Require Pillow >= 9.4.0 to avoid AttributeError when loading image dataset
Require Pillow >= 9.4.0 to avoid AttributeError when loading image dataset.

The `PIL.Image.ExifTags` that we use in our code was implemented in Pillow-9.4.0: https://github.com/python-pillow/Pillow/commit/24a5405a9f7ea22f28f9c98b3e407292ea5ee1d3

The bug #6881 was introduced in datasets-2.19.0 by this PR:
- #6739

Fix #6881.
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6883). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. Do you think this is worth making a patch release for?
CC: @huggingface/datasets  <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) | 0.005764 / 0.011353 (-0.005589) | 0.004182 / 0.011008 (-0.006826) | 0.064520 / 0.038508 (0.026012) | 0.034260 / 0.023109 (0.011151) | 0.245677 / 0.275898 (-0.030221) | 0.277889 / 0.323480 (-0.045591) | 0.004569 / 0.007986 (-0.003417) | 0.002905 / 0.004328 (-0.001423) | 0.049346 / 0.004250 (0.045095) | 0.050529 / 0.037052 (0.013476) | 0.264718 / 0.258489 (0.006229) | 0.295705 / 0.293841 (0.001864) | 0.028144 / 0.128546 (-0.100402) | 0.011048 / 0.075646 (-0.064598) | 0.206290 / 0.419271 (-0.212982) | 0.035886 / 0.043533 (-0.007647) | 0.245038 / 0.255139 (-0.010101) | 0.269835 / 0.283200 (-0.013365) | 0.018927 / 0.141683 (-0.122756) | 1.136536 / 1.452155 (-0.315619) | 1.183256 / 1.492716 (-0.309460) |

### 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) | 0.115372 / 0.018006 (0.097366) | 0.315471 / 0.000490 (0.314982) | 0.000238 / 0.000200 (0.000038) | 0.000043 / 0.000054 (-0.000012) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021201 / 0.037411 (-0.016210) | 0.070374 / 0.014526 (0.055848) | 0.077557 / 0.176557 (-0.099000) | 0.124713 / 0.737135 (-0.612423) | 0.078850 / 0.296338 (-0.217489) |

### 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) | 0.278674 / 0.215209 (0.063465) | 2.739597 / 2.077655 (0.661942) | 1.438214 / 1.504120 (-0.065906) | 1.326373 / 1.541195 (-0.214822) | 1.370961 / 1.468490 (-0.097529) | 0.569160 / 4.584777 (-4.015617) | 2.411890 / 3.745712 (-1.333822) | 2.954073 / 5.269862 (-2.315788) | 1.816883 / 4.565676 (-2.748794) | 0.063123 / 0.424275 (-0.361152) | 0.005531 / 0.007607 (-0.002076) | 0.328184 / 0.226044 (0.102140) | 3.263083 / 2.268929 (0.994155) | 1.809159 / 55.444624 (-53.635465) | 1.535257 / 6.876477 (-5.341220) | 1.583428 / 2.142072 (-0.558644) | 0.642950 / 4.805227 (-4.162277) | 0.122240 / 6.500664 (-6.378424) | 0.044596 / 0.075469 (-0.030873) |

### 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) | 0.999993 / 1.841788 (-0.841795) | 12.941508 / 8.074308 (4.867200) | 10.417519 / 10.191392 (0.226127) | 0.134345 / 0.680424 (-0.546079) | 0.014651 / 0.534201 (-0.519550) | 0.288660 / 0.579283 (-0.290623) | 0.274550 / 0.434364 (-0.159814) | 0.327785 / 0.540337 (-0.212553) | 0.422954 / 1.386936 (-0.963982) |

</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) | 0.006051 / 0.011353 (-0.005302) | 0.003926 / 0.011008 (-0.007082) | 0.051480 / 0.038508 (0.012972) | 0.036102 / 0.023109 (0.012992) | 0.273358 / 0.275898 (-0.002540) | 0.293261 / 0.323480 (-0.030219) | 0.004562 / 0.007986 (-0.003424) | 0.002918 / 0.004328 (-0.001410) | 0.050386 / 0.004250 (0.046135) | 0.048427 / 0.037052 (0.011375) | 0.280178 / 0.258489 (0.021689) | 0.314599 / 0.293841 (0.020758) | 0.030876 / 0.128546 (-0.097670) | 0.010571 / 0.075646 (-0.065076) | 0.058555 / 0.419271 (-0.360717) | 0.034974 / 0.043533 (-0.008559) | 0.266604 / 0.255139 (0.011465) | 0.284712 / 0.283200 (0.001512) | 0.020296 / 0.141683 (-0.121387) | 1.116760 / 1.452155 (-0.335395) | 1.157794 / 1.492716 (-0.334922) |

### 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) | 0.103777 / 0.018006 (0.085771) | 0.314267 / 0.000490 (0.313778) | 0.000226 / 0.000200 (0.000026) | 0.000047 / 0.000054 (-0.000008) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023837 / 0.037411 (-0.013574) | 0.082145 / 0.014526 (0.067619) | 0.090434 / 0.176557 (-0.086123) | 0.132096 / 0.737135 (-0.605040) | 0.092426 / 0.296338 (-0.203913) |

### 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) | 0.299554 / 0.215209 (0.084345) | 2.932382 / 2.077655 (0.854727) | 1.549994 / 1.504120 (0.045874) | 1.454944 / 1.541195 (-0.086251) | 1.474987 / 1.468490 (0.006497) | 0.586149 / 4.584777 (-3.998628) | 0.972118 / 3.745712 (-2.773594) | 2.991719 / 5.269862 (-2.278142) | 1.876365 / 4.565676 (-2.689311) | 0.065178 / 0.424275 (-0.359098) | 0.005114 / 0.007607 (-0.002493) | 0.353704 / 0.226044 (0.127660) | 3.500940 / 2.268929 (1.232012) | 1.965581 / 55.444624 (-53.479043) | 1.662594 / 6.876477 (-5.213883) | 1.702761 / 2.142072 (-0.439311) | 0.663879 / 4.805227 (-4.141348) | 0.120036 / 6.500664 (-6.380628) | 0.043195 / 0.075469 (-0.032274) |

### 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) | 0.997690 / 1.841788 (-0.844098) | 13.448914 / 8.074308 (5.374606) | 10.132469 / 10.191392 (-0.058923) | 0.148493 / 0.680424 (-0.531930) | 0.016670 / 0.534201 (-0.517531) | 0.289708 / 0.579283 (-0.289575) | 0.132938 / 0.434364 (-0.301425) | 0.411425 / 0.540337 (-0.128913) | 0.430748 / 1.386936 (-0.956188) |

</details>
</details>

![](https://cml.dev/watermark.png#70e38090f070d323d452b5e746686f31b1086bd8 "CML watermark")
 maybe not super important since it was not reported by users, this can be included in the next release I observed the same AttributeError with Pillow == 10.3.0, while 9.4.0 works for me. What's the error you're getting @Eric2i ?

On my side on 10.3.0 I could run this without errors:

```python
import PIL.Image
PIL.Image.ExifTags.Base.Orientation is not None  # True
``` Sorry, false alarm. I double-checked that 10.3.0 is also good on my side. Thanks for your sample codes. I just faced the same bug after installing recent versions of Huggingface and datasets in a new environment. I solved it by uninstalling the recent version of Pillow and sticking to 9.4.0.
`pip uninstall Pillow`
`pip install Pillow==9.4.0` 
