[TokenClassification] Label realignment for subword aggregation
# What does this PR do?

Tentative to replace #11622 

- Added `AggregationStrategy`
- `ignore_subwords` and `grouped_entities` arguments are now fused
  into `aggregation_strategy`. It makes more sense anyway because
  `ignore_subwords=True` with `grouped_entities=False` did not have a
  meaning anyway.
- Added 2 new ways to aggregate which are MAX, and AVERAGE
- AVERAGE requires a bit more information than the others, for now this
case is slightly specific, we should keep that in mind for future
changes.
- Testing has been modified to reflect new argument, and to check the
correct deprecation and the new aggregation_strategy.
- Put the testing argument and testing results for aggregation_strategy,
close together, so that readers can understand what is supposed to
happen.
- `aggregate` is now only tested on a small model as it does not mean
anything to test it globally for all models.
- Previous tests are unchanged in desired output.
- Added a new test case that showcases better the difference between the
  FIRST, MAX and AVERAGE strategies.

Fixes #10263, #10763
See also #10568

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## Who can review?


@LysandreJik

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Thank you @Narsil, I'll take a look!

@francescorubbo, @elk-cloner, this PR originated from yours and is based on the same approach. If you approve of this PR, can I add you as co-authors, as you've greatly contributed to its current shape? <|||||>> @francescorubbo, @elk-cloner, this PR originated from yours and is based on the same approach. If you approve of this PR, can I add you as co-authors, as you've greatly contributed to its current shape?

Sure!
<|||||>> Thank you @Narsil, I'll take a look!
> 
> @francescorubbo, @elk-cloner, this PR originated from yours and is based on the same approach. If you approve of this PR, can I add you as co-authors, as you've greatly contributed to its current shape?

Sure!<|||||>Great, I will! 

Pinging also @joshdevins and @cceyda for feedback<|||||>Looking good, thank you for all the work! I'm wondering if you can include test cases for the original 3 examples provided in https://github.com/huggingface/transformers/issues/10263#issue-811193366 ? The new test examples here look correct but I'm not sure they cover the scope of the first examples. Maybe just stub out a real model and test with the labels for each sub-word token as provided in the example. This will exercise just the aggregation logic then as in theory a model could output any of these example labels.<|||||>@joshdevins 

Do you mind giving an example displaying the issue ?

I tried this, but I don't think it exhibits what you mention in the original issue:  ( https://github.com/huggingface/transformers/issues/10263#issue-811193366)

```python
        NER_MODEL = "elastic/distilbert-base-cased-finetuned-conll03-english"
        model = AutoModelForTokenClassification.from_pretrained(NER_MODEL)
        tokenizer = AutoTokenizer.from_pretrained(NER_MODEL, use_fast=True)
        sentence = """Accenture is a company. Max Mustermann is someone, Elasticsearch is something."""
        nlp_ner = pipeline("ner", model=model, tokenizer=tokenizer)
        output = nlp_ner(sentence)
        print(output)
        self.assertEqual(
            nested_simplify(output),
            [
                {"entity": "B-PER", "score": 0.9953969, "index": 9, "word": "Max", "start": 24, "end": 27},
                {"entity": "I-PER", "score": 0.9773876, "index": 10, "word": "Must", "start": 28, "end": 32},
                {"entity": "I-PER", "score": 0.9924896, "index": 11, "word": "##erman", "start": 32, "end": 37},
                {"entity": "I-PER", "score": 0.9860034, "index": 12, "word": "##n", "start": 37, "end": 38},
                {"entity": "B-ORG", "score": 0.99201995, "index": 16, "word": "El", "start": 51, "end": 53},
                {"entity": "B-ORG", "score": 0.99391395, "index": 17, "word": "##astic", "start": 53, "end": 58},
                {"entity": "B-ORG", "score": 0.9962443, "index": 18, "word": "##sea", "start": 58, "end": 61},
                {"entity": "B-ORG", "score": 0.9924281, "index": 19, "word": "##rch", "start": 61, "end": 64},
            ],
        )
```<|||||>I think we should just wait for the test with `elastic`  model and we' re good to go.<|||||>@Narsil I've since retrained that model (by labelling all sub-word tokens instead of padding) and it appears to work better with new domain data like "Elasticsearch".

The point was more to decouple the fix from a specific model and to be robust to possible outputs of a model particularly for words/sub-words that are out-of-domain for a model and relying on sub-word token classification which can be less predictable (in my experience). This was why I suggested stubbing out the model and just putting in the sub-word labels directly to the aggregator to see if the expected behaviour matches the actual new behaviour.<|||||>@joshdevins Yes, then I think I already added those tests here:

```python
    def test_aggregation_strategy_example2(self):
        model_name = self.small_models[0]
        tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
        nlp = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
        # Just to understand scores indexes in this test
        self.assertEqual(
            nlp.model.config.id2label,
            {0: "O", 1: "B-MISC", 2: "I-MISC", 3: "B-PER", 4: "I-PER", 5: "B-ORG", 6: "I-ORG", 7: "B-LOC", 8: "I-LOC"},
        )
        example = [
            {
                # Necessary for AVERAGE
                "scores": np.array([0, 0.55, 0, 0.45, 0, 0, 0, 0, 0, 0]),
                "is_subword": False,
                "index": 1,
                "word": "Ra",
                "start": 0,
                "end": 2,
            },
            {
                "scores": np.array([0, 0, 0, 0.2, 0, 0, 0, 0.8, 0, 0]),
                "is_subword": True,
                "word": "##ma",
                "start": 2,
                "end": 4,
                "index": 2,
            },
            {
                # 4th score will have the higher average
                # 4th score is B-PER for this model
                # It's does not correspond to any of the subtokens.
                "scores": np.array([0, 0, 0, 0.4, 0, 0, 0.6, 0, 0, 0]),
                "is_subword": True,
                "word": "##zotti",
                "start": 11,
                "end": 13,
                "index": 3,
            },
        ]
        self.assertEqual(
            nlp.aggregate(example, AggregationStrategy.NONE),
            [
                {"end": 2, "entity": "B-MISC", "score": 0.55, "start": 0, "word": "Ra", "index": 1},
                {"end": 4, "entity": "B-LOC", "score": 0.8, "start": 2, "word": "##ma", "index": 2},
                {"end": 13, "entity": "I-ORG", "score": 0.6, "start": 11, "word": "##zotti", "index": 3},
            ],
        )

        self.assertEqual(
            nlp.aggregate(example, AggregationStrategy.FIRST),
            [{"entity_group": "MISC", "score": 0.55, "word": "Ramazotti", "start": 0, "end": 13}],
        )
        self.assertEqual(
            nlp.aggregate(example, AggregationStrategy.MAX),
            [{"entity_group": "LOC", "score": 0.8, "word": "Ramazotti", "start": 0, "end": 13}],
        )
        self.assertEqual(
            nested_simplify(nlp.aggregate(example, AggregationStrategy.AVERAGE)),
            [{"entity_group": "PER", "score": 0.35, "word": "Ramazotti", "start": 0, "end": 13}],
        )
```
<|||||>@Narsil Ah cool, I missed those examples in my read-through. LGTM 🎉<|||||>@LysandreJik I changed the co-authors, I'll merge after you check I've done it correctly.<|||||>Looks good to me, feel free to merge!