Running tests...
Capturing agent diff from: /logs/artifacts/testbed
rm 'datasets/atomic/dummy/atomic/1.1.0/dummy_data.zip'
Removed binary: datasets/atomic/dummy/atomic/1.1.0/dummy_data.zip
rm 'datasets/conceptnet5/dummy/conceptnet5/5.7.0/dummy_data.zip'
Removed binary: datasets/conceptnet5/dummy/conceptnet5/5.7.0/dummy_data.zip
rm 'datasets/conceptnet5/dummy/omcs_sentences_free/5.7.0/dummy_data.zip'
Removed binary: datasets/conceptnet5/dummy/omcs_sentences_free/5.7.0/dummy_data.zip
rm 'datasets/conceptnet5/dummy/omcs_sentences_more/5.7.0/dummy_data.zip'
Removed binary: datasets/conceptnet5/dummy/omcs_sentences_more/5.7.0/dummy_data.zip
rm 'datasets/lama/dummy/conceptnet/1.1.0/dummy_data.zip'
Removed binary: datasets/lama/dummy/conceptnet/1.1.0/dummy_data.zip
rm 'datasets/lama/dummy/google_re/1.1.0/dummy_data.zip'
Removed binary: datasets/lama/dummy/google_re/1.1.0/dummy_data.zip
rm 'datasets/lama/dummy/squad/1.1.0/dummy_data.zip'
Removed binary: datasets/lama/dummy/squad/1.1.0/dummy_data.zip
rm 'datasets/nell/dummy/nell_belief/1115.0.0/dummy_data.zip'
Removed binary: datasets/nell/dummy/nell_belief/1115.0.0/dummy_data.zip
rm 'datasets/nell/dummy/nell_belief_sentences/1115.0.0/dummy_data.zip'
Removed binary: datasets/nell/dummy/nell_belief_sentences/1115.0.0/dummy_data.zip
rm 'datasets/nell/dummy/nell_candidate/1110.0.0/dummy_data.zip'
Removed binary: datasets/nell/dummy/nell_candidate/1110.0.0/dummy_data.zip
rm 'datasets/nell/dummy/nell_candidate_sentences/1110.0.0/dummy_data.zip'
Removed binary: datasets/nell/dummy/nell_candidate_sentences/1110.0.0/dummy_data.zip
rm 'datasets/ollie/dummy/ollie_lemmagrep/1.1.0/dummy_data.zip'
Removed binary: datasets/ollie/dummy/ollie_lemmagrep/1.1.0/dummy_data.zip
rm 'datasets/ollie/dummy/ollie_patterned/1.1.0/dummy_data.zip'
Removed binary: datasets/ollie/dummy/ollie_patterned/1.1.0/dummy_data.zip
Patch size: 8929 bytes
HEAD is now at 99c5ad9 base
Baked origin refs missing; fetching from origin at verify time (network_mode=public)...
From https://github.com/huggingface/datasets
 * [new branch]          1.18.X                 -> origin/1.18.X
 * [new branch]          2.10                   -> origin/2.10
 * [new branch]          2.13                   -> origin/2.13
 * [new branch]          2.14                   -> origin/2.14
 * [new branch]          2.19                   -> origin/2.19
 * [new branch]          2.21                   -> origin/2.21
 * [new branch]          2.21-bis               -> origin/2.21-bis
 * [new branch]          2.6                    -> origin/2.6
 * [new branch]          2.7                    -> origin/2.7
 * [new branch]          PathLike-save_to_disk  -> origin/PathLike-save_to_disk
 * [new branch]          add-allow-empty-for-parquet-conversion -> origin/add-allow-empty-for-parquet-conversion
 * [new branch]          add-error_bad_chunk-to-json-loader -> origin/add-error_bad_chunk-to-json-loader
 * [new branch]          add-iterable-dataset-decode -> origin/add-iterable-dataset-decode
 * [new branch]          add-pq-extension       -> origin/add-pq-extension
 * [new branch]          add-repo_id-info       -> origin/add-repo_id-info
 * [new branch]          align-remove_columns-in-formatted-case -> origin/align-remove_columns-in-formatted-case
 * [new branch]          cfahlgren1/add-jsonl-sample-by-document -> origin/cfahlgren1/add-jsonl-sample-by-document
 * [new branch]          cfahlgren1/agent-traces-prompt-sent-at -> origin/cfahlgren1/agent-traces-prompt-sent-at
 * [new branch]          chore/add-dependabot-github-actions -> origin/chore/add-dependabot-github-actions
 * [new branch]          ci-csv-path            -> origin/ci-csv-path
 * [new branch]          ci-fix-test_load_dataset_distributed_with_script -> origin/ci-fix-test_load_dataset_distributed_with_script
 * [new branch]          ci-test-2.21           -> origin/ci-test-2.21
 * [new branch]          ci-test-20240523       -> origin/ci-test-20240523
 * [new branch]          ci-test-20240723       -> origin/ci-test-20240723
 * [new branch]          ci-test-array-cast     -> origin/ci-test-array-cast
 * [new branch]          ci-test-cast           -> origin/ci-test-cast
 * [new branch]          ci-test-ci             -> origin/ci-test-ci
 * [new branch]          ci-test-errors         -> origin/ci-test-errors
 * [new branch]          ci-test-fsspec         -> origin/ci-test-fsspec
 * [new branch]          ci-test-fsspec-2023.10.0 -> origin/ci-test-fsspec-2023.10.0
 * [new branch]          ci-test-fsspec-2023.12.1 -> origin/ci-test-fsspec-2023.12.1
 * [new branch]          ci-test-hf-folder-auth-token -> origin/ci-test-hf-folder-auth-token
 * [new branch]          ci-test-hub-20240708   -> origin/ci-test-hub-20240708
 * [new branch]          ci-test-huggingface-hub-0.29.0.rc6 -> origin/ci-test-huggingface-hub-0.29.0.rc6
 * [new branch]          ci-test-huggingface-hub-0.30.0.rc1 -> origin/ci-test-huggingface-hub-0.30.0.rc1
 * [new branch]          ci-test-huggingface-hub-1.10.0.rc0-release -> origin/ci-test-huggingface-hub-1.10.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.10.0.rc1-release -> origin/ci-test-huggingface-hub-1.10.0.rc1-release
 * [new branch]          ci-test-huggingface-hub-1.11.0.rc0-release -> origin/ci-test-huggingface-hub-1.11.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.12.0.rc0-release -> origin/ci-test-huggingface-hub-1.12.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.12.0.rc1-release -> origin/ci-test-huggingface-hub-1.12.0.rc1-release
 * [new branch]          ci-test-huggingface-hub-1.13.0.rc0-release -> origin/ci-test-huggingface-hub-1.13.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.14.0.rc0-release -> origin/ci-test-huggingface-hub-1.14.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.15.0.rc0-release -> origin/ci-test-huggingface-hub-1.15.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.16.0.rc0-release -> origin/ci-test-huggingface-hub-1.16.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.17.0.rc0-release -> origin/ci-test-huggingface-hub-1.17.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.18.0.rc0-release -> origin/ci-test-huggingface-hub-1.18.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.19.0.rc1-release -> origin/ci-test-huggingface-hub-1.19.0.rc1-release
 * [new branch]          ci-test-huggingface-hub-1.20.0.rc0-release -> origin/ci-test-huggingface-hub-1.20.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.21.0.rc0-release -> origin/ci-test-huggingface-hub-1.21.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.7.0.rc0-release -> origin/ci-test-huggingface-hub-1.7.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.7.0.rc1-release -> origin/ci-test-huggingface-hub-1.7.0.rc1-release
 * [new branch]          ci-test-huggingface-hub-1.8.0.rc0-release -> origin/ci-test-huggingface-hub-1.8.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-1.9.0.rc0-release -> origin/ci-test-huggingface-hub-1.9.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v0.16.0.rc0 -> origin/ci-test-huggingface-hub-v0.16.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.17.0.rc0 -> origin/ci-test-huggingface-hub-v0.17.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.18.0.rc0 -> origin/ci-test-huggingface-hub-v0.18.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.19.0.rc0 -> origin/ci-test-huggingface-hub-v0.19.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.20.0.rc1 -> origin/ci-test-huggingface-hub-v0.20.0.rc1
 * [new branch]          ci-test-huggingface-hub-v0.21.0.rc0 -> origin/ci-test-huggingface-hub-v0.21.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.22.0.rc0 -> origin/ci-test-huggingface-hub-v0.22.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.22.0.rc1 -> origin/ci-test-huggingface-hub-v0.22.0.rc1
 * [new branch]          ci-test-huggingface-hub-v0.23.0.rc0 -> origin/ci-test-huggingface-hub-v0.23.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.23.0.rc1 -> origin/ci-test-huggingface-hub-v0.23.0.rc1
 * [new branch]          ci-test-huggingface-hub-v0.24.0.rc0 -> origin/ci-test-huggingface-hub-v0.24.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.25.0.rc0 -> origin/ci-test-huggingface-hub-v0.25.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.25.0.rc1 -> origin/ci-test-huggingface-hub-v0.25.0.rc1
 * [new branch]          ci-test-huggingface-hub-v0.26.0.rc0 -> origin/ci-test-huggingface-hub-v0.26.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.27.0.rc0 -> origin/ci-test-huggingface-hub-v0.27.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.28.0.rc0 -> origin/ci-test-huggingface-hub-v0.28.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.29.0.rc0 -> origin/ci-test-huggingface-hub-v0.29.0.rc0
 * [new branch]          ci-test-huggingface-hub-v0.29.0.rc1 -> origin/ci-test-huggingface-hub-v0.29.0.rc1
 * [new branch]          ci-test-huggingface-hub-v0.29.0.rc2 -> origin/ci-test-huggingface-hub-v0.29.0.rc2
 * [new branch]          ci-test-huggingface-hub-v0.29.0.rc5 -> origin/ci-test-huggingface-hub-v0.29.0.rc5
 * [new branch]          ci-test-huggingface-hub-v0.29.0.rc7 -> origin/ci-test-huggingface-hub-v0.29.0.rc7
 * [new branch]          ci-test-huggingface-hub-v0.29.3.rc0 -> origin/ci-test-huggingface-hub-v0.29.3.rc0
 * [new branch]          ci-test-huggingface-hub-v0.30.0.rc3-release -> origin/ci-test-huggingface-hub-v0.30.0.rc3-release
 * [new branch]          ci-test-huggingface-hub-v0.31.0.rc0-release -> origin/ci-test-huggingface-hub-v0.31.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v0.32.0.rc0-release -> origin/ci-test-huggingface-hub-v0.32.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v0.32.0.rc1-release -> origin/ci-test-huggingface-hub-v0.32.0.rc1-release
 * [new branch]          ci-test-huggingface-hub-v0.33.0.rc0-release -> origin/ci-test-huggingface-hub-v0.33.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v0.34.0.rc0-release -> origin/ci-test-huggingface-hub-v0.34.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v0.35.0.rc0-release -> origin/ci-test-huggingface-hub-v0.35.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v0.35.0.rc1-release -> origin/ci-test-huggingface-hub-v0.35.0.rc1-release
 * [new branch]          ci-test-huggingface-hub-v0.36.0.rc0-release -> origin/ci-test-huggingface-hub-v0.36.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v1.0.0.rc0-release -> origin/ci-test-huggingface-hub-v1.0.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v1.0.0.rc1-release -> origin/ci-test-huggingface-hub-v1.0.0.rc1-release
 * [new branch]          ci-test-huggingface-hub-v1.0.0.rc2-release -> origin/ci-test-huggingface-hub-v1.0.0.rc2-release
 * [new branch]          ci-test-huggingface-hub-v1.0.0.rc3-release -> origin/ci-test-huggingface-hub-v1.0.0.rc3-release
 * [new branch]          ci-test-huggingface-hub-v1.0.0.rc4-release -> origin/ci-test-huggingface-hub-v1.0.0.rc4-release
 * [new branch]          ci-test-huggingface-hub-v1.0.0.rc5-release -> origin/ci-test-huggingface-hub-v1.0.0.rc5-release
 * [new branch]          ci-test-huggingface-hub-v1.0.0.rc6-release -> origin/ci-test-huggingface-hub-v1.0.0.rc6-release
 * [new branch]          ci-test-huggingface-hub-v1.0.0.rc7-release -> origin/ci-test-huggingface-hub-v1.0.0.rc7-release
 * [new branch]          ci-test-huggingface-hub-v1.1.0.rc0-release -> origin/ci-test-huggingface-hub-v1.1.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v1.1.0.rc1-release -> origin/ci-test-huggingface-hub-v1.1.0.rc1-release
 * [new branch]          ci-test-huggingface-hub-v1.1.0.rc2-release -> origin/ci-test-huggingface-hub-v1.1.0.rc2-release
 * [new branch]          ci-test-huggingface-hub-v1.2.0.rc0-release -> origin/ci-test-huggingface-hub-v1.2.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v1.3.0.rc0-release -> origin/ci-test-huggingface-hub-v1.3.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v1.4.0.rc0-release -> origin/ci-test-huggingface-hub-v1.4.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v1.5.0.rc0-release -> origin/ci-test-huggingface-hub-v1.5.0.rc0-release
 * [new branch]          ci-test-huggingface-hub-v1.6.0.rc0-release -> origin/ci-test-huggingface-hub-v1.6.0.rc0-release
 * [new branch]          ci-test-node-down      -> origin/ci-test-node-down
 * [new branch]          ci-test-np-2           -> origin/ci-test-np-2
 * [new branch]          ci-test-num-examples   -> origin/ci-test-num-examples
 * [new branch]          ci-test-numpy-2.0.0    -> origin/ci-test-numpy-2.0.0
 * [new branch]          ci-test-pyarrow-vuln   -> origin/ci-test-pyarrow-vuln
 * [new branch]          ci-test-sequence-dtype -> origin/ci-test-sequence-dtype
 * [new branch]          ci-test-tuple-protocol -> origin/ci-test-tuple-protocol
 * [new branch]          conda-4.6.0            -> origin/conda-4.6.0
 * [new branch]          conda-release-3.1.0    -> origin/conda-release-3.1.0
 * [new branch]          data-page-v2           -> origin/data-page-v2
 * [new branch]          datasets-2.19.1-hotfix -> origin/datasets-2.19.1-hotfix
 * [new branch]          demo-lazy-decoding     -> origin/demo-lazy-decoding
 * [new branch]          deprecate-search       -> origin/deprecate-search
 * [new branch]          dev-3.0                -> origin/dev-3.0
 * [new branch]          download-partial-dataset -> origin/download-partial-dataset
 * [new branch]          download_and_prepare-if-missing-splits -> origin/download_and_prepare-if-missing-splits
 * [new branch]          drop-data_files_duplicates -> origin/drop-data_files_duplicates
 * [new branch]          dummy                  -> origin/dummy
 * [new branch]          eliebak-patch-1        -> origin/eliebak-patch-1
 * [new branch]          fix-4796               -> origin/fix-4796
 * [new branch]          fix-6147               -> origin/fix-6147
 * [new branch]          fix-6880               -> origin/fix-6880
 * [new branch]          fix-7037               -> origin/fix-7037
 * [new branch]          fix-ci-numpy-numba     -> origin/fix-ci-numpy-numba
 * [new branch]          fix-expected-splits-when-passing-data_files-or-dir -> origin/fix-expected-splits-when-passing-data_files-or-dir
 * [new branch]          fix-set_default        -> origin/fix-set_default
 * [new branch]          fix-spawning-on-macos  -> origin/fix-spawning-on-macos
 * [new branch]          fix-zero-proba-interleave_datasets -> origin/fix-zero-proba-interleave_datasets
 * [new branch]          fix-zstd               -> origin/fix-zstd
 * [new branch]          fsspec-fixes           -> origin/fsspec-fixes
 * [new branch]          image-type-inference   -> origin/image-type-inference
 * [new branch]          large_convert_to_parquet -> origin/large_convert_to_parquet
 * [new branch]          lazy-data_files_resolution -> origin/lazy-data_files_resolution
 * [new branch]          main                   -> origin/main
 * [new branch]          map-decode             -> origin/map-decode
 * [new branch]          move-exceptions-to-utils -> origin/move-exceptions-to-utils
 * [new branch]          overload-load_dataset  -> origin/overload-load_dataset
 * [new branch]          release-2.14.5         -> origin/release-2.14.5
 * [new branch]          release-conda          -> origin/release-conda
 * [new branch]          release-conda-4.2.0    -> origin/release-conda-4.2.0
 * [new branch]          release-conda-5.0.0    -> origin/release-conda-5.0.0
 * [new branch]          resolve-data_files-by-split-name -> origin/resolve-data_files-by-split-name
 * [new branch]          retrieve-cached-no-script-datasets -> origin/retrieve-cached-no-script-datasets
 * [new branch]          retry-streaming-hub-error -> origin/retry-streaming-hub-error
 * [new branch]          security-fix/-github-workflows-release-conda-yml-1780939856 -> origin/security-fix/-github-workflows-release-conda-yml-1780939856
 * [new branch]          speedup_file_downloads -> origin/speedup_file_downloads
 * [new branch]          split_dataset_by_node-same-number-of-examples -> origin/split_dataset_by_node-same-number-of-examples
 * [new branch]          stas00-patch-1         -> origin/stas00-patch-1
 * [new branch]          stevhliu-patch-1       -> origin/stevhliu-patch-1
 * [new branch]          test-convert-parquet   -> origin/test-convert-parquet
 * [new branch]          test-disable-transformer-containers-in-docs-ci -> origin/test-disable-transformer-containers-in-docs-ci
 * [new branch]          v2.13-release          -> origin/v2.13-release
 * [new branch]          v2.14-release          -> origin/v2.14-release
 * [new branch]          v2.14.6-patch          -> origin/v2.14.6-patch
 * [new branch]          v2.14.7-patch          -> origin/v2.14.7-patch
 * [new branch]          v2.21.0-release        -> origin/v2.21.0-release
 * [new tag]             0.0.2                  -> 0.0.2
 * [new tag]             0.0.3                  -> 0.0.3
 * [new tag]             0.1.0                  -> 0.1.0
 * [new tag]             0.2.0                  -> 0.2.0
 * [new tag]             0.2.1                  -> 0.2.1
 * [new tag]             0.3.0                  -> 0.3.0
 * [new tag]             0.4.0                  -> 0.4.0
 * [new tag]             1.0.0                  -> 1.0.0
 * [new tag]             1.0.1                  -> 1.0.1
 * [new tag]             1.0.2                  -> 1.0.2
 * [new tag]             1.1.0                  -> 1.1.0
 * [new tag]             1.1.1                  -> 1.1.1
 * [new tag]             1.1.2                  -> 1.1.2
 * [new tag]             1.1.3                  -> 1.1.3
 * [new tag]             1.10.0                 -> 1.10.0
 * [new tag]             1.10.1                 -> 1.10.1
 * [new tag]             1.10.2                 -> 1.10.2
 * [new tag]             1.11.0                 -> 1.11.0
 * [new tag]             1.12.0                 -> 1.12.0
 * [new tag]             1.12.1                 -> 1.12.1
 * [new tag]             1.13.0                 -> 1.13.0
 * [new tag]             1.13.1                 -> 1.13.1
 * [new tag]             1.13.2                 -> 1.13.2
 * [new tag]             1.13.3                 -> 1.13.3
 * [new tag]             1.14.0                 -> 1.14.0
 * [new tag]             1.15.0                 -> 1.15.0
 * [new tag]             1.15.1                 -> 1.15.1
 * [new tag]             1.16.0                 -> 1.16.0
 * [new tag]             1.16.1                 -> 1.16.1
 * [new tag]             1.17.0                 -> 1.17.0
 * [new tag]             1.18.0                 -> 1.18.0
 * [new tag]             1.18.1                 -> 1.18.1
 * [new tag]             1.18.2                 -> 1.18.2
 * [new tag]             1.18.3                 -> 1.18.3
 * [new tag]             1.18.4                 -> 1.18.4
 * [new tag]             1.2.0                  -> 1.2.0
 * [new tag]             1.2.1                  -> 1.2.1
 * [new tag]             1.3.0                  -> 1.3.0
 * [new tag]             1.4.0                  -> 1.4.0
 * [new tag]             1.4.1                  -> 1.4.1
 * [new tag]             1.5.0                  -> 1.5.0
 * [new tag]             1.6.0                  -> 1.6.0
 * [new tag]             1.6.1                  -> 1.6.1
 * [new tag]             1.6.2                  -> 1.6.2
 * [new tag]             1.7.0                  -> 1.7.0
 * [new tag]             1.8.0                  -> 1.8.0
 * [new tag]             1.9.0                  -> 1.9.0
 * [new tag]             2.0.0                  -> 2.0.0
 * [new tag]             2.1.0                  -> 2.1.0
 * [new tag]             2.10.0                 -> 2.10.0
 * [new tag]             2.10.1                 -> 2.10.1
 * [new tag]             2.11.0                 -> 2.11.0
 * [new tag]             2.12.0                 -> 2.12.0
 * [new tag]             2.13.0                 -> 2.13.0
 * [new tag]             2.13.1                 -> 2.13.1
 * [new tag]             2.13.2                 -> 2.13.2
 * [new tag]             2.14.0                 -> 2.14.0
 * [new tag]             2.14.1                 -> 2.14.1
 * [new tag]             2.14.2                 -> 2.14.2
 * [new tag]             2.14.3                 -> 2.14.3
 * [new tag]             2.14.4                 -> 2.14.4
 * [new tag]             2.14.5                 -> 2.14.5
 * [new tag]             2.14.6                 -> 2.14.6
 * [new tag]             2.14.7                 -> 2.14.7
 * [new tag]             2.15.0                 -> 2.15.0
 * [new tag]             2.16.0                 -> 2.16.0
 * [new tag]             2.16.1                 -> 2.16.1
 * [new tag]             2.17.0                 -> 2.17.0
 * [new tag]             2.17.1                 -> 2.17.1
 * [new tag]             2.18.0                 -> 2.18.0
 * [new tag]             2.19.0                 -> 2.19.0
 * [new tag]             2.19.1                 -> 2.19.1
 * [new tag]             2.19.2                 -> 2.19.2
 * [new tag]             2.2.0                  -> 2.2.0
 * [new tag]             2.2.1                  -> 2.2.1
 * [new tag]             2.2.2                  -> 2.2.2
 * [new tag]             2.20.0                 -> 2.20.0
 * [new tag]             2.21.0                 -> 2.21.0
 * [new tag]             2.3.0                  -> 2.3.0
 * [new tag]             2.3.1                  -> 2.3.1
 * [new tag]             2.3.2                  -> 2.3.2
 * [new tag]             2.4.0                  -> 2.4.0
 * [new tag]             2.5.0                  -> 2.5.0
 * [new tag]             2.5.1                  -> 2.5.1
 * [new tag]             2.5.2                  -> 2.5.2
 * [new tag]             2.6.0                  -> 2.6.0
 * [new tag]             2.6.1                  -> 2.6.1
 * [new tag]             2.6.2                  -> 2.6.2
 * [new tag]             2.7.0                  -> 2.7.0
 * [new tag]             2.7.1                  -> 2.7.1
 * [new tag]             2.8.0                  -> 2.8.0
 * [new tag]             2.9.0                  -> 2.9.0
 * [new tag]             3.0.0                  -> 3.0.0
 * [new tag]             3.0.1                  -> 3.0.1
 * [new tag]             3.0.2                  -> 3.0.2
 * [new tag]             3.1.0                  -> 3.1.0
 * [new tag]             3.2.0                  -> 3.2.0
 * [new tag]             3.3.0                  -> 3.3.0
 * [new tag]             3.3.1                  -> 3.3.1
 * [new tag]             3.3.2                  -> 3.3.2
 * [new tag]             3.4.0                  -> 3.4.0
 * [new tag]             3.4.1                  -> 3.4.1
 * [new tag]             3.5.0                  -> 3.5.0
 * [new tag]             3.5.1                  -> 3.5.1
 * [new tag]             3.6.0                  -> 3.6.0
 * [new tag]             4.0.0                  -> 4.0.0
 * [new tag]             4.1.0                  -> 4.1.0
 * [new tag]             4.1.1                  -> 4.1.1
 * [new tag]             4.2.0                  -> 4.2.0
 * [new tag]             4.3.0                  -> 4.3.0
 * [new tag]             4.4.0                  -> 4.4.0
 * [new tag]             4.4.1                  -> 4.4.1
 * [new tag]             4.4.2                  -> 4.4.2
 * [new tag]             4.5.0                  -> 4.5.0
 * [new tag]             4.6.0                  -> 4.6.0
 * [new tag]             4.6.1                  -> 4.6.1
 * [new tag]             4.7.0                  -> 4.7.0
 * [new tag]             4.8.0                  -> 4.8.0
 * [new tag]             4.8.1                  -> 4.8.1
 * [new tag]             4.8.2                  -> 4.8.2
 * [new tag]             4.8.3                  -> 4.8.3
 * [new tag]             4.8.4                  -> 4.8.4
 * [new tag]             4.8.5                  -> 4.8.5
 * [new tag]             5.0.0                  -> 5.0.0
 * [new tag]             alpha                  -> alpha
 * [new tag]             delete                 -> delete
Removing datasets/flores/dummy/sien/1.1.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/encs/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enda/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ende/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enel/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enes/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enet/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enfi/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enfr/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enga/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enhr/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enhu/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enit/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enlt/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enlv/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enmt/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ennl/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enpl/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enpt/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enro/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ensk/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ensl/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ensv/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/aztr_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/be_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/beru_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/es_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/fr_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/gl_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/glpt_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/he_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/it_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/pt_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/ru_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/ru_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/tr_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/xtreme/dummy/XQuAD.ar/1.0.0/dummy_data-zip-extracted/
HEAD is now at b713dcdff Fix CI: commit operation equality (hfh 1.20.0) and pytest parametrize collection error (#8283)
Checking out base repo...
Note: switching to '599403601739e7a73e8ebbc8653d246e07207265^'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at e60c99fbb [Docs] How to use with PyTorch page (#4474)
Installing repo...
Using CPython 3.9.20
Creating virtual environment at: .venv
/testbed/.venv/bin/python
Python 3.9.20
Resolved 34 packages in 958ms
Prepared 34 packages in 823ms
Installed 34 packages in 96ms
 + aiohappyeyeballs==2.6.1
 + aiohttp==3.13.5
 + aiosignal==1.4.0
 + async-timeout==5.0.1
 + attrs==26.1.0
 + certifi==2026.6.17
 + charset-normalizer==3.4.7
 + datasets==2.2.3.dev0 (from file:///testbed)
 + dill==0.3.5.1
 + filelock==3.19.1
 + frozenlist==1.8.0
 + fsspec==2025.10.0
 + hf-xet==1.5.1
 + huggingface-hub==0.36.2
 + idna==3.18
 + multidict==6.7.1
 + multiprocess==0.70.13
 + numpy==2.0.2
 + packaging==26.2
 + pandas==2.3.3
 + propcache==0.4.1
 + pyarrow==21.0.0
 + python-dateutil==2.9.0.post0
 + pytz==2026.2
 + pyyaml==6.0.3
 + requests==2.32.5
 + responses==0.18.0
 + six==1.17.0
 + tqdm==4.68.3
 + typing-extensions==4.15.0
 + tzdata==2026.2
 + urllib3==2.6.3
 + xxhash==3.7.1
 + yarl==1.22.0
Resolved 1 package in 31ms
Prepared 1 package in 252ms
Uninstalled 1 package in 61ms
Installed 1 package in 41ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 154ms
Prepared 6 packages in 65ms
Installed 6 packages in 18ms
 + absl-py==2.3.1
 + decorator==5.3.1
 + greenlet==3.2.5
 + pillow==11.3.0
 + sqlalchemy==2.0.51
 + zstandard==0.25.0
Name: datasets
Version: 2.2.3.dev0
Location: /testbed/.venv/lib/python3.9/site-packages
Requires: aiohttp, dill, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, requests, responses, tqdm, xxhash
Required-by:
>>>>> Init Succeeded
Running performance test before patch...
Running test /tests/gso_test_0.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_1.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_2.py 5 times...
  Iteration 1/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7b51705ddac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x751552c9dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x71c46849dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7005bde9dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x76c76d29cac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_3.py 5 times...
  Iteration 1/5
  Iteration 2/5
  Iteration 3/5
  Iteration 4/5
  Iteration 5/5
>>>>> Tests Passed
Running test /tests/gso_test_4.py 5 times...
  Iteration 1/5
  Iteration 2/5
  Iteration 3/5
  Iteration 4/5
  Iteration 5/5
>>>>> Tests Passed
Running test /tests/gso_test_5.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_6.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7acec4643a50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x721bf2e5fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7084f47e0a50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7ed623e9fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x73c8e7a5fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 6/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x72d0a189fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 7/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x74a4ace9fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 8/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7c9f0405fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 9/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x753493b1fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 10/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x76a44e4e0a50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_7.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_8.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x743c9591da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x714354f1ca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x70dea3cdda50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x752b1c05da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x75a1e729da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 6/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7fb49e25da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 7/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x739bac05da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 8/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x753216a40a50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 9/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7e1b4691da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 10/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x75d31bc9da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_9.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_10.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7d66fae9b9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x743cfb11b9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x797f253db9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x76a07671b9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x74154c31b9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 6/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x72370b71b9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 7/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x777b74b1b9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 8/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7de0d165b9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 9/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x737b2405b9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 10/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7903034da9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_11.py 5 times...
  Iteration 1/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7d4f88a5da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7e35dd91da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7ebb1d05ca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7a4d64e9da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7fda1131da50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_12.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Applying patch...
Skipped patch 'datasets/atomic/dataset_infos.json'.
Skipped patch 'datasets/conceptnet5/dataset_infos.json'.
Skipped patch 'datasets/lama/dataset_infos.json'.
Skipped patch 'datasets/nell/dataset_infos.json'.
Skipped patch 'datasets/ollie/dataset_infos.json'.
Checking patch .circleci/deploy.sh...
Checking patch convert_dataset.sh...
Checking patch datasets/atomic/README.md...
Checking patch datasets/atomic/atomic.py...
Checking patch datasets/conceptnet5/README.md...
Checking patch datasets/conceptnet5/conceptnet5.py...
Checking patch datasets/generics_kb/generics_kb.py...
Checking patch datasets/lama/README.md...
Checking patch datasets/lama/lama.py...
Checking patch datasets/nell/README.md...
Checking patch datasets/nell/nell.py...
Checking patch datasets/ollie/README.md...
Checking patch datasets/ollie/ollie.py...
Checking patch datasets/proto_qa/proto_qa.py...
Checking patch datasets/ro_sts/ro_sts.py...
Checking patch datasets/ro_sts_parallel/README.md...
Checking patch datasets/ro_sts_parallel/ro_sts_parallel.py...
Checking patch src/datasets/arrow_dataset.py...
Checking patch src/datasets/fingerprint.py...
Checking patch src/datasets/formatting/formatting.py...
Applied patch .circleci/deploy.sh cleanly.
Applied patch convert_dataset.sh cleanly.
Applied patch datasets/atomic/README.md cleanly.
Applied patch datasets/atomic/atomic.py cleanly.
Applied patch datasets/conceptnet5/README.md cleanly.
Applied patch datasets/conceptnet5/conceptnet5.py cleanly.
Applied patch datasets/generics_kb/generics_kb.py cleanly.
Applied patch datasets/lama/README.md cleanly.
Applied patch datasets/lama/lama.py cleanly.
Applied patch datasets/nell/README.md cleanly.
Applied patch datasets/nell/nell.py cleanly.
Applied patch datasets/ollie/README.md cleanly.
Applied patch datasets/ollie/ollie.py cleanly.
Applied patch datasets/proto_qa/proto_qa.py cleanly.
Applied patch datasets/ro_sts/ro_sts.py cleanly.
Applied patch datasets/ro_sts_parallel/README.md cleanly.
Applied patch datasets/ro_sts_parallel/ro_sts_parallel.py cleanly.
Applied patch src/datasets/arrow_dataset.py cleanly.
Applied patch src/datasets/fingerprint.py cleanly.
Applied patch src/datasets/formatting/formatting.py cleanly.
Successfully applied patch using git apply --verbose
>>>>> Applied Patch
Installing repo...
Using CPython 3.9.20
Creating virtual environment at: .venv
/testbed/.venv/bin/python
Python 3.9.20
Resolved 34 packages in 969ms
Prepared 34 packages in 843ms
Installed 34 packages in 99ms
 + aiohappyeyeballs==2.6.1
 + aiohttp==3.13.5
 + aiosignal==1.4.0
 + async-timeout==5.0.1
 + attrs==26.1.0
 + certifi==2026.6.17
 + charset-normalizer==3.4.7
 + datasets==2.2.3.dev0 (from file:///testbed)
 + dill==0.3.5.1
 + filelock==3.19.1
 + frozenlist==1.8.0
 + fsspec==2025.10.0
 + hf-xet==1.5.1
 + huggingface-hub==0.36.2
 + idna==3.18
 + multidict==6.7.1
 + multiprocess==0.70.13
 + numpy==2.0.2
 + packaging==26.2
 + pandas==2.3.3
 + propcache==0.4.1
 + pyarrow==21.0.0
 + python-dateutil==2.9.0.post0
 + pytz==2026.2
 + pyyaml==6.0.3
 + requests==2.32.5
 + responses==0.18.0
 + six==1.17.0
 + tqdm==4.68.3
 + typing-extensions==4.15.0
 + tzdata==2026.2
 + urllib3==2.6.3
 + xxhash==3.7.1
 + yarl==1.22.0
Resolved 1 package in 29ms
Prepared 1 package in 188ms
Uninstalled 1 package in 58ms
Installed 1 package in 38ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 167ms
Prepared 6 packages in 58ms
Installed 6 packages in 9ms
 + absl-py==2.3.1
 + decorator==5.3.1
 + greenlet==3.2.5
 + pillow==11.3.0
 + sqlalchemy==2.0.51
 + zstandard==0.25.0
Name: datasets
Version: 2.2.3.dev0
Location: /testbed/.venv/lib/python3.9/site-packages
Requires: aiohttp, dill, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, requests, responses, tqdm, xxhash
Required-by:
>>>>> Init Succeeded
Running performance test after patch...
Running test /tests/gso_test_0.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_1.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_2.py 5 times...
  Iteration 1/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x79f8a7c9ea50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7a1541f1fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7b23a83dfa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7aa241b1fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7e4d6769fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_3.py 5 times...
  Iteration 1/5
  Iteration 2/5
  Iteration 3/5
  Iteration 4/5
  Iteration 5/5
>>>>> Tests Passed
Running test /tests/gso_test_4.py 5 times...
  Iteration 1/5
  Iteration 2/5
  Iteration 3/5
  Iteration 4/5
  Iteration 5/5
>>>>> Tests Passed
Running test /tests/gso_test_5.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_6.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x78aaf2f219e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x76579b8629e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7d84a1b229e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x71517c0a19e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x789fdc7219e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 6/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x75706e1e29e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 7/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x779002ea29e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 8/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7fdd697219e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 9/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7252ce8a29e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 10/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7fa006d229e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_7.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_8.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x76449905e9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x74b658f1e9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7495ea89f9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7170e5d1e9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7892285de9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 6/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x77add105e9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 7/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x703cbf6de9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 8/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7879d769e9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 9/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7203fe65d9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 10/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7d603085e9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_9.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_10.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x741ee209f970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x723edcdde970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x729a2e8de970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x72de2dcdf970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7f600885e970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 6/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x78d2cfe9e970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 7/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x70a5c1edf970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 8/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7917e26de970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 9/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7a5403d1e970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 10/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x77d604b1e970> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_11.py 5 times...
  Iteration 1/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7c05e4bde9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7c00654df9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7a7110bdd9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x79b1acd1e9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x75012d11f9e0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_12.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
>>>>> Start Base Output
>>>>> Test 0
Execution time: 0.215343s
Execution time: 0.213316s
Execution time: 0.223152s
Execution time: 0.221639s
Execution time: 0.215770s
Execution time: 0.214839s
Execution time: 0.213199s
Execution time: 0.211889s
Execution time: 0.212781s
Execution time: 0.214063s
>>>>> Test 1
Execution time: 0.003959s
Execution time: 0.003940s
Execution time: 0.003971s
Execution time: 0.003924s
Execution time: 0.003888s
Execution time: 0.004029s
Execution time: 0.003985s
Execution time: 0.003872s
Execution time: 0.003887s
Execution time: 0.003893s
>>>>> Test 2
Execution time: 0.782344s
Execution time: 0.790712s
Execution time: 0.800785s
Execution time: 0.770788s
Execution time: 0.796523s
>>>>> Test 3
Execution time: 0.420510s
Execution time: 0.418653s
Execution time: 0.423746s
Execution time: 0.418325s
Execution time: 0.424452s
>>>>> Test 4
Execution time: 0.049851s
Execution time: 0.049366s
Execution time: 0.050850s
Execution time: 0.049953s
Execution time: 0.049903s
>>>>> Test 5
Execution time: 0.004002s
Execution time: 0.004046s
Execution time: 0.004159s
Execution time: 0.004063s
Execution time: 0.004020s
Execution time: 0.004049s
Execution time: 0.004125s
Execution time: 0.004061s
Execution time: 0.004098s
Execution time: 0.004078s
>>>>> Test 6
Execution time: 0.004399s
Execution time: 0.004490s
Execution time: 0.004516s
Execution time: 0.004463s
Execution time: 0.004541s
Execution time: 0.004818s
Execution time: 0.004630s
Execution time: 0.004482s
Execution time: 0.004418s
Execution time: 0.004428s
>>>>> Test 7
Execution time: 0.005699s
Execution time: 0.006026s
Execution time: 0.006097s
Execution time: 0.005708s
Execution time: 0.005657s
Execution time: 0.005684s
Execution time: 0.005609s
Execution time: 0.005800s
Execution time: 0.007994s
Execution time: 0.005731s
>>>>> Test 8
Execution time: 0.687364s
Execution time: 0.697882s
Execution time: 0.675389s
Execution time: 0.674902s
Execution time: 0.673456s
Execution time: 0.670703s
Execution time: 0.690545s
Execution time: 0.706373s
Execution time: 0.676313s
Execution time: 0.692943s
>>>>> Test 9
Execution time: 0.032610s
Execution time: 0.032997s
Execution time: 0.033372s
Execution time: 0.032553s
Execution time: 0.032629s
Execution time: 0.032499s
Execution time: 0.032832s
Execution time: 0.032601s
Execution time: 0.032390s
Execution time: 0.032706s
>>>>> Test 10
Execution time: 0.001996s
Execution time: 0.002018s
Execution time: 0.002013s
Execution time: 0.002011s
Execution time: 0.002078s
Execution time: 0.002055s
Execution time: 0.002013s
Execution time: 0.002007s
Execution time: 0.002001s
Execution time: 0.001999s
>>>>> Test 11
Execution time: 0.068333s
Execution time: 0.068792s
Execution time: 0.068124s
Execution time: 0.068566s
Execution time: 0.069006s
>>>>> Test 12
Execution time: 0.261546s
Execution time: 0.268488s
Execution time: 0.262938s
Execution time: 0.267932s
Execution time: 0.270315s
Execution time: 0.270368s
Execution time: 0.272059s
Execution time: 0.279740s
Execution time: 0.264430s
Execution time: 0.264407s
>>>>> End Base Output
>>>>> Start Patch Output
>>>>> Test 0
Execution time: 0.004542s
Execution time: 0.004403s
Execution time: 0.004452s
Execution time: 0.004435s
Execution time: 0.004639s
Execution time: 0.004140s
Execution time: 0.004129s
Execution time: 0.004125s
Execution time: 0.004408s
Execution time: 0.004089s
>>>>> Test 1
Execution time: 0.000602s
Execution time: 0.000561s
Execution time: 0.000553s
Execution time: 0.000794s
Execution time: 0.000625s
Execution time: 0.000578s
Execution time: 0.000565s
Execution time: 0.000567s
Execution time: 0.000578s
Execution time: 0.000751s
>>>>> Test 2
Execution time: 0.329573s
Execution time: 0.324949s
Execution time: 0.316931s
Execution time: 0.315204s
Execution time: 0.338753s
>>>>> Test 3
Execution time: 0.139727s
Execution time: 0.137896s
Execution time: 0.139883s
Execution time: 0.130907s
Execution time: 0.144087s
>>>>> Test 4
Execution time: 0.023282s
Execution time: 0.024022s
Execution time: 0.024358s
Execution time: 0.026522s
Execution time: 0.026863s
>>>>> Test 5
Execution time: 0.002667s
Execution time: 0.002625s
Execution time: 0.002680s
Execution time: 0.002630s
Execution time: 0.002581s
Execution time: 0.002648s
Execution time: 0.002689s
Execution time: 0.002691s
Execution time: 0.002631s
Execution time: 0.002625s
>>>>> Test 6
Execution time: 0.003116s
Execution time: 0.003395s
Execution time: 0.003060s
Execution time: 0.003099s
Execution time: 0.003016s
Execution time: 0.003103s
Execution time: 0.003086s
Execution time: 0.003128s
Execution time: 0.003103s
Execution time: 0.003083s
>>>>> Test 7
Execution time: 0.004067s
Execution time: 0.004122s
Execution time: 0.004075s
Execution time: 0.004105s
Execution time: 0.004177s
Execution time: 0.004081s
Execution time: 0.004106s
Execution time: 0.005323s
Execution time: 0.004149s
Execution time: 0.004923s
>>>>> Test 8
Execution time: 0.598213s
Execution time: 0.606416s
Execution time: 0.605923s
Execution time: 0.582870s
Execution time: 0.641444s
Execution time: 0.589865s
Execution time: 0.593883s
Execution time: 0.613183s
Execution time: 0.624958s
Execution time: 0.602619s
>>>>> Test 9
Execution time: 0.028986s
Execution time: 0.028318s
Execution time: 0.028041s
Execution time: 0.028929s
Execution time: 0.028977s
Execution time: 0.028866s
Execution time: 0.027968s
Execution time: 0.030416s
Execution time: 0.028363s
Execution time: 0.030554s
>>>>> Test 10
Execution time: 0.001638s
Execution time: 0.001653s
Execution time: 0.001597s
Execution time: 0.001605s
Execution time: 0.001585s
Execution time: 0.001626s
Execution time: 0.001620s
Execution time: 0.001620s
Execution time: 0.001591s
Execution time: 0.001646s
>>>>> Test 11
Execution time: 0.053889s
Execution time: 0.052317s
Execution time: 0.053224s
Execution time: 0.053087s
Execution time: 0.052359s
>>>>> Test 12
Execution time: 0.249811s
Execution time: 0.245058s
Execution time: 0.246261s
Execution time: 0.246095s
Execution time: 0.249684s
Execution time: 0.240621s
Execution time: 0.251962s
Execution time: 0.241300s
Execution time: 0.246268s
Execution time: 0.247546s
>>>>> End Patch Output
Removing base_0.txt
Removing base_1.txt
Removing base_10.txt
Removing base_11.txt
Removing base_12.txt
Removing base_2.txt
Removing base_3.txt
Removing base_4.txt
Removing base_5.txt
Removing base_6.txt
Removing base_7.txt
Removing base_8.txt
Removing base_9.txt
Removing build/
Removing gso_0_result.json
Removing gso_10_result.json
Removing gso_11_result.json
Removing gso_12_result.json
Removing gso_1_result.json
Removing gso_2_result.json
Removing gso_3_result.json
Removing gso_4_result.json
Removing gso_5_result.json
Removing gso_6_result.json
Removing gso_7_result.json
Removing gso_8_result.json
Removing gso_9_result.json
Removing result_0.txt
Removing result_1.txt
Removing result_10.txt
Removing result_11.txt
Removing result_12.txt
Removing result_2.txt
Removing result_3.txt
Removing result_4.txt
Removing result_5.txt
Removing result_6.txt
Removing result_7.txt
Removing result_8.txt
Removing result_9.txt
Removing src/datasets.egg-info/
HEAD is now at b713dcdff Fix CI: commit operation equality (hfh 1.20.0) and pytest parametrize collection error (#8283)
Checking out commit...
Previous HEAD position was b713dcdff Fix CI: commit operation equality (hfh 1.20.0) and pytest parametrize collection error (#8283)
HEAD is now at 599403601 Optimize contiguous shard and select (#4466)
Installing repo...
Using CPython 3.9.20
Creating virtual environment at: .venv
/testbed/.venv/bin/python
Python 3.9.20
Resolved 34 packages in 952ms
Prepared 34 packages in 690ms
Installed 34 packages in 84ms
 + aiohappyeyeballs==2.6.1
 + aiohttp==3.13.5
 + aiosignal==1.4.0
 + async-timeout==5.0.1
 + attrs==26.1.0
 + certifi==2026.6.17
 + charset-normalizer==3.4.7
 + datasets==2.2.3.dev0 (from file:///testbed)
 + dill==0.3.5.1
 + filelock==3.19.1
 + frozenlist==1.8.0
 + fsspec==2025.10.0
 + hf-xet==1.5.1
 + huggingface-hub==0.36.2
 + idna==3.18
 + multidict==6.7.1
 + multiprocess==0.70.13
 + numpy==2.0.2
 + packaging==26.2
 + pandas==2.3.3
 + propcache==0.4.1
 + pyarrow==21.0.0
 + python-dateutil==2.9.0.post0
 + pytz==2026.2
 + pyyaml==6.0.3
 + requests==2.32.5
 + responses==0.18.0
 + six==1.17.0
 + tqdm==4.68.3
 + typing-extensions==4.15.0
 + tzdata==2026.2
 + urllib3==2.6.3
 + xxhash==3.7.1
 + yarl==1.22.0
Resolved 1 package in 18ms
Prepared 1 package in 221ms
Uninstalled 1 package in 56ms
Installed 1 package in 38ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 175ms
Prepared 6 packages in 74ms
Installed 6 packages in 18ms
 + absl-py==2.3.1
 + decorator==5.3.1
 + greenlet==3.2.5
 + pillow==11.3.0
 + sqlalchemy==2.0.51
 + zstandard==0.25.0
Name: datasets
Version: 2.2.3.dev0
Location: /testbed/.venv/lib/python3.9/site-packages
Requires: aiohttp, dill, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, requests, responses, tqdm, xxhash
Required-by:
>>>>> Init Succeeded
Running performance test for commit...
Running test /tests/gso_test_0.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_1.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_2.py 5 times...
  Iteration 1/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7ebc66a9dc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x712c6d9ddc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x73bd4591dc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7f1fa2e9dc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7f172889dc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_3.py 5 times...
  Iteration 1/5
  Iteration 2/5
  Iteration 3/5
  Iteration 4/5
  Iteration 5/5
>>>>> Tests Passed
Running test /tests/gso_test_4.py 5 times...
  Iteration 1/5
  Iteration 2/5
  Iteration 3/5
  Iteration 4/5
  Iteration 5/5
>>>>> Tests Passed
Running test /tests/gso_test_5.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_6.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7ad332320ba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x702df999fba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x704d844e0ba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7231966a0ba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x78b3c6860ba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 6/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7895b5ea0ba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 7/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x71c042120ba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 8/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7256ad51fba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 9/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7e5956cdeba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 10/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x720d53520ba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_7.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_8.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x708972a9eba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7d4e8b9deba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7f56e8ea0ba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x75bf9f4deba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7e370a2deba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 6/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x725b9f5dfba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 7/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x724278b1eba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 8/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x79485f31fba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 9/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7bfc818deba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 10/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7b3b11a9dba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_9.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_10.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x772ecdc9db30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x78c7c829cb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x72fdd385cb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7c731e39bb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x78d71109cb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 6/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x72a693cdcb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 7/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x78056811bb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 8/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x765ad5cdcb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 9/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x717b4539bb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 10/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7a179f89cb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_11.py 5 times...
  Iteration 1/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7aa3d31deba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 2/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x719d01b1fba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 3/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7cea8efdfba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 4/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x7affa7a9eba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
  Iteration 5/5
Parameter 'indices'=<generator object setup.<locals>.<genexpr> at 0x76c1a889eba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_12.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
>>>>> Start Commit Output
>>>>> Test 0
Execution time: 0.028764s
Execution time: 0.027172s
Execution time: 0.028131s
Execution time: 0.028952s
Execution time: 0.029729s
Execution time: 0.035452s
Execution time: 0.027542s
Execution time: 0.027809s
Execution time: 0.029420s
Execution time: 0.028986s
>>>>> Test 1
Execution time: 0.000792s
Execution time: 0.000781s
Execution time: 0.000776s
Execution time: 0.000787s
Execution time: 0.000771s
Execution time: 0.000725s
Execution time: 0.000749s
Execution time: 0.000802s
Execution time: 0.000753s
Execution time: 0.000747s
>>>>> Test 2
Execution time: 0.249958s
Execution time: 0.247607s
Execution time: 0.279549s
Execution time: 0.243135s
Execution time: 0.243869s
>>>>> Test 3
Execution time: 0.147386s
Execution time: 0.146181s
Execution time: 0.138751s
Execution time: 0.147056s
Execution time: 0.140405s
>>>>> Test 4
Execution time: 0.023858s
Execution time: 0.025700s
Execution time: 0.027162s
Execution time: 0.025170s
Execution time: 0.024323s
>>>>> Test 5
Execution time: 0.002599s
Execution time: 0.002636s
Execution time: 0.002497s
Execution time: 0.002506s
Execution time: 0.002541s
Execution time: 0.002445s
Execution time: 0.002437s
Execution time: 0.002461s
Execution time: 0.003260s
Execution time: 0.002485s
>>>>> Test 6
Execution time: 0.002937s
Execution time: 0.002905s
Execution time: 0.003123s
Execution time: 0.003015s
Execution time: 0.002987s
Execution time: 0.002972s
Execution time: 0.003022s
Execution time: 0.002976s
Execution time: 0.002865s
Execution time: 0.002912s
>>>>> Test 7
Execution time: 0.004057s
Execution time: 0.004379s
Execution time: 0.004055s
Execution time: 0.004216s
Execution time: 0.003996s
Execution time: 0.004116s
Execution time: 0.004085s
Execution time: 0.004013s
Execution time: 0.004038s
Execution time: 0.003984s
>>>>> Test 8
Execution time: 0.519014s
Execution time: 0.523146s
Execution time: 0.519625s
Execution time: 0.513714s
Execution time: 0.530486s
Execution time: 0.526861s
Execution time: 0.519055s
Execution time: 0.540614s
Execution time: 0.510920s
Execution time: 0.523456s
>>>>> Test 9
Execution time: 0.025646s
Execution time: 0.025539s
Execution time: 0.028226s
Execution time: 0.025737s
Execution time: 0.025708s
Execution time: 0.029518s
Execution time: 0.025088s
Execution time: 0.025924s
Execution time: 0.025713s
Execution time: 0.027347s
>>>>> Test 10
Execution time: 0.001545s
Execution time: 0.001507s
Execution time: 0.001534s
Execution time: 0.001539s
Execution time: 0.001527s
Execution time: 0.001473s
Execution time: 0.001523s
Execution time: 0.001594s
Execution time: 0.001557s
Execution time: 0.001559s
>>>>> Test 11
Execution time: 0.050658s
Execution time: 0.051370s
Execution time: 0.052099s
Execution time: 0.053137s
Execution time: 0.050875s
>>>>> Test 12
Execution time: 0.273711s
Execution time: 0.239941s
Execution time: 0.225463s
Execution time: 0.222087s
Execution time: 0.220082s
Execution time: 0.230854s
Execution time: 0.225805s
Execution time: 0.220128s
Execution time: 0.221276s
Execution time: 0.248901s
>>>>> End Commit Output
opt_commit: True, binary_reward: 1, reward: 1.115921388412643
