Dedicated RNG object for fingerprinting

Closes https://github.com/huggingface/datasets/issues/6604, closes https://github.com/huggingface/datasets/issues/2775

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6606). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<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) | 0.005625 / 0.011353 (-0.005728) | 0.003313 / 0.011008 (-0.007695) | 0.063997 / 0.038508 (0.025489) | 0.028949 / 0.023109 (0.005839) | 0.250069 / 0.275898 (-0.025829) | 0.271412 / 0.323480 (-0.052068) | 0.003837 / 0.007986 (-0.004148) | 0.002632 / 0.004328 (-0.001697) | 0.048351 / 0.004250 (0.044100) | 0.040664 / 0.037052 (0.003612) | 0.267540 / 0.258489 (0.009051) | 0.285237 / 0.293841 (-0.008604) | 0.026962 / 0.128546 (-0.101584) | 0.010417 / 0.075646 (-0.065229) | 0.211430 / 0.419271 (-0.207842) | 0.035411 / 0.043533 (-0.008122) | 0.258867 / 0.255139 (0.003728) | 0.278562 / 0.283200 (-0.004638) | 0.017690 / 0.141683 (-0.123993) | 1.128813 / 1.452155 (-0.323342) | 1.169384 / 1.492716 (-0.323333) |

### 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) | 0.091322 / 0.018006 (0.073316) | 0.303272 / 0.000490 (0.302782) | 0.000202 / 0.000200 (0.000002) | 0.000044 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.017551 / 0.037411 (-0.019861) | 0.060027 / 0.014526 (0.045502) | 0.073431 / 0.176557 (-0.103125) | 0.120550 / 0.737135 (-0.616585) | 0.073107 / 0.296338 (-0.223231) |

### 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) | 0.283064 / 0.215209 (0.067855) | 2.754593 / 2.077655 (0.676938) | 1.477303 / 1.504120 (-0.026817) | 1.341072 / 1.541195 (-0.200123) | 1.366625 / 1.468490 (-0.101865) | 0.573467 / 4.584777 (-4.011310) | 2.395225 / 3.745712 (-1.350487) | 2.777021 / 5.269862 (-2.492841) | 1.720733 / 4.565676 (-2.844944) | 0.063339 / 0.424275 (-0.360936) | 0.004954 / 0.007607 (-0.002653) | 0.350359 / 0.226044 (0.124315) | 3.376221 / 2.268929 (1.107293) | 1.835539 / 55.444624 (-53.609086) | 1.558064 / 6.876477 (-5.318413) | 1.582778 / 2.142072 (-0.559294) | 0.649918 / 4.805227 (-4.155309) | 0.117761 / 6.500664 (-6.382903) | 0.041771 / 0.075469 (-0.033698) |

### 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) | 0.950202 / 1.841788 (-0.891586) | 11.476160 / 8.074308 (3.401852) | 10.290618 / 10.191392 (0.099226) | 0.140659 / 0.680424 (-0.539765) | 0.014525 / 0.534201 (-0.519676) | 0.287253 / 0.579283 (-0.292030) | 0.266204 / 0.434364 (-0.168160) | 0.327818 / 0.540337 (-0.212519) | 0.431680 / 1.386936 (-0.955256) |

</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) | 0.005096 / 0.011353 (-0.006257) | 0.003460 / 0.011008 (-0.007548) | 0.049474 / 0.038508 (0.010966) | 0.031063 / 0.023109 (0.007954) | 0.272899 / 0.275898 (-0.002999) | 0.291859 / 0.323480 (-0.031621) | 0.004858 / 0.007986 (-0.003128) | 0.002598 / 0.004328 (-0.001731) | 0.049074 / 0.004250 (0.044824) | 0.044722 / 0.037052 (0.007669) | 0.285262 / 0.258489 (0.026772) | 0.314168 / 0.293841 (0.020327) | 0.046346 / 0.128546 (-0.082200) | 0.010384 / 0.075646 (-0.065262) | 0.058331 / 0.419271 (-0.360940) | 0.033728 / 0.043533 (-0.009805) | 0.276217 / 0.255139 (0.021078) | 0.295465 / 0.283200 (0.012265) | 0.018215 / 0.141683 (-0.123467) | 1.163847 / 1.452155 (-0.288308) | 1.213901 / 1.492716 (-0.278816) |

### 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) | 0.091953 / 0.018006 (0.073947) | 0.299977 / 0.000490 (0.299487) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022031 / 0.037411 (-0.015381) | 0.075067 / 0.014526 (0.060541) | 0.087305 / 0.176557 (-0.089251) | 0.125530 / 0.737135 (-0.611605) | 0.088761 / 0.296338 (-0.207578) |

### 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) | 0.302682 / 0.215209 (0.087473) | 2.941509 / 2.077655 (0.863854) | 1.643399 / 1.504120 (0.139280) | 1.530148 / 1.541195 (-0.011046) | 1.542067 / 1.468490 (0.073577) | 0.575883 / 4.584777 (-4.008894) | 2.434320 / 3.745712 (-1.311392) | 2.761683 / 5.269862 (-2.508179) | 1.732068 / 4.565676 (-2.833609) | 0.063543 / 0.424275 (-0.360732) | 0.005089 / 0.007607 (-0.002518) | 0.351314 / 0.226044 (0.125269) | 3.494572 / 2.268929 (1.225643) | 2.032503 / 55.444624 (-53.412121) | 1.697949 / 6.876477 (-5.178528) | 1.700392 / 2.142072 (-0.441680) | 0.650757 / 4.805227 (-4.154471) | 0.116719 / 6.500664 (-6.383945) | 0.040559 / 0.075469 (-0.034910) |

### 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) | 0.978218 / 1.841788 (-0.863570) | 11.972379 / 8.074308 (3.898071) | 10.725735 / 10.191392 (0.534343) | 0.130564 / 0.680424 (-0.549860) | 0.015396 / 0.534201 (-0.518805) | 0.286900 / 0.579283 (-0.292383) | 0.279633 / 0.434364 (-0.154730) | 0.327483 / 0.540337 (-0.212854) | 0.417848 / 1.386936 (-0.969088) |

</details>
</details>

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