Re-enable import sorting disabled by flake8:noqa directive when using ruff linter

[albertvillanova]

Re-enable import sorting that was wrongly disabled by flake8: noqa directive after switching to ruff linter in datasets-2.10.0 PR:

• Lint code with ruff #5519

Note that after the linter switch, we wrongly replaced flake8: noqa with ruff: noqa in datasets-2.17.0 PR:

• Migrate from setup.cfg to pyproject.toml #6619

That replacement was wrong because we kept the isort: skip directives although they were indeed disabled by flake8: noqa first and by ruff: noqa afterwards. See for example __init__.py file after the linter switch:

• We kept the flake8: noqa directive
  

  datasets/src/datasets/__init__.py

  Line 1 in 06ae3f6

  # flake8: noqa

• Whereas we also kept the isort: skip directives (that were disabled)
  

  datasets/src/datasets/__init__.py

  Lines 82 to 84 in 06ae3f6

  	from datasets import arrow_dataset as _arrow_dataset # isort:skip
  	from datasets import utils as _utils # isort:skip
  	from datasets.utils import download_manager as _deprecated_download_manager # isort:skip

Fix #6942.

[HuggingFaceDocBuilderDev]

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[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)	0.004847 / 0.011353 (-0.006506)	0.003199 / 0.011008 (-0.007810)	0.060677 / 0.038508 (0.022169)	0.030544 / 0.023109 (0.007435)	0.240870 / 0.275898 (-0.035028)	0.261320 / 0.323480 (-0.062160)	0.002816 / 0.007986 (-0.005170)	0.002483 / 0.004328 (-0.001845)	0.048527 / 0.004250 (0.044277)	0.045496 / 0.037052 (0.008444)	0.251296 / 0.258489 (-0.007193)	0.285746 / 0.293841 (-0.008095)	0.025076 / 0.128546 (-0.103470)	0.009417 / 0.075646 (-0.066229)	0.191361 / 0.419271 (-0.227911)	0.033778 / 0.043533 (-0.009755)	0.235581 / 0.255139 (-0.019558)	0.261069 / 0.283200 (-0.022131)	0.018255 / 0.141683 (-0.123428)	1.098437 / 1.452155 (-0.353718)	1.127124 / 1.492716 (-0.365592)

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)	0.004479 / 0.018006 (-0.013527)	0.283706 / 0.000490 (0.283216)	0.000214 / 0.000200 (0.000014)	0.000043 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018364 / 0.037411 (-0.019048)	0.058398 / 0.014526 (0.043872)	0.073056 / 0.176557 (-0.103501)	0.117147 / 0.737135 (-0.619989)	0.073683 / 0.296338 (-0.222656)

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)	0.265121 / 0.215209 (0.049912)	2.636981 / 2.077655 (0.559327)	1.380192 / 1.504120 (-0.123928)	1.270779 / 1.541195 (-0.270416)	1.295729 / 1.468490 (-0.172762)	0.523768 / 4.584777 (-4.061009)	2.295720 / 3.745712 (-1.449992)	2.519211 / 5.269862 (-2.750650)	1.618712 / 4.565676 (-2.946965)	0.058321 / 0.424275 (-0.365954)	0.004492 / 0.007607 (-0.003115)	0.316101 / 0.226044 (0.090057)	3.169913 / 2.268929 (0.900984)	1.793412 / 55.444624 (-53.651213)	1.473784 / 6.876477 (-5.402693)	1.565325 / 2.142072 (-0.576748)	0.592734 / 4.805227 (-4.212493)	0.109333 / 6.500664 (-6.391331)	0.039063 / 0.075469 (-0.036406)

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)	0.935504 / 1.841788 (-0.906284)	10.865520 / 8.074308 (2.791212)	9.219337 / 10.191392 (-0.972055)	0.135284 / 0.680424 (-0.545140)	0.013664 / 0.534201 (-0.520537)	0.271601 / 0.579283 (-0.307682)	0.260456 / 0.434364 (-0.173908)	0.302931 / 0.540337 (-0.237406)	0.414643 / 1.386936 (-0.972293)

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)	0.004801 / 0.011353 (-0.006552)	0.003092 / 0.011008 (-0.007917)	0.046471 / 0.038508 (0.007963)	0.031337 / 0.023109 (0.008228)	0.258920 / 0.275898 (-0.016978)	0.269842 / 0.323480 (-0.053638)	0.003976 / 0.007986 (-0.004009)	0.002661 / 0.004328 (-0.001668)	0.045676 / 0.004250 (0.041426)	0.038199 / 0.037052 (0.001146)	0.277382 / 0.258489 (0.018893)	0.289351 / 0.293841 (-0.004490)	0.028452 / 0.128546 (-0.100094)	0.009737 / 0.075646 (-0.065910)	0.055201 / 0.419271 (-0.364071)	0.032686 / 0.043533 (-0.010847)	0.259617 / 0.255139 (0.004478)	0.277163 / 0.283200 (-0.006037)	0.017825 / 0.141683 (-0.123858)	1.102797 / 1.452155 (-0.349357)	1.105018 / 1.492716 (-0.387699)

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)	0.094844 / 0.018006 (0.076838)	0.290519 / 0.000490 (0.290029)	0.000211 / 0.000200 (0.000012)	0.000050 / 0.000054 (-0.000004)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.021917 / 0.037411 (-0.015494)	0.075278 / 0.014526 (0.060753)	0.085971 / 0.176557 (-0.090586)	0.127072 / 0.737135 (-0.610063)	0.088244 / 0.296338 (-0.208095)

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)	0.276704 / 0.215209 (0.061495)	2.736960 / 2.077655 (0.659305)	1.519634 / 1.504120 (0.015514)	1.403026 / 1.541195 (-0.138168)	1.418465 / 1.468490 (-0.050025)	0.552425 / 4.584777 (-4.032352)	0.955244 / 3.745712 (-2.790468)	2.556563 / 5.269862 (-2.713298)	1.705095 / 4.565676 (-2.860582)	0.061212 / 0.424275 (-0.363063)	0.004707 / 0.007607 (-0.002900)	0.326284 / 0.226044 (0.100239)	3.253911 / 2.268929 (0.984983)	1.868649 / 55.444624 (-53.575976)	1.598697 / 6.876477 (-5.277780)	1.682617 / 2.142072 (-0.459455)	0.606379 / 4.805227 (-4.198848)	0.114126 / 6.500664 (-6.386538)	0.038869 / 0.075469 (-0.036601)

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)	0.966354 / 1.841788 (-0.875433)	11.575918 / 8.074308 (3.501609)	9.816597 / 10.191392 (-0.374795)	0.141492 / 0.680424 (-0.538932)	0.015375 / 0.534201 (-0.518826)	0.276027 / 0.579283 (-0.303256)	0.118979 / 0.434364 (-0.315385)	0.313467 / 0.540337 (-0.226870)	0.403539 / 1.386936 (-0.983397)