Extract data on the fly in packaged builders

Instead of waiting for data files to be extracted in the packaged builders, we can prepend the compression prefix and extract them as they are being read (using `fsspec`). This saves disk space (deleting extracted archives is not set by default) and slightly speeds up dataset generation (less disk reads)

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6784). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

CI failures are unrelated, so this is ready for the review

<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) | 0.005130 / 0.011353 (-0.006223) | 0.003784 / 0.011008 (-0.007224) | 0.064899 / 0.038508 (0.026391) | 0.029456 / 0.023109 (0.006347) | 0.253384 / 0.275898 (-0.022514) | 0.273509 / 0.323480 (-0.049971) | 0.004116 / 0.007986 (-0.003870) | 0.002713 / 0.004328 (-0.001615) | 0.053984 / 0.004250 (0.049733) | 0.043538 / 0.037052 (0.006485) | 0.264696 / 0.258489 (0.006207) | 0.298321 / 0.293841 (0.004480) | 0.027916 / 0.128546 (-0.100630) | 0.010734 / 0.075646 (-0.064912) | 0.208284 / 0.419271 (-0.210988) | 0.035873 / 0.043533 (-0.007659) | 0.251028 / 0.255139 (-0.004111) | 0.270835 / 0.283200 (-0.012364) | 0.017475 / 0.141683 (-0.124208) | 1.130728 / 1.452155 (-0.321426) | 1.188672 / 1.492716 (-0.304044) |

### 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) | 0.094191 / 0.018006 (0.076185) | 0.304064 / 0.000490 (0.303575) | 0.000251 / 0.000200 (0.000051) | 0.000058 / 0.000054 (0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018414 / 0.037411 (-0.018998) | 0.061550 / 0.014526 (0.047024) | 0.074200 / 0.176557 (-0.102357) | 0.120250 / 0.737135 (-0.616885) | 0.076018 / 0.296338 (-0.220321) |

### 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) | 0.302517 / 0.215209 (0.087308) | 2.943936 / 2.077655 (0.866282) | 1.584847 / 1.504120 (0.080727) | 1.464501 / 1.541195 (-0.076694) | 1.472402 / 1.468490 (0.003912) | 0.570971 / 4.584777 (-4.013806) | 2.383207 / 3.745712 (-1.362505) | 2.811520 / 5.269862 (-2.458342) | 1.746997 / 4.565676 (-2.818680) | 0.063391 / 0.424275 (-0.360884) | 0.005296 / 0.007607 (-0.002311) | 0.358948 / 0.226044 (0.132903) | 3.604704 / 2.268929 (1.335776) | 1.935813 / 55.444624 (-53.508812) | 1.659944 / 6.876477 (-5.216533) | 1.687151 / 2.142072 (-0.454922) | 0.658044 / 4.805227 (-4.147183) | 0.120425 / 6.500664 (-6.380240) | 0.042694 / 0.075469 (-0.032775) |

### 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) | 0.986308 / 1.841788 (-0.855479) | 11.727945 / 8.074308 (3.653637) | 9.532785 / 10.191392 (-0.658607) | 0.140071 / 0.680424 (-0.540352) | 0.013472 / 0.534201 (-0.520729) | 0.285828 / 0.579283 (-0.293455) | 0.261571 / 0.434364 (-0.172793) | 0.323114 / 0.540337 (-0.217223) | 0.418132 / 1.386936 (-0.968804) |

</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) | 0.005428 / 0.011353 (-0.005925) | 0.003954 / 0.011008 (-0.007054) | 0.050336 / 0.038508 (0.011828) | 0.029941 / 0.023109 (0.006831) | 0.281483 / 0.275898 (0.005585) | 0.304822 / 0.323480 (-0.018658) | 0.004151 / 0.007986 (-0.003835) | 0.002862 / 0.004328 (-0.001466) | 0.049196 / 0.004250 (0.044945) | 0.040266 / 0.037052 (0.003213) | 0.293515 / 0.258489 (0.035026) | 0.319165 / 0.293841 (0.025324) | 0.029186 / 0.128546 (-0.099360) | 0.010838 / 0.075646 (-0.064809) | 0.058789 / 0.419271 (-0.360483) | 0.032847 / 0.043533 (-0.010686) | 0.280164 / 0.255139 (0.025025) | 0.299609 / 0.283200 (0.016410) | 0.018291 / 0.141683 (-0.123392) | 1.153858 / 1.452155 (-0.298297) | 1.219108 / 1.492716 (-0.273608) |

### 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) | 0.093783 / 0.018006 (0.075777) | 0.301526 / 0.000490 (0.301037) | 0.000211 / 0.000200 (0.000011) | 0.000055 / 0.000054 (0.000001) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022105 / 0.037411 (-0.015306) | 0.074844 / 0.014526 (0.060318) | 0.087147 / 0.176557 (-0.089409) | 0.127678 / 0.737135 (-0.609457) | 0.088630 / 0.296338 (-0.207709) |

### 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) | 0.286805 / 0.215209 (0.071596) | 2.828664 / 2.077655 (0.751009) | 1.579771 / 1.504120 (0.075651) | 1.463137 / 1.541195 (-0.078058) | 1.509238 / 1.468490 (0.040748) | 0.583425 / 4.584777 (-4.001352) | 2.424905 / 3.745712 (-1.320807) | 2.819354 / 5.269862 (-2.450508) | 1.784695 / 4.565676 (-2.780981) | 0.063374 / 0.424275 (-0.360901) | 0.005337 / 0.007607 (-0.002270) | 0.342291 / 0.226044 (0.116247) | 3.404319 / 2.268929 (1.135390) | 1.956909 / 55.444624 (-53.487716) | 1.694317 / 6.876477 (-5.182160) | 1.696256 / 2.142072 (-0.445817) | 0.655748 / 4.805227 (-4.149480) | 0.116785 / 6.500664 (-6.383879) | 0.040930 / 0.075469 (-0.034539) |

### 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) | 1.034463 / 1.841788 (-0.807325) | 12.252041 / 8.074308 (4.177733) | 10.593960 / 10.191392 (0.402568) | 0.139311 / 0.680424 (-0.541112) | 0.016177 / 0.534201 (-0.518023) | 0.288910 / 0.579283 (-0.290373) | 0.281588 / 0.434364 (-0.152776) | 0.323066 / 0.540337 (-0.217272) | 0.427604 / 1.386936 (-0.959332) |

</details>
</details>

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