Add `BioGPTForSequenceClassification`
Author: awinml
Pull request: yes

# What does this PR do?

Add Sequence Classification support for BioGPT.

Fixes #21530 
Fixes #21535 
This PR completes the stalled PR #21535.
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## Who can review?
@ArthurZucker @younesbelkada @NielsRogge @sgugger 

Comments:
_The documentation is not available anymore as the PR was closed or merged._

@NielsRogge @sgugger  Is there a way to skip the check for specific lines when I run `make repo-consistency`.

It gives an error when I add this:
`# Copied from transformers.models.opt.modeling_opt.OPTForSequenceClassification with OPT->BioGpt`.
There are some attributes like word_embed_proj_dim which do not exist for the BioGpt model.
Also it changes the case of the docstring variable, which leads to a variable not found error.

Should I drop the copy attribution comment?


If some attributes do not exist, let's just add the `# Adapted from` mention, and put the `# Copied from` only where it properly fits! 

@younesbelkada You're right, I haven't figured out how to solve this failing test.

@ArthurZucker  Any suggestions as to how to fix this failing test?

I went through #18123. The code is extremely similar, but I still don't get why the test is failing. Maybe I am missing something. I need help to fix it.


```python
_____________________________ BioGptModelTest.test_load_with_mismatched_shapes _____________________________

self = <tests.models.biogpt.test_modeling_biogpt.BioGptModelTest testMethod=test_load_with_mismatched_shapes>

    def test_load_with_mismatched_shapes(self):
        if not self.test_mismatched_shapes:
            return
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
    
        for model_class in self.all_model_classes:
            if model_class.__name__ not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES):
                continue
    
            with self.subTest(msg=f"Testing {model_class}"):
                with tempfile.TemporaryDirectory() as tmp_dir:
                    model = model_class(config)
                    model.save_pretrained(tmp_dir)
    
                    # Fails when we don't set ignore_mismatched_sizes=True
                    with self.assertRaises(RuntimeError):
                        new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
                    with self.assertRaises(RuntimeError):
>                       new_model_without_prefix = AutoModel.from_pretrained(tmp_dir, vocab_size=10)
E                       AssertionError: RuntimeError not raised

tests/test_modeling_common.py:2640: AssertionError

```



Hey! I'll try to have a look, it looks like setting the `vocab_size` does not change the shape of the model which means that it does not raise an error when it should! 

@ArthurZucker Thanks! The `vocab_size` argument had my suspicion as well. Since we inherit from `BioGptModel`, I thought that already does the needful. I could not figure out what I was missing. Looking forward to your suggestions.

@ArthurZucker Those changes seemed to do the trick, all the CI tests pass now. Thanks for your help!