Add CLI function to convert script-dataset to Parquet
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The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6795). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.
2024-04-09T14:50:17Z
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@huggingface/datasets once this PR is merged, I would suggest making a release. Do you agree?
2024-04-12T11:50:28Z
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<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) | 0.005367 / 0.011353 (-0.005986) | 0.003161 / 0.011008 (-0.007847) | 0.063259 / 0.038508 (0.024751) | 0.030550 / 0.023109 (0.007441) | 0.243789 / 0.275898 (-0.032109) | 0.262474 / 0.323480 (-0.061006) | 0.003157 / 0.007986 (-0.004829) | 0.002586 / 0.004328 (-0.001742) | 0.049336 / 0.004250 (0.045085) | 0.046434 / 0.037052 (0.009382) | 0.249142 / 0.258489 (-0.009347) | 0.282953 / 0.293841 (-0.010888) | 0.027881 / 0.128546 (-0.100666) | 0.010069 / 0.075646 (-0.065578) | 0.207937 / 0.419271 (-0.211334) | 0.036005 / 0.043533 (-0.007528) | 0.251850 / 0.255139 (-0.003288) | 0.265156 / 0.283200 (-0.018044) | 0.019780 / 0.141683 (-0.121903) | 1.124301 / 1.452155 (-0.327853) | 1.177392 / 1.492716 (-0.315324) |

### 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) | 0.091045 / 0.018006 (0.073039) | 0.301258 / 0.000490 (0.300769) | 0.000214 / 0.000200 (0.000014) | 0.000048 / 0.000054 (-0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018726 / 0.037411 (-0.018686) | 0.061623 / 0.014526 (0.047097) | 0.073905 / 0.176557 (-0.102651) | 0.119444 / 0.737135 (-0.617692) | 0.074614 / 0.296338 (-0.221725) |

### 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) | 0.287313 / 0.215209 (0.072104) | 2.772864 / 2.077655 (0.695209) | 1.465267 / 1.504120 (-0.038853) | 1.343666 / 1.541195 (-0.197528) | 1.329390 / 1.468490 (-0.139100) | 0.570222 / 4.584777 (-4.014555) | 2.421835 / 3.745712 (-1.323877) | 2.747282 / 5.269862 (-2.522579) | 1.728733 / 4.565676 (-2.836943) | 0.063671 / 0.424275 (-0.360604) | 0.005343 / 0.007607 (-0.002264) | 0.335078 / 0.226044 (0.109033) | 3.334305 / 2.268929 (1.065376) | 1.779496 / 55.444624 (-53.665129) | 1.496475 / 6.876477 (-5.380002) | 1.507848 / 2.142072 (-0.634224) | 0.653653 / 4.805227 (-4.151575) | 0.118373 / 6.500664 (-6.382291) | 0.041727 / 0.075469 (-0.033742) |

### 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) | 0.981985 / 1.841788 (-0.859803) | 11.290978 / 8.074308 (3.216670) | 9.499217 / 10.191392 (-0.692175) | 0.131353 / 0.680424 (-0.549071) | 0.014416 / 0.534201 (-0.519785) | 0.288381 / 0.579283 (-0.290902) | 0.265483 / 0.434364 (-0.168880) | 0.323438 / 0.540337 (-0.216900) | 0.417946 / 1.386936 (-0.968990) |

</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) | 0.005272 / 0.011353 (-0.006081) | 0.003551 / 0.011008 (-0.007457) | 0.050173 / 0.038508 (0.011665) | 0.031291 / 0.023109 (0.008182) | 0.278658 / 0.275898 (0.002760) | 0.301812 / 0.323480 (-0.021668) | 0.004237 / 0.007986 (-0.003748) | 0.002713 / 0.004328 (-0.001615) | 0.049483 / 0.004250 (0.045233) | 0.039995 / 0.037052 (0.002943) | 0.293101 / 0.258489 (0.034612) | 0.319956 / 0.293841 (0.026116) | 0.029127 / 0.128546 (-0.099419) | 0.010247 / 0.075646 (-0.065400) | 0.057929 / 0.419271 (-0.361342) | 0.032942 / 0.043533 (-0.010591) | 0.281677 / 0.255139 (0.026538) | 0.297937 / 0.283200 (0.014737) | 0.018285 / 0.141683 (-0.123398) | 1.272858 / 1.452155 (-0.179297) | 1.213375 / 1.492716 (-0.279342) |

### 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) | 0.091110 / 0.018006 (0.073104) | 0.302589 / 0.000490 (0.302099) | 0.000214 / 0.000200 (0.000014) | 0.000070 / 0.000054 (0.000015) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021520 / 0.037411 (-0.015891) | 0.075013 / 0.014526 (0.060487) | 0.088695 / 0.176557 (-0.087862) | 0.128281 / 0.737135 (-0.608854) | 0.090611 / 0.296338 (-0.205727) |

### 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) | 0.297457 / 0.215209 (0.082248) | 2.928612 / 2.077655 (0.850957) | 1.613245 / 1.504120 (0.109125) | 1.485263 / 1.541195 (-0.055931) | 1.496885 / 1.468490 (0.028395) | 0.570120 / 4.584777 (-4.014657) | 2.487532 / 3.745712 (-1.258180) | 2.761552 / 5.269862 (-2.508309) | 1.731864 / 4.565676 (-2.833812) | 0.062989 / 0.424275 (-0.361286) | 0.005428 / 0.007607 (-0.002179) | 0.354932 / 0.226044 (0.128888) | 3.524475 / 2.268929 (1.255547) | 1.977684 / 55.444624 (-53.466941) | 1.692568 / 6.876477 (-5.183909) | 1.673003 / 2.142072 (-0.469069) | 0.643976 / 4.805227 (-4.161251) | 0.116499 / 6.500664 (-6.384165) | 0.040772 / 0.075469 (-0.034697) |

### 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) | 1.020354 / 1.841788 (-0.821434) | 12.143991 / 8.074308 (4.069683) | 10.354058 / 10.191392 (0.162666) | 0.145460 / 0.680424 (-0.534964) | 0.015356 / 0.534201 (-0.518845) | 0.307190 / 0.579283 (-0.272093) | 0.276664 / 0.434364 (-0.157699) | 0.350068 / 0.540337 (-0.190269) | 0.440824 / 1.386936 (-0.946112) |

</details>
</details>

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2024-04-12T15:33:10Z
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Close #6690.