Title: Unpin hfh
State: closed

Needed to use those in dataset-viewer:

- dev version of hfh https://github.com/huggingface/dataset-viewer/pull/2781: don't span the hub with /paths-info requests
- dev version of datasets at https://github.com/huggingface/datasets/pull/6875: don't write too big logs in the viewer

close https://github.com/huggingface/datasets/issues/6863

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6876). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

transformers 4.40.2 was release yesterday but not sure if it contains the fix

@lhoestq yes I knew transformers 4.40.2 was released yesterday, but I had checked that it does not contain the fix: only 2 bug fixes. That is why our CI continues failing in this PR. We will have to wait until the next minor version.

> If we urgently need some dev feature for dataset-viewer, I would suggest pushing the feature (cherry-picked) to a dedicated branch with 2.19.1 as its starting point (without opening a PR), and install datasets from that branch.

I have done so:
- Created a branch from 2.19.1: https://github.com/huggingface/datasets/tree/datasets-2.19.1-hotfix
- Cherry-picked the commit in this PR: https://github.com/huggingface/datasets/commit/3638183e2f7e0dce8924e46e7cc21bf6d5d7adfb
- Opened a PR in dataset-viewer to update datasets to this revision: https://github.com/huggingface/dataset-viewer/pull/2783

hfh 0.23.1 and transformers 4.41.0 as are out out, let's unpin no ?

I have re-run the CI to check that is green before.

The errors were coming from `transformers` having FutureWarning when loading models or tokenizers. I disabled the warnings for the `transformers`-related calls since they're not related to `datasets`

I opened an issue in transformers:
- https://github.com/huggingface/transformers/issues/31002

It's because the error from the FutureWarning happened when running `cache_file()` from `transformers`, which has some code that try/except and re-raise an OSError

Opened https://github.com/huggingface/transformers/pull/31007 to fix the FutureWarning in transformers. Sorry, thought it was fixed by https://github.com/huggingface/transformers/issues/30618 but clearly an oversight from my side.

Regarding the pytest config, yes I remember adding it and in general I still think it's a good idea to have it. Will be more careful next time to update `transformers` before `huggingface_hub`'s release and not the other way around (first time it happens since I've set this value :grimacing:). For a temporary fix in `datasets` I would rather temporarily disable the filterwarnings in `datasets` rather then adding filters in the test code. 

alright I disabled the errors on FutureWarning, do you see anything else @albertvillanova or we can merge ?

<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005165 / 0.011353 (-0.006188) | 0.003991 / 0.011008 (-0.007017) | 0.064029 / 0.038508 (0.025521) | 0.031578 / 0.023109 (0.008468) | 0.242646 / 0.275898 (-0.033252) | 0.261834 / 0.323480 (-0.061646) | 0.003032 / 0.007986 (-0.004953) | 0.002659 / 0.004328 (-0.001670) | 0.049868 / 0.004250 (0.045618) | 0.047607 / 0.037052 (0.010555) | 0.250537 / 0.258489 (-0.007952) | 0.289460 / 0.293841 (-0.004381) | 0.027225 / 0.128546 (-0.101321) | 0.010496 / 0.075646 (-0.065151) | 0.208455 / 0.419271 (-0.210816) | 0.036813 / 0.043533 (-0.006720) | 0.243361 / 0.255139 (-0.011778) | 0.267477 / 0.283200 (-0.015723) | 0.020402 / 0.141683 (-0.121281) | 1.117118 / 1.452155 (-0.335037) | 1.154868 / 1.492716 (-0.337849) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.096796 / 0.018006 (0.078790) | 0.304588 / 0.000490 (0.304098) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019221 / 0.037411 (-0.018190) | 0.062897 / 0.014526 (0.048371) | 0.076446 / 0.176557 (-0.100111) | 0.124476 / 0.737135 (-0.612659) | 0.079921 / 0.296338 (-0.216418) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.284442 / 0.215209 (0.069233) | 2.799419 / 2.077655 (0.721764) | 1.468022 / 1.504120 (-0.036098) | 1.354013 / 1.541195 (-0.187182) | 1.379985 / 1.468490 (-0.088505) | 0.561723 / 4.584777 (-4.023054) | 2.408887 / 3.745712 (-1.336825) | 2.712591 / 5.269862 (-2.557271) | 1.803132 / 4.565676 (-2.762544) | 0.063010 / 0.424275 (-0.361265) | 0.005030 / 0.007607 (-0.002577) | 0.339065 / 0.226044 (0.113021) | 3.373667 / 2.268929 (1.104738) | 1.861569 / 55.444624 (-53.583056) | 1.551357 / 6.876477 (-5.325120) | 1.701885 / 2.142072 (-0.440187) | 0.645685 / 4.805227 (-4.159543) | 0.117915 / 6.500664 (-6.382749) | 0.042656 / 0.075469 (-0.032814) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.957397 / 1.841788 (-0.884391) | 11.544300 / 8.074308 (3.469992) | 9.761814 / 10.191392 (-0.429578) | 0.134766 / 0.680424 (-0.545658) | 0.015387 / 0.534201 (-0.518814) | 0.285692 / 0.579283 (-0.293591) | 0.269201 / 0.434364 (-0.165163) | 0.328198 / 0.540337 (-0.212140) | 0.422315 / 1.386936 (-0.964621) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005333 / 0.011353 (-0.006020) | 0.003638 / 0.011008 (-0.007370) | 0.050503 / 0.038508 (0.011994) | 0.032240 / 0.023109 (0.009130) | 0.267602 / 0.275898 (-0.008296) | 0.293125 / 0.323480 (-0.030355) | 0.004275 / 0.007986 (-0.003710) | 0.002714 / 0.004328 (-0.001615) | 0.049341 / 0.004250 (0.045090) | 0.040364 / 0.037052 (0.003311) | 0.281096 / 0.258489 (0.022607) | 0.312615 / 0.293841 (0.018774) | 0.029981 / 0.128546 (-0.098565) | 0.010230 / 0.075646 (-0.065416) | 0.059218 / 0.419271 (-0.360054) | 0.033360 / 0.043533 (-0.010172) | 0.269518 / 0.255139 (0.014379) | 0.287559 / 0.283200 (0.004360) | 0.018159 / 0.141683 (-0.123524) | 1.107148 / 1.452155 (-0.345006) | 1.170731 / 1.492716 (-0.321985) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.095942 / 0.018006 (0.077936) | 0.304914 / 0.000490 (0.304425) | 0.000227 / 0.000200 (0.000027) | 0.000051 / 0.000054 (-0.000003) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022609 / 0.037411 (-0.014803) | 0.076455 / 0.014526 (0.061929) | 0.088170 / 0.176557 (-0.088386) | 0.128485 / 0.737135 (-0.608651) | 0.092471 / 0.296338 (-0.203867) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.291471 / 0.215209 (0.076262) | 2.822666 / 2.077655 (0.745012) | 1.531679 / 1.504120 (0.027559) | 1.405931 / 1.541195 (-0.135263) | 1.418893 / 1.468490 (-0.049597) | 0.576128 / 4.584777 (-4.008649) | 0.969466 / 3.745712 (-2.776246) | 2.831998 / 5.269862 (-2.437863) | 1.788814 / 4.565676 (-2.776863) | 0.064141 / 0.424275 (-0.360134) | 0.005126 / 0.007607 (-0.002482) | 0.341699 / 0.226044 (0.115654) | 3.320551 / 2.268929 (1.051622) | 1.903350 / 55.444624 (-53.541274) | 1.611809 / 6.876477 (-5.264668) | 1.729355 / 2.142072 (-0.412717) | 0.654622 / 4.805227 (-4.150605) | 0.118739 / 6.500664 (-6.381925) | 0.041453 / 0.075469 (-0.034016) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.017635 / 1.841788 (-0.824153) | 12.275948 / 8.074308 (4.201640) | 10.416224 / 10.191392 (0.224832) | 0.142288 / 0.680424 (-0.538135) | 0.015591 / 0.534201 (-0.518610) | 0.286515 / 0.579283 (-0.292768) | 0.128661 / 0.434364 (-0.305703) | 0.325728 / 0.540337 (-0.214609) | 0.415827 / 1.386936 (-0.971109) |

</details>
</details>

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