Add `fsspec` support for `to_json`, `to_csv`, and `to_parquet`
Hi to whoever is reading this! 🤗 (Most likely @mariosasko)

## What's in this PR?

This PR replaces the `open` from Python with `fsspec.open` and adds the argument `storage_options` for the methods `to_json`, `to_csv`, and `to_parquet`, to allow users to export any 🤗`Dataset` into a file in a file-system as requested at #6086.

## What's missing in this PR?

As per `to_json`, `to_csv`, and `to_parquet` docstrings for the recently included `storage_options` arg, I've scoped it to 2.15.0, so we should check that before merging in case we want to scope that for 2.14.2 instead.

Additionally, should we also add `fsspec` support for the `from_csv`, `from_json`, and `from_parquet` methods? If you want me to do so @mariosasko just let me know and I'll create another PR to support that too!

Fix #6086.
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6096). All of your documentation changes will be reflected on that endpoint. Hi here @lhoestq @mariosasko I just realised this PR is still open, should we close it in case this is something not to include within `datasets`, or should we merge? Let me know whatever you decide 🤗 <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) | 0.005011 / 0.011353 (-0.006342) | 0.003203 / 0.011008 (-0.007806) | 0.064033 / 0.038508 (0.025524) | 0.029152 / 0.023109 (0.006043) | 0.242884 / 0.275898 (-0.033014) | 0.263517 / 0.323480 (-0.059963) | 0.004088 / 0.007986 (-0.003898) | 0.002570 / 0.004328 (-0.001759) | 0.049061 / 0.004250 (0.044811) | 0.040170 / 0.037052 (0.003117) | 0.263305 / 0.258489 (0.004816) | 0.286255 / 0.293841 (-0.007586) | 0.028206 / 0.128546 (-0.100340) | 0.010337 / 0.075646 (-0.065309) | 0.206235 / 0.419271 (-0.213036) | 0.038182 / 0.043533 (-0.005351) | 0.246486 / 0.255139 (-0.008653) | 0.263077 / 0.283200 (-0.020122) | 0.017850 / 0.141683 (-0.123833) | 1.173921 / 1.452155 (-0.278234) | 1.255583 / 1.492716 (-0.237133) |

### 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) | 0.090278 / 0.018006 (0.072272) | 0.298146 / 0.000490 (0.297657) | 0.000215 / 0.000200 (0.000015) | 0.000043 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018021 / 0.037411 (-0.019390) | 0.061434 / 0.014526 (0.046908) | 0.072617 / 0.176557 (-0.103939) | 0.119063 / 0.737135 (-0.618072) | 0.073997 / 0.296338 (-0.222341) |

### 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) | 0.288496 / 0.215209 (0.073287) | 2.794943 / 2.077655 (0.717288) | 1.538299 / 1.504120 (0.034179) | 1.399164 / 1.541195 (-0.142031) | 1.419104 / 1.468490 (-0.049386) | 0.566147 / 4.584777 (-4.018630) | 2.386687 / 3.745712 (-1.359025) | 2.723584 / 5.269862 (-2.546278) | 1.699161 / 4.565676 (-2.866515) | 0.062526 / 0.424275 (-0.361750) | 0.004927 / 0.007607 (-0.002680) | 0.345132 / 0.226044 (0.119087) | 3.389634 / 2.268929 (1.120706) | 1.898012 / 55.444624 (-53.546612) | 1.599050 / 6.876477 (-5.277427) | 1.614289 / 2.142072 (-0.527783) | 0.656716 / 4.805227 (-4.148511) | 0.118480 / 6.500664 (-6.382184) | 0.041913 / 0.075469 (-0.033557) |

### 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) | 0.968676 / 1.841788 (-0.873111) | 11.184668 / 8.074308 (3.110360) | 9.249912 / 10.191392 (-0.941480) | 0.141139 / 0.680424 (-0.539285) | 0.014207 / 0.534201 (-0.519994) | 0.287603 / 0.579283 (-0.291680) | 0.262792 / 0.434364 (-0.171572) | 0.340239 / 0.540337 (-0.200099) | 0.437471 / 1.386936 (-0.949465) |

</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) | 0.005268 / 0.011353 (-0.006085) | 0.003142 / 0.011008 (-0.007866) | 0.049333 / 0.038508 (0.010825) | 0.029558 / 0.023109 (0.006449) | 0.270716 / 0.275898 (-0.005182) | 0.293834 / 0.323480 (-0.029646) | 0.004285 / 0.007986 (-0.003701) | 0.002703 / 0.004328 (-0.001626) | 0.048857 / 0.004250 (0.044607) | 0.043456 / 0.037052 (0.006404) | 0.286058 / 0.258489 (0.027569) | 0.313491 / 0.293841 (0.019650) | 0.029336 / 0.128546 (-0.099210) | 0.010287 / 0.075646 (-0.065360) | 0.057753 / 0.419271 (-0.361518) | 0.050867 / 0.043533 (0.007334) | 0.271717 / 0.255139 (0.016578) | 0.291468 / 0.283200 (0.008268) | 0.018668 / 0.141683 (-0.123015) | 1.137399 / 1.452155 (-0.314755) | 1.186315 / 1.492716 (-0.306401) |

### 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) | 0.090289 / 0.018006 (0.072283) | 0.297987 / 0.000490 (0.297497) | 0.000227 / 0.000200 (0.000027) | 0.000044 / 0.000054 (-0.000010) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021012 / 0.037411 (-0.016399) | 0.075046 / 0.014526 (0.060520) | 0.085295 / 0.176557 (-0.091261) | 0.123879 / 0.737135 (-0.613257) | 0.086572 / 0.296338 (-0.209766) |

### 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) | 0.293350 / 0.215209 (0.078141) | 2.875958 / 2.077655 (0.798304) | 1.586460 / 1.504120 (0.082340) | 1.467950 / 1.541195 (-0.073245) | 1.453478 / 1.468490 (-0.015012) | 0.566083 / 4.584777 (-4.018693) | 2.462582 / 3.745712 (-1.283130) | 2.609367 / 5.269862 (-2.660495) | 1.709691 / 4.565676 (-2.855985) | 0.062928 / 0.424275 (-0.361347) | 0.005040 / 0.007607 (-0.002567) | 0.337997 / 0.226044 (0.111952) | 3.347235 / 2.268929 (1.078306) | 1.923940 / 55.444624 (-53.520684) | 1.657731 / 6.876477 (-5.218746) | 1.747469 / 2.142072 (-0.394604) | 0.657061 / 4.805227 (-4.148167) | 0.116655 / 6.500664 (-6.384009) | 0.040363 / 0.075469 (-0.035106) |

### 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) | 1.011171 / 1.841788 (-0.830617) | 11.705905 / 8.074308 (3.631597) | 10.064391 / 10.191392 (-0.127001) | 0.141681 / 0.680424 (-0.538743) | 0.014763 / 0.534201 (-0.519438) | 0.286425 / 0.579283 (-0.292858) | 0.271036 / 0.434364 (-0.163328) | 0.321393 / 0.540337 (-0.218944) | 0.424539 / 1.386936 (-0.962397) |

</details>
</details>

![](https://cml.dev/watermark.png#e52f4d0cf1cb89a9719b42fff13a891f92d51e04 "CML watermark")
 Thanks @alvarobartt. 

I am linking this PR to the corresponding issue (on the right column, under "Development") and closing the issue.

For future contributions, please add to the PR description the word "fix" followed by the issue number, e.g.:
```
Fix #6086.
```
- I have edited the PR description to add this. > Thanks @alvarobartt.
> 
> I am linking this PR to the corresponding issue (on the right column, under "Development") and closing the issue.
> 
> For future contributions, please add to the PR description the word "fix" followed by the issue number, e.g.:
> 
> ```
> Fix #6086.
> ```
> 
> * I have edited the PR description to add this.

Hi @albertvillanova, fair, I missed that, thanks for the edit and the heads up!