Read GeoParquet files using parquet reader

Let GeoParquet files with the file extension `*.geoparquet` or `*.gpq` be readable by the default parquet reader.

Those two file extensions are the ones most commonly used for GeoParquet files, and is included in the `gpq` validator tool at https://github.com/planetlabs/gpq/blob/e5576b4ee7306b4d2259d56c879465a9364dab90/cmd/gpq/command/convert.go#L73-L75

Addresses https://github.com/huggingface/datasets/issues/6438

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6508). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Cool ! Do you mind writing a test using a geoparquet file in `tests/io/test_parquet.py` ?

I'm not too familiar with geoparquet, does it use e.g. pyarrow extension types ? or schema metadata ?

> Geometry columns MUST be stored using the BYTE_ARRAY parquet type. They MUST be encoded as [WKB](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry#Well-known_binary).

https://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md#geometry-columns

It has metadata:

> File metadata indicating things like the version of this specification used
> Column metadata with additional metadata for each geometry column

https://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md#metadata

The specification is very short by the way:

https://github.com/opengeospatial/geoparquet/blob/main/format-specs/geoparquet.md

https://github.com/opengeospatial/geoparquet/blob/main/format-specs/compatible-parquet.md is also worth reading for this PR

> Cool ! Do you mind writing a test using a geoparquet file in `tests/io/test_parquet.py` ?

Yep, let me do that do that later today!

> I'm not too familiar with geoparquet, does it use e.g. pyarrow extension types ? or schema metadata ?

GeoParquet is a Parquet file with a `geometry` column that is encoded in a Binary format (technically WKB as @severo mentioned above). It is not a pyarrow extension type (though that would be cool). Regular `parquet` readers such as `pyarrow` would thus see the column as a binary column, while libraries such as `geopandas` which implement a GeoParquet reader would look at the schema metadata.

E.g. taking this [file](https://huggingface.co/datasets/weiji14/clay_vector_embeddings/resolve/862b1602f326421adc99375912c08603a9f2cc5c/32VLM_v01.gpq) as an example, this is how the 'geo' schema looks like:

```python
import pyarrow.parquet as pq

schema = pq.read_schema(where="32VLM_v01.gpq")
print(schema.metadata[b"geo"])
```

```
{
    "primary_column": "geometry",
    "columns": {
        "geometry": {
            "encoding": "WKB",
            "crs": {
                "$schema": "https://proj.org/schemas/v0.7/projjson.schema.json",
                "type": "GeographicCRS",
                "name": "WGS 84 (CRS84)",
                "datum_ensemble": {
                    "name": "World Geodetic System 1984 ensemble",
                    "members": [
                        {"name": "World Geodetic System 1984 (Transit)"},
                        {"name": "World Geodetic System 1984 (G730)"},
                        {"name": "World Geodetic System 1984 (G873)"},
                        {"name": "World Geodetic System 1984 (G1150)"},
                        {"name": "World Geodetic System 1984 (G1674)"},
                        {"name": "World Geodetic System 1984 (G1762)"},
                        {"name": "World Geodetic System 1984 (G2139)"},
                    ],
                    "ellipsoid": {
                        "name": "WGS 84",
                        "semi_major_axis": 6378137,
                        "inverse_flattening": 298.257223563,
                    },
                    "accuracy": "2.0",
                    "id": {"authority": "EPSG", "code": 6326},
                },
                "coordinate_system": {
                    "subtype": "ellipsoidal",
                    "axis": [
                        {
                            "name": "Geodetic longitude",
                            "abbreviation": "Lon",
                            "direction": "east",
                            "unit": "degree",
                        },
                        {
                            "name": "Geodetic latitude",
                            "abbreviation": "Lat",
                            "direction": "north",
                            "unit": "degree",
                        },
                    ],
                },
                "scope": "Not known.",
                "area": "World.",
                "bbox": {
                    "south_latitude": -90,
                    "west_longitude": -180,
                    "north_latitude": 90,
                    "east_longitude": 180,
                },
                "id": {"authority": "OGC", "code": "CRS84"},
            },
            "geometry_types": ["Polygon"],
            "bbox": [
                5.370542846111244,
                59.42344573656881,
                7.368267282586697,
                60.42591328670696,
            ],
        }
    },
    "version": "1.0.0",
    "creator": {"library": "geopandas", "version": "0.14.1"},
}
```

We can continue the discussion on how to handle this extra 'geo' schema metadata in #6438. I'd like to keep this PR small by just piggy-backing off the default Parquet reader for now, which would just show the 'geometry' column as a binary column.

Thanks ! Also if you can make sure that doing `ds.to_parquet("path/to/output.geoparquet")` also saves as a valid geoparquet files (including the schema metadata) that would be awesome.

It would also enable `push_to_hub` to save geoparquet files

> Thanks ! Also if you can make sure that doing `ds.to_parquet("path/to/output.geoparquet")` also saves as a valid geoparquet files (including the schema metadata) that would be awesome.
> 
> It would also enable `push_to_hub` to save geoparquet files

Hmm, it should be possible to let PyArrow save a Parquet file with a geometry WKB column, but saving the GeoParquet schema metadata won't be easy without introducing [`geopandas`](https://github.com/geopandas/geopandas) as a dependency. Does this need to be done in this PR, or can it be a separate one?

I see, then let's keep it like this for now.
I just checked and it would require to add support for keeping the schema metadata in `Features` anyway.

Feel free to fix your code formatting using

```
make style
```

and we can merge this PR :)



Cool, linted to remove the extra blank line at 7088f585557807a63673cdc58900d7ce56146cf7. :rocket:

The previous CI failure at https://github.com/huggingface/datasets/actions/runs/7482863299/job/20668381959#step:6:5299 says `datasets.exceptions.DefunctDatasetError: Dataset 'eli5' is defunct and no longer accessible due to unavailability of the source data` which seems unrelated, might be to do with https://github.com/huggingface/datasets/issues/6605. I've updated the PR branch with changes from `main` again, could someone re-run the tests and merge if ok? Thanks!

sorry, it took me some time to fix the CI on the `main` branch

will merge once it's green :)

<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) | 0.005467 / 0.011353 (-0.005886) | 0.003696 / 0.011008 (-0.007313) | 0.063298 / 0.038508 (0.024790) | 0.032209 / 0.023109 (0.009100) | 0.246307 / 0.275898 (-0.029591) | 0.276864 / 0.323480 (-0.046616) | 0.003941 / 0.007986 (-0.004044) | 0.002616 / 0.004328 (-0.001713) | 0.049543 / 0.004250 (0.045292) | 0.044886 / 0.037052 (0.007833) | 0.266502 / 0.258489 (0.008013) | 0.288401 / 0.293841 (-0.005440) | 0.027911 / 0.128546 (-0.100635) | 0.011011 / 0.075646 (-0.064636) | 0.207972 / 0.419271 (-0.211299) | 0.036324 / 0.043533 (-0.007209) | 0.259450 / 0.255139 (0.004311) | 0.267317 / 0.283200 (-0.015883) | 0.018857 / 0.141683 (-0.122826) | 1.145350 / 1.452155 (-0.306805) | 1.204204 / 1.492716 (-0.288513) |

### 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) | 0.103864 / 0.018006 (0.085858) | 0.306941 / 0.000490 (0.306451) | 0.000218 / 0.000200 (0.000018) | 0.000044 / 0.000054 (-0.000011) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018391 / 0.037411 (-0.019020) | 0.064600 / 0.014526 (0.050074) | 0.075454 / 0.176557 (-0.101102) | 0.120913 / 0.737135 (-0.616223) | 0.076998 / 0.296338 (-0.219341) |

### 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) | 0.279491 / 0.215209 (0.064282) | 2.742471 / 2.077655 (0.664816) | 1.447980 / 1.504120 (-0.056140) | 1.328202 / 1.541195 (-0.212992) | 1.397291 / 1.468490 (-0.071199) | 0.585726 / 4.584777 (-3.999051) | 2.385132 / 3.745712 (-1.360580) | 2.874888 / 5.269862 (-2.394974) | 1.820177 / 4.565676 (-2.745500) | 0.063876 / 0.424275 (-0.360399) | 0.004946 / 0.007607 (-0.002661) | 0.336445 / 0.226044 (0.110401) | 3.396813 / 2.268929 (1.127885) | 1.832644 / 55.444624 (-53.611981) | 1.581304 / 6.876477 (-5.295172) | 1.607243 / 2.142072 (-0.534829) | 0.662752 / 4.805227 (-4.142476) | 0.119494 / 6.500664 (-6.381170) | 0.042573 / 0.075469 (-0.032896) |

### 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) | 0.936784 / 1.841788 (-0.905003) | 12.154288 / 8.074308 (4.079980) | 10.944835 / 10.191392 (0.753443) | 0.132856 / 0.680424 (-0.547568) | 0.015197 / 0.534201 (-0.519004) | 0.290647 / 0.579283 (-0.288636) | 0.273498 / 0.434364 (-0.160866) | 0.324893 / 0.540337 (-0.215444) | 0.427905 / 1.386936 (-0.959032) |

</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) | 0.005695 / 0.011353 (-0.005658) | 0.003562 / 0.011008 (-0.007446) | 0.050117 / 0.038508 (0.011608) | 0.033876 / 0.023109 (0.010767) | 0.275514 / 0.275898 (-0.000384) | 0.298460 / 0.323480 (-0.025020) | 0.004240 / 0.007986 (-0.003745) | 0.002738 / 0.004328 (-0.001591) | 0.048518 / 0.004250 (0.044268) | 0.049064 / 0.037052 (0.012012) | 0.287094 / 0.258489 (0.028605) | 0.314281 / 0.293841 (0.020440) | 0.057861 / 0.128546 (-0.070686) | 0.010893 / 0.075646 (-0.064753) | 0.062251 / 0.419271 (-0.357020) | 0.036788 / 0.043533 (-0.006745) | 0.272431 / 0.255139 (0.017292) | 0.292022 / 0.283200 (0.008822) | 0.019874 / 0.141683 (-0.121809) | 1.156939 / 1.452155 (-0.295216) | 1.237966 / 1.492716 (-0.254751) |

### 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) | 0.096156 / 0.018006 (0.078150) | 0.306652 / 0.000490 (0.306162) | 0.000230 / 0.000200 (0.000031) | 0.000059 / 0.000054 (0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022965 / 0.037411 (-0.014447) | 0.081349 / 0.014526 (0.066823) | 0.089035 / 0.176557 (-0.087521) | 0.128831 / 0.737135 (-0.608304) | 0.090321 / 0.296338 (-0.206017) |

### 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) | 0.293110 / 0.215209 (0.077901) | 2.884493 / 2.077655 (0.806839) | 1.582522 / 1.504120 (0.078402) | 1.518977 / 1.541195 (-0.022218) | 1.528449 / 1.468490 (0.059959) | 0.577369 / 4.584777 (-4.007408) | 2.473060 / 3.745712 (-1.272652) | 3.104363 / 5.269862 (-2.165499) | 1.916529 / 4.565676 (-2.649147) | 0.064594 / 0.424275 (-0.359682) | 0.005386 / 0.007607 (-0.002221) | 0.353336 / 0.226044 (0.127292) | 3.471914 / 2.268929 (1.202985) | 1.959222 / 55.444624 (-53.485402) | 1.677153 / 6.876477 (-5.199324) | 1.716961 / 2.142072 (-0.425112) | 0.658355 / 4.805227 (-4.146873) | 0.117296 / 6.500664 (-6.383368) | 0.041139 / 0.075469 (-0.034330) |

### 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) | 1.025220 / 1.841788 (-0.816567) | 14.510987 / 8.074308 (6.436679) | 11.851428 / 10.191392 (1.660036) | 0.143759 / 0.680424 (-0.536665) | 0.015644 / 0.534201 (-0.518557) | 0.296824 / 0.579283 (-0.282459) | 0.281566 / 0.434364 (-0.152798) | 0.335094 / 0.540337 (-0.205244) | 0.425199 / 1.386936 (-0.961737) |

</details>
</details>

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