Title: Fix ConnectionError for gated datasets and unauthenticated users
State: closed

Fix `ConnectionError` for gated datasets and unauthenticated users. See:
- https://github.com/huggingface/dataset-viewer/issues/3025

Note that a recent change in the Hub returns dataset info for gated datasets and unauthenticated users, instead of raising a `GatedRepoError` as before. See:
- https://github.com/huggingface/huggingface_hub/issues/2457

This PR adds an additional check (/auth-check) for gated datasets and raises `DatasetNotFoundError` for unauthenticated users, as it was the case before the change in the Hub.
- Fix suggested by @Pierrci (thanks @Wauplin for pointing it out).

Fix #7109.

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7110). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.Note that the CI error is unrelated to this PR and should be addressed in another PR. See:
- #7111<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) | 0.005354 / 0.011353 (-0.005999) | 0.004031 / 0.011008 (-0.006977) | 0.062470 / 0.038508 (0.023962) | 0.030882 / 0.023109 (0.007773) | 0.244816 / 0.275898 (-0.031082) | 0.264324 / 0.323480 (-0.059156) | 0.004164 / 0.007986 (-0.003822) | 0.002858 / 0.004328 (-0.001471) | 0.049008 / 0.004250 (0.044758) | 0.042139 / 0.037052 (0.005086) | 0.279496 / 0.258489 (0.021007) | 0.279408 / 0.293841 (-0.014433) | 0.029701 / 0.128546 (-0.098845) | 0.012501 / 0.075646 (-0.063145) | 0.203267 / 0.419271 (-0.216004) | 0.035964 / 0.043533 (-0.007569) | 0.239361 / 0.255139 (-0.015778) | 0.258942 / 0.283200 (-0.024257) | 0.017956 / 0.141683 (-0.123727) | 1.160468 / 1.452155 (-0.291687) | 1.203475 / 1.492716 (-0.289242) |

### 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) | 0.004639 / 0.018006 (-0.013367) | 0.298020 / 0.000490 (0.297530) | 0.000212 / 0.000200 (0.000012) | 0.000043 / 0.000054 (-0.000012) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019371 / 0.037411 (-0.018040) | 0.063311 / 0.014526 (0.048785) | 0.076412 / 0.176557 (-0.100145) | 0.122574 / 0.737135 (-0.614561) | 0.078076 / 0.296338 (-0.218263) |

### 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) | 0.275381 / 0.215209 (0.060172) | 2.713220 / 2.077655 (0.635565) | 1.441940 / 1.504120 (-0.062179) | 1.325545 / 1.541195 (-0.215650) | 1.363859 / 1.468490 (-0.104631) | 0.715147 / 4.584777 (-3.869630) | 2.356482 / 3.745712 (-1.389230) | 2.882792 / 5.269862 (-2.387069) | 1.833399 / 4.565676 (-2.732278) | 0.077872 / 0.424275 (-0.346403) | 0.005172 / 0.007607 (-0.002435) | 0.326361 / 0.226044 (0.100316) | 3.239202 / 2.268929 (0.970273) | 1.837745 / 55.444624 (-53.606879) | 1.517299 / 6.876477 (-5.359178) | 1.552938 / 2.142072 (-0.589134) | 0.801496 / 4.805227 (-4.003731) | 0.133351 / 6.500664 (-6.367314) | 0.042052 / 0.075469 (-0.033418) |

### 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) | 0.957887 / 1.841788 (-0.883901) | 11.625291 / 8.074308 (3.550983) | 9.679413 / 10.191392 (-0.511979) | 0.140271 / 0.680424 (-0.540153) | 0.013991 / 0.534201 (-0.520210) | 0.299874 / 0.579283 (-0.279409) | 0.267164 / 0.434364 (-0.167200) | 0.338143 / 0.540337 (-0.202194) | 0.434105 / 1.386936 (-0.952831) |

</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) | 0.005833 / 0.011353 (-0.005520) | 0.003761 / 0.011008 (-0.007247) | 0.049699 / 0.038508 (0.011191) | 0.032786 / 0.023109 (0.009677) | 0.265100 / 0.275898 (-0.010798) | 0.291045 / 0.323480 (-0.032435) | 0.004281 / 0.007986 (-0.003705) | 0.002737 / 0.004328 (-0.001591) | 0.048524 / 0.004250 (0.044274) | 0.040783 / 0.037052 (0.003731) | 0.281122 / 0.258489 (0.022633) | 0.311349 / 0.293841 (0.017508) | 0.032143 / 0.128546 (-0.096403) | 0.011747 / 0.075646 (-0.063899) | 0.059432 / 0.419271 (-0.359840) | 0.034362 / 0.043533 (-0.009171) | 0.261061 / 0.255139 (0.005922) | 0.279536 / 0.283200 (-0.003663) | 0.019172 / 0.141683 (-0.122510) | 1.160069 / 1.452155 (-0.292086) | 1.224160 / 1.492716 (-0.268556) |

### 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) | 0.093596 / 0.018006 (0.075590) | 0.302862 / 0.000490 (0.302372) | 0.000208 / 0.000200 (0.000008) | 0.000047 / 0.000054 (-0.000007) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022785 / 0.037411 (-0.014626) | 0.079263 / 0.014526 (0.064737) | 0.091340 / 0.176557 (-0.085216) | 0.129453 / 0.737135 (-0.607682) | 0.091349 / 0.296338 (-0.204989) |

### 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) | 0.298166 / 0.215209 (0.082957) | 3.003146 / 2.077655 (0.925491) | 1.575903 / 1.504120 (0.071783) | 1.445231 / 1.541195 (-0.095963) | 1.477116 / 1.468490 (0.008625) | 0.726496 / 4.584777 (-3.858281) | 0.959827 / 3.745712 (-2.785885) | 2.941142 / 5.269862 (-2.328720) | 1.878581 / 4.565676 (-2.687096) | 0.078475 / 0.424275 (-0.345800) | 0.005137 / 0.007607 (-0.002470) | 0.352078 / 0.226044 (0.126034) | 3.486113 / 2.268929 (1.217184) | 1.965024 / 55.444624 (-53.479600) | 1.667223 / 6.876477 (-5.209254) | 1.665254 / 2.142072 (-0.476819) | 0.803543 / 4.805227 (-4.001684) | 0.133003 / 6.500664 (-6.367661) | 0.041462 / 0.075469 (-0.034008) |

### 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) | 1.045534 / 1.841788 (-0.796254) | 12.124988 / 8.074308 (4.050680) | 10.418723 / 10.191392 (0.227331) | 0.142453 / 0.680424 (-0.537971) | 0.015686 / 0.534201 (-0.518515) | 0.300557 / 0.579283 (-0.278726) | 0.119851 / 0.434364 (-0.314512) | 0.342297 / 0.540337 (-0.198040) | 0.441263 / 1.386936 (-0.945673) |

</details>
</details>

![](https://cml.dev/watermark.png#90b1d94ef419cb26f0bb24d982897dca39aa8a46 "CML watermark")
lgtm!