Remove deprecated code
Opened by Wauplin (closed)

What does this PR do?
1. remove `list_files_info` in favor of `list_repo_tree`. As of `0.23`, `list_files_info` will be removed for good. `datasets` had a utility to support both pre-0.20 and post-0.20 versions. Since `hfh` version is already pinned to `>=0.21.2`, I removed the legacy part.
2. `preupload_lfs_files` had also a different behavior between `<0.20` and `>=0.20`. I remove it since huggingface_hub is now pinned to `>=0.21.2`
3. `hf_hub_url` is overwritten to default to the dataset repo_type. I do think it is misleading to keep the same method naming for it. I renamed it to `get_dataset_url` for clarity. Let me know if you prefer to see this change reverted.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6761). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Thanks for cleaning this :) I'm also fine with renaming `hf_dataset_url` (and not `get_dataset_url` as you said in your OP)

(Yep, `hf_dataset_url` is fine, made a mistake writing the PR description)

@albertvillanova Sorry about that, tests are now fixed! :)

<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) | 0.005357 / 0.011353 (-0.005995) | 0.003788 / 0.011008 (-0.007220) | 0.063630 / 0.038508 (0.025122) | 0.031353 / 0.023109 (0.008244) | 0.247525 / 0.275898 (-0.028373) | 0.282052 / 0.323480 (-0.041428) | 0.004247 / 0.007986 (-0.003739) | 0.002750 / 0.004328 (-0.001579) | 0.049467 / 0.004250 (0.045217) | 0.046663 / 0.037052 (0.009610) | 0.266440 / 0.258489 (0.007951) | 0.295230 / 0.293841 (0.001389) | 0.028271 / 0.128546 (-0.100276) | 0.011116 / 0.075646 (-0.064530) | 0.222092 / 0.419271 (-0.197179) | 0.036627 / 0.043533 (-0.006906) | 0.252607 / 0.255139 (-0.002532) | 0.271231 / 0.283200 (-0.011969) | 0.019070 / 0.141683 (-0.122613) | 1.152645 / 1.452155 (-0.299509) | 1.211267 / 1.492716 (-0.281449) |

### 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) | 0.095002 / 0.018006 (0.076996) | 0.304054 / 0.000490 (0.303564) | 0.000212 / 0.000200 (0.000012) | 0.000056 / 0.000054 (0.000001) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018251 / 0.037411 (-0.019161) | 0.061929 / 0.014526 (0.047403) | 0.074641 / 0.176557 (-0.101916) | 0.122643 / 0.737135 (-0.614492) | 0.076744 / 0.296338 (-0.219594) |

### 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) | 0.284605 / 0.215209 (0.069396) | 2.774638 / 2.077655 (0.696984) | 1.473907 / 1.504120 (-0.030213) | 1.351054 / 1.541195 (-0.190141) | 1.348840 / 1.468490 (-0.119650) | 0.576243 / 4.584777 (-4.008534) | 2.444110 / 3.745712 (-1.301602) | 2.814741 / 5.269862 (-2.455121) | 1.762666 / 4.565676 (-2.803010) | 0.063959 / 0.424275 (-0.360316) | 0.005011 / 0.007607 (-0.002596) | 0.338406 / 0.226044 (0.112361) | 3.361213 / 2.268929 (1.092284) | 1.832674 / 55.444624 (-53.611950) | 1.564229 / 6.876477 (-5.312248) | 1.570843 / 2.142072 (-0.571230) | 0.657134 / 4.805227 (-4.148093) | 0.120041 / 6.500664 (-6.380623) | 0.048594 / 0.075469 (-0.026875) |

### 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) | 0.965328 / 1.841788 (-0.876460) | 11.704441 / 8.074308 (3.630133) | 9.895462 / 10.191392 (-0.295930) | 0.131913 / 0.680424 (-0.548511) | 0.015175 / 0.534201 (-0.519026) | 0.292022 / 0.579283 (-0.287261) | 0.269752 / 0.434364 (-0.164612) | 0.330453 / 0.540337 (-0.209884) | 0.421659 / 1.386936 (-0.965277) |

</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) | 0.005472 / 0.011353 (-0.005881) | 0.003809 / 0.011008 (-0.007199) | 0.049594 / 0.038508 (0.011086) | 0.031858 / 0.023109 (0.008748) | 0.277622 / 0.275898 (0.001724) | 0.296092 / 0.323480 (-0.027388) | 0.004209 / 0.007986 (-0.003777) | 0.002726 / 0.004328 (-0.001603) | 0.048057 / 0.004250 (0.043806) | 0.043317 / 0.037052 (0.006265) | 0.288371 / 0.258489 (0.029882) | 0.312847 / 0.293841 (0.019007) | 0.029110 / 0.128546 (-0.099437) | 0.010792 / 0.075646 (-0.064854) | 0.058694 / 0.419271 (-0.360577) | 0.033315 / 0.043533 (-0.010218) | 0.281225 / 0.255139 (0.026086) | 0.297044 / 0.283200 (0.013844) | 0.018897 / 0.141683 (-0.122786) | 1.156417 / 1.452155 (-0.295738) | 1.221393 / 1.492716 (-0.271323) |

### 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) | 0.095065 / 0.018006 (0.077059) | 0.304107 / 0.000490 (0.303618) | 0.000213 / 0.000200 (0.000014) | 0.000043 / 0.000054 (-0.000012) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021658 / 0.037411 (-0.015753) | 0.075948 / 0.014526 (0.061423) | 0.087019 / 0.176557 (-0.089537) | 0.127309 / 0.737135 (-0.609827) | 0.092251 / 0.296338 (-0.204087) |

### 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) | 0.291906 / 0.215209 (0.076697) | 2.865007 / 2.077655 (0.787352) | 1.591647 / 1.504120 (0.087527) | 1.474499 / 1.541195 (-0.066696) | 1.496644 / 1.468490 (0.028154) | 0.575337 / 4.584777 (-4.009440) | 2.569426 / 3.745712 (-1.176287) | 2.872611 / 5.269862 (-2.397251) | 1.804278 / 4.565676 (-2.761399) | 0.064225 / 0.424275 (-0.360050) | 0.005574 / 0.007607 (-0.002033) | 0.347724 / 0.226044 (0.121680) | 3.426418 / 2.268929 (1.157490) | 1.966270 / 55.444624 (-53.478355) | 1.687790 / 6.876477 (-5.188686) | 1.728530 / 2.142072 (-0.413542) | 0.650251 / 4.805227 (-4.154977) | 0.118381 / 6.500664 (-6.382283) | 0.041693 / 0.075469 (-0.033776) |

### 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) | 1.014203 / 1.841788 (-0.827585) | 12.219496 / 8.074308 (4.145188) | 10.469677 / 10.191392 (0.278285) | 0.141840 / 0.680424 (-0.538584) | 0.015104 / 0.534201 (-0.519097) | 0.288453 / 0.579283 (-0.290830) | 0.287467 / 0.434364 (-0.146897) | 0.331046 / 0.540337 (-0.209292) | 0.423731 / 1.386936 (-0.963205) |

</details>
</details>

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