Fix multiprocessing with spawn in iterable datasets
_The documentation is not available anymore as the PR was closed or merged._ @lhoestq 
A test is failing, but I don't think it is due to my changes Good catch ! Could you add a test to make sure transformed IterableDataset objects are still picklable ?

Something like `test_pickle_after_many_transforms` in in `test_iterable_dataset.py` that does a bunch or rename, map, take on a dataset and checks that the dataset can be pickled at the end and the reloaded dataset returns the same elements @lhoestq 
I added the test and fixed one last method <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) | 0.006537 / 0.011353 (-0.004816) | 0.003960 / 0.011008 (-0.007048) | 0.085135 / 0.038508 (0.046627) | 0.079271 / 0.023109 (0.056162) | 0.383743 / 0.275898 (0.107845) | 0.414622 / 0.323480 (0.091143) | 0.004202 / 0.007986 (-0.003784) | 0.003537 / 0.004328 (-0.000791) | 0.065758 / 0.004250 (0.061508) | 0.054225 / 0.037052 (0.017173) | 0.395715 / 0.258489 (0.137226) | 0.438985 / 0.293841 (0.145144) | 0.030590 / 0.128546 (-0.097956) | 0.008754 / 0.075646 (-0.066892) | 0.288415 / 0.419271 (-0.130857) | 0.051863 / 0.043533 (0.008330) | 0.382501 / 0.255139 (0.127363) | 0.414428 / 0.283200 (0.131228) | 0.024084 / 0.141683 (-0.117599) | 1.478726 / 1.452155 (0.026572) | 1.544763 / 1.492716 (0.052047) |

### 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) | 0.285143 / 0.018006 (0.267136) | 0.603859 / 0.000490 (0.603369) | 0.004330 / 0.000200 (0.004131) | 0.000108 / 0.000054 (0.000054) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027856 / 0.037411 (-0.009555) | 0.081963 / 0.014526 (0.067437) | 0.104106 / 0.176557 (-0.072451) | 0.151378 / 0.737135 (-0.585757) | 0.096476 / 0.296338 (-0.199862) |

### 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) | 0.402938 / 0.215209 (0.187729) | 4.042312 / 2.077655 (1.964657) | 2.068421 / 1.504120 (0.564301) | 1.877870 / 1.541195 (0.336675) | 1.947643 / 1.468490 (0.479153) | 0.482031 / 4.584777 (-4.102746) | 3.554747 / 3.745712 (-0.190965) | 3.307811 / 5.269862 (-1.962050) | 2.082886 / 4.565676 (-2.482791) | 0.056853 / 0.424275 (-0.367422) | 0.007535 / 0.007607 (-0.000072) | 0.483694 / 0.226044 (0.257649) | 4.827906 / 2.268929 (2.558978) | 2.567572 / 55.444624 (-52.877052) | 2.167206 / 6.876477 (-4.709271) | 2.414442 / 2.142072 (0.272369) | 0.579472 / 4.805227 (-4.225755) | 0.132976 / 6.500664 (-6.367688) | 0.059315 / 0.075469 (-0.016154) |

### 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) | 1.260086 / 1.841788 (-0.581702) | 19.438297 / 8.074308 (11.363989) | 14.188161 / 10.191392 (3.996769) | 0.168534 / 0.680424 (-0.511890) | 0.018070 / 0.534201 (-0.516131) | 0.394241 / 0.579283 (-0.185043) | 0.411057 / 0.434364 (-0.023307) | 0.461123 / 0.540337 (-0.079215) | 0.626844 / 1.386936 (-0.760092) |

</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) | 0.006896 / 0.011353 (-0.004457) | 0.004207 / 0.011008 (-0.006801) | 0.064981 / 0.038508 (0.026473) | 0.080261 / 0.023109 (0.057152) | 0.399403 / 0.275898 (0.123505) | 0.433099 / 0.323480 (0.109619) | 0.005697 / 0.007986 (-0.002288) | 0.003601 / 0.004328 (-0.000728) | 0.065924 / 0.004250 (0.061673) | 0.058868 / 0.037052 (0.021815) | 0.403705 / 0.258489 (0.145216) | 0.439218 / 0.293841 (0.145377) | 0.032789 / 0.128546 (-0.095757) | 0.008675 / 0.075646 (-0.066971) | 0.071217 / 0.419271 (-0.348055) | 0.048487 / 0.043533 (0.004954) | 0.399878 / 0.255139 (0.144739) | 0.412816 / 0.283200 (0.129616) | 0.023905 / 0.141683 (-0.117778) | 1.541402 / 1.452155 (0.089247) | 1.588080 / 1.492716 (0.095364) |

### 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) | 0.322863 / 0.018006 (0.304856) | 0.530291 / 0.000490 (0.529802) | 0.004862 / 0.000200 (0.004662) | 0.000097 / 0.000054 (0.000042) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.032697 / 0.037411 (-0.004715) | 0.092416 / 0.014526 (0.077891) | 0.107355 / 0.176557 (-0.069201) | 0.160217 / 0.737135 (-0.576918) | 0.109286 / 0.296338 (-0.187052) |

### 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) | 0.437375 / 0.215209 (0.222166) | 4.362644 / 2.077655 (2.284990) | 2.335404 / 1.504120 (0.831284) | 2.173215 / 1.541195 (0.632020) | 2.254061 / 1.468490 (0.785571) | 0.493906 / 4.584777 (-4.090871) | 3.609025 / 3.745712 (-0.136687) | 3.352380 / 5.269862 (-1.917481) | 2.074185 / 4.565676 (-2.491492) | 0.057863 / 0.424275 (-0.366412) | 0.007297 / 0.007607 (-0.000310) | 0.512464 / 0.226044 (0.286420) | 5.135921 / 2.268929 (2.866993) | 2.788889 / 55.444624 (-52.655736) | 2.479097 / 6.876477 (-4.397379) | 2.717848 / 2.142072 (0.575776) | 0.590442 / 4.805227 (-4.214785) | 0.133721 / 6.500664 (-6.366943) | 0.061491 / 0.075469 (-0.013978) |

### 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) | 1.429564 / 1.841788 (-0.412224) | 20.628733 / 8.074308 (12.554425) | 15.299571 / 10.191392 (5.108179) | 0.171032 / 0.680424 (-0.509392) | 0.019995 / 0.534201 (-0.514206) | 0.401283 / 0.579283 (-0.178000) | 0.416504 / 0.434364 (-0.017860) | 0.471219 / 0.540337 (-0.069118) | 0.641299 / 1.386936 (-0.745637) |

</details>
</details>

![](https://cml.dev/watermark.png#5503e7beb5a31926aec03c6c9b24813f9f83cd7b "CML watermark")

The "Spawn" method is preferred when multiprocessing on macOS or Windows systems, instead of the "Fork" method on linux systems.

This causes some methods of Iterable Datasets to break when using a dataloader with more than 0 workers.

I fixed the issue by replacing lambda and local methods which are not pickle-able.

See the example below:

```python
from datasets import load_dataset
from torch.utils.data import DataLoader


if __name__ == "__main__":
    dataset = load_dataset("lhoestq/demo1", split="train")
    dataset = dataset.to_iterable_dataset(num_shards=3)

    dataset = dataset.remove_columns(["package_name"])
    dataset = dataset.rename_columns({
        "review": "review1"
    })
    dataset = dataset.rename_column("date", "date1")
    for sample in DataLoader(dataset, batch_size=None, num_workers=3):
        print(sample)
```

To notice the fix on a linux system, adding these lines should do the trick:

```python
import multiprocessing
multiprocessing.set_start_method('spawn')
```

I also removed what looks like code duplication between rename_colums and rename_column
