Fix deprecation of use_auth_token in DownloadConfig

[albertvillanova]

This PR fixes an issue with the deprecation of use_auth_token in DownloadConfig introduced by:

• Deprecate use_auth_token in favor of token #5996

In [1]: from datasets import DownloadConfig

In [2]: DownloadConfig(use_auth_token=False)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-41927b449e72> in <module>
----> 1 DownloadConfig(use_auth_token=False)

TypeError: __init__() got an unexpected keyword argument 'use_auth_token'

In [1]: from datasets import get_dataset_config_names
In [2]: get_dataset_config_names("squad", use_auth_token=False)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-22-4671992ead50> in <module>
----> 1 get_dataset_config_names("squad", use_auth_token=False)

~/huggingface/datasets/src/datasets/inspect.py in get_dataset_config_names(path, revision, download_config, download_mode, dynamic_modules_path, data_files, **download_kwargs)
    349     ```
    350     """
--> 351     dataset_module = dataset_module_factory(
    352         path,
    353         revision=revision,

~/huggingface/datasets/src/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs)
   1374     """
   1375     if download_config is None:
-> 1376         download_config = DownloadConfig(**download_kwargs)
   1377     download_mode = DownloadMode(download_mode or DownloadMode.REUSE_DATASET_IF_EXISTS)
   1378     download_config.extract_compressed_file = True

TypeError: __init__() got an unexpected keyword argument 'use_auth_token'

[HuggingFaceDocBuilderDev]

The documentation is not available anymore as the PR was closed or merged.

[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)	0.008996 / 0.011353 (-0.002357)	0.004976 / 0.011008 (-0.006033)	0.114495 / 0.038508 (0.075987)	0.083958 / 0.023109 (0.060849)	0.408395 / 0.275898 (0.132497)	0.456757 / 0.323480 (0.133278)	0.006396 / 0.007986 (-0.001589)	0.004315 / 0.004328 (-0.000014)	0.093558 / 0.004250 (0.089307)	0.062067 / 0.037052 (0.025014)	0.423452 / 0.258489 (0.164963)	0.463947 / 0.293841 (0.170106)	0.049934 / 0.128546 (-0.078613)	0.013937 / 0.075646 (-0.061709)	0.365809 / 0.419271 (-0.053463)	0.067382 / 0.043533 (0.023849)	0.418860 / 0.255139 (0.163721)	0.463264 / 0.283200 (0.180065)	0.034392 / 0.141683 (-0.107291)	1.870685 / 1.452155 (0.418530)	1.975313 / 1.492716 (0.482597)

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)	0.261748 / 0.018006 (0.243742)	0.645510 / 0.000490 (0.645020)	0.000376 / 0.000200 (0.000176)	0.000077 / 0.000054 (0.000023)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.032129 / 0.037411 (-0.005282)	0.104309 / 0.014526 (0.089783)	0.113154 / 0.176557 (-0.063403)	0.186795 / 0.737135 (-0.550341)	0.115584 / 0.296338 (-0.180755)

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)	0.577755 / 0.215209 (0.362546)	5.984988 / 2.077655 (3.907333)	2.581967 / 1.504120 (1.077848)	2.305744 / 1.541195 (0.764549)	2.359618 / 1.468490 (0.891128)	0.882892 / 4.584777 (-3.701885)	5.755578 / 3.745712 (2.009866)	8.718373 / 5.269862 (3.448511)	5.217586 / 4.565676 (0.651909)	0.099785 / 0.424275 (-0.324490)	0.009008 / 0.007607 (0.001401)	0.730937 / 0.226044 (0.504892)	7.265309 / 2.268929 (4.996381)	3.487167 / 55.444624 (-51.957457)	2.750090 / 6.876477 (-4.126386)	3.060198 / 2.142072 (0.918125)	1.069945 / 4.805227 (-3.735282)	0.227143 / 6.500664 (-6.273521)	0.083601 / 0.075469 (0.008132)

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)	1.754375 / 1.841788 (-0.087412)	25.448731 / 8.074308 (17.374423)	22.385943 / 10.191392 (12.194551)	0.249921 / 0.680424 (-0.430503)	0.034138 / 0.534201 (-0.500063)	0.535170 / 0.579283 (-0.044113)	0.605474 / 0.434364 (0.171110)	0.580025 / 0.540337 (0.039688)	0.810537 / 1.386936 (-0.576399)

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)	0.009117 / 0.011353 (-0.002236)	0.005029 / 0.011008 (-0.005979)	0.082200 / 0.038508 (0.043691)	0.082386 / 0.023109 (0.059277)	0.491869 / 0.275898 (0.215971)	0.546735 / 0.323480 (0.223255)	0.006893 / 0.007986 (-0.001093)	0.004571 / 0.004328 (0.000243)	0.085361 / 0.004250 (0.081111)	0.063342 / 0.037052 (0.026290)	0.522522 / 0.258489 (0.264033)	0.560784 / 0.293841 (0.266943)	0.047685 / 0.128546 (-0.080861)	0.017741 / 0.075646 (-0.057905)	0.098204 / 0.419271 (-0.321067)	0.062919 / 0.043533 (0.019386)	0.504005 / 0.255139 (0.248866)	0.547022 / 0.283200 (0.263823)	0.033731 / 0.141683 (-0.107952)	1.869765 / 1.452155 (0.417610)	1.935867 / 1.492716 (0.443151)

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)	0.304756 / 0.018006 (0.286750)	0.623647 / 0.000490 (0.623157)	0.000508 / 0.000200 (0.000308)	0.000090 / 0.000054 (0.000035)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.043627 / 0.037411 (0.006216)	0.107183 / 0.014526 (0.092657)	0.119304 / 0.176557 (-0.057253)	0.192651 / 0.737135 (-0.544485)	0.125118 / 0.296338 (-0.171221)

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)	0.669980 / 0.215209 (0.454771)	6.566068 / 2.077655 (4.488413)	3.136271 / 1.504120 (1.632152)	2.964643 / 1.541195 (1.423448)	2.936772 / 1.468490 (1.468282)	0.885205 / 4.584777 (-3.699572)	5.539062 / 3.745712 (1.793350)	5.006133 / 5.269862 (-0.263729)	3.313697 / 4.565676 (-1.251979)	0.102975 / 0.424275 (-0.321301)	0.010759 / 0.007607 (0.003152)	0.791176 / 0.226044 (0.565132)	7.822195 / 2.268929 (5.553266)	3.982315 / 55.444624 (-51.462309)	3.357026 / 6.876477 (-3.519451)	3.561307 / 2.142072 (1.419234)	1.056966 / 4.805227 (-3.748261)	0.220476 / 6.500664 (-6.280188)	0.090535 / 0.075469 (0.015066)

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)	1.897984 / 1.841788 (0.056196)	26.411411 / 8.074308 (18.337103)	22.951939 / 10.191392 (12.760547)	0.216091 / 0.680424 (-0.464333)	0.037005 / 0.534201 (-0.497196)	0.505585 / 0.579283 (-0.073698)	0.617794 / 0.434364 (0.183430)	0.604631 / 0.540337 (0.064293)	0.826356 / 1.386936 (-0.560580)

[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)	0.006850 / 0.011353 (-0.004503)	0.004062 / 0.011008 (-0.006947)	0.086587 / 0.038508 (0.048079)	0.079587 / 0.023109 (0.056478)	0.353601 / 0.275898 (0.077702)	0.396399 / 0.323480 (0.072919)	0.004182 / 0.007986 (-0.003804)	0.004445 / 0.004328 (0.000117)	0.065100 / 0.004250 (0.060849)	0.057386 / 0.037052 (0.020334)	0.356945 / 0.258489 (0.098456)	0.407093 / 0.293841 (0.113252)	0.031949 / 0.128546 (-0.096597)	0.008525 / 0.075646 (-0.067121)	0.291310 / 0.419271 (-0.127961)	0.053638 / 0.043533 (0.010105)	0.359381 / 0.255139 (0.104242)	0.399473 / 0.283200 (0.116273)	0.025880 / 0.141683 (-0.115803)	1.487604 / 1.452155 (0.035449)	1.550528 / 1.492716 (0.057812)

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)	0.201106 / 0.018006 (0.183099)	0.457538 / 0.000490 (0.457048)	0.003995 / 0.000200 (0.003795)	0.000087 / 0.000054 (0.000032)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.030365 / 0.037411 (-0.007046)	0.088064 / 0.014526 (0.073538)	0.096432 / 0.176557 (-0.080124)	0.158063 / 0.737135 (-0.579072)	0.098258 / 0.296338 (-0.198080)

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)	0.405351 / 0.215209 (0.190142)	4.032639 / 2.077655 (1.954984)	2.018357 / 1.504120 (0.514237)	1.848493 / 1.541195 (0.307298)	1.929401 / 1.468490 (0.460910)	0.488729 / 4.584777 (-4.096048)	3.586114 / 3.745712 (-0.159598)	5.279054 / 5.269862 (0.009193)	3.113275 / 4.565676 (-1.452402)	0.057373 / 0.424275 (-0.366902)	0.007416 / 0.007607 (-0.000191)	0.485514 / 0.226044 (0.259470)	4.854389 / 2.268929 (2.585461)	2.493113 / 55.444624 (-52.951512)	2.128836 / 6.876477 (-4.747641)	2.383669 / 2.142072 (0.241596)	0.588266 / 4.805227 (-4.216962)	0.133603 / 6.500664 (-6.367061)	0.061812 / 0.075469 (-0.013657)

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)	1.260841 / 1.841788 (-0.580947)	20.086954 / 8.074308 (12.012646)	14.620932 / 10.191392 (4.429540)	0.161525 / 0.680424 (-0.518899)	0.018102 / 0.534201 (-0.516099)	0.393810 / 0.579283 (-0.185473)	0.406974 / 0.434364 (-0.027390)	0.462732 / 0.540337 (-0.077606)	0.634221 / 1.386936 (-0.752715)

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)	0.006692 / 0.011353 (-0.004661)	0.004068 / 0.011008 (-0.006940)	0.068009 / 0.038508 (0.029501)	0.081298 / 0.023109 (0.058189)	0.363531 / 0.275898 (0.087633)	0.408482 / 0.323480 (0.085002)	0.005601 / 0.007986 (-0.002384)	0.003385 / 0.004328 (-0.000943)	0.068043 / 0.004250 (0.063792)	0.059739 / 0.037052 (0.022687)	0.374043 / 0.258489 (0.115553)	0.407219 / 0.293841 (0.113378)	0.031194 / 0.128546 (-0.097352)	0.008630 / 0.075646 (-0.067017)	0.073755 / 0.419271 (-0.345517)	0.049831 / 0.043533 (0.006298)	0.363664 / 0.255139 (0.108525)	0.381515 / 0.283200 (0.098315)	0.026331 / 0.141683 (-0.115352)	1.507771 / 1.452155 (0.055617)	1.554403 / 1.492716 (0.061686)

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)	0.226309 / 0.018006 (0.208302)	0.452428 / 0.000490 (0.451938)	0.000937 / 0.000200 (0.000737)	0.000069 / 0.000054 (0.000015)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.031899 / 0.037411 (-0.005513)	0.092090 / 0.014526 (0.077564)	0.100838 / 0.176557 (-0.075718)	0.153722 / 0.737135 (-0.583413)	0.101950 / 0.296338 (-0.194389)

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)	0.417879 / 0.215209 (0.202669)	4.171939 / 2.077655 (2.094284)	2.312937 / 1.504120 (0.808817)	2.209991 / 1.541195 (0.668796)	2.329469 / 1.468490 (0.860979)	0.484576 / 4.584777 (-4.100201)	3.659198 / 3.745712 (-0.086514)	5.255227 / 5.269862 (-0.014634)	3.047430 / 4.565676 (-1.518247)	0.057029 / 0.424275 (-0.367246)	0.007735 / 0.007607 (0.000127)	0.499962 / 0.226044 (0.273918)	4.991655 / 2.268929 (2.722727)	2.755999 / 55.444624 (-52.688625)	2.374034 / 6.876477 (-4.502443)	2.599759 / 2.142072 (0.457687)	0.600319 / 4.805227 (-4.204908)	0.146176 / 6.500664 (-6.354488)	0.062328 / 0.075469 (-0.013142)

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)	1.346065 / 1.841788 (-0.495722)	20.430343 / 8.074308 (12.356035)	14.632959 / 10.191392 (4.441567)	0.167007 / 0.680424 (-0.513417)	0.018588 / 0.534201 (-0.515613)	0.396015 / 0.579283 (-0.183268)	0.429384 / 0.434364 (-0.004980)	0.467746 / 0.540337 (-0.072591)	0.615166 / 1.386936 (-0.771770)