Issue: Use `huggingface_hub` cache
State: closed
Author: lhoestq

- use `hf_hub_download()` from `huggingface_hub` for HF files
- `datasets` cache_dir is still used for:
  - caching datasets as Arrow files (that back `Dataset` objects)
  - extracted archives, uncompressed files
  - files downloaded via http (datasets with scripts)
- I removed code that were made for http files (and also the dummy_data / mock_download_manager stuff that happened to rely on them and have been legacy for a while now)

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7105). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Nice

<img width="141" alt="Capture d’écran 2024-08-19 à 15 25 00" src="https://github.com/user-attachments/assets/18c7b3ec-a57e-45d7-9b19-0b12df9feccd">


fyi the CI failure on test_py310_numpy2 is unrelated to this PR (it's a dependency install failure)

<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) | 0.005677 / 0.011353 (-0.005676) | 0.004054 / 0.011008 (-0.006954) | 0.063101 / 0.038508 (0.024592) | 0.031665 / 0.023109 (0.008556) | 0.243332 / 0.275898 (-0.032566) | 0.271067 / 0.323480 (-0.052413) | 0.004283 / 0.007986 (-0.003703) | 0.002889 / 0.004328 (-0.001440) | 0.049269 / 0.004250 (0.045018) | 0.048707 / 0.037052 (0.011654) | 0.258599 / 0.258489 (0.000110) | 0.307715 / 0.293841 (0.013874) | 0.029850 / 0.128546 (-0.098696) | 0.012299 / 0.075646 (-0.063347) | 0.207616 / 0.419271 (-0.211656) | 0.037655 / 0.043533 (-0.005878) | 0.246602 / 0.255139 (-0.008537) | 0.268518 / 0.283200 (-0.014682) | 0.018128 / 0.141683 (-0.123555) | 1.181569 / 1.452155 (-0.270586) | 1.250641 / 1.492716 (-0.242075) |

### 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) | 0.143911 / 0.018006 (0.125905) | 0.305608 / 0.000490 (0.305118) | 0.000250 / 0.000200 (0.000050) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019208 / 0.037411 (-0.018204) | 0.062502 / 0.014526 (0.047976) | 0.075896 / 0.176557 (-0.100661) | 0.123422 / 0.737135 (-0.613713) | 0.077311 / 0.296338 (-0.219028) |

### 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) | 0.283108 / 0.215209 (0.067899) | 2.783509 / 2.077655 (0.705855) | 1.466358 / 1.504120 (-0.037762) | 1.350989 / 1.541195 (-0.190206) | 1.370517 / 1.468490 (-0.097973) | 0.732706 / 4.584777 (-3.852071) | 2.366710 / 3.745712 (-1.379002) | 2.988913 / 5.269862 (-2.280949) | 1.892204 / 4.565676 (-2.673473) | 0.079077 / 0.424275 (-0.345198) | 0.005158 / 0.007607 (-0.002449) | 0.336620 / 0.226044 (0.110576) | 3.423556 / 2.268929 (1.154628) | 1.848732 / 55.444624 (-53.595892) | 1.544996 / 6.876477 (-5.331480) | 1.550051 / 2.142072 (-0.592022) | 0.798235 / 4.805227 (-4.006993) | 0.132945 / 6.500664 (-6.367719) | 0.041785 / 0.075469 (-0.033684) |

### 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) | 0.963359 / 1.841788 (-0.878429) | 11.699994 / 8.074308 (3.625686) | 9.311998 / 10.191392 (-0.879394) | 0.140493 / 0.680424 (-0.539931) | 0.013834 / 0.534201 (-0.520367) | 0.302569 / 0.579283 (-0.276714) | 0.267377 / 0.434364 (-0.166987) | 0.341093 / 0.540337 (-0.199244) | 0.431941 / 1.386936 (-0.954995) |

</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) | 0.005744 / 0.011353 (-0.005608) | 0.003668 / 0.011008 (-0.007340) | 0.049837 / 0.038508 (0.011329) | 0.032051 / 0.023109 (0.008941) | 0.271725 / 0.275898 (-0.004173) | 0.302612 / 0.323480 (-0.020867) | 0.004455 / 0.007986 (-0.003531) | 0.002816 / 0.004328 (-0.001512) | 0.049036 / 0.004250 (0.044785) | 0.041233 / 0.037052 (0.004181) | 0.287900 / 0.258489 (0.029411) | 0.326204 / 0.293841 (0.032363) | 0.032027 / 0.128546 (-0.096519) | 0.012033 / 0.075646 (-0.063613) | 0.060822 / 0.419271 (-0.358449) | 0.033830 / 0.043533 (-0.009703) | 0.274855 / 0.255139 (0.019716) | 0.294191 / 0.283200 (0.010992) | 0.017979 / 0.141683 (-0.123704) | 1.151353 / 1.452155 (-0.300801) | 1.215384 / 1.492716 (-0.277333) |

### 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) | 0.102552 / 0.018006 (0.084546) | 0.314148 / 0.000490 (0.313658) | 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) | 0.024565 / 0.037411 (-0.012846) | 0.076968 / 0.014526 (0.062442) | 0.087982 / 0.176557 (-0.088574) | 0.129844 / 0.737135 (-0.607292) | 0.091370 / 0.296338 (-0.204968) |

### 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) | 0.296767 / 0.215209 (0.081558) | 2.910716 / 2.077655 (0.833062) | 1.579526 / 1.504120 (0.075406) | 1.453457 / 1.541195 (-0.087737) | 1.466296 / 1.468490 (-0.002194) | 0.728372 / 4.584777 (-3.856405) | 0.963852 / 3.745712 (-2.781861) | 2.946582 / 5.269862 (-2.323280) | 1.936199 / 4.565676 (-2.629478) | 0.078886 / 0.424275 (-0.345389) | 0.005537 / 0.007607 (-0.002071) | 0.346315 / 0.226044 (0.120270) | 3.440774 / 2.268929 (1.171845) | 1.937549 / 55.444624 (-53.507076) | 1.649507 / 6.876477 (-5.226970) | 1.653386 / 2.142072 (-0.488686) | 0.806598 / 4.805227 (-3.998629) | 0.133384 / 6.500664 (-6.367280) | 0.040552 / 0.075469 (-0.034917) |

### 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) | 1.030515 / 1.841788 (-0.811272) | 12.129888 / 8.074308 (4.055580) | 10.287069 / 10.191392 (0.095677) | 0.141512 / 0.680424 (-0.538912) | 0.015483 / 0.534201 (-0.518718) | 0.300053 / 0.579283 (-0.279230) | 0.120825 / 0.434364 (-0.313539) | 0.342681 / 0.540337 (-0.197656) | 0.470616 / 1.386936 (-0.916320) |

</details>
</details>

![](https://cml.dev/watermark.png#28780197dd3e4c125defae29ac8ef5346c41350a "CML watermark")


yay! is this in a shipped release?

we can do one in the coming days once @albertvillanova is back

We have made a release and this feature is now included.