Minor changes in JAX-formatting docstrings & type-hints
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P.S. For more context, I'm currently exploring the integration of 🤗`datasets` with JAX, so in case you need any help or want me to try something specific just let me know! (`jnp.asarray`/`jnp.array(..., copy=False)` still no zero-copy 😭)
2023-02-10T19:06:21Z
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_The documentation is not available anymore as the PR was closed or merged._
2023-02-10T19:09:24Z
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> Hi ! Thanks for improving this :)

Glad to help, @lhoestq! Also, regarding the questions in the `## What's missing?` can I have your input? Thanks 🤗 
2023-02-13T08:43:20Z
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Whoops forgot to reply to these matters - sorry x)

Yea a JAX guide would be welcome in the documentation ! This can be done in a separate PR if you want :)

Pyarrow is always imported with `datasets`, so it doesn't really matter if it's under TYPE_CHECKING or not.

Regarding the license : yes indeed it should be in every file, thanks for reporting.

No big preference between jnp.array and jnp.asarray, unless one offers better performance
2023-02-13T10:07:02Z
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> Whoops forgot to reply to these matters - sorry x)
> 
> Yea a JAX guide would be welcome in the documentation ! This can be done in a separate PR if you want :)
> 
> Pyarrow is always imported with `datasets`, so it doesn't really matter if it's under TYPE_CHECKING or not.
> 
> Regarding the license : yes indeed it should be in every file, thanks for reporting.
> 
> No big preference between jnp.array and jnp.asarray, unless one offers better performance

Cool @lhoestq thanks for the input there!

1. I can create a separate PR for JAX-format usage
2. Regarding that, makes sense, we can just not put it there, unless it's more clear that in that file `pyarrow` is just required for typing?
3. Do you want me to add the License? In this PR? In a separate one?
4. Ideally `jnp.asarray` is similar to `np.asarray` which in the case of `numpy` tends to be more efficient as it does zero-copy when possible, while `np.array` has `copy=True` by default, anyway as I mentioned before (and as you already know) the copy from `numpy` to `jax` is not zero-copy, while the other way around (`jax` to `numpy`) it is
2023-02-13T13:47:11Z
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Thanks, feel free to create separate PRs for the docs and the license.

I guess you can move the `pyarrow` import back to where it was for consistency with the other files and we can merge this one ;)
2023-02-13T18:11:01Z
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> Thanks, feel free to create separate PRs for the docs and the license.
> 
> I guess you can move the `pyarrow` import back to where it was for consistency with the other files and we can merge this one ;)

Cool thanks I'll do that! 👍🏻 
2023-02-13T19:27:38Z
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Actually I just checked and there are still tens of thousands of users with jax 0.3.25 - so we need to support older versions as well. I guess it comes from `transformers` which doesn't support jax 0.4 (and doesn't want to until the jax team stops breaking the lib all the time).

Could you make sure your changes work with older versions as well ? Sorry for not spotting this earlier.
If we have `"jax>=0.2.8,!=0.3.2,<=0.4.3"` that'b be nice, and we can update the latest supported release from time to time.

In the CI you can add `jax==0.2.8` for the `deps-minimum` job, and use `jax~=0.4.1` for the `deps-latest`.
2023-02-14T13:13:42Z
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> Actually I just checked and there are still tens of thousands of users with jax 0.3.25 - so we need to support older versions as well. I guess it comes from `transformers` which doesn't support jax 0.4 (and doesn't want to until the jax team stops breaking the lib all the time).
> 
> Could you make sure your changes work with older versions as well ? Sorry for not spotting this earlier. If we have `"jax>=0.2.8,!=0.3.2,<=0.4.3"` that'b be nice, and we can update the latest supported release from time to time.
> 
> In the CI you can add `jax==0.2.8` for the `deps-minimum` job, and use `jax~=0.4.1` for the `deps-latest`.

Ok, didn't know that @lhoestq thanks for the detailed context! Sure, I'll update it and make sure it's also compatible with older versions.
2023-02-14T14:42:51Z
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Oops forgot to add you as co-author of the last commit @lhoestq my bad 😞 
2023-02-14T19:37:21Z
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So it should be fixed right now @lhoestq! The thing is that `jax` doesn't provide support for Python 3.7 due to its EOL next June (more information at https://endoflife.date/python)...

Anyway, I can confirm that `jax.Array` type works with 0.3.25 and that the following code works fine:

```python
import jax
import jax.numpy as jnp

x = jnp.ones((1, 10), dtype=jnp.float32)  # Is a `jnp.DeviceArray`
assert isinstance(x, jax.Array)  # Is `True`
```

So we can still use 0.3.25 as the maximum supported version, as well as 0.3.6 for `jaxlib` so as to be consistent with 🤗`transformers`.

Thanks for your comments @lhoestq those were really useful!
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Sorry for the spam, pinning versions leads to failure runs (not related to the type-hinting); I'll check that locally instead of here to avoid spam... Not pinning the dependencies work but I'll check the minimum required versions for both `jax` and `jaxlib` in Python 3.7
2023-02-14T20:40:59Z
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> Cool ! Thanks for trying to make the CI support it, but it's maybe not worth spending more time on this for now ^^
> 
> merging :)

Do you want me to work on the CI in a separate branch? Thanks for merging and for your help as always :)
2023-02-15T13:20:34Z
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> Do you want me to work on the CI in a separate branch? Thanks for merging and for your help as always :)

In the end I think we can keep it as is since we didn't modify the core code for jax. Maybe later if we do further changes and need to make sure we don't break anything ;) For example when we decide to add support for more recent versions
2023-02-15T13:25:13Z
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<details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<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) | 0.010798 / 0.011353 (-0.000555) | 0.005690 / 0.011008 (-0.005318) | 0.116840 / 0.038508 (0.078332) | 0.041376 / 0.023109 (0.018266) | 0.345616 / 0.275898 (0.069718) | 0.413914 / 0.323480 (0.090434) | 0.009237 / 0.007986 (0.001252) | 0.004490 / 0.004328 (0.000162) | 0.085833 / 0.004250 (0.081582) | 0.050231 / 0.037052 (0.013179) | 0.367276 / 0.258489 (0.108787) | 0.393735 / 0.293841 (0.099894) | 0.043775 / 0.128546 (-0.084772) | 0.013215 / 0.075646 (-0.062432) | 0.391020 / 0.419271 (-0.028252) | 0.055102 / 0.043533 (0.011569) | 0.360333 / 0.255139 (0.105194) | 0.370531 / 0.283200 (0.087331) | 0.115484 / 0.141683 (-0.026199) | 1.694779 / 1.452155 (0.242625) | 1.756249 / 1.492716 (0.263532) |

### 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) | 0.230508 / 0.018006 (0.212501) | 0.478681 / 0.000490 (0.478191) | 0.010305 / 0.000200 (0.010105) | 0.000147 / 0.000054 (0.000093) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030953 / 0.037411 (-0.006459) | 0.124320 / 0.014526 (0.109794) | 0.140417 / 0.176557 (-0.036140) | 0.189522 / 0.737135 (-0.547613) | 0.143635 / 0.296338 (-0.152704) |

### 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) | 0.485995 / 0.215209 (0.270786) | 4.799668 / 2.077655 (2.722014) | 2.195655 / 1.504120 (0.691535) | 1.940073 / 1.541195 (0.398879) | 2.053853 / 1.468490 (0.585363) | 0.825399 / 4.584777 (-3.759378) | 4.522180 / 3.745712 (0.776468) | 2.484626 / 5.269862 (-2.785236) | 1.727617 / 4.565676 (-2.838059) | 0.098808 / 0.424275 (-0.325467) | 0.014753 / 0.007607 (0.007146) | 0.606798 / 0.226044 (0.380754) | 5.918090 / 2.268929 (3.649162) | 2.668124 / 55.444624 (-52.776500) | 2.300447 / 6.876477 (-4.576030) | 2.411203 / 2.142072 (0.269130) | 0.999826 / 4.805227 (-3.805401) | 0.193683 / 6.500664 (-6.306981) | 0.069341 / 0.075469 (-0.006129) |

### 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) | 1.455816 / 1.841788 (-0.385972) | 17.176476 / 8.074308 (9.102168) | 16.359100 / 10.191392 (6.167708) | 0.199669 / 0.680424 (-0.480755) | 0.033456 / 0.534201 (-0.500745) | 0.512478 / 0.579283 (-0.066805) | 0.526350 / 0.434364 (0.091986) | 0.637669 / 0.540337 (0.097332) | 0.753821 / 1.386936 (-0.633115) |

</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) | 0.008176 / 0.011353 (-0.003177) | 0.005862 / 0.011008 (-0.005147) | 0.086123 / 0.038508 (0.047615) | 0.037144 / 0.023109 (0.014035) | 0.398328 / 0.275898 (0.122430) | 0.439126 / 0.323480 (0.115647) | 0.006455 / 0.007986 (-0.001531) | 0.004575 / 0.004328 (0.000246) | 0.083396 / 0.004250 (0.079146) | 0.052827 / 0.037052 (0.015775) | 0.401039 / 0.258489 (0.142550) | 0.441374 / 0.293841 (0.147533) | 0.041671 / 0.128546 (-0.086875) | 0.014098 / 0.075646 (-0.061548) | 0.100873 / 0.419271 (-0.318398) | 0.058690 / 0.043533 (0.015157) | 0.395817 / 0.255139 (0.140678) | 0.409226 / 0.283200 (0.126026) | 0.119804 / 0.141683 (-0.021879) | 1.704583 / 1.452155 (0.252428) | 1.782527 / 1.492716 (0.289811) |

### 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) | 0.255166 / 0.018006 (0.237160) | 0.485091 / 0.000490 (0.484601) | 0.007458 / 0.000200 (0.007258) | 0.000116 / 0.000054 (0.000061) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.034531 / 0.037411 (-0.002880) | 0.134332 / 0.014526 (0.119806) | 0.144944 / 0.176557 (-0.031613) | 0.199352 / 0.737135 (-0.537783) | 0.152243 / 0.296338 (-0.144095) |

### 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) | 0.495361 / 0.215209 (0.280152) | 4.895144 / 2.077655 (2.817489) | 2.350419 / 1.504120 (0.846299) | 2.112131 / 1.541195 (0.570937) | 2.234469 / 1.468490 (0.765978) | 0.815862 / 4.584777 (-3.768915) | 4.531638 / 3.745712 (0.785926) | 2.405186 / 5.269862 (-2.864676) | 1.559020 / 4.565676 (-3.006656) | 0.100432 / 0.424275 (-0.323843) | 0.014217 / 0.007607 (0.006610) | 0.614622 / 0.226044 (0.388577) | 5.984541 / 2.268929 (3.715613) | 2.929897 / 55.444624 (-52.514727) | 2.484010 / 6.876477 (-4.392467) | 2.533538 / 2.142072 (0.391466) | 0.972119 / 4.805227 (-3.833108) | 0.193630 / 6.500664 (-6.307034) | 0.073694 / 0.075469 (-0.001775) |

### 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) | 1.503725 / 1.841788 (-0.338063) | 17.421529 / 8.074308 (9.347221) | 15.686433 / 10.191392 (5.495041) | 0.216688 / 0.680424 (-0.463736) | 0.020929 / 0.534201 (-0.513272) | 0.512523 / 0.579283 (-0.066760) | 0.499878 / 0.434364 (0.065514) | 0.639238 / 0.540337 (0.098900) | 0.769598 / 1.386936 (-0.617338) |

</details>
</details>

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2023-02-15T13:27:13Z
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> > Do you want me to work on the CI in a separate branch? Thanks for merging and for your help as always :)
> 
> In the end I think we can keep it as is since we didn't modify the core code for jax. Maybe later if we do further changes and need to make sure we don't break anything ;) For example when we decide to add support for more recent versions

Makes sense, thank you @lhoestq!
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Hi to whoever is reading this! 🤗

## What's in this PR?

I was exploring the code regarding the `JaxFormatter` implemented in 🤗`datasets`, and found some things that IMO could be changed. Those are mainly regarding the docstrings and the type-hints based on `jax`'s 0.4.1 release where `jax.Array` was introduced as the default type for JAX-arrays (instead of `jnp.DeviceArray`, `jnp.SharedDeviceArray`, and `jnp.GlobalDeviceArray`). Even though `isinstance(..., jax.Array)` also works with lower versions such as e.g. `0.3.25`.

More information about the latter at [`jax` v0.4.1 - Release Notes](https://github.com/google/jax/releases/tag/jax-v0.4.1) and [jax.Array migration - JAX documentation](https://jax.readthedocs.io/en/latest/jax_array_migration.html).

## What's missing?

* Do you want me to write an entry in the documentation on how to use 🤗`datasets` with JAX as https://huggingface.co/docs/datasets/use_with_pytorch with PyTorch?
* Do we need to actually include `pyarrow` under the `TYPE_CHECKING` when needed? I just did it for JAX, but if we are OK with that, I can do that with the rest of the formatters, just LMK.
* Should the License header be included in `datasets.formatting.np_formatter`? If so, do I include the one from 2020 e.g. https://github.com/huggingface/datasets/blob/b065547654efa0ec633cf373ac1512884c68b2e1/src/datasets/formatting/tf_formatter.py#L1-L13
* Is there any reason why `jnp.array` is being used instead of `jnp.asarray`? There's no difference between both, just that `jnp.asarray` has `copy=False` as default, even though `numpy` to `jax.numpy` conversion is not zero-copy, but just asking :)