Lazy data files resolution and offline cache reload
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6493). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. > Naive question: is there any breaking change when loading?

No breaking changes except that the cache folders are different

e.g. for glue sst2 (has parquet export)

```
This branch (new format is config/version/commit_sha)
~/.cache/huggingface/datasets/glue/sst2/1.0.0/fd8e86499fa5c264fcaad392a8f49ddf58bf4037
On main
~/.cache/huggingface/datasets/glue/sst2/0.0.0/74a75637ac4acd3f
On 2.15.0
~/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad
```

e.g. for wikimedia/wikipedia 20231101.ab (has metadata configs)


```
This branch (new format is config/version/commit_sha)
~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/4cb9b0d719291f1a10f96f67d609c5d442980dc9
On main (takes ages to load)
~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/cfa627e27933df13
On 2.15.0 (takes ages to load)
~/.cache/huggingface/datasets/wikimedia___wikipedia/20231101.ab/0.0.0/e92ee7a91c466564
```


e.g. for lhoestq/demo1 (no metadata configs)


```
This branch (new format is config/version/commit_sha)
~/.cache/huggingface/datasets/lhoestq___demo1/default/0.0.0/87ecf163bedca9d80598b528940a9c4f99e14c11
On main
~/.cache/huggingface/datasets/lhoestq___demo1/default-8a4a0b7a240d3c5e/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d
On 2.15.0
~/.cache/huggingface/datasets/lhoestq___demo1/default-59d4029e0bb36ae0/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d
``` There was a last bug I just fixed: if you modify a dataset and reload it from the hub it won't download the new version - I think I need to use another hash to name the cache directory
edit: fixed I switched to using the git commit sha for the cache directory, which is now `config/version/commit_sha` :) much cleaner than before.

And for local file it's a hash that takes into account the resolved files (and their last modified dates) I also ran the `transformers` CI on this branch and it's green FYI `huggingface_hub` will have a release on tuesday/wednesday (will speed up load_dataset data files resolution which is now needed for datasets loaded from parquet export) so we can aim on merging this around the same time and do a release on thursday Merging this one, and hopefully the cache backward compatibility PR soon too :)

Then it will be release time <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) | 0.005444 / 0.011353 (-0.005909) | 0.003562 / 0.011008 (-0.007446) | 0.063183 / 0.038508 (0.024675) | 0.048885 / 0.023109 (0.025776) | 0.248422 / 0.275898 (-0.027476) | 0.277844 / 0.323480 (-0.045636) | 0.003019 / 0.007986 (-0.004966) | 0.002660 / 0.004328 (-0.001669) | 0.048928 / 0.004250 (0.044677) | 0.044850 / 0.037052 (0.007798) | 0.248505 / 0.258489 (-0.009984) | 0.282231 / 0.293841 (-0.011610) | 0.028302 / 0.128546 (-0.100244) | 0.010829 / 0.075646 (-0.064818) | 0.206738 / 0.419271 (-0.212533) | 0.035485 / 0.043533 (-0.008048) | 0.244575 / 0.255139 (-0.010564) | 0.281411 / 0.283200 (-0.001789) | 0.019563 / 0.141683 (-0.122120) | 1.113769 / 1.452155 (-0.338386) | 1.176831 / 1.492716 (-0.315885) |

### 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) | 0.004718 / 0.018006 (-0.013288) | 0.304103 / 0.000490 (0.303614) | 0.000214 / 0.000200 (0.000014) | 0.000052 / 0.000054 (-0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019642 / 0.037411 (-0.017769) | 0.060275 / 0.014526 (0.045749) | 0.073072 / 0.176557 (-0.103484) | 0.119789 / 0.737135 (-0.617346) | 0.074535 / 0.296338 (-0.221804) |

### 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) | 0.278799 / 0.215209 (0.063590) | 2.725320 / 2.077655 (0.647665) | 1.419048 / 1.504120 (-0.085071) | 1.335041 / 1.541195 (-0.206154) | 1.373029 / 1.468490 (-0.095461) | 0.566774 / 4.584777 (-4.018003) | 2.383796 / 3.745712 (-1.361916) | 2.734804 / 5.269862 (-2.535057) | 1.712277 / 4.565676 (-2.853399) | 0.062119 / 0.424275 (-0.362156) | 0.004949 / 0.007607 (-0.002658) | 0.336126 / 0.226044 (0.110082) | 3.298602 / 2.268929 (1.029674) | 1.842815 / 55.444624 (-53.601809) | 1.544028 / 6.876477 (-5.332449) | 1.566717 / 2.142072 (-0.575355) | 0.643006 / 4.805227 (-4.162221) | 0.118241 / 6.500664 (-6.382423) | 0.042453 / 0.075469 (-0.033016) |

### 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) | 0.949015 / 1.841788 (-0.892773) | 11.717958 / 8.074308 (3.643649) | 10.482448 / 10.191392 (0.291056) | 0.128564 / 0.680424 (-0.551860) | 0.014792 / 0.534201 (-0.519408) | 0.288636 / 0.579283 (-0.290647) | 0.263345 / 0.434364 (-0.171019) | 0.325753 / 0.540337 (-0.214584) | 0.421294 / 1.386936 (-0.965642) |

</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) | 0.005367 / 0.011353 (-0.005985) | 0.003802 / 0.011008 (-0.007206) | 0.049322 / 0.038508 (0.010814) | 0.055201 / 0.023109 (0.032092) | 0.287811 / 0.275898 (0.011913) | 0.305141 / 0.323480 (-0.018339) | 0.004095 / 0.007986 (-0.003890) | 0.002733 / 0.004328 (-0.001595) | 0.049508 / 0.004250 (0.045258) | 0.039199 / 0.037052 (0.002147) | 0.282719 / 0.258489 (0.024230) | 0.311156 / 0.293841 (0.017315) | 0.029469 / 0.128546 (-0.099077) | 0.010709 / 0.075646 (-0.064937) | 0.057646 / 0.419271 (-0.361626) | 0.032696 / 0.043533 (-0.010837) | 0.285087 / 0.255139 (0.029948) | 0.294142 / 0.283200 (0.010942) | 0.019779 / 0.141683 (-0.121904) | 1.176844 / 1.452155 (-0.275310) | 1.190925 / 1.492716 (-0.301792) |

### 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) | 0.092885 / 0.018006 (0.074879) | 0.301129 / 0.000490 (0.300640) | 0.000232 / 0.000200 (0.000032) | 0.000049 / 0.000054 (-0.000005) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023202 / 0.037411 (-0.014210) | 0.076850 / 0.014526 (0.062325) | 0.090058 / 0.176557 (-0.086499) | 0.128091 / 0.737135 (-0.609045) | 0.091098 / 0.296338 (-0.205240) |

### 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) | 0.292973 / 0.215209 (0.077764) | 2.876022 / 2.077655 (0.798367) | 1.672115 / 1.504120 (0.167995) | 1.555103 / 1.541195 (0.013909) | 1.559832 / 1.468490 (0.091342) | 0.558017 / 4.584777 (-4.026760) | 2.428448 / 3.745712 (-1.317264) | 2.812024 / 5.269862 (-2.457837) | 1.738470 / 4.565676 (-2.827207) | 0.062669 / 0.424275 (-0.361607) | 0.005071 / 0.007607 (-0.002536) | 0.351804 / 0.226044 (0.125759) | 3.412207 / 2.268929 (1.143279) | 2.023478 / 55.444624 (-53.421147) | 1.761281 / 6.876477 (-5.115195) | 1.770789 / 2.142072 (-0.371283) | 0.643062 / 4.805227 (-4.162165) | 0.116616 / 6.500664 (-6.384048) | 0.041816 / 0.075469 (-0.033653) |

### 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) | 0.988430 / 1.841788 (-0.853357) | 12.278636 / 8.074308 (4.204328) | 11.066185 / 10.191392 (0.874793) | 0.141191 / 0.680424 (-0.539233) | 0.015547 / 0.534201 (-0.518654) | 0.288045 / 0.579283 (-0.291238) | 0.279651 / 0.434364 (-0.154713) | 0.329869 / 0.540337 (-0.210469) | 0.420391 / 1.386936 (-0.966545) |

</details>
</details>

![](https://cml.dev/watermark.png#ef3b5dd3633995c95d77f35fb17f89ff44990bc4 "CML watermark")

Includes both https://github.com/huggingface/datasets/pull/6458 and https://github.com/huggingface/datasets/pull/6459

This PR should be merged instead of the two individually, since they are conflicting

## Offline cache reload

 it can reload datasets that were pushed to hub if they exist in the cache.

example:

```python
>>> Dataset.from_dict({"a": [1, 2]}).push_to_hub("lhoestq/tmp")
>>> load_dataset("lhoestq/tmp")
DatasetDict({
    train: Dataset({
        features: ['a'],
        num_rows: 2
    })
})
```

and later, without connection:

```python
>>> load_dataset("lhoestq/tmp")
Using the latest cached version of the dataset since lhoestq/tmp couldn't be found on the Hugging Face Hub
Found the latest cached dataset configuration 'default' at /Users/quentinlhoest/.cache/huggingface/datasets/lhoestq___tmp/default/0.0.0/da0e902a945afeb9 (last modified on Wed Dec 13 14:55:52 2023).
DatasetDict({
    train: Dataset({
        features: ['a'],
        num_rows: 2
    })
})
```

- Updated `CachedDatasetModuleFactory` to look for datasets in the cache at `<namespace>___<dataset_name>/<config_id>`
- Since the metadata configs parameters are not available in offline mode, we don't know which folder to load (config_id and hash change), so I simply load the latest one
  - I instantiate a BuilderConfig even if there is no metadata config with the right config_name
  - Its config_id is equal to the config_name to be able to retrieve it in the cache (no more suffix for configs from metadata configs)
  - We can reload this config if offline mode by specifying the right config_name (same as online !)
- Consequences of this change:
  - Only when there are user's parameters it creates a custom builder config with config_id = config_name + user parameters hash
  - the hash used to name the cache folder takes into account the metadata config and the dataset info, so that the right cache can be reloaded when there is internet connection without redownloading the data or resolving the data files. For local directories I hash the builder configs and dataset info, and for datasets on the hub I use the commit sha as hash.
  - cache directories now look like `config/version/commit_sha` for hub datasets which is clean :)

Fix https://github.com/huggingface/datasets/issues/3547

## Lazy data files resolution

this makes this code run in 2sec instead of >10sec

```python
from datasets import load_dataset

ds = load_dataset("glue", "sst2", streaming=True, trust_remote_code=False)
```

For some datasets with many configs and files it can be up to 100x faster.
This is particularly important now that some datasets will be loaded from the Parquet export instead of the scripts.

The data files are only resolved in the builder `__init__`. To do so I added DataFilesPatternsList and DataFilesPatternsDict that have `.resolve()` to return resolved DataFilesList and DataFilesDict
