Pass down storage options
_The documentation is not available anymore as the PR was closed or merged._ > download_and_prepare is not called when streaming a dataset, so we may need to have storage_options in the DatasetBuilder.__init__ ? This way it could also be passed later to as_streaming_dataset and the StreamingDownloadManager

> Currently the storage_options parameter in download_and_prepare are for the target filesystem where the dataset must be downloaded and prepared as arrow files

Ah, I noted this when looking for ways to plumb down `storage_options` although I think I was looking at adding to `BuilderConfig`. The `DatasetBuilder` constructor looks more appropriate for this, will get that added in a future commit Noting as experimental SGTM. The only tests I can think of to add at the moment would be mocks that assert the storage options get passed all the way down using `mock.assert_called_with` but if Hugging Face has some S3/GCS buckets for testing, maybe those would be better in a future PR. Let me know what you think I think adding tests with the mockfs fixture will do the job. Tests and docs can be added when request_etag and is_remote_url support fsspec (right now they would fail with mockfs).

Let's see in a subsequent PR, this is exciting ! :) <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) | 0.009217 / 0.011353 (-0.002136) | 0.006275 / 0.011008 (-0.004733) | 0.124361 / 0.038508 (0.085853) | 0.035680 / 0.023109 (0.012570) | 0.395255 / 0.275898 (0.119357) | 0.426104 / 0.323480 (0.102624) | 0.006822 / 0.007986 (-0.001163) | 0.004467 / 0.004328 (0.000138) | 0.099404 / 0.004250 (0.095153) | 0.051919 / 0.037052 (0.014867) | 0.388286 / 0.258489 (0.129797) | 0.426361 / 0.293841 (0.132520) | 0.053100 / 0.128546 (-0.075446) | 0.019453 / 0.075646 (-0.056194) | 0.433139 / 0.419271 (0.013867) | 0.063240 / 0.043533 (0.019707) | 0.381175 / 0.255139 (0.126036) | 0.411686 / 0.283200 (0.128487) | 0.104843 / 0.141683 (-0.036840) | 1.853582 / 1.452155 (0.401427) | 1.935644 / 1.492716 (0.442928) |

### 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) | 0.218969 / 0.018006 (0.200963) | 0.515011 / 0.000490 (0.514522) | 0.004017 / 0.000200 (0.003818) | 0.000097 / 0.000054 (0.000043) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.028975 / 0.037411 (-0.008437) | 0.125239 / 0.014526 (0.110713) | 0.131371 / 0.176557 (-0.045185) | 0.203864 / 0.737135 (-0.533271) | 0.140784 / 0.296338 (-0.155554) |

### 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) | 0.620701 / 0.215209 (0.405492) | 6.263557 / 2.077655 (4.185903) | 2.510058 / 1.504120 (1.005938) | 2.085892 / 1.541195 (0.544697) | 2.170362 / 1.468490 (0.701872) | 1.325600 / 4.584777 (-3.259177) | 5.583355 / 3.745712 (1.837642) | 5.092791 / 5.269862 (-0.177071) | 2.814766 / 4.565676 (-1.750911) | 0.153568 / 0.424275 (-0.270707) | 0.014850 / 0.007607 (0.007243) | 0.787011 / 0.226044 (0.560967) | 7.948813 / 2.268929 (5.679885) | 3.320831 / 55.444624 (-52.123793) | 2.526327 / 6.876477 (-4.350150) | 2.691651 / 2.142072 (0.549579) | 1.521199 / 4.805227 (-3.284028) | 0.269738 / 6.500664 (-6.230926) | 0.082959 / 0.075469 (0.007490) |

### 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) | 1.740056 / 1.841788 (-0.101732) | 17.699732 / 8.074308 (9.625424) | 22.450689 / 10.191392 (12.259297) | 0.229350 / 0.680424 (-0.451073) | 0.027486 / 0.534201 (-0.506715) | 0.536153 / 0.579283 (-0.043130) | 0.608166 / 0.434364 (0.173802) | 0.629144 / 0.540337 (0.088807) | 0.732671 / 1.386936 (-0.654265) |

</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) | 0.010147 / 0.011353 (-0.001206) | 0.006484 / 0.011008 (-0.004524) | 0.098664 / 0.038508 (0.060156) | 0.036400 / 0.023109 (0.013291) | 0.432895 / 0.275898 (0.156997) | 0.466433 / 0.323480 (0.142954) | 0.008102 / 0.007986 (0.000117) | 0.004554 / 0.004328 (0.000225) | 0.100466 / 0.004250 (0.096216) | 0.054066 / 0.037052 (0.017013) | 0.439177 / 0.258489 (0.180688) | 0.502907 / 0.293841 (0.209066) | 0.059210 / 0.128546 (-0.069336) | 0.020220 / 0.075646 (-0.055426) | 0.124671 / 0.419271 (-0.294600) | 0.064278 / 0.043533 (0.020746) | 0.435659 / 0.255139 (0.180520) | 0.459670 / 0.283200 (0.176471) | 0.115574 / 0.141683 (-0.026109) | 1.826360 / 1.452155 (0.374205) | 1.943199 / 1.492716 (0.450483) |

### 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) | 0.238463 / 0.018006 (0.220457) | 0.534889 / 0.000490 (0.534400) | 0.000404 / 0.000200 (0.000204) | 0.000092 / 0.000054 (0.000038) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.033210 / 0.037411 (-0.004201) | 0.133529 / 0.014526 (0.119003) | 0.143813 / 0.176557 (-0.032743) | 0.213079 / 0.737135 (-0.524056) | 0.148427 / 0.296338 (-0.147912) |

### 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) | 0.656819 / 0.215209 (0.441610) | 6.414860 / 2.077655 (4.337205) | 2.756182 / 1.504120 (1.252062) | 2.405268 / 1.541195 (0.864073) | 2.436418 / 1.468490 (0.967928) | 1.289828 / 4.584777 (-3.294949) | 5.572731 / 3.745712 (1.827018) | 3.185432 / 5.269862 (-2.084429) | 2.093220 / 4.565676 (-2.472457) | 0.144817 / 0.424275 (-0.279458) | 0.015674 / 0.007607 (0.008067) | 0.801238 / 0.226044 (0.575194) | 7.955925 / 2.268929 (5.686996) | 3.605670 / 55.444624 (-51.838955) | 2.837568 / 6.876477 (-4.038908) | 2.873848 / 2.142072 (0.731775) | 1.493512 / 4.805227 (-3.311715) | 0.266251 / 6.500664 (-6.234413) | 0.082417 / 0.075469 (0.006948) |

### 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) | 1.608685 / 1.841788 (-0.233103) | 18.587875 / 8.074308 (10.513567) | 21.786119 / 10.191392 (11.594727) | 0.261748 / 0.680424 (-0.418675) | 0.026228 / 0.534201 (-0.507973) | 0.553538 / 0.579283 (-0.025745) | 0.599780 / 0.434364 (0.165416) | 0.665663 / 0.540337 (0.125325) | 0.792785 / 1.386936 (-0.594151) |

</details>
</details>

![](https://cml.dev/watermark.png#1520e017a9bb6f80e82a38b578213e418ad7e845 "CML watermark")

Remove implementation-specific kwargs from `file_utils.fsspec_get` and `file_utils.fsspec_head`, instead allowing them to be passed down via `storage_options`. This fixes an issue where s3fs did not recognize a timeout arg as well as fixes an issue mentioned in https://github.com/huggingface/datasets/issues/5281 by allowing users to pass down `storage_options` all the way from `datasets.load_dataset` to support implementation-specific credentials

Supports something like the following to provide credentials explicitly instead of relying on boto's methods of locating them

```
load_dataset(..., data_files=["s3://..."], storage_options={"profile": "..."})
```