Added support for other features for already supported models
 # What does this PR do?

This PR adds support for almost all the features available for already supported models.

Main contributions:

- `OnnxSeq2SeqConfigWithPast`: a new class  inheriting from `OnnxConfigWithPast` designed specifically for seq2seq models, this should make things easier for the community to contribute.
- Tests refactoring and parameterization: now every (model, feature) export pair is tested, and is considered as a standalone test (compared to before when everything was considered to be one big test).
- A lot of new features (a feature is a task plus the choice or not to use `past_key_values`), that have been requested by the community (check the list of supported feautres below)

Features now supported:

- For BERT like models: default, sequence-classification, token-classification and question-answering (multiple-choice will be added later).
- For causal language models (GPT-2 and GPT-neo): default, default-with-past, causal-lm, causal-lm-with-past, sequence-classification and token-classification (only for GPT2).
- For Seq2Seq models (T5, BART, mBART): 
     - T5, BART, mBART: default, default-with-past, seq2seq-lm, seq2seq-lm-with-past
     - BART, mBART: causal-lm, causal-lm-with-past, sequence-classification, question-answering

@michaelbenayoun @lewtun @Albertobegue 
any idea when this PR will be merged ?<|||||>> @michaelbenayoun @lewtun @Albertobegue any idea when this PR will be merged ?

Hey @girishnadiger-gep this PR was superseded by #14700 which has been merged sometime ago. Is there a specific issue or feature missing that you're interested in?<|||||>Hi @lewtun ,
Thanks for getting back. I was trying to implement BART Summarization on onnx. Facing a weird issue where the inputs to the model are 4 `('input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask')`, but unable to figure out how to get `'decoder_input_ids'` and `'decoder_attention_mask'` values to feed to the onnx model.

I've converted a BART Large model to onnx model using 'seq2seq-lm' feature, but thought i'm missing something here so asked in this forum.<|||||>Ah for that you can probably adapt the example that I used for the Marian PR in https://github.com/huggingface/transformers/pull/14586

FYI we also have a forum (https://discuss.huggingface.co/) which is better suited for these type of questions - we try to use GitHub issues for bug reports / feature requests