Remove deprecated code

[Wauplin]

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.

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[lhoestq]

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

[Wauplin]

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

[albertvillanova]

Great! Thank you. We forgot to remove all this dead code.

There are however some red CI tests to be fixed...

[Wauplin]

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

[albertvillanova]

Great! Thanks again.

[github-actions]

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

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)

PyArrow==latest

Show updated benchmarks!

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)