Avoid calling http_head for non-HTTP URLs

[albertvillanova]

Avoid calling http_head for non-HTTP URLs, by adding and else statement.

Currently, it makes an unnecessary HTTP call (which adds latency) for non-HTTP protocols, like FTP, S3,...

I discovered this while working in an unrelated issue.

[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)	0.005591 / 0.011353 (-0.005761)	0.003992 / 0.011008 (-0.007016)	0.063932 / 0.038508 (0.025424)	0.034572 / 0.023109 (0.011463)	0.252532 / 0.275898 (-0.023366)	0.271233 / 0.323480 (-0.052247)	0.005146 / 0.007986 (-0.002840)	0.002844 / 0.004328 (-0.001484)	0.049555 / 0.004250 (0.045305)	0.044111 / 0.037052 (0.007059)	0.270131 / 0.258489 (0.011642)	0.318109 / 0.293841 (0.024269)	0.030247 / 0.128546 (-0.098300)	0.012438 / 0.075646 (-0.063209)	0.205160 / 0.419271 (-0.214112)	0.036228 / 0.043533 (-0.007305)	0.250664 / 0.255139 (-0.004475)	0.263884 / 0.283200 (-0.019315)	0.018141 / 0.141683 (-0.123541)	1.128504 / 1.452155 (-0.323650)	1.182543 / 1.492716 (-0.310173)

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)	0.094576 / 0.018006 (0.076570)	0.301153 / 0.000490 (0.300664)	0.000246 / 0.000200 (0.000046)	0.000065 / 0.000054 (0.000011)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.019143 / 0.037411 (-0.018268)	0.062788 / 0.014526 (0.048262)	0.074688 / 0.176557 (-0.101869)	0.121799 / 0.737135 (-0.615336)	0.076200 / 0.296338 (-0.220138)

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)	0.277002 / 0.215209 (0.061793)	2.735738 / 2.077655 (0.658083)	1.430408 / 1.504120 (-0.073712)	1.309795 / 1.541195 (-0.231400)	1.339083 / 1.468490 (-0.129407)	0.702540 / 4.584777 (-3.882237)	2.352468 / 3.745712 (-1.393244)	2.913698 / 5.269862 (-2.356164)	1.871739 / 4.565676 (-2.693938)	0.077054 / 0.424275 (-0.347221)	0.005055 / 0.007607 (-0.002552)	0.330550 / 0.226044 (0.104505)	3.272556 / 2.268929 (1.003627)	1.805268 / 55.444624 (-53.639356)	1.504791 / 6.876477 (-5.371686)	1.511361 / 2.142072 (-0.630712)	0.784451 / 4.805227 (-4.020776)	0.132182 / 6.500664 (-6.368482)	0.042516 / 0.075469 (-0.032954)

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)	0.946939 / 1.841788 (-0.894849)	11.369607 / 8.074308 (3.295299)	9.667350 / 10.191392 (-0.524042)	0.138689 / 0.680424 (-0.541735)	0.014416 / 0.534201 (-0.519785)	0.300685 / 0.579283 (-0.278598)	0.259709 / 0.434364 (-0.174655)	0.341271 / 0.540337 (-0.199066)	0.435609 / 1.386936 (-0.951327)

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)	0.005726 / 0.011353 (-0.005627)	0.004071 / 0.011008 (-0.006937)	0.050837 / 0.038508 (0.012329)	0.047000 / 0.023109 (0.023890)	0.278543 / 0.275898 (0.002645)	0.300526 / 0.323480 (-0.022954)	0.004483 / 0.007986 (-0.003503)	0.002835 / 0.004328 (-0.001494)	0.050925 / 0.004250 (0.046675)	0.041834 / 0.037052 (0.004782)	0.285059 / 0.258489 (0.026570)	0.324557 / 0.293841 (0.030716)	0.038949 / 0.128546 (-0.089597)	0.012145 / 0.075646 (-0.063501)	0.061791 / 0.419271 (-0.357481)	0.034493 / 0.043533 (-0.009040)	0.274034 / 0.255139 (0.018895)	0.295886 / 0.283200 (0.012686)	0.018524 / 0.141683 (-0.123159)	1.148766 / 1.452155 (-0.303388)	1.207966 / 1.492716 (-0.284750)

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)	0.094078 / 0.018006 (0.076071)	0.307850 / 0.000490 (0.307361)	0.000224 / 0.000200 (0.000024)	0.000079 / 0.000054 (0.000025)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.023502 / 0.037411 (-0.013910)	0.077321 / 0.014526 (0.062795)	0.091147 / 0.176557 (-0.085410)	0.131111 / 0.737135 (-0.606025)	0.090906 / 0.296338 (-0.205432)

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)	0.290700 / 0.215209 (0.075491)	2.833655 / 2.077655 (0.756001)	1.546371 / 1.504120 (0.042251)	1.415337 / 1.541195 (-0.125858)	1.445752 / 1.468490 (-0.022738)	0.737880 / 4.584777 (-3.846897)	0.961549 / 3.745712 (-2.784164)	2.844021 / 5.269862 (-2.425841)	2.023547 / 4.565676 (-2.542130)	0.079791 / 0.424275 (-0.344484)	0.005449 / 0.007607 (-0.002158)	0.356381 / 0.226044 (0.130337)	3.515555 / 2.268929 (1.246627)	1.920407 / 55.444624 (-53.524217)	1.628637 / 6.876477 (-5.247839)	1.752995 / 2.142072 (-0.389077)	0.807264 / 4.805227 (-3.997963)	0.133627 / 6.500664 (-6.367037)	0.041861 / 0.075469 (-0.033609)

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)	1.035643 / 1.841788 (-0.806144)	12.114792 / 8.074308 (4.040484)	10.185844 / 10.191392 (-0.005548)	0.142354 / 0.680424 (-0.538070)	0.015466 / 0.534201 (-0.518734)	0.304681 / 0.579283 (-0.274603)	0.124297 / 0.434364 (-0.310067)	0.339907 / 0.540337 (-0.200430)	0.436266 / 1.386936 (-0.950670)