ChunkPipeline (batch_size enabled on `zero-cls` and `qa` pipelines.
# What does this PR do?

It enables `batch_size` on the two pipeline where it wasn't possible previously.

The main roadblock for these is that `preprocess` would already return tensors that would contain part of the `batch_size`, meaning we couldn't use `Pipeline.batch_size` independently of the processed data. Both concepts were not orthogonal.

It also means there was no way to limit the size of the batch on a very large input item, by chunking the inputs being fed to the model.


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The problem is being solved by changing some assumptions about the format of `Pipeline`, and by creating `ChunkPipeline` (since we didn't want to change ALL pipelines for these two).

This modifications should yield exactly 0 breaking changes, and really just enable the batching for these two pipelines.

They differ from `Pipeline` simply because `preprocess` will not return just one item, but an iterator instead (SquadFeatures for `qa`, and a list of [sentence, hypothesis]  for `zero-shot`).

```python
item = ...
model_inputs = pipe.preprocess(item)
model_outputs = pipe._forward(model_inputs)
outputs = pipe.postprocess(model_outputs)
```

Now becomes:

```python
item = ...
all = []
for model_inputs in pipe.preprocess(item):
    model_outputs = pipe._forward(model_inputs)
    all.append(model_outputs)
output = pipe.postprocess(all)
```
When doing batching this becomes a bit more convoluted since we need to keep track of 2 things, where we are in the batch currently, and do we still need to accumulate in the `all` variable or not.

Everything is handled in iterators but it's equivalent to:

```python
item = ....
batch = []
all_model_outputs = []
for item in all_items:
    for model_inputs in pipe.preprocess(item):
        batch.append(model_inputs)
        if len(batch) != batch_size:
           continue

         model_outputs = pipe._forward(collate(batch))
         batch = []
        for model_output in model_outputs:
             if model_outputs["is_last"]:
                 output = model.postprocess(all_model_outputs)
                 all_model_outputs = []


                 yield output
             else:
                 all_model_outputs.append(model_output)
```

We are doing that using a special `is_last` boolean value that needs to be sent by the `preprocess` function and passed along (as a tensor) by the `_forward` function. That's because it's a bit hard for the iterators to deduce that information on their own if it's not propagated by the `_forward` function. Making it explicit makes reasoning about this clearer as the pipeline is not responsible both for sending it and passing it along (it's still magically accumulated back as a list before sending to `postprocess`).

This could be entirely magically inferred but it feels the complexity incurred by not be worth it (it's already complex enough).

<table>
  <tr>
    <td>
Pipeline
<img src="https://user-images.githubusercontent.com/204321/140971945-5e20a775-c291-4640-8c2e-f349337a4fea.jpeg" width=200>
</td>
<td>
Pipeline + batch_size
<img src="https://user-images.githubusercontent.com/204321/140971950-6ebabf33-6e93-4975-b397-26d640aa6321.jpeg" width=200>
</td>
</tr>
<tr>
<td>
ChunkPipeline
<img src="https://user-images.githubusercontent.com/204321/140971957-e9278c13-0847-4c64-85c9-3e03fbb66e9b.jpeg" width=200>
</td>
<td>
ChunkPipeline + batch_size
<img src="https://user-images.githubusercontent.com/204321/140971955-550ee033-e26e-41f4-b81f-28bb2195879b.jpeg" width=200>
</td>
</tr>
</table>


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@LysandreJik I split this PR in 2, were 1 holds the ASR part, and this just enables `batch_size` for QA and zero-shot.

It has benefits, but also increases complexity of the overall code. Not 100% sure about this.<|||||>Ok thanks, this is a big PR, will review it in the coming days.<|||||>@LysandreJik I added a bunch of documentation as comments for now.

When this pass it could be helpful to add stuff in the main documentation (mainly for pipeline creation).
<|||||>Thanks for adding comments, this looks good. Would you like to merge it now or to include the documentation changes in it before merging?<|||||>I want to add some documentation for this now<|||||>@LysandreJik I think it's better.

For the documentation, I kept it slightly minimal. Actually I think part of the documentation on `pipeline` belongs to this page https://huggingface.co/docs/transformers/task_summary but it seems that this page talks about a little more than just the pipeline, and also is missing quite a few tasks (and there's also the great work of @merveenoyan on tasks https://github.com/huggingface/huggingface_hub/pull/509 that we might want to link to and/or refer)