Running tests...
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 * [new tag]             alpha                  -> alpha
 * [new tag]             delete                 -> delete
Removing datasets/flores/dummy/sien/1.1.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/encs/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enda/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ende/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enel/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enes/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enet/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enfi/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enfr/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enga/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enhr/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enhu/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enit/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enlt/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enlv/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enmt/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ennl/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enpl/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enpt/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/enro/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ensk/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ensl/1.0.0/dummy_data-zip-extracted/
Removing datasets/para_crawl/dummy/ensv/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/aztr_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/be_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/beru_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/es_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/fr_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/gl_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/glpt_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/he_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/it_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/pt_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/ru_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/ru_to_pt/1.0.0/dummy_data-zip-extracted/
Removing datasets/ted_hrlr/dummy/tr_to_en/1.0.0/dummy_data-zip-extracted/
Removing datasets/xtreme/dummy/XQuAD.ar/1.0.0/dummy_data-zip-extracted/
HEAD is now at b713dcdff Fix CI: commit operation equality (hfh 1.20.0) and pytest parametrize collection error (#8283)
Checking out base repo...
Note: switching to '599403601739e7a73e8ebbc8653d246e07207265^'.

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

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

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

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

HEAD is now at e60c99fbb [Docs] How to use with PyTorch page (#4474)
Installing repo...
Using CPython 3.9.20
Creating virtual environment at: .venv
/testbed/.venv/bin/python
Python 3.9.20
Resolved 34 packages in 1.30s
Prepared 34 packages in 897ms
Installed 34 packages in 98ms
 + 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 114ms
Prepared 1 package in 187ms
Uninstalled 1 package in 65ms
Installed 1 package in 41ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 352ms
Prepared 6 packages in 58ms
Installed 6 packages in 19ms
 + 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 0x758f54b1dac0> 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 0x70b24849dac0> 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 0x75d04345dac0> 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 0x77097be5dac0> 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 0x7792b90ddac0> 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 0x711fb8b1fa50> 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 0x7e5d62ddfa50> 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 0x79b5f4c5fa50> 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 0x73ed0c19fa50> 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 0x7aa2a991fa50> 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 0x711ea611fa50> 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 0x78c52671fa50> 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 0x7b8c3de9fa50> 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 0x7294b785fa50> 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 0x718d87c9fa50> 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 0x73553e51da50> 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 0x7ed0072dda50> 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 0x764ac669da50> 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 0x7b30f849da50> 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 0x7bbfc271da50> 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 0x7e85b351da50> 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 0x7755d985da50> 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 0x7c0e68fdda50> 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 0x7939010dda50> 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 0x72656851ca50> 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 0x7c7d68e5b9e0> 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 0x7df1828da9e0> 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 0x7ba88c91b9e0> 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 0x74442a29b9e0> 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 0x7b93e74999e0> 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 0x7cf86089b9e0> 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 0x73405329b9e0> 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 0x72b04611b9e0> 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 0x7453414db9e0> 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 0x73030489b9e0> 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 0x7859c769ca50> 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 0x76bae0a9ca50> 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 0x7b1e9d9dda50> 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 0x79811009ea50> 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 0x7c9883e9ca50> 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...
Checking patch src/datasets/arrow_dataset.py...
Checking patch src/datasets/formatting/formatting.py...
Applied patch .circleci/deploy.sh cleanly.
Applied patch convert_dataset.sh cleanly.
Applied patch datasets/atomic/README.md cleanly.
Applied patch datasets/atomic/atomic.py cleanly.
Applied patch datasets/conceptnet5/README.md cleanly.
Applied patch datasets/conceptnet5/conceptnet5.py cleanly.
Applied patch datasets/generics_kb/generics_kb.py cleanly.
Applied patch datasets/lama/README.md cleanly.
Applied patch datasets/lama/lama.py cleanly.
Applied patch datasets/nell/README.md cleanly.
Applied patch datasets/nell/nell.py cleanly.
Applied patch datasets/ollie/README.md cleanly.
Applied patch datasets/ollie/ollie.py cleanly.
Applied patch datasets/proto_qa/proto_qa.py cleanly.
Applied patch datasets/ro_sts/ro_sts.py cleanly.
Applied patch datasets/ro_sts_parallel/README.md cleanly.
Applied patch datasets/ro_sts_parallel/ro_sts_parallel.py cleanly.
Applied patch src/datasets/arrow_dataset.py cleanly.
Applied patch src/datasets/formatting/formatting.py cleanly.
Successfully applied patch using git apply --verbose
>>>>> Applied Patch
Installing repo...
Using CPython 3.9.20
Creating virtual environment at: .venv
/testbed/.venv/bin/python
Python 3.9.20
Resolved 34 packages in 1.26s
Prepared 34 packages in 787ms
Installed 34 packages in 114ms
 + 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 127ms
Prepared 1 package in 278ms
Uninstalled 1 package in 55ms
Installed 1 package in 41ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 308ms
Prepared 6 packages in 54ms
Installed 6 packages in 19ms
 + 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 0x74b6b8edeac0> 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 0x7dc7dd91eac0> 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 0x75f2eaa9eac0> 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 0x725c5e09cac0> 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 0x7fddb7f1eac0> 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 0x74f0c9e42a50> 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 0x75071f7dfa50> 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 0x7a69f825fa50> 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 0x7a07ce91fa50> 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 0x74f0e431fa50> 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 0x7f4576a9fa50> 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 0x7cad7c49ea50> 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 0x7f90d405fa50> 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 0x77fadd8dfa50> 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 0x7c4efd51fa50> 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 0x7a479dc5ea50> 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 0x7c293a49ea50> 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 0x75b757b1ea50> 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 0x7f3d22a9ea50> 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 0x7d887171ea50> 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 0x76ff8411ea50> 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 0x7eb7c4c9ea50> 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 0x7d5b5f4dea50> 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 0x720e1a29ea50> 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 0x7b1a8a09ea50> 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 0x7d6d1ed1d9e0> 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 0x71552e5dd9e0> 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 0x7d66a7c5d9e0> 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 0x7dcff485d9e0> 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 0x7ca8ee71d9e0> 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 0x790009e9d9e0> 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 0x758ac0e409e0> 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 0x737691d1d9e0> 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 0x7bc89dc9d9e0> 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 0x75fd2969d9e0> 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 0x7ccaa9f1da50> 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 0x73d4a83dda50> 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 0x70d9f529da50> 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 0x72897ee9ca50> 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 0x729edd89da50> 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: 0.215820s
Execution time: 0.215998s
Execution time: 0.214455s
Execution time: 0.217484s
Execution time: 0.215267s
Execution time: 0.240517s
Execution time: 0.220819s
Execution time: 0.214148s
Execution time: 0.215541s
Execution time: 0.218936s
>>>>> Test 1
Execution time: 0.003937s
Execution time: 0.003925s
Execution time: 0.004047s
Execution time: 0.004088s
Execution time: 0.003956s
Execution time: 0.003897s
Execution time: 0.003909s
Execution time: 0.004056s
Execution time: 0.003929s
Execution time: 0.003966s
>>>>> Test 2
Execution time: 0.800797s
Execution time: 0.793312s
Execution time: 0.794480s
Execution time: 0.780242s
Execution time: 0.794072s
>>>>> Test 3
Execution time: 0.418593s
Execution time: 0.434110s
Execution time: 0.422639s
Execution time: 0.420639s
Execution time: 0.416169s
>>>>> Test 4
Execution time: 0.050120s
Execution time: 0.049867s
Execution time: 0.050366s
Execution time: 0.049731s
Execution time: 0.050244s
>>>>> Test 5
Execution time: 0.004047s
Execution time: 0.004108s
Execution time: 0.004160s
Execution time: 0.004210s
Execution time: 0.004033s
Execution time: 0.004042s
Execution time: 0.004091s
Execution time: 0.004179s
Execution time: 0.004018s
Execution time: 0.004067s
>>>>> Test 6
Execution time: 0.004436s
Execution time: 0.004446s
Execution time: 0.004447s
Execution time: 0.004487s
Execution time: 0.004335s
Execution time: 0.004486s
Execution time: 0.004475s
Execution time: 0.004439s
Execution time: 0.004476s
Execution time: 0.004619s
>>>>> Test 7
Execution time: 0.005738s
Execution time: 0.005728s
Execution time: 0.005761s
Execution time: 0.005735s
Execution time: 0.005604s
Execution time: 0.005829s
Execution time: 0.005702s
Execution time: 0.005665s
Execution time: 0.005665s
Execution time: 0.005698s
>>>>> Test 8
Execution time: 0.679678s
Execution time: 0.682570s
Execution time: 0.661800s
Execution time: 0.699867s
Execution time: 0.681112s
Execution time: 0.684953s
Execution time: 0.686200s
Execution time: 0.687596s
Execution time: 0.679018s
Execution time: 0.696681s
>>>>> Test 9
Execution time: 0.032536s
Execution time: 0.033284s
Execution time: 0.032593s
Execution time: 0.032483s
Execution time: 0.032691s
Execution time: 0.032405s
Execution time: 0.034073s
Execution time: 0.032627s
Execution time: 0.032367s
Execution time: 0.032351s
>>>>> Test 10
Execution time: 0.002012s
Execution time: 0.001996s
Execution time: 0.002037s
Execution time: 0.002016s
Execution time: 0.002040s
Execution time: 0.002029s
Execution time: 0.001990s
Execution time: 0.001968s
Execution time: 0.002032s
Execution time: 0.002091s
>>>>> Test 11
Execution time: 0.076649s
Execution time: 0.069101s
Execution time: 0.071376s
Execution time: 0.070459s
Execution time: 0.069319s
>>>>> Test 12
Execution time: 0.262682s
Execution time: 0.263502s
Execution time: 0.257561s
Execution time: 0.264519s
Execution time: 0.273982s
Execution time: 0.260858s
Execution time: 0.261530s
Execution time: 0.261606s
Execution time: 0.270091s
Execution time: 0.261640s
>>>>> End Base Output
>>>>> Start Patch Output
>>>>> Test 0
Execution time: 0.004571s
Execution time: 0.004549s
Execution time: 0.004470s
Execution time: 0.004281s
Execution time: 0.004560s
Execution time: 0.004205s
Execution time: 0.004550s
Execution time: 0.004521s
Execution time: 0.004701s
Execution time: 0.004573s
>>>>> Test 1
Execution time: 0.000699s
Execution time: 0.000675s
Execution time: 0.000686s
Execution time: 0.000928s
Execution time: 0.000695s
Execution time: 0.000700s
Execution time: 0.000702s
Execution time: 0.000680s
Execution time: 0.000683s
Execution time: 0.000708s
>>>>> Test 2
Execution time: 0.381493s
Execution time: 0.390100s
Execution time: 0.401058s
Execution time: 0.385770s
Execution time: 0.385514s
>>>>> Test 3
Execution time: 0.156556s
Execution time: 0.153343s
Execution time: 0.154595s
Execution time: 0.173096s
Execution time: 0.156415s
>>>>> Test 4
Execution time: 0.023817s
Execution time: 0.024122s
Execution time: 0.024111s
Execution time: 0.024142s
Execution time: 0.023935s
>>>>> Test 5
Execution time: 0.002835s
Execution time: 0.002908s
Execution time: 0.002831s
Execution time: 0.002896s
Execution time: 0.002982s
Execution time: 0.002841s
Execution time: 0.002897s
Execution time: 0.002833s
Execution time: 0.002840s
Execution time: 0.002860s
>>>>> Test 6
Execution time: 0.003232s
Execution time: 0.003284s
Execution time: 0.003191s
Execution time: 0.003241s
Execution time: 0.003375s
Execution time: 0.003251s
Execution time: 0.003214s
Execution time: 0.003226s
Execution time: 0.003267s
Execution time: 0.003182s
>>>>> Test 7
Execution time: 0.004213s
Execution time: 0.004284s
Execution time: 0.004248s
Execution time: 0.004294s
Execution time: 0.004410s
Execution time: 0.004350s
Execution time: 0.004236s
Execution time: 0.004188s
Execution time: 0.004202s
Execution time: 0.004240s
>>>>> Test 8
Execution time: 0.595246s
Execution time: 0.600843s
Execution time: 0.629386s
Execution time: 0.598644s
Execution time: 0.596254s
Execution time: 0.585925s
Execution time: 0.600090s
Execution time: 0.591201s
Execution time: 0.591247s
Execution time: 0.583325s
>>>>> Test 9
Execution time: 0.028357s
Execution time: 0.028248s
Execution time: 0.043904s
Execution time: 0.040323s
Execution time: 0.027760s
Execution time: 0.028486s
Execution time: 0.027875s
Execution time: 0.028597s
Execution time: 0.027606s
Execution time: 0.028012s
>>>>> Test 10
Execution time: 0.001747s
Execution time: 0.001732s
Execution time: 0.001769s
Execution time: 0.001754s
Execution time: 0.001683s
Execution time: 0.001748s
Execution time: 0.001763s
Execution time: 0.001730s
Execution time: 0.001719s
Execution time: 0.001714s
>>>>> Test 11
Execution time: 0.053749s
Execution time: 0.053073s
Execution time: 0.053649s
Execution time: 0.052930s
Execution time: 0.052371s
>>>>> Test 12
Execution time: 0.236422s
Execution time: 0.243466s
Execution time: 0.240526s
Execution time: 0.239557s
Execution time: 0.243705s
Execution time: 0.242660s
Execution time: 0.238496s
Execution time: 0.240786s
Execution time: 0.240839s
Execution time: 0.240304s
>>>>> 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
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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
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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 1.06s
Prepared 34 packages in 700ms
Installed 34 packages in 82ms
 + 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 118ms
Prepared 1 package in 229ms
Uninstalled 1 package in 60ms
Installed 1 package in 29ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 186ms
Prepared 6 packages in 57ms
Installed 6 packages in 15ms
 + 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
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  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 0x71fa8fbddc10> 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 0x7a30ad11cc10> 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 0x7d1ff045dc10> 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 0x706648a9dc10> 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 0x72349bf1dc10> 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 0x7c9ea79dfba0> 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 0x7ab0aae9fba0> 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 0x7ad0dba60ba0> 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 0x7508fb49fba0> 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 0x7b69e1ae0ba0> 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 0x78f21c5e0ba0> 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 0x76876b3e0ba0> 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 0x7e5619adfba0> 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 0x7d1ce7660ba0> 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 0x793db7ee0ba0> 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 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 0x7c454131fba0> 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 0x7d5a41241ba0> 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 0x70d77231eba0> 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 0x7663b7e5fba0> 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 0x70860169eba0> 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 0x75700829fba0> 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 0x7cebecb1fba0> 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 0x7b1aa811eba0> 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 0x71722bfdfba0> 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 0x77d9d6edfba0> 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 0x789d1c45cb30> 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 0x7bd65831db30> 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 0x7290fa71cb30> 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 0x7ee79ee9bb30> 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 0x7fce6ac3fb30> 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 0x72c77971cb30> 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 0x7545c9bdcb30> 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 0x72f1b2a9cb30> 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 0x7f156ae5db30> 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 0x73503069db30> 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 0x7e9cb5e9eba0> 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 0x7aa9f2e9dba0> 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 0x769ef64deba0> 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 0x7c62dcb1eba0> 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 0x76f6bcc5eba0> 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: 0.028474s
Execution time: 0.026935s
Execution time: 0.028089s
Execution time: 0.027550s
Execution time: 0.027616s
Execution time: 0.027026s
Execution time: 0.029688s
Execution time: 0.027639s
Execution time: 0.027292s
Execution time: 0.027510s
>>>>> Test 1
Execution time: 0.000742s
Execution time: 0.000736s
Execution time: 0.000725s
Execution time: 0.000721s
Execution time: 0.000734s
Execution time: 0.000713s
Execution time: 0.000708s
Execution time: 0.000729s
Execution time: 0.000721s
Execution time: 0.000739s
>>>>> Test 2
Execution time: 0.237302s
Execution time: 0.249096s
Execution time: 0.239776s
Execution time: 0.246541s
Execution time: 0.245617s
>>>>> Test 3
Execution time: 0.139363s
Execution time: 0.143428s
Execution time: 0.145132s
Execution time: 0.140468s
Execution time: 0.139346s
>>>>> Test 4
Execution time: 0.023082s
Execution time: 0.023570s
Execution time: 0.023674s
Execution time: 0.023884s
Execution time: 0.023445s
>>>>> Test 5
Execution time: 0.002436s
Execution time: 0.002434s
Execution time: 0.002532s
Execution time: 0.002483s
Execution time: 0.002443s
Execution time: 0.002459s
Execution time: 0.002552s
Execution time: 0.002626s
Execution time: 0.002440s
Execution time: 0.002436s
>>>>> Test 6
Execution time: 0.002970s
Execution time: 0.002910s
Execution time: 0.002886s
Execution time: 0.002879s
Execution time: 0.002971s
Execution time: 0.003058s
Execution time: 0.002822s
Execution time: 0.003030s
Execution time: 0.002863s
Execution time: 0.002880s
>>>>> Test 7
Execution time: 0.003906s
Execution time: 0.004019s
Execution time: 0.004057s
Execution time: 0.003937s
Execution time: 0.004063s
Execution time: 0.004075s
Execution time: 0.004015s
Execution time: 0.004323s
Execution time: 0.003962s
Execution time: 0.003987s
>>>>> Test 8
Execution time: 0.519555s
Execution time: 0.506295s
Execution time: 0.524878s
Execution time: 0.512155s
Execution time: 0.517547s
Execution time: 0.522261s
Execution time: 0.515255s
Execution time: 0.515559s
Execution time: 0.509067s
Execution time: 0.522343s
>>>>> Test 9
Execution time: 0.025574s
Execution time: 0.025231s
Execution time: 0.025494s
Execution time: 0.026238s
Execution time: 0.025267s
Execution time: 0.025771s
Execution time: 0.025658s
Execution time: 0.026221s
Execution time: 0.025687s
Execution time: 0.026616s
>>>>> Test 10
Execution time: 0.001678s
Execution time: 0.001511s
Execution time: 0.001519s
Execution time: 0.001545s
Execution time: 0.001507s
Execution time: 0.001485s
Execution time: 0.001557s
Execution time: 0.001532s
Execution time: 0.001490s
Execution time: 0.001517s
>>>>> Test 11
Execution time: 0.051262s
Execution time: 0.050990s
Execution time: 0.052242s
Execution time: 0.050849s
Execution time: 0.052516s
>>>>> Test 12
Execution time: 0.220054s
Execution time: 0.220701s
Execution time: 0.219885s
Execution time: 0.218780s
Execution time: 0.217618s
Execution time: 0.215979s
Execution time: 0.223324s
Execution time: 0.216724s
Execution time: 0.218395s
Execution time: 0.215045s
>>>>> End Commit Output
opt_commit: False, binary_reward: 0, reward: 1.0274178923893003
