Allow SplitDict setitem to replace existing SplitInfo
Opened by lhoestq (closed)

Fix this code provided by @clefourrier 

```python
import datasets
import os

token = os.getenv("TOKEN")

results = datasets.load_dataset("gaia-benchmark/results_public", "2023", token=token, download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)
results["test"] = datasets.Dataset.from_list([row for row in results["test"] if row["model"] != "StateFlow"])
results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test")
```

```
ValueError                                Traceback (most recent call last)
Cell In[43], line 1
----> 1 results["test"].push_to_hub("gaia-benchmark/results_public", "2023", token=token, split="test")

File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/arrow_dataset.py:5498, in Dataset.push_to_hub(self, repo_id, config_name, split, private, token, branch, max_shard_size, num_shards, embed_external_files)
   5496         repo_info.dataset_size = (repo_info.dataset_size or 0) + dataset_nbytes
   5497         repo_info.size_in_bytes = repo_info.download_size + repo_info.dataset_size
-> 5498         repo_info.splits[split] = SplitInfo(
   5499             split, num_bytes=dataset_nbytes, num_examples=len(self), dataset_name=dataset_name
   5500         )
   5501         info_to_dump = repo_info
   5502 # create the metadata configs if it was uploaded with push_to_hub before metadata configs existed

File ~/miniconda3/envs/default310/lib/python3.10/site-packages/datasets/splits.py:541, in SplitDict.__setitem__(self, key, value)
    539     raise ValueError(f"Cannot add elem. (key mismatch: '{key}' != '{value.name}')")
    540 if key in self:
--> 541     raise ValueError(f"Split {key} already present")
    542 super().__setitem__(key, value)

ValueError: Split test already present
```

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6665). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<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) | 0.004968 / 0.011353 (-0.006385) | 0.003732 / 0.011008 (-0.007276) | 0.063672 / 0.038508 (0.025164) | 0.027066 / 0.023109 (0.003957) | 0.253306 / 0.275898 (-0.022592) | 0.283382 / 0.323480 (-0.040098) | 0.004217 / 0.007986 (-0.003768) | 0.002865 / 0.004328 (-0.001464) | 0.048672 / 0.004250 (0.044421) | 0.040740 / 0.037052 (0.003688) | 0.271848 / 0.258489 (0.013359) | 0.293162 / 0.293841 (-0.000679) | 0.027410 / 0.128546 (-0.101136) | 0.010605 / 0.075646 (-0.065042) | 0.210545 / 0.419271 (-0.208726) | 0.036085 / 0.043533 (-0.007447) | 0.259807 / 0.255139 (0.004668) | 0.274056 / 0.283200 (-0.009144) | 0.018812 / 0.141683 (-0.122871) | 1.116687 / 1.452155 (-0.335468) | 1.164276 / 1.492716 (-0.328440) |

### 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) | 0.092874 / 0.018006 (0.074868) | 0.355897 / 0.000490 (0.355407) | 0.000224 / 0.000200 (0.000024) | 0.000051 / 0.000054 (-0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018461 / 0.037411 (-0.018950) | 0.062061 / 0.014526 (0.047535) | 0.072353 / 0.176557 (-0.104203) | 0.119162 / 0.737135 (-0.617974) | 0.082974 / 0.296338 (-0.213364) |

### 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) | 0.291631 / 0.215209 (0.076422) | 2.861495 / 2.077655 (0.783841) | 1.496753 / 1.504120 (-0.007367) | 1.371164 / 1.541195 (-0.170031) | 1.415473 / 1.468490 (-0.053018) | 0.566778 / 4.584777 (-4.017999) | 2.376209 / 3.745712 (-1.369503) | 2.812326 / 5.269862 (-2.457535) | 1.765640 / 4.565676 (-2.800037) | 0.063274 / 0.424275 (-0.361001) | 0.004933 / 0.007607 (-0.002674) | 0.342345 / 0.226044 (0.116301) | 3.407487 / 2.268929 (1.138558) | 1.856646 / 55.444624 (-53.587978) | 1.590284 / 6.876477 (-5.286193) | 1.610068 / 2.142072 (-0.532004) | 0.656007 / 4.805227 (-4.149220) | 0.118310 / 6.500664 (-6.382354) | 0.042596 / 0.075469 (-0.032873) |

### 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) | 0.991392 / 1.841788 (-0.850395) | 11.612397 / 8.074308 (3.538089) | 9.627836 / 10.191392 (-0.563556) | 0.130575 / 0.680424 (-0.549848) | 0.014152 / 0.534201 (-0.520049) | 0.289736 / 0.579283 (-0.289548) | 0.260041 / 0.434364 (-0.174323) | 0.339730 / 0.540337 (-0.200608) | 0.447529 / 1.386936 (-0.939407) |

</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) | 0.005315 / 0.011353 (-0.006038) | 0.003955 / 0.011008 (-0.007053) | 0.049618 / 0.038508 (0.011110) | 0.030404 / 0.023109 (0.007295) | 0.258727 / 0.275898 (-0.017171) | 0.282020 / 0.323480 (-0.041460) | 0.004356 / 0.007986 (-0.003629) | 0.002866 / 0.004328 (-0.001462) | 0.049122 / 0.004250 (0.044872) | 0.045534 / 0.037052 (0.008482) | 0.269560 / 0.258489 (0.011071) | 0.301225 / 0.293841 (0.007384) | 0.029786 / 0.128546 (-0.098761) | 0.010433 / 0.075646 (-0.065213) | 0.058222 / 0.419271 (-0.361049) | 0.052968 / 0.043533 (0.009435) | 0.256605 / 0.255139 (0.001467) | 0.279899 / 0.283200 (-0.003300) | 0.018233 / 0.141683 (-0.123450) | 1.164060 / 1.452155 (-0.288095) | 1.218049 / 1.492716 (-0.274667) |

### 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) | 0.093646 / 0.018006 (0.075639) | 0.288804 / 0.000490 (0.288314) | 0.000224 / 0.000200 (0.000024) | 0.000051 / 0.000054 (-0.000003) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022193 / 0.037411 (-0.015219) | 0.075507 / 0.014526 (0.060981) | 0.086091 / 0.176557 (-0.090465) | 0.127433 / 0.737135 (-0.609703) | 0.087064 / 0.296338 (-0.209274) |

### 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) | 0.292459 / 0.215209 (0.077250) | 2.842430 / 2.077655 (0.764776) | 1.505824 / 1.504120 (0.001704) | 1.377052 / 1.541195 (-0.164143) | 1.408757 / 1.468490 (-0.059733) | 0.571705 / 4.584777 (-4.013072) | 2.459798 / 3.745712 (-1.285914) | 2.714826 / 5.269862 (-2.555035) | 1.782064 / 4.565676 (-2.783613) | 0.063113 / 0.424275 (-0.361162) | 0.005099 / 0.007607 (-0.002509) | 0.343624 / 0.226044 (0.117579) | 3.415806 / 2.268929 (1.146878) | 1.853253 / 55.444624 (-53.591371) | 1.584392 / 6.876477 (-5.292084) | 1.720384 / 2.142072 (-0.421689) | 0.646637 / 4.805227 (-4.158590) | 0.118072 / 6.500664 (-6.382593) | 0.041362 / 0.075469 (-0.034107) |

### 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) | 1.020086 / 1.841788 (-0.821701) | 12.303980 / 8.074308 (4.229672) | 10.322869 / 10.191392 (0.131477) | 0.140959 / 0.680424 (-0.539465) | 0.015372 / 0.534201 (-0.518829) | 0.288552 / 0.579283 (-0.290731) | 0.278243 / 0.434364 (-0.156121) | 0.328399 / 0.540337 (-0.211939) | 0.433618 / 1.386936 (-0.953318) |

</details>
</details>

![](https://cml.dev/watermark.png#9469092d88ff7bb4d3f7fe6c2de0109ca458b5da "CML watermark")