Running tests...
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Removing .pytest_cache/
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 1.02s
Prepared 34 packages in 747ms
Installed 34 packages in 83ms
 + 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 123ms
Prepared 1 package in 231ms
Uninstalled 1 package in 65ms
Installed 1 package in 43ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 187ms
Prepared 6 packages in 65ms
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 before patch...
Running test /tests/gso_test_0.py 10 times...
  Iteration 1/10
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>>>>> Tests Passed
Running test /tests/gso_test_1.py 10 times...
  Iteration 1/10
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>>>>> Tests Passed
Running test /tests/gso_test_2.py 5 times...
  Iteration 1/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x78dd8265cb30> 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 0x7e0c0ce5db30> 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 0x78abf0b1cb30> 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 0x772b2ac5db30> 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 0x7d56b09ddb30> 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
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>>>>> Tests Passed
Running test /tests/gso_test_5.py 10 times...
  Iteration 1/10
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>>>>> Tests Passed
Running test /tests/gso_test_6.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7ef8b02dfac0> 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 0x7c774e29fac0> 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 0x7a189211fac0> 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 0x71b23531fac0> 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 0x7539cb71fac0> 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 0x7b26e7f1fac0> 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 0x7bb2a425fac0> 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 0x7f42459dfac0> 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 0x755208adfac0> 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 0x701afbe5fac0> 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
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>>>>> Tests Passed
Running test /tests/gso_test_8.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7c3e5811dac0> 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 0x771af229dac0> 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 0x7d144bf1cac0> 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 0x75eace65dac0> 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 0x752c9e89dac0> 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 0x7189dae5dac0> 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 0x78d5a549dac0> 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 0x78a3074ddac0> 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 0x7e8d6ca5dac0> 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 0x75b9ae25dac0> 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...
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>>>>> Tests Passed
Running test /tests/gso_test_10.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x78f6b2c3ea50> 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 0x7b0b7789ba50> 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 0x7c3b1fa9ba50> 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 0x74a15529ba50> 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 0x78023a91aa50> 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 0x78e02909ba50> 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 0x75c36809ba50> 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 0x7aa6d511ba50> 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 0x7e7d32f1ba50> 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 0x7568cfb1ba50> 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 0x7422d1a9cac0> 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 0x75c2a26dcac0> 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 0x7d0fb571dac0> 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 0x73753709dac0> 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 0x7393d709dac0> 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...
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>>>>> 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...
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.
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 1.57s
Prepared 34 packages in 846ms
Installed 34 packages in 104ms
 + 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 129ms
Prepared 1 package in 154ms
Uninstalled 1 package in 34ms
Installed 1 package in 31ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 149ms
Prepared 6 packages in 60ms
Installed 6 packages in 7ms
 + 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...
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>>>>> Tests Passed
Running test /tests/gso_test_1.py 10 times...
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>>>>> Tests Passed
Running test /tests/gso_test_2.py 5 times...
  Iteration 1/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x7abd637ddb30> 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 0x7a375169cb30> 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 0x73a48571db30> 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 0x70801809db30> 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 0x7f8d04e9db30> 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
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>>>>> Tests Passed
Running test /tests/gso_test_4.py 5 times...
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>>>>> Tests Passed
Running test /tests/gso_test_5.py 10 times...
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>>>>> Tests Passed
Running test /tests/gso_test_6.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x77894571fac0> 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 0x777986e9fac0> 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 0x761c66cdfac0> 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 0x7871dc9dfac0> 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 0x7b45a931fac0> 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 0x778019c9fac0> 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 0x7d65bea9fac0> 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 0x74e77f9dfac0> 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 0x7dfb77241ac0> 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 0x7230f549fac0> 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...
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>>>>> Tests Passed
Running test /tests/gso_test_8.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x7a4b50c5dac0> 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 0x7c61bf5ddac0> 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 0x7c9ba371dac0> 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 0x7ed5de5ddac0> 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 0x779b9689dac0> 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 0x7b00f611dac0> 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 0x7893de71dac0> 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 0x714ef985dac0> 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 0x7428392ddac0> 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 0x7108a4b9dac0> 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...
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>>>>> Tests Passed
Running test /tests/gso_test_10.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x76865b51ba50> 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 0x7d3bf0b1ba50> 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 0x7343b3adba50> 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 0x7600b88dba50> 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 0x700672c5ba50> 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 0x701a0625ba50> 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 0x7504db29ba50> 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 0x7d009405ba50> 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 0x7b533729ba50> 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 0x7656c2f1ba50> 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 0x710a6fc5eac0> 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 0x7219e945dac0> 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 0x73b31dfdcac0> 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 0x737c16bdcac0> 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 0x7da25bd1dac0> 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: 0.222278s
Execution time: 0.215296s
Execution time: 0.223576s
Execution time: 0.218497s
Execution time: 0.219027s
Execution time: 0.212509s
Execution time: 0.223991s
Execution time: 0.212192s
Execution time: 0.213784s
Execution time: 0.217725s
>>>>> Test 1
Execution time: 0.003875s
Execution time: 0.003883s
Execution time: 0.004110s
Execution time: 0.003908s
Execution time: 0.003915s
Execution time: 0.003849s
Execution time: 0.003963s
Execution time: 0.004111s
Execution time: 0.003891s
Execution time: 0.003925s
>>>>> Test 2
Execution time: 0.769170s
Execution time: 0.769794s
Execution time: 0.773432s
Execution time: 0.814931s
Execution time: 0.768420s
>>>>> Test 3
Execution time: 0.420840s
Execution time: 0.419643s
Execution time: 0.421516s
Execution time: 0.427928s
Execution time: 0.438404s
>>>>> Test 4
Execution time: 0.050381s
Execution time: 0.052373s
Execution time: 0.050015s
Execution time: 0.050977s
Execution time: 0.051024s
>>>>> Test 5
Execution time: 0.004066s
Execution time: 0.004052s
Execution time: 0.004473s
Execution time: 0.004094s
Execution time: 0.004109s
Execution time: 0.004159s
Execution time: 0.004326s
Execution time: 0.004223s
Execution time: 0.004126s
Execution time: 0.004035s
>>>>> Test 6
Execution time: 0.004505s
Execution time: 0.004420s
Execution time: 0.004442s
Execution time: 0.004430s
Execution time: 0.004507s
Execution time: 0.004420s
Execution time: 0.004512s
Execution time: 0.004520s
Execution time: 0.004393s
Execution time: 0.004429s
>>>>> Test 7
Execution time: 0.005590s
Execution time: 0.005674s
Execution time: 0.005785s
Execution time: 0.005645s
Execution time: 0.005673s
Execution time: 0.008199s
Execution time: 0.005726s
Execution time: 0.005653s
Execution time: 0.005794s
Execution time: 0.005664s
>>>>> Test 8
Execution time: 0.676210s
Execution time: 0.682271s
Execution time: 0.706164s
Execution time: 0.706542s
Execution time: 0.691112s
Execution time: 0.680220s
Execution time: 0.698431s
Execution time: 0.693901s
Execution time: 0.707213s
Execution time: 0.687770s
>>>>> Test 9
Execution time: 0.033408s
Execution time: 0.033209s
Execution time: 0.032984s
Execution time: 0.032604s
Execution time: 0.034088s
Execution time: 0.032206s
Execution time: 0.033135s
Execution time: 0.032356s
Execution time: 0.040085s
Execution time: 0.033101s
>>>>> Test 10
Execution time: 0.002277s
Execution time: 0.001971s
Execution time: 0.002085s
Execution time: 0.001947s
Execution time: 0.002011s
Execution time: 0.002045s
Execution time: 0.002031s
Execution time: 0.002002s
Execution time: 0.002069s
Execution time: 0.001993s
>>>>> Test 11
Execution time: 0.068402s
Execution time: 0.071419s
Execution time: 0.068947s
Execution time: 0.070167s
Execution time: 0.069941s
>>>>> Test 12
Execution time: 0.269260s
Execution time: 0.265307s
Execution time: 0.266566s
Execution time: 0.264811s
Execution time: 0.291146s
Execution time: 0.260587s
Execution time: 0.265949s
Execution time: 0.264886s
Execution time: 0.260466s
Execution time: 0.270266s
>>>>> End Base Output
>>>>> Start Patch Output
>>>>> Test 0
Execution time: 0.215802s
Execution time: 0.216505s
Execution time: 0.219765s
Execution time: 0.212896s
Execution time: 0.211883s
Execution time: 0.222841s
Execution time: 0.247354s
Execution time: 0.220678s
Execution time: 0.215404s
Execution time: 0.217341s
>>>>> Test 1
Execution time: 0.003873s
Execution time: 0.004000s
Execution time: 0.004028s
Execution time: 0.004228s
Execution time: 0.004013s
Execution time: 0.004024s
Execution time: 0.004047s
Execution time: 0.003893s
Execution time: 0.003950s
Execution time: 0.003924s
>>>>> Test 2
Execution time: 0.779866s
Execution time: 0.830776s
Execution time: 0.796669s
Execution time: 0.797518s
Execution time: 0.783245s
>>>>> Test 3
Execution time: 0.420858s
Execution time: 0.427376s
Execution time: 0.424408s
Execution time: 0.432411s
Execution time: 0.416318s
>>>>> Test 4
Execution time: 0.050737s
Execution time: 0.049602s
Execution time: 0.050119s
Execution time: 0.052486s
Execution time: 0.049918s
>>>>> Test 5
Execution time: 0.004138s
Execution time: 0.004122s
Execution time: 0.004260s
Execution time: 0.004014s
Execution time: 0.004105s
Execution time: 0.004168s
Execution time: 0.004071s
Execution time: 0.004172s
Execution time: 0.004054s
Execution time: 0.004223s
>>>>> Test 6
Execution time: 0.004489s
Execution time: 0.004493s
Execution time: 0.004512s
Execution time: 0.004549s
Execution time: 0.004513s
Execution time: 0.004538s
Execution time: 0.004529s
Execution time: 0.004476s
Execution time: 0.004484s
Execution time: 0.004624s
>>>>> Test 7
Execution time: 0.006564s
Execution time: 0.006332s
Execution time: 0.005789s
Execution time: 0.005792s
Execution time: 0.005763s
Execution time: 0.005708s
Execution time: 0.008303s
Execution time: 0.005702s
Execution time: 0.005728s
Execution time: 0.005763s
>>>>> Test 8
Execution time: 0.698973s
Execution time: 0.679429s
Execution time: 0.680906s
Execution time: 0.690356s
Execution time: 0.690599s
Execution time: 0.701845s
Execution time: 0.689124s
Execution time: 0.684414s
Execution time: 0.711479s
Execution time: 0.673516s
>>>>> Test 9
Execution time: 0.032124s
Execution time: 0.033554s
Execution time: 0.036058s
Execution time: 0.034785s
Execution time: 0.032592s
Execution time: 0.034171s
Execution time: 0.033456s
Execution time: 0.033949s
Execution time: 0.033451s
Execution time: 0.036806s
>>>>> Test 10
Execution time: 0.002075s
Execution time: 0.002031s
Execution time: 0.002324s
Execution time: 0.002069s
Execution time: 0.002359s
Execution time: 0.002152s
Execution time: 0.002162s
Execution time: 0.002795s
Execution time: 0.002074s
Execution time: 0.002011s
>>>>> Test 11
Execution time: 0.077898s
Execution time: 0.071200s
Execution time: 0.074829s
Execution time: 0.071914s
Execution time: 0.098750s
>>>>> Test 12
Execution time: 0.272115s
Execution time: 0.265620s
Execution time: 0.266558s
Execution time: 0.266150s
Execution time: 0.268041s
Execution time: 0.274306s
Execution time: 0.268194s
Execution time: 0.266415s
Execution time: 0.262402s
Execution time: 0.268709s
>>>>> 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
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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 1.16s
Prepared 34 packages in 890ms
Installed 34 packages in 119ms
 + 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 124ms
Prepared 1 package in 250ms
Uninstalled 1 package in 52ms
Installed 1 package in 38ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 247ms
Prepared 6 packages in 58ms
Installed 6 packages in 13ms
 + 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 0x726568a9dc80> 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 0x7d408709dc80> 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 0x76dc63e9dc80> 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 0x7fcd5f71cc80> 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 0x7185e51ddc80> 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 0x79fb5b8a0c10> 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 0x729b84860c10> 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 0x706186e5fc10> 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 0x735be0720c10> 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 0x75539dfe0c10> 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 0x7eac8cc9fc10> 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 0x74b03bea0c10> 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 0x78234e660c10> 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 0x7c322dce0c10> 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 0x76f3142dfc10> 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
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  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 0x7eaa6bf1dc10> 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 0x7d9c41fdfc10> 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 0x773469cdfc10> 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 0x7dd1e649dc10> 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 0x7661490ddc10> 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 0x70d84b49ec10> 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 0x75c6fee9dc10> 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 0x7a70c365dc10> 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 0x7b2ea831ec10> 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 0x7613d349dc10> 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 0x7d06c35dcba0> 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 0x7070e451cba0> 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 0x785cf511cba0> 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 0x70eba759bba0> 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 0x74dbc1d1dba0> 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 0x6ffbe725cba0> 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 0x78419c0dbba0> 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 0x7d7f0f25dba0> 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 0x7186cd71dba0> 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 0x7f6757adcba0> 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 0x7416b42dfc10> 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 0x7e3e9dc9fc10> 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 0x7027eb9dfc10> 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 0x79058951ec10> 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 0x706c9e8dec10> 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: 0.029376s
Execution time: 0.027477s
Execution time: 0.028376s
Execution time: 0.028090s
Execution time: 0.027538s
Execution time: 0.027927s
Execution time: 0.027723s
Execution time: 0.028451s
Execution time: 0.029695s
Execution time: 0.028243s
>>>>> Test 1
Execution time: 0.000789s
Execution time: 0.000804s
Execution time: 0.000812s
Execution time: 0.000737s
Execution time: 0.000736s
Execution time: 0.000740s
Execution time: 0.000714s
Execution time: 0.000737s
Execution time: 0.000766s
Execution time: 0.000748s
>>>>> Test 2
Execution time: 0.246698s
Execution time: 0.242613s
Execution time: 0.240902s
Execution time: 0.252122s
Execution time: 0.244717s
>>>>> Test 3
Execution time: 0.139601s
Execution time: 0.141797s
Execution time: 0.139207s
Execution time: 0.141323s
Execution time: 0.140360s
>>>>> Test 4
Execution time: 0.024647s
Execution time: 0.025253s
Execution time: 0.023249s
Execution time: 0.024082s
Execution time: 0.023897s
>>>>> Test 5
Execution time: 0.002457s
Execution time: 0.002513s
Execution time: 0.002416s
Execution time: 0.002506s
Execution time: 0.002464s
Execution time: 0.002476s
Execution time: 0.002494s
Execution time: 0.002477s
Execution time: 0.002508s
Execution time: 0.002509s
>>>>> Test 6
Execution time: 0.003227s
Execution time: 0.002942s
Execution time: 0.003006s
Execution time: 0.002954s
Execution time: 0.004264s
Execution time: 0.003050s
Execution time: 0.002915s
Execution time: 0.003390s
Execution time: 0.002978s
Execution time: 0.002874s
>>>>> Test 7
Execution time: 0.004072s
Execution time: 0.003961s
Execution time: 0.003953s
Execution time: 0.004352s
Execution time: 0.004115s
Execution time: 0.003984s
Execution time: 0.004000s
Execution time: 0.004032s
Execution time: 0.003969s
Execution time: 0.004035s
>>>>> Test 8
Execution time: 0.516347s
Execution time: 0.528166s
Execution time: 0.527894s
Execution time: 0.524769s
Execution time: 0.514341s
Execution time: 0.530486s
Execution time: 0.531148s
Execution time: 0.522273s
Execution time: 0.558280s
Execution time: 0.521947s
>>>>> Test 9
Execution time: 0.026068s
Execution time: 0.029703s
Execution time: 0.025914s
Execution time: 0.027450s
Execution time: 0.026057s
Execution time: 0.025910s
Execution time: 0.035111s
Execution time: 0.031985s
Execution time: 0.025976s
Execution time: 0.027738s
>>>>> Test 10
Execution time: 0.001543s
Execution time: 0.001532s
Execution time: 0.001650s
Execution time: 0.001623s
Execution time: 0.001576s
Execution time: 0.001586s
Execution time: 0.001518s
Execution time: 0.001528s
Execution time: 0.001523s
Execution time: 0.001532s
>>>>> Test 11
Execution time: 0.052027s
Execution time: 0.054209s
Execution time: 0.052463s
Execution time: 0.052023s
Execution time: 0.052362s
>>>>> Test 12
Execution time: 0.218122s
Execution time: 0.232803s
Execution time: 0.218545s
Execution time: 0.231009s
Execution time: 0.216405s
Execution time: 0.219341s
Execution time: 0.223313s
Execution time: 0.228887s
Execution time: 0.217265s
Execution time: 0.219461s
>>>>> End Commit Output
opt_commit: False, binary_reward: 0, reward: 0.4847228309881771
