Running tests...
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 * [new tag]             2.2.2                  -> 2.2.2
 * [new tag]             2.20.0                 -> 2.20.0
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 * [new tag]             3.0.0                  -> 3.0.0
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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 1.33s
Prepared 34 packages in 865ms
Installed 34 packages in 134ms
 + aiohappyeyeballs==2.6.1
 + aiohttp==3.13.5
 + aiosignal==1.4.0
 + async-timeout==5.0.1
 + attrs==26.1.0
 + certifi==2026.6.17
 + charset-normalizer==3.4.7
 + datasets==2.2.3.dev0 (from file:///testbed)
 + dill==0.3.5.1
 + filelock==3.19.1
 + frozenlist==1.8.0
 + fsspec==2025.10.0
 + hf-xet==1.5.1
 + huggingface-hub==0.36.2
 + idna==3.18
 + multidict==6.7.1
 + multiprocess==0.70.13
 + numpy==2.0.2
 + packaging==26.2
 + pandas==2.3.3
 + propcache==0.4.1
 + pyarrow==21.0.0
 + python-dateutil==2.9.0.post0
 + pytz==2026.2
 + pyyaml==6.0.3
 + requests==2.32.5
 + responses==0.18.0
 + six==1.17.0
 + tqdm==4.68.3
 + typing-extensions==4.15.0
 + tzdata==2026.2
 + urllib3==2.6.3
 + xxhash==3.7.1
 + yarl==1.22.0
Resolved 1 package in 124ms
Prepared 1 package in 248ms
Uninstalled 1 package in 68ms
Installed 1 package in 38ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 339ms
Prepared 6 packages in 80ms
Installed 6 packages in 11ms
 + 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 0x79be7f71cb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7efebb7ddb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x76937e31db30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7f4ff385cb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x71878251db30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 0x78a8d8ddfac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x754a7a59fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7efb8711fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7c910205fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7e0bb151fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x704084c5fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x77bfba31fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x703b4b45eac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7d3950fd5ac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7499bfe9eac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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
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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 0x7dac0689dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x798b82e9dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7b46824ddac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7db24745dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x70920f05dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7ded4ba9dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x76f7fe09dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7029926ddac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7c4d9afddac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x784f79b1dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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
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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 0x7d2d7b09ba50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7518f485ba50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x774005ddba50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x74514791ba50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x74d21769ba50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x78f1c2733a50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7b78d265aa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x767e20edba50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7b040aa5ba50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x706efba5ba50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 0x7b255251dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7e5559e9cac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7f9cf949dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7a2da95dcac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x79422069eac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 1.33s
Prepared 34 packages in 921ms
Installed 34 packages in 85ms
 + 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 121ms
Prepared 1 package in 294ms
Uninstalled 1 package in 29ms
Installed 1 package in 40ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 328ms
Prepared 6 packages in 51ms
Installed 6 packages in 24ms
 + 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 0x722c5be01b30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7fbf89e9cb30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x74a20325db30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7759b648db30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7e2f9f89db30> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 0x7ab1f9c9fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7710f4d1fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x72c9e009fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x744706f1fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x736ae0487ac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7611cc09fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x77dbe231fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x72f40c9deac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7eac8b25fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x6ffdaf31fac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 0x7bc9bf85eac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7b414bb1eac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7aa41a11eac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7de3f5f1eac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x755dbb9deac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x716893e9dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x718862f1eac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x710f92e5eac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x74c7a93deac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x74339ef1dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 0x79f614adca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7b9088a3fa50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x77daa5a9ca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7b6457cdca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x73175aa5ca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7fe5ac09ca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x710c5989ca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7389bc51ca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x73c69ba5ca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7e0654c5ca50> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 0x7eb72065dac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7ce6589deac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7a295329cac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x739995e5cac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7d3627fdcac0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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: 0.221458s
Execution time: 0.212817s
Execution time: 0.214496s
Execution time: 0.224755s
Execution time: 0.221567s
Execution time: 0.271352s
Execution time: 0.225535s
Execution time: 0.225439s
Execution time: 0.220186s
Execution time: 0.233079s
>>>>> Test 1
Execution time: 0.004171s
Execution time: 0.004062s
Execution time: 0.003982s
Execution time: 0.004552s
Execution time: 0.004227s
Execution time: 0.004873s
Execution time: 0.004046s
Execution time: 0.004009s
Execution time: 0.003935s
Execution time: 0.004030s
>>>>> Test 2
Execution time: 0.819095s
Execution time: 0.813506s
Execution time: 0.907543s
Execution time: 0.829614s
Execution time: 0.957492s
>>>>> Test 3
Execution time: 0.439364s
Execution time: 0.436376s
Execution time: 0.528415s
Execution time: 0.441058s
Execution time: 0.439667s
>>>>> Test 4
Execution time: 0.052853s
Execution time: 0.051592s
Execution time: 0.051307s
Execution time: 0.050950s
Execution time: 0.052278s
>>>>> Test 5
Execution time: 0.004130s
Execution time: 0.004093s
Execution time: 0.004167s
Execution time: 0.004170s
Execution time: 0.004109s
Execution time: 0.004094s
Execution time: 0.004110s
Execution time: 0.004162s
Execution time: 0.004137s
Execution time: 0.004084s
>>>>> Test 6
Execution time: 0.004484s
Execution time: 0.004475s
Execution time: 0.004649s
Execution time: 0.004622s
Execution time: 0.004639s
Execution time: 0.004668s
Execution time: 0.004627s
Execution time: 0.004603s
Execution time: 0.006459s
Execution time: 0.004745s
>>>>> Test 7
Execution time: 0.006031s
Execution time: 0.005818s
Execution time: 0.005825s
Execution time: 0.005795s
Execution time: 0.005831s
Execution time: 0.005915s
Execution time: 0.005860s
Execution time: 0.006478s
Execution time: 0.005776s
Execution time: 0.005838s
>>>>> Test 8
Execution time: 0.730144s
Execution time: 0.804364s
Execution time: 0.686822s
Execution time: 0.698422s
Execution time: 0.706158s
Execution time: 0.713404s
Execution time: 0.695039s
Execution time: 0.719071s
Execution time: 0.738995s
Execution time: 0.700252s
>>>>> Test 9
Execution time: 0.033996s
Execution time: 0.033122s
Execution time: 0.045150s
Execution time: 0.034939s
Execution time: 0.034246s
Execution time: 0.033798s
Execution time: 0.033445s
Execution time: 0.034074s
Execution time: 0.033823s
Execution time: 0.033277s
>>>>> Test 10
Execution time: 0.002031s
Execution time: 0.002223s
Execution time: 0.002047s
Execution time: 0.002300s
Execution time: 0.002349s
Execution time: 0.002232s
Execution time: 0.002063s
Execution time: 0.002123s
Execution time: 0.002780s
Execution time: 0.002220s
>>>>> Test 11
Execution time: 0.069915s
Execution time: 0.069742s
Execution time: 0.070513s
Execution time: 0.080551s
Execution time: 0.079029s
>>>>> Test 12
Execution time: 0.279102s
Execution time: 0.273846s
Execution time: 0.275995s
Execution time: 0.270650s
Execution time: 0.268091s
Execution time: 0.285886s
Execution time: 0.267767s
Execution time: 0.271510s
Execution time: 0.283054s
Execution time: 0.292250s
>>>>> End Base Output
>>>>> Start Patch Output
>>>>> Test 0
Execution time: 0.004520s
Execution time: 0.005495s
Execution time: 0.004078s
Execution time: 0.004319s
Execution time: 0.004073s
Execution time: 0.004402s
Execution time: 0.004300s
Execution time: 0.004492s
Execution time: 0.004372s
Execution time: 0.004440s
>>>>> Test 1
Execution time: 0.000520s
Execution time: 0.000512s
Execution time: 0.000502s
Execution time: 0.000543s
Execution time: 0.000525s
Execution time: 0.000516s
Execution time: 0.000510s
Execution time: 0.000500s
Execution time: 0.000512s
Execution time: 0.000512s
>>>>> Test 2
Execution time: 0.406749s
Execution time: 0.410466s
Execution time: 0.385676s
Execution time: 0.386527s
Execution time: 0.404263s
>>>>> Test 3
Execution time: 0.154704s
Execution time: 0.159746s
Execution time: 0.165691s
Execution time: 0.155875s
Execution time: 0.152779s
>>>>> Test 4
Execution time: 0.023988s
Execution time: 0.022921s
Execution time: 0.022898s
Execution time: 0.025604s
Execution time: 0.023852s
>>>>> Test 5
Execution time: 0.002685s
Execution time: 0.002564s
Execution time: 0.002581s
Execution time: 0.002566s
Execution time: 0.002830s
Execution time: 0.002593s
Execution time: 0.003060s
Execution time: 0.002553s
Execution time: 0.002771s
Execution time: 0.002540s
>>>>> Test 6
Execution time: 0.003075s
Execution time: 0.003067s
Execution time: 0.003155s
Execution time: 0.003055s
Execution time: 0.004660s
Execution time: 0.003017s
Execution time: 0.003066s
Execution time: 0.003049s
Execution time: 0.002999s
Execution time: 0.003039s
>>>>> Test 7
Execution time: 0.004976s
Execution time: 0.004193s
Execution time: 0.004329s
Execution time: 0.004208s
Execution time: 0.004149s
Execution time: 0.004821s
Execution time: 0.004182s
Execution time: 0.004089s
Execution time: 0.004128s
Execution time: 0.004103s
>>>>> Test 8
Execution time: 0.624576s
Execution time: 0.611756s
Execution time: 0.631086s
Execution time: 0.606791s
Execution time: 0.600461s
Execution time: 0.622499s
Execution time: 0.612716s
Execution time: 0.643121s
Execution time: 0.610864s
Execution time: 0.615844s
>>>>> Test 9
Execution time: 0.028301s
Execution time: 0.028011s
Execution time: 0.028297s
Execution time: 0.028279s
Execution time: 0.028388s
Execution time: 0.027944s
Execution time: 0.028969s
Execution time: 0.032586s
Execution time: 0.028307s
Execution time: 0.028133s
>>>>> Test 10
Execution time: 0.001646s
Execution time: 0.002194s
Execution time: 0.001654s
Execution time: 0.001533s
Execution time: 0.001559s
Execution time: 0.001676s
Execution time: 0.001568s
Execution time: 0.001673s
Execution time: 0.001858s
Execution time: 0.001605s
>>>>> Test 11
Execution time: 0.058313s
Execution time: 0.055438s
Execution time: 0.054115s
Execution time: 0.053890s
Execution time: 0.053839s
>>>>> Test 12
Execution time: 0.264864s
Execution time: 0.269886s
Execution time: 0.263763s
Execution time: 0.270942s
Execution time: 0.254992s
Execution time: 0.279093s
Execution time: 0.255070s
Execution time: 0.266687s
Execution time: 0.253987s
Execution time: 0.255638s
>>>>> End Patch Output
Removing base_0.txt
Removing base_1.txt
Removing base_10.txt
Removing base_11.txt
Removing base_12.txt
Removing base_2.txt
Removing base_3.txt
Removing base_4.txt
Removing base_5.txt
Removing base_6.txt
Removing base_7.txt
Removing base_8.txt
Removing base_9.txt
Removing build/
Removing gso_0_result.json
Removing gso_10_result.json
Removing gso_11_result.json
Removing gso_12_result.json
Removing gso_1_result.json
Removing gso_2_result.json
Removing gso_3_result.json
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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
Removing result_1.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 1.20s
Prepared 34 packages in 698ms
Installed 34 packages in 58ms
 + 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 102ms
Prepared 1 package in 215ms
Uninstalled 1 package in 25ms
Installed 1 package in 19ms
 - pyarrow==21.0.0
 + pyarrow==20.0.0
Resolved 13 packages in 291ms
Prepared 6 packages in 32ms
Installed 6 packages in 8ms
 + absl-py==2.3.1
 + decorator==5.3.1
 + greenlet==3.2.5
 + pillow==11.3.0
 + sqlalchemy==2.0.51
 + zstandard==0.25.0
Name: datasets
Version: 2.2.3.dev0
Location: /testbed/.venv/lib/python3.9/site-packages
Requires: aiohttp, dill, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, requests, responses, tqdm, xxhash
Required-by:
>>>>> Init Succeeded
Running performance test for commit...
Running test /tests/gso_test_0.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_1.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_2.py 5 times...
  Iteration 1/5
Parameter 'indices'=<generator object experiment.<locals>.<genexpr> at 0x76db5fb1dc80> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7bc9d83ddc80> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x742f08dddc80> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7319ffb9dc80> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x744613a9ec80> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 0x71110325fc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7e0054a5fc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7d3f2651fc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x76d3115dfc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x737e4ae5fc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x757c3b842c10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x758766920c10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7aee8d69fc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x6ffbb7ce0c10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7ff433f1fc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
>>>>> Tests Passed
Running test /tests/gso_test_7.py 10 times...
  Iteration 1/10
  Iteration 2/10
  Iteration 3/10
  Iteration 4/10
  Iteration 5/10
  Iteration 6/10
  Iteration 7/10
  Iteration 8/10
  Iteration 9/10
  Iteration 10/10
>>>>> Tests Passed
Running test /tests/gso_test_8.py 10 times...
  Iteration 1/10
Parameter 'indices'=<generator object experiment.<locals>.contiguous_generator at 0x733a27c9dc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7f65c009cc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x74a6c971ec10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x790fbb8dfc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x74cd0925dc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x747860c9ec10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7b2bd2c9dc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7c0a6cb1dc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x70baa509ec10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x768e705ddc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 0x7ef00631cba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7a1521a9bba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7772d5fdcba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x764d7ca9dba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7d18ec85aba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x70f2cac5cba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7be142adcba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x77f72c49cba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x762d0fa9cba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7b378585cba0> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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 0x73c3fa1dec10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7f2f68f85c10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7f91bf89fc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x72995a3dfc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it 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 0x7ebb62e9fc10> of the transform datasets.arrow_dataset.Dataset.select couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider 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: 0.029590s
Execution time: 0.028205s
Execution time: 0.029376s
Execution time: 0.027965s
Execution time: 0.028750s
Execution time: 0.027930s
Execution time: 0.028981s
Execution time: 0.028383s
Execution time: 0.032840s
Execution time: 0.028330s
>>>>> Test 1
Execution time: 0.000776s
Execution time: 0.000741s
Execution time: 0.000763s
Execution time: 0.000764s
Execution time: 0.000727s
Execution time: 0.001012s
Execution time: 0.000752s
Execution time: 0.000766s
Execution time: 0.000776s
Execution time: 0.000752s
>>>>> Test 2
Execution time: 0.248018s
Execution time: 0.250648s
Execution time: 0.247881s
Execution time: 0.245669s
Execution time: 0.260773s
>>>>> Test 3
Execution time: 0.156122s
Execution time: 0.161768s
Execution time: 0.161560s
Execution time: 0.175974s
Execution time: 0.148000s
>>>>> Test 4
Execution time: 0.023907s
Execution time: 0.023825s
Execution time: 0.030049s
Execution time: 0.029381s
Execution time: 0.024175s
>>>>> Test 5
Execution time: 0.002535s
Execution time: 0.003973s
Execution time: 0.002459s
Execution time: 0.002933s
Execution time: 0.002498s
Execution time: 0.002629s
Execution time: 0.004314s
Execution time: 0.002507s
Execution time: 0.002547s
Execution time: 0.003870s
>>>>> Test 6
Execution time: 0.003712s
Execution time: 0.003082s
Execution time: 0.003056s
Execution time: 0.004017s
Execution time: 0.003117s
Execution time: 0.003026s
Execution time: 0.003182s
Execution time: 0.002908s
Execution time: 0.003031s
Execution time: 0.005049s
>>>>> Test 7
Execution time: 0.004262s
Execution time: 0.004025s
Execution time: 0.006492s
Execution time: 0.006079s
Execution time: 0.004208s
Execution time: 0.003975s
Execution time: 0.004215s
Execution time: 0.004981s
Execution time: 0.004273s
Execution time: 0.004016s
>>>>> Test 8
Execution time: 0.560348s
Execution time: 0.538313s
Execution time: 0.556773s
Execution time: 0.573126s
Execution time: 0.565193s
Execution time: 0.527547s
Execution time: 0.572412s
Execution time: 0.556943s
Execution time: 0.532590s
Execution time: 0.539628s
>>>>> Test 9
Execution time: 0.025520s
Execution time: 0.025984s
Execution time: 0.025957s
Execution time: 0.025905s
Execution time: 0.027758s
Execution time: 0.025994s
Execution time: 0.026021s
Execution time: 0.026656s
Execution time: 0.025803s
Execution time: 0.025824s
>>>>> Test 10
Execution time: 0.001694s
Execution time: 0.001502s
Execution time: 0.001506s
Execution time: 0.002580s
Execution time: 0.001524s
Execution time: 0.001656s
Execution time: 0.002472s
Execution time: 0.001514s
Execution time: 0.001672s
Execution time: 0.001580s
>>>>> Test 11
Execution time: 0.056255s
Execution time: 0.054115s
Execution time: 0.056731s
Execution time: 0.054451s
Execution time: 0.064873s
>>>>> Test 12
Execution time: 0.218042s
Execution time: 0.229650s
Execution time: 0.223710s
Execution time: 0.233420s
Execution time: 0.218626s
Execution time: 0.216978s
Execution time: 0.236064s
Execution time: 0.218208s
Execution time: 0.221313s
Execution time: 0.221877s
>>>>> End Commit Output
opt_commit: True, binary_reward: 1, reward: 1.1586526050934998
