torch_dtype_from_dict_config(config: dict[str, Any]) -> torch.dtype:\n """"""Extract the appropriate torch dtype from a dictionary configuration.\n\n Args:\n config: Dictionary containing configuration parameters\n\n Returns:\n torch.dtype: The appropriate torch dtype (float16, bfloat16, or float32)\n """"""\n if config[""fp16""]:\n return torch.float16\n elif config[""bf16""]:\n return torch.bfloat16\n else:\n return torch.float\n\n\n@dataclass\nclass GPTConfig(TransformerConfig, io.IOMixin):\n """"""Configuration class for GPT models.\n\n Extends TransformerConfig with additional parameters specific to GPT models\n and provides utility methods for model configuration.\n """"""\n\n # From megatron.core.models.gpt.gpt_model.GPTModel\n fp16_lm_cross_entropy: bool = False\n parallel_output: bool = True\n share_embeddings_and_output_weights: bool = True\n make_vocab_size_divisible_by: int = 128\n position_embedding_type: Literal[""learned_absolute"", ""rope""] = ""learned_absolute""\n rotary_base: int = 10000\n rotary_percent: float = 1.0\n seq_len_interpolation_factor: Optional[float] = None\n seq_length: int = 1024\n attention_softmax_in_fp32: bool = False\n masked_softmax_fusion: bool = True\n cross_entropy_loss_fusion: bool = True\n gradient_accumulation_fusion: bool = _grad_accum_fusion_available\n deallocate_pipeline_outputs: bool = True\n scatter_embedding_sequence_parallel: bool = True\n tp_only_amax_red: bool = False\n\n use_transformer_engine_full_layer_spec: bool = False\n transformer_layer_spec: Union[ModuleSpec, Callable[[""GPTConfig""], ModuleSpec]] = default_layer_spec\n\n forward_step_fn: Callable = gpt_forward_step\n data_step_fn: Callable = gpt_data_step\n generation_config: Optional[""GenerationConfig""] = None\n\n vocab_size: Optional[int] = None\n tp_comm_overlap_cfg: Optional[Union[str, dict[str, Any]]] = None\n\n def configure_model(self, tokenizer, pre_process=None, post_process=None, vp_stage=None) -> ""MCoreGPTModel"":\n """"""Configure and instantiate a Megatron Core GPT model based on this configuration.\n\n Args:\n tokenizer: Tokenizer used with the model\n pre_process: Whether to include pre-processing in the model, defaults to first pipeline stage\n post_process: Whether to include post-processing in the model, defaults to last pipeline stage\n vp_stage: Virtual pipeline stage\n\n Returns:\n MCoreGPTModel: Configured Megatron Core GPT model instance\n """"""\n # Enable per-Transformer layer cuda graph.\n if self.enable_cuda_graph and self.cuda_graph_scope != ""full_iteration"":\n assert HAVE_TE, ""Transformer Engine is required for cudagraphs.""\n assert getattr(self, ""use_te_rng_tracker"", False), (\n ""Transformer engine's RNG tracker is required for cudagraphs, it can be ""\n ""enabled with use_te_rng_tracker=True'.""\n )\n\n vp_size = self.virtual_pipeline_model_parallel_size\n is_pipeline_asymmetric = getattr(self, ""account_for_embedding_in_pipeline_split"", False) or getattr(\n self, ""account_for_loss_in_pipeline_split"", False\n )\n is_pipeline_asymmetric |= (\n getattr(self, ""num_layers_in_first_pipeline_stage"", None)\n or getattr(self, ""num_layers_in_last_pipeline_stage"", None)\n ) is not None\n is_flexible_pp_layout = is_pipeline_asymmetric or (\n getattr(self, ""pipeline_model_parallel_layout"", None) is not None\n )\n if vp_size and not is_flexible_pp_layout:\n p_size = self.pipeline_model_parallel_size\n assert (\n self.num_layers // p_size\n ) % vp_size == 0, ""Make sure the number of model chunks is the same across all pipeline stages.""\n\n import inspect\n\n from megatron.core import parallel_state\n\n # During fake lightning initialization, pass 0 to bypass the assertion that vp_stage must be\n # non-None when using virtual pipeline model parallelism\n vp_stage = vp_stage or 0\n\n transformer_layer_spec = self.transformer_layer_spec\n if not isinstance(transformer_layer_spec, ModuleSpec):\n # Check if the transformer_layer_spec function accepts vp_stage parameter\n if 'vp_stage' in inspect.signature(transformer_layer_spec).parameters:\n transformer_layer_spec = transformer_layer_spec(self, vp_stage=vp_stage)\n else:\n transformer_layer_spec = transformer_layer_spec(self)\n\n if self.vocab_size is not None:\n vocab_size = self.vocab_size\n if tokenizer is not None:\n logging.info(\n f""Use preset vocab_size: {vocab_size}, original vocab_size: {tokenizer.vocab_size}, dummy tokens:""\n f"" {vocab_size - tokenizer.vocab_size}.""\n )\n else:\n vocab_size = get_vocab_size(self, tokenizer.vocab_size, self.make_vocab_size_divisible_by)\n # Initialize model as meta data instead of allocating data on a device\n model_init_device_context = contextlib.nullcontext\n if self.init_model_with_meta_device:\n model_init_device_context = partial(torch.device, device='meta')\n\n if 'mtp_block_spec' in inspect.signature(MCoreGPTModel.__init__).parameters:\n kwargs = {""mtp_block_spec"": mtp_block_spec(self, vp_stage=vp_stage)}\n else:\n kwargs = {}\n\n if self.attention_backend == AttnBackend.local:\n if hasattr(transformer_layer_spec, 'submodules'):\n transformer_layer_spec.submodules.self_attention.submodules.core_attention = MCoreDotProductAttention\n with model_init_device_context():\n model = MCoreGPTModel(\n self,\n transformer_layer_spec=transformer_layer_spec,\n vocab_size=vocab_size,\n max_sequence_length=self.seq_length,\n fp16_lm_cross_entropy=self.fp16_lm_cross_entropy,\n parallel_output=self.parallel_output,\n share_embeddings_and_output_weights=self.share_embeddings_and_output_weights,\n position_embedding_type=self.position_embedding_type,\n rotary_percent=self.rotary_percent,\n rotary_base=self.rotary_base,\n seq_len_interpolation_factor=self.seq_len_interpolation_factor,\n pre_process=pre_process\n or parallel_state.is_pipeline_first_stage(ignore_virtual=False, vp_stage=vp_stage),\n post_process=post_process\n or parallel_state.is_pipeline_last_stage(ignore_virtual=False, vp_stage=vp_stage),\n scatter_embedding_sequence_parallel=self.scatter_embedding_sequence_parallel,\n vp_stage=vp_stage,\n **kwargs,\n )\n\n # If using full TE layer, need to set TP, CP group since the module call\n # is not routed through megatron core, which normally handles passing the\n # TP, CP group to the TE modules.\n # Deep iterate but skip self to avoid infinite recursion.\n if HAVE_TE and self.use_transformer_engine_full_layer_spec:\n # Copied from:\n # https://github.com/NVIDIA/TransformerEngine/blob/main/transformer_engine/pytorch/transformer.py\n if parallel_state.get_tensor_model_parallel_world_size() > 1:\n for index, child in enumerate(model.modules()):\n if index == 0:\n continue\n if hasattr(child, ""set_tensor_parallel_group""):\n tp_group = parallel_state.get_tensor_model_parallel_group()\n child.set_tensor_parallel_group(tp_group)\n\n if parallel_state.get_context_parallel_world_size() > 1:\n cp_stream = torch.cuda.Stream()\n for module in self.get_model_module_list():\n for index, child in enumerate(module.modules()):\n if index == 0:\n continue\n if hasattr(child, ""set_context_parallel_group""):\n child.set_context_parallel_group(\n parallel_state.get_context_parallel_group(),\n parallel_state.get_context_parallel_global_ranks(),\n cp_stream,\n )\n\n return model\n\n\n@dataclass\nclass GPTConfig126M(GPTConfig):\n """"""Configuration for a 126M parameter GPT model.\n\n Predefined configuration for a small GPT model with 12 layers,\n 768 hidden size, and 12 attention heads.\n """"""\n\n seq_length: int = 2048\n num_layers: int = 12\n hidden_size: int = 768\n ffn_hidden_size: int = 3072\n num_attention_heads: int = 12\n bias_activation_fusion: bool = True\n bias_dropout_add_fusion: bool = True\n use_transformer_engine_full_layer_spec: bool = True\n\n\n@dataclass\nclass GPTConfig5B(GPTConfig):\n """"""Configuration for a 5B parameter GPT model.\n\n Predefined configuration for a medium-sized GPT model with 24 layers,\n 4096 hidden size, and 32 attention heads.\n """"""\n\n seq_length: int = 2048\n num_layers: int = 24\n hidden_size: int = 4096\n ffn_hidden_size: int = 16384\n num_attention_heads: int = 32\n bias_activation_fusion: bool = True\n bias_dropout_add_fusion: bool = True\n use_transformer_engine_full_layer_spec: bool = True\n\n\n@dataclass\nclass GPTConfig7B(GPTConfig):\n """"""Configuration for a 7B parameter GPT model.\n\n Predefined configuration for a medium-sized GPT model with 32 layers,\n 4096 hidden size, and 32 attention heads.\n """"""\n\n seq_length: int = 2048\n num_layers: int = 32\n hidden_size: int = 4096\n ffn_hidden_size: int = 10880\n num_attention_heads: int = 32\n bias_activation_fusion: bool = True\n bias_dropout_add_fusion: bool = True\n use_transformer_engine_full_layer_spec: bool = True\n\n\n@dataclass\nclass GPTConfig20B(GPTConfig):\n """"""Configuration for a 20B parameter GPT model.\n\n Predefined configuration for a large GPT model with 44 layers,\n 6144 hidden size, and 48 attention heads.\n """"""\n\n seq_length: int = 2048\n num_layers: int = 44\n hidden_size: int = 6144\n ffn_hidden_size: int = 24576\n num_attention_heads: int = 48\n bias_activation_fusion: bool = True\n bias_dropout_add_fusion: bool = True\n use_transformer_engine_full_layer_spec: bool = True\n\n\n@dataclass\nclass GPTConfig40B(GPTConfig):\n """"""Configuration for a 40B parameter GPT model.\n\n Predefined configuration for a large GPT model with 48 layers,\n 8192 hidden size, and 64 attention heads.\n """"""\n\n seq_length: int = 2048\n num_layers: int = 48\n hidden_size: int = 8192\n ffn_hidden_size: int = 32768\n num_attention_heads: int = 64\n bias_activation_fusion: bool = True\n bias_dropout_add_fusion: bool = True\n use_transformer_engine_full_layer_spec: bool = True\n\n\n@dataclass\nclass GPTConfig175B(GPTConfig):\n """"""Configuration for a 175B parameter GPT model.\n\n Predefined configuration for a massive GPT model with 96 layers,\n 12288 hidden size, and 96 attention heads.\n """"""\n\n seq_length: int = 2048\n num_layers: int = 96\n hidden_size: int = 12288\n ffn_hidden_size: int = 49152\n num_attention_heads: int = 96\n hidden_dropout: float = 0.0\n attention_dropout: float = 0.0\n bias_activation_fusion: bool = True\n bias_dropout_add_fusion: bool = True\n use_transformer_engine_full_layer_spec: bool = True\n layernorm_zero_centered_gamma: bool = True\n\n\nclass GPTModel(L.LightningModule, io.IOMixin, io.ConnectorMixin, fn.FNMixin):\n """"""GPT model implementation using Megatron Core and PyTorch Lightning.\n\n This class provides a high-level interface for training and using GPT models\n with proper integration with NeMo's infrastructure.\n """"""\n\n def __init__(\n self,\n config: GPTConfig,\n # TODO: Add transformer_layer_spec when we update mcore\n optim: Optional[OptimizerModule] = None,\n tokenizer: Optional[""TokenizerSpec""] = None,\n model_transform: Optional[Callable[[nn.Module], nn.Module]] = None,\n model_context_managers: Optional[list] = [],\n ):\n """"""Initialize the GPT model.\n\n Args:\n config: Configuration for the GPT model\n optim: Optional optimizer module\n tokenizer: Optional tokenizer specification\n model_transform: Optional function to transform the model after initialization\n model_context_managers: Optional list of context managers to apply when configuring and instantiating\n the model.\n """"""\n super().__init__()\n self.config = config\n self.tokenizer = tokenizer\n self.optim = optim or MegatronOptimizerModule(config=OptimizerConfig(lr=1e-4, use_distributed_optimizer=True))\n self.optim.connect(self) # This will bind the `configure_optimizers` method\n self.model_transform = model_transform\n self.model_context_managers = model_context_managers\n self._training_loss_reduction = None\n self._validation_loss_reduction = None\n\n def configure_model(self, vp_stage: Optional[int] = None) -> None:\n """"""Configure the underlying model if not already configured.\n\n This method ensures the model is instantiated from the configuration.\n """"""\n from nemo.collections.llm.modelopt.model_utils import restore_modelopt_state\n\n if not hasattr(self, ""module""):\n with contextlib.ExitStack() as stack:\n # Apply requested context managers for this block\n for cm in self.model_context_managers:\n stack.enter_context(cm)\n\n self.module = self.config.configure_model(self.tokenizer, vp_stage=vp_stage)\n\n # Restore ModelOpt state if it exists.\n # NOTE: Also called in MegatronStrategy.load_checkpoint but we do it for GPTModel here first,\n # for transformations which add new parameters to the model that need to be included in the optimizer.\n # TODO: Add to other models when needed.\n restore_modelopt_state(self.module, trainer=self._trainer) # `self.trainer` throws exception if not set\n\n def forward(\n self,\n input_ids: torch.Tensor,\n position_ids: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n labels: Optional[torch.Tensor] = None,\n decoder_input: Optional[torch.Tensor] = None,\n inference_context=None,\n packed_seq_params=None,\n ) -> torch.Tensor:\n """"""Forward pass through the GPT model.\n\n Args:\n input_ids: Input token IDs\n position_ids: Position IDs for the input\n attention_mask: Optional attention mask\n labels: Optional labels for computing loss\n decoder_input: Optional decoder input\n inference_context: Optional parameters for inference\n packed_seq_params: Optional parameters for packed sequence processing\n\n Returns:\n torch.Tensor: Output tensor from the model\n """"""\n extra_kwargs = {""packed_seq_params"": packed_seq_params} if packed_seq_params is not None else {}\n output_tensor = self.module(\n input_ids,\n position_ids,\n attention_mask,\n decoder_input=decoder_input,\n labels=labels,\n inference_context=inference_context,\n **extra_kwargs,\n )\n\n return output_tensor\n\n def data_step(self, dataloader_iter) -> dict[str, torch.Tensor]:\n """"""Process a batch of data from the dataloader.\n\n Args:\n dataloader_iter: Iterator over the dataloader\n\n Returns:\n dict[str, torch.Tensor]: Processed batch\n """"""\n return self.config.data_step_fn(dataloader_iter)\n\n def forward_step(self, batch) -> torch.Tensor:\n """"""Execute a forward step using the provided batch.\n\n Args:\n batch: Input batch\n\n Returns:\n torch.Tensor: Output from the forward pass\n """"""\n return self.config.forward_step_fn(self, batch)\n\n def training_step(self, batch, batch_idx=None) -> torch.Tensor:\n """"""Execute a training step.\n\n Args:\n batch: Input batch\n batch_idx: Optional batch index\n\n Returns:\n torch.Tensor: Loss value\n """"""\n # In mcore the loss-function is part of the forward-pass (when labels are provided)\n return self.forward_step(batch)\n\n def validation_step(self, batch, batch_idx=None) -> torch.Tensor:\n """"""Execute a validation step.\n\n Args:\n batch: Input batch\n batch_idx: Optional batch index\n\n Returns:\n torch.Tensor: Loss value\n """"""\n # In mcore the loss-function is part of the forward-pass (when labels are provided)\n\n return self.forward_step(batch)\n\n def get_inference_wrapper(\n self,\n params_dtype: torch.dtype,\n inference_batch_times_seqlen_threshold: int,\n inference_max_seq_length: int = 2560,\n ) -> GPTInferenceWrapper:\n """"""Get an inference wrapper for the model.\n\n Creates and configures a GPTInferenceWrapper around the model for efficient inference.\n\n Args:\n params_dtype: Data type for parameters\n inference_batch_times_seqlen_threshold: Threshold for optimizing inference\n inference_max_seq_length: Maximum sequence length for inference (prefill and decode)\n\n Returns:\n GPTInferenceWrapper: Wrapped model for inference\n """"""\n # This is to get the MCore model required in GPTInferenceWrapper.\n mcore_model = self.module\n while mcore_model:\n if type(mcore_model) is MCoreGPTModel:\n break\n mcore_model = getattr(mcore_model, ""module"", None)\n if mcore_model is None or type(mcore_model) is not MCoreGPTModel:\n raise ValueError(""Exact McoreGPTModel instance not found in the model structure."")\n\n vocab_size = None\n if hasattr(self.config, ""vocab_size""):\n vocab_size = self.config.vocab_size\n elif self.tokenizer is not None:\n vocab_size = self.tokenizer.vocab_size\n else:\n raise ValueError(\n ""Unable to find vocab size.""\n "" Either pass in a tokenizer with vocab size, or set vocab size in the model config""\n )\n\n inference_wrapper_config = InferenceWrapperConfig(\n hidden_size=mcore_model.config.hidden_size,\n params_dtype=params_dtype,\n inference_batch_times_seqlen_threshold=inference_batch_times_seqlen_threshold,\n padded_vocab_size=vocab_size,\n inference_max_seq_length=inference_max_seq_length,\n )\n\n model_inference_wrapper = GPTInferenceWrapper(mcore_model, inference_wrapper_config)\n return model_inference_wrapper\n\n @property\n def training_loss_reduction(self) -> MaskedTokenLossReduction:\n """"""Get the loss reduction module for training.\n\n Returns:\n MaskedTokenLossReduction: Loss reduction module for training\n """"""\n if not self._training_loss_reduction:\n self._training_loss_reduction = MaskedTokenLossReduction()\n\n return self._training_loss_reduction\n\n @property\n def validation_loss_reduction(self) -> MaskedTokenLossReduction:\n """"""Get the loss reduction module for validation.\n\n Returns:\n MaskedTokenLossReduction: Loss reduction module for validation\n """"""\n if not self._validation_loss_reduction:\n self._validation_loss_reduction = MaskedTokenLossReduction(validation_step=True)\n\n return self._validation_loss_reduction\n\n\ndef get_packed_seq_params(batch):\n """"""Extract packed sequence parameters from the batch.\n\n Creates and returns a PackedSeqParams object with appropriate parameters\n for packed sequence processing.\n\n Args:\n batch: Input batch containing packed sequence information\n\n Returns:\n PackedSeqParams: Parameters for packed sequence processing\n """"""\n from megatron.core.packed_seq_params import PackedSeqParams\n\n cu_seqlens = batch[""cu_seqlens""].squeeze() # remove batch size dimension (mbs=1)\n # remove -1 ""paddings"" added in collate_fn\n if (cu_seqlens_argmin := batch.get(""cu_seqlens_argmin"", None)) is not None:\n # pre-compute cu_seqlens_argmin in dataset class for perf\n cu_seqlens = cu_seqlens[: cu_seqlens_argmin.item()]\n else:\n cu_seqlens = cu_seqlens[: torch.argmin(cu_seqlens)]\n\n # pre-compute max_seqlens in dataset class for perf\n max_seqlen = batch[""max_seqlen""].squeeze() if ""max_seqlen"" in batch else None\n\n # these args are passed eventually into TEDotProductAttention.forward()\n return PackedSeqParams(\n cu_seqlens_q=cu_seqlens,\n cu_seqlens_kv=cu_seqlens,\n max_seqlen_q=max_seqlen,\n max_seqlen_kv=max_seqlen,\n qkv_format=""thd"",\n )\n\n\n__all__ = [\n ""GPTModel"",\n ""GPTConfig"",\n ""gpt_data_step"",\n ""gpt_forward_step"",\n ""transformer_engine_layer_spec"",\n ""local_layer_spec"",\n]\n",python,tab +92,150194,"nemo/collections/llm/recipes/finetune_default.py",0,0,"",python,tab +93,151318,"tests/lightning/_io/artifacts/model.yaml",0,0,"",yaml,tab +94,257319,"tests/lightning/_io/artifacts/model.yaml",5011,0,"",yaml,selection_command +95,304285,"nemo/collections/llm/recipes/finetune_default.py",0,0,"",python,tab +96,330219,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"",python,tab +97,332288,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9906,37," recipe.optim.config.lr = 1e-4",python,selection_command +98,338831,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9914,0,"",python,selection_command 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+161,6803237,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2771,0,"",python,selection_command +162,6803687,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",4563,0,"",python,selection_command +163,6804415,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",6545,0,"",python,selection_command +164,6817637,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",6563,0,"",python,selection_command +165,6817841,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2771,0,"",python,selection_command +166,6818636,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",4563,0,"",python,selection_command +167,6819065,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",6545,0,"",python,selection_command +168,6819491,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",6563,0,"",python,selection_command +169,6819699,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2771,0,"",python,selection_command +170,6824996,"nemo/collections/llm/recipes/qwen3.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom lightning.pytorch.callbacks.callback import Callback\nfrom megatron.core.distributed import DistributedDataParallelConfig\n\nfrom nemo import lightning as nl\nfrom nemo.collections.llm.gpt.model.qwen3 import (\n Qwen3Config1P7B,\n Qwen3Config4B,\n Qwen3Config8B,\n Qwen3Config14B,\n Qwen3Config30B_A3B,\n Qwen3Config32B,\n Qwen3Config235B_A22B,\n Qwen3Config600M,\n Qwen3Model,\n)\nfrom nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed, fp16_mixed\n\n\ndef qwen3_model(version: str) -> run.Config[pl.LightningModule]:\n """"""\n A function to create a qwen3 models.\n\n Args:\n version (str): The version of the qwen3 model to create. One of [""qwen3_600m"", ""qwen3_1p7b"",\n ""qwen3_4b"", ""qwen3_8b"", ""qwen3_14b"", ""qwen3_32b"", ""qwen3_30b_a3b"", ""qwen3_235b_a22b""].\n\n Returns:\n run.Config[pl.LightningModule]: Configuration for the qwen3 model.\n """"""\n config = None\n if version == ""qwen3_600m"":\n config = run.Config(Qwen3Config600M)\n elif version == ""qwen3_1p7b"":\n config = run.Config(Qwen3Config1P7B)\n elif version == ""qwen3_4b"":\n config = run.Config(Qwen3Config4B)\n elif version == ""qwen3_8b"":\n config = run.Config(Qwen3Config8B)\n elif version == ""qwen3_14b"":\n config = run.Config(Qwen3Config14B)\n elif version == ""qwen3_32b"":\n config = run.Config(Qwen3Config32B)\n elif version == ""qwen3_30b_a3b"":\n config = run.Config(Qwen3Config30B_A3B)\n elif version == ""qwen3_235b_a22b"":\n config = run.Config(Qwen3Config235B_A22B)\n\n assert config is not None, f""Invalid version: {version}""\n return run.Config(Qwen3Model, config=config)\n\n\ndef qwen3_trainer(\n tensor_parallelism: int = 1,\n pipeline_parallelism: int = 1,\n pipeline_parallelism_type: Optional[torch.dtype] = None,\n virtual_pipeline_parallelism: Optional[int] = None,\n context_parallelism: int = 1,\n sequence_parallelism: bool = False,\n expert_parallelism: int = 1,\n account_for_embedding_in_pipeline_split: bool = False,\n account_for_loss_in_pipeline_split: bool = False,\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n max_steps: int = 1168251,\n precision: str = ""bf16-mixed"",\n accumulate_grad_batches: int = 1,\n limit_test_batches: int = 32,\n limit_val_batches: int = 32,\n log_every_n_steps: int = 10,\n val_check_interval: int = 2000,\n callbacks: Optional[list[run.Config[Callback]]] = None,\n) -> run.Config[nl.Trainer]:\n """"""\n Configure the NeMo Lightning Trainer for qwen3 models.\n\n This function sets up the distributed training strategy and other training parameters.\n\n Args:\n tensor_parallelism (int): Degree of tensor model parallelism.\n pipeline_parallelism (int): Degree of pipeline model parallelism.\n pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n context_parallelism (int): Degree of context parallelism.\n sequence_parallelism (bool): Whether to use sequence parallelism.\n expert_parallelism (Optional[int]): Degree of expert parallelism.\n account_for_embedding_in_pipeline_split (bool): Whether to treat input embedding layer as a standard\n transformer layer in the context of partition and placement for pipeline parallelism.\n account_for_loss_in_pipeline_split (bool): Whether to treat loss layer as a standard transformer\n layer in the context of partition and placement for pipeline parallelism.\n account_for_loss_in_pipeline_split (bool): = False,\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n max_steps (int): Maximum number of training steps.\n precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n accumulate_grad_batches (int): Number of steps per gradient accumulation.\n limit_test_batches (int): Limit the number of test batches.\n limit_val_batches (int): Limit the number of validation batches.\n log_every_n_steps (int): Log every n steps.\n val_check_interval (int): Run validation every N steps.\n callbacks (Optional[list[run.Config[Callback]]]): List of callback configurations.\n\n Returns:\n run.Config[nl.Trainer]: Configuration for the NeMo Lightning Trainer.\n """"""\n strategy = run.Config(\n nl.MegatronStrategy,\n tensor_model_parallel_size=tensor_parallelism,\n pipeline_model_parallel_size=pipeline_parallelism,\n pipeline_dtype=pipeline_parallelism_type,\n virtual_pipeline_model_parallel_size=virtual_pipeline_parallelism,\n context_parallel_size=context_parallelism,\n sequence_parallel=sequence_parallelism,\n expert_model_parallel_size=expert_parallelism,\n expert_tensor_parallel_size=1,\n account_for_embedding_in_pipeline_split=account_for_embedding_in_pipeline_split,\n account_for_loss_in_pipeline_split=account_for_loss_in_pipeline_split,\n gradient_as_bucket_view=True,\n ckpt_include_optimizer=True,\n ckpt_async_save=True,\n ckpt_parallel_load=True,\n ddp=run.Config(\n DistributedDataParallelConfig,\n check_for_nan_in_grad=True,\n grad_reduce_in_fp32=True,\n overlap_grad_reduce=True,\n overlap_param_gather=True,\n average_in_collective=True, # Not supported for custom FSDP for now, need to be set to False if using FSDP\n data_parallel_sharding_strategy=""optim_grads_params"", # For custom FSDP only\n ),\n )\n\n precision_plugin = None\n if precision == ""16-mixed"":\n precision_plugin = fp16_mixed()\n elif precision == ""bf16-mixed"":\n precision_plugin = bf16_mixed()\n\n trainer = run.Config(\n nl.Trainer,\n accelerator=""gpu"",\n callbacks=callbacks,\n devices=num_gpus_per_node,\n accumulate_grad_batches=accumulate_grad_batches,\n limit_test_batches=limit_test_batches,\n limit_val_batches=limit_val_batches,\n log_every_n_steps=log_every_n_steps,\n max_steps=max_steps,\n num_nodes=num_nodes,\n plugins=precision_plugin,\n strategy=strategy,\n use_distributed_sampler=False,\n val_check_interval=val_check_interval,\n )\n\n return trainer\n",python,tab +171,6825512,"nemo/collections/llm/recipes/qwen3.py",0,0,"",python,selection_command +172,6828239,"nemo/collections/llm/gpt/model/qwen3.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom dataclasses import dataclass\nfrom functools import cached_property, partial\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Annotated, Callable, Optional\n\nimport torch\nfrom torch import nn\n\nfrom nemo.collections.llm.gpt.model.base import GPTModel, torch_dtype_from_mcore_config\nfrom nemo.collections.llm.gpt.model.qwen2 import Qwen2Config\nfrom nemo.collections.llm.utils import Config\nfrom nemo.lightning import OptimizerModule, io, teardown\nfrom nemo.lightning.io.state import TransformFns\nfrom nemo.lightning.pytorch.utils import dtype_from_hf\n\nif TYPE_CHECKING:\n from transformers import AutoModelForCausalLM\n from transformers import Qwen3Config as HFQwen3Config\n\n from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec\n\n\n@dataclass\nclass Qwen3Config(Qwen2Config):\n """"""\n Base config for Qwen 3 Models\n """"""\n\n add_qkv_bias: bool = False\n qk_layernorm: bool = True\n kv_channels: Optional[int] = 128\n num_query_groups: int = 8\n max_position_embeddings: int = 40960\n vocab_size: int = 151936\n\n\n@dataclass\nclass Qwen3MoEConfig(Qwen3Config):\n """"""\n Base config for Qwen 3 MoE Models\n """"""\n\n num_moe_experts: int = 128\n moe_router_load_balancing_type: str = ""aux_loss""\n moe_aux_loss_coeff: float = 1e-3\n moe_router_topk: int = 8\n moe_router_pre_softmax: bool = False\n moe_grouped_gemm: bool = True\n moe_token_dispatcher_type: str = ""alltoall""\n moe_permute_fusion: bool = True\n\n\n@dataclass\nclass Qwen3Config600M(Qwen3Config):\n """"""\n Config for Qwen 3 0.6B: https://huggingface.co/Qwen/Qwen3-0.6B\n """"""\n\n num_layers: int = 28\n hidden_size: int = 1024\n num_attention_heads: int = 16\n ffn_hidden_size: int = 3072\n share_embeddings_and_output_weights: bool = True\n\n\n@dataclass\nclass Qwen3Config1P7B(Qwen3Config):\n """"""\n Config for Qwen 3 1.7B: https://huggingface.co/Qwen/Qwen3-1.7B\n """"""\n\n num_layers: int = 28\n hidden_size: int = 2048\n num_attention_heads: int = 16\n ffn_hidden_size: int = 6144\n share_embeddings_and_output_weights: bool = True\n\n\n@dataclass\nclass Qwen3Config4B(Qwen3Config):\n """"""\n Config for Qwen 3 4B: https://huggingface.co/Qwen/Qwen3-4B\n """"""\n\n num_layers: int = 36\n hidden_size: int = 2560\n num_attention_heads: int = 32\n ffn_hidden_size: int = 9728\n share_embeddings_and_output_weights: bool = True\n\n\n@dataclass\nclass Qwen3Config8B(Qwen3Config):\n """"""\n Config for Qwen 3 8B: https://huggingface.co/Qwen/Qwen3-8B\n """"""\n\n num_layers: int = 36\n hidden_size: int = 4096\n num_attention_heads: int = 32\n ffn_hidden_size: int = 12288\n\n\n@dataclass\nclass Qwen3Config14B(Qwen3Config):\n """"""\n Config for Qwen 3 14B: https://huggingface.co/Qwen/Qwen3-14B\n """"""\n\n num_layers: int = 40\n hidden_size: int = 5120\n num_attention_heads: int = 40\n ffn_hidden_size: int = 17408\n\n\n@dataclass\nclass Qwen3Config32B(Qwen3Config):\n """"""\n Config for Qwen 3 32B: https://huggingface.co/Qwen/Qwen3-32B\n """"""\n\n num_layers: int = 64\n hidden_size: int = 5120\n num_attention_heads: int = 64\n ffn_hidden_size: int = 25600\n\n\n@dataclass\nclass Qwen3Config30B_A3B(Qwen3MoEConfig):\n """"""\n Config for Qwen 3 30B-A3B: https://huggingface.co/Qwen/Qwen3-30B-A3B\n """"""\n\n num_layers: int = 48\n hidden_size: int = 2048\n num_attention_heads: int = 32\n num_query_groups: int = 4\n ffn_hidden_size: int = 6144\n moe_ffn_hidden_size: int = 768\n\n\n@dataclass\nclass Qwen3Config235B_A22B(Qwen3MoEConfig):\n """"""\n Config for Qwen 3 235B-A22B: https://huggingface.co/Qwen/Qwen3-235B-A22B\n """"""\n\n num_layers: int = 94\n hidden_size: int = 4096\n num_attention_heads: int = 64\n num_query_groups: int = 4\n ffn_hidden_size: int = 12288\n moe_ffn_hidden_size: int = 1536\n\n\nclass Qwen3Model(GPTModel):\n """"""\n Base model for Qwen 3\n """"""\n\n def __init__(\n self,\n config: Annotated[Optional[Qwen3Config], Config[Qwen3Config]] = None,\n optim: Optional[OptimizerModule] = None,\n tokenizer: Optional[""TokenizerSpec""] = None,\n model_transform: Optional[Callable[[nn.Module], nn.Module]] = None,\n ):\n super().__init__(config or Qwen3Config(), optim=optim, tokenizer=tokenizer, model_transform=model_transform)\n\n\n@io.model_importer(Qwen3Model, ""hf"")\nclass HFQwen3Importer(io.ModelConnector[""AutoModelForCausalLM"", Qwen3Model]):\n # pylint: disable=C0115,C0116\n def init(self) -> Qwen3Model:\n return Qwen3Model(self.config, tokenizer=self.tokenizer)\n\n def apply(self, output_path: Path) -> Path:\n from transformers import AutoModelForCausalLM\n\n # logging.setLevel(logging.DEBUG)\n source = AutoModelForCausalLM.from_pretrained(str(self), torch_dtype='auto', trust_remote_code=True)\n target = self.init()\n trainer = self.nemo_setup(target)\n self.convert_state(source, target)\n self.nemo_save(output_path, trainer)\n\n print(f""Converted Qwen 3 model to Nemo, model saved to {output_path}"")\n\n teardown(trainer, target)\n del trainer, target\n\n return output_path\n\n def convert_state(self, source, target):\n mapping = {\n ""model.embed_tokens.weight"": ""embedding.word_embeddings.weight"",\n ""**.self_attn.o_proj.weight"": ""**.self_attention.linear_proj.weight"",\n ""**.self_attn.q_norm.weight"": ""**.self_attention.q_layernorm.weight"",\n ""**.self_attn.k_norm.weight"": ""**.self_attention.k_layernorm.weight"",\n ""**.input_layernorm.weight"": ""**.self_attention.linear_qkv.layer_norm_weight"",\n ""model.norm.weight"": ""decoder.final_layernorm.weight"",\n ""lm_head.weight"": ""output_layer.weight"",\n }\n is_moe = self.config.num_moe_experts is not None\n if is_moe:\n mapping.update(\n {\n ""**.mlp.experts.*.down_proj.weight"": ""**.mlp.experts.linear_fc2.weight*"",\n ""**.mlp.gate.weight"": ""**.mlp.router.weight"",\n ""**.post_attention_layernorm.weight"": ""**.pre_mlp_layernorm.weight"",\n }\n )\n else:\n mapping.update(\n {\n ""**.mlp.down_proj.weight"": ""**.mlp.linear_fc2.weight"",\n ""**.post_attention_layernorm.weight"": ""**.mlp.linear_fc1.layer_norm_weight"",\n }\n )\n\n if getattr(source.config, ""tie_word_embeddings"", False):\n del mapping[""lm_head.weight""]\n\n transforms = [\n io.state_transform(\n source_key=(\n ""**.self_attn.q_proj.weight"",\n ""**.self_attn.k_proj.weight"",\n ""**.self_attn.v_proj.weight"",\n ),\n target_key=""**.self_attention.linear_qkv.weight"",\n fn=TransformFns.merge_qkv,\n ),\n (\n io.state_transform(\n source_key=(""**.mlp.gate_proj.weight"", ""**.mlp.up_proj.weight""),\n target_key=""**.mlp.linear_fc1.weight"",\n fn=TransformFns.merge_fc1,\n )\n if not is_moe\n else io.state_transform(\n source_key=(""**.mlp.experts.*.gate_proj.weight"", ""**.mlp.experts.*.up_proj.weight""),\n target_key=""**.mlp.experts.linear_fc1.weight*"",\n fn=TransformFns.merge_fc1,\n )\n ),\n ]\n return io.apply_transforms(source, target, mapping=mapping, transforms=transforms)\n\n @cached_property\n def tokenizer(self) -> ""AutoTokenizer"":\n from nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\n return AutoTokenizer(self.save_hf_tokenizer_assets(str(self)), trust_remote_code=True)\n\n @cached_property\n def config(self) -> Qwen3Config:\n from transformers import AutoConfig as HFAutoConfig\n from transformers import GenerationConfig\n\n source = HFAutoConfig.from_pretrained(str(self), trust_remote_code=True)\n generation_config = GenerationConfig.from_pretrained(str(self))\n\n is_moe = getattr(source, ""num_experts"", None) is not None\n if is_moe:\n qwen3_config_cls = partial(Qwen3MoEConfig, moe_ffn_hidden_size=source.moe_intermediate_size)\n else:\n qwen3_config_cls = Qwen3Config\n output = qwen3_config_cls(\n num_layers=source.num_hidden_layers,\n hidden_size=source.hidden_size,\n ffn_hidden_size=source.intermediate_size,\n num_attention_heads=source.num_attention_heads,\n num_query_groups=source.num_key_value_heads,\n init_method_std=source.initializer_range,\n layernorm_epsilon=source.rms_norm_eps,\n vocab_size=source.vocab_size,\n make_vocab_size_divisible_by=1187,\n rotary_base=source.rope_theta,\n share_embeddings_and_output_weights=getattr(source, ""tie_word_embeddings"", False),\n fp16=(dtype_from_hf(source) == torch.float16),\n bf16=(dtype_from_hf(source) == torch.bfloat16),\n params_dtype=dtype_from_hf(source),\n generation_config=generation_config,\n )\n\n return output\n\n\n@io.model_exporter(Qwen3Model, ""hf"")\nclass HFQwen3Exporter(io.ModelConnector[Qwen3Model, ""AutoModelForCausalLM""]):\n # pylint: disable=C0115,C0116\n def init(self, dtype=torch.bfloat16) -> ""AutoModelForCausalLM"":\n from transformers import AutoModelForCausalLM\n from transformers.modeling_utils import no_init_weights\n\n with no_init_weights():\n return AutoModelForCausalLM.from_config(self.config, trust_remote_code=True, torch_dtype=dtype)\n\n def apply(self, output_path: Path) -> Path:\n source, _ = self.nemo_load(str(self))\n target = self.init(torch_dtype_from_mcore_config(source.config))\n target = self.convert_state(source, target)\n\n target = target.cpu()\n target.save_pretrained(output_path)\n self.tokenizer.save_pretrained(output_path)\n\n return output_path\n\n def convert_state(self, source, target):\n mapping = {\n ""**.self_attention.linear_proj.weight"": ""**.self_attn.o_proj.weight"",\n ""**.self_attention.linear_qkv.layer_norm_weight"": ""**.input_layernorm.weight"",\n ""**.self_attention.q_layernorm.weight"": ""**.self_attn.q_norm.weight"",\n ""**.self_attention.k_layernorm.weight"": ""**.self_attn.k_norm.weight"",\n ""decoder.final_layernorm.weight"": ""model.norm.weight"",\n }\n is_moe = getattr(self.config, ""num_experts"", 0) > 0\n if is_moe:\n mapping.update(\n {\n ""**.mlp.experts.linear_fc2.weight*"": ""**.mlp.experts.*.down_proj.weight"",\n ""**.mlp.router.weight"": ""**.mlp.gate.weight"",\n ""**.pre_mlp_layernorm.weight"": ""**.post_attention_layernorm.weight"",\n }\n )\n else:\n mapping.update(\n {\n ""**.mlp.linear_fc2.weight"": ""**.mlp.down_proj.weight"",\n ""**.mlp.linear_fc1.layer_norm_weight"": ""**.post_attention_layernorm.weight"",\n }\n )\n transforms = [\n io.state_transform(\n source_key=""**.self_attention.linear_qkv.weight"",\n target_key=(\n ""**.self_attn.q_proj.weight"",\n ""**.self_attn.k_proj.weight"",\n ""**.self_attn.v_proj.weight"",\n ),\n fn=TransformFns.split_qkv,\n ),\n (\n io.state_transform(\n source_key=""**.mlp.linear_fc1.weight"",\n target_key=(""**.mlp.gate_proj.weight"", ""**.mlp.up_proj.weight""),\n fn=TransformFns.split_fc1,\n )\n if not is_moe\n else io.state_transform(\n source_key=""**.mlp.experts.linear_fc1.weight*"",\n target_key=(""**.mlp.experts.*.gate_proj.weight"", ""**.mlp.experts.*.up_proj.weight""),\n fn=TransformFns.split_fc1,\n )\n ),\n io.state_transform(\n source_key=""embedding.word_embeddings.weight"",\n target_key=""model.embed_tokens.weight"",\n fn=TransformFns.prune_padding,\n ),\n ]\n if not self.config.tie_word_embeddings:\n transforms.append(\n io.state_transform(\n source_key=""output_layer.weight"",\n target_key=""lm_head.weight"",\n fn=TransformFns.prune_padding,\n )\n )\n\n return io.apply_transforms(\n source,\n target,\n mapping=mapping,\n transforms=transforms,\n )\n\n @property\n def tokenizer(self):\n return io.load_context(str(self)).model.tokenizer.tokenizer\n\n @property\n def config(self) -> ""HFQwen3Config"":\n from transformers import Qwen3Config as HFQwen3Config\n from transformers import Qwen3MoeConfig as HFQwen3MoeConfig\n\n source: Qwen3Config = io.load_context(str(self), subpath=""model.config"")\n is_moe = source.num_moe_experts is not None\n hf_config_cls = (\n partial(\n HFQwen3MoeConfig,\n moe_intermediate_size=source.moe_ffn_hidden_size,\n num_experts=source.num_moe_experts,\n num_experts_per_tok=source.moe_router_topk,\n router_aux_loss_coef=source.moe_aux_loss_coeff,\n norm_topk_prob=True,\n )\n if is_moe\n else HFQwen3Config\n )\n\n return hf_config_cls(\n architectures=[""Qwen3ForCausalLM""],\n num_hidden_layers=source.num_layers,\n hidden_size=source.hidden_size,\n intermediate_size=source.ffn_hidden_size,\n num_attention_heads=source.num_attention_heads,\n head_dim=source.kv_channels,\n 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+235,7436861,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9187,0,"",python,selection_command +236,7437968,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9124,0,"",python,selection_command +237,7439297,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9187,0,"",python,selection_command +238,7439437,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9250,0,"",python,selection_command +239,7439577,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9314,0,"",python,selection_command +240,7511369,"nemo/collections/llm/recipes/finetune_default.py",0,0,"",python,tab +241,8536523,"nemo/collections/llm/recipes/qwen3_600m.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_600m""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n """"""\n Factory function to create a Qwen3 600M model configuration.\n\n Returns:\n run.Config[pl.LightningModule]: Configuration for the Qwen3 600M model.\n\n Examples:\n CLI usage:\n $ nemo llm pretrain model=qwen3_600m ...\n\n Python API usage:\n >>> model_config = model()\n >>> print(model_config)\n """"""\n return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n # General\n dir: Optional[str] = None,\n name: str = ""default"",\n # Trainer\n tensor_parallelism: int = 1,\n pipeline_parallelism: int = 1,\n pipeline_parallelism_type: Optional[torch.dtype] = None,\n virtual_pipeline_parallelism: Optional[int] = None,\n context_parallelism: int = 1,\n sequence_parallelism: bool = False,\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n max_steps: int = 300000,\n precision: str = ""bf16-mixed"",\n accumulate_grad_batches: int = 1,\n gradient_clip_val: float = 1.0,\n limit_test_batches: int = 32,\n limit_val_batches: int = 32,\n log_every_n_steps: int = 10,\n val_check_interval: int = 500,\n # Data\n global_batch_size=32,\n micro_batch_size=2,\n seq_length=4096,\n # Optimizer\n warmup_steps=500,\n constant_steps=0,\n min_lr=3e-5,\n max_lr=3e-4,\n # Training function\n fn=pretrain,\n) -> run.Partial:\n """"""\n Create a pre-training recipe for Qwen3 600M model.\n\n This function sets up a complete configuration for pre-training, including\n model, trainer, data, logging, optimization, and resumption settings.\n\n Args:\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the pre-training run.\n tensor_parallelism (int): Degree of tensor model parallelism.\n pipeline_parallelism (int): Degree of pipeline model parallelism.\n pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n context_parallelism (int): Degree of context parallelism.\n sequence_parallelism (bool): Whether to use sequence parallelism.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n max_steps (int): Maximum number of training steps.\n precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n accumulate_grad_batches (int): Number of steps per gradient accumulation.\n gradient_clip_val (float): Value for gradient clipping.\n limit_test_batches (int): Limit the number of test batches.\n limit_val_batches (int): Limit the number of validation batches.\n log_every_n_steps (int): Log every n steps.\n val_check_interval (int): Run validation every N steps.\n global_batch_size (int): Global batch size.\n micro_batch_size (int): Micro batch size.\n seq_length (int): Sequence length.\n warmup_steps (int): Number of warmup steps.\n constant_steps (int): Number of constant steps.\n min_lr (float): Minimum learning rate.\n max_lr (float): Maximum learning rate.\n fn (Callable): The pre-training function to use.\n\n Returns:\n run.Partial: Partial configuration for pre-training.\n\n Examples:\n CLI usage:\n $ nemo llm pretrain --factory qwen3_600m\n $ nemo llm pretrain --factory ""qwen3_600m(num_nodes=1, name='my_qwen3_pretrain')""\n\n Python API usage:\n >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n >>> print(recipe)\n\n Note:\n This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n """"""\n return run.Partial(\n fn,\n model=model(),\n trainer=qwen3_trainer(\n tensor_parallelism=tensor_parallelism,\n pipeline_parallelism=pipeline_parallelism,\n pipeline_parallelism_type=pipeline_parallelism_type,\n virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n context_parallelism=context_parallelism,\n sequence_parallelism=sequence_parallelism,\n num_nodes=num_nodes,\n num_gpus_per_node=num_gpus_per_node,\n max_steps=max_steps,\n precision=precision,\n accumulate_grad_batches=accumulate_grad_batches,\n limit_test_batches=limit_test_batches,\n limit_val_batches=limit_val_batches,\n log_every_n_steps=log_every_n_steps,\n val_check_interval=val_check_interval,\n callbacks=[run.Config(TimingCallback)],\n ),\n data=run.Config(\n MockDataModule,\n seq_length=seq_length,\n global_batch_size=global_batch_size,\n micro_batch_size=micro_batch_size,\n ),\n log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n optim=distributed_fused_adam_with_cosine_annealing(\n precision=precision,\n warmup_steps=warmup_steps,\n constant_steps=constant_steps,\n min_lr=min_lr,\n max_lr=max_lr,\n clip_grad=gradient_clip_val,\n ),\n resume=default_resume(),\n )\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n dir: Optional[str] = None,\n name: str = ""default"",\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n peft_scheme: Optional[str] = 'lora',\n packed_sequence: bool = False,\n) -> run.Partial:\n """"""\n Create a fine-tuning recipe for Qwen3 600M model.\n\n This function sets up a complete configuration for fine-tuning, including\n model, trainer, data, logging, optimization, and resumption settings.\n The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n\n Args:\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the fine-tuning run.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n Allowed values: 'lora'/'dora'/'none'/None.\n packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n efficiency. Default sequence length is 2048.\n\n Returns:\n run.Partial: Partial configuration for fine-tuning.\n\n Examples:\n CLI usage:\n $ nemo llm finetune --factory qwen3_600m\n\n Python API usage:\n >>> recipe = finetune_recipe(name=""qwen3_600m_finetune"", num_nodes=2)\n >>> print(recipe)\n\n Note:\n This recipe uses the SQuAD dataset for fine-tuning.\n """"""\n recipe = default_finetune_recipe(\n model(), ""Qwen/Qwen3-0.6B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n )\n\n if peft_scheme is None or peft_scheme.lower() == 'none':\n recipe.optim.config.lr = 5e-6\n elif peft_scheme.lower() in ['lora', 'dora']:\n recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n recipe.optim.config.lr = 1e-4\n else:\n raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n return recipe\n",python,tab +242,8593512,"nemo/collections/llm/recipes/qwen3_600m.py",1780,0,"",python,selection_keyboard +243,8594713,"nemo/collections/llm/recipes/qwen3_600m.py",3438,0,"",python,selection_keyboard +244,8595636,"nemo/collections/llm/recipes/qwen3_600m.py",3346,0,"",python,selection_command +245,8595889,"nemo/collections/llm/recipes/qwen3_600m.py",3251,0,"",python,selection_command +246,8595928,"nemo/collections/llm/recipes/qwen3_600m.py",3177,0,"",python,selection_command +247,8595960,"nemo/collections/llm/recipes/qwen3_600m.py",3107,0,"",python,selection_command +248,8595985,"nemo/collections/llm/recipes/qwen3_600m.py",3057,0,"",python,selection_command +249,8596018,"nemo/collections/llm/recipes/qwen3_600m.py",2985,0,"",python,selection_command +250,8596052,"nemo/collections/llm/recipes/qwen3_600m.py",2975,0,"",python,selection_command +251,8596085,"nemo/collections/llm/recipes/qwen3_600m.py",2974,0,"",python,selection_command +252,8596119,"nemo/collections/llm/recipes/qwen3_600m.py",2900,0,"",python,selection_command +253,8596152,"nemo/collections/llm/recipes/qwen3_600m.py",2821,0,"",python,selection_command +254,8596186,"nemo/collections/llm/recipes/qwen3_600m.py",2820,0,"",python,selection_command +255,8596220,"nemo/collections/llm/recipes/qwen3_600m.py",2765,0,"",python,selection_command +256,8596257,"nemo/collections/llm/recipes/qwen3_600m.py",2757,0,"",python,selection_command +257,8596289,"nemo/collections/llm/recipes/qwen3_600m.py",2739,0,"",python,selection_command +258,8596322,"nemo/collections/llm/recipes/qwen3_600m.py",2722,0,"",python,selection_command +259,8621282,"nemo/collections/llm/__init__.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# This is here to import it once, which improves the speed of launch when in debug-mode\nfrom nemo.utils.import_utils import safe_import\n\nsafe_import(""transformer_engine"")\n\nfrom nemo.collections.llm import peft\nfrom nemo.collections.llm.bert.data import BERTMockDataModule, BERTPreTrainingDataModule, SpecterDataModule\nfrom nemo.collections.llm.bert.model import (\n BertConfig,\n BertEmbeddingLargeConfig,\n BertEmbeddingMiniConfig,\n BertEmbeddingModel,\n BertModel,\n HuggingFaceBertBaseConfig,\n HuggingFaceBertConfig,\n HuggingFaceBertLargeConfig,\n HuggingFaceBertModel,\n MegatronBertBaseConfig,\n MegatronBertConfig,\n MegatronBertLargeConfig,\n)\nfrom nemo.collections.llm.gpt.data import ( # noqa: F401\n AlpacaDataModule,\n ChatDataModule,\n CustomReRankerDataModule,\n CustomRetrievalDataModule,\n DollyDataModule,\n FineTuningDataModule,\n HFDatasetDataModule,\n HFDatasetDataModulePacked,\n HFMockDataModule,\n MockDataModule,\n PreTrainingDataModule,\n SpecterReRankerDataModule,\n SquadDataModule,\n)\nfrom nemo.collections.llm.gpt.data.api import dolly, hf_dataset, mock, squad\nfrom nemo.collections.llm.gpt.model import ( # noqa: F401\n Baichuan2Config,\n Baichuan2Config7B,\n Baichuan2Model,\n BaseMambaConfig1_3B,\n BaseMambaConfig2_7B,\n BaseMambaConfig130M,\n BaseMambaConfig370M,\n BaseMambaConfig780M,\n ChatGLM2Config6B,\n ChatGLM3Config6B,\n ChatGLMConfig,\n ChatGLMModel,\n CodeGemmaConfig2B,\n CodeGemmaConfig7B,\n CodeLlamaConfig7B,\n CodeLlamaConfig13B,\n CodeLlamaConfig34B,\n CodeLlamaConfig70B,\n DeepSeekModel,\n DeepSeekV2Config,\n DeepSeekV2LiteConfig,\n DeepSeekV3Config,\n Gemma2Config,\n Gemma2Config2B,\n Gemma2Config9B,\n Gemma2Config27B,\n Gemma2Model,\n Gemma3Config1B,\n Gemma3Config4B,\n Gemma3Config12B,\n Gemma3Config27B,\n Gemma3Model,\n GemmaConfig,\n GemmaConfig2B,\n GemmaConfig7B,\n GemmaModel,\n GPTConfig,\n GPTConfig5B,\n GPTConfig7B,\n GPTConfig20B,\n GPTConfig40B,\n GPTConfig126M,\n GPTConfig175B,\n GPTModel,\n GPTOSSConfig,\n GPTOSSConfig20B,\n GPTOSSConfig120B,\n GPTOSSModel,\n Hyena1bConfig,\n Hyena7bARCLongContextConfig,\n Hyena7bConfig,\n Hyena40bARCLongContextConfig,\n Hyena40bConfig,\n HyenaConfig,\n HyenaModel,\n HyenaNV1bConfig,\n HyenaNV7bConfig,\n HyenaNV40bConfig,\n HyenaNVTestConfig,\n HyenaTestConfig,\n Llama2Config7B,\n Llama2Config13B,\n Llama2Config70B,\n Llama3Config8B,\n Llama3Config70B,\n Llama4Config,\n Llama4Experts16Config,\n Llama4Experts128Config,\n Llama31Config8B,\n Llama31Config70B,\n Llama31Config405B,\n Llama31Nemotron70BConfig,\n Llama31NemotronNano8BConfig,\n Llama31NemotronUltra253BConfig,\n Llama32Config1B,\n Llama32Config3B,\n Llama32EmbeddingConfig1B,\n Llama32EmbeddingConfig3B,\n Llama32Reranker1BConfig,\n Llama32Reranker500MConfig,\n Llama33NemotronSuper49BConfig,\n LlamaConfig,\n LlamaEmbeddingModel,\n LlamaModel,\n LlamaNemotronModel,\n MambaModel,\n MaskedTokenLossReduction,\n MistralConfig7B,\n MistralModel,\n MistralNeMoConfig12B,\n MistralSmall3Config24B,\n MixtralConfig,\n MixtralConfig8x3B,\n MixtralConfig8x7B,\n MixtralConfig8x22B,\n MixtralModel,\n Nemotron3Config4B,\n Nemotron3Config8B,\n Nemotron3Config22B,\n Nemotron4Config15B,\n Nemotron4Config340B,\n NemotronConfig,\n NemotronHConfig4B,\n NemotronHConfig8B,\n NemotronHConfig47B,\n NemotronHConfig56B,\n NemotronModel,\n NemotronNano9Bv2,\n NemotronNano12Bv2,\n NVIDIAMambaConfig8B,\n NVIDIAMambaHybridConfig8B,\n Phi3Config,\n Phi3ConfigMini,\n Phi3Model,\n Qwen2Config,\n Qwen2Config1P5B,\n Qwen2Config7B,\n Qwen2Config72B,\n Qwen2Config500M,\n Qwen2Model,\n Qwen3Config,\n Qwen3Config1P7B,\n Qwen3Config4B,\n Qwen3Config8B,\n Qwen3Config14B,\n Qwen3Config30B_A3B,\n Qwen3Config32B,\n Qwen3Config235B_A22B,\n Qwen3Config600M,\n Qwen3Model,\n Qwen25Config1P5B,\n Qwen25Config3B,\n Qwen25Config7B,\n Qwen25Config14B,\n Qwen25Config32B,\n Qwen25Config72B,\n Qwen25Config500M,\n ReRankerModel,\n SSMConfig,\n Starcoder2Config,\n Starcoder2Config3B,\n Starcoder2Config7B,\n Starcoder2Config15B,\n Starcoder2Model,\n StarcoderConfig,\n StarcoderConfig15B,\n StarcoderModel,\n gpt_data_step,\n gpt_forward_step,\n)\nfrom nemo.collections.llm.t5.data import FineTuningDataModule as T5FineTuningDataModule\nfrom nemo.collections.llm.t5.data import MockDataModule as T5MockDataModule\nfrom nemo.collections.llm.t5.data import PreTrainingDataModule as T5PreTrainingDataModule\nfrom nemo.collections.llm.t5.data import SquadDataModule as T5SquadDataModule\nfrom nemo.collections.llm.t5.model import (\n T5Config,\n T5Config3B,\n T5Config11B,\n T5Config220M,\n T5Model,\n t5_data_step,\n t5_forward_step,\n)\n\n__all__ = [\n ""MockDataModule"",\n ""T5MockDataModule"",\n ""CustomRetrievalDataModule"",\n ""CustomReRankerDataModule"",\n ""SpecterReRankerDataModule"",\n ""GPTModel"",\n ""GPTConfig"",\n ""HyenaTestConfig"",\n ""Hyena7bConfig"",\n ""Hyena40bConfig"",\n ""Hyena7bARCLongContextConfig"",\n ""Hyena40bARCLongContextConfig"",\n ""HyenaNVTestConfig"",\n ""HyenaNV40bConfig"",\n ""HyenaNV7bConfig"",\n ""HyenaConfig"",\n ""HyenaModel"",\n ""Hyena1bConfig"",\n ""HyenaNV1bConfig"",\n ""gpt_data_step"",\n ""gpt_forward_step"",\n ""T5Model"",\n ""T5Config"",\n ""T5Config220M"",\n ""T5Config3B"",\n ""T5Config11B"",\n ""BertConfig"",\n ""BertEmbeddingModel"",\n ""BertModel"",\n ""BertEmbeddingLargeConfig"",\n ""BertEmbeddingMiniConfig"",\n ""t5_data_step"",\n ""t5_forward_step"",\n ""MaskedTokenLossReduction"",\n ""MistralConfig7B"",\n ""MistralNeMoConfig12B"",\n ""MistralSmall3Config24B"",\n ""MistralModel"",\n ""MixtralConfig"",\n ""MixtralConfig8x3B"",\n ""MixtralConfig8x7B"",\n ""MixtralConfig8x22B"",\n ""MixtralModel"",\n ""Starcoder2Config15B"",\n ""Starcoder2Config"",\n ""Starcoder2Model"",\n ""NemotronModel"",\n ""Nemotron3Config4B"",\n ""Nemotron3Config8B"",\n ""Nemotron3Config22B"",\n ""Nemotron4Config15B"",\n ""Nemotron4Config340B"",\n ""NemotronConfig"",\n ""LlamaEmbeddingModel"",\n ""Llama32EmbeddingConfig1B"",\n ""Llama32EmbeddingConfig3B"",\n ""Phi3Config"",\n ""Phi3ConfigMini"",\n ""Phi3Model"",\n ""SSMConfig"",\n ""BaseMambaConfig130M"",\n ""BaseMambaConfig370M"",\n ""BaseMambaConfig780M"",\n ""BaseMambaConfig1_3B"",\n ""BaseMambaConfig2_7B"",\n ""NVIDIAMambaConfig8B"",\n ""NVIDIAMambaHybridConfig8B"",\n ""NemotronHConfig4B"",\n ""NemotronHConfig8B"",\n ""NemotronHConfig47B"",\n ""NemotronHConfig56B"",\n ""NemotronNano9Bv2"",\n ""NemotronNano12Bv2"",\n ""MambaModel"",\n ""LlamaConfig"",\n ""Llama2Config7B"",\n ""Llama2Config13B"",\n ""Llama2Config70B"",\n ""Llama3Config8B"",\n ""Llama3Config70B"",\n ""Llama31Config8B"",\n ""Llama31Config70B"",\n ""Llama31Config405B"",\n ""Llama32Config1B"",\n ""Llama32Config3B"",\n ""Llama4Experts16Config"",\n ""Llama4Experts128Config"",\n ""Llama4Config"",\n ""Llama31NemotronNano8BConfig"",\n ""Llama31Nemotron70BConfig"",\n ""Llama33NemotronSuper49BConfig"",\n ""Llama31NemotronUltra253BConfig"",\n ""Llama32Reranker500MConfig"",\n ""Llama32Reranker1BConfig"",\n ""CodeLlamaConfig7B"",\n ""CodeLlamaConfig13B"",\n ""CodeLlamaConfig34B"",\n ""CodeLlamaConfig70B"",\n ""LlamaModel"",\n ""LlamaNemotronModel"",\n ""GPTOSSConfig"",\n ""GPTOSSConfig120B"",\n ""GPTOSSConfig20B"",\n ""GPTOSSModel"",\n ""GemmaConfig"",\n ""GemmaConfig2B"",\n ""GemmaConfig7B"",\n ""CodeGemmaConfig2B"",\n ""CodeGemmaConfig7B"",\n ""GemmaModel"",\n ""Gemma2Model"",\n ""Gemma2Config9B"",\n ""Gemma2Config"",\n ""Gemma2Config27B"",\n ""Gemma2Config2B"",\n ""Gemma3Model"",\n ""Gemma3Config1B"",\n ""Gemma3Config4B"",\n ""Gemma3Config12B"",\n ""Gemma3Config27B"",\n ""Baichuan2Config"",\n ""Baichuan2Config7B"",\n ""Baichuan2Model"",\n ""ChatGLMConfig"",\n ""ChatGLM2Config6B"",\n ""ChatGLM3Config6B"",\n ""ChatGLMModel"",\n ""Qwen2Model"",\n ""Qwen2Config7B"",\n ""Qwen2Config"",\n ""Qwen2Config500M"",\n ""Qwen2Config1P5B"",\n ""Qwen25Config3B"",\n ""Qwen2Config72B"",\n ""Qwen25Config500M"",\n ""Qwen25Config1P5B"",\n ""Qwen25Config7B"",\n ""Qwen25Config14B"",\n ""Qwen25Config32B"",\n ""Qwen25Config72B"",\n ""Qwen3Config"",\n ""Qwen3Config600M"",\n ""Qwen3Config1P7B"",\n ""Qwen3Config4B"",\n ""Qwen3Config8B"",\n ""Qwen3Config14B"",\n ""Qwen3Config32B"",\n ""Qwen3Config30B_A3B"",\n ""Qwen3Config235B_A22B"",\n ""Qwen3Model"",\n ""PreTrainingDataModule"",\n ""FineTuningDataModule"",\n ""ChatDataModule"",\n ""SquadDataModule"",\n ""T5PreTrainingDataModule"",\n ""T5FineTuningDataModule"",\n ""T5SquadDataModule"",\n ""T5MockDataModule"",\n ""DeepSeekModel"",\n ""DeepSeekV2Config"",\n ""DeepSeekV2LiteConfig"",\n ""DeepSeekV3Config"",\n ""HuggingFaceBertBaseConfig"",\n ""HuggingFaceBertConfig"",\n ""HuggingFaceBertLargeConfig"",\n ""HuggingFaceBertModel"",\n ""MegatronBertBaseConfig"",\n ""MegatronBertConfig"",\n ""MegatronBertLargeConfig"",\n ""BERTMockDataModule"",\n ""BERTPreTrainingDataModule"",\n ""SpecterDataModule"",\n ""DollyDataModule"",\n ""tokenizer"",\n ""mock"",\n ""squad"",\n ""dolly"",\n ""peft"",\n ""hf_dataset"",\n ""HFMockDataModule"",\n]\n\n\nfrom nemo.utils import logging\n\ntry:\n import nemo_run as run # noqa: F401\n\n from nemo.collections.llm.api import ( # noqa: F401\n distill,\n export_ckpt,\n finetune,\n generate,\n import_ckpt,\n pretrain,\n prune,\n ptq,\n train,\n validate,\n )\n from nemo.collections.llm.recipes import * # noqa\n\n __all__.extend(\n [\n ""train"",\n ""import_ckpt"",\n ""export_ckpt"",\n ""pretrain"",\n ""validate"",\n ""finetune"",\n ""generate"",\n ""prune"",\n ""ptq"",\n ""distill"",\n ]\n )\nexcept ImportError as error:\n logging.warning(f""Failed to import nemo.collections.llm.[api,recipes]: {error}"")\n\ntry:\n from nemo.collections.llm.api import deploy # noqa: F401\n\n __all__.append(""deploy"")\nexcept ImportError as error:\n logging.warning(f""The deploy module could not be imported: {error}"")\n\ntry:\n from nemo.collections.llm.api import evaluate # noqa: F401\n\n __all__.append(""evaluate"")\nexcept ImportError as error:\n logging.warning(f""The evaluate module could not be imported: {error}"")\n",python,tab +260,8626049,"nemo/collections/llm/__init__.py",4446,0,"",python,selection_command +261,8626789,"nemo/collections/llm/__init__.py",4463,0,"",python,selection_command +262,8627040,"nemo/collections/llm/__init__.py",4484,0,"",python,selection_command +263,8627079,"nemo/collections/llm/__init__.py",4503,0,"",python,selection_command +264,8627103,"nemo/collections/llm/__init__.py",4522,0,"",python,selection_command +265,8627137,"nemo/collections/llm/__init__.py",4542,0,"",python,selection_command +266,8627487,"nemo/collections/llm/__init__.py",4522,0,"",python,selection_command +267,8627638,"nemo/collections/llm/__init__.py",4503,0,"",python,selection_command +268,8627780,"nemo/collections/llm/__init__.py",4484,0,"",python,selection_command +269,8628089,"nemo/collections/llm/__init__.py",4463,0,"",python,selection_command +270,8629239,"nemo/collections/llm/__init__.py",4484,0,"",python,selection_command +271,8629489,"nemo/collections/llm/__init__.py",4503,0,"",python,selection_command +272,8629524,"nemo/collections/llm/__init__.py",4522,0,"",python,selection_command +273,8629555,"nemo/collections/llm/__init__.py",4542,0,"",python,selection_command +274,8629588,"nemo/collections/llm/__init__.py",4566,0,"",python,selection_command +275,8629694,"nemo/collections/llm/__init__.py",4586,0,"",python,selection_command +276,8629854,"nemo/collections/llm/__init__.py",4612,0,"",python,selection_command +277,8630212,"nemo/collections/llm/__init__.py",4612,1,"Q",python,selection_command +278,8630338,"nemo/collections/llm/__init__.py",4612,15,"Qwen3Config600M",python,selection_command +279,8630919,"nemo/collections/llm/__init__.py",4626,0,"",python,selection_command +280,8941201,"nemo/collections/llm/recipes/qwen3_600m.py",0,0,"",python,tab +281,184621360,"tests/lightning/_io/artifacts/model.yaml",0,0,"",yaml,tab +282,271675338,"tests/lightning/_io/artifacts/model.yaml",994,0,"",yaml,selection_command +283,271675455,"tests/lightning/_io/artifacts/model.yaml",1080,0,"",yaml,selection_command +284,271675609,"tests/lightning/_io/artifacts/model.yaml",1484,0,"",yaml,selection_command +285,271675787,"tests/lightning/_io/artifacts/model.yaml",1573,0,"",yaml,selection_command +286,271676144,"tests/lightning/_io/artifacts/model.yaml",5108,0,"",yaml,selection_command 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+1,3,"src/extension/completions-core/vscode-node/lib/src/postInsertion.ts",0,0,"/*---------------------------------------------------------------------------------------------\n * Copyright (c) Microsoft Corporation. All rights reserved.\n * Licensed under the MIT License. See License.txt in the project root for license information.\n *--------------------------------------------------------------------------------------------*/\nimport { IInstantiationService, ServicesAccessor } from '../../../../../util/vs/platform/instantiation/common/instantiation';\nimport { ICompletionsTelemetryService } from '../../bridge/src/completionsTelemetryServiceBridge';\nimport { CopilotTokenManager } from './auth/copilotTokenManager';\nimport { ChangeTracker } from './changeTracker';\nimport { CitationManager, IPCitationDetail } from './citationManager';\nimport { createCompletionState } from './completionState';\nimport { ICompletionsContextService } from './context';\nimport { FileReader } from './fileReader';\nimport { PostInsertionCategory, telemetryAccepted, telemetryRejected } from './ghostText/telemetry';\nimport { LogTarget, Logger } from './logger';\nimport { Fetcher } from './networking';\nimport { CopilotNamedAnnotationList } from './openai/stream';\nimport { contextIndentationFromText, indentationBlockFinished } from './prompt/parseBlock';\nimport { Prompt, extractPrompt } from './prompt/prompt';\nimport { fetchCitations } from './snippy/handlePostInsertion';\nimport { editDistance, lexEditDistance } from './suggestions/editDistance';\nimport { SuggestionStatus, computeCompletionText } from './suggestions/partialSuggestions';\nimport { TelemetryStore, TelemetryWithExp, telemetry, telemetryCatch } from './telemetry';\nimport { TextDocumentManager } from './textDocumentManager';\nimport { ICompletionsPromiseQueueService } from './util/promiseQueue';\nimport { ICompletionsRuntimeModeService } from './util/runtimeMode';\n\nconst postInsertionLogger = new Logger('postInsertion');\n\ntype Timeout = {\n\tseconds: number;\n\tcaptureCode: boolean;\n\tcaptureRejection: boolean;\n};\n// windows for telemetry checks, in seconds\n// captureCode = capture the code after acceptance,\n// captureRejection = capture the code after rejection\nconst captureTimeouts: Timeout[] = [\n\t{ seconds: 15, captureCode: false, captureRejection: false },\n\t{ seconds: 30, captureCode: true, captureRejection: true },\n\t{ seconds: 120, captureCode: false, captureRejection: false },\n\t{ seconds: 300, captureCode: false, captureRejection: false },\n\t{ seconds: 600, captureCode: false, captureRejection: false },\n];\n\n// No. of chars before/after insertion point to look for the completion\nconst stillInCodeNearMargin = 50;\nconst stillInCodeFarMargin = 1500;\n\n// If lex edit distance is below this fraction of completion length it is considered\n// in the code\nconst stillInCodeFraction = 0.5;\n\n// Number of characters captured after the insertion point.\n// Used only if we couldn't detect termination point with indent-based parsing.\nconst captureCodeMargin = 500;\n\nconst postInsertConfiguration: {\n\ttriggerPostInsertionSynchroneously: boolean;\n\tcaptureCode: boolean;\n\tcaptureRejection: boolean;\n} = {\n\ttriggerPostInsertionSynchroneously: false,\n\tcaptureCode: false,\n\tcaptureRejection: false,\n};\n\nasync function captureCode(\n\taccessor: ServicesAccessor,\n\turi: string,\n\tcompletionTelemetry: TelemetryWithExp,\n\toffset: number,\n\tsuffixOffset?: number\n): Promise<{ prompt: Prompt; capturedCode: string; terminationOffset: number }> {\n\tconst ctx = accessor.get(ICompletionsContextService);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst logTarget = ctx.get(LogTarget);\n\tconst result = await ctx.get(FileReader).getOrReadTextDocumentWithFakeClientProperties({ uri });\n\tif (result.status !== 'valid') {\n\t\tpostInsertionLogger.info(logTarget, `Could not get document for ${uri}. Maybe it was closed by the editor.`);\n\t\treturn {\n\t\t\tprompt: {\n\t\t\t\tprefix: '',\n\t\t\t\tsuffix: '',\n\t\t\t\tisFimEnabled: false,\n\t\t\t},\n\t\t\tcapturedCode: '',\n\t\t\tterminationOffset: 0,\n\t\t};\n\t}\n\tconst document = result.document;\n\tconst documentText = document.getText();\n\tconst documentTextBefore = documentText.substring(0, offset);\n\tconst position = document.positionAt(offset);\n\n\t// Treat the code before offset as the hypothetical prompt\n\tconst hypotheticalPromptResponse = await instantiationService.invokeFunction(extractPrompt,\n\t\tcompletionTelemetry.properties.headerRequestId,\n\t\tcreateCompletionState(document, position),\n\t\tcompletionTelemetry\n\t);\n\tconst hypotheticalPrompt =\n\t\thypotheticalPromptResponse.type === 'prompt'\n\t\t\t? hypotheticalPromptResponse.prompt\n\t\t\t: {\n\t\t\t\tprefix: documentTextBefore,\n\t\t\t\tsuffix: '',\n\t\t\t\tisFimEnabled: false,\n\t\t\t}; // TODO(eaftan): Pass an actual suffix when we're ready to support it\n\n\tif (hypotheticalPrompt.isFimEnabled && suffixOffset !== undefined) {\n\t\t// With FIM enabled, we can exactly determine capturedCode, suffix and prefix by propertly initialized trackers. No need to guess.\n\t\tconst capturedCode = documentText.substring(offset, suffixOffset);\n\t\thypotheticalPrompt.suffix = documentText.substring(suffixOffset);\n\n\t\treturn { prompt: hypotheticalPrompt, capturedCode, terminationOffset: 0 };\n\t} else {\n\t\t//Everything after the insertion point is hypothetical response we could get from AI\n\t\tconst hypotheticalResponse = documentText.substring(offset);\n\n\t\t//Try to find the termination offset in the hypothetical response using indentation based parsing\n\t\tconst contextIndent = contextIndentationFromText(documentTextBefore, offset, document.detectedLanguageId);\n\t\tconst indentTerminationFunction = indentationBlockFinished(contextIndent, undefined);\n\t\tconst terminationResult = indentTerminationFunction(hypotheticalResponse);\n\n\t\t//If we could detect termination of the indentation block, capture 2x length of detected suggestion\n\t\t//Otherwise capture a lot of characters\n\t\tconst maxOffset = Math.min(\n\t\t\tdocumentText.length,\n\t\t\toffset + (terminationResult ? terminationResult * 2 : captureCodeMargin)\n\t\t);\n\n\t\tconst capturedCode = documentText.substring(offset, maxOffset);\n\n\t\treturn { prompt: hypotheticalPrompt, capturedCode, terminationOffset: terminationResult ?? -1 };\n\t}\n}\n\nexport function postRejectionTasks(\n\taccessor: ServicesAccessor,\n\tinsertionCategory: PostInsertionCategory,\n\tinsertionOffset: number,\n\turi: string,\n\tcompletions: { completionText: string; completionTelemetryData: TelemetryWithExp }[]\n) {\n\tconst logTarget = accessor.get(ICompletionsContextService).get(LogTarget);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst telemetryService = accessor.get(ICompletionsTelemetryService);\n\tconst promiseQueueService = accessor.get(ICompletionsPromiseQueueService);\n\n\t//Send `.rejected` telemetry event for each rejected completion\n\tcompletions.forEach(({ completionText, completionTelemetryData }) => {\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`${insertionCategory}.rejected choiceIndex: ${completionTelemetryData.properties.choiceIndex}`\n\t\t);\n\t\tinstantiationService.invokeFunction(telemetryRejected, insertionCategory, completionTelemetryData);\n\n\t\t// Fire-and-forget external logging of rejected completion\n\t\tvoid (async () => {\n\t\t\ttry {\n\t\t\t\tconst url = (globalThis as any).process?.env?.CROWD_CODE_API_GATEWAY_URL as (string | undefined);\n\t\t\t\tif (!url) { return; }\n\t\t\t\tconst { prompt } = await instantiationService.invokeFunction(captureCode,\n\t\t\t\t\turi,\n\t\t\t\t\tcompletionTelemetryData,\n\t\t\t\t\tinsertionOffset\n\t\t\t\t);\n\t\t\t\tconst fetcher = accessor.get(ICompletionsContextService).get(Fetcher);\n\t\t\t\tconst payload = {\n\t\t\t\t\ttype: 'tab',\n\t\t\t\t\tevent: 'rejected',\n\t\t\t\t\trequestId: completionTelemetryData.properties.headerRequestId,\n\t\t\t\t\tchoiceIndex: completionTelemetryData.properties.choiceIndex,\n\t\t\t\t\turi,\n\t\t\t\t\tinsertionOffset,\n\t\t\t\t\tprompt,\n\t\t\t\t\ttext: completionText\n\t\t\t\t} as any;\n\t\t\t\tawait fetcher.fetch(url, { method: 'POST', headers: { 'Content-Type': 'application/json' }, json: payload });\n\t\t\t} catch (e) {\n\t\t\t\tpostInsertionLogger.debug(logTarget, 'External tab log (rejected) failed');\n\t\t\t}\n\t\t})();\n\t});\n\tconst positionTracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset - 1);\n\tconst suffixTracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset);\n\n\tconst checkInCode = async (t: Timeout) => {\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`Original offset: ${insertionOffset}, Tracked offset: ${positionTracker.offset}`\n\t\t);\n\t\tconst { completionTelemetryData } = completions[0];\n\n\t\tconst { prompt, capturedCode, terminationOffset } = await instantiationService.invokeFunction(captureCode,\n\t\t\turi,\n\t\t\tcompletionTelemetryData,\n\t\t\tpositionTracker.offset + 1,\n\t\t\tsuffixTracker.offset\n\t\t);\n\n\t\tconst promptTelemetry = {\n\t\t\thypotheticalPromptJson: JSON.stringify({ prefix: prompt.prefix, context: prompt.context }),\n\t\t\thypotheticalPromptSuffixJson: JSON.stringify(prompt.suffix),\n\t\t};\n\n\t\tconst customTelemetryData = completionTelemetryData.extendedBy(\n\t\t\t{\n\t\t\t\t...promptTelemetry,\n\t\t\t\tcapturedCodeJson: JSON.stringify(capturedCode),\n\t\t\t},\n\t\t\t{\n\t\t\t\ttimeout: t.seconds,\n\t\t\t\tinsertionOffset: insertionOffset,\n\t\t\t\ttrackedOffset: positionTracker.offset,\n\t\t\t\tterminationOffsetInCapturedCode: terminationOffset,\n\t\t\t}\n\t\t);\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`${insertionCategory}.capturedAfterRejected choiceIndex: ${completionTelemetryData.properties.choiceIndex}`,\n\t\t\tcustomTelemetryData\n\t\t);\n\t\tinstantiationService.invokeFunction(telemetry, insertionCategory + '.capturedAfterRejected', customTelemetryData, TelemetryStore.Enhanced);\n\t};\n\t// Capture the code typed after we detected that completion was rejected,\n\t// Uses first displayed completion as the source/seed of telemetry information.\n\tcaptureTimeouts\n\t\t.filter(t => t.captureRejection)\n\t\t.map(t =>\n\t\t\tpositionTracker.push(\n\t\t\t\ttelemetryCatch(telemetryService, promiseQueueService, () => checkInCode(t), 'postRejectionTasks'),\n\t\t\t\tt.seconds * 1000\n\t\t\t)\n\t\t);\n}\n\nexport function postInsertionTasks(\n\taccessor: ServicesAccessor,\n\tinsertionCategory: PostInsertionCategory,\n\tcompletionText: string,\n\tinsertionOffset: number,\n\turi: string,\n\ttelemetryData: TelemetryWithExp,\n\tsuggestionStatus: SuggestionStatus,\n\tcopilotAnnotations?: CopilotNamedAnnotationList\n) {\n\tconst logTarget = accessor.get(ICompletionsContextService).get(LogTarget);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst promiseQueueService = accessor.get(ICompletionsPromiseQueueService);\n\tconst telemetryService = accessor.get(ICompletionsTelemetryService);\n\tconst runtimeModeService = accessor.get(ICompletionsRuntimeModeService);\n\n\tconst telemetryDataWithStatus = telemetryData.extendedBy(\n\t\t{\n\t\t\tcompType: suggestionStatus.compType,\n\t\t},\n\t\t{\n\t\t\tcompCharLen: suggestionStatus.acceptedLength,\n\t\t\tnumLines: suggestionStatus.acceptedLines,\n\t\t}\n\t);\n\t// send "".accepted"" telemetry\n\tpostInsertionLogger.debug(\n\t\tlogTarget,\n\t\t`${insertionCategory}.accepted choiceIndex: ${telemetryDataWithStatus.properties.choiceIndex}`\n\t);\n\tinstantiationService.invokeFunction(telemetryAccepted, insertionCategory, telemetryDataWithStatus);\n\n\tconst fullCompletionText = completionText;\n\tcompletionText = computeCompletionText(completionText, suggestionStatus);\n\tconst trimmedCompletion = completionText.trim();\n\tconst tracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset);\n\tconst suffixTracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset + completionText.length);\n\n\t// Fire-and-forget external logging of accepted completion with prompt input\n\tvoid (async () => {\n\t\ttry {\n\t\t\tconst url = (globalThis as any).process?.env?.CROWD_CODE_API_GATEWAY_URL as (string | undefined);\n\t\t\tif (!url) { return; }\n\t\t\tconst { prompt } = await instantiationService.invokeFunction(captureCode,\n\t\t\t\turi,\n\t\t\t\ttelemetryDataWithStatus,\n\t\t\t\tinsertionOffset,\n\t\t\t\tsuffixTracker.offset\n\t\t\t);\n\t\t\tconst fetcher = accessor.get(ICompletionsContextService).get(Fetcher);\n\t\t\tconst payload = {\n\t\t\t\ttype: 'tab',\n\t\t\t\tevent: 'accepted',\n\t\t\t\trequestId: telemetryDataWithStatus.properties.headerRequestId,\n\t\t\t\tchoiceIndex: telemetryDataWithStatus.properties.choiceIndex,\n\t\t\t\turi,\n\t\t\t\tinsertionOffset,\n\t\t\t\tprompt,\n\t\t\t\taccepted: {\n\t\t\t\t\ttext: fullCompletionText,\n\t\t\t\t\tacceptedLength: suggestionStatus.acceptedLength,\n\t\t\t\t\tacceptedLines: suggestionStatus.acceptedLines\n\t\t\t\t}\n\t\t\t} as any;\n\t\t\tawait fetcher.fetch(url, { method: 'POST', headers: { 'Content-Type': 'application/json' }, json: payload });\n\t\t} catch (e) {\n\t\t\tpostInsertionLogger.debug(logTarget, 'External tab log (accepted) failed');\n\t\t}\n\t})();\n\n\tconst stillInCodeCheck = async (timeout: Timeout) => {\n\t\tconst check = instantiationService.invokeFunction(checkStillInCode,\n\t\t\tinsertionCategory,\n\t\t\ttrimmedCompletion,\n\t\t\tinsertionOffset,\n\t\t\turi,\n\t\t\ttimeout,\n\t\t\ttelemetryDataWithStatus,\n\t\t\ttracker,\n\t\t\tsuffixTracker\n\t\t);\n\t\tawait check;\n\t};\n\n\t// For test purposes, we add one set of these telemetry events synchronously to allow asserting the telemetry\n\tif (postInsertConfiguration.triggerPostInsertionSynchroneously && runtimeModeService.isRunningInTest()) {\n\t\tconst check = stillInCodeCheck({\n\t\t\tseconds: 0,\n\t\t\tcaptureCode: postInsertConfiguration.captureCode,\n\t\t\tcaptureRejection: postInsertConfiguration.captureRejection,\n\t\t});\n\t\tpromiseQueueService.register(check);\n\t} else {\n\t\tcaptureTimeouts.map(timeout =>\n\t\t\ttracker.push(\n\t\t\t\ttelemetryCatch(telemetryService, promiseQueueService, () => stillInCodeCheck(timeout), 'postInsertionTasks'),\n\t\t\t\ttimeout.seconds * 1000\n\t\t\t)\n\t\t);\n\t}\n\n\tinstantiationService.invokeFunction(acc => telemetryCatch(telemetryService, promiseQueueService, citationCheck, 'post insertion citation check')(\n\t\tacc,\n\t\turi,\n\t\tfullCompletionText,\n\t\tcompletionText,\n\t\tinsertionOffset,\n\t\tcopilotAnnotations\n\t));\n}\n\nasync function citationCheck(\n\taccessor: ServicesAccessor,\n\turi: string,\n\tfullCompletionText: string,\n\tinsertedText: string,\n\tinsertionOffset: number,\n\tcopilotAnnotations?: CopilotNamedAnnotationList\n) {\n\tconst logTarget = accessor.get(ICompletionsContextService).get(LogTarget);\n\tconst textDocumentManagerService = accessor.get(ICompletionsContextService).get(TextDocumentManager);\n\tconst copilotTokenManager = accessor.get(ICompletionsContextService).get(CopilotTokenManager);\n\tconst citationManagerService = accessor.get(ICompletionsContextService).get(CitationManager);\n\n\t// If there are no citations, request directly from the snippy service\n\tif (!copilotAnnotations || (copilotAnnotations.ip_code_citations?.length ?? 0) < 1) {\n\t\t// Do not request citations if in blocking mode\n\t\tif (copilotTokenManager.getLastToken()?.getTokenValue('sn') === '1') { return; }\n\t\tawait fetchCitations(accessor, uri, insertedText, insertionOffset);\n\t\treturn;\n\t}\n\n\tconst doc = await textDocumentManagerService.getTextDocument({ uri });\n\n\t// in the CLS, if the editor does not wait to send document updates until the\n\t// acceptance function returns, we could be in a race condition with ongoing\n\t// edits. This searches for the completion text so that hopefully we're providing\n\t// an exact location in a known version of the document.\n\tif (doc) {\n\t\tconst found = find(doc.getText(), insertedText, stillInCodeNearMargin, insertionOffset);\n\t\tif (found.stillInCodeHeuristic) {\n\t\t\tinsertionOffset = found.foundOffset;\n\t\t}\n\t}\n\n\tfor (const citation of copilotAnnotations.ip_code_citations) {\n\t\tconst citationStart = computeCitationStart(\n\t\t\tfullCompletionText.length,\n\t\t\tinsertedText.length,\n\t\t\tcitation.start_offset\n\t\t);\n\t\tif (citationStart === undefined) {\n\t\t\tpostInsertionLogger.info(\n\t\t\t\tlogTarget,\n\t\t\t\t`Full completion for ${uri} contains a reference matching public code, but the partially inserted text did not include the match.`\n\t\t\t);\n\t\t\tcontinue;\n\t\t}\n\t\tconst offsetStart = insertionOffset + citationStart;\n\t\tconst start = doc?.positionAt(offsetStart);\n\t\tconst offsetEnd =\n\t\t\tinsertionOffset + computeCitationEnd(fullCompletionText.length, insertedText.length, citation.stop_offset);\n\t\tconst end = doc?.positionAt(offsetEnd);\n\t\tconst text = start && end ? doc?.getText({ start, end }) : '';\n\n\t\tawait citationManagerService.handleIPCodeCitation({\n\t\t\tinDocumentUri: uri,\n\t\t\toffsetStart,\n\t\t\toffsetEnd,\n\t\t\tversion: doc?.version,\n\t\t\tlocation: start && end ? { start, end } : undefined,\n\t\t\tmatchingText: text,\n\t\t\tdetails: citation.details.citations as IPCitationDetail[],\n\t\t});\n\t}\n}\n\nfunction computeCitationStart(\n\tcompletionLength: number,\n\tinsertedLength: number,\n\tcitationStartOffset: number\n): number | undefined {\n\tif (insertedLength < completionLength && citationStartOffset > insertedLength) {\n\t\treturn undefined;\n\t}\n\treturn citationStartOffset;\n}\n\nfunction computeCitationEnd(completionLength: number, insertedLength: number, citationStopOffset: number): number {\n\tif (insertedLength < completionLength) {\n\t\treturn Math.min(citationStopOffset, insertedLength);\n\t}\n\treturn citationStopOffset;\n}\n\nfunction find(documentText: string, completion: string, margin: number, offset: number) {\n\t// Compute the best alignment between a window of the document text and the completion\n\tconst window = documentText.substring(\n\t\tMath.max(0, offset - margin),\n\t\tMath.min(documentText.length, offset + completion.length + margin)\n\t);\n\tconst lexAlignment = lexEditDistance(window, completion);\n\tconst fraction = lexAlignment.lexDistance / lexAlignment.needleLexLength;\n\tconst { distance: charEditDistance } = editDistance(\n\t\twindow.substring(lexAlignment.startOffset, lexAlignment.endOffset),\n\t\tcompletion\n\t);\n\treturn {\n\t\trelativeLexEditDistance: fraction,\n\t\tcharEditDistance,\n\t\tcompletionLexLength: lexAlignment.needleLexLength,\n\t\tfoundOffset: lexAlignment.startOffset + Math.max(0, offset - margin),\n\t\tlexEditDistance: lexAlignment.lexDistance,\n\t\tstillInCodeHeuristic: fraction <= stillInCodeFraction ? 1 : 0,\n\t};\n}\n\nasync function checkStillInCode(\n\taccessor: ServicesAccessor,\n\tinsertionCategory: string,\n\tcompletion: string,\n\tinsertionOffset: number, // offset where the completion was inserted to\n\turi: string,\n\ttimeout: Timeout,\n\ttelemetryData: TelemetryWithExp,\n\ttracker: ChangeTracker,\n\tsuffixTracker: ChangeTracker\n) {\n\t// Get contents of file from file system\n\tconst ctx = accessor.get(ICompletionsContextService);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst logTarget = ctx.get(LogTarget);\n\tconst result = await ctx.get(FileReader).getOrReadTextDocument({ uri });\n\tif (result.status === 'valid') {\n\t\tconst document = result.document;\n\t\tconst documentText = document.getText();\n\n\t\t// We try twice, first very close to the insertion point, then a bit\n\t\t// further. This is to increase accuracy for short completions,\n\t\t// where the completion might appear elsewhere.\n\t\tlet finding = find(documentText, completion, stillInCodeNearMargin, tracker.offset);\n\t\tif (!finding.stillInCodeHeuristic) {\n\t\t\tfinding = find(documentText, completion, stillInCodeFarMargin, tracker.offset);\n\t\t}\n\t\t// Debug and log a binary decision\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`stillInCode: ${finding.stillInCodeHeuristic ? 'Found' : 'Not found'}! Completion '${completion}' in file ${uri\n\t\t\t}. lexEditDistance fraction was ${finding.relativeLexEditDistance}. Char edit distance was ${finding.charEditDistance\n\t\t\t}. Inserted at ${insertionOffset}, tracked at ${tracker.offset}, found at ${finding.foundOffset\n\t\t\t}. choiceIndex: ${telemetryData.properties.choiceIndex}`\n\t\t);\n\t\t// Log all the details for analysis\n\t\tconst customTelemetryData = telemetryData\n\t\t\t.extendedBy({}, { timeout: timeout.seconds, insertionOffset: insertionOffset, trackedOffset: tracker.offset })\n\t\t\t.extendedBy({}, finding);\n\t\tinstantiationService.invokeFunction(telemetry, insertionCategory + '.stillInCode', customTelemetryData);\n\n\t\tif (timeout.captureCode) {\n\t\t\tconst { prompt, capturedCode, terminationOffset } = await instantiationService.invokeFunction(\n\t\t\t\tcaptureCode,\n\t\t\t\turi,\n\t\t\t\tcustomTelemetryData,\n\t\t\t\ttracker.offset,\n\t\t\t\tsuffixTracker.offset\n\t\t\t);\n\t\t\tconst promptTelemetry = {\n\t\t\t\thypotheticalPromptJson: JSON.stringify({ prefix: prompt.prefix, context: prompt.context }),\n\t\t\t\thypotheticalPromptSuffixJson: JSON.stringify(prompt.suffix),\n\t\t\t};\n\n\t\t\tconst afterAcceptedTelemetry = telemetryData.extendedBy(\n\t\t\t\t{\n\t\t\t\t\t...promptTelemetry,\n\t\t\t\t\tcapturedCodeJson: JSON.stringify(capturedCode),\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\ttimeout: timeout.seconds,\n\t\t\t\t\tinsertionOffset: insertionOffset,\n\t\t\t\t\ttrackedOffset: tracker.offset,\n\t\t\t\t\tterminationOffsetInCapturedCode: terminationOffset,\n\t\t\t\t}\n\t\t\t);\n\t\t\tpostInsertionLogger.debug(\n\t\t\t\tlogTarget,\n\t\t\t\t`${insertionCategory}.capturedAfterAccepted choiceIndex: ${telemetryData.properties.choiceIndex}`,\n\t\t\t\tcustomTelemetryData\n\t\t\t);\n\t\t\tinstantiationService.invokeFunction(\n\t\t\t\ttelemetry,\n\t\t\t\tinsertionCategory + '.capturedAfterAccepted',\n\t\t\t\tafterAcceptedTelemetry,\n\t\t\t\tTelemetryStore.Enhanced\n\t\t\t);\n\t\t}\n\t}\n}\n",typescript,tab +2,453,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"2:45:14 PM [info] Activating crowd-code\n2:45:14 PM [info] Recording started\n2:45:14 PM [info] Initializing git provider using file system watchers...\n2:45:14 PM [info] Git repository found\n2:45:14 PM [info] Git provider initialized successfully\n2:45:14 PM [info] Initial git state: [object Object]\n",Log,tab +3,1807,"src/extension/completions-core/vscode-node/lib/src/postInsertion.ts",0,0,"",typescript,tab +4,5024,"src/extension/completions-core/vscode-node/lib/src/postInsertion.ts",1068,11397,"import { CopilotNamedAnnotationList } from './openai/stream';\nimport { contextIndentationFromText, indentationBlockFinished } from './prompt/parseBlock';\nimport { Prompt, extractPrompt } from './prompt/prompt';\nimport { fetchCitations } from './snippy/handlePostInsertion';\nimport { editDistance, lexEditDistance } from './suggestions/editDistance';\nimport { SuggestionStatus, computeCompletionText } from './suggestions/partialSuggestions';\nimport { TelemetryStore, TelemetryWithExp, telemetry, telemetryCatch } from './telemetry';\nimport { TextDocumentManager } from './textDocumentManager';\nimport { ICompletionsPromiseQueueService } from './util/promiseQueue';\nimport { ICompletionsRuntimeModeService } from './util/runtimeMode';\n\nconst postInsertionLogger = new Logger('postInsertion');\n\ntype Timeout = {\n\tseconds: number;\n\tcaptureCode: boolean;\n\tcaptureRejection: boolean;\n};\n// windows for telemetry checks, in seconds\n// captureCode = capture the code after acceptance,\n// captureRejection = capture the code after rejection\nconst captureTimeouts: Timeout[] = [\n\t{ seconds: 15, captureCode: false, captureRejection: false },\n\t{ seconds: 30, captureCode: true, captureRejection: true },\n\t{ seconds: 120, captureCode: false, captureRejection: false },\n\t{ seconds: 300, captureCode: false, captureRejection: false },\n\t{ seconds: 600, captureCode: false, captureRejection: false },\n];\n\n// No. of chars before/after insertion point to look for the completion\nconst stillInCodeNearMargin = 50;\nconst stillInCodeFarMargin = 1500;\n\n// If lex edit distance is below this fraction of completion length it is considered\n// in the code\nconst stillInCodeFraction = 0.5;\n\n// Number of characters captured after the insertion point.\n// Used only if we couldn't detect termination point with indent-based parsing.\nconst captureCodeMargin = 500;\n\nconst postInsertConfiguration: {\n\ttriggerPostInsertionSynchroneously: boolean;\n\tcaptureCode: boolean;\n\tcaptureRejection: boolean;\n} = {\n\ttriggerPostInsertionSynchroneously: false,\n\tcaptureCode: false,\n\tcaptureRejection: false,\n};\n\nasync function captureCode(\n\taccessor: ServicesAccessor,\n\turi: string,\n\tcompletionTelemetry: TelemetryWithExp,\n\toffset: number,\n\tsuffixOffset?: number\n): Promise<{ prompt: Prompt; capturedCode: string; terminationOffset: number }> {\n\tconst ctx = accessor.get(ICompletionsContextService);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst logTarget = ctx.get(LogTarget);\n\tconst result = await ctx.get(FileReader).getOrReadTextDocumentWithFakeClientProperties({ uri });\n\tif (result.status !== 'valid') {\n\t\tpostInsertionLogger.info(logTarget, `Could not get document for ${uri}. Maybe it was closed by the editor.`);\n\t\treturn {\n\t\t\tprompt: {\n\t\t\t\tprefix: '',\n\t\t\t\tsuffix: '',\n\t\t\t\tisFimEnabled: false,\n\t\t\t},\n\t\t\tcapturedCode: '',\n\t\t\tterminationOffset: 0,\n\t\t};\n\t}\n\tconst document = result.document;\n\tconst documentText = document.getText();\n\tconst documentTextBefore = documentText.substring(0, offset);\n\tconst position = document.positionAt(offset);\n\n\t// Treat the code before offset as the hypothetical prompt\n\tconst hypotheticalPromptResponse = await instantiationService.invokeFunction(extractPrompt,\n\t\tcompletionTelemetry.properties.headerRequestId,\n\t\tcreateCompletionState(document, position),\n\t\tcompletionTelemetry\n\t);\n\tconst hypotheticalPrompt =\n\t\thypotheticalPromptResponse.type === 'prompt'\n\t\t\t? hypotheticalPromptResponse.prompt\n\t\t\t: {\n\t\t\t\tprefix: documentTextBefore,\n\t\t\t\tsuffix: '',\n\t\t\t\tisFimEnabled: false,\n\t\t\t}; // TODO(eaftan): Pass an actual suffix when we're ready to support it\n\n\tif (hypotheticalPrompt.isFimEnabled && suffixOffset !== undefined) {\n\t\t// With FIM enabled, we can exactly determine capturedCode, suffix and prefix by propertly initialized trackers. No need to guess.\n\t\tconst capturedCode = documentText.substring(offset, suffixOffset);\n\t\thypotheticalPrompt.suffix = documentText.substring(suffixOffset);\n\n\t\treturn { prompt: hypotheticalPrompt, capturedCode, terminationOffset: 0 };\n\t} else {\n\t\t//Everything after the insertion point is hypothetical response we could get from AI\n\t\tconst hypotheticalResponse = documentText.substring(offset);\n\n\t\t//Try to find the termination offset in the hypothetical response using indentation based parsing\n\t\tconst contextIndent = contextIndentationFromText(documentTextBefore, offset, document.detectedLanguageId);\n\t\tconst indentTerminationFunction = indentationBlockFinished(contextIndent, undefined);\n\t\tconst terminationResult = indentTerminationFunction(hypotheticalResponse);\n\n\t\t//If we could detect termination of the indentation block, capture 2x length of detected suggestion\n\t\t//Otherwise capture a lot of characters\n\t\tconst maxOffset = Math.min(\n\t\t\tdocumentText.length,\n\t\t\toffset + (terminationResult ? terminationResult * 2 : captureCodeMargin)\n\t\t);\n\n\t\tconst capturedCode = documentText.substring(offset, maxOffset);\n\n\t\treturn { prompt: hypotheticalPrompt, capturedCode, terminationOffset: terminationResult ?? -1 };\n\t}\n}\n\nexport function postRejectionTasks(\n\taccessor: ServicesAccessor,\n\tinsertionCategory: PostInsertionCategory,\n\tinsertionOffset: number,\n\turi: string,\n\tcompletions: { completionText: string; completionTelemetryData: TelemetryWithExp }[]\n) {\n\tconst logTarget = accessor.get(ICompletionsContextService).get(LogTarget);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst telemetryService = accessor.get(ICompletionsTelemetryService);\n\tconst promiseQueueService = accessor.get(ICompletionsPromiseQueueService);\n\n\t//Send `.rejected` telemetry event for each rejected completion\n\tcompletions.forEach(({ completionText, completionTelemetryData }) => {\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`${insertionCategory}.rejected choiceIndex: ${completionTelemetryData.properties.choiceIndex}`\n\t\t);\n\t\tinstantiationService.invokeFunction(telemetryRejected, insertionCategory, completionTelemetryData);\n\t});\n\tconst positionTracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset - 1);\n\tconst suffixTracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset);\n\n\tconst checkInCode = async (t: Timeout) => {\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`Original offset: ${insertionOffset}, Tracked offset: ${positionTracker.offset}`\n\t\t);\n\t\tconst { completionTelemetryData } = completions[0];\n\n\t\tconst { prompt, capturedCode, terminationOffset } = await instantiationService.invokeFunction(captureCode,\n\t\t\turi,\n\t\t\tcompletionTelemetryData,\n\t\t\tpositionTracker.offset + 1,\n\t\t\tsuffixTracker.offset\n\t\t);\n\n\t\tconst promptTelemetry = {\n\t\t\thypotheticalPromptJson: JSON.stringify({ prefix: prompt.prefix, context: prompt.context }),\n\t\t\thypotheticalPromptSuffixJson: JSON.stringify(prompt.suffix),\n\t\t};\n\n\t\tconst customTelemetryData = completionTelemetryData.extendedBy(\n\t\t\t{\n\t\t\t\t...promptTelemetry,\n\t\t\t\tcapturedCodeJson: JSON.stringify(capturedCode),\n\t\t\t},\n\t\t\t{\n\t\t\t\ttimeout: t.seconds,\n\t\t\t\tinsertionOffset: insertionOffset,\n\t\t\t\ttrackedOffset: positionTracker.offset,\n\t\t\t\tterminationOffsetInCapturedCode: terminationOffset,\n\t\t\t}\n\t\t);\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`${insertionCategory}.capturedAfterRejected choiceIndex: ${completionTelemetryData.properties.choiceIndex}`,\n\t\t\tcustomTelemetryData\n\t\t);\n\t\tinstantiationService.invokeFunction(telemetry, insertionCategory + '.capturedAfterRejected', customTelemetryData, TelemetryStore.Enhanced);\n\t};\n\t// Capture the code typed after we detected that completion was rejected,\n\t// Uses first displayed completion as the source/seed of telemetry information.\n\tcaptureTimeouts\n\t\t.filter(t => t.captureRejection)\n\t\t.map(t =>\n\t\t\tpositionTracker.push(\n\t\t\t\ttelemetryCatch(telemetryService, promiseQueueService, () => checkInCode(t), 'postRejectionTasks'),\n\t\t\t\tt.seconds * 1000\n\t\t\t)\n\t\t);\n}\n\nexport function postInsertionTasks(\n\taccessor: ServicesAccessor,\n\tinsertionCategory: PostInsertionCategory,\n\tcompletionText: string,\n\tinsertionOffset: number,\n\turi: string,\n\ttelemetryData: TelemetryWithExp,\n\tsuggestionStatus: SuggestionStatus,\n\tcopilotAnnotations?: CopilotNamedAnnotationList\n) {\n\tconst logTarget = accessor.get(ICompletionsContextService).get(LogTarget);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst promiseQueueService = accessor.get(ICompletionsPromiseQueueService);\n\tconst telemetryService = accessor.get(ICompletionsTelemetryService);\n\tconst runtimeModeService = accessor.get(ICompletionsRuntimeModeService);\n\n\tconst telemetryDataWithStatus = telemetryData.extendedBy(\n\t\t{\n\t\t\tcompType: suggestionStatus.compType,\n\t\t},\n\t\t{\n\t\t\tcompCharLen: suggestionStatus.acceptedLength,\n\t\t\tnumLines: suggestionStatus.acceptedLines,\n\t\t}\n\t);\n\t// send "".accepted"" telemetry\n\tpostInsertionLogger.debug(\n\t\tlogTarget,\n\t\t`${insertionCategory}.accepted choiceIndex: ${telemetryDataWithStatus.properties.choiceIndex}`\n\t);\n\tinstantiationService.invokeFunction(telemetryAccepted, insertionCategory, telemetryDataWithStatus);\n\n\tconst fullCompletionText = completionText;\n\tcompletionText = computeCompletionText(completionText, suggestionStatus);\n\tconst trimmedCompletion = completionText.trim();\n\tconst tracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset);\n\tconst suffixTracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset + completionText.length);\n",typescript,content +5,5117,"TERMINAL",0,0,"",,terminal_command +6,5224,"src/extension/completions-core/vscode-node/lib/src/postInsertion.ts",0,0,"Switched from branch 'capture-tab-and-chat-interaction' to 'main'",typescript,git_branch_checkout +7,12049,"TERMINAL",0,0,"",,terminal_command +8,25220,"src/extension/completions-core/vscode-node/lib/src/postInsertion.ts",0,0,"Switched from branch 'main' to 'expose-oai-endpoint'",typescript,git_branch_checkout +9,28949,"package.json",0,0,"{\n\t""name"": ""copilot-chat"",\n\t""displayName"": ""GitHub Copilot Chat"",\n\t""description"": ""AI chat features powered by Copilot"",\n\t""version"": ""0.33.0"",\n\t""build"": ""1"",\n\t""internalAIKey"": ""1058ec22-3c95-4951-8443-f26c1f325911"",\n\t""completionsCoreVersion"": ""1.378.1799"",\n\t""internalLargeStorageAriaKey"": ""ec712b3202c5462fb6877acae7f1f9d7-c19ad55e-3e3c-4f99-984b-827f6d95bd9e-6917"",\n\t""ariaKey"": ""0c6ae279ed8443289764825290e4f9e2-1a736e7c-1324-4338-be46-fc2a58ae4d14-7255"",\n\t""buildType"": ""dev"",\n\t""publisher"": ""GitHub"",\n\t""homepage"": ""https://github.com/features/copilot?editor=vscode"",\n\t""license"": ""SEE LICENSE IN LICENSE.txt"",\n\t""repository"": {\n\t\t""type"": ""git"",\n\t\t""url"": ""https://github.com/microsoft/vscode-copilot-chat""\n\t},\n\t""bugs"": {\n\t\t""url"": ""https://github.com/microsoft/vscode/issues""\n\t},\n\t""qna"": ""https://github.com/github-community/community/discussions/categories/copilot"",\n\t""icon"": ""assets/copilot.png"",\n\t""pricing"": ""Trial"",\n\t""engines"": {\n\t\t""vscode"": ""^1.106.0-20251030"",\n\t\t""npm"": "">=9.0.0"",\n\t\t""node"": "">=22.14.0""\n\t},\n\t""categories"": [\n\t\t""AI"",\n\t\t""Chat"",\n\t\t""Programming Languages"",\n\t\t""Machine Learning""\n\t],\n\t""keywords"": [\n\t\t""ai"",\n\t\t""openai"",\n\t\t""codex"",\n\t\t""pilot"",\n\t\t""snippets"",\n\t\t""documentation"",\n\t\t""autocomplete"",\n\t\t""intellisense"",\n\t\t""refactor"",\n\t\t""javascript"",\n\t\t""python"",\n\t\t""typescript"",\n\t\t""php"",\n\t\t""go"",\n\t\t""golang"",\n\t\t""ruby"",\n\t\t""c++"",\n\t\t""c#"",\n\t\t""java"",\n\t\t""kotlin"",\n\t\t""co-pilot""\n\t],\n\t""badges"": [\n\t\t{\n\t\t\t""url"": ""https://img.shields.io/badge/GitHub%20Copilot-Subscription%20Required-orange"",\n\t\t\t""href"": ""https://github.com/github-copilot/signup?editor=vscode"",\n\t\t\t""description"": ""%github.copilot.badge.signUp%""\n\t\t},\n\t\t{\n\t\t\t""url"": ""https://img.shields.io/github/stars/github/copilot-docs?style=social"",\n\t\t\t""href"": ""https://github.com/github/copilot-docs"",\n\t\t\t""description"": ""%github.copilot.badge.star%""\n\t\t},\n\t\t{\n\t\t\t""url"": ""https://img.shields.io/youtube/channel/views/UC7c3Kb6jYCRj4JOHHZTxKsQ?style=social"",\n\t\t\t""href"": ""https://www.youtube.com/@GitHub/search?query=copilot"",\n\t\t\t""description"": ""%github.copilot.badge.youtube%""\n\t\t},\n\t\t{\n\t\t\t""url"": ""https://img.shields.io/twitter/follow/github?style=social"",\n\t\t\t""href"": ""https://twitter.com/github"",\n\t\t\t""description"": ""%github.copilot.badge.twitter%""\n\t\t}\n\t],\n\t""activationEvents"": [\n\t\t""onStartupFinished"",\n\t\t""onLanguageModelChat:copilot"",\n\t\t""onUri"",\n\t\t""onFileSystem:ccreq"",\n\t\t""onFileSystem:ccsettings""\n\t],\n\t""main"": ""./dist/extension"",\n\t""l10n"": ""./l10n"",\n\t""enabledApiProposals"": [\n\t\t""extensionsAny"",\n\t\t""newSymbolNamesProvider"",\n\t\t""interactive"",\n\t\t""codeActionAI"",\n\t\t""activeComment"",\n\t\t""commentReveal"",\n\t\t""contribCommentThreadAdditionalMenu"",\n\t\t""contribCommentsViewThreadMenus"",\n\t\t""documentFiltersExclusive"",\n\t\t""embeddings"",\n\t\t""findTextInFiles"",\n\t\t""findTextInFiles2"",\n\t\t""findFiles2@2"",\n\t\t""textSearchProvider"",\n\t\t""terminalDataWriteEvent"",\n\t\t""terminalExecuteCommandEvent"",\n\t\t""terminalSelection"",\n\t\t""terminalQuickFixProvider"",\n\t\t""mappedEditsProvider"",\n\t\t""aiRelatedInformation"",\n\t\t""aiSettingsSearch"",\n\t\t""chatParticipantAdditions"",\n\t\t""chatEditing"",\n\t\t""defaultChatParticipant@4"",\n\t\t""contribSourceControlInputBoxMenu"",\n\t\t""authLearnMore"",\n\t\t""testObserver"",\n\t\t""aiTextSearchProvider@2"",\n\t\t""chatParticipantPrivate@11"",\n\t\t""chatProvider@4"",\n\t\t""contribDebugCreateConfiguration"",\n\t\t""chatReferenceDiagnostic"",\n\t\t""textSearchProvider2"",\n\t\t""chatReferenceBinaryData"",\n\t\t""languageModelSystem"",\n\t\t""languageModelCapabilities"",\n\t\t""inlineCompletionsAdditions"",\n\t\t""chatStatusItem"",\n\t\t""taskProblemMatcherStatus"",\n\t\t""contribLanguageModelToolSets"",\n\t\t""textDocumentChangeReason"",\n\t\t""resolvers"",\n\t\t""taskExecutionTerminal"",\n\t\t""dataChannels"",\n\t\t""languageModelThinkingPart"",\n\t\t""chatSessionsProvider@3"",\n\t\t""devDeviceId"",\n\t\t""contribEditorContentMenu""\n\t],\n\t""contributes"": {\n\t\t""languageModelTools"": [\n\t\t\t{\n\t\t\t\t""name"": ""copilot_searchCodebase"",\n\t\t\t\t""toolReferenceName"": ""codebase"",\n\t\t\t\t""displayName"": ""%copilot.tools.searchCodebase.name%"",\n\t\t\t\t""icon"": ""$(folder)"",\n\t\t\t\t""userDescription"": ""%copilot.codebase.tool.description%"",\n\t\t\t\t""modelDescription"": ""Run a natural language search for relevant code or documentation comments from the user's current workspace. Returns relevant code snippets from the user's current workspace if it is large, or the full contents of the workspace if it is small."",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""codesearch"",\n\t\t\t\t\t""vscode_codesearch""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""query"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The query to search the codebase for. Should contain all relevant context. Should ideally be text that might appear in the codebase, such as function names, variable names, or comments.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""query""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""runSubagent"",\n\t\t\t\t""toolReferenceName"": ""runSubagent"",\n\t\t\t\t""displayName"": ""%copilot.tools.runSubagent.name%"",\n\t\t\t\t""canBeReferencedInPrompt"": true,\n\t\t\t\t""userDescription"": ""%copilot.tools.runSubagent.description%"",\n\t\t\t\t""modelDescription"": ""Launch a new agent to handle complex, multi-step tasks autonomously. This tool is good at researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries, use this agent to perform the search for you.\n\n- Agents do not run async or in the background, you will wait for the agent's result.\n- When the agent is done, it will return a single message back to you. The result returned by the agent is not visible to the user. To show the user the result, you should send a text message back to the user with a concise summary of the result.\n - Each agent invocation is stateless. You will not be able to send additional messages to the agent, nor will the agent be able to communicate with you outside of its final report. Therefore, your prompt should contain a highly detailed task description for the agent to perform autonomously and you should specify exactly what information the agent should return back to you in its final and only message to you.\n - The agent's outputs should generally be trusted\n - Clearly tell the agent whether you expect it to write code or just to do research (search, file reads, web fetches, etc.), since it is not aware of the user's intent"",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""prompt"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""A detailed description of the task for the agent to perform""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""description"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""A short (3-5 word) description of the task""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""prompt"",\n\t\t\t\t\t\t""description""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_searchWorkspaceSymbols"",\n\t\t\t\t""toolReferenceName"": ""symbols"",\n\t\t\t\t""displayName"": ""%copilot.tools.searchWorkspaceSymbols.name%"",\n\t\t\t\t""icon"": ""$(symbol)"",\n\t\t\t\t""userDescription"": ""%copilot.workspaceSymbols.tool.description%"",\n\t\t\t\t""modelDescription"": ""Search the user's workspace for code symbols using language services. Use this tool when the user is looking for a specific symbol in their workspace."",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""vscode_codesearch""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""symbolName"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The symbol to search for, such as a function name, class name, or variable name.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""symbolName""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_listCodeUsages"",\n\t\t\t\t""toolReferenceName"": ""usages"",\n\t\t\t\t""displayName"": ""%copilot.tools.listCodeUsages.name%"",\n\t\t\t\t""icon"": ""$(references)"",\n\t\t\t\t""canBeReferencedInPrompt"": true,\n\t\t\t\t""userDescription"": ""%copilot.listCodeUsages.tool.description%"",\n\t\t\t\t""modelDescription"": ""Request to list all usages (references, definitions, implementations etc) of a function, class, method, variable etc. Use this tool when \n1. Looking for a sample implementation of an interface or class\n2. Checking how a function is used throughout the codebase.\n3. Including and updating all usages when changing a function, method, or constructor"",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""vscode_codesearch""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""symbolName"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The name of the symbol, such as a function name, class name, method name, variable name, etc.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""filePaths"": {\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""description"": ""One or more file paths which likely contain the definition of the symbol. For instance the file which declares a class or function. This is optional but will speed up the invocation of this tool and improve the quality of its output."",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""symbolName""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_getVSCodeAPI"",\n\t\t\t\t""toolReferenceName"": ""vscodeAPI"",\n\t\t\t\t""displayName"": ""%copilot.tools.getVSCodeAPI.name%"",\n\t\t\t\t""icon"": ""$(references)"",\n\t\t\t\t""userDescription"": ""%copilot.vscode.tool.description%"",\n\t\t\t\t""modelDescription"": ""Get comprehensive VS Code API documentation and references for extension development. This tool provides authoritative documentation for VS Code's extensive API surface, including proposed APIs, contribution points, and best practices. Use this tool for understanding complex VS Code API interactions.\n\nWhen to use this tool:\n- User asks about specific VS Code APIs, interfaces, or extension capabilities\n- Need documentation for VS Code extension contribution points (commands, views, settings, etc.)\n- Questions about proposed APIs and their usage patterns\n- Understanding VS Code extension lifecycle, activation events, and packaging\n- Best practices for VS Code extension development architecture\n- API examples and code patterns for extension features\n- Troubleshooting extension-specific issues or API limitations\n\nWhen NOT to use this tool:\n- Creating simple standalone files or scripts unrelated to VS Code extensions\n- General programming questions not specific to VS Code extension development\n- Questions about using VS Code as an editor (user-facing features)\n- Non-extension related development tasks\n- File creation or editing that doesn't involve VS Code extension APIs\n\nCRITICAL usage guidelines:\n1. Always include specific API names, interfaces, or concepts in your query\n2. Mention the extension feature you're trying to implement\n3. Include context about proposed vs stable APIs when relevant\n4. Reference specific contribution points when asking about extension manifest\n5. Be specific about the VS Code version or API version when known\n\nScope: This tool is for EXTENSION DEVELOPMENT ONLY - building tools that extend VS Code itself, not for general file creation or non-extension programming tasks."",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""query"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The query to search vscode documentation for. Should contain all relevant context.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""query""\n\t\t\t\t\t]\n\t\t\t\t},\n\t\t\t\t""tags"": [],\n\t\t\t\t""canBeReferencedInPrompt"": true\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_findFiles"",\n\t\t\t\t""toolReferenceName"": ""fileSearch"",\n\t\t\t\t""displayName"": ""%copilot.tools.findFiles.name%"",\n\t\t\t\t""modelDescription"": ""Search for files in the workspace by glob pattern. This only returns the paths of matching files. Use this tool when you know the exact filename pattern of the files you're searching for. Glob patterns match from the root of the workspace folder. Examples:\n- **/*.{js,ts} to match all js/ts files in the workspace.\n- src/** to match all files under the top-level src folder.\n- **/foo/**/*.js to match all js files under any foo folder in the workspace."",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""vscode_codesearch""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""query"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""Search for files with names or paths matching this glob pattern.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""maxResults"": {\n\t\t\t\t\t\t\t""type"": ""number"",\n\t\t\t\t\t\t\t""description"": ""The maximum number of results to return. Do not use this unless necessary, it can slow things down. By default, only some matches are returned. If you use this and don't see what you're looking for, you can try again with a more specific query or a larger maxResults.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""query""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_findTextInFiles"",\n\t\t\t\t""toolReferenceName"": ""textSearch"",\n\t\t\t\t""displayName"": ""%copilot.tools.findTextInFiles.name%"",\n\t\t\t\t""modelDescription"": ""Do a fast text search in the workspace. Use this tool when you want to search with an exact string or regex. If you are not sure what words will appear in the workspace, prefer using regex patterns with alternation (|) or character classes to search for multiple potential words at once instead of making separate searches. For example, use 'function|method|procedure' to look for all of those words at once. Use includePattern to search within files matching a specific pattern, or in a specific file, using a relative path. Use this tool when you want to see an overview of a particular file, instead of using read_file many times to look for code within a file."",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""vscode_codesearch""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""query"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The pattern to search for in files in the workspace. Use regex with alternation (e.g., 'word1|word2|word3') or character classes to find multiple potential words in a single search. Be sure to set the isRegexp property properly to declare whether it's a regex or plain text pattern. Is case-insensitive.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""isRegexp"": {\n\t\t\t\t\t\t\t""type"": ""boolean"",\n\t\t\t\t\t\t\t""description"": ""Whether the pattern is a regex.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""includePattern"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""Search files matching this glob pattern. Will be applied to the relative path of files within the workspace. To search recursively inside a folder, use a proper glob pattern like \""src/folder/**\"". Do not use | in includePattern.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""maxResults"": {\n\t\t\t\t\t\t\t""type"": ""number"",\n\t\t\t\t\t\t\t""description"": ""The maximum number of results to return. Do not use this unless necessary, it can slow things down. By default, only some matches are returned. If you use this and don't see what you're looking for, you can try again with a more specific query or a larger maxResults.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""query"",\n\t\t\t\t\t\t""isRegexp""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_applyPatch"",\n\t\t\t\t""displayName"": ""%copilot.tools.applyPatch.name%"",\n\t\t\t\t""toolReferenceName"": ""applyPatch"",\n\t\t\t\t""userDescription"": ""%copilot.tools.applyPatch.description%"",\n\t\t\t\t""modelDescription"": ""Edit text files. Do not use this tool to edit Jupyter notebooks. `apply_patch` allows you to execute a diff/patch against a text file, but the format of the diff specification is unique to this task, so pay careful attention to these instructions. To use the `apply_patch` command, you should pass a message of the following structure as \""input\"":\n\n*** Begin Patch\n[YOUR_PATCH]\n*** End Patch\n\nWhere [YOUR_PATCH] is the actual content of your patch, specified in the following V4A diff format.\n\n*** [ACTION] File: [/absolute/path/to/file] -> ACTION can be one of Add, Update, or Delete.\nAn example of a message that you might pass as \""input\"" to this function, in order to apply a patch, is shown below.\n\n*** Begin Patch\n*** Update File: /Users/someone/pygorithm/searching/binary_search.py\n@@class BaseClass\n@@ def search():\n- pass\n+ raise NotImplementedError()\n\n@@class Subclass\n@@ def search():\n- pass\n+ raise NotImplementedError()\n\n*** End Patch\nDo not use line numbers in this diff format."",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""input"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The edit patch to apply.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""explanation"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""A short description of what the tool call is aiming to achieve.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""input"",\n\t\t\t\t\t\t""explanation""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_readFile"",\n\t\t\t\t""toolReferenceName"": ""readFile"",\n\t\t\t\t""displayName"": ""%copilot.tools.readFile.name%"",\n\t\t\t\t""modelDescription"": ""Read the contents of a file.\n\nYou must specify the line range you're interested in. Line numbers are 1-indexed. If the file contents returned are insufficient for your task, you may call this tool again to retrieve more content. Prefer reading larger ranges over doing many small reads."",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""vscode_codesearch""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePath"": {\n\t\t\t\t\t\t\t""description"": ""The absolute path of the file to read."",\n\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""startLine"": {\n\t\t\t\t\t\t\t""type"": ""number"",\n\t\t\t\t\t\t\t""description"": ""The line number to start reading from, 1-based.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""endLine"": {\n\t\t\t\t\t\t\t""type"": ""number"",\n\t\t\t\t\t\t\t""description"": ""The inclusive line number to end reading at, 1-based.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""filePath"",\n\t\t\t\t\t\t""startLine"",\n\t\t\t\t\t\t""endLine""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_listDirectory"",\n\t\t\t\t""toolReferenceName"": ""listDirectory"",\n\t\t\t\t""displayName"": ""%copilot.tools.listDirectory.name%"",\n\t\t\t\t""modelDescription"": ""List the contents of a directory. Result will have the name of the child. If the name ends in /, it's a folder, otherwise a file"",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""vscode_codesearch""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""path"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The absolute path to the directory to list.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""path""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_getErrors"",\n\t\t\t\t""displayName"": ""%copilot.tools.getErrors.name%"",\n\t\t\t\t""toolReferenceName"": ""problems"",\n\t\t\t\t""canBeReferencedInPrompt"": true,\n\t\t\t\t""icon"": ""$(error)"",\n\t\t\t\t""userDescription"": ""%copilot.tools.errors.description%"",\n\t\t\t\t""modelDescription"": ""Get any compile or lint errors in a specific file or across all files. If the user mentions errors or problems in a file, they may be referring to these. Use the tool to see the same errors that the user is seeing. If the user asks you to analyze all errors, or does not specify a file, use this tool to gather errors for all files. Also use this tool after editing a file to validate the change."",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePaths"": {\n\t\t\t\t\t\t\t""description"": ""The absolute paths to the files or folders to check for errors. Omit 'filePaths' when retrieving all errors."",\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_readProjectStructure"",\n\t\t\t\t""displayName"": ""%copilot.tools.readProjectStructure.name%"",\n\t\t\t\t""modelDescription"": ""Get a file tree representation of the workspace."",\n\t\t\t\t""tags"": []\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_getChangedFiles"",\n\t\t\t\t""displayName"": ""%copilot.tools.getChangedFiles.name%"",\n\t\t\t\t""toolReferenceName"": ""changes"",\n\t\t\t\t""icon"": ""$(diff)"",\n\t\t\t\t""canBeReferencedInPrompt"": true,\n\t\t\t\t""userDescription"": ""%copilot.tools.changes.description%"",\n\t\t\t\t""modelDescription"": ""Get git diffs of current file changes in a git repository. Don't forget that you can use run_in_terminal to run git commands in a terminal as well."",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""vscode_codesearch""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""repositoryPath"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The absolute path to the git repository to look for changes in. If not provided, the active git repository will be used.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""sourceControlState"": {\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t""enum"": [\n\t\t\t\t\t\t\t\t\t""staged"",\n\t\t\t\t\t\t\t\t\t""unstaged"",\n\t\t\t\t\t\t\t\t\t""merge-conflicts""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t""description"": ""The kinds of git state to filter by. Allowed values are: 'staged', 'unstaged', and 'merge-conflicts'. If not provided, all states will be included.""\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_testFailure"",\n\t\t\t\t""toolReferenceName"": ""testFailure"",\n\t\t\t\t""displayName"": ""%copilot.tools.testFailure.name%"",\n\t\t\t\t""icon"": ""$(beaker)"",\n\t\t\t\t""userDescription"": ""%copilot.testFailure.tool.description%"",\n\t\t\t\t""modelDescription"": ""Includes test failure information in the prompt."",\n\t\t\t\t""inputSchema"": {},\n\t\t\t\t""tags"": [\n\t\t\t\t\t""vscode_editing_with_tests"",\n\t\t\t\t\t""enable_other_tool_copilot_readFile"",\n\t\t\t\t\t""enable_other_tool_copilot_listDirectory"",\n\t\t\t\t\t""enable_other_tool_copilot_findFiles"",\n\t\t\t\t\t""enable_other_tool_copilot_runTests""\n\t\t\t\t],\n\t\t\t\t""canBeReferencedInPrompt"": true\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_updateUserPreferences"",\n\t\t\t\t""toolReferenceName"": ""updateUserPreferences"",\n\t\t\t\t""displayName"": ""%copilot.tools.updateUserPreferences.name%"",\n\t\t\t\t""modelDescription"": ""Update the user's preferences file with new information about the user and their coding preferences, based on the current chat history."",\n\t\t\t\t""canBeReferencedInPrompt"": true,\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""facts"": {\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t""description"": ""An array of new user preferences to remember.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""facts""\n\t\t\t\t\t]\n\t\t\t\t},\n\t\t\t\t""when"": ""config.github.copilot.chat.enableUserPreferences""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_createNewWorkspace"",\n\t\t\t\t""displayName"": ""%github.copilot.tools.createNewWorkspace.name%"",\n\t\t\t\t""toolReferenceName"": ""newWorkspace"",\n\t\t\t\t""icon"": ""$(new-folder)"",\n\t\t\t\t""userDescription"": ""%github.copilot.tools.createNewWorkspace.userDescription%"",\n\t\t\t\t""when"": ""config.github.copilot.chat.newWorkspaceCreation.enabled"",\n\t\t\t\t""modelDescription"": ""Get comprehensive setup steps to help the user create complete project structures in a VS Code workspace. This tool is designed for full project initialization and scaffolding, not for creating individual files.\n\nWhen to use this tool:\n- User wants to create a new complete project from scratch\n- Setting up entire project frameworks (TypeScript projects, React apps, Node.js servers, etc.)\n- Initializing Model Context Protocol (MCP) servers with full structure\n- Creating VS Code extensions with proper scaffolding\n- Setting up Next.js, Vite, or other framework-based projects\n- User asks for \""new project\"", \""create a workspace\"", \""set up a [framework] project\""\n- Need to establish complete development environment with dependencies, config files, and folder structure\n\nWhen NOT to use this tool:\n- Creating single files or small code snippets\n- Adding individual files to existing projects\n- Making modifications to existing codebases\n- User asks to \""create a file\"" or \""add a component\""\n- Simple code examples or demonstrations\n- Debugging or fixing existing code\n\nThis tool provides complete project setup including:\n- Folder structure creation\n- Package.json and dependency management\n- Configuration files (tsconfig, eslint, etc.)\n- Initial boilerplate code\n- Development environment setup\n- Build and run instructions\n\nUse other file creation tools for individual files within existing projects."",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""query"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The query to use to generate the new workspace. This should be a clear and concise description of the workspace the user wants to create.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""query""\n\t\t\t\t\t]\n\t\t\t\t},\n\t\t\t\t""tags"": [\n\t\t\t\t\t""enable_other_tool_install_extension"",\n\t\t\t\t\t""enable_other_tool_get_project_setup_info""\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_getProjectSetupInfo"",\n\t\t\t\t""displayName"": ""%github.copilot.tools.getProjectSetupInfo.name%"",\n\t\t\t\t""when"": ""config.github.copilot.chat.newWorkspaceCreation.enabled"",\n\t\t\t\t""toolReferenceName"": ""getProjectSetupInfo"",\n\t\t\t\t""modelDescription"": ""Do not call this tool without first calling the tool to create a workspace. This tool provides a project setup information for a Visual Studio Code workspace based on a project type and programming language."",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""projectType"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The type of project to create. Supported values are: 'python-script', 'python-project', 'mcp-server', 'model-context-protocol-server', 'vscode-extension', 'next-js', 'vite' and 'other'""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""projectType""\n\t\t\t\t\t]\n\t\t\t\t},\n\t\t\t\t""tags"": []\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_installExtension"",\n\t\t\t\t""displayName"": ""Install Extension in VS Code"",\n\t\t\t\t""when"": ""config.github.copilot.chat.newWorkspaceCreation.enabled"",\n\t\t\t\t""toolReferenceName"": ""installExtension"",\n\t\t\t\t""modelDescription"": ""Install an extension in VS Code. Use this tool to install an extension in Visual Studio Code as part of a new workspace creation process only."",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""id"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The ID of the extension to install. This should be in the format ..""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""name"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The name of the extension to install. This should be a clear and concise description of the extension.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""id"",\n\t\t\t\t\t\t""name""\n\t\t\t\t\t]\n\t\t\t\t},\n\t\t\t\t""tags"": []\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_runVscodeCommand"",\n\t\t\t\t""displayName"": ""Run VS Code Command"",\n\t\t\t\t""when"": ""config.github.copilot.chat.newWorkspaceCreation.enabled"",\n\t\t\t\t""toolReferenceName"": ""runVscodeCommand"",\n\t\t\t\t""modelDescription"": ""Run a command in VS Code. Use this tool to run a command in Visual Studio Code as part of a new workspace creation process only."",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""commandId"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The ID of the command to execute. This should be in the format .""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""name"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The name of the command to execute. This should be a clear and concise description of the command.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""args"": {\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""description"": ""The arguments to pass to the command. This should be an array of strings."",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""commandId"",\n\t\t\t\t\t\t""name""\n\t\t\t\t\t]\n\t\t\t\t},\n\t\t\t\t""tags"": []\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_createNewJupyterNotebook"",\n\t\t\t\t""displayName"": ""Create New Jupyter Notebook"",\n\t\t\t\t""icon"": ""$(notebook)"",\n\t\t\t\t""toolReferenceName"": ""newJupyterNotebook"",\n\t\t\t\t""modelDescription"": ""Generates a new Jupyter Notebook (.ipynb) in VS Code. Jupyter Notebooks are interactive documents commonly used for data exploration, analysis, visualization, and combining code with narrative text. Prefer creating plain Python files or similar unless a user explicitly requests creating a new Jupyter Notebook or already has a Jupyter Notebook opened or exists in the workspace."",\n\t\t\t\t""userDescription"": ""%copilot.tools.newJupyterNotebook.description%"",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""query"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The query to use to generate the jupyter notebook. This should be a clear and concise description of the notebook the user wants to create.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""query""\n\t\t\t\t\t]\n\t\t\t\t},\n\t\t\t\t""tags"": []\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_insertEdit"",\n\t\t\t\t""toolReferenceName"": ""insertEdit"",\n\t\t\t\t""displayName"": ""%copilot.tools.insertEdit.name%"",\n\t\t\t\t""modelDescription"": ""Insert new code into an existing file in the workspace. Use this tool once per file that needs to be modified, even if there are multiple changes for a file. Generate the \""explanation\"" property first.\nThe system is very smart and can understand how to apply your edits to the files, you just need to provide minimal hints.\nAvoid repeating existing code, instead use comments to represent regions of unchanged code. Be as concise as possible. For example:\n// ...existing code...\n{ changed code }\n// ...existing code...\n{ changed code }\n// ...existing code...\n\nHere is an example of how you should use format an edit to an existing Person class:\nclass Person {\n\t// ...existing code...\n\tage: number;\n\t// ...existing code...\n\tgetAge() {\n\treturn this.age;\n\t}\n}"",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""explanation"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""A short explanation of the edit being made.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""filePath"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""An absolute path to the file to edit.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""code"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The code change to apply to the file.\nThe system is very smart and can understand how to apply your edits to the files, you just need to provide minimal hints.\nAvoid repeating existing code, instead use comments to represent regions of unchanged code. Be as concise as possible. For example:\n// ...existing code...\n{ changed code }\n// ...existing code...\n{ changed code }\n// ...existing code...\n\nHere is an example of how you should use format an edit to an existing Person class:\nclass Person {\n\t// ...existing code...\n\tage: number;\n\t// ...existing code...\n\tgetAge() {\n\t\treturn this.age;\n\t}\n}""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""explanation"",\n\t\t\t\t\t\t""filePath"",\n\t\t\t\t\t\t""code""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_createFile"",\n\t\t\t\t""toolReferenceName"": ""createFile"",\n\t\t\t\t""displayName"": ""%copilot.tools.createFile.name%"",\n\t\t\t\t""userDescription"": ""%copilot.tools.createFile.description%"",\n\t\t\t\t""modelDescription"": ""This is a tool for creating a new file in the workspace. The file will be created with the specified content. The directory will be created if it does not already exist. Never use this tool to edit a file that already exists."",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePath"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The absolute path to the file to create.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""content"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The content to write to the file.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""filePath"",\n\t\t\t\t\t\t""content""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_createDirectory"",\n\t\t\t\t""toolReferenceName"": ""createDirectory"",\n\t\t\t\t""displayName"": ""%copilot.tools.createDirectory.name%"",\n\t\t\t\t""userDescription"": ""%copilot.tools.createDirectory.description%"",\n\t\t\t\t""modelDescription"": ""Create a new directory structure in the workspace. Will recursively create all directories in the path, like mkdir -p. You do not need to use this tool before using create_file, that tool will automatically create the needed directories."",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""dirPath"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The absolute path to the directory to create.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""dirPath""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_openSimpleBrowser"",\n\t\t\t\t""displayName"": ""%copilot.tools.openSimpleBrowser.name%"",\n\t\t\t\t""modelDescription"": ""Preview a website or open a URL in the editor's Simple Browser. Useful for quickly viewing locally hosted websites, demos, or resources without leaving the coding environment."",\n\t\t\t\t""userDescription"": ""%copilot.tools.openSimpleBrowser.description%"",\n\t\t\t\t""toolReferenceName"": ""openSimpleBrowser"",\n\t\t\t\t""canBeReferencedInPrompt"": true,\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""url"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The website URL to preview or open in the Simple Browser inside the editor. Must be either an http or https URL""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""url""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_replaceString"",\n\t\t\t\t""toolReferenceName"": ""replaceString"",\n\t\t\t\t""displayName"": ""%copilot.tools.replaceString.name%"",\n\t\t\t\t""modelDescription"": ""This is a tool for making edits in an existing file in the workspace. For moving or renaming files, use run in terminal tool with the 'mv' command instead. For larger edits, split them into smaller edits and call the edit tool multiple times to ensure accuracy. Before editing, always ensure you have the context to understand the file's contents and context. To edit a file, provide: 1) filePath (absolute path), 2) oldString (MUST be the exact literal text to replace including all whitespace, indentation, newlines, and surrounding code etc), and 3) newString (MUST be the exact literal text to replace \\`oldString\\` with (also including all whitespace, indentation, newlines, and surrounding code etc.). Ensure the resulting code is correct and idiomatic.). Each use of this tool replaces exactly ONE occurrence of oldString.\n\nCRITICAL for \\`oldString\\`: Must uniquely identify the single instance to change. Include at least 3 lines of context BEFORE and AFTER the target text, matching whitespace and indentation precisely. If this string matches multiple locations, or does not match exactly, the tool will fail. Never use 'Lines 123-456 omitted' from summarized documents or ...existing code... comments in the oldString or newString."",\n\t\t\t\t""when"": ""!config.github.copilot.chat.disableReplaceTool"",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePath"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""An absolute path to the file to edit.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""oldString"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The exact literal text to replace, preferably unescaped. For single replacements (default), include at least 3 lines of context BEFORE and AFTER the target text, matching whitespace and indentation precisely. For multiple replacements, specify expected_replacements parameter. If this string is not the exact literal text (i.e. you escaped it) or does not match exactly, the tool will fail.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""newString"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The exact literal text to replace `old_string` with, preferably unescaped. Provide the EXACT text. Ensure the resulting code is correct and idiomatic.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""filePath"",\n\t\t\t\t\t\t""oldString"",\n\t\t\t\t\t\t""newString""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_multiReplaceString"",\n\t\t\t\t""toolReferenceName"": ""multiReplaceString"",\n\t\t\t\t""displayName"": ""%copilot.tools.multiReplaceString.name%"",\n\t\t\t\t""modelDescription"": ""This tool allows you to apply multiple replace_string_in_file operations in a single call, which is more efficient than calling replace_string_in_file multiple times. It takes an array of replacement operations and applies them sequentially. Each replacement operation has the same parameters as replace_string_in_file: filePath, oldString, newString, and explanation. This tool is ideal when you need to make multiple edits across different files or multiple edits in the same file. The tool will provide a summary of successful and failed operations."",\n\t\t\t\t""when"": ""!config.github.copilot.chat.disableReplaceTool"",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""explanation"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""A brief explanation of what the multi-replace operation will accomplish.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""replacements"": {\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""description"": ""An array of replacement operations to apply sequentially."",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t""explanation"": {\n\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t""description"": ""A brief explanation of this specific replacement operation.""\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""filePath"": {\n\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t""description"": ""An absolute path to the file to edit.""\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""oldString"": {\n\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t""description"": ""The exact literal text to replace, preferably unescaped. Include at least 3 lines of context BEFORE and AFTER the target text, matching whitespace and indentation precisely. If this string is not the exact literal text or does not match exactly, this replacement will fail.""\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""newString"": {\n\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t""description"": ""The exact literal text to replace `oldString` with, preferably unescaped. Provide the EXACT text. Ensure the resulting code is correct and idiomatic.""\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t""explanation"",\n\t\t\t\t\t\t\t\t\t""filePath"",\n\t\t\t\t\t\t\t\t\t""oldString"",\n\t\t\t\t\t\t\t\t\t""newString""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t""minItems"": 1\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""explanation"",\n\t\t\t\t\t\t""replacements""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_editNotebook"",\n\t\t\t\t""toolReferenceName"": ""editNotebook"",\n\t\t\t\t""displayName"": ""%copilot.tools.editNotebook.name%"",\n\t\t\t\t""modelDescription"": ""This is a tool for editing an existing Notebook file in the workspace. Generate the \""explanation\"" property first.\nThe system is very smart and can understand how to apply your edits to the notebooks.\nWhen updating the content of an existing cell, ensure newCode preserves whitespace and indentation exactly and does NOT include any code markers such as (...existing code...)."",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""enable_other_tool_copilot_getNotebookSummary""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePath"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""An absolute path to the notebook file to edit, or the URI of a untitled, not yet named, file, such as `untitled:Untitled-1.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""cellId"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""Id of the cell that needs to be deleted or edited. Use the value `TOP`, `BOTTOM` when inserting a cell at the top or bottom of the notebook, else provide the id of the cell after which a new cell is to be inserted. Remember, if a cellId is provided and editType=insert, then a cell will be inserted after the cell with the provided cellId.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""newCode"": {\n\t\t\t\t\t\t\t""anyOf"": [\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t""description"": ""The code for the new or existing cell to be edited. Code should not be wrapped within tags. Do NOT include code markers such as (...existing code...) to indicate existing code.""\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t""description"": ""The code for the new or existing cell to be edited. Code should not be wrapped within tags""\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""language"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The language of the cell. `markdown`, `python`, `javascript`, `julia`, etc.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""editType"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""enum"": [\n\t\t\t\t\t\t\t\t""insert"",\n\t\t\t\t\t\t\t\t""delete"",\n\t\t\t\t\t\t\t\t""edit""\n\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t""description"": ""The operation peformed on the cell, whether `insert`, `delete` or `edit`.\nUse the `editType` field to specify the operation: `insert` to add a new cell, `edit` to modify an existing cell's content, and `delete` to remove a cell.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""filePath"",\n\t\t\t\t\t\t""editType"",\n\t\t\t\t\t\t""cellId""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_runNotebookCell"",\n\t\t\t\t""displayName"": ""%copilot.tools.runNotebookCell.name%"",\n\t\t\t\t""toolReferenceName"": ""runCell"",\n\t\t\t\t""icon"": ""$(play)"",\n\t\t\t\t""modelDescription"": ""This is a tool for running a code cell in a notebook file directly in the notebook editor. The output from the execution will be returned. Code cells should be run as they are added or edited when working through a problem to bring the kernel state up to date and ensure the code executes successfully. Code cells are ready to run and don't require any pre-processing. If asked to run the first cell in a notebook, you should run the first code cell since markdown cells cannot be executed. NOTE: Avoid executing Markdown cells or providing Markdown cell IDs, as Markdown cells cannot be executed."",\n\t\t\t\t""userDescription"": ""%copilot.tools.runNotebookCell.description%"",\n\t\t\t\t""tags"": [\n\t\t\t\t\t""enable_other_tool_copilot_getNotebookSummary""\n\t\t\t\t],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePath"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""An absolute path to the notebook file with the cell to run, or the URI of a untitled, not yet named, file, such as `untitled:Untitled-1.ipynb""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""reason"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""An optional explanation of why the cell is being run. This will be shown to the user before the tool is run and is not necessary if it's self-explanatory.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""cellId"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The ID for the code cell to execute. Avoid providing markdown cell IDs as nothing will be executed.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""continueOnError"": {\n\t\t\t\t\t\t\t""type"": ""boolean"",\n\t\t\t\t\t\t\t""description"": ""Whether or not execution should continue for remaining cells if an error is encountered. Default to false unless instructed otherwise.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""filePath"",\n\t\t\t\t\t\t""cellId""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_getNotebookSummary"",\n\t\t\t\t""toolReferenceName"": ""getNotebookSummary"",\n\t\t\t\t""displayName"": ""Get the structure of a notebook"",\n\t\t\t\t""modelDescription"": ""This is a tool returns the list of the Notebook cells along with the id, cell types, line ranges, language, execution information and output mime types for each cell. This is useful to get Cell Ids when executing a notebook or determine what cells have been executed and what order, or what cells have outputs. If required to read contents of a cell use this to determine the line range of a cells, and then use read_file tool to read a specific line range. Requery this tool if the contents of the notebook change."",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePath"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""An absolute path to the notebook file with the cell to run, or the URI of a untitled, not yet named, file, such as `untitled:Untitled-1.ipynb""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""filePath""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_readNotebookCellOutput"",\n\t\t\t\t""displayName"": ""%copilot.tools.getNotebookCellOutput.name%"",\n\t\t\t\t""toolReferenceName"": ""readNotebookCellOutput"",\n\t\t\t\t""icon"": ""$(notebook-render-output)"",\n\t\t\t\t""modelDescription"": ""This tool will retrieve the output for a notebook cell from its most recent execution or restored from disk. The cell may have output even when it has not been run in the current kernel session. This tool has a higher token limit for output length than the runNotebookCell tool."",\n\t\t\t\t""userDescription"": ""%copilot.tools.getNotebookCellOutput.description%"",\n\t\t\t\t""when"": ""userHasOpenedNotebook"",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePath"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""An absolute path to the notebook file with the cell to run, or the URI of a untitled, not yet named, file, such as `untitled:Untitled-1.ipynb""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""cellId"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The ID of the cell for which output should be retrieved.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""filePath"",\n\t\t\t\t\t\t""cellId""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_fetchWebPage"",\n\t\t\t\t""displayName"": ""%copilot.tools.fetchWebPage.name%"",\n\t\t\t\t""toolReferenceName"": ""fetch"",\n\t\t\t\t""when"": ""!isWeb"",\n\t\t\t\t""canBeReferencedInPrompt"": true,\n\t\t\t\t""icon"": ""$(globe)"",\n\t\t\t\t""userDescription"": ""%copilot.tools.fetchWebPage.description%"",\n\t\t\t\t""modelDescription"": ""Fetches the main content from a web page. This tool is useful for summarizing or analyzing the content of a webpage. You should use this tool when you think the user is looking for information from a specific webpage."",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""urls"": {\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t""description"": ""An array of URLs to fetch content from.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""query"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The query to search for in the web page's content. This should be a clear and concise description of the content you want to find.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""urls"",\n\t\t\t\t\t\t""query""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_findTestFiles"",\n\t\t\t\t""displayName"": ""%copilot.tools.findTestFiles.name%"",\n\t\t\t\t""icon"": ""$(beaker)"",\n\t\t\t\t""canBeReferencedInPrompt"": false,\n\t\t\t\t""toolReferenceName"": ""findTestFiles"",\n\t\t\t\t""userDescription"": ""%copilot.tools.findTestFiles.description%"",\n\t\t\t\t""modelDescription"": ""For a source code file, find the file that contains the tests. For a test file find the file that contains the code under test."",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePaths"": {\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""filePaths""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_getDocInfo"",\n\t\t\t\t""displayName"": ""%copilot.tools.getDocInfo.name%"",\n\t\t\t\t""icon"": ""$(beaker)"",\n\t\t\t\t""canBeReferencedInPrompt"": false,\n\t\t\t\t""toolReferenceName"": ""docInfo"",\n\t\t\t\t""userDescription"": ""%copilot.tools.getDocInfo.description%"",\n\t\t\t\t""modelDescription"": ""Find information about how to document it a symbol like a class or function. This tool is useful for generating documentation comments for code symbols. You should use this tool when you think the user is looking for information about how to document a specific code symbol."",\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""filePaths"": {\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t""description"": ""The file paths for which documentation information is needed.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""filePaths""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_getSearchResults"",\n\t\t\t\t""toolReferenceName"": ""searchResults"",\n\t\t\t\t""displayName"": ""%github.copilot.tools.searchResults.name%"",\n\t\t\t\t""icon"": ""$(search)"",\n\t\t\t\t""userDescription"": ""%github.copilot.tools.searchResults.description%"",\n\t\t\t\t""modelDescription"": ""The results from the search view""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_githubRepo"",\n\t\t\t\t""toolReferenceName"": ""githubRepo"",\n\t\t\t\t""displayName"": ""%github.copilot.tools.githubRepo.name%"",\n\t\t\t\t""modelDescription"": ""Searches a GitHub repository for relevant source code snippets. Only use this tool if the user is very clearly asking for code snippets from a specific GitHub repository. Do not use this tool for Github repos that the user has open in their workspace."",\n\t\t\t\t""userDescription"": ""%github.copilot.tools.githubRepo.userDescription%"",\n\t\t\t\t""icon"": ""$(repo)"",\n\t\t\t\t""canBeReferencedInPrompt"": true,\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""repo"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The name of the Github repository to search for code in. Should must be formatted as '/'.""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""query"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The query to search for repo. Should contain all relevant context.""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""repo"",\n\t\t\t\t\t\t""query""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_toolReplay"",\n\t\t\t\t""modelDescription"": ""Replays a tool call from a previous chat session."",\n\t\t\t\t""displayName"": ""tool replay"",\n\t\t\t\t""when"": ""false"",\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""toolCallId"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""the id of the tool original tool call""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""toolName"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""the name of the tool being replayed""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""toolCallArgs"": {\n\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t""description"": ""the arguments of the tool call""\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_memory"",\n\t\t\t\t""toolReferenceName"": ""memory"",\n\t\t\t\t""displayName"": ""%copilot.tools.memory.name%"",\n\t\t\t\t""userDescription"": ""%copilot.tools.memory.description%"",\n\t\t\t\t""modelDescription"": ""Manage persistent memory across conversations. This tool allows you to create, view, update, and delete memory files that persist between chat sessions. Use this to remember important information about the user, their preferences, project context, or anything that should be recalled in future conversations. Available commands: view (list/read memories), create (new memory file), str_replace (edit content), insert (add content), delete (remove memory), rename (change filename)."",\n\t\t\t\t""icon"": ""$(database)"",\n\t\t\t\t""when"": ""config.github.copilot.chat.tools.memory.enabled"",\n\t\t\t\t""canBeReferencedInPrompt"": true,\n\t\t\t\t""tags"": [],\n\t\t\t\t""inputSchema"": {\n\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t""command"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""enum"": [\n\t\t\t\t\t\t\t\t""view"",\n\t\t\t\t\t\t\t\t""create"",\n\t\t\t\t\t\t\t\t""str_replace"",\n\t\t\t\t\t\t\t\t""insert"",\n\t\t\t\t\t\t\t\t""delete"",\n\t\t\t\t\t\t\t\t""rename""\n\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t""description"": ""The memory operation to perform: view (list/read), create (new file), str_replace (edit), insert (add content), delete (remove), rename (change filename)""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""path"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""The path to the memory file (must start with /memories, e.g., /memories/notes.md or /memories/project/info.txt)""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""view_range"": {\n\t\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t""type"": ""number""\n\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t""description"": ""Optional: view specific line range [start, end] for view command""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""file_text"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""Content for create operation""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""old_str"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""String to replace in str_replace operation""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""new_str"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""Replacement string in str_replace operation""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""insert_line"": {\n\t\t\t\t\t\t\t""type"": ""number"",\n\t\t\t\t\t\t\t""description"": ""Line number for insert operation""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""insert_text"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""Text to insert at specified line for insert operation""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""old_path"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""Source path for rename operation""\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""new_path"": {\n\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t""description"": ""Destination path for rename operation""\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\t""required"": [\n\t\t\t\t\t\t""command""\n\t\t\t\t\t]\n\t\t\t\t}\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""copilot_editFiles"",\n\t\t\t\t""modelDescription"": ""This is a placeholder tool, do not use"",\n\t\t\t\t""userDescription"": ""Edit files"",\n\t\t\t\t""icon"": ""$(pencil)"",\n\t\t\t\t""displayName"": ""Edit Files"",\n\t\t\t\t""toolReferenceName"": ""editFiles""\n\t\t\t}\n\t\t],\n\t\t""languageModelToolSets"": [\n\t\t\t{\n\t\t\t\t""name"": ""edit"",\n\t\t\t\t""description"": ""%copilot.toolSet.editing.description%"",\n\t\t\t\t""icon"": ""$(pencil)"",\n\t\t\t\t""tools"": [\n\t\t\t\t\t""createFile"",\n\t\t\t\t\t""createDirectory"",\n\t\t\t\t\t""editNotebook"",\n\t\t\t\t\t""newJupyterNotebook"",\n\t\t\t\t\t""editFiles""\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""runNotebooks"",\n\t\t\t\t""description"": ""%copilot.toolSet.runNotebook.description%"",\n\t\t\t\t""icon"": ""$(notebook)"",\n\t\t\t\t""tools"": [\n\t\t\t\t\t""runCell"",\n\t\t\t\t\t""getNotebookSummary"",\n\t\t\t\t\t""readNotebookCellOutput""\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""search"",\n\t\t\t\t""description"": ""%copilot.toolSet.search.description%"",\n\t\t\t\t""icon"": ""$(search)"",\n\t\t\t\t""tools"": [\n\t\t\t\t\t""fileSearch"",\n\t\t\t\t\t""textSearch"",\n\t\t\t\t\t""listDirectory"",\n\t\t\t\t\t""readFile"",\n\t\t\t\t\t""codebase"",\n\t\t\t\t\t""searchResults""\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""name"": ""new"",\n\t\t\t\t""description"": ""%copilot.toolSet.new.description%"",\n\t\t\t\t""icon"": ""$(new-folder)"",\n\t\t\t\t""tools"": [\n\t\t\t\t\t""newWorkspace"",\n\t\t\t\t\t""runVscodeCommand"",\n\t\t\t\t\t""getProjectSetupInfo"",\n\t\t\t\t\t""installExtension""\n\t\t\t\t]\n\t\t\t}\n\t\t],\n\t\t""chatParticipants"": [\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.default"",\n\t\t\t\t""name"": ""GitHubCopilot"",\n\t\t\t\t""fullName"": ""GitHub Copilot"",\n\t\t\t\t""description"": ""%copilot.description%"",\n\t\t\t\t""isDefault"": true,\n\t\t\t\t""locations"": [\n\t\t\t\t\t""panel""\n\t\t\t\t],\n\t\t\t\t""modes"": [\n\t\t\t\t\t""ask""\n\t\t\t\t],\n\t\t\t\t""disambiguation"": [\n\t\t\t\t\t{\n\t\t\t\t\t\t""category"": ""generate_code_sample"",\n\t\t\t\t\t\t""description"": ""The user wants to generate code snippets without referencing the contents of the current workspace. This category does not include generating entire projects."",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t""Write an example of computing a SHA256 hash.""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""category"": ""add_feature_to_file"",\n\t\t\t\t\t\t""description"": ""The user wants to change code in a file that is provided in their request, without referencing the contents of the current workspace. This category does not include generating entire projects."",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t""Add a refresh button to the table widget.""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""category"": ""question_about_specific_files"",\n\t\t\t\t\t\t""description"": ""The user has a question about a specific file or code snippet that they have provided as part of their query, and the question does not require additional workspace context to answer."",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t""What does this file do?""\n\t\t\t\t\t\t]\n\t\t\t\t\t}\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.editingSession"",\n\t\t\t\t""name"": ""GitHubCopilot"",\n\t\t\t\t""fullName"": ""GitHub Copilot"",\n\t\t\t\t""description"": ""%copilot.edits.description%"",\n\t\t\t\t""isDefault"": true,\n\t\t\t\t""locations"": [\n\t\t\t\t\t""panel""\n\t\t\t\t],\n\t\t\t\t""modes"": [\n\t\t\t\t\t""edit""\n\t\t\t\t],\n\t\t\t\t""when"": ""!config.chat.edits2.enabled""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.editingSessionEditor"",\n\t\t\t\t""name"": ""GitHubCopilot"",\n\t\t\t\t""fullName"": ""GitHub Copilot"",\n\t\t\t\t""description"": ""%copilot.edits.description%"",\n\t\t\t\t""isDefault"": true,\n\t\t\t\t""when"": ""config.inlineChat.enableV2"",\n\t\t\t\t""locations"": [\n\t\t\t\t\t""editor""\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.editingSession2"",\n\t\t\t\t""name"": ""GitHubCopilot"",\n\t\t\t\t""fullName"": ""GitHub Copilot"",\n\t\t\t\t""description"": ""%copilot.edits.description%"",\n\t\t\t\t""isDefault"": true,\n\t\t\t\t""locations"": [\n\t\t\t\t\t""panel""\n\t\t\t\t],\n\t\t\t\t""modes"": [\n\t\t\t\t\t""edit""\n\t\t\t\t],\n\t\t\t\t""when"": ""config.chat.edits2.enabled""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.editsAgent"",\n\t\t\t\t""name"": ""agent"",\n\t\t\t\t""fullName"": ""GitHub Copilot"",\n\t\t\t\t""description"": ""%copilot.agent.description%"",\n\t\t\t\t""locations"": [\n\t\t\t\t\t""panel""\n\t\t\t\t],\n\t\t\t\t""modes"": [\n\t\t\t\t\t""agent""\n\t\t\t\t],\n\t\t\t\t""isEngine"": true,\n\t\t\t\t""isDefault"": true,\n\t\t\t\t""isAgent"": true,\n\t\t\t\t""when"": ""config.chat.agent.enabled"",\n\t\t\t\t""commands"": [\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""list""\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""error"",\n\t\t\t\t\t\t""description"": ""Make a model request which will result in an error"",\n\t\t\t\t\t\t""when"": ""github.copilot.chat.debug""\n\t\t\t\t\t}\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.editor"",\n\t\t\t\t""name"": ""Copilot"",\n\t\t\t\t""fullName"": ""GitHub Copilot"",\n\t\t\t\t""description"": ""%copilot.description%"",\n\t\t\t\t""isDefault"": true,\n\t\t\t\t""locations"": [\n\t\t\t\t\t""editor""\n\t\t\t\t],\n\t\t\t\t""when"": ""!config.inlineChat.enableV2"",\n\t\t\t\t""disambiguation"": [\n\t\t\t\t\t{\n\t\t\t\t\t\t""category"": ""unknown"",\n\t\t\t\t\t\t""description"": ""Intent of this command is unclear or is not related to information technologies"",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t""Add a dog to this comment.""\n\t\t\t\t\t\t]\n\t\t\t\t\t}\n\t\t\t\t],\n\t\t\t\t""commands"": [\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""generate"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.generate.description%"",\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""generate"",\n\t\t\t\t\t\t\t\t""description"": ""Generate new code"",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""Add a function that returns the sum of two numbers""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""edit"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.edit.inline.description%"",\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""edit"",\n\t\t\t\t\t\t\t\t""description"": ""Make changes to existing code"",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""Change this method to use async/await""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""doc"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.doc.description%"",\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""doc"",\n\t\t\t\t\t\t\t\t""description"": ""Add documentation comment for this symbol"",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""Add jsdoc to this method""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""fix"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.fix.description%"",\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""fix"",\n\t\t\t\t\t\t\t\t""description"": ""Propose a fix for the problems in the selected code"",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""There is a problem in this code. Rewrite the code to show it with the bug fixed.""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""explain"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.explain.description%"",\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""explain"",\n\t\t\t\t\t\t\t\t""description"": ""Explain how the code in your active editor works"",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""Write an explanation for the code above as paragraphs of text.""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""review"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.review.description%"",\n\t\t\t\t\t\t""when"": ""github.copilot.advanced.review.intent""\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""tests"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.tests.description%"",\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""tests"",\n\t\t\t\t\t\t\t\t""description"": ""Generate unit tests for the selected code. The user does not want to fix their existing tests."",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""Write a set of detailed unit test functions for the code above.""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t}\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.notebook"",\n\t\t\t\t""name"": ""GitHubCopilot"",\n\t\t\t\t""fullName"": ""GitHub Copilot"",\n\t\t\t\t""description"": ""%copilot.description%"",\n\t\t\t\t""isDefault"": true,\n\t\t\t\t""locations"": [\n\t\t\t\t\t""notebook""\n\t\t\t\t],\n\t\t\t\t""when"": ""!config.inlineChat.notebookAgent"",\n\t\t\t\t""commands"": [\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""fix"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.fix.description%""\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""explain"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.explain.description%""\n\t\t\t\t\t}\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.notebookEditorAgent"",\n\t\t\t\t""name"": ""GitHubCopilot"",\n\t\t\t\t""fullName"": ""GitHub Copilot"",\n\t\t\t\t""description"": ""%copilot.description%"",\n\t\t\t\t""isDefault"": true,\n\t\t\t\t""locations"": [\n\t\t\t\t\t""notebook""\n\t\t\t\t],\n\t\t\t\t""when"": ""config.inlineChat.notebookAgent"",\n\t\t\t\t""commands"": [\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""fix"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.fix.description%""\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""explain"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.explain.description%""\n\t\t\t\t\t}\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.workspace"",\n\t\t\t\t""name"": ""workspace"",\n\t\t\t\t""fullName"": ""Workspace"",\n\t\t\t\t""description"": ""%copilot.workspace.description%"",\n\t\t\t\t""when"": ""!github.copilot.interactiveSession.disabled"",\n\t\t\t\t""sampleRequest"": ""%copilot.workspace.sampleRequest%"",\n\t\t\t\t""locations"": [\n\t\t\t\t\t""panel""\n\t\t\t\t],\n\t\t\t\t""disambiguation"": [\n\t\t\t\t\t{\n\t\t\t\t\t\t""category"": ""workspace_project_questions"",\n\t\t\t\t\t\t""description"": ""The user wants to learn about or update the code or files in their current workspace. Questions in this category may be about understanding what the whole workspace does or locating the implementation of some code. This does not include generating or updating tests."",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t""What does this project do?""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""category"": ""find_code_in_workspace"",\n\t\t\t\t\t\t""description"": ""The user wants to locate the implementation of some functionality in their current workspace."",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t""Where is the tree widget implemented?""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""category"": ""generate_with_workspace_context"",\n\t\t\t\t\t\t""description"": ""The user wants to generate code based on multiple files in the workspace and did not specify which files to reference."",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t""Create a README for this project.""\n\t\t\t\t\t\t]\n\t\t\t\t\t}\n\t\t\t\t],\n\t\t\t\t""commands"": [\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""explain"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.explain.description%""\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""review"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.review.description%"",\n\t\t\t\t\t\t""when"": ""github.copilot.advanced.review.intent""\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""tests"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.tests.description%"",\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""create_tests"",\n\t\t\t\t\t\t\t\t""description"": ""The user wants to generate unit tests."",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""Generate tests for my selection using pytest.""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""fix"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.fix.description%"",\n\t\t\t\t\t\t""sampleRequest"": ""%copilot.workspace.fix.sampleRequest%""\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""new"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.new.description%"",\n\t\t\t\t\t\t""sampleRequest"": ""%copilot.workspace.new.sampleRequest%"",\n\t\t\t\t\t\t""isSticky"": true,\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""create_new_workspace_or_extension"",\n\t\t\t\t\t\t\t\t""description"": ""The user wants to create a complete Visual Studio Code workspace from scratch, such as a new application or a Visual Studio Code extension. Use this category only if the question relates to generating or creating new workspaces in Visual Studio Code. Do not use this category for updating existing code or generating sample code snippets"",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""Scaffold a Node server."",\n\t\t\t\t\t\t\t\t\t""Create a sample project which uses the fileSystemProvider API."",\n\t\t\t\t\t\t\t\t\t""react application""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""newNotebook"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.newNotebook.description%"",\n\t\t\t\t\t\t""sampleRequest"": ""%copilot.workspace.newNotebook.sampleRequest%"",\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""create_jupyter_notebook"",\n\t\t\t\t\t\t\t\t""description"": ""The user wants to create a new Jupyter notebook in Visual Studio Code."",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""Create a notebook to analyze this CSV file.""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""semanticSearch"",\n\t\t\t\t\t\t""description"": ""%copilot.workspace.semanticSearch.description%"",\n\t\t\t\t\t\t""sampleRequest"": ""%copilot.workspace.semanticSearch.sampleRequest%"",\n\t\t\t\t\t\t""when"": ""config.github.copilot.semanticSearch.enabled""\n\t\t\t\t\t},\n\t\t\t\t\t{\n\t\t\t\t\t\t""name"": ""setupTests"",\n\t\t\t\t\t\t""description"": ""%copilot.vscode.setupTests.description%"",\n\t\t\t\t\t\t""sampleRequest"": ""%copilot.vscode.setupTests.sampleRequest%"",\n\t\t\t\t\t\t""when"": ""config.github.copilot.chat.setupTests.enabled"",\n\t\t\t\t\t\t""disambiguation"": [\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t""category"": ""set_up_tests"",\n\t\t\t\t\t\t\t\t""description"": ""The user wants to configure project test setup, framework, or test runner. The user does not want to fix their existing tests."",\n\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t""Set up tests for this project.""\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t]\n\t\t\t\t\t}\n\t\t\t\t]\n\t\t\t},\n\t\t\t{\n\t\t\t\t""id"": ""github.copilot.vscode"",\n\t\t\t\t""name"": ""vscode"",\n\t\t\t\t""fullName"": ""VS Code"",\n\t\t\t\t""description"": ""%copilot.vscode.description%"",\n\t\t\t\t""when"": ""!github.copilot.interactiveSession.disabled"",\n\t\t\t\t""sampleRequest"": ""%copilot.vscode.sampleRequest%"",\n\t\t\t\t""locations"": [\n\t\t\t\t\t""panel""\n\t\t\t\t],\n\t\t\t\t""disambiguation"": [\n\t\t\t\t\t{\n\t\t\t\t\t\t""category"": ""vscode_configuration_questions"",\n\t\t\t\t\t\t""description"": ""The user wants to learn about, use, or configure the Visual Studio Code. Use this category if the users question is specifically about commands, settings, keybindings, extensions and other features available in Visual Studio Code. 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""github.copilot.buildRemoteWorkspaceIndex"",\n\t\t\t\t""title"": ""%github.copilot.command.buildRemoteWorkspaceIndex%"",\n\t\t\t\t""category"": ""Chat"",\n\t\t\t\t""enablement"": ""github.copilot-chat.activated""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.report"",\n\t\t\t\t""title"": ""Report Issue"",\n\t\t\t\t""category"": ""Chat""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.chat.rerunWithCopilotDebug"",\n\t\t\t\t""title"": ""%github.copilot.command.rerunWithCopilotDebug%"",\n\t\t\t\t""category"": ""Chat""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.chat.startCopilotDebugCommand"",\n\t\t\t\t""title"": ""Start Copilot Debug""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.chat.clearTemporalContext"",\n\t\t\t\t""title"": ""Clear Temporal Context"",\n\t\t\t\t""category"": ""Developer""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.search.markHelpful"",\n\t\t\t\t""title"": ""Helpful"",\n\t\t\t\t""icon"": 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""github.copilot.chat.notebook.enableFollowCellExecution"",\n\t\t\t\t""title"": ""Enable Follow Cell Execution from Chat"",\n\t\t\t\t""shortTitle"": ""Follow"",\n\t\t\t\t""icon"": ""$(pinned)""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.chat.notebook.disableFollowCellExecution"",\n\t\t\t\t""title"": ""Disable Follow Cell Execution from Chat"",\n\t\t\t\t""shortTitle"": ""Unfollow"",\n\t\t\t\t""icon"": ""$(pinned-dirty)""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.chat.manageBYOK"",\n\t\t\t\t""title"": ""Manage Bring Your Own Key Vendor"",\n\t\t\t\t""enablement"": ""false""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.chat.manageBYOKAPIKey"",\n\t\t\t\t""title"": ""Manage Bring Your Own Key API Key"",\n\t\t\t\t""enablement"": ""false""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.cloud.sessions.refresh"",\n\t\t\t\t""title"": ""%github.copilot.command.refreshAgentSessions%"",\n\t\t\t\t""icon"": ""$(refresh)""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.cloud.sessions.openInBrowser"",\n\t\t\t\t""title"": ""%github.copilot.command.openCopilotAgentSessionsInBrowser%"",\n\t\t\t\t""icon"": ""$(link-external)""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.cloud.sessions.proxy.closeChatSessionPullRequest"",\n\t\t\t\t""title"": ""%github.copilot.command.closeChatSessionPullRequest.title%""\n\t\t\t},\n\t\t\t{\n\t\t\t\t""command"": ""github.copilot.chat.openSuggestionsPanel"",\n ""title"": ""Open Completions Panel"",\n ""enablement"": ""github.copilot.extensionUnification.activated && !isWeb"",\n\t\t\t\t""category"": ""GitHub Copilot""\n\t\t\t},\n\t\t\t{\n ""command"": ""github.copilot.chat.toggleStatusMenu"",\n ""title"": ""Open Status Menu"",\n\t\t\t\t""enablement"": ""github.copilot.extensionUnification.activated"",\n ""category"": ""GitHub Copilot""\n },\n\t\t\t{\n ""command"": ""github.copilot.chat.completions.disable"",\n ""title"": ""Disable Completions"",\n ""enablement"": ""github.copilot.extensionUnification.activated && github.copilot.activated && config.editor.inlineSuggest.enabled && github.copilot.completions.enabled"",\n ""category"": ""GitHub Copilot""\n },\n {\n ""command"": ""github.copilot.chat.completions.enable"",\n ""title"": ""Enable Completions"",\n ""enablement"": ""github.copilot.extensionUnification.activated && github.copilot.activated && !(config.editor.inlineSuggest.enabled && github.copilot.completions.enabled)"",\n ""category"": ""GitHub Copilot""\n },\n {\n ""command"": ""github.copilot.chat.completions.toggle"",\n ""title"": ""Toggle (Enable/Disable) Completions"",\n ""enablement"": ""github.copilot.extensionUnification.activated && github.copilot.activated"",\n ""category"": ""GitHub Copilot""\n }\n\t\t],\n\t\t""configuration"": [\n\t\t\t{\n\t\t\t\t""title"": ""GitHub Copilot Chat"",\n\t\t\t\t""id"": ""stable"",\n\t\t\t\t""properties"": 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""object"",\n\t\t\t\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.reviewSelection.instruction.file%"",\n\t\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t\t""file"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t\t\t"".copilot-review-instructions.md""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t\t""language"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""file"": "".copilot-review-instructions.md""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""file""\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.reviewSelection.instruction.text%"",\n\t\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t\t""text"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t\t\t""Use underscore for field names.""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t\t""language"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""text""\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""text"": ""Use underscore for field names.""\n\t\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""text"": ""Resolve all TODO tasks.""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""default"": [],\n\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.reviewSelection.instructions%"",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""file"": 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[\n\t\t\t\t\t\t\t\t\t\t\t\t"".copilot-codeGeneration-instructions.md""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t\t""language"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""file"": "".copilot-codeGeneration-instructions.md""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""file""\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.codeGeneration.instruction.text%"",\n\t\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t\t""text"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t\t\t""Use underscore for field names.""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t\t""language"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""text""\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""text"": ""Use underscore for field names.""\n\t\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""text"": ""Always add a comment: 'Generated by Copilot'.""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""default"": [],\n\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.codeGeneration.instructions%"",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""file"": "".copilot-codeGeneration-instructions.md""\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""text"": ""Always add a comment: 'Generated by Copilot'.""\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t],\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.testGeneration.instructions"": {\n\t\t\t\t\t\t""markdownDeprecationMessage"": ""%github.copilot.config.testGeneration.instructions.deprecated%"",\n\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t""oneOf"": [\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.experimental.testGeneration.instruction.file%"",\n\t\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t\t""file"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t\t\t"".copilot-test-instructions.md""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t\t""language"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""file"": "".copilot-test-instructions.md""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""file""\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.experimental.testGeneration.instruction.text%"",\n\t\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t\t""text"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t\t\t""Use suite and test instead of describe and it.""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t\t""language"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""text""\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""text"": ""Always try uniting related tests in a suite.""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""default"": [],\n\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.testGeneration.instructions%"",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""file"": "".copilot-test-instructions.md""\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""text"": ""Always try uniting related tests in a suite.""\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t],\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.commitMessageGeneration.instructions"": {\n\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t""oneOf"": [\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.commitMessageGeneration.instruction.file%"",\n\t\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t\t""file"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t\t\t"".copilot-commit-message-instructions.md""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""file"": "".copilot-commit-message-instructions.md""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""file""\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.commitMessageGeneration.instruction.text%"",\n\t\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t\t""text"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t\t\t""Use conventional commit message format.""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""text""\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""text"": ""Use conventional commit message format.""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""default"": [],\n\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.commitMessageGeneration.instructions%"",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""file"": "".copilot-commit-message-instructions.md""\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""text"": ""Use conventional commit message format.""\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t],\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.pullRequestDescriptionGeneration.instructions"": {\n\t\t\t\t\t\t""type"": ""array"",\n\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t""oneOf"": [\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.pullRequestDescriptionGeneration.instruction.file%"",\n\t\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t\t""file"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t\t\t"".copilot-pull-request-description-instructions.md""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""file"": "".copilot-pull-request-description-instructions.md""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""file""\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.pullRequestDescriptionGeneration.instruction.text%"",\n\t\t\t\t\t\t\t\t\t""properties"": {\n\t\t\t\t\t\t\t\t\t\t""text"": {\n\t\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t\t\t""Include every commit message in the pull request description.""\n\t\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t\t""required"": [\n\t\t\t\t\t\t\t\t\t\t""text""\n\t\t\t\t\t\t\t\t\t],\n\t\t\t\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t""text"": ""Include every commit message in the pull request description.""\n\t\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t},\n\t\t\t\t\t\t""default"": [],\n\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.pullRequestDescriptionGeneration.instructions%"",\n\t\t\t\t\t\t""examples"": [\n\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""file"": "".copilot-pull-request-description-instructions.md""\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t""text"": ""Use conventional commit message format.""\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t],\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.generateTests.codeLens"": {\n\t\t\t\t\t\t""type"": ""boolean"",\n\t\t\t\t\t\t""default"": false,\n\t\t\t\t\t\t""description"": ""%github.copilot.config.generateTests.codeLens%"",\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.edits.temporalContext.enabled"": {\n\t\t\t\t\t\t""type"": ""boolean"",\n\t\t\t\t\t\t""default"": false,\n\t\t\t\t\t\t""description"": ""%github.copilot.chat.edits.temporalContext.enabled%"",\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental"",\n\t\t\t\t\t\t\t""onExp""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.editor.temporalContext.enabled"": {\n\t\t\t\t\t\t""type"": ""boolean"",\n\t\t\t\t\t\t""default"": false,\n\t\t\t\t\t\t""description"": ""%github.copilot.chat.editor.temporalContext.enabled%"",\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental"",\n\t\t\t\t\t\t\t""onExp""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.setupTests.enabled"": {\n\t\t\t\t\t\t""type"": ""boolean"",\n\t\t\t\t\t\t""default"": true,\n\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.config.setupTests.enabled%"",\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental""\n\t\t\t\t\t\t]\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.languageContext.typescript.enabled"": {\n\t\t\t\t\t\t""type"": ""boolean"",\n\t\t\t\t\t\t""default"": false,\n\t\t\t\t\t\t""scope"": ""resource"",\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental"",\n\t\t\t\t\t\t\t""onExP""\n\t\t\t\t\t\t],\n\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.chat.languageContext.typescript.enabled%""\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.languageContext.typescript.items"": {\n\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t""enum"": [\n\t\t\t\t\t\t\t""minimal"",\n\t\t\t\t\t\t\t""double"",\n\t\t\t\t\t\t\t""fillHalf"",\n\t\t\t\t\t\t\t""fill""\n\t\t\t\t\t\t],\n\t\t\t\t\t\t""default"": ""minimal"",\n\t\t\t\t\t\t""scope"": ""resource"",\n\t\t\t\t\t\t""tags"": [\n\t\t\t\t\t\t\t""experimental"",\n\t\t\t\t\t\t\t""onExP""\n\t\t\t\t\t\t],\n\t\t\t\t\t\t""markdownDescription"": ""%github.copilot.chat.languageContext.typescript.items%""\n\t\t\t\t\t},\n\t\t\t\t\t""github.copilot.chat.languageContext.typescript.includeDocumentation"": {\n\t\t\t\t\t\t""type"": ""boolean"",\n\t\t\t\t\t\t""default"": false,\n\t\t\t\t\t\t""scope"": ""resource"",\n\t\t\t\t\t\t""tags"": 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If this is not configured, the editor will try multiple edit tools and pick the best one.\n\n- 'find-replace': Find and replace text in a document.\n- 'multi-find-replace': Find and replace text in a document.\n- 'apply-patch': A file-oriented diff format used by some OpenAI models\n- 'code-rewrite': A general but slower editing tool that allows the model to rewrite and code snippet and provide only the replacement to the editor."",\n\t\t\t\t\t\t\t\t\t""items"": {\n\t\t\t\t\t\t\t\t\t\t""type"": ""string"",\n\t\t\t\t\t\t\t\t\t\t""enum"": [\n\t\t\t\t\t\t\t\t\t\t\t""find-replace"",\n\t\t\t\t\t\t\t\t\t\t\t""multi-find-replace"",\n\t\t\t\t\t\t\t\t\t\t\t""apply-patch"",\n\t\t\t\t\t\t\t\t\t\t\t""code-rewrite""\n\t\t\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t""thinking"": {\n\t\t\t\t\t\t\t\t\t""type"": ""boolean"",\n\t\t\t\t\t\t\t\t\t""default"": false,\n\t\t\t\t\t\t\t\t\t""description"": ""Whether the model supports thinking capabilities""\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\t""requestHeaders"": {\n\t\t\t\t\t\t\t\t\t""type"": ""object"",\n\t\t\t\t\t\t\t\t\t""description"": ""Additional HTTP headers to include with requests to this model. These reserved headers are not allowed and ignored if present: forbidden request headers (https://developer.mozilla.org/en-US/docs/Glossary/Forbidden_request_header), forwarding headers ('forwarded', 'x-forwarded-for', 'x-forwarded-host', 'x-forwarded-proto'), and others ('api-key', 'authorization', 'content-type', 'openai-intent', 'x-github-api-version', 'x-initiator', 'x-interaction-id', 'x-interaction-type', 'x-onbehalf-extension-id', 'x-request-id', 'x-vscode-user-agent-library-version'). 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Microsoft Corporation. All rights reserved.\n * Licensed under the MIT License. See License.txt in the project root for license information.\n *--------------------------------------------------------------------------------------------*/\nimport { IInstantiationService, ServicesAccessor } from '../../../../../util/vs/platform/instantiation/common/instantiation';\nimport { ICompletionsTelemetryService } from '../../bridge/src/completionsTelemetryServiceBridge';\nimport { CopilotTokenManager } from './auth/copilotTokenManager';\nimport { ChangeTracker } from './changeTracker';\nimport { CitationManager, IPCitationDetail } from './citationManager';\nimport { createCompletionState } from './completionState';\nimport { ICompletionsContextService } from './context';\nimport { FileReader } from './fileReader';\nimport { PostInsertionCategory, telemetryAccepted, telemetryRejected } from './ghostText/telemetry';\nimport { LogTarget, Logger } from './logger';\nimport { CopilotNamedAnnotationList } from './openai/stream';\nimport { contextIndentationFromText, indentationBlockFinished } from './prompt/parseBlock';\nimport { Prompt, extractPrompt } from './prompt/prompt';\nimport { fetchCitations } from './snippy/handlePostInsertion';\nimport { editDistance, lexEditDistance } from './suggestions/editDistance';\nimport { SuggestionStatus, computeCompletionText } from './suggestions/partialSuggestions';\nimport { TelemetryStore, TelemetryWithExp, telemetry, telemetryCatch } from './telemetry';\nimport { TextDocumentManager } from './textDocumentManager';\nimport { ICompletionsPromiseQueueService } from './util/promiseQueue';\nimport { ICompletionsRuntimeModeService } from './util/runtimeMode';\n\nconst postInsertionLogger = new Logger('postInsertion');\n\ntype Timeout = {\n\tseconds: number;\n\tcaptureCode: boolean;\n\tcaptureRejection: boolean;\n};\n// windows for telemetry checks, in seconds\n// captureCode = capture the code after acceptance,\n// captureRejection = capture the code after rejection\nconst captureTimeouts: Timeout[] = [\n\t{ seconds: 15, captureCode: false, captureRejection: false },\n\t{ seconds: 30, captureCode: true, captureRejection: true },\n\t{ seconds: 120, captureCode: false, captureRejection: false },\n\t{ seconds: 300, captureCode: false, captureRejection: false },\n\t{ seconds: 600, captureCode: false, captureRejection: false },\n];\n\n// No. of chars before/after insertion point to look for the completion\nconst stillInCodeNearMargin = 50;\nconst stillInCodeFarMargin = 1500;\n\n// If lex edit distance is below this fraction of completion length it is considered\n// in the code\nconst stillInCodeFraction = 0.5;\n\n// Number of characters captured after the insertion point.\n// Used only if we couldn't detect termination point with indent-based parsing.\nconst captureCodeMargin = 500;\n\nconst postInsertConfiguration: {\n\ttriggerPostInsertionSynchroneously: boolean;\n\tcaptureCode: boolean;\n\tcaptureRejection: boolean;\n} = {\n\ttriggerPostInsertionSynchroneously: false,\n\tcaptureCode: false,\n\tcaptureRejection: false,\n};\n\nasync function captureCode(\n\taccessor: ServicesAccessor,\n\turi: string,\n\tcompletionTelemetry: TelemetryWithExp,\n\toffset: number,\n\tsuffixOffset?: number\n): Promise<{ prompt: Prompt; capturedCode: string; terminationOffset: number }> {\n\tconst ctx = accessor.get(ICompletionsContextService);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst logTarget = ctx.get(LogTarget);\n\tconst result = await ctx.get(FileReader).getOrReadTextDocumentWithFakeClientProperties({ uri });\n\tif (result.status !== 'valid') {\n\t\tpostInsertionLogger.info(logTarget, `Could not get document for ${uri}. Maybe it was closed by the editor.`);\n\t\treturn {\n\t\t\tprompt: {\n\t\t\t\tprefix: '',\n\t\t\t\tsuffix: '',\n\t\t\t\tisFimEnabled: false,\n\t\t\t},\n\t\t\tcapturedCode: '',\n\t\t\tterminationOffset: 0,\n\t\t};\n\t}\n\tconst document = result.document;\n\tconst documentText = document.getText();\n\tconst documentTextBefore = documentText.substring(0, offset);\n\tconst position = document.positionAt(offset);\n\n\t// Treat the code before offset as the hypothetical prompt\n\tconst hypotheticalPromptResponse = await instantiationService.invokeFunction(extractPrompt,\n\t\tcompletionTelemetry.properties.headerRequestId,\n\t\tcreateCompletionState(document, position),\n\t\tcompletionTelemetry\n\t);\n\tconst hypotheticalPrompt =\n\t\thypotheticalPromptResponse.type === 'prompt'\n\t\t\t? hypotheticalPromptResponse.prompt\n\t\t\t: {\n\t\t\t\tprefix: documentTextBefore,\n\t\t\t\tsuffix: '',\n\t\t\t\tisFimEnabled: false,\n\t\t\t}; // TODO(eaftan): Pass an actual suffix when we're ready to support it\n\n\tif (hypotheticalPrompt.isFimEnabled && suffixOffset !== undefined) {\n\t\t// With FIM enabled, we can exactly determine capturedCode, suffix and prefix by propertly initialized trackers. No need to guess.\n\t\tconst capturedCode = documentText.substring(offset, suffixOffset);\n\t\thypotheticalPrompt.suffix = documentText.substring(suffixOffset);\n\n\t\treturn { prompt: hypotheticalPrompt, capturedCode, terminationOffset: 0 };\n\t} else {\n\t\t//Everything after the insertion point is hypothetical response we could get from AI\n\t\tconst hypotheticalResponse = documentText.substring(offset);\n\n\t\t//Try to find the termination offset in the hypothetical response using indentation based parsing\n\t\tconst contextIndent = contextIndentationFromText(documentTextBefore, offset, document.detectedLanguageId);\n\t\tconst indentTerminationFunction = indentationBlockFinished(contextIndent, undefined);\n\t\tconst terminationResult = indentTerminationFunction(hypotheticalResponse);\n\n\t\t//If we could detect termination of the indentation block, capture 2x length of detected suggestion\n\t\t//Otherwise capture a lot of characters\n\t\tconst maxOffset = Math.min(\n\t\t\tdocumentText.length,\n\t\t\toffset + (terminationResult ? terminationResult * 2 : captureCodeMargin)\n\t\t);\n\n\t\tconst capturedCode = documentText.substring(offset, maxOffset);\n\n\t\treturn { prompt: hypotheticalPrompt, capturedCode, terminationOffset: terminationResult ?? -1 };\n\t}\n}\n\nexport function postRejectionTasks(\n\taccessor: ServicesAccessor,\n\tinsertionCategory: PostInsertionCategory,\n\tinsertionOffset: number,\n\turi: string,\n\tcompletions: { completionText: string; completionTelemetryData: TelemetryWithExp }[]\n) {\n\tconst logTarget = accessor.get(ICompletionsContextService).get(LogTarget);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst telemetryService = accessor.get(ICompletionsTelemetryService);\n\tconst promiseQueueService = accessor.get(ICompletionsPromiseQueueService);\n\n\t//Send `.rejected` telemetry event for each rejected completion\n\tcompletions.forEach(({ completionText, completionTelemetryData }) => {\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`${insertionCategory}.rejected choiceIndex: ${completionTelemetryData.properties.choiceIndex}`\n\t\t);\n\t\tinstantiationService.invokeFunction(telemetryRejected, insertionCategory, completionTelemetryData);\n\t});\n\tconst positionTracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset - 1);\n\tconst suffixTracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset);\n\n\tconst checkInCode = async (t: Timeout) => {\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`Original offset: ${insertionOffset}, Tracked offset: ${positionTracker.offset}`\n\t\t);\n\t\tconst { completionTelemetryData } = completions[0];\n\n\t\tconst { prompt, capturedCode, terminationOffset } = await instantiationService.invokeFunction(captureCode,\n\t\t\turi,\n\t\t\tcompletionTelemetryData,\n\t\t\tpositionTracker.offset + 1,\n\t\t\tsuffixTracker.offset\n\t\t);\n\n\t\tconst promptTelemetry = {\n\t\t\thypotheticalPromptJson: JSON.stringify({ prefix: prompt.prefix, context: prompt.context }),\n\t\t\thypotheticalPromptSuffixJson: JSON.stringify(prompt.suffix),\n\t\t};\n\n\t\tconst customTelemetryData = completionTelemetryData.extendedBy(\n\t\t\t{\n\t\t\t\t...promptTelemetry,\n\t\t\t\tcapturedCodeJson: JSON.stringify(capturedCode),\n\t\t\t},\n\t\t\t{\n\t\t\t\ttimeout: t.seconds,\n\t\t\t\tinsertionOffset: insertionOffset,\n\t\t\t\ttrackedOffset: positionTracker.offset,\n\t\t\t\tterminationOffsetInCapturedCode: terminationOffset,\n\t\t\t}\n\t\t);\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`${insertionCategory}.capturedAfterRejected choiceIndex: ${completionTelemetryData.properties.choiceIndex}`,\n\t\t\tcustomTelemetryData\n\t\t);\n\t\tinstantiationService.invokeFunction(telemetry, insertionCategory + '.capturedAfterRejected', customTelemetryData, TelemetryStore.Enhanced);\n\t};\n\t// Capture the code typed after we detected that completion was rejected,\n\t// Uses first displayed completion as the source/seed of telemetry information.\n\tcaptureTimeouts\n\t\t.filter(t => t.captureRejection)\n\t\t.map(t =>\n\t\t\tpositionTracker.push(\n\t\t\t\ttelemetryCatch(telemetryService, promiseQueueService, () => checkInCode(t), 'postRejectionTasks'),\n\t\t\t\tt.seconds * 1000\n\t\t\t)\n\t\t);\n}\n\nexport function postInsertionTasks(\n\taccessor: ServicesAccessor,\n\tinsertionCategory: PostInsertionCategory,\n\tcompletionText: string,\n\tinsertionOffset: number,\n\turi: string,\n\ttelemetryData: TelemetryWithExp,\n\tsuggestionStatus: SuggestionStatus,\n\tcopilotAnnotations?: CopilotNamedAnnotationList\n) {\n\tconst logTarget = accessor.get(ICompletionsContextService).get(LogTarget);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst promiseQueueService = accessor.get(ICompletionsPromiseQueueService);\n\tconst telemetryService = accessor.get(ICompletionsTelemetryService);\n\tconst runtimeModeService = accessor.get(ICompletionsRuntimeModeService);\n\n\tconst telemetryDataWithStatus = telemetryData.extendedBy(\n\t\t{\n\t\t\tcompType: suggestionStatus.compType,\n\t\t},\n\t\t{\n\t\t\tcompCharLen: suggestionStatus.acceptedLength,\n\t\t\tnumLines: suggestionStatus.acceptedLines,\n\t\t}\n\t);\n\t// send "".accepted"" telemetry\n\tpostInsertionLogger.debug(\n\t\tlogTarget,\n\t\t`${insertionCategory}.accepted choiceIndex: ${telemetryDataWithStatus.properties.choiceIndex}`\n\t);\n\tinstantiationService.invokeFunction(telemetryAccepted, insertionCategory, telemetryDataWithStatus);\n\n\tconst fullCompletionText = completionText;\n\tcompletionText = computeCompletionText(completionText, suggestionStatus);\n\tconst trimmedCompletion = completionText.trim();\n\tconst tracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset);\n\tconst suffixTracker = instantiationService.createInstance(ChangeTracker, uri, insertionOffset + completionText.length);\n\n\tconst stillInCodeCheck = async (timeout: Timeout) => {\n\t\tconst check = instantiationService.invokeFunction(checkStillInCode,\n\t\t\tinsertionCategory,\n\t\t\ttrimmedCompletion,\n\t\t\tinsertionOffset,\n\t\t\turi,\n\t\t\ttimeout,\n\t\t\ttelemetryDataWithStatus,\n\t\t\ttracker,\n\t\t\tsuffixTracker\n\t\t);\n\t\tawait check;\n\t};\n\n\t// For test purposes, we add one set of these telemetry events synchronously to allow asserting the telemetry\n\tif (postInsertConfiguration.triggerPostInsertionSynchroneously && runtimeModeService.isRunningInTest()) {\n\t\tconst check = stillInCodeCheck({\n\t\t\tseconds: 0,\n\t\t\tcaptureCode: postInsertConfiguration.captureCode,\n\t\t\tcaptureRejection: postInsertConfiguration.captureRejection,\n\t\t});\n\t\tpromiseQueueService.register(check);\n\t} else {\n\t\tcaptureTimeouts.map(timeout =>\n\t\t\ttracker.push(\n\t\t\t\ttelemetryCatch(telemetryService, promiseQueueService, () => stillInCodeCheck(timeout), 'postInsertionTasks'),\n\t\t\t\ttimeout.seconds * 1000\n\t\t\t)\n\t\t);\n\t}\n\n\tinstantiationService.invokeFunction(acc => telemetryCatch(telemetryService, promiseQueueService, citationCheck, 'post insertion citation check')(\n\t\tacc,\n\t\turi,\n\t\tfullCompletionText,\n\t\tcompletionText,\n\t\tinsertionOffset,\n\t\tcopilotAnnotations\n\t));\n}\n\nasync function citationCheck(\n\taccessor: ServicesAccessor,\n\turi: string,\n\tfullCompletionText: string,\n\tinsertedText: string,\n\tinsertionOffset: number,\n\tcopilotAnnotations?: CopilotNamedAnnotationList\n) {\n\tconst logTarget = accessor.get(ICompletionsContextService).get(LogTarget);\n\tconst textDocumentManagerService = accessor.get(ICompletionsContextService).get(TextDocumentManager);\n\tconst copilotTokenManager = accessor.get(ICompletionsContextService).get(CopilotTokenManager);\n\tconst citationManagerService = accessor.get(ICompletionsContextService).get(CitationManager);\n\n\t// If there are no citations, request directly from the snippy service\n\tif (!copilotAnnotations || (copilotAnnotations.ip_code_citations?.length ?? 0) < 1) {\n\t\t// Do not request citations if in blocking mode\n\t\tif (copilotTokenManager.getLastToken()?.getTokenValue('sn') === '1') { return; }\n\t\tawait fetchCitations(accessor, uri, insertedText, insertionOffset);\n\t\treturn;\n\t}\n\n\tconst doc = await textDocumentManagerService.getTextDocument({ uri });\n\n\t// in the CLS, if the editor does not wait to send document updates until the\n\t// acceptance function returns, we could be in a race condition with ongoing\n\t// edits. This searches for the completion text so that hopefully we're providing\n\t// an exact location in a known version of the document.\n\tif (doc) {\n\t\tconst found = find(doc.getText(), insertedText, stillInCodeNearMargin, insertionOffset);\n\t\tif (found.stillInCodeHeuristic) {\n\t\t\tinsertionOffset = found.foundOffset;\n\t\t}\n\t}\n\n\tfor (const citation of copilotAnnotations.ip_code_citations) {\n\t\tconst citationStart = computeCitationStart(\n\t\t\tfullCompletionText.length,\n\t\t\tinsertedText.length,\n\t\t\tcitation.start_offset\n\t\t);\n\t\tif (citationStart === undefined) {\n\t\t\tpostInsertionLogger.info(\n\t\t\t\tlogTarget,\n\t\t\t\t`Full completion for ${uri} contains a reference matching public code, but the partially inserted text did not include the match.`\n\t\t\t);\n\t\t\tcontinue;\n\t\t}\n\t\tconst offsetStart = insertionOffset + citationStart;\n\t\tconst start = doc?.positionAt(offsetStart);\n\t\tconst offsetEnd =\n\t\t\tinsertionOffset + computeCitationEnd(fullCompletionText.length, insertedText.length, citation.stop_offset);\n\t\tconst end = doc?.positionAt(offsetEnd);\n\t\tconst text = start && end ? doc?.getText({ start, end }) : '';\n\n\t\tawait citationManagerService.handleIPCodeCitation({\n\t\t\tinDocumentUri: uri,\n\t\t\toffsetStart,\n\t\t\toffsetEnd,\n\t\t\tversion: doc?.version,\n\t\t\tlocation: start && end ? { start, end } : undefined,\n\t\t\tmatchingText: text,\n\t\t\tdetails: citation.details.citations as IPCitationDetail[],\n\t\t});\n\t}\n}\n\nfunction computeCitationStart(\n\tcompletionLength: number,\n\tinsertedLength: number,\n\tcitationStartOffset: number\n): number | undefined {\n\tif (insertedLength < completionLength && citationStartOffset > insertedLength) {\n\t\treturn undefined;\n\t}\n\treturn citationStartOffset;\n}\n\nfunction computeCitationEnd(completionLength: number, insertedLength: number, citationStopOffset: number): number {\n\tif (insertedLength < completionLength) {\n\t\treturn Math.min(citationStopOffset, insertedLength);\n\t}\n\treturn citationStopOffset;\n}\n\nfunction find(documentText: string, completion: string, margin: number, offset: number) {\n\t// Compute the best alignment between a window of the document text and the completion\n\tconst window = documentText.substring(\n\t\tMath.max(0, offset - margin),\n\t\tMath.min(documentText.length, offset + completion.length + margin)\n\t);\n\tconst lexAlignment = lexEditDistance(window, completion);\n\tconst fraction = lexAlignment.lexDistance / lexAlignment.needleLexLength;\n\tconst { distance: charEditDistance } = editDistance(\n\t\twindow.substring(lexAlignment.startOffset, lexAlignment.endOffset),\n\t\tcompletion\n\t);\n\treturn {\n\t\trelativeLexEditDistance: fraction,\n\t\tcharEditDistance,\n\t\tcompletionLexLength: lexAlignment.needleLexLength,\n\t\tfoundOffset: lexAlignment.startOffset + Math.max(0, offset - margin),\n\t\tlexEditDistance: lexAlignment.lexDistance,\n\t\tstillInCodeHeuristic: fraction <= stillInCodeFraction ? 1 : 0,\n\t};\n}\n\nasync function checkStillInCode(\n\taccessor: ServicesAccessor,\n\tinsertionCategory: string,\n\tcompletion: string,\n\tinsertionOffset: number, // offset where the completion was inserted to\n\turi: string,\n\ttimeout: Timeout,\n\ttelemetryData: TelemetryWithExp,\n\ttracker: ChangeTracker,\n\tsuffixTracker: ChangeTracker\n) {\n\t// Get contents of file from file system\n\tconst ctx = accessor.get(ICompletionsContextService);\n\tconst instantiationService = accessor.get(IInstantiationService);\n\tconst logTarget = ctx.get(LogTarget);\n\tconst result = await ctx.get(FileReader).getOrReadTextDocument({ uri });\n\tif (result.status === 'valid') {\n\t\tconst document = result.document;\n\t\tconst documentText = document.getText();\n\n\t\t// We try twice, first very close to the insertion point, then a bit\n\t\t// further. This is to increase accuracy for short completions,\n\t\t// where the completion might appear elsewhere.\n\t\tlet finding = find(documentText, completion, stillInCodeNearMargin, tracker.offset);\n\t\tif (!finding.stillInCodeHeuristic) {\n\t\t\tfinding = find(documentText, completion, stillInCodeFarMargin, tracker.offset);\n\t\t}\n\t\t// Debug and log a binary decision\n\t\tpostInsertionLogger.debug(\n\t\t\tlogTarget,\n\t\t\t`stillInCode: ${finding.stillInCodeHeuristic ? 'Found' : 'Not found'}! Completion '${completion}' in file ${uri\n\t\t\t}. lexEditDistance fraction was ${finding.relativeLexEditDistance}. Char edit distance was ${finding.charEditDistance\n\t\t\t}. Inserted at ${insertionOffset}, tracked at ${tracker.offset}, found at ${finding.foundOffset\n\t\t\t}. choiceIndex: ${telemetryData.properties.choiceIndex}`\n\t\t);\n\t\t// Log all the details for analysis\n\t\tconst customTelemetryData = telemetryData\n\t\t\t.extendedBy({}, { timeout: timeout.seconds, insertionOffset: insertionOffset, trackedOffset: tracker.offset })\n\t\t\t.extendedBy({}, finding);\n\t\tinstantiationService.invokeFunction(telemetry, insertionCategory + '.stillInCode', customTelemetryData);\n\n\t\tif (timeout.captureCode) {\n\t\t\tconst { prompt, capturedCode, terminationOffset } = await instantiationService.invokeFunction(\n\t\t\t\tcaptureCode,\n\t\t\t\turi,\n\t\t\t\tcustomTelemetryData,\n\t\t\t\ttracker.offset,\n\t\t\t\tsuffixTracker.offset\n\t\t\t);\n\t\t\tconst promptTelemetry = {\n\t\t\t\thypotheticalPromptJson: JSON.stringify({ prefix: prompt.prefix, context: prompt.context }),\n\t\t\t\thypotheticalPromptSuffixJson: JSON.stringify(prompt.suffix),\n\t\t\t};\n\n\t\t\tconst afterAcceptedTelemetry = telemetryData.extendedBy(\n\t\t\t\t{\n\t\t\t\t\t...promptTelemetry,\n\t\t\t\t\tcapturedCodeJson: JSON.stringify(capturedCode),\n\t\t\t\t},\n\t\t\t\t{\n\t\t\t\t\ttimeout: timeout.seconds,\n\t\t\t\t\tinsertionOffset: insertionOffset,\n\t\t\t\t\ttrackedOffset: tracker.offset,\n\t\t\t\t\tterminationOffsetInCapturedCode: terminationOffset,\n\t\t\t\t}\n\t\t\t);\n\t\t\tpostInsertionLogger.debug(\n\t\t\t\tlogTarget,\n\t\t\t\t`${insertionCategory}.capturedAfterAccepted choiceIndex: ${telemetryData.properties.choiceIndex}`,\n\t\t\t\tcustomTelemetryData\n\t\t\t);\n\t\t\tinstantiationService.invokeFunction(\n\t\t\t\ttelemetry,\n\t\t\t\tinsertionCategory + '.capturedAfterAccepted',\n\t\t\t\tafterAcceptedTelemetry,\n\t\t\t\tTelemetryStore.Enhanced\n\t\t\t);\n\t\t}\n\t}\n}\n",typescript,tab +11,44025,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"2:45:14 PM [info] Activating crowd-code\n2:45:14 PM [info] Recording started\n2:45:14 PM [info] Initializing git provider using file system watchers...\n2:45:14 PM [info] Git repository found\n2:45:14 PM [info] Git provider initialized successfully\n2:45:14 PM [info] Initial git state: [object Object]\n2:45:19 PM [info] Branch checkout detected: capture-tab-and-chat-interaction -> main\n2:45:19 PM [info] Recording git checkout: Switched from branch 'capture-tab-and-chat-interaction' to 'main'\n2:45:19 PM [info] Resetting file cache due to branch checkout\n2:45:39 PM [info] Branch checkout detected: main -> expose-oai-endpoint\n2:45:39 PM [info] Recording git checkout: Switched from branch 'main' to 'expose-oai-endpoint'\n2:45:39 PM [info] Resetting file cache due to branch checkout\n",Log,tab +12,45667,"TERMINAL",0,0,"",,terminal_focus +13,45668,"src/extension/completions-core/vscode-node/lib/src/postInsertion.ts",0,0,"",typescript,tab +14,56465,"TERMINAL",0,0,"module load node",,terminal_command +15,56504,"TERMINAL",0,0,"]633;C",,terminal_output +16,58035,"TERMINAL",0,0,"Lmod has detected the following error:  The following module(s) are unknown: ""node""\r\n\r\nPlease check the spelling or version number. Also try ""module spider ...""\r\nIt is also possible your cache file is out-of-date; it may help to try:\r\n $ module --ignore_cache load ""node""\r\n\r\nAlso make sure that all modulefiles written in TCL start with the string #%Module\r\n\r\n\r\n\r\n]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat",,terminal_output +17,62622,"TERMINAL",0,0,"module spider node",,terminal_command +18,62686,"TERMINAL",0,0,"]633;C",,terminal_output +19,63233,"TERMINAL",0,0,"\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n Tree::DAG_Node:\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n Versions:\r\n Tree::DAG_Node/1.32 (E)\r\n\r\nNames marked by a trailing (E) are extensions provided by another module.\r\n\r\n\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n For detailed information about a specific ""Tree::DAG_Node"" package (including how to load the modules) use the module's full name.\r\n Note that names that have a trailing (E) are extensions provided by other modules.\r\n For example:\r\n\r\n $ module spider Tree::DAG_Node/1.32\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n hatch_nodejs_version:\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n Versions:\r\n hatch_nodejs_version/0.3.1 (E)\r\n\r\nNames marked by a trailing (E) are extensions provided by another module.\r\n\r\n\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n For detailed information about a specific ""hatch_nodejs_version"" package (including how to load the modules) use the module's full name.\r\n Note that names that have a trailing (E) are extensions provided by other modules.\r\n For example:\r\n\r\n $ module spider hatch_nodejs_version/0.3.1\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n--More--",,terminal_output +20,68702,"TERMINAL",0,0,"\r\r\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n nodejs:\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n Description:\r\n Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications. Node.js uses an\r\n event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run\r\n across distributed devices.\r\n\r\n Versions:\r\n nodejs/18.17.1-GCCcore-12.3.0\r\n nodejs/20.13.1-GCCcore-13.3.0\r\n\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n For detailed information about a specific ""nodejs"" package (including how to load the modules) use the module's full name.\r\n Note that names that have a trailing (E) are extensions provided by other modules.\r\n For example:\r\n\r\n $ module spider nodejs/20.13.1-GCCcore-13.3.0\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n\r\n \r\n\r\n]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat",,terminal_output +21,73093,"TERMINAL",0,0,"module spider nodejs",,terminal_command +22,73164,"TERMINAL",0,0,"]633;C",,terminal_output +23,73651,"TERMINAL",0,0,"\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n nodejs:\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n Description:\r\n Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications. Node.js uses an\r\n event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run\r\n across distributed devices.\r\n\r\n Versions:\r\n nodejs/18.17.1-GCCcore-12.3.0\r\n nodejs/20.13.1-GCCcore-13.3.0\r\n\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n For detailed information about a specific ""nodejs"" package (including how to load the modules) use the module's full name.\r\n Note that names that have a trailing (E) are extensions provided by other modules.\r\n For example:\r\n\r\n $ module spider nodejs/20.13.1-GCCcore-13.3.0\r\n------------------------------------------------------------------------------------------------------------------------------------------------\r\n\r\n \r\n\r\n]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat",,terminal_output +24,77423,"TERMINAL",0,0,"module load nodejs",,terminal_command +25,77479,"TERMINAL",0,0,"]633;C",,terminal_output +26,77502,"TERMINAL",0,0,"]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat",,terminal_output +27,83804,"TERMINAL",0,0,"npm install",,terminal_command +28,83843,"TERMINAL",0,0,"]633;C",,terminal_output +29,87169,"TERMINAL",0,0,"npm ERR! code EBADENGINE\r\nnpm ERR! engine Unsupported engine\r\nnpm ERR! engine Not compatible with your version of node/npm: copilot-chat@0.33.0\r\nnpm ERR! notsup Not compatible with your version of node/npm: copilot-chat@0.33.0\r\nnpm ERR! notsup Required: {""vscode"":""^1.106.0-20251030"",""npm"":"">=9.0.0"",""node"":"">=22.14.0""}\r\nnpm ERR! notsup Actual: {""npm"":""10.5.2"",""node"":""v20.13.1""}\r\n\r\nnpm ERR! A complete log of this run can be found in: /home/franz.srambical/.npm/_logs/2025-11-03T13_46_39_640Z-debug-0.log\r\n]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat",,terminal_output +30,112309,"TERMINAL",0,0,"npm --version",,terminal_command +31,112360,"TERMINAL",0,0,"]633;C",,terminal_output +32,112751,"TERMINAL",0,0,"10.5.2\r\n]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat",,terminal_output +33,118827,"TERMINAL",0,0,"node --version",,terminal_command +34,118879,"TERMINAL",0,0,"]633;C",,terminal_output +35,118978,"TERMINAL",0,0,"v20.13.1\r\n]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat",,terminal_output +36,119789,"TERMINAL",0,0,"",,terminal_command +37,1197730,"TERMINAL",0,0,"bash",,terminal_focus +38,1199269,"TERMINAL",0,0,"ensure-deps",,terminal_focus +39,1200217,"TERMINAL",0,0,"bash",,terminal_focus +40,1206900,"TERMINAL",0,0,"squeue",,terminal_command +41,1206922,"TERMINAL",0,0,"]633;C JOBID USER PARTITION NODES CPUS ST SUBMIT_TIME START_TIME TIME TIME_LIMIT NODELIST(REASON)\r\n 33317 xiao.liu interacti 1 128 R 2025-11-02T17:43:38 2025-11-02T17:43:38 21:21:43 23:59:00 hai006\r\n 33350 nishant.ku standard 1 8 R 2025-11-03T14:45:30 2025-11-03T14:45:37 19:44 1-00:00:00 hai001\r\n 33328 kalyan.nad standard 1 64 R 2025-11-03T11:56:23 2025-11-03T11:56:38 3:08:43 1-00:00:00 hai002\r\n 33318 xiao.liu standard 1 128 R 2025-11-02T19:29:40 2025-11-02T19:30:38 19:34:43 23:59:00 hai004\r\n]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat",,terminal_output +42,1211022,"TERMINAL",0,0,"bash",,terminal_focus +43,1219029,"TERMINAL",0,0,"salloc --gpus=1 --ntasks-per-node=1 --cpus-per-task=10 --mem=100G",,terminal_command +44,1219099,"TERMINAL",0,0,"]633;Csalloc: Granted job allocation 33358\r\n",,terminal_output +45,1219215,"TERMINAL",0,0,"salloc: Nodes hai003 are ready for job\r\n",,terminal_output +46,1219567,"TERMINAL",0,0,"Running inside SLURM, Job ID 33358.\r\n",,terminal_output +47,1219682,"TERMINAL",0,0,"]0;franz.srambical@hai-login1:/fast/home/franz.srambical/vscode-crowd-pilot-chat[?2004h[franz.srambical@hai003.haicore.berlin:/fast/home/franz.srambical/vscode-crowd-pilot-chat] $ ",,terminal_output +48,1224028,"TERMINAL",0,0,"c",,terminal_output +49,1224289,"TERMINAL",0,0,"d",,terminal_output +50,1224355,"TERMINAL",0,0,"\r\n[?2004l\r]0;franz.srambical@hai-login1:~[?2004h[franz.srambical@hai003.haicore.berlin:~] $ ",,terminal_output +51,1224575,"TERMINAL",0,0,"c",,terminal_output +52,1224867,"TERMINAL",0,0,"d",,terminal_output +53,1224966,"TERMINAL",0,0," ",,terminal_output +54,1225827,"TERMINAL",0,0,"c",,terminal_output +55,1226200,"TERMINAL",0,0,"",,terminal_output +56,1226269,"TERMINAL",0,0,"v",,terminal_output +57,1226461,"TERMINAL",0,0,"s",,terminal_output +58,1226606,"TERMINAL",0,0,"code-crowd-pilot-chat/",,terminal_output +59,1227060,"TERMINAL",0,0,"\r\n[?2004l\r]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat[?2004h[franz.srambical@hai003.haicore.berlin:~/vscode-crowd-pilot-chat] $ ",,terminal_output +60,1229480,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +61,1229693,"TERMINAL",0,0,"s': cd vscode-crowd-pilot-chat/o': . ""/fast/home/franz.srambical/.cursor-server/bin/3ccce8f55d8cca49f6d28b491a844c699b8719a0/out/vs/workbench/contrib/terminal/common/scripts/shellIntegration-bash.sh""\ru': source .venv/bin/activate\r\n\r",,terminal_output +62,1229821,"TERMINAL",0,0,"[1@r': sour",,terminal_output +63,1230609,"TERMINAL",0,0,"\r[42@[franz.srambical@hai003.haicore.berlin:~/vscode-crowd-pilot-chat] $ sour\r\n[?2004l\rbash: .venv/bin/activate: No such file or directory\r\n]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat[?2004h[franz.srambical@hai003.haicore.berlin:~/vscode-crowd-pilot-chat] $ ",,terminal_output +64,1232732,"TERMINAL",0,0,"c",,terminal_output +65,1232970,"TERMINAL",0,0,"d",,terminal_output +66,1233059,"TERMINAL",0,0," ",,terminal_output +67,1233468,"TERMINAL",0,0,"\r\n[?2004l\r]0;franz.srambical@hai-login1:~[?2004h[franz.srambical@hai003.haicore.berlin:~] $ ",,terminal_output +68,1234351,"TERMINAL",0,0,"c",,terminal_output +69,1234661,"TERMINAL",0,0,"d",,terminal_output +70,1234732,"TERMINAL",0,0," ",,terminal_output +71,1235002,"TERMINAL",0,0,"c",,terminal_output +72,1236659,"TERMINAL",0,0,"r",,terminal_output +73,1236820,"TERMINAL",0,0,"ow",,terminal_output +74,1237038,"TERMINAL",0,0,"d-",,terminal_output +75,1237435,"TERMINAL",0,0,"\r\n[?2004l\rbash: cd: crowd-: No such file or directory\r\n]0;franz.srambical@hai-login1:~[?2004h[franz.srambical@hai003.haicore.berlin:~] $ ",,terminal_output +76,1238253,"TERMINAL",0,0,"cd crowd-",,terminal_output +77,1238716,"TERMINAL",0,0,"p",,terminal_output +78,1238987,"TERMINAL",0,0,"ilot/",,terminal_output +79,1239317,"TERMINAL",0,0,"\r\n[?2004l\r]0;franz.srambical@hai-login1:~/crowd-pilot[?2004h[franz.srambical@hai003.haicore.berlin:~/crowd-pilot] $ ",,terminal_output +80,1240079,"TERMINAL",0,0,"[franz.srambical@hai003.haicore.berlin:~/crowd-pilot] $ ",,terminal_output +81,1240623,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +82,1240845,"TERMINAL",0,0,"s': source .venv/bin/activate\r[1@o': so",,terminal_output +83,1240942,"TERMINAL",0,0,"[1@u': sou",,terminal_output +84,1240990,"TERMINAL",0,0,"[1@r': sour",,terminal_output +85,1241255,"TERMINAL",0,0,"\r[30@[franz.srambical@hai003.haicore.berlin:~/crowd-pilot] $ sour\r\n[?2004l\r]0;franz.srambical@hai-login1:~/crowd-pilot[?2004h(crowd-pilot) [franz.srambical@hai003.haicore.berlin:~/crowd-pilot] $ ",,terminal_output +86,1241598,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +87,1242293,"TERMINAL",0,0,"s': source .venv/bin/activate\rg': python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0\r",,terminal_output +88,1243176,"TERMINAL",0,0,"\rcrowd-pilot) [franz.srambical@hai003.haicore.berlin:~/crowd-pilot] $ python3 -m sglang.launch_server --model-path qwen/qwen2.5-0.5b-instruct --host 0.0.0.0\r\n\r",,terminal_output +89,1243821,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +90,1256338,"TERMINAL",0,0,"2025-11-03 15:06:10.917404: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n",,terminal_output +91,1256424,"TERMINAL",0,0,"2025-11-03 15:06:10.974069: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n",,terminal_output +92,1260111,"TERMINAL",0,0,"2025-11-03 15:06:14.709115: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n",,terminal_output +93,1268996,"TERMINAL",0,0,"[2025-11-03 15:06:23] WARNING server_args.py:1104: Attention backend not explicitly specified. Use fa3 backend by default.\r\n[2025-11-03 15:06:23] INFO trace.py:48: opentelemetry package is not installed, tracing disabled\r\n",,terminal_output +94,1269546,"TERMINAL",0,0,"[2025-11-03 15:06:24] server_args=ServerArgs(model_path='qwen/qwen2.5-0.5b-instruct', tokenizer_path='qwen/qwen2.5-0.5b-instruct', tokenizer_mode='auto', tokenizer_worker_num=1, skip_tokenizer_init=False, load_format='auto', model_loader_extra_config='{}', trust_remote_code=False, context_length=None, is_embedding=False, enable_multimodal=None, revision=None, model_impl='auto', host='0.0.0.0', port=30000, grpc_mode=False, skip_server_warmup=False, warmups=None, nccl_port=None, checkpoint_engine_wait_weights_before_ready=False, dtype='auto', quantization=None, quantization_param_path=None, kv_cache_dtype='auto', enable_fp32_lm_head=False, modelopt_quant=None, modelopt_checkpoint_restore_path=None, modelopt_checkpoint_save_path=None, modelopt_export_path=None, quantize_and_serve=False, mem_fraction_static=0.835, max_running_requests=None, max_queued_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='fcfs', enable_priority_scheduling=False, abort_on_priority_when_disabled=False, schedule_low_priority_values_first=False, priority_scheduling_preemption_threshold=10, schedule_conservativeness=1.0, page_size=1, hybrid_kvcache_ratio=None, swa_full_tokens_ratio=0.8, disable_hybrid_swa_memory=False, radix_eviction_policy='lru', device='cuda', tp_size=1, pp_size=1, pp_max_micro_batch_size=None, stream_interval=1, stream_output=False, random_seed=916590368, constrained_json_whitespace_pattern=None, constrained_json_disable_any_whitespace=False, watchdog_timeout=300, dist_timeout=None, download_dir=None, base_gpu_id=0, gpu_id_step=1, sleep_on_idle=False, log_level='info', log_level_http=None, log_requests=False, log_requests_level=2, crash_dump_folder=None, show_time_cost=False, enable_metrics=False, enable_metrics_for_all_schedulers=False, tokenizer_metrics_custom_labels_header='x-custom-labels', tokenizer_metrics_allowed_custom_labels=None, bucket_time_to_first_token=None, bucket_inter_token_latency=None, bucket_e2e_request_latency=None, collect_tokens_histogram=False, prompt_tokens_buckets=None, generation_tokens_buckets=None, gc_warning_threshold_secs=0.0, decode_log_interval=40, enable_request_time_stats_logging=False, kv_events_config=None, enable_trace=False, oltp_traces_endpoint='localhost:4317', api_key=None, served_model_name='qwen/qwen2.5-0.5b-instruct', weight_version='default', chat_template=None, completion_template=None, file_storage_path='sglang_storage', enable_cache_report=False, reasoning_parser=None, tool_call_parser=None, tool_server=None, sampling_defaults='model', dp_size=1, load_balance_method='round_robin', load_watch_interval=0.1, prefill_round_robin_balance=False, dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', preferred_sampling_params=None, enable_lora=None, max_lora_rank=None, lora_target_modules=None, lora_paths=None, max_loaded_loras=None, max_loras_per_batch=8, lora_eviction_policy='lru', lora_backend='triton', max_lora_chunk_size=16, attention_backend='fa3', decode_attention_backend=None, prefill_attention_backend=None, sampling_backend='flashinfer', grammar_backend='xgrammar', mm_attention_backend=None, nsa_prefill_backend='flashmla_sparse', nsa_decode_backend='fa3', speculative_algorithm=None, speculative_draft_model_path=None, speculative_draft_model_revision=None, speculative_draft_load_format=None, speculative_num_steps=None, speculative_eagle_topk=None, speculative_num_draft_tokens=None, speculative_accept_threshold_single=1.0, speculative_accept_threshold_acc=1.0, speculative_token_map=None, speculative_attention_mode='prefill', speculative_ngram_min_match_window_size=1, speculative_ngram_max_match_window_size=12, speculative_ngram_min_bfs_breadth=1, speculative_ngram_max_bfs_breadth=10, speculative_ngram_match_type='BFS', speculative_ngram_branch_length=18, speculative_ngram_capacity=10000000, ep_size=1, moe_a2a_backend='none', moe_runner_backend='auto', flashinfer_mxfp4_moe_precision='default', enable_flashinfer_allreduce_fusion=False, deepep_mode='auto', ep_num_redundant_experts=0, ep_dispatch_algorithm='static', init_expert_location='trivial', enable_eplb=False, eplb_algorithm='auto', eplb_rebalance_num_iterations=1000, eplb_rebalance_layers_per_chunk=None, eplb_min_rebalancing_utilization_threshold=1.0, expert_distribution_recorder_mode=None, expert_distribution_recorder_buffer_size=1000, enable_expert_distribution_metrics=False, deepep_config=None, moe_dense_tp_size=None, elastic_ep_backend=None, mooncake_ib_device=None, max_mamba_cache_size=None, mamba_ssm_dtype='float32', mamba_full_memory_ratio=0.9, enable_hierarchical_cache=False, hicache_ratio=2.0, hicache_size=0, hicache_write_policy='write_through', hicache_io_backend='kernel', hicache_mem_layout='layer_first', hicache_storage_backend=None, hicache_storage_prefetch_policy='best_effort', hicache_storage_backend_extra_config=None, enable_lmcache=False, kt_amx_weight_path=None, kt_amx_method='AMXINT4', kt_cpuinfer=None, kt_threadpool_count=2, kt_num_gpu_experts=None, enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, cpu_offload_gb=0, offload_group_size=-1, offload_num_in_group=1, offload_prefetch_step=1, offload_mode='cpu', multi_item_scoring_delimiter=None, disable_radix_cache=False, cuda_graph_max_bs=256, cuda_graph_bs=[1, 2, 4, 8, 12, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256], disable_cuda_graph=False, disable_cuda_graph_padding=False, enable_profile_cuda_graph=False, enable_cudagraph_gc=False, enable_nccl_nvls=False, enable_symm_mem=False, disable_flashinfer_cutlass_moe_fp4_allgather=False, enable_tokenizer_batch_encode=False, disable_tokenizer_batch_decode=False, disable_outlines_disk_cache=False, disable_custom_all_reduce=False, enable_mscclpp=False, enable_torch_symm_mem=False, disable_overlap_schedule=False, enable_mixed_chunk=False, enable_dp_attention=False, enable_dp_lm_head=False, enable_two_batch_overlap=False, enable_single_batch_overlap=False, tbo_token_distribution_threshold=0.48, enable_torch_compile=False, enable_piecewise_cuda_graph=False, torch_compile_max_bs=32, piecewise_cuda_graph_max_tokens=4096, piecewise_cuda_graph_tokens=[4, 8, 12, 16, 20, 24, 28, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256, 288, 320, 352, 384, 416, 448, 480, 512, 640, 768, 896, 1024, 1152, 1280, 1408, 1536, 1664, 1792, 1920, 2048, 2176, 2304, 2432, 2560, 2688, 2816, 2944, 3072, 3200, 3328, 3456, 3584, 3712, 3840, 3968, 4096], piecewise_cuda_graph_compiler='eager', torchao_config='', enable_nan_detection=False, enable_p2p_check=False, triton_attention_reduce_in_fp32=False, triton_attention_num_kv_splits=8, triton_attention_split_tile_size=None, num_continuous_decode_steps=1, delete_ckpt_after_loading=False, enable_memory_saver=False, enable_weights_cpu_backup=False, allow_auto_truncate=False, enable_custom_logit_processor=False, flashinfer_mla_disable_ragged=False, disable_shared_experts_fusion=False, disable_chunked_prefix_cache=False, disable_fast_image_processor=False, keep_mm_feature_on_device=False, enable_return_hidden_states=False, scheduler_recv_interval=1, numa_node=None, enable_deterministic_inference=False, rl_on_policy_target=None, enable_dynamic_batch_tokenizer=False, dynamic_batch_tokenizer_batch_size=32, dynamic_batch_tokenizer_batch_timeout=0.002, debug_tensor_dump_output_folder=None, debug_tensor_dump_input_file=None, debug_tensor_dump_inject=False, disaggregation_mode='null', disaggregation_transfer_backend='mooncake', disaggregation_bootstrap_port=8998, disaggregation_decode_tp=None, disaggregation_decode_dp=None, disaggregation_prefill_pp=1, disaggregation_ib_device=None, disaggregation_decode_enable_offload_kvcache=False, num_reserved_decode_tokens=512, disaggregation_decode_polling_interval=1, custom_weight_loader=[], weight_loader_disable_mmap=False, remote_instance_weight_loader_seed_instance_ip=None, remote_instance_weight_loader_seed_instance_service_port=None, remote_instance_weight_loader_send_weights_group_ports=None, enable_pdmux=False, pdmux_config_path=None, sm_group_num=8)\r\n",,terminal_output +95,1270581,"TERMINAL",0,0,"[2025-11-03 15:06:25] Using default HuggingFace chat template with detected content format: string\r\n",,terminal_output +96,1276785,"TERMINAL",0,0,"bash",,terminal_focus +97,1282178,"TERMINAL",0,0,"squeue",,terminal_command +98,1282210,"TERMINAL",0,0,"]633;C JOBID USER PARTITION NODES CPUS ST SUBMIT_TIME START_TIME TIME TIME_LIMIT NODELIST(REASON)\r\n 33358 franz.sram interacti 1 20 R 2025-11-03T15:05:33 2025-11-03T15:05:33 1:03 1-00:00:00 hai003\r\n 33317 xiao.liu interacti 1 128 R 2025-11-02T17:43:38 2025-11-02T17:43:38 21:22:58 23:59:00 hai006\r\n 33350 nishant.ku standard 1 8 R 2025-11-03T14:45:30 2025-11-03T14:45:37 20:59 1-00:00:00 hai001\r\n 33328 kalyan.nad standard 1 64 R 2025-11-03T11:56:23 2025-11-03T11:56:38 3:09:58 1-00:00:00 hai002\r\n 33318 xiao.liu standard 1 128 R 2025-11-02T19:29:40 2025-11-02T19:30:38 19:35:58 23:59:00 hai004\r\n]0;franz.srambical@hai-login1:~/vscode-crowd-pilot-chat",,terminal_output +99,1285870,"TERMINAL",0,0,"[2025-11-03 15:06:40] INFO trace.py:48: opentelemetry package is not installed, tracing disabled\r\n",,terminal_output +100,1286728,"TERMINAL",0,0,"srun",,terminal_focus +101,1288033,"TERMINAL",0,0,"[2025-11-03 15:06:42] Init torch distributed begin.\r\n",,terminal_output +102,1288250,"TERMINAL",0,0,"[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\r\n[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\r\n[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\r\n[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\r\n[2025-11-03 15:06:42] Init torch distributed ends. mem usage=0.00 GB\r\n",,terminal_output +103,1288299,"TERMINAL",0,0,"[2025-11-03 15:06:42] MOE_RUNNER_BACKEND is not initialized, the backend will be automatically selected\r\n",,terminal_output +104,1289767,"TERMINAL",0,0,"[2025-11-03 15:06:44] Load weight begin. avail mem=78.68 GB\r\n",,terminal_output +105,1289871,"TERMINAL",0,0,"[2025-11-03 15:06:44] TensorFlow version 2.20.0 available.\r\n",,terminal_output +106,1291023,"TERMINAL",0,0,"[2025-11-03 15:06:45] Using model weights format ['*.safetensors']\r\n",,terminal_output +107,1291548,"TERMINAL",0,0,"[2025-11-03 15:06:46] No model.safetensors.index.json found in remote.\r\n\rLoading safetensors checkpoint shards: 0% Completed | 0/1 [00:00.\n */\n\n#include QMK_KEYBOARD_H\n\nenum anne_pro_layers {\n BASE,\n FN1,\n FN2,\n};\n\n// clang-format off\n// Key symbols are based on QMK. Use them to remap your keyboard\n/*\n* Layer BASE\n* ,-----------------------------------------------------------------------------------------.\n* | esc | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 0 | - | = | Bksp |\n* |-----------------------------------------------------------------------------------------+\n* | Tab | q | w | e | r | t | y | u | i | o | p | [ | ] | \ |\n* |-----------------------------------------------------------------------------------------+\n* | Caps | a | s | d | f | g | h | j | k | l | ; | ' | Enter |\n* |-----------------------------------------------------------------------------------------+\n* | Shift | z | x | c | v | b | n | m | , | . | / | Shift |\n* |-----------------------------------------------------------------------------------------+\n* | Ctrl | L1 | Alt | space | Alt | FN1 | FN2 | Ctrl |\n* \-----------------------------------------------------------------------------------------/\n* Layer TAP in BASE\n* ,-----------------------------------------------------------------------------------------.\n* | | | | | | | | | | | | | | |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | | | | | | | | | |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | | | | | | | | |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | | | | | | | UP |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | LEFT | DOWN | RIGHT |\n* \-----------------------------------------------------------------------------------------/\n*/\n const uint16_t keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n [BASE] = LAYOUT_60_ansi( /* Base */\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n LT(FN1, KC_CAPS), KC_A, KC_S, KC_D, KC_F, KC_G, KC_H, KC_J, KC_K, KC_L, KC_SCLN, KC_QUOT, KC_ENT,\n KC_LSFT, KC_Z, KC_X, KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_UP),\n KC_LCTL, KC_LGUI, KC_LALT, KC_SPC, KC_RALT, LT(FN1, KC_LEFT), LT(FN2, KC_DOWN), RCTL_T(KC_RGHT)\n),\n /*\n * Layer FN1\n * ,-----------------------------------------------------------------------------------------.\n * | ` | F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | F10 | F11 | F12 | DELETE |\n * |-----------------------------------------------------------------------------------------+\n * | Tab | q | UP | e | r | t | y | u | i | o | PS | HOME | END | \ |\n * |-----------------------------------------------------------------------------------------+\n * | Esc |LEFT |DOWN |RIGHT| f | g | h | j | k | l | PGUP|PGDN | Enter |\n * |-----------------------------------------------------------------------------------------+\n * | Shift |V-UP |V-DWN|MUTE | v | b | n | m | , |INSRT| DEL | Shift |\n * |-----------------------------------------------------------------------------------------+\n * | Ctrl | L1 | Alt | space | Alt | FN1 | FN2 | Ctrl |\n * \-----------------------------------------------------------------------------------------/\n *\n */\n [FN1] = LAYOUT_60_ansi( /* FN1 */\n KC_GRV, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, KC_F12, KC_DEL,\n _______, _______, KC_UP, _______, _______, _______, _______, _______, _______, _______, KC_PSCR, KC_HOME, KC_END, _______,\n _______, KC_LEFT, KC_DOWN, KC_RGHT, _______, _______, _______, _______, _______, _______, KC_PGUP, KC_PGDN, _______,\n _______, KC_VOLU, KC_VOLD, KC_MUTE, _______, _______, _______, _______, _______, KC_INS, KC_DEL, _______,\n _______, _______, _______, _______, _______, _______, MO(FN2), _______\n),\n /*\n * Layer FN2\n * ,-----------------------------------------------------------------------------------------.\n * | ~ | BT1 | BT2 | BT3 | BT4 | F5 | F6 | F7 | F8 | MOD | TOG | BRI- | BRI+ | Bksp |\n * |-----------------------------------------------------------------------------------------+\n * | Tab | q | UP | e | r | t | y | u | i | o | PS | HOME | END | \ |\n * |-----------------------------------------------------------------------------------------+\n * | Esc |LEFT |DOWN |RIGHT| f | g | h | j | k | l | PGUP|PGDN | Enter |\n * |-----------------------------------------------------------------------------------------+\n * | Shift | z | x | c | v | b | n | m | , |INSRT| DEL | Shift |\n * |-----------------------------------------------------------------------------------------+\n * | Ctrl | L1 | Alt | space | Alt | FN1 | FN2 | Ctrl |\n * \-----------------------------------------------------------------------------------------/\n *\n */\n [FN2] = LAYOUT_60_ansi( /* FN2 */\n _______, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, _______, _______, _______, _______, KC_AP_RGB_MOD, KC_AP_RGB_TOG, KC_AP_RGB_VAD, KC_AP_RGB_VAI, _______,\n _______, _______, KC_UP, _______, _______, _______, _______, _______, _______, _______, KC_PSCR, KC_HOME, KC_END, _______,\n _______, KC_LEFT, KC_DOWN, KC_RGHT, _______, _______, _______, _______, _______, _______, KC_PGUP, KC_PGDN, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, KC_INS, KC_DEL, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n};\n// clang-format on\n",c,tab +5,19235,"keyboards/annepro2/keymaps/default/keymap.c",787,0,"",c,selection_mouse +6,19242,"keyboards/annepro2/keymaps/default/keymap.c",786,0,"",c,selection_command +7,74109,"keyboards/annepro2/keymaps/default/keymap.c",787,0,"",c,selection_mouse +8,74116,"keyboards/annepro2/keymaps/default/keymap.c",786,0,"",c,selection_command +9,74698,"keyboards/annepro2/keymaps/default/keymap.c",0,7451," /* Copyright 2021 OpenAnnePro community\n *\n * This program is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 2 of the License, or\n * (at your option) any later version.\n *\n * This program is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with this program. If not, see .\n */\n\n#include QMK_KEYBOARD_H\n\nenum anne_pro_layers {\n BASE,\n FN1,\n FN2,\n};\n\n// clang-format off\n// Key symbols are based on QMK. Use them to remap your keyboard\n/*\n* Layer BASE\n* ,-----------------------------------------------------------------------------------------.\n* | esc | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 0 | - | = | Bksp |\n* |-----------------------------------------------------------------------------------------+\n* | Tab | q | w | e | r | t | y | u | i | o | p | [ | ] | \ |\n* |-----------------------------------------------------------------------------------------+\n* | Caps | a | s | d | f | g | h | j | k | l | ; | ' | Enter |\n* |-----------------------------------------------------------------------------------------+\n* | Shift | z | x | c | v | b | n | m | , | . | / | Shift |\n* |-----------------------------------------------------------------------------------------+\n* | Ctrl | L1 | Alt | space | Alt | FN1 | FN2 | Ctrl |\n* \-----------------------------------------------------------------------------------------/\n* Layer TAP in BASE\n* ,-----------------------------------------------------------------------------------------.\n* | | | | | | | | | | | | | | |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | | | | | | | | | |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | | | | | | | | |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | | | | | | | UP |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | LEFT | DOWN | RIGHT |\n* \-----------------------------------------------------------------------------------------/\n*/\n const uint16_t keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n [BASE] = LAYOUT_60_ansi( /* Base */\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n LT(FN1, KC_CAPS), KC_A, KC_S, KC_D, KC_F, KC_G, KC_H, KC_J, KC_K, KC_L, KC_SCLN, KC_QUOT, KC_ENT,\n KC_LSFT, KC_Z, KC_X, KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_UP),\n KC_LCTL, KC_LGUI, KC_LALT, KC_SPC, KC_RALT, LT(FN1, KC_LEFT), LT(FN2, KC_DOWN), RCTL_T(KC_RGHT)\n),\n /*\n * Layer FN1\n * ,-----------------------------------------------------------------------------------------.\n * | ` | F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | F10 | F11 | F12 | DELETE |\n * |-----------------------------------------------------------------------------------------+\n * | Tab | q | UP | e | r | t | y | u | i | o | PS | HOME | END | \ |\n * |-----------------------------------------------------------------------------------------+\n * | Esc |LEFT |DOWN |RIGHT| f | g | h | j | k | l | PGUP|PGDN | Enter |\n * |-----------------------------------------------------------------------------------------+\n * | Shift |V-UP |V-DWN|MUTE | v | b | n | m | , |INSRT| DEL | Shift |\n * |-----------------------------------------------------------------------------------------+\n * | Ctrl | L1 | Alt | space | Alt | FN1 | FN2 | Ctrl |\n * \-----------------------------------------------------------------------------------------/\n *\n */\n [FN1] = LAYOUT_60_ansi( /* FN1 */\n KC_GRV, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, KC_F12, KC_DEL,\n _______, _______, KC_UP, _______, _______, _______, _______, _______, _______, _______, KC_PSCR, KC_HOME, KC_END, _______,\n _______, KC_LEFT, KC_DOWN, KC_RGHT, _______, _______, _______, _______, _______, _______, KC_PGUP, KC_PGDN, _______,\n _______, KC_VOLU, KC_VOLD, KC_MUTE, _______, _______, _______, _______, _______, KC_INS, KC_DEL, _______,\n _______, _______, _______, _______, _______, _______, MO(FN2), _______\n),\n /*\n * Layer FN2\n * ,-----------------------------------------------------------------------------------------.\n * | ~ | BT1 | BT2 | BT3 | BT4 | F5 | F6 | F7 | F8 | MOD | TOG | BRI- | BRI+ | Bksp |\n * |-----------------------------------------------------------------------------------------+\n * | Tab | q | UP | e | r | t | y | u | i | o | PS | HOME | END | \ |\n * |-----------------------------------------------------------------------------------------+\n * | Esc |LEFT |DOWN |RIGHT| f | g | h | j | k | l | PGUP|PGDN | Enter |\n * |-----------------------------------------------------------------------------------------+\n * | Shift | z | x | c | v | b | n | m | , |INSRT| DEL | Shift |\n * |-----------------------------------------------------------------------------------------+\n * | Ctrl | L1 | Alt | space | Alt | FN1 | FN2 | Ctrl |\n * \-----------------------------------------------------------------------------------------/\n *\n */\n [FN2] = LAYOUT_60_ansi( /* FN2 */\n _______, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, _______, _______, _______, _______, KC_AP_RGB_MOD, KC_AP_RGB_TOG, KC_AP_RGB_VAD, KC_AP_RGB_VAI, _______,\n _______, _______, KC_UP, _______, _______, _______, _______, _______, _______, _______, KC_PSCR, KC_HOME, KC_END, _______,\n _______, KC_LEFT, KC_DOWN, KC_RGHT, _______, _______, _______, _______, _______, _______, KC_PGUP, KC_PGDN, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, KC_INS, KC_DEL, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n};\n// clang-format on\n",c,selection_command +10,74940,"keyboards/annepro2/keymaps/default/keymap.c",7451,0,"",c,selection_command +11,76114,"keyboards/annepro2/keymaps/miryoku/keymap.c",0,0," /* Copyright 2021 OpenAnnePro community\n *\n * This program is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 2 of the License, or\n * (at your option) any later version.\n *\n * This program is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with this program. If not, see .\n */\n\n #include QMK_KEYBOARD_H\n\n enum layers {\n BASE,\n NAV,\n MOUSE,\n MEDIA,\n NUMBERS,\n SYMBOLS,\n FUN,\n };\n \n // Helper: use S(KC_...) for shifted symbols where needed.\n \n const uint16_t PROGMEM keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n \n /* BASE\n - home-row mod-taps from your ZMK config (mt left/right mods)\n - thumb cluster uses LT(...) to switch into NAV/MOUSE/MEDIA/NUMBERS/SYMBOLS/FUN\n */\n [BASE] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n /* Row 2 */\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n /* Row 3 (home row with mod-taps) */\n LCTL_T(KC_ESC), LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), KC_SCLN, RGUI_T(KC_QUOT), KC_ENT,\n /* Row 4 */\n KC_LSFT, KC_Z, KC_X, KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_UP),\n /* Thumb cluster (8 keys) -- mapped from your < positions:\n LT(MEDIA, ESC) ; LT(NAV, SPACE) ; LT(MOUSE, TAB); transparent\n LT(SYMBOLS, ENTER) ; LT(NUMBERS, BSPC) ; transparent ; RCTL_T(RIGHT)\n */\n LT(MEDIA, KC_ESC), LT(NAV, KC_SPC), LT(MOUSE, KC_TAB), KC_TRNS, LT(SYMBOLS, KC_ENT), LT(NUMBERS, KC_BSPC), KC_TRNS, RCTL_T(KC_RGHT)\n ),\n \n /* NAV layer (ZMK ""Nav"")\n - clipboard shortcuts, arrows, caps, insert/page/home/end\n - bootloader mapped to RESET\n */\n [NAV] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, QK_BOOT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, LCTL(KC_Y), LCTL(KC_V), LCTL(KC_C), LCTL(KC_X), LCTL(KC_Z), KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_LGUI, KC_LALT, KC_LCTL, KC_LSFT, KC_TRNS, KC_LEFT, KC_DOWN, KC_UP, KC_RIGHT, KC_CAPS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_INS, KC_PGDN, KC_PGUP, KC_HOME, KC_END, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_ENT, KC_BSPC, KC_DEL, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* MOUSE layer (ZMK ""Mouse"")\n - mouse movement and wheel, mouse buttons, and keep clipboard shortcuts on top row\n */\n [MOUSE] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, QK_BOOT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, LCTL(KC_Y), LCTL(KC_V), LCTL(KC_C), LCTL(KC_X), LCTL(KC_Z), KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_LGUI, KC_LALT, KC_LCTL, KC_LSFT, KC_TRNS, KC_MS_LEFT, KC_MS_DOWN, KC_MS_UP, KC_MS_RIGHT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_BTN2, KC_BTN1, KC_BTN3, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* MEDIA layer (ZMK ""Media"")\n - media controls, volume, playlist, Bluetooth select (kept Anne Pro special codes),\n - brightness/RGB mapped to AP-specific codes (as seen in stock AnnePro2 keymap)\n */\n [MEDIA] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_GRV, QK_BOOT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_AP_RGB_VAD, KC_AP_RGB_VAI, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_LGUI, KC_LALT, KC_LCTL, KC_LSFT, KC_TRNS, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_MUTE, KC_MPLY, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* NUMBERS layer (ZMK ""Numbers"")\n - numbers/numpad-like layout on alpha rows per your ZMK mapping\n */\n [NUMBERS] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, KC_LBRC, KC_7, KC_8, KC_9, KC_RBRC, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_SCLN, KC_4, KC_5, KC_6, KC_EQL, KC_TRNS, KC_RSFT, KC_RCTL, KC_RALT, KC_RGUI, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_GRV, KC_1, KC_2, KC_3, KC_BSLS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_DOT, KC_0, KC_MINS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* SYMBOLS layer (ZMK ""Symbols"")\n - uses S(KC_...) where sensible (shifted versions of existing keys)\n */\n [SYMBOLS] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, S(KC_LBRC), S(KC_7), S(KC_8), S(KC_9), S(KC_RBRC), KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_COLN, S(KC_4), S(KC_5), S(KC_6), S(KC_EQL), KC_TRNS, KC_RSFT, KC_RCTL, KC_RALT, KC_RGUI, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, S(KC_GRV), KC_EXLM, KC_AT, KC_HASH, KC_PIPE, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 4 */\n S(KC_9), S(KC_0), S(KC_MINS), KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* FUN layer (ZMK ""Fun"")\n - F1..F12 and application/menu key etc.\n */\n [FUN] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, KC_F12, KC_F7, KC_F8, KC_F9, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_F11, KC_F4, KC_F5, KC_F6, KC_TRNS, KC_TRNS, KC_RSFT, KC_RCTL, KC_RALT, KC_RGUI, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_F10, KC_F1, KC_F2, KC_F3, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_APP, KC_SPC, KC_TAB, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n )\n }; // keymaps\n \n ",c,tab +12,76473,"keyboards/annepro2/keymaps/miryoku/keymap.c",849,0,"",c,selection_mouse +13,76480,"keyboards/annepro2/keymaps/miryoku/keymap.c",848,0,"",c,selection_command +14,76694,"keyboards/annepro2/keymaps/miryoku/keymap.c",0,6939," /* Copyright 2021 OpenAnnePro community\n *\n * This program is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 2 of the License, or\n * (at your option) any later version.\n *\n * This program is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with this program. If not, see .\n */\n\n #include QMK_KEYBOARD_H\n\n enum layers {\n BASE,\n NAV,\n MOUSE,\n MEDIA,\n NUMBERS,\n SYMBOLS,\n FUN,\n };\n \n // Helper: use S(KC_...) for shifted symbols where needed.\n \n const uint16_t PROGMEM keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n \n /* BASE\n - home-row mod-taps from your ZMK config (mt left/right mods)\n - thumb cluster uses LT(...) to switch into NAV/MOUSE/MEDIA/NUMBERS/SYMBOLS/FUN\n */\n [BASE] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n /* Row 2 */\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n /* Row 3 (home row with mod-taps) */\n LCTL_T(KC_ESC), LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), KC_SCLN, RGUI_T(KC_QUOT), KC_ENT,\n /* Row 4 */\n KC_LSFT, KC_Z, KC_X, KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_UP),\n /* Thumb cluster (8 keys) -- mapped from your < positions:\n LT(MEDIA, ESC) ; LT(NAV, SPACE) ; LT(MOUSE, TAB); transparent\n LT(SYMBOLS, ENTER) ; LT(NUMBERS, BSPC) ; transparent ; RCTL_T(RIGHT)\n */\n LT(MEDIA, KC_ESC), LT(NAV, KC_SPC), LT(MOUSE, KC_TAB), KC_TRNS, LT(SYMBOLS, KC_ENT), LT(NUMBERS, KC_BSPC), KC_TRNS, RCTL_T(KC_RGHT)\n ),\n \n /* NAV layer (ZMK ""Nav"")\n - clipboard shortcuts, arrows, caps, insert/page/home/end\n - bootloader mapped to RESET\n */\n [NAV] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, QK_BOOT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, LCTL(KC_Y), LCTL(KC_V), LCTL(KC_C), LCTL(KC_X), LCTL(KC_Z), KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_LGUI, KC_LALT, KC_LCTL, KC_LSFT, KC_TRNS, KC_LEFT, KC_DOWN, KC_UP, KC_RIGHT, KC_CAPS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_INS, KC_PGDN, KC_PGUP, KC_HOME, KC_END, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_ENT, KC_BSPC, KC_DEL, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* MOUSE layer (ZMK ""Mouse"")\n - mouse movement and wheel, mouse buttons, and keep clipboard shortcuts on top row\n */\n [MOUSE] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, QK_BOOT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, LCTL(KC_Y), LCTL(KC_V), LCTL(KC_C), LCTL(KC_X), LCTL(KC_Z), KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_LGUI, KC_LALT, KC_LCTL, KC_LSFT, KC_TRNS, KC_MS_LEFT, KC_MS_DOWN, KC_MS_UP, KC_MS_RIGHT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_BTN2, KC_BTN1, KC_BTN3, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* MEDIA layer (ZMK ""Media"")\n - media controls, volume, playlist, Bluetooth select (kept Anne Pro special codes),\n - brightness/RGB mapped to AP-specific codes (as seen in stock AnnePro2 keymap)\n */\n [MEDIA] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_GRV, QK_BOOT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_AP_RGB_VAD, KC_AP_RGB_VAI, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_LGUI, KC_LALT, KC_LCTL, KC_LSFT, KC_TRNS, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_MUTE, KC_MPLY, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* NUMBERS layer (ZMK ""Numbers"")\n - numbers/numpad-like layout on alpha rows per your ZMK mapping\n */\n [NUMBERS] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, KC_LBRC, KC_7, KC_8, KC_9, KC_RBRC, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_SCLN, KC_4, KC_5, KC_6, KC_EQL, KC_TRNS, KC_RSFT, KC_RCTL, KC_RALT, KC_RGUI, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_GRV, KC_1, KC_2, KC_3, KC_BSLS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_DOT, KC_0, KC_MINS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* SYMBOLS layer (ZMK ""Symbols"")\n - uses S(KC_...) where sensible (shifted versions of existing keys)\n */\n [SYMBOLS] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, S(KC_LBRC), S(KC_7), S(KC_8), S(KC_9), S(KC_RBRC), KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_COLN, S(KC_4), S(KC_5), S(KC_6), S(KC_EQL), KC_TRNS, KC_RSFT, KC_RCTL, KC_RALT, KC_RGUI, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, S(KC_GRV), KC_EXLM, KC_AT, KC_HASH, KC_PIPE, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 4 */\n S(KC_9), S(KC_0), S(KC_MINS), KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n ),\n \n /* FUN layer (ZMK ""Fun"")\n - F1..F12 and application/menu key etc.\n */\n [FUN] = LAYOUT_60_ansi(\n /* Row 1 */\n KC_TRNS, KC_F12, KC_F7, KC_F8, KC_F9, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 2 */\n KC_TRNS, KC_F11, KC_F4, KC_F5, KC_F6, KC_TRNS, KC_TRNS, KC_RSFT, KC_RCTL, KC_RALT, KC_RGUI, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 3 */\n KC_TRNS, KC_F10, KC_F1, KC_F2, KC_F3, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Row 4 */\n KC_APP, KC_SPC, KC_TAB, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS,\n /* Thumbs */\n KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS, KC_TRNS\n )\n }; // keymaps\n \n ",c,selection_command +15,76976,"keyboards/annepro2/keymaps/miryoku/keymap.c",0,6939," /* Copyright 2021 OpenAnnePro community\n *\n * This program is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 2 of the License, or\n * (at your option) any later version.\n *\n * This program is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with this program. If not, see .\n */\n\n#include QMK_KEYBOARD_H\n\nenum anne_pro_layers {\n BASE,\n FN1,\n FN2,\n};\n\n// clang-format off\n// Key symbols are based on QMK. Use them to remap your keyboard\n/*\n* Layer BASE\n* ,-----------------------------------------------------------------------------------------.\n* | esc | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 0 | - | = | Bksp |\n* |-----------------------------------------------------------------------------------------+\n* | Tab | q | w | e | r | t | y | u | i | o | p | [ | ] | \ |\n* |-----------------------------------------------------------------------------------------+\n* | Caps | a | s | d | f | g | h | j | k | l | ; | ' | Enter |\n* |-----------------------------------------------------------------------------------------+\n* | Shift | z | x | c | v | b | n | m | , | . | / | Shift |\n* |-----------------------------------------------------------------------------------------+\n* | Ctrl | L1 | Alt | space | Alt | FN1 | FN2 | Ctrl |\n* \-----------------------------------------------------------------------------------------/\n* Layer TAP in BASE\n* ,-----------------------------------------------------------------------------------------.\n* | | | | | | | | | | | | | | |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | | | | | | | | | |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | | | | | | | | |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | | | | | | | UP |\n* |-----------------------------------------------------------------------------------------+\n* | | | | | | LEFT | DOWN | RIGHT |\n* \-----------------------------------------------------------------------------------------/\n*/\n const uint16_t keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n [BASE] = LAYOUT_60_ansi( /* Base */\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n LT(FN1, KC_CAPS), KC_A, KC_S, KC_D, KC_F, KC_G, KC_H, KC_J, KC_K, KC_L, KC_SCLN, KC_QUOT, KC_ENT,\n KC_LSFT, KC_Z, KC_X, KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_UP),\n KC_LCTL, KC_LGUI, KC_LALT, KC_SPC, KC_RALT, LT(FN1, KC_LEFT), LT(FN2, KC_DOWN), RCTL_T(KC_RGHT)\n),\n /*\n * Layer FN1\n * ,-----------------------------------------------------------------------------------------.\n * | ` | F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | F10 | F11 | F12 | DELETE |\n * |-----------------------------------------------------------------------------------------+\n * | Tab | q | UP | e | r | t | y | u | i | o | PS | HOME | END | \ |\n * |-----------------------------------------------------------------------------------------+\n * | Esc |LEFT |DOWN |RIGHT| f | g | h | j | k | l | PGUP|PGDN | Enter |\n * |-----------------------------------------------------------------------------------------+\n * | Shift |V-UP |V-DWN|MUTE | v | b | n | m | , |INSRT| DEL | Shift |\n * |-----------------------------------------------------------------------------------------+\n * | Ctrl | L1 | Alt | space | Alt | FN1 | FN2 | Ctrl |\n * \-----------------------------------------------------------------------------------------/\n *\n */\n [FN1] = LAYOUT_60_ansi( /* FN1 */\n KC_GRV, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, KC_F12, KC_DEL,\n _______, _______, KC_UP, _______, _______, _______, _______, _______, _______, _______, KC_PSCR, KC_HOME, KC_END, _______,\n _______, KC_LEFT, KC_DOWN, KC_RGHT, _______, _______, _______, _______, _______, _______, KC_PGUP, KC_PGDN, _______,\n _______, KC_VOLU, KC_VOLD, KC_MUTE, _______, _______, _______, _______, _______, KC_INS, KC_DEL, _______,\n _______, _______, _______, _______, _______, _______, MO(FN2), _______\n),\n /*\n * Layer FN2\n * ,-----------------------------------------------------------------------------------------.\n * | ~ | BT1 | BT2 | BT3 | BT4 | 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&kp S &kp D &kp F &kp G &kp H &kp J &kp K &kp L &mt RIGHT_CONTROL SEMICOLON &trans\n&mt LEFT_SHIFT F13 < 3 Z < 4 X &kp C &kp V &kp B &kp N &kp M &kp COMMA &kp DOT &mt RIGHT_SHIFT SLASH &trans\n&mt LEFT_ALT TAB &kp SPACE &kp LEFT_GUI &trans < 2 ENTER < 1 BACKSPACE &trans\n >;\n };\n\n Special-Chars {\n bindings = <\n&trans &trans &kp DQT &kp APOSTROPHE &trans &trans &trans &kp LEFT_BRACKET &kp RIGHT_BRACKET &kp BSLH &kp MINUS &trans\n&trans &kp EXCL &kp AT &kp POUND &kp DOLLAR &kp PERCENT &kp CARET &kp AMPERSAND &kp ASTERISK &kp LPAR &kp RPAR &trans\n&trans &kp NUMBER_1 &kp N2 &kp N3 &kp N4 &kp N5 &kp N6 &kp N7 &kp N8 &kp N9 &kp N0 &trans\n&trans &trans &trans &trans &trans &trans &trans\n >;\n };\n\n Arrows {\n bindings = <\n&trans &trans &trans &trans &trans &trans &trans &trans &trans &trans &trans &trans\n&trans &trans &kp TILDE &kp PIPE &kp GRAVE &trans &kp LEFT &kp DOWN_ARROW &kp UP_ARROW &kp RIGHT &trans &trans\n&trans &trans &trans &trans &trans &trans &trans &trans &trans &trans &trans &trans\n&trans &trans &trans &trans &trans &bootloader &bootloader\n >;\n };\n\n Hyper {\n bindings = <\n&trans &kp LS(LA(Q)) &kp LS(LA(W)) &kp LS(LA(F)) &kp LS(LA(P)) &kp LS(LA(B)) &mkp MCLK &kp TAB &kp LS(TAB) &kp LS(LA(Y)) &kp LS(LA(SLASH)) &trans\n&trans &kp LS(LA(A)) &kp LS(LA(R)) &kp TAB &kp LS(TAB) &kp LS(LA(G)) &mmv MOVE_LEFT &mmv MOVE_DOWN &mmv MOVE_UP &mmv MOVE_RIGHT &kp LS(LA(O)) &trans\n&trans &kp LS(LA(Z)) &kp LS(LA(X)) &kp LA(LS(C)) &kp LS(LA(D)) &kp LA(LS(V)) &msc SCRL_LEFT &msc SCRL_DOWN &msc SCRL_UP &msc SCRL_RIGHT &kp LS(LA(MINUS)) &trans\n&kp LS(LA(F7)) &kp LS(LA(F8)) &kp LCMD &kp LS(LA(F10)) &mkp LCLK &mkp RCLK &kp LS(LA(F13))\n >;\n };\n\n Numpad {\n bindings = <\n&trans &trans &trans &trans &trans &trans &trans &kp N7 &kp N8 &kp N9 &trans &trans\n&trans &trans &trans &kp FSLH &kp EQUAL &trans &trans &kp N4 &kp N5 &kp N6 &kp PLUS &trans\n&trans &trans &trans &trans &kp STAR &trans &kp NUMBER_0 &kp N1 &kp N2 &kp N3 &kp MINUS &trans\n&trans &trans &trans &trans &trans &trans &trans\n >;\n };\n\n FKeys-Media {\n bindings = <\n&trans &kp F1 &kp F2 &kp F3 &kp F4 &kp F5 &kp F6 &kp F7 &kp F8 &kp F9 &kp F10 &kp F11\n&trans &trans &trans &trans &trans &trans &trans &kp C_BRIGHTNESS_DEC &kp C_BRIGHTNESS_INC &trans &trans &bootloader\n&trans &trans &trans &trans &trans &trans &trans &kp C_MUTE &trans &trans &trans &trans\n&kp C_PREV &kp C_PLAY_PAUSE &kp C_NEXT &trans &kp C_VOLUME_DOWN &kp C_VOLUME_UP &trans\n >;\n };\n\n Numbers {\n bindings = <\n&trans &trans &trans &trans &trans &trans &trans &trans &trans &trans &trans &trans\n&trans &kp NUMBER_1 &kp NUMBER_2 &kp N3 &kp N4 &kp N5 &kp N6 &kp N7 &kp N8 &kp N9 &kp N0 &trans\n&trans &trans &trans &trans &trans &trans &trans &trans &trans &trans &trans &trans\n&trans &trans &trans &trans &trans &trans &trans\n >;\n };\n\n Bluetooth {\n bindings = <\n&trans &bt BT_CLR &bt BT_NXT &bt BT_PRV &bt BT_SEL 0 &trans &trans &trans &trans &trans &trans &trans\n&trans &trans &trans &trans &bt BT_SEL 1 &bt BT_SEL 2 &bt BT_SEL 3 &bt BT_SEL 4 &trans &trans &trans &trans\n&trans &trans &trans &trans &to 0 &trans &trans &trans &trans &trans &trans &trans\n&trans &trans &trans &trans &trans &trans &trans\n >;\n };\n };\n};\n",plaintext,tab +121,301005,"/Users/franzsrambical/zmk-config-calmar-one/config/calmar-one.keymap",0,59,"#define ZMK_POINTING_DEFAULT_MOVE_VAL 1500 // default: 600",plaintext,selection_command +122,307663,"/Users/franzsrambical/zmk-config-calmar-one/config/calmar-one.keymap",249,0,"",plaintext,selection_mouse +123,307671,"/Users/franzsrambical/zmk-config-calmar-one/config/calmar-one.keymap",248,0,"",plaintext,selection_command +124,309354,"/Users/franzsrambical/zmk-config-calmar-one/config/calmar-one.json",0,0,"",json,tab +125,313515,"/Users/franzsrambical/zmk-config-calmar-one/config/calmar-one.keymap",0,0,"",plaintext,tab +126,836461,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"/* Calmar One keymap for Anne Pro 2\n *\n * Approximates the user's ZMK layout:\n * - Layers: BASE, SYM (Special-Chars), NAV (Arrows), HYPER (Mouse/Hyper combos), NUM (Numpad), FKEY (F-keys/Media), NUMBERS, BT (Bluetooth)\n */\n\n#include QMK_KEYBOARD_H\n\nenum anne_pro_layers {\n BASE,\n SYM, // Special-Chars\n NAV, // Arrows / system\n HYPER, // Mouse + Hyper combos\n NUM, // Numpad\n FKEY, // F-keys / Media / Brightness\n NUMBERS, // Number row on home row\n BT // Bluetooth controls\n};\n\n// Convenience alias for transparent\n#define _______ KC_TRNS\n\n// clang-format off\nconst uint16_t keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n /* BASE (QWERTY-ish, with mods and layer-taps approximating ZMK) */\n [BASE] = LAYOUT_60_ansi(\n LT(FKEY, KC_DEL), KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n MT(MOD_LALT, KC_TAB), KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n MT(MOD_LCTL, KC_ESC), KC_A, KC_S, KC_D, KC_F, KC_G, KC_H, KC_J, KC_K, KC_L, RCTL_T(KC_SCLN), KC_QUOT, KC_ENT,\n LSFT_T(KC_F13), LT(HYPER, KC_Z), LT(NUM, KC_X), KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, RSFT_T(KC_SLSH), RSFT_T(KC_SLSH),\n KC_LCTL, KC_LGUI, KC_LALT, KC_SPC, KC_RALT, LT(NAV, KC_ENT), LT(SYM, KC_BSPC), RCTL_T(KC_RGHT)\n ),\n\n /* SYM (Special-Chars) */\n [SYM] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, KC_EXLM, KC_AT, KC_HASH, KC_DLR, KC_PERC, KC_CIRC, KC_AMPR, KC_LPRN, KC_RPRN, KC_MINS, KC_LBRC, KC_RBRC, KC_BSLS,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_EQL, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n\n /* NAV (Arrows / System) */\n [NAV] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, _______, _______, _______,\n _______, KC_BRID, KC_BRIU, KC_MUTE, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n\n /* HYPER (Mouse / Hyper combos) */\n [HYPER] = LAYOUT_60_ansi(\n _______, S(A(KC_Q)), S(A(KC_W)), S(A(KC_F)), S(A(KC_P)), S(A(KC_B)), KC_BTN3, KC_TAB, S(KC_TAB), S(A(KC_Y)), S(A(KC_SLSH)), _______, _______,\n _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______,\n _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______,\n S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), KC_BTN1, KC_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n\n /* NUM (Numpad on right side) */\n [NUM] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, KC_P7, KC_P8, KC_P9, _______, _______, _______, _______,\n _______, _______, _______, KC_PSLS, KC_EQL, _______, _______, KC_P4, KC_P5, KC_P6, KC_PPLS, _______, _______, _______,\n _______, _______, _______, _______, KC_PAST, _______, KC_P0, KC_P1, KC_P2, KC_P3, KC_PMNS, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n\n /* FKEY (F-Keys / Media / Brightness) */\n [FKEY] = LAYOUT_60_ansi(\n _______, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_BRID, KC_BRIU, _______, _______, QK_BOOT, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_MUTE, _______, _______, _______, _______, _______,\n KC_MPRV, KC_MPLY, KC_MNXT, _______, KC_VOLD, KC_VOLU, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n\n /* NUMBERS (Numbers on home row) */\n [NUMBERS] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n\n /* BT (Bluetooth controls for Anne Pro 2) */\n [BT] = LAYOUT_60_ansi(\n _______, KC_AP2_BT_UNPAIR, _______, _______, KC_AP2_USB, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n};\n// clang-format on\n\n\n",c,tab +127,843893,"keyboards/annepro2/keymaps/calmar_one/rules.mk",0,0,"MOUSEKEY_ENABLE = yes\nEXTRAKEY_ENABLE = yes\nCONSOLE_ENABLE = no\nCOMMAND_ENABLE = no\n\n",makefile,tab +128,849620,"keyboards/annepro2/keymaps/calmar_one/config.h",0,0,"#pragma once\n\n#define TAPPING_TERM 200\n#define PERMISSIVE_HOLD\n\n// Mouse keys: keep defaults; adjust if needed after testing\n\n",c,tab +129,854047,"keyboards/annepro2/keymaps/calmar_one/config.h",126,0,"",c,selection_mouse +130,902794,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab +131,904411,"keyboards/annepro2/keymaps/calmar_one/keymap.c",523,0,"",c,selection_mouse +132,1011899,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4315,120," _______, _______, _______, _______, _______, _______, _______, KC_BRID, KC_BRIU, _______, _______, QK_BOOT, _______, _______,",c,content +133,1011899,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2781,439," _______, S(A(KC_Q)), S(A(KC_W)), S(A(KC_F)), S(A(KC_P)), S(A(KC_B)), KC_BTN3, KC_TAB, S(KC_TAB), S(A(KC_Y)), S(A(KC_SLSH)), _______, _______, _______,\n _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______, _______,\n _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______, _______,",c,content +134,1027048,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4812,0,"",c,selection_mouse +135,1056026,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4812,0,"franzsrambical@MBF6N9WFVKFV qmk_firmware % make annepro2/c18:calmar_one\nMaking annepro2/c18 with keymap calmar_one\n\narm-none-eabi-gcc (Arm GNU Toolchain 14.3.Rel1 (Build arm-14.174)) 14.3.1 20250623\nCopyright (C) 2024 Free Software Foundation, Inc.\nThis is free software; see the source for copying conditions. There is NO\nwarranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n\nCompiling: quantum/keymap_introspection.c In file included from ./keyboards/annepro2/keymaps/calmar_one/keymap.c:7,\n from quantum/keymap_introspection.c:9:\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:54:74: error: 'KC_BTN3' undeclared here (not in a function); did you mean 'MS_BTN3'?\n 54 | _______, S(A(KC_Q)), S(A(KC_W)), S(A(KC_F)), S(A(KC_P)), S(A(KC_B)), KC_BTN3, KC_TAB, S(KC_TAB), S(A(KC_Y)), S(A(KC_SLSH)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:35:37: note: in definition of macro 'LAYOUT_60_ansi'\n 35 | { k0A, k0B, k0C, k0D, k0E, k0F, k0G, k0H, k0I, k0J, k0K, k0L, k0M, k0N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:55:75: error: 'KC_MS_L' undeclared here (not in a function); did you mean 'KC_MSEL'?\n 55 | _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:36:37: note: in definition of macro 'LAYOUT_60_ansi'\n 36 | { k1A, k1B, k1C, k1D, k1E, k1F, k1G, k1H, k1I, k1J, k1K, k1L, k1M, k1N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:55:85: error: 'KC_MS_D' undeclared here (not in a function); did you mean 'KC_MRWD'?\n 55 | _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:36:42: note: in definition of macro 'LAYOUT_60_ansi'\n 36 | { k1A, k1B, k1C, k1D, k1E, k1F, k1G, k1H, k1I, k1J, k1K, k1L, k1M, k1N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:55:95: error: 'KC_MS_U' undeclared here (not in a function); did you mean 'KC_MSEL'?\n 55 | _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:36:47: note: in definition of macro 'LAYOUT_60_ansi'\n 36 | { k1A, k1B, k1C, k1D, k1E, k1F, k1G, k1H, k1I, k1J, k1K, k1L, k1M, k1N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:55:106: error: 'KC_MS_R' undeclared here (not in a function); did you mean 'KC_MSEL'?\n 55 | _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:36:52: note: in definition of macro 'LAYOUT_60_ansi'\n 36 | { k1A, k1B, k1C, k1D, k1E, k1F, k1G, k1H, k1I, k1J, k1K, k1L, k1M, k1N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:56:75: error: 'KC_WH_L' undeclared here (not in a function); did you mean 'KC_WHOM'?\n 56 | _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:37:37: note: in definition of macro 'LAYOUT_60_ansi'\n 37 | { k2A, k2B, k2C, k2D, k2E, k2F, k2G, k2H, k2I, k2J, k2K, k2L, k2M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:56:85: error: 'KC_WH_D' undeclared here (not in a function); did you mean 'KC_WFWD'?\n 56 | _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:37:42: note: in definition of macro 'LAYOUT_60_ansi'\n 37 | { k2A, k2B, k2C, k2D, k2E, k2F, k2G, k2H, k2I, k2J, k2K, k2L, k2M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:56:95: error: 'KC_WH_U' undeclared here (not in a function); did you mean 'KC_WHOM'?\n 56 | _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:37:47: note: in definition of macro 'LAYOUT_60_ansi'\n 37 | { k2A, k2B, k2C, k2D, k2E, k2F, k2G, k2H, k2I, k2J, k2K, k2L, k2M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:56:106: error: 'KC_WH_R' undeclared here (not in a function); did you mean 'KC_WHOM'?\n 56 | _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:37:52: note: in definition of macro 'LAYOUT_60_ansi'\n 37 | { k2A, k2B, k2C, k2D, k2E, k2F, k2G, k2H, k2I, k2J, k2K, k2L, k2M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:57:64: error: 'KC_BTN1' undeclared here (not in a function); did you mean 'MS_BTN1'?\n 57 | S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), KC_BTN1, KC_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:38:32: note: in definition of macro 'LAYOUT_60_ansi'\n 38 | { k3A, XXX, k3C, k3D, k3E, k3F, k3G, k3H, k3I, k3J, k3K, k3L, k3M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:57:75: error: 'KC_BTN2' undeclared here (not in a function); did you mean 'MS_BTN2'?\n 57 | S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), KC_BTN1, KC_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:38:37: note: in definition of macro 'LAYOUT_60_ansi'\n 38 | { k3A, XXX, k3C, k3D, k3E, k3F, k3G, k3H, k3I, k3J, k3K, k3L, k3M, XXX }, \\n | ^~~\n [ERRORS]\n | \n | \n | \nmake[1]: *** [.build/obj_annepro2_c18_calmar_one/quantum/keymap_introspection.o] Error 1\nmake: Make finished with errors\n",c,content +136,1056037,"keyboards/annepro2/keymaps/calmar_one/keymap.c",12723,0," ",c,content 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+302,1059341,"keyboards/annepro2/keymaps/calmar_one/keymap.c",12593,0,"",c,selection_mouse +303,1059356,"keyboards/annepro2/keymaps/calmar_one/keymap.c",12592,0,"",c,selection_command +304,1060163,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4812,7911,"",c,content +305,1064519,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4812,0,"franzsrambical@MBF6N9WFVKFV qmk_firmware % make annepro2/c18:calmar_one\nMaking annepro2/c18 with keymap calmar_one\n\narm-none-eabi-gcc (Arm GNU Toolchain 14.3.Rel1 (Build arm-14.174)) 14.3.1 20250623\nCopyright (C) 2024 Free Software Foundation, Inc.\nThis is free software; see the source for copying conditions. There is NO\nwarranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n\nCompiling: quantum/keymap_introspection.c In file included from ./keyboards/annepro2/keymaps/calmar_one/keymap.c:7,\n from quantum/keymap_introspection.c:9:\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:54:74: error: 'KC_BTN3' undeclared here (not in a function); did you mean 'MS_BTN3'?\n 54 | _______, S(A(KC_Q)), S(A(KC_W)), S(A(KC_F)), S(A(KC_P)), S(A(KC_B)), KC_BTN3, KC_TAB, S(KC_TAB), S(A(KC_Y)), S(A(KC_SLSH)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:35:37: note: in definition of macro 'LAYOUT_60_ansi'\n 35 | { k0A, k0B, k0C, k0D, k0E, k0F, k0G, k0H, k0I, k0J, k0K, k0L, k0M, k0N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:55:75: error: 'KC_MS_L' undeclared here (not in a function); did you mean 'KC_MSEL'?\n 55 | _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:36:37: note: in definition of macro 'LAYOUT_60_ansi'\n 36 | { k1A, k1B, k1C, k1D, k1E, k1F, k1G, k1H, k1I, k1J, k1K, k1L, k1M, k1N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:55:85: error: 'KC_MS_D' undeclared here (not in a function); did you mean 'KC_MRWD'?\n 55 | _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:36:42: note: in definition of macro 'LAYOUT_60_ansi'\n 36 | { k1A, k1B, k1C, k1D, k1E, k1F, k1G, k1H, k1I, k1J, k1K, k1L, k1M, k1N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:55:95: error: 'KC_MS_U' undeclared here (not in a function); did you mean 'KC_MSEL'?\n 55 | _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:36:47: note: in definition of macro 'LAYOUT_60_ansi'\n 36 | { k1A, k1B, k1C, k1D, k1E, k1F, k1G, k1H, k1I, k1J, k1K, k1L, k1M, k1N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:55:106: error: 'KC_MS_R' undeclared here (not in a function); did you mean 'KC_MSEL'?\n 55 | _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), KC_MS_L, KC_MS_D, KC_MS_U, KC_MS_R, S(A(KC_O)), _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:36:52: note: in definition of macro 'LAYOUT_60_ansi'\n 36 | { k1A, k1B, k1C, k1D, k1E, k1F, k1G, k1H, k1I, k1J, k1K, k1L, k1M, k1N }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:56:75: error: 'KC_WH_L' undeclared here (not in a function); did you mean 'KC_WHOM'?\n 56 | _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:37:37: note: in definition of macro 'LAYOUT_60_ansi'\n 37 | { k2A, k2B, k2C, k2D, k2E, k2F, k2G, k2H, k2I, k2J, k2K, k2L, k2M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:56:85: error: 'KC_WH_D' undeclared here (not in a function); did you mean 'KC_WFWD'?\n 56 | _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:37:42: note: in definition of macro 'LAYOUT_60_ansi'\n 37 | { k2A, k2B, k2C, k2D, k2E, k2F, k2G, k2H, k2I, k2J, k2K, k2L, k2M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:56:95: error: 'KC_WH_U' undeclared here (not in a function); did you mean 'KC_WHOM'?\n 56 | _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:37:47: note: in definition of macro 'LAYOUT_60_ansi'\n 37 | { k2A, k2B, k2C, k2D, k2E, k2F, k2G, k2H, k2I, k2J, k2K, k2L, k2M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:56:106: error: 'KC_WH_R' undeclared here (not in a function); did you mean 'KC_WHOM'?\n 56 | _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), KC_WH_L, KC_WH_D, KC_WH_U, KC_WH_R, S(A(KC_MINS)), _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:37:52: note: in definition of macro 'LAYOUT_60_ansi'\n 37 | { k2A, k2B, k2C, k2D, k2E, k2F, k2G, k2H, k2I, k2J, k2K, k2L, k2M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:57:64: error: 'KC_BTN1' undeclared here (not in a function); did you mean 'MS_BTN1'?\n 57 | S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), KC_BTN1, KC_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:38:32: note: in definition of macro 'LAYOUT_60_ansi'\n 38 | { k3A, XXX, k3C, k3D, k3E, k3F, k3G, k3H, k3I, k3J, k3K, k3L, k3M, XXX }, \\n | ^~~\n./keyboards/annepro2/keymaps/calmar_one/keymap.c:57:75: error: 'KC_BTN2' undeclared here (not in a function); did you mean 'MS_BTN2'?\n 57 | S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), KC_BTN1, KC_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,\n | ^~~~~~~\n./.build/obj_annepro2_c18_calmar_one/src/default_keyboard.h:38:37: note: in definition of macro 'LAYOUT_60_ansi'\n 38 | { k3A, XXX, k3C, k3D, k3E, k3F, k3G, k3H, k3I, k3J, k3K, k3L, k3M, XXX }, \\n | ^~~\n [ERRORS]\n | \n | \n | \nmake[1]: *** [.build/obj_annepro2_c18_calmar_one/quantum/keymap_introspection.o] Error 1\nmake: Make finished with errors\n",c,content +306,1065276,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4812,7911,"",c,content +307,1135001,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2781,609," _______, S(A(KC_Q)), S(A(KC_W)), S(A(KC_F)), S(A(KC_P)), S(A(KC_B)), MS_BTN3, KC_TAB, S(KC_TAB), S(A(KC_Y)), S(A(KC_SLSH)), _______, _______, _______,\n _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, S(A(KC_O)), _______, _______, _______,\n _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, S(A(KC_MINS)), _______, _______,\n S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), MS_BTN1, MS_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,",c,content +308,1274075,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab +309,1274678,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab +310,1275871,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4813,0,"",c,selection_command +311,1276475,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4851,0,"",c,selection_command +312,1276815,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4881,0,"",c,selection_command +313,1278737,"keyboards/annepro2/keymaps/calmar_one/keymap.c",5011,0,"",c,selection_command +314,1278963,"keyboards/annepro2/keymaps/calmar_one/keymap.c",5141,0,"",c,selection_command +315,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",5383,101," KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL",c,content +316,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4813,327," /* FUN (Left-hand tertiary: function keys; duplicate thumbs) */\n [FUN] = LAYOUT_60_ansi(\n _______, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, KC_F12, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, QK_BOOT, _______,",c,content +317,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4154,652," /* SYM (Left-hand secondary: shifted symbols; duplicate thumbs) */\n [SYM] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, KC_EXLM, KC_AT, KC_HASH, KC_DLR, KC_PERC, KC_CIRC, KC_AMPR, KC_LPRN, KC_RPRN, KC_MINS, KC_LBRC, KC_RBRC, KC_BSLS,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_EQL, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL",c,content +318,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4046,101," KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL",c,content +319,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3502,34," /* NUM (Left-hand primary: numerals/symbols; duplicate thumbs) */",c,content +320,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2716,779," /* MEDIA (Right-hand: media; RGB and BT controls inline) */\n [MEDIA] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, _______, _______, _______,\n _______, RGB_VAI, RGB_VAD, KC_MUTE, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_MPLY, KC_MSTP, KC_MUTE, KC_RCTL",c,content +321,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2367,342," _______, _______, _______, _______, _______, _______, MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, _______, _______, _______,\n _______, MS_WHLU, MS_WHLD, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, MS_BTN1, MS_BTN2, MS_BTN3, KC_RCTL",c,content +322,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2051,55," /* MOUSE (Right-hand: mouse; movement mirrors NAV; buttons on right thumbs) */\n [MOUSE] = LAYOUT_60_ansi(",c,content +323,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1572,472," _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, _______, _______, _______,\n _______, KC_BRID, KC_BRIU, KC_MUTE, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL",c,content +324,2197140,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1388,53," /* NAV (Right-hand: arrows/edit; duplicate thumb taps) */\n [NAV] = LAYOUT_60_ansi(",c,content +325,2197141,"keyboards/annepro2/keymaps/calmar_one/keymap.c",758,623," KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT,\n KC_LSFT, KC_Z, KC_X, KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_SLSH),\n LT(MEDIA, KC_ESC), LT(MOUSE, KC_TAB), KC_LALT, LT(NAV, KC_SPC), LT(NUM, KC_BSPC), LT(SYM, KC_ENT), LT(FUN, KC_DEL), KC_RCTL",c,content +326,2197141,"keyboards/annepro2/keymaps/calmar_one/keymap.c",661,69," /* BASE (QWERTY with Miryoku-style home row mods and thumb LTs) */",c,content +327,2197141,"keyboards/annepro2/keymaps/calmar_one/keymap.c",280,204," NAV, // Right-hand: navigation/edit\n MOUSE, // Right-hand: mouse emulation\n MEDIA, // Right-hand: media / RGB / BT controls\n NUM, // Left-hand: numbers/symbols\n SYM, // Left-hand: shifted symbols\n FUN, // Left-hand: function/system",c,content +328,2504403,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3319,120," _______, _______, _______, KC_MUTE, _______, _______, _______, _______, _______, _______, _______, _______,",c,content +329,2504404,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1022,147," KC_CAPS, LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT,",c,content +330,3221006,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2627,120," _______, _______, _______, _______, _______, _______, _______, MS_WHLD, MS_WHLU, _______, _______, _______,",c,content +331,3586092,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2627,120," _______, MS_WHLU, MS_WHLD, _______, _______, _______, _______, _______, _______, _______, _______, _______,",c,content +332,3586123,"keyboards/annepro2/keymaps/calmar_one/keymap.c",5622,0," /* NUMBERS (Separate: numbers on home row; duplicate thumbs) */\n [NUMBERS] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n",c,content +333,3586123,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2627,120," _______, _______, _______, _______, _______, _______, _______, MS_WHLD, MS_WHLU, _______, _______, _______,",c,content +334,3586123,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2376,129," _______, _______, _______, _______, _______, _______, LCTL(KC_Z), LCTL(KC_Y), LCTL(KC_X), LCTL(KC_C), LCTL(KC_V), _______, _______, _______,",c,content +335,3586123,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2137,0," /* HYPER (Right-hand: Hyper combos + mouse movement/wheel; mouse buttons on thumbs) */\n [HYPER] = LAYOUT_60_ansi(\n _______, S(A(KC_Q)), S(A(KC_W)), S(A(KC_F)), S(A(KC_P)), S(A(KC_B)), _______, KC_TAB, S(KC_TAB), S(A(KC_Y)), S(A(KC_SLSH)), _______, _______, _______,\n _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, S(A(KC_O)), _______, _______, _______,\n _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, S(A(KC_MINS)), _______, _______,\n S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), MS_BTN1, MS_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, MS_BTN1, MS_BTN2, MS_BTN3, KC_RCTL\n ),\n\n",c,content +336,3586123,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1659,129," _______, _______, _______, _______, _______, _______, LCTL(KC_Z), LCTL(KC_Y), LCTL(KC_X), LCTL(KC_C), LCTL(KC_V), _______, _______, _______,",c,content +337,3586123,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1179,257," KC_LSFT, LT(HYPER, KC_Z), LT(NUM, KC_X), KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_SLSH),\n LT(MEDIA, KC_ESC), LT(MOUSE, KC_TAB), KC_LGUI, LT(NAV, KC_SPC), LT(NUM, KC_BSPC), LT(SYM, KC_ENT), LT(FUN, KC_DEL), KC_RCTL",c,content +338,3586123,"keyboards/annepro2/keymaps/calmar_one/keymap.c",551,0," NUMBERS, // Separate numbers on home row\n",c,content +339,3586123,"keyboards/annepro2/keymaps/calmar_one/keymap.c",368,96," MEDIA, // Right-hand: media / BT controls\n NUM, // Left-hand: numpad",c,content +340,3586123,"keyboards/annepro2/keymaps/calmar_one/keymap.c",324,0," HYPER, // Right-hand: Hyper combos + mouse movement/wheel\n",c,content +341,4071678,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab +342,4072538,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab +343,4087938,"keyboards/annepro2/keymaps/calmar_one/keymap.c",6012,0,"",c,selection_command +344,4088846,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab +345,4089129,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab +346,4094844,"keyboards/annepro2/keymaps/calmar_one/keymap.c",6007,0,"",c,selection_mouse +347,4094855,"keyboards/annepro2/keymaps/calmar_one/keymap.c",6006,0,"",c,selection_command +348,4094861,"keyboards/annepro2/keymaps/calmar_one/keymap.c",6006,1,"(",c,selection_mouse +349,4094869,"keyboards/annepro2/keymaps/calmar_one/keymap.c",6007,0,"",c,selection_command +350,4096093,"keyboards/annepro2/keymaps/calmar_one/keymap.c",6006,0,"",c,selection_command +351,4097522,"keyboards/annepro2/keymaps/calmar_one/keymap.c",7258,34,"",c,content +352,4097540,"keyboards/annepro2/keymaps/calmar_one/keymap.c",7258,0,"_______, _______, _______, _______",c,content +353,4097542,"keyboards/annepro2/keymaps/calmar_one/keymap.c",7240,7,"",c,content 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+521,4097758,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1451,0,"_______,",c,content +522,4097760,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1445,4,"",c,content +523,4097760,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1445,0,"RGHT)\n ),\n\n /* SYM (Special-Chars) */\n [SYM] = LAYOUT_60_ansi(\n _______",c,content +524,4097761,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1442,0,"RCTL_T(",c,content +525,4097762,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1439,0,"SPC",c,content +526,4097762,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1436,2,"",c,content +527,4097763,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1427,4,"",c,content +528,4097764,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1426,0,"SY",c,content +529,4097764,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1420,0,", KC_RALT, LT(NAV, KC_ENT",c,content +530,4097765,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1419,0,"P",c,content +531,4097769,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1417,1,"",c,content +532,4097772,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1414,0," ",c,content +533,4097773,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1404,8,"",c,content +534,4097776,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1404,0,"KC_LCTL, KC_LGUI, KC_LALT",c,content +535,4097776,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1381,0,")",c,content +536,4097777,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1374,0,"RSFT_T(",c,content +537,4097777,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1280,8,"",c,content +538,4097778,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1280,0,"F13),",c,content +539,4097779,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1277,0,"LSFT_T(",c,content +540,4097779,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1240,3,"",c,content +541,4097779,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1240,0,"CTL",c,content +542,4097780,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1236,1,"",c,content +543,4097780,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1225,7,"",c,content +544,4097789,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1222,1,"",c,content +545,4097790,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1211,7,"",c,content +546,4097791,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1208,1,"",c,content +547,4097792,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1197,7,"",c,content +548,4097792,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1182,1,"",c,content +549,4097793,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1171,7,"",c,content +550,4097794,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1171,0," ",c,content +551,4097795,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1168,1,"",c,content +552,4097796,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1157,7,"",c,content +553,4097797,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1157,0," ",c,content +554,4097798,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1154,1,"",c,content +555,4097798,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1143,7,"",c,content +556,4097799,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1140,1,"",c,content +557,4097799,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1129,7,"",c,content +558,4097799,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1123,4,"",c,content +559,4097801,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1123,0,"ESC)",c,content +560,4097802,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1120,0,"MT(MOD_LCTL, ",c,content +561,4097822,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1089,0," ",c,content +562,4097825,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1047,0," ",c,content +563,4097825,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1040,0," ",c,content +564,4097826,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1027,1,"",c,content +565,4097827,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1026,0,")",c,content +566,4097827,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1020,0,"MT(MOD_LALT, ",c,content +567,4097827,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1007,0," ",c,content +568,4097828,"keyboards/annepro2/keymaps/calmar_one/keymap.c",990,0," ",c,content +569,4097829,"keyboards/annepro2/keymaps/calmar_one/keymap.c",947,0," ",c,content +570,4097830,"keyboards/annepro2/keymaps/calmar_one/keymap.c",942,0," ",c,content +571,4097830,"keyboards/annepro2/keymaps/calmar_one/keymap.c",935,0," ",c,content +572,4097831,"keyboards/annepro2/keymaps/calmar_one/keymap.c",924,5,"",c,content +573,4097831,"keyboards/annepro2/keymaps/calmar_one/keymap.c",924,0,"DEL),",c,content +574,4097832,"keyboards/annepro2/keymaps/calmar_one/keymap.c",921,0,"LT(FKEY, ",c,content +575,4097832,"keyboards/annepro2/keymaps/calmar_one/keymap.c",876,9,"",c,content +576,4097833,"keyboards/annepro2/keymaps/calmar_one/keymap.c",876,0,"layer-taps approximating ZMK",c,content +577,4097833,"keyboards/annepro2/keymaps/calmar_one/keymap.c",838,28,"",c,content +578,4097834,"keyboards/annepro2/keymaps/calmar_one/keymap.c",838,0,"-ish, with",c,content +579,4097834,"keyboards/annepro2/keymaps/calmar_one/keymap.c",631,1,"",c,content +580,4097842,"keyboards/annepro2/keymaps/calmar_one/keymap.c",631,0," row",c,content +581,4097843,"keyboards/annepro2/keymaps/calmar_one/keymap.c",616,10,"",c,content +582,4097843,"keyboards/annepro2/keymaps/calmar_one/keymap.c",616,0,"N",c,content +583,4097844,"keyboards/annepro2/keymaps/calmar_one/keymap.c",530,69,"",c,content +584,4097845,"keyboards/annepro2/keymaps/calmar_one/keymap.c",530,0,"F-keys / Media / Brightness",c,content +585,4097845,"keyboards/annepro2/keymaps/calmar_one/keymap.c",516,5,"",c,content +586,4097846,"keyboards/annepro2/keymaps/calmar_one/keymap.c",516,0,"FKEY,",c,content +587,4097848,"keyboards/annepro2/keymaps/calmar_one/keymap.c",496,12,"",c,content +588,4097850,"keyboards/annepro2/keymaps/calmar_one/keymap.c",496,0,"N",c,content +589,4097854,"keyboards/annepro2/keymaps/calmar_one/keymap.c",422,56,"",c,content +590,4097855,"keyboards/annepro2/keymaps/calmar_one/keymap.c",422,0,"+ Hyper combo",c,content +591,4097855,"keyboards/annepro2/keymaps/calmar_one/keymap.c",404,13,"",c,content +592,4097856,"keyboards/annepro2/keymaps/calmar_one/keymap.c",404,0,"M",c,content +593,4097856,"keyboards/annepro2/keymaps/calmar_one/keymap.c",340,55,"",c,content +594,4097870,"keyboards/annepro2/keymaps/calmar_one/keymap.c",340,0,"Arrows / system\n HYPER",c,content +595,4097871,"keyboards/annepro2/keymaps/calmar_one/keymap.c",296,36,"",c,content +596,4097873,"keyboards/annepro2/keymaps/calmar_one/keymap.c",296,0,"Special-Chars\n NAV, ",c,content +597,4097874,"keyboards/annepro2/keymaps/calmar_one/keymap.c",282,3,"",c,content +598,4097874,"keyboards/annepro2/keymaps/calmar_one/keymap.c",282,0,"SYM",c,content +599,4097875,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3322,2,"",c,content +600,4097875,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3322,0,"KC",c,content +601,4097876,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3311,2,"",c,content +602,4097876,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3311,0,"KC",c,content +603,4097877,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3203,6,"",c,content +604,4097877,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3203,0,"KC_WH_",c,content +605,4097878,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3192,6,"",c,content +606,4097878,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3192,0,"KC_WH_",c,content +607,4097879,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3182,6,"",c,content +608,4097879,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3182,0,"KC_WH_",c,content +609,4097880,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3172,6,"",c,content +610,4097887,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3172,0,"KC_WH_",c,content +611,4097888,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3048,3,"",c,content +612,4097888,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3044,0,"KC_",c,content +613,4097889,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3039,2,"",c,content +614,4097890,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3037,1,"",c,content +615,4097890,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3033,0,"KC_",c,content +616,4097891,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3027,3,"",c,content +617,4097891,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3023,0,"KC_",c,content +618,4097891,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3017,3,"",c,content +619,4097892,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3013,0,"KC_",c,content +620,4097892,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2854,2,"",c,content +621,4097893,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2854,0,"KC",c,content +622,4097894,"keyboards/annepro2/keymaps/calmar_one/keymap.c",5729,0,"",c,selection_command +623,4114203,"keyboards/annepro2/keymaps/calmar_one/keymap.c",280,5205," NAV, // Right-hand: navigation/edit\n HYPER, // Right-hand: Hyper combos + mouse movement/wheel\n MOUSE, // Right-hand: mouse emulation\n MEDIA, // Right-hand: media / BT controls\n NUM, // Left-hand: numpad\n SYM, // Left-hand: shifted symbols\n FUN, // Left-hand: function/system\n NUMBERS, // Separate numbers on home row\n BT // Bluetooth controls\n};\n\n// Convenience alias for transparent\n#define _______ KC_TRNS\n\n// clang-format off\nconst uint16_t keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n /* BASE (QWERTY with Miryoku-style home row mods and thumb LTs) */\n [BASE] = LAYOUT_60_ansi(\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n KC_CAPS, LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT,\n KC_LSFT, LT(HYPER, KC_Z), LT(NUM, KC_X), KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_SLSH),\n LT(MEDIA, KC_ESC), LT(MOUSE, KC_TAB), KC_LGUI, LT(NAV, KC_SPC), LT(NUM, KC_BSPC), LT(SYM, KC_ENT), LT(FUN, KC_DEL), KC_RCTL\n ),\n\n /* NAV (Right-hand: arrows/edit; duplicate thumb taps) */\n [NAV] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, LCTL(KC_Z), LCTL(KC_Y), LCTL(KC_X), LCTL(KC_C), LCTL(KC_V), _______, _______, _______,\n _______, _______, _______, _______, _______, _______, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, _______, _______, _______,\n _______, KC_BRID, KC_BRIU, KC_MUTE, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* HYPER (Right-hand: Hyper combos + mouse movement/wheel; mouse buttons on thumbs) */\n [HYPER] = LAYOUT_60_ansi(\n _______, S(A(KC_Q)), S(A(KC_W)), S(A(KC_F)), S(A(KC_P)), S(A(KC_B)), _______, KC_TAB, S(KC_TAB), S(A(KC_Y)), S(A(KC_SLSH)), _______, _______, _______,\n _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, S(A(KC_O)), _______, _______, _______,\n _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, S(A(KC_MINS)), _______, _______,\n S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), MS_BTN1, MS_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, MS_BTN1, MS_BTN2, MS_BTN3, KC_RCTL\n ),\n\n /* MOUSE (Right-hand: mouse; movement mirrors NAV; buttons on right thumbs) */\n [MOUSE] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, LCTL(KC_Z), LCTL(KC_Y), LCTL(KC_X), LCTL(KC_C), LCTL(KC_V), _______, _______, _______,\n _______, _______, _______, _______, _______, _______, MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, MS_WHLD, MS_WHLU, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, MS_BTN1, MS_BTN2, MS_BTN3, KC_RCTL\n ),\n\n /* MEDIA (Right-hand: media; RGB and BT controls inline) */\n [MEDIA] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, _______, _______, _______,\n _______, _______, _______, KC_MUTE, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_MPLY, KC_MSTP, KC_MUTE, KC_RCTL\n ),\n\n /* NUM (Left-hand primary: numerals/symbols; duplicate thumbs) */\n [NUM] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, KC_P7, KC_P8, KC_P9, _______, _______, _______, _______,\n _______, _______, _______, KC_PSLS, KC_EQL, _______, _______, KC_P4, KC_P5, KC_P6, KC_PPLS, _______, _______, _______,\n _______, _______, _______, _______, KC_PAST, _______, KC_P0, KC_P1, KC_P2, KC_P3, KC_PMNS, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* SYM (Left-hand secondary: shifted symbols; duplicate thumbs) */\n [SYM] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, KC_EXLM, KC_AT, KC_HASH, KC_DLR, KC_PERC, KC_CIRC, KC_AMPR, KC_LPRN, KC_RPRN, KC_MINS, KC_LBRC, KC_RBRC, KC_BSLS,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_EQL, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* FUN (Left-hand tertiary: function keys; duplicate thumbs) */\n [FUN] = LAYOUT_60_ansi(\n _______, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, KC_F12, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, QK_BOOT, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* NUMBERS (Separate: numbers on home row; duplicate thumbs) */\n [NUMBERS] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n",c,content +624,4114247,"/Users/franzsrambical/zmk-config-calmar-one/config/calmar-one.keymap",0,0,"",plaintext,tab +625,4128392,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab 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KC_DLR, KC_PERC, KC_CIRC, KC_AMPR, KC_ASTR, KC_LPRN, KC_RPRN, _______, _______, _______,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,",c,content +634,4885143,"keyboards/annepro2/keymaps/calmar_one/keymap.c",3920,604," _______, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_BRID, KC_BRIU, _______, _______, QK_BOOT, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_MUTE, _______, _______, _______, _______, _______,\n KC_MPRV, KC_MPLY, KC_MNXT, _______, KC_VOLD, KC_VOLU, _______, _______, _______, _______, _______, _______,\n KC_MPRV, KC_MPLY, KC_MNXT, _______, KC_VOLD, KC_VOLU, _______, KC_RCTL",c,content +635,4885143,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2038,120," _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, QK_BOOT, QK_BOOT,",c,content +636,4885143,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1773,144," _______, _______, KC_TILD, KC_PIPE, KC_GRV, _______, LCTL(KC_Z), LCTL(KC_Y), LCTL(KC_X), LCTL(KC_C), LCTL(KC_V), _______, _______, _______,",c,content +637,5108867,"keyboards/annepro2/keymaps/calmar_one/keymap.c",4399,103," KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_MPLY, KC_MSTP, KC_MUTE, KC_RCTL",c,content +638,5108867,"keyboards/annepro2/keymaps/calmar_one/keymap.c",2037,120," _______, _______, _______, _______, _______, _______, _______, KC_HOME, KC_PGDN, KC_PGUP, KC_END, _______,",c,content +639,5108867,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1643,129," _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, QK_BOOT,",c,content +640,5527192,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1400,150," MT(MOD_LALT, KC_TAB), KC_SPC, KC_LGUI, _______, LT(NAV, KC_ENT), LT(SYM, KC_BSPC), _______, _______",c,content +641,5643122,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1400,126," LT(MEDIA, KC_ESC), KC_TAB, LT(MOUSE, KC_LGUI), LT(NAV, KC_SPC), LT(NUM, KC_BSPC), LT(SYM, KC_ENT), LT(FUN, KC_DEL), KC_RCTL",c,content +642,5643145,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1400,150," MT(MOD_LALT, KC_TAB), KC_SPC, LT(MOUSE, KC_LGUI), KC_SPC, LT(NAV, KC_ENT), LT(SYM, KC_BSPC), _______, KC_RCTL",c,content +643,9768256,"/Users/franzsrambical/Downloads/calmar_one(1).json",0,0,"{\n ""version"": 1,\n ""notes"": """",\n ""documentation"": ""\""This file is a QMK Configurator export. You can import this at . It can also be used directly with QMK's source code.\n\nTo setup your QMK environment check out the tutorial: \n\nYou can convert this file to a keymap.c using this command: `qmk json2c {keymap}`\n\nYou can compile this keymap using this command: `qmk compile {keymap}`\""\n"",\n ""keyboard"": ""annepro2/c18"",\n ""keymap"": ""calmar_one"",\n ""layout"": ""LAYOUT_60_ansi"",\n ""layers"": [\n [\n ""KC_ESC"",\n ""KC_1"",\n ""KC_2"",\n ""KC_3"",\n ""KC_4"",\n ""KC_5"",\n ""KC_6"",\n ""KC_7"",\n ""KC_8"",\n ""KC_9"",\n ""KC_0"",\n ""KC_MINS"",\n ""KC_EQL"",\n ""KC_BSPC"",\n ""KC_TAB"",\n ""KC_Q"",\n ""KC_W"",\n ""KC_E"",\n ""KC_R"",\n ""KC_T"",\n ""KC_Y"",\n ""KC_U"",\n ""KC_I"",\n ""KC_O"",\n ""KC_P"",\n ""KC_LBRC"",\n ""KC_RBRC"",\n ""KC_BSLS"",\n ""KC_RCTL"",\n ""LGUI(KC_A)"",\n ""LALT(KC_S)"",\n ""LCTL(KC_D)"",\n ""LSFT(KC_F)"",\n ""KC_G"",\n ""KC_H"",\n ""RSFT(KC_J)"",\n ""RCTL(KC_K)"",\n ""RALT(KC_K)"",\n ""RGUI(KC_SCLN)"",\n ""KC_QUOT"",\n ""KC_ENT"",\n ""KC_LSFT"",\n ""KC_Z"",\n ""LT(1,KC_X)"",\n ""LT(2,KC_C)"",\n ""LT(3,KC_V)"",\n ""KC_B"",\n ""KC_N"",\n ""LT(4,KC_M)"",\n ""LT(5,KC_COMM)"",\n ""KC_DOT"",\n ""KC_SLSH"",\n ""KC_UP"",\n ""KC_LCTL"",\n ""KC_LALT"",\n ""KC_LGUI"",\n ""KC_SPC"",\n ""KC_RALT"",\n ""KC_LEFT"",\n ""KC_DOWN"",\n ""KC_RGHT""\n ],\n [\n ""KC_NO"",\n ""ANY(KC_AP2_BT1)"",\n ""ANY(KC_AP2_BT2)"",\n ""ANY(KC_AP2_BT3)"",\n ""ANY(KC_AP2_BT4)"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_BRID"",\n ""KC_BRIU"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_MPRV"",\n ""KC_VOLD"",\n ""KC_VOLU"",\n ""KC_MNXT"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_MUTE"",\n ""KC_MPLY"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO""\n ],\n [\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_LEFT"",\n ""KC_DOWN"",\n ""KC_UP"",\n ""KC_RGHT"",\n ""KC_CAPS"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_INS"",\n ""KC_PGDN"",\n ""KC_PGUP"",\n ""KC_HOME"",\n ""KC_END"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO""\n ],\n [\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""MS_BTN1"",\n ""MS_BTN2"",\n ""MS_BTN3"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""MS_LEFT"",\n ""MS_DOWN"",\n ""MS_UP"",\n ""MS_RGHT"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""MS_WHLL"",\n ""MS_WHLD"",\n ""MS_WHLU"",\n ""MS_WHLR"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO""\n ],\n [\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_LPRN"",\n ""KC_RPRN"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_LCBR"",\n ""KC_AMPR"",\n ""KC_ASTR"",\n ""KC_NO"",\n ""KC_RCBR"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_COLN"",\n ""KC_DLR"",\n ""KC_PERC"",\n ""KC_CIRC"",\n ""KC_EQL"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_TILD"",\n ""KC_EXLM"",\n ""KC_AT"",\n ""KC_HASH"",\n ""KC_PIPE"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO""\n ],\n [\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_LBRC"",\n ""KC_7"",\n ""KC_8"",\n ""KC_9"",\n ""KC_RBRC"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_SCLN"",\n ""KC_4"",\n ""KC_5"",\n ""KC_6"",\n ""KC_EQL"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_GRV"",\n ""KC_1"",\n ""KC_2"",\n ""KC_3"",\n ""KC_BSLS"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO"",\n ""KC_NO""\n ]\n ],\n ""author"": """"\n}",json,tab +644,9769712,"/Users/franzsrambical/Downloads/calmar_one(1).json",569,0,"",json,selection_mouse +645,9769723,"/Users/franzsrambical/Downloads/calmar_one(1).json",568,0,"",json,selection_command +646,9807833,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",0,0,"/* Calmar One keymap for Anne Pro 2\n *\n * Approximates the user's ZMK layout:\n * - Layers: BASE, SYM (Special-Chars), NAV (Arrows), HYPER (Mouse/Hyper combos), NUM (Numpad), FKEY (F-keys/Media), NUMBERS, BT (Bluetooth)\n */\n\n#include QMK_KEYBOARD_H\n\nenum anne_pro_layers {\n BASE,\n NAV, // Right-hand: navigation/edit\n HYPER, // Right-hand: Hyper combos + mouse movement/wheel\n MOUSE, // Right-hand: mouse emulation\n MEDIA, // Right-hand: media / BT controls\n NUM, // Left-hand: numpad\n SYM, // Left-hand: shifted symbols\n FUN, // Left-hand: function/system\n NUMBERS, // Separate numbers on home row\n BT // Bluetooth controls\n};\n\n// Convenience alias for transparent\n#define _______ KC_TRNS\n\n// clang-format off\nconst uint16_t keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n /* BASE (QWERTY with Miryoku-style home row mods and thumb LTs) */\n [BASE] = LAYOUT_60_ansi(\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n KC_CAPS, LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT,\n KC_LSFT, LT(HYPER, KC_Z), LT(NUM, KC_X), KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_SLSH),\n MT(MOD_LALT, KC_TAB), KC_SPC, LT(MOUSE, KC_LGUI), KC_SPC, LT(NAV, KC_ENT), LT(SYM, KC_BSPC), _______, KC_RCTL\n ),\n\n /* NAV (Right-hand: arrows/edit; duplicate thumb taps) */\n [NAV] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, QK_BOOT,\n _______, _______, KC_TILD, KC_PIPE, KC_GRV, _______, LCTL(KC_Z), LCTL(KC_Y), LCTL(KC_X), LCTL(KC_C), LCTL(KC_V), _______, _______, _______,\n _______, _______, _______, _______, _______, _______, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_HOME, KC_PGDN, KC_PGUP, KC_END, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* HYPER (Right-hand: Hyper combos + mouse movement/wheel; mouse buttons on thumbs) */\n [HYPER] = LAYOUT_60_ansi(\n _______, S(A(KC_Q)), S(A(KC_W)), S(A(KC_F)), S(A(KC_P)), S(A(KC_B)), _______, KC_TAB, S(KC_TAB), S(A(KC_Y)), S(A(KC_SLSH)), _______, _______, _______,\n _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, S(A(KC_O)), _______, _______, _______,\n _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, S(A(KC_MINS)), _______, _______,\n S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), MS_BTN1, MS_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, MS_BTN1, MS_BTN2, MS_BTN3, KC_RCTL\n ),\n\n /* MOUSE (Right-hand: mouse; movement mirrors NAV; buttons on right thumbs) */\n [MOUSE] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, LCTL(KC_Z), LCTL(KC_Y), LCTL(KC_X), LCTL(KC_C), LCTL(KC_V), _______, _______, _______,\n _______, _______, _______, _______, _______, _______, MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, MS_WHLD, MS_WHLU, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, MS_BTN1, MS_BTN2, MS_BTN3, KC_RCTL\n ),\n\n /* MEDIA (Right-hand: media; RGB and BT controls inline) */\n [MEDIA] = LAYOUT_60_ansi(\n _______, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_BRID, KC_BRIU, _______, _______, QK_BOOT, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_MUTE, _______, _______, _______, _______, _______,\n KC_MPRV, KC_MPLY, KC_MNXT, _______, KC_VOLD, KC_VOLU, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_MPLY, KC_MSTP, KC_MUTE, KC_RCTL\n ),\n\n /* NUM (Left-hand primary: numerals/symbols; duplicate thumbs) */\n [NUM] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, KC_P7, KC_P8, KC_P9, _______, _______, _______, _______,\n _______, _______, _______, KC_PSLS, KC_EQL, _______, _______, KC_P4, KC_P5, KC_P6, KC_PPLS, _______, _______, _______,\n _______, _______, _______, _______, KC_PAST, _______, KC_P0, KC_P1, KC_P2, KC_P3, KC_PMNS, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* SYM (Left-hand secondary: shifted symbols; duplicate thumbs) */\n [SYM] = LAYOUT_60_ansi(\n _______, _______, S(KC_QUOT), KC_QUOT, _______, _______, _______, _______, KC_LBRC, KC_RBRC, KC_BSLS, KC_MINS, _______, _______,\n _______, KC_EXLM, KC_AT, KC_HASH, KC_DLR, KC_PERC, KC_CIRC, KC_AMPR, KC_ASTR, KC_LPRN, KC_RPRN, _______, _______, _______,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* FUN (Left-hand tertiary: function keys; duplicate thumbs) */\n [FUN] = LAYOUT_60_ansi(\n _______, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, KC_F12, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, QK_BOOT, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* NUMBERS (Separate: numbers on home row; duplicate thumbs) */\n [NUMBERS] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* BT (Bluetooth controls for Anne Pro 2) */\n [BT] = LAYOUT_60_ansi(\n _______, KC_AP2_BT_UNPAIR, _______, _______, KC_AP2_USB, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______\n ),\n};\n// clang-format on\n\n\n",c,tab +647,9809407,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",323,0,"",c,selection_mouse +648,9809418,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",322,0,"",c,selection_command +649,9824531,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",556,0,"",c,selection_mouse +650,9824538,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",555,0,"",c,selection_command +651,9825323,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",514,42," SYM, // Left-hand: shifted symbols",c,selection_command +652,9843484,"/Users/franzsrambical/Downloads/calmar_one(1).json",0,0,"",json,tab +653,9843970,"/Users/franzsrambical/Downloads/calmar_one(1).json",625,0,"",json,selection_mouse +654,9843976,"/Users/franzsrambical/Downloads/calmar_one(1).json",624,0,"",json,selection_command +655,9844463,"/Users/franzsrambical/Downloads/calmar_one(1).json",612,13," ""KC_4"",",json,selection_command +656,9846642,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",0,0,"",c,tab +657,9849986,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",248,0,"",c,selection_mouse +658,10020771,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",249,0,"// clang-format off\nconst uint16_t keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n [0] = LAYOUT_60_ansi(\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n KC_RCTL, LGUI(KC_A), LALT(KC_S), LCTL(KC_D), LSFT(KC_F), KC_G, KC_H, RSFT(KC_J), RCTL(KC_K), RALT(KC_K), RGUI(KC_SCLN), KC_QUOT, KC_ENT,\n KC_LSFT, KC_Z, LT(1,KC_X), LT(2,KC_C), LT(3,KC_V), KC_B, KC_N, LT(4,KC_M), LT(5,KC_COMM), KC_DOT, KC_SLSH, KC_UP,\n KC_LCTL, KC_LALT, KC_LGUI, KC_SPC, KC_RALT, KC_LEFT, KC_DOWN, KC_RGHT\n ),\n\n [1] = LAYOUT_60_ansi(\n KC_NO, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_BRID, KC_BRIU, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MUTE, KC_MPLY, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n\n [2] = LAYOUT_60_ansi(\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, KC_CAPS, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_INS, KC_PGDN, KC_PGUP, KC_HOME, KC_END, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n\n [3] = LAYOUT_60_ansi(\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_BTN1, MS_BTN2, MS_BTN3, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n\n [4] = LAYOUT_60_ansi(\n KC_NO, KC_NO, KC_NO, KC_LPRN, KC_RPRN, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_LCBR, KC_AMPR, KC_ASTR, KC_NO, KC_RCBR, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_COLN, KC_DLR, KC_PERC, KC_CIRC, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_TILD, KC_EXLM, KC_AT, KC_HASH, KC_PIPE, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n\n [5] = LAYOUT_60_ansi(\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_LBRC, KC_7, KC_8, KC_9, KC_RBRC, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_SCLN, KC_4, KC_5, KC_6, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_GRV, KC_1, KC_2, KC_3, KC_BSLS, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n};\n// clang-format on\n\n\n",c,content +659,10020771,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",0,222,"/* This keymap implements exactly the layout from the provided JSON export. */",c,content +660,10020793,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",3243,7753,"",c,content +661,10292183,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",3243,0,"\n",c,content +662,10292183,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",2744,470," /* BT (Bluetooth controls for Anne Pro 2) */\n [BT] = LAYOUT_60_ansi(\n _______, KC_AP2_BT_UNPAIR, _______, _______, KC_AP2_USB, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______",c,content +663,10292183,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",2240,497," /* NUMBERS (Separate: numbers on home row; duplicate thumbs) */\n [NUMBERS] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL",c,content +664,10292183,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",1744,489," /* MOUSE (Right-hand: mouse; movement mirrors NAV; buttons on right thumbs) */\n [MOUSE] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, LCTL(KC_Z), LCTL(KC_Y), LCTL(KC_X), LCTL(KC_C), LCTL(KC_V), _______, _______, _______,\n _______, _______, _______, _______, _______, _______, MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, MS_WHLD, MS_WHLU, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, MS_BTN1, MS_BTN2, MS_BTN3, KC_RCTL\n ),\n\n /* MEDIA (Right-hand: media; RGB and BT controls inline) */\n [MEDIA] = LAYOUT_60_ansi(\n _______, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_BRID, KC_BRIU, _______, _______, QK_BOOT, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_MUTE, _______, _______, _______, _______, _______,\n KC_MPRV, KC_MPLY, KC_MNXT, _______, KC_VOLD, KC_VOLU, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_MPLY, KC_MSTP, KC_MUTE, KC_RCTL\n ),\n\n /* NUM (Left-hand primary: numerals/symbols; duplicate thumbs) */\n [NUM] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, KC_P7, KC_P8, KC_P9, _______, _______, _______, _______,\n _______, _______, _______, KC_PSLS, KC_EQL, _______, _______, KC_P4, KC_P5, KC_P6, KC_PPLS, _______, _______, _______,\n _______, _______, _______, _______, KC_PAST, _______, KC_P0, KC_P1, KC_P2, KC_P3, KC_PMNS, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* SYM (Left-hand secondary: shifted symbols; duplicate thumbs) */\n [SYM] = LAYOUT_60_ansi(\n _______, _______, S(KC_QUOT), KC_QUOT, _______, _______, _______, _______, KC_LBRC, KC_RBRC, KC_BSLS, KC_MINS, _______, _______,\n _______, KC_EXLM, KC_AT, KC_HASH, KC_DLR, KC_PERC, KC_CIRC, KC_AMPR, KC_ASTR, KC_LPRN, KC_RPRN, _______, _______, _______,\n _______, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL\n ),\n\n /* FUN (Left-hand tertiary: function keys; duplicate thumbs) */\n [FUN] = LAYOUT_60_ansi(\n _______, KC_F1, KC_F2, KC_F3, KC_F4, KC_F5, KC_F6, KC_F7, KC_F8, KC_F9, KC_F10, KC_F11, KC_F12, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, QK_BOOT, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL",c,content +665,10292183,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",1252,485," /* HYPER (Right-hand: Hyper combos + mouse movement/wheel; mouse buttons on thumbs) */\n [HYPER] = LAYOUT_60_ansi(\n _______, S(A(KC_Q)), S(A(KC_W)), S(A(KC_F)), S(A(KC_P)), S(A(KC_B)), _______, KC_TAB, S(KC_TAB), S(A(KC_Y)), S(A(KC_SLSH)), _______, _______, _______,\n _______, S(A(KC_A)), S(A(KC_R)), KC_TAB, S(KC_TAB), S(A(KC_G)), MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, S(A(KC_O)), _______, _______, _______,\n _______, S(A(KC_Z)), S(A(KC_X)), A(S(KC_C)), S(A(KC_D)), A(S(KC_V)), MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, S(A(KC_MINS)), _______, _______,\n S(A(KC_F7)), S(A(KC_F8)), KC_LGUI, S(A(KC_F10)), MS_BTN1, MS_BTN2, S(A(KC_F13)), _______, _______, _______, _______, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, MS_BTN1, MS_BTN2, MS_BTN3, KC_RCTL",c,content +666,10292183,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",740,505," /* NAV (Right-hand: arrows/edit; duplicate thumb taps) */\n [NAV] = LAYOUT_60_ansi(\n _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, _______, QK_BOOT,\n _______, _______, KC_TILD, KC_PIPE, KC_GRV, _______, LCTL(KC_Z), LCTL(KC_Y), LCTL(KC_X), LCTL(KC_C), LCTL(KC_V), _______, _______, _______,\n _______, _______, _______, _______, _______, _______, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, _______, _______, _______,\n _______, _______, _______, _______, _______, _______, _______, KC_HOME, KC_PGDN, KC_PGUP, KC_END, _______,\n KC_ESC, KC_TAB, KC_LALT, KC_SPC, KC_BSPC, KC_ENT, KC_DEL, KC_RCTL",c,content +667,10292183,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",180,553," /* BASE (QWERTY with Miryoku-style home row mods and thumb LTs) */\n [BASE] = LAYOUT_60_ansi(\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n KC_CAPS, LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT,\n KC_LSFT, LT(HYPER, KC_Z), LT(NUM, KC_X), KC_C, KC_V, KC_B, KC_N, KC_M, KC_COMM, KC_DOT, KC_SLSH, RSFT_T(KC_SLSH),\n MT(MOD_LALT, KC_TAB), KC_SPC, LT(MOUSE, KC_LGUI), KC_SPC, LT(NAV, KC_ENT), LT(SYM, KC_BSPC), _______, KC_RCTL",c,content +668,10292183,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",105,0,"enum anne_pro_layers {\n BASE,\n NAV, // Right-hand: navigation/edit\n HYPER, // Right-hand: Hyper combos + mouse movement/wheel\n MOUSE, // Right-hand: mouse emulation\n MEDIA, // Right-hand: media / BT controls\n NUM, // Left-hand: numpad\n SYM, // Left-hand: shifted symbols\n FUN, // Left-hand: function/system\n NUMBERS, // Separate numbers on home row\n BT // Bluetooth controls\n};\n\n// Convenience alias for transparent\n#define _______ KC_TRNS\n\n",c,content +669,10292183,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",0,78,"/* Calmar One keymap for Anne Pro 2\n *\n * Approximates the user's ZMK layout:\n * - Layers: BASE, SYM (Special-Chars), NAV (Arrows), HYPER (Mouse/Hyper combos), NUM (Numpad), FKEY (F-keys/Media), NUMBERS, BT (Bluetooth)\n */",c,content +670,10292281,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",249,0,"// clang-format off\nconst uint16_t keymaps[][MATRIX_ROWS][MATRIX_COLS] = {\n [0] = LAYOUT_60_ansi(\n KC_ESC, KC_1, KC_2, KC_3, KC_4, KC_5, KC_6, KC_7, KC_8, KC_9, KC_0, KC_MINS, KC_EQL, KC_BSPC,\n KC_TAB, KC_Q, KC_W, KC_E, KC_R, KC_T, KC_Y, KC_U, KC_I, KC_O, KC_P, KC_LBRC, KC_RBRC, KC_BSLS,\n KC_RCTL, LGUI_T(KC_A), LALT_T(KC_S), LCTL_T(KC_D), LSFT_T(KC_F), KC_G, KC_H, RSFT_T(KC_J), RCTL_T(KC_K), RALT_T(KC_L), RGUI_T(KC_SCLN), KC_QUOT, KC_ENT,\n KC_LSFT, KC_Z, LT(1,KC_X), LT(2,KC_C), LT(3,KC_V), KC_B, KC_N, LT(4,KC_M), LT(5,KC_COMM), KC_DOT, KC_SLSH, KC_UP,\n KC_LCTL, KC_LALT, KC_LGUI, KC_SPC, KC_RALT, KC_LEFT, KC_DOWN, KC_RGHT\n ),\n\n [1] = LAYOUT_60_ansi(\n KC_NO, KC_AP2_BT1, KC_AP2_BT2, KC_AP2_BT3, KC_AP2_BT4, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_BRID, KC_BRIU, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MPRV, KC_VOLD, KC_VOLU, KC_MNXT, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_MUTE, KC_MPLY, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n\n [2] = LAYOUT_60_ansi(\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_LEFT, KC_DOWN, KC_UP, KC_RGHT, KC_CAPS, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_INS, KC_PGDN, KC_PGUP, KC_HOME, KC_END, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n\n [3] = LAYOUT_60_ansi(\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_BTN1, MS_BTN2, MS_BTN3, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_LEFT, MS_DOWN, MS_UP, MS_RGHT, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, MS_WHLL, MS_WHLD, MS_WHLU, MS_WHLR, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n\n [4] = LAYOUT_60_ansi(\n KC_NO, KC_NO, KC_NO, KC_LPRN, KC_RPRN, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_LCBR, KC_AMPR, KC_ASTR, KC_NO, KC_RCBR, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_COLN, KC_DLR, KC_PERC, KC_CIRC, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_TILD, KC_EXLM, KC_AT, KC_HASH, KC_PIPE, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n\n [5] = LAYOUT_60_ansi(\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_LBRC, KC_7, KC_8, KC_9, KC_RBRC, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_SCLN, KC_4, KC_5, KC_6, KC_EQL, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_GRV, KC_1, KC_2, KC_3, KC_BSLS, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO,\n KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO, KC_NO\n ),\n};\n// clang-format on\n\n\n",c,content +671,10292281,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",0,222,"/* This keymap implements exactly the layout from the provided JSON export. */",c,content +672,10292299,"keyboards/annepro2/keymaps/calmar_one-backup/keymap.c",3259,7753,"",c,content +673,38385939,"keyboards/annepro2/keymaps/calmar_one/keymap.c",0,0,"",c,tab +674,38385966,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1400,0,"",c,selection_command +675,38400101,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1401,0,"",c,selection_command +676,38400181,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1401,0,"a",c,content +677,38400189,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1402,0,"",c,selection_keyboard +678,38400427,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1402,0,"a",c,content +679,38400432,"keyboards/annepro2/keymaps/calmar_one/keymap.c",1403,0,"",c,selection_keyboard 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[info] Recording started\n8:19:17 PM [info] Initializing git provider using file system watchers...\n8:19:17 PM [info] Git repository found\n8:19:17 PM [info] Git provider initialized successfully\n8:19:17 PM [info] Initial git state: [object Object]\n8:19:32 PM [info] Branch checkout detected: main -> crowd-pilot-extension-submodule\n8:19:32 PM [info] Recording git checkout: Switched from branch 'main' to 'crowd-pilot-extension-submodule'\n8:19:32 PM [info] Resetting file cache due to branch checkout\n",Log,tab +7,17666,"TERMINAL",0,0,"",,terminal_focus +8,24113,".gitmodules",0,0,"[submodule ""maxtext""]\n\tpath = maxtext\n\turl = https://github.com/AI-Hypercomputer/maxtext\n[submodule ""crowd-pilot-extension""]\n\tpath = crowd-pilot-extension\n\turl = git@github.com:p-doom/crowd-pilot-extension.git\n",properties,tab +9,24132,".gitmodules",89,0,"",properties,selection_command diff --git 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*/\nREPLACED LINE 1\nREPLACED LINE 2",plaintext,content +47,250330,"Untitled-1",97,92,"",plaintext,content +48,250872,"Untitled-1",2,0,"/* crowd-pilot: insert start */\nline A\nline B\n/* crowd-pilot: insert end */\n",plaintext,content +49,251166,"Untitled-1",34,13,"/* crowd-pilot: replacement */\nREPLACED LINE 1\nREPLACED LINE 2",plaintext,content +50,251422,"Untitled-1",97,92,"",plaintext,content +51,251686,"Untitled-1",2,0,"/* crowd-pilot: insert start */\nline A\nline B\n/* crowd-pilot: insert end */\n",plaintext,content +52,252037,"Untitled-1",34,13,"/* crowd-pilot: replacement */\nREPLACED LINE 1\nREPLACED LINE 2",plaintext,content +53,252224,"Untitled-1",97,92,"",plaintext,content +54,252883,"Untitled-1",2,0,"/* crowd-pilot: insert start */\nline A\nline B\n/* crowd-pilot: insert end */\n",plaintext,content +55,253347,"Untitled-1",34,13,"/* crowd-pilot: replacement */\nREPLACED LINE 1\nREPLACED LINE 2",plaintext,content +56,254207,"Untitled-1",97,92,"",plaintext,content diff --git a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c540a54c-40c8-4e46-b509-2316a8a981721765978775045-2025_12_17-14.39.41.212/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c540a54c-40c8-4e46-b509-2316a8a981721765978775045-2025_12_17-14.39.41.212/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..c9622e332ccf66763482f8ff05c265f9bf012e89 --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c540a54c-40c8-4e46-b509-2316a8a981721765978775045-2025_12_17-14.39.41.212/source.csv @@ -0,0 +1,750 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,3,"crates/core/src/pipeline.rs",0,0,"//! Pipeline for processing CSV sessions into conversations.\n\nuse std::path::Path;\nuse std::sync::atomic::{AtomicUsize, Ordering};\n\nuse rayon::prelude::*;\nuse serde::{Deserialize, Serialize};\nuse walkdir::WalkDir;\n\nuse crate::conversation::{ConversationStateManager, ConversationStateManagerConfig, FinalizedConversation};\nuse crate::Tokenizer;\n\n/// A row from the CSV file.\n#[derive(Debug, Deserialize)]\n#[serde(rename_all = ""PascalCase"")]\nstruct CsvRow {\n #[serde(rename = ""Sequence"")]\n _sequence: Option,\n #[serde(rename = ""Time"")]\n _time: Option,\n file: String,\n range_offset: Option,\n range_length: Option,\n text: Option,\n #[serde(rename = ""Language"")]\n _language: Option,\n #[serde(rename = ""Type"")]\n event_type: String,\n}\n\n/// Configuration for the pipeline.\n#[derive(Debug, Clone)]\npub struct PipelineConfig {\n pub max_tokens_per_conversation: usize,\n pub max_tokens_per_message: usize,\n pub min_conversation_messages: usize,\n pub viewport_radius: usize,\n pub coalesce_radius: usize,\n pub val_ratio: f64,\n}\n\nimpl Default for PipelineConfig {\n fn default() -> Self {\n Self {\n max_tokens_per_conversation: 8192,\n max_tokens_per_message: 2048,\n min_conversation_messages: 5,\n viewport_radius: 10,\n coalesce_radius: 5,\n val_ratio: 0.1,\n }\n }\n}\n\n/// Result of processing a single session.\n#[derive(Debug)]\npub struct SessionResult {\n pub conversations: Vec,\n pub source_path: String,\n}\n\n/// Result of processing all sessions.\n#[derive(Debug, Serialize)]\npub struct PipelineResult {\n pub total_sessions: usize,\n pub total_conversations: usize,\n pub train_conversations: usize,\n pub val_conversations: usize,\n pub total_messages: usize,\n pub total_tokens: usize,\n}\n\n/// NeMo conversation record format.\n#[derive(Debug, Serialize)]\npub struct NemoRecord {\n pub mask: String,\n pub system: String,\n pub conversations: Vec,\n}\n\n/// A message in NeMo format.\n#[derive(Debug, Serialize)]\npub struct NemoMessage {\n pub from: String,\n pub value: String,\n}\n\n/// Discover all CSV files in a directory.\npub fn discover_csv_files(root: &Path) -> Vec {\n let mut paths: Vec = WalkDir::new(root)\n .into_iter()\n .filter_map(|e| e.ok())\n .filter(|e| e.path().extension().map_or(false, |ext| ext == ""csv""))\n .map(|e| e.path().to_path_buf())\n .collect();\n paths.sort();\n paths\n}\n\n/// Process a single CSV session file.\npub fn process_session(\n csv_path: &Path,\n tokenizer: &T,\n config: &PipelineConfig,\n) -> Result, Box>\nwhere\n T: Tokenizer,\n{\n let manager_config = ConversationStateManagerConfig {\n viewport_radius: config.viewport_radius,\n coalesce_radius: config.coalesce_radius,\n max_tokens_per_message: config.max_tokens_per_message,\n max_tokens_per_terminal_output: 256,\n max_tokens_per_conversation: Some(config.max_tokens_per_conversation),\n min_conversation_messages: config.min_conversation_messages,\n };\n\n let mut manager = ConversationStateManager::new(tokenizer, manager_config);\n\n let mut reader = csv::Reader::from_path(csv_path)?;\n \n for result in reader.deserialize() {\n let row: CsvRow = result?;\n \n match row.event_type.as_str() {\n ""tab"" => {\n manager.handle_tab_event(&row.file, row.text.as_deref());\n }\n ""content"" => {\n let offset = row.range_offset.expect(""content event missing RangeOffset"") as usize;\n let length = row.range_length.expect(""content event missing RangeLength"") as usize;\n let text = row.text.as_deref().unwrap_or("""");\n manager.handle_content_event(&row.file, offset, length, text);\n }\n ""selection_command"" | ""selection_mouse"" | ""selection_keyboard"" => {\n let offset = row.range_offset.expect(""selection event missing RangeOffset"") as usize;\n manager.handle_selection_event(&row.file, offset);\n }\n ""terminal_command"" => {\n let command = row.text.as_deref().unwrap_or_else(|| {\n eprintln!(""Warning: terminal_command event missing Text in {:?}"", csv_path);\n """"\n });\n manager.handle_terminal_command_event(command);\n }\n ""terminal_output"" => {\n let output = row.text.as_deref().unwrap_or_else(|| {\n eprintln!(""Warning: terminal_output event missing Text in {:?}"", csv_path);\n """"\n });\n manager.handle_terminal_output_event(output);\n }\n ""terminal_focus"" => {\n manager.handle_terminal_focus_event();\n }\n ""git_branch_checkout"" => {\n let branch_info = row.text.as_deref().unwrap_or_else(|| {\n eprintln!(""Warning: git_branch_checkout event missing Text in {:?}"", csv_path);\n """"\n });\n manager.handle_git_branch_checkout_event(branch_info);\n }\n other => {\n eprintln!(""Warning: Unknown event type '{}' in {:?}"", other, csv_path);\n }\n }\n }\n\n Ok(manager.get_conversations())\n}\n\n/// Process all CSV sessions in a directory in parallel.\n///\n/// Uses rayon for parallel processing. The tokenizer must be `Sync + Send`\n/// to be shared across threads.\npub fn process_all_sessions(\n csv_root: &Path,\n tokenizer: &T,\n config: &PipelineConfig,\n) -> Result, Box>\nwhere\n T: Tokenizer + Sync + Send,\n{\n let csv_files = discover_csv_files(csv_root);\n\n if csv_files.is_empty() {\n return Err(format!(""No CSV files found under {:?}"", csv_root).into());\n }\n\n let total_files = csv_files.len();\n let processed_count = AtomicUsize::new(0);\n let error_count = AtomicUsize::new(0);\n\n let results: Vec = csv_files\n .into_iter()\n .filter_map(|csv_path| {\n let result = process_session(&csv_path, tokenizer, config);\n let count = processed_count.fetch_add(1, Ordering::Relaxed) + 1;\n\n match result {\n Ok(conversations) => {\n if count % 100 == 0 || count == total_files {\n eprintln!(""Processed {}/{} sessions..."", count, total_files);\n }\n Some(SessionResult {\n conversations,\n source_path: csv_path.to_string_lossy().to_string(),\n })\n }\n Err(e) => {\n error_count.fetch_add(1, Ordering::Relaxed);\n eprintln!(""Error processing {:?}: {}"", csv_path, e);\n None\n }\n }\n })\n .collect();\n\n let errors = error_count.load(Ordering::Relaxed);\n if errors > 0 {\n eprintln!(""Warning: {} sessions failed to process"", errors);\n }\n\n Ok(results)\n}\n\n/// Write conversations to JSONL files (training and validation).\npub fn write_jsonl_output(\n session_results: Vec,\n output_dir: &Path,\n val_ratio: f64,\n system_prompt: &str,\n) -> Result> {\n use std::fs::File;\n use std::io::{BufWriter, Write};\n\n std::fs::create_dir_all(output_dir)?;\n\n // Shuffle sessions for train/val split (using simple deterministic shuffle)\n let mut sessions: Vec<_> = session_results.into_iter().enumerate().collect();\n // Simple deterministic shuffle based on index\n sessions.sort_by(|(i, a), (j, b)| {\n let hash_a = (i * 2654435761) % 1000;\n let hash_b = (j * 2654435761) % 1000;\n hash_a.cmp(&hash_b).then_with(|| a.source_path.cmp(&b.source_path))\n });\n\n let total_sessions = sessions.len();\n let val_count = (total_sessions as f64 * val_ratio).round() as usize;\n let train_count = total_sessions - val_count;\n\n let train_path = output_dir.join(""training.jsonl"");\n let val_path = output_dir.join(""validation.jsonl"");\n\n let mut train_file = BufWriter::new(File::create(&train_path)?);\n let mut val_file = BufWriter::new(File::create(&val_path)?);\n\n let mut train_conversations = 0;\n let mut val_conversations = 0;\n let mut total_messages = 0;\n let mut total_tokens = 0;\n\n for (idx, (_, session)) in sessions.into_iter().enumerate() {\n let is_validation = idx >= train_count;\n \n for conv in session.conversations {\n let nemo_messages: Vec = conv\n .messages\n .iter()\n .map(|m| NemoMessage {\n from: m.from.clone(),\n value: m.value.clone(),\n })\n .collect();\n\n let record = NemoRecord {\n mask: ""User"".to_string(),\n system: system_prompt.to_string(),\n conversations: nemo_messages,\n };\n\n let json_line = serde_json::to_string(&record)?;\n \n if is_validation {\n writeln!(val_file, ""{}"", json_line)?;\n val_conversations += 1;\n } else {\n writeln!(train_file, ""{}"", json_line)?;\n train_conversations += 1;\n }\n\n total_messages += conv.messages.len();\n total_tokens += conv.token_count;\n }\n }\n\n train_file.flush()?;\n val_file.flush()?;\n\n Ok(PipelineResult {\n total_sessions,\n total_conversations: train_conversations + val_conversations,\n train_conversations,\n val_conversations,\n total_messages,\n total_tokens,\n })\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use std::io::Write;\n use tempfile::TempDir;\n\n /// Character-based approximate tokenizer for tests.\n struct CharApproxTokenizer;\n\n impl Tokenizer for CharApproxTokenizer {\n fn count_tokens(&self, text: &str) -> usize {\n text.len() / 4\n }\n\n fn truncate_to_max_tokens(&self, text: &str, max_tokens: usize) -> String {\n text.chars().take(max_tokens * 4).collect()\n }\n }\n\n #[test]\n fn test_discover_csv_files() {\n let temp = TempDir::new().unwrap();\n let csv1 = temp.path().join(""session1.csv"");\n let csv2 = temp.path().join(""subdir/session2.csv"");\n \n std::fs::create_dir_all(temp.path().join(""subdir"")).unwrap();\n std::fs::write(&csv1, ""header\n"").unwrap();\n std::fs::write(&csv2, ""header\n"").unwrap();\n\n let files = discover_csv_files(temp.path());\n assert_eq!(files.len(), 2);\n }\n\n #[test]\n fn test_process_session() {\n let temp = TempDir::new().unwrap();\n let csv_path = temp.path().join(""test.csv"");\n \n let mut file = std::fs::File::create(&csv_path).unwrap();\n writeln!(file, ""Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type"").unwrap();\n writeln!(file, ""1,2024-01-01,/test/file.rs,0,0,\""fn main() {{}}\"",rust,tab"").unwrap();\n writeln!(file, ""2,2024-01-01,/test/file.rs,0,0,echo hello,bash,terminal_command"").unwrap();\n writeln!(file, ""3,2024-01-01,/test/file.rs,0,0,hello world,bash,terminal_output"").unwrap();\n\n let config = PipelineConfig {\n min_conversation_messages: 2,\n ..Default::default()\n };\n\n let tokenizer = CharApproxTokenizer;\n let conversations = process_session(&csv_path, &tokenizer, &config).unwrap();\n \n // Should have at least one conversation with messages\n assert!(!conversations.is_empty() || conversations.iter().any(|c| !c.messages.is_empty()));\n }\n}\n\n",rust,tab +2,139,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"2:39:41 PM [info] Activating crowd-code\n2:39:41 PM [info] Recording started\n2:39:41 PM [info] Initializing git provider using file system watchers...\n",Log,tab +3,176,"TERMINAL",0,0,"",,terminal_focus +4,220,"extension-output-pdoom-org.crowd-code-#1-crowd-code",150,0,"2:39:41 PM [info] Git repository found\n2:39:41 PM [info] Git provider initialized successfully\n2:39:41 PM [info] Initial git state: [object Object]\n",Log,content +5,1831,"crates/core/src/pipeline.rs",0,0,"",rust,tab +6,5754,"TERMINAL",0,0,"bash",,terminal_focus +7,8543,"TERMINAL",0,0,"squeue",,terminal_command +8,8557,"TERMINAL",0,0,"]633;C JOBID USER PARTITION NODES CPUS ST SUBMIT_TIME START_TIME TIME TIME_LIMIT NODELIST(REASON)\r\n 36723 franz.sram interacti 1 200 R 2025-12-17T13:01:48 2025-12-17T13:01:48 1:38:01 1-00:00:00 hai008\r\n 36722 mihir.maha interacti 1 2 R 2025-12-17T12:51:04 2025-12-17T12:51:04 1:48:45 2:00:00 hai008\r\n 36701 xiao.liu interacti 1 128 R 2025-12-17T04:00:33 2025-12-17T04:00:33 10:39:16 23:59:00 hai005\r\n 36700 xiao.liu interacti 1 128 R 2025-12-17T01:06:53 2025-12-17T01:06:53 13:32:56 23:59:00 hai006\r\n 36730 florian.mu standard 1 2 R 2025-12-17T14:29:13 2025-12-17T14:29:13 10:36 1-00:00:00 hai007\r\n 36729 florian.mu standard 1 24 R 2025-12-17T14:27:06 2025-12-17T14:27:07 12:42 10:00:00 hai007\r\n 36704 nishant.ku standard 3 624 R 2025-12-17T09:38:39 2025-12-17T09:38:39 5:01:10 1-00:00:00 hai[002-004]\r\n 36691 kalyan.nad standard 1 128 R 2025-12-16T17:27:47 2025-12-16T17:27:58 21:11:51 1-00:00:00 hai001\r\n]0;franz.srambical@hai-login2:~",,terminal_output +9,10709,"TERMINAL",0,0,"salloc --gpus=1 --ntasks-per-node=1 --cpus-per-task=10 --mem=400G --qos=normal",,terminal_command +10,10771,"TERMINAL",0,0,"]633;Csalloc: Granted job allocation 36731\r\n",,terminal_output +11,10869,"TERMINAL",0,0,"salloc: Nodes hai006 are ready for job\r\n",,terminal_output +12,11142,"TERMINAL",0,0,"Running inside SLURM, Job ID 36731.\r\n",,terminal_output +13,11234,"TERMINAL",0,0,"]0;franz.srambical@hai-login2:~[?2004h[franz.srambical@hai006.haicore.berlin:~] $ ",,terminal_output +14,12073,"TERMINAL",0,0,"bash",,terminal_focus +15,26051,"TERMINAL",0,0,"srun",,terminal_focus +16,26759,"TERMINAL",0,0,"e",,terminal_output +17,27043,"TERMINAL",0,0,"x",,terminal_output +18,27210,"TERMINAL",0,0,"i",,terminal_output +19,27372,"TERMINAL",0,0,"t",,terminal_output +20,27486,"TERMINAL",0,0,"\r\n[?2004l\rexit\r\nsalloc: Relinquishing job allocation 36731\r\nsalloc: Job allocation 36731 has been revoked.\r\n]0;franz.srambical@hai-login2:~",,terminal_output +21,29136,"TERMINAL",0,0,"bash",,terminal_focus +22,40768,"crates/core/src/pipeline.rs",0,0,"",rust,tab +23,40769,"crates/core/src/pipeline.rs",6254,0,"",rust,selection_command +24,42910,"crates/cli/src/main.rs",0,0,"//! CLI tool for serializing crowd-pilot IDE interaction data.\n//!\n//! This tool processes CSV session files and outputs JSONL format suitable for\n//! NeMo SFT training. It uses the HuggingFace tokenizers Rust library for\n//! accurate token counting.\n\nuse std::path::PathBuf;\n\nuse clap::Parser;\nuse tokenizers::Tokenizer as HfTokenizer;\n\nuse crowd_pilot_serializer_core::{\n pipeline::{PipelineConfig, PipelineResult},\n process_all_sessions, write_jsonl_output, Tokenizer,\n};\n\n/// Serialize crowd-pilot CSV sessions to NeMo JSONL format.\n#[derive(Parser, Debug)]\n#[command(name = ""crowd-pilot-serialize"")]\n#[command(author, version, about, long_about = None)]\nstruct Args {\n /// Root directory containing CSV session files\n #[arg(long)]\n csv_root: PathBuf,\n\n /// Output directory for JSONL files\n #[arg(long)]\n output_dir: PathBuf,\n\n /// HuggingFace tokenizer model name or path\n #[arg(long)]\n tokenizer: String,\n\n /// Maximum tokens per conversation chunk\n #[arg(long, default_value = ""8192"")]\n max_tokens_per_conversation: usize,\n\n /// Maximum tokens per message\n #[arg(long, default_value = ""2048"")]\n max_tokens_per_message: usize,\n\n /// Minimum messages required to keep a conversation\n #[arg(long, default_value = ""5"")]\n min_conversation_messages: usize,\n\n /// Viewport radius (lines above/below cursor)\n #[arg(long, default_value = ""10"")]\n viewport_radius: usize,\n\n /// Coalesce radius for grouping nearby edits\n #[arg(long, default_value = ""5"")]\n coalesce_radius: usize,\n\n /// Fraction of sessions for validation (0.0-1.0)\n #[arg(long, default_value = ""0.1"")]\n val_ratio: f64,\n\n /// Custom system prompt (optional)\n #[arg(long)]\n system_prompt: Option,\n}\n\nconst DEFAULT_SYSTEM_PROMPT: &str = r#""You are a helpful assistant that can interact multiple times with a computer shell to solve programming tasks.\nYour response must contain exactly ONE bash code block with ONE command (or commands connected with && or ||).\n\nFormat your response as shown in .\n\n\n```bash\nyour_command_here\n```\n\n\nFailure to follow these rules will cause your response to be rejected.""#;\n\n/// Wrapper around HuggingFace tokenizers for token counting and truncation.\n///\n/// This uses the Rust-native tokenizers library, which is `Send + Sync`\n/// and enables true parallel tokenization without the Python GIL.\nstruct RustTokenizer {\n inner: HfTokenizer,\n}\n\nimpl RustTokenizer {\n /// Load a HuggingFace tokenizer from a model name or path.\n fn load(model_name: &str) -> Result> {\n let inner = HfTokenizer::from_pretrained(model_name, None)\n .map_err(|e| e as Box)?;\n Ok(Self { inner })\n }\n}\n\nimpl Tokenizer for RustTokenizer {\n fn count_tokens(&self, text: &str) -> usize {\n self.inner\n .encode(text, false)\n .map(|enc| enc.get_ids().len())\n .unwrap_or(0)\n }\n\n fn truncate_to_max_tokens(&self, text: &str, max_tokens: usize) -> String {\n match self.inner.encode(text, false) {\n Ok(encoding) => {\n let ids = encoding.get_ids();\n if ids.len() <= max_tokens {\n return text.to_string();\n }\n let truncated_ids: Vec = ids[..max_tokens].to_vec();\n self.inner\n .decode(&truncated_ids, true)\n .unwrap_or_else(|_| text.chars().take(max_tokens * 4).collect())\n }\n Err(_) => text.chars().take(max_tokens * 4).collect(),\n }\n }\n}\n\nfn main() -> Result<(), Box> {\n let args = Args::parse();\n\n println!(""Loading tokenizer from {}..."", args.tokenizer);\n let tokenizer = RustTokenizer::load(&args.tokenizer)?;\n\n let config = PipelineConfig {\n max_tokens_per_conversation: args.max_tokens_per_conversation,\n max_tokens_per_message: args.max_tokens_per_message,\n min_conversation_messages: args.min_conversation_messages,\n viewport_radius: args.viewport_radius,\n coalesce_radius: args.coalesce_radius,\n val_ratio: args.val_ratio,\n };\n\n println!(""Processing CSV files from {:?}..."", args.csv_root);\n let session_results = process_all_sessions(\n &args.csv_root,\n &tokenizer,\n &config,\n )?;\n\n let total_sessions = session_results.len();\n println!(""Processed {} sessions"", total_sessions);\n\n let system_prompt = args.system_prompt.as_deref().unwrap_or(DEFAULT_SYSTEM_PROMPT);\n\n println!(""Writing output to {:?}..."", args.output_dir);\n let result: PipelineResult = write_jsonl_output(\n session_results,\n &args.output_dir,\n args.val_ratio,\n system_prompt,\n )?;\n\n let metadata_path = args.output_dir.join(""metadata.json"");\n let metadata = serde_json::json!({\n ""config"": {\n ""csv_root"": args.csv_root.to_string_lossy(),\n ""output_dir"": args.output_dir.to_string_lossy(),\n ""tokenizer"": args.tokenizer,\n ""max_tokens_per_conversation"": args.max_tokens_per_conversation,\n ""max_tokens_per_message"": args.max_tokens_per_message,\n ""min_conversation_messages"": args.min_conversation_messages,\n ""viewport_radius"": args.viewport_radius,\n ""coalesce_radius"": args.coalesce_radius,\n ""val_ratio"": args.val_ratio,\n },\n ""counts"": {\n ""total_sessions"": result.total_sessions,\n ""total_conversations"": result.total_conversations,\n ""train_conversations"": result.train_conversations,\n ""val_conversations"": result.val_conversations,\n },\n ""stats"": {\n ""total_messages"": result.total_messages,\n ""total_tokens"": result.total_tokens,\n ""avg_messages_per_conversation"": if result.total_conversations > 0 {\n result.total_messages as f64 / result.total_conversations as f64\n } else {\n 0.0\n },\n ""avg_tokens_per_conversation"": if result.total_conversations > 0 {\n result.total_tokens as f64 / result.total_conversations as f64\n } else {\n 0.0\n },\n },\n ""files"": {\n ""train_path"": args.output_dir.join(""training.jsonl"").to_string_lossy(),\n ""val_path"": args.output_dir.join(""validation.jsonl"").to_string_lossy(),\n },\n });\n std::fs::write(&metadata_path, serde_json::to_string_pretty(&metadata)?)?;\n\n println!(""\n[summary]"");\n println!("" Total sessions processed: {}"", result.total_sessions);\n println!("" Train conversations: {}"", result.train_conversations);\n println!("" Val conversations: {}"", result.val_conversations);\n println!("" Total messages: {}"", result.total_messages);\n println!("" Total tokens: {}"", result.total_tokens);\n println!("" Output: {:?}/{{training,validation}}.jsonl"", args.output_dir);\n println!("" Metadata: {:?}"", metadata_path);\n\n Ok(())\n}\n",rust,tab +25,42911,"crates/cli/src/main.rs",147,0,"",rust,selection_command +26,80276,"crates/cli/src/main.rs",2739,0,"",rust,selection_mouse +27,93687,"crates/cli/src/main.rs",2672,0,"",rust,selection_command +28,94304,"crates/cli/src/main.rs",2718,0,"",rust,selection_command +29,96203,"crates/cli/src/main.rs",2653,0,"",rust,selection_command +30,96984,"crates/cli/src/main.rs",2720,0,"",rust,selection_command +31,97167,"crates/cli/src/main.rs",2780,0,"",rust,selection_command +32,98335,"crates/cli/src/main.rs",2807,0,"",rust,selection_command +33,98501,"crates/cli/src/main.rs",2813,0,"",rust,selection_command +34,98751,"crates/cli/src/main.rs",2815,0,"",rust,selection_command 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""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Processed 349/349 sessions...Processed 349 sessionsWriting output to ""test_output_dir""...[summary] Total sessions processed: 349 Train conversations: 4036 Val conversations: 403 Total messages: 129517 Total tokens: 33411160 Output: ""test_output_dir""/{training,validation}.jsonl Metadata: ""test_output_dir/metadata.json""real 1m28.496suser 73m28.352ssys 1m56.252s[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ [franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ cargo build --release -p crowd-pilot-serialize Compiling crowd-pilot-serializer-core v0.1.0 (/fast/home/franz.srambical/crowd-pilot-serializer/crates/core)warning: unused import: `rayon::prelude` --> crates/core/src/pipeline.rs:6:5 |6 | use rayon::prelude::*; | ^^^^^^^^^^^^^^ | = note: `#[warn(unused_imports)]` (part of `#[warn(unused)]`) on by defaultwarning: `crowd-pilot-serializer-core` (lib) generated 1 warning Compiling crowd-pilot-serialize v0.1.0 (/fast/home/franz.srambical/crowd-pilot-serializer/crates/cli) Finished `release` profile [optimized] target(s) in 5.92s[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ time bash /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.shLoading tokenizer from Qwen/Qwen3-8B...Processing CSV files from ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/""...Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-4457e5d2-f5e8-4b15-95aa-bafa247369991751528947759-2025_07_03-09.50.10.663/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-7879e034-f897-48e8-8481-1a87a73b0dc81752135543307-2025_07_10-10.19.09.565/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-81dc70dc-8e01-48a6-9a00-9349b9f9a4171751541780271-2025_07_03-13.23.33.804/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-ebfebb4b-ef7a-46a0-8725-dd76b91fd2891752048248358-2025_07_09-10.04.56.945/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-fbd09e27-2302-4b0c-83a4-a77b7bc2e3dc1751440721102-2025_07_02-09.19.16.832/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-0d5e2cbe-83a2-48a0-b2d9-a8e41912bfb61753114173405-2025_07_21-18.09.45.514/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-2f5b13c7-61f7-4340-b581-9edac6a53f1f1753015255059-2025_07_20-14.41.09.478/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-3e3ce02e-664a-4f58-9d7f-0f56e32c7def1753363875204-2025_07_24-15.31.23.202/source.csv""Processed 100/349 sessions...Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-46a4cc9d-ac37-44ff-ae8d-547db76d96f31752072213286-2025_07_09-16.43.57.848/source.csv""^Creal 2m26.399suser 2m21.383ssys 0m4.368s[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ time bash /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.shLoading tokenizer from Qwen/Qwen3-8B...Processing CSV files from ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/""...Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-4457e5d2-f5e8-4b15-95aa-bafa247369991751528947759-2025_07_03-09.50.10.663/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-7879e034-f897-48e8-8481-1a87a73b0dc81752135543307-2025_07_10-10.19.09.565/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-81dc70dc-8e01-48a6-9a00-9349b9f9a4171751541780271-2025_07_03-13.23.33.804/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-ebfebb4b-ef7a-46a0-8725-dd76b91fd2891752048248358-2025_07_09-10.04.56.945/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-fbd09e27-2302-4b0c-83a4-a77b7bc2e3dc1751440721102-2025_07_02-09.19.16.832/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-0d5e2cbe-83a2-48a0-b2d9-a8e41912bfb61753114173405-2025_07_21-18.09.45.514/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-2f5b13c7-61f7-4340-b581-9edac6a53f1f1753015255059-2025_07_20-14.41.09.478/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-3e3ce02e-664a-4f58-9d7f-0f56e32c7def1753363875204-2025_07_24-15.31.23.202/source.csv""Processed 100/349 sessions...Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-46a4cc9d-ac37-44ff-ae8d-547db76d96f31752072213286-2025_07_09-16.43.57.848/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-7879e034-f897-48e8-8481-1a87a73b0dc81752135543307-2025_07_10-10.19.09.565/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-81dc70dc-8e01-48a6-9a00-9349b9f9a4171751541780271-2025_07_03-13.23.33.804/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-8e67a739-7b65-4646-afc1-42e9766880571751607756007-2025_07_04-07.43.31.602/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-8e67a739-7b65-4646-afc1-42e9766880571751607756007-2025_07_04-07.43.31.602/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-29e2cbae-7056-4585-b457-f48bd451c3fd1750644341589-2025_06_22-19.05.43.270/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-8e0958c9-e396-41d9-b3d4-8a748cefa1701750701699946-2025_06_23-11.01.41.744/source.csv""Processed 200/349 sessions...Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-0f5513f7-8bc9-4c5d-856d-79d92f75113d1751284706913-2025_06_30-13.59.01.459/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-2d2437e1-caa5-4315-a7d9-4d9478073a161750944609503-2025_06_26-15.30.55.51/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-6791460b-ec38-4da2-872f-193943c12d601753274780799-2025_07_23-14.47.19.396/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-92ab1593-f937-4cc4-a174-544581a6ac991751909174142-2025_07_07-19.26.40.736/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-92ab1593-f937-4cc4-a174-544581a6ac991751909174142-2025_07_07-19.26.40.736/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-9d03fc6f-7387-447c-9d61-dfb41a6cd5d41752168236691-2025_07_10-19.24.24.908/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-d80c259c-238d-4ab7-8a8a-2d0fc8e345961753345086680-2025_07_24-10.19.00.46/source.csv""Processed 300/349 sessions...Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-fe3e4a6d-15dd-460a-96b3-1f7a60db202a1753178306285-2025_07_22-12.23.53.192/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-fe3e4a6d-15dd-460a-96b3-1f7a60db202a1753178306285-2025_07_22-12.23.53.192/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/e8b08c312d88206805b92191af1ee2a660f8f0e59d3990233d6a3f81cdab43f4/crowd-code-9c6bfb6d-25d7-401c-a2eb-18ae18179f4b1750244015795-2025_06_18-12.58.06.505/source.csv""Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/e8b08c312d88206805b92191af1ee2a660f8f0e59d3990233d6a3f81cdab43f4/crowd-code-b329b9f2-ee04-44c3-8e37-55401c64da7f1750160908319-2025_06_17-13.49.11.314/source.csv""Processed 349/349 sessions...Processed 349 sessionsWriting output to ""test_output_dir""...[summary] Total sessions processed: 349 Train conversations: 4036 Val conversations: 403 Total messages: 129517 Total tokens: 33411160 Output: ""test_output_dir""/{training,validation}.jsonl Metadata: ""test_output_dir/metadata.json""real 5m12.107suser 4m53.516ssys 0m8.006s[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ exitexitsalloc: Relinquishing job allocation 36723",,terminal_command +210,409413,"TERMINAL",0,0,"salloc --gpus=1 --ntasks-per-node=1 --cpus-per-task=100 --mem=400G --qos=normal",,terminal_command +211,409463,"TERMINAL",0,0,"]633;Csalloc: Granted job allocation 36733\r\n",,terminal_output +212,409560,"TERMINAL",0,0,"salloc: Nodes hai008 are ready for job\r\n",,terminal_output +213,409821,"TERMINAL",0,0,"Running inside SLURM, Job ID 36733.\r\n",,terminal_output +214,410499,"TERMINAL",0,0,"]0;franz.srambical@hai-login2:~/miles[?2004h[franz.srambical@hai008.haicore.berlin:~/miles] $ ",,terminal_output +215,411112,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +216,411456,"TERMINAL",0,0,"c': time bash /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",,terminal_output +217,411724,"TERMINAL",0,0,"\ra': time bash /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh\r\rr': cargo build --release -p crowd-pilot-serialize\r",,terminal_output +218,411846,"TERMINAL",0,0,"[1@g': carg",,terminal_output +219,411920,"TERMINAL",0,0,"[1@o': cargo",,terminal_output +220,412868,"TERMINAL",0,0,"\r[23@[franz.srambical@hai008.haicore.berlin:~/miles] $ cargo\r\n[?2004l\r",,terminal_output +221,412959,"TERMINAL",0,0,"error: could not find `Cargo.toml` in `/fast/home/franz.srambical/miles` or any parent directory\r\n]0;franz.srambical@hai-login2:~/miles[?2004h[franz.srambical@hai008.haicore.berlin:~/miles] $ ",,terminal_output +222,415267,"TERMINAL",0,0,"c",,terminal_output +223,415524,"TERMINAL",0,0,"d",,terminal_output +224,415616,"TERMINAL",0,0," ",,terminal_output +225,415806,"TERMINAL",0,0,".",,terminal_output +226,415939,"TERMINAL",0,0,".",,terminal_output +227,416117,"TERMINAL",0,0,"/",,terminal_output +228,417793,"TERMINAL",0,0,"c",,terminal_output +229,417963,"TERMINAL",0,0,"r",,terminal_output +230,418096,"TERMINAL",0,0,"o",,terminal_output +231,418273,"TERMINAL",0,0,"",,terminal_output +232,419314,"TERMINAL",0,0,"c",,terminal_output +233,419548,"TERMINAL",0,0,"r",,terminal_output +234,419877,"TERMINAL",0,0,"",,terminal_output +235,420023,"TERMINAL",0,0,"",,terminal_output +236,420152,"TERMINAL",0,0,".",,terminal_output +237,420294,"TERMINAL",0,0,".",,terminal_output +238,420545,"TERMINAL",0,0,"/",,terminal_output +239,420629,"TERMINAL",0,0,"c",,terminal_output +240,420804,"TERMINAL",0,0,"r",,terminal_output +241,420958,"TERMINAL",0,0,"w",,terminal_output +242,421040,"TERMINAL",0,0,"o",,terminal_output +243,421260,"TERMINAL",0,0,"d",,terminal_output +244,421357,"TERMINAL",0,0,"",,terminal_output +245,421771,"TERMINAL",0,0,"",,terminal_output +246,421918,"TERMINAL",0,0,"",,terminal_output +247,422076,"TERMINAL",0,0,"",,terminal_output +248,422222,"TERMINAL",0,0,"o",,terminal_output +249,422322,"TERMINAL",0,0,"w",,terminal_output +250,422422,"TERMINAL",0,0,"d",,terminal_output +251,422484,"TERMINAL",0,0,"-",,terminal_output +252,423028,"TERMINAL",0,0,"p",,terminal_output +253,423162,"TERMINAL",0,0,"ilot",,terminal_output +254,423544,"TERMINAL",0,0,"-",,terminal_output +255,423812,"TERMINAL",0,0,"e",,terminal_output +256,424025,"TERMINAL",0,0,"xtension/",,terminal_output +257,424421,"TERMINAL",0,0,"",,terminal_output +258,424746,"TERMINAL",0,0,"",,terminal_output +259,425197,"TERMINAL",0,0,"",,terminal_output +260,425314,"TERMINAL",0,0,"",,terminal_output +261,425667,"TERMINAL",0,0,"ser",,terminal_output +262,425831,"TERMINAL",0,0,"ializer/",,terminal_output +263,426046,"TERMINAL",0,0,"\r\n[?2004l\r]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +264,427497,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +265,427650,"TERMINAL",0,0,"c': cd ../crowd-pilot-serializer/",,terminal_output +266,427856,"TERMINAL",0,0,"a': cargo build --release -p crowd-pilot-serialize\r[1@r': car",,terminal_output +267,428005,"TERMINAL",0,0,"[1@g': carg",,terminal_output +268,428152,"TERMINAL",0,0,"[1@o': cargo",,terminal_output +269,428507,"TERMINAL",0,0,"\r[40@[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ cargo",,terminal_output +270,428729,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +271,430113,"TERMINAL",0,0," Compiling crowd-pilot-serializer-core v0.1.0 (/fast/home/franz.srambical/crowd-pilot-serializer/crates/core)\r\n Building [=======================> ] 195/197: crowd-pilot-serializer-core \r",,terminal_output +272,431012,"TERMINAL",0,0," Compiling crowd-pilot-serialize v0.1.0 (/fast/home/franz.srambical/crowd-pilot-serializer/crates/cli)\r\n Building [=======================> ] 196/197: crowd-pilot-serialize(bin) \r",,terminal_output +273,434679,"TERMINAL",0,0," Finished ]8;;https://doc.rust-lang.org/cargo/reference/profiles.html#default-profiles\`release` profile [optimized]]8;;\ target(s) in 5.86s\r\n]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +274,436357,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",0,0,"source /home/franz.srambical/crowd-pilot-serializer/.venv/bin/activate\nexport LD_LIBRARY_PATH=$(python -c ""import sysconfig; print(sysconfig.get_config_var('LIBDIR'))""):$LD_LIBRARY_PATH\n./target/release/crowd-pilot-serialize --csv-root=""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/"" --output-dir=test_output_dir --tokenizer=""Qwen/Qwen3-8B""",shellscript,tab +275,438009,"crates/cli/src/main.rs",0,0,"",rust,tab +276,441955,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",0,0,"",shellscript,tab +277,442592,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",94,0,"",shellscript,selection_command +278,442777,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",23,0,"",shellscript,selection_command +279,443366,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",0,71,"",shellscript,content +280,445381,"TERMINAL",0,0,"r",,terminal_output +281,445461,"TERMINAL",0,0,"m",,terminal_output +282,445670,"TERMINAL",0,0," ",,terminal_output +283,445894,"TERMINAL",0,0,"-",,terminal_output +284,446094,"TERMINAL",0,0,"r",,terminal_output +285,446372,"TERMINAL",0,0," ",,terminal_output +286,446552,"TERMINAL",0,0,".v",,terminal_output +287,446626,"TERMINAL",0,0,"e",,terminal_output +288,446917,"TERMINAL",0,0,"nv/",,terminal_output +289,448027,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +290,459484,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",0,115,"",shellscript,content +291,463100,"TERMINAL",0,0,"]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +292,465115,"TERMINAL",0,0,"b",,terminal_output +293,465388,"TERMINAL",0,0,"as",,terminal_output +294,465540,"TERMINAL",0,0,"h",,terminal_output +295,465749,"TERMINAL",0,0," ",,terminal_output +296,465997,"TERMINAL",0,0,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",,terminal_output +297,466342,"TERMINAL",0,0,"\r/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh\r",,terminal_output +298,466573,"TERMINAL",0,0,"[1@ ",,terminal_output +299,466935,"TERMINAL",0,0,"",,terminal_output +300,467104,"TERMINAL",0,0,"[1@t",,terminal_output +301,467207,"TERMINAL",0,0,"[1@i",,terminal_output +302,467278,"TERMINAL",0,0,"[1@m",,terminal_output +303,467351,"TERMINAL",0,0,"[1@e",,terminal_output +304,467494,"TERMINAL",0,0,"[1@ ",,terminal_output +305,467999,"TERMINAL",0,0,"",,terminal_output +306,468229,"TERMINAL",0,0,"\r\n[?2004l\rLoading tokenizer from Qwen/Qwen3-8B...\r\n",,terminal_output +307,468507,"TERMINAL",0,0,"Processing CSV files from ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/""...\r\n",,terminal_output +308,468739,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-0f5513f7-8bc9-4c5d-856d-79d92f75113d1751284706913-2025_06_30-13.59.01.459/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-d80c259c-238d-4ab7-8a8a-2d0fc8e345961753345086680-2025_07_24-10.19.00.46/source.csv""\r\n",,terminal_output +309,468868,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-4457e5d2-f5e8-4b15-95aa-bafa247369991751528947759-2025_07_03-09.50.10.663/source.csv""\r\n",,terminal_output +310,469311,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +311,469626,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-2f5b13c7-61f7-4340-b581-9edac6a53f1f1753015255059-2025_07_20-14.41.09.478/source.csv""\r\n",,terminal_output +312,469941,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-29e2cbae-7056-4585-b457-f48bd451c3fd1750644341589-2025_06_22-19.05.43.270/source.csv""\r\n",,terminal_output +313,470531,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-8e0958c9-e396-41d9-b3d4-8a748cefa1701750701699946-2025_06_23-11.01.41.744/source.csv""\r\n",,terminal_output +314,470902,"TERMINAL",0,0,"Processed 100/349 sessions...\r\n",,terminal_output +315,471453,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +316,471745,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +317,471871,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +318,472296,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-81dc70dc-8e01-48a6-9a00-9349b9f9a4171751541780271-2025_07_03-13.23.33.804/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +319,473704,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""\r\n",,terminal_output +320,473933,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/e8b08c312d88206805b92191af1ee2a660f8f0e59d3990233d6a3f81cdab43f4/crowd-code-b329b9f2-ee04-44c3-8e37-55401c64da7f1750160908319-2025_06_17-13.49.11.314/source.csv""\r\n",,terminal_output +321,474070,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/e8b08c312d88206805b92191af1ee2a660f8f0e59d3990233d6a3f81cdab43f4/crowd-code-9c6bfb6d-25d7-401c-a2eb-18ae18179f4b1750244015795-2025_06_18-12.58.06.505/source.csv""\r\n",,terminal_output +322,474265,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-0d5e2cbe-83a2-48a0-b2d9-a8e41912bfb61753114173405-2025_07_21-18.09.45.514/source.csv""\r\n",,terminal_output +323,474626,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +324,475338,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-81dc70dc-8e01-48a6-9a00-9349b9f9a4171751541780271-2025_07_03-13.23.33.804/source.csv""\r\n",,terminal_output +325,475653,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +326,475907,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-fe3e4a6d-15dd-460a-96b3-1f7a60db202a1753178306285-2025_07_22-12.23.53.192/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-fe3e4a6d-15dd-460a-96b3-1f7a60db202a1753178306285-2025_07_22-12.23.53.192/source.csv""\r\n",,terminal_output +327,476473,"crates/cli/src/main.rs",0,0,"",rust,tab +328,477026,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""\r\n",,terminal_output +329,477679,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-3e3ce02e-664a-4f58-9d7f-0f56e32c7def1753363875204-2025_07_24-15.31.23.202/source.csv""\r\n",,terminal_output +330,479824,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +331,479907,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +332,480495,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-2d2437e1-caa5-4315-a7d9-4d9478073a161750944609503-2025_06_26-15.30.55.51/source.csv""\r\n",,terminal_output +333,480602,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +334,480678,"TERMINAL",0,0,"Processed 200/349 sessions...\r\n",,terminal_output +335,481052,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +336,482111,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +337,482347,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +338,483785,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-92ab1593-f937-4cc4-a174-544581a6ac991751909174142-2025_07_07-19.26.40.736/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-92ab1593-f937-4cc4-a174-544581a6ac991751909174142-2025_07_07-19.26.40.736/source.csv""\r\n",,terminal_output +339,485115,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +340,485287,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +341,486409,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +342,486485,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +343,487096,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +344,488695,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +345,489055,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +346,490484,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-6791460b-ec38-4da2-872f-193943c12d601753274780799-2025_07_23-14.47.19.396/source.csv""\r\n",,terminal_output +347,490936,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-fbd09e27-2302-4b0c-83a4-a77b7bc2e3dc1751440721102-2025_07_02-09.19.16.832/source.csv""\r\n",,terminal_output +348,494445,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +349,496078,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-46a4cc9d-ac37-44ff-ae8d-547db76d96f31752072213286-2025_07_09-16.43.57.848/source.csv""\r\n",,terminal_output +350,499097,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +351,500104,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +352,500619,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +353,500934,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +354,504096,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-9d03fc6f-7387-447c-9d61-dfb41a6cd5d41752168236691-2025_07_10-19.24.24.908/source.csv""\r\n",,terminal_output +355,504701,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""\r\n",,terminal_output +356,505285,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""\r\n",,terminal_output +357,507700,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-ebfebb4b-ef7a-46a0-8725-dd76b91fd2891752048248358-2025_07_09-10.04.56.945/source.csv""\r\n",,terminal_output +358,509162,"crates/cli/src/main.rs",3124,0,"",rust,selection_command 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""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\n",,terminal_output +366,513937,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-8e67a739-7b65-4646-afc1-42e9766880571751607756007-2025_07_04-07.43.31.602/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-8e67a739-7b65-4646-afc1-42e9766880571751607756007-2025_07_04-07.43.31.602/source.csv""\r\n",,terminal_output +367,514862,"crates/cli/src/main.rs",3182,0,"",rust,selection_command +368,515313,"crates/cli/src/main.rs",3180,0,"",rust,selection_command +369,515433,"crates/cli/src/main.rs",3172,0,"",rust,selection_command +370,516751,"TERMINAL",0,0,"Processed 300/349 sessions...\r\n",,terminal_output +371,517213,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\n",,terminal_output +372,528917,"crates/cli/src/main.rs",3202,0,"",rust,selection_command +373,529070,"crates/cli/src/main.rs",3248,0,"",rust,selection_command +374,529734,"crates/cli/src/main.rs",3249,0,"",rust,selection_command +375,534916,"crates/cli/src/main.rs",3294,0,"",rust,selection_command +376,535160,"crates/cli/src/main.rs",3298,0,"",rust,selection_command +377,535319,"crates/cli/src/main.rs",3305,0,"",rust,selection_command +378,535484,"crates/cli/src/main.rs",3309,0,"",rust,selection_command +379,535634,"crates/cli/src/main.rs",3310,0,"",rust,selection_command +380,537413,"TERMINAL",0,0,"Processed 349/349 sessions...\r\nProcessed 349 sessions\r\nWriting output to ""test_output_dir""...\r\n",,terminal_output +381,538034,"crates/cli/src/main.rs",3309,0,"",rust,selection_command +382,538175,"crates/cli/src/main.rs",3305,0,"",rust,selection_command +383,540620,"crates/cli/src/main.rs",3309,0,"",rust,selection_command +384,540964,"crates/cli/src/main.rs",3278,44," return text.to_string();",rust,selection_command +385,547676,"TERMINAL",0,0,"\r\n[summary]\r\n Total sessions processed: 349\r\n Train conversations: 4036\r\n Val conversations: 403\r\n Total messages: 129517\r\n Total tokens: 33411160\r\n Output: ""test_output_dir""/{training,validation}.jsonl\r\n Metadata: ""test_output_dir/metadata.json""\r\n",,terminal_output +386,548429,"TERMINAL",0,0,"\r\nreal\t1m20.227s\r\nuser\t62m40.945s\r\nsys\t1m53.099s\r\n]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +387,550310,"crates/cli/src/main.rs",3309,0,"",rust,selection_command +388,580626,"crates/cli/src/main.rs",3339,0,"",rust,selection_command +389,580773,"crates/cli/src/main.rs",3372,0,"",rust,selection_command +390,581612,"crates/cli/src/main.rs",3361,0,"",rust,selection_command +391,603268,"crates/cli/src/main.rs",3374,0,"",rust,selection_command +392,603537,"crates/cli/src/main.rs",3376,0,"",rust,selection_command +393,603560,"crates/cli/src/main.rs",3379,0,"",rust,selection_command +394,603598,"crates/cli/src/main.rs",3380,0,"",rust,selection_command +395,603625,"crates/cli/src/main.rs",3383,0,"",rust,selection_command +396,603928,"crates/cli/src/main.rs",3385,0,"",rust,selection_command +397,604237,"crates/cli/src/main.rs",3387,0,"",rust,selection_command +398,607810,"crates/cli/src/main.rs",3390,0,"",rust,selection_command +399,608007,"crates/cli/src/main.rs",3393,0,"",rust,selection_command +400,608237,"crates/cli/src/main.rs",3403,0,"",rust,selection_command +401,608645,"crates/cli/src/main.rs",3393,0,"",rust,selection_command +402,610039,"crates/cli/src/main.rs",3403,0,"",rust,selection_command +403,610240,"crates/cli/src/main.rs",3405,0,"",rust,selection_command +404,610434,"crates/cli/src/main.rs",3411,0,"",rust,selection_command +405,610767,"crates/cli/src/main.rs",3405,0,"",rust,selection_command +406,614484,"crates/cli/src/main.rs",3403,0,"",rust,selection_command +407,614625,"crates/cli/src/main.rs",3393,0,"",rust,selection_command +408,614886,"crates/cli/src/main.rs",3390,0,"",rust,selection_command +409,614916,"crates/cli/src/main.rs",3387,0,"",rust,selection_command +410,615301,"crates/cli/src/main.rs",3390,0,"",rust,selection_command +411,615722,"crates/cli/src/main.rs",3387,3,"ids",rust,selection_command +412,616303,"crates/cli/src/main.rs",3389,0,"",rust,selection_command +413,616433,"crates/cli/src/main.rs",3387,0,"",rust,selection_command +414,620391,"crates/cli/src/main.rs",3357,0,"",rust,selection_command +415,626998,"crates/cli/src/main.rs",3361,0,"",rust,selection_command +416,683056,"crates/cli/src/main.rs",3435,0,"",rust,selection_command +417,683478,"crates/cli/src/main.rs",3462,0,"",rust,selection_command +418,684225,"crates/cli/src/main.rs",3463,0,"",rust,selection_command +419,685999,"crates/cli/src/main.rs",3513,0,"",rust,selection_command +420,686933,"crates/cli/src/main.rs",3527,0,"",rust,selection_command +421,687174,"crates/cli/src/main.rs",3529,0,"",rust,selection_command +422,716496,"crates/cli/src/main.rs",3492,84," .unwrap_or_else(|_| text.chars().take(max_tokens * 4).collect())",rust,selection_command +423,755117,"crates/cli/src/main.rs",3529,0,"",rust,selection_command +424,759499,"crates/cli/src/main.rs",3575,0,"",rust,selection_command +425,759876,"crates/cli/src/main.rs",3573,0,"",rust,selection_command +426,760005,"crates/cli/src/main.rs",3566,0,"",rust,selection_command +427,762888,"crates/cli/src/main.rs",3564,0,"",rust,selection_command +428,763140,"crates/cli/src/main.rs",3563,0,"",rust,selection_command +429,763164,"crates/cli/src/main.rs",3561,0,"",rust,selection_command +430,763209,"crates/cli/src/main.rs",3550,0,"",rust,selection_command +431,763240,"crates/cli/src/main.rs",3549,0,"",rust,selection_command +432,763307,"crates/cli/src/main.rs",3545,0,"",rust,selection_command +433,763308,"crates/cli/src/main.rs",3542,0,"",rust,selection_command +434,763330,"crates/cli/src/main.rs",3537,0,"",rust,selection_command +435,763369,"crates/cli/src/main.rs",3536,0,"",rust,selection_command +436,763410,"crates/cli/src/main.rs",3532,0,"",rust,selection_command +437,763431,"crates/cli/src/main.rs",3530,0,"",rust,selection_command +438,763624,"crates/cli/src/main.rs",3529,0,"",rust,selection_command +439,763874,"crates/cli/src/main.rs",3527,0,"",rust,selection_command +440,763905,"crates/cli/src/main.rs",3513,0,"",rust,selection_command +441,764024,"crates/cli/src/main.rs",3512,0,"",rust,selection_command +442,764372,"crates/cli/src/main.rs",3513,0,"",rust,selection_command +443,764548,"crates/cli/src/main.rs",3527,0,"",rust,selection_command +444,767713,"crates/cli/src/main.rs",3110,557," let encoding = self.inner\n .encode(text, false)\n .expect(""Failed to encode text with tokenizer"");\n \n let ids = encoding.get_ids();\n if ids.len() <= max_tokens {\n return text.to_string();\n }\n \n let truncated_ids: Vec = ids[..max_tokens].to_vec();\n self.inner\n .decode(&truncated_ids, true)\n .expect(""Failed to decode truncated tokens"")",rust,content +445,773033,"crates/cli/src/main.rs",3503,0,"",rust,selection_command +446,773249,"crates/cli/src/main.rs",3461,0,"",rust,selection_command +447,773402,"crates/cli/src/main.rs",3434,0,"",rust,selection_command +448,773703,"crates/cli/src/main.rs",3376,0,"",rust,selection_command +449,773864,"crates/cli/src/main.rs",3367,0,"",rust,selection_command +450,774119,"crates/cli/src/main.rs",3357,0,"",rust,selection_command +451,774305,"crates/cli/src/main.rs",3320,0,"",rust,selection_command +452,774557,"crates/cli/src/main.rs",3283,0,"",rust,selection_command +453,774588,"crates/cli/src/main.rs",3245,0,"",rust,selection_command +454,774758,"crates/cli/src/main.rs",3233,0,"",rust,selection_command +455,774947,"crates/cli/src/main.rs",3175,0,"",rust,selection_command +456,775266,"crates/cli/src/main.rs",3142,0,"",rust,selection_command +457,775724,"crates/cli/src/main.rs",3175,0,"",rust,selection_command +458,776097,"crates/cli/src/main.rs",3233,0,"",rust,selection_command +459,777156,"crates/cli/src/main.rs",3177,0,"",rust,selection_command +460,779998,"crates/cli/src/main.rs",3189,0,"",rust,selection_command +461,781808,"crates/cli/src/main.rs",3110,451," match self.inner.encode(text, false) {\n Ok(encoding) => {\n let ids = encoding.get_ids();\n if ids.len() <= max_tokens {\n return text.to_string();\n }\n let truncated_ids: Vec = ids[..max_tokens].to_vec();\n self.inner\n .decode(&truncated_ids, true)\n .unwrap_or_else(|_| text.chars().take(max_tokens * 4).collect())\n }\n Err(_) => text.chars().take(max_tokens * 4).collect(),\n }",rust,content +462,781859,"crates/cli/src/main.rs",3110,557," let encoding = self.inner\n .encode(text, false)\n .expect(""Failed to encode text with tokenizer"");\n \n let ids = encoding.get_ids();\n if ids.len() <= max_tokens {\n return text.to_string();\n }\n \n let truncated_ids: Vec = ids[..max_tokens].to_vec();\n self.inner\n .decode(&truncated_ids, true)\n .expect(""Failed to decode truncated tokens"")",rust,content +463,781859,"crates/cli/src/main.rs",2953,69," .expect(""Failed to encode text with tokenizer"")\n .get_ids()\n .len()",rust,content +464,793333,"crates/cli/src/main.rs",0,0,"",rust,tab +465,794085,"crates/cli/src/main.rs",0,0,"",rust,selection_command +466,795518,"crates/cli/src/main.rs",1439,0,"",rust,selection_keyboard +467,795797,"crates/cli/src/main.rs",3036,0,"",rust,selection_keyboard +468,806741,"crates/cli/src/main.rs",3013,0,"",rust,selection_command +469,806865,"crates/cli/src/main.rs",2953,0,"",rust,selection_command +470,810420,"crates/cli/src/main.rs",3142,0,"",rust,selection_command +471,816160,"crates/cli/src/main.rs",3176,0,"",rust,selection_command +472,816326,"crates/cli/src/main.rs",3209,0,"",rust,selection_command +473,816469,"crates/cli/src/main.rs",3270,0,"",rust,selection_command +474,816715,"crates/cli/src/main.rs",3279,0,"",rust,selection_command +475,819091,"crates/cli/src/main.rs",3317,0,"",rust,selection_command +476,819319,"crates/cli/src/main.rs",3354,0,"",rust,selection_command +477,819351,"crates/cli/src/main.rs",3391,0,"",rust,selection_command +478,819385,"crates/cli/src/main.rs",3401,0,"",rust,selection_command +479,819417,"crates/cli/src/main.rs",3410,0,"",rust,selection_command +480,819948,"crates/cli/src/main.rs",3401,0,"",rust,selection_command +481,820114,"crates/cli/src/main.rs",3391,0,"",rust,selection_command +482,820222,"crates/cli/src/main.rs",3401,0,"",rust,selection_command +483,820401,"crates/cli/src/main.rs",3410,0,"",rust,selection_command +484,822970,"crates/cli/src/main.rs",3476,0,"",rust,selection_command +485,823138,"crates/cli/src/main.rs",3495,0,"",rust,selection_command +486,824839,"crates/cli/src/main.rs",3476,0,"",rust,selection_command +487,825009,"crates/cli/src/main.rs",3484,0,"",rust,selection_command +488,825227,"crates/cli/src/main.rs",3488,0,"",rust,selection_command +489,827418,"crates/cli/src/main.rs",3507,0,"",rust,selection_command +490,827569,"crates/cli/src/main.rs",3549,0,"",rust,selection_command +491,828153,"crates/cli/src/main.rs",3537,0,"",rust,selection_command +492,833206,"TERMINAL",0,0,"time bash /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",,terminal_output +493,833640,"TERMINAL",0,0,"\rrm -r .venv/",,terminal_output +494,834281,"TERMINAL",0,0,"cargo build --release -p crowd-pilot-serialize",,terminal_output +495,835373,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +496,836628,"TERMINAL",0,0," Compiling crowd-pilot-serialize v0.1.0 (/fast/home/franz.srambical/crowd-pilot-serializer/crates/cli)\r\n Building [=======================> ] 196/197: crowd-pilot-serialize(bin) \r",,terminal_output +497,840327,"TERMINAL",0,0," Finished ]8;;https://doc.rust-lang.org/cargo/reference/profiles.html#default-profiles\`release` profile [optimized]]8;;\ target(s) in 4.84s\r\n]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +498,844481,"TERMINAL",0,0,"cargo build --release -p crowd-pilot-serialize",,terminal_output +499,844537,"TERMINAL",0,0,"time bash /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",,terminal_output +500,845216,"TERMINAL",0,0,"\r\n[?2004l\rLoading tokenizer from Qwen/Qwen3-8B...\r\n",,terminal_output +501,845491,"TERMINAL",0,0,"Processing CSV files from ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/""...\r\n",,terminal_output +502,845897,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-4457e5d2-f5e8-4b15-95aa-bafa247369991751528947759-2025_07_03-09.50.10.663/source.csv""\r\n",,terminal_output +503,846080,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-29e2cbae-7056-4585-b457-f48bd451c3fd1750644341589-2025_06_22-19.05.43.270/source.csv""\r\n",,terminal_output +504,846280,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +505,846444,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-0f5513f7-8bc9-4c5d-856d-79d92f75113d1751284706913-2025_06_30-13.59.01.459/source.csv""\r\n",,terminal_output +506,847663,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-8e0958c9-e396-41d9-b3d4-8a748cefa1701750701699946-2025_06_23-11.01.41.744/source.csv""\r\n",,terminal_output +507,847753,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +508,847892,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +509,848550,"TERMINAL",0,0,"Processed 100/349 sessions...\r\n",,terminal_output +510,848603,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/e8b08c312d88206805b92191af1ee2a660f8f0e59d3990233d6a3f81cdab43f4/crowd-code-b329b9f2-ee04-44c3-8e37-55401c64da7f1750160908319-2025_06_17-13.49.11.314/source.csv""\r\n",,terminal_output +511,848950,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-0d5e2cbe-83a2-48a0-b2d9-a8e41912bfb61753114173405-2025_07_21-18.09.45.514/source.csv""\r\n",,terminal_output +512,849027,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-fe3e4a6d-15dd-460a-96b3-1f7a60db202a1753178306285-2025_07_22-12.23.53.192/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-fe3e4a6d-15dd-460a-96b3-1f7a60db202a1753178306285-2025_07_22-12.23.53.192/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +513,849338,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-d80c259c-238d-4ab7-8a8a-2d0fc8e345961753345086680-2025_07_24-10.19.00.46/source.csv""\r\n",,terminal_output +514,849795,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +515,850126,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-81dc70dc-8e01-48a6-9a00-9349b9f9a4171751541780271-2025_07_03-13.23.33.804/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +516,850348,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""\r\n",,terminal_output +517,851121,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-2f5b13c7-61f7-4340-b581-9edac6a53f1f1753015255059-2025_07_20-14.41.09.478/source.csv""\r\n",,terminal_output +518,851927,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-81dc70dc-8e01-48a6-9a00-9349b9f9a4171751541780271-2025_07_03-13.23.33.804/source.csv""\r\n",,terminal_output +519,852529,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/e8b08c312d88206805b92191af1ee2a660f8f0e59d3990233d6a3f81cdab43f4/crowd-code-9c6bfb6d-25d7-401c-a2eb-18ae18179f4b1750244015795-2025_06_18-12.58.06.505/source.csv""\r\n",,terminal_output +520,853595,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""\r\n",,terminal_output +521,853697,"crates/cli/src/main.rs",0,0,"",rust,tab +522,853698,"crates/cli/src/main.rs",147,0,"",rust,selection_command +523,857776,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +524,857894,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +525,858022,"TERMINAL",0,0,"Processed 200/349 sessions...\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +526,858154,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +527,858700,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +528,858935,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-3e3ce02e-664a-4f58-9d7f-0f56e32c7def1753363875204-2025_07_24-15.31.23.202/source.csv""\r\n",,terminal_output +529,859148,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +530,859637,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +531,859693,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-2d2437e1-caa5-4315-a7d9-4d9478073a161750944609503-2025_06_26-15.30.55.51/source.csv""\r\n",,terminal_output +532,860131,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +533,860360,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +534,860811,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +535,861101,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-92ab1593-f937-4cc4-a174-544581a6ac991751909174142-2025_07_07-19.26.40.736/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-92ab1593-f937-4cc4-a174-544581a6ac991751909174142-2025_07_07-19.26.40.736/source.csv""\r\n",,terminal_output +536,861367,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-6791460b-ec38-4da2-872f-193943c12d601753274780799-2025_07_23-14.47.19.396/source.csv""\r\n",,terminal_output +537,866033,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +538,866480,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-fbd09e27-2302-4b0c-83a4-a77b7bc2e3dc1751440721102-2025_07_02-09.19.16.832/source.csv""\r\n",,terminal_output +539,869106,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +540,869682,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +541,869993,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +542,870217,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +543,872375,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-46a4cc9d-ac37-44ff-ae8d-547db76d96f31752072213286-2025_07_09-16.43.57.848/source.csv""\r\n",,terminal_output +544,876690,"crates/cli/Cargo.toml",0,0,"[package]\nname = ""crowd-pilot-serialize""\nversion.workspace = true\nedition.workspace = true\nlicense.workspace = true\ndescription = ""CLI tool for serializing crowd-pilot's IDE interaction data""\n\n[[bin]]\nname = ""crowd-pilot-serialize""\npath = ""src/main.rs""\n\n[dependencies]\ncrowd-pilot-serializer-core = { path = ""../core"" }\nclap = { version = ""4.5"", features = [""derive""] }\ntokenizers = { version = ""0.21"", features = [""http""] }\nserde_json = { workspace = true }\n\n",plaintext,tab +545,876691,"crates/cli/Cargo.toml",370,0,"",plaintext,selection_command +546,880948,"crates/cli/Cargo.toml",0,0,"[package]\nname = ""crowd-pilot-serialize""\nversion.workspace = true\nedition.workspace = true\nlicense.workspace = true\ndescription = ""CLI tool for serializing crowd-pilot's IDE interaction data""\n\n[[bin]]\nname = ""crowd-pilot-serialize""\npath = ""src/main.rs""\n\n[dependencies]\ncrowd-pilot-serializer-core = { path = ""../core"" }\nclap = { version = ""4.5"", features = [""derive""] }\ntokenizers = { version = ""0.21"", features = [""http""] }\nserde_json = { workspace = true }\n\n",plaintext,tab +547,880948,"crates/cli/Cargo.toml",336,0,"",plaintext,selection_mouse +548,880963,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""\r\n",,terminal_output +549,881370,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""\r\n",,terminal_output +550,882133,"crates/cli/Cargo.toml",0,0,"",plaintext,tab +551,882135,"crates/cli/src/main.rs",0,0,"",rust,tab +552,883274,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-7879e034-f897-48e8-8481-1a87a73b0dc81752135543307-2025_07_10-10.19.09.565/source.csv""\r\n",,terminal_output +553,883912,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-8e67a739-7b65-4646-afc1-42e9766880571751607756007-2025_07_04-07.43.31.602/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-8e67a739-7b65-4646-afc1-42e9766880571751607756007-2025_07_04-07.43.31.602/source.csv""\r\n",,terminal_output +554,884181,"crates/cli/src/main.rs",0,0,"",rust,tab +555,884791,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-9d03fc6f-7387-447c-9d61-dfb41a6cd5d41752168236691-2025_07_10-19.24.24.908/source.csv""\r\n",,terminal_output +556,885331,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\n",,terminal_output +557,886013,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-ebfebb4b-ef7a-46a0-8725-dd76b91fd2891752048248358-2025_07_09-10.04.56.945/source.csv""\r\n",,terminal_output +558,886127,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-7879e034-f897-48e8-8481-1a87a73b0dc81752135543307-2025_07_10-10.19.09.565/source.csv""\r\n",,terminal_output +559,892462,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\n",,terminal_output +560,894040,"TERMINAL",0,0,"Processed 300/349 sessions...\r\n",,terminal_output +561,913573,"TERMINAL",0,0,"Processed 349/349 sessions...\r\nProcessed 349 sessions\r\nWriting output to ""test_output_dir""...\r\n",,terminal_output +562,923261,"TERMINAL",0,0,"\r\n[summary]\r\n Total sessions processed: 349\r\n Train conversations: 4036\r\n Val conversations: 403\r\n Total messages: 129517\r\n Total tokens: 33411160\r\n Output: ""test_output_dir""/{training,validation}.jsonl\r\n Metadata: ""test_output_dir/metadata.json""\r\n",,terminal_output +563,924031,"TERMINAL",0,0,"\r\nreal\t1m18.850s\r\nuser\t62m27.949s\r\nsys\t1m48.527s\r\n]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +564,3901206,"TERMINAL",0,0,"\r[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +565,3911235,"TERMINAL",0,0,"c",,terminal_output +566,3911489,"TERMINAL",0,0,"at",,terminal_output +567,3911701,"TERMINAL",0,0," t",,terminal_output +568,3911752,"TERMINAL",0,0,"e",,terminal_output +569,3911922,"TERMINAL",0,0,"st",,terminal_output +570,3912108,"TERMINAL",0,0,"_output_dir/",,terminal_output +571,3913026,"TERMINAL",0,0,"m",,terminal_output +572,3913114,"TERMINAL",0,0,"e",,terminal_output +573,3913292,"TERMINAL",0,0,"tadata.json ",,terminal_output +574,3913674,"TERMINAL",0,0,"\r\n[?2004l\r{\r\n ""config"": {\r\n ""coalesce_radius"": 5,\r\n ""csv_root"": ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/"",\r\n ""max_tokens_per_conversation"": 8192,\r\n ""max_tokens_per_message"": 2048,\r\n ""min_conversation_messages"": 5,\r\n ""output_dir"": ""test_output_dir"",\r\n ""tokenizer"": ""Qwen/Qwen3-8B"",\r\n ""val_ratio"": 0.1,\r\n ""viewport_radius"": 10\r\n },\r\n ""counts"": {\r\n ""total_conversations"": 4439,\r\n ""total_sessions"": 349,\r\n ""train_conversations"": 4036,\r\n ""val_conversations"": 403\r\n },\r\n ""files"": {\r\n ""train_path"": ""test_output_dir/training.jsonl"",\r\n ""val_path"": ""test_output_dir/validation.jsonl""\r\n },\r\n ""stats"": {\r\n ""avg_messages_per_conversation"": 29.177066906961027,\r\n ""avg_tokens_per_conversation"": 7526.731245776075,\r\n ""total_messages"": 129517,\r\n ""total_tokens"": 33411160\r\n }\r\n}]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +575,3935256,"TERMINAL",0,0,"m",,terminal_output +576,3935387,"TERMINAL",0,0,"v",,terminal_output +577,3935556,"TERMINAL",0,0," ",,terminal_output +578,3935824,"TERMINAL",0,0,".",,terminal_output +579,3936012,"TERMINAL",0,0,".",,terminal_output +580,3936138,"TERMINAL",0,0,"/",,terminal_output +581,3936284,"TERMINAL",0,0,"c",,terminal_output +582,3936417,"TERMINAL",0,0,"r",,terminal_output +583,3936544,"TERMINAL",0,0,"o",,terminal_output +584,3936599,"TERMINAL",0,0,"w",,terminal_output +585,3936743,"TERMINAL",0,0,"d",,terminal_output +586,3936826,"TERMINAL",0,0,"-",,terminal_output +587,3937156,"TERMINAL",0,0,"pi",,terminal_output +588,3937387,"TERMINAL",0,0,"lot",,terminal_output +589,3938144,"TERMINAL",0,0," ",,terminal_output +590,3938318,"TERMINAL",0,0,".",,terminal_output +591,3938496,"TERMINAL",0,0,".",,terminal_output +592,3938688,"TERMINAL",0,0,"/",,terminal_output +593,3938872,"TERMINAL",0,0,"c",,terminal_output +594,3939098,"TERMINAL",0,0,"r",,terminal_output +595,3939158,"TERMINAL",0,0,"o",,terminal_output +596,3939248,"TERMINAL",0,0,"w",,terminal_output +597,3939363,"TERMINAL",0,0,"d",,terminal_output +598,3939504,"TERMINAL",0,0,"-",,terminal_output +599,3939956,"TERMINAL",0,0,"p",,terminal_output +600,3940015,"TERMINAL",0,0,"i",,terminal_output +601,3940221,"TERMINAL",0,0,"lot",,terminal_output +602,3941128,"TERMINAL",0,0,"-",,terminal_output +603,3941513,"TERMINAL",0,0,"ser",,terminal_output +604,3941660,"TERMINAL",0,0,"ializer/",,terminal_output +605,3942531,"TERMINAL",0,0,"",,terminal_output +606,3942799,"TERMINAL",0,0,"-",,terminal_output +607,3943048,"TERMINAL",0,0,"le",,terminal_output +608,3943131,"TERMINAL",0,0,"g",,terminal_output +609,3943304,"TERMINAL",0,0,"a",,terminal_output +610,3943415,"TERMINAL",0,0,"c",,terminal_output +611,3943534,"TERMINAL",0,0,"y",,terminal_output +612,3943755,"TERMINAL",0,0,"\r\n[?2004l\r]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +613,3953356,"/home/franz.srambical/crowd-pilot-serializer-legacy/crowd_pilot_serializer/serialization_utils.py",0,0,"#!/usr/bin/env python3\n""""""\nCommon utilities for dataset serialization scripts.\n""""""\n\nfrom __future__ import annotations\n\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import List, Optional, Tuple, Dict, Any\n\nimport difflib\nimport re\nimport pandas as pd\nfrom datasets import Dataset, load_dataset\n\n\n_ANSI_CSI_RE = re.compile(r""\x1b\[[0-9;?]*[ -/]*[@-~]"")\n_ANSI_OSC_TERMINATED_RE = re.compile(r""\x1b\][\s\S]*?(?:\x07|\x1b\\)"")\n_ANSI_OSC_LINE_FALLBACK_RE = re.compile(r""\x1b\][^\n]*$"")\n_BRACKETED_PASTE_ENABLE = ""\x1b[?2004h""\n_BRACKETED_PASTE_DISABLE = ""\x1b[?2004l""\n_OSC_633 = ""\x1b]633;""\n_OSC_0 = ""\x1b]0;""\n\n\n\n@dataclass\nclass ConversationState:\n """"""\n Mutable state used while constructing conversations.\n """"""\n conversations: List[List[Dict[str, str]]]\n max_tokens_per_conversation: int\n max_tokens_per_message: int\n min_conversation_messages: int\n tokenizer: Any\n conversation_token_counts: List[int] = field(default_factory=list)\n current_conversation: List[Dict[str, str]] = field(default_factory=list)\n current_tokens: int = 0\n files_opened_in_conversation: set[str] = field(default_factory=set)\n\n def finalize_conversation(self) -> None:\n """"""\n Finalize the current conversation: check constraints and append if valid.\n Then reset state for the next conversation.\n """"""\n if self.current_conversation:\n is_long_enough = len(self.current_conversation) >= self.min_conversation_messages\n has_user = any(msg.get(""from"") == ""User"" for msg in self.current_conversation)\n has_assistant = any(msg.get(""from"") == ""Assistant"" for msg in self.current_conversation)\n\n if is_long_enough and has_user and has_assistant:\n self.conversations.append(self.current_conversation)\n self.conversation_token_counts.append(self.current_tokens)\n \n self.current_conversation = []\n self.current_tokens = 0\n self.files_opened_in_conversation.clear()\n\n def append_message(self, message: Dict[str, str]) -> None:\n value = message[""value""]\n \n tokens = self.tokenizer.encode(value)\n num_tokens = len(tokens)\n\n if num_tokens > self.max_tokens_per_message:\n tokens = tokens[:self.max_tokens_per_message]\n value = self.tokenizer.decode(tokens)\n message[""value""] = value\n num_tokens = self.max_tokens_per_message\n\n if self.current_tokens + num_tokens > self.max_tokens_per_conversation:\n self.finalize_conversation()\n\n self.current_conversation.append(message)\n self.current_tokens += num_tokens\n\n def maybe_capture_file_contents(\n self,\n file_path: str,\n content: str,\n ) -> None:\n """"""\n Capture the contents of the given file in the current conversation if it hasn't been opened yet.\n """"""\n if file_path in self.files_opened_in_conversation:\n return\n cmd = f""cat -n {file_path}""\n self.append_message({\n ""from"": ""Assistant"",\n ""value"": _fenced_block(""bash"", _clean_text(cmd)),\n })\n output = _line_numbered_output(content)\n self.append_message({\n ""from"": ""User"",\n ""value"": f""\n{output}\n"",\n })\n self.files_opened_in_conversation.add(file_path)\n\n\ndef _clean_text(text: str) -> str:\n # Normalize line endings and strip trailing spaces; preserve tabs/newlines.\n return text.replace(""\r\n"", ""\n"").replace(""\r"", ""\n"").rstrip()\n\n\ndef _fenced_block(language: Optional[str], content: str) -> str:\n lang = (language or """").lower()\n return f""```{lang}\n{content}\n```\n""\n\n\ndef _apply_change(content: str, offset: int, length: int, new_text: str) -> str:\n # Mirrors crowd_code_player.replay_file.apply_change\n base = str(content)\n text = str(new_text) if pd.notna(new_text) else """"\n text = text.replace(""\\n"", ""\n"").replace(""\\r"", ""\r"")\n if offset > len(base):\n base = base + ("" "" * (offset - len(base)))\n return base[:offset] + text + base[offset + length:]\n\n\ndef _apply_backspaces(text: str) -> str:\n out: List[str] = []\n for ch in text:\n if ch == ""\b"": # \x08\n if out:\n out.pop()\n else:\n out.append(ch)\n return """".join(out)\n\n\ndef _normalize_terminal_output(raw: str) -> str:\n """"""\n Normalize PTY/terminal output for training:\n - Apply backspaces (\x08)\n - Strip OSC (window title/shell integration) first, keeping BEL/ST terminators intact\n - Resolve carriage returns (\r) by keeping the last rewrite per line\n - Strip CSI (coloring etc.)\n - Finally drop any remaining BEL (\x07)\n """"""\n if not raw:\n return raw\n s = _apply_backspaces(raw)\n # Remove OSC sequences that are properly terminated (BEL or ST)\n s = _ANSI_OSC_TERMINATED_RE.sub("""", s)\n # Fallback: drop any unterminated OSC up to end-of-line only\n s = ""\n"".join(_ANSI_OSC_LINE_FALLBACK_RE.sub("""", line) for line in s.split(""\n""))\n # Resolve carriage returns per line:\n # - If there are multiple rewrites, keep the last non-empty conversation\n # - If it's CRLF (ending with '\r' before '\n'), keep the content before '\r'\n resolved_lines: List[str] = []\n for seg in s.split(""\n""):\n parts = seg.split(""\r"")\n chosen = """"\n # pick last non-empty part if available; else last part\n for p in reversed(parts):\n if p != """":\n chosen = p\n break\n if chosen == """" and parts:\n chosen = parts[-1]\n resolved_lines.append(chosen)\n s = ""\n"".join(resolved_lines)\n # Strip ANSI escape sequences\n s = _ANSI_CSI_RE.sub("""", s)\n # Remove any remaining BEL beeps\n s = s.replace(""\x07"", """")\n return s\n\n\ndef _line_numbered_output(content: str, start_line: Optional[int] = None, end_line: Optional[int] = None) -> str:\n # FIXME (f.srambical): check whether this corresponds **exactly** to the output of cat -n {file_path} | sed -n '{vstart},{vend}p'\n lines = content.splitlines()\n total = len(lines)\n if total == 0:\n return """"\n s = 1 if start_line is None else max(1, min(start_line, total))\n e = total if end_line is None else max(1, min(end_line, total))\n assert e >= s, ""End line number cannot be less than start line number! Likely a bug in the line numbering computation.""\n buf: List[str] = []\n for idx in range(s, e + 1):\n buf.append(f""{idx:6}\t{lines[idx - 1]}"")\n return ""\n"".join(buf)\n\n\ndef _compute_viewport(total_lines: int, center_line: int, radius: int) -> Tuple[int, int]:\n if total_lines <= 0:\n return (1, 0)\n start = max(1, center_line - radius)\n end = min(total_lines, center_line + radius)\n assert end >= start, ""Viewport cannot have negative width! Likely a bug in the viewport computation.""\n return (start, end)\n\n\ndef _escape_single_quotes_for_sed(text: str) -> str:\n # Close quote, add an escaped single quote, reopen quote: '""'""'\n return text.replace(""'"", ""'\""'\""'"")\n\n\ndef _compute_changed_block_lines(\n before: str, after: str\n) -> Tuple[int, int, int, int, List[str]]:\n """"""\n Return 1-based start and end line numbers in 'before' that should be\n replaced, 1-based start and end line numbers in 'after' that contain\n the replacement, and the replacement lines from 'after'.\n\n For pure deletions, the replacement list may be empty.\n """"""\n before_lines = before.splitlines()\n after_lines = after.splitlines()\n sm = difflib.SequenceMatcher(a=before_lines, b=after_lines, autojunk=False)\n opcodes = [op for op in sm.get_opcodes() if op[0] != ""equal""]\n assert opcodes, ""Opcode list cannot be empty! Likely a bug in the diff computation.""\n\n first = opcodes[0]\n last = opcodes[-1]\n # i1/i2 refer to 'before' indices, j1/j2 to 'after'\n start_before = max(1, first[1] + 1)\n end_before = last[2] # no increment since we go from 'exclusive' to 'inclusive' indexing\n start_after = max(1, first[3] + 1)\n end_after = last[4]\n replacement_lines = after_lines[first[3] : last[4]]\n return (start_before, end_before, start_after, end_after, replacement_lines)\n\n\ndef session_to_nemo_conversations(\n df: pd.DataFrame,\n max_tokens_per_conversation: int,\n max_tokens_per_message: int,\n min_conversation_messages: int,\n tokenizer: Any,\n viewport_radius: int = 10,\n normalize_terminal_output: bool = True,\n coalesce_radius: int = 5,\n) -> Tuple[List[List[Dict[str, str]]], List[int]]:\n """"""\n Convert a session DataFrame to one or more NeMo conversations.\n\n - Conversations are created by approximately limiting the total tokens\n across all `value` fields to `max_tokens_per_conversation`.\n - When a new conversation starts (after the first), the first time a file is\n referenced in that conversation we re-log the full file contents with\n `cat -n ` and numbered output so that each conversation is self-contained.\n """"""\n file_states: Dict[str, str] = {}\n per_file_viewport: Dict[str, Optional[Tuple[int, int]]] = {}\n\n conversations: List[List[Dict[str, str]]] = []\n conversation_token_counts: List[int] = []\n conversation_state = ConversationState(\n conversations=conversations,\n conversation_token_counts=conversation_token_counts,\n max_tokens_per_conversation=max_tokens_per_conversation,\n max_tokens_per_message=max_tokens_per_message,\n min_conversation_messages=min_conversation_messages,\n tokenizer=tokenizer,\n )\n\n terminal_output_buffer: List[str] = []\n pending_edits_before: Dict[str, Optional[str]] = {}\n pending_edit_regions: Dict[str, Optional[Tuple[int, int]]] = {}\n\n def _flush_terminal_output_buffer() -> None:\n if not terminal_output_buffer:\n return\n aggregated = """".join(terminal_output_buffer)\n out = aggregated\n if normalize_terminal_output:\n out = _normalize_terminal_output(out)\n cleaned = _clean_text(out)\n if cleaned.strip():\n conversation_state.append_message({\n ""from"": ""User"",\n ""value"": f""\n{cleaned}\n"",\n })\n terminal_output_buffer.clear()\n\n def _flush_pending_edit_for_file(target_file: str) -> None:\n before_snapshot = pending_edits_before.get(target_file)\n if before_snapshot is None:\n return\n after_state = file_states.get(target_file, """")\n if before_snapshot.rstrip(""\n"") == after_state.rstrip(""\n""):\n pending_edits_before[target_file] = None\n pending_edit_regions[target_file] = None\n return\n (\n start_before,\n end_before,\n start_after,\n end_after,\n repl_lines,\n ) = _compute_changed_block_lines(before_snapshot, after_state)\n before_total_lines = len(before_snapshot.splitlines())\n if end_before < start_before:\n escaped_lines = [_escape_single_quotes_for_sed(line) for line in repl_lines]\n sed_payload = ""\n"".join(escaped_lines)\n if start_before <= max(1, before_total_lines):\n sed_cmd = f""sed -i '{start_before}i\\\n{sed_payload}' {target_file}""\n else:\n sed_cmd = f""sed -i '$a\\\n{sed_payload}' {target_file}""\n elif not repl_lines:\n sed_cmd = f""sed -i '{start_before},{end_before}d' {target_file}""\n else:\n escaped_lines = [_escape_single_quotes_for_sed(line) for line in repl_lines]\n sed_payload = ""\n"".join(escaped_lines)\n sed_cmd = f""sed -i '{start_before},{end_before}c\\\n{sed_payload}' {target_file}""\n total_lines = len(after_state.splitlines())\n center = (start_after + end_after) // 2\n vp = _compute_viewport(total_lines, center, viewport_radius)\n per_file_viewport[target_file] = vp\n vstart, vend = vp\n conversation_state.maybe_capture_file_contents(target_file, before_snapshot)\n chained_cmd = f""{sed_cmd} && cat -n {target_file} | sed -n '{vstart},{vend}p'""\n conversation_state.append_message({\n ""from"": ""Assistant"",\n ""value"": _fenced_block(""bash"", _clean_text(chained_cmd)),\n })\n viewport_output = _line_numbered_output(after_state, vstart, vend)\n conversation_state.append_message({\n ""from"": ""User"",\n ""value"": f""\n{viewport_output}\n"",\n })\n pending_edits_before[target_file] = None\n pending_edit_regions[target_file] = None\n\n def _flush_all_pending_edits() -> None:\n for fname in list(pending_edits_before.keys()):\n _flush_pending_edit_for_file(fname)\n\n for i in range(len(df)):\n row = df.iloc[i]\n file_path: str = row[""File""]\n event_type = row[""Type""]\n\n match event_type:\n case ""tab"":\n _flush_all_pending_edits()\n _flush_terminal_output_buffer()\n text = row[""Text""]\n if pd.notna(text):\n content = str(text).replace(""\\n"", ""\n"").replace(""\\r"", ""\r"")\n file_states[file_path] = content\n cmd = f""cat -n {file_path}""\n conversation_state.append_message({\n ""from"": ""Assistant"",\n ""value"": _fenced_block(""bash"", _clean_text(cmd)),\n })\n output = _line_numbered_output(content)\n conversation_state.append_message({\n ""from"": ""User"",\n ""value"": f""\n{output}\n"",\n })\n conversation_state.files_opened_in_conversation.add(file_path)\n else:\n # File switch without content snapshot: show current viewport only\n content = file_states.get(file_path, """")\n total_lines = len(content.splitlines())\n vp = per_file_viewport.get(file_path)\n if not vp or vp[1] == 0:\n vp = _compute_viewport(total_lines, 1, viewport_radius)\n per_file_viewport[file_path] = vp\n if vp and vp[1] >= vp[0]:\n vstart, vend = vp\n conversation_state.maybe_capture_file_contents(file_path, content)\n cmd = f""cat -n {file_path} | sed -n '{vstart},{vend}p'""\n conversation_state.append_message({\n ""from"": ""Assistant"",\n ""value"": _fenced_block(""bash"", _clean_text(cmd)),\n })\n viewport_output = _line_numbered_output(content, vstart, vend)\n conversation_state.append_message({\n ""from"": ""User"",\n ""value"": f""\n{viewport_output}\n"",\n })\n\n case ""content"":\n _flush_terminal_output_buffer()\n offset = int(row[""RangeOffset""])\n length = int(row[""RangeLength""])\n new_text = row[""Text""]\n before = file_states.get(file_path, """")\n # Approximate current edit region in line space\n new_text_str = str(new_text) if pd.notna(new_text) else """"\n start_line_current = before[:offset].count(""\n"") + 1\n deleted_conversation = before[offset:offset + length]\n lines_added = new_text_str.count(""\n"")\n lines_deleted = deleted_conversation.count(""\n"")\n region_start = start_line_current\n region_end = start_line_current + max(lines_added, lines_deleted, 0)\n # Flush pending edits if this edit is far from the pending region\n current_region = pending_edit_regions.get(file_path)\n if current_region is not None:\n rstart, rend = current_region\n if region_start < (rstart - coalesce_radius) or region_start > (rend + coalesce_radius):\n _flush_pending_edit_for_file(file_path)\n current_region = None\n after = _apply_change(before, offset, length, new_text)\n if pending_edits_before.get(file_path) is None:\n pending_edits_before[file_path] = before\n # Update/initialize region union\n if current_region is None:\n pending_edit_regions[file_path] = (region_start, max(region_start, region_end))\n else:\n rstart, rend = current_region\n pending_edit_regions[file_path] = (min(rstart, region_start), max(rend, region_end))\n file_states[file_path] = after\n\n case ""selection_command"" | ""selection_mouse"" | ""selection_keyboard"":\n # During an edit burst (pending edits), suppress flush and viewport emissions\n if pending_edits_before.get(file_path) is None:\n _flush_terminal_output_buffer()\n else:\n # Skip emitting viewport while edits are pending to avoid per-keystroke sed/cat spam\n continue\n offset = int(row[""RangeOffset""])\n content = file_states.get(file_path, """")\n total_lines = len(content.splitlines())\n target_line = content[:offset].count(""\n"") + 1\n vp = per_file_viewport.get(file_path)\n should_emit = False\n if not vp or vp[1] == 0:\n vp = _compute_viewport(total_lines, target_line, viewport_radius)\n per_file_viewport[file_path] = vp\n should_emit = True\n else:\n vstart, vend = vp\n if target_line < vstart or target_line > vend:\n vp = _compute_viewport(total_lines, target_line, viewport_radius)\n per_file_viewport[file_path] = vp\n should_emit = True\n if should_emit and vp and vp[1] >= vp[0]:\n vstart, vend = vp\n conversation_state.maybe_capture_file_contents(file_path, content)\n cmd = f""cat -n {file_path} | sed -n '{vstart},{vend}p'""\n conversation_state.append_message({\n ""from"": ""Assistant"",\n ""value"": _fenced_block(""bash"", _clean_text(cmd)),\n })\n viewport_output = _line_numbered_output(content, vstart, vend)\n conversation_state.append_message({\n ""from"": ""User"",\n ""value"": f""\n{viewport_output}\n"",\n })\n\n case ""terminal_command"":\n _flush_all_pending_edits()\n _flush_terminal_output_buffer()\n command = row[""Text""]\n command_str = str(command).replace(""\\n"", ""\n"").replace(""\\r"", ""\r"")\n conversation_state.append_message({\n ""from"": ""Assistant"",\n ""value"": _fenced_block(""bash"", _clean_text(command_str)),\n })\n\n case ""terminal_output"":\n output = row[""Text""]\n raw_output = str(output).replace(""\\n"", ""\n"").replace(""\\r"", ""\r"")\n terminal_output_buffer.append(raw_output)\n\n case ""terminal_focus"":\n _flush_all_pending_edits()\n _flush_terminal_output_buffer()\n # No-op for bash transcript; focus changes don't emit commands/output\n pass\n\n case ""git_branch_checkout"":\n _flush_all_pending_edits()\n _flush_terminal_output_buffer()\n branch_info = row[""Text""]\n branch_str = str(branch_info).replace(""\\n"", ""\n"").replace(""\\r"", ""\r"")\n cleaned = _clean_text(branch_str)\n m = re.search(r""to '([^']+)'"", cleaned)\n if not m:\n raise ValueError(f""Could not extract branch name from git checkout message: {cleaned}"")\n branch_name = m.group(1).strip()\n # Safe-quote branch if it contains special characters\n if re.search(r""[^A-Za-z0-9._/\\-]"", branch_name):\n branch_name = ""'"" + branch_name.replace(""'"", ""'\""'\""'"") + ""'""\n cmd = f""git checkout {branch_name}""\n conversation_state.append_message({\n ""from"": ""Assistant"",\n ""value"": _fenced_block(""bash"", _clean_text(cmd)),\n })\n\n case _:\n raise ValueError(f""Unknown event type: {event_type}"")\n\n _flush_all_pending_edits()\n _flush_terminal_output_buffer()\n conversation_state.finalize_conversation()\n return conversations, conversation_token_counts\n\n\n\ndef load_hf_csv(hf_path: str, split: str) -> Dataset:\n loaded = load_dataset(hf_path, split=split)\n\n assert isinstance(loaded, Dataset), ""Expected a Dataset from load_dataset""\n return loaded",python,tab +614,3974333,"crates/cli/src/main.rs",0,0,"",rust,tab +615,4012375,"crates/cli/src/main.rs",7050,0,"",rust,selection_command +616,4013435,"crates/cli/src/main.rs",0,0,"",rust,selection_command +617,4015269,"crates/cli/src/main.rs",1439,0,"",rust,selection_keyboard +618,4053409,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/hf_part/generate_jsonl_qwen_4k.sh",0,0,"#!/bin/bash\n\nset -uex\n\nOUTPUT_DIR=""/fast/project/HFMI_SynergyUnit/tab_model/data/nemo_hf_part_jsonl_4k_tokens/""\nCSV_ROOT=""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/""\n\nMAX_TOKENS_PER_CONVERSATION=4096\nTOKENIZER_MODEL=""Qwen/Qwen3-Coder-30B-A3B-Instruct""\n\nuv run crowd_pilot/serialize_dataset_nemo_json.py --csv_root=$CSV_ROOT --output_dir=$OUTPUT_DIR --max_tokens_per_conversation=$MAX_TOKENS_PER_CONVERSATION --tokenizer_model=$TOKENIZER_MODEL",shellscript,tab +619,4058563,"crates/cli/src/main.rs",0,0,"",rust,tab +620,4060334,"crates/cli/src/main.rs",1438,0,"",rust,selection_command +621,4060583,"crates/cli/src/main.rs",1410,0,"",rust,selection_command +622,4060615,"crates/cli/src/main.rs",1371,0,"",rust,selection_command +623,4060647,"crates/cli/src/main.rs",1320,0,"",rust,selection_command +624,4060682,"crates/cli/src/main.rs",1319,0,"",rust,selection_command +625,4060713,"crates/cli/src/main.rs",1281,0,"",rust,selection_command +626,4060746,"crates/cli/src/main.rs",1243,0,"",rust,selection_command +627,4060781,"crates/cli/src/main.rs",1186,0,"",rust,selection_command +628,4061030,"crates/cli/src/main.rs",1185,0,"",rust,selection_command +629,4061183,"crates/cli/src/main.rs",1150,0,"",rust,selection_command +630,4089610,"crates/cli/src/main.rs",1109,0,"",rust,selection_command +631,4214179,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/hf_part/generate_jsonl_qwen_4k.sh",0,0,"",shellscript,tab +632,4216135,"crates/cli/src/main.rs",0,0,"",rust,tab +633,4225278,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/hf_part/generate_jsonl_qwen_4k.sh",0,0,"",shellscript,tab +634,4226519,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/hf_part/generate_jsonl_qwen_4k.sh",182,0,"",shellscript,selection_command +635,4226760,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/hf_part/generate_jsonl_qwen_4k.sh",179,0,"",shellscript,selection_command +636,4227412,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/hf_part/generate_jsonl_qwen_4k.sh",182,0,"",shellscript,selection_command +637,4227514,"/home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/nemo/dataset/hf_part/generate_jsonl_qwen_4k.sh",214,0,"",shellscript,selection_command +638,4228637,"crates/cli/src/main.rs",0,0,"",rust,tab +639,4229190,"crates/cli/src/main.rs",1113,0,"",rust,selection_command +640,4229342,"crates/cli/src/main.rs",1115,0,"",rust,selection_command +641,4229603,"crates/cli/src/main.rs",1118,0,"",rust,selection_command +642,4229638,"crates/cli/src/main.rs",1119,0,"",rust,selection_command +643,4229700,"crates/cli/src/main.rs",1123,0,"",rust,selection_command +644,4229702,"crates/cli/src/main.rs",1125,0,"",rust,selection_command +645,4229737,"crates/cli/src/main.rs",1139,0,"",rust,selection_command +646,4229956,"crates/cli/src/main.rs",1141,0,"",rust,selection_command +647,4230174,"crates/cli/src/main.rs",1142,0,"",rust,selection_command +648,4230747,"crates/cli/src/main.rs",1107,0,"",rust,selection_command +649,4230817,"crates/cli/src/main.rs",1073,0,"",rust,selection_command +650,4230935,"crates/cli/src/main.rs",1066,0,"",rust,selection_command +651,4231095,"crates/cli/src/main.rs",1025,0,"",rust,selection_command +652,4231606,"crates/cli/src/main.rs",1025,4,"",rust,content +653,4231917,"crates/cli/src/main.rs",1025,0,"4",rust,content +654,4231917,"crates/cli/src/main.rs",1026,0,"",rust,selection_keyboard +655,4231992,"crates/cli/src/main.rs",1026,0,"0",rust,content +656,4231993,"crates/cli/src/main.rs",1027,0,"",rust,selection_keyboard +657,4232422,"crates/cli/src/main.rs",1027,0,"6",rust,content +658,4232422,"crates/cli/src/main.rs",1028,0,"",rust,selection_keyboard +659,4232832,"crates/cli/src/main.rs",1027,1,"",rust,content +660,4233003,"crates/cli/src/main.rs",1027,0,"9",rust,content +661,4233003,"crates/cli/src/main.rs",1028,0,"",rust,selection_keyboard +662,4233116,"crates/cli/src/main.rs",1028,0,"6",rust,content +663,4233116,"crates/cli/src/main.rs",1029,0,"",rust,selection_keyboard +664,4233441,"crates/cli/src/main.rs",1028,0,"",rust,selection_command +665,4234531,"TERMINAL",0,0,"\r[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +666,4235310,"TERMINAL",0,0,"mv ../crowd-pilot ../crowd-pilot-serializer-legacy",,terminal_output +667,4236374,"TERMINAL",0,0,"",,terminal_output +668,4239278,"TERMINAL",0,0,"mv ../crowd-pilot ../crowd-pilot-serializer-legacy",,terminal_output +669,4239434,"TERMINAL",0,0,"cat test_output_dir/metadata.json ",,terminal_output +670,4239579,"TERMINAL",0,0,"time bash /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",,terminal_output +671,4239733,"TERMINAL",0,0,"\rcargo build --release -p crowd-pilot-serialize",,terminal_output +672,4240874,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +673,4242192,"TERMINAL",0,0," Compiling crowd-pilot-serialize v0.1.0 (/fast/home/franz.srambical/crowd-pilot-serializer/crates/cli)\r\n Building [=======================> ] 196/197: crowd-pilot-serialize(bin) \r",,terminal_output +674,4245918,"TERMINAL",0,0," Finished ]8;;https://doc.rust-lang.org/cargo/reference/profiles.html#default-profiles\`release` profile [optimized]]8;;\ target(s) in 4.92s\r\n]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +675,4278050,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +676,4278343,"TERMINAL",0,0,"c': cargo build --release -p crowd-pilot-serialize",,terminal_output +677,4278581,"TERMINAL",0,0,"\rr': cargo build --release -p crowd-pilot-serialize",,terminal_output +678,4278706,"TERMINAL",0,0,"\ro': cargo build --release -p crowd-pilot-serialize\rw': cargo build --release -p crowd-pilot-serialize",,terminal_output +679,4278819,"TERMINAL",0,0,"\rd': cargo build --release -p crowd-pilot-serialize",,terminal_output +680,4279155,"TERMINAL",0,0,"mv ../crowd-pilot ../crowd-pilot-serializer-legacy",,terminal_output +681,4279969,"TERMINAL",0,0,"crowd-pilot ../crowd-pilot-serializer-legacy\r",,terminal_output +682,4280445,"TERMINAL",0,0,"time bash /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",,terminal_output +683,4281641,"TERMINAL",0,0,"\r[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ time bash /home/franz.srambical/slurm/dev/franz/berlin/crowd-pilot/crowd_pilot_serializer/serialize.sh",,terminal_output +684,4282014,"TERMINAL",0,0,"\r\n[?2004l\rLoading tokenizer from Qwen/Qwen3-8B...\r\n",,terminal_output +685,4282342,"TERMINAL",0,0,"Processing CSV files from ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/""...\r\n",,terminal_output +686,4282454,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-4457e5d2-f5e8-4b15-95aa-bafa247369991751528947759-2025_07_03-09.50.10.663/source.csv""\r\n",,terminal_output +687,4282552,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-d80c259c-238d-4ab7-8a8a-2d0fc8e345961753345086680-2025_07_24-10.19.00.46/source.csv""\r\n",,terminal_output +688,4282780,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-8e0958c9-e396-41d9-b3d4-8a748cefa1701750701699946-2025_06_23-11.01.41.744/source.csv""\r\n",,terminal_output +689,4283381,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +690,4283401,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +691,4283614,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-2f5b13c7-61f7-4340-b581-9edac6a53f1f1753015255059-2025_07_20-14.41.09.478/source.csv""\r\n",,terminal_output +692,4285626,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +693,4286489,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +694,4286741,"TERMINAL",0,0,"Processed 100/349 sessions...\r\n",,terminal_output +695,4287575,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-81dc70dc-8e01-48a6-9a00-9349b9f9a4171751541780271-2025_07_03-13.23.33.804/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/e8b08c312d88206805b92191af1ee2a660f8f0e59d3990233d6a3f81cdab43f4/crowd-code-b329b9f2-ee04-44c3-8e37-55401c64da7f1750160908319-2025_06_17-13.49.11.314/source.csv""\r\n",,terminal_output +696,4287742,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-0d5e2cbe-83a2-48a0-b2d9-a8e41912bfb61753114173405-2025_07_21-18.09.45.514/source.csv""\r\n",,terminal_output +697,4287984,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-81dc70dc-8e01-48a6-9a00-9349b9f9a4171751541780271-2025_07_03-13.23.33.804/source.csv""\r\n",,terminal_output +698,4288061,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-0f5513f7-8bc9-4c5d-856d-79d92f75113d1751284706913-2025_06_30-13.59.01.459/source.csv""\r\n",,terminal_output +699,4290319,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/e8b08c312d88206805b92191af1ee2a660f8f0e59d3990233d6a3f81cdab43f4/crowd-code-9c6bfb6d-25d7-401c-a2eb-18ae18179f4b1750244015795-2025_06_18-12.58.06.505/source.csv""\r\n",,terminal_output +700,4290672,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""\r\n",,terminal_output +701,4290833,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-29e2cbae-7056-4585-b457-f48bd451c3fd1750644341589-2025_06_22-19.05.43.270/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-fe3e4a6d-15dd-460a-96b3-1f7a60db202a1753178306285-2025_07_22-12.23.53.192/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-fe3e4a6d-15dd-460a-96b3-1f7a60db202a1753178306285-2025_07_22-12.23.53.192/source.csv""\r\n",,terminal_output +702,4291085,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +703,4291159,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +704,4291825,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +705,4292680,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +706,4294734,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-bec33357-731a-4f4a-afb4-3d538f18fd451751530719479-2025_07_03-10.19.48.863/source.csv""\r\n",,terminal_output +707,4297516,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-2d2437e1-caa5-4315-a7d9-4d9478073a161750944609503-2025_06_26-15.30.55.51/source.csv""\r\n",,terminal_output +708,4299402,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +709,4299953,"TERMINAL",0,0,"Processed 200/349 sessions...\r\n",,terminal_output +710,4300874,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-92ab1593-f937-4cc4-a174-544581a6ac991751909174142-2025_07_07-19.26.40.736/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-92ab1593-f937-4cc4-a174-544581a6ac991751909174142-2025_07_07-19.26.40.736/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +711,4301021,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +712,4301195,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +713,4301260,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +714,4301750,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +715,4302825,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +716,4303242,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +717,4305144,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-3e3ce02e-664a-4f58-9d7f-0f56e32c7def1753363875204-2025_07_24-15.31.23.202/source.csv""\r\n",,terminal_output +718,4305452,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +719,4305812,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-6791460b-ec38-4da2-872f-193943c12d601753274780799-2025_07_23-14.47.19.396/source.csv""\r\n",,terminal_output +720,4307587,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +721,4310134,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +722,4311317,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-172d798b-bee8-455a-b904-9dd3fe6387d51754411154298-2025_08_05-18.25.56.221/source.csv""\r\n",,terminal_output +723,4313654,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +724,4317545,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +725,4318269,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +726,4319340,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +727,4319718,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-cdede756-87c5-47af-85f8-bc9bf1c41bac1750785125700-2025_06_24-22.05.07.988/source.csv""\r\n",,terminal_output +728,4321367,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-fbd09e27-2302-4b0c-83a4-a77b7bc2e3dc1751440721102-2025_07_02-09.19.16.832/source.csv""\r\n",,terminal_output +729,4329641,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-8e67a739-7b65-4646-afc1-42e9766880571751607756007-2025_07_04-07.43.31.602/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-8e67a739-7b65-4646-afc1-42e9766880571751607756007-2025_07_04-07.43.31.602/source.csv""\r\n",,terminal_output +730,4331002,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-46a4cc9d-ac37-44ff-ae8d-547db76d96f31752072213286-2025_07_09-16.43.57.848/source.csv""\r\n",,terminal_output +731,4331246,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-7879e034-f897-48e8-8481-1a87a73b0dc81752135543307-2025_07_10-10.19.09.565/source.csv""\r\n",,terminal_output +732,4335040,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-9d03fc6f-7387-447c-9d61-dfb41a6cd5d41752168236691-2025_07_10-19.24.24.908/source.csv""\r\n",,terminal_output +733,4335345,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-7879e034-f897-48e8-8481-1a87a73b0dc81752135543307-2025_07_10-10.19.09.565/source.csv""\r\n",,terminal_output +734,4341669,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\n",,terminal_output +735,4341830,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\nWarning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-76073275-4388-463f-8e12-ce34ee46fad51752495312029-2025_07_14-14.15.14.704/source.csv""\r\n",,terminal_output +736,4345700,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-ebfebb4b-ef7a-46a0-8725-dd76b91fd2891752048248358-2025_07_09-10.04.56.945/source.csv""\r\n",,terminal_output +737,4350649,"TERMINAL",0,0,"Processed 300/349 sessions...\r\n",,terminal_output +738,4364854,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""\r\n",,terminal_output +739,4365197,"TERMINAL",0,0,"Warning: terminal_command event missing Text in ""/fast/project/HFMI_SynergyUnit/tab_model/data/hf_part_csv/1de052c516cab686515c107385aaf7c3a7e3e5c23c9bc3c0be0cff3df28cd64d/crowd-code-3dde1b0c-c963-467e-aa73-fb6c54df3ae41751963426964-2025_07_08-10.30.57.271/source.csv""\r\n",,terminal_output +740,4384097,"TERMINAL",0,0,"Processed 349/349 sessions...\r\nProcessed 349 sessions\r\nWriting output to ""test_output_dir""...\r\n",,terminal_output +741,4404313,"TERMINAL",0,0,"\r\n[summary]\r\n Total sessions processed: 349\r\n Train conversations: 9564\r\n Val conversations: 945\r\n Total messages: 131234\r\n Total tokens: 36988408\r\n Output: ""test_output_dir""/{training,validation}.jsonl\r\n Metadata: ""test_output_dir/metadata.json""\r\n",,terminal_output +742,4405067,"TERMINAL",0,0,"\r\nreal\t2m3.083s\r\nuser\t92m54.340s\r\nsys\t2m7.583s\r\n]0;franz.srambical@hai-login2:~/crowd-pilot-serializer[?2004h[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +743,4451914,"crates/cli/src/main.rs",1025,4,"8192",rust,content +744,4451918,"crates/cli/src/main.rs",1025,0,"",rust,selection_command +745,5673880,"TERMINAL",0,0,"\r[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +746,5677399,"TERMINAL",0,0,"\r[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +747,7914396,"TERMINAL",0,0,"\r[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +748,7918716,"TERMINAL",0,0,"\r[franz.srambical@hai008.haicore.berlin:~/crowd-pilot-serializer] $ ",,terminal_output +749,8726050,"crates/cli/src/main.rs",992,0,"",rust,selection_command diff --git a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c7b553b9-9f39-49d5-9921-ace297dd945c1764453653385-2025_11_29-23.00.57.494/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c7b553b9-9f39-49d5-9921-ace297dd945c1764453653385-2025_11_29-23.00.57.494/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..d36d57cf15403e105c8f2bdc3366682a3c080f10 --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c7b553b9-9f39-49d5-9921-ace297dd945c1764453653385-2025_11_29-23.00.57.494/source.csv @@ -0,0 +1,65 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,12,"Untitled-1",0,0,"",plaintext,tab +2,106,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"11:00:57 PM [info] Activating crowd-code\n11:00:57 PM [info] Recording started\n11:00:57 PM [info] Initializing git provider using file system watchers...\n11:00:57 PM [info] No workspace folder found\n",Log,tab +3,879,"Untitled-1",0,0,"",plaintext,tab +4,7510,"Untitled-1",0,0,"\n",plaintext,content +5,8198,"Untitled-1",0,0,"",plaintext,selection_command +6,9546,"Untitled-1",1,0,"",plaintext,selection_command +7,9662,"Untitled-1",0,0,"",plaintext,selection_command +8,11565,"Untitled-1",1,0,"",plaintext,selection_command +9,12165,"Untitled-1",0,0,"",plaintext,selection_command +10,12576,"Untitled-1",1,0,"",plaintext,selection_command +11,13855,"Untitled-1",0,1,"",plaintext,content +12,20208,"Untitled-1",0,0,"\n",plaintext,content +13,20705,"Untitled-1",0,1,"",plaintext,content +14,25013,"Untitled-1",0,0,"\n",plaintext,content +15,26025,"Untitled-1",0,0,"",plaintext,selection_command +16,26644,"Untitled-1",1,0,"",plaintext,selection_command +17,27236,"Untitled-1",0,0,"",plaintext,selection_command +18,27970,"Untitled-1",1,0,"",plaintext,selection_command +19,32009,"Untitled-1",0,0,"",plaintext,selection_command +20,44217,"Untitled-1",1,0,"",plaintext,selection_command +21,47229,"Untitled-1",0,0,"",plaintext,selection_command +22,48041,"Untitled-1",1,0,"",plaintext,selection_command +23,51061,"Untitled-1",0,0,"",plaintext,selection_command +24,51215,"Untitled-1",1,0,"",plaintext,selection_command +25,51533,"Untitled-1",0,0,"",plaintext,selection_command +26,52546,"Untitled-1",1,0,"",plaintext,selection_command +27,53160,"Untitled-1",0,0,"",plaintext,selection_command 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+60,267178,"Untitled-1",37,0,"",plaintext,selection_command +61,268523,"Untitled-1",5,0,"",plaintext,selection_command +62,268710,"Untitled-1",4,0,"",plaintext,selection_command +63,268859,"Untitled-1",3,0,"",plaintext,selection_command +64,269024,"Untitled-1",2,0,"",plaintext,selection_command diff --git a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c80bdedd-9d77-4f25-87da-5184ca5a8b3f1763540961734-2025_11_19-09.29.24.625/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c80bdedd-9d77-4f25-87da-5184ca5a8b3f1763540961734-2025_11_19-09.29.24.625/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..6b37cc16d713626a39c1503e41489a8fa11802b1 --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c80bdedd-9d77-4f25-87da-5184ca5a8b3f1763540961734-2025_11_19-09.29.24.625/source.csv @@ -0,0 +1,18 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,1,"nemo_run/config.py",0,0,"# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import annotations\n\nimport copy\nimport dataclasses\nimport inspect\nimport os\nimport re\nimport sys\nimport typing\nfrom pathlib import Path\nfrom types import MappingProxyType\nfrom typing import Any, Callable, Generic, Iterable, Optional, Type, TypeVar, Union, get_args\n\nimport fiddle as fdl\nimport fiddle._src.experimental.dataclasses as fdl_dc\nimport graphviz\nfrom fiddle._src import config, daglish, daglish_extensions\nfrom fiddle._src.casting import register_supported_cast\nfrom fiddle._src.config import TypeOrCallableProducingT\nfrom fiddle.graphviz import render, render_diff\nfrom typing_extensions import Annotated, ParamSpec, Self\n\nimport nemo_run.exceptions as run_exceptions\n\nParams = ParamSpec(""Params"")\nReturnType = TypeVar(""ReturnType"")\n\n_T = TypeVar(""_T"")\n_BuildableT = TypeVar(""_BuildableT"", bound=fdl.Buildable)\n\nRECURSIVE_TYPES = (typing.Union, typing.Optional)\n_NEMORUN_HOME = os.environ.get(""NEMORUN_HOME"", os.path.expanduser(""~/.nemo_run""))\nRUNDIR_NAME = ""nemo_run""\nRUNDIR_SPECIAL_NAME = ""/$nemo_run""\nSCRIPTS_DIR = ""scripts""\n\n# Metadata keys\nUSE_WITH_RAY_CLUSTER_KEY = ""use_with_ray_cluster""\n\n\ndef get_nemorun_home() -> str:\n """"""\n Get the current NEMORUN_HOME directory path.\n\n Returns:\n The path to the NEMORUN_HOME directory.\n """"""\n return _NEMORUN_HOME\n\n\ndef set_nemorun_home(path: str) -> None:\n """"""\n Set the NEMORUN_HOME directory path.\n\n Args:\n path: The new path for NEMORUN_HOME.\n """"""\n global _NEMORUN_HOME\n _NEMORUN_HOME = os.path.expanduser(path)\n\n\ndef get_type_namespace(typ: Type | Callable) -> str:\n """"""\n Get the namespace of a type or callable.\n\n Args:\n typ: The type or callable to get the namespace for.\n\n Returns:\n A string representing the namespace of the type or callable.\n\n Examples:\n >>> class MyClass:\n ... pass\n >>> get_type_namespace(MyClass)\n 'your_module.MyClass'\n """"""\n module = typ.__module__\n if module == ""__main__"":\n # Get the filename without extension\n main_module = sys.modules[""__main__""]\n filename = os.path.basename(main_module.__file__)\n module = os.path.splitext(filename)[0]\n\n if isinstance(typ, fdl.Buildable):\n typ = typ.__fn_or_cls__\n\n _name = getattr(typ, ""__qualname__"", str(typ))\n if _name.startswith(""ForwardRef""):\n _name = _name.split(""."")[-1]\n return f""{module}.{_name}""\n\n\ndef get_underlying_types(type_hint: typing.Any) -> typing.Set[typing.Type]:\n if isinstance(type_hint, typing._GenericAlias): # type: ignore\n if str(type_hint).startswith(""typing.Annotated""):\n origin = type_hint.__origin__\n if hasattr(origin, ""__origin__""):\n origin = origin.__origin__\n else:\n origin = type_hint.__origin__\n if origin in RECURSIVE_TYPES:\n types = set()\n for arg in type_hint.__args__:\n types.update(get_underlying_types(arg))\n return types\n return {type_hint}\n\n\ndef from_dict(raw_data: dict | list | str | float | int | bool, cls: Type[_T]) -> _T:\n if isinstance(raw_data, dict):\n underlying_types = get_underlying_types(cls)\n underlying_types = [tp for tp in underlying_types if tp is not type(None)]\n assert len(underlying_types) == 1, (\n f""Unable to load {cls}. Nested union types are not currently supported.""\n )\n cls = underlying_types[0] # type: ignore\n\n if dataclasses.is_dataclass(cls):\n fields_dict = {\n f.name: from_dict(raw_data.get(f.name), f.type) # type: ignore\n for f in dataclasses.fields(cls)\n if f.init\n }\n return cls(**fields_dict) # type: ignore\n elif isinstance(raw_data, list):\n return [from_dict(item, cls.__args__[0]) for item in raw_data] # type: ignore\n else:\n return raw_data # type: ignore\n\n\ndef set_value(cfg: config.Buildable, key: str, value: Any) -> None:\n """"""Set an attribute's value.\n\n Args:\n cfg: A `fdl.Buildable` whose attribute is to be overridden.\n assignment: String representing attribute's override expression. Of the form\n `attribute=value`.\n """"""\n *parents, last = _parse_path(key)\n\n walk = typing.cast(Any, cfg)\n try:\n for parent in parents:\n walk = parent.follow(walk)\n except Exception as e:\n raise run_exceptions.SetValueError(f'Invalid path ""{key}"".') from e\n\n try:\n if isinstance(last, daglish.Attr):\n setattr(walk, last.name, value)\n elif isinstance(last, daglish.Key):\n walk[last.key] = value\n else:\n raise run_exceptions.SetValueError(f""Unexpected path element {last}."")\n except Exception as e:\n raise run_exceptions.SetValueError(f'Could not set ""{key}"" to ""{value}"".') from e\n\n\nclass _CloneAndFNMixin:\n def clone(self):\n """"""Returns a deep clone of the object.""""""\n return copy.deepcopy(self)\n\n def walk(self: _BuildableT, **kwargs) -> _BuildableT: # type: ignore\n """"""\n Recursively applies a transformation function to attributes within the configuration object\n and its children that match the keys provided in kwargs. Attributes not listed in kwargs\n are not modified.\n\n Args:\n **kwargs (dict): A dictionary where keys are attribute names and values are functions\n that take the current attribute value and return a new value.\n\n Returns\n -------\n Config: A new Config instance with selectively modified attributes.\n\n Examples\n --------\n >>> config = Config(model=ModelConfig(seq_length=128))\n >>> new_config = config.walk(seq_length=lambda cfg: cfg.seq_length * 2)\n >>> new_config.model.seq_length\n 256\n """"""\n return _try_set_all(self, _walk=True, **kwargs)\n\n def broadcast(self: _BuildableT, **kwargs) -> _BuildableT: # type: ignore\n """"""\n Sets new values to attributes within the configuration object and its children that match\n the keys provided in kwargs. Attributes not listed in kwargs are not modified.\n\n Args:\n **kwargs (dict): A dictionary where keys are attribute names and values are the new\n values to be set.\n\n Returns\n -------\n Config: A new Config instance with selectively updated attributes.\n\n Examples\n --------\n >>> config = Config(model=ModelConfig(tensor_model_parallel_size=1))\n >>> new_config = config.broadcast(tensor_model_parallel_size=2)\n >>> new_config.model.tensor_model_parallel_size\n 2\n """"""\n return _try_set_all(self, **kwargs)\n\n\nclass _VisualizeMixin:\n def visualize(self, **kwargs) -> graphviz.Graph:\n return render(self, **kwargs)\n\n def diff(self, old: Self, trim=True, **kwargs):\n return render_diff(old=old, new=self, trim=trim, **kwargs)\n\n def save_config_img(self, path_str: str) -> None:\n """"""\n Saves the configuration to a file.\n\n Args:\n path (str): The file path where the configuration should be saved.\n fdl_fn (Partial): The function descriptor library function to save.\n\n Example:\n >>> save_config_img(""path/to/dir"", some_fdl_fn)\n """"""\n path: Path = Path(path_str)\n\n if not path.suffix:\n path = path / ""config.png""\n elif path.suffix != "".png"":\n raise ValueError(""The file extension must be .png"")\n\n path.parent.mkdir(parents=True, exist_ok=True)\n\n with path.open(""wb"") as f:\n f.write(self.visualize().pipe(""png""))\n\n def _repr_svg_(self):\n """"""Special method used by Jupyter to represent an object as SVG.\n\n Returns\n -------\n str: SVG representation of the Config object if Graphviz can render it.\n If Graphviz rendering fails or is not available, it returns None.\n """"""\n try:\n # Attempt to render using Graphviz and return SVG representation\n return self.visualize().pipe(format=""svg"").decode(""utf-8"")\n except Exception as e:\n # If rendering fails, log the exception or handle it as needed\n print(f""Graphviz rendering failed: {e}"")\n return self.__repr__()\n\n\nclass Config(Generic[_T], fdl.Config[_T], _CloneAndFNMixin, _VisualizeMixin):\n """"""\n Wrapper around fdl.Config with nemo_run specific functionality.\n See `fdl.Config `_ for more.\n """"""\n\n def __init__(\n self,\n fn_or_cls: Union[fdl.Buildable[_T], TypeOrCallableProducingT[_T]],\n *args,\n bind_args: bool = True,\n **kwargs,\n ):\n # Handle dict types by converting to _kwargs_to_dict function\n if fn_or_cls == {} or (hasattr(fn_or_cls, ""__origin__"") and fn_or_cls.__origin__ is dict):\n fn_or_cls = dict # type: ignore\n bind_args = False\n\n new_kwargs = kwargs\n if bind_args and not isinstance(fn_or_cls, fdl.Buildable):\n try:\n new_kwargs = _bind_args(fn_or_cls, *args, **kwargs)\n except Exception:\n new_kwargs = kwargs\n\n super().__init__(fn_or_cls, *args, **new_kwargs)\n\n @classmethod\n def __unflatten__(\n cls,\n values: Iterable[Any],\n metadata: config.BuildableTraverserMetadata,\n ):\n # If this is a dictionary config, reconstruct it with the arguments\n if metadata.fn_or_cls is dict:\n return cls(**metadata.arguments(values))\n return super().__unflatten__(values, metadata)\n\n\nclass Partial(Generic[_T], fdl.Partial[_T], _CloneAndFNMixin, _VisualizeMixin):\n """"""\n Wrapper around fdl.Partial with nemo_run specific functionality.\n See `fdl.Partial `_ for more.\n """"""\n\n def __init__(\n self,\n fn_or_cls: Union[fdl.Buildable[_T], TypeOrCallableProducingT[_T]],\n *args,\n bind_args: bool = True,\n **kwargs,\n ):\n new_kwargs = kwargs\n if bind_args and not isinstance(fn_or_cls, fdl.Buildable):\n try:\n new_kwargs = _bind_args(fn_or_cls, **kwargs)\n except Exception:\n new_kwargs = kwargs\n\n super().__init__(fn_or_cls, *args, **new_kwargs)\n\n\nregister_supported_cast(fdl.Config, Config)\nregister_supported_cast(fdl.Partial, Partial)\nregister_supported_cast(Config, Config)\nregister_supported_cast(Partial, Partial)\n\n\nclass ConfigurableMixin(_VisualizeMixin):\n """"""\n A mixin class that provides configuration and visualization functionality.\n\n This mixin adds methods for converting objects to Config instances,\n visualizing configurations, and comparing configurations.\n\n For classes that are not dataclasses, the `to_config` method needs to be\n overridden to provide custom conversion logic to Config instances.\n """"""\n\n def diff(self, old: Self, trim=True, **kwargs):\n """"""\n Generate a visual difference between this configuration and an old one.\n\n Args:\n old (Self): The old configuration to compare against.\n trim (bool, optional): Whether to trim unchanged parts. Defaults to True.\n **kwargs: Additional arguments to pass to render_diff.\n\n Returns:\n graphviz.Digraph: A graph representing the differences between configurations.\n """"""\n return render_diff(old=old.to_config(), new=self.to_config(), trim=trim, **kwargs)\n\n def to_config(self) -> Config[Self]:\n """"""\n Convert the current object to a Config instance.\n\n This method automatically converts dataclasses to Config instances.\n For classes that are not dataclasses, this method needs to be overridden\n to provide custom conversion logic.\n\n Returns:\n Config: A Config representation of the current object.\n\n Raises:\n NotImplementedError: If the object type cannot be converted to Config\n or if the method is not overridden for non-dataclass types.\n\n Note:\n For classes that are not dataclasses, you must override this method\n to define how the object should be converted to a Config instance.\n """"""\n if dataclasses.is_dataclass(self):\n try:\n return fdl.cast(\n Config, fdl_dc.convert_dataclasses_to_configs(self, allow_post_init=True)\n )\n except Exception as e:\n raise NotImplementedError(\n f""Cannot convert type {type(self)} to Config"",\n f""Please implement a method `to_config` on {type(self)}."",\n ) from e\n elif isinstance(self, (list, tuple, dict)):\n return self # type: ignore\n else:\n raise NotImplementedError(\n f""Cannot convert type {type(self)} to Config. ""\n f""Please override the `to_config` method for {type(self)}.""\n )\n\n def _repr_svg_(self):\n """"""\n Generate an SVG representation of the object for Jupyter notebooks.\n\n Returns:\n str: SVG representation of the object if it can be rendered,\n otherwise returns the string representation.\n """"""\n if isinstance(self, (list, tuple, dict)):\n try:\n return render(self).pipe(format=""svg"").decode(""utf-8"")\n except Exception as e:\n print(f""Graphviz rendering failed: {e}"")\n return self.__repr__()\n\n return self.to_config()._repr_svg_()\n\n\n@dataclasses.dataclass\nclass Script(ConfigurableMixin):\n """"""\n Dataclass to configure raw scripts.\n\n Examples:\n\n .. code-block:: python\n\n file_based_script = run.Script(""./scripts/echo.sh"")\n\n inline_script = run.Script(\n inline=\""\""\""\n env\n echo ""Hello 1""\n echo ""Hello 2""\n \""\""\""\n )\n\n """"""\n\n #: Path to your script\n path: str = """"\n #: Inline contents of the script. Either path or inline needs to be set.\n inline: str = """"\n #: Args to pass to your scripts, only applicable when path is set.\n args: list[str] = dataclasses.field(default_factory=list)\n #: Environment variables to set when running the script.\n env: dict[str, str] = dataclasses.field(default_factory=dict)\n #: Entrypoint to use, defaults to bash.\n entrypoint: str = ""bash""\n #: Whether to use ``python -m`` when executing via python.\n m: bool = False\n\n metadata: dict[str, Any] = dataclasses.field(default_factory=dict)\n\n def __post_init__(self):\n assert self.path or self.inline\n assert self.entrypoint, ""Need to provide an entrypoint for script.""\n if self.m:\n assert ""python"" in self.entrypoint, ""-m can only be used with python""\n\n def get_name(self):\n if self.inline:\n name = self.inline.strip()[:10]\n return re.sub(""[^0-9a-zA-Z]+"", ""_"", name)\n else:\n return os.path.basename(self.path)\n\n def to_command(\n self, with_entrypoint: bool = False, filename: Optional[str] = None, is_local: bool = False\n ) -> list[str]:\n if self.inline:\n if filename:\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n with open(filename, ""w"") as f:\n f.write(""#!/usr/bin/bash\n"" + self.inline)\n\n if is_local:\n cmd = [filename]\n else:\n cmd = [os.path.join(f""/{RUNDIR_NAME}"", SCRIPTS_DIR, Path(filename).name)]\n\n if with_entrypoint:\n cmd = [self.entrypoint] + cmd\n\n return cmd\n\n inline = self.inline.replace('""', '\\""')\n cmd = [""-c"", f'""{inline}""']\n if with_entrypoint:\n cmd = [self.entrypoint] + cmd\n\n return cmd\n\n args = [self.path] + self.args\n if self.m:\n cmd = [""-m""] + args\n else:\n cmd = args\n\n if with_entrypoint:\n if self.entrypoint:\n cmd = [self.entrypoint] + cmd\n else:\n raise ValueError(""Cannot use with_entrypoint=True without specifying entrypoint"")\n\n return cmd\n\n\n# A type alias for an optional type that is annotated with a Config.\n# This is useful for when you want to specify a type is Optional but\n# always want to provide a default config.\nOptionalDefaultConfig = Annotated[Optional[_T], Config[_T]]\nOptionalDefaultPartial = Annotated[Optional[_T], Partial[_T]]\n\n\ndef _parse_path(path: str) -> daglish.Path:\n """"""Parses a path into a list of either attributes or index lookups.""""""\n if not path.startswith(""["") and not path.startswith("".""):\n path = f"".{path}"" # Add a leading `.` to make parsing work properly.\n\n return daglish_extensions.parse_path(path)\n\n\ndef _bind_args(\n fn_or_cls: TypeOrCallableProducingT,\n *fn_args: fdl.Config | str | Callable,\n **fn_kwargs: fdl.Config | str | Callable,\n) -> dict[str, fdl.Config | str | Callable]:\n sig = inspect.signature(fn_or_cls)\n params = sig.parameters\n\n if set(fn_kwargs) > set(params):\n raise TypeError(\n f""{set(fn_kwargs) - set(params)} does not exist as args in {fn_or_cls.__module__}:{fn_or_cls.__name__}. Please remove them.""\n )\n\n final_args = _construct_args(fn_or_cls, params, fn_kwargs)\n final_args = fn_kwargs | final_args\n sig.bind(*fn_args, **final_args)\n return final_args\n\n\ndef _construct_args(\n fn_or_cls: TypeOrCallableProducingT,\n params: MappingProxyType[str, inspect.Parameter],\n kwargs: dict[str, fdl.Config | str | Callable],\n):\n final_args = {}\n\n primitive = [str, float, int, bool, bytes]\n primitive.extend([Optional[t] for t in primitive])\n\n for name, parameter in params.items():\n arg = kwargs.get(name, None)\n\n if arg:\n if dataclasses.is_dataclass(arg):\n final_args[name] = fdl.cast(\n Config,\n fdl_dc.convert_dataclasses_to_configs(arg, allow_post_init=True),\n )\n else:\n final_args[name] = arg\n elif str(parameter.annotation).startswith(""typing.Annotated""):\n args = get_args(parameter.annotation)\n if str(args[0]).startswith(""typing.Optional"") and len(args) > 1:\n cfg_type = get_args(args[0])[0]\n buildable = args[1].__origin__\n if issubclass(buildable, fdl.Buildable):\n final_args[name] = buildable(cfg_type)\n\n return final_args\n\n\nConfigT = TypeVar(""ConfigT"", Config, Partial)\n\n\ndef _try_set_all(config: _BuildableT, _walk: bool = False, **kwargs) -> _BuildableT:\n for key, val in kwargs.items():\n if hasattr(config, key):\n _val = val(config) if _walk else val\n setattr(config, key, _val)\n\n for attr_name in dir(config):\n try:\n if hasattr(config, attr_name):\n attr = getattr(config, attr_name)\n if isinstance(attr, (fdl.Config, fdl.Partial)):\n _try_set_all(attr, _walk=_walk, **kwargs)\n except ValueError:\n pass\n\n return config\n",python,tab +2,45,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"9:29:24 AM [info] Activating crowd-code\n9:29:24 AM [info] Recording started\n9:29:24 AM [info] Initializing git provider using file system watchers...\n9:29:24 AM [info] Git repository found\n9:29:24 AM [info] Git provider initialized successfully\n9:29:24 AM [info] Initial git state: [object Object]\n",Log,tab +3,1027,"nemo_run/config.py",0,0,"",python,tab +4,11239,"nemo_run/config.py",10462,13,"class Partial",python,selection_command +5,13234,"nemo_run/config.py",10549,0,"",python,selection_mouse +6,13255,"nemo_run/config.py",10548,0,"",python,selection_command +7,15762,"nemo_run/config.py",10556,0,"",python,selection_command +8,16108,"nemo_run/config.py",10562,0,"",python,selection_command +9,16323,"nemo_run/config.py",10569,0,"",python,selection_command +10,16505,"nemo_run/config.py",10572,0,"",python,selection_command +11,17021,"nemo_run/config.py",10569,0,"",python,selection_command +12,17673,"nemo_run/config.py",10489,0,"",python,selection_command +13,19772,"nemo_run/config.py",0,0,"",python,selection_command +14,20756,"nemo_run/config.py",979,0,"",python,selection_command +15,22098,"nemo_run/config.py",0,0,"",python,selection_command +16,22680,"nemo_run/config.py",10489,0,"",python,selection_command +17,448036,"nemo_run/config.py",10460,0,"",python,selection_mouse diff --git a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-cd6287e2-4cd4-444c-aab8-dbf1b52d201d1763483346410-2025_11_18-17.29.09.568/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-cd6287e2-4cd4-444c-aab8-dbf1b52d201d1763483346410-2025_11_18-17.29.09.568/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..a71df7b4bb72140bbb42cda99d8e949c2667ce5e --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-cd6287e2-4cd4-444c-aab8-dbf1b52d201d1763483346410-2025_11_18-17.29.09.568/source.csv @@ -0,0 +1,256 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,1,"nemo/collections/llm/recipes/finetune_default.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import TYPE_CHECKING, Any, Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\n\nimport nemo.lightning as nl\nfrom nemo.collections import llm\nfrom nemo.collections.llm.gpt.data.packed_sequence import PackedSequenceSpecs\nfrom nemo.collections.llm.peft import DoRA, LoRA\nfrom nemo.collections.llm.recipes.log.default import tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed\nfrom nemo.lightning.pytorch.callbacks import PEFT\nfrom nemo.utils.exp_manager import TimingCallback\n\nif TYPE_CHECKING:\n from lightning.pytorch.loggers import TensorBoardLogger, WandbLogger\n\nTokenizerType = Any\n\n\ndef default_finetune_recipe(\n model: run.Config[pl.LightningModule],\n resume_path: str,\n dir: Optional[str] = None,\n name: str = ""default"",\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n packed_sequence: bool = False, # once packing recipe is well tested, change this default to true\n tokenizer: Optional[TokenizerType] = ""model"",\n) -> run.Partial:\n """"""\n Create a default fine-tuning recipe for any model.\n\n This function sets up a template for a complete configuration for fine-tuning, including\n model, trainer, data, logging, optimization, and resumption settings.\n\n Args:\n model (run.Config[pl.LightningModule]): Configuration for a NeMo model.\n resume_path (str): Path to the Huggingface model or pretrained distributed checkpoint for resume\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the fine-tuning run.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n packed_sequence (bool): Whether to use packed sequence.\n tokenizer (Optional[TokenizerType]): Tokenizer setting to be applied. Can be 'data' or 'model'\n or an instance of TokenizerSpec.\n\n Returns:\n run.Partial: Partial configuration for fine-tuning.\n\n See usages of this recipe for further details.\n """"""\n if packed_sequence:\n datamodule = run.Config(\n llm.SquadDataModule,\n seq_length=2048,\n global_batch_size=8,\n micro_batch_size=1,\n packed_sequence_specs=PackedSequenceSpecs(packed_sequence_size=2048),\n )\n else:\n datamodule = run.Config(llm.SquadDataModule, seq_length=2048, global_batch_size=128, micro_batch_size=1)\n recipe = run.Partial(\n llm.finetune,\n model=model,\n trainer=default_finetune_trainer(\n num_nodes=num_nodes,\n num_gpus_per_node=num_gpus_per_node,\n ),\n data=datamodule,\n log=default_finetune_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n optim=distributed_fused_adam_with_cosine_annealing(max_lr=1e-4, min_lr=0, warmup_steps=50, adam_beta2=0.98),\n resume=nemo_resume(resume_path),\n tokenizer=tokenizer,\n )\n\n return recipe\n\n\ndef default_finetune_trainer(\n tensor_parallelism=1,\n pipeline_parallelism=1,\n pipeline_parallelism_type=torch.bfloat16,\n virtual_pipeline_parallelism=None,\n context_parallelism=1,\n sequence_parallelism=False,\n num_nodes=1,\n num_gpus_per_node=8,\n max_steps=1000,\n limit_test_batches=None,\n limit_val_batches=None,\n val_check_interval=30,\n):\n """"""\n Create a default fine-tuning trainer for any model.\n\n This function sets up a template for strategy and trainer.\n\n Args:\n See docstrings of MegatronStrategy and Trainer.\n\n Returns:\n run.Config: Config for a finetuning trainer.\n\n See usages of this in recipes for further details.\n """"""\n strategy = run.Config(\n nl.MegatronStrategy,\n tensor_model_parallel_size=tensor_parallelism,\n pipeline_model_parallel_size=pipeline_parallelism,\n pipeline_dtype=pipeline_parallelism_type,\n virtual_pipeline_model_parallel_size=virtual_pipeline_parallelism,\n context_parallel_size=context_parallelism,\n sequence_parallel=sequence_parallelism,\n gradient_as_bucket_view=True,\n ckpt_load_strictness=""log_all"",\n )\n\n trainer = run.Config(\n nl.Trainer,\n accelerator=""gpu"",\n accumulate_grad_batches=1,\n devices=num_gpus_per_node,\n limit_test_batches=limit_test_batches,\n limit_val_batches=limit_val_batches,\n log_every_n_steps=1,\n max_steps=max_steps,\n num_nodes=num_nodes,\n plugins=bf16_mixed(),\n strategy=strategy,\n use_distributed_sampler=False,\n val_check_interval=val_check_interval,\n callbacks=[run.Config(TimingCallback)],\n )\n\n return trainer\n\n\ndef default_finetune_log(\n dir: Optional[str] = None,\n name: str = ""default"",\n tensorboard_logger: Optional[run.Config['TensorBoardLogger']] = None,\n wandb_logger: Optional[run.Config['WandbLogger']] = None,\n) -> run.Config[nl.NeMoLogger]:\n """"""\n Create a default fine-tuning logger for any model.\n\n This function sets up a template for ModelCheckpoint and NeMoLogger.\n\n Args:\n See docstrings of ModelCheckpoint and NeMoLogger.\n\n Returns:\n run.Config: Config for a finetuning NeMoLogger.\n\n See usages of this in recipes for further details.\n """"""\n\n ckpt = run.Config(\n nl.ModelCheckpoint,\n save_last=""link"",\n save_top_k=2,\n every_n_train_steps=50,\n filename=""{model_name}--{val_loss:.2f}-{step}-{consumed_samples}"",\n )\n\n return run.Config(\n nl.NeMoLogger,\n ckpt=ckpt,\n name=name,\n tensorboard=tensorboard_logger,\n wandb=wandb_logger,\n log_dir=dir,\n )\n\n\ndef nemo_resume(model_id: str) -> run.Config[nl.AutoResume]:\n """"""\n Configure automatic resumption from a NeMo checkpoint converted from Huggingface for\n https://huggingface.co/{model_id}.\n\n This NeMo checkpoint should be converted from Huggingface beforehand, using nemo.collections.llm.import_ckpt.\n When converting the checkpoint, the NeMo checkpoint will be saved in NEMO_HOME (set to ~/.cache/nemo by default).\n\n This function sets up the configuration to resume training from path nemo://{model_id}.\n This translates to the full path {NEMO_HOME}/models/{model_id}.\n\n Args:\n model_id (str): Path to the Huggingface model or pretrained distributed checkpoint for resume\n\n Returns:\n run.Config[nl.AutoResume]: Configuration for resuming from NeMo checkpoint.\n """"""\n return run.Config(\n nl.AutoResume,\n restore_config=run.Config(nl.RestoreConfig, path=f""nemo://{model_id}""),\n )\n\n\n@run.cli.factory(name='lora')\ndef lora() -> run.Config[PEFT]:\n """"""\n Factory function to create a LoRA configuration.\n\n Returns:\n run.Config[PEFT]: Configuration for the LoRA class.\n\n Examples:\n CLI usage:\n $ nemo llm finetune -f llama3_8b peft=lora\n\n Python API usage:\n >>> lora_config = lora()\n >>> print(lora_config)\n """"""\n return run.Config(LoRA)\n\n\n@run.cli.factory(name='dora')\ndef dora() -> run.Config[PEFT]:\n """"""\n Factory function to create a DoRA configuration.\n\n Returns:\n run.Config[PEFT]: Configuration for the DoRA class.\n\n Examples:\n CLI usage:\n $ nemo llm finetune -f llama3_8b peft=dora\n\n Python API usage:\n >>> dora_config = dora()\n >>> print(dora_config)\n """"""\n return run.Config(DoRA)\n",python,tab +2,56,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"5:29:09 PM [info] Activating crowd-code\n5:29:09 PM [info] Recording started\n5:29:09 PM [info] Initializing git provider using file system watchers...\n5:29:09 PM [info] Git repository found\n5:29:09 PM [info] Git provider initialized successfully\n",Log,tab +3,126,"extension-output-pdoom-org.crowd-code-#1-crowd-code",245,0,"5:29:09 PM [info] Initial git state: [object Object]\n",Log,content +4,913,"nemo/collections/llm/recipes/finetune_default.py",0,0,"",python,tab +5,1882,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n """"""\n Factory function to create a Qwen3 30B-A3B model configuration.\n This is a MoE (Mixture of Experts) model with 128 experts.\n\n Returns:\n run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n Examples:\n CLI usage:\n $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n Python API usage:\n >>> model_config = model()\n >>> print(model_config)\n """"""\n return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n # General\n dir: Optional[str] = None,\n name: str = ""default"",\n # Trainer\n tensor_parallelism: int = 4, # Default for 30B-A3B model\n pipeline_parallelism: int = 2,\n pipeline_parallelism_type: Optional[torch.dtype] = None,\n virtual_pipeline_parallelism: Optional[int] = None,\n context_parallelism: int = 1,\n expert_parallelism: Optional[int] = 4,\n sequence_parallelism: bool = True,\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n max_steps: int = 300000,\n precision: str = ""bf16-mixed"",\n accumulate_grad_batches: int = 1,\n gradient_clip_val: float = 1.0,\n limit_test_batches: int = 32,\n limit_val_batches: int = 32,\n log_every_n_steps: int = 10,\n val_check_interval: int = 500,\n # Data\n global_batch_size=32,\n micro_batch_size=2,\n seq_length=4096,\n # Optimizer\n warmup_steps=500,\n constant_steps=0,\n min_lr=3e-5,\n max_lr=3e-4,\n # Training function\n fn=pretrain,\n) -> run.Partial:\n """"""\n Create a pre-training recipe for Qwen3 30B-A3B model.\n\n This function sets up a complete configuration for pre-training, including\n model, trainer, data, logging, optimization, and resumption settings.\n This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n Args:\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the pre-training run.\n tensor_parallelism (int): Degree of tensor model parallelism.\n pipeline_parallelism (int): Degree of pipeline model parallelism.\n pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n context_parallelism (int): Degree of context parallelism.\n sequence_parallelism (bool): Whether to use sequence parallelism.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n max_steps (int): Maximum number of training steps.\n precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n accumulate_grad_batches (int): Number of steps per gradient accumulation.\n gradient_clip_val (float): Value for gradient clipping.\n limit_test_batches (int): Limit the number of test batches.\n limit_val_batches (int): Limit the number of validation batches.\n log_every_n_steps (int): Log every n steps.\n val_check_interval (int): Run validation every N steps.\n global_batch_size (int): Global batch size.\n micro_batch_size (int): Micro batch size.\n seq_length (int): Sequence length.\n warmup_steps (int): Number of warmup steps.\n constant_steps (int): Number of constant steps.\n min_lr (float): Minimum learning rate.\n max_lr (float): Maximum learning rate.\n fn (Callable): The pre-training function to use.\n\n Returns:\n run.Partial: Partial configuration for pre-training.\n\n Examples:\n CLI usage:\n $ nemo llm pretrain --factory qwen3_30b_a3b\n $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n Python API usage:\n >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n >>> print(recipe)\n\n Note:\n This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n """"""\n recipe = run.Partial(\n fn,\n model=model(),\n trainer=qwen3_trainer(\n tensor_parallelism=tensor_parallelism,\n pipeline_parallelism=pipeline_parallelism,\n pipeline_parallelism_type=pipeline_parallelism_type,\n virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n context_parallelism=context_parallelism,\n sequence_parallelism=sequence_parallelism,\n expert_parallelism=expert_parallelism,\n num_nodes=num_nodes,\n num_gpus_per_node=num_gpus_per_node,\n max_steps=max_steps,\n precision=precision,\n accumulate_grad_batches=accumulate_grad_batches,\n limit_test_batches=limit_test_batches,\n limit_val_batches=limit_val_batches,\n log_every_n_steps=log_every_n_steps,\n val_check_interval=val_check_interval,\n callbacks=[run.Config(TimingCallback)],\n ),\n data=run.Config(\n MockDataModule,\n seq_length=seq_length,\n global_batch_size=global_batch_size,\n micro_batch_size=micro_batch_size,\n tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n ),\n log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n optim=distributed_fused_adam_with_cosine_annealing(\n precision=precision,\n warmup_steps=warmup_steps,\n constant_steps=constant_steps,\n min_lr=min_lr,\n max_lr=max_lr,\n clip_grad=gradient_clip_val,\n ),\n resume=default_resume(),\n )\n recipe.model.config.recompute_granularity = ""full""\n recipe.model.config.recompute_method = ""uniform""\n recipe.model.config.recompute_num_layers = 1\n return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n dir: Optional[str] = None,\n name: str = ""default"",\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n peft_scheme: Optional[str] = 'lora',\n packed_sequence: bool = False,\n) -> run.Partial:\n """"""\n Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n This function sets up a complete configuration for fine-tuning, including\n model, trainer, data, logging, optimization, and resumption settings.\n The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n Args:\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the fine-tuning run.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n Allowed values: 'lora'/'dora'/'none'/None.\n packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n efficiency. Default sequence length is 2048.\n\n Returns:\n run.Partial: Partial configuration for fine-tuning.\n\n Examples:\n CLI usage:\n $ nemo llm finetune --factory qwen3_30b_a3b\n\n Python API usage:\n >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n >>> print(recipe)\n\n Note:\n This recipe uses the SQuAD dataset for fine-tuning.\n """"""\n recipe = default_finetune_recipe(\n model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n )\n if peft_scheme is None or peft_scheme.lower() == 'none':\n recipe.trainer.strategy.tensor_model_parallel_size = 4\n recipe.trainer.strategy.expert_model_parallel_size = 4\n recipe.trainer.strategy.expert_tensor_parallel_size = 1\n recipe.trainer.strategy.pipeline_model_parallel_size = 2\n recipe.trainer.strategy.sequence_parallel = True\n recipe.optim.config.lr = 5e-6\n elif peft_scheme.lower() in ['lora', 'dora']:\n recipe.trainer.strategy.tensor_model_parallel_size = 4\n recipe.trainer.strategy.expert_model_parallel_size = 4\n recipe.trainer.strategy.expert_tensor_parallel_size = 1\n recipe.trainer.strategy.sequence_parallel = True\n recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n recipe.optim.config.lr = 1e-4\n else:\n raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n return recipe\n",python,tab +6,3679,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2687,0,"",python,selection_keyboard +7,3985,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",4959,0,"",python,selection_keyboard +8,9297,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10041,0,"",python,selection_command +9,14593,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10023,0,"",python,selection_command +10,14839,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9954,0,"",python,selection_command +11,14872,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",9944,0,"",python,selection_command 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+124,3210780,"nemo/collections/llm/recipes/finetune_default.py",2789,32," datamodule = run.Config(",python,selection_command +125,3436136,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"",python,tab +126,3436411,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,10041,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n """"""\n Factory function to create a Qwen3 30B-A3B model configuration.\n This is a MoE (Mixture of Experts) model with 128 experts.\n\n Returns:\n run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n Examples:\n CLI usage:\n $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n Python API usage:\n >>> model_config = model()\n >>> print(model_config)\n """"""\n return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n # General\n dir: Optional[str] = None,\n name: str = ""default"",\n # Trainer\n tensor_parallelism: int = 4, # Default for 30B-A3B model\n pipeline_parallelism: int = 2,\n pipeline_parallelism_type: Optional[torch.dtype] = None,\n virtual_pipeline_parallelism: Optional[int] = None,\n context_parallelism: int = 1,\n expert_parallelism: Optional[int] = 4,\n sequence_parallelism: bool = True,\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n max_steps: int = 300000,\n precision: str = ""bf16-mixed"",\n accumulate_grad_batches: int = 1,\n gradient_clip_val: float = 1.0,\n limit_test_batches: int = 32,\n limit_val_batches: int = 32,\n log_every_n_steps: int = 10,\n val_check_interval: int = 500,\n # Data\n global_batch_size=32,\n micro_batch_size=2,\n seq_length=4096,\n # Optimizer\n warmup_steps=500,\n constant_steps=0,\n min_lr=3e-5,\n max_lr=3e-4,\n # Training function\n fn=pretrain,\n) -> run.Partial:\n """"""\n Create a pre-training recipe for Qwen3 30B-A3B model.\n\n This function sets up a complete configuration for pre-training, including\n model, trainer, data, logging, optimization, and resumption settings.\n This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n Args:\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the pre-training run.\n tensor_parallelism (int): Degree of tensor model parallelism.\n pipeline_parallelism (int): Degree of pipeline model parallelism.\n pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n context_parallelism (int): Degree of context parallelism.\n sequence_parallelism (bool): Whether to use sequence parallelism.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n max_steps (int): Maximum number of training steps.\n precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n accumulate_grad_batches (int): Number of steps per gradient accumulation.\n gradient_clip_val (float): Value for gradient clipping.\n limit_test_batches (int): Limit the number of test batches.\n limit_val_batches (int): Limit the number of validation batches.\n log_every_n_steps (int): Log every n steps.\n val_check_interval (int): Run validation every N steps.\n global_batch_size (int): Global batch size.\n micro_batch_size (int): Micro batch size.\n seq_length (int): Sequence length.\n warmup_steps (int): Number of warmup steps.\n constant_steps (int): Number of constant steps.\n min_lr (float): Minimum learning rate.\n max_lr (float): Maximum learning rate.\n fn (Callable): The pre-training function to use.\n\n Returns:\n run.Partial: Partial configuration for pre-training.\n\n Examples:\n CLI usage:\n $ nemo llm pretrain --factory qwen3_30b_a3b\n $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n Python API usage:\n >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n >>> print(recipe)\n\n Note:\n This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n """"""\n recipe = run.Partial(\n fn,\n model=model(),\n trainer=qwen3_trainer(\n tensor_parallelism=tensor_parallelism,\n pipeline_parallelism=pipeline_parallelism,\n pipeline_parallelism_type=pipeline_parallelism_type,\n virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n context_parallelism=context_parallelism,\n sequence_parallelism=sequence_parallelism,\n expert_parallelism=expert_parallelism,\n num_nodes=num_nodes,\n num_gpus_per_node=num_gpus_per_node,\n max_steps=max_steps,\n precision=precision,\n accumulate_grad_batches=accumulate_grad_batches,\n limit_test_batches=limit_test_batches,\n limit_val_batches=limit_val_batches,\n log_every_n_steps=log_every_n_steps,\n val_check_interval=val_check_interval,\n callbacks=[run.Config(TimingCallback)],\n ),\n data=run.Config(\n MockDataModule,\n seq_length=seq_length,\n global_batch_size=global_batch_size,\n micro_batch_size=micro_batch_size,\n tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n ),\n log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n optim=distributed_fused_adam_with_cosine_annealing(\n precision=precision,\n warmup_steps=warmup_steps,\n constant_steps=constant_steps,\n min_lr=min_lr,\n max_lr=max_lr,\n clip_grad=gradient_clip_val,\n ),\n resume=default_resume(),\n )\n recipe.model.config.recompute_granularity = ""full""\n recipe.model.config.recompute_method = ""uniform""\n recipe.model.config.recompute_num_layers = 1\n return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n dir: Optional[str] = None,\n name: str = ""default"",\n num_nodes: int = 1,\n num_gpus_per_node: int = 8,\n peft_scheme: Optional[str] = 'lora',\n packed_sequence: bool = False,\n) -> run.Partial:\n """"""\n Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n This function sets up a complete configuration for fine-tuning, including\n model, trainer, data, logging, optimization, and resumption settings.\n The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n Args:\n dir (Optional[str]): Directory for saving logs and checkpoints.\n name (str): Name of the fine-tuning run.\n num_nodes (int): Number of compute nodes to use.\n num_gpus_per_node (int): Number of GPUs per node.\n peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n Allowed values: 'lora'/'dora'/'none'/None.\n packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n efficiency. Default sequence length is 2048.\n\n Returns:\n run.Partial: Partial configuration for fine-tuning.\n\n Examples:\n CLI usage:\n $ nemo llm finetune --factory qwen3_30b_a3b\n\n Python API usage:\n >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n >>> print(recipe)\n\n Note:\n This recipe uses the SQuAD dataset for fine-tuning.\n """"""\n recipe = default_finetune_recipe(\n model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n )\n if peft_scheme is None or peft_scheme.lower() == 'none':\n recipe.trainer.strategy.tensor_model_parallel_size = 4\n recipe.trainer.strategy.expert_model_parallel_size = 4\n recipe.trainer.strategy.expert_tensor_parallel_size = 1\n recipe.trainer.strategy.pipeline_model_parallel_size = 2\n recipe.trainer.strategy.sequence_parallel = True\n recipe.optim.config.lr = 5e-6\n elif peft_scheme.lower() in ['lora', 'dora']:\n recipe.trainer.strategy.tensor_model_parallel_size = 4\n recipe.trainer.strategy.expert_model_parallel_size = 4\n recipe.trainer.strategy.expert_tensor_parallel_size = 1\n recipe.trainer.strategy.sequence_parallel = True\n recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n recipe.optim.config.lr = 1e-4\n else:\n raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n return recipe\n",python,selection_command +127,3436648,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10041,0,"",python,selection_command +128,3901672,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"",python,selection_command +129,3903628,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",818,0,"",python,selection_command +130,3905550,"nemo/collections/llm/__init__.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# This is here to import it once, which improves the speed of launch when in debug-mode\nfrom nemo.utils.import_utils import safe_import\n\nsafe_import(""transformer_engine"")\n\nfrom nemo.collections.llm import peft\nfrom nemo.collections.llm.bert.data import BERTMockDataModule, BERTPreTrainingDataModule, SpecterDataModule\nfrom nemo.collections.llm.bert.model import (\n BertConfig,\n BertEmbeddingLargeConfig,\n BertEmbeddingMiniConfig,\n BertEmbeddingModel,\n BertModel,\n HuggingFaceBertBaseConfig,\n HuggingFaceBertConfig,\n HuggingFaceBertLargeConfig,\n HuggingFaceBertModel,\n MegatronBertBaseConfig,\n MegatronBertConfig,\n MegatronBertLargeConfig,\n)\nfrom nemo.collections.llm.gpt.data import ( # noqa: F401\n AlpacaDataModule,\n ChatDataModule,\n CustomReRankerDataModule,\n CustomRetrievalDataModule,\n DollyDataModule,\n FineTuningDataModule,\n HFDatasetDataModule,\n HFDatasetDataModulePacked,\n HFMockDataModule,\n MockDataModule,\n PreTrainingDataModule,\n SpecterReRankerDataModule,\n SquadDataModule,\n)\nfrom nemo.collections.llm.gpt.data.api import dolly, hf_dataset, mock, squad\nfrom nemo.collections.llm.gpt.model import ( # noqa: F401\n Baichuan2Config,\n Baichuan2Config7B,\n Baichuan2Model,\n BaseMambaConfig1_3B,\n BaseMambaConfig2_7B,\n BaseMambaConfig130M,\n BaseMambaConfig370M,\n BaseMambaConfig780M,\n ChatGLM2Config6B,\n ChatGLM3Config6B,\n ChatGLMConfig,\n ChatGLMModel,\n CodeGemmaConfig2B,\n CodeGemmaConfig7B,\n CodeLlamaConfig7B,\n CodeLlamaConfig13B,\n CodeLlamaConfig34B,\n CodeLlamaConfig70B,\n DeepSeekModel,\n DeepSeekV2Config,\n DeepSeekV2LiteConfig,\n DeepSeekV3Config,\n Gemma2Config,\n Gemma2Config2B,\n Gemma2Config9B,\n Gemma2Config27B,\n Gemma2Model,\n Gemma3Config1B,\n Gemma3Config4B,\n Gemma3Config12B,\n Gemma3Config27B,\n Gemma3Model,\n GemmaConfig,\n GemmaConfig2B,\n GemmaConfig7B,\n GemmaModel,\n GPTConfig,\n GPTConfig5B,\n GPTConfig7B,\n GPTConfig20B,\n GPTConfig40B,\n GPTConfig126M,\n GPTConfig175B,\n GPTModel,\n GPTOSSConfig,\n GPTOSSConfig20B,\n GPTOSSConfig120B,\n GPTOSSModel,\n HFAutoModelForCausalLM,\n Hyena1bConfig,\n Hyena7bARCLongContextConfig,\n Hyena7bConfig,\n Hyena40bARCLongContextConfig,\n Hyena40bConfig,\n HyenaConfig,\n HyenaModel,\n HyenaNV1bConfig,\n HyenaNV7bConfig,\n HyenaNV40bConfig,\n HyenaNVTestConfig,\n HyenaTestConfig,\n Llama2Config7B,\n Llama2Config13B,\n Llama2Config70B,\n Llama3Config8B,\n Llama3Config70B,\n Llama4Config,\n Llama4Experts16Config,\n Llama4Experts128Config,\n Llama31Config8B,\n Llama31Config70B,\n Llama31Config405B,\n Llama31Nemotron70BConfig,\n Llama31NemotronNano8BConfig,\n Llama31NemotronUltra253BConfig,\n Llama32Config1B,\n Llama32Config3B,\n Llama32EmbeddingConfig1B,\n Llama32EmbeddingConfig3B,\n Llama32Reranker1BConfig,\n Llama32Reranker500MConfig,\n Llama33NemotronSuper49BConfig,\n LlamaConfig,\n LlamaEmbeddingModel,\n LlamaModel,\n LlamaNemotronModel,\n MambaModel,\n MaskedTokenLossReduction,\n MistralConfig7B,\n MistralModel,\n MistralNeMoConfig12B,\n MistralSmall3Config24B,\n MixtralConfig,\n MixtralConfig8x3B,\n MixtralConfig8x7B,\n MixtralConfig8x22B,\n MixtralModel,\n Nemotron3Config4B,\n Nemotron3Config8B,\n Nemotron3Config22B,\n Nemotron4Config15B,\n Nemotron4Config340B,\n NemotronConfig,\n NemotronHConfig4B,\n NemotronHConfig8B,\n NemotronHConfig47B,\n NemotronHConfig56B,\n NemotronModel,\n NemotronNano9Bv2,\n NemotronNano12Bv2,\n NVIDIAMambaConfig8B,\n NVIDIAMambaHybridConfig8B,\n Phi3Config,\n Phi3ConfigMini,\n Phi3Model,\n Qwen2Config,\n Qwen2Config1P5B,\n Qwen2Config7B,\n Qwen2Config72B,\n Qwen2Config500M,\n Qwen2Model,\n Qwen3Config,\n Qwen3Config1P7B,\n Qwen3Config4B,\n Qwen3Config8B,\n Qwen3Config14B,\n Qwen3Config30B_A3B,\n Qwen3Config32B,\n Qwen3Config235B_A22B,\n Qwen3Config600M,\n Qwen3Model,\n Qwen25Config1P5B,\n Qwen25Config3B,\n Qwen25Config7B,\n Qwen25Config14B,\n Qwen25Config32B,\n Qwen25Config72B,\n Qwen25Config500M,\n ReRankerModel,\n SSMConfig,\n Starcoder2Config,\n Starcoder2Config3B,\n Starcoder2Config7B,\n Starcoder2Config15B,\n Starcoder2Model,\n StarcoderConfig,\n StarcoderConfig15B,\n StarcoderModel,\n gpt_data_step,\n gpt_forward_step,\n)\nfrom nemo.collections.llm.t5.data import FineTuningDataModule as T5FineTuningDataModule\nfrom nemo.collections.llm.t5.data import MockDataModule as T5MockDataModule\nfrom nemo.collections.llm.t5.data import PreTrainingDataModule as T5PreTrainingDataModule\nfrom nemo.collections.llm.t5.data import SquadDataModule as T5SquadDataModule\nfrom nemo.collections.llm.t5.model import (\n T5Config,\n T5Config3B,\n T5Config11B,\n T5Config220M,\n T5Model,\n t5_data_step,\n t5_forward_step,\n)\n\n__all__ = [\n ""MockDataModule"",\n ""T5MockDataModule"",\n ""CustomRetrievalDataModule"",\n ""CustomReRankerDataModule"",\n ""SpecterReRankerDataModule"",\n ""GPTModel"",\n ""GPTConfig"",\n ""HyenaTestConfig"",\n ""Hyena7bConfig"",\n ""Hyena40bConfig"",\n ""Hyena7bARCLongContextConfig"",\n ""Hyena40bARCLongContextConfig"",\n ""HyenaNVTestConfig"",\n ""HyenaNV40bConfig"",\n ""HyenaNV7bConfig"",\n ""HyenaConfig"",\n ""HyenaModel"",\n ""Hyena1bConfig"",\n ""HyenaNV1bConfig"",\n ""gpt_data_step"",\n ""gpt_forward_step"",\n ""T5Model"",\n ""T5Config"",\n ""T5Config220M"",\n ""T5Config3B"",\n ""T5Config11B"",\n ""BertConfig"",\n ""BertEmbeddingModel"",\n ""BertModel"",\n ""BertEmbeddingLargeConfig"",\n ""BertEmbeddingMiniConfig"",\n ""t5_data_step"",\n ""t5_forward_step"",\n ""MaskedTokenLossReduction"",\n ""MistralConfig7B"",\n ""MistralNeMoConfig12B"",\n ""MistralSmall3Config24B"",\n ""MistralModel"",\n ""MixtralConfig"",\n ""MixtralConfig8x3B"",\n ""MixtralConfig8x7B"",\n ""MixtralConfig8x22B"",\n ""MixtralModel"",\n ""Starcoder2Config15B"",\n ""Starcoder2Config"",\n ""Starcoder2Model"",\n ""NemotronModel"",\n ""Nemotron3Config4B"",\n ""Nemotron3Config8B"",\n ""Nemotron3Config22B"",\n ""Nemotron4Config15B"",\n ""Nemotron4Config340B"",\n ""NemotronConfig"",\n ""LlamaEmbeddingModel"",\n ""Llama32EmbeddingConfig1B"",\n ""Llama32EmbeddingConfig3B"",\n ""Phi3Config"",\n ""Phi3ConfigMini"",\n ""Phi3Model"",\n ""SSMConfig"",\n ""BaseMambaConfig130M"",\n ""BaseMambaConfig370M"",\n ""BaseMambaConfig780M"",\n ""BaseMambaConfig1_3B"",\n ""BaseMambaConfig2_7B"",\n ""NVIDIAMambaConfig8B"",\n ""NVIDIAMambaHybridConfig8B"",\n ""NemotronHConfig4B"",\n ""NemotronHConfig8B"",\n ""NemotronHConfig47B"",\n ""NemotronHConfig56B"",\n ""NemotronNano9Bv2"",\n ""NemotronNano12Bv2"",\n ""MambaModel"",\n ""LlamaConfig"",\n ""Llama2Config7B"",\n ""Llama2Config13B"",\n ""Llama2Config70B"",\n ""Llama3Config8B"",\n ""Llama3Config70B"",\n ""Llama31Config8B"",\n ""Llama31Config70B"",\n ""Llama31Config405B"",\n ""Llama32Config1B"",\n ""Llama32Config3B"",\n ""Llama4Experts16Config"",\n ""Llama4Experts128Config"",\n ""Llama4Config"",\n ""Llama31NemotronNano8BConfig"",\n ""Llama31Nemotron70BConfig"",\n ""Llama33NemotronSuper49BConfig"",\n ""Llama31NemotronUltra253BConfig"",\n ""Llama32Reranker500MConfig"",\n ""Llama32Reranker1BConfig"",\n ""CodeLlamaConfig7B"",\n ""CodeLlamaConfig13B"",\n ""CodeLlamaConfig34B"",\n ""CodeLlamaConfig70B"",\n ""LlamaModel"",\n ""LlamaNemotronModel"",\n ""GPTOSSConfig"",\n ""GPTOSSConfig120B"",\n ""GPTOSSConfig20B"",\n ""GPTOSSModel"",\n ""GemmaConfig"",\n ""GemmaConfig2B"",\n ""GemmaConfig7B"",\n ""CodeGemmaConfig2B"",\n ""CodeGemmaConfig7B"",\n ""GemmaModel"",\n ""Gemma2Model"",\n ""Gemma2Config9B"",\n ""Gemma2Config"",\n ""Gemma2Config27B"",\n ""Gemma2Config2B"",\n ""Gemma3Model"",\n ""Gemma3Config1B"",\n ""Gemma3Config4B"",\n ""Gemma3Config12B"",\n ""Gemma3Config27B"",\n ""Baichuan2Config"",\n ""Baichuan2Config7B"",\n ""Baichuan2Model"",\n ""ChatGLMConfig"",\n ""ChatGLM2Config6B"",\n ""ChatGLM3Config6B"",\n ""ChatGLMModel"",\n ""Qwen2Model"",\n ""Qwen2Config7B"",\n ""Qwen2Config"",\n ""Qwen2Config500M"",\n ""Qwen2Config1P5B"",\n ""Qwen25Config3B"",\n ""Qwen2Config72B"",\n ""Qwen25Config500M"",\n ""Qwen25Config1P5B"",\n ""Qwen25Config7B"",\n ""Qwen25Config14B"",\n ""Qwen25Config32B"",\n ""Qwen25Config72B"",\n ""Qwen3Config"",\n ""Qwen3Config600M"",\n ""Qwen3Config1P7B"",\n ""Qwen3Config4B"",\n ""Qwen3Config8B"",\n ""Qwen3Config14B"",\n ""Qwen3Config32B"",\n ""Qwen3Config30B_A3B"",\n ""Qwen3Config235B_A22B"",\n ""Qwen3Model"",\n ""PreTrainingDataModule"",\n ""FineTuningDataModule"",\n ""ChatDataModule"",\n ""SquadDataModule"",\n ""T5PreTrainingDataModule"",\n ""T5FineTuningDataModule"",\n ""T5SquadDataModule"",\n ""T5MockDataModule"",\n ""DeepSeekModel"",\n ""DeepSeekV2Config"",\n ""DeepSeekV2LiteConfig"",\n ""DeepSeekV3Config"",\n ""HuggingFaceBertBaseConfig"",\n ""HuggingFaceBertConfig"",\n ""HuggingFaceBertLargeConfig"",\n ""HuggingFaceBertModel"",\n ""MegatronBertBaseConfig"",\n ""MegatronBertConfig"",\n ""MegatronBertLargeConfig"",\n ""BERTMockDataModule"",\n ""BERTPreTrainingDataModule"",\n ""SpecterDataModule"",\n ""DollyDataModule"",\n ""tokenizer"",\n ""mock"",\n ""squad"",\n ""dolly"",\n ""peft"",\n ""hf_dataset"",\n ""HFAutoModelForCausalLM"",\n ""HFMockDataModule"",\n]\n\n\nfrom nemo.utils import logging\n\ntry:\n import nemo_run as run # noqa: F401\n\n from nemo.collections.llm.api import ( # noqa: F401\n distill,\n export_ckpt,\n finetune,\n generate,\n import_ckpt,\n pretrain,\n prune,\n ptq,\n train,\n validate,\n )\n from nemo.collections.llm.recipes import * # noqa\n\n __all__.extend(\n [\n ""train"",\n ""import_ckpt"",\n ""export_ckpt"",\n ""pretrain"",\n ""validate"",\n ""finetune"",\n ""generate"",\n ""prune"",\n ""ptq"",\n ""distill"",\n ]\n )\nexcept ImportError as error:\n logging.warning(f""Failed to import nemo.collections.llm.[api,recipes]: {error}"")\n\ntry:\n from nemo.collections.llm.api import deploy # noqa: F401\n\n __all__.append(""deploy"")\nexcept ImportError as error:\n logging.warning(f""The deploy module could not be imported: {error}"")\n\ntry:\n from nemo.collections.llm.api import evaluate # noqa: F401\n\n __all__.append(""evaluate"")\nexcept ImportError as error:\n logging.warning(f""The evaluate module could not be imported: {error}"")\n",python,tab +131,3910853,"nemo/collections/llm/__init__.py",1372,0,"",python,selection_command +132,3911438,"nemo/collections/llm/gpt/data/chat.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom functools import lru_cache\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any, Dict, List, Optional, Union\n\nfrom nemo.collections.llm.gpt.data.core import create_sft_dataset\nfrom nemo.collections.llm.gpt.data.fine_tuning import FineTuningDataModule\n\nif TYPE_CHECKING:\n from nemo.collections.common.tokenizers import TokenizerSpec\n from nemo.collections.llm.gpt.data.packed_sequence import PackedSequenceSpecs\n\n\nclass ChatDataModule(FineTuningDataModule):\n """"""\n Base class for fine-tuning an LLM on chat datasets.\n This class calls `GPTSFTChatDataset` for chat template processing\n\n See base class `FineTuningDataModule` for more details.\n """"""\n\n def __init__(\n self,\n dataset_root: Union[str, Path],\n seq_length: int = 2048,\n tokenizer: Optional[""TokenizerSpec""] = None,\n micro_batch_size: int = 4,\n global_batch_size: int = 8,\n rampup_batch_size: Optional[List[int]] = None,\n seed: int = 1234,\n memmap_workers: int = 1,\n num_workers: int = 8,\n pin_memory: bool = True,\n persistent_workers: bool = False,\n packed_sequence_specs: Optional[""PackedSequenceSpecs""] = None,\n dataset_kwargs: Optional[Dict[str, Any]] = None,\n use_hf_tokenizer_chat_template: bool = False,\n ):\n """"""Data module for finetuning on chat datasets.\n See base class `FineTuningDataModule` for more details of the arguments.\n\n Args:\n use_hf_tokenizer_chat_template: Whether to use the chat template from the HuggingFace tokenizer. If True,\n uses the tokenizer's built-in chat template. If False, uses default chat template from\n GPTSFTChatDataset. Defaults to False.\n """"""\n super().__init__(\n dataset_root,\n seq_length,\n tokenizer,\n micro_batch_size,\n global_batch_size,\n rampup_batch_size,\n seed,\n memmap_workers,\n num_workers,\n pin_memory,\n persistent_workers,\n packed_sequence_specs,\n dataset_kwargs,\n )\n self.use_hf_tokenizer_chat_template = use_hf_tokenizer_chat_template\n\n @lru_cache\n def _create_dataset(self, path, pack_metadata_path=None, is_test=False, **kwargs):\n # pylint: disable=C0115,C0116\n return create_sft_dataset(\n path,\n tokenizer=self.tokenizer,\n seq_length=(self.seq_length if is_test or self.packed_sequence_size <= 0 else self.packed_sequence_size),\n memmap_workers=self.memmap_workers,\n seed=self.seed,\n chat=True,\n is_test=is_test,\n pack_metadata_file_path=None, # packing is not supported\n pad_cu_seqlens=False,\n use_hf_tokenizer_chat_template=self.use_hf_tokenizer_chat_template,\n **kwargs,\n )\n",python,tab +133,3911440,"nemo/collections/llm/gpt/data/chat.py",1051,0,"",python,selection_command +134,3922774,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"",python,tab +135,3925020,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2832,0,"",python,selection_command +136,3925651,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2832,1,"4",python,selection_command +137,3925809,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2832,4,"4096",python,selection_command +138,3926234,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2835,0,"",python,selection_command +139,3934172,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2811,0,"",python,selection_command +140,3934321,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2785,0,"",python,selection_command +141,3934605,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2788,0,"",python,selection_command +142,3934779,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",2789,0,"",python,selection_command 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All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport json\nimport warnings\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any, Callable, Optional, Union\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom megatron.core import parallel_state\nfrom rich.console import Console\nfrom torch.distributed import all_gather_object\nfrom typing_extensions import Annotated\n\nimport nemo.lightning as nl\nfrom nemo.collections.llm import GPTModel, HFAutoModelForCausalLM\nfrom nemo.collections.llm.gpt.data.fine_tuning import FineTuningDataModule\nfrom nemo.collections.llm.modelopt import (\n DistillationGPTModel,\n ExportConfig,\n PruningConfig,\n QuantizationConfig,\n Quantizer,\n prune_language_model,\n save_pruned_model,\n set_modelopt_spec_if_exists_in_ckpt,\n setup_trainer_and_restore_model_with_modelopt_spec,\n)\nfrom nemo.lightning import (\n AutoResume,\n NeMoLogger,\n OptimizerModule,\n Trainer,\n configure_no_restart_validation_training_loop,\n io,\n)\nfrom nemo.lightning.base import NEMO_MODELS_CACHE\nfrom nemo.lightning.callback_group import CallbackGroup\nfrom nemo.lightning.ckpt_utils import ckpt_to_context_subdir\nfrom nemo.lightning.pytorch.callbacks import PEFT, JitTransform, ModelTransform\nfrom nemo.utils import logging\nfrom nemo.utils.get_rank import is_global_rank_zero\n\nif TYPE_CHECKING:\n from megatron.core.inference.common_inference_params import CommonInferenceParams\n from megatron.core.inference.inference_request import InferenceRequest\n\n\nTokenizerType = Any\nAnyPath = Union[Path, str]\n\n\n@run.cli.entrypoint(namespace=""llm"")\ndef train(\n model: Union[pl.LightningModule, AnyPath],\n data: pl.LightningDataModule,\n trainer: Trainer,\n log: Annotated[Optional[NeMoLogger], run.Config[NeMoLogger]] = None,\n resume: Annotated[Optional[AutoResume], run.Config[AutoResume]] = None,\n optim: Optional[OptimizerModule] = None,\n tokenizer: Optional[TokenizerType] = None,\n model_transform: Optional[Union[PEFT, ModelTransform, Callable]] = None,\n # TODO: Fix export export: Optional[str] = None,\n) -> Path:\n """"""\n Trains a model using the specified data and trainer, with optional tokenizer, source, and export.\n\n Args:\n model (Union[pl.LightningModule, AnyPath]): The model to be trained or a path to the NeMo 2 checkpoint.\n data (pl.LightningDataModule): The data module containing training data.\n trainer (Trainer): The trainer instance configured with a MegatronStrategy.\n log (NeMoLogger): A nemologger instance.\n resume (Optional[Union[AutoResume, Resume]]): Resume training from a checkpoint.\n optim (Optional[OptimizerModule]): The optimizer module to be used. If not provided, the default optimizer\n from the model will be used.\n tokenizer (Optional[TokenizerType]): Tokenizer setting to be applied. Can be 'data' or 'model'\n or an instance of TokenizerSpec.\n export (Optional[str]): Filename to save the exported checkpoint after training.\n model_transform (Optional[Union[Callable[[nn.Module], nn.Module], PEFT]]): A model transform to be applied.\n\n Returns\n -------\n Path: The directory path where training artifacts are saved.\n\n Examples\n --------\n >>> from nemo.collections import llm\n >>> from nemo import lightning as nl\n >>> model = llm.MistralModel()\n >>> data = llm.SquadDataModule(seq_length=4096, global_batch_size=16, micro_batch_size=2)\n >>> precision = nl.MegatronMixedPrecision(precision=""bf16-mixed"")\n >>> trainer = nl.Trainer(strategy=nl.MegatronStrategy(tensor_model_parallel_size=2), plugins=precision)\n >>> llm.train(model, data, trainer, tokenizer=""data"")\n PosixPath('/path/to/log_dir')\n """"""\n model = _load_model_from_path(model)\n\n # [ModelOpt]: If modelopt_state exists, overwrite transformer_layer_spec to modelopt spec\n if resume:\n if resume.restore_config and resume.restore_config.path:\n set_modelopt_spec_if_exists_in_ckpt(model, resume.restore_config.path)\n elif resume.resume_from_path:\n set_modelopt_spec_if_exists_in_ckpt(model, resume.resume_from_path)\n\n app_state = _setup(\n model=model,\n data=data,\n trainer=trainer,\n log=log,\n resume=resume,\n optim=optim,\n tokenizer=tokenizer,\n model_transform=model_transform,\n )\n\n trainer.fit(model, data)\n\n return app_state.exp_dir\n\n\n@run.cli.entrypoint(namespace=""llm"")\ndef pretrain(\n model: Union[pl.LightningModule, AnyPath],\n data: pl.LightningDataModule,\n trainer: Trainer,\n log: Annotated[Optional[NeMoLogger], run.Config[NeMoLogger]] = None,\n resume: Annotated[Optional[AutoResume], run.Config[AutoResume]] = None,\n optim: Optional[OptimizerModule] = None,\n) -> Path:\n """"""\n Pretrains a model using the specified data and trainer, with optional logging, resuming, and optimization.\n\n This function is a wrapper around the `train` function, specifically configured for pretraining tasks.\n Note, by default it will use the tokenizer from the model.\n\n Args:\n model (Union[pl.LightningModule, AnyPath]): The model to be pretrained or a path to the NeMo 2 checkpoint.\n data (pl.LightningDataModule): The data module containing pretraining data.\n trainer (Trainer): The trainer instance configured with a MegatronStrategy.\n log (NeMoLogger): A nemologger instance.\n resume (Optional[AutoResume]): Resume training from a checkpoint.\n optim (Optional[OptimizerModule]): The optimizer module to be used. If not provided, the default\n optimizer from the model will be used.\n\n Returns:\n Path: The directory path where pretraining artifacts are saved.\n\n Examples:\n >>> from nemo.collections import llm\n >>> from nemo import lightning as nl\n >>> model = llm.MistralModel()\n >>> data = llm.PretrainingDataModule(paths=[...], seq_length=4096, global_batch_size=16, micro_batch_size=2)\n >>> precision = nl.MegatronMixedPrecision(precision=""bf16-mixed"")\n >>> trainer = nl.Trainer(strategy=nl.MegatronStrategy(tensor_model_parallel_size=2), plugins=precision)\n >>> llm.pretrain(model, data, trainer)\n PosixPath('/path/to/log_dir')\n """"""\n model = _load_model_from_path(model)\n _validate_config(model, data, trainer, log=log, resume=resume, optim=optim)\n\n return train(\n model=model,\n data=data,\n trainer=trainer,\n log=log,\n resume=resume,\n optim=optim,\n tokenizer=""data"",\n )\n\n\n@run.cli.entrypoint(namespace=""llm"")\ndef finetune(\n model: Union[pl.LightningModule, AnyPath],\n data: pl.LightningDataModule,\n trainer: Trainer,\n log: Annotated[Optional[NeMoLogger], run.Config[NeMoLogger]] = None,\n resume: Annotated[Optional[AutoResume], run.Config[AutoResume]] = None,\n optim: Optional[OptimizerModule] = None,\n peft: Optional[Union[PEFT, ModelTransform, Callable]] = None,\n tokenizer: Optional[TokenizerType] = ""model"",\n) -> Path:\n """"""\n Finetunes a model using the specified data and trainer, with optional logging, resuming, and PEFT.\n\n Note, by default it will use the tokenizer from the model.\n\n Args:\n model (Union[pl.LightningModule, AnyPath]): The model to be finetuned.\n data (pl.LightningDataModule): The data module containing finetuning data.\n trainer (Trainer): The trainer instance configured with a MegatronStrategy.\n log (NeMoLogger): A nemologger instance.\n resume (Optional[AutoResume]): Resume training from a checkpoint.\n optim (Optional[OptimizerModule]): The optimizer module to be used. If not provided, the default\n optimizer from the model will be used.\n peft (Optional[PEFT]): A PEFT (Parameter-Efficient Fine-Tuning) configuration to be applied.\n tokenizer (Optional[TokenizerType]): Tokenizer setting to be applied. Can be 'data' or 'model'\n or an instance of TokenizerSpec. If 'data' uses the data loader's tokenizer instead of the tokenizer\n from the model checkpoint, which is useful for expanding vocabulary or adding special tokens\n (such as chat template tokens).\n\n Returns:\n Path: The directory path where finetuning artifacts are saved.\n\n Examples:\n >>> from nemo.collections import llm\n >>> from nemo import lightning as nl\n >>> model = llm.MistralModel()\n >>> data = llm.SquadDataModule(seq_length=4096, global_batch_size=16, micro_batch_size=2)\n >>> precision = nl.MegatronMixedPrecision(precision=""bf16-mixed"")\n >>> trainer = nl.Trainer(strategy=nl.MegatronStrategy(tensor_model_parallel_size=2), plugins=precision)\n >>> llm.finetune(model, data, trainer, peft=llm.peft.LoRA()])\n PosixPath('/path/to/log_dir')\n """"""\n model = _load_model_from_path(model)\n _validate_config(model, data, trainer, log=log, resume=resume, optim=optim, model_transform=peft)\n return train(\n model=model,\n data=data,\n trainer=trainer,\n log=log,\n resume=resume,\n optim=optim,\n tokenizer=tokenizer,\n model_transform=peft,\n )\n\n\n@run.cli.entrypoint(namespace=""llm"")\ndef validate(\n model: pl.LightningModule,\n data: pl.LightningDataModule,\n trainer: Trainer,\n log: Annotated[Optional[NeMoLogger], run.Config[NeMoLogger]] = None,\n resume: Annotated[Optional[AutoResume], run.Config[AutoResume]] = None,\n optim: Optional[OptimizerModule] = None,\n tokenizer: Optional[TokenizerType] = None,\n model_transform: Optional[Union[PEFT, ModelTransform, Callable]] = None,\n) -> Path:\n """"""\n Validates a model using the specified data and trainer, with optional logging, resuming, and model transformations.\n\n Args:\n model (pl.LightningModule): The model to be validated.\n data (pl.LightningDataModule): The data module containing validation data.\n trainer (Trainer): The trainer instance configured with a MegatronStrategy.\n log (NeMoLogger): A nemologger instance.\n resume (Optional[AutoResume]): Resume from a checkpoint for validation.\n optim (Optional[OptimizerModule]): The optimizer module to be used. If not provided, the default optimizer\n from the model will be used.\n tokenizer (Optional[TokenizerType]): Tokenizer setting to be applied. Can be 'data' or 'model'\n or an instance of TokenizerSpec.\n model_transform (Optional[Union[Callable[[nn.Module], nn.Module], PEFT]]): A model transform to be applied.\n\n Returns:\n Path: The directory path where validation artifacts are saved.\n\n Examples:\n >>> from nemo.collections import llm\n >>> from nemo import lightning as nl\n >>> model = llm.MistralModel()\n >>> data = llm.SquadDataModule(seq_length=4096, global_batch_size=16, micro_batch_size=2)\n >>> precision = nl.MegatronMixedPrecision(precision=""bf16-mixed"")\n >>> trainer = nl.Trainer(strategy=nl.MegatronStrategy(tensor_model_parallel_size=2), plugins=precision)\n >>> llm.validate(model, data, trainer, tokenizer=""data"")\n PosixPath('/path/to/log_dir')\n """"""\n app_state = _setup(\n model=model,\n data=data,\n trainer=trainer,\n log=log,\n resume=resume,\n optim=optim,\n tokenizer=tokenizer,\n model_transform=model_transform,\n )\n\n trainer.validate(model, data)\n\n return app_state.exp_dir\n\n\n@run.cli.entrypoint(name=""prune"", namespace=""llm"")\ndef prune(\n nemo_checkpoint: str,\n save_path: str,\n pruning_config: PruningConfig,\n devices: int = 1,\n num_nodes: int = 1,\n tp_size: int = 1,\n pp_size: int = 1,\n num_layers_in_first_pipeline_stage: int | None = None,\n num_layers_in_last_pipeline_stage: int | None = None,\n num_train_samples: int = 1024,\n data: pl.LightningDataModule | None = None,\n tokenizer_path: str | None = None,\n legacy_ckpt: bool = False,\n) -> str:\n """"""\n Prunes a model using the specified data and trainer. Currently only supports GPT models.\n\n Args:\n nemo_checkpoint (str): The path to the NeMo checkpoint to be pruned.\n save_path (str): The path to save the pruned NeMo checkpoint.\n pruning_config (PruningConfig): The pruning configuration.\n devices (int): The number of devices to use for pruning.\n num_nodes (int): The number of nodes to use for pruning.\n tp_size (int): The tensor parallel size.\n pp_size (int): The pipeline parallel size.\n num_train_samples (int): Number of training samples for importance estimation using forward pass.\n num_layers_in_first_pipeline_stage (int): The number of layers in the first pipeline stage.\n num_layers_in_last_pipeline_stage (int): The number of layers in the last pipeline stage.\n data (pl.LightningDataModule): The data module for forward pass.\n Required if not dropping layers.\n tokenizer_path (str): Path to the tokenizer if not using model's tokenizer.\n legacy_ckpt (bool): If True, allow loading ckpt saved with older version of TE.\n Use for cases like missing state dict keys ending with `_extra_state`.\n\n Returns:\n str: The path to the pruned NeMo checkpoint.\n\n Examples:\n >>> from nemo.collections import llm\n >>> from nemo.collections.llm.modelopt.prune import PruningConfig\n >>> data = llm.PretrainingDataModule(\n paths=[""1.0"", ""path/to/tokenized/data""],\n seq_length=256,\n global_batch_size=1,\n micro_batch_size=1,\n )\n >>> llm.prune(\n nemo_checkpoint=""path/to/llama3.1-8b"",\n save_path=""path/to/pruned_llama_model"",\n pruning_config=PruningConfig(target_ffn_hidden_size=9216, target_hidden_size=3072),\n data=data\n )\n """"""\n if data is not None:\n assert data.global_batch_size == data.micro_batch_size, ""Global batch size must be equal to micro batch size""\n steps = num_train_samples // data.global_batch_size\n else:\n steps = num_train_samples\n\n model, trainer = setup_trainer_and_restore_model_with_modelopt_spec(\n model_path=nemo_checkpoint,\n tensor_model_parallel_size=tp_size,\n pipeline_model_parallel_size=pp_size,\n num_layers_in_first_pipeline_stage=num_layers_in_first_pipeline_stage,\n num_layers_in_last_pipeline_stage=num_layers_in_last_pipeline_stage,\n devices=devices,\n num_nodes=num_nodes,\n inference_only=True,\n tokenizer_path=tokenizer_path,\n legacy_ckpt=legacy_ckpt,\n strategy_kwargs={""sequence_parallel"": False, ""replace_progress_bar"": False},\n trainer_kwargs={""max_steps"": steps, ""limit_val_batches"": steps, ""val_check_interval"": steps},\n model_config_overrides={""sequence_parallel"": False},\n )\n prune_language_model(model, pruning_config, data, trainer)\n save_pruned_model(trainer, save_path)\n\n console = Console()\n console.print(f""[green]โœ“ Pruning succeded, pruned checkpoint saved to {save_path}[/green]"")\n\n return save_path\n\n\n@run.cli.entrypoint(name=""distill"", namespace=""llm"")\ndef distill(\n student_model_path: AnyPath,\n teacher_model_path: AnyPath,\n data: pl.LightningDataModule,\n trainer: Trainer,\n distillation_config_path: Optional[AnyPath] = None,\n log: Annotated[Optional[NeMoLogger], run.Config[NeMoLogger]] = None,\n resume: Annotated[Optional[AutoResume], run.Config[AutoResume]] = None,\n optim: Optional[OptimizerModule] = None,\n tokenizer: Optional[TokenizerType] = None,\n model_transform: Optional[Union[PEFT, ModelTransform, Callable]] = None,\n) -> Path:\n """"""\n Distills a teacher model into a student model using special Knowledge-Distillation losses.\n\n Note that this requires an existing NeMo 2.0 checkpoint of the student model as well, as\n the model class is not known beforehand.\n This script currently supports instances of ``nemo.collections.llm.GPTModel`` for now.\n\n Args:\n student_model_path (Path): Path to student model NeMo checkpoint to be trained.\n teacher_model_path (Path): Path to teacher model NeMo checkpoint to distill from.\n data (pl.LightningDataModule): The data module containing training data.\n trainer (Trainer): The trainer instance configured with a MegatronStrategy.\n distillation_config_path (Optional[Path]): Path to distillation config YAML file.\n If not provided, by default will perform logits-only distillation.\n log (NeMoLogger): A nemologger instance.\n resume (Optional[Union[AutoResume, Resume]]): Resume training from a checkpoint.\n optim (Optional[OptimizerModule]): The optimizer module to be used. If not provided, the default optimizer\n from the model will be used.\n tokenizer (Optional[TokenizerType]): Tokenizer setting to be applied. Can be 'data' or 'model'\n or an instance of TokenizerSpec.\n export (Optional[str]): Filename to save the exported checkpoint after training.\n model_transform (Optional[Union[Callable[[nn.Module], nn.Module], PEFT]]): A model transform to be applied.\n\n Returns\n -------\n Path: The directory path where training artifacts are saved.\n\n Examples\n --------\n >>> from nemo.collections import llm\n >>> from nemo import lightning as nl\n >>> student = ""/path/to/student/nemo/ckpt"" # <-- change me\n >>> teacher = ""/path/to/teacher/nemo/ckpt"" # <-- change me\n >>> data = llm.SquadDataModule(seq_length=4096, global_batch_size=16, micro_batch_size=2)\n >>> precision = nl.MegatronMixedPrecision(precision=""bf16-mixed"")\n >>> trainer = nl.Trainer(strategy=nl.MegatronStrategy(tensor_model_parallel_size=2), plugins=precision)\n >>> llm.distill(student, teacher, data, trainer, tokenizer=""model"")\n PosixPath('/path/to/log_dir')\n """"""\n _student_model = io.load_context(ckpt_to_context_subdir(student_model_path), subpath=""model"")\n _teacher_model = io.load_context(ckpt_to_context_subdir(teacher_model_path), subpath=""model"")\n assert isinstance(_student_model, GPTModel), ""Only models based on `llm.GPTModel` are supported currently.""\n assert isinstance(_teacher_model, GPTModel), ""Only models based on `llm.GPTModel` are supported currently.""\n\n if tokenizer is None:\n tokenizer = getattr(_student_model, ""tokenizer"", None) or getattr(_teacher_model, ""tokenizer"", None)\n assert tokenizer is not None, ""Tokenizer neither provided nor found in models.""\n\n model = DistillationGPTModel(\n _student_model.config,\n _teacher_model.config,\n teacher_ckpt_path=teacher_model_path,\n distillation_config_path=distillation_config_path,\n )\n model.__io__ = _student_model.__io__\n\n if resume is None:\n resume = AutoResume()\n if resume.restore_config is None:\n resume.restore_config = nl.RestoreConfig(path=student_model_path)\n\n return train(\n model=model,\n data=data,\n optim=optim,\n tokenizer=tokenizer,\n trainer=trainer,\n log=log,\n resume=resume,\n model_transform=model_transform,\n )\n\n\n@run.cli.entrypoint(name=""ptq"", namespace=""llm"")\ndef ptq(\n model_path: str,\n export_config: ExportConfig,\n calibration_tp: int = 1,\n calibration_pp: int = 1,\n calibration_ep: int = 1,\n num_layers_in_first_pipeline_stage: int | None = None,\n num_layers_in_last_pipeline_stage: int | None = None,\n devices: int | None = None,\n num_nodes: int | None = None,\n quantization_config: Annotated[Optional[QuantizationConfig], run.Config[QuantizationConfig]] = None,\n forward_loop: Callable | None = None,\n tokenizer_path: str | None = None,\n legacy_ckpt: bool = False,\n trust_remote_code: bool = False,\n) -> Path:\n """"""\n Applies Post-Training Quantization (PTQ) for a model using the specified quantization and export configs. It runs\n calibration for a small dataset to collect scaling factors low-precision GEMMs used by desired quantization method.\n By default, this function produces TensorRT-LLM checkpoint ready for deployment using the Export-Deploy repository\n (https://github.com/NVIDIA-NeMo/Export-Deploy) or directly using TensorRT-LLM library.\n\n The function can be used through the NeMo CLI in the following way:\n ```bash\n # Run calibration using tensor parallel set to 8 and export quantized checkpoint with tensor parallel equal 2\n nemo llm ptq run.executor=torchrun run.executor.ntasks_per_node=8 \\n model_path=/models/Llama-3-70B \\n export_config.path=/models/Llama-3-70B-FP8 \\n calibration_tp=8 \\n export_config.inference_tp=2\n\n # Choose different quantization method, for example, INT8 SmoothQuant\n nemo llm ptq run.executor=torchrun run.executor.ntasks_per_node=1 \\n model_path=/models/Llama-3-8B \\n export_config.path=/models/Llama-3-8B-INT8_SQ \\n quantization_config.algorithm=int8_sq\n\n # Export as NeMo checkpoint instead\n nemo llm ptq run.executor=torchrun \\n model_path=/models/Llama-3-8B \\n export_config.path=/models/Llama-3-8B-INT8_SQ \\n quantization_config.algorithm=int8_sq \\n export_config.export_format=nemo\n\n # Quantize HF AutoModel checkpoint.\n nemo llm ptq run.executor=torchrun run.executor.ntasks_per_node=1 \\n model_path=/models/Llama-3-70B-HF \\n export_config.path=/models/Llama-3-70B-HF-FP8 \\n export_config.export_format=hf\n ```\n\n Args:\n model_path (str): The path to model to be quantized.\n calibration_tp (int): Calibration tensor parallelism.\n calibration_pp (int): Calibration pipeline parallelism.\n num_layers_in_first_pipeline_stage (int): Number of layers in the first pipeline stage.\n num_layers_in_last_pipeline_stage (int): Number of layers in the last pipeline stage.\n export_config (ExportConfig): Export configuration for output checkpoint.\n devices (int): Number of devices to use for calibration. Default: calibration_tp.\n num_nodes (int): Number of nodes to use for calibration. Default: calibration_pp.\n quantization_config (QuantizationConfig): Configuration for quantization algorithm.\n forward_loop (Callable): Forward loop to use for calibration.\n If not provided, a forward loop will be created using the calibration dataset.\n tokenizer_path (str): Path to the tokenizer if not using model's tokenizer.\n legacy_ckpt (bool): If True, allow loading ckpt saved with older version of TE.\n trust_remote_code (bool): Trust remote code when loading HuggingFace models.\n\n Returns:\n Path: The path where the quantized checkpoint has been saved after calibration.\n """"""\n if not quantization_config:\n quantization_config = QuantizationConfig()\n if devices is None:\n devices = calibration_tp\n if num_nodes is None:\n num_nodes = calibration_pp\n\n quantizer = Quantizer(quantization_config, export_config)\n assert Path(model_path).exists(), f""Path {model_path} does not exist""\n is_automodel = (Path(model_path) / 'config.json').exists()\n\n trainer = None\n if is_automodel:\n assert export_config.export_format != ""nemo"", ""Automodel PTQ does not support export format nemo""\n model = HFAutoModelForCausalLM(model_name=model_path, trust_remote_code=trust_remote_code, device_map=""auto"")\n model.configure_model()\n else:\n model, trainer = setup_trainer_and_restore_model_with_modelopt_spec(\n model_path=model_path,\n tensor_model_parallel_size=calibration_tp,\n pipeline_model_parallel_size=calibration_pp,\n num_layers_in_first_pipeline_stage=num_layers_in_first_pipeline_stage,\n num_layers_in_last_pipeline_stage=num_layers_in_last_pipeline_stage,\n expert_model_parallel_size=calibration_ep,\n devices=devices,\n num_nodes=num_nodes,\n inference_only=True,\n tokenizer_path=tokenizer_path,\n legacy_ckpt=legacy_ckpt,\n strategy_kwargs={""sequence_parallel"": False, ""lazy_init"": True},\n trainer_kwargs={},\n model_config_overrides={""sequence_parallel"": False},\n )\n\n model = quantizer.quantize(model, forward_loop)\n quantizer.export(model, model_path, trainer)\n\n if is_global_rank_zero():\n console = Console()\n console.print(f""[green]โœ“ PTQ succeded, quantized checkpoint exported to {export_config.path}[/green]"")\n return export_config.path\n\n\n@run.cli.entrypoint(name=""import"", namespace=""llm"")\ndef import_ckpt(\n model: pl.LightningModule,\n source: str,\n output_path: Optional[AnyPath] = None,\n overwrite: bool = False,\n **kwargs,\n) -> Path:\n """"""\n Imports a checkpoint into a model using the model's associated importer, typically for\n the purpose of fine-tuning a community model trained in an external framework, such as\n Hugging Face.\n\n This function can be used both programmatically and through the NeMo CLI:\n\n CLI Usage:\n ```bash\n # Import Llama 3 8B from HuggingFace (saves to $NEMO_MODELS_CACHE)\n nemo llm import model=llama3_8b source=""hf://meta-llama/Llama-3.1-8B""\n\n # Import with custom output path\n nemo llm import model=llama3_8b source=""hf://meta-llama/Llama-3.1-8B"" output_path=""/path/to/save""\n\n # Force overwrite existing checkpoint\n nemo llm import model=llama3_8b source=""hf://meta-llama/Llama-3.1-8B"" overwrite=true\n ```\n\n Python Usage:\n ```python\n model = Mistral7BModel()\n imported_path = import_ckpt(model, ""hf://mistralai/Mistral-7B-v0.1"")\n ```\n\n The importer component of the model reads the checkpoint data from the specified source\n and transforms it into the right format. This is particularly useful for adapting\n models that have been pre-trained in different environments or frameworks to be fine-tuned\n or further developed within the current system.\n\n For instance, using `import_ckpt(Mistral7BModel(), ""hf"")` initiates the import process\n by searching for a registered model importer tagged with ""hf"". In NeMo, `HFMistral7BImporter`\n is registered under this tag via:\n `@io.model_importer(Mistral7BModel, ""hf"", default_path=""mistralai/Mistral-7B-v0.1"")`.\n This links `Mistral7BModel` to `HFMistral7BImporter`, designed for HuggingFace checkpoints.\n\n Args:\n model (pl.LightningModule): The model into which the checkpoint will be imported.\n This model must implement the ConnectorMixin.\n source (str): The source from which the checkpoint will be imported. This can be\n a file path, URL, or any other string identifier that the model's importer\n can recognize.\n output_path (Optional[Path]): The path where the imported checkpoint will be stored.\n If not specified, the checkpoint will be saved to $NEMO_MODELS_CACHE\n (defaults to ~/.cache/nemo/models/ if the environment variable is not set).\n overwrite (bool): If set to True, existing files at the output path will be overwritten.\n This is useful for model updates where retaining old checkpoint files is not required.\n\n Returns:\n Path: The path where the checkpoint has been saved after import.\n\n Raises:\n ValueError: If the model does not implement ConnectorMixin, indicating a lack of\n necessary importer functionality.\n FileExistsError: If the output path is provided (that is, when not using models cache)\n and it exists and overwrite is not set to True.\n """"""\n if output_path:\n output_path = Path(output_path)\n if output_path.exists() and not overwrite:\n raise FileExistsError(f""Output path {output_path} exists. Use overwrite=True to force overwrite."")\n\n output = io.import_ckpt(model=model, source=source, output_path=output_path, overwrite=overwrite, **kwargs)\n\n console = Console()\n if output_path:\n console.print(f""[green]โœ“ Checkpoint imported to {output}[/green]"")\n else:\n console.print(f""[green] $NEMO_MODELS_CACHE={NEMO_MODELS_CACHE} [/green]"")\n\n # Display directory structure as a tree\n dir_tree = _build_directory_tree(output, root_name=""Imported Checkpoint"")\n console.print(dir_tree)\n\n return output\n\n\ndef load_connector_from_trainer_ckpt(path: AnyPath, target: str) -> io.ModelConnector:\n # pylint: disable=C0116\n if not isinstance(path, Path):\n path = Path(path)\n return io.load_context(path, subpath=""model"").exporter(target, path)\n\n\n@run.cli.entrypoint(name=""export"", namespace=""llm"")\ndef export_ckpt(\n path: AnyPath,\n target: str,\n output_path: Optional[AnyPath] = None,\n overwrite: bool = False,\n load_connector: Callable[[Path, str], io.ModelConnector] = load_connector_from_trainer_ckpt,\n modelopt_export_kwargs: dict[str, Any] = None,\n **kwargs,\n) -> Path:\n """"""\n Exports a checkpoint from a model using the model's associated exporter, typically for\n the purpose of sharing a model that has been fine-tuned or customized within NeMo.\n\n This function can be used both programmatically and through the NeMo CLI:\n\n CLI Usage:\n ```bash\n # Export model to HuggingFace format (saves to {checkpoint_path}/hf/)\n nemo llm export path=/path/to/model.nemo target=""hf""\n\n # Export with custom output path\n nemo llm export path=/path/to/model.nemo target=""hf"" output_path=""/path/to/save""\n\n # Force overwrite existing export\n nemo llm export path=/path/to/model.nemo target=""hf"" overwrite=true\n ```\n\n Python Usage:\n ```python\n nemo_ckpt_path = Path(""/path/to/model.nemo"")\n export_path = export_ckpt(nemo_ckpt_path, ""hf"")\n ```\n\n The exporter component of the model reads the model's state from the specified path and\n exports it into the format specified by the 'target' identifier. This is particularly\n useful for adapting models that have been developed or fine-tuned within NeMo to be\n compatible with other environments or frameworks.\n\n Args:\n path (Path): The path to the model's checkpoint file from which data will be exported.\n target (str): The identifier for the exporter that defines the format of the export\n (e.g., ""hf"" for HuggingFace format).\n output_path (Optional[Path]): The path where the exported checkpoint will be saved.\n If not specified, defaults to {checkpoint_path}/{target}/.\n overwrite (bool): If set to True, existing files at the output path will be overwritten.\n This is useful for model updates where retaining old checkpoint files is not required.\n load_connector (Callable[[Path, str], ModelConnector]): A function to load the appropriate\n exporter based on the model and target format. Defaults to `load_connector_from_trainer_ckpt`.\n modelopt_export_kwargs (Dict[str, Any]): Additional keyword arguments for ModelOpt export to HuggingFace.\n\n Returns:\n Path: The path where the checkpoint has been saved after export.\n\n Raises:\n ValueError: If the model does not implement ConnectorMixin, indicating a lack of\n necessary exporter functionality.\n FileExistsError: If the output path is provided (that is, when not using models cache)\n and it exists and overwrite is not set to True.\n """"""\n if not isinstance(path, Path):\n path = Path(path)\n if output_path and not isinstance(output_path, Path):\n output_path = Path(output_path)\n if output_path.exists() and not overwrite:\n raise FileExistsError(f""Output path {output_path} exists. Use overwrite=True to force overwrite."")\n\n output = io.export_ckpt(path, target, output_path, overwrite, load_connector, modelopt_export_kwargs, **kwargs)\n\n console = Console()\n console.print(f""[green]โœ“ Checkpoint exported to {output}[/green]"")\n\n return output\n\n\n@run.cli.entrypoint(name=""generate"", namespace=""llm"")\ndef generate(\n path: AnyPath,\n trainer: nl.Trainer,\n prompts: Optional[list[str]] = None,\n encoder_prompts: Optional[list[str]] = None,\n input_dataset: Optional[Union[pl.LightningDataModule, str]] = None,\n params_dtype: torch.dtype = torch.bfloat16,\n add_BOS: bool = False,\n max_batch_size: int = 4,\n random_seed: Optional[int] = None,\n inference_batch_times_seqlen_threshold: int = 1000,\n inference_params: Optional[""CommonInferenceParams""] = None,\n text_only: bool = False,\n output_path: Optional[AnyPath] = None,\n enable_flash_decode: bool = True,\n **kwargs,\n) -> list[Union[""InferenceRequest"", str]]:\n """"""\n Generates text using a NeMo LLM model.\n\n This function takes a checkpoint path and a list of prompts,\n and generates text based on the loaded model and parameters.\n It returns a list of generated text, either as a string or as an InferenceRequest object.\n\n Python Usage:\n ```python\n strategy = nl.MegatronStrategy(\n tensor_model_parallel_size=2,\n pipeline_model_parallel_size=1,\n context_parallel_size=1,\n sequence_parallel=False,\n setup_optimizers=False,\n store_optimizer_states=False,\n )\n\n trainer = nl.Trainer(\n accelerator=""gpu"",\n devices=2,\n num_nodes=1,\n strategy=strategy,\n plugins=nl.MegatronMixedPrecision(\n precision=""bf16-mixed"",\n params_dtype=torch.bfloat16,\n pipeline_dtype=torch.bfloat16,\n autocast_enabled=False,\n grad_reduce_in_fp32=False,\n ),\n )\n prompts = [\n ""Hello, how are you?"",\n ""How many r's are in the word 'strawberry'?"",\n ""Which number is bigger? 10.119 or 10.19?"",\n ]\n\n if __name__ == ""__main__"":\n results = api.generate(\n path=os.path.join(os.environ[""NEMO_HOME""], ""models"", ""meta-llama/Meta-Llama-3-8B""),\n prompts=prompts,\n trainer=trainer,\n inference_params=CommonInferenceParams(temperature=0.1, top_k=10, num_tokens_to_generate=512),\n text_only=True,\n )\n ```\n\n Args:\n path (Union[Path, str]): The path to the model checkpoint.\n prompts (list[str]): The list of prompts to generate text for.\n trainer (nl.Trainer): The trainer object.\n encoder_prompts (Optional[list[str]], optional): The list of encoder prompts. Defaults to None.\n input_dataset (Optional[Union[pl.LightningDataModule, str]], optional): The input data module or jsonl file.\n Test set will be used for generation for data modules. Defaults to None.\n params_dtype (torch.dtype, optional): The data type of the model parameters. Defaults to torch.bfloat16.\n add_BOS (bool, optional): Whether to add the beginning of sequence token. Defaults to False.\n max_batch_size (int, optional): The maximum batch size. Defaults to 4.\n random_seed (Optional[int], optional): The random seed. Defaults to None.\n inference_batch_times_seqlen_threshold (int, optional): If batch-size times sequence-length is smaller than\n this threshold then we will not use pipelining, otherwise we will. Defaults to 1000.\n inference_params (Optional[""CommonInferenceParams""], optional): The inference parameters defined in\n Mcore's CommonInferenceParams. Defaults to None.\n text_only (bool, optional): Whether to return only the generated text as a string. Defaults to False.\n output_path (Optional[Union[Path, str]], optional): The path to save the generated text or test dataset\n predictions. Defaults to None.\n enable_flash_decode (bool, optional): Whether to enable flash decode. Defaults to True.\n **kwargs: Additional keyword arguments passed to setup_model_and_tokenizer.\n\n Returns:\n list[Union[""InferenceRequest"", str]]: A list of generated text,\n either as a string or as an InferenceRequest object.\n """"""\n from nemo.collections.llm import inference\n\n if input_dataset is not None:\n input_path = input_dataset if isinstance(input_dataset, str) else input_dataset.test_path\n with open(input_path) as f:\n dataset = [json.loads(sample) for sample in f.readlines()]\n inputs = [sample[""input""] for sample in dataset]\n elif prompts is not None:\n inputs = prompts\n else:\n raise ValueError(""Either prompts or input_dataset must be provided."")\n\n inference_wrapped_model, mcore_tokenizer = inference.setup_model_and_tokenizer(\n path=path,\n trainer=trainer,\n params_dtype=params_dtype,\n inference_batch_times_seqlen_threshold=inference_batch_times_seqlen_threshold,\n enable_flash_decode=enable_flash_decode,\n **kwargs,\n )\n\n max_seq_length = inference_params.num_tokens_to_generate + max(len(mcore_tokenizer.tokenize(p)) for p in inputs)\n # set kv cache allocation to only num tokens in prompt + max tokens to generate\n inference_wrapped_model.inference_wrapper_config.inference_max_seq_length = max_seq_length\n inference_wrapped_model.inference_context.max_sequence_length = max_seq_length\n\n if trainer.strategy.expert_model_parallel_size > 1:\n inputs_on_this_dp_rank = inputs\n else:\n dp_size = trainer.strategy.distributed_sampler_kwargs['num_replicas']\n dp_rank = trainer.strategy.distributed_sampler_kwargs['rank']\n chunk_size = (len(inputs) + dp_size - 1) // dp_size\n start_idx = dp_rank * chunk_size\n end_idx = min(start_idx + chunk_size, len(inputs))\n inputs_on_this_dp_rank = inputs[start_idx:end_idx]\n\n results_on_this_dp_rank = inference.generate(\n model=inference_wrapped_model,\n tokenizer=mcore_tokenizer,\n prompts=inputs_on_this_dp_rank,\n encoder_prompts=encoder_prompts,\n add_BOS=add_BOS,\n max_batch_size=max_batch_size,\n random_seed=random_seed,\n inference_params=inference_params,\n )\n\n if trainer.strategy.expert_model_parallel_size > 1:\n gathered_results = [r.generated_text if text_only else r for r in results_on_this_dp_rank]\n else:\n gathered_results = [None] * dp_size\n\n all_gather_object(\n gathered_results,\n [r.generated_text if text_only else r for r in results_on_this_dp_rank],\n group=parallel_state.get_data_parallel_group(),\n )\n gathered_results = [result for sublist in gathered_results for result in sublist]\n\n assert len(gathered_results) == len(inputs)\n\n if output_path is not None and is_global_rank_zero():\n with open(output_path, ""w"") as f:\n for sample, pred in zip(dataset if input_dataset else inputs, gathered_results):\n if type(sample) == dict:\n sample[""label""] = sample.pop(""output"", None)\n sample[""prediction""] = pred if text_only else pred.generated_text\n elif type(sample) == str:\n sample = {""input"": sample, ""prediction"": pred if text_only else pred.generated_text}\n f.write(json.dumps(sample) + ""\n"")\n logging.info(f""Predictions written to {output_path}"")\n\n return gathered_results\n\n\ndef _use_tokenizer(model: pl.LightningModule, data: pl.LightningDataModule, tokenizer: TokenizerType) -> None:\n if tokenizer == ""data"":\n _set_with_io(model, ""tokenizer"", data.tokenizer)\n elif tokenizer == ""model"":\n _set_with_io(data, ""tokenizer"", model.tokenizer)\n else:\n try:\n from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec\n\n if isinstance(tokenizer, TokenizerSpec):\n _set_with_io(model, ""tokenizer"", tokenizer)\n _set_with_io(data, ""tokenizer"", tokenizer)\n else:\n raise ValueError(f""Expected TokenizerSpec or 'data' or 'model', got: {tokenizer}"")\n except ImportError:\n raise ValueError(""TokenizerSpec is not available"")\n\n\ndef _setup(\n model: pl.LightningModule,\n data: pl.LightningDataModule,\n trainer: Trainer,\n log: Optional[NeMoLogger],\n resume: Optional[AutoResume],\n optim: Optional[OptimizerModule],\n tokenizer: Optional[TokenizerType],\n model_transform: Optional[Union[PEFT, ModelTransform, Callable]],\n) -> Any: # Return type is Any because app_state's type is not specified\n configure_no_restart_validation_training_loop(trainer)\n _log = log or NeMoLogger()\n if resume and isinstance(model_transform, PEFT) and _log.ckpt:\n logging.info(""Disabling try_restore_best_ckpt restoration for adapters"")\n _log.ckpt.try_restore_best_ckpt = False\n\n app_state = _log.setup(\n trainer,\n resume_if_exists=getattr(resume, ""resume_if_exists"", False),\n task_config=getattr(train, ""__io__"", None),\n )\n\n # Configure telemetry via CallbackGroup\n CallbackGroup.get_instance().update_config(nemo_version='v2', trainer=trainer, data=data)\n\n if resume is not None:\n CallbackGroup.get_instance().on_load_checkpoint_start()\n resume.setup(trainer, model)\n CallbackGroup.get_instance().on_load_checkpoint_end()\n\n if optim:\n CallbackGroup.get_instance().on_optimizer_init_start()\n optim.connect(model)\n CallbackGroup.get_instance().on_optimizer_init_end()\n if tokenizer: # TODO: Improve this\n _use_tokenizer(model, data, tokenizer)\n\n if model_transform:\n _set_with_io(model, ""model_transform"", model_transform)\n\n # Add ModelTransform callback to Trainer if needed\n if getattr(model, ""model_transform"", None):\n if not any(isinstance(cb, ModelTransform) for cb in trainer.callbacks):\n if isinstance(model_transform, ModelTransform):\n trainer.callbacks.append(model_transform)\n else:\n trainer.callbacks.append(ModelTransform())\n # Move jit callback at the end ensure it's applied on top of any model transformations (peft)\n jit_cb = None\n for i, cb in enumerate(trainer.callbacks):\n if isinstance(cb, JitTransform):\n assert jit_cb is None\n jit_cb = trainer.callbacks.pop(i)\n if jit_cb is not None:\n trainer.callbacks.append(jit_cb)\n return app_state\n\n\ndef _set_with_io(obj, attr, value):\n setattr(obj, attr, value)\n if hasattr(obj, ""__io__"") and hasattr(value, ""__io__""):\n setattr(obj.__io__, attr, deepcopy(value.__io__))\n\n\ndef _validate_config(\n model: pl.LightningModule,\n data: pl.LightningDataModule,\n trainer: Trainer,\n log: Optional[NeMoLogger] = None,\n resume: Optional[AutoResume] = None,\n optim: Optional[OptimizerModule] = None,\n tokenizer: Optional[TokenizerType] = None,\n model_transform: Optional[Union[PEFT, ModelTransform, Callable]] = None,\n) -> None:\n\n # Model validation\n if hasattr(model, ""config""):\n assert getattr(model.config, ""seq_length"", 1) > 0\n assert getattr(model.config, ""max_position_embeddings"", 1) > 0\n assert model.config.num_layers > 0\n assert model.config.hidden_size > 0\n assert model.config.num_attention_heads > 0\n assert model.config.ffn_hidden_size > 0\n else:\n assert not isinstance(trainer.strategy, nl.MegatronStrategy), ""Expected model.config to exist""\n\n # Data validation\n assert data.micro_batch_size > 0\n if isinstance(trainer.strategy, nl.MegatronStrategy):\n assert data.global_batch_size > 0\n assert data.seq_length > 0\n\n assert (\n data.global_batch_size % data.micro_batch_size == 0\n ), ""Global batch size must be divisible by micro batch size in data module.""\n\n # Trainer validation\n\n # MegatronStrategy validation\n if isinstance(trainer.strategy, nl.MegatronStrategy):\n # Basic validation\n assert trainer.strategy.tensor_model_parallel_size > 0\n assert trainer.strategy.pipeline_model_parallel_size > 0\n assert trainer.strategy.context_parallel_size > 0\n\n # DP validation\n assert (trainer.num_devices * trainer.num_nodes) % (\n trainer.strategy.tensor_model_parallel_size\n * trainer.strategy.pipeline_model_parallel_size\n * trainer.strategy.context_parallel_size\n ) == 0, ""Number of GPUs must be divisible by the product of all parallelism sizes for data parallel.""\n\n assert (\n data.global_batch_size\n % (\n data.micro_batch_size\n * (\n (trainer.num_devices * trainer.num_nodes)\n / (\n trainer.strategy.tensor_model_parallel_size\n * trainer.strategy.pipeline_model_parallel_size\n * trainer.strategy.context_parallel_size\n )\n )\n )\n == 0\n ), ""Global batch size must be divisible by the product of micro batch size and data parallel size""\n\n # TP/SP validation\n if trainer.strategy.tensor_model_parallel_size == 1:\n if trainer.strategy.sequence_parallel == True:\n warnings.warn(""Disabling sequence parallelism because tensor model parallelism is disabled"")\n trainer.strategy.sequence_parallel = False\n\n # PP/VP validation\n if trainer.strategy.pipeline_model_parallel_size > 1:\n assert (\n trainer.strategy.pipeline_dtype is not None\n ), ""pipeline_dtype must be set if pipeline model parallelism is enabled""\n else:\n if trainer.strategy.virtual_pipeline_model_parallel_size is not None:\n warnings.warn(""Disabling virtual pipeline parallelism because pipeline model parallelism is disabled"")\n trainer.strategy.virtual_pipeline_model_parallel_size = None\n if trainer.strategy.pipeline_dtype is not None:\n warnings.warn(""Setting pipeline dtype to None because pipeline model parallelism is disabled"")\n trainer.strategy.pipeline_dtype = None\n\n # CP validation\n if trainer.strategy.context_parallel_size > 1:\n if hasattr(model, ""config""):\n if model.config.seq_length is not None:\n assert (\n model.config.seq_length % (trainer.strategy.context_parallel_size * 2) == 0\n ), 'Sequence length must be divisible by 2 * context parallel size if context parallel is used.'\n if isinstance(data, FineTuningDataModule):\n # check calculate_per_token_loss to be True\n # check average_in_collective to be False\n # for context parallel to solve the issue of nan loss on ranks with all tokens masked\n # (only happens in SFT)\n assert (\n model.config.calculate_per_token_loss\n ), ""When finetuning with CP>1, model.config.calculate_per_token_loss must be True""\n assert (\n not trainer.strategy.ddp_config.average_in_collective\n ), ""When finetuning with CP>1, average_in_collective must be False""\n\n # EP validation\n if trainer.strategy.expert_model_parallel_size > 1:\n if hasattr(model, ""config""):\n assert (\n model.config.num_moe_experts is not None\n ), ""num_experts must be non None to use expert model parallelism""\n assert (\n model.config.num_moe_experts % trainer.strategy.expert_model_parallel_size == 0\n ), ""Number of experts should be a multiple of expert model parallel_size.""\n\n\ndef _build_directory_tree(path, tree=None, root_name=None):\n """"""Build a Rich Tree representation of a directory structure.""""""\n from rich.tree import Tree\n\n path = Path(path)\n if tree is None:\n tree = Tree(f""[bold blue]{root_name or path.name}[/bold blue]"")\n\n # Sort to have directories first, then files\n items = sorted(path.iterdir(), key=lambda x: (not x.is_dir(), x.name))\n\n for item in items:\n if item.is_dir():\n branch = tree.add(f""[bold cyan]{item.name}/[/bold cyan]"")\n _build_directory_tree(item, branch)\n else:\n # Color differently based on file extension\n if item.suffix in ('.json', '.jsonl'):\n tree.add(f""[yellow]{item.name}[/yellow]"")\n elif item.suffix in ('.pt', '.bin', '.ckpt', '.nemo'):\n tree.add(f""[magenta]{item.name}[/magenta]"")\n elif item.suffix in ('.py', '.sh'):\n tree.add(f""[green]{item.name}[/green]"")\n else:\n tree.add(f""[white]{item.name}[/white]"")\n\n return tree\n\n\ndef _load_model_from_path(model: Union[pl.LightningModule, AnyPath]):\n if isinstance(model, AnyPath):\n model = io.load_context(ckpt_to_context_subdir(model), subpath=""model"")\n return model\n",python,tab +233,57568647,"nemo/collections/llm/api.py",7280,0,"",python,selection_command +234,57591453,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"",python,tab +235,57591454,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",7327,0,"",python,selection_command 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a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-d574b592-36c1-470c-87c1-c12b951e96361762425248759-2025_11_06-11.34.11.675/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-d574b592-36c1-470c-87c1-c12b951e96361762425248759-2025_11_06-11.34.11.675/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..b773eb9674e0e2557d92d4a11dc177316ad8485f --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-d574b592-36c1-470c-87c1-c12b951e96361762425248759-2025_11_06-11.34.11.675/source.csv @@ -0,0 +1,94 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,4,"src/extension.ts",0,0,"// The module 'vscode' contains the VS Code extensibility API\n// Import the module and reference it with the alias vscode in your code below\nimport * as vscode from 'vscode';\n\n// This method is called when your extension is activated\n// Your extension is activated the very first time the command is executed\nexport function activate(context: vscode.ExtensionContext) {\n\n\tconsole.log('Crowd Pilot extension activated');\n\n\t// Configure terminal to allow tab keybinding to work\n\t// This makes the command skip the shell so VS Code can intercept tab in terminals\n\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\tconst commandsToSkipShell = config.get('commandsToSkipShell', []);\n\tif (!commandsToSkipShell.includes('crowd-pilot.testRun')) {\n\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\tconfig.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\tconsole.log('Added testRun to commandsToSkipShell');\n\t}\n\n\tconst testRun = vscode.commands.registerCommand('crowd-pilot.testRun', async () => {\n\t\tconst editor = vscode.window.activeTextEditor;\n\t\tconst doc = editor!.document;\n\t\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\t\tconst git = vscode.extensions.getExtension('vscode.git')?.exports?.getAPI(1);\n\t\tconst repo = git?.repositories?.[0];\n\t\n\t\t// Emit a few actions:\n\t\tawait vscode.window.showTextDocument(doc);\n\t\teditor!.selections = [new vscode.Selection(0, 0, 0, 0)];\n\t\tawait editor!.edit(e => e.insert(new vscode.Position(0, 0), 'hello world\n'));\n\t\tterm.show();\n\t\tterm.sendText('echo VSCode test');\n\t\t//await repo?.pull();\n\t\n\t\tvscode.window.showInformationMessage('All actions emitted');\n\t });\n\n\tcontext.subscriptions.push(testRun);\n}\n\n// This method is called when your extension is deactivated\nexport function deactivate() {}\n",typescript,tab +2,160,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"11:34:11 AM [info] Activating crowd-code\n11:34:11 AM [info] Recording started\n11:34:11 AM [info] Initializing git provider using file system watchers...\n11:34:11 AM [info] Git repository found\n11:34:11 AM [info] Git provider initialized successfully\n11:34:11 AM [info] Initial git state: [object Object]\n",Log,tab +3,1020,"src/extension.ts",0,0,"",typescript,tab +4,2502,"TERMINAL",0,0,"",,terminal_focus +5,30907,"src/extension.ts",635,0,"",typescript,selection_command +6,31146,"src/extension.ts",713,0,"",typescript,selection_command +7,31177,"src/extension.ts",774,0,"",typescript,selection_command +8,31209,"src/extension.ts",825,0,"",typescript,selection_command +9,31344,"src/extension.ts",921,0,"",typescript,selection_command +10,102622,"src/extension.ts",773,201,"\t\tconst updated = [...commandsToSkipShell, 'crowd-pilot.testRun'];\n\t\tconfig.update('commandsToSkipShell', updated, vscode.ConfigurationTarget.Global, false)\n\t\t\t.then(() => {\n\t\t\t\tconsole.log('[Crowd Pilot] Successfully updated commandsToSkipShell');\n\t\t\t\t// Verify it was set\n\t\t\t\tconst verifyConfig = vscode.workspace.getConfiguration('terminal.integrated');\n\t\t\t\tconst verify = verifyConfig.get('commandsToSkipShell', []);\n\t\t\t\tconsole.log('[Crowd Pilot] Verified commandsToSkipShell:', JSON.stringify(verify));\n\t\t\t\tif (!verify.includes('crowd-pilot.testRun')) {\n\t\t\t\t\tconsole.error('[Crowd Pilot] ERROR: Configuration update did not persist!');\n\t\t\t\t}\n\t\t\t})\n\t\t\t.catch((err) => {\n\t\t\t\tconsole.error('[Crowd Pilot] ERROR updating commandsToSkipShell:', err);\n\t\t\t});\n\t} else {\n\t\tconsole.log('[Crowd Pilot] testRun already in commandsToSkipShell');",typescript,content +11,102622,"src/extension.ts",712,0,"\tconsole.log('[Crowd Pilot] Current commandsToSkipShell:', JSON.stringify(commandsToSkipShell));\n\t\n",typescript,content +12,102622,"src/extension.ts",371,48,"\tconsole.log('[Crowd Pilot] Extension activated');",typescript,content +13,110500,"src/extension.ts",874,849,"\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\tconfig.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\tconsole.log('Added testRun to commandsToSkipShell');",typescript,content +14,110500,"src/extension.ts",714,99,"",typescript,content +15,110500,"src/extension.ts",371,50,"\tconsole.log('Crowd Pilot extension activated');",typescript,content +16,110559,"src/extension.ts",1747,0,"\tcontext.subscriptions.push(checkConfig);\n",typescript,content +17,110559,"src/extension.ts",1709,0,"\t// Diagnostic command to check keybinding configuration\n\tconst checkConfig = vscode.commands.registerCommand('crowd-pilot.checkConfig', () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get('commandsToSkipShell', []);\n\t\tconst hasCommand = commandsToSkipShell.includes('crowd-pilot.testRun');\n\t\t\n\t\tconst message = `commandsToSkipShell: ${JSON.stringify(commandsToSkipShell)}\n` +\n\t\t\t`Contains 'crowd-pilot.testRun': ${hasCommand}`;\n\t\t\n\t\tconsole.log('[Crowd Pilot] Config check:', message);\n\t\tvscode.window.showInformationMessage(message, { modal: true });\n\t});\n\n",typescript,content +18,110559,"src/extension.ts",1065,0,"\t\tconsole.log('[Crowd Pilot] testRun command executed');\n",typescript,content +19,110559,"src/extension.ts",773,201,"\t\tconst updated = [...commandsToSkipShell, 'crowd-pilot.testRun'];\n\t\tconfig.update('commandsToSkipShell', updated, vscode.ConfigurationTarget.Global, false)\n\t\t\t.then(() => {\n\t\t\t\tconsole.log('[Crowd Pilot] Successfully updated commandsToSkipShell');\n\t\t\t\t// Verify it was set\n\t\t\t\tconst verifyConfig = vscode.workspace.getConfiguration('terminal.integrated');\n\t\t\t\tconst verify = verifyConfig.get('commandsToSkipShell', []);\n\t\t\t\tconsole.log('[Crowd Pilot] Verified commandsToSkipShell:', JSON.stringify(verify));\n\t\t\t\tif (!verify.includes('crowd-pilot.testRun')) {\n\t\t\t\t\tconsole.error('[Crowd Pilot] ERROR: Configuration update did not persist!');\n\t\t\t\t}\n\t\t\t})\n\t\t\t.catch((err) => {\n\t\t\t\tconsole.error('[Crowd Pilot] ERROR updating commandsToSkipShell:', err);\n\t\t\t});\n\t} else {\n\t\tconsole.log('[Crowd Pilot] testRun already in commandsToSkipShell');",typescript,content +20,110559,"src/extension.ts",712,0,"\tconsole.log('[Crowd Pilot] Current commandsToSkipShell:', JSON.stringify(commandsToSkipShell));\n\t\n",typescript,content +21,110559,"src/extension.ts",371,48,"\tconsole.log('[Crowd Pilot] Extension activated');",typescript,content +22,128376,"src/extension.ts",3195,42,"",typescript,content +23,128376,"src/extension.ts",2515,642,"",typescript,content +24,128376,"src/extension.ts",1814,57,"",typescript,content +25,128376,"src/extension.ts",874,849,"\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\tconfig.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\tconsole.log('Added testRun to commandsToSkipShell');",typescript,content +26,128376,"src/extension.ts",714,99,"",typescript,content +27,128376,"src/extension.ts",371,50,"\tconsole.log('Crowd Pilot extension activated');",typescript,content +28,128418,"src/extension.ts",1747,0,"\tcontext.subscriptions.push(checkConfig);\n",typescript,content +29,128418,"src/extension.ts",1709,0,"\t// Diagnostic command to check keybinding configuration\n\tconst checkConfig = vscode.commands.registerCommand('crowd-pilot.checkConfig', () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get('commandsToSkipShell', []);\n\t\tconst hasCommand = commandsToSkipShell.includes('crowd-pilot.testRun');\n\t\t\n\t\tconst message = `commandsToSkipShell: ${JSON.stringify(commandsToSkipShell)}\n` +\n\t\t\t`Contains 'crowd-pilot.testRun': ${hasCommand}`;\n\t\t\n\t\tconsole.log('[Crowd Pilot] Config check:', message);\n\t\tvscode.window.showInformationMessage(message, { modal: true });\n\t});\n\n",typescript,content +30,128418,"src/extension.ts",1065,0,"\t\tconsole.log('[Crowd Pilot] testRun command executed');\n",typescript,content +31,128418,"src/extension.ts",773,201,"\t\tconst updated = [...commandsToSkipShell, 'crowd-pilot.testRun'];\n\t\tconfig.update('commandsToSkipShell', updated, vscode.ConfigurationTarget.Global, false)\n\t\t\t.then(() => {\n\t\t\t\tconsole.log('[Crowd Pilot] Successfully updated commandsToSkipShell');\n\t\t\t\t// Verify it was set\n\t\t\t\tconst verifyConfig = vscode.workspace.getConfiguration('terminal.integrated');\n\t\t\t\tconst verify = verifyConfig.get('commandsToSkipShell', []);\n\t\t\t\tconsole.log('[Crowd Pilot] Verified commandsToSkipShell:', JSON.stringify(verify));\n\t\t\t\tif (!verify.includes('crowd-pilot.testRun')) {\n\t\t\t\t\tconsole.error('[Crowd Pilot] ERROR: Configuration update did not persist!');\n\t\t\t\t}\n\t\t\t}, (err: unknown) => {\n\t\t\t\tconsole.error('[Crowd Pilot] ERROR updating commandsToSkipShell:', err);\n\t\t\t});\n\t} else {\n\t\tconsole.log('[Crowd Pilot] testRun already in commandsToSkipShell');",typescript,content +32,128418,"src/extension.ts",712,0,"\tconsole.log('[Crowd Pilot] Current commandsToSkipShell:', JSON.stringify(commandsToSkipShell));\n\t\n",typescript,content +33,128418,"src/extension.ts",371,48,"\tconsole.log('[Crowd Pilot] Extension activated');",typescript,content +34,135218,"package.json",0,0,"{\n ""name"": ""crowd-pilot"",\n ""displayName"": ""crowd-pilot-extension"",\n ""description"": ""Teaching language models to code like humans."",\n ""version"": ""0.0.1"",\n ""engines"": {\n ""vscode"": ""^1.99.3""\n },\n ""categories"": [\n ""Other""\n ],\n ""activationEvents"": [],\n ""main"": ""./out/extension.js"",\n ""contributes"": {\n ""commands"": [\n {\n ""command"": ""crowd-pilot.testRun"",\n ""title"": ""Test Run""\n },\n {\n ""command"": ""crowd-pilot.checkConfig"",\n ""title"": ""Check Keybinding Config""\n }\n ],\n ""keybindings"": [\n {\n ""command"": ""crowd-pilot.testRun"",\n ""key"": ""tab"",\n ""mac"": ""tab"",\n ""when"": ""editorTextFocus || terminalFocus""\n }\n ]\n },\n ""scripts"": {\n ""vscode:prepublish"": ""npm run compile"",\n ""compile"": ""tsc -p ./"",\n ""watch"": ""tsc -watch -p ./"",\n ""pretest"": ""npm run compile && npm run lint"",\n ""lint"": ""eslint src"",\n ""test"": ""vscode-test""\n },\n ""devDependencies"": {\n ""@types/vscode"": ""^1.105.0"",\n ""@types/mocha"": ""^10.0.10"",\n ""@types/node"": ""22.x"",\n ""@typescript-eslint/eslint-plugin"": ""^8.45.0"",\n ""@typescript-eslint/parser"": ""^8.45.0"",\n ""eslint"": ""^9.36.0"",\n ""typescript"": ""^5.9.3"",\n ""@vscode/test-cli"": ""^0.0.11"",\n ""@vscode/test-electron"": ""^2.5.2""\n }\n}\n",json,tab +35,137453,"src/extension.ts",0,0,"",typescript,tab +36,177116,"src/extension.ts",1721,0,"",typescript,selection_command +37,213229,"package.json",0,0,"",json,tab +38,216261,"src/extension.ts",0,0,"",typescript,tab +39,286905,"src/extension.ts",1734,0,"",typescript,selection_command +40,287709,"src/extension.ts",1726,0,"",typescript,selection_command +41,287778,"src/extension.ts",1724,0,"",typescript,selection_command +42,288152,"src/extension.ts",1659,0,"",typescript,selection_command +43,318683,"src/extension.ts",3194,42,"",typescript,content +44,318683,"src/extension.ts",2514,642,"",typescript,content +45,318683,"src/extension.ts",1813,57,"",typescript,content +46,318683,"src/extension.ts",874,848,"\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\tconfig.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\tconsole.log('Added testRun to commandsToSkipShell');",typescript,content +47,318683,"src/extension.ts",714,99,"",typescript,content +48,318683,"src/extension.ts",371,50,"\tconsole.log('Crowd Pilot extension activated');",typescript,content +49,318735,"src/extension.ts",1747,0,"\tcontext.subscriptions.push(checkConfig);\n",typescript,content +50,318735,"src/extension.ts",1709,0,"\t// Diagnostic command to check keybinding configuration\n\tconst checkConfig = vscode.commands.registerCommand('crowd-pilot.checkConfig', () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get('commandsToSkipShell', []);\n\t\tconst hasCommand = commandsToSkipShell.includes('crowd-pilot.testRun');\n\t\t\n\t\tconst message = `commandsToSkipShell: ${JSON.stringify(commandsToSkipShell)}\n` +\n\t\t\t`Contains 'crowd-pilot.testRun': ${hasCommand}`;\n\t\t\n\t\tconsole.log('[Crowd Pilot] Config check:', message);\n\t\tvscode.window.showInformationMessage(message, { modal: true });\n\t});\n\n",typescript,content +51,318735,"src/extension.ts",1065,0,"\t\t// Log context information\n\t\tconst activeEditor = vscode.window.activeTextEditor;\n\t\tconst activeTerminal = vscode.window.activeTerminal;\n\t\tconst contextInfo = {\n\t\t\thasActiveEditor: !!activeEditor,\n\t\t\thasActiveTerminal: !!activeTerminal,\n\t\t\tactiveTerminalName: activeTerminal?.name,\n\t\t\tterminalCount: vscode.window.terminals.length,\n\t\t};\n\t\tconsole.log('[Crowd Pilot] testRun command executed', JSON.stringify(contextInfo));\n\t\t\n",typescript,content +52,318735,"src/extension.ts",773,201,"\t\tconst updated = [...commandsToSkipShell, 'crowd-pilot.testRun'];\n\t\tconfig.update('commandsToSkipShell', updated, vscode.ConfigurationTarget.Global, false)\n\t\t\t.then(() => {\n\t\t\t\tconsole.log('[Crowd Pilot] Successfully updated commandsToSkipShell');\n\t\t\t\t// Verify it was set\n\t\t\t\tconst verifyConfig = vscode.workspace.getConfiguration('terminal.integrated');\n\t\t\t\tconst verify = verifyConfig.get('commandsToSkipShell', []);\n\t\t\t\tconsole.log('[Crowd Pilot] Verified commandsToSkipShell:', JSON.stringify(verify));\n\t\t\t\tif (!verify.includes('crowd-pilot.testRun')) {\n\t\t\t\t\tconsole.error('[Crowd Pilot] ERROR: Configuration update did not persist!');\n\t\t\t\t}\n\t\t\t}, (err: unknown) => {\n\t\t\t\tconsole.error('[Crowd Pilot] ERROR updating commandsToSkipShell:', err);\n\t\t\t});\n\t} else {\n\t\tconsole.log('[Crowd Pilot] testRun already in commandsToSkipShell');",typescript,content +53,318735,"src/extension.ts",712,0,"\tconsole.log('[Crowd Pilot] Current commandsToSkipShell:', JSON.stringify(commandsToSkipShell));\n\t\n",typescript,content +54,318735,"src/extension.ts",371,48,"\tconsole.log('[Crowd Pilot] Extension activated');",typescript,content +55,323409,"src/extension.ts",3565,42,"",typescript,content +56,323409,"src/extension.ts",2885,642,"",typescript,content +57,323409,"src/extension.ts",1813,428,"",typescript,content +58,323409,"src/extension.ts",874,848,"\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\tconfig.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\tconsole.log('Added testRun to commandsToSkipShell');",typescript,content +59,323409,"src/extension.ts",714,99,"",typescript,content +60,323409,"src/extension.ts",371,50,"\tconsole.log('Crowd Pilot extension activated');",typescript,content +61,323453,"src/extension.ts",1747,0,"\tcontext.subscriptions.push(checkConfig);\n",typescript,content +62,323454,"src/extension.ts",1709,0,"\t// Diagnostic command to check keybinding configuration and context\n\tconst checkConfig = vscode.commands.registerCommand('crowd-pilot.checkConfig', () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get('commandsToSkipShell', []);\n\t\tconst hasCommand = commandsToSkipShell.includes('crowd-pilot.testRun');\n\t\t\n\t\tconst activeEditor = vscode.window.activeTextEditor;\n\t\tconst activeTerminal = vscode.window.activeTerminal;\n\t\t\n\t\tconst message = `commandsToSkipShell: ${JSON.stringify(commandsToSkipShell)}\n` +\n\t\t\t`Contains 'crowd-pilot.testRun': ${hasCommand}\n` +\n\t\t\t`Active Editor: ${activeEditor ? 'Yes' : 'No'}\n` +\n\t\t\t`Active Terminal: ${activeTerminal ? activeTerminal.name : 'No'}\n` +\n\t\t\t`Terminal Count: ${vscode.window.terminals.length}`;\n\t\t\n\t\tconsole.log('[Crowd Pilot] Config check:', message);\n\t\tvscode.window.showInformationMessage(message, { modal: true });\n\t});\n\n",typescript,content +63,323454,"src/extension.ts",1065,0,"\t\t// Log context information\n\t\tconst activeEditor = vscode.window.activeTextEditor;\n\t\tconst activeTerminal = vscode.window.activeTerminal;\n\t\tconst contextInfo = {\n\t\t\thasActiveEditor: !!activeEditor,\n\t\t\thasActiveTerminal: !!activeTerminal,\n\t\t\tactiveTerminalName: activeTerminal?.name,\n\t\t\tterminalCount: vscode.window.terminals.length,\n\t\t};\n\t\tconsole.log('[Crowd Pilot] testRun command executed', JSON.stringify(contextInfo));\n\t\t\n",typescript,content +64,323454,"src/extension.ts",773,201,"\t\tconst updated = [...commandsToSkipShell, 'crowd-pilot.testRun'];\n\t\tconfig.update('commandsToSkipShell', updated, vscode.ConfigurationTarget.Global, false)\n\t\t\t.then(() => {\n\t\t\t\tconsole.log('[Crowd Pilot] Successfully updated commandsToSkipShell');\n\t\t\t\t// Verify it was set\n\t\t\t\tconst verifyConfig = vscode.workspace.getConfiguration('terminal.integrated');\n\t\t\t\tconst verify = verifyConfig.get('commandsToSkipShell', []);\n\t\t\t\tconsole.log('[Crowd Pilot] Verified commandsToSkipShell:', JSON.stringify(verify));\n\t\t\t\tif (!verify.includes('crowd-pilot.testRun')) {\n\t\t\t\t\tconsole.error('[Crowd Pilot] ERROR: Configuration update did not persist!');\n\t\t\t\t}\n\t\t\t}, (err: unknown) => {\n\t\t\t\tconsole.error('[Crowd Pilot] ERROR updating commandsToSkipShell:', err);\n\t\t\t});\n\t} else {\n\t\tconsole.log('[Crowd Pilot] testRun already in commandsToSkipShell');",typescript,content +65,323454,"src/extension.ts",712,0,"\tconsole.log('[Crowd Pilot] Current commandsToSkipShell:', JSON.stringify(commandsToSkipShell));\n\t\n",typescript,content +66,323454,"src/extension.ts",371,48,"\tconsole.log('[Crowd Pilot] Extension activated');",typescript,content +67,433961,"src/extension.ts",3877,42,"",typescript,content +68,433961,"src/extension.ts",2885,954,"",typescript,content +69,433961,"src/extension.ts",1813,428,"",typescript,content +70,433962,"src/extension.ts",874,848,"\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\tconfig.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\tconsole.log('Added testRun to commandsToSkipShell');",typescript,content +71,433962,"src/extension.ts",714,99,"",typescript,content +72,433962,"src/extension.ts",371,50,"\tconsole.log('Crowd Pilot extension activated');",typescript,content +73,434008,"src/extension.ts",1747,0,"\tcontext.subscriptions.push(checkConfig);\n",typescript,content +74,434008,"src/extension.ts",1709,0,"\t// Diagnostic command to check keybinding configuration and context\n\tconst checkConfig = vscode.commands.registerCommand('crowd-pilot.checkConfig', () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get('commandsToSkipShell', []);\n\t\tconst hasCommand = commandsToSkipShell.includes('crowd-pilot.testRun');\n\t\t\n\t\tconst activeEditor = vscode.window.activeTextEditor;\n\t\tconst activeTerminal = vscode.window.activeTerminal;\n\t\t\n\t\tconst message = `commandsToSkipShell: ${JSON.stringify(commandsToSkipShell)}\n` +\n\t\t\t`Contains 'crowd-pilot.testRun': ${hasCommand}\n` +\n\t\t\t`Active Editor: ${activeEditor ? 'Yes' : 'No'}\n` +\n\t\t\t`Active Terminal: ${activeTerminal ? activeTerminal.name : 'No'}\n` +\n\t\t\t`Terminal Count: ${vscode.window.terminals.length}`;\n\t\t\n\t\tconsole.log('[Crowd Pilot] Config check:', message);\n\t\tvscode.window.showInformationMessage(message, { modal: true });\n\t});\n\n",typescript,content +75,434008,"src/extension.ts",1560,14,"\t\tterm.show(false); // ensure terminal gets focus (preserveFocus: false)\n\t\t// Small delay to ensure terminal has focus before sending text\n\t\tawait new Promise(resolve => setTimeout(resolve, 100));",typescript,content +76,434008,"src/extension.ts",1375,44,"\t\tawait vscode.window.showTextDocument(doc, undefined, true); // preserveFocus: true",typescript,content +77,434008,"src/extension.ts",1065,0,"\t\t// Log context information\n\t\tconst activeEditor = vscode.window.activeTextEditor;\n\t\tconst activeTerminal = vscode.window.activeTerminal;\n\t\tconst contextInfo = {\n\t\t\thasActiveEditor: !!activeEditor,\n\t\t\thasActiveTerminal: !!activeTerminal,\n\t\t\tactiveTerminalName: activeTerminal?.name,\n\t\t\tterminalCount: vscode.window.terminals.length,\n\t\t};\n\t\tconsole.log('[Crowd Pilot] testRun command executed', JSON.stringify(contextInfo));\n\t\t\n",typescript,content +78,434008,"src/extension.ts",773,201,"\t\tconst updated = [...commandsToSkipShell, 'crowd-pilot.testRun'];\n\t\tconfig.update('commandsToSkipShell', updated, vscode.ConfigurationTarget.Global, false)\n\t\t\t.then(() => {\n\t\t\t\tconsole.log('[Crowd Pilot] Successfully updated commandsToSkipShell');\n\t\t\t\t// Verify it was set\n\t\t\t\tconst verifyConfig = vscode.workspace.getConfiguration('terminal.integrated');\n\t\t\t\tconst verify = verifyConfig.get('commandsToSkipShell', []);\n\t\t\t\tconsole.log('[Crowd Pilot] Verified commandsToSkipShell:', JSON.stringify(verify));\n\t\t\t\tif (!verify.includes('crowd-pilot.testRun')) {\n\t\t\t\t\tconsole.error('[Crowd Pilot] ERROR: Configuration update did not persist!');\n\t\t\t\t}\n\t\t\t}, (err: unknown) => {\n\t\t\t\tconsole.error('[Crowd Pilot] ERROR updating commandsToSkipShell:', err);\n\t\t\t});\n\t} else {\n\t\tconsole.log('[Crowd Pilot] testRun already in commandsToSkipShell');",typescript,content +79,434008,"src/extension.ts",712,0,"\tconsole.log('[Crowd Pilot] Current commandsToSkipShell:', JSON.stringify(commandsToSkipShell));\n\t\n",typescript,content +80,434008,"src/extension.ts",371,48,"\tconsole.log('[Crowd Pilot] Extension activated');",typescript,content +81,442669,"src/extension.ts",635,0,"",typescript,selection_mouse +82,442679,"src/extension.ts",634,0,"",typescript,selection_command +83,465304,"src/extension.ts",0,4236,"// The module 'vscode' contains the VS Code extensibility API\n// Import the module and reference it with the alias vscode in your code below\nimport * as vscode from 'vscode';\n\n// This method is called when your extension is activated\n// Your extension is activated the very first time the command is executed\nexport function activate(context: vscode.ExtensionContext) {\n\n\tconsole.log('Crowd Pilot extension activated');\n\n\t// Configure terminal to allow tab keybinding to work\n\t// This makes the command skip the shell so VS Code can intercept tab in terminals\n\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\tconst commandsToSkipShell = config.get('commandsToSkipShell', []);\n\tif (!commandsToSkipShell.includes('crowd-pilot.testRun')) {\n\t\tcommandsToSkipShell.push('crowd-pilot.testRun');\n\t\tconfig.update('commandsToSkipShell', commandsToSkipShell, vscode.ConfigurationTarget.Global);\n\t\tconsole.log('Added testRun to commandsToSkipShell');\n\t}\n\n\tconst testRun = vscode.commands.registerCommand('crowd-pilot.testRun', async () => {\n\t\tconst editor = vscode.window.activeTextEditor;\n\t\tconst doc = editor!.document;\n\t\tconst term = vscode.window.terminals[0] ?? vscode.window.createTerminal('Test');\n\t\tconst git = vscode.extensions.getExtension('vscode.git')?.exports?.getAPI(1);\n\t\tconst repo = git?.repositories?.[0];\n\t\n\t\t// Emit a few actions:\n\t\tawait vscode.window.showTextDocument(doc);\n\t\teditor!.selections = [new vscode.Selection(0, 0, 0, 0)];\n\t\tawait editor!.edit(e => e.insert(new vscode.Position(0, 0), 'hello world\n'));\n\t\tterm.show();\n\t\tterm.sendText('echo VSCode test');\n\t\t//await repo?.pull();\n\t\n\t\tvscode.window.showInformationMessage('All actions emitted');\n\t });\n\n\tcontext.subscriptions.push(testRun);\n}\n\n// This method is called when your extension is deactivated\nexport function deactivate() {}\n",typescript,content +84,465319,"src/extension.ts",1747,0,"\tcontext.subscriptions.push(checkConfig);\n",typescript,content +85,465319,"src/extension.ts",1709,0,"\t// Diagnostic command to check keybinding configuration and context\n\tconst checkConfig = vscode.commands.registerCommand('crowd-pilot.checkConfig', () => {\n\t\tconst config = vscode.workspace.getConfiguration('terminal.integrated');\n\t\tconst commandsToSkipShell = config.get('commandsToSkipShell', []);\n\t\tconst hasCommand = commandsToSkipShell.includes('crowd-pilot.testRun');\n\t\t\n\t\tconst activeEditor = vscode.window.activeTextEditor;\n\t\tconst activeTerminal = vscode.window.activeTerminal;\n\t\t\n\t\tconst message = `commandsToSkipShell: ${JSON.stringify(commandsToSkipShell)}\n` +\n\t\t\t`Contains 'crowd-pilot.testRun': ${hasCommand}\n` +\n\t\t\t`Active Editor: ${activeEditor ? 'Yes' : 'No'}\n` +\n\t\t\t`Active Terminal: ${activeTerminal ? activeTerminal.name : 'No'}\n` +\n\t\t\t`Terminal Count: ${vscode.window.terminals.length}`;\n\t\t\n\t\tconsole.log('[Crowd Pilot] Config check:', message);\n\t\tvscode.window.showInformationMessage(message, { modal: true });\n\t});\n\n",typescript,content +86,465319,"src/extension.ts",1065,0,"\t\t// Log context information\n\t\tconst activeEditor = vscode.window.activeTextEditor;\n\t\tconst activeTerminal = vscode.window.activeTerminal;\n\t\tconst contextInfo = {\n\t\t\thasActiveEditor: !!activeEditor,\n\t\t\thasActiveTerminal: !!activeTerminal,\n\t\t\tactiveTerminalName: activeTerminal?.name,\n\t\t\tterminalCount: vscode.window.terminals.length,\n\t\t};\n\t\tconsole.log('[Crowd Pilot] testRun command executed', JSON.stringify(contextInfo));\n\t\t\n",typescript,content +87,465319,"src/extension.ts",773,201,"\t\tconst updated = [...commandsToSkipShell, 'crowd-pilot.testRun'];\n\t\tconfig.update('commandsToSkipShell', updated, vscode.ConfigurationTarget.Global, false)\n\t\t\t.then(() => {\n\t\t\t\tconsole.log('[Crowd Pilot] Successfully updated commandsToSkipShell');\n\t\t\t\t// Verify it was set\n\t\t\t\tconst verifyConfig = vscode.workspace.getConfiguration('terminal.integrated');\n\t\t\t\tconst verify = verifyConfig.get('commandsToSkipShell', []);\n\t\t\t\tconsole.log('[Crowd Pilot] Verified commandsToSkipShell:', JSON.stringify(verify));\n\t\t\t\tif (!verify.includes('crowd-pilot.testRun')) {\n\t\t\t\t\tconsole.error('[Crowd Pilot] ERROR: Configuration update did not persist!');\n\t\t\t\t}\n\t\t\t}, (err: unknown) => {\n\t\t\t\tconsole.error('[Crowd Pilot] ERROR updating commandsToSkipShell:', err);\n\t\t\t});\n\t} else {\n\t\tconsole.log('[Crowd Pilot] testRun already in commandsToSkipShell');",typescript,content +88,465319,"src/extension.ts",712,0,"\tconsole.log('[Crowd Pilot] Current commandsToSkipShell:', JSON.stringify(commandsToSkipShell));\n\t\n",typescript,content +89,465319,"src/extension.ts",371,48,"\tconsole.log('[Crowd Pilot] Extension activated');",typescript,content +90,466096,"src/extension.ts",610,0,"",typescript,selection_mouse +91,6223328,".vscode/launch.json",0,0,"// A launch configuration that compiles the extension and then opens it inside a new window\n// Use IntelliSense to learn about possible attributes.\n// Hover to view descriptions of existing attributes.\n// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387\n{\n\t""version"": ""0.2.0"",\n\t""configurations"": [\n\t\t{\n\t\t\t""name"": ""Run Extension"",\n\t\t\t""type"": ""extensionHost"",\n\t\t\t""request"": ""launch"",\n\t\t\t""args"": [\n\t\t\t\t""--extensionDevelopmentPath=${workspaceFolder}""\n\t\t\t],\n\t\t\t""timeout"": 20000, // Increase the timeout to 20 seconds\n\t\t\t""outFiles"": [\n\t\t\t\t""${workspaceFolder}/out/**/*.js""\n\t\t\t],\n\t\t\t""preLaunchTask"": ""${defaultBuildTask}""\n\t\t}\n\t]\n}\n",jsonc,tab +92,6574889,"src/extension.ts",0,0,"",typescript,tab +93,6574918,"src/extension.ts",423,0,"",typescript,selection_command diff --git a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-e1a5354c-3f18-4c4b-94a1-64c1669feac51757270084100-2025_09_07-20.34.50.955/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-e1a5354c-3f18-4c4b-94a1-64c1669feac51757270084100-2025_09_07-20.34.50.955/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..458894696f3d9502139939a038a4e9eb48416a1d --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-e1a5354c-3f18-4c4b-94a1-64c1669feac51757270084100-2025_09_07-20.34.50.955/source.csv @@ -0,0 +1,43 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,3,"utils/nn.py",0,0,"import math\nfrom typing import Tuple, Callable, List\n\nfrom flax import nnx\nimport jax\nimport jax.numpy as jnp\nimport einops\n\n\nclass SpatioTemporalPositionalEncoding(nnx.Module):\n """"""\n Applies separate sinusoidal positional encodings to the temporal and spatial dimensions.\n """"""\n\n def __init__(self, d_model: int, max_len: int = 5000):\n self.d_model = d_model\n self.max_len = max_len\n\n pe = jnp.zeros((self.max_len, self.d_model))\n position = jnp.arange(0, self.max_len, dtype=jnp.float32)[:, None]\n div_term = jnp.exp(\n jnp.arange(0, self.d_model, 2) * (-math.log(10000.0) / self.d_model)\n )\n pe = pe.at[:, 0::2].set(jnp.sin(position * div_term))\n pe = pe.at[:, 1::2].set(jnp.cos(position * div_term))\n self.pe = nnx.Variable(pe)\n\n def __call__(self, x: jax.Array) -> jax.Array:\n """"""\n Args:\n x: The input tensor of shape (Batch, Time, Space, Dimension).\n\n Returns:\n The input tensor with positional encodings added.\n """"""\n assert x.ndim == 4, f""Input must be 4-dimensional, but got shape {x.shape}""\n\n num_timesteps = x.shape[1]\n num_spatial_patches = x.shape[2]\n\n # Temporal positional encoding: (1, T, 1, D)\n temporal_pe = self.pe.value[None, :num_timesteps, None, :]\n x = x + temporal_pe\n\n # Spatial positional encoding: (1, 1, S, D)\n spatial_pe = self.pe.value[None, None, :num_spatial_patches, :]\n x = x + spatial_pe\n\n return x\n\n\nclass STBlock(nnx.Module):\n def __init__(\n self,\n dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n sow_weights: bool,\n sow_activations: bool,\n ):\n self.dim = dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=False\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(self, x_BTNM: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z_BTNM = self.spatial_norm(x_BTNM)\n z_BTNM = self.spatial_attention(z_BTNM, sow_weights=self.sow_weights)\n x_BTNM = x_BTNM + z_BTNM\n\n # --- Temporal attention ---\n x_BNTM = x_BTNM.swapaxes(1, 2)\n z_BNTM = self.temporal_norm(x_BNTM)\n z_BNTM = self.temporal_attention(z_BNTM, sow_weights=self.sow_weights)\n x_BNTM = x_BNTM + z_BNTM\n x_BTNM = x_BNTM.swapaxes(1, 2)\n\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n if self.sow_activations:\n self.sow(nnx.Intermediate, ""activations"", x_BTNM)\n return x_BTNM\n\n\nclass STTransformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n V: vocabulary size\n """"""\n\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n sow_weights: bool = False,\n sow_activations: bool = False,\n sow_logits: bool = False,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_logits = sow_logits\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n\n self.pos_enc = SpatioTemporalPositionalEncoding(self.model_dim, max_len=max_len)\n\n self.blocks = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n STBlock(\n dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n sow_weights=self.sow_weights,\n sow_activations=self.sow_activations,\n )\n )\n\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(self, x_BTNI: jax.Array) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM)\n\n x_BTNV = self.output_dense(x_BTNM)\n if self.sow_logits:\n self.sow(nnx.Intermediate, ""logits"", x_BTNV)\n return x_BTNV\n\n\nclass TransformerBlock(nnx.Module):\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n sow_weights: bool,\n sow_activations: bool,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.decode = decode\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(\n self, x_BTNM: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None\n ) -> jax.Array:\n # --- Spatial attention ---\n B, T, N, M = x_BTNM.shape\n z_FNM = einops.rearrange(x_BTNM, ""b t n m -> (b t) n m"")\n z_FNM = self.spatial_norm(z_FNM)\n z_FNM = self.spatial_attention(z_FNM, sow_weights=self.sow_weights)\n z_BTNM = einops.rearrange(z_FNM, ""(b t) n m -> b t n m"", t=T)\n x_BTNM = x_BTNM + z_BTNM\n # --- Temporal attention ---\n z_PTM = einops.rearrange(x_BTNM, ""b t n m -> (b n) t m"")\n z_PTM = self.temporal_norm(z_PTM)\n z_PTM = self.temporal_attention(z_PTM, sow_weights=self.sow_weights)\n z_BTNM = einops.rearrange(z_PTM, ""(b n) t m -> b t n m"", n=N)\n x_BTNM = x_BTNM + z_BTNM\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n if self.sow_activations:\n self.sow(nnx.Intermediate, ""activations"", x_BTNM)\n\n return x_BTNM\n\n\nclass Transformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n V: vocabulary size\n F: number of frames in batch\n P: number of patch positions in batch\n """"""\n\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n sow_logits: bool = False,\n sow_weights: bool = False,\n sow_activations: bool = False,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_logits = sow_logits\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n\n self.pos_enc = SpatioTemporalPositionalEncoding(self.model_dim, max_len=max_len)\n\n self.blocks: List[TransformerBlock] = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n TransformerBlock(\n model_dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n sow_weights=self.sow_weights,\n sow_activations=self.sow_activations,\n rngs=rngs,\n )\n )\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self, x_BTNI: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None\n ) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM, pos_index)\n\n x_BTNV = self.output_dense(x_BTNM)\n if self.sow_logits:\n self.sow(nnx.Intermediate, ""logits"", x_BTNV)\n return x_BTNV\n\n\ndef normalize(x: jax.Array) -> jax.Array:\n return x / (jnp.linalg.norm(x, ord=2, axis=-1, keepdims=True) + 1e-8)\n\n\nclass VectorQuantizer(nnx.Module):\n """"""\n Dimension keys:\n D: B * T * N\n K: number of latents\n L: latent dimension\n """"""\n\n def __init__(\n self,\n latent_dim: int,\n num_latents: int,\n dropout: float,\n dtype: jnp.dtype,\n rngs: nnx.Rngs,\n ):\n self.latent_dim = latent_dim\n self.num_latents = num_latents\n self.dropout = dropout\n self.dtype = dtype\n\n self.codebook = nnx.Param(\n normalize(\n nnx.initializers.lecun_uniform()(\n rngs.params(), (self.num_latents, self.latent_dim)\n )\n )\n )\n self.drop = nnx.Dropout(self.dropout, rngs=rngs)\n\n def __call__(\n self, x_DL: jax.Array, training: bool\n ) -> Tuple[jax.Array, jax.Array, jax.Array, jax.Array]:\n # --- Compute distances ---\n x_DL = x_DL.astype(self.dtype)\n codebook = self.codebook.value.astype(self.dtype)\n\n x_normalized_DL = normalize(x_DL)\n normalized_codebook_KL = normalize(codebook)\n distance_DK = -jnp.matmul(x_normalized_DL, normalized_codebook_KL.T)\n if training:\n distance_DK = self.drop(distance_DK)\n\n # --- Get indices and embeddings ---\n indices_D = jnp.argmin(distance_DK, axis=-1)\n z_DL = codebook[indices_D]\n\n # --- Straight through estimator ---\n z_q_DL = x_DL + jax.lax.stop_gradient(z_DL - x_DL)\n return z_q_DL, z_DL, x_DL, indices_D\n\n def get_codes(self, indices_E: jax.Array) -> jax.Array:\n return self.codebook[indices_E]\n\n\ndef _create_flash_attention_fn(use_flash_attention: bool, is_causal: bool) -> Callable:\n """"""\n Create an attention function that uses flash attention if enabled.\n\n flax.nnx.MultiHeadAttention provides tensors with shape (batch..., length, num_heads, head_dim),\n but jax.nn.dot_product_attention expects (batch, length, num_heads, head_dim). We reshape to\n ensure compatibility. cuDNN's flash attention additionally requires a sequence length that\n is a multiple of 4. We pad the sequence length to the nearest multiple of 4 and mask\n accordingly. Note that cuDNN requires the mask to be broadcast before calling the attention\n function due to strict shape checking.\n """"""\n\n def attention_fn(\n query_BTHD, key_BSHD, value_BSHD, bias=None, mask_B111=None, **kwargs\n ):\n implementation = ""cudnn"" if use_flash_attention else None\n\n def _merge_batch_dims(x):\n return einops.rearrange(x, ""... l h k -> (...) l h k"")\n\n def _pad(x, pad_size):\n return jnp.pad(x, ((0, 0), (0, pad_size), (0, 0), (0, 0)))\n\n original_shape = query_BTHD.shape\n T = query_BTHD.shape[-3]\n S = key_BSHD.shape[-3]\n\n # Pad to nearest multiple of 4\n Q = ((T + 3) // 4) * 4\n pad_size_Q = Q - T\n K = ((S + 3) // 4) * 4\n pad_size_K = K - S\n\n query_BQHD = _pad(_merge_batch_dims(query_BTHD), pad_size_Q)\n key_BKHD = _pad(_merge_batch_dims(key_BSHD), pad_size_K)\n value_BKHD = _pad(_merge_batch_dims(value_BSHD), pad_size_K)\n\n attention_mask = jnp.ones((Q, K), dtype=jnp.bool_)\n attention_mask = attention_mask.at[T:, :].set(False)\n attention_mask = attention_mask.at[:, S:].set(False)\n\n mask_11TS = attention_mask[jnp.newaxis, jnp.newaxis, :, :]\n\n bias_4d = (\n jnp.pad(\n _merge_batch_dims(bias),\n ((0, 0), (0, 0), (0, pad_size_Q), (0, pad_size_K)),\n )\n if bias is not None\n else None\n )\n\n # NOTE: jax.nn.dot_product_attention does not support dropout\n output_4d = jax.nn.dot_product_attention(\n query=query_BQHD,\n key=key_BKHD,\n value=value_BKHD,\n bias=bias_4d,\n mask=mask_11TS,\n implementation=implementation,\n is_causal=is_causal,\n )\n return output_4d[..., :T, :, :].reshape(original_shape)\n\n return attention_fn\n",python,tab +2,149,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"8:34:50 PM [info] Activating crowd-code\n8:34:50 PM [info] Recording started\n8:34:50 PM [info] Initializing git provider using file system watchers...\n",Log,tab +3,187,"extension-output-pdoom-org.crowd-code-#1-crowd-code",150,0,"8:34:51 PM [info] Git repository found\n8:34:51 PM [info] Git provider initialized successfully\n8:34:51 PM [info] Initial git state: [object Object]\n",Log,content +4,3399,"utils/nn.py",0,0,"",python,tab +5,5944,"utils/nn.py",16950,0,"",python,selection_command +6,6119,"utils/nn.py",16918,0,"",python,selection_command +7,6363,"utils/nn.py",16879,0,"",python,selection_command +8,6384,"utils/nn.py",16843,0,"",python,selection_command +9,6419,"utils/nn.py",16783,0,"",python,selection_command +10,6454,"utils/nn.py",16737,0,"",python,selection_command +11,6487,"utils/nn.py",16709,0,"",python,selection_command +12,6520,"utils/nn.py",16692,0,"",python,selection_command +13,6551,"utils/nn.py",16661,0,"",python,selection_command +14,6861,"utils/nn.py",16692,0,"",python,selection_command +15,7020,"utils/nn.py",16709,0,"",python,selection_command +16,7314,"utils/nn.py",16692,0,"",python,selection_command +17,21332,"utils/nn.py",16661,0,"",python,selection_command +18,21589,"utils/nn.py",16692,0,"",python,selection_command +19,23753,"utils/nn.py",16709,0,"",python,selection_command +20,23936,"utils/nn.py",16737,0,"",python,selection_command +21,24089,"utils/nn.py",16783,0,"",python,selection_command +22,24270,"utils/nn.py",16843,0,"",python,selection_command +23,24431,"utils/nn.py",16879,0,"",python,selection_command +24,24599,"utils/nn.py",16918,0,"",python,selection_command +25,24737,"utils/nn.py",16950,0,"",python,selection_command +26,26706,"utils/nn.py",16918,0,"",python,selection_command +27,26890,"utils/nn.py",16879,0,"",python,selection_command +28,35158,"utils/nn.py",15971,0,"",python,selection_command +29,38308,"models/tokenizer.py",0,0,"from typing import Dict, Tuple\n\nimport flax.nnx as nnx\nimport jax.numpy as jnp\nimport jax\n\nfrom utils.preprocess import patchify, unpatchify\nfrom utils.nn import STTransformer, VectorQuantizer\n\n\nclass TokenizerVQVAE(nnx.Module):\n """"""\n ST-ViVit VQ-VAE\n\n Dimension keys:\n B: batch size\n T: sequence length\n N: number of patches per frame\n L: latent dimension\n D: B * T * N\n H: height\n W: width\n C: number of channels\n P: patch token dimension (patch_size^2 * C)\n """"""\n\n def __init__(\n self,\n in_dim: int,\n model_dim: int,\n ffn_dim: int,\n latent_dim: int,\n num_latents: int,\n patch_size: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n codebook_dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n ):\n self.in_dim = in_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.latent_dim = latent_dim\n self.num_latents = num_latents\n self.patch_size = patch_size\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.codebook_dropout = codebook_dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.encoder = STTransformer(\n self.in_dim * self.patch_size**2,\n self.model_dim,\n self.ffn_dim,\n self.latent_dim,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.vq = VectorQuantizer(\n self.latent_dim,\n self.num_latents,\n self.codebook_dropout,\n self.dtype,\n rngs=rngs,\n )\n self.out_dim = self.in_dim * self.patch_size**2\n self.decoder = STTransformer(\n self.latent_dim,\n self.model_dim,\n self.ffn_dim,\n self.out_dim,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n\n def __call__(\n self, batch: Dict[str, jax.Array], training: bool = True\n ) -> Dict[str, jax.Array]:\n H, W = batch[""videos""].shape[2:4]\n videos_BTHWC = batch[""videos""]\n outputs = self.vq_encode(videos_BTHWC, training)\n z_q_BTNL = outputs[""z_q""]\n recon_BTHWC = self.decoder(z_q_BTNL)\n recon_BTHWC = recon_BTHWC.astype(jnp.float32)\n recon_BTHWC = nnx.sigmoid(recon_BTHWC)\n recon_BTHWC = recon_BTHWC.astype(self.dtype)\n recon_BTHWC = unpatchify(recon_BTHWC, self.patch_size, H, W)\n outputs[""recon""] = recon_BTHWC\n return outputs\n\n def vq_encode(\n self, videos: jax.Array, training: bool = True\n ) -> Dict[str, jax.Array]:\n # --- Preprocess + encode ---\n B, T = videos.shape[:2]\n patch_BTNP = patchify(videos, self.patch_size)\n N = patch_BTNP.shape[2]\n x_BTNL = self.encoder(patch_BTNP)\n\n # --- Vector quantize ---\n x_DL = x_BTNL.reshape(B * T * N, self.latent_dim)\n z_q_DL, z_DL, emb_DL, indices_D = self.vq(x_DL, training)\n z_q_BTNL = z_q_DL.reshape(B, T, N, self.latent_dim)\n indices_BTN = indices_D.reshape(B, T, N)\n return dict(z_q=z_q_BTNL, z=z_DL, emb=emb_DL, indices=indices_BTN)\n\n def decode(self, indices_BTN: jax.Array, video_hw: Tuple[int, int]) -> jax.Array:\n z_BTNL = self.vq.codebook[indices_BTN]\n recon_BTNP = self.decoder(z_BTNL)\n recon_BTNP = recon_BTNP.astype(jnp.float32)\n recon_BTNP = nnx.sigmoid(recon_BTNP)\n recon_BTNP = recon_BTNP.astype(self.dtype)\n return unpatchify(recon_BTNP, self.patch_size, *video_hw)\n",python,tab +30,38309,"models/tokenizer.py",1841,15,"VectorQuantizer",python,selection_command +31,38548,"models/tokenizer.py",1855,0,"",python,selection_command +32,38739,"models/tokenizer.py",1841,0,"",python,selection_command +33,38880,"models/tokenizer.py",1839,0,"",python,selection_command +34,39039,"models/tokenizer.py",1836,0,"",python,selection_command +35,39473,"models/tokenizer.py",3502,0,"",python,selection_command +36,40428,"models/tokenizer.py",3501,0,"",python,selection_command +37,40583,"models/tokenizer.py",3497,0,"",python,selection_command +38,40772,"models/tokenizer.py",3495,0,"",python,selection_command +39,41280,"models/tokenizer.py",3437,0,"",python,selection_command +40,41895,"models/tokenizer.py",3395,0,"",python,selection_command +41,42041,"models/tokenizer.py",3362,0,"",python,selection_command +42,42240,"models/tokenizer.py",3360,0,"",python,selection_command diff --git a/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-f30029d7-35fb-408d-b8cf-377e2b67278b1762084766653-2025_11_02-12.59.29.208/source.csv b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-f30029d7-35fb-408d-b8cf-377e2b67278b1762084766653-2025_11_02-12.59.29.208/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..8ba4f7d9151ddc359fe832c52513daabe7296113 --- /dev/null +++ b/4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-f30029d7-35fb-408d-b8cf-377e2b67278b1762084766653-2025_11_02-12.59.29.208/source.csv @@ -0,0 +1,10591 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,2,"examples/crowd_code.html",0,0,"\n\n\n\n \n \n \n \n\n\n\n \n \n \n \n \n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function definitions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

FS worked on conceptualization of the project and implemented the IDE extension. MM implemented the server-side data collection pipeline. All authors contributed technically. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

\n \n \n \n \n\n \n\n\n",html,tab +2,48,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"12:59:29 PM [info] Activating crowd-code\n12:59:29 PM [info] Recording started\n12:59:29 PM [info] Initializing git provider using file system watchers...\n12:59:29 PM [info] Git repository found\n12:59:29 PM [info] Git provider initialized successfully\n",Log,tab +3,130,"extension-output-pdoom-org.crowd-code-#1-crowd-code",250,0,"12:59:29 PM [info] Initial git state: [object Object]\n",Log,content +4,2223,"examples/crowd_code.html",0,0,"",html,tab +5,39886,"examples/crowd-clone.html",0,0,"",html,tab +6,97829,"examples/crowd_code.html",0,0,"",html,tab +7,102143,"examples/crowd_code.html",593,0,"",html,selection_mouse +8,102151,"examples/crowd_code.html",592,0,"",html,selection_command +9,102545,"examples/crowd_code.html",594,0,"",html,selection_mouse +10,103467,"examples/crowd_code.html",0,6713,"\n\n\n\n \n \n \n \n\n\n\n \n \n \n \n \n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function definitions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

FS worked on conceptualization of the project and implemented the IDE extension. MM implemented the server-side data collection pipeline. All authors contributed technically. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

\n \n \n \n \n\n \n\n\n",html,selection_command +11,103679,"examples/crowd_code.html",6713,0,"",html,selection_command +12,104512,"examples/crowd-clone.html",0,0,"",html,tab +13,105405,"examples/crowd-clone.html",0,0,"\n\n\n\n \n \n \n \n\n\n\n \n \n \n \n \n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function definitions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

FS worked on conceptualization of the project and implemented the IDE extension. MM implemented the server-side data collection pipeline. All authors contributed technically. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

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+534,192863,"examples/crowd-clone.html",949,7,"crowd-c",html,selection_command +535,193216,"examples/crowd-clone.html",949,12,"crowd-clone:",html,selection_command +536,193945,"examples/crowd-clone.html",949,11,"crowd-clone",html,selection_command +537,194117,"examples/crowd-clone.html",949,11,"",html,content +538,195912,"examples/crowd-clone.html",948,0,"",html,selection_command +539,238007,"examples/crowd-clone.html",949,0,"crowd-clone",html,content +540,238017,"examples/crowd-clone.html",949,0,"",html,selection_command +541,238503,"examples/crowd-clone.html",1015,65,"",html,content +542,238511,"examples/crowd-clone.html",1015,0,"",html,selection_command +543,239233,"examples/crowd-clone.html",1015,0,"We introduce crowd-clone, a continually growing dataset of human ",html,content +544,239245,"examples/crowd-clone.html",1015,0,"",html,selection_command +545,239560,"examples/crowd-clone.html",949,11,"",html,content +546,239567,"examples/crowd-clone.html",949,0,"",html,selection_command 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\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function definitions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

FS worked on conceptualization of the project and implemented the IDE extension. MM implemented the server-side data collection pipeline. All authors contributed technically. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

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\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function definitions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

FS worked on conceptualization of the project and implemented the IDE extension. MM implemented the server-side data collection pipeline. All authors contributed technically. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

\n \n \n \n \n\n \n\n\n",html,selection_command +595,259368,"examples/crowd_code.html",6713,0,"",html,selection_command +596,260213,"examples/crowd-clone.html",0,0,"",html,tab +597,260877,"examples/crowd-clone.html",0,0,"\n\n\n\n \n \n \n \n\n\n\n \n \n \n \n \n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function definitions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

FS worked on conceptualization of the project and implemented the IDE extension. MM implemented the server-side data collection pipeline. All authors contributed technically. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

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\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function definitions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

FS worked on conceptualization of the project and implemented the IDE extension. MM implemented the server-side data collection pipeline. All authors contributed technically. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

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\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n Install once, and forget about it.\n

\n \n \n \n 1\n

Data Is The New Oil

\n

\n Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.\n Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.\n

\n

\n The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,\n in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt\n at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping\n to function definitions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.\n

\n 2\n

Going Beyond Open-Source Towards Open-Engineering

\n

\n Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the\n developer's time is not just captured by the code they produce, but also by the mere act of engineering.\n

\n

\n We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.\n We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is \n install the crowd-code extension, and forget about it.\n

\n
\n
Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.
\n

\n The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the\n public under the most permissive Creative Commons license (CC0).\n An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply\n press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for\n consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to\n make data collection and release more transparent.\n

\n

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n \n\n \n\n

Contributions

\n

FS worked on conceptualization of the project and implemented the IDE extension. MM implemented the server-side data collection pipeline. All authors contributed technically. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.

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+1499,8966317,"TERMINAL",0,0,"^Cโ ™% \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004h",,terminal_output +1500,8966520,"TERMINAL",0,0,"npm run dev",,terminal_output +1501,8966612,"TERMINAL",0,0,"cp examples/* dist",,terminal_output +1502,8966866,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +1503,8966867,"TERMINAL",0,0,"[?2004l\r\r\n]633;E;cp examples/* dist;280736e1-4344-414b-b289-7b3558692a37",,terminal_output +1504,8966881,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +1505,8967390,"TERMINAL",0,0,"npm run dev",,terminal_command +1506,8967441,"TERMINAL",0,0,"]633;C",,terminal_output +1507,8967602,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +1508,8967782,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +1509,8968224,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 443ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +1510,8968718,"TERMINAL",0,0,"created dist/transforms.v2.js in 493ms\r\n\r\n[2025-11-02 15:28:57] waiting for changes...\r\n",,terminal_output +1511,8977332,"examples/agi_cast.html",1728,0,"",html,selection_command +1512,8977586,"examples/agi_cast.html",1741,0,"",html,selection_command +1513,8977618,"examples/agi_cast.html",1765,0,"",html,selection_command +1514,8977645,"examples/agi_cast.html",1779,0,"",html,selection_command +1515,8977679,"examples/agi_cast.html",1811,0,"",html,selection_command +1516,8977713,"examples/agi_cast.html",1885,0,"",html,selection_command +1517,8977966,"examples/agi_cast.html",1811,0,"",html,selection_command +1518,8978214,"examples/agi_cast.html",1779,0,"",html,selection_command +1519,8978246,"examples/agi_cast.html",1765,0,"",html,selection_command +1520,8978281,"examples/agi_cast.html",1741,0,"",html,selection_command +1521,8978314,"examples/agi_cast.html",1728,0,"",html,selection_command +1522,8978578,"examples/agi_cast.html",1627,0,"",html,selection_command +1523,8978804,"examples/agi_cast.html",1595,0,"",html,selection_command +1524,8979012,"examples/agi_cast.html",1627,0,"",html,selection_command +1525,8980821,"TERMINAL",0,0,"^P",,terminal_output +1526,8980900,"TERMINAL",0,0,"^P",,terminal_output +1527,8981459,"TERMINAL",0,0,"^Cโ ™% \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004h",,terminal_output +1528,8981736,"TERMINAL",0,0,"npm run devcp examples/* dist",,terminal_output +1529,8981977,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +1530,8981977,"TERMINAL",0,0,"[?2004l\r\r\n]633;E;cp examples/* dist;280736e1-4344-414b-b289-7b3558692a37",,terminal_output +1531,8981995,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +1532,8982474,"TERMINAL",0,0,"npm run dev",,terminal_command +1533,8982526,"TERMINAL",0,0,"]633;C",,terminal_output +1534,8982635,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +1535,8982789,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +1536,8983163,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n",,terminal_output +1537,8983257,"TERMINAL",0,0,"(!) 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",html,selection_command +1583,9039608,"examples/agi_cast.html",2008,183,"

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet ",html,selection_command +1584,9039725,"examples/agi_cast.html",2008,358,"

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data",html,selection_command +1585,9039907,"examples/agi_cast.html",2008,399,"

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.",html,selection_command +1586,9040027,"examples/agi_cast.html",2008,408,"

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

",html,selection_command +1587,9040176,"examples/agi_cast.html",2008,416,"

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

",html,selection_command +1588,9040327,"examples/agi_cast.html",2008,586,"

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.",html,selection_command +1589,9040475,"examples/agi_cast.html",2008,595,"

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

",html,selection_command +1590,9040632,"examples/agi_cast.html",2008,612,"

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

\n ",html,selection_command +1591,9041002,"examples/agi_cast.html",2008,595,"

\n Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet \n using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data\n for software engineering agents.\n

\n

\n We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. Together.\n

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\n We introduce Jasmine, a production-ready JAX-based codebase for world modeling from unlabeled videos.\n Scale from single hosts to hundreds of xPUs thanks to XLA.\n

\n \n \n \n \n
\n
Figure 1: Jasmine in action.
\n 1\n

Introduction

\n

\n We are at the cusp of an intelligence revolution. Neural networks are able to clone the behaviour of peak human intellectual performance \n given enough compute, data, and the right algorithms . While an increasing amount of capital expenditure is allocated to compute clusters, and a well-working\n recipe of equipping models with the required priors and capacity to reason is publicly available, the path to human-level intelligence with the ability to automate\n large fractions of the economy will increasingly be shaped by paradigms that are able to find and efficiently use untouched data troves.\n

\n

\n While product-feedback-loops constitute an adaptive data trove, many domains like robotics are not mature enough to yield a product with wide enough\n adoption to create a feedback-loop of sufficient magnitude, prompting the search for alternatives.\n One paradigm proposed by the research community to overcome the data scarcity in those domains is that of world models. While world models can help frontier model\n development in numerous ways, an ambitious goal of the community is to train a world model to act as a simulation of the world , in order to\n train an agent in that simulation, via an adaptive curriculum or otherwise.\n

\n

Deriving Empirical Environment Complexity Scaling Trends

\n

\n While numerous previous works have investigated large-scale world modeling and its application to robotics , world modeling for agent training calls for a vastly different treatment.\n Such regime requires the compounding error of world models to be orders of magnitude smaller than when solely used for short-term look-ahead. The feasibility of such a world model in its truest sense is entirely\n understudied, and Jasmine, a world modeling codebase, is our first milestone towards studying the setting using rigorous evaluations. Specifically, we want to develop Empirical Environment Complexity Scaling Trends, where we train world models to full convergence\n in environments of increasing complexity (Atari , RetroGym , Craftax , Minecraft )\n and under the synthetic infinite-data regime. Subsequently, we want to evaluate those models two-fold: i) via a taxonomy of granular benchmarks probing\n specific world modeling capabilities (reconstruction quality, environment dynamics at the body/tail of the data distribution, long-horizon consistency) , and ii) by training reinforcement learning (RL) agents in both\n the world model and the corresponding ground-truth environment, and measuring the performance difference between those agents.\n

\n

\n Ultimately, such treatment permits us to derive empirical estimates of compute and data requirements to model environments of increasing complexity sufficiently well (as determined by our evaluation procedure). Only given such estimates can we try to draw conclusions\n about the feasibility of world modeling of environments as complex as the real world for agent training. If our empirical estimates show resource requirement trends that are feasible under the assumption of the continuation of Moore's Law and increased capital\n expenditure, that would manifest world modeling as a paradigm with high likelihood of success in overcoming the data-scarcity in domains as general as (humanoid) robotics. Otherwise, the world modeling research community must realign its direction with downstream goals\n that are feasible.\n

\n

A batteries-included foundation for world modeling research

\n

\n Jasmine, our first milestone towards deriving Empirical Environment Complexity Scaling Trends, is the result of weeks of infrastructure work to make large-scale world modeling research more accessible. What started off as a fork of\n Jafar grew into a full-fledged world\n modeling codebase amenable to large-scale training, implementing multiple dynamics model baselines, asynchronous checkpointing, process-parallel dataloading, checkpointing of model weights, optimizer and dataloader states, checkpointing policies, full reproducibility with identical\n training curves, mixed precision training, optimized FlashAttention (via cuDNN SDPA), activation checkpointing, DDP\n (with FSDP/HSDP requiring changing a singe LoC), WSD schedule, index-shuffling during dataloading, and native Treescope support. Jasmine implements the new\n flax.nnx API and strictly adheres to Noam Shazeer's shape suffix convention, thereby providing\n a didactic implementation of world modeling architectures. Jasmine solely depends\n on battle-tested libraries from the Google ecosystem (Flax, Optax, Orbax, Grain,\n PIX, ArrayRecord).\n

\n

Releasing a dataset of fine-grained research engineering

\n

\n We captured every step of the research engineering process behind Jasmine using crowd-code ,\n a VS Code/ Cursor extension that captures fine-grained IDE interactions (character-level edits, navigation, debugging patterns, terminal usage) and allows researchers to contribute their \n engineering process to a crowd-sourced dataset. Today, we release crowd-code-0.1, our first dataset of dense IDE interactions, which encompasses the entire development of Jasmine.\n crowd-code-0.1 is unfiltered, uncleaned, and uncurated, but only contains IDE interactions of the Jasmine authors. We are actively working on cleaning and curating the full dataset,\n which will be released in the future.\n

\n \n\n \n\n

Contributions

\n

MM, AN and FS worked on research, ideation and implementation. FS wrote the manuscript. SB provided feedback and guidance.

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Figure 1: Jasmine in action.
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Figure 1: Jasmine in action.
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2023-04-20},\n}\n\n@article{shazeer2017outrageously,\n title = {Outrageously large neural networks: The sparsely-gated\n mixture-of-experts layer},\n author = {Shazeer, Noam and Mirhoseini, Azalia and Maziarz, Krzysztof and\n Davis, Andy and Le, Quoc and Hinton, Geoffrey and Dean, Jeff},\n journal = {arXiv preprint arXiv:1701.06538},\n year = {2017},\n}\n\n@article{fedus2022switch,\n title = {Switch transformers: Scaling to trillion parameter models with simple\n and efficient sparsity},\n author = {Fedus, William and Zoph, Barret and Shazeer, Noam},\n journal = {Journal of Machine Learning Research},\n volume = {23},\n number = {120},\n pages = {1--39},\n year = {2022},\n}\n\n@article{schulman2015high,\n title = {High-dimensional continuous control using generalized advantage\n estimation},\n author = {Schulman, John and Moritz, Philipp and Levine, Sergey and Jordan,\n Michael and Abbeel, Pieter},\n journal = {arXiv preprint arXiv:1506.02438},\n year = {2015},\n}\n\n@article{srambical2025ppo,\n author = {Srambical, Franz},\n title = {PPO Is Secretly Using Monte Carlo Advantage Estimation In LLM\n Post-Training},\n journal = {p(doom) blog},\n year = {2025},\n note = {https://pdoom.org/blog.html},\n}\n\n@article{williams1992simple,\n title = {Simple statistical gradient-following algorithms for connectionist\n reinforcement learning},\n author = {Williams, Ronald J},\n journal = {Machine learning},\n volume = {8},\n pages = {229--256},\n year = {1992},\n publisher = {Springer},\n}\n\n@software{deepmind2020jax,\n title = {The {D}eep{M}ind {JAX} {E}cosystem},\n author = {DeepMind and Babuschkin, Igor and Baumli, Kate and Bell, Alison and\n Bhupatiraju, Surya and Bruce, Jake and Buchlovsky, Peter and Budden,\n David and Cai, Trevor and Clark, Aidan and Danihelka, Ivo and Dedieu,\n Antoine and Fantacci, Claudio and Godwin, Jonathan and Jones, Chris\n and Hemsley, Ross and Hennigan, Tom and Hessel, Matteo and Hou,\n Shaobo and Kapturowski, Steven and Keck, Thomas and Kemaev, Iurii and\n King, Michael and Kunesch, Markus and Martens, Lena and Merzic, Hamza\n and Mikulik, Vladimir and Norman, Tamara and Papamakarios, George and\n Quan, John and Ring, Roman and Ruiz, Francisco and Sanchez, Alvaro\n and Sartran, Laurent and Schneider, Rosalia and Sezener, Eren and\n Spencer, Stephen and Srinivasan, Srivatsan and Stanojevi\'{c}, Milo\v\n {s} and Stokowiec, Wojciech and Wang, Luyu and Zhou, Guangyao and\n Viola, Fabio},\n url = {http://github.com/deepmind},\n year = {2020},\n}\n\n@misc{jax2025jit,\n title = {JAX: Just-in-time compilation},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/jit-compilation.html},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{jax2025callbacks,\n title = {JAX: External callbacks},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/external-callbacks.html},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{jax2025checkify,\n title = {JAX: The `checkify` transformation},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/debugging/checkify_guide.html},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{jax2025key,\n title = {JAX: Key concepts},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/key-concepts.html},\n note = {Accessed: 2025-03-26},\n}\n\n@software{deepmind2020chex,\n title = {Chex},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n url = {http://github.com/google-deepmind/chex},\n year = {2020},\n}\n\n@misc{jax2025control,\n title = {JAX: Control flow and logical operators with JIT},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://docs.jax.dev/en/latest/control-flow.html},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{xla2025conditional,\n title = {XLA:Operation Semantics:Conditional},\n author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James\n Johnson and Chris Leary and Dougal Maclaurin and George Necula and\n Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao\n Zhang},\n year = {2025},\n url = {https://openxla.org/xla/operation_semantics#conditional},\n note = {Accessed: 2025-03-26},\n}\n\n@misc{ayaka76822025error,\n author = {ayaka7682},\n title = {Message on public Discord server: Try this:\n \n import jax from jax._src.error_check import set_error_if, raise_if_error\n \n \n import jax.numpy as jnp\n \n \n @jax.jit\n \n \n def f(x, y):\n \n \n set_error_if(x != 0, 'x must be 0')\n \n \n return jnp.multiply(x, y)\n \n \n f(0, 0)\n \n \n raise_if_error()\n },\n year = {2025},\n url = {\n https://discord.com/channels/1107832795377713302/1107832795688083561/1354171414596419854\n },\n note = {Accessed: 2025-03-26},\n}\n\n@book{sutton1998reinforcement,\n title={Reinforcement learning: An introduction},\n author={Sutton, Richard S and Barto, Andrew G and others},\n volume={1},\n number={1},\n year={1998},\n publisher={MIT press Cambridge}\n}\n\n@article{sutton1999policy,\n title={Policy gradient methods for reinforcement learning with function approximation},\n author={Sutton, Richard S and McAllester, David and Singh, Satinder and Mansour, Yishay},\n journal={Advances in neural information processing systems},\n volume={12},\n year={1999}\n}\n\n@article{degris2012off,\n title={Off-policy actor-critic},\n author={Degris, Thomas and White, Martha and Sutton, Richard S},\n journal={arXiv preprint arXiv:1205.4839},\n year={2012}\n}\n\n@article{schulman2017proximal,\n title={Proximal policy optimization algorithms},\n author={Schulman, John and Wolski, Filip and Dhariwal, Prafulla and Radford, Alec and Klimov, Oleg},\n journal={arXiv preprint arXiv:1707.06347},\n year={2017}\n}\n\n@article{ouyang2022training,\n title={Training language models to follow instructions with human feedback},\n author={Ouyang, Long and Wu, Jeffrey and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others},\n journal={Advances in neural information processing systems},\n volume={35},\n pages={27730--27744},\n year={2022}\n}\n\n\n@misc{openai2025imo,\n title = {We achieved gold medal-level performance ๐Ÿฅ‡on the 2025 International Mathematical Olympiad with a general-purpose reasoning LLM!},\n author = {OpenAI},\n year = {2025},\n url = {https://x.com/OpenAI/status/1946594928945148246},\n note = {Accessed: 2025-08-05},\n}\n\n@misc{deepmind2025imo,\n title = {Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad},\n author = {Luong, Thang and Lockhart, Edward},\n year = {2025},\n url = {https://deepmind.google/discover/blog/advanced-version-of-gemini-with-deep-think-officially-achieves-gold-medal-standard-at-the-international-mathematical-olympiad/},\n note = {DeepMind Blog, July 21, 2025},\n}\n\n@misc{deepseekai2025r1,\n title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, \n author={DeepSeek-AI},\n year={2025},\n eprint={2501.12948},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2501.12948}, \n}\n\n@misc{cursor2025tab,\n title = {A New Tab Model},\n author = {Cursor},\n year = {2025},\n url = {https://cursor.com/blog/tab-update},\n note = {Accessed: 2025-08-05},\n}\n\n@inproceedings{bruce2024genie,\n title={Genie: Generative Interactive Environments},\n author={Jake Bruce and Michael D Dennis and Ashley Edwards and Jack Parker-Holder and Yuge Shi and Edward Hughes and Matthew Lai and Aditi Mavalankar and Richie Steigerwald and Chris Apps and Yusuf Aytar and Sarah Maria Elisabeth Bechtle and Feryal Behbahani and Stephanie C.Y. Chan and Nicolas Heess and Lucy Gonzalez and Simon Osindero and Sherjil Ozair and Scott Reed and Jingwei Zhang and Konrad Zolna and Jeff Clune and Nando de Freitas and Satinder Singh and Tim Rockt{\""a}schel},\n booktitle={Forty-first International Conference on Machine Learning},\n year={2024},\n url={https://openreview.net/forum?id=bJbSbJskOS}\n}\n\n@article{parkerholder2024genie2,\n title = {Genie 2: A Large-Scale Foundation World Model},\n author = {Jack Parker-Holder and Philip Ball and Jake Bruce and Vibhavari Dasagi and Kristian Holsheimer and Christos Kaplanis and Alexandre Moufarek and Guy Scully and Jeremy Shar and Jimmy Shi and Stephen Spencer and Jessica Yung and Michael Dennis and Sultan Kenjeyev and Shangbang Long and Vlad Mnih and Harris Chan and Maxime Gazeau and Bonnie Li and Fabio Pardo and Luyu Wang and Lei Zhang and Frederic Besse and Tim Harley and Anna Mitenkova and Jane Wang and Jeff Clune and Demis Hassabis and Raia Hadsell and Adrian Bolton and Satinder Singh and Tim Rockt{\""a}schel},\n year = {2024},\n url = {https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/}\n}\n\n@article{deepmind2025genie3,\n title = {Genie 3: A New Frontier for World Models},\n author = {Philip J. Ball and Jakob Bauer and Frank Belletti and Bethanie Brownfield and Ariel Ephrat and Shlomi Fruchter and Agrim Gupta and Kristian Holsheimer and Aleksander Holynski and Jiri Hron and Christos Kaplanis and Marjorie Limont and Matt McGill and Yanko Oliveira and Jack Parker-Holder and Frank Perbet and Guy Scully and Jeremy Shar and Stephen Spencer and Omer Tov and Ruben Villegas and Emma Wang and Jessica Yung and Cip Baetu and Jordi Berbel and David Bridson and Jake Bruce and Gavin Buttimore and Sarah Chakera and Bilva Chandra and Paul Collins and Alex Cullum and Bogdan Damoc and Vibha Dasagi and Maxime Gazeau and Charles Gbadamosi and Woohyun Han and Ed Hirst and Ashyana Kachra and Lucie Kerley and Kristian Kjems and Eva Knoepfel and Vika Koriakin and Jessica Lo and Cong Lu and Zeb Mehring and Alex Moufarek and Henna Nandwani and Valeria Oliveira and Fabio Pardo and Jane Park and Andrew Pierson and Ben Poole and Helen Ran and Tim Salimans and Manuel Sanchez and Igor Saprykin and Amy Shen and Sailesh Sidhwani and Duncan Smith and Joe Stanton and Hamish Tomlinson and Dimple Vijaykumar and Luyu Wang and Piers Wingfield and Nat Wong and Keyang Xu and Christopher Yew and Nick Young and Vadim Zubov and Douglas Eck and Dumitru Erhan and Koray Kavukcuoglu and Demis Hassabis and Zoubin Gharamani and Raia Hadsell and A{\""a}ron van den Oord and Inbar Mosseri and Adrian Bolton and Satinder Singh and Tim Rockt{\""a}schel},\n year = {2025},\n url = {}\n}\n\n@InProceedings{parkerholder2022evolving,\n title = \t {Evolving Curricula with Regret-Based Environment Design},\n author = {Parker-Holder, Jack and Jiang, Minqi and Dennis, Michael and Samvelyan, Mikayel and Foerster, Jakob and Grefenstette, Edward and Rockt{\""a}schel, Tim},\n booktitle = \t {Proceedings of the 39th International Conference on Machine Learning},\n pages = \t {17473--17498},\n year = \t {2022},\n editor = \t {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},\n volume = \t {162},\n series = \t {Proceedings of Machine Learning Research},\n month = \t {17--23 Jul},\n publisher = {PMLR},\n pdf = \t {https://proceedings.mlr.press/v162/parker-holder22a/parker-holder22a.pdf},\n url = \t {https://proceedings.mlr.press/v162/parker-holder22a.html},\n abstract = \t {Training generally-capable agents with reinforcement learning (RL) remains a significant challenge. A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agentโ€™s capabilities. These methods benefit from theoretical robustness guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces in practice. By contrast, evolutionary approaches incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. This work proposes harnessing the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agentโ€™s capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing significant empirical gains in a diverse set of environments. An interactive version of this paper is available at https://accelagent.github.io.}\n}\n\n@article{agarwal2025cosmos,\n title = {Cosmos World Foundation Model Platform for Physical AI},\n author = {Agarwal, Niket and others},\n journal = {arXiv preprint arXiv:2501.03575},\n year = {2025}\n}\n\n@article{bellemare2013arcade,\n title = {The arcade learning environment: An evaluation platform for general agents},\n author = {Bellemare, Marc G and others},\n journal = {Journal of artificial intelligence research},\n volume = {47},\n pages = {253--279},\n year = {2013}\n}\n\n@article{nichol2018retro,\n title={Gotta Learn Fast: A New Benchmark for Generalization in RL},\n author={Nichol, Alex and Pfau, Vicki and Hesse, Christopher and Klimov, Oleg and Schulman, John},\n journal={arXiv preprint arXiv:1804.03720},\n year={2018}\n}\n\n@inproceedings{matthews2024craftax,\n author={Michael Matthews and Michael Beukman and Benjamin Ellis and Mikayel Samvelyan and Matthew Jackson and Samuel Coward and Jakob Foerster},\n title = {Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning},\n booktitle = {International Conference on Machine Learning ({ICML})},\n year = {2024}\n}\n\n@inproceedings{NEURIPS2022_9c7008af,\n author = {Baker, Bowen and Akkaya, Ilge and Zhokov, Peter and Huizinga, Joost and Tang, Jie and Ecoffet, Adrien and Houghton, Brandon and Sampedro, Raul and Clune, Jeff},\n booktitle = {Advances in Neural Information Processing Systems},\n editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},\n pages = {24639--24654},\n publisher = {Curran Associates, Inc.},\n title = {Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos},\n url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/9c7008aff45b5d8f0973b23e1a22ada0-Paper-Conference.pdf},\n volume = {35},\n year = {2022}\n}\n\n@inproceedings{osband2020bsuite,\n title={Behaviour Suite for Reinforcement Learning},\n author={Osband, Ian and\n Doron, Yotam and\n Hessel, Matteo and\n Aslanides, John and\n Sezener, Eren and\n Saraiva, Andre and\n McKinney, Katrina and\n Lattimore, Tor and\n {Sz}epesv{\'a}ri, Csaba and\n Singh, Satinder and\n Van Roy, Benjamin and\n Sutton, Richard and\n Silver, David and\n van Hasselt, Hado},\n booktitle={International Conference on Learning Representations},\n year={2020},\n url={https://openreview.net/forum?id=rygf-kSYwH}\n}\n\n@article{nguyen2025crowd-sourcing,\n author = {Nguyen, Alfred and Mahajan, Mihir and Srambical, Franz},\n title = {Crowd-Sourcing A Dataset To Make Agents Code Like Humans},\n journal = {p(doom) blog},\n year = {2025},\n note = {https://pdoom.org/blog.html}\n}",bibtex,tab +3440,10826367,"examples/bibliography.bib",990,0,"",bibtex,selection_mouse +3441,10827314,"examples/bibliography.bib",27113,0,"",bibtex,selection_command +3442,10861963,"examples/bibliography.bib",27114,0,"\n",bibtex,content +3443,10862180,"examples/bibliography.bib",27115,0,"\n",bibtex,content +3444,10862477,"examples/bibliography.bib",27116,0,"@misc{LuongLockhart2025GeminiIMO,\n author = {Thang Luong and Edward Lockhart},\n title = {Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad},\n url = {https://deepmind.google/discover/blog/advanced-version-of-gemini-with-deep-think-officially-achieves-gold-medal-standard-at-the-international-mathematical-olympiad/},\n year = {2025},\n}\n\n@misc{LinCheng2025GeminiICPC,\n author = {Hanzhao (Maggie) Lin and Heng-Tze Cheng},\n title = 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\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5082,12541339,"examples/agi_cast.html",1781,1254,"
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5083,12541373,"examples/agi_cast.html",1767,1268," \n
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5084,12541408,"examples/agi_cast.html",1743,1292," \n \n
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5085,12541441,"examples/agi_cast.html",1730,1305," \n \n \n
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5086,12541476,"examples/agi_cast.html",1721,1314,"

\n \n \n \n
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5087,12541595,"examples/agi_cast.html",1597,1438," We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of AGI research.\n

\n \n \n \n
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5088,12541744,"examples/agi_cast.html",1589,1446,"

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of AGI research.\n

\n \n \n \n
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5089,12541876,"examples/agi_cast.html",1577,1458," \n

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of AGI research.\n

\n \n \n \n
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5090,12542384,"examples/agi_cast.html",1589,1446,"

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of AGI research.\n

\n \n \n \n
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

",html,selection_command +5091,12542570,"examples/agi_cast.html",1577,1458," \n

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of AGI research.\n

\n \n \n \n
\n
Figure 1: AGI-CAST in action.
\n 1\n

Behaviour-Cloning AGI Research

\n

\n Internet-scale pre-training, preference modeling, and reinforcement learning using verification signals offer a compelling pathway for language models\n to attain human-level performance\n , yet data is increasingly bottlenecking progress from\n spiky towards general intelligence. A natural way to extend the current paradigm to automation of arbitrary knowledge work is to behaviour-clone\n from screen recordings of human practitioners. This\n requires moving from predominantly text-based towards video-based behaviour-cloning, a nascent field.

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Circular dependencies\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/array.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-interpolate/src/value.js -> node_modules/d3-interpolate/src/object.js -> node_modules/d3-interpolate/src/value.js\r\nnode_modules/d3-selection/src/selection/index.js -> node_modules/d3-selection/src/selection/select.js -> node_modules/d3-selection/src/selection/index.js\r\n...and 7 more\r\ncreated dist/template.v2.js in 392ms\r\nbundles src/transforms.js โ†’ dist/transforms.v2.js...\r\n",,terminal_output +5173,12977764,"TERMINAL",0,0,"created dist/transforms.v2.js in 665ms\r\n\r\n[2025-11-02 16:35:46] waiting for changes...\r\n",,terminal_output +5174,12985811,"examples/agi_cast.html",2977,0,"",html,selection_command +5175,12986056,"examples/agi_cast.html",2976,0,"",html,selection_command +5176,12986088,"examples/agi_cast.html",2975,0,"",html,selection_command +5177,12986118,"examples/agi_cast.html",2971,0,"",html,selection_command +5178,12986151,"examples/agi_cast.html",2958,0,"",html,selection_command +5179,12986184,"examples/agi_cast.html",2956,0,"",html,selection_command +5180,12986218,"examples/agi_cast.html",2953,0,"",html,selection_command +5181,12986251,"examples/agi_cast.html",2948,0,"",html,selection_command +5182,12986284,"examples/agi_cast.html",2947,0,"",html,selection_command +5183,12986319,"examples/agi_cast.html",2946,0,"",html,selection_command +5184,12986351,"examples/agi_cast.html",2945,0,"",html,selection_command +5185,12986726,"examples/agi_cast.html",2944,0,"",html,selection_command +5186,12986915,"examples/agi_cast.html",2944,1,"",html,content +5187,12993517,"TERMINAL",0,0,"^Cโ ™% \r \r]633;D;0]633;P;Cwd=/Users/franzsrambical/Documents/pdoom/pdoom.org\r]633;Afranzsrambical@MBF6N9WFVKFV pdoom.org % ]633;B[?2004h",,terminal_output +5188,12993799,"TERMINAL",0,0,"npm run dev",,terminal_output +5189,12993856,"TERMINAL",0,0,"cp examples/* dist",,terminal_output +5190,12994112,"TERMINAL",0,0,"cp examples/* dist",,terminal_command +5191,12994112,"TERMINAL",0,0,"[?2004l\r\r\n]633;E;cp examples/* dist;280736e1-4344-414b-b289-7b3558692a37",,terminal_output +5192,12994141,"TERMINAL",0,0,"]633;C% \r \r",,terminal_output +5193,12994727,"TERMINAL",0,0,"npm run dev",,terminal_command +5194,12994779,"TERMINAL",0,0,"]633;C",,terminal_output +5195,12994911,"TERMINAL",0,0,"\r\n> distill-template@2.8.0 dev\r\n> rollup -c rollup.config.dev.js -w\r\n\r\n",,terminal_output +5196,12995074,"TERMINAL",0,0,"crollup v2.7.3\r\nbundles src/components.js โ†’ dist/template.v2.js...\r\n",,terminal_output +5197,12995495,"TERMINAL",0,0,"http://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/dist\r\nhttp://localhost:8088 -> /Users/franzsrambical/Documents/pdoom/pdoom.org/examples\r\n(!) 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",html,selection_command +9671,15479637,"examples/agi_cast.html",1589,216,"

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of long-horizon AGI research.",html,selection_command +9672,15479890,"examples/agi_cast.html",1589,225,"

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of long-horizon AGI research.\n

",html,selection_command +9673,15479923,"examples/agi_cast.html",1589,238,"

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of long-horizon AGI research.\n

\n ",html,selection_command +9674,15479955,"examples/agi_cast.html",1589,262,"

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of long-horizon AGI research.\n

\n \n ",html,selection_command +9675,15479988,"examples/agi_cast.html",1589,276,"

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of long-horizon AGI research.\n

\n \n \n ",html,selection_command +9676,15480023,"examples/agi_cast.html",1589,396,"

\n We introduce AGI-CAST, a continually growing dataset of unlabeled screen recordings of long-horizon AGI research.\n

\n \n \n \n