Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails
EDIT:
I think in this case, the metadata files (either README or JSON) should not be read (i.e. `self.info.splits` should be None).

One idea: 
- I think ideally we should set this behavior when we pass `--save_info` to the CLI `test`
- However, currently, the builder is unaware of this: `save_info` arg is not passed to it > I think in this case

@albertvillanova  You mean in cases when the script was changed? 

I suggest that we:
* add a check on the slice (like 'split_name[n%]) kind of format here: https://github.com/huggingface/datasets/blob/main/src/datasets/splits.py#L523 to catch things like this. 
* Error here happens before splits verification, but in `_prepare_split`, and `_prepare_split` doesn't perform any verification and don't know about it. so we can pass this parameter and take splits from `split_generator`, not from `split.info` in case when `verify_infos` is False
* we can check if split **names** from split_generators and self.info.splits are the same **before** preparing splits (if `verify_info=True`) so that we don't spend time on generating unwanted data. 
* provide some user-friendly warnings about `ignore_verifications` parameter so that users know that if something is not matching they can ignore it

I started it here: https://github.com/huggingface/datasets/pull/5327/files

What do you think @albertvillanova ? I edited my previous comment:
- First I proposed setting `self.info.splits` to None when `ignore_verifications=True`
  - I thought it was the easiest implementation because `ignore_verifications` is passed to `DatasetBuilder.download_and_prepare`
  - However, afterwards, I realized this might not be a good idea for this use case:
    - A user wants to optimize the loading of the dataset, and passes `ignore_verifications=False` to avoid all the verifications
      - In this case, we want `self.info.splits` to be read from metadata file
- Then, I thought that it might be better to set `self.info.splits` to None when we pass `--save_info` to the CLI test: if we are going to save the info to the metadata file, it makes no sense to read the info from the metadata file
  - This implementation is not so easy because the Builder knows nothing about `--save_info`

I agree with you there are 2 things to be addressed here:
- One is what I have just commented: `self.info.splits` should be None in this case
- The other, a validation should be implemented when calling `make_file_instructions` and/or `SplitDict.__getitem__`, so that when passing "training" to it, we get a more descriptive error other than `TypeError: expected str, bytes or os.PathLike object, not NoneType` 
### Describe the bug

If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails.

That's what happened in https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/discussions/2#6385fd1269634850f8ddff48.

### Steps to reproduce the bug

1. create a dataset with a custom split that returns, for example, only `"train"` split in `_splits_generators'`. specifically, if really want to reproduce, copy `https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/food_vision_199_classes.py 
2. run `datasets-cli test dataset_script.py --save_info --all_configs` - this would generate metadata yaml in `README.md` that would contain info about splits, for example, like this:
```
  splits:
  - name: train
    num_bytes: 2973286
    num_examples: 19747
```
3. make changes to your script so that it returns another set of splits, for example, `"train"` and `"test"` (uncomment [these lines](https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/food_vision_199_classes.py#L271))
4. run `load_dataset` and get the following error:
```python
Traceback (most recent call last):
  File "/home/daniel/code/pytorch/env/bin/datasets-cli", line 8, in <module>
    sys.exit(main())
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/commands/datasets_cli.py", line 39, in main
    service.run()
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/commands/test.py", line 141, in run
    builder.download_and_prepare(
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare
    self._download_and_prepare(
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare
    super()._download_and_prepare(
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 913, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 1356, in _prepare_split
    split_info = self.info.splits[split_generator.name]
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/splits.py", line 525, in __getitem__
    instructions = make_file_instructions(
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/arrow_reader.py", line 111, in make_file_instructions
    name2filenames = {
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/arrow_reader.py", line 112, in <dictcomp>
    info.name: filenames_for_dataset_split(
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/naming.py", line 78, in filenames_for_dataset_split
    prefix = filename_prefix_for_split(dataset_name, split)
  File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/naming.py", line 57, in filename_prefix_for_split
    if os.path.basename(name) != name:
  File "/home/daniel/code/pytorch/env/lib/python3.8/posixpath.py", line 143, in basename
    p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
5. bonus: try to regenerate metadata in `README.md` with `datasets-cli` as in step 2 and get the same error.


This is because `dataset.info.splits` contains only `"train"` split so when we are doing `self.info.splits[split_generator.name]` it tries to infer smth like `info.splits['train[50%]']` and that's not the case and it fails.

### Expected behavior

to be discussed? 

This can be solved by removing splits information from metadata file first. But I wonder if there is a better way. 

### Environment info

- Datasets version: 2.7.1
- Python version: 3.8.13